code stringlengths 86 54.5k | code_codestyle int64 0 371 | style_context stringlengths 87 49.2k | style_context_codestyle int64 0 349 | label int64 0 1 |
|---|---|---|---|---|
'''simple docstring'''
a : List[str] = "0.21.0"
from .accelerator import Accelerator
from .big_modeling import (
cpu_offload,
cpu_offload_with_hook,
disk_offload,
dispatch_model,
init_empty_weights,
init_on_device,
load_checkpoint_and_dispatch,
)
from .data_loader import skip_first_batches
from .launchers import debug_launcher, notebook_launcher
from .state import PartialState
from .utils import (
DeepSpeedPlugin,
DistributedDataParallelKwargs,
DistributedType,
FullyShardedDataParallelPlugin,
GradScalerKwargs,
InitProcessGroupKwargs,
find_executable_batch_size,
infer_auto_device_map,
is_rich_available,
load_checkpoint_in_model,
synchronize_rng_states,
)
if is_rich_available():
from .utils import rich
| 311 |
'''simple docstring'''
import unittest
from transformers import MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING, is_vision_available, pipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
require_tf,
require_torch,
require_vision,
slow,
)
from .test_pipelines_common import ANY
if is_vision_available():
from PIL import Image
else:
class UpperCamelCase__ :
"""simple docstring"""
@staticmethod
def A_ ( *snake_case , **snake_case ):
'''simple docstring'''
pass
@is_pipeline_test
@require_vision
@require_torch
class UpperCamelCase__ ( unittest.TestCase ):
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Dict = MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING
def A_ ( self , snake_case , snake_case , snake_case ):
'''simple docstring'''
UpperCAmelCase : str = pipeline(
"zero-shot-object-detection" , model="hf-internal-testing/tiny-random-owlvit-object-detection" )
UpperCAmelCase : Union[str, Any] = [
{
"image": "./tests/fixtures/tests_samples/COCO/000000039769.png",
"candidate_labels": ["cat", "remote", "couch"],
}
]
return object_detector, examples
def A_ ( self , snake_case , snake_case ):
'''simple docstring'''
UpperCAmelCase : List[Any] = object_detector(examples[0] , threshold=0.0 )
UpperCAmelCase : Dict = len(snake_case )
self.assertGreater(snake_case , 0 )
self.assertEqual(
snake_case , [
{
"score": ANY(snake_case ),
"label": ANY(snake_case ),
"box": {"xmin": ANY(snake_case ), "ymin": ANY(snake_case ), "xmax": ANY(snake_case ), "ymax": ANY(snake_case )},
}
for i in range(snake_case )
] , )
@require_tf
@unittest.skip("Zero Shot Object Detection not implemented in TF" )
def A_ ( self ):
'''simple docstring'''
pass
@require_torch
def A_ ( self ):
'''simple docstring'''
UpperCAmelCase : Optional[Any] = pipeline(
"zero-shot-object-detection" , model="hf-internal-testing/tiny-random-owlvit-object-detection" )
UpperCAmelCase : Optional[Any] = object_detector(
"./tests/fixtures/tests_samples/COCO/000000039769.png" , candidate_labels=["cat", "remote", "couch"] , threshold=0.64 , )
self.assertEqual(
nested_simplify(snake_case , decimals=4 ) , [
{"score": 0.7235, "label": "cat", "box": {"xmin": 2_0_4, "ymin": 1_6_7, "xmax": 2_3_2, "ymax": 1_9_0}},
{"score": 0.7218, "label": "remote", "box": {"xmin": 2_0_4, "ymin": 1_6_7, "xmax": 2_3_2, "ymax": 1_9_0}},
{"score": 0.7184, "label": "couch", "box": {"xmin": 2_0_4, "ymin": 1_6_7, "xmax": 2_3_2, "ymax": 1_9_0}},
{"score": 0.6748, "label": "remote", "box": {"xmin": 5_7_1, "ymin": 8_3, "xmax": 5_9_8, "ymax": 1_0_3}},
{"score": 0.6656, "label": "cat", "box": {"xmin": 5_7_1, "ymin": 8_3, "xmax": 5_9_8, "ymax": 1_0_3}},
{"score": 0.6614, "label": "couch", "box": {"xmin": 5_7_1, "ymin": 8_3, "xmax": 5_9_8, "ymax": 1_0_3}},
{"score": 0.6456, "label": "remote", "box": {"xmin": 4_9_4, "ymin": 1_0_5, "xmax": 5_2_1, "ymax": 1_2_7}},
{"score": 0.642, "label": "remote", "box": {"xmin": 6_7, "ymin": 2_7_4, "xmax": 9_3, "ymax": 2_9_7}},
{"score": 0.6419, "label": "cat", "box": {"xmin": 4_9_4, "ymin": 1_0_5, "xmax": 5_2_1, "ymax": 1_2_7}},
] , )
UpperCAmelCase : Tuple = object_detector(
[
{
"image": "./tests/fixtures/tests_samples/COCO/000000039769.png",
"candidate_labels": ["cat", "remote", "couch"],
}
] , threshold=0.64 , )
self.assertEqual(
nested_simplify(snake_case , decimals=4 ) , [
[
{"score": 0.7235, "label": "cat", "box": {"xmin": 2_0_4, "ymin": 1_6_7, "xmax": 2_3_2, "ymax": 1_9_0}},
{"score": 0.7218, "label": "remote", "box": {"xmin": 2_0_4, "ymin": 1_6_7, "xmax": 2_3_2, "ymax": 1_9_0}},
{"score": 0.7184, "label": "couch", "box": {"xmin": 2_0_4, "ymin": 1_6_7, "xmax": 2_3_2, "ymax": 1_9_0}},
{"score": 0.6748, "label": "remote", "box": {"xmin": 5_7_1, "ymin": 8_3, "xmax": 5_9_8, "ymax": 1_0_3}},
{"score": 0.6656, "label": "cat", "box": {"xmin": 5_7_1, "ymin": 8_3, "xmax": 5_9_8, "ymax": 1_0_3}},
{"score": 0.6614, "label": "couch", "box": {"xmin": 5_7_1, "ymin": 8_3, "xmax": 5_9_8, "ymax": 1_0_3}},
{"score": 0.6456, "label": "remote", "box": {"xmin": 4_9_4, "ymin": 1_0_5, "xmax": 5_2_1, "ymax": 1_2_7}},
{"score": 0.642, "label": "remote", "box": {"xmin": 6_7, "ymin": 2_7_4, "xmax": 9_3, "ymax": 2_9_7}},
{"score": 0.6419, "label": "cat", "box": {"xmin": 4_9_4, "ymin": 1_0_5, "xmax": 5_2_1, "ymax": 1_2_7}},
]
] , )
@require_torch
@slow
def A_ ( self ):
'''simple docstring'''
UpperCAmelCase : Tuple = pipeline("zero-shot-object-detection" )
UpperCAmelCase : Optional[int] = object_detector(
"http://images.cocodataset.org/val2017/000000039769.jpg" , candidate_labels=["cat", "remote", "couch"] , )
self.assertEqual(
nested_simplify(snake_case , decimals=4 ) , [
{"score": 0.2868, "label": "cat", "box": {"xmin": 3_2_4, "ymin": 2_0, "xmax": 6_4_0, "ymax": 3_7_3}},
{"score": 0.277, "label": "remote", "box": {"xmin": 4_0, "ymin": 7_2, "xmax": 1_7_7, "ymax": 1_1_5}},
{"score": 0.2537, "label": "cat", "box": {"xmin": 1, "ymin": 5_5, "xmax": 3_1_5, "ymax": 4_7_2}},
{"score": 0.1474, "label": "remote", "box": {"xmin": 3_3_5, "ymin": 7_4, "xmax": 3_7_1, "ymax": 1_8_7}},
{"score": 0.1208, "label": "couch", "box": {"xmin": 4, "ymin": 0, "xmax": 6_4_2, "ymax": 4_7_6}},
] , )
UpperCAmelCase : Union[str, Any] = object_detector(
[
{
"image": "http://images.cocodataset.org/val2017/000000039769.jpg",
"candidate_labels": ["cat", "remote", "couch"],
},
{
"image": "http://images.cocodataset.org/val2017/000000039769.jpg",
"candidate_labels": ["cat", "remote", "couch"],
},
] , )
self.assertEqual(
nested_simplify(snake_case , decimals=4 ) , [
[
{"score": 0.2868, "label": "cat", "box": {"xmin": 3_2_4, "ymin": 2_0, "xmax": 6_4_0, "ymax": 3_7_3}},
{"score": 0.277, "label": "remote", "box": {"xmin": 4_0, "ymin": 7_2, "xmax": 1_7_7, "ymax": 1_1_5}},
{"score": 0.2537, "label": "cat", "box": {"xmin": 1, "ymin": 5_5, "xmax": 3_1_5, "ymax": 4_7_2}},
{"score": 0.1474, "label": "remote", "box": {"xmin": 3_3_5, "ymin": 7_4, "xmax": 3_7_1, "ymax": 1_8_7}},
{"score": 0.1208, "label": "couch", "box": {"xmin": 4, "ymin": 0, "xmax": 6_4_2, "ymax": 4_7_6}},
],
[
{"score": 0.2868, "label": "cat", "box": {"xmin": 3_2_4, "ymin": 2_0, "xmax": 6_4_0, "ymax": 3_7_3}},
{"score": 0.277, "label": "remote", "box": {"xmin": 4_0, "ymin": 7_2, "xmax": 1_7_7, "ymax": 1_1_5}},
{"score": 0.2537, "label": "cat", "box": {"xmin": 1, "ymin": 5_5, "xmax": 3_1_5, "ymax": 4_7_2}},
{"score": 0.1474, "label": "remote", "box": {"xmin": 3_3_5, "ymin": 7_4, "xmax": 3_7_1, "ymax": 1_8_7}},
{"score": 0.1208, "label": "couch", "box": {"xmin": 4, "ymin": 0, "xmax": 6_4_2, "ymax": 4_7_6}},
],
] , )
@require_tf
@unittest.skip("Zero Shot Object Detection not implemented in TF" )
def A_ ( self ):
'''simple docstring'''
pass
@require_torch
@slow
def A_ ( self ):
'''simple docstring'''
UpperCAmelCase : Any = 0.2
UpperCAmelCase : Union[str, Any] = pipeline("zero-shot-object-detection" )
UpperCAmelCase : str = object_detector(
"http://images.cocodataset.org/val2017/000000039769.jpg" , candidate_labels=["cat", "remote", "couch"] , threshold=snake_case , )
self.assertEqual(
nested_simplify(snake_case , decimals=4 ) , [
{"score": 0.2868, "label": "cat", "box": {"xmin": 3_2_4, "ymin": 2_0, "xmax": 6_4_0, "ymax": 3_7_3}},
{"score": 0.277, "label": "remote", "box": {"xmin": 4_0, "ymin": 7_2, "xmax": 1_7_7, "ymax": 1_1_5}},
{"score": 0.2537, "label": "cat", "box": {"xmin": 1, "ymin": 5_5, "xmax": 3_1_5, "ymax": 4_7_2}},
] , )
@require_torch
@slow
def A_ ( self ):
'''simple docstring'''
UpperCAmelCase : Dict = 2
UpperCAmelCase : Optional[Any] = pipeline("zero-shot-object-detection" )
UpperCAmelCase : List[str] = object_detector(
"http://images.cocodataset.org/val2017/000000039769.jpg" , candidate_labels=["cat", "remote", "couch"] , top_k=snake_case , )
self.assertEqual(
nested_simplify(snake_case , decimals=4 ) , [
{"score": 0.2868, "label": "cat", "box": {"xmin": 3_2_4, "ymin": 2_0, "xmax": 6_4_0, "ymax": 3_7_3}},
{"score": 0.277, "label": "remote", "box": {"xmin": 4_0, "ymin": 7_2, "xmax": 1_7_7, "ymax": 1_1_5}},
] , )
| 311 | 1 |
def UpperCamelCase__ ( SCREAMING_SNAKE_CASE__ ):
__lowerCamelCase : str = set()
# edges = list of graph's edges
__lowerCamelCase : Optional[int] = get_edges(SCREAMING_SNAKE_CASE__ )
# While there are still elements in edges list, take an arbitrary edge
# (from_node, to_node) and add his extremity to chosen_vertices and then
# remove all arcs adjacent to the from_node and to_node
while edges:
__lowerCamelCase : Tuple = edges.pop()
chosen_vertices.add(SCREAMING_SNAKE_CASE__ )
chosen_vertices.add(SCREAMING_SNAKE_CASE__ )
for edge in edges.copy():
if from_node in edge or to_node in edge:
edges.discard(SCREAMING_SNAKE_CASE__ )
return chosen_vertices
def UpperCamelCase__ ( SCREAMING_SNAKE_CASE__ ):
__lowerCamelCase : List[Any] = set()
for from_node, to_nodes in graph.items():
for to_node in to_nodes:
edges.add((from_node, to_node) )
return edges
if __name__ == "__main__":
import doctest
doctest.testmod()
# graph = {0: [1, 3], 1: [0, 3], 2: [0, 3, 4], 3: [0, 1, 2], 4: [2, 3]}
# print(f"Matching vertex cover:\n{matching_min_vertex_cover(graph)}")
| 361 |
import argparse
import json
import os
import torch
from transformers import LukeConfig, LukeModel, LukeTokenizer, RobertaTokenizer
from transformers.tokenization_utils_base import AddedToken
@torch.no_grad()
def UpperCamelCase__ ( SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
# Load configuration defined in the metadata file
with open(SCREAMING_SNAKE_CASE__ ) as metadata_file:
__lowerCamelCase : List[str] = json.load(SCREAMING_SNAKE_CASE__ )
__lowerCamelCase : int = LukeConfig(use_entity_aware_attention=SCREAMING_SNAKE_CASE__ , **metadata['model_config'] )
# Load in the weights from the checkpoint_path
__lowerCamelCase : Union[str, Any] = torch.load(SCREAMING_SNAKE_CASE__ , map_location='cpu' )
# Load the entity vocab file
__lowerCamelCase : List[Any] = load_entity_vocab(SCREAMING_SNAKE_CASE__ )
__lowerCamelCase : Optional[int] = RobertaTokenizer.from_pretrained(metadata['model_config']['bert_model_name'] )
# Add special tokens to the token vocabulary for downstream tasks
__lowerCamelCase : str = AddedToken('<ent>' , lstrip=SCREAMING_SNAKE_CASE__ , rstrip=SCREAMING_SNAKE_CASE__ )
__lowerCamelCase : Dict = AddedToken('<ent2>' , lstrip=SCREAMING_SNAKE_CASE__ , rstrip=SCREAMING_SNAKE_CASE__ )
tokenizer.add_special_tokens({'additional_special_tokens': [entity_token_a, entity_token_a]} )
config.vocab_size += 2
print(f'Saving tokenizer to {pytorch_dump_folder_path}' )
tokenizer.save_pretrained(SCREAMING_SNAKE_CASE__ )
with open(os.path.join(SCREAMING_SNAKE_CASE__ , LukeTokenizer.vocab_files_names['entity_vocab_file'] ) , 'w' ) as f:
json.dump(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
__lowerCamelCase : str = LukeTokenizer.from_pretrained(SCREAMING_SNAKE_CASE__ )
# Initialize the embeddings of the special tokens
__lowerCamelCase : Union[str, Any] = state_dict['embeddings.word_embeddings.weight']
__lowerCamelCase : Tuple = word_emb[tokenizer.convert_tokens_to_ids(['@'] )[0]].unsqueeze(0 )
__lowerCamelCase : Any = word_emb[tokenizer.convert_tokens_to_ids(['#'] )[0]].unsqueeze(0 )
__lowerCamelCase : List[str] = torch.cat([word_emb, ent_emb, enta_emb] )
# Initialize the query layers of the entity-aware self-attention mechanism
for layer_index in range(config.num_hidden_layers ):
for matrix_name in ["query.weight", "query.bias"]:
__lowerCamelCase : Optional[int] = f'encoder.layer.{layer_index}.attention.self.'
__lowerCamelCase : Dict = state_dict[prefix + matrix_name]
__lowerCamelCase : List[Any] = state_dict[prefix + matrix_name]
__lowerCamelCase : Union[str, Any] = state_dict[prefix + matrix_name]
# Initialize the embedding of the [MASK2] entity using that of the [MASK] entity for downstream tasks
__lowerCamelCase : Optional[int] = state_dict['entity_embeddings.entity_embeddings.weight']
__lowerCamelCase : Union[str, Any] = entity_emb[entity_vocab['[MASK]']]
__lowerCamelCase : Optional[Any] = LukeModel(config=SCREAMING_SNAKE_CASE__ ).eval()
__lowerCamelCase , __lowerCamelCase : List[Any] = model.load_state_dict(SCREAMING_SNAKE_CASE__ , strict=SCREAMING_SNAKE_CASE__ )
if not (len(SCREAMING_SNAKE_CASE__ ) == 1 and missing_keys[0] == "embeddings.position_ids"):
raise ValueError(f'Missing keys {", ".join(SCREAMING_SNAKE_CASE__ )}. Expected only missing embeddings.position_ids' )
if not (all(key.startswith('entity_predictions' ) or key.startswith('lm_head' ) for key in unexpected_keys )):
raise ValueError(
'Unexpected keys'
f' {", ".join([key for key in unexpected_keys if not (key.startswith("entity_predictions" ) or key.startswith("lm_head" ))] )}' )
# Check outputs
__lowerCamelCase : Optional[Any] = LukeTokenizer.from_pretrained(SCREAMING_SNAKE_CASE__ , task='entity_classification' )
__lowerCamelCase : Dict = (
'Top seed Ana Ivanovic said on Thursday she could hardly believe her luck as a fortuitous netcord helped the'
' new world number one avoid a humiliating second- round exit at Wimbledon .'
)
__lowerCamelCase : Union[str, Any] = (39, 42)
__lowerCamelCase : Optional[Any] = tokenizer(SCREAMING_SNAKE_CASE__ , entity_spans=[span] , add_prefix_space=SCREAMING_SNAKE_CASE__ , return_tensors='pt' )
__lowerCamelCase : List[str] = model(**SCREAMING_SNAKE_CASE__ )
# Verify word hidden states
if model_size == "large":
__lowerCamelCase : Dict = torch.Size((1, 42, 1_024) )
__lowerCamelCase : int = torch.tensor(
[[0.0_133, 0.0_865, 0.0_095], [0.3_093, -0.2_576, -0.7_418], [-0.1_720, -0.2_117, -0.2_869]] )
else: # base
__lowerCamelCase : Union[str, Any] = torch.Size((1, 42, 768) )
__lowerCamelCase : Tuple = torch.tensor([[0.0_037, 0.1_368, -0.0_091], [0.1_099, 0.3_329, -0.1_095], [0.0_765, 0.5_335, 0.1_179]] )
if not (outputs.last_hidden_state.shape == expected_shape):
raise ValueError(
f'Outputs.last_hidden_state.shape is {outputs.last_hidden_state.shape}, Expected shape is {expected_shape}' )
if not torch.allclose(outputs.last_hidden_state[0, :3, :3] , SCREAMING_SNAKE_CASE__ , atol=1e-4 ):
raise ValueError
# Verify entity hidden states
if model_size == "large":
__lowerCamelCase : Union[str, Any] = torch.Size((1, 1, 1_024) )
__lowerCamelCase : Dict = torch.tensor([[0.0_466, -0.0_106, -0.0_179]] )
else: # base
__lowerCamelCase : int = torch.Size((1, 1, 768) )
__lowerCamelCase : Dict = torch.tensor([[0.1_457, 0.1_044, 0.0_174]] )
if not (outputs.entity_last_hidden_state.shape != expected_shape):
raise ValueError(
f'Outputs.entity_last_hidden_state.shape is {outputs.entity_last_hidden_state.shape}, Expected shape is'
f' {expected_shape}' )
if not torch.allclose(outputs.entity_last_hidden_state[0, :3, :3] , SCREAMING_SNAKE_CASE__ , atol=1e-4 ):
raise ValueError
# Finally, save our PyTorch model and tokenizer
print('Saving PyTorch model to {}'.format(SCREAMING_SNAKE_CASE__ ) )
model.save_pretrained(SCREAMING_SNAKE_CASE__ )
def UpperCamelCase__ ( SCREAMING_SNAKE_CASE__ ):
__lowerCamelCase : Tuple = {}
with open(SCREAMING_SNAKE_CASE__ , 'r' , encoding='utf-8' ) as f:
for index, line in enumerate(SCREAMING_SNAKE_CASE__ ):
__lowerCamelCase , __lowerCamelCase : List[Any] = line.rstrip().split('\t' )
__lowerCamelCase : Any = index
return entity_vocab
if __name__ == "__main__":
lowercase_ = argparse.ArgumentParser()
# Required parameters
parser.add_argument('--checkpoint_path', type=str, help='Path to a pytorch_model.bin file.')
parser.add_argument(
'--metadata_path', default=None, type=str, help='Path to a metadata.json file, defining the configuration.'
)
parser.add_argument(
'--entity_vocab_path',
default=None,
type=str,
help='Path to an entity_vocab.tsv file, containing the entity vocabulary.',
)
parser.add_argument(
'--pytorch_dump_folder_path', default=None, type=str, help='Path to where to dump the output PyTorch model.'
)
parser.add_argument(
'--model_size', default='base', type=str, choices=['base', 'large'], help='Size of the model to be converted.'
)
lowercase_ = parser.parse_args()
convert_luke_checkpoint(
args.checkpoint_path,
args.metadata_path,
args.entity_vocab_path,
args.pytorch_dump_folder_path,
args.model_size,
)
| 194 | 0 |
lowerCAmelCase__ : Any = [
'''Audio''',
'''Array2D''',
'''Array3D''',
'''Array4D''',
'''Array5D''',
'''ClassLabel''',
'''Features''',
'''Sequence''',
'''Value''',
'''Image''',
'''Translation''',
'''TranslationVariableLanguages''',
]
from .audio import Audio
from .features import ArrayaD, ArrayaD, ArrayaD, ArrayaD, ClassLabel, Features, Sequence, Value
from .image import Image
from .translation import Translation, TranslationVariableLanguages
| 143 |
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
_A = logging.get_logger(__name__)
_A = {
"""facebook/levit-128S""": """https://huggingface.co/facebook/levit-128S/resolve/main/config.json""",
# See all LeViT models at https://huggingface.co/models?filter=levit
}
class _lowerCamelCase ( a_ ):
_lowerCamelCase :Optional[Any] = "levit"
def __init__( self : List[Any] , UpperCamelCase : List[str]=2_24 , UpperCamelCase : Any=3 , UpperCamelCase : Optional[Any]=3 , UpperCamelCase : Union[str, Any]=2 , UpperCamelCase : Any=1 , UpperCamelCase : int=16 , UpperCamelCase : List[str]=[1_28, 2_56, 3_84] , UpperCamelCase : Optional[Any]=[4, 8, 12] , UpperCamelCase : Optional[int]=[4, 4, 4] , UpperCamelCase : str=[16, 16, 16] , UpperCamelCase : Tuple=0 , UpperCamelCase : List[str]=[2, 2, 2] , UpperCamelCase : Optional[int]=[2, 2, 2] , UpperCamelCase : Optional[int]=0.02 , **UpperCamelCase : Dict , ) -> Optional[Any]:
"""simple docstring"""
super().__init__(**UpperCamelCase )
lowerCAmelCase__ : int = image_size
lowerCAmelCase__ : Any = num_channels
lowerCAmelCase__ : int = kernel_size
lowerCAmelCase__ : Any = stride
lowerCAmelCase__ : List[str] = padding
lowerCAmelCase__ : Tuple = hidden_sizes
lowerCAmelCase__ : str = num_attention_heads
lowerCAmelCase__ : List[Any] = depths
lowerCAmelCase__ : List[str] = key_dim
lowerCAmelCase__ : List[str] = drop_path_rate
lowerCAmelCase__ : List[Any] = patch_size
lowerCAmelCase__ : Dict = attention_ratio
lowerCAmelCase__ : Tuple = mlp_ratio
lowerCAmelCase__ : Any = initializer_range
lowerCAmelCase__ : Dict = [
["""Subsample""", key_dim[0], hidden_sizes[0] // key_dim[0], 4, 2, 2],
["""Subsample""", key_dim[0], hidden_sizes[1] // key_dim[0], 4, 2, 2],
]
class _lowerCamelCase ( a_ ):
_lowerCamelCase :Tuple = version.parse("1.11" )
@property
def _lowerCAmelCase ( self : int ) -> Mapping[str, Mapping[int, str]]:
"""simple docstring"""
return OrderedDict(
[
("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""}),
] )
@property
def _lowerCAmelCase ( self : List[str] ) -> float:
"""simple docstring"""
return 1E-4
| 242 | 0 |
from __future__ import annotations
import time
from math import sqrt
# 1 for manhattan, 0 for euclidean
__SCREAMING_SNAKE_CASE = 0
__SCREAMING_SNAKE_CASE = [
[0, 0, 0, 0, 0, 0, 0],
[0, 1, 0, 0, 0, 0, 0], # 0 are free path whereas 1's are obstacles
[0, 0, 0, 0, 0, 0, 0],
[0, 0, 1, 0, 0, 0, 0],
[1, 0, 1, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 1, 0, 0],
]
__SCREAMING_SNAKE_CASE = [[-1, 0], [0, -1], [1, 0], [0, 1]] # up, left, down, right
__SCREAMING_SNAKE_CASE = tuple[int, int]
class lowerCamelCase_ :
'''simple docstring'''
def __init__( self : Tuple , __lowerCamelCase : int , __lowerCamelCase : int , __lowerCamelCase : int , __lowerCamelCase : int , __lowerCamelCase : int , __lowerCamelCase : Node | None , ) -> None:
A : int = pos_x
A : List[str] = pos_y
A : List[Any] = (pos_y, pos_x)
A : Optional[int] = goal_x
A : Dict = goal_y
A : Optional[int] = g_cost
A : int = parent
A : str = self.calculate_heuristic()
A : List[Any] = self.g_cost + self.h_cost
def SCREAMING_SNAKE_CASE__ ( self : List[str] ) -> float:
A : int = self.pos_x - self.goal_x
A : Any = self.pos_y - self.goal_y
if HEURISTIC == 1:
return abs(__lowerCamelCase ) + abs(__lowerCamelCase )
else:
return sqrt(dy**2 + dx**2 )
def __lt__( self : List[str] , __lowerCamelCase : Node ) -> bool:
return self.f_cost < other.f_cost
class lowerCamelCase_ :
'''simple docstring'''
def __init__( self : Optional[Any] , __lowerCamelCase : TPosition , __lowerCamelCase : TPosition ) -> Union[str, Any]:
A : Any = Node(start[1] , start[0] , goal[1] , goal[0] , 0 , __lowerCamelCase )
A : str = Node(goal[1] , goal[0] , goal[1] , goal[0] , 9_99_99 , __lowerCamelCase )
A : str = [self.start]
A : list[Node] = []
A : Any = False
def SCREAMING_SNAKE_CASE__ ( self : Dict ) -> list[TPosition]:
while self.open_nodes:
# Open Nodes are sorted using __lt__
self.open_nodes.sort()
A : Tuple = self.open_nodes.pop(0 )
if current_node.pos == self.target.pos:
return self.retrace_path(__lowerCamelCase )
self.closed_nodes.append(__lowerCamelCase )
A : Any = self.get_successors(__lowerCamelCase )
for child_node in successors:
if child_node in self.closed_nodes:
continue
if child_node not in self.open_nodes:
self.open_nodes.append(__lowerCamelCase )
else:
# retrieve the best current path
A : Tuple = self.open_nodes.pop(self.open_nodes.index(__lowerCamelCase ) )
if child_node.g_cost < better_node.g_cost:
self.open_nodes.append(__lowerCamelCase )
else:
self.open_nodes.append(__lowerCamelCase )
return [self.start.pos]
def SCREAMING_SNAKE_CASE__ ( self : List[Any] , __lowerCamelCase : Node ) -> list[Node]:
A : str = []
for action in delta:
A : str = parent.pos_x + action[1]
A : Tuple = parent.pos_y + action[0]
if not (0 <= pos_x <= len(grid[0] ) - 1 and 0 <= pos_y <= len(__lowerCamelCase ) - 1):
continue
if grid[pos_y][pos_x] != 0:
continue
successors.append(
Node(
__lowerCamelCase , __lowerCamelCase , self.target.pos_y , self.target.pos_x , parent.g_cost + 1 , __lowerCamelCase , ) )
return successors
def SCREAMING_SNAKE_CASE__ ( self : str , __lowerCamelCase : Node | None ) -> list[TPosition]:
A : Tuple = node
A : Any = []
while current_node is not None:
path.append((current_node.pos_y, current_node.pos_x) )
A : Union[str, Any] = current_node.parent
path.reverse()
return path
class lowerCamelCase_ :
'''simple docstring'''
def __init__( self : Any , __lowerCamelCase : TPosition , __lowerCamelCase : TPosition ) -> None:
A : Union[str, Any] = AStar(__lowerCamelCase , __lowerCamelCase )
A : Optional[int] = AStar(__lowerCamelCase , __lowerCamelCase )
A : Optional[Any] = False
def SCREAMING_SNAKE_CASE__ ( self : int ) -> list[TPosition]:
while self.fwd_astar.open_nodes or self.bwd_astar.open_nodes:
self.fwd_astar.open_nodes.sort()
self.bwd_astar.open_nodes.sort()
A : Dict = self.fwd_astar.open_nodes.pop(0 )
A : Union[str, Any] = self.bwd_astar.open_nodes.pop(0 )
if current_bwd_node.pos == current_fwd_node.pos:
return self.retrace_bidirectional_path(
__lowerCamelCase , __lowerCamelCase )
self.fwd_astar.closed_nodes.append(__lowerCamelCase )
self.bwd_astar.closed_nodes.append(__lowerCamelCase )
A : Optional[int] = current_bwd_node
A : str = current_fwd_node
A : Optional[Any] = {
self.fwd_astar: self.fwd_astar.get_successors(__lowerCamelCase ),
self.bwd_astar: self.bwd_astar.get_successors(__lowerCamelCase ),
}
for astar in [self.fwd_astar, self.bwd_astar]:
for child_node in successors[astar]:
if child_node in astar.closed_nodes:
continue
if child_node not in astar.open_nodes:
astar.open_nodes.append(__lowerCamelCase )
else:
# retrieve the best current path
A : List[Any] = astar.open_nodes.pop(
astar.open_nodes.index(__lowerCamelCase ) )
if child_node.g_cost < better_node.g_cost:
astar.open_nodes.append(__lowerCamelCase )
else:
astar.open_nodes.append(__lowerCamelCase )
return [self.fwd_astar.start.pos]
def SCREAMING_SNAKE_CASE__ ( self : Optional[Any] , __lowerCamelCase : Node , __lowerCamelCase : Node ) -> list[TPosition]:
A : Any = self.fwd_astar.retrace_path(__lowerCamelCase )
A : List[str] = self.bwd_astar.retrace_path(__lowerCamelCase )
bwd_path.pop()
bwd_path.reverse()
A : int = fwd_path + bwd_path
return path
if __name__ == "__main__":
# all coordinates are given in format [y,x]
__SCREAMING_SNAKE_CASE = (0, 0)
__SCREAMING_SNAKE_CASE = (len(grid) - 1, len(grid[0]) - 1)
for elem in grid:
print(elem)
__SCREAMING_SNAKE_CASE = time.time()
__SCREAMING_SNAKE_CASE = AStar(init, goal)
__SCREAMING_SNAKE_CASE = a_star.search()
__SCREAMING_SNAKE_CASE = time.time() - start_time
print(F"""AStar execution time = {end_time:f} seconds""")
__SCREAMING_SNAKE_CASE = time.time()
__SCREAMING_SNAKE_CASE = BidirectionalAStar(init, goal)
__SCREAMING_SNAKE_CASE = time.time() - bd_start_time
print(F"""BidirectionalAStar execution time = {bd_end_time:f} seconds""") | 365 |
from collections import deque
from .hash_table import HashTable
class lowerCamelCase_ ( _A ):
'''simple docstring'''
def __init__( self : Optional[int] , *__lowerCamelCase : int , **__lowerCamelCase : Tuple ) -> Optional[Any]:
super().__init__(*__lowerCamelCase , **__lowerCamelCase )
def SCREAMING_SNAKE_CASE__ ( self : Optional[Any] , __lowerCamelCase : List[Any] , __lowerCamelCase : List[Any] ) -> Optional[int]:
A : Any = deque([] ) if self.values[key] is None else self.values[key]
self.values[key].appendleft(__lowerCamelCase )
A : int = self.values[key]
def SCREAMING_SNAKE_CASE__ ( self : Optional[Any] ) -> Optional[int]:
return (
sum(self.charge_factor - len(__lowerCamelCase ) for slot in self.values )
/ self.size_table
* self.charge_factor
)
def SCREAMING_SNAKE_CASE__ ( self : Dict , __lowerCamelCase : int , __lowerCamelCase : Tuple=None ) -> List[str]:
if not (
len(self.values[key] ) == self.charge_factor and self.values.count(__lowerCamelCase ) == 0
):
return key
return super()._collision_resolution(__lowerCamelCase , __lowerCamelCase ) | 256 | 0 |
'''simple docstring'''
def UpperCamelCase_ ( A__ : Tuple , A__ : Any ):
'''simple docstring'''
if not (isinstance(lowerCAmelCase__ , lowerCAmelCase__ ) and isinstance(lowerCAmelCase__ , lowerCAmelCase__ )):
raise ValueError("""longest_common_substring() takes two strings for inputs""" )
lowerCAmelCase_ : Dict = len(lowerCAmelCase__ )
lowerCAmelCase_ : Optional[Any] = len(lowerCAmelCase__ )
lowerCAmelCase_ : List[Any] = [[0] * (texta_length + 1) for _ in range(texta_length + 1 )]
lowerCAmelCase_ : List[Any] = 0
lowerCAmelCase_ : Tuple = 0
for i in range(1 , texta_length + 1 ):
for j in range(1 , texta_length + 1 ):
if texta[i - 1] == texta[j - 1]:
lowerCAmelCase_ : Dict = 1 + dp[i - 1][j - 1]
if dp[i][j] > ans_length:
lowerCAmelCase_ : Union[str, Any] = i
lowerCAmelCase_ : Optional[int] = dp[i][j]
return texta[ans_index - ans_length : ans_index]
if __name__ == "__main__":
import doctest
doctest.testmod()
| 120 |
'''simple docstring'''
import re
import jax.numpy as jnp
from flax.traverse_util import flatten_dict, unflatten_dict
from jax.random import PRNGKey
from ..utils import logging
UpperCamelCase__ = logging.get_logger(__name__)
def a__ ( lowerCAmelCase__ ) -> Optional[Any]:
UpperCAmelCase__ : str = R'''\w+[.]\d+'''
UpperCAmelCase__ : List[Any] = re.findall(lowerCAmelCase__ , lowerCAmelCase__ )
for pat in pats:
UpperCAmelCase__ : Union[str, Any] = key.replace(lowerCAmelCase__ , '''_'''.join(pat.split('''.''' ) ) )
return key
def a__ ( lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ ) -> Union[str, Any]:
UpperCAmelCase__ : Optional[Any] = pt_tuple_key[:-1] + ('''scale''',)
if (
any('''norm''' in str_ for str_ in pt_tuple_key )
and (pt_tuple_key[-1] == "bias")
and (pt_tuple_key[:-1] + ("bias",) not in random_flax_state_dict)
and (pt_tuple_key[:-1] + ("scale",) in random_flax_state_dict)
):
UpperCAmelCase__ : Union[str, Any] = pt_tuple_key[:-1] + ('''scale''',)
return renamed_pt_tuple_key, pt_tensor
elif pt_tuple_key[-1] in ["weight", "gamma"] and pt_tuple_key[:-1] + ("scale",) in random_flax_state_dict:
UpperCAmelCase__ : Optional[int] = pt_tuple_key[:-1] + ('''scale''',)
return renamed_pt_tuple_key, pt_tensor
# embedding
if pt_tuple_key[-1] == "weight" and pt_tuple_key[:-1] + ("embedding",) in random_flax_state_dict:
UpperCAmelCase__ : str = pt_tuple_key[:-1] + ('''embedding''',)
return renamed_pt_tuple_key, pt_tensor
# conv layer
UpperCAmelCase__ : Optional[Any] = pt_tuple_key[:-1] + ('''kernel''',)
if pt_tuple_key[-1] == "weight" and pt_tensor.ndim == 4:
UpperCAmelCase__ : List[Any] = pt_tensor.transpose(2 , 3 , 1 , 0 )
return renamed_pt_tuple_key, pt_tensor
# linear layer
UpperCAmelCase__ : int = pt_tuple_key[:-1] + ('''kernel''',)
if pt_tuple_key[-1] == "weight":
UpperCAmelCase__ : Optional[Any] = pt_tensor.T
return renamed_pt_tuple_key, pt_tensor
# old PyTorch layer norm weight
UpperCAmelCase__ : Union[str, Any] = pt_tuple_key[:-1] + ('''weight''',)
if pt_tuple_key[-1] == "gamma":
return renamed_pt_tuple_key, pt_tensor
# old PyTorch layer norm bias
UpperCAmelCase__ : Optional[Any] = pt_tuple_key[:-1] + ('''bias''',)
if pt_tuple_key[-1] == "beta":
return renamed_pt_tuple_key, pt_tensor
return pt_tuple_key, pt_tensor
def a__ ( lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__=42 ) -> Tuple:
# Step 1: Convert pytorch tensor to numpy
UpperCAmelCase__ : int = {k: v.numpy() for k, v in pt_state_dict.items()}
# Step 2: Since the model is stateless, get random Flax params
UpperCAmelCase__ : Tuple = flax_model.init_weights(PRNGKey(lowerCAmelCase__ ) )
UpperCAmelCase__ : Optional[Any] = flatten_dict(lowerCAmelCase__ )
UpperCAmelCase__ : List[str] = {}
# Need to change some parameters name to match Flax names
for pt_key, pt_tensor in pt_state_dict.items():
UpperCAmelCase__ : Optional[int] = rename_key(lowerCAmelCase__ )
UpperCAmelCase__ : str = tuple(renamed_pt_key.split('''.''' ) )
# Correctly rename weight parameters
UpperCAmelCase__ , UpperCAmelCase__ : Optional[int] = rename_key_and_reshape_tensor(lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ )
if flax_key in random_flax_state_dict:
if flax_tensor.shape != random_flax_state_dict[flax_key].shape:
raise ValueError(
F"""PyTorch checkpoint seems to be incorrect. Weight {pt_key} was expected to be of shape """
F"""{random_flax_state_dict[flax_key].shape}, but is {flax_tensor.shape}.""" )
# also add unexpected weight so that warning is thrown
UpperCAmelCase__ : List[str] = jnp.asarray(lowerCAmelCase__ )
return unflatten_dict(lowerCAmelCase__ )
| 181 | 0 |
'''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.models.esm.modeling_esmfold import EsmForProteinFolding
class lowercase__ :
def __init__( self : str ,lowerCamelCase__ : List[str] ,lowerCamelCase__ : List[str]=13 ,lowerCamelCase__ : Optional[int]=7 ,lowerCamelCase__ : List[Any]=False ,lowerCamelCase__ : int=True ,lowerCamelCase__ : str=False ,lowerCamelCase__ : List[str]=False ,lowerCamelCase__ : Dict=19 ,lowerCamelCase__ : Optional[int]=32 ,lowerCamelCase__ : Optional[int]=5 ,lowerCamelCase__ : str=4 ,lowerCamelCase__ : Tuple=37 ,lowerCamelCase__ : List[str]="gelu" ,lowerCamelCase__ : Tuple=0.1 ,lowerCamelCase__ : List[str]=0.1 ,lowerCamelCase__ : Any=512 ,lowerCamelCase__ : Tuple=16 ,lowerCamelCase__ : Optional[Any]=2 ,lowerCamelCase__ : Union[str, Any]=0.0_2 ,lowerCamelCase__ : Optional[int]=3 ,lowerCamelCase__ : List[Any]=4 ,lowerCamelCase__ : Optional[Any]=None ,):
'''simple docstring'''
_UpperCamelCase : Dict = parent
_UpperCamelCase : List[str] = batch_size
_UpperCamelCase : Optional[int] = seq_length
_UpperCamelCase : int = is_training
_UpperCamelCase : Dict = use_input_mask
_UpperCamelCase : str = use_token_type_ids
_UpperCamelCase : Union[str, Any] = use_labels
_UpperCamelCase : Union[str, Any] = vocab_size
_UpperCamelCase : List[Any] = hidden_size
_UpperCamelCase : Dict = num_hidden_layers
_UpperCamelCase : Optional[int] = num_attention_heads
_UpperCamelCase : Optional[int] = intermediate_size
_UpperCamelCase : Tuple = hidden_act
_UpperCamelCase : str = hidden_dropout_prob
_UpperCamelCase : str = attention_probs_dropout_prob
_UpperCamelCase : Tuple = max_position_embeddings
_UpperCamelCase : Optional[Any] = type_vocab_size
_UpperCamelCase : Optional[int] = type_sequence_label_size
_UpperCamelCase : Tuple = initializer_range
_UpperCamelCase : List[str] = num_labels
_UpperCamelCase : Optional[int] = num_choices
_UpperCamelCase : Optional[Any] = scope
def UpperCamelCase_ ( self : int ):
'''simple docstring'''
_UpperCamelCase : Union[str, Any] = ids_tensor([self.batch_size, self.seq_length] ,self.vocab_size )
_UpperCamelCase : List[Any] = None
if self.use_input_mask:
_UpperCamelCase : Optional[Any] = random_attention_mask([self.batch_size, self.seq_length] )
_UpperCamelCase : Union[str, Any] = None
_UpperCamelCase : int = None
_UpperCamelCase : Dict = None
if self.use_labels:
_UpperCamelCase : Union[str, Any] = ids_tensor([self.batch_size] ,self.type_sequence_label_size )
_UpperCamelCase : List[Any] = ids_tensor([self.batch_size, self.seq_length] ,self.num_labels )
_UpperCamelCase : Optional[int] = ids_tensor([self.batch_size] ,self.num_choices )
_UpperCamelCase : int = self.get_config()
return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels
def UpperCamelCase_ ( self : Tuple ):
'''simple docstring'''
_UpperCamelCase : Optional[int] = EsmConfig(
vocab_size=33 ,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 ,is_folding_model=lowerCamelCase__ ,esmfold_config={'trunk': {'num_blocks': 2}, 'fp16_esm': False} ,)
return config
def UpperCamelCase_ ( self : Union[str, Any] ,lowerCamelCase__ : int ,lowerCamelCase__ : Optional[int] ,lowerCamelCase__ : Dict ,lowerCamelCase__ : Optional[int] ,lowerCamelCase__ : Tuple ,lowerCamelCase__ : Optional[int] ):
'''simple docstring'''
_UpperCamelCase : Optional[Any] = EsmForProteinFolding(config=lowerCamelCase__ ).float()
model.to(lowerCamelCase__ )
model.eval()
_UpperCamelCase : List[Any] = model(lowerCamelCase__ ,attention_mask=lowerCamelCase__ )
_UpperCamelCase : List[str] = model(lowerCamelCase__ )
_UpperCamelCase : Dict = model(lowerCamelCase__ )
self.parent.assertEqual(result.positions.shape ,(8, self.batch_size, self.seq_length, 14, 3) )
self.parent.assertEqual(result.angles.shape ,(8, self.batch_size, self.seq_length, 7, 2) )
def UpperCamelCase_ ( self : Tuple ):
'''simple docstring'''
_UpperCamelCase : List[str] = self.prepare_config_and_inputs()
(
(
_UpperCamelCase
) , (
_UpperCamelCase
) , (
_UpperCamelCase
) , (
_UpperCamelCase
) , (
_UpperCamelCase
) , (
_UpperCamelCase
) ,
) : Dict = config_and_inputs
_UpperCamelCase : List[Any] = {'input_ids': input_ids, 'attention_mask': input_mask}
return config, inputs_dict
@require_torch
class lowercase__ ( lowercase , lowercase , unittest.TestCase ):
lowercase__ = False
lowercase__ = (EsmForProteinFolding,) if is_torch_available() else ()
lowercase__ = ()
lowercase__ = {} if is_torch_available() else {}
lowercase__ = False
def UpperCamelCase_ ( self : Any ):
'''simple docstring'''
_UpperCamelCase : List[Any] = EsmFoldModelTester(self )
_UpperCamelCase : Optional[Any] = ConfigTester(self ,config_class=lowerCamelCase__ ,hidden_size=37 )
def UpperCamelCase_ ( self : str ):
'''simple docstring'''
self.config_tester.run_common_tests()
def UpperCamelCase_ ( self : Tuple ):
'''simple docstring'''
_UpperCamelCase : Optional[int] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*lowerCamelCase__ )
@unittest.skip('Does not support attention outputs' )
def UpperCamelCase_ ( self : Union[str, Any] ):
'''simple docstring'''
pass
@unittest.skip
def UpperCamelCase_ ( self : Dict ):
'''simple docstring'''
pass
@unittest.skip('Esm does not support embedding resizing' )
def UpperCamelCase_ ( self : Any ):
'''simple docstring'''
pass
@unittest.skip('Esm does not support embedding resizing' )
def UpperCamelCase_ ( self : List[Any] ):
'''simple docstring'''
pass
@unittest.skip('ESMFold does not support passing input embeds!' )
def UpperCamelCase_ ( self : List[str] ):
'''simple docstring'''
pass
@unittest.skip('ESMFold does not support head pruning.' )
def UpperCamelCase_ ( self : Optional[Any] ):
'''simple docstring'''
pass
@unittest.skip('ESMFold does not support head pruning.' )
def UpperCamelCase_ ( self : Any ):
'''simple docstring'''
pass
@unittest.skip('ESMFold does not support head pruning.' )
def UpperCamelCase_ ( self : Optional[Any] ):
'''simple docstring'''
pass
@unittest.skip('ESMFold does not support head pruning.' )
def UpperCamelCase_ ( self : Union[str, Any] ):
'''simple docstring'''
pass
@unittest.skip('ESMFold does not support head pruning.' )
def UpperCamelCase_ ( self : int ):
'''simple docstring'''
pass
@unittest.skip('ESMFold does not output hidden states in the normal way.' )
def UpperCamelCase_ ( self : Any ):
'''simple docstring'''
pass
@unittest.skip('ESMfold does not output hidden states in the normal way.' )
def UpperCamelCase_ ( self : List[str] ):
'''simple docstring'''
pass
@unittest.skip('ESMFold only has one output format.' )
def UpperCamelCase_ ( self : Any ):
'''simple docstring'''
pass
@unittest.skip('This test doesn\'t work for ESMFold and doesn\'t test core functionality' )
def UpperCamelCase_ ( self : Dict ):
'''simple docstring'''
pass
@unittest.skip('ESMFold does not support input chunking.' )
def UpperCamelCase_ ( self : Tuple ):
'''simple docstring'''
pass
@unittest.skip('ESMFold doesn\'t respect you and it certainly doesn\'t respect your initialization arguments.' )
def UpperCamelCase_ ( self : Tuple ):
'''simple docstring'''
pass
@unittest.skip('ESMFold doesn\'t support torchscript compilation.' )
def UpperCamelCase_ ( self : Union[str, Any] ):
'''simple docstring'''
pass
@unittest.skip('ESMFold doesn\'t support torchscript compilation.' )
def UpperCamelCase_ ( self : Any ):
'''simple docstring'''
pass
@unittest.skip('ESMFold doesn\'t support torchscript compilation.' )
def UpperCamelCase_ ( self : Any ):
'''simple docstring'''
pass
@unittest.skip('ESMFold doesn\'t support data parallel.' )
def UpperCamelCase_ ( self : Optional[int] ):
'''simple docstring'''
pass
@unittest.skip('Will be fixed soon by reducing the size of the model used for common tests.' )
def UpperCamelCase_ ( self : Optional[Any] ):
'''simple docstring'''
pass
@require_torch
class lowercase__ ( lowercase ):
@slow
def UpperCamelCase_ ( self : Optional[Any] ):
'''simple docstring'''
_UpperCamelCase : Optional[int] = EsmForProteinFolding.from_pretrained('facebook/esmfold_v1' ).float()
model.eval()
_UpperCamelCase : str = torch.tensor([[0, 6, 4, 13, 5, 4, 16, 12, 11, 7, 2]] )
_UpperCamelCase : Union[str, Any] = model(lowerCamelCase__ )['positions']
_UpperCamelCase : Optional[Any] = torch.tensor([2.5_8_2_8, 0.7_9_9_3, -1_0.9_3_3_4] ,dtype=torch.floataa )
self.assertTrue(torch.allclose(position_outputs[0, 0, 0, 0] ,lowerCamelCase__ ,atol=1E-4 ) )
| 236 |
'''simple docstring'''
from torch import nn
def A__ ( UpperCAmelCase_ ):
if act_fn in ["swish", "silu"]:
return nn.SiLU()
elif act_fn == "mish":
return nn.Mish()
elif act_fn == "gelu":
return nn.GELU()
else:
raise ValueError(f'Unsupported activation function: {act_fn}' )
| 236 | 1 |
import json
import os
import unittest
from transformers import OpenAIGPTTokenizer, OpenAIGPTTokenizerFast
from transformers.models.openai.tokenization_openai import VOCAB_FILES_NAMES
from transformers.testing_utils import require_ftfy, require_spacy, require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class UpperCamelCase__ ( lowerCAmelCase_ , unittest.TestCase ):
'''simple docstring'''
__snake_case : str = OpenAIGPTTokenizer
__snake_case : List[str] = OpenAIGPTTokenizerFast
__snake_case : Tuple = True
__snake_case : int = False
def SCREAMING_SNAKE_CASE__ ( self : Dict ) -> List[str]:
'''simple docstring'''
super().setUp()
# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
SCREAMING_SNAKE_CASE = [
"""l""",
"""o""",
"""w""",
"""e""",
"""r""",
"""s""",
"""t""",
"""i""",
"""d""",
"""n""",
"""w</w>""",
"""r</w>""",
"""t</w>""",
"""lo""",
"""low""",
"""er</w>""",
"""low</w>""",
"""lowest</w>""",
"""newer</w>""",
"""wider</w>""",
"""<unk>""",
]
SCREAMING_SNAKE_CASE = dict(zip(lowerCamelCase__ ,range(len(lowerCamelCase__ ) ) ) )
SCREAMING_SNAKE_CASE = ["""#version: 0.2""", """l o""", """lo w""", """e r</w>""", """"""]
SCREAMING_SNAKE_CASE = os.path.join(self.tmpdirname ,VOCAB_FILES_NAMES["""vocab_file"""] )
SCREAMING_SNAKE_CASE = os.path.join(self.tmpdirname ,VOCAB_FILES_NAMES["""merges_file"""] )
with open(self.vocab_file ,"""w""" ) as fp:
fp.write(json.dumps(lowerCamelCase__ ) )
with open(self.merges_file ,"""w""" ) as fp:
fp.write("""\n""".join(lowerCamelCase__ ) )
def SCREAMING_SNAKE_CASE__ ( self : Tuple ,lowerCamelCase__ : Dict ) -> Optional[int]:
'''simple docstring'''
return "lower newer", "lower newer"
def SCREAMING_SNAKE_CASE__ ( self : List[str] ) -> Union[str, Any]:
'''simple docstring'''
SCREAMING_SNAKE_CASE = OpenAIGPTTokenizer(self.vocab_file ,self.merges_file )
SCREAMING_SNAKE_CASE = """lower"""
SCREAMING_SNAKE_CASE = ["""low""", """er</w>"""]
SCREAMING_SNAKE_CASE = tokenizer.tokenize(lowerCamelCase__ )
self.assertListEqual(lowerCamelCase__ ,lowerCamelCase__ )
SCREAMING_SNAKE_CASE = tokens + ["""<unk>"""]
SCREAMING_SNAKE_CASE = [14, 15, 20]
self.assertListEqual(tokenizer.convert_tokens_to_ids(lowerCamelCase__ ) ,lowerCamelCase__ )
def SCREAMING_SNAKE_CASE__ ( self : Dict ,lowerCamelCase__ : List[Any]=15 ) -> Tuple:
'''simple docstring'''
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(F"""{tokenizer.__class__.__name__} ({pretrained_name})""" ):
SCREAMING_SNAKE_CASE = self.rust_tokenizer_class.from_pretrained(lowerCamelCase__ ,**lowerCamelCase__ )
# Simple input
SCREAMING_SNAKE_CASE = """This is a simple input"""
SCREAMING_SNAKE_CASE = ["""This is a simple input 1""", """This is a simple input 2"""]
SCREAMING_SNAKE_CASE = ("""This is a simple input""", """This is a pair""")
SCREAMING_SNAKE_CASE = [
("""This is a simple input 1""", """This is a simple input 2"""),
("""This is a simple pair 1""", """This is a simple pair 2"""),
]
# Simple input tests
self.assertRaises(lowerCamelCase__ ,tokenizer_r.encode ,lowerCamelCase__ ,max_length=lowerCamelCase__ ,padding="""max_length""" )
# Simple input
self.assertRaises(lowerCamelCase__ ,tokenizer_r.encode_plus ,lowerCamelCase__ ,max_length=lowerCamelCase__ ,padding="""max_length""" )
# Simple input
self.assertRaises(
lowerCamelCase__ ,tokenizer_r.batch_encode_plus ,lowerCamelCase__ ,max_length=lowerCamelCase__ ,padding="""max_length""" ,)
# Pair input
self.assertRaises(lowerCamelCase__ ,tokenizer_r.encode ,lowerCamelCase__ ,max_length=lowerCamelCase__ ,padding="""max_length""" )
# Pair input
self.assertRaises(lowerCamelCase__ ,tokenizer_r.encode_plus ,lowerCamelCase__ ,max_length=lowerCamelCase__ ,padding="""max_length""" )
# Pair input
self.assertRaises(
lowerCamelCase__ ,tokenizer_r.batch_encode_plus ,lowerCamelCase__ ,max_length=lowerCamelCase__ ,padding="""max_length""" ,)
def SCREAMING_SNAKE_CASE__ ( self : str ) -> List[str]:
'''simple docstring'''
pass
@require_ftfy
@require_spacy
@require_tokenizers
class UpperCamelCase__ ( lowerCAmelCase_ ):
'''simple docstring'''
pass
| 296 |
import importlib
import json
import os
import sys
import tempfile
import unittest
from pathlib import Path
import transformers
import transformers.models.auto
from transformers.models.auto.configuration_auto import CONFIG_MAPPING, AutoConfig
from transformers.models.bert.configuration_bert import BertConfig
from transformers.models.roberta.configuration_roberta import RobertaConfig
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER, get_tests_dir
sys.path.append(str(Path(__file__).parent.parent.parent.parent / """utils"""))
from test_module.custom_configuration import CustomConfig # noqa E402
SCREAMING_SNAKE_CASE_ = get_tests_dir("""fixtures/dummy-config.json""")
class UpperCamelCase__ ( unittest.TestCase ):
'''simple docstring'''
def SCREAMING_SNAKE_CASE__ ( self : Optional[Any] ) -> Optional[Any]:
'''simple docstring'''
SCREAMING_SNAKE_CASE = 0
def SCREAMING_SNAKE_CASE__ ( self : Any ) -> str:
'''simple docstring'''
self.assertIsNotNone(transformers.models.auto.__spec__ )
self.assertIsNotNone(importlib.util.find_spec("""transformers.models.auto""" ) )
def SCREAMING_SNAKE_CASE__ ( self : Union[str, Any] ) -> List[Any]:
'''simple docstring'''
SCREAMING_SNAKE_CASE = AutoConfig.from_pretrained("""bert-base-uncased""" )
self.assertIsInstance(lowerCamelCase__ ,lowerCamelCase__ )
def SCREAMING_SNAKE_CASE__ ( self : int ) -> Optional[Any]:
'''simple docstring'''
SCREAMING_SNAKE_CASE = AutoConfig.from_pretrained(lowerCamelCase__ )
self.assertIsInstance(lowerCamelCase__ ,lowerCamelCase__ )
def SCREAMING_SNAKE_CASE__ ( self : Union[str, Any] ) -> List[Any]:
'''simple docstring'''
SCREAMING_SNAKE_CASE = AutoConfig.from_pretrained(lowerCamelCase__ )
self.assertIsInstance(lowerCamelCase__ ,lowerCamelCase__ )
def SCREAMING_SNAKE_CASE__ ( self : Tuple ) -> Optional[int]:
'''simple docstring'''
SCREAMING_SNAKE_CASE = AutoConfig.for_model("""roberta""" )
self.assertIsInstance(lowerCamelCase__ ,lowerCamelCase__ )
def SCREAMING_SNAKE_CASE__ ( self : int ) -> int:
'''simple docstring'''
with tempfile.TemporaryDirectory() as tmp_dir:
# This model name contains bert and roberta, but roberta ends up being picked.
SCREAMING_SNAKE_CASE = os.path.join(lowerCamelCase__ ,"""fake-roberta""" )
os.makedirs(lowerCamelCase__ ,exist_ok=lowerCamelCase__ )
with open(os.path.join(lowerCamelCase__ ,"""config.json""" ) ,"""w""" ) as f:
f.write(json.dumps({} ) )
SCREAMING_SNAKE_CASE = AutoConfig.from_pretrained(lowerCamelCase__ )
self.assertEqual(type(lowerCamelCase__ ) ,lowerCamelCase__ )
def SCREAMING_SNAKE_CASE__ ( self : Dict ) -> str:
'''simple docstring'''
try:
AutoConfig.register("""custom""" ,lowerCamelCase__ )
# Wrong model type will raise an error
with self.assertRaises(lowerCamelCase__ ):
AutoConfig.register("""model""" ,lowerCamelCase__ )
# Trying to register something existing in the Transformers library will raise an error
with self.assertRaises(lowerCamelCase__ ):
AutoConfig.register("""bert""" ,lowerCamelCase__ )
# Now that the config is registered, it can be used as any other config with the auto-API
SCREAMING_SNAKE_CASE = CustomConfig()
with tempfile.TemporaryDirectory() as tmp_dir:
config.save_pretrained(lowerCamelCase__ )
SCREAMING_SNAKE_CASE = AutoConfig.from_pretrained(lowerCamelCase__ )
self.assertIsInstance(lowerCamelCase__ ,lowerCamelCase__ )
finally:
if "custom" in CONFIG_MAPPING._extra_content:
del CONFIG_MAPPING._extra_content["custom"]
def SCREAMING_SNAKE_CASE__ ( self : str ) -> Dict:
'''simple docstring'''
with self.assertRaisesRegex(
lowerCamelCase__ ,"""bert-base is not a local folder and is not a valid model identifier""" ):
SCREAMING_SNAKE_CASE = AutoConfig.from_pretrained("""bert-base""" )
def SCREAMING_SNAKE_CASE__ ( self : Dict ) -> str:
'''simple docstring'''
with self.assertRaisesRegex(
lowerCamelCase__ ,R"""aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)""" ):
SCREAMING_SNAKE_CASE = AutoConfig.from_pretrained(lowerCamelCase__ ,revision="""aaaaaa""" )
def SCREAMING_SNAKE_CASE__ ( self : Tuple ) -> List[Any]:
'''simple docstring'''
with self.assertRaisesRegex(
lowerCamelCase__ ,"""hf-internal-testing/no-config-test-repo does not appear to have a file named config.json.""" ,):
SCREAMING_SNAKE_CASE = AutoConfig.from_pretrained("""hf-internal-testing/no-config-test-repo""" )
def SCREAMING_SNAKE_CASE__ ( self : Optional[int] ) -> Union[str, Any]:
'''simple docstring'''
with self.assertRaises(lowerCamelCase__ ):
SCREAMING_SNAKE_CASE = AutoConfig.from_pretrained("""hf-internal-testing/test_dynamic_model""" )
# If remote code is disabled, we can't load this config.
with self.assertRaises(lowerCamelCase__ ):
SCREAMING_SNAKE_CASE = AutoConfig.from_pretrained("""hf-internal-testing/test_dynamic_model""" ,trust_remote_code=lowerCamelCase__ )
SCREAMING_SNAKE_CASE = AutoConfig.from_pretrained("""hf-internal-testing/test_dynamic_model""" ,trust_remote_code=lowerCamelCase__ )
self.assertEqual(config.__class__.__name__ ,"""NewModelConfig""" )
# Test config can be reloaded.
with tempfile.TemporaryDirectory() as tmp_dir:
config.save_pretrained(lowerCamelCase__ )
SCREAMING_SNAKE_CASE = AutoConfig.from_pretrained(lowerCamelCase__ ,trust_remote_code=lowerCamelCase__ )
self.assertEqual(reloaded_config.__class__.__name__ ,"""NewModelConfig""" )
def SCREAMING_SNAKE_CASE__ ( self : Dict ) -> Union[str, Any]:
'''simple docstring'''
class UpperCamelCase__ ( lowerCAmelCase_ ):
'''simple docstring'''
__snake_case : Union[str, Any] = "new-model"
try:
AutoConfig.register("""new-model""" ,lowerCamelCase__ )
# If remote code is not set, the default is to use local
SCREAMING_SNAKE_CASE = AutoConfig.from_pretrained("""hf-internal-testing/test_dynamic_model""" )
self.assertEqual(config.__class__.__name__ ,"""NewModelConfigLocal""" )
# If remote code is disabled, we load the local one.
SCREAMING_SNAKE_CASE = AutoConfig.from_pretrained("""hf-internal-testing/test_dynamic_model""" ,trust_remote_code=lowerCamelCase__ )
self.assertEqual(config.__class__.__name__ ,"""NewModelConfigLocal""" )
# If remote is enabled, we load from the Hub
SCREAMING_SNAKE_CASE = AutoConfig.from_pretrained("""hf-internal-testing/test_dynamic_model""" ,trust_remote_code=lowerCamelCase__ )
self.assertEqual(config.__class__.__name__ ,"""NewModelConfig""" )
finally:
if "new-model" in CONFIG_MAPPING._extra_content:
del CONFIG_MAPPING._extra_content["new-model"]
| 296 | 1 |
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 _SCREAMING_SNAKE_CASE ( __SCREAMING_SNAKE_CASE ):
'''simple docstring'''
lowercase_ = "longformer"
def __init__(self : Optional[Any] , 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_ : Union[str, Any] , ) ->Optional[int]:
'''simple docstring'''
super().__init__(pad_token_id=UpperCAmelCase_ , **UpperCAmelCase_)
lowerCamelCase__: Any =attention_window
lowerCamelCase__: List[str] =sep_token_id
lowerCamelCase__: str =bos_token_id
lowerCamelCase__: Any =eos_token_id
lowerCamelCase__: Optional[Any] =vocab_size
lowerCamelCase__: List[str] =hidden_size
lowerCamelCase__: str =num_hidden_layers
lowerCamelCase__: Union[str, Any] =num_attention_heads
lowerCamelCase__: Union[str, Any] =hidden_act
lowerCamelCase__: List[str] =intermediate_size
lowerCamelCase__: Union[str, Any] =hidden_dropout_prob
lowerCamelCase__: Optional[int] =attention_probs_dropout_prob
lowerCamelCase__: Union[str, Any] =max_position_embeddings
lowerCamelCase__: List[Any] =type_vocab_size
lowerCamelCase__: int =initializer_range
lowerCamelCase__: List[Any] =layer_norm_eps
lowerCamelCase__: int =onnx_export
class _SCREAMING_SNAKE_CASE ( __SCREAMING_SNAKE_CASE ):
'''simple docstring'''
def __init__(self : Tuple , UpperCAmelCase_ : "PretrainedConfig" , UpperCAmelCase_ : str = "default" , UpperCAmelCase_ : "List[PatchingSpec]" = None) ->Tuple:
'''simple docstring'''
super().__init__(UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_)
lowerCamelCase__: Dict =True
@property
def SCREAMING_SNAKE_CASE_ (self : List[Any]) ->Mapping[str, Mapping[int, str]]:
'''simple docstring'''
if self.task == "multiple-choice":
lowerCamelCase__: Any ={0: "batch", 1: "choice", 2: "sequence"}
else:
lowerCamelCase__: Tuple ={0: "batch", 1: "sequence"}
return OrderedDict(
[
("input_ids", dynamic_axis),
("attention_mask", dynamic_axis),
("global_attention_mask", dynamic_axis),
])
@property
def SCREAMING_SNAKE_CASE_ (self : Union[str, Any]) ->Mapping[str, Mapping[int, str]]:
'''simple docstring'''
lowerCamelCase__: Dict =super().outputs
if self.task == "default":
lowerCamelCase__: List[str] ={0: "batch"}
return outputs
@property
def SCREAMING_SNAKE_CASE_ (self : List[Any]) ->float:
'''simple docstring'''
return 1E-4
@property
def SCREAMING_SNAKE_CASE_ (self : Dict) ->int:
'''simple docstring'''
return max(super().default_onnx_opset , 14)
def SCREAMING_SNAKE_CASE_ (self : Any , UpperCAmelCase_ : "PreTrainedTokenizerBase" , UpperCAmelCase_ : int = -1 , UpperCAmelCase_ : int = -1 , UpperCAmelCase_ : bool = False , UpperCAmelCase_ : Optional[TensorType] = None , ) ->Mapping[str, Any]:
'''simple docstring'''
lowerCamelCase__: str =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
lowerCamelCase__: Any =torch.zeros_like(inputs["input_ids"])
# make every second token global
lowerCamelCase__: Dict =1
return inputs
| 273 |
def lowerCAmelCase_ ( __a ) -> str:
"""simple docstring"""
if isinstance(__a , __a ):
raise TypeError("'float' object cannot be interpreted as an integer" )
if isinstance(__a , __a ):
raise TypeError("'str' object cannot be interpreted as an integer" )
if num == 0:
return "0b0"
lowerCamelCase__: Optional[int] =False
if num < 0:
lowerCamelCase__: Optional[Any] =True
lowerCamelCase__: List[Any] =-num
lowerCamelCase__: list[int] =[]
while num > 0:
binary.insert(0 , num % 2 )
num >>= 1
if negative:
return "-0b" + "".join(str(__a ) for e in binary )
return "0b" + "".join(str(__a ) for e in binary )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 273 | 1 |
import datasets
from .evaluate import evaluate
UpperCamelCase = "\\n@article{hendrycks2021cuad,\n title={CUAD: An Expert-Annotated NLP Dataset for Legal Contract Review},\n author={Dan Hendrycks and Collin Burns and Anya Chen and Spencer Ball},\n journal={arXiv preprint arXiv:2103.06268},\n year={2021}\n}\n"
UpperCamelCase = "\nThis metric wrap the official scoring script for version 1 of the Contract\nUnderstanding Atticus Dataset (CUAD).\nContract Understanding Atticus Dataset (CUAD) v1 is a corpus of more than 13,000 labels in 510\ncommercial legal contracts that have been manually labeled to identify 41 categories of important\nclauses that lawyers look for when reviewing contracts in connection with corporate transactions.\n"
UpperCamelCase = "\nComputes CUAD scores (EM, F1, AUPR, Precision@80%Recall, and Precision@90%Recall).\nArgs:\n predictions: List of question-answers dictionaries with the following key-values:\n - 'id': id of the question-answer pair as given in the references (see below)\n - 'prediction_text': list of possible texts for the answer, as a list of strings\n depending on a threshold on the confidence probability of each prediction.\n references: List of question-answers dictionaries with the following key-values:\n - 'id': id of the question-answer pair (see above),\n - 'answers': a Dict in the CUAD dataset format\n {\n 'text': list of possible texts for the answer, as a list of strings\n 'answer_start': list of start positions for the answer, as a list of ints\n }\n Note that answer_start values are not taken into account to compute the metric.\nReturns:\n 'exact_match': Exact match (the normalized answer exactly match the gold answer)\n 'f1': The F-score of predicted tokens versus the gold answer\n 'aupr': Area Under the Precision-Recall curve\n 'prec_at_80_recall': Precision at 80% recall\n 'prec_at_90_recall': Precision at 90% recall\nExamples:\n >>> predictions = [{'prediction_text': ['The seller:', 'The buyer/End-User: Shenzhen LOHAS Supply Chain Management Co., Ltd.'], 'id': 'LohaCompanyltd_20191209_F-1_EX-10.16_11917878_EX-10.16_Supply Agreement__Parties'}]\n >>> references = [{'answers': {'answer_start': [143, 49], 'text': ['The seller:', 'The buyer/End-User: Shenzhen LOHAS Supply Chain Management Co., Ltd.']}, 'id': 'LohaCompanyltd_20191209_F-1_EX-10.16_11917878_EX-10.16_Supply Agreement__Parties'}]\n >>> cuad_metric = datasets.load_metric(\"cuad\")\n >>> results = cuad_metric.compute(predictions=predictions, references=references)\n >>> print(results)\n {'exact_match': 100.0, 'f1': 100.0, 'aupr': 0.0, 'prec_at_80_recall': 1.0, 'prec_at_90_recall': 1.0}\n"
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class _lowerCamelCase ( datasets.Metric ):
"""simple docstring"""
def _snake_case ( self )->Optional[int]:
'''simple docstring'''
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
'''predictions''': {
'''id''': datasets.Value('''string''' ),
'''prediction_text''': datasets.features.Sequence(datasets.Value('''string''' ) ),
},
'''references''': {
'''id''': datasets.Value('''string''' ),
'''answers''': datasets.features.Sequence(
{
'''text''': datasets.Value('''string''' ),
'''answer_start''': datasets.Value('''int32''' ),
} ),
},
} ) , codebase_urls=['''https://www.atticusprojectai.org/cuad'''] , reference_urls=['''https://www.atticusprojectai.org/cuad'''] , )
def _snake_case ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )->Optional[int]:
'''simple docstring'''
A_ : Tuple = {prediction["id"]: prediction["prediction_text"] for prediction in predictions}
A_ : Any = [
{
"paragraphs": [
{
"qas": [
{
"answers": [{"text": answer_text} for answer_text in ref["answers"]["text"]],
"id": ref["id"],
}
for ref in references
]
}
]
}
]
A_ : str = evaluate(dataset=_SCREAMING_SNAKE_CASE , predictions=_SCREAMING_SNAKE_CASE )
return score
| 186 |
import warnings
from ...utils import logging
from .image_processing_beit import BeitImageProcessor
_snake_case : Optional[Any] = logging.get_logger(__name__)
class a (_lowerCAmelCase ):
"""simple docstring"""
def __init__( self : Optional[Any] , *lowerCamelCase : Dict , **lowerCamelCase : List[Any] ) -> None:
warnings.warn(
"The class BeitFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please"
" use BeitImageProcessor instead." , lowerCamelCase , )
super().__init__(*lowerCamelCase , **lowerCamelCase )
| 123 | 0 |
#
# This a `torch.distributed` diagnostics script that checks that all GPUs in the cluster (one or
# many nodes) can talk to each other via nccl and allocate gpu memory.
#
# To run first adjust the number of processes and nodes:
#
# python -m torch.distributed.run --nproc_per_node 2 --nnodes 1 torch-distributed-gpu-test.py
#
# You may need to add --master_addr $MASTER_ADDR --master_port $MASTER_PORT if using a custom addr:port
#
# You can also use the rdzv API: --rdzv_endpoint $MASTER_ADDR:$MASTER_PORT --rdzv_backend c10d
#
# use torch.distributed.launch instead of torch.distributed.run for torch < 1.9
#
# If you get a hanging in `barrier` calls you have some network issues, you may try to debug this with:
#
# NCCL_DEBUG=INFO python -m torch.distributed.run --nproc_per_node 2 --nnodes 1 torch-distributed-gpu-test.py
#
# which should tell you what's going on behind the scenes.
#
#
# This script can be run via `srun` in the SLURM environment as well. Here is a SLURM script that
# runs on 2 nodes of 4 gpus per node:
#
# #SBATCH --job-name=test-nodes # name
# #SBATCH --nodes=2 # nodes
# #SBATCH --ntasks-per-node=1 # crucial - only 1 task per dist per node!
# #SBATCH --cpus-per-task=10 # number of cores per tasks
# #SBATCH --gres=gpu:4 # number of gpus
# #SBATCH --time 0:05:00 # maximum execution time (HH:MM:SS)
# #SBATCH --output=%x-%j.out # output file name
#
# GPUS_PER_NODE=4
# MASTER_ADDR=$(scontrol show hostnames $SLURM_JOB_NODELIST | head -n 1)
# MASTER_PORT=6000
#
# srun --jobid $SLURM_JOBID bash -c 'python -m torch.distributed.run \
# --nproc_per_node $GPUS_PER_NODE --nnodes $SLURM_NNODES --node_rank $SLURM_PROCID \
# --master_addr $MASTER_ADDR --master_port $MASTER_PORT \
# torch-distributed-gpu-test.py'
#
import fcntl
import os
import socket
import torch
import torch.distributed as dist
def lowerCamelCase ( *a_ ) -> List[Any]:
with open(a_ , 'r' ) as fh:
fcntl.flock(a_ , fcntl.LOCK_EX )
try:
print(*a_ )
finally:
fcntl.flock(a_ , fcntl.LOCK_UN )
lowerCamelCase_ = int(os.environ["""LOCAL_RANK"""])
torch.cuda.set_device(local_rank)
lowerCamelCase_ = torch.device("""cuda""", local_rank)
lowerCamelCase_ = socket.gethostname()
lowerCamelCase_ = f'''[{hostname}-{local_rank}]'''
try:
# test distributed
dist.init_process_group("""nccl""")
dist.all_reduce(torch.ones(1).to(device), op=dist.ReduceOp.SUM)
dist.barrier()
# test cuda is available and can allocate memory
torch.cuda.is_available()
torch.ones(1).cuda(local_rank)
# global rank
lowerCamelCase_ = dist.get_rank()
lowerCamelCase_ = dist.get_world_size()
printflock(f'''{gpu} is OK (global rank: {rank}/{world_size})''')
dist.barrier()
if rank == 0:
printflock(f'''pt={torch.__version__}, cuda={torch.version.cuda}, nccl={torch.cuda.nccl.version()}''')
except Exception:
printflock(f'''{gpu} is broken''')
raise
| 14 |
from __future__ import annotations
lowerCamelCase_ = 1_0
def lowerCamelCase ( a_ ) -> list[int]:
lowerCAmelCase_ = 1
lowerCAmelCase_ = max(a_ )
while placement <= max_digit:
# declare and initialize empty buckets
lowerCAmelCase_ = [[] for _ in range(a_ )]
# split list_of_ints between the buckets
for i in list_of_ints:
lowerCAmelCase_ = int((i / placement) % RADIX )
buckets[tmp].append(a_ )
# put each buckets' contents into list_of_ints
lowerCAmelCase_ = 0
for b in range(a_ ):
for i in buckets[b]:
lowerCAmelCase_ = i
a += 1
# move to next
placement *= RADIX
return list_of_ints
if __name__ == "__main__":
import doctest
doctest.testmod()
| 14 | 1 |
import json
import os
import unittest
from transformers import BatchEncoding, MvpTokenizer, MvpTokenizerFast
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.utils import cached_property
from ...test_tokenization_common import TokenizerTesterMixin, filter_roberta_detectors
@require_tokenizers
class __lowerCAmelCase ( a_, unittest.TestCase ):
lowerCamelCase_ : Optional[Any] = MvpTokenizer
lowerCamelCase_ : List[Any] = MvpTokenizerFast
lowerCamelCase_ : Dict = True
lowerCamelCase_ : Optional[Any] = filter_roberta_detectors
def lowerCamelCase (self ) -> Union[str, Any]:
'''simple docstring'''
super().setUp()
snake_case_ : Dict = [
"""l""",
"""o""",
"""w""",
"""e""",
"""r""",
"""s""",
"""t""",
"""i""",
"""d""",
"""n""",
"""\u0120""",
"""\u0120l""",
"""\u0120n""",
"""\u0120lo""",
"""\u0120low""",
"""er""",
"""\u0120lowest""",
"""\u0120newer""",
"""\u0120wider""",
"""<unk>""",
]
snake_case_ : int = dict(zip(_lowercase , range(len(_lowercase ) ) ) )
snake_case_ : List[Any] = ["""#version: 0.2""", """\u0120 l""", """\u0120l o""", """\u0120lo w""", """e r""", """"""]
snake_case_ : Optional[int] = {"""unk_token""": """<unk>"""}
snake_case_ : List[Any] = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['''vocab_file'''] )
snake_case_ : List[str] = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['''merges_file'''] )
with open(self.vocab_file , '''w''' , encoding='''utf-8''' ) as fp:
fp.write(json.dumps(_lowercase ) + '''\n''' )
with open(self.merges_file , '''w''' , encoding='''utf-8''' ) as fp:
fp.write('''\n'''.join(_lowercase ) )
def lowerCamelCase (self , **__magic_name__ ) -> Optional[Any]:
'''simple docstring'''
kwargs.update(self.special_tokens_map )
return self.tokenizer_class.from_pretrained(self.tmpdirname , **_lowercase )
def lowerCamelCase (self , **__magic_name__ ) -> Union[str, Any]:
'''simple docstring'''
kwargs.update(self.special_tokens_map )
return self.rust_tokenizer_class.from_pretrained(self.tmpdirname , **_lowercase )
def lowerCamelCase (self , __magic_name__ ) -> int:
'''simple docstring'''
return "lower newer", "lower newer"
@cached_property
def lowerCamelCase (self ) -> List[Any]:
'''simple docstring'''
return MvpTokenizer.from_pretrained('''RUCAIBox/mvp''' )
@cached_property
def lowerCamelCase (self ) -> Union[str, Any]:
'''simple docstring'''
return MvpTokenizerFast.from_pretrained('''RUCAIBox/mvp''' )
@require_torch
def lowerCamelCase (self ) -> List[str]:
'''simple docstring'''
snake_case_ : List[Any] = ["""A long paragraph for summarization.""", """Another paragraph for summarization."""]
snake_case_ : List[str] = [0, 250, 251, 1_7818, 13, 3_9186, 1938, 4, 2]
for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
snake_case_ : Optional[Any] = tokenizer(_lowercase , max_length=len(_lowercase ) , padding=_lowercase , return_tensors='''pt''' )
self.assertIsInstance(_lowercase , _lowercase )
self.assertEqual((2, 9) , batch.input_ids.shape )
self.assertEqual((2, 9) , batch.attention_mask.shape )
snake_case_ : Dict = batch.input_ids.tolist()[0]
self.assertListEqual(_lowercase , _lowercase )
# Test that special tokens are reset
@require_torch
def lowerCamelCase (self ) -> Union[str, Any]:
'''simple docstring'''
snake_case_ : List[str] = ["""A long paragraph for summarization.""", """Another paragraph for summarization."""]
for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
snake_case_ : List[str] = tokenizer(_lowercase , padding=_lowercase , return_tensors='''pt''' )
# check if input_ids are returned and no labels
self.assertIn('''input_ids''' , _lowercase )
self.assertIn('''attention_mask''' , _lowercase )
self.assertNotIn('''labels''' , _lowercase )
self.assertNotIn('''decoder_attention_mask''' , _lowercase )
@require_torch
def lowerCamelCase (self ) -> Union[str, Any]:
'''simple docstring'''
snake_case_ : List[Any] = [
"""Summary of the text.""",
"""Another summary.""",
]
for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
snake_case_ : int = tokenizer(text_target=_lowercase , max_length=32 , padding='''max_length''' , return_tensors='''pt''' )
self.assertEqual(32 , targets['''input_ids'''].shape[1] )
@require_torch
def lowerCamelCase (self ) -> Any:
'''simple docstring'''
for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
snake_case_ : int = tokenizer(
['''I am a small frog''' * 1024, '''I am a small frog'''] , padding=_lowercase , truncation=_lowercase , return_tensors='''pt''' )
self.assertIsInstance(_lowercase , _lowercase )
self.assertEqual(batch.input_ids.shape , (2, 1024) )
@require_torch
def lowerCamelCase (self ) -> List[str]:
'''simple docstring'''
snake_case_ : Tuple = ["""A long paragraph for summarization."""]
snake_case_ : Optional[Any] = [
"""Summary of the text.""",
]
for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
snake_case_ : List[Any] = tokenizer(_lowercase , text_target=_lowercase , return_tensors='''pt''' )
snake_case_ : Union[str, Any] = inputs["""input_ids"""]
snake_case_ : Dict = inputs["""labels"""]
self.assertTrue((input_ids[:, 0] == tokenizer.bos_token_id).all().item() )
self.assertTrue((labels[:, 0] == tokenizer.bos_token_id).all().item() )
self.assertTrue((input_ids[:, -1] == tokenizer.eos_token_id).all().item() )
self.assertTrue((labels[:, -1] == tokenizer.eos_token_id).all().item() )
def lowerCamelCase (self ) -> int:
'''simple docstring'''
pass
def lowerCamelCase (self ) -> Union[str, Any]:
'''simple docstring'''
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(F'''{tokenizer.__class__.__name__} ({pretrained_name})''' ):
snake_case_ : List[str] = self.rust_tokenizer_class.from_pretrained(_lowercase , **_lowercase )
snake_case_ : List[str] = self.tokenizer_class.from_pretrained(_lowercase , **_lowercase )
snake_case_ : Optional[int] = """A, <mask> AllenNLP sentence."""
snake_case_ : Union[str, Any] = tokenizer_r.encode_plus(_lowercase , add_special_tokens=_lowercase , return_token_type_ids=_lowercase )
snake_case_ : Tuple = tokenizer_p.encode_plus(_lowercase , add_special_tokens=_lowercase , return_token_type_ids=_lowercase )
# token_type_ids should put 0 everywhere
self.assertEqual(sum(tokens_r['''token_type_ids'''] ) , sum(tokens_p['''token_type_ids'''] ) )
# attention_mask should put 1 everywhere, so sum over length should be 1
self.assertEqual(
sum(tokens_r['''attention_mask'''] ) / len(tokens_r['''attention_mask'''] ) , sum(tokens_p['''attention_mask'''] ) / len(tokens_p['''attention_mask'''] ) , )
snake_case_ : List[Any] = tokenizer_r.convert_ids_to_tokens(tokens_r['''input_ids'''] )
snake_case_ : str = tokenizer_p.convert_ids_to_tokens(tokens_p['''input_ids'''] )
# Rust correctly handles the space before the mask while python doesnt
self.assertSequenceEqual(tokens_p['''input_ids'''] , [0, 250, 6, 5_0264, 3823, 487, 2_1992, 3645, 4, 2] )
self.assertSequenceEqual(tokens_r['''input_ids'''] , [0, 250, 6, 5_0264, 3823, 487, 2_1992, 3645, 4, 2] )
self.assertSequenceEqual(
_lowercase , ['''<s>''', '''A''', ''',''', '''<mask>''', '''ĠAllen''', '''N''', '''LP''', '''Ġsentence''', '''.''', '''</s>'''] )
self.assertSequenceEqual(
_lowercase , ['''<s>''', '''A''', ''',''', '''<mask>''', '''ĠAllen''', '''N''', '''LP''', '''Ġsentence''', '''.''', '''</s>'''] )
| 279 |
from dataclasses import dataclass
from typing import Optional, Tuple
import torch
from torch import nn
from transformers import RobertaPreTrainedModel, XLMRobertaConfig, XLMRobertaModel
from transformers.utils import ModelOutput
@dataclass
class A__(a_ ):
"""simple docstring"""
_A : Optional[torch.FloatTensor] = None
_A : torch.FloatTensor = None
_A : Optional[Tuple[torch.FloatTensor]] = None
_A : Optional[Tuple[torch.FloatTensor]] = None
class A__(a_ ):
"""simple docstring"""
def __init__( self , _lowercase=1 , _lowercase=0 , _lowercase=2 , _lowercase=512 , _lowercase="cls" , _lowercase=False , _lowercase=True , **_lowercase , ) -> Union[str, Any]:
super().__init__(pad_token_id=_lowercase , bos_token_id=_lowercase , eos_token_id=_lowercase , **_lowercase )
a_ : str = project_dim
a_ : List[Any] = pooler_fn
a_ : Union[str, Any] = learn_encoder
a_ : List[str] = use_attention_mask
class A__(a_ ):
"""simple docstring"""
_A : Any = [r'''pooler''', r'''logit_scale''']
_A : List[str] = [r'''position_ids''', r'''predictions.decoder.bias''']
_A : List[str] = '''roberta'''
_A : Union[str, Any] = RobertaSeriesConfig
def __init__( self , _lowercase ) -> Optional[Any]:
super().__init__(_lowercase )
a_ : Optional[int] = XLMRobertaModel(_lowercase )
a_ : Any = nn.Linear(config.hidden_size , config.project_dim )
a_ : Union[str, Any] = getattr(_lowercase , """has_pre_transformation""" , _lowercase )
if self.has_pre_transformation:
a_ : int = nn.Linear(config.hidden_size , config.project_dim )
a_ : Union[str, Any] = nn.LayerNorm(config.hidden_size , eps=config.layer_norm_eps )
self.post_init()
def UpperCamelCase__ ( self , _lowercase = None , _lowercase = None , _lowercase = None , _lowercase = None , _lowercase = None , _lowercase = None , _lowercase = None , _lowercase = None , _lowercase = None , _lowercase = None , _lowercase = None , ) -> Any:
a_ : str = return_dict if return_dict is not None else self.config.use_return_dict
a_ : Any = self.base_model(
input_ids=_lowercase , attention_mask=_lowercase , token_type_ids=_lowercase , position_ids=_lowercase , head_mask=_lowercase , inputs_embeds=_lowercase , encoder_hidden_states=_lowercase , encoder_attention_mask=_lowercase , output_attentions=_lowercase , output_hidden_states=True if self.has_pre_transformation else output_hidden_states , return_dict=_lowercase , )
if self.has_pre_transformation:
a_ : str = outputs["""hidden_states"""][-2]
a_ : Tuple = self.pre_LN(_lowercase )
a_ : List[str] = self.transformation_pre(_lowercase )
return TransformationModelOutput(
projection_state=_lowercase , last_hidden_state=outputs.last_hidden_state , hidden_states=outputs.hidden_states , attentions=outputs.attentions , )
else:
a_ : Union[str, Any] = self.transformation(outputs.last_hidden_state )
return TransformationModelOutput(
projection_state=_lowercase , last_hidden_state=outputs.last_hidden_state , hidden_states=outputs.hidden_states , attentions=outputs.attentions , )
| 248 | 0 |
'''simple docstring'''
def UpperCamelCase ( a ) -> bool:
'''simple docstring'''
return str(a ) == str(a )[::-1]
def UpperCamelCase ( a ) -> int:
'''simple docstring'''
return int(a ) + int(str(a )[::-1] )
def UpperCamelCase ( a = 1_0000 ) -> int:
'''simple docstring'''
__magic_name__ = []
for num in range(1 , a ):
__magic_name__ = 0
__magic_name__ = num
while iterations < 50:
__magic_name__ = sum_reverse(a )
iterations += 1
if is_palindrome(a ):
break
else:
lychrel_nums.append(a )
return len(a )
if __name__ == "__main__":
print(F'''{solution() = }''')
| 98 |
'''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 _SCREAMING_SNAKE_CASE ( __a ,__a ):
__SCREAMING_SNAKE_CASE :Optional[Any] = 1
@register_to_config
def __init__( self : Optional[int] , a__ : int = 1000 , a__ : Optional[Union[np.ndarray, List[float]]] = None ):
# set `betas`, `alphas`, `timesteps`
self.set_timesteps(a__ )
# standard deviation of the initial noise distribution
__magic_name__ = 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.
__magic_name__ = 4
# running values
__magic_name__ = []
def snake_case__ ( self : Dict , a__ : int , a__ : Union[str, torch.device] = None ):
__magic_name__ = num_inference_steps
__magic_name__ = torch.linspace(1 , 0 , num_inference_steps + 1 )[:-1]
__magic_name__ = torch.cat([steps, torch.tensor([0.0] )] )
if self.config.trained_betas is not None:
__magic_name__ = torch.tensor(self.config.trained_betas , dtype=torch.floataa )
else:
__magic_name__ = torch.sin(steps * math.pi / 2 ) ** 2
__magic_name__ = (1.0 - self.betas**2) ** 0.5
__magic_name__ = (torch.atana(self.betas , self.alphas ) / math.pi * 2)[:-1]
__magic_name__ = timesteps.to(a__ )
__magic_name__ = []
def snake_case__ ( self : Union[str, Any] , a__ : torch.FloatTensor , a__ : int , a__ : torch.FloatTensor , a__ : 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''' )
__magic_name__ = (self.timesteps == timestep).nonzero().item()
__magic_name__ = timestep_index + 1
__magic_name__ = sample * self.betas[timestep_index] + model_output * self.alphas[timestep_index]
self.ets.append(a__ )
if len(self.ets ) == 1:
__magic_name__ = self.ets[-1]
elif len(self.ets ) == 2:
__magic_name__ = (3 * self.ets[-1] - self.ets[-2]) / 2
elif len(self.ets ) == 3:
__magic_name__ = (23 * self.ets[-1] - 16 * self.ets[-2] + 5 * self.ets[-3]) / 12
else:
__magic_name__ = (1 / 24) * (55 * self.ets[-1] - 59 * self.ets[-2] + 37 * self.ets[-3] - 9 * self.ets[-4])
__magic_name__ = self._get_prev_sample(a__ , a__ , a__ , a__ )
if not return_dict:
return (prev_sample,)
return SchedulerOutput(prev_sample=a__ )
def snake_case__ ( self : Any , a__ : torch.FloatTensor , *a__ : Optional[int] , **a__ : Union[str, Any] ):
return sample
def snake_case__ ( self : str , a__ : Optional[int] , a__ : int , a__ : List[Any] , a__ : List[str] ):
__magic_name__ = self.alphas[timestep_index]
__magic_name__ = self.betas[timestep_index]
__magic_name__ = self.alphas[prev_timestep_index]
__magic_name__ = self.betas[prev_timestep_index]
__magic_name__ = (sample - sigma * ets) / max(a__ , 1E-8 )
__magic_name__ = next_alpha * pred + ets * next_sigma
return prev_sample
def __len__( self : int ):
return self.config.num_train_timesteps
| 98 | 1 |
'''simple docstring'''
import argparse
import json
import logging
import os
import sys
from unittest.mock import patch
from transformers.testing_utils import TestCasePlus, get_gpu_count, slow
A =[
os.path.join(os.path.dirname(__file__), dirname)
for dirname in [
'text-classification',
'language-modeling',
'summarization',
'token-classification',
'question-answering',
]
]
sys.path.extend(SRC_DIRS)
if SRC_DIRS is not None:
import run_clm_flax
import run_flax_glue
import run_flax_ner
import run_mlm_flax
import run_qa
import run_summarization_flax
import run_ta_mlm_flax
logging.basicConfig(level=logging.DEBUG)
A =logging.getLogger()
def snake_case_ ():
UpperCAmelCase = argparse.ArgumentParser()
parser.add_argument('''-f''' )
UpperCAmelCase = parser.parse_args()
return args.f
def snake_case_ (_a : List[str] , _a : Union[str, Any]="eval" ):
UpperCAmelCase = os.path.join(_a , F"{split}_results.json" )
if os.path.exists(_a ):
with open(_a , '''r''' ) as f:
return json.load(_a )
raise ValueError(F"can't find {path}" )
A =logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)
class _a ( __a ):
def A ( self : Any ):
'''simple docstring'''
UpperCAmelCase = self.get_auto_remove_tmp_dir()
UpperCAmelCase = f"\n run_glue.py\n --model_name_or_path distilbert-base-uncased\n --output_dir {tmp_dir}\n --train_file ./tests/fixtures/tests_samples/MRPC/train.csv\n --validation_file ./tests/fixtures/tests_samples/MRPC/dev.csv\n --per_device_train_batch_size=2\n --per_device_eval_batch_size=1\n --learning_rate=1e-4\n --eval_steps=2\n --warmup_steps=2\n --seed=42\n --max_seq_length=128\n ".split()
with patch.object(lowercase , '''argv''' , lowercase ):
run_flax_glue.main()
UpperCAmelCase = get_results(lowercase )
self.assertGreaterEqual(result['''eval_accuracy'''] , 0.75 )
@slow
def A ( self : Any ):
'''simple docstring'''
UpperCAmelCase = self.get_auto_remove_tmp_dir()
UpperCAmelCase = f"\n run_clm_flax.py\n --model_name_or_path distilgpt2\n --train_file ./tests/fixtures/sample_text.txt\n --validation_file ./tests/fixtures/sample_text.txt\n --do_train\n --do_eval\n --block_size 128\n --per_device_train_batch_size 4\n --per_device_eval_batch_size 4\n --num_train_epochs 2\n --logging_steps 2 --eval_steps 2\n --output_dir {tmp_dir}\n --overwrite_output_dir\n ".split()
with patch.object(lowercase , '''argv''' , lowercase ):
run_clm_flax.main()
UpperCAmelCase = get_results(lowercase )
self.assertLess(result['''eval_perplexity'''] , 100 )
@slow
def A ( self : str ):
'''simple docstring'''
UpperCAmelCase = self.get_auto_remove_tmp_dir()
UpperCAmelCase = f"\n run_summarization.py\n --model_name_or_path t5-small\n --train_file tests/fixtures/tests_samples/xsum/sample.json\n --validation_file tests/fixtures/tests_samples/xsum/sample.json\n --test_file tests/fixtures/tests_samples/xsum/sample.json\n --output_dir {tmp_dir}\n --overwrite_output_dir\n --num_train_epochs=3\n --warmup_steps=8\n --do_train\n --do_eval\n --do_predict\n --learning_rate=2e-4\n --per_device_train_batch_size=2\n --per_device_eval_batch_size=1\n --predict_with_generate\n ".split()
with patch.object(lowercase , '''argv''' , lowercase ):
run_summarization_flax.main()
UpperCAmelCase = get_results(lowercase , split='''test''' )
self.assertGreaterEqual(result['''test_rouge1'''] , 10 )
self.assertGreaterEqual(result['''test_rouge2'''] , 2 )
self.assertGreaterEqual(result['''test_rougeL'''] , 7 )
self.assertGreaterEqual(result['''test_rougeLsum'''] , 7 )
@slow
def A ( self : int ):
'''simple docstring'''
UpperCAmelCase = self.get_auto_remove_tmp_dir()
UpperCAmelCase = f"\n run_mlm.py\n --model_name_or_path distilroberta-base\n --train_file ./tests/fixtures/sample_text.txt\n --validation_file ./tests/fixtures/sample_text.txt\n --output_dir {tmp_dir}\n --overwrite_output_dir\n --max_seq_length 128\n --per_device_train_batch_size 4\n --per_device_eval_batch_size 4\n --logging_steps 2 --eval_steps 2\n --do_train\n --do_eval\n --num_train_epochs=1\n ".split()
with patch.object(lowercase , '''argv''' , lowercase ):
run_mlm_flax.main()
UpperCAmelCase = get_results(lowercase )
self.assertLess(result['''eval_perplexity'''] , 42 )
@slow
def A ( self : Tuple ):
'''simple docstring'''
UpperCAmelCase = self.get_auto_remove_tmp_dir()
UpperCAmelCase = f"\n run_t5_mlm_flax.py\n --model_name_or_path t5-small\n --train_file ./tests/fixtures/sample_text.txt\n --validation_file ./tests/fixtures/sample_text.txt\n --do_train\n --do_eval\n --max_seq_length 128\n --per_device_train_batch_size 4\n --per_device_eval_batch_size 4\n --num_train_epochs 2\n --logging_steps 2 --eval_steps 2\n --output_dir {tmp_dir}\n --overwrite_output_dir\n ".split()
with patch.object(lowercase , '''argv''' , lowercase ):
run_ta_mlm_flax.main()
UpperCAmelCase = get_results(lowercase )
self.assertGreaterEqual(result['''eval_accuracy'''] , 0.42 )
@slow
def A ( self : Tuple ):
'''simple docstring'''
UpperCAmelCase = 7 if get_gpu_count() > 1 else 2
UpperCAmelCase = self.get_auto_remove_tmp_dir()
UpperCAmelCase = f"\n run_flax_ner.py\n --model_name_or_path bert-base-uncased\n --train_file tests/fixtures/tests_samples/conll/sample.json\n --validation_file tests/fixtures/tests_samples/conll/sample.json\n --output_dir {tmp_dir}\n --overwrite_output_dir\n --do_train\n --do_eval\n --warmup_steps=2\n --learning_rate=2e-4\n --logging_steps 2 --eval_steps 2\n --per_device_train_batch_size=2\n --per_device_eval_batch_size=2\n --num_train_epochs={epochs}\n --seed 7\n ".split()
with patch.object(lowercase , '''argv''' , lowercase ):
run_flax_ner.main()
UpperCAmelCase = get_results(lowercase )
self.assertGreaterEqual(result['''eval_accuracy'''] , 0.75 )
self.assertGreaterEqual(result['''eval_f1'''] , 0.3 )
@slow
def A ( self : Union[str, Any] ):
'''simple docstring'''
UpperCAmelCase = self.get_auto_remove_tmp_dir()
UpperCAmelCase = f"\n run_qa.py\n --model_name_or_path bert-base-uncased\n --version_2_with_negative\n --train_file tests/fixtures/tests_samples/SQUAD/sample.json\n --validation_file tests/fixtures/tests_samples/SQUAD/sample.json\n --output_dir {tmp_dir}\n --overwrite_output_dir\n --num_train_epochs=3\n --warmup_steps=2\n --do_train\n --do_eval\n --logging_steps 2 --eval_steps 2\n --learning_rate=2e-4\n --per_device_train_batch_size=2\n --per_device_eval_batch_size=1\n ".split()
with patch.object(lowercase , '''argv''' , lowercase ):
run_qa.main()
UpperCAmelCase = get_results(lowercase )
self.assertGreaterEqual(result['''eval_f1'''] , 30 )
self.assertGreaterEqual(result['''eval_exact'''] , 30 )
| 34 |
'''simple docstring'''
import warnings
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class _a ( __a ):
__a : int = ["""image_processor""", """tokenizer"""]
__a : Union[str, Any] = """ChineseCLIPImageProcessor"""
__a : List[Any] = ("""BertTokenizer""", """BertTokenizerFast""")
def __init__( self : Dict , lowercase : Union[str, Any]=None , lowercase : Dict=None , **lowercase : Optional[Any] ):
'''simple docstring'''
UpperCAmelCase = None
if "feature_extractor" in kwargs:
warnings.warn(
'''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
''' instead.''' , lowercase , )
UpperCAmelCase = kwargs.pop('''feature_extractor''' )
UpperCAmelCase = image_processor if image_processor is not None else feature_extractor
if image_processor is None:
raise ValueError('''You need to specify an `image_processor`.''' )
if tokenizer is None:
raise ValueError('''You need to specify a `tokenizer`.''' )
super().__init__(lowercase , lowercase )
UpperCAmelCase = self.image_processor
def __call__( self : Tuple , lowercase : Optional[Any]=None , lowercase : Union[str, Any]=None , lowercase : int=None , **lowercase : Dict ):
'''simple docstring'''
if text is None and images is None:
raise ValueError('''You have to specify either text or images. Both cannot be none.''' )
if text is not None:
UpperCAmelCase = self.tokenizer(lowercase , return_tensors=lowercase , **lowercase )
if images is not None:
UpperCAmelCase = self.image_processor(lowercase , return_tensors=lowercase , **lowercase )
if text is not None and images is not None:
UpperCAmelCase = image_features.pixel_values
return encoding
elif text is not None:
return encoding
else:
return BatchEncoding(data=dict(**lowercase ) , tensor_type=lowercase )
def A ( self : int , *lowercase : Tuple , **lowercase : List[str] ):
'''simple docstring'''
return self.tokenizer.batch_decode(*lowercase , **lowercase )
def A ( self : Optional[Any] , *lowercase : int , **lowercase : Optional[int] ):
'''simple docstring'''
return self.tokenizer.decode(*lowercase , **lowercase )
@property
def A ( self : Optional[int] ):
'''simple docstring'''
UpperCAmelCase = self.tokenizer.model_input_names
UpperCAmelCase = self.image_processor.model_input_names
return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names ) )
@property
def A ( self : List[Any] ):
'''simple docstring'''
warnings.warn(
'''`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.''' , lowercase , )
return self.image_processor_class
| 34 | 1 |
'''simple docstring'''
import unittest
from transformers import LiltConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
LiltForQuestionAnswering,
LiltForSequenceClassification,
LiltForTokenClassification,
LiltModel,
)
from transformers.models.lilt.modeling_lilt import LILT_PRETRAINED_MODEL_ARCHIVE_LIST
class __UpperCamelCase :
"""simple docstring"""
def __init__( self , __a , __a=13 , __a=7 , __a=True , __a=True , __a=True , __a=True , __a=99 , __a=24 , __a=2 , __a=6 , __a=37 , __a="gelu" , __a=0.1 , __a=0.1 , __a=512 , __a=16 , __a=2 , __a=0.02 , __a=3 , __a=None , __a=1000 , ):
'''simple docstring'''
__a : str = parent
__a : Optional[int] = batch_size
__a : List[Any] = seq_length
__a : Dict = is_training
__a : Any = use_input_mask
__a : Optional[int] = use_token_type_ids
__a : Optional[int] = use_labels
__a : List[str] = vocab_size
__a : Dict = hidden_size
__a : Union[str, Any] = num_hidden_layers
__a : List[str] = num_attention_heads
__a : Any = intermediate_size
__a : Any = hidden_act
__a : Optional[Any] = hidden_dropout_prob
__a : int = attention_probs_dropout_prob
__a : Dict = max_position_embeddings
__a : str = type_vocab_size
__a : Dict = type_sequence_label_size
__a : str = initializer_range
__a : List[Any] = num_labels
__a : str = scope
__a : Tuple = range_bbox
def __UpperCAmelCase ( self ):
'''simple docstring'''
__a : Any = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
__a : int = ids_tensor([self.batch_size, self.seq_length, 4] , self.range_bbox )
# Ensure that bbox is legal
for i in range(bbox.shape[0] ):
for j in range(bbox.shape[1] ):
if bbox[i, j, 3] < bbox[i, j, 1]:
__a : Optional[int] = bbox[i, j, 3]
__a : Optional[int] = bbox[i, j, 1]
__a : List[str] = t
if bbox[i, j, 2] < bbox[i, j, 0]:
__a : Optional[int] = bbox[i, j, 2]
__a : Optional[Any] = bbox[i, j, 0]
__a : int = t
__a : Any = None
if self.use_input_mask:
__a : Any = ids_tensor([self.batch_size, self.seq_length] , vocab_size=2 )
__a : List[Any] = None
if self.use_token_type_ids:
__a : Union[str, Any] = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
__a : Tuple = None
__a : int = None
if self.use_labels:
__a : Any = ids_tensor([self.batch_size] , self.type_sequence_label_size )
__a : Any = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
__a : str = self.get_config()
return config, input_ids, bbox, token_type_ids, input_mask, sequence_labels, token_labels
def __UpperCAmelCase ( self ):
'''simple docstring'''
return LiltConfig(
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 , initializer_range=self.initializer_range , )
def __UpperCAmelCase ( self , __a , __a , __a , __a , __a , __a , __a , ):
'''simple docstring'''
__a : Optional[int] = LiltModel(config=__a )
model.to(__a )
model.eval()
__a : Any = model(__a , bbox=__a , attention_mask=__a , token_type_ids=__a )
__a : List[str] = model(__a , bbox=__a , token_type_ids=__a )
__a : Any = model(__a , bbox=__a )
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 __UpperCAmelCase ( self , __a , __a , __a , __a , __a , __a , __a , ):
'''simple docstring'''
__a : Optional[int] = self.num_labels
__a : str = LiltForTokenClassification(config=__a )
model.to(__a )
model.eval()
__a : List[Any] = model(
__a , bbox=__a , attention_mask=__a , token_type_ids=__a , labels=__a )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def __UpperCAmelCase ( self , __a , __a , __a , __a , __a , __a , __a , ):
'''simple docstring'''
__a : str = LiltForQuestionAnswering(config=__a )
model.to(__a )
model.eval()
__a : int = model(
__a , bbox=__a , attention_mask=__a , token_type_ids=__a , start_positions=__a , end_positions=__a , )
self.parent.assertEqual(result.start_logits.shape , (self.batch_size, self.seq_length) )
self.parent.assertEqual(result.end_logits.shape , (self.batch_size, self.seq_length) )
def __UpperCAmelCase ( self ):
'''simple docstring'''
__a : Optional[int] = self.prepare_config_and_inputs()
(
__a
) : Tuple = config_and_inputs
__a : int = {
'input_ids': input_ids,
'bbox': bbox,
'token_type_ids': token_type_ids,
'attention_mask': input_mask,
}
return config, inputs_dict
@require_torch
class __UpperCamelCase ( lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , unittest.TestCase ):
"""simple docstring"""
A_ = (
(
LiltModel,
LiltForSequenceClassification,
LiltForTokenClassification,
LiltForQuestionAnswering,
)
if is_torch_available()
else ()
)
A_ = (
{
"feature-extraction": LiltModel,
"question-answering": LiltForQuestionAnswering,
"text-classification": LiltForSequenceClassification,
"token-classification": LiltForTokenClassification,
"zero-shot": LiltForSequenceClassification,
}
if is_torch_available()
else {}
)
A_ = False
A_ = False
def __UpperCAmelCase ( self , __a , __a , __a , __a , __a ):
'''simple docstring'''
return True
def __UpperCAmelCase ( self ):
'''simple docstring'''
__a : Dict = LiltModelTester(self )
__a : Union[str, Any] = ConfigTester(self , config_class=__a , hidden_size=37 )
def __UpperCAmelCase ( self ):
'''simple docstring'''
self.config_tester.run_common_tests()
def __UpperCAmelCase ( self ):
'''simple docstring'''
__a : Optional[int] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*__a )
def __UpperCAmelCase ( self ):
'''simple docstring'''
__a : Optional[int] = self.model_tester.prepare_config_and_inputs()
for type in ["absolute", "relative_key", "relative_key_query"]:
__a : Dict = type
self.model_tester.create_and_check_model(*__a )
def __UpperCAmelCase ( self ):
'''simple docstring'''
__a : Optional[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_token_classification(*__a )
def __UpperCAmelCase ( self ):
'''simple docstring'''
__a : Dict = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_question_answering(*__a )
@slow
def __UpperCAmelCase ( self ):
'''simple docstring'''
for model_name in LILT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
__a : Optional[int] = LiltModel.from_pretrained(__a )
self.assertIsNotNone(__a )
@require_torch
@slow
class __UpperCamelCase ( unittest.TestCase ):
"""simple docstring"""
def __UpperCAmelCase ( self ):
'''simple docstring'''
__a : Union[str, Any] = LiltModel.from_pretrained('SCUT-DLVCLab/lilt-roberta-en-base' ).to(__a )
__a : List[Any] = torch.tensor([[1, 2]] , device=__a )
__a : List[Any] = torch.tensor([[[1, 2, 3, 4], [5, 6, 7, 8]]] , device=__a )
# forward pass
with torch.no_grad():
__a : Union[str, Any] = model(input_ids=__a , bbox=__a )
__a : Union[str, Any] = torch.Size([1, 2, 768] )
__a : Dict = torch.tensor(
[[-0.0653, 0.0950, -0.0061], [-0.0545, 0.0926, -0.0324]] , device=__a , )
self.assertTrue(outputs.last_hidden_state.shape , __a )
self.assertTrue(torch.allclose(outputs.last_hidden_state[0, :, :3] , __a , atol=1E-3 ) )
| 352 |
'''simple docstring'''
from dataclasses import dataclass
from typing import Optional, Tuple
import torch
from torch import nn
from transformers import RobertaPreTrainedModel, XLMRobertaConfig, XLMRobertaModel
from transformers.utils import ModelOutput
@dataclass
class __UpperCamelCase ( lowerCAmelCase_ ):
A_ = None
A_ = None
A_ = None
A_ = None
class __UpperCamelCase ( lowerCAmelCase_ ):
def __init__( self , __a=1 , __a=0 , __a=2 , __a=512 , __a="cls" , __a=False , __a=True , **__a , ):
'''simple docstring'''
super().__init__(pad_token_id=__a , bos_token_id=__a , eos_token_id=__a , **__a )
__a : Any = project_dim
__a : Optional[Any] = pooler_fn
__a : int = learn_encoder
__a : str = use_attention_mask
class __UpperCamelCase ( lowerCAmelCase_ ):
A_ = [r"pooler", r"logit_scale"]
A_ = [r"position_ids", r"predictions.decoder.bias"]
A_ = "roberta"
A_ = RobertaSeriesConfig
def __init__( self , __a ):
'''simple docstring'''
super().__init__(__a )
__a : Optional[Any] = XLMRobertaModel(__a )
__a : str = nn.Linear(config.hidden_size , config.project_dim )
__a : Optional[int] = getattr(__a , 'has_pre_transformation' , __a )
if self.has_pre_transformation:
__a : int = nn.Linear(config.hidden_size , config.project_dim )
__a : List[str] = nn.LayerNorm(config.hidden_size , eps=config.layer_norm_eps )
self.post_init()
def __UpperCAmelCase ( self , __a = None , __a = None , __a = None , __a = None , __a = None , __a = None , __a = None , __a = None , __a = None , __a = None , __a = None , ):
'''simple docstring'''
__a : Optional[Any] = return_dict if return_dict is not None else self.config.use_return_dict
__a : Tuple = self.base_model(
input_ids=__a , attention_mask=__a , token_type_ids=__a , position_ids=__a , head_mask=__a , inputs_embeds=__a , encoder_hidden_states=__a , encoder_attention_mask=__a , output_attentions=__a , output_hidden_states=True if self.has_pre_transformation else output_hidden_states , return_dict=__a , )
if self.has_pre_transformation:
__a : Optional[Any] = outputs['hidden_states'][-2]
__a : Optional[int] = self.pre_LN(__a )
__a : Union[str, Any] = self.transformation_pre(__a )
return TransformationModelOutput(
projection_state=__a , last_hidden_state=outputs.last_hidden_state , hidden_states=outputs.hidden_states , attentions=outputs.attentions , )
else:
__a : Optional[Any] = self.transformation(outputs.last_hidden_state )
return TransformationModelOutput(
projection_state=__a , last_hidden_state=outputs.last_hidden_state , hidden_states=outputs.hidden_states , attentions=outputs.attentions , )
| 294 | 0 |
import tempfile
import torch
from diffusers import PNDMScheduler
from .test_schedulers import SchedulerCommonTest
class __snake_case ( lowerCAmelCase ):
_a : str= (PNDMScheduler,)
_a : List[Any]= (("num_inference_steps", 50),)
def _SCREAMING_SNAKE_CASE ( self ,**snake_case ):
'''simple docstring'''
lowercase : Dict = {
"""num_train_timesteps""": 1000,
"""beta_start""": 0.0_001,
"""beta_end""": 0.02,
"""beta_schedule""": """linear""",
}
config.update(**snake_case )
return config
def _SCREAMING_SNAKE_CASE ( self ,snake_case=0 ,**snake_case ):
'''simple docstring'''
lowercase : Union[str, Any] = dict(self.forward_default_kwargs )
lowercase : List[str] = kwargs.pop("""num_inference_steps""" ,snake_case )
lowercase : Optional[Any] = self.dummy_sample
lowercase : Optional[int] = 0.1 * sample
lowercase : Dict = [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]
for scheduler_class in self.scheduler_classes:
lowercase : str = self.get_scheduler_config(**snake_case )
lowercase : Union[str, Any] = scheduler_class(**snake_case )
scheduler.set_timesteps(snake_case )
# copy over dummy past residuals
lowercase : Dict = dummy_past_residuals[:]
with tempfile.TemporaryDirectory() as tmpdirname:
scheduler.save_config(snake_case )
lowercase : str = scheduler_class.from_pretrained(snake_case )
new_scheduler.set_timesteps(snake_case )
# copy over dummy past residuals
lowercase : Dict = dummy_past_residuals[:]
lowercase : Optional[int] = scheduler.step_prk(snake_case ,snake_case ,snake_case ,**snake_case ).prev_sample
lowercase : Tuple = new_scheduler.step_prk(snake_case ,snake_case ,snake_case ,**snake_case ).prev_sample
assert torch.sum(torch.abs(output - new_output ) ) < 1e-5, "Scheduler outputs are not identical"
lowercase : Tuple = scheduler.step_plms(snake_case ,snake_case ,snake_case ,**snake_case ).prev_sample
lowercase : Union[str, Any] = new_scheduler.step_plms(snake_case ,snake_case ,snake_case ,**snake_case ).prev_sample
assert torch.sum(torch.abs(output - new_output ) ) < 1e-5, "Scheduler outputs are not identical"
def _SCREAMING_SNAKE_CASE ( self ):
'''simple docstring'''
pass
def _SCREAMING_SNAKE_CASE ( self ,snake_case=0 ,**snake_case ):
'''simple docstring'''
lowercase : int = dict(self.forward_default_kwargs )
lowercase : int = kwargs.pop("""num_inference_steps""" ,snake_case )
lowercase : Dict = self.dummy_sample
lowercase : Dict = 0.1 * sample
lowercase : Any = [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]
for scheduler_class in self.scheduler_classes:
lowercase : Optional[Any] = self.get_scheduler_config()
lowercase : Tuple = scheduler_class(**snake_case )
scheduler.set_timesteps(snake_case )
# copy over dummy past residuals (must be after setting timesteps)
lowercase : Any = dummy_past_residuals[:]
with tempfile.TemporaryDirectory() as tmpdirname:
scheduler.save_config(snake_case )
lowercase : Any = scheduler_class.from_pretrained(snake_case )
# copy over dummy past residuals
new_scheduler.set_timesteps(snake_case )
# copy over dummy past residual (must be after setting timesteps)
lowercase : str = dummy_past_residuals[:]
lowercase : List[Any] = scheduler.step_prk(snake_case ,snake_case ,snake_case ,**snake_case ).prev_sample
lowercase : List[str] = new_scheduler.step_prk(snake_case ,snake_case ,snake_case ,**snake_case ).prev_sample
assert torch.sum(torch.abs(output - new_output ) ) < 1e-5, "Scheduler outputs are not identical"
lowercase : Any = scheduler.step_plms(snake_case ,snake_case ,snake_case ,**snake_case ).prev_sample
lowercase : Optional[int] = new_scheduler.step_plms(snake_case ,snake_case ,snake_case ,**snake_case ).prev_sample
assert torch.sum(torch.abs(output - new_output ) ) < 1e-5, "Scheduler outputs are not identical"
def _SCREAMING_SNAKE_CASE ( self ,**snake_case ):
'''simple docstring'''
lowercase : Optional[Any] = self.scheduler_classes[0]
lowercase : Tuple = self.get_scheduler_config(**snake_case )
lowercase : str = scheduler_class(**snake_case )
lowercase : Tuple = 10
lowercase : Union[str, Any] = self.dummy_model()
lowercase : List[Any] = self.dummy_sample_deter
scheduler.set_timesteps(snake_case )
for i, t in enumerate(scheduler.prk_timesteps ):
lowercase : Tuple = model(snake_case ,snake_case )
lowercase : int = scheduler.step_prk(snake_case ,snake_case ,snake_case ).prev_sample
for i, t in enumerate(scheduler.plms_timesteps ):
lowercase : List[Any] = model(snake_case ,snake_case )
lowercase : Optional[int] = scheduler.step_plms(snake_case ,snake_case ,snake_case ).prev_sample
return sample
def _SCREAMING_SNAKE_CASE ( self ):
'''simple docstring'''
lowercase : List[str] = dict(self.forward_default_kwargs )
lowercase : Tuple = kwargs.pop("""num_inference_steps""" ,snake_case )
for scheduler_class in self.scheduler_classes:
lowercase : List[str] = self.get_scheduler_config()
lowercase : Optional[int] = scheduler_class(**snake_case )
lowercase : Optional[Any] = self.dummy_sample
lowercase : List[str] = 0.1 * sample
if num_inference_steps is not None and hasattr(snake_case ,"""set_timesteps""" ):
scheduler.set_timesteps(snake_case )
elif num_inference_steps is not None and not hasattr(snake_case ,"""set_timesteps""" ):
lowercase : Optional[Any] = num_inference_steps
# copy over dummy past residuals (must be done after set_timesteps)
lowercase : Tuple = [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]
lowercase : Optional[Any] = dummy_past_residuals[:]
lowercase : Tuple = scheduler.step_prk(snake_case ,0 ,snake_case ,**snake_case ).prev_sample
lowercase : Union[str, Any] = scheduler.step_prk(snake_case ,1 ,snake_case ,**snake_case ).prev_sample
self.assertEqual(output_a.shape ,sample.shape )
self.assertEqual(output_a.shape ,output_a.shape )
lowercase : List[str] = scheduler.step_plms(snake_case ,0 ,snake_case ,**snake_case ).prev_sample
lowercase : Any = scheduler.step_plms(snake_case ,1 ,snake_case ,**snake_case ).prev_sample
self.assertEqual(output_a.shape ,sample.shape )
self.assertEqual(output_a.shape ,output_a.shape )
def _SCREAMING_SNAKE_CASE ( self ):
'''simple docstring'''
for timesteps in [100, 1000]:
self.check_over_configs(num_train_timesteps=snake_case )
def _SCREAMING_SNAKE_CASE ( self ):
'''simple docstring'''
for steps_offset in [0, 1]:
self.check_over_configs(steps_offset=snake_case )
lowercase : Optional[Any] = self.scheduler_classes[0]
lowercase : Optional[Any] = self.get_scheduler_config(steps_offset=1 )
lowercase : Optional[int] = scheduler_class(**snake_case )
scheduler.set_timesteps(10 )
assert torch.equal(
scheduler.timesteps ,torch.LongTensor(
[901, 851, 851, 801, 801, 751, 751, 701, 701, 651, 651, 601, 601, 501, 401, 301, 201, 101, 1] ) ,)
def _SCREAMING_SNAKE_CASE ( self ):
'''simple docstring'''
for beta_start, beta_end in zip([0.0_001, 0.001] ,[0.002, 0.02] ):
self.check_over_configs(beta_start=snake_case ,beta_end=snake_case )
def _SCREAMING_SNAKE_CASE ( self ):
'''simple docstring'''
for schedule in ["linear", "squaredcos_cap_v2"]:
self.check_over_configs(beta_schedule=snake_case )
def _SCREAMING_SNAKE_CASE ( self ):
'''simple docstring'''
for prediction_type in ["epsilon", "v_prediction"]:
self.check_over_configs(prediction_type=snake_case )
def _SCREAMING_SNAKE_CASE ( self ):
'''simple docstring'''
for t in [1, 5, 10]:
self.check_over_forward(time_step=snake_case )
def _SCREAMING_SNAKE_CASE ( self ):
'''simple docstring'''
for t, num_inference_steps in zip([1, 5, 10] ,[10, 50, 100] ):
self.check_over_forward(num_inference_steps=snake_case )
def _SCREAMING_SNAKE_CASE ( self ):
'''simple docstring'''
lowercase : Union[str, Any] = 27
for scheduler_class in self.scheduler_classes:
lowercase : List[str] = self.dummy_sample
lowercase : Dict = 0.1 * sample
lowercase : Any = self.get_scheduler_config()
lowercase : int = scheduler_class(**snake_case )
scheduler.set_timesteps(snake_case )
# before power of 3 fix, would error on first step, so we only need to do two
for i, t in enumerate(scheduler.prk_timesteps[:2] ):
lowercase : int = scheduler.step_prk(snake_case ,snake_case ,snake_case ).prev_sample
def _SCREAMING_SNAKE_CASE ( self ):
'''simple docstring'''
with self.assertRaises(snake_case ):
lowercase : List[Any] = self.scheduler_classes[0]
lowercase : int = self.get_scheduler_config()
lowercase : List[Any] = scheduler_class(**snake_case )
scheduler.step_plms(self.dummy_sample ,1 ,self.dummy_sample ).prev_sample
def _SCREAMING_SNAKE_CASE ( self ):
'''simple docstring'''
lowercase : Optional[int] = self.full_loop()
lowercase : Optional[Any] = torch.sum(torch.abs(snake_case ) )
lowercase : str = torch.mean(torch.abs(snake_case ) )
assert abs(result_sum.item() - 198.1_318 ) < 1e-2
assert abs(result_mean.item() - 0.2_580 ) < 1e-3
def _SCREAMING_SNAKE_CASE ( self ):
'''simple docstring'''
lowercase : List[str] = self.full_loop(prediction_type="""v_prediction""" )
lowercase : str = torch.sum(torch.abs(snake_case ) )
lowercase : Optional[int] = torch.mean(torch.abs(snake_case ) )
assert abs(result_sum.item() - 67.3_986 ) < 1e-2
assert abs(result_mean.item() - 0.0_878 ) < 1e-3
def _SCREAMING_SNAKE_CASE ( self ):
'''simple docstring'''
lowercase : Tuple = self.full_loop(set_alpha_to_one=snake_case ,beta_start=0.01 )
lowercase : Optional[int] = torch.sum(torch.abs(snake_case ) )
lowercase : Union[str, Any] = torch.mean(torch.abs(snake_case ) )
assert abs(result_sum.item() - 230.0_399 ) < 1e-2
assert abs(result_mean.item() - 0.2_995 ) < 1e-3
def _SCREAMING_SNAKE_CASE ( self ):
'''simple docstring'''
lowercase : Any = self.full_loop(set_alpha_to_one=snake_case ,beta_start=0.01 )
lowercase : str = torch.sum(torch.abs(snake_case ) )
lowercase : Any = torch.mean(torch.abs(snake_case ) )
assert abs(result_sum.item() - 186.9_482 ) < 1e-2
assert abs(result_mean.item() - 0.2_434 ) < 1e-3
| 20 |
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:
_lowercase : List[str] =None
_lowercase : Union[str, Any] =logging.get_logger(__name__)
_lowercase : Optional[int] ={"vocab_file": "sentencepiece.bpe.model", "tokenizer_file": "tokenizer.json"}
_lowercase : Dict ={
"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"
),
},
}
_lowercase : str ={
"moussaKam/mbarthez": 1024,
"moussaKam/barthez": 1024,
"moussaKam/barthez-orangesum-title": 1024,
}
_lowercase : Dict ="▁"
class snake_case__ (A__ ):
"""simple docstring"""
__lowerCAmelCase :Union[str, Any] = VOCAB_FILES_NAMES
__lowerCAmelCase :Optional[int] = PRETRAINED_VOCAB_FILES_MAP
__lowerCAmelCase :int = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
__lowerCAmelCase :Any = ["input_ids", "attention_mask"]
__lowerCAmelCase :Any = BarthezTokenizer
def __init__( self , __lowercase=None , __lowercase=None , __lowercase="<s>" , __lowercase="</s>" , __lowercase="</s>" , __lowercase="<s>" , __lowercase="<unk>" , __lowercase="<pad>" , __lowercase="<mask>" , **__lowercase , ) -> str:
"""simple docstring"""
a__ : int = AddedToken(__lowercase , lstrip=__lowercase , rstrip=__lowercase ) if isinstance(__lowercase , __lowercase ) else mask_token
super().__init__(
__lowercase , tokenizer_file=__lowercase , bos_token=__lowercase , eos_token=__lowercase , unk_token=__lowercase , sep_token=__lowercase , cls_token=__lowercase , pad_token=__lowercase , mask_token=__lowercase , **__lowercase , )
a__ : List[str] = vocab_file
a__ : List[Any] = False if not self.vocab_file else True
def SCREAMING_SNAKE_CASE__( self , __lowercase , __lowercase = None ) -> List[int]:
"""simple docstring"""
if token_ids_a is None:
return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
a__ : Tuple = [self.cls_token_id]
a__ : List[str] = [self.sep_token_id]
return cls + token_ids_a + sep + sep + token_ids_a + sep
def SCREAMING_SNAKE_CASE__( self , __lowercase , __lowercase = None ) -> List[int]:
"""simple docstring"""
a__ : List[Any] = [self.sep_token_id]
a__ : str = [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 SCREAMING_SNAKE_CASE__( self , __lowercase , __lowercase = None ) -> Tuple[str]:
"""simple docstring"""
if not self.can_save_slow_tokenizer:
raise ValueError(
"""Your fast tokenizer does not have the necessary information to save the vocabulary for a slow """
"""tokenizer.""" )
if not os.path.isdir(__lowercase ):
logger.error(F'''Vocabulary path ({save_directory}) should be a directory''' )
return
a__ : Tuple = os.path.join(
__lowercase , (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(__lowercase ):
copyfile(self.vocab_file , __lowercase )
return (out_vocab_file,)
| 170 | 0 |
import unittest
import numpy as np
import torch
from diffusers import PNDMPipeline, PNDMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device
enable_full_determinism()
class a_ ( unittest.TestCase ):
"""simple docstring"""
@property
def __lowerCAmelCase ( self ) ->List[Any]:
torch.manual_seed(0 )
SCREAMING_SNAKE_CASE : Optional[Any] = UNetaDModel(
block_out_channels=(32, 64) , layers_per_block=2 , sample_size=32 , in_channels=3 , out_channels=3 , down_block_types=('''DownBlock2D''', '''AttnDownBlock2D''') , up_block_types=('''AttnUpBlock2D''', '''UpBlock2D''') , )
return model
def __lowerCAmelCase ( self ) ->Optional[Any]:
SCREAMING_SNAKE_CASE : Optional[int] = self.dummy_uncond_unet
SCREAMING_SNAKE_CASE : Tuple = PNDMScheduler()
SCREAMING_SNAKE_CASE : List[Any] = PNDMPipeline(unet=_lowerCamelCase , scheduler=_lowerCamelCase )
pndm.to(_lowerCamelCase )
pndm.set_progress_bar_config(disable=_lowerCamelCase )
SCREAMING_SNAKE_CASE : Dict = torch.manual_seed(0 )
SCREAMING_SNAKE_CASE : List[Any] = pndm(generator=_lowerCamelCase , num_inference_steps=20 , output_type='''numpy''' ).images
SCREAMING_SNAKE_CASE : str = torch.manual_seed(0 )
SCREAMING_SNAKE_CASE : int = pndm(generator=_lowerCamelCase , num_inference_steps=20 , output_type='''numpy''' , return_dict=_lowerCamelCase )[0]
SCREAMING_SNAKE_CASE : Optional[int] = image[0, -3:, -3:, -1]
SCREAMING_SNAKE_CASE : Optional[Any] = image_from_tuple[0, -3:, -3:, -1]
assert image.shape == (1, 32, 32, 3)
SCREAMING_SNAKE_CASE : int = np.array([1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
assert np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 1e-2
@slow
@require_torch
class a_ ( unittest.TestCase ):
"""simple docstring"""
def __lowerCAmelCase ( self ) ->Tuple:
SCREAMING_SNAKE_CASE : str = """google/ddpm-cifar10-32"""
SCREAMING_SNAKE_CASE : Dict = UNetaDModel.from_pretrained(_lowerCamelCase )
SCREAMING_SNAKE_CASE : Dict = PNDMScheduler()
SCREAMING_SNAKE_CASE : Union[str, Any] = PNDMPipeline(unet=_lowerCamelCase , scheduler=_lowerCamelCase )
pndm.to(_lowerCamelCase )
pndm.set_progress_bar_config(disable=_lowerCamelCase )
SCREAMING_SNAKE_CASE : Any = torch.manual_seed(0 )
SCREAMING_SNAKE_CASE : Union[str, Any] = pndm(generator=_lowerCamelCase , output_type='''numpy''' ).images
SCREAMING_SNAKE_CASE : Optional[int] = image[0, -3:, -3:, -1]
assert image.shape == (1, 32, 32, 3)
SCREAMING_SNAKE_CASE : List[Any] = np.array([0.1_5_6_4, 0.1_4_6_4_5, 0.1_4_0_6, 0.1_4_7_1_5, 0.1_2_4_2_5, 0.1_4_0_4_5, 0.1_3_1_1_5, 0.1_2_1_7_5, 0.1_2_5] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
| 350 |
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
a__ : Tuple = '''▁'''
a__ : List[Any] = {'''vocab_file''': '''spiece.model'''}
a__ : Optional[Any] = {
'''vocab_file''': {'''google/pegasus-xsum''': '''https://huggingface.co/google/pegasus-xsum/resolve/main/spiece.model'''}
}
a__ : str = {
'''google/pegasus-xsum''': 512,
}
a__ : str = logging.get_logger(__name__)
class a_ ( a__ ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE : str = VOCAB_FILES_NAMES
__SCREAMING_SNAKE_CASE : str = VOCAB_FILES_NAMES
__SCREAMING_SNAKE_CASE : Tuple = PRETRAINED_VOCAB_FILES_MAP
__SCREAMING_SNAKE_CASE : Optional[int] = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
__SCREAMING_SNAKE_CASE : str = ['input_ids', 'attention_mask']
def __init__( self , _lowerCamelCase , _lowerCamelCase="<pad>" , _lowerCamelCase="</s>" , _lowerCamelCase="<unk>" , _lowerCamelCase="<mask_2>" , _lowerCamelCase="<mask_1>" , _lowerCamelCase=None , _lowerCamelCase=103 , _lowerCamelCase = None , **_lowerCamelCase , ) ->None:
SCREAMING_SNAKE_CASE : Dict = offset
if additional_special_tokens is not None:
if not isinstance(_lowerCamelCase , _lowerCamelCase ):
raise TypeError(
F"""additional_special_tokens should be of type {type(_lowerCamelCase )}, but is"""
F""" {type(_lowerCamelCase )}""" )
SCREAMING_SNAKE_CASE : List[Any] = (
([mask_token_sent] + additional_special_tokens)
if mask_token_sent not in additional_special_tokens and mask_token_sent is not None
else additional_special_tokens
)
# fill additional tokens with ..., <unk_token_102> in case not all additional tokens are already taken
additional_special_tokens_extended += [
F"""<unk_{i}>""" for i in range(len(_lowerCamelCase ) , self.offset - 1 )
]
if len(set(_lowerCamelCase ) ) != len(_lowerCamelCase ):
raise ValueError(
'''Please make sure that the provided additional_special_tokens do not contain an incorrectly'''
F""" shifted list of <unk_x> tokens. Found {additional_special_tokens_extended}.""" )
SCREAMING_SNAKE_CASE : Dict = additional_special_tokens_extended
else:
SCREAMING_SNAKE_CASE : str = [mask_token_sent] if mask_token_sent is not None else []
additional_special_tokens += [F"""<unk_{i}>""" for i in range(2 , self.offset )]
SCREAMING_SNAKE_CASE : Union[str, Any] = {} if sp_model_kwargs is None else sp_model_kwargs
super().__init__(
eos_token=_lowerCamelCase , unk_token=_lowerCamelCase , mask_token=_lowerCamelCase , pad_token=_lowerCamelCase , mask_token_sent=_lowerCamelCase , offset=_lowerCamelCase , additional_special_tokens=_lowerCamelCase , sp_model_kwargs=self.sp_model_kwargs , **_lowerCamelCase , )
SCREAMING_SNAKE_CASE : List[str] = mask_token_sent
SCREAMING_SNAKE_CASE : Optional[int] = vocab_file
SCREAMING_SNAKE_CASE : Tuple = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(_lowerCamelCase )
# add special tokens to encoder dict
SCREAMING_SNAKE_CASE : Dict[int, str] = {
0: self.pad_token,
1: self.eos_token,
}
if self.mask_token_sent is not None:
self.encoder.update(
{
2: self.mask_token_sent,
3: self.mask_token,
} )
if self.offset > 0:
# entries 2-104 are only used for pretraining and called <mask_1>, <mask_2>, unk_2, ...unk_102
# mask_token_sent is already added to list -> so start at 1
self.encoder.update({i + 3: additional_special_tokens[i] for i in range(1 , self.offset - 1 )} )
SCREAMING_SNAKE_CASE : Dict[str, int] = {v: k for k, v in self.encoder.items()}
@property
def __lowerCAmelCase ( self ) ->int:
return len(self.sp_model ) + self.offset
def __lowerCAmelCase ( self ) ->Dict[str, int]:
SCREAMING_SNAKE_CASE : Union[str, Any] = {self.convert_ids_to_tokens(_lowerCamelCase ): i for i in range(self.vocab_size )}
vocab.update(self.added_tokens_encoder )
return vocab
def __getstate__( self ) ->Optional[Any]:
SCREAMING_SNAKE_CASE : Optional[int] = self.__dict__.copy()
SCREAMING_SNAKE_CASE : str = None
return state
def __setstate__( self , _lowerCamelCase ) ->Union[str, Any]:
SCREAMING_SNAKE_CASE : Optional[Any] = d
# for backward compatibility
if not hasattr(self , '''sp_model_kwargs''' ):
SCREAMING_SNAKE_CASE : List[str] = {}
SCREAMING_SNAKE_CASE : str = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(self.vocab_file )
def __lowerCAmelCase ( self , _lowerCamelCase ) ->List[str]:
return self.sp_model.encode(_lowerCamelCase , out_type=_lowerCamelCase )
def __lowerCAmelCase ( self , _lowerCamelCase ) ->int:
if token in self.decoder:
return self.decoder[token]
elif token in self.added_tokens_decoder:
return self.added_tokens_decoder[token]
SCREAMING_SNAKE_CASE : List[str] = self.sp_model.piece_to_id(_lowerCamelCase )
return sp_id + self.offset
def __lowerCAmelCase ( self , _lowerCamelCase ) ->str:
if index in self.encoder:
return self.encoder[index]
elif index in self.added_tokens_encoder:
return self.added_tokens_encoder[index]
else:
SCREAMING_SNAKE_CASE : Dict = self.sp_model.IdToPiece(index - self.offset )
return token
def __lowerCAmelCase ( self , _lowerCamelCase ) ->Union[str, Any]:
SCREAMING_SNAKE_CASE : Dict = []
SCREAMING_SNAKE_CASE : int = ''''''
for token in tokens:
# make sure that special tokens are not decoded using sentencepiece model
if token in self.all_special_tokens:
out_string += self.sp_model.decode(_lowerCamelCase ) + token
SCREAMING_SNAKE_CASE : Optional[Any] = []
else:
current_sub_tokens.append(_lowerCamelCase )
out_string += self.sp_model.decode(_lowerCamelCase )
return out_string.strip()
def __lowerCAmelCase ( self , _lowerCamelCase=False ) ->str:
return 1
def __lowerCAmelCase ( self , _lowerCamelCase ) ->int:
SCREAMING_SNAKE_CASE : Dict = set(self.all_special_ids ) # call it once instead of inside list comp
all_special_ids.remove(self.unk_token_id ) # <unk> is only sometimes special
return [1 if x in all_special_ids else 0 for x in seq]
def __lowerCAmelCase ( self , _lowerCamelCase , _lowerCamelCase = None , _lowerCamelCase = False ) ->List[int]:
if already_has_special_tokens:
return self._special_token_mask(_lowerCamelCase )
elif token_ids_a is None:
return self._special_token_mask(_lowerCamelCase ) + [1]
else:
return self._special_token_mask(token_ids_a + token_ids_a ) + [1]
def __lowerCAmelCase ( self , _lowerCamelCase , _lowerCamelCase=None ) ->List[int]:
if token_ids_a is None:
return token_ids_a + [self.eos_token_id]
# We don't expect to process pairs, but leave the pair logic for API consistency
return token_ids_a + token_ids_a + [self.eos_token_id]
def __lowerCAmelCase ( self , _lowerCamelCase , _lowerCamelCase = None ) ->Tuple[str]:
if not os.path.isdir(_lowerCamelCase ):
logger.error(F"""Vocabulary path ({save_directory}) should be a directory""" )
return
SCREAMING_SNAKE_CASE : int = os.path.join(
_lowerCamelCase , (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file'''] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(_lowerCamelCase ) and os.path.isfile(self.vocab_file ):
copyfile(self.vocab_file , _lowerCamelCase )
elif not os.path.isfile(self.vocab_file ):
with open(_lowerCamelCase , '''wb''' ) as fi:
SCREAMING_SNAKE_CASE : Tuple = self.sp_model.serialized_model_proto()
fi.write(_lowerCamelCase )
return (out_vocab_file,)
| 19 | 0 |
"""simple docstring"""
from __future__ import annotations
lowerCAmelCase__ = '''#'''
class SCREAMING_SNAKE_CASE__ :
"""simple docstring"""
def __init__( self ):
"""simple docstring"""
lowerCAmelCase : dict = {}
def lowercase__ ( self , snake_case__ ):
"""simple docstring"""
lowerCAmelCase : List[str] = self._trie
for char in text:
if char not in trie:
lowerCAmelCase : List[Any] = {}
lowerCAmelCase : List[str] = trie[char]
lowerCAmelCase : Optional[Any] = True
def lowercase__ ( self , snake_case__ ):
"""simple docstring"""
lowerCAmelCase : Any = self._trie
for char in prefix:
if char in trie:
lowerCAmelCase : List[str] = trie[char]
else:
return []
return self._elements(snake_case__ )
def lowercase__ ( self , snake_case__ ):
"""simple docstring"""
lowerCAmelCase : Optional[Any] = []
for c, v in d.items():
lowerCAmelCase : Dict = [" "] if c == END else [(c + s) for s in self._elements(snake_case__ )]
result.extend(snake_case__ )
return tuple(snake_case__ )
lowerCAmelCase__ = Trie()
lowerCAmelCase__ = ('''depart''', '''detergent''', '''daring''', '''dog''', '''deer''', '''deal''')
for word in words:
trie.insert_word(word)
def a__ ( SCREAMING_SNAKE_CASE : str ):
'''simple docstring'''
lowerCAmelCase : Union[str, Any] = trie.find_word(SCREAMING_SNAKE_CASE )
return tuple(string + word for word in suffixes )
def a__ ( ):
'''simple docstring'''
print(autocomplete_using_trie("de" ) )
if __name__ == "__main__":
import doctest
doctest.testmod()
main()
| 108 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__lowerCAmelCase : List[Any] = logging.get_logger(__name__)
__lowerCAmelCase : int = {
'microsoft/biogpt': 'https://huggingface.co/microsoft/biogpt/resolve/main/config.json',
# See all BioGPT models at https://huggingface.co/models?filter=biogpt
}
class UpperCAmelCase_ ( _A ):
'''simple docstring'''
a__ = """biogpt"""
def __init__( self : List[str] , UpperCamelCase__ : Optional[Any]=4_2384 , UpperCamelCase__ : Union[str, Any]=1024 , UpperCamelCase__ : Any=24 , UpperCamelCase__ : Union[str, Any]=16 , UpperCamelCase__ : Tuple=4096 , UpperCamelCase__ : Tuple="gelu" , UpperCamelCase__ : Dict=0.1 , UpperCamelCase__ : Dict=0.1 , UpperCamelCase__ : str=1024 , UpperCamelCase__ : Any=0.02 , UpperCamelCase__ : List[str]=1E-12 , UpperCamelCase__ : Dict=True , UpperCamelCase__ : Optional[int]=True , UpperCamelCase__ : Union[str, Any]=0.0 , UpperCamelCase__ : Any=0.0 , UpperCamelCase__ : List[str]=1 , UpperCamelCase__ : Dict=0 , UpperCamelCase__ : List[str]=2 , **UpperCamelCase__ : Optional[int] , ) -> Tuple:
"""simple docstring"""
__magic_name__ = vocab_size
__magic_name__ = max_position_embeddings
__magic_name__ = hidden_size
__magic_name__ = num_hidden_layers
__magic_name__ = num_attention_heads
__magic_name__ = intermediate_size
__magic_name__ = hidden_act
__magic_name__ = hidden_dropout_prob
__magic_name__ = attention_probs_dropout_prob
__magic_name__ = initializer_range
__magic_name__ = layer_norm_eps
__magic_name__ = scale_embedding
__magic_name__ = use_cache
__magic_name__ = layerdrop
__magic_name__ = activation_dropout
super().__init__(pad_token_id=UpperCamelCase__ , bos_token_id=UpperCamelCase__ , eos_token_id=UpperCamelCase__ , **UpperCamelCase__ )
| 88 | 0 |
"""simple docstring"""
__A = "Input must be a string of 8 numbers plus letter"
__A = "TRWAGMYFPDXBNJZSQVHLCKE"
def lowerCamelCase_ ( UpperCamelCase__ : str ) -> bool:
"""simple docstring"""
if not isinstance(UpperCamelCase__ , UpperCamelCase__ ):
__lowerCamelCase = F"""Expected string as input, found {type(UpperCamelCase__ ).__name__}"""
raise TypeError(UpperCamelCase__ )
__lowerCamelCase = spanish_id.replace('-' , '' ).upper()
if len(UpperCamelCase__ ) != 9:
raise ValueError(UpperCamelCase__ )
try:
__lowerCamelCase = int(spanish_id_clean[0:8] )
__lowerCamelCase = spanish_id_clean[8]
except ValueError as ex:
raise ValueError(UpperCamelCase__ ) from ex
if letter.isdigit():
raise ValueError(UpperCamelCase__ )
return letter == LOOKUP_LETTERS[number % 23]
if __name__ == "__main__":
import doctest
doctest.testmod()
| 356 |
from dataclasses import dataclass
from typing import Dict, Optional, Union
import torch
import torch.nn.functional as F
from torch import nn
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .attention import BasicTransformerBlock
from .attention_processor import AttentionProcessor, AttnProcessor
from .embeddings import TimestepEmbedding, Timesteps
from .modeling_utils import ModelMixin
@dataclass
class __lowerCAmelCase ( __magic_name__ ):
"""simple docstring"""
snake_case_ = 42
class __lowerCAmelCase ( __magic_name__ , __magic_name__ ):
"""simple docstring"""
@register_to_config
def __init__( self , lowerCamelCase__ = 32 , lowerCamelCase__ = 64 , lowerCamelCase__ = 20 , lowerCamelCase__ = 768 , lowerCamelCase__=77 , lowerCamelCase__=4 , lowerCamelCase__ = 0.0 , lowerCamelCase__ = "silu" , lowerCamelCase__ = None , lowerCamelCase__ = None , lowerCamelCase__ = "linear" , lowerCamelCase__ = "prd" , lowerCamelCase__ = None , lowerCamelCase__ = None , lowerCamelCase__ = None , ) -> Tuple:
'''simple docstring'''
super().__init__()
__lowerCamelCase = num_attention_heads
__lowerCamelCase = attention_head_dim
__lowerCamelCase = num_attention_heads * attention_head_dim
__lowerCamelCase = additional_embeddings
__lowerCamelCase = time_embed_dim or inner_dim
__lowerCamelCase = embedding_proj_dim or embedding_dim
__lowerCamelCase = clip_embed_dim or embedding_dim
__lowerCamelCase = Timesteps(lowerCamelCase__ , lowerCamelCase__ , 0 )
__lowerCamelCase = TimestepEmbedding(lowerCamelCase__ , lowerCamelCase__ , out_dim=lowerCamelCase__ , act_fn=lowerCamelCase__ )
__lowerCamelCase = nn.Linear(lowerCamelCase__ , lowerCamelCase__ )
if embedding_proj_norm_type is None:
__lowerCamelCase = None
elif embedding_proj_norm_type == "layer":
__lowerCamelCase = nn.LayerNorm(lowerCamelCase__ )
else:
raise ValueError(f"""unsupported embedding_proj_norm_type: {embedding_proj_norm_type}""" )
__lowerCamelCase = nn.Linear(lowerCamelCase__ , lowerCamelCase__ )
if encoder_hid_proj_type is None:
__lowerCamelCase = None
elif encoder_hid_proj_type == "linear":
__lowerCamelCase = nn.Linear(lowerCamelCase__ , lowerCamelCase__ )
else:
raise ValueError(f"""unsupported encoder_hid_proj_type: {encoder_hid_proj_type}""" )
__lowerCamelCase = nn.Parameter(torch.zeros(1 , num_embeddings + additional_embeddings , lowerCamelCase__ ) )
if added_emb_type == "prd":
__lowerCamelCase = nn.Parameter(torch.zeros(1 , 1 , lowerCamelCase__ ) )
elif added_emb_type is None:
__lowerCamelCase = None
else:
raise ValueError(
f"""`added_emb_type`: {added_emb_type} is not supported. Make sure to choose one of `'prd'` or `None`.""" )
__lowerCamelCase = nn.ModuleList(
[
BasicTransformerBlock(
lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , dropout=lowerCamelCase__ , activation_fn='gelu' , attention_bias=lowerCamelCase__ , )
for d in range(lowerCamelCase__ )
] )
if norm_in_type == "layer":
__lowerCamelCase = nn.LayerNorm(lowerCamelCase__ )
elif norm_in_type is None:
__lowerCamelCase = None
else:
raise ValueError(f"""Unsupported norm_in_type: {norm_in_type}.""" )
__lowerCamelCase = nn.LayerNorm(lowerCamelCase__ )
__lowerCamelCase = nn.Linear(lowerCamelCase__ , lowerCamelCase__ )
__lowerCamelCase = torch.full(
[num_embeddings + additional_embeddings, num_embeddings + additional_embeddings] , -1_00_00.0 )
causal_attention_mask.triu_(1 )
__lowerCamelCase = causal_attention_mask[None, ...]
self.register_buffer('causal_attention_mask' , lowerCamelCase__ , persistent=lowerCamelCase__ )
__lowerCamelCase = nn.Parameter(torch.zeros(1 , lowerCamelCase__ ) )
__lowerCamelCase = nn.Parameter(torch.zeros(1 , lowerCamelCase__ ) )
@property
# Copied from diffusers.models.unet_2d_condition.UNet2DConditionModel.attn_processors
def lowercase_ ( self ) -> Dict[str, AttentionProcessor]:
'''simple docstring'''
__lowerCamelCase = {}
def fn_recursive_add_processors(lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ ):
if hasattr(lowerCamelCase__ , 'set_processor' ):
__lowerCamelCase = module.processor
for sub_name, child in module.named_children():
fn_recursive_add_processors(f"""{name}.{sub_name}""" , lowerCamelCase__ , lowerCamelCase__ )
return processors
for name, module in self.named_children():
fn_recursive_add_processors(lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ )
return processors
def lowercase_ ( self , lowerCamelCase__ ) -> Tuple:
'''simple docstring'''
__lowerCamelCase = len(self.attn_processors.keys() )
if isinstance(lowerCamelCase__ , lowerCamelCase__ ) and len(lowerCamelCase__ ) != count:
raise ValueError(
f"""A dict of processors was passed, but the number of processors {len(lowerCamelCase__ )} does not match the"""
f""" number of attention layers: {count}. Please make sure to pass {count} processor classes.""" )
def fn_recursive_attn_processor(lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ ):
if hasattr(lowerCamelCase__ , 'set_processor' ):
if not isinstance(lowerCamelCase__ , lowerCamelCase__ ):
module.set_processor(lowerCamelCase__ )
else:
module.set_processor(processor.pop(f"""{name}.processor""" ) )
for sub_name, child in module.named_children():
fn_recursive_attn_processor(f"""{name}.{sub_name}""" , lowerCamelCase__ , lowerCamelCase__ )
for name, module in self.named_children():
fn_recursive_attn_processor(lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ )
def lowercase_ ( self ) -> Optional[int]:
'''simple docstring'''
self.set_attn_processor(AttnProcessor() )
def lowercase_ ( self , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ = None , lowerCamelCase__ = None , lowerCamelCase__ = True , ) -> int:
'''simple docstring'''
__lowerCamelCase = hidden_states.shape[0]
__lowerCamelCase = timestep
if not torch.is_tensor(lowerCamelCase__ ):
__lowerCamelCase = torch.tensor([timesteps] , dtype=torch.long , device=hidden_states.device )
elif torch.is_tensor(lowerCamelCase__ ) and len(timesteps.shape ) == 0:
__lowerCamelCase = timesteps[None].to(hidden_states.device )
# broadcast to batch dimension in a way that's compatible with ONNX/Core ML
__lowerCamelCase = timesteps * torch.ones(lowerCamelCase__ , dtype=timesteps.dtype , device=timesteps.device )
__lowerCamelCase = self.time_proj(lowerCamelCase__ )
# timesteps does not contain any weights and will always return f32 tensors
# but time_embedding might be fp16, so we need to cast here.
__lowerCamelCase = timesteps_projected.to(dtype=self.dtype )
__lowerCamelCase = self.time_embedding(lowerCamelCase__ )
if self.embedding_proj_norm is not None:
__lowerCamelCase = self.embedding_proj_norm(lowerCamelCase__ )
__lowerCamelCase = self.embedding_proj(lowerCamelCase__ )
if self.encoder_hidden_states_proj is not None and encoder_hidden_states is not None:
__lowerCamelCase = self.encoder_hidden_states_proj(lowerCamelCase__ )
elif self.encoder_hidden_states_proj is not None and encoder_hidden_states is None:
raise ValueError('`encoder_hidden_states_proj` requires `encoder_hidden_states` to be set' )
__lowerCamelCase = self.proj_in(lowerCamelCase__ )
__lowerCamelCase = self.positional_embedding.to(hidden_states.dtype )
__lowerCamelCase = []
__lowerCamelCase = 0
if encoder_hidden_states is not None:
additional_embeds.append(lowerCamelCase__ )
additional_embeddings_len += encoder_hidden_states.shape[1]
if len(proj_embeddings.shape ) == 2:
__lowerCamelCase = proj_embeddings[:, None, :]
if len(hidden_states.shape ) == 2:
__lowerCamelCase = hidden_states[:, None, :]
__lowerCamelCase = additional_embeds + [
proj_embeddings,
time_embeddings[:, None, :],
hidden_states,
]
if self.prd_embedding is not None:
__lowerCamelCase = self.prd_embedding.to(hidden_states.dtype ).expand(lowerCamelCase__ , -1 , -1 )
additional_embeds.append(lowerCamelCase__ )
__lowerCamelCase = torch.cat(
lowerCamelCase__ , dim=1 , )
# Allow positional_embedding to not include the `addtional_embeddings` and instead pad it with zeros for these additional tokens
__lowerCamelCase = additional_embeddings_len + proj_embeddings.shape[1] + 1
if positional_embeddings.shape[1] < hidden_states.shape[1]:
__lowerCamelCase = F.pad(
lowerCamelCase__ , (
0,
0,
additional_embeddings_len,
self.prd_embedding.shape[1] if self.prd_embedding is not None else 0,
) , value=0.0 , )
__lowerCamelCase = hidden_states + positional_embeddings
if attention_mask is not None:
__lowerCamelCase = (1 - attention_mask.to(hidden_states.dtype )) * -1_00_00.0
__lowerCamelCase = F.pad(lowerCamelCase__ , (0, self.additional_embeddings) , value=0.0 )
__lowerCamelCase = (attention_mask[:, None, :] + self.causal_attention_mask).to(hidden_states.dtype )
__lowerCamelCase = attention_mask.repeat_interleave(self.config.num_attention_heads , dim=0 )
if self.norm_in is not None:
__lowerCamelCase = self.norm_in(lowerCamelCase__ )
for block in self.transformer_blocks:
__lowerCamelCase = block(lowerCamelCase__ , attention_mask=lowerCamelCase__ )
__lowerCamelCase = self.norm_out(lowerCamelCase__ )
if self.prd_embedding is not None:
__lowerCamelCase = hidden_states[:, -1]
else:
__lowerCamelCase = hidden_states[:, additional_embeddings_len:]
__lowerCamelCase = self.proj_to_clip_embeddings(lowerCamelCase__ )
if not return_dict:
return (predicted_image_embedding,)
return PriorTransformerOutput(predicted_image_embedding=lowerCamelCase__ )
def lowercase_ ( self , lowerCamelCase__ ) -> List[Any]:
'''simple docstring'''
__lowerCamelCase = (prior_latents * self.clip_std) + self.clip_mean
return prior_latents
| 348 | 0 |
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 , snake_case__ , snake_case__=13 , snake_case__=30 , snake_case__=2 , snake_case__=3 , snake_case__=True , snake_case__=True , snake_case__=32 , snake_case__=5 , snake_case__=4 , snake_case__=37 , snake_case__="gelu" , snake_case__=0.1 , snake_case__=0.1 , snake_case__=10 , snake_case__=0.02 , snake_case__=3 , snake_case__=0.6 , snake_case__=None , ):
'''simple docstring'''
UpperCamelCase_ = parent
UpperCamelCase_ = batch_size
UpperCamelCase_ = image_size
UpperCamelCase_ = patch_size
UpperCamelCase_ = num_channels
UpperCamelCase_ = is_training
UpperCamelCase_ = use_labels
UpperCamelCase_ = hidden_size
UpperCamelCase_ = num_hidden_layers
UpperCamelCase_ = num_attention_heads
UpperCamelCase_ = intermediate_size
UpperCamelCase_ = hidden_act
UpperCamelCase_ = hidden_dropout_prob
UpperCamelCase_ = attention_probs_dropout_prob
UpperCamelCase_ = type_sequence_label_size
UpperCamelCase_ = initializer_range
UpperCamelCase_ = mask_ratio
UpperCamelCase_ = scope
# in ViTMAE, the expected sequence length = (num_patches + 1) * (1 - config.mask_ratio), rounded above
# (we add 1 for the [CLS] token)
UpperCamelCase_ = (image_size // patch_size) ** 2
UpperCamelCase_ = int(math.ceil((1 - mask_ratio) * (num_patches + 1) ) )
def _lowerCamelCase ( self ):
'''simple docstring'''
UpperCamelCase_ = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
UpperCamelCase_ = None
if self.use_labels:
UpperCamelCase_ = ids_tensor([self.batch_size] , self.type_sequence_label_size )
UpperCamelCase_ = self.get_config()
return config, pixel_values, labels
def _lowerCamelCase ( self ):
'''simple docstring'''
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=_lowerCAmelCase , initializer_range=self.initializer_range , mask_ratio=self.mask_ratio , )
def _lowerCamelCase ( self , snake_case__ , snake_case__ , snake_case__ ):
'''simple docstring'''
UpperCamelCase_ = ViTMAEModel(config=_lowerCAmelCase )
model.to(_lowerCAmelCase )
model.eval()
UpperCamelCase_ = model(_lowerCAmelCase )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def _lowerCamelCase ( self , snake_case__ , snake_case__ , snake_case__ ):
'''simple docstring'''
UpperCamelCase_ = ViTMAEForPreTraining(_lowerCAmelCase )
model.to(_lowerCAmelCase )
model.eval()
UpperCamelCase_ = model(_lowerCAmelCase )
UpperCamelCase_ = (self.image_size // self.patch_size) ** 2
UpperCamelCase_ = self.patch_size**2 * self.num_channels
self.parent.assertEqual(result.logits.shape , (self.batch_size, num_patches, expected_num_channels) )
# test greyscale images
UpperCamelCase_ = 1
UpperCamelCase_ = ViTMAEForPreTraining(_lowerCAmelCase )
model.to(_lowerCAmelCase )
model.eval()
UpperCamelCase_ = floats_tensor([self.batch_size, 1, self.image_size, self.image_size] )
UpperCamelCase_ = model(_lowerCAmelCase )
UpperCamelCase_ = self.patch_size**2
self.parent.assertEqual(result.logits.shape , (self.batch_size, num_patches, expected_num_channels) )
def _lowerCamelCase ( self ):
'''simple docstring'''
UpperCamelCase_ = self.prepare_config_and_inputs()
UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ = config_and_inputs
UpperCamelCase_ = {"pixel_values": pixel_values}
return config, inputs_dict
@require_torch
class _lowercase (a_ , a_ , unittest.TestCase ):
'''simple docstring'''
lowercase__ = (ViTMAEModel, ViTMAEForPreTraining) if is_torch_available() else ()
lowercase__ = {"feature-extraction": ViTMAEModel} if is_torch_available() else {}
lowercase__ = False
lowercase__ = False
lowercase__ = False
lowercase__ = False
def _lowerCamelCase ( self ):
'''simple docstring'''
UpperCamelCase_ = ViTMAEModelTester(self )
UpperCamelCase_ = ConfigTester(self , config_class=_lowerCAmelCase , has_text_modality=_lowerCAmelCase , hidden_size=37 )
def _lowerCamelCase ( self ):
'''simple docstring'''
self.config_tester.run_common_tests()
@unittest.skip(reason="ViTMAE does not use inputs_embeds" )
def _lowerCamelCase ( self ):
'''simple docstring'''
pass
def _lowerCamelCase ( self ):
'''simple docstring'''
UpperCamelCase_ , UpperCamelCase_ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
UpperCamelCase_ = model_class(_lowerCAmelCase )
self.assertIsInstance(model.get_input_embeddings() , (nn.Module) )
UpperCamelCase_ = model.get_output_embeddings()
self.assertTrue(x is None or isinstance(_lowerCAmelCase , nn.Linear ) )
def _lowerCamelCase ( self ):
'''simple docstring'''
UpperCamelCase_ , UpperCamelCase_ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
UpperCamelCase_ = model_class(_lowerCAmelCase )
UpperCamelCase_ = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
UpperCamelCase_ = [*signature.parameters.keys()]
UpperCamelCase_ = ["pixel_values"]
self.assertListEqual(arg_names[:1] , _lowerCAmelCase )
def _lowerCamelCase ( self ):
'''simple docstring'''
UpperCamelCase_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*_lowerCAmelCase )
def _lowerCamelCase ( self ):
'''simple docstring'''
UpperCamelCase_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_pretraining(*_lowerCAmelCase )
def _lowerCamelCase ( self , snake_case__ , snake_case__ , snake_case__ ):
'''simple docstring'''
np.random.seed(2 )
UpperCamelCase_ = int((pt_model.config.image_size // pt_model.config.patch_size) ** 2 )
UpperCamelCase_ = np.random.uniform(size=(self.model_tester.batch_size, num_patches) )
UpperCamelCase_ = torch.from_numpy(_lowerCAmelCase )
# Add `noise` argument.
# PT inputs will be prepared in `super().check_pt_tf_models()` with this added `noise` argument
UpperCamelCase_ = pt_noise
super().check_pt_tf_models(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase )
def _lowerCamelCase ( self ):
'''simple docstring'''
UpperCamelCase_ , UpperCamelCase_ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
UpperCamelCase_ = model_class(_lowerCAmelCase )
model.to(_lowerCAmelCase )
model.eval()
# make random mask reproducible
torch.manual_seed(2 )
with torch.no_grad():
UpperCamelCase_ = model(**self._prepare_for_class(_lowerCAmelCase , _lowerCAmelCase ) )
UpperCamelCase_ = outputs[0].cpu().numpy()
UpperCamelCase_ = 0
with tempfile.TemporaryDirectory() as tmpdirname:
model.save_pretrained(_lowerCAmelCase )
UpperCamelCase_ = model_class.from_pretrained(_lowerCAmelCase )
model.to(_lowerCAmelCase )
# make random mask reproducible
torch.manual_seed(2 )
with torch.no_grad():
UpperCamelCase_ = model(**self._prepare_for_class(_lowerCAmelCase , _lowerCAmelCase ) )
# Make sure we don't have nans
UpperCamelCase_ = after_outputs[0].cpu().numpy()
UpperCamelCase_ = 0
UpperCamelCase_ = np.amax(np.abs(out_a - out_a ) )
self.assertLessEqual(_lowerCAmelCase , 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 ):
'''simple docstring'''
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 ):
'''simple docstring'''
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 ):
'''simple docstring'''
pass
@unittest.skip(reason="ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load" )
def _lowerCamelCase ( self ):
'''simple docstring'''
pass
@unittest.skip("Will be fixed soon by reducing the size of the model used for common tests." )
def _lowerCamelCase ( self ):
'''simple docstring'''
pass
@slow
def _lowerCamelCase ( self ):
'''simple docstring'''
for model_name in VIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
UpperCamelCase_ = ViTMAEModel.from_pretrained(_lowerCAmelCase )
self.assertIsNotNone(_lowerCAmelCase )
def _lowerCAmelCase ():
UpperCamelCase_ = 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 ):
'''simple docstring'''
return ViTImageProcessor.from_pretrained("facebook/vit-mae-base" ) if is_vision_available() else None
@slow
def _lowerCamelCase ( self ):
'''simple docstring'''
np.random.seed(2 )
UpperCamelCase_ = ViTMAEForPreTraining.from_pretrained("facebook/vit-mae-base" ).to(_lowerCAmelCase )
UpperCamelCase_ = self.default_image_processor
UpperCamelCase_ = prepare_img()
UpperCamelCase_ = image_processor(images=_lowerCAmelCase , return_tensors="pt" ).to(_lowerCAmelCase )
# prepare a noise vector that will be also used for testing the TF model
# (this way we can ensure that the PT and TF models operate on the same inputs)
UpperCamelCase_ = ViTMAEConfig()
UpperCamelCase_ = int((vit_mae_config.image_size // vit_mae_config.patch_size) ** 2 )
UpperCamelCase_ = np.random.uniform(size=(1, num_patches) )
# forward pass
with torch.no_grad():
UpperCamelCase_ = model(**_lowerCAmelCase , noise=torch.from_numpy(_lowerCAmelCase ).to(device=_lowerCAmelCase ) )
# verify the logits
UpperCamelCase_ = torch.Size((1, 196, 768) )
self.assertEqual(outputs.logits.shape , _lowerCAmelCase )
UpperCamelCase_ = torch.tensor(
[[-0.0_548, -1.7_023, -0.9_325], [0.3_721, -0.5_670, -0.2_233], [0.8_235, -1.3_878, -0.3_524]] )
self.assertTrue(torch.allclose(outputs.logits[0, :3, :3] , expected_slice.to(_lowerCAmelCase ) , atol=1e-4 ) )
| 128 |
'''simple docstring'''
import tempfile
import unittest
import numpy as np
import transformers
from transformers import GPTaTokenizer, GPTJConfig, is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax, tooslow
from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask
if is_flax_available():
import jax
import jax.numpy as jnp
from transformers.modeling_flax_pytorch_utils import (
convert_pytorch_state_dict_to_flax,
load_flax_weights_in_pytorch_model,
)
from transformers.models.gptj.modeling_flax_gptj import FlaxGPTJForCausalLM, FlaxGPTJModel
if is_torch_available():
import torch
class lowerCAmelCase_ :
def __init__( self , _lowerCAmelCase , _lowerCAmelCase=14 , _lowerCAmelCase=7 , _lowerCAmelCase=True , _lowerCAmelCase=True , _lowerCAmelCase=False , _lowerCAmelCase=True , _lowerCAmelCase=99 , _lowerCAmelCase=32 , _lowerCAmelCase=4 , _lowerCAmelCase=4 , _lowerCAmelCase=4 , _lowerCAmelCase=37 , _lowerCAmelCase="gelu" , _lowerCAmelCase=0.1 , _lowerCAmelCase=0.1 , _lowerCAmelCase=512 , _lowerCAmelCase=0.02 , ) -> Any:
_lowerCAmelCase = parent
_lowerCAmelCase = batch_size
_lowerCAmelCase = seq_length
_lowerCAmelCase = is_training
_lowerCAmelCase = use_input_mask
_lowerCAmelCase = use_token_type_ids
_lowerCAmelCase = use_labels
_lowerCAmelCase = vocab_size
_lowerCAmelCase = hidden_size
_lowerCAmelCase = rotary_dim
_lowerCAmelCase = num_hidden_layers
_lowerCAmelCase = num_attention_heads
_lowerCAmelCase = intermediate_size
_lowerCAmelCase = hidden_act
_lowerCAmelCase = hidden_dropout_prob
_lowerCAmelCase = attention_probs_dropout_prob
_lowerCAmelCase = max_position_embeddings
_lowerCAmelCase = initializer_range
_lowerCAmelCase = None
_lowerCAmelCase = vocab_size - 1
_lowerCAmelCase = vocab_size - 1
_lowerCAmelCase = vocab_size - 1
def _snake_case ( self ) -> str:
_lowerCAmelCase = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
_lowerCAmelCase = None
if self.use_input_mask:
_lowerCAmelCase = random_attention_mask([self.batch_size, self.seq_length] )
_lowerCAmelCase = GPTJConfig(
vocab_size=self.vocab_size , n_embd=self.hidden_size , n_layer=self.num_hidden_layers , n_head=self.num_attention_heads , n_positions=self.max_position_embeddings , use_cache=_lowerCAmelCase , bos_token_id=self.bos_token_id , eos_token_id=self.eos_token_id , pad_token_id=self.pad_token_id , rotary_dim=self.rotary_dim , )
return (config, input_ids, input_mask)
def _snake_case ( self ) -> Any:
_lowerCAmelCase = self.prepare_config_and_inputs()
_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase = config_and_inputs
_lowerCAmelCase = {"input_ids": input_ids, "attention_mask": attention_mask}
return config, inputs_dict
def _snake_case ( self , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase ) -> Dict:
_lowerCAmelCase = 20
_lowerCAmelCase = model_class_name(_lowerCAmelCase )
_lowerCAmelCase = model.init_cache(input_ids.shape[0] , _lowerCAmelCase )
_lowerCAmelCase = jnp.ones((input_ids.shape[0], max_decoder_length) , dtype="i4" )
_lowerCAmelCase = jnp.broadcast_to(
jnp.arange(input_ids.shape[-1] - 1 )[None, :] , (input_ids.shape[0], input_ids.shape[-1] - 1) )
_lowerCAmelCase = model(
input_ids[:, :-1] , attention_mask=_lowerCAmelCase , past_key_values=_lowerCAmelCase , position_ids=_lowerCAmelCase , )
_lowerCAmelCase = jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]] , dtype="i4" )
_lowerCAmelCase = model(
input_ids[:, -1:] , attention_mask=_lowerCAmelCase , past_key_values=outputs_cache.past_key_values , position_ids=_lowerCAmelCase , )
_lowerCAmelCase = model(_lowerCAmelCase )
_lowerCAmelCase = np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5]) ) )
self.parent.assertTrue(diff < 1E-3 , msg=f'''Max diff is {diff}''' )
def _snake_case ( self , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase ) -> int:
_lowerCAmelCase = 20
_lowerCAmelCase = model_class_name(_lowerCAmelCase )
_lowerCAmelCase = jnp.concatenate(
[attention_mask, jnp.zeros((attention_mask.shape[0], max_decoder_length - attention_mask.shape[1]) )] , axis=-1 , )
_lowerCAmelCase = model.init_cache(input_ids.shape[0] , _lowerCAmelCase )
_lowerCAmelCase = jnp.broadcast_to(
jnp.arange(input_ids.shape[-1] - 1 )[None, :] , (input_ids.shape[0], input_ids.shape[-1] - 1) )
_lowerCAmelCase = model(
input_ids[:, :-1] , attention_mask=_lowerCAmelCase , past_key_values=_lowerCAmelCase , position_ids=_lowerCAmelCase , )
_lowerCAmelCase = jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]] , dtype="i4" )
_lowerCAmelCase = model(
input_ids[:, -1:] , past_key_values=outputs_cache.past_key_values , attention_mask=_lowerCAmelCase , position_ids=_lowerCAmelCase , )
_lowerCAmelCase = model(_lowerCAmelCase , attention_mask=_lowerCAmelCase )
_lowerCAmelCase = np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5]) ) )
self.parent.assertTrue(diff < 1E-3 , msg=f'''Max diff is {diff}''' )
@require_flax
class lowerCAmelCase_ ( __magic_name__ ,__magic_name__ ,unittest.TestCase ):
__lowerCamelCase : str = (FlaxGPTJModel, FlaxGPTJForCausalLM) if is_flax_available() else ()
__lowerCamelCase : Optional[Any] = (FlaxGPTJForCausalLM,) if is_flax_available() else ()
def _snake_case ( self ) -> List[str]:
_lowerCAmelCase = FlaxGPTJModelTester(self )
def _snake_case ( self ) -> List[str]:
for model_class_name in self.all_model_classes:
_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.check_use_cache_forward(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase )
def _snake_case ( self ) -> int:
for model_class_name in self.all_model_classes:
_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.check_use_cache_forward_with_attn_mask(
_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase )
@tooslow
def _snake_case ( self ) -> Any:
_lowerCAmelCase = GPTaTokenizer.from_pretrained("gpt2" , pad_token="<|endoftext|>" , padding_side="left" )
_lowerCAmelCase = tokenizer(["Hello this is a long string", "Hey"] , return_tensors="np" , padding=_lowerCAmelCase , truncation=_lowerCAmelCase )
_lowerCAmelCase = FlaxGPTJForCausalLM.from_pretrained("EleutherAI/gpt-j-6B" )
_lowerCAmelCase = False
_lowerCAmelCase = model.config.eos_token_id
_lowerCAmelCase = jax.jit(model.generate )
_lowerCAmelCase = jit_generate(
inputs["input_ids"] , attention_mask=inputs["attention_mask"] , pad_token_id=tokenizer.pad_token_id ).sequences
_lowerCAmelCase = tokenizer.batch_decode(_lowerCAmelCase , skip_special_tokens=_lowerCAmelCase )
_lowerCAmelCase = [
"Hello this is a long string of text.\n\nI'm trying to get the text of the",
"Hey, I'm a little late to the party. I'm going to",
]
self.assertListEqual(_lowerCAmelCase , _lowerCAmelCase )
@is_pt_flax_cross_test
def _snake_case ( self ) -> Optional[Any]:
_lowerCAmelCase , _lowerCAmelCase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
with self.subTest(model_class.__name__ ):
# prepare inputs
_lowerCAmelCase = self._prepare_for_class(_lowerCAmelCase , _lowerCAmelCase )
_lowerCAmelCase = {k: torch.tensor(v.tolist() ) for k, v in prepared_inputs_dict.items()}
# load corresponding PyTorch class
_lowerCAmelCase = model_class.__name__[4:] # Skip the "Flax" at the beginning
_lowerCAmelCase = getattr(_lowerCAmelCase , _lowerCAmelCase )
_lowerCAmelCase , _lowerCAmelCase = pt_inputs["input_ids"].shape
_lowerCAmelCase = np.random.randint(0 , seq_length - 1 , size=(batch_size,) )
for batch_idx, start_index in enumerate(_lowerCAmelCase ):
_lowerCAmelCase = 0
_lowerCAmelCase = 1
_lowerCAmelCase = 0
_lowerCAmelCase = 1
_lowerCAmelCase = pt_model_class(_lowerCAmelCase ).eval()
_lowerCAmelCase = model_class(_lowerCAmelCase , dtype=jnp.floataa )
_lowerCAmelCase = convert_pytorch_state_dict_to_flax(pt_model.state_dict() , _lowerCAmelCase )
_lowerCAmelCase = fx_state
with torch.no_grad():
_lowerCAmelCase = pt_model(**_lowerCAmelCase ).to_tuple()
_lowerCAmelCase = fx_model(**_lowerCAmelCase ).to_tuple()
self.assertEqual(len(_lowerCAmelCase ) , len(_lowerCAmelCase ) , "Output lengths differ between Flax and PyTorch" )
for fx_output, pt_output in zip(_lowerCAmelCase , _lowerCAmelCase ):
self.assert_almost_equals(fx_output[:, -1] , pt_output[:, -1].numpy() , 4E-2 )
with tempfile.TemporaryDirectory() as tmpdirname:
pt_model.save_pretrained(_lowerCAmelCase )
_lowerCAmelCase = model_class.from_pretrained(_lowerCAmelCase , from_pt=_lowerCAmelCase )
_lowerCAmelCase = fx_model_loaded(**_lowerCAmelCase ).to_tuple()
self.assertEqual(
len(_lowerCAmelCase ) , len(_lowerCAmelCase ) , "Output lengths differ between Flax and PyTorch" )
for fx_output_loaded, pt_output in zip(_lowerCAmelCase , _lowerCAmelCase ):
self.assert_almost_equals(fx_output_loaded[:, -1] , pt_output[:, -1].numpy() , 4E-2 )
@is_pt_flax_cross_test
def _snake_case ( self ) -> Dict:
_lowerCAmelCase , _lowerCAmelCase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
with self.subTest(model_class.__name__ ):
# prepare inputs
_lowerCAmelCase = self._prepare_for_class(_lowerCAmelCase , _lowerCAmelCase )
_lowerCAmelCase = {k: torch.tensor(v.tolist() ) for k, v in prepared_inputs_dict.items()}
# load corresponding PyTorch class
_lowerCAmelCase = model_class.__name__[4:] # Skip the "Flax" at the beginning
_lowerCAmelCase = getattr(_lowerCAmelCase , _lowerCAmelCase )
_lowerCAmelCase = pt_model_class(_lowerCAmelCase ).eval()
_lowerCAmelCase = model_class(_lowerCAmelCase , dtype=jnp.floataa )
_lowerCAmelCase = load_flax_weights_in_pytorch_model(_lowerCAmelCase , fx_model.params )
_lowerCAmelCase , _lowerCAmelCase = pt_inputs["input_ids"].shape
_lowerCAmelCase = np.random.randint(0 , seq_length - 1 , size=(batch_size,) )
for batch_idx, start_index in enumerate(_lowerCAmelCase ):
_lowerCAmelCase = 0
_lowerCAmelCase = 1
_lowerCAmelCase = 0
_lowerCAmelCase = 1
# make sure weights are tied in PyTorch
pt_model.tie_weights()
with torch.no_grad():
_lowerCAmelCase = pt_model(**_lowerCAmelCase ).to_tuple()
_lowerCAmelCase = fx_model(**_lowerCAmelCase ).to_tuple()
self.assertEqual(len(_lowerCAmelCase ) , len(_lowerCAmelCase ) , "Output lengths differ between Flax and PyTorch" )
for fx_output, pt_output in zip(_lowerCAmelCase , _lowerCAmelCase ):
self.assert_almost_equals(fx_output[:, -1] , pt_output[:, -1].numpy() , 4E-2 )
with tempfile.TemporaryDirectory() as tmpdirname:
fx_model.save_pretrained(_lowerCAmelCase )
_lowerCAmelCase = pt_model_class.from_pretrained(_lowerCAmelCase , from_flax=_lowerCAmelCase )
with torch.no_grad():
_lowerCAmelCase = pt_model_loaded(**_lowerCAmelCase ).to_tuple()
self.assertEqual(
len(_lowerCAmelCase ) , len(_lowerCAmelCase ) , "Output lengths differ between Flax and PyTorch" )
for fx_output, pt_output in zip(_lowerCAmelCase , _lowerCAmelCase ):
self.assert_almost_equals(fx_output[:, -1] , pt_output[:, -1].numpy() , 4E-2 )
@tooslow
def _snake_case ( self ) -> Union[str, Any]:
for model_class_name in self.all_model_classes:
_lowerCAmelCase = model_class_name.from_pretrained("EleutherAI/gpt-j-6B" )
_lowerCAmelCase = model(np.ones((1, 1) ) )
self.assertIsNotNone(_lowerCAmelCase )
| 158 | 0 |
from __future__ import annotations
__snake_case : Optional[int] =list[list[int]]
# assigning initial values to the grid
__snake_case : Matrix =[
[3, 0, 6, 5, 0, 8, 4, 0, 0],
[5, 2, 0, 0, 0, 0, 0, 0, 0],
[0, 8, 7, 0, 0, 0, 0, 3, 1],
[0, 0, 3, 0, 1, 0, 0, 8, 0],
[9, 0, 0, 8, 6, 3, 0, 0, 5],
[0, 5, 0, 0, 9, 0, 6, 0, 0],
[1, 3, 0, 0, 0, 0, 2, 5, 0],
[0, 0, 0, 0, 0, 0, 0, 7, 4],
[0, 0, 5, 2, 0, 6, 3, 0, 0],
]
# a grid with no solution
__snake_case : Matrix =[
[5, 0, 6, 5, 0, 8, 4, 0, 3],
[5, 2, 0, 0, 0, 0, 0, 0, 2],
[1, 8, 7, 0, 0, 0, 0, 3, 1],
[0, 0, 3, 0, 1, 0, 0, 8, 0],
[9, 0, 0, 8, 6, 3, 0, 0, 5],
[0, 5, 0, 0, 9, 0, 6, 0, 0],
[1, 3, 0, 0, 0, 0, 2, 5, 0],
[0, 0, 0, 0, 0, 0, 0, 7, 4],
[0, 0, 5, 2, 0, 6, 3, 0, 0],
]
def lowerCAmelCase__ ( lowerCamelCase_ : Matrix ,lowerCamelCase_ : int ,lowerCamelCase_ : int ,lowerCamelCase_ : int):
'''simple docstring'''
for i in range(9):
if grid[row][i] == n or grid[i][column] == n:
return False
for i in range(3):
for j in range(3):
if grid[(row - row % 3) + i][(column - column % 3) + j] == n:
return False
return True
def lowerCAmelCase__ ( lowerCamelCase_ : Matrix):
'''simple docstring'''
for i in range(9):
for j in range(9):
if grid[i][j] == 0:
return i, j
return None
def lowerCAmelCase__ ( lowerCamelCase_ : Matrix):
'''simple docstring'''
if location := find_empty_location(lowerCamelCase_):
lowerCAmelCase__ , lowerCAmelCase__ : Optional[Any] = location
else:
# If the location is ``None``, then the grid is solved.
return grid
for digit in range(1 ,10):
if is_safe(lowerCamelCase_ ,lowerCamelCase_ ,lowerCamelCase_ ,lowerCamelCase_):
lowerCAmelCase__ : Optional[Any] = digit
if sudoku(lowerCamelCase_) is not None:
return grid
lowerCAmelCase__ : Dict = 0
return None
def lowerCAmelCase__ ( lowerCamelCase_ : Matrix):
'''simple docstring'''
for row in grid:
for cell in row:
print(lowerCamelCase_ ,end=''' ''')
print()
if __name__ == "__main__":
# make a copy of grid so that you can compare with the unmodified grid
for example_grid in (initial_grid, no_solution):
print('\nExample grid:\n' + '=' * 2_0)
print_solution(example_grid)
print('\nExample grid solution:')
__snake_case : int =sudoku(example_grid)
if solution is not None:
print_solution(solution)
else:
print('Cannot find a solution.')
| 94 |
from math import factorial
def lowerCAmelCase__ ( lowerCamelCase_ : int = 100):
'''simple docstring'''
return sum(map(lowerCamelCase_ ,str(factorial(lowerCamelCase_))))
if __name__ == "__main__":
print(solution(int(input('Enter the Number: ').strip())))
| 94 | 1 |
from datetime import datetime
import requests
from bsa import BeautifulSoup
if __name__ == "__main__":
snake_case : int = input('''Enter image url: ''').strip()
print(F"""Downloading image from {url} ...""")
snake_case : List[Any] = BeautifulSoup(requests.get(url).content, '''html.parser''')
# The image URL is in the content field of the first meta tag with property og:image
snake_case : List[Any] = soup.find('''meta''', {'''property''': '''og:image'''})['''content''']
snake_case : List[Any] = requests.get(image_url).content
snake_case : str = F"""{datetime.now():%Y-%m-%d_%H:%M:%S}.jpg"""
with open(file_name, '''wb''') as fp:
fp.write(image_data)
print(F"""Done. Image saved to disk as {file_name}.""")
| 94 |
"""simple docstring"""
import inspect
from typing import Optional, Union
import numpy as np
import PIL
import torch
from torch.nn import functional as F
from torchvision import transforms
from transformers import CLIPFeatureExtractor, CLIPModel, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
DiffusionPipeline,
DPMSolverMultistepScheduler,
LMSDiscreteScheduler,
PNDMScheduler,
UNetaDConditionModel,
)
from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion import StableDiffusionPipelineOutput
from diffusers.utils import (
PIL_INTERPOLATION,
randn_tensor,
)
def lowerCamelCase__ ( __snake_case, __snake_case, __snake_case ) -> int:
"""simple docstring"""
if isinstance(__snake_case, torch.Tensor ):
return image
elif isinstance(__snake_case, PIL.Image.Image ):
_UpperCamelCase = [image]
if isinstance(image[0], PIL.Image.Image ):
_UpperCamelCase = [np.array(i.resize((w, h), resample=PIL_INTERPOLATION['''lanczos'''] ) )[None, :] for i in image]
_UpperCamelCase = np.concatenate(__snake_case, axis=0 )
_UpperCamelCase = np.array(__snake_case ).astype(np.floataa ) / 255.0
_UpperCamelCase = image.transpose(0, 3, 1, 2 )
_UpperCamelCase = 2.0 * image - 1.0
_UpperCamelCase = torch.from_numpy(__snake_case )
elif isinstance(image[0], torch.Tensor ):
_UpperCamelCase = torch.cat(__snake_case, dim=0 )
return image
def lowerCamelCase__ ( __snake_case, __snake_case, __snake_case, __snake_case=0.9995 ) -> List[Any]:
"""simple docstring"""
if not isinstance(__snake_case, np.ndarray ):
_UpperCamelCase = True
_UpperCamelCase = va.device
_UpperCamelCase = va.cpu().numpy()
_UpperCamelCase = va.cpu().numpy()
_UpperCamelCase = np.sum(va * va / (np.linalg.norm(__snake_case ) * np.linalg.norm(__snake_case )) )
if np.abs(__snake_case ) > DOT_THRESHOLD:
_UpperCamelCase = (1 - t) * va + t * va
else:
_UpperCamelCase = np.arccos(__snake_case )
_UpperCamelCase = np.sin(__snake_case )
_UpperCamelCase = theta_a * t
_UpperCamelCase = np.sin(__snake_case )
_UpperCamelCase = np.sin(theta_a - theta_t ) / sin_theta_a
_UpperCamelCase = sin_theta_t / sin_theta_a
_UpperCamelCase = sa * va + sa * va
if inputs_are_torch:
_UpperCamelCase = torch.from_numpy(__snake_case ).to(__snake_case )
return va
def lowerCamelCase__ ( __snake_case, __snake_case ) -> List[str]:
"""simple docstring"""
_UpperCamelCase = F.normalize(__snake_case, dim=-1 )
_UpperCamelCase = F.normalize(__snake_case, dim=-1 )
return (x - y).norm(dim=-1 ).div(2 ).arcsin().pow(2 ).mul(2 )
def lowerCamelCase__ ( __snake_case, __snake_case ) -> Optional[int]:
"""simple docstring"""
for param in model.parameters():
_UpperCamelCase = value
class _UpperCAmelCase( lowerCamelCase ):
def __init__( self , __a , __a , __a , __a , __a , __a , __a , __a=None , __a=None , __a=None , ) -> List[str]:
'''simple docstring'''
super().__init__()
self.register_modules(
vae=__a , text_encoder=__a , clip_model=__a , tokenizer=__a , unet=__a , scheduler=__a , feature_extractor=__a , coca_model=__a , coca_tokenizer=__a , coca_transform=__a , )
_UpperCamelCase = (
feature_extractor.size
if isinstance(feature_extractor.size , __a)
else feature_extractor.size['''shortest_edge''']
)
_UpperCamelCase = transforms.Normalize(mean=feature_extractor.image_mean , std=feature_extractor.image_std)
set_requires_grad(self.text_encoder , __a)
set_requires_grad(self.clip_model , __a)
def UpperCAmelCase ( self , __a = "auto") -> Union[str, Any]:
'''simple docstring'''
if slice_size == "auto":
# half the attention head size is usually a good trade-off between
# speed and memory
_UpperCamelCase = self.unet.config.attention_head_dim // 2
self.unet.set_attention_slice(__a)
def UpperCAmelCase ( self) -> Optional[int]:
'''simple docstring'''
self.enable_attention_slicing(__a)
def UpperCAmelCase ( self) -> int:
'''simple docstring'''
set_requires_grad(self.vae , __a)
def UpperCAmelCase ( self) -> Union[str, Any]:
'''simple docstring'''
set_requires_grad(self.vae , __a)
def UpperCAmelCase ( self) -> Tuple:
'''simple docstring'''
set_requires_grad(self.unet , __a)
def UpperCAmelCase ( self) -> int:
'''simple docstring'''
set_requires_grad(self.unet , __a)
def UpperCAmelCase ( self , __a , __a , __a) -> Any:
'''simple docstring'''
# get the original timestep using init_timestep
_UpperCamelCase = min(int(num_inference_steps * strength) , __a)
_UpperCamelCase = max(num_inference_steps - init_timestep , 0)
_UpperCamelCase = self.scheduler.timesteps[t_start:]
return timesteps, num_inference_steps - t_start
def UpperCAmelCase ( self , __a , __a , __a , __a , __a , __a=None) -> Tuple:
'''simple docstring'''
if not isinstance(__a , torch.Tensor):
raise ValueError(F'''`image` has to be of type `torch.Tensor` but is {type(__a)}''')
_UpperCamelCase = image.to(device=__a , dtype=__a)
if isinstance(__a , __a):
_UpperCamelCase = [
self.vae.encode(image[i : i + 1]).latent_dist.sample(generator[i]) for i in range(__a)
]
_UpperCamelCase = torch.cat(__a , dim=0)
else:
_UpperCamelCase = self.vae.encode(__a).latent_dist.sample(__a)
# Hardcode 0.18215 because stable-diffusion-2-base has not self.vae.config.scaling_factor
_UpperCamelCase = 0.1_8215 * init_latents
_UpperCamelCase = init_latents.repeat_interleave(__a , dim=0)
_UpperCamelCase = randn_tensor(init_latents.shape , generator=__a , device=__a , dtype=__a)
# get latents
_UpperCamelCase = self.scheduler.add_noise(__a , __a , __a)
_UpperCamelCase = init_latents
return latents
def UpperCAmelCase ( self , __a) -> str:
'''simple docstring'''
_UpperCamelCase = self.coca_transform(__a).unsqueeze(0)
with torch.no_grad(), torch.cuda.amp.autocast():
_UpperCamelCase = self.coca_model.generate(transformed_image.to(device=self.device , dtype=self.coca_model.dtype))
_UpperCamelCase = self.coca_tokenizer.decode(generated[0].cpu().numpy())
return generated.split('''<end_of_text>''')[0].replace('''<start_of_text>''' , '''''').rstrip(''' .,''')
def UpperCAmelCase ( self , __a , __a) -> Optional[int]:
'''simple docstring'''
_UpperCamelCase = self.feature_extractor.preprocess(__a)
_UpperCamelCase = torch.from_numpy(clip_image_input['''pixel_values'''][0]).unsqueeze(0).to(self.device).half()
_UpperCamelCase = self.clip_model.get_image_features(__a)
_UpperCamelCase = image_embeddings_clip / image_embeddings_clip.norm(p=2 , dim=-1 , keepdim=__a)
_UpperCamelCase = image_embeddings_clip.repeat_interleave(__a , dim=0)
return image_embeddings_clip
@torch.enable_grad()
def UpperCAmelCase ( self , __a , __a , __a , __a , __a , __a , __a , ) -> Tuple:
'''simple docstring'''
_UpperCamelCase = latents.detach().requires_grad_()
_UpperCamelCase = self.scheduler.scale_model_input(__a , __a)
# predict the noise residual
_UpperCamelCase = self.unet(__a , __a , encoder_hidden_states=__a).sample
if isinstance(self.scheduler , (PNDMScheduler, DDIMScheduler, DPMSolverMultistepScheduler)):
_UpperCamelCase = self.scheduler.alphas_cumprod[timestep]
_UpperCamelCase = 1 - alpha_prod_t
# compute predicted original sample from predicted noise also called
# "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
_UpperCamelCase = (latents - beta_prod_t ** 0.5 * noise_pred) / alpha_prod_t ** 0.5
_UpperCamelCase = torch.sqrt(__a)
_UpperCamelCase = pred_original_sample * (fac) + latents * (1 - fac)
elif isinstance(self.scheduler , __a):
_UpperCamelCase = self.scheduler.sigmas[index]
_UpperCamelCase = latents - sigma * noise_pred
else:
raise ValueError(F'''scheduler type {type(self.scheduler)} not supported''')
# Hardcode 0.18215 because stable-diffusion-2-base has not self.vae.config.scaling_factor
_UpperCamelCase = 1 / 0.1_8215 * sample
_UpperCamelCase = self.vae.decode(__a).sample
_UpperCamelCase = (image / 2 + 0.5).clamp(0 , 1)
_UpperCamelCase = transforms.Resize(self.feature_extractor_size)(__a)
_UpperCamelCase = self.normalize(__a).to(latents.dtype)
_UpperCamelCase = self.clip_model.get_image_features(__a)
_UpperCamelCase = image_embeddings_clip / image_embeddings_clip.norm(p=2 , dim=-1 , keepdim=__a)
_UpperCamelCase = spherical_dist_loss(__a , __a).mean() * clip_guidance_scale
_UpperCamelCase = -torch.autograd.grad(__a , __a)[0]
if isinstance(self.scheduler , __a):
_UpperCamelCase = latents.detach() + grads * (sigma**2)
_UpperCamelCase = noise_pred_original
else:
_UpperCamelCase = noise_pred_original - torch.sqrt(__a) * grads
return noise_pred, latents
@torch.no_grad()
def __call__( self , __a , __a , __a = None , __a = None , __a = 5_12 , __a = 5_12 , __a = 0.6 , __a = 50 , __a = 7.5 , __a = 1 , __a = 0.0 , __a = 1_00 , __a = None , __a = "pil" , __a = True , __a = 0.8 , __a = 0.1 , __a = 0.1 , ) -> Dict:
'''simple docstring'''
if isinstance(__a , __a) and len(__a) != batch_size:
raise ValueError(F'''You have passed {batch_size} batch_size, but only {len(__a)} generators.''')
if height % 8 != 0 or width % 8 != 0:
raise ValueError(F'''`height` and `width` have to be divisible by 8 but are {height} and {width}.''')
if isinstance(__a , torch.Generator) and batch_size > 1:
_UpperCamelCase = [generator] + [None] * (batch_size - 1)
_UpperCamelCase = [
('''model''', self.coca_model is None),
('''tokenizer''', self.coca_tokenizer is None),
('''transform''', self.coca_transform is None),
]
_UpperCamelCase = [x[0] for x in coca_is_none if x[1]]
_UpperCamelCase = ''', '''.join(__a)
# generate prompts with coca model if prompt is None
if content_prompt is None:
if len(__a):
raise ValueError(
F'''Content prompt is None and CoCa [{coca_is_none_str}] is None.'''
F'''Set prompt or pass Coca [{coca_is_none_str}] to DiffusionPipeline.''')
_UpperCamelCase = self.get_image_description(__a)
if style_prompt is None:
if len(__a):
raise ValueError(
F'''Style prompt is None and CoCa [{coca_is_none_str}] is None.'''
F''' Set prompt or pass Coca [{coca_is_none_str}] to DiffusionPipeline.''')
_UpperCamelCase = self.get_image_description(__a)
# get prompt text embeddings for content and style
_UpperCamelCase = self.tokenizer(
__a , padding='''max_length''' , max_length=self.tokenizer.model_max_length , truncation=__a , return_tensors='''pt''' , )
_UpperCamelCase = self.text_encoder(content_text_input.input_ids.to(self.device))[0]
_UpperCamelCase = self.tokenizer(
__a , padding='''max_length''' , max_length=self.tokenizer.model_max_length , truncation=__a , return_tensors='''pt''' , )
_UpperCamelCase = self.text_encoder(style_text_input.input_ids.to(self.device))[0]
_UpperCamelCase = slerp(__a , __a , __a)
# duplicate text embeddings for each generation per prompt
_UpperCamelCase = text_embeddings.repeat_interleave(__a , dim=0)
# set timesteps
_UpperCamelCase = '''offset''' in set(inspect.signature(self.scheduler.set_timesteps).parameters.keys())
_UpperCamelCase = {}
if accepts_offset:
_UpperCamelCase = 1
self.scheduler.set_timesteps(__a , **__a)
# Some schedulers like PNDM have timesteps as arrays
# It's more optimized to move all timesteps to correct device beforehand
self.scheduler.timesteps.to(self.device)
_UpperCamelCase , _UpperCamelCase = self.get_timesteps(__a , __a , self.device)
_UpperCamelCase = timesteps[:1].repeat(__a)
# Preprocess image
_UpperCamelCase = preprocess(__a , __a , __a)
_UpperCamelCase = self.prepare_latents(
__a , __a , __a , text_embeddings.dtype , self.device , __a)
_UpperCamelCase = preprocess(__a , __a , __a)
_UpperCamelCase = self.prepare_latents(
__a , __a , __a , text_embeddings.dtype , self.device , __a)
_UpperCamelCase = slerp(__a , __a , __a)
if clip_guidance_scale > 0:
_UpperCamelCase = self.get_clip_image_embeddings(__a , __a)
_UpperCamelCase = self.get_clip_image_embeddings(__a , __a)
_UpperCamelCase = slerp(
__a , __a , __a)
# here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
# of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
# corresponds to doing no classifier free guidance.
_UpperCamelCase = guidance_scale > 1.0
# get unconditional embeddings for classifier free guidance
if do_classifier_free_guidance:
_UpperCamelCase = content_text_input.input_ids.shape[-1]
_UpperCamelCase = self.tokenizer([''''''] , padding='''max_length''' , max_length=__a , return_tensors='''pt''')
_UpperCamelCase = self.text_encoder(uncond_input.input_ids.to(self.device))[0]
# duplicate unconditional embeddings for each generation per prompt
_UpperCamelCase = uncond_embeddings.repeat_interleave(__a , dim=0)
# For classifier free guidance, we need to do two forward passes.
# Here we concatenate the unconditional and text embeddings into a single batch
# to avoid doing two forward passes
_UpperCamelCase = torch.cat([uncond_embeddings, text_embeddings])
# get the initial random noise unless the user supplied it
# Unlike in other pipelines, latents need to be generated in the target device
# for 1-to-1 results reproducibility with the CompVis implementation.
# However this currently doesn't work in `mps`.
_UpperCamelCase = (batch_size, self.unet.config.in_channels, height // 8, width // 8)
_UpperCamelCase = text_embeddings.dtype
if latents is None:
if self.device.type == "mps":
# randn does not work reproducibly on mps
_UpperCamelCase = torch.randn(__a , generator=__a , device='''cpu''' , dtype=__a).to(
self.device)
else:
_UpperCamelCase = torch.randn(__a , generator=__a , device=self.device , dtype=__a)
else:
if latents.shape != latents_shape:
raise ValueError(F'''Unexpected latents shape, got {latents.shape}, expected {latents_shape}''')
_UpperCamelCase = latents.to(self.device)
# scale the initial noise by the standard deviation required by the scheduler
_UpperCamelCase = 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]
_UpperCamelCase = '''eta''' in set(inspect.signature(self.scheduler.step).parameters.keys())
_UpperCamelCase = {}
if accepts_eta:
_UpperCamelCase = eta
# check if the scheduler accepts generator
_UpperCamelCase = '''generator''' in set(inspect.signature(self.scheduler.step).parameters.keys())
if accepts_generator:
_UpperCamelCase = generator
with self.progress_bar(total=__a):
for i, t in enumerate(__a):
# expand the latents if we are doing classifier free guidance
_UpperCamelCase = torch.cat([latents] * 2) if do_classifier_free_guidance else latents
_UpperCamelCase = self.scheduler.scale_model_input(__a , __a)
# predict the noise residual
_UpperCamelCase = self.unet(__a , __a , encoder_hidden_states=__a).sample
# perform classifier free guidance
if do_classifier_free_guidance:
_UpperCamelCase , _UpperCamelCase = noise_pred.chunk(2)
_UpperCamelCase = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
# perform clip guidance
if clip_guidance_scale > 0:
_UpperCamelCase = (
text_embeddings.chunk(2)[1] if do_classifier_free_guidance else text_embeddings
)
_UpperCamelCase , _UpperCamelCase = self.cond_fn(
__a , __a , __a , __a , __a , __a , __a , )
# compute the previous noisy sample x_t -> x_t-1
_UpperCamelCase = self.scheduler.step(__a , __a , __a , **__a).prev_sample
# Hardcode 0.18215 because stable-diffusion-2-base has not self.vae.config.scaling_factor
_UpperCamelCase = 1 / 0.1_8215 * latents
_UpperCamelCase = self.vae.decode(__a).sample
_UpperCamelCase = (image / 2 + 0.5).clamp(0 , 1)
_UpperCamelCase = image.cpu().permute(0 , 2 , 3 , 1).numpy()
if output_type == "pil":
_UpperCamelCase = self.numpy_to_pil(__a)
if not return_dict:
return (image, None)
return StableDiffusionPipelineOutput(images=__a , nsfw_content_detected=__a)
| 194 | 0 |
__A = {
"joule": 1.0,
"kilojoule": 10_00,
"megajoule": 1_00_00_00,
"gigajoule": 10_00_00_00_00,
"wattsecond": 1.0,
"watthour": 36_00,
"kilowatthour": 3_60_00_00,
"newtonmeter": 1.0,
"calorie_nutr": 41_86.8,
"kilocalorie_nutr": 4_18_68_00.00,
"electronvolt": 1.6_0_2_1_7_6_6_3_4e-1_9,
"britishthermalunit_it": 10_55.0_55_85,
"footpound": 1.3_5_5_8_1_8,
}
def lowerCamelCase_ ( UpperCamelCase__ : str , UpperCamelCase__ : str , UpperCamelCase__ : float ):
"""simple docstring"""
if to_type not in ENERGY_CONVERSION or from_type not in ENERGY_CONVERSION:
__lowerCamelCase = (
F"""Incorrect 'from_type' or 'to_type' value: {from_type!r}, {to_type!r}\n"""
F"""Valid values are: {', '.join(UpperCamelCase__ )}"""
)
raise ValueError(UpperCamelCase__ )
return value * ENERGY_CONVERSION[from_type] / ENERGY_CONVERSION[to_type]
if __name__ == "__main__":
import doctest
doctest.testmod()
| 359 |
import requests
__A = "https://newsapi.org/v1/articles?source=bbc-news&sortBy=top&apiKey="
def lowerCamelCase_ ( UpperCamelCase__ : str ) -> None:
"""simple docstring"""
__lowerCamelCase = requests.get(_NEWS_API + bbc_news_api_key ).json()
# each article in the list is a dict
for i, article in enumerate(bbc_news_page['articles'] , 1 ):
print(F"""{i}.) {article['title']}""" )
if __name__ == "__main__":
fetch_bbc_news(bbc_news_api_key="<Your BBC News API key goes here>")
| 348 | 0 |
"""simple docstring"""
A_ = [0, 2, 4, 6, 8]
A_ = [1, 3, 5, 7, 9]
def UpperCAmelCase__ (snake_case__ : int , snake_case__ : int , snake_case__ : list[int] , snake_case__ : int ):
"""simple docstring"""
if remaining_length == 0:
if digits[0] == 0 or digits[-1] == 0:
return 0
for i in range(length // 2 - 1 , -1 , -1 ):
remainder += digits[i] + digits[length - i - 1]
if remainder % 2 == 0:
return 0
remainder //= 10
return 1
if remaining_length == 1:
if remainder % 2 == 0:
return 0
_snake_case : Dict = 0
for digit in range(10 ):
_snake_case : Optional[int] = digit
result += reversible_numbers(
0 , (remainder + 2 * digit) // 10 , a__ , a__ )
return result
_snake_case : str = 0
for digita in range(10 ):
_snake_case : List[str] = digita
if (remainder + digita) % 2 == 0:
_snake_case : List[str] = ODD_DIGITS
else:
_snake_case : str = EVEN_DIGITS
for digita in other_parity_digits:
_snake_case : str = digita
result += reversible_numbers(
remaining_length - 2 , (remainder + digita + digita) // 10 , a__ , a__ , )
return result
def UpperCAmelCase__ (snake_case__ : int = 9 ):
"""simple docstring"""
_snake_case : str = 0
for length in range(1 , max_power + 1 ):
result += reversible_numbers(a__ , 0 , [0] * length , a__ )
return result
if __name__ == "__main__":
print(F'''{solution() = }''')
| 64 | """simple docstring"""
import os
import sys
import unittest
UpperCAmelCase = os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, """utils"""))
import check_dummies # noqa: E402
from check_dummies import create_dummy_files, create_dummy_object, find_backend, read_init # noqa: E402
# Align TRANSFORMERS_PATH in check_dummies with the current path
UpperCAmelCase = os.path.join(git_repo_path, """src""", """diffusers""")
class UpperCAmelCase_ ( unittest.TestCase):
def _UpperCamelCase ( self : Tuple ) -> str:
_UpperCamelCase = find_backend(''' if not is_torch_available():''' )
self.assertEqual(__UpperCamelCase , '''torch''' )
# backend_with_underscore = find_backend(" if not is_tensorflow_text_available():")
# self.assertEqual(backend_with_underscore, "tensorflow_text")
_UpperCamelCase = find_backend(''' if not (is_torch_available() and is_transformers_available()):''' )
self.assertEqual(__UpperCamelCase , '''torch_and_transformers''' )
# double_backend_with_underscore = find_backend(
# " if not (is_sentencepiece_available() and is_tensorflow_text_available()):"
# )
# self.assertEqual(double_backend_with_underscore, "sentencepiece_and_tensorflow_text")
_UpperCamelCase = find_backend(
''' if not (is_torch_available() and is_transformers_available() and is_onnx_available()):''' )
self.assertEqual(__UpperCamelCase , '''torch_and_transformers_and_onnx''' )
def _UpperCamelCase ( self : Optional[Any] ) -> Union[str, Any]:
_UpperCamelCase = read_init()
# We don't assert on the exact list of keys to allow for smooth grow of backend-specific objects
self.assertIn('''torch''' , __UpperCamelCase )
self.assertIn('''torch_and_transformers''' , __UpperCamelCase )
self.assertIn('''flax_and_transformers''' , __UpperCamelCase )
self.assertIn('''torch_and_transformers_and_onnx''' , __UpperCamelCase )
# Likewise, we can't assert on the exact content of a key
self.assertIn('''UNet2DModel''' , objects['''torch'''] )
self.assertIn('''FlaxUNet2DConditionModel''' , objects['''flax'''] )
self.assertIn('''StableDiffusionPipeline''' , objects['''torch_and_transformers'''] )
self.assertIn('''FlaxStableDiffusionPipeline''' , objects['''flax_and_transformers'''] )
self.assertIn('''LMSDiscreteScheduler''' , objects['''torch_and_scipy'''] )
self.assertIn('''OnnxStableDiffusionPipeline''' , objects['''torch_and_transformers_and_onnx'''] )
def _UpperCamelCase ( self : Tuple ) -> Optional[int]:
_UpperCamelCase = create_dummy_object('''CONSTANT''' , '''\'torch\'''' )
self.assertEqual(__UpperCamelCase , '''\nCONSTANT = None\n''' )
_UpperCamelCase = create_dummy_object('''function''' , '''\'torch\'''' )
self.assertEqual(
__UpperCamelCase , '''\ndef function(*args, **kwargs):\n requires_backends(function, \'torch\')\n''' )
_UpperCamelCase = '''
class FakeClass(metaclass=DummyObject):
_backends = \'torch\'
def __init__(self, *args, **kwargs):
requires_backends(self, \'torch\')
@classmethod
def from_config(cls, *args, **kwargs):
requires_backends(cls, \'torch\')
@classmethod
def from_pretrained(cls, *args, **kwargs):
requires_backends(cls, \'torch\')
'''
_UpperCamelCase = create_dummy_object('''FakeClass''' , '''\'torch\'''' )
self.assertEqual(__UpperCamelCase , __UpperCamelCase )
def _UpperCamelCase ( self : Any ) -> Any:
_UpperCamelCase = '''# This file is autogenerated by the command `make fix-copies`, do not edit.
from ..utils import DummyObject, requires_backends
CONSTANT = None
def function(*args, **kwargs):
requires_backends(function, ["torch"])
class FakeClass(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"])
@classmethod
def from_config(cls, *args, **kwargs):
requires_backends(cls, ["torch"])
@classmethod
def from_pretrained(cls, *args, **kwargs):
requires_backends(cls, ["torch"])
'''
_UpperCamelCase = create_dummy_files({'''torch''': ['''CONSTANT''', '''function''', '''FakeClass''']} )
self.assertEqual(dummy_files['''torch'''] , __UpperCamelCase )
| 256 | 0 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__lowerCamelCase = logging.get_logger(__name__)
__lowerCamelCase = {
'''facebook/dpr-ctx_encoder-single-nq-base''': (
'''https://huggingface.co/facebook/dpr-ctx_encoder-single-nq-base/resolve/main/config.json'''
),
'''facebook/dpr-question_encoder-single-nq-base''': (
'''https://huggingface.co/facebook/dpr-question_encoder-single-nq-base/resolve/main/config.json'''
),
'''facebook/dpr-reader-single-nq-base''': (
'''https://huggingface.co/facebook/dpr-reader-single-nq-base/resolve/main/config.json'''
),
'''facebook/dpr-ctx_encoder-multiset-base''': (
'''https://huggingface.co/facebook/dpr-ctx_encoder-multiset-base/resolve/main/config.json'''
),
'''facebook/dpr-question_encoder-multiset-base''': (
'''https://huggingface.co/facebook/dpr-question_encoder-multiset-base/resolve/main/config.json'''
),
'''facebook/dpr-reader-multiset-base''': (
'''https://huggingface.co/facebook/dpr-reader-multiset-base/resolve/main/config.json'''
),
}
class A__ ( _snake_case ):
lowercase = "dpr"
def __init__( self , UpperCamelCase__=30522 , UpperCamelCase__=768 , UpperCamelCase__=12 , UpperCamelCase__=12 , UpperCamelCase__=3072 , UpperCamelCase__="gelu" , UpperCamelCase__=0.1 , UpperCamelCase__=0.1 , UpperCamelCase__=512 , UpperCamelCase__=2 , UpperCamelCase__=0.02 , UpperCamelCase__=1e-1_2 , UpperCamelCase__=0 , UpperCamelCase__="absolute" , UpperCamelCase__ = 0 , **UpperCamelCase__ , ) -> Any:
'''simple docstring'''
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_ = projection_dim
A_ = position_embedding_type
| 101 |
'''simple docstring'''
import argparse
import glob
import logging
import os
import sys
import time
from collections import defaultdict
from pathlib import Path
from typing import Dict, List, Tuple
import numpy as np
import pytorch_lightning as pl
import torch
from callbacks import SeqaSeqLoggingCallback, get_checkpoint_callback, get_early_stopping_callback
from torch import nn
from torch.utils.data import DataLoader
from transformers import MBartTokenizer, TaForConditionalGeneration
from transformers.models.bart.modeling_bart import shift_tokens_right
from utils import (
ROUGE_KEYS,
LegacySeqaSeqDataset,
SeqaSeqDataset,
assert_all_frozen,
calculate_bleu,
calculate_rouge,
check_output_dir,
flatten_list,
freeze_embeds,
freeze_params,
get_git_info,
label_smoothed_nll_loss,
lmap,
pickle_save,
save_git_info,
save_json,
use_task_specific_params,
)
# need the parent dir module
sys.path.insert(2, str(Path(__file__).resolve().parents[1]))
from lightning_base import BaseTransformer, add_generic_args, generic_train # noqa
__lowerCamelCase = logging.getLogger(__name__)
class A__ ( _snake_case ):
lowercase = "summarization"
lowercase = ["loss"]
lowercase = ROUGE_KEYS
lowercase = "rouge2"
def __init__( self , UpperCamelCase__ , **UpperCamelCase__ ) -> Dict:
'''simple docstring'''
if hparams.sortish_sampler and hparams.gpus > 1:
A_ = False
elif hparams.max_tokens_per_batch is not None:
if hparams.gpus > 1:
raise NotImplementedError("""Dynamic Batch size does not work for multi-gpu training""" )
if hparams.sortish_sampler:
raise ValueError("""--sortish_sampler and --max_tokens_per_batch may not be used simultaneously""" )
super().__init__(UpperCamelCase__ , num_labels=UpperCamelCase__ , mode=self.mode , **UpperCamelCase__ )
use_task_specific_params(self.model , """summarization""" )
save_git_info(self.hparams.output_dir )
A_ = Path(self.output_dir ) / """metrics.json"""
A_ = Path(self.output_dir ) / """hparams.pkl"""
pickle_save(self.hparams , self.hparams_save_path )
A_ = 0
A_ = defaultdict(UpperCamelCase__ )
A_ = self.config.model_type
A_ = self.config.tgt_vocab_size if self.model_type == """fsmt""" else self.config.vocab_size
A_ = {
"data_dir": self.hparams.data_dir,
"max_source_length": self.hparams.max_source_length,
"prefix": self.model.config.prefix or "",
}
A_ = {
"""train""": self.hparams.n_train,
"""val""": self.hparams.n_val,
"""test""": self.hparams.n_test,
}
A_ = {k: v if v >= 0 else None for k, v in n_observations_per_split.items()}
A_ = {
"""train""": self.hparams.max_target_length,
"""val""": self.hparams.val_max_target_length,
"""test""": self.hparams.test_max_target_length,
}
assert self.target_lens["train"] <= self.target_lens["val"], f'''target_lens: {self.target_lens}'''
assert self.target_lens["train"] <= self.target_lens["test"], f'''target_lens: {self.target_lens}'''
if self.hparams.freeze_embeds:
freeze_embeds(self.model )
if self.hparams.freeze_encoder:
freeze_params(self.model.get_encoder() )
assert_all_frozen(self.model.get_encoder() )
A_ = get_git_info()["""repo_sha"""]
A_ = hparams.num_workers
A_ = None # default to config
if self.model.config.decoder_start_token_id is None and isinstance(self.tokenizer , UpperCamelCase__ ):
A_ = self.tokenizer.lang_code_to_id[hparams.tgt_lang]
A_ = self.decoder_start_token_id
A_ = (
SeqaSeqDataset if hasattr(self.tokenizer , """prepare_seq2seq_batch""" ) else LegacySeqaSeqDataset
)
A_ = False
A_ = self.model.config.num_beams if self.hparams.eval_beams is None else self.hparams.eval_beams
if self.hparams.eval_max_gen_length is not None:
A_ = self.hparams.eval_max_gen_length
else:
A_ = self.model.config.max_length
A_ = self.default_val_metric if self.hparams.val_metric is None else self.hparams.val_metric
def snake_case_ ( self , UpperCamelCase__ ) -> Dict[str, List[str]]:
'''simple docstring'''
A_ = {
k: self.tokenizer.batch_decode(v.tolist() ) if """mask""" not in k else v.shape for k, v in batch.items()
}
save_json(UpperCamelCase__ , Path(self.output_dir ) / """text_batch.json""" )
save_json({k: v.tolist() for k, v in batch.items()} , Path(self.output_dir ) / """tok_batch.json""" )
A_ = True
return readable_batch
def snake_case_ ( self , UpperCamelCase__ , **UpperCamelCase__ ) -> str:
'''simple docstring'''
return self.model(UpperCamelCase__ , **UpperCamelCase__ )
def snake_case_ ( self , UpperCamelCase__ ) -> List[str]:
'''simple docstring'''
A_ = self.tokenizer.batch_decode(
UpperCamelCase__ , skip_special_tokens=UpperCamelCase__ , clean_up_tokenization_spaces=UpperCamelCase__ )
return lmap(str.strip , UpperCamelCase__ )
def snake_case_ ( self , UpperCamelCase__ ) -> Tuple:
'''simple docstring'''
A_ = self.tokenizer.pad_token_id
A_ , A_ = batch["""input_ids"""], batch["""attention_mask"""]
A_ = batch["""labels"""]
if isinstance(self.model , UpperCamelCase__ ):
A_ = self.model._shift_right(UpperCamelCase__ )
else:
A_ = shift_tokens_right(UpperCamelCase__ , UpperCamelCase__ )
if not self.already_saved_batch: # This would be slightly better if it only happened on rank zero
A_ = decoder_input_ids
self.save_readable_batch(UpperCamelCase__ )
A_ = self(UpperCamelCase__ , attention_mask=UpperCamelCase__ , decoder_input_ids=UpperCamelCase__ , use_cache=UpperCamelCase__ )
A_ = outputs["""logits"""]
if self.hparams.label_smoothing == 0:
# Same behavior as modeling_bart.py, besides ignoring pad_token_id
A_ = nn.CrossEntropyLoss(ignore_index=UpperCamelCase__ )
assert lm_logits.shape[-1] == self.vocab_size
A_ = ce_loss_fct(lm_logits.view(-1 , lm_logits.shape[-1] ) , tgt_ids.view(-1 ) )
else:
A_ = nn.functional.log_softmax(UpperCamelCase__ , dim=-1 )
A_ , A_ = label_smoothed_nll_loss(
UpperCamelCase__ , UpperCamelCase__ , self.hparams.label_smoothing , ignore_index=UpperCamelCase__ )
return (loss,)
@property
def snake_case_ ( self ) -> int:
'''simple docstring'''
return self.tokenizer.pad_token_id
def snake_case_ ( self , UpperCamelCase__ , UpperCamelCase__ ) -> Dict:
'''simple docstring'''
A_ = self._step(UpperCamelCase__ )
A_ = dict(zip(self.loss_names , UpperCamelCase__ ) )
# tokens per batch
A_ = batch["""input_ids"""].ne(self.pad ).sum() + batch["""labels"""].ne(self.pad ).sum()
A_ = batch["""input_ids"""].shape[0]
A_ = batch["""input_ids"""].eq(self.pad ).sum()
A_ = batch["""input_ids"""].eq(self.pad ).float().mean()
# TODO(SS): make a wandb summary metric for this
return {"loss": loss_tensors[0], "log": logs}
def snake_case_ ( self , UpperCamelCase__ , UpperCamelCase__ ) -> Dict:
'''simple docstring'''
return self._generative_step(UpperCamelCase__ )
def snake_case_ ( self , UpperCamelCase__ , UpperCamelCase__="val" ) -> Dict:
'''simple docstring'''
self.step_count += 1
A_ = {k: torch.stack([x[k] for x in outputs] ).mean() for k in self.loss_names}
A_ = losses["""loss"""]
A_ = {
k: np.array([x[k] for x in outputs] ).mean() for k in self.metric_names + ["""gen_time""", """gen_len"""]
}
A_ = (
generative_metrics[self.val_metric] if self.val_metric in generative_metrics else losses[self.val_metric]
)
A_ = torch.tensor(UpperCamelCase__ ).type_as(UpperCamelCase__ )
generative_metrics.update({k: v.item() for k, v in losses.items()} )
losses.update(UpperCamelCase__ )
A_ = {f'''{prefix}_avg_{k}''': x for k, x in losses.items()}
A_ = self.step_count
self.metrics[prefix].append(UpperCamelCase__ ) # callback writes this to self.metrics_save_path
A_ = flatten_list([x["""preds"""] for x in outputs] )
return {
"log": all_metrics,
"preds": preds,
f'''{prefix}_loss''': loss,
f'''{prefix}_{self.val_metric}''': metric_tensor,
}
def snake_case_ ( self , UpperCamelCase__ , UpperCamelCase__ ) -> Dict:
'''simple docstring'''
return calculate_rouge(UpperCamelCase__ , UpperCamelCase__ )
def snake_case_ ( self , UpperCamelCase__ ) -> dict:
'''simple docstring'''
A_ = time.time()
# parser.add_argument('--eval_max_gen_length', type=int, default=None, help='never generate more than n tokens')
A_ = self.model.generate(
batch["""input_ids"""] , attention_mask=batch["""attention_mask"""] , use_cache=UpperCamelCase__ , decoder_start_token_id=self.decoder_start_token_id , num_beams=self.eval_beams , max_length=self.eval_max_length , )
A_ = (time.time() - ta) / batch["""input_ids"""].shape[0]
A_ = self.ids_to_clean_text(UpperCamelCase__ )
A_ = self.ids_to_clean_text(batch["""labels"""] )
A_ = self._step(UpperCamelCase__ )
A_ = dict(zip(self.loss_names , UpperCamelCase__ ) )
A_ = self.calc_generative_metrics(UpperCamelCase__ , UpperCamelCase__ )
A_ = np.mean(lmap(UpperCamelCase__ , UpperCamelCase__ ) )
base_metrics.update(gen_time=UpperCamelCase__ , gen_len=UpperCamelCase__ , preds=UpperCamelCase__ , target=UpperCamelCase__ , **UpperCamelCase__ )
return base_metrics
def snake_case_ ( self , UpperCamelCase__ , UpperCamelCase__ ) -> Tuple:
'''simple docstring'''
return self._generative_step(UpperCamelCase__ )
def snake_case_ ( self , UpperCamelCase__ ) -> Dict:
'''simple docstring'''
return self.validation_epoch_end(UpperCamelCase__ , prefix="""test""" )
def snake_case_ ( self , UpperCamelCase__ ) -> SeqaSeqDataset:
'''simple docstring'''
A_ = self.n_obs[type_path]
A_ = self.target_lens[type_path]
A_ = self.dataset_class(
self.tokenizer , type_path=UpperCamelCase__ , n_obs=UpperCamelCase__ , max_target_length=UpperCamelCase__ , **self.dataset_kwargs , )
return dataset
def snake_case_ ( self , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ = False ) -> DataLoader:
'''simple docstring'''
A_ = self.get_dataset(UpperCamelCase__ )
if self.hparams.sortish_sampler and type_path != "test" and type_path != "val":
A_ = dataset.make_sortish_sampler(UpperCamelCase__ , distributed=self.hparams.gpus > 1 )
return DataLoader(
UpperCamelCase__ , batch_size=UpperCamelCase__ , collate_fn=dataset.collate_fn , shuffle=UpperCamelCase__ , num_workers=self.num_workers , sampler=UpperCamelCase__ , )
elif self.hparams.max_tokens_per_batch is not None and type_path != "test" and type_path != "val":
A_ = dataset.make_dynamic_sampler(
self.hparams.max_tokens_per_batch , distributed=self.hparams.gpus > 1 )
return DataLoader(
UpperCamelCase__ , batch_sampler=UpperCamelCase__ , collate_fn=dataset.collate_fn , num_workers=self.num_workers , )
else:
return DataLoader(
UpperCamelCase__ , batch_size=UpperCamelCase__ , collate_fn=dataset.collate_fn , shuffle=UpperCamelCase__ , num_workers=self.num_workers , sampler=UpperCamelCase__ , )
def snake_case_ ( self ) -> DataLoader:
'''simple docstring'''
A_ = self.get_dataloader("""train""" , batch_size=self.hparams.train_batch_size , shuffle=UpperCamelCase__ )
return dataloader
def snake_case_ ( self ) -> DataLoader:
'''simple docstring'''
return self.get_dataloader("""val""" , batch_size=self.hparams.eval_batch_size )
def snake_case_ ( self ) -> DataLoader:
'''simple docstring'''
return self.get_dataloader("""test""" , batch_size=self.hparams.eval_batch_size )
@staticmethod
def snake_case_ ( UpperCamelCase__ , UpperCamelCase__ ) -> List[Any]:
'''simple docstring'''
BaseTransformer.add_model_specific_args(UpperCamelCase__ , UpperCamelCase__ )
add_generic_args(UpperCamelCase__ , UpperCamelCase__ )
parser.add_argument(
"""--max_source_length""" , default=1024 , type=UpperCamelCase__ , help=(
"""The maximum total input sequence length after tokenization. Sequences longer """
"""than this will be truncated, sequences shorter will be padded."""
) , )
parser.add_argument(
"""--max_target_length""" , default=56 , type=UpperCamelCase__ , help=(
"""The maximum total input sequence length after tokenization. Sequences longer """
"""than this will be truncated, sequences shorter will be padded."""
) , )
parser.add_argument(
"""--val_max_target_length""" , default=142 , type=UpperCamelCase__ , help=(
"""The maximum total input sequence length after tokenization. Sequences longer """
"""than this will be truncated, sequences shorter will be padded."""
) , )
parser.add_argument(
"""--test_max_target_length""" , default=142 , type=UpperCamelCase__ , help=(
"""The maximum total input sequence length after tokenization. Sequences longer """
"""than this will be truncated, sequences shorter will be padded."""
) , )
parser.add_argument("""--freeze_encoder""" , action="""store_true""" )
parser.add_argument("""--freeze_embeds""" , action="""store_true""" )
parser.add_argument("""--sortish_sampler""" , action="""store_true""" , default=UpperCamelCase__ )
parser.add_argument("""--overwrite_output_dir""" , action="""store_true""" , default=UpperCamelCase__ )
parser.add_argument("""--max_tokens_per_batch""" , type=UpperCamelCase__ , default=UpperCamelCase__ )
parser.add_argument("""--logger_name""" , type=UpperCamelCase__ , choices=["""default""", """wandb""", """wandb_shared"""] , default="""default""" )
parser.add_argument("""--n_train""" , type=UpperCamelCase__ , default=-1 , required=UpperCamelCase__ , help="""# examples. -1 means use all.""" )
parser.add_argument("""--n_val""" , type=UpperCamelCase__ , default=500 , required=UpperCamelCase__ , help="""# examples. -1 means use all.""" )
parser.add_argument("""--n_test""" , type=UpperCamelCase__ , default=-1 , required=UpperCamelCase__ , help="""# examples. -1 means use all.""" )
parser.add_argument(
"""--task""" , type=UpperCamelCase__ , default="""summarization""" , required=UpperCamelCase__ , help="""# examples. -1 means use all.""" )
parser.add_argument("""--label_smoothing""" , type=UpperCamelCase__ , default=0.0 , required=UpperCamelCase__ )
parser.add_argument("""--src_lang""" , type=UpperCamelCase__ , default="""""" , required=UpperCamelCase__ )
parser.add_argument("""--tgt_lang""" , type=UpperCamelCase__ , default="""""" , required=UpperCamelCase__ )
parser.add_argument("""--eval_beams""" , type=UpperCamelCase__ , default=UpperCamelCase__ , required=UpperCamelCase__ )
parser.add_argument(
"""--val_metric""" , type=UpperCamelCase__ , default=UpperCamelCase__ , required=UpperCamelCase__ , choices=["""bleu""", """rouge2""", """loss""", None] )
parser.add_argument("""--eval_max_gen_length""" , type=UpperCamelCase__ , default=UpperCamelCase__ , help="""never generate more than n tokens""" )
parser.add_argument("""--save_top_k""" , type=UpperCamelCase__ , default=1 , required=UpperCamelCase__ , help="""How many checkpoints to save""" )
parser.add_argument(
"""--early_stopping_patience""" , type=UpperCamelCase__ , default=-1 , required=UpperCamelCase__ , help=(
"""-1 means never early stop. early_stopping_patience is measured in validation checks, not epochs. So"""
""" val_check_interval will effect it."""
) , )
return parser
class A__ ( _snake_case ):
lowercase = "translation"
lowercase = ["loss"]
lowercase = ["bleu"]
lowercase = "bleu"
def __init__( self , UpperCamelCase__ , **UpperCamelCase__ ) -> Optional[int]:
'''simple docstring'''
super().__init__(UpperCamelCase__ , **UpperCamelCase__ )
A_ = hparams.src_lang
A_ = hparams.tgt_lang
def snake_case_ ( self , UpperCamelCase__ , UpperCamelCase__ ) -> dict:
'''simple docstring'''
return calculate_bleu(UpperCamelCase__ , UpperCamelCase__ )
def UpperCAmelCase__ ( UpperCAmelCase__, UpperCAmelCase__=None ) -> SummarizationModule:
Path(args.output_dir ).mkdir(exist_ok=UpperCAmelCase__ )
check_output_dir(UpperCAmelCase__, expected_items=3 )
if model is None:
if "summarization" in args.task:
A_ = SummarizationModule(UpperCAmelCase__ )
else:
A_ = TranslationModule(UpperCAmelCase__ )
A_ = Path(args.data_dir ).name
if (
args.logger_name == "default"
or args.fast_dev_run
or str(args.output_dir ).startswith("""/tmp""" )
or str(args.output_dir ).startswith("""/var""" )
):
A_ = True # don't pollute wandb logs unnecessarily
elif args.logger_name == "wandb":
from pytorch_lightning.loggers import WandbLogger
A_ = os.environ.get("""WANDB_PROJECT""", UpperCAmelCase__ )
A_ = WandbLogger(name=model.output_dir.name, project=UpperCAmelCase__ )
elif args.logger_name == "wandb_shared":
from pytorch_lightning.loggers import WandbLogger
A_ = WandbLogger(name=model.output_dir.name, project=F'''hf_{dataset}''' )
if args.early_stopping_patience >= 0:
A_ = get_early_stopping_callback(model.val_metric, args.early_stopping_patience )
else:
A_ = False
A_ = args.val_metric == """loss"""
A_ = generic_train(
UpperCAmelCase__, UpperCAmelCase__, logging_callback=SeqaSeqLoggingCallback(), checkpoint_callback=get_checkpoint_callback(
args.output_dir, model.val_metric, args.save_top_k, UpperCAmelCase__ ), early_stopping_callback=UpperCAmelCase__, logger=UpperCAmelCase__, )
pickle_save(model.hparams, model.output_dir / """hparams.pkl""" )
if not args.do_predict:
return model
A_ = """"""
A_ = sorted(glob.glob(os.path.join(args.output_dir, """*.ckpt""" ), recursive=UpperCAmelCase__ ) )
if checkpoints:
A_ = checkpoints[-1]
A_ = checkpoints[-1]
trainer.logger.log_hyperparams(model.hparams )
# test() without a model tests using the best checkpoint automatically
trainer.test()
return model
if __name__ == "__main__":
__lowerCamelCase = argparse.ArgumentParser()
__lowerCamelCase = pl.Trainer.add_argparse_args(parser)
__lowerCamelCase = SummarizationModule.add_model_specific_args(parser, os.getcwd())
__lowerCamelCase = parser.parse_args()
main(args)
| 101 | 1 |
import math
from collections import defaultdict
from typing import List, Optional, Tuple, Union
import numpy as np
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin, SchedulerOutput
def UpperCAmelCase__ ( lowerCamelCase, lowerCamelCase=0.999, lowerCamelCase="cosine", ):
if alpha_transform_type == "cosine":
def alpha_bar_fn(lowerCamelCase ):
return math.cos((t + 0.008) / 1.008 * math.pi / 2 ) ** 2
elif alpha_transform_type == "exp":
def alpha_bar_fn(lowerCamelCase ):
return math.exp(t * -12.0 )
else:
raise ValueError(F"Unsupported alpha_tranform_type: {alpha_transform_type}" )
lowercase :List[str] = []
for i in range(lowerCamelCase ):
lowercase :Tuple = i / num_diffusion_timesteps
lowercase :Optional[Any] = (i + 1) / num_diffusion_timesteps
betas.append(min(1 - alpha_bar_fn(lowerCamelCase ) / alpha_bar_fn(lowerCamelCase ), lowerCamelCase ) )
return torch.tensor(lowerCamelCase, dtype=torch.floataa )
class __lowerCAmelCase ( lowerCAmelCase , lowerCAmelCase):
_a = [e.name for e in KarrasDiffusionSchedulers]
_a = 2
@register_to_config
def __init__( self: Any , _lowerCAmelCase: int = 10_00 , _lowerCAmelCase: float = 0.0_00_85 , _lowerCAmelCase: float = 0.0_12 , _lowerCAmelCase: str = "linear" , _lowerCAmelCase: Optional[Union[np.ndarray, List[float]]] = None , _lowerCAmelCase: str = "epsilon" , _lowerCAmelCase: Optional[bool] = False , _lowerCAmelCase: Optional[bool] = False , _lowerCAmelCase: float = 1.0 , _lowerCAmelCase: str = "linspace" , _lowerCAmelCase: int = 0 , ):
if trained_betas is not None:
lowercase :str = torch.tensor(_lowerCAmelCase , dtype=torch.floataa )
elif beta_schedule == "linear":
lowercase :Optional[Any] = torch.linspace(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , dtype=torch.floataa )
elif beta_schedule == "scaled_linear":
# this schedule is very specific to the latent diffusion model.
lowercase :List[Any] = (
torch.linspace(beta_start**0.5 , beta_end**0.5 , _lowerCAmelCase , dtype=torch.floataa ) ** 2
)
elif beta_schedule == "squaredcos_cap_v2":
# Glide cosine schedule
lowercase :Optional[int] = betas_for_alpha_bar(_lowerCAmelCase , alpha_transform_type="cosine" )
elif beta_schedule == "exp":
lowercase :Tuple = betas_for_alpha_bar(_lowerCAmelCase , alpha_transform_type="exp" )
else:
raise NotImplementedError(F"{beta_schedule} does is not implemented for {self.__class__}" )
lowercase :List[Any] = 1.0 - self.betas
lowercase :Union[str, Any] = torch.cumprod(self.alphas , dim=0 )
# set all values
self.set_timesteps(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase )
lowercase :Any = use_karras_sigmas
def SCREAMING_SNAKE_CASE ( self: Union[str, Any] , _lowerCAmelCase: List[str] , _lowerCAmelCase: Optional[Any]=None ):
if schedule_timesteps is None:
lowercase :Tuple = self.timesteps
lowercase :Any = (schedule_timesteps == timestep).nonzero()
# The sigma index that is taken for the **very** first `step`
# is always the second index (or the last index if there is only 1)
# This way we can ensure we don't accidentally skip a sigma in
# case we start in the middle of the denoising schedule (e.g. for image-to-image)
if len(self._index_counter ) == 0:
lowercase :Tuple = 1 if len(_lowerCAmelCase ) > 1 else 0
else:
lowercase :str = timestep.cpu().item() if torch.is_tensor(_lowerCAmelCase ) else timestep
lowercase :Union[str, Any] = self._index_counter[timestep_int]
return indices[pos].item()
@property
def SCREAMING_SNAKE_CASE ( self: Union[str, Any] ):
# standard deviation of the initial noise distribution
if self.config.timestep_spacing in ["linspace", "trailing"]:
return self.sigmas.max()
return (self.sigmas.max() ** 2 + 1) ** 0.5
def SCREAMING_SNAKE_CASE ( self: int , _lowerCAmelCase: torch.FloatTensor , _lowerCAmelCase: Union[float, torch.FloatTensor] , ):
lowercase :Optional[Any] = self.index_for_timestep(_lowerCAmelCase )
lowercase :Optional[Any] = self.sigmas[step_index]
lowercase :str = sample / ((sigma**2 + 1) ** 0.5)
return sample
def SCREAMING_SNAKE_CASE ( self: Dict , _lowerCAmelCase: int , _lowerCAmelCase: Union[str, torch.device] = None , _lowerCAmelCase: Optional[int] = None , ):
lowercase :List[str] = num_inference_steps
lowercase :Optional[int] = num_train_timesteps or self.config.num_train_timesteps
# "linspace", "leading", "trailing" corresponds to annotation of Table 2. of https://arxiv.org/abs/2305.08891
if self.config.timestep_spacing == "linspace":
lowercase :int = np.linspace(0 , num_train_timesteps - 1 , _lowerCAmelCase , dtype=_lowerCAmelCase )[::-1].copy()
elif self.config.timestep_spacing == "leading":
lowercase :List[str] = num_train_timesteps // self.num_inference_steps
# creates integer timesteps by multiplying by ratio
# casting to int to avoid issues when num_inference_step is power of 3
lowercase :List[str] = (np.arange(0 , _lowerCAmelCase ) * step_ratio).round()[::-1].copy().astype(_lowerCAmelCase )
timesteps += self.config.steps_offset
elif self.config.timestep_spacing == "trailing":
lowercase :List[str] = num_train_timesteps / self.num_inference_steps
# creates integer timesteps by multiplying by ratio
# casting to int to avoid issues when num_inference_step is power of 3
lowercase :List[str] = (np.arange(_lowerCAmelCase , 0 , -step_ratio )).round().copy().astype(_lowerCAmelCase )
timesteps -= 1
else:
raise ValueError(
F"{self.config.timestep_spacing} is not supported. Please make sure to choose one of 'linspace', 'leading' or 'trailing'." )
lowercase :str = np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5 )
lowercase :int = np.log(_lowerCAmelCase )
lowercase :Optional[Any] = np.interp(_lowerCAmelCase , np.arange(0 , len(_lowerCAmelCase ) ) , _lowerCAmelCase )
if self.config.use_karras_sigmas:
lowercase :str = self._convert_to_karras(in_sigmas=_lowerCAmelCase , num_inference_steps=self.num_inference_steps )
lowercase :Any = np.array([self._sigma_to_t(_lowerCAmelCase , _lowerCAmelCase ) for sigma in sigmas] )
lowercase :str = np.concatenate([sigmas, [0.0]] ).astype(np.floataa )
lowercase :Optional[int] = torch.from_numpy(_lowerCAmelCase ).to(device=_lowerCAmelCase )
lowercase :Union[str, Any] = torch.cat([sigmas[:1], sigmas[1:-1].repeat_interleave(2 ), sigmas[-1:]] )
lowercase :Optional[int] = torch.from_numpy(_lowerCAmelCase )
lowercase :str = torch.cat([timesteps[:1], timesteps[1:].repeat_interleave(2 )] )
if str(_lowerCAmelCase ).startswith("mps" ):
# mps does not support float64
lowercase :Tuple = timesteps.to(_lowerCAmelCase , dtype=torch.floataa )
else:
lowercase :Tuple = timesteps.to(device=_lowerCAmelCase )
# empty dt and derivative
lowercase :List[Any] = None
lowercase :str = None
# for exp beta schedules, such as the one for `pipeline_shap_e.py`
# we need an index counter
lowercase :Optional[int] = defaultdict(_lowerCAmelCase )
def SCREAMING_SNAKE_CASE ( self: Dict , _lowerCAmelCase: int , _lowerCAmelCase: Tuple ):
# get log sigma
lowercase :Optional[int] = np.log(_lowerCAmelCase )
# get distribution
lowercase :List[str] = log_sigma - log_sigmas[:, np.newaxis]
# get sigmas range
lowercase :Dict = np.cumsum((dists >= 0) , axis=0 ).argmax(axis=0 ).clip(max=log_sigmas.shape[0] - 2 )
lowercase :Optional[Any] = low_idx + 1
lowercase :Optional[int] = log_sigmas[low_idx]
lowercase :int = log_sigmas[high_idx]
# interpolate sigmas
lowercase :str = (low - log_sigma) / (low - high)
lowercase :Union[str, Any] = np.clip(_lowerCAmelCase , 0 , 1 )
# transform interpolation to time range
lowercase :Optional[Any] = (1 - w) * low_idx + w * high_idx
lowercase :List[Any] = t.reshape(sigma.shape )
return t
def SCREAMING_SNAKE_CASE ( self: List[Any] , _lowerCAmelCase: torch.FloatTensor , _lowerCAmelCase: Union[str, Any] ):
lowercase :float = in_sigmas[-1].item()
lowercase :float = in_sigmas[0].item()
lowercase :str = 7.0 # 7.0 is the value used in the paper
lowercase :Any = np.linspace(0 , 1 , _lowerCAmelCase )
lowercase :str = sigma_min ** (1 / rho)
lowercase :Optional[Any] = sigma_max ** (1 / rho)
lowercase :Optional[Any] = (max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
return sigmas
@property
def SCREAMING_SNAKE_CASE ( self: Union[str, Any] ):
return self.dt is None
def SCREAMING_SNAKE_CASE ( self: Optional[Any] , _lowerCAmelCase: Union[torch.FloatTensor, np.ndarray] , _lowerCAmelCase: Union[float, torch.FloatTensor] , _lowerCAmelCase: Union[torch.FloatTensor, np.ndarray] , _lowerCAmelCase: bool = True , ):
lowercase :str = self.index_for_timestep(_lowerCAmelCase )
# advance index counter by 1
lowercase :int = timestep.cpu().item() if torch.is_tensor(_lowerCAmelCase ) else timestep
self._index_counter[timestep_int] += 1
if self.state_in_first_order:
lowercase :Dict = self.sigmas[step_index]
lowercase :Dict = self.sigmas[step_index + 1]
else:
# 2nd order / Heun's method
lowercase :List[str] = self.sigmas[step_index - 1]
lowercase :List[str] = self.sigmas[step_index]
# currently only gamma=0 is supported. This usually works best anyways.
# We can support gamma in the future but then need to scale the timestep before
# passing it to the model which requires a change in API
lowercase :Any = 0
lowercase :Optional[int] = sigma * (gamma + 1) # Note: sigma_hat == sigma for now
# 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
if self.config.prediction_type == "epsilon":
lowercase :Any = sigma_hat if self.state_in_first_order else sigma_next
lowercase :Union[str, Any] = sample - sigma_input * model_output
elif self.config.prediction_type == "v_prediction":
lowercase :List[Any] = sigma_hat if self.state_in_first_order else sigma_next
lowercase :str = model_output * (-sigma_input / (sigma_input**2 + 1) ** 0.5) + (
sample / (sigma_input**2 + 1)
)
elif self.config.prediction_type == "sample":
lowercase :Dict = model_output
else:
raise ValueError(
F"prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`" )
if self.config.clip_sample:
lowercase :List[Any] = pred_original_sample.clamp(
-self.config.clip_sample_range , self.config.clip_sample_range )
if self.state_in_first_order:
# 2. Convert to an ODE derivative for 1st order
lowercase :List[Any] = (sample - pred_original_sample) / sigma_hat
# 3. delta timestep
lowercase :Dict = sigma_next - sigma_hat
# store for 2nd order step
lowercase :Dict = derivative
lowercase :Optional[Any] = dt
lowercase :Union[str, Any] = sample
else:
# 2. 2nd order / Heun's method
lowercase :Optional[Any] = (sample - pred_original_sample) / sigma_next
lowercase :str = (self.prev_derivative + derivative) / 2
# 3. take prev timestep & sample
lowercase :List[Any] = self.dt
lowercase :Any = self.sample
# free dt and derivative
# Note, this puts the scheduler in "first order mode"
lowercase :List[Any] = None
lowercase :str = None
lowercase :int = None
lowercase :Optional[Any] = sample + derivative * dt
if not return_dict:
return (prev_sample,)
return SchedulerOutput(prev_sample=_lowerCAmelCase )
def SCREAMING_SNAKE_CASE ( self: Union[str, Any] , _lowerCAmelCase: torch.FloatTensor , _lowerCAmelCase: torch.FloatTensor , _lowerCAmelCase: torch.FloatTensor , ):
# Make sure sigmas and timesteps have the same device and dtype as original_samples
lowercase :List[Any] = self.sigmas.to(device=original_samples.device , dtype=original_samples.dtype )
if original_samples.device.type == "mps" and torch.is_floating_point(_lowerCAmelCase ):
# mps does not support float64
lowercase :List[str] = self.timesteps.to(original_samples.device , dtype=torch.floataa )
lowercase :List[str] = timesteps.to(original_samples.device , dtype=torch.floataa )
else:
lowercase :Dict = self.timesteps.to(original_samples.device )
lowercase :Tuple = timesteps.to(original_samples.device )
lowercase :int = [self.index_for_timestep(_lowerCAmelCase , _lowerCAmelCase ) for t in timesteps]
lowercase :Dict = sigmas[step_indices].flatten()
while len(sigma.shape ) < len(original_samples.shape ):
lowercase :Union[str, Any] = sigma.unsqueeze(-1 )
lowercase :Union[str, Any] = original_samples + noise * sigma
return noisy_samples
def __len__( self: Tuple ):
return self.config.num_train_timesteps
| 236 |
import unittest
from transformers import GPTNeoXJapaneseConfig, is_torch_available
from transformers.models.gpt_neox_japanese.tokenization_gpt_neox_japanese import GPTNeoXJapaneseTokenizer
from transformers.testing_utils import require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import GPTNeoXJapaneseForCausalLM, GPTNeoXJapaneseModel
class __lowerCAmelCase :
def __init__( self: List[str] , _lowerCAmelCase: Dict , _lowerCAmelCase: List[str]=13 , _lowerCAmelCase: Dict=7 , _lowerCAmelCase: Tuple=True , _lowerCAmelCase: Dict=True , _lowerCAmelCase: Optional[Any]=True , _lowerCAmelCase: Union[str, Any]=True , _lowerCAmelCase: str=99 , _lowerCAmelCase: Dict=32 , _lowerCAmelCase: Dict=5 , _lowerCAmelCase: int=4 , _lowerCAmelCase: Optional[Any]=4 , _lowerCAmelCase: Any="gelu" , _lowerCAmelCase: Any=0.0 , _lowerCAmelCase: List[Any]=0.1 , _lowerCAmelCase: Optional[int]=True , _lowerCAmelCase: Any=5_12 , _lowerCAmelCase: Optional[Any]=16 , _lowerCAmelCase: str=2 , _lowerCAmelCase: Tuple=0.02 , _lowerCAmelCase: str=3 , _lowerCAmelCase: List[str]=4 , _lowerCAmelCase: Optional[Any]=None , ):
lowercase :List[str] = parent
lowercase :List[str] = batch_size
lowercase :List[str] = seq_length
lowercase :Dict = is_training
lowercase :Union[str, Any] = use_input_mask
lowercase :Optional[int] = use_token_type_ids
lowercase :Dict = use_labels
lowercase :Any = vocab_size
lowercase :List[Any] = hidden_size
lowercase :Optional[int] = num_hidden_layers
lowercase :Union[str, Any] = num_attention_heads
lowercase :Any = intermediate_multiple_size
lowercase :str = hidden_act
lowercase :List[Any] = hidden_dropout
lowercase :Optional[Any] = attention_dropout
lowercase :int = weight_tying
lowercase :Tuple = max_position_embeddings
lowercase :str = type_vocab_size
lowercase :Union[str, Any] = type_sequence_label_size
lowercase :Dict = initializer_range
lowercase :Dict = num_labels
lowercase :Dict = num_choices
lowercase :Any = scope
def SCREAMING_SNAKE_CASE ( self: str ):
lowercase :List[Any] = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
lowercase :Optional[Any] = None
if self.use_input_mask:
lowercase :str = random_attention_mask([self.batch_size, self.seq_length] )
lowercase :int = None
if self.use_labels:
lowercase :Dict = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
lowercase :Tuple = self.get_config()
return config, input_ids, input_mask, token_labels
def SCREAMING_SNAKE_CASE ( self: str ):
return GPTNeoXJapaneseConfig(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_multiple_size=self.intermediate_multiple_size , hidden_act=self.hidden_act , hidden_dropout=self.hidden_dropout , attention_dropout=self.attention_dropout , weight_tying=self.weight_tying , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , is_decoder=_lowerCAmelCase , initializer_range=self.initializer_range , )
def SCREAMING_SNAKE_CASE ( self: Optional[int] ):
lowercase , lowercase , lowercase , lowercase :Any = self.prepare_config_and_inputs()
lowercase :Tuple = True
return config, input_ids, input_mask, token_labels
def SCREAMING_SNAKE_CASE ( self: List[Any] , _lowerCAmelCase: Optional[Any] , _lowerCAmelCase: Dict , _lowerCAmelCase: List[str] ):
lowercase :List[str] = GPTNeoXJapaneseModel(config=_lowerCAmelCase )
model.to(_lowerCAmelCase )
model.eval()
lowercase :Optional[int] = model(_lowerCAmelCase , attention_mask=_lowerCAmelCase )
lowercase :Dict = model(_lowerCAmelCase )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def SCREAMING_SNAKE_CASE ( self: Optional[int] , _lowerCAmelCase: Dict , _lowerCAmelCase: List[Any] , _lowerCAmelCase: int ):
lowercase :Dict = True
lowercase :Optional[Any] = GPTNeoXJapaneseModel(_lowerCAmelCase )
model.to(_lowerCAmelCase )
model.eval()
lowercase :List[str] = model(_lowerCAmelCase , attention_mask=_lowerCAmelCase )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def SCREAMING_SNAKE_CASE ( self: int , _lowerCAmelCase: str , _lowerCAmelCase: Dict , _lowerCAmelCase: int , _lowerCAmelCase: List[Any] ):
lowercase :str = GPTNeoXJapaneseForCausalLM(config=_lowerCAmelCase )
model.to(_lowerCAmelCase )
model.eval()
lowercase :Tuple = model(_lowerCAmelCase , attention_mask=_lowerCAmelCase , labels=_lowerCAmelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def SCREAMING_SNAKE_CASE ( self: Dict , _lowerCAmelCase: List[Any] , _lowerCAmelCase: int , _lowerCAmelCase: Any ):
lowercase :Optional[Any] = True
lowercase :List[str] = GPTNeoXJapaneseForCausalLM(config=_lowerCAmelCase )
model.to(_lowerCAmelCase )
model.eval()
# first forward pass
lowercase :str = model(_lowerCAmelCase , attention_mask=_lowerCAmelCase , use_cache=_lowerCAmelCase )
lowercase :Tuple = outputs.past_key_values
# create hypothetical multiple next token and extent to next_input_ids
lowercase :Optional[int] = ids_tensor((self.batch_size, 3) , config.vocab_size )
lowercase :Tuple = ids_tensor((self.batch_size, 3) , vocab_size=2 )
# append to next input_ids and
lowercase :Tuple = torch.cat([input_ids, next_tokens] , dim=-1 )
lowercase :List[Any] = torch.cat([input_mask, next_mask] , dim=-1 )
lowercase :Dict = model(_lowerCAmelCase , attention_mask=_lowerCAmelCase , output_hidden_states=_lowerCAmelCase )
lowercase :List[str] = output_from_no_past["hidden_states"][0]
lowercase :str = model(
_lowerCAmelCase , attention_mask=_lowerCAmelCase , past_key_values=_lowerCAmelCase , output_hidden_states=_lowerCAmelCase , )["hidden_states"][0]
# select random slice
lowercase :Dict = ids_tensor((1,) , output_from_past.shape[-1] ).item()
lowercase :str = output_from_no_past[:, -3:, random_slice_idx].detach()
lowercase :List[str] = output_from_past[:, :, random_slice_idx].detach()
self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1] )
# test that outputs are equal for slice
self.parent.assertTrue(torch.allclose(_lowerCAmelCase , _lowerCAmelCase , atol=1e-3 ) )
def SCREAMING_SNAKE_CASE ( self: Dict ):
lowercase :str = self.prepare_config_and_inputs()
lowercase , lowercase , lowercase , lowercase :Tuple = config_and_inputs
lowercase :Union[str, Any] = {"input_ids": input_ids, "attention_mask": input_mask}
return config, inputs_dict
@require_torch
class __lowerCAmelCase ( lowerCAmelCase , lowerCAmelCase , unittest.TestCase):
_a = (GPTNeoXJapaneseModel, GPTNeoXJapaneseForCausalLM) if is_torch_available() else ()
_a = (GPTNeoXJapaneseForCausalLM,) if is_torch_available() else ()
_a = (
{'''feature-extraction''': GPTNeoXJapaneseModel, '''text-generation''': GPTNeoXJapaneseForCausalLM}
if is_torch_available()
else {}
)
_a = False
_a = False
_a = False
_a = False
def SCREAMING_SNAKE_CASE ( self: List[str] ):
lowercase :Any = GPTNeoXJapaneseModelTester(self )
lowercase :List[str] = ConfigTester(self , config_class=_lowerCAmelCase , hidden_size=37 )
def SCREAMING_SNAKE_CASE ( self: Tuple ):
self.config_tester.run_common_tests()
def SCREAMING_SNAKE_CASE ( self: Dict ):
lowercase , lowercase , lowercase , lowercase :Tuple = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase )
def SCREAMING_SNAKE_CASE ( self: Tuple ):
lowercase , lowercase , lowercase , lowercase :Optional[Any] = self.model_tester.prepare_config_and_inputs_for_decoder()
self.model_tester.create_and_check_model_as_decoder(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase )
def SCREAMING_SNAKE_CASE ( self: Optional[int] ):
# This regression test was failing with PyTorch < 1.3
lowercase , lowercase , lowercase , lowercase :Tuple = self.model_tester.prepare_config_and_inputs_for_decoder()
lowercase :Union[str, Any] = None
self.model_tester.create_and_check_model_as_decoder(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase )
def SCREAMING_SNAKE_CASE ( self: Union[str, Any] ):
lowercase , lowercase , lowercase , lowercase :List[str] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_decoder_model_past_large_inputs(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase )
def SCREAMING_SNAKE_CASE ( self: str ):
lowercase :Optional[int] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_causal_lm(*_lowerCAmelCase )
@slow
def SCREAMING_SNAKE_CASE ( self: str ):
lowercase :int = "abeja/gpt-neox-japanese-2.7b"
lowercase :Optional[int] = ["データサイエンティストとは、", "100年後に必要とされる会社は、", "フルリモートの環境で働くために必要なことは、", "国境の長いトンネルを抜けると", "美味しい日本食といえば、"]
lowercase :int = [
"データサイエンティストとは、データを分析し、ビジネスに役立つ知見を導き出す専門家のことです。",
"100年後に必要とされる会社は、「人」が中心の会社です。",
"フルリモートの環境で働くために必要なことは、「自分の時間をコントロールする」ことです。",
"国境の長いトンネルを抜けると、そこは雪国だった。",
"美味しい日本食といえば、やっぱりお寿司ですよね。",
]
lowercase :List[str] = GPTNeoXJapaneseTokenizer.from_pretrained(_lowerCAmelCase )
lowercase :Tuple = GPTNeoXJapaneseForCausalLM.from_pretrained(_lowerCAmelCase )
lowercase :List[str] = []
for prompt in prompts:
lowercase :Optional[int] = tokenizer(_lowerCAmelCase , return_tensors="pt" ).input_ids
lowercase :Union[str, Any] = model.generate(_lowerCAmelCase , max_length=50 )
lowercase :List[str] = tokenizer.batch_decode(_lowerCAmelCase , skip_special_tokens=_lowerCAmelCase )
predicted_outputs += generated_string
self.assertListEqual(_lowerCAmelCase , _lowerCAmelCase )
| 236 | 1 |
"""simple docstring"""
class __A :
"""simple docstring"""
def __init__( self , __A ) -> None:
a =len(__A )
a =[0] * len_array
if len_array > 0:
a =array[0]
for i in range(1 , __A ):
a =self.prefix_sum[i - 1] + array[i]
def SCREAMING_SNAKE_CASE ( self , __A , __A ) -> int:
if start == 0:
return self.prefix_sum[end]
return self.prefix_sum[end] - self.prefix_sum[start - 1]
def SCREAMING_SNAKE_CASE ( self , __A ) -> bool:
a ={0}
for sum_item in self.prefix_sum:
if sum_item - target_sum in sums:
return True
sums.add(__A )
return False
if __name__ == "__main__":
import doctest
doctest.testmod() | 215 |
"""simple docstring"""
def _A ( lowercase = 2_00_00_00 ):
"""simple docstring"""
a =[0 for i in range(n + 1 )]
a =1
a =1
for i in range(2 , int(n**0.5 ) + 1 ):
if primality_list[i] == 0:
for j in range(i * i , n + 1 , lowercase ):
a =1
a =0
for i in range(lowercase ):
if primality_list[i] == 0:
sum_of_primes += i
return sum_of_primes
if __name__ == "__main__":
print(F'{solution() = }') | 215 | 1 |
from typing import Optional, Tuple, Union
import tensorflow as tf
from ...activations_tf import ACTaFN
from ...file_utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward
from ...modeling_tf_outputs import (
TFBaseModelOutputWithNoAttention,
TFBaseModelOutputWithPoolingAndNoAttention,
TFSequenceClassifierOutput,
)
from ...modeling_tf_utils import TFPreTrainedModel, TFSequenceClassificationLoss, keras_serializable, unpack_inputs
from ...tf_utils import shape_list
from ...utils import logging
from .configuration_regnet import RegNetConfig
__A : Optional[Any] = logging.get_logger(__name__)
# General docstring
__A : Optional[int] = "RegNetConfig"
# Base docstring
__A : str = "facebook/regnet-y-040"
__A : str = [1, 1_088, 7, 7]
# Image classification docstring
__A : List[str] = "facebook/regnet-y-040"
__A : Optional[Any] = "tabby, tabby cat"
__A : Optional[Any] = [
"facebook/regnet-y-040",
# See all regnet models at https://huggingface.co/models?filter=regnet
]
class A_ (tf.keras.layers.Layer ):
def __init__( self , _A , _A = 3 , _A = 1 , _A = 1 , _A = "relu" , **_A , ):
'''simple docstring'''
super().__init__(**_A )
# The padding and conv has been verified in
# https://colab.research.google.com/gist/sayakpaul/854bc10eeaf21c9ee2119e0b9f3841a7/scratchpad.ipynb
UpperCAmelCase = tf.keras.layers.ZeroPaddingaD(padding=kernel_size // 2 )
UpperCAmelCase = tf.keras.layers.ConvaD(
filters=_A , kernel_size=_A , strides=_A , padding='''VALID''' , groups=_A , use_bias=_A , name='''convolution''' , )
UpperCAmelCase = tf.keras.layers.BatchNormalization(epsilon=1E-5 , momentum=0.9 , name='''normalization''' )
UpperCAmelCase = ACTaFN[activation] if activation is not None else tf.identity
def _lowercase ( self , _A ):
'''simple docstring'''
UpperCAmelCase = self.convolution(self.padding(_A ) )
UpperCAmelCase = self.normalization(_A )
UpperCAmelCase = self.activation(_A )
return hidden_state
class A_ (tf.keras.layers.Layer ):
def __init__( self , _A , **_A ):
'''simple docstring'''
super().__init__(**_A )
UpperCAmelCase = config.num_channels
UpperCAmelCase = TFRegNetConvLayer(
out_channels=config.embedding_size , kernel_size=3 , stride=2 , activation=config.hidden_act , name='''embedder''' , )
def _lowercase ( self , _A ):
'''simple docstring'''
UpperCAmelCase = shape_list(_A )[1]
if tf.executing_eagerly() and num_channels != self.num_channels:
raise ValueError(
'''Make sure that the channel dimension of the pixel values match with the one set in the configuration.''' )
# When running on CPU, `tf.keras.layers.Conv2D` doesn't support `NCHW` format.
# So change the input format from `NCHW` to `NHWC`.
# shape = (batch_size, in_height, in_width, in_channels=num_channels)
UpperCAmelCase = tf.transpose(_A , perm=(0, 2, 3, 1) )
UpperCAmelCase = self.embedder(_A )
return hidden_state
class A_ (tf.keras.layers.Layer ):
def __init__( self , _A , _A = 2 , **_A ):
'''simple docstring'''
super().__init__(**_A )
UpperCAmelCase = tf.keras.layers.ConvaD(
filters=_A , kernel_size=1 , strides=_A , use_bias=_A , name='''convolution''' )
UpperCAmelCase = tf.keras.layers.BatchNormalization(epsilon=1E-5 , momentum=0.9 , name='''normalization''' )
def _lowercase ( self , _A , _A = False ):
'''simple docstring'''
return self.normalization(self.convolution(_A ) , training=_A )
class A_ (tf.keras.layers.Layer ):
def __init__( self , _A , _A , **_A ):
'''simple docstring'''
super().__init__(**_A )
UpperCAmelCase = tf.keras.layers.GlobalAveragePoolingaD(keepdims=_A , name='''pooler''' )
UpperCAmelCase = [
tf.keras.layers.ConvaD(filters=_A , kernel_size=1 , activation='''relu''' , name='''attention.0''' ),
tf.keras.layers.ConvaD(filters=_A , kernel_size=1 , activation='''sigmoid''' , name='''attention.2''' ),
]
def _lowercase ( self , _A ):
'''simple docstring'''
UpperCAmelCase = self.pooler(_A )
for layer_module in self.attention:
UpperCAmelCase = layer_module(_A )
UpperCAmelCase = hidden_state * pooled
return hidden_state
class A_ (tf.keras.layers.Layer ):
def __init__( self , _A , _A , _A , _A = 1 , **_A ):
'''simple docstring'''
super().__init__(**_A )
UpperCAmelCase = in_channels != out_channels or stride != 1
UpperCAmelCase = max(1 , out_channels // config.groups_width )
UpperCAmelCase = (
TFRegNetShortCut(_A , stride=_A , name='''shortcut''' )
if should_apply_shortcut
else tf.keras.layers.Activation('''linear''' , name='''shortcut''' )
)
# `self.layers` instead of `self.layer` because that is a reserved argument.
UpperCAmelCase = [
TFRegNetConvLayer(_A , kernel_size=1 , activation=config.hidden_act , name='''layer.0''' ),
TFRegNetConvLayer(
_A , stride=_A , groups=_A , activation=config.hidden_act , name='''layer.1''' ),
TFRegNetConvLayer(_A , kernel_size=1 , activation=_A , name='''layer.2''' ),
]
UpperCAmelCase = ACTaFN[config.hidden_act]
def _lowercase ( self , _A ):
'''simple docstring'''
UpperCAmelCase = hidden_state
for layer_module in self.layers:
UpperCAmelCase = layer_module(_A )
UpperCAmelCase = self.shortcut(_A )
hidden_state += residual
UpperCAmelCase = self.activation(_A )
return hidden_state
class A_ (tf.keras.layers.Layer ):
def __init__( self , _A , _A , _A , _A = 1 , **_A ):
'''simple docstring'''
super().__init__(**_A )
UpperCAmelCase = in_channels != out_channels or stride != 1
UpperCAmelCase = max(1 , out_channels // config.groups_width )
UpperCAmelCase = (
TFRegNetShortCut(_A , stride=_A , name='''shortcut''' )
if should_apply_shortcut
else tf.keras.layers.Activation('''linear''' , name='''shortcut''' )
)
UpperCAmelCase = [
TFRegNetConvLayer(_A , kernel_size=1 , activation=config.hidden_act , name='''layer.0''' ),
TFRegNetConvLayer(
_A , stride=_A , groups=_A , activation=config.hidden_act , name='''layer.1''' ),
TFRegNetSELayer(_A , reduced_channels=int(round(in_channels / 4 ) ) , name='''layer.2''' ),
TFRegNetConvLayer(_A , kernel_size=1 , activation=_A , name='''layer.3''' ),
]
UpperCAmelCase = ACTaFN[config.hidden_act]
def _lowercase ( self , _A ):
'''simple docstring'''
UpperCAmelCase = hidden_state
for layer_module in self.layers:
UpperCAmelCase = layer_module(_A )
UpperCAmelCase = self.shortcut(_A )
hidden_state += residual
UpperCAmelCase = self.activation(_A )
return hidden_state
class A_ (tf.keras.layers.Layer ):
def __init__( self , _A , _A , _A , _A = 2 , _A = 2 , **_A ):
'''simple docstring'''
super().__init__(**_A )
UpperCAmelCase = TFRegNetXLayer if config.layer_type == '''x''' else TFRegNetYLayer
UpperCAmelCase = [
# downsampling is done in the first layer with stride of 2
layer(_A , _A , _A , stride=_A , name='''layers.0''' ),
*[layer(_A , _A , _A , name=F"""layers.{i+1}""" ) for i in range(depth - 1 )],
]
def _lowercase ( self , _A ):
'''simple docstring'''
for layer_module in self.layers:
UpperCAmelCase = layer_module(_A )
return hidden_state
class A_ (tf.keras.layers.Layer ):
def __init__( self , _A , **_A ):
'''simple docstring'''
super().__init__(**_A )
UpperCAmelCase = []
# based on `downsample_in_first_stage`, the first layer of the first stage may or may not downsample the input
self.stages.append(
TFRegNetStage(
_A , config.embedding_size , config.hidden_sizes[0] , stride=2 if config.downsample_in_first_stage else 1 , depth=config.depths[0] , name='''stages.0''' , ) )
UpperCAmelCase = zip(config.hidden_sizes , config.hidden_sizes[1:] )
for i, ((in_channels, out_channels), depth) in enumerate(zip(_A , config.depths[1:] ) ):
self.stages.append(TFRegNetStage(_A , _A , _A , depth=_A , name=F"""stages.{i+1}""" ) )
def _lowercase ( self , _A , _A = False , _A = True ):
'''simple docstring'''
UpperCAmelCase = () if output_hidden_states else None
for stage_module in self.stages:
if output_hidden_states:
UpperCAmelCase = hidden_states + (hidden_state,)
UpperCAmelCase = stage_module(_A )
if output_hidden_states:
UpperCAmelCase = hidden_states + (hidden_state,)
if not return_dict:
return tuple(v for v in [hidden_state, hidden_states] if v is not None )
return TFBaseModelOutputWithNoAttention(last_hidden_state=_A , hidden_states=_A )
@keras_serializable
class A_ (tf.keras.layers.Layer ):
UpperCAmelCase__ = RegNetConfig
def __init__( self , _A , **_A ):
'''simple docstring'''
super().__init__(**_A )
UpperCAmelCase = config
UpperCAmelCase = TFRegNetEmbeddings(_A , name='''embedder''' )
UpperCAmelCase = TFRegNetEncoder(_A , name='''encoder''' )
UpperCAmelCase = tf.keras.layers.GlobalAveragePoolingaD(keepdims=_A , name='''pooler''' )
@unpack_inputs
def _lowercase ( self , _A , _A = None , _A = None , _A = False , ):
'''simple docstring'''
UpperCAmelCase = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
UpperCAmelCase = return_dict if return_dict is not None else self.config.use_return_dict
UpperCAmelCase = self.embedder(_A , training=_A )
UpperCAmelCase = self.encoder(
_A , output_hidden_states=_A , return_dict=_A , training=_A )
UpperCAmelCase = encoder_outputs[0]
UpperCAmelCase = self.pooler(_A )
# Change to NCHW output format have uniformity in the modules
UpperCAmelCase = tf.transpose(_A , perm=(0, 3, 1, 2) )
UpperCAmelCase = tf.transpose(_A , perm=(0, 3, 1, 2) )
# Change the other hidden state outputs to NCHW as well
if output_hidden_states:
UpperCAmelCase = tuple([tf.transpose(_A , perm=(0, 3, 1, 2) ) for h in encoder_outputs[1]] )
if not return_dict:
return (last_hidden_state, pooled_output) + encoder_outputs[1:]
return TFBaseModelOutputWithPoolingAndNoAttention(
last_hidden_state=_A , pooler_output=_A , hidden_states=hidden_states if output_hidden_states else encoder_outputs.hidden_states , )
class A_ (a_ ):
UpperCAmelCase__ = RegNetConfig
UpperCAmelCase__ = '''regnet'''
UpperCAmelCase__ = '''pixel_values'''
@property
def _lowercase ( self ):
'''simple docstring'''
return {"pixel_values": tf.TensorSpec(shape=(None, self.config.num_channels, 2_2_4, 2_2_4) , dtype=tf.floataa )}
__A : str = R"\n Parameters:\n This model is a Tensorflow\n [tf.keras.layers.Layer](https://www.tensorflow.org/api_docs/python/tf/keras/layers/Layer) sub-class. Use it as a\n regular Tensorflow Module and refer to the Tensorflow documentation for all matter related to general usage and\n behavior.\n config ([`RegNetConfig`]): Model configuration class with all the parameters of the model.\n Initializing with a config file does not load the weights associated with the model, only the\n configuration. Check out the [`~TFPreTrainedModel.from_pretrained`] method to load the model weights.\n"
__A : List[Any] = R"\n Args:\n pixel_values (`tf.Tensor` of shape `(batch_size, num_channels, height, width)`):\n Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. See\n [`ConveNextImageProcessor.__call__`] for details.\n output_hidden_states (`bool`, *optional*):\n Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for\n more detail.\n return_dict (`bool`, *optional*):\n Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.\n"
@add_start_docstrings(
'''The bare RegNet model outputting raw features without any specific head on top.''' , a_ , )
class A_ (a_ ):
def __init__( self , _A , *_A , **_A ):
'''simple docstring'''
super().__init__(_A , *_A , **_A )
UpperCAmelCase = TFRegNetMainLayer(_A , name='''regnet''' )
@unpack_inputs
@add_start_docstrings_to_model_forward(_A )
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_DOC , output_type=_A , config_class=_CONFIG_FOR_DOC , modality='''vision''' , expected_output=_EXPECTED_OUTPUT_SHAPE , )
def _lowercase ( self , _A , _A = None , _A = None , _A=False , ):
'''simple docstring'''
UpperCAmelCase = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
UpperCAmelCase = return_dict if return_dict is not None else self.config.use_return_dict
UpperCAmelCase = self.regnet(
pixel_values=_A , output_hidden_states=_A , return_dict=_A , training=_A , )
if not return_dict:
return (outputs[0],) + outputs[1:]
return TFBaseModelOutputWithPoolingAndNoAttention(
last_hidden_state=outputs.last_hidden_state , pooler_output=outputs.pooler_output , hidden_states=outputs.hidden_states , )
@add_start_docstrings(
'''
RegNet Model with an image classification head on top (a linear layer on top of the pooled features), e.g. for
ImageNet.
''' , a_ , )
class A_ (a_ , a_ ):
def __init__( self , _A , *_A , **_A ):
'''simple docstring'''
super().__init__(_A , *_A , **_A )
UpperCAmelCase = config.num_labels
UpperCAmelCase = TFRegNetMainLayer(_A , name='''regnet''' )
# classification head
UpperCAmelCase = [
tf.keras.layers.Flatten(),
tf.keras.layers.Dense(config.num_labels , name='''classifier.1''' ) if config.num_labels > 0 else tf.identity,
]
@unpack_inputs
@add_start_docstrings_to_model_forward(_A )
@add_code_sample_docstrings(
checkpoint=_IMAGE_CLASS_CHECKPOINT , output_type=_A , config_class=_CONFIG_FOR_DOC , expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT , )
def _lowercase ( self , _A = None , _A = None , _A = None , _A = None , _A=False , ):
'''simple docstring'''
UpperCAmelCase = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
UpperCAmelCase = return_dict if return_dict is not None else self.config.use_return_dict
UpperCAmelCase = self.regnet(
_A , output_hidden_states=_A , return_dict=_A , training=_A )
UpperCAmelCase = outputs.pooler_output if return_dict else outputs[1]
UpperCAmelCase = self.classifier[0](_A )
UpperCAmelCase = self.classifier[1](_A )
UpperCAmelCase = None if labels is None else self.hf_compute_loss(labels=_A , logits=_A )
if not return_dict:
UpperCAmelCase = (logits,) + outputs[2:]
return ((loss,) + output) if loss is not None else output
return TFSequenceClassifierOutput(loss=_A , logits=_A , hidden_states=outputs.hidden_states )
| 273 |
from __future__ import annotations
from collections.abc import Callable
def __SCREAMING_SNAKE_CASE ( UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ = 100 , ) -> float:
'''simple docstring'''
UpperCAmelCase = x_start
UpperCAmelCase = fnc(UpperCamelCase__ )
UpperCAmelCase = 0.0
for _ in range(UpperCamelCase__ ):
# Approximates small segments of curve as linear and solve
# for trapezoidal area
UpperCAmelCase = (x_end - x_start) / steps + xa
UpperCAmelCase = fnc(UpperCamelCase__ )
area += abs(fxa + fxa ) * (xa - xa) / 2
# Increment step
UpperCAmelCase = xa
UpperCAmelCase = fxa
return area
if __name__ == "__main__":
def __SCREAMING_SNAKE_CASE ( UpperCamelCase__ ) -> str:
'''simple docstring'''
return x**3 + x**2
print("f(x) = x^3 + x^2")
print("The area between the curve, x = -5, x = 5 and the x axis is:")
__A : List[Any] = 10
while i <= 100_000:
print(F'with {i} steps: {trapezoidal_area(f, -5, 5, i)}')
i *= 10
| 273 | 1 |
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from .tokenization_electra import ElectraTokenizer
_UpperCAmelCase : int = {"vocab_file": "vocab.txt", "tokenizer_file": "tokenizer.json"}
_UpperCAmelCase : str = {
"vocab_file": {
"google/electra-small-generator": (
"https://huggingface.co/google/electra-small-generator/resolve/main/vocab.txt"
),
"google/electra-base-generator": "https://huggingface.co/google/electra-base-generator/resolve/main/vocab.txt",
"google/electra-large-generator": (
"https://huggingface.co/google/electra-large-generator/resolve/main/vocab.txt"
),
"google/electra-small-discriminator": (
"https://huggingface.co/google/electra-small-discriminator/resolve/main/vocab.txt"
),
"google/electra-base-discriminator": (
"https://huggingface.co/google/electra-base-discriminator/resolve/main/vocab.txt"
),
"google/electra-large-discriminator": (
"https://huggingface.co/google/electra-large-discriminator/resolve/main/vocab.txt"
),
},
"tokenizer_file": {
"google/electra-small-generator": (
"https://huggingface.co/google/electra-small-generator/resolve/main/tokenizer.json"
),
"google/electra-base-generator": (
"https://huggingface.co/google/electra-base-generator/resolve/main/tokenizer.json"
),
"google/electra-large-generator": (
"https://huggingface.co/google/electra-large-generator/resolve/main/tokenizer.json"
),
"google/electra-small-discriminator": (
"https://huggingface.co/google/electra-small-discriminator/resolve/main/tokenizer.json"
),
"google/electra-base-discriminator": (
"https://huggingface.co/google/electra-base-discriminator/resolve/main/tokenizer.json"
),
"google/electra-large-discriminator": (
"https://huggingface.co/google/electra-large-discriminator/resolve/main/tokenizer.json"
),
},
}
_UpperCAmelCase : Optional[int] = {
"google/electra-small-generator": 512,
"google/electra-base-generator": 512,
"google/electra-large-generator": 512,
"google/electra-small-discriminator": 512,
"google/electra-base-discriminator": 512,
"google/electra-large-discriminator": 512,
}
_UpperCAmelCase : List[str] = {
"google/electra-small-generator": {"do_lower_case": True},
"google/electra-base-generator": {"do_lower_case": True},
"google/electra-large-generator": {"do_lower_case": True},
"google/electra-small-discriminator": {"do_lower_case": True},
"google/electra-base-discriminator": {"do_lower_case": True},
"google/electra-large-discriminator": {"do_lower_case": True},
}
class __lowerCAmelCase ( lowerCAmelCase):
_a = VOCAB_FILES_NAMES
_a = PRETRAINED_VOCAB_FILES_MAP
_a = PRETRAINED_INIT_CONFIGURATION
_a = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
_a = ElectraTokenizer
def __init__( self: str , _lowerCAmelCase: List[str]=None , _lowerCAmelCase: Any=None , _lowerCAmelCase: Optional[int]=True , _lowerCAmelCase: Union[str, Any]="[UNK]" , _lowerCAmelCase: Dict="[SEP]" , _lowerCAmelCase: Tuple="[PAD]" , _lowerCAmelCase: Optional[Any]="[CLS]" , _lowerCAmelCase: Optional[Any]="[MASK]" , _lowerCAmelCase: List[str]=True , _lowerCAmelCase: Union[str, Any]=None , **_lowerCAmelCase: List[str] , ):
super().__init__(
_lowerCAmelCase , tokenizer_file=_lowerCAmelCase , do_lower_case=_lowerCAmelCase , unk_token=_lowerCAmelCase , sep_token=_lowerCAmelCase , pad_token=_lowerCAmelCase , cls_token=_lowerCAmelCase , mask_token=_lowerCAmelCase , tokenize_chinese_chars=_lowerCAmelCase , strip_accents=_lowerCAmelCase , **_lowerCAmelCase , )
lowercase :Optional[int] = json.loads(self.backend_tokenizer.normalizer.__getstate__() )
if (
normalizer_state.get("lowercase" , _lowerCAmelCase ) != do_lower_case
or normalizer_state.get("strip_accents" , _lowerCAmelCase ) != strip_accents
or normalizer_state.get("handle_chinese_chars" , _lowerCAmelCase ) != tokenize_chinese_chars
):
lowercase :Optional[int] = getattr(_lowerCAmelCase , normalizer_state.pop("type" ) )
lowercase :str = do_lower_case
lowercase :Dict = strip_accents
lowercase :Optional[Any] = tokenize_chinese_chars
lowercase :Union[str, Any] = normalizer_class(**_lowerCAmelCase )
lowercase :List[str] = do_lower_case
def SCREAMING_SNAKE_CASE ( self: Tuple , _lowerCAmelCase: Union[str, Any] , _lowerCAmelCase: str=None ):
lowercase :List[Any] = [self.cls_token_id] + token_ids_a + [self.sep_token_id]
if token_ids_a:
output += token_ids_a + [self.sep_token_id]
return output
def SCREAMING_SNAKE_CASE ( self: str , _lowerCAmelCase: List[int] , _lowerCAmelCase: Optional[List[int]] = None ):
lowercase :Optional[Any] = [self.sep_token_id]
lowercase :str = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep ) * [0] + len(token_ids_a + sep ) * [1]
def SCREAMING_SNAKE_CASE ( self: List[Any] , _lowerCAmelCase: str , _lowerCAmelCase: Optional[str] = None ):
lowercase :List[Any] = self._tokenizer.model.save(_lowerCAmelCase , name=_lowerCAmelCase )
return tuple(_lowerCAmelCase )
| 361 |
import logging
import os
import threading
import time
try:
import warnings
except ImportError:
_UpperCAmelCase : List[str] = None
try:
import msvcrt
except ImportError:
_UpperCAmelCase : Tuple = None
try:
import fcntl
except ImportError:
_UpperCAmelCase : Optional[Any] = None
# Backward compatibility
# ------------------------------------------------
try:
TimeoutError
except NameError:
_UpperCAmelCase : Tuple = OSError
# Data
# ------------------------------------------------
_UpperCAmelCase : Optional[int] = [
"Timeout",
"BaseFileLock",
"WindowsFileLock",
"UnixFileLock",
"SoftFileLock",
"FileLock",
]
_UpperCAmelCase : Optional[Any] = "3.0.12"
_UpperCAmelCase : int = None
def UpperCAmelCase__ ( ):
global _logger
lowercase :List[str] = _logger or logging.getLogger(__name__ )
return _logger
class __lowerCAmelCase ( lowerCAmelCase):
def __init__( self: int , _lowerCAmelCase: Dict ):
lowercase :Any = lock_file
return None
def __str__( self: Dict ):
lowercase :str = F"The file lock '{self.lock_file}' could not be acquired."
return temp
class __lowerCAmelCase :
def __init__( self: Tuple , _lowerCAmelCase: Any ):
lowercase :Optional[Any] = lock
return None
def __enter__( self: List[Any] ):
return self.lock
def __exit__( self: Dict , _lowerCAmelCase: Optional[Any] , _lowerCAmelCase: List[Any] , _lowerCAmelCase: Optional[int] ):
self.lock.release()
return None
class __lowerCAmelCase :
def __init__( self: Optional[Any] , _lowerCAmelCase: Union[str, Any] , _lowerCAmelCase: Tuple=-1 , _lowerCAmelCase: int=None ):
lowercase :Any = max_filename_length if max_filename_length is not None else 2_55
# Hash the filename if it's too long
lowercase :int = self.hash_filename_if_too_long(_lowerCAmelCase , _lowerCAmelCase )
# The path to the lock file.
lowercase :List[Any] = lock_file
# The file descriptor for the *_lock_file* as it is returned by the
# os.open() function.
# This file lock is only NOT None, if the object currently holds the
# lock.
lowercase :Any = None
# The default timeout value.
lowercase :Any = timeout
# We use this lock primarily for the lock counter.
lowercase :Optional[int] = threading.Lock()
# The lock counter is used for implementing the nested locking
# mechanism. Whenever the lock is acquired, the counter is increased and
# the lock is only released, when this value is 0 again.
lowercase :Optional[int] = 0
return None
@property
def SCREAMING_SNAKE_CASE ( self: Union[str, Any] ):
return self._lock_file
@property
def SCREAMING_SNAKE_CASE ( self: Optional[Any] ):
return self._timeout
@timeout.setter
def SCREAMING_SNAKE_CASE ( self: Tuple , _lowerCAmelCase: List[str] ):
lowercase :Tuple = float(_lowerCAmelCase )
return None
def SCREAMING_SNAKE_CASE ( self: int ):
raise NotImplementedError()
def SCREAMING_SNAKE_CASE ( self: int ):
raise NotImplementedError()
@property
def SCREAMING_SNAKE_CASE ( self: Optional[Any] ):
return self._lock_file_fd is not None
def SCREAMING_SNAKE_CASE ( self: Union[str, Any] , _lowerCAmelCase: List[Any]=None , _lowerCAmelCase: Union[str, Any]=0.05 ):
# Use the default timeout, if no timeout is provided.
if timeout is None:
lowercase :List[str] = self.timeout
# Increment the number right at the beginning.
# We can still undo it, if something fails.
with self._thread_lock:
self._lock_counter += 1
lowercase :Any = id(self )
lowercase :Optional[int] = self._lock_file
lowercase :Optional[Any] = time.time()
try:
while True:
with self._thread_lock:
if not self.is_locked:
logger().debug(F"Attempting to acquire lock {lock_id} on {lock_filename}" )
self._acquire()
if self.is_locked:
logger().debug(F"Lock {lock_id} acquired on {lock_filename}" )
break
elif timeout >= 0 and time.time() - start_time > timeout:
logger().debug(F"Timeout on acquiring lock {lock_id} on {lock_filename}" )
raise Timeout(self._lock_file )
else:
logger().debug(
F"Lock {lock_id} not acquired on {lock_filename}, waiting {poll_intervall} seconds ..." )
time.sleep(_lowerCAmelCase )
except: # noqa
# Something did go wrong, so decrement the counter.
with self._thread_lock:
lowercase :Union[str, Any] = max(0 , self._lock_counter - 1 )
raise
return _Acquire_ReturnProxy(lock=self )
def SCREAMING_SNAKE_CASE ( self: Tuple , _lowerCAmelCase: Tuple=False ):
with self._thread_lock:
if self.is_locked:
self._lock_counter -= 1
if self._lock_counter == 0 or force:
lowercase :Union[str, Any] = id(self )
lowercase :str = self._lock_file
logger().debug(F"Attempting to release lock {lock_id} on {lock_filename}" )
self._release()
lowercase :List[str] = 0
logger().debug(F"Lock {lock_id} released on {lock_filename}" )
return None
def __enter__( self: Tuple ):
self.acquire()
return self
def __exit__( self: Union[str, Any] , _lowerCAmelCase: List[Any] , _lowerCAmelCase: List[str] , _lowerCAmelCase: Dict ):
self.release()
return None
def __del__( self: Optional[Any] ):
self.release(force=_lowerCAmelCase )
return None
def SCREAMING_SNAKE_CASE ( self: Union[str, Any] , _lowerCAmelCase: str , _lowerCAmelCase: int ):
lowercase :Union[str, Any] = os.path.basename(_lowerCAmelCase )
if len(_lowerCAmelCase ) > max_length and max_length > 0:
lowercase :Dict = os.path.dirname(_lowerCAmelCase )
lowercase :Any = str(hash(_lowerCAmelCase ) )
lowercase :Union[str, Any] = filename[: max_length - len(_lowerCAmelCase ) - 8] + "..." + hashed_filename + ".lock"
return os.path.join(_lowerCAmelCase , _lowerCAmelCase )
else:
return path
class __lowerCAmelCase ( lowerCAmelCase):
def __init__( self: int , _lowerCAmelCase: int , _lowerCAmelCase: Optional[Any]=-1 , _lowerCAmelCase: List[Any]=None ):
from .file_utils import relative_to_absolute_path
super().__init__(_lowerCAmelCase , timeout=_lowerCAmelCase , max_filename_length=_lowerCAmelCase )
lowercase :Optional[int] = "\\\\?\\" + relative_to_absolute_path(self.lock_file )
def SCREAMING_SNAKE_CASE ( self: Any ):
lowercase :int = os.O_RDWR | os.O_CREAT | os.O_TRUNC
try:
lowercase :Tuple = os.open(self._lock_file , _lowerCAmelCase )
except OSError:
pass
else:
try:
msvcrt.locking(_lowerCAmelCase , msvcrt.LK_NBLCK , 1 )
except OSError:
os.close(_lowerCAmelCase )
else:
lowercase :Any = fd
return None
def SCREAMING_SNAKE_CASE ( self: Union[str, Any] ):
lowercase :Any = self._lock_file_fd
lowercase :Tuple = None
msvcrt.locking(_lowerCAmelCase , msvcrt.LK_UNLCK , 1 )
os.close(_lowerCAmelCase )
try:
os.remove(self._lock_file )
# Probably another instance of the application
# that acquired the file lock.
except OSError:
pass
return None
class __lowerCAmelCase ( lowerCAmelCase):
def __init__( self: str , _lowerCAmelCase: Tuple , _lowerCAmelCase: Dict=-1 , _lowerCAmelCase: Tuple=None ):
lowercase :List[str] = os.statvfs(os.path.dirname(_lowerCAmelCase ) ).f_namemax
super().__init__(_lowerCAmelCase , timeout=_lowerCAmelCase , max_filename_length=_lowerCAmelCase )
def SCREAMING_SNAKE_CASE ( self: int ):
lowercase :Any = os.O_RDWR | os.O_CREAT | os.O_TRUNC
lowercase :Optional[int] = os.open(self._lock_file , _lowerCAmelCase )
try:
fcntl.flock(_lowerCAmelCase , fcntl.LOCK_EX | fcntl.LOCK_NB )
except OSError:
os.close(_lowerCAmelCase )
else:
lowercase :Optional[Any] = fd
return None
def SCREAMING_SNAKE_CASE ( self: Union[str, Any] ):
# Do not remove the lockfile:
#
# https://github.com/benediktschmitt/py-filelock/issues/31
# https://stackoverflow.com/questions/17708885/flock-removing-locked-file-without-race-condition
lowercase :Dict = self._lock_file_fd
lowercase :Union[str, Any] = None
fcntl.flock(_lowerCAmelCase , fcntl.LOCK_UN )
os.close(_lowerCAmelCase )
return None
class __lowerCAmelCase ( lowerCAmelCase):
def SCREAMING_SNAKE_CASE ( self: List[Any] ):
lowercase :str = os.O_WRONLY | os.O_CREAT | os.O_EXCL | os.O_TRUNC
try:
lowercase :List[Any] = os.open(self._lock_file , _lowerCAmelCase )
except OSError:
pass
else:
lowercase :int = fd
return None
def SCREAMING_SNAKE_CASE ( self: Optional[Any] ):
os.close(self._lock_file_fd )
lowercase :int = None
try:
os.remove(self._lock_file )
# The file is already deleted and that's what we want.
except OSError:
pass
return None
_UpperCAmelCase : Tuple = None
if msvcrt:
_UpperCAmelCase : str = WindowsFileLock
elif fcntl:
_UpperCAmelCase : List[Any] = UnixFileLock
else:
_UpperCAmelCase : Optional[int] = SoftFileLock
if warnings is not None:
warnings.warn("only soft file lock is available")
| 158 | 0 |
#
# This a `torch.distributed` diagnostics script that checks that all GPUs in the cluster (one or
# many nodes) can talk to each other via nccl and allocate gpu memory.
#
# To run first adjust the number of processes and nodes:
#
# python -m torch.distributed.run --nproc_per_node 2 --nnodes 1 torch-distributed-gpu-test.py
#
# You may need to add --master_addr $MASTER_ADDR --master_port $MASTER_PORT if using a custom addr:port
#
# You can also use the rdzv API: --rdzv_endpoint $MASTER_ADDR:$MASTER_PORT --rdzv_backend c10d
#
# use torch.distributed.launch instead of torch.distributed.run for torch < 1.9
#
# If you get a hanging in `barrier` calls you have some network issues, you may try to debug this with:
#
# NCCL_DEBUG=INFO python -m torch.distributed.run --nproc_per_node 2 --nnodes 1 torch-distributed-gpu-test.py
#
# which should tell you what's going on behind the scenes.
#
#
# This script can be run via `srun` in the SLURM environment as well. Here is a SLURM script that
# runs on 2 nodes of 4 gpus per node:
#
# #SBATCH --job-name=test-nodes # name
# #SBATCH --nodes=2 # nodes
# #SBATCH --ntasks-per-node=1 # crucial - only 1 task per dist per node!
# #SBATCH --cpus-per-task=10 # number of cores per tasks
# #SBATCH --gres=gpu:4 # number of gpus
# #SBATCH --time 0:05:00 # maximum execution time (HH:MM:SS)
# #SBATCH --output=%x-%j.out # output file name
#
# GPUS_PER_NODE=4
# MASTER_ADDR=$(scontrol show hostnames $SLURM_JOB_NODELIST | head -n 1)
# MASTER_PORT=6000
#
# srun --jobid $SLURM_JOBID bash -c 'python -m torch.distributed.run \
# --nproc_per_node $GPUS_PER_NODE --nnodes $SLURM_NNODES --node_rank $SLURM_PROCID \
# --master_addr $MASTER_ADDR --master_port $MASTER_PORT \
# torch-distributed-gpu-test.py'
#
import fcntl
import os
import socket
import torch
import torch.distributed as dist
def SCREAMING_SNAKE_CASE ( *lowercase_ ) -> Dict:
"""simple docstring"""
with open(lowercase_ , '''r''' ) as fh:
fcntl.flock(lowercase_ , fcntl.LOCK_EX )
try:
print(*lowercase_ )
finally:
fcntl.flock(lowercase_ , fcntl.LOCK_UN )
_lowerCamelCase : Optional[int] = int(os.environ["""LOCAL_RANK"""])
torch.cuda.set_device(local_rank)
_lowerCamelCase : List[Any] = torch.device("""cuda""", local_rank)
_lowerCamelCase : Union[str, Any] = socket.gethostname()
_lowerCamelCase : str = F'''[{hostname}-{local_rank}]'''
try:
# test distributed
dist.init_process_group("""nccl""")
dist.all_reduce(torch.ones(1).to(device), op=dist.ReduceOp.SUM)
dist.barrier()
# test cuda is available and can allocate memory
torch.cuda.is_available()
torch.ones(1).cuda(local_rank)
# global rank
_lowerCamelCase : Any = dist.get_rank()
_lowerCamelCase : int = dist.get_world_size()
printflock(F'''{gpu} is OK (global rank: {rank}/{world_size})''')
dist.barrier()
if rank == 0:
printflock(F'''pt={torch.__version__}, cuda={torch.version.cuda}, nccl={torch.cuda.nccl.version()}''')
except Exception:
printflock(F'''{gpu} is broken''')
raise
| 14 |
import os
import sys
import unittest
_lowerCamelCase : Optional[Any] = 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,
)
_lowerCamelCase : Any = os.path.join("""tests""", """models""", """bert""", """test_modeling_bert.py""")
_lowerCamelCase : str = os.path.join("""tests""", """models""", """blip""", """test_modeling_blip.py""")
class UpperCamelCase_ ( unittest.TestCase ):
'''simple docstring'''
def SCREAMING_SNAKE_CASE ( self : Tuple) ->Tuple:
'''simple docstring'''
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 SCREAMING_SNAKE_CASE ( self : Tuple) ->List[Any]:
'''simple docstring'''
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 SCREAMING_SNAKE_CASE ( self : Optional[Any]) ->str:
'''simple docstring'''
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__)
| 14 | 1 |
def lowerCamelCase__ ( a = 50 ) -> int:
_A: int = [1] * (length + 1)
for row_length in range(length + 1 ):
for tile_length in range(2 , 5 ):
for tile_start in range(row_length - tile_length + 1 ):
ways_number[row_length] += ways_number[
row_length - tile_start - tile_length
]
return ways_number[length]
if __name__ == "__main__":
print(F"""{solution() = }""")
| 357 |
import math
import random
from typing import Any
from .hill_climbing import SearchProblem
def lowerCamelCase__ ( a , a = True , a = math.inf , a = -math.inf , a = math.inf , a = -math.inf , a = False , a = 1_00 , a = 0.01 , a = 1 , ) -> Any:
_A: Optional[Any] = False
_A: Dict = search_prob
_A: str = start_temperate
_A: Optional[int] = []
_A: int = 0
_A: Dict = None
while not search_end:
_A: Dict = current_state.score()
if best_state is None or current_score > best_state.score():
_A: List[Any] = current_state
scores.append(a )
iterations += 1
_A: List[str] = None
_A: str = current_state.get_neighbors()
while (
next_state is None and neighbors
): # till we do not find a neighbor that we can move to
_A: Any = random.randint(0 , len(a ) - 1 ) # picking a random neighbor
_A: Union[str, Any] = neighbors.pop(a )
_A: List[str] = picked_neighbor.score() - current_score
if (
picked_neighbor.x > max_x
or picked_neighbor.x < min_x
or picked_neighbor.y > max_y
or picked_neighbor.y < min_y
):
continue # neighbor outside our bounds
if not find_max:
_A: Optional[Any] = change * -1 # in case we are finding minimum
if change > 0: # improves the solution
_A: str = picked_neighbor
else:
_A: Tuple = (math.e) ** (
change / current_temp
) # probability generation function
if random.random() < probability: # random number within probability
_A: Optional[int] = picked_neighbor
_A: Dict = current_temp - (current_temp * rate_of_decrease)
if current_temp < threshold_temp or next_state is None:
# temperature below threshold, or could not find a suitable neighbor
_A: Any = True
else:
_A: List[Any] = next_state
if visualization:
from matplotlib import pyplot as plt
plt.plot(range(a ) , a )
plt.xlabel('''Iterations''' )
plt.ylabel('''Function values''' )
plt.show()
return best_state
if __name__ == "__main__":
def lowerCamelCase__ ( a , a ) -> Optional[Any]:
return (x**2) + (y**2)
# starting the problem with initial coordinates (12, 47)
UpperCAmelCase__ : Optional[int] = SearchProblem(x=12, y=47, step_size=1, function_to_optimize=test_fa)
UpperCAmelCase__ : Optional[Any] = simulated_annealing(
prob, find_max=False, max_x=100, min_x=5, max_y=50, min_y=-5, visualization=True
)
print(
'The minimum score for f(x, y) = x^2 + y^2 with the domain 100 > x > 5 '
F"""and 50 > y > - 5 found via hill climbing: {local_min.score()}"""
)
# starting the problem with initial coordinates (12, 47)
UpperCAmelCase__ : Optional[Any] = SearchProblem(x=12, y=47, step_size=1, function_to_optimize=test_fa)
UpperCAmelCase__ : List[str] = simulated_annealing(
prob, find_max=True, max_x=100, min_x=5, max_y=50, min_y=-5, visualization=True
)
print(
'The maximum score for f(x, y) = x^2 + y^2 with the domain 100 > x > 5 '
F"""and 50 > y > - 5 found via hill climbing: {local_min.score()}"""
)
def lowerCamelCase__ ( a , a ) -> Optional[Any]:
return (3 * x**2) - (6 * y)
UpperCAmelCase__ : Union[str, Any] = SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_fa)
UpperCAmelCase__ : List[str] = simulated_annealing(prob, find_max=False, visualization=True)
print(
'The minimum score for f(x, y) = 3*x^2 - 6*y found via hill climbing: '
F"""{local_min.score()}"""
)
UpperCAmelCase__ : Optional[Any] = SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_fa)
UpperCAmelCase__ : List[Any] = simulated_annealing(prob, find_max=True, visualization=True)
print(
'The maximum score for f(x, y) = 3*x^2 - 6*y found via hill climbing: '
F"""{local_min.score()}"""
)
| 301 | 0 |
"""simple docstring"""
def a_ ( ):
return [list(range(1_0_0_0 - i , -1_0_0_0 - i , -1 ) ) for i in range(1_0_0_0 )]
lowerCAmelCase__ : Any = generate_large_matrix()
lowerCAmelCase__ : Any = (
[[4, 3, 2, -1], [3, 2, 1, -1], [1, 1, -1, -2], [-1, -1, -2, -3]],
[[3, 2], [1, 0]],
[[7, 7, 6]],
[[7, 7, 6], [-1, -2, -3]],
grid,
)
def a_ ( lowerCamelCase ):
assert all(row == sorted(lowerCamelCase , reverse=lowerCamelCase ) for row in grid )
assert all(list(lowerCamelCase ) == sorted(lowerCamelCase , reverse=lowerCamelCase ) for col in zip(*lowerCamelCase ) )
def a_ ( lowerCamelCase ):
UpperCAmelCase__ = 0
UpperCAmelCase__ = len(lowerCamelCase ) - 1
# Edge cases such as no values or all numbers are negative.
if not array or array[0] < 0:
return 0
while right + 1 > left:
UpperCAmelCase__ = (left + right) // 2
UpperCAmelCase__ = array[mid]
# Num must be negative and the index must be greater than or equal to 0.
if num < 0 and array[mid - 1] >= 0:
return mid
if num >= 0:
UpperCAmelCase__ = mid + 1
else:
UpperCAmelCase__ = mid - 1
# No negative numbers so return the last index of the array + 1 which is the length.
return len(lowerCamelCase )
def a_ ( lowerCamelCase ):
UpperCAmelCase__ = 0
UpperCAmelCase__ = len(grid[0] )
for i in range(len(lowerCamelCase ) ):
UpperCAmelCase__ = find_negative_index(grid[i][:bound] )
total += bound
return (len(lowerCamelCase ) * len(grid[0] )) - total
def a_ ( lowerCamelCase ):
return len([number for row in grid for number in row if number < 0] )
def a_ ( lowerCamelCase ):
UpperCAmelCase__ = 0
for row in grid:
for i, number in enumerate(lowerCamelCase ):
if number < 0:
total += len(lowerCamelCase ) - i
break
return total
def a_ ( ):
from timeit import timeit
print('Running benchmarks' )
UpperCAmelCase__ = (
'from __main__ import count_negatives_binary_search, '
'count_negatives_brute_force, count_negatives_brute_force_with_break, grid'
)
for func in (
"count_negatives_binary_search", # took 0.7727 seconds
"count_negatives_brute_force_with_break", # took 4.6505 seconds
"count_negatives_brute_force", # took 12.8160 seconds
):
UpperCAmelCase__ = timeit(f'''{func}(grid=grid)''' , setup=lowerCamelCase , number=5_0_0 )
print(f'''{func}() took {time:0.4f} seconds''' )
if __name__ == "__main__":
import doctest
doctest.testmod()
benchmark()
| 98 | """simple docstring"""
import os
import numpy
import onnx
def a_ ( lowerCamelCase , lowerCamelCase ):
UpperCAmelCase__ = a.name
UpperCAmelCase__ = b.name
UpperCAmelCase__ = ''
UpperCAmelCase__ = ''
UpperCAmelCase__ = a == b
UpperCAmelCase__ = name_a
UpperCAmelCase__ = name_b
return res
def a_ ( lowerCamelCase , lowerCamelCase , lowerCamelCase ):
for i, input_name in enumerate(node_proto.input ):
if input_name == name:
node_proto.input.insert(lowerCamelCase , lowerCamelCase )
node_proto.input.pop(i + 1 )
if node_proto.op_type == "If":
_graph_replace_input_with(node_proto.attribute[0].g , lowerCamelCase , lowerCamelCase )
_graph_replace_input_with(node_proto.attribute[1].g , lowerCamelCase , lowerCamelCase )
if node_proto.op_type == "Loop":
_graph_replace_input_with(node_proto.attribute[0].g , lowerCamelCase , lowerCamelCase )
def a_ ( lowerCamelCase , lowerCamelCase , lowerCamelCase ):
for n in graph_proto.node:
_node_replace_input_with(lowerCamelCase , lowerCamelCase , lowerCamelCase )
def a_ ( lowerCamelCase , lowerCamelCase , lowerCamelCase ):
UpperCAmelCase__ = list(model.graph.initializer )
UpperCAmelCase__ = list(model_without_ext.graph.initializer )
for i, ref_i in ind_to_replace:
assert inits_with_data[i].name == inits[i].name
assert inits_with_data[ref_i].name == inits[ref_i].name
assert i > ref_i
UpperCAmelCase__ = inits[i].name
UpperCAmelCase__ = inits[ref_i].name
model_without_ext.graph.initializer.remove(inits[i] )
# for n in model.graph.node:
_graph_replace_input_with(model_without_ext.graph , lowerCamelCase , lowerCamelCase )
def a_ ( lowerCamelCase ):
UpperCAmelCase__ = os.path.dirname(lowerCamelCase )
UpperCAmelCase__ = os.path.basename(lowerCamelCase )
UpperCAmelCase__ = onnx.load(os.path.join(lowerCamelCase , lowerCamelCase ) )
UpperCAmelCase__ = list(model.graph.initializer )
UpperCAmelCase__ = set()
UpperCAmelCase__ = {}
UpperCAmelCase__ = []
UpperCAmelCase__ = 0
for i in range(len(lowerCamelCase ) ):
if i in dup_set:
continue
for j in range(i + 1 , len(lowerCamelCase ) ):
if j in dup_set:
continue
if _is_equal_tensor_proto(inits[i] , inits[j] ):
dup_set.add(lowerCamelCase )
dup_set.add(lowerCamelCase )
UpperCAmelCase__ = inits[j].data_type
UpperCAmelCase__ = numpy.prod(inits[j].dims )
if dtype == 1:
mem_size *= 4
elif dtype == 6:
mem_size *= 4
elif dtype == 7 or dtype == 1_1:
mem_size *= 8
else:
print('unexpected data type: ' , lowerCamelCase )
total_reduced_size += mem_size
UpperCAmelCase__ = inits[i].name
UpperCAmelCase__ = inits[j].name
if name_i in dup_map:
dup_map[name_i].append(lowerCamelCase )
else:
UpperCAmelCase__ = [name_j]
ind_to_replace.append((j, i) )
print('total reduced size: ' , total_reduced_size / 1_0_2_4 / 1_0_2_4 / 1_0_2_4 , 'GB' )
UpperCAmelCase__ = sorted(lowerCamelCase )
_remove_dup_initializers_from_model(lowerCamelCase , lowerCamelCase , lowerCamelCase )
UpperCAmelCase__ = 'optimized_' + model_file_name
UpperCAmelCase__ = os.path.join(lowerCamelCase , lowerCamelCase )
onnx.save(lowerCamelCase , lowerCamelCase )
return new_model
| 98 | 1 |
'''simple docstring'''
import argparse
import json
import gdown
import numpy as np
import torch
from huggingface_hub import hf_hub_download
from transformers import (
VideoMAEConfig,
VideoMAEForPreTraining,
VideoMAEForVideoClassification,
VideoMAEImageProcessor,
)
def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ : int ):
'''simple docstring'''
UpperCAmelCase__ = VideoMAEConfig()
set_architecture_configs(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
if "finetuned" not in model_name:
UpperCAmelCase__ = False
if "finetuned" in model_name:
UpperCAmelCase__ = """huggingface/label-files"""
if "kinetics" in model_name:
UpperCAmelCase__ = 400
UpperCAmelCase__ = """kinetics400-id2label.json"""
elif "ssv2" in model_name:
UpperCAmelCase__ = 174
UpperCAmelCase__ = """something-something-v2-id2label.json"""
else:
raise ValueError("""Model name should either contain 'kinetics' or 'ssv2' in case it's fine-tuned.""" )
UpperCAmelCase__ = json.load(open(hf_hub_download(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , repo_type="""dataset""" ) , """r""" ) )
UpperCAmelCase__ = {int(SCREAMING_SNAKE_CASE__ ): v for k, v in idalabel.items()}
UpperCAmelCase__ = idalabel
UpperCAmelCase__ = {v: k for k, v in idalabel.items()}
return config
def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ : List[Any] , SCREAMING_SNAKE_CASE__ : str ):
'''simple docstring'''
if "small" in model_name:
UpperCAmelCase__ = 384
UpperCAmelCase__ = 1536
UpperCAmelCase__ = 12
UpperCAmelCase__ = 16
UpperCAmelCase__ = 12
UpperCAmelCase__ = 3
UpperCAmelCase__ = 192
UpperCAmelCase__ = 768
elif "large" in model_name:
UpperCAmelCase__ = 1024
UpperCAmelCase__ = 4096
UpperCAmelCase__ = 24
UpperCAmelCase__ = 16
UpperCAmelCase__ = 12
UpperCAmelCase__ = 8
UpperCAmelCase__ = 512
UpperCAmelCase__ = 2048
elif "huge" in model_name:
UpperCAmelCase__ = 1280
UpperCAmelCase__ = 5120
UpperCAmelCase__ = 32
UpperCAmelCase__ = 16
UpperCAmelCase__ = 12
UpperCAmelCase__ = 8
UpperCAmelCase__ = 640
UpperCAmelCase__ = 2560
elif "base" not in model_name:
raise ValueError("""Model name should include either \"small\", \"base\", \"large\", or \"huge\"""" )
def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ : Tuple ):
'''simple docstring'''
if "encoder." in name:
UpperCAmelCase__ = name.replace("""encoder.""" , """""" )
if "cls_token" in name:
UpperCAmelCase__ = name.replace("""cls_token""" , """videomae.embeddings.cls_token""" )
if "decoder_pos_embed" in name:
UpperCAmelCase__ = name.replace("""decoder_pos_embed""" , """decoder.decoder_pos_embed""" )
if "pos_embed" in name and "decoder" not in name:
UpperCAmelCase__ = name.replace("""pos_embed""" , """videomae.embeddings.position_embeddings""" )
if "patch_embed.proj" in name:
UpperCAmelCase__ = name.replace("""patch_embed.proj""" , """videomae.embeddings.patch_embeddings.projection""" )
if "patch_embed.norm" in name:
UpperCAmelCase__ = name.replace("""patch_embed.norm""" , """videomae.embeddings.norm""" )
if "decoder.blocks" in name:
UpperCAmelCase__ = name.replace("""decoder.blocks""" , """decoder.decoder_layers""" )
if "blocks" in name:
UpperCAmelCase__ = name.replace("""blocks""" , """videomae.encoder.layer""" )
if "attn.proj" in name:
UpperCAmelCase__ = name.replace("""attn.proj""" , """attention.output.dense""" )
if "attn" in name and "bias" not in name:
UpperCAmelCase__ = name.replace("""attn""" , """attention.self""" )
if "attn" in name:
UpperCAmelCase__ = name.replace("""attn""" , """attention.attention""" )
if "norm1" in name:
UpperCAmelCase__ = name.replace("""norm1""" , """layernorm_before""" )
if "norm2" in name:
UpperCAmelCase__ = name.replace("""norm2""" , """layernorm_after""" )
if "mlp.fc1" in name:
UpperCAmelCase__ = name.replace("""mlp.fc1""" , """intermediate.dense""" )
if "mlp.fc2" in name:
UpperCAmelCase__ = name.replace("""mlp.fc2""" , """output.dense""" )
if "decoder_embed" in name:
UpperCAmelCase__ = name.replace("""decoder_embed""" , """decoder.decoder_embed""" )
if "decoder_norm" in name:
UpperCAmelCase__ = name.replace("""decoder_norm""" , """decoder.decoder_norm""" )
if "decoder_pred" in name:
UpperCAmelCase__ = name.replace("""decoder_pred""" , """decoder.decoder_pred""" )
if "norm.weight" in name and "decoder" not in name and "fc" not in name:
UpperCAmelCase__ = name.replace("""norm.weight""" , """videomae.layernorm.weight""" )
if "norm.bias" in name and "decoder" not in name and "fc" not in name:
UpperCAmelCase__ = name.replace("""norm.bias""" , """videomae.layernorm.bias""" )
if "head" in name and "decoder" not in name:
UpperCAmelCase__ = name.replace("""head""" , """classifier""" )
return name
def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ : Dict , SCREAMING_SNAKE_CASE__ : Optional[int] ):
'''simple docstring'''
for key in orig_state_dict.copy().keys():
UpperCAmelCase__ = orig_state_dict.pop(SCREAMING_SNAKE_CASE__ )
if key.startswith("""encoder.""" ):
UpperCAmelCase__ = key.replace("""encoder.""" , """""" )
if "qkv" in key:
UpperCAmelCase__ = key.split(""".""" )
if key.startswith("""decoder.blocks""" ):
UpperCAmelCase__ = config.decoder_hidden_size
UpperCAmelCase__ = int(key_split[2] )
UpperCAmelCase__ = """decoder.decoder_layers."""
if "weight" in key:
UpperCAmelCase__ = val[:dim, :]
UpperCAmelCase__ = val[dim : dim * 2, :]
UpperCAmelCase__ = val[-dim:, :]
else:
UpperCAmelCase__ = config.hidden_size
UpperCAmelCase__ = int(key_split[1] )
UpperCAmelCase__ = """videomae.encoder.layer."""
if "weight" in key:
UpperCAmelCase__ = val[:dim, :]
UpperCAmelCase__ = val[dim : dim * 2, :]
UpperCAmelCase__ = val[-dim:, :]
else:
UpperCAmelCase__ = val
return orig_state_dict
def _UpperCamelCase ( ):
'''simple docstring'''
UpperCAmelCase__ = hf_hub_download(
repo_id="""hf-internal-testing/spaghetti-video""" , filename="""eating_spaghetti.npy""" , repo_type="""dataset""" )
UpperCAmelCase__ = np.load(SCREAMING_SNAKE_CASE__ )
return list(SCREAMING_SNAKE_CASE__ )
def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ : List[Any] , SCREAMING_SNAKE_CASE__ : str , SCREAMING_SNAKE_CASE__ : List[Any] , SCREAMING_SNAKE_CASE__ : int ):
'''simple docstring'''
UpperCAmelCase__ = get_videomae_config(SCREAMING_SNAKE_CASE__ )
if "finetuned" in model_name:
UpperCAmelCase__ = VideoMAEForVideoClassification(SCREAMING_SNAKE_CASE__ )
else:
UpperCAmelCase__ = VideoMAEForPreTraining(SCREAMING_SNAKE_CASE__ )
# download original checkpoint, hosted on Google Drive
UpperCAmelCase__ = """pytorch_model.bin"""
gdown.cached_download(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , quiet=SCREAMING_SNAKE_CASE__ )
UpperCAmelCase__ = torch.load(SCREAMING_SNAKE_CASE__ , map_location="""cpu""" )
if "model" in files:
UpperCAmelCase__ = files["""model"""]
else:
UpperCAmelCase__ = files["""module"""]
UpperCAmelCase__ = convert_state_dict(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
model.load_state_dict(SCREAMING_SNAKE_CASE__ )
model.eval()
# verify model on basic input
UpperCAmelCase__ = VideoMAEImageProcessor(image_mean=[0.5, 0.5, 0.5] , image_std=[0.5, 0.5, 0.5] )
UpperCAmelCase__ = prepare_video()
UpperCAmelCase__ = image_processor(SCREAMING_SNAKE_CASE__ , return_tensors="""pt""" )
if "finetuned" not in model_name:
UpperCAmelCase__ = hf_hub_download(repo_id="""hf-internal-testing/bool-masked-pos""" , filename="""bool_masked_pos.pt""" )
UpperCAmelCase__ = torch.load(SCREAMING_SNAKE_CASE__ )
UpperCAmelCase__ = model(**SCREAMING_SNAKE_CASE__ )
UpperCAmelCase__ = outputs.logits
UpperCAmelCase__ = [
"""videomae-small-finetuned-kinetics""",
"""videomae-small-finetuned-ssv2""",
# Kinetics-400 checkpoints (short = pretrained only for 800 epochs instead of 1600)
"""videomae-base-short""",
"""videomae-base-short-finetuned-kinetics""",
"""videomae-base""",
"""videomae-base-finetuned-kinetics""",
"""videomae-large""",
"""videomae-large-finetuned-kinetics""",
"""videomae-huge-finetuned-kinetics""",
# Something-Something-v2 checkpoints (short = pretrained only for 800 epochs instead of 2400)
"""videomae-base-short-ssv2""",
"""videomae-base-short-finetuned-ssv2""",
"""videomae-base-ssv2""",
"""videomae-base-finetuned-ssv2""",
]
# NOTE: logits were tested with image_mean and image_std equal to [0.5, 0.5, 0.5] and [0.5, 0.5, 0.5]
if model_name == "videomae-small-finetuned-kinetics":
UpperCAmelCase__ = torch.Size([1, 400] )
UpperCAmelCase__ = torch.tensor([-0.92_91, -0.40_61, -0.93_07] )
elif model_name == "videomae-small-finetuned-ssv2":
UpperCAmelCase__ = torch.Size([1, 174] )
UpperCAmelCase__ = torch.tensor([0.26_71, -0.46_89, -0.82_35] )
elif model_name == "videomae-base":
UpperCAmelCase__ = torch.Size([1, 1408, 1536] )
UpperCAmelCase__ = torch.tensor([[0.77_39, 0.79_68, 0.70_89], [0.67_01, 0.74_87, 0.62_09], [0.42_87, 0.51_58, 0.47_73]] )
elif model_name == "videomae-base-short":
UpperCAmelCase__ = torch.Size([1, 1408, 1536] )
UpperCAmelCase__ = torch.tensor([[0.79_94, 0.96_12, 0.85_08], [0.74_01, 0.89_58, 0.83_02], [0.58_62, 0.74_68, 0.73_25]] )
# we verified the loss both for normalized and unnormalized targets for this one
UpperCAmelCase__ = torch.tensor([0.51_42] ) if config.norm_pix_loss else torch.tensor([0.64_69] )
elif model_name == "videomae-large":
UpperCAmelCase__ = torch.Size([1, 1408, 1536] )
UpperCAmelCase__ = torch.tensor([[0.71_49, 0.79_97, 0.69_66], [0.67_68, 0.78_69, 0.69_48], [0.51_39, 0.62_21, 0.56_05]] )
elif model_name == "videomae-large-finetuned-kinetics":
UpperCAmelCase__ = torch.Size([1, 400] )
UpperCAmelCase__ = torch.tensor([0.07_71, 0.00_11, -0.36_25] )
elif model_name == "videomae-huge-finetuned-kinetics":
UpperCAmelCase__ = torch.Size([1, 400] )
UpperCAmelCase__ = torch.tensor([0.24_33, 0.16_32, -0.48_94] )
elif model_name == "videomae-base-short-finetuned-kinetics":
UpperCAmelCase__ = torch.Size([1, 400] )
UpperCAmelCase__ = torch.tensor([0.65_88, 0.09_90, -0.24_93] )
elif model_name == "videomae-base-finetuned-kinetics":
UpperCAmelCase__ = torch.Size([1, 400] )
UpperCAmelCase__ = torch.tensor([0.36_69, -0.06_88, -0.24_21] )
elif model_name == "videomae-base-short-ssv2":
UpperCAmelCase__ = torch.Size([1, 1408, 1536] )
UpperCAmelCase__ = torch.tensor([[0.47_12, 0.52_96, 0.57_86], [0.22_78, 0.27_29, 0.40_26], [0.03_52, 0.07_30, 0.25_06]] )
elif model_name == "videomae-base-short-finetuned-ssv2":
UpperCAmelCase__ = torch.Size([1, 174] )
UpperCAmelCase__ = torch.tensor([-0.05_37, -0.15_39, -0.32_66] )
elif model_name == "videomae-base-ssv2":
UpperCAmelCase__ = torch.Size([1, 1408, 1536] )
UpperCAmelCase__ = torch.tensor([[0.81_31, 0.87_27, 0.85_46], [0.73_66, 0.93_77, 0.88_70], [0.59_35, 0.88_74, 0.85_64]] )
elif model_name == "videomae-base-finetuned-ssv2":
UpperCAmelCase__ = torch.Size([1, 174] )
UpperCAmelCase__ = torch.tensor([0.19_61, -0.83_37, -0.63_89] )
else:
raise ValueError(F'''Model name not supported. Should be one of {model_names}''' )
# verify logits
assert logits.shape == expected_shape
if "finetuned" in model_name:
assert torch.allclose(logits[0, :3] , SCREAMING_SNAKE_CASE__ , atol=1e-4 )
else:
print("""Logits:""" , logits[0, :3, :3] )
assert torch.allclose(logits[0, :3, :3] , SCREAMING_SNAKE_CASE__ , atol=1e-4 )
print("""Logits ok!""" )
# verify loss, if applicable
if model_name == "videomae-base-short":
UpperCAmelCase__ = outputs.loss
assert torch.allclose(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , atol=1e-4 )
print("""Loss ok!""" )
if pytorch_dump_folder_path is not None:
print(F'''Saving model and image processor to {pytorch_dump_folder_path}''' )
image_processor.save_pretrained(SCREAMING_SNAKE_CASE__ )
model.save_pretrained(SCREAMING_SNAKE_CASE__ )
if push_to_hub:
print("""Pushing to the hub...""" )
model.push_to_hub(SCREAMING_SNAKE_CASE__ , organization="""nielsr""" )
if __name__ == "__main__":
UpperCAmelCase_ = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'--checkpoint_url',
default='https://drive.google.com/u/1/uc?id=1tEhLyskjb755TJ65ptsrafUG2llSwQE1&export=download&confirm=t&uuid=aa3276eb-fb7e-482a-adec-dc7171df14c4',
type=str,
help=(
'URL of the original PyTorch checkpoint (on Google Drive) you\'d like to convert. Should be a direct'
' download link.'
),
)
parser.add_argument(
'--pytorch_dump_folder_path',
default='/Users/nielsrogge/Documents/VideoMAE/Test',
type=str,
help='Path to the output PyTorch model directory.',
)
parser.add_argument('--model_name', default='videomae-base', type=str, help='Name of the model.')
parser.add_argument(
'--push_to_hub', action='store_true', help='Whether or not to push the converted model to the 🤗 hub.'
)
UpperCAmelCase_ = parser.parse_args()
convert_videomae_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.model_name, args.push_to_hub)
| 362 |
'''simple docstring'''
import importlib.util
import json
import os
import warnings
from dataclasses import dataclass, field
import torch
from ..training_args import TrainingArguments
from ..utils import cached_property, is_sagemaker_dp_enabled, logging
UpperCAmelCase_ = logging.get_logger(__name__)
def _UpperCamelCase ( ):
'''simple docstring'''
UpperCAmelCase__ = os.getenv("""SM_HP_MP_PARAMETERS""" , """{}""" )
try:
# Parse it and check the field "partitions" is included, it is required for model parallel.
UpperCAmelCase__ = json.loads(SCREAMING_SNAKE_CASE__ )
if "partitions" not in smp_options:
return False
except json.JSONDecodeError:
return False
# Get the sagemaker specific framework parameters from mpi_options variable.
UpperCAmelCase__ = os.getenv("""SM_FRAMEWORK_PARAMS""" , """{}""" )
try:
# Parse it and check the field "sagemaker_distributed_dataparallel_enabled".
UpperCAmelCase__ = json.loads(SCREAMING_SNAKE_CASE__ )
if not mpi_options.get("""sagemaker_mpi_enabled""" , SCREAMING_SNAKE_CASE__ ):
return False
except json.JSONDecodeError:
return False
# Lastly, check if the `smdistributed` module is present.
return importlib.util.find_spec("""smdistributed""" ) is not None
if is_sagemaker_model_parallel_available():
import smdistributed.modelparallel.torch as smp
smp.init()
@dataclass
class lowerCAmelCase_ ( lowerCamelCase_ ):
'''simple docstring'''
lowerCAmelCase_ : str = field(
default="""""" , metadata={"""help""": """Used by the SageMaker launcher to send mp-specific args. Ignored in SageMakerTrainer"""} , )
def SCREAMING_SNAKE_CASE__ ( self : Tuple ):
"""simple docstring"""
super().__post_init__()
warnings.warn(
"""`SageMakerTrainingArguments` is deprecated and will be removed in v5 of Transformers. You can use """
"""`TrainingArguments` instead.""" , _UpperCAmelCase , )
@cached_property
def SCREAMING_SNAKE_CASE__ ( self : Union[str, Any] ):
"""simple docstring"""
logger.info("""PyTorch: setting up devices""" )
if torch.distributed.is_available() and torch.distributed.is_initialized() and self.local_rank == -1:
logger.warning(
"""torch.distributed process group is initialized, but local_rank == -1. """
"""In order to use Torch DDP, launch your script with `python -m torch.distributed.launch""" )
if self.no_cuda:
UpperCAmelCase__ = torch.device("""cpu""" )
UpperCAmelCase__ = 0
elif is_sagemaker_model_parallel_available():
UpperCAmelCase__ = smp.local_rank()
UpperCAmelCase__ = torch.device("""cuda""" , _UpperCAmelCase )
UpperCAmelCase__ = 1
elif is_sagemaker_dp_enabled():
import smdistributed.dataparallel.torch.torch_smddp # noqa: F401
torch.distributed.init_process_group(backend="""smddp""" , timeout=self.ddp_timeout_delta )
UpperCAmelCase__ = int(os.getenv("""SMDATAPARALLEL_LOCAL_RANK""" ) )
UpperCAmelCase__ = torch.device("""cuda""" , self.local_rank )
UpperCAmelCase__ = 1
elif self.local_rank == -1:
# if n_gpu is > 1 we'll use nn.DataParallel.
# If you only want to use a specific subset of GPUs use `CUDA_VISIBLE_DEVICES=0`
# Explicitly set CUDA to the first (index 0) CUDA device, otherwise `set_device` will
# trigger an error that a device index is missing. Index 0 takes into account the
# GPUs available in the environment, so `CUDA_VISIBLE_DEVICES=1,2` with `cuda:0`
# will use the first GPU in that env, i.e. GPU#1
UpperCAmelCase__ = torch.device("""cuda:0""" if torch.cuda.is_available() else """cpu""" )
# Sometimes the line in the postinit has not been run before we end up here, so just checking we're not at
# the default value.
UpperCAmelCase__ = torch.cuda.device_count()
else:
# Here, we'll use torch.distributed.
# Initializes the distributed backend which will take care of synchronizing nodes/GPUs
if not torch.distributed.is_initialized():
torch.distributed.init_process_group(backend="""nccl""" , timeout=self.ddp_timeout_delta )
UpperCAmelCase__ = torch.device("""cuda""" , self.local_rank )
UpperCAmelCase__ = 1
if device.type == "cuda":
torch.cuda.set_device(_UpperCAmelCase )
return device
@property
def SCREAMING_SNAKE_CASE__ ( self : List[Any] ):
"""simple docstring"""
if is_sagemaker_model_parallel_available():
return smp.dp_size()
return super().world_size
@property
def SCREAMING_SNAKE_CASE__ ( self : Dict ):
"""simple docstring"""
return not is_sagemaker_model_parallel_available()
@property
def SCREAMING_SNAKE_CASE__ ( self : Optional[int] ):
"""simple docstring"""
return False
| 61 | 0 |
"""simple docstring"""
from __future__ import annotations
import time
lowerCAmelCase__ = list[tuple[int, int]]
lowerCAmelCase__ = [
[0, 0, 0, 0, 0, 0, 0],
[0, 1, 0, 0, 0, 0, 0], # 0 are free path whereas 1's are obstacles
[0, 0, 0, 0, 0, 0, 0],
[0, 0, 1, 0, 0, 0, 0],
[1, 0, 1, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 1, 0, 0],
]
lowerCAmelCase__ = [[-1, 0], [0, -1], [1, 0], [0, 1]] # up, left, down, right
class __snake_case :
def __init__( self : List[Any] , __lowerCAmelCase : int , __lowerCAmelCase : int , __lowerCAmelCase : int , __lowerCAmelCase : int , __lowerCAmelCase : Node | None ):
"""simple docstring"""
_lowerCamelCase : List[Any] = pos_x
_lowerCamelCase : List[Any] = pos_y
_lowerCamelCase : str = (pos_y, pos_x)
_lowerCamelCase : Tuple = goal_x
_lowerCamelCase : int = goal_y
_lowerCamelCase : int = parent
class __snake_case :
def __init__( self : Any , __lowerCAmelCase : tuple[int, int] , __lowerCAmelCase : tuple[int, int] ):
"""simple docstring"""
_lowerCamelCase : Optional[Any] = Node(start[1] , start[0] , goal[1] , goal[0] , __lowerCAmelCase )
_lowerCamelCase : List[str] = Node(goal[1] , goal[0] , goal[1] , goal[0] , __lowerCAmelCase )
_lowerCamelCase : int = [self.start]
_lowerCamelCase : Optional[Any] = False
def SCREAMING_SNAKE_CASE ( self : Optional[Any] ):
"""simple docstring"""
while self.node_queue:
_lowerCamelCase : Dict = self.node_queue.pop(0 )
if current_node.pos == self.target.pos:
_lowerCamelCase : Tuple = True
return self.retrace_path(__lowerCAmelCase )
_lowerCamelCase : List[str] = self.get_successors(__lowerCAmelCase )
for node in successors:
self.node_queue.append(__lowerCAmelCase )
if not self.reached:
return [self.start.pos]
return None
def SCREAMING_SNAKE_CASE ( self : str , __lowerCAmelCase : Node ):
"""simple docstring"""
_lowerCamelCase : Union[str, Any] = []
for action in delta:
_lowerCamelCase : List[str] = parent.pos_x + action[1]
_lowerCamelCase : Optional[int] = parent.pos_y + action[0]
if not (0 <= pos_x <= len(grid[0] ) - 1 and 0 <= pos_y <= len(__lowerCAmelCase ) - 1):
continue
if grid[pos_y][pos_x] != 0:
continue
successors.append(
Node(__lowerCAmelCase , __lowerCAmelCase , self.target.pos_y , self.target.pos_x , __lowerCAmelCase ) )
return successors
def SCREAMING_SNAKE_CASE ( self : Optional[Any] , __lowerCAmelCase : Node | None ):
"""simple docstring"""
_lowerCamelCase : Tuple = node
_lowerCamelCase : Any = []
while current_node is not None:
path.append((current_node.pos_y, current_node.pos_x) )
_lowerCamelCase : Optional[int] = current_node.parent
path.reverse()
return path
class __snake_case :
def __init__( self : Dict , __lowerCAmelCase : str , __lowerCAmelCase : Optional[int] ):
"""simple docstring"""
_lowerCamelCase : Any = BreadthFirstSearch(__lowerCAmelCase , __lowerCAmelCase )
_lowerCamelCase : Tuple = BreadthFirstSearch(__lowerCAmelCase , __lowerCAmelCase )
_lowerCamelCase : int = False
def SCREAMING_SNAKE_CASE ( self : str ):
"""simple docstring"""
while self.fwd_bfs.node_queue or self.bwd_bfs.node_queue:
_lowerCamelCase : Optional[Any] = self.fwd_bfs.node_queue.pop(0 )
_lowerCamelCase : List[str] = self.bwd_bfs.node_queue.pop(0 )
if current_bwd_node.pos == current_fwd_node.pos:
_lowerCamelCase : Optional[Any] = True
return self.retrace_bidirectional_path(
__lowerCAmelCase , __lowerCAmelCase )
_lowerCamelCase : str = current_bwd_node
_lowerCamelCase : Union[str, Any] = current_fwd_node
_lowerCamelCase : str = {
self.fwd_bfs: self.fwd_bfs.get_successors(__lowerCAmelCase ),
self.bwd_bfs: self.bwd_bfs.get_successors(__lowerCAmelCase ),
}
for bfs in [self.fwd_bfs, self.bwd_bfs]:
for node in successors[bfs]:
bfs.node_queue.append(__lowerCAmelCase )
if not self.reached:
return [self.fwd_bfs.start.pos]
return None
def SCREAMING_SNAKE_CASE ( self : List[Any] , __lowerCAmelCase : Node , __lowerCAmelCase : Node ):
"""simple docstring"""
_lowerCamelCase : Optional[Any] = self.fwd_bfs.retrace_path(__lowerCAmelCase )
_lowerCamelCase : Optional[Any] = self.bwd_bfs.retrace_path(__lowerCAmelCase )
bwd_path.pop()
bwd_path.reverse()
_lowerCamelCase : str = fwd_path + bwd_path
return path
if __name__ == "__main__":
# all coordinates are given in format [y,x]
import doctest
doctest.testmod()
lowerCAmelCase__ = (0, 0)
lowerCAmelCase__ = (len(grid) - 1, len(grid[0]) - 1)
for elem in grid:
print(elem)
lowerCAmelCase__ = time.time()
lowerCAmelCase__ = BreadthFirstSearch(init, goal)
lowerCAmelCase__ = bfs.search()
lowerCAmelCase__ = time.time() - start_bfs_time
print('''Unidirectional BFS computation time : ''', bfs_time)
lowerCAmelCase__ = time.time()
lowerCAmelCase__ = BidirectionalBreadthFirstSearch(init, goal)
lowerCAmelCase__ = bd_bfs.search()
lowerCAmelCase__ = time.time() - start_bd_bfs_time
print('''Bidirectional BFS computation time : ''', bd_bfs_time)
| 72 |
from abc import ABC, abstractmethod
from typing import List, Optional
class a_ ( a__ ):
"""simple docstring"""
def __init__( self ) ->List[str]:
# test for the above condition
self.test()
def __lowerCAmelCase ( self ) ->List[str]:
SCREAMING_SNAKE_CASE : int = 0
SCREAMING_SNAKE_CASE : Union[str, Any] = False
while not completed:
if counter == 1:
self.reset()
SCREAMING_SNAKE_CASE : List[Any] = self.advance()
if not self.does_advance(_lowerCamelCase ):
raise Exception(
'''Custom Constraint is not defined correctly. self.does_advance(self.advance()) must be true.''' )
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE : Dict = self.update(_lowerCamelCase )
counter += 1
if counter > 1_0000:
raise Exception('''update() does not fulfill the constraint.''' )
if self.remaining() != 0:
raise Exception('''Custom Constraint is not defined correctly.''' )
@abstractmethod
def __lowerCAmelCase ( self ) ->Optional[int]:
raise NotImplementedError(
F"""{self.__class__} is an abstract class. Only classes inheriting this class can be called.""" )
@abstractmethod
def __lowerCAmelCase ( self , _lowerCamelCase ) ->Optional[Any]:
raise NotImplementedError(
F"""{self.__class__} is an abstract class. Only classes inheriting this class can be called.""" )
@abstractmethod
def __lowerCAmelCase ( self , _lowerCamelCase ) ->Optional[Any]:
raise NotImplementedError(
F"""{self.__class__} is an abstract class. Only classes inheriting this class can be called.""" )
@abstractmethod
def __lowerCAmelCase ( self ) ->Optional[Any]:
raise NotImplementedError(
F"""{self.__class__} is an abstract class. Only classes inheriting this class can be called.""" )
@abstractmethod
def __lowerCAmelCase ( self ) ->Union[str, Any]:
raise NotImplementedError(
F"""{self.__class__} is an abstract class. Only classes inheriting this class can be called.""" )
@abstractmethod
def __lowerCAmelCase ( self , _lowerCamelCase=False ) ->Any:
raise NotImplementedError(
F"""{self.__class__} is an abstract class. Only classes inheriting this class can be called.""" )
class a_ ( a__ ):
"""simple docstring"""
def __init__( self , _lowerCamelCase ) ->int:
super(_lowerCamelCase , self ).__init__()
if not isinstance(_lowerCamelCase , _lowerCamelCase ) or len(_lowerCamelCase ) == 0:
raise ValueError(F"""`token_ids` has to be a non-empty list, but is {token_ids}.""" )
if any((not isinstance(_lowerCamelCase , _lowerCamelCase ) or token_id < 0) for token_id in token_ids ):
raise ValueError(F"""Each list in `token_ids` has to be a list of positive integers, but is {token_ids}.""" )
SCREAMING_SNAKE_CASE : Optional[Any] = token_ids
SCREAMING_SNAKE_CASE : Union[str, Any] = len(self.token_ids )
SCREAMING_SNAKE_CASE : Any = -1 # the index of the currently fulfilled step
SCREAMING_SNAKE_CASE : Any = False
def __lowerCAmelCase ( self ) ->List[Any]:
if self.completed:
return None
return self.token_ids[self.fulfilled_idx + 1]
def __lowerCAmelCase ( self , _lowerCamelCase ) ->Union[str, Any]:
if not isinstance(_lowerCamelCase , _lowerCamelCase ):
raise ValueError(F"""`token_id` has to be an `int`, but is {token_id} of type {type(_lowerCamelCase )}""" )
if self.completed:
return False
return token_id == self.token_ids[self.fulfilled_idx + 1]
def __lowerCAmelCase ( self , _lowerCamelCase ) ->List[str]:
if not isinstance(_lowerCamelCase , _lowerCamelCase ):
raise ValueError(F"""`token_id` has to be an `int`, but is {token_id} of type {type(_lowerCamelCase )}""" )
SCREAMING_SNAKE_CASE : str = False
SCREAMING_SNAKE_CASE : Any = False
SCREAMING_SNAKE_CASE : List[Any] = False
if self.does_advance(_lowerCamelCase ):
self.fulfilled_idx += 1
SCREAMING_SNAKE_CASE : str = True
if self.fulfilled_idx == (self.seqlen - 1):
SCREAMING_SNAKE_CASE : Any = True
SCREAMING_SNAKE_CASE : Union[str, Any] = completed
else:
# failed to make progress.
SCREAMING_SNAKE_CASE : Dict = True
self.reset()
return stepped, completed, reset
def __lowerCAmelCase ( self ) ->List[Any]:
SCREAMING_SNAKE_CASE : List[Any] = False
SCREAMING_SNAKE_CASE : Union[str, Any] = 0
def __lowerCAmelCase ( self ) ->Any:
return self.seqlen - (self.fulfilled_idx + 1)
def __lowerCAmelCase ( self , _lowerCamelCase=False ) ->Dict:
SCREAMING_SNAKE_CASE : Any = PhrasalConstraint(self.token_ids )
if stateful:
SCREAMING_SNAKE_CASE : Dict = self.seqlen
SCREAMING_SNAKE_CASE : int = self.fulfilled_idx
SCREAMING_SNAKE_CASE : Tuple = self.completed
return new_constraint
class a_ :
"""simple docstring"""
def __init__( self , _lowerCamelCase , _lowerCamelCase=True ) ->Dict:
SCREAMING_SNAKE_CASE : Any = max([len(_lowerCamelCase ) for one in nested_token_ids] )
SCREAMING_SNAKE_CASE : List[str] = {}
for token_ids in nested_token_ids:
SCREAMING_SNAKE_CASE : Optional[Any] = root
for tidx, token_id in enumerate(_lowerCamelCase ):
if token_id not in level:
SCREAMING_SNAKE_CASE : Any = {}
SCREAMING_SNAKE_CASE : Tuple = level[token_id]
if no_subsets and self.has_subsets(_lowerCamelCase , _lowerCamelCase ):
raise ValueError(
'''Each list in `nested_token_ids` can\'t be a complete subset of another list, but is'''
F""" {nested_token_ids}.""" )
SCREAMING_SNAKE_CASE : List[Any] = root
def __lowerCAmelCase ( self , _lowerCamelCase ) ->int:
SCREAMING_SNAKE_CASE : List[Any] = self.trie
for current_token in current_seq:
SCREAMING_SNAKE_CASE : int = start[current_token]
SCREAMING_SNAKE_CASE : Optional[int] = list(start.keys() )
return next_tokens
def __lowerCAmelCase ( self , _lowerCamelCase ) ->Dict:
SCREAMING_SNAKE_CASE : Any = self.next_tokens(_lowerCamelCase )
return len(_lowerCamelCase ) == 0
def __lowerCAmelCase ( self , _lowerCamelCase ) ->Optional[Any]:
SCREAMING_SNAKE_CASE : Any = list(root.values() )
if len(_lowerCamelCase ) == 0:
return 1
else:
return sum([self.count_leaves(_lowerCamelCase ) for nn in next_nodes] )
def __lowerCAmelCase ( self , _lowerCamelCase , _lowerCamelCase ) ->Dict:
SCREAMING_SNAKE_CASE : List[str] = self.count_leaves(_lowerCamelCase )
return len(_lowerCamelCase ) != leaf_count
class a_ ( a__ ):
"""simple docstring"""
def __init__( self , _lowerCamelCase ) ->str:
super(_lowerCamelCase , self ).__init__()
if not isinstance(_lowerCamelCase , _lowerCamelCase ) or len(_lowerCamelCase ) == 0:
raise ValueError(F"""`nested_token_ids` has to be a non-empty list, but is {nested_token_ids}.""" )
if any(not isinstance(_lowerCamelCase , _lowerCamelCase ) for token_ids in nested_token_ids ):
raise ValueError(F"""`nested_token_ids` has to be a list of lists, but is {nested_token_ids}.""" )
if any(
any((not isinstance(_lowerCamelCase , _lowerCamelCase ) or token_id < 0) for token_id in token_ids )
for token_ids in nested_token_ids ):
raise ValueError(
F"""Each list in `nested_token_ids` has to be a list of positive integers, but is {nested_token_ids}.""" )
SCREAMING_SNAKE_CASE : List[Any] = DisjunctiveTrie(_lowerCamelCase )
SCREAMING_SNAKE_CASE : Any = nested_token_ids
SCREAMING_SNAKE_CASE : Optional[int] = self.trie.max_height
SCREAMING_SNAKE_CASE : Union[str, Any] = []
SCREAMING_SNAKE_CASE : Optional[int] = False
def __lowerCAmelCase ( self ) ->int:
SCREAMING_SNAKE_CASE : str = self.trie.next_tokens(self.current_seq )
if len(_lowerCamelCase ) == 0:
return None
else:
return token_list
def __lowerCAmelCase ( self , _lowerCamelCase ) ->Dict:
if not isinstance(_lowerCamelCase , _lowerCamelCase ):
raise ValueError(F"""`token_id` is supposed to be type `int`, but is {token_id} of type {type(_lowerCamelCase )}""" )
SCREAMING_SNAKE_CASE : List[str] = self.trie.next_tokens(self.current_seq )
return token_id in next_tokens
def __lowerCAmelCase ( self , _lowerCamelCase ) ->Any:
if not isinstance(_lowerCamelCase , _lowerCamelCase ):
raise ValueError(F"""`token_id` is supposed to be type `int`, but is {token_id} of type {type(_lowerCamelCase )}""" )
SCREAMING_SNAKE_CASE : int = False
SCREAMING_SNAKE_CASE : List[Any] = False
SCREAMING_SNAKE_CASE : Union[str, Any] = False
if self.does_advance(_lowerCamelCase ):
self.current_seq.append(_lowerCamelCase )
SCREAMING_SNAKE_CASE : Union[str, Any] = True
else:
SCREAMING_SNAKE_CASE : Dict = True
self.reset()
SCREAMING_SNAKE_CASE : Any = self.trie.reached_leaf(self.current_seq )
SCREAMING_SNAKE_CASE : List[Any] = completed
return stepped, completed, reset
def __lowerCAmelCase ( self ) ->Optional[Any]:
SCREAMING_SNAKE_CASE : Any = False
SCREAMING_SNAKE_CASE : List[Any] = []
def __lowerCAmelCase ( self ) ->Optional[Any]:
if self.completed:
# since this can be completed without reaching max height
return 0
else:
return self.seqlen - len(self.current_seq )
def __lowerCAmelCase ( self , _lowerCamelCase=False ) ->List[str]:
SCREAMING_SNAKE_CASE : str = DisjunctiveConstraint(self.token_ids )
if stateful:
SCREAMING_SNAKE_CASE : str = self.seqlen
SCREAMING_SNAKE_CASE : int = self.current_seq
SCREAMING_SNAKE_CASE : Optional[int] = self.completed
return new_constraint
class a_ :
"""simple docstring"""
def __init__( self , _lowerCamelCase ) ->Union[str, Any]:
SCREAMING_SNAKE_CASE : List[Any] = constraints
# max # of steps required to fulfill a given constraint
SCREAMING_SNAKE_CASE : str = max([c.seqlen for c in constraints] )
SCREAMING_SNAKE_CASE : List[str] = len(_lowerCamelCase )
SCREAMING_SNAKE_CASE : int = False
self.init_state()
def __lowerCAmelCase ( self ) ->int:
SCREAMING_SNAKE_CASE : Any = []
SCREAMING_SNAKE_CASE : List[Any] = None
SCREAMING_SNAKE_CASE : Tuple = [constraint.copy(stateful=_lowerCamelCase ) for constraint in self.constraints]
def __lowerCAmelCase ( self ) ->str:
SCREAMING_SNAKE_CASE : str = 0
if self.inprogress_constraint:
# extra points for having a constraint mid-fulfilled
add += self.max_seqlen - self.inprogress_constraint.remaining()
return (len(self.complete_constraints ) * self.max_seqlen) + add
def __lowerCAmelCase ( self ) ->Optional[int]:
SCREAMING_SNAKE_CASE : Tuple = []
if self.inprogress_constraint is None:
for constraint in self.pending_constraints: # "pending" == "unfulfilled yet"
SCREAMING_SNAKE_CASE : Optional[int] = constraint.advance()
if isinstance(_lowerCamelCase , _lowerCamelCase ):
token_list.append(_lowerCamelCase )
elif isinstance(_lowerCamelCase , _lowerCamelCase ):
token_list.extend(_lowerCamelCase )
else:
SCREAMING_SNAKE_CASE : List[str] = self.inprogress_constraint.advance()
if isinstance(_lowerCamelCase , _lowerCamelCase ):
token_list.append(_lowerCamelCase )
elif isinstance(_lowerCamelCase , _lowerCamelCase ):
token_list.extend(_lowerCamelCase )
if len(_lowerCamelCase ) == 0:
return None
else:
return token_list
def __lowerCAmelCase ( self , _lowerCamelCase ) ->Union[str, Any]:
self.init_state()
if token_ids is not None:
for token in token_ids:
# completes or steps **one** constraint
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE : List[str] = self.add(_lowerCamelCase )
# the entire list of constraints are fulfilled
if self.completed:
break
def __lowerCAmelCase ( self , _lowerCamelCase ) ->List[Any]:
if not isinstance(_lowerCamelCase , _lowerCamelCase ):
raise ValueError(F"""`token_id` should be an `int`, but is `{token_id}`.""" )
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE : Optional[Any] = False, False
if self.completed:
SCREAMING_SNAKE_CASE : List[str] = True
SCREAMING_SNAKE_CASE : Optional[int] = False
return complete, stepped
if self.inprogress_constraint is not None:
# In the middle of fulfilling a constraint. If the `token_id` *does* makes an incremental progress to current
# job, simply update the state
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE : int = self.inprogress_constraint.update(_lowerCamelCase )
if reset:
# 1. If the next token breaks the progress, then we must restart.
# e.g. constraint = "I love pies" and sequence so far is "I love" but `token_id` == "books".
# But that doesn't mean we self.init_state(), since we only reset the state for this particular
# constraint, not the full list of constraints.
self.pending_constraints.append(self.inprogress_constraint.copy(stateful=_lowerCamelCase ) )
SCREAMING_SNAKE_CASE : Optional[int] = None
if complete:
# 2. If the next token completes the constraint, move it to completed list, set
# inprogress to None. If there are no pending constraints either, then this full list of constraints
# is complete.
self.complete_constraints.append(self.inprogress_constraint )
SCREAMING_SNAKE_CASE : str = None
if len(self.pending_constraints ) == 0:
# we're done!
SCREAMING_SNAKE_CASE : Optional[Any] = True
else:
# Not in the middle of fulfilling a constraint. So does this `token_id` helps us step towards any of our list
# of constraints?
for cidx, pending_constraint in enumerate(self.pending_constraints ):
if pending_constraint.does_advance(_lowerCamelCase ):
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE : Tuple = pending_constraint.update(_lowerCamelCase )
if not stepped:
raise Exception(
'''`constraint.update(token_id)` is not yielding incremental progress, '''
'''even though `constraint.does_advance(token_id)` is true.''' )
if complete:
self.complete_constraints.append(_lowerCamelCase )
SCREAMING_SNAKE_CASE : str = None
if not complete and stepped:
SCREAMING_SNAKE_CASE : Optional[Any] = pending_constraint
if complete or stepped:
# If we made any progress at all, then it's at least not a "pending constraint".
SCREAMING_SNAKE_CASE : Union[str, Any] = (
self.pending_constraints[:cidx] + self.pending_constraints[cidx + 1 :]
)
if len(self.pending_constraints ) == 0 and self.inprogress_constraint is None:
# If there's no longer any pending after this and no inprogress either, then we must be
# complete.
SCREAMING_SNAKE_CASE : str = True
break # prevent accidentally stepping through multiple constraints with just one token.
return complete, stepped
def __lowerCAmelCase ( self , _lowerCamelCase=True ) ->str:
SCREAMING_SNAKE_CASE : Dict = ConstraintListState(self.constraints ) # we actually never though self.constraints objects
# throughout this process. So it's at initialization state.
if stateful:
SCREAMING_SNAKE_CASE : str = [
constraint.copy(stateful=_lowerCamelCase ) for constraint in self.complete_constraints
]
if self.inprogress_constraint is not None:
SCREAMING_SNAKE_CASE : Optional[int] = self.inprogress_constraint.copy(stateful=_lowerCamelCase )
SCREAMING_SNAKE_CASE : Optional[int] = [constraint.copy() for constraint in self.pending_constraints]
return new_state
| 313 | 0 |
'''simple docstring'''
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_squeezebert import SqueezeBertTokenizer
lowercase : str = logging.get_logger(__name__)
lowercase : Any = {'vocab_file': 'vocab.txt', 'tokenizer_file': 'tokenizer.json'}
lowercase : Tuple = {
'vocab_file': {
'squeezebert/squeezebert-uncased': (
'https://huggingface.co/squeezebert/squeezebert-uncased/resolve/main/vocab.txt'
),
'squeezebert/squeezebert-mnli': 'https://huggingface.co/squeezebert/squeezebert-mnli/resolve/main/vocab.txt',
'squeezebert/squeezebert-mnli-headless': (
'https://huggingface.co/squeezebert/squeezebert-mnli-headless/resolve/main/vocab.txt'
),
},
'tokenizer_file': {
'squeezebert/squeezebert-uncased': (
'https://huggingface.co/squeezebert/squeezebert-uncased/resolve/main/tokenizer.json'
),
'squeezebert/squeezebert-mnli': (
'https://huggingface.co/squeezebert/squeezebert-mnli/resolve/main/tokenizer.json'
),
'squeezebert/squeezebert-mnli-headless': (
'https://huggingface.co/squeezebert/squeezebert-mnli-headless/resolve/main/tokenizer.json'
),
},
}
lowercase : List[str] = {
'squeezebert/squeezebert-uncased': 5_12,
'squeezebert/squeezebert-mnli': 5_12,
'squeezebert/squeezebert-mnli-headless': 5_12,
}
lowercase : List[Any] = {
'squeezebert/squeezebert-uncased': {'do_lower_case': True},
'squeezebert/squeezebert-mnli': {'do_lower_case': True},
'squeezebert/squeezebert-mnli-headless': {'do_lower_case': True},
}
class A ( __snake_case ):
__magic_name__ = VOCAB_FILES_NAMES
__magic_name__ = PRETRAINED_VOCAB_FILES_MAP
__magic_name__ = PRETRAINED_INIT_CONFIGURATION
__magic_name__ = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
__magic_name__ = SqueezeBertTokenizer
def __init__( self , SCREAMING_SNAKE_CASE=None , SCREAMING_SNAKE_CASE=None , SCREAMING_SNAKE_CASE=True , SCREAMING_SNAKE_CASE="[UNK]" , SCREAMING_SNAKE_CASE="[SEP]" , SCREAMING_SNAKE_CASE="[PAD]" , SCREAMING_SNAKE_CASE="[CLS]" , SCREAMING_SNAKE_CASE="[MASK]" , SCREAMING_SNAKE_CASE=True , SCREAMING_SNAKE_CASE=None , **SCREAMING_SNAKE_CASE , ) -> Optional[int]:
"""simple docstring"""
super().__init__(
SCREAMING_SNAKE_CASE , tokenizer_file=SCREAMING_SNAKE_CASE , do_lower_case=SCREAMING_SNAKE_CASE , unk_token=SCREAMING_SNAKE_CASE , sep_token=SCREAMING_SNAKE_CASE , pad_token=SCREAMING_SNAKE_CASE , cls_token=SCREAMING_SNAKE_CASE , mask_token=SCREAMING_SNAKE_CASE , tokenize_chinese_chars=SCREAMING_SNAKE_CASE , strip_accents=SCREAMING_SNAKE_CASE , **SCREAMING_SNAKE_CASE , )
A : Any = json.loads(self.backend_tokenizer.normalizer.__getstate__() )
if (
normalizer_state.get('''lowercase''' , SCREAMING_SNAKE_CASE ) != do_lower_case
or normalizer_state.get('''strip_accents''' , SCREAMING_SNAKE_CASE ) != strip_accents
or normalizer_state.get('''handle_chinese_chars''' , SCREAMING_SNAKE_CASE ) != tokenize_chinese_chars
):
A : Tuple = getattr(SCREAMING_SNAKE_CASE , normalizer_state.pop('''type''' ) )
A : Optional[Any] = do_lower_case
A : Any = strip_accents
A : Tuple = tokenize_chinese_chars
A : Dict = normalizer_class(**SCREAMING_SNAKE_CASE )
A : Optional[Any] = do_lower_case
def __lowerCAmelCase ( self , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE=None ) -> Tuple:
"""simple docstring"""
A : Dict = [self.cls_token_id] + token_ids_a + [self.sep_token_id]
if token_ids_a:
output += token_ids_a + [self.sep_token_id]
return output
def __lowerCAmelCase ( self , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE = None ) -> List[int]:
"""simple docstring"""
A : int = [self.sep_token_id]
A : List[Any] = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep ) * [0] + len(token_ids_a + sep ) * [1]
def __lowerCAmelCase ( self , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE = None ) -> Tuple[str]:
"""simple docstring"""
A : Union[str, Any] = self._tokenizer.model.save(SCREAMING_SNAKE_CASE , name=SCREAMING_SNAKE_CASE )
return tuple(SCREAMING_SNAKE_CASE )
| 311 |
'''simple docstring'''
from typing import List, Optional, Tuple, Union
import torch
from ...schedulers import DDIMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class A ( __snake_case ):
def __init__( self , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ) -> Any:
"""simple docstring"""
super().__init__()
# make sure scheduler can always be converted to DDIM
A : Dict = DDIMScheduler.from_config(scheduler.config )
self.register_modules(unet=SCREAMING_SNAKE_CASE , scheduler=SCREAMING_SNAKE_CASE )
@torch.no_grad()
def __call__( self , SCREAMING_SNAKE_CASE = 1 , SCREAMING_SNAKE_CASE = None , SCREAMING_SNAKE_CASE = 0.0 , SCREAMING_SNAKE_CASE = 50 , SCREAMING_SNAKE_CASE = None , SCREAMING_SNAKE_CASE = "pil" , SCREAMING_SNAKE_CASE = True , ) -> Union[ImagePipelineOutput, Tuple]:
"""simple docstring"""
if isinstance(self.unet.config.sample_size , SCREAMING_SNAKE_CASE ):
A : List[Any] = (
batch_size,
self.unet.config.in_channels,
self.unet.config.sample_size,
self.unet.config.sample_size,
)
else:
A : Optional[int] = (batch_size, self.unet.config.in_channels, *self.unet.config.sample_size)
if isinstance(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ) and len(SCREAMING_SNAKE_CASE ) != batch_size:
raise ValueError(
F'You have passed a list of generators of length {len(SCREAMING_SNAKE_CASE )}, but requested an effective batch'
F' size of {batch_size}. Make sure the batch size matches the length of the generators.' )
A : str = randn_tensor(SCREAMING_SNAKE_CASE , generator=SCREAMING_SNAKE_CASE , device=self.device , dtype=self.unet.dtype )
# set step values
self.scheduler.set_timesteps(SCREAMING_SNAKE_CASE )
for t in self.progress_bar(self.scheduler.timesteps ):
# 1. predict noise model_output
A : Any = self.unet(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ).sample
# 2. predict previous mean of image x_t-1 and add variance depending on eta
# eta corresponds to η in paper and should be between [0, 1]
# do x_t -> x_t-1
A : int = self.scheduler.step(
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , eta=SCREAMING_SNAKE_CASE , use_clipped_model_output=SCREAMING_SNAKE_CASE , generator=SCREAMING_SNAKE_CASE ).prev_sample
A : Dict = (image / 2 + 0.5).clamp(0 , 1 )
A : Optional[int] = image.cpu().permute(0 , 2 , 3 , 1 ).numpy()
if output_type == "pil":
A : int = self.numpy_to_pil(SCREAMING_SNAKE_CASE )
if not return_dict:
return (image,)
return ImagePipelineOutput(images=SCREAMING_SNAKE_CASE )
| 311 | 1 |
import inspect
import unittest
import numpy as np
from transformers import ViTConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor
if is_flax_available():
import jax
from transformers.models.vit.modeling_flax_vit import FlaxViTForImageClassification, FlaxViTModel
class UpperCAmelCase_ ( unittest.TestCase ):
'''simple docstring'''
def __init__( self : Optional[int] , UpperCamelCase__ : Dict , UpperCamelCase__ : List[str]=13 , UpperCamelCase__ : int=30 , UpperCamelCase__ : Optional[int]=2 , UpperCamelCase__ : List[Any]=3 , UpperCamelCase__ : str=True , UpperCamelCase__ : Tuple=True , UpperCamelCase__ : Optional[Any]=32 , UpperCamelCase__ : int=5 , UpperCamelCase__ : List[Any]=4 , UpperCamelCase__ : List[str]=37 , UpperCamelCase__ : Any="gelu" , UpperCamelCase__ : List[Any]=0.1 , UpperCamelCase__ : Union[str, Any]=0.1 , UpperCamelCase__ : Dict=10 , UpperCamelCase__ : List[Any]=0.02 , ) -> Dict:
"""simple docstring"""
__magic_name__ = parent
__magic_name__ = batch_size
__magic_name__ = image_size
__magic_name__ = patch_size
__magic_name__ = num_channels
__magic_name__ = is_training
__magic_name__ = use_labels
__magic_name__ = hidden_size
__magic_name__ = num_hidden_layers
__magic_name__ = num_attention_heads
__magic_name__ = intermediate_size
__magic_name__ = hidden_act
__magic_name__ = hidden_dropout_prob
__magic_name__ = attention_probs_dropout_prob
__magic_name__ = type_sequence_label_size
__magic_name__ = initializer_range
# in ViT, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
__magic_name__ = (image_size // patch_size) ** 2
__magic_name__ = num_patches + 1
def _lowercase ( self : Any ) -> Optional[int]:
"""simple docstring"""
__magic_name__ = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
__magic_name__ = ViTConfig(
image_size=self.image_size , patch_size=self.patch_size , num_channels=self.num_channels , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , is_decoder=UpperCamelCase__ , initializer_range=self.initializer_range , )
return config, pixel_values
def _lowercase ( self : List[Any] , UpperCamelCase__ : Union[str, Any] , UpperCamelCase__ : int ) -> List[Any]:
"""simple docstring"""
__magic_name__ = FlaxViTModel(config=UpperCamelCase__ )
__magic_name__ = model(UpperCamelCase__ )
# expected sequence length = num_patches + 1 (we add 1 for the [CLS] token)
__magic_name__ = (self.image_size, self.image_size)
__magic_name__ = (self.patch_size, self.patch_size)
__magic_name__ = (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, num_patches + 1, self.hidden_size) )
def _lowercase ( self : Optional[int] , UpperCamelCase__ : str , UpperCamelCase__ : List[Any] ) -> Any:
"""simple docstring"""
__magic_name__ = self.type_sequence_label_size
__magic_name__ = FlaxViTForImageClassification(config=UpperCamelCase__ )
__magic_name__ = model(UpperCamelCase__ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size) )
# test greyscale images
__magic_name__ = 1
__magic_name__ = FlaxViTForImageClassification(UpperCamelCase__ )
__magic_name__ = floats_tensor([self.batch_size, 1, self.image_size, self.image_size] )
__magic_name__ = model(UpperCamelCase__ )
def _lowercase ( self : int ) -> int:
"""simple docstring"""
__magic_name__ = self.prepare_config_and_inputs()
(
(
__magic_name__
) , (
__magic_name__
) ,
) = config_and_inputs
__magic_name__ = {"""pixel_values""": pixel_values}
return config, inputs_dict
@require_flax
class UpperCAmelCase_ ( _A , unittest.TestCase ):
'''simple docstring'''
a__ = (FlaxViTModel, FlaxViTForImageClassification) if is_flax_available() else ()
def _lowercase ( self : Tuple ) -> None:
"""simple docstring"""
__magic_name__ = FlaxViTModelTester(self )
__magic_name__ = ConfigTester(self , config_class=UpperCamelCase__ , has_text_modality=UpperCamelCase__ , hidden_size=37 )
def _lowercase ( self : int ) -> Dict:
"""simple docstring"""
self.config_tester.run_common_tests()
def _lowercase ( self : Optional[Any] ) -> str:
"""simple docstring"""
__magic_name__ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*UpperCamelCase__ )
def _lowercase ( self : Dict ) -> List[str]:
"""simple docstring"""
__magic_name__ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*UpperCamelCase__ )
def _lowercase ( self : str ) -> Union[str, Any]:
"""simple docstring"""
__magic_name__ , __magic_name__ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
__magic_name__ = model_class(UpperCamelCase__ )
__magic_name__ = inspect.signature(model.__call__ )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
__magic_name__ = [*signature.parameters.keys()]
__magic_name__ = ["""pixel_values"""]
self.assertListEqual(arg_names[:1] , UpperCamelCase__ )
def _lowercase ( self : Dict ) -> Optional[int]:
"""simple docstring"""
__magic_name__ , __magic_name__ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
with self.subTest(model_class.__name__ ):
__magic_name__ = self._prepare_for_class(UpperCamelCase__ , UpperCamelCase__ )
__magic_name__ = model_class(UpperCamelCase__ )
@jax.jit
def model_jitted(UpperCamelCase__ : Optional[Any] , **UpperCamelCase__ : Dict ):
return model(pixel_values=UpperCamelCase__ , **UpperCamelCase__ )
with self.subTest("""JIT Enabled""" ):
__magic_name__ = model_jitted(**UpperCamelCase__ ).to_tuple()
with self.subTest("""JIT Disabled""" ):
with jax.disable_jit():
__magic_name__ = 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 )
@slow
def _lowercase ( self : Tuple ) -> Any:
"""simple docstring"""
for model_class_name in self.all_model_classes:
__magic_name__ = model_class_name.from_pretrained("""google/vit-base-patch16-224""" )
__magic_name__ = model(np.ones((1, 3, 224, 224) ) )
self.assertIsNotNone(UpperCamelCase__ )
| 88 |
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from tokenizers import processors
from ...tokenization_utils import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_mbart import MBartTokenizer
else:
__A =None
__A =logging.get_logger(__name__)
__A ={'''vocab_file''': '''sentencepiece.bpe.model''', '''tokenizer_file''': '''tokenizer.json'''}
__A ={
'''vocab_file''': {
'''facebook/mbart-large-en-ro''': (
'''https://huggingface.co/facebook/mbart-large-en-ro/resolve/main/sentencepiece.bpe.model'''
),
'''facebook/mbart-large-cc25''': (
'''https://huggingface.co/facebook/mbart-large-cc25/resolve/main/sentencepiece.bpe.model'''
),
},
'''tokenizer_file''': {
'''facebook/mbart-large-en-ro''': '''https://huggingface.co/facebook/mbart-large-en-ro/resolve/main/tokenizer.json''',
'''facebook/mbart-large-cc25''': '''https://huggingface.co/facebook/mbart-large-cc25/resolve/main/tokenizer.json''',
},
}
__A ={
'''facebook/mbart-large-en-ro''': 1_0_2_4,
'''facebook/mbart-large-cc25''': 1_0_2_4,
}
# fmt: off
__A =['''ar_AR''', '''cs_CZ''', '''de_DE''', '''en_XX''', '''es_XX''', '''et_EE''', '''fi_FI''', '''fr_XX''', '''gu_IN''', '''hi_IN''', '''it_IT''', '''ja_XX''', '''kk_KZ''', '''ko_KR''', '''lt_LT''', '''lv_LV''', '''my_MM''', '''ne_NP''', '''nl_XX''', '''ro_RO''', '''ru_RU''', '''si_LK''', '''tr_TR''', '''vi_VN''', '''zh_CN''']
class _SCREAMING_SNAKE_CASE ( snake_case_ ):
lowerCAmelCase__ = VOCAB_FILES_NAMES
lowerCAmelCase__ = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
lowerCAmelCase__ = PRETRAINED_VOCAB_FILES_MAP
lowerCAmelCase__ = ['input_ids', 'attention_mask']
lowerCAmelCase__ = MBartTokenizer
lowerCAmelCase__ = []
lowerCAmelCase__ = []
def __init__( self , lowercase=None , lowercase=None , lowercase="<s>" , lowercase="</s>" , lowercase="</s>" , lowercase="<s>" , lowercase="<unk>" , lowercase="<pad>" , lowercase="<mask>" , lowercase=None , lowercase=None , lowercase=None , **lowercase , ) -> Dict:
# Mask token behave like a normal word, i.e. include the space before it
lowerCamelCase_ = AddedToken(lowercase , lstrip=lowercase , rstrip=lowercase ) if isinstance(lowercase , lowercase ) else mask_token
super().__init__(
vocab_file=lowercase , tokenizer_file=lowercase , bos_token=lowercase , eos_token=lowercase , sep_token=lowercase , cls_token=lowercase , unk_token=lowercase , pad_token=lowercase , mask_token=lowercase , src_lang=lowercase , tgt_lang=lowercase , additional_special_tokens=lowercase , **lowercase , )
lowerCamelCase_ = vocab_file
lowerCamelCase_ = False if not self.vocab_file else True
lowerCamelCase_ = FAIRSEQ_LANGUAGE_CODES.copy()
if additional_special_tokens is not None:
# Only add those special tokens if they are not already there.
_additional_special_tokens.extend(
[t for t in additional_special_tokens if t not in _additional_special_tokens] )
self.add_special_tokens({"additional_special_tokens": _additional_special_tokens} )
lowerCamelCase_ = {
lang_code: self.convert_tokens_to_ids(lowercase ) for lang_code in FAIRSEQ_LANGUAGE_CODES
}
lowerCamelCase_ = src_lang if src_lang is not None else "en_XX"
lowerCamelCase_ = self.convert_tokens_to_ids(self._src_lang )
lowerCamelCase_ = tgt_lang
self.set_src_lang_special_tokens(self._src_lang )
@property
def SCREAMING_SNAKE_CASE_( self ) -> str:
return self._src_lang
@src_lang.setter
def SCREAMING_SNAKE_CASE_( self , lowercase ) -> None:
lowerCamelCase_ = new_src_lang
self.set_src_lang_special_tokens(self._src_lang )
def SCREAMING_SNAKE_CASE_( self , lowercase , lowercase = None ) -> List[int]:
if token_ids_a is None:
return self.prefix_tokens + token_ids_a + self.suffix_tokens
# We don't expect to process pairs, but leave the pair logic for API consistency
return self.prefix_tokens + token_ids_a + token_ids_a + self.suffix_tokens
def SCREAMING_SNAKE_CASE_( self , lowercase , lowercase = None ) -> List[int]:
lowerCamelCase_ = [self.sep_token_id]
lowerCamelCase_ = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep + sep + token_ids_a + sep ) * [0]
def SCREAMING_SNAKE_CASE_( self , lowercase , lowercase , lowercase , lowercase , **lowercase ) -> List[Any]:
if src_lang is None or tgt_lang is None:
raise ValueError("Translation requires a `src_lang` and a `tgt_lang` for this model" )
lowerCamelCase_ = src_lang
lowerCamelCase_ = self(lowercase , add_special_tokens=lowercase , return_tensors=lowercase , **lowercase )
lowerCamelCase_ = self.convert_tokens_to_ids(lowercase )
lowerCamelCase_ = tgt_lang_id
return inputs
def SCREAMING_SNAKE_CASE_( self , lowercase , lowercase = "en_XX" , lowercase = None , lowercase = "ro_RO" , **lowercase , ) -> BatchEncoding:
lowerCamelCase_ = src_lang
lowerCamelCase_ = tgt_lang
return super().prepare_seqaseq_batch(lowercase , lowercase , **lowercase )
def SCREAMING_SNAKE_CASE_( self ) -> Dict:
return self.set_src_lang_special_tokens(self.src_lang )
def SCREAMING_SNAKE_CASE_( self ) -> Optional[Any]:
return self.set_tgt_lang_special_tokens(self.tgt_lang )
def SCREAMING_SNAKE_CASE_( self , lowercase ) -> None:
lowerCamelCase_ = self.convert_tokens_to_ids(lowercase )
lowerCamelCase_ = []
lowerCamelCase_ = [self.eos_token_id, self.cur_lang_code]
lowerCamelCase_ = self.convert_ids_to_tokens(self.prefix_tokens )
lowerCamelCase_ = self.convert_ids_to_tokens(self.suffix_tokens )
lowerCamelCase_ = processors.TemplateProcessing(
single=prefix_tokens_str + ["$A"] + suffix_tokens_str , pair=prefix_tokens_str + ["$A", "$B"] + suffix_tokens_str , special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str , self.prefix_tokens + self.suffix_tokens ) ) , )
def SCREAMING_SNAKE_CASE_( self , lowercase ) -> None:
lowerCamelCase_ = self.convert_tokens_to_ids(lowercase )
lowerCamelCase_ = []
lowerCamelCase_ = [self.eos_token_id, self.cur_lang_code]
lowerCamelCase_ = self.convert_ids_to_tokens(self.prefix_tokens )
lowerCamelCase_ = self.convert_ids_to_tokens(self.suffix_tokens )
lowerCamelCase_ = processors.TemplateProcessing(
single=prefix_tokens_str + ["$A"] + suffix_tokens_str , pair=prefix_tokens_str + ["$A", "$B"] + suffix_tokens_str , special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str , self.prefix_tokens + self.suffix_tokens ) ) , )
def SCREAMING_SNAKE_CASE_( self , lowercase , lowercase = None ) -> Tuple[str]:
if not self.can_save_slow_tokenizer:
raise ValueError(
"Your fast tokenizer does not have the necessary information to save the vocabulary for a slow "
"tokenizer." )
if not os.path.isdir(lowercase ):
logger.error(f'Vocabulary path ({save_directory}) should be a directory.' )
return
lowerCamelCase_ = os.path.join(
lowercase , (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(lowercase ):
copyfile(self.vocab_file , lowercase )
return (out_vocab_file,)
| 19 | 0 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
_lowercase: List[str] = logging.get_logger(__name__)
_lowercase: Union[str, Any] = {
"andreasmadsen/efficient_mlm_m0.40": (
"https://huggingface.co/andreasmadsen/efficient_mlm_m0.40/resolve/main/config.json"
),
}
class lowercase ( lowerCAmelCase ):
"""simple docstring"""
__A = "roberta-prelayernorm"
def __init__(self , lowerCamelCase_=50265 , lowerCamelCase_=768 , lowerCamelCase_=12 , lowerCamelCase_=12 , lowerCamelCase_=3072 , lowerCamelCase_="gelu" , lowerCamelCase_=0.1 , lowerCamelCase_=0.1 , lowerCamelCase_=512 , lowerCamelCase_=2 , lowerCamelCase_=0.02 , lowerCamelCase_=1E-1_2 , lowerCamelCase_=1 , lowerCamelCase_=0 , lowerCamelCase_=2 , lowerCamelCase_="absolute" , lowerCamelCase_=True , lowerCamelCase_=None , **lowerCamelCase_ , ):
"""simple docstring"""
super().__init__(pad_token_id=lowerCamelCase_ , bos_token_id=lowerCamelCase_ , eos_token_id=lowerCamelCase_ , **lowerCamelCase_ )
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
a = classifier_dropout
class lowercase ( lowerCAmelCase ):
"""simple docstring"""
@property
def UpperCamelCase_ (self ):
"""simple docstring"""
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),
] )
| 363 |
import random
import timeit
from functools import wraps
from typing import Callable, Optional
from ..configuration_utils import PretrainedConfig
from ..models.auto.modeling_tf_auto import TF_MODEL_MAPPING, TF_MODEL_WITH_LM_HEAD_MAPPING
from ..utils import is_pyanvml_available, is_tf_available, logging
from .benchmark_utils import (
Benchmark,
Memory,
MemorySummary,
measure_peak_memory_cpu,
start_memory_tracing,
stop_memory_tracing,
)
if is_tf_available():
import tensorflow as tf
from tensorflow.python.framework.errors_impl import ResourceExhaustedError
from .benchmark_args_tf import TensorFlowBenchmarkArguments
if is_pyanvml_available():
import pyanvml.pyanvml as nvml
_lowercase: Any = logging.get_logger(__name__)
def a( A : bool , A : bool ) -> List[str]:
"""simple docstring"""
def run_func(A : Union[str, Any] ):
@wraps(A )
def run_in_eager_mode(*A : int , **A : List[str] ):
return func(*A , **A )
@wraps(A )
@tf.function(experimental_compile=A )
def run_in_graph_mode(*A : List[str] , **A : Optional[int] ):
return func(*A , **A )
if do_eager_mode is True:
if use_xla is not False:
raise ValueError(
"Cannot run model in XLA, if `args.eager_mode` is set to `True`. Please set `args.eager_mode=False`." )
return run_in_eager_mode
else:
return run_in_graph_mode
return run_func
def a( A : int , A : int , A : int ) -> ["tf.Tensor"]:
"""simple docstring"""
a = random.Random()
a = [rng.randint(0 , vocab_size - 1 ) for i in range(batch_size * sequence_length )]
return tf.constant(A , shape=(batch_size, sequence_length) , dtype=tf.intaa )
class _lowercase ( lowerCAmelCase ):
"""simple docstring"""
__A = 42
__A = 42
__A = "TensorFlow"
@property
def UpperCamelCase_ (self ):
"""simple docstring"""
return tf.__version__
def UpperCamelCase_ (self , lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ ):
"""simple docstring"""
a = self.args.strategy
if strategy is None:
raise ValueError("A device strategy has to be initialized before using TensorFlow." )
a = self._prepare_inference_func(lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ )
return self._measure_speed(_inference )
def UpperCamelCase_ (self , lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ ):
"""simple docstring"""
a = self.args.strategy
if strategy is None:
raise ValueError("A device strategy has to be initialized before using TensorFlow." )
a = self._prepare_train_func(lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ )
return self._measure_speed(_train )
def UpperCamelCase_ (self , lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ ):
"""simple docstring"""
if self.args.is_gpu:
tf.config.experimental.set_memory_growth(self.args.gpu_list[self.args.device_idx] , lowerCamelCase_ )
a = self.args.strategy
if strategy is None:
raise ValueError("A device strategy has to be initialized before using TensorFlow." )
a = self._prepare_inference_func(lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ )
return self._measure_memory(_inference )
def UpperCamelCase_ (self , lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ ):
"""simple docstring"""
if self.args.is_gpu:
tf.config.experimental.set_memory_growth(self.args.gpu_list[self.args.device_idx] , lowerCamelCase_ )
a = self.args.strategy
if strategy is None:
raise ValueError("A device strategy has to be initialized before using TensorFlow." )
a = self._prepare_train_func(lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ )
return self._measure_memory(_train )
def UpperCamelCase_ (self , lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ ):
"""simple docstring"""
a = self.config_dict[model_name]
if self.args.fpaa:
raise NotImplementedError("Mixed precision is currently not supported." )
a = (
hasattr(lowerCamelCase_ , "architectures" )
and isinstance(config.architectures , lowerCamelCase_ )
and len(config.architectures ) > 0
)
if not self.args.only_pretrain_model and has_model_class_in_config:
try:
a = "TF" + config.architectures[0] # prepend 'TF' for tensorflow model
a = __import__("transformers" , fromlist=[model_class] )
a = getattr(lowerCamelCase_ , lowerCamelCase_ )
a = model_cls(lowerCamelCase_ )
except ImportError:
raise ImportError(
F'''{model_class} does not exist. If you just want to test the pretrained model, you might want to'''
" set `--only_pretrain_model` or `args.only_pretrain_model=True`." )
else:
a = TF_MODEL_MAPPING[config.__class__](lowerCamelCase_ )
# encoder-decoder has vocab size saved differently
a = config.vocab_size if hasattr(lowerCamelCase_ , "vocab_size" ) else config.encoder.vocab_size
a = random_input_ids(lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ )
@run_with_tf_optimizations(self.args.eager_mode , self.args.use_xla )
def encoder_decoder_forward():
return model(lowerCamelCase_ , decoder_input_ids=lowerCamelCase_ , training=lowerCamelCase_ )
@run_with_tf_optimizations(self.args.eager_mode , self.args.use_xla )
def encoder_forward():
return model(lowerCamelCase_ , training=lowerCamelCase_ )
a = encoder_decoder_forward if config.is_encoder_decoder else encoder_forward
return _inference
def UpperCamelCase_ (self , lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ ):
"""simple docstring"""
a = self.config_dict[model_name]
if self.args.eager_mode is not False:
raise ValueError("Training cannot be done in eager mode. Please make sure that `args.eager_mode = False`." )
if self.args.fpaa:
raise NotImplementedError("Mixed precision is currently not supported." )
a = (
hasattr(lowerCamelCase_ , "architectures" )
and isinstance(config.architectures , lowerCamelCase_ )
and len(config.architectures ) > 0
)
if not self.args.only_pretrain_model and has_model_class_in_config:
try:
a = "TF" + config.architectures[0] # prepend 'TF' for tensorflow model
a = __import__("transformers" , fromlist=[model_class] )
a = getattr(lowerCamelCase_ , lowerCamelCase_ )
a = model_cls(lowerCamelCase_ )
except ImportError:
raise ImportError(
F'''{model_class} does not exist. If you just want to test the pretrained model, you might want to'''
" set `--only_pretrain_model` or `args.only_pretrain_model=True`." )
else:
a = TF_MODEL_WITH_LM_HEAD_MAPPING[config.__class__](lowerCamelCase_ )
# encoder-decoder has vocab size saved differently
a = config.vocab_size if hasattr(lowerCamelCase_ , "vocab_size" ) else config.encoder.vocab_size
a = random_input_ids(lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ )
@run_with_tf_optimizations(self.args.eager_mode , self.args.use_xla )
def encoder_decoder_train():
a = model(lowerCamelCase_ , decoder_input_ids=lowerCamelCase_ , labels=lowerCamelCase_ , training=lowerCamelCase_ )[0]
a = tf.gradients(lowerCamelCase_ , model.trainable_variables )
return gradients
@run_with_tf_optimizations(self.args.eager_mode , self.args.use_xla )
def encoder_train():
a = model(lowerCamelCase_ , labels=lowerCamelCase_ , training=lowerCamelCase_ )[0]
a = tf.gradients(lowerCamelCase_ , model.trainable_variables )
return gradients
a = encoder_decoder_train if config.is_encoder_decoder else encoder_train
return _train
def UpperCamelCase_ (self , lowerCamelCase_ ):
"""simple docstring"""
with self.args.strategy.scope():
try:
if self.args.is_tpu or self.args.use_xla:
# run additional 10 times to stabilize compilation for tpu
logger.info("Do inference on TPU. Running model 5 times to stabilize compilation" )
timeit.repeat(lowerCamelCase_ , repeat=1 , number=5 )
# as written in https://docs.python.org/2/library/timeit.html#timeit.Timer.repeat, min should be taken rather than the average
a = timeit.repeat(
lowerCamelCase_ , repeat=self.args.repeat , number=10 , )
return min(lowerCamelCase_ ) / 10.0
except ResourceExhaustedError as e:
self.print_fn(F'''Doesn\'t fit on GPU. {e}''' )
def UpperCamelCase_ (self , lowerCamelCase_ ):
"""simple docstring"""
logger.info(
"Note that TensorFlow allocates more memory than "
"it might need to speed up computation. "
"The memory reported here corresponds to the memory "
"reported by `nvidia-smi`, which can vary depending "
"on total available memory on the GPU that is used." )
with self.args.strategy.scope():
try:
if self.args.trace_memory_line_by_line:
if not self.args.eager_mode:
raise ValueError(
"`args.eager_mode` is set to `False`. Make sure to run model in eager mode to measure memory"
" consumption line by line." )
a = start_memory_tracing("transformers" )
if self.args.is_tpu:
# tpu
raise NotImplementedError(
"Memory Benchmarking is currently not implemented for TPU. Please disable memory benchmarking"
" with `args.memory=False`" )
elif self.args.is_gpu:
# gpu
if not is_pyanvml_available():
logger.warning(
"py3nvml not installed, we won't log GPU memory usage. "
"Install py3nvml (pip install py3nvml) to log information about GPU." )
a = "N/A"
else:
logger.info(
"Measuring total GPU usage on GPU device. Make sure to not have additional processes"
" running on the same GPU." )
# init nvml
nvml.nvmlInit()
func()
a = nvml.nvmlDeviceGetHandleByIndex(self.args.device_idx )
a = nvml.nvmlDeviceGetMemoryInfo(lowerCamelCase_ )
a = meminfo.used
a = Memory(lowerCamelCase_ )
# shutdown nvml
nvml.nvmlShutdown()
else:
# cpu
if self.args.trace_memory_line_by_line:
logger.info(
"When enabling line by line tracing, the max peak memory for CPU is inaccurate in"
" TensorFlow." )
a = None
else:
a = measure_peak_memory_cpu(lowerCamelCase_ )
a = Memory(lowerCamelCase_ ) if isinstance(lowerCamelCase_ , lowerCamelCase_ ) else memory_bytes
if self.args.trace_memory_line_by_line:
a = stop_memory_tracing(lowerCamelCase_ )
if memory is None:
a = summary.total
else:
a = None
return memory, summary
except ResourceExhaustedError as e:
self.print_fn(F'''Doesn\'t fit on GPU. {e}''' )
return "N/A", None
| 71 | 0 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
lowercase_ = logging.get_logger(__name__)
lowercase_ = {
'facebook/xlm-roberta-xl': 'https://huggingface.co/facebook/xlm-roberta-xl/resolve/main/config.json',
'facebook/xlm-roberta-xxl': 'https://huggingface.co/facebook/xlm-roberta-xxl/resolve/main/config.json',
# See all XLM-RoBERTa-XL models at https://huggingface.co/models?filter=xlm-roberta-xl
}
class __lowerCAmelCase ( lowerCamelCase__ ):
_a = """xlm-roberta-xl"""
def __init__( self , lowerCAmelCase=250_880 , lowerCAmelCase=2_560 , lowerCAmelCase=36 , lowerCAmelCase=32 , lowerCAmelCase=10_240 , lowerCAmelCase="gelu" , lowerCAmelCase=0.1 , lowerCAmelCase=0.1 , lowerCAmelCase=514 , lowerCAmelCase=1 , lowerCAmelCase=0.02 , lowerCAmelCase=1e-05 , lowerCAmelCase=1 , lowerCAmelCase=0 , lowerCAmelCase=2 , lowerCAmelCase="absolute" , lowerCAmelCase=True , lowerCAmelCase=None , **lowerCAmelCase , ) -> Optional[int]:
'''simple docstring'''
super().__init__(pad_token_id=snake_case__ , bos_token_id=snake_case__ , eos_token_id=snake_case__ , **snake_case__ )
_lowercase =vocab_size
_lowercase =hidden_size
_lowercase =num_hidden_layers
_lowercase =num_attention_heads
_lowercase =hidden_act
_lowercase =intermediate_size
_lowercase =hidden_dropout_prob
_lowercase =attention_probs_dropout_prob
_lowercase =max_position_embeddings
_lowercase =type_vocab_size
_lowercase =initializer_range
_lowercase =layer_norm_eps
_lowercase =position_embedding_type
_lowercase =use_cache
_lowercase =classifier_dropout
class __lowerCAmelCase ( lowerCamelCase__ ):
@property
def A__ ( self ) -> Mapping[str, Mapping[int, str]]:
'''simple docstring'''
if self.task == "multiple-choice":
_lowercase ={0: '''batch''', 1: '''choice''', 2: '''sequence'''}
else:
_lowercase ={0: '''batch''', 1: '''sequence'''}
return OrderedDict(
[
('input_ids', dynamic_axis),
('attention_mask', dynamic_axis),
] )
| 205 | import os
from argparse import ArgumentParser
from typing import List
import torch.utils.data
from datasets import Dataset, IterableDataset
from datasets.distributed import split_dataset_by_node
__snake_case = 4
__snake_case = 3
class __snake_case ( lowerCamelCase__ ):
pass
def lowerCAmelCase_ ( __lowerCAmelCase )-> List[str]:
'''simple docstring'''
for shard in shards:
for i in range(__lowerCAmelCase ):
yield {"i": i, "shard": shard}
def lowerCAmelCase_ ( )-> Optional[int]:
'''simple docstring'''
UpperCAmelCase : List[str] =int(os.environ['''RANK'''] )
UpperCAmelCase : Optional[Any] =int(os.environ['''WORLD_SIZE'''] )
UpperCAmelCase : List[Any] =ArgumentParser()
parser.add_argument('''--streaming''' , type=__lowerCAmelCase )
parser.add_argument('''--local_rank''' , type=__lowerCAmelCase )
parser.add_argument('''--num_workers''' , type=__lowerCAmelCase , default=0 )
UpperCAmelCase : Any =parser.parse_args()
UpperCAmelCase : List[str] =args.streaming
UpperCAmelCase : Tuple =args.num_workers
UpperCAmelCase : int ={'''shards''': [f'''shard_{shard_idx}''' for shard_idx in range(__lowerCAmelCase )]}
UpperCAmelCase : Optional[int] =IterableDataset.from_generator(__lowerCAmelCase , gen_kwargs=__lowerCAmelCase )
if not streaming:
UpperCAmelCase : List[Any] =Dataset.from_list(list(__lowerCAmelCase ) )
UpperCAmelCase : Dict =split_dataset_by_node(__lowerCAmelCase , rank=__lowerCAmelCase , world_size=__lowerCAmelCase )
UpperCAmelCase : List[Any] =torch.utils.data.DataLoader(__lowerCAmelCase , num_workers=__lowerCAmelCase )
UpperCAmelCase : Dict =NUM_SHARDS * NUM_ITEMS_PER_SHARD
UpperCAmelCase : str =full_size // world_size
expected_local_size += int(rank < (full_size % world_size) )
UpperCAmelCase : List[Any] =sum(1 for _ in dataloader )
if local_size != expected_local_size:
raise FailedTestError(f'''local_size {local_size} != expected_local_size {expected_local_size}''' )
if __name__ == "__main__":
main()
| 348 | 0 |
'''simple docstring'''
from __future__ import annotations
import unittest
from transformers import LEDConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import tensorflow as tf
from transformers import TFLEDForConditionalGeneration, TFLEDModel
@require_tf
class UpperCamelCase_ :
"""simple docstring"""
_lowerCAmelCase = LEDConfig
_lowerCAmelCase = {}
_lowerCAmelCase = 'gelu'
def __init__( self : List[Any] , _lowerCamelCase : Optional[Any] , _lowerCamelCase : List[Any]=13 , _lowerCamelCase : Dict=7 , _lowerCamelCase : Dict=True , _lowerCamelCase : int=False , _lowerCamelCase : Optional[Any]=99 , _lowerCamelCase : List[Any]=32 , _lowerCamelCase : List[Any]=2 , _lowerCamelCase : Optional[int]=4 , _lowerCamelCase : Optional[int]=37 , _lowerCamelCase : str=0.1 , _lowerCamelCase : Union[str, Any]=0.1 , _lowerCamelCase : List[str]=20 , _lowerCamelCase : int=2 , _lowerCamelCase : Optional[Any]=1 , _lowerCamelCase : Tuple=0 , _lowerCamelCase : Any=4 , ):
"""simple docstring"""
A_ : List[Any] = parent
A_ : Tuple = batch_size
A_ : List[Any] = seq_length
A_ : List[Any] = is_training
A_ : str = use_labels
A_ : List[str] = vocab_size
A_ : Union[str, Any] = hidden_size
A_ : List[str] = num_hidden_layers
A_ : List[Any] = num_attention_heads
A_ : Optional[Any] = intermediate_size
A_ : str = hidden_dropout_prob
A_ : Any = attention_probs_dropout_prob
A_ : Optional[Any] = max_position_embeddings
A_ : List[Any] = eos_token_id
A_ : str = pad_token_id
A_ : Tuple = bos_token_id
A_ : List[Any] = attention_window
# `ModelTesterMixin.test_attention_outputs` is expecting attention tensors to be of size
# [num_attention_heads, encoder_seq_length, encoder_key_length], but TFLongformerSelfAttention
# returns attention of shape [num_attention_heads, encoder_seq_length, self.attention_window + 1]
# because its local attention only attends to `self.attention_window` and one before and one after
A_ : Optional[Any] = self.attention_window + 2
# because of padding `encoder_seq_length`, is different from `seq_length`. Relevant for
# the `test_attention_outputs` and `test_hidden_states_output` tests
A_ : Optional[int] = (
self.seq_length + (self.attention_window - self.seq_length % self.attention_window) % self.attention_window
)
def _a ( self : Optional[Any] ):
"""simple docstring"""
A_ : List[str] = ids_tensor([self.batch_size, self.seq_length - 1] , self.vocab_size )
A_ : Any = tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size ) , 1 )
A_ : Union[str, Any] = tf.concat([input_ids, eos_tensor] , axis=1 )
A_ : int = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
A_ : List[Any] = 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 , attention_window=self.attention_window , **self.config_updates , )
A_ : Dict = prepare_led_inputs_dict(_lowerCamelCase , _lowerCamelCase , _lowerCamelCase )
A_ : Optional[Any] = tf.concat(
[tf.zeros_like(_lowerCamelCase )[:, :-1], tf.ones_like(_lowerCamelCase )[:, -1:]] , axis=-1 , )
A_ : Optional[int] = global_attention_mask
return config, inputs_dict
def _a ( self : Optional[Any] , _lowerCamelCase : str , _lowerCamelCase : Optional[int] ):
"""simple docstring"""
A_ : Optional[int] = TFLEDModel(config=_lowerCamelCase ).get_decoder()
A_ : str = inputs_dict['''input_ids''']
A_ : Optional[Any] = input_ids[:1, :]
A_ : Optional[Any] = inputs_dict['''attention_mask'''][:1, :]
A_ : List[Any] = 1
# first forward pass
A_ : Optional[int] = model(_lowerCamelCase , attention_mask=_lowerCamelCase , use_cache=_lowerCamelCase )
A_ : List[str] = outputs.to_tuple()
# create hypothetical next token and extent to next_input_ids
A_ : List[Any] = ids_tensor((self.batch_size, 3) , config.vocab_size )
A_ : Any = tf.cast(ids_tensor((self.batch_size, 3) , 2 ) , tf.inta )
# append to next input_ids and
A_ : List[Any] = tf.concat([input_ids, next_tokens] , axis=-1 )
A_ : Tuple = tf.concat([attention_mask, next_attn_mask] , axis=-1 )
A_ : Optional[int] = model(_lowerCamelCase , attention_mask=_lowerCamelCase )[0]
A_ : Dict = model(_lowerCamelCase , attention_mask=_lowerCamelCase , past_key_values=_lowerCamelCase )[0]
self.parent.assertEqual(next_tokens.shape[1] , output_from_past.shape[1] )
# select random slice
A_ : List[str] = int(ids_tensor((1,) , output_from_past.shape[-1] ) )
A_ : List[str] = output_from_no_past[:, -3:, random_slice_idx]
A_ : List[str] = output_from_past[:, :, random_slice_idx]
# test that outputs are equal for slice
tf.debugging.assert_near(_lowerCamelCase , _lowerCamelCase , rtol=1E-3 )
def snake_case__ ( lowerCamelCase__ : Optional[Any] , lowerCamelCase__ : Dict , lowerCamelCase__ : Tuple , lowerCamelCase__ : int=None , lowerCamelCase__ : int=None , lowerCamelCase__ : List[Any]=None , lowerCamelCase__ : Dict=None , ) -> Dict:
if attention_mask is None:
A_ : List[str] = tf.cast(tf.math.not_equal(lowerCamelCase__ , config.pad_token_id ) , tf.inta )
if decoder_attention_mask is None:
A_ : List[str] = 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_ : Union[str, Any] = tf.ones((config.encoder_layers, config.encoder_attention_heads) )
if decoder_head_mask is None:
A_ : List[str] = tf.ones((config.decoder_layers, config.decoder_attention_heads) )
return {
"input_ids": input_ids,
"attention_mask": attention_mask,
"decoder_input_ids": decoder_input_ids,
"decoder_attention_mask": decoder_attention_mask,
"head_mask": head_mask,
"decoder_head_mask": decoder_head_mask,
}
@require_tf
class UpperCamelCase_ (a__, a__, unittest.TestCase ):
"""simple docstring"""
_lowerCAmelCase = (TFLEDForConditionalGeneration, TFLEDModel) if is_tf_available() else ()
_lowerCAmelCase = (TFLEDForConditionalGeneration,) if is_tf_available() else ()
_lowerCAmelCase = (
{
'conversational': TFLEDForConditionalGeneration,
'feature-extraction': TFLEDModel,
'summarization': TFLEDForConditionalGeneration,
'text2text-generation': TFLEDForConditionalGeneration,
'translation': TFLEDForConditionalGeneration,
}
if is_tf_available()
else {}
)
_lowerCAmelCase = True
_lowerCAmelCase = False
_lowerCAmelCase = False
_lowerCAmelCase = False
def _a ( self : List[Any] ):
"""simple docstring"""
A_ : Tuple = TFLEDModelTester(self )
A_ : Tuple = ConfigTester(self , config_class=_lowerCamelCase )
def _a ( self : Union[str, Any] ):
"""simple docstring"""
self.config_tester.run_common_tests()
def _a ( self : Any ):
"""simple docstring"""
A_ : List[str] = self.model_tester.prepare_config_and_inputs_for_common()
self.model_tester.check_decoder_model_past_large_inputs(*_lowerCamelCase )
def _a ( self : int ):
"""simple docstring"""
A_ : List[str] = self.model_tester.prepare_config_and_inputs_for_common()
A_ : str = tf.zeros_like(inputs_dict['''attention_mask'''] )
A_ : str = 2
A_ : Dict = tf.where(
tf.range(self.model_tester.seq_length )[None, :] < num_global_attn_indices , 1 , inputs_dict['''global_attention_mask'''] , )
A_ : str = True
A_ : Dict = self.model_tester.seq_length
A_ : Dict = self.model_tester.encoder_seq_length
def check_decoder_attentions_output(_lowerCamelCase : Union[str, Any] ):
A_ : str = outputs.decoder_attentions
self.assertEqual(len(_lowerCamelCase ) , self.model_tester.num_hidden_layers )
self.assertListEqual(
list(decoder_attentions[0].shape[-3:] ) , [self.model_tester.num_attention_heads, seq_length, seq_length] , )
def check_encoder_attentions_output(_lowerCamelCase : Union[str, Any] ):
A_ : Optional[int] = [t.numpy() for t in outputs.encoder_attentions]
A_ : Any = [t.numpy() for t in outputs.encoder_global_attentions]
self.assertEqual(len(_lowerCamelCase ) , self.model_tester.num_hidden_layers )
self.assertEqual(len(_lowerCamelCase ) , self.model_tester.num_hidden_layers )
self.assertListEqual(
list(attentions[0].shape[-3:] ) , [self.model_tester.num_attention_heads, seq_length, seq_length] , )
self.assertListEqual(
list(global_attentions[0].shape[-3:] ) , [self.model_tester.num_attention_heads, encoder_seq_length, num_global_attn_indices] , )
for model_class in self.all_model_classes:
A_ : Optional[int] = True
A_ : str = False
A_ : Optional[Any] = False
A_ : List[Any] = model_class(_lowerCamelCase )
A_ : str = model(self._prepare_for_class(_lowerCamelCase , _lowerCamelCase ) )
A_ : int = len(_lowerCamelCase )
self.assertEqual(config.output_hidden_states , _lowerCamelCase )
check_encoder_attentions_output(_lowerCamelCase )
if self.is_encoder_decoder:
A_ : str = model_class(_lowerCamelCase )
A_ : Dict = model(self._prepare_for_class(_lowerCamelCase , _lowerCamelCase ) )
self.assertEqual(config.output_hidden_states , _lowerCamelCase )
check_decoder_attentions_output(_lowerCamelCase )
# Check that output attentions can also be changed via the config
del inputs_dict["output_attentions"]
A_ : List[Any] = True
A_ : Optional[int] = model_class(_lowerCamelCase )
A_ : Dict = model(self._prepare_for_class(_lowerCamelCase , _lowerCamelCase ) )
self.assertEqual(config.output_hidden_states , _lowerCamelCase )
check_encoder_attentions_output(_lowerCamelCase )
# Check attention is always last and order is fine
A_ : Tuple = True
A_ : Dict = True
A_ : Union[str, Any] = model_class(_lowerCamelCase )
A_ : List[str] = model(self._prepare_for_class(_lowerCamelCase , _lowerCamelCase ) )
self.assertEqual(out_len + (2 if self.is_encoder_decoder else 1) , len(_lowerCamelCase ) )
self.assertEqual(model.config.output_hidden_states , _lowerCamelCase )
check_encoder_attentions_output(_lowerCamelCase )
@unittest.skip('''LED keeps using potentially symbolic tensors in conditionals and breaks tracing.''' )
def _a ( self : int ):
"""simple docstring"""
pass
def _a ( self : Optional[int] ):
"""simple docstring"""
pass
def snake_case__ ( lowerCamelCase__ : List[str] ) -> Dict:
return tf.constant(lowerCamelCase__ , dtype=tf.intaa )
snake_case__ = 1e-4
@slow
@require_tf
class UpperCamelCase_ (unittest.TestCase ):
"""simple docstring"""
def _a ( self : Optional[int] ):
"""simple docstring"""
A_ : List[Any] = TFLEDForConditionalGeneration.from_pretrained('''allenai/led-base-16384''' ).led
# change to intended input here
A_ : Any = _long_tensor([512 * [0, 31414, 232, 328, 740, 1140, 12695, 69]] )
A_ : List[Any] = _long_tensor([128 * [0, 31414, 232, 328, 740, 1140, 12695, 69]] )
A_ : List[Any] = prepare_led_inputs_dict(model.config , _lowerCamelCase , _lowerCamelCase )
A_ : Tuple = model(**_lowerCamelCase )[0]
A_ : Dict = (1, 1024, 768)
self.assertEqual(output.shape , _lowerCamelCase )
# change to expected output here
A_ : Optional[Any] = tf.convert_to_tensor(
[[2.30_50, 2.82_79, 0.65_31], [-1.84_57, -0.14_55, -3.56_61], [-1.01_86, 0.45_86, -2.20_43]] , )
tf.debugging.assert_near(output[:, :3, :3] , _lowerCamelCase , atol=1E-3 )
def _a ( self : Tuple ):
"""simple docstring"""
A_ : Tuple = TFLEDForConditionalGeneration.from_pretrained('''allenai/led-base-16384''' )
# change to intended input here
A_ : Optional[Any] = _long_tensor([512 * [0, 31414, 232, 328, 740, 1140, 12695, 69]] )
A_ : List[Any] = _long_tensor([128 * [0, 31414, 232, 328, 740, 1140, 12695, 69]] )
A_ : Union[str, Any] = prepare_led_inputs_dict(model.config , _lowerCamelCase , _lowerCamelCase )
A_ : Any = model(**_lowerCamelCase )[0]
A_ : Optional[int] = (1, 1024, model.config.vocab_size)
self.assertEqual(output.shape , _lowerCamelCase )
# change to expected output here
A_ : int = tf.convert_to_tensor(
[[33.65_07, 6.45_72, 16.80_89], [5.87_39, -2.42_38, 11.29_02], [-3.21_39, -4.31_49, 4.27_83]] , )
tf.debugging.assert_near(output[:, :3, :3] , _lowerCamelCase , atol=1E-3 , rtol=1E-3 )
| 355 |
'''simple docstring'''
def snake_case__ ( lowerCamelCase__ : list[int] , lowerCamelCase__ : list[int] , lowerCamelCase__ : int ) -> bool:
return not any(
neighbour == 1 and colored_vertices[i] == color
for i, neighbour in enumerate(lowerCamelCase__ ) )
def snake_case__ ( lowerCamelCase__ : list[list[int]] , lowerCamelCase__ : int , lowerCamelCase__ : list[int] , lowerCamelCase__ : int ) -> bool:
# Base Case
if index == len(lowerCamelCase__ ):
return True
# Recursive Step
for i in range(lowerCamelCase__ ):
if valid_coloring(graph[index] , lowerCamelCase__ , lowerCamelCase__ ):
# Color current vertex
A_ : int = i
# Validate coloring
if util_color(lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , index + 1 ):
return True
# Backtrack
A_ : str = -1
return False
def snake_case__ ( lowerCamelCase__ : list[list[int]] , lowerCamelCase__ : int ) -> list[int]:
A_ : List[str] = [-1] * len(lowerCamelCase__ )
if util_color(lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , 0 ):
return colored_vertices
return []
| 4 | 0 |
snake_case : str = '''
# Transformers installation
! pip install transformers datasets
# To install from source instead of the last release, comment the command above and uncomment the following one.
# ! pip install git+https://github.com/huggingface/transformers.git
'''
snake_case : List[Any] = [{'''type''': '''code''', '''content''': INSTALL_CONTENT}]
snake_case : int = {
'''{processor_class}''': '''FakeProcessorClass''',
'''{model_class}''': '''FakeModelClass''',
'''{object_class}''': '''FakeObjectClass''',
}
| 94 |
import sys
from .dependency_versions_table import deps
from .utils.versions import require_version, require_version_core
# define which module versions we always want to check at run time
# (usually the ones defined in `install_requires` in setup.py)
#
# order specific notes:
# - tqdm must be checked before tokenizers
snake_case : Union[str, Any] = '''python tqdm regex requests packaging filelock numpy tokenizers'''.split()
if sys.version_info < (3, 7):
pkgs_to_check_at_runtime.append('''dataclasses''')
if sys.version_info < (3, 8):
pkgs_to_check_at_runtime.append('''importlib_metadata''')
for pkg in pkgs_to_check_at_runtime:
if pkg in deps:
if pkg == "tokenizers":
# must be loaded here, or else tqdm check may fail
from .utils import is_tokenizers_available
if not is_tokenizers_available():
continue # not required, check version only if installed
require_version_core(deps[pkg])
else:
raise ValueError(F"""can't find {pkg} in {deps.keys()}, check dependency_versions_table.py""")
def __lowerCamelCase ( UpperCAmelCase_ : Any , UpperCAmelCase_ : Optional[int]=None ):
"""simple docstring"""
require_version(deps[pkg] , UpperCAmelCase_ )
| 94 | 1 |
import numpy as np
import torch
import tqdm
from ...models.unet_ad import UNetaDModel
from ...pipelines import DiffusionPipeline
from ...utils import randn_tensor
from ...utils.dummy_pt_objects import DDPMScheduler
class lowercase_ ( lowercase ):
'''simple docstring'''
def __init__( self : Union[str, Any] , __UpperCAmelCase : UNetaDModel , __UpperCAmelCase : UNetaDModel , __UpperCAmelCase : DDPMScheduler , __UpperCAmelCase : Optional[int] , ) ->List[str]:
"""simple docstring"""
super().__init__()
a = value_function
a = unet
a = scheduler
a = env
a = env.get_dataset()
a = {}
for key in self.data.keys():
try:
a = self.data[key].mean()
except: # noqa: E722
pass
a = {}
for key in self.data.keys():
try:
a = self.data[key].std()
except: # noqa: E722
pass
a = env.observation_space.shape[0]
a = env.action_space.shape[0]
def __lowerCAmelCase ( self : List[Any] , __UpperCAmelCase : List[str] , __UpperCAmelCase : Optional[int] ) ->Dict:
"""simple docstring"""
return (x_in - self.means[key]) / self.stds[key]
def __lowerCAmelCase ( self : Optional[int] , __UpperCAmelCase : Dict , __UpperCAmelCase : Dict ) ->List[str]:
"""simple docstring"""
return x_in * self.stds[key] + self.means[key]
def __lowerCAmelCase ( self : int , __UpperCAmelCase : int ) ->List[str]:
"""simple docstring"""
if type(__UpperCAmelCase ) is dict:
return {k: self.to_torch(__UpperCAmelCase ) for k, v in x_in.items()}
elif torch.is_tensor(__UpperCAmelCase ):
return x_in.to(self.unet.device )
return torch.tensor(__UpperCAmelCase , device=self.unet.device )
def __lowerCAmelCase ( self : Optional[int] , __UpperCAmelCase : int , __UpperCAmelCase : Union[str, Any] , __UpperCAmelCase : Tuple ) ->int:
"""simple docstring"""
for key, val in cond.items():
a = val.clone()
return x_in
def __lowerCAmelCase ( self : Optional[Any] , __UpperCAmelCase : List[str] , __UpperCAmelCase : str , __UpperCAmelCase : List[str] , __UpperCAmelCase : Optional[Any] ) ->Tuple:
"""simple docstring"""
a = x.shape[0]
a = None
for i in tqdm.tqdm(self.scheduler.timesteps ):
# create batch of timesteps to pass into model
a = torch.full((batch_size,) , __UpperCAmelCase , device=self.unet.device , dtype=torch.long )
for _ in range(__UpperCAmelCase ):
with torch.enable_grad():
x.requires_grad_()
# permute to match dimension for pre-trained models
a = self.value_function(x.permute(0 , 2 , 1 ) , __UpperCAmelCase ).sample
a = torch.autograd.grad([y.sum()] , [x] )[0]
a = self.scheduler._get_variance(__UpperCAmelCase )
a = torch.exp(0.5 * posterior_variance )
a = model_std * grad
a = 0
a = x.detach()
a = x + scale * grad
a = self.reset_xa(__UpperCAmelCase , __UpperCAmelCase , self.action_dim )
a = self.unet(x.permute(0 , 2 , 1 ) , __UpperCAmelCase ).sample.permute(0 , 2 , 1 )
# TODO: verify deprecation of this kwarg
a = self.scheduler.step(__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , predict_epsilon=__UpperCAmelCase )['''prev_sample''']
# apply conditions to the trajectory (set the initial state)
a = self.reset_xa(__UpperCAmelCase , __UpperCAmelCase , self.action_dim )
a = self.to_torch(__UpperCAmelCase )
return x, y
def __call__( self : Union[str, Any] , __UpperCAmelCase : Dict , __UpperCAmelCase : Optional[int]=64 , __UpperCAmelCase : int=32 , __UpperCAmelCase : Optional[Any]=2 , __UpperCAmelCase : str=0.1 ) ->List[str]:
"""simple docstring"""
a = self.normalize(__UpperCAmelCase , '''observations''' )
a = obs[None].repeat(__UpperCAmelCase , axis=0 )
a = {0: self.to_torch(__UpperCAmelCase )}
a = (batch_size, planning_horizon, self.state_dim + self.action_dim)
# generate initial noise and apply our conditions (to make the trajectories start at current state)
a = randn_tensor(__UpperCAmelCase , device=self.unet.device )
a = self.reset_xa(__UpperCAmelCase , __UpperCAmelCase , self.action_dim )
a = self.to_torch(__UpperCAmelCase )
# run the diffusion process
a , a = self.run_diffusion(__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase )
# sort output trajectories by value
a = y.argsort(0 , descending=__UpperCAmelCase ).squeeze()
a = x[sorted_idx]
a = sorted_values[:, :, : self.action_dim]
a = actions.detach().cpu().numpy()
a = self.de_normalize(__UpperCAmelCase , key='''actions''' )
# select the action with the highest value
if y is not None:
a = 0
else:
# if we didn't run value guiding, select a random action
a = np.random.randint(0 , __UpperCAmelCase )
a = denorm_actions[selected_index, 0]
return denorm_actions
| 26 |
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_distilbert import DistilBertTokenizer
UpperCAmelCase__ = logging.get_logger(__name__)
UpperCAmelCase__ = {"vocab_file": "vocab.txt", "tokenizer_file": "tokenizer.json"}
UpperCAmelCase__ = {
"vocab_file": {
"distilbert-base-uncased": "https://huggingface.co/distilbert-base-uncased/resolve/main/vocab.txt",
"distilbert-base-uncased-distilled-squad": (
"https://huggingface.co/distilbert-base-uncased-distilled-squad/resolve/main/vocab.txt"
),
"distilbert-base-cased": "https://huggingface.co/distilbert-base-cased/resolve/main/vocab.txt",
"distilbert-base-cased-distilled-squad": (
"https://huggingface.co/distilbert-base-cased-distilled-squad/resolve/main/vocab.txt"
),
"distilbert-base-german-cased": "https://huggingface.co/distilbert-base-german-cased/resolve/main/vocab.txt",
"distilbert-base-multilingual-cased": (
"https://huggingface.co/distilbert-base-multilingual-cased/resolve/main/vocab.txt"
),
},
"tokenizer_file": {
"distilbert-base-uncased": "https://huggingface.co/distilbert-base-uncased/resolve/main/tokenizer.json",
"distilbert-base-uncased-distilled-squad": (
"https://huggingface.co/distilbert-base-uncased-distilled-squad/resolve/main/tokenizer.json"
),
"distilbert-base-cased": "https://huggingface.co/distilbert-base-cased/resolve/main/tokenizer.json",
"distilbert-base-cased-distilled-squad": (
"https://huggingface.co/distilbert-base-cased-distilled-squad/resolve/main/tokenizer.json"
),
"distilbert-base-german-cased": (
"https://huggingface.co/distilbert-base-german-cased/resolve/main/tokenizer.json"
),
"distilbert-base-multilingual-cased": (
"https://huggingface.co/distilbert-base-multilingual-cased/resolve/main/tokenizer.json"
),
},
}
UpperCAmelCase__ = {
"distilbert-base-uncased": 512,
"distilbert-base-uncased-distilled-squad": 512,
"distilbert-base-cased": 512,
"distilbert-base-cased-distilled-squad": 512,
"distilbert-base-german-cased": 512,
"distilbert-base-multilingual-cased": 512,
}
UpperCAmelCase__ = {
"distilbert-base-uncased": {"do_lower_case": True},
"distilbert-base-uncased-distilled-squad": {"do_lower_case": True},
"distilbert-base-cased": {"do_lower_case": False},
"distilbert-base-cased-distilled-squad": {"do_lower_case": False},
"distilbert-base-german-cased": {"do_lower_case": False},
"distilbert-base-multilingual-cased": {"do_lower_case": False},
}
class lowercase_ ( lowercase ):
'''simple docstring'''
__snake_case = VOCAB_FILES_NAMES
__snake_case = PRETRAINED_VOCAB_FILES_MAP
__snake_case = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
__snake_case = PRETRAINED_INIT_CONFIGURATION
__snake_case = ['''input_ids''', '''attention_mask''']
__snake_case = DistilBertTokenizer
def __init__( self : Dict , __UpperCAmelCase : List[Any]=None , __UpperCAmelCase : Optional[int]=None , __UpperCAmelCase : Optional[int]=True , __UpperCAmelCase : Optional[int]="[UNK]" , __UpperCAmelCase : str="[SEP]" , __UpperCAmelCase : Tuple="[PAD]" , __UpperCAmelCase : Any="[CLS]" , __UpperCAmelCase : int="[MASK]" , __UpperCAmelCase : Optional[int]=True , __UpperCAmelCase : Optional[Any]=None , **__UpperCAmelCase : str , ) ->Optional[int]:
"""simple docstring"""
super().__init__(
__UpperCAmelCase , tokenizer_file=__UpperCAmelCase , do_lower_case=__UpperCAmelCase , unk_token=__UpperCAmelCase , sep_token=__UpperCAmelCase , pad_token=__UpperCAmelCase , cls_token=__UpperCAmelCase , mask_token=__UpperCAmelCase , tokenize_chinese_chars=__UpperCAmelCase , strip_accents=__UpperCAmelCase , **__UpperCAmelCase , )
a = json.loads(self.backend_tokenizer.normalizer.__getstate__() )
if (
normalizer_state.get('''lowercase''' , __UpperCAmelCase ) != do_lower_case
or normalizer_state.get('''strip_accents''' , __UpperCAmelCase ) != strip_accents
or normalizer_state.get('''handle_chinese_chars''' , __UpperCAmelCase ) != tokenize_chinese_chars
):
a = getattr(__UpperCAmelCase , normalizer_state.pop('''type''' ) )
a = do_lower_case
a = strip_accents
a = tokenize_chinese_chars
a = normalizer_class(**__UpperCAmelCase )
a = do_lower_case
def __lowerCAmelCase ( self : Optional[int] , __UpperCAmelCase : Optional[Any] , __UpperCAmelCase : Optional[int]=None ) ->Optional[Any]:
"""simple docstring"""
a = [self.cls_token_id] + token_ids_a + [self.sep_token_id]
if token_ids_a:
output += token_ids_a + [self.sep_token_id]
return output
def __lowerCAmelCase ( self : List[Any] , __UpperCAmelCase : List[int] , __UpperCAmelCase : Optional[List[int]] = None ) ->List[int]:
"""simple docstring"""
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 ) ->Tuple[str]:
"""simple docstring"""
a = self._tokenizer.model.save(__UpperCAmelCase , name=__UpperCAmelCase )
return tuple(__UpperCAmelCase )
| 26 | 1 |
'''simple docstring'''
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available
if is_vision_available():
from PIL import Image
from transformers import CLIPImageProcessor, CLIPProcessor
@require_vision
class lowerCAmelCase_ ( unittest.TestCase ):
'''simple docstring'''
def SCREAMING_SNAKE_CASE__ ( self : Any ):
"""simple docstring"""
UpperCAmelCase__ = tempfile.mkdtemp()
# fmt: off
UpperCAmelCase__ = ['''l''', '''o''', '''w''', '''e''', '''r''', '''s''', '''t''', '''i''', '''d''', '''n''', '''lo''', '''l</w>''', '''w</w>''', '''r</w>''', '''t</w>''', '''low</w>''', '''er</w>''', '''lowest</w>''', '''newer</w>''', '''wider''', '''<unk>''', '''<|startoftext|>''', '''<|endoftext|>''']
# fmt: on
UpperCAmelCase__ = dict(zip(snake_case__ , range(len(snake_case__ ) ) ) )
UpperCAmelCase__ = ['''#version: 0.2''', '''l o''', '''lo w</w>''', '''e r</w>''', '''''']
UpperCAmelCase__ = {'''unk_token''': '''<unk>'''}
UpperCAmelCase__ = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES["""vocab_file"""] )
UpperCAmelCase__ = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES["""merges_file"""] )
with open(self.vocab_file , """w""" , encoding="""utf-8""" ) as fp:
fp.write(json.dumps(snake_case__ ) + """\n""" )
with open(self.merges_file , """w""" , encoding="""utf-8""" ) as fp:
fp.write("""\n""".join(snake_case__ ) )
UpperCAmelCase__ = {
'''do_resize''': True,
'''size''': 20,
'''do_center_crop''': True,
'''crop_size''': 18,
'''do_normalize''': True,
'''image_mean''': [0.4814_5466, 0.457_8275, 0.4082_1073],
'''image_std''': [0.2686_2954, 0.2613_0258, 0.2757_7711],
}
UpperCAmelCase__ = os.path.join(self.tmpdirname , snake_case__ )
with open(self.image_processor_file , """w""" , encoding="""utf-8""" ) as fp:
json.dump(snake_case__ , snake_case__ )
def SCREAMING_SNAKE_CASE__ ( self : List[str] , **_UpperCAmelCase : List[str] ):
"""simple docstring"""
return CLIPTokenizer.from_pretrained(self.tmpdirname , **snake_case__ )
def SCREAMING_SNAKE_CASE__ ( self : List[str] , **_UpperCAmelCase : str ):
"""simple docstring"""
return CLIPTokenizerFast.from_pretrained(self.tmpdirname , **snake_case__ )
def SCREAMING_SNAKE_CASE__ ( self : str , **_UpperCAmelCase : Tuple ):
"""simple docstring"""
return CLIPImageProcessor.from_pretrained(self.tmpdirname , **snake_case__ )
def SCREAMING_SNAKE_CASE__ ( self : Tuple ):
"""simple docstring"""
shutil.rmtree(self.tmpdirname )
def SCREAMING_SNAKE_CASE__ ( self : List[Any] ):
"""simple docstring"""
UpperCAmelCase__ = [np.random.randint(2_55 , size=(3, 30, 4_00) , dtype=np.uinta )]
UpperCAmelCase__ = [Image.fromarray(np.moveaxis(snake_case__ , 0 , -1 ) ) for x in image_inputs]
return image_inputs
def SCREAMING_SNAKE_CASE__ ( self : Tuple ):
"""simple docstring"""
UpperCAmelCase__ = self.get_tokenizer()
UpperCAmelCase__ = self.get_rust_tokenizer()
UpperCAmelCase__ = self.get_image_processor()
UpperCAmelCase__ = CLIPProcessor(tokenizer=snake_case__ , image_processor=snake_case__ )
processor_slow.save_pretrained(self.tmpdirname )
UpperCAmelCase__ = CLIPProcessor.from_pretrained(self.tmpdirname , use_fast=snake_case__ )
UpperCAmelCase__ = CLIPProcessor(tokenizer=snake_case__ , image_processor=snake_case__ )
processor_fast.save_pretrained(self.tmpdirname )
UpperCAmelCase__ = CLIPProcessor.from_pretrained(self.tmpdirname )
self.assertEqual(processor_slow.tokenizer.get_vocab() , tokenizer_slow.get_vocab() )
self.assertEqual(processor_fast.tokenizer.get_vocab() , tokenizer_fast.get_vocab() )
self.assertEqual(tokenizer_slow.get_vocab() , tokenizer_fast.get_vocab() )
self.assertIsInstance(processor_slow.tokenizer , snake_case__ )
self.assertIsInstance(processor_fast.tokenizer , snake_case__ )
self.assertEqual(processor_slow.image_processor.to_json_string() , image_processor.to_json_string() )
self.assertEqual(processor_fast.image_processor.to_json_string() , image_processor.to_json_string() )
self.assertIsInstance(processor_slow.image_processor , snake_case__ )
self.assertIsInstance(processor_fast.image_processor , snake_case__ )
def SCREAMING_SNAKE_CASE__ ( self : Any ):
"""simple docstring"""
UpperCAmelCase__ = CLIPProcessor(tokenizer=self.get_tokenizer() , image_processor=self.get_image_processor() )
processor.save_pretrained(self.tmpdirname )
UpperCAmelCase__ = self.get_tokenizer(bos_token="""(BOS)""" , eos_token="""(EOS)""" )
UpperCAmelCase__ = self.get_image_processor(do_normalize=snake_case__ , padding_value=1.0 )
UpperCAmelCase__ = CLIPProcessor.from_pretrained(
self.tmpdirname , bos_token="""(BOS)""" , eos_token="""(EOS)""" , do_normalize=snake_case__ , padding_value=1.0 )
self.assertEqual(processor.tokenizer.get_vocab() , tokenizer_add_kwargs.get_vocab() )
self.assertIsInstance(processor.tokenizer , snake_case__ )
self.assertEqual(processor.image_processor.to_json_string() , image_processor_add_kwargs.to_json_string() )
self.assertIsInstance(processor.image_processor , snake_case__ )
def SCREAMING_SNAKE_CASE__ ( self : int ):
"""simple docstring"""
UpperCAmelCase__ = self.get_image_processor()
UpperCAmelCase__ = self.get_tokenizer()
UpperCAmelCase__ = CLIPProcessor(tokenizer=snake_case__ , image_processor=snake_case__ )
UpperCAmelCase__ = self.prepare_image_inputs()
UpperCAmelCase__ = image_processor(snake_case__ , return_tensors="""np""" )
UpperCAmelCase__ = processor(images=snake_case__ , return_tensors="""np""" )
for key in input_image_proc.keys():
self.assertAlmostEqual(input_image_proc[key].sum() , input_processor[key].sum() , delta=1E-2 )
def SCREAMING_SNAKE_CASE__ ( self : Union[str, Any] ):
"""simple docstring"""
UpperCAmelCase__ = self.get_image_processor()
UpperCAmelCase__ = self.get_tokenizer()
UpperCAmelCase__ = CLIPProcessor(tokenizer=snake_case__ , image_processor=snake_case__ )
UpperCAmelCase__ = '''lower newer'''
UpperCAmelCase__ = processor(text=snake_case__ )
UpperCAmelCase__ = tokenizer(snake_case__ )
for key in encoded_tok.keys():
self.assertListEqual(encoded_tok[key] , encoded_processor[key] )
def SCREAMING_SNAKE_CASE__ ( self : str ):
"""simple docstring"""
UpperCAmelCase__ = self.get_image_processor()
UpperCAmelCase__ = self.get_tokenizer()
UpperCAmelCase__ = CLIPProcessor(tokenizer=snake_case__ , image_processor=snake_case__ )
UpperCAmelCase__ = '''lower newer'''
UpperCAmelCase__ = self.prepare_image_inputs()
UpperCAmelCase__ = processor(text=snake_case__ , images=snake_case__ )
self.assertListEqual(list(inputs.keys() ) , ["""input_ids""", """attention_mask""", """pixel_values"""] )
# test if it raises when no input is passed
with pytest.raises(snake_case__ ):
processor()
def SCREAMING_SNAKE_CASE__ ( self : List[str] ):
"""simple docstring"""
UpperCAmelCase__ = self.get_image_processor()
UpperCAmelCase__ = self.get_tokenizer()
UpperCAmelCase__ = CLIPProcessor(tokenizer=snake_case__ , image_processor=snake_case__ )
UpperCAmelCase__ = [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]
UpperCAmelCase__ = processor.batch_decode(snake_case__ )
UpperCAmelCase__ = tokenizer.batch_decode(snake_case__ )
self.assertListEqual(snake_case__ , snake_case__ )
def SCREAMING_SNAKE_CASE__ ( self : List[str] ):
"""simple docstring"""
UpperCAmelCase__ = self.get_image_processor()
UpperCAmelCase__ = self.get_tokenizer()
UpperCAmelCase__ = CLIPProcessor(tokenizer=snake_case__ , image_processor=snake_case__ )
UpperCAmelCase__ = '''lower newer'''
UpperCAmelCase__ = self.prepare_image_inputs()
UpperCAmelCase__ = processor(text=snake_case__ , images=snake_case__ )
self.assertListEqual(list(inputs.keys() ) , processor.model_input_names )
| 346 | from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
__snake_case = {
'''configuration_bloom''': ['''BLOOM_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''BloomConfig''', '''BloomOnnxConfig'''],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__snake_case = ['''BloomTokenizerFast''']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__snake_case = [
'''BLOOM_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''BloomForCausalLM''',
'''BloomModel''',
'''BloomPreTrainedModel''',
'''BloomForSequenceClassification''',
'''BloomForTokenClassification''',
'''BloomForQuestionAnswering''',
]
if TYPE_CHECKING:
from .configuration_bloom import BLOOM_PRETRAINED_CONFIG_ARCHIVE_MAP, BloomConfig, BloomOnnxConfig
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_bloom_fast import BloomTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_bloom import (
BLOOM_PRETRAINED_MODEL_ARCHIVE_LIST,
BloomForCausalLM,
BloomForQuestionAnswering,
BloomForSequenceClassification,
BloomForTokenClassification,
BloomModel,
BloomPreTrainedModel,
)
else:
import sys
__snake_case = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 348 | 0 |
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
__UpperCamelCase = logging.get_logger(__name__)
__UpperCamelCase = {
'''microsoft/resnet-50''': '''https://huggingface.co/microsoft/resnet-50/blob/main/config.json''',
}
class UpperCamelCase ( lowerCAmelCase__ , lowerCAmelCase__ ):
SCREAMING_SNAKE_CASE_ = "resnet"
SCREAMING_SNAKE_CASE_ = ["basic", "bottleneck"]
def __init__( self, lowerCAmelCase__=3, lowerCAmelCase__=64, lowerCAmelCase__=[256, 512, 1024, 2048], lowerCAmelCase__=[3, 4, 6, 3], lowerCAmelCase__="bottleneck", lowerCAmelCase__="relu", lowerCAmelCase__=False, lowerCAmelCase__=None, lowerCAmelCase__=None, **lowerCAmelCase__, ) -> Dict:
super().__init__(**lowerCAmelCase__)
if layer_type not in self.layer_types:
raise ValueError(f'layer_type={layer_type} is not one of {",".join(self.layer_types)}')
snake_case_ = num_channels
snake_case_ = embedding_size
snake_case_ = hidden_sizes
snake_case_ = depths
snake_case_ = layer_type
snake_case_ = hidden_act
snake_case_ = downsample_in_first_stage
snake_case_ = ['stem'] + [f'stage{idx}' for idx in range(1, len(lowerCAmelCase__) + 1)]
snake_case_ , snake_case_ = get_aligned_output_features_output_indices(
out_features=lowerCAmelCase__, out_indices=lowerCAmelCase__, stage_names=self.stage_names)
class UpperCamelCase ( lowerCAmelCase__ ):
SCREAMING_SNAKE_CASE_ = version.parse("1.11" )
@property
def a_ ( self) -> Mapping[str, Mapping[int, str]]:
return OrderedDict(
[
('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'}),
])
@property
def a_ ( self) -> float:
return 1e-3
| 365 | """simple docstring"""
from __future__ import annotations
def UpperCAmelCase ( UpperCAmelCase , UpperCAmelCase ) -> list[str]:
if partitions <= 0:
raise ValueError('partitions must be a positive number!' )
if partitions > number_of_bytes:
raise ValueError('partitions can not > number_of_bytes!' )
snake_case_ = number_of_bytes // partitions
snake_case_ = []
for i in range(UpperCAmelCase ):
snake_case_ = i * bytes_per_partition + 1
snake_case_ = (
number_of_bytes if i == partitions - 1 else (i + 1) * bytes_per_partition
)
allocation_list.append(f'{start_bytes}-{end_bytes}' )
return allocation_list
if __name__ == "__main__":
import doctest
doctest.testmod()
| 312 | 0 |
from diffusers.utils.testing_utils import require_onnxruntime
@require_onnxruntime
class lowercase :
pass
| 101 |
import inspect
from typing import Callable, List, Optional, Union
import torch
from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer
from diffusers import DiffusionPipeline
from diffusers.models import AutoencoderKL, UNetaDConditionModel
from diffusers.pipelines.stable_diffusion import StableDiffusionPipelineOutput
from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
from diffusers.schedulers import DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler
from diffusers.utils import logging
lowercase__ :Optional[int] = logging.get_logger(__name__) # pylint: disable=invalid-name
class lowercase ( SCREAMING_SNAKE_CASE__ ):
def __init__( self ,A__ ,A__ ,A__ ,A__ ,A__ ,A__ ,A__ ,):
super().__init__()
self.register_modules(
vae=A__ ,text_encoder=A__ ,tokenizer=A__ ,unet=A__ ,scheduler=A__ ,safety_checker=A__ ,feature_extractor=A__ ,)
def A__ ( self ,A__ = "auto"):
if slice_size == "auto":
# half the attention head size is usually a good trade-off between
# speed and memory
lowercase = self.unet.config.attention_head_dim // 2
self.unet.set_attention_slice(A__)
def A__ ( self):
self.enable_attention_slicing(A__)
@torch.no_grad()
def __call__( self ,A__ ,A__ = 5_1_2 ,A__ = 5_1_2 ,A__ = 5_0 ,A__ = 7.5 ,A__ = None ,A__ = 1 ,A__ = 0.0 ,A__ = None ,A__ = None ,A__ = "pil" ,A__ = True ,A__ = None ,A__ = 1 ,A__ = None ,**A__ ,):
if isinstance(A__ ,A__):
lowercase = 1
elif isinstance(A__ ,A__):
lowercase = len(A__)
else:
raise ValueError(f'`prompt` has to be of type `str` or `list` but is {type(A__)}')
if height % 8 != 0 or width % 8 != 0:
raise ValueError(f'`height` and `width` have to be divisible by 8 but are {height} and {width}.')
if (callback_steps is None) or (
callback_steps is not None and (not isinstance(A__ ,A__) or callback_steps <= 0)
):
raise ValueError(
f'`callback_steps` has to be a positive integer but is {callback_steps} of type'
f' {type(A__)}.')
# get prompt text embeddings
lowercase = self.tokenizer(
A__ ,padding='''max_length''' ,max_length=self.tokenizer.model_max_length ,return_tensors='''pt''' ,)
lowercase = text_inputs.input_ids
if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
lowercase = self.tokenizer.batch_decode(text_input_ids[:, self.tokenizer.model_max_length :])
logger.warning(
'''The following part of your input was truncated because CLIP can only handle sequences up to'''
f' {self.tokenizer.model_max_length} tokens: {removed_text}')
lowercase = text_input_ids[:, : self.tokenizer.model_max_length]
if text_embeddings is None:
lowercase = self.text_encoder(text_input_ids.to(self.device))[0]
# duplicate text embeddings for each generation per prompt, using mps friendly method
lowercase , lowercase , lowercase = text_embeddings.shape
lowercase = text_embeddings.repeat(1 ,A__ ,1)
lowercase = text_embeddings.view(bs_embed * num_images_per_prompt ,A__ ,-1)
# here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
# of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
# corresponds to doing no classifier free guidance.
lowercase = guidance_scale > 1.0
# get unconditional embeddings for classifier free guidance
if do_classifier_free_guidance:
lowercase = 42
if negative_prompt is None:
lowercase = ['''''']
elif type(A__) is not type(A__):
raise TypeError(
f'`negative_prompt` should be the same type to `prompt`, but got {type(A__)} !='
f' {type(A__)}.')
elif isinstance(A__ ,A__):
lowercase = [negative_prompt]
elif batch_size != len(A__):
raise ValueError(
f'`negative_prompt`: {negative_prompt} has batch size {len(A__)}, but `prompt`:'
f' {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches'
''' the batch size of `prompt`.''')
else:
lowercase = negative_prompt
lowercase = text_input_ids.shape[-1]
lowercase = self.tokenizer(
A__ ,padding='''max_length''' ,max_length=A__ ,truncation=A__ ,return_tensors='''pt''' ,)
lowercase = self.text_encoder(uncond_input.input_ids.to(self.device))[0]
# duplicate unconditional embeddings for each generation per prompt, using mps friendly method
lowercase = uncond_embeddings.shape[1]
lowercase = uncond_embeddings.repeat(A__ ,A__ ,1)
lowercase = uncond_embeddings.view(batch_size * num_images_per_prompt ,A__ ,-1)
# For classifier free guidance, we need to do two forward passes.
# Here we concatenate the unconditional and text embeddings into a single batch
# to avoid doing two forward passes
lowercase = torch.cat([uncond_embeddings, text_embeddings])
# get the initial random noise unless the user supplied it
# Unlike in other pipelines, latents need to be generated in the target device
# for 1-to-1 results reproducibility with the CompVis implementation.
# However this currently doesn't work in `mps`.
lowercase = (batch_size * num_images_per_prompt, self.unet.config.in_channels, height // 8, width // 8)
lowercase = (batch_size * num_images_per_prompt, self.unet.config.in_channels, 6_4, 6_4)
lowercase = text_embeddings.dtype
if latents is None:
if self.device.type == "mps":
# randn does not exist on mps
lowercase = torch.randn(
A__ ,generator=A__ ,device='''cpu''' ,dtype=A__).to(self.device)
lowercase = torch.randn(A__ ,generator=A__ ,device='''cpu''' ,dtype=A__).to(
self.device)
else:
lowercase = torch.randn(
A__ ,generator=A__ ,device=self.device ,dtype=A__)
lowercase = torch.randn(A__ ,generator=A__ ,device=self.device ,dtype=A__)
else:
if latents_reference.shape != latents_shape:
raise ValueError(f'Unexpected latents shape, got {latents.shape}, expected {latents_shape}')
lowercase = latents_reference.to(self.device)
lowercase = latents.to(self.device)
# This is the key part of the pipeline where we
# try to ensure that the generated images w/ the same seed
# but different sizes actually result in similar images
lowercase = (latents_shape[3] - latents_shape_reference[3]) // 2
lowercase = (latents_shape[2] - latents_shape_reference[2]) // 2
lowercase = latents_shape_reference[3] if dx >= 0 else latents_shape_reference[3] + 2 * dx
lowercase = latents_shape_reference[2] if dy >= 0 else latents_shape_reference[2] + 2 * dy
lowercase = 0 if dx < 0 else dx
lowercase = 0 if dy < 0 else dy
lowercase = max(-dx ,0)
lowercase = max(-dy ,0)
# import pdb
# pdb.set_trace()
lowercase = latents_reference[:, :, dy : dy + h, dx : dx + w]
# set timesteps
self.scheduler.set_timesteps(A__)
# Some schedulers like PNDM have timesteps as arrays
# It's more optimized to move all timesteps to correct device beforehand
lowercase = self.scheduler.timesteps.to(self.device)
# scale the initial noise by the standard deviation required by the scheduler
lowercase = 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]
lowercase = '''eta''' in set(inspect.signature(self.scheduler.step).parameters.keys())
lowercase = {}
if accepts_eta:
lowercase = eta
for i, t in enumerate(self.progress_bar(A__)):
# expand the latents if we are doing classifier free guidance
lowercase = torch.cat([latents] * 2) if do_classifier_free_guidance else latents
lowercase = self.scheduler.scale_model_input(A__ ,A__)
# predict the noise residual
lowercase = self.unet(A__ ,A__ ,encoder_hidden_states=A__).sample
# perform guidance
if do_classifier_free_guidance:
lowercase , lowercase = noise_pred.chunk(2)
lowercase = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
# compute the previous noisy sample x_t -> x_t-1
lowercase = self.scheduler.step(A__ ,A__ ,A__ ,**A__).prev_sample
# call the callback, if provided
if callback is not None and i % callback_steps == 0:
callback(A__ ,A__ ,A__)
lowercase = 1 / 0.18215 * latents
lowercase = self.vae.decode(A__).sample
lowercase = (image / 2 + 0.5).clamp(0 ,1)
# we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
lowercase = image.cpu().permute(0 ,2 ,3 ,1).float().numpy()
if self.safety_checker is not None:
lowercase = self.feature_extractor(self.numpy_to_pil(A__) ,return_tensors='''pt''').to(
self.device)
lowercase , lowercase = self.safety_checker(
images=A__ ,clip_input=safety_checker_input.pixel_values.to(text_embeddings.dtype))
else:
lowercase = None
if output_type == "pil":
lowercase = self.numpy_to_pil(A__)
if not return_dict:
return (image, has_nsfw_concept)
return StableDiffusionPipelineOutput(images=A__ ,nsfw_content_detected=A__)
| 101 | 1 |
from __future__ import annotations
import unittest
import numpy as np
from transformers import BlipTextConfig
from transformers.testing_utils import require_tf, slow
from transformers.utils import is_tf_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
if is_tf_available():
import tensorflow as tf
from transformers import TFBlipTextModel
from transformers.models.blip.modeling_tf_blip import TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST
class lowerCamelCase_ :
'''simple docstring'''
def __init__( self : Union[str, Any] , __lowerCamelCase : Tuple , __lowerCamelCase : Optional[int]=12 , __lowerCamelCase : Tuple=7 , __lowerCamelCase : Union[str, Any]=True , __lowerCamelCase : int=True , __lowerCamelCase : Dict=True , __lowerCamelCase : int=99 , __lowerCamelCase : int=32 , __lowerCamelCase : List[Any]=32 , __lowerCamelCase : Dict=2 , __lowerCamelCase : List[str]=4 , __lowerCamelCase : Optional[int]=37 , __lowerCamelCase : Any=0.1 , __lowerCamelCase : Any=0.1 , __lowerCamelCase : Tuple=5_12 , __lowerCamelCase : str=0.02 , __lowerCamelCase : Optional[Any]=0 , __lowerCamelCase : List[Any]=None , ) -> Union[str, Any]:
A : Optional[Any] = parent
A : int = batch_size
A : Union[str, Any] = seq_length
A : Union[str, Any] = is_training
A : Any = use_input_mask
A : List[str] = use_labels
A : Union[str, Any] = vocab_size
A : Dict = hidden_size
A : int = projection_dim
A : Dict = num_hidden_layers
A : Optional[int] = num_attention_heads
A : Optional[int] = intermediate_size
A : Optional[int] = dropout
A : List[Any] = attention_dropout
A : Optional[int] = max_position_embeddings
A : int = initializer_range
A : List[str] = scope
A : str = bos_token_id
def SCREAMING_SNAKE_CASE__ ( self : Any ) -> Optional[int]:
A : Optional[Any] = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
A : Union[str, Any] = None
if self.use_input_mask:
A : Tuple = random_attention_mask([self.batch_size, self.seq_length] )
if input_mask is not None:
A : Dict = input_mask.numpy()
A , A : Optional[Any] = input_mask.shape
A : int = np.random.randint(1 , seq_length - 1 , size=(batch_size,) )
for batch_idx, start_index in enumerate(__lowerCamelCase ):
A : str = 1
A : int = 0
A : str = self.get_config()
return config, input_ids, tf.convert_to_tensor(__lowerCamelCase )
def SCREAMING_SNAKE_CASE__ ( self : str ) -> Any:
return BlipTextConfig(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , projection_dim=self.projection_dim , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , dropout=self.dropout , attention_dropout=self.attention_dropout , max_position_embeddings=self.max_position_embeddings , initializer_range=self.initializer_range , bos_token_id=self.bos_token_id , )
def SCREAMING_SNAKE_CASE__ ( self : Any , __lowerCamelCase : List[Any] , __lowerCamelCase : List[Any] , __lowerCamelCase : List[Any] ) -> Optional[int]:
A : List[str] = TFBlipTextModel(config=__lowerCamelCase )
A : Optional[int] = model(__lowerCamelCase , attention_mask=__lowerCamelCase , training=__lowerCamelCase )
A : Optional[Any] = model(__lowerCamelCase , training=__lowerCamelCase )
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 SCREAMING_SNAKE_CASE__ ( self : Tuple ) -> Optional[int]:
A : Optional[int] = self.prepare_config_and_inputs()
A , A , A : List[str] = config_and_inputs
A : List[Any] = {"input_ids": input_ids, "attention_mask": input_mask}
return config, inputs_dict
@require_tf
class lowerCamelCase_ ( _A ,unittest.TestCase ):
'''simple docstring'''
a__ = (TFBlipTextModel,) if is_tf_available() else ()
a__ = False
a__ = False
a__ = False
def SCREAMING_SNAKE_CASE__ ( self : Optional[int] ) -> Tuple:
A : int = BlipTextModelTester(self )
A : List[str] = ConfigTester(self , config_class=__lowerCamelCase , hidden_size=37 )
def SCREAMING_SNAKE_CASE__ ( self : Any ) -> Optional[Any]:
self.config_tester.run_common_tests()
def SCREAMING_SNAKE_CASE__ ( self : Union[str, Any] ) -> List[str]:
A : str = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*__lowerCamelCase )
def SCREAMING_SNAKE_CASE__ ( self : List[Any] ) -> Any:
pass
def SCREAMING_SNAKE_CASE__ ( self : Union[str, Any] ) -> Dict:
pass
@unittest.skip(reason="Blip does not use inputs_embeds" )
def SCREAMING_SNAKE_CASE__ ( self : str ) -> List[str]:
pass
@unittest.skip(reason="BlipTextModel has no base class and is not available in MODEL_MAPPING" )
def SCREAMING_SNAKE_CASE__ ( self : List[Any] ) -> Optional[int]:
pass
@unittest.skip(reason="BlipTextModel has no base class and is not available in MODEL_MAPPING" )
def SCREAMING_SNAKE_CASE__ ( self : List[str] ) -> Optional[Any]:
pass
@slow
def SCREAMING_SNAKE_CASE__ ( self : List[Any] ) -> List[Any]:
for model_name in TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
A : Optional[int] = TFBlipTextModel.from_pretrained(__lowerCamelCase )
self.assertIsNotNone(__lowerCamelCase )
def SCREAMING_SNAKE_CASE__ ( self : int , __lowerCamelCase : Any=True ) -> List[str]:
super().test_pt_tf_model_equivalence(allow_missing_keys=__lowerCamelCase ) | 256 |
import re
import jax.numpy as jnp
from flax.traverse_util import flatten_dict, unflatten_dict
from jax.random import PRNGKey
from ..utils import logging
__SCREAMING_SNAKE_CASE = logging.get_logger(__name__)
def UpperCAmelCase ( _lowerCamelCase ):
A : List[Any] = R"\w+[.]\d+"
A : Optional[Any] = re.findall(_lowerCamelCase , _lowerCamelCase )
for pat in pats:
A : int = key.replace(_lowerCamelCase , "_".join(pat.split("." ) ) )
return key
def UpperCAmelCase ( _lowerCamelCase , _lowerCamelCase , _lowerCamelCase ):
A : Union[str, Any] = pt_tuple_key[:-1] + ("scale",)
if (
any("norm" in str_ for str_ in pt_tuple_key )
and (pt_tuple_key[-1] == "bias")
and (pt_tuple_key[:-1] + ("bias",) not in random_flax_state_dict)
and (pt_tuple_key[:-1] + ("scale",) in random_flax_state_dict)
):
A : List[Any] = pt_tuple_key[:-1] + ("scale",)
return renamed_pt_tuple_key, pt_tensor
elif pt_tuple_key[-1] in ["weight", "gamma"] and pt_tuple_key[:-1] + ("scale",) in random_flax_state_dict:
A : int = pt_tuple_key[:-1] + ("scale",)
return renamed_pt_tuple_key, pt_tensor
# embedding
if pt_tuple_key[-1] == "weight" and pt_tuple_key[:-1] + ("embedding",) in random_flax_state_dict:
A : List[Any] = pt_tuple_key[:-1] + ("embedding",)
return renamed_pt_tuple_key, pt_tensor
# conv layer
A : Optional[int] = pt_tuple_key[:-1] + ("kernel",)
if pt_tuple_key[-1] == "weight" and pt_tensor.ndim == 4:
A : Union[str, Any] = pt_tensor.transpose(2 , 3 , 1 , 0 )
return renamed_pt_tuple_key, pt_tensor
# linear layer
A : List[Any] = pt_tuple_key[:-1] + ("kernel",)
if pt_tuple_key[-1] == "weight":
A : int = pt_tensor.T
return renamed_pt_tuple_key, pt_tensor
# old PyTorch layer norm weight
A : List[str] = pt_tuple_key[:-1] + ("weight",)
if pt_tuple_key[-1] == "gamma":
return renamed_pt_tuple_key, pt_tensor
# old PyTorch layer norm bias
A : Optional[Any] = pt_tuple_key[:-1] + ("bias",)
if pt_tuple_key[-1] == "beta":
return renamed_pt_tuple_key, pt_tensor
return pt_tuple_key, pt_tensor
def UpperCAmelCase ( _lowerCamelCase , _lowerCamelCase , _lowerCamelCase=42 ):
# Step 1: Convert pytorch tensor to numpy
A : Union[str, Any] = {k: v.numpy() for k, v in pt_state_dict.items()}
# Step 2: Since the model is stateless, get random Flax params
A : Dict = flax_model.init_weights(PRNGKey(_lowerCamelCase ) )
A : Dict = flatten_dict(_lowerCamelCase )
A : Dict = {}
# Need to change some parameters name to match Flax names
for pt_key, pt_tensor in pt_state_dict.items():
A : Tuple = rename_key(_lowerCamelCase )
A : List[str] = tuple(renamed_pt_key.split("." ) )
# Correctly rename weight parameters
A , A : str = rename_key_and_reshape_tensor(_lowerCamelCase , _lowerCamelCase , _lowerCamelCase )
if flax_key in random_flax_state_dict:
if flax_tensor.shape != random_flax_state_dict[flax_key].shape:
raise ValueError(
f"""PyTorch checkpoint seems to be incorrect. Weight {pt_key} was expected to be of shape """
f"""{random_flax_state_dict[flax_key].shape}, but is {flax_tensor.shape}.""" )
# also add unexpected weight so that warning is thrown
A : Union[str, Any] = jnp.asarray(_lowerCamelCase )
return unflatten_dict(_lowerCamelCase ) | 256 | 1 |
'''simple docstring'''
import random
def snake_case_ ( lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ )-> str:
'''simple docstring'''
_UpperCAmelCase : List[Any] = a[left_index]
_UpperCAmelCase : str = left_index + 1
for j in range(left_index + 1 , lowerCAmelCase_ ):
if a[j] < pivot:
_UpperCAmelCase ,_UpperCAmelCase : List[Any] = a[i], a[j]
i += 1
_UpperCAmelCase ,_UpperCAmelCase : Any = a[i - 1], a[left_index]
return i - 1
def snake_case_ ( lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ )-> Dict:
'''simple docstring'''
if left < right:
_UpperCAmelCase : List[Any] = random.randint(lowerCAmelCase_ , right - 1 )
_UpperCAmelCase ,_UpperCAmelCase : str = (
a[left],
a[pivot],
) # switches the pivot with the left most bound
_UpperCAmelCase : Tuple = partition(lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ )
quick_sort_random(
lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ ) # recursive quicksort to the left of the pivot point
quick_sort_random(
lowerCAmelCase_ , pivot_index + 1 , lowerCAmelCase_ ) # recursive quicksort to the right of the pivot point
def snake_case_ ( )-> Optional[Any]:
'''simple docstring'''
_UpperCAmelCase : Union[str, Any] = input("""Enter numbers separated by a comma:\n""" ).strip()
_UpperCAmelCase : Union[str, Any] = [int(lowerCAmelCase_ ) for item in user_input.split(""",""" )]
quick_sort_random(lowerCAmelCase_ , 0 , len(lowerCAmelCase_ ) )
print(lowerCAmelCase_ )
if __name__ == "__main__":
main()
| 215 |
'''simple docstring'''
from __future__ import annotations
import unittest
from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
if is_tf_available():
import numpy as np
import tensorflow as tf
from transformers import TFCamembertModel
@require_tf
@require_sentencepiece
@require_tokenizers
class lowercase ( unittest.TestCase ):
"""simple docstring"""
@slow
def _snake_case ( self ) -> Any:
_UpperCAmelCase : Dict = TFCamembertModel.from_pretrained("""jplu/tf-camembert-base""" )
_UpperCAmelCase : Optional[int] = tf.convert_to_tensor(
[[5, 121, 11, 660, 16, 730, 25_543, 110, 83, 6]] ,dtype=tf.intaa ,) # J'aime le camembert !"
_UpperCAmelCase : Dict = model(a_ )["""last_hidden_state"""]
_UpperCAmelCase : Dict = tf.TensorShape((1, 10, 768) )
self.assertEqual(output.shape ,a_ )
# compare the actual values for a slice.
_UpperCAmelCase : Tuple = tf.convert_to_tensor(
[[[-0.0254, 0.0235, 0.1027], [0.0606, -0.1811, -0.0418], [-0.1561, -0.1127, 0.2687]]] ,dtype=tf.floataa ,)
# camembert = torch.hub.load('pytorch/fairseq', 'camembert.v0')
# camembert.eval()
# expected_slice = roberta.model.forward(input_ids)[0][:, :3, :3].detach()
self.assertTrue(np.allclose(output[:, :3, :3].numpy() ,expected_slice.numpy() ,atol=1E-4 ) )
| 215 | 1 |
from __future__ import annotations
from collections.abc import Generator
import requests
from bsa import BeautifulSoup
_SCREAMING_SNAKE_CASE = 'https://www.indeed.co.in/jobs?q=mobile+app+development&l='
def snake_case ( snake_case__ :str = "mumbai") -> Generator[tuple[str, str], None, None]:
_A = BeautifulSoup(requests.get(url + location).content , """html.parser""")
# This attribute finds out all the specifics listed in a job
for job in soup.find_all("""div""" , attrs={"""data-tn-component""": """organicJob"""}):
_A = job.find("""a""" , attrs={"""data-tn-element""": """jobTitle"""}).text.strip()
_A = job.find("""span""" , {"""class""": """company"""}).text.strip()
yield job_title, company_name
if __name__ == "__main__":
for i, job in enumerate(fetch_jobs('Bangalore'), 1):
print(F'''Job {i:>2} is {job[0]} at {job[1]}''')
| 81 | import torch
from diffusers import UnCLIPScheduler
from .test_schedulers import SchedulerCommonTest
class a ( __lowerCAmelCase ):
"""simple docstring"""
lowerCamelCase :int = (UnCLIPScheduler,)
def UpperCAmelCase ( self , **lowerCAmelCase_ ) -> List[Any]:
_A = {
"""num_train_timesteps""": 10_00,
"""variance_type""": """fixed_small_log""",
"""clip_sample""": True,
"""clip_sample_range""": 1.0,
"""prediction_type""": """epsilon""",
}
config.update(**lowerCAmelCase_ )
return config
def UpperCAmelCase ( self ) -> Union[str, Any]:
for timesteps in [1, 5, 1_00, 10_00]:
self.check_over_configs(num_train_timesteps=lowerCAmelCase_ )
def UpperCAmelCase ( self ) -> Union[str, Any]:
for variance in ["fixed_small_log", "learned_range"]:
self.check_over_configs(variance_type=lowerCAmelCase_ )
def UpperCAmelCase ( self ) -> int:
for clip_sample in [True, False]:
self.check_over_configs(clip_sample=lowerCAmelCase_ )
def UpperCAmelCase ( self ) -> List[Any]:
for clip_sample_range in [1, 5, 10, 20]:
self.check_over_configs(clip_sample_range=lowerCAmelCase_ )
def UpperCAmelCase ( self ) -> Any:
for prediction_type in ["epsilon", "sample"]:
self.check_over_configs(prediction_type=lowerCAmelCase_ )
def UpperCAmelCase ( self ) -> Optional[int]:
for time_step in [0, 5_00, 9_99]:
for prev_timestep in [None, 5, 1_00, 2_50, 5_00, 7_50]:
if prev_timestep is not None and prev_timestep >= time_step:
continue
self.check_over_forward(time_step=lowerCAmelCase_ , prev_timestep=lowerCAmelCase_ )
def UpperCAmelCase ( self ) -> Union[str, Any]:
_A = self.scheduler_classes[0]
_A = self.get_scheduler_config(variance_type="""fixed_small_log""" )
_A = scheduler_class(**lowerCAmelCase_ )
assert torch.sum(torch.abs(scheduler._get_variance(0 ) - 1.0000E-10 ) ) < 1E-5
assert torch.sum(torch.abs(scheduler._get_variance(4_87 ) - 0.054_9625 ) ) < 1E-5
assert torch.sum(torch.abs(scheduler._get_variance(9_99 ) - 0.999_4987 ) ) < 1E-5
def UpperCAmelCase ( self ) -> Optional[int]:
_A = self.scheduler_classes[0]
_A = self.get_scheduler_config(variance_type="""learned_range""" )
_A = scheduler_class(**lowerCAmelCase_ )
_A = 0.5
assert scheduler._get_variance(1 , predicted_variance=lowerCAmelCase_ ) - -10.171_2790 < 1E-5
assert scheduler._get_variance(4_87 , predicted_variance=lowerCAmelCase_ ) - -5.799_8052 < 1E-5
assert scheduler._get_variance(9_99 , predicted_variance=lowerCAmelCase_ ) - -0.001_0011 < 1E-5
def UpperCAmelCase ( self ) -> List[Any]:
_A = self.scheduler_classes[0]
_A = self.get_scheduler_config()
_A = scheduler_class(**lowerCAmelCase_ )
_A = scheduler.timesteps
_A = self.dummy_model()
_A = self.dummy_sample_deter
_A = torch.manual_seed(0 )
for i, t in enumerate(lowerCAmelCase_ ):
# 1. predict noise residual
_A = model(lowerCAmelCase_ , lowerCAmelCase_ )
# 2. predict previous mean of sample x_t-1
_A = scheduler.step(lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , generator=lowerCAmelCase_ ).prev_sample
_A = pred_prev_sample
_A = torch.sum(torch.abs(lowerCAmelCase_ ) )
_A = torch.mean(torch.abs(lowerCAmelCase_ ) )
assert abs(result_sum.item() - 252.268_2495 ) < 1E-2
assert abs(result_mean.item() - 0.328_4743 ) < 1E-3
def UpperCAmelCase ( self ) -> Optional[int]:
_A = self.scheduler_classes[0]
_A = self.get_scheduler_config()
_A = scheduler_class(**lowerCAmelCase_ )
scheduler.set_timesteps(25 )
_A = scheduler.timesteps
_A = self.dummy_model()
_A = self.dummy_sample_deter
_A = torch.manual_seed(0 )
for i, t in enumerate(lowerCAmelCase_ ):
# 1. predict noise residual
_A = model(lowerCAmelCase_ , lowerCAmelCase_ )
if i + 1 == timesteps.shape[0]:
_A = None
else:
_A = timesteps[i + 1]
# 2. predict previous mean of sample x_t-1
_A = scheduler.step(
lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , prev_timestep=lowerCAmelCase_ , generator=lowerCAmelCase_ ).prev_sample
_A = pred_prev_sample
_A = torch.sum(torch.abs(lowerCAmelCase_ ) )
_A = torch.mean(torch.abs(lowerCAmelCase_ ) )
assert abs(result_sum.item() - 258.204_4983 ) < 1E-2
assert abs(result_mean.item() - 0.336_2038 ) < 1E-3
def UpperCAmelCase ( self ) -> Dict:
pass
def UpperCAmelCase ( self ) -> List[Any]:
pass
| 81 | 1 |
from typing import List
from .keymap import KEYMAP, get_character
def _lowerCAmelCase (_lowerCAmelCase):
def decorator(_lowerCAmelCase):
UpperCamelCase_ = getattr(SCREAMING_SNAKE_CASE_ , "handle_key" , [])
handle += [key]
setattr(SCREAMING_SNAKE_CASE_ , "handle_key" , SCREAMING_SNAKE_CASE_)
return func
return decorator
def _lowerCAmelCase (*_lowerCAmelCase):
def decorator(_lowerCAmelCase):
UpperCamelCase_ = getattr(SCREAMING_SNAKE_CASE_ , "handle_key" , [])
handle += keys
setattr(SCREAMING_SNAKE_CASE_ , "handle_key" , SCREAMING_SNAKE_CASE_)
return func
return decorator
class _lowercase (a_ ):
'''simple docstring'''
def __new__( cls , snake_case__ , snake_case__ , snake_case__ ):
'''simple docstring'''
UpperCamelCase_ = super().__new__(cls , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase )
if not hasattr(_lowerCAmelCase , "key_handler" ):
setattr(_lowerCAmelCase , "key_handler" , {} )
setattr(_lowerCAmelCase , "handle_input" , KeyHandler.handle_input )
for value in attrs.values():
UpperCamelCase_ = getattr(_lowerCAmelCase , "handle_key" , [] )
for key in handled_keys:
UpperCamelCase_ = value
return new_cls
@staticmethod
def _lowerCamelCase ( cls ):
'''simple docstring'''
UpperCamelCase_ = get_character()
if char != KEYMAP["undefined"]:
UpperCamelCase_ = ord(_lowerCAmelCase )
UpperCamelCase_ = cls.key_handler.get(_lowerCAmelCase )
if handler:
UpperCamelCase_ = char
return handler(cls )
else:
return None
def _lowerCAmelCase (cls):
return KeyHandler(cls.__name__ , cls.__bases__ , cls.__dict__.copy())
| 128 |
'''simple docstring'''
import argparse
import torch
from transformers import FunnelBaseModel, FunnelConfig, FunnelModel, load_tf_weights_in_funnel
from transformers.utils import logging
logging.set_verbosity_info()
def __a(SCREAMING_SNAKE_CASE_ : Any , SCREAMING_SNAKE_CASE_ : str , SCREAMING_SNAKE_CASE_ : Any , SCREAMING_SNAKE_CASE_ : str ):
'''simple docstring'''
_lowerCAmelCase = FunnelConfig.from_json_file(SCREAMING_SNAKE_CASE_ )
print(F'''Building PyTorch model from configuration: {config}''' )
_lowerCAmelCase = FunnelBaseModel(SCREAMING_SNAKE_CASE_ ) if base_model else FunnelModel(SCREAMING_SNAKE_CASE_ )
# Load weights from tf checkpoint
load_tf_weights_in_funnel(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
# Save pytorch-model
print(F'''Save PyTorch model to {pytorch_dump_path}''' )
torch.save(model.state_dict() , SCREAMING_SNAKE_CASE_ )
if __name__ == "__main__":
_SCREAMING_SNAKE_CASE = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--tf_checkpoint_path", default=None, type=str, required=True, help="Path to the TensorFlow checkpoint path."
)
parser.add_argument(
"--config_file",
default=None,
type=str,
required=True,
help="The config json file corresponding to the pre-trained model. \nThis specifies the model architecture.",
)
parser.add_argument(
"--pytorch_dump_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
)
parser.add_argument(
"--base_model", action="store_true", help="Whether you want just the base model (no decoder) or not."
)
_SCREAMING_SNAKE_CASE = parser.parse_args()
convert_tf_checkpoint_to_pytorch(
args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path, args.base_model
)
| 158 | 0 |
"""simple docstring"""
import logging
import os
from logging import (
CRITICAL, # NOQA
DEBUG, # NOQA
ERROR, # NOQA
FATAL, # NOQA
INFO, # NOQA
NOTSET, # NOQA
WARN, # NOQA
WARNING, # NOQA
)
from typing import Optional
from tqdm import auto as tqdm_lib
lowerCAmelCase__ = {
'''debug''': logging.DEBUG,
'''info''': logging.INFO,
'''warning''': logging.WARNING,
'''error''': logging.ERROR,
'''critical''': logging.CRITICAL,
}
lowerCAmelCase__ = logging.WARNING
def a__ ( ):
'''simple docstring'''
lowerCAmelCase : List[str] = os.getenv("DATASETS_VERBOSITY" , SCREAMING_SNAKE_CASE )
if env_level_str:
if env_level_str in log_levels:
return log_levels[env_level_str]
else:
logging.getLogger().warning(
f"""Unknown option DATASETS_VERBOSITY={env_level_str}, """
f"""has to be one of: { ", ".join(log_levels.keys() ) }""" )
return _default_log_level
def a__ ( ):
'''simple docstring'''
return __name__.split("." )[0]
def a__ ( ):
'''simple docstring'''
return logging.getLogger(_get_library_name() )
def a__ ( ):
'''simple docstring'''
lowerCAmelCase : Tuple = _get_library_root_logger()
library_root_logger.setLevel(_get_default_logging_level() )
def a__ ( ):
'''simple docstring'''
lowerCAmelCase : Optional[int] = _get_library_root_logger()
library_root_logger.setLevel(logging.NOTSET )
def a__ ( SCREAMING_SNAKE_CASE : Optional[str] = None ):
'''simple docstring'''
if name is None:
lowerCAmelCase : Any = _get_library_name()
return logging.getLogger(SCREAMING_SNAKE_CASE )
def a__ ( ):
'''simple docstring'''
return _get_library_root_logger().getEffectiveLevel()
def a__ ( SCREAMING_SNAKE_CASE : int ):
'''simple docstring'''
_get_library_root_logger().setLevel(SCREAMING_SNAKE_CASE )
def a__ ( ):
'''simple docstring'''
return set_verbosity(SCREAMING_SNAKE_CASE )
def a__ ( ):
'''simple docstring'''
return set_verbosity(SCREAMING_SNAKE_CASE )
def a__ ( ):
'''simple docstring'''
return set_verbosity(SCREAMING_SNAKE_CASE )
def a__ ( ):
'''simple docstring'''
return set_verbosity(SCREAMING_SNAKE_CASE )
def a__ ( ):
'''simple docstring'''
lowerCAmelCase : Union[str, Any] = False
def a__ ( ):
'''simple docstring'''
lowerCAmelCase : List[str] = True
# Configure the library root logger at the module level (singleton-like)
_configure_library_root_logger()
class SCREAMING_SNAKE_CASE__ :
"""simple docstring"""
def __init__( self , *snake_case__ , **snake_case__ ): # pylint: disable=unused-argument
"""simple docstring"""
lowerCAmelCase : Any = args[0] if args else None
def __iter__( self ):
"""simple docstring"""
return iter(self._iterator )
def __getattr__( self , snake_case__ ):
"""simple docstring"""
def empty_fn(*snake_case__ , **snake_case__ ): # pylint: disable=unused-argument
return
return empty_fn
def __enter__( self ):
"""simple docstring"""
return self
def __exit__( self , snake_case__ , snake_case__ , snake_case__ ):
"""simple docstring"""
return
lowerCAmelCase__ = True
class SCREAMING_SNAKE_CASE__ :
"""simple docstring"""
def __call__( self , *snake_case__ , snake_case__=False , **snake_case__ ):
"""simple docstring"""
if _tqdm_active and not disable:
return tqdm_lib.tqdm(*snake_case__ , **snake_case__ )
else:
return EmptyTqdm(*snake_case__ , **snake_case__ )
def lowercase__ ( self , *snake_case__ , **snake_case__ ):
"""simple docstring"""
lowerCAmelCase : List[str] = None
if _tqdm_active:
return tqdm_lib.tqdm.set_lock(*snake_case__ , **snake_case__ )
def lowercase__ ( self ):
"""simple docstring"""
if _tqdm_active:
return tqdm_lib.tqdm.get_lock()
lowerCAmelCase__ = _tqdm_cls()
def a__ ( ):
'''simple docstring'''
global _tqdm_active
return bool(_tqdm_active )
def a__ ( ):
'''simple docstring'''
global _tqdm_active
lowerCAmelCase : Any = True
def a__ ( ):
'''simple docstring'''
global _tqdm_active
lowerCAmelCase : int = False
| 133 |
"""simple docstring"""
import re
from filelock import FileLock
try:
import nltk
lowerCAmelCase__ = True
except (ImportError, ModuleNotFoundError):
lowerCAmelCase__ = False
if NLTK_AVAILABLE:
with FileLock('''.lock''') as lock:
nltk.download('''punkt''', quiet=True)
def a__ ( SCREAMING_SNAKE_CASE : str ):
'''simple docstring'''
re.sub("<n>" , "" , SCREAMING_SNAKE_CASE ) # remove pegasus newline char
assert NLTK_AVAILABLE, "nltk must be installed to separate newlines between sentences. (pip install nltk)"
return "\n".join(nltk.sent_tokenize(SCREAMING_SNAKE_CASE ) )
| 133 | 1 |
'''simple docstring'''
import faiss # noqa: F401 # Here to have a nice missing dependency error message early on
import numpy # noqa: F401 # Here to have a nice missing dependency error message early on
import requests # noqa: F401 # Here to have a nice missing dependency error message early on
import sklearn # noqa: F401 # Here to have a nice missing dependency error message early on
import tqdm # noqa: F401 # Here to have a nice missing dependency error message early on
from mauve import compute_mauve # From: mauve-text
import datasets
__a = '\\n@inproceedings{pillutla-etal:mauve:neurips2021,\n title={MAUVE: Measuring the Gap Between Neural Text and Human Text using Divergence Frontiers},\n author={Pillutla, Krishna and Swayamdipta, Swabha and Zellers, Rowan and Thickstun, John and Welleck, Sean and Choi, Yejin and Harchaoui, Zaid},\n booktitle = {NeurIPS},\n year = {2021}\n}\n\n'
__a = '\\nMAUVE is a library built on PyTorch and HuggingFace Transformers to measure the gap between neural text and human text with the eponymous MAUVE measure.\n\nMAUVE summarizes both Type I and Type II errors measured softly using Kullback–Leibler (KL) divergences.\n\nFor details, see the MAUVE paper: https://arxiv.org/abs/2102.01454 (Neurips, 2021).\n\nThis metrics is a wrapper around the official implementation of MAUVE:\nhttps://github.com/krishnap25/mauve\n'
__a = '\nCalculates MAUVE scores between two lists of generated text and reference text.\nArgs:\n predictions: list of generated text to score. Each predictions\n should be a string with tokens separated by spaces.\n references: list of reference for each prediction. Each\n reference should be a string with tokens separated by spaces.\nOptional Args:\n num_buckets: the size of the histogram to quantize P and Q. Options: \'auto\' (default) or an integer\n pca_max_data: the number data points to use for PCA dimensionality reduction prior to clustering. If -1, use all the data. Default -1\n kmeans_explained_var: amount of variance of the data to keep in dimensionality reduction by PCA. Default 0.9\n kmeans_num_redo: number of times to redo k-means clustering (the best objective is kept). Default 5\n kmeans_max_iter: maximum number of k-means iterations. Default 500\n featurize_model_name: name of the model from which features are obtained. Default \'gpt2-large\' Use one of [\'gpt2\', \'gpt2-medium\', \'gpt2-large\', \'gpt2-xl\'].\n device_id: Device for featurization. Supply a GPU id (e.g. 0 or 3) to use GPU. If no GPU with this id is found, use CPU\n max_text_length: maximum number of tokens to consider. Default 1024\n divergence_curve_discretization_size: Number of points to consider on the divergence curve. Default 25\n mauve_scaling_factor: "c" from the paper. Default 5.\n verbose: If True (default), print running time updates\n seed: random seed to initialize k-means cluster assignments.\nReturns:\n mauve: MAUVE score, a number between 0 and 1. Larger values indicate that P and Q are closer,\n frontier_integral: Frontier Integral, a number between 0 and 1. Smaller values indicate that P and Q are closer,\n divergence_curve: a numpy.ndarray of shape (m, 2); plot it with matplotlib to view the divergence curve,\n p_hist: a discrete distribution, which is a quantized version of the text distribution p_text,\n q_hist: same as above, but with q_text.\nExamples:\n\n >>> # faiss segfaults in doctest for some reason, so the .compute call is not tested with doctest\n >>> import datasets\n >>> mauve = datasets.load_metric(\'mauve\')\n >>> predictions = ["hello there", "general kenobi"]\n >>> references = ["hello there", "general kenobi"]\n >>> out = mauve.compute(predictions=predictions, references=references) # doctest: +SKIP\n >>> print(out.mauve) # doctest: +SKIP\n 1.0\n'
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class A__ ( datasets.Metric ):
"""simple docstring"""
def _lowerCAmelCase ( self : int ) -> Optional[int]:
"""simple docstring"""
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , homepage="https://github.com/krishnap25/mauve" , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
"predictions": datasets.Value("string" , id="sequence" ),
"references": datasets.Value("string" , id="sequence" ),
} ) , codebase_urls=["https://github.com/krishnap25/mauve"] , reference_urls=[
"https://arxiv.org/abs/2102.01454",
"https://github.com/krishnap25/mauve",
] , )
def _lowerCAmelCase ( self : Tuple , lowerCAmelCase__ : Union[str, Any] , lowerCAmelCase__ : str , lowerCAmelCase__ : Optional[Any]=None , lowerCAmelCase__ : Any=None , lowerCAmelCase__ : Tuple=None , lowerCAmelCase__ : Tuple=None , lowerCAmelCase__ : List[Any]="auto" , lowerCAmelCase__ : Tuple=-1 , lowerCAmelCase__ : Any=0.9 , lowerCAmelCase__ : Optional[int]=5 , lowerCAmelCase__ : str=5_0_0 , lowerCAmelCase__ : Any="gpt2-large" , lowerCAmelCase__ : int=-1 , lowerCAmelCase__ : Tuple=1_0_2_4 , lowerCAmelCase__ : Optional[int]=2_5 , lowerCAmelCase__ : Any=5 , lowerCAmelCase__ : int=True , lowerCAmelCase__ : Optional[int]=2_5 , ) -> str:
"""simple docstring"""
_UpperCAmelCase : Any = compute_mauve(
p_text=snake_case_ , q_text=snake_case_ , p_features=snake_case_ , q_features=snake_case_ , p_tokens=snake_case_ , q_tokens=snake_case_ , num_buckets=snake_case_ , pca_max_data=snake_case_ , kmeans_explained_var=snake_case_ , kmeans_num_redo=snake_case_ , kmeans_max_iter=snake_case_ , featurize_model_name=snake_case_ , device_id=snake_case_ , max_text_length=snake_case_ , divergence_curve_discretization_size=snake_case_ , mauve_scaling_factor=snake_case_ , verbose=snake_case_ , seed=snake_case_ , )
return out | 145 |
"""simple docstring"""
from math import pi, sqrt
def lowercase (_lowerCAmelCase ):
if num <= 0:
raise ValueError("""math domain error""" )
if num > 171.5:
raise OverflowError("""math range error""" )
elif num - int(_lowerCAmelCase ) not in (0, 0.5):
raise NotImplementedError("""num must be an integer or a half-integer""" )
elif num == 0.5:
return sqrt(_lowerCAmelCase )
else:
return 1.0 if num == 1 else (num - 1) * gamma(num - 1 )
def lowercase ():
assert gamma(0.5 ) == sqrt(_lowerCAmelCase )
assert gamma(1 ) == 1.0
assert gamma(2 ) == 1.0
if __name__ == "__main__":
from doctest import testmod
testmod()
SCREAMING_SNAKE_CASE_ = 1.0
while num:
SCREAMING_SNAKE_CASE_ = float(input('''Gamma of: '''))
print(F"gamma({num}) = {gamma(num)}")
print('''\nEnter 0 to exit...''')
| 301 | 0 |
"""simple docstring"""
import torch
from transformers import AutoModel
class __snake_case ( torch.nn.Module ):
def __init__( self , lowercase="sayef/fsner-bert-base-uncased") -> Union[str, Any]:
'''simple docstring'''
super(lowercase , self).__init__()
a__: Any = AutoModel.from_pretrained(lowercase , return_dict=lowercase)
a__: Dict = torch.nn.CosineSimilarity(3 , 1e-08)
a__: List[Any] = torch.nn.Softmax(dim=1)
def lowerCamelCase_ ( self , **lowercase) -> Dict:
'''simple docstring'''
return self.bert(**lowercase).last_hidden_state
def lowerCamelCase_ ( self , lowercase) -> List[str]:
'''simple docstring'''
return token_embeddings.sum(2 , keepdim=lowercase)
def lowerCamelCase_ ( self , lowercase , lowercase , lowercase=1) -> int:
'''simple docstring'''
return self.softmax(T * self.cos(lowercase , lowercase))
def lowerCamelCase_ ( self , lowercase , lowercase) -> Any:
'''simple docstring'''
a__: Dict = W_supports['sizes'].tolist()
a__: Dict = W_supports['start_token_id'].item()
a__: Dict = W_supports['end_token_id'].item()
del W_supports["sizes"]
del W_supports["start_token_id"]
del W_supports["end_token_id"]
a__: str = self.BERT(**lowercase)
a__: Any = self.BERT(**lowercase)
a__: Optional[int] = None
a__: Optional[Any] = None
a__: Optional[Any] = W_supports['input_ids'] == start_token_id
a__: str = W_supports['input_ids'] == end_token_id
for i, size in enumerate(lowercase):
if i == 0:
a__: Union[str, Any] = 0
else:
a__: Union[str, Any] = support_sizes[i - 1]
a__: Union[str, Any] = S[s : s + size][start_token_masks[s : s + size]]
a__: str = S[s : s + size][end_token_masks[s : s + size]]
a__: Any = torch.matmul(q[i] , s_start.T).sum(1).softmax(0)
a__: Dict = torch.matmul(q[i] , s_end.T).sum(1).softmax(0)
if p_starts is not None:
a__: str = torch.vstack((p_starts, p_start))
a__: Tuple = torch.vstack((p_ends, p_end))
else:
a__: Union[str, Any] = p_start
a__: Union[str, Any] = p_end
return p_starts, p_ends
| 203 | """simple docstring"""
from __future__ import annotations
class __snake_case :
def __init__( self , lowercase=None) -> Optional[Any]:
'''simple docstring'''
a__: int = data
a__: str = None
def __repr__( self) -> List[str]:
'''simple docstring'''
a__: Optional[Any] = []
a__: Union[str, Any] = self
while temp:
string_rep.append(f'{temp.data}')
a__: Tuple = temp.next
return "->".join(lowercase)
def __a ( _SCREAMING_SNAKE_CASE ) ->str:
if not elements_list:
raise Exception('The Elements List is empty' )
a__: Any = Node(elements_list[0] )
for i in range(1 , len(_SCREAMING_SNAKE_CASE ) ):
a__: Optional[Any] = Node(elements_list[i] )
a__: Tuple = current.next
return head
def __a ( _SCREAMING_SNAKE_CASE ) ->None:
if head_node is not None and isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
print_reverse(head_node.next )
print(head_node.data )
def __a ( ) ->Optional[Any]:
from doctest import testmod
testmod()
a__: Tuple = make_linked_list([14, 52, 14, 12, 43] )
print('Linked List:' )
print(_SCREAMING_SNAKE_CASE )
print('Elements in Reverse:' )
print_reverse(_SCREAMING_SNAKE_CASE )
if __name__ == "__main__":
main()
| 203 | 1 |
import numpy as np
class UpperCamelCase__ :
'''simple docstring'''
def __init__( self : str ,lowerCamelCase__ : Optional[Any]=None ,lowerCamelCase__ : Union[str, Any]=None ,lowerCamelCase__ : Dict=None ,lowerCamelCase__ : Dict=None ,lowerCamelCase__ : Dict=None ) -> int:
'''simple docstring'''
self.set_matricies(red=lowercase_ ,green=lowercase_ ,blue=lowercase_ ,red_edge=lowercase_ ,nir=lowercase_ )
def SCREAMING_SNAKE_CASE__ ( self : Union[str, Any] ,lowerCamelCase__ : int=None ,lowerCamelCase__ : Tuple=None ,lowerCamelCase__ : List[str]=None ,lowerCamelCase__ : Optional[int]=None ,lowerCamelCase__ : Any=None ) -> Tuple:
'''simple docstring'''
if red is not None:
SCREAMING_SNAKE_CASE = red
if green is not None:
SCREAMING_SNAKE_CASE = green
if blue is not None:
SCREAMING_SNAKE_CASE = blue
if red_edge is not None:
SCREAMING_SNAKE_CASE = red_edge
if nir is not None:
SCREAMING_SNAKE_CASE = nir
return True
def SCREAMING_SNAKE_CASE__ ( self : Dict ,lowerCamelCase__ : Union[str, Any]="" ,lowerCamelCase__ : Any=None ,lowerCamelCase__ : List[str]=None ,lowerCamelCase__ : List[Any]=None ,lowerCamelCase__ : Any=None ,lowerCamelCase__ : Dict=None ) -> List[str]:
'''simple docstring'''
self.set_matricies(red=lowercase_ ,green=lowercase_ ,blue=lowercase_ ,red_edge=lowercase_ ,nir=lowercase_ )
SCREAMING_SNAKE_CASE = {
"ARVI2": self.arvaa,
"CCCI": self.ccci,
"CVI": self.cvi,
"GLI": self.gli,
"NDVI": self.ndvi,
"BNDVI": self.bndvi,
"redEdgeNDVI": self.red_edge_ndvi,
"GNDVI": self.gndvi,
"GBNDVI": self.gbndvi,
"GRNDVI": self.grndvi,
"RBNDVI": self.rbndvi,
"PNDVI": self.pndvi,
"ATSAVI": self.atsavi,
"BWDRVI": self.bwdrvi,
"CIgreen": self.ci_green,
"CIrededge": self.ci_rededge,
"CI": self.ci,
"CTVI": self.ctvi,
"GDVI": self.gdvi,
"EVI": self.evi,
"GEMI": self.gemi,
"GOSAVI": self.gosavi,
"GSAVI": self.gsavi,
"Hue": self.hue,
"IVI": self.ivi,
"IPVI": self.ipvi,
"I": self.i,
"RVI": self.rvi,
"MRVI": self.mrvi,
"MSAVI": self.m_savi,
"NormG": self.norm_g,
"NormNIR": self.norm_nir,
"NormR": self.norm_r,
"NGRDI": self.ngrdi,
"RI": self.ri,
"S": self.s,
"IF": self._if,
"DVI": self.dvi,
"TVI": self.tvi,
"NDRE": self.ndre,
}
try:
return funcs[index]()
except KeyError:
print("""Index not in the list!""" )
return False
def SCREAMING_SNAKE_CASE__ ( self : Union[str, Any] ) -> str:
'''simple docstring'''
return -0.18 + (1.17 * ((self.nir - self.red) / (self.nir + self.red)))
def SCREAMING_SNAKE_CASE__ ( self : Dict ) -> List[str]:
'''simple docstring'''
return ((self.nir - self.redEdge) / (self.nir + self.redEdge)) / (
(self.nir - self.red) / (self.nir + self.red)
)
def SCREAMING_SNAKE_CASE__ ( self : List[Any] ) -> List[Any]:
'''simple docstring'''
return self.nir * (self.red / (self.green**2))
def SCREAMING_SNAKE_CASE__ ( self : str ) -> Dict:
'''simple docstring'''
return (2 * self.green - self.red - self.blue) / (
2 * self.green + self.red + self.blue
)
def SCREAMING_SNAKE_CASE__ ( self : Tuple ) -> List[str]:
'''simple docstring'''
return (self.nir - self.red) / (self.nir + self.red)
def SCREAMING_SNAKE_CASE__ ( self : Union[str, Any] ) -> List[Any]:
'''simple docstring'''
return (self.nir - self.blue) / (self.nir + self.blue)
def SCREAMING_SNAKE_CASE__ ( self : int ) -> str:
'''simple docstring'''
return (self.redEdge - self.red) / (self.redEdge + self.red)
def SCREAMING_SNAKE_CASE__ ( self : List[str] ) -> str:
'''simple docstring'''
return (self.nir - self.green) / (self.nir + self.green)
def SCREAMING_SNAKE_CASE__ ( self : Optional[Any] ) -> Tuple:
'''simple docstring'''
return (self.nir - (self.green + self.blue)) / (
self.nir + (self.green + self.blue)
)
def SCREAMING_SNAKE_CASE__ ( self : List[str] ) -> List[Any]:
'''simple docstring'''
return (self.nir - (self.green + self.red)) / (
self.nir + (self.green + self.red)
)
def SCREAMING_SNAKE_CASE__ ( self : int ) -> List[str]:
'''simple docstring'''
return (self.nir - (self.blue + self.red)) / (self.nir + (self.blue + self.red))
def SCREAMING_SNAKE_CASE__ ( self : Any ) -> Optional[Any]:
'''simple docstring'''
return (self.nir - (self.green + self.red + self.blue)) / (
self.nir + (self.green + self.red + self.blue)
)
def SCREAMING_SNAKE_CASE__ ( self : Any ,lowerCamelCase__ : List[Any]=0.08 ,lowerCamelCase__ : List[str]=1.22 ,lowerCamelCase__ : Union[str, Any]=0.03 ) -> Optional[int]:
'''simple docstring'''
return a * (
(self.nir - a * self.red - b)
/ (a * self.nir + self.red - a * b + x * (1 + a**2))
)
def SCREAMING_SNAKE_CASE__ ( self : List[str] ) -> List[str]:
'''simple docstring'''
return (0.1 * self.nir - self.blue) / (0.1 * self.nir + self.blue)
def SCREAMING_SNAKE_CASE__ ( self : Optional[int] ) -> Union[str, Any]:
'''simple docstring'''
return (self.nir / self.green) - 1
def SCREAMING_SNAKE_CASE__ ( self : List[Any] ) -> int:
'''simple docstring'''
return (self.nir / self.redEdge) - 1
def SCREAMING_SNAKE_CASE__ ( self : List[Any] ) -> List[Any]:
'''simple docstring'''
return (self.red - self.blue) / self.red
def SCREAMING_SNAKE_CASE__ ( self : Union[str, Any] ) -> int:
'''simple docstring'''
SCREAMING_SNAKE_CASE = self.ndvi()
return ((ndvi + 0.5) / (abs(ndvi + 0.5 ))) * (abs(ndvi + 0.5 ) ** (1 / 2))
def SCREAMING_SNAKE_CASE__ ( self : Tuple ) -> List[Any]:
'''simple docstring'''
return self.nir - self.green
def SCREAMING_SNAKE_CASE__ ( self : Union[str, Any] ) -> Optional[Any]:
'''simple docstring'''
return 2.5 * (
(self.nir - self.red) / (self.nir + 6 * self.red - 7.5 * self.blue + 1)
)
def SCREAMING_SNAKE_CASE__ ( self : Union[str, Any] ) -> Optional[Any]:
'''simple docstring'''
SCREAMING_SNAKE_CASE = (2 * (self.nir**2 - self.red**2) + 1.5 * self.nir + 0.5 * self.red) / (
self.nir + self.red + 0.5
)
return n * (1 - 0.25 * n) - (self.red - 0.125) / (1 - self.red)
def SCREAMING_SNAKE_CASE__ ( self : Dict ,lowerCamelCase__ : Optional[Any]=0.16 ) -> Optional[int]:
'''simple docstring'''
return (self.nir - self.green) / (self.nir + self.green + y)
def SCREAMING_SNAKE_CASE__ ( self : Dict ,lowerCamelCase__ : Union[str, Any]=0.5 ) -> Dict:
'''simple docstring'''
return ((self.nir - self.green) / (self.nir + self.green + n)) * (1 + n)
def SCREAMING_SNAKE_CASE__ ( self : List[str] ) -> str:
'''simple docstring'''
return np.arctan(
((2 * self.red - self.green - self.blue) / 30.5) * (self.green - self.blue) )
def SCREAMING_SNAKE_CASE__ ( self : List[Any] ,lowerCamelCase__ : int=None ,lowerCamelCase__ : Union[str, Any]=None ) -> List[str]:
'''simple docstring'''
return (self.nir - b) / (a * self.red)
def SCREAMING_SNAKE_CASE__ ( self : List[str] ) -> List[str]:
'''simple docstring'''
return (self.nir / ((self.nir + self.red) / 2)) * (self.ndvi() + 1)
def SCREAMING_SNAKE_CASE__ ( self : Dict ) -> Any:
'''simple docstring'''
return (self.red + self.green + self.blue) / 30.5
def SCREAMING_SNAKE_CASE__ ( self : Tuple ) -> Optional[int]:
'''simple docstring'''
return self.nir / self.red
def SCREAMING_SNAKE_CASE__ ( self : Optional[int] ) -> Any:
'''simple docstring'''
return (self.rvi() - 1) / (self.rvi() + 1)
def SCREAMING_SNAKE_CASE__ ( self : Optional[int] ) -> str:
'''simple docstring'''
return (
(2 * self.nir + 1)
- ((2 * self.nir + 1) ** 2 - 8 * (self.nir - self.red)) ** (1 / 2)
) / 2
def SCREAMING_SNAKE_CASE__ ( self : Any ) -> Tuple:
'''simple docstring'''
return self.green / (self.nir + self.red + self.green)
def SCREAMING_SNAKE_CASE__ ( self : str ) -> Optional[Any]:
'''simple docstring'''
return self.nir / (self.nir + self.red + self.green)
def SCREAMING_SNAKE_CASE__ ( self : Dict ) -> Any:
'''simple docstring'''
return self.red / (self.nir + self.red + self.green)
def SCREAMING_SNAKE_CASE__ ( self : Union[str, Any] ) -> Any:
'''simple docstring'''
return (self.green - self.red) / (self.green + self.red)
def SCREAMING_SNAKE_CASE__ ( self : int ) -> Optional[int]:
'''simple docstring'''
return (self.red - self.green) / (self.red + self.green)
def SCREAMING_SNAKE_CASE__ ( self : List[str] ) -> int:
'''simple docstring'''
SCREAMING_SNAKE_CASE = np.max([np.max(self.red ), np.max(self.green ), np.max(self.blue )] )
SCREAMING_SNAKE_CASE = np.min([np.min(self.red ), np.min(self.green ), np.min(self.blue )] )
return (max_value - min_value) / max_value
def SCREAMING_SNAKE_CASE__ ( self : Optional[Any] ) -> Tuple:
'''simple docstring'''
return (2 * self.red - self.green - self.blue) / (self.green - self.blue)
def SCREAMING_SNAKE_CASE__ ( self : Optional[Any] ) -> List[str]:
'''simple docstring'''
return self.nir / self.red
def SCREAMING_SNAKE_CASE__ ( self : Optional[int] ) -> List[str]:
'''simple docstring'''
return (self.ndvi() + 0.5) ** (1 / 2)
def SCREAMING_SNAKE_CASE__ ( self : int ) -> Optional[int]:
'''simple docstring'''
return (self.nir - self.redEdge) / (self.nir + self.redEdge)
| 296 |
"""simple docstring"""
import os
_a = {'I': 1, 'V': 5, 'X': 10, 'L': 50, 'C': 100, 'D': 500, 'M': 1_000}
def __a ( __lowerCamelCase ):
UpperCAmelCase_ : Union[str, Any] = 0
UpperCAmelCase_ : List[str] = 0
while index < len(__lowerCamelCase ) - 1:
UpperCAmelCase_ : Tuple = SYMBOLS[numerals[index]]
UpperCAmelCase_ : List[str] = SYMBOLS[numerals[index + 1]]
if current_value < next_value:
total_value -= current_value
else:
total_value += current_value
index += 1
total_value += SYMBOLS[numerals[index]]
return total_value
def __a ( __lowerCamelCase ):
UpperCAmelCase_ : List[str] = ""
UpperCAmelCase_ : Any = num // 1000
numerals += m_count * "M"
num %= 1000
UpperCAmelCase_ : Any = num // 100
if c_count == 9:
numerals += "CM"
c_count -= 9
elif c_count == 4:
numerals += "CD"
c_count -= 4
if c_count >= 5:
numerals += "D"
c_count -= 5
numerals += c_count * "C"
num %= 100
UpperCAmelCase_ : str = num // 10
if x_count == 9:
numerals += "XC"
x_count -= 9
elif x_count == 4:
numerals += "XL"
x_count -= 4
if x_count >= 5:
numerals += "L"
x_count -= 5
numerals += x_count * "X"
num %= 10
if num == 9:
numerals += "IX"
num -= 9
elif num == 4:
numerals += "IV"
num -= 4
if num >= 5:
numerals += "V"
num -= 5
numerals += num * "I"
return numerals
def __a ( __lowerCamelCase = "/p089_roman.txt" ):
UpperCAmelCase_ : int = 0
with open(os.path.dirname(__lowerCamelCase ) + roman_numerals_filename ) as filea:
UpperCAmelCase_ : Optional[Any] = filea.readlines()
for line in lines:
UpperCAmelCase_ : Tuple = line.strip()
UpperCAmelCase_ : Optional[Any] = parse_roman_numerals(__lowerCamelCase )
UpperCAmelCase_ : Tuple = generate_roman_numerals(__lowerCamelCase )
savings += len(__lowerCamelCase ) - len(__lowerCamelCase )
return savings
if __name__ == "__main__":
print(f"""{solution() = }""")
| 61 | 0 |
import inspect
import unittest
from transformers import YolosConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from torch import nn
from transformers import YolosForObjectDetection, YolosModel
from transformers.models.yolos.modeling_yolos import YOLOS_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import AutoImageProcessor
class __snake_case :
def __init__( self : Dict , _snake_case : Tuple , _snake_case : Any=13 , _snake_case : Any=[30, 30] , _snake_case : Union[str, Any]=2 , _snake_case : Tuple=3 , _snake_case : Tuple=True , _snake_case : Union[str, Any]=True , _snake_case : int=32 , _snake_case : List[str]=5 , _snake_case : Union[str, Any]=4 , _snake_case : Any=37 , _snake_case : List[Any]="gelu" , _snake_case : List[str]=0.1 , _snake_case : int=0.1 , _snake_case : Optional[Any]=10 , _snake_case : Dict=0.0_2 , _snake_case : str=3 , _snake_case : Optional[int]=None , _snake_case : Optional[Any]=8 , _snake_case : List[Any]=10 , ):
"""simple docstring"""
UpperCAmelCase_ = parent
UpperCAmelCase_ = batch_size
UpperCAmelCase_ = image_size
UpperCAmelCase_ = patch_size
UpperCAmelCase_ = num_channels
UpperCAmelCase_ = is_training
UpperCAmelCase_ = use_labels
UpperCAmelCase_ = hidden_size
UpperCAmelCase_ = num_hidden_layers
UpperCAmelCase_ = num_attention_heads
UpperCAmelCase_ = intermediate_size
UpperCAmelCase_ = hidden_act
UpperCAmelCase_ = hidden_dropout_prob
UpperCAmelCase_ = attention_probs_dropout_prob
UpperCAmelCase_ = type_sequence_label_size
UpperCAmelCase_ = initializer_range
UpperCAmelCase_ = num_labels
UpperCAmelCase_ = scope
UpperCAmelCase_ = n_targets
UpperCAmelCase_ = num_detection_tokens
# we set the expected sequence length (which is used in several tests)
# expected sequence length = num_patches + 1 (we add 1 for the [CLS] token) + num_detection_tokens
UpperCAmelCase_ = (image_size[1] // patch_size) * (image_size[0] // patch_size)
UpperCAmelCase_ = num_patches + 1 + self.num_detection_tokens
def lowerCamelCase ( self : int):
"""simple docstring"""
UpperCAmelCase_ = floats_tensor([self.batch_size, self.num_channels, self.image_size[0], self.image_size[1]])
UpperCAmelCase_ = None
if self.use_labels:
# labels is a list of Dict (each Dict being the labels for a given example in the batch)
UpperCAmelCase_ = []
for i in range(self.batch_size):
UpperCAmelCase_ = {}
UpperCAmelCase_ = torch.randint(
high=self.num_labels , size=(self.n_targets,) , device=_snake_case)
UpperCAmelCase_ = torch.rand(self.n_targets , 4 , device=_snake_case)
labels.append(_snake_case)
UpperCAmelCase_ = self.get_config()
return config, pixel_values, labels
def lowerCamelCase ( self : Dict):
"""simple docstring"""
return YolosConfig(
image_size=self.image_size , patch_size=self.patch_size , num_channels=self.num_channels , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , is_decoder=_snake_case , initializer_range=self.initializer_range , num_detection_tokens=self.num_detection_tokens , num_labels=self.num_labels , )
def lowerCamelCase ( self : List[Any] , _snake_case : Optional[Any] , _snake_case : int , _snake_case : Tuple):
"""simple docstring"""
UpperCAmelCase_ = YolosModel(config=_snake_case)
model.to(_snake_case)
model.eval()
UpperCAmelCase_ = model(_snake_case)
self.parent.assertEqual(
result.last_hidden_state.shape , (self.batch_size, self.expected_seq_len, self.hidden_size))
def lowerCamelCase ( self : int , _snake_case : Tuple , _snake_case : List[Any] , _snake_case : Any):
"""simple docstring"""
UpperCAmelCase_ = YolosForObjectDetection(_snake_case)
model.to(_snake_case)
model.eval()
UpperCAmelCase_ = model(pixel_values=_snake_case)
UpperCAmelCase_ = model(_snake_case)
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_detection_tokens, self.num_labels + 1))
self.parent.assertEqual(result.pred_boxes.shape , (self.batch_size, self.num_detection_tokens, 4))
UpperCAmelCase_ = model(pixel_values=_snake_case , labels=_snake_case)
self.parent.assertEqual(result.loss.shape , ())
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_detection_tokens, self.num_labels + 1))
self.parent.assertEqual(result.pred_boxes.shape , (self.batch_size, self.num_detection_tokens, 4))
def lowerCamelCase ( self : Optional[int]):
"""simple docstring"""
UpperCAmelCase_ = self.prepare_config_and_inputs()
UpperCAmelCase_ = config_and_inputs
UpperCAmelCase_ = {'pixel_values': pixel_values}
return config, inputs_dict
@require_torch
class __snake_case ( SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , unittest.TestCase ):
UpperCAmelCase__ : str = (YolosModel, YolosForObjectDetection) if is_torch_available() else ()
UpperCAmelCase__ : int = (
{'''feature-extraction''': YolosModel, '''object-detection''': YolosForObjectDetection} if is_torch_available() else {}
)
UpperCAmelCase__ : List[Any] = False
UpperCAmelCase__ : List[str] = False
UpperCAmelCase__ : Optional[Any] = False
UpperCAmelCase__ : Dict = False
def lowerCamelCase ( self : int , _snake_case : Any , _snake_case : List[str] , _snake_case : List[str]=False):
"""simple docstring"""
UpperCAmelCase_ = super()._prepare_for_class(_snake_case , _snake_case , return_labels=_snake_case)
if return_labels:
if model_class.__name__ == "YolosForObjectDetection":
UpperCAmelCase_ = []
for i in range(self.model_tester.batch_size):
UpperCAmelCase_ = {}
UpperCAmelCase_ = torch.ones(
size=(self.model_tester.n_targets,) , device=_snake_case , dtype=torch.long)
UpperCAmelCase_ = torch.ones(
self.model_tester.n_targets , 4 , device=_snake_case , dtype=torch.float)
labels.append(_snake_case)
UpperCAmelCase_ = labels
return inputs_dict
def lowerCamelCase ( self : Union[str, Any]):
"""simple docstring"""
UpperCAmelCase_ = YolosModelTester(self)
UpperCAmelCase_ = ConfigTester(self , config_class=_snake_case , has_text_modality=_snake_case , hidden_size=37)
def lowerCamelCase ( self : Dict):
"""simple docstring"""
self.config_tester.run_common_tests()
def lowerCamelCase ( self : Union[str, Any]):
"""simple docstring"""
pass
def lowerCamelCase ( self : int):
"""simple docstring"""
UpperCAmelCase_ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
UpperCAmelCase_ = model_class(_snake_case)
self.assertIsInstance(model.get_input_embeddings() , (nn.Module))
UpperCAmelCase_ = model.get_output_embeddings()
self.assertTrue(x is None or isinstance(_snake_case , nn.Linear))
def lowerCamelCase ( self : Any):
"""simple docstring"""
UpperCAmelCase_ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
UpperCAmelCase_ = model_class(_snake_case)
UpperCAmelCase_ = inspect.signature(model.forward)
# signature.parameters is an OrderedDict => so arg_names order is deterministic
UpperCAmelCase_ = [*signature.parameters.keys()]
UpperCAmelCase_ = ['pixel_values']
self.assertListEqual(arg_names[:1] , _snake_case)
def lowerCamelCase ( self : Tuple):
"""simple docstring"""
UpperCAmelCase_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*_snake_case)
def lowerCamelCase ( self : Optional[Any]):
"""simple docstring"""
UpperCAmelCase_ = self.model_tester.prepare_config_and_inputs_for_common()
UpperCAmelCase_ = True
# in YOLOS, the seq_len is different
UpperCAmelCase_ = self.model_tester.expected_seq_len
for model_class in self.all_model_classes:
UpperCAmelCase_ = True
UpperCAmelCase_ = False
UpperCAmelCase_ = True
UpperCAmelCase_ = model_class(_snake_case)
model.to(_snake_case)
model.eval()
with torch.no_grad():
UpperCAmelCase_ = model(**self._prepare_for_class(_snake_case , _snake_case))
UpperCAmelCase_ = outputs.attentions
self.assertEqual(len(_snake_case) , self.model_tester.num_hidden_layers)
# check that output_attentions also work using config
del inputs_dict["output_attentions"]
UpperCAmelCase_ = True
UpperCAmelCase_ = model_class(_snake_case)
model.to(_snake_case)
model.eval()
with torch.no_grad():
UpperCAmelCase_ = model(**self._prepare_for_class(_snake_case , _snake_case))
UpperCAmelCase_ = outputs.attentions
self.assertEqual(len(_snake_case) , self.model_tester.num_hidden_layers)
self.assertListEqual(
list(attentions[0].shape[-3:]) , [self.model_tester.num_attention_heads, seq_len, seq_len] , )
UpperCAmelCase_ = len(_snake_case)
# Check attention is always last and order is fine
UpperCAmelCase_ = True
UpperCAmelCase_ = True
UpperCAmelCase_ = model_class(_snake_case)
model.to(_snake_case)
model.eval()
with torch.no_grad():
UpperCAmelCase_ = model(**self._prepare_for_class(_snake_case , _snake_case))
UpperCAmelCase_ = 1
self.assertEqual(out_len + added_hidden_states , len(_snake_case))
UpperCAmelCase_ = outputs.attentions
self.assertEqual(len(_snake_case) , self.model_tester.num_hidden_layers)
self.assertListEqual(
list(self_attentions[0].shape[-3:]) , [self.model_tester.num_attention_heads, seq_len, seq_len] , )
def lowerCamelCase ( self : List[Any]):
"""simple docstring"""
def check_hidden_states_output(_snake_case : List[str] , _snake_case : Any , _snake_case : List[str]):
UpperCAmelCase_ = model_class(_snake_case)
model.to(_snake_case)
model.eval()
with torch.no_grad():
UpperCAmelCase_ = model(**self._prepare_for_class(_snake_case , _snake_case))
UpperCAmelCase_ = outputs.hidden_states
UpperCAmelCase_ = getattr(
self.model_tester , '''expected_num_hidden_layers''' , self.model_tester.num_hidden_layers + 1)
self.assertEqual(len(_snake_case) , _snake_case)
# YOLOS has a different seq_length
UpperCAmelCase_ = self.model_tester.expected_seq_len
self.assertListEqual(
list(hidden_states[0].shape[-2:]) , [seq_length, self.model_tester.hidden_size] , )
UpperCAmelCase_ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
UpperCAmelCase_ = True
check_hidden_states_output(_snake_case , _snake_case , _snake_case)
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
UpperCAmelCase_ = True
check_hidden_states_output(_snake_case , _snake_case , _snake_case)
def lowerCamelCase ( self : Dict):
"""simple docstring"""
UpperCAmelCase_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_object_detection(*_snake_case)
@slow
def lowerCamelCase ( self : Tuple):
"""simple docstring"""
for model_name in YOLOS_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
UpperCAmelCase_ = YolosModel.from_pretrained(_snake_case)
self.assertIsNotNone(_snake_case)
def A () -> Optional[Any]:
"""simple docstring"""
UpperCAmelCase_ = Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
return image
@require_torch
@require_vision
class __snake_case ( unittest.TestCase ):
@cached_property
def lowerCamelCase ( self : Any):
"""simple docstring"""
return AutoImageProcessor.from_pretrained('''hustvl/yolos-small''') if is_vision_available() else None
@slow
def lowerCamelCase ( self : int):
"""simple docstring"""
UpperCAmelCase_ = YolosForObjectDetection.from_pretrained('''hustvl/yolos-small''').to(_snake_case)
UpperCAmelCase_ = self.default_image_processor
UpperCAmelCase_ = prepare_img()
UpperCAmelCase_ = image_processor(images=_snake_case , return_tensors='''pt''').to(_snake_case)
# forward pass
with torch.no_grad():
UpperCAmelCase_ = model(inputs.pixel_values)
# verify outputs
UpperCAmelCase_ = torch.Size((1, 100, 92))
self.assertEqual(outputs.logits.shape , _snake_case)
UpperCAmelCase_ = torch.tensor(
[[-2_4.0_2_4_8, -1_0.3_0_2_4, -1_4.8_2_9_0], [-4_2.0_3_9_2, -1_6.8_2_0_0, -2_7.4_3_3_4], [-2_7.2_7_4_3, -1_1.8_1_5_4, -1_8.7_1_4_8]] , device=_snake_case , )
UpperCAmelCase_ = torch.tensor(
[[0.2_5_5_9, 0.5_4_5_5, 0.4_7_0_6], [0.2_9_8_9, 0.7_2_7_9, 0.1_8_7_5], [0.7_7_3_2, 0.4_0_1_7, 0.4_4_6_2]] , device=_snake_case)
self.assertTrue(torch.allclose(outputs.logits[0, :3, :3] , _snake_case , atol=1e-4))
self.assertTrue(torch.allclose(outputs.pred_boxes[0, :3, :3] , _snake_case , atol=1e-4))
# verify postprocessing
UpperCAmelCase_ = image_processor.post_process_object_detection(
_snake_case , threshold=0.3 , target_sizes=[image.size[::-1]])[0]
UpperCAmelCase_ = torch.tensor([0.9_9_9_4, 0.9_7_9_0, 0.9_9_6_4, 0.9_9_7_2, 0.9_8_6_1]).to(_snake_case)
UpperCAmelCase_ = [75, 75, 17, 63, 17]
UpperCAmelCase_ = torch.tensor([3_3_5.0_6_0_9, 7_9.3_8_4_8, 3_7_5.4_2_1_6, 1_8_7.2_4_9_5]).to(_snake_case)
self.assertEqual(len(results['''scores''']) , 5)
self.assertTrue(torch.allclose(results['''scores'''] , _snake_case , atol=1e-4))
self.assertSequenceEqual(results['''labels'''].tolist() , _snake_case)
self.assertTrue(torch.allclose(results['''boxes'''][0, :] , _snake_case))
| 358 |
import sys
def A (__A : int ) -> Dict:
"""simple docstring"""
UpperCAmelCase_ = len(__A )
UpperCAmelCase_ = [[0 for x in range(__A )] for x in range(__A )]
UpperCAmelCase_ = [[0 for x in range(__A )] for x in range(__A )]
for chain_length in range(2 , __A ):
for a in range(1 , n - chain_length + 1 ):
UpperCAmelCase_ = a + chain_length - 1
UpperCAmelCase_ = sys.maxsize
for c in range(__A , __A ):
UpperCAmelCase_ = (
matrix[a][c] + matrix[c + 1][b] + array[a - 1] * array[c] * array[b]
)
if cost < matrix[a][b]:
UpperCAmelCase_ = cost
UpperCAmelCase_ = c
return matrix, sol
def A (__A : Any , __A : Dict , __A : Optional[int] ) -> Optional[int]:
"""simple docstring"""
if i == j:
print('''A''' + str(__A ) , end=''' ''' )
else:
print('''(''' , end=''' ''' )
print_optiomal_solution(__A , __A , optimal_solution[i][j] )
print_optiomal_solution(__A , optimal_solution[i][j] + 1 , __A )
print(''')''' , end=''' ''' )
def A () -> List[str]:
"""simple docstring"""
UpperCAmelCase_ = [30, 35, 15, 5, 10, 20, 25]
UpperCAmelCase_ = len(__A )
# Size of matrix created from above array will be
# 30*35 35*15 15*5 5*10 10*20 20*25
UpperCAmelCase_ , UpperCAmelCase_ = matrix_chain_order(__A )
print('''No. of Operation required: ''' + str(matrix[1][n - 1] ) )
print_optiomal_solution(__A , 1 , n - 1 )
if __name__ == "__main__":
main()
| 7 | 0 |
'''simple docstring'''
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_squeezebert import SqueezeBertTokenizer
a : List[Any] = logging.get_logger(__name__)
a : Dict = {"vocab_file": "vocab.txt", "tokenizer_file": "tokenizer.json"}
a : List[Any] = {
"vocab_file": {
"squeezebert/squeezebert-uncased": (
"https://huggingface.co/squeezebert/squeezebert-uncased/resolve/main/vocab.txt"
),
"squeezebert/squeezebert-mnli": "https://huggingface.co/squeezebert/squeezebert-mnli/resolve/main/vocab.txt",
"squeezebert/squeezebert-mnli-headless": (
"https://huggingface.co/squeezebert/squeezebert-mnli-headless/resolve/main/vocab.txt"
),
},
"tokenizer_file": {
"squeezebert/squeezebert-uncased": (
"https://huggingface.co/squeezebert/squeezebert-uncased/resolve/main/tokenizer.json"
),
"squeezebert/squeezebert-mnli": (
"https://huggingface.co/squeezebert/squeezebert-mnli/resolve/main/tokenizer.json"
),
"squeezebert/squeezebert-mnli-headless": (
"https://huggingface.co/squeezebert/squeezebert-mnli-headless/resolve/main/tokenizer.json"
),
},
}
a : List[str] = {
"squeezebert/squeezebert-uncased": 5_12,
"squeezebert/squeezebert-mnli": 5_12,
"squeezebert/squeezebert-mnli-headless": 5_12,
}
a : Dict = {
"squeezebert/squeezebert-uncased": {"do_lower_case": True},
"squeezebert/squeezebert-mnli": {"do_lower_case": True},
"squeezebert/squeezebert-mnli-headless": {"do_lower_case": True},
}
class UpperCamelCase__ ( lowercase__ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Union[str, Any] = VOCAB_FILES_NAMES
SCREAMING_SNAKE_CASE__ : List[Any] = PRETRAINED_VOCAB_FILES_MAP
SCREAMING_SNAKE_CASE__ : int = PRETRAINED_INIT_CONFIGURATION
SCREAMING_SNAKE_CASE__ : Any = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
SCREAMING_SNAKE_CASE__ : Dict = SqueezeBertTokenizer
def __init__( self , snake_case=None , snake_case=None , snake_case=True , snake_case="[UNK]" , snake_case="[SEP]" , snake_case="[PAD]" , snake_case="[CLS]" , snake_case="[MASK]" , snake_case=True , snake_case=None , **snake_case , ):
'''simple docstring'''
super().__init__(
snake_case , tokenizer_file=snake_case , do_lower_case=snake_case , unk_token=snake_case , sep_token=snake_case , pad_token=snake_case , cls_token=snake_case , mask_token=snake_case , tokenize_chinese_chars=snake_case , strip_accents=snake_case , **snake_case , )
UpperCAmelCase : Optional[Any] = json.loads(self.backend_tokenizer.normalizer.__getstate__() )
if (
normalizer_state.get("lowercase" , snake_case ) != do_lower_case
or normalizer_state.get("strip_accents" , snake_case ) != strip_accents
or normalizer_state.get("handle_chinese_chars" , snake_case ) != tokenize_chinese_chars
):
UpperCAmelCase : Dict = getattr(snake_case , normalizer_state.pop("type" ) )
UpperCAmelCase : Optional[int] = do_lower_case
UpperCAmelCase : Optional[int] = strip_accents
UpperCAmelCase : Any = tokenize_chinese_chars
UpperCAmelCase : Any = normalizer_class(**snake_case )
UpperCAmelCase : Optional[int] = do_lower_case
def A_ ( self , snake_case , snake_case=None ):
'''simple docstring'''
UpperCAmelCase : str = [self.cls_token_id] + token_ids_a + [self.sep_token_id]
if token_ids_a:
output += token_ids_a + [self.sep_token_id]
return output
def A_ ( self , snake_case , snake_case = None ):
'''simple docstring'''
UpperCAmelCase : Optional[int] = [self.sep_token_id]
UpperCAmelCase : str = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep ) * [0] + len(token_ids_a + sep ) * [1]
def A_ ( self , snake_case , snake_case = None ):
'''simple docstring'''
UpperCAmelCase : Any = self._tokenizer.model.save(snake_case , name=snake_case )
return tuple(snake_case )
| 311 |
'''simple docstring'''
import argparse
import logging
import pickle
import random
import time
import numpy as np
from transformers import BertTokenizer, GPTaTokenizer, RobertaTokenizer
logging.basicConfig(
format="%(asctime)s - %(levelname)s - %(name)s - %(message)s", datefmt="%m/%d/%Y %H:%M:%S", level=logging.INFO
)
a : Tuple = logging.getLogger(__name__)
def lowercase ( ):
'''simple docstring'''
UpperCAmelCase : Any = argparse.ArgumentParser(
description="Preprocess the data to avoid re-doing it several times by (tokenization + token_to_ids)." )
parser.add_argument("--file_path" , type=__magic_name__ , default="data/dump.txt" , help="The path to the data." )
parser.add_argument("--tokenizer_type" , type=__magic_name__ , default="bert" , choices=["bert", "roberta", "gpt2"] )
parser.add_argument("--tokenizer_name" , type=__magic_name__ , default="bert-base-uncased" , help="The tokenizer to use." )
parser.add_argument("--dump_file" , type=__magic_name__ , default="data/dump" , help="The dump file prefix." )
UpperCAmelCase : List[Any] = parser.parse_args()
logger.info(F"Loading Tokenizer ({args.tokenizer_name})" )
if args.tokenizer_type == "bert":
UpperCAmelCase : Any = BertTokenizer.from_pretrained(args.tokenizer_name )
UpperCAmelCase : Optional[int] = tokenizer.special_tokens_map["cls_token"] # `[CLS]`
UpperCAmelCase : Any = tokenizer.special_tokens_map["sep_token"] # `[SEP]`
elif args.tokenizer_type == "roberta":
UpperCAmelCase : List[Any] = RobertaTokenizer.from_pretrained(args.tokenizer_name )
UpperCAmelCase : Tuple = tokenizer.special_tokens_map["cls_token"] # `<s>`
UpperCAmelCase : Optional[int] = tokenizer.special_tokens_map["sep_token"] # `</s>`
elif args.tokenizer_type == "gpt2":
UpperCAmelCase : List[str] = GPTaTokenizer.from_pretrained(args.tokenizer_name )
UpperCAmelCase : Optional[Any] = tokenizer.special_tokens_map["bos_token"] # `<|endoftext|>`
UpperCAmelCase : List[Any] = tokenizer.special_tokens_map["eos_token"] # `<|endoftext|>`
logger.info(F"Loading text from {args.file_path}" )
with open(args.file_path , "r" , encoding="utf8" ) as fp:
UpperCAmelCase : str = fp.readlines()
logger.info("Start encoding" )
logger.info(F"{len(__magic_name__ )} examples to process." )
UpperCAmelCase : int = []
UpperCAmelCase : int = 0
UpperCAmelCase : Union[str, Any] = 1_0000
UpperCAmelCase : Union[str, Any] = time.time()
for text in data:
UpperCAmelCase : Dict = F"{bos} {text.strip()} {sep}"
UpperCAmelCase : Tuple = tokenizer.encode(__magic_name__ , add_special_tokens=__magic_name__ )
rslt.append(__magic_name__ )
iter += 1
if iter % interval == 0:
UpperCAmelCase : Dict = time.time()
logger.info(F"{iter} examples processed. - {(end-start):.2f}s/{interval}expl" )
UpperCAmelCase : Any = time.time()
logger.info("Finished binarization" )
logger.info(F"{len(__magic_name__ )} examples processed." )
UpperCAmelCase : str = F"{args.dump_file}.{args.tokenizer_name}.pickle"
UpperCAmelCase : List[str] = tokenizer.vocab_size
if vocab_size < (1 << 16):
UpperCAmelCase : int = [np.uintaa(__magic_name__ ) for d in rslt]
else:
UpperCAmelCase : int = [np.intaa(__magic_name__ ) for d in rslt]
random.shuffle(rslt_ )
logger.info(F"Dump to {dp_file}" )
with open(__magic_name__ , "wb" ) as handle:
pickle.dump(rslt_ , __magic_name__ , protocol=pickle.HIGHEST_PROTOCOL )
if __name__ == "__main__":
main()
| 311 | 1 |
from random import shuffle
import tensorflow as tf
from numpy import array
def lowerCamelCase__ ( UpperCamelCase__ : str , UpperCamelCase__ : List[str] ) -> Optional[Any]:
'''simple docstring'''
_snake_case = int(lowercase_ )
assert noofclusters < len(lowercase_ )
# Find out the dimensionality
_snake_case = len(vectors[0] )
# Will help select random centroids from among the available vectors
_snake_case = list(range(len(lowercase_ ) ) )
shuffle(lowercase_ )
# GRAPH OF COMPUTATION
# We initialize a new graph and set it as the default during each run
# of this algorithm. This ensures that as this function is called
# multiple times, the default graph doesn't keep getting crowded with
# unused ops and Variables from previous function calls.
_snake_case = tf.Graph()
with graph.as_default():
# SESSION OF COMPUTATION
_snake_case = tf.Session()
##CONSTRUCTING THE ELEMENTS OF COMPUTATION
##First lets ensure we have a Variable vector for each centroid,
##initialized to one of the vectors from the available data points
_snake_case = [
tf.Variable(vectors[vector_indices[i]] ) for i in range(lowercase_ )
]
##These nodes will assign the centroid Variables the appropriate
##values
_snake_case = tf.placeholder('float64' , [dim] )
_snake_case = []
for centroid in centroids:
cent_assigns.append(tf.assign(lowercase_ , lowercase_ ) )
##Variables for cluster assignments of individual vectors(initialized
##to 0 at first)
_snake_case = [tf.Variable(0 ) for i in range(len(lowercase_ ) )]
##These nodes will assign an assignment Variable the appropriate
##value
_snake_case = tf.placeholder('int32' )
_snake_case = []
for assignment in assignments:
cluster_assigns.append(tf.assign(lowercase_ , lowercase_ ) )
##Now lets construct the node that will compute the mean
# The placeholder for the input
_snake_case = tf.placeholder('float' , [None, dim] )
# The Node/op takes the input and computes a mean along the 0th
# dimension, i.e. the list of input vectors
_snake_case = tf.reduce_mean(lowercase_ , 0 )
##Node for computing Euclidean distances
# Placeholders for input
_snake_case = tf.placeholder('float' , [dim] )
_snake_case = tf.placeholder('float' , [dim] )
_snake_case = tf.sqrt(tf.reduce_sum(tf.pow(tf.sub(lowercase_ , lowercase_ ) , 2 ) ) )
##This node will figure out which cluster to assign a vector to,
##based on Euclidean distances of the vector from the centroids.
# Placeholder for input
_snake_case = tf.placeholder('float' , [noofclusters] )
_snake_case = tf.argmin(lowercase_ , 0 )
##INITIALIZING STATE VARIABLES
##This will help initialization of all Variables defined with respect
##to the graph. The Variable-initializer should be defined after
##all the Variables have been constructed, so that each of them
##will be included in the initialization.
_snake_case = tf.initialize_all_variables()
# Initialize all variables
sess.run(lowercase_ )
##CLUSTERING ITERATIONS
# Now perform the Expectation-Maximization steps of K-Means clustering
# iterations. To keep things simple, we will only do a set number of
# iterations, instead of using a Stopping Criterion.
_snake_case = 100
for _ in range(lowercase_ ):
##EXPECTATION STEP
##Based on the centroid locations till last iteration, compute
##the _expected_ centroid assignments.
# Iterate over each vector
for vector_n in range(len(lowercase_ ) ):
_snake_case = vectors[vector_n]
# Compute Euclidean distance between this vector and each
# centroid. Remember that this list cannot be named
#'centroid_distances', since that is the input to the
# cluster assignment node.
_snake_case = [
sess.run(lowercase_ , feed_dict={va: vect, va: sess.run(lowercase_ )} )
for centroid in centroids
]
# Now use the cluster assignment node, with the distances
# as the input
_snake_case = sess.run(
lowercase_ , feed_dict={centroid_distances: distances} )
# Now assign the value to the appropriate state variable
sess.run(
cluster_assigns[vector_n] , feed_dict={assignment_value: assignment} )
##MAXIMIZATION STEP
# Based on the expected state computed from the Expectation Step,
# compute the locations of the centroids so as to maximize the
# overall objective of minimizing within-cluster Sum-of-Squares
for cluster_n in range(lowercase_ ):
# Collect all the vectors assigned to this cluster
_snake_case = [
vectors[i]
for i in range(len(lowercase_ ) )
if sess.run(assignments[i] ) == cluster_n
]
# Compute new centroid location
_snake_case = sess.run(
lowercase_ , feed_dict={mean_input: array(lowercase_ )} )
# Assign value to appropriate variable
sess.run(
cent_assigns[cluster_n] , feed_dict={centroid_value: new_location} )
# Return centroids and assignments
_snake_case = sess.run(lowercase_ )
_snake_case = sess.run(lowercase_ )
return centroids, assignments
| 357 |
import unittest
from typing import Tuple
import torch
from diffusers.utils import floats_tensor, randn_tensor, torch_all_close, torch_device
from diffusers.utils.testing_utils import require_torch
@require_torch
class UpperCamelCase_ :
@property
def lowerCAmelCase ( self ) -> int:
return self.get_dummy_input()
@property
def lowerCAmelCase ( self ) -> Optional[Any]:
if self.block_type == "down":
return (4, 32, 16, 16)
elif self.block_type == "mid":
return (4, 32, 32, 32)
elif self.block_type == "up":
return (4, 32, 64, 64)
raise ValueError(F'''\'{self.block_type}\' is not a supported block_type. Set it to \'up\', \'mid\', or \'down\'.''' )
def lowerCAmelCase ( self , lowerCAmelCase_=True , lowerCAmelCase_=False , lowerCAmelCase_=False , lowerCAmelCase_=False , ) -> List[str]:
_snake_case = 4
_snake_case = 32
_snake_case = (32, 32)
_snake_case = torch.manual_seed(0 )
_snake_case = torch.device(lowerCAmelCase_ )
_snake_case = (batch_size, num_channels) + sizes
_snake_case = randn_tensor(lowerCAmelCase_ , generator=lowerCAmelCase_ , device=lowerCAmelCase_ )
_snake_case = {'hidden_states': hidden_states}
if include_temb:
_snake_case = 128
_snake_case = randn_tensor((batch_size, temb_channels) , generator=lowerCAmelCase_ , device=lowerCAmelCase_ )
if include_res_hidden_states_tuple:
_snake_case = torch.manual_seed(1 )
_snake_case = (randn_tensor(lowerCAmelCase_ , generator=lowerCAmelCase_ , device=lowerCAmelCase_ ),)
if include_encoder_hidden_states:
_snake_case = floats_tensor((batch_size, 32, 32) ).to(lowerCAmelCase_ )
if include_skip_sample:
_snake_case = randn_tensor(((batch_size, 3) + sizes) , generator=lowerCAmelCase_ , device=lowerCAmelCase_ )
return dummy_input
def lowerCAmelCase ( self ) -> Tuple:
_snake_case = {
'in_channels': 32,
'out_channels': 32,
'temb_channels': 128,
}
if self.block_type == "up":
_snake_case = 32
if self.block_type == "mid":
init_dict.pop('out_channels' )
_snake_case = self.dummy_input
return init_dict, inputs_dict
def lowerCAmelCase ( self , lowerCAmelCase_ ) -> Optional[int]:
_snake_case , _snake_case = self.prepare_init_args_and_inputs_for_common()
_snake_case = self.block_class(**lowerCAmelCase_ )
unet_block.to(lowerCAmelCase_ )
unet_block.eval()
with torch.no_grad():
_snake_case = unet_block(**lowerCAmelCase_ )
if isinstance(lowerCAmelCase_ , lowerCAmelCase_ ):
_snake_case = output[0]
self.assertEqual(output.shape , self.output_shape )
_snake_case = output[0, -1, -3:, -3:]
_snake_case = torch.tensor(lowerCAmelCase_ ).to(lowerCAmelCase_ )
assert torch_all_close(output_slice.flatten() , lowerCAmelCase_ , atol=5E-3 )
@unittest.skipIf(torch_device == 'mps' , 'Training is not supported in mps' )
def lowerCAmelCase ( self ) -> Tuple:
_snake_case , _snake_case = self.prepare_init_args_and_inputs_for_common()
_snake_case = self.block_class(**lowerCAmelCase_ )
model.to(lowerCAmelCase_ )
model.train()
_snake_case = model(**lowerCAmelCase_ )
if isinstance(lowerCAmelCase_ , lowerCAmelCase_ ):
_snake_case = output[0]
_snake_case = torch.device(lowerCAmelCase_ )
_snake_case = randn_tensor(output.shape , device=lowerCAmelCase_ )
_snake_case = torch.nn.functional.mse_loss(lowerCAmelCase_ , lowerCAmelCase_ )
loss.backward()
| 295 | 0 |
"""simple docstring"""
import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional
import numpy as np
import torch
from datasets import load_dataset
from torchvision.transforms import Compose, Lambda, Normalize, RandomHorizontalFlip, RandomResizedCrop, ToTensor
import transformers
from transformers import (
CONFIG_MAPPING,
IMAGE_PROCESSOR_MAPPING,
MODEL_FOR_MASKED_IMAGE_MODELING_MAPPING,
AutoConfig,
AutoImageProcessor,
AutoModelForMaskedImageModeling,
HfArgumentParser,
Trainer,
TrainingArguments,
)
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
lowerCamelCase_ = 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")
lowerCamelCase_ = list(MODEL_FOR_MASKED_IMAGE_MODELING_MAPPING.keys())
lowerCamelCase_ = tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)
@dataclass
class _SCREAMING_SNAKE_CASE:
SCREAMING_SNAKE_CASE_ : Optional[str] = field(
default='''cifar10''' , metadata={'''help''': '''Name of a dataset from the datasets package'''} )
SCREAMING_SNAKE_CASE_ : Optional[str] = field(
default=A , metadata={'''help''': '''The configuration name of the dataset to use (via the datasets library).'''} )
SCREAMING_SNAKE_CASE_ : Optional[str] = field(
default=A , metadata={'''help''': '''The column name of the images in the files. If not set, will try to use \'image\' or \'img\'.'''} , )
SCREAMING_SNAKE_CASE_ : Optional[str] = field(default=A , metadata={'''help''': '''A folder containing the training data.'''} )
SCREAMING_SNAKE_CASE_ : Optional[str] = field(default=A , metadata={'''help''': '''A folder containing the validation data.'''} )
SCREAMING_SNAKE_CASE_ : Optional[float] = field(
default=0.15 , metadata={'''help''': '''Percent to split off of train for validation.'''} )
SCREAMING_SNAKE_CASE_ : int = field(default=32 , metadata={'''help''': '''The size of the square patches to use for masking.'''} )
SCREAMING_SNAKE_CASE_ : float = field(
default=0.6 , metadata={'''help''': '''Percentage of patches to mask.'''} , )
SCREAMING_SNAKE_CASE_ : Optional[int] = field(
default=A , metadata={
'''help''': (
'''For debugging purposes or quicker training, truncate the number of training examples to this '''
'''value if set.'''
)
} , )
SCREAMING_SNAKE_CASE_ : Optional[int] = field(
default=A , metadata={
'''help''': (
'''For debugging purposes or quicker training, truncate the number of evaluation examples to this '''
'''value if set.'''
)
} , )
def _UpperCamelCase ( self ) -> List[Any]:
"""simple docstring"""
__SCREAMING_SNAKE_CASE :int = {}
if self.train_dir is not None:
__SCREAMING_SNAKE_CASE :Dict = self.train_dir
if self.validation_dir is not None:
__SCREAMING_SNAKE_CASE :Any = self.validation_dir
__SCREAMING_SNAKE_CASE :Dict = data_files if data_files else None
@dataclass
class _SCREAMING_SNAKE_CASE:
SCREAMING_SNAKE_CASE_ : str = field(
default=A , metadata={
'''help''': (
'''The model checkpoint for weights initialization. Can be a local path to a pytorch_model.bin or a '''
'''checkpoint identifier on the hub. '''
'''Don\'t set if you want to train a model from scratch.'''
)
} , )
SCREAMING_SNAKE_CASE_ : Optional[str] = field(
default=A , metadata={'''help''': '''If training from scratch, pass a model type from the list: ''' + ''', '''.join(A )} , )
SCREAMING_SNAKE_CASE_ : Optional[str] = field(
default=A , metadata={'''help''': '''Pretrained config name or path if not the same as model_name'''} )
SCREAMING_SNAKE_CASE_ : Optional[str] = field(
default=A , 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'''
)
} , )
SCREAMING_SNAKE_CASE_ : Optional[str] = field(
default=A , metadata={'''help''': '''Where do you want to store (cache) the pretrained models/datasets downloaded from the hub'''} , )
SCREAMING_SNAKE_CASE_ : str = field(
default='''main''' , metadata={'''help''': '''The specific model version to use (can be a branch name, tag name or commit id).'''} , )
SCREAMING_SNAKE_CASE_ : str = field(default=A , metadata={'''help''': '''Name or path of preprocessor config.'''} )
SCREAMING_SNAKE_CASE_ : bool = field(
default=A , metadata={
'''help''': (
'''Will use the token generated when running `huggingface-cli login` (necessary to use this script '''
'''with private models).'''
)
} , )
SCREAMING_SNAKE_CASE_ : Optional[int] = field(
default=A , metadata={
'''help''': (
'''The size (resolution) of each image. If not specified, will use `image_size` of the configuration.'''
)
} , )
SCREAMING_SNAKE_CASE_ : Optional[int] = field(
default=A , metadata={
'''help''': (
'''The size (resolution) of each patch. If not specified, will use `patch_size` of the configuration.'''
)
} , )
SCREAMING_SNAKE_CASE_ : Optional[int] = field(
default=A , metadata={'''help''': '''Stride to use for the encoder.'''} , )
class _SCREAMING_SNAKE_CASE:
def __init__( self ,SCREAMING_SNAKE_CASE__=1_92 ,SCREAMING_SNAKE_CASE__=32 ,SCREAMING_SNAKE_CASE__=4 ,SCREAMING_SNAKE_CASE__=0.6 ) -> List[str]:
"""simple docstring"""
__SCREAMING_SNAKE_CASE :int = input_size
__SCREAMING_SNAKE_CASE :Any = mask_patch_size
__SCREAMING_SNAKE_CASE :List[Any] = model_patch_size
__SCREAMING_SNAKE_CASE :Union[str, Any] = mask_ratio
if self.input_size % self.mask_patch_size != 0:
raise ValueError('''Input size must be divisible by mask patch size''' )
if self.mask_patch_size % self.model_patch_size != 0:
raise ValueError('''Mask patch size must be divisible by model patch size''' )
__SCREAMING_SNAKE_CASE :Any = self.input_size // self.mask_patch_size
__SCREAMING_SNAKE_CASE :Dict = self.mask_patch_size // self.model_patch_size
__SCREAMING_SNAKE_CASE :List[Any] = self.rand_size**2
__SCREAMING_SNAKE_CASE :List[Any] = int(np.ceil(self.token_count * self.mask_ratio ) )
def __call__( self ) -> List[str]:
"""simple docstring"""
__SCREAMING_SNAKE_CASE :str = np.random.permutation(self.token_count )[: self.mask_count]
__SCREAMING_SNAKE_CASE :Optional[int] = np.zeros(self.token_count ,dtype=lowerCamelCase__ )
__SCREAMING_SNAKE_CASE :int = 1
__SCREAMING_SNAKE_CASE :Any = mask.reshape((self.rand_size, self.rand_size) )
__SCREAMING_SNAKE_CASE :Tuple = mask.repeat(self.scale ,axis=0 ).repeat(self.scale ,axis=1 )
return torch.tensor(mask.flatten() )
def __lowerCamelCase ( a_ : List[str] ) -> int:
__SCREAMING_SNAKE_CASE :List[str] = torch.stack([example['''pixel_values'''] for example in examples] )
__SCREAMING_SNAKE_CASE :Dict = torch.stack([example['''mask'''] for example in examples] )
return {"pixel_values": pixel_values, "bool_masked_pos": mask}
def __lowerCamelCase ( ) -> List[str]:
# See all possible arguments in src/transformers/training_args.py
# or by passing the --help flag to this script.
# We now keep distinct sets of args, for a cleaner separation of concerns.
__SCREAMING_SNAKE_CASE :str = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments) )
if len(sys.argv ) == 2 and sys.argv[1].endswith('''.json''' ):
# If we pass only one argument to the script and it's the path to a json file,
# let's parse it to get our arguments.
__SCREAMING_SNAKE_CASE :Tuple = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1] ) )
else:
__SCREAMING_SNAKE_CASE :Dict = 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_mim''' , a_ , a_ )
# 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()
__SCREAMING_SNAKE_CASE :Optional[Any] = training_args.get_process_log_level()
logger.setLevel(a_ )
transformers.utils.logging.set_verbosity(a_ )
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.
__SCREAMING_SNAKE_CASE :Any = None
if os.path.isdir(training_args.output_dir ) and training_args.do_train and not training_args.overwrite_output_dir:
__SCREAMING_SNAKE_CASE :int = 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.
__SCREAMING_SNAKE_CASE :Union[str, Any] = 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.
__SCREAMING_SNAKE_CASE :int = None if 'validation' in ds.keys() else data_args.train_val_split
if isinstance(data_args.train_val_split , a_ ) and data_args.train_val_split > 0.0:
__SCREAMING_SNAKE_CASE :int = ds['train'].train_test_split(data_args.train_val_split )
__SCREAMING_SNAKE_CASE :Optional[Any] = split['train']
__SCREAMING_SNAKE_CASE :List[str] = split['test']
# Create config
# Distributed training:
# The .from_pretrained methods guarantee that only one local process can concurrently
# download model & vocab.
__SCREAMING_SNAKE_CASE :Dict = {
'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_or_path:
__SCREAMING_SNAKE_CASE :Tuple = AutoConfig.from_pretrained(model_args.config_name_or_path , **a_ )
elif model_args.model_name_or_path:
__SCREAMING_SNAKE_CASE :List[str] = AutoConfig.from_pretrained(model_args.model_name_or_path , **a_ )
else:
__SCREAMING_SNAKE_CASE :Any = CONFIG_MAPPING[model_args.model_type]()
logger.warning('''You are instantiating a new config instance from scratch.''' )
if model_args.config_overrides is not None:
logger.info(f'''Overriding config: {model_args.config_overrides}''' )
config.update_from_string(model_args.config_overrides )
logger.info(f'''New config: {config}''' )
# make sure the decoder_type is "simmim" (only relevant for BEiT)
if hasattr(a_ , '''decoder_type''' ):
__SCREAMING_SNAKE_CASE :List[str] = 'simmim'
# adapt config
__SCREAMING_SNAKE_CASE :Dict = model_args.image_size if model_args.image_size is not None else config.image_size
__SCREAMING_SNAKE_CASE :Optional[Any] = model_args.patch_size if model_args.patch_size is not None else config.patch_size
__SCREAMING_SNAKE_CASE :int = (
model_args.encoder_stride if model_args.encoder_stride is not None else config.encoder_stride
)
config.update(
{
'''image_size''': model_args.image_size,
'''patch_size''': model_args.patch_size,
'''encoder_stride''': model_args.encoder_stride,
} )
# create image processor
if model_args.image_processor_name:
__SCREAMING_SNAKE_CASE :Optional[Any] = AutoImageProcessor.from_pretrained(model_args.image_processor_name , **a_ )
elif model_args.model_name_or_path:
__SCREAMING_SNAKE_CASE :Tuple = AutoImageProcessor.from_pretrained(model_args.model_name_or_path , **a_ )
else:
__SCREAMING_SNAKE_CASE :List[str] = {
conf.model_type: image_processor_class for conf, image_processor_class in IMAGE_PROCESSOR_MAPPING.items()
}
__SCREAMING_SNAKE_CASE :Dict = IMAGE_PROCESSOR_TYPES[model_args.model_type]()
# create model
if model_args.model_name_or_path:
__SCREAMING_SNAKE_CASE :List[Any] = AutoModelForMaskedImageModeling.from_pretrained(
model_args.model_name_or_path , from_tf=bool('''.ckpt''' in model_args.model_name_or_path ) , config=a_ , cache_dir=model_args.cache_dir , revision=model_args.model_revision , use_auth_token=True if model_args.use_auth_token else None , )
else:
logger.info('''Training new model from scratch''' )
__SCREAMING_SNAKE_CASE :Tuple = AutoModelForMaskedImageModeling.from_config(a_ )
if training_args.do_train:
__SCREAMING_SNAKE_CASE :Union[str, Any] = ds['train'].column_names
else:
__SCREAMING_SNAKE_CASE :Dict = ds['validation'].column_names
if data_args.image_column_name is not None:
__SCREAMING_SNAKE_CASE :Optional[Any] = data_args.image_column_name
elif "image" in column_names:
__SCREAMING_SNAKE_CASE :List[Any] = 'image'
elif "img" in column_names:
__SCREAMING_SNAKE_CASE :Optional[int] = 'img'
else:
__SCREAMING_SNAKE_CASE :List[Any] = column_names[0]
# transformations as done in original SimMIM paper
# source: https://github.com/microsoft/SimMIM/blob/main/data/data_simmim.py
__SCREAMING_SNAKE_CASE :int = Compose(
[
Lambda(lambda a_ : img.convert('''RGB''' ) if img.mode != "RGB" else img ),
RandomResizedCrop(model_args.image_size , scale=(0.67, 1.0) , ratio=(3.0 / 4.0, 4.0 / 3.0) ),
RandomHorizontalFlip(),
ToTensor(),
Normalize(mean=image_processor.image_mean , std=image_processor.image_std ),
] )
# create mask generator
__SCREAMING_SNAKE_CASE :Tuple = MaskGenerator(
input_size=model_args.image_size , mask_patch_size=data_args.mask_patch_size , model_patch_size=model_args.patch_size , mask_ratio=data_args.mask_ratio , )
def preprocess_images(a_ : Tuple ):
__SCREAMING_SNAKE_CASE :Dict = [transforms(a_ ) for image in examples[image_column_name]]
__SCREAMING_SNAKE_CASE :int = [mask_generator() for i in range(len(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:
__SCREAMING_SNAKE_CASE :Optional[int] = ds['train'].shuffle(seed=training_args.seed ).select(range(data_args.max_train_samples ) )
# Set the training transforms
ds["train"].set_transform(a_ )
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:
__SCREAMING_SNAKE_CASE :int = (
ds['validation'].shuffle(seed=training_args.seed ).select(range(data_args.max_eval_samples ) )
)
# Set the validation transforms
ds["validation"].set_transform(a_ )
# Initialize our trainer
__SCREAMING_SNAKE_CASE :int = Trainer(
model=a_ , args=a_ , train_dataset=ds['''train'''] if training_args.do_train else None , eval_dataset=ds['''validation'''] if training_args.do_eval else None , tokenizer=a_ , data_collator=a_ , )
# Training
if training_args.do_train:
__SCREAMING_SNAKE_CASE :Optional[Any] = None
if training_args.resume_from_checkpoint is not None:
__SCREAMING_SNAKE_CASE :List[str] = training_args.resume_from_checkpoint
elif last_checkpoint is not None:
__SCREAMING_SNAKE_CASE :List[Any] = last_checkpoint
__SCREAMING_SNAKE_CASE :List[str] = trainer.train(resume_from_checkpoint=a_ )
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:
__SCREAMING_SNAKE_CASE :Optional[int] = trainer.evaluate()
trainer.log_metrics('''eval''' , a_ )
trainer.save_metrics('''eval''' , a_ )
# Write model card and (optionally) push to hub
__SCREAMING_SNAKE_CASE :Tuple = {
'finetuned_from': model_args.model_name_or_path,
'tasks': 'masked-image-modeling',
'dataset': data_args.dataset_name,
'tags': ['masked-image-modeling'],
}
if training_args.push_to_hub:
trainer.push_to_hub(**a_ )
else:
trainer.create_model_card(**a_ )
if __name__ == "__main__":
main() | 191 |
import gc
import random
import tempfile
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMInverseScheduler,
DDIMScheduler,
DPMSolverMultistepInverseScheduler,
DPMSolverMultistepScheduler,
StableDiffusionDiffEditPipeline,
UNetaDConditionModel,
)
from diffusers.utils import load_image, slow
from diffusers.utils.testing_utils import enable_full_determinism, floats_tensor, require_torch_gpu, torch_device
from ..pipeline_params import TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS, TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin
enable_full_determinism()
class __A ( a , a , unittest.TestCase ):
"""simple docstring"""
UpperCamelCase__ : Optional[Any] =StableDiffusionDiffEditPipeline
UpperCamelCase__ : str =TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {"""height""", """width""", """image"""} | {"""image_latents"""}
UpperCamelCase__ : Optional[Any] =TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS - {"""image"""} | {"""image_latents"""}
UpperCamelCase__ : Dict =frozenset(
[] ) # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
UpperCamelCase__ : Any =frozenset([] )
def __lowercase ( self ):
"""simple docstring"""
torch.manual_seed(0 )
__UpperCamelCase : Dict =UNetaDConditionModel(
block_out_channels=(32, 64) , layers_per_block=2 , sample_size=32 , in_channels=4 , out_channels=4 , down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D') , up_block_types=('CrossAttnUpBlock2D', 'UpBlock2D') , cross_attention_dim=32 , attention_head_dim=(2, 4) , use_linear_projection=lowerCamelCase__ , )
__UpperCamelCase : List[str] =DDIMScheduler(
beta_start=0.00_085 , beta_end=0.012 , beta_schedule='scaled_linear' , clip_sample=lowerCamelCase__ , set_alpha_to_one=lowerCamelCase__ , )
__UpperCamelCase : Union[str, Any] =DDIMInverseScheduler(
beta_start=0.00_085 , beta_end=0.012 , beta_schedule='scaled_linear' , clip_sample=lowerCamelCase__ , set_alpha_to_zero=lowerCamelCase__ , )
torch.manual_seed(0 )
__UpperCamelCase : Optional[int] =AutoencoderKL(
block_out_channels=[32, 64] , in_channels=3 , out_channels=3 , down_block_types=['DownEncoderBlock2D', 'DownEncoderBlock2D'] , up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D'] , latent_channels=4 , sample_size=128 , )
torch.manual_seed(0 )
__UpperCamelCase : Tuple =CLIPTextConfig(
bos_token_id=0 , eos_token_id=2 , hidden_size=32 , intermediate_size=37 , layer_norm_eps=1E-05 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=1000 , hidden_act='gelu' , projection_dim=512 , )
__UpperCamelCase : Any =CLIPTextModel(lowerCamelCase__ )
__UpperCamelCase : int =CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip' )
__UpperCamelCase : Union[str, Any] ={
'unet': unet,
'scheduler': scheduler,
'inverse_scheduler': inverse_scheduler,
'vae': vae,
'text_encoder': text_encoder,
'tokenizer': tokenizer,
'safety_checker': None,
'feature_extractor': None,
}
return components
def __lowercase ( self , lowerCamelCase__ , lowerCamelCase__=0 ):
"""simple docstring"""
__UpperCamelCase : int =floats_tensor((1, 16, 16) , rng=random.Random(lowerCamelCase__ ) ).to(lowerCamelCase__ )
__UpperCamelCase : Any =floats_tensor((1, 2, 4, 16, 16) , rng=random.Random(lowerCamelCase__ ) ).to(lowerCamelCase__ )
if str(lowerCamelCase__ ).startswith('mps' ):
__UpperCamelCase : Any =torch.manual_seed(lowerCamelCase__ )
else:
__UpperCamelCase : Optional[int] =torch.Generator(device=lowerCamelCase__ ).manual_seed(lowerCamelCase__ )
__UpperCamelCase : Dict ={
'prompt': 'a dog and a newt',
'mask_image': mask,
'image_latents': latents,
'generator': generator,
'num_inference_steps': 2,
'inpaint_strength': 1.0,
'guidance_scale': 6.0,
'output_type': 'numpy',
}
return inputs
def __lowercase ( self , lowerCamelCase__ , lowerCamelCase__=0 ):
"""simple docstring"""
__UpperCamelCase : Tuple =floats_tensor((1, 3, 32, 32) , rng=random.Random(lowerCamelCase__ ) ).to(lowerCamelCase__ )
__UpperCamelCase : int =image.cpu().permute(0 , 2 , 3 , 1 )[0]
__UpperCamelCase : Optional[Any] =Image.fromarray(np.uinta(lowerCamelCase__ ) ).convert('RGB' )
if str(lowerCamelCase__ ).startswith('mps' ):
__UpperCamelCase : List[Any] =torch.manual_seed(lowerCamelCase__ )
else:
__UpperCamelCase : Any =torch.Generator(device=lowerCamelCase__ ).manual_seed(lowerCamelCase__ )
__UpperCamelCase : Optional[int] ={
'image': image,
'source_prompt': 'a cat and a frog',
'target_prompt': 'a dog and a newt',
'generator': generator,
'num_inference_steps': 2,
'num_maps_per_mask': 2,
'mask_encode_strength': 1.0,
'guidance_scale': 6.0,
'output_type': 'numpy',
}
return inputs
def __lowercase ( self , lowerCamelCase__ , lowerCamelCase__=0 ):
"""simple docstring"""
__UpperCamelCase : str =floats_tensor((1, 3, 32, 32) , rng=random.Random(lowerCamelCase__ ) ).to(lowerCamelCase__ )
__UpperCamelCase : Any =image.cpu().permute(0 , 2 , 3 , 1 )[0]
__UpperCamelCase : int =Image.fromarray(np.uinta(lowerCamelCase__ ) ).convert('RGB' )
if str(lowerCamelCase__ ).startswith('mps' ):
__UpperCamelCase : Any =torch.manual_seed(lowerCamelCase__ )
else:
__UpperCamelCase : int =torch.Generator(device=lowerCamelCase__ ).manual_seed(lowerCamelCase__ )
__UpperCamelCase : Optional[int] ={
'image': image,
'prompt': 'a cat and a frog',
'generator': generator,
'num_inference_steps': 2,
'inpaint_strength': 1.0,
'guidance_scale': 6.0,
'decode_latents': True,
'output_type': 'numpy',
}
return inputs
def __lowercase ( self ):
"""simple docstring"""
if not hasattr(self.pipeline_class , '_optional_components' ):
return
__UpperCamelCase : Optional[Any] =self.get_dummy_components()
__UpperCamelCase : List[str] =self.pipeline_class(**lowerCamelCase__ )
pipe.to(lowerCamelCase__ )
pipe.set_progress_bar_config(disable=lowerCamelCase__ )
# set all optional components to None and update pipeline config accordingly
for optional_component in pipe._optional_components:
setattr(lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ )
pipe.register_modules(**{optional_component: None for optional_component in pipe._optional_components} )
__UpperCamelCase : Union[str, Any] =self.get_dummy_inputs(lowerCamelCase__ )
__UpperCamelCase : List[Any] =pipe(**lowerCamelCase__ )[0]
with tempfile.TemporaryDirectory() as tmpdir:
pipe.save_pretrained(lowerCamelCase__ )
__UpperCamelCase : Tuple =self.pipeline_class.from_pretrained(lowerCamelCase__ )
pipe_loaded.to(lowerCamelCase__ )
pipe_loaded.set_progress_bar_config(disable=lowerCamelCase__ )
for optional_component in pipe._optional_components:
self.assertTrue(
getattr(lowerCamelCase__ , lowerCamelCase__ ) is None , f'`{optional_component}` did not stay set to None after loading.' , )
__UpperCamelCase : str =self.get_dummy_inputs(lowerCamelCase__ )
__UpperCamelCase : Union[str, Any] =pipe_loaded(**lowerCamelCase__ )[0]
__UpperCamelCase : Tuple =np.abs(output - output_loaded ).max()
self.assertLess(lowerCamelCase__ , 1E-4 )
def __lowercase ( self ):
"""simple docstring"""
__UpperCamelCase : Any ='cpu'
__UpperCamelCase : Union[str, Any] =self.get_dummy_components()
__UpperCamelCase : Any =self.pipeline_class(**lowerCamelCase__ )
pipe.to(lowerCamelCase__ )
pipe.set_progress_bar_config(disable=lowerCamelCase__ )
__UpperCamelCase : int =self.get_dummy_mask_inputs(lowerCamelCase__ )
__UpperCamelCase : Union[str, Any] =pipe.generate_mask(**lowerCamelCase__ )
__UpperCamelCase : int =mask[0, -3:, -3:]
self.assertEqual(mask.shape , (1, 16, 16) )
__UpperCamelCase : Tuple =np.array([0] * 9 )
__UpperCamelCase : str =np.abs(mask_slice.flatten() - expected_slice ).max()
self.assertLessEqual(lowerCamelCase__ , 1E-3 )
self.assertEqual(mask[0, -3, -4] , 0 )
def __lowercase ( self ):
"""simple docstring"""
__UpperCamelCase : int ='cpu'
__UpperCamelCase : Union[str, Any] =self.get_dummy_components()
__UpperCamelCase : Optional[Any] =self.pipeline_class(**lowerCamelCase__ )
pipe.to(lowerCamelCase__ )
pipe.set_progress_bar_config(disable=lowerCamelCase__ )
__UpperCamelCase : Dict =self.get_dummy_inversion_inputs(lowerCamelCase__ )
__UpperCamelCase : List[Any] =pipe.invert(**lowerCamelCase__ ).images
__UpperCamelCase : Optional[Any] =image[0, -1, -3:, -3:]
self.assertEqual(image.shape , (2, 32, 32, 3) )
__UpperCamelCase : List[str] =np.array(
[0.5_150, 0.5_134, 0.5_043, 0.5_376, 0.4_694, 0.51_050, 0.5_015, 0.4_407, 0.4_799] , )
__UpperCamelCase : int =np.abs(image_slice.flatten() - expected_slice ).max()
self.assertLessEqual(lowerCamelCase__ , 1E-3 )
def __lowercase ( self ):
"""simple docstring"""
super().test_inference_batch_single_identical(expected_max_diff=5E-3 )
def __lowercase ( self ):
"""simple docstring"""
__UpperCamelCase : List[str] ='cpu'
__UpperCamelCase : int =self.get_dummy_components()
__UpperCamelCase : str ={'beta_start': 0.00_085, 'beta_end': 0.012, 'beta_schedule': 'scaled_linear'}
__UpperCamelCase : str =DPMSolverMultistepScheduler(**lowerCamelCase__ )
__UpperCamelCase : Dict =DPMSolverMultistepInverseScheduler(**lowerCamelCase__ )
__UpperCamelCase : Any =self.pipeline_class(**lowerCamelCase__ )
pipe.to(lowerCamelCase__ )
pipe.set_progress_bar_config(disable=lowerCamelCase__ )
__UpperCamelCase : Tuple =self.get_dummy_inversion_inputs(lowerCamelCase__ )
__UpperCamelCase : str =pipe.invert(**lowerCamelCase__ ).images
__UpperCamelCase : List[Any] =image[0, -1, -3:, -3:]
self.assertEqual(image.shape , (2, 32, 32, 3) )
__UpperCamelCase : List[str] =np.array(
[0.5_150, 0.5_134, 0.5_043, 0.5_376, 0.4_694, 0.51_050, 0.5_015, 0.4_407, 0.4_799] , )
__UpperCamelCase : Optional[Any] =np.abs(image_slice.flatten() - expected_slice ).max()
self.assertLessEqual(lowerCamelCase__ , 1E-3 )
@require_torch_gpu
@slow
class __A ( unittest.TestCase ):
"""simple docstring"""
def __lowercase ( self ):
"""simple docstring"""
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
@classmethod
def __lowercase ( cls ):
"""simple docstring"""
__UpperCamelCase : Optional[int] =load_image(
'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/diffedit/fruit.png' )
__UpperCamelCase : Union[str, Any] =raw_image.convert('RGB' ).resize((768, 768) )
__UpperCamelCase : List[Any] =raw_image
def __lowercase ( self ):
"""simple docstring"""
__UpperCamelCase : Optional[int] =torch.manual_seed(0 )
__UpperCamelCase : Dict =StableDiffusionDiffEditPipeline.from_pretrained(
'stabilityai/stable-diffusion-2-1' , safety_checker=lowerCamelCase__ , torch_dtype=torch.floataa )
__UpperCamelCase : List[str] =DDIMScheduler.from_config(pipe.scheduler.config )
__UpperCamelCase : List[str] =DDIMInverseScheduler.from_config(pipe.scheduler.config )
pipe.enable_model_cpu_offload()
pipe.set_progress_bar_config(disable=lowerCamelCase__ )
__UpperCamelCase : List[str] ='a bowl of fruit'
__UpperCamelCase : Dict ='a bowl of pears'
__UpperCamelCase : Tuple =pipe.generate_mask(
image=self.raw_image , source_prompt=lowerCamelCase__ , target_prompt=lowerCamelCase__ , generator=lowerCamelCase__ , )
__UpperCamelCase : int =pipe.invert(
prompt=lowerCamelCase__ , image=self.raw_image , inpaint_strength=0.7 , generator=lowerCamelCase__ ).latents
__UpperCamelCase : Dict =pipe(
prompt=lowerCamelCase__ , mask_image=lowerCamelCase__ , image_latents=lowerCamelCase__ , generator=lowerCamelCase__ , negative_prompt=lowerCamelCase__ , inpaint_strength=0.7 , output_type='numpy' , ).images[0]
__UpperCamelCase : str =(
np.array(
load_image(
'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
'/diffedit/pears.png' ).resize((768, 768) ) )
/ 255
)
assert np.abs((expected_image - image).max() ) < 5E-1
def __lowercase ( self ):
"""simple docstring"""
__UpperCamelCase : Any =torch.manual_seed(0 )
__UpperCamelCase : List[Any] =StableDiffusionDiffEditPipeline.from_pretrained(
'stabilityai/stable-diffusion-2-1' , safety_checker=lowerCamelCase__ , torch_dtype=torch.floataa )
__UpperCamelCase : Optional[Any] =DPMSolverMultistepScheduler.from_config(pipe.scheduler.config )
__UpperCamelCase : Optional[int] =DPMSolverMultistepInverseScheduler.from_config(pipe.scheduler.config )
pipe.enable_model_cpu_offload()
pipe.set_progress_bar_config(disable=lowerCamelCase__ )
__UpperCamelCase : Optional[Any] ='a bowl of fruit'
__UpperCamelCase : int ='a bowl of pears'
__UpperCamelCase : str =pipe.generate_mask(
image=self.raw_image , source_prompt=lowerCamelCase__ , target_prompt=lowerCamelCase__ , generator=lowerCamelCase__ , )
__UpperCamelCase : List[str] =pipe.invert(
prompt=lowerCamelCase__ , image=self.raw_image , inpaint_strength=0.7 , generator=lowerCamelCase__ , num_inference_steps=25 , ).latents
__UpperCamelCase : List[str] =pipe(
prompt=lowerCamelCase__ , mask_image=lowerCamelCase__ , image_latents=lowerCamelCase__ , generator=lowerCamelCase__ , negative_prompt=lowerCamelCase__ , inpaint_strength=0.7 , num_inference_steps=25 , output_type='numpy' , ).images[0]
__UpperCamelCase : Tuple =(
np.array(
load_image(
'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
'/diffedit/pears.png' ).resize((768, 768) ) )
/ 255
)
assert np.abs((expected_image - image).max() ) < 5E-1
| 71 | 0 |
"""simple docstring"""
import math_equivalence # From: git+https://github.com/hendrycks/math.git
import datasets
__lowerCamelCase = "\\n@article{hendrycksmath2021,\n title={Measuring Mathematical Problem Solving With the MATH Dataset},\n author={Dan Hendrycks\n and Collin Burns\n and Saurav Kadavath\n and Akul Arora\n and Steven Basart\n and Eric Tang\n and Dawn Song\n and Jacob Steinhardt},\n journal={arXiv preprint arXiv:2103.03874},\n year={2021}\n}\n"
__lowerCamelCase = "\\nThis metric is used to assess performance on the Mathematics Aptitude Test of Heuristics (MATH) dataset.\nIt first canonicalizes the inputs (e.g., converting \"1/2\" to \"\\frac{1}{2}\") and then computes accuracy.\n"
__lowerCamelCase = R"\nCalculates accuracy after canonicalizing inputs.\n\nArgs:\n predictions: list of predictions to score. Each prediction\n is a string that contains natural language and LaTex.\n references: list of reference for each prediction. Each\n reference is a string that contains natural language\n and LaTex.\nReturns:\n accuracy: accuracy after canonicalizing inputs\n (e.g., converting \"1/2\" to \"\\frac{1}{2}\")\n\nExamples:\n >>> metric = datasets.load_metric(\"competition_math\")\n >>> results = metric.compute(references=[\"\\frac{1}{2}\"], predictions=[\"1/2\"])\n >>> print(results)\n {'accuracy': 1.0}\n"
@datasets.utils.file_utils.add_end_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class UpperCamelCase__( datasets.Metric ):
def snake_case__ ( self ) -> Dict:
return datasets.MetricInfo(
description=_DESCRIPTION ,citation=_CITATION ,inputs_description=_KWARGS_DESCRIPTION ,features=datasets.Features(
{
'predictions': datasets.Value('string' ),
'references': datasets.Value('string' ),
} ) ,homepage='https://github.com/hendrycks/math' ,codebase_urls=['https://github.com/hendrycks/math'] ,)
def snake_case__ ( self ,__UpperCAmelCase ,__UpperCAmelCase ) -> List[str]:
A__ = 0.0
for i, j in zip(__UpperCAmelCase ,__UpperCAmelCase ):
n_correct += 1.0 if math_equivalence.is_equiv(__UpperCAmelCase ,__UpperCAmelCase ) else 0.0
A__ = n_correct / len(__UpperCAmelCase )
return {
"accuracy": accuracy,
}
| 357 | """simple docstring"""
import numpy as np
from cva import COLOR_BGR2GRAY, cvtColor, imread
from numpy import array, uinta
from PIL import Image
from digital_image_processing import change_contrast as cc
from digital_image_processing import convert_to_negative as cn
from digital_image_processing import sepia as sp
from digital_image_processing.dithering import burkes as bs
from digital_image_processing.edge_detection import canny
from digital_image_processing.filters import convolve as conv
from digital_image_processing.filters import gaussian_filter as gg
from digital_image_processing.filters import local_binary_pattern as lbp
from digital_image_processing.filters import median_filter as med
from digital_image_processing.filters import sobel_filter as sob
from digital_image_processing.resize import resize as rs
__lowerCamelCase = imread(R"digital_image_processing/image_data/lena_small.jpg")
__lowerCamelCase = cvtColor(img, COLOR_BGR2GRAY)
def UpperCAmelCase ( ):
"""simple docstring"""
A__ = cn.convert_to_negative(UpperCamelCase__ )
# assert negative_img array for at least one True
assert negative_img.any()
def UpperCAmelCase ( ):
"""simple docstring"""
with Image.open('digital_image_processing/image_data/lena_small.jpg' ) as img:
# Work around assertion for response
assert str(cc.change_contrast(UpperCamelCase__ , 110 ) ).startswith(
'<PIL.Image.Image image mode=RGB size=100x100 at' )
def UpperCAmelCase ( ):
"""simple docstring"""
A__ = canny.gen_gaussian_kernel(9 , sigma=1.4 )
# Assert ambiguous array
assert resp.all()
def UpperCAmelCase ( ):
"""simple docstring"""
A__ = imread('digital_image_processing/image_data/lena_small.jpg' , 0 )
# assert ambiguous array for all == True
assert canny_img.all()
A__ = canny.canny(UpperCamelCase__ )
# assert canny array for at least one True
assert canny_array.any()
def UpperCAmelCase ( ):
"""simple docstring"""
assert gg.gaussian_filter(UpperCamelCase__ , 5 , sigma=0.9 ).all()
def UpperCAmelCase ( ):
"""simple docstring"""
A__ = array([[0.2_5, 0.5, 0.2_5], [0.5, -3, 0.5], [0.2_5, 0.5, 0.2_5]] )
A__ = conv.img_convolve(UpperCamelCase__ , UpperCamelCase__ ).astype(UpperCamelCase__ )
assert res.any()
def UpperCAmelCase ( ):
"""simple docstring"""
assert med.median_filter(UpperCamelCase__ , 3 ).any()
def UpperCAmelCase ( ):
"""simple docstring"""
A__ , A__ = sob.sobel_filter(UpperCamelCase__ )
assert grad.any() and theta.any()
def UpperCAmelCase ( ):
"""simple docstring"""
A__ = sp.make_sepia(UpperCamelCase__ , 20 )
assert sepia.all()
def UpperCAmelCase ( UpperCamelCase__ = "digital_image_processing/image_data/lena_small.jpg" ):
"""simple docstring"""
A__ = bs.Burkes(imread(UpperCamelCase__ , 1 ) , 120 )
burkes.process()
assert burkes.output_img.any()
def UpperCAmelCase ( UpperCamelCase__ = "digital_image_processing/image_data/lena_small.jpg" , ):
"""simple docstring"""
A__ = rs.NearestNeighbour(imread(UpperCamelCase__ , 1 ) , 400 , 200 )
nn.process()
assert nn.output.any()
def UpperCAmelCase ( ):
"""simple docstring"""
A__ = 'digital_image_processing/image_data/lena.jpg'
# Reading the image and converting it to grayscale.
A__ = imread(UpperCamelCase__ , 0 )
# Test for get_neighbors_pixel function() return not None
A__ = 0
A__ = 0
A__ = image[x_coordinate][y_coordinate]
A__ = lbp.get_neighbors_pixel(
UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ )
assert neighbors_pixels is not None
# Test for local_binary_pattern function()
# Create a numpy array as the same height and width of read image
A__ = np.zeros((image.shape[0], image.shape[1]) )
# Iterating through the image and calculating the local binary pattern value
# for each pixel.
for i in range(0 , image.shape[0] ):
for j in range(0 , image.shape[1] ):
A__ = lbp.local_binary_value(UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ )
assert lbp_image.any()
| 154 | 0 |
"""simple docstring"""
def lowercase__ ( snake_case_ :str , snake_case_ :str ):
def get_matched_characters(snake_case_ :str , snake_case_ :str ) -> str:
__UpperCAmelCase = []
__UpperCAmelCase = min(len(_stra ) , len(_stra ) ) // 2
for i, l in enumerate(_stra ):
__UpperCAmelCase = int(max(0 , i - limit ) )
__UpperCAmelCase = int(min(i + limit + 1 , len(_stra ) ) )
if l in _stra[left:right]:
matched.append(snake_case_ )
__UpperCAmelCase = F'''{_stra[0:_stra.index(snake_case_ )]} {_stra[_stra.index(snake_case_ ) + 1:]}'''
return "".join(snake_case_ )
# matching characters
__UpperCAmelCase = get_matched_characters(snake_case_ , snake_case_ )
__UpperCAmelCase = get_matched_characters(snake_case_ , snake_case_ )
__UpperCAmelCase = len(snake_case_ )
# transposition
__UpperCAmelCase = (
len([(ca, ca) for ca, ca in zip(snake_case_ , snake_case_ ) if ca != ca] ) // 2
)
if not match_count:
__UpperCAmelCase = 0.0
else:
__UpperCAmelCase = (
1
/ 3
* (
match_count / len(snake_case_ )
+ match_count / len(snake_case_ )
+ (match_count - transpositions) / match_count
)
)
# common prefix up to 4 characters
__UpperCAmelCase = 0
for ca, ca in zip(stra[:4] , stra[:4] ):
if ca == ca:
prefix_len += 1
else:
break
return jaro + 0.1 * prefix_len * (1 - jaro)
if __name__ == "__main__":
import doctest
doctest.testmod()
print(jaro_winkler('hello', 'world'))
| 332 |
'''simple docstring'''
import os
__snake_case ={"""I""": 1, """V""": 5, """X""": 10, """L""": 50, """C""": 100, """D""": 500, """M""": 1_000}
def a_ ( lowerCamelCase : str ):
lowerCAmelCase = 0
lowerCAmelCase = 0
while index < len(lowerCamelCase ) - 1:
lowerCAmelCase = SYMBOLS[numerals[index]]
lowerCAmelCase = SYMBOLS[numerals[index + 1]]
if current_value < next_value:
total_value -= current_value
else:
total_value += current_value
index += 1
total_value += SYMBOLS[numerals[index]]
return total_value
def a_ ( lowerCamelCase : int ):
lowerCAmelCase = ''
lowerCAmelCase = num // 1000
numerals += m_count * "M"
num %= 1000
lowerCAmelCase = num // 100
if c_count == 9:
numerals += "CM"
c_count -= 9
elif c_count == 4:
numerals += "CD"
c_count -= 4
if c_count >= 5:
numerals += "D"
c_count -= 5
numerals += c_count * "C"
num %= 100
lowerCAmelCase = num // 10
if x_count == 9:
numerals += "XC"
x_count -= 9
elif x_count == 4:
numerals += "XL"
x_count -= 4
if x_count >= 5:
numerals += "L"
x_count -= 5
numerals += x_count * "X"
num %= 10
if num == 9:
numerals += "IX"
num -= 9
elif num == 4:
numerals += "IV"
num -= 4
if num >= 5:
numerals += "V"
num -= 5
numerals += num * "I"
return numerals
def a_ ( lowerCamelCase : str = "/p089_roman.txt" ):
lowerCAmelCase = 0
with open(os.path.dirname(lowerCamelCase ) + roman_numerals_filename ) as filea:
lowerCAmelCase = filea.readlines()
for line in lines:
lowerCAmelCase = line.strip()
lowerCAmelCase = parse_roman_numerals(lowerCamelCase )
lowerCAmelCase = generate_roman_numerals(lowerCamelCase )
savings += len(lowerCamelCase ) - len(lowerCamelCase )
return savings
if __name__ == "__main__":
print(F'''{solution() = }''')
| 4 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_torch_available,
is_vision_available,
)
lowerCamelCase_ = {'''configuration_beit''': ['''BEIT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''BeitConfig''', '''BeitOnnxConfig''']}
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCamelCase_ = ['''BeitFeatureExtractor''']
lowerCamelCase_ = ['''BeitImageProcessor''']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCamelCase_ = [
'''BEIT_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''BeitForImageClassification''',
'''BeitForMaskedImageModeling''',
'''BeitForSemanticSegmentation''',
'''BeitModel''',
'''BeitPreTrainedModel''',
]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCamelCase_ = [
'''FlaxBeitForImageClassification''',
'''FlaxBeitForMaskedImageModeling''',
'''FlaxBeitModel''',
'''FlaxBeitPreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_beit import BEIT_PRETRAINED_CONFIG_ARCHIVE_MAP, BeitConfig, BeitOnnxConfig
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .feature_extraction_beit import BeitFeatureExtractor
from .image_processing_beit import BeitImageProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_beit import (
BEIT_PRETRAINED_MODEL_ARCHIVE_LIST,
BeitForImageClassification,
BeitForMaskedImageModeling,
BeitForSemanticSegmentation,
BeitModel,
BeitPreTrainedModel,
)
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_beit import (
FlaxBeitForImageClassification,
FlaxBeitForMaskedImageModeling,
FlaxBeitModel,
FlaxBeitPreTrainedModel,
)
else:
import sys
lowerCamelCase_ = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__) | 352 |
from __future__ import annotations
import csv
import requests
from bsa import BeautifulSoup
def UpperCamelCase( lowercase_ = "" ) -> dict[str, float]:
'''simple docstring'''
snake_case_ = url or """https://www.imdb.com/chart/top/?ref_=nv_mv_250"""
snake_case_ = BeautifulSoup(requests.get(lowercase_ ).text , """html.parser""" )
snake_case_ = soup.find_all("""td""" , attrs="""titleColumn""" )
snake_case_ = soup.find_all("""td""" , class_="""ratingColumn imdbRating""" )
return {
title.a.text: float(rating.strong.text )
for title, rating in zip(lowercase_ , lowercase_ )
}
def UpperCamelCase( lowercase_ = "IMDb_Top_250_Movies.csv" ) -> None:
'''simple docstring'''
snake_case_ = get_imdb_top_aaa_movies()
with open(lowercase_ , """w""" , newline="""""" ) as out_file:
snake_case_ = csv.writer(lowercase_ )
writer.writerow(["""Movie title""", """IMDb rating"""] )
for title, rating in movies.items():
writer.writerow([title, rating] )
if __name__ == "__main__":
write_movies() | 34 | 0 |
import logging
import math
from functools import partial
from typing import Any, Callable, Dict, Iterable, List, Optional, Sequence, Tuple, Union
import torch
from .tensor_utils import tensor_tree_map, tree_map
def lowerCAmelCase_ ( snake_case_ ):
_A : Optional[int] = []
if isinstance(snake_case_,snake_case_ ):
for v in tree.values():
shapes.extend(_fetch_dims(snake_case_ ) )
elif isinstance(snake_case_,(list, tuple) ):
for t in tree:
shapes.extend(_fetch_dims(snake_case_ ) )
elif isinstance(snake_case_,torch.Tensor ):
shapes.append(tree.shape )
else:
raise ValueError("""Not supported""" )
return shapes
@torch.jit.ignore
def lowerCAmelCase_ ( snake_case_,snake_case_ ):
_A : int = []
for d in reversed(snake_case_ ):
idx.append(flat_idx % d )
_A : List[str] = flat_idx // d
return tuple(reversed(snake_case_ ) )
@torch.jit.ignore
def lowerCAmelCase_ ( snake_case_,snake_case_,snake_case_,snake_case_ = None,snake_case_ = None,):
# start_edges and end_edges both indicate whether, starting from any given
# dimension, the start/end index is at the top/bottom edge of the
# corresponding tensor, modeled as a tree
def reduce_edge_list(snake_case_ ) -> None:
_A : List[str] = True
for i in range(len(snake_case_ ) ):
_A : List[str] = -1 * (i + 1)
l[reversed_idx] &= tally
_A : List[Any] = l[reversed_idx]
if start_edges is None:
_A : str = [s == 0 for s in start]
reduce_edge_list(snake_case_ )
if end_edges is None:
_A : Any = [e == (d - 1) for e, d in zip(snake_case_,snake_case_ )]
reduce_edge_list(snake_case_ )
# Base cases. Either start/end are empty and we're done, or the final,
# one-dimensional tensor can be simply sliced
if len(snake_case_ ) == 0:
return [()]
elif len(snake_case_ ) == 1:
return [(slice(start[0],end[0] + 1 ),)]
_A : List[Tuple[slice, ...]] = []
_A : List[slice] = []
# Dimensions common to start and end can be selected directly
for s, e in zip(snake_case_,snake_case_ ):
if s == e:
path_list.append(slice(snake_case_,s + 1 ) )
else:
break
_A : Tuple[slice, ...] = tuple(snake_case_ )
_A : str = len(snake_case_ )
# start == end, and we're done
if divergence_idx == len(snake_case_ ):
return [path]
def upper() -> Tuple[Tuple[slice, ...], ...]:
assert start_edges is not None
assert end_edges is not None
_A : Any = start[divergence_idx]
return tuple(
path + (slice(snake_case_,sdi + 1 ),) + s
for s in _get_minimal_slice_set(
start[divergence_idx + 1 :],[d - 1 for d in dims[divergence_idx + 1 :]],dims[divergence_idx + 1 :],start_edges=start_edges[divergence_idx + 1 :],end_edges=[True for _ in end_edges[divergence_idx + 1 :]],) )
def lower() -> Tuple[Tuple[slice, ...], ...]:
assert start_edges is not None
assert end_edges is not None
_A : Union[str, Any] = end[divergence_idx]
return tuple(
path + (slice(snake_case_,edi + 1 ),) + s
for s in _get_minimal_slice_set(
[0 for _ in start[divergence_idx + 1 :]],end[divergence_idx + 1 :],dims[divergence_idx + 1 :],start_edges=[True for _ in start_edges[divergence_idx + 1 :]],end_edges=end_edges[divergence_idx + 1 :],) )
# If both start and end are at the edges of the subtree rooted at
# divergence_idx, we can just select the whole subtree at once
if start_edges[divergence_idx] and end_edges[divergence_idx]:
slices.append(path + (slice(start[divergence_idx],end[divergence_idx] + 1 ),) )
# If just start is at the edge, we can grab almost all of the subtree,
# treating only the ragged bottom edge as an edge case
elif start_edges[divergence_idx]:
slices.append(path + (slice(start[divergence_idx],end[divergence_idx] ),) )
slices.extend(lower() )
# Analogous to the previous case, but the top is ragged this time
elif end_edges[divergence_idx]:
slices.extend(upper() )
slices.append(path + (slice(start[divergence_idx] + 1,end[divergence_idx] + 1 ),) )
# If both sides of the range are ragged, we need to handle both sides
# separately. If there's contiguous meat in between them, we can index it
# in one big chunk
else:
slices.extend(upper() )
_A : List[str] = end[divergence_idx] - start[divergence_idx]
if middle_ground > 1:
slices.append(path + (slice(start[divergence_idx] + 1,end[divergence_idx] ),) )
slices.extend(lower() )
return slices
@torch.jit.ignore
def lowerCAmelCase_ ( snake_case_,snake_case_,snake_case_,snake_case_ ):
_A : Optional[int] = t.shape[:no_batch_dims]
_A : Tuple = list(_flat_idx_to_idx(snake_case_,snake_case_ ) )
# _get_minimal_slice_set is inclusive
_A : str = list(_flat_idx_to_idx(flat_end - 1,snake_case_ ) )
# Get an ordered list of slices to perform
_A : int = _get_minimal_slice_set(
snake_case_,snake_case_,snake_case_,)
_A : Optional[int] = [t[s] for s in slices]
return torch.cat([s.view((-1,) + t.shape[no_batch_dims:] ) for s in sliced_tensors] )
def lowerCAmelCase_ ( snake_case_,snake_case_,snake_case_,snake_case_,snake_case_ = False,snake_case_ = None,snake_case_ = False,):
if not (len(snake_case_ ) > 0):
raise ValueError("""Must provide at least one input""" )
_A : int = [shape[:no_batch_dims] for shape in _fetch_dims(snake_case_ )]
_A : Tuple = tuple([max(snake_case_ ) for s in zip(*snake_case_ )] )
def _prep_inputs(snake_case_ ) -> torch.Tensor:
if not low_mem:
if not sum(t.shape[:no_batch_dims] ) == no_batch_dims:
_A : Dict = t.expand(orig_batch_dims + t.shape[no_batch_dims:] )
_A : Union[str, Any] = t.reshape(-1,*t.shape[no_batch_dims:] )
else:
_A : Dict = t.expand(orig_batch_dims + t.shape[no_batch_dims:] )
return t
_A : Dict[str, Any] = tensor_tree_map(_prep_inputs,snake_case_ )
_A : Tuple = None
if _out is not None:
_A : Optional[int] = tensor_tree_map(lambda snake_case_ : t.view([-1] + list(t.shape[no_batch_dims:] ) ),_out )
_A : Optional[Any] = 1
for d in orig_batch_dims:
flat_batch_dim *= d
_A : str = flat_batch_dim // chunk_size + (flat_batch_dim % chunk_size != 0)
def _select_chunk(snake_case_ ) -> torch.Tensor:
return t[i : i + chunk_size] if t.shape[0] != 1 else t
_A : Any = 0
_A : List[str] = prepped_outputs
for _ in range(snake_case_ ):
# Chunk the input
if not low_mem:
_A : List[Any] = _select_chunk
else:
_A : str = partial(
_chunk_slice,flat_start=snake_case_,flat_end=min(snake_case_,i + chunk_size ),no_batch_dims=len(snake_case_ ),)
_A : Dict[str, Any] = tensor_tree_map(snake_case_,snake_case_ )
# Run the layer on the chunk
_A : Tuple = layer(**snake_case_ )
# Allocate space for the output
if out is None:
_A : Optional[Any] = tensor_tree_map(lambda snake_case_ : t.new_zeros((flat_batch_dim,) + t.shape[1:] ),snake_case_ )
# Put the chunk in its pre-allocated space
if isinstance(snake_case_,snake_case_ ):
def assign(snake_case_,snake_case_ ) -> None:
for k, v in da.items():
if isinstance(snake_case_,snake_case_ ):
assign(snake_case_,da[k] )
else:
if _add_into_out:
v[i : i + chunk_size] += da[k]
else:
_A : List[Any] = da[k]
assign(snake_case_,snake_case_ )
elif isinstance(snake_case_,snake_case_ ):
for xa, xa in zip(snake_case_,snake_case_ ):
if _add_into_out:
xa[i : i + chunk_size] += xa
else:
_A : Tuple = xa
elif isinstance(snake_case_,torch.Tensor ):
if _add_into_out:
out[i : i + chunk_size] += output_chunk
else:
_A : Optional[int] = output_chunk
else:
raise ValueError("""Not supported""" )
i += chunk_size
_A : int = tensor_tree_map(lambda snake_case_ : t.view(orig_batch_dims + t.shape[1:] ),snake_case_ )
return out
class lowercase :
def __init__( self , _a = 512 , ) -> str:
_A : Union[str, Any] = max_chunk_size
_A : Optional[int] = None
_A : Optional[tuple] = None
def a__ ( self , _a , _a , _a ) -> int:
logging.info("""Tuning chunk size...""" )
if min_chunk_size >= self.max_chunk_size:
return min_chunk_size
_A : List[int] = [2**l for l in range(int(math.log(self.max_chunk_size , 2 ) ) + 1 )]
_A : List[Any] = [c for c in candidates if c > min_chunk_size]
_A : Optional[Any] = [min_chunk_size] + candidates
candidates[-1] += 4
def test_chunk_size(_a ) -> bool:
try:
with torch.no_grad():
fn(*_a , chunk_size=_a )
return True
except RuntimeError:
return False
_A : Optional[int] = 0
_A : str = len(_a ) - 1
while i > min_viable_chunk_size_index:
_A : Optional[Any] = test_chunk_size(candidates[i] )
if not viable:
_A : Union[str, Any] = (min_viable_chunk_size_index + i) // 2
else:
_A : Union[str, Any] = i
_A : Optional[Any] = (i + len(_a ) - 1) // 2
return candidates[min_viable_chunk_size_index]
def a__ ( self , _a , _a ) -> bool:
_A : Optional[Any] = True
for aa, aa in zip(_a , _a ):
assert type(_a ) == type(_a )
if isinstance(_a , (list, tuple) ):
consistent &= self._compare_arg_caches(_a , _a )
elif isinstance(_a , _a ):
_A : Union[str, Any] = [v for _, v in sorted(aa.items() , key=lambda _a : x[0] )]
_A : Union[str, Any] = [v for _, v in sorted(aa.items() , key=lambda _a : x[0] )]
consistent &= self._compare_arg_caches(_a , _a )
else:
consistent &= aa == aa
return consistent
def a__ ( self , _a , _a , _a , ) -> int:
_A : Tuple = True
_A : tuple = tree_map(lambda _a : a.shape if isinstance(_a , torch.Tensor ) else a , _a , _a )
if self.cached_arg_data is not None:
# If args have changed shape/value, we need to re-tune
assert len(self.cached_arg_data ) == len(_a )
_A : List[Any] = self._compare_arg_caches(self.cached_arg_data , _a )
else:
# Otherwise, we can reuse the precomputed value
_A : Tuple = False
if not consistent:
_A : int = self._determine_favorable_chunk_size(
_a , _a , _a , )
_A : int = arg_data
assert self.cached_chunk_size is not None
return self.cached_chunk_size
| 26 |
import multiprocessing
from typing import TYPE_CHECKING, Optional, Union
from .. import Dataset, Features, config
from ..formatting import query_table
from ..packaged_modules.sql.sql import Sql
from ..utils import logging
from .abc import AbstractDatasetInputStream
if TYPE_CHECKING:
import sqlitea
import sqlalchemy
class lowercase ( UpperCamelCase__ ):
def __init__( self , _a , _a , _a = None , _a = None , _a = False , **_a , ) -> int:
super().__init__(features=_a , cache_dir=_a , keep_in_memory=_a , **_a )
_A : Optional[int] = Sql(
cache_dir=_a , features=_a , sql=_a , con=_a , **_a , )
def a__ ( self ) -> Optional[Any]:
_A : Tuple = None
_A : int = None
_A : Tuple = None
_A : Union[str, Any] = None
self.builder.download_and_prepare(
download_config=_a , download_mode=_a , verification_mode=_a , base_path=_a , )
# Build dataset for splits
_A : int = self.builder.as_dataset(
split="""train""" , verification_mode=_a , in_memory=self.keep_in_memory )
return dataset
class lowercase :
def __init__( self , _a , _a , _a , _a = None , _a = None , **_a , ) -> Union[str, Any]:
if num_proc is not None and num_proc <= 0:
raise ValueError(F'''num_proc {num_proc} must be an integer > 0.''' )
_A : Dict = dataset
_A : int = name
_A : Union[str, Any] = con
_A : str = batch_size if batch_size else config.DEFAULT_MAX_BATCH_SIZE
_A : str = num_proc
_A : Optional[Any] = to_sql_kwargs
def a__ ( self ) -> int:
_A : Any = self.to_sql_kwargs.pop("""sql""" , _a )
_A : List[str] = self.to_sql_kwargs.pop("""con""" , _a )
_A : int = self.to_sql_kwargs.pop("""index""" , _a )
_A : List[str] = self._write(index=_a , **self.to_sql_kwargs )
return written
def a__ ( self , _a ) -> Optional[int]:
_A , _A , _A : List[str] = args
_A : int = {**to_sql_kwargs, """if_exists""": """append"""} if offset > 0 else to_sql_kwargs
_A : str = query_table(
table=self.dataset.data , key=slice(_a , offset + self.batch_size ) , indices=self.dataset._indices , )
_A : Tuple = batch.to_pandas()
_A : Union[str, Any] = df.to_sql(self.name , self.con , index=_a , **_a )
return num_rows or len(_a )
def a__ ( self , _a , **_a ) -> int:
_A : Any = 0
if self.num_proc is None or self.num_proc == 1:
for offset in logging.tqdm(
range(0 , len(self.dataset ) , self.batch_size ) , unit="""ba""" , disable=not logging.is_progress_bar_enabled() , desc="""Creating SQL from Arrow format""" , ):
written += self._batch_sql((offset, index, to_sql_kwargs) )
else:
_A , _A : Tuple = len(self.dataset ), self.batch_size
with multiprocessing.Pool(self.num_proc ) as pool:
for num_rows in logging.tqdm(
pool.imap(
self._batch_sql , [(offset, index, to_sql_kwargs) for offset in range(0 , _a , _a )] , ) , total=(num_rows // batch_size) + 1 if num_rows % batch_size else num_rows // batch_size , unit="""ba""" , disable=not logging.is_progress_bar_enabled() , desc="""Creating SQL from Arrow format""" , ):
written += num_rows
return written
| 26 | 1 |
'''simple docstring'''
import math
def __lowerCamelCase ( lowerCAmelCase_ , lowerCAmelCase_ ) -> float:
if initial_intensity < 0:
raise ValueError('The value of intensity cannot be negative' )
# handling of negative values of initial intensity
if angle < 0 or angle > 360:
raise ValueError('In Malus Law, the angle is in the range 0-360 degrees' )
# handling of values out of allowed range
return initial_intensity * (math.cos(math.radians(lowerCAmelCase_ ) ) ** 2)
if __name__ == "__main__":
import doctest
doctest.testmod(name='''malus_law''')
| 107 |
'''simple docstring'''
import enum
import warnings
from ..tokenization_utils import TruncationStrategy
from ..utils import add_end_docstrings, is_tf_available, is_torch_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_tf_available():
import tensorflow as tf
from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
if is_torch_available():
from ..models.auto.modeling_auto import MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
__lowerCAmelCase = logging.get_logger(__name__)
class __magic_name__ ( enum.Enum ):
lowerCAmelCase : Union[str, Any] = 0
lowerCAmelCase : Dict = 1
@add_end_docstrings(_UpperCamelCase )
class __magic_name__ ( _UpperCamelCase ):
lowerCAmelCase : int = 'generated'
def __init__( self : List[Any] ,*_UpperCAmelCase : Any ,**_UpperCAmelCase : Optional[int] ):
super().__init__(*_UpperCAmelCase ,**_UpperCAmelCase )
self.check_model_type(
TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
if self.framework == 'tf'
else MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING )
def __lowercase ( self : Optional[int] ,_UpperCAmelCase : List[Any]=None ,_UpperCAmelCase : Tuple=None ,_UpperCAmelCase : List[Any]=None ,_UpperCAmelCase : Union[str, Any]=None ,_UpperCAmelCase : Dict=None ,_UpperCAmelCase : Optional[int]=None ,**_UpperCAmelCase : List[Any] ,):
_a : Tuple = {}
if truncation is not None:
_a : Union[str, Any] = truncation
_a : List[str] = generate_kwargs
_a : Optional[Any] = {}
if return_tensors is not None and return_type is None:
_a : Optional[int] = ReturnType.TENSORS if return_tensors else ReturnType.TEXT
if return_type is not None:
_a : List[Any] = return_type
if clean_up_tokenization_spaces is not None:
_a : Optional[int] = clean_up_tokenization_spaces
if stop_sequence is not None:
_a : List[str] = self.tokenizer.encode(_UpperCAmelCase ,add_special_tokens=_UpperCAmelCase )
if len(_UpperCAmelCase ) > 1:
warnings.warn(
'Stopping on a multiple token sequence is not yet supported on transformers. The first token of'
' the stop sequence will be used as the stop sequence string in the interim.' )
_a : List[Any] = stop_sequence_ids[0]
return preprocess_params, forward_params, postprocess_params
def __lowercase ( self : Any ,_UpperCAmelCase : int ,_UpperCAmelCase : int ,_UpperCAmelCase : int ):
return True
def __lowercase ( self : str ,*_UpperCAmelCase : Any ,_UpperCAmelCase : Union[str, Any] ):
_a : Optional[Any] = self.model.config.prefix if self.model.config.prefix is not None else ''
if isinstance(args[0] ,_UpperCAmelCase ):
if self.tokenizer.pad_token_id is None:
raise ValueError('Please make sure that the tokenizer has a pad_token_id when using a batch input' )
_a : Optional[Any] = ([prefix + arg for arg in args[0]],)
_a : Any = True
elif isinstance(args[0] ,_UpperCAmelCase ):
_a : List[str] = (prefix + args[0],)
_a : str = False
else:
raise ValueError(
F""" `args[0]`: {args[0]} have the wrong format. The should be either of type `str` or type `list`""" )
_a : Optional[Any] = self.tokenizer(*_UpperCAmelCase ,padding=_UpperCAmelCase ,truncation=_UpperCAmelCase ,return_tensors=self.framework )
# This is produced by tokenizers but is an invalid generate kwargs
if "token_type_ids" in inputs:
del inputs["token_type_ids"]
return inputs
def __call__( self : Optional[int] ,*_UpperCAmelCase : List[Any] ,**_UpperCAmelCase : Any ):
_a : Tuple = super().__call__(*_UpperCAmelCase ,**_UpperCAmelCase )
if (
isinstance(args[0] ,_UpperCAmelCase )
and all(isinstance(_UpperCAmelCase ,_UpperCAmelCase ) for el in args[0] )
and all(len(_UpperCAmelCase ) == 1 for res in result )
):
return [res[0] for res in result]
return result
def __lowercase ( self : Tuple ,_UpperCAmelCase : Union[str, Any] ,_UpperCAmelCase : List[str]=TruncationStrategy.DO_NOT_TRUNCATE ,**_UpperCAmelCase : int ):
_a : Dict = self._parse_and_tokenize(_UpperCAmelCase ,truncation=_UpperCAmelCase ,**_UpperCAmelCase )
return inputs
def __lowercase ( self : Any ,_UpperCAmelCase : str ,**_UpperCAmelCase : Tuple ):
if self.framework == "pt":
_a , _a : int = model_inputs['input_ids'].shape
elif self.framework == "tf":
_a , _a : Dict = tf.shape(model_inputs['input_ids'] ).numpy()
_a : Optional[Any] = generate_kwargs.get('min_length' ,self.model.config.min_length )
_a : Optional[int] = generate_kwargs.get('max_length' ,self.model.config.max_length )
self.check_inputs(_UpperCAmelCase ,generate_kwargs['min_length'] ,generate_kwargs['max_length'] )
_a : List[str] = self.model.generate(**_UpperCAmelCase ,**_UpperCAmelCase )
_a : int = output_ids.shape[0]
if self.framework == "pt":
_a : int = output_ids.reshape(_UpperCAmelCase ,out_b // in_b ,*output_ids.shape[1:] )
elif self.framework == "tf":
_a : Any = tf.reshape(_UpperCAmelCase ,(in_b, out_b // in_b, *output_ids.shape[1:]) )
return {"output_ids": output_ids}
def __lowercase ( self : Dict ,_UpperCAmelCase : Tuple ,_UpperCAmelCase : Any=ReturnType.TEXT ,_UpperCAmelCase : Dict=False ):
_a : Union[str, Any] = []
for output_ids in model_outputs["output_ids"][0]:
if return_type == ReturnType.TENSORS:
_a : int = {F"""{self.return_name}_token_ids""": output_ids}
elif return_type == ReturnType.TEXT:
_a : str = {
F"""{self.return_name}_text""": self.tokenizer.decode(
_UpperCAmelCase ,skip_special_tokens=_UpperCAmelCase ,clean_up_tokenization_spaces=_UpperCAmelCase ,)
}
records.append(_UpperCAmelCase )
return records
@add_end_docstrings(_UpperCamelCase )
class __magic_name__ ( _UpperCamelCase ):
lowerCAmelCase : Any = 'summary'
def __call__( self : Any ,*_UpperCAmelCase : Union[str, Any] ,**_UpperCAmelCase : List[str] ):
return super().__call__(*_UpperCAmelCase ,**_UpperCAmelCase )
def __lowercase ( self : Optional[Any] ,_UpperCAmelCase : int ,_UpperCAmelCase : int ,_UpperCAmelCase : int ):
if max_length < min_length:
logger.warning(F"""Your min_length={min_length} must be inferior than your max_length={max_length}.""" )
if input_length < max_length:
logger.warning(
F"""Your max_length is set to {max_length}, but your input_length is only {input_length}. Since this is """
'a summarization task, where outputs shorter than the input are typically wanted, you might '
F"""consider decreasing max_length manually, e.g. summarizer('...', max_length={input_length//2})""" )
@add_end_docstrings(_UpperCamelCase )
class __magic_name__ ( _UpperCamelCase ):
lowerCAmelCase : List[Any] = 'translation'
def __lowercase ( self : Optional[int] ,_UpperCAmelCase : int ,_UpperCAmelCase : int ,_UpperCAmelCase : int ):
if input_length > 0.9 * max_length:
logger.warning(
F"""Your input_length: {input_length} is bigger than 0.9 * max_length: {max_length}. You might consider """
'increasing your max_length manually, e.g. translator(\'...\', max_length=400)' )
return True
def __lowercase ( self : Any ,*_UpperCAmelCase : Any ,_UpperCAmelCase : List[str]=TruncationStrategy.DO_NOT_TRUNCATE ,_UpperCAmelCase : Optional[int]=None ,_UpperCAmelCase : Any=None ):
if getattr(self.tokenizer ,'_build_translation_inputs' ,_UpperCAmelCase ):
return self.tokenizer._build_translation_inputs(
*_UpperCAmelCase ,return_tensors=self.framework ,truncation=_UpperCAmelCase ,src_lang=_UpperCAmelCase ,tgt_lang=_UpperCAmelCase )
else:
return super()._parse_and_tokenize(*_UpperCAmelCase ,truncation=_UpperCAmelCase )
def __lowercase ( self : Tuple ,_UpperCAmelCase : List[Any]=None ,_UpperCAmelCase : List[str]=None ,**_UpperCAmelCase : Dict ):
_a , _a , _a : str = super()._sanitize_parameters(**_UpperCAmelCase )
if src_lang is not None:
_a : Optional[int] = src_lang
if tgt_lang is not None:
_a : List[Any] = tgt_lang
if src_lang is None and tgt_lang is None:
# Backward compatibility, direct arguments use is preferred.
_a : int = kwargs.get('task' ,self.task )
_a : int = task.split('_' )
if task and len(_UpperCAmelCase ) == 4:
# translation, XX, to YY
_a : List[Any] = items[1]
_a : Any = items[3]
return preprocess_params, forward_params, postprocess_params
def __call__( self : Optional[Any] ,*_UpperCAmelCase : Tuple ,**_UpperCAmelCase : Optional[int] ):
return super().__call__(*_UpperCAmelCase ,**_UpperCAmelCase )
| 107 | 1 |
"""simple docstring"""
import pytest
import datasets.config
from datasets.utils.info_utils import is_small_dataset
@pytest.mark.parametrize("dataset_size" , [None, 400 * 2**20, 600 * 2**20] )
@pytest.mark.parametrize("input_in_memory_max_size" , ["default", 0, 100 * 2**20, 900 * 2**20] )
def A__ ( UpperCamelCase , UpperCamelCase , UpperCamelCase ):
if input_in_memory_max_size != "default":
monkeypatch.setattr(datasets.config , "IN_MEMORY_MAX_SIZE" , UpperCamelCase )
A = datasets.config.IN_MEMORY_MAX_SIZE
if input_in_memory_max_size == "default":
assert in_memory_max_size == 0
else:
assert in_memory_max_size == input_in_memory_max_size
if dataset_size and in_memory_max_size:
A = dataset_size < in_memory_max_size
else:
A = False
A = is_small_dataset(UpperCamelCase )
assert result == expected
| 292 |
"""simple docstring"""
import json
import os
import tempfile
from transformers.testing_utils import check_json_file_has_correct_format
class _UpperCAmelCase :
UpperCamelCase = None
def lowerCamelCase ( self :List[Any] ):
A = self.feature_extraction_class(**self.feat_extract_dict )
A = json.loads(feat_extract.to_json_string() )
for key, value in self.feat_extract_dict.items():
self.assertEqual(obj[key] , __UpperCamelCase )
def lowerCamelCase ( self :Dict ):
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 )
self.assertEqual(feat_extract_second.to_dict() , feat_extract_first.to_dict() )
def lowerCamelCase ( self :Dict ):
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 )
self.assertEqual(feat_extract_second.to_dict() , feat_extract_first.to_dict() )
def lowerCamelCase ( self :Tuple ):
A = self.feature_extraction_class()
self.assertIsNotNone(__UpperCamelCase )
| 292 | 1 |
'''simple docstring'''
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_available():
import torch
if is_vision_available():
from PIL import Image
from transformers import LevitImageProcessor
class snake_case__ ( unittest.TestCase ):
"""simple docstring"""
def __init__( self : str , UpperCamelCase__ : Union[str, Any] , UpperCamelCase__ : Optional[Any]=7 , UpperCamelCase__ : Optional[int]=3 , UpperCamelCase__ : Optional[Any]=18 , UpperCamelCase__ : List[Any]=30 , UpperCamelCase__ : Optional[Any]=400 , UpperCamelCase__ : Dict=True , UpperCamelCase__ : Optional[int]=None , UpperCamelCase__ : Tuple=True , UpperCamelCase__ : List[Any]=None , UpperCamelCase__ : Dict=True , UpperCamelCase__ : Tuple=[0.5, 0.5, 0.5] , UpperCamelCase__ : Optional[Any]=[0.5, 0.5, 0.5] , ) -> List[str]:
"""simple docstring"""
snake_case : List[str] = size if size is not None else {'''shortest_edge''': 18}
snake_case : List[str] = crop_size if crop_size is not None else {'''height''': 18, '''width''': 18}
snake_case : Union[str, Any] = parent
snake_case : Optional[Any] = batch_size
snake_case : Optional[int] = num_channels
snake_case : int = image_size
snake_case : Dict = min_resolution
snake_case : List[str] = max_resolution
snake_case : List[Any] = do_resize
snake_case : List[Any] = size
snake_case : Dict = do_center_crop
snake_case : Optional[int] = crop_size
snake_case : int = do_normalize
snake_case : Optional[Any] = image_mean
snake_case : int = image_std
def lowerCAmelCase ( self : int ) -> str:
"""simple docstring"""
return {
"image_mean": self.image_mean,
"image_std": self.image_std,
"do_normalize": self.do_normalize,
"do_resize": self.do_resize,
"do_center_crop": self.do_center_crop,
"size": self.size,
"crop_size": self.crop_size,
}
@require_torch
@require_vision
class snake_case__ ( __SCREAMING_SNAKE_CASE , unittest.TestCase ):
"""simple docstring"""
lowerCamelCase = LevitImageProcessor if is_vision_available() else None
def lowerCAmelCase ( self : Tuple ) -> Union[str, Any]:
"""simple docstring"""
snake_case : List[Any] = LevitImageProcessingTester(self )
@property
def lowerCAmelCase ( self : List[Any] ) -> Union[str, Any]:
"""simple docstring"""
return self.image_processor_tester.prepare_image_processor_dict()
def lowerCAmelCase ( self : List[Any] ) -> Dict:
"""simple docstring"""
snake_case : Any = self.image_processing_class(**self.image_processor_dict )
self.assertTrue(hasattr(UpperCamelCase__ , '''image_mean''' ) )
self.assertTrue(hasattr(UpperCamelCase__ , '''image_std''' ) )
self.assertTrue(hasattr(UpperCamelCase__ , '''do_normalize''' ) )
self.assertTrue(hasattr(UpperCamelCase__ , '''do_resize''' ) )
self.assertTrue(hasattr(UpperCamelCase__ , '''do_center_crop''' ) )
self.assertTrue(hasattr(UpperCamelCase__ , '''size''' ) )
def lowerCAmelCase ( self : Any ) -> Dict:
"""simple docstring"""
snake_case : Optional[Any] = self.image_processing_class.from_dict(self.image_processor_dict )
self.assertEqual(image_processor.size , {'''shortest_edge''': 18} )
self.assertEqual(image_processor.crop_size , {'''height''': 18, '''width''': 18} )
snake_case : str = self.image_processing_class.from_dict(self.image_processor_dict , size=42 , crop_size=84 )
self.assertEqual(image_processor.size , {'''shortest_edge''': 42} )
self.assertEqual(image_processor.crop_size , {'''height''': 84, '''width''': 84} )
def lowerCAmelCase ( self : List[str] ) -> Tuple:
"""simple docstring"""
pass
def lowerCAmelCase ( self : List[Any] ) -> Union[str, Any]:
"""simple docstring"""
snake_case : str = self.image_processing_class(**self.image_processor_dict )
# create random PIL images
snake_case : Dict = prepare_image_inputs(self.image_processor_tester , equal_resolution=UpperCamelCase__ )
for image in image_inputs:
self.assertIsInstance(UpperCamelCase__ , Image.Image )
# Test not batched input
snake_case : Optional[Any] = image_processing(image_inputs[0] , return_tensors='''pt''' ).pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size['''height'''],
self.image_processor_tester.crop_size['''width'''],
) , )
# Test batched
snake_case : int = image_processing(UpperCamelCase__ , return_tensors='''pt''' ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size['''height'''],
self.image_processor_tester.crop_size['''width'''],
) , )
def lowerCAmelCase ( self : Optional[Any] ) -> List[Any]:
"""simple docstring"""
snake_case : List[Any] = self.image_processing_class(**self.image_processor_dict )
# create random numpy tensors
snake_case : Union[str, Any] = prepare_image_inputs(self.image_processor_tester , equal_resolution=UpperCamelCase__ , numpify=UpperCamelCase__ )
for image in image_inputs:
self.assertIsInstance(UpperCamelCase__ , np.ndarray )
# Test not batched input
snake_case : Tuple = image_processing(image_inputs[0] , return_tensors='''pt''' ).pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size['''height'''],
self.image_processor_tester.crop_size['''width'''],
) , )
# Test batched
snake_case : Tuple = image_processing(UpperCamelCase__ , return_tensors='''pt''' ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size['''height'''],
self.image_processor_tester.crop_size['''width'''],
) , )
def lowerCAmelCase ( self : List[Any] ) -> Union[str, Any]:
"""simple docstring"""
snake_case : Any = self.image_processing_class(**self.image_processor_dict )
# create random PyTorch tensors
snake_case : Optional[int] = prepare_image_inputs(self.image_processor_tester , equal_resolution=UpperCamelCase__ , torchify=UpperCamelCase__ )
for image in image_inputs:
self.assertIsInstance(UpperCamelCase__ , torch.Tensor )
# Test not batched input
snake_case : int = image_processing(image_inputs[0] , return_tensors='''pt''' ).pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size['''height'''],
self.image_processor_tester.crop_size['''width'''],
) , )
# Test batched
snake_case : List[Any] = image_processing(UpperCamelCase__ , return_tensors='''pt''' ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size['''height'''],
self.image_processor_tester.crop_size['''width'''],
) , )
| 352 |
'''simple docstring'''
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
PNDMScheduler,
StableDiffusionLDMaDPipeline,
UNetaDConditionModel,
)
from diffusers.utils import nightly, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu
from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
enable_full_determinism()
class snake_case__ ( unittest.TestCase ):
"""simple docstring"""
lowerCamelCase = StableDiffusionLDMaDPipeline
lowerCamelCase = TEXT_TO_IMAGE_PARAMS
lowerCamelCase = TEXT_TO_IMAGE_BATCH_PARAMS
lowerCamelCase = TEXT_TO_IMAGE_IMAGE_PARAMS
def lowerCAmelCase ( self : Optional[Any] ) -> Union[str, Any]:
"""simple docstring"""
torch.manual_seed(0 )
snake_case : str = 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 , )
snake_case : Dict = DDIMScheduler(
beta_start=0.00_085 , beta_end=0.012 , beta_schedule='''scaled_linear''' , clip_sample=UpperCamelCase__ , set_alpha_to_one=UpperCamelCase__ , )
torch.manual_seed(0 )
snake_case : int = AutoencoderKL(
block_out_channels=[32, 64] , in_channels=6 , out_channels=6 , down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D'''] , up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D'''] , latent_channels=4 , )
torch.manual_seed(0 )
snake_case : Tuple = 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=1000 , )
snake_case : int = CLIPTextModel(UpperCamelCase__ )
snake_case : Tuple = CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip''' )
snake_case : str = {
'''unet''': unet,
'''scheduler''': scheduler,
'''vae''': vae,
'''text_encoder''': text_encoder,
'''tokenizer''': tokenizer,
'''safety_checker''': None,
'''feature_extractor''': None,
}
return components
def lowerCAmelCase ( self : Optional[int] , UpperCamelCase__ : Union[str, Any] , UpperCamelCase__ : str=0 ) -> Optional[int]:
"""simple docstring"""
if str(UpperCamelCase__ ).startswith('''mps''' ):
snake_case : Optional[int] = torch.manual_seed(UpperCamelCase__ )
else:
snake_case : Optional[int] = torch.Generator(device=UpperCamelCase__ ).manual_seed(UpperCamelCase__ )
snake_case : Tuple = {
'''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 ) -> str:
"""simple docstring"""
snake_case : int = '''cpu''' # ensure determinism for the device-dependent torch.Generator
snake_case : List[Any] = self.get_dummy_components()
snake_case : str = StableDiffusionLDMaDPipeline(**UpperCamelCase__ )
snake_case : Union[str, Any] = ldmad_pipe.to(UpperCamelCase__ )
ldmad_pipe.set_progress_bar_config(disable=UpperCamelCase__ )
snake_case : List[str] = self.get_dummy_inputs(UpperCamelCase__ )
snake_case : Tuple = ldmad_pipe(**UpperCamelCase__ )
snake_case ,snake_case : int = output.rgb, output.depth
snake_case : str = rgb[0, -3:, -3:, -1]
snake_case : Union[str, Any] = depth[0, -3:, -1]
assert rgb.shape == (1, 64, 64, 3)
assert depth.shape == (1, 64, 64)
snake_case : int = np.array(
[0.37_338_176, 0.70_247, 0.74_203_193, 0.51_643_604, 0.58_256_793, 0.60_932_136, 0.4_181_095, 0.48_355_877, 0.46_535_262] )
snake_case : str = np.array([103.46_727, 85.812_004, 87.849_236] )
assert np.abs(image_slice_rgb.flatten() - expected_slice_rgb ).max() < 1e-2
assert np.abs(image_slice_depth.flatten() - expected_slice_depth ).max() < 1e-2
def lowerCAmelCase ( self : Dict ) -> int:
"""simple docstring"""
snake_case : int = self.get_dummy_components()
snake_case : Any = StableDiffusionLDMaDPipeline(**UpperCamelCase__ )
snake_case : Optional[Any] = ldmad_pipe.to(UpperCamelCase__ )
ldmad_pipe.set_progress_bar_config(disable=UpperCamelCase__ )
snake_case : int = self.get_dummy_inputs(UpperCamelCase__ )
snake_case : str = 3 * [inputs['''prompt''']]
# forward
snake_case : Union[str, Any] = ldmad_pipe(**UpperCamelCase__ )
snake_case ,snake_case : Union[str, Any] = output.rgb, output.depth
snake_case : Tuple = rgb_slice_a[0, -3:, -3:, -1]
snake_case : List[str] = depth_slice_a[0, -3:, -1]
snake_case : int = self.get_dummy_inputs(UpperCamelCase__ )
snake_case : Optional[int] = 3 * [inputs.pop('''prompt''' )]
snake_case : Dict = ldmad_pipe.tokenizer(
UpperCamelCase__ , padding='''max_length''' , max_length=ldmad_pipe.tokenizer.model_max_length , truncation=UpperCamelCase__ , return_tensors='''pt''' , )
snake_case : Optional[Any] = text_inputs['''input_ids'''].to(UpperCamelCase__ )
snake_case : Any = ldmad_pipe.text_encoder(UpperCamelCase__ )[0]
snake_case : Tuple = prompt_embeds
# forward
snake_case : List[Any] = ldmad_pipe(**UpperCamelCase__ )
snake_case ,snake_case : Dict = output.rgb, output.depth
snake_case : Any = rgb_slice_a[0, -3:, -3:, -1]
snake_case : Dict = depth_slice_a[0, -3:, -1]
assert np.abs(rgb_slice_a.flatten() - rgb_slice_a.flatten() ).max() < 1e-4
assert np.abs(depth_slice_a.flatten() - depth_slice_a.flatten() ).max() < 1e-4
def lowerCAmelCase ( self : str ) -> List[Any]:
"""simple docstring"""
snake_case : str = '''cpu''' # ensure determinism for the device-dependent torch.Generator
snake_case : Dict = self.get_dummy_components()
snake_case : List[str] = PNDMScheduler(skip_prk_steps=UpperCamelCase__ )
snake_case : Optional[Any] = StableDiffusionLDMaDPipeline(**UpperCamelCase__ )
snake_case : Dict = ldmad_pipe.to(UpperCamelCase__ )
ldmad_pipe.set_progress_bar_config(disable=UpperCamelCase__ )
snake_case : Dict = self.get_dummy_inputs(UpperCamelCase__ )
snake_case : str = '''french fries'''
snake_case : List[str] = ldmad_pipe(**UpperCamelCase__ , negative_prompt=UpperCamelCase__ )
snake_case ,snake_case : Union[str, Any] = output.rgb, output.depth
snake_case : Union[str, Any] = rgb[0, -3:, -3:, -1]
snake_case : int = depth[0, -3:, -1]
assert rgb.shape == (1, 64, 64, 3)
assert depth.shape == (1, 64, 64)
snake_case : Dict = np.array(
[0.37_044, 0.71_811_503, 0.7_223_251, 0.48_603_675, 0.5_638_391, 0.6_364_948, 0.42_833_704, 0.4_901_315, 0.47_926_217] )
snake_case : Any = np.array([107.84_738, 84.62_802, 89.962_135] )
assert np.abs(rgb_slice.flatten() - expected_slice_rgb ).max() < 1e-2
assert np.abs(depth_slice.flatten() - expected_slice_depth ).max() < 1e-2
@slow
@require_torch_gpu
class snake_case__ ( unittest.TestCase ):
"""simple docstring"""
def lowerCAmelCase ( self : Optional[int] ) -> List[str]:
"""simple docstring"""
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def lowerCAmelCase ( self : Union[str, Any] , UpperCamelCase__ : Any , UpperCamelCase__ : Union[str, Any]="cpu" , UpperCamelCase__ : Optional[int]=torch.floataa , UpperCamelCase__ : Union[str, Any]=0 ) -> Any:
"""simple docstring"""
snake_case : Any = torch.Generator(device=UpperCamelCase__ ).manual_seed(UpperCamelCase__ )
snake_case : Optional[Any] = np.random.RandomState(UpperCamelCase__ ).standard_normal((1, 4, 64, 64) )
snake_case : Any = torch.from_numpy(UpperCamelCase__ ).to(device=UpperCamelCase__ , dtype=UpperCamelCase__ )
snake_case : List[Any] = {
'''prompt''': '''a photograph of an astronaut riding a horse''',
'''latents''': latents,
'''generator''': generator,
'''num_inference_steps''': 3,
'''guidance_scale''': 7.5,
'''output_type''': '''numpy''',
}
return inputs
def lowerCAmelCase ( self : Union[str, Any] ) -> Union[str, Any]:
"""simple docstring"""
snake_case : Dict = StableDiffusionLDMaDPipeline.from_pretrained('''Intel/ldm3d''' )
snake_case : Optional[Any] = ldmad_pipe.to(UpperCamelCase__ )
ldmad_pipe.set_progress_bar_config(disable=UpperCamelCase__ )
snake_case : Any = self.get_inputs(UpperCamelCase__ )
snake_case : str = ldmad_pipe(**UpperCamelCase__ )
snake_case ,snake_case : List[Any] = output.rgb, output.depth
snake_case : Optional[Any] = rgb[0, -3:, -3:, -1].flatten()
snake_case : Tuple = rgb[0, -3:, -1].flatten()
assert rgb.shape == (1, 512, 512, 3)
assert depth.shape == (1, 512, 512)
snake_case : Optional[Any] = np.array(
[0.53_805_465, 0.56_707_305, 0.5_486_515, 0.57_012_236, 0.5_814_511, 0.56_253_487, 0.54_843_014, 0.55_092_263, 0.6_459_706] )
snake_case : str = np.array(
[0.9_263_781, 0.6_678_672, 0.5_486_515, 0.92_202_145, 0.67_831_135, 0.56_253_487, 0.9_241_694, 0.7_551_478, 0.6_459_706] )
assert np.abs(rgb_slice - expected_slice_rgb ).max() < 3e-3
assert np.abs(depth_slice - expected_slice_depth ).max() < 3e-3
@nightly
@require_torch_gpu
class snake_case__ ( unittest.TestCase ):
"""simple docstring"""
def lowerCAmelCase ( self : Union[str, Any] ) -> Optional[int]:
"""simple docstring"""
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def lowerCAmelCase ( self : Optional[Any] , UpperCamelCase__ : Any , UpperCamelCase__ : Any="cpu" , UpperCamelCase__ : Optional[int]=torch.floataa , UpperCamelCase__ : Optional[int]=0 ) -> str:
"""simple docstring"""
snake_case : Dict = torch.Generator(device=UpperCamelCase__ ).manual_seed(UpperCamelCase__ )
snake_case : Optional[Any] = np.random.RandomState(UpperCamelCase__ ).standard_normal((1, 4, 64, 64) )
snake_case : int = torch.from_numpy(UpperCamelCase__ ).to(device=UpperCamelCase__ , dtype=UpperCamelCase__ )
snake_case : List[str] = {
'''prompt''': '''a photograph of an astronaut riding a horse''',
'''latents''': latents,
'''generator''': generator,
'''num_inference_steps''': 50,
'''guidance_scale''': 7.5,
'''output_type''': '''numpy''',
}
return inputs
def lowerCAmelCase ( self : Optional[Any] ) -> Tuple:
"""simple docstring"""
snake_case : Optional[Any] = StableDiffusionLDMaDPipeline.from_pretrained('''Intel/ldm3d''' ).to(UpperCamelCase__ )
ldmad_pipe.set_progress_bar_config(disable=UpperCamelCase__ )
snake_case : Dict = self.get_inputs(UpperCamelCase__ )
snake_case : str = ldmad_pipe(**UpperCamelCase__ )
snake_case ,snake_case : Union[str, Any] = output.rgb, output.depth
snake_case : Union[str, Any] = 0.495_586
snake_case : Tuple = 0.33_795_515
snake_case : Dict = 112.48_518
snake_case : Optional[int] = 98.489_746
assert np.abs(expected_rgb_mean - rgb.mean() ) < 1e-3
assert np.abs(expected_rgb_std - rgb.std() ) < 1e-3
assert np.abs(expected_depth_mean - depth.mean() ) < 1e-3
assert np.abs(expected_depth_std - depth.std() ) < 1e-3
def lowerCAmelCase ( self : Dict ) -> Dict:
"""simple docstring"""
snake_case : Optional[int] = StableDiffusionLDMaDPipeline.from_pretrained('''Intel/ldm3d-4c''' ).to(UpperCamelCase__ )
ldmad_pipe.set_progress_bar_config(disable=UpperCamelCase__ )
snake_case : int = self.get_inputs(UpperCamelCase__ )
snake_case : List[Any] = ldmad_pipe(**UpperCamelCase__ )
snake_case ,snake_case : Union[str, Any] = output.rgb, output.depth
snake_case : Tuple = 0.4_194_127
snake_case : Optional[Any] = 0.35_375_586
snake_case : Any = 0.5_638_502
snake_case : int = 0.34_686_103
assert rgb.shape == (1, 512, 512, 3)
assert depth.shape == (1, 512, 512, 1)
assert np.abs(expected_rgb_mean - rgb.mean() ) < 1e-3
assert np.abs(expected_rgb_std - rgb.std() ) < 1e-3
assert np.abs(expected_depth_mean - depth.mean() ) < 1e-3
assert np.abs(expected_depth_std - depth.std() ) < 1e-3
| 83 | 0 |
"""simple docstring"""
import inspect
import warnings
from typing import Any, Dict, Optional, Union
from packaging import version
def lowercase ( *a__ : Union[str, Any] , a__ : Optional[Union[Dict, Any]] = None , a__ : int=True , a__ : Tuple=2 ) -> Optional[int]:
from .. import __version__
_UpperCamelCase = take_from
_UpperCamelCase = ()
if not isinstance(args[0] , a__ ):
_UpperCamelCase = (args,)
for attribute, version_name, message in args:
if version.parse(version.parse(a__ ).base_version ) >= version.parse(a__ ):
raise ValueError(
F'''The deprecation tuple {(attribute, version_name, message)} should be removed since diffusers\''''
F''' version {__version__} is >= {version_name}''' )
_UpperCamelCase = None
if isinstance(a__ , a__ ) and attribute in deprecated_kwargs:
values += (deprecated_kwargs.pop(a__ ),)
_UpperCamelCase = F'''The `{attribute}` argument is deprecated and will be removed in version {version_name}.'''
elif hasattr(a__ , a__ ):
values += (getattr(a__ , a__ ),)
_UpperCamelCase = F'''The `{attribute}` attribute is deprecated and will be removed in version {version_name}.'''
elif deprecated_kwargs is None:
_UpperCamelCase = F'''`{attribute}` is deprecated and will be removed in version {version_name}.'''
if warning is not None:
_UpperCamelCase = warning + ''' ''' if standard_warn else ''''''
warnings.warn(warning + message , a__ , stacklevel=a__ )
if isinstance(a__ , a__ ) and len(a__ ) > 0:
_UpperCamelCase = inspect.getouterframes(inspect.currentframe() )[1]
_UpperCamelCase = call_frame.filename
_UpperCamelCase = call_frame.lineno
_UpperCamelCase = call_frame.function
_UpperCamelCase , _UpperCamelCase = next(iter(deprecated_kwargs.items() ) )
raise TypeError(F'''{function} in {filename} line {line_number-1} got an unexpected keyword argument `{key}`''' )
if len(a__ ) == 0:
return
elif len(a__ ) == 1:
return values[0]
return values
| 256 | """simple docstring"""
import argparse
import json
import logging
import os
import shutil
import sys
import tempfile
import unittest
from unittest import mock
import torch
from accelerate.utils import write_basic_config
from transformers.testing_utils import TestCasePlus, get_gpu_count, run_command, slow, torch_device
from transformers.utils import is_apex_available
logging.basicConfig(level=logging.DEBUG)
UpperCAmelCase = logging.getLogger()
def lowercase ( ) -> List[str]:
_UpperCamelCase = argparse.ArgumentParser()
parser.add_argument('''-f''' )
_UpperCamelCase = parser.parse_args()
return args.f
def lowercase ( a__ : List[Any] ) -> Optional[Any]:
_UpperCamelCase = {}
_UpperCamelCase = os.path.join(a__ , '''all_results.json''' )
if os.path.exists(a__ ):
with open(a__ , '''r''' ) as f:
_UpperCamelCase = json.load(a__ )
else:
raise ValueError(F'''can\'t find {path}''' )
return results
def lowercase ( ) -> str:
_UpperCamelCase = torch.cuda.is_available() and torch_device == '''cuda'''
return is_using_cuda and is_apex_available()
UpperCAmelCase = logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)
class UpperCAmelCase_ ( _lowercase):
@classmethod
def _UpperCamelCase ( cls : Any ) -> List[Any]:
# Write Accelerate config, will pick up on CPU, GPU, and multi-GPU
_UpperCamelCase = tempfile.mkdtemp()
_UpperCamelCase = os.path.join(cls.tmpdir , '''default_config.yml''' )
write_basic_config(save_location=cls.configPath )
_UpperCamelCase = ['''accelerate''', '''launch''', '''--config_file''', cls.configPath]
@classmethod
def _UpperCamelCase ( cls : int ) -> str:
shutil.rmtree(cls.tmpdir )
@mock.patch.dict(os.environ , {'''WANDB_MODE''': '''offline'''} )
def _UpperCamelCase ( self : Union[str, Any] ) -> Tuple:
_UpperCamelCase = self.get_auto_remove_tmp_dir()
_UpperCamelCase = F'''
{self.examples_dir}/pytorch/text-classification/run_glue_no_trainer.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
--seed=42
--checkpointing_steps epoch
--with_tracking
'''.split()
if is_cuda_and_apex_available():
testargs.append('''--fp16''' )
run_command(self._launch_args + testargs )
_UpperCamelCase = get_results(__UpperCamelCase )
self.assertGreaterEqual(result['''eval_accuracy'''] , 0.7_5 )
self.assertTrue(os.path.exists(os.path.join(__UpperCamelCase , '''epoch_0''' ) ) )
self.assertTrue(os.path.exists(os.path.join(__UpperCamelCase , '''glue_no_trainer''' ) ) )
@mock.patch.dict(os.environ , {'''WANDB_MODE''': '''offline'''} )
def _UpperCamelCase ( self : str ) -> Dict:
_UpperCamelCase = self.get_auto_remove_tmp_dir()
_UpperCamelCase = F'''
{self.examples_dir}/pytorch/language-modeling/run_clm_no_trainer.py
--model_name_or_path distilgpt2
--train_file ./tests/fixtures/sample_text.txt
--validation_file ./tests/fixtures/sample_text.txt
--block_size 128
--per_device_train_batch_size 5
--per_device_eval_batch_size 5
--num_train_epochs 2
--output_dir {tmp_dir}
--checkpointing_steps epoch
--with_tracking
'''.split()
if torch.cuda.device_count() > 1:
# Skipping because there are not enough batches to train the model + would need a drop_last to work.
return
run_command(self._launch_args + testargs )
_UpperCamelCase = get_results(__UpperCamelCase )
self.assertLess(result['''perplexity'''] , 100 )
self.assertTrue(os.path.exists(os.path.join(__UpperCamelCase , '''epoch_0''' ) ) )
self.assertTrue(os.path.exists(os.path.join(__UpperCamelCase , '''clm_no_trainer''' ) ) )
@mock.patch.dict(os.environ , {'''WANDB_MODE''': '''offline'''} )
def _UpperCamelCase ( self : List[str] ) -> Tuple:
_UpperCamelCase = self.get_auto_remove_tmp_dir()
_UpperCamelCase = F'''
{self.examples_dir}/pytorch/language-modeling/run_mlm_no_trainer.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}
--num_train_epochs=1
--checkpointing_steps epoch
--with_tracking
'''.split()
run_command(self._launch_args + testargs )
_UpperCamelCase = get_results(__UpperCamelCase )
self.assertLess(result['''perplexity'''] , 42 )
self.assertTrue(os.path.exists(os.path.join(__UpperCamelCase , '''epoch_0''' ) ) )
self.assertTrue(os.path.exists(os.path.join(__UpperCamelCase , '''mlm_no_trainer''' ) ) )
@mock.patch.dict(os.environ , {'''WANDB_MODE''': '''offline'''} )
def _UpperCamelCase ( self : List[str] ) -> str:
# with so little data distributed training needs more epochs to get the score on par with 0/1 gpu
_UpperCamelCase = 7 if get_gpu_count() > 1 else 2
_UpperCamelCase = self.get_auto_remove_tmp_dir()
_UpperCamelCase = F'''
{self.examples_dir}/pytorch/token-classification/run_ner_no_trainer.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}
--learning_rate=2e-4
--per_device_train_batch_size=2
--per_device_eval_batch_size=2
--num_train_epochs={epochs}
--seed 7
--checkpointing_steps epoch
--with_tracking
'''.split()
run_command(self._launch_args + testargs )
_UpperCamelCase = get_results(__UpperCamelCase )
self.assertGreaterEqual(result['''eval_accuracy'''] , 0.7_5 )
self.assertLess(result['''train_loss'''] , 0.5 )
self.assertTrue(os.path.exists(os.path.join(__UpperCamelCase , '''epoch_0''' ) ) )
self.assertTrue(os.path.exists(os.path.join(__UpperCamelCase , '''ner_no_trainer''' ) ) )
@unittest.skip(reason='''Fix me @muellerzr''' )
@mock.patch.dict(os.environ , {'''WANDB_MODE''': '''offline'''} )
def _UpperCamelCase ( self : Optional[Any] ) -> Optional[Any]:
_UpperCamelCase = self.get_auto_remove_tmp_dir()
_UpperCamelCase = F'''
{self.examples_dir}/pytorch/question-answering/run_qa_no_trainer.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}
--seed=42
--max_train_steps=10
--num_warmup_steps=2
--learning_rate=2e-4
--per_device_train_batch_size=2
--per_device_eval_batch_size=1
--checkpointing_steps epoch
--with_tracking
'''.split()
run_command(self._launch_args + testargs )
_UpperCamelCase = get_results(__UpperCamelCase )
# Because we use --version_2_with_negative the testing script uses SQuAD v2 metrics.
self.assertGreaterEqual(result['''eval_f1'''] , 28 )
self.assertGreaterEqual(result['''eval_exact'''] , 28 )
self.assertTrue(os.path.exists(os.path.join(__UpperCamelCase , '''epoch_0''' ) ) )
self.assertTrue(os.path.exists(os.path.join(__UpperCamelCase , '''qa_no_trainer''' ) ) )
@mock.patch.dict(os.environ , {'''WANDB_MODE''': '''offline'''} )
def _UpperCamelCase ( self : Optional[Any] ) -> str:
_UpperCamelCase = self.get_auto_remove_tmp_dir()
_UpperCamelCase = F'''
{self.examples_dir}/pytorch/multiple-choice/run_swag_no_trainer.py
--model_name_or_path bert-base-uncased
--train_file tests/fixtures/tests_samples/swag/sample.json
--validation_file tests/fixtures/tests_samples/swag/sample.json
--output_dir {tmp_dir}
--max_train_steps=20
--num_warmup_steps=2
--learning_rate=2e-4
--per_device_train_batch_size=2
--per_device_eval_batch_size=1
--with_tracking
'''.split()
run_command(self._launch_args + testargs )
_UpperCamelCase = get_results(__UpperCamelCase )
self.assertGreaterEqual(result['''eval_accuracy'''] , 0.8 )
self.assertTrue(os.path.exists(os.path.join(__UpperCamelCase , '''swag_no_trainer''' ) ) )
@slow
@mock.patch.dict(os.environ , {'''WANDB_MODE''': '''offline'''} )
def _UpperCamelCase ( self : int ) -> Optional[Any]:
_UpperCamelCase = self.get_auto_remove_tmp_dir()
_UpperCamelCase = F'''
{self.examples_dir}/pytorch/summarization/run_summarization_no_trainer.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
--output_dir {tmp_dir}
--max_train_steps=50
--num_warmup_steps=8
--learning_rate=2e-4
--per_device_train_batch_size=2
--per_device_eval_batch_size=1
--checkpointing_steps epoch
--with_tracking
'''.split()
run_command(self._launch_args + testargs )
_UpperCamelCase = get_results(__UpperCamelCase )
self.assertGreaterEqual(result['''eval_rouge1'''] , 10 )
self.assertGreaterEqual(result['''eval_rouge2'''] , 2 )
self.assertGreaterEqual(result['''eval_rougeL'''] , 7 )
self.assertGreaterEqual(result['''eval_rougeLsum'''] , 7 )
self.assertTrue(os.path.exists(os.path.join(__UpperCamelCase , '''epoch_0''' ) ) )
self.assertTrue(os.path.exists(os.path.join(__UpperCamelCase , '''summarization_no_trainer''' ) ) )
@slow
@mock.patch.dict(os.environ , {'''WANDB_MODE''': '''offline'''} )
def _UpperCamelCase ( self : str ) -> str:
_UpperCamelCase = self.get_auto_remove_tmp_dir()
_UpperCamelCase = F'''
{self.examples_dir}/pytorch/translation/run_translation_no_trainer.py
--model_name_or_path sshleifer/student_marian_en_ro_6_1
--source_lang en
--target_lang ro
--train_file tests/fixtures/tests_samples/wmt16/sample.json
--validation_file tests/fixtures/tests_samples/wmt16/sample.json
--output_dir {tmp_dir}
--max_train_steps=50
--num_warmup_steps=8
--num_beams=6
--learning_rate=3e-3
--per_device_train_batch_size=2
--per_device_eval_batch_size=1
--source_lang en_XX
--target_lang ro_RO
--checkpointing_steps epoch
--with_tracking
'''.split()
run_command(self._launch_args + testargs )
_UpperCamelCase = get_results(__UpperCamelCase )
self.assertGreaterEqual(result['''eval_bleu'''] , 30 )
self.assertTrue(os.path.exists(os.path.join(__UpperCamelCase , '''epoch_0''' ) ) )
self.assertTrue(os.path.exists(os.path.join(__UpperCamelCase , '''translation_no_trainer''' ) ) )
@slow
def _UpperCamelCase ( self : Any ) -> List[Any]:
_UpperCamelCase = logging.StreamHandler(sys.stdout )
logger.addHandler(__UpperCamelCase )
_UpperCamelCase = self.get_auto_remove_tmp_dir()
_UpperCamelCase = F'''
{self.examples_dir}/pytorch/semantic-segmentation/run_semantic_segmentation_no_trainer.py
--dataset_name huggingface/semantic-segmentation-test-sample
--output_dir {tmp_dir}
--max_train_steps=10
--num_warmup_steps=2
--learning_rate=2e-4
--per_device_train_batch_size=2
--per_device_eval_batch_size=1
--checkpointing_steps epoch
'''.split()
run_command(self._launch_args + testargs )
_UpperCamelCase = get_results(__UpperCamelCase )
self.assertGreaterEqual(result['''eval_overall_accuracy'''] , 0.1_0 )
@mock.patch.dict(os.environ , {'''WANDB_MODE''': '''offline'''} )
def _UpperCamelCase ( self : List[Any] ) -> Optional[Any]:
_UpperCamelCase = self.get_auto_remove_tmp_dir()
_UpperCamelCase = F'''
{self.examples_dir}/pytorch/image-classification/run_image_classification_no_trainer.py
--model_name_or_path google/vit-base-patch16-224-in21k
--dataset_name hf-internal-testing/cats_vs_dogs_sample
--learning_rate 1e-4
--per_device_train_batch_size 2
--per_device_eval_batch_size 1
--max_train_steps 2
--train_val_split 0.1
--seed 42
--output_dir {tmp_dir}
--with_tracking
--checkpointing_steps 1
'''.split()
if is_cuda_and_apex_available():
testargs.append('''--fp16''' )
run_command(self._launch_args + testargs )
_UpperCamelCase = get_results(__UpperCamelCase )
# The base model scores a 25%
self.assertGreaterEqual(result['''eval_accuracy'''] , 0.6 )
self.assertTrue(os.path.exists(os.path.join(__UpperCamelCase , '''step_1''' ) ) )
self.assertTrue(os.path.exists(os.path.join(__UpperCamelCase , '''image_classification_no_trainer''' ) ) )
| 256 | 1 |
"""simple docstring"""
import argparse
import OmegaConf
import torch
from diffusers import DDIMScheduler, LDMPipeline, UNetLDMModel, VQModel
def _snake_case ( UpperCAmelCase_ : str , UpperCAmelCase_ : Tuple , UpperCAmelCase_ : Any ):
A__ = OmegaConf.load(UpperCAmelCase_ )
A__ = torch.load(UpperCAmelCase_ , map_location="""cpu""" )["""model"""]
A__ = list(state_dict.keys() )
# extract state_dict for VQVAE
A__ = {}
A__ = """first_stage_model."""
for key in keys:
if key.startswith(UpperCAmelCase_ ):
A__ = state_dict[key]
# extract state_dict for UNetLDM
A__ = {}
A__ = """model.diffusion_model."""
for key in keys:
if key.startswith(UpperCAmelCase_ ):
A__ = state_dict[key]
A__ = config.model.params.first_stage_config.params
A__ = config.model.params.unet_config.params
A__ = VQModel(**UpperCAmelCase_ ).eval()
vqvae.load_state_dict(UpperCAmelCase_ )
A__ = UNetLDMModel(**UpperCAmelCase_ ).eval()
unet.load_state_dict(UpperCAmelCase_ )
A__ = DDIMScheduler(
timesteps=config.model.params.timesteps , beta_schedule="""scaled_linear""" , beta_start=config.model.params.linear_start , beta_end=config.model.params.linear_end , clip_sample=UpperCAmelCase_ , )
A__ = LDMPipeline(UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ )
pipeline.save_pretrained(UpperCAmelCase_ )
if __name__ == "__main__":
SCREAMING_SNAKE_CASE_ : Optional[int] = argparse.ArgumentParser()
parser.add_argument('--checkpoint_path', type=str, required=True)
parser.add_argument('--config_path', type=str, required=True)
parser.add_argument('--output_path', type=str, required=True)
SCREAMING_SNAKE_CASE_ : Optional[int] = parser.parse_args()
convert_ldm_original(args.checkpoint_path, args.config_path, args.output_path)
| 69 |
"""simple docstring"""
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
EulerAncestralDiscreteScheduler,
LMSDiscreteScheduler,
PNDMScheduler,
StableDiffusionInstructPixaPixPipeline,
UNetaDConditionModel,
)
from diffusers.image_processor import VaeImageProcessor
from diffusers.utils import floats_tensor, load_image, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu
from ..pipeline_params import (
IMAGE_TO_IMAGE_IMAGE_PARAMS,
TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS,
TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin
enable_full_determinism()
class a ( _lowerCamelCase, _lowerCamelCase, _lowerCamelCase, unittest.TestCase ):
"""simple docstring"""
UpperCAmelCase = StableDiffusionInstructPixaPixPipeline
UpperCAmelCase = TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"height", "width", "cross_attention_kwargs"}
UpperCAmelCase = TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
UpperCAmelCase = IMAGE_TO_IMAGE_IMAGE_PARAMS
UpperCAmelCase = IMAGE_TO_IMAGE_IMAGE_PARAMS
def UpperCamelCase ( self: Tuple ):
"""simple docstring"""
torch.manual_seed(0 )
A__ = UNetaDConditionModel(
block_out_channels=(32, 64) , layers_per_block=2 , sample_size=32 , in_channels=8 , out_channels=4 , down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""") , up_block_types=("""CrossAttnUpBlock2D""", """UpBlock2D""") , cross_attention_dim=32 , )
A__ = PNDMScheduler(skip_prk_steps=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=10_00 , )
A__ = CLIPTextModel(UpperCamelCase )
A__ = CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip""" )
A__ = {
"""unet""": unet,
"""scheduler""": scheduler,
"""vae""": vae,
"""text_encoder""": text_encoder,
"""tokenizer""": tokenizer,
"""safety_checker""": None,
"""feature_extractor""": None,
}
return components
def UpperCamelCase ( self: str , UpperCamelCase: List[str] , UpperCamelCase: Optional[Any]=0 ):
"""simple docstring"""
A__ = floats_tensor((1, 3, 32, 32) , rng=random.Random(UpperCamelCase ) ).to(UpperCamelCase )
A__ = image.cpu().permute(0 , 2 , 3 , 1 )[0]
A__ = Image.fromarray(np.uinta(UpperCamelCase ) ).convert("""RGB""" )
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""",
"""image""": image,
"""generator""": generator,
"""num_inference_steps""": 2,
"""guidance_scale""": 6.0,
"""image_guidance_scale""": 1,
"""output_type""": """numpy""",
}
return inputs
def UpperCamelCase ( self: int ):
"""simple docstring"""
A__ = """cpu""" # ensure determinism for the device-dependent torch.Generator
A__ = self.get_dummy_components()
A__ = StableDiffusionInstructPixaPixPipeline(**UpperCamelCase )
A__ = sd_pipe.to(UpperCamelCase )
sd_pipe.set_progress_bar_config(disable=UpperCamelCase )
A__ = self.get_dummy_inputs(UpperCamelCase )
A__ = sd_pipe(**UpperCamelCase ).images
A__ = image[0, -3:, -3:, -1]
assert image.shape == (1, 32, 32, 3)
A__ = np.array([0.7_526, 0.3_750, 0.4_547, 0.6_117, 0.5_866, 0.5_016, 0.4_327, 0.5_642, 0.4_815] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-3
def UpperCamelCase ( self: List[str] ):
"""simple docstring"""
A__ = """cpu""" # ensure determinism for the device-dependent torch.Generator
A__ = self.get_dummy_components()
A__ = StableDiffusionInstructPixaPixPipeline(**UpperCamelCase )
A__ = sd_pipe.to(UpperCamelCase )
sd_pipe.set_progress_bar_config(disable=UpperCamelCase )
A__ = self.get_dummy_inputs(UpperCamelCase )
A__ = """french fries"""
A__ = sd_pipe(**UpperCamelCase , negative_prompt=UpperCamelCase )
A__ = output.images
A__ = image[0, -3:, -3:, -1]
assert image.shape == (1, 32, 32, 3)
A__ = np.array([0.7_511, 0.3_642, 0.4_553, 0.6_236, 0.5_797, 0.5_013, 0.4_343, 0.5_611, 0.4_831] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-3
def UpperCamelCase ( self: List[Any] ):
"""simple docstring"""
A__ = """cpu""" # ensure determinism for the device-dependent torch.Generator
A__ = self.get_dummy_components()
A__ = StableDiffusionInstructPixaPixPipeline(**UpperCamelCase )
A__ = sd_pipe.to(UpperCamelCase )
sd_pipe.set_progress_bar_config(disable=UpperCamelCase )
A__ = self.get_dummy_inputs(UpperCamelCase )
A__ = [inputs["""prompt"""]] * 2
A__ = np.array(inputs["""image"""] ).astype(np.floataa ) / 255.0
A__ = torch.from_numpy(UpperCamelCase ).unsqueeze(0 ).to(UpperCamelCase )
A__ = image / 2 + 0.5
A__ = image.permute(0 , 3 , 1 , 2 )
A__ = image.repeat(2 , 1 , 1 , 1 )
A__ = sd_pipe(**UpperCamelCase ).images
A__ = image[-1, -3:, -3:, -1]
assert image.shape == (2, 32, 32, 3)
A__ = np.array([0.5_812, 0.5_748, 0.5_222, 0.5_908, 0.5_695, 0.7_174, 0.6_804, 0.5_523, 0.5_579] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-3
def UpperCamelCase ( self: Optional[Any] ):
"""simple docstring"""
A__ = """cpu""" # ensure determinism for the device-dependent torch.Generator
A__ = self.get_dummy_components()
A__ = EulerAncestralDiscreteScheduler(
beta_start=0.00_085 , beta_end=0.012 , beta_schedule="""scaled_linear""" )
A__ = StableDiffusionInstructPixaPixPipeline(**UpperCamelCase )
A__ = sd_pipe.to(UpperCamelCase )
sd_pipe.set_progress_bar_config(disable=UpperCamelCase )
A__ = self.get_dummy_inputs(UpperCamelCase )
A__ = sd_pipe(**UpperCamelCase ).images
A__ = image[0, -3:, -3:, -1]
A__ = [round(UpperCamelCase , 4 ) for x in image_slice.flatten().tolist()]
print(""",""".join([str(UpperCamelCase ) for x in slice] ) )
assert image.shape == (1, 32, 32, 3)
A__ = np.array([0.7_417, 0.3_842, 0.4_732, 0.5_776, 0.5_891, 0.5_139, 0.4_052, 0.5_673, 0.4_986] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-3
def UpperCamelCase ( self: str ):
"""simple docstring"""
super().test_inference_batch_single_identical(expected_max_diff=3e-3 )
def UpperCamelCase ( self: int ):
"""simple docstring"""
A__ = self.get_dummy_components()
A__ = StableDiffusionInstructPixaPixPipeline(**UpperCamelCase )
A__ = VaeImageProcessor(do_resize=UpperCamelCase , do_normalize=UpperCamelCase )
A__ = pipe.to(UpperCamelCase )
pipe.set_progress_bar_config(disable=UpperCamelCase )
A__ = pipe(**self.get_dummy_inputs_by_type(UpperCamelCase , input_image_type="""pt""" ) )[0]
A__ = components["""vae"""]
A__ = self.get_dummy_inputs_by_type(UpperCamelCase , input_image_type="""pt""" )
for image_param in self.image_latents_params:
if image_param in inputs.keys():
A__ = vae.encode(inputs[image_param] ).latent_dist.mode()
A__ = pipe(**UpperCamelCase )[0]
A__ = np.abs(out - out_latents_inputs ).max()
self.assertLess(UpperCamelCase , 1e-4 , """passing latents as image input generate different result from passing image""" )
@slow
@require_torch_gpu
class a ( unittest.TestCase ):
"""simple docstring"""
def UpperCamelCase ( self: Tuple ):
"""simple docstring"""
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def UpperCamelCase ( self: List[Any] , UpperCamelCase: List[str]=0 ):
"""simple docstring"""
A__ = torch.manual_seed(UpperCamelCase )
A__ = load_image(
"""https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/stable_diffusion_pix2pix/example.jpg""" )
A__ = {
"""prompt""": """turn him into a cyborg""",
"""image""": image,
"""generator""": generator,
"""num_inference_steps""": 3,
"""guidance_scale""": 7.5,
"""image_guidance_scale""": 1.0,
"""output_type""": """numpy""",
}
return inputs
def UpperCamelCase ( self: str ):
"""simple docstring"""
A__ = StableDiffusionInstructPixaPixPipeline.from_pretrained(
"""timbrooks/instruct-pix2pix""" , safety_checker=UpperCamelCase )
pipe.to(UpperCamelCase )
pipe.set_progress_bar_config(disable=UpperCamelCase )
pipe.enable_attention_slicing()
A__ = self.get_inputs()
A__ = pipe(**UpperCamelCase ).images
A__ = image[0, -3:, -3:, -1].flatten()
assert image.shape == (1, 5_12, 5_12, 3)
A__ = np.array([0.5_902, 0.6_015, 0.6_027, 0.5_983, 0.6_092, 0.6_061, 0.5_765, 0.5_785, 0.5_555] )
assert np.abs(expected_slice - image_slice ).max() < 1e-3
def UpperCamelCase ( self: Any ):
"""simple docstring"""
A__ = StableDiffusionInstructPixaPixPipeline.from_pretrained(
"""timbrooks/instruct-pix2pix""" , safety_checker=UpperCamelCase )
A__ = LMSDiscreteScheduler.from_config(pipe.scheduler.config )
pipe.to(UpperCamelCase )
pipe.set_progress_bar_config(disable=UpperCamelCase )
pipe.enable_attention_slicing()
A__ = self.get_inputs()
A__ = pipe(**UpperCamelCase ).images
A__ = image[0, -3:, -3:, -1].flatten()
assert image.shape == (1, 5_12, 5_12, 3)
A__ = np.array([0.6_578, 0.6_817, 0.6_972, 0.6_761, 0.6_856, 0.6_916, 0.6_428, 0.6_516, 0.6_301] )
assert np.abs(expected_slice - image_slice ).max() < 1e-3
def UpperCamelCase ( self: Optional[Any] ):
"""simple docstring"""
A__ = StableDiffusionInstructPixaPixPipeline.from_pretrained(
"""timbrooks/instruct-pix2pix""" , safety_checker=UpperCamelCase )
A__ = DDIMScheduler.from_config(pipe.scheduler.config )
pipe.to(UpperCamelCase )
pipe.set_progress_bar_config(disable=UpperCamelCase )
pipe.enable_attention_slicing()
A__ = self.get_inputs()
A__ = pipe(**UpperCamelCase ).images
A__ = image[0, -3:, -3:, -1].flatten()
assert image.shape == (1, 5_12, 5_12, 3)
A__ = np.array([0.3_828, 0.3_834, 0.3_818, 0.3_792, 0.3_865, 0.3_752, 0.3_792, 0.3_847, 0.3_753] )
assert np.abs(expected_slice - image_slice ).max() < 1e-3
def UpperCamelCase ( self: Union[str, Any] ):
"""simple docstring"""
A__ = 0
def callback_fn(UpperCamelCase: int , UpperCamelCase: int , UpperCamelCase: torch.FloatTensor ) -> None:
A__ = True
nonlocal number_of_steps
number_of_steps += 1
if step == 1:
A__ = latents.detach().cpu().numpy()
assert latents.shape == (1, 4, 64, 64)
A__ = latents[0, -3:, -3:, -1]
A__ = np.array([-0.2_463, -0.4_644, -0.9_756, 1.5_176, 1.4_414, 0.7_866, 0.9_897, 0.8_521, 0.7_983] )
assert np.abs(latents_slice.flatten() - expected_slice ).max() < 5e-2
elif step == 2:
A__ = latents.detach().cpu().numpy()
assert latents.shape == (1, 4, 64, 64)
A__ = latents[0, -3:, -3:, -1]
A__ = np.array([-0.2_644, -0.4_626, -0.9_653, 1.5_176, 1.4_551, 0.7_686, 0.9_805, 0.8_452, 0.8_115] )
assert np.abs(latents_slice.flatten() - expected_slice ).max() < 5e-2
A__ = False
A__ = StableDiffusionInstructPixaPixPipeline.from_pretrained(
"""timbrooks/instruct-pix2pix""" , safety_checker=UpperCamelCase , torch_dtype=torch.floataa )
A__ = pipe.to(UpperCamelCase )
pipe.set_progress_bar_config(disable=UpperCamelCase )
pipe.enable_attention_slicing()
A__ = self.get_inputs()
pipe(**UpperCamelCase , callback=UpperCamelCase , callback_steps=1 )
assert callback_fn.has_been_called
assert number_of_steps == 3
def UpperCamelCase ( self: Optional[Any] ):
"""simple docstring"""
torch.cuda.empty_cache()
torch.cuda.reset_max_memory_allocated()
torch.cuda.reset_peak_memory_stats()
A__ = StableDiffusionInstructPixaPixPipeline.from_pretrained(
"""timbrooks/instruct-pix2pix""" , safety_checker=UpperCamelCase , torch_dtype=torch.floataa )
A__ = pipe.to(UpperCamelCase )
pipe.set_progress_bar_config(disable=UpperCamelCase )
pipe.enable_attention_slicing(1 )
pipe.enable_sequential_cpu_offload()
A__ = self.get_inputs()
A__ = pipe(**UpperCamelCase )
A__ = torch.cuda.max_memory_allocated()
# make sure that less than 2.2 GB is allocated
assert mem_bytes < 2.2 * 10**9
def UpperCamelCase ( self: List[str] ):
"""simple docstring"""
A__ = self.get_inputs()
# resize to resolution that is divisible by 8 but not 16 or 32
A__ = inputs["""image"""].resize((5_04, 5_04) )
A__ = """timbrooks/instruct-pix2pix"""
A__ = StableDiffusionInstructPixaPixPipeline.from_pretrained(
UpperCamelCase , safety_checker=UpperCamelCase , )
pipe.to(UpperCamelCase )
pipe.set_progress_bar_config(disable=UpperCamelCase )
pipe.enable_attention_slicing()
A__ = pipe(**UpperCamelCase )
A__ = output.images[0]
A__ = image[2_55:2_58, 3_83:3_86, -1]
assert image.shape == (5_04, 5_04, 3)
A__ = np.array([0.2_726, 0.2_529, 0.2_664, 0.2_655, 0.2_641, 0.2_642, 0.2_591, 0.2_649, 0.2_590] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 5e-3
| 69 | 1 |
"""simple docstring"""
def _A ( lowercase , lowercase ):
"""simple docstring"""
return number | (1 << position)
def _A ( lowercase , lowercase ):
"""simple docstring"""
return number & ~(1 << position)
def _A ( lowercase , lowercase ):
"""simple docstring"""
return number ^ (1 << position)
def _A ( lowercase , lowercase ):
"""simple docstring"""
return ((number >> position) & 1) == 1
def _A ( lowercase , lowercase ):
"""simple docstring"""
return int((number & (1 << position)) != 0 )
if __name__ == "__main__":
import doctest
doctest.testmod() | 81 |
"""simple docstring"""
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
lowerCamelCase_ : Any = logging.get_logger(__name__)
lowerCamelCase_ : Optional[Any] = """▁"""
lowerCamelCase_ : Union[str, Any] = {"""vocab_file""": """sentencepiece.bpe.model"""}
lowerCamelCase_ : Any = {
"""vocab_file""": {
"""xlm-roberta-base""": """https://huggingface.co/xlm-roberta-base/resolve/main/sentencepiece.bpe.model""",
"""xlm-roberta-large""": """https://huggingface.co/xlm-roberta-large/resolve/main/sentencepiece.bpe.model""",
"""xlm-roberta-large-finetuned-conll02-dutch""": (
"""https://huggingface.co/xlm-roberta-large-finetuned-conll02-dutch/resolve/main/sentencepiece.bpe.model"""
),
"""xlm-roberta-large-finetuned-conll02-spanish""": (
"""https://huggingface.co/xlm-roberta-large-finetuned-conll02-spanish/resolve/main/sentencepiece.bpe.model"""
),
"""xlm-roberta-large-finetuned-conll03-english""": (
"""https://huggingface.co/xlm-roberta-large-finetuned-conll03-english/resolve/main/sentencepiece.bpe.model"""
),
"""xlm-roberta-large-finetuned-conll03-german""": (
"""https://huggingface.co/xlm-roberta-large-finetuned-conll03-german/resolve/main/sentencepiece.bpe.model"""
),
}
}
lowerCamelCase_ : Tuple = {
"""xlm-roberta-base""": 5_1_2,
"""xlm-roberta-large""": 5_1_2,
"""xlm-roberta-large-finetuned-conll02-dutch""": 5_1_2,
"""xlm-roberta-large-finetuned-conll02-spanish""": 5_1_2,
"""xlm-roberta-large-finetuned-conll03-english""": 5_1_2,
"""xlm-roberta-large-finetuned-conll03-german""": 5_1_2,
}
class __A ( _SCREAMING_SNAKE_CASE ):
"""simple docstring"""
__lowerCAmelCase = VOCAB_FILES_NAMES
__lowerCAmelCase = PRETRAINED_VOCAB_FILES_MAP
__lowerCAmelCase = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
__lowerCAmelCase = ["input_ids", "attention_mask"]
def __init__( self , __A , __A="<s>" , __A="</s>" , __A="</s>" , __A="<s>" , __A="<unk>" , __A="<pad>" , __A="<mask>" , __A = None , **__A , ) -> None:
# Mask token behave like a normal word, i.e. include the space before it
a =AddedToken(__A , lstrip=__A , rstrip=__A ) if isinstance(__A , __A ) else mask_token
a ={} if sp_model_kwargs is None else sp_model_kwargs
super().__init__(
bos_token=__A , eos_token=__A , unk_token=__A , sep_token=__A , cls_token=__A , pad_token=__A , mask_token=__A , sp_model_kwargs=self.sp_model_kwargs , **__A , )
a =spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(str(__A ) )
a =vocab_file
# Original fairseq vocab and spm vocab must be "aligned":
# Vocab | 0 | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9
# -------- | ------- | ------- | ------ | ------- | --- | --- | --- | ----- | ----- | ----
# fairseq | '<s>' | '<pad>' | '</s>' | '<unk>' | ',' | '.' | '▁' | 's' | '▁de' | '-'
# spm | '<unk>' | '<s>' | '</s>' | ',' | '.' | '▁' | 's' | '▁de' | '-' | '▁a'
# Mimic fairseq token-to-id alignment for the first 4 token
a ={'''<s>''': 0, '''<pad>''': 1, '''</s>''': 2, '''<unk>''': 3}
# The first "real" token "," has position 4 in the original fairseq vocab and position 3 in the spm vocab
a =1
a =len(self.sp_model ) + self.fairseq_offset
a ={v: k for k, v in self.fairseq_tokens_to_ids.items()}
def __getstate__( self ) -> Any:
a =self.__dict__.copy()
a =None
a =self.sp_model.serialized_model_proto()
return state
def __setstate__( self , __A ) -> List[Any]:
a =d
# for backward compatibility
if not hasattr(self , '''sp_model_kwargs''' ):
a ={}
a =spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.LoadFromSerializedProto(self.sp_model_proto )
def SCREAMING_SNAKE_CASE ( self , __A , __A = None ) -> List[int]:
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 SCREAMING_SNAKE_CASE ( self , __A , __A = None , __A = False ) -> List[int]:
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=__A , token_ids_a=__A , already_has_special_tokens=__A )
if token_ids_a is None:
return [1] + ([0] * len(__A )) + [1]
return [1] + ([0] * len(__A )) + [1, 1] + ([0] * len(__A )) + [1]
def SCREAMING_SNAKE_CASE ( self , __A , __A = None ) -> List[int]:
a =[self.sep_token_id]
a =[self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep + sep + token_ids_a + sep ) * [0]
@property
def SCREAMING_SNAKE_CASE ( self ) -> List[str]:
return len(self.sp_model ) + self.fairseq_offset + 1 # Add the <mask> token
def SCREAMING_SNAKE_CASE ( self ) -> Optional[int]:
a ={self.convert_ids_to_tokens(__A ): i for i in range(self.vocab_size )}
vocab.update(self.added_tokens_encoder )
return vocab
def SCREAMING_SNAKE_CASE ( self , __A ) -> List[str]:
return self.sp_model.encode(__A , out_type=__A )
def SCREAMING_SNAKE_CASE ( self , __A ) -> int:
if token in self.fairseq_tokens_to_ids:
return self.fairseq_tokens_to_ids[token]
a =self.sp_model.PieceToId(__A )
# Need to return unknown token if the SP model returned 0
return spm_id + self.fairseq_offset if spm_id else self.unk_token_id
def SCREAMING_SNAKE_CASE ( self , __A ) -> List[str]:
if index in self.fairseq_ids_to_tokens:
return self.fairseq_ids_to_tokens[index]
return self.sp_model.IdToPiece(index - self.fairseq_offset )
def SCREAMING_SNAKE_CASE ( self , __A ) -> Optional[Any]:
a =''''''.join(__A ).replace(__A , ''' ''' ).strip()
return out_string
def SCREAMING_SNAKE_CASE ( self , __A , __A = None ) -> Tuple[str]:
if not os.path.isdir(__A ):
logger.error(f'''Vocabulary path ({save_directory}) should be a directory''' )
return
a =os.path.join(
__A , (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file'''] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(__A ) and os.path.isfile(self.vocab_file ):
copyfile(self.vocab_file , __A )
elif not os.path.isfile(self.vocab_file ):
with open(__A , '''wb''' ) as fi:
a =self.sp_model.serialized_model_proto()
fi.write(__A )
return (out_vocab_file,) | 81 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
a : Optional[Any] = {}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a : Tuple = ['NllbTokenizer']
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a : List[Any] = ['NllbTokenizerFast']
if TYPE_CHECKING:
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_nllb import NllbTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_nllb_fast import NllbTokenizerFast
else:
import sys
a : Optional[Any] = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 361 |
'''simple docstring'''
a : Dict = """ABCDEFGHIJKLMNOPQRSTUVWXYZ"""
def __lowerCamelCase ( ) -> None:
UpperCAmelCase : Optional[int] = input("""Enter message: """ )
UpperCAmelCase : Dict = input("""Enter key [alphanumeric]: """ )
UpperCAmelCase : Optional[Any] = input("""Encrypt/Decrypt [e/d]: """ )
if mode.lower().startswith("""e""" ):
UpperCAmelCase : List[str] = """encrypt"""
UpperCAmelCase : List[str] = encrypt_message(_lowercase , _lowercase )
elif mode.lower().startswith("""d""" ):
UpperCAmelCase : Tuple = """decrypt"""
UpperCAmelCase : str = decrypt_message(_lowercase , _lowercase )
print(F'''\n{mode.title()}ed message:''' )
print(_lowercase )
def __lowerCamelCase ( _lowercase , _lowercase ) -> str:
return translate_message(_lowercase , _lowercase , """encrypt""" )
def __lowerCamelCase ( _lowercase , _lowercase ) -> str:
return translate_message(_lowercase , _lowercase , """decrypt""" )
def __lowerCamelCase ( _lowercase , _lowercase , _lowercase ) -> str:
UpperCAmelCase : Optional[int] = []
UpperCAmelCase : Optional[Any] = 0
UpperCAmelCase : Tuple = key.upper()
for symbol in message:
UpperCAmelCase : Dict = LETTERS.find(symbol.upper() )
if num != -1:
if mode == "encrypt":
num += LETTERS.find(key[key_index] )
elif mode == "decrypt":
num -= LETTERS.find(key[key_index] )
num %= len(_lowercase )
if symbol.isupper():
translated.append(LETTERS[num] )
elif symbol.islower():
translated.append(LETTERS[num].lower() )
key_index += 1
if key_index == len(_lowercase ):
UpperCAmelCase : Optional[int] = 0
else:
translated.append(_lowercase )
return "".join(_lowercase )
if __name__ == "__main__":
main()
| 338 | 0 |
import unittest
import numpy as np
import torch
from torch import nn
from transformers import (
CLIPImageProcessor,
CLIPTextConfig,
CLIPTextModelWithProjection,
CLIPTokenizer,
CLIPVisionConfig,
CLIPVisionModelWithProjection,
)
from diffusers import KandinskyVaaPriorPipeline, PriorTransformer, UnCLIPScheduler
from diffusers.utils import torch_device
from diffusers.utils.testing_utils import enable_full_determinism, skip_mps
from ..test_pipelines_common import PipelineTesterMixin
enable_full_determinism()
class __lowerCAmelCase ( UpperCAmelCase__ , unittest.TestCase ):
snake_case_ : Any = KandinskyVaaPriorPipeline
snake_case_ : List[str] = ["prompt"]
snake_case_ : int = ["prompt", "negative_prompt"]
snake_case_ : Optional[int] = [
"num_images_per_prompt",
"generator",
"num_inference_steps",
"latents",
"negative_prompt",
"guidance_scale",
"output_type",
"return_dict",
]
snake_case_ : List[Any] = False
@property
def UpperCamelCase ( self : Union[str, Any] ):
"""simple docstring"""
return 32
@property
def UpperCamelCase ( self : Tuple ):
"""simple docstring"""
return 32
@property
def UpperCamelCase ( self : List[Any] ):
"""simple docstring"""
return self.time_input_dim
@property
def UpperCamelCase ( self : Optional[int] ):
"""simple docstring"""
return self.time_input_dim * 4
@property
def UpperCamelCase ( self : List[Any] ):
"""simple docstring"""
return 100
@property
def UpperCamelCase ( self : Any ):
"""simple docstring"""
_UpperCAmelCase = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip" )
return tokenizer
@property
def UpperCamelCase ( self : List[str] ):
"""simple docstring"""
torch.manual_seed(0 )
_UpperCAmelCase = CLIPTextConfig(
bos_token_id=0 , eos_token_id=2 , hidden_size=self.text_embedder_hidden_size , projection_dim=self.text_embedder_hidden_size , intermediate_size=37 , layer_norm_eps=1e-05 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=1_000 , )
return CLIPTextModelWithProjection(snake_case__ )
@property
def UpperCamelCase ( self : List[str] ):
"""simple docstring"""
torch.manual_seed(0 )
_UpperCAmelCase = {
"num_attention_heads": 2,
"attention_head_dim": 12,
"embedding_dim": self.text_embedder_hidden_size,
"num_layers": 1,
}
_UpperCAmelCase = PriorTransformer(**snake_case__ )
# clip_std and clip_mean is initialized to be 0 so PriorTransformer.post_process_latents will always return 0 - set clip_std to be 1 so it won't return 0
_UpperCAmelCase = nn.Parameter(torch.ones(model.clip_std.shape ) )
return model
@property
def UpperCamelCase ( self : str ):
"""simple docstring"""
torch.manual_seed(0 )
_UpperCAmelCase = CLIPVisionConfig(
hidden_size=self.text_embedder_hidden_size , image_size=224 , projection_dim=self.text_embedder_hidden_size , intermediate_size=37 , num_attention_heads=4 , num_channels=3 , num_hidden_layers=5 , patch_size=14 , )
_UpperCAmelCase = CLIPVisionModelWithProjection(snake_case__ )
return model
@property
def UpperCamelCase ( self : Dict ):
"""simple docstring"""
_UpperCAmelCase = CLIPImageProcessor(
crop_size=224 , do_center_crop=snake_case__ , do_normalize=snake_case__ , do_resize=snake_case__ , image_mean=[0.48_145_466, 0.4_578_275, 0.40_821_073] , image_std=[0.26_862_954, 0.26_130_258, 0.27_577_711] , resample=3 , size=224 , )
return image_processor
def UpperCamelCase ( self : Dict ):
"""simple docstring"""
_UpperCAmelCase = self.dummy_prior
_UpperCAmelCase = self.dummy_image_encoder
_UpperCAmelCase = self.dummy_text_encoder
_UpperCAmelCase = self.dummy_tokenizer
_UpperCAmelCase = self.dummy_image_processor
_UpperCAmelCase = UnCLIPScheduler(
variance_type="fixed_small_log" , prediction_type="sample" , num_train_timesteps=1_000 , clip_sample=snake_case__ , clip_sample_range=10.0 , )
_UpperCAmelCase = {
"prior": prior,
"image_encoder": image_encoder,
"text_encoder": text_encoder,
"tokenizer": tokenizer,
"scheduler": scheduler,
"image_processor": image_processor,
}
return components
def UpperCamelCase ( self : str , snake_case__ : int , snake_case__ : Optional[Any]=0 ):
"""simple docstring"""
if str(snake_case__ ).startswith("mps" ):
_UpperCAmelCase = torch.manual_seed(snake_case__ )
else:
_UpperCAmelCase = torch.Generator(device=snake_case__ ).manual_seed(snake_case__ )
_UpperCAmelCase = {
"prompt": "horse",
"generator": generator,
"guidance_scale": 4.0,
"num_inference_steps": 2,
"output_type": "np",
}
return inputs
def UpperCamelCase ( self : List[str] ):
"""simple docstring"""
_UpperCAmelCase = "cpu"
_UpperCAmelCase = self.get_dummy_components()
_UpperCAmelCase = self.pipeline_class(**snake_case__ )
_UpperCAmelCase = pipe.to(snake_case__ )
pipe.set_progress_bar_config(disable=snake_case__ )
_UpperCAmelCase = pipe(**self.get_dummy_inputs(snake_case__ ) )
_UpperCAmelCase = output.image_embeds
_UpperCAmelCase = pipe(
**self.get_dummy_inputs(snake_case__ ) , return_dict=snake_case__ , )[0]
_UpperCAmelCase = image[0, -10:]
_UpperCAmelCase = image_from_tuple[0, -10:]
assert image.shape == (1, 32)
_UpperCAmelCase = np.array(
[-0.0_532, 1.7_120, 0.3_656, -1.0_852, -0.8_946, -1.1_756, 0.4_348, 0.2_482, 0.5_146, -0.1_156] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
assert np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 1e-2
@skip_mps
def UpperCamelCase ( self : Dict ):
"""simple docstring"""
_UpperCAmelCase = torch_device == "cpu"
_UpperCAmelCase = True
_UpperCAmelCase = False
self._test_inference_batch_single_identical(
test_max_difference=snake_case__ , relax_max_difference=snake_case__ , test_mean_pixel_difference=snake_case__ , )
@skip_mps
def UpperCamelCase ( self : int ):
"""simple docstring"""
_UpperCAmelCase = torch_device == "cpu"
_UpperCAmelCase = False
self._test_attention_slicing_forward_pass(
test_max_difference=snake_case__ , test_mean_pixel_difference=snake_case__ , )
| 133 |
def __SCREAMING_SNAKE_CASE ( snake_case_ ):
'''simple docstring'''
_UpperCAmelCase = len(snake_case_ )
for i in range(snake_case_ ):
for j in range(i + 1 , snake_case_ ):
if numbers[j] < numbers[i]:
_UpperCAmelCase , _UpperCAmelCase = numbers[j], numbers[i]
return numbers
if __name__ == "__main__":
lowercase_ : Optional[Any] = input('Enter numbers separated by a comma:\n').strip()
lowercase_ : Dict = [int(item) for item in user_input.split(',')]
print(exchange_sort(unsorted))
| 133 | 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
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
_snake_case = logging.get_logger(__name__)
_snake_case = {
'microsoft/resnet-50': 'https://huggingface.co/microsoft/resnet-50/blob/main/config.json',
}
class a__ ( lowerCamelCase_ , lowerCamelCase_ ):
_SCREAMING_SNAKE_CASE : Union[str, Any] = 'resnet'
_SCREAMING_SNAKE_CASE : Union[str, Any] = ['basic', 'bottleneck']
def __init__( self , _UpperCamelCase=3 , _UpperCamelCase=64 , _UpperCamelCase=[256, 512, 1024, 2048] , _UpperCamelCase=[3, 4, 6, 3] , _UpperCamelCase="bottleneck" , _UpperCamelCase="relu" , _UpperCamelCase=False , _UpperCamelCase=None , _UpperCamelCase=None , **_UpperCamelCase , ):
"""simple docstring"""
super().__init__(**_UpperCamelCase )
if layer_type not in self.layer_types:
raise ValueError(f'''layer_type={layer_type} is not one of {','.join(self.layer_types )}''' )
_lowercase : List[str] = num_channels
_lowercase : List[str] = embedding_size
_lowercase : Dict = hidden_sizes
_lowercase : List[str] = depths
_lowercase : Union[str, Any] = layer_type
_lowercase : str = hidden_act
_lowercase : Optional[Any] = downsample_in_first_stage
_lowercase : Optional[int] = ["stem"] + [f'''stage{idx}''' for idx in range(1 , len(_UpperCamelCase ) + 1 )]
_lowercase , _lowercase : Tuple = get_aligned_output_features_output_indices(
out_features=_UpperCamelCase , out_indices=_UpperCamelCase , stage_names=self.stage_names )
class a__ ( lowerCamelCase_ ):
_SCREAMING_SNAKE_CASE : Tuple = version.parse('1.11' )
@property
def _lowerCamelCase ( self ):
"""simple docstring"""
return OrderedDict(
[
("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
] )
@property
def _lowerCamelCase ( self ):
"""simple docstring"""
return 1E-3
| 199 |
'''simple docstring'''
import gc
import random
import unittest
import numpy as np
import torch
from transformers import (
CLIPImageProcessor,
CLIPTextConfig,
CLIPTextModel,
CLIPTokenizer,
CLIPVisionConfig,
CLIPVisionModelWithProjection,
)
from diffusers import AutoencoderKL, DDIMScheduler, DDPMScheduler, StableUnCLIPImgaImgPipeline, UNetaDConditionModel
from diffusers.pipelines.pipeline_utils import DiffusionPipeline
from diffusers.pipelines.stable_diffusion.stable_unclip_image_normalizer import StableUnCLIPImageNormalizer
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import (
enable_full_determinism,
floats_tensor,
load_image,
load_numpy,
require_torch_gpu,
skip_mps,
slow,
torch_device,
)
from ..pipeline_params import TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS, TEXT_GUIDED_IMAGE_VARIATION_PARAMS
from ..test_pipelines_common import (
PipelineKarrasSchedulerTesterMixin,
PipelineLatentTesterMixin,
PipelineTesterMixin,
assert_mean_pixel_difference,
)
enable_full_determinism()
class a__ ( lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ , unittest.TestCase ):
_SCREAMING_SNAKE_CASE : Dict = StableUnCLIPImgaImgPipeline
_SCREAMING_SNAKE_CASE : int = TEXT_GUIDED_IMAGE_VARIATION_PARAMS
_SCREAMING_SNAKE_CASE : List[str] = TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
_SCREAMING_SNAKE_CASE : Any = frozenset(
[] ) # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
_SCREAMING_SNAKE_CASE : List[Any] = frozenset([] )
def _lowerCamelCase ( self ):
"""simple docstring"""
_lowercase : List[Any] = 32
_lowercase : Any = embedder_hidden_size
# image encoding components
_lowercase : Optional[int] = CLIPImageProcessor(crop_size=32 , size=32 )
torch.manual_seed(0 )
_lowercase : Dict = CLIPVisionModelWithProjection(
CLIPVisionConfig(
hidden_size=_UpperCamelCase , projection_dim=_UpperCamelCase , num_hidden_layers=5 , num_attention_heads=4 , image_size=32 , intermediate_size=37 , patch_size=1 , ) )
# regular denoising components
torch.manual_seed(0 )
_lowercase : str = StableUnCLIPImageNormalizer(embedding_dim=_UpperCamelCase )
_lowercase : Dict = DDPMScheduler(beta_schedule="squaredcos_cap_v2" )
torch.manual_seed(0 )
_lowercase : str = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip" )
torch.manual_seed(0 )
_lowercase : Optional[Any] = CLIPTextModel(
CLIPTextConfig(
bos_token_id=0 , eos_token_id=2 , hidden_size=_UpperCamelCase , projection_dim=32 , intermediate_size=37 , layer_norm_eps=1E-0_5 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=1000 , ) )
torch.manual_seed(0 )
_lowercase : List[str] = UNetaDConditionModel(
sample_size=32 , in_channels=4 , out_channels=4 , down_block_types=("CrossAttnDownBlock2D", "DownBlock2D") , up_block_types=("UpBlock2D", "CrossAttnUpBlock2D") , block_out_channels=(32, 64) , attention_head_dim=(2, 4) , class_embed_type="projection" , projection_class_embeddings_input_dim=embedder_projection_dim * 2 , cross_attention_dim=_UpperCamelCase , layers_per_block=1 , upcast_attention=_UpperCamelCase , use_linear_projection=_UpperCamelCase , )
torch.manual_seed(0 )
_lowercase : int = DDIMScheduler(
beta_schedule="scaled_linear" , beta_start=0.0_0_0_8_5 , beta_end=0.0_1_2 , prediction_type="v_prediction" , set_alpha_to_one=_UpperCamelCase , steps_offset=1 , )
torch.manual_seed(0 )
_lowercase : Dict = AutoencoderKL()
_lowercase : int = {
# image encoding components
"feature_extractor": feature_extractor,
"image_encoder": image_encoder.eval(),
# image noising components
"image_normalizer": image_normalizer.eval(),
"image_noising_scheduler": image_noising_scheduler,
# regular denoising components
"tokenizer": tokenizer,
"text_encoder": text_encoder.eval(),
"unet": unet.eval(),
"scheduler": scheduler,
"vae": vae.eval(),
}
return components
def _lowerCamelCase ( self , _UpperCamelCase , _UpperCamelCase=0 , _UpperCamelCase=True ):
"""simple docstring"""
if str(_UpperCamelCase ).startswith("mps" ):
_lowercase : List[Any] = torch.manual_seed(_UpperCamelCase )
else:
_lowercase : List[str] = torch.Generator(device=_UpperCamelCase ).manual_seed(_UpperCamelCase )
_lowercase : List[str] = floats_tensor((1, 3, 32, 32) , rng=random.Random(_UpperCamelCase ) ).to(_UpperCamelCase )
if pil_image:
_lowercase : Optional[Any] = input_image * 0.5 + 0.5
_lowercase : Optional[int] = input_image.clamp(0 , 1 )
_lowercase : str = input_image.cpu().permute(0 , 2 , 3 , 1 ).float().numpy()
_lowercase : int = DiffusionPipeline.numpy_to_pil(_UpperCamelCase )[0]
return {
"prompt": "An anime racoon running a marathon",
"image": input_image,
"generator": generator,
"num_inference_steps": 2,
"output_type": "np",
}
@skip_mps
def _lowerCamelCase ( self ):
"""simple docstring"""
_lowercase : Dict = "cpu" # ensure determinism for the device-dependent torch.Generator
_lowercase : List[str] = self.get_dummy_components()
_lowercase : str = StableUnCLIPImgaImgPipeline(**_UpperCamelCase )
_lowercase : int = sd_pipe.to(_UpperCamelCase )
sd_pipe.set_progress_bar_config(disable=_UpperCamelCase )
_lowercase : str = self.get_dummy_inputs(_UpperCamelCase )
inputs.update({"image_embeds": None} )
_lowercase : Tuple = sd_pipe(**_UpperCamelCase ).images
_lowercase : str = image[0, -3:, -3:, -1]
assert image.shape == (1, 32, 32, 3)
_lowercase : Tuple = np.array([0.3_8_7_2, 0.7_2_2_4, 0.5_6_0_1, 0.4_7_4_1, 0.6_8_7_2, 0.5_8_1_4, 0.4_6_3_6, 0.3_8_6_7, 0.5_0_7_8] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-3
def _lowerCamelCase ( self ):
"""simple docstring"""
_lowercase : List[Any] = torch_device in ["cpu", "mps"]
self._test_attention_slicing_forward_pass(test_max_difference=_UpperCamelCase )
def _lowerCamelCase ( self ):
"""simple docstring"""
_lowercase : Dict = torch_device in ["cpu", "mps"]
self._test_inference_batch_single_identical(test_max_difference=_UpperCamelCase )
@unittest.skipIf(
torch_device != "cuda" or not is_xformers_available() , reason="XFormers attention is only available with CUDA and `xformers` installed" , )
def _lowerCamelCase ( self ):
"""simple docstring"""
self._test_xformers_attention_forwardGenerator_pass(test_max_difference=_UpperCamelCase )
@slow
@require_torch_gpu
class a__ ( unittest.TestCase ):
def _lowerCamelCase ( self ):
"""simple docstring"""
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def _lowerCamelCase ( self ):
"""simple docstring"""
_lowercase : Any = load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/turtle.png" )
_lowercase : Any = load_numpy(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/stable_unclip_2_1_l_img2img_anime_turtle_fp16.npy" )
_lowercase : List[str] = StableUnCLIPImgaImgPipeline.from_pretrained(
"fusing/stable-unclip-2-1-l-img2img" , torch_dtype=torch.floataa )
pipe.to(_UpperCamelCase )
pipe.set_progress_bar_config(disable=_UpperCamelCase )
# stable unclip will oom when integration tests are run on a V100,
# so turn on memory savings
pipe.enable_attention_slicing()
pipe.enable_sequential_cpu_offload()
_lowercase : int = torch.Generator(device="cpu" ).manual_seed(0 )
_lowercase : List[Any] = pipe(_UpperCamelCase , "anime turle" , generator=_UpperCamelCase , output_type="np" )
_lowercase : str = output.images[0]
assert image.shape == (768, 768, 3)
assert_mean_pixel_difference(_UpperCamelCase , _UpperCamelCase )
def _lowerCamelCase ( self ):
"""simple docstring"""
_lowercase : Optional[int] = load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/turtle.png" )
_lowercase : Any = load_numpy(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/stable_unclip_2_1_h_img2img_anime_turtle_fp16.npy" )
_lowercase : Any = StableUnCLIPImgaImgPipeline.from_pretrained(
"fusing/stable-unclip-2-1-h-img2img" , torch_dtype=torch.floataa )
pipe.to(_UpperCamelCase )
pipe.set_progress_bar_config(disable=_UpperCamelCase )
# stable unclip will oom when integration tests are run on a V100,
# so turn on memory savings
pipe.enable_attention_slicing()
pipe.enable_sequential_cpu_offload()
_lowercase : Optional[int] = torch.Generator(device="cpu" ).manual_seed(0 )
_lowercase : Any = pipe(_UpperCamelCase , "anime turle" , generator=_UpperCamelCase , output_type="np" )
_lowercase : Tuple = output.images[0]
assert image.shape == (768, 768, 3)
assert_mean_pixel_difference(_UpperCamelCase , _UpperCamelCase )
def _lowerCamelCase ( self ):
"""simple docstring"""
_lowercase : List[str] = load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/turtle.png" )
torch.cuda.empty_cache()
torch.cuda.reset_max_memory_allocated()
torch.cuda.reset_peak_memory_stats()
_lowercase : Optional[Any] = StableUnCLIPImgaImgPipeline.from_pretrained(
"fusing/stable-unclip-2-1-h-img2img" , torch_dtype=torch.floataa )
_lowercase : Tuple = pipe.to(_UpperCamelCase )
pipe.set_progress_bar_config(disable=_UpperCamelCase )
pipe.enable_attention_slicing()
pipe.enable_sequential_cpu_offload()
_lowercase : Optional[int] = pipe(
_UpperCamelCase , "anime turtle" , num_inference_steps=2 , output_type="np" , )
_lowercase : str = torch.cuda.max_memory_allocated()
# make sure that less than 7 GB is allocated
assert mem_bytes < 7 * 10**9
| 199 | 1 |
"""simple docstring"""
import argparse
import re
import requests
import torch
# git clone https://github.com/salesforce/BLIP.git
from models.blip import blip_decoder
from models.blip_itm import blip_itm
from models.blip_vqa import blip_vqa
from PIL import Image
from torchvision import transforms
from torchvision.transforms.functional import InterpolationMode
from transformers import (
BertTokenizer,
BlipConfig,
BlipForConditionalGeneration,
BlipForImageTextRetrieval,
BlipForQuestionAnswering,
)
def __lowerCAmelCase ( lowercase : Dict , lowercase : int ) -> Optional[Any]:
"""simple docstring"""
snake_case : List[str] = "https://storage.googleapis.com/sfr-vision-language-research/BLIP/demo.jpg"
snake_case : Any = Image.open(requests.get(lowercase , stream=lowercase ).raw ).convert("RGB" )
snake_case : int = transforms.Compose(
[
transforms.Resize((image_size, image_size) , interpolation=InterpolationMode.BICUBIC ),
transforms.ToTensor(),
transforms.Normalize((0.4814_5466, 0.457_8275, 0.4082_1073) , (0.2686_2954, 0.2613_0258, 0.2757_7711) ),
] )
snake_case : str = transform(lowercase ).unsqueeze(0 ).to(lowercase )
return image
def __lowerCAmelCase ( lowercase : str ) -> List[Any]:
"""simple docstring"""
if "visual_encoder" in key:
snake_case : str = re.sub("visual_encoder*" , "vision_model.encoder" , lowercase )
if "blocks" in key:
snake_case : Dict = re.sub(R"blocks" , "layers" , lowercase )
if "attn" in key:
snake_case : Optional[int] = re.sub(R"attn" , "self_attn" , lowercase )
if "norm1" in key:
snake_case : List[str] = re.sub(R"norm1" , "layer_norm1" , lowercase )
if "norm2" in key:
snake_case : str = re.sub(R"norm2" , "layer_norm2" , lowercase )
if "encoder.norm" in key:
snake_case : Dict = re.sub(R"encoder.norm" , "post_layernorm" , lowercase )
if "encoder.patch_embed.proj" in key:
snake_case : List[Any] = re.sub(R"encoder.patch_embed.proj" , "embeddings.patch_embedding" , lowercase )
if "encoder.pos_embed" in key:
snake_case : Union[str, Any] = re.sub(R"encoder.pos_embed" , "embeddings.position_embedding" , lowercase )
if "encoder.cls_token" in key:
snake_case : List[str] = re.sub(R"encoder.cls_token" , "embeddings.class_embedding" , lowercase )
if "self_attn" in key:
snake_case : Any = re.sub(R"self_attn.proj" , "self_attn.projection" , lowercase )
return key
@torch.no_grad()
def __lowerCAmelCase ( lowercase : List[Any] , lowercase : str=None ) -> List[Any]:
"""simple docstring"""
if config_path is not None:
snake_case : str = BlipConfig.from_pretrained(lowercase )
else:
snake_case : int = BlipConfig(projection_dim=512 , text_config={} , vision_config={} )
snake_case : Tuple = BlipForConditionalGeneration(lowercase ).eval()
snake_case : Dict = "https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base_capfilt_large.pth"
snake_case : Optional[int] = blip_decoder(pretrained=lowercase , image_size=384 , vit="base" )
snake_case : Union[str, Any] = pt_model.eval()
snake_case : Optional[Any] = pt_model.state_dict()
for key in modified_state_dict.copy():
snake_case : str = modified_state_dict.pop(lowercase )
snake_case : Optional[Any] = rename_key(lowercase )
snake_case : Any = value
hf_model.load_state_dict(lowercase )
snake_case : str = 384
snake_case : Dict = load_demo_image(image_size=lowercase , device="cpu" )
snake_case : int = BertTokenizer.from_pretrained("bert-base-uncased" )
snake_case : Any = tokenizer(["a picture of"] ).input_ids
snake_case : str = hf_model.generate(lowercase , lowercase )
assert out[0].tolist() == [3_0522, 1037, 3861, 1997, 1037, 2450, 3564, 2006, 1996, 3509, 2007, 2014, 3899, 102]
snake_case : int = hf_model.generate(lowercase )
assert out[0].tolist() == [3_0522, 1037, 2450, 3564, 2006, 1996, 3509, 2007, 2014, 3899, 102]
if pytorch_dump_folder_path is not None:
hf_model.save_pretrained(lowercase )
# model_url = 'https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_vqa.pth'
snake_case : Optional[Any] = (
"https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base_vqa_capfilt_large.pth"
)
snake_case : str = blip_vqa(pretrained=lowercase , image_size=lowercase , vit="base" )
vqa_model.eval()
snake_case : Tuple = vqa_model.state_dict()
for key in modified_state_dict.copy():
snake_case : List[str] = modified_state_dict.pop(lowercase )
snake_case : Any = rename_key(lowercase )
snake_case : Union[str, Any] = value
snake_case : List[Any] = BlipForQuestionAnswering(lowercase )
hf_vqa_model.load_state_dict(lowercase )
snake_case : Optional[int] = ["How many dogs are in this image?"]
snake_case : Any = tokenizer(lowercase , return_tensors="pt" ).input_ids
snake_case : Any = hf_vqa_model.generate(lowercase , lowercase )
print(tokenizer.decode(answer[0] ) )
assert tokenizer.decode(answer[0] ) == "[UNK] 1 [SEP]"
if pytorch_dump_folder_path is not None:
hf_vqa_model.save_pretrained(pytorch_dump_folder_path + "_vqa" )
snake_case : Union[str, Any] = "https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base_retrieval_coco.pth"
snake_case : Optional[Any] = blip_itm(pretrained=lowercase , image_size=lowercase , vit="base" )
itm_model.eval()
snake_case : int = itm_model.state_dict()
for key in modified_state_dict.copy():
snake_case : Any = modified_state_dict.pop(lowercase )
snake_case : Optional[Any] = rename_key(lowercase )
snake_case : Optional[Any] = value
snake_case : List[str] = BlipForImageTextRetrieval(lowercase )
snake_case : Optional[Any] = ["A picture of a woman with a dog sitting in a beach"]
snake_case : List[Any] = tokenizer(
lowercase , return_tensors="pt" , padding="max_length" , truncation=lowercase , max_length=35 , ).input_ids
hf_itm_model.load_state_dict(lowercase )
hf_itm_model.eval()
snake_case : int = hf_itm_model(lowercase , lowercase , use_itm_head=lowercase )
snake_case : List[str] = hf_itm_model(lowercase , lowercase , use_itm_head=lowercase )
assert out[0].item() == 0.2110_6874_9427_7954
assert torch.nn.functional.softmax(out_itm[0] , dim=1 )[:, 1].item() == 0.4_5698_8453_8650_5127
if pytorch_dump_folder_path is not None:
hf_itm_model.save_pretrained(pytorch_dump_folder_path + "_itm" )
if __name__ == "__main__":
__snake_case = argparse.ArgumentParser()
parser.add_argument("""--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model.""")
parser.add_argument("""--config_path""", default=None, type=str, help="""Path to hf config.json of model to convert""")
__snake_case = parser.parse_args()
convert_blip_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path)
| 203 |
"""simple docstring"""
import argparse
import json
import logging
import os
import shutil
import sys
import tempfile
import unittest
from unittest import mock
import torch
from accelerate.utils import write_basic_config
from transformers.testing_utils import TestCasePlus, get_gpu_count, run_command, slow, torch_device
from transformers.utils import is_apex_available
logging.basicConfig(level=logging.DEBUG)
__snake_case = logging.getLogger()
def __lowerCAmelCase ( ) -> str:
"""simple docstring"""
snake_case : List[str] = argparse.ArgumentParser()
parser.add_argument("-f" )
snake_case : Tuple = parser.parse_args()
return args.f
def __lowerCAmelCase ( lowercase : Optional[int] ) -> Dict:
"""simple docstring"""
snake_case : Any = {}
snake_case : int = os.path.join(lowercase , "all_results.json" )
if os.path.exists(lowercase ):
with open(lowercase , "r" ) as f:
snake_case : Optional[int] = json.load(lowercase )
else:
raise ValueError(F'can\'t find {path}' )
return results
def __lowerCAmelCase ( ) -> Tuple:
"""simple docstring"""
snake_case : Union[str, Any] = torch.cuda.is_available() and torch_device == "cuda"
return is_using_cuda and is_apex_available()
__snake_case = logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)
class _lowerCAmelCase ( snake_case_ ):
@classmethod
def lowerCamelCase ( cls ) -> Optional[Any]:
'''simple docstring'''
snake_case : Optional[int] = tempfile.mkdtemp()
snake_case : int = os.path.join(cls.tmpdir , "default_config.yml" )
write_basic_config(save_location=cls.configPath )
snake_case : Union[str, Any] = ["accelerate", "launch", "--config_file", cls.configPath]
@classmethod
def lowerCamelCase ( cls ) -> Optional[Any]:
'''simple docstring'''
shutil.rmtree(cls.tmpdir )
@mock.patch.dict(os.environ , {"WANDB_MODE": "offline"} )
def lowerCamelCase ( self ) -> int:
'''simple docstring'''
snake_case : str = self.get_auto_remove_tmp_dir()
snake_case : Dict = F'\n {self.examples_dir}/pytorch/text-classification/run_glue_no_trainer.py\n --model_name_or_path distilbert-base-uncased\n --output_dir {tmp_dir}\n --train_file ./tests/fixtures/tests_samples/MRPC/train.csv\n --validation_file ./tests/fixtures/tests_samples/MRPC/dev.csv\n --per_device_train_batch_size=2\n --per_device_eval_batch_size=1\n --learning_rate=1e-4\n --seed=42\n --checkpointing_steps epoch\n --with_tracking\n '.split()
if is_cuda_and_apex_available():
testargs.append("--fp16" )
run_command(self._launch_args + testargs )
snake_case : Union[str, Any] = get_results(UpperCamelCase__ )
self.assertGreaterEqual(result["eval_accuracy"] , 0.75 )
self.assertTrue(os.path.exists(os.path.join(UpperCamelCase__ , "epoch_0" ) ) )
self.assertTrue(os.path.exists(os.path.join(UpperCamelCase__ , "glue_no_trainer" ) ) )
@mock.patch.dict(os.environ , {"WANDB_MODE": "offline"} )
def lowerCamelCase ( self ) -> int:
'''simple docstring'''
snake_case : Dict = self.get_auto_remove_tmp_dir()
snake_case : Dict = F'\n {self.examples_dir}/pytorch/language-modeling/run_clm_no_trainer.py\n --model_name_or_path distilgpt2\n --train_file ./tests/fixtures/sample_text.txt\n --validation_file ./tests/fixtures/sample_text.txt\n --block_size 128\n --per_device_train_batch_size 5\n --per_device_eval_batch_size 5\n --num_train_epochs 2\n --output_dir {tmp_dir}\n --checkpointing_steps epoch\n --with_tracking\n '.split()
if torch.cuda.device_count() > 1:
# Skipping because there are not enough batches to train the model + would need a drop_last to work.
return
run_command(self._launch_args + testargs )
snake_case : Any = get_results(UpperCamelCase__ )
self.assertLess(result["perplexity"] , 100 )
self.assertTrue(os.path.exists(os.path.join(UpperCamelCase__ , "epoch_0" ) ) )
self.assertTrue(os.path.exists(os.path.join(UpperCamelCase__ , "clm_no_trainer" ) ) )
@mock.patch.dict(os.environ , {"WANDB_MODE": "offline"} )
def lowerCamelCase ( self ) -> Any:
'''simple docstring'''
snake_case : Union[str, Any] = self.get_auto_remove_tmp_dir()
snake_case : str = F'\n {self.examples_dir}/pytorch/language-modeling/run_mlm_no_trainer.py\n --model_name_or_path distilroberta-base\n --train_file ./tests/fixtures/sample_text.txt\n --validation_file ./tests/fixtures/sample_text.txt\n --output_dir {tmp_dir}\n --num_train_epochs=1\n --checkpointing_steps epoch\n --with_tracking\n '.split()
run_command(self._launch_args + testargs )
snake_case : Dict = get_results(UpperCamelCase__ )
self.assertLess(result["perplexity"] , 42 )
self.assertTrue(os.path.exists(os.path.join(UpperCamelCase__ , "epoch_0" ) ) )
self.assertTrue(os.path.exists(os.path.join(UpperCamelCase__ , "mlm_no_trainer" ) ) )
@mock.patch.dict(os.environ , {"WANDB_MODE": "offline"} )
def lowerCamelCase ( self ) -> Optional[Any]:
'''simple docstring'''
snake_case : Optional[Any] = 7 if get_gpu_count() > 1 else 2
snake_case : Optional[int] = self.get_auto_remove_tmp_dir()
snake_case : List[str] = F'\n {self.examples_dir}/pytorch/token-classification/run_ner_no_trainer.py\n --model_name_or_path bert-base-uncased\n --train_file tests/fixtures/tests_samples/conll/sample.json\n --validation_file tests/fixtures/tests_samples/conll/sample.json\n --output_dir {tmp_dir}\n --learning_rate=2e-4\n --per_device_train_batch_size=2\n --per_device_eval_batch_size=2\n --num_train_epochs={epochs}\n --seed 7\n --checkpointing_steps epoch\n --with_tracking\n '.split()
run_command(self._launch_args + testargs )
snake_case : str = get_results(UpperCamelCase__ )
self.assertGreaterEqual(result["eval_accuracy"] , 0.75 )
self.assertLess(result["train_loss"] , 0.5 )
self.assertTrue(os.path.exists(os.path.join(UpperCamelCase__ , "epoch_0" ) ) )
self.assertTrue(os.path.exists(os.path.join(UpperCamelCase__ , "ner_no_trainer" ) ) )
@unittest.skip(reason="Fix me @muellerzr" )
@mock.patch.dict(os.environ , {"WANDB_MODE": "offline"} )
def lowerCamelCase ( self ) -> str:
'''simple docstring'''
snake_case : List[Any] = self.get_auto_remove_tmp_dir()
snake_case : Dict = F'\n {self.examples_dir}/pytorch/question-answering/run_qa_no_trainer.py\n --model_name_or_path bert-base-uncased\n --version_2_with_negative\n --train_file tests/fixtures/tests_samples/SQUAD/sample.json\n --validation_file tests/fixtures/tests_samples/SQUAD/sample.json\n --output_dir {tmp_dir}\n --seed=42\n --max_train_steps=10\n --num_warmup_steps=2\n --learning_rate=2e-4\n --per_device_train_batch_size=2\n --per_device_eval_batch_size=1\n --checkpointing_steps epoch\n --with_tracking\n '.split()
run_command(self._launch_args + testargs )
snake_case : Dict = get_results(UpperCamelCase__ )
# Because we use --version_2_with_negative the testing script uses SQuAD v2 metrics.
self.assertGreaterEqual(result["eval_f1"] , 28 )
self.assertGreaterEqual(result["eval_exact"] , 28 )
self.assertTrue(os.path.exists(os.path.join(UpperCamelCase__ , "epoch_0" ) ) )
self.assertTrue(os.path.exists(os.path.join(UpperCamelCase__ , "qa_no_trainer" ) ) )
@mock.patch.dict(os.environ , {"WANDB_MODE": "offline"} )
def lowerCamelCase ( self ) -> int:
'''simple docstring'''
snake_case : Tuple = self.get_auto_remove_tmp_dir()
snake_case : List[str] = F'\n {self.examples_dir}/pytorch/multiple-choice/run_swag_no_trainer.py\n --model_name_or_path bert-base-uncased\n --train_file tests/fixtures/tests_samples/swag/sample.json\n --validation_file tests/fixtures/tests_samples/swag/sample.json\n --output_dir {tmp_dir}\n --max_train_steps=20\n --num_warmup_steps=2\n --learning_rate=2e-4\n --per_device_train_batch_size=2\n --per_device_eval_batch_size=1\n --with_tracking\n '.split()
run_command(self._launch_args + testargs )
snake_case : Optional[Any] = get_results(UpperCamelCase__ )
self.assertGreaterEqual(result["eval_accuracy"] , 0.8 )
self.assertTrue(os.path.exists(os.path.join(UpperCamelCase__ , "swag_no_trainer" ) ) )
@slow
@mock.patch.dict(os.environ , {"WANDB_MODE": "offline"} )
def lowerCamelCase ( self ) -> str:
'''simple docstring'''
snake_case : Tuple = self.get_auto_remove_tmp_dir()
snake_case : Dict = F'\n {self.examples_dir}/pytorch/summarization/run_summarization_no_trainer.py\n --model_name_or_path t5-small\n --train_file tests/fixtures/tests_samples/xsum/sample.json\n --validation_file tests/fixtures/tests_samples/xsum/sample.json\n --output_dir {tmp_dir}\n --max_train_steps=50\n --num_warmup_steps=8\n --learning_rate=2e-4\n --per_device_train_batch_size=2\n --per_device_eval_batch_size=1\n --checkpointing_steps epoch\n --with_tracking\n '.split()
run_command(self._launch_args + testargs )
snake_case : Any = get_results(UpperCamelCase__ )
self.assertGreaterEqual(result["eval_rouge1"] , 10 )
self.assertGreaterEqual(result["eval_rouge2"] , 2 )
self.assertGreaterEqual(result["eval_rougeL"] , 7 )
self.assertGreaterEqual(result["eval_rougeLsum"] , 7 )
self.assertTrue(os.path.exists(os.path.join(UpperCamelCase__ , "epoch_0" ) ) )
self.assertTrue(os.path.exists(os.path.join(UpperCamelCase__ , "summarization_no_trainer" ) ) )
@slow
@mock.patch.dict(os.environ , {"WANDB_MODE": "offline"} )
def lowerCamelCase ( self ) -> List[Any]:
'''simple docstring'''
snake_case : Optional[Any] = self.get_auto_remove_tmp_dir()
snake_case : int = F'\n {self.examples_dir}/pytorch/translation/run_translation_no_trainer.py\n --model_name_or_path sshleifer/student_marian_en_ro_6_1\n --source_lang en\n --target_lang ro\n --train_file tests/fixtures/tests_samples/wmt16/sample.json\n --validation_file tests/fixtures/tests_samples/wmt16/sample.json\n --output_dir {tmp_dir}\n --max_train_steps=50\n --num_warmup_steps=8\n --num_beams=6\n --learning_rate=3e-3\n --per_device_train_batch_size=2\n --per_device_eval_batch_size=1\n --source_lang en_XX\n --target_lang ro_RO\n --checkpointing_steps epoch\n --with_tracking\n '.split()
run_command(self._launch_args + testargs )
snake_case : Optional[int] = get_results(UpperCamelCase__ )
self.assertGreaterEqual(result["eval_bleu"] , 30 )
self.assertTrue(os.path.exists(os.path.join(UpperCamelCase__ , "epoch_0" ) ) )
self.assertTrue(os.path.exists(os.path.join(UpperCamelCase__ , "translation_no_trainer" ) ) )
@slow
def lowerCamelCase ( self ) -> Optional[int]:
'''simple docstring'''
snake_case : str = logging.StreamHandler(sys.stdout )
logger.addHandler(UpperCamelCase__ )
snake_case : Dict = self.get_auto_remove_tmp_dir()
snake_case : Dict = F'\n {self.examples_dir}/pytorch/semantic-segmentation/run_semantic_segmentation_no_trainer.py\n --dataset_name huggingface/semantic-segmentation-test-sample\n --output_dir {tmp_dir}\n --max_train_steps=10\n --num_warmup_steps=2\n --learning_rate=2e-4\n --per_device_train_batch_size=2\n --per_device_eval_batch_size=1\n --checkpointing_steps epoch\n '.split()
run_command(self._launch_args + testargs )
snake_case : Optional[Any] = get_results(UpperCamelCase__ )
self.assertGreaterEqual(result["eval_overall_accuracy"] , 0.10 )
@mock.patch.dict(os.environ , {"WANDB_MODE": "offline"} )
def lowerCamelCase ( self ) -> Union[str, Any]:
'''simple docstring'''
snake_case : Dict = self.get_auto_remove_tmp_dir()
snake_case : str = F'\n {self.examples_dir}/pytorch/image-classification/run_image_classification_no_trainer.py\n --model_name_or_path google/vit-base-patch16-224-in21k\n --dataset_name hf-internal-testing/cats_vs_dogs_sample\n --learning_rate 1e-4\n --per_device_train_batch_size 2\n --per_device_eval_batch_size 1\n --max_train_steps 2\n --train_val_split 0.1\n --seed 42\n --output_dir {tmp_dir}\n --with_tracking\n --checkpointing_steps 1\n '.split()
if is_cuda_and_apex_available():
testargs.append("--fp16" )
run_command(self._launch_args + testargs )
snake_case : str = get_results(UpperCamelCase__ )
# The base model scores a 25%
self.assertGreaterEqual(result["eval_accuracy"] , 0.6 )
self.assertTrue(os.path.exists(os.path.join(UpperCamelCase__ , "step_1" ) ) )
self.assertTrue(os.path.exists(os.path.join(UpperCamelCase__ , "image_classification_no_trainer" ) ) )
| 203 | 1 |
import cmath
import math
def a__ ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase ):
SCREAMING_SNAKE_CASE_ = math.radians(__UpperCamelCase )
SCREAMING_SNAKE_CASE_ = math.radians(__UpperCamelCase )
# Convert voltage and current to rectangular form
SCREAMING_SNAKE_CASE_ = cmath.rect(__UpperCamelCase , __UpperCamelCase )
SCREAMING_SNAKE_CASE_ = cmath.rect(__UpperCamelCase , __UpperCamelCase )
# Calculate apparent power
return voltage_rect * current_rect
if __name__ == "__main__":
import doctest
doctest.testmod()
| 366 | # Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import argparse
import os
from accelerate.utils import ComputeEnvironment
from .cluster import get_cluster_input
from .config_args import cache_dir, default_config_file, default_yaml_config_file, load_config_from_file # noqa: F401
from .config_utils import _ask_field, _ask_options, _convert_compute_environment # noqa: F401
from .sagemaker import get_sagemaker_input
A : Union[str, Any] = "Launches a series of prompts to create and save a `default_config.yaml` configuration file for your training system. Should always be ran first on your machine"
def a__ ( ):
SCREAMING_SNAKE_CASE_ = _ask_options(
"In which compute environment are you running?" , ["This machine", "AWS (Amazon SageMaker)"] , _convert_compute_environment , )
if compute_environment == ComputeEnvironment.AMAZON_SAGEMAKER:
SCREAMING_SNAKE_CASE_ = get_sagemaker_input()
else:
SCREAMING_SNAKE_CASE_ = get_cluster_input()
return config
def a__ ( __UpperCamelCase=None ):
if subparsers is not None:
SCREAMING_SNAKE_CASE_ = subparsers.add_parser("config" , description=__UpperCamelCase )
else:
SCREAMING_SNAKE_CASE_ = argparse.ArgumentParser("Accelerate config command" , description=__UpperCamelCase )
parser.add_argument(
"--config_file" , default=__UpperCamelCase , help=(
"The path to use to store the config file. Will default to a file named default_config.yaml in the cache "
"location, which is the content of the environment `HF_HOME` suffixed with 'accelerate', or if you don't have "
"such an environment variable, your cache directory ('~/.cache' or the content of `XDG_CACHE_HOME`) suffixed "
"with 'huggingface'."
) , )
if subparsers is not None:
parser.set_defaults(func=__UpperCamelCase )
return parser
def a__ ( __UpperCamelCase ):
SCREAMING_SNAKE_CASE_ = get_user_input()
if args.config_file is not None:
SCREAMING_SNAKE_CASE_ = args.config_file
else:
if not os.path.isdir(__UpperCamelCase ):
os.makedirs(__UpperCamelCase )
SCREAMING_SNAKE_CASE_ = default_yaml_config_file
if config_file.endswith(".json" ):
config.to_json_file(__UpperCamelCase )
else:
config.to_yaml_file(__UpperCamelCase )
print(F'''accelerate configuration saved at {config_file}''' )
def a__ ( ):
SCREAMING_SNAKE_CASE_ = config_command_parser()
SCREAMING_SNAKE_CASE_ = parser.parse_args()
config_command(__UpperCamelCase )
if __name__ == "__main__":
main()
| 305 | 0 |
'''simple docstring'''
import argparse
import importlib
from pathlib import Path
# Test all the extensions added in the setup
a__ : Tuple =[
'''kernels/rwkv/wkv_cuda.cu''',
'''kernels/rwkv/wkv_op.cpp''',
'''kernels/deformable_detr/ms_deform_attn.h''',
'''kernels/deformable_detr/cuda/ms_deform_im2col_cuda.cuh''',
'''models/graphormer/algos_graphormer.pyx''',
]
def lowercase__ ( __lowercase : Union[str, Any] ) -> Any:
"""simple docstring"""
for file in FILES_TO_FIND:
if not (transformers_path / file).exists():
return False
return True
if __name__ == "__main__":
a__ : Tuple =argparse.ArgumentParser()
parser.add_argument('''--check_lib''', action='''store_true''', help='''Whether to check the build or the actual package.''')
a__ : Union[str, Any] =parser.parse_args()
if args.check_lib:
a__ : Optional[int] =importlib.import_module('''transformers''')
a__ : List[str] =Path(transformers_module.__file__).parent
else:
a__ : Tuple =Path.cwd() / '''build/lib/transformers'''
if not test_custom_files_are_present(transformers_path):
raise ValueError('''The built release does not contain the custom files. Fix this before going further!''')
| 53 |
class A :
"""simple docstring"""
def __init__( self : Any,lowercase_ : Tuple,lowercase_ : Any,lowercase_ : List[str] )-> List[Any]:
'''simple docstring'''
A__ = name
A__ = value
A__ = weight
def __repr__( self : int )-> Tuple:
'''simple docstring'''
return F'{self.__class__.__name__}({self.name}, {self.value}, {self.weight})'
def snake_case__ ( self : Any )-> str:
'''simple docstring'''
return self.value
def snake_case__ ( self : Any )-> Tuple:
'''simple docstring'''
return self.name
def snake_case__ ( self : Any )-> Dict:
'''simple docstring'''
return self.weight
def snake_case__ ( self : Union[str, Any] )-> Optional[Any]:
'''simple docstring'''
return self.value / self.weight
def _snake_case( SCREAMING_SNAKE_CASE__ : Optional[Any] , SCREAMING_SNAKE_CASE__ : List[str] , SCREAMING_SNAKE_CASE__ : List[Any] ) -> List[Any]:
'''simple docstring'''
A__ = []
for i in range(len(SCREAMING_SNAKE_CASE__ ) ):
menu.append(Things(name[i] , value[i] , weight[i] ) )
return menu
def _snake_case( SCREAMING_SNAKE_CASE__ : Dict , SCREAMING_SNAKE_CASE__ : List[str] , SCREAMING_SNAKE_CASE__ : int ) -> Any:
'''simple docstring'''
A__ = sorted(SCREAMING_SNAKE_CASE__ , key=SCREAMING_SNAKE_CASE__ , reverse=SCREAMING_SNAKE_CASE__ )
A__ = []
A__ , A__ = 0.0, 0.0
for i in range(len(SCREAMING_SNAKE_CASE__ ) ):
if (total_cost + items_copy[i].get_weight()) <= max_cost:
result.append(items_copy[i] )
total_cost += items_copy[i].get_weight()
total_value += items_copy[i].get_value()
return (result, total_value)
def _snake_case( ) -> Any:
'''simple docstring'''
if __name__ == "__main__":
import doctest
doctest.testmod()
| 7 | 0 |
"""simple docstring"""
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow
if is_torch_available():
import torch
from transformers import XLMRobertaModel
@require_sentencepiece
@require_tokenizers
@require_torch
class UpperCamelCase ( unittest.TestCase ):
@slow
def _UpperCAmelCase ( self ) -> str:
'''simple docstring'''
lowercase_ : Optional[Any] = XLMRobertaModel.from_pretrained('xlm-roberta-base' )
lowercase_ : Dict = torch.tensor([[0, 581, 1_0269, 83, 9_9942, 136, 6_0742, 23, 70, 8_0583, 1_8276, 2]] )
# The dog is cute and lives in the garden house
lowercase_ : Dict = torch.Size((1, 12, 768) ) # batch_size, sequence_length, embedding_vector_dim
lowercase_ : List[str] = torch.tensor(
[[-0.0101, 0.1218, -0.0803, 0.0801, 0.1327, 0.0776, -0.1215, 0.2383, 0.3338, 0.3106, 0.0300, 0.0252]] )
# xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.base')
# xlmr.eval()
# expected_output_values_last_dim = xlmr.extract_features(input_ids[0])[:, :, -1]
with torch.no_grad():
lowercase_ : Union[str, Any] = model(__UpperCamelCase )['last_hidden_state'].detach()
self.assertEqual(output.shape ,__UpperCamelCase )
# compare the actual values for a slice of last dim
self.assertTrue(torch.allclose(output[:, :, -1] ,__UpperCamelCase ,atol=1e-3 ) )
@slow
def _UpperCAmelCase ( self ) -> Union[str, Any]:
'''simple docstring'''
lowercase_ : Any = XLMRobertaModel.from_pretrained('xlm-roberta-large' )
lowercase_ : Optional[Any] = torch.tensor([[0, 581, 1_0269, 83, 9_9942, 136, 6_0742, 23, 70, 8_0583, 1_8276, 2]] )
# The dog is cute and lives in the garden house
lowercase_ : Dict = torch.Size((1, 12, 1024) ) # batch_size, sequence_length, embedding_vector_dim
lowercase_ : Tuple = torch.tensor(
[[-0.0699, -0.0318, 0.0705, -0.1241, 0.0999, -0.0520, 0.1004, -0.1838, -0.4704, 0.1437, 0.0821, 0.0126]] )
# xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.large')
# xlmr.eval()
# expected_output_values_last_dim = xlmr.extract_features(input_ids[0])[:, :, -1]
with torch.no_grad():
lowercase_ : List[Any] = model(__UpperCamelCase )['last_hidden_state'].detach()
self.assertEqual(output.shape ,__UpperCamelCase )
# compare the actual values for a slice of last dim
self.assertTrue(torch.allclose(output[:, :, -1] ,__UpperCamelCase ,atol=1e-3 ) )
| 321 | """simple docstring"""
from collections import namedtuple
import requests
from lxml import html # type: ignore
__SCREAMING_SNAKE_CASE =namedtuple("covid_data", "cases deaths recovered")
def lowercase__( __SCREAMING_SNAKE_CASE : str = "https://www.worldometers.info/coronavirus/" ):
lowercase_ : Union[str, Any] = '//div[@class = "maincounter-number"]/span/text()'
return covid_data(*html.fromstring(requests.get(__SCREAMING_SNAKE_CASE ).content ).xpath(__SCREAMING_SNAKE_CASE ) )
__SCREAMING_SNAKE_CASE ="Total COVID-19 cases in the world: {}\nTotal deaths due to COVID-19 in the world: {}\nTotal COVID-19 patients recovered in the world: {}"
print(fmt.format(*covid_stats()))
| 321 | 1 |
import argparse
import os
from io import BytesIO
from pathlib import Path
import requests
from clip_retrieval.clip_client import ClipClient
from PIL import Image
from tqdm import tqdm
def lowercase_ ( _lowerCamelCase : Tuple , _lowerCamelCase : List[str] , _lowerCamelCase : Optional[Any]):
lowercase__ : str = 1.5
lowercase__ : Any = int(factor * num_class_images)
lowercase__ : Optional[Any] = ClipClient(
url="https://knn.laion.ai/knn-service" , indice_name="laion_400m" , num_images=_lowerCamelCase , aesthetic_weight=0.1)
os.makedirs(f'''{class_data_dir}/images''' , exist_ok=_lowerCamelCase)
if len(list(Path(f'''{class_data_dir}/images''').iterdir())) >= num_class_images:
return
while True:
lowercase__ : Dict = client.query(text=_lowerCamelCase)
if len(_lowerCamelCase) >= factor * num_class_images or num_images > 1E4:
break
else:
lowercase__ : List[Any] = int(factor * num_images)
lowercase__ : Any = ClipClient(
url="https://knn.laion.ai/knn-service" , indice_name="laion_400m" , num_images=_lowerCamelCase , aesthetic_weight=0.1 , )
lowercase__ : List[str] = 0
lowercase__ : Dict = 0
lowercase__ : int = tqdm(desc="downloading real regularization images" , total=_lowerCamelCase)
with open(f'''{class_data_dir}/caption.txt''' , "w") as fa, open(f'''{class_data_dir}/urls.txt''' , "w") as fa, open(
f'''{class_data_dir}/images.txt''' , "w") as fa:
while total < num_class_images:
lowercase__ : List[str] = class_images[count]
count += 1
try:
lowercase__ : Union[str, Any] = requests.get(images["url"])
if img.status_code == 200:
lowercase__ : List[str] = Image.open(BytesIO(img.content))
with open(f'''{class_data_dir}/images/{total}.jpg''' , "wb") as f:
f.write(img.content)
fa.write(images["caption"] + "\n")
fa.write(images["url"] + "\n")
fa.write(f'''{class_data_dir}/images/{total}.jpg''' + "\n")
total += 1
pbar.update(1)
else:
continue
except Exception:
continue
return
def lowercase_ ( ):
lowercase__ : Optional[int] = argparse.ArgumentParser("" , add_help=_lowerCamelCase)
parser.add_argument("--class_prompt" , help="text prompt to retrieve images" , required=_lowerCamelCase , type=_lowerCamelCase)
parser.add_argument("--class_data_dir" , help="path to save images" , required=_lowerCamelCase , type=_lowerCamelCase)
parser.add_argument("--num_class_images" , help="number of images to download" , default=200 , type=_lowerCamelCase)
return parser.parse_args()
if __name__ == "__main__":
UpperCamelCase = parse_args()
retrieve(args.class_prompt, args.class_data_dir, args.num_class_images)
| 87 |
import inspect
import unittest
import torch
import torch.nn as nn
from accelerate.hooks import (
AlignDevicesHook,
ModelHook,
SequentialHook,
add_hook_to_module,
attach_align_device_hook,
remove_hook_from_module,
remove_hook_from_submodules,
)
from accelerate.test_utils import require_multi_gpu
class A ( nn.Module ):
def __init__(self ):
super().__init__()
__lowercase= nn.Linear(3 , 4 )
__lowercase= nn.BatchNormad(4 )
__lowercase= nn.Linear(4 , 5 )
def _A (self , lowerCAmelCase ):
return self.lineara(self.batchnorm(self.lineara(lowerCAmelCase ) ) )
class A ( A_ ):
def _A (self , lowerCAmelCase , *lowerCAmelCase , **lowerCAmelCase ):
return (args[0] + 1,) + args[1:], kwargs
class A ( A_ ):
def _A (self , lowerCAmelCase , lowerCAmelCase ):
return output + 1
class A ( unittest.TestCase ):
def _A (self ):
__lowercase= ModelForTest()
__lowercase= ModelHook()
add_hook_to_module(lowerCAmelCase , lowerCAmelCase )
self.assertEqual(test_model._hf_hook , lowerCAmelCase )
self.assertTrue(hasattr(lowerCAmelCase , '_old_forward' ) )
# Check adding the hook did not change the name or the signature
self.assertEqual(test_model.forward.__name__ , 'forward' )
self.assertListEqual(list(inspect.signature(test_model.forward ).parameters ) , ['x'] )
remove_hook_from_module(lowerCAmelCase )
self.assertFalse(hasattr(lowerCAmelCase , '_hf_hook' ) )
self.assertFalse(hasattr(lowerCAmelCase , '_old_forward' ) )
def _A (self ):
__lowercase= ModelForTest()
__lowercase= ModelHook()
add_hook_to_module(lowerCAmelCase , lowerCAmelCase )
add_hook_to_module(lowerCAmelCase , lowerCAmelCase , append=lowerCAmelCase )
self.assertEqual(isinstance(test_model._hf_hook , lowerCAmelCase ) , lowerCAmelCase )
self.assertEqual(len(test_model._hf_hook.hooks ) , 2 )
self.assertTrue(hasattr(lowerCAmelCase , '_old_forward' ) )
# Check adding the hook did not change the name or the signature
self.assertEqual(test_model.forward.__name__ , 'forward' )
self.assertListEqual(list(inspect.signature(test_model.forward ).parameters ) , ['x'] )
remove_hook_from_module(lowerCAmelCase )
self.assertFalse(hasattr(lowerCAmelCase , '_hf_hook' ) )
self.assertFalse(hasattr(lowerCAmelCase , '_old_forward' ) )
def _A (self ):
__lowercase= ModelForTest()
__lowercase= torch.randn(2 , 3 )
__lowercase= test_model(x + 1 )
__lowercase= test_model(x + 2 )
__lowercase= PreForwardHook()
add_hook_to_module(lowerCAmelCase , lowerCAmelCase )
__lowercase= test_model(lowerCAmelCase )
self.assertTrue(torch.allclose(lowerCAmelCase , lowerCAmelCase , atol=1E-5 ) )
# Attaching a hook to a model when it already has one replaces, does not chain
__lowercase= PreForwardHook()
add_hook_to_module(lowerCAmelCase , lowerCAmelCase )
__lowercase= test_model(lowerCAmelCase )
self.assertTrue(torch.allclose(lowerCAmelCase , lowerCAmelCase , atol=1E-5 ) )
# You need to use the sequential hook to chain two or more hooks
__lowercase= SequentialHook(PreForwardHook() , PreForwardHook() )
add_hook_to_module(lowerCAmelCase , lowerCAmelCase )
__lowercase= test_model(lowerCAmelCase )
assert torch.allclose(lowerCAmelCase , lowerCAmelCase , atol=1E-5 )
def _A (self ):
__lowercase= ModelForTest()
__lowercase= torch.randn(2 , 3 )
__lowercase= test_model(lowerCAmelCase )
__lowercase= PostForwardHook()
add_hook_to_module(lowerCAmelCase , lowerCAmelCase )
__lowercase= test_model(lowerCAmelCase )
self.assertTrue(torch.allclose(lowerCAmelCase , output + 1 , atol=1E-5 ) )
# Attaching a hook to a model when it already has one replaces, does not chain
__lowercase= PostForwardHook()
add_hook_to_module(lowerCAmelCase , lowerCAmelCase )
__lowercase= test_model(lowerCAmelCase )
self.assertTrue(torch.allclose(lowerCAmelCase , output + 1 , atol=1E-5 ) )
# You need to use the sequential hook to chain two or more hooks
__lowercase= SequentialHook(PostForwardHook() , PostForwardHook() )
add_hook_to_module(lowerCAmelCase , lowerCAmelCase )
__lowercase= test_model(lowerCAmelCase )
assert torch.allclose(lowerCAmelCase , output + 2 , atol=1E-5 )
def _A (self ):
__lowercase= ModelForTest()
__lowercase= torch.randn(2 , 3 )
__lowercase= test_model(lowerCAmelCase )
__lowercase= PostForwardHook()
add_hook_to_module(lowerCAmelCase , lowerCAmelCase )
__lowercase= test_model(lowerCAmelCase )
self.assertTrue(torch.allclose(lowerCAmelCase , output + 1 ) )
self.assertTrue(outputa.requires_grad )
__lowercase= True
__lowercase= test_model(lowerCAmelCase )
self.assertFalse(outputa.requires_grad )
@require_multi_gpu
def _A (self ):
__lowercase= ModelForTest()
# Everything is on CPU
self.assertEqual(model.lineara.weight.device , torch.device('cpu' ) )
self.assertEqual(model.batchnorm.weight.device , torch.device('cpu' ) )
self.assertEqual(model.lineara.weight.device , torch.device('cpu' ) )
# This will move each submodule on different devices
add_hook_to_module(model.lineara , AlignDevicesHook(execution_device=0 ) )
add_hook_to_module(model.batchnorm , AlignDevicesHook(execution_device=0 ) )
add_hook_to_module(model.lineara , AlignDevicesHook(execution_device=1 ) )
self.assertEqual(model.lineara.weight.device , torch.device(0 ) )
self.assertEqual(model.batchnorm.weight.device , torch.device(0 ) )
self.assertEqual(model.batchnorm.running_mean.device , torch.device(0 ) )
self.assertEqual(model.lineara.weight.device , torch.device(1 ) )
# We can still make a forward pass. The input does not need to be on any particular device
__lowercase= torch.randn(2 , 3 )
__lowercase= model(lowerCAmelCase )
self.assertEqual(output.device , torch.device(1 ) )
# We can add a general hook to put back output on same device as input.
add_hook_to_module(lowerCAmelCase , AlignDevicesHook(io_same_device=lowerCAmelCase ) )
__lowercase= torch.randn(2 , 3 ).to(0 )
__lowercase= model(lowerCAmelCase )
self.assertEqual(output.device , torch.device(0 ) )
def _A (self ):
__lowercase= ModelForTest()
# Everything is on CPU
self.assertEqual(model.lineara.weight.device , torch.device('cpu' ) )
self.assertEqual(model.batchnorm.weight.device , torch.device('cpu' ) )
self.assertEqual(model.lineara.weight.device , torch.device('cpu' ) )
# This will move each submodule on different devices
__lowercase= {'execution_device': 0 if torch.cuda.is_available() else 'cpu', 'offload': True}
add_hook_to_module(model.lineara , AlignDevicesHook(**lowerCAmelCase ) )
add_hook_to_module(model.batchnorm , AlignDevicesHook(**lowerCAmelCase ) )
add_hook_to_module(model.lineara , AlignDevicesHook(**lowerCAmelCase ) )
# Parameters have been offloaded, so on the meta device
self.assertEqual(model.lineara.weight.device , torch.device('meta' ) )
self.assertEqual(model.batchnorm.weight.device , torch.device('meta' ) )
self.assertEqual(model.lineara.weight.device , torch.device('meta' ) )
# Buffers are not included in the offload by default, so are on the execution device
__lowercase= torch.device(hook_kwargs['execution_device'] )
self.assertEqual(model.batchnorm.running_mean.device , lowerCAmelCase )
__lowercase= torch.randn(2 , 3 )
__lowercase= model(lowerCAmelCase )
self.assertEqual(output.device , lowerCAmelCase )
# Removing hooks loads back the weights in the model.
remove_hook_from_module(model.lineara )
remove_hook_from_module(model.batchnorm )
remove_hook_from_module(model.lineara )
self.assertEqual(model.lineara.weight.device , torch.device('cpu' ) )
self.assertEqual(model.batchnorm.weight.device , torch.device('cpu' ) )
self.assertEqual(model.lineara.weight.device , torch.device('cpu' ) )
# Now test with buffers included in the offload
__lowercase= {
'execution_device': 0 if torch.cuda.is_available() else 'cpu',
'offload': True,
'offload_buffers': True,
}
add_hook_to_module(model.lineara , AlignDevicesHook(**lowerCAmelCase ) )
add_hook_to_module(model.batchnorm , AlignDevicesHook(**lowerCAmelCase ) )
add_hook_to_module(model.lineara , AlignDevicesHook(**lowerCAmelCase ) )
# Parameters have been offloaded, so on the meta device, buffers included
self.assertEqual(model.lineara.weight.device , torch.device('meta' ) )
self.assertEqual(model.batchnorm.weight.device , torch.device('meta' ) )
self.assertEqual(model.lineara.weight.device , torch.device('meta' ) )
self.assertEqual(model.batchnorm.running_mean.device , torch.device('meta' ) )
__lowercase= torch.randn(2 , 3 )
__lowercase= model(lowerCAmelCase )
self.assertEqual(output.device , lowerCAmelCase )
# Removing hooks loads back the weights in the model.
remove_hook_from_module(model.lineara )
remove_hook_from_module(model.batchnorm )
remove_hook_from_module(model.lineara )
self.assertEqual(model.lineara.weight.device , torch.device('cpu' ) )
self.assertEqual(model.batchnorm.weight.device , torch.device('cpu' ) )
self.assertEqual(model.lineara.weight.device , torch.device('cpu' ) )
def _A (self ):
__lowercase= ModelForTest()
# Everything is on CPU
self.assertEqual(model.lineara.weight.device , torch.device('cpu' ) )
self.assertEqual(model.batchnorm.weight.device , torch.device('cpu' ) )
self.assertEqual(model.lineara.weight.device , torch.device('cpu' ) )
# This will move each submodule on different devices
__lowercase= 0 if torch.cuda.is_available() else 'cpu'
attach_align_device_hook(lowerCAmelCase , execution_device=lowerCAmelCase , offload=lowerCAmelCase )
# Parameters have been offloaded, so on the meta device
self.assertEqual(model.lineara.weight.device , torch.device('meta' ) )
self.assertEqual(model.batchnorm.weight.device , torch.device('meta' ) )
self.assertEqual(model.lineara.weight.device , torch.device('meta' ) )
# Buffers are not included in the offload by default, so are on the execution device
__lowercase= torch.device(lowerCAmelCase )
self.assertEqual(model.batchnorm.running_mean.device , lowerCAmelCase )
__lowercase= torch.randn(2 , 3 )
__lowercase= model(lowerCAmelCase )
self.assertEqual(output.device , lowerCAmelCase )
# Removing hooks loads back the weights in the model.
remove_hook_from_submodules(lowerCAmelCase )
self.assertEqual(model.lineara.weight.device , torch.device('cpu' ) )
self.assertEqual(model.batchnorm.weight.device , torch.device('cpu' ) )
self.assertEqual(model.lineara.weight.device , torch.device('cpu' ) )
# Now test with buffers included in the offload
attach_align_device_hook(lowerCAmelCase , execution_device=lowerCAmelCase , offload=lowerCAmelCase , offload_buffers=lowerCAmelCase )
# Parameters have been offloaded, so on the meta device, buffers included
self.assertEqual(model.lineara.weight.device , torch.device('meta' ) )
self.assertEqual(model.batchnorm.weight.device , torch.device('meta' ) )
self.assertEqual(model.lineara.weight.device , torch.device('meta' ) )
self.assertEqual(model.batchnorm.running_mean.device , torch.device('meta' ) )
__lowercase= torch.randn(2 , 3 )
__lowercase= model(lowerCAmelCase )
self.assertEqual(output.device , lowerCAmelCase )
# Removing hooks loads back the weights in the model.
remove_hook_from_submodules(lowerCAmelCase )
self.assertEqual(model.lineara.weight.device , torch.device('cpu' ) )
self.assertEqual(model.batchnorm.weight.device , torch.device('cpu' ) )
self.assertEqual(model.lineara.weight.device , torch.device('cpu' ) )
def _A (self ):
__lowercase= ModelForTest()
# Everything is on CPU
self.assertEqual(model.lineara.weight.device , torch.device('cpu' ) )
self.assertEqual(model.batchnorm.weight.device , torch.device('cpu' ) )
self.assertEqual(model.lineara.weight.device , torch.device('cpu' ) )
# This will move each submodule on different devices
__lowercase= 0 if torch.cuda.is_available() else 'cpu'
attach_align_device_hook(
lowerCAmelCase , execution_device=lowerCAmelCase , offload=lowerCAmelCase , weights_map=model.state_dict() )
# Parameters have been offloaded, so on the meta device
self.assertEqual(model.lineara.weight.device , torch.device('meta' ) )
self.assertEqual(model.batchnorm.weight.device , torch.device('meta' ) )
self.assertEqual(model.lineara.weight.device , torch.device('meta' ) )
# Buffers are not included in the offload by default, so are on the execution device
__lowercase= torch.device(lowerCAmelCase )
self.assertEqual(model.batchnorm.running_mean.device , lowerCAmelCase )
__lowercase= torch.randn(2 , 3 )
__lowercase= model(lowerCAmelCase )
self.assertEqual(output.device , lowerCAmelCase )
# Removing hooks loads back the weights in the model.
remove_hook_from_submodules(lowerCAmelCase )
self.assertEqual(model.lineara.weight.device , torch.device('cpu' ) )
self.assertEqual(model.batchnorm.weight.device , torch.device('cpu' ) )
self.assertEqual(model.lineara.weight.device , torch.device('cpu' ) )
# Now test with buffers included in the offload
attach_align_device_hook(
lowerCAmelCase , execution_device=lowerCAmelCase , offload=lowerCAmelCase , weights_map=model.state_dict() , offload_buffers=lowerCAmelCase , )
# Parameters have been offloaded, so on the meta device, buffers included
self.assertEqual(model.lineara.weight.device , torch.device('meta' ) )
self.assertEqual(model.batchnorm.weight.device , torch.device('meta' ) )
self.assertEqual(model.lineara.weight.device , torch.device('meta' ) )
self.assertEqual(model.batchnorm.running_mean.device , torch.device('meta' ) )
__lowercase= torch.randn(2 , 3 )
__lowercase= model(lowerCAmelCase )
self.assertEqual(output.device , lowerCAmelCase )
# Removing hooks loads back the weights in the model.
remove_hook_from_submodules(lowerCAmelCase )
self.assertEqual(model.lineara.weight.device , torch.device('cpu' ) )
self.assertEqual(model.batchnorm.weight.device , torch.device('cpu' ) )
self.assertEqual(model.lineara.weight.device , torch.device('cpu' ) )
| 295 | 0 |
import copy
import unittest
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, 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, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
MODEL_FOR_MULTIPLE_CHOICE_MAPPING,
MODEL_FOR_QUESTION_ANSWERING_MAPPING,
MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
LayoutLMvaConfig,
LayoutLMvaForQuestionAnswering,
LayoutLMvaForSequenceClassification,
LayoutLMvaForTokenClassification,
LayoutLMvaModel,
)
from transformers.models.layoutlmva.modeling_layoutlmva import LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import LayoutLMvaImageProcessor
class lowerCAmelCase_ :
def __init__( self, SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_=2, SCREAMING_SNAKE_CASE_=3, SCREAMING_SNAKE_CASE_=4, SCREAMING_SNAKE_CASE_=2, SCREAMING_SNAKE_CASE_=7, SCREAMING_SNAKE_CASE_=True, SCREAMING_SNAKE_CASE_=True, SCREAMING_SNAKE_CASE_=True, SCREAMING_SNAKE_CASE_=True, SCREAMING_SNAKE_CASE_=99, SCREAMING_SNAKE_CASE_=36, SCREAMING_SNAKE_CASE_=3, SCREAMING_SNAKE_CASE_=4, SCREAMING_SNAKE_CASE_=37, SCREAMING_SNAKE_CASE_="gelu", SCREAMING_SNAKE_CASE_=0.1, SCREAMING_SNAKE_CASE_=0.1, SCREAMING_SNAKE_CASE_=512, SCREAMING_SNAKE_CASE_=16, SCREAMING_SNAKE_CASE_=2, SCREAMING_SNAKE_CASE_=0.02, SCREAMING_SNAKE_CASE_=6, SCREAMING_SNAKE_CASE_=6, SCREAMING_SNAKE_CASE_=3, SCREAMING_SNAKE_CASE_=4, SCREAMING_SNAKE_CASE_=None, SCREAMING_SNAKE_CASE_=1000, ) -> Optional[int]:
UpperCamelCase : Any = parent
UpperCamelCase : int = batch_size
UpperCamelCase : Dict = num_channels
UpperCamelCase : Any = image_size
UpperCamelCase : Any = patch_size
UpperCamelCase : Any = text_seq_length
UpperCamelCase : Optional[Any] = is_training
UpperCamelCase : Any = use_input_mask
UpperCamelCase : Dict = use_token_type_ids
UpperCamelCase : Optional[Any] = use_labels
UpperCamelCase : str = vocab_size
UpperCamelCase : List[str] = hidden_size
UpperCamelCase : Tuple = num_hidden_layers
UpperCamelCase : Union[str, Any] = num_attention_heads
UpperCamelCase : str = intermediate_size
UpperCamelCase : Union[str, Any] = hidden_act
UpperCamelCase : Optional[Any] = hidden_dropout_prob
UpperCamelCase : Optional[int] = attention_probs_dropout_prob
UpperCamelCase : Dict = max_position_embeddings
UpperCamelCase : Union[str, Any] = type_vocab_size
UpperCamelCase : Union[str, Any] = type_sequence_label_size
UpperCamelCase : Optional[Any] = initializer_range
UpperCamelCase : List[Any] = coordinate_size
UpperCamelCase : Union[str, Any] = shape_size
UpperCamelCase : Any = num_labels
UpperCamelCase : int = num_choices
UpperCamelCase : Optional[int] = scope
UpperCamelCase : Dict = range_bbox
# LayoutLMv3's sequence length equals the number of text tokens + number of patches + 1 (we add 1 for the CLS token)
UpperCamelCase : List[str] = text_seq_length
UpperCamelCase : Any = (image_size // patch_size) ** 2 + 1
UpperCamelCase : Optional[int] = self.text_seq_length + self.image_seq_length
def snake_case_ ( self ) -> Dict:
UpperCamelCase : str = ids_tensor([self.batch_size, self.text_seq_length], self.vocab_size )
UpperCamelCase : Any = ids_tensor([self.batch_size, self.text_seq_length, 4], self.range_bbox )
# Ensure that bbox is legal
for i in range(bbox.shape[0] ):
for j in range(bbox.shape[1] ):
if bbox[i, j, 3] < bbox[i, j, 1]:
UpperCamelCase : List[Any] = bbox[i, j, 3]
UpperCamelCase : Any = bbox[i, j, 1]
UpperCamelCase : List[str] = t
if bbox[i, j, 2] < bbox[i, j, 0]:
UpperCamelCase : Optional[Any] = bbox[i, j, 2]
UpperCamelCase : List[Any] = bbox[i, j, 0]
UpperCamelCase : Optional[int] = t
UpperCamelCase : Union[str, Any] = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
UpperCamelCase : Dict = None
if self.use_input_mask:
UpperCamelCase : Optional[Any] = random_attention_mask([self.batch_size, self.text_seq_length] )
UpperCamelCase : List[Any] = None
if self.use_token_type_ids:
UpperCamelCase : List[Any] = ids_tensor([self.batch_size, self.text_seq_length], self.type_vocab_size )
UpperCamelCase : Tuple = None
UpperCamelCase : List[str] = None
if self.use_labels:
UpperCamelCase : Optional[Any] = ids_tensor([self.batch_size], self.type_sequence_label_size )
UpperCamelCase : Any = ids_tensor([self.batch_size, self.text_seq_length], self.num_labels )
UpperCamelCase : List[str] = LayoutLMvaConfig(
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, initializer_range=self.initializer_range, coordinate_size=self.coordinate_size, shape_size=self.shape_size, input_size=self.image_size, patch_size=self.patch_size, )
return config, input_ids, bbox, pixel_values, token_type_ids, input_mask, sequence_labels, token_labels
def snake_case_ ( self, SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_ ) -> int:
UpperCamelCase : Tuple = LayoutLMvaModel(config=SCREAMING_SNAKE_CASE_ )
model.to(SCREAMING_SNAKE_CASE_ )
model.eval()
# text + image
UpperCamelCase : List[str] = model(SCREAMING_SNAKE_CASE_, pixel_values=SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Any = model(
SCREAMING_SNAKE_CASE_, bbox=SCREAMING_SNAKE_CASE_, pixel_values=SCREAMING_SNAKE_CASE_, attention_mask=SCREAMING_SNAKE_CASE_, token_type_ids=SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Tuple = model(SCREAMING_SNAKE_CASE_, bbox=SCREAMING_SNAKE_CASE_, pixel_values=SCREAMING_SNAKE_CASE_, token_type_ids=SCREAMING_SNAKE_CASE_ )
UpperCamelCase : List[str] = model(SCREAMING_SNAKE_CASE_, bbox=SCREAMING_SNAKE_CASE_, pixel_values=SCREAMING_SNAKE_CASE_ )
self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size) )
# text only
UpperCamelCase : Tuple = model(SCREAMING_SNAKE_CASE_ )
self.parent.assertEqual(
result.last_hidden_state.shape, (self.batch_size, self.text_seq_length, self.hidden_size) )
# image only
UpperCamelCase : List[str] = model(pixel_values=SCREAMING_SNAKE_CASE_ )
self.parent.assertEqual(
result.last_hidden_state.shape, (self.batch_size, self.image_seq_length, self.hidden_size) )
def snake_case_ ( self, SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_ ) -> Tuple:
UpperCamelCase : Optional[int] = self.num_labels
UpperCamelCase : List[str] = LayoutLMvaForSequenceClassification(SCREAMING_SNAKE_CASE_ )
model.to(SCREAMING_SNAKE_CASE_ )
model.eval()
UpperCamelCase : List[str] = model(
SCREAMING_SNAKE_CASE_, bbox=SCREAMING_SNAKE_CASE_, pixel_values=SCREAMING_SNAKE_CASE_, attention_mask=SCREAMING_SNAKE_CASE_, token_type_ids=SCREAMING_SNAKE_CASE_, labels=SCREAMING_SNAKE_CASE_, )
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_labels) )
def snake_case_ ( self, SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_ ) -> List[str]:
UpperCamelCase : Tuple = self.num_labels
UpperCamelCase : Tuple = LayoutLMvaForTokenClassification(config=SCREAMING_SNAKE_CASE_ )
model.to(SCREAMING_SNAKE_CASE_ )
model.eval()
UpperCamelCase : Dict = model(
SCREAMING_SNAKE_CASE_, bbox=SCREAMING_SNAKE_CASE_, pixel_values=SCREAMING_SNAKE_CASE_, attention_mask=SCREAMING_SNAKE_CASE_, token_type_ids=SCREAMING_SNAKE_CASE_, labels=SCREAMING_SNAKE_CASE_, )
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.text_seq_length, self.num_labels) )
def snake_case_ ( self, SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_ ) -> Optional[int]:
UpperCamelCase : Optional[int] = LayoutLMvaForQuestionAnswering(config=SCREAMING_SNAKE_CASE_ )
model.to(SCREAMING_SNAKE_CASE_ )
model.eval()
UpperCamelCase : Any = model(
SCREAMING_SNAKE_CASE_, bbox=SCREAMING_SNAKE_CASE_, pixel_values=SCREAMING_SNAKE_CASE_, attention_mask=SCREAMING_SNAKE_CASE_, token_type_ids=SCREAMING_SNAKE_CASE_, start_positions=SCREAMING_SNAKE_CASE_, end_positions=SCREAMING_SNAKE_CASE_, )
self.parent.assertEqual(result.start_logits.shape, (self.batch_size, self.seq_length) )
self.parent.assertEqual(result.end_logits.shape, (self.batch_size, self.seq_length) )
def snake_case_ ( self ) -> List[str]:
UpperCamelCase : Tuple = self.prepare_config_and_inputs()
(
(
UpperCamelCase
) , (
UpperCamelCase
) , (
UpperCamelCase
) , (
UpperCamelCase
) , (
UpperCamelCase
) , (
UpperCamelCase
) , (
UpperCamelCase
) , (
UpperCamelCase
) ,
) : Optional[int] = config_and_inputs
UpperCamelCase : str = {
'input_ids': input_ids,
'bbox': bbox,
'pixel_values': pixel_values,
'token_type_ids': token_type_ids,
'attention_mask': input_mask,
}
return config, inputs_dict
@require_torch
class lowerCAmelCase_ ( a__ , a__ , unittest.TestCase ):
UpperCAmelCase__ : Any = False
UpperCAmelCase__ : List[str] = False
UpperCAmelCase__ : Union[str, Any] = False
UpperCAmelCase__ : str = (
(
LayoutLMvaModel,
LayoutLMvaForSequenceClassification,
LayoutLMvaForTokenClassification,
LayoutLMvaForQuestionAnswering,
)
if is_torch_available()
else ()
)
UpperCAmelCase__ : Tuple = (
{"document-question-answering": LayoutLMvaForQuestionAnswering, "feature-extraction": LayoutLMvaModel}
if is_torch_available()
else {}
)
def snake_case_ ( self, SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_ ) -> List[Any]:
# `DocumentQuestionAnsweringPipeline` is expected to work with this model, but it combines the text and visual
# embedding along the sequence dimension (dim 1), which causes an error during post-processing as `p_mask` has
# the sequence dimension of the text embedding only.
# (see the line `embedding_output = torch.cat([embedding_output, visual_embeddings], dim=1)`)
return True
def snake_case_ ( self ) -> Optional[Any]:
UpperCamelCase : Optional[int] = LayoutLMvaModelTester(self )
UpperCamelCase : List[str] = ConfigTester(self, config_class=SCREAMING_SNAKE_CASE_, hidden_size=37 )
def snake_case_ ( self, SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_=False ) -> str:
UpperCamelCase : Optional[int] = copy.deepcopy(SCREAMING_SNAKE_CASE_ )
if model_class in get_values(SCREAMING_SNAKE_CASE_ ):
UpperCamelCase : Tuple = {
k: v.unsqueeze(1 ).expand(-1, self.model_tester.num_choices, -1 ).contiguous()
if isinstance(SCREAMING_SNAKE_CASE_, torch.Tensor ) and v.ndim > 1
else v
for k, v in inputs_dict.items()
}
if return_labels:
if model_class in get_values(SCREAMING_SNAKE_CASE_ ):
UpperCamelCase : Tuple = torch.ones(self.model_tester.batch_size, dtype=torch.long, device=SCREAMING_SNAKE_CASE_ )
elif model_class in get_values(SCREAMING_SNAKE_CASE_ ):
UpperCamelCase : Tuple = torch.zeros(
self.model_tester.batch_size, dtype=torch.long, device=SCREAMING_SNAKE_CASE_ )
UpperCamelCase : List[Any] = torch.zeros(
self.model_tester.batch_size, dtype=torch.long, device=SCREAMING_SNAKE_CASE_ )
elif model_class in [
*get_values(SCREAMING_SNAKE_CASE_ ),
]:
UpperCamelCase : Union[str, Any] = torch.zeros(
self.model_tester.batch_size, dtype=torch.long, device=SCREAMING_SNAKE_CASE_ )
elif model_class in [
*get_values(SCREAMING_SNAKE_CASE_ ),
]:
UpperCamelCase : List[str] = torch.zeros(
(self.model_tester.batch_size, self.model_tester.text_seq_length), dtype=torch.long, device=SCREAMING_SNAKE_CASE_, )
return inputs_dict
def snake_case_ ( self ) -> Tuple:
self.config_tester.run_common_tests()
def snake_case_ ( self ) -> List[Any]:
UpperCamelCase : List[str] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*SCREAMING_SNAKE_CASE_ )
def snake_case_ ( self ) -> Optional[int]:
UpperCamelCase : List[Any] = self.model_tester.prepare_config_and_inputs()
for type in ["absolute", "relative_key", "relative_key_query"]:
UpperCamelCase : int = type
self.model_tester.create_and_check_model(*SCREAMING_SNAKE_CASE_ )
def snake_case_ ( self ) -> Tuple:
UpperCamelCase : int = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_sequence_classification(*SCREAMING_SNAKE_CASE_ )
def snake_case_ ( self ) -> Optional[Any]:
UpperCamelCase : int = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_token_classification(*SCREAMING_SNAKE_CASE_ )
def snake_case_ ( self ) -> Any:
UpperCamelCase : Optional[int] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_question_answering(*SCREAMING_SNAKE_CASE_ )
@slow
def snake_case_ ( self ) -> Optional[Any]:
for model_name in LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
UpperCamelCase : Dict = LayoutLMvaModel.from_pretrained(SCREAMING_SNAKE_CASE_ )
self.assertIsNotNone(SCREAMING_SNAKE_CASE_ )
def UpperCamelCase ( ) -> Dict:
UpperCamelCase : Optional[int] = Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png' )
return image
@require_torch
class lowerCAmelCase_ ( unittest.TestCase ):
@cached_property
def snake_case_ ( self ) -> str:
return LayoutLMvaImageProcessor(apply_ocr=SCREAMING_SNAKE_CASE_ ) if is_vision_available() else None
@slow
def snake_case_ ( self ) -> Optional[int]:
UpperCamelCase : Any = LayoutLMvaModel.from_pretrained('microsoft/layoutlmv3-base' ).to(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Any = self.default_image_processor
UpperCamelCase : Optional[int] = prepare_img()
UpperCamelCase : Optional[Any] = image_processor(images=SCREAMING_SNAKE_CASE_, return_tensors='pt' ).pixel_values.to(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : str = torch.tensor([[1, 2]] )
UpperCamelCase : str = torch.tensor([[1, 2, 3, 4], [5, 6, 7, 8]] ).unsqueeze(0 )
# forward pass
UpperCamelCase : Any = model(
input_ids=input_ids.to(SCREAMING_SNAKE_CASE_ ), bbox=bbox.to(SCREAMING_SNAKE_CASE_ ), pixel_values=pixel_values.to(SCREAMING_SNAKE_CASE_ ), )
# verify the logits
UpperCamelCase : str = torch.Size((1, 199, 768) )
self.assertEqual(outputs.last_hidden_state.shape, SCREAMING_SNAKE_CASE_ )
UpperCamelCase : int = torch.tensor(
[[-0.05_29, 0.36_18, 0.16_32], [-0.15_87, -0.16_67, -0.04_00], [-0.15_57, -0.16_71, -0.05_05]] ).to(SCREAMING_SNAKE_CASE_ )
self.assertTrue(torch.allclose(outputs.last_hidden_state[0, :3, :3], SCREAMING_SNAKE_CASE_, atol=1e-4 ) )
| 103 |
import unittest
import numpy as np
from transformers import is_flax_available
from transformers.testing_utils import require_flax
from ..test_modeling_flax_common import ids_tensor
if is_flax_available():
import jax
import jax.numpy as jnp
from transformers.generation import (
FlaxForcedBOSTokenLogitsProcessor,
FlaxForcedEOSTokenLogitsProcessor,
FlaxLogitsProcessorList,
FlaxMinLengthLogitsProcessor,
FlaxTemperatureLogitsWarper,
FlaxTopKLogitsWarper,
FlaxTopPLogitsWarper,
)
@require_flax
class lowerCAmelCase_ ( unittest.TestCase ):
def snake_case_ ( self, SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_ ) -> Union[str, Any]:
UpperCamelCase : List[str] = jnp.ones((batch_size, length) ) / length
return scores
def snake_case_ ( self ) -> Optional[Any]:
UpperCamelCase : Optional[Any] = None
UpperCamelCase : Optional[int] = 20
UpperCamelCase : Optional[Any] = self._get_uniform_logits(batch_size=2, length=SCREAMING_SNAKE_CASE_ )
# tweak scores to not be uniform anymore
UpperCamelCase : Dict = scores.at[1, 5].set((1 / length) + 0.1 ) # peak, 1st batch
UpperCamelCase : Any = scores.at[1, 10].set((1 / length) - 0.4 ) # valley, 1st batch
# compute softmax
UpperCamelCase : List[str] = jax.nn.softmax(SCREAMING_SNAKE_CASE_, axis=-1 )
UpperCamelCase : List[Any] = FlaxTemperatureLogitsWarper(temperature=0.5 )
UpperCamelCase : int = FlaxTemperatureLogitsWarper(temperature=1.3 )
UpperCamelCase : Tuple = jax.nn.softmax(temp_dist_warper_sharper(SCREAMING_SNAKE_CASE_, scores.copy(), cur_len=SCREAMING_SNAKE_CASE_ ), axis=-1 )
UpperCamelCase : Any = jax.nn.softmax(temp_dist_warper_smoother(SCREAMING_SNAKE_CASE_, scores.copy(), cur_len=SCREAMING_SNAKE_CASE_ ), axis=-1 )
# uniform distribution stays uniform
self.assertTrue(jnp.allclose(probs[0, :], warped_prob_sharp[0, :], atol=1e-3 ) )
self.assertTrue(jnp.allclose(probs[0, :], warped_prob_smooth[0, :], atol=1e-3 ) )
# sharp peaks get higher, valleys get lower
self.assertLess(probs[1, :].max(), warped_prob_sharp[1, :].max() )
self.assertGreater(probs[1, :].min(), warped_prob_sharp[1, :].min() )
# smooth peaks get lower, valleys get higher
self.assertGreater(probs[1, :].max(), warped_prob_smooth[1, :].max() )
self.assertLess(probs[1, :].min(), warped_prob_smooth[1, :].min() )
def snake_case_ ( self ) -> Optional[Any]:
UpperCamelCase : Dict = None
UpperCamelCase : Any = 10
UpperCamelCase : Any = 2
# create ramp distribution
UpperCamelCase : List[Any] = np.broadcast_to(np.arange(SCREAMING_SNAKE_CASE_ )[None, :], (batch_size, vocab_size) ).copy()
UpperCamelCase : Tuple = ramp_logits[1:, : vocab_size // 2] + vocab_size
UpperCamelCase : Dict = FlaxTopKLogitsWarper(3 )
UpperCamelCase : Tuple = top_k_warp(SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_, cur_len=SCREAMING_SNAKE_CASE_ )
# check that correct tokens are filtered
self.assertListEqual(jnp.isinf(scores[0] ).tolist(), 7 * [True] + 3 * [False] )
self.assertListEqual(jnp.isinf(scores[1] ).tolist(), 2 * [True] + 3 * [False] + 5 * [True] )
# check special case
UpperCamelCase : Optional[int] = 5
UpperCamelCase : Optional[int] = FlaxTopKLogitsWarper(top_k=1, filter_value=0.0, min_tokens_to_keep=3 )
UpperCamelCase : Union[str, Any] = np.broadcast_to(np.arange(SCREAMING_SNAKE_CASE_ )[None, :], (batch_size, length) ).copy()
UpperCamelCase : List[str] = top_k_warp_safety_check(SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_, cur_len=SCREAMING_SNAKE_CASE_ )
# min_tokens overwrites k: 3 tokens are kept => 2 tokens are nullified
self.assertListEqual((scores == 0.0).sum(axis=-1 ).tolist(), [2, 2] )
def snake_case_ ( self ) -> Union[str, Any]:
UpperCamelCase : int = None
UpperCamelCase : List[str] = 10
UpperCamelCase : Optional[Any] = 2
# create distribution and take log (inverse to Softmax as taken in TopPLogitsWarper)
UpperCamelCase : Optional[int] = np.log(np.array([[0.3, 0.1, 0.1, 0.5], [0.15, 0.3, 0.3, 0.25]] ) )
UpperCamelCase : Optional[Any] = FlaxTopPLogitsWarper(0.8 )
UpperCamelCase : int = np.exp(top_p_warp(SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_, cur_len=SCREAMING_SNAKE_CASE_ ) )
# dist should be filtered to keep min num values so that sum is >= top_p
# exp (-inf) => 0
UpperCamelCase : Any = np.array([[0.3, 0.0, 0.0, 0.5], [0.0, 0.3, 0.3, 0.25]] )
self.assertTrue(np.allclose(SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_, atol=1e-3 ) )
# check edge cases with negative and extreme logits
UpperCamelCase : Optional[Any] = np.broadcast_to(np.arange(SCREAMING_SNAKE_CASE_ )[None, :], (batch_size, vocab_size) ).copy() - (
vocab_size // 2
)
# make ramp_logits more extreme
UpperCamelCase : Tuple = ramp_logits[1] * 1_00.0
# make sure at least 2 tokens are kept
UpperCamelCase : int = FlaxTopPLogitsWarper(0.9, min_tokens_to_keep=2, filter_value=0.0 )
UpperCamelCase : List[str] = top_p_warp(SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_, cur_len=SCREAMING_SNAKE_CASE_ )
# first batch should keep three tokens, second batch would keep only 1, but due to `min_tokens_to_keep=2` keeps 2.
self.assertListEqual((filtered_dist != 0.0).sum(axis=-1 ).tolist(), [3, 2] )
def snake_case_ ( self ) -> List[Any]:
UpperCamelCase : Union[str, Any] = 20
UpperCamelCase : Union[str, Any] = 4
UpperCamelCase : Optional[int] = 0
UpperCamelCase : Dict = FlaxMinLengthLogitsProcessor(min_length=10, eos_token_id=SCREAMING_SNAKE_CASE_ )
# check that min length is applied at length 5
UpperCamelCase : List[str] = ids_tensor((batch_size, 20), vocab_size=20 )
UpperCamelCase : Any = 5
UpperCamelCase : Tuple = self._get_uniform_logits(SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Tuple = min_dist_processor(SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_, cur_len=SCREAMING_SNAKE_CASE_ )
self.assertListEqual(scores_before_min_length[:, eos_token_id].tolist(), 4 * [-float('inf' )] )
# check that min length is not applied anymore at length 15
UpperCamelCase : Any = self._get_uniform_logits(SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Union[str, Any] = 15
UpperCamelCase : str = min_dist_processor(SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_, cur_len=SCREAMING_SNAKE_CASE_ )
self.assertFalse(jnp.isinf(SCREAMING_SNAKE_CASE_ ).any() )
def snake_case_ ( self ) -> Dict:
UpperCamelCase : str = 20
UpperCamelCase : List[Any] = 4
UpperCamelCase : List[str] = 0
UpperCamelCase : int = FlaxForcedBOSTokenLogitsProcessor(bos_token_id=SCREAMING_SNAKE_CASE_ )
# check that all scores are -inf except the bos_token_id score
UpperCamelCase : Any = ids_tensor((batch_size, 1), vocab_size=20 )
UpperCamelCase : List[Any] = 1
UpperCamelCase : Tuple = self._get_uniform_logits(SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Optional[Any] = logits_processor(SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_, cur_len=SCREAMING_SNAKE_CASE_ )
self.assertTrue(jnp.isneginf(scores[:, bos_token_id + 1 :] ).all() )
self.assertListEqual(scores[:, bos_token_id].tolist(), 4 * [0] ) # score for bos_token_id shold be zero
# check that bos_token_id is not forced if current length is greater than 1
UpperCamelCase : Dict = 3
UpperCamelCase : str = self._get_uniform_logits(SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_ )
UpperCamelCase : int = logits_processor(SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_, cur_len=SCREAMING_SNAKE_CASE_ )
self.assertFalse(jnp.isinf(SCREAMING_SNAKE_CASE_ ).any() )
def snake_case_ ( self ) -> List[str]:
UpperCamelCase : Union[str, Any] = 20
UpperCamelCase : Optional[Any] = 4
UpperCamelCase : List[Any] = 0
UpperCamelCase : int = 5
UpperCamelCase : Tuple = FlaxForcedEOSTokenLogitsProcessor(max_length=SCREAMING_SNAKE_CASE_, eos_token_id=SCREAMING_SNAKE_CASE_ )
# check that all scores are -inf except the eos_token_id when max_length is reached
UpperCamelCase : str = ids_tensor((batch_size, 4), vocab_size=20 )
UpperCamelCase : Tuple = 4
UpperCamelCase : Union[str, Any] = self._get_uniform_logits(SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Any = logits_processor(SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_, cur_len=SCREAMING_SNAKE_CASE_ )
self.assertTrue(jnp.isneginf(scores[:, eos_token_id + 1 :] ).all() )
self.assertListEqual(scores[:, eos_token_id].tolist(), 4 * [0] ) # score for eos_token_id should be zero
# check that eos_token_id is not forced if max_length is not reached
UpperCamelCase : str = 3
UpperCamelCase : List[Any] = self._get_uniform_logits(SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Union[str, Any] = logits_processor(SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_, cur_len=SCREAMING_SNAKE_CASE_ )
self.assertFalse(jnp.isinf(SCREAMING_SNAKE_CASE_ ).any() )
def snake_case_ ( self ) -> int:
UpperCamelCase : int = 4
UpperCamelCase : Tuple = 10
UpperCamelCase : str = 15
UpperCamelCase : List[str] = 2
UpperCamelCase : Any = 1
UpperCamelCase : List[str] = 15
# dummy input_ids and scores
UpperCamelCase : Dict = ids_tensor((batch_size, sequence_length), SCREAMING_SNAKE_CASE_ )
UpperCamelCase : List[Any] = input_ids.copy()
UpperCamelCase : Any = self._get_uniform_logits(SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Any = scores.copy()
# instantiate all dist processors
UpperCamelCase : List[str] = FlaxTemperatureLogitsWarper(temperature=0.5 )
UpperCamelCase : Optional[Any] = FlaxTopKLogitsWarper(3 )
UpperCamelCase : Optional[int] = FlaxTopPLogitsWarper(0.8 )
# instantiate all logits processors
UpperCamelCase : Optional[Any] = FlaxMinLengthLogitsProcessor(min_length=10, eos_token_id=SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Dict = FlaxForcedBOSTokenLogitsProcessor(bos_token_id=SCREAMING_SNAKE_CASE_ )
UpperCamelCase : List[Any] = FlaxForcedEOSTokenLogitsProcessor(max_length=SCREAMING_SNAKE_CASE_, eos_token_id=SCREAMING_SNAKE_CASE_ )
UpperCamelCase : List[str] = 10
# no processor list
UpperCamelCase : Any = temp_dist_warp(SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_, cur_len=SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Dict = top_k_warp(SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_, cur_len=SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Dict = top_p_warp(SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_, cur_len=SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Dict = min_dist_proc(SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_, cur_len=SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Union[str, Any] = bos_dist_proc(SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_, cur_len=SCREAMING_SNAKE_CASE_ )
UpperCamelCase : List[str] = eos_dist_proc(SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_, cur_len=SCREAMING_SNAKE_CASE_ )
# with processor list
UpperCamelCase : List[str] = FlaxLogitsProcessorList(
[temp_dist_warp, top_k_warp, top_p_warp, min_dist_proc, bos_dist_proc, eos_dist_proc] )
UpperCamelCase : Optional[Any] = processor(SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_, cur_len=SCREAMING_SNAKE_CASE_ )
# scores should be equal
self.assertTrue(jnp.allclose(SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_, atol=1e-3 ) )
# input_ids should never be changed
self.assertListEqual(input_ids.tolist(), input_ids_comp.tolist() )
def snake_case_ ( self ) -> int:
UpperCamelCase : Optional[Any] = 4
UpperCamelCase : Tuple = 10
UpperCamelCase : Union[str, Any] = 15
UpperCamelCase : Union[str, Any] = 2
UpperCamelCase : Optional[Any] = 1
UpperCamelCase : int = 15
# dummy input_ids and scores
UpperCamelCase : Dict = ids_tensor((batch_size, sequence_length), SCREAMING_SNAKE_CASE_ )
UpperCamelCase : str = input_ids.copy()
UpperCamelCase : Optional[int] = self._get_uniform_logits(SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Union[str, Any] = scores.copy()
# instantiate all dist processors
UpperCamelCase : Dict = FlaxTemperatureLogitsWarper(temperature=0.5 )
UpperCamelCase : Optional[Any] = FlaxTopKLogitsWarper(3 )
UpperCamelCase : Union[str, Any] = FlaxTopPLogitsWarper(0.8 )
# instantiate all logits processors
UpperCamelCase : str = FlaxMinLengthLogitsProcessor(min_length=10, eos_token_id=SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Any = FlaxForcedBOSTokenLogitsProcessor(bos_token_id=SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Optional[int] = FlaxForcedEOSTokenLogitsProcessor(max_length=SCREAMING_SNAKE_CASE_, eos_token_id=SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Any = 10
# no processor list
def run_no_processor_list(SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_ ):
UpperCamelCase : Optional[Any] = temp_dist_warp(SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_, cur_len=SCREAMING_SNAKE_CASE_ )
UpperCamelCase : str = top_k_warp(SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_, cur_len=SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Any = top_p_warp(SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_, cur_len=SCREAMING_SNAKE_CASE_ )
UpperCamelCase : List[str] = min_dist_proc(SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_, cur_len=SCREAMING_SNAKE_CASE_ )
UpperCamelCase : List[str] = bos_dist_proc(SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_, cur_len=SCREAMING_SNAKE_CASE_ )
UpperCamelCase : List[str] = eos_dist_proc(SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_, cur_len=SCREAMING_SNAKE_CASE_ )
return scores
# with processor list
def run_processor_list(SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_ ):
UpperCamelCase : Tuple = FlaxLogitsProcessorList(
[temp_dist_warp, top_k_warp, top_p_warp, min_dist_proc, bos_dist_proc, eos_dist_proc] )
UpperCamelCase : Union[str, Any] = processor(SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_, cur_len=SCREAMING_SNAKE_CASE_ )
return scores
UpperCamelCase : Dict = jax.jit(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : List[str] = jax.jit(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : int = jitted_run_no_processor_list(SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_ )
UpperCamelCase : int = jitted_run_processor_list(SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_ )
# scores should be equal
self.assertTrue(jnp.allclose(SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_, atol=1e-3 ) )
# input_ids should never be changed
self.assertListEqual(input_ids.tolist(), input_ids_comp.tolist() )
| 103 | 1 |
from __future__ import annotations
from random import choice
def a( A : str ) -> int:
"""simple docstring"""
return choice(_A )
def a( A : list[int] , A : int ) -> int:
"""simple docstring"""
a = random_pivot(_A )
# partition based on pivot
# linear time
a = [e for e in lst if e < pivot]
a = [e for e in lst if e > pivot]
# if we get lucky, pivot might be the element we want.
# we can easily see this:
# small (elements smaller than k)
# + pivot (kth element)
# + big (elements larger than k)
if len(_A ) == k - 1:
return pivot
# pivot is in elements bigger than k
elif len(_A ) < k - 1:
return kth_number(_A , k - len(_A ) - 1 )
# pivot is in elements smaller than k
else:
return kth_number(_A , _A )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 227 |
import warnings
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class _SCREAMING_SNAKE_CASE ( lowerCAmelCase__):
_UpperCamelCase:Optional[int] = ["image_processor", "tokenizer"]
_UpperCamelCase:Tuple = "ChineseCLIPImageProcessor"
_UpperCamelCase:List[str] = ("BertTokenizer", "BertTokenizerFast")
def __init__( self , _SCREAMING_SNAKE_CASE=None , _SCREAMING_SNAKE_CASE=None , **_SCREAMING_SNAKE_CASE )-> Tuple:
lowerCamelCase_ =None
if "feature_extractor" in kwargs:
warnings.warn(
"""The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"""
""" instead.""" , _SCREAMING_SNAKE_CASE , )
lowerCamelCase_ =kwargs.pop("""feature_extractor""" )
lowerCamelCase_ =image_processor if image_processor is not None else feature_extractor
if image_processor is None:
raise ValueError("""You need to specify an `image_processor`.""" )
if tokenizer is None:
raise ValueError("""You need to specify a `tokenizer`.""" )
super().__init__(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
lowerCamelCase_ =self.image_processor
def __call__( self , _SCREAMING_SNAKE_CASE=None , _SCREAMING_SNAKE_CASE=None , _SCREAMING_SNAKE_CASE=None , **_SCREAMING_SNAKE_CASE )-> Optional[Any]:
if text is None and images is None:
raise ValueError("""You have to specify either text or images. Both cannot be none.""" )
if text is not None:
lowerCamelCase_ =self.tokenizer(_SCREAMING_SNAKE_CASE , return_tensors=_SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE )
if images is not None:
lowerCamelCase_ =self.image_processor(_SCREAMING_SNAKE_CASE , return_tensors=_SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE )
if text is not None and images is not None:
lowerCamelCase_ =image_features.pixel_values
return encoding
elif text is not None:
return encoding
else:
return BatchEncoding(data=dict(**_SCREAMING_SNAKE_CASE ) , tensor_type=_SCREAMING_SNAKE_CASE )
def _snake_case ( self , *_SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE )-> List[str]:
return self.tokenizer.batch_decode(*_SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE )
def _snake_case ( self , *_SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE )-> Any:
return self.tokenizer.decode(*_SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE )
@property
def _snake_case ( self )-> Union[str, Any]:
lowerCamelCase_ =self.tokenizer.model_input_names
lowerCamelCase_ =self.image_processor.model_input_names
return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names ) )
@property
def _snake_case ( self )-> int:
warnings.warn(
"""`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.""" , _SCREAMING_SNAKE_CASE , )
return self.image_processor_class
| 154 | 0 |
"""simple docstring"""
import argparse
import importlib
from pathlib import Path
# Test all the extensions added in the setup
_A = [
"""kernels/rwkv/wkv_cuda.cu""",
"""kernels/rwkv/wkv_op.cpp""",
"""kernels/deformable_detr/ms_deform_attn.h""",
"""kernels/deformable_detr/cuda/ms_deform_im2col_cuda.cuh""",
"""models/graphormer/algos_graphormer.pyx""",
]
def a__ ( lowerCAmelCase ) -> Dict:
# Test all the extensions added in the setup
for file in FILES_TO_FIND:
if not (transformers_path / file).exists():
return False
return True
if __name__ == "__main__":
_A = argparse.ArgumentParser()
parser.add_argument("""--check_lib""", action="""store_true""", help="""Whether to check the build or the actual package.""")
_A = parser.parse_args()
if args.check_lib:
_A = importlib.import_module("""transformers""")
_A = Path(transformers_module.__file__).parent
else:
_A = Path.cwd() / """build/lib/transformers"""
if not test_custom_files_are_present(transformers_path):
raise ValueError("""The built release does not contain the custom files. Fix this before going further!""")
| 166 |
"""simple docstring"""
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import torch
import torch.nn as nn
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .embeddings import GaussianFourierProjection, TimestepEmbedding, Timesteps
from .modeling_utils import ModelMixin
from .unet_ad_blocks import get_down_block, get_mid_block, get_out_block, get_up_block
@dataclass
class lowerCamelCase ( lowerCAmelCase__ ):
'''simple docstring'''
SCREAMING_SNAKE_CASE = 42
class lowerCamelCase ( lowerCAmelCase__ , lowerCAmelCase__ ):
'''simple docstring'''
@register_to_config
def __init__(self , _lowerCamelCase = 65536 , _lowerCamelCase = None , _lowerCamelCase = 2 , _lowerCamelCase = 2 , _lowerCamelCase = 0 , _lowerCamelCase = "fourier" , _lowerCamelCase = True , _lowerCamelCase = False , _lowerCamelCase = 0.0 , _lowerCamelCase = ("DownBlock1DNoSkip", "DownBlock1D", "AttnDownBlock1D") , _lowerCamelCase = ("AttnUpBlock1D", "UpBlock1D", "UpBlock1DNoSkip") , _lowerCamelCase = "UNetMidBlock1D" , _lowerCamelCase = None , _lowerCamelCase = (32, 32, 64) , _lowerCamelCase = None , _lowerCamelCase = 8 , _lowerCamelCase = 1 , _lowerCamelCase = False , ):
"""simple docstring"""
super().__init__()
UpperCAmelCase__ : str = sample_size
# time
if time_embedding_type == "fourier":
UpperCAmelCase__ : Any = GaussianFourierProjection(
embedding_size=8 , set_W_to_weight=_lowerCamelCase , log=_lowerCamelCase , flip_sin_to_cos=_lowerCamelCase )
UpperCAmelCase__ : Tuple = 2 * block_out_channels[0]
elif time_embedding_type == "positional":
UpperCAmelCase__ : List[str] = Timesteps(
block_out_channels[0] , flip_sin_to_cos=_lowerCamelCase , downscale_freq_shift=_lowerCamelCase )
UpperCAmelCase__ : Optional[Any] = block_out_channels[0]
if use_timestep_embedding:
UpperCAmelCase__ : Optional[Any] = block_out_channels[0] * 4
UpperCAmelCase__ : Any = TimestepEmbedding(
in_channels=_lowerCamelCase , time_embed_dim=_lowerCamelCase , act_fn=_lowerCamelCase , out_dim=block_out_channels[0] , )
UpperCAmelCase__ : Optional[Any] = nn.ModuleList([] )
UpperCAmelCase__ : int = None
UpperCAmelCase__ : str = nn.ModuleList([] )
UpperCAmelCase__ : Optional[int] = None
# down
UpperCAmelCase__ : List[str] = in_channels
for i, down_block_type in enumerate(_lowerCamelCase ):
UpperCAmelCase__ : Optional[Any] = output_channel
UpperCAmelCase__ : List[str] = block_out_channels[i]
if i == 0:
input_channel += extra_in_channels
UpperCAmelCase__ : Any = i == len(_lowerCamelCase ) - 1
UpperCAmelCase__ : Dict = get_down_block(
_lowerCamelCase , num_layers=_lowerCamelCase , in_channels=_lowerCamelCase , out_channels=_lowerCamelCase , temb_channels=block_out_channels[0] , add_downsample=not is_final_block or downsample_each_block , )
self.down_blocks.append(_lowerCamelCase )
# mid
UpperCAmelCase__ : Optional[Any] = get_mid_block(
_lowerCamelCase , in_channels=block_out_channels[-1] , mid_channels=block_out_channels[-1] , out_channels=block_out_channels[-1] , embed_dim=block_out_channels[0] , num_layers=_lowerCamelCase , add_downsample=_lowerCamelCase , )
# up
UpperCAmelCase__ : Tuple = list(reversed(_lowerCamelCase ) )
UpperCAmelCase__ : List[str] = reversed_block_out_channels[0]
if out_block_type is None:
UpperCAmelCase__ : int = out_channels
else:
UpperCAmelCase__ : List[Any] = block_out_channels[0]
for i, up_block_type in enumerate(_lowerCamelCase ):
UpperCAmelCase__ : Any = output_channel
UpperCAmelCase__ : Dict = (
reversed_block_out_channels[i + 1] if i < len(_lowerCamelCase ) - 1 else final_upsample_channels
)
UpperCAmelCase__ : Union[str, Any] = i == len(_lowerCamelCase ) - 1
UpperCAmelCase__ : Optional[int] = get_up_block(
_lowerCamelCase , num_layers=_lowerCamelCase , in_channels=_lowerCamelCase , out_channels=_lowerCamelCase , temb_channels=block_out_channels[0] , add_upsample=not is_final_block , )
self.up_blocks.append(_lowerCamelCase )
UpperCAmelCase__ : Dict = output_channel
# out
UpperCAmelCase__ : Optional[int] = norm_num_groups if norm_num_groups is not None else min(block_out_channels[0] // 4 , 32 )
UpperCAmelCase__ : int = get_out_block(
out_block_type=_lowerCamelCase , num_groups_out=_lowerCamelCase , embed_dim=block_out_channels[0] , out_channels=_lowerCamelCase , act_fn=_lowerCamelCase , fc_dim=block_out_channels[-1] // 4 , )
def _a (self , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase = True , ):
"""simple docstring"""
UpperCAmelCase__ : Union[str, Any] = timestep
if not torch.is_tensor(_lowerCamelCase ):
UpperCAmelCase__ : Dict = torch.tensor([timesteps] , dtype=torch.long , device=sample.device )
elif torch.is_tensor(_lowerCamelCase ) and len(timesteps.shape ) == 0:
UpperCAmelCase__ : List[str] = timesteps[None].to(sample.device )
UpperCAmelCase__ : Optional[Any] = self.time_proj(_lowerCamelCase )
if self.config.use_timestep_embedding:
UpperCAmelCase__ : Dict = self.time_mlp(_lowerCamelCase )
else:
UpperCAmelCase__ : int = timestep_embed[..., None]
UpperCAmelCase__ : List[str] = timestep_embed.repeat([1, 1, sample.shape[2]] ).to(sample.dtype )
UpperCAmelCase__ : Dict = timestep_embed.broadcast_to((sample.shape[:1] + timestep_embed.shape[1:]) )
# 2. down
UpperCAmelCase__ : Optional[Any] = ()
for downsample_block in self.down_blocks:
UpperCAmelCase__ , UpperCAmelCase__ : Dict = downsample_block(hidden_states=_lowerCamelCase , temb=_lowerCamelCase )
down_block_res_samples += res_samples
# 3. mid
if self.mid_block:
UpperCAmelCase__ : Optional[Any] = self.mid_block(_lowerCamelCase , _lowerCamelCase )
# 4. up
for i, upsample_block in enumerate(self.up_blocks ):
UpperCAmelCase__ : int = down_block_res_samples[-1:]
UpperCAmelCase__ : Dict = down_block_res_samples[:-1]
UpperCAmelCase__ : str = upsample_block(_lowerCamelCase , res_hidden_states_tuple=_lowerCamelCase , temb=_lowerCamelCase )
# 5. post-process
if self.out_block:
UpperCAmelCase__ : str = self.out_block(_lowerCamelCase , _lowerCamelCase )
if not return_dict:
return (sample,)
return UNetaDOutput(sample=_lowerCamelCase )
| 166 | 1 |
from collections import OrderedDict
from typing import Any, List, Mapping, Optional
from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from ...utils import logging
_a = logging.get_logger(__name__)
_a = {
'''EleutherAI/gpt-j-6B''': '''https://huggingface.co/EleutherAI/gpt-j-6B/resolve/main/config.json''',
# See all GPT-J models at https://huggingface.co/models?filter=gpt_j
}
class __lowerCamelCase ( __a):
"""simple docstring"""
UpperCamelCase__ = """gptj"""
UpperCamelCase__ = {
"""max_position_embeddings""": """n_positions""",
"""hidden_size""": """n_embd""",
"""num_attention_heads""": """n_head""",
"""num_hidden_layers""": """n_layer""",
}
def __init__( self , UpperCAmelCase=5_0400 , UpperCAmelCase=2048 , UpperCAmelCase=4096 , UpperCAmelCase=28 , UpperCAmelCase=16 , UpperCAmelCase=64 , UpperCAmelCase=None , UpperCAmelCase="gelu_new" , UpperCAmelCase=0.0 , UpperCAmelCase=0.0 , UpperCAmelCase=0.0 , UpperCAmelCase=1e-5 , UpperCAmelCase=0.02 , UpperCAmelCase=True , UpperCAmelCase=5_0256 , UpperCAmelCase=5_0256 , UpperCAmelCase=False , **UpperCAmelCase , ):
"""simple docstring"""
_UpperCAmelCase = vocab_size
_UpperCAmelCase = n_positions
_UpperCAmelCase = n_embd
_UpperCAmelCase = n_layer
_UpperCAmelCase = n_head
_UpperCAmelCase = n_inner
_UpperCAmelCase = rotary_dim
_UpperCAmelCase = activation_function
_UpperCAmelCase = resid_pdrop
_UpperCAmelCase = embd_pdrop
_UpperCAmelCase = attn_pdrop
_UpperCAmelCase = layer_norm_epsilon
_UpperCAmelCase = initializer_range
_UpperCAmelCase = use_cache
_UpperCAmelCase = bos_token_id
_UpperCAmelCase = eos_token_id
super().__init__(
bos_token_id=UpperCAmelCase , eos_token_id=UpperCAmelCase , tie_word_embeddings=UpperCAmelCase , **UpperCAmelCase )
class __lowerCamelCase ( __a):
"""simple docstring"""
def __init__( self , UpperCAmelCase , UpperCAmelCase = "default" , UpperCAmelCase = None , UpperCAmelCase = False , ):
"""simple docstring"""
super().__init__(UpperCAmelCase , task=UpperCAmelCase , patching_specs=UpperCAmelCase , use_past=UpperCAmelCase )
if not getattr(self._config , 'pad_token_id' , UpperCAmelCase ):
# TODO: how to do that better?
_UpperCAmelCase = 0
@property
def UpperCamelCase ( self ):
"""simple docstring"""
_UpperCAmelCase = OrderedDict({'input_ids': {0: 'batch', 1: 'sequence'}} )
if self.use_past:
self.fill_with_past_key_values_(UpperCAmelCase , direction='inputs' )
_UpperCAmelCase = {0: 'batch', 1: 'past_sequence + sequence'}
else:
_UpperCAmelCase = {0: 'batch', 1: 'sequence'}
return common_inputs
@property
def UpperCamelCase ( self ):
"""simple docstring"""
return self._config.n_layer
@property
def UpperCamelCase ( self ):
"""simple docstring"""
return self._config.n_head
def UpperCamelCase ( self , UpperCAmelCase , UpperCAmelCase = -1 , UpperCAmelCase = -1 , UpperCAmelCase = False , UpperCAmelCase = None , ):
"""simple docstring"""
_UpperCAmelCase = super(UpperCAmelCase , self ).generate_dummy_inputs(
UpperCAmelCase , batch_size=UpperCAmelCase , seq_length=UpperCAmelCase , is_pair=UpperCAmelCase , framework=UpperCAmelCase )
# We need to order the input in the way they appears in the forward()
_UpperCAmelCase = OrderedDict({'input_ids': common_inputs['input_ids']} )
# Need to add the past_keys
if self.use_past:
if not is_torch_available():
raise ValueError('Cannot generate dummy past_keys inputs without PyTorch installed.' )
else:
import torch
_UpperCAmelCase , _UpperCAmelCase = common_inputs['input_ids'].shape
# Not using the same length for past_key_values
_UpperCAmelCase = seqlen + 2
_UpperCAmelCase = (
batch,
self.num_attention_heads,
past_key_values_length,
self._config.hidden_size // self.num_attention_heads,
)
_UpperCAmelCase = [
(torch.zeros(UpperCAmelCase ), torch.zeros(UpperCAmelCase )) for _ in range(self.num_layers )
]
_UpperCAmelCase = common_inputs['attention_mask']
if self.use_past:
_UpperCAmelCase = ordered_inputs['attention_mask'].dtype
_UpperCAmelCase = torch.cat(
[ordered_inputs['attention_mask'], torch.ones(UpperCAmelCase , UpperCAmelCase , dtype=UpperCAmelCase )] , dim=1 )
return ordered_inputs
@property
def UpperCamelCase ( self ):
"""simple docstring"""
return 13
| 39 |
'''simple docstring'''
import os
from distutils.util import strtobool
def snake_case_ (_a : Union[str, Any] , _a : List[Any] ):
for e in env_keys:
UpperCAmelCase = int(os.environ.get(_a , -1 ) )
if val >= 0:
return val
return default
def snake_case_ (_a : Dict , _a : Any=False ):
UpperCAmelCase = os.environ.get(_a , str(_a ) )
return strtobool(_a ) == 1 # As its name indicates `strtobool` actually returns an int...
def snake_case_ (_a : str , _a : Optional[Any]="no" ):
UpperCAmelCase = os.environ.get(_a , str(_a ) )
return value
| 34 | 0 |
def lowerCamelCase__ ( a ) -> "list[int]":
if upper_limit < 0:
raise ValueError('''Limit for the Catalan sequence must be ≥ 0''' )
_A: Optional[Any] = [0] * (upper_limit + 1)
# Base case: C(0) = C(1) = 1
_A: str = 1
if upper_limit > 0:
_A: Optional[Any] = 1
# Recurrence relation: C(i) = sum(C(j).C(i-j-1)), from j = 0 to i
for i in range(2 , upper_limit + 1 ):
for j in range(lowercase_ ):
catalan_list[i] += catalan_list[j] * catalan_list[i - j - 1]
return catalan_list
if __name__ == "__main__":
print('\n********* Catalan Numbers Using Dynamic Programming ************\n')
print('\n*** Enter -1 at any time to quit ***')
print('\nEnter the upper limit (≥ 0) for the Catalan number sequence: ', end='')
try:
while True:
UpperCAmelCase__ : int = int(input().strip())
if N < 0:
print('\n********* Goodbye!! ************')
break
else:
print(F"""The Catalan numbers from 0 through {N} are:""")
print(catalan_numbers(N))
print('Try another upper limit for the sequence: ', end='')
except (NameError, ValueError):
print('\n********* Invalid input, goodbye! ************\n')
import doctest
doctest.testmod()
| 357 |
import math
import random
from typing import Any
from .hill_climbing import SearchProblem
def lowerCamelCase__ ( a , a = True , a = math.inf , a = -math.inf , a = math.inf , a = -math.inf , a = False , a = 1_00 , a = 0.01 , a = 1 , ) -> Any:
_A: Optional[Any] = False
_A: Dict = search_prob
_A: str = start_temperate
_A: Optional[int] = []
_A: int = 0
_A: Dict = None
while not search_end:
_A: Dict = current_state.score()
if best_state is None or current_score > best_state.score():
_A: List[Any] = current_state
scores.append(a )
iterations += 1
_A: List[str] = None
_A: str = current_state.get_neighbors()
while (
next_state is None and neighbors
): # till we do not find a neighbor that we can move to
_A: Any = random.randint(0 , len(a ) - 1 ) # picking a random neighbor
_A: Union[str, Any] = neighbors.pop(a )
_A: List[str] = picked_neighbor.score() - current_score
if (
picked_neighbor.x > max_x
or picked_neighbor.x < min_x
or picked_neighbor.y > max_y
or picked_neighbor.y < min_y
):
continue # neighbor outside our bounds
if not find_max:
_A: Optional[Any] = change * -1 # in case we are finding minimum
if change > 0: # improves the solution
_A: str = picked_neighbor
else:
_A: Tuple = (math.e) ** (
change / current_temp
) # probability generation function
if random.random() < probability: # random number within probability
_A: Optional[int] = picked_neighbor
_A: Dict = current_temp - (current_temp * rate_of_decrease)
if current_temp < threshold_temp or next_state is None:
# temperature below threshold, or could not find a suitable neighbor
_A: Any = True
else:
_A: List[Any] = next_state
if visualization:
from matplotlib import pyplot as plt
plt.plot(range(a ) , a )
plt.xlabel('''Iterations''' )
plt.ylabel('''Function values''' )
plt.show()
return best_state
if __name__ == "__main__":
def lowerCamelCase__ ( a , a ) -> Optional[Any]:
return (x**2) + (y**2)
# starting the problem with initial coordinates (12, 47)
UpperCAmelCase__ : Optional[int] = SearchProblem(x=12, y=47, step_size=1, function_to_optimize=test_fa)
UpperCAmelCase__ : Optional[Any] = simulated_annealing(
prob, find_max=False, max_x=100, min_x=5, max_y=50, min_y=-5, visualization=True
)
print(
'The minimum score for f(x, y) = x^2 + y^2 with the domain 100 > x > 5 '
F"""and 50 > y > - 5 found via hill climbing: {local_min.score()}"""
)
# starting the problem with initial coordinates (12, 47)
UpperCAmelCase__ : Optional[Any] = SearchProblem(x=12, y=47, step_size=1, function_to_optimize=test_fa)
UpperCAmelCase__ : List[str] = simulated_annealing(
prob, find_max=True, max_x=100, min_x=5, max_y=50, min_y=-5, visualization=True
)
print(
'The maximum score for f(x, y) = x^2 + y^2 with the domain 100 > x > 5 '
F"""and 50 > y > - 5 found via hill climbing: {local_min.score()}"""
)
def lowerCamelCase__ ( a , a ) -> Optional[Any]:
return (3 * x**2) - (6 * y)
UpperCAmelCase__ : Union[str, Any] = SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_fa)
UpperCAmelCase__ : List[str] = simulated_annealing(prob, find_max=False, visualization=True)
print(
'The minimum score for f(x, y) = 3*x^2 - 6*y found via hill climbing: '
F"""{local_min.score()}"""
)
UpperCAmelCase__ : Optional[Any] = SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_fa)
UpperCAmelCase__ : List[Any] = simulated_annealing(prob, find_max=True, visualization=True)
print(
'The maximum score for f(x, y) = 3*x^2 - 6*y found via hill climbing: '
F"""{local_min.score()}"""
)
| 301 | 0 |
'''simple docstring'''
from __future__ import annotations
import unittest
from transformers import RoFormerConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import tensorflow as tf
from transformers import (
TFRoFormerForCausalLM,
TFRoFormerForMaskedLM,
TFRoFormerForMultipleChoice,
TFRoFormerForQuestionAnswering,
TFRoFormerForSequenceClassification,
TFRoFormerForTokenClassification,
TFRoFormerModel,
)
from transformers.models.roformer.modeling_tf_roformer import (
TFRoFormerSelfAttention,
TFRoFormerSinusoidalPositionalEmbedding,
)
class a_ :
def __init__( self , snake_case_ , snake_case_=1_3 , snake_case_=7 , snake_case_=True , snake_case_=True , snake_case_=True , snake_case_=True , snake_case_=9_9 , snake_case_=3_2 , snake_case_=2 , snake_case_=4 , snake_case_=3_7 , snake_case_="gelu" , snake_case_=0.1 , snake_case_=0.1 , snake_case_=5_1_2 , snake_case_=1_6 , snake_case_=2 , snake_case_=0.02 , snake_case_=3 , snake_case_=4 , snake_case_=None , ):
_lowerCAmelCase : Tuple = parent
_lowerCAmelCase : Dict = 1_3
_lowerCAmelCase : List[str] = 7
_lowerCAmelCase : Union[str, Any] = True
_lowerCAmelCase : Any = True
_lowerCAmelCase : Optional[Any] = True
_lowerCAmelCase : Optional[int] = True
_lowerCAmelCase : List[Any] = 9_9
_lowerCAmelCase : Any = 3_2
_lowerCAmelCase : Optional[int] = 2
_lowerCAmelCase : Optional[Any] = 4
_lowerCAmelCase : Any = 3_7
_lowerCAmelCase : Tuple = '''gelu'''
_lowerCAmelCase : Any = 0.1
_lowerCAmelCase : Union[str, Any] = 0.1
_lowerCAmelCase : List[Any] = 5_1_2
_lowerCAmelCase : str = 1_6
_lowerCAmelCase : Union[str, Any] = 2
_lowerCAmelCase : List[Any] = 0.02
_lowerCAmelCase : Optional[Any] = 3
_lowerCAmelCase : Union[str, Any] = 4
_lowerCAmelCase : Optional[int] = None
def __UpperCamelCase ( self ):
_lowerCAmelCase : Tuple = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
_lowerCAmelCase : List[Any] = None
if self.use_input_mask:
_lowerCAmelCase : Any = random_attention_mask([self.batch_size, self.seq_length] )
_lowerCAmelCase : List[Any] = None
if self.use_token_type_ids:
_lowerCAmelCase : List[str] = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
_lowerCAmelCase : Tuple = None
_lowerCAmelCase : Optional[int] = None
_lowerCAmelCase : int = None
if self.use_labels:
_lowerCAmelCase : Union[str, Any] = ids_tensor([self.batch_size] , self.type_sequence_label_size )
_lowerCAmelCase : Union[str, Any] = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
_lowerCAmelCase : Tuple = ids_tensor([self.batch_size] , self.num_choices )
_lowerCAmelCase : Tuple = RoFormerConfig(
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 , initializer_range=self.initializer_range , return_dict=lowercase_ , )
return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def __UpperCamelCase ( self , snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ ):
_lowerCAmelCase : Optional[int] = TFRoFormerModel(config=lowercase_ )
_lowerCAmelCase : Optional[Any] = {'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}
_lowerCAmelCase : str = [input_ids, input_mask]
_lowerCAmelCase : str = model(lowercase_ )
_lowerCAmelCase : Optional[int] = model(lowercase_ )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def __UpperCamelCase ( self , snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ ):
_lowerCAmelCase : Dict = True
_lowerCAmelCase : Tuple = TFRoFormerForCausalLM(config=lowercase_ )
_lowerCAmelCase : List[str] = {
'''input_ids''': input_ids,
'''attention_mask''': input_mask,
'''token_type_ids''': token_type_ids,
}
_lowerCAmelCase : Dict = model(lowercase_ )['''logits''']
self.parent.assertListEqual(
list(prediction_scores.numpy().shape ) , [self.batch_size, self.seq_length, self.vocab_size] )
def __UpperCamelCase ( self , snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ ):
_lowerCAmelCase : Optional[Any] = TFRoFormerForMaskedLM(config=lowercase_ )
_lowerCAmelCase : Dict = {
'''input_ids''': input_ids,
'''attention_mask''': input_mask,
'''token_type_ids''': token_type_ids,
}
_lowerCAmelCase : Union[str, Any] = model(lowercase_ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def __UpperCamelCase ( self , snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ ):
_lowerCAmelCase : List[str] = self.num_labels
_lowerCAmelCase : List[str] = TFRoFormerForSequenceClassification(config=lowercase_ )
_lowerCAmelCase : int = {
'''input_ids''': input_ids,
'''attention_mask''': input_mask,
'''token_type_ids''': token_type_ids,
}
_lowerCAmelCase : str = model(lowercase_ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def __UpperCamelCase ( self , snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ ):
_lowerCAmelCase : List[Any] = self.num_choices
_lowerCAmelCase : Tuple = TFRoFormerForMultipleChoice(config=lowercase_ )
_lowerCAmelCase : List[str] = tf.tile(tf.expand_dims(lowercase_ , 1 ) , (1, self.num_choices, 1) )
_lowerCAmelCase : Tuple = tf.tile(tf.expand_dims(lowercase_ , 1 ) , (1, self.num_choices, 1) )
_lowerCAmelCase : Tuple = tf.tile(tf.expand_dims(lowercase_ , 1 ) , (1, self.num_choices, 1) )
_lowerCAmelCase : Optional[Any] = {
'''input_ids''': multiple_choice_inputs_ids,
'''attention_mask''': multiple_choice_input_mask,
'''token_type_ids''': multiple_choice_token_type_ids,
}
_lowerCAmelCase : Union[str, Any] = model(lowercase_ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_choices) )
def __UpperCamelCase ( self , snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ ):
_lowerCAmelCase : Any = self.num_labels
_lowerCAmelCase : List[Any] = TFRoFormerForTokenClassification(config=lowercase_ )
_lowerCAmelCase : Optional[int] = {
'''input_ids''': input_ids,
'''attention_mask''': input_mask,
'''token_type_ids''': token_type_ids,
}
_lowerCAmelCase : Optional[int] = model(lowercase_ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def __UpperCamelCase ( self , snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ ):
_lowerCAmelCase : List[Any] = TFRoFormerForQuestionAnswering(config=lowercase_ )
_lowerCAmelCase : Optional[int] = {
'''input_ids''': input_ids,
'''attention_mask''': input_mask,
'''token_type_ids''': token_type_ids,
}
_lowerCAmelCase : str = model(lowercase_ )
self.parent.assertEqual(result.start_logits.shape , (self.batch_size, self.seq_length) )
self.parent.assertEqual(result.end_logits.shape , (self.batch_size, self.seq_length) )
def __UpperCamelCase ( self ):
_lowerCAmelCase : Optional[Any] = self.prepare_config_and_inputs()
(
_lowerCAmelCase
) : Optional[int] = config_and_inputs
_lowerCAmelCase : List[Any] = {'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': input_mask}
return config, inputs_dict
@require_tf
class a_ (a_ , a_ , unittest.TestCase ):
__lowerCAmelCase : Union[str, Any] = (
(
TFRoFormerModel,
TFRoFormerForCausalLM,
TFRoFormerForMaskedLM,
TFRoFormerForQuestionAnswering,
TFRoFormerForSequenceClassification,
TFRoFormerForTokenClassification,
TFRoFormerForMultipleChoice,
)
if is_tf_available()
else ()
)
__lowerCAmelCase : Any = (
{
"""feature-extraction""": TFRoFormerModel,
"""fill-mask""": TFRoFormerForMaskedLM,
"""question-answering""": TFRoFormerForQuestionAnswering,
"""text-classification""": TFRoFormerForSequenceClassification,
"""text-generation""": TFRoFormerForCausalLM,
"""token-classification""": TFRoFormerForTokenClassification,
"""zero-shot""": TFRoFormerForSequenceClassification,
}
if is_tf_available()
else {}
)
__lowerCAmelCase : Any = False
__lowerCAmelCase : Tuple = False
def __UpperCamelCase ( self , snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ ):
if pipeline_test_casse_name == "TextGenerationPipelineTests":
return True
return False
def __UpperCamelCase ( self ):
_lowerCAmelCase : Any = TFRoFormerModelTester(self )
_lowerCAmelCase : Any = ConfigTester(self , config_class=lowercase_ , hidden_size=3_7 )
def __UpperCamelCase ( self ):
self.config_tester.run_common_tests()
def __UpperCamelCase ( self ):
_lowerCAmelCase : Tuple = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*lowercase_ )
def __UpperCamelCase ( self ):
_lowerCAmelCase : str = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_lm(*lowercase_ )
def __UpperCamelCase ( self ):
_lowerCAmelCase : Optional[int] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_lm_head(*lowercase_ )
def __UpperCamelCase ( self ):
_lowerCAmelCase : Dict = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_multiple_choice(*lowercase_ )
def __UpperCamelCase ( self ):
_lowerCAmelCase : Union[str, Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_question_answering(*lowercase_ )
def __UpperCamelCase ( self ):
_lowerCAmelCase : List[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_sequence_classification(*lowercase_ )
def __UpperCamelCase ( self ):
_lowerCAmelCase : List[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_token_classification(*lowercase_ )
@slow
def __UpperCamelCase ( self ):
_lowerCAmelCase : Dict = TFRoFormerModel.from_pretrained("""junnyu/roformer_chinese_base""" )
self.assertIsNotNone(lowercase_ )
@require_tf
class a_ (unittest.TestCase ):
@slow
def __UpperCamelCase ( self ):
_lowerCAmelCase : Dict = TFRoFormerForMaskedLM.from_pretrained("""junnyu/roformer_chinese_base""" )
_lowerCAmelCase : Dict = tf.constant([[0, 1, 2, 3, 4, 5]] )
_lowerCAmelCase : Optional[int] = model(lowercase_ )[0]
# TODO Replace vocab size
_lowerCAmelCase : int = 5_0_0_0_0
_lowerCAmelCase : int = [1, 6, vocab_size]
self.assertEqual(output.shape , lowercase_ )
print(output[:, :3, :3] )
# TODO Replace values below with what was printed above.
_lowerCAmelCase : Optional[int] = tf.constant(
[
[
[-0.1205_3341, -1.026_4901, 0.2922_1946],
[-1.513_3783, 0.19_7433, 0.1519_0607],
[-5.013_5403, -3.90_0256, -0.8403_8764],
]
] )
tf.debugging.assert_near(output[:, :3, :3] , lowercase_ , atol=1E-4 )
@require_tf
class a_ (unittest.TestCase ):
__lowerCAmelCase : Tuple = 1E-4
def __UpperCamelCase ( self ):
_lowerCAmelCase : List[Any] = tf.constant([[4, 1_0]] )
_lowerCAmelCase : Optional[int] = TFRoFormerSinusoidalPositionalEmbedding(num_positions=6 , embedding_dim=6 )
_lowerCAmelCase : List[Any] = emba(input_ids.shape )
_lowerCAmelCase : int = tf.constant(
[[0.0000, 0.0000, 0.0000, 1.0000, 1.0000, 1.0000], [0.8415, 0.0464, 0.0022, 0.5403, 0.9989, 1.0000]] )
tf.debugging.assert_near(lowercase_ , lowercase_ , atol=self.tolerance )
def __UpperCamelCase ( self ):
_lowerCAmelCase : Tuple = tf.constant(
[
[0.0000, 0.0000, 0.0000, 0.0000, 0.0000],
[0.8415, 0.8219, 0.8020, 0.7819, 0.7617],
[0.9093, 0.9364, 0.9581, 0.9749, 0.9870],
] )
_lowerCAmelCase : Tuple = TFRoFormerSinusoidalPositionalEmbedding(num_positions=5_1_2 , embedding_dim=5_1_2 )
emba([2, 1_6, 5_1_2] )
_lowerCAmelCase : int = emba.weight[:3, :5]
tf.debugging.assert_near(lowercase_ , lowercase_ , atol=self.tolerance )
@require_tf
class a_ (unittest.TestCase ):
__lowerCAmelCase : Union[str, Any] = 1E-4
def __UpperCamelCase ( self ):
# 2,12,16,64
_lowerCAmelCase : Optional[Any] = tf.reshape(tf.range(2 * 1_2 * 1_6 * 6_4 , dtype=tf.floataa ) , shape=(2, 1_2, 1_6, 6_4) ) / 1_0_0
_lowerCAmelCase : Optional[Any] = -tf.reshape(tf.range(2 * 1_2 * 1_6 * 6_4 , dtype=tf.floataa ) , shape=(2, 1_2, 1_6, 6_4) ) / 1_0_0
_lowerCAmelCase : Optional[int] = TFRoFormerSinusoidalPositionalEmbedding(num_positions=3_2 , embedding_dim=6_4 )
_lowerCAmelCase : str = embed_positions([2, 1_6, 7_6_8] )[None, None, :, :]
_lowerCAmelCase : Any = TFRoFormerSelfAttention.apply_rotary_position_embeddings(
lowercase_ , lowercase_ , lowercase_ )
_lowerCAmelCase : str = tf.constant(
[
[0.0000, 0.0100, 0.0200, 0.0300, 0.0400, 0.0500, 0.0600, 0.0700],
[-0.2012, 0.8897, 0.0263, 0.9401, 0.2074, 0.9463, 0.3481, 0.9343],
[-1.7057, 0.6271, -1.2145, 1.3897, -0.6303, 1.7647, -0.1173, 1.8985],
[-2.1731, -1.6397, -2.7358, 0.2854, -2.1840, 1.7183, -1.3018, 2.4871],
[0.2717, -3.6173, -2.9206, -2.1988, -3.6638, 0.3858, -2.9155, 2.2980],
[3.9859, -2.1580, -0.7984, -4.4904, -4.1181, -2.0252, -4.4782, 1.1253],
] )
_lowerCAmelCase : Union[str, Any] = tf.constant(
[
[0.0000, -0.0100, -0.0200, -0.0300, -0.0400, -0.0500, -0.0600, -0.0700],
[0.2012, -0.8897, -0.0263, -0.9401, -0.2074, -0.9463, -0.3481, -0.9343],
[1.7057, -0.6271, 1.2145, -1.3897, 0.6303, -1.7647, 0.1173, -1.8985],
[2.1731, 1.6397, 2.7358, -0.2854, 2.1840, -1.7183, 1.3018, -2.4871],
[-0.2717, 3.6173, 2.9206, 2.1988, 3.6638, -0.3858, 2.9155, -2.2980],
[-3.9859, 2.1580, 0.7984, 4.4904, 4.1181, 2.0252, 4.4782, -1.1253],
] )
tf.debugging.assert_near(query_layer[0, 0, :6, :8] , lowercase_ , atol=self.tolerance )
tf.debugging.assert_near(key_layer[0, 0, :6, :8] , lowercase_ , atol=self.tolerance )
| 309 |
"""simple docstring"""
from __future__ import annotations
import sys
from collections import deque
from typing import Generic, TypeVar
__UpperCamelCase : Tuple = TypeVar('''T''')
class SCREAMING_SNAKE_CASE ( Generic[T] ):
"""simple docstring"""
lowercase__ = 42 # Cache store of keys
lowercase__ = 42 # References of the keys in cache
lowercase__ = 10 # Maximum capacity of cache
def __init__( self : Dict ,lowercase_ : int ):
lowerCAmelCase__ : str = deque()
lowerCAmelCase__ : Any = set()
if not n:
lowerCAmelCase__ : Optional[Any] = sys.maxsize
elif n < 0:
raise ValueError('''n should be an integer greater than 0.''' )
else:
lowerCAmelCase__ : int = n
def __lowerCAmelCase ( self : str ,lowercase_ : T ):
if x not in self.key_reference:
if len(self.dq_store ) == LRUCache._MAX_CAPACITY:
lowerCAmelCase__ : Any = self.dq_store.pop()
self.key_reference.remove(lowercase_ )
else:
self.dq_store.remove(lowercase_ )
self.dq_store.appendleft(lowercase_ )
self.key_reference.add(lowercase_ )
def __lowerCAmelCase ( self : int ):
for k in self.dq_store:
print(lowercase_ )
def __repr__( self : Tuple ):
return F'LRUCache({self._MAX_CAPACITY}) => {list(self.dq_store )}'
if __name__ == "__main__":
import doctest
doctest.testmod()
__UpperCamelCase : LRUCache[str | int] = LRUCache(4)
lru_cache.refer('''A''')
lru_cache.refer(2)
lru_cache.refer(3)
lru_cache.refer('''A''')
lru_cache.refer(4)
lru_cache.refer(5)
lru_cache.display()
print(lru_cache)
assert str(lru_cache) == "LRUCache(4) => [5, 4, 'A', 3]"
| 106 | 0 |
import re
from filelock import FileLock
try:
import nltk
__lowerCamelCase : Dict = True
except (ImportError, ModuleNotFoundError):
__lowerCamelCase : Optional[Any] = False
if NLTK_AVAILABLE:
with FileLock('''.lock''') as lock:
nltk.download('''punkt''', quiet=True)
def __SCREAMING_SNAKE_CASE ( __UpperCamelCase : List[str] ) -> str:
"""simple docstring"""
re.sub("""<n>""" , """""" , __lowerCAmelCase ) # remove pegasus newline char
assert NLTK_AVAILABLE, "nltk must be installed to separate newlines between sentences. (pip install nltk)"
return "\n".join(nltk.sent_tokenize(__lowerCAmelCase ) )
| 362 | from __future__ import annotations
import copy
import inspect
import unittest
import numpy as np
from transformers import is_tf_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_tf, slow
from transformers.utils import cached_property
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import tensorflow as tf
from transformers import (
TF_LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST,
TF_MODEL_FOR_MULTIPLE_CHOICE_MAPPING,
TF_MODEL_FOR_QUESTION_ANSWERING_MAPPING,
TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
TF_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
LayoutLMvaConfig,
TFLayoutLMvaForQuestionAnswering,
TFLayoutLMvaForSequenceClassification,
TFLayoutLMvaForTokenClassification,
TFLayoutLMvaModel,
)
if is_vision_available():
from PIL import Image
from transformers import LayoutLMvaImageProcessor
class __snake_case :
def __init__( self : Any , _lowercase : Tuple , _lowercase : str=2 , _lowercase : List[Any]=3 , _lowercase : Optional[Any]=4 , _lowercase : Optional[Any]=2 , _lowercase : str=7 , _lowercase : Dict=True , _lowercase : List[str]=True , _lowercase : Union[str, Any]=True , _lowercase : Optional[int]=True , _lowercase : Dict=99 , _lowercase : Dict=36 , _lowercase : Tuple=2 , _lowercase : Optional[int]=4 , _lowercase : int=37 , _lowercase : Tuple="gelu" , _lowercase : Optional[Any]=0.1 , _lowercase : Tuple=0.1 , _lowercase : str=5_12 , _lowercase : Dict=16 , _lowercase : int=2 , _lowercase : int=0.02 , _lowercase : Any=6 , _lowercase : List[Any]=6 , _lowercase : List[Any]=3 , _lowercase : List[Any]=4 , _lowercase : int=None , _lowercase : Optional[int]=10_00 , ):
"""simple docstring"""
SCREAMING_SNAKE_CASE__ = parent
SCREAMING_SNAKE_CASE__ = batch_size
SCREAMING_SNAKE_CASE__ = num_channels
SCREAMING_SNAKE_CASE__ = image_size
SCREAMING_SNAKE_CASE__ = patch_size
SCREAMING_SNAKE_CASE__ = is_training
SCREAMING_SNAKE_CASE__ = use_input_mask
SCREAMING_SNAKE_CASE__ = use_token_type_ids
SCREAMING_SNAKE_CASE__ = use_labels
SCREAMING_SNAKE_CASE__ = vocab_size
SCREAMING_SNAKE_CASE__ = hidden_size
SCREAMING_SNAKE_CASE__ = num_hidden_layers
SCREAMING_SNAKE_CASE__ = num_attention_heads
SCREAMING_SNAKE_CASE__ = intermediate_size
SCREAMING_SNAKE_CASE__ = hidden_act
SCREAMING_SNAKE_CASE__ = hidden_dropout_prob
SCREAMING_SNAKE_CASE__ = attention_probs_dropout_prob
SCREAMING_SNAKE_CASE__ = max_position_embeddings
SCREAMING_SNAKE_CASE__ = type_vocab_size
SCREAMING_SNAKE_CASE__ = type_sequence_label_size
SCREAMING_SNAKE_CASE__ = initializer_range
SCREAMING_SNAKE_CASE__ = coordinate_size
SCREAMING_SNAKE_CASE__ = shape_size
SCREAMING_SNAKE_CASE__ = num_labels
SCREAMING_SNAKE_CASE__ = num_choices
SCREAMING_SNAKE_CASE__ = scope
SCREAMING_SNAKE_CASE__ = range_bbox
# LayoutLMv3's sequence length equals the number of text tokens + number of patches + 1 (we add 1 for the CLS token)
SCREAMING_SNAKE_CASE__ = text_seq_length
SCREAMING_SNAKE_CASE__ = (image_size // patch_size) ** 2 + 1
SCREAMING_SNAKE_CASE__ = self.text_seq_length + self.image_seq_length
def __a ( self : List[str] ):
"""simple docstring"""
SCREAMING_SNAKE_CASE__ = ids_tensor([self.batch_size, self.text_seq_length] , self.vocab_size )
SCREAMING_SNAKE_CASE__ = ids_tensor([self.batch_size, self.text_seq_length, 4] , self.range_bbox )
SCREAMING_SNAKE_CASE__ = bbox.numpy()
# Ensure that bbox is legal
for i in range(bbox.shape[0] ):
for j in range(bbox.shape[1] ):
if bbox[i, j, 3] < bbox[i, j, 1]:
SCREAMING_SNAKE_CASE__ = bbox[i, j, 3]
SCREAMING_SNAKE_CASE__ = bbox[i, j, 1]
SCREAMING_SNAKE_CASE__ = tmp_coordinate
if bbox[i, j, 2] < bbox[i, j, 0]:
SCREAMING_SNAKE_CASE__ = bbox[i, j, 2]
SCREAMING_SNAKE_CASE__ = bbox[i, j, 0]
SCREAMING_SNAKE_CASE__ = tmp_coordinate
SCREAMING_SNAKE_CASE__ = tf.constant(_lowercase )
SCREAMING_SNAKE_CASE__ = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
SCREAMING_SNAKE_CASE__ = None
if self.use_input_mask:
SCREAMING_SNAKE_CASE__ = random_attention_mask([self.batch_size, self.text_seq_length] )
SCREAMING_SNAKE_CASE__ = None
if self.use_token_type_ids:
SCREAMING_SNAKE_CASE__ = ids_tensor([self.batch_size, self.text_seq_length] , self.type_vocab_size )
SCREAMING_SNAKE_CASE__ = None
SCREAMING_SNAKE_CASE__ = None
if self.use_labels:
SCREAMING_SNAKE_CASE__ = ids_tensor([self.batch_size] , self.type_sequence_label_size )
SCREAMING_SNAKE_CASE__ = ids_tensor([self.batch_size, self.text_seq_length] , self.num_labels )
SCREAMING_SNAKE_CASE__ = LayoutLMvaConfig(
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 , initializer_range=self.initializer_range , coordinate_size=self.coordinate_size , shape_size=self.shape_size , input_size=self.image_size , patch_size=self.patch_size , )
return config, input_ids, bbox, pixel_values, token_type_ids, input_mask, sequence_labels, token_labels
def __a ( self : List[str] , _lowercase : Dict , _lowercase : List[Any] , _lowercase : str , _lowercase : Optional[int] , _lowercase : Union[str, Any] , _lowercase : Tuple ):
"""simple docstring"""
SCREAMING_SNAKE_CASE__ = TFLayoutLMvaModel(config=_lowercase )
# text + image
SCREAMING_SNAKE_CASE__ = model(_lowercase , pixel_values=_lowercase , training=_lowercase )
SCREAMING_SNAKE_CASE__ = model(
_lowercase , bbox=_lowercase , pixel_values=_lowercase , attention_mask=_lowercase , token_type_ids=_lowercase , training=_lowercase , )
SCREAMING_SNAKE_CASE__ = model(_lowercase , bbox=_lowercase , pixel_values=_lowercase , training=_lowercase )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
# text only
SCREAMING_SNAKE_CASE__ = model(_lowercase , training=_lowercase )
self.parent.assertEqual(
result.last_hidden_state.shape , (self.batch_size, self.text_seq_length, self.hidden_size) )
# image only
SCREAMING_SNAKE_CASE__ = model({"""pixel_values""": pixel_values} , training=_lowercase )
self.parent.assertEqual(
result.last_hidden_state.shape , (self.batch_size, self.image_seq_length, self.hidden_size) )
def __a ( self : int , _lowercase : int , _lowercase : Optional[int] , _lowercase : Optional[Any] , _lowercase : Optional[int] , _lowercase : Tuple , _lowercase : List[Any] , _lowercase : Tuple ):
"""simple docstring"""
SCREAMING_SNAKE_CASE__ = self.num_labels
SCREAMING_SNAKE_CASE__ = TFLayoutLMvaForSequenceClassification(config=_lowercase )
SCREAMING_SNAKE_CASE__ = model(
_lowercase , bbox=_lowercase , pixel_values=_lowercase , attention_mask=_lowercase , token_type_ids=_lowercase , labels=_lowercase , training=_lowercase , )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def __a ( self : Any , _lowercase : Dict , _lowercase : Tuple , _lowercase : int , _lowercase : Optional[int] , _lowercase : Optional[int] , _lowercase : Union[str, Any] , _lowercase : Tuple ):
"""simple docstring"""
SCREAMING_SNAKE_CASE__ = self.num_labels
SCREAMING_SNAKE_CASE__ = TFLayoutLMvaForTokenClassification(config=_lowercase )
SCREAMING_SNAKE_CASE__ = model(
_lowercase , bbox=_lowercase , pixel_values=_lowercase , attention_mask=_lowercase , token_type_ids=_lowercase , labels=_lowercase , training=_lowercase , )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.text_seq_length, self.num_labels) )
def __a ( self : str , _lowercase : int , _lowercase : List[str] , _lowercase : str , _lowercase : str , _lowercase : List[str] , _lowercase : List[str] , _lowercase : Union[str, Any] ):
"""simple docstring"""
SCREAMING_SNAKE_CASE__ = 2
SCREAMING_SNAKE_CASE__ = TFLayoutLMvaForQuestionAnswering(config=_lowercase )
SCREAMING_SNAKE_CASE__ = model(
_lowercase , bbox=_lowercase , pixel_values=_lowercase , attention_mask=_lowercase , token_type_ids=_lowercase , start_positions=_lowercase , end_positions=_lowercase , training=_lowercase , )
self.parent.assertEqual(result.start_logits.shape , (self.batch_size, self.seq_length) )
self.parent.assertEqual(result.end_logits.shape , (self.batch_size, self.seq_length) )
def __a ( self : List[Any] ):
"""simple docstring"""
SCREAMING_SNAKE_CASE__ = self.prepare_config_and_inputs()
((SCREAMING_SNAKE_CASE__) , (SCREAMING_SNAKE_CASE__) , (SCREAMING_SNAKE_CASE__) , (SCREAMING_SNAKE_CASE__) , (SCREAMING_SNAKE_CASE__) , (SCREAMING_SNAKE_CASE__) , (SCREAMING_SNAKE_CASE__) , (SCREAMING_SNAKE_CASE__)) = config_and_inputs
SCREAMING_SNAKE_CASE__ = {
"""input_ids""": input_ids,
"""bbox""": bbox,
"""pixel_values""": pixel_values,
"""token_type_ids""": token_type_ids,
"""attention_mask""": input_mask,
}
return config, inputs_dict
@require_tf
class __snake_case ( lowerCamelCase_ , lowerCamelCase_ , unittest.TestCase ):
lowerCAmelCase_ = (
(
TFLayoutLMvaModel,
TFLayoutLMvaForQuestionAnswering,
TFLayoutLMvaForSequenceClassification,
TFLayoutLMvaForTokenClassification,
)
if is_tf_available()
else ()
)
lowerCAmelCase_ = (
{"document-question-answering": TFLayoutLMvaForQuestionAnswering, "feature-extraction": TFLayoutLMvaModel}
if is_tf_available()
else {}
)
lowerCAmelCase_ = False
lowerCAmelCase_ = False
lowerCAmelCase_ = False
def __a ( self : Union[str, Any] , _lowercase : Optional[int] , _lowercase : List[str] , _lowercase : Optional[Any] , _lowercase : Optional[int] , _lowercase : List[Any] ):
"""simple docstring"""
return True
def __a ( self : List[str] , _lowercase : List[Any] , _lowercase : str , _lowercase : str=False ):
"""simple docstring"""
SCREAMING_SNAKE_CASE__ = copy.deepcopy(_lowercase )
if model_class in get_values(_lowercase ):
SCREAMING_SNAKE_CASE__ = {
k: tf.tile(tf.expand_dims(_lowercase , 1 ) , (1, self.model_tester.num_choices) + (1,) * (v.ndim - 1) )
if isinstance(_lowercase , tf.Tensor ) and v.ndim > 0
else v
for k, v in inputs_dict.items()
}
if return_labels:
if model_class in get_values(_lowercase ):
SCREAMING_SNAKE_CASE__ = tf.ones(self.model_tester.batch_size , dtype=tf.intaa )
elif model_class in get_values(_lowercase ):
SCREAMING_SNAKE_CASE__ = tf.zeros(self.model_tester.batch_size , dtype=tf.intaa )
SCREAMING_SNAKE_CASE__ = tf.zeros(self.model_tester.batch_size , dtype=tf.intaa )
elif model_class in get_values(_lowercase ):
SCREAMING_SNAKE_CASE__ = tf.zeros(self.model_tester.batch_size , dtype=tf.intaa )
elif model_class in get_values(_lowercase ):
SCREAMING_SNAKE_CASE__ = tf.zeros(
(self.model_tester.batch_size, self.model_tester.text_seq_length) , dtype=tf.intaa )
return inputs_dict
def __a ( self : Tuple ):
"""simple docstring"""
SCREAMING_SNAKE_CASE__ = TFLayoutLMvaModelTester(self )
SCREAMING_SNAKE_CASE__ = ConfigTester(self , config_class=_lowercase , hidden_size=37 )
def __a ( self : Any ):
"""simple docstring"""
self.config_tester.run_common_tests()
def __a ( self : Dict ):
"""simple docstring"""
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
SCREAMING_SNAKE_CASE__ = model_class(_lowercase )
if getattr(_lowercase , """hf_compute_loss""" , _lowercase ):
# The number of elements in the loss should be the same as the number of elements in the label
SCREAMING_SNAKE_CASE__ = self._prepare_for_class(inputs_dict.copy() , _lowercase , return_labels=_lowercase )
SCREAMING_SNAKE_CASE__ = prepared_for_class[
sorted(prepared_for_class.keys() - inputs_dict.keys() , reverse=_lowercase )[0]
]
SCREAMING_SNAKE_CASE__ = added_label.shape.as_list()[:1]
# Test that model correctly compute the loss with kwargs
SCREAMING_SNAKE_CASE__ = self._prepare_for_class(inputs_dict.copy() , _lowercase , return_labels=_lowercase )
SCREAMING_SNAKE_CASE__ = prepared_for_class.pop("""input_ids""" )
SCREAMING_SNAKE_CASE__ = model(_lowercase , **_lowercase )[0]
self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1] )
# Test that model correctly compute the loss when we mask some positions
SCREAMING_SNAKE_CASE__ = self._prepare_for_class(inputs_dict.copy() , _lowercase , return_labels=_lowercase )
SCREAMING_SNAKE_CASE__ = prepared_for_class.pop("""input_ids""" )
if "labels" in prepared_for_class:
SCREAMING_SNAKE_CASE__ = prepared_for_class["""labels"""].numpy()
if len(labels.shape ) > 1 and labels.shape[1] != 1:
SCREAMING_SNAKE_CASE__ = -1_00
SCREAMING_SNAKE_CASE__ = tf.convert_to_tensor(_lowercase )
SCREAMING_SNAKE_CASE__ = model(_lowercase , **_lowercase )[0]
self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1] )
self.assertTrue(not np.any(np.isnan(loss.numpy() ) ) )
# Test that model correctly compute the loss with a dict
SCREAMING_SNAKE_CASE__ = self._prepare_for_class(inputs_dict.copy() , _lowercase , return_labels=_lowercase )
SCREAMING_SNAKE_CASE__ = model(_lowercase )[0]
self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1] )
# Test that model correctly compute the loss with a tuple
SCREAMING_SNAKE_CASE__ = self._prepare_for_class(inputs_dict.copy() , _lowercase , return_labels=_lowercase )
# Get keys that were added with the _prepare_for_class function
SCREAMING_SNAKE_CASE__ = prepared_for_class.keys() - inputs_dict.keys()
SCREAMING_SNAKE_CASE__ = inspect.signature(model.call ).parameters
SCREAMING_SNAKE_CASE__ = list(signature.keys() )
# Create a dictionary holding the location of the tensors in the tuple
SCREAMING_SNAKE_CASE__ = {0: """input_ids"""}
for label_key in label_keys:
SCREAMING_SNAKE_CASE__ = signature_names.index(_lowercase )
SCREAMING_SNAKE_CASE__ = label_key
SCREAMING_SNAKE_CASE__ = sorted(tuple_index_mapping.items() )
# Initialize a list with their default values, update the values and convert to a tuple
SCREAMING_SNAKE_CASE__ = []
for name in signature_names:
if name != "kwargs":
list_input.append(signature[name].default )
for index, value in sorted_tuple_index_mapping:
SCREAMING_SNAKE_CASE__ = prepared_for_class[value]
SCREAMING_SNAKE_CASE__ = tuple(_lowercase )
# Send to model
SCREAMING_SNAKE_CASE__ = model(tuple_input[:-1] )[0]
self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1] )
def __a ( self : Optional[int] ):
"""simple docstring"""
(
(
SCREAMING_SNAKE_CASE__
) , (
SCREAMING_SNAKE_CASE__
) , (
SCREAMING_SNAKE_CASE__
) , (
SCREAMING_SNAKE_CASE__
) , (
SCREAMING_SNAKE_CASE__
) , (
SCREAMING_SNAKE_CASE__
) , (
SCREAMING_SNAKE_CASE__
) , (
SCREAMING_SNAKE_CASE__
) ,
) = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(_lowercase , _lowercase , _lowercase , _lowercase , _lowercase , _lowercase )
def __a ( self : int ):
"""simple docstring"""
(
(
SCREAMING_SNAKE_CASE__
) , (
SCREAMING_SNAKE_CASE__
) , (
SCREAMING_SNAKE_CASE__
) , (
SCREAMING_SNAKE_CASE__
) , (
SCREAMING_SNAKE_CASE__
) , (
SCREAMING_SNAKE_CASE__
) , (
SCREAMING_SNAKE_CASE__
) , (
SCREAMING_SNAKE_CASE__
) ,
) = self.model_tester.prepare_config_and_inputs()
for type in ["absolute", "relative_key", "relative_key_query"]:
SCREAMING_SNAKE_CASE__ = type
self.model_tester.create_and_check_model(_lowercase , _lowercase , _lowercase , _lowercase , _lowercase , _lowercase )
def __a ( self : int ):
"""simple docstring"""
(
(
SCREAMING_SNAKE_CASE__
) , (
SCREAMING_SNAKE_CASE__
) , (
SCREAMING_SNAKE_CASE__
) , (
SCREAMING_SNAKE_CASE__
) , (
SCREAMING_SNAKE_CASE__
) , (
SCREAMING_SNAKE_CASE__
) , (
SCREAMING_SNAKE_CASE__
) , (
SCREAMING_SNAKE_CASE__
) ,
) = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_sequence_classification(
_lowercase , _lowercase , _lowercase , _lowercase , _lowercase , _lowercase , _lowercase )
def __a ( self : str ):
"""simple docstring"""
(
(
SCREAMING_SNAKE_CASE__
) , (
SCREAMING_SNAKE_CASE__
) , (
SCREAMING_SNAKE_CASE__
) , (
SCREAMING_SNAKE_CASE__
) , (
SCREAMING_SNAKE_CASE__
) , (
SCREAMING_SNAKE_CASE__
) , (
SCREAMING_SNAKE_CASE__
) , (
SCREAMING_SNAKE_CASE__
) ,
) = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_token_classification(
_lowercase , _lowercase , _lowercase , _lowercase , _lowercase , _lowercase , _lowercase )
def __a ( self : List[str] ):
"""simple docstring"""
(
(
SCREAMING_SNAKE_CASE__
) , (
SCREAMING_SNAKE_CASE__
) , (
SCREAMING_SNAKE_CASE__
) , (
SCREAMING_SNAKE_CASE__
) , (
SCREAMING_SNAKE_CASE__
) , (
SCREAMING_SNAKE_CASE__
) , (
SCREAMING_SNAKE_CASE__
) , (
SCREAMING_SNAKE_CASE__
) ,
) = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_question_answering(
_lowercase , _lowercase , _lowercase , _lowercase , _lowercase , _lowercase , _lowercase )
@slow
def __a ( self : Tuple ):
"""simple docstring"""
for model_name in TF_LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
SCREAMING_SNAKE_CASE__ = TFLayoutLMvaModel.from_pretrained(_lowercase )
self.assertIsNotNone(_lowercase )
def __SCREAMING_SNAKE_CASE ( ) -> List[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ = Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""" )
return image
@require_tf
class __snake_case ( unittest.TestCase ):
@cached_property
def __a ( self : Optional[int] ):
"""simple docstring"""
return LayoutLMvaImageProcessor(apply_ocr=_lowercase ) if is_vision_available() else None
@slow
def __a ( self : Tuple ):
"""simple docstring"""
SCREAMING_SNAKE_CASE__ = TFLayoutLMvaModel.from_pretrained("""microsoft/layoutlmv3-base""" )
SCREAMING_SNAKE_CASE__ = self.default_image_processor
SCREAMING_SNAKE_CASE__ = prepare_img()
SCREAMING_SNAKE_CASE__ = image_processor(images=_lowercase , return_tensors="""tf""" ).pixel_values
SCREAMING_SNAKE_CASE__ = tf.constant([[1, 2]] )
SCREAMING_SNAKE_CASE__ = tf.expand_dims(tf.constant([[1, 2, 3, 4], [5, 6, 7, 8]] ) , axis=0 )
# forward pass
SCREAMING_SNAKE_CASE__ = model(input_ids=_lowercase , bbox=_lowercase , pixel_values=_lowercase , training=_lowercase )
# verify the logits
SCREAMING_SNAKE_CASE__ = (1, 1_99, 7_68)
self.assertEqual(outputs.last_hidden_state.shape , _lowercase )
SCREAMING_SNAKE_CASE__ = tf.constant(
[[-0.05_29, 0.36_18, 0.16_32], [-0.15_87, -0.16_67, -0.04_00], [-0.15_57, -0.16_71, -0.05_05]] )
self.assertTrue(np.allclose(outputs.last_hidden_state[0, :3, :3] , _lowercase , atol=1E-4 ) )
| 204 | 0 |
"""simple docstring"""
def snake_case_ ( A_ : Dict = 1, A_ : List[str] = 10_00 ):
'''simple docstring'''
_lowerCamelCase : int = 1
_lowerCamelCase : Union[str, Any] = 0
for divide_by_number in range(UpperCAmelCase_, digit + 1 ):
_lowerCamelCase : list[int] = []
_lowerCamelCase : int = numerator
for _ in range(1, digit + 1 ):
if now_divide in has_been_divided:
if longest_list_length < len(UpperCAmelCase_ ):
_lowerCamelCase : Optional[Any] = len(UpperCAmelCase_ )
_lowerCamelCase : List[Any] = divide_by_number
else:
has_been_divided.append(UpperCAmelCase_ )
_lowerCamelCase : str = now_divide * 10 % divide_by_number
return the_digit
# Tests
if __name__ == "__main__":
import doctest
doctest.testmod()
| 72 |
'''simple docstring'''
import subprocess
import sys
from transformers import BertConfig, BertModel, BertTokenizer, pipeline
from transformers.testing_utils import TestCasePlus, require_torch
class lowercase__ ( lowercase ):
@require_torch
def UpperCamelCase_ ( self : Dict ):
'''simple docstring'''
# this test is a bit tricky since TRANSFORMERS_OFFLINE can only be changed before
# `transformers` is loaded, and it's too late for inside pytest - so we are changing it
# while running an external program
# python one-liner segments
# this must be loaded before socket.socket is monkey-patched
_UpperCamelCase : Any = '\nfrom transformers import BertConfig, BertModel, BertTokenizer, pipeline\n '
_UpperCamelCase : Dict = '\nmname = "hf-internal-testing/tiny-random-bert"\nBertConfig.from_pretrained(mname)\nBertModel.from_pretrained(mname)\nBertTokenizer.from_pretrained(mname)\npipe = pipeline(task="fill-mask", model=mname)\nprint("success")\n '
_UpperCamelCase : Optional[Any] = '\nimport socket\ndef offline_socket(*args, **kwargs): raise RuntimeError("Offline mode is enabled, we shouldn\'t access internet")\nsocket.socket = offline_socket\n '
# Force fetching the files so that we can use the cache
_UpperCamelCase : Optional[Any] = 'hf-internal-testing/tiny-random-bert'
BertConfig.from_pretrained(lowerCamelCase__ )
BertModel.from_pretrained(lowerCamelCase__ )
BertTokenizer.from_pretrained(lowerCamelCase__ )
pipeline(task='fill-mask' ,model=lowerCamelCase__ )
# baseline - just load from_pretrained with normal network
_UpperCamelCase : Optional[int] = [sys.executable, '-c', '\n'.join([load, run, mock] )]
# should succeed
_UpperCamelCase : Dict = self.get_env()
# should succeed as TRANSFORMERS_OFFLINE=1 tells it to use local files
_UpperCamelCase : str = '1'
_UpperCamelCase : Union[str, Any] = subprocess.run(lowerCamelCase__ ,env=lowerCamelCase__ ,check=lowerCamelCase__ ,capture_output=lowerCamelCase__ )
self.assertEqual(result.returncode ,0 ,result.stderr )
self.assertIn('success' ,result.stdout.decode() )
@require_torch
def UpperCamelCase_ ( self : Tuple ):
'''simple docstring'''
# python one-liner segments
# this must be loaded before socket.socket is monkey-patched
_UpperCamelCase : Any = '\nfrom transformers import BertConfig, BertModel, BertTokenizer, pipeline\n '
_UpperCamelCase : Any = '\nmname = "hf-internal-testing/tiny-random-bert"\nBertConfig.from_pretrained(mname)\nBertModel.from_pretrained(mname)\nBertTokenizer.from_pretrained(mname)\npipe = pipeline(task="fill-mask", model=mname)\nprint("success")\n '
_UpperCamelCase : Any = '\nimport socket\ndef offline_socket(*args, **kwargs): raise socket.error("Faking flaky internet")\nsocket.socket = offline_socket\n '
# Force fetching the files so that we can use the cache
_UpperCamelCase : List[Any] = 'hf-internal-testing/tiny-random-bert'
BertConfig.from_pretrained(lowerCamelCase__ )
BertModel.from_pretrained(lowerCamelCase__ )
BertTokenizer.from_pretrained(lowerCamelCase__ )
pipeline(task='fill-mask' ,model=lowerCamelCase__ )
# baseline - just load from_pretrained with normal network
_UpperCamelCase : Union[str, Any] = [sys.executable, '-c', '\n'.join([load, run, mock] )]
# should succeed
_UpperCamelCase : List[Any] = self.get_env()
_UpperCamelCase : Dict = subprocess.run(lowerCamelCase__ ,env=lowerCamelCase__ ,check=lowerCamelCase__ ,capture_output=lowerCamelCase__ )
self.assertEqual(result.returncode ,0 ,result.stderr )
self.assertIn('success' ,result.stdout.decode() )
@require_torch
def UpperCamelCase_ ( self : Union[str, Any] ):
'''simple docstring'''
# this test is a bit tricky since TRANSFORMERS_OFFLINE can only be changed before
# `transformers` is loaded, and it's too late for inside pytest - so we are changing it
# while running an external program
# python one-liner segments
# this must be loaded before socket.socket is monkey-patched
_UpperCamelCase : Optional[Any] = '\nfrom transformers import BertConfig, BertModel, BertTokenizer\n '
_UpperCamelCase : str = '\nmname = "hf-internal-testing/tiny-random-bert-sharded"\nBertConfig.from_pretrained(mname)\nBertModel.from_pretrained(mname)\nprint("success")\n '
_UpperCamelCase : Any = '\nimport socket\ndef offline_socket(*args, **kwargs): raise ValueError("Offline mode is enabled")\nsocket.socket = offline_socket\n '
# baseline - just load from_pretrained with normal network
_UpperCamelCase : Optional[int] = [sys.executable, '-c', '\n'.join([load, run] )]
# should succeed
_UpperCamelCase : Optional[Any] = self.get_env()
_UpperCamelCase : int = subprocess.run(lowerCamelCase__ ,env=lowerCamelCase__ ,check=lowerCamelCase__ ,capture_output=lowerCamelCase__ )
self.assertEqual(result.returncode ,0 ,result.stderr )
self.assertIn('success' ,result.stdout.decode() )
# next emulate no network
_UpperCamelCase : Dict = [sys.executable, '-c', '\n'.join([load, mock, run] )]
# Doesn't fail anymore since the model is in the cache due to other tests, so commenting this.
# env["TRANSFORMERS_OFFLINE"] = "0"
# result = subprocess.run(cmd, env=env, check=False, capture_output=True)
# self.assertEqual(result.returncode, 1, result.stderr)
# should succeed as TRANSFORMERS_OFFLINE=1 tells it to use local files
_UpperCamelCase : Dict = '1'
_UpperCamelCase : Dict = subprocess.run(lowerCamelCase__ ,env=lowerCamelCase__ ,check=lowerCamelCase__ ,capture_output=lowerCamelCase__ )
self.assertEqual(result.returncode ,0 ,result.stderr )
self.assertIn('success' ,result.stdout.decode() )
@require_torch
def UpperCamelCase_ ( self : str ):
'''simple docstring'''
_UpperCamelCase : int = '\nfrom transformers import pipeline\n '
_UpperCamelCase : str = '\nmname = "hf-internal-testing/tiny-random-bert"\npipe = pipeline(model=mname)\n '
_UpperCamelCase : Optional[Any] = '\nimport socket\ndef offline_socket(*args, **kwargs): raise socket.error("Offline mode is enabled")\nsocket.socket = offline_socket\n '
_UpperCamelCase : Union[str, Any] = self.get_env()
_UpperCamelCase : List[Any] = '1'
_UpperCamelCase : Tuple = [sys.executable, '-c', '\n'.join([load, mock, run] )]
_UpperCamelCase : int = subprocess.run(lowerCamelCase__ ,env=lowerCamelCase__ ,check=lowerCamelCase__ ,capture_output=lowerCamelCase__ )
self.assertEqual(result.returncode ,1 ,result.stderr )
self.assertIn(
'You cannot infer task automatically within `pipeline` when using offline mode' ,result.stderr.decode().replace('\n' ,'' ) ,)
@require_torch
def UpperCamelCase_ ( self : Dict ):
'''simple docstring'''
_UpperCamelCase : Optional[int] = '\nfrom transformers import AutoModel\n '
_UpperCamelCase : int = '\nmname = "hf-internal-testing/test_dynamic_model"\nAutoModel.from_pretrained(mname, trust_remote_code=True)\nprint("success")\n '
# baseline - just load from_pretrained with normal network
_UpperCamelCase : Any = [sys.executable, '-c', '\n'.join([load, run] )]
# should succeed
_UpperCamelCase : Optional[Any] = self.get_env()
_UpperCamelCase : Optional[int] = subprocess.run(lowerCamelCase__ ,env=lowerCamelCase__ ,check=lowerCamelCase__ ,capture_output=lowerCamelCase__ )
self.assertEqual(result.returncode ,0 ,result.stderr )
self.assertIn('success' ,result.stdout.decode() )
# should succeed as TRANSFORMERS_OFFLINE=1 tells it to use local files
_UpperCamelCase : List[Any] = '1'
_UpperCamelCase : Dict = subprocess.run(lowerCamelCase__ ,env=lowerCamelCase__ ,check=lowerCamelCase__ ,capture_output=lowerCamelCase__ )
self.assertEqual(result.returncode ,0 ,result.stderr )
self.assertIn('success' ,result.stdout.decode() )
| 83 | 0 |
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 A ( unittest.TestCase ):
def __init__( self, UpperCamelCase__, UpperCamelCase__=100, UpperCamelCase__=13, UpperCamelCase__=30, UpperCamelCase__=2, UpperCamelCase__=3, UpperCamelCase__=True, UpperCamelCase__=True, UpperCamelCase__=32, UpperCamelCase__=5, UpperCamelCase__=4, UpperCamelCase__=37, UpperCamelCase__="gelu", UpperCamelCase__=0.1, UpperCamelCase__=0.1, UpperCamelCase__=10, UpperCamelCase__=0.02, UpperCamelCase__=3, ):
"""simple docstring"""
lowerCAmelCase_ = parent
lowerCAmelCase_ = vocab_size
lowerCAmelCase_ = batch_size
lowerCAmelCase_ = image_size
lowerCAmelCase_ = patch_size
lowerCAmelCase_ = num_channels
lowerCAmelCase_ = is_training
lowerCAmelCase_ = use_labels
lowerCAmelCase_ = hidden_size
lowerCAmelCase_ = num_hidden_layers
lowerCAmelCase_ = num_attention_heads
lowerCAmelCase_ = intermediate_size
lowerCAmelCase_ = hidden_act
lowerCAmelCase_ = hidden_dropout_prob
lowerCAmelCase_ = attention_probs_dropout_prob
lowerCAmelCase_ = type_sequence_label_size
lowerCAmelCase_ = initializer_range
# in BeiT, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
lowerCAmelCase_ = (image_size // patch_size) ** 2
lowerCAmelCase_ = num_patches + 1
def SCREAMING_SNAKE_CASE__ ( self ):
"""simple docstring"""
lowerCAmelCase_ = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
lowerCAmelCase_ = None
if self.use_labels:
lowerCAmelCase_ = ids_tensor([self.batch_size], self.type_sequence_label_size )
lowerCAmelCase_ = 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 SCREAMING_SNAKE_CASE__ ( self, UpperCamelCase__, UpperCamelCase__, UpperCamelCase__ ):
"""simple docstring"""
lowerCAmelCase_ = FlaxBeitModel(config=UpperCamelCase__ )
lowerCAmelCase_ = model(UpperCamelCase__ )
self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size) )
def SCREAMING_SNAKE_CASE__ ( self, UpperCamelCase__, UpperCamelCase__, UpperCamelCase__ ):
"""simple docstring"""
lowerCAmelCase_ = FlaxBeitForMaskedImageModeling(config=UpperCamelCase__ )
lowerCAmelCase_ = model(UpperCamelCase__ )
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length - 1, self.vocab_size) )
def SCREAMING_SNAKE_CASE__ ( self, UpperCamelCase__, UpperCamelCase__, UpperCamelCase__ ):
"""simple docstring"""
lowerCAmelCase_ = self.type_sequence_label_size
lowerCAmelCase_ = FlaxBeitForImageClassification(config=UpperCamelCase__ )
lowerCAmelCase_ = model(UpperCamelCase__ )
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.type_sequence_label_size) )
# test greyscale images
lowerCAmelCase_ = 1
lowerCAmelCase_ = FlaxBeitForImageClassification(UpperCamelCase__ )
lowerCAmelCase_ = floats_tensor([self.batch_size, 1, self.image_size, self.image_size] )
lowerCAmelCase_ = model(UpperCamelCase__ )
def SCREAMING_SNAKE_CASE__ ( self ):
"""simple docstring"""
lowerCAmelCase_ = self.prepare_config_and_inputs()
(
(
lowerCAmelCase_
) , (
lowerCAmelCase_
) , (
lowerCAmelCase_
) ,
) = config_and_inputs
lowerCAmelCase_ = {'''pixel_values''': pixel_values}
return config, inputs_dict
@require_flax
class A ( __UpperCAmelCase , unittest.TestCase ):
__snake_case = (
(FlaxBeitModel, FlaxBeitForImageClassification, FlaxBeitForMaskedImageModeling) if is_flax_available() else ()
)
def SCREAMING_SNAKE_CASE__ ( self ):
"""simple docstring"""
lowerCAmelCase_ = FlaxBeitModelTester(self )
lowerCAmelCase_ = ConfigTester(self, config_class=UpperCamelCase__, has_text_modality=UpperCamelCase__, hidden_size=37 )
def SCREAMING_SNAKE_CASE__ ( self ):
"""simple docstring"""
self.config_tester.run_common_tests()
def SCREAMING_SNAKE_CASE__ ( self ):
"""simple docstring"""
lowerCAmelCase_ , lowerCAmelCase_ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
lowerCAmelCase_ = model_class(UpperCamelCase__ )
lowerCAmelCase_ = inspect.signature(model.__call__ )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
lowerCAmelCase_ = [*signature.parameters.keys()]
lowerCAmelCase_ = ['''pixel_values''']
self.assertListEqual(arg_names[:1], UpperCamelCase__ )
def SCREAMING_SNAKE_CASE__ ( self ):
"""simple docstring"""
lowerCAmelCase_ , lowerCAmelCase_ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
with self.subTest(model_class.__name__ ):
lowerCAmelCase_ = self._prepare_for_class(UpperCamelCase__, UpperCamelCase__ )
lowerCAmelCase_ = model_class(UpperCamelCase__ )
@jax.jit
def model_jitted(UpperCamelCase__, **UpperCamelCase__ ):
return model(pixel_values=UpperCamelCase__, **UpperCamelCase__ )
with self.subTest('''JIT Enabled''' ):
lowerCAmelCase_ = model_jitted(**UpperCamelCase__ ).to_tuple()
with self.subTest('''JIT Disabled''' ):
with jax.disable_jit():
lowerCAmelCase_ = 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 SCREAMING_SNAKE_CASE__ ( self ):
"""simple docstring"""
lowerCAmelCase_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*UpperCamelCase__ )
def SCREAMING_SNAKE_CASE__ ( self ):
"""simple docstring"""
lowerCAmelCase_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_lm(*UpperCamelCase__ )
def SCREAMING_SNAKE_CASE__ ( self ):
"""simple docstring"""
lowerCAmelCase_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*UpperCamelCase__ )
@slow
def SCREAMING_SNAKE_CASE__ ( self ):
"""simple docstring"""
for model_class_name in self.all_model_classes:
lowerCAmelCase_ = model_class_name.from_pretrained('''microsoft/beit-base-patch16-224''' )
lowerCAmelCase_ = model(np.ones((1, 3, 224, 224) ) )
self.assertIsNotNone(UpperCamelCase__ )
def __UpperCamelCase ( ):
lowerCAmelCase_ = Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
return image
@require_vision
@require_flax
class A ( unittest.TestCase ):
@cached_property
def SCREAMING_SNAKE_CASE__ ( self ):
"""simple docstring"""
return BeitImageProcessor.from_pretrained('''microsoft/beit-base-patch16-224''' ) if is_vision_available() else None
@slow
def SCREAMING_SNAKE_CASE__ ( self ):
"""simple docstring"""
lowerCAmelCase_ = FlaxBeitForMaskedImageModeling.from_pretrained('''microsoft/beit-base-patch16-224-pt22k''' )
lowerCAmelCase_ = self.default_image_processor
lowerCAmelCase_ = prepare_img()
lowerCAmelCase_ = image_processor(images=UpperCamelCase__, return_tensors='''np''' ).pixel_values
# prepare bool_masked_pos
lowerCAmelCase_ = np.ones((1, 196), dtype=UpperCamelCase__ )
# forward pass
lowerCAmelCase_ = model(pixel_values=UpperCamelCase__, bool_masked_pos=UpperCamelCase__ )
lowerCAmelCase_ = outputs.logits
# verify the logits
lowerCAmelCase_ = (1, 196, 8192)
self.assertEqual(logits.shape, UpperCamelCase__ )
lowerCAmelCase_ = np.array(
[[-3.2_437, 0.5_072, -13.9_174], [-3.2_456, 0.4_948, -13.9_401], [-3.2_033, 0.5_121, -13.8_550]] )
self.assertTrue(np.allclose(logits[bool_masked_pos][:3, :3], UpperCamelCase__, atol=1E-2 ) )
@slow
def SCREAMING_SNAKE_CASE__ ( self ):
"""simple docstring"""
lowerCAmelCase_ = FlaxBeitForImageClassification.from_pretrained('''microsoft/beit-base-patch16-224''' )
lowerCAmelCase_ = self.default_image_processor
lowerCAmelCase_ = prepare_img()
lowerCAmelCase_ = image_processor(images=UpperCamelCase__, return_tensors='''np''' )
# forward pass
lowerCAmelCase_ = model(**UpperCamelCase__ )
lowerCAmelCase_ = outputs.logits
# verify the logits
lowerCAmelCase_ = (1, 1000)
self.assertEqual(logits.shape, UpperCamelCase__ )
lowerCAmelCase_ = np.array([-1.2_385, -1.0_987, -1.0_108] )
self.assertTrue(np.allclose(logits[0, :3], UpperCamelCase__, atol=1E-4 ) )
lowerCAmelCase_ = 281
self.assertEqual(logits.argmax(-1 ).item(), UpperCamelCase__ )
@slow
def SCREAMING_SNAKE_CASE__ ( self ):
"""simple docstring"""
lowerCAmelCase_ = FlaxBeitForImageClassification.from_pretrained('''microsoft/beit-large-patch16-224-pt22k-ft22k''' )
lowerCAmelCase_ = self.default_image_processor
lowerCAmelCase_ = prepare_img()
lowerCAmelCase_ = image_processor(images=UpperCamelCase__, return_tensors='''np''' )
# forward pass
lowerCAmelCase_ = model(**UpperCamelCase__ )
lowerCAmelCase_ = outputs.logits
# verify the logits
lowerCAmelCase_ = (1, 2_1841)
self.assertEqual(logits.shape, UpperCamelCase__ )
lowerCAmelCase_ = np.array([1.6_881, -0.2_787, 0.5_901] )
self.assertTrue(np.allclose(logits[0, :3], UpperCamelCase__, atol=1E-4 ) )
lowerCAmelCase_ = 2396
self.assertEqual(logits.argmax(-1 ).item(), UpperCamelCase__ )
| 167 |
_A = {
"joule": 1.0,
"kilojoule": 1_000,
"megajoule": 1_000_000,
"gigajoule": 1_000_000_000,
"wattsecond": 1.0,
"watthour": 3_600,
"kilowatthour": 3_600_000,
"newtonmeter": 1.0,
"calorie_nutr": 4_186.8,
"kilocalorie_nutr": 4_186_800.00,
"electronvolt": 1.602_176_634e-19,
"britishthermalunit_it": 1_055.05_585,
"footpound": 1.355_818,
}
def __UpperCamelCase ( _A , _A , _A ):
if to_type not in ENERGY_CONVERSION or from_type not in ENERGY_CONVERSION:
lowerCAmelCase_ = (
f"Incorrect 'from_type' or 'to_type' value: {from_type!r}, {to_type!r}\n"
f"Valid values are: {', '.join(_A )}"
)
raise ValueError(_A )
return value * ENERGY_CONVERSION[from_type] / ENERGY_CONVERSION[to_type]
if __name__ == "__main__":
import doctest
doctest.testmod()
| 167 | 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
__UpperCamelCase = logging.get_logger(__name__)
__UpperCamelCase = {
'''facebook/levit-128S''': '''https://huggingface.co/facebook/levit-128S/resolve/main/config.json''',
# See all LeViT models at https://huggingface.co/models?filter=levit
}
class UpperCamelCase ( lowerCAmelCase__ ):
SCREAMING_SNAKE_CASE_ = "levit"
def __init__( self, lowerCAmelCase__=224, lowerCAmelCase__=3, lowerCAmelCase__=3, lowerCAmelCase__=2, lowerCAmelCase__=1, lowerCAmelCase__=16, lowerCAmelCase__=[128, 256, 384], lowerCAmelCase__=[4, 8, 12], lowerCAmelCase__=[4, 4, 4], lowerCAmelCase__=[16, 16, 16], lowerCAmelCase__=0, lowerCAmelCase__=[2, 2, 2], lowerCAmelCase__=[2, 2, 2], lowerCAmelCase__=0.02, **lowerCAmelCase__, ) -> Optional[Any]:
super().__init__(**lowerCAmelCase__)
snake_case_ = image_size
snake_case_ = num_channels
snake_case_ = kernel_size
snake_case_ = stride
snake_case_ = padding
snake_case_ = hidden_sizes
snake_case_ = num_attention_heads
snake_case_ = depths
snake_case_ = key_dim
snake_case_ = drop_path_rate
snake_case_ = patch_size
snake_case_ = attention_ratio
snake_case_ = mlp_ratio
snake_case_ = initializer_range
snake_case_ = [
['Subsample', key_dim[0], hidden_sizes[0] // key_dim[0], 4, 2, 2],
['Subsample', key_dim[0], hidden_sizes[1] // key_dim[0], 4, 2, 2],
]
class UpperCamelCase ( lowerCAmelCase__ ):
SCREAMING_SNAKE_CASE_ = version.parse("1.11" )
@property
def a_ ( self) -> Mapping[str, Mapping[int, str]]:
return OrderedDict(
[
('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'}),
])
@property
def a_ ( self) -> float:
return 1e-4
| 69 | """simple docstring"""
from typing import TYPE_CHECKING
from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__UpperCamelCase = {
'''configuration_mctct''': ['''MCTCT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''MCTCTConfig'''],
'''feature_extraction_mctct''': ['''MCTCTFeatureExtractor'''],
'''processing_mctct''': ['''MCTCTProcessor'''],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__UpperCamelCase = [
'''MCTCT_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''MCTCTForCTC''',
'''MCTCTModel''',
'''MCTCTPreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_mctct import MCTCT_PRETRAINED_CONFIG_ARCHIVE_MAP, MCTCTConfig
from .feature_extraction_mctct import MCTCTFeatureExtractor
from .processing_mctct import MCTCTProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_mctct import MCTCT_PRETRAINED_MODEL_ARCHIVE_LIST, MCTCTForCTC, MCTCTModel, MCTCTPreTrainedModel
else:
import sys
__UpperCamelCase = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 69 | 1 |
"""simple docstring"""
import functools
def UpperCamelCase ( UpperCAmelCase , UpperCAmelCase ) ->int:
"""simple docstring"""
a_ = len(UpperCAmelCase )
a_ = len(UpperCAmelCase )
@functools.cache
def min_distance(UpperCAmelCase , UpperCAmelCase ) -> int:
# if first word index is overflow - delete all from the second word
if indexa >= len_worda:
return len_worda - indexa
# if second word index is overflow - delete all from the first word
if indexa >= len_worda:
return len_worda - indexa
a_ = int(worda[indexa] != worda[indexa] ) # current letters not identical
return min(
1 + min_distance(indexa + 1 , UpperCAmelCase ) , 1 + min_distance(UpperCAmelCase , indexa + 1 ) , diff + min_distance(indexa + 1 , indexa + 1 ) , )
return min_distance(0 , 0 )
if __name__ == "__main__":
import doctest
doctest.testmod() | 367 |
"""simple docstring"""
from __future__ import annotations
import inspect
import unittest
from math import floor
import numpy as np
from transformers import CvtConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import tensorflow as tf
from transformers import TFCvtForImageClassification, TFCvtModel
from transformers.models.cvt.modeling_tf_cvt import TF_CVT_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import AutoImageProcessor
class snake_case ( SCREAMING_SNAKE_CASE_ ):
def UpperCAmelCase__ ( self) ->Any:
a_ = self.config_class(**self.inputs_dict)
self.parent.assertTrue(hasattr(__UpperCAmelCase , "embed_dim"))
self.parent.assertTrue(hasattr(__UpperCAmelCase , "num_heads"))
class snake_case :
def __init__( self , __UpperCAmelCase , __UpperCAmelCase=13 , __UpperCAmelCase=64 , __UpperCAmelCase=3 , __UpperCAmelCase=[16, 48, 96] , __UpperCAmelCase=[1, 3, 6] , __UpperCAmelCase=[1, 2, 10] , __UpperCAmelCase=[7, 3, 3] , __UpperCAmelCase=[4, 2, 2] , __UpperCAmelCase=[2, 1, 1] , __UpperCAmelCase=[2, 2, 2] , __UpperCAmelCase=[False, False, True] , __UpperCAmelCase=[0.0, 0.0, 0.0] , __UpperCAmelCase=0.02 , __UpperCAmelCase=1E-12 , __UpperCAmelCase=True , __UpperCAmelCase=True , __UpperCAmelCase=2 , ) ->Optional[int]:
a_ = parent
a_ = batch_size
a_ = image_size
a_ = patch_sizes
a_ = patch_stride
a_ = patch_padding
a_ = is_training
a_ = use_labels
a_ = num_labels
a_ = num_channels
a_ = embed_dim
a_ = num_heads
a_ = stride_kv
a_ = depth
a_ = cls_token
a_ = attention_drop_rate
a_ = initializer_range
a_ = layer_norm_eps
def UpperCAmelCase__ ( self) ->Any:
a_ = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])
a_ = None
if self.use_labels:
# create a random int32 tensor of given shape
a_ = ids_tensor([self.batch_size] , self.num_labels)
a_ = self.get_config()
return config, pixel_values, labels
def UpperCAmelCase__ ( self) ->Union[str, Any]:
return CvtConfig(
image_size=self.image_size , num_labels=self.num_labels , num_channels=self.num_channels , embed_dim=self.embed_dim , num_heads=self.num_heads , patch_sizes=self.patch_sizes , patch_padding=self.patch_padding , patch_stride=self.patch_stride , stride_kv=self.stride_kv , depth=self.depth , cls_token=self.cls_token , attention_drop_rate=self.attention_drop_rate , initializer_range=self.initializer_range , )
def UpperCAmelCase__ ( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase) ->Optional[Any]:
a_ = TFCvtModel(config=__UpperCAmelCase)
a_ = model(__UpperCAmelCase , training=__UpperCAmelCase)
a_ = (self.image_size, self.image_size)
a_ , a_ = image_size[0], image_size[1]
for i in range(len(self.depth)):
a_ = floor(((height + 2 * self.patch_padding[i] - self.patch_sizes[i]) / self.patch_stride[i]) + 1)
a_ = floor(((width + 2 * self.patch_padding[i] - self.patch_sizes[i]) / self.patch_stride[i]) + 1)
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.embed_dim[-1], height, width))
def UpperCAmelCase__ ( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase) ->str:
a_ = self.num_labels
a_ = TFCvtForImageClassification(__UpperCAmelCase)
a_ = model(__UpperCAmelCase , labels=__UpperCAmelCase , training=__UpperCAmelCase)
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels))
def UpperCAmelCase__ ( self) ->Tuple:
a_ = self.prepare_config_and_inputs()
a_ , a_ , a_ = config_and_inputs
a_ = {"pixel_values": pixel_values}
return config, inputs_dict
@require_tf
class snake_case ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , unittest.TestCase ):
a_ : Union[str, Any] = (TFCvtModel, TFCvtForImageClassification) if is_tf_available() else ()
a_ : List[Any] = (
{"""feature-extraction""": TFCvtModel, """image-classification""": TFCvtForImageClassification}
if is_tf_available()
else {}
)
a_ : Any = False
a_ : Dict = False
a_ : Optional[int] = False
a_ : List[Any] = False
a_ : List[Any] = False
def UpperCAmelCase__ ( self) ->List[str]:
a_ = TFCvtModelTester(self)
a_ = TFCvtConfigTester(self , config_class=__UpperCAmelCase , has_text_modality=__UpperCAmelCase , hidden_size=37)
def UpperCAmelCase__ ( self) ->List[str]:
self.config_tester.create_and_test_config_common_properties()
self.config_tester.create_and_test_config_to_json_string()
self.config_tester.create_and_test_config_to_json_file()
self.config_tester.create_and_test_config_from_and_save_pretrained()
self.config_tester.create_and_test_config_with_num_labels()
self.config_tester.check_config_can_be_init_without_params()
self.config_tester.check_config_arguments_init()
@unittest.skip(reason="Cvt does not output attentions")
def UpperCAmelCase__ ( self) ->Dict:
pass
@unittest.skip(reason="Cvt does not use inputs_embeds")
def UpperCAmelCase__ ( self) ->List[str]:
pass
@unittest.skip(reason="Cvt does not support input and output embeddings")
def UpperCAmelCase__ ( self) ->Optional[Any]:
pass
@unittest.skipIf(
not is_tf_available() or len(tf.config.list_physical_devices("GPU")) == 0 , reason="TF does not support backprop for grouped convolutions on CPU." , )
def UpperCAmelCase__ ( self) ->Dict:
super().test_dataset_conversion()
@unittest.skipIf(
not is_tf_available() or len(tf.config.list_physical_devices("GPU")) == 0 , reason="TF does not support backprop for grouped convolutions on CPU." , )
@slow
def UpperCAmelCase__ ( self) ->List[str]:
super().test_keras_fit()
@unittest.skip(reason="Get `Failed to determine best cudnn convolution algo.` error after using TF 2.12+cuda 11.8")
def UpperCAmelCase__ ( self) ->Dict:
a_ = tf.keras.mixed_precision.Policy("mixed_float16")
tf.keras.mixed_precision.set_global_policy(__UpperCAmelCase)
super().test_keras_fit()
tf.keras.mixed_precision.set_global_policy("float32")
def UpperCAmelCase__ ( self) ->Optional[int]:
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 UpperCAmelCase__ ( self) ->Optional[int]:
def check_hidden_states_output(__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase):
a_ = model_class(__UpperCAmelCase)
a_ = model(**self._prepare_for_class(__UpperCAmelCase , __UpperCAmelCase))
a_ = outputs.hidden_states
a_ = len(self.model_tester.depth)
self.assertEqual(len(__UpperCAmelCase) , __UpperCAmelCase)
# verify the first hidden states (first block)
self.assertListEqual(
list(hidden_states[0].shape[-3:]) , [
self.model_tester.embed_dim[0],
self.model_tester.image_size // 4,
self.model_tester.image_size // 4,
] , )
a_ , a_ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
a_ = True
check_hidden_states_output(__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase)
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
a_ = True
check_hidden_states_output(__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase)
def UpperCAmelCase__ ( self) ->Dict:
a_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*__UpperCAmelCase)
def UpperCAmelCase__ ( self) ->List[str]:
a_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*__UpperCAmelCase)
@slow
def UpperCAmelCase__ ( self) ->str:
for model_name in TF_CVT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
a_ = TFCvtModel.from_pretrained(__UpperCAmelCase)
self.assertIsNotNone(__UpperCAmelCase)
def UpperCamelCase ( ) ->Dict:
"""simple docstring"""
a_ = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png" )
return image
@require_tf
@require_vision
class snake_case ( unittest.TestCase ):
@cached_property
def UpperCAmelCase__ ( self) ->int:
return AutoImageProcessor.from_pretrained(TF_CVT_PRETRAINED_MODEL_ARCHIVE_LIST[0])
@slow
def UpperCAmelCase__ ( self) ->Any:
a_ = TFCvtForImageClassification.from_pretrained(TF_CVT_PRETRAINED_MODEL_ARCHIVE_LIST[0])
a_ = self.default_image_processor
a_ = prepare_img()
a_ = image_processor(images=__UpperCAmelCase , return_tensors="tf")
# forward pass
a_ = model(**__UpperCAmelCase)
# verify the logits
a_ = tf.TensorShape((1, 10_00))
self.assertEqual(outputs.logits.shape , __UpperCAmelCase)
a_ = tf.constant([0.9_285, 0.9_015, -0.3_150])
self.assertTrue(np.allclose(outputs.logits[0, :3].numpy() , __UpperCAmelCase , atol=1E-4)) | 303 | 0 |
"""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'''
from queue import Queue
from typing import TYPE_CHECKING, Optional
if TYPE_CHECKING:
from ..models.auto import AutoTokenizer
class __SCREAMING_SNAKE_CASE :
"""simple docstring"""
def UpperCamelCase__ ( self : Dict , __a : Any ):
raise NotImplementedError()
def UpperCamelCase__ ( self : int ):
raise NotImplementedError()
class __SCREAMING_SNAKE_CASE (A_ ):
"""simple docstring"""
def __init__( self : Tuple , __a : "AutoTokenizer" , __a : bool = False , **__a : List[Any] ):
_a = tokenizer
_a = skip_prompt
_a = decode_kwargs
# variables used in the streaming process
_a = []
_a = 0
_a = True
def UpperCamelCase__ ( self : Dict , __a : Union[str, Any] ):
if len(value.shape ) > 1 and value.shape[0] > 1:
raise ValueError("TextStreamer only supports batch size 1" )
elif len(value.shape ) > 1:
_a = value[0]
if self.skip_prompt and self.next_tokens_are_prompt:
_a = False
return
# Add the new token to the cache and decodes the entire thing.
self.token_cache.extend(value.tolist() )
_a = self.tokenizer.decode(self.token_cache , **self.decode_kwargs )
# After the symbol for a new line, we flush the cache.
if text.endswith("\n" ):
_a = text[self.print_len :]
_a = []
_a = 0
# If the last token is a CJK character, we print the characters.
elif len(snake_case__ ) > 0 and self._is_chinese_char(ord(text[-1] ) ):
_a = text[self.print_len :]
self.print_len += len(snake_case__ )
# Otherwise, prints until the last space char (simple heuristic to avoid printing incomplete words,
# which may change with the subsequent token -- there are probably smarter ways to do this!)
else:
_a = text[self.print_len : text.rfind(" " ) + 1]
self.print_len += len(snake_case__ )
self.on_finalized_text(snake_case__ )
def UpperCamelCase__ ( self : str ):
if len(self.token_cache ) > 0:
_a = self.tokenizer.decode(self.token_cache , **self.decode_kwargs )
_a = text[self.print_len :]
_a = []
_a = 0
else:
_a = ""
_a = True
self.on_finalized_text(snake_case__ , stream_end=snake_case__ )
def UpperCamelCase__ ( self : List[str] , __a : str , __a : bool = False ):
print(snake_case__ , flush=snake_case__ , end="" if not stream_end else None )
def UpperCamelCase__ ( self : str , __a : Optional[int] ):
if (
(cp >= 0X4E_00 and cp <= 0X9F_FF)
or (cp >= 0X34_00 and cp <= 0X4D_BF) #
or (cp >= 0X2_00_00 and cp <= 0X2_A6_DF) #
or (cp >= 0X2_A7_00 and cp <= 0X2_B7_3F) #
or (cp >= 0X2_B7_40 and cp <= 0X2_B8_1F) #
or (cp >= 0X2_B8_20 and cp <= 0X2_CE_AF) #
or (cp >= 0XF9_00 and cp <= 0XFA_FF)
or (cp >= 0X2_F8_00 and cp <= 0X2_FA_1F) #
): #
return True
return False
class __SCREAMING_SNAKE_CASE (A_ ):
"""simple docstring"""
def __init__( self : List[Any] , __a : "AutoTokenizer" , __a : bool = False , __a : Optional[float] = None , **__a : List[str] ):
super().__init__(snake_case__ , snake_case__ , **snake_case__ )
_a = Queue()
_a = None
_a = timeout
def UpperCamelCase__ ( self : Any , __a : str , __a : bool = False ):
self.text_queue.put(snake_case__ , timeout=self.timeout )
if stream_end:
self.text_queue.put(self.stop_signal , timeout=self.timeout )
def __iter__( self : Optional[int] ):
return self
def UpperCamelCase__ ( self : Optional[Any] ):
_a = self.text_queue.get(timeout=self.timeout )
if value == self.stop_signal:
raise StopIteration()
else:
return value
| 360 |
'''simple docstring'''
import requests
lowerCAmelCase_ : List[Any] = 'YOUR API KEY'
def _lowerCamelCase ( lowercase : str , lowercase : str = giphy_api_key ) -> list:
_a = "+".join(query.split() )
_a = F'https://api.giphy.com/v1/gifs/search?q={formatted_query}&api_key={api_key}'
_a = requests.get(lowercase ).json()["data"]
return [gif["url"] for gif in gifs]
if __name__ == "__main__":
print('\n'.join(get_gifs('space ship')))
| 346 | 0 |
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxSeqaSeqConfigWithPast
from ...utils import logging
lowerCamelCase = logging.get_logger(__name__)
lowerCamelCase = {
'google/umt5-small': 'https://huggingface.co/google/umt5-small/resolve/main/config.json',
# See all umt5 models at https://huggingface.co/models?filter=umt5
}
class A ( UpperCamelCase_ ):
UpperCamelCase__ : Optional[Any] ='umt5'
UpperCamelCase__ : Optional[Any] =['past_key_values']
def __init__( self : Any , lowercase_ : Optional[Any]=25_0112 , lowercase_ : Optional[Any]=512 , lowercase_ : Dict=64 , lowercase_ : str=1024 , lowercase_ : Optional[int]=8 , lowercase_ : Union[str, Any]=None , lowercase_ : Union[str, Any]=6 , lowercase_ : List[str]=32 , lowercase_ : Dict=128 , lowercase_ : List[Any]=0.1 , lowercase_ : List[Any]=1E-6 , lowercase_ : Tuple=1.0 , lowercase_ : str="gated-gelu" , lowercase_ : int=True , lowercase_ : List[Any]=True , lowercase_ : Optional[int]="T5Tokenizer" , lowercase_ : List[str]=True , lowercase_ : Optional[Any]=0 , lowercase_ : str=1 , lowercase_ : int=0 , **lowercase_ : List[Any] , ) -> Tuple:
"""simple docstring"""
super().__init__(
is_encoder_decoder=lowercase_ , tokenizer_class=lowercase_ , tie_word_embeddings=lowercase_ , pad_token_id=lowercase_ , eos_token_id=lowercase_ , decoder_start_token_id=lowercase_ , **lowercase_ , )
_lowerCamelCase : List[str] =vocab_size
_lowerCamelCase : int =d_model
_lowerCamelCase : Any =d_kv
_lowerCamelCase : str =d_ff
_lowerCamelCase : Union[str, Any] =num_layers
_lowerCamelCase : Union[str, Any] =(
num_decoder_layers if num_decoder_layers is not None else self.num_layers
) # default = symmetry
_lowerCamelCase : Any =num_heads
_lowerCamelCase : List[str] =relative_attention_num_buckets
_lowerCamelCase : Union[str, Any] =relative_attention_max_distance
_lowerCamelCase : List[Any] =dropout_rate
_lowerCamelCase : str =layer_norm_epsilon
_lowerCamelCase : Union[str, Any] =initializer_factor
_lowerCamelCase : Optional[Any] =feed_forward_proj
_lowerCamelCase : List[str] =use_cache
_lowerCamelCase : Tuple =self.feed_forward_proj.split('-' )
_lowerCamelCase : Union[str, Any] =act_info[-1]
_lowerCamelCase : Any =act_info[0] == 'gated'
if len(lowercase_ ) > 1 and act_info[0] != "gated" or len(lowercase_ ) > 2:
raise ValueError(
F'''`feed_forward_proj`: {feed_forward_proj} is not a valid activation function of the dense layer.'''
'Please make sure `feed_forward_proj` is of the format `gated-{ACT_FN}` or `{ACT_FN}`, e.g. '
'\'gated-gelu\' or \'relu\'' )
if feed_forward_proj == "gated-gelu":
_lowerCamelCase : int ='gelu_new'
@property
def lowerCamelCase ( self : List[str] ) -> Any:
"""simple docstring"""
return self.d_model
@property
def lowerCamelCase ( self : Optional[int] ) -> Optional[int]:
"""simple docstring"""
return self.num_heads
@property
def lowerCamelCase ( self : int ) -> int:
"""simple docstring"""
return self.num_layers
class A ( UpperCamelCase_ ):
@property
# Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.inputs
def lowerCamelCase ( self : str ) -> Mapping[str, Mapping[int, str]]:
"""simple docstring"""
_lowerCamelCase : Union[str, Any] ={
'input_ids': {0: 'batch', 1: 'encoder_sequence'},
'attention_mask': {0: 'batch', 1: 'encoder_sequence'},
}
if self.use_past:
_lowerCamelCase : str ='past_encoder_sequence + sequence'
_lowerCamelCase : Union[str, Any] ={0: 'batch'}
_lowerCamelCase : int ={0: 'batch', 1: 'past_decoder_sequence + sequence'}
else:
_lowerCamelCase : List[str] ={0: 'batch', 1: 'decoder_sequence'}
_lowerCamelCase : List[str] ={0: 'batch', 1: 'decoder_sequence'}
if self.use_past:
self.fill_with_past_key_values_(lowercase_ , direction='inputs' )
return common_inputs
@property
# Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.default_onnx_opset
def lowerCamelCase ( self : Tuple ) -> int:
"""simple docstring"""
return 13
@property
def lowerCamelCase ( self : Optional[int] ) -> float:
"""simple docstring"""
return 5E-4
| 199 |
from sklearn.metrics import mean_squared_error
import datasets
lowerCamelCase = '\\n@article{scikit-learn,\n title={Scikit-learn: Machine Learning in {P}ython},\n author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.\n and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P.\n and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and\n Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.},\n journal={Journal of Machine Learning Research},\n volume={12},\n pages={2825--2830},\n year={2011}\n}\n'
lowerCamelCase = '\\nMean Squared Error(MSE) is the average of the square of difference between the predicted\nand actual values.\n'
lowerCamelCase = '\nArgs:\n predictions: array-like of shape (n_samples,) or (n_samples, n_outputs)\n Estimated target values.\n references: array-like of shape (n_samples,) or (n_samples, n_outputs)\n Ground truth (correct) target values.\n sample_weight: array-like of shape (n_samples,), default=None\n Sample weights.\n multioutput: {"raw_values", "uniform_average"} or array-like of shape (n_outputs,), default="uniform_average"\n Defines aggregating of multiple output values. Array-like value defines weights used to average errors.\n\n "raw_values" : Returns a full set of errors in case of multioutput input.\n\n "uniform_average" : Errors of all outputs are averaged with uniform weight.\n\n squared : bool, default=True\n If True returns MSE value, if False returns RMSE (Root Mean Squared Error) value.\n\nReturns:\n mse : mean squared error.\nExamples:\n\n >>> mse_metric = datasets.load_metric("mse")\n >>> predictions = [2.5, 0.0, 2, 8]\n >>> references = [3, -0.5, 2, 7]\n >>> results = mse_metric.compute(predictions=predictions, references=references)\n >>> print(results)\n {\'mse\': 0.375}\n >>> rmse_result = mse_metric.compute(predictions=predictions, references=references, squared=False)\n >>> print(rmse_result)\n {\'mse\': 0.6123724356957945}\n\n If you\'re using multi-dimensional lists, then set the config as follows :\n\n >>> mse_metric = datasets.load_metric("mse", "multilist")\n >>> predictions = [[0.5, 1], [-1, 1], [7, -6]]\n >>> references = [[0, 2], [-1, 2], [8, -5]]\n >>> results = mse_metric.compute(predictions=predictions, references=references)\n >>> print(results)\n {\'mse\': 0.7083333333333334}\n >>> results = mse_metric.compute(predictions=predictions, references=references, multioutput=\'raw_values\')\n >>> print(results) # doctest: +NORMALIZE_WHITESPACE\n {\'mse\': array([0.41666667, 1. ])}\n'
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class A ( datasets.Metric ):
def lowerCamelCase ( self : List[Any] ) -> List[str]:
"""simple docstring"""
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(self._get_feature_types() ) , reference_urls=[
'https://scikit-learn.org/stable/modules/generated/sklearn.metrics.mean_squared_error.html'
] , )
def lowerCamelCase ( self : Tuple ) -> Optional[int]:
"""simple docstring"""
if self.config_name == "multilist":
return {
"predictions": datasets.Sequence(datasets.Value('float' ) ),
"references": datasets.Sequence(datasets.Value('float' ) ),
}
else:
return {
"predictions": datasets.Value('float' ),
"references": datasets.Value('float' ),
}
def lowerCamelCase ( self : List[Any] , lowercase_ : List[Any] , lowercase_ : Tuple , lowercase_ : List[str]=None , lowercase_ : Union[str, Any]="uniform_average" , lowercase_ : Tuple=True ) -> Any:
"""simple docstring"""
_lowerCamelCase : List[str] =mean_squared_error(
lowercase_ , lowercase_ , sample_weight=lowercase_ , multioutput=lowercase_ , squared=lowercase_ )
return {"mse": mse}
| 199 | 1 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
lowerCAmelCase__ : Any = logging.get_logger(__name__)
lowerCAmelCase__ : Optional[int] = {
"facebook/convnextv2-tiny-1k-224": "https://huggingface.co/facebook/convnextv2-tiny-1k-224/resolve/main/config.json",
}
class SCREAMING_SNAKE_CASE__ ( snake_case__ ,snake_case__ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE = '''convnextv2'''
def __init__( self : List[str] , UpperCAmelCase_ : Union[str, Any]=3 , UpperCAmelCase_ : Optional[int]=4 , UpperCAmelCase_ : Any=4 , UpperCAmelCase_ : List[Any]=None , UpperCAmelCase_ : str=None , UpperCAmelCase_ : Dict="gelu" , UpperCAmelCase_ : Any=0.02 , UpperCAmelCase_ : Optional[int]=1e-12 , UpperCAmelCase_ : Dict=0.0 , UpperCAmelCase_ : Optional[Any]=224 , UpperCAmelCase_ : int=None , UpperCAmelCase_ : Union[str, Any]=None , **UpperCAmelCase_ : Any , ):
"""simple docstring"""
super().__init__(**UpperCAmelCase_ )
__UpperCAmelCase : List[str] = num_channels
__UpperCAmelCase : List[str] = patch_size
__UpperCAmelCase : str = num_stages
__UpperCAmelCase : Any = [96, 192, 384, 768] if hidden_sizes is None else hidden_sizes
__UpperCAmelCase : Any = [3, 3, 9, 3] if depths is None else depths
__UpperCAmelCase : Optional[int] = hidden_act
__UpperCAmelCase : Optional[Any] = initializer_range
__UpperCAmelCase : Union[str, Any] = layer_norm_eps
__UpperCAmelCase : Optional[Any] = drop_path_rate
__UpperCAmelCase : List[Any] = image_size
__UpperCAmelCase : Optional[int] = ["stem"] + [f"stage{idx}" for idx in range(1 , len(self.depths ) + 1 )]
__UpperCAmelCase , __UpperCAmelCase : str = get_aligned_output_features_output_indices(
out_features=UpperCAmelCase_ , out_indices=UpperCAmelCase_ , stage_names=self.stage_names )
| 37 |
'''simple docstring'''
import unittest
from transformers import PegasusTokenizer, PegasusTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, require_torch, slow
from transformers.utils import cached_property
from ...test_tokenization_common import TokenizerTesterMixin
lowerCAmelCase__ : Any = get_tests_dir("fixtures/test_sentencepiece_no_bos.model")
@require_sentencepiece
@require_tokenizers
class SCREAMING_SNAKE_CASE__ ( snake_case__ ,unittest.TestCase ):
"""simple docstring"""
SCREAMING_SNAKE_CASE = PegasusTokenizer
SCREAMING_SNAKE_CASE = PegasusTokenizerFast
SCREAMING_SNAKE_CASE = True
SCREAMING_SNAKE_CASE = True
def lowerCamelCase_ ( self : int ):
"""simple docstring"""
super().setUp()
# We have a SentencePiece fixture for testing
__UpperCAmelCase : Tuple = PegasusTokenizer(UpperCAmelCase_ )
tokenizer.save_pretrained(self.tmpdirname )
@cached_property
def lowerCamelCase_ ( self : Dict ):
"""simple docstring"""
return PegasusTokenizer.from_pretrained("google/pegasus-large" )
def lowerCamelCase_ ( self : List[Any] , **UpperCAmelCase_ : List[str] ):
"""simple docstring"""
return PegasusTokenizer.from_pretrained(self.tmpdirname , **UpperCAmelCase_ )
def lowerCamelCase_ ( self : str , UpperCAmelCase_ : int ):
"""simple docstring"""
return ("This is a test", "This is a test")
def lowerCamelCase_ ( self : Optional[Any] ):
"""simple docstring"""
__UpperCAmelCase : List[str] = "</s>"
__UpperCAmelCase : Union[str, Any] = 1
self.assertEqual(self.get_tokenizer()._convert_token_to_id(UpperCAmelCase_ ) , UpperCAmelCase_ )
self.assertEqual(self.get_tokenizer()._convert_id_to_token(UpperCAmelCase_ ) , UpperCAmelCase_ )
def lowerCamelCase_ ( self : Any ):
"""simple docstring"""
__UpperCAmelCase : int = list(self.get_tokenizer().get_vocab().keys() )
self.assertEqual(vocab_keys[0] , "<pad>" )
self.assertEqual(vocab_keys[1] , "</s>" )
self.assertEqual(vocab_keys[-1] , "v" )
self.assertEqual(len(UpperCAmelCase_ ) , 1_103 )
def lowerCamelCase_ ( self : Tuple ):
"""simple docstring"""
self.assertEqual(self.get_tokenizer().vocab_size , 1_103 )
def lowerCamelCase_ ( self : str ):
"""simple docstring"""
__UpperCAmelCase : str = self.rust_tokenizer_class.from_pretrained(self.tmpdirname )
__UpperCAmelCase : int = self.tokenizer_class.from_pretrained(self.tmpdirname )
__UpperCAmelCase : Tuple = (
"Let's see which <unk> is the better <unk_token_11> one <mask_1> It seems like this <mask_2> was important"
" </s> <pad> <pad> <pad>"
)
__UpperCAmelCase : List[str] = rust_tokenizer([raw_input_str] , return_tensors=UpperCAmelCase_ , add_special_tokens=UpperCAmelCase_ ).input_ids[0]
__UpperCAmelCase : int = py_tokenizer([raw_input_str] , return_tensors=UpperCAmelCase_ , add_special_tokens=UpperCAmelCase_ ).input_ids[0]
self.assertListEqual(UpperCAmelCase_ , UpperCAmelCase_ )
def lowerCamelCase_ ( self : Dict ):
"""simple docstring"""
__UpperCAmelCase : Any = self._large_tokenizer
# <mask_1> masks whole sentence while <mask_2> masks single word
__UpperCAmelCase : Tuple = "<mask_1> To ensure a <mask_2> flow of bank resolutions."
__UpperCAmelCase : Optional[Any] = [2, 413, 615, 114, 3, 1_971, 113, 1_679, 10_710, 107, 1]
__UpperCAmelCase : Optional[Any] = tokenizer([raw_input_str] , return_tensors=UpperCAmelCase_ ).input_ids[0]
self.assertListEqual(UpperCAmelCase_ , UpperCAmelCase_ )
def lowerCamelCase_ ( self : Any ):
"""simple docstring"""
__UpperCAmelCase : Dict = self._large_tokenizer
# The tracebacks for the following asserts are **better** without messages or self.assertEqual
assert tokenizer.vocab_size == 96_103
assert tokenizer.pad_token_id == 0
assert tokenizer.eos_token_id == 1
assert tokenizer.offset == 103
assert tokenizer.unk_token_id == tokenizer.offset + 2 == 105
assert tokenizer.unk_token == "<unk>"
assert tokenizer.model_max_length == 1_024
__UpperCAmelCase : Tuple = "To ensure a smooth flow of bank resolutions."
__UpperCAmelCase : str = [413, 615, 114, 2_291, 1_971, 113, 1_679, 10_710, 107, 1]
__UpperCAmelCase : Union[str, Any] = tokenizer([raw_input_str] , return_tensors=UpperCAmelCase_ ).input_ids[0]
self.assertListEqual(UpperCAmelCase_ , UpperCAmelCase_ )
assert tokenizer.convert_ids_to_tokens([0, 1, 2, 3] ) == ["<pad>", "</s>", "<mask_1>", "<mask_2>"]
@require_torch
def lowerCamelCase_ ( self : str ):
"""simple docstring"""
__UpperCAmelCase : List[Any] = ["This is going to be way too long." * 150, "short example"]
__UpperCAmelCase : Optional[int] = ["not super long but more than 5 tokens", "tiny"]
__UpperCAmelCase : str = self._large_tokenizer(UpperCAmelCase_ , padding=UpperCAmelCase_ , truncation=UpperCAmelCase_ , return_tensors="pt" )
__UpperCAmelCase : Union[str, Any] = self._large_tokenizer(
text_target=UpperCAmelCase_ , max_length=5 , padding=UpperCAmelCase_ , truncation=UpperCAmelCase_ , return_tensors="pt" )
assert batch.input_ids.shape == (2, 1_024)
assert batch.attention_mask.shape == (2, 1_024)
assert targets["input_ids"].shape == (2, 5)
assert len(UpperCAmelCase_ ) == 2 # input_ids, attention_mask.
@slow
def lowerCamelCase_ ( self : Any ):
"""simple docstring"""
# fmt: off
__UpperCAmelCase : Tuple = {"input_ids": [[38_979, 143, 18_485, 606, 130, 26_669, 87_686, 121, 54_189, 1_129, 111, 26_669, 87_686, 121, 9_114, 14_787, 121, 13_249, 158, 592, 956, 121, 14_621, 31_576, 143, 62_613, 108, 9_688, 930, 43_430, 11_562, 62_613, 304, 108, 11_443, 897, 108, 9_314, 17_415, 63_399, 108, 11_443, 7_614, 18_316, 118, 4_284, 7_148, 12_430, 143, 1_400, 25_703, 158, 111, 4_284, 7_148, 11_772, 143, 21_297, 1_064, 158, 122, 204, 3_506, 1_754, 1_133, 14_787, 1_581, 115, 33_224, 4_482, 111, 1_355, 110, 29_173, 317, 50_833, 108, 20_147, 94_665, 111, 77_198, 107, 1], [110, 62_613, 117, 638, 112, 1_133, 121, 20_098, 1_355, 79_050, 13_872, 135, 1_596, 53_541, 1_352, 141, 13_039, 5_542, 124, 302, 518, 111, 268, 2_956, 115, 149, 4_427, 107, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [139, 1_235, 2_799, 18_289, 17_780, 204, 109, 9_474, 1_296, 107, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], "attention_mask": [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]} # noqa: E501
# fmt: on
self.tokenizer_integration_test_util(
expected_encoding=UpperCAmelCase_ , model_name="google/bigbird-pegasus-large-arxiv" , revision="ba85d0851d708441f91440d509690f1ab6353415" , )
@require_sentencepiece
@require_tokenizers
class SCREAMING_SNAKE_CASE__ ( snake_case__ ,unittest.TestCase ):
"""simple docstring"""
SCREAMING_SNAKE_CASE = PegasusTokenizer
SCREAMING_SNAKE_CASE = PegasusTokenizerFast
SCREAMING_SNAKE_CASE = True
SCREAMING_SNAKE_CASE = True
def lowerCamelCase_ ( self : Optional[Any] ):
"""simple docstring"""
super().setUp()
# We have a SentencePiece fixture for testing
__UpperCAmelCase : List[str] = PegasusTokenizer(UpperCAmelCase_ , offset=0 , mask_token_sent=UpperCAmelCase_ , mask_token="[MASK]" )
tokenizer.save_pretrained(self.tmpdirname )
@cached_property
def lowerCamelCase_ ( self : List[Any] ):
"""simple docstring"""
return PegasusTokenizer.from_pretrained("google/bigbird-pegasus-large-arxiv" )
def lowerCamelCase_ ( self : Union[str, Any] , **UpperCAmelCase_ : int ):
"""simple docstring"""
return PegasusTokenizer.from_pretrained(self.tmpdirname , **UpperCAmelCase_ )
def lowerCamelCase_ ( self : str , UpperCAmelCase_ : Union[str, Any] ):
"""simple docstring"""
return ("This is a test", "This is a test")
def lowerCamelCase_ ( self : Optional[Any] ):
"""simple docstring"""
__UpperCAmelCase : Optional[Any] = self.rust_tokenizer_class.from_pretrained(self.tmpdirname )
__UpperCAmelCase : Optional[Any] = self.tokenizer_class.from_pretrained(self.tmpdirname )
__UpperCAmelCase : List[str] = (
"Let's see which <unk> is the better <unk_token> one [MASK] It seems like this [MASK] was important </s>"
" <pad> <pad> <pad>"
)
__UpperCAmelCase : str = rust_tokenizer([raw_input_str] , return_tensors=UpperCAmelCase_ , add_special_tokens=UpperCAmelCase_ ).input_ids[0]
__UpperCAmelCase : int = py_tokenizer([raw_input_str] , return_tensors=UpperCAmelCase_ , add_special_tokens=UpperCAmelCase_ ).input_ids[0]
self.assertListEqual(UpperCAmelCase_ , UpperCAmelCase_ )
@require_torch
def lowerCamelCase_ ( self : List[Any] ):
"""simple docstring"""
__UpperCAmelCase : Any = ["This is going to be way too long." * 1_000, "short example"]
__UpperCAmelCase : List[Any] = ["not super long but more than 5 tokens", "tiny"]
__UpperCAmelCase : int = self._large_tokenizer(UpperCAmelCase_ , padding=UpperCAmelCase_ , truncation=UpperCAmelCase_ , return_tensors="pt" )
__UpperCAmelCase : List[Any] = self._large_tokenizer(
text_target=UpperCAmelCase_ , max_length=5 , padding=UpperCAmelCase_ , truncation=UpperCAmelCase_ , return_tensors="pt" )
assert batch.input_ids.shape == (2, 4_096)
assert batch.attention_mask.shape == (2, 4_096)
assert targets["input_ids"].shape == (2, 5)
assert len(UpperCAmelCase_ ) == 2 # input_ids, attention_mask.
def lowerCamelCase_ ( self : Tuple ):
"""simple docstring"""
__UpperCAmelCase : List[Any] = (
"This is an example string that is used to test the original TF implementation against the HF"
" implementation"
)
__UpperCAmelCase : int = self._large_tokenizer(UpperCAmelCase_ ).input_ids
self.assertListEqual(
UpperCAmelCase_ , [182, 117, 142, 587, 4_211, 120, 117, 263, 112, 804, 109, 856, 25_016, 3_137, 464, 109, 26_955, 3_137, 1] , )
| 37 | 1 |
"""simple docstring"""
from ... import PretrainedConfig
SCREAMING_SNAKE_CASE_ : int = {
'sijunhe/nezha-cn-base': 'https://huggingface.co/sijunhe/nezha-cn-base/resolve/main/config.json',
}
class a ( UpperCAmelCase__ ):
"""simple docstring"""
UpperCAmelCase = NEZHA_PRETRAINED_CONFIG_ARCHIVE_MAP
UpperCAmelCase = "nezha"
def __init__( self: str , UpperCamelCase: List[Any]=2_11_28 , UpperCamelCase: int=7_68 , UpperCamelCase: Optional[int]=12 , UpperCamelCase: int=12 , UpperCamelCase: Tuple=30_72 , UpperCamelCase: Optional[int]="gelu" , UpperCamelCase: List[Any]=0.1 , UpperCamelCase: Optional[Any]=0.1 , UpperCamelCase: Optional[Any]=5_12 , UpperCamelCase: str=64 , UpperCamelCase: Dict=2 , UpperCamelCase: int=0.02 , UpperCamelCase: Union[str, Any]=1e-1_2 , UpperCamelCase: Dict=0.1 , UpperCamelCase: List[str]=0 , UpperCamelCase: str=2 , UpperCamelCase: str=3 , UpperCamelCase: List[str]=True , **UpperCamelCase: Optional[int] , ):
"""simple docstring"""
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__ = max_relative_position
A__ = type_vocab_size
A__ = initializer_range
A__ = layer_norm_eps
A__ = classifier_dropout
A__ = use_cache
| 335 |
from typing import TYPE_CHECKING
from ...file_utils import _LazyModule, is_tokenizers_available, is_torch_available, is_vision_available
from ...utils import OptionalDependencyNotAvailable
A : int = {'configuration_dpt': ['DPT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'DPTConfig']}
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A : Union[str, Any] = ['DPTFeatureExtractor']
A : int = ['DPTImageProcessor']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A : Tuple = [
'DPT_PRETRAINED_MODEL_ARCHIVE_LIST',
'DPTForDepthEstimation',
'DPTForSemanticSegmentation',
'DPTModel',
'DPTPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_dpt import DPT_PRETRAINED_CONFIG_ARCHIVE_MAP, DPTConfig
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .feature_extraction_dpt import DPTFeatureExtractor
from .image_processing_dpt import DPTImageProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_dpt import (
DPT_PRETRAINED_MODEL_ARCHIVE_LIST,
DPTForDepthEstimation,
DPTForSemanticSegmentation,
DPTModel,
DPTPreTrainedModel,
)
else:
import sys
A : str = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 305 | 0 |
"""simple docstring"""
def A_ ( A__ ) -> list:
return [
txt[:a] + txt[a].upper() + txt[a + 1 :]
for a in range(len(A__ ) )
if txt[a].isalpha()
]
if __name__ == "__main__":
__import__("""doctest""").testmod()
| 367 |
import unittest
from transformers import AutoTokenizer, NystromformerConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
NystromformerForMaskedLM,
NystromformerForMultipleChoice,
NystromformerForQuestionAnswering,
NystromformerForSequenceClassification,
NystromformerForTokenClassification,
NystromformerModel,
)
from transformers.models.nystromformer.modeling_nystromformer import NYSTROMFORMER_PRETRAINED_MODEL_ARCHIVE_LIST
class A__ :
"""simple docstring"""
def __init__( self , lowercase , lowercase=13 , lowercase=7 , lowercase=True , lowercase=True , lowercase=True , lowercase=True , lowercase=99 , lowercase=32 , lowercase=5 , lowercase=4 , lowercase=37 , lowercase="gelu" , lowercase=0.1 , lowercase=0.1 , lowercase=512 , lowercase=16 , lowercase=2 , lowercase=0.02 , lowercase=3 , lowercase=4 , lowercase=None , ) -> List[Any]:
'''simple docstring'''
a__ : Any = parent
a__ : int = batch_size
a__ : Dict = seq_length
a__ : Tuple = is_training
a__ : Any = use_input_mask
a__ : Optional[Any] = use_token_type_ids
a__ : Dict = use_labels
a__ : Optional[int] = vocab_size
a__ : List[Any] = hidden_size
a__ : int = num_hidden_layers
a__ : Optional[Any] = num_attention_heads
a__ : str = intermediate_size
a__ : Optional[int] = hidden_act
a__ : Dict = hidden_dropout_prob
a__ : Optional[int] = attention_probs_dropout_prob
a__ : Tuple = max_position_embeddings
a__ : Dict = type_vocab_size
a__ : Any = type_sequence_label_size
a__ : List[str] = initializer_range
a__ : List[str] = num_labels
a__ : Optional[Any] = num_choices
a__ : str = scope
def __lowercase ( self) -> Union[str, Any]:
'''simple docstring'''
a__ : Union[str, Any] = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size)
a__ : Tuple = None
if self.use_input_mask:
a__ : Optional[int] = random_attention_mask([self.batch_size, self.seq_length])
a__ : Any = None
if self.use_token_type_ids:
a__ : List[Any] = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size)
a__ : str = None
a__ : List[Any] = None
a__ : List[str] = None
if self.use_labels:
a__ : Any = ids_tensor([self.batch_size] , self.type_sequence_label_size)
a__ : str = ids_tensor([self.batch_size, self.seq_length] , self.num_labels)
a__ : str = ids_tensor([self.batch_size] , self.num_choices)
a__ : Union[str, Any] = self.get_config()
return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def __lowercase ( self) -> Optional[int]:
'''simple docstring'''
return NystromformerConfig(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , is_decoder=lowercase , initializer_range=self.initializer_range , )
def __lowercase ( self , lowercase , lowercase , lowercase , lowercase , lowercase , lowercase , lowercase) -> Optional[int]:
'''simple docstring'''
a__ : Union[str, Any] = NystromformerModel(config=lowercase)
model.to(lowercase)
model.eval()
a__ : List[Any] = model(lowercase , attention_mask=lowercase , token_type_ids=lowercase)
a__ : int = model(lowercase , token_type_ids=lowercase)
a__ : Optional[Any] = model(lowercase)
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size))
def __lowercase ( self , lowercase , lowercase , lowercase , lowercase , lowercase , lowercase , lowercase) -> str:
'''simple docstring'''
a__ : List[str] = NystromformerForMaskedLM(config=lowercase)
model.to(lowercase)
model.eval()
a__ : int = model(lowercase , attention_mask=lowercase , token_type_ids=lowercase , labels=lowercase)
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size))
def __lowercase ( self , lowercase , lowercase , lowercase , lowercase , lowercase , lowercase , lowercase) -> Union[str, Any]:
'''simple docstring'''
a__ : Any = NystromformerForQuestionAnswering(config=lowercase)
model.to(lowercase)
model.eval()
a__ : str = model(
lowercase , attention_mask=lowercase , token_type_ids=lowercase , start_positions=lowercase , end_positions=lowercase , )
self.parent.assertEqual(result.start_logits.shape , (self.batch_size, self.seq_length))
self.parent.assertEqual(result.end_logits.shape , (self.batch_size, self.seq_length))
def __lowercase ( self , lowercase , lowercase , lowercase , lowercase , lowercase , lowercase , lowercase) -> Optional[Any]:
'''simple docstring'''
a__ : int = self.num_labels
a__ : Optional[Any] = NystromformerForSequenceClassification(lowercase)
model.to(lowercase)
model.eval()
a__ : Tuple = model(lowercase , attention_mask=lowercase , token_type_ids=lowercase , labels=lowercase)
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels))
def __lowercase ( self , lowercase , lowercase , lowercase , lowercase , lowercase , lowercase , lowercase) -> List[str]:
'''simple docstring'''
a__ : Tuple = self.num_labels
a__ : int = NystromformerForTokenClassification(config=lowercase)
model.to(lowercase)
model.eval()
a__ : str = model(lowercase , attention_mask=lowercase , token_type_ids=lowercase , labels=lowercase)
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels))
def __lowercase ( self , lowercase , lowercase , lowercase , lowercase , lowercase , lowercase , lowercase) -> Any:
'''simple docstring'''
a__ : Optional[int] = self.num_choices
a__ : Tuple = NystromformerForMultipleChoice(config=lowercase)
model.to(lowercase)
model.eval()
a__ : Optional[Any] = input_ids.unsqueeze(1).expand(-1 , self.num_choices , -1).contiguous()
a__ : Tuple = token_type_ids.unsqueeze(1).expand(-1 , self.num_choices , -1).contiguous()
a__ : str = input_mask.unsqueeze(1).expand(-1 , self.num_choices , -1).contiguous()
a__ : Optional[int] = model(
lowercase , attention_mask=lowercase , token_type_ids=lowercase , labels=lowercase , )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_choices))
def __lowercase ( self) -> Union[str, Any]:
'''simple docstring'''
a__ : List[Any] = self.prepare_config_and_inputs()
(
(
a__
) , (
a__
) , (
a__
) , (
a__
) , (
a__
) , (
a__
) , (
a__
) ,
) : str = config_and_inputs
a__ : Any = {'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': input_mask}
return config, inputs_dict
@require_torch
class A__ ( __UpperCAmelCase , __UpperCAmelCase , unittest.TestCase ):
"""simple docstring"""
__A : Any = (
(
NystromformerModel,
NystromformerForMaskedLM,
NystromformerForMultipleChoice,
NystromformerForQuestionAnswering,
NystromformerForSequenceClassification,
NystromformerForTokenClassification,
)
if is_torch_available()
else ()
)
__A : str = (
{
'''feature-extraction''': NystromformerModel,
'''fill-mask''': NystromformerForMaskedLM,
'''question-answering''': NystromformerForQuestionAnswering,
'''text-classification''': NystromformerForSequenceClassification,
'''token-classification''': NystromformerForTokenClassification,
'''zero-shot''': NystromformerForSequenceClassification,
}
if is_torch_available()
else {}
)
__A : Optional[Any] = False
__A : Tuple = False
def __lowercase ( self) -> Optional[Any]:
'''simple docstring'''
a__ : int = NystromformerModelTester(self)
a__ : Any = ConfigTester(self , config_class=lowercase , hidden_size=37)
def __lowercase ( self) -> Any:
'''simple docstring'''
self.config_tester.run_common_tests()
def __lowercase ( self) -> int:
'''simple docstring'''
a__ : str = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*lowercase)
def __lowercase ( self) -> Tuple:
'''simple docstring'''
a__ : Tuple = self.model_tester.prepare_config_and_inputs()
for type in ["absolute", "relative_key", "relative_key_query"]:
a__ : Optional[Any] = type
self.model_tester.create_and_check_model(*lowercase)
def __lowercase ( self) -> Tuple:
'''simple docstring'''
a__ : List[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_lm(*lowercase)
def __lowercase ( self) -> Any:
'''simple docstring'''
a__ : List[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_multiple_choice(*lowercase)
def __lowercase ( self) -> Any:
'''simple docstring'''
a__ : Union[str, Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_question_answering(*lowercase)
def __lowercase ( self) -> Union[str, Any]:
'''simple docstring'''
a__ : Union[str, Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_sequence_classification(*lowercase)
def __lowercase ( self) -> int:
'''simple docstring'''
a__ : List[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_token_classification(*lowercase)
@slow
def __lowercase ( self) -> Optional[int]:
'''simple docstring'''
for model_name in NYSTROMFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
a__ : int = NystromformerModel.from_pretrained(lowercase)
self.assertIsNotNone(lowercase)
@require_torch
class A__ ( unittest.TestCase ):
"""simple docstring"""
@slow
def __lowercase ( self) -> Optional[Any]:
'''simple docstring'''
a__ : List[str] = NystromformerModel.from_pretrained('uw-madison/nystromformer-512')
a__ : Tuple = torch.tensor([[0, 1, 2, 3, 4, 5]])
with torch.no_grad():
a__ : List[Any] = model(lowercase)[0]
a__ : str = torch.Size((1, 6, 768))
self.assertEqual(output.shape , lowercase)
a__ : str = torch.tensor(
[[[-0.45_32, -0.09_36, 0.51_37], [-0.26_76, 0.06_28, 0.61_86], [-0.36_29, -0.17_26, 0.47_16]]])
self.assertTrue(torch.allclose(output[:, :3, :3] , lowercase , atol=1e-4))
@slow
def __lowercase ( self) -> Optional[int]:
'''simple docstring'''
a__ : Any = 'the [MASK] of Belgium is Brussels'
a__ : List[str] = AutoTokenizer.from_pretrained('uw-madison/nystromformer-512')
a__ : Optional[int] = NystromformerForMaskedLM.from_pretrained('uw-madison/nystromformer-512')
a__ : List[Any] = tokenizer(lowercase , return_tensors='pt')
with torch.no_grad():
a__ : Union[str, Any] = model(encoding.input_ids).logits
a__ : str = token_logits[:, 2, :].argmax(-1)[0]
self.assertEqual(tokenizer.decode(lowercase) , 'capital')
| 225 | 0 |
'''simple docstring'''
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow
if is_torch_available():
import torch
from transformers import XLMRobertaModel
@require_sentencepiece
@require_tokenizers
@require_torch
class a_ ( unittest.TestCase ):
@slow
def A__ ( self ) -> List[Any]:
"""simple docstring"""
UpperCamelCase = XLMRobertaModel.from_pretrained("""xlm-roberta-base""" )
UpperCamelCase = torch.tensor([[0, 581, 10269, 83, 99942, 136, 60742, 23, 70, 80583, 18276, 2]] )
# The dog is cute and lives in the garden house
UpperCamelCase = torch.Size((1, 12, 768) ) # batch_size, sequence_length, embedding_vector_dim
UpperCamelCase = torch.tensor(
[[-0.0_1_0_1, 0.1_2_1_8, -0.0_8_0_3, 0.0_8_0_1, 0.1_3_2_7, 0.0_7_7_6, -0.1_2_1_5, 0.2_3_8_3, 0.3_3_3_8, 0.3_1_0_6, 0.0_3_0_0, 0.0_2_5_2]] )
# xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.base')
# xlmr.eval()
# expected_output_values_last_dim = xlmr.extract_features(input_ids[0])[:, :, -1]
with torch.no_grad():
UpperCamelCase = model(_SCREAMING_SNAKE_CASE )["""last_hidden_state"""].detach()
self.assertEqual(output.shape , _SCREAMING_SNAKE_CASE )
# compare the actual values for a slice of last dim
self.assertTrue(torch.allclose(output[:, :, -1] , _SCREAMING_SNAKE_CASE , atol=1e-3 ) )
@slow
def A__ ( self ) -> Any:
"""simple docstring"""
UpperCamelCase = XLMRobertaModel.from_pretrained("""xlm-roberta-large""" )
UpperCamelCase = torch.tensor([[0, 581, 10269, 83, 99942, 136, 60742, 23, 70, 80583, 18276, 2]] )
# The dog is cute and lives in the garden house
UpperCamelCase = torch.Size((1, 12, 1024) ) # batch_size, sequence_length, embedding_vector_dim
UpperCamelCase = torch.tensor(
[[-0.0_6_9_9, -0.0_3_1_8, 0.0_7_0_5, -0.1_2_4_1, 0.0_9_9_9, -0.0_5_2_0, 0.1_0_0_4, -0.1_8_3_8, -0.4_7_0_4, 0.1_4_3_7, 0.0_8_2_1, 0.0_1_2_6]] )
# xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.large')
# xlmr.eval()
# expected_output_values_last_dim = xlmr.extract_features(input_ids[0])[:, :, -1]
with torch.no_grad():
UpperCamelCase = model(_SCREAMING_SNAKE_CASE )["""last_hidden_state"""].detach()
self.assertEqual(output.shape , _SCREAMING_SNAKE_CASE )
# compare the actual values for a slice of last dim
self.assertTrue(torch.allclose(output[:, :, -1] , _SCREAMING_SNAKE_CASE , atol=1e-3 ) )
| 321 |
'''simple docstring'''
import argparse
from collections import defaultdict
import yaml
SCREAMING_SNAKE_CASE__ = 'docs/source/en/_toctree.yml'
def lowercase__ ( __UpperCamelCase )-> Optional[Any]:
UpperCamelCase = defaultdict(__UpperCamelCase )
UpperCamelCase = []
UpperCamelCase = []
for doc in doc_list:
if "local" in doc:
counts[doc["local"]] += 1
if doc["title"].lower() == "overview":
overview_doc.append({"""local""": doc["""local"""], """title""": doc["""title"""]} )
else:
new_doc_list.append(__UpperCamelCase )
UpperCamelCase = new_doc_list
UpperCamelCase = [key for key, value in counts.items() if value > 1]
UpperCamelCase = []
for duplicate_key in duplicates:
UpperCamelCase = list({doc["""title"""] for doc in doc_list if doc["""local"""] == duplicate_key} )
if len(__UpperCamelCase ) > 1:
raise ValueError(
F"{duplicate_key} is present several times in the documentation table of content at "
"""`docs/source/en/_toctree.yml` with different *Title* values. Choose one of those and remove the """
"""others.""" )
# Only add this once
new_doc.append({"""local""": duplicate_key, """title""": titles[0]} )
# Add none duplicate-keys
new_doc.extend([doc for doc in doc_list if """local""" not in counts or counts[doc["""local"""]] == 1] )
UpperCamelCase = sorted(__UpperCamelCase , key=lambda __UpperCamelCase : s["title"].lower() )
# "overview" gets special treatment and is always first
if len(__UpperCamelCase ) > 1:
raise ValueError("""{doc_list} has two 'overview' docs which is not allowed.""" )
overview_doc.extend(__UpperCamelCase )
# Sort
return overview_doc
def lowercase__ ( __UpperCamelCase=False )-> List[str]:
with open(__UpperCamelCase , encoding="""utf-8""" ) as f:
UpperCamelCase = yaml.safe_load(f.read() )
# Get to the API doc
UpperCamelCase = 0
while content[api_idx]["title"] != "API":
api_idx += 1
UpperCamelCase = content[api_idx]["""sections"""]
# Then to the model doc
UpperCamelCase = 0
while api_doc[scheduler_idx]["title"] != "Schedulers":
scheduler_idx += 1
UpperCamelCase = api_doc[scheduler_idx]["""sections"""]
UpperCamelCase = clean_doc_toc(__UpperCamelCase )
UpperCamelCase = False
if new_scheduler_doc != scheduler_doc:
UpperCamelCase = True
if overwrite:
UpperCamelCase = new_scheduler_doc
if diff:
if overwrite:
UpperCamelCase = api_doc
with open(__UpperCamelCase , """w""" , encoding="""utf-8""" ) as f:
f.write(yaml.dump(__UpperCamelCase , allow_unicode=__UpperCamelCase ) )
else:
raise ValueError(
"""The model doc part of the table of content is not properly sorted, run `make style` to fix this.""" )
def lowercase__ ( __UpperCamelCase=False )-> Tuple:
with open(__UpperCamelCase , encoding="""utf-8""" ) as f:
UpperCamelCase = yaml.safe_load(f.read() )
# Get to the API doc
UpperCamelCase = 0
while content[api_idx]["title"] != "API":
api_idx += 1
UpperCamelCase = content[api_idx]["""sections"""]
# Then to the model doc
UpperCamelCase = 0
while api_doc[pipeline_idx]["title"] != "Pipelines":
pipeline_idx += 1
UpperCamelCase = False
UpperCamelCase = api_doc[pipeline_idx]["""sections"""]
UpperCamelCase = []
# sort sub pipeline docs
for pipeline_doc in pipeline_docs:
if "section" in pipeline_doc:
UpperCamelCase = pipeline_doc["""section"""]
UpperCamelCase = clean_doc_toc(__UpperCamelCase )
if overwrite:
UpperCamelCase = new_sub_pipeline_doc
new_pipeline_docs.append(__UpperCamelCase )
# sort overall pipeline doc
UpperCamelCase = clean_doc_toc(__UpperCamelCase )
if new_pipeline_docs != pipeline_docs:
UpperCamelCase = True
if overwrite:
UpperCamelCase = new_pipeline_docs
if diff:
if overwrite:
UpperCamelCase = api_doc
with open(__UpperCamelCase , """w""" , encoding="""utf-8""" ) as f:
f.write(yaml.dump(__UpperCamelCase , allow_unicode=__UpperCamelCase ) )
else:
raise ValueError(
"""The model doc part of the table of content is not properly sorted, run `make style` to fix this.""" )
if __name__ == "__main__":
SCREAMING_SNAKE_CASE__ = argparse.ArgumentParser()
parser.add_argument('--fix_and_overwrite', action='store_true', help='Whether to fix inconsistencies.')
SCREAMING_SNAKE_CASE__ = parser.parse_args()
check_scheduler_doc(args.fix_and_overwrite)
check_pipeline_doc(args.fix_and_overwrite)
| 321 | 1 |
'''simple docstring'''
import darl # noqa
import gym
import tqdm
from diffusers.experimental import ValueGuidedRLPipeline
__lowercase: Dict = {
"n_samples": 64,
"horizon": 32,
"num_inference_steps": 20,
"n_guide_steps": 2, # can set to 0 for faster sampling, does not use value network
"scale_grad_by_std": True,
"scale": 0.1,
"eta": 0.0,
"t_grad_cutoff": 2,
"device": "cpu",
}
if __name__ == "__main__":
__lowercase: Optional[int] = "hopper-medium-v2"
__lowercase: str = gym.make(env_name)
__lowercase: Optional[Any] = ValueGuidedRLPipeline.from_pretrained(
"bglick13/hopper-medium-v2-value-function-hor32",
env=env,
)
env.seed(0)
__lowercase: Optional[Any] = env.reset()
__lowercase: Tuple = 0
__lowercase: int = 0
__lowercase: List[Any] = 1_000
__lowercase: Union[str, Any] = [obs.copy()]
try:
for t in tqdm.tqdm(range(T)):
# call the policy
__lowercase: Dict = pipeline(obs, planning_horizon=32)
# execute action in environment
__lowercase ,__lowercase ,__lowercase ,__lowercase: Dict = env.step(denorm_actions)
__lowercase: List[Any] = env.get_normalized_score(total_reward)
# update return
total_reward += reward
total_score += score
print(
F"""Step: {t}, Reward: {reward}, Total Reward: {total_reward}, Score: {score}, Total Score:"""
F""" {total_score}"""
)
# save observations for rendering
rollout.append(next_observation.copy())
__lowercase: str = next_observation
except KeyboardInterrupt:
pass
print(F"""Total reward: {total_reward}""") | 31 |
'''simple docstring'''
from ..utils import DummyObject, requires_backends
class UpperCAmelCase ( metaclass=SCREAMING_SNAKE_CASE__):
_lowerCamelCase : str = ['torch', 'scipy']
def __init__( self : List[str], *a_ : Optional[int], **a_ : int ):
"""simple docstring"""
requires_backends(self, ["torch", "scipy"] )
@classmethod
def lowercase_ ( cls : Dict, *a_ : Tuple, **a_ : Dict ):
"""simple docstring"""
requires_backends(cls, ["torch", "scipy"] )
@classmethod
def lowercase_ ( cls : Optional[Any], *a_ : List[Any], **a_ : Any ):
"""simple docstring"""
requires_backends(cls, ["torch", "scipy"] ) | 31 | 1 |
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class __snake_case ( UpperCamelCase_ ):
_a = ['''image_processor''', '''tokenizer''']
_a = '''AutoImageProcessor'''
_a = '''AutoTokenizer'''
def __init__( self : Tuple , A_ : int , A_ : int):
super().__init__(A_ , A_)
lowerCAmelCase_ : Optional[int] = self.image_processor
def __call__( self : List[str] , A_ : Tuple=None , A_ : List[Any]=None , A_ : Any=None , **A_ : List[str]):
if text is None and images is None:
raise ValueError('''You have to specify either text or images. Both cannot be none.''')
if text is not None:
lowerCAmelCase_ : List[Any] = self.tokenizer(A_ , return_tensors=A_ , **A_)
if images is not None:
lowerCAmelCase_ : List[Any] = self.image_processor(A_ , return_tensors=A_ , **A_)
if text is not None and images is not None:
lowerCAmelCase_ : Tuple = image_features.pixel_values
return encoding
elif text is not None:
return encoding
else:
return BatchEncoding(data=dict(**A_) , tensor_type=A_)
def UpperCAmelCase__ ( self : Tuple , *A_ : Any , **A_ : int):
return self.tokenizer.batch_decode(*A_ , **A_)
def UpperCAmelCase__ ( self : str , *A_ : Optional[Any] , **A_ : Optional[Any]):
return self.tokenizer.decode(*A_ , **A_)
@property
def UpperCAmelCase__ ( self : int):
return ["input_ids", "attention_mask", "pixel_values"]
| 103 |
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_base import BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import PaddingStrategy, logging
from .tokenization_realm import RealmTokenizer
A__ : str = logging.get_logger(__name__)
A__ : Any = {'''vocab_file''': '''vocab.txt''', '''tokenizer_file''': '''tokenizer.json'''}
A__ : str = {
'''vocab_file''': {
'''google/realm-cc-news-pretrained-embedder''': (
'''https://huggingface.co/google/realm-cc-news-pretrained-embedder/resolve/main/vocab.txt'''
),
'''google/realm-cc-news-pretrained-encoder''': (
'''https://huggingface.co/google/realm-cc-news-pretrained-encoder/resolve/main/vocab.txt'''
),
'''google/realm-cc-news-pretrained-scorer''': (
'''https://huggingface.co/google/realm-cc-news-pretrained-scorer/resolve/main/vocab.txt'''
),
'''google/realm-cc-news-pretrained-openqa''': (
'''https://huggingface.co/google/realm-cc-news-pretrained-openqa/aresolve/main/vocab.txt'''
),
'''google/realm-orqa-nq-openqa''': '''https://huggingface.co/google/realm-orqa-nq-openqa/resolve/main/vocab.txt''',
'''google/realm-orqa-nq-reader''': '''https://huggingface.co/google/realm-orqa-nq-reader/resolve/main/vocab.txt''',
'''google/realm-orqa-wq-openqa''': '''https://huggingface.co/google/realm-orqa-wq-openqa/resolve/main/vocab.txt''',
'''google/realm-orqa-wq-reader''': '''https://huggingface.co/google/realm-orqa-wq-reader/resolve/main/vocab.txt''',
},
'''tokenizer_file''': {
'''google/realm-cc-news-pretrained-embedder''': (
'''https://huggingface.co/google/realm-cc-news-pretrained-embedder/resolve/main/tokenizer.jsont'''
),
'''google/realm-cc-news-pretrained-encoder''': (
'''https://huggingface.co/google/realm-cc-news-pretrained-encoder/resolve/main/tokenizer.json'''
),
'''google/realm-cc-news-pretrained-scorer''': (
'''https://huggingface.co/google/realm-cc-news-pretrained-scorer/resolve/main/tokenizer.json'''
),
'''google/realm-cc-news-pretrained-openqa''': (
'''https://huggingface.co/google/realm-cc-news-pretrained-openqa/aresolve/main/tokenizer.json'''
),
'''google/realm-orqa-nq-openqa''': (
'''https://huggingface.co/google/realm-orqa-nq-openqa/resolve/main/tokenizer.json'''
),
'''google/realm-orqa-nq-reader''': (
'''https://huggingface.co/google/realm-orqa-nq-reader/resolve/main/tokenizer.json'''
),
'''google/realm-orqa-wq-openqa''': (
'''https://huggingface.co/google/realm-orqa-wq-openqa/resolve/main/tokenizer.json'''
),
'''google/realm-orqa-wq-reader''': (
'''https://huggingface.co/google/realm-orqa-wq-reader/resolve/main/tokenizer.json'''
),
},
}
A__ : Union[str, Any] = {
'''google/realm-cc-news-pretrained-embedder''': 512,
'''google/realm-cc-news-pretrained-encoder''': 512,
'''google/realm-cc-news-pretrained-scorer''': 512,
'''google/realm-cc-news-pretrained-openqa''': 512,
'''google/realm-orqa-nq-openqa''': 512,
'''google/realm-orqa-nq-reader''': 512,
'''google/realm-orqa-wq-openqa''': 512,
'''google/realm-orqa-wq-reader''': 512,
}
A__ : Dict = {
'''google/realm-cc-news-pretrained-embedder''': {'''do_lower_case''': True},
'''google/realm-cc-news-pretrained-encoder''': {'''do_lower_case''': True},
'''google/realm-cc-news-pretrained-scorer''': {'''do_lower_case''': True},
'''google/realm-cc-news-pretrained-openqa''': {'''do_lower_case''': True},
'''google/realm-orqa-nq-openqa''': {'''do_lower_case''': True},
'''google/realm-orqa-nq-reader''': {'''do_lower_case''': True},
'''google/realm-orqa-wq-openqa''': {'''do_lower_case''': True},
'''google/realm-orqa-wq-reader''': {'''do_lower_case''': True},
}
class __snake_case ( UpperCamelCase_ ):
_a = VOCAB_FILES_NAMES
_a = PRETRAINED_VOCAB_FILES_MAP
_a = PRETRAINED_INIT_CONFIGURATION
_a = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
_a = RealmTokenizer
def __init__( self : int , A_ : Optional[int]=None , A_ : Optional[Any]=None , A_ : Optional[Any]=True , A_ : Optional[int]="[UNK]" , A_ : List[Any]="[SEP]" , A_ : List[Any]="[PAD]" , A_ : Optional[Any]="[CLS]" , A_ : Dict="[MASK]" , A_ : List[Any]=True , A_ : List[str]=None , **A_ : List[str] , ):
super().__init__(
A_ , tokenizer_file=A_ , do_lower_case=A_ , unk_token=A_ , sep_token=A_ , pad_token=A_ , cls_token=A_ , mask_token=A_ , tokenize_chinese_chars=A_ , strip_accents=A_ , **A_ , )
lowerCAmelCase_ : Optional[int] = json.loads(self.backend_tokenizer.normalizer.__getstate__())
if (
normalizer_state.get('''lowercase''' , A_) != do_lower_case
or normalizer_state.get('''strip_accents''' , A_) != strip_accents
or normalizer_state.get('''handle_chinese_chars''' , A_) != tokenize_chinese_chars
):
lowerCAmelCase_ : int = getattr(A_ , normalizer_state.pop('''type'''))
lowerCAmelCase_ : str = do_lower_case
lowerCAmelCase_ : Dict = strip_accents
lowerCAmelCase_ : Optional[Any] = tokenize_chinese_chars
lowerCAmelCase_ : Union[str, Any] = normalizer_class(**A_)
lowerCAmelCase_ : Any = do_lower_case
def UpperCAmelCase__ ( self : Optional[Any] , A_ : Optional[Any] , **A_ : Tuple):
lowerCAmelCase_ : List[str] = PaddingStrategy.MAX_LENGTH
lowerCAmelCase_ : str = text
lowerCAmelCase_ : int = kwargs.pop('''text_pair''' , A_)
lowerCAmelCase_ : str = kwargs.pop('''return_tensors''' , A_)
lowerCAmelCase_ : int = {
'''input_ids''': [],
'''attention_mask''': [],
'''token_type_ids''': [],
}
for idx, candidate_text in enumerate(A_):
if batch_text_pair is not None:
lowerCAmelCase_ : List[Any] = batch_text_pair[idx]
else:
lowerCAmelCase_ : List[Any] = None
lowerCAmelCase_ : int = super().__call__(A_ , A_ , return_tensors=A_ , **A_)
lowerCAmelCase_ : Optional[Any] = encoded_candidates.get('''input_ids''')
lowerCAmelCase_ : List[str] = encoded_candidates.get('''attention_mask''')
lowerCAmelCase_ : Optional[Any] = encoded_candidates.get('''token_type_ids''')
if encoded_input_ids is not None:
output_data["input_ids"].append(A_)
if encoded_attention_mask is not None:
output_data["attention_mask"].append(A_)
if encoded_token_type_ids is not None:
output_data["token_type_ids"].append(A_)
lowerCAmelCase_ : List[str] = {key: item for key, item in output_data.items() if len(A_) != 0}
return BatchEncoding(A_ , tensor_type=A_)
def UpperCAmelCase__ ( self : List[str] , A_ : Tuple , A_ : List[Any]=None):
lowerCAmelCase_ : Optional[Any] = [self.cls_token_id] + token_ids_a + [self.sep_token_id]
if token_ids_a:
output += token_ids_a + [self.sep_token_id]
return output
def UpperCAmelCase__ ( self : Tuple , A_ : List[int] , A_ : Optional[List[int]] = None):
lowerCAmelCase_ : Tuple = [self.sep_token_id]
lowerCAmelCase_ : Union[str, Any] = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep) * [0]
return len(cls + token_ids_a + sep) * [0] + len(token_ids_a + sep) * [1]
def UpperCAmelCase__ ( self : List[str] , A_ : str , A_ : Optional[str] = None):
lowerCAmelCase_ : List[str] = self._tokenizer.model.save(A_ , name=A_)
return tuple(A_)
| 103 | 1 |
'''simple docstring'''
def lowerCamelCase ( lowerCAmelCase : str , lowerCAmelCase : Any , lowerCAmelCase : Any=False ):
"""simple docstring"""
if isinstance(lowerCAmelCase , lowerCAmelCase ) and isinstance(lowerCAmelCase , lowerCAmelCase ):
__magic_name__ : str = len(set_a.intersection(lowerCAmelCase ) )
if alternative_union:
__magic_name__ : List[str] = len(lowerCAmelCase ) + len(lowerCAmelCase )
else:
__magic_name__ : Any = len(set_a.union(lowerCAmelCase ) )
return intersection / union
if isinstance(lowerCAmelCase , (list, tuple) ) and isinstance(lowerCAmelCase , (list, tuple) ):
__magic_name__ : str = [element for element in set_a if element in set_b]
if alternative_union:
__magic_name__ : Dict = len(lowerCAmelCase ) + len(lowerCAmelCase )
return len(lowerCAmelCase ) / union
else:
__magic_name__ : Any = set_a + [element for element in set_b if element not in set_a]
return len(lowerCAmelCase ) / len(lowerCAmelCase )
return len(lowerCAmelCase ) / len(lowerCAmelCase )
return None
if __name__ == "__main__":
lowerCAmelCase :Dict = {'''a''', '''b''', '''c''', '''d''', '''e'''}
lowerCAmelCase :Tuple = {'''c''', '''d''', '''e''', '''f''', '''h''', '''i'''}
print(jaccard_similarity(set_a, set_b)) | 364 |
'''simple docstring'''
import os
import unittest
from transformers import LxmertTokenizer, LxmertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class _lowerCamelCase ( lowercase__ , unittest.TestCase ):
'''simple docstring'''
A_ : Optional[Any] = LxmertTokenizer
A_ : List[Any] = LxmertTokenizerFast
A_ : int = True
A_ : Any = True
def __lowerCAmelCase ( self : List[str] ) -> Tuple:
super().setUp()
__magic_name__ : str = [
'[UNK]',
'[CLS]',
'[SEP]',
'want',
'##want',
'##ed',
'wa',
'un',
'runn',
'##ing',
',',
'low',
'lowest',
]
__magic_name__ : List[str] = 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 : Any , _A : str ) -> List[Any]:
__magic_name__ : Dict = 'UNwant\u00E9d,running'
__magic_name__ : Dict = 'unwanted, running'
return input_text, output_text
def __lowerCAmelCase ( self : Tuple ) -> Optional[int]:
__magic_name__ : Optional[Any] = self.tokenizer_class(self.vocab_file )
__magic_name__ : List[str] = tokenizer.tokenize('UNwant\u00E9d,running' )
self.assertListEqual(_A , ['un', '##want', '##ed', ',', 'runn', '##ing'] )
self.assertListEqual(tokenizer.convert_tokens_to_ids(_A ) , [7, 4, 5, 10, 8, 9] )
def __lowerCAmelCase ( self : int ) -> List[Any]:
if not self.test_rust_tokenizer:
return
__magic_name__ : Any = self.get_tokenizer()
__magic_name__ : Optional[Any] = self.get_rust_tokenizer()
__magic_name__ : Union[str, Any] = 'I was born in 92000, and this is falsé.'
__magic_name__ : List[Any] = tokenizer.tokenize(_A )
__magic_name__ : Dict = rust_tokenizer.tokenize(_A )
self.assertListEqual(_A , _A )
__magic_name__ : int = tokenizer.encode(_A , add_special_tokens=_A )
__magic_name__ : Union[str, Any] = rust_tokenizer.encode(_A , add_special_tokens=_A )
self.assertListEqual(_A , _A )
__magic_name__ : List[Any] = self.get_rust_tokenizer()
__magic_name__ : str = tokenizer.encode(_A )
__magic_name__ : Optional[int] = rust_tokenizer.encode(_A )
self.assertListEqual(_A , _A ) | 275 | 0 |
'''simple docstring'''
from __future__ import annotations
from collections.abc import Generator
def _A ( ):
"""simple docstring"""
__lowercase ={}
__lowercase =2
while True:
__lowercase =factor_map.pop(_lowerCAmelCase , _lowerCAmelCase )
if factor:
__lowercase =factor + prime
while x in factor_map:
x += factor
__lowercase =factor
else:
__lowercase =prime
yield prime
prime += 1
def _A ( _lowerCAmelCase = 1e1_0 ):
"""simple docstring"""
__lowercase =sieve()
__lowercase =1
while True:
__lowercase =next(_lowerCAmelCase )
if (2 * prime * n) > limit:
return n
# Ignore the next prime as the reminder will be 2.
next(_lowerCAmelCase )
n += 2
if __name__ == "__main__":
print(solution())
| 166 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowerCamelCase = {
"""configuration_mctct""": ["""MCTCT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """MCTCTConfig"""],
"""feature_extraction_mctct""": ["""MCTCTFeatureExtractor"""],
"""processing_mctct""": ["""MCTCTProcessor"""],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCamelCase = [
"""MCTCT_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""MCTCTForCTC""",
"""MCTCTModel""",
"""MCTCTPreTrainedModel""",
]
if TYPE_CHECKING:
from .configuration_mctct import MCTCT_PRETRAINED_CONFIG_ARCHIVE_MAP, MCTCTConfig
from .feature_extraction_mctct import MCTCTFeatureExtractor
from .processing_mctct import MCTCTProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_mctct import MCTCT_PRETRAINED_MODEL_ARCHIVE_LIST, MCTCTForCTC, MCTCTModel, MCTCTPreTrainedModel
else:
import sys
lowerCamelCase = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 166 | 1 |
'''simple docstring'''
from unittest import TestCase
from datasets import Sequence, Value
from datasets.arrow_dataset import Dataset
class lowerCamelCase ( lowercase_ ):
'''simple docstring'''
def lowercase__ ( self : List[Any] ) -> Union[str, Any]:
'''simple docstring'''
return [
{"col_1": 3, "col_2": "a"},
{"col_1": 2, "col_2": "b"},
{"col_1": 1, "col_2": "c"},
{"col_1": 0, "col_2": "d"},
]
def lowercase__ ( self : int ) -> List[Any]:
'''simple docstring'''
A__ : Union[str, Any] ={"""col_1""": [3, 2, 1, 0], """col_2""": ["""a""", """b""", """c""", """d"""]}
return Dataset.from_dict(lowerCAmelCase_ )
def lowercase__ ( self : Optional[Any] ) -> int:
'''simple docstring'''
A__ : str =self._create_example_records()
A__ : Dict =Dataset.from_list(lowerCAmelCase_ )
self.assertListEqual(dset.column_names , ["""col_1""", """col_2"""] )
for i, r in enumerate(lowerCAmelCase_ ):
self.assertDictEqual(lowerCAmelCase_ , example_records[i] )
def lowercase__ ( self : Union[str, Any] ) -> Union[str, Any]:
'''simple docstring'''
A__ : Any =self._create_example_records()
A__ : List[Any] =Dataset.from_list(lowerCAmelCase_ )
A__ : Union[str, Any] =Dataset.from_dict({k: [r[k] for r in example_records] for k in example_records[0]} )
self.assertEqual(dset.info , dset_from_dict.info )
def lowercase__ ( self : Dict ) -> str: # checks what happens with missing columns
'''simple docstring'''
A__ : Optional[int] =[{"""col_1""": 1}, {"""col_2""": """x"""}]
A__ : int =Dataset.from_list(lowerCAmelCase_ )
self.assertDictEqual(dset[0] , {"""col_1""": 1} )
self.assertDictEqual(dset[1] , {"""col_1""": None} ) # NB: first record is used for columns
def lowercase__ ( self : Union[str, Any] ) -> Dict: # checks if the type can be inferred from the second record
'''simple docstring'''
A__ : List[Any] =[{"""col_1""": []}, {"""col_1""": [1, 2]}]
A__ : List[Any] =Dataset.from_list(lowerCAmelCase_ )
self.assertEqual(dset.info.features["""col_1"""] , Sequence(Value("""int64""" ) ) )
def lowercase__ ( self : Any ) -> Any:
'''simple docstring'''
A__ : Dict =Dataset.from_list([] )
self.assertEqual(len(lowerCAmelCase_ ) , 0 )
self.assertListEqual(dset.column_names , [] )
| 366 |
'''simple docstring'''
import torch
from torch import nn
class lowerCamelCase ( nn.Module ):
'''simple docstring'''
def __init__( self : Any , lowerCAmelCase_ : List[str] , lowerCAmelCase_ : List[Any] , lowerCAmelCase_ : Dict , lowerCAmelCase_ : str , lowerCAmelCase_ : Optional[int]=1 , lowerCAmelCase_ : str=False ) -> List[str]:
'''simple docstring'''
super().__init__()
A__ : Any =n_token
A__ : int =d_embed
A__ : Any =d_proj
A__ : Tuple =cutoffs + [n_token]
A__ : Optional[Any] =[0] + self.cutoffs
A__ : Dict =div_val
A__ : str =self.cutoffs[0]
A__ : Optional[Any] =len(self.cutoffs ) - 1
A__ : List[Any] =self.shortlist_size + self.n_clusters
if self.n_clusters > 0:
A__ : Any =nn.Parameter(torch.zeros(self.n_clusters , self.d_embed ) )
A__ : str =nn.Parameter(torch.zeros(self.n_clusters ) )
A__ : Union[str, Any] =nn.ModuleList()
A__ : Optional[int] =nn.ParameterList()
if div_val == 1:
for i in range(len(self.cutoffs ) ):
if d_proj != d_embed:
self.out_projs.append(nn.Parameter(torch.FloatTensor(lowerCAmelCase_ , lowerCAmelCase_ ) ) )
else:
self.out_projs.append(lowerCAmelCase_ )
self.out_layers.append(nn.Linear(lowerCAmelCase_ , lowerCAmelCase_ ) )
else:
for i in range(len(self.cutoffs ) ):
A__ , A__ : Optional[int] =self.cutoff_ends[i], self.cutoff_ends[i + 1]
A__ : Tuple =d_embed // (div_val**i)
self.out_projs.append(nn.Parameter(torch.FloatTensor(lowerCAmelCase_ , lowerCAmelCase_ ) ) )
self.out_layers.append(nn.Linear(lowerCAmelCase_ , r_idx - l_idx ) )
A__ : Optional[int] =keep_order
def lowercase__ ( self : Dict , lowerCAmelCase_ : int , lowerCAmelCase_ : Tuple , lowerCAmelCase_ : Union[str, Any] , lowerCAmelCase_ : Any ) -> Union[str, Any]:
'''simple docstring'''
if proj is None:
A__ : Optional[int] =nn.functional.linear(lowerCAmelCase_ , lowerCAmelCase_ , bias=lowerCAmelCase_ )
else:
# if CUDA_MAJOR <= 9 and CUDA_MINOR <= 1:
A__ : Optional[int] =nn.functional.linear(lowerCAmelCase_ , proj.t().contiguous() )
A__ : Union[str, Any] =nn.functional.linear(lowerCAmelCase_ , lowerCAmelCase_ , bias=lowerCAmelCase_ )
# else:
# logit = torch.einsum('bd,de,ev->bv', (hidden, proj, weight.t()))
# if bias is not None:
# logit = logit + bias
return logit
def lowercase__ ( self : Tuple , lowerCAmelCase_ : List[str] , lowerCAmelCase_ : List[Any]=None , lowerCAmelCase_ : Dict=False ) -> Optional[int]:
'''simple docstring'''
if labels is not None:
# Shift so that tokens < n predict n
A__ : Optional[Any] =hidden[..., :-1, :].contiguous()
A__ : List[Any] =labels[..., 1:].contiguous()
A__ : Optional[int] =hidden.view(-1 , hidden.size(-1 ) )
A__ : str =labels.view(-1 )
if hidden.size(0 ) != labels.size(0 ):
raise RuntimeError("""Input and labels should have the same size in the batch dimension.""" )
else:
A__ : Optional[int] =hidden.view(-1 , hidden.size(-1 ) )
if self.n_clusters == 0:
A__ : Optional[Any] =self._compute_logit(lowerCAmelCase_ , self.out_layers[0].weight , self.out_layers[0].bias , self.out_projs[0] )
if labels is not None:
A__ : Tuple =labels != -1_00
A__ : int =torch.zeros_like(lowerCAmelCase_ , dtype=hidden.dtype , device=hidden.device )
A__ : Union[str, Any] =(
-nn.functional.log_softmax(lowerCAmelCase_ , dim=-1 )[mask].gather(1 , labels[mask].unsqueeze(1 ) ).squeeze(1 )
)
else:
A__ : List[Any] =nn.functional.log_softmax(lowerCAmelCase_ , dim=-1 )
else:
# construct weights and biases
A__ , A__ : Any =[], []
for i in range(len(self.cutoffs ) ):
if self.div_val == 1:
A__ , A__ : Optional[int] =self.cutoff_ends[i], self.cutoff_ends[i + 1]
A__ : int =self.out_layers[0].weight[l_idx:r_idx]
A__ : List[str] =self.out_layers[0].bias[l_idx:r_idx]
else:
A__ : List[str] =self.out_layers[i].weight
A__ : Union[str, Any] =self.out_layers[i].bias
if i == 0:
A__ : Tuple =torch.cat([weight_i, self.cluster_weight] , dim=0 )
A__ : List[str] =torch.cat([bias_i, self.cluster_bias] , dim=0 )
weights.append(lowerCAmelCase_ )
biases.append(lowerCAmelCase_ )
A__ , A__ , A__ : Tuple =weights[0], biases[0], self.out_projs[0]
A__ : List[Any] =self._compute_logit(lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ )
A__ : int =nn.functional.log_softmax(lowerCAmelCase_ , dim=1 )
if labels is None:
A__ : Union[str, Any] =hidden.new_empty((head_logit.size(0 ), self.n_token) )
else:
A__ : Union[str, Any] =torch.zeros_like(lowerCAmelCase_ , dtype=hidden.dtype , device=hidden.device )
A__ : Any =0
A__ : Tuple =[0] + self.cutoffs
for i in range(len(lowerCAmelCase_ ) - 1 ):
A__ , A__ : Tuple =cutoff_values[i], cutoff_values[i + 1]
if labels is not None:
A__ : Tuple =(labels >= l_idx) & (labels < r_idx)
A__ : Any =mask_i.nonzero().squeeze()
if indices_i.numel() == 0:
continue
A__ : int =labels.index_select(0 , lowerCAmelCase_ ) - l_idx
A__ : List[str] =head_logprob.index_select(0 , lowerCAmelCase_ )
A__ : str =hidden.index_select(0 , lowerCAmelCase_ )
else:
A__ : Optional[Any] =hidden
if i == 0:
if labels is not None:
A__ : Optional[Any] =head_logprob_i.gather(1 , target_i[:, None] ).squeeze(1 )
else:
A__ : Union[str, Any] =head_logprob[:, : self.cutoffs[0]]
else:
A__ , A__ , A__ : Dict =weights[i], biases[i], self.out_projs[i]
A__ : List[Any] =self._compute_logit(lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ )
A__ : List[Any] =nn.functional.log_softmax(lowerCAmelCase_ , dim=1 )
A__ : Optional[Any] =self.cutoffs[0] + i - 1 # No probability for the head cluster
if labels is not None:
A__ : Union[str, Any] =head_logprob_i[:, cluster_prob_idx] + tail_logprob_i.gather(
1 , target_i[:, None] ).squeeze(1 )
else:
A__ : List[str] =head_logprob[:, cluster_prob_idx, None] + tail_logprob_i
A__ : Tuple =logprob_i
if labels is not None:
if (hasattr(self , """keep_order""" ) and self.keep_order) or keep_order:
out.index_copy_(0 , lowerCAmelCase_ , -logprob_i )
else:
out[offset : offset + logprob_i.size(0 )].copy_(-logprob_i )
offset += logprob_i.size(0 )
return out
def lowercase__ ( self : List[str] , lowerCAmelCase_ : Optional[Any] ) -> Any:
'''simple docstring'''
if self.n_clusters == 0:
A__ : List[str] =self._compute_logit(lowerCAmelCase_ , self.out_layers[0].weight , self.out_layers[0].bias , self.out_projs[0] )
return nn.functional.log_softmax(lowerCAmelCase_ , dim=-1 )
else:
# construct weights and biases
A__ , A__ : List[str] =[], []
for i in range(len(self.cutoffs ) ):
if self.div_val == 1:
A__ , A__ : int =self.cutoff_ends[i], self.cutoff_ends[i + 1]
A__ : List[str] =self.out_layers[0].weight[l_idx:r_idx]
A__ : List[Any] =self.out_layers[0].bias[l_idx:r_idx]
else:
A__ : Dict =self.out_layers[i].weight
A__ : Any =self.out_layers[i].bias
if i == 0:
A__ : List[str] =torch.cat([weight_i, self.cluster_weight] , dim=0 )
A__ : Tuple =torch.cat([bias_i, self.cluster_bias] , dim=0 )
weights.append(lowerCAmelCase_ )
biases.append(lowerCAmelCase_ )
A__ , A__ , A__ : Optional[int] =weights[0], biases[0], self.out_projs[0]
A__ : Any =self._compute_logit(lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ )
A__ : Dict =hidden.new_empty((head_logit.size(0 ), self.n_token) )
A__ : Dict =nn.functional.log_softmax(lowerCAmelCase_ , dim=1 )
A__ : Tuple =[0] + self.cutoffs
for i in range(len(lowerCAmelCase_ ) - 1 ):
A__ , A__ : List[Any] =cutoff_values[i], cutoff_values[i + 1]
if i == 0:
A__ : Tuple =head_logprob[:, : self.cutoffs[0]]
else:
A__ , A__ , A__ : Any =weights[i], biases[i], self.out_projs[i]
A__ : Dict =self._compute_logit(lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ )
A__ : str =nn.functional.log_softmax(lowerCAmelCase_ , dim=1 )
A__ : str =head_logprob[:, -i] + tail_logprob_i
A__ : List[Any] =logprob_i
return out
| 136 | 0 |
"""simple docstring"""
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import DetrConfig, DetrForObjectDetection, DetrForSegmentation, DetrImageProcessor, ResNetConfig
from transformers.utils import logging
logging.set_verbosity_info()
_lowercase = logging.get_logger(__name__)
def _snake_case ( snake_case__ : Dict ):
# initialize config
if "resnet-50" in model_name:
A = ResNetConfig.from_pretrained('microsoft/resnet-50' )
elif "resnet-101" in model_name:
A = ResNetConfig.from_pretrained('microsoft/resnet-101' )
else:
raise ValueError('Model name should include either resnet50 or resnet101' )
A = DetrConfig(use_timm_backbone=snake_case__ , backbone_config=snake_case__ )
# set label attributes
A = 'panoptic' in model_name
if is_panoptic:
A = 250
else:
A = 91
A = 'huggingface/label-files'
A = 'coco-detection-id2label.json'
A = json.load(open(hf_hub_download(snake_case__ , snake_case__ , repo_type='dataset' ) , 'r' ) )
A = {int(snake_case__ ): v for k, v in idalabel.items()}
A = idalabel
A = {v: k for k, v in idalabel.items()}
return config, is_panoptic
def _snake_case ( snake_case__ : int ):
# here we list all keys to be renamed (original name on the left, our name on the right)
A = []
# stem
# fmt: off
rename_keys.append(('backbone.0.body.conv1.weight', 'backbone.conv_encoder.model.embedder.embedder.convolution.weight') )
rename_keys.append(('backbone.0.body.bn1.weight', 'backbone.conv_encoder.model.embedder.embedder.normalization.weight') )
rename_keys.append(('backbone.0.body.bn1.bias', 'backbone.conv_encoder.model.embedder.embedder.normalization.bias') )
rename_keys.append(('backbone.0.body.bn1.running_mean', 'backbone.conv_encoder.model.embedder.embedder.normalization.running_mean') )
rename_keys.append(('backbone.0.body.bn1.running_var', 'backbone.conv_encoder.model.embedder.embedder.normalization.running_var') )
# stages
for stage_idx in range(len(config.backbone_config.depths ) ):
for layer_idx in range(config.backbone_config.depths[stage_idx] ):
# shortcut
if layer_idx == 0:
rename_keys.append(
(
F'backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.0.weight',
F'backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.convolution.weight',
) )
rename_keys.append(
(
F'backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.weight',
F'backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.weight',
) )
rename_keys.append(
(
F'backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.bias',
F'backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.bias',
) )
rename_keys.append(
(
F'backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.running_mean',
F'backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.running_mean',
) )
rename_keys.append(
(
F'backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.running_var',
F'backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.running_var',
) )
# 3 convs
for i in range(3 ):
rename_keys.append(
(
F'backbone.0.body.layer{stage_idx + 1}.{layer_idx}.conv{i+1}.weight',
F'backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.convolution.weight',
) )
rename_keys.append(
(
F'backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.weight',
F'backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.weight',
) )
rename_keys.append(
(
F'backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.bias',
F'backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.bias',
) )
rename_keys.append(
(
F'backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.running_mean',
F'backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.running_mean',
) )
rename_keys.append(
(
F'backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.running_var',
F'backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.running_var',
) )
# fmt: on
for i in range(config.encoder_layers ):
# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
rename_keys.append(
(
F'transformer.encoder.layers.{i}.self_attn.out_proj.weight',
F'encoder.layers.{i}.self_attn.out_proj.weight',
) )
rename_keys.append(
(F'transformer.encoder.layers.{i}.self_attn.out_proj.bias', F'encoder.layers.{i}.self_attn.out_proj.bias') )
rename_keys.append((F'transformer.encoder.layers.{i}.linear1.weight', F'encoder.layers.{i}.fc1.weight') )
rename_keys.append((F'transformer.encoder.layers.{i}.linear1.bias', F'encoder.layers.{i}.fc1.bias') )
rename_keys.append((F'transformer.encoder.layers.{i}.linear2.weight', F'encoder.layers.{i}.fc2.weight') )
rename_keys.append((F'transformer.encoder.layers.{i}.linear2.bias', F'encoder.layers.{i}.fc2.bias') )
rename_keys.append(
(F'transformer.encoder.layers.{i}.norm1.weight', F'encoder.layers.{i}.self_attn_layer_norm.weight') )
rename_keys.append(
(F'transformer.encoder.layers.{i}.norm1.bias', F'encoder.layers.{i}.self_attn_layer_norm.bias') )
rename_keys.append(
(F'transformer.encoder.layers.{i}.norm2.weight', F'encoder.layers.{i}.final_layer_norm.weight') )
rename_keys.append((F'transformer.encoder.layers.{i}.norm2.bias', F'encoder.layers.{i}.final_layer_norm.bias') )
# decoder layers: 2 times output projection, 2 feedforward neural networks and 3 layernorms
rename_keys.append(
(
F'transformer.decoder.layers.{i}.self_attn.out_proj.weight',
F'decoder.layers.{i}.self_attn.out_proj.weight',
) )
rename_keys.append(
(F'transformer.decoder.layers.{i}.self_attn.out_proj.bias', F'decoder.layers.{i}.self_attn.out_proj.bias') )
rename_keys.append(
(
F'transformer.decoder.layers.{i}.multihead_attn.out_proj.weight',
F'decoder.layers.{i}.encoder_attn.out_proj.weight',
) )
rename_keys.append(
(
F'transformer.decoder.layers.{i}.multihead_attn.out_proj.bias',
F'decoder.layers.{i}.encoder_attn.out_proj.bias',
) )
rename_keys.append((F'transformer.decoder.layers.{i}.linear1.weight', F'decoder.layers.{i}.fc1.weight') )
rename_keys.append((F'transformer.decoder.layers.{i}.linear1.bias', F'decoder.layers.{i}.fc1.bias') )
rename_keys.append((F'transformer.decoder.layers.{i}.linear2.weight', F'decoder.layers.{i}.fc2.weight') )
rename_keys.append((F'transformer.decoder.layers.{i}.linear2.bias', F'decoder.layers.{i}.fc2.bias') )
rename_keys.append(
(F'transformer.decoder.layers.{i}.norm1.weight', F'decoder.layers.{i}.self_attn_layer_norm.weight') )
rename_keys.append(
(F'transformer.decoder.layers.{i}.norm1.bias', F'decoder.layers.{i}.self_attn_layer_norm.bias') )
rename_keys.append(
(F'transformer.decoder.layers.{i}.norm2.weight', F'decoder.layers.{i}.encoder_attn_layer_norm.weight') )
rename_keys.append(
(F'transformer.decoder.layers.{i}.norm2.bias', F'decoder.layers.{i}.encoder_attn_layer_norm.bias') )
rename_keys.append(
(F'transformer.decoder.layers.{i}.norm3.weight', F'decoder.layers.{i}.final_layer_norm.weight') )
rename_keys.append((F'transformer.decoder.layers.{i}.norm3.bias', F'decoder.layers.{i}.final_layer_norm.bias') )
# convolutional projection + query embeddings + layernorm of decoder + class and bounding box heads
rename_keys.extend(
[
('input_proj.weight', 'input_projection.weight'),
('input_proj.bias', 'input_projection.bias'),
('query_embed.weight', 'query_position_embeddings.weight'),
('transformer.decoder.norm.weight', 'decoder.layernorm.weight'),
('transformer.decoder.norm.bias', 'decoder.layernorm.bias'),
('class_embed.weight', 'class_labels_classifier.weight'),
('class_embed.bias', 'class_labels_classifier.bias'),
('bbox_embed.layers.0.weight', 'bbox_predictor.layers.0.weight'),
('bbox_embed.layers.0.bias', 'bbox_predictor.layers.0.bias'),
('bbox_embed.layers.1.weight', 'bbox_predictor.layers.1.weight'),
('bbox_embed.layers.1.bias', 'bbox_predictor.layers.1.bias'),
('bbox_embed.layers.2.weight', 'bbox_predictor.layers.2.weight'),
('bbox_embed.layers.2.bias', 'bbox_predictor.layers.2.bias'),
] )
return rename_keys
def _snake_case ( snake_case__ : List[Any] , snake_case__ : Optional[Any] , snake_case__ : List[Any] ):
A = state_dict.pop(snake_case__ )
A = val
def _snake_case ( snake_case__ : int , snake_case__ : Optional[int]=False ):
A = ''
if is_panoptic:
A = 'detr.'
# first: transformer encoder
for i in range(6 ):
# read in weights + bias of input projection layer (in PyTorch's MultiHeadAttention, this is a single matrix + bias)
A = state_dict.pop(F'{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight' )
A = state_dict.pop(F'{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_bias' )
# next, add query, keys and values (in that order) to the state dict
A = in_proj_weight[:256, :]
A = in_proj_bias[:256]
A = in_proj_weight[256:512, :]
A = in_proj_bias[256:512]
A = in_proj_weight[-256:, :]
A = in_proj_bias[-256:]
# next: transformer decoder (which is a bit more complex because it also includes cross-attention)
for i in range(6 ):
# read in weights + bias of input projection layer of self-attention
A = state_dict.pop(F'{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_weight' )
A = state_dict.pop(F'{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_bias' )
# next, add query, keys and values (in that order) to the state dict
A = in_proj_weight[:256, :]
A = in_proj_bias[:256]
A = in_proj_weight[256:512, :]
A = in_proj_bias[256:512]
A = in_proj_weight[-256:, :]
A = in_proj_bias[-256:]
# read in weights + bias of input projection layer of cross-attention
A = state_dict.pop(
F'{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_weight' )
A = state_dict.pop(F'{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_bias' )
# next, add query, keys and values (in that order) of cross-attention to the state dict
A = in_proj_weight_cross_attn[:256, :]
A = in_proj_bias_cross_attn[:256]
A = in_proj_weight_cross_attn[256:512, :]
A = in_proj_bias_cross_attn[256:512]
A = in_proj_weight_cross_attn[-256:, :]
A = in_proj_bias_cross_attn[-256:]
def _snake_case ( ):
A = 'http://images.cocodataset.org/val2017/000000039769.jpg'
A = Image.open(requests.get(snake_case__ , stream=snake_case__ ).raw )
return im
@torch.no_grad()
def _snake_case ( snake_case__ : Any , snake_case__ : List[Any]=None , snake_case__ : int=False ):
A , A = get_detr_config(snake_case__ )
# load original model from torch hub
A = {
'detr-resnet-50': 'detr_resnet50',
'detr-resnet-101': 'detr_resnet101',
}
logger.info(F'Converting model {model_name}...' )
A = torch.hub.load('facebookresearch/detr' , model_name_to_original_name[model_name] , pretrained=snake_case__ ).eval()
A = detr.state_dict()
# rename keys
for src, dest in create_rename_keys(snake_case__ ):
if is_panoptic:
A = 'detr.' + src
rename_key(snake_case__ , snake_case__ , snake_case__ )
# query, key and value matrices need special treatment
read_in_q_k_v(snake_case__ , is_panoptic=snake_case__ )
# important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
A = 'detr.model.' if is_panoptic else 'model.'
for key in state_dict.copy().keys():
if is_panoptic:
if (
key.startswith('detr' )
and not key.startswith('class_labels_classifier' )
and not key.startswith('bbox_predictor' )
):
A = state_dict.pop(snake_case__ )
A = val
elif "class_labels_classifier" in key or "bbox_predictor" in key:
A = state_dict.pop(snake_case__ )
A = val
elif key.startswith('bbox_attention' ) or key.startswith('mask_head' ):
continue
else:
A = state_dict.pop(snake_case__ )
A = val
else:
if not key.startswith('class_labels_classifier' ) and not key.startswith('bbox_predictor' ):
A = state_dict.pop(snake_case__ )
A = val
# finally, create HuggingFace model and load state dict
A = DetrForSegmentation(snake_case__ ) if is_panoptic else DetrForObjectDetection(snake_case__ )
model.load_state_dict(snake_case__ )
model.eval()
# verify our conversion on an image
A = 'coco_panoptic' if is_panoptic else 'coco_detection'
A = DetrImageProcessor(format=snake_case__ )
A = processor(images=prepare_img() , return_tensors='pt' )
A = encoding['pixel_values']
A = detr(snake_case__ )
A = model(snake_case__ )
assert torch.allclose(outputs.logits , original_outputs['pred_logits'] , atol=1e-3 )
assert torch.allclose(outputs.pred_boxes , original_outputs['pred_boxes'] , atol=1e-3 )
if is_panoptic:
assert torch.allclose(outputs.pred_masks , original_outputs['pred_masks'] , atol=1e-4 )
print('Looks ok!' )
if pytorch_dump_folder_path is not None:
# Save model and image processor
logger.info(F'Saving PyTorch model and image processor to {pytorch_dump_folder_path}...' )
Path(snake_case__ ).mkdir(exist_ok=snake_case__ )
model.save_pretrained(snake_case__ )
processor.save_pretrained(snake_case__ )
if push_to_hub:
# Upload model and image processor to the hub
logger.info('Uploading PyTorch model and image processor to the hub...' )
model.push_to_hub(F'nielsr/{model_name}' )
processor.push_to_hub(F'nielsr/{model_name}' )
if __name__ == "__main__":
_lowercase = argparse.ArgumentParser()
parser.add_argument(
'''--model_name''',
default='''detr-resnet-50''',
type=str,
choices=['''detr-resnet-50''', '''detr-resnet-101'''],
help='''Name of the DETR model you\'d like to convert.''',
)
parser.add_argument(
'''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the folder to output PyTorch model.'''
)
parser.add_argument('''--push_to_hub''', action='''store_true''', help='''Whether to push the model to the hub or not.''')
_lowercase = parser.parse_args()
convert_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub) | 74 |
"""simple docstring"""
from math import pi, sqrt
def lowercase (_lowerCAmelCase ):
if num <= 0:
raise ValueError("""math domain error""" )
if num > 171.5:
raise OverflowError("""math range error""" )
elif num - int(_lowerCAmelCase ) not in (0, 0.5):
raise NotImplementedError("""num must be an integer or a half-integer""" )
elif num == 0.5:
return sqrt(_lowerCAmelCase )
else:
return 1.0 if num == 1 else (num - 1) * gamma(num - 1 )
def lowercase ():
assert gamma(0.5 ) == sqrt(_lowerCAmelCase )
assert gamma(1 ) == 1.0
assert gamma(2 ) == 1.0
if __name__ == "__main__":
from doctest import testmod
testmod()
SCREAMING_SNAKE_CASE_ = 1.0
while num:
SCREAMING_SNAKE_CASE_ = float(input('''Gamma of: '''))
print(F"gamma({num}) = {gamma(num)}")
print('''\nEnter 0 to exit...''')
| 301 | 0 |
'''simple docstring'''
import math
def UpperCamelCase_ ( ):
lowerCAmelCase_ : Union[str, Any] = input("""Enter message: """ )
lowerCAmelCase_ : Optional[Any] = int(input(f'Enter key [2-{len(A__ ) - 1}]: ' ) )
lowerCAmelCase_ : Union[str, Any] = input("""Encryption/Decryption [e/d]: """ )
if mode.lower().startswith("""e""" ):
lowerCAmelCase_ : List[str] = encrypt_message(A__ , A__ )
elif mode.lower().startswith("""d""" ):
lowerCAmelCase_ : Dict = decrypt_message(A__ , A__ )
# Append pipe symbol (vertical bar) to identify spaces at the end.
print(f'Output:\n{text + "|"}' )
def UpperCamelCase_ ( A__ : int , A__ : str ):
lowerCAmelCase_ : List[Any] = [""""""] * key
for col in range(A__ ):
lowerCAmelCase_ : Tuple = col
while pointer < len(A__ ):
cipher_text[col] += message[pointer]
pointer += key
return "".join(A__ )
def UpperCamelCase_ ( A__ : int , A__ : str ):
lowerCAmelCase_ : Tuple = math.ceil(len(A__ ) / key )
lowerCAmelCase_ : int = key
lowerCAmelCase_ : List[str] = (num_cols * num_rows) - len(A__ )
lowerCAmelCase_ : Any = [""""""] * num_cols
lowerCAmelCase_ : Dict = 0
lowerCAmelCase_ : Any = 0
for symbol in message:
plain_text[col] += symbol
col += 1
if (
(col == num_cols)
or (col == num_cols - 1)
and (row >= num_rows - num_shaded_boxes)
):
lowerCAmelCase_ : Optional[int] = 0
row += 1
return "".join(A__ )
if __name__ == "__main__":
import doctest
doctest.testmod()
main()
| 368 |
'''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 __snake_case :
"""simple docstring"""
def __init__( self : Optional[Any] , lowerCamelCase : Tuple , lowerCamelCase : Tuple=13 , lowerCamelCase : Dict=30 , lowerCamelCase : Dict=2 , lowerCamelCase : Optional[int]=3 , lowerCamelCase : List[Any]=True , lowerCamelCase : Any=True , lowerCamelCase : str=32 , lowerCamelCase : Any=5 , lowerCamelCase : int=4 , lowerCamelCase : List[str]=37 , lowerCamelCase : Any="gelu" , lowerCamelCase : Optional[Any]=0.1 , lowerCamelCase : List[str]=0.1 , lowerCamelCase : str=10 , lowerCamelCase : Optional[Any]=0.02 , lowerCamelCase : List[str]=3 , lowerCamelCase : Union[str, Any]=0.6 , lowerCamelCase : List[Any]=None , ) -> Optional[int]:
lowerCAmelCase_ : Optional[Any] = parent
lowerCAmelCase_ : Optional[int] = batch_size
lowerCAmelCase_ : int = image_size
lowerCAmelCase_ : List[Any] = patch_size
lowerCAmelCase_ : int = num_channels
lowerCAmelCase_ : Any = is_training
lowerCAmelCase_ : Tuple = use_labels
lowerCAmelCase_ : Optional[Any] = hidden_size
lowerCAmelCase_ : List[Any] = num_hidden_layers
lowerCAmelCase_ : Optional[Any] = num_attention_heads
lowerCAmelCase_ : Dict = intermediate_size
lowerCAmelCase_ : Union[str, Any] = hidden_act
lowerCAmelCase_ : Union[str, Any] = hidden_dropout_prob
lowerCAmelCase_ : Any = attention_probs_dropout_prob
lowerCAmelCase_ : List[Any] = type_sequence_label_size
lowerCAmelCase_ : Dict = initializer_range
lowerCAmelCase_ : List[str] = mask_ratio
lowerCAmelCase_ : Tuple = scope
# in ViTMAE, the expected sequence length = (num_patches + 1) * (1 - config.mask_ratio), rounded above
# (we add 1 for the [CLS] token)
lowerCAmelCase_ : Union[str, Any] = (image_size // patch_size) ** 2
lowerCAmelCase_ : Any = int(math.ceil((1 - mask_ratio) * (num_patches + 1) ) )
def __lowercase ( self : Optional[int] ) -> str:
lowerCAmelCase_ : str = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
lowerCAmelCase_ : Optional[int] = None
if self.use_labels:
lowerCAmelCase_ : int = ids_tensor([self.batch_size] , self.type_sequence_label_size )
lowerCAmelCase_ : str = self.get_config()
return config, pixel_values, labels
def __lowercase ( self : Optional[int] ) -> Optional[int]:
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=lowerCamelCase , initializer_range=self.initializer_range , mask_ratio=self.mask_ratio , )
def __lowercase ( self : Any , lowerCamelCase : Any , lowerCamelCase : Union[str, Any] , lowerCamelCase : Dict ) -> Tuple:
lowerCAmelCase_ : Tuple = ViTMAEModel(config=lowerCamelCase )
model.to(lowerCamelCase )
model.eval()
lowerCAmelCase_ : Dict = model(lowerCamelCase )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def __lowercase ( self : List[str] , lowerCamelCase : Union[str, Any] , lowerCamelCase : List[str] , lowerCamelCase : Union[str, Any] ) -> Dict:
lowerCAmelCase_ : Tuple = ViTMAEForPreTraining(lowerCamelCase )
model.to(lowerCamelCase )
model.eval()
lowerCAmelCase_ : List[str] = model(lowerCamelCase )
lowerCAmelCase_ : int = (self.image_size // self.patch_size) ** 2
lowerCAmelCase_ : int = self.patch_size**2 * self.num_channels
self.parent.assertEqual(result.logits.shape , (self.batch_size, num_patches, expected_num_channels) )
# test greyscale images
lowerCAmelCase_ : List[Any] = 1
lowerCAmelCase_ : List[str] = ViTMAEForPreTraining(lowerCamelCase )
model.to(lowerCamelCase )
model.eval()
lowerCAmelCase_ : Optional[Any] = floats_tensor([self.batch_size, 1, self.image_size, self.image_size] )
lowerCAmelCase_ : Tuple = model(lowerCamelCase )
lowerCAmelCase_ : List[Any] = self.patch_size**2
self.parent.assertEqual(result.logits.shape , (self.batch_size, num_patches, expected_num_channels) )
def __lowercase ( self : Optional[int] ) -> str:
lowerCAmelCase_ : Any = self.prepare_config_and_inputs()
lowerCAmelCase_, lowerCAmelCase_, lowerCAmelCase_ : List[Any] = config_and_inputs
lowerCAmelCase_ : Any = {"""pixel_values""": pixel_values}
return config, inputs_dict
@require_torch
class __snake_case ( _SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,unittest.TestCase):
"""simple docstring"""
lowercase = (ViTMAEModel, ViTMAEForPreTraining) if is_torch_available() else ()
lowercase = {'feature-extraction': ViTMAEModel} if is_torch_available() else {}
lowercase = False
lowercase = False
lowercase = False
lowercase = False
def __lowercase ( self : Optional[Any] ) -> List[Any]:
lowerCAmelCase_ : Optional[int] = ViTMAEModelTester(self )
lowerCAmelCase_ : Optional[int] = ConfigTester(self , config_class=lowerCamelCase , has_text_modality=lowerCamelCase , hidden_size=37 )
def __lowercase ( self : Dict ) -> Tuple:
self.config_tester.run_common_tests()
@unittest.skip(reason="""ViTMAE does not use inputs_embeds""" )
def __lowercase ( self : Optional[int] ) -> Optional[int]:
pass
def __lowercase ( self : List[str] ) -> Tuple:
lowerCAmelCase_, lowerCAmelCase_ : Optional[Any] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
lowerCAmelCase_ : List[str] = model_class(lowerCamelCase )
self.assertIsInstance(model.get_input_embeddings() , (nn.Module) )
lowerCAmelCase_ : Tuple = model.get_output_embeddings()
self.assertTrue(x is None or isinstance(lowerCamelCase , nn.Linear ) )
def __lowercase ( self : Optional[Any] ) -> Any:
lowerCAmelCase_, lowerCAmelCase_ : List[Any] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
lowerCAmelCase_ : Optional[int] = model_class(lowerCamelCase )
lowerCAmelCase_ : Optional[Any] = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
lowerCAmelCase_ : Any = [*signature.parameters.keys()]
lowerCAmelCase_ : Optional[Any] = ["""pixel_values"""]
self.assertListEqual(arg_names[:1] , lowerCamelCase )
def __lowercase ( self : Tuple ) -> str:
lowerCAmelCase_ : Any = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*lowerCamelCase )
def __lowercase ( self : Optional[int] ) -> str:
lowerCAmelCase_ : Tuple = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_pretraining(*lowerCamelCase )
def __lowercase ( self : Optional[int] , lowerCamelCase : Dict , lowerCamelCase : List[Any] , lowerCamelCase : Optional[Any] ) -> str:
# make masks reproducible
np.random.seed(2 )
lowerCAmelCase_ : Tuple = int((pt_model.config.image_size // pt_model.config.patch_size) ** 2 )
lowerCAmelCase_ : Optional[int] = np.random.uniform(size=(self.model_tester.batch_size, num_patches) )
lowerCAmelCase_ : Optional[Any] = torch.from_numpy(lowerCamelCase )
# Add `noise` argument.
# PT inputs will be prepared in `super().check_pt_tf_models()` with this added `noise` argument
lowerCAmelCase_ : int = pt_noise
super().check_pt_tf_models(lowerCamelCase , lowerCamelCase , lowerCamelCase )
def __lowercase ( self : int ) -> Dict:
lowerCAmelCase_, lowerCAmelCase_ : Optional[Any] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
lowerCAmelCase_ : Optional[int] = model_class(lowerCamelCase )
model.to(lowerCamelCase )
model.eval()
# make random mask reproducible
torch.manual_seed(2 )
with torch.no_grad():
lowerCAmelCase_ : Any = model(**self._prepare_for_class(lowerCamelCase , lowerCamelCase ) )
lowerCAmelCase_ : Any = outputs[0].cpu().numpy()
lowerCAmelCase_ : List[str] = 0
with tempfile.TemporaryDirectory() as tmpdirname:
model.save_pretrained(lowerCamelCase )
lowerCAmelCase_ : int = model_class.from_pretrained(lowerCamelCase )
model.to(lowerCamelCase )
# make random mask reproducible
torch.manual_seed(2 )
with torch.no_grad():
lowerCAmelCase_ : str = model(**self._prepare_for_class(lowerCamelCase , lowerCamelCase ) )
# Make sure we don't have nans
lowerCAmelCase_ : Optional[Any] = after_outputs[0].cpu().numpy()
lowerCAmelCase_ : str = 0
lowerCAmelCase_ : List[Any] = np.amax(np.abs(out_a - out_a ) )
self.assertLessEqual(lowerCamelCase , 1E-5 )
@unittest.skip(
reason="""ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load
to get deterministic results.""" )
def __lowercase ( self : Optional[int] ) -> List[Any]:
pass
@unittest.skip(
reason="""ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load
to get deterministic results.""" )
def __lowercase ( self : Union[str, Any] ) -> str:
pass
@unittest.skip(
reason="""ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load
to get deterministic results.""" )
def __lowercase ( self : Optional[Any] ) -> Union[str, Any]:
pass
@unittest.skip(reason="""ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load""" )
def __lowercase ( self : Tuple ) -> Optional[Any]:
pass
@unittest.skip("""Will be fixed soon by reducing the size of the model used for common tests.""" )
def __lowercase ( self : List[Any] ) -> str:
pass
@slow
def __lowercase ( self : List[str] ) -> List[Any]:
for model_name in VIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
lowerCAmelCase_ : List[Any] = ViTMAEModel.from_pretrained(lowerCamelCase )
self.assertIsNotNone(lowerCamelCase )
def UpperCamelCase_ ( ):
'''simple docstring'''
lowerCAmelCase_ : List[str] = Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""" )
return image
@require_torch
@require_vision
class __snake_case ( unittest.TestCase):
"""simple docstring"""
@cached_property
def __lowercase ( self : Union[str, Any] ) -> Union[str, Any]:
return ViTImageProcessor.from_pretrained("""facebook/vit-mae-base""" ) if is_vision_available() else None
@slow
def __lowercase ( self : int ) -> List[Any]:
# make random mask reproducible across the PT and TF model
np.random.seed(2 )
lowerCAmelCase_ : Dict = ViTMAEForPreTraining.from_pretrained("""facebook/vit-mae-base""" ).to(lowerCamelCase )
lowerCAmelCase_ : Union[str, Any] = self.default_image_processor
lowerCAmelCase_ : Union[str, Any] = prepare_img()
lowerCAmelCase_ : Dict = image_processor(images=lowerCamelCase , return_tensors="""pt""" ).to(lowerCamelCase )
# 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)
lowerCAmelCase_ : Optional[int] = ViTMAEConfig()
lowerCAmelCase_ : Optional[Any] = int((vit_mae_config.image_size // vit_mae_config.patch_size) ** 2 )
lowerCAmelCase_ : Optional[int] = np.random.uniform(size=(1, num_patches) )
# forward pass
with torch.no_grad():
lowerCAmelCase_ : str = model(**lowerCamelCase , noise=torch.from_numpy(lowerCamelCase ).to(device=lowerCamelCase ) )
# verify the logits
lowerCAmelCase_ : str = torch.Size((1, 1_96, 7_68) )
self.assertEqual(outputs.logits.shape , lowerCamelCase )
lowerCAmelCase_ : str = torch.tensor(
[[-0.0_548, -1.7_023, -0.9_325], [0.3_721, -0.5_670, -0.2_233], [0.8_235, -1.3_878, -0.3_524]] )
self.assertTrue(torch.allclose(outputs.logits[0, :3, :3] , expected_slice.to(lowerCamelCase ) , atol=1E-4 ) )
| 89 | 0 |
def A_ ( _lowerCAmelCase , _lowerCAmelCase ) -> str:
if not (isinstance(_lowerCAmelCase , _lowerCAmelCase ) and isinstance(_lowerCAmelCase , _lowerCAmelCase )):
raise ValueError("longest_common_substring() takes two strings for inputs" )
UpperCamelCase : str = len(_lowerCAmelCase )
UpperCamelCase : Union[str, Any] = len(_lowerCAmelCase )
UpperCamelCase : Any = [[0] * (texta_length + 1) for _ in range(texta_length + 1 )]
UpperCamelCase : Optional[Any] = 0
UpperCamelCase : List[Any] = 0
for i in range(1 , texta_length + 1 ):
for j in range(1 , texta_length + 1 ):
if texta[i - 1] == texta[j - 1]:
UpperCamelCase : Optional[int] = 1 + dp[i - 1][j - 1]
if dp[i][j] > ans_length:
UpperCamelCase : Optional[Any] = i
UpperCamelCase : Optional[int] = dp[i][j]
return texta[ans_index - ans_length : ans_index]
if __name__ == "__main__":
import doctest
doctest.testmod()
| 52 |
def _SCREAMING_SNAKE_CASE ( lowercase : int = 10_00 ):
'''simple docstring'''
lowerCamelCase_ = 2**power
lowerCamelCase_ = 0
while n:
lowerCamelCase_ , lowerCamelCase_ = r + n % 10, n // 10
return r
if __name__ == "__main__":
print(solution(int(str(input()).strip())))
| 204 | 0 |
import socket
def A ( ) -> Union[str, Any]:
'''simple docstring'''
UpperCAmelCase_ = socket.socket(socket.AF_INET , socket.SOCK_STREAM )
UpperCAmelCase_ = socket.gethostname()
UpperCAmelCase_ = 1_2312
sock.connect((host, port) )
sock.send(b'''Hello server!''' )
with open('''Received_file''' , '''wb''' ) as out_file:
print('''File opened''' )
print('''Receiving data...''' )
while True:
UpperCAmelCase_ = sock.recv(1024 )
if not data:
break
out_file.write(_lowerCamelCase )
print('''Successfully received the file''' )
sock.close()
print('''Connection closed''' )
if __name__ == "__main__":
main()
| 363 |
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available
if is_vision_available():
from PIL import Image
from transformers import (
AutoProcessor,
BertTokenizerFast,
BlipImageProcessor,
GPTaTokenizer,
InstructBlipProcessor,
PreTrainedTokenizerFast,
)
@require_vision
class a_ ( unittest.TestCase ):
def __a ( self :Optional[Any]) -> int:
UpperCAmelCase_ = tempfile.mkdtemp()
UpperCAmelCase_ = BlipImageProcessor()
UpperCAmelCase_ = GPTaTokenizer.from_pretrained('''hf-internal-testing/tiny-random-GPT2Model''')
UpperCAmelCase_ = BertTokenizerFast.from_pretrained('''hf-internal-testing/tiny-random-bert''')
UpperCAmelCase_ = InstructBlipProcessor(_lowercase , _lowercase , _lowercase)
processor.save_pretrained(self.tmpdirname)
def __a ( self :List[Any] , **_lowercase :Dict) -> str:
return AutoProcessor.from_pretrained(self.tmpdirname , **_lowercase).tokenizer
def __a ( self :Optional[Any] , **_lowercase :Optional[Any]) -> Optional[int]:
return AutoProcessor.from_pretrained(self.tmpdirname , **_lowercase).image_processor
def __a ( self :Dict , **_lowercase :Tuple) -> str:
return AutoProcessor.from_pretrained(self.tmpdirname , **_lowercase).qformer_tokenizer
def __a ( self :Optional[int]) -> str:
shutil.rmtree(self.tmpdirname)
def __a ( self :Any) -> List[str]:
UpperCAmelCase_ = [np.random.randint(255 , size=(3, 30, 400) , dtype=np.uinta)]
UpperCAmelCase_ = [Image.fromarray(np.moveaxis(_lowercase , 0 , -1)) for x in image_inputs]
return image_inputs
def __a ( self :Tuple) -> int:
UpperCAmelCase_ = InstructBlipProcessor(
tokenizer=self.get_tokenizer() , image_processor=self.get_image_processor() , qformer_tokenizer=self.get_qformer_tokenizer() , )
processor.save_pretrained(self.tmpdirname)
UpperCAmelCase_ = self.get_tokenizer(bos_token='''(BOS)''' , eos_token='''(EOS)''')
UpperCAmelCase_ = self.get_image_processor(do_normalize=_lowercase , padding_value=1.0)
UpperCAmelCase_ = InstructBlipProcessor.from_pretrained(
self.tmpdirname , bos_token='''(BOS)''' , eos_token='''(EOS)''' , do_normalize=_lowercase , padding_value=1.0)
self.assertEqual(processor.tokenizer.get_vocab() , tokenizer_add_kwargs.get_vocab())
self.assertIsInstance(processor.tokenizer , _lowercase)
self.assertEqual(processor.image_processor.to_json_string() , image_processor_add_kwargs.to_json_string())
self.assertIsInstance(processor.image_processor , _lowercase)
self.assertIsInstance(processor.qformer_tokenizer , _lowercase)
def __a ( self :Dict) -> Any:
UpperCAmelCase_ = self.get_image_processor()
UpperCAmelCase_ = self.get_tokenizer()
UpperCAmelCase_ = self.get_qformer_tokenizer()
UpperCAmelCase_ = InstructBlipProcessor(
tokenizer=_lowercase , image_processor=_lowercase , qformer_tokenizer=_lowercase)
UpperCAmelCase_ = self.prepare_image_inputs()
UpperCAmelCase_ = image_processor(_lowercase , return_tensors='''np''')
UpperCAmelCase_ = processor(images=_lowercase , return_tensors='''np''')
for key in input_feat_extract.keys():
self.assertAlmostEqual(input_feat_extract[key].sum() , input_processor[key].sum() , delta=1E-2)
def __a ( self :Union[str, Any]) -> Dict:
UpperCAmelCase_ = self.get_image_processor()
UpperCAmelCase_ = self.get_tokenizer()
UpperCAmelCase_ = self.get_qformer_tokenizer()
UpperCAmelCase_ = InstructBlipProcessor(
tokenizer=_lowercase , image_processor=_lowercase , qformer_tokenizer=_lowercase)
UpperCAmelCase_ = '''lower newer'''
UpperCAmelCase_ = processor(text=_lowercase)
UpperCAmelCase_ = tokenizer(_lowercase , return_token_type_ids=_lowercase)
UpperCAmelCase_ = qformer_tokenizer(_lowercase , return_token_type_ids=_lowercase)
for key in encoded_tokens.keys():
self.assertListEqual(encoded_tokens[key] , encoded_processor[key])
for key in encoded_tokens_qformer.keys():
self.assertListEqual(encoded_tokens_qformer[key] , encoded_processor['''qformer_''' + key])
def __a ( self :Dict) -> Optional[Any]:
UpperCAmelCase_ = self.get_image_processor()
UpperCAmelCase_ = self.get_tokenizer()
UpperCAmelCase_ = self.get_qformer_tokenizer()
UpperCAmelCase_ = InstructBlipProcessor(
tokenizer=_lowercase , image_processor=_lowercase , qformer_tokenizer=_lowercase)
UpperCAmelCase_ = '''lower newer'''
UpperCAmelCase_ = self.prepare_image_inputs()
UpperCAmelCase_ = processor(text=_lowercase , images=_lowercase)
self.assertListEqual(
list(inputs.keys()) , ['''input_ids''', '''attention_mask''', '''qformer_input_ids''', '''qformer_attention_mask''', '''pixel_values'''] , )
# test if it raises when no input is passed
with pytest.raises(_lowercase):
processor()
def __a ( self :Optional[int]) -> Optional[Any]:
UpperCAmelCase_ = self.get_image_processor()
UpperCAmelCase_ = self.get_tokenizer()
UpperCAmelCase_ = self.get_qformer_tokenizer()
UpperCAmelCase_ = InstructBlipProcessor(
tokenizer=_lowercase , image_processor=_lowercase , qformer_tokenizer=_lowercase)
UpperCAmelCase_ = [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]
UpperCAmelCase_ = processor.batch_decode(_lowercase)
UpperCAmelCase_ = tokenizer.batch_decode(_lowercase)
self.assertListEqual(_lowercase , _lowercase)
def __a ( self :str) -> int:
UpperCAmelCase_ = self.get_image_processor()
UpperCAmelCase_ = self.get_tokenizer()
UpperCAmelCase_ = self.get_qformer_tokenizer()
UpperCAmelCase_ = InstructBlipProcessor(
tokenizer=_lowercase , image_processor=_lowercase , qformer_tokenizer=_lowercase)
UpperCAmelCase_ = '''lower newer'''
UpperCAmelCase_ = self.prepare_image_inputs()
UpperCAmelCase_ = processor(text=_lowercase , images=_lowercase)
self.assertListEqual(
list(inputs.keys()) , ['''input_ids''', '''attention_mask''', '''qformer_input_ids''', '''qformer_attention_mask''', '''pixel_values'''] , )
| 344 | 0 |
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