code stringlengths 87 55.2k | code_codestyle int64 0 349 | style_context stringlengths 135 49.1k | style_context_codestyle int64 0 349 | label int64 0 1 |
|---|---|---|---|---|
class lowercase_ :
'''simple docstring'''
def __init__( self : Dict ) ->List[str]:
"""simple docstring"""
a = {}
def __lowerCAmelCase ( self : str ) ->None:
"""simple docstring"""
print(self.vertex )
for i in self.vertex:
print(__UpperCAmelCase , ''' -> ''' , ''' -> '''.join([str(__UpperCAmelCase ) for j in self.vertex[i]] ) )
def __lowerCAmelCase ( self : Dict , __UpperCAmelCase : int , __UpperCAmelCase : int ) ->None:
"""simple docstring"""
if from_vertex in self.vertex:
self.vertex[from_vertex].append(__UpperCAmelCase )
else:
# else make a new vertex
a = [to_vertex]
def __lowerCAmelCase ( self : Optional[Any] ) ->None:
"""simple docstring"""
a = [False] * len(self.vertex )
# call the recursive helper function
for i in range(len(self.vertex ) ):
if not visited[i]:
self.dfs_recursive(__UpperCAmelCase , __UpperCAmelCase )
def __lowerCAmelCase ( self : str , __UpperCAmelCase : int , __UpperCAmelCase : list ) ->None:
"""simple docstring"""
a = True
print(__UpperCAmelCase , end=''' ''' )
# Recur for all the vertices that are adjacent to this node
for i in self.vertex:
if not visited[i]:
self.dfs_recursive(__UpperCAmelCase , __UpperCAmelCase )
if __name__ == "__main__":
UpperCAmelCase__ = Graph()
g.add_edge(0, 1)
g.add_edge(0, 2)
g.add_edge(1, 2)
g.add_edge(2, 0)
g.add_edge(2, 3)
g.add_edge(3, 3)
g.print_graph()
print("DFS:")
g.dfs()
# OUTPUT:
# 0 -> 1 -> 2
# 1 -> 2
# 2 -> 0 -> 3
# 3 -> 3
# DFS:
# 0 1 2 3
| 0 |
'''simple docstring'''
import argparse
from collections import OrderedDict
from pathlib import Path
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from torchvision.transforms import functional as F
from transformers import DetrImageProcessor, TableTransformerConfig, TableTransformerForObjectDetection
from transformers.utils import logging
logging.set_verbosity_info()
__snake_case = logging.get_logger(__name__)
# here we list all keys to be renamed (original name on the left, our name on the right)
__snake_case = []
for i in range(6):
# 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 encoder + 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.encoder.norm.weight''', '''encoder.layernorm.weight'''),
('''transformer.encoder.norm.bias''', '''encoder.layernorm.bias'''),
('''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'''),
]
)
def a ( __a , __a , __a ) -> List[str]:
'''simple docstring'''
UpperCamelCase__ :List[Any] = state_dict.pop(__a )
UpperCamelCase__ :int = val
def a ( __a ) -> Any:
'''simple docstring'''
UpperCamelCase__ :Tuple = OrderedDict()
for key, value in state_dict.items():
if "backbone.0.body" in key:
UpperCamelCase__ :Dict = key.replace('''backbone.0.body''' , '''backbone.conv_encoder.model''' )
UpperCamelCase__ :List[str] = value
else:
UpperCamelCase__ :Dict = value
return new_state_dict
def a ( __a ) -> Optional[Any]:
'''simple docstring'''
UpperCamelCase__ :Optional[Any] = ''''''
# 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)
UpperCamelCase__ :Optional[Any] = state_dict.pop(f'''{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight''' )
UpperCamelCase__ :str = 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
UpperCamelCase__ :Any = in_proj_weight[:256, :]
UpperCamelCase__ :Tuple = in_proj_bias[:256]
UpperCamelCase__ :Optional[int] = in_proj_weight[256:512, :]
UpperCamelCase__ :Optional[Any] = in_proj_bias[256:512]
UpperCamelCase__ :Tuple = in_proj_weight[-256:, :]
UpperCamelCase__ :Dict = 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
UpperCamelCase__ :List[str] = state_dict.pop(f'''{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_weight''' )
UpperCamelCase__ :Optional[Any] = 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
UpperCamelCase__ :Any = in_proj_weight[:256, :]
UpperCamelCase__ :Optional[int] = in_proj_bias[:256]
UpperCamelCase__ :Tuple = in_proj_weight[256:512, :]
UpperCamelCase__ :Dict = in_proj_bias[256:512]
UpperCamelCase__ :Any = in_proj_weight[-256:, :]
UpperCamelCase__ :Dict = in_proj_bias[-256:]
# read in weights + bias of input projection layer of cross-attention
UpperCamelCase__ :List[str] = state_dict.pop(
f'''{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_weight''' )
UpperCamelCase__ :Any = 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
UpperCamelCase__ :Optional[Any] = in_proj_weight_cross_attn[:256, :]
UpperCamelCase__ :Any = in_proj_bias_cross_attn[:256]
UpperCamelCase__ :Any = in_proj_weight_cross_attn[256:512, :]
UpperCamelCase__ :Dict = in_proj_bias_cross_attn[256:512]
UpperCamelCase__ :str = in_proj_weight_cross_attn[-256:, :]
UpperCamelCase__ :Tuple = in_proj_bias_cross_attn[-256:]
def a ( __a , __a ) -> Optional[int]:
'''simple docstring'''
UpperCamelCase__ , UpperCamelCase__ :str = image.size
UpperCamelCase__ :Optional[Any] = max(__a , __a )
UpperCamelCase__ :List[Any] = 800 if '''detection''' in checkpoint_url else 1000
UpperCamelCase__ :Dict = target_max_size / current_max_size
UpperCamelCase__ :Any = image.resize((int(round(scale * width ) ), int(round(scale * height ) )) )
return resized_image
def a ( __a ) -> int:
'''simple docstring'''
UpperCamelCase__ :Any = F.to_tensor(__a )
UpperCamelCase__ :int = F.normalize(__a , mean=[0.4_8_5, 0.4_5_6, 0.4_0_6] , std=[0.2_2_9, 0.2_2_4, 0.2_2_5] )
return image
@torch.no_grad()
def a ( __a , __a , __a ) -> Dict:
'''simple docstring'''
logger.info('''Converting model...''' )
# load original state dict
UpperCamelCase__ :Optional[Any] = torch.hub.load_state_dict_from_url(__a , map_location='''cpu''' )
# rename keys
for src, dest in rename_keys:
rename_key(__a , __a , __a )
UpperCamelCase__ :Any = rename_backbone_keys(__a )
# query, key and value matrices need special treatment
read_in_q_k_v(__a )
# important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
UpperCamelCase__ :Dict = '''model.'''
for key in state_dict.copy().keys():
if not key.startswith('''class_labels_classifier''' ) and not key.startswith('''bbox_predictor''' ):
UpperCamelCase__ :Optional[Any] = state_dict.pop(__a )
UpperCamelCase__ :int = val
# create HuggingFace model and load state dict
UpperCamelCase__ :str = TableTransformerConfig(
backbone='''resnet18''' , mask_loss_coefficient=1 , dice_loss_coefficient=1 , ce_loss_coefficient=1 , bbox_loss_coefficient=5 , giou_loss_coefficient=2 , eos_coefficient=0.4 , class_cost=1 , bbox_cost=5 , giou_cost=2 , )
if "detection" in checkpoint_url:
UpperCamelCase__ :List[str] = 15
UpperCamelCase__ :int = 2
UpperCamelCase__ :Tuple = {0: '''table''', 1: '''table rotated'''}
UpperCamelCase__ :int = idalabel
UpperCamelCase__ :Dict = {v: k for k, v in idalabel.items()}
else:
UpperCamelCase__ :int = 125
UpperCamelCase__ :List[str] = 6
UpperCamelCase__ :Optional[Any] = {
0: '''table''',
1: '''table column''',
2: '''table row''',
3: '''table column header''',
4: '''table projected row header''',
5: '''table spanning cell''',
}
UpperCamelCase__ :Dict = idalabel
UpperCamelCase__ :Optional[Any] = {v: k for k, v in idalabel.items()}
UpperCamelCase__ :List[Any] = DetrImageProcessor(
format='''coco_detection''' , max_size=800 if '''detection''' in checkpoint_url else 1000 )
UpperCamelCase__ :int = TableTransformerForObjectDetection(__a )
model.load_state_dict(__a )
model.eval()
# verify our conversion
UpperCamelCase__ :Dict = '''example_pdf.png''' if '''detection''' in checkpoint_url else '''example_table.png'''
UpperCamelCase__ :Optional[Any] = hf_hub_download(repo_id='''nielsr/example-pdf''' , repo_type='''dataset''' , filename=__a )
UpperCamelCase__ :Tuple = Image.open(__a ).convert('''RGB''' )
UpperCamelCase__ :int = normalize(resize(__a , __a ) ).unsqueeze(0 )
UpperCamelCase__ :Optional[int] = model(__a )
if "detection" in checkpoint_url:
UpperCamelCase__ :Dict = (1, 15, 3)
UpperCamelCase__ :List[Any] = torch.tensor(
[[-6.7_8_9_7, -1_6.9_9_8_5, 6.7_9_3_7], [-8.0_1_8_6, -2_2.2_1_9_2, 6.9_6_7_7], [-7.3_1_1_7, -2_1.0_7_0_8, 7.4_0_5_5]] )
UpperCamelCase__ :Tuple = torch.tensor([[0.4_8_6_7, 0.1_7_6_7, 0.6_7_3_2], [0.6_7_1_8, 0.4_4_7_9, 0.3_8_3_0], [0.4_7_1_6, 0.1_7_6_0, 0.6_3_6_4]] )
else:
UpperCamelCase__ :Optional[Any] = (1, 125, 7)
UpperCamelCase__ :Dict = torch.tensor(
[[-1_8.1_4_3_0, -8.3_2_1_4, 4.8_2_7_4], [-1_8.4_6_8_5, -7.1_3_6_1, -4.2_6_6_7], [-2_6.3_6_9_3, -9.3_4_2_9, -4.9_9_6_2]] )
UpperCamelCase__ :List[Any] = torch.tensor([[0.4_9_8_3, 0.5_5_9_5, 0.9_4_4_0], [0.4_9_1_6, 0.6_3_1_5, 0.5_9_5_4], [0.6_1_0_8, 0.8_6_3_7, 0.1_1_3_5]] )
assert outputs.logits.shape == expected_shape
assert torch.allclose(outputs.logits[0, :3, :3] , __a , atol=1e-4 )
assert torch.allclose(outputs.pred_boxes[0, :3, :3] , __a , 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(__a ).mkdir(exist_ok=__a )
model.save_pretrained(__a )
image_processor.save_pretrained(__a )
if push_to_hub:
# Push model to HF hub
logger.info('''Pushing model to the hub...''' )
UpperCamelCase__ :Union[str, Any] = (
'''microsoft/table-transformer-detection'''
if '''detection''' in checkpoint_url
else '''microsoft/table-transformer-structure-recognition'''
)
model.push_to_hub(__a )
image_processor.push_to_hub(__a )
if __name__ == "__main__":
__snake_case = argparse.ArgumentParser()
parser.add_argument(
'''--checkpoint_url''',
default='''https://pubtables1m.blob.core.windows.net/model/pubtables1m_detection_detr_r18.pth''',
type=str,
choices=[
'''https://pubtables1m.blob.core.windows.net/model/pubtables1m_detection_detr_r18.pth''',
'''https://pubtables1m.blob.core.windows.net/model/pubtables1m_structure_detr_r18.pth''',
],
help='''URL of the Table Transformer checkpoint 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 or not to push the converted model to the 🤗 hub.'''
)
__snake_case = parser.parse_args()
convert_table_transformer_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub) | 97 | 0 |
'''simple docstring'''
import json
from typing import List, Optional, Tuple
from tokenizers import pre_tokenizers, processors
from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_bart import BartTokenizer
SCREAMING_SNAKE_CASE_: Any =logging.get_logger(__name__)
SCREAMING_SNAKE_CASE_: Union[str, Any] ={'vocab_file': 'vocab.json', 'merges_file': 'merges.txt', 'tokenizer_file': 'tokenizer.json'}
# See all BART models at https://huggingface.co/models?filter=bart
SCREAMING_SNAKE_CASE_: str ={
'vocab_file': {
'facebook/bart-base': 'https://huggingface.co/facebook/bart-base/resolve/main/vocab.json',
'facebook/bart-large': 'https://huggingface.co/facebook/bart-large/resolve/main/vocab.json',
'facebook/bart-large-mnli': 'https://huggingface.co/facebook/bart-large-mnli/resolve/main/vocab.json',
'facebook/bart-large-cnn': 'https://huggingface.co/facebook/bart-large-cnn/resolve/main/vocab.json',
'facebook/bart-large-xsum': 'https://huggingface.co/facebook/bart-large-xsum/resolve/main/vocab.json',
'yjernite/bart_eli5': 'https://huggingface.co/yjernite/bart_eli5/resolve/main/vocab.json',
},
'merges_file': {
'facebook/bart-base': 'https://huggingface.co/facebook/bart-base/resolve/main/merges.txt',
'facebook/bart-large': 'https://huggingface.co/facebook/bart-large/resolve/main/merges.txt',
'facebook/bart-large-mnli': 'https://huggingface.co/facebook/bart-large-mnli/resolve/main/merges.txt',
'facebook/bart-large-cnn': 'https://huggingface.co/facebook/bart-large-cnn/resolve/main/merges.txt',
'facebook/bart-large-xsum': 'https://huggingface.co/facebook/bart-large-xsum/resolve/main/merges.txt',
'yjernite/bart_eli5': 'https://huggingface.co/yjernite/bart_eli5/resolve/main/merges.txt',
},
'tokenizer_file': {
'facebook/bart-base': 'https://huggingface.co/facebook/bart-base/resolve/main/tokenizer.json',
'facebook/bart-large': 'https://huggingface.co/facebook/bart-large/resolve/main/tokenizer.json',
'facebook/bart-large-mnli': 'https://huggingface.co/facebook/bart-large-mnli/resolve/main/tokenizer.json',
'facebook/bart-large-cnn': 'https://huggingface.co/facebook/bart-large-cnn/resolve/main/tokenizer.json',
'facebook/bart-large-xsum': 'https://huggingface.co/facebook/bart-large-xsum/resolve/main/tokenizer.json',
'yjernite/bart_eli5': 'https://huggingface.co/yjernite/bart_eli5/resolve/main/tokenizer.json',
},
}
SCREAMING_SNAKE_CASE_: int ={
'facebook/bart-base': 10_24,
'facebook/bart-large': 10_24,
'facebook/bart-large-mnli': 10_24,
'facebook/bart-large-cnn': 10_24,
'facebook/bart-large-xsum': 10_24,
'yjernite/bart_eli5': 10_24,
}
class __A ( UpperCamelCase__ ):
a__ : Dict = VOCAB_FILES_NAMES
a__ : Any = PRETRAINED_VOCAB_FILES_MAP
a__ : Union[str, Any] = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
a__ : int = ["""input_ids""", """attention_mask"""]
a__ : str = BartTokenizer
def __init__(self : List[str] , __a : List[Any]=None , __a : str=None , __a : List[str]=None , __a : Union[str, Any]="replace" , __a : List[str]="<s>" , __a : List[Any]="</s>" , __a : Dict="</s>" , __a : Optional[int]="<s>" , __a : Any="<unk>" , __a : List[Any]="<pad>" , __a : List[str]="<mask>" , __a : Tuple=False , __a : Optional[Any]=True , **__a : Optional[Any] , ):
super().__init__(
__a , __a , tokenizer_file=__a , errors=__a , bos_token=__a , eos_token=__a , sep_token=__a , cls_token=__a , unk_token=__a , pad_token=__a , mask_token=__a , add_prefix_space=__a , trim_offsets=__a , **__a , )
UpperCAmelCase_ = json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__() )
if pre_tok_state.get("add_prefix_space" , __a ) != add_prefix_space:
UpperCAmelCase_ = getattr(__a , pre_tok_state.pop("type" ) )
UpperCAmelCase_ = add_prefix_space
UpperCAmelCase_ = pre_tok_class(**__a )
UpperCAmelCase_ = add_prefix_space
# the pre_tokenizer is already updated in the GPT2TokenizerFast `__init__`
UpperCAmelCase_ = "post_processor"
UpperCAmelCase_ = getattr(self.backend_tokenizer , __a , __a )
if tokenizer_component_instance:
UpperCAmelCase_ = json.loads(tokenizer_component_instance.__getstate__() )
# The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
if "sep" in state:
UpperCAmelCase_ = tuple(state["sep"] )
if "cls" in state:
UpperCAmelCase_ = tuple(state["cls"] )
UpperCAmelCase_ = False
if state.get("add_prefix_space" , __a ) != add_prefix_space:
UpperCAmelCase_ = add_prefix_space
UpperCAmelCase_ = True
if state.get("trim_offsets" , __a ) != trim_offsets:
UpperCAmelCase_ = trim_offsets
UpperCAmelCase_ = True
if changes_to_apply:
UpperCAmelCase_ = getattr(__a , state.pop("type" ) )
UpperCAmelCase_ = component_class(**__a )
setattr(self.backend_tokenizer , __a , __a )
@property
def _lowercase (self : Dict ):
if self._mask_token is None:
if self.verbose:
logger.error("Using mask_token, but it is not set yet." )
return None
return str(self._mask_token )
@mask_token.setter
def _lowercase (self : Optional[int] , __a : List[Any] ):
UpperCAmelCase_ = AddedToken(__a , lstrip=__a , rstrip=__a ) if isinstance(__a , __a ) else value
UpperCAmelCase_ = value
def _lowercase (self : Any , *__a : List[Any] , **__a : Any ):
UpperCAmelCase_ = kwargs.get("is_split_into_words" , __a )
if is_split_into_words and not self.add_prefix_space:
raise ValueError(
f"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True """
"to use it with pretokenized inputs." )
return super()._batch_encode_plus(*__a , **__a )
def _lowercase (self : List[str] , *__a : Optional[Any] , **__a : Dict ):
UpperCAmelCase_ = kwargs.get("is_split_into_words" , __a )
if is_split_into_words and not self.add_prefix_space:
raise ValueError(
f"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True """
"to use it with pretokenized inputs." )
return super()._encode_plus(*__a , **__a )
def _lowercase (self : str , __a : str , __a : Optional[str] = None ):
UpperCAmelCase_ = self._tokenizer.model.save(__a , name=__a )
return tuple(__a )
def _lowercase (self : Any , __a : Optional[int] , __a : str=None ):
UpperCAmelCase_ = [self.bos_token_id] + token_ids_a + [self.eos_token_id]
if token_ids_a is None:
return output
return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]
def _lowercase (self : Union[str, Any] , __a : List[int] , __a : Optional[List[int]] = None ):
UpperCAmelCase_ = [self.sep_token_id]
UpperCAmelCase_ = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep + sep + token_ids_a + sep ) * [0]
| 1 |
'''simple docstring'''
from __future__ import annotations
from fractions import Fraction
from math import gcd, sqrt
def a ( __a ) -> bool:
'''simple docstring'''
UpperCamelCase__ :int = int(number**0.5 )
return number == sq * sq
def a ( __a , __a , __a , __a , __a , __a ) -> tuple[int, int]:
'''simple docstring'''
UpperCamelCase__ :int = x_num * y_den * z_den + y_num * x_den * z_den + z_num * x_den * y_den
UpperCamelCase__ :int = x_den * y_den * z_den
UpperCamelCase__ :int = gcd(__a , __a )
top //= hcf
bottom //= hcf
return top, bottom
def a ( __a = 35 ) -> int:
'''simple docstring'''
UpperCamelCase__ :set = set()
UpperCamelCase__ :int
UpperCamelCase__ :Fraction = Fraction(0 )
UpperCamelCase__ :tuple[int, int]
for x_num in range(1 , order + 1 ):
for x_den in range(x_num + 1 , order + 1 ):
for y_num in range(1 , order + 1 ):
for y_den in range(y_num + 1 , order + 1 ):
# n=1
UpperCamelCase__ :int = x_num * y_den + x_den * y_num
UpperCamelCase__ :Any = x_den * y_den
UpperCamelCase__ :Tuple = gcd(__a , __a )
z_num //= hcf
z_den //= hcf
if 0 < z_num < z_den <= order:
UpperCamelCase__ :Tuple = add_three(
__a , __a , __a , __a , __a , __a )
unique_s.add(__a )
# n=2
UpperCamelCase__ :List[str] = (
x_num * x_num * y_den * y_den + x_den * x_den * y_num * y_num
)
UpperCamelCase__ :Dict = x_den * x_den * y_den * y_den
if is_sq(__a ) and is_sq(__a ):
UpperCamelCase__ :Any = int(sqrt(__a ) )
UpperCamelCase__ :Optional[int] = int(sqrt(__a ) )
UpperCamelCase__ :int = gcd(__a , __a )
z_num //= hcf
z_den //= hcf
if 0 < z_num < z_den <= order:
UpperCamelCase__ :Tuple = add_three(
__a , __a , __a , __a , __a , __a )
unique_s.add(__a )
# n=-1
UpperCamelCase__ :Tuple = x_num * y_num
UpperCamelCase__ :Union[str, Any] = x_den * y_num + x_num * y_den
UpperCamelCase__ :List[str] = gcd(__a , __a )
z_num //= hcf
z_den //= hcf
if 0 < z_num < z_den <= order:
UpperCamelCase__ :Union[str, Any] = add_three(
__a , __a , __a , __a , __a , __a )
unique_s.add(__a )
# n=2
UpperCamelCase__ :Optional[Any] = x_num * x_num * y_num * y_num
UpperCamelCase__ :Tuple = (
x_den * x_den * y_num * y_num + x_num * x_num * y_den * y_den
)
if is_sq(__a ) and is_sq(__a ):
UpperCamelCase__ :str = int(sqrt(__a ) )
UpperCamelCase__ :Any = int(sqrt(__a ) )
UpperCamelCase__ :Dict = gcd(__a , __a )
z_num //= hcf
z_den //= hcf
if 0 < z_num < z_den <= order:
UpperCamelCase__ :int = add_three(
__a , __a , __a , __a , __a , __a )
unique_s.add(__a )
for num, den in unique_s:
total += Fraction(__a , __a )
return total.denominator + total.numerator
if __name__ == "__main__":
print(F"""{solution() = }""") | 97 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowerCamelCase : Union[str, Any] = {'configuration_vit_msn': ['VIT_MSN_PRETRAINED_CONFIG_ARCHIVE_MAP', 'ViTMSNConfig']}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCamelCase : Tuple = [
'VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST',
'ViTMSNModel',
'ViTMSNForImageClassification',
'ViTMSNPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_vit_msn import VIT_MSN_PRETRAINED_CONFIG_ARCHIVE_MAP, ViTMSNConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_vit_msn import (
VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST,
ViTMSNForImageClassification,
ViTMSNModel,
ViTMSNPreTrainedModel,
)
else:
import sys
lowerCamelCase : Tuple = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 2 |
'''simple docstring'''
def a ( ) -> Union[str, Any]:
'''simple docstring'''
UpperCamelCase__ :Optional[int] = []
UpperCamelCase__ :int = 1
while len(__a ) < 1e6:
constant.append(str(__a ) )
i += 1
UpperCamelCase__ :Union[str, Any] = ''''''.join(__a )
return (
int(constant[0] )
* int(constant[9] )
* int(constant[99] )
* int(constant[999] )
* int(constant[9999] )
* int(constant[99999] )
* int(constant[999999] )
)
if __name__ == "__main__":
print(solution()) | 97 | 0 |
'''simple docstring'''
import unittest
import numpy as np
import requests
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_available():
import torch
from transformers.pytorch_utils import is_torch_greater_or_equal_than_1_11
else:
lowercase : str = False
if is_vision_available():
from PIL import Image
from transformers import PixaStructImageProcessor
class A ( unittest.TestCase ):
def __init__( self , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE=7 , SCREAMING_SNAKE_CASE=3 , SCREAMING_SNAKE_CASE=18 , SCREAMING_SNAKE_CASE=30 , SCREAMING_SNAKE_CASE=400 , SCREAMING_SNAKE_CASE=None , SCREAMING_SNAKE_CASE=True , SCREAMING_SNAKE_CASE=True , SCREAMING_SNAKE_CASE=None , ) -> List[str]:
"""simple docstring"""
A : Any = size if size is not None else {'''height''': 20, '''width''': 20}
A : List[Any] = parent
A : Dict = batch_size
A : Optional[Any] = num_channels
A : str = image_size
A : List[Any] = min_resolution
A : Optional[int] = max_resolution
A : Union[str, Any] = size
A : Tuple = do_normalize
A : Tuple = do_convert_rgb
A : Union[str, Any] = [512, 1024, 2048, 4096]
A : Optional[int] = patch_size if patch_size is not None else {'''height''': 16, '''width''': 16}
def __lowerCAmelCase ( self ) -> str:
"""simple docstring"""
return {"do_normalize": self.do_normalize, "do_convert_rgb": self.do_convert_rgb}
def __lowerCAmelCase ( self ) -> int:
"""simple docstring"""
A : str = '''https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/australia.jpg'''
A : List[str] = Image.open(requests.get(SCREAMING_SNAKE_CASE , stream=SCREAMING_SNAKE_CASE ).raw ).convert('''RGB''' )
return raw_image
@unittest.skipIf(
not is_torch_greater_or_equal_than_1_11 , reason='''`Pix2StructImageProcessor` requires `torch>=1.11.0`.''' , )
@require_torch
@require_vision
class A ( __snake_case , unittest.TestCase ):
__magic_name__ = PixaStructImageProcessor if is_vision_available() else None
def __lowerCAmelCase ( self ) -> Optional[Any]:
"""simple docstring"""
A : Optional[int] = PixaStructImageProcessingTester(self )
@property
def __lowerCAmelCase ( self ) -> Tuple:
"""simple docstring"""
return self.image_processor_tester.prepare_image_processor_dict()
def __lowerCAmelCase ( self ) -> Tuple:
"""simple docstring"""
A : List[str] = self.image_processing_class(**self.image_processor_dict )
self.assertTrue(hasattr(SCREAMING_SNAKE_CASE , '''do_normalize''' ) )
self.assertTrue(hasattr(SCREAMING_SNAKE_CASE , '''do_convert_rgb''' ) )
def __lowerCAmelCase ( self ) -> Union[str, Any]:
"""simple docstring"""
A : Dict = self.image_processor_tester.prepare_dummy_image()
A : Union[str, Any] = self.image_processing_class(**self.image_processor_dict )
A : int = 2048
A : Tuple = image_processor(SCREAMING_SNAKE_CASE , return_tensors='''pt''' , max_patches=SCREAMING_SNAKE_CASE )
self.assertTrue(torch.allclose(inputs.flattened_patches.mean() , torch.tensor(0.0_606 ) , atol=1e-3 , rtol=1e-3 ) )
def __lowerCAmelCase ( self ) -> List[Any]:
"""simple docstring"""
A : Optional[int] = self.image_processing_class(**self.image_processor_dict )
# create random PIL images
A : int = prepare_image_inputs(self.image_processor_tester , equal_resolution=SCREAMING_SNAKE_CASE )
for image in image_inputs:
self.assertIsInstance(SCREAMING_SNAKE_CASE , Image.Image )
# Test not batched input
A : str = (
(self.image_processor_tester.patch_size['''height'''] * self.image_processor_tester.patch_size['''width'''])
* self.image_processor_tester.num_channels
) + 2
for max_patch in self.image_processor_tester.max_patches:
# Test not batched input
A : Optional[int] = image_processor(
image_inputs[0] , return_tensors='''pt''' , max_patches=SCREAMING_SNAKE_CASE ).flattened_patches
self.assertEqual(
encoded_images.shape , (1, max_patch, expected_hidden_dim) , )
# Test batched
A : Union[str, Any] = image_processor(
SCREAMING_SNAKE_CASE , return_tensors='''pt''' , max_patches=SCREAMING_SNAKE_CASE ).flattened_patches
self.assertEqual(
encoded_images.shape , (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim) , )
def __lowerCAmelCase ( self ) -> Union[str, Any]:
"""simple docstring"""
A : List[Any] = self.image_processing_class(**self.image_processor_dict )
# create random PIL images
A : Any = prepare_image_inputs(self.image_processor_tester , equal_resolution=SCREAMING_SNAKE_CASE )
for image in image_inputs:
self.assertIsInstance(SCREAMING_SNAKE_CASE , Image.Image )
# Test not batched input
A : List[Any] = (
(self.image_processor_tester.patch_size['''height'''] * self.image_processor_tester.patch_size['''width'''])
* self.image_processor_tester.num_channels
) + 2
A : Optional[int] = True
for max_patch in self.image_processor_tester.max_patches:
# Test not batched input
with self.assertRaises(SCREAMING_SNAKE_CASE ):
A : Any = image_processor(
image_inputs[0] , return_tensors='''pt''' , max_patches=SCREAMING_SNAKE_CASE ).flattened_patches
A : Any = '''Hello'''
A : Any = image_processor(
image_inputs[0] , return_tensors='''pt''' , max_patches=SCREAMING_SNAKE_CASE , header_text=SCREAMING_SNAKE_CASE ).flattened_patches
self.assertEqual(
encoded_images.shape , (1, max_patch, expected_hidden_dim) , )
# Test batched
A : Optional[int] = image_processor(
SCREAMING_SNAKE_CASE , return_tensors='''pt''' , max_patches=SCREAMING_SNAKE_CASE , header_text=SCREAMING_SNAKE_CASE ).flattened_patches
self.assertEqual(
encoded_images.shape , (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim) , )
def __lowerCAmelCase ( self ) -> Dict:
"""simple docstring"""
A : Dict = self.image_processing_class(**self.image_processor_dict )
# create random numpy tensors
A : int = prepare_image_inputs(self.image_processor_tester , equal_resolution=SCREAMING_SNAKE_CASE , numpify=SCREAMING_SNAKE_CASE )
for image in image_inputs:
self.assertIsInstance(SCREAMING_SNAKE_CASE , np.ndarray )
A : Tuple = (
(self.image_processor_tester.patch_size['''height'''] * self.image_processor_tester.patch_size['''width'''])
* self.image_processor_tester.num_channels
) + 2
for max_patch in self.image_processor_tester.max_patches:
# Test not batched input
A : List[Any] = image_processor(
image_inputs[0] , return_tensors='''pt''' , max_patches=SCREAMING_SNAKE_CASE ).flattened_patches
self.assertEqual(
encoded_images.shape , (1, max_patch, expected_hidden_dim) , )
# Test batched
A : str = image_processor(
SCREAMING_SNAKE_CASE , return_tensors='''pt''' , max_patches=SCREAMING_SNAKE_CASE ).flattened_patches
self.assertEqual(
encoded_images.shape , (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim) , )
def __lowerCAmelCase ( self ) -> List[str]:
"""simple docstring"""
A : str = self.image_processing_class(**self.image_processor_dict )
# create random PyTorch tensors
A : Optional[Any] = prepare_image_inputs(self.image_processor_tester , equal_resolution=SCREAMING_SNAKE_CASE , torchify=SCREAMING_SNAKE_CASE )
for image in image_inputs:
self.assertIsInstance(SCREAMING_SNAKE_CASE , torch.Tensor )
# Test not batched input
A : int = (
(self.image_processor_tester.patch_size['''height'''] * self.image_processor_tester.patch_size['''width'''])
* self.image_processor_tester.num_channels
) + 2
for max_patch in self.image_processor_tester.max_patches:
# Test not batched input
A : Optional[Any] = image_processor(
image_inputs[0] , return_tensors='''pt''' , max_patches=SCREAMING_SNAKE_CASE ).flattened_patches
self.assertEqual(
encoded_images.shape , (1, max_patch, expected_hidden_dim) , )
# Test batched
A : Dict = image_processor(
SCREAMING_SNAKE_CASE , return_tensors='''pt''' , max_patches=SCREAMING_SNAKE_CASE ).flattened_patches
self.assertEqual(
encoded_images.shape , (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim) , )
@unittest.skipIf(
not is_torch_greater_or_equal_than_1_11 , reason='''`Pix2StructImageProcessor` requires `torch>=1.11.0`.''' , )
@require_torch
@require_vision
class A ( __snake_case , unittest.TestCase ):
__magic_name__ = PixaStructImageProcessor if is_vision_available() else None
def __lowerCAmelCase ( self ) -> str:
"""simple docstring"""
A : Union[str, Any] = PixaStructImageProcessingTester(self , num_channels=4 )
A : Optional[Any] = 3
@property
def __lowerCAmelCase ( self ) -> Tuple:
"""simple docstring"""
return self.image_processor_tester.prepare_image_processor_dict()
def __lowerCAmelCase ( self ) -> Union[str, Any]:
"""simple docstring"""
A : str = self.image_processing_class(**self.image_processor_dict )
self.assertTrue(hasattr(SCREAMING_SNAKE_CASE , '''do_normalize''' ) )
self.assertTrue(hasattr(SCREAMING_SNAKE_CASE , '''do_convert_rgb''' ) )
def __lowerCAmelCase ( self ) -> Optional[Any]:
"""simple docstring"""
A : List[Any] = self.image_processing_class(**self.image_processor_dict )
# create random PIL images
A : Optional[Any] = prepare_image_inputs(self.image_processor_tester , equal_resolution=SCREAMING_SNAKE_CASE )
for image in image_inputs:
self.assertIsInstance(SCREAMING_SNAKE_CASE , Image.Image )
# Test not batched input
A : int = (
(self.image_processor_tester.patch_size['''height'''] * self.image_processor_tester.patch_size['''width'''])
* (self.image_processor_tester.num_channels - 1)
) + 2
for max_patch in self.image_processor_tester.max_patches:
# Test not batched input
A : Any = image_processor(
image_inputs[0] , return_tensors='''pt''' , max_patches=SCREAMING_SNAKE_CASE ).flattened_patches
self.assertEqual(
encoded_images.shape , (1, max_patch, expected_hidden_dim) , )
# Test batched
A : int = image_processor(
SCREAMING_SNAKE_CASE , return_tensors='''pt''' , max_patches=SCREAMING_SNAKE_CASE ).flattened_patches
self.assertEqual(
encoded_images.shape , (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim) , )
| 3 |
'''simple docstring'''
from PIL import Image
def a ( __a , __a ) -> Image:
'''simple docstring'''
def brightness(__a ) -> float:
return 128 + level + (c - 128)
if not -2_5_5.0 <= level <= 2_5_5.0:
raise ValueError('''level must be between -255.0 (black) and 255.0 (white)''' )
return img.point(__a )
if __name__ == "__main__":
# Load image
with Image.open('''image_data/lena.jpg''') as img:
# Change brightness to 100
__snake_case = change_brightness(img, 100)
brigt_img.save('''image_data/lena_brightness.png''', format='''png''') | 97 | 0 |
'''simple docstring'''
import argparse
from pathlib import Path
import torch
from packaging import version
from torch.onnx import export
from diffusers import AutoencoderKL
__snake_case =version.parse(version.parse(torch.__version__).base_version) < version.parse("""1.11""")
def a_ ( lowerCamelCase : Dict , lowerCamelCase : tuple , lowerCamelCase : Path , lowerCamelCase : Dict , lowerCamelCase : List[str] , lowerCamelCase : Dict , lowerCamelCase : Optional[Any] , lowerCamelCase : Optional[int]=False , ):
output_path.parent.mkdir(parents=lowerCamelCase , exist_ok=lowerCamelCase )
# PyTorch deprecated the `enable_onnx_checker` and `use_external_data_format` arguments in v1.11,
# so we check the torch version for backwards compatibility
if is_torch_less_than_1_11:
export(
lowerCamelCase , lowerCamelCase , f=output_path.as_posix() , input_names=lowerCamelCase , output_names=lowerCamelCase , dynamic_axes=lowerCamelCase , do_constant_folding=lowerCamelCase , use_external_data_format=lowerCamelCase , enable_onnx_checker=lowerCamelCase , opset_version=lowerCamelCase , )
else:
export(
lowerCamelCase , lowerCamelCase , f=output_path.as_posix() , input_names=lowerCamelCase , output_names=lowerCamelCase , dynamic_axes=lowerCamelCase , do_constant_folding=lowerCamelCase , opset_version=lowerCamelCase , )
@torch.no_grad()
def a_ ( lowerCamelCase : str , lowerCamelCase : str , lowerCamelCase : int , lowerCamelCase : bool = False ):
lowerCAmelCase = torch.floataa if fpaa else torch.floataa
if fpaa and torch.cuda.is_available():
lowerCAmelCase = 'cuda'
elif fpaa and not torch.cuda.is_available():
raise ValueError('`float16` model export is only supported on GPUs with CUDA' )
else:
lowerCAmelCase = 'cpu'
lowerCAmelCase = Path(lowerCamelCase )
# VAE DECODER
lowerCAmelCase = AutoencoderKL.from_pretrained(model_path + '/vae' )
lowerCAmelCase = vae_decoder.config.latent_channels
# forward only through the decoder part
lowerCAmelCase = vae_decoder.decode
onnx_export(
lowerCamelCase , model_args=(
torch.randn(1 , lowerCamelCase , 25 , 25 ).to(device=lowerCamelCase , dtype=lowerCamelCase ),
False,
) , output_path=output_path / 'vae_decoder' / 'model.onnx' , ordered_input_names=['latent_sample', 'return_dict'] , output_names=['sample'] , dynamic_axes={
'latent_sample': {0: 'batch', 1: 'channels', 2: 'height', 3: 'width'},
} , opset=lowerCamelCase , )
del vae_decoder
if __name__ == "__main__":
__snake_case =argparse.ArgumentParser()
parser.add_argument(
"""--model_path""",
type=str,
required=True,
help="""Path to the `diffusers` checkpoint to convert (either a local directory or on the Hub).""",
)
parser.add_argument("""--output_path""", type=str, required=True, help="""Path to the output model.""")
parser.add_argument(
"""--opset""",
default=14,
type=int,
help="""The version of the ONNX operator set to use.""",
)
parser.add_argument("""--fp16""", action="""store_true""", default=False, help="""Export the models in `float16` mode""")
__snake_case =parser.parse_args()
print(args.output_path)
convert_models(args.model_path, args.output_path, args.opset, args.fpaa)
print("""SD: Done: ONNX""")
| 4 |
'''simple docstring'''
from datetime import datetime as dt
import os
from github import Github
__snake_case = [
'''good first issue''',
'''good second issue''',
'''good difficult issue''',
'''feature request''',
'''new model''',
'''wip''',
]
def a ( ) -> List[str]:
'''simple docstring'''
UpperCamelCase__ :List[str] = Github(os.environ['''GITHUB_TOKEN'''] )
UpperCamelCase__ :Tuple = g.get_repo('''huggingface/transformers''' )
UpperCamelCase__ :Union[str, Any] = repo.get_issues(state='''open''' )
for issue in open_issues:
UpperCamelCase__ :List[Any] = sorted([comment for comment in issue.get_comments()] , key=lambda __a : i.created_at , reverse=__a )
UpperCamelCase__ :List[Any] = comments[0] if len(__a ) > 0 else None
if (
last_comment is not None
and last_comment.user.login == "github-actions[bot]"
and (dt.utcnow() - issue.updated_at).days > 7
and (dt.utcnow() - issue.created_at).days >= 30
and not any(label.name.lower() in LABELS_TO_EXEMPT for label in issue.get_labels() )
):
# print(f"Would close issue {issue.number} since it has been 7 days of inactivity since bot mention.")
issue.edit(state='''closed''' )
elif (
(dt.utcnow() - issue.updated_at).days > 23
and (dt.utcnow() - issue.created_at).days >= 30
and not any(label.name.lower() in LABELS_TO_EXEMPT for label in issue.get_labels() )
):
# print(f"Would add stale comment to {issue.number}")
issue.create_comment(
'''This issue has been automatically marked as stale because it has not had '''
'''recent activity. If you think this still needs to be addressed '''
'''please comment on this thread.\n\nPlease note that issues that do not follow the '''
'''[contributing guidelines](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md) '''
'''are likely to be ignored.''' )
if __name__ == "__main__":
main() | 97 | 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 lowerCamelCase__ :
def __init__(self , UpperCAmelCase , UpperCAmelCase=1_3 , UpperCAmelCase=[3_0, 3_0] , UpperCAmelCase=2 , UpperCAmelCase=3 , UpperCAmelCase=True , UpperCAmelCase=True , UpperCAmelCase=3_2 , UpperCAmelCase=5 , UpperCAmelCase=4 , UpperCAmelCase=3_7 , UpperCAmelCase="gelu" , UpperCAmelCase=0.1 , UpperCAmelCase=0.1 , UpperCAmelCase=1_0 , UpperCAmelCase=0.02 , UpperCAmelCase=3 , UpperCAmelCase=None , UpperCAmelCase=8 , UpperCAmelCase=1_0 , ) -> Tuple:
_lowercase =parent
_lowercase =batch_size
_lowercase =image_size
_lowercase =patch_size
_lowercase =num_channels
_lowercase =is_training
_lowercase =use_labels
_lowercase =hidden_size
_lowercase =num_hidden_layers
_lowercase =num_attention_heads
_lowercase =intermediate_size
_lowercase =hidden_act
_lowercase =hidden_dropout_prob
_lowercase =attention_probs_dropout_prob
_lowercase =type_sequence_label_size
_lowercase =initializer_range
_lowercase =num_labels
_lowercase =scope
_lowercase =n_targets
_lowercase =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
_lowercase =(image_size[1] // patch_size) * (image_size[0] // patch_size)
_lowercase =num_patches + 1 + self.num_detection_tokens
def __A (self ) -> str:
_lowercase =floats_tensor([self.batch_size, self.num_channels, self.image_size[0], self.image_size[1]] )
_lowercase =None
if self.use_labels:
# labels is a list of Dict (each Dict being the labels for a given example in the batch)
_lowercase =[]
for i in range(self.batch_size ):
_lowercase ={}
_lowercase =torch.randint(
high=self.num_labels , size=(self.n_targets,) , device=UpperCAmelCase )
_lowercase =torch.rand(self.n_targets , 4 , device=UpperCAmelCase )
labels.append(UpperCAmelCase )
_lowercase =self.get_config()
return config, pixel_values, labels
def __A (self ) -> Dict:
return YolosConfig(
image_size=self.image_size , patch_size=self.patch_size , num_channels=self.num_channels , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , is_decoder=UpperCAmelCase , initializer_range=self.initializer_range , num_detection_tokens=self.num_detection_tokens , num_labels=self.num_labels , )
def __A (self , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase ) -> Dict:
_lowercase =YolosModel(config=UpperCAmelCase )
model.to(UpperCAmelCase )
model.eval()
_lowercase =model(UpperCAmelCase )
self.parent.assertEqual(
result.last_hidden_state.shape , (self.batch_size, self.expected_seq_len, self.hidden_size) )
def __A (self , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase ) -> List[str]:
_lowercase =YolosForObjectDetection(UpperCAmelCase )
model.to(UpperCAmelCase )
model.eval()
_lowercase =model(pixel_values=UpperCAmelCase )
_lowercase =model(UpperCAmelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_detection_tokens, self.num_labels + 1) )
self.parent.assertEqual(result.pred_boxes.shape , (self.batch_size, self.num_detection_tokens, 4) )
_lowercase =model(pixel_values=UpperCAmelCase , labels=UpperCAmelCase )
self.parent.assertEqual(result.loss.shape , () )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_detection_tokens, self.num_labels + 1) )
self.parent.assertEqual(result.pred_boxes.shape , (self.batch_size, self.num_detection_tokens, 4) )
def __A (self ) -> List[Any]:
_lowercase =self.prepare_config_and_inputs()
_lowercase , _lowercase , _lowercase =config_and_inputs
_lowercase ={'''pixel_values''': pixel_values}
return config, inputs_dict
@require_torch
class lowerCamelCase__ ( lowerCAmelCase , lowerCAmelCase , unittest.TestCase):
SCREAMING_SNAKE_CASE__ = (YolosModel, YolosForObjectDetection) if is_torch_available() else ()
SCREAMING_SNAKE_CASE__ = (
{'''feature-extraction''': YolosModel, '''object-detection''': YolosForObjectDetection} if is_torch_available() else {}
)
SCREAMING_SNAKE_CASE__ = False
SCREAMING_SNAKE_CASE__ = False
SCREAMING_SNAKE_CASE__ = False
SCREAMING_SNAKE_CASE__ = False
def __A (self , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase=False ) -> Optional[int]:
_lowercase =super()._prepare_for_class(UpperCAmelCase , UpperCAmelCase , return_labels=UpperCAmelCase )
if return_labels:
if model_class.__name__ == "YolosForObjectDetection":
_lowercase =[]
for i in range(self.model_tester.batch_size ):
_lowercase ={}
_lowercase =torch.ones(
size=(self.model_tester.n_targets,) , device=UpperCAmelCase , dtype=torch.long )
_lowercase =torch.ones(
self.model_tester.n_targets , 4 , device=UpperCAmelCase , dtype=torch.float )
labels.append(UpperCAmelCase )
_lowercase =labels
return inputs_dict
def __A (self ) -> List[Any]:
_lowercase =YolosModelTester(self )
_lowercase =ConfigTester(self , config_class=UpperCAmelCase , has_text_modality=UpperCAmelCase , hidden_size=3_7 )
def __A (self ) -> List[str]:
self.config_tester.run_common_tests()
def __A (self ) -> Dict:
# YOLOS does not use inputs_embeds
pass
def __A (self ) -> Any:
_lowercase , _lowercase =self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
_lowercase =model_class(UpperCAmelCase )
self.assertIsInstance(model.get_input_embeddings() , (nn.Module) )
_lowercase =model.get_output_embeddings()
self.assertTrue(x is None or isinstance(UpperCAmelCase , nn.Linear ) )
def __A (self ) -> int:
_lowercase , _lowercase =self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
_lowercase =model_class(UpperCAmelCase )
_lowercase =inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
_lowercase =[*signature.parameters.keys()]
_lowercase =['''pixel_values''']
self.assertListEqual(arg_names[:1] , UpperCAmelCase )
def __A (self ) -> Tuple:
_lowercase =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*UpperCAmelCase )
def __A (self ) -> Tuple:
_lowercase , _lowercase =self.model_tester.prepare_config_and_inputs_for_common()
_lowercase =True
# in YOLOS, the seq_len is different
_lowercase =self.model_tester.expected_seq_len
for model_class in self.all_model_classes:
_lowercase =True
_lowercase =False
_lowercase =True
_lowercase =model_class(UpperCAmelCase )
model.to(UpperCAmelCase )
model.eval()
with torch.no_grad():
_lowercase =model(**self._prepare_for_class(UpperCAmelCase , UpperCAmelCase ) )
_lowercase =outputs.attentions
self.assertEqual(len(UpperCAmelCase ) , self.model_tester.num_hidden_layers )
# check that output_attentions also work using config
del inputs_dict["output_attentions"]
_lowercase =True
_lowercase =model_class(UpperCAmelCase )
model.to(UpperCAmelCase )
model.eval()
with torch.no_grad():
_lowercase =model(**self._prepare_for_class(UpperCAmelCase , UpperCAmelCase ) )
_lowercase =outputs.attentions
self.assertEqual(len(UpperCAmelCase ) , self.model_tester.num_hidden_layers )
self.assertListEqual(
list(attentions[0].shape[-3:] ) , [self.model_tester.num_attention_heads, seq_len, seq_len] , )
_lowercase =len(UpperCAmelCase )
# Check attention is always last and order is fine
_lowercase =True
_lowercase =True
_lowercase =model_class(UpperCAmelCase )
model.to(UpperCAmelCase )
model.eval()
with torch.no_grad():
_lowercase =model(**self._prepare_for_class(UpperCAmelCase , UpperCAmelCase ) )
_lowercase =1
self.assertEqual(out_len + added_hidden_states , len(UpperCAmelCase ) )
_lowercase =outputs.attentions
self.assertEqual(len(UpperCAmelCase ) , self.model_tester.num_hidden_layers )
self.assertListEqual(
list(self_attentions[0].shape[-3:] ) , [self.model_tester.num_attention_heads, seq_len, seq_len] , )
def __A (self ) -> Any:
def check_hidden_states_output(UpperCAmelCase , UpperCAmelCase , UpperCAmelCase ):
_lowercase =model_class(UpperCAmelCase )
model.to(UpperCAmelCase )
model.eval()
with torch.no_grad():
_lowercase =model(**self._prepare_for_class(UpperCAmelCase , UpperCAmelCase ) )
_lowercase =outputs.hidden_states
_lowercase =getattr(
self.model_tester , '''expected_num_hidden_layers''' , self.model_tester.num_hidden_layers + 1 )
self.assertEqual(len(UpperCAmelCase ) , UpperCAmelCase )
# YOLOS has a different seq_length
_lowercase =self.model_tester.expected_seq_len
self.assertListEqual(
list(hidden_states[0].shape[-2:] ) , [seq_length, self.model_tester.hidden_size] , )
_lowercase , _lowercase =self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
_lowercase =True
check_hidden_states_output(UpperCAmelCase , UpperCAmelCase , UpperCAmelCase )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
_lowercase =True
check_hidden_states_output(UpperCAmelCase , UpperCAmelCase , UpperCAmelCase )
def __A (self ) -> List[Any]:
_lowercase =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_object_detection(*UpperCAmelCase )
@slow
def __A (self ) -> int:
for model_name in YOLOS_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
_lowercase =YolosModel.from_pretrained(UpperCAmelCase )
self.assertIsNotNone(UpperCAmelCase )
def UpperCAmelCase_ ( ) -> List[str]:
"""simple docstring"""
_lowercase =Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
return image
@require_torch
@require_vision
class lowerCamelCase__ ( unittest.TestCase):
@cached_property
def __A (self ) -> Optional[int]:
return AutoImageProcessor.from_pretrained('''hustvl/yolos-small''' ) if is_vision_available() else None
@slow
def __A (self ) -> Optional[int]:
_lowercase =YolosForObjectDetection.from_pretrained('''hustvl/yolos-small''' ).to(UpperCAmelCase )
_lowercase =self.default_image_processor
_lowercase =prepare_img()
_lowercase =image_processor(images=UpperCAmelCase , return_tensors='''pt''' ).to(UpperCAmelCase )
# forward pass
with torch.no_grad():
_lowercase =model(inputs.pixel_values )
# verify outputs
_lowercase =torch.Size((1, 1_0_0, 9_2) )
self.assertEqual(outputs.logits.shape , UpperCAmelCase )
_lowercase =torch.tensor(
[[-24.0248, -10.3024, -14.8290], [-42.0392, -16.8200, -27.4334], [-27.2743, -11.8154, -18.7148]] , device=UpperCAmelCase , )
_lowercase =torch.tensor(
[[0.2559, 0.5455, 0.4706], [0.2989, 0.7279, 0.1875], [0.7732, 0.4017, 0.4462]] , device=UpperCAmelCase )
self.assertTrue(torch.allclose(outputs.logits[0, :3, :3] , UpperCAmelCase , atol=1e-4 ) )
self.assertTrue(torch.allclose(outputs.pred_boxes[0, :3, :3] , UpperCAmelCase , atol=1e-4 ) )
# verify postprocessing
_lowercase =image_processor.post_process_object_detection(
UpperCAmelCase , threshold=0.3 , target_sizes=[image.size[::-1]] )[0]
_lowercase =torch.tensor([0.9994, 0.9790, 0.9964, 0.9972, 0.9861] ).to(UpperCAmelCase )
_lowercase =[7_5, 7_5, 1_7, 6_3, 1_7]
_lowercase =torch.tensor([335.0609, 79.3848, 375.4216, 187.2495] ).to(UpperCAmelCase )
self.assertEqual(len(results['''scores'''] ) , 5 )
self.assertTrue(torch.allclose(results['''scores'''] , UpperCAmelCase , atol=1e-4 ) )
self.assertSequenceEqual(results['''labels'''].tolist() , UpperCAmelCase )
self.assertTrue(torch.allclose(results['''boxes'''][0, :] , UpperCAmelCase ) )
| 5 |
'''simple docstring'''
import re
from filelock import FileLock
try:
import nltk
__snake_case = True
except (ImportError, ModuleNotFoundError):
__snake_case = False
if NLTK_AVAILABLE:
with FileLock('''.lock''') as lock:
nltk.download('''punkt''', quiet=True)
def a ( __a ) -> str:
'''simple docstring'''
re.sub('''<n>''' , '''''' , __a ) # 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(__a ) ) | 97 | 0 |
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A : List[str] = logging.get_logger(__name__)
A : Dict = {
'facebook/wav2vec2-base-960h': 'https://huggingface.co/facebook/wav2vec2-base-960h/resolve/main/config.json',
# See all Wav2Vec2 models at https://huggingface.co/models?filter=wav2vec2
}
class __A( a ):
snake_case_ = '''wav2vec2'''
def __init__( self , _snake_case=32 , _snake_case=768 , _snake_case=12 , _snake_case=12 , _snake_case=3_072 , _snake_case="gelu" , _snake_case=0.1 , _snake_case=0.1 , _snake_case=0.1 , _snake_case=0.0 , _snake_case=0.0 , _snake_case=0.1 , _snake_case=0.1 , _snake_case=0.02 , _snake_case=1E-5 , _snake_case="group" , _snake_case="gelu" , _snake_case=(512, 512, 512, 512, 512, 512, 512) , _snake_case=(5, 2, 2, 2, 2, 2, 2) , _snake_case=(10, 3, 3, 3, 3, 2, 2) , _snake_case=False , _snake_case=128 , _snake_case=16 , _snake_case=False , _snake_case=True , _snake_case=0.05 , _snake_case=10 , _snake_case=2 , _snake_case=0.0 , _snake_case=10 , _snake_case=0 , _snake_case=320 , _snake_case=2 , _snake_case=0.1 , _snake_case=100 , _snake_case=256 , _snake_case=256 , _snake_case=0.1 , _snake_case="sum" , _snake_case=False , _snake_case=False , _snake_case=256 , _snake_case=(512, 512, 512, 512, 1_500) , _snake_case=(5, 3, 3, 1, 1) , _snake_case=(1, 2, 3, 1, 1) , _snake_case=512 , _snake_case=0 , _snake_case=1 , _snake_case=2 , _snake_case=False , _snake_case=3 , _snake_case=2 , _snake_case=3 , _snake_case=None , _snake_case=None , **_snake_case , ) -> Optional[Any]:
'''simple docstring'''
super().__init__(**_snake_case , pad_token_id=_snake_case , bos_token_id=_snake_case , eos_token_id=_snake_case )
__a = hidden_size
__a = feat_extract_norm
__a = feat_extract_activation
__a = list(_snake_case )
__a = list(_snake_case )
__a = list(_snake_case )
__a = conv_bias
__a = num_conv_pos_embeddings
__a = num_conv_pos_embedding_groups
__a = len(self.conv_dim )
__a = num_hidden_layers
__a = intermediate_size
__a = hidden_act
__a = num_attention_heads
__a = hidden_dropout
__a = attention_dropout
__a = activation_dropout
__a = feat_proj_dropout
__a = final_dropout
__a = layerdrop
__a = layer_norm_eps
__a = initializer_range
__a = vocab_size
__a = do_stable_layer_norm
__a = use_weighted_layer_sum
if (
(len(self.conv_stride ) != self.num_feat_extract_layers)
or (len(self.conv_kernel ) != self.num_feat_extract_layers)
or (len(self.conv_dim ) != self.num_feat_extract_layers)
):
raise ValueError(
'''Configuration for convolutional layers is incorrect. It is required that `len(config.conv_dim)` =='''
''' `len(config.conv_stride)` == `len(config.conv_kernel)`, but is `len(config.conv_dim) ='''
F""" {len(self.conv_dim )}`, `len(config.conv_stride) = {len(self.conv_stride )}`,"""
F""" `len(config.conv_kernel) = {len(self.conv_kernel )}`.""" )
# fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
__a = apply_spec_augment
__a = mask_time_prob
__a = mask_time_length
__a = mask_time_min_masks
__a = mask_feature_prob
__a = mask_feature_length
__a = mask_feature_min_masks
# parameters for pretraining with codevector quantized representations
__a = num_codevectors_per_group
__a = num_codevector_groups
__a = contrastive_logits_temperature
__a = feat_quantizer_dropout
__a = num_negatives
__a = codevector_dim
__a = proj_codevector_dim
__a = diversity_loss_weight
# ctc loss
__a = ctc_loss_reduction
__a = ctc_zero_infinity
# adapter
__a = add_adapter
__a = adapter_kernel_size
__a = adapter_stride
__a = num_adapter_layers
__a = output_hidden_size or hidden_size
__a = adapter_attn_dim
# SequenceClassification-specific parameter. Feel free to ignore for other classes.
__a = classifier_proj_size
# XVector-specific parameters. Feel free to ignore for other classes.
__a = list(_snake_case )
__a = list(_snake_case )
__a = list(_snake_case )
__a = xvector_output_dim
@property
def SCREAMING_SNAKE_CASE_ ( self ) -> Dict:
'''simple docstring'''
return functools.reduce(operator.mul , self.conv_stride , 1 ) | 6 |
'''simple docstring'''
from pathlib import Path
import fire
from tqdm import tqdm
def a ( __a="ro" , __a="en" , __a="wmt16" , __a=None ) -> None:
'''simple docstring'''
try:
import datasets
except (ModuleNotFoundError, ImportError):
raise ImportError('''run pip install datasets''' )
UpperCamelCase__ :int = f'''{src_lang}-{tgt_lang}'''
print(f'''Converting {dataset}-{pair}''' )
UpperCamelCase__ :Tuple = datasets.load_dataset(__a , __a )
if save_dir is None:
UpperCamelCase__ :Any = f'''{dataset}-{pair}'''
UpperCamelCase__ :Dict = Path(__a )
save_dir.mkdir(exist_ok=__a )
for split in ds.keys():
print(f'''Splitting {split} with {ds[split].num_rows} records''' )
# to save to val.source, val.target like summary datasets
UpperCamelCase__ :Dict = '''val''' if split == '''validation''' else split
UpperCamelCase__ :List[Any] = save_dir.joinpath(f'''{fn}.source''' )
UpperCamelCase__ :int = save_dir.joinpath(f'''{fn}.target''' )
UpperCamelCase__ :Union[str, Any] = src_path.open('''w+''' )
UpperCamelCase__ :Tuple = tgt_path.open('''w+''' )
# reader is the bottleneck so writing one record at a time doesn't slow things down
for x in tqdm(ds[split] ):
UpperCamelCase__ :Union[str, Any] = x['''translation''']
src_fp.write(ex[src_lang] + '''\n''' )
tgt_fp.write(ex[tgt_lang] + '''\n''' )
print(f'''Saved {dataset} dataset to {save_dir}''' )
if __name__ == "__main__":
fire.Fire(download_wmt_dataset) | 97 | 0 |
from ...utils import (
OptionalDependencyNotAvailable,
is_torch_available,
is_transformers_available,
is_transformers_version,
)
try:
if not (is_transformers_available() and is_torch_available() and is_transformers_version(">=", "4.25.0")):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ...utils.dummy_torch_and_transformers_objects import UnCLIPImageVariationPipeline, UnCLIPPipeline
else:
from .pipeline_unclip import UnCLIPPipeline
from .pipeline_unclip_image_variation import UnCLIPImageVariationPipeline
from .text_proj import UnCLIPTextProjModel
| 7 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...file_utils import _LazyModule, is_tokenizers_available, is_torch_available, is_vision_available
from ...utils import OptionalDependencyNotAvailable
__snake_case = {'''configuration_dpt''': ['''DPT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''DPTConfig''']}
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__snake_case = ['''DPTFeatureExtractor''']
__snake_case = ['''DPTImageProcessor''']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__snake_case = [
'''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
__snake_case = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__) | 97 | 0 |
def __SCREAMING_SNAKE_CASE (SCREAMING_SNAKE_CASE__ ):
if length <= 0 or not isinstance(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
raise ValueError('''Length must be a positive integer.''' )
return [n * (2 * n - 1) for n in range(SCREAMING_SNAKE_CASE__ )]
if __name__ == "__main__":
print(hexagonal_numbers(length=5))
print(hexagonal_numbers(length=10)) | 8 |
'''simple docstring'''
def a ( __a , __a ) -> int:
'''simple docstring'''
if len(__a ) != len(__a ):
raise ValueError('''String lengths must match!''' )
UpperCamelCase__ :Union[str, Any] = 0
for chara, chara in zip(__a , __a ):
if chara != chara:
count += 1
return count
if __name__ == "__main__":
import doctest
doctest.testmod() | 97 | 0 |
from __future__ import annotations
import math
def _UpperCamelCase ( lowercase__ , lowercase__ ):
if len(lowercase__ ) != 2 or len(a[0] ) != 2 or len(lowercase__ ) != 2 or len(b[0] ) != 2:
raise Exception('''Matrices are not 2x2''' )
__SCREAMING_SNAKE_CASE : str = [
[a[0][0] * b[0][0] + a[0][1] * b[1][0], a[0][0] * b[0][1] + a[0][1] * b[1][1]],
[a[1][0] * b[0][0] + a[1][1] * b[1][0], a[1][0] * b[0][1] + a[1][1] * b[1][1]],
]
return new_matrix
def _UpperCamelCase ( lowercase__ , lowercase__ ):
return [
[matrix_a[row][col] + matrix_b[row][col] for col in range(len(matrix_a[row] ) )]
for row in range(len(lowercase__ ) )
]
def _UpperCamelCase ( lowercase__ , lowercase__ ):
return [
[matrix_a[row][col] - matrix_b[row][col] for col in range(len(matrix_a[row] ) )]
for row in range(len(lowercase__ ) )
]
def _UpperCamelCase ( lowercase__ ):
if len(lowercase__ ) % 2 != 0 or len(a[0] ) % 2 != 0:
raise Exception('''Odd matrices are not supported!''' )
__SCREAMING_SNAKE_CASE : int = len(lowercase__ )
__SCREAMING_SNAKE_CASE : List[str] = matrix_length // 2
__SCREAMING_SNAKE_CASE : Tuple = [[a[i][j] for j in range(lowercase__ , lowercase__ )] for i in range(lowercase__ )]
__SCREAMING_SNAKE_CASE : Optional[Any] = [
[a[i][j] for j in range(lowercase__ , lowercase__ )] for i in range(lowercase__ , lowercase__ )
]
__SCREAMING_SNAKE_CASE : int = [[a[i][j] for j in range(lowercase__ )] for i in range(lowercase__ )]
__SCREAMING_SNAKE_CASE : int = [[a[i][j] for j in range(lowercase__ )] for i in range(lowercase__ , lowercase__ )]
return top_left, top_right, bot_left, bot_right
def _UpperCamelCase ( lowercase__ ):
return len(lowercase__ ), len(matrix[0] )
def _UpperCamelCase ( lowercase__ ):
print('''\n'''.join(str(lowercase__ ) for line in matrix ) )
def _UpperCamelCase ( lowercase__ , lowercase__ ):
if matrix_dimensions(lowercase__ ) == (2, 2):
return default_matrix_multiplication(lowercase__ , lowercase__ )
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE : List[str] = split_matrix(lowercase__ )
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE : Optional[int] = split_matrix(lowercase__ )
__SCREAMING_SNAKE_CASE : str = actual_strassen(lowercase__ , matrix_subtraction(lowercase__ , lowercase__ ) )
__SCREAMING_SNAKE_CASE : List[str] = actual_strassen(matrix_addition(lowercase__ , lowercase__ ) , lowercase__ )
__SCREAMING_SNAKE_CASE : List[str] = actual_strassen(matrix_addition(lowercase__ , lowercase__ ) , lowercase__ )
__SCREAMING_SNAKE_CASE : int = actual_strassen(lowercase__ , matrix_subtraction(lowercase__ , lowercase__ ) )
__SCREAMING_SNAKE_CASE : int = actual_strassen(matrix_addition(lowercase__ , lowercase__ ) , matrix_addition(lowercase__ , lowercase__ ) )
__SCREAMING_SNAKE_CASE : Optional[Any] = actual_strassen(matrix_subtraction(lowercase__ , lowercase__ ) , matrix_addition(lowercase__ , lowercase__ ) )
__SCREAMING_SNAKE_CASE : Optional[Any] = actual_strassen(matrix_subtraction(lowercase__ , lowercase__ ) , matrix_addition(lowercase__ , lowercase__ ) )
__SCREAMING_SNAKE_CASE : Optional[int] = matrix_addition(matrix_subtraction(matrix_addition(lowercase__ , lowercase__ ) , lowercase__ ) , lowercase__ )
__SCREAMING_SNAKE_CASE : Tuple = matrix_addition(lowercase__ , lowercase__ )
__SCREAMING_SNAKE_CASE : Optional[int] = matrix_addition(lowercase__ , lowercase__ )
__SCREAMING_SNAKE_CASE : Optional[Any] = matrix_subtraction(matrix_subtraction(matrix_addition(lowercase__ , lowercase__ ) , lowercase__ ) , lowercase__ )
# construct the new matrix from our 4 quadrants
__SCREAMING_SNAKE_CASE : int = []
for i in range(len(lowercase__ ) ):
new_matrix.append(top_left[i] + top_right[i] )
for i in range(len(lowercase__ ) ):
new_matrix.append(bot_left[i] + bot_right[i] )
return new_matrix
def _UpperCamelCase ( lowercase__ , lowercase__ ):
if matrix_dimensions(lowercase__ )[1] != matrix_dimensions(lowercase__ )[0]:
__SCREAMING_SNAKE_CASE : str = (
'''Unable to multiply these matrices, please check the dimensions.\n'''
F'''Matrix A: {matrixa}\n'''
F'''Matrix B: {matrixa}'''
)
raise Exception(lowercase__ )
__SCREAMING_SNAKE_CASE : int = matrix_dimensions(lowercase__ )
__SCREAMING_SNAKE_CASE : int = matrix_dimensions(lowercase__ )
if dimensiona[0] == dimensiona[1] and dimensiona[0] == dimensiona[1]:
return [matrixa, matrixa]
__SCREAMING_SNAKE_CASE : Optional[int] = max(*lowercase__ , *lowercase__ )
__SCREAMING_SNAKE_CASE : Tuple = int(math.pow(2 , math.ceil(math.loga(lowercase__ ) ) ) )
__SCREAMING_SNAKE_CASE : str = matrixa
__SCREAMING_SNAKE_CASE : List[str] = matrixa
# Adding zeros to the matrices so that the arrays dimensions are the same and also
# power of 2
for i in range(0 , lowercase__ ):
if i < dimensiona[0]:
for _ in range(dimensiona[1] , lowercase__ ):
new_matrixa[i].append(0 )
else:
new_matrixa.append([0] * maxim )
if i < dimensiona[0]:
for _ in range(dimensiona[1] , lowercase__ ):
new_matrixa[i].append(0 )
else:
new_matrixa.append([0] * maxim )
__SCREAMING_SNAKE_CASE : int = actual_strassen(lowercase__ , lowercase__ )
# Removing the additional zeros
for i in range(0 , lowercase__ ):
if i < dimensiona[0]:
for _ in range(dimensiona[1] , lowercase__ ):
final_matrix[i].pop()
else:
final_matrix.pop()
return final_matrix
if __name__ == "__main__":
__lowerCAmelCase : str =[
[2, 3, 4, 5],
[6, 4, 3, 1],
[2, 3, 6, 7],
[3, 1, 2, 4],
[2, 3, 4, 5],
[6, 4, 3, 1],
[2, 3, 6, 7],
[3, 1, 2, 4],
[2, 3, 4, 5],
[6, 2, 3, 1],
]
__lowerCAmelCase : Any =[[0, 2, 1, 1], [1_6, 2, 3, 3], [2, 2, 7, 7], [1_3, 1_1, 2_2, 4]]
print(strassen(matrixa, matrixa))
| 9 |
'''simple docstring'''
def a ( __a ) -> "list[int]":
'''simple docstring'''
if upper_limit < 0:
raise ValueError('''Limit for the Catalan sequence must be ≥ 0''' )
UpperCamelCase__ :Optional[Any] = [0] * (upper_limit + 1)
# Base case: C(0) = C(1) = 1
UpperCamelCase__ :int = 1
if upper_limit > 0:
UpperCamelCase__ :int = 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(__a ):
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:
__snake_case = 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() | 97 | 0 |
import os
import string
import sys
__A = 1 << 8
__A = {
"tab": ord("\t"),
"newline": ord("\r"),
"esc": 27,
"up": 65 + ARROW_KEY_FLAG,
"down": 66 + ARROW_KEY_FLAG,
"right": 67 + ARROW_KEY_FLAG,
"left": 68 + ARROW_KEY_FLAG,
"mod_int": 91,
"undefined": sys.maxsize,
"interrupt": 3,
"insert": 50,
"delete": 51,
"pg_up": 53,
"pg_down": 54,
}
__A = KEYMAP["up"]
__A = KEYMAP["left"]
if sys.platform == "win32":
__A = []
__A = {
b"\xe0H": KEYMAP["up"] - ARROW_KEY_FLAG,
b"\x00H": KEYMAP["up"] - ARROW_KEY_FLAG,
b"\xe0P": KEYMAP["down"] - ARROW_KEY_FLAG,
b"\x00P": KEYMAP["down"] - ARROW_KEY_FLAG,
b"\xe0M": KEYMAP["right"] - ARROW_KEY_FLAG,
b"\x00M": KEYMAP["right"] - ARROW_KEY_FLAG,
b"\xe0K": KEYMAP["left"] - ARROW_KEY_FLAG,
b"\x00K": KEYMAP["left"] - ARROW_KEY_FLAG,
}
for i in range(10):
__A = ord(str(i))
def lowerCAmelCase_ ( ) -> Dict:
"""simple docstring"""
if os.name == "nt":
import msvcrt
lowerCamelCase__: Dict ="mbcs"
# Flush the keyboard buffer
while msvcrt.kbhit():
msvcrt.getch()
if len(__a ) == 0:
# Read the keystroke
lowerCamelCase__: Any =msvcrt.getch()
# If it is a prefix char, get second part
if ch in (b"\x00", b"\xe0"):
lowerCamelCase__: Any =ch + msvcrt.getch()
# Translate actual Win chars to bullet char types
try:
lowerCamelCase__: int =chr(WIN_KEYMAP[cha] )
WIN_CH_BUFFER.append(chr(KEYMAP["mod_int"] ) )
WIN_CH_BUFFER.append(__a )
if ord(__a ) in (
KEYMAP["insert"] - 1 << 9,
KEYMAP["delete"] - 1 << 9,
KEYMAP["pg_up"] - 1 << 9,
KEYMAP["pg_down"] - 1 << 9,
):
WIN_CH_BUFFER.append(chr(126 ) )
lowerCamelCase__: Any =chr(KEYMAP["esc"] )
except KeyError:
lowerCamelCase__: Optional[Any] =cha[1]
else:
lowerCamelCase__: Dict =ch.decode(__a )
else:
lowerCamelCase__: Tuple =WIN_CH_BUFFER.pop(0 )
elif os.name == "posix":
import termios
import tty
lowerCamelCase__: List[Any] =sys.stdin.fileno()
lowerCamelCase__: Dict =termios.tcgetattr(__a )
try:
tty.setraw(__a )
lowerCamelCase__: str =sys.stdin.read(1 )
finally:
termios.tcsetattr(__a , termios.TCSADRAIN , __a )
return ch
def lowerCAmelCase_ ( ) -> List[Any]:
"""simple docstring"""
lowerCamelCase__: Any =get_raw_chars()
if ord(__a ) in [KEYMAP["interrupt"], KEYMAP["newline"]]:
return char
elif ord(__a ) == KEYMAP["esc"]:
lowerCamelCase__: Optional[int] =get_raw_chars()
if ord(__a ) == KEYMAP["mod_int"]:
lowerCamelCase__: Any =get_raw_chars()
if ord(__a ) >= KEYMAP["arrow_begin"] - ARROW_KEY_FLAG and ord(__a ) <= KEYMAP["arrow_end"] - ARROW_KEY_FLAG:
return chr(ord(__a ) + ARROW_KEY_FLAG )
else:
return KEYMAP["undefined"]
else:
return get_raw_chars()
else:
if char in string.printable:
return char
else:
return KEYMAP["undefined"]
| 10 |
'''simple docstring'''
import io
import json
import fsspec
import pytest
from datasets import Dataset, DatasetDict, Features, NamedSplit, Value
from datasets.io.json import JsonDatasetReader, JsonDatasetWriter
from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases
def a ( __a , __a ) -> Optional[int]:
'''simple docstring'''
assert isinstance(__a , __a )
assert dataset.num_rows == 4
assert dataset.num_columns == 3
assert dataset.column_names == ["col_1", "col_2", "col_3"]
for feature, expected_dtype in expected_features.items():
assert dataset.features[feature].dtype == expected_dtype
@pytest.mark.parametrize('''keep_in_memory''' , [False, True] )
def a ( __a , __a , __a ) -> Optional[Any]:
'''simple docstring'''
UpperCamelCase__ :Union[str, Any] = tmp_path / '''cache'''
UpperCamelCase__ :Dict = {'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
UpperCamelCase__ :Tuple = JsonDatasetReader(__a , cache_dir=__a , keep_in_memory=__a ).read()
_check_json_dataset(__a , __a )
@pytest.mark.parametrize(
'''features''' , [
None,
{'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''},
{'''col_1''': '''string''', '''col_2''': '''string''', '''col_3''': '''string'''},
{'''col_1''': '''int32''', '''col_2''': '''int32''', '''col_3''': '''int32'''},
{'''col_1''': '''float32''', '''col_2''': '''float32''', '''col_3''': '''float32'''},
] , )
def a ( __a , __a , __a ) -> Any:
'''simple docstring'''
UpperCamelCase__ :Union[str, Any] = tmp_path / '''cache'''
UpperCamelCase__ :Optional[Any] = {'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
UpperCamelCase__ :Optional[Any] = features.copy() if features else default_expected_features
UpperCamelCase__ :Tuple = (
Features({feature: Value(__a ) for feature, dtype in features.items()} ) if features is not None else None
)
UpperCamelCase__ :int = JsonDatasetReader(__a , features=__a , cache_dir=__a ).read()
_check_json_dataset(__a , __a )
@pytest.mark.parametrize(
'''features''' , [
None,
{'''col_3''': '''float64''', '''col_1''': '''string''', '''col_2''': '''int64'''},
] , )
def a ( __a , __a , __a ) -> Tuple:
'''simple docstring'''
UpperCamelCase__ :int = tmp_path / '''cache'''
UpperCamelCase__ :str = {'''col_3''': '''float64''', '''col_1''': '''string''', '''col_2''': '''int64'''}
UpperCamelCase__ :Any = features.copy() if features else default_expected_features
UpperCamelCase__ :Union[str, Any] = (
Features({feature: Value(__a ) for feature, dtype in features.items()} ) if features is not None else None
)
UpperCamelCase__ :Any = JsonDatasetReader(__a , features=__a , cache_dir=__a ).read()
assert isinstance(__a , __a )
assert dataset.num_rows == 2
assert dataset.num_columns == 3
assert dataset.column_names == ["col_3", "col_1", "col_2"]
for feature, expected_dtype in expected_features.items():
assert dataset.features[feature].dtype == expected_dtype
def a ( __a , __a ) -> List[Any]:
'''simple docstring'''
UpperCamelCase__ :Any = {'''col_2''': '''int64''', '''col_3''': '''float64''', '''col_1''': '''string'''}
UpperCamelCase__ :int = features.copy()
UpperCamelCase__ :List[Any] = (
Features({feature: Value(__a ) for feature, dtype in features.items()} ) if features is not None else None
)
UpperCamelCase__ :Optional[int] = tmp_path / '''cache'''
UpperCamelCase__ :Dict = JsonDatasetReader(__a , features=__a , cache_dir=__a ).read()
assert isinstance(__a , __a )
assert dataset.num_rows == 2
assert dataset.num_columns == 3
assert dataset.column_names == ["col_2", "col_3", "col_1"]
for feature, expected_dtype in expected_features.items():
assert dataset.features[feature].dtype == expected_dtype
@pytest.mark.parametrize('''split''' , [None, NamedSplit('''train''' ), '''train''', '''test'''] )
def a ( __a , __a , __a ) -> List[Any]:
'''simple docstring'''
UpperCamelCase__ :Union[str, Any] = tmp_path / '''cache'''
UpperCamelCase__ :Optional[Any] = {'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
UpperCamelCase__ :List[Any] = JsonDatasetReader(__a , cache_dir=__a , split=__a ).read()
_check_json_dataset(__a , __a )
assert dataset.split == split if split else "train"
@pytest.mark.parametrize('''path_type''' , [str, list] )
def a ( __a , __a , __a ) -> Any:
'''simple docstring'''
if issubclass(__a , __a ):
UpperCamelCase__ :Union[str, Any] = jsonl_path
elif issubclass(__a , __a ):
UpperCamelCase__ :int = [jsonl_path]
UpperCamelCase__ :Dict = tmp_path / '''cache'''
UpperCamelCase__ :Any = {'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
UpperCamelCase__ :List[str] = JsonDatasetReader(__a , cache_dir=__a ).read()
_check_json_dataset(__a , __a )
def a ( __a , __a , __a=("train",) ) -> Optional[Any]:
'''simple docstring'''
assert isinstance(__a , __a )
for split in splits:
UpperCamelCase__ :Optional[int] = dataset_dict[split]
assert dataset.num_rows == 4
assert dataset.num_columns == 3
assert dataset.column_names == ["col_1", "col_2", "col_3"]
for feature, expected_dtype in expected_features.items():
assert dataset.features[feature].dtype == expected_dtype
@pytest.mark.parametrize('''keep_in_memory''' , [False, True] )
def a ( __a , __a , __a ) -> List[str]:
'''simple docstring'''
UpperCamelCase__ :List[str] = tmp_path / '''cache'''
UpperCamelCase__ :Dict = {'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
UpperCamelCase__ :str = JsonDatasetReader({'''train''': jsonl_path} , cache_dir=__a , keep_in_memory=__a ).read()
_check_json_datasetdict(__a , __a )
@pytest.mark.parametrize(
'''features''' , [
None,
{'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''},
{'''col_1''': '''string''', '''col_2''': '''string''', '''col_3''': '''string'''},
{'''col_1''': '''int32''', '''col_2''': '''int32''', '''col_3''': '''int32'''},
{'''col_1''': '''float32''', '''col_2''': '''float32''', '''col_3''': '''float32'''},
] , )
def a ( __a , __a , __a ) -> int:
'''simple docstring'''
UpperCamelCase__ :Tuple = tmp_path / '''cache'''
UpperCamelCase__ :Any = {'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
UpperCamelCase__ :Optional[int] = features.copy() if features else default_expected_features
UpperCamelCase__ :str = (
Features({feature: Value(__a ) for feature, dtype in features.items()} ) if features is not None else None
)
UpperCamelCase__ :Dict = JsonDatasetReader({'''train''': jsonl_path} , features=__a , cache_dir=__a ).read()
_check_json_datasetdict(__a , __a )
@pytest.mark.parametrize('''split''' , [None, NamedSplit('''train''' ), '''train''', '''test'''] )
def a ( __a , __a , __a ) -> str:
'''simple docstring'''
if split:
UpperCamelCase__ :List[str] = {split: jsonl_path}
else:
UpperCamelCase__ :int = '''train'''
UpperCamelCase__ :int = {'''train''': jsonl_path, '''test''': jsonl_path}
UpperCamelCase__ :Any = tmp_path / '''cache'''
UpperCamelCase__ :Union[str, Any] = {'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
UpperCamelCase__ :Any = JsonDatasetReader(__a , cache_dir=__a ).read()
_check_json_datasetdict(__a , __a , splits=list(path.keys() ) )
assert all(dataset[split].split == split for split in path.keys() )
def a ( __a ) -> Union[str, Any]:
'''simple docstring'''
return json.load(__a )
def a ( __a ) -> int:
'''simple docstring'''
return [json.loads(__a ) for line in buffer]
class lowercase :
"""simple docstring"""
@pytest.mark.parametrize('''lines, load_json_function''' , [(True, load_json_lines), (False, load_json)] )
def lowerCAmelCase__ ( self , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ ):
'''simple docstring'''
with io.BytesIO() as buffer:
JsonDatasetWriter(UpperCamelCase_ , UpperCamelCase_ , lines=UpperCamelCase_ ).write()
buffer.seek(0 )
UpperCamelCase__ :List[Any] = load_json_function(UpperCamelCase_ )
assert isinstance(UpperCamelCase_ , UpperCamelCase_ )
assert isinstance(exported_content[0] , UpperCamelCase_ )
assert len(UpperCamelCase_ ) == 10
@pytest.mark.parametrize(
'''orient, container, keys, len_at''' , [
('''records''', list, {'''tokens''', '''labels''', '''answers''', '''id'''}, None),
('''split''', dict, {'''columns''', '''data'''}, '''data'''),
('''index''', dict, set('''0123456789''' ), None),
('''columns''', dict, {'''tokens''', '''labels''', '''answers''', '''id'''}, '''tokens'''),
('''values''', list, None, None),
('''table''', dict, {'''schema''', '''data'''}, '''data'''),
] , )
def lowerCAmelCase__ ( self , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ ):
'''simple docstring'''
with io.BytesIO() as buffer:
JsonDatasetWriter(UpperCamelCase_ , UpperCamelCase_ , lines=UpperCamelCase_ , orient=UpperCamelCase_ ).write()
buffer.seek(0 )
UpperCamelCase__ :Optional[int] = load_json(UpperCamelCase_ )
assert isinstance(UpperCamelCase_ , UpperCamelCase_ )
if keys:
if container is dict:
assert exported_content.keys() == keys
else:
assert exported_content[0].keys() == keys
else:
assert not hasattr(UpperCamelCase_ , '''keys''' ) and not hasattr(exported_content[0] , '''keys''' )
if len_at:
assert len(exported_content[len_at] ) == 10
else:
assert len(UpperCamelCase_ ) == 10
@pytest.mark.parametrize('''lines, load_json_function''' , [(True, load_json_lines), (False, load_json)] )
def lowerCAmelCase__ ( self , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ ):
'''simple docstring'''
with io.BytesIO() as buffer:
JsonDatasetWriter(UpperCamelCase_ , UpperCamelCase_ , lines=UpperCamelCase_ , num_proc=2 ).write()
buffer.seek(0 )
UpperCamelCase__ :Union[str, Any] = load_json_function(UpperCamelCase_ )
assert isinstance(UpperCamelCase_ , UpperCamelCase_ )
assert isinstance(exported_content[0] , UpperCamelCase_ )
assert len(UpperCamelCase_ ) == 10
@pytest.mark.parametrize(
'''orient, container, keys, len_at''' , [
('''records''', list, {'''tokens''', '''labels''', '''answers''', '''id'''}, None),
('''split''', dict, {'''columns''', '''data'''}, '''data'''),
('''index''', dict, set('''0123456789''' ), None),
('''columns''', dict, {'''tokens''', '''labels''', '''answers''', '''id'''}, '''tokens'''),
('''values''', list, None, None),
('''table''', dict, {'''schema''', '''data'''}, '''data'''),
] , )
def lowerCAmelCase__ ( self , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ ):
'''simple docstring'''
with io.BytesIO() as buffer:
JsonDatasetWriter(UpperCamelCase_ , UpperCamelCase_ , lines=UpperCamelCase_ , orient=UpperCamelCase_ , num_proc=2 ).write()
buffer.seek(0 )
UpperCamelCase__ :int = load_json(UpperCamelCase_ )
assert isinstance(UpperCamelCase_ , UpperCamelCase_ )
if keys:
if container is dict:
assert exported_content.keys() == keys
else:
assert exported_content[0].keys() == keys
else:
assert not hasattr(UpperCamelCase_ , '''keys''' ) and not hasattr(exported_content[0] , '''keys''' )
if len_at:
assert len(exported_content[len_at] ) == 10
else:
assert len(UpperCamelCase_ ) == 10
def lowerCAmelCase__ ( self , UpperCamelCase_ ):
'''simple docstring'''
with pytest.raises(UpperCamelCase_ ):
with io.BytesIO() as buffer:
JsonDatasetWriter(UpperCamelCase_ , UpperCamelCase_ , num_proc=0 )
@pytest.mark.parametrize('''compression, extension''' , [('''gzip''', '''gz'''), ('''bz2''', '''bz2'''), ('''xz''', '''xz''')] )
def lowerCAmelCase__ ( self , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ ):
'''simple docstring'''
UpperCamelCase__ :Tuple = tmp_path_factory.mktemp('''data''' ) / F'''test.json.{extension}'''
UpperCamelCase__ :Union[str, Any] = str(shared_datadir / F'''test_file.json.{extension}''' )
JsonDatasetWriter(UpperCamelCase_ , UpperCamelCase_ , compression=UpperCamelCase_ ).write()
with fsspec.open(UpperCamelCase_ , '''rb''' , compression='''infer''' ) as f:
UpperCamelCase__ :Dict = f.read()
with fsspec.open(UpperCamelCase_ , '''rb''' , compression='''infer''' ) as f:
UpperCamelCase__ :int = f.read()
assert exported_content == original_content | 97 | 0 |
import math
class lowerCAmelCase__ :
'''simple docstring'''
def __init__( self , __lowerCamelCase=0) -> str: # a graph with Node 0,1,...,N-1
_A : Tuple = n
_A : Optional[int] = [
[math.inf for j in range(0 , __lowerCamelCase)] for i in range(0 , __lowerCamelCase)
] # adjacency matrix for weight
_A : List[str] = [
[math.inf for j in range(0 , __lowerCamelCase)] for i in range(0 , __lowerCamelCase)
] # dp[i][j] stores minimum distance from i to j
def _lowerCamelCase ( self , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase) -> int:
_A : Dict = w
def _lowerCamelCase ( self) -> Union[str, Any]:
for k in range(0 , self.n):
for i in range(0 , self.n):
for j in range(0 , self.n):
_A : Optional[Any] = min(self.dp[i][j] , self.dp[i][k] + self.dp[k][j])
def _lowerCamelCase ( self , __lowerCamelCase , __lowerCamelCase) -> Optional[int]:
return self.dp[u][v]
if __name__ == "__main__":
lowerCAmelCase__ = Graph(5)
graph.add_edge(0, 2, 9)
graph.add_edge(0, 4, 10)
graph.add_edge(1, 3, 5)
graph.add_edge(2, 3, 7)
graph.add_edge(3, 0, 10)
graph.add_edge(3, 1, 2)
graph.add_edge(3, 2, 1)
graph.add_edge(3, 4, 6)
graph.add_edge(4, 1, 3)
graph.add_edge(4, 2, 4)
graph.add_edge(4, 3, 9)
graph.floyd_warshall()
graph.show_min(1, 4)
graph.show_min(0, 3)
| 11 |
'''simple docstring'''
import unittest
from dataclasses import dataclass
import pytest
from accelerate.commands.config.config_args import SageMakerConfig
from accelerate.utils import ComputeEnvironment
from accelerate.utils.launch import _convert_nargs_to_dict
@dataclass
class lowercase ( A__ ):
"""simple docstring"""
_a = ComputeEnvironment.AMAZON_SAGEMAKER
_a = True
_a = 'ml.p3.2xlarge'
_a = 'accelerate_sagemaker_execution_role'
_a = 'hf-sm'
_a = 'us-east-1'
_a = 1
_a = 'accelerate-sagemaker-1'
_a = '1.6'
_a = '4.4'
_a = 'train.py'
_a = [
'--model_name_or_path',
'bert',
'--do_train',
'False',
'--epochs',
'3',
'--learning_rate',
'5e-5',
'--max_steps',
'50.5',
]
_a = [
'--model_name_or_path',
'bert',
'--do_train',
'--do_test',
'False',
'--do_predict',
'--epochs',
'3',
'--learning_rate',
'5e-5',
'--max_steps',
'50.5',
]
class lowercase ( unittest.TestCase ):
"""simple docstring"""
def lowerCAmelCase__ ( self ):
'''simple docstring'''
UpperCamelCase__ :Union[str, Any] = _convert_nargs_to_dict(MockLaunchConfig.success_training_script_args )
assert isinstance(converted_args['''model_name_or_path'''] , UpperCamelCase_ )
assert isinstance(converted_args['''do_train'''] , UpperCamelCase_ )
assert isinstance(converted_args['''epochs'''] , UpperCamelCase_ )
assert isinstance(converted_args['''learning_rate'''] , UpperCamelCase_ )
assert isinstance(converted_args['''max_steps'''] , UpperCamelCase_ )
with pytest.raises(UpperCamelCase_ ):
_convert_nargs_to_dict(MockLaunchConfig.fail_training_script_args ) | 97 | 0 |
from __future__ import annotations
def lowerCamelCase__ ( A__ : int , A__ : int ):
'''simple docstring'''
__lowerCamelCase = []
create_all_state(1 , A__ , A__ , [] , A__ )
return result
def lowerCamelCase__ ( A__ : int , A__ : int , A__ : int , A__ : list[int] , A__ : list[list[int]] , ):
'''simple docstring'''
if level == 0:
total_list.append(current_list[:] )
return
for i in range(A__ , total_number - level + 2 ):
current_list.append(A__ )
create_all_state(i + 1 , A__ , level - 1 , A__ , A__ )
current_list.pop()
def lowerCamelCase__ ( A__ : list[list[int]] ):
'''simple docstring'''
for i in total_list:
print(*A__ )
if __name__ == "__main__":
UpperCAmelCase_ = 4
UpperCAmelCase_ = 2
UpperCAmelCase_ = generate_all_combinations(n, k)
print_all_state(total_list)
| 12 |
'''simple docstring'''
from datetime import datetime
import matplotlib.pyplot as plt
import torch
def a ( __a ) -> int:
'''simple docstring'''
for param in module.parameters():
UpperCamelCase__ :Dict = False
def a ( ) -> Union[str, Any]:
'''simple docstring'''
UpperCamelCase__ :List[Any] = '''cuda''' if torch.cuda.is_available() else '''cpu'''
if torch.backends.mps.is_available() and torch.backends.mps.is_built():
UpperCamelCase__ :Optional[int] = '''mps'''
if device == "mps":
print(
'''WARNING: MPS currently doesn\'t seem to work, and messes up backpropagation without any visible torch'''
''' errors. I recommend using CUDA on a colab notebook or CPU instead if you\'re facing inexplicable issues'''
''' with generations.''' )
return device
def a ( __a ) -> Any:
'''simple docstring'''
UpperCamelCase__ :Dict = plt.imshow(__a )
fig.axes.get_xaxis().set_visible(__a )
fig.axes.get_yaxis().set_visible(__a )
plt.show()
def a ( ) -> str:
'''simple docstring'''
UpperCamelCase__ :int = datetime.now()
UpperCamelCase__ :str = current_time.strftime('''%H:%M:%S''' )
return timestamp | 97 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
lowerCAmelCase : int = {"""configuration_swin""": ["""SWIN_PRETRAINED_CONFIG_ARCHIVE_MAP""", """SwinConfig""", """SwinOnnxConfig"""]}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase : Dict = [
"""SWIN_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""SwinForImageClassification""",
"""SwinForMaskedImageModeling""",
"""SwinModel""",
"""SwinPreTrainedModel""",
"""SwinBackbone""",
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase : str = [
"""TF_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""TFSwinForImageClassification""",
"""TFSwinForMaskedImageModeling""",
"""TFSwinModel""",
"""TFSwinPreTrainedModel""",
]
if TYPE_CHECKING:
from .configuration_swin import SWIN_PRETRAINED_CONFIG_ARCHIVE_MAP, SwinConfig, SwinOnnxConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_swin import (
SWIN_PRETRAINED_MODEL_ARCHIVE_LIST,
SwinBackbone,
SwinForImageClassification,
SwinForMaskedImageModeling,
SwinModel,
SwinPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_swin import (
TF_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST,
TFSwinForImageClassification,
TFSwinForMaskedImageModeling,
TFSwinModel,
TFSwinPreTrainedModel,
)
else:
import sys
lowerCAmelCase : Any = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 13 |
'''simple docstring'''
from scipy.stats import pearsonr
import datasets
__snake_case = '''
Pearson correlation coefficient and p-value for testing non-correlation.
The Pearson correlation coefficient measures the linear relationship between two datasets. The calculation of the p-value relies on the assumption that each dataset is normally distributed. Like other correlation coefficients, this one varies between -1 and +1 with 0 implying no correlation. Correlations of -1 or +1 imply an exact linear relationship. Positive correlations imply that as x increases, so does y. Negative correlations imply that as x increases, y decreases.
The p-value roughly indicates the probability of an uncorrelated system producing datasets that have a Pearson correlation at least as extreme as the one computed from these datasets.
'''
__snake_case = '''
Args:
predictions (`list` of `int`): Predicted class labels, as returned by a model.
references (`list` of `int`): Ground truth labels.
return_pvalue (`boolean`): If `True`, returns the p-value, along with the correlation coefficient. If `False`, returns only the correlation coefficient. Defaults to `False`.
Returns:
pearsonr (`float`): Pearson correlation coefficient. Minimum possible value is -1. Maximum possible value is 1. Values of 1 and -1 indicate exact linear positive and negative relationships, respectively. A value of 0 implies no correlation.
p-value (`float`): P-value, which roughly indicates the probability of an The p-value roughly indicates the probability of an uncorrelated system producing datasets that have a Pearson correlation at least as extreme as the one computed from these datasets. Minimum possible value is 0. Maximum possible value is 1. Higher values indicate higher probabilities.
Examples:
Example 1-A simple example using only predictions and references.
>>> pearsonr_metric = datasets.load_metric("pearsonr")
>>> results = pearsonr_metric.compute(predictions=[10, 9, 2.5, 6, 4], references=[1, 2, 3, 4, 5])
>>> print(round(results[\'pearsonr\'], 2))
-0.74
Example 2-The same as Example 1, but that also returns the `p-value`.
>>> pearsonr_metric = datasets.load_metric("pearsonr")
>>> results = pearsonr_metric.compute(predictions=[10, 9, 2.5, 6, 4], references=[1, 2, 3, 4, 5], return_pvalue=True)
>>> print(sorted(list(results.keys())))
[\'p-value\', \'pearsonr\']
>>> print(round(results[\'pearsonr\'], 2))
-0.74
>>> print(round(results[\'p-value\'], 2))
0.15
'''
__snake_case = '''
@article{2020SciPy-NMeth,
author = {Virtanen, Pauli and Gommers, Ralf and Oliphant, Travis E. and
Haberland, Matt and Reddy, Tyler and Cournapeau, David and
Burovski, Evgeni and Peterson, Pearu and Weckesser, Warren and
Bright, Jonathan and {van der Walt}, St{\'e}fan J. and
Brett, Matthew and Wilson, Joshua and Millman, K. Jarrod and
Mayorov, Nikolay and Nelson, Andrew R. J. and Jones, Eric and
Kern, Robert and Larson, Eric and Carey, C J and
Polat, Ilhan and Feng, Yu and Moore, Eric W. and
{VanderPlas}, Jake and Laxalde, Denis and Perktold, Josef and
Cimrman, Robert and Henriksen, Ian and Quintero, E. A. and
Harris, Charles R. and Archibald, Anne M. and
Ribeiro, Antonio H. and Pedregosa, Fabian and
{van Mulbregt}, Paul and {SciPy 1.0 Contributors}},
title = {{{SciPy} 1.0: Fundamental Algorithms for Scientific
Computing in Python}},
journal = {Nature Methods},
year = {2020},
volume = {17},
pages = {261--272},
adsurl = {https://rdcu.be/b08Wh},
doi = {10.1038/s41592-019-0686-2},
}
'''
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class lowercase ( datasets.Metric ):
"""simple docstring"""
def lowerCAmelCase__ ( self ):
'''simple docstring'''
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
'''predictions''': datasets.Value('''float''' ),
'''references''': datasets.Value('''float''' ),
} ) , reference_urls=['''https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.pearsonr.html'''] , )
def lowerCAmelCase__ ( self , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_=False ):
'''simple docstring'''
if return_pvalue:
UpperCamelCase__ :Any = pearsonr(UpperCamelCase_ , UpperCamelCase_ )
return {"pearsonr": results[0], "p-value": results[1]}
else:
return {"pearsonr": float(pearsonr(UpperCamelCase_ , UpperCamelCase_ )[0] )} | 97 | 0 |
import argparse
import json
from collections import OrderedDict
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
SegformerConfig,
SegformerForImageClassification,
SegformerForSemanticSegmentation,
SegformerImageProcessor,
)
from transformers.utils import logging
logging.set_verbosity_info()
_lowerCamelCase : Tuple = logging.get_logger(__name__)
def SCREAMING_SNAKE_CASE ( lowercase_ , lowercase_=False ) -> Union[str, Any]:
"""simple docstring"""
A__ = OrderedDict()
for key, value in state_dict.items():
if encoder_only and not key.startswith('''head''' ):
A__ = '''segformer.encoder.''' + key
if key.startswith('''backbone''' ):
A__ = key.replace('''backbone''' , '''segformer.encoder''' )
if "patch_embed" in key:
# replace for example patch_embed1 by patch_embeddings.0
A__ = key[key.find('''patch_embed''' ) + len('''patch_embed''' )]
A__ = key.replace(f"""patch_embed{idx}""" , f"""patch_embeddings.{int(lowercase_ )-1}""" )
if "norm" in key:
A__ = key.replace('''norm''' , '''layer_norm''' )
if "segformer.encoder.layer_norm" in key:
# replace for example layer_norm1 by layer_norm.0
A__ = key[key.find('''segformer.encoder.layer_norm''' ) + len('''segformer.encoder.layer_norm''' )]
A__ = key.replace(f"""layer_norm{idx}""" , f"""layer_norm.{int(lowercase_ )-1}""" )
if "layer_norm1" in key:
A__ = key.replace('''layer_norm1''' , '''layer_norm_1''' )
if "layer_norm2" in key:
A__ = key.replace('''layer_norm2''' , '''layer_norm_2''' )
if "block" in key:
# replace for example block1 by block.0
A__ = key[key.find('''block''' ) + len('''block''' )]
A__ = key.replace(f"""block{idx}""" , f"""block.{int(lowercase_ )-1}""" )
if "attn.q" in key:
A__ = key.replace('''attn.q''' , '''attention.self.query''' )
if "attn.proj" in key:
A__ = key.replace('''attn.proj''' , '''attention.output.dense''' )
if "attn" in key:
A__ = key.replace('''attn''' , '''attention.self''' )
if "fc1" in key:
A__ = key.replace('''fc1''' , '''dense1''' )
if "fc2" in key:
A__ = key.replace('''fc2''' , '''dense2''' )
if "linear_pred" in key:
A__ = key.replace('''linear_pred''' , '''classifier''' )
if "linear_fuse" in key:
A__ = key.replace('''linear_fuse.conv''' , '''linear_fuse''' )
A__ = key.replace('''linear_fuse.bn''' , '''batch_norm''' )
if "linear_c" in key:
# replace for example linear_c4 by linear_c.3
A__ = key[key.find('''linear_c''' ) + len('''linear_c''' )]
A__ = key.replace(f"""linear_c{idx}""" , f"""linear_c.{int(lowercase_ )-1}""" )
if key.startswith('''head''' ):
A__ = key.replace('''head''' , '''classifier''' )
A__ = value
return new_state_dict
def SCREAMING_SNAKE_CASE ( lowercase_ , lowercase_ ) -> str:
"""simple docstring"""
for i in range(config.num_encoder_blocks ):
for j in range(config.depths[i] ):
# read in weights + bias of keys and values (which is a single matrix in the original implementation)
A__ = state_dict.pop(f"""segformer.encoder.block.{i}.{j}.attention.self.kv.weight""" )
A__ = state_dict.pop(f"""segformer.encoder.block.{i}.{j}.attention.self.kv.bias""" )
# next, add keys and values (in that order) to the state dict
A__ = kv_weight[
: config.hidden_sizes[i], :
]
A__ = kv_bias[: config.hidden_sizes[i]]
A__ = kv_weight[
config.hidden_sizes[i] :, :
]
A__ = kv_bias[
config.hidden_sizes[i] :
]
def SCREAMING_SNAKE_CASE ( ) -> Optional[Any]:
"""simple docstring"""
A__ = '''http://images.cocodataset.org/val2017/000000039769.jpg'''
A__ = Image.open(requests.get(lowercase_ , stream=lowercase_ ).raw )
return image
@torch.no_grad()
def SCREAMING_SNAKE_CASE ( lowercase_ , lowercase_ , lowercase_ ) -> List[str]:
"""simple docstring"""
A__ = SegformerConfig()
A__ = False
# set attributes based on model_name
A__ = '''huggingface/label-files'''
if "segformer" in model_name:
A__ = model_name[len('''segformer.''' ) : len('''segformer.''' ) + 2]
if "ade" in model_name:
A__ = 150
A__ = '''ade20k-id2label.json'''
A__ = (1, 150, 128, 128)
elif "city" in model_name:
A__ = 19
A__ = '''cityscapes-id2label.json'''
A__ = (1, 19, 128, 128)
else:
raise ValueError(f"""Model {model_name} not supported""" )
elif "mit" in model_name:
A__ = True
A__ = model_name[4:6]
A__ = 1_000
A__ = '''imagenet-1k-id2label.json'''
A__ = (1, 1_000)
else:
raise ValueError(f"""Model {model_name} not supported""" )
# set config attributes
A__ = json.load(open(hf_hub_download(lowercase_ , lowercase_ , repo_type='''dataset''' ) , '''r''' ) )
A__ = {int(lowercase_ ): v for k, v in idalabel.items()}
A__ = idalabel
A__ = {v: k for k, v in idalabel.items()}
if size == "b0":
pass
elif size == "b1":
A__ = [64, 128, 320, 512]
A__ = 256
elif size == "b2":
A__ = [64, 128, 320, 512]
A__ = 768
A__ = [3, 4, 6, 3]
elif size == "b3":
A__ = [64, 128, 320, 512]
A__ = 768
A__ = [3, 4, 18, 3]
elif size == "b4":
A__ = [64, 128, 320, 512]
A__ = 768
A__ = [3, 8, 27, 3]
elif size == "b5":
A__ = [64, 128, 320, 512]
A__ = 768
A__ = [3, 6, 40, 3]
else:
raise ValueError(f"""Size {size} not supported""" )
# load image processor (only resize + normalize)
A__ = SegformerImageProcessor(
image_scale=(512, 512) , keep_ratio=lowercase_ , align=lowercase_ , do_random_crop=lowercase_ )
# prepare image
A__ = prepare_img()
A__ = image_processor(images=lowercase_ , return_tensors='''pt''' ).pixel_values
logger.info(f"""Converting model {model_name}...""" )
# load original state dict
if encoder_only:
A__ = torch.load(lowercase_ , map_location=torch.device('''cpu''' ) )
else:
A__ = torch.load(lowercase_ , map_location=torch.device('''cpu''' ) )['''state_dict''']
# rename keys
A__ = rename_keys(lowercase_ , encoder_only=lowercase_ )
if not encoder_only:
del state_dict["decode_head.conv_seg.weight"]
del state_dict["decode_head.conv_seg.bias"]
# key and value matrices need special treatment
read_in_k_v(lowercase_ , lowercase_ )
# create HuggingFace model and load state dict
if encoder_only:
A__ = False
A__ = SegformerForImageClassification(lowercase_ )
else:
A__ = SegformerForSemanticSegmentation(lowercase_ )
model.load_state_dict(lowercase_ )
model.eval()
# forward pass
A__ = model(lowercase_ )
A__ = outputs.logits
# set expected_slice based on model name
# ADE20k checkpoints
if model_name == "segformer.b0.512x512.ade.160k":
A__ = torch.tensor(
[
[[-4.63_10, -5.52_32, -6.23_56], [-5.19_21, -6.14_44, -6.59_96], [-5.44_24, -6.27_90, -6.75_74]],
[[-12.13_91, -13.31_22, -13.95_54], [-12.87_32, -13.93_52, -14.35_63], [-12.94_38, -13.82_26, -14.25_13]],
[[-12.51_34, -13.46_86, -14.49_15], [-12.86_69, -14.43_43, -14.77_58], [-13.25_23, -14.58_19, -15.06_94]],
] )
elif model_name == "segformer.b1.512x512.ade.160k":
A__ = torch.tensor(
[
[[-7.58_20, -8.72_31, -8.32_15], [-8.06_00, -10.35_29, -10.03_04], [-7.52_08, -9.41_03, -9.62_39]],
[[-12.69_18, -13.89_94, -13.71_37], [-13.31_96, -15.75_23, -15.47_89], [-12.93_43, -14.87_57, -14.96_89]],
[[-11.19_11, -11.94_21, -11.32_43], [-11.33_42, -13.68_39, -13.35_81], [-10.39_09, -12.18_32, -12.48_58]],
] )
elif model_name == "segformer.b2.512x512.ade.160k":
A__ = torch.tensor(
[
[[-11.81_73, -14.38_50, -16.31_28], [-14.56_48, -16.58_04, -18.65_68], [-14.72_23, -15.73_87, -18.42_18]],
[[-15.72_90, -17.91_71, -19.44_23], [-18.31_05, -19.94_48, -21.46_61], [-17.92_96, -18.64_97, -20.79_10]],
[[-15.07_83, -17.03_36, -18.27_89], [-16.87_71, -18.68_70, -20.16_12], [-16.24_54, -17.14_26, -19.50_55]],
] )
elif model_name == "segformer.b3.512x512.ade.160k":
A__ = torch.tensor(
[
[[-9.08_78, -10.20_81, -10.18_91], [-9.31_44, -10.79_41, -10.98_43], [-9.22_94, -10.38_55, -10.57_04]],
[[-12.23_16, -13.90_68, -13.61_02], [-12.91_61, -14.37_02, -14.32_35], [-12.52_33, -13.71_74, -13.79_32]],
[[-14.62_75, -15.24_90, -14.97_27], [-14.34_00, -15.96_87, -16.28_27], [-14.14_84, -15.40_33, -15.89_37]],
] )
elif model_name == "segformer.b4.512x512.ade.160k":
A__ = torch.tensor(
[
[[-12.31_44, -13.24_47, -14.08_02], [-13.36_14, -14.58_16, -15.61_17], [-13.33_40, -14.44_33, -16.22_19]],
[[-19.27_81, -20.41_28, -20.75_06], [-20.61_53, -21.65_66, -22.09_98], [-19.98_00, -21.04_30, -22.14_94]],
[[-18.87_39, -19.78_04, -21.18_34], [-20.12_33, -21.67_65, -23.29_44], [-20.03_15, -21.26_41, -23.69_44]],
] )
elif model_name == "segformer.b5.640x640.ade.160k":
A__ = torch.tensor(
[
[[-9.55_24, -12.08_35, -11.73_48], [-10.52_29, -13.64_46, -14.56_62], [-9.58_42, -12.88_51, -13.94_14]],
[[-15.34_32, -17.53_23, -17.08_18], [-16.33_30, -18.92_55, -19.21_01], [-15.13_40, -17.78_48, -18.39_71]],
[[-12.60_72, -14.94_86, -14.66_31], [-13.76_29, -17.09_07, -17.77_45], [-12.78_99, -16.16_95, -17.16_71]],
] )
# Cityscapes checkpoints
elif model_name == "segformer.b0.1024x1024.city.160k":
A__ = torch.tensor(
[
[[-11.92_95, -13.40_57, -14.81_06], [-13.34_31, -14.81_79, -15.37_81], [-14.28_36, -15.59_42, -16.15_88]],
[[-11.49_06, -12.80_67, -13.65_64], [-13.11_89, -14.05_00, -14.15_43], [-13.87_48, -14.51_36, -14.87_89]],
[[0.53_74, 0.10_67, -0.47_42], [0.11_41, -0.22_55, -0.70_99], [-0.30_00, -0.59_24, -1.31_05]],
] )
elif model_name == "segformer.b0.512x1024.city.160k":
A__ = torch.tensor(
[
[[-7.82_17, -9.87_67, -10.17_17], [-9.44_38, -10.90_58, -11.40_47], [-9.79_39, -12.34_95, -12.10_79]],
[[-7.15_14, -9.53_36, -10.08_60], [-9.77_76, -11.68_22, -11.84_39], [-10.14_11, -12.76_55, -12.89_72]],
[[0.30_21, 0.08_05, -0.23_10], [-0.03_28, -0.16_05, -0.27_14], [-0.14_08, -0.54_77, -0.69_76]],
] )
elif model_name == "segformer.b0.640x1280.city.160k":
A__ = torch.tensor(
[
[
[-1.1_3_7_2E0_1, -1.2_7_8_7E0_1, -1.3_4_7_7E0_1],
[-1.2_5_3_6E0_1, -1.4_1_9_4E0_1, -1.4_4_0_9E0_1],
[-1.3_2_1_7E0_1, -1.4_8_8_8E0_1, -1.5_3_2_7E0_1],
],
[
[-1.4_7_9_1E0_1, -1.7_1_2_2E0_1, -1.8_2_7_7E0_1],
[-1.7_1_6_3E0_1, -1.9_1_9_2E0_1, -1.9_5_3_3E0_1],
[-1.7_8_9_7E0_1, -1.9_9_9_1E0_1, -2.0_3_1_5E0_1],
],
[
[7.6_7_2_3E-0_1, 4.1_9_2_1E-0_1, -7.7_8_7_8E-0_2],
[4.7_7_7_2E-0_1, 9.5_5_5_7E-0_3, -2.8_0_8_2E-0_1],
[3.6_0_3_2E-0_1, -2.4_8_2_6E-0_1, -5.1_1_6_8E-0_1],
],
] )
elif model_name == "segformer.b0.768x768.city.160k":
A__ = torch.tensor(
[
[[-9.49_59, -11.30_87, -11.74_79], [-11.00_25, -12.65_40, -12.33_19], [-11.40_64, -13.04_87, -12.99_05]],
[[-9.89_05, -11.30_84, -12.08_54], [-11.17_26, -12.76_98, -12.95_83], [-11.59_85, -13.32_78, -14.17_74]],
[[0.22_13, 0.01_92, -0.24_66], [-0.17_31, -0.42_13, -0.48_74], [-0.31_26, -0.65_41, -1.13_89]],
] )
elif model_name == "segformer.b1.1024x1024.city.160k":
A__ = torch.tensor(
[
[[-13.57_48, -13.91_11, -12.65_00], [-14.35_00, -15.36_83, -14.23_28], [-14.75_32, -16.04_24, -15.60_87]],
[[-17.16_51, -15.87_25, -12.96_53], [-17.25_80, -17.37_18, -14.82_23], [-16.60_58, -16.87_83, -16.74_52]],
[[-3.64_56, -3.02_09, -1.42_03], [-3.07_97, -3.19_59, -2.00_00], [-1.87_57, -1.92_17, -1.69_97]],
] )
elif model_name == "segformer.b2.1024x1024.city.160k":
A__ = torch.tensor(
[
[[-16.09_76, -16.48_56, -17.39_62], [-16.62_34, -19.03_42, -19.76_85], [-16.09_00, -18.06_61, -19.11_80]],
[[-18.47_50, -18.84_88, -19.50_74], [-19.40_30, -22.15_70, -22.59_77], [-19.11_91, -20.84_86, -22.37_83]],
[[-4.51_78, -5.50_37, -6.51_09], [-5.08_84, -7.21_74, -8.03_34], [-4.41_56, -5.81_17, -7.29_70]],
] )
elif model_name == "segformer.b3.1024x1024.city.160k":
A__ = torch.tensor(
[
[[-14.20_81, -14.47_32, -14.19_77], [-14.58_67, -16.44_23, -16.63_56], [-13.44_41, -14.96_85, -16.86_96]],
[[-14.45_76, -14.70_73, -15.04_51], [-15.08_16, -17.62_37, -17.98_73], [-14.42_13, -16.01_99, -18.59_92]],
[[-4.73_49, -4.95_88, -5.09_66], [-4.32_10, -6.93_25, -7.25_91], [-3.43_12, -4.74_84, -7.19_17]],
] )
elif model_name == "segformer.b4.1024x1024.city.160k":
A__ = torch.tensor(
[
[[-11.77_37, -11.95_26, -11.32_73], [-13.66_92, -14.45_74, -13.88_78], [-13.89_37, -14.69_24, -15.93_45]],
[[-14.67_06, -14.53_30, -14.13_06], [-16.15_02, -16.81_80, -16.42_69], [-16.83_38, -17.89_39, -20.17_46]],
[[1.04_91, 0.82_89, 1.03_10], [1.10_44, 0.52_19, 0.80_55], [1.08_99, 0.69_26, 0.55_90]],
] )
elif model_name == "segformer.b5.1024x1024.city.160k":
A__ = torch.tensor(
[
[[-12.56_41, -13.47_77, -13.06_84], [-13.95_87, -15.89_83, -16.65_57], [-13.31_09, -15.73_50, -16.31_41]],
[[-14.70_74, -15.43_52, -14.59_44], [-16.63_53, -18.16_63, -18.61_20], [-15.17_02, -18.03_29, -18.15_47]],
[[-1.79_90, -2.09_51, -1.77_84], [-2.63_97, -3.82_45, -3.96_86], [-1.52_64, -2.81_26, -2.93_16]],
] )
else:
A__ = logits.argmax(-1 ).item()
print('''Predicted class:''' , model.config.idalabel[predicted_class_idx] )
# verify logits
if not encoder_only:
assert logits.shape == expected_shape
assert torch.allclose(logits[0, :3, :3, :3] , lowercase_ , atol=1E-2 )
# finally, save model and image processor
logger.info(f"""Saving PyTorch model and image processor to {pytorch_dump_folder_path}...""" )
Path(lowercase_ ).mkdir(exist_ok=lowercase_ )
model.save_pretrained(lowercase_ )
image_processor.save_pretrained(lowercase_ )
if __name__ == "__main__":
_lowerCamelCase : Any = argparse.ArgumentParser()
parser.add_argument(
"""--model_name""",
default="""segformer.b0.512x512.ade.160k""",
type=str,
help="""Name of the model you'd like to convert.""",
)
parser.add_argument(
"""--checkpoint_path""", default=None, type=str, help="""Path to the original PyTorch checkpoint (.pth file)."""
)
parser.add_argument(
"""--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the folder to output PyTorch model."""
)
_lowerCamelCase : Union[str, Any] = parser.parse_args()
convert_segformer_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path)
| 14 |
'''simple docstring'''
from typing import List, Optional
from tokenizers import ByteLevelBPETokenizer
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_blenderbot_small import BlenderbotSmallTokenizer
__snake_case = logging.get_logger(__name__)
__snake_case = {
'''vocab_file''': '''vocab.json''',
'''merges_file''': '''merges.txt''',
'''tokenizer_config_file''': '''tokenizer_config.json''',
}
__snake_case = {
'''vocab_file''': {
'''facebook/blenderbot_small-90M''': '''https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/vocab.json'''
},
'''merges_file''': {
'''facebook/blenderbot_small-90M''': '''https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/merges.txt'''
},
'''tokenizer_config_file''': {
'''facebook/blenderbot_small-90M''': (
'''https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/tokenizer_config.json'''
)
},
}
__snake_case = {
'''facebook/blenderbot_small-90M''': 512,
}
class lowercase ( A__ ):
"""simple docstring"""
_a = VOCAB_FILES_NAMES
_a = PRETRAINED_VOCAB_FILES_MAP
_a = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
_a = BlenderbotSmallTokenizer
def __init__( self , UpperCamelCase_=None , UpperCamelCase_=None , UpperCamelCase_="<|endoftext|>" , UpperCamelCase_="<|endoftext|>" , UpperCamelCase_="<|endoftext|>" , UpperCamelCase_=False , UpperCamelCase_=True , **UpperCamelCase_ , ):
'''simple docstring'''
super().__init__(
ByteLevelBPETokenizer(
vocab=UpperCamelCase_ , merges=UpperCamelCase_ , add_prefix_space=UpperCamelCase_ , trim_offsets=UpperCamelCase_ , ) , bos_token=UpperCamelCase_ , eos_token=UpperCamelCase_ , unk_token=UpperCamelCase_ , **UpperCamelCase_ , )
UpperCamelCase__ :Union[str, Any] = add_prefix_space
def lowerCAmelCase__ ( self , UpperCamelCase_ , UpperCamelCase_=None ):
'''simple docstring'''
UpperCamelCase__ :List[Any] = [self.bos_token_id] + token_ids_a + [self.eos_token_id]
if token_ids_a is None:
return output
return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]
def lowerCAmelCase__ ( self , UpperCamelCase_ , UpperCamelCase_ = None ):
'''simple docstring'''
UpperCamelCase__ :Optional[int] = [self.sep_token_id]
UpperCamelCase__ :Any = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep + sep + token_ids_a + sep ) * [0] | 97 | 0 |
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
SCREAMING_SNAKE_CASE :List[str] = [
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)
SCREAMING_SNAKE_CASE :List[str] = logging.getLogger()
def UpperCAmelCase ( ) -> List[str]:
"""simple docstring"""
__A = argparse.ArgumentParser()
parser.add_argument("-f" )
__A = parser.parse_args()
return args.f
def UpperCAmelCase ( a_ , a_="eval" ) -> Dict:
"""simple docstring"""
__A = 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}''' )
SCREAMING_SNAKE_CASE :Any = logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)
class UpperCAmelCase ( __SCREAMING_SNAKE_CASE ):
'''simple docstring'''
def UpperCamelCase_ ( self : Any ):
__A = self.get_auto_remove_tmp_dir()
__A = f'''
run_glue.py
--model_name_or_path distilbert-base-uncased
--output_dir {tmp_dir}
--train_file ./tests/fixtures/tests_samples/MRPC/train.csv
--validation_file ./tests/fixtures/tests_samples/MRPC/dev.csv
--per_device_train_batch_size=2
--per_device_eval_batch_size=1
--learning_rate=1e-4
--eval_steps=2
--warmup_steps=2
--seed=42
--max_seq_length=128
'''.split()
with patch.object(A ,"argv" ,A ):
run_flax_glue.main()
__A = get_results(A )
self.assertGreaterEqual(result["eval_accuracy"] ,0.75 )
@slow
def UpperCamelCase_ ( self : List[str] ):
__A = self.get_auto_remove_tmp_dir()
__A = f'''
run_clm_flax.py
--model_name_or_path distilgpt2
--train_file ./tests/fixtures/sample_text.txt
--validation_file ./tests/fixtures/sample_text.txt
--do_train
--do_eval
--block_size 128
--per_device_train_batch_size 4
--per_device_eval_batch_size 4
--num_train_epochs 2
--logging_steps 2 --eval_steps 2
--output_dir {tmp_dir}
--overwrite_output_dir
'''.split()
with patch.object(A ,"argv" ,A ):
run_clm_flax.main()
__A = get_results(A )
self.assertLess(result["eval_perplexity"] ,1_00 )
@slow
def UpperCamelCase_ ( self : int ):
__A = self.get_auto_remove_tmp_dir()
__A = f'''
run_summarization.py
--model_name_or_path t5-small
--train_file tests/fixtures/tests_samples/xsum/sample.json
--validation_file tests/fixtures/tests_samples/xsum/sample.json
--test_file tests/fixtures/tests_samples/xsum/sample.json
--output_dir {tmp_dir}
--overwrite_output_dir
--num_train_epochs=3
--warmup_steps=8
--do_train
--do_eval
--do_predict
--learning_rate=2e-4
--per_device_train_batch_size=2
--per_device_eval_batch_size=1
--predict_with_generate
'''.split()
with patch.object(A ,"argv" ,A ):
run_summarization_flax.main()
__A = get_results(A ,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 UpperCamelCase_ ( self : int ):
__A = self.get_auto_remove_tmp_dir()
__A = f'''
run_mlm.py
--model_name_or_path distilroberta-base
--train_file ./tests/fixtures/sample_text.txt
--validation_file ./tests/fixtures/sample_text.txt
--output_dir {tmp_dir}
--overwrite_output_dir
--max_seq_length 128
--per_device_train_batch_size 4
--per_device_eval_batch_size 4
--logging_steps 2 --eval_steps 2
--do_train
--do_eval
--num_train_epochs=1
'''.split()
with patch.object(A ,"argv" ,A ):
run_mlm_flax.main()
__A = get_results(A )
self.assertLess(result["eval_perplexity"] ,42 )
@slow
def UpperCamelCase_ ( self : Any ):
__A = self.get_auto_remove_tmp_dir()
__A = f'''
run_t5_mlm_flax.py
--model_name_or_path t5-small
--train_file ./tests/fixtures/sample_text.txt
--validation_file ./tests/fixtures/sample_text.txt
--do_train
--do_eval
--max_seq_length 128
--per_device_train_batch_size 4
--per_device_eval_batch_size 4
--num_train_epochs 2
--logging_steps 2 --eval_steps 2
--output_dir {tmp_dir}
--overwrite_output_dir
'''.split()
with patch.object(A ,"argv" ,A ):
run_ta_mlm_flax.main()
__A = get_results(A )
self.assertGreaterEqual(result["eval_accuracy"] ,0.42 )
@slow
def UpperCamelCase_ ( self : Any ):
# with so little data distributed training needs more epochs to get the score on par with 0/1 gpu
__A = 7 if get_gpu_count() > 1 else 2
__A = self.get_auto_remove_tmp_dir()
__A = f'''
run_flax_ner.py
--model_name_or_path bert-base-uncased
--train_file tests/fixtures/tests_samples/conll/sample.json
--validation_file tests/fixtures/tests_samples/conll/sample.json
--output_dir {tmp_dir}
--overwrite_output_dir
--do_train
--do_eval
--warmup_steps=2
--learning_rate=2e-4
--logging_steps 2 --eval_steps 2
--per_device_train_batch_size=2
--per_device_eval_batch_size=2
--num_train_epochs={epochs}
--seed 7
'''.split()
with patch.object(A ,"argv" ,A ):
run_flax_ner.main()
__A = get_results(A )
self.assertGreaterEqual(result["eval_accuracy"] ,0.75 )
self.assertGreaterEqual(result["eval_f1"] ,0.3 )
@slow
def UpperCamelCase_ ( self : int ):
__A = self.get_auto_remove_tmp_dir()
__A = f'''
run_qa.py
--model_name_or_path bert-base-uncased
--version_2_with_negative
--train_file tests/fixtures/tests_samples/SQUAD/sample.json
--validation_file tests/fixtures/tests_samples/SQUAD/sample.json
--output_dir {tmp_dir}
--overwrite_output_dir
--num_train_epochs=3
--warmup_steps=2
--do_train
--do_eval
--logging_steps 2 --eval_steps 2
--learning_rate=2e-4
--per_device_train_batch_size=2
--per_device_eval_batch_size=1
'''.split()
with patch.object(A ,"argv" ,A ):
run_qa.main()
__A = get_results(A )
self.assertGreaterEqual(result["eval_f1"] ,30 )
self.assertGreaterEqual(result["eval_exact"] ,30 )
| 15 |
'''simple docstring'''
from ...utils import (
OptionalDependencyNotAvailable,
is_flax_available,
is_torch_available,
is_transformers_available,
)
try:
if not (is_transformers_available() and is_torch_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ...utils.dummy_torch_and_transformers_objects import * # noqa F403
else:
from .multicontrolnet import MultiControlNetModel
from .pipeline_controlnet import StableDiffusionControlNetPipeline
from .pipeline_controlnet_imgaimg import StableDiffusionControlNetImgaImgPipeline
from .pipeline_controlnet_inpaint import StableDiffusionControlNetInpaintPipeline
if is_transformers_available() and is_flax_available():
from .pipeline_flax_controlnet import FlaxStableDiffusionControlNetPipeline | 97 | 0 |
"""simple docstring"""
from collections.abc import Callable
import numpy as np
def __UpperCAmelCase ( __lowerCamelCase , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase ) -> np.ndarray:
lowercase__ : Optional[int] = int(np.ceil((x_end - xa) / step_size ) )
lowercase__ : Optional[int] = np.zeros((n + 1,) )
lowercase__ : List[Any] = ya
lowercase__ : Optional[Any] = xa
for k in range(__lowerCamelCase ):
lowercase__ : str = y[k] + step_size * ode_func(__lowerCamelCase , y[k] )
x += step_size
return y
if __name__ == "__main__":
import doctest
doctest.testmod()
| 16 |
'''simple docstring'''
from typing import Callable, Optional
from .. import Features
from ..packaged_modules.generator.generator import Generator
from .abc import AbstractDatasetInputStream
class lowercase ( A__ ):
"""simple docstring"""
def __init__( self , UpperCamelCase_ , UpperCamelCase_ = None , UpperCamelCase_ = None , UpperCamelCase_ = False , UpperCamelCase_ = False , UpperCamelCase_ = None , UpperCamelCase_ = None , **UpperCamelCase_ , ):
'''simple docstring'''
super().__init__(
features=UpperCamelCase_ , cache_dir=UpperCamelCase_ , keep_in_memory=UpperCamelCase_ , streaming=UpperCamelCase_ , num_proc=UpperCamelCase_ , **UpperCamelCase_ , )
UpperCamelCase__ :Any = Generator(
cache_dir=UpperCamelCase_ , features=UpperCamelCase_ , generator=UpperCamelCase_ , gen_kwargs=UpperCamelCase_ , **UpperCamelCase_ , )
def lowerCAmelCase__ ( self ):
'''simple docstring'''
if self.streaming:
UpperCamelCase__ :Optional[Any] = self.builder.as_streaming_dataset(split='''train''' )
# Build regular (map-style) dataset
else:
UpperCamelCase__ :Optional[int] = None
UpperCamelCase__ :int = None
UpperCamelCase__ :Any = None
UpperCamelCase__ :Any = None
self.builder.download_and_prepare(
download_config=UpperCamelCase_ , download_mode=UpperCamelCase_ , verification_mode=UpperCamelCase_ , base_path=UpperCamelCase_ , num_proc=self.num_proc , )
UpperCamelCase__ :List[Any] = self.builder.as_dataset(
split='''train''' , verification_mode=UpperCamelCase_ , in_memory=self.keep_in_memory )
return dataset | 97 | 0 |
"""simple docstring"""
import unittest
from knapsack import knapsack as k
class _lowerCAmelCase ( unittest.TestCase ):
"""simple docstring"""
def _lowercase ( self : Optional[Any] ):
__lowercase = 0
__lowercase = [0]
__lowercase = [0]
__lowercase = len(UpperCAmelCase__ )
self.assertEqual(k.knapsack(UpperCAmelCase__, UpperCAmelCase__, UpperCAmelCase__, UpperCAmelCase__ ), 0 )
__lowercase = [6_0]
__lowercase = [1_0]
__lowercase = len(UpperCAmelCase__ )
self.assertEqual(k.knapsack(UpperCAmelCase__, UpperCAmelCase__, UpperCAmelCase__, UpperCAmelCase__ ), 0 )
def _lowercase ( self : Union[str, Any] ):
__lowercase = 3
__lowercase = [1, 2, 3]
__lowercase = [3, 2, 1]
__lowercase = len(UpperCAmelCase__ )
self.assertEqual(k.knapsack(UpperCAmelCase__, UpperCAmelCase__, UpperCAmelCase__, UpperCAmelCase__ ), 5 )
def _lowercase ( self : Dict ):
__lowercase = 5_0
__lowercase = [6_0, 1_0_0, 1_2_0]
__lowercase = [1_0, 2_0, 3_0]
__lowercase = len(UpperCAmelCase__ )
self.assertEqual(k.knapsack(UpperCAmelCase__, UpperCAmelCase__, UpperCAmelCase__, UpperCAmelCase__ ), 2_2_0 )
if __name__ == "__main__":
unittest.main()
| 17 |
'''simple docstring'''
__snake_case = 65521
def a ( __a ) -> int:
'''simple docstring'''
UpperCamelCase__ :Tuple = 1
UpperCamelCase__ :Any = 0
for plain_chr in plain_text:
UpperCamelCase__ :List[str] = (a + ord(__a )) % MOD_ADLER
UpperCamelCase__ :Tuple = (b + a) % MOD_ADLER
return (b << 16) | a | 97 | 0 |
from unittest import TestCase
from datasets import Sequence, Value
from datasets.arrow_dataset import Dataset
class a__ ( A__ ):
def __UpperCamelCase ( self : List[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 __UpperCamelCase ( self : int ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : int = {"col_1": [3, 2, 1, 0], "col_2": ["a", "b", "c", "d"]}
return Dataset.from_dict(_A )
def __UpperCamelCase ( self : List[str] ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : List[str] = self._create_example_records()
SCREAMING_SNAKE_CASE_ : List[str] = Dataset.from_list(_A )
self.assertListEqual(dset.column_names,["col_1", "col_2"] )
for i, r in enumerate(_A ):
self.assertDictEqual(_A,example_records[i] )
def __UpperCamelCase ( self : Dict ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Union[str, Any] = self._create_example_records()
SCREAMING_SNAKE_CASE_ : Any = Dataset.from_list(_A )
SCREAMING_SNAKE_CASE_ : Dict = 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 __UpperCamelCase ( self : Tuple ): # checks what happens with missing columns
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Dict = [{"col_1": 1}, {"col_2": "x"}]
SCREAMING_SNAKE_CASE_ : Tuple = Dataset.from_list(_A )
self.assertDictEqual(dset[0],{"col_1": 1} )
self.assertDictEqual(dset[1],{"col_1": None} ) # NB: first record is used for columns
def __UpperCamelCase ( self : Any ): # checks if the type can be inferred from the second record
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : int = [{"col_1": []}, {"col_1": [1, 2]}]
SCREAMING_SNAKE_CASE_ : Optional[Any] = Dataset.from_list(_A )
self.assertEqual(dset.info.features["col_1"],Sequence(Value("int64" ) ) )
def __UpperCamelCase ( self : List[Any] ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Optional[int] = Dataset.from_list([] )
self.assertEqual(len(_A ),0 )
self.assertListEqual(dset.column_names,[] )
| 18 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
__snake_case = logging.get_logger(__name__)
__snake_case = {
'''camembert-base''': '''https://huggingface.co/camembert-base/resolve/main/config.json''',
'''umberto-commoncrawl-cased-v1''': (
'''https://huggingface.co/Musixmatch/umberto-commoncrawl-cased-v1/resolve/main/config.json'''
),
'''umberto-wikipedia-uncased-v1''': (
'''https://huggingface.co/Musixmatch/umberto-wikipedia-uncased-v1/resolve/main/config.json'''
),
}
class lowercase ( A__ ):
"""simple docstring"""
_a = 'camembert'
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-12 , UpperCamelCase_=1 , UpperCamelCase_=0 , UpperCamelCase_=2 , UpperCamelCase_="absolute" , UpperCamelCase_=True , UpperCamelCase_=None , **UpperCamelCase_ , ):
'''simple docstring'''
super().__init__(pad_token_id=UpperCamelCase_ , bos_token_id=UpperCamelCase_ , eos_token_id=UpperCamelCase_ , **UpperCamelCase_ )
UpperCamelCase__ :int = vocab_size
UpperCamelCase__ :Optional[int] = hidden_size
UpperCamelCase__ :Optional[int] = num_hidden_layers
UpperCamelCase__ :List[Any] = num_attention_heads
UpperCamelCase__ :Union[str, Any] = hidden_act
UpperCamelCase__ :List[Any] = intermediate_size
UpperCamelCase__ :int = hidden_dropout_prob
UpperCamelCase__ :Tuple = attention_probs_dropout_prob
UpperCamelCase__ :Union[str, Any] = max_position_embeddings
UpperCamelCase__ :Tuple = type_vocab_size
UpperCamelCase__ :int = initializer_range
UpperCamelCase__ :List[str] = layer_norm_eps
UpperCamelCase__ :int = position_embedding_type
UpperCamelCase__ :Any = use_cache
UpperCamelCase__ :Any = classifier_dropout
class lowercase ( A__ ):
"""simple docstring"""
@property
def lowerCAmelCase__ ( self ):
'''simple docstring'''
if self.task == "multiple-choice":
UpperCamelCase__ :List[str] = {0: '''batch''', 1: '''choice''', 2: '''sequence'''}
else:
UpperCamelCase__ :Tuple = {0: '''batch''', 1: '''sequence'''}
return OrderedDict(
[
('''input_ids''', dynamic_axis),
('''attention_mask''', dynamic_axis),
] ) | 97 | 0 |
import copy
import tempfile
import unittest
from transformers import MaMaaaConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from transformers.utils import cached_property
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import MaMaaaForConditionalGeneration, MaMaaaModel, MaMaaaTokenizer
from transformers.models.mam_aaa.modeling_mam_aaa import MaMaaaDecoder, MaMaaaEncoder
def lowerCamelCase_ ( lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__=None , lowerCamelCase__=None , lowerCamelCase__=None , lowerCamelCase__=None , lowerCamelCase__=None , ):
if attention_mask is None:
lowerCamelCase_ = input_ids.ne(config.pad_token_id )
if decoder_attention_mask is None:
lowerCamelCase_ = decoder_input_ids.ne(config.pad_token_id )
if head_mask is None:
lowerCamelCase_ = torch.ones(config.encoder_layers , config.encoder_attention_heads , device=lowerCamelCase__ )
if decoder_head_mask is None:
lowerCamelCase_ = torch.ones(config.decoder_layers , config.decoder_attention_heads , device=lowerCamelCase__ )
if cross_attn_head_mask is None:
lowerCamelCase_ = torch.ones(config.decoder_layers , config.decoder_attention_heads , device=lowerCamelCase__ )
return {
"input_ids": input_ids,
"decoder_input_ids": decoder_input_ids,
"attention_mask": attention_mask,
"decoder_attention_mask": attention_mask,
"head_mask": head_mask,
"decoder_head_mask": decoder_head_mask,
"cross_attn_head_mask": cross_attn_head_mask,
}
class _SCREAMING_SNAKE_CASE :
def __init__( self , lowercase , lowercase=13 , lowercase=7 , lowercase=True , lowercase=False , lowercase=99 , lowercase=16 , lowercase=2 , lowercase=4 , lowercase=4 , lowercase="relu" , lowercase=0.1 , lowercase=0.1 , lowercase=0.0 , lowercase=0.0 , lowercase=20 , lowercase=2 , lowercase=1 , lowercase=0 , ) -> int:
lowerCamelCase_ = parent
lowerCamelCase_ = batch_size
lowerCamelCase_ = seq_length
lowerCamelCase_ = is_training
lowerCamelCase_ = use_labels
lowerCamelCase_ = vocab_size
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_ = encoder_layerdrop
lowerCamelCase_ = decoder_layerdrop
lowerCamelCase_ = max_position_embeddings
lowerCamelCase_ = eos_token_id
lowerCamelCase_ = pad_token_id
lowerCamelCase_ = bos_token_id
def SCREAMING_SNAKE_CASE_( self ) -> Optional[int]:
lowerCamelCase_ = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
lowerCamelCase_ = self.eos_token_id # Eos Token
lowerCamelCase_ = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
# we need to clamp the input ids here to avoid having pad token in between
# this is because for M2M100 the position_ids are prepared such that
# all pad tokens have pos id = 2 and rest are between 2..seq_length
# and the seq_length here is seq_length - num_pad_tokens
# but when using past, there is no way of knowing if the past input ids had
# pad tokens in them, which results in incorrect seq_lenth and which in turn results in
# position_ids being off by num_pad_tokens in past input
lowerCamelCase_ = input_ids.clamp(self.pad_token_id + 1 )
lowerCamelCase_ = decoder_input_ids.clamp(self.pad_token_id + 1 )
lowerCamelCase_ = self.get_config()
lowerCamelCase_ = prepare_mam_aaa_inputs_dict(lowercase , lowercase , lowercase )
return config, inputs_dict
def SCREAMING_SNAKE_CASE_( self ) -> Any:
return MaMaaaConfig(
vocab_size=self.vocab_size , d_model=self.hidden_size , encoder_layers=self.num_hidden_layers , decoder_layers=self.num_hidden_layers , encoder_attention_heads=self.num_attention_heads , decoder_attention_heads=self.num_attention_heads , encoder_ffn_dim=self.intermediate_size , decoder_ffn_dim=self.intermediate_size , dropout=self.hidden_dropout_prob , attention_dropout=self.attention_probs_dropout_prob , encoder_layerdrop=self.encoder_layerdrop , decoder_layerdrop=self.decoder_layerdrop , max_position_embeddings=self.max_position_embeddings , eos_token_id=self.eos_token_id , bos_token_id=self.bos_token_id , pad_token_id=self.pad_token_id , )
def SCREAMING_SNAKE_CASE_( self ) -> Tuple:
lowerCamelCase_ , lowerCamelCase_ = self.prepare_config_and_inputs()
return config, inputs_dict
def SCREAMING_SNAKE_CASE_( self , lowercase , lowercase ) -> Any:
lowerCamelCase_ = MaMaaaModel(config=lowercase ).get_decoder().to(lowercase ).eval()
lowerCamelCase_ = inputs_dict["input_ids"]
lowerCamelCase_ = inputs_dict["attention_mask"]
lowerCamelCase_ = inputs_dict["head_mask"]
# first forward pass
lowerCamelCase_ = model(lowercase , attention_mask=lowercase , head_mask=lowercase , use_cache=lowercase )
lowerCamelCase_ , lowerCamelCase_ = outputs.to_tuple()
# create hypothetical multiple next token and extent to next_input_ids
lowerCamelCase_ = ids_tensor((self.batch_size, 3) , config.vocab_size )
lowerCamelCase_ = ids_tensor((self.batch_size, 3) , 2 )
# append to next input_ids and
lowerCamelCase_ = torch.cat([input_ids, next_tokens] , dim=-1 )
lowerCamelCase_ = torch.cat([attention_mask, next_attn_mask] , dim=-1 )
lowerCamelCase_ = model(lowercase , attention_mask=lowercase )["last_hidden_state"]
lowerCamelCase_ = model(lowercase , attention_mask=lowercase , past_key_values=lowercase )[
"last_hidden_state"
]
# select random slice
lowerCamelCase_ = ids_tensor((1,) , output_from_past.shape[-1] ).item()
lowerCamelCase_ = output_from_no_past[:, -3:, random_slice_idx].detach()
lowerCamelCase_ = output_from_past[:, :, random_slice_idx].detach()
self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1] )
# test that outputs are equal for slice
self.parent.assertTrue(torch.allclose(lowercase , lowercase , atol=1e-2 ) )
def SCREAMING_SNAKE_CASE_( self , lowercase , lowercase ) -> Union[str, Any]:
lowerCamelCase_ = MaMaaaModel(config=lowercase ).to(lowercase ).eval()
lowerCamelCase_ = model(**lowercase )
lowerCamelCase_ = outputs.encoder_last_hidden_state
lowerCamelCase_ = outputs.last_hidden_state
with tempfile.TemporaryDirectory() as tmpdirname:
lowerCamelCase_ = model.get_encoder()
encoder.save_pretrained(lowercase )
lowerCamelCase_ = MaMaaaEncoder.from_pretrained(lowercase ).to(lowercase )
lowerCamelCase_ = encoder(inputs_dict["input_ids"] , attention_mask=inputs_dict["attention_mask"] )[
0
]
self.parent.assertTrue((encoder_last_hidden_state_a - encoder_last_hidden_state).abs().max().item() < 1e-3 )
with tempfile.TemporaryDirectory() as tmpdirname:
lowerCamelCase_ = model.get_decoder()
decoder.save_pretrained(lowercase )
lowerCamelCase_ = MaMaaaDecoder.from_pretrained(lowercase ).to(lowercase )
lowerCamelCase_ = decoder(
input_ids=inputs_dict["decoder_input_ids"] , attention_mask=inputs_dict["decoder_attention_mask"] , encoder_hidden_states=lowercase , encoder_attention_mask=inputs_dict["attention_mask"] , )[0]
self.parent.assertTrue((last_hidden_state_a - last_hidden_state).abs().max().item() < 1e-3 )
@require_torch
class _SCREAMING_SNAKE_CASE ( snake_case_ , snake_case_ , snake_case_ , unittest.TestCase ):
lowerCAmelCase__ = (
(
MaMaaaModel,
MaMaaaForConditionalGeneration,
)
if is_torch_available()
else ()
)
lowerCAmelCase__ = (MaMaaaForConditionalGeneration,) if is_torch_available() else ()
lowerCAmelCase__ = (
{
'conversational': MaMaaaForConditionalGeneration,
'feature-extraction': MaMaaaModel,
'summarization': MaMaaaForConditionalGeneration,
'text2text-generation': MaMaaaForConditionalGeneration,
'translation': MaMaaaForConditionalGeneration,
}
if is_torch_available()
else {}
)
lowerCAmelCase__ = True
lowerCAmelCase__ = True
lowerCAmelCase__ = False
lowerCAmelCase__ = False
def SCREAMING_SNAKE_CASE_( self , lowercase , lowercase , lowercase , lowercase , lowercase ) -> int:
if pipeline_test_casse_name == "TranslationPipelineTests":
# Get `ValueError: Translation requires a `src_lang` and a `tgt_lang` for this model`.
# `M2M100Config` was never used in pipeline tests: cannot create a simple tokenizer.
return True
return False
def SCREAMING_SNAKE_CASE_( self ) -> Optional[Any]:
lowerCamelCase_ = MaMaaaModelTester(self )
lowerCamelCase_ = ConfigTester(self , config_class=lowercase )
def SCREAMING_SNAKE_CASE_( self ) -> str:
self.config_tester.run_common_tests()
def SCREAMING_SNAKE_CASE_( self ) -> Optional[int]:
lowerCamelCase_ , lowerCamelCase_ = self.model_tester.prepare_config_and_inputs()
for model_class in self.all_model_classes:
lowerCamelCase_ = model_class(lowercase )
with tempfile.TemporaryDirectory() as tmpdirname:
model.save_pretrained(lowercase )
lowerCamelCase_ , lowerCamelCase_ = model_class.from_pretrained(lowercase , output_loading_info=lowercase )
self.assertEqual(info["missing_keys"] , [] )
def SCREAMING_SNAKE_CASE_( self ) -> int:
lowerCamelCase_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_decoder_model_past_large_inputs(*lowercase )
def SCREAMING_SNAKE_CASE_( self ) -> Any:
lowerCamelCase_ = self.model_tester.prepare_config_and_inputs_for_common()
self.model_tester.check_encoder_decoder_model_standalone(*lowercase )
def SCREAMING_SNAKE_CASE_( self ) -> Optional[Any]:
lowerCamelCase_ , lowerCamelCase_ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in (MaMaaaModel, MaMaaaForConditionalGeneration):
lowerCamelCase_ = model_class(lowercase )
model.to(lowercase )
model.eval()
lowerCamelCase_ = copy.deepcopy(self._prepare_for_class(lowercase , lowercase ) )
if not self.is_encoder_decoder:
lowerCamelCase_ = inputs["input_ids"]
del inputs["input_ids"]
else:
lowerCamelCase_ = inputs["input_ids"]
lowerCamelCase_ = inputs.get("decoder_input_ids" , lowercase )
del inputs["input_ids"]
inputs.pop("decoder_input_ids" , lowercase )
lowerCamelCase_ = model.get_input_embeddings()
if not self.is_encoder_decoder:
lowerCamelCase_ = wte(lowercase )
else:
lowerCamelCase_ = wte(lowercase )
lowerCamelCase_ = wte(lowercase )
with torch.no_grad():
model(**lowercase )[0]
def SCREAMING_SNAKE_CASE_( self ) -> int:
lowerCamelCase_ , lowerCamelCase_ = self.model_tester.prepare_config_and_inputs()
lowerCamelCase_ = input_dict["input_ids"]
lowerCamelCase_ = input_ids.ne(1 ).to(lowercase )
lowerCamelCase_ = MaMaaaForConditionalGeneration(lowercase ).eval().to(lowercase )
if torch_device == "cuda":
model.half()
model.generate(lowercase , attention_mask=lowercase )
model.generate(num_beams=4 , do_sample=lowercase , early_stopping=lowercase , num_return_sequences=3 )
def lowerCamelCase_ ( lowerCamelCase__ ):
return torch.tensor(lowerCamelCase__ , dtype=torch.long , device=lowerCamelCase__ )
__A =1E-4
@require_torch
@require_sentencepiece
@require_tokenizers
@slow
class _SCREAMING_SNAKE_CASE ( unittest.TestCase ):
@cached_property
def SCREAMING_SNAKE_CASE_( self ) -> Optional[int]:
return MaMaaaTokenizer.from_pretrained("facebook/m2m100_418M" )
def SCREAMING_SNAKE_CASE_( self ) -> List[Any]:
lowerCamelCase_ = MaMaaaModel.from_pretrained("facebook/m2m100_418M" ).to(lowercase )
lowerCamelCase_ = _long_tensor([[128028, 98, 12, 30527, 2732, 159, 7755, 61904, 39144, 38, 2]] )
lowerCamelCase_ = _long_tensor([[2, 128028, 98, 12, 30527, 2732, 159, 7755, 61904, 39144, 38]] )
lowerCamelCase_ = prepare_mam_aaa_inputs_dict(model.config , lowercase , lowercase )
with torch.no_grad():
lowerCamelCase_ = model(**lowercase )[0]
lowerCamelCase_ = torch.Size((1, 11, 1024) )
self.assertEqual(output.shape , lowercase )
# change to expected output here
lowerCamelCase_ = torch.tensor(
[[-0.7_7_8_0, -0.1_6_7_6, 0.1_0_3_8], [-6.7_5_5_6, -1.3_9_9_2, 0.0_5_6_7], [-7.5_3_8_3, -0.5_9_2_0, -0.2_7_7_9]] , device=lowercase )
self.assertTrue(torch.allclose(output[:, :3, :3] , lowercase , atol=lowercase ) )
def SCREAMING_SNAKE_CASE_( self ) -> Optional[int]:
lowerCamelCase_ = MaMaaaForConditionalGeneration.from_pretrained("facebook/m2m100_418M" ).to(lowercase )
# change to intended input
lowerCamelCase_ = _long_tensor([[128028, 98, 12, 30527, 2732, 159, 7755, 61904, 39144, 38, 2]] )
lowerCamelCase_ = _long_tensor([[2, 128028, 98, 12, 30527, 2732, 159, 7755, 61904, 39144, 38]] )
lowerCamelCase_ = prepare_mam_aaa_inputs_dict(model.config , lowercase , lowercase )
with torch.no_grad():
lowerCamelCase_ = model(**lowercase )[0]
lowerCamelCase_ = torch.Size((1, 11, model.config.vocab_size) )
self.assertEqual(output.shape , lowercase )
# change to expected output here
lowerCamelCase_ = torch.tensor(
[[-1.0_4_4_8, -1.0_4_1_1, 3.7_9_9_2], [-3.2_1_9_1, -3.2_3_8_6, -1.3_4_5_1], [-3.6_2_1_0, -3.5_9_9_3, 0.4_9_2_5]] , device=lowercase )
self.assertTrue(torch.allclose(output[:, :3, :3] , lowercase , atol=lowercase ) )
def SCREAMING_SNAKE_CASE_( self ) -> Union[str, Any]:
lowerCamelCase_ = MaMaaaForConditionalGeneration.from_pretrained("facebook/m2m100_418M" ).to(lowercase )
lowerCamelCase_ = MaMaaaTokenizer.from_pretrained("facebook/m2m100_418M" , src_lang="fr" , tgt_lang="en" )
lowerCamelCase_ = [
"L'affaire NSA souligne l'absence totale de débat sur le renseignement",
"Selon moi, il y a deux niveaux de réponse de la part du gouvernement français.",
"Lorsque François Hollande téléphone à Barack Obama ou quand le ministre des affaires étrangères Laurent"
" Fabius convoque l'ambassadeur des Etats-Unis, ils réagissent à une vraie découverte, qui est celle de"
" l'ampleur de la surveillance américaine sur l'ensemble des communications en France.",
]
# The below article tests that we don't add any hypotheses outside of the top n_beams
lowerCamelCase_ = tokenizer(lowercase , padding=lowercase , return_tensors="pt" )
lowerCamelCase_ = model.generate(
input_ids=dct["input_ids"].to(lowercase ) , attention_mask=dct["attention_mask"].to(lowercase ) , num_beams=5 , forced_bos_token_id=tokenizer.get_lang_id("en" ) , )
lowerCamelCase_ = [
"The NSA case highlights the total absence of intelligence debate",
"I think there are two levels of response from the French government.",
"When François Hollande calls Barack Obama or when Foreign Minister Laurent Fabius calls the U.S."
" Ambassador, they respond to a real discovery, which is that of the scale of U.S. surveillance on all"
" communications in France.",
]
lowerCamelCase_ = tokenizer.batch_decode(
hypotheses_batch.tolist() , clean_up_tokenization_spaces=lowercase , skip_special_tokens=lowercase )
assert generated == expected_en
| 19 |
'''simple docstring'''
import json
import pathlib
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_available():
import torch
if is_vision_available():
from PIL import Image
from transformers import ConditionalDetrImageProcessor
class lowercase ( unittest.TestCase ):
"""simple docstring"""
def __init__( self , UpperCamelCase_ , UpperCamelCase_=7 , UpperCamelCase_=3 , UpperCamelCase_=30 , UpperCamelCase_=400 , UpperCamelCase_=True , UpperCamelCase_=None , UpperCamelCase_=True , UpperCamelCase_=[0.5, 0.5, 0.5] , UpperCamelCase_=[0.5, 0.5, 0.5] , UpperCamelCase_=True , UpperCamelCase_=1 / 255 , UpperCamelCase_=True , ):
'''simple docstring'''
UpperCamelCase__ :Dict = size if size is not None else {'''shortest_edge''': 18, '''longest_edge''': 1333}
UpperCamelCase__ :str = parent
UpperCamelCase__ :List[Any] = batch_size
UpperCamelCase__ :Dict = num_channels
UpperCamelCase__ :str = min_resolution
UpperCamelCase__ :Optional[Any] = max_resolution
UpperCamelCase__ :int = do_resize
UpperCamelCase__ :Optional[Any] = size
UpperCamelCase__ :Tuple = do_normalize
UpperCamelCase__ :List[Any] = image_mean
UpperCamelCase__ :Dict = image_std
UpperCamelCase__ :Union[str, Any] = do_rescale
UpperCamelCase__ :Union[str, Any] = rescale_factor
UpperCamelCase__ :Union[str, Any] = do_pad
def lowerCAmelCase__ ( self ):
'''simple docstring'''
return {
"do_resize": self.do_resize,
"size": self.size,
"do_normalize": self.do_normalize,
"image_mean": self.image_mean,
"image_std": self.image_std,
"do_rescale": self.do_rescale,
"rescale_factor": self.rescale_factor,
"do_pad": self.do_pad,
}
def lowerCAmelCase__ ( self , UpperCamelCase_ , UpperCamelCase_=False ):
'''simple docstring'''
if not batched:
UpperCamelCase__ :List[str] = image_inputs[0]
if isinstance(UpperCamelCase_ , Image.Image ):
UpperCamelCase__ , UpperCamelCase__ :List[str] = image.size
else:
UpperCamelCase__ , UpperCamelCase__ :List[Any] = image.shape[1], image.shape[2]
if w < h:
UpperCamelCase__ :int = int(self.size['''shortest_edge'''] * h / w )
UpperCamelCase__ :Dict = self.size['''shortest_edge''']
elif w > h:
UpperCamelCase__ :int = self.size['''shortest_edge''']
UpperCamelCase__ :Tuple = int(self.size['''shortest_edge'''] * w / h )
else:
UpperCamelCase__ :str = self.size['''shortest_edge''']
UpperCamelCase__ :str = self.size['''shortest_edge''']
else:
UpperCamelCase__ :Any = []
for image in image_inputs:
UpperCamelCase__ , UpperCamelCase__ :Dict = self.get_expected_values([image] )
expected_values.append((expected_height, expected_width) )
UpperCamelCase__ :List[Any] = max(UpperCamelCase_ , key=lambda UpperCamelCase_ : item[0] )[0]
UpperCamelCase__ :Optional[int] = max(UpperCamelCase_ , key=lambda UpperCamelCase_ : item[1] )[1]
return expected_height, expected_width
@require_torch
@require_vision
class lowercase ( A__ , unittest.TestCase ):
"""simple docstring"""
_a = ConditionalDetrImageProcessor if is_vision_available() else None
def lowerCAmelCase__ ( self ):
'''simple docstring'''
UpperCamelCase__ :List[Any] = ConditionalDetrImageProcessingTester(self )
@property
def lowerCAmelCase__ ( self ):
'''simple docstring'''
return self.image_processor_tester.prepare_image_processor_dict()
def lowerCAmelCase__ ( self ):
'''simple docstring'''
UpperCamelCase__ :Tuple = 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_ , '''size''' ) )
def lowerCAmelCase__ ( self ):
'''simple docstring'''
UpperCamelCase__ :Optional[int] = self.image_processing_class.from_dict(self.image_processor_dict )
self.assertEqual(image_processor.size , {'''shortest_edge''': 18, '''longest_edge''': 1333} )
self.assertEqual(image_processor.do_pad , UpperCamelCase_ )
UpperCamelCase__ :List[str] = self.image_processing_class.from_dict(
self.image_processor_dict , size=42 , max_size=84 , pad_and_return_pixel_mask=UpperCamelCase_ )
self.assertEqual(image_processor.size , {'''shortest_edge''': 42, '''longest_edge''': 84} )
self.assertEqual(image_processor.do_pad , UpperCamelCase_ )
def lowerCAmelCase__ ( self ):
'''simple docstring'''
pass
def lowerCAmelCase__ ( self ):
'''simple docstring'''
UpperCamelCase__ :Dict = self.image_processing_class(**self.image_processor_dict )
# create random PIL images
UpperCamelCase__ :List[str] = prepare_image_inputs(self.image_processor_tester , equal_resolution=UpperCamelCase_ )
for image in image_inputs:
self.assertIsInstance(UpperCamelCase_ , Image.Image )
# Test not batched input
UpperCamelCase__ :Any = image_processing(image_inputs[0] , return_tensors='''pt''' ).pixel_values
UpperCamelCase__ , UpperCamelCase__ :str = self.image_processor_tester.get_expected_values(UpperCamelCase_ )
self.assertEqual(
encoded_images.shape , (1, self.image_processor_tester.num_channels, expected_height, expected_width) , )
# Test batched
UpperCamelCase__ , UpperCamelCase__ :str = self.image_processor_tester.get_expected_values(UpperCamelCase_ , batched=UpperCamelCase_ )
UpperCamelCase__ :List[str] = 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,
expected_height,
expected_width,
) , )
def lowerCAmelCase__ ( self ):
'''simple docstring'''
UpperCamelCase__ :List[Any] = self.image_processing_class(**self.image_processor_dict )
# create random numpy tensors
UpperCamelCase__ :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
UpperCamelCase__ :Union[str, Any] = image_processing(image_inputs[0] , return_tensors='''pt''' ).pixel_values
UpperCamelCase__ , UpperCamelCase__ :List[Any] = self.image_processor_tester.get_expected_values(UpperCamelCase_ )
self.assertEqual(
encoded_images.shape , (1, self.image_processor_tester.num_channels, expected_height, expected_width) , )
# Test batched
UpperCamelCase__ :Union[str, Any] = image_processing(UpperCamelCase_ , return_tensors='''pt''' ).pixel_values
UpperCamelCase__ , UpperCamelCase__ :str = self.image_processor_tester.get_expected_values(UpperCamelCase_ , batched=UpperCamelCase_ )
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
expected_height,
expected_width,
) , )
def lowerCAmelCase__ ( self ):
'''simple docstring'''
UpperCamelCase__ :List[str] = self.image_processing_class(**self.image_processor_dict )
# create random PyTorch tensors
UpperCamelCase__ :Any = 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
UpperCamelCase__ :str = image_processing(image_inputs[0] , return_tensors='''pt''' ).pixel_values
UpperCamelCase__ , UpperCamelCase__ :Dict = self.image_processor_tester.get_expected_values(UpperCamelCase_ )
self.assertEqual(
encoded_images.shape , (1, self.image_processor_tester.num_channels, expected_height, expected_width) , )
# Test batched
UpperCamelCase__ :List[str] = image_processing(UpperCamelCase_ , return_tensors='''pt''' ).pixel_values
UpperCamelCase__ , UpperCamelCase__ :Optional[int] = self.image_processor_tester.get_expected_values(UpperCamelCase_ , batched=UpperCamelCase_ )
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
expected_height,
expected_width,
) , )
@slow
def lowerCAmelCase__ ( self ):
'''simple docstring'''
UpperCamelCase__ :Optional[Any] = Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
with open('''./tests/fixtures/tests_samples/COCO/coco_annotations.txt''' , '''r''' ) as f:
UpperCamelCase__ :Optional[int] = json.loads(f.read() )
UpperCamelCase__ :Any = {'''image_id''': 39769, '''annotations''': target}
# encode them
UpperCamelCase__ :str = ConditionalDetrImageProcessor.from_pretrained('''microsoft/conditional-detr-resnet-50''' )
UpperCamelCase__ :List[Any] = image_processing(images=UpperCamelCase_ , annotations=UpperCamelCase_ , return_tensors='''pt''' )
# verify pixel values
UpperCamelCase__ :List[str] = torch.Size([1, 3, 800, 1066] )
self.assertEqual(encoding['''pixel_values'''].shape , UpperCamelCase_ )
UpperCamelCase__ :str = torch.tensor([0.2796, 0.3138, 0.3481] )
self.assertTrue(torch.allclose(encoding['''pixel_values'''][0, 0, 0, :3] , UpperCamelCase_ , atol=1e-4 ) )
# verify area
UpperCamelCase__ :str = torch.tensor([5887.9600, 11250.2061, 489353.8438, 837122.7500, 147967.5156, 165732.3438] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''area'''] , UpperCamelCase_ ) )
# verify boxes
UpperCamelCase__ :Optional[Any] = torch.Size([6, 4] )
self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape , UpperCamelCase_ )
UpperCamelCase__ :Optional[Any] = torch.tensor([0.5503, 0.2765, 0.0604, 0.2215] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''boxes'''][0] , UpperCamelCase_ , atol=1e-3 ) )
# verify image_id
UpperCamelCase__ :List[Any] = torch.tensor([39769] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id'''] , UpperCamelCase_ ) )
# verify is_crowd
UpperCamelCase__ :int = torch.tensor([0, 0, 0, 0, 0, 0] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd'''] , UpperCamelCase_ ) )
# verify class_labels
UpperCamelCase__ :List[str] = torch.tensor([75, 75, 63, 65, 17, 17] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels'''] , UpperCamelCase_ ) )
# verify orig_size
UpperCamelCase__ :Tuple = torch.tensor([480, 640] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size'''] , UpperCamelCase_ ) )
# verify size
UpperCamelCase__ :Union[str, Any] = torch.tensor([800, 1066] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size'''] , UpperCamelCase_ ) )
@slow
def lowerCAmelCase__ ( self ):
'''simple docstring'''
UpperCamelCase__ :List[str] = Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
with open('''./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt''' , '''r''' ) as f:
UpperCamelCase__ :Tuple = json.loads(f.read() )
UpperCamelCase__ :List[str] = {'''file_name''': '''000000039769.png''', '''image_id''': 39769, '''segments_info''': target}
UpperCamelCase__ :Any = pathlib.Path('''./tests/fixtures/tests_samples/COCO/coco_panoptic''' )
# encode them
UpperCamelCase__ :List[Any] = ConditionalDetrImageProcessor(format='''coco_panoptic''' )
UpperCamelCase__ :Dict = image_processing(images=UpperCamelCase_ , annotations=UpperCamelCase_ , masks_path=UpperCamelCase_ , return_tensors='''pt''' )
# verify pixel values
UpperCamelCase__ :str = torch.Size([1, 3, 800, 1066] )
self.assertEqual(encoding['''pixel_values'''].shape , UpperCamelCase_ )
UpperCamelCase__ :Optional[int] = torch.tensor([0.2796, 0.3138, 0.3481] )
self.assertTrue(torch.allclose(encoding['''pixel_values'''][0, 0, 0, :3] , UpperCamelCase_ , atol=1e-4 ) )
# verify area
UpperCamelCase__ :Tuple = torch.tensor([147979.6875, 165527.0469, 484638.5938, 11292.9375, 5879.6562, 7634.1147] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''area'''] , UpperCamelCase_ ) )
# verify boxes
UpperCamelCase__ :Any = torch.Size([6, 4] )
self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape , UpperCamelCase_ )
UpperCamelCase__ :List[Any] = torch.tensor([0.2625, 0.5437, 0.4688, 0.8625] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''boxes'''][0] , UpperCamelCase_ , atol=1e-3 ) )
# verify image_id
UpperCamelCase__ :List[str] = torch.tensor([39769] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id'''] , UpperCamelCase_ ) )
# verify is_crowd
UpperCamelCase__ :Union[str, Any] = torch.tensor([0, 0, 0, 0, 0, 0] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd'''] , UpperCamelCase_ ) )
# verify class_labels
UpperCamelCase__ :str = torch.tensor([17, 17, 63, 75, 75, 93] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels'''] , UpperCamelCase_ ) )
# verify masks
UpperCamelCase__ :Optional[Any] = 822873
self.assertEqual(encoding['''labels'''][0]['''masks'''].sum().item() , UpperCamelCase_ )
# verify orig_size
UpperCamelCase__ :List[str] = torch.tensor([480, 640] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size'''] , UpperCamelCase_ ) )
# verify size
UpperCamelCase__ :List[Any] = torch.tensor([800, 1066] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size'''] , UpperCamelCase_ ) ) | 97 | 0 |
from __future__ import annotations
from typing import Any
def _snake_case( SCREAMING_SNAKE_CASE__ ) -> int:
if not postfix_notation:
return 0
lowercase : int = {"""+""", """-""", """*""", """/"""}
lowercase : list[Any] = []
for token in postfix_notation:
if token in operations:
lowercase , lowercase : Dict = stack.pop(), stack.pop()
if token == "+":
stack.append(a + b )
elif token == "-":
stack.append(a - b )
elif token == "*":
stack.append(a * b )
else:
if a * b < 0 and a % b != 0:
stack.append(a // b + 1 )
else:
stack.append(a // b )
else:
stack.append(int(SCREAMING_SNAKE_CASE__ ) )
return stack.pop()
if __name__ == "__main__":
import doctest
doctest.testmod()
| 20 |
'''simple docstring'''
from collections import defaultdict
class lowercase :
"""simple docstring"""
def __init__( self , UpperCamelCase_ , UpperCamelCase_ ):
'''simple docstring'''
UpperCamelCase__ :List[Any] = total # total no of tasks (N)
# DP table will have a dimension of (2^M)*N
# initially all values are set to -1
UpperCamelCase__ :Union[str, Any] = [
[-1 for i in range(total + 1 )] for j in range(2 ** len(UpperCamelCase_ ) )
]
UpperCamelCase__ :str = defaultdict(UpperCamelCase_ ) # stores the list of persons for each task
# final_mask is used to check if all persons are included by setting all bits
# to 1
UpperCamelCase__ :Optional[int] = (1 << len(UpperCamelCase_ )) - 1
def lowerCAmelCase__ ( self , UpperCamelCase_ , UpperCamelCase_ ):
'''simple docstring'''
if mask == self.final_mask:
return 1
# if not everyone gets the task and no more tasks are available, return 0
if task_no > self.total_tasks:
return 0
# if case already considered
if self.dp[mask][task_no] != -1:
return self.dp[mask][task_no]
# Number of ways when we don't this task in the arrangement
UpperCamelCase__ :str = self.count_ways_until(UpperCamelCase_ , task_no + 1 )
# now assign the tasks one by one to all possible persons and recursively
# assign for the remaining tasks.
if task_no in self.task:
for p in self.task[task_no]:
# if p is already given a task
if mask & (1 << p):
continue
# assign this task to p and change the mask value. And recursively
# assign tasks with the new mask value.
total_ways_util += self.count_ways_until(mask | (1 << p) , task_no + 1 )
# save the value.
UpperCamelCase__ :Optional[int] = total_ways_util
return self.dp[mask][task_no]
def lowerCAmelCase__ ( self , UpperCamelCase_ ):
'''simple docstring'''
for i in range(len(UpperCamelCase_ ) ):
for j in task_performed[i]:
self.task[j].append(UpperCamelCase_ )
# call the function to fill the DP table, final answer is stored in dp[0][1]
return self.count_ways_until(0 , 1 )
if __name__ == "__main__":
__snake_case = 5 # total no of tasks (the value of N)
# the list of tasks that can be done by M persons.
__snake_case = [[1, 3, 4], [1, 2, 5], [3, 4]]
print(
AssignmentUsingBitmask(task_performed, total_tasks).count_no_of_ways(
task_performed
)
) | 97 | 0 |
from typing import Dict, List, Optional, Union
import numpy as np
from transformers.utils import is_vision_available
from transformers.utils.generic import TensorType
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_dimension_format,
)
from ...image_utils import (
IMAGENET_STANDARD_MEAN,
IMAGENET_STANDARD_STD,
ChannelDimension,
ImageInput,
PILImageResampling,
is_valid_image,
to_numpy_array,
valid_images,
)
from ...utils import logging
if is_vision_available():
import PIL
SCREAMING_SNAKE_CASE : List[str] = logging.get_logger(__name__)
def UpperCamelCase_( lowerCamelCase_ ) -> List[List[ImageInput]]:
if isinstance(lowerCamelCase_ , (list, tuple) ) and isinstance(videos[0] , (list, tuple) ) and is_valid_image(videos[0][0] ):
return videos
elif isinstance(lowerCamelCase_ , (list, tuple) ) and is_valid_image(videos[0] ):
return [videos]
elif is_valid_image(lowerCamelCase_ ):
return [[videos]]
raise ValueError(F'''Could not make batched video from {videos}''' )
class _lowerCamelCase( _a ):
lowercase_ : Union[str, Any] = ["""pixel_values"""]
def __init__( self, lowerCamelCase = True, lowerCamelCase = None, lowerCamelCase = PILImageResampling.BILINEAR, lowerCamelCase = True, lowerCamelCase = None, lowerCamelCase = True, lowerCamelCase = 1 / 2_55, lowerCamelCase = True, lowerCamelCase = True, lowerCamelCase = None, lowerCamelCase = None, **lowerCamelCase, ) -> None:
"""simple docstring"""
super().__init__(**lowerCamelCase)
_lowercase : str = size if size is not None else {'shortest_edge': 2_56}
_lowercase : Any = get_size_dict(lowerCamelCase, default_to_square=lowerCamelCase)
_lowercase : List[str] = crop_size if crop_size is not None else {'height': 2_24, 'width': 2_24}
_lowercase : Optional[Any] = get_size_dict(lowerCamelCase, param_name='crop_size')
_lowercase : Optional[int] = do_resize
_lowercase : Tuple = size
_lowercase : Any = do_center_crop
_lowercase : Any = crop_size
_lowercase : int = resample
_lowercase : int = do_rescale
_lowercase : str = rescale_factor
_lowercase : Tuple = offset
_lowercase : List[str] = do_normalize
_lowercase : Tuple = image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
_lowercase : Any = image_std if image_std is not None else IMAGENET_STANDARD_STD
def UpperCamelCase ( self, lowerCamelCase, lowerCamelCase, lowerCamelCase = PILImageResampling.BILINEAR, lowerCamelCase = None, **lowerCamelCase, ) -> np.ndarray:
"""simple docstring"""
_lowercase : Union[str, Any] = get_size_dict(lowerCamelCase, default_to_square=lowerCamelCase)
if "shortest_edge" in size:
_lowercase : Dict = get_resize_output_image_size(lowerCamelCase, size['shortest_edge'], default_to_square=lowerCamelCase)
elif "height" in size and "width" in size:
_lowercase : List[Any] = (size['height'], size['width'])
else:
raise ValueError(F'''Size must have \'height\' and \'width\' or \'shortest_edge\' as keys. Got {size.keys()}''')
return resize(lowerCamelCase, size=lowerCamelCase, resample=lowerCamelCase, data_format=lowerCamelCase, **lowerCamelCase)
def UpperCamelCase ( self, lowerCamelCase, lowerCamelCase, lowerCamelCase = None, **lowerCamelCase, ) -> np.ndarray:
"""simple docstring"""
_lowercase : Dict = get_size_dict(lowerCamelCase)
if "height" not in size or "width" not in size:
raise ValueError(F'''Size must have \'height\' and \'width\' as keys. Got {size.keys()}''')
return center_crop(lowerCamelCase, size=(size['height'], size['width']), data_format=lowerCamelCase, **lowerCamelCase)
def UpperCamelCase ( self, lowerCamelCase, lowerCamelCase, lowerCamelCase = True, lowerCamelCase = None, **lowerCamelCase, ) -> Dict:
"""simple docstring"""
_lowercase : str = image.astype(np.floataa)
if offset:
_lowercase : List[str] = image - (scale / 2)
return rescale(lowerCamelCase, scale=lowerCamelCase, data_format=lowerCamelCase, **lowerCamelCase)
def UpperCamelCase ( self, lowerCamelCase, lowerCamelCase, lowerCamelCase, lowerCamelCase = None, **lowerCamelCase, ) -> np.ndarray:
"""simple docstring"""
return normalize(lowerCamelCase, mean=lowerCamelCase, std=lowerCamelCase, data_format=lowerCamelCase, **lowerCamelCase)
def UpperCamelCase ( self, lowerCamelCase, lowerCamelCase = None, lowerCamelCase = None, lowerCamelCase = None, lowerCamelCase = None, lowerCamelCase = None, lowerCamelCase = None, lowerCamelCase = None, lowerCamelCase = None, lowerCamelCase = None, lowerCamelCase = None, lowerCamelCase = None, lowerCamelCase = ChannelDimension.FIRST, ) -> np.ndarray:
"""simple docstring"""
if do_resize and size is None or resample is None:
raise ValueError('Size and resample must be specified if do_resize is True.')
if do_center_crop and crop_size is None:
raise ValueError('Crop size must be specified if do_center_crop is True.')
if do_rescale and rescale_factor is None:
raise ValueError('Rescale factor must be specified if do_rescale is True.')
if do_normalize and (image_mean is None or image_std is None):
raise ValueError('Image mean and std must be specified if do_normalize is True.')
if offset and not do_rescale:
raise ValueError('For offset, do_rescale must also be set to True.')
# All transformations expect numpy arrays.
_lowercase : Tuple = to_numpy_array(lowerCamelCase)
if do_resize:
_lowercase : Optional[Any] = self.resize(image=lowerCamelCase, size=lowerCamelCase, resample=lowerCamelCase)
if do_center_crop:
_lowercase : Dict = self.center_crop(lowerCamelCase, size=lowerCamelCase)
if do_rescale:
_lowercase : Dict = self.rescale(image=lowerCamelCase, scale=lowerCamelCase, offset=lowerCamelCase)
if do_normalize:
_lowercase : Optional[Any] = self.normalize(image=lowerCamelCase, mean=lowerCamelCase, std=lowerCamelCase)
_lowercase : Tuple = to_channel_dimension_format(lowerCamelCase, lowerCamelCase)
return image
def UpperCamelCase ( self, lowerCamelCase, lowerCamelCase = None, lowerCamelCase = None, lowerCamelCase = None, lowerCamelCase = None, lowerCamelCase = None, lowerCamelCase = None, lowerCamelCase = None, lowerCamelCase = None, lowerCamelCase = None, lowerCamelCase = None, lowerCamelCase = None, lowerCamelCase = None, lowerCamelCase = ChannelDimension.FIRST, **lowerCamelCase, ) -> PIL.Image.Image:
"""simple docstring"""
_lowercase : Any = do_resize if do_resize is not None else self.do_resize
_lowercase : Optional[int] = resample if resample is not None else self.resample
_lowercase : Dict = do_center_crop if do_center_crop is not None else self.do_center_crop
_lowercase : Optional[int] = do_rescale if do_rescale is not None else self.do_rescale
_lowercase : Dict = rescale_factor if rescale_factor is not None else self.rescale_factor
_lowercase : Optional[int] = offset if offset is not None else self.offset
_lowercase : List[Any] = do_normalize if do_normalize is not None else self.do_normalize
_lowercase : Any = image_mean if image_mean is not None else self.image_mean
_lowercase : Union[str, Any] = image_std if image_std is not None else self.image_std
_lowercase : Union[str, Any] = size if size is not None else self.size
_lowercase : Any = get_size_dict(lowerCamelCase, default_to_square=lowerCamelCase)
_lowercase : List[str] = crop_size if crop_size is not None else self.crop_size
_lowercase : Any = get_size_dict(lowerCamelCase, param_name='crop_size')
if not valid_images(lowerCamelCase):
raise ValueError(
'Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '
'torch.Tensor, tf.Tensor or jax.ndarray.')
_lowercase : List[str] = make_batched(lowerCamelCase)
_lowercase : Any = [
[
self._preprocess_image(
image=lowerCamelCase, do_resize=lowerCamelCase, size=lowerCamelCase, resample=lowerCamelCase, do_center_crop=lowerCamelCase, crop_size=lowerCamelCase, do_rescale=lowerCamelCase, rescale_factor=lowerCamelCase, offset=lowerCamelCase, do_normalize=lowerCamelCase, image_mean=lowerCamelCase, image_std=lowerCamelCase, data_format=lowerCamelCase, )
for img in video
]
for video in videos
]
_lowercase : Dict = {'pixel_values': videos}
return BatchFeature(data=lowerCamelCase, tensor_type=lowerCamelCase)
| 21 |
'''simple docstring'''
import csv
import tweepy
# Twitter API credentials
__snake_case = ''''''
__snake_case = ''''''
__snake_case = ''''''
__snake_case = ''''''
def a ( __a ) -> None:
'''simple docstring'''
UpperCamelCase__ :List[Any] = tweepy.OAuthHandler(__a , __a )
auth.set_access_token(__a , __a )
UpperCamelCase__ :List[str] = tweepy.API(__a )
# initialize a list to hold all the tweepy Tweets
UpperCamelCase__ :Dict = []
# make initial request for most recent tweets (200 is the maximum allowed count)
UpperCamelCase__ :Tuple = api.user_timeline(screen_name=__a , count=200 )
# save most recent tweets
alltweets.extend(__a )
# save the id of the oldest tweet less one
UpperCamelCase__ :Union[str, Any] = alltweets[-1].id - 1
# keep grabbing tweets until there are no tweets left to grab
while len(__a ) > 0:
print(f'''getting tweets before {oldest}''' )
# all subsequent requests use the max_id param to prevent duplicates
UpperCamelCase__ :Union[str, Any] = api.user_timeline(
screen_name=__a , count=200 , max_id=__a )
# save most recent tweets
alltweets.extend(__a )
# update the id of the oldest tweet less one
UpperCamelCase__ :Tuple = alltweets[-1].id - 1
print(f'''...{len(__a )} tweets downloaded so far''' )
# transform the tweepy tweets into a 2D array that will populate the csv
UpperCamelCase__ :int = [[tweet.id_str, tweet.created_at, tweet.text] for tweet in alltweets]
# write the csv
with open(f'''new_{screen_name}_tweets.csv''' , '''w''' ) as f:
UpperCamelCase__ :Tuple = csv.writer(__a )
writer.writerow(['''id''', '''created_at''', '''text'''] )
writer.writerows(__a )
if __name__ == "__main__":
# pass in the username of the account you want to download
get_all_tweets('''FirePing32''') | 97 | 0 |
'''simple docstring'''
import json
import os
from typing import Optional
import numpy as np
from ...feature_extraction_utils import BatchFeature
from ...processing_utils import ProcessorMixin
from ...utils import logging
from ...utils.hub import get_file_from_repo
from ..auto import AutoTokenizer
__SCREAMING_SNAKE_CASE :str = logging.get_logger(__name__)
class A_ ( lowerCAmelCase_ ):
_lowerCamelCase : Optional[Any] = """AutoTokenizer"""
_lowerCamelCase : int = ["""tokenizer"""]
_lowerCamelCase : Dict = {
"""semantic_prompt""": 1,
"""coarse_prompt""": 2,
"""fine_prompt""": 2,
}
def __init__( self : List[str] , snake_case_ : int , snake_case_ : Optional[Any]=None ):
super().__init__(snake_case_ )
_UpperCAmelCase = speaker_embeddings
@classmethod
def lowercase ( cls : int , snake_case_ : Optional[int] , snake_case_ : List[Any]="speaker_embeddings_path.json" , **snake_case_ : Dict ):
if speaker_embeddings_dict_path is not None:
_UpperCAmelCase = get_file_from_repo(
snake_case_ , snake_case_ , subfolder=kwargs.pop("subfolder" , snake_case_ ) , cache_dir=kwargs.pop("cache_dir" , snake_case_ ) , force_download=kwargs.pop("force_download" , snake_case_ ) , proxies=kwargs.pop("proxies" , snake_case_ ) , resume_download=kwargs.pop("resume_download" , snake_case_ ) , local_files_only=kwargs.pop("local_files_only" , snake_case_ ) , use_auth_token=kwargs.pop("use_auth_token" , snake_case_ ) , revision=kwargs.pop("revision" , snake_case_ ) , )
if speaker_embeddings_path is None:
logger.warning(
f'`{os.path.join(snake_case_ , snake_case_ )}` does not exists\n , no preloaded speaker embeddings will be used - Make sure to provide a correct path to the json\n dictionnary if wanted, otherwise set `speaker_embeddings_dict_path=None`.' )
_UpperCAmelCase = None
else:
with open(snake_case_ ) as speaker_embeddings_json:
_UpperCAmelCase = json.load(snake_case_ )
else:
_UpperCAmelCase = None
_UpperCAmelCase = AutoTokenizer.from_pretrained(snake_case_ , **snake_case_ )
return cls(tokenizer=snake_case_ , speaker_embeddings=snake_case_ )
def lowercase ( self : Tuple , snake_case_ : List[Any] , snake_case_ : Dict="speaker_embeddings_path.json" , snake_case_ : List[str]="speaker_embeddings" , snake_case_ : bool = False , **snake_case_ : Any , ):
if self.speaker_embeddings is not None:
os.makedirs(os.path.join(snake_case_ , snake_case_ , "v2" ) , exist_ok=snake_case_ )
_UpperCAmelCase = {}
_UpperCAmelCase = save_directory
for prompt_key in self.speaker_embeddings:
if prompt_key != "repo_or_path":
_UpperCAmelCase = self._load_voice_preset(snake_case_ )
_UpperCAmelCase = {}
for key in self.speaker_embeddings[prompt_key]:
np.save(
os.path.join(
embeddings_dict["repo_or_path"] , snake_case_ , f'{prompt_key}_{key}' ) , voice_preset[key] , allow_pickle=snake_case_ , )
_UpperCAmelCase = os.path.join(snake_case_ , f'{prompt_key}_{key}.npy' )
_UpperCAmelCase = tmp_dict
with open(os.path.join(snake_case_ , snake_case_ ) , "w" ) as fp:
json.dump(snake_case_ , snake_case_ )
super().save_pretrained(snake_case_ , snake_case_ , **snake_case_ )
def lowercase ( self : str , snake_case_ : str = None , **snake_case_ : Tuple ):
_UpperCAmelCase = self.speaker_embeddings[voice_preset]
_UpperCAmelCase = {}
for key in ["semantic_prompt", "coarse_prompt", "fine_prompt"]:
if key not in voice_preset_paths:
raise ValueError(
f'Voice preset unrecognized, missing {key} as a key in self.speaker_embeddings[{voice_preset}].' )
_UpperCAmelCase = get_file_from_repo(
self.speaker_embeddings.get("repo_or_path" , "/" ) , voice_preset_paths[key] , subfolder=kwargs.pop("subfolder" , snake_case_ ) , cache_dir=kwargs.pop("cache_dir" , snake_case_ ) , force_download=kwargs.pop("force_download" , snake_case_ ) , proxies=kwargs.pop("proxies" , snake_case_ ) , resume_download=kwargs.pop("resume_download" , snake_case_ ) , local_files_only=kwargs.pop("local_files_only" , snake_case_ ) , use_auth_token=kwargs.pop("use_auth_token" , snake_case_ ) , revision=kwargs.pop("revision" , snake_case_ ) , )
if path is None:
raise ValueError(
f'`{os.path.join(self.speaker_embeddings.get("repo_or_path" , "/" ) , voice_preset_paths[key] )}` does not exists\n , no preloaded voice preset will be used - Make sure to provide correct paths to the {voice_preset}\n embeddings.' )
_UpperCAmelCase = np.load(snake_case_ )
return voice_preset_dict
def lowercase ( self : List[Any] , snake_case_ : Optional[dict] = None ):
for key in ["semantic_prompt", "coarse_prompt", "fine_prompt"]:
if key not in voice_preset:
raise ValueError(f'Voice preset unrecognized, missing {key} as a key.' )
if not isinstance(voice_preset[key] , np.ndarray ):
raise ValueError(f'{key} voice preset must be a {str(self.preset_shape[key] )}D ndarray.' )
if len(voice_preset[key].shape ) != self.preset_shape[key]:
raise ValueError(f'{key} voice preset must be a {str(self.preset_shape[key] )}D ndarray.' )
def __call__( self : List[Any] , snake_case_ : Tuple=None , snake_case_ : Any=None , snake_case_ : Any="pt" , snake_case_ : List[str]=2_5_6 , snake_case_ : str=False , snake_case_ : Dict=True , snake_case_ : str=False , **snake_case_ : Optional[int] , ):
if voice_preset is not None and not isinstance(snake_case_ , snake_case_ ):
if (
isinstance(snake_case_ , snake_case_ )
and self.speaker_embeddings is not None
and voice_preset in self.speaker_embeddings
):
_UpperCAmelCase = self._load_voice_preset(snake_case_ )
else:
if isinstance(snake_case_ , snake_case_ ) and not voice_preset.endswith(".npz" ):
_UpperCAmelCase = voice_preset + ".npz"
_UpperCAmelCase = np.load(snake_case_ )
if voice_preset is not None:
self._validate_voice_preset_dict(snake_case_ , **snake_case_ )
_UpperCAmelCase = BatchFeature(data=snake_case_ , tensor_type=snake_case_ )
_UpperCAmelCase = self.tokenizer(
snake_case_ , return_tensors=snake_case_ , padding="max_length" , max_length=snake_case_ , return_attention_mask=snake_case_ , return_token_type_ids=snake_case_ , add_special_tokens=snake_case_ , **snake_case_ , )
if voice_preset is not None:
_UpperCAmelCase = voice_preset
return encoded_text
| 22 |
'''simple docstring'''
import argparse
from collections import OrderedDict
from pathlib import Path
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from torchvision.transforms import functional as F
from transformers import DetrImageProcessor, TableTransformerConfig, TableTransformerForObjectDetection
from transformers.utils import logging
logging.set_verbosity_info()
__snake_case = logging.get_logger(__name__)
# here we list all keys to be renamed (original name on the left, our name on the right)
__snake_case = []
for i in range(6):
# 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 encoder + 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.encoder.norm.weight''', '''encoder.layernorm.weight'''),
('''transformer.encoder.norm.bias''', '''encoder.layernorm.bias'''),
('''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'''),
]
)
def a ( __a , __a , __a ) -> List[str]:
'''simple docstring'''
UpperCamelCase__ :List[Any] = state_dict.pop(__a )
UpperCamelCase__ :int = val
def a ( __a ) -> Any:
'''simple docstring'''
UpperCamelCase__ :Tuple = OrderedDict()
for key, value in state_dict.items():
if "backbone.0.body" in key:
UpperCamelCase__ :Dict = key.replace('''backbone.0.body''' , '''backbone.conv_encoder.model''' )
UpperCamelCase__ :List[str] = value
else:
UpperCamelCase__ :Dict = value
return new_state_dict
def a ( __a ) -> Optional[Any]:
'''simple docstring'''
UpperCamelCase__ :Optional[Any] = ''''''
# 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)
UpperCamelCase__ :Optional[Any] = state_dict.pop(f'''{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight''' )
UpperCamelCase__ :str = 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
UpperCamelCase__ :Any = in_proj_weight[:256, :]
UpperCamelCase__ :Tuple = in_proj_bias[:256]
UpperCamelCase__ :Optional[int] = in_proj_weight[256:512, :]
UpperCamelCase__ :Optional[Any] = in_proj_bias[256:512]
UpperCamelCase__ :Tuple = in_proj_weight[-256:, :]
UpperCamelCase__ :Dict = 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
UpperCamelCase__ :List[str] = state_dict.pop(f'''{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_weight''' )
UpperCamelCase__ :Optional[Any] = 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
UpperCamelCase__ :Any = in_proj_weight[:256, :]
UpperCamelCase__ :Optional[int] = in_proj_bias[:256]
UpperCamelCase__ :Tuple = in_proj_weight[256:512, :]
UpperCamelCase__ :Dict = in_proj_bias[256:512]
UpperCamelCase__ :Any = in_proj_weight[-256:, :]
UpperCamelCase__ :Dict = in_proj_bias[-256:]
# read in weights + bias of input projection layer of cross-attention
UpperCamelCase__ :List[str] = state_dict.pop(
f'''{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_weight''' )
UpperCamelCase__ :Any = 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
UpperCamelCase__ :Optional[Any] = in_proj_weight_cross_attn[:256, :]
UpperCamelCase__ :Any = in_proj_bias_cross_attn[:256]
UpperCamelCase__ :Any = in_proj_weight_cross_attn[256:512, :]
UpperCamelCase__ :Dict = in_proj_bias_cross_attn[256:512]
UpperCamelCase__ :str = in_proj_weight_cross_attn[-256:, :]
UpperCamelCase__ :Tuple = in_proj_bias_cross_attn[-256:]
def a ( __a , __a ) -> Optional[int]:
'''simple docstring'''
UpperCamelCase__ , UpperCamelCase__ :str = image.size
UpperCamelCase__ :Optional[Any] = max(__a , __a )
UpperCamelCase__ :List[Any] = 800 if '''detection''' in checkpoint_url else 1000
UpperCamelCase__ :Dict = target_max_size / current_max_size
UpperCamelCase__ :Any = image.resize((int(round(scale * width ) ), int(round(scale * height ) )) )
return resized_image
def a ( __a ) -> int:
'''simple docstring'''
UpperCamelCase__ :Any = F.to_tensor(__a )
UpperCamelCase__ :int = F.normalize(__a , mean=[0.4_8_5, 0.4_5_6, 0.4_0_6] , std=[0.2_2_9, 0.2_2_4, 0.2_2_5] )
return image
@torch.no_grad()
def a ( __a , __a , __a ) -> Dict:
'''simple docstring'''
logger.info('''Converting model...''' )
# load original state dict
UpperCamelCase__ :Optional[Any] = torch.hub.load_state_dict_from_url(__a , map_location='''cpu''' )
# rename keys
for src, dest in rename_keys:
rename_key(__a , __a , __a )
UpperCamelCase__ :Any = rename_backbone_keys(__a )
# query, key and value matrices need special treatment
read_in_q_k_v(__a )
# important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
UpperCamelCase__ :Dict = '''model.'''
for key in state_dict.copy().keys():
if not key.startswith('''class_labels_classifier''' ) and not key.startswith('''bbox_predictor''' ):
UpperCamelCase__ :Optional[Any] = state_dict.pop(__a )
UpperCamelCase__ :int = val
# create HuggingFace model and load state dict
UpperCamelCase__ :str = TableTransformerConfig(
backbone='''resnet18''' , mask_loss_coefficient=1 , dice_loss_coefficient=1 , ce_loss_coefficient=1 , bbox_loss_coefficient=5 , giou_loss_coefficient=2 , eos_coefficient=0.4 , class_cost=1 , bbox_cost=5 , giou_cost=2 , )
if "detection" in checkpoint_url:
UpperCamelCase__ :List[str] = 15
UpperCamelCase__ :int = 2
UpperCamelCase__ :Tuple = {0: '''table''', 1: '''table rotated'''}
UpperCamelCase__ :int = idalabel
UpperCamelCase__ :Dict = {v: k for k, v in idalabel.items()}
else:
UpperCamelCase__ :int = 125
UpperCamelCase__ :List[str] = 6
UpperCamelCase__ :Optional[Any] = {
0: '''table''',
1: '''table column''',
2: '''table row''',
3: '''table column header''',
4: '''table projected row header''',
5: '''table spanning cell''',
}
UpperCamelCase__ :Dict = idalabel
UpperCamelCase__ :Optional[Any] = {v: k for k, v in idalabel.items()}
UpperCamelCase__ :List[Any] = DetrImageProcessor(
format='''coco_detection''' , max_size=800 if '''detection''' in checkpoint_url else 1000 )
UpperCamelCase__ :int = TableTransformerForObjectDetection(__a )
model.load_state_dict(__a )
model.eval()
# verify our conversion
UpperCamelCase__ :Dict = '''example_pdf.png''' if '''detection''' in checkpoint_url else '''example_table.png'''
UpperCamelCase__ :Optional[Any] = hf_hub_download(repo_id='''nielsr/example-pdf''' , repo_type='''dataset''' , filename=__a )
UpperCamelCase__ :Tuple = Image.open(__a ).convert('''RGB''' )
UpperCamelCase__ :int = normalize(resize(__a , __a ) ).unsqueeze(0 )
UpperCamelCase__ :Optional[int] = model(__a )
if "detection" in checkpoint_url:
UpperCamelCase__ :Dict = (1, 15, 3)
UpperCamelCase__ :List[Any] = torch.tensor(
[[-6.7_8_9_7, -1_6.9_9_8_5, 6.7_9_3_7], [-8.0_1_8_6, -2_2.2_1_9_2, 6.9_6_7_7], [-7.3_1_1_7, -2_1.0_7_0_8, 7.4_0_5_5]] )
UpperCamelCase__ :Tuple = torch.tensor([[0.4_8_6_7, 0.1_7_6_7, 0.6_7_3_2], [0.6_7_1_8, 0.4_4_7_9, 0.3_8_3_0], [0.4_7_1_6, 0.1_7_6_0, 0.6_3_6_4]] )
else:
UpperCamelCase__ :Optional[Any] = (1, 125, 7)
UpperCamelCase__ :Dict = torch.tensor(
[[-1_8.1_4_3_0, -8.3_2_1_4, 4.8_2_7_4], [-1_8.4_6_8_5, -7.1_3_6_1, -4.2_6_6_7], [-2_6.3_6_9_3, -9.3_4_2_9, -4.9_9_6_2]] )
UpperCamelCase__ :List[Any] = torch.tensor([[0.4_9_8_3, 0.5_5_9_5, 0.9_4_4_0], [0.4_9_1_6, 0.6_3_1_5, 0.5_9_5_4], [0.6_1_0_8, 0.8_6_3_7, 0.1_1_3_5]] )
assert outputs.logits.shape == expected_shape
assert torch.allclose(outputs.logits[0, :3, :3] , __a , atol=1e-4 )
assert torch.allclose(outputs.pred_boxes[0, :3, :3] , __a , 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(__a ).mkdir(exist_ok=__a )
model.save_pretrained(__a )
image_processor.save_pretrained(__a )
if push_to_hub:
# Push model to HF hub
logger.info('''Pushing model to the hub...''' )
UpperCamelCase__ :Union[str, Any] = (
'''microsoft/table-transformer-detection'''
if '''detection''' in checkpoint_url
else '''microsoft/table-transformer-structure-recognition'''
)
model.push_to_hub(__a )
image_processor.push_to_hub(__a )
if __name__ == "__main__":
__snake_case = argparse.ArgumentParser()
parser.add_argument(
'''--checkpoint_url''',
default='''https://pubtables1m.blob.core.windows.net/model/pubtables1m_detection_detr_r18.pth''',
type=str,
choices=[
'''https://pubtables1m.blob.core.windows.net/model/pubtables1m_detection_detr_r18.pth''',
'''https://pubtables1m.blob.core.windows.net/model/pubtables1m_structure_detr_r18.pth''',
],
help='''URL of the Table Transformer checkpoint 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 or not to push the converted model to the 🤗 hub.'''
)
__snake_case = parser.parse_args()
convert_table_transformer_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub) | 97 | 0 |
'''simple docstring'''
import math
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import numpy as np
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin
@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->UnCLIP
class SCREAMING_SNAKE_CASE( A__ ):
"""simple docstring"""
lowerCamelCase__ = 42
lowerCamelCase__ = None
def snake_case_ ( _lowerCAmelCase : Optional[int] , _lowerCAmelCase : Any=0.9_9_9 , _lowerCAmelCase : Optional[Any]="cosine" , ) -> List[Any]:
if alpha_transform_type == "cosine":
def alpha_bar_fn(_lowerCAmelCase : List[Any] ):
return math.cos((t + 0.0_0_8) / 1.0_0_8 * math.pi / 2 ) ** 2
elif alpha_transform_type == "exp":
def alpha_bar_fn(_lowerCAmelCase : Any ):
return math.exp(t * -1_2.0 )
else:
raise ValueError(f"""Unsupported alpha_tranform_type: {alpha_transform_type}""" )
UpperCAmelCase : Any = []
for i in range(_lowerCAmelCase ):
UpperCAmelCase : Dict = i / num_diffusion_timesteps
UpperCAmelCase : List[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 SCREAMING_SNAKE_CASE( A__ , A__ ):
"""simple docstring"""
@register_to_config
def __init__( self : Union[str, Any] , __snake_case : int = 1000 , __snake_case : str = "fixed_small_log" , __snake_case : bool = True , __snake_case : Optional[float] = 1.0 , __snake_case : str = "epsilon" , __snake_case : str = "squaredcos_cap_v2" , ) -> str:
if beta_schedule != "squaredcos_cap_v2":
raise ValueError('''UnCLIPScheduler only supports `beta_schedule`: \'squaredcos_cap_v2\'''' )
UpperCAmelCase : Union[str, Any] = betas_for_alpha_bar(__snake_case )
UpperCAmelCase : List[Any] = 1.0 - self.betas
UpperCAmelCase : List[Any] = torch.cumprod(self.alphas , dim=0 )
UpperCAmelCase : List[Any] = torch.tensor(1.0 )
# standard deviation of the initial noise distribution
UpperCAmelCase : int = 1.0
# setable values
UpperCAmelCase : str = None
UpperCAmelCase : Union[str, Any] = torch.from_numpy(np.arange(0 , __snake_case )[::-1].copy() )
UpperCAmelCase : Optional[Any] = variance_type
def A ( self : Optional[Any] , __snake_case : torch.FloatTensor , __snake_case : Optional[int] = None ) -> torch.FloatTensor:
return sample
def A ( self : Dict , __snake_case : int , __snake_case : Union[str, torch.device] = None ) -> Optional[Any]:
UpperCAmelCase : List[str] = num_inference_steps
UpperCAmelCase : Optional[int] = (self.config.num_train_timesteps - 1) / (self.num_inference_steps - 1)
UpperCAmelCase : str = (np.arange(0 , __snake_case ) * step_ratio).round()[::-1].copy().astype(np.intaa )
UpperCAmelCase : Optional[int] = torch.from_numpy(__snake_case ).to(__snake_case )
def A ( self : Any , __snake_case : str , __snake_case : List[str]=None , __snake_case : str=None , __snake_case : List[str]=None ) -> int:
if prev_timestep is None:
UpperCAmelCase : Optional[int] = t - 1
UpperCAmelCase : Any = self.alphas_cumprod[t]
UpperCAmelCase : Any = self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.one
UpperCAmelCase : Any = 1 - alpha_prod_t
UpperCAmelCase : int = 1 - alpha_prod_t_prev
if prev_timestep == t - 1:
UpperCAmelCase : Optional[int] = self.betas[t]
else:
UpperCAmelCase : List[str] = 1 - alpha_prod_t / alpha_prod_t_prev
# For t > 0, compute predicted variance βt (see formula (6) and (7) from https://arxiv.org/pdf/2006.11239.pdf)
# and sample from it to get previous sample
# x_{t-1} ~ N(pred_prev_sample, variance) == add variance to pred_sample
UpperCAmelCase : int = beta_prod_t_prev / beta_prod_t * beta
if variance_type is None:
UpperCAmelCase : Any = self.config.variance_type
# hacks - were probably added for training stability
if variance_type == "fixed_small_log":
UpperCAmelCase : Optional[Any] = torch.log(torch.clamp(__snake_case , min=1E-20 ) )
UpperCAmelCase : int = torch.exp(0.5 * variance )
elif variance_type == "learned_range":
# NOTE difference with DDPM scheduler
UpperCAmelCase : Tuple = variance.log()
UpperCAmelCase : List[Any] = beta.log()
UpperCAmelCase : List[Any] = (predicted_variance + 1) / 2
UpperCAmelCase : List[Any] = frac * max_log + (1 - frac) * min_log
return variance
def A ( self : Union[str, Any] , __snake_case : torch.FloatTensor , __snake_case : int , __snake_case : torch.FloatTensor , __snake_case : Optional[int] = None , __snake_case : int=None , __snake_case : bool = True , ) -> Union[UnCLIPSchedulerOutput, Tuple]:
UpperCAmelCase : Optional[int] = timestep
if model_output.shape[1] == sample.shape[1] * 2 and self.variance_type == "learned_range":
UpperCAmelCase , UpperCAmelCase : Tuple = torch.split(__snake_case , sample.shape[1] , dim=1 )
else:
UpperCAmelCase : int = None
# 1. compute alphas, betas
if prev_timestep is None:
UpperCAmelCase : Optional[Any] = t - 1
UpperCAmelCase : str = self.alphas_cumprod[t]
UpperCAmelCase : Dict = self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.one
UpperCAmelCase : Union[str, Any] = 1 - alpha_prod_t
UpperCAmelCase : Dict = 1 - alpha_prod_t_prev
if prev_timestep == t - 1:
UpperCAmelCase : Tuple = self.betas[t]
UpperCAmelCase : Optional[Any] = self.alphas[t]
else:
UpperCAmelCase : List[str] = 1 - alpha_prod_t / alpha_prod_t_prev
UpperCAmelCase : Union[str, Any] = 1 - beta
# 2. compute predicted original sample from predicted noise also called
# "predicted x_0" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf
if self.config.prediction_type == "epsilon":
UpperCAmelCase : Dict = (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
elif self.config.prediction_type == "sample":
UpperCAmelCase : Union[str, Any] = model_output
else:
raise ValueError(
F"""prediction_type given as {self.config.prediction_type} must be one of `epsilon` or `sample`"""
''' for the UnCLIPScheduler.''' )
# 3. Clip "predicted x_0"
if self.config.clip_sample:
UpperCAmelCase : int = torch.clamp(
__snake_case , -self.config.clip_sample_range , self.config.clip_sample_range )
# 4. Compute coefficients for pred_original_sample x_0 and current sample x_t
# See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
UpperCAmelCase : Union[str, Any] = (alpha_prod_t_prev ** 0.5 * beta) / beta_prod_t
UpperCAmelCase : Optional[int] = alpha ** 0.5 * beta_prod_t_prev / beta_prod_t
# 5. Compute predicted previous sample µ_t
# See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
UpperCAmelCase : Union[str, Any] = pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample
# 6. Add noise
UpperCAmelCase : int = 0
if t > 0:
UpperCAmelCase : Union[str, Any] = randn_tensor(
model_output.shape , dtype=model_output.dtype , generator=__snake_case , device=model_output.device )
UpperCAmelCase : Optional[Any] = self._get_variance(
__snake_case , predicted_variance=__snake_case , prev_timestep=__snake_case , )
if self.variance_type == "fixed_small_log":
UpperCAmelCase : Tuple = variance
elif self.variance_type == "learned_range":
UpperCAmelCase : List[Any] = (0.5 * variance).exp()
else:
raise ValueError(
F"""variance_type given as {self.variance_type} must be one of `fixed_small_log` or `learned_range`"""
''' for the UnCLIPScheduler.''' )
UpperCAmelCase : Dict = variance * variance_noise
UpperCAmelCase : Tuple = pred_prev_sample + variance
if not return_dict:
return (pred_prev_sample,)
return UnCLIPSchedulerOutput(prev_sample=__snake_case , pred_original_sample=__snake_case )
def A ( self : Optional[Any] , __snake_case : torch.FloatTensor , __snake_case : torch.FloatTensor , __snake_case : torch.IntTensor , ) -> torch.FloatTensor:
# Make sure alphas_cumprod and timestep have same device and dtype as original_samples
UpperCAmelCase : Dict = self.alphas_cumprod.to(device=original_samples.device , dtype=original_samples.dtype )
UpperCAmelCase : Tuple = timesteps.to(original_samples.device )
UpperCAmelCase : int = alphas_cumprod[timesteps] ** 0.5
UpperCAmelCase : Optional[int] = sqrt_alpha_prod.flatten()
while len(sqrt_alpha_prod.shape ) < len(original_samples.shape ):
UpperCAmelCase : Optional[int] = sqrt_alpha_prod.unsqueeze(-1 )
UpperCAmelCase : Any = (1 - alphas_cumprod[timesteps]) ** 0.5
UpperCAmelCase : Any = sqrt_one_minus_alpha_prod.flatten()
while len(sqrt_one_minus_alpha_prod.shape ) < len(original_samples.shape ):
UpperCAmelCase : int = sqrt_one_minus_alpha_prod.unsqueeze(-1 )
UpperCAmelCase : Any = sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
return noisy_samples
| 23 |
'''simple docstring'''
from __future__ import annotations
from fractions import Fraction
from math import gcd, sqrt
def a ( __a ) -> bool:
'''simple docstring'''
UpperCamelCase__ :int = int(number**0.5 )
return number == sq * sq
def a ( __a , __a , __a , __a , __a , __a ) -> tuple[int, int]:
'''simple docstring'''
UpperCamelCase__ :int = x_num * y_den * z_den + y_num * x_den * z_den + z_num * x_den * y_den
UpperCamelCase__ :int = x_den * y_den * z_den
UpperCamelCase__ :int = gcd(__a , __a )
top //= hcf
bottom //= hcf
return top, bottom
def a ( __a = 35 ) -> int:
'''simple docstring'''
UpperCamelCase__ :set = set()
UpperCamelCase__ :int
UpperCamelCase__ :Fraction = Fraction(0 )
UpperCamelCase__ :tuple[int, int]
for x_num in range(1 , order + 1 ):
for x_den in range(x_num + 1 , order + 1 ):
for y_num in range(1 , order + 1 ):
for y_den in range(y_num + 1 , order + 1 ):
# n=1
UpperCamelCase__ :int = x_num * y_den + x_den * y_num
UpperCamelCase__ :Any = x_den * y_den
UpperCamelCase__ :Tuple = gcd(__a , __a )
z_num //= hcf
z_den //= hcf
if 0 < z_num < z_den <= order:
UpperCamelCase__ :Tuple = add_three(
__a , __a , __a , __a , __a , __a )
unique_s.add(__a )
# n=2
UpperCamelCase__ :List[str] = (
x_num * x_num * y_den * y_den + x_den * x_den * y_num * y_num
)
UpperCamelCase__ :Dict = x_den * x_den * y_den * y_den
if is_sq(__a ) and is_sq(__a ):
UpperCamelCase__ :Any = int(sqrt(__a ) )
UpperCamelCase__ :Optional[int] = int(sqrt(__a ) )
UpperCamelCase__ :int = gcd(__a , __a )
z_num //= hcf
z_den //= hcf
if 0 < z_num < z_den <= order:
UpperCamelCase__ :Tuple = add_three(
__a , __a , __a , __a , __a , __a )
unique_s.add(__a )
# n=-1
UpperCamelCase__ :Tuple = x_num * y_num
UpperCamelCase__ :Union[str, Any] = x_den * y_num + x_num * y_den
UpperCamelCase__ :List[str] = gcd(__a , __a )
z_num //= hcf
z_den //= hcf
if 0 < z_num < z_den <= order:
UpperCamelCase__ :Union[str, Any] = add_three(
__a , __a , __a , __a , __a , __a )
unique_s.add(__a )
# n=2
UpperCamelCase__ :Optional[Any] = x_num * x_num * y_num * y_num
UpperCamelCase__ :Tuple = (
x_den * x_den * y_num * y_num + x_num * x_num * y_den * y_den
)
if is_sq(__a ) and is_sq(__a ):
UpperCamelCase__ :str = int(sqrt(__a ) )
UpperCamelCase__ :Any = int(sqrt(__a ) )
UpperCamelCase__ :Dict = gcd(__a , __a )
z_num //= hcf
z_den //= hcf
if 0 < z_num < z_den <= order:
UpperCamelCase__ :int = add_three(
__a , __a , __a , __a , __a , __a )
unique_s.add(__a )
for num, den in unique_s:
total += Fraction(__a , __a )
return total.denominator + total.numerator
if __name__ == "__main__":
print(F"""{solution() = }""") | 97 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
snake_case_ = {
'configuration_x_clip': [
'XCLIP_PRETRAINED_CONFIG_ARCHIVE_MAP',
'XCLIPConfig',
'XCLIPTextConfig',
'XCLIPVisionConfig',
],
'processing_x_clip': ['XCLIPProcessor'],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
snake_case_ = [
'XCLIP_PRETRAINED_MODEL_ARCHIVE_LIST',
'XCLIPModel',
'XCLIPPreTrainedModel',
'XCLIPTextModel',
'XCLIPVisionModel',
]
if TYPE_CHECKING:
from .configuration_x_clip import (
XCLIP_PRETRAINED_CONFIG_ARCHIVE_MAP,
XCLIPConfig,
XCLIPTextConfig,
XCLIPVisionConfig,
)
from .processing_x_clip import XCLIPProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_x_clip import (
XCLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
XCLIPModel,
XCLIPPreTrainedModel,
XCLIPTextModel,
XCLIPVisionModel,
)
else:
import sys
snake_case_ = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 24 |
'''simple docstring'''
def a ( ) -> Union[str, Any]:
'''simple docstring'''
UpperCamelCase__ :Optional[int] = []
UpperCamelCase__ :int = 1
while len(__a ) < 1e6:
constant.append(str(__a ) )
i += 1
UpperCamelCase__ :Union[str, Any] = ''''''.join(__a )
return (
int(constant[0] )
* int(constant[9] )
* int(constant[99] )
* int(constant[999] )
* int(constant[9999] )
* int(constant[99999] )
* int(constant[999999] )
)
if __name__ == "__main__":
print(solution()) | 97 | 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 import EsmForMaskedLM, EsmForSequenceClassification, EsmForTokenClassification, EsmModel
from transformers.models.esm.modeling_esm import (
ESM_PRETRAINED_MODEL_ARCHIVE_LIST,
EsmEmbeddings,
create_position_ids_from_input_ids,
)
class lowerCAmelCase_ :
"""simple docstring"""
def __init__(self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__=13 , SCREAMING_SNAKE_CASE__=7 , SCREAMING_SNAKE_CASE__=False , SCREAMING_SNAKE_CASE__=True , SCREAMING_SNAKE_CASE__=False , SCREAMING_SNAKE_CASE__=True , SCREAMING_SNAKE_CASE__=33 , SCREAMING_SNAKE_CASE__=32 , SCREAMING_SNAKE_CASE__=5 , 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__=5_12 , SCREAMING_SNAKE_CASE__=16 , SCREAMING_SNAKE_CASE__=2 , SCREAMING_SNAKE_CASE__=0.02 , SCREAMING_SNAKE_CASE__=3 , SCREAMING_SNAKE_CASE__=4 , SCREAMING_SNAKE_CASE__=None , ) -> Optional[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Dict = parent
SCREAMING_SNAKE_CASE__ : str = batch_size
SCREAMING_SNAKE_CASE__ : Optional[int] = seq_length
SCREAMING_SNAKE_CASE__ : List[str] = is_training
SCREAMING_SNAKE_CASE__ : Dict = use_input_mask
SCREAMING_SNAKE_CASE__ : int = use_token_type_ids
SCREAMING_SNAKE_CASE__ : Dict = use_labels
SCREAMING_SNAKE_CASE__ : Tuple = vocab_size
SCREAMING_SNAKE_CASE__ : Dict = hidden_size
SCREAMING_SNAKE_CASE__ : Any = num_hidden_layers
SCREAMING_SNAKE_CASE__ : List[str] = num_attention_heads
SCREAMING_SNAKE_CASE__ : str = intermediate_size
SCREAMING_SNAKE_CASE__ : Optional[Any] = hidden_act
SCREAMING_SNAKE_CASE__ : Optional[int] = hidden_dropout_prob
SCREAMING_SNAKE_CASE__ : List[Any] = attention_probs_dropout_prob
SCREAMING_SNAKE_CASE__ : Optional[Any] = max_position_embeddings
SCREAMING_SNAKE_CASE__ : Dict = type_vocab_size
SCREAMING_SNAKE_CASE__ : Any = type_sequence_label_size
SCREAMING_SNAKE_CASE__ : Optional[Any] = initializer_range
SCREAMING_SNAKE_CASE__ : Union[str, Any] = num_labels
SCREAMING_SNAKE_CASE__ : List[str] = num_choices
SCREAMING_SNAKE_CASE__ : Tuple = scope
def __magic_name__ (self ) -> Union[str, Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : str = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
SCREAMING_SNAKE_CASE__ : Optional[int] = None
if self.use_input_mask:
SCREAMING_SNAKE_CASE__ : Union[str, Any] = random_attention_mask([self.batch_size, self.seq_length] )
SCREAMING_SNAKE_CASE__ : List[str] = None
SCREAMING_SNAKE_CASE__ : Optional[Any] = None
SCREAMING_SNAKE_CASE__ : List[Any] = None
if self.use_labels:
SCREAMING_SNAKE_CASE__ : Tuple = ids_tensor([self.batch_size] , self.type_sequence_label_size )
SCREAMING_SNAKE_CASE__ : Any = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
SCREAMING_SNAKE_CASE__ : Optional[int] = ids_tensor([self.batch_size] , self.num_choices )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = self.get_config()
return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels
def __magic_name__ (self ) -> List[str]:
"""simple docstring"""
return EsmConfig(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , pad_token_id=1 , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , initializer_range=self.initializer_range , )
def __magic_name__ (self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) -> List[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Dict = EsmModel(config=SCREAMING_SNAKE_CASE__ )
model.to(SCREAMING_SNAKE_CASE__ )
model.eval()
SCREAMING_SNAKE_CASE__ : Optional[Any] = model(SCREAMING_SNAKE_CASE__ , attention_mask=SCREAMING_SNAKE_CASE__ )
SCREAMING_SNAKE_CASE__ : List[Any] = model(SCREAMING_SNAKE_CASE__ )
SCREAMING_SNAKE_CASE__ : Optional[Any] = model(SCREAMING_SNAKE_CASE__ )
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 __magic_name__ (self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) -> Union[str, Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Union[str, Any] = EsmForMaskedLM(config=SCREAMING_SNAKE_CASE__ )
model.to(SCREAMING_SNAKE_CASE__ )
model.eval()
SCREAMING_SNAKE_CASE__ : List[Any] = model(SCREAMING_SNAKE_CASE__ , attention_mask=SCREAMING_SNAKE_CASE__ , labels=SCREAMING_SNAKE_CASE__ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def __magic_name__ (self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) -> Optional[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Any = self.num_labels
SCREAMING_SNAKE_CASE__ : Tuple = EsmForTokenClassification(config=SCREAMING_SNAKE_CASE__ )
model.to(SCREAMING_SNAKE_CASE__ )
model.eval()
SCREAMING_SNAKE_CASE__ : Optional[int] = model(SCREAMING_SNAKE_CASE__ , attention_mask=SCREAMING_SNAKE_CASE__ , labels=SCREAMING_SNAKE_CASE__ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def __magic_name__ (self ) -> List[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Dict = self.prepare_config_and_inputs()
(
(
SCREAMING_SNAKE_CASE__
) , (
SCREAMING_SNAKE_CASE__
) , (
SCREAMING_SNAKE_CASE__
) , (
SCREAMING_SNAKE_CASE__
) , (
SCREAMING_SNAKE_CASE__
) , (
SCREAMING_SNAKE_CASE__
) ,
) : Optional[int] = config_and_inputs
SCREAMING_SNAKE_CASE__ : List[str] = {"""input_ids""": input_ids, """attention_mask""": input_mask}
return config, inputs_dict
@require_torch
class lowerCAmelCase_ (a__ , a__ , unittest.TestCase ):
"""simple docstring"""
__UpperCamelCase : Optional[Any] = False
__UpperCamelCase : int = (
(
EsmForMaskedLM,
EsmModel,
EsmForSequenceClassification,
EsmForTokenClassification,
)
if is_torch_available()
else ()
)
__UpperCamelCase : Optional[int] = ()
__UpperCamelCase : List[Any] = (
{
'''feature-extraction''': EsmModel,
'''fill-mask''': EsmForMaskedLM,
'''text-classification''': EsmForSequenceClassification,
'''token-classification''': EsmForTokenClassification,
'''zero-shot''': EsmForSequenceClassification,
}
if is_torch_available()
else {}
)
__UpperCamelCase : Any = True
def __magic_name__ (self ) -> Dict:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Tuple = EsmModelTester(self )
SCREAMING_SNAKE_CASE__ : int = ConfigTester(self , config_class=SCREAMING_SNAKE_CASE__ , hidden_size=37 )
def __magic_name__ (self ) -> str:
"""simple docstring"""
self.config_tester.run_common_tests()
def __magic_name__ (self ) -> Union[str, Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Tuple = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*SCREAMING_SNAKE_CASE__ )
def __magic_name__ (self ) -> Tuple:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : int = self.model_tester.prepare_config_and_inputs()
for type in ["absolute", "relative_key", "relative_key_query"]:
SCREAMING_SNAKE_CASE__ : Optional[Any] = type
self.model_tester.create_and_check_model(*SCREAMING_SNAKE_CASE__ )
def __magic_name__ (self ) -> Tuple:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : int = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_lm(*SCREAMING_SNAKE_CASE__ )
def __magic_name__ (self ) -> Optional[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : List[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_token_classification(*SCREAMING_SNAKE_CASE__ )
@slow
def __magic_name__ (self ) -> List[Any]:
"""simple docstring"""
for model_name in ESM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
SCREAMING_SNAKE_CASE__ : Tuple = EsmModel.from_pretrained(SCREAMING_SNAKE_CASE__ )
self.assertIsNotNone(SCREAMING_SNAKE_CASE__ )
def __magic_name__ (self ) -> Tuple:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Tuple = self.model_tester.prepare_config_and_inputs()[0]
SCREAMING_SNAKE_CASE__ : Dict = EsmEmbeddings(config=SCREAMING_SNAKE_CASE__ )
SCREAMING_SNAKE_CASE__ : List[Any] = torch.as_tensor([[12, 31, 13, model.padding_idx]] )
SCREAMING_SNAKE_CASE__ : List[Any] = torch.as_tensor(
[
[
0 + model.padding_idx + 1,
1 + model.padding_idx + 1,
2 + model.padding_idx + 1,
model.padding_idx,
]
] )
SCREAMING_SNAKE_CASE__ : List[Any] = create_position_ids_from_input_ids(SCREAMING_SNAKE_CASE__ , model.padding_idx )
self.assertEqual(position_ids.shape , expected_positions.shape )
self.assertTrue(torch.all(torch.eq(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) ) )
def __magic_name__ (self ) -> List[str]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : int = self.model_tester.prepare_config_and_inputs()[0]
SCREAMING_SNAKE_CASE__ : Any = EsmEmbeddings(config=SCREAMING_SNAKE_CASE__ )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = torch.empty(2 , 4 , 30 )
SCREAMING_SNAKE_CASE__ : Optional[Any] = [
0 + embeddings.padding_idx + 1,
1 + embeddings.padding_idx + 1,
2 + embeddings.padding_idx + 1,
3 + embeddings.padding_idx + 1,
]
SCREAMING_SNAKE_CASE__ : Optional[int] = torch.as_tensor([expected_single_positions, expected_single_positions] )
SCREAMING_SNAKE_CASE__ : Optional[Any] = embeddings.create_position_ids_from_inputs_embeds(SCREAMING_SNAKE_CASE__ )
self.assertEqual(position_ids.shape , expected_positions.shape )
self.assertTrue(torch.all(torch.eq(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) ) )
@unittest.skip("""Esm does not support embedding resizing""" )
def __magic_name__ (self ) -> List[Any]:
"""simple docstring"""
pass
@unittest.skip("""Esm does not support embedding resizing""" )
def __magic_name__ (self ) -> Optional[int]:
"""simple docstring"""
pass
@unittest.skip("""Will be fixed soon by reducing the size of the model used for common tests.""" )
def __magic_name__ (self ) -> Tuple:
"""simple docstring"""
pass
@require_torch
class lowerCAmelCase_ (a__ ):
"""simple docstring"""
@slow
def __magic_name__ (self ) -> Any:
"""simple docstring"""
with torch.no_grad():
SCREAMING_SNAKE_CASE__ : Optional[int] = EsmForMaskedLM.from_pretrained("""facebook/esm2_t6_8M_UR50D""" )
model.eval()
SCREAMING_SNAKE_CASE__ : Optional[int] = torch.tensor([[0, 1, 2, 3, 4, 5]] )
SCREAMING_SNAKE_CASE__ : Optional[int] = model(SCREAMING_SNAKE_CASE__ )[0]
SCREAMING_SNAKE_CASE__ : Tuple = 33
SCREAMING_SNAKE_CASE__ : List[str] = torch.Size((1, 6, vocab_size) )
self.assertEqual(output.shape , SCREAMING_SNAKE_CASE__ )
SCREAMING_SNAKE_CASE__ : Dict = torch.tensor(
[[[8.9215, -10.5898, -6.4671], [-6.3967, -13.9114, -1.1212], [-7.7812, -13.9516, -3.7406]]] )
self.assertTrue(torch.allclose(output[:, :3, :3] , SCREAMING_SNAKE_CASE__ , atol=1E-4 ) )
@slow
def __magic_name__ (self ) -> List[str]:
"""simple docstring"""
with torch.no_grad():
SCREAMING_SNAKE_CASE__ : int = EsmModel.from_pretrained("""facebook/esm2_t6_8M_UR50D""" )
model.eval()
SCREAMING_SNAKE_CASE__ : Union[str, Any] = torch.tensor([[0, 6, 4, 13, 5, 4, 16, 12, 11, 7, 2]] )
SCREAMING_SNAKE_CASE__ : Tuple = model(SCREAMING_SNAKE_CASE__ )[0]
# compare the actual values for a slice.
SCREAMING_SNAKE_CASE__ : Optional[Any] = torch.tensor(
[[[0.1444, 0.5413, 0.3248], [0.3034, 0.0053, 0.3108], [0.3228, -0.2499, 0.3415]]] )
self.assertTrue(torch.allclose(output[:, :3, :3] , SCREAMING_SNAKE_CASE__ , atol=1E-4 ) )
| 25 |
'''simple docstring'''
from PIL import Image
def a ( __a , __a ) -> Image:
'''simple docstring'''
def brightness(__a ) -> float:
return 128 + level + (c - 128)
if not -2_5_5.0 <= level <= 2_5_5.0:
raise ValueError('''level must be between -255.0 (black) and 255.0 (white)''' )
return img.point(__a )
if __name__ == "__main__":
# Load image
with Image.open('''image_data/lena.jpg''') as img:
# Change brightness to 100
__snake_case = change_brightness(img, 100)
brigt_img.save('''image_data/lena_brightness.png''', format='''png''') | 97 | 0 |
# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import argparse
import os
from accelerate.test_utils import execute_subprocess_async
def lowerCAmelCase_ ( snake_case_=None ):
if subparsers is not None:
_A : List[str] = subparsers.add_parser("""test""" )
else:
_A : str = argparse.ArgumentParser("""Accelerate test command""" )
parser.add_argument(
"""--config_file""",default=snake_case_,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=snake_case_ )
return parser
def lowerCAmelCase_ ( snake_case_ ):
_A : str = os.path.sep.join(__file__.split(os.path.sep )[:-2] + ["""test_utils""", """scripts""", """test_script.py"""] )
if args.config_file is None:
_A : Tuple = script_name
else:
_A : Tuple = f'''--config_file={args.config_file} {script_name}'''
_A : int = ["""accelerate-launch"""] + test_args.split()
_A : str = execute_subprocess_async(snake_case_,env=os.environ.copy() )
if result.returncode == 0:
print("""Test is a success! You are ready for your distributed training!""" )
def lowerCAmelCase_ ( ):
_A : List[Any] = test_command_parser()
_A : Any = parser.parse_args()
test_command(snake_case_ )
if __name__ == "__main__":
main()
| 26 |
'''simple docstring'''
from datetime import datetime as dt
import os
from github import Github
__snake_case = [
'''good first issue''',
'''good second issue''',
'''good difficult issue''',
'''feature request''',
'''new model''',
'''wip''',
]
def a ( ) -> List[str]:
'''simple docstring'''
UpperCamelCase__ :List[str] = Github(os.environ['''GITHUB_TOKEN'''] )
UpperCamelCase__ :Tuple = g.get_repo('''huggingface/transformers''' )
UpperCamelCase__ :Union[str, Any] = repo.get_issues(state='''open''' )
for issue in open_issues:
UpperCamelCase__ :List[Any] = sorted([comment for comment in issue.get_comments()] , key=lambda __a : i.created_at , reverse=__a )
UpperCamelCase__ :List[Any] = comments[0] if len(__a ) > 0 else None
if (
last_comment is not None
and last_comment.user.login == "github-actions[bot]"
and (dt.utcnow() - issue.updated_at).days > 7
and (dt.utcnow() - issue.created_at).days >= 30
and not any(label.name.lower() in LABELS_TO_EXEMPT for label in issue.get_labels() )
):
# print(f"Would close issue {issue.number} since it has been 7 days of inactivity since bot mention.")
issue.edit(state='''closed''' )
elif (
(dt.utcnow() - issue.updated_at).days > 23
and (dt.utcnow() - issue.created_at).days >= 30
and not any(label.name.lower() in LABELS_TO_EXEMPT for label in issue.get_labels() )
):
# print(f"Would add stale comment to {issue.number}")
issue.create_comment(
'''This issue has been automatically marked as stale because it has not had '''
'''recent activity. If you think this still needs to be addressed '''
'''please comment on this thread.\n\nPlease note that issues that do not follow the '''
'''[contributing guidelines](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md) '''
'''are likely to be ignored.''' )
if __name__ == "__main__":
main() | 97 | 0 |
'''simple docstring'''
import json
import os
import shutil
import sys
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path
from huggingface_hub import HfFolder, delete_repo
from requests.exceptions import HTTPError
from transformers import AutoConfig, BertConfig, GPTaConfig
from transformers.configuration_utils import PretrainedConfig
from transformers.testing_utils import TOKEN, USER, is_staging_test
sys.path.append(str(Path(__file__).parent.parent / 'utils'))
from test_module.custom_configuration import CustomConfig # noqa E402
__lowercase : Dict = {
'return_dict': False,
'output_hidden_states': True,
'output_attentions': True,
'torchscript': True,
'torch_dtype': 'float16',
'use_bfloat16': True,
'tf_legacy_loss': True,
'pruned_heads': {'a': 1},
'tie_word_embeddings': False,
'is_decoder': True,
'cross_attention_hidden_size': 1_28,
'add_cross_attention': True,
'tie_encoder_decoder': True,
'max_length': 50,
'min_length': 3,
'do_sample': True,
'early_stopping': True,
'num_beams': 3,
'num_beam_groups': 3,
'diversity_penalty': 0.5,
'temperature': 2.0,
'top_k': 10,
'top_p': 0.7,
'typical_p': 0.2,
'repetition_penalty': 0.8,
'length_penalty': 0.8,
'no_repeat_ngram_size': 5,
'encoder_no_repeat_ngram_size': 5,
'bad_words_ids': [1, 2, 3],
'num_return_sequences': 3,
'chunk_size_feed_forward': 5,
'output_scores': True,
'return_dict_in_generate': True,
'forced_bos_token_id': 2,
'forced_eos_token_id': 3,
'remove_invalid_values': True,
'architectures': ['BertModel'],
'finetuning_task': 'translation',
'id2label': {0: 'label'},
'label2id': {'label': '0'},
'tokenizer_class': 'BertTokenizerFast',
'prefix': 'prefix',
'bos_token_id': 6,
'pad_token_id': 7,
'eos_token_id': 8,
'sep_token_id': 9,
'decoder_start_token_id': 10,
'exponential_decay_length_penalty': (5, 1.01),
'suppress_tokens': [0, 1],
'begin_suppress_tokens': 2,
'task_specific_params': {'translation': 'some_params'},
'problem_type': 'regression',
}
@is_staging_test
class __UpperCamelCase ( unittest.TestCase ):
@classmethod
def __UpperCAmelCase ( cls ):
'''simple docstring'''
__a : int = TOKEN
HfFolder.save_token(__a )
@classmethod
def __UpperCAmelCase ( cls ):
'''simple docstring'''
try:
delete_repo(token=cls._token , repo_id='test-config' )
except HTTPError:
pass
try:
delete_repo(token=cls._token , repo_id='valid_org/test-config-org' )
except HTTPError:
pass
try:
delete_repo(token=cls._token , repo_id='test-dynamic-config' )
except HTTPError:
pass
def __UpperCAmelCase ( self ):
'''simple docstring'''
__a : int = BertConfig(
vocab_size=99 , hidden_size=32 , num_hidden_layers=5 , num_attention_heads=4 , intermediate_size=37 )
config.push_to_hub('test-config' , use_auth_token=self._token )
__a : Dict = BertConfig.from_pretrained(f"""{USER}/test-config""" )
for k, v in config.to_dict().items():
if k != "transformers_version":
self.assertEqual(__a , getattr(__a , __a ) )
# Reset repo
delete_repo(token=self._token , repo_id='test-config' )
# Push to hub via save_pretrained
with tempfile.TemporaryDirectory() as tmp_dir:
config.save_pretrained(__a , repo_id='test-config' , push_to_hub=__a , use_auth_token=self._token )
__a : List[str] = BertConfig.from_pretrained(f"""{USER}/test-config""" )
for k, v in config.to_dict().items():
if k != "transformers_version":
self.assertEqual(__a , getattr(__a , __a ) )
def __UpperCAmelCase ( self ):
'''simple docstring'''
__a : Any = BertConfig(
vocab_size=99 , hidden_size=32 , num_hidden_layers=5 , num_attention_heads=4 , intermediate_size=37 )
config.push_to_hub('valid_org/test-config-org' , use_auth_token=self._token )
__a : List[Any] = BertConfig.from_pretrained('valid_org/test-config-org' )
for k, v in config.to_dict().items():
if k != "transformers_version":
self.assertEqual(__a , getattr(__a , __a ) )
# Reset repo
delete_repo(token=self._token , repo_id='valid_org/test-config-org' )
# Push to hub via save_pretrained
with tempfile.TemporaryDirectory() as tmp_dir:
config.save_pretrained(
__a , repo_id='valid_org/test-config-org' , push_to_hub=__a , use_auth_token=self._token )
__a : List[Any] = BertConfig.from_pretrained('valid_org/test-config-org' )
for k, v in config.to_dict().items():
if k != "transformers_version":
self.assertEqual(__a , getattr(__a , __a ) )
def __UpperCAmelCase ( self ):
'''simple docstring'''
CustomConfig.register_for_auto_class()
__a : str = CustomConfig(attribute=42 )
config.push_to_hub('test-dynamic-config' , use_auth_token=self._token )
# This has added the proper auto_map field to the config
self.assertDictEqual(config.auto_map , {'AutoConfig': 'custom_configuration.CustomConfig'} )
__a : Tuple = AutoConfig.from_pretrained(f"""{USER}/test-dynamic-config""" , trust_remote_code=__a )
# Can't make an isinstance check because the new_config is from the FakeConfig class of a dynamic module
self.assertEqual(new_config.__class__.__name__ , 'CustomConfig' )
self.assertEqual(new_config.attribute , 42 )
class __UpperCamelCase ( unittest.TestCase ):
def __UpperCAmelCase ( self ):
'''simple docstring'''
__a : Dict = GPTaConfig()
# attempt to modify each of int/float/bool/str config records and verify they were updated
__a : Dict = c.n_embd + 1 # int
__a : List[str] = c.resid_pdrop + 1.0 # float
__a : Optional[int] = not c.scale_attn_weights # bool
__a : Union[str, Any] = c.summary_type + 'foo' # str
c.update_from_string(
f"""n_embd={n_embd},resid_pdrop={resid_pdrop},scale_attn_weights={scale_attn_weights},summary_type={summary_type}""" )
self.assertEqual(__a , c.n_embd , 'mismatch for key: n_embd' )
self.assertEqual(__a , c.resid_pdrop , 'mismatch for key: resid_pdrop' )
self.assertEqual(__a , c.scale_attn_weights , 'mismatch for key: scale_attn_weights' )
self.assertEqual(__a , c.summary_type , 'mismatch for key: summary_type' )
def __UpperCAmelCase ( self ):
'''simple docstring'''
__a : List[str] = PretrainedConfig()
__a : Dict = [key for key in base_config.__dict__ if key not in config_common_kwargs]
# If this part of the test fails, you have arguments to addin config_common_kwargs above.
self.assertListEqual(
__a , ['is_encoder_decoder', '_name_or_path', '_commit_hash', 'transformers_version'] )
__a : int = [key for key, value in config_common_kwargs.items() if value == getattr(__a , __a )]
if len(__a ) > 0:
raise ValueError(
'The following keys are set with the default values in'
' `test_configuration_common.config_common_kwargs` pick another value for them:'
f""" {", ".join(__a )}.""" )
def __UpperCAmelCase ( self ):
'''simple docstring'''
with self.assertRaises(__a ):
# config is in subfolder, the following should not work without specifying the subfolder
__a : List[Any] = BertConfig.from_pretrained('hf-internal-testing/tiny-random-bert-subfolder' )
__a : Optional[int] = BertConfig.from_pretrained('hf-internal-testing/tiny-random-bert-subfolder' , subfolder='bert' )
self.assertIsNotNone(__a )
def __UpperCAmelCase ( self ):
'''simple docstring'''
__a : List[str] = mock.Mock()
__a : Dict = 500
__a : Any = {}
__a : int = HTTPError
__a : Tuple = {}
# Download this model to make sure it's in the cache.
__a : Union[str, Any] = BertConfig.from_pretrained('hf-internal-testing/tiny-random-bert' )
# Under the mock environment we get a 500 error when trying to reach the model.
with mock.patch('requests.Session.request' , return_value=__a ) as mock_head:
__a : Any = BertConfig.from_pretrained('hf-internal-testing/tiny-random-bert' )
# This check we did call the fake head request
mock_head.assert_called()
def __UpperCAmelCase ( self ):
'''simple docstring'''
__a : Union[str, Any] = BertConfig.from_pretrained(
'https://huggingface.co/hf-internal-testing/tiny-random-bert/resolve/main/config.json' )
def __UpperCAmelCase ( self ):
'''simple docstring'''
__a : str = AutoConfig.from_pretrained('bert-base-cased' )
__a : Optional[Any] = ['config.4.0.0.json']
with tempfile.TemporaryDirectory() as tmp_dir:
configuration.save_pretrained(__a )
__a : List[str] = 2
json.dump(configuration.to_dict() , open(os.path.join(__a , 'config.4.0.0.json' ) , 'w' ) )
# This should pick the new configuration file as the version of Transformers is > 4.0.0
__a : Optional[int] = AutoConfig.from_pretrained(__a )
self.assertEqual(new_configuration.hidden_size , 2 )
# Will need to be adjusted if we reach v42 and this test is still here.
# Should pick the old configuration file as the version of Transformers is < 4.42.0
__a : str = ['config.42.0.0.json']
__a : List[Any] = 768
configuration.save_pretrained(__a )
shutil.move(os.path.join(__a , 'config.4.0.0.json' ) , os.path.join(__a , 'config.42.0.0.json' ) )
__a : List[str] = AutoConfig.from_pretrained(__a )
self.assertEqual(new_configuration.hidden_size , 768 )
def __UpperCAmelCase ( self ):
'''simple docstring'''
__a : int = 'hf-internal-testing/test-two-configs'
import transformers as new_transformers
__a : Optional[Any] = 'v4.0.0'
__a , __a : Any = new_transformers.models.auto.AutoConfig.from_pretrained(
__a , return_unused_kwargs=__a )
self.assertEqual(new_configuration.hidden_size , 2 )
# This checks `_configuration_file` ia not kept in the kwargs by mistake.
self.assertDictEqual(__a , {} )
# Testing an older version by monkey-patching the version in the module it's used.
import transformers as old_transformers
__a : Dict = 'v3.0.0'
__a : str = old_transformers.models.auto.AutoConfig.from_pretrained(__a )
self.assertEqual(old_configuration.hidden_size , 768 )
| 27 |
'''simple docstring'''
import re
from filelock import FileLock
try:
import nltk
__snake_case = True
except (ImportError, ModuleNotFoundError):
__snake_case = False
if NLTK_AVAILABLE:
with FileLock('''.lock''') as lock:
nltk.download('''punkt''', quiet=True)
def a ( __a ) -> str:
'''simple docstring'''
re.sub('''<n>''' , '''''' , __a ) # 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(__a ) ) | 97 | 0 |
'''simple docstring'''
import math
import qiskit
def __lowerCamelCase ( A__ = 1 , A__ = 1 , A__ = 1 ) -> qiskit.result.counts.Counts:
"""simple docstring"""
if (
isinstance(A__ , A__ )
or isinstance(A__ , A__ )
or isinstance(A__ , A__ )
):
raise TypeError('inputs must be integers.' )
if (input_a < 0) or (input_a < 0) or (carry_in < 0):
raise ValueError('inputs must be positive.' )
if (
(math.floor(A__ ) != input_a)
or (math.floor(A__ ) != input_a)
or (math.floor(A__ ) != carry_in)
):
raise ValueError('inputs must be exact integers.' )
if (input_a > 2) or (input_a > 2) or (carry_in > 2):
raise ValueError('inputs must be less or equal to 2.' )
# build registers
UpperCamelCase = qiskit.QuantumRegister(4 , 'qr' )
UpperCamelCase = qiskit.ClassicalRegister(2 , 'cr' )
# list the entries
UpperCamelCase = [input_a, input_a, carry_in]
UpperCamelCase = qiskit.QuantumCircuit(A__ , A__ )
for i in range(0 , 3 ):
if entry[i] == 2:
quantum_circuit.h(A__ ) # for hadamard entries
elif entry[i] == 1:
quantum_circuit.x(A__ ) # for 1 entries
elif entry[i] == 0:
quantum_circuit.i(A__ ) # for 0 entries
# build the circuit
quantum_circuit.ccx(0 , 1 , 3 ) # ccx = toffoli gate
quantum_circuit.cx(0 , 1 )
quantum_circuit.ccx(1 , 2 , 3 )
quantum_circuit.cx(1 , 2 )
quantum_circuit.cx(0 , 1 )
quantum_circuit.measure([2, 3] , A__ ) # measure the last two qbits
UpperCamelCase = qiskit.Aer.get_backend('aer_simulator' )
UpperCamelCase = qiskit.execute(A__ , A__ , shots=1_000 )
return job.result().get_counts(A__ )
if __name__ == "__main__":
print(f'''Total sum count for state is: {quantum_full_adder(1, 1, 1)}''')
| 28 |
'''simple docstring'''
from pathlib import Path
import fire
from tqdm import tqdm
def a ( __a="ro" , __a="en" , __a="wmt16" , __a=None ) -> None:
'''simple docstring'''
try:
import datasets
except (ModuleNotFoundError, ImportError):
raise ImportError('''run pip install datasets''' )
UpperCamelCase__ :int = f'''{src_lang}-{tgt_lang}'''
print(f'''Converting {dataset}-{pair}''' )
UpperCamelCase__ :Tuple = datasets.load_dataset(__a , __a )
if save_dir is None:
UpperCamelCase__ :Any = f'''{dataset}-{pair}'''
UpperCamelCase__ :Dict = Path(__a )
save_dir.mkdir(exist_ok=__a )
for split in ds.keys():
print(f'''Splitting {split} with {ds[split].num_rows} records''' )
# to save to val.source, val.target like summary datasets
UpperCamelCase__ :Dict = '''val''' if split == '''validation''' else split
UpperCamelCase__ :List[Any] = save_dir.joinpath(f'''{fn}.source''' )
UpperCamelCase__ :int = save_dir.joinpath(f'''{fn}.target''' )
UpperCamelCase__ :Union[str, Any] = src_path.open('''w+''' )
UpperCamelCase__ :Tuple = tgt_path.open('''w+''' )
# reader is the bottleneck so writing one record at a time doesn't slow things down
for x in tqdm(ds[split] ):
UpperCamelCase__ :Union[str, Any] = x['''translation''']
src_fp.write(ex[src_lang] + '''\n''' )
tgt_fp.write(ex[tgt_lang] + '''\n''' )
print(f'''Saved {dataset} dataset to {save_dir}''' )
if __name__ == "__main__":
fire.Fire(download_wmt_dataset) | 97 | 0 |
from math import sqrt
def lowercase__ ( __snake_case : int = 1_000_000 ):
'''simple docstring'''
UpperCAmelCase_ : int = 0
UpperCAmelCase_ : int = 0
UpperCAmelCase_ : int
while num_cuboids <= limit:
max_cuboid_size += 1
for sum_shortest_sides in range(2 , 2 * max_cuboid_size + 1 ):
if sqrt(sum_shortest_sides**2 + max_cuboid_size**2 ).is_integer():
num_cuboids += (
min(__snake_case , sum_shortest_sides // 2 )
- max(1 , sum_shortest_sides - max_cuboid_size )
+ 1
)
return max_cuboid_size
if __name__ == "__main__":
print(F'{solution() = }')
| 29 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...file_utils import _LazyModule, is_tokenizers_available, is_torch_available, is_vision_available
from ...utils import OptionalDependencyNotAvailable
__snake_case = {'''configuration_dpt''': ['''DPT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''DPTConfig''']}
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__snake_case = ['''DPTFeatureExtractor''']
__snake_case = ['''DPTImageProcessor''']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__snake_case = [
'''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
__snake_case = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__) | 97 | 0 |
import mpmath # for roots of unity
import numpy as np
class lowercase__:
"""simple docstring"""
def __init__( self : Dict , SCREAMING_SNAKE_CASE_ : str=None , SCREAMING_SNAKE_CASE_ : Tuple=None ) -> List[Any]:
# Input as list
lowercase_ = list(poly_a or [0] )[:]
lowercase_ = list(poly_b or [0] )[:]
# Remove leading zero coefficients
while self.polyA[-1] == 0:
self.polyA.pop()
lowercase_ = len(self.polyA )
while self.polyB[-1] == 0:
self.polyB.pop()
lowercase_ = len(self.polyB )
# Add 0 to make lengths equal a power of 2
lowercase_ = int(
2 ** np.ceil(np.loga(len(self.polyA ) + len(self.polyB ) - 1 ) ) )
while len(self.polyA ) < self.c_max_length:
self.polyA.append(0 )
while len(self.polyB ) < self.c_max_length:
self.polyB.append(0 )
# A complex root used for the fourier transform
lowercase_ = complex(mpmath.root(x=1 , n=self.c_max_length , k=1 ) )
# The product
lowercase_ = self.__multiply()
def _lowercase ( self : Optional[int] , SCREAMING_SNAKE_CASE_ : str ) -> List[Any]:
lowercase_ = [[x] for x in self.polyA] if which == '''A''' else [[x] for x in self.polyB]
# Corner case
if len(SCREAMING_SNAKE_CASE_ ) <= 1:
return dft[0]
#
lowercase_ = self.c_max_length // 2
while next_ncol > 0:
lowercase_ = [[] for i in range(SCREAMING_SNAKE_CASE_ )]
lowercase_ = self.root**next_ncol
# First half of next step
lowercase_ = 1
for j in range(self.c_max_length // (next_ncol * 2) ):
for i in range(SCREAMING_SNAKE_CASE_ ):
new_dft[i].append(dft[i][j] + current_root * dft[i + next_ncol][j] )
current_root *= root
# Second half of next step
lowercase_ = 1
for j in range(self.c_max_length // (next_ncol * 2) ):
for i in range(SCREAMING_SNAKE_CASE_ ):
new_dft[i].append(dft[i][j] - current_root * dft[i + next_ncol][j] )
current_root *= root
# Update
lowercase_ = new_dft
lowercase_ = next_ncol // 2
return dft[0]
def _lowercase ( self : int ) -> Dict:
lowercase_ = self.__dft('''A''' )
lowercase_ = self.__dft('''B''' )
lowercase_ = [[dft_a[i] * dft_b[i] for i in range(self.c_max_length )]]
del dft_a
del dft_b
# Corner Case
if len(inverce_c[0] ) <= 1:
return inverce_c[0]
# Inverse DFT
lowercase_ = 2
while next_ncol <= self.c_max_length:
lowercase_ = [[] for i in range(SCREAMING_SNAKE_CASE_ )]
lowercase_ = self.root ** (next_ncol // 2)
lowercase_ = 1
# First half of next step
for j in range(self.c_max_length // next_ncol ):
for i in range(next_ncol // 2 ):
# Even positions
new_inverse_c[i].append(
(
inverce_c[i][j]
+ inverce_c[i][j + self.c_max_length // next_ncol]
)
/ 2 )
# Odd positions
new_inverse_c[i + next_ncol // 2].append(
(
inverce_c[i][j]
- inverce_c[i][j + self.c_max_length // next_ncol]
)
/ (2 * current_root) )
current_root *= root
# Update
lowercase_ = new_inverse_c
next_ncol *= 2
# Unpack
lowercase_ = [round(x[0].real , 8 ) + round(x[0].imag , 8 ) * 1J for x in inverce_c]
# Remove leading 0's
while inverce_c[-1] == 0:
inverce_c.pop()
return inverce_c
def __str__( self : str ) -> Optional[int]:
lowercase_ = '''A = ''' + ''' + '''.join(
f'''{coef}*x^{i}''' for coef, i in enumerate(self.polyA[: self.len_A] ) )
lowercase_ = '''B = ''' + ''' + '''.join(
f'''{coef}*x^{i}''' for coef, i in enumerate(self.polyB[: self.len_B] ) )
lowercase_ = '''A*B = ''' + ''' + '''.join(
f'''{coef}*x^{i}''' for coef, i in enumerate(self.product ) )
return f'''{a}\n{b}\n{c}'''
# Unit tests
if __name__ == "__main__":
import doctest
doctest.testmod()
| 30 |
'''simple docstring'''
def a ( __a , __a ) -> int:
'''simple docstring'''
if len(__a ) != len(__a ):
raise ValueError('''String lengths must match!''' )
UpperCamelCase__ :Union[str, Any] = 0
for chara, chara in zip(__a , __a ):
if chara != chara:
count += 1
return count
if __name__ == "__main__":
import doctest
doctest.testmod() | 97 | 0 |
'''simple docstring'''
import math
import unittest
from transformers import BioGptConfig, 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, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
BioGptForCausalLM,
BioGptForSequenceClassification,
BioGptForTokenClassification,
BioGptModel,
BioGptTokenizer,
)
from transformers.models.biogpt.modeling_biogpt import BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST
class lowerCamelCase_ :
'''simple docstring'''
def __init__( self : List[Any] , A : Dict , A : Optional[Any]=13 , A : Optional[Any]=7 , A : Union[str, Any]=True , A : Optional[Any]=True , A : int=False , A : str=True , A : Optional[Any]=99 , A : Union[str, Any]=32 , A : int=5 , A : Tuple=4 , A : Union[str, Any]=37 , A : Dict="gelu" , A : Union[str, Any]=0.1 , A : str=0.1 , A : Union[str, Any]=512 , A : int=16 , A : List[str]=2 , A : Tuple=0.02 , A : int=3 , A : List[str]=4 , A : str=None , ):
_UpperCAmelCase : List[Any] = parent
_UpperCAmelCase : Any = batch_size
_UpperCAmelCase : int = seq_length
_UpperCAmelCase : Union[str, Any] = is_training
_UpperCAmelCase : Any = use_input_mask
_UpperCAmelCase : Optional[Any] = use_token_type_ids
_UpperCAmelCase : str = use_labels
_UpperCAmelCase : Union[str, Any] = vocab_size
_UpperCAmelCase : Tuple = hidden_size
_UpperCAmelCase : Union[str, Any] = num_hidden_layers
_UpperCAmelCase : Optional[Any] = num_attention_heads
_UpperCAmelCase : Union[str, Any] = intermediate_size
_UpperCAmelCase : Union[str, Any] = hidden_act
_UpperCAmelCase : List[Any] = hidden_dropout_prob
_UpperCAmelCase : List[Any] = attention_probs_dropout_prob
_UpperCAmelCase : Optional[int] = max_position_embeddings
_UpperCAmelCase : str = type_vocab_size
_UpperCAmelCase : str = type_sequence_label_size
_UpperCAmelCase : int = initializer_range
_UpperCAmelCase : Optional[Any] = num_labels
_UpperCAmelCase : List[str] = num_choices
_UpperCAmelCase : List[str] = scope
def _A ( self : Optional[int] ):
_UpperCAmelCase : Dict = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
_UpperCAmelCase : Union[str, Any] = None
if self.use_input_mask:
_UpperCAmelCase : List[Any] = random_attention_mask([self.batch_size, self.seq_length] )
_UpperCAmelCase : Any = None
if self.use_token_type_ids:
_UpperCAmelCase : List[str] = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
_UpperCAmelCase : Optional[int] = None
_UpperCAmelCase : Any = None
_UpperCAmelCase : Optional[int] = None
if self.use_labels:
_UpperCAmelCase : Optional[Any] = ids_tensor([self.batch_size] , self.type_sequence_label_size )
_UpperCAmelCase : Tuple = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
_UpperCAmelCase : List[str] = ids_tensor([self.batch_size] , self.num_choices )
_UpperCAmelCase : List[Any] = self.get_config()
return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def _A ( self : Dict ):
return BioGptConfig(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , is_decoder=A , initializer_range=self.initializer_range , )
def _A ( self : int , A : List[Any] , A : Any , A : int , A : Union[str, Any] , A : Dict , A : List[Any] , A : Dict ):
_UpperCAmelCase : List[str] = BioGptModel(config=A )
model.to(A )
model.eval()
_UpperCAmelCase : Tuple = model(A , attention_mask=A )
_UpperCAmelCase : int = model(A )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def _A ( self : List[Any] , A : str , A : List[Any] , A : Dict , A : List[Any] , A : List[str] , A : Union[str, Any] , A : int , A : List[str] , A : Dict , ):
_UpperCAmelCase : Optional[int] = BioGptForCausalLM(config=A )
model.to(A )
model.eval()
_UpperCAmelCase : Optional[int] = model(A , attention_mask=A , token_type_ids=A , labels=A )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def _A ( self : List[Any] , A : str , A : str , A : str , A : Any , A : List[str] , *A : Optional[int] ):
_UpperCAmelCase : str = BioGptModel(config=A )
model.to(A )
model.eval()
# create attention mask
_UpperCAmelCase : List[Any] = torch.ones(input_ids.shape , dtype=torch.long , device=A )
_UpperCAmelCase : Optional[int] = self.seq_length // 2
_UpperCAmelCase : List[Any] = 0
# first forward pass
_UpperCAmelCase , _UpperCAmelCase : List[str] = model(A , attention_mask=A ).to_tuple()
# create hypothetical next token and extent to next_input_ids
_UpperCAmelCase : List[str] = ids_tensor((self.batch_size, 1) , config.vocab_size )
# change a random masked slice from input_ids
_UpperCAmelCase : List[str] = ids_tensor((1,) , A ).item() + 1
_UpperCAmelCase : str = ids_tensor((self.batch_size, 1) , config.vocab_size ).squeeze(-1 )
_UpperCAmelCase : Any = random_other_next_tokens
# append to next input_ids and attn_mask
_UpperCAmelCase : List[Any] = torch.cat([input_ids, next_tokens] , dim=-1 )
_UpperCAmelCase : Optional[int] = torch.cat(
[attn_mask, torch.ones((attn_mask.shape[0], 1) , dtype=torch.long , device=A )] , dim=1 , )
# get two different outputs
_UpperCAmelCase : List[Any] = model(A , attention_mask=A )["last_hidden_state"]
_UpperCAmelCase : Optional[Any] = model(A , past_key_values=A , attention_mask=A )["last_hidden_state"]
# select random slice
_UpperCAmelCase : Any = ids_tensor((1,) , output_from_past.shape[-1] ).item()
_UpperCAmelCase : Optional[Any] = output_from_no_past[:, -1, random_slice_idx].detach()
_UpperCAmelCase : Any = output_from_past[:, 0, random_slice_idx].detach()
# test that outputs are equal for slice
self.parent.assertTrue(torch.allclose(A , A , atol=1E-3 ) )
def _A ( self : int , A : Dict , A : str , A : Dict , A : Union[str, Any] , A : Any , *A : Union[str, Any] ):
_UpperCAmelCase : Optional[Any] = BioGptModel(config=A ).to(A ).eval()
_UpperCAmelCase : List[Any] = torch.ones(input_ids.shape , dtype=torch.long , device=A )
# first forward pass
_UpperCAmelCase : Union[str, Any] = model(A , attention_mask=A , use_cache=A )
_UpperCAmelCase , _UpperCAmelCase : Dict = outputs.to_tuple()
# create hypothetical multiple next token and extent to next_input_ids
_UpperCAmelCase : str = ids_tensor((self.batch_size, 3) , config.vocab_size )
_UpperCAmelCase : Any = ids_tensor((self.batch_size, 3) , 2 )
# append to next input_ids and
_UpperCAmelCase : Optional[Any] = torch.cat([input_ids, next_tokens] , dim=-1 )
_UpperCAmelCase : Dict = torch.cat([attention_mask, next_attn_mask] , dim=-1 )
_UpperCAmelCase : Any = model(A , attention_mask=A )["last_hidden_state"]
_UpperCAmelCase : Dict = model(A , attention_mask=A , past_key_values=A )[
"last_hidden_state"
]
# select random slice
_UpperCAmelCase : Dict = ids_tensor((1,) , output_from_past.shape[-1] ).item()
_UpperCAmelCase : Dict = output_from_no_past[:, -3:, random_slice_idx].detach()
_UpperCAmelCase : Any = output_from_past[:, :, random_slice_idx].detach()
self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1] )
# test that outputs are equal for slice
self.parent.assertTrue(torch.allclose(A , A , atol=1E-3 ) )
def _A ( self : Optional[Any] , A : Tuple , A : List[str] , A : Tuple , A : Dict , A : List[Any] , *A : Tuple , A : List[str]=False ):
_UpperCAmelCase : Optional[int] = BioGptForCausalLM(A )
model.to(A )
if gradient_checkpointing:
model.gradient_checkpointing_enable()
_UpperCAmelCase : Union[str, Any] = model(A , labels=A )
self.parent.assertEqual(result.loss.shape , () )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
result.loss.backward()
def _A ( self : Optional[Any] , A : Any , *A : Optional[Any] ):
_UpperCAmelCase : Tuple = BioGptModel(A )
_UpperCAmelCase : int = model.config.initializer_range / math.sqrt(2 * model.config.num_hidden_layers )
for key in model.state_dict().keys():
if "c_proj" in key and "weight" in key:
self.parent.assertLessEqual(abs(torch.std(model.state_dict()[key] ) - model_std ) , 0.001 )
self.parent.assertLessEqual(abs(torch.mean(model.state_dict()[key] ) - 0.0 ) , 0.01 )
def _A ( self : Optional[int] , A : Dict , A : Tuple , A : Optional[int] , A : int , A : List[str] , *A : Dict ):
_UpperCAmelCase : Any = self.num_labels
_UpperCAmelCase : Any = BioGptForTokenClassification(A )
model.to(A )
model.eval()
_UpperCAmelCase : Optional[int] = model(A , attention_mask=A , token_type_ids=A )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def _A ( self : int ):
_UpperCAmelCase : Dict = self.prepare_config_and_inputs()
(
(
_UpperCAmelCase
) , (
_UpperCAmelCase
) , (
_UpperCAmelCase
) , (
_UpperCAmelCase
) , (
_UpperCAmelCase
) , (
_UpperCAmelCase
) , (
_UpperCAmelCase
) ,
) : List[str] = config_and_inputs
_UpperCAmelCase : Optional[int] = {"input_ids": input_ids, "attention_mask": input_mask}
return config, inputs_dict
@require_torch
class lowerCamelCase_ (snake_case__ , snake_case__ , snake_case__ , unittest.TestCase ):
'''simple docstring'''
__UpperCamelCase: List[str] = (
(BioGptModel, BioGptForCausalLM, BioGptForSequenceClassification, BioGptForTokenClassification)
if is_torch_available()
else ()
)
__UpperCamelCase: List[str] = (BioGptForCausalLM,) if is_torch_available() else ()
__UpperCamelCase: str = (
{
"feature-extraction": BioGptModel,
"text-classification": BioGptForSequenceClassification,
"text-generation": BioGptForCausalLM,
"token-classification": BioGptForTokenClassification,
"zero-shot": BioGptForSequenceClassification,
}
if is_torch_available()
else {}
)
__UpperCamelCase: Union[str, Any] = False
def _A ( self : Optional[Any] ):
_UpperCAmelCase : List[Any] = BioGptModelTester(self )
_UpperCAmelCase : str = ConfigTester(self , config_class=A , hidden_size=37 )
def _A ( self : Union[str, Any] ):
self.config_tester.run_common_tests()
def _A ( self : Any ):
_UpperCAmelCase : Any = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*A )
def _A ( self : Any ):
_UpperCAmelCase : Optional[int] = self.model_tester.prepare_config_and_inputs()
for type in ["absolute", "relative_key", "relative_key_query"]:
_UpperCAmelCase : Tuple = type
self.model_tester.create_and_check_model(*A )
def _A ( self : int ):
_UpperCAmelCase : Optional[int] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_biogpt_model_attention_mask_past(*A )
def _A ( self : Union[str, Any] ):
_UpperCAmelCase : List[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_forward_and_backwards(*A , gradient_checkpointing=A )
def _A ( self : Union[str, Any] ):
_UpperCAmelCase : Dict = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_biogpt_model_past_large_inputs(*A )
def _A ( self : Dict ):
_UpperCAmelCase : Dict = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_biogpt_weight_initialization(*A )
def _A ( self : Dict ):
_UpperCAmelCase : Dict = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_biogpt_for_token_classification(*A )
@slow
def _A ( self : List[str] ):
_UpperCAmelCase : Optional[Any] = BioGptForCausalLM.from_pretrained("microsoft/biogpt" )
model.to(A )
_UpperCAmelCase : Tuple = BioGptTokenizer.from_pretrained("microsoft/biogpt" )
_UpperCAmelCase : str = "left"
# Define PAD Token = EOS Token = 50256
_UpperCAmelCase : Any = tokenizer.eos_token
_UpperCAmelCase : int = model.config.eos_token_id
# use different length sentences to test batching
_UpperCAmelCase : Any = [
"Hello, my dog is a little",
"Today, I",
]
_UpperCAmelCase : Tuple = tokenizer(A , return_tensors="pt" , padding=A )
_UpperCAmelCase : Optional[Any] = inputs["input_ids"].to(A )
_UpperCAmelCase : Any = model.generate(
input_ids=A , attention_mask=inputs["attention_mask"].to(A ) , )
_UpperCAmelCase : int = tokenizer(sentences[0] , return_tensors="pt" ).input_ids.to(A )
_UpperCAmelCase : List[Any] = model.generate(input_ids=A )
_UpperCAmelCase : List[Any] = inputs_non_padded.shape[-1] - inputs["attention_mask"][-1].long().sum().cpu().item()
_UpperCAmelCase : int = tokenizer(sentences[1] , return_tensors="pt" ).input_ids.to(A )
_UpperCAmelCase : int = model.generate(input_ids=A , max_length=model.config.max_length - num_paddings )
_UpperCAmelCase : Dict = tokenizer.batch_decode(A , skip_special_tokens=A )
_UpperCAmelCase : Any = tokenizer.decode(output_non_padded[0] , skip_special_tokens=A )
_UpperCAmelCase : Optional[int] = tokenizer.decode(output_padded[0] , skip_special_tokens=A )
_UpperCAmelCase : str = [
"Hello, my dog is a little bit bigger than a little bit.",
"Today, I have a good idea of how to use the information",
]
self.assertListEqual(A , A )
self.assertListEqual(A , [non_padded_sentence, padded_sentence] )
@slow
def _A ( self : str ):
for model_name in BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
_UpperCAmelCase : Optional[Any] = BioGptModel.from_pretrained(A )
self.assertIsNotNone(A )
def _A ( self : List[str] ):
_UpperCAmelCase , _UpperCAmelCase : int = self.model_tester.prepare_config_and_inputs_for_common()
_UpperCAmelCase : str = 3
_UpperCAmelCase : List[str] = input_dict["input_ids"]
_UpperCAmelCase : Dict = input_ids.ne(1 ).to(A )
_UpperCAmelCase : List[Any] = ids_tensor([self.model_tester.batch_size] , self.model_tester.type_sequence_label_size )
_UpperCAmelCase : List[str] = BioGptForSequenceClassification(A )
model.to(A )
model.eval()
_UpperCAmelCase : List[str] = model(A , attention_mask=A , labels=A )
self.assertEqual(result.logits.shape , (self.model_tester.batch_size, self.model_tester.num_labels) )
def _A ( self : int ):
_UpperCAmelCase , _UpperCAmelCase : Optional[Any] = self.model_tester.prepare_config_and_inputs_for_common()
_UpperCAmelCase : int = 3
_UpperCAmelCase : Dict = "multi_label_classification"
_UpperCAmelCase : Optional[Any] = input_dict["input_ids"]
_UpperCAmelCase : Optional[int] = input_ids.ne(1 ).to(A )
_UpperCAmelCase : Tuple = ids_tensor(
[self.model_tester.batch_size, config.num_labels] , self.model_tester.type_sequence_label_size ).to(torch.float )
_UpperCAmelCase : Optional[Any] = BioGptForSequenceClassification(A )
model.to(A )
model.eval()
_UpperCAmelCase : Tuple = model(A , attention_mask=A , labels=A )
self.assertEqual(result.logits.shape , (self.model_tester.batch_size, self.model_tester.num_labels) )
@require_torch
class lowerCamelCase_ (unittest.TestCase ):
'''simple docstring'''
@slow
def _A ( self : List[Any] ):
_UpperCAmelCase : Optional[Any] = BioGptForCausalLM.from_pretrained("microsoft/biogpt" )
_UpperCAmelCase : List[str] = torch.tensor([[2, 4805, 9, 656, 21]] )
_UpperCAmelCase : List[Any] = model(A )[0]
_UpperCAmelCase : int = 42384
_UpperCAmelCase : int = torch.Size((1, 5, vocab_size) )
self.assertEqual(output.shape , A )
_UpperCAmelCase : Any = torch.tensor(
[[[-9.5_236, -9.8_918, 10.4_557], [-11.0_469, -9.6_423, 8.1_022], [-8.8_664, -7.8_826, 5.5_325]]] )
self.assertTrue(torch.allclose(output[:, :3, :3] , A , atol=1E-4 ) )
@slow
def _A ( self : Any ):
_UpperCAmelCase : str = BioGptTokenizer.from_pretrained("microsoft/biogpt" )
_UpperCAmelCase : Tuple = BioGptForCausalLM.from_pretrained("microsoft/biogpt" )
model.to(A )
torch.manual_seed(0 )
_UpperCAmelCase : Optional[Any] = tokenizer("COVID-19 is" , return_tensors="pt" ).to(A )
_UpperCAmelCase : Dict = model.generate(
**A , min_length=100 , max_length=1024 , num_beams=5 , early_stopping=A , )
_UpperCAmelCase : Optional[Any] = tokenizer.decode(output_ids[0] , skip_special_tokens=A )
_UpperCAmelCase : List[str] = (
"COVID-19 is a global pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the"
" causative agent of coronavirus disease 2019 (COVID-19), which has spread to more than 200 countries and"
" territories, including the United States (US), Canada, Australia, New Zealand, the United Kingdom (UK),"
" and the United States of America (USA), as of March 11, 2020, with more than 800,000 confirmed cases and"
" more than 800,000 deaths."
)
self.assertEqual(A , A )
| 31 |
'''simple docstring'''
def a ( __a ) -> "list[int]":
'''simple docstring'''
if upper_limit < 0:
raise ValueError('''Limit for the Catalan sequence must be ≥ 0''' )
UpperCamelCase__ :Optional[Any] = [0] * (upper_limit + 1)
# Base case: C(0) = C(1) = 1
UpperCamelCase__ :int = 1
if upper_limit > 0:
UpperCamelCase__ :int = 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(__a ):
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:
__snake_case = 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() | 97 | 0 |
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
DPMSolverMultistepScheduler,
TextToVideoSDPipeline,
UNetaDConditionModel,
)
from diffusers.utils import is_xformers_available, load_numpy, skip_mps, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism
from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineTesterMixin
enable_full_determinism()
@skip_mps
class SCREAMING_SNAKE_CASE__ ( lowercase__ , unittest.TestCase ):
snake_case__ : Optional[Any] = TextToVideoSDPipeline
snake_case__ : Optional[int] = TEXT_TO_IMAGE_PARAMS
snake_case__ : str = TEXT_TO_IMAGE_BATCH_PARAMS
# No `output_type`.
snake_case__ : Optional[Any] = frozenset(
[
'''num_inference_steps''',
'''generator''',
'''latents''',
'''return_dict''',
'''callback''',
'''callback_steps''',
] )
def SCREAMING_SNAKE_CASE ( self : str ) -> Union[str, Any]:
torch.manual_seed(0 )
a_ : Optional[int] = UNetaDConditionModel(
block_out_channels=(3_2, 6_4, 6_4, 6_4) , layers_per_block=2 , sample_size=3_2 , in_channels=4 , out_channels=4 , down_block_types=('CrossAttnDownBlock3D', 'CrossAttnDownBlock3D', 'CrossAttnDownBlock3D', 'DownBlock3D') , up_block_types=('UpBlock3D', 'CrossAttnUpBlock3D', 'CrossAttnUpBlock3D', 'CrossAttnUpBlock3D') , cross_attention_dim=3_2 , attention_head_dim=4 , )
a_ : int = DDIMScheduler(
beta_start=0.00085 , beta_end=0.012 , beta_schedule='scaled_linear' , clip_sample=SCREAMING_SNAKE_CASE__ , set_alpha_to_one=SCREAMING_SNAKE_CASE__ , )
torch.manual_seed(0 )
a_ : int = AutoencoderKL(
block_out_channels=[3_2, 6_4] , in_channels=3 , out_channels=3 , down_block_types=['DownEncoderBlock2D', 'DownEncoderBlock2D'] , up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D'] , latent_channels=4 , sample_size=1_2_8 , )
torch.manual_seed(0 )
a_ : Optional[int] = CLIPTextConfig(
bos_token_id=0 , eos_token_id=2 , hidden_size=3_2 , intermediate_size=3_7 , layer_norm_eps=1E-05 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=1_0_0_0 , hidden_act='gelu' , projection_dim=5_1_2 , )
a_ : Dict = CLIPTextModel(SCREAMING_SNAKE_CASE__ )
a_ : Union[str, Any] = CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip' )
a_ : Union[str, Any] = {
'unet': unet,
'scheduler': scheduler,
'vae': vae,
'text_encoder': text_encoder,
'tokenizer': tokenizer,
}
return components
def SCREAMING_SNAKE_CASE ( self : List[Any] , SCREAMING_SNAKE_CASE__ : str , SCREAMING_SNAKE_CASE__ : List[Any]=0 ) -> List[str]:
if str(SCREAMING_SNAKE_CASE__ ).startswith('mps' ):
a_ : Dict = torch.manual_seed(SCREAMING_SNAKE_CASE__ )
else:
a_ : Optional[Any] = torch.Generator(device=SCREAMING_SNAKE_CASE__ ).manual_seed(SCREAMING_SNAKE_CASE__ )
a_ : int = {
'prompt': 'A painting of a squirrel eating a burger',
'generator': generator,
'num_inference_steps': 2,
'guidance_scale': 6.0,
'output_type': 'pt',
}
return inputs
def SCREAMING_SNAKE_CASE ( self : Union[str, Any] ) -> Tuple:
a_ : List[Any] = 'cpu' # ensure determinism for the device-dependent torch.Generator
a_ : Dict = self.get_dummy_components()
a_ : str = TextToVideoSDPipeline(**SCREAMING_SNAKE_CASE__ )
a_ : Union[str, Any] = sd_pipe.to(SCREAMING_SNAKE_CASE__ )
sd_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__ )
a_ : Tuple = self.get_dummy_inputs(SCREAMING_SNAKE_CASE__ )
a_ : Dict = 'np'
a_ : Dict = sd_pipe(**SCREAMING_SNAKE_CASE__ ).frames
a_ : int = frames[0][-3:, -3:, -1]
assert frames[0].shape == (6_4, 6_4, 3)
a_ : Union[str, Any] = np.array([158.0, 160.0, 153.0, 125.0, 100.0, 121.0, 111.0, 93.0, 113.0] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
def SCREAMING_SNAKE_CASE ( self : Any ) -> List[Any]:
self._test_attention_slicing_forward_pass(test_mean_pixel_difference=SCREAMING_SNAKE_CASE__ , expected_max_diff=3E-3 )
@unittest.skipIf(
torch_device != 'cuda' or not is_xformers_available() , reason='XFormers attention is only available with CUDA and `xformers` installed' , )
def SCREAMING_SNAKE_CASE ( self : Any ) -> str:
self._test_xformers_attention_forwardGenerator_pass(test_mean_pixel_difference=SCREAMING_SNAKE_CASE__ , expected_max_diff=1E-2 )
@unittest.skip(reason='Batching needs to be properly figured out first for this pipeline.' )
def SCREAMING_SNAKE_CASE ( self : Dict ) -> Union[str, Any]:
pass
@unittest.skip(reason='Batching needs to be properly figured out first for this pipeline.' )
def SCREAMING_SNAKE_CASE ( self : str ) -> List[str]:
pass
@unittest.skip(reason='`num_images_per_prompt` argument is not supported for this pipeline.' )
def SCREAMING_SNAKE_CASE ( self : int ) -> List[Any]:
pass
def SCREAMING_SNAKE_CASE ( self : Dict ) -> Union[str, Any]:
return super().test_progress_bar()
@slow
@skip_mps
class SCREAMING_SNAKE_CASE__ ( unittest.TestCase ):
def SCREAMING_SNAKE_CASE ( self : Optional[Any] ) -> List[Any]:
a_ : str = load_numpy(
'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/text_to_video/video.npy' )
a_ : Any = TextToVideoSDPipeline.from_pretrained('damo-vilab/text-to-video-ms-1.7b' )
a_ : Optional[Any] = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config )
a_ : Optional[Any] = pipe.to('cuda' )
a_ : Any = 'Spiderman is surfing'
a_ : List[Any] = torch.Generator(device='cpu' ).manual_seed(0 )
a_ : Optional[Any] = pipe(SCREAMING_SNAKE_CASE__ , generator=SCREAMING_SNAKE_CASE__ , num_inference_steps=2_5 , output_type='pt' ).frames
a_ : str = video_frames.cpu().numpy()
assert np.abs(expected_video - video ).mean() < 5E-2
def SCREAMING_SNAKE_CASE ( self : Any ) -> Any:
a_ : Dict = load_numpy(
'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/text_to_video/video_2step.npy' )
a_ : Tuple = TextToVideoSDPipeline.from_pretrained('damo-vilab/text-to-video-ms-1.7b' )
a_ : Tuple = pipe.to('cuda' )
a_ : Any = 'Spiderman is surfing'
a_ : List[str] = torch.Generator(device='cpu' ).manual_seed(0 )
a_ : List[Any] = pipe(SCREAMING_SNAKE_CASE__ , generator=SCREAMING_SNAKE_CASE__ , num_inference_steps=2 , output_type='pt' ).frames
a_ : List[str] = video_frames.cpu().numpy()
assert np.abs(expected_video - video ).mean() < 5E-2
| 32 |
'''simple docstring'''
import io
import json
import fsspec
import pytest
from datasets import Dataset, DatasetDict, Features, NamedSplit, Value
from datasets.io.json import JsonDatasetReader, JsonDatasetWriter
from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases
def a ( __a , __a ) -> Optional[int]:
'''simple docstring'''
assert isinstance(__a , __a )
assert dataset.num_rows == 4
assert dataset.num_columns == 3
assert dataset.column_names == ["col_1", "col_2", "col_3"]
for feature, expected_dtype in expected_features.items():
assert dataset.features[feature].dtype == expected_dtype
@pytest.mark.parametrize('''keep_in_memory''' , [False, True] )
def a ( __a , __a , __a ) -> Optional[Any]:
'''simple docstring'''
UpperCamelCase__ :Union[str, Any] = tmp_path / '''cache'''
UpperCamelCase__ :Dict = {'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
UpperCamelCase__ :Tuple = JsonDatasetReader(__a , cache_dir=__a , keep_in_memory=__a ).read()
_check_json_dataset(__a , __a )
@pytest.mark.parametrize(
'''features''' , [
None,
{'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''},
{'''col_1''': '''string''', '''col_2''': '''string''', '''col_3''': '''string'''},
{'''col_1''': '''int32''', '''col_2''': '''int32''', '''col_3''': '''int32'''},
{'''col_1''': '''float32''', '''col_2''': '''float32''', '''col_3''': '''float32'''},
] , )
def a ( __a , __a , __a ) -> Any:
'''simple docstring'''
UpperCamelCase__ :Union[str, Any] = tmp_path / '''cache'''
UpperCamelCase__ :Optional[Any] = {'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
UpperCamelCase__ :Optional[Any] = features.copy() if features else default_expected_features
UpperCamelCase__ :Tuple = (
Features({feature: Value(__a ) for feature, dtype in features.items()} ) if features is not None else None
)
UpperCamelCase__ :int = JsonDatasetReader(__a , features=__a , cache_dir=__a ).read()
_check_json_dataset(__a , __a )
@pytest.mark.parametrize(
'''features''' , [
None,
{'''col_3''': '''float64''', '''col_1''': '''string''', '''col_2''': '''int64'''},
] , )
def a ( __a , __a , __a ) -> Tuple:
'''simple docstring'''
UpperCamelCase__ :int = tmp_path / '''cache'''
UpperCamelCase__ :str = {'''col_3''': '''float64''', '''col_1''': '''string''', '''col_2''': '''int64'''}
UpperCamelCase__ :Any = features.copy() if features else default_expected_features
UpperCamelCase__ :Union[str, Any] = (
Features({feature: Value(__a ) for feature, dtype in features.items()} ) if features is not None else None
)
UpperCamelCase__ :Any = JsonDatasetReader(__a , features=__a , cache_dir=__a ).read()
assert isinstance(__a , __a )
assert dataset.num_rows == 2
assert dataset.num_columns == 3
assert dataset.column_names == ["col_3", "col_1", "col_2"]
for feature, expected_dtype in expected_features.items():
assert dataset.features[feature].dtype == expected_dtype
def a ( __a , __a ) -> List[Any]:
'''simple docstring'''
UpperCamelCase__ :Any = {'''col_2''': '''int64''', '''col_3''': '''float64''', '''col_1''': '''string'''}
UpperCamelCase__ :int = features.copy()
UpperCamelCase__ :List[Any] = (
Features({feature: Value(__a ) for feature, dtype in features.items()} ) if features is not None else None
)
UpperCamelCase__ :Optional[int] = tmp_path / '''cache'''
UpperCamelCase__ :Dict = JsonDatasetReader(__a , features=__a , cache_dir=__a ).read()
assert isinstance(__a , __a )
assert dataset.num_rows == 2
assert dataset.num_columns == 3
assert dataset.column_names == ["col_2", "col_3", "col_1"]
for feature, expected_dtype in expected_features.items():
assert dataset.features[feature].dtype == expected_dtype
@pytest.mark.parametrize('''split''' , [None, NamedSplit('''train''' ), '''train''', '''test'''] )
def a ( __a , __a , __a ) -> List[Any]:
'''simple docstring'''
UpperCamelCase__ :Union[str, Any] = tmp_path / '''cache'''
UpperCamelCase__ :Optional[Any] = {'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
UpperCamelCase__ :List[Any] = JsonDatasetReader(__a , cache_dir=__a , split=__a ).read()
_check_json_dataset(__a , __a )
assert dataset.split == split if split else "train"
@pytest.mark.parametrize('''path_type''' , [str, list] )
def a ( __a , __a , __a ) -> Any:
'''simple docstring'''
if issubclass(__a , __a ):
UpperCamelCase__ :Union[str, Any] = jsonl_path
elif issubclass(__a , __a ):
UpperCamelCase__ :int = [jsonl_path]
UpperCamelCase__ :Dict = tmp_path / '''cache'''
UpperCamelCase__ :Any = {'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
UpperCamelCase__ :List[str] = JsonDatasetReader(__a , cache_dir=__a ).read()
_check_json_dataset(__a , __a )
def a ( __a , __a , __a=("train",) ) -> Optional[Any]:
'''simple docstring'''
assert isinstance(__a , __a )
for split in splits:
UpperCamelCase__ :Optional[int] = dataset_dict[split]
assert dataset.num_rows == 4
assert dataset.num_columns == 3
assert dataset.column_names == ["col_1", "col_2", "col_3"]
for feature, expected_dtype in expected_features.items():
assert dataset.features[feature].dtype == expected_dtype
@pytest.mark.parametrize('''keep_in_memory''' , [False, True] )
def a ( __a , __a , __a ) -> List[str]:
'''simple docstring'''
UpperCamelCase__ :List[str] = tmp_path / '''cache'''
UpperCamelCase__ :Dict = {'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
UpperCamelCase__ :str = JsonDatasetReader({'''train''': jsonl_path} , cache_dir=__a , keep_in_memory=__a ).read()
_check_json_datasetdict(__a , __a )
@pytest.mark.parametrize(
'''features''' , [
None,
{'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''},
{'''col_1''': '''string''', '''col_2''': '''string''', '''col_3''': '''string'''},
{'''col_1''': '''int32''', '''col_2''': '''int32''', '''col_3''': '''int32'''},
{'''col_1''': '''float32''', '''col_2''': '''float32''', '''col_3''': '''float32'''},
] , )
def a ( __a , __a , __a ) -> int:
'''simple docstring'''
UpperCamelCase__ :Tuple = tmp_path / '''cache'''
UpperCamelCase__ :Any = {'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
UpperCamelCase__ :Optional[int] = features.copy() if features else default_expected_features
UpperCamelCase__ :str = (
Features({feature: Value(__a ) for feature, dtype in features.items()} ) if features is not None else None
)
UpperCamelCase__ :Dict = JsonDatasetReader({'''train''': jsonl_path} , features=__a , cache_dir=__a ).read()
_check_json_datasetdict(__a , __a )
@pytest.mark.parametrize('''split''' , [None, NamedSplit('''train''' ), '''train''', '''test'''] )
def a ( __a , __a , __a ) -> str:
'''simple docstring'''
if split:
UpperCamelCase__ :List[str] = {split: jsonl_path}
else:
UpperCamelCase__ :int = '''train'''
UpperCamelCase__ :int = {'''train''': jsonl_path, '''test''': jsonl_path}
UpperCamelCase__ :Any = tmp_path / '''cache'''
UpperCamelCase__ :Union[str, Any] = {'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
UpperCamelCase__ :Any = JsonDatasetReader(__a , cache_dir=__a ).read()
_check_json_datasetdict(__a , __a , splits=list(path.keys() ) )
assert all(dataset[split].split == split for split in path.keys() )
def a ( __a ) -> Union[str, Any]:
'''simple docstring'''
return json.load(__a )
def a ( __a ) -> int:
'''simple docstring'''
return [json.loads(__a ) for line in buffer]
class lowercase :
"""simple docstring"""
@pytest.mark.parametrize('''lines, load_json_function''' , [(True, load_json_lines), (False, load_json)] )
def lowerCAmelCase__ ( self , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ ):
'''simple docstring'''
with io.BytesIO() as buffer:
JsonDatasetWriter(UpperCamelCase_ , UpperCamelCase_ , lines=UpperCamelCase_ ).write()
buffer.seek(0 )
UpperCamelCase__ :List[Any] = load_json_function(UpperCamelCase_ )
assert isinstance(UpperCamelCase_ , UpperCamelCase_ )
assert isinstance(exported_content[0] , UpperCamelCase_ )
assert len(UpperCamelCase_ ) == 10
@pytest.mark.parametrize(
'''orient, container, keys, len_at''' , [
('''records''', list, {'''tokens''', '''labels''', '''answers''', '''id'''}, None),
('''split''', dict, {'''columns''', '''data'''}, '''data'''),
('''index''', dict, set('''0123456789''' ), None),
('''columns''', dict, {'''tokens''', '''labels''', '''answers''', '''id'''}, '''tokens'''),
('''values''', list, None, None),
('''table''', dict, {'''schema''', '''data'''}, '''data'''),
] , )
def lowerCAmelCase__ ( self , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ ):
'''simple docstring'''
with io.BytesIO() as buffer:
JsonDatasetWriter(UpperCamelCase_ , UpperCamelCase_ , lines=UpperCamelCase_ , orient=UpperCamelCase_ ).write()
buffer.seek(0 )
UpperCamelCase__ :Optional[int] = load_json(UpperCamelCase_ )
assert isinstance(UpperCamelCase_ , UpperCamelCase_ )
if keys:
if container is dict:
assert exported_content.keys() == keys
else:
assert exported_content[0].keys() == keys
else:
assert not hasattr(UpperCamelCase_ , '''keys''' ) and not hasattr(exported_content[0] , '''keys''' )
if len_at:
assert len(exported_content[len_at] ) == 10
else:
assert len(UpperCamelCase_ ) == 10
@pytest.mark.parametrize('''lines, load_json_function''' , [(True, load_json_lines), (False, load_json)] )
def lowerCAmelCase__ ( self , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ ):
'''simple docstring'''
with io.BytesIO() as buffer:
JsonDatasetWriter(UpperCamelCase_ , UpperCamelCase_ , lines=UpperCamelCase_ , num_proc=2 ).write()
buffer.seek(0 )
UpperCamelCase__ :Union[str, Any] = load_json_function(UpperCamelCase_ )
assert isinstance(UpperCamelCase_ , UpperCamelCase_ )
assert isinstance(exported_content[0] , UpperCamelCase_ )
assert len(UpperCamelCase_ ) == 10
@pytest.mark.parametrize(
'''orient, container, keys, len_at''' , [
('''records''', list, {'''tokens''', '''labels''', '''answers''', '''id'''}, None),
('''split''', dict, {'''columns''', '''data'''}, '''data'''),
('''index''', dict, set('''0123456789''' ), None),
('''columns''', dict, {'''tokens''', '''labels''', '''answers''', '''id'''}, '''tokens'''),
('''values''', list, None, None),
('''table''', dict, {'''schema''', '''data'''}, '''data'''),
] , )
def lowerCAmelCase__ ( self , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ ):
'''simple docstring'''
with io.BytesIO() as buffer:
JsonDatasetWriter(UpperCamelCase_ , UpperCamelCase_ , lines=UpperCamelCase_ , orient=UpperCamelCase_ , num_proc=2 ).write()
buffer.seek(0 )
UpperCamelCase__ :int = load_json(UpperCamelCase_ )
assert isinstance(UpperCamelCase_ , UpperCamelCase_ )
if keys:
if container is dict:
assert exported_content.keys() == keys
else:
assert exported_content[0].keys() == keys
else:
assert not hasattr(UpperCamelCase_ , '''keys''' ) and not hasattr(exported_content[0] , '''keys''' )
if len_at:
assert len(exported_content[len_at] ) == 10
else:
assert len(UpperCamelCase_ ) == 10
def lowerCAmelCase__ ( self , UpperCamelCase_ ):
'''simple docstring'''
with pytest.raises(UpperCamelCase_ ):
with io.BytesIO() as buffer:
JsonDatasetWriter(UpperCamelCase_ , UpperCamelCase_ , num_proc=0 )
@pytest.mark.parametrize('''compression, extension''' , [('''gzip''', '''gz'''), ('''bz2''', '''bz2'''), ('''xz''', '''xz''')] )
def lowerCAmelCase__ ( self , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ ):
'''simple docstring'''
UpperCamelCase__ :Tuple = tmp_path_factory.mktemp('''data''' ) / F'''test.json.{extension}'''
UpperCamelCase__ :Union[str, Any] = str(shared_datadir / F'''test_file.json.{extension}''' )
JsonDatasetWriter(UpperCamelCase_ , UpperCamelCase_ , compression=UpperCamelCase_ ).write()
with fsspec.open(UpperCamelCase_ , '''rb''' , compression='''infer''' ) as f:
UpperCamelCase__ :Dict = f.read()
with fsspec.open(UpperCamelCase_ , '''rb''' , compression='''infer''' ) as f:
UpperCamelCase__ :int = f.read()
assert exported_content == original_content | 97 | 0 |
"""simple docstring"""
from __future__ import annotations
def lowercase ( __snake_case : str , __snake_case : list[str] | None = None ):
lowercase_ : Dict = word_bank or []
# create a table
lowercase_ : int = len(__snake_case ) + 1
lowercase_ : list[list[list[str]]] = []
for _ in range(__snake_case ):
table.append([] )
# seed value
lowercase_ : str = [[]] # because empty string has empty combination
# iterate through the indices
for i in range(__snake_case ):
# condition
if table[i] != []:
for word in word_bank:
# slice condition
if target[i : i + len(__snake_case )] == word:
lowercase_ : list[list[str]] = [
[word, *way] for way in table[i]
]
# adds the word to every combination the current position holds
# now,push that combination to the table[i+len(word)]
table[i + len(__snake_case )] += new_combinations
# combinations are in reverse order so reverse for better output
for combination in table[len(__snake_case )]:
combination.reverse()
return table[len(__snake_case )]
if __name__ == "__main__":
print(all_construct('''jwajalapa''', ['''jwa''', '''j''', '''w''', '''a''', '''la''', '''lapa''']))
print(all_construct('''rajamati''', ['''s''', '''raj''', '''amat''', '''raja''', '''ma''', '''i''', '''t''']))
print(
all_construct(
'''hexagonosaurus''',
['''h''', '''ex''', '''hex''', '''ag''', '''ago''', '''ru''', '''auru''', '''rus''', '''go''', '''no''', '''o''', '''s'''],
)
)
| 33 |
'''simple docstring'''
import unittest
from dataclasses import dataclass
import pytest
from accelerate.commands.config.config_args import SageMakerConfig
from accelerate.utils import ComputeEnvironment
from accelerate.utils.launch import _convert_nargs_to_dict
@dataclass
class lowercase ( A__ ):
"""simple docstring"""
_a = ComputeEnvironment.AMAZON_SAGEMAKER
_a = True
_a = 'ml.p3.2xlarge'
_a = 'accelerate_sagemaker_execution_role'
_a = 'hf-sm'
_a = 'us-east-1'
_a = 1
_a = 'accelerate-sagemaker-1'
_a = '1.6'
_a = '4.4'
_a = 'train.py'
_a = [
'--model_name_or_path',
'bert',
'--do_train',
'False',
'--epochs',
'3',
'--learning_rate',
'5e-5',
'--max_steps',
'50.5',
]
_a = [
'--model_name_or_path',
'bert',
'--do_train',
'--do_test',
'False',
'--do_predict',
'--epochs',
'3',
'--learning_rate',
'5e-5',
'--max_steps',
'50.5',
]
class lowercase ( unittest.TestCase ):
"""simple docstring"""
def lowerCAmelCase__ ( self ):
'''simple docstring'''
UpperCamelCase__ :Union[str, Any] = _convert_nargs_to_dict(MockLaunchConfig.success_training_script_args )
assert isinstance(converted_args['''model_name_or_path'''] , UpperCamelCase_ )
assert isinstance(converted_args['''do_train'''] , UpperCamelCase_ )
assert isinstance(converted_args['''epochs'''] , UpperCamelCase_ )
assert isinstance(converted_args['''learning_rate'''] , UpperCamelCase_ )
assert isinstance(converted_args['''max_steps'''] , UpperCamelCase_ )
with pytest.raises(UpperCamelCase_ ):
_convert_nargs_to_dict(MockLaunchConfig.fail_training_script_args ) | 97 | 0 |
'''simple docstring'''
import inspect
import unittest
from transformers import DPTConfig
from transformers.file_utils import is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from torch import nn
from transformers import MODEL_MAPPING, DPTForDepthEstimation, DPTForSemanticSegmentation, DPTModel
from transformers.models.dpt.modeling_dpt import DPT_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import DPTImageProcessor
class _a :
def __init__( self : str , lowercase : List[Any] , lowercase : Dict=2 , lowercase : str=32 , lowercase : Optional[Any]=16 , lowercase : Optional[Any]=3 , lowercase : Union[str, Any]=True , lowercase : List[Any]=True , lowercase : Optional[int]=32 , lowercase : Any=4 , lowercase : str=[0, 1, 2, 3] , lowercase : List[Any]=4 , lowercase : str=37 , lowercase : Optional[Any]="gelu" , lowercase : Tuple=0.1 , lowercase : Tuple=0.1 , lowercase : Union[str, Any]=0.02 , lowercase : int=3 , lowercase : int=[1, 384, 24, 24] , lowercase : str=True , lowercase : List[Any]=None , ):
'''simple docstring'''
UpperCAmelCase = parent
UpperCAmelCase = batch_size
UpperCAmelCase = image_size
UpperCAmelCase = patch_size
UpperCAmelCase = num_channels
UpperCAmelCase = is_training
UpperCAmelCase = use_labels
UpperCAmelCase = hidden_size
UpperCAmelCase = num_hidden_layers
UpperCAmelCase = backbone_out_indices
UpperCAmelCase = num_attention_heads
UpperCAmelCase = intermediate_size
UpperCAmelCase = hidden_act
UpperCAmelCase = hidden_dropout_prob
UpperCAmelCase = attention_probs_dropout_prob
UpperCAmelCase = initializer_range
UpperCAmelCase = num_labels
UpperCAmelCase = backbone_featmap_shape
UpperCAmelCase = scope
UpperCAmelCase = is_hybrid
# sequence length of DPT = num_patches + 1 (we add 1 for the [CLS] token)
UpperCAmelCase = (image_size // patch_size) ** 2
UpperCAmelCase = num_patches + 1
def A ( self : int ):
'''simple docstring'''
UpperCAmelCase = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
UpperCAmelCase = None
if self.use_labels:
UpperCAmelCase = ids_tensor([self.batch_size, self.image_size, self.image_size] , self.num_labels )
UpperCAmelCase = self.get_config()
return config, pixel_values, labels
def A ( self : str ):
'''simple docstring'''
UpperCAmelCase = {
'''global_padding''': '''same''',
'''layer_type''': '''bottleneck''',
'''depths''': [3, 4, 9],
'''out_features''': ['''stage1''', '''stage2''', '''stage3'''],
'''embedding_dynamic_padding''': True,
'''hidden_sizes''': [96, 192, 384, 768],
'''num_groups''': 2,
}
return DPTConfig(
image_size=self.image_size , patch_size=self.patch_size , num_channels=self.num_channels , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , backbone_out_indices=self.backbone_out_indices , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , is_decoder=lowercase , initializer_range=self.initializer_range , is_hybrid=self.is_hybrid , backbone_config=lowercase , backbone_featmap_shape=self.backbone_featmap_shape , )
def A ( self : Optional[int] , lowercase : str , lowercase : Optional[Any] , lowercase : List[str] ):
'''simple docstring'''
UpperCAmelCase = DPTModel(config=lowercase )
model.to(lowercase )
model.eval()
UpperCAmelCase = model(lowercase )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def A ( self : List[str] , lowercase : Tuple , lowercase : Optional[int] , lowercase : List[str] ):
'''simple docstring'''
UpperCAmelCase = self.num_labels
UpperCAmelCase = DPTForDepthEstimation(lowercase )
model.to(lowercase )
model.eval()
UpperCAmelCase = model(lowercase )
self.parent.assertEqual(result.predicted_depth.shape , (self.batch_size, self.image_size, self.image_size) )
def A ( self : int , lowercase : Dict , lowercase : Union[str, Any] , lowercase : int ):
'''simple docstring'''
UpperCAmelCase = self.num_labels
UpperCAmelCase = DPTForSemanticSegmentation(lowercase )
model.to(lowercase )
model.eval()
UpperCAmelCase = model(lowercase , labels=lowercase )
self.parent.assertEqual(
result.logits.shape , (self.batch_size, self.num_labels, self.image_size, self.image_size) )
def A ( self : Tuple ):
'''simple docstring'''
UpperCAmelCase = self.prepare_config_and_inputs()
UpperCAmelCase , UpperCAmelCase , UpperCAmelCase = config_and_inputs
UpperCAmelCase = {'''pixel_values''': pixel_values}
return config, inputs_dict
@require_torch
class _a ( __a , __a , unittest.TestCase ):
__a : Dict = (DPTModel, DPTForDepthEstimation, DPTForSemanticSegmentation) if is_torch_available() else ()
__a : Optional[int] = (
{
"""depth-estimation""": DPTForDepthEstimation,
"""feature-extraction""": DPTModel,
"""image-segmentation""": DPTForSemanticSegmentation,
}
if is_torch_available()
else {}
)
__a : Any = False
__a : List[Any] = False
__a : Dict = False
def A ( self : int ):
'''simple docstring'''
UpperCAmelCase = DPTModelTester(self )
UpperCAmelCase = ConfigTester(self , config_class=lowercase , has_text_modality=lowercase , hidden_size=37 )
def A ( self : int ):
'''simple docstring'''
self.config_tester.run_common_tests()
@unittest.skip(reason='''DPT does not use inputs_embeds''' )
def A ( self : List[str] ):
'''simple docstring'''
pass
def A ( self : int ):
'''simple docstring'''
UpperCAmelCase , UpperCAmelCase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
UpperCAmelCase = model_class(lowercase )
self.assertIsInstance(model.get_input_embeddings() , (nn.Module) )
UpperCAmelCase = model.get_output_embeddings()
self.assertTrue(x is None or isinstance(lowercase , nn.Linear ) )
def A ( self : Any ):
'''simple docstring'''
UpperCAmelCase , UpperCAmelCase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
UpperCAmelCase = model_class(lowercase )
UpperCAmelCase = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
UpperCAmelCase = [*signature.parameters.keys()]
UpperCAmelCase = ['''pixel_values''']
self.assertListEqual(arg_names[:1] , lowercase )
def A ( self : Tuple ):
'''simple docstring'''
UpperCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*lowercase )
def A ( self : Dict ):
'''simple docstring'''
UpperCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_depth_estimation(*lowercase )
def A ( self : Any ):
'''simple docstring'''
UpperCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_semantic_segmentation(*lowercase )
def A ( self : List[str] ):
'''simple docstring'''
for model_class in self.all_model_classes:
if model_class.__name__ == "DPTForDepthEstimation":
continue
UpperCAmelCase , UpperCAmelCase = self.model_tester.prepare_config_and_inputs_for_common()
UpperCAmelCase = True
if model_class in get_values(lowercase ):
continue
UpperCAmelCase = model_class(lowercase )
model.to(lowercase )
model.train()
UpperCAmelCase = self._prepare_for_class(lowercase , lowercase , return_labels=lowercase )
UpperCAmelCase = model(**lowercase ).loss
loss.backward()
def A ( self : str ):
'''simple docstring'''
for model_class in self.all_model_classes:
if model_class.__name__ == "DPTForDepthEstimation":
continue
UpperCAmelCase , UpperCAmelCase = self.model_tester.prepare_config_and_inputs_for_common()
UpperCAmelCase = False
UpperCAmelCase = True
if model_class in get_values(lowercase ) or not model_class.supports_gradient_checkpointing:
continue
UpperCAmelCase = model_class(lowercase )
model.to(lowercase )
model.gradient_checkpointing_enable()
model.train()
UpperCAmelCase = self._prepare_for_class(lowercase , lowercase , return_labels=lowercase )
UpperCAmelCase = model(**lowercase ).loss
loss.backward()
def A ( self : Optional[Any] ):
'''simple docstring'''
UpperCAmelCase , UpperCAmelCase = self.model_tester.prepare_config_and_inputs_for_common()
UpperCAmelCase = _config_zero_init(lowercase )
for model_class in self.all_model_classes:
UpperCAmelCase = model_class(config=lowercase )
# Skip the check for the backbone
UpperCAmelCase = []
for name, module in model.named_modules():
if module.__class__.__name__ == "DPTViTHybridEmbeddings":
UpperCAmelCase = [f"{name}.{key}" for key in module.state_dict().keys()]
break
for name, param in model.named_parameters():
if param.requires_grad:
if name in backbone_params:
continue
self.assertIn(
((param.data.mean() * 1E9).round() / 1E9).item() , [0.0, 1.0] , msg=f"Parameter {name} of model {model_class} seems not properly initialized" , )
@unittest.skip('''Will be fixed soon by reducing the size of the model used for common tests.''' )
def A ( self : int ):
'''simple docstring'''
pass
@slow
def A ( self : Any ):
'''simple docstring'''
for model_name in DPT_PRETRAINED_MODEL_ARCHIVE_LIST[1:]:
UpperCAmelCase = DPTModel.from_pretrained(lowercase )
self.assertIsNotNone(lowercase )
def A ( self : str ):
'''simple docstring'''
UpperCAmelCase , UpperCAmelCase = self.model_tester.prepare_config_and_inputs_for_common()
UpperCAmelCase = '''add'''
with self.assertRaises(lowercase ):
UpperCAmelCase = DPTForDepthEstimation(lowercase )
def snake_case_ ():
UpperCAmelCase = Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
return image
@require_torch
@require_vision
@slow
class _a ( unittest.TestCase ):
def A ( self : Dict ):
'''simple docstring'''
UpperCAmelCase = DPTImageProcessor.from_pretrained('''Intel/dpt-hybrid-midas''' )
UpperCAmelCase = DPTForDepthEstimation.from_pretrained('''Intel/dpt-hybrid-midas''' ).to(lowercase )
UpperCAmelCase = prepare_img()
UpperCAmelCase = image_processor(images=lowercase , return_tensors='''pt''' ).to(lowercase )
# forward pass
with torch.no_grad():
UpperCAmelCase = model(**lowercase )
UpperCAmelCase = outputs.predicted_depth
# verify the predicted depth
UpperCAmelCase = torch.Size((1, 384, 384) )
self.assertEqual(predicted_depth.shape , lowercase )
UpperCAmelCase = torch.tensor(
[[[5.6437, 5.6146, 5.6511], [5.4371, 5.5649, 5.5958], [5.5215, 5.5184, 5.5293]]] ).to(lowercase )
self.assertTrue(torch.allclose(outputs.predicted_depth[:3, :3, :3] / 100 , lowercase , atol=1E-4 ) )
| 34 |
'''simple docstring'''
from datetime import datetime
import matplotlib.pyplot as plt
import torch
def a ( __a ) -> int:
'''simple docstring'''
for param in module.parameters():
UpperCamelCase__ :Dict = False
def a ( ) -> Union[str, Any]:
'''simple docstring'''
UpperCamelCase__ :List[Any] = '''cuda''' if torch.cuda.is_available() else '''cpu'''
if torch.backends.mps.is_available() and torch.backends.mps.is_built():
UpperCamelCase__ :Optional[int] = '''mps'''
if device == "mps":
print(
'''WARNING: MPS currently doesn\'t seem to work, and messes up backpropagation without any visible torch'''
''' errors. I recommend using CUDA on a colab notebook or CPU instead if you\'re facing inexplicable issues'''
''' with generations.''' )
return device
def a ( __a ) -> Any:
'''simple docstring'''
UpperCamelCase__ :Dict = plt.imshow(__a )
fig.axes.get_xaxis().set_visible(__a )
fig.axes.get_yaxis().set_visible(__a )
plt.show()
def a ( ) -> str:
'''simple docstring'''
UpperCamelCase__ :int = datetime.now()
UpperCamelCase__ :str = current_time.strftime('''%H:%M:%S''' )
return timestamp | 97 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
__a = {
"configuration_encodec": [
"ENCODEC_PRETRAINED_CONFIG_ARCHIVE_MAP",
"EncodecConfig",
],
"feature_extraction_encodec": ["EncodecFeatureExtractor"],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__a = [
"ENCODEC_PRETRAINED_MODEL_ARCHIVE_LIST",
"EncodecModel",
"EncodecPreTrainedModel",
]
if TYPE_CHECKING:
from .configuration_encodec import (
ENCODEC_PRETRAINED_CONFIG_ARCHIVE_MAP,
EncodecConfig,
)
from .feature_extraction_encodec import EncodecFeatureExtractor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_encodec import (
ENCODEC_PRETRAINED_MODEL_ARCHIVE_LIST,
EncodecModel,
EncodecPreTrainedModel,
)
else:
import sys
__a = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 35 |
'''simple docstring'''
from scipy.stats import pearsonr
import datasets
__snake_case = '''
Pearson correlation coefficient and p-value for testing non-correlation.
The Pearson correlation coefficient measures the linear relationship between two datasets. The calculation of the p-value relies on the assumption that each dataset is normally distributed. Like other correlation coefficients, this one varies between -1 and +1 with 0 implying no correlation. Correlations of -1 or +1 imply an exact linear relationship. Positive correlations imply that as x increases, so does y. Negative correlations imply that as x increases, y decreases.
The p-value roughly indicates the probability of an uncorrelated system producing datasets that have a Pearson correlation at least as extreme as the one computed from these datasets.
'''
__snake_case = '''
Args:
predictions (`list` of `int`): Predicted class labels, as returned by a model.
references (`list` of `int`): Ground truth labels.
return_pvalue (`boolean`): If `True`, returns the p-value, along with the correlation coefficient. If `False`, returns only the correlation coefficient. Defaults to `False`.
Returns:
pearsonr (`float`): Pearson correlation coefficient. Minimum possible value is -1. Maximum possible value is 1. Values of 1 and -1 indicate exact linear positive and negative relationships, respectively. A value of 0 implies no correlation.
p-value (`float`): P-value, which roughly indicates the probability of an The p-value roughly indicates the probability of an uncorrelated system producing datasets that have a Pearson correlation at least as extreme as the one computed from these datasets. Minimum possible value is 0. Maximum possible value is 1. Higher values indicate higher probabilities.
Examples:
Example 1-A simple example using only predictions and references.
>>> pearsonr_metric = datasets.load_metric("pearsonr")
>>> results = pearsonr_metric.compute(predictions=[10, 9, 2.5, 6, 4], references=[1, 2, 3, 4, 5])
>>> print(round(results[\'pearsonr\'], 2))
-0.74
Example 2-The same as Example 1, but that also returns the `p-value`.
>>> pearsonr_metric = datasets.load_metric("pearsonr")
>>> results = pearsonr_metric.compute(predictions=[10, 9, 2.5, 6, 4], references=[1, 2, 3, 4, 5], return_pvalue=True)
>>> print(sorted(list(results.keys())))
[\'p-value\', \'pearsonr\']
>>> print(round(results[\'pearsonr\'], 2))
-0.74
>>> print(round(results[\'p-value\'], 2))
0.15
'''
__snake_case = '''
@article{2020SciPy-NMeth,
author = {Virtanen, Pauli and Gommers, Ralf and Oliphant, Travis E. and
Haberland, Matt and Reddy, Tyler and Cournapeau, David and
Burovski, Evgeni and Peterson, Pearu and Weckesser, Warren and
Bright, Jonathan and {van der Walt}, St{\'e}fan J. and
Brett, Matthew and Wilson, Joshua and Millman, K. Jarrod and
Mayorov, Nikolay and Nelson, Andrew R. J. and Jones, Eric and
Kern, Robert and Larson, Eric and Carey, C J and
Polat, Ilhan and Feng, Yu and Moore, Eric W. and
{VanderPlas}, Jake and Laxalde, Denis and Perktold, Josef and
Cimrman, Robert and Henriksen, Ian and Quintero, E. A. and
Harris, Charles R. and Archibald, Anne M. and
Ribeiro, Antonio H. and Pedregosa, Fabian and
{van Mulbregt}, Paul and {SciPy 1.0 Contributors}},
title = {{{SciPy} 1.0: Fundamental Algorithms for Scientific
Computing in Python}},
journal = {Nature Methods},
year = {2020},
volume = {17},
pages = {261--272},
adsurl = {https://rdcu.be/b08Wh},
doi = {10.1038/s41592-019-0686-2},
}
'''
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class lowercase ( datasets.Metric ):
"""simple docstring"""
def lowerCAmelCase__ ( self ):
'''simple docstring'''
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
'''predictions''': datasets.Value('''float''' ),
'''references''': datasets.Value('''float''' ),
} ) , reference_urls=['''https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.pearsonr.html'''] , )
def lowerCAmelCase__ ( self , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_=False ):
'''simple docstring'''
if return_pvalue:
UpperCamelCase__ :Any = pearsonr(UpperCamelCase_ , UpperCamelCase_ )
return {"pearsonr": results[0], "p-value": results[1]}
else:
return {"pearsonr": float(pearsonr(UpperCamelCase_ , UpperCamelCase_ )[0] )} | 97 | 0 |
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import BeitConfig, BeitForImageClassification, BeitForMaskedImageModeling, BeitImageProcessor
from transformers.image_utils import PILImageResampling
from transformers.utils import logging
logging.set_verbosity_info()
_snake_case = logging.get_logger(__name__)
def A ( _lowerCamelCase , _lowerCamelCase=False , _lowerCamelCase=False ):
'''simple docstring'''
_lowerCAmelCase : int = "backbone." if is_semantic else ""
_lowerCAmelCase : Optional[int] = []
for i in range(config.num_hidden_layers ):
# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
rename_keys.append((F"{prefix}blocks.{i}.norm1.weight", F"beit.encoder.layer.{i}.layernorm_before.weight") )
rename_keys.append((F"{prefix}blocks.{i}.norm1.bias", F"beit.encoder.layer.{i}.layernorm_before.bias") )
rename_keys.append(
(F"{prefix}blocks.{i}.attn.proj.weight", F"beit.encoder.layer.{i}.attention.output.dense.weight") )
rename_keys.append(
(F"{prefix}blocks.{i}.attn.proj.bias", F"beit.encoder.layer.{i}.attention.output.dense.bias") )
rename_keys.append((F"{prefix}blocks.{i}.norm2.weight", F"beit.encoder.layer.{i}.layernorm_after.weight") )
rename_keys.append((F"{prefix}blocks.{i}.norm2.bias", F"beit.encoder.layer.{i}.layernorm_after.bias") )
rename_keys.append((F"{prefix}blocks.{i}.mlp.fc1.weight", F"beit.encoder.layer.{i}.intermediate.dense.weight") )
rename_keys.append((F"{prefix}blocks.{i}.mlp.fc1.bias", F"beit.encoder.layer.{i}.intermediate.dense.bias") )
rename_keys.append((F"{prefix}blocks.{i}.mlp.fc2.weight", F"beit.encoder.layer.{i}.output.dense.weight") )
rename_keys.append((F"{prefix}blocks.{i}.mlp.fc2.bias", F"beit.encoder.layer.{i}.output.dense.bias") )
# projection layer + position embeddings
rename_keys.extend(
[
(F"{prefix}cls_token", "beit.embeddings.cls_token"),
(F"{prefix}patch_embed.proj.weight", "beit.embeddings.patch_embeddings.projection.weight"),
(F"{prefix}patch_embed.proj.bias", "beit.embeddings.patch_embeddings.projection.bias"),
(F"{prefix}pos_embed", "beit.embeddings.position_embeddings"),
] )
if has_lm_head:
# mask token + layernorm
rename_keys.extend(
[
("mask_token", "beit.embeddings.mask_token"),
("norm.weight", "layernorm.weight"),
("norm.bias", "layernorm.bias"),
] )
else:
# layernorm + classification head
rename_keys.extend(
[
("fc_norm.weight", "beit.pooler.layernorm.weight"),
("fc_norm.bias", "beit.pooler.layernorm.bias"),
("head.weight", "classifier.weight"),
("head.bias", "classifier.bias"),
] )
return rename_keys
def A ( _lowerCamelCase , _lowerCamelCase , _lowerCamelCase=False , _lowerCamelCase=False ):
'''simple docstring'''
for i in range(config.num_hidden_layers ):
_lowerCAmelCase : Tuple = "backbone." if is_semantic else ""
# queries, keys and values
_lowerCAmelCase : int = state_dict.pop(F"{prefix}blocks.{i}.attn.qkv.weight" )
_lowerCAmelCase : Any = state_dict.pop(F"{prefix}blocks.{i}.attn.q_bias" )
_lowerCAmelCase : int = state_dict.pop(F"{prefix}blocks.{i}.attn.v_bias" )
_lowerCAmelCase : Any = in_proj_weight[
: config.hidden_size, :
]
_lowerCAmelCase : Optional[int] = q_bias
_lowerCAmelCase : Tuple = in_proj_weight[
config.hidden_size : config.hidden_size * 2, :
]
_lowerCAmelCase : str = in_proj_weight[
-config.hidden_size :, :
]
_lowerCAmelCase : int = v_bias
# gamma_1 and gamma_2
# we call them lambda because otherwise they are renamed when using .from_pretrained
_lowerCAmelCase : Optional[Any] = state_dict.pop(F"{prefix}blocks.{i}.gamma_1" )
_lowerCAmelCase : Tuple = state_dict.pop(F"{prefix}blocks.{i}.gamma_2" )
_lowerCAmelCase : Dict = gamma_a
_lowerCAmelCase : Union[str, Any] = gamma_a
def A ( _lowerCamelCase , _lowerCamelCase , _lowerCamelCase ):
'''simple docstring'''
_lowerCAmelCase : Optional[int] = dct.pop(_lowerCamelCase )
_lowerCAmelCase : int = val
def A ( ):
'''simple docstring'''
_lowerCAmelCase : Union[str, Any] = "http://images.cocodataset.org/val2017/000000039769.jpg"
_lowerCAmelCase : Tuple = Image.open(requests.get(_lowerCamelCase , stream=_lowerCamelCase ).raw )
return im
@torch.no_grad()
def A ( _lowerCamelCase , _lowerCamelCase , _lowerCamelCase=False ):
'''simple docstring'''
_lowerCAmelCase : List[str] = False if "rvlcdip" in checkpoint_url else True
_lowerCAmelCase : str = BeitConfig(use_absolute_position_embeddings=_lowerCamelCase , use_mask_token=_lowerCamelCase )
# size of the architecture
if "large" in checkpoint_url or "dit-l" in checkpoint_url:
_lowerCAmelCase : List[str] = 1_024
_lowerCAmelCase : List[Any] = 4_096
_lowerCAmelCase : Tuple = 24
_lowerCAmelCase : Any = 16
# labels
if "rvlcdip" in checkpoint_url:
_lowerCAmelCase : List[Any] = 16
_lowerCAmelCase : int = "huggingface/label-files"
_lowerCAmelCase : Optional[Any] = "rvlcdip-id2label.json"
_lowerCAmelCase : Any = json.load(open(hf_hub_download(_lowerCamelCase , _lowerCamelCase , repo_type="dataset" ) , "r" ) )
_lowerCAmelCase : str = {int(_lowerCamelCase ): v for k, v in idalabel.items()}
_lowerCAmelCase : Dict = idalabel
_lowerCAmelCase : Any = {v: k for k, v in idalabel.items()}
# load state_dict of original model, remove and rename some keys
_lowerCAmelCase : List[str] = torch.hub.load_state_dict_from_url(_lowerCamelCase , map_location="cpu" )["model"]
_lowerCAmelCase : List[str] = create_rename_keys(_lowerCamelCase , has_lm_head=_lowerCamelCase )
for src, dest in rename_keys:
rename_key(_lowerCamelCase , _lowerCamelCase , _lowerCamelCase )
read_in_q_k_v(_lowerCamelCase , _lowerCamelCase , has_lm_head=_lowerCamelCase )
# load HuggingFace model
_lowerCAmelCase : Dict = BeitForMaskedImageModeling(_lowerCamelCase ) if has_lm_head else BeitForImageClassification(_lowerCamelCase )
model.eval()
model.load_state_dict(_lowerCamelCase )
# Check outputs on an image
_lowerCAmelCase : Optional[Any] = BeitImageProcessor(
size=config.image_size , resample=PILImageResampling.BILINEAR , do_center_crop=_lowerCamelCase )
_lowerCAmelCase : Tuple = prepare_img()
_lowerCAmelCase : Optional[Any] = image_processor(images=_lowerCamelCase , return_tensors="pt" )
_lowerCAmelCase : Optional[int] = encoding["pixel_values"]
_lowerCAmelCase : Any = model(_lowerCamelCase )
_lowerCAmelCase : List[Any] = outputs.logits
# verify logits
_lowerCAmelCase : Optional[Any] = [1, 16] if "rvlcdip" in checkpoint_url else [1, 196, 8_192]
assert logits.shape == torch.Size(_lowerCamelCase ), "Shape of logits not as expected"
Path(_lowerCamelCase ).mkdir(exist_ok=_lowerCamelCase )
print(F"Saving model to {pytorch_dump_folder_path}" )
model.save_pretrained(_lowerCamelCase )
print(F"Saving image processor to {pytorch_dump_folder_path}" )
image_processor.save_pretrained(_lowerCamelCase )
if push_to_hub:
if has_lm_head:
_lowerCAmelCase : Optional[int] = "dit-base" if "base" in checkpoint_url else "dit-large"
else:
_lowerCAmelCase : Dict = "dit-base-finetuned-rvlcdip" if "dit-b" in checkpoint_url else "dit-large-finetuned-rvlcdip"
image_processor.push_to_hub(
repo_path_or_name=Path(_lowerCamelCase , _lowerCamelCase ) , organization="nielsr" , commit_message="Add image processor" , use_temp_dir=_lowerCamelCase , )
model.push_to_hub(
repo_path_or_name=Path(_lowerCamelCase , _lowerCamelCase ) , organization="nielsr" , commit_message="Add model" , use_temp_dir=_lowerCamelCase , )
if __name__ == "__main__":
_snake_case = argparse.ArgumentParser()
parser.add_argument(
"--checkpoint_url",
default="https://layoutlm.blob.core.windows.net/dit/dit-pts/dit-base-224-p16-500k-62d53a.pth",
type=str,
help="URL to the original PyTorch checkpoint (.pth file).",
)
parser.add_argument(
"--pytorch_dump_folder_path", default=None, type=str, help="Path to the folder to output PyTorch model."
)
parser.add_argument(
"--push_to_hub",
action="store_true",
)
_snake_case = parser.parse_args()
convert_dit_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)
| 36 |
'''simple docstring'''
from typing import List, Optional
from tokenizers import ByteLevelBPETokenizer
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_blenderbot_small import BlenderbotSmallTokenizer
__snake_case = logging.get_logger(__name__)
__snake_case = {
'''vocab_file''': '''vocab.json''',
'''merges_file''': '''merges.txt''',
'''tokenizer_config_file''': '''tokenizer_config.json''',
}
__snake_case = {
'''vocab_file''': {
'''facebook/blenderbot_small-90M''': '''https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/vocab.json'''
},
'''merges_file''': {
'''facebook/blenderbot_small-90M''': '''https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/merges.txt'''
},
'''tokenizer_config_file''': {
'''facebook/blenderbot_small-90M''': (
'''https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/tokenizer_config.json'''
)
},
}
__snake_case = {
'''facebook/blenderbot_small-90M''': 512,
}
class lowercase ( A__ ):
"""simple docstring"""
_a = VOCAB_FILES_NAMES
_a = PRETRAINED_VOCAB_FILES_MAP
_a = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
_a = BlenderbotSmallTokenizer
def __init__( self , UpperCamelCase_=None , UpperCamelCase_=None , UpperCamelCase_="<|endoftext|>" , UpperCamelCase_="<|endoftext|>" , UpperCamelCase_="<|endoftext|>" , UpperCamelCase_=False , UpperCamelCase_=True , **UpperCamelCase_ , ):
'''simple docstring'''
super().__init__(
ByteLevelBPETokenizer(
vocab=UpperCamelCase_ , merges=UpperCamelCase_ , add_prefix_space=UpperCamelCase_ , trim_offsets=UpperCamelCase_ , ) , bos_token=UpperCamelCase_ , eos_token=UpperCamelCase_ , unk_token=UpperCamelCase_ , **UpperCamelCase_ , )
UpperCamelCase__ :Union[str, Any] = add_prefix_space
def lowerCAmelCase__ ( self , UpperCamelCase_ , UpperCamelCase_=None ):
'''simple docstring'''
UpperCamelCase__ :List[Any] = [self.bos_token_id] + token_ids_a + [self.eos_token_id]
if token_ids_a is None:
return output
return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]
def lowerCAmelCase__ ( self , UpperCamelCase_ , UpperCamelCase_ = None ):
'''simple docstring'''
UpperCamelCase__ :Optional[int] = [self.sep_token_id]
UpperCamelCase__ :Any = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep + sep + token_ids_a + sep ) * [0] | 97 | 0 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowerCAmelCase = logging.get_logger(__name__)
_lowerCAmelCase = {
'''tiiuae/falcon-40b''': '''https://huggingface.co/tiiuae/falcon-40b/resolve/main/config.json''',
'''tiiuae/falcon-7b''': '''https://huggingface.co/tiiuae/falcon-7b/resolve/main/config.json''',
}
class lowerCAmelCase_( SCREAMING_SNAKE_CASE_ ):
'''simple docstring'''
__lowercase : Optional[Any] = '''falcon'''
__lowercase : Optional[int] = ['''past_key_values''']
def __init__( self ,__UpperCAmelCase=6_5024 ,__UpperCAmelCase=4544 ,__UpperCAmelCase=32 ,__UpperCAmelCase=71 ,__UpperCAmelCase=1E-5 ,__UpperCAmelCase=0.0_2 ,__UpperCAmelCase=True ,__UpperCAmelCase=0.0 ,__UpperCAmelCase=0.0 ,__UpperCAmelCase=None ,__UpperCAmelCase=False ,__UpperCAmelCase=False ,__UpperCAmelCase=True ,__UpperCAmelCase=True ,__UpperCAmelCase=False ,__UpperCAmelCase=11 ,__UpperCAmelCase=11 ,**__UpperCAmelCase ,) -> Union[str, Any]:
lowerCAmelCase__ : List[str] = vocab_size
# Backward compatibility with n_embed kwarg
lowerCAmelCase__ : List[str] = kwargs.pop("""n_embed""" ,__UpperCAmelCase )
lowerCAmelCase__ : List[Any] = hidden_size if n_embed is None else n_embed
lowerCAmelCase__ : List[Any] = num_hidden_layers
lowerCAmelCase__ : Optional[Any] = num_attention_heads
lowerCAmelCase__ : str = layer_norm_epsilon
lowerCAmelCase__ : int = initializer_range
lowerCAmelCase__ : str = use_cache
lowerCAmelCase__ : str = hidden_dropout
lowerCAmelCase__ : Tuple = attention_dropout
lowerCAmelCase__ : Tuple = bos_token_id
lowerCAmelCase__ : Union[str, Any] = eos_token_id
lowerCAmelCase__ : Any = num_attention_heads if num_kv_heads is None else num_kv_heads
lowerCAmelCase__ : int = alibi
lowerCAmelCase__ : Any = new_decoder_architecture
lowerCAmelCase__ : Optional[int] = multi_query # Ignored when new_decoder_architecture is True
lowerCAmelCase__ : Union[str, Any] = parallel_attn
lowerCAmelCase__ : str = bias
super().__init__(bos_token_id=__UpperCAmelCase ,eos_token_id=__UpperCAmelCase ,**__UpperCAmelCase )
@property
def UpperCAmelCase_ ( self ) -> str:
return self.hidden_size // self.num_attention_heads
@property
def UpperCAmelCase_ ( self ) -> Tuple:
return not self.alibi
| 37 |
'''simple docstring'''
from ...utils import (
OptionalDependencyNotAvailable,
is_flax_available,
is_torch_available,
is_transformers_available,
)
try:
if not (is_transformers_available() and is_torch_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ...utils.dummy_torch_and_transformers_objects import * # noqa F403
else:
from .multicontrolnet import MultiControlNetModel
from .pipeline_controlnet import StableDiffusionControlNetPipeline
from .pipeline_controlnet_imgaimg import StableDiffusionControlNetImgaImgPipeline
from .pipeline_controlnet_inpaint import StableDiffusionControlNetInpaintPipeline
if is_transformers_available() and is_flax_available():
from .pipeline_flax_controlnet import FlaxStableDiffusionControlNetPipeline | 97 | 0 |
import inspect
import os
import torch
from transformers import AutoModel
from transformers.testing_utils import mockenv_context
from transformers.trainer_utils import set_seed
import accelerate
from accelerate.accelerator import Accelerator
from accelerate.state import AcceleratorState
from accelerate.test_utils.testing import (
AccelerateTestCase,
TempDirTestCase,
execute_subprocess_async,
require_cuda,
require_fsdp,
require_multi_gpu,
slow,
)
from accelerate.utils.constants import (
FSDP_AUTO_WRAP_POLICY,
FSDP_BACKWARD_PREFETCH,
FSDP_SHARDING_STRATEGY,
FSDP_STATE_DICT_TYPE,
)
from accelerate.utils.dataclasses import FullyShardedDataParallelPlugin
from accelerate.utils.other import patch_environment
set_seed(42)
UpperCAmelCase_ : int = '''bert-base-cased'''
UpperCAmelCase_ : Any = '''fp16'''
UpperCAmelCase_ : str = '''bf16'''
UpperCAmelCase_ : int = [FPaa, BFaa]
@require_fsdp
@require_cuda
class _SCREAMING_SNAKE_CASE ( _a ):
def _A ( self : List[Any] ):
super().setUp()
UpperCamelCase :Tuple = dict(
ACCELERATE_USE_FSDP="""true""" , MASTER_ADDR="""localhost""" , MASTER_PORT="""10999""" , RANK="""0""" , LOCAL_RANK="""0""" , WORLD_SIZE="""1""" , )
def _A ( self : List[str] ):
from torch.distributed.fsdp.fully_sharded_data_parallel import ShardingStrategy
for i, strategy in enumerate(__lowerCamelCase ):
UpperCamelCase :Union[str, Any] = self.dist_env.copy()
UpperCamelCase :List[Any] = F"""{i + 1}"""
UpperCamelCase :List[Any] = strategy
with mockenv_context(**__lowerCamelCase ):
UpperCamelCase :List[str] = FullyShardedDataParallelPlugin()
self.assertEqual(fsdp_plugin.sharding_strategy , ShardingStrategy(i + 1 ) )
def _A ( self : str ):
from torch.distributed.fsdp.fully_sharded_data_parallel import BackwardPrefetch
for i, prefetch_policy in enumerate(__lowerCamelCase ):
UpperCamelCase :str = self.dist_env.copy()
UpperCamelCase :List[Any] = prefetch_policy
with mockenv_context(**__lowerCamelCase ):
UpperCamelCase :Optional[int] = FullyShardedDataParallelPlugin()
if prefetch_policy == "NO_PREFETCH":
self.assertIsNone(fsdp_plugin.backward_prefetch )
else:
self.assertEqual(fsdp_plugin.backward_prefetch , BackwardPrefetch(i + 1 ) )
def _A ( self : Union[str, Any] ):
from torch.distributed.fsdp.fully_sharded_data_parallel import StateDictType
for i, state_dict_type in enumerate(__lowerCamelCase ):
UpperCamelCase :Any = self.dist_env.copy()
UpperCamelCase :Tuple = state_dict_type
with mockenv_context(**__lowerCamelCase ):
UpperCamelCase :Dict = FullyShardedDataParallelPlugin()
self.assertEqual(fsdp_plugin.state_dict_type , StateDictType(i + 1 ) )
if state_dict_type == "FULL_STATE_DICT":
self.assertTrue(fsdp_plugin.state_dict_config.offload_to_cpu )
self.assertTrue(fsdp_plugin.state_dict_config.ranka_only )
def _A ( self : Tuple ):
UpperCamelCase :int = AutoModel.from_pretrained(__lowerCamelCase )
for policy in FSDP_AUTO_WRAP_POLICY:
UpperCamelCase :Any = self.dist_env.copy()
UpperCamelCase :Dict = policy
if policy == "TRANSFORMER_BASED_WRAP":
UpperCamelCase :Any = """BertLayer"""
elif policy == "SIZE_BASED_WRAP":
UpperCamelCase :Optional[Any] = """2000"""
with mockenv_context(**__lowerCamelCase ):
UpperCamelCase :int = FullyShardedDataParallelPlugin()
fsdp_plugin.set_auto_wrap_policy(__lowerCamelCase )
if policy == "NO_WRAP":
self.assertIsNone(fsdp_plugin.auto_wrap_policy )
else:
self.assertIsNotNone(fsdp_plugin.auto_wrap_policy )
UpperCamelCase :List[Any] = self.dist_env.copy()
UpperCamelCase :Optional[int] = """TRANSFORMER_BASED_WRAP"""
UpperCamelCase :int = """T5Layer"""
with mockenv_context(**__lowerCamelCase ):
UpperCamelCase :Any = FullyShardedDataParallelPlugin()
with self.assertRaises(__lowerCamelCase ) as cm:
fsdp_plugin.set_auto_wrap_policy(__lowerCamelCase )
self.assertTrue("""Could not find the transformer layer class to wrap in the model.""" in str(cm.exception ) )
UpperCamelCase :List[str] = self.dist_env.copy()
UpperCamelCase :str = """SIZE_BASED_WRAP"""
UpperCamelCase :int = """0"""
with mockenv_context(**__lowerCamelCase ):
UpperCamelCase :Optional[Any] = FullyShardedDataParallelPlugin()
fsdp_plugin.set_auto_wrap_policy(__lowerCamelCase )
self.assertIsNone(fsdp_plugin.auto_wrap_policy )
def _A ( self : str ):
from torch.distributed.fsdp.fully_sharded_data_parallel import MixedPrecision
from torch.distributed.fsdp.sharded_grad_scaler import ShardedGradScaler
for mp_dtype in dtypes:
UpperCamelCase :List[Any] = self.dist_env.copy()
UpperCamelCase :Union[str, Any] = mp_dtype
with mockenv_context(**__lowerCamelCase ):
UpperCamelCase :Dict = Accelerator()
if mp_dtype == "fp16":
UpperCamelCase :int = torch.floataa
elif mp_dtype == "bf16":
UpperCamelCase :Union[str, Any] = torch.bfloataa
UpperCamelCase :Dict = MixedPrecision(param_dtype=__lowerCamelCase , reduce_dtype=__lowerCamelCase , buffer_dtype=__lowerCamelCase )
self.assertEqual(accelerator.state.fsdp_plugin.mixed_precision_policy , __lowerCamelCase )
if mp_dtype == FPaa:
self.assertTrue(isinstance(accelerator.scaler , __lowerCamelCase ) )
elif mp_dtype == BFaa:
self.assertIsNone(accelerator.scaler )
AcceleratorState._reset_state(__lowerCamelCase )
def _A ( self : Dict ):
from torch.distributed.fsdp.fully_sharded_data_parallel import CPUOffload
for flag in [True, False]:
UpperCamelCase :Union[str, Any] = self.dist_env.copy()
UpperCamelCase :Union[str, Any] = str(__lowerCamelCase ).lower()
with mockenv_context(**__lowerCamelCase ):
UpperCamelCase :int = FullyShardedDataParallelPlugin()
self.assertEqual(fsdp_plugin.cpu_offload , CPUOffload(offload_params=__lowerCamelCase ) )
@require_fsdp
@require_multi_gpu
@slow
class _SCREAMING_SNAKE_CASE ( _a ):
def _A ( self : List[Any] ):
super().setUp()
UpperCamelCase :Optional[int] = 0.82
UpperCamelCase :Any = [
"""fsdp_shard_grad_op_transformer_based_wrap""",
"""fsdp_full_shard_transformer_based_wrap""",
]
UpperCamelCase :List[Any] = {
"""multi_gpu_fp16""": 3_200,
"""fsdp_shard_grad_op_transformer_based_wrap_fp16""": 2_000,
"""fsdp_full_shard_transformer_based_wrap_fp16""": 1_900,
# Disabling below test as it overwhelms the RAM memory usage
# on CI self-hosted runner leading to tests getting killed.
# "fsdp_full_shard_cpu_offload_transformer_based_wrap_fp32": 1500, # fp16 was leading to indefinite hang
}
UpperCamelCase :Optional[int] = 160
UpperCamelCase :Union[str, Any] = 160
UpperCamelCase :Tuple = inspect.getfile(accelerate.test_utils )
UpperCamelCase :str = os.path.sep.join(mod_file.split(os.path.sep )[:-1] + ["""scripts""", """external_deps"""] )
def _A ( self : Optional[Any] ):
UpperCamelCase :Optional[int] = os.path.join(self.test_scripts_folder , """test_performance.py""" )
UpperCamelCase :Any = ["""accelerate""", """launch""", """--num_processes=2""", """--num_machines=1""", """--machine_rank=0""", """--use_fsdp"""]
for config in self.performance_configs:
UpperCamelCase :Optional[Any] = cmd.copy()
for i, strategy in enumerate(__lowerCamelCase ):
if strategy.lower() in config:
cmd_config.append(F"""--fsdp_sharding_strategy={i+1}""" )
break
if "fp32" in config:
cmd_config.append("""--mixed_precision=no""" )
else:
cmd_config.append("""--mixed_precision=fp16""" )
if "cpu_offload" in config:
cmd_config.append("""--fsdp_offload_params=True""" )
for policy in FSDP_AUTO_WRAP_POLICY:
if policy.lower() in config:
cmd_config.append(F"""--fsdp_auto_wrap_policy={policy}""" )
break
if policy == "TRANSFORMER_BASED_WRAP":
cmd_config.append("""--fsdp_transformer_layer_cls_to_wrap=BertLayer""" )
elif policy == "SIZE_BASED_WRAP":
cmd_config.append("""--fsdp_min_num_params=2000""" )
cmd_config.extend(
[
self.test_file_path,
F"""--output_dir={self.tmpdir}""",
F"""--performance_lower_bound={self.performance_lower_bound}""",
] )
with patch_environment(omp_num_threads=1 ):
execute_subprocess_async(__lowerCamelCase , env=os.environ.copy() )
def _A ( self : int ):
UpperCamelCase :Dict = os.path.join(self.test_scripts_folder , """test_checkpointing.py""" )
UpperCamelCase :List[str] = [
"""accelerate""",
"""launch""",
"""--num_processes=2""",
"""--num_machines=1""",
"""--machine_rank=0""",
"""--use_fsdp""",
"""--mixed_precision=fp16""",
"""--fsdp_transformer_layer_cls_to_wrap=BertLayer""",
]
for i, strategy in enumerate(__lowerCamelCase ):
UpperCamelCase :List[str] = cmd.copy()
cmd_config.append(F"""--fsdp_sharding_strategy={i+1}""" )
if strategy != "FULL_SHARD":
continue
UpperCamelCase :Union[str, Any] = len(__lowerCamelCase )
for state_dict_type in FSDP_STATE_DICT_TYPE:
UpperCamelCase :Dict = cmd_config[:state_dict_config_index]
cmd_config.append(F"""--fsdp_state_dict_type={state_dict_type}""" )
cmd_config.extend(
[
self.test_file_path,
F"""--output_dir={self.tmpdir}""",
"""--partial_train_epoch=1""",
] )
with patch_environment(omp_num_threads=1 ):
execute_subprocess_async(__lowerCamelCase , env=os.environ.copy() )
UpperCamelCase :Optional[Any] = cmd_config[:-1]
UpperCamelCase :int = os.path.join(self.tmpdir , """epoch_0""" )
cmd_config.extend(
[
F"""--resume_from_checkpoint={resume_from_checkpoint}""",
] )
with patch_environment(omp_num_threads=1 ):
execute_subprocess_async(__lowerCamelCase , env=os.environ.copy() )
def _A ( self : Optional[Any] ):
UpperCamelCase :List[Any] = os.path.join(self.test_scripts_folder , """test_peak_memory_usage.py""" )
UpperCamelCase :Union[str, Any] = [
"""accelerate""",
"""launch""",
"""--num_processes=2""",
"""--num_machines=1""",
"""--machine_rank=0""",
]
for spec, peak_mem_upper_bound in self.peak_memory_usage_upper_bound.items():
UpperCamelCase :List[str] = cmd.copy()
if "fp16" in spec:
cmd_config.extend(["""--mixed_precision=fp16"""] )
else:
cmd_config.extend(["""--mixed_precision=no"""] )
if "multi_gpu" in spec:
continue
else:
cmd_config.extend(["""--use_fsdp"""] )
for i, strategy in enumerate(__lowerCamelCase ):
if strategy.lower() in spec:
cmd_config.append(F"""--fsdp_sharding_strategy={i+1}""" )
break
if "cpu_offload" in spec:
cmd_config.append("""--fsdp_offload_params=True""" )
for policy in FSDP_AUTO_WRAP_POLICY:
if policy.lower() in spec:
cmd_config.append(F"""--fsdp_auto_wrap_policy={policy}""" )
break
if policy == "TRANSFORMER_BASED_WRAP":
cmd_config.append("""--fsdp_transformer_layer_cls_to_wrap=BertLayer""" )
elif policy == "SIZE_BASED_WRAP":
cmd_config.append("""--fsdp_min_num_params=2000""" )
cmd_config.extend(
[
self.test_file_path,
F"""--output_dir={self.tmpdir}""",
F"""--peak_memory_upper_bound={peak_mem_upper_bound}""",
F"""--n_train={self.n_train}""",
F"""--n_val={self.n_val}""",
] )
with patch_environment(omp_num_threads=1 ):
execute_subprocess_async(__lowerCamelCase , env=os.environ.copy() )
| 38 |
'''simple docstring'''
from typing import Callable, Optional
from .. import Features
from ..packaged_modules.generator.generator import Generator
from .abc import AbstractDatasetInputStream
class lowercase ( A__ ):
"""simple docstring"""
def __init__( self , UpperCamelCase_ , UpperCamelCase_ = None , UpperCamelCase_ = None , UpperCamelCase_ = False , UpperCamelCase_ = False , UpperCamelCase_ = None , UpperCamelCase_ = None , **UpperCamelCase_ , ):
'''simple docstring'''
super().__init__(
features=UpperCamelCase_ , cache_dir=UpperCamelCase_ , keep_in_memory=UpperCamelCase_ , streaming=UpperCamelCase_ , num_proc=UpperCamelCase_ , **UpperCamelCase_ , )
UpperCamelCase__ :Any = Generator(
cache_dir=UpperCamelCase_ , features=UpperCamelCase_ , generator=UpperCamelCase_ , gen_kwargs=UpperCamelCase_ , **UpperCamelCase_ , )
def lowerCAmelCase__ ( self ):
'''simple docstring'''
if self.streaming:
UpperCamelCase__ :Optional[Any] = self.builder.as_streaming_dataset(split='''train''' )
# Build regular (map-style) dataset
else:
UpperCamelCase__ :Optional[int] = None
UpperCamelCase__ :int = None
UpperCamelCase__ :Any = None
UpperCamelCase__ :Any = None
self.builder.download_and_prepare(
download_config=UpperCamelCase_ , download_mode=UpperCamelCase_ , verification_mode=UpperCamelCase_ , base_path=UpperCamelCase_ , num_proc=self.num_proc , )
UpperCamelCase__ :List[Any] = self.builder.as_dataset(
split='''train''' , verification_mode=UpperCamelCase_ , in_memory=self.keep_in_memory )
return dataset | 97 | 0 |
import gc
import tempfile
import unittest
import numpy as np
import torch
from diffusers import VersatileDiffusionTextToImagePipeline
from diffusers.utils.testing_utils import nightly, require_torch_gpu, torch_device
_a = False
class __lowerCamelCase ( unittest.TestCase):
"""simple docstring"""
pass
@nightly
@require_torch_gpu
class __lowerCamelCase ( unittest.TestCase):
"""simple docstring"""
def UpperCamelCase ( self ):
"""simple docstring"""
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def UpperCamelCase ( self ):
"""simple docstring"""
_UpperCAmelCase = VersatileDiffusionTextToImagePipeline.from_pretrained('shi-labs/versatile-diffusion' )
# remove text_unet
pipe.remove_unused_weights()
pipe.to(UpperCAmelCase )
pipe.set_progress_bar_config(disable=UpperCAmelCase )
_UpperCAmelCase = 'A painting of a squirrel eating a burger '
_UpperCAmelCase = torch.manual_seed(0 )
_UpperCAmelCase = pipe(
prompt=UpperCAmelCase , generator=UpperCAmelCase , guidance_scale=7.5 , num_inference_steps=2 , output_type='numpy' ).images
with tempfile.TemporaryDirectory() as tmpdirname:
pipe.save_pretrained(UpperCAmelCase )
_UpperCAmelCase = VersatileDiffusionTextToImagePipeline.from_pretrained(UpperCAmelCase )
pipe.to(UpperCAmelCase )
pipe.set_progress_bar_config(disable=UpperCAmelCase )
_UpperCAmelCase = generator.manual_seed(0 )
_UpperCAmelCase = pipe(
prompt=UpperCAmelCase , generator=UpperCAmelCase , guidance_scale=7.5 , num_inference_steps=2 , output_type='numpy' ).images
assert np.abs(image - new_image ).sum() < 1e-5, "Models don't have the same forward pass"
def UpperCamelCase ( self ):
"""simple docstring"""
_UpperCAmelCase = VersatileDiffusionTextToImagePipeline.from_pretrained(
'shi-labs/versatile-diffusion' , torch_dtype=torch.floataa )
pipe.to(UpperCAmelCase )
pipe.set_progress_bar_config(disable=UpperCAmelCase )
_UpperCAmelCase = 'A painting of a squirrel eating a burger '
_UpperCAmelCase = torch.manual_seed(0 )
_UpperCAmelCase = pipe(
prompt=UpperCAmelCase , generator=UpperCAmelCase , guidance_scale=7.5 , num_inference_steps=50 , output_type='numpy' ).images
_UpperCAmelCase = image[0, 253:256, 253:256, -1]
assert image.shape == (1, 512, 512, 3)
_UpperCAmelCase = np.array([0.33_67, 0.31_69, 0.26_56, 0.38_70, 0.47_90, 0.37_96, 0.40_09, 0.48_78, 0.47_78] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
| 39 |
'''simple docstring'''
__snake_case = 65521
def a ( __a ) -> int:
'''simple docstring'''
UpperCamelCase__ :Tuple = 1
UpperCamelCase__ :Any = 0
for plain_chr in plain_text:
UpperCamelCase__ :List[str] = (a + ord(__a )) % MOD_ADLER
UpperCamelCase__ :Tuple = (b + a) % MOD_ADLER
return (b << 16) | a | 97 | 0 |
"""simple docstring"""
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin
if is_torch_available():
import torch
if is_vision_available():
from PIL import Image
from transformers import ChineseCLIPImageProcessor
class _A ( unittest.TestCase ):
"""simple docstring"""
def __init__( self : Optional[Any] , __UpperCAmelCase : Dict , __UpperCAmelCase : str=7 , __UpperCAmelCase : str=3 , __UpperCAmelCase : Tuple=18 , __UpperCAmelCase : Dict=30 , __UpperCAmelCase : Any=400 , __UpperCAmelCase : List[Any]=True , __UpperCAmelCase : Dict=None , __UpperCAmelCase : Any=True , __UpperCAmelCase : Optional[Any]=None , __UpperCAmelCase : Tuple=True , __UpperCAmelCase : Tuple=[0.48_145_466, 0.4_578_275, 0.40_821_073] , __UpperCAmelCase : Optional[Any]=[0.26_862_954, 0.26_130_258, 0.27_577_711] , __UpperCAmelCase : Union[str, Any]=True , ):
a : int = size if size is not None else {"height": 224, "width": 224}
a : List[str] = crop_size if crop_size is not None else {"height": 18, "width": 18}
a : List[Any] = parent
a : Any = batch_size
a : str = num_channels
a : Optional[int] = image_size
a : Tuple = min_resolution
a : str = max_resolution
a : Dict = do_resize
a : Any = size
a : Dict = do_center_crop
a : List[str] = crop_size
a : str = do_normalize
a : Optional[int] = image_mean
a : Tuple = image_std
a : Any = do_convert_rgb
def __snake_case ( self : Union[str, Any]):
return {
"do_resize": self.do_resize,
"size": self.size,
"do_center_crop": self.do_center_crop,
"crop_size": self.crop_size,
"do_normalize": self.do_normalize,
"image_mean": self.image_mean,
"image_std": self.image_std,
"do_convert_rgb": self.do_convert_rgb,
}
def __snake_case ( self : Dict , __UpperCAmelCase : Optional[Any]=False , __UpperCAmelCase : List[Any]=False , __UpperCAmelCase : Any=False):
assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"
if equal_resolution:
a : str = []
for i in range(self.batch_size):
image_inputs.append(
np.random.randint(
255 , size=(self.num_channels, self.max_resolution, self.max_resolution) , dtype=np.uinta))
else:
a : Tuple = []
for i in range(self.batch_size):
a , a : List[str] = np.random.choice(np.arange(self.min_resolution , self.max_resolution) , 2)
image_inputs.append(np.random.randint(255 , size=(self.num_channels, width, height) , dtype=np.uinta))
if not numpify and not torchify:
# PIL expects the channel dimension as last dimension
a : List[Any] = [Image.fromarray(np.moveaxis(__UpperCAmelCase , 0 , -1)) for x in image_inputs]
if torchify:
a : Optional[Any] = [torch.from_numpy(__UpperCAmelCase) for x in image_inputs]
return image_inputs
@require_torch
@require_vision
class _A ( _a ,unittest.TestCase ):
"""simple docstring"""
UpperCAmelCase : Union[str, Any] = ChineseCLIPImageProcessor if is_vision_available() else None
def __snake_case ( self : List[str]):
a : Dict = ChineseCLIPImageProcessingTester(self , do_center_crop=__UpperCAmelCase)
@property
def __snake_case ( self : Union[str, Any]):
return self.image_processor_tester.prepare_image_processor_dict()
def __snake_case ( self : str):
a : Optional[int] = self.image_processing_class(**self.image_processor_dict)
self.assertTrue(hasattr(__UpperCAmelCase , "do_resize"))
self.assertTrue(hasattr(__UpperCAmelCase , "size"))
self.assertTrue(hasattr(__UpperCAmelCase , "do_center_crop"))
self.assertTrue(hasattr(__UpperCAmelCase , "center_crop"))
self.assertTrue(hasattr(__UpperCAmelCase , "do_normalize"))
self.assertTrue(hasattr(__UpperCAmelCase , "image_mean"))
self.assertTrue(hasattr(__UpperCAmelCase , "image_std"))
self.assertTrue(hasattr(__UpperCAmelCase , "do_convert_rgb"))
def __snake_case ( self : Any):
a : Union[str, Any] = self.image_processing_class.from_dict(self.image_processor_dict)
self.assertEqual(image_processor.size , {"height": 224, "width": 224})
self.assertEqual(image_processor.crop_size , {"height": 18, "width": 18})
a : int = 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 __snake_case ( self : str):
pass
def __snake_case ( self : Tuple):
# Initialize image_processing
a : Union[str, Any] = self.image_processing_class(**self.image_processor_dict)
# create random PIL images
a : Any = self.image_processor_tester.prepare_inputs(equal_resolution=__UpperCAmelCase)
for image in image_inputs:
self.assertIsInstance(__UpperCAmelCase , Image.Image)
# Test not batched input
a : str = image_processing(image_inputs[0] , return_tensors="pt").pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size["height"],
self.image_processor_tester.crop_size["width"],
) , )
# Test batched
a : Dict = 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 __snake_case ( self : List[Any]):
# Initialize image_processing
a : str = self.image_processing_class(**self.image_processor_dict)
# create random numpy tensors
a : Any = self.image_processor_tester.prepare_inputs(equal_resolution=__UpperCAmelCase , numpify=__UpperCAmelCase)
for image in image_inputs:
self.assertIsInstance(__UpperCAmelCase , np.ndarray)
# Test not batched input
a : 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
a : Union[str, 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"],
) , )
def __snake_case ( self : List[str]):
# Initialize image_processing
a : str = self.image_processing_class(**self.image_processor_dict)
# create random PyTorch tensors
a : Tuple = self.image_processor_tester.prepare_inputs(equal_resolution=__UpperCAmelCase , torchify=__UpperCAmelCase)
for image in image_inputs:
self.assertIsInstance(__UpperCAmelCase , torch.Tensor)
# Test not batched input
a : List[str] = image_processing(image_inputs[0] , return_tensors="pt").pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size["height"],
self.image_processor_tester.crop_size["width"],
) , )
# Test batched
a : str = 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"],
) , )
@require_torch
@require_vision
class _A ( _a ,unittest.TestCase ):
"""simple docstring"""
UpperCAmelCase : Optional[int] = ChineseCLIPImageProcessor if is_vision_available() else None
def __snake_case ( self : Union[str, Any]):
a : str = ChineseCLIPImageProcessingTester(self , num_channels=4 , do_center_crop=__UpperCAmelCase)
a : Dict = 3
@property
def __snake_case ( self : Optional[Any]):
return self.image_processor_tester.prepare_image_processor_dict()
def __snake_case ( self : Optional[int]):
a : Tuple = self.image_processing_class(**self.image_processor_dict)
self.assertTrue(hasattr(__UpperCAmelCase , "do_resize"))
self.assertTrue(hasattr(__UpperCAmelCase , "size"))
self.assertTrue(hasattr(__UpperCAmelCase , "do_center_crop"))
self.assertTrue(hasattr(__UpperCAmelCase , "center_crop"))
self.assertTrue(hasattr(__UpperCAmelCase , "do_normalize"))
self.assertTrue(hasattr(__UpperCAmelCase , "image_mean"))
self.assertTrue(hasattr(__UpperCAmelCase , "image_std"))
self.assertTrue(hasattr(__UpperCAmelCase , "do_convert_rgb"))
def __snake_case ( self : Any):
pass
def __snake_case ( self : Union[str, Any]):
# Initialize image_processing
a : List[Any] = self.image_processing_class(**self.image_processor_dict)
# create random PIL images
a : str = self.image_processor_tester.prepare_inputs(equal_resolution=__UpperCAmelCase)
for image in image_inputs:
self.assertIsInstance(__UpperCAmelCase , Image.Image)
# Test not batched input
a : Tuple = image_processing(image_inputs[0] , return_tensors="pt").pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.expected_encoded_image_num_channels,
self.image_processor_tester.crop_size["height"],
self.image_processor_tester.crop_size["width"],
) , )
# Test batched
a : Optional[Any] = image_processing(__UpperCAmelCase , return_tensors="pt").pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.expected_encoded_image_num_channels,
self.image_processor_tester.crop_size["height"],
self.image_processor_tester.crop_size["width"],
) , )
| 40 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
__snake_case = logging.get_logger(__name__)
__snake_case = {
'''camembert-base''': '''https://huggingface.co/camembert-base/resolve/main/config.json''',
'''umberto-commoncrawl-cased-v1''': (
'''https://huggingface.co/Musixmatch/umberto-commoncrawl-cased-v1/resolve/main/config.json'''
),
'''umberto-wikipedia-uncased-v1''': (
'''https://huggingface.co/Musixmatch/umberto-wikipedia-uncased-v1/resolve/main/config.json'''
),
}
class lowercase ( A__ ):
"""simple docstring"""
_a = 'camembert'
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-12 , UpperCamelCase_=1 , UpperCamelCase_=0 , UpperCamelCase_=2 , UpperCamelCase_="absolute" , UpperCamelCase_=True , UpperCamelCase_=None , **UpperCamelCase_ , ):
'''simple docstring'''
super().__init__(pad_token_id=UpperCamelCase_ , bos_token_id=UpperCamelCase_ , eos_token_id=UpperCamelCase_ , **UpperCamelCase_ )
UpperCamelCase__ :int = vocab_size
UpperCamelCase__ :Optional[int] = hidden_size
UpperCamelCase__ :Optional[int] = num_hidden_layers
UpperCamelCase__ :List[Any] = num_attention_heads
UpperCamelCase__ :Union[str, Any] = hidden_act
UpperCamelCase__ :List[Any] = intermediate_size
UpperCamelCase__ :int = hidden_dropout_prob
UpperCamelCase__ :Tuple = attention_probs_dropout_prob
UpperCamelCase__ :Union[str, Any] = max_position_embeddings
UpperCamelCase__ :Tuple = type_vocab_size
UpperCamelCase__ :int = initializer_range
UpperCamelCase__ :List[str] = layer_norm_eps
UpperCamelCase__ :int = position_embedding_type
UpperCamelCase__ :Any = use_cache
UpperCamelCase__ :Any = classifier_dropout
class lowercase ( A__ ):
"""simple docstring"""
@property
def lowerCAmelCase__ ( self ):
'''simple docstring'''
if self.task == "multiple-choice":
UpperCamelCase__ :List[str] = {0: '''batch''', 1: '''choice''', 2: '''sequence'''}
else:
UpperCamelCase__ :Tuple = {0: '''batch''', 1: '''sequence'''}
return OrderedDict(
[
('''input_ids''', dynamic_axis),
('''attention_mask''', dynamic_axis),
] ) | 97 | 0 |
'''simple docstring'''
from __future__ import annotations
import inspect
import unittest
import numpy as np
from transformers import ResNetConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import tensorflow as tf
from transformers import TFResNetForImageClassification, TFResNetModel
from transformers.models.resnet.modeling_tf_resnet import TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import AutoImageProcessor
class _lowercase :
def __init__( self: List[Any] , UpperCamelCase__: Optional[Any] , UpperCamelCase__: int=3 , UpperCamelCase__: Union[str, Any]=32 , UpperCamelCase__: Any=3 , UpperCamelCase__: Optional[int]=10 , UpperCamelCase__: List[str]=[10, 20, 30, 40] , UpperCamelCase__: Tuple=[1, 1, 2, 1] , UpperCamelCase__: Union[str, Any]=True , UpperCamelCase__: List[str]=True , UpperCamelCase__: str="relu" , UpperCamelCase__: Any=3 , UpperCamelCase__: Optional[int]=None , ):
lowerCamelCase__ : List[str] = parent
lowerCamelCase__ : str = batch_size
lowerCamelCase__ : str = image_size
lowerCamelCase__ : List[str] = num_channels
lowerCamelCase__ : List[str] = embeddings_size
lowerCamelCase__ : Dict = hidden_sizes
lowerCamelCase__ : Optional[Any] = depths
lowerCamelCase__ : Dict = is_training
lowerCamelCase__ : str = use_labels
lowerCamelCase__ : Any = hidden_act
lowerCamelCase__ : str = num_labels
lowerCamelCase__ : List[str] = scope
lowerCamelCase__ : Optional[int] = len(UpperCamelCase__ )
def lowerCamelCase_ ( self: str ):
lowerCamelCase__ : Dict = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
lowerCamelCase__ : int = None
if self.use_labels:
lowerCamelCase__ : Dict = ids_tensor([self.batch_size] , self.num_labels )
lowerCamelCase__ : str = self.get_config()
return config, pixel_values, labels
def lowerCamelCase_ ( self: Any ):
return ResNetConfig(
num_channels=self.num_channels , embeddings_size=self.embeddings_size , hidden_sizes=self.hidden_sizes , depths=self.depths , hidden_act=self.hidden_act , num_labels=self.num_labels , image_size=self.image_size , )
def lowerCamelCase_ ( self: Union[str, Any] , UpperCamelCase__: str , UpperCamelCase__: str , UpperCamelCase__: int ):
lowerCamelCase__ : Union[str, Any] = TFResNetModel(config=UpperCamelCase__ )
lowerCamelCase__ : int = model(UpperCamelCase__ )
# expected last hidden states: B, C, H // 32, W // 32
self.parent.assertEqual(
result.last_hidden_state.shape , (self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32) , )
def lowerCamelCase_ ( self: Any , UpperCamelCase__: List[Any] , UpperCamelCase__: List[str] , UpperCamelCase__: int ):
lowerCamelCase__ : Union[str, Any] = self.num_labels
lowerCamelCase__ : Dict = TFResNetForImageClassification(UpperCamelCase__ )
lowerCamelCase__ : int = model(UpperCamelCase__ , labels=UpperCamelCase__ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def lowerCamelCase_ ( self: Optional[int] ):
lowerCamelCase__ : Union[str, Any] = self.prepare_config_and_inputs()
lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ : Optional[int] = config_and_inputs
lowerCamelCase__ : Union[str, Any] = {"""pixel_values""": pixel_values}
return config, inputs_dict
@require_tf
class _lowercase ( _lowercase , _lowercase , unittest.TestCase ):
a = (TFResNetModel, TFResNetForImageClassification) if is_tf_available() else ()
a = (
{"""feature-extraction""": TFResNetModel, """image-classification""": TFResNetForImageClassification}
if is_tf_available()
else {}
)
a = False
a = False
a = False
a = False
a = False
def lowerCamelCase_ ( self: Any ):
lowerCamelCase__ : Union[str, Any] = TFResNetModelTester(self )
lowerCamelCase__ : Any = ConfigTester(self , config_class=UpperCamelCase__ , has_text_modality=UpperCamelCase__ )
def lowerCamelCase_ ( self: Optional[int] ):
self.create_and_test_config_common_properties()
self.config_tester.create_and_test_config_to_json_string()
self.config_tester.create_and_test_config_to_json_file()
self.config_tester.create_and_test_config_from_and_save_pretrained()
self.config_tester.create_and_test_config_with_num_labels()
self.config_tester.check_config_can_be_init_without_params()
self.config_tester.check_config_arguments_init()
def lowerCamelCase_ ( self: int ):
return
@unittest.skip(reason="""ResNet does not use inputs_embeds""" )
def lowerCamelCase_ ( self: Optional[int] ):
pass
@unittest.skip(reason="""ResNet does not support input and output embeddings""" )
def lowerCamelCase_ ( self: Union[str, Any] ):
pass
def lowerCamelCase_ ( self: Optional[int] ):
lowerCamelCase__ , lowerCamelCase__ : List[str] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
lowerCamelCase__ : Optional[Any] = model_class(UpperCamelCase__ )
lowerCamelCase__ : List[str] = inspect.signature(model.call )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
lowerCamelCase__ : Union[str, Any] = [*signature.parameters.keys()]
lowerCamelCase__ : Dict = ["""pixel_values"""]
self.assertListEqual(arg_names[:1] , UpperCamelCase__ )
def lowerCamelCase_ ( self: int ):
lowerCamelCase__ : Any = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*UpperCamelCase__ )
def lowerCamelCase_ ( self: Union[str, Any] ):
def check_hidden_states_output(UpperCamelCase__: Optional[Any] , UpperCamelCase__: Optional[int] , UpperCamelCase__: int ):
lowerCamelCase__ : List[str] = model_class(UpperCamelCase__ )
lowerCamelCase__ : Tuple = model(**self._prepare_for_class(UpperCamelCase__ , UpperCamelCase__ ) )
lowerCamelCase__ : Tuple = outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states
lowerCamelCase__ : Optional[int] = self.model_tester.num_stages
self.assertEqual(len(UpperCamelCase__ ) , expected_num_stages + 1 )
# ResNet's feature maps are of shape (batch_size, num_channels, height, width)
self.assertListEqual(
list(hidden_states[0].shape[-2:] ) , [self.model_tester.image_size // 4, self.model_tester.image_size // 4] , )
lowerCamelCase__ , lowerCamelCase__ : Optional[Any] = self.model_tester.prepare_config_and_inputs_for_common()
lowerCamelCase__ : Tuple = ["""basic""", """bottleneck"""]
for model_class in self.all_model_classes:
for layer_type in layers_type:
lowerCamelCase__ : Tuple = layer_type
lowerCamelCase__ : Optional[Any] = True
check_hidden_states_output(UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
lowerCamelCase__ : Union[str, Any] = True
check_hidden_states_output(UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ )
def lowerCamelCase_ ( self: List[Any] ):
lowerCamelCase__ : str = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*UpperCamelCase__ )
@slow
def lowerCamelCase_ ( self: List[Any] ):
for model_name in TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
lowerCamelCase__ : List[str] = TFResNetModel.from_pretrained(UpperCamelCase__ )
self.assertIsNotNone(UpperCamelCase__ )
def SCREAMING_SNAKE_CASE_ () -> Union[str, Any]:
lowerCamelCase__ : List[str] = Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""" )
return image
@require_tf
@require_vision
class _lowercase ( unittest.TestCase ):
@cached_property
def lowerCamelCase_ ( self: int ):
return (
AutoImageProcessor.from_pretrained(TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[0] )
if is_vision_available()
else None
)
@slow
def lowerCamelCase_ ( self: Optional[int] ):
lowerCamelCase__ : List[str] = TFResNetForImageClassification.from_pretrained(TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[0] )
lowerCamelCase__ : Union[str, Any] = self.default_image_processor
lowerCamelCase__ : Optional[Any] = prepare_img()
lowerCamelCase__ : Optional[Any] = image_processor(images=UpperCamelCase__ , return_tensors="""tf""" )
# forward pass
lowerCamelCase__ : Optional[int] = model(**UpperCamelCase__ )
# verify the logits
lowerCamelCase__ : List[Any] = tf.TensorShape((1, 1_000) )
self.assertEqual(outputs.logits.shape , UpperCamelCase__ )
lowerCamelCase__ : Tuple = tf.constant([-11.1_069, -9.7_877, -8.3_777] )
self.assertTrue(np.allclose(outputs.logits[0, :3].numpy() , UpperCamelCase__ , atol=1e-4 ) )
| 41 |
'''simple docstring'''
import json
import pathlib
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_available():
import torch
if is_vision_available():
from PIL import Image
from transformers import ConditionalDetrImageProcessor
class lowercase ( unittest.TestCase ):
"""simple docstring"""
def __init__( self , UpperCamelCase_ , UpperCamelCase_=7 , UpperCamelCase_=3 , UpperCamelCase_=30 , UpperCamelCase_=400 , UpperCamelCase_=True , UpperCamelCase_=None , UpperCamelCase_=True , UpperCamelCase_=[0.5, 0.5, 0.5] , UpperCamelCase_=[0.5, 0.5, 0.5] , UpperCamelCase_=True , UpperCamelCase_=1 / 255 , UpperCamelCase_=True , ):
'''simple docstring'''
UpperCamelCase__ :Dict = size if size is not None else {'''shortest_edge''': 18, '''longest_edge''': 1333}
UpperCamelCase__ :str = parent
UpperCamelCase__ :List[Any] = batch_size
UpperCamelCase__ :Dict = num_channels
UpperCamelCase__ :str = min_resolution
UpperCamelCase__ :Optional[Any] = max_resolution
UpperCamelCase__ :int = do_resize
UpperCamelCase__ :Optional[Any] = size
UpperCamelCase__ :Tuple = do_normalize
UpperCamelCase__ :List[Any] = image_mean
UpperCamelCase__ :Dict = image_std
UpperCamelCase__ :Union[str, Any] = do_rescale
UpperCamelCase__ :Union[str, Any] = rescale_factor
UpperCamelCase__ :Union[str, Any] = do_pad
def lowerCAmelCase__ ( self ):
'''simple docstring'''
return {
"do_resize": self.do_resize,
"size": self.size,
"do_normalize": self.do_normalize,
"image_mean": self.image_mean,
"image_std": self.image_std,
"do_rescale": self.do_rescale,
"rescale_factor": self.rescale_factor,
"do_pad": self.do_pad,
}
def lowerCAmelCase__ ( self , UpperCamelCase_ , UpperCamelCase_=False ):
'''simple docstring'''
if not batched:
UpperCamelCase__ :List[str] = image_inputs[0]
if isinstance(UpperCamelCase_ , Image.Image ):
UpperCamelCase__ , UpperCamelCase__ :List[str] = image.size
else:
UpperCamelCase__ , UpperCamelCase__ :List[Any] = image.shape[1], image.shape[2]
if w < h:
UpperCamelCase__ :int = int(self.size['''shortest_edge'''] * h / w )
UpperCamelCase__ :Dict = self.size['''shortest_edge''']
elif w > h:
UpperCamelCase__ :int = self.size['''shortest_edge''']
UpperCamelCase__ :Tuple = int(self.size['''shortest_edge'''] * w / h )
else:
UpperCamelCase__ :str = self.size['''shortest_edge''']
UpperCamelCase__ :str = self.size['''shortest_edge''']
else:
UpperCamelCase__ :Any = []
for image in image_inputs:
UpperCamelCase__ , UpperCamelCase__ :Dict = self.get_expected_values([image] )
expected_values.append((expected_height, expected_width) )
UpperCamelCase__ :List[Any] = max(UpperCamelCase_ , key=lambda UpperCamelCase_ : item[0] )[0]
UpperCamelCase__ :Optional[int] = max(UpperCamelCase_ , key=lambda UpperCamelCase_ : item[1] )[1]
return expected_height, expected_width
@require_torch
@require_vision
class lowercase ( A__ , unittest.TestCase ):
"""simple docstring"""
_a = ConditionalDetrImageProcessor if is_vision_available() else None
def lowerCAmelCase__ ( self ):
'''simple docstring'''
UpperCamelCase__ :List[Any] = ConditionalDetrImageProcessingTester(self )
@property
def lowerCAmelCase__ ( self ):
'''simple docstring'''
return self.image_processor_tester.prepare_image_processor_dict()
def lowerCAmelCase__ ( self ):
'''simple docstring'''
UpperCamelCase__ :Tuple = 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_ , '''size''' ) )
def lowerCAmelCase__ ( self ):
'''simple docstring'''
UpperCamelCase__ :Optional[int] = self.image_processing_class.from_dict(self.image_processor_dict )
self.assertEqual(image_processor.size , {'''shortest_edge''': 18, '''longest_edge''': 1333} )
self.assertEqual(image_processor.do_pad , UpperCamelCase_ )
UpperCamelCase__ :List[str] = self.image_processing_class.from_dict(
self.image_processor_dict , size=42 , max_size=84 , pad_and_return_pixel_mask=UpperCamelCase_ )
self.assertEqual(image_processor.size , {'''shortest_edge''': 42, '''longest_edge''': 84} )
self.assertEqual(image_processor.do_pad , UpperCamelCase_ )
def lowerCAmelCase__ ( self ):
'''simple docstring'''
pass
def lowerCAmelCase__ ( self ):
'''simple docstring'''
UpperCamelCase__ :Dict = self.image_processing_class(**self.image_processor_dict )
# create random PIL images
UpperCamelCase__ :List[str] = prepare_image_inputs(self.image_processor_tester , equal_resolution=UpperCamelCase_ )
for image in image_inputs:
self.assertIsInstance(UpperCamelCase_ , Image.Image )
# Test not batched input
UpperCamelCase__ :Any = image_processing(image_inputs[0] , return_tensors='''pt''' ).pixel_values
UpperCamelCase__ , UpperCamelCase__ :str = self.image_processor_tester.get_expected_values(UpperCamelCase_ )
self.assertEqual(
encoded_images.shape , (1, self.image_processor_tester.num_channels, expected_height, expected_width) , )
# Test batched
UpperCamelCase__ , UpperCamelCase__ :str = self.image_processor_tester.get_expected_values(UpperCamelCase_ , batched=UpperCamelCase_ )
UpperCamelCase__ :List[str] = 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,
expected_height,
expected_width,
) , )
def lowerCAmelCase__ ( self ):
'''simple docstring'''
UpperCamelCase__ :List[Any] = self.image_processing_class(**self.image_processor_dict )
# create random numpy tensors
UpperCamelCase__ :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
UpperCamelCase__ :Union[str, Any] = image_processing(image_inputs[0] , return_tensors='''pt''' ).pixel_values
UpperCamelCase__ , UpperCamelCase__ :List[Any] = self.image_processor_tester.get_expected_values(UpperCamelCase_ )
self.assertEqual(
encoded_images.shape , (1, self.image_processor_tester.num_channels, expected_height, expected_width) , )
# Test batched
UpperCamelCase__ :Union[str, Any] = image_processing(UpperCamelCase_ , return_tensors='''pt''' ).pixel_values
UpperCamelCase__ , UpperCamelCase__ :str = self.image_processor_tester.get_expected_values(UpperCamelCase_ , batched=UpperCamelCase_ )
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
expected_height,
expected_width,
) , )
def lowerCAmelCase__ ( self ):
'''simple docstring'''
UpperCamelCase__ :List[str] = self.image_processing_class(**self.image_processor_dict )
# create random PyTorch tensors
UpperCamelCase__ :Any = 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
UpperCamelCase__ :str = image_processing(image_inputs[0] , return_tensors='''pt''' ).pixel_values
UpperCamelCase__ , UpperCamelCase__ :Dict = self.image_processor_tester.get_expected_values(UpperCamelCase_ )
self.assertEqual(
encoded_images.shape , (1, self.image_processor_tester.num_channels, expected_height, expected_width) , )
# Test batched
UpperCamelCase__ :List[str] = image_processing(UpperCamelCase_ , return_tensors='''pt''' ).pixel_values
UpperCamelCase__ , UpperCamelCase__ :Optional[int] = self.image_processor_tester.get_expected_values(UpperCamelCase_ , batched=UpperCamelCase_ )
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
expected_height,
expected_width,
) , )
@slow
def lowerCAmelCase__ ( self ):
'''simple docstring'''
UpperCamelCase__ :Optional[Any] = Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
with open('''./tests/fixtures/tests_samples/COCO/coco_annotations.txt''' , '''r''' ) as f:
UpperCamelCase__ :Optional[int] = json.loads(f.read() )
UpperCamelCase__ :Any = {'''image_id''': 39769, '''annotations''': target}
# encode them
UpperCamelCase__ :str = ConditionalDetrImageProcessor.from_pretrained('''microsoft/conditional-detr-resnet-50''' )
UpperCamelCase__ :List[Any] = image_processing(images=UpperCamelCase_ , annotations=UpperCamelCase_ , return_tensors='''pt''' )
# verify pixel values
UpperCamelCase__ :List[str] = torch.Size([1, 3, 800, 1066] )
self.assertEqual(encoding['''pixel_values'''].shape , UpperCamelCase_ )
UpperCamelCase__ :str = torch.tensor([0.2796, 0.3138, 0.3481] )
self.assertTrue(torch.allclose(encoding['''pixel_values'''][0, 0, 0, :3] , UpperCamelCase_ , atol=1e-4 ) )
# verify area
UpperCamelCase__ :str = torch.tensor([5887.9600, 11250.2061, 489353.8438, 837122.7500, 147967.5156, 165732.3438] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''area'''] , UpperCamelCase_ ) )
# verify boxes
UpperCamelCase__ :Optional[Any] = torch.Size([6, 4] )
self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape , UpperCamelCase_ )
UpperCamelCase__ :Optional[Any] = torch.tensor([0.5503, 0.2765, 0.0604, 0.2215] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''boxes'''][0] , UpperCamelCase_ , atol=1e-3 ) )
# verify image_id
UpperCamelCase__ :List[Any] = torch.tensor([39769] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id'''] , UpperCamelCase_ ) )
# verify is_crowd
UpperCamelCase__ :int = torch.tensor([0, 0, 0, 0, 0, 0] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd'''] , UpperCamelCase_ ) )
# verify class_labels
UpperCamelCase__ :List[str] = torch.tensor([75, 75, 63, 65, 17, 17] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels'''] , UpperCamelCase_ ) )
# verify orig_size
UpperCamelCase__ :Tuple = torch.tensor([480, 640] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size'''] , UpperCamelCase_ ) )
# verify size
UpperCamelCase__ :Union[str, Any] = torch.tensor([800, 1066] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size'''] , UpperCamelCase_ ) )
@slow
def lowerCAmelCase__ ( self ):
'''simple docstring'''
UpperCamelCase__ :List[str] = Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
with open('''./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt''' , '''r''' ) as f:
UpperCamelCase__ :Tuple = json.loads(f.read() )
UpperCamelCase__ :List[str] = {'''file_name''': '''000000039769.png''', '''image_id''': 39769, '''segments_info''': target}
UpperCamelCase__ :Any = pathlib.Path('''./tests/fixtures/tests_samples/COCO/coco_panoptic''' )
# encode them
UpperCamelCase__ :List[Any] = ConditionalDetrImageProcessor(format='''coco_panoptic''' )
UpperCamelCase__ :Dict = image_processing(images=UpperCamelCase_ , annotations=UpperCamelCase_ , masks_path=UpperCamelCase_ , return_tensors='''pt''' )
# verify pixel values
UpperCamelCase__ :str = torch.Size([1, 3, 800, 1066] )
self.assertEqual(encoding['''pixel_values'''].shape , UpperCamelCase_ )
UpperCamelCase__ :Optional[int] = torch.tensor([0.2796, 0.3138, 0.3481] )
self.assertTrue(torch.allclose(encoding['''pixel_values'''][0, 0, 0, :3] , UpperCamelCase_ , atol=1e-4 ) )
# verify area
UpperCamelCase__ :Tuple = torch.tensor([147979.6875, 165527.0469, 484638.5938, 11292.9375, 5879.6562, 7634.1147] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''area'''] , UpperCamelCase_ ) )
# verify boxes
UpperCamelCase__ :Any = torch.Size([6, 4] )
self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape , UpperCamelCase_ )
UpperCamelCase__ :List[Any] = torch.tensor([0.2625, 0.5437, 0.4688, 0.8625] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''boxes'''][0] , UpperCamelCase_ , atol=1e-3 ) )
# verify image_id
UpperCamelCase__ :List[str] = torch.tensor([39769] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id'''] , UpperCamelCase_ ) )
# verify is_crowd
UpperCamelCase__ :Union[str, Any] = torch.tensor([0, 0, 0, 0, 0, 0] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd'''] , UpperCamelCase_ ) )
# verify class_labels
UpperCamelCase__ :str = torch.tensor([17, 17, 63, 75, 75, 93] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels'''] , UpperCamelCase_ ) )
# verify masks
UpperCamelCase__ :Optional[Any] = 822873
self.assertEqual(encoding['''labels'''][0]['''masks'''].sum().item() , UpperCamelCase_ )
# verify orig_size
UpperCamelCase__ :List[str] = torch.tensor([480, 640] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size'''] , UpperCamelCase_ ) )
# verify size
UpperCamelCase__ :List[Any] = torch.tensor([800, 1066] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size'''] , UpperCamelCase_ ) ) | 97 | 0 |
'''simple docstring'''
import argparse
import os
import torch
from transformers.utils import WEIGHTS_NAME
lowercase : Dict = ["small", "medium", "large"]
lowercase : Any = "lm_head.decoder.weight"
lowercase : Optional[Any] = "lm_head.weight"
def SCREAMING_SNAKE_CASE__ ( __A , __A ) -> List[str]:
_snake_case = torch.load(__A )
_snake_case = d.pop(__A )
os.makedirs(__A , exist_ok=__A )
torch.save(__A , os.path.join(__A , __A ) )
if __name__ == "__main__":
lowercase : Union[str, Any] = argparse.ArgumentParser()
parser.add_argument("--dialogpt_path", default=".", type=str)
lowercase : int = parser.parse_args()
for MODEL in DIALOGPT_MODELS:
lowercase : List[Any] = os.path.join(args.dialogpt_path, F'''{MODEL}_ft.pkl''')
lowercase : Union[str, Any] = F'''./DialoGPT-{MODEL}'''
convert_dialogpt_checkpoint(
checkpoint_path,
pytorch_dump_folder_path,
)
| 42 |
'''simple docstring'''
from collections import defaultdict
class lowercase :
"""simple docstring"""
def __init__( self , UpperCamelCase_ , UpperCamelCase_ ):
'''simple docstring'''
UpperCamelCase__ :List[Any] = total # total no of tasks (N)
# DP table will have a dimension of (2^M)*N
# initially all values are set to -1
UpperCamelCase__ :Union[str, Any] = [
[-1 for i in range(total + 1 )] for j in range(2 ** len(UpperCamelCase_ ) )
]
UpperCamelCase__ :str = defaultdict(UpperCamelCase_ ) # stores the list of persons for each task
# final_mask is used to check if all persons are included by setting all bits
# to 1
UpperCamelCase__ :Optional[int] = (1 << len(UpperCamelCase_ )) - 1
def lowerCAmelCase__ ( self , UpperCamelCase_ , UpperCamelCase_ ):
'''simple docstring'''
if mask == self.final_mask:
return 1
# if not everyone gets the task and no more tasks are available, return 0
if task_no > self.total_tasks:
return 0
# if case already considered
if self.dp[mask][task_no] != -1:
return self.dp[mask][task_no]
# Number of ways when we don't this task in the arrangement
UpperCamelCase__ :str = self.count_ways_until(UpperCamelCase_ , task_no + 1 )
# now assign the tasks one by one to all possible persons and recursively
# assign for the remaining tasks.
if task_no in self.task:
for p in self.task[task_no]:
# if p is already given a task
if mask & (1 << p):
continue
# assign this task to p and change the mask value. And recursively
# assign tasks with the new mask value.
total_ways_util += self.count_ways_until(mask | (1 << p) , task_no + 1 )
# save the value.
UpperCamelCase__ :Optional[int] = total_ways_util
return self.dp[mask][task_no]
def lowerCAmelCase__ ( self , UpperCamelCase_ ):
'''simple docstring'''
for i in range(len(UpperCamelCase_ ) ):
for j in task_performed[i]:
self.task[j].append(UpperCamelCase_ )
# call the function to fill the DP table, final answer is stored in dp[0][1]
return self.count_ways_until(0 , 1 )
if __name__ == "__main__":
__snake_case = 5 # total no of tasks (the value of N)
# the list of tasks that can be done by M persons.
__snake_case = [[1, 3, 4], [1, 2, 5], [3, 4]]
print(
AssignmentUsingBitmask(task_performed, total_tasks).count_no_of_ways(
task_performed
)
) | 97 | 0 |
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 lowerCamelCase_ ( UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ , unittest.TestCase ):
'''simple docstring'''
a__ : int = StableUnCLIPImgaImgPipeline
a__ : Optional[int] = TEXT_GUIDED_IMAGE_VARIATION_PARAMS
a__ : Union[str, Any] = TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
a__ : Optional[Any] = frozenset(
[] ) # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
a__ : int = frozenset([] )
def UpperCamelCase__ ( self) -> Tuple:
__UpperCamelCase :Tuple = 32
__UpperCamelCase :Optional[int] = embedder_hidden_size
# image encoding components
__UpperCamelCase :Union[str, Any] = CLIPImageProcessor(crop_size=32 , size=32)
torch.manual_seed(0)
__UpperCamelCase :Union[str, Any] = CLIPVisionModelWithProjection(
CLIPVisionConfig(
hidden_size=__lowercase , projection_dim=__lowercase , num_hidden_layers=5 , num_attention_heads=4 , image_size=32 , intermediate_size=37 , patch_size=1 , ))
# regular denoising components
torch.manual_seed(0)
__UpperCamelCase :str = StableUnCLIPImageNormalizer(embedding_dim=__lowercase)
__UpperCamelCase :Optional[int] = DDPMScheduler(beta_schedule='''squaredcos_cap_v2''')
torch.manual_seed(0)
__UpperCamelCase :Union[str, Any] = CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip''')
torch.manual_seed(0)
__UpperCamelCase :Dict = CLIPTextModel(
CLIPTextConfig(
bos_token_id=0 , eos_token_id=2 , hidden_size=__lowercase , 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=1_000 , ))
torch.manual_seed(0)
__UpperCamelCase :List[Any] = 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=__lowercase , layers_per_block=1 , upcast_attention=__lowercase , use_linear_projection=__lowercase , )
torch.manual_seed(0)
__UpperCamelCase :Tuple = DDIMScheduler(
beta_schedule='''scaled_linear''' , beta_start=0.0_00_85 , beta_end=0.0_12 , prediction_type='''v_prediction''' , set_alpha_to_one=__lowercase , steps_offset=1 , )
torch.manual_seed(0)
__UpperCamelCase :List[str] = AutoencoderKL()
__UpperCamelCase :Tuple = {
# 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 UpperCamelCase__ ( self , __lowercase , __lowercase=0 , __lowercase=True) -> str:
if str(__lowercase).startswith('''mps'''):
__UpperCamelCase :Union[str, Any] = torch.manual_seed(__lowercase)
else:
__UpperCamelCase :int = torch.Generator(device=__lowercase).manual_seed(__lowercase)
__UpperCamelCase :int = floats_tensor((1, 3, 32, 32) , rng=random.Random(__lowercase)).to(__lowercase)
if pil_image:
__UpperCamelCase :List[Any] = input_image * 0.5 + 0.5
__UpperCamelCase :Optional[Any] = input_image.clamp(0 , 1)
__UpperCamelCase :int = input_image.cpu().permute(0 , 2 , 3 , 1).float().numpy()
__UpperCamelCase :Optional[Any] = DiffusionPipeline.numpy_to_pil(__lowercase)[0]
return {
"prompt": "An anime racoon running a marathon",
"image": input_image,
"generator": generator,
"num_inference_steps": 2,
"output_type": "np",
}
@skip_mps
def UpperCamelCase__ ( self) -> Union[str, Any]:
__UpperCamelCase :Dict = '''cpu''' # ensure determinism for the device-dependent torch.Generator
__UpperCamelCase :Tuple = self.get_dummy_components()
__UpperCamelCase :Any = StableUnCLIPImgaImgPipeline(**__lowercase)
__UpperCamelCase :Optional[Any] = sd_pipe.to(__lowercase)
sd_pipe.set_progress_bar_config(disable=__lowercase)
__UpperCamelCase :List[Any] = self.get_dummy_inputs(__lowercase)
inputs.update({'''image_embeds''': None})
__UpperCamelCase :Any = sd_pipe(**__lowercase).images
__UpperCamelCase :List[str] = image[0, -3:, -3:, -1]
assert image.shape == (1, 32, 32, 3)
__UpperCamelCase :List[Any] = np.array([0.38_72, 0.72_24, 0.56_01, 0.47_41, 0.68_72, 0.58_14, 0.46_36, 0.38_67, 0.50_78])
assert np.abs(image_slice.flatten() - expected_slice).max() < 1E-3
def UpperCamelCase__ ( self) -> str:
__UpperCamelCase :Optional[Any] = torch_device in ['''cpu''', '''mps''']
self._test_attention_slicing_forward_pass(test_max_difference=__lowercase)
def UpperCamelCase__ ( self) -> List[Any]:
__UpperCamelCase :Optional[Any] = torch_device in ['''cpu''', '''mps''']
self._test_inference_batch_single_identical(test_max_difference=__lowercase)
@unittest.skipIf(
torch_device != '''cuda''' or not is_xformers_available() , reason='''XFormers attention is only available with CUDA and `xformers` installed''' , )
def UpperCamelCase__ ( self) -> Union[str, Any]:
self._test_xformers_attention_forwardGenerator_pass(test_max_difference=__lowercase)
@slow
@require_torch_gpu
class lowerCamelCase_ ( unittest.TestCase ):
'''simple docstring'''
def UpperCamelCase__ ( self) -> Union[str, Any]:
# clean up the VRAM after each test
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def UpperCamelCase__ ( self) -> Union[str, Any]:
__UpperCamelCase :int = load_image(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/turtle.png''')
__UpperCamelCase :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''')
__UpperCamelCase :List[Any] = StableUnCLIPImgaImgPipeline.from_pretrained(
'''fusing/stable-unclip-2-1-l-img2img''' , torch_dtype=torch.floataa)
pipe.to(__lowercase)
pipe.set_progress_bar_config(disable=__lowercase)
# 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()
__UpperCamelCase :int = torch.Generator(device='''cpu''').manual_seed(0)
__UpperCamelCase :Dict = pipe(__lowercase , '''anime turle''' , generator=__lowercase , output_type='''np''')
__UpperCamelCase :Dict = output.images[0]
assert image.shape == (768, 768, 3)
assert_mean_pixel_difference(__lowercase , __lowercase)
def UpperCamelCase__ ( self) -> List[str]:
__UpperCamelCase :Optional[Any] = load_image(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/turtle.png''')
__UpperCamelCase :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''')
__UpperCamelCase :Any = StableUnCLIPImgaImgPipeline.from_pretrained(
'''fusing/stable-unclip-2-1-h-img2img''' , torch_dtype=torch.floataa)
pipe.to(__lowercase)
pipe.set_progress_bar_config(disable=__lowercase)
# 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()
__UpperCamelCase :int = torch.Generator(device='''cpu''').manual_seed(0)
__UpperCamelCase :Optional[int] = pipe(__lowercase , '''anime turle''' , generator=__lowercase , output_type='''np''')
__UpperCamelCase :List[Any] = output.images[0]
assert image.shape == (768, 768, 3)
assert_mean_pixel_difference(__lowercase , __lowercase)
def UpperCamelCase__ ( self) -> List[str]:
__UpperCamelCase :Dict = 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()
__UpperCamelCase :List[Any] = StableUnCLIPImgaImgPipeline.from_pretrained(
'''fusing/stable-unclip-2-1-h-img2img''' , torch_dtype=torch.floataa)
__UpperCamelCase :Union[str, Any] = pipe.to(__lowercase)
pipe.set_progress_bar_config(disable=__lowercase)
pipe.enable_attention_slicing()
pipe.enable_sequential_cpu_offload()
__UpperCamelCase :Optional[Any] = pipe(
__lowercase , '''anime turtle''' , num_inference_steps=2 , output_type='''np''' , )
__UpperCamelCase :int = torch.cuda.max_memory_allocated()
# make sure that less than 7 GB is allocated
assert mem_bytes < 7 * 10**9
| 43 |
'''simple docstring'''
import csv
import tweepy
# Twitter API credentials
__snake_case = ''''''
__snake_case = ''''''
__snake_case = ''''''
__snake_case = ''''''
def a ( __a ) -> None:
'''simple docstring'''
UpperCamelCase__ :List[Any] = tweepy.OAuthHandler(__a , __a )
auth.set_access_token(__a , __a )
UpperCamelCase__ :List[str] = tweepy.API(__a )
# initialize a list to hold all the tweepy Tweets
UpperCamelCase__ :Dict = []
# make initial request for most recent tweets (200 is the maximum allowed count)
UpperCamelCase__ :Tuple = api.user_timeline(screen_name=__a , count=200 )
# save most recent tweets
alltweets.extend(__a )
# save the id of the oldest tweet less one
UpperCamelCase__ :Union[str, Any] = alltweets[-1].id - 1
# keep grabbing tweets until there are no tweets left to grab
while len(__a ) > 0:
print(f'''getting tweets before {oldest}''' )
# all subsequent requests use the max_id param to prevent duplicates
UpperCamelCase__ :Union[str, Any] = api.user_timeline(
screen_name=__a , count=200 , max_id=__a )
# save most recent tweets
alltweets.extend(__a )
# update the id of the oldest tweet less one
UpperCamelCase__ :Tuple = alltweets[-1].id - 1
print(f'''...{len(__a )} tweets downloaded so far''' )
# transform the tweepy tweets into a 2D array that will populate the csv
UpperCamelCase__ :int = [[tweet.id_str, tweet.created_at, tweet.text] for tweet in alltweets]
# write the csv
with open(f'''new_{screen_name}_tweets.csv''' , '''w''' ) as f:
UpperCamelCase__ :Tuple = csv.writer(__a )
writer.writerow(['''id''', '''created_at''', '''text'''] )
writer.writerows(__a )
if __name__ == "__main__":
# pass in the username of the account you want to download
get_all_tweets('''FirePing32''') | 97 | 0 |
"""simple docstring"""
import unittest
from transformers import DebertaVaConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
DebertaVaForMaskedLM,
DebertaVaForMultipleChoice,
DebertaVaForQuestionAnswering,
DebertaVaForSequenceClassification,
DebertaVaForTokenClassification,
DebertaVaModel,
)
from transformers.models.deberta_va.modeling_deberta_va import DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST
class __A ( SCREAMING_SNAKE_CASE_ ):
def __init__( self , a__ , a__=13 , a__=7 , a__=True , a__=True , a__=True , a__=True , a__=99 , a__=32 , a__=5 , a__=4 , a__=37 , a__="gelu" , a__=0.1 , a__=0.1 , a__=512 , a__=16 , a__=2 , a__=0.0_2 , a__=False , a__=True , a__="None" , a__=3 , a__=4 , a__=None , ):
_lowerCAmelCase : Dict = parent
_lowerCAmelCase : str = batch_size
_lowerCAmelCase : List[Any] = seq_length
_lowerCAmelCase : Dict = is_training
_lowerCAmelCase : Dict = use_input_mask
_lowerCAmelCase : int = use_token_type_ids
_lowerCAmelCase : int = use_labels
_lowerCAmelCase : Optional[int] = vocab_size
_lowerCAmelCase : Tuple = hidden_size
_lowerCAmelCase : Tuple = num_hidden_layers
_lowerCAmelCase : Dict = num_attention_heads
_lowerCAmelCase : Union[str, Any] = intermediate_size
_lowerCAmelCase : str = hidden_act
_lowerCAmelCase : Any = hidden_dropout_prob
_lowerCAmelCase : List[str] = attention_probs_dropout_prob
_lowerCAmelCase : List[str] = max_position_embeddings
_lowerCAmelCase : List[str] = type_vocab_size
_lowerCAmelCase : Tuple = type_sequence_label_size
_lowerCAmelCase : List[Any] = initializer_range
_lowerCAmelCase : Union[str, Any] = num_labels
_lowerCAmelCase : Optional[Any] = num_choices
_lowerCAmelCase : Tuple = relative_attention
_lowerCAmelCase : Tuple = position_biased_input
_lowerCAmelCase : Dict = pos_att_type
_lowerCAmelCase : Any = scope
def __A ( self ):
_lowerCAmelCase : int = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
_lowerCAmelCase : Optional[Any] = None
if self.use_input_mask:
_lowerCAmelCase : str = ids_tensor([self.batch_size, self.seq_length] , vocab_size=2 )
_lowerCAmelCase : str = None
if self.use_token_type_ids:
_lowerCAmelCase : List[str] = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
_lowerCAmelCase : Union[str, Any] = None
_lowerCAmelCase : Union[str, Any] = None
_lowerCAmelCase : Any = None
if self.use_labels:
_lowerCAmelCase : str = ids_tensor([self.batch_size] , self.type_sequence_label_size )
_lowerCAmelCase : int = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
_lowerCAmelCase : str = ids_tensor([self.batch_size] , self.num_choices )
_lowerCAmelCase : List[str] = self.get_config()
return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def __A ( self ):
return DebertaVaConfig(
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 , relative_attention=self.relative_attention , position_biased_input=self.position_biased_input , pos_att_type=self.pos_att_type , )
def __A ( self , a__ ):
self.parent.assertListEqual(list(result.loss.size() ) , [] )
def __A ( self , a__ , a__ , a__ , a__ , a__ , a__ , a__ ):
_lowerCAmelCase : Union[str, Any] = DebertaVaModel(config=a__ )
model.to(a__ )
model.eval()
_lowerCAmelCase : List[Any] = model(a__ , attention_mask=a__ , token_type_ids=a__ )[0]
_lowerCAmelCase : List[Any] = model(a__ , token_type_ids=a__ )[0]
_lowerCAmelCase : Any = model(a__ )[0]
self.parent.assertListEqual(list(sequence_output.size() ) , [self.batch_size, self.seq_length, self.hidden_size] )
def __A ( self , a__ , a__ , a__ , a__ , a__ , a__ , a__ ):
_lowerCAmelCase : List[str] = DebertaVaForMaskedLM(config=a__ )
model.to(a__ )
model.eval()
_lowerCAmelCase : Optional[Any] = model(a__ , attention_mask=a__ , token_type_ids=a__ , labels=a__ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def __A ( self , a__ , a__ , a__ , a__ , a__ , a__ , a__ ):
_lowerCAmelCase : int = self.num_labels
_lowerCAmelCase : int = DebertaVaForSequenceClassification(a__ )
model.to(a__ )
model.eval()
_lowerCAmelCase : Optional[Any] = model(a__ , attention_mask=a__ , token_type_ids=a__ , labels=a__ )
self.parent.assertListEqual(list(result.logits.size() ) , [self.batch_size, self.num_labels] )
self.check_loss_output(a__ )
def __A ( self , a__ , a__ , a__ , a__ , a__ , a__ , a__ ):
_lowerCAmelCase : List[Any] = self.num_labels
_lowerCAmelCase : str = DebertaVaForTokenClassification(config=a__ )
model.to(a__ )
model.eval()
_lowerCAmelCase : Optional[Any] = model(a__ , attention_mask=a__ , token_type_ids=a__ , labels=a__ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def __A ( self , a__ , a__ , a__ , a__ , a__ , a__ , a__ ):
_lowerCAmelCase : Any = DebertaVaForQuestionAnswering(config=a__ )
model.to(a__ )
model.eval()
_lowerCAmelCase : Dict = model(
a__ , attention_mask=a__ , token_type_ids=a__ , start_positions=a__ , end_positions=a__ , )
self.parent.assertEqual(result.start_logits.shape , (self.batch_size, self.seq_length) )
self.parent.assertEqual(result.end_logits.shape , (self.batch_size, self.seq_length) )
def __A ( self , a__ , a__ , a__ , a__ , a__ , a__ , a__ ):
_lowerCAmelCase : Union[str, Any] = DebertaVaForMultipleChoice(config=a__ )
model.to(a__ )
model.eval()
_lowerCAmelCase : Optional[int] = input_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
_lowerCAmelCase : Optional[Any] = token_type_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
_lowerCAmelCase : str = input_mask.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
_lowerCAmelCase : List[str] = model(
a__ , attention_mask=a__ , token_type_ids=a__ , labels=a__ , )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_choices) )
def __A ( self ):
_lowerCAmelCase : Tuple = self.prepare_config_and_inputs()
(
(
_lowerCAmelCase
) , (
_lowerCAmelCase
) , (
_lowerCAmelCase
) , (
_lowerCAmelCase
) , (
_lowerCAmelCase
) , (
_lowerCAmelCase
) , (
_lowerCAmelCase
) ,
) : Union[str, Any] = config_and_inputs
_lowerCAmelCase : Union[str, Any] = {"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": input_mask}
return config, inputs_dict
@require_torch
class __A ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , unittest.TestCase ):
_UpperCamelCase : Union[str, Any] = (
(
DebertaVaModel,
DebertaVaForMaskedLM,
DebertaVaForSequenceClassification,
DebertaVaForTokenClassification,
DebertaVaForQuestionAnswering,
DebertaVaForMultipleChoice,
)
if is_torch_available()
else ()
)
_UpperCamelCase : str = (
{
"feature-extraction": DebertaVaModel,
"fill-mask": DebertaVaForMaskedLM,
"question-answering": DebertaVaForQuestionAnswering,
"text-classification": DebertaVaForSequenceClassification,
"token-classification": DebertaVaForTokenClassification,
"zero-shot": DebertaVaForSequenceClassification,
}
if is_torch_available()
else {}
)
_UpperCamelCase : Optional[Any] = True
_UpperCamelCase : List[Any] = False
_UpperCamelCase : List[Any] = False
_UpperCamelCase : Dict = False
_UpperCamelCase : Tuple = False
def __A ( self ):
_lowerCAmelCase : Optional[Any] = DebertaVaModelTester(self )
_lowerCAmelCase : Any = ConfigTester(self , config_class=a__ , hidden_size=37 )
def __A ( self ):
self.config_tester.run_common_tests()
def __A ( self ):
_lowerCAmelCase : Optional[int] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_deberta_model(*a__ )
def __A ( self ):
_lowerCAmelCase : List[str] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_deberta_for_sequence_classification(*a__ )
def __A ( self ):
_lowerCAmelCase : Any = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_deberta_for_masked_lm(*a__ )
def __A ( self ):
_lowerCAmelCase : List[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_deberta_for_question_answering(*a__ )
def __A ( self ):
_lowerCAmelCase : List[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_deberta_for_token_classification(*a__ )
def __A ( self ):
_lowerCAmelCase : Union[str, Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_deberta_for_multiple_choice(*a__ )
@slow
def __A ( self ):
for model_name in DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
_lowerCAmelCase : Tuple = DebertaVaModel.from_pretrained(a__ )
self.assertIsNotNone(a__ )
@require_torch
@require_sentencepiece
@require_tokenizers
class __A ( unittest.TestCase ):
@unittest.skip(reason="""Model not available yet""" )
def __A ( self ):
pass
@slow
def __A ( self ):
_lowerCAmelCase : Union[str, Any] = DebertaVaModel.from_pretrained("""microsoft/deberta-v2-xlarge""" )
_lowerCAmelCase : Dict = torch.tensor([[0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]] )
_lowerCAmelCase : Any = torch.tensor([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]] )
with torch.no_grad():
_lowerCAmelCase : Optional[Any] = model(a__ , attention_mask=a__ )[0]
# compare the actual values for a slice.
_lowerCAmelCase : str = torch.tensor(
[[[0.2_3_5_6, 0.1_9_4_8, 0.0_3_6_9], [-0.1_0_6_3, 0.3_5_8_6, -0.5_1_5_2], [-0.6_3_9_9, -0.0_2_5_9, -0.2_5_2_5]]] )
self.assertTrue(torch.allclose(output[:, 1:4, 1:4] , a__ , atol=1e-4 ) , F"{output[:, 1:4, 1:4]}" )
| 44 |
'''simple docstring'''
import argparse
from collections import OrderedDict
from pathlib import Path
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from torchvision.transforms import functional as F
from transformers import DetrImageProcessor, TableTransformerConfig, TableTransformerForObjectDetection
from transformers.utils import logging
logging.set_verbosity_info()
__snake_case = logging.get_logger(__name__)
# here we list all keys to be renamed (original name on the left, our name on the right)
__snake_case = []
for i in range(6):
# 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 encoder + 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.encoder.norm.weight''', '''encoder.layernorm.weight'''),
('''transformer.encoder.norm.bias''', '''encoder.layernorm.bias'''),
('''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'''),
]
)
def a ( __a , __a , __a ) -> List[str]:
'''simple docstring'''
UpperCamelCase__ :List[Any] = state_dict.pop(__a )
UpperCamelCase__ :int = val
def a ( __a ) -> Any:
'''simple docstring'''
UpperCamelCase__ :Tuple = OrderedDict()
for key, value in state_dict.items():
if "backbone.0.body" in key:
UpperCamelCase__ :Dict = key.replace('''backbone.0.body''' , '''backbone.conv_encoder.model''' )
UpperCamelCase__ :List[str] = value
else:
UpperCamelCase__ :Dict = value
return new_state_dict
def a ( __a ) -> Optional[Any]:
'''simple docstring'''
UpperCamelCase__ :Optional[Any] = ''''''
# 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)
UpperCamelCase__ :Optional[Any] = state_dict.pop(f'''{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight''' )
UpperCamelCase__ :str = 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
UpperCamelCase__ :Any = in_proj_weight[:256, :]
UpperCamelCase__ :Tuple = in_proj_bias[:256]
UpperCamelCase__ :Optional[int] = in_proj_weight[256:512, :]
UpperCamelCase__ :Optional[Any] = in_proj_bias[256:512]
UpperCamelCase__ :Tuple = in_proj_weight[-256:, :]
UpperCamelCase__ :Dict = 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
UpperCamelCase__ :List[str] = state_dict.pop(f'''{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_weight''' )
UpperCamelCase__ :Optional[Any] = 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
UpperCamelCase__ :Any = in_proj_weight[:256, :]
UpperCamelCase__ :Optional[int] = in_proj_bias[:256]
UpperCamelCase__ :Tuple = in_proj_weight[256:512, :]
UpperCamelCase__ :Dict = in_proj_bias[256:512]
UpperCamelCase__ :Any = in_proj_weight[-256:, :]
UpperCamelCase__ :Dict = in_proj_bias[-256:]
# read in weights + bias of input projection layer of cross-attention
UpperCamelCase__ :List[str] = state_dict.pop(
f'''{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_weight''' )
UpperCamelCase__ :Any = 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
UpperCamelCase__ :Optional[Any] = in_proj_weight_cross_attn[:256, :]
UpperCamelCase__ :Any = in_proj_bias_cross_attn[:256]
UpperCamelCase__ :Any = in_proj_weight_cross_attn[256:512, :]
UpperCamelCase__ :Dict = in_proj_bias_cross_attn[256:512]
UpperCamelCase__ :str = in_proj_weight_cross_attn[-256:, :]
UpperCamelCase__ :Tuple = in_proj_bias_cross_attn[-256:]
def a ( __a , __a ) -> Optional[int]:
'''simple docstring'''
UpperCamelCase__ , UpperCamelCase__ :str = image.size
UpperCamelCase__ :Optional[Any] = max(__a , __a )
UpperCamelCase__ :List[Any] = 800 if '''detection''' in checkpoint_url else 1000
UpperCamelCase__ :Dict = target_max_size / current_max_size
UpperCamelCase__ :Any = image.resize((int(round(scale * width ) ), int(round(scale * height ) )) )
return resized_image
def a ( __a ) -> int:
'''simple docstring'''
UpperCamelCase__ :Any = F.to_tensor(__a )
UpperCamelCase__ :int = F.normalize(__a , mean=[0.4_8_5, 0.4_5_6, 0.4_0_6] , std=[0.2_2_9, 0.2_2_4, 0.2_2_5] )
return image
@torch.no_grad()
def a ( __a , __a , __a ) -> Dict:
'''simple docstring'''
logger.info('''Converting model...''' )
# load original state dict
UpperCamelCase__ :Optional[Any] = torch.hub.load_state_dict_from_url(__a , map_location='''cpu''' )
# rename keys
for src, dest in rename_keys:
rename_key(__a , __a , __a )
UpperCamelCase__ :Any = rename_backbone_keys(__a )
# query, key and value matrices need special treatment
read_in_q_k_v(__a )
# important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
UpperCamelCase__ :Dict = '''model.'''
for key in state_dict.copy().keys():
if not key.startswith('''class_labels_classifier''' ) and not key.startswith('''bbox_predictor''' ):
UpperCamelCase__ :Optional[Any] = state_dict.pop(__a )
UpperCamelCase__ :int = val
# create HuggingFace model and load state dict
UpperCamelCase__ :str = TableTransformerConfig(
backbone='''resnet18''' , mask_loss_coefficient=1 , dice_loss_coefficient=1 , ce_loss_coefficient=1 , bbox_loss_coefficient=5 , giou_loss_coefficient=2 , eos_coefficient=0.4 , class_cost=1 , bbox_cost=5 , giou_cost=2 , )
if "detection" in checkpoint_url:
UpperCamelCase__ :List[str] = 15
UpperCamelCase__ :int = 2
UpperCamelCase__ :Tuple = {0: '''table''', 1: '''table rotated'''}
UpperCamelCase__ :int = idalabel
UpperCamelCase__ :Dict = {v: k for k, v in idalabel.items()}
else:
UpperCamelCase__ :int = 125
UpperCamelCase__ :List[str] = 6
UpperCamelCase__ :Optional[Any] = {
0: '''table''',
1: '''table column''',
2: '''table row''',
3: '''table column header''',
4: '''table projected row header''',
5: '''table spanning cell''',
}
UpperCamelCase__ :Dict = idalabel
UpperCamelCase__ :Optional[Any] = {v: k for k, v in idalabel.items()}
UpperCamelCase__ :List[Any] = DetrImageProcessor(
format='''coco_detection''' , max_size=800 if '''detection''' in checkpoint_url else 1000 )
UpperCamelCase__ :int = TableTransformerForObjectDetection(__a )
model.load_state_dict(__a )
model.eval()
# verify our conversion
UpperCamelCase__ :Dict = '''example_pdf.png''' if '''detection''' in checkpoint_url else '''example_table.png'''
UpperCamelCase__ :Optional[Any] = hf_hub_download(repo_id='''nielsr/example-pdf''' , repo_type='''dataset''' , filename=__a )
UpperCamelCase__ :Tuple = Image.open(__a ).convert('''RGB''' )
UpperCamelCase__ :int = normalize(resize(__a , __a ) ).unsqueeze(0 )
UpperCamelCase__ :Optional[int] = model(__a )
if "detection" in checkpoint_url:
UpperCamelCase__ :Dict = (1, 15, 3)
UpperCamelCase__ :List[Any] = torch.tensor(
[[-6.7_8_9_7, -1_6.9_9_8_5, 6.7_9_3_7], [-8.0_1_8_6, -2_2.2_1_9_2, 6.9_6_7_7], [-7.3_1_1_7, -2_1.0_7_0_8, 7.4_0_5_5]] )
UpperCamelCase__ :Tuple = torch.tensor([[0.4_8_6_7, 0.1_7_6_7, 0.6_7_3_2], [0.6_7_1_8, 0.4_4_7_9, 0.3_8_3_0], [0.4_7_1_6, 0.1_7_6_0, 0.6_3_6_4]] )
else:
UpperCamelCase__ :Optional[Any] = (1, 125, 7)
UpperCamelCase__ :Dict = torch.tensor(
[[-1_8.1_4_3_0, -8.3_2_1_4, 4.8_2_7_4], [-1_8.4_6_8_5, -7.1_3_6_1, -4.2_6_6_7], [-2_6.3_6_9_3, -9.3_4_2_9, -4.9_9_6_2]] )
UpperCamelCase__ :List[Any] = torch.tensor([[0.4_9_8_3, 0.5_5_9_5, 0.9_4_4_0], [0.4_9_1_6, 0.6_3_1_5, 0.5_9_5_4], [0.6_1_0_8, 0.8_6_3_7, 0.1_1_3_5]] )
assert outputs.logits.shape == expected_shape
assert torch.allclose(outputs.logits[0, :3, :3] , __a , atol=1e-4 )
assert torch.allclose(outputs.pred_boxes[0, :3, :3] , __a , 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(__a ).mkdir(exist_ok=__a )
model.save_pretrained(__a )
image_processor.save_pretrained(__a )
if push_to_hub:
# Push model to HF hub
logger.info('''Pushing model to the hub...''' )
UpperCamelCase__ :Union[str, Any] = (
'''microsoft/table-transformer-detection'''
if '''detection''' in checkpoint_url
else '''microsoft/table-transformer-structure-recognition'''
)
model.push_to_hub(__a )
image_processor.push_to_hub(__a )
if __name__ == "__main__":
__snake_case = argparse.ArgumentParser()
parser.add_argument(
'''--checkpoint_url''',
default='''https://pubtables1m.blob.core.windows.net/model/pubtables1m_detection_detr_r18.pth''',
type=str,
choices=[
'''https://pubtables1m.blob.core.windows.net/model/pubtables1m_detection_detr_r18.pth''',
'''https://pubtables1m.blob.core.windows.net/model/pubtables1m_structure_detr_r18.pth''',
],
help='''URL of the Table Transformer checkpoint 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 or not to push the converted model to the 🤗 hub.'''
)
__snake_case = parser.parse_args()
convert_table_transformer_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub) | 97 | 0 |
"""simple docstring"""
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from diffusers import (
DDIMScheduler,
KandinskyVaaInpaintPipeline,
KandinskyVaaPriorPipeline,
UNetaDConditionModel,
VQModel,
)
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu
from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference
enable_full_determinism()
class __lowerCAmelCase ( __SCREAMING_SNAKE_CASE , unittest.TestCase ):
'''simple docstring'''
__UpperCAmelCase : Union[str, Any] = KandinskyVaaInpaintPipeline
__UpperCAmelCase : List[Any] = ['image_embeds', 'negative_image_embeds', 'image', 'mask_image']
__UpperCAmelCase : Dict = [
'image_embeds',
'negative_image_embeds',
'image',
'mask_image',
]
__UpperCAmelCase : List[str] = [
'generator',
'height',
'width',
'latents',
'guidance_scale',
'num_inference_steps',
'return_dict',
'guidance_scale',
'num_images_per_prompt',
'output_type',
'return_dict',
]
__UpperCAmelCase : Dict = False
@property
def __UpperCAmelCase ( self ):
return 32
@property
def __UpperCAmelCase ( self ):
return 32
@property
def __UpperCAmelCase ( self ):
return self.time_input_dim
@property
def __UpperCAmelCase ( self ):
return self.time_input_dim * 4
@property
def __UpperCAmelCase ( self ):
return 100
@property
def __UpperCAmelCase ( self ):
torch.manual_seed(0 )
__a = {
'''in_channels''': 9,
# Out channels is double in channels because predicts mean and variance
'''out_channels''': 8,
'''addition_embed_type''': '''image''',
'''down_block_types''': ('''ResnetDownsampleBlock2D''', '''SimpleCrossAttnDownBlock2D'''),
'''up_block_types''': ('''SimpleCrossAttnUpBlock2D''', '''ResnetUpsampleBlock2D'''),
'''mid_block_type''': '''UNetMidBlock2DSimpleCrossAttn''',
'''block_out_channels''': (self.block_out_channels_a, self.block_out_channels_a * 2),
'''layers_per_block''': 1,
'''encoder_hid_dim''': self.text_embedder_hidden_size,
'''encoder_hid_dim_type''': '''image_proj''',
'''cross_attention_dim''': self.cross_attention_dim,
'''attention_head_dim''': 4,
'''resnet_time_scale_shift''': '''scale_shift''',
'''class_embed_type''': None,
}
__a = UNetaDConditionModel(**_a )
return model
@property
def __UpperCAmelCase ( self ):
return {
"block_out_channels": [32, 64],
"down_block_types": ["DownEncoderBlock2D", "AttnDownEncoderBlock2D"],
"in_channels": 3,
"latent_channels": 4,
"layers_per_block": 1,
"norm_num_groups": 8,
"norm_type": "spatial",
"num_vq_embeddings": 12,
"out_channels": 3,
"up_block_types": [
"AttnUpDecoderBlock2D",
"UpDecoderBlock2D",
],
"vq_embed_dim": 4,
}
@property
def __UpperCAmelCase ( self ):
torch.manual_seed(0 )
__a = VQModel(**self.dummy_movq_kwargs )
return model
def __UpperCAmelCase ( self ):
__a = self.dummy_unet
__a = self.dummy_movq
__a = DDIMScheduler(
num_train_timesteps=1_000 , beta_schedule='''linear''' , beta_start=0.0_0085 , beta_end=0.012 , clip_sample=_a , set_alpha_to_one=_a , steps_offset=1 , prediction_type='''epsilon''' , thresholding=_a , )
__a = {
'''unet''': unet,
'''scheduler''': scheduler,
'''movq''': movq,
}
return components
def __UpperCAmelCase ( self , _a , _a=0 ):
__a = floats_tensor((1, self.text_embedder_hidden_size) , rng=random.Random(_a ) ).to(_a )
__a = floats_tensor((1, self.text_embedder_hidden_size) , rng=random.Random(seed + 1 ) ).to(
_a )
# create init_image
__a = floats_tensor((1, 3, 64, 64) , rng=random.Random(_a ) ).to(_a )
__a = image.cpu().permute(0 , 2 , 3 , 1 )[0]
__a = Image.fromarray(np.uinta(_a ) ).convert('''RGB''' ).resize((256, 256) )
# create mask
__a = np.ones((64, 64) , dtype=np.floataa )
__a = 0
if str(_a ).startswith('''mps''' ):
__a = torch.manual_seed(_a )
else:
__a = torch.Generator(device=_a ).manual_seed(_a )
__a = {
'''image''': init_image,
'''mask_image''': mask,
'''image_embeds''': image_embeds,
'''negative_image_embeds''': negative_image_embeds,
'''generator''': generator,
'''height''': 64,
'''width''': 64,
'''num_inference_steps''': 2,
'''guidance_scale''': 4.0,
'''output_type''': '''np''',
}
return inputs
def __UpperCAmelCase ( self ):
__a = '''cpu'''
__a = self.get_dummy_components()
__a = self.pipeline_class(**_a )
__a = pipe.to(_a )
pipe.set_progress_bar_config(disable=_a )
__a = pipe(**self.get_dummy_inputs(_a ) )
__a = output.images
__a = pipe(
**self.get_dummy_inputs(_a ) , return_dict=_a , )[0]
__a = image[0, -3:, -3:, -1]
__a = image_from_tuple[0, -3:, -3:, -1]
print(f'''image.shape {image.shape}''' )
assert image.shape == (1, 64, 64, 3)
__a = np.array(
[0.5077_5903, 0.4952_7195, 0.4882_4543, 0.5019_2237, 0.4864_4906, 0.4937_3814, 0.478_0598, 0.4723_4827, 0.4832_7848] )
assert (
np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
), f''' expected_slice {expected_slice}, but got {image_slice.flatten()}'''
assert (
np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 1E-2
), f''' expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}'''
def __UpperCAmelCase ( self ):
super().test_inference_batch_single_identical(expected_max_diff=3E-3 )
@slow
@require_torch_gpu
class __lowerCAmelCase ( unittest.TestCase ):
'''simple docstring'''
def __UpperCAmelCase ( self ):
# clean up the VRAM after each test
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def __UpperCAmelCase ( self ):
__a = load_numpy(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
'''/kandinskyv22/kandinskyv22_inpaint_cat_with_hat_fp16.npy''' )
__a = load_image(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main''' '''/kandinsky/cat.png''' )
__a = np.ones((768, 768) , dtype=np.floataa )
__a = 0
__a = '''a hat'''
__a = KandinskyVaaPriorPipeline.from_pretrained(
'''kandinsky-community/kandinsky-2-2-prior''' , torch_dtype=torch.floataa )
pipe_prior.to(_a )
__a = KandinskyVaaInpaintPipeline.from_pretrained(
'''kandinsky-community/kandinsky-2-2-decoder-inpaint''' , torch_dtype=torch.floataa )
__a = pipeline.to(_a )
pipeline.set_progress_bar_config(disable=_a )
__a = torch.Generator(device='''cpu''' ).manual_seed(0 )
__a , __a = pipe_prior(
_a , generator=_a , num_inference_steps=5 , negative_prompt='''''' , ).to_tuple()
__a = pipeline(
image=_a , mask_image=_a , image_embeds=_a , negative_image_embeds=_a , generator=_a , num_inference_steps=100 , height=768 , width=768 , output_type='''np''' , )
__a = output.images[0]
assert image.shape == (768, 768, 3)
assert_mean_pixel_difference(_a , _a )
| 45 |
'''simple docstring'''
from __future__ import annotations
from fractions import Fraction
from math import gcd, sqrt
def a ( __a ) -> bool:
'''simple docstring'''
UpperCamelCase__ :int = int(number**0.5 )
return number == sq * sq
def a ( __a , __a , __a , __a , __a , __a ) -> tuple[int, int]:
'''simple docstring'''
UpperCamelCase__ :int = x_num * y_den * z_den + y_num * x_den * z_den + z_num * x_den * y_den
UpperCamelCase__ :int = x_den * y_den * z_den
UpperCamelCase__ :int = gcd(__a , __a )
top //= hcf
bottom //= hcf
return top, bottom
def a ( __a = 35 ) -> int:
'''simple docstring'''
UpperCamelCase__ :set = set()
UpperCamelCase__ :int
UpperCamelCase__ :Fraction = Fraction(0 )
UpperCamelCase__ :tuple[int, int]
for x_num in range(1 , order + 1 ):
for x_den in range(x_num + 1 , order + 1 ):
for y_num in range(1 , order + 1 ):
for y_den in range(y_num + 1 , order + 1 ):
# n=1
UpperCamelCase__ :int = x_num * y_den + x_den * y_num
UpperCamelCase__ :Any = x_den * y_den
UpperCamelCase__ :Tuple = gcd(__a , __a )
z_num //= hcf
z_den //= hcf
if 0 < z_num < z_den <= order:
UpperCamelCase__ :Tuple = add_three(
__a , __a , __a , __a , __a , __a )
unique_s.add(__a )
# n=2
UpperCamelCase__ :List[str] = (
x_num * x_num * y_den * y_den + x_den * x_den * y_num * y_num
)
UpperCamelCase__ :Dict = x_den * x_den * y_den * y_den
if is_sq(__a ) and is_sq(__a ):
UpperCamelCase__ :Any = int(sqrt(__a ) )
UpperCamelCase__ :Optional[int] = int(sqrt(__a ) )
UpperCamelCase__ :int = gcd(__a , __a )
z_num //= hcf
z_den //= hcf
if 0 < z_num < z_den <= order:
UpperCamelCase__ :Tuple = add_three(
__a , __a , __a , __a , __a , __a )
unique_s.add(__a )
# n=-1
UpperCamelCase__ :Tuple = x_num * y_num
UpperCamelCase__ :Union[str, Any] = x_den * y_num + x_num * y_den
UpperCamelCase__ :List[str] = gcd(__a , __a )
z_num //= hcf
z_den //= hcf
if 0 < z_num < z_den <= order:
UpperCamelCase__ :Union[str, Any] = add_three(
__a , __a , __a , __a , __a , __a )
unique_s.add(__a )
# n=2
UpperCamelCase__ :Optional[Any] = x_num * x_num * y_num * y_num
UpperCamelCase__ :Tuple = (
x_den * x_den * y_num * y_num + x_num * x_num * y_den * y_den
)
if is_sq(__a ) and is_sq(__a ):
UpperCamelCase__ :str = int(sqrt(__a ) )
UpperCamelCase__ :Any = int(sqrt(__a ) )
UpperCamelCase__ :Dict = gcd(__a , __a )
z_num //= hcf
z_den //= hcf
if 0 < z_num < z_den <= order:
UpperCamelCase__ :int = add_three(
__a , __a , __a , __a , __a , __a )
unique_s.add(__a )
for num, den in unique_s:
total += Fraction(__a , __a )
return total.denominator + total.numerator
if __name__ == "__main__":
print(F"""{solution() = }""") | 97 | 0 |
"""simple docstring"""
import warnings
from ...utils import logging
from .image_processing_flava import FlavaImageProcessor
SCREAMING_SNAKE_CASE__ = logging.get_logger(__name__)
class lowercase ( _UpperCAmelCase ):
def __init__( self , *lowercase , **lowercase ) -> None:
warnings.warn(
"""The class FlavaFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please"""
""" use FlavaImageProcessor instead.""" , lowercase , )
super().__init__(*lowercase , **lowercase )
| 46 |
'''simple docstring'''
def a ( ) -> Union[str, Any]:
'''simple docstring'''
UpperCamelCase__ :Optional[int] = []
UpperCamelCase__ :int = 1
while len(__a ) < 1e6:
constant.append(str(__a ) )
i += 1
UpperCamelCase__ :Union[str, Any] = ''''''.join(__a )
return (
int(constant[0] )
* int(constant[9] )
* int(constant[99] )
* int(constant[999] )
* int(constant[9999] )
* int(constant[99999] )
* int(constant[999999] )
)
if __name__ == "__main__":
print(solution()) | 97 | 0 |
'''simple docstring'''
import inspect
import os
import unittest
import torch
import accelerate
from accelerate import debug_launcher
from accelerate.test_utils import (
execute_subprocess_async,
require_cpu,
require_huggingface_suite,
require_multi_gpu,
require_single_gpu,
)
from accelerate.utils import patch_environment
@require_huggingface_suite
class A__ ( unittest.TestCase ):
def A ( self : Optional[Any] ) -> Tuple:
'''simple docstring'''
_SCREAMING_SNAKE_CASE =inspect.getfile(accelerate.test_utils )
_SCREAMING_SNAKE_CASE =os.path.sep.join(
mod_file.split(os.path.sep )[:-1] + ['scripts', 'external_deps', 'test_metrics.py'] )
from accelerate.test_utils.scripts.external_deps import test_metrics # noqa: F401
_SCREAMING_SNAKE_CASE =test_metrics
@require_cpu
def A ( self : Optional[int] ) -> str:
'''simple docstring'''
debug_launcher(self.test_metrics.main , num_processes=1 )
@require_cpu
def A ( self : Optional[Any] ) -> Union[str, Any]:
'''simple docstring'''
debug_launcher(self.test_metrics.main )
@require_single_gpu
def A ( self : Optional[int] ) -> List[str]:
'''simple docstring'''
self.test_metrics.main()
@require_multi_gpu
def A ( self : List[Any] ) -> Union[str, Any]:
'''simple docstring'''
print(f"Found {torch.cuda.device_count()} devices." )
_SCREAMING_SNAKE_CASE =['torchrun', f"--nproc_per_node={torch.cuda.device_count()}", self.test_file_path]
with patch_environment(omp_num_threads=1 ):
execute_subprocess_async(_a , env=os.environ.copy() )
| 47 |
'''simple docstring'''
from PIL import Image
def a ( __a , __a ) -> Image:
'''simple docstring'''
def brightness(__a ) -> float:
return 128 + level + (c - 128)
if not -2_5_5.0 <= level <= 2_5_5.0:
raise ValueError('''level must be between -255.0 (black) and 255.0 (white)''' )
return img.point(__a )
if __name__ == "__main__":
# Load image
with Image.open('''image_data/lena.jpg''') as img:
# Change brightness to 100
__snake_case = change_brightness(img, 100)
brigt_img.save('''image_data/lena_brightness.png''', format='''png''') | 97 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_torch_available
SCREAMING_SNAKE_CASE__ : Dict = {'configuration_speech_encoder_decoder': ['SpeechEncoderDecoderConfig']}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
SCREAMING_SNAKE_CASE__ : Optional[int] = ['SpeechEncoderDecoderModel']
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
SCREAMING_SNAKE_CASE__ : List[Any] = ['FlaxSpeechEncoderDecoderModel']
if TYPE_CHECKING:
from .configuration_speech_encoder_decoder import SpeechEncoderDecoderConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_speech_encoder_decoder import SpeechEncoderDecoderModel
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_speech_encoder_decoder import FlaxSpeechEncoderDecoderModel
else:
import sys
SCREAMING_SNAKE_CASE__ : Dict = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 48 |
'''simple docstring'''
from datetime import datetime as dt
import os
from github import Github
__snake_case = [
'''good first issue''',
'''good second issue''',
'''good difficult issue''',
'''feature request''',
'''new model''',
'''wip''',
]
def a ( ) -> List[str]:
'''simple docstring'''
UpperCamelCase__ :List[str] = Github(os.environ['''GITHUB_TOKEN'''] )
UpperCamelCase__ :Tuple = g.get_repo('''huggingface/transformers''' )
UpperCamelCase__ :Union[str, Any] = repo.get_issues(state='''open''' )
for issue in open_issues:
UpperCamelCase__ :List[Any] = sorted([comment for comment in issue.get_comments()] , key=lambda __a : i.created_at , reverse=__a )
UpperCamelCase__ :List[Any] = comments[0] if len(__a ) > 0 else None
if (
last_comment is not None
and last_comment.user.login == "github-actions[bot]"
and (dt.utcnow() - issue.updated_at).days > 7
and (dt.utcnow() - issue.created_at).days >= 30
and not any(label.name.lower() in LABELS_TO_EXEMPT for label in issue.get_labels() )
):
# print(f"Would close issue {issue.number} since it has been 7 days of inactivity since bot mention.")
issue.edit(state='''closed''' )
elif (
(dt.utcnow() - issue.updated_at).days > 23
and (dt.utcnow() - issue.created_at).days >= 30
and not any(label.name.lower() in LABELS_TO_EXEMPT for label in issue.get_labels() )
):
# print(f"Would add stale comment to {issue.number}")
issue.create_comment(
'''This issue has been automatically marked as stale because it has not had '''
'''recent activity. If you think this still needs to be addressed '''
'''please comment on this thread.\n\nPlease note that issues that do not follow the '''
'''[contributing guidelines](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md) '''
'''are likely to be ignored.''' )
if __name__ == "__main__":
main() | 97 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
__snake_case :List[str] = {
'''configuration_xlm''': ['''XLM_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''XLMConfig''', '''XLMOnnxConfig'''],
'''tokenization_xlm''': ['''XLMTokenizer'''],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__snake_case :Dict = [
'''XLM_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''XLMForMultipleChoice''',
'''XLMForQuestionAnswering''',
'''XLMForQuestionAnsweringSimple''',
'''XLMForSequenceClassification''',
'''XLMForTokenClassification''',
'''XLMModel''',
'''XLMPreTrainedModel''',
'''XLMWithLMHeadModel''',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__snake_case :Union[str, Any] = [
'''TF_XLM_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''TFXLMForMultipleChoice''',
'''TFXLMForQuestionAnsweringSimple''',
'''TFXLMForSequenceClassification''',
'''TFXLMForTokenClassification''',
'''TFXLMMainLayer''',
'''TFXLMModel''',
'''TFXLMPreTrainedModel''',
'''TFXLMWithLMHeadModel''',
]
if TYPE_CHECKING:
from .configuration_xlm import XLM_PRETRAINED_CONFIG_ARCHIVE_MAP, XLMConfig, XLMOnnxConfig
from .tokenization_xlm import XLMTokenizer
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_xlm import (
XLM_PRETRAINED_MODEL_ARCHIVE_LIST,
XLMForMultipleChoice,
XLMForQuestionAnswering,
XLMForQuestionAnsweringSimple,
XLMForSequenceClassification,
XLMForTokenClassification,
XLMModel,
XLMPreTrainedModel,
XLMWithLMHeadModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_xlm import (
TF_XLM_PRETRAINED_MODEL_ARCHIVE_LIST,
TFXLMForMultipleChoice,
TFXLMForQuestionAnsweringSimple,
TFXLMForSequenceClassification,
TFXLMForTokenClassification,
TFXLMMainLayer,
TFXLMModel,
TFXLMPreTrainedModel,
TFXLMWithLMHeadModel,
)
else:
import sys
__snake_case :List[Any] = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 49 |
'''simple docstring'''
import re
from filelock import FileLock
try:
import nltk
__snake_case = True
except (ImportError, ModuleNotFoundError):
__snake_case = False
if NLTK_AVAILABLE:
with FileLock('''.lock''') as lock:
nltk.download('''punkt''', quiet=True)
def a ( __a ) -> str:
'''simple docstring'''
re.sub('''<n>''' , '''''' , __a ) # 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(__a ) ) | 97 | 0 |
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_albert import AlbertTokenizer
else:
_UpperCAmelCase : str = None
_UpperCAmelCase : List[Any] = logging.get_logger(__name__)
_UpperCAmelCase : List[str] = {"""vocab_file""": """spiece.model""", """tokenizer_file""": """tokenizer.json"""}
_UpperCAmelCase : Optional[int] = {
"""vocab_file""": {
"""albert-base-v1""": """https://huggingface.co/albert-base-v1/resolve/main/spiece.model""",
"""albert-large-v1""": """https://huggingface.co/albert-large-v1/resolve/main/spiece.model""",
"""albert-xlarge-v1""": """https://huggingface.co/albert-xlarge-v1/resolve/main/spiece.model""",
"""albert-xxlarge-v1""": """https://huggingface.co/albert-xxlarge-v1/resolve/main/spiece.model""",
"""albert-base-v2""": """https://huggingface.co/albert-base-v2/resolve/main/spiece.model""",
"""albert-large-v2""": """https://huggingface.co/albert-large-v2/resolve/main/spiece.model""",
"""albert-xlarge-v2""": """https://huggingface.co/albert-xlarge-v2/resolve/main/spiece.model""",
"""albert-xxlarge-v2""": """https://huggingface.co/albert-xxlarge-v2/resolve/main/spiece.model""",
},
"""tokenizer_file""": {
"""albert-base-v1""": """https://huggingface.co/albert-base-v1/resolve/main/tokenizer.json""",
"""albert-large-v1""": """https://huggingface.co/albert-large-v1/resolve/main/tokenizer.json""",
"""albert-xlarge-v1""": """https://huggingface.co/albert-xlarge-v1/resolve/main/tokenizer.json""",
"""albert-xxlarge-v1""": """https://huggingface.co/albert-xxlarge-v1/resolve/main/tokenizer.json""",
"""albert-base-v2""": """https://huggingface.co/albert-base-v2/resolve/main/tokenizer.json""",
"""albert-large-v2""": """https://huggingface.co/albert-large-v2/resolve/main/tokenizer.json""",
"""albert-xlarge-v2""": """https://huggingface.co/albert-xlarge-v2/resolve/main/tokenizer.json""",
"""albert-xxlarge-v2""": """https://huggingface.co/albert-xxlarge-v2/resolve/main/tokenizer.json""",
},
}
_UpperCAmelCase : Optional[Any] = {
"""albert-base-v1""": 5_12,
"""albert-large-v1""": 5_12,
"""albert-xlarge-v1""": 5_12,
"""albert-xxlarge-v1""": 5_12,
"""albert-base-v2""": 5_12,
"""albert-large-v2""": 5_12,
"""albert-xlarge-v2""": 5_12,
"""albert-xxlarge-v2""": 5_12,
}
_UpperCAmelCase : Optional[int] = """▁"""
class lowerCAmelCase ( __UpperCamelCase ):
UpperCAmelCase__ = VOCAB_FILES_NAMES
UpperCAmelCase__ = PRETRAINED_VOCAB_FILES_MAP
UpperCAmelCase__ = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
UpperCAmelCase__ = AlbertTokenizer
def __init__( self : Optional[Any] , UpperCAmelCase : Tuple=None , UpperCAmelCase : Optional[Any]=None , UpperCAmelCase : Optional[Any]=True , UpperCAmelCase : Optional[Any]=True , UpperCAmelCase : List[Any]=False , UpperCAmelCase : Tuple="[CLS]" , UpperCAmelCase : str="[SEP]" , UpperCAmelCase : Any="<unk>" , UpperCAmelCase : Any="[SEP]" , UpperCAmelCase : List[Any]="<pad>" , UpperCAmelCase : List[str]="[CLS]" , UpperCAmelCase : Optional[int]="[MASK]" , **UpperCAmelCase : int , ) -> Dict:
# Mask token behave like a normal word, i.e. include the space before it and
# is included in the raw text, there should be a match in a non-normalized sentence.
lowerCamelCase__ : Any = (
AddedToken(UpperCAmelCase , lstrip=UpperCAmelCase , rstrip=UpperCAmelCase , normalized=UpperCAmelCase )
if isinstance(UpperCAmelCase , UpperCAmelCase )
else mask_token
)
super().__init__(
UpperCAmelCase , tokenizer_file=UpperCAmelCase , do_lower_case=UpperCAmelCase , remove_space=UpperCAmelCase , keep_accents=UpperCAmelCase , bos_token=UpperCAmelCase , eos_token=UpperCAmelCase , unk_token=UpperCAmelCase , sep_token=UpperCAmelCase , pad_token=UpperCAmelCase , cls_token=UpperCAmelCase , mask_token=UpperCAmelCase , **UpperCAmelCase , )
lowerCamelCase__ : Union[str, Any] = do_lower_case
lowerCamelCase__ : List[Any] = remove_space
lowerCamelCase__ : Optional[Any] = keep_accents
lowerCamelCase__ : Tuple = vocab_file
lowerCamelCase__ : Tuple = False if not self.vocab_file else True
def A_ ( self : List[str] , UpperCAmelCase : List[int] , UpperCAmelCase : Optional[List[int]] = None ) -> List[int]:
lowerCamelCase__ : Optional[Any] = [self.sep_token_id]
lowerCamelCase__ : List[Any] = [self.cls_token_id]
if token_ids_a is None:
return cls + token_ids_a + sep
return cls + token_ids_a + sep + token_ids_a + sep
def A_ ( self : List[Any] , UpperCAmelCase : List[int] , UpperCAmelCase : Optional[List[int]] = None ) -> List[int]:
lowerCamelCase__ : List[str] = [self.sep_token_id]
lowerCamelCase__ : int = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep ) * [0] + len(token_ids_a + sep ) * [1]
def A_ ( self : Any , UpperCAmelCase : str , UpperCAmelCase : Optional[str] = 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(UpperCAmelCase ):
logger.error(F"""Vocabulary path ({save_directory}) should be a directory""" )
return
lowerCamelCase__ : List[str] = os.path.join(
UpperCAmelCase , (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file'] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(UpperCAmelCase ):
copyfile(self.vocab_file , UpperCAmelCase )
return (out_vocab_file,)
| 50 |
'''simple docstring'''
from pathlib import Path
import fire
from tqdm import tqdm
def a ( __a="ro" , __a="en" , __a="wmt16" , __a=None ) -> None:
'''simple docstring'''
try:
import datasets
except (ModuleNotFoundError, ImportError):
raise ImportError('''run pip install datasets''' )
UpperCamelCase__ :int = f'''{src_lang}-{tgt_lang}'''
print(f'''Converting {dataset}-{pair}''' )
UpperCamelCase__ :Tuple = datasets.load_dataset(__a , __a )
if save_dir is None:
UpperCamelCase__ :Any = f'''{dataset}-{pair}'''
UpperCamelCase__ :Dict = Path(__a )
save_dir.mkdir(exist_ok=__a )
for split in ds.keys():
print(f'''Splitting {split} with {ds[split].num_rows} records''' )
# to save to val.source, val.target like summary datasets
UpperCamelCase__ :Dict = '''val''' if split == '''validation''' else split
UpperCamelCase__ :List[Any] = save_dir.joinpath(f'''{fn}.source''' )
UpperCamelCase__ :int = save_dir.joinpath(f'''{fn}.target''' )
UpperCamelCase__ :Union[str, Any] = src_path.open('''w+''' )
UpperCamelCase__ :Tuple = tgt_path.open('''w+''' )
# reader is the bottleneck so writing one record at a time doesn't slow things down
for x in tqdm(ds[split] ):
UpperCamelCase__ :Union[str, Any] = x['''translation''']
src_fp.write(ex[src_lang] + '''\n''' )
tgt_fp.write(ex[tgt_lang] + '''\n''' )
print(f'''Saved {dataset} dataset to {save_dir}''' )
if __name__ == "__main__":
fire.Fire(download_wmt_dataset) | 97 | 0 |
from ..utils import is_flax_available, is_torch_available
if is_torch_available():
from .autoencoder_kl import AutoencoderKL
from .controlnet import ControlNetModel
from .dual_transformer_ad import DualTransformeraDModel
from .modeling_utils import ModelMixin
from .prior_transformer import PriorTransformer
from .ta_film_transformer import TaFilmDecoder
from .transformer_ad import TransformeraDModel
from .unet_ad import UNetaDModel
from .unet_ad import UNetaDModel
from .unet_ad_condition import UNetaDConditionModel
from .unet_ad_condition import UNetaDConditionModel
from .vq_model import VQModel
if is_flax_available():
from .controlnet_flax import FlaxControlNetModel
from .unet_ad_condition_flax import FlaxUNetaDConditionModel
from .vae_flax import FlaxAutoencoderKL
| 51 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...file_utils import _LazyModule, is_tokenizers_available, is_torch_available, is_vision_available
from ...utils import OptionalDependencyNotAvailable
__snake_case = {'''configuration_dpt''': ['''DPT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''DPTConfig''']}
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__snake_case = ['''DPTFeatureExtractor''']
__snake_case = ['''DPTImageProcessor''']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__snake_case = [
'''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
__snake_case = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__) | 97 | 0 |
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
if TYPE_CHECKING:
from ... import FeatureExtractionMixin, PreTrainedTokenizerBase, TensorType
__lowerCamelCase : Optional[int] = logging.get_logger(__name__)
__lowerCamelCase : List[Any] = {
"""microsoft/deberta-v2-xlarge""": """https://huggingface.co/microsoft/deberta-v2-xlarge/resolve/main/config.json""",
"""microsoft/deberta-v2-xxlarge""": """https://huggingface.co/microsoft/deberta-v2-xxlarge/resolve/main/config.json""",
"""microsoft/deberta-v2-xlarge-mnli""": (
"""https://huggingface.co/microsoft/deberta-v2-xlarge-mnli/resolve/main/config.json"""
),
"""microsoft/deberta-v2-xxlarge-mnli""": (
"""https://huggingface.co/microsoft/deberta-v2-xxlarge-mnli/resolve/main/config.json"""
),
}
class A__ ( __snake_case ):
_UpperCAmelCase :Dict = 'deberta-v2'
def __init__( self , A_=12_8100 , A_=1536 , A_=24 , A_=24 , A_=6144 , A_="gelu" , A_=0.1 , A_=0.1 , A_=512 , A_=0 , A_=0.02 , A_=1e-7 , A_=False , A_=-1 , A_=0 , A_=True , A_=None , A_=0 , A_="gelu" , **A_ , ):
'''simple docstring'''
super().__init__(**A_ )
UpperCamelCase : Optional[Any] = hidden_size
UpperCamelCase : Optional[int] = num_hidden_layers
UpperCamelCase : Tuple = num_attention_heads
UpperCamelCase : Optional[int] = intermediate_size
UpperCamelCase : str = hidden_act
UpperCamelCase : List[Any] = hidden_dropout_prob
UpperCamelCase : List[Any] = attention_probs_dropout_prob
UpperCamelCase : List[str] = max_position_embeddings
UpperCamelCase : Optional[Any] = type_vocab_size
UpperCamelCase : Dict = initializer_range
UpperCamelCase : str = relative_attention
UpperCamelCase : Union[str, Any] = max_relative_positions
UpperCamelCase : List[Any] = pad_token_id
UpperCamelCase : Tuple = position_biased_input
# Backwards compatibility
if type(A_ ) == str:
UpperCamelCase : Any = [x.strip() for x in pos_att_type.lower().split("|" )]
UpperCamelCase : Any = pos_att_type
UpperCamelCase : Optional[Any] = vocab_size
UpperCamelCase : Optional[int] = layer_norm_eps
UpperCamelCase : List[str] = kwargs.get("pooler_hidden_size" , A_ )
UpperCamelCase : Any = pooler_dropout
UpperCamelCase : Optional[Any] = pooler_hidden_act
class A__ ( __snake_case ):
@property
def __UpperCamelCase( self ):
'''simple docstring'''
if self.task == "multiple-choice":
UpperCamelCase : str = {0: "batch", 1: "choice", 2: "sequence"}
else:
UpperCamelCase : List[Any] = {0: "batch", 1: "sequence"}
if self._config.type_vocab_size > 0:
return OrderedDict(
[("input_ids", dynamic_axis), ("attention_mask", dynamic_axis), ("token_type_ids", dynamic_axis)] )
else:
return OrderedDict([("input_ids", dynamic_axis), ("attention_mask", dynamic_axis)] )
@property
def __UpperCamelCase( self ):
'''simple docstring'''
return 12
def __UpperCamelCase( self , A_ , A_ = -1 , A_ = -1 , A_ = -1 , A_ = False , A_ = None , A_ = 3 , A_ = 40 , A_ = 40 , A_ = None , ):
'''simple docstring'''
UpperCamelCase : List[Any] = super().generate_dummy_inputs(preprocessor=A_ , framework=A_ )
if self._config.type_vocab_size == 0 and "token_type_ids" in dummy_inputs:
del dummy_inputs["token_type_ids"]
return dummy_inputs
| 52 |
'''simple docstring'''
def a ( __a , __a ) -> int:
'''simple docstring'''
if len(__a ) != len(__a ):
raise ValueError('''String lengths must match!''' )
UpperCamelCase__ :Union[str, Any] = 0
for chara, chara in zip(__a , __a ):
if chara != chara:
count += 1
return count
if __name__ == "__main__":
import doctest
doctest.testmod() | 97 | 0 |
'''simple docstring'''
import json
import os
import re
import shutil
import tempfile
import unittest
from typing import Tuple
from transformers import AddedToken, BatchEncoding, PerceiverTokenizer
from transformers.utils import cached_property, is_tf_available, is_torch_available
from ...test_tokenization_common import TokenizerTesterMixin
if is_torch_available():
a__ : Any ='''pt'''
elif is_tf_available():
a__ : Tuple ='''tf'''
else:
a__ : List[str] ='''jax'''
class snake_case ( __lowerCamelCase , unittest.TestCase ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Any =PerceiverTokenizer
SCREAMING_SNAKE_CASE_ : Optional[int] =False
def _lowerCamelCase ( self : int ):
super().setUp()
__UpperCamelCase = PerceiverTokenizer()
tokenizer.save_pretrained(self.tmpdirname )
@cached_property
def _lowerCamelCase ( self : Optional[Any] ):
return PerceiverTokenizer.from_pretrained('deepmind/language-perceiver' )
def _lowerCamelCase ( self : str , **__A : Tuple ):
return self.tokenizer_class.from_pretrained(self.tmpdirname , **__A )
def _lowerCamelCase ( self : List[str] , __A : Union[str, Any] , __A : Dict=False , __A : Any=2_0 , __A : Tuple=5 ):
# XXX The default common tokenizer tests assume that every ID is decodable on its own.
# This assumption is invalid for Perceiver because single bytes might not be
# valid utf-8 (byte 128 for instance).
# Here we're overriding the smallest possible method to provide
# a clean sequence without making the same assumption.
__UpperCamelCase = []
for i in range(len(__A ) ):
try:
__UpperCamelCase = tokenizer.decode([i] , clean_up_tokenization_spaces=__A )
except UnicodeDecodeError:
pass
toks.append((i, tok) )
__UpperCamelCase = list(filter(lambda __A : re.match(R'^[ a-zA-Z]+$' , t[1] ) , __A ) )
__UpperCamelCase = list(filter(lambda __A : [t[0]] == tokenizer.encode(t[1] , add_special_tokens=__A ) , __A ) )
if max_length is not None and len(__A ) > max_length:
__UpperCamelCase = toks[:max_length]
if min_length is not None and len(__A ) < min_length and len(__A ) > 0:
while len(__A ) < min_length:
__UpperCamelCase = toks + toks
# toks_str = [t[1] for t in toks]
__UpperCamelCase = [t[0] for t in toks]
# Ensure consistency
__UpperCamelCase = tokenizer.decode(__A , clean_up_tokenization_spaces=__A )
if " " not in output_txt and len(__A ) > 1:
__UpperCamelCase = (
tokenizer.decode([toks_ids[0]] , clean_up_tokenization_spaces=__A )
+ ' '
+ tokenizer.decode(toks_ids[1:] , clean_up_tokenization_spaces=__A )
)
if with_prefix_space:
__UpperCamelCase = ' ' + output_txt
__UpperCamelCase = tokenizer.encode(__A , add_special_tokens=__A )
return output_txt, output_ids
def _lowerCamelCase ( self : Union[str, Any] ):
__UpperCamelCase = self.perceiver_tokenizer
__UpperCamelCase = 'Unicode €.'
__UpperCamelCase = tokenizer(__A )
__UpperCamelCase = [4, 9_1, 1_1_6, 1_1_1, 1_0_5, 1_1_7, 1_0_6, 1_0_7, 3_8, 2_3_2, 1_3_6, 1_7_8, 5_2, 5]
self.assertEqual(encoded['input_ids'] , __A )
# decoding
__UpperCamelCase = tokenizer.decode(__A )
self.assertEqual(__A , '[CLS]Unicode €.[SEP]' )
__UpperCamelCase = tokenizer('e è é ê ë' )
__UpperCamelCase = [4, 1_0_7, 3_8, 2_0_1, 1_7_4, 3_8, 2_0_1, 1_7_5, 3_8, 2_0_1, 1_7_6, 3_8, 2_0_1, 1_7_7, 5]
self.assertEqual(encoded['input_ids'] , __A )
# decoding
__UpperCamelCase = tokenizer.decode(__A )
self.assertEqual(__A , '[CLS]e è é ê ë[SEP]' )
# encode/decode, but with `encode` instead of `__call__`
self.assertEqual(tokenizer.decode(tokenizer.encode('e è é ê ë' ) ) , '[CLS]e è é ê ë[SEP]' )
def _lowerCamelCase ( self : List[Any] ):
__UpperCamelCase = self.perceiver_tokenizer
__UpperCamelCase = ['A long paragraph for summarization.', 'Another paragraph for summarization.']
# fmt: off
__UpperCamelCase = [4, 7_1, 3_8, 1_1_4, 1_1_7, 1_1_6, 1_0_9, 3_8, 1_1_8, 1_0_3, 1_2_0, 1_0_3, 1_0_9, 1_2_0, 1_0_3, 1_1_8, 1_1_0, 3_8, 1_0_8, 1_1_7, 1_2_0, 3_8, 1_2_1, 1_2_3, 1_1_5, 1_1_5, 1_0_3, 1_2_0, 1_1_1, 1_2_8, 1_0_3, 1_2_2, 1_1_1, 1_1_7, 1_1_6, 5_2, 5, 0]
# fmt: on
__UpperCamelCase = tokenizer(__A , padding=__A , return_tensors=__A )
self.assertIsInstance(__A , __A )
if FRAMEWORK != "jax":
__UpperCamelCase = list(batch.input_ids.numpy()[0] )
else:
__UpperCamelCase = list(batch.input_ids.tolist()[0] )
self.assertListEqual(__A , __A )
self.assertEqual((2, 3_8) , batch.input_ids.shape )
self.assertEqual((2, 3_8) , batch.attention_mask.shape )
def _lowerCamelCase ( self : Dict ):
__UpperCamelCase = self.perceiver_tokenizer
__UpperCamelCase = ['A long paragraph for summarization.', 'Another paragraph for summarization.']
__UpperCamelCase = tokenizer(__A , padding=__A , return_tensors=__A )
# check if input_ids are returned and no decoder_input_ids
self.assertIn('input_ids' , __A )
self.assertIn('attention_mask' , __A )
self.assertNotIn('decoder_input_ids' , __A )
self.assertNotIn('decoder_attention_mask' , __A )
def _lowerCamelCase ( self : List[Any] ):
__UpperCamelCase = self.perceiver_tokenizer
__UpperCamelCase = [
'Summary of the text.',
'Another summary.',
]
__UpperCamelCase = tokenizer(
text_target=__A , max_length=3_2 , padding='max_length' , truncation=__A , return_tensors=__A )
self.assertEqual(3_2 , targets['input_ids'].shape[1] )
def _lowerCamelCase ( self : Dict ):
# safety check on max_len default value so we are sure the test works
__UpperCamelCase = self.get_tokenizers()
for tokenizer in tokenizers:
with self.subTest(f'''{tokenizer.__class__.__name__}''' ):
self.assertNotEqual(tokenizer.model_max_length , 4_2 )
# Now let's start the test
__UpperCamelCase = self.get_tokenizers()
for tokenizer in tokenizers:
with self.subTest(f'''{tokenizer.__class__.__name__}''' ):
# Isolate this from the other tests because we save additional tokens/etc
__UpperCamelCase = tempfile.mkdtemp()
__UpperCamelCase = ' He is very happy, UNwant\u00E9d,running'
__UpperCamelCase = tokenizer.encode(__A , add_special_tokens=__A )
tokenizer.save_pretrained(__A )
__UpperCamelCase = tokenizer.__class__.from_pretrained(__A )
__UpperCamelCase = after_tokenizer.encode(__A , add_special_tokens=__A )
self.assertListEqual(__A , __A )
shutil.rmtree(__A )
__UpperCamelCase = self.get_tokenizers(model_max_length=4_2 )
for tokenizer in tokenizers:
with self.subTest(f'''{tokenizer.__class__.__name__}''' ):
# Isolate this from the other tests because we save additional tokens/etc
__UpperCamelCase = tempfile.mkdtemp()
__UpperCamelCase = ' He is very happy, UNwant\u00E9d,running'
tokenizer.add_tokens(['bim', 'bambam'] )
__UpperCamelCase = tokenizer.additional_special_tokens
additional_special_tokens.append('new_additional_special_token' )
tokenizer.add_special_tokens({'additional_special_tokens': additional_special_tokens} )
__UpperCamelCase = tokenizer.encode(__A , add_special_tokens=__A )
tokenizer.save_pretrained(__A )
__UpperCamelCase = tokenizer.__class__.from_pretrained(__A )
__UpperCamelCase = after_tokenizer.encode(__A , add_special_tokens=__A )
self.assertListEqual(__A , __A )
self.assertIn('new_additional_special_token' , after_tokenizer.additional_special_tokens )
self.assertEqual(after_tokenizer.model_max_length , 4_2 )
__UpperCamelCase = tokenizer.__class__.from_pretrained(__A , model_max_length=4_3 )
self.assertEqual(tokenizer.model_max_length , 4_3 )
shutil.rmtree(__A )
def _lowerCamelCase ( self : Any ):
__UpperCamelCase = []
if self.test_slow_tokenizer:
tokenizer_list.append((self.tokenizer_class, self.get_tokenizer()) )
if self.test_rust_tokenizer:
tokenizer_list.append((self.rust_tokenizer_class, self.get_rust_tokenizer()) )
for tokenizer_class, tokenizer_utils in tokenizer_list:
with tempfile.TemporaryDirectory() as tmp_dir:
tokenizer_utils.save_pretrained(__A )
with open(os.path.join(__A , 'special_tokens_map.json' ) , encoding='utf-8' ) as json_file:
__UpperCamelCase = json.load(__A )
with open(os.path.join(__A , 'tokenizer_config.json' ) , encoding='utf-8' ) as json_file:
__UpperCamelCase = json.load(__A )
__UpperCamelCase = [f'''<extra_id_{i}>''' for i in range(1_2_5 )]
__UpperCamelCase = added_tokens_extra_ids + [
'an_additional_special_token'
]
__UpperCamelCase = added_tokens_extra_ids + [
'an_additional_special_token'
]
with open(os.path.join(__A , 'special_tokens_map.json' ) , 'w' , encoding='utf-8' ) as outfile:
json.dump(__A , __A )
with open(os.path.join(__A , 'tokenizer_config.json' ) , 'w' , encoding='utf-8' ) as outfile:
json.dump(__A , __A )
# the following checks allow us to verify that our test works as expected, i.e. that the tokenizer takes
# into account the new value of additional_special_tokens given in the "tokenizer_config.json" and
# "special_tokens_map.json" files
__UpperCamelCase = tokenizer_class.from_pretrained(
__A , )
self.assertIn(
'an_additional_special_token' , tokenizer_without_change_in_init.additional_special_tokens )
self.assertEqual(
['an_additional_special_token'] , tokenizer_without_change_in_init.convert_ids_to_tokens(
tokenizer_without_change_in_init.convert_tokens_to_ids(['an_additional_special_token'] ) ) , )
# Now we test that we can change the value of additional_special_tokens in the from_pretrained
__UpperCamelCase = added_tokens_extra_ids + [AddedToken('a_new_additional_special_token' , lstrip=__A )]
__UpperCamelCase = tokenizer_class.from_pretrained(
__A , additional_special_tokens=__A , )
self.assertIn('a_new_additional_special_token' , tokenizer.additional_special_tokens )
self.assertEqual(
['a_new_additional_special_token'] , tokenizer.convert_ids_to_tokens(
tokenizer.convert_tokens_to_ids(['a_new_additional_special_token'] ) ) , )
def _lowerCamelCase ( self : Dict ):
__UpperCamelCase = self.perceiver_tokenizer
self.assertEqual(tokenizer.decode([1_7_8] ) , '�' )
def _lowerCamelCase ( self : Dict ):
pass
def _lowerCamelCase ( self : List[str] ):
pass
def _lowerCamelCase ( self : Tuple ):
pass
def _lowerCamelCase ( self : Optional[Any] ):
pass
def _lowerCamelCase ( self : str ):
# The default common tokenizer tests uses invalid tokens for Perceiver that can only accept one-character
# strings and special added tokens as tokens
__UpperCamelCase = self.get_tokenizers(fast=__A , do_lower_case=__A )
for tokenizer in tokenizers:
with self.subTest(f'''{tokenizer.__class__.__name__}''' ):
__UpperCamelCase = ['[CLS]', 't', 'h', 'i', 's', ' ', 'i', 's', ' ', 'a', ' ', 't', 'e', 's', 't', '[SEP]']
__UpperCamelCase = tokenizer.convert_tokens_to_string(__A )
self.assertIsInstance(__A , __A )
| 53 |
'''simple docstring'''
def a ( __a ) -> "list[int]":
'''simple docstring'''
if upper_limit < 0:
raise ValueError('''Limit for the Catalan sequence must be ≥ 0''' )
UpperCamelCase__ :Optional[Any] = [0] * (upper_limit + 1)
# Base case: C(0) = C(1) = 1
UpperCamelCase__ :int = 1
if upper_limit > 0:
UpperCamelCase__ :int = 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(__a ):
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:
__snake_case = 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() | 97 | 0 |
"""simple docstring"""
import argparse
import shlex
import runhouse as rh
if __name__ == "__main__":
# Refer to https://runhouse-docs.readthedocs-hosted.com/en/latest/api/python/cluster.html#hardware-setup for cloud access
# setup instructions, if using on-demand hardware
# If user passes --user <user> --host <host> --key_path <key_path> <example> <args>, fill them in as BYO cluster
# If user passes --instance <instance> --provider <provider> <example> <args>, fill them in as on-demand cluster
# Throw an error if user passes both BYO and on-demand cluster args
# Otherwise, use default values
a__ : List[Any] = argparse.ArgumentParser()
parser.add_argument('''--user''', type=str, default='''ubuntu''')
parser.add_argument('''--host''', type=str, default='''localhost''')
parser.add_argument('''--key_path''', type=str, default=None)
parser.add_argument('''--instance''', type=str, default='''V100:1''')
parser.add_argument('''--provider''', type=str, default='''cheapest''')
parser.add_argument('''--use_spot''', type=bool, default=False)
parser.add_argument('''--example''', type=str, default='''pytorch/text-generation/run_generation.py''')
a__ , a__ : Optional[Any] = parser.parse_known_args()
if args.host != "localhost":
if args.instance != "V100:1" or args.provider != "cheapest":
raise ValueError('''Cannot specify both BYO and on-demand cluster args''')
a__ : List[str] = rh.cluster(
name='''rh-cluster''', ips=[args.host], ssh_creds={'''ssh_user''': args.user, '''ssh_private_key''': args.key_path}
)
else:
a__ : Union[str, Any] = rh.cluster(
name='''rh-cluster''', instance_type=args.instance, provider=args.provider, use_spot=args.use_spot
)
a__ : Any = args.example.rsplit('''/''', 1)[0]
# Set up remote environment
cluster.install_packages(['''pip:./''']) # Installs transformers from local source
# Note transformers is copied into the home directory on the remote machine, so we can install from there
cluster.run([F"pip install -r transformers/examples/{example_dir}/requirements.txt"])
cluster.run(['''pip install torch --upgrade --extra-index-url https://download.pytorch.org/whl/cu117'''])
# Run example. You can bypass the CLI wrapper and paste your own code here.
cluster.run([F"python transformers/examples/{args.example} {' '.join(shlex.quote(arg) for arg in unknown)}"])
# Alternatively, we can just import and run a training function (especially if there's no wrapper CLI):
# from my_script... import train
# reqs = ['pip:./', 'torch', 'datasets', 'accelerate', 'evaluate', 'tqdm', 'scipy', 'scikit-learn', 'tensorboard']
# launch_train_gpu = rh.function(fn=train,
# system=gpu,
# reqs=reqs,
# name='train_bert_glue')
#
# We can pass in arguments just like we would to a function:
# launch_train_gpu(num_epochs = 3, lr = 2e-5, seed = 42, batch_size = 16
# stream_logs=True)
| 54 |
'''simple docstring'''
import io
import json
import fsspec
import pytest
from datasets import Dataset, DatasetDict, Features, NamedSplit, Value
from datasets.io.json import JsonDatasetReader, JsonDatasetWriter
from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases
def a ( __a , __a ) -> Optional[int]:
'''simple docstring'''
assert isinstance(__a , __a )
assert dataset.num_rows == 4
assert dataset.num_columns == 3
assert dataset.column_names == ["col_1", "col_2", "col_3"]
for feature, expected_dtype in expected_features.items():
assert dataset.features[feature].dtype == expected_dtype
@pytest.mark.parametrize('''keep_in_memory''' , [False, True] )
def a ( __a , __a , __a ) -> Optional[Any]:
'''simple docstring'''
UpperCamelCase__ :Union[str, Any] = tmp_path / '''cache'''
UpperCamelCase__ :Dict = {'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
UpperCamelCase__ :Tuple = JsonDatasetReader(__a , cache_dir=__a , keep_in_memory=__a ).read()
_check_json_dataset(__a , __a )
@pytest.mark.parametrize(
'''features''' , [
None,
{'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''},
{'''col_1''': '''string''', '''col_2''': '''string''', '''col_3''': '''string'''},
{'''col_1''': '''int32''', '''col_2''': '''int32''', '''col_3''': '''int32'''},
{'''col_1''': '''float32''', '''col_2''': '''float32''', '''col_3''': '''float32'''},
] , )
def a ( __a , __a , __a ) -> Any:
'''simple docstring'''
UpperCamelCase__ :Union[str, Any] = tmp_path / '''cache'''
UpperCamelCase__ :Optional[Any] = {'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
UpperCamelCase__ :Optional[Any] = features.copy() if features else default_expected_features
UpperCamelCase__ :Tuple = (
Features({feature: Value(__a ) for feature, dtype in features.items()} ) if features is not None else None
)
UpperCamelCase__ :int = JsonDatasetReader(__a , features=__a , cache_dir=__a ).read()
_check_json_dataset(__a , __a )
@pytest.mark.parametrize(
'''features''' , [
None,
{'''col_3''': '''float64''', '''col_1''': '''string''', '''col_2''': '''int64'''},
] , )
def a ( __a , __a , __a ) -> Tuple:
'''simple docstring'''
UpperCamelCase__ :int = tmp_path / '''cache'''
UpperCamelCase__ :str = {'''col_3''': '''float64''', '''col_1''': '''string''', '''col_2''': '''int64'''}
UpperCamelCase__ :Any = features.copy() if features else default_expected_features
UpperCamelCase__ :Union[str, Any] = (
Features({feature: Value(__a ) for feature, dtype in features.items()} ) if features is not None else None
)
UpperCamelCase__ :Any = JsonDatasetReader(__a , features=__a , cache_dir=__a ).read()
assert isinstance(__a , __a )
assert dataset.num_rows == 2
assert dataset.num_columns == 3
assert dataset.column_names == ["col_3", "col_1", "col_2"]
for feature, expected_dtype in expected_features.items():
assert dataset.features[feature].dtype == expected_dtype
def a ( __a , __a ) -> List[Any]:
'''simple docstring'''
UpperCamelCase__ :Any = {'''col_2''': '''int64''', '''col_3''': '''float64''', '''col_1''': '''string'''}
UpperCamelCase__ :int = features.copy()
UpperCamelCase__ :List[Any] = (
Features({feature: Value(__a ) for feature, dtype in features.items()} ) if features is not None else None
)
UpperCamelCase__ :Optional[int] = tmp_path / '''cache'''
UpperCamelCase__ :Dict = JsonDatasetReader(__a , features=__a , cache_dir=__a ).read()
assert isinstance(__a , __a )
assert dataset.num_rows == 2
assert dataset.num_columns == 3
assert dataset.column_names == ["col_2", "col_3", "col_1"]
for feature, expected_dtype in expected_features.items():
assert dataset.features[feature].dtype == expected_dtype
@pytest.mark.parametrize('''split''' , [None, NamedSplit('''train''' ), '''train''', '''test'''] )
def a ( __a , __a , __a ) -> List[Any]:
'''simple docstring'''
UpperCamelCase__ :Union[str, Any] = tmp_path / '''cache'''
UpperCamelCase__ :Optional[Any] = {'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
UpperCamelCase__ :List[Any] = JsonDatasetReader(__a , cache_dir=__a , split=__a ).read()
_check_json_dataset(__a , __a )
assert dataset.split == split if split else "train"
@pytest.mark.parametrize('''path_type''' , [str, list] )
def a ( __a , __a , __a ) -> Any:
'''simple docstring'''
if issubclass(__a , __a ):
UpperCamelCase__ :Union[str, Any] = jsonl_path
elif issubclass(__a , __a ):
UpperCamelCase__ :int = [jsonl_path]
UpperCamelCase__ :Dict = tmp_path / '''cache'''
UpperCamelCase__ :Any = {'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
UpperCamelCase__ :List[str] = JsonDatasetReader(__a , cache_dir=__a ).read()
_check_json_dataset(__a , __a )
def a ( __a , __a , __a=("train",) ) -> Optional[Any]:
'''simple docstring'''
assert isinstance(__a , __a )
for split in splits:
UpperCamelCase__ :Optional[int] = dataset_dict[split]
assert dataset.num_rows == 4
assert dataset.num_columns == 3
assert dataset.column_names == ["col_1", "col_2", "col_3"]
for feature, expected_dtype in expected_features.items():
assert dataset.features[feature].dtype == expected_dtype
@pytest.mark.parametrize('''keep_in_memory''' , [False, True] )
def a ( __a , __a , __a ) -> List[str]:
'''simple docstring'''
UpperCamelCase__ :List[str] = tmp_path / '''cache'''
UpperCamelCase__ :Dict = {'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
UpperCamelCase__ :str = JsonDatasetReader({'''train''': jsonl_path} , cache_dir=__a , keep_in_memory=__a ).read()
_check_json_datasetdict(__a , __a )
@pytest.mark.parametrize(
'''features''' , [
None,
{'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''},
{'''col_1''': '''string''', '''col_2''': '''string''', '''col_3''': '''string'''},
{'''col_1''': '''int32''', '''col_2''': '''int32''', '''col_3''': '''int32'''},
{'''col_1''': '''float32''', '''col_2''': '''float32''', '''col_3''': '''float32'''},
] , )
def a ( __a , __a , __a ) -> int:
'''simple docstring'''
UpperCamelCase__ :Tuple = tmp_path / '''cache'''
UpperCamelCase__ :Any = {'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
UpperCamelCase__ :Optional[int] = features.copy() if features else default_expected_features
UpperCamelCase__ :str = (
Features({feature: Value(__a ) for feature, dtype in features.items()} ) if features is not None else None
)
UpperCamelCase__ :Dict = JsonDatasetReader({'''train''': jsonl_path} , features=__a , cache_dir=__a ).read()
_check_json_datasetdict(__a , __a )
@pytest.mark.parametrize('''split''' , [None, NamedSplit('''train''' ), '''train''', '''test'''] )
def a ( __a , __a , __a ) -> str:
'''simple docstring'''
if split:
UpperCamelCase__ :List[str] = {split: jsonl_path}
else:
UpperCamelCase__ :int = '''train'''
UpperCamelCase__ :int = {'''train''': jsonl_path, '''test''': jsonl_path}
UpperCamelCase__ :Any = tmp_path / '''cache'''
UpperCamelCase__ :Union[str, Any] = {'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
UpperCamelCase__ :Any = JsonDatasetReader(__a , cache_dir=__a ).read()
_check_json_datasetdict(__a , __a , splits=list(path.keys() ) )
assert all(dataset[split].split == split for split in path.keys() )
def a ( __a ) -> Union[str, Any]:
'''simple docstring'''
return json.load(__a )
def a ( __a ) -> int:
'''simple docstring'''
return [json.loads(__a ) for line in buffer]
class lowercase :
"""simple docstring"""
@pytest.mark.parametrize('''lines, load_json_function''' , [(True, load_json_lines), (False, load_json)] )
def lowerCAmelCase__ ( self , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ ):
'''simple docstring'''
with io.BytesIO() as buffer:
JsonDatasetWriter(UpperCamelCase_ , UpperCamelCase_ , lines=UpperCamelCase_ ).write()
buffer.seek(0 )
UpperCamelCase__ :List[Any] = load_json_function(UpperCamelCase_ )
assert isinstance(UpperCamelCase_ , UpperCamelCase_ )
assert isinstance(exported_content[0] , UpperCamelCase_ )
assert len(UpperCamelCase_ ) == 10
@pytest.mark.parametrize(
'''orient, container, keys, len_at''' , [
('''records''', list, {'''tokens''', '''labels''', '''answers''', '''id'''}, None),
('''split''', dict, {'''columns''', '''data'''}, '''data'''),
('''index''', dict, set('''0123456789''' ), None),
('''columns''', dict, {'''tokens''', '''labels''', '''answers''', '''id'''}, '''tokens'''),
('''values''', list, None, None),
('''table''', dict, {'''schema''', '''data'''}, '''data'''),
] , )
def lowerCAmelCase__ ( self , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ ):
'''simple docstring'''
with io.BytesIO() as buffer:
JsonDatasetWriter(UpperCamelCase_ , UpperCamelCase_ , lines=UpperCamelCase_ , orient=UpperCamelCase_ ).write()
buffer.seek(0 )
UpperCamelCase__ :Optional[int] = load_json(UpperCamelCase_ )
assert isinstance(UpperCamelCase_ , UpperCamelCase_ )
if keys:
if container is dict:
assert exported_content.keys() == keys
else:
assert exported_content[0].keys() == keys
else:
assert not hasattr(UpperCamelCase_ , '''keys''' ) and not hasattr(exported_content[0] , '''keys''' )
if len_at:
assert len(exported_content[len_at] ) == 10
else:
assert len(UpperCamelCase_ ) == 10
@pytest.mark.parametrize('''lines, load_json_function''' , [(True, load_json_lines), (False, load_json)] )
def lowerCAmelCase__ ( self , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ ):
'''simple docstring'''
with io.BytesIO() as buffer:
JsonDatasetWriter(UpperCamelCase_ , UpperCamelCase_ , lines=UpperCamelCase_ , num_proc=2 ).write()
buffer.seek(0 )
UpperCamelCase__ :Union[str, Any] = load_json_function(UpperCamelCase_ )
assert isinstance(UpperCamelCase_ , UpperCamelCase_ )
assert isinstance(exported_content[0] , UpperCamelCase_ )
assert len(UpperCamelCase_ ) == 10
@pytest.mark.parametrize(
'''orient, container, keys, len_at''' , [
('''records''', list, {'''tokens''', '''labels''', '''answers''', '''id'''}, None),
('''split''', dict, {'''columns''', '''data'''}, '''data'''),
('''index''', dict, set('''0123456789''' ), None),
('''columns''', dict, {'''tokens''', '''labels''', '''answers''', '''id'''}, '''tokens'''),
('''values''', list, None, None),
('''table''', dict, {'''schema''', '''data'''}, '''data'''),
] , )
def lowerCAmelCase__ ( self , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ ):
'''simple docstring'''
with io.BytesIO() as buffer:
JsonDatasetWriter(UpperCamelCase_ , UpperCamelCase_ , lines=UpperCamelCase_ , orient=UpperCamelCase_ , num_proc=2 ).write()
buffer.seek(0 )
UpperCamelCase__ :int = load_json(UpperCamelCase_ )
assert isinstance(UpperCamelCase_ , UpperCamelCase_ )
if keys:
if container is dict:
assert exported_content.keys() == keys
else:
assert exported_content[0].keys() == keys
else:
assert not hasattr(UpperCamelCase_ , '''keys''' ) and not hasattr(exported_content[0] , '''keys''' )
if len_at:
assert len(exported_content[len_at] ) == 10
else:
assert len(UpperCamelCase_ ) == 10
def lowerCAmelCase__ ( self , UpperCamelCase_ ):
'''simple docstring'''
with pytest.raises(UpperCamelCase_ ):
with io.BytesIO() as buffer:
JsonDatasetWriter(UpperCamelCase_ , UpperCamelCase_ , num_proc=0 )
@pytest.mark.parametrize('''compression, extension''' , [('''gzip''', '''gz'''), ('''bz2''', '''bz2'''), ('''xz''', '''xz''')] )
def lowerCAmelCase__ ( self , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ ):
'''simple docstring'''
UpperCamelCase__ :Tuple = tmp_path_factory.mktemp('''data''' ) / F'''test.json.{extension}'''
UpperCamelCase__ :Union[str, Any] = str(shared_datadir / F'''test_file.json.{extension}''' )
JsonDatasetWriter(UpperCamelCase_ , UpperCamelCase_ , compression=UpperCamelCase_ ).write()
with fsspec.open(UpperCamelCase_ , '''rb''' , compression='''infer''' ) as f:
UpperCamelCase__ :Dict = f.read()
with fsspec.open(UpperCamelCase_ , '''rb''' , compression='''infer''' ) as f:
UpperCamelCase__ :int = f.read()
assert exported_content == original_content | 97 | 0 |
'''simple docstring'''
import inspect
import tempfile
import unittest
from huggingface_hub import hf_hub_download
from transformers import is_torch_available
from transformers.testing_utils import is_flaky, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
a_ : List[Any] = 1e-4
if is_torch_available():
import torch
from transformers import AutoformerConfig, AutoformerForPrediction, AutoformerModel
from transformers.models.autoformer.modeling_autoformer import AutoformerDecoder, AutoformerEncoder
@require_torch
class snake_case :
"""simple docstring"""
def __init__( self , UpperCamelCase , UpperCamelCase=16 , UpperCamelCase=13 , UpperCamelCase=7 , UpperCamelCase=14 , UpperCamelCase=10 , UpperCamelCase=19 , UpperCamelCase=5 , UpperCamelCase=4 , UpperCamelCase=True , UpperCamelCase=16 , UpperCamelCase=2 , UpperCamelCase=4 , UpperCamelCase=4 , UpperCamelCase="gelu" , UpperCamelCase=0.1 , UpperCamelCase=0.1 , UpperCamelCase=[1, 2, 3, 4, 5] , UpperCamelCase=25 , UpperCamelCase=5 , ):
"""simple docstring"""
lowerCamelCase_ = d_model
lowerCamelCase_ = parent
lowerCamelCase_ = batch_size
lowerCamelCase_ = prediction_length
lowerCamelCase_ = context_length
lowerCamelCase_ = cardinality
lowerCamelCase_ = num_time_features
lowerCamelCase_ = lags_sequence
lowerCamelCase_ = embedding_dimension
lowerCamelCase_ = is_training
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_ = context_length
lowerCamelCase_ = prediction_length + label_length
lowerCamelCase_ = label_length
lowerCamelCase_ = moving_average
lowerCamelCase_ = autocorrelation_factor
def snake_case ( self ):
"""simple docstring"""
return AutoformerConfig(
d_model=self.d_model , 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 , prediction_length=self.prediction_length , context_length=self.context_length , label_length=self.label_length , lags_sequence=self.lags_sequence , num_time_features=self.num_time_features , num_static_categorical_features=1 , cardinality=[self.cardinality] , embedding_dimension=[self.embedding_dimension] , moving_average=self.moving_average , )
def snake_case ( self , UpperCamelCase ):
"""simple docstring"""
lowerCamelCase_ = config.context_length + max(config.lags_sequence )
lowerCamelCase_ = ids_tensor([self.batch_size, 1] , config.cardinality[0] )
lowerCamelCase_ = floats_tensor([self.batch_size, _past_length, config.num_time_features] )
lowerCamelCase_ = floats_tensor([self.batch_size, _past_length] )
lowerCamelCase_ = floats_tensor([self.batch_size, _past_length] ) > 0.5
# decoder inputs
lowerCamelCase_ = floats_tensor([self.batch_size, config.prediction_length, config.num_time_features] )
lowerCamelCase_ = floats_tensor([self.batch_size, config.prediction_length] )
lowerCamelCase_ = {
"past_values": past_values,
"static_categorical_features": static_categorical_features,
"past_time_features": past_time_features,
"past_observed_mask": past_observed_mask,
"future_time_features": future_time_features,
"future_values": future_values,
}
return inputs_dict
def snake_case ( self ):
"""simple docstring"""
lowerCamelCase_ = self.get_config()
lowerCamelCase_ = self.prepare_autoformer_inputs_dict(UpperCamelCase )
return config, inputs_dict
def snake_case ( self ):
"""simple docstring"""
lowerCamelCase_ ,lowerCamelCase_ = self.prepare_config_and_inputs()
return config, inputs_dict
def snake_case ( self , UpperCamelCase , UpperCamelCase ):
"""simple docstring"""
lowerCamelCase_ = AutoformerModel(config=UpperCamelCase ).to(UpperCamelCase ).eval()
lowerCamelCase_ = model(**UpperCamelCase )
lowerCamelCase_ = outputs.encoder_last_hidden_state
lowerCamelCase_ = outputs.last_hidden_state
with tempfile.TemporaryDirectory() as tmpdirname:
lowerCamelCase_ = model.get_encoder()
encoder.save_pretrained(UpperCamelCase )
lowerCamelCase_ = AutoformerEncoder.from_pretrained(UpperCamelCase ).to(UpperCamelCase )
lowerCamelCase_ ,lowerCamelCase_ ,lowerCamelCase_ ,lowerCamelCase_ ,lowerCamelCase_ = model.create_network_inputs(**UpperCamelCase )
lowerCamelCase_ ,lowerCamelCase_ = model.decomposition_layer(transformer_inputs[:, : config.context_length, ...] )
lowerCamelCase_ = torch.cat(
(transformer_inputs[:, : config.context_length, ...], feature[:, : config.context_length, ...]) , dim=-1 , )
lowerCamelCase_ = encoder(inputs_embeds=UpperCamelCase )[0]
self.parent.assertTrue((encoder_last_hidden_state_a - encoder_last_hidden_state).abs().max().item() < 1e-3 )
lowerCamelCase_ = (
torch.mean(transformer_inputs[:, : config.context_length, ...] , dim=1 )
.unsqueeze(1 )
.repeat(1 , config.prediction_length , 1 )
)
lowerCamelCase_ = torch.zeros(
[transformer_inputs.shape[0], config.prediction_length, transformer_inputs.shape[2]] , device=enc_input.device , )
lowerCamelCase_ = torch.cat(
(
torch.cat((seasonal_input[:, -config.label_length :, ...], zeros) , dim=1 ),
feature[:, config.context_length - config.label_length :, ...],
) , dim=-1 , )
lowerCamelCase_ = torch.cat(
(
torch.cat((trend_input[:, -config.label_length :, ...], mean) , dim=1 ),
feature[:, config.context_length - config.label_length :, ...],
) , dim=-1 , )
with tempfile.TemporaryDirectory() as tmpdirname:
lowerCamelCase_ = model.get_decoder()
decoder.save_pretrained(UpperCamelCase )
lowerCamelCase_ = AutoformerDecoder.from_pretrained(UpperCamelCase ).to(UpperCamelCase )
lowerCamelCase_ = decoder(
trend=UpperCamelCase , inputs_embeds=UpperCamelCase , encoder_hidden_states=UpperCamelCase , )[0]
self.parent.assertTrue((last_hidden_state_a - last_hidden_state).abs().max().item() < 1e-3 )
@require_torch
class snake_case ( lowercase , lowercase , unittest.TestCase ):
"""simple docstring"""
_lowerCamelCase = (AutoformerModel, AutoformerForPrediction) if is_torch_available() else ()
_lowerCamelCase = (AutoformerForPrediction,) if is_torch_available() else ()
_lowerCamelCase = {"feature-extraction": AutoformerModel} if is_torch_available() else {}
_lowerCamelCase = False
_lowerCamelCase = False
_lowerCamelCase = False
_lowerCamelCase = False
_lowerCamelCase = False
_lowerCamelCase = False
def snake_case ( self ):
"""simple docstring"""
lowerCamelCase_ = AutoformerModelTester(self )
lowerCamelCase_ = ConfigTester(self , config_class=UpperCamelCase , has_text_modality=UpperCamelCase )
def snake_case ( self ):
"""simple docstring"""
self.config_tester.run_common_tests()
def snake_case ( self ):
"""simple docstring"""
lowerCamelCase_ ,lowerCamelCase_ = self.model_tester.prepare_config_and_inputs()
for model_class in self.all_model_classes:
lowerCamelCase_ = model_class(UpperCamelCase )
with tempfile.TemporaryDirectory() as tmpdirname:
model.save_pretrained(UpperCamelCase )
lowerCamelCase_ ,lowerCamelCase_ = model_class.from_pretrained(UpperCamelCase , output_loading_info=UpperCamelCase )
self.assertEqual(info["missing_keys"] , [] )
def snake_case ( self ):
"""simple docstring"""
lowerCamelCase_ = self.model_tester.prepare_config_and_inputs_for_common()
self.model_tester.check_encoder_decoder_model_standalone(*UpperCamelCase )
@unittest.skip(reason="Model has no tokens embeddings" )
def snake_case ( self ):
"""simple docstring"""
pass
def snake_case ( self ):
"""simple docstring"""
lowerCamelCase_ = inspect.signature(getattr(UpperCamelCase , "forward" ) )
# The main input is the name of the argument after `self`
lowerCamelCase_ = list(model_signature.parameters.keys() )[1]
self.assertEqual(AutoformerModel.main_input_name , UpperCamelCase )
def 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.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
lowerCamelCase_ = [*signature.parameters.keys()]
lowerCamelCase_ = [
"past_values",
"past_time_features",
"past_observed_mask",
"static_categorical_features",
"static_real_features",
"future_values",
"future_time_features",
]
if model.__class__.__name__ in ["AutoformerForPrediction"]:
expected_arg_names.append("future_observed_mask" )
expected_arg_names.extend(
[
"decoder_attention_mask",
"head_mask",
"decoder_head_mask",
"cross_attn_head_mask",
"encoder_outputs",
"past_key_values",
"output_hidden_states",
"output_attentions",
"use_cache",
"return_dict",
] )
self.assertListEqual(arg_names[: len(UpperCamelCase )] , UpperCamelCase )
def snake_case ( self ):
"""simple docstring"""
lowerCamelCase_ ,lowerCamelCase_ = self.model_tester.prepare_config_and_inputs_for_common()
lowerCamelCase_ = True
lowerCamelCase_ = getattr(self.model_tester , "seq_length" , UpperCamelCase )
lowerCamelCase_ = getattr(self.model_tester , "decoder_seq_length" , UpperCamelCase )
lowerCamelCase_ = getattr(self.model_tester , "encoder_seq_length" , UpperCamelCase )
lowerCamelCase_ = getattr(self.model_tester , "d_model" , UpperCamelCase )
lowerCamelCase_ = getattr(self.model_tester , "num_attention_heads" , UpperCamelCase )
lowerCamelCase_ = d_model // num_attention_heads
for model_class in self.all_model_classes:
lowerCamelCase_ = True
lowerCamelCase_ = False
lowerCamelCase_ = True
lowerCamelCase_ = model_class(UpperCamelCase )
model.to(UpperCamelCase )
model.eval()
with torch.no_grad():
lowerCamelCase_ = model(**self._prepare_for_class(UpperCamelCase , UpperCamelCase ) )
lowerCamelCase_ = outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
self.assertEqual(len(UpperCamelCase ) , self.model_tester.num_hidden_layers )
# check that output_attentions also work using config
del inputs_dict["output_attentions"]
lowerCamelCase_ = True
lowerCamelCase_ = model_class(UpperCamelCase )
model.to(UpperCamelCase )
model.eval()
with torch.no_grad():
lowerCamelCase_ = model(**self._prepare_for_class(UpperCamelCase , UpperCamelCase ) )
lowerCamelCase_ = outputs.encoder_attentions
self.assertEqual(len(UpperCamelCase ) , self.model_tester.num_hidden_layers )
self.assertListEqual(
list(attentions[0].shape[-3:] ) , [self.model_tester.num_attention_heads, encoder_seq_length, dim] , )
lowerCamelCase_ = len(UpperCamelCase )
lowerCamelCase_ = 7
if "last_hidden_state" in outputs:
correct_outlen += 1
if "trend" in outputs:
correct_outlen += 1
if "past_key_values" in outputs:
correct_outlen += 1 # past_key_values have been returned
if "loss" in outputs:
correct_outlen += 1
if "params" in outputs:
correct_outlen += 1
self.assertEqual(UpperCamelCase , UpperCamelCase )
# decoder attentions
lowerCamelCase_ = outputs.decoder_attentions
self.assertIsInstance(UpperCamelCase , (list, tuple) )
self.assertEqual(len(UpperCamelCase ) , self.model_tester.num_hidden_layers )
self.assertListEqual(
list(decoder_attentions[0].shape[-3:] ) , [self.model_tester.num_attention_heads, decoder_seq_length, dim] , )
# cross attentions
lowerCamelCase_ = outputs.cross_attentions
self.assertIsInstance(UpperCamelCase , (list, tuple) )
self.assertEqual(len(UpperCamelCase ) , self.model_tester.num_hidden_layers )
self.assertListEqual(
list(cross_attentions[0].shape[-3:] ) , [self.model_tester.num_attention_heads, decoder_seq_length, dim] , )
# Check attention is always last and order is fine
lowerCamelCase_ = True
lowerCamelCase_ = True
lowerCamelCase_ = model_class(UpperCamelCase )
model.to(UpperCamelCase )
model.eval()
with torch.no_grad():
lowerCamelCase_ = model(**self._prepare_for_class(UpperCamelCase , UpperCamelCase ) )
self.assertEqual(out_len + 2 , len(UpperCamelCase ) )
lowerCamelCase_ = outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
self.assertEqual(len(UpperCamelCase ) , self.model_tester.num_hidden_layers )
self.assertListEqual(
list(self_attentions[0].shape[-3:] ) , [self.model_tester.num_attention_heads, encoder_seq_length, dim] , )
@is_flaky()
def snake_case ( self ):
"""simple docstring"""
super().test_retain_grad_hidden_states_attentions()
def __snake_case ( UpperCAmelCase_ : int="train-batch.pt" ):
lowerCamelCase_ = hf_hub_download(repo_id="hf-internal-testing/tourism-monthly-batch" , filename=UpperCAmelCase_ , repo_type="dataset" )
lowerCamelCase_ = torch.load(UpperCAmelCase_ , map_location=UpperCAmelCase_ )
return batch
@require_torch
@slow
class snake_case ( unittest.TestCase ):
"""simple docstring"""
def snake_case ( self ):
"""simple docstring"""
lowerCamelCase_ = AutoformerModel.from_pretrained("huggingface/autoformer-tourism-monthly" ).to(UpperCamelCase )
lowerCamelCase_ = prepare_batch()
with torch.no_grad():
lowerCamelCase_ = model(
past_values=batch["past_values"] , past_time_features=batch["past_time_features"] , past_observed_mask=batch["past_observed_mask"] , static_categorical_features=batch["static_categorical_features"] , future_values=batch["future_values"] , future_time_features=batch["future_time_features"] , )[0]
lowerCamelCase_ = torch.Size(
(64, model.config.prediction_length + model.config.label_length, model.config.feature_size) )
self.assertEqual(output.shape , UpperCamelCase )
lowerCamelCase_ = torch.tensor(
[[0.3_593, -1.3_398, 0.6_330], [0.2_279, 1.5_396, -0.1_792], [0.0_450, 1.3_225, -0.2_335]] , device=UpperCamelCase )
self.assertTrue(torch.allclose(output[0, :3, :3] , UpperCamelCase , atol=UpperCamelCase ) )
def snake_case ( self ):
"""simple docstring"""
lowerCamelCase_ = AutoformerForPrediction.from_pretrained("huggingface/autoformer-tourism-monthly" ).to(UpperCamelCase )
lowerCamelCase_ = prepare_batch("val-batch.pt" )
with torch.no_grad():
lowerCamelCase_ = model(
past_values=batch["past_values"] , past_time_features=batch["past_time_features"] , past_observed_mask=batch["past_observed_mask"] , static_categorical_features=batch["static_categorical_features"] , ).encoder_last_hidden_state
lowerCamelCase_ = torch.Size((64, model.config.context_length, model.config.d_model) )
self.assertEqual(output.shape , UpperCamelCase )
lowerCamelCase_ = torch.tensor(
[[-0.0_734, -0.9_036, 0.8_358], [4.7_186, 2.4_113, 1.9_581], [1.7_953, 2.3_558, 1.2_970]] , device=UpperCamelCase )
self.assertTrue(torch.allclose(output[0, :3, :3] , UpperCamelCase , atol=UpperCamelCase ) )
def snake_case ( self ):
"""simple docstring"""
lowerCamelCase_ = AutoformerForPrediction.from_pretrained("huggingface/autoformer-tourism-monthly" ).to(UpperCamelCase )
lowerCamelCase_ = prepare_batch("val-batch.pt" )
with torch.no_grad():
lowerCamelCase_ = model.generate(
static_categorical_features=batch["static_categorical_features"] , past_time_features=batch["past_time_features"] , past_values=batch["past_values"] , future_time_features=batch["future_time_features"] , past_observed_mask=batch["past_observed_mask"] , )
lowerCamelCase_ = torch.Size((64, model.config.num_parallel_samples, model.config.prediction_length) )
self.assertEqual(outputs.sequences.shape , UpperCamelCase )
lowerCamelCase_ = torch.tensor([3_130.6_763, 4_056.5_293, 7_053.0_786] , device=UpperCamelCase )
lowerCamelCase_ = outputs.sequences.mean(dim=1 )
self.assertTrue(torch.allclose(mean_prediction[0, -3:] , UpperCamelCase , rtol=1e-1 ) )
| 55 |
'''simple docstring'''
import unittest
from dataclasses import dataclass
import pytest
from accelerate.commands.config.config_args import SageMakerConfig
from accelerate.utils import ComputeEnvironment
from accelerate.utils.launch import _convert_nargs_to_dict
@dataclass
class lowercase ( A__ ):
"""simple docstring"""
_a = ComputeEnvironment.AMAZON_SAGEMAKER
_a = True
_a = 'ml.p3.2xlarge'
_a = 'accelerate_sagemaker_execution_role'
_a = 'hf-sm'
_a = 'us-east-1'
_a = 1
_a = 'accelerate-sagemaker-1'
_a = '1.6'
_a = '4.4'
_a = 'train.py'
_a = [
'--model_name_or_path',
'bert',
'--do_train',
'False',
'--epochs',
'3',
'--learning_rate',
'5e-5',
'--max_steps',
'50.5',
]
_a = [
'--model_name_or_path',
'bert',
'--do_train',
'--do_test',
'False',
'--do_predict',
'--epochs',
'3',
'--learning_rate',
'5e-5',
'--max_steps',
'50.5',
]
class lowercase ( unittest.TestCase ):
"""simple docstring"""
def lowerCAmelCase__ ( self ):
'''simple docstring'''
UpperCamelCase__ :Union[str, Any] = _convert_nargs_to_dict(MockLaunchConfig.success_training_script_args )
assert isinstance(converted_args['''model_name_or_path'''] , UpperCamelCase_ )
assert isinstance(converted_args['''do_train'''] , UpperCamelCase_ )
assert isinstance(converted_args['''epochs'''] , UpperCamelCase_ )
assert isinstance(converted_args['''learning_rate'''] , UpperCamelCase_ )
assert isinstance(converted_args['''max_steps'''] , UpperCamelCase_ )
with pytest.raises(UpperCamelCase_ ):
_convert_nargs_to_dict(MockLaunchConfig.fail_training_script_args ) | 97 | 0 |
'''simple docstring'''
from transformers import HfArgumentParser, TensorFlowBenchmark, TensorFlowBenchmarkArguments
def __magic_name__ ( ) -> List[Any]:
'''simple docstring'''
snake_case_ = HfArgumentParser(__UpperCAmelCase )
snake_case_ = parser.parse_args_into_dataclasses()[0]
snake_case_ = TensorFlowBenchmark(args=__UpperCAmelCase )
try:
snake_case_ = parser.parse_args_into_dataclasses()[0]
except ValueError as e:
snake_case_ = '''Arg --no_{0} is no longer used, please use --no-{0} instead.'''
snake_case_ = ''' '''.join(str(__UpperCAmelCase ).split(''' ''' )[:-1] )
snake_case_ = ''''''
snake_case_ = eval(str(__UpperCAmelCase ).split(''' ''' )[-1] )
snake_case_ = []
for arg in depreciated_args:
# arg[2:] removes '--'
if arg[2:] in TensorFlowBenchmark.deprecated_args:
# arg[5:] removes '--no_'
full_error_msg += arg_error_msg.format(arg[5:] )
else:
wrong_args.append(__UpperCAmelCase )
if len(__UpperCAmelCase ) > 0:
snake_case_ = full_error_msg + begin_error_msg + str(__UpperCAmelCase )
raise ValueError(__UpperCAmelCase )
benchmark.run()
if __name__ == "__main__":
main()
| 56 |
'''simple docstring'''
from datetime import datetime
import matplotlib.pyplot as plt
import torch
def a ( __a ) -> int:
'''simple docstring'''
for param in module.parameters():
UpperCamelCase__ :Dict = False
def a ( ) -> Union[str, Any]:
'''simple docstring'''
UpperCamelCase__ :List[Any] = '''cuda''' if torch.cuda.is_available() else '''cpu'''
if torch.backends.mps.is_available() and torch.backends.mps.is_built():
UpperCamelCase__ :Optional[int] = '''mps'''
if device == "mps":
print(
'''WARNING: MPS currently doesn\'t seem to work, and messes up backpropagation without any visible torch'''
''' errors. I recommend using CUDA on a colab notebook or CPU instead if you\'re facing inexplicable issues'''
''' with generations.''' )
return device
def a ( __a ) -> Any:
'''simple docstring'''
UpperCamelCase__ :Dict = plt.imshow(__a )
fig.axes.get_xaxis().set_visible(__a )
fig.axes.get_yaxis().set_visible(__a )
plt.show()
def a ( ) -> str:
'''simple docstring'''
UpperCamelCase__ :int = datetime.now()
UpperCamelCase__ :str = current_time.strftime('''%H:%M:%S''' )
return timestamp | 97 | 0 |
"""simple docstring"""
import warnings
from typing import Dict, List, Optional, Tuple
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
A : str = logging.get_logger(__name__)
class _UpperCamelCase ( lowerCAmelCase__ ):
'''simple docstring'''
__UpperCAmelCase : str =["""input_ids""", """attention_mask"""]
def __init__( self , __a="</s>" , __a="<unk>" , __a="<pad>" , __a=1_25 , __a=None , **__a , ):
# Add extra_ids to the special token list
if extra_ids > 0 and additional_special_tokens is None:
__lowerCAmelCase = [f"<extra_id_{i}>" for i in range(__a )]
elif extra_ids > 0 and additional_special_tokens is not None:
# Check that we have the right number of extra_id special tokens
__lowerCAmelCase = len(set(filter(lambda __a : bool("extra_id" in str(__a ) ) , __a ) ) )
if extra_tokens != extra_ids:
raise ValueError(
f"Both extra_ids ({extra_ids}) and additional_special_tokens ({additional_special_tokens}) are"
" provided to ByT5Tokenizer. In this case the additional_special_tokens must include the"
" extra_ids tokens" )
__lowerCAmelCase = AddedToken(__a , lstrip=__a , rstrip=__a ) if isinstance(__a , __a ) else pad_token
__lowerCAmelCase = AddedToken(__a , lstrip=__a , rstrip=__a ) if isinstance(__a , __a ) else eos_token
__lowerCAmelCase = AddedToken(__a , lstrip=__a , rstrip=__a ) if isinstance(__a , __a ) else unk_token
super().__init__(
eos_token=__a , unk_token=__a , pad_token=__a , extra_ids=__a , additional_special_tokens=__a , **__a , )
__lowerCAmelCase = extra_ids
__lowerCAmelCase = 2**8 # utf is 8 bits
# define special tokens dict
__lowerCAmelCase = {
self.pad_token: 0,
self.eos_token: 1,
self.unk_token: 2,
}
__lowerCAmelCase = len(self.special_tokens_encoder )
__lowerCAmelCase = len(__a )
for i, token in enumerate(__a ):
__lowerCAmelCase = self.vocab_size + i - n
__lowerCAmelCase = {v: k for k, v in self.special_tokens_encoder.items()}
@property
def snake_case ( self ):
return self._utf_vocab_size + self._num_special_tokens + self._extra_ids
def snake_case ( self , __a , __a = None , __a = False ):
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=__a , token_ids_a=__a , already_has_special_tokens=__a )
# normal case: some special tokens
if token_ids_a is None:
return ([0] * len(__a )) + [1]
return ([0] * len(__a )) + [1] + ([0] * len(__a )) + [1]
def snake_case ( self , __a ):
if len(__a ) > 0 and token_ids[-1] == self.eos_token_id:
warnings.warn(
f"This sequence already has {self.eos_token}. In future versions this behavior may lead to duplicated"
" eos tokens being added." )
return token_ids
else:
return token_ids + [self.eos_token_id]
def snake_case ( self , __a , __a = None ):
__lowerCAmelCase = [self.eos_token_id]
if token_ids_a is None:
return len(token_ids_a + eos ) * [0]
return len(token_ids_a + eos + token_ids_a + eos ) * [0]
def snake_case ( self , __a , __a = None ):
__lowerCAmelCase = self._add_eos_if_not_present(__a )
if token_ids_a is None:
return token_ids_a
else:
__lowerCAmelCase = self._add_eos_if_not_present(__a )
return token_ids_a + token_ids_a
def snake_case ( self , __a ):
__lowerCAmelCase = [chr(__a ) for i in text.encode("utf-8" )]
return tokens
def snake_case ( self , __a ):
if token in self.special_tokens_encoder:
__lowerCAmelCase = self.special_tokens_encoder[token]
elif token in self.added_tokens_encoder:
__lowerCAmelCase = self.added_tokens_encoder[token]
elif len(__a ) != 1:
__lowerCAmelCase = self.unk_token_id
else:
__lowerCAmelCase = ord(__a ) + self._num_special_tokens
return token_id
def snake_case ( self , __a ):
if index in self.special_tokens_decoder:
__lowerCAmelCase = self.special_tokens_decoder[index]
else:
__lowerCAmelCase = chr(index - self._num_special_tokens )
return token
def snake_case ( self , __a ):
__lowerCAmelCase = B""
for token in tokens:
if token in self.special_tokens_decoder:
__lowerCAmelCase = self.special_tokens_decoder[token].encode("utf-8" )
elif token in self.added_tokens_decoder:
__lowerCAmelCase = self.special_tokens_decoder[token].encode("utf-8" )
elif token in self.special_tokens_encoder:
__lowerCAmelCase = token.encode("utf-8" )
elif token in self.added_tokens_encoder:
__lowerCAmelCase = token.encode("utf-8" )
else:
__lowerCAmelCase = bytes([ord(__a )] )
bstring += tok_string
__lowerCAmelCase = bstring.decode("utf-8" , errors="ignore" )
return string
def snake_case ( self , __a , __a = None ):
return ()
| 57 |
'''simple docstring'''
from scipy.stats import pearsonr
import datasets
__snake_case = '''
Pearson correlation coefficient and p-value for testing non-correlation.
The Pearson correlation coefficient measures the linear relationship between two datasets. The calculation of the p-value relies on the assumption that each dataset is normally distributed. Like other correlation coefficients, this one varies between -1 and +1 with 0 implying no correlation. Correlations of -1 or +1 imply an exact linear relationship. Positive correlations imply that as x increases, so does y. Negative correlations imply that as x increases, y decreases.
The p-value roughly indicates the probability of an uncorrelated system producing datasets that have a Pearson correlation at least as extreme as the one computed from these datasets.
'''
__snake_case = '''
Args:
predictions (`list` of `int`): Predicted class labels, as returned by a model.
references (`list` of `int`): Ground truth labels.
return_pvalue (`boolean`): If `True`, returns the p-value, along with the correlation coefficient. If `False`, returns only the correlation coefficient. Defaults to `False`.
Returns:
pearsonr (`float`): Pearson correlation coefficient. Minimum possible value is -1. Maximum possible value is 1. Values of 1 and -1 indicate exact linear positive and negative relationships, respectively. A value of 0 implies no correlation.
p-value (`float`): P-value, which roughly indicates the probability of an The p-value roughly indicates the probability of an uncorrelated system producing datasets that have a Pearson correlation at least as extreme as the one computed from these datasets. Minimum possible value is 0. Maximum possible value is 1. Higher values indicate higher probabilities.
Examples:
Example 1-A simple example using only predictions and references.
>>> pearsonr_metric = datasets.load_metric("pearsonr")
>>> results = pearsonr_metric.compute(predictions=[10, 9, 2.5, 6, 4], references=[1, 2, 3, 4, 5])
>>> print(round(results[\'pearsonr\'], 2))
-0.74
Example 2-The same as Example 1, but that also returns the `p-value`.
>>> pearsonr_metric = datasets.load_metric("pearsonr")
>>> results = pearsonr_metric.compute(predictions=[10, 9, 2.5, 6, 4], references=[1, 2, 3, 4, 5], return_pvalue=True)
>>> print(sorted(list(results.keys())))
[\'p-value\', \'pearsonr\']
>>> print(round(results[\'pearsonr\'], 2))
-0.74
>>> print(round(results[\'p-value\'], 2))
0.15
'''
__snake_case = '''
@article{2020SciPy-NMeth,
author = {Virtanen, Pauli and Gommers, Ralf and Oliphant, Travis E. and
Haberland, Matt and Reddy, Tyler and Cournapeau, David and
Burovski, Evgeni and Peterson, Pearu and Weckesser, Warren and
Bright, Jonathan and {van der Walt}, St{\'e}fan J. and
Brett, Matthew and Wilson, Joshua and Millman, K. Jarrod and
Mayorov, Nikolay and Nelson, Andrew R. J. and Jones, Eric and
Kern, Robert and Larson, Eric and Carey, C J and
Polat, Ilhan and Feng, Yu and Moore, Eric W. and
{VanderPlas}, Jake and Laxalde, Denis and Perktold, Josef and
Cimrman, Robert and Henriksen, Ian and Quintero, E. A. and
Harris, Charles R. and Archibald, Anne M. and
Ribeiro, Antonio H. and Pedregosa, Fabian and
{van Mulbregt}, Paul and {SciPy 1.0 Contributors}},
title = {{{SciPy} 1.0: Fundamental Algorithms for Scientific
Computing in Python}},
journal = {Nature Methods},
year = {2020},
volume = {17},
pages = {261--272},
adsurl = {https://rdcu.be/b08Wh},
doi = {10.1038/s41592-019-0686-2},
}
'''
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class lowercase ( datasets.Metric ):
"""simple docstring"""
def lowerCAmelCase__ ( self ):
'''simple docstring'''
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
'''predictions''': datasets.Value('''float''' ),
'''references''': datasets.Value('''float''' ),
} ) , reference_urls=['''https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.pearsonr.html'''] , )
def lowerCAmelCase__ ( self , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_=False ):
'''simple docstring'''
if return_pvalue:
UpperCamelCase__ :Any = pearsonr(UpperCamelCase_ , UpperCamelCase_ )
return {"pearsonr": results[0], "p-value": results[1]}
else:
return {"pearsonr": float(pearsonr(UpperCamelCase_ , UpperCamelCase_ )[0] )} | 97 | 0 |
'''simple docstring'''
def lowerCamelCase ( __lowerCamelCase : int = 1000 ) ->int:
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = 1, 1
_SCREAMING_SNAKE_CASE = []
for i in range(1 , n + 1 ):
_SCREAMING_SNAKE_CASE = prev_numerator + 2 * prev_denominator
_SCREAMING_SNAKE_CASE = prev_numerator + prev_denominator
if len(str(__lowerCamelCase ) ) > len(str(__lowerCamelCase ) ):
result.append(__lowerCamelCase )
_SCREAMING_SNAKE_CASE = numerator
_SCREAMING_SNAKE_CASE = denominator
return len(__lowerCamelCase )
if __name__ == "__main__":
print(f"""{solution() = }""")
| 58 |
'''simple docstring'''
from typing import List, Optional
from tokenizers import ByteLevelBPETokenizer
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_blenderbot_small import BlenderbotSmallTokenizer
__snake_case = logging.get_logger(__name__)
__snake_case = {
'''vocab_file''': '''vocab.json''',
'''merges_file''': '''merges.txt''',
'''tokenizer_config_file''': '''tokenizer_config.json''',
}
__snake_case = {
'''vocab_file''': {
'''facebook/blenderbot_small-90M''': '''https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/vocab.json'''
},
'''merges_file''': {
'''facebook/blenderbot_small-90M''': '''https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/merges.txt'''
},
'''tokenizer_config_file''': {
'''facebook/blenderbot_small-90M''': (
'''https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/tokenizer_config.json'''
)
},
}
__snake_case = {
'''facebook/blenderbot_small-90M''': 512,
}
class lowercase ( A__ ):
"""simple docstring"""
_a = VOCAB_FILES_NAMES
_a = PRETRAINED_VOCAB_FILES_MAP
_a = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
_a = BlenderbotSmallTokenizer
def __init__( self , UpperCamelCase_=None , UpperCamelCase_=None , UpperCamelCase_="<|endoftext|>" , UpperCamelCase_="<|endoftext|>" , UpperCamelCase_="<|endoftext|>" , UpperCamelCase_=False , UpperCamelCase_=True , **UpperCamelCase_ , ):
'''simple docstring'''
super().__init__(
ByteLevelBPETokenizer(
vocab=UpperCamelCase_ , merges=UpperCamelCase_ , add_prefix_space=UpperCamelCase_ , trim_offsets=UpperCamelCase_ , ) , bos_token=UpperCamelCase_ , eos_token=UpperCamelCase_ , unk_token=UpperCamelCase_ , **UpperCamelCase_ , )
UpperCamelCase__ :Union[str, Any] = add_prefix_space
def lowerCAmelCase__ ( self , UpperCamelCase_ , UpperCamelCase_=None ):
'''simple docstring'''
UpperCamelCase__ :List[Any] = [self.bos_token_id] + token_ids_a + [self.eos_token_id]
if token_ids_a is None:
return output
return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]
def lowerCAmelCase__ ( self , UpperCamelCase_ , UpperCamelCase_ = None ):
'''simple docstring'''
UpperCamelCase__ :Optional[int] = [self.sep_token_id]
UpperCamelCase__ :Any = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep + sep + token_ids_a + sep ) * [0] | 97 | 0 |
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_mobilebert import MobileBertTokenizer
__lowerCamelCase = logging.get_logger(__name__)
__lowerCamelCase = {"""vocab_file""": """vocab.txt""", """tokenizer_file""": """tokenizer.json"""}
__lowerCamelCase = {
"""vocab_file""": {"""mobilebert-uncased""": """https://huggingface.co/google/mobilebert-uncased/resolve/main/vocab.txt"""},
"""tokenizer_file""": {
"""mobilebert-uncased""": """https://huggingface.co/google/mobilebert-uncased/resolve/main/tokenizer.json"""
},
}
__lowerCamelCase = {"""mobilebert-uncased""": 5_12}
__lowerCamelCase = {}
class UpperCAmelCase ( A_ ):
A__ : Union[str, Any] = VOCAB_FILES_NAMES
A__ : Dict = PRETRAINED_VOCAB_FILES_MAP
A__ : Any = PRETRAINED_INIT_CONFIGURATION
A__ : int = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
A__ : Optional[Any] = MobileBertTokenizer
def __init__(self : str , snake_case__ : List[str]=None , snake_case__ : int=None , snake_case__ : List[str]=True , snake_case__ : str="[UNK]" , snake_case__ : int="[SEP]" , snake_case__ : Optional[Any]="[PAD]" , snake_case__ : Any="[CLS]" , snake_case__ : Tuple="[MASK]" , snake_case__ : Tuple=True , snake_case__ : str=None , **snake_case__ : Optional[Any] , ) -> Optional[int]:
'''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__ , )
snake_case : Dict = 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
):
snake_case : Tuple = getattr(snake_case__ , normalizer_state.pop("type" ) )
snake_case : Optional[Any] = do_lower_case
snake_case : Dict = strip_accents
snake_case : Union[str, Any] = tokenize_chinese_chars
snake_case : Tuple = normalizer_class(**snake_case__ )
snake_case : Optional[Any] = do_lower_case
def _SCREAMING_SNAKE_CASE (self : Dict , snake_case__ : Union[str, Any] , snake_case__ : List[str]=None ) -> Union[str, Any]:
'''simple docstring'''
snake_case : 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 _SCREAMING_SNAKE_CASE (self : Union[str, Any] , snake_case__ : List[int] , snake_case__ : Optional[List[int]] = None ) -> List[int]:
'''simple docstring'''
snake_case : Dict = [self.sep_token_id]
snake_case : Dict = [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[str] , snake_case__ : str , snake_case__ : Optional[str] = None ) -> Tuple[str]:
'''simple docstring'''
snake_case : List[Any] = self._tokenizer.model.save(snake_case__ , name=snake_case__ )
return tuple(snake_case__ )
| 59 |
'''simple docstring'''
from ...utils import (
OptionalDependencyNotAvailable,
is_flax_available,
is_torch_available,
is_transformers_available,
)
try:
if not (is_transformers_available() and is_torch_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ...utils.dummy_torch_and_transformers_objects import * # noqa F403
else:
from .multicontrolnet import MultiControlNetModel
from .pipeline_controlnet import StableDiffusionControlNetPipeline
from .pipeline_controlnet_imgaimg import StableDiffusionControlNetImgaImgPipeline
from .pipeline_controlnet_inpaint import StableDiffusionControlNetInpaintPipeline
if is_transformers_available() and is_flax_available():
from .pipeline_flax_controlnet import FlaxStableDiffusionControlNetPipeline | 97 | 0 |
"""simple docstring"""
import warnings
from ...configuration_utils import PretrainedConfig
from ...utils import logging
snake_case__ : int = logging.get_logger(__name__)
snake_case__ : Dict = {
'''RUCAIBox/mvp''': '''https://huggingface.co/RUCAIBox/mvp/resolve/main/config.json''',
}
class snake_case_( a__ ):
__UpperCamelCase = '''mvp'''
__UpperCamelCase = ['''past_key_values''']
__UpperCamelCase = {'''num_attention_heads''': '''encoder_attention_heads''', '''hidden_size''': '''d_model'''}
def __init__( self : Dict , UpperCamelCase_ : Union[str, Any]=5_0_2_6_7 , UpperCamelCase_ : Tuple=1_0_2_4 , UpperCamelCase_ : int=1_2 , UpperCamelCase_ : Optional[int]=4_0_9_6 , UpperCamelCase_ : Union[str, Any]=1_6 , UpperCamelCase_ : List[Any]=1_2 , UpperCamelCase_ : Dict=4_0_9_6 , UpperCamelCase_ : Union[str, Any]=1_6 , UpperCamelCase_ : List[str]=0.0 , UpperCamelCase_ : Optional[int]=0.0 , UpperCamelCase_ : List[str]="gelu" , UpperCamelCase_ : List[Any]=1_0_2_4 , UpperCamelCase_ : List[Any]=0.1 , UpperCamelCase_ : List[str]=0.0 , UpperCamelCase_ : List[str]=0.0 , UpperCamelCase_ : List[Any]=0.02 , UpperCamelCase_ : List[str]=0.0 , UpperCamelCase_ : List[str]=False , UpperCamelCase_ : Union[str, Any]=True , UpperCamelCase_ : str=1 , UpperCamelCase_ : Tuple=0 , UpperCamelCase_ : List[Any]=2 , UpperCamelCase_ : Dict=True , UpperCamelCase_ : Union[str, Any]=2 , UpperCamelCase_ : Tuple=2 , UpperCamelCase_ : List[Any]=False , UpperCamelCase_ : Optional[int]=1_0_0 , UpperCamelCase_ : Dict=8_0_0 , **UpperCamelCase_ : Any , ):
lowerCAmelCase : str = vocab_size
lowerCAmelCase : str = max_position_embeddings
lowerCAmelCase : Union[str, Any] = d_model
lowerCAmelCase : List[Any] = encoder_ffn_dim
lowerCAmelCase : Tuple = encoder_layers
lowerCAmelCase : Tuple = encoder_attention_heads
lowerCAmelCase : Any = decoder_ffn_dim
lowerCAmelCase : Optional[int] = decoder_layers
lowerCAmelCase : Any = decoder_attention_heads
lowerCAmelCase : Optional[Any] = dropout
lowerCAmelCase : Tuple = attention_dropout
lowerCAmelCase : Union[str, Any] = activation_dropout
lowerCAmelCase : Union[str, Any] = activation_function
lowerCAmelCase : int = init_std
lowerCAmelCase : str = encoder_layerdrop
lowerCAmelCase : str = decoder_layerdrop
lowerCAmelCase : Dict = classifier_dropout
lowerCAmelCase : List[str] = use_cache
lowerCAmelCase : Optional[Any] = encoder_layers
lowerCAmelCase : Union[str, Any] = scale_embedding # scale factor will be sqrt(d_model) if True
lowerCAmelCase : Optional[Any] = use_prompt
lowerCAmelCase : Tuple = prompt_length
lowerCAmelCase : int = prompt_mid_dim
super().__init__(
pad_token_id=UpperCamelCase_ , bos_token_id=UpperCamelCase_ , eos_token_id=UpperCamelCase_ , is_encoder_decoder=UpperCamelCase_ , decoder_start_token_id=UpperCamelCase_ , forced_eos_token_id=UpperCamelCase_ , **UpperCamelCase_ , )
if self.forced_bos_token_id is None and kwargs.get('''force_bos_token_to_be_generated''' , UpperCamelCase_ ):
lowerCAmelCase : int = self.bos_token_id
warnings.warn(
F'''Please make sure the config includes `forced_bos_token_id={self.bos_token_id}` in future versions. '''
'''The config can simply be saved and uploaded again to be fixed.''' )
| 60 |
'''simple docstring'''
from typing import Callable, Optional
from .. import Features
from ..packaged_modules.generator.generator import Generator
from .abc import AbstractDatasetInputStream
class lowercase ( A__ ):
"""simple docstring"""
def __init__( self , UpperCamelCase_ , UpperCamelCase_ = None , UpperCamelCase_ = None , UpperCamelCase_ = False , UpperCamelCase_ = False , UpperCamelCase_ = None , UpperCamelCase_ = None , **UpperCamelCase_ , ):
'''simple docstring'''
super().__init__(
features=UpperCamelCase_ , cache_dir=UpperCamelCase_ , keep_in_memory=UpperCamelCase_ , streaming=UpperCamelCase_ , num_proc=UpperCamelCase_ , **UpperCamelCase_ , )
UpperCamelCase__ :Any = Generator(
cache_dir=UpperCamelCase_ , features=UpperCamelCase_ , generator=UpperCamelCase_ , gen_kwargs=UpperCamelCase_ , **UpperCamelCase_ , )
def lowerCAmelCase__ ( self ):
'''simple docstring'''
if self.streaming:
UpperCamelCase__ :Optional[Any] = self.builder.as_streaming_dataset(split='''train''' )
# Build regular (map-style) dataset
else:
UpperCamelCase__ :Optional[int] = None
UpperCamelCase__ :int = None
UpperCamelCase__ :Any = None
UpperCamelCase__ :Any = None
self.builder.download_and_prepare(
download_config=UpperCamelCase_ , download_mode=UpperCamelCase_ , verification_mode=UpperCamelCase_ , base_path=UpperCamelCase_ , num_proc=self.num_proc , )
UpperCamelCase__ :List[Any] = self.builder.as_dataset(
split='''train''' , verification_mode=UpperCamelCase_ , in_memory=self.keep_in_memory )
return dataset | 97 | 0 |
"""simple docstring"""
import argparse
import logging
import pickle
from collections import Counter
logging.basicConfig(
format='%(asctime)s - %(levelname)s - %(name)s - %(message)s', datefmt='%m/%d/%Y %H:%M:%S', level=logging.INFO
)
_a = logging.getLogger(__name__)
if __name__ == "__main__":
_a = argparse.ArgumentParser(
description='Token Counts for smoothing the masking probabilities in MLM (cf XLM/word2vec)'
)
parser.add_argument(
'--data_file', type=str, default='data/dump.bert-base-uncased.pickle', help='The binarized dataset.'
)
parser.add_argument(
'--token_counts_dump', type=str, default='data/token_counts.bert-base-uncased.pickle', help='The dump file.'
)
parser.add_argument('--vocab_size', default=30_522, type=int)
_a = parser.parse_args()
logger.info(f"""Loading data from {args.data_file}""")
with open(args.data_file, 'rb') as fp:
_a = pickle.load(fp)
logger.info('Counting occurrences for MLM.')
_a = Counter()
for tk_ids in data:
counter.update(tk_ids)
_a = [0] * args.vocab_size
for k, v in counter.items():
_a = v
logger.info(f"""Dump to {args.token_counts_dump}""")
with open(args.token_counts_dump, 'wb') as handle:
pickle.dump(counts, handle, protocol=pickle.HIGHEST_PROTOCOL)
| 61 |
'''simple docstring'''
__snake_case = 65521
def a ( __a ) -> int:
'''simple docstring'''
UpperCamelCase__ :Tuple = 1
UpperCamelCase__ :Any = 0
for plain_chr in plain_text:
UpperCamelCase__ :List[str] = (a + ord(__a )) % MOD_ADLER
UpperCamelCase__ :Tuple = (b + a) % MOD_ADLER
return (b << 16) | a | 97 | 0 |
import argparse
import json
import subprocess
def _UpperCAmelCase ( SCREAMING_SNAKE_CASE__ : List[str] , SCREAMING_SNAKE_CASE__ : Tuple ):
__UpperCamelCase =[]
__UpperCamelCase =(
F'curl -H "Accept: application/vnd.github+json" -H "Authorization: Bearer {token}"'
' https://api.github.com/repos/huggingface/transformers/actions/runners'
)
__UpperCamelCase =subprocess.run(SCREAMING_SNAKE_CASE__ , shell=SCREAMING_SNAKE_CASE__ , stdout=subprocess.PIPE )
__UpperCamelCase =output.stdout.decode('utf-8' )
__UpperCamelCase =json.loads(SCREAMING_SNAKE_CASE__ )
__UpperCamelCase =status['runners']
for runner in runners:
if runner["name"] in target_runners:
if runner["status"] == "offline":
offline_runners.append(SCREAMING_SNAKE_CASE__ )
# save the result so we can report them on Slack
with open('offline_runners.txt' , 'w' ) as fp:
fp.write(json.dumps(SCREAMING_SNAKE_CASE__ ) )
if len(SCREAMING_SNAKE_CASE__ ) > 0:
__UpperCamelCase ='\n'.join([x['name'] for x in offline_runners] )
raise ValueError(F'The following runners are offline:\n{failed}' )
if __name__ == "__main__":
def _UpperCAmelCase ( SCREAMING_SNAKE_CASE__ : int ):
return values.split(',' )
_A = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'--target_runners',
default=None,
type=list_str,
required=True,
help='Comma-separated list of runners to check status.',
)
parser.add_argument(
'--token', default=None, type=str, required=True, help='A token that has actions:read permission.'
)
_A = parser.parse_args()
get_runner_status(args.target_runners, args.token)
| 62 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
__snake_case = logging.get_logger(__name__)
__snake_case = {
'''camembert-base''': '''https://huggingface.co/camembert-base/resolve/main/config.json''',
'''umberto-commoncrawl-cased-v1''': (
'''https://huggingface.co/Musixmatch/umberto-commoncrawl-cased-v1/resolve/main/config.json'''
),
'''umberto-wikipedia-uncased-v1''': (
'''https://huggingface.co/Musixmatch/umberto-wikipedia-uncased-v1/resolve/main/config.json'''
),
}
class lowercase ( A__ ):
"""simple docstring"""
_a = 'camembert'
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-12 , UpperCamelCase_=1 , UpperCamelCase_=0 , UpperCamelCase_=2 , UpperCamelCase_="absolute" , UpperCamelCase_=True , UpperCamelCase_=None , **UpperCamelCase_ , ):
'''simple docstring'''
super().__init__(pad_token_id=UpperCamelCase_ , bos_token_id=UpperCamelCase_ , eos_token_id=UpperCamelCase_ , **UpperCamelCase_ )
UpperCamelCase__ :int = vocab_size
UpperCamelCase__ :Optional[int] = hidden_size
UpperCamelCase__ :Optional[int] = num_hidden_layers
UpperCamelCase__ :List[Any] = num_attention_heads
UpperCamelCase__ :Union[str, Any] = hidden_act
UpperCamelCase__ :List[Any] = intermediate_size
UpperCamelCase__ :int = hidden_dropout_prob
UpperCamelCase__ :Tuple = attention_probs_dropout_prob
UpperCamelCase__ :Union[str, Any] = max_position_embeddings
UpperCamelCase__ :Tuple = type_vocab_size
UpperCamelCase__ :int = initializer_range
UpperCamelCase__ :List[str] = layer_norm_eps
UpperCamelCase__ :int = position_embedding_type
UpperCamelCase__ :Any = use_cache
UpperCamelCase__ :Any = classifier_dropout
class lowercase ( A__ ):
"""simple docstring"""
@property
def lowerCAmelCase__ ( self ):
'''simple docstring'''
if self.task == "multiple-choice":
UpperCamelCase__ :List[str] = {0: '''batch''', 1: '''choice''', 2: '''sequence'''}
else:
UpperCamelCase__ :Tuple = {0: '''batch''', 1: '''sequence'''}
return OrderedDict(
[
('''input_ids''', dynamic_axis),
('''attention_mask''', dynamic_axis),
] ) | 97 | 0 |
'''simple docstring'''
import unittest
from queue import Empty
from threading import Thread
from transformers import AutoTokenizer, TextIteratorStreamer, TextStreamer, is_torch_available
from transformers.testing_utils import CaptureStdout, require_torch, torch_device
from ..test_modeling_common import ids_tensor
if is_torch_available():
import torch
from transformers import AutoModelForCausalLM
@require_torch
class __SCREAMING_SNAKE_CASE (unittest.TestCase ):
"""simple docstring"""
def UpperCamelCase__ ( self : Union[str, Any] ):
_a = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-gpt2" )
_a = AutoModelForCausalLM.from_pretrained("hf-internal-testing/tiny-random-gpt2" ).to(__a )
_a = -1
_a = ids_tensor((1, 5) , vocab_size=model.config.vocab_size ).to(__a )
_a = model.generate(__a , max_new_tokens=10 , do_sample=__a )
_a = tokenizer.decode(greedy_ids[0] )
with CaptureStdout() as cs:
_a = TextStreamer(__a )
model.generate(__a , max_new_tokens=10 , do_sample=__a , streamer=__a )
# The greedy text should be printed to stdout, except for the final "\n" in the streamer
_a = cs.out[:-1]
self.assertEqual(__a , __a )
def UpperCamelCase__ ( self : Optional[int] ):
_a = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-gpt2" )
_a = AutoModelForCausalLM.from_pretrained("hf-internal-testing/tiny-random-gpt2" ).to(__a )
_a = -1
_a = ids_tensor((1, 5) , vocab_size=model.config.vocab_size ).to(__a )
_a = model.generate(__a , max_new_tokens=10 , do_sample=__a )
_a = tokenizer.decode(greedy_ids[0] )
_a = TextIteratorStreamer(__a )
_a = {"input_ids": input_ids, "max_new_tokens": 10, "do_sample": False, "streamer": streamer}
_a = Thread(target=model.generate , kwargs=__a )
thread.start()
_a = ""
for new_text in streamer:
streamer_text += new_text
self.assertEqual(__a , __a )
def UpperCamelCase__ ( self : str ):
_a = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-gpt2" )
_a = AutoModelForCausalLM.from_pretrained("hf-internal-testing/tiny-random-gpt2" ).to(__a )
_a = -1
_a = ids_tensor((1, 5) , vocab_size=model.config.vocab_size ).to(__a )
_a = model.generate(__a , max_new_tokens=10 , do_sample=__a )
_a = greedy_ids[:, input_ids.shape[1] :]
_a = tokenizer.decode(new_greedy_ids[0] )
with CaptureStdout() as cs:
_a = TextStreamer(__a , skip_prompt=__a )
model.generate(__a , max_new_tokens=10 , do_sample=__a , streamer=__a )
# The greedy text should be printed to stdout, except for the final "\n" in the streamer
_a = cs.out[:-1]
self.assertEqual(__a , __a )
def UpperCamelCase__ ( self : int ):
# Tests that we can pass `decode_kwargs` to the streamer to control how the tokens are decoded. Must be tested
# with actual models -- the dummy models' tokenizers are not aligned with their models, and
# `skip_special_tokens=True` has no effect on them
_a = AutoTokenizer.from_pretrained("distilgpt2" )
_a = AutoModelForCausalLM.from_pretrained("distilgpt2" ).to(__a )
_a = -1
_a = torch.ones((1, 5) , device=__a ).long() * model.config.bos_token_id
with CaptureStdout() as cs:
_a = TextStreamer(__a , skip_special_tokens=__a )
model.generate(__a , max_new_tokens=1 , do_sample=__a , streamer=__a )
# The prompt contains a special token, so the streamer should not print it. As such, the output text, when
# re-tokenized, must only contain one token
_a = cs.out[:-1] # Remove the final "\n"
_a = tokenizer(__a , return_tensors="pt" )
self.assertEqual(streamer_text_tokenized.input_ids.shape , (1, 1) )
def UpperCamelCase__ ( self : Any ):
_a = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-gpt2" )
_a = AutoModelForCausalLM.from_pretrained("hf-internal-testing/tiny-random-gpt2" ).to(__a )
_a = -1
_a = ids_tensor((1, 5) , vocab_size=model.config.vocab_size ).to(__a )
_a = TextIteratorStreamer(__a , timeout=0.001 )
_a = {"input_ids": input_ids, "max_new_tokens": 10, "do_sample": False, "streamer": streamer}
_a = Thread(target=model.generate , kwargs=__a )
thread.start()
# The streamer will timeout after 0.001 seconds, so an exception will be raised
with self.assertRaises(__a ):
_a = ""
for new_text in streamer:
streamer_text += new_text
| 63 |
'''simple docstring'''
import json
import pathlib
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_available():
import torch
if is_vision_available():
from PIL import Image
from transformers import ConditionalDetrImageProcessor
class lowercase ( unittest.TestCase ):
"""simple docstring"""
def __init__( self , UpperCamelCase_ , UpperCamelCase_=7 , UpperCamelCase_=3 , UpperCamelCase_=30 , UpperCamelCase_=400 , UpperCamelCase_=True , UpperCamelCase_=None , UpperCamelCase_=True , UpperCamelCase_=[0.5, 0.5, 0.5] , UpperCamelCase_=[0.5, 0.5, 0.5] , UpperCamelCase_=True , UpperCamelCase_=1 / 255 , UpperCamelCase_=True , ):
'''simple docstring'''
UpperCamelCase__ :Dict = size if size is not None else {'''shortest_edge''': 18, '''longest_edge''': 1333}
UpperCamelCase__ :str = parent
UpperCamelCase__ :List[Any] = batch_size
UpperCamelCase__ :Dict = num_channels
UpperCamelCase__ :str = min_resolution
UpperCamelCase__ :Optional[Any] = max_resolution
UpperCamelCase__ :int = do_resize
UpperCamelCase__ :Optional[Any] = size
UpperCamelCase__ :Tuple = do_normalize
UpperCamelCase__ :List[Any] = image_mean
UpperCamelCase__ :Dict = image_std
UpperCamelCase__ :Union[str, Any] = do_rescale
UpperCamelCase__ :Union[str, Any] = rescale_factor
UpperCamelCase__ :Union[str, Any] = do_pad
def lowerCAmelCase__ ( self ):
'''simple docstring'''
return {
"do_resize": self.do_resize,
"size": self.size,
"do_normalize": self.do_normalize,
"image_mean": self.image_mean,
"image_std": self.image_std,
"do_rescale": self.do_rescale,
"rescale_factor": self.rescale_factor,
"do_pad": self.do_pad,
}
def lowerCAmelCase__ ( self , UpperCamelCase_ , UpperCamelCase_=False ):
'''simple docstring'''
if not batched:
UpperCamelCase__ :List[str] = image_inputs[0]
if isinstance(UpperCamelCase_ , Image.Image ):
UpperCamelCase__ , UpperCamelCase__ :List[str] = image.size
else:
UpperCamelCase__ , UpperCamelCase__ :List[Any] = image.shape[1], image.shape[2]
if w < h:
UpperCamelCase__ :int = int(self.size['''shortest_edge'''] * h / w )
UpperCamelCase__ :Dict = self.size['''shortest_edge''']
elif w > h:
UpperCamelCase__ :int = self.size['''shortest_edge''']
UpperCamelCase__ :Tuple = int(self.size['''shortest_edge'''] * w / h )
else:
UpperCamelCase__ :str = self.size['''shortest_edge''']
UpperCamelCase__ :str = self.size['''shortest_edge''']
else:
UpperCamelCase__ :Any = []
for image in image_inputs:
UpperCamelCase__ , UpperCamelCase__ :Dict = self.get_expected_values([image] )
expected_values.append((expected_height, expected_width) )
UpperCamelCase__ :List[Any] = max(UpperCamelCase_ , key=lambda UpperCamelCase_ : item[0] )[0]
UpperCamelCase__ :Optional[int] = max(UpperCamelCase_ , key=lambda UpperCamelCase_ : item[1] )[1]
return expected_height, expected_width
@require_torch
@require_vision
class lowercase ( A__ , unittest.TestCase ):
"""simple docstring"""
_a = ConditionalDetrImageProcessor if is_vision_available() else None
def lowerCAmelCase__ ( self ):
'''simple docstring'''
UpperCamelCase__ :List[Any] = ConditionalDetrImageProcessingTester(self )
@property
def lowerCAmelCase__ ( self ):
'''simple docstring'''
return self.image_processor_tester.prepare_image_processor_dict()
def lowerCAmelCase__ ( self ):
'''simple docstring'''
UpperCamelCase__ :Tuple = 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_ , '''size''' ) )
def lowerCAmelCase__ ( self ):
'''simple docstring'''
UpperCamelCase__ :Optional[int] = self.image_processing_class.from_dict(self.image_processor_dict )
self.assertEqual(image_processor.size , {'''shortest_edge''': 18, '''longest_edge''': 1333} )
self.assertEqual(image_processor.do_pad , UpperCamelCase_ )
UpperCamelCase__ :List[str] = self.image_processing_class.from_dict(
self.image_processor_dict , size=42 , max_size=84 , pad_and_return_pixel_mask=UpperCamelCase_ )
self.assertEqual(image_processor.size , {'''shortest_edge''': 42, '''longest_edge''': 84} )
self.assertEqual(image_processor.do_pad , UpperCamelCase_ )
def lowerCAmelCase__ ( self ):
'''simple docstring'''
pass
def lowerCAmelCase__ ( self ):
'''simple docstring'''
UpperCamelCase__ :Dict = self.image_processing_class(**self.image_processor_dict )
# create random PIL images
UpperCamelCase__ :List[str] = prepare_image_inputs(self.image_processor_tester , equal_resolution=UpperCamelCase_ )
for image in image_inputs:
self.assertIsInstance(UpperCamelCase_ , Image.Image )
# Test not batched input
UpperCamelCase__ :Any = image_processing(image_inputs[0] , return_tensors='''pt''' ).pixel_values
UpperCamelCase__ , UpperCamelCase__ :str = self.image_processor_tester.get_expected_values(UpperCamelCase_ )
self.assertEqual(
encoded_images.shape , (1, self.image_processor_tester.num_channels, expected_height, expected_width) , )
# Test batched
UpperCamelCase__ , UpperCamelCase__ :str = self.image_processor_tester.get_expected_values(UpperCamelCase_ , batched=UpperCamelCase_ )
UpperCamelCase__ :List[str] = 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,
expected_height,
expected_width,
) , )
def lowerCAmelCase__ ( self ):
'''simple docstring'''
UpperCamelCase__ :List[Any] = self.image_processing_class(**self.image_processor_dict )
# create random numpy tensors
UpperCamelCase__ :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
UpperCamelCase__ :Union[str, Any] = image_processing(image_inputs[0] , return_tensors='''pt''' ).pixel_values
UpperCamelCase__ , UpperCamelCase__ :List[Any] = self.image_processor_tester.get_expected_values(UpperCamelCase_ )
self.assertEqual(
encoded_images.shape , (1, self.image_processor_tester.num_channels, expected_height, expected_width) , )
# Test batched
UpperCamelCase__ :Union[str, Any] = image_processing(UpperCamelCase_ , return_tensors='''pt''' ).pixel_values
UpperCamelCase__ , UpperCamelCase__ :str = self.image_processor_tester.get_expected_values(UpperCamelCase_ , batched=UpperCamelCase_ )
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
expected_height,
expected_width,
) , )
def lowerCAmelCase__ ( self ):
'''simple docstring'''
UpperCamelCase__ :List[str] = self.image_processing_class(**self.image_processor_dict )
# create random PyTorch tensors
UpperCamelCase__ :Any = 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
UpperCamelCase__ :str = image_processing(image_inputs[0] , return_tensors='''pt''' ).pixel_values
UpperCamelCase__ , UpperCamelCase__ :Dict = self.image_processor_tester.get_expected_values(UpperCamelCase_ )
self.assertEqual(
encoded_images.shape , (1, self.image_processor_tester.num_channels, expected_height, expected_width) , )
# Test batched
UpperCamelCase__ :List[str] = image_processing(UpperCamelCase_ , return_tensors='''pt''' ).pixel_values
UpperCamelCase__ , UpperCamelCase__ :Optional[int] = self.image_processor_tester.get_expected_values(UpperCamelCase_ , batched=UpperCamelCase_ )
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
expected_height,
expected_width,
) , )
@slow
def lowerCAmelCase__ ( self ):
'''simple docstring'''
UpperCamelCase__ :Optional[Any] = Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
with open('''./tests/fixtures/tests_samples/COCO/coco_annotations.txt''' , '''r''' ) as f:
UpperCamelCase__ :Optional[int] = json.loads(f.read() )
UpperCamelCase__ :Any = {'''image_id''': 39769, '''annotations''': target}
# encode them
UpperCamelCase__ :str = ConditionalDetrImageProcessor.from_pretrained('''microsoft/conditional-detr-resnet-50''' )
UpperCamelCase__ :List[Any] = image_processing(images=UpperCamelCase_ , annotations=UpperCamelCase_ , return_tensors='''pt''' )
# verify pixel values
UpperCamelCase__ :List[str] = torch.Size([1, 3, 800, 1066] )
self.assertEqual(encoding['''pixel_values'''].shape , UpperCamelCase_ )
UpperCamelCase__ :str = torch.tensor([0.2796, 0.3138, 0.3481] )
self.assertTrue(torch.allclose(encoding['''pixel_values'''][0, 0, 0, :3] , UpperCamelCase_ , atol=1e-4 ) )
# verify area
UpperCamelCase__ :str = torch.tensor([5887.9600, 11250.2061, 489353.8438, 837122.7500, 147967.5156, 165732.3438] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''area'''] , UpperCamelCase_ ) )
# verify boxes
UpperCamelCase__ :Optional[Any] = torch.Size([6, 4] )
self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape , UpperCamelCase_ )
UpperCamelCase__ :Optional[Any] = torch.tensor([0.5503, 0.2765, 0.0604, 0.2215] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''boxes'''][0] , UpperCamelCase_ , atol=1e-3 ) )
# verify image_id
UpperCamelCase__ :List[Any] = torch.tensor([39769] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id'''] , UpperCamelCase_ ) )
# verify is_crowd
UpperCamelCase__ :int = torch.tensor([0, 0, 0, 0, 0, 0] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd'''] , UpperCamelCase_ ) )
# verify class_labels
UpperCamelCase__ :List[str] = torch.tensor([75, 75, 63, 65, 17, 17] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels'''] , UpperCamelCase_ ) )
# verify orig_size
UpperCamelCase__ :Tuple = torch.tensor([480, 640] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size'''] , UpperCamelCase_ ) )
# verify size
UpperCamelCase__ :Union[str, Any] = torch.tensor([800, 1066] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size'''] , UpperCamelCase_ ) )
@slow
def lowerCAmelCase__ ( self ):
'''simple docstring'''
UpperCamelCase__ :List[str] = Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
with open('''./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt''' , '''r''' ) as f:
UpperCamelCase__ :Tuple = json.loads(f.read() )
UpperCamelCase__ :List[str] = {'''file_name''': '''000000039769.png''', '''image_id''': 39769, '''segments_info''': target}
UpperCamelCase__ :Any = pathlib.Path('''./tests/fixtures/tests_samples/COCO/coco_panoptic''' )
# encode them
UpperCamelCase__ :List[Any] = ConditionalDetrImageProcessor(format='''coco_panoptic''' )
UpperCamelCase__ :Dict = image_processing(images=UpperCamelCase_ , annotations=UpperCamelCase_ , masks_path=UpperCamelCase_ , return_tensors='''pt''' )
# verify pixel values
UpperCamelCase__ :str = torch.Size([1, 3, 800, 1066] )
self.assertEqual(encoding['''pixel_values'''].shape , UpperCamelCase_ )
UpperCamelCase__ :Optional[int] = torch.tensor([0.2796, 0.3138, 0.3481] )
self.assertTrue(torch.allclose(encoding['''pixel_values'''][0, 0, 0, :3] , UpperCamelCase_ , atol=1e-4 ) )
# verify area
UpperCamelCase__ :Tuple = torch.tensor([147979.6875, 165527.0469, 484638.5938, 11292.9375, 5879.6562, 7634.1147] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''area'''] , UpperCamelCase_ ) )
# verify boxes
UpperCamelCase__ :Any = torch.Size([6, 4] )
self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape , UpperCamelCase_ )
UpperCamelCase__ :List[Any] = torch.tensor([0.2625, 0.5437, 0.4688, 0.8625] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''boxes'''][0] , UpperCamelCase_ , atol=1e-3 ) )
# verify image_id
UpperCamelCase__ :List[str] = torch.tensor([39769] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id'''] , UpperCamelCase_ ) )
# verify is_crowd
UpperCamelCase__ :Union[str, Any] = torch.tensor([0, 0, 0, 0, 0, 0] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd'''] , UpperCamelCase_ ) )
# verify class_labels
UpperCamelCase__ :str = torch.tensor([17, 17, 63, 75, 75, 93] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels'''] , UpperCamelCase_ ) )
# verify masks
UpperCamelCase__ :Optional[Any] = 822873
self.assertEqual(encoding['''labels'''][0]['''masks'''].sum().item() , UpperCamelCase_ )
# verify orig_size
UpperCamelCase__ :List[str] = torch.tensor([480, 640] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size'''] , UpperCamelCase_ ) )
# verify size
UpperCamelCase__ :List[Any] = torch.tensor([800, 1066] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size'''] , UpperCamelCase_ ) ) | 97 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
A_ = {
'''configuration_xlm''': ['''XLM_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''XLMConfig''', '''XLMOnnxConfig'''],
'''tokenization_xlm''': ['''XLMTokenizer'''],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A_ = [
'''XLM_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''XLMForMultipleChoice''',
'''XLMForQuestionAnswering''',
'''XLMForQuestionAnsweringSimple''',
'''XLMForSequenceClassification''',
'''XLMForTokenClassification''',
'''XLMModel''',
'''XLMPreTrainedModel''',
'''XLMWithLMHeadModel''',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A_ = [
'''TF_XLM_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''TFXLMForMultipleChoice''',
'''TFXLMForQuestionAnsweringSimple''',
'''TFXLMForSequenceClassification''',
'''TFXLMForTokenClassification''',
'''TFXLMMainLayer''',
'''TFXLMModel''',
'''TFXLMPreTrainedModel''',
'''TFXLMWithLMHeadModel''',
]
if TYPE_CHECKING:
from .configuration_xlm import XLM_PRETRAINED_CONFIG_ARCHIVE_MAP, XLMConfig, XLMOnnxConfig
from .tokenization_xlm import XLMTokenizer
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_xlm import (
XLM_PRETRAINED_MODEL_ARCHIVE_LIST,
XLMForMultipleChoice,
XLMForQuestionAnswering,
XLMForQuestionAnsweringSimple,
XLMForSequenceClassification,
XLMForTokenClassification,
XLMModel,
XLMPreTrainedModel,
XLMWithLMHeadModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_xlm import (
TF_XLM_PRETRAINED_MODEL_ARCHIVE_LIST,
TFXLMForMultipleChoice,
TFXLMForQuestionAnsweringSimple,
TFXLMForSequenceClassification,
TFXLMForTokenClassification,
TFXLMMainLayer,
TFXLMModel,
TFXLMPreTrainedModel,
TFXLMWithLMHeadModel,
)
else:
import sys
A_ = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 64 |
'''simple docstring'''
from collections import defaultdict
class lowercase :
"""simple docstring"""
def __init__( self , UpperCamelCase_ , UpperCamelCase_ ):
'''simple docstring'''
UpperCamelCase__ :List[Any] = total # total no of tasks (N)
# DP table will have a dimension of (2^M)*N
# initially all values are set to -1
UpperCamelCase__ :Union[str, Any] = [
[-1 for i in range(total + 1 )] for j in range(2 ** len(UpperCamelCase_ ) )
]
UpperCamelCase__ :str = defaultdict(UpperCamelCase_ ) # stores the list of persons for each task
# final_mask is used to check if all persons are included by setting all bits
# to 1
UpperCamelCase__ :Optional[int] = (1 << len(UpperCamelCase_ )) - 1
def lowerCAmelCase__ ( self , UpperCamelCase_ , UpperCamelCase_ ):
'''simple docstring'''
if mask == self.final_mask:
return 1
# if not everyone gets the task and no more tasks are available, return 0
if task_no > self.total_tasks:
return 0
# if case already considered
if self.dp[mask][task_no] != -1:
return self.dp[mask][task_no]
# Number of ways when we don't this task in the arrangement
UpperCamelCase__ :str = self.count_ways_until(UpperCamelCase_ , task_no + 1 )
# now assign the tasks one by one to all possible persons and recursively
# assign for the remaining tasks.
if task_no in self.task:
for p in self.task[task_no]:
# if p is already given a task
if mask & (1 << p):
continue
# assign this task to p and change the mask value. And recursively
# assign tasks with the new mask value.
total_ways_util += self.count_ways_until(mask | (1 << p) , task_no + 1 )
# save the value.
UpperCamelCase__ :Optional[int] = total_ways_util
return self.dp[mask][task_no]
def lowerCAmelCase__ ( self , UpperCamelCase_ ):
'''simple docstring'''
for i in range(len(UpperCamelCase_ ) ):
for j in task_performed[i]:
self.task[j].append(UpperCamelCase_ )
# call the function to fill the DP table, final answer is stored in dp[0][1]
return self.count_ways_until(0 , 1 )
if __name__ == "__main__":
__snake_case = 5 # total no of tasks (the value of N)
# the list of tasks that can be done by M persons.
__snake_case = [[1, 3, 4], [1, 2, 5], [3, 4]]
print(
AssignmentUsingBitmask(task_performed, total_tasks).count_no_of_ways(
task_performed
)
) | 97 | 0 |
from ...utils import is_torch_available, is_transformers_available
if is_transformers_available() and is_torch_available():
from .pipeline_vq_diffusion import LearnedClassifierFreeSamplingEmbeddings, VQDiffusionPipeline
| 65 |
'''simple docstring'''
import csv
import tweepy
# Twitter API credentials
__snake_case = ''''''
__snake_case = ''''''
__snake_case = ''''''
__snake_case = ''''''
def a ( __a ) -> None:
'''simple docstring'''
UpperCamelCase__ :List[Any] = tweepy.OAuthHandler(__a , __a )
auth.set_access_token(__a , __a )
UpperCamelCase__ :List[str] = tweepy.API(__a )
# initialize a list to hold all the tweepy Tweets
UpperCamelCase__ :Dict = []
# make initial request for most recent tweets (200 is the maximum allowed count)
UpperCamelCase__ :Tuple = api.user_timeline(screen_name=__a , count=200 )
# save most recent tweets
alltweets.extend(__a )
# save the id of the oldest tweet less one
UpperCamelCase__ :Union[str, Any] = alltweets[-1].id - 1
# keep grabbing tweets until there are no tweets left to grab
while len(__a ) > 0:
print(f'''getting tweets before {oldest}''' )
# all subsequent requests use the max_id param to prevent duplicates
UpperCamelCase__ :Union[str, Any] = api.user_timeline(
screen_name=__a , count=200 , max_id=__a )
# save most recent tweets
alltweets.extend(__a )
# update the id of the oldest tweet less one
UpperCamelCase__ :Tuple = alltweets[-1].id - 1
print(f'''...{len(__a )} tweets downloaded so far''' )
# transform the tweepy tweets into a 2D array that will populate the csv
UpperCamelCase__ :int = [[tweet.id_str, tweet.created_at, tweet.text] for tweet in alltweets]
# write the csv
with open(f'''new_{screen_name}_tweets.csv''' , '''w''' ) as f:
UpperCamelCase__ :Tuple = csv.writer(__a )
writer.writerow(['''id''', '''created_at''', '''text'''] )
writer.writerows(__a )
if __name__ == "__main__":
# pass in the username of the account you want to download
get_all_tweets('''FirePing32''') | 97 | 0 |
"""simple docstring"""
def A_ ( _lowercase ):
'''simple docstring'''
return sum(i for i in range(1, number // 2 + 1 ) if number % i == 0 ) == number
if __name__ == "__main__":
print("Program to check whether a number is a Perfect number or not...")
__a = int(input("Enter number: ").strip())
print(F"""{number} is {'' if perfect(number) else 'not '}a Perfect Number.""")
| 66 |
'''simple docstring'''
import argparse
from collections import OrderedDict
from pathlib import Path
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from torchvision.transforms import functional as F
from transformers import DetrImageProcessor, TableTransformerConfig, TableTransformerForObjectDetection
from transformers.utils import logging
logging.set_verbosity_info()
__snake_case = logging.get_logger(__name__)
# here we list all keys to be renamed (original name on the left, our name on the right)
__snake_case = []
for i in range(6):
# 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 encoder + 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.encoder.norm.weight''', '''encoder.layernorm.weight'''),
('''transformer.encoder.norm.bias''', '''encoder.layernorm.bias'''),
('''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'''),
]
)
def a ( __a , __a , __a ) -> List[str]:
'''simple docstring'''
UpperCamelCase__ :List[Any] = state_dict.pop(__a )
UpperCamelCase__ :int = val
def a ( __a ) -> Any:
'''simple docstring'''
UpperCamelCase__ :Tuple = OrderedDict()
for key, value in state_dict.items():
if "backbone.0.body" in key:
UpperCamelCase__ :Dict = key.replace('''backbone.0.body''' , '''backbone.conv_encoder.model''' )
UpperCamelCase__ :List[str] = value
else:
UpperCamelCase__ :Dict = value
return new_state_dict
def a ( __a ) -> Optional[Any]:
'''simple docstring'''
UpperCamelCase__ :Optional[Any] = ''''''
# 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)
UpperCamelCase__ :Optional[Any] = state_dict.pop(f'''{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight''' )
UpperCamelCase__ :str = 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
UpperCamelCase__ :Any = in_proj_weight[:256, :]
UpperCamelCase__ :Tuple = in_proj_bias[:256]
UpperCamelCase__ :Optional[int] = in_proj_weight[256:512, :]
UpperCamelCase__ :Optional[Any] = in_proj_bias[256:512]
UpperCamelCase__ :Tuple = in_proj_weight[-256:, :]
UpperCamelCase__ :Dict = 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
UpperCamelCase__ :List[str] = state_dict.pop(f'''{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_weight''' )
UpperCamelCase__ :Optional[Any] = 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
UpperCamelCase__ :Any = in_proj_weight[:256, :]
UpperCamelCase__ :Optional[int] = in_proj_bias[:256]
UpperCamelCase__ :Tuple = in_proj_weight[256:512, :]
UpperCamelCase__ :Dict = in_proj_bias[256:512]
UpperCamelCase__ :Any = in_proj_weight[-256:, :]
UpperCamelCase__ :Dict = in_proj_bias[-256:]
# read in weights + bias of input projection layer of cross-attention
UpperCamelCase__ :List[str] = state_dict.pop(
f'''{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_weight''' )
UpperCamelCase__ :Any = 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
UpperCamelCase__ :Optional[Any] = in_proj_weight_cross_attn[:256, :]
UpperCamelCase__ :Any = in_proj_bias_cross_attn[:256]
UpperCamelCase__ :Any = in_proj_weight_cross_attn[256:512, :]
UpperCamelCase__ :Dict = in_proj_bias_cross_attn[256:512]
UpperCamelCase__ :str = in_proj_weight_cross_attn[-256:, :]
UpperCamelCase__ :Tuple = in_proj_bias_cross_attn[-256:]
def a ( __a , __a ) -> Optional[int]:
'''simple docstring'''
UpperCamelCase__ , UpperCamelCase__ :str = image.size
UpperCamelCase__ :Optional[Any] = max(__a , __a )
UpperCamelCase__ :List[Any] = 800 if '''detection''' in checkpoint_url else 1000
UpperCamelCase__ :Dict = target_max_size / current_max_size
UpperCamelCase__ :Any = image.resize((int(round(scale * width ) ), int(round(scale * height ) )) )
return resized_image
def a ( __a ) -> int:
'''simple docstring'''
UpperCamelCase__ :Any = F.to_tensor(__a )
UpperCamelCase__ :int = F.normalize(__a , mean=[0.4_8_5, 0.4_5_6, 0.4_0_6] , std=[0.2_2_9, 0.2_2_4, 0.2_2_5] )
return image
@torch.no_grad()
def a ( __a , __a , __a ) -> Dict:
'''simple docstring'''
logger.info('''Converting model...''' )
# load original state dict
UpperCamelCase__ :Optional[Any] = torch.hub.load_state_dict_from_url(__a , map_location='''cpu''' )
# rename keys
for src, dest in rename_keys:
rename_key(__a , __a , __a )
UpperCamelCase__ :Any = rename_backbone_keys(__a )
# query, key and value matrices need special treatment
read_in_q_k_v(__a )
# important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
UpperCamelCase__ :Dict = '''model.'''
for key in state_dict.copy().keys():
if not key.startswith('''class_labels_classifier''' ) and not key.startswith('''bbox_predictor''' ):
UpperCamelCase__ :Optional[Any] = state_dict.pop(__a )
UpperCamelCase__ :int = val
# create HuggingFace model and load state dict
UpperCamelCase__ :str = TableTransformerConfig(
backbone='''resnet18''' , mask_loss_coefficient=1 , dice_loss_coefficient=1 , ce_loss_coefficient=1 , bbox_loss_coefficient=5 , giou_loss_coefficient=2 , eos_coefficient=0.4 , class_cost=1 , bbox_cost=5 , giou_cost=2 , )
if "detection" in checkpoint_url:
UpperCamelCase__ :List[str] = 15
UpperCamelCase__ :int = 2
UpperCamelCase__ :Tuple = {0: '''table''', 1: '''table rotated'''}
UpperCamelCase__ :int = idalabel
UpperCamelCase__ :Dict = {v: k for k, v in idalabel.items()}
else:
UpperCamelCase__ :int = 125
UpperCamelCase__ :List[str] = 6
UpperCamelCase__ :Optional[Any] = {
0: '''table''',
1: '''table column''',
2: '''table row''',
3: '''table column header''',
4: '''table projected row header''',
5: '''table spanning cell''',
}
UpperCamelCase__ :Dict = idalabel
UpperCamelCase__ :Optional[Any] = {v: k for k, v in idalabel.items()}
UpperCamelCase__ :List[Any] = DetrImageProcessor(
format='''coco_detection''' , max_size=800 if '''detection''' in checkpoint_url else 1000 )
UpperCamelCase__ :int = TableTransformerForObjectDetection(__a )
model.load_state_dict(__a )
model.eval()
# verify our conversion
UpperCamelCase__ :Dict = '''example_pdf.png''' if '''detection''' in checkpoint_url else '''example_table.png'''
UpperCamelCase__ :Optional[Any] = hf_hub_download(repo_id='''nielsr/example-pdf''' , repo_type='''dataset''' , filename=__a )
UpperCamelCase__ :Tuple = Image.open(__a ).convert('''RGB''' )
UpperCamelCase__ :int = normalize(resize(__a , __a ) ).unsqueeze(0 )
UpperCamelCase__ :Optional[int] = model(__a )
if "detection" in checkpoint_url:
UpperCamelCase__ :Dict = (1, 15, 3)
UpperCamelCase__ :List[Any] = torch.tensor(
[[-6.7_8_9_7, -1_6.9_9_8_5, 6.7_9_3_7], [-8.0_1_8_6, -2_2.2_1_9_2, 6.9_6_7_7], [-7.3_1_1_7, -2_1.0_7_0_8, 7.4_0_5_5]] )
UpperCamelCase__ :Tuple = torch.tensor([[0.4_8_6_7, 0.1_7_6_7, 0.6_7_3_2], [0.6_7_1_8, 0.4_4_7_9, 0.3_8_3_0], [0.4_7_1_6, 0.1_7_6_0, 0.6_3_6_4]] )
else:
UpperCamelCase__ :Optional[Any] = (1, 125, 7)
UpperCamelCase__ :Dict = torch.tensor(
[[-1_8.1_4_3_0, -8.3_2_1_4, 4.8_2_7_4], [-1_8.4_6_8_5, -7.1_3_6_1, -4.2_6_6_7], [-2_6.3_6_9_3, -9.3_4_2_9, -4.9_9_6_2]] )
UpperCamelCase__ :List[Any] = torch.tensor([[0.4_9_8_3, 0.5_5_9_5, 0.9_4_4_0], [0.4_9_1_6, 0.6_3_1_5, 0.5_9_5_4], [0.6_1_0_8, 0.8_6_3_7, 0.1_1_3_5]] )
assert outputs.logits.shape == expected_shape
assert torch.allclose(outputs.logits[0, :3, :3] , __a , atol=1e-4 )
assert torch.allclose(outputs.pred_boxes[0, :3, :3] , __a , 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(__a ).mkdir(exist_ok=__a )
model.save_pretrained(__a )
image_processor.save_pretrained(__a )
if push_to_hub:
# Push model to HF hub
logger.info('''Pushing model to the hub...''' )
UpperCamelCase__ :Union[str, Any] = (
'''microsoft/table-transformer-detection'''
if '''detection''' in checkpoint_url
else '''microsoft/table-transformer-structure-recognition'''
)
model.push_to_hub(__a )
image_processor.push_to_hub(__a )
if __name__ == "__main__":
__snake_case = argparse.ArgumentParser()
parser.add_argument(
'''--checkpoint_url''',
default='''https://pubtables1m.blob.core.windows.net/model/pubtables1m_detection_detr_r18.pth''',
type=str,
choices=[
'''https://pubtables1m.blob.core.windows.net/model/pubtables1m_detection_detr_r18.pth''',
'''https://pubtables1m.blob.core.windows.net/model/pubtables1m_structure_detr_r18.pth''',
],
help='''URL of the Table Transformer checkpoint 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 or not to push the converted model to the 🤗 hub.'''
)
__snake_case = parser.parse_args()
convert_table_transformer_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub) | 97 | 0 |
'''simple docstring'''
import math
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__UpperCAmelCase =logging.get_logger(__name__)
__UpperCAmelCase ={
"facebook/data2vec-base-960h": "https://huggingface.co/facebook/data2vec-audio-base-960h/resolve/main/config.json",
# See all Data2VecAudio models at https://huggingface.co/models?filter=data2vec-audio
}
class a__ ( UpperCAmelCase__ ):
lowerCamelCase : Optional[int] ="data2vec-audio"
def __init__( self : Optional[Any] , a : int=32 , a : int=7_68 , a : Optional[Any]=12 , a : Any=12 , a : str=30_72 , a : Tuple="gelu" , a : List[Any]=0.1 , a : int=0.1 , a : List[str]=0.1 , a : List[Any]=0.0 , a : Dict=0.1 , a : Optional[int]=0.1 , a : int=0.02 , a : List[Any]=1e-5 , a : List[str]="gelu" , a : List[str]=(5_12, 5_12, 5_12, 5_12, 5_12, 5_12, 5_12) , a : Optional[Any]=(5, 2, 2, 2, 2, 2, 2) , a : Any=(10, 3, 3, 3, 3, 2, 2) , a : Optional[int]=False , a : List[Any]=16 , a : Optional[Any]=19 , a : Optional[int]=5 , a : str=0.05 , a : Union[str, Any]=10 , a : Tuple=2 , a : int=0.0 , a : List[str]=10 , a : Union[str, Any]=0 , a : List[Any]="sum" , a : Dict=False , a : List[Any]=False , a : Dict=2_56 , a : Tuple=(5_12, 5_12, 5_12, 5_12, 15_00) , a : Any=(5, 3, 3, 1, 1) , a : Optional[Any]=(1, 2, 3, 1, 1) , a : Optional[int]=5_12 , a : str=0 , a : Dict=1 , a : Optional[int]=2 , a : Optional[int]=False , a : List[Any]=3 , a : int=2 , a : Any=3 , a : Optional[int]=None , **a : str , ):
"""simple docstring"""
super().__init__(**a , pad_token_id=a , bos_token_id=a , eos_token_id=a )
__lowerCamelCase = hidden_size
__lowerCamelCase = feat_extract_activation
__lowerCamelCase = list(a )
__lowerCamelCase = list(a )
__lowerCamelCase = list(a )
__lowerCamelCase = conv_bias
__lowerCamelCase = num_conv_pos_embeddings
__lowerCamelCase = num_conv_pos_embedding_groups
__lowerCamelCase = conv_pos_kernel_size
__lowerCamelCase = len(self.conv_dim )
__lowerCamelCase = num_hidden_layers
__lowerCamelCase = intermediate_size
__lowerCamelCase = hidden_act
__lowerCamelCase = num_attention_heads
__lowerCamelCase = hidden_dropout
__lowerCamelCase = attention_dropout
__lowerCamelCase = activation_dropout
__lowerCamelCase = feat_proj_dropout
__lowerCamelCase = final_dropout
__lowerCamelCase = layerdrop
__lowerCamelCase = layer_norm_eps
__lowerCamelCase = initializer_range
__lowerCamelCase = vocab_size
__lowerCamelCase = use_weighted_layer_sum
if (
(len(self.conv_stride ) != self.num_feat_extract_layers)
or (len(self.conv_kernel ) != self.num_feat_extract_layers)
or (len(self.conv_dim ) != self.num_feat_extract_layers)
):
raise ValueError(
'''Configuration for convolutional layers is incorrect. It is required that `len(config.conv_dim)` =='''
''' `len(config.conv_stride)` == `len(config.conv_kernel)`, but is `len(config.conv_dim) ='''
f""" {len(self.conv_dim )}`, `len(config.conv_stride) = {len(self.conv_stride )}`,"""
f""" `len(config.conv_kernel) = {len(self.conv_kernel )}`.""" )
# fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
__lowerCamelCase = mask_time_prob
__lowerCamelCase = mask_time_length
__lowerCamelCase = mask_time_min_masks
__lowerCamelCase = mask_feature_prob
__lowerCamelCase = mask_feature_length
__lowerCamelCase = mask_feature_min_masks
# ctc loss
__lowerCamelCase = ctc_loss_reduction
__lowerCamelCase = ctc_zero_infinity
# adapter
__lowerCamelCase = add_adapter
__lowerCamelCase = adapter_kernel_size
__lowerCamelCase = adapter_stride
__lowerCamelCase = num_adapter_layers
__lowerCamelCase = output_hidden_size or hidden_size
# SequenceClassification-specific parameter. Feel free to ignore for other classes.
__lowerCamelCase = classifier_proj_size
# XVector-specific parameters. Feel free to ignore for other classes.
__lowerCamelCase = list(a )
__lowerCamelCase = list(a )
__lowerCamelCase = list(a )
__lowerCamelCase = xvector_output_dim
@property
def SCREAMING_SNAKE_CASE__ ( self : int ):
"""simple docstring"""
return math.prod(self.conv_stride )
| 67 |
'''simple docstring'''
from __future__ import annotations
from fractions import Fraction
from math import gcd, sqrt
def a ( __a ) -> bool:
'''simple docstring'''
UpperCamelCase__ :int = int(number**0.5 )
return number == sq * sq
def a ( __a , __a , __a , __a , __a , __a ) -> tuple[int, int]:
'''simple docstring'''
UpperCamelCase__ :int = x_num * y_den * z_den + y_num * x_den * z_den + z_num * x_den * y_den
UpperCamelCase__ :int = x_den * y_den * z_den
UpperCamelCase__ :int = gcd(__a , __a )
top //= hcf
bottom //= hcf
return top, bottom
def a ( __a = 35 ) -> int:
'''simple docstring'''
UpperCamelCase__ :set = set()
UpperCamelCase__ :int
UpperCamelCase__ :Fraction = Fraction(0 )
UpperCamelCase__ :tuple[int, int]
for x_num in range(1 , order + 1 ):
for x_den in range(x_num + 1 , order + 1 ):
for y_num in range(1 , order + 1 ):
for y_den in range(y_num + 1 , order + 1 ):
# n=1
UpperCamelCase__ :int = x_num * y_den + x_den * y_num
UpperCamelCase__ :Any = x_den * y_den
UpperCamelCase__ :Tuple = gcd(__a , __a )
z_num //= hcf
z_den //= hcf
if 0 < z_num < z_den <= order:
UpperCamelCase__ :Tuple = add_three(
__a , __a , __a , __a , __a , __a )
unique_s.add(__a )
# n=2
UpperCamelCase__ :List[str] = (
x_num * x_num * y_den * y_den + x_den * x_den * y_num * y_num
)
UpperCamelCase__ :Dict = x_den * x_den * y_den * y_den
if is_sq(__a ) and is_sq(__a ):
UpperCamelCase__ :Any = int(sqrt(__a ) )
UpperCamelCase__ :Optional[int] = int(sqrt(__a ) )
UpperCamelCase__ :int = gcd(__a , __a )
z_num //= hcf
z_den //= hcf
if 0 < z_num < z_den <= order:
UpperCamelCase__ :Tuple = add_three(
__a , __a , __a , __a , __a , __a )
unique_s.add(__a )
# n=-1
UpperCamelCase__ :Tuple = x_num * y_num
UpperCamelCase__ :Union[str, Any] = x_den * y_num + x_num * y_den
UpperCamelCase__ :List[str] = gcd(__a , __a )
z_num //= hcf
z_den //= hcf
if 0 < z_num < z_den <= order:
UpperCamelCase__ :Union[str, Any] = add_three(
__a , __a , __a , __a , __a , __a )
unique_s.add(__a )
# n=2
UpperCamelCase__ :Optional[Any] = x_num * x_num * y_num * y_num
UpperCamelCase__ :Tuple = (
x_den * x_den * y_num * y_num + x_num * x_num * y_den * y_den
)
if is_sq(__a ) and is_sq(__a ):
UpperCamelCase__ :str = int(sqrt(__a ) )
UpperCamelCase__ :Any = int(sqrt(__a ) )
UpperCamelCase__ :Dict = gcd(__a , __a )
z_num //= hcf
z_den //= hcf
if 0 < z_num < z_den <= order:
UpperCamelCase__ :int = add_three(
__a , __a , __a , __a , __a , __a )
unique_s.add(__a )
for num, den in unique_s:
total += Fraction(__a , __a )
return total.denominator + total.numerator
if __name__ == "__main__":
print(F"""{solution() = }""") | 97 | 0 |
from collections import OrderedDict
from ...utils import logging
from .auto_factory import _BaseAutoModelClass, _LazyAutoMapping, auto_class_update
from .configuration_auto import CONFIG_MAPPING_NAMES
lowerCAmelCase__ = logging.get_logger(__name__)
lowerCAmelCase__ = OrderedDict(
[
# Base model mapping
("""albert""", """FlaxAlbertModel"""),
("""bart""", """FlaxBartModel"""),
("""beit""", """FlaxBeitModel"""),
("""bert""", """FlaxBertModel"""),
("""big_bird""", """FlaxBigBirdModel"""),
("""blenderbot""", """FlaxBlenderbotModel"""),
("""blenderbot-small""", """FlaxBlenderbotSmallModel"""),
("""clip""", """FlaxCLIPModel"""),
("""distilbert""", """FlaxDistilBertModel"""),
("""electra""", """FlaxElectraModel"""),
("""gpt-sw3""", """FlaxGPT2Model"""),
("""gpt2""", """FlaxGPT2Model"""),
("""gpt_neo""", """FlaxGPTNeoModel"""),
("""gptj""", """FlaxGPTJModel"""),
("""longt5""", """FlaxLongT5Model"""),
("""marian""", """FlaxMarianModel"""),
("""mbart""", """FlaxMBartModel"""),
("""mt5""", """FlaxMT5Model"""),
("""opt""", """FlaxOPTModel"""),
("""pegasus""", """FlaxPegasusModel"""),
("""regnet""", """FlaxRegNetModel"""),
("""resnet""", """FlaxResNetModel"""),
("""roberta""", """FlaxRobertaModel"""),
("""roberta-prelayernorm""", """FlaxRobertaPreLayerNormModel"""),
("""roformer""", """FlaxRoFormerModel"""),
("""t5""", """FlaxT5Model"""),
("""vision-text-dual-encoder""", """FlaxVisionTextDualEncoderModel"""),
("""vit""", """FlaxViTModel"""),
("""wav2vec2""", """FlaxWav2Vec2Model"""),
("""whisper""", """FlaxWhisperModel"""),
("""xglm""", """FlaxXGLMModel"""),
("""xlm-roberta""", """FlaxXLMRobertaModel"""),
]
)
lowerCAmelCase__ = OrderedDict(
[
# Model for pre-training mapping
("""albert""", """FlaxAlbertForPreTraining"""),
("""bart""", """FlaxBartForConditionalGeneration"""),
("""bert""", """FlaxBertForPreTraining"""),
("""big_bird""", """FlaxBigBirdForPreTraining"""),
("""electra""", """FlaxElectraForPreTraining"""),
("""longt5""", """FlaxLongT5ForConditionalGeneration"""),
("""mbart""", """FlaxMBartForConditionalGeneration"""),
("""mt5""", """FlaxMT5ForConditionalGeneration"""),
("""roberta""", """FlaxRobertaForMaskedLM"""),
("""roberta-prelayernorm""", """FlaxRobertaPreLayerNormForMaskedLM"""),
("""roformer""", """FlaxRoFormerForMaskedLM"""),
("""t5""", """FlaxT5ForConditionalGeneration"""),
("""wav2vec2""", """FlaxWav2Vec2ForPreTraining"""),
("""whisper""", """FlaxWhisperForConditionalGeneration"""),
("""xlm-roberta""", """FlaxXLMRobertaForMaskedLM"""),
]
)
lowerCAmelCase__ = OrderedDict(
[
# Model for Masked LM mapping
("""albert""", """FlaxAlbertForMaskedLM"""),
("""bart""", """FlaxBartForConditionalGeneration"""),
("""bert""", """FlaxBertForMaskedLM"""),
("""big_bird""", """FlaxBigBirdForMaskedLM"""),
("""distilbert""", """FlaxDistilBertForMaskedLM"""),
("""electra""", """FlaxElectraForMaskedLM"""),
("""mbart""", """FlaxMBartForConditionalGeneration"""),
("""roberta""", """FlaxRobertaForMaskedLM"""),
("""roberta-prelayernorm""", """FlaxRobertaPreLayerNormForMaskedLM"""),
("""roformer""", """FlaxRoFormerForMaskedLM"""),
("""xlm-roberta""", """FlaxXLMRobertaForMaskedLM"""),
]
)
lowerCAmelCase__ = OrderedDict(
[
# Model for Seq2Seq Causal LM mapping
("""bart""", """FlaxBartForConditionalGeneration"""),
("""blenderbot""", """FlaxBlenderbotForConditionalGeneration"""),
("""blenderbot-small""", """FlaxBlenderbotSmallForConditionalGeneration"""),
("""encoder-decoder""", """FlaxEncoderDecoderModel"""),
("""longt5""", """FlaxLongT5ForConditionalGeneration"""),
("""marian""", """FlaxMarianMTModel"""),
("""mbart""", """FlaxMBartForConditionalGeneration"""),
("""mt5""", """FlaxMT5ForConditionalGeneration"""),
("""pegasus""", """FlaxPegasusForConditionalGeneration"""),
("""t5""", """FlaxT5ForConditionalGeneration"""),
]
)
lowerCAmelCase__ = OrderedDict(
[
# Model for Image-classsification
("""beit""", """FlaxBeitForImageClassification"""),
("""regnet""", """FlaxRegNetForImageClassification"""),
("""resnet""", """FlaxResNetForImageClassification"""),
("""vit""", """FlaxViTForImageClassification"""),
]
)
lowerCAmelCase__ = OrderedDict(
[
("""vision-encoder-decoder""", """FlaxVisionEncoderDecoderModel"""),
]
)
lowerCAmelCase__ = OrderedDict(
[
# Model for Causal LM mapping
("""bart""", """FlaxBartForCausalLM"""),
("""bert""", """FlaxBertForCausalLM"""),
("""big_bird""", """FlaxBigBirdForCausalLM"""),
("""electra""", """FlaxElectraForCausalLM"""),
("""gpt-sw3""", """FlaxGPT2LMHeadModel"""),
("""gpt2""", """FlaxGPT2LMHeadModel"""),
("""gpt_neo""", """FlaxGPTNeoForCausalLM"""),
("""gptj""", """FlaxGPTJForCausalLM"""),
("""opt""", """FlaxOPTForCausalLM"""),
("""roberta""", """FlaxRobertaForCausalLM"""),
("""roberta-prelayernorm""", """FlaxRobertaPreLayerNormForCausalLM"""),
("""xglm""", """FlaxXGLMForCausalLM"""),
("""xlm-roberta""", """FlaxXLMRobertaForCausalLM"""),
]
)
lowerCAmelCase__ = OrderedDict(
[
# Model for Sequence Classification mapping
("""albert""", """FlaxAlbertForSequenceClassification"""),
("""bart""", """FlaxBartForSequenceClassification"""),
("""bert""", """FlaxBertForSequenceClassification"""),
("""big_bird""", """FlaxBigBirdForSequenceClassification"""),
("""distilbert""", """FlaxDistilBertForSequenceClassification"""),
("""electra""", """FlaxElectraForSequenceClassification"""),
("""mbart""", """FlaxMBartForSequenceClassification"""),
("""roberta""", """FlaxRobertaForSequenceClassification"""),
("""roberta-prelayernorm""", """FlaxRobertaPreLayerNormForSequenceClassification"""),
("""roformer""", """FlaxRoFormerForSequenceClassification"""),
("""xlm-roberta""", """FlaxXLMRobertaForSequenceClassification"""),
]
)
lowerCAmelCase__ = OrderedDict(
[
# Model for Question Answering mapping
("""albert""", """FlaxAlbertForQuestionAnswering"""),
("""bart""", """FlaxBartForQuestionAnswering"""),
("""bert""", """FlaxBertForQuestionAnswering"""),
("""big_bird""", """FlaxBigBirdForQuestionAnswering"""),
("""distilbert""", """FlaxDistilBertForQuestionAnswering"""),
("""electra""", """FlaxElectraForQuestionAnswering"""),
("""mbart""", """FlaxMBartForQuestionAnswering"""),
("""roberta""", """FlaxRobertaForQuestionAnswering"""),
("""roberta-prelayernorm""", """FlaxRobertaPreLayerNormForQuestionAnswering"""),
("""roformer""", """FlaxRoFormerForQuestionAnswering"""),
("""xlm-roberta""", """FlaxXLMRobertaForQuestionAnswering"""),
]
)
lowerCAmelCase__ = OrderedDict(
[
# Model for Token Classification mapping
("""albert""", """FlaxAlbertForTokenClassification"""),
("""bert""", """FlaxBertForTokenClassification"""),
("""big_bird""", """FlaxBigBirdForTokenClassification"""),
("""distilbert""", """FlaxDistilBertForTokenClassification"""),
("""electra""", """FlaxElectraForTokenClassification"""),
("""roberta""", """FlaxRobertaForTokenClassification"""),
("""roberta-prelayernorm""", """FlaxRobertaPreLayerNormForTokenClassification"""),
("""roformer""", """FlaxRoFormerForTokenClassification"""),
("""xlm-roberta""", """FlaxXLMRobertaForTokenClassification"""),
]
)
lowerCAmelCase__ = OrderedDict(
[
# Model for Multiple Choice mapping
("""albert""", """FlaxAlbertForMultipleChoice"""),
("""bert""", """FlaxBertForMultipleChoice"""),
("""big_bird""", """FlaxBigBirdForMultipleChoice"""),
("""distilbert""", """FlaxDistilBertForMultipleChoice"""),
("""electra""", """FlaxElectraForMultipleChoice"""),
("""roberta""", """FlaxRobertaForMultipleChoice"""),
("""roberta-prelayernorm""", """FlaxRobertaPreLayerNormForMultipleChoice"""),
("""roformer""", """FlaxRoFormerForMultipleChoice"""),
("""xlm-roberta""", """FlaxXLMRobertaForMultipleChoice"""),
]
)
lowerCAmelCase__ = OrderedDict(
[
("""bert""", """FlaxBertForNextSentencePrediction"""),
]
)
lowerCAmelCase__ = OrderedDict(
[
("""speech-encoder-decoder""", """FlaxSpeechEncoderDecoderModel"""),
("""whisper""", """FlaxWhisperForConditionalGeneration"""),
]
)
lowerCAmelCase__ = OrderedDict(
[
("""whisper""", """FlaxWhisperForAudioClassification"""),
]
)
lowerCAmelCase__ = _LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_MAPPING_NAMES)
lowerCAmelCase__ = _LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_PRETRAINING_MAPPING_NAMES)
lowerCAmelCase__ = _LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_MASKED_LM_MAPPING_NAMES)
lowerCAmelCase__ = _LazyAutoMapping(
CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES
)
lowerCAmelCase__ = _LazyAutoMapping(
CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING_NAMES
)
lowerCAmelCase__ = _LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_VISION_2_SEQ_MAPPING_NAMES)
lowerCAmelCase__ = _LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_CAUSAL_LM_MAPPING_NAMES)
lowerCAmelCase__ = _LazyAutoMapping(
CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING_NAMES
)
lowerCAmelCase__ = _LazyAutoMapping(
CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_QUESTION_ANSWERING_MAPPING_NAMES
)
lowerCAmelCase__ = _LazyAutoMapping(
CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING_NAMES
)
lowerCAmelCase__ = _LazyAutoMapping(
CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_MULTIPLE_CHOICE_MAPPING_NAMES
)
lowerCAmelCase__ = _LazyAutoMapping(
CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING_NAMES
)
lowerCAmelCase__ = _LazyAutoMapping(
CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING_NAMES
)
lowerCAmelCase__ = _LazyAutoMapping(
CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING_NAMES
)
class a__ ( _BaseAutoModelClass ):
"""simple docstring"""
__lowerCamelCase = FLAX_MODEL_MAPPING
lowerCAmelCase__ = auto_class_update(FlaxAutoModel)
class a__ ( _BaseAutoModelClass ):
"""simple docstring"""
__lowerCamelCase = FLAX_MODEL_FOR_PRETRAINING_MAPPING
lowerCAmelCase__ = auto_class_update(FlaxAutoModelForPreTraining, head_doc="""pretraining""")
class a__ ( _BaseAutoModelClass ):
"""simple docstring"""
__lowerCamelCase = FLAX_MODEL_FOR_CAUSAL_LM_MAPPING
lowerCAmelCase__ = auto_class_update(FlaxAutoModelForCausalLM, head_doc="""causal language modeling""")
class a__ ( _BaseAutoModelClass ):
"""simple docstring"""
__lowerCamelCase = FLAX_MODEL_FOR_MASKED_LM_MAPPING
lowerCAmelCase__ = auto_class_update(FlaxAutoModelForMaskedLM, head_doc="""masked language modeling""")
class a__ ( _BaseAutoModelClass ):
"""simple docstring"""
__lowerCamelCase = FLAX_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
lowerCAmelCase__ = auto_class_update(
FlaxAutoModelForSeqaSeqLM, head_doc="""sequence-to-sequence language modeling""", checkpoint_for_example="""t5-base"""
)
class a__ ( _BaseAutoModelClass ):
"""simple docstring"""
__lowerCamelCase = FLAX_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
lowerCAmelCase__ = auto_class_update(
FlaxAutoModelForSequenceClassification, head_doc="""sequence classification"""
)
class a__ ( _BaseAutoModelClass ):
"""simple docstring"""
__lowerCamelCase = FLAX_MODEL_FOR_QUESTION_ANSWERING_MAPPING
lowerCAmelCase__ = auto_class_update(FlaxAutoModelForQuestionAnswering, head_doc="""question answering""")
class a__ ( _BaseAutoModelClass ):
"""simple docstring"""
__lowerCamelCase = FLAX_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING
lowerCAmelCase__ = auto_class_update(
FlaxAutoModelForTokenClassification, head_doc="""token classification"""
)
class a__ ( _BaseAutoModelClass ):
"""simple docstring"""
__lowerCamelCase = FLAX_MODEL_FOR_MULTIPLE_CHOICE_MAPPING
lowerCAmelCase__ = auto_class_update(FlaxAutoModelForMultipleChoice, head_doc="""multiple choice""")
class a__ ( _BaseAutoModelClass ):
"""simple docstring"""
__lowerCamelCase = FLAX_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING
lowerCAmelCase__ = auto_class_update(
FlaxAutoModelForNextSentencePrediction, head_doc="""next sentence prediction"""
)
class a__ ( _BaseAutoModelClass ):
"""simple docstring"""
__lowerCamelCase = FLAX_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING
lowerCAmelCase__ = auto_class_update(
FlaxAutoModelForImageClassification, head_doc="""image classification"""
)
class a__ ( _BaseAutoModelClass ):
"""simple docstring"""
__lowerCamelCase = FLAX_MODEL_FOR_VISION_2_SEQ_MAPPING
lowerCAmelCase__ = auto_class_update(FlaxAutoModelForVisionaSeq, head_doc="""vision-to-text modeling""")
class a__ ( _BaseAutoModelClass ):
"""simple docstring"""
__lowerCamelCase = FLAX_MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING
lowerCAmelCase__ = auto_class_update(
FlaxAutoModelForSpeechSeqaSeq, head_doc="""sequence-to-sequence speech-to-text modeling"""
)
| 68 |
'''simple docstring'''
def a ( ) -> Union[str, Any]:
'''simple docstring'''
UpperCamelCase__ :Optional[int] = []
UpperCamelCase__ :int = 1
while len(__a ) < 1e6:
constant.append(str(__a ) )
i += 1
UpperCamelCase__ :Union[str, Any] = ''''''.join(__a )
return (
int(constant[0] )
* int(constant[9] )
* int(constant[99] )
* int(constant[999] )
* int(constant[9999] )
* int(constant[99999] )
* int(constant[999999] )
)
if __name__ == "__main__":
print(solution()) | 97 | 0 |
"""simple docstring"""
from collections import deque
def UpperCAmelCase ( UpperCAmelCase ) -> int:
snake_case_ = len(UpperCAmelCase )
snake_case_ = deque()
snake_case_ = [False for _ in range(UpperCAmelCase )]
snake_case_ = [-1 for _ in range(UpperCAmelCase )]
snake_case_ = index_of[:]
def strong_connect(UpperCAmelCase , UpperCAmelCase , UpperCAmelCase ):
snake_case_ = index # the number when this node is seen
snake_case_ = index # lowest rank node reachable from here
index += 1
stack.append(UpperCAmelCase )
snake_case_ = True
for w in g[v]:
if index_of[w] == -1:
snake_case_ = strong_connect(UpperCAmelCase , UpperCAmelCase , UpperCAmelCase )
snake_case_ = (
lowlink_of[w] if lowlink_of[w] < lowlink_of[v] else lowlink_of[v]
)
elif on_stack[w]:
snake_case_ = (
lowlink_of[w] if lowlink_of[w] < lowlink_of[v] else lowlink_of[v]
)
if lowlink_of[v] == index_of[v]:
snake_case_ = []
snake_case_ = stack.pop()
snake_case_ = False
component.append(UpperCAmelCase )
while w != v:
snake_case_ = stack.pop()
snake_case_ = False
component.append(UpperCAmelCase )
components.append(UpperCAmelCase )
return index
snake_case_ = []
for v in range(UpperCAmelCase ):
if index_of[v] == -1:
strong_connect(UpperCAmelCase , 0 , UpperCAmelCase )
return components
def UpperCAmelCase ( UpperCAmelCase , UpperCAmelCase ) -> Optional[int]:
snake_case_ = [[] for _ in range(UpperCAmelCase )]
for u, v in edges:
g[u].append(UpperCAmelCase )
return g
if __name__ == "__main__":
# Test
__UpperCamelCase = 7
__UpperCamelCase = [0, 0, 1, 2, 3, 3, 4, 4, 6]
__UpperCamelCase = [1, 3, 2, 0, 1, 4, 5, 6, 5]
__UpperCamelCase = [(u, v) for u, v in zip(source, target)]
__UpperCamelCase = create_graph(n_vertices, edges)
assert [[5], [6], [4], [3, 2, 1, 0]] == tarjan(g)
| 69 |
'''simple docstring'''
from PIL import Image
def a ( __a , __a ) -> Image:
'''simple docstring'''
def brightness(__a ) -> float:
return 128 + level + (c - 128)
if not -2_5_5.0 <= level <= 2_5_5.0:
raise ValueError('''level must be between -255.0 (black) and 255.0 (white)''' )
return img.point(__a )
if __name__ == "__main__":
# Load image
with Image.open('''image_data/lena.jpg''') as img:
# Change brightness to 100
__snake_case = change_brightness(img, 100)
brigt_img.save('''image_data/lena_brightness.png''', format='''png''') | 97 | 0 |
'''simple docstring'''
# This script creates a super tiny model that is useful inside tests, when we just want to test that
# the machinery works, without needing to the check the quality of the outcomes.
#
# This version creates a tiny vocab first, and then a tiny model - so the outcome is truly tiny -
# all files ~60KB. As compared to taking a full-size model, reducing to the minimum its layers and
# emb dimensions, but keeping the full vocab + merges files, leading to ~3MB in total for all files.
# The latter is done by `fsmt-make-super-tiny-model.py`.
#
# It will be used then as "stas/tiny-wmt19-en-ru"
from pathlib import Path
import json
import tempfile
from transformers import FSMTTokenizer, FSMTConfig, FSMTForConditionalGeneration
from transformers.models.fsmt.tokenization_fsmt import VOCAB_FILES_NAMES
A__ : Any ='''tiny-wmt19-en-ru'''
# Build
# borrowed from a test
A__ : Optional[Any] =[
'''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>''',
]
A__ : Dict =dict(zip(vocab, range(len(vocab))))
A__ : Tuple =['''l o 123''', '''lo w 1456''', '''e r</w> 1789''', '''''']
with tempfile.TemporaryDirectory() as tmpdirname:
A__ : str =Path(tmpdirname)
A__ : Union[str, Any] =build_dir / VOCAB_FILES_NAMES['''src_vocab_file''']
A__ : Union[str, Any] =build_dir / VOCAB_FILES_NAMES['''tgt_vocab_file''']
A__ : str =build_dir / VOCAB_FILES_NAMES['''merges_file''']
with open(src_vocab_file, '''w''') as fp:
fp.write(json.dumps(vocab_tokens))
with open(tgt_vocab_file, '''w''') as fp:
fp.write(json.dumps(vocab_tokens))
with open(merges_file, '''w''') as fp:
fp.write('''\n'''.join(merges))
A__ : Any =FSMTTokenizer(
langs=['''en''', '''ru'''],
src_vocab_size=len(vocab),
tgt_vocab_size=len(vocab),
src_vocab_file=src_vocab_file,
tgt_vocab_file=tgt_vocab_file,
merges_file=merges_file,
)
A__ : Optional[Any] =FSMTConfig(
langs=['''ru''', '''en'''],
src_vocab_size=10_00,
tgt_vocab_size=10_00,
d_model=4,
encoder_layers=1,
decoder_layers=1,
encoder_ffn_dim=4,
decoder_ffn_dim=4,
encoder_attention_heads=1,
decoder_attention_heads=1,
)
A__ : List[Any] =FSMTForConditionalGeneration(config)
print(F"""num of params {tiny_model.num_parameters()}""")
# Test
A__ : Union[str, Any] =tokenizer(['''Making tiny model'''], return_tensors='''pt''')
A__ : List[str] =tiny_model(**batch)
print('''test output:''', len(outputs.logits[0]))
# Save
tiny_model.half() # makes it smaller
tiny_model.save_pretrained(mname_tiny)
tokenizer.save_pretrained(mname_tiny)
print(F"""Generated {mname_tiny}""")
# Upload
# transformers-cli upload tiny-wmt19-en-ru
| 70 |
'''simple docstring'''
from datetime import datetime as dt
import os
from github import Github
__snake_case = [
'''good first issue''',
'''good second issue''',
'''good difficult issue''',
'''feature request''',
'''new model''',
'''wip''',
]
def a ( ) -> List[str]:
'''simple docstring'''
UpperCamelCase__ :List[str] = Github(os.environ['''GITHUB_TOKEN'''] )
UpperCamelCase__ :Tuple = g.get_repo('''huggingface/transformers''' )
UpperCamelCase__ :Union[str, Any] = repo.get_issues(state='''open''' )
for issue in open_issues:
UpperCamelCase__ :List[Any] = sorted([comment for comment in issue.get_comments()] , key=lambda __a : i.created_at , reverse=__a )
UpperCamelCase__ :List[Any] = comments[0] if len(__a ) > 0 else None
if (
last_comment is not None
and last_comment.user.login == "github-actions[bot]"
and (dt.utcnow() - issue.updated_at).days > 7
and (dt.utcnow() - issue.created_at).days >= 30
and not any(label.name.lower() in LABELS_TO_EXEMPT for label in issue.get_labels() )
):
# print(f"Would close issue {issue.number} since it has been 7 days of inactivity since bot mention.")
issue.edit(state='''closed''' )
elif (
(dt.utcnow() - issue.updated_at).days > 23
and (dt.utcnow() - issue.created_at).days >= 30
and not any(label.name.lower() in LABELS_TO_EXEMPT for label in issue.get_labels() )
):
# print(f"Would add stale comment to {issue.number}")
issue.create_comment(
'''This issue has been automatically marked as stale because it has not had '''
'''recent activity. If you think this still needs to be addressed '''
'''please comment on this thread.\n\nPlease note that issues that do not follow the '''
'''[contributing guidelines](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md) '''
'''are likely to be ignored.''' )
if __name__ == "__main__":
main() | 97 | 0 |
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 A ( a_ ,a_=0.999 ,a_="cosine" ,) -> List[str]:
if alpha_transform_type == "cosine":
def alpha_bar_fn(a_ ):
return math.cos((t + 0.008) / 1.008 * math.pi / 2 ) ** 2
elif alpha_transform_type == "exp":
def alpha_bar_fn(a_ ):
return math.exp(t * -12.0 )
else:
raise ValueError(F'Unsupported alpha_tranform_type: {alpha_transform_type}' )
__UpperCamelCase : List[Any] =[]
for i in range(a_ ):
__UpperCamelCase : List[str] =i / num_diffusion_timesteps
__UpperCamelCase : Optional[int] =(i + 1) / num_diffusion_timesteps
betas.append(min(1 - alpha_bar_fn(a_ ) / alpha_bar_fn(a_ ) ,a_ ) )
return torch.tensor(a_ ,dtype=torch.floataa )
class __A ( a , a ):
"""simple docstring"""
UpperCamelCase__ : Optional[Any] =[e.name for e in KarrasDiffusionSchedulers]
UpperCamelCase__ : Optional[int] =2
@register_to_config
def __init__( self , lowerCamelCase__ = 1000 , lowerCamelCase__ = 0.00_085 , lowerCamelCase__ = 0.012 , lowerCamelCase__ = "linear" , lowerCamelCase__ = None , lowerCamelCase__ = "epsilon" , lowerCamelCase__ = False , lowerCamelCase__ = False , lowerCamelCase__ = 1.0 , lowerCamelCase__ = "linspace" , lowerCamelCase__ = 0 , ):
"""simple docstring"""
if trained_betas is not None:
__UpperCamelCase : Optional[int] =torch.tensor(lowerCamelCase__ , dtype=torch.floataa )
elif beta_schedule == "linear":
__UpperCamelCase : str =torch.linspace(lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , dtype=torch.floataa )
elif beta_schedule == "scaled_linear":
# this schedule is very specific to the latent diffusion model.
__UpperCamelCase : Optional[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
__UpperCamelCase : Optional[int] =betas_for_alpha_bar(lowerCamelCase__ , alpha_transform_type='cosine' )
elif beta_schedule == "exp":
__UpperCamelCase : str =betas_for_alpha_bar(lowerCamelCase__ , alpha_transform_type='exp' )
else:
raise NotImplementedError(f'{beta_schedule} does is not implemented for {self.__class__}' )
__UpperCamelCase : Union[str, Any] =1.0 - self.betas
__UpperCamelCase : str =torch.cumprod(self.alphas , dim=0 )
# set all values
self.set_timesteps(lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ )
__UpperCamelCase : str =use_karras_sigmas
def __lowercase ( self , lowerCamelCase__ , lowerCamelCase__=None ):
"""simple docstring"""
if schedule_timesteps is None:
__UpperCamelCase : Union[str, Any] =self.timesteps
__UpperCamelCase : Tuple =(schedule_timesteps == timestep).nonzero()
# The sigma index that is taken for the **very** first `step`
# is always the second index (or the last index if there is only 1)
# This way we can ensure we don't accidentally skip a sigma in
# case we start in the middle of the denoising schedule (e.g. for image-to-image)
if len(self._index_counter ) == 0:
__UpperCamelCase : Tuple =1 if len(lowerCamelCase__ ) > 1 else 0
else:
__UpperCamelCase : Union[str, Any] =timestep.cpu().item() if torch.is_tensor(lowerCamelCase__ ) else timestep
__UpperCamelCase : List[str] =self._index_counter[timestep_int]
return indices[pos].item()
@property
def __lowercase ( self ):
"""simple docstring"""
if self.config.timestep_spacing in ["linspace", "trailing"]:
return self.sigmas.max()
return (self.sigmas.max() ** 2 + 1) ** 0.5
def __lowercase ( self , lowerCamelCase__ , lowerCamelCase__ , ):
"""simple docstring"""
__UpperCamelCase : List[str] =self.index_for_timestep(lowerCamelCase__ )
__UpperCamelCase : List[str] =self.sigmas[step_index]
__UpperCamelCase : Optional[Any] =sample / ((sigma**2 + 1) ** 0.5)
return sample
def __lowercase ( self , lowerCamelCase__ , lowerCamelCase__ = None , lowerCamelCase__ = None , ):
"""simple docstring"""
__UpperCamelCase : List[str] =num_inference_steps
__UpperCamelCase : Union[str, Any] =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":
__UpperCamelCase : Dict =np.linspace(0 , num_train_timesteps - 1 , lowerCamelCase__ , dtype=lowerCamelCase__ )[::-1].copy()
elif self.config.timestep_spacing == "leading":
__UpperCamelCase : 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
__UpperCamelCase : List[str] =(np.arange(0 , lowerCamelCase__ ) * step_ratio).round()[::-1].copy().astype(lowerCamelCase__ )
timesteps += self.config.steps_offset
elif self.config.timestep_spacing == "trailing":
__UpperCamelCase : Optional[Any] =num_train_timesteps / self.num_inference_steps
# creates integer timesteps by multiplying by ratio
# casting to int to avoid issues when num_inference_step is power of 3
__UpperCamelCase : Any =(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\'.' )
__UpperCamelCase : List[Any] =np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5 )
__UpperCamelCase : int =np.log(lowerCamelCase__ )
__UpperCamelCase : str =np.interp(lowerCamelCase__ , np.arange(0 , len(lowerCamelCase__ ) ) , lowerCamelCase__ )
if self.config.use_karras_sigmas:
__UpperCamelCase : Optional[Any] =self._convert_to_karras(in_sigmas=lowerCamelCase__ , num_inference_steps=self.num_inference_steps )
__UpperCamelCase : List[Any] =np.array([self._sigma_to_t(lowerCamelCase__ , lowerCamelCase__ ) for sigma in sigmas] )
__UpperCamelCase : List[Any] =np.concatenate([sigmas, [0.0]] ).astype(np.floataa )
__UpperCamelCase : List[str] =torch.from_numpy(lowerCamelCase__ ).to(device=lowerCamelCase__ )
__UpperCamelCase : Optional[int] =torch.cat([sigmas[:1], sigmas[1:-1].repeat_interleave(2 ), sigmas[-1:]] )
__UpperCamelCase : List[Any] =torch.from_numpy(lowerCamelCase__ )
__UpperCamelCase : str =torch.cat([timesteps[:1], timesteps[1:].repeat_interleave(2 )] )
if str(lowerCamelCase__ ).startswith('mps' ):
# mps does not support float64
__UpperCamelCase : Optional[int] =timesteps.to(lowerCamelCase__ , dtype=torch.floataa )
else:
__UpperCamelCase : List[Any] =timesteps.to(device=lowerCamelCase__ )
# empty dt and derivative
__UpperCamelCase : Dict =None
__UpperCamelCase : Optional[Any] =None
# for exp beta schedules, such as the one for `pipeline_shap_e.py`
# we need an index counter
__UpperCamelCase : List[str] =defaultdict(lowerCamelCase__ )
def __lowercase ( self , lowerCamelCase__ , lowerCamelCase__ ):
"""simple docstring"""
__UpperCamelCase : Any =np.log(lowerCamelCase__ )
# get distribution
__UpperCamelCase : Any =log_sigma - log_sigmas[:, np.newaxis]
# get sigmas range
__UpperCamelCase : Any =np.cumsum((dists >= 0) , axis=0 ).argmax(axis=0 ).clip(max=log_sigmas.shape[0] - 2 )
__UpperCamelCase : Optional[int] =low_idx + 1
__UpperCamelCase : Optional[int] =log_sigmas[low_idx]
__UpperCamelCase : Optional[int] =log_sigmas[high_idx]
# interpolate sigmas
__UpperCamelCase : Any =(low - log_sigma) / (low - high)
__UpperCamelCase : int =np.clip(lowerCamelCase__ , 0 , 1 )
# transform interpolation to time range
__UpperCamelCase : Tuple =(1 - w) * low_idx + w * high_idx
__UpperCamelCase : Optional[int] =t.reshape(sigma.shape )
return t
def __lowercase ( self , lowerCamelCase__ , lowerCamelCase__ ):
"""simple docstring"""
__UpperCamelCase : float =in_sigmas[-1].item()
__UpperCamelCase : float =in_sigmas[0].item()
__UpperCamelCase : Dict =7.0 # 7.0 is the value used in the paper
__UpperCamelCase : str =np.linspace(0 , 1 , lowerCamelCase__ )
__UpperCamelCase : int =sigma_min ** (1 / rho)
__UpperCamelCase : Tuple =sigma_max ** (1 / rho)
__UpperCamelCase : Dict =(max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
return sigmas
@property
def __lowercase ( self ):
"""simple docstring"""
return self.dt is None
def __lowercase ( self , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ = True , ):
"""simple docstring"""
__UpperCamelCase : List[str] =self.index_for_timestep(lowerCamelCase__ )
# advance index counter by 1
__UpperCamelCase : Optional[int] =timestep.cpu().item() if torch.is_tensor(lowerCamelCase__ ) else timestep
self._index_counter[timestep_int] += 1
if self.state_in_first_order:
__UpperCamelCase : List[str] =self.sigmas[step_index]
__UpperCamelCase : Tuple =self.sigmas[step_index + 1]
else:
# 2nd order / Heun's method
__UpperCamelCase : Union[str, Any] =self.sigmas[step_index - 1]
__UpperCamelCase : int =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
__UpperCamelCase : Any =0
__UpperCamelCase : Union[str, Any] =sigma * (gamma + 1) # Note: sigma_hat == sigma for now
# 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
if self.config.prediction_type == "epsilon":
__UpperCamelCase : Optional[int] =sigma_hat if self.state_in_first_order else sigma_next
__UpperCamelCase : Tuple =sample - sigma_input * model_output
elif self.config.prediction_type == "v_prediction":
__UpperCamelCase : Dict =sigma_hat if self.state_in_first_order else sigma_next
__UpperCamelCase : Union[str, Any] =model_output * (-sigma_input / (sigma_input**2 + 1) ** 0.5) + (
sample / (sigma_input**2 + 1)
)
elif self.config.prediction_type == "sample":
__UpperCamelCase : 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:
__UpperCamelCase : 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
__UpperCamelCase : int =(sample - pred_original_sample) / sigma_hat
# 3. delta timestep
__UpperCamelCase : List[str] =sigma_next - sigma_hat
# store for 2nd order step
__UpperCamelCase : Optional[Any] =derivative
__UpperCamelCase : Optional[Any] =dt
__UpperCamelCase : Optional[int] =sample
else:
# 2. 2nd order / Heun's method
__UpperCamelCase : Any =(sample - pred_original_sample) / sigma_next
__UpperCamelCase : List[str] =(self.prev_derivative + derivative) / 2
# 3. take prev timestep & sample
__UpperCamelCase : Optional[Any] =self.dt
__UpperCamelCase : Union[str, Any] =self.sample
# free dt and derivative
# Note, this puts the scheduler in "first order mode"
__UpperCamelCase : Optional[Any] =None
__UpperCamelCase : Union[str, Any] =None
__UpperCamelCase : str =None
__UpperCamelCase : str =sample + derivative * dt
if not return_dict:
return (prev_sample,)
return SchedulerOutput(prev_sample=lowerCamelCase__ )
def __lowercase ( self , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , ):
"""simple docstring"""
__UpperCamelCase : Optional[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
__UpperCamelCase : Tuple =self.timesteps.to(original_samples.device , dtype=torch.floataa )
__UpperCamelCase : Tuple =timesteps.to(original_samples.device , dtype=torch.floataa )
else:
__UpperCamelCase : Optional[Any] =self.timesteps.to(original_samples.device )
__UpperCamelCase : Tuple =timesteps.to(original_samples.device )
__UpperCamelCase : List[str] =[self.index_for_timestep(lowerCamelCase__ , lowerCamelCase__ ) for t in timesteps]
__UpperCamelCase : Optional[int] =sigmas[step_indices].flatten()
while len(sigma.shape ) < len(original_samples.shape ):
__UpperCamelCase : List[str] =sigma.unsqueeze(-1 )
__UpperCamelCase : Tuple =original_samples + noise * sigma
return noisy_samples
def __len__( self ):
"""simple docstring"""
return self.config.num_train_timesteps
| 71 |
'''simple docstring'''
import re
from filelock import FileLock
try:
import nltk
__snake_case = True
except (ImportError, ModuleNotFoundError):
__snake_case = False
if NLTK_AVAILABLE:
with FileLock('''.lock''') as lock:
nltk.download('''punkt''', quiet=True)
def a ( __a ) -> str:
'''simple docstring'''
re.sub('''<n>''' , '''''' , __a ) # 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(__a ) ) | 97 | 0 |
"""simple docstring"""
import gc
import random
import unittest
import numpy as np
import torch
from diffusers import DDIMScheduler, KandinskyVaaPipeline, KandinskyVaaPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.utils import floats_tensor, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu
from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference
enable_full_determinism()
class __snake_case ( _lowercase , unittest.TestCase):
snake_case__ : Any = KandinskyVaaPipeline
snake_case__ : List[str] = [
"image_embeds",
"negative_image_embeds",
]
snake_case__ : Optional[int] = ["image_embeds", "negative_image_embeds"]
snake_case__ : Any = [
"generator",
"height",
"width",
"latents",
"guidance_scale",
"num_inference_steps",
"return_dict",
"guidance_scale",
"num_images_per_prompt",
"output_type",
"return_dict",
]
snake_case__ : Union[str, Any] = False
@property
def SCREAMING_SNAKE_CASE ( self : Dict ):
"""simple docstring"""
return 3_2
@property
def SCREAMING_SNAKE_CASE ( self : Dict ):
"""simple docstring"""
return 3_2
@property
def SCREAMING_SNAKE_CASE ( self : Dict ):
"""simple docstring"""
return self.time_input_dim
@property
def SCREAMING_SNAKE_CASE ( self : Tuple ):
"""simple docstring"""
return self.time_input_dim * 4
@property
def SCREAMING_SNAKE_CASE ( self : Optional[Any] ):
"""simple docstring"""
return 1_0_0
@property
def SCREAMING_SNAKE_CASE ( self : str ):
"""simple docstring"""
torch.manual_seed(0 )
_lowerCamelCase : Any = {
'''in_channels''': 4,
# Out channels is double in channels because predicts mean and variance
'''out_channels''': 8,
'''addition_embed_type''': '''image''',
'''down_block_types''': ('''ResnetDownsampleBlock2D''', '''SimpleCrossAttnDownBlock2D'''),
'''up_block_types''': ('''SimpleCrossAttnUpBlock2D''', '''ResnetUpsampleBlock2D'''),
'''mid_block_type''': '''UNetMidBlock2DSimpleCrossAttn''',
'''block_out_channels''': (self.block_out_channels_a, self.block_out_channels_a * 2),
'''layers_per_block''': 1,
'''encoder_hid_dim''': self.text_embedder_hidden_size,
'''encoder_hid_dim_type''': '''image_proj''',
'''cross_attention_dim''': self.cross_attention_dim,
'''attention_head_dim''': 4,
'''resnet_time_scale_shift''': '''scale_shift''',
'''class_embed_type''': None,
}
_lowerCamelCase : Tuple = UNetaDConditionModel(**__lowerCAmelCase )
return model
@property
def SCREAMING_SNAKE_CASE ( self : Tuple ):
"""simple docstring"""
return {
"block_out_channels": [3_2, 6_4],
"down_block_types": ["DownEncoderBlock2D", "AttnDownEncoderBlock2D"],
"in_channels": 3,
"latent_channels": 4,
"layers_per_block": 1,
"norm_num_groups": 8,
"norm_type": "spatial",
"num_vq_embeddings": 1_2,
"out_channels": 3,
"up_block_types": [
"AttnUpDecoderBlock2D",
"UpDecoderBlock2D",
],
"vq_embed_dim": 4,
}
@property
def SCREAMING_SNAKE_CASE ( self : List[Any] ):
"""simple docstring"""
torch.manual_seed(0 )
_lowerCamelCase : Any = VQModel(**self.dummy_movq_kwargs )
return model
def SCREAMING_SNAKE_CASE ( self : Union[str, Any] ):
"""simple docstring"""
_lowerCamelCase : Optional[Any] = self.dummy_unet
_lowerCamelCase : Optional[Any] = self.dummy_movq
_lowerCamelCase : Optional[int] = DDIMScheduler(
num_train_timesteps=1_0_0_0 , beta_schedule='''linear''' , beta_start=0.0_00_85 , beta_end=0.0_12 , clip_sample=__lowerCAmelCase , set_alpha_to_one=__lowerCAmelCase , steps_offset=1 , prediction_type='''epsilon''' , thresholding=__lowerCAmelCase , )
_lowerCamelCase : Optional[int] = {
'''unet''': unet,
'''scheduler''': scheduler,
'''movq''': movq,
}
return components
def SCREAMING_SNAKE_CASE ( self : Union[str, Any] , __lowerCAmelCase : Tuple , __lowerCAmelCase : Optional[Any]=0 ):
"""simple docstring"""
_lowerCamelCase : Tuple = floats_tensor((1, self.text_embedder_hidden_size) , rng=random.Random(__lowerCAmelCase ) ).to(__lowerCAmelCase )
_lowerCamelCase : Union[str, Any] = floats_tensor((1, self.text_embedder_hidden_size) , rng=random.Random(seed + 1 ) ).to(
__lowerCAmelCase )
if str(__lowerCAmelCase ).startswith('''mps''' ):
_lowerCamelCase : Union[str, Any] = torch.manual_seed(__lowerCAmelCase )
else:
_lowerCamelCase : Any = torch.Generator(device=__lowerCAmelCase ).manual_seed(__lowerCAmelCase )
_lowerCamelCase : List[str] = {
'''image_embeds''': image_embeds,
'''negative_image_embeds''': negative_image_embeds,
'''generator''': generator,
'''height''': 6_4,
'''width''': 6_4,
'''guidance_scale''': 4.0,
'''num_inference_steps''': 2,
'''output_type''': '''np''',
}
return inputs
def SCREAMING_SNAKE_CASE ( self : Any ):
"""simple docstring"""
_lowerCamelCase : Optional[Any] = '''cpu'''
_lowerCamelCase : Any = self.get_dummy_components()
_lowerCamelCase : Union[str, Any] = self.pipeline_class(**__lowerCAmelCase )
_lowerCamelCase : Tuple = pipe.to(__lowerCAmelCase )
pipe.set_progress_bar_config(disable=__lowerCAmelCase )
_lowerCamelCase : Optional[int] = pipe(**self.get_dummy_inputs(__lowerCAmelCase ) )
_lowerCamelCase : List[Any] = output.images
_lowerCamelCase : str = pipe(
**self.get_dummy_inputs(__lowerCAmelCase ) , return_dict=__lowerCAmelCase , )[0]
_lowerCamelCase : Any = image[0, -3:, -3:, -1]
_lowerCamelCase : Optional[Any] = image_from_tuple[0, -3:, -3:, -1]
assert image.shape == (1, 6_4, 6_4, 3)
_lowerCamelCase : int = np.array(
[0.6_23_79_76, 1.0, 0.36_44_13_32, 1.0, 0.70_63_96_34, 0.29_87_71_86, 0.85_65_21_25, 0.5_21_68_43, 0.54_45_40_46] )
assert (
np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
), f''' expected_slice {expected_slice}, but got {image_slice.flatten()}'''
assert (
np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 1E-2
), f''' expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}'''
@slow
@require_torch_gpu
class __snake_case ( unittest.TestCase):
def SCREAMING_SNAKE_CASE ( self : List[Any] ):
"""simple docstring"""
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def SCREAMING_SNAKE_CASE ( self : Union[str, Any] ):
"""simple docstring"""
_lowerCamelCase : List[Any] = load_numpy(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
'''/kandinskyv22/kandinskyv22_text2img_cat_fp16.npy''' )
_lowerCamelCase : Dict = KandinskyVaaPriorPipeline.from_pretrained(
'''kandinsky-community/kandinsky-2-2-prior''' , torch_dtype=torch.floataa )
pipe_prior.to(__lowerCAmelCase )
_lowerCamelCase : Any = KandinskyVaaPipeline.from_pretrained(
'''kandinsky-community/kandinsky-2-2-decoder''' , torch_dtype=torch.floataa )
_lowerCamelCase : int = pipeline.to(__lowerCAmelCase )
pipeline.set_progress_bar_config(disable=__lowerCAmelCase )
_lowerCamelCase : int = '''red cat, 4k photo'''
_lowerCamelCase : Any = torch.Generator(device='''cuda''' ).manual_seed(0 )
_lowerCamelCase , _lowerCamelCase : int = pipe_prior(
__lowerCAmelCase , generator=__lowerCAmelCase , num_inference_steps=5 , negative_prompt='''''' , ).to_tuple()
_lowerCamelCase : List[Any] = torch.Generator(device='''cuda''' ).manual_seed(0 )
_lowerCamelCase : Dict = pipeline(
image_embeds=__lowerCAmelCase , negative_image_embeds=__lowerCAmelCase , generator=__lowerCAmelCase , num_inference_steps=1_0_0 , output_type='''np''' , )
_lowerCamelCase : Optional[int] = output.images[0]
assert image.shape == (5_1_2, 5_1_2, 3)
assert_mean_pixel_difference(__lowerCAmelCase , __lowerCAmelCase )
| 72 |
'''simple docstring'''
from pathlib import Path
import fire
from tqdm import tqdm
def a ( __a="ro" , __a="en" , __a="wmt16" , __a=None ) -> None:
'''simple docstring'''
try:
import datasets
except (ModuleNotFoundError, ImportError):
raise ImportError('''run pip install datasets''' )
UpperCamelCase__ :int = f'''{src_lang}-{tgt_lang}'''
print(f'''Converting {dataset}-{pair}''' )
UpperCamelCase__ :Tuple = datasets.load_dataset(__a , __a )
if save_dir is None:
UpperCamelCase__ :Any = f'''{dataset}-{pair}'''
UpperCamelCase__ :Dict = Path(__a )
save_dir.mkdir(exist_ok=__a )
for split in ds.keys():
print(f'''Splitting {split} with {ds[split].num_rows} records''' )
# to save to val.source, val.target like summary datasets
UpperCamelCase__ :Dict = '''val''' if split == '''validation''' else split
UpperCamelCase__ :List[Any] = save_dir.joinpath(f'''{fn}.source''' )
UpperCamelCase__ :int = save_dir.joinpath(f'''{fn}.target''' )
UpperCamelCase__ :Union[str, Any] = src_path.open('''w+''' )
UpperCamelCase__ :Tuple = tgt_path.open('''w+''' )
# reader is the bottleneck so writing one record at a time doesn't slow things down
for x in tqdm(ds[split] ):
UpperCamelCase__ :Union[str, Any] = x['''translation''']
src_fp.write(ex[src_lang] + '''\n''' )
tgt_fp.write(ex[tgt_lang] + '''\n''' )
print(f'''Saved {dataset} dataset to {save_dir}''' )
if __name__ == "__main__":
fire.Fire(download_wmt_dataset) | 97 | 0 |
import argparse
import os
import torch
from diffusers import (
CMStochasticIterativeScheduler,
ConsistencyModelPipeline,
UNetaDModel,
)
a ={
"""sample_size""": 32,
"""in_channels""": 3,
"""out_channels""": 3,
"""layers_per_block""": 2,
"""num_class_embeds""": 1000,
"""block_out_channels""": [32, 64],
"""attention_head_dim""": 8,
"""down_block_types""": [
"""ResnetDownsampleBlock2D""",
"""AttnDownBlock2D""",
],
"""up_block_types""": [
"""AttnUpBlock2D""",
"""ResnetUpsampleBlock2D""",
],
"""resnet_time_scale_shift""": """scale_shift""",
"""upsample_type""": """resnet""",
"""downsample_type""": """resnet""",
}
a ={
"""sample_size""": 64,
"""in_channels""": 3,
"""out_channels""": 3,
"""layers_per_block""": 3,
"""num_class_embeds""": 1000,
"""block_out_channels""": [192, 192 * 2, 192 * 3, 192 * 4],
"""attention_head_dim""": 64,
"""down_block_types""": [
"""ResnetDownsampleBlock2D""",
"""AttnDownBlock2D""",
"""AttnDownBlock2D""",
"""AttnDownBlock2D""",
],
"""up_block_types""": [
"""AttnUpBlock2D""",
"""AttnUpBlock2D""",
"""AttnUpBlock2D""",
"""ResnetUpsampleBlock2D""",
],
"""resnet_time_scale_shift""": """scale_shift""",
"""upsample_type""": """resnet""",
"""downsample_type""": """resnet""",
}
a ={
"""sample_size""": 256,
"""in_channels""": 3,
"""out_channels""": 3,
"""layers_per_block""": 2,
"""num_class_embeds""": None,
"""block_out_channels""": [256, 256, 256 * 2, 256 * 2, 256 * 4, 256 * 4],
"""attention_head_dim""": 64,
"""down_block_types""": [
"""ResnetDownsampleBlock2D""",
"""ResnetDownsampleBlock2D""",
"""ResnetDownsampleBlock2D""",
"""AttnDownBlock2D""",
"""AttnDownBlock2D""",
"""AttnDownBlock2D""",
],
"""up_block_types""": [
"""AttnUpBlock2D""",
"""AttnUpBlock2D""",
"""AttnUpBlock2D""",
"""ResnetUpsampleBlock2D""",
"""ResnetUpsampleBlock2D""",
"""ResnetUpsampleBlock2D""",
],
"""resnet_time_scale_shift""": """default""",
"""upsample_type""": """resnet""",
"""downsample_type""": """resnet""",
}
a ={
"""num_train_timesteps""": 40,
"""sigma_min""": 0.0_02,
"""sigma_max""": 80.0,
}
a ={
"""num_train_timesteps""": 201,
"""sigma_min""": 0.0_02,
"""sigma_max""": 80.0,
}
a ={
"""num_train_timesteps""": 151,
"""sigma_min""": 0.0_02,
"""sigma_max""": 80.0,
}
def SCREAMING_SNAKE_CASE__ ( lowerCamelCase__ ) -> Optional[int]:
if isinstance(lowerCamelCase__ , lowerCamelCase__ ):
return v
if v.lower() in ("yes", "true", "t", "y", "1"):
return True
elif v.lower() in ("no", "false", "f", "n", "0"):
return False
else:
raise argparse.ArgumentTypeError('boolean value expected' )
def SCREAMING_SNAKE_CASE__ ( lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__=False ) -> Tuple:
__lowerCamelCase : List[str] = checkpoint[F"{old_prefix}.in_layers.0.weight"]
__lowerCamelCase : Any = checkpoint[F"{old_prefix}.in_layers.0.bias"]
__lowerCamelCase : Optional[int] = checkpoint[F"{old_prefix}.in_layers.2.weight"]
__lowerCamelCase : Tuple = checkpoint[F"{old_prefix}.in_layers.2.bias"]
__lowerCamelCase : Optional[Any] = checkpoint[F"{old_prefix}.emb_layers.1.weight"]
__lowerCamelCase : List[Any] = checkpoint[F"{old_prefix}.emb_layers.1.bias"]
__lowerCamelCase : List[Any] = checkpoint[F"{old_prefix}.out_layers.0.weight"]
__lowerCamelCase : str = checkpoint[F"{old_prefix}.out_layers.0.bias"]
__lowerCamelCase : Optional[Any] = checkpoint[F"{old_prefix}.out_layers.3.weight"]
__lowerCamelCase : Optional[int] = checkpoint[F"{old_prefix}.out_layers.3.bias"]
if has_skip:
__lowerCamelCase : str = checkpoint[F"{old_prefix}.skip_connection.weight"]
__lowerCamelCase : Optional[int] = checkpoint[F"{old_prefix}.skip_connection.bias"]
return new_checkpoint
def SCREAMING_SNAKE_CASE__ ( lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__=None ) -> Tuple:
__lowerCamelCase , __lowerCamelCase , __lowerCamelCase : List[str] = checkpoint[F"{old_prefix}.qkv.weight"].chunk(3 , dim=0 )
__lowerCamelCase , __lowerCamelCase , __lowerCamelCase : List[Any] = checkpoint[F"{old_prefix}.qkv.bias"].chunk(3 , dim=0 )
__lowerCamelCase : int = checkpoint[F"{old_prefix}.norm.weight"]
__lowerCamelCase : List[Any] = checkpoint[F"{old_prefix}.norm.bias"]
__lowerCamelCase : Dict = weight_q.squeeze(-1 ).squeeze(-1 )
__lowerCamelCase : int = bias_q.squeeze(-1 ).squeeze(-1 )
__lowerCamelCase : Optional[Any] = weight_k.squeeze(-1 ).squeeze(-1 )
__lowerCamelCase : Any = bias_k.squeeze(-1 ).squeeze(-1 )
__lowerCamelCase : Tuple = weight_v.squeeze(-1 ).squeeze(-1 )
__lowerCamelCase : Any = bias_v.squeeze(-1 ).squeeze(-1 )
__lowerCamelCase : Union[str, Any] = (
checkpoint[F"{old_prefix}.proj_out.weight"].squeeze(-1 ).squeeze(-1 )
)
__lowerCamelCase : Union[str, Any] = checkpoint[F"{old_prefix}.proj_out.bias"].squeeze(-1 ).squeeze(-1 )
return new_checkpoint
def SCREAMING_SNAKE_CASE__ ( lowerCamelCase__ , lowerCamelCase__ ) -> List[Any]:
__lowerCamelCase : int = torch.load(lowerCamelCase__ , map_location='cpu' )
__lowerCamelCase : Optional[int] = {}
__lowerCamelCase : Dict = checkpoint['time_embed.0.weight']
__lowerCamelCase : Optional[Any] = checkpoint['time_embed.0.bias']
__lowerCamelCase : Dict = checkpoint['time_embed.2.weight']
__lowerCamelCase : int = checkpoint['time_embed.2.bias']
if unet_config["num_class_embeds"] is not None:
__lowerCamelCase : Optional[Any] = checkpoint['label_emb.weight']
__lowerCamelCase : str = checkpoint['input_blocks.0.0.weight']
__lowerCamelCase : List[Any] = checkpoint['input_blocks.0.0.bias']
__lowerCamelCase : Tuple = unet_config['down_block_types']
__lowerCamelCase : Optional[Any] = unet_config['layers_per_block']
__lowerCamelCase : Any = unet_config['attention_head_dim']
__lowerCamelCase : Any = unet_config['block_out_channels']
__lowerCamelCase : Union[str, Any] = 1
__lowerCamelCase : Tuple = channels_list[0]
for i, layer_type in enumerate(lowerCamelCase__ ):
__lowerCamelCase : str = channels_list[i]
__lowerCamelCase : List[str] = current_channels != prev_channels
if layer_type == "ResnetDownsampleBlock2D":
for j in range(lowerCamelCase__ ):
__lowerCamelCase : List[Any] = F"down_blocks.{i}.resnets.{j}"
__lowerCamelCase : int = F"input_blocks.{current_layer}.0"
__lowerCamelCase : int = True if j == 0 and downsample_block_has_skip else False
__lowerCamelCase : List[Any] = convert_resnet(lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , has_skip=lowerCamelCase__ )
current_layer += 1
elif layer_type == "AttnDownBlock2D":
for j in range(lowerCamelCase__ ):
__lowerCamelCase : Union[str, Any] = F"down_blocks.{i}.resnets.{j}"
__lowerCamelCase : Optional[int] = F"input_blocks.{current_layer}.0"
__lowerCamelCase : Optional[Any] = True if j == 0 and downsample_block_has_skip else False
__lowerCamelCase : List[Any] = convert_resnet(lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , has_skip=lowerCamelCase__ )
__lowerCamelCase : Any = F"down_blocks.{i}.attentions.{j}"
__lowerCamelCase : Union[str, Any] = F"input_blocks.{current_layer}.1"
__lowerCamelCase : List[Any] = convert_attention(
lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ )
current_layer += 1
if i != len(lowerCamelCase__ ) - 1:
__lowerCamelCase : Tuple = F"down_blocks.{i}.downsamplers.0"
__lowerCamelCase : Any = F"input_blocks.{current_layer}.0"
__lowerCamelCase : Any = convert_resnet(lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ )
current_layer += 1
__lowerCamelCase : Union[str, Any] = current_channels
# hardcoded the mid-block for now
__lowerCamelCase : Optional[Any] = 'mid_block.resnets.0'
__lowerCamelCase : Any = 'middle_block.0'
__lowerCamelCase : Any = convert_resnet(lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ )
__lowerCamelCase : str = 'mid_block.attentions.0'
__lowerCamelCase : Union[str, Any] = 'middle_block.1'
__lowerCamelCase : str = convert_attention(lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ )
__lowerCamelCase : Optional[Any] = 'mid_block.resnets.1'
__lowerCamelCase : Optional[int] = 'middle_block.2'
__lowerCamelCase : Union[str, Any] = convert_resnet(lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ )
__lowerCamelCase : str = 0
__lowerCamelCase : Union[str, Any] = unet_config['up_block_types']
for i, layer_type in enumerate(lowerCamelCase__ ):
if layer_type == "ResnetUpsampleBlock2D":
for j in range(layers_per_block + 1 ):
__lowerCamelCase : Optional[int] = F"up_blocks.{i}.resnets.{j}"
__lowerCamelCase : str = F"output_blocks.{current_layer}.0"
__lowerCamelCase : Union[str, Any] = convert_resnet(lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , has_skip=lowerCamelCase__ )
current_layer += 1
if i != len(lowerCamelCase__ ) - 1:
__lowerCamelCase : List[str] = F"up_blocks.{i}.upsamplers.0"
__lowerCamelCase : str = F"output_blocks.{current_layer-1}.1"
__lowerCamelCase : Dict = convert_resnet(lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ )
elif layer_type == "AttnUpBlock2D":
for j in range(layers_per_block + 1 ):
__lowerCamelCase : Dict = F"up_blocks.{i}.resnets.{j}"
__lowerCamelCase : int = F"output_blocks.{current_layer}.0"
__lowerCamelCase : Optional[int] = convert_resnet(lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , has_skip=lowerCamelCase__ )
__lowerCamelCase : List[str] = F"up_blocks.{i}.attentions.{j}"
__lowerCamelCase : Dict = F"output_blocks.{current_layer}.1"
__lowerCamelCase : Dict = convert_attention(
lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ )
current_layer += 1
if i != len(lowerCamelCase__ ) - 1:
__lowerCamelCase : int = F"up_blocks.{i}.upsamplers.0"
__lowerCamelCase : str = F"output_blocks.{current_layer-1}.2"
__lowerCamelCase : Optional[int] = convert_resnet(lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ )
__lowerCamelCase : Tuple = checkpoint['out.0.weight']
__lowerCamelCase : Dict = checkpoint['out.0.bias']
__lowerCamelCase : Optional[int] = checkpoint['out.2.weight']
__lowerCamelCase : List[str] = checkpoint['out.2.bias']
return new_checkpoint
if __name__ == "__main__":
a =argparse.ArgumentParser()
parser.add_argument("""--unet_path""", default=None, type=str, required=True, help="""Path to the unet.pt to convert.""")
parser.add_argument(
"""--dump_path""", default=None, type=str, required=True, help="""Path to output the converted UNet model."""
)
parser.add_argument("""--class_cond""", default=True, type=str, help="""Whether the model is class-conditional.""")
a =parser.parse_args()
a =strabool(args.class_cond)
a =os.path.basename(args.unet_path)
print(F"""Checkpoint: {ckpt_name}""")
# Get U-Net config
if "imagenet64" in ckpt_name:
a =IMAGENET_64_UNET_CONFIG
elif "256" in ckpt_name and (("bedroom" in ckpt_name) or ("cat" in ckpt_name)):
a =LSUN_256_UNET_CONFIG
elif "test" in ckpt_name:
a =TEST_UNET_CONFIG
else:
raise ValueError(F"""Checkpoint type {ckpt_name} is not currently supported.""")
if not args.class_cond:
a =None
a =con_pt_to_diffuser(args.unet_path, unet_config)
a =UNetaDModel(**unet_config)
image_unet.load_state_dict(converted_unet_ckpt)
# Get scheduler config
if "cd" in ckpt_name or "test" in ckpt_name:
a =CD_SCHEDULER_CONFIG
elif "ct" in ckpt_name and "imagenet64" in ckpt_name:
a =CT_IMAGENET_64_SCHEDULER_CONFIG
elif "ct" in ckpt_name and "256" in ckpt_name and (("bedroom" in ckpt_name) or ("cat" in ckpt_name)):
a =CT_LSUN_256_SCHEDULER_CONFIG
else:
raise ValueError(F"""Checkpoint type {ckpt_name} is not currently supported.""")
a =CMStochasticIterativeScheduler(**scheduler_config)
a =ConsistencyModelPipeline(unet=image_unet, scheduler=cm_scheduler)
consistency_model.save_pretrained(args.dump_path)
| 73 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...file_utils import _LazyModule, is_tokenizers_available, is_torch_available, is_vision_available
from ...utils import OptionalDependencyNotAvailable
__snake_case = {'''configuration_dpt''': ['''DPT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''DPTConfig''']}
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__snake_case = ['''DPTFeatureExtractor''']
__snake_case = ['''DPTImageProcessor''']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__snake_case = [
'''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
__snake_case = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__) | 97 | 0 |
"""simple docstring"""
from ...processing_utils import ProcessorMixin
class lowerCAmelCase_ ( _lowercase ):
'''simple docstring'''
_lowerCamelCase: Any = '''SpeechT5FeatureExtractor'''
_lowerCamelCase: List[Any] = '''SpeechT5Tokenizer'''
def __init__( self : Optional[Any] ,A_ : str ,A_ : List[str] ) -> Union[str, Any]:
super().__init__(A_ ,A_ )
def __call__( self : List[str] ,*A_ : Any ,**A_ : Union[str, Any] ) -> Dict:
A = kwargs.pop('audio' ,A_ )
A = kwargs.pop('text' ,A_ )
A = kwargs.pop('text_target' ,A_ )
A = kwargs.pop('audio_target' ,A_ )
A = kwargs.pop('sampling_rate' ,A_ )
if audio is not None and text is not None:
raise ValueError(
'Cannot process both `audio` and `text` inputs. Did you mean `audio_target` or `text_target`?' )
if audio_target is not None and text_target is not None:
raise ValueError(
'Cannot process both `audio_target` and `text_target` inputs. Did you mean `audio` or `text`?' )
if audio is None and audio_target is None and text is None and text_target is None:
raise ValueError(
'You need to specify either an `audio`, `audio_target`, `text`, or `text_target` input to process.' )
if audio is not None:
A = self.feature_extractor(A_ ,*A_ ,sampling_rate=A_ ,**A_ )
elif text is not None:
A = self.tokenizer(A_ ,**A_ )
else:
A = None
if audio_target is not None:
A = self.feature_extractor(audio_target=A_ ,*A_ ,sampling_rate=A_ ,**A_ )
A = targets['input_values']
elif text_target is not None:
A = self.tokenizer(A_ ,**A_ )
A = targets['input_ids']
else:
A = None
if inputs is None:
return targets
if targets is not None:
A = labels
A = targets.get('attention_mask' )
if decoder_attention_mask is not None:
A = decoder_attention_mask
return inputs
def _SCREAMING_SNAKE_CASE ( self : List[Any] ,*A_ : Optional[int] ,**A_ : Tuple ) -> int:
A = kwargs.pop('input_values' ,A_ )
A = kwargs.pop('input_ids' ,A_ )
A = kwargs.pop('labels' ,A_ )
if input_values is not None and input_ids is not None:
raise ValueError('Cannot process both `input_values` and `input_ids` inputs.' )
if input_values is None and input_ids is None and labels is None:
raise ValueError(
'You need to specify either an `input_values`, `input_ids`, or `labels` input to be padded.' )
if input_values is not None:
A = self.feature_extractor.pad(A_ ,*A_ ,**A_ )
elif input_ids is not None:
A = self.tokenizer.pad(A_ ,**A_ )
else:
A = None
if labels is not None:
if "input_ids" in labels or (isinstance(A_ ,A_ ) and "input_ids" in labels[0]):
A = self.tokenizer.pad(A_ ,**A_ )
A = targets['input_ids']
else:
A = self.feature_extractor.feature_size
A = self.feature_extractor.num_mel_bins
A = self.feature_extractor.pad(A_ ,*A_ ,**A_ )
A = feature_size_hack
A = targets['input_values']
else:
A = None
if inputs is None:
return targets
if targets is not None:
A = labels
A = targets.get('attention_mask' )
if decoder_attention_mask is not None:
A = decoder_attention_mask
return inputs
def _SCREAMING_SNAKE_CASE ( self : int ,*A_ : str ,**A_ : Any ) -> Dict:
return self.tokenizer.batch_decode(*A_ ,**A_ )
def _SCREAMING_SNAKE_CASE ( self : Dict ,*A_ : Any ,**A_ : Any ) -> Dict:
return self.tokenizer.decode(*A_ ,**A_ ) | 74 |
'''simple docstring'''
def a ( __a , __a ) -> int:
'''simple docstring'''
if len(__a ) != len(__a ):
raise ValueError('''String lengths must match!''' )
UpperCamelCase__ :Union[str, Any] = 0
for chara, chara in zip(__a , __a ):
if chara != chara:
count += 1
return count
if __name__ == "__main__":
import doctest
doctest.testmod() | 97 | 0 |
'''simple docstring'''
import shutil
import tempfile
import unittest
from unittest.mock import patch
from transformers import (
DefaultFlowCallback,
IntervalStrategy,
PrinterCallback,
ProgressCallback,
Trainer,
TrainerCallback,
TrainingArguments,
is_torch_available,
)
from transformers.testing_utils import require_torch
if is_torch_available():
from transformers.trainer import DEFAULT_CALLBACKS
from .test_trainer import RegressionDataset, RegressionModelConfig, RegressionPreTrainedModel
class __UpperCamelCase ( lowerCamelCase__ ):
def __init__( self ):
"""simple docstring"""
lowerCamelCase_ =[]
def lowercase__ ( self, lowerCAmelCase, lowerCAmelCase, lowerCAmelCase, **lowerCAmelCase ):
"""simple docstring"""
self.events.append('''on_init_end''' )
def lowercase__ ( self, lowerCAmelCase, lowerCAmelCase, lowerCAmelCase, **lowerCAmelCase ):
"""simple docstring"""
self.events.append('''on_train_begin''' )
def lowercase__ ( self, lowerCAmelCase, lowerCAmelCase, lowerCAmelCase, **lowerCAmelCase ):
"""simple docstring"""
self.events.append('''on_train_end''' )
def lowercase__ ( self, lowerCAmelCase, lowerCAmelCase, lowerCAmelCase, **lowerCAmelCase ):
"""simple docstring"""
self.events.append('''on_epoch_begin''' )
def lowercase__ ( self, lowerCAmelCase, lowerCAmelCase, lowerCAmelCase, **lowerCAmelCase ):
"""simple docstring"""
self.events.append('''on_epoch_end''' )
def lowercase__ ( self, lowerCAmelCase, lowerCAmelCase, lowerCAmelCase, **lowerCAmelCase ):
"""simple docstring"""
self.events.append('''on_step_begin''' )
def lowercase__ ( self, lowerCAmelCase, lowerCAmelCase, lowerCAmelCase, **lowerCAmelCase ):
"""simple docstring"""
self.events.append('''on_step_end''' )
def lowercase__ ( self, lowerCAmelCase, lowerCAmelCase, lowerCAmelCase, **lowerCAmelCase ):
"""simple docstring"""
self.events.append('''on_evaluate''' )
def lowercase__ ( self, lowerCAmelCase, lowerCAmelCase, lowerCAmelCase, **lowerCAmelCase ):
"""simple docstring"""
self.events.append('''on_predict''' )
def lowercase__ ( self, lowerCAmelCase, lowerCAmelCase, lowerCAmelCase, **lowerCAmelCase ):
"""simple docstring"""
self.events.append('''on_save''' )
def lowercase__ ( self, lowerCAmelCase, lowerCAmelCase, lowerCAmelCase, **lowerCAmelCase ):
"""simple docstring"""
self.events.append('''on_log''' )
def lowercase__ ( self, lowerCAmelCase, lowerCAmelCase, lowerCAmelCase, **lowerCAmelCase ):
"""simple docstring"""
self.events.append('''on_prediction_step''' )
@require_torch
class __UpperCamelCase ( unittest.TestCase ):
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ =tempfile.mkdtemp()
def lowercase__ ( self ):
"""simple docstring"""
shutil.rmtree(self.output_dir )
def lowercase__ ( self, lowerCAmelCase=0, lowerCAmelCase=0, lowerCAmelCase=64, lowerCAmelCase=64, lowerCAmelCase=None, lowerCAmelCase=False, **lowerCAmelCase ):
"""simple docstring"""
lowerCamelCase_ =RegressionDataset(length=lowerCAmelCase )
lowerCamelCase_ =RegressionDataset(length=lowerCAmelCase )
lowerCamelCase_ =RegressionModelConfig(a=lowerCAmelCase, b=lowerCAmelCase )
lowerCamelCase_ =RegressionPreTrainedModel(lowerCAmelCase )
lowerCamelCase_ =TrainingArguments(self.output_dir, disable_tqdm=lowerCAmelCase, report_to=[], **lowerCAmelCase )
return Trainer(
lowerCAmelCase, lowerCAmelCase, train_dataset=lowerCAmelCase, eval_dataset=lowerCAmelCase, callbacks=lowerCAmelCase, )
def lowercase__ ( self, lowerCAmelCase, lowerCAmelCase ):
"""simple docstring"""
self.assertEqual(len(lowerCAmelCase ), len(lowerCAmelCase ) )
# Order doesn't matter
lowerCamelCase_ =sorted(lowerCAmelCase, key=lambda lowerCAmelCase : cb.__name__ if isinstance(lowerCAmelCase, lowerCAmelCase ) else cb.__class__.__name__ )
lowerCamelCase_ =sorted(lowerCAmelCase, key=lambda lowerCAmelCase : cb.__name__ if isinstance(lowerCAmelCase, lowerCAmelCase ) else cb.__class__.__name__ )
for cba, cba in zip(lowerCAmelCase, lowerCAmelCase ):
if isinstance(lowerCAmelCase, lowerCAmelCase ) and isinstance(lowerCAmelCase, lowerCAmelCase ):
self.assertEqual(lowerCAmelCase, lowerCAmelCase )
elif isinstance(lowerCAmelCase, lowerCAmelCase ) and not isinstance(lowerCAmelCase, lowerCAmelCase ):
self.assertEqual(lowerCAmelCase, cba.__class__ )
elif not isinstance(lowerCAmelCase, lowerCAmelCase ) and isinstance(lowerCAmelCase, lowerCAmelCase ):
self.assertEqual(cba.__class__, lowerCAmelCase )
else:
self.assertEqual(lowerCAmelCase, lowerCAmelCase )
def lowercase__ ( self, lowerCAmelCase ):
"""simple docstring"""
lowerCamelCase_ =['''on_init_end''', '''on_train_begin''']
lowerCamelCase_ =0
lowerCamelCase_ =len(trainer.get_eval_dataloader() )
lowerCamelCase_ =['''on_prediction_step'''] * len(trainer.get_eval_dataloader() ) + ['''on_log''', '''on_evaluate''']
for _ in range(trainer.state.num_train_epochs ):
expected_events.append('''on_epoch_begin''' )
for _ in range(lowerCAmelCase ):
step += 1
expected_events += ["on_step_begin", "on_step_end"]
if step % trainer.args.logging_steps == 0:
expected_events.append('''on_log''' )
if trainer.args.evaluation_strategy == IntervalStrategy.STEPS and step % trainer.args.eval_steps == 0:
expected_events += evaluation_events.copy()
if step % trainer.args.save_steps == 0:
expected_events.append('''on_save''' )
expected_events.append('''on_epoch_end''' )
if trainer.args.evaluation_strategy == IntervalStrategy.EPOCH:
expected_events += evaluation_events.copy()
expected_events += ["on_log", "on_train_end"]
return expected_events
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ =self.get_trainer()
lowerCamelCase_ =DEFAULT_CALLBACKS.copy() + [ProgressCallback]
self.check_callbacks_equality(trainer.callback_handler.callbacks, lowerCAmelCase )
# Callbacks passed at init are added to the default callbacks
lowerCamelCase_ =self.get_trainer(callbacks=[MyTestTrainerCallback] )
expected_callbacks.append(lowerCAmelCase )
self.check_callbacks_equality(trainer.callback_handler.callbacks, lowerCAmelCase )
# TrainingArguments.disable_tqdm controls if use ProgressCallback or PrinterCallback
lowerCamelCase_ =self.get_trainer(disable_tqdm=lowerCAmelCase )
lowerCamelCase_ =DEFAULT_CALLBACKS.copy() + [PrinterCallback]
self.check_callbacks_equality(trainer.callback_handler.callbacks, lowerCAmelCase )
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ =DEFAULT_CALLBACKS.copy() + [ProgressCallback]
lowerCamelCase_ =self.get_trainer()
# We can add, pop, or remove by class name
trainer.remove_callback(lowerCAmelCase )
expected_callbacks.remove(lowerCAmelCase )
self.check_callbacks_equality(trainer.callback_handler.callbacks, lowerCAmelCase )
lowerCamelCase_ =self.get_trainer()
lowerCamelCase_ =trainer.pop_callback(lowerCAmelCase )
self.assertEqual(cb.__class__, lowerCAmelCase )
self.check_callbacks_equality(trainer.callback_handler.callbacks, lowerCAmelCase )
trainer.add_callback(lowerCAmelCase )
expected_callbacks.insert(0, lowerCAmelCase )
self.check_callbacks_equality(trainer.callback_handler.callbacks, lowerCAmelCase )
# We can also add, pop, or remove by instance
lowerCamelCase_ =self.get_trainer()
lowerCamelCase_ =trainer.callback_handler.callbacks[0]
trainer.remove_callback(lowerCAmelCase )
expected_callbacks.remove(lowerCAmelCase )
self.check_callbacks_equality(trainer.callback_handler.callbacks, lowerCAmelCase )
lowerCamelCase_ =self.get_trainer()
lowerCamelCase_ =trainer.callback_handler.callbacks[0]
lowerCamelCase_ =trainer.pop_callback(lowerCAmelCase )
self.assertEqual(lowerCAmelCase, lowerCAmelCase )
self.check_callbacks_equality(trainer.callback_handler.callbacks, lowerCAmelCase )
trainer.add_callback(lowerCAmelCase )
expected_callbacks.insert(0, lowerCAmelCase )
self.check_callbacks_equality(trainer.callback_handler.callbacks, lowerCAmelCase )
def lowercase__ ( self ):
"""simple docstring"""
import warnings
# XXX: for now ignore scatter_gather warnings in this test since it's not relevant to what's being tested
warnings.simplefilter(action='''ignore''', category=lowerCAmelCase )
lowerCamelCase_ =self.get_trainer(callbacks=[MyTestTrainerCallback] )
trainer.train()
lowerCamelCase_ =trainer.callback_handler.callbacks[-2].events
self.assertEqual(lowerCAmelCase, self.get_expected_events(lowerCAmelCase ) )
# Independent log/save/eval
lowerCamelCase_ =self.get_trainer(callbacks=[MyTestTrainerCallback], logging_steps=5 )
trainer.train()
lowerCamelCase_ =trainer.callback_handler.callbacks[-2].events
self.assertEqual(lowerCAmelCase, self.get_expected_events(lowerCAmelCase ) )
lowerCamelCase_ =self.get_trainer(callbacks=[MyTestTrainerCallback], save_steps=5 )
trainer.train()
lowerCamelCase_ =trainer.callback_handler.callbacks[-2].events
self.assertEqual(lowerCAmelCase, self.get_expected_events(lowerCAmelCase ) )
lowerCamelCase_ =self.get_trainer(callbacks=[MyTestTrainerCallback], eval_steps=5, evaluation_strategy='''steps''' )
trainer.train()
lowerCamelCase_ =trainer.callback_handler.callbacks[-2].events
self.assertEqual(lowerCAmelCase, self.get_expected_events(lowerCAmelCase ) )
lowerCamelCase_ =self.get_trainer(callbacks=[MyTestTrainerCallback], evaluation_strategy='''epoch''' )
trainer.train()
lowerCamelCase_ =trainer.callback_handler.callbacks[-2].events
self.assertEqual(lowerCAmelCase, self.get_expected_events(lowerCAmelCase ) )
# A bit of everything
lowerCamelCase_ =self.get_trainer(
callbacks=[MyTestTrainerCallback], logging_steps=3, save_steps=10, eval_steps=5, evaluation_strategy='''steps''', )
trainer.train()
lowerCamelCase_ =trainer.callback_handler.callbacks[-2].events
self.assertEqual(lowerCAmelCase, self.get_expected_events(lowerCAmelCase ) )
# warning should be emitted for duplicated callbacks
with patch('''transformers.trainer_callback.logger.warning''' ) as warn_mock:
lowerCamelCase_ =self.get_trainer(
callbacks=[MyTestTrainerCallback, MyTestTrainerCallback], )
assert str(lowerCAmelCase ) in warn_mock.call_args[0][0]
| 75 |
'''simple docstring'''
def a ( __a ) -> "list[int]":
'''simple docstring'''
if upper_limit < 0:
raise ValueError('''Limit for the Catalan sequence must be ≥ 0''' )
UpperCamelCase__ :Optional[Any] = [0] * (upper_limit + 1)
# Base case: C(0) = C(1) = 1
UpperCamelCase__ :int = 1
if upper_limit > 0:
UpperCamelCase__ :int = 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(__a ):
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:
__snake_case = 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() | 97 | 0 |
import os
from collections.abc import Iterator
def lowerCamelCase__ ( _a = "."):
for dir_path, dir_names, filenames in os.walk(_a):
SCREAMING_SNAKE_CASE : Dict = [d for d in dir_names if d != "scripts" and d[0] not in "._"]
for filename in filenames:
if filename == "__init__.py":
continue
if os.path.splitext(_a)[1] in (".py", ".ipynb"):
yield os.path.join(_a , _a).lstrip("./")
def lowerCamelCase__ ( _a):
return f"{i * ' '}*" if i else "\n##"
def lowerCamelCase__ ( _a , _a):
SCREAMING_SNAKE_CASE : int = old_path.split(os.sep)
for i, new_part in enumerate(new_path.split(os.sep)):
if (i + 1 > len(_a) or old_parts[i] != new_part) and new_part:
print(f"{md_prefix(_a)} {new_part.replace('_' , ' ').title()}")
return new_path
def lowerCamelCase__ ( _a = "."):
SCREAMING_SNAKE_CASE : Dict = ""
for filepath in sorted(good_file_paths(_a)):
SCREAMING_SNAKE_CASE ,SCREAMING_SNAKE_CASE : Optional[int] = os.path.split(_a)
if filepath != old_path:
SCREAMING_SNAKE_CASE : Optional[int] = print_path(_a , _a)
SCREAMING_SNAKE_CASE : Dict = (filepath.count(os.sep) + 1) if filepath else 0
SCREAMING_SNAKE_CASE : int = f"{filepath}/{filename}".replace(" " , "%20")
SCREAMING_SNAKE_CASE : str = os.path.splitext(filename.replace("_" , " ").title())[0]
print(f"{md_prefix(_a)} [{filename}]({url})")
if __name__ == "__main__":
print_directory_md('.') | 76 |
'''simple docstring'''
import io
import json
import fsspec
import pytest
from datasets import Dataset, DatasetDict, Features, NamedSplit, Value
from datasets.io.json import JsonDatasetReader, JsonDatasetWriter
from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases
def a ( __a , __a ) -> Optional[int]:
'''simple docstring'''
assert isinstance(__a , __a )
assert dataset.num_rows == 4
assert dataset.num_columns == 3
assert dataset.column_names == ["col_1", "col_2", "col_3"]
for feature, expected_dtype in expected_features.items():
assert dataset.features[feature].dtype == expected_dtype
@pytest.mark.parametrize('''keep_in_memory''' , [False, True] )
def a ( __a , __a , __a ) -> Optional[Any]:
'''simple docstring'''
UpperCamelCase__ :Union[str, Any] = tmp_path / '''cache'''
UpperCamelCase__ :Dict = {'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
UpperCamelCase__ :Tuple = JsonDatasetReader(__a , cache_dir=__a , keep_in_memory=__a ).read()
_check_json_dataset(__a , __a )
@pytest.mark.parametrize(
'''features''' , [
None,
{'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''},
{'''col_1''': '''string''', '''col_2''': '''string''', '''col_3''': '''string'''},
{'''col_1''': '''int32''', '''col_2''': '''int32''', '''col_3''': '''int32'''},
{'''col_1''': '''float32''', '''col_2''': '''float32''', '''col_3''': '''float32'''},
] , )
def a ( __a , __a , __a ) -> Any:
'''simple docstring'''
UpperCamelCase__ :Union[str, Any] = tmp_path / '''cache'''
UpperCamelCase__ :Optional[Any] = {'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
UpperCamelCase__ :Optional[Any] = features.copy() if features else default_expected_features
UpperCamelCase__ :Tuple = (
Features({feature: Value(__a ) for feature, dtype in features.items()} ) if features is not None else None
)
UpperCamelCase__ :int = JsonDatasetReader(__a , features=__a , cache_dir=__a ).read()
_check_json_dataset(__a , __a )
@pytest.mark.parametrize(
'''features''' , [
None,
{'''col_3''': '''float64''', '''col_1''': '''string''', '''col_2''': '''int64'''},
] , )
def a ( __a , __a , __a ) -> Tuple:
'''simple docstring'''
UpperCamelCase__ :int = tmp_path / '''cache'''
UpperCamelCase__ :str = {'''col_3''': '''float64''', '''col_1''': '''string''', '''col_2''': '''int64'''}
UpperCamelCase__ :Any = features.copy() if features else default_expected_features
UpperCamelCase__ :Union[str, Any] = (
Features({feature: Value(__a ) for feature, dtype in features.items()} ) if features is not None else None
)
UpperCamelCase__ :Any = JsonDatasetReader(__a , features=__a , cache_dir=__a ).read()
assert isinstance(__a , __a )
assert dataset.num_rows == 2
assert dataset.num_columns == 3
assert dataset.column_names == ["col_3", "col_1", "col_2"]
for feature, expected_dtype in expected_features.items():
assert dataset.features[feature].dtype == expected_dtype
def a ( __a , __a ) -> List[Any]:
'''simple docstring'''
UpperCamelCase__ :Any = {'''col_2''': '''int64''', '''col_3''': '''float64''', '''col_1''': '''string'''}
UpperCamelCase__ :int = features.copy()
UpperCamelCase__ :List[Any] = (
Features({feature: Value(__a ) for feature, dtype in features.items()} ) if features is not None else None
)
UpperCamelCase__ :Optional[int] = tmp_path / '''cache'''
UpperCamelCase__ :Dict = JsonDatasetReader(__a , features=__a , cache_dir=__a ).read()
assert isinstance(__a , __a )
assert dataset.num_rows == 2
assert dataset.num_columns == 3
assert dataset.column_names == ["col_2", "col_3", "col_1"]
for feature, expected_dtype in expected_features.items():
assert dataset.features[feature].dtype == expected_dtype
@pytest.mark.parametrize('''split''' , [None, NamedSplit('''train''' ), '''train''', '''test'''] )
def a ( __a , __a , __a ) -> List[Any]:
'''simple docstring'''
UpperCamelCase__ :Union[str, Any] = tmp_path / '''cache'''
UpperCamelCase__ :Optional[Any] = {'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
UpperCamelCase__ :List[Any] = JsonDatasetReader(__a , cache_dir=__a , split=__a ).read()
_check_json_dataset(__a , __a )
assert dataset.split == split if split else "train"
@pytest.mark.parametrize('''path_type''' , [str, list] )
def a ( __a , __a , __a ) -> Any:
'''simple docstring'''
if issubclass(__a , __a ):
UpperCamelCase__ :Union[str, Any] = jsonl_path
elif issubclass(__a , __a ):
UpperCamelCase__ :int = [jsonl_path]
UpperCamelCase__ :Dict = tmp_path / '''cache'''
UpperCamelCase__ :Any = {'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
UpperCamelCase__ :List[str] = JsonDatasetReader(__a , cache_dir=__a ).read()
_check_json_dataset(__a , __a )
def a ( __a , __a , __a=("train",) ) -> Optional[Any]:
'''simple docstring'''
assert isinstance(__a , __a )
for split in splits:
UpperCamelCase__ :Optional[int] = dataset_dict[split]
assert dataset.num_rows == 4
assert dataset.num_columns == 3
assert dataset.column_names == ["col_1", "col_2", "col_3"]
for feature, expected_dtype in expected_features.items():
assert dataset.features[feature].dtype == expected_dtype
@pytest.mark.parametrize('''keep_in_memory''' , [False, True] )
def a ( __a , __a , __a ) -> List[str]:
'''simple docstring'''
UpperCamelCase__ :List[str] = tmp_path / '''cache'''
UpperCamelCase__ :Dict = {'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
UpperCamelCase__ :str = JsonDatasetReader({'''train''': jsonl_path} , cache_dir=__a , keep_in_memory=__a ).read()
_check_json_datasetdict(__a , __a )
@pytest.mark.parametrize(
'''features''' , [
None,
{'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''},
{'''col_1''': '''string''', '''col_2''': '''string''', '''col_3''': '''string'''},
{'''col_1''': '''int32''', '''col_2''': '''int32''', '''col_3''': '''int32'''},
{'''col_1''': '''float32''', '''col_2''': '''float32''', '''col_3''': '''float32'''},
] , )
def a ( __a , __a , __a ) -> int:
'''simple docstring'''
UpperCamelCase__ :Tuple = tmp_path / '''cache'''
UpperCamelCase__ :Any = {'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
UpperCamelCase__ :Optional[int] = features.copy() if features else default_expected_features
UpperCamelCase__ :str = (
Features({feature: Value(__a ) for feature, dtype in features.items()} ) if features is not None else None
)
UpperCamelCase__ :Dict = JsonDatasetReader({'''train''': jsonl_path} , features=__a , cache_dir=__a ).read()
_check_json_datasetdict(__a , __a )
@pytest.mark.parametrize('''split''' , [None, NamedSplit('''train''' ), '''train''', '''test'''] )
def a ( __a , __a , __a ) -> str:
'''simple docstring'''
if split:
UpperCamelCase__ :List[str] = {split: jsonl_path}
else:
UpperCamelCase__ :int = '''train'''
UpperCamelCase__ :int = {'''train''': jsonl_path, '''test''': jsonl_path}
UpperCamelCase__ :Any = tmp_path / '''cache'''
UpperCamelCase__ :Union[str, Any] = {'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
UpperCamelCase__ :Any = JsonDatasetReader(__a , cache_dir=__a ).read()
_check_json_datasetdict(__a , __a , splits=list(path.keys() ) )
assert all(dataset[split].split == split for split in path.keys() )
def a ( __a ) -> Union[str, Any]:
'''simple docstring'''
return json.load(__a )
def a ( __a ) -> int:
'''simple docstring'''
return [json.loads(__a ) for line in buffer]
class lowercase :
"""simple docstring"""
@pytest.mark.parametrize('''lines, load_json_function''' , [(True, load_json_lines), (False, load_json)] )
def lowerCAmelCase__ ( self , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ ):
'''simple docstring'''
with io.BytesIO() as buffer:
JsonDatasetWriter(UpperCamelCase_ , UpperCamelCase_ , lines=UpperCamelCase_ ).write()
buffer.seek(0 )
UpperCamelCase__ :List[Any] = load_json_function(UpperCamelCase_ )
assert isinstance(UpperCamelCase_ , UpperCamelCase_ )
assert isinstance(exported_content[0] , UpperCamelCase_ )
assert len(UpperCamelCase_ ) == 10
@pytest.mark.parametrize(
'''orient, container, keys, len_at''' , [
('''records''', list, {'''tokens''', '''labels''', '''answers''', '''id'''}, None),
('''split''', dict, {'''columns''', '''data'''}, '''data'''),
('''index''', dict, set('''0123456789''' ), None),
('''columns''', dict, {'''tokens''', '''labels''', '''answers''', '''id'''}, '''tokens'''),
('''values''', list, None, None),
('''table''', dict, {'''schema''', '''data'''}, '''data'''),
] , )
def lowerCAmelCase__ ( self , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ ):
'''simple docstring'''
with io.BytesIO() as buffer:
JsonDatasetWriter(UpperCamelCase_ , UpperCamelCase_ , lines=UpperCamelCase_ , orient=UpperCamelCase_ ).write()
buffer.seek(0 )
UpperCamelCase__ :Optional[int] = load_json(UpperCamelCase_ )
assert isinstance(UpperCamelCase_ , UpperCamelCase_ )
if keys:
if container is dict:
assert exported_content.keys() == keys
else:
assert exported_content[0].keys() == keys
else:
assert not hasattr(UpperCamelCase_ , '''keys''' ) and not hasattr(exported_content[0] , '''keys''' )
if len_at:
assert len(exported_content[len_at] ) == 10
else:
assert len(UpperCamelCase_ ) == 10
@pytest.mark.parametrize('''lines, load_json_function''' , [(True, load_json_lines), (False, load_json)] )
def lowerCAmelCase__ ( self , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ ):
'''simple docstring'''
with io.BytesIO() as buffer:
JsonDatasetWriter(UpperCamelCase_ , UpperCamelCase_ , lines=UpperCamelCase_ , num_proc=2 ).write()
buffer.seek(0 )
UpperCamelCase__ :Union[str, Any] = load_json_function(UpperCamelCase_ )
assert isinstance(UpperCamelCase_ , UpperCamelCase_ )
assert isinstance(exported_content[0] , UpperCamelCase_ )
assert len(UpperCamelCase_ ) == 10
@pytest.mark.parametrize(
'''orient, container, keys, len_at''' , [
('''records''', list, {'''tokens''', '''labels''', '''answers''', '''id'''}, None),
('''split''', dict, {'''columns''', '''data'''}, '''data'''),
('''index''', dict, set('''0123456789''' ), None),
('''columns''', dict, {'''tokens''', '''labels''', '''answers''', '''id'''}, '''tokens'''),
('''values''', list, None, None),
('''table''', dict, {'''schema''', '''data'''}, '''data'''),
] , )
def lowerCAmelCase__ ( self , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ ):
'''simple docstring'''
with io.BytesIO() as buffer:
JsonDatasetWriter(UpperCamelCase_ , UpperCamelCase_ , lines=UpperCamelCase_ , orient=UpperCamelCase_ , num_proc=2 ).write()
buffer.seek(0 )
UpperCamelCase__ :int = load_json(UpperCamelCase_ )
assert isinstance(UpperCamelCase_ , UpperCamelCase_ )
if keys:
if container is dict:
assert exported_content.keys() == keys
else:
assert exported_content[0].keys() == keys
else:
assert not hasattr(UpperCamelCase_ , '''keys''' ) and not hasattr(exported_content[0] , '''keys''' )
if len_at:
assert len(exported_content[len_at] ) == 10
else:
assert len(UpperCamelCase_ ) == 10
def lowerCAmelCase__ ( self , UpperCamelCase_ ):
'''simple docstring'''
with pytest.raises(UpperCamelCase_ ):
with io.BytesIO() as buffer:
JsonDatasetWriter(UpperCamelCase_ , UpperCamelCase_ , num_proc=0 )
@pytest.mark.parametrize('''compression, extension''' , [('''gzip''', '''gz'''), ('''bz2''', '''bz2'''), ('''xz''', '''xz''')] )
def lowerCAmelCase__ ( self , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ ):
'''simple docstring'''
UpperCamelCase__ :Tuple = tmp_path_factory.mktemp('''data''' ) / F'''test.json.{extension}'''
UpperCamelCase__ :Union[str, Any] = str(shared_datadir / F'''test_file.json.{extension}''' )
JsonDatasetWriter(UpperCamelCase_ , UpperCamelCase_ , compression=UpperCamelCase_ ).write()
with fsspec.open(UpperCamelCase_ , '''rb''' , compression='''infer''' ) as f:
UpperCamelCase__ :Dict = f.read()
with fsspec.open(UpperCamelCase_ , '''rb''' , compression='''infer''' ) as f:
UpperCamelCase__ :int = f.read()
assert exported_content == original_content | 97 | 0 |
"""simple docstring"""
from collections import defaultdict
from math import gcd
def a_ ( _lowerCAmelCase : int = 150_0000 ):
'''simple docstring'''
lowercase__ : defaultdict = defaultdict(_lowerCAmelCase )
lowercase__ : int = 2
while 2 * euclid_m * (euclid_m + 1) <= limit:
for euclid_n in range((euclid_m % 2) + 1 , _lowerCAmelCase , 2 ):
if gcd(_lowerCAmelCase , _lowerCAmelCase ) > 1:
continue
lowercase__ : Tuple = 2 * euclid_m * (euclid_m + euclid_n)
for perimeter in range(_lowerCAmelCase , limit + 1 , _lowerCAmelCase ):
frequencies[perimeter] += 1
euclid_m += 1
return sum(1 for frequency in frequencies.values() if frequency == 1 )
if __name__ == "__main__":
print(f'''{solution() = }''')
| 77 |
'''simple docstring'''
import unittest
from dataclasses import dataclass
import pytest
from accelerate.commands.config.config_args import SageMakerConfig
from accelerate.utils import ComputeEnvironment
from accelerate.utils.launch import _convert_nargs_to_dict
@dataclass
class lowercase ( A__ ):
"""simple docstring"""
_a = ComputeEnvironment.AMAZON_SAGEMAKER
_a = True
_a = 'ml.p3.2xlarge'
_a = 'accelerate_sagemaker_execution_role'
_a = 'hf-sm'
_a = 'us-east-1'
_a = 1
_a = 'accelerate-sagemaker-1'
_a = '1.6'
_a = '4.4'
_a = 'train.py'
_a = [
'--model_name_or_path',
'bert',
'--do_train',
'False',
'--epochs',
'3',
'--learning_rate',
'5e-5',
'--max_steps',
'50.5',
]
_a = [
'--model_name_or_path',
'bert',
'--do_train',
'--do_test',
'False',
'--do_predict',
'--epochs',
'3',
'--learning_rate',
'5e-5',
'--max_steps',
'50.5',
]
class lowercase ( unittest.TestCase ):
"""simple docstring"""
def lowerCAmelCase__ ( self ):
'''simple docstring'''
UpperCamelCase__ :Union[str, Any] = _convert_nargs_to_dict(MockLaunchConfig.success_training_script_args )
assert isinstance(converted_args['''model_name_or_path'''] , UpperCamelCase_ )
assert isinstance(converted_args['''do_train'''] , UpperCamelCase_ )
assert isinstance(converted_args['''epochs'''] , UpperCamelCase_ )
assert isinstance(converted_args['''learning_rate'''] , UpperCamelCase_ )
assert isinstance(converted_args['''max_steps'''] , UpperCamelCase_ )
with pytest.raises(UpperCamelCase_ ):
_convert_nargs_to_dict(MockLaunchConfig.fail_training_script_args ) | 97 | 0 |
"""simple docstring"""
from PIL import Image
def _lowerCAmelCase ( lowercase_ , lowercase_ ):
UpperCAmelCase = (259 * (level + 255)) / (255 * (259 - level))
def contrast(lowercase_ ) -> int:
return int(128 + factor * (c - 128) )
return img.point(lowercase_ )
if __name__ == "__main__":
# Load image
with Image.open("""image_data/lena.jpg""") as img:
# Change contrast to 170
snake_case_ = change_contrast(img, 170)
cont_img.save("""image_data/lena_high_contrast.png""", format="""png""")
| 78 |
'''simple docstring'''
from datetime import datetime
import matplotlib.pyplot as plt
import torch
def a ( __a ) -> int:
'''simple docstring'''
for param in module.parameters():
UpperCamelCase__ :Dict = False
def a ( ) -> Union[str, Any]:
'''simple docstring'''
UpperCamelCase__ :List[Any] = '''cuda''' if torch.cuda.is_available() else '''cpu'''
if torch.backends.mps.is_available() and torch.backends.mps.is_built():
UpperCamelCase__ :Optional[int] = '''mps'''
if device == "mps":
print(
'''WARNING: MPS currently doesn\'t seem to work, and messes up backpropagation without any visible torch'''
''' errors. I recommend using CUDA on a colab notebook or CPU instead if you\'re facing inexplicable issues'''
''' with generations.''' )
return device
def a ( __a ) -> Any:
'''simple docstring'''
UpperCamelCase__ :Dict = plt.imshow(__a )
fig.axes.get_xaxis().set_visible(__a )
fig.axes.get_yaxis().set_visible(__a )
plt.show()
def a ( ) -> str:
'''simple docstring'''
UpperCamelCase__ :int = datetime.now()
UpperCamelCase__ :str = current_time.strftime('''%H:%M:%S''' )
return timestamp | 97 | 0 |
'''simple docstring'''
import collections
import gzip
import os
import urllib
import numpy
from tensorflow.python.framework import dtypes, random_seed
from tensorflow.python.platform import gfile
from tensorflow.python.util.deprecation import deprecated
lowerCamelCase_ = collections.namedtuple('''_Datasets''', ['''train''', '''validation''', '''test'''])
# CVDF mirror of http://yann.lecun.com/exdb/mnist/
lowerCamelCase_ = '''https://storage.googleapis.com/cvdf-datasets/mnist/'''
def __lowercase ( __lowercase ) -> Optional[Any]:
'''simple docstring'''
_A = numpy.dtype(numpy.uintaa ).newbyteorder(">" )
return numpy.frombuffer(bytestream.read(4 ) , dtype=__lowercase )[0]
@deprecated(__lowercase , "Please use tf.data to implement this functionality." )
def __lowercase ( __lowercase ) -> List[Any]:
'''simple docstring'''
print("Extracting" , f.name )
with gzip.GzipFile(fileobj=__lowercase ) as bytestream:
_A = _readaa(__lowercase )
if magic != 2051:
raise ValueError(
"Invalid magic number %d in MNIST image file: %s" % (magic, f.name) )
_A = _readaa(__lowercase )
_A = _readaa(__lowercase )
_A = _readaa(__lowercase )
_A = bytestream.read(rows * cols * num_images )
_A = numpy.frombuffer(__lowercase , dtype=numpy.uinta )
_A = data.reshape(__lowercase , __lowercase , __lowercase , 1 )
return data
@deprecated(__lowercase , "Please use tf.one_hot on tensors." )
def __lowercase ( __lowercase , __lowercase ) -> int:
'''simple docstring'''
_A = labels_dense.shape[0]
_A = numpy.arange(__lowercase ) * num_classes
_A = numpy.zeros((num_labels, num_classes) )
_A = 1
return labels_one_hot
@deprecated(__lowercase , "Please use tf.data to implement this functionality." )
def __lowercase ( __lowercase , __lowercase=False , __lowercase=10 ) -> List[Any]:
'''simple docstring'''
print("Extracting" , f.name )
with gzip.GzipFile(fileobj=__lowercase ) as bytestream:
_A = _readaa(__lowercase )
if magic != 2049:
raise ValueError(
"Invalid magic number %d in MNIST label file: %s" % (magic, f.name) )
_A = _readaa(__lowercase )
_A = bytestream.read(__lowercase )
_A = numpy.frombuffer(__lowercase , dtype=numpy.uinta )
if one_hot:
return _dense_to_one_hot(__lowercase , __lowercase )
return labels
class _UpperCAmelCase :
"""simple docstring"""
@deprecated(
__UpperCAmelCase , "Please use alternatives such as official/mnist/_DataSet.py"
" from tensorflow/models." , )
def __init__( self : str , __UpperCAmelCase : int , __UpperCAmelCase : Optional[int] , __UpperCAmelCase : List[str]=False , __UpperCAmelCase : List[str]=False , __UpperCAmelCase : Optional[Any]=dtypes.floataa , __UpperCAmelCase : Tuple=True , __UpperCAmelCase : Optional[int]=None , ):
'''simple docstring'''
_A , _A = random_seed.get_seed(__UpperCAmelCase )
# If op level seed is not set, use whatever graph level seed is returned
numpy.random.seed(seeda if seed is None else seeda )
_A = dtypes.as_dtype(__UpperCAmelCase ).base_dtype
if dtype not in (dtypes.uinta, dtypes.floataa):
raise TypeError("Invalid image dtype %r, expected uint8 or float32" % dtype )
if fake_data:
_A = 10000
_A = one_hot
else:
assert (
images.shape[0] == labels.shape[0]
), f'''images.shape: {images.shape} labels.shape: {labels.shape}'''
_A = images.shape[0]
# Convert shape from [num examples, rows, columns, depth]
# to [num examples, rows*columns] (assuming depth == 1)
if reshape:
assert images.shape[3] == 1
_A = images.reshape(
images.shape[0] , images.shape[1] * images.shape[2] )
if dtype == dtypes.floataa:
# Convert from [0, 255] -> [0.0, 1.0].
_A = images.astype(numpy.floataa )
_A = numpy.multiply(__UpperCAmelCase , 1.0 / 255.0 )
_A = images
_A = labels
_A = 0
_A = 0
@property
def lowerCAmelCase ( self : int ):
'''simple docstring'''
return self._images
@property
def lowerCAmelCase ( self : Optional[int] ):
'''simple docstring'''
return self._labels
@property
def lowerCAmelCase ( self : Union[str, Any] ):
'''simple docstring'''
return self._num_examples
@property
def lowerCAmelCase ( self : Optional[int] ):
'''simple docstring'''
return self._epochs_completed
def lowerCAmelCase ( self : Any , __UpperCAmelCase : List[str] , __UpperCAmelCase : Any=False , __UpperCAmelCase : int=True ):
'''simple docstring'''
if fake_data:
_A = [1] * 784
_A = [1] + [0] * 9 if self.one_hot else 0
return (
[fake_image for _ in range(__UpperCAmelCase )],
[fake_label for _ in range(__UpperCAmelCase )],
)
_A = self._index_in_epoch
# Shuffle for the first epoch
if self._epochs_completed == 0 and start == 0 and shuffle:
_A = numpy.arange(self._num_examples )
numpy.random.shuffle(__UpperCAmelCase )
_A = self.images[perma]
_A = self.labels[perma]
# Go to the next epoch
if start + batch_size > self._num_examples:
# Finished epoch
self._epochs_completed += 1
# Get the rest examples in this epoch
_A = self._num_examples - start
_A = self._images[start : self._num_examples]
_A = self._labels[start : self._num_examples]
# Shuffle the data
if shuffle:
_A = numpy.arange(self._num_examples )
numpy.random.shuffle(__UpperCAmelCase )
_A = self.images[perm]
_A = self.labels[perm]
# Start next epoch
_A = 0
_A = batch_size - rest_num_examples
_A = self._index_in_epoch
_A = self._images[start:end]
_A = self._labels[start:end]
return (
numpy.concatenate((images_rest_part, images_new_part) , axis=0 ),
numpy.concatenate((labels_rest_part, labels_new_part) , axis=0 ),
)
else:
self._index_in_epoch += batch_size
_A = self._index_in_epoch
return self._images[start:end], self._labels[start:end]
@deprecated(__lowercase , "Please write your own downloading logic." )
def __lowercase ( __lowercase , __lowercase , __lowercase ) -> Union[str, Any]:
'''simple docstring'''
if not gfile.Exists(__lowercase ):
gfile.MakeDirs(__lowercase )
_A = os.path.join(__lowercase , __lowercase )
if not gfile.Exists(__lowercase ):
urllib.request.urlretrieve(__lowercase , __lowercase ) # noqa: S310
with gfile.GFile(__lowercase ) as f:
_A = f.size()
print("Successfully downloaded" , __lowercase , __lowercase , "bytes." )
return filepath
@deprecated(
__lowercase , "Please use alternatives such as:" " tensorflow_datasets.load('mnist')" )
def __lowercase ( __lowercase , __lowercase=False , __lowercase=False , __lowercase=dtypes.floataa , __lowercase=True , __lowercase=5000 , __lowercase=None , __lowercase=DEFAULT_SOURCE_URL , ) -> List[str]:
'''simple docstring'''
if fake_data:
def fake():
return _DataSet(
[] , [] , fake_data=__lowercase , one_hot=__lowercase , dtype=__lowercase , seed=__lowercase )
_A = fake()
_A = fake()
_A = fake()
return _Datasets(train=__lowercase , validation=__lowercase , test=__lowercase )
if not source_url: # empty string check
_A = DEFAULT_SOURCE_URL
_A = "train-images-idx3-ubyte.gz"
_A = "train-labels-idx1-ubyte.gz"
_A = "t10k-images-idx3-ubyte.gz"
_A = "t10k-labels-idx1-ubyte.gz"
_A = _maybe_download(
__lowercase , __lowercase , source_url + train_images_file )
with gfile.Open(__lowercase , "rb" ) as f:
_A = _extract_images(__lowercase )
_A = _maybe_download(
__lowercase , __lowercase , source_url + train_labels_file )
with gfile.Open(__lowercase , "rb" ) as f:
_A = _extract_labels(__lowercase , one_hot=__lowercase )
_A = _maybe_download(
__lowercase , __lowercase , source_url + test_images_file )
with gfile.Open(__lowercase , "rb" ) as f:
_A = _extract_images(__lowercase )
_A = _maybe_download(
__lowercase , __lowercase , source_url + test_labels_file )
with gfile.Open(__lowercase , "rb" ) as f:
_A = _extract_labels(__lowercase , one_hot=__lowercase )
if not 0 <= validation_size <= len(__lowercase ):
_A = (
"Validation size should be between 0 and "
F'''{len(__lowercase )}. Received: {validation_size}.'''
)
raise ValueError(__lowercase )
_A = train_images[:validation_size]
_A = train_labels[:validation_size]
_A = train_images[validation_size:]
_A = train_labels[validation_size:]
_A = {"dtype": dtype, "reshape": reshape, "seed": seed}
_A = _DataSet(__lowercase , __lowercase , **__lowercase )
_A = _DataSet(__lowercase , __lowercase , **__lowercase )
_A = _DataSet(__lowercase , __lowercase , **__lowercase )
return _Datasets(train=__lowercase , validation=__lowercase , test=__lowercase )
| 79 |
'''simple docstring'''
from scipy.stats import pearsonr
import datasets
__snake_case = '''
Pearson correlation coefficient and p-value for testing non-correlation.
The Pearson correlation coefficient measures the linear relationship between two datasets. The calculation of the p-value relies on the assumption that each dataset is normally distributed. Like other correlation coefficients, this one varies between -1 and +1 with 0 implying no correlation. Correlations of -1 or +1 imply an exact linear relationship. Positive correlations imply that as x increases, so does y. Negative correlations imply that as x increases, y decreases.
The p-value roughly indicates the probability of an uncorrelated system producing datasets that have a Pearson correlation at least as extreme as the one computed from these datasets.
'''
__snake_case = '''
Args:
predictions (`list` of `int`): Predicted class labels, as returned by a model.
references (`list` of `int`): Ground truth labels.
return_pvalue (`boolean`): If `True`, returns the p-value, along with the correlation coefficient. If `False`, returns only the correlation coefficient. Defaults to `False`.
Returns:
pearsonr (`float`): Pearson correlation coefficient. Minimum possible value is -1. Maximum possible value is 1. Values of 1 and -1 indicate exact linear positive and negative relationships, respectively. A value of 0 implies no correlation.
p-value (`float`): P-value, which roughly indicates the probability of an The p-value roughly indicates the probability of an uncorrelated system producing datasets that have a Pearson correlation at least as extreme as the one computed from these datasets. Minimum possible value is 0. Maximum possible value is 1. Higher values indicate higher probabilities.
Examples:
Example 1-A simple example using only predictions and references.
>>> pearsonr_metric = datasets.load_metric("pearsonr")
>>> results = pearsonr_metric.compute(predictions=[10, 9, 2.5, 6, 4], references=[1, 2, 3, 4, 5])
>>> print(round(results[\'pearsonr\'], 2))
-0.74
Example 2-The same as Example 1, but that also returns the `p-value`.
>>> pearsonr_metric = datasets.load_metric("pearsonr")
>>> results = pearsonr_metric.compute(predictions=[10, 9, 2.5, 6, 4], references=[1, 2, 3, 4, 5], return_pvalue=True)
>>> print(sorted(list(results.keys())))
[\'p-value\', \'pearsonr\']
>>> print(round(results[\'pearsonr\'], 2))
-0.74
>>> print(round(results[\'p-value\'], 2))
0.15
'''
__snake_case = '''
@article{2020SciPy-NMeth,
author = {Virtanen, Pauli and Gommers, Ralf and Oliphant, Travis E. and
Haberland, Matt and Reddy, Tyler and Cournapeau, David and
Burovski, Evgeni and Peterson, Pearu and Weckesser, Warren and
Bright, Jonathan and {van der Walt}, St{\'e}fan J. and
Brett, Matthew and Wilson, Joshua and Millman, K. Jarrod and
Mayorov, Nikolay and Nelson, Andrew R. J. and Jones, Eric and
Kern, Robert and Larson, Eric and Carey, C J and
Polat, Ilhan and Feng, Yu and Moore, Eric W. and
{VanderPlas}, Jake and Laxalde, Denis and Perktold, Josef and
Cimrman, Robert and Henriksen, Ian and Quintero, E. A. and
Harris, Charles R. and Archibald, Anne M. and
Ribeiro, Antonio H. and Pedregosa, Fabian and
{van Mulbregt}, Paul and {SciPy 1.0 Contributors}},
title = {{{SciPy} 1.0: Fundamental Algorithms for Scientific
Computing in Python}},
journal = {Nature Methods},
year = {2020},
volume = {17},
pages = {261--272},
adsurl = {https://rdcu.be/b08Wh},
doi = {10.1038/s41592-019-0686-2},
}
'''
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class lowercase ( datasets.Metric ):
"""simple docstring"""
def lowerCAmelCase__ ( self ):
'''simple docstring'''
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
'''predictions''': datasets.Value('''float''' ),
'''references''': datasets.Value('''float''' ),
} ) , reference_urls=['''https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.pearsonr.html'''] , )
def lowerCAmelCase__ ( self , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_=False ):
'''simple docstring'''
if return_pvalue:
UpperCamelCase__ :Any = pearsonr(UpperCamelCase_ , UpperCamelCase_ )
return {"pearsonr": results[0], "p-value": results[1]}
else:
return {"pearsonr": float(pearsonr(UpperCamelCase_ , UpperCamelCase_ )[0] )} | 97 | 0 |
'''simple docstring'''
import enum
import warnings
from .. import MODEL_FOR_CAUSAL_LM_MAPPING, TF_MODEL_FOR_CAUSAL_LM_MAPPING
from ..utils import add_end_docstrings, is_tf_available
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_tf_available():
import tensorflow as tf
class lowercase_ ( enum.Enum ):
__UpperCAmelCase = 0
__UpperCAmelCase = 1
__UpperCAmelCase = 2
@add_end_docstrings(a__ )
class lowercase_ ( a__ ):
__UpperCAmelCase = '\n In 1991, the remains of Russian Tsar Nicholas II and his family (except for Alexei and Maria) are discovered. The\n voice of Nicholas\'s young son, Tsarevich Alexei Nikolaevich, narrates the remainder of the story. 1883 Western\n Siberia, a young Grigori Rasputin is asked by his father and a group of men to perform magic. Rasputin has a vision\n and denounces one of the men as a horse thief. Although his father initially slaps him for making such an\n accusation, Rasputin watches as the man is chased outside and beaten. Twenty years later, Rasputin sees a vision of\n the Virgin Mary, prompting him to become a priest. Rasputin quickly becomes famous, with people, even a bishop,\n begging for his blessing. <eod> </s> <eos>\n '
def __init__( self , *a , **a ):
super().__init__(*a , **a )
self.check_model_type(
TF_MODEL_FOR_CAUSAL_LM_MAPPING if self.framework == "tf" else MODEL_FOR_CAUSAL_LM_MAPPING )
if "prefix" not in self._preprocess_params:
# This is very specific. The logic is quite complex and needs to be done
# as a "default".
# It also defines both some preprocess_kwargs and generate_kwargs
# which is why we cannot put them in their respective methods.
UpperCamelCase__ = None
if self.model.config.prefix is not None:
UpperCamelCase__ = self.model.config.prefix
if prefix is None and self.model.__class__.__name__ in [
"XLNetLMHeadModel",
"TransfoXLLMHeadModel",
"TFXLNetLMHeadModel",
"TFTransfoXLLMHeadModel",
]:
# For XLNet and TransformerXL we add an article to the prompt to give more state to the model.
UpperCamelCase__ = self.XL_PREFIX
if prefix is not None:
# Recalculate some generate_kwargs linked to prefix.
UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ = self._sanitize_parameters(prefix=a , **self._forward_params )
UpperCamelCase__ = {**self._preprocess_params, **preprocess_params}
UpperCamelCase__ = {**self._forward_params, **forward_params}
def __a ( self , a=None , a=None , a=None , a=None , a=None , a=None , a=None , a=None , **a , ):
UpperCamelCase__ = {}
if prefix is not None:
UpperCamelCase__ = prefix
if prefix:
UpperCamelCase__ = self.tokenizer(
a , padding=a , add_special_tokens=a , return_tensors=self.framework )
UpperCamelCase__ = prefix_inputs["input_ids"].shape[-1]
if handle_long_generation is not None:
if handle_long_generation not in {"hole"}:
raise ValueError(
f'''{handle_long_generation} is not a valid value for `handle_long_generation` parameter expected'''
" [None, 'hole']" )
UpperCamelCase__ = handle_long_generation
preprocess_params.update(a )
UpperCamelCase__ = generate_kwargs
UpperCamelCase__ = {}
if return_full_text is not None and return_type is None:
if return_text is not None:
raise ValueError("`return_text` is mutually exclusive with `return_full_text`" )
if return_tensors is not None:
raise ValueError("`return_full_text` is mutually exclusive with `return_tensors`" )
UpperCamelCase__ = ReturnType.FULL_TEXT if return_full_text else ReturnType.NEW_TEXT
if return_tensors is not None and return_type is None:
if return_text is not None:
raise ValueError("`return_text` is mutually exclusive with `return_tensors`" )
UpperCamelCase__ = ReturnType.TENSORS
if return_type is not None:
UpperCamelCase__ = return_type
if clean_up_tokenization_spaces is not None:
UpperCamelCase__ = clean_up_tokenization_spaces
if stop_sequence is not None:
UpperCamelCase__ = self.tokenizer.encode(a , add_special_tokens=a )
if len(a ) > 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." )
UpperCamelCase__ = stop_sequence_ids[0]
return preprocess_params, forward_params, postprocess_params
def __a ( self , *a , **a ):
# Parse arguments
if self.model.__class__.__name__ in ["TransfoXLLMHeadModel"]:
kwargs.update({"add_space_before_punct_symbol": True} )
return super()._parse_and_tokenize(*a , **a )
def __call__( self , a , **a ):
return super().__call__(a , **a )
def __a ( self , a , a="" , a=None , **a ):
UpperCamelCase__ = self.tokenizer(
prefix + prompt_text , padding=a , add_special_tokens=a , return_tensors=self.framework )
UpperCamelCase__ = prompt_text
if handle_long_generation == "hole":
UpperCamelCase__ = inputs["input_ids"].shape[-1]
if "max_new_tokens" in generate_kwargs:
UpperCamelCase__ = generate_kwargs["max_new_tokens"]
else:
UpperCamelCase__ = generate_kwargs.get("max_length" , self.model.config.max_length ) - cur_len
if new_tokens < 0:
raise ValueError("We cannot infer how many new tokens are expected" )
if cur_len + new_tokens > self.tokenizer.model_max_length:
UpperCamelCase__ = self.tokenizer.model_max_length - new_tokens
if keep_length <= 0:
raise ValueError(
"We cannot use `hole` to handle this generation the number of desired tokens exceeds the"
" models max length" )
UpperCamelCase__ = inputs["input_ids"][:, -keep_length:]
if "attention_mask" in inputs:
UpperCamelCase__ = inputs["attention_mask"][:, -keep_length:]
return inputs
def __a ( self , a , **a ):
UpperCamelCase__ = model_inputs["input_ids"]
UpperCamelCase__ = model_inputs.get("attention_mask" , a )
# Allow empty prompts
if input_ids.shape[1] == 0:
UpperCamelCase__ = None
UpperCamelCase__ = None
UpperCamelCase__ = 1
else:
UpperCamelCase__ = input_ids.shape[0]
UpperCamelCase__ = model_inputs.pop("prompt_text" )
# If there is a prefix, we may need to adjust the generation length. Do so without permanently modifying
# generate_kwargs, as some of the parameterization may come from the initialization of the pipeline.
UpperCamelCase__ = generate_kwargs.pop("prefix_length" , 0 )
if prefix_length > 0:
UpperCamelCase__ = "max_new_tokens" in generate_kwargs or (
"generation_config" in generate_kwargs
and generate_kwargs["generation_config"].max_new_tokens is not None
)
if not has_max_new_tokens:
UpperCamelCase__ = generate_kwargs.get("max_length" ) or self.model.config.max_length
generate_kwargs["max_length"] += prefix_length
UpperCamelCase__ = "min_new_tokens" in generate_kwargs or (
"generation_config" in generate_kwargs
and generate_kwargs["generation_config"].min_new_tokens is not None
)
if not has_min_new_tokens and "min_length" in generate_kwargs:
generate_kwargs["min_length"] += prefix_length
# BS x SL
UpperCamelCase__ = self.model.generate(input_ids=a , attention_mask=a , **a )
UpperCamelCase__ = generated_sequence.shape[0]
if self.framework == "pt":
UpperCamelCase__ = generated_sequence.reshape(a , out_b // in_b , *generated_sequence.shape[1:] )
elif self.framework == "tf":
UpperCamelCase__ = tf.reshape(a , (in_b, out_b // in_b, *generated_sequence.shape[1:]) )
return {"generated_sequence": generated_sequence, "input_ids": input_ids, "prompt_text": prompt_text}
def __a ( self , a , a=ReturnType.FULL_TEXT , a=True ):
UpperCamelCase__ = model_outputs["generated_sequence"][0]
UpperCamelCase__ = model_outputs["input_ids"]
UpperCamelCase__ = model_outputs["prompt_text"]
UpperCamelCase__ = generated_sequence.numpy().tolist()
UpperCamelCase__ = []
for sequence in generated_sequence:
if return_type == ReturnType.TENSORS:
UpperCamelCase__ = {"generated_token_ids": sequence}
elif return_type in {ReturnType.NEW_TEXT, ReturnType.FULL_TEXT}:
# Decode text
UpperCamelCase__ = self.tokenizer.decode(
a , skip_special_tokens=a , clean_up_tokenization_spaces=a , )
# Remove PADDING prompt of the sequence if XLNet or Transfo-XL model is used
if input_ids is None:
UpperCamelCase__ = 0
else:
UpperCamelCase__ = len(
self.tokenizer.decode(
input_ids[0] , skip_special_tokens=a , clean_up_tokenization_spaces=a , ) )
if return_type == ReturnType.FULL_TEXT:
UpperCamelCase__ = prompt_text + text[prompt_length:]
else:
UpperCamelCase__ = text[prompt_length:]
UpperCamelCase__ = {"generated_text": all_text}
records.append(a )
return records
| 80 |
'''simple docstring'''
from typing import List, Optional
from tokenizers import ByteLevelBPETokenizer
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_blenderbot_small import BlenderbotSmallTokenizer
__snake_case = logging.get_logger(__name__)
__snake_case = {
'''vocab_file''': '''vocab.json''',
'''merges_file''': '''merges.txt''',
'''tokenizer_config_file''': '''tokenizer_config.json''',
}
__snake_case = {
'''vocab_file''': {
'''facebook/blenderbot_small-90M''': '''https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/vocab.json'''
},
'''merges_file''': {
'''facebook/blenderbot_small-90M''': '''https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/merges.txt'''
},
'''tokenizer_config_file''': {
'''facebook/blenderbot_small-90M''': (
'''https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/tokenizer_config.json'''
)
},
}
__snake_case = {
'''facebook/blenderbot_small-90M''': 512,
}
class lowercase ( A__ ):
"""simple docstring"""
_a = VOCAB_FILES_NAMES
_a = PRETRAINED_VOCAB_FILES_MAP
_a = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
_a = BlenderbotSmallTokenizer
def __init__( self , UpperCamelCase_=None , UpperCamelCase_=None , UpperCamelCase_="<|endoftext|>" , UpperCamelCase_="<|endoftext|>" , UpperCamelCase_="<|endoftext|>" , UpperCamelCase_=False , UpperCamelCase_=True , **UpperCamelCase_ , ):
'''simple docstring'''
super().__init__(
ByteLevelBPETokenizer(
vocab=UpperCamelCase_ , merges=UpperCamelCase_ , add_prefix_space=UpperCamelCase_ , trim_offsets=UpperCamelCase_ , ) , bos_token=UpperCamelCase_ , eos_token=UpperCamelCase_ , unk_token=UpperCamelCase_ , **UpperCamelCase_ , )
UpperCamelCase__ :Union[str, Any] = add_prefix_space
def lowerCAmelCase__ ( self , UpperCamelCase_ , UpperCamelCase_=None ):
'''simple docstring'''
UpperCamelCase__ :List[Any] = [self.bos_token_id] + token_ids_a + [self.eos_token_id]
if token_ids_a is None:
return output
return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]
def lowerCAmelCase__ ( self , UpperCamelCase_ , UpperCamelCase_ = None ):
'''simple docstring'''
UpperCamelCase__ :Optional[int] = [self.sep_token_id]
UpperCamelCase__ :Any = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep + sep + token_ids_a + sep ) * [0] | 97 | 0 |
"""simple docstring"""
from __future__ import annotations
import unittest
from transformers import is_tf_available, is_torch_available
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER, SMALL_MODEL_IDENTIFIER, is_pt_tf_cross_test, slow
if is_tf_available():
from transformers import (
AutoConfig,
BertConfig,
GPTaConfig,
TaConfig,
TFAutoModel,
TFAutoModelForCausalLM,
TFAutoModelForMaskedLM,
TFAutoModelForPreTraining,
TFAutoModelForQuestionAnswering,
TFAutoModelForSeqaSeqLM,
TFAutoModelForSequenceClassification,
TFAutoModelWithLMHead,
TFBertForMaskedLM,
TFBertForPreTraining,
TFBertForQuestionAnswering,
TFBertForSequenceClassification,
TFBertModel,
TFGPTaLMHeadModel,
TFRobertaForMaskedLM,
TFTaForConditionalGeneration,
)
from transformers.models.bert.modeling_tf_bert import TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST
from transformers.models.gpta.modeling_tf_gpta import TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST
from transformers.models.ta.modeling_tf_ta import TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST
if is_torch_available():
from transformers import (
AutoModel,
AutoModelForCausalLM,
AutoModelForMaskedLM,
AutoModelForPreTraining,
AutoModelForQuestionAnswering,
AutoModelForSeqaSeqLM,
AutoModelForSequenceClassification,
AutoModelWithLMHead,
BertForMaskedLM,
BertForPreTraining,
BertForQuestionAnswering,
BertForSequenceClassification,
BertModel,
GPTaLMHeadModel,
RobertaForMaskedLM,
TaForConditionalGeneration,
)
@is_pt_tf_cross_test
class __A ( unittest.TestCase ):
"""simple docstring"""
@slow
def SCREAMING_SNAKE_CASE ( self ) -> Tuple:
# for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
for model_name in ["bert-base-uncased"]:
a =AutoConfig.from_pretrained(__A )
self.assertIsNotNone(__A )
self.assertIsInstance(__A , __A )
a =TFAutoModel.from_pretrained(__A , from_pt=__A )
self.assertIsNotNone(__A )
self.assertIsInstance(__A , __A )
a =AutoModel.from_pretrained(__A , from_tf=__A )
self.assertIsNotNone(__A )
self.assertIsInstance(__A , __A )
@slow
def SCREAMING_SNAKE_CASE ( self ) -> Tuple:
# for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
for model_name in ["bert-base-uncased"]:
a =AutoConfig.from_pretrained(__A )
self.assertIsNotNone(__A )
self.assertIsInstance(__A , __A )
a =TFAutoModelForPreTraining.from_pretrained(__A , from_pt=__A )
self.assertIsNotNone(__A )
self.assertIsInstance(__A , __A )
a =AutoModelForPreTraining.from_pretrained(__A , from_tf=__A )
self.assertIsNotNone(__A )
self.assertIsInstance(__A , __A )
@slow
def SCREAMING_SNAKE_CASE ( self ) -> int:
for model_name in TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
a =AutoConfig.from_pretrained(__A )
self.assertIsNotNone(__A )
self.assertIsInstance(__A , __A )
a =TFAutoModelForCausalLM.from_pretrained(__A , from_pt=__A )
a , a =TFAutoModelForCausalLM.from_pretrained(
__A , output_loading_info=__A , from_pt=__A )
self.assertIsNotNone(__A )
self.assertIsInstance(__A , __A )
a =AutoModelForCausalLM.from_pretrained(__A , from_tf=__A )
a , a =AutoModelForCausalLM.from_pretrained(
__A , output_loading_info=__A , from_tf=__A )
self.assertIsNotNone(__A )
self.assertIsInstance(__A , __A )
@slow
def SCREAMING_SNAKE_CASE ( self ) -> str:
for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
a =AutoConfig.from_pretrained(__A )
self.assertIsNotNone(__A )
self.assertIsInstance(__A , __A )
a =TFAutoModelWithLMHead.from_pretrained(__A , from_pt=__A )
self.assertIsNotNone(__A )
self.assertIsInstance(__A , __A )
a =AutoModelWithLMHead.from_pretrained(__A , from_tf=__A )
self.assertIsNotNone(__A )
self.assertIsInstance(__A , __A )
@slow
def SCREAMING_SNAKE_CASE ( self ) -> List[Any]:
for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
a =AutoConfig.from_pretrained(__A )
self.assertIsNotNone(__A )
self.assertIsInstance(__A , __A )
a =TFAutoModelForMaskedLM.from_pretrained(__A , from_pt=__A )
a , a =TFAutoModelForMaskedLM.from_pretrained(
__A , output_loading_info=__A , from_pt=__A )
self.assertIsNotNone(__A )
self.assertIsInstance(__A , __A )
a =AutoModelForMaskedLM.from_pretrained(__A , from_tf=__A )
a , a =AutoModelForMaskedLM.from_pretrained(
__A , output_loading_info=__A , from_tf=__A )
self.assertIsNotNone(__A )
self.assertIsInstance(__A , __A )
@slow
def SCREAMING_SNAKE_CASE ( self ) -> Optional[int]:
for model_name in TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
a =AutoConfig.from_pretrained(__A )
self.assertIsNotNone(__A )
self.assertIsInstance(__A , __A )
a =TFAutoModelForSeqaSeqLM.from_pretrained(__A , from_pt=__A )
a , a =TFAutoModelForSeqaSeqLM.from_pretrained(
__A , output_loading_info=__A , from_pt=__A )
self.assertIsNotNone(__A )
self.assertIsInstance(__A , __A )
a =AutoModelForSeqaSeqLM.from_pretrained(__A , from_tf=__A )
a , a =AutoModelForSeqaSeqLM.from_pretrained(
__A , output_loading_info=__A , from_tf=__A )
self.assertIsNotNone(__A )
self.assertIsInstance(__A , __A )
@slow
def SCREAMING_SNAKE_CASE ( self ) -> Tuple:
# for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
for model_name in ["bert-base-uncased"]:
a =AutoConfig.from_pretrained(__A )
self.assertIsNotNone(__A )
self.assertIsInstance(__A , __A )
a =TFAutoModelForSequenceClassification.from_pretrained(__A , from_pt=__A )
self.assertIsNotNone(__A )
self.assertIsInstance(__A , __A )
a =AutoModelForSequenceClassification.from_pretrained(__A , from_tf=__A )
self.assertIsNotNone(__A )
self.assertIsInstance(__A , __A )
@slow
def SCREAMING_SNAKE_CASE ( self ) -> str:
# for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
for model_name in ["bert-base-uncased"]:
a =AutoConfig.from_pretrained(__A )
self.assertIsNotNone(__A )
self.assertIsInstance(__A , __A )
a =TFAutoModelForQuestionAnswering.from_pretrained(__A , from_pt=__A )
self.assertIsNotNone(__A )
self.assertIsInstance(__A , __A )
a =AutoModelForQuestionAnswering.from_pretrained(__A , from_tf=__A )
self.assertIsNotNone(__A )
self.assertIsInstance(__A , __A )
def SCREAMING_SNAKE_CASE ( self ) -> Optional[Any]:
a =TFAutoModelWithLMHead.from_pretrained(__A , from_pt=__A )
self.assertIsInstance(__A , __A )
self.assertEqual(model.num_parameters() , 1_4410 )
self.assertEqual(model.num_parameters(only_trainable=__A ) , 1_4410 )
a =AutoModelWithLMHead.from_pretrained(__A , from_tf=__A )
self.assertIsInstance(__A , __A )
self.assertEqual(model.num_parameters() , 1_4410 )
self.assertEqual(model.num_parameters(only_trainable=__A ) , 1_4410 )
def SCREAMING_SNAKE_CASE ( self ) -> List[Any]:
a =TFAutoModelWithLMHead.from_pretrained(__A , from_pt=__A )
self.assertIsInstance(__A , __A )
self.assertEqual(model.num_parameters() , 1_4410 )
self.assertEqual(model.num_parameters(only_trainable=__A ) , 1_4410 )
a =AutoModelWithLMHead.from_pretrained(__A , from_tf=__A )
self.assertIsInstance(__A , __A )
self.assertEqual(model.num_parameters() , 1_4410 )
self.assertEqual(model.num_parameters(only_trainable=__A ) , 1_4410 ) | 81 |
'''simple docstring'''
from ...utils import (
OptionalDependencyNotAvailable,
is_flax_available,
is_torch_available,
is_transformers_available,
)
try:
if not (is_transformers_available() and is_torch_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ...utils.dummy_torch_and_transformers_objects import * # noqa F403
else:
from .multicontrolnet import MultiControlNetModel
from .pipeline_controlnet import StableDiffusionControlNetPipeline
from .pipeline_controlnet_imgaimg import StableDiffusionControlNetImgaImgPipeline
from .pipeline_controlnet_inpaint import StableDiffusionControlNetInpaintPipeline
if is_transformers_available() and is_flax_available():
from .pipeline_flax_controlnet import FlaxStableDiffusionControlNetPipeline | 97 | 0 |
from __future__ import annotations
import random
import unittest
from transformers import TransfoXLConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import tensorflow as tf
from transformers import (
TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST,
TFTransfoXLForSequenceClassification,
TFTransfoXLLMHeadModel,
TFTransfoXLModel,
)
class __lowerCAmelCase :
def __init__( self , _snake_case , ):
"""simple docstring"""
_lowerCAmelCase = parent
_lowerCAmelCase = 13
_lowerCAmelCase = 7
_lowerCAmelCase = 30
_lowerCAmelCase = self.seq_length + self.mem_len
_lowerCAmelCase = 15
_lowerCAmelCase = True
_lowerCAmelCase = True
_lowerCAmelCase = 99
_lowerCAmelCase = [10, 50, 80]
_lowerCAmelCase = 32
_lowerCAmelCase = 32
_lowerCAmelCase = 4
_lowerCAmelCase = 8
_lowerCAmelCase = 128
_lowerCAmelCase = 2
_lowerCAmelCase = 2
_lowerCAmelCase = None
_lowerCAmelCase = 1
_lowerCAmelCase = 0
_lowerCAmelCase = 3
_lowerCAmelCase = self.vocab_size - 1
_lowerCAmelCase = 0.01
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
_lowerCAmelCase = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
_lowerCAmelCase = None
if self.use_labels:
_lowerCAmelCase = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
_lowerCAmelCase = TransfoXLConfig(
vocab_size=self.vocab_size , mem_len=self.mem_len , clamp_len=self.clamp_len , cutoffs=self.cutoffs , d_model=self.hidden_size , d_embed=self.d_embed , n_head=self.num_attention_heads , d_head=self.d_head , d_inner=self.d_inner , div_val=self.div_val , n_layer=self.num_hidden_layers , eos_token_id=self.eos_token_id , pad_token_id=self.vocab_size - 1 , init_range=self.init_range , num_labels=self.num_labels , )
return (config, input_ids_a, input_ids_a, lm_labels)
def snake_case ( self ):
"""simple docstring"""
random.seed(self.seed )
tf.random.set_seed(self.seed )
def snake_case ( self , _snake_case , _snake_case , _snake_case , _snake_case ):
"""simple docstring"""
_lowerCAmelCase = TFTransfoXLModel(_snake_case )
_lowerCAmelCase , _lowerCAmelCase = model(_snake_case ).to_tuple()
_lowerCAmelCase = {"""input_ids""": input_ids_a, """mems""": mems_a}
_lowerCAmelCase , _lowerCAmelCase = model(_snake_case ).to_tuple()
self.parent.assertEqual(hidden_states_a.shape , (self.batch_size, self.seq_length, self.hidden_size) )
self.parent.assertEqual(hidden_states_a.shape , (self.batch_size, self.seq_length, self.hidden_size) )
self.parent.assertListEqual(
[mem.shape for mem in mems_a] , [(self.mem_len, self.batch_size, self.hidden_size)] * self.num_hidden_layers , )
self.parent.assertListEqual(
[mem.shape for mem in mems_a] , [(self.mem_len, self.batch_size, self.hidden_size)] * self.num_hidden_layers , )
def snake_case ( self , _snake_case , _snake_case , _snake_case , _snake_case ):
"""simple docstring"""
_lowerCAmelCase = TFTransfoXLLMHeadModel(_snake_case )
_lowerCAmelCase , _lowerCAmelCase = model(_snake_case ).to_tuple()
_lowerCAmelCase = {"""input_ids""": input_ids_a, """labels""": lm_labels}
_lowerCAmelCase , _lowerCAmelCase = model(_snake_case ).to_tuple()
_lowerCAmelCase , _lowerCAmelCase = model([input_ids_a, mems_a] ).to_tuple()
_lowerCAmelCase = {"""input_ids""": input_ids_a, """mems""": mems_a, """labels""": lm_labels}
_lowerCAmelCase , _lowerCAmelCase = model(_snake_case ).to_tuple()
self.parent.assertEqual(lm_logits_a.shape , (self.batch_size, self.seq_length, self.vocab_size) )
self.parent.assertListEqual(
[mem.shape for mem in mems_a] , [(self.mem_len, self.batch_size, self.hidden_size)] * self.num_hidden_layers , )
self.parent.assertEqual(lm_logits_a.shape , (self.batch_size, self.seq_length, self.vocab_size) )
self.parent.assertListEqual(
[mem.shape for mem in mems_a] , [(self.mem_len, self.batch_size, self.hidden_size)] * self.num_hidden_layers , )
def snake_case ( self , _snake_case , _snake_case , _snake_case , _snake_case ):
"""simple docstring"""
_lowerCAmelCase = TFTransfoXLForSequenceClassification(_snake_case )
_lowerCAmelCase = model(_snake_case )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = self.prepare_config_and_inputs()
((_lowerCAmelCase) , (_lowerCAmelCase) , (_lowerCAmelCase) , (_lowerCAmelCase)) = config_and_inputs
_lowerCAmelCase = {"""input_ids""": input_ids_a}
return config, inputs_dict
@require_tf
class __lowerCAmelCase ( lowerCamelCase__ , lowerCamelCase__ , unittest.TestCase ):
__lowerCamelCase = (
(TFTransfoXLModel, TFTransfoXLLMHeadModel, TFTransfoXLForSequenceClassification) if is_tf_available() else ()
)
__lowerCamelCase = () if is_tf_available() else ()
__lowerCamelCase = (
{
'''feature-extraction''': TFTransfoXLModel,
'''text-classification''': TFTransfoXLForSequenceClassification,
'''text-generation''': TFTransfoXLLMHeadModel,
'''zero-shot''': TFTransfoXLForSequenceClassification,
}
if is_tf_available()
else {}
)
# TODO: add this test when TFTransfoXLLMHead has a linear output layer implemented
__lowerCamelCase = False
__lowerCamelCase = False
__lowerCamelCase = False
__lowerCamelCase = False
def snake_case ( self , _snake_case , _snake_case , _snake_case , _snake_case , _snake_case ):
"""simple docstring"""
if pipeline_test_casse_name == "TextGenerationPipelineTests":
# Get `ValueError: AttributeError: 'NoneType' object has no attribute 'new_ones'` or `AssertionError`.
# `TransfoXLConfig` was never used in pipeline tests: cannot create a simple
# tokenizer.
return True
return False
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = TFTransfoXLModelTester(self )
_lowerCAmelCase = ConfigTester(self , config_class=_snake_case , d_embed=37 )
def snake_case ( self ):
"""simple docstring"""
self.config_tester.run_common_tests()
def snake_case ( self ):
"""simple docstring"""
self.model_tester.set_seed()
_lowerCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_transfo_xl_model(*_snake_case )
def snake_case ( self ):
"""simple docstring"""
self.model_tester.set_seed()
_lowerCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_transfo_xl_lm_head(*_snake_case )
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_transfo_xl_for_sequence_classification(*_snake_case )
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase , _lowerCAmelCase = self.model_tester.prepare_config_and_inputs_for_common()
_lowerCAmelCase = [TFTransfoXLForSequenceClassification]
for model_class in self.all_model_classes:
_lowerCAmelCase = model_class(_snake_case )
assert isinstance(model.get_input_embeddings() , tf.keras.layers.Layer )
if model_class in list_other_models_with_output_ebd:
_lowerCAmelCase = model.get_output_embeddings()
assert isinstance(_snake_case , tf.keras.layers.Layer )
_lowerCAmelCase = model.get_bias()
assert name is None
else:
_lowerCAmelCase = model.get_output_embeddings()
assert x is None
_lowerCAmelCase = model.get_bias()
assert name is None
def snake_case ( self ):
"""simple docstring"""
pass
@slow
def snake_case ( self ):
"""simple docstring"""
for model_name in TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
_lowerCAmelCase = TFTransfoXLModel.from_pretrained(_snake_case )
self.assertIsNotNone(_snake_case )
@unittest.skip(reason="""This model doesn't play well with fit() due to not returning a single loss.""" )
def snake_case ( self ):
"""simple docstring"""
pass
@require_tf
class __lowerCAmelCase ( unittest.TestCase ):
@unittest.skip("""Skip test until #12651 is resolved.""" )
@slow
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = TFTransfoXLLMHeadModel.from_pretrained("""transfo-xl-wt103""" )
# fmt: off
_lowerCAmelCase = tf.convert_to_tensor([[33,1297,2,1,1009,4,1109,11739,4762,358,5,25,245,22,1706,17,20098,5,3215,21,37,1110,3,13,1041,4,24,603,490,2,71477,20098,104447,2,20961,1,2604,4,1,329,3,6224,831,16002,2,8,603,78967,29546,23,803,20,25,416,5,8,232,4,277,6,1855,4601,3,29546,54,8,3609,5,57211,49,4,1,277,18,8,1755,15691,3,341,25,416,693,42573,71,17,401,94,31,17919,2,29546,7873,18,1,435,23,11011,755,5,5167,3,7983,98,84,2,29546,3267,8,3609,4,1,4865,1075,2,6087,71,6,346,8,5854,3,29546,824,1400,1868,2,19,160,2,311,8,5496,2,20920,17,25,15097,3,24,24,0]] , dtype=tf.intaa ) # noqa: E231
# fmt: on
# In 1991 , the remains of Russian Tsar Nicholas II and his family
# ( except for Alexei and Maria ) are discovered .
# The voice of Nicholas's young son , Tsarevich Alexei Nikolaevich , narrates the
# remainder of the story . 1883 Western Siberia ,
# a young Grigori Rasputin is asked by his father and a group of men to perform magic .
# Rasputin has a vision and denounces one of the men as a horse thief . Although his
# father initially slaps him for making such an accusation , Rasputin watches as the
# man is chased outside and beaten . Twenty years later , Rasputin sees a vision of
# the Virgin Mary , prompting him to become a priest . Rasputin quickly becomes famous ,
# with people , even a bishop , begging for his blessing . <eod> </s> <eos>
# fmt: off
_lowerCAmelCase = [33,1297,2,1,1009,4,1109,11739,4762,358,5,25,245,22,1706,17,20098,5,3215,21,37,1110,3,13,1041,4,24,603,490,2,71477,20098,104447,2,20961,1,2604,4,1,329,3,6224,831,16002,2,8,603,78967,29546,23,803,20,25,416,5,8,232,4,277,6,1855,4601,3,29546,54,8,3609,5,57211,49,4,1,277,18,8,1755,15691,3,341,25,416,693,42573,71,17,401,94,31,17919,2,29546,7873,18,1,435,23,11011,755,5,5167,3,7983,98,84,2,29546,3267,8,3609,4,1,4865,1075,2,6087,71,6,346,8,5854,3,29546,824,1400,1868,2,19,160,2,311,8,5496,2,20920,17,25,15097,3,24,24,0,33,1,1857,2,1,1009,4,1109,11739,4762,358,5,25,245,28,1110,3,13,1041,4,24,603,490,2,71477,20098,104447,2,20961,1,2604,4,1,329,3,0] # noqa: E231
# fmt: on
# In 1991, the remains of Russian Tsar Nicholas II and his family (
# except for Alexei and Maria ) are discovered. The voice of young son,
# Tsarevich Alexei Nikolaevich, narrates the remainder of the story.
# 1883 Western Siberia, a young Grigori Rasputin is asked by his father
# and a group of men to perform magic. Rasputin has a vision and
# denounces one of the men as a horse thief. Although his father initially
# slaps him for making such an accusation, Rasputin watches as the man
# is chased outside and beaten. Twenty years later, Rasputin sees a vision
# of the Virgin Mary, prompting him to become a priest.
# Rasputin quickly becomes famous, with people, even a bishop, begging for
# his blessing. <unk> <unk> <eos> In the 1990s, the remains of Russian Tsar
# Nicholas II and his family were discovered. The voice of <unk> young son,
# Tsarevich Alexei Nikolaevich, narrates the remainder of the story.<eos>
_lowerCAmelCase = model.generate(_snake_case , max_length=200 , do_sample=_snake_case )
self.assertListEqual(output_ids[0].numpy().tolist() , _snake_case )
| 82 |
'''simple docstring'''
from typing import Callable, Optional
from .. import Features
from ..packaged_modules.generator.generator import Generator
from .abc import AbstractDatasetInputStream
class lowercase ( A__ ):
"""simple docstring"""
def __init__( self , UpperCamelCase_ , UpperCamelCase_ = None , UpperCamelCase_ = None , UpperCamelCase_ = False , UpperCamelCase_ = False , UpperCamelCase_ = None , UpperCamelCase_ = None , **UpperCamelCase_ , ):
'''simple docstring'''
super().__init__(
features=UpperCamelCase_ , cache_dir=UpperCamelCase_ , keep_in_memory=UpperCamelCase_ , streaming=UpperCamelCase_ , num_proc=UpperCamelCase_ , **UpperCamelCase_ , )
UpperCamelCase__ :Any = Generator(
cache_dir=UpperCamelCase_ , features=UpperCamelCase_ , generator=UpperCamelCase_ , gen_kwargs=UpperCamelCase_ , **UpperCamelCase_ , )
def lowerCAmelCase__ ( self ):
'''simple docstring'''
if self.streaming:
UpperCamelCase__ :Optional[Any] = self.builder.as_streaming_dataset(split='''train''' )
# Build regular (map-style) dataset
else:
UpperCamelCase__ :Optional[int] = None
UpperCamelCase__ :int = None
UpperCamelCase__ :Any = None
UpperCamelCase__ :Any = None
self.builder.download_and_prepare(
download_config=UpperCamelCase_ , download_mode=UpperCamelCase_ , verification_mode=UpperCamelCase_ , base_path=UpperCamelCase_ , num_proc=self.num_proc , )
UpperCamelCase__ :List[Any] = self.builder.as_dataset(
split='''train''' , verification_mode=UpperCamelCase_ , in_memory=self.keep_in_memory )
return dataset | 97 | 0 |
'''simple docstring'''
from datetime import datetime
import matplotlib.pyplot as plt
import torch
def A__ ( UpperCAmelCase_ ):
for param in module.parameters():
_UpperCamelCase : Dict = False
def A__ ( ):
_UpperCamelCase : Dict = 'cuda' if torch.cuda.is_available() else 'cpu'
if torch.backends.mps.is_available() and torch.backends.mps.is_built():
_UpperCamelCase : Tuple = 'mps'
if device == "mps":
print(
'WARNING: MPS currently doesn\'t seem to work, and messes up backpropagation without any visible torch'
' errors. I recommend using CUDA on a colab notebook or CPU instead if you\'re facing inexplicable issues'
' with generations.' )
return device
def A__ ( UpperCAmelCase_ ):
_UpperCamelCase : Optional[Any] = plt.imshow(UpperCAmelCase_ )
fig.axes.get_xaxis().set_visible(UpperCAmelCase_ )
fig.axes.get_yaxis().set_visible(UpperCAmelCase_ )
plt.show()
def A__ ( ):
_UpperCamelCase : int = datetime.now()
_UpperCamelCase : Tuple = current_time.strftime('%H:%M:%S' )
return timestamp
| 83 |
'''simple docstring'''
__snake_case = 65521
def a ( __a ) -> int:
'''simple docstring'''
UpperCamelCase__ :Tuple = 1
UpperCamelCase__ :Any = 0
for plain_chr in plain_text:
UpperCamelCase__ :List[str] = (a + ord(__a )) % MOD_ADLER
UpperCamelCase__ :Tuple = (b + a) % MOD_ADLER
return (b << 16) | a | 97 | 0 |
"""simple docstring"""
import unittest
import numpy as np
def _snake_case ( lowercase__ : np.ndarray , lowercase__ : np.ndarray , lowercase__ : np.ndarray , lowercase__ : np.ndarray | None = None , ) -> np.ndarray:
'''simple docstring'''
lowerCAmelCase_ :Any = np.shape(lowercase__ )
lowerCAmelCase_ :int = np.shape(lowercase__ )
lowerCAmelCase_ :List[str] = np.shape(lowercase__ )
if shape_a[0] != shape_b[0]:
lowerCAmelCase_ :Tuple = (
"""Expected the same number of rows for A and B. """
f"""Instead found A of size {shape_a} and B of size {shape_b}"""
)
raise ValueError(lowercase__ )
if shape_b[1] != shape_c[1]:
lowerCAmelCase_ :Optional[Any] = (
"""Expected the same number of columns for B and C. """
f"""Instead found B of size {shape_b} and C of size {shape_c}"""
)
raise ValueError(lowercase__ )
lowerCAmelCase_ :Optional[int] = pseudo_inv
if a_inv is None:
try:
lowerCAmelCase_ :Optional[Any] = np.linalg.inv(lowercase__ )
except np.linalg.LinAlgError:
raise ValueError(
"""Input matrix A is not invertible. Cannot compute Schur complement.""" )
return mat_c - mat_b.T @ a_inv @ mat_b
class _SCREAMING_SNAKE_CASE ( unittest.TestCase ):
def __lowerCAmelCase ( self ) -> None:
lowerCAmelCase_ :Any = np.array([[1, 2, 1], [2, 1, 2], [3, 2, 4]] )
lowerCAmelCase_ :int = np.array([[0, 3], [3, 0], [2, 3]] )
lowerCAmelCase_ :List[str] = np.array([[2, 1], [6, 3]] )
lowerCAmelCase_ :Optional[Any] = schur_complement(__A , __A , __A )
lowerCAmelCase_ :Optional[Any] = np.block([[a, b], [b.T, c]] )
lowerCAmelCase_ :int = np.linalg.det(__A )
lowerCAmelCase_ :Union[str, Any] = np.linalg.det(__A )
lowerCAmelCase_ :List[Any] = np.linalg.det(__A )
self.assertAlmostEqual(__A , det_a * det_s )
def __lowerCAmelCase ( self ) -> None:
lowerCAmelCase_ :str = np.array([[1, 2, 1], [2, 1, 2], [3, 2, 4]] )
lowerCAmelCase_ :Dict = np.array([[0, 3], [3, 0], [2, 3]] )
lowerCAmelCase_ :int = np.array([[2, 1], [6, 3]] )
with self.assertRaises(__A ):
schur_complement(__A , __A , __A )
def __lowerCAmelCase ( self ) -> None:
lowerCAmelCase_ :Optional[int] = np.array([[1, 2, 1], [2, 1, 2], [3, 2, 4]] )
lowerCAmelCase_ :str = np.array([[0, 3], [3, 0], [2, 3]] )
lowerCAmelCase_ :int = np.array([[2, 1, 3], [6, 3, 5]] )
with self.assertRaises(__A ):
schur_complement(__A , __A , __A )
if __name__ == "__main__":
import doctest
doctest.testmod()
unittest.main()
| 84 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
__snake_case = logging.get_logger(__name__)
__snake_case = {
'''camembert-base''': '''https://huggingface.co/camembert-base/resolve/main/config.json''',
'''umberto-commoncrawl-cased-v1''': (
'''https://huggingface.co/Musixmatch/umberto-commoncrawl-cased-v1/resolve/main/config.json'''
),
'''umberto-wikipedia-uncased-v1''': (
'''https://huggingface.co/Musixmatch/umberto-wikipedia-uncased-v1/resolve/main/config.json'''
),
}
class lowercase ( A__ ):
"""simple docstring"""
_a = 'camembert'
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-12 , UpperCamelCase_=1 , UpperCamelCase_=0 , UpperCamelCase_=2 , UpperCamelCase_="absolute" , UpperCamelCase_=True , UpperCamelCase_=None , **UpperCamelCase_ , ):
'''simple docstring'''
super().__init__(pad_token_id=UpperCamelCase_ , bos_token_id=UpperCamelCase_ , eos_token_id=UpperCamelCase_ , **UpperCamelCase_ )
UpperCamelCase__ :int = vocab_size
UpperCamelCase__ :Optional[int] = hidden_size
UpperCamelCase__ :Optional[int] = num_hidden_layers
UpperCamelCase__ :List[Any] = num_attention_heads
UpperCamelCase__ :Union[str, Any] = hidden_act
UpperCamelCase__ :List[Any] = intermediate_size
UpperCamelCase__ :int = hidden_dropout_prob
UpperCamelCase__ :Tuple = attention_probs_dropout_prob
UpperCamelCase__ :Union[str, Any] = max_position_embeddings
UpperCamelCase__ :Tuple = type_vocab_size
UpperCamelCase__ :int = initializer_range
UpperCamelCase__ :List[str] = layer_norm_eps
UpperCamelCase__ :int = position_embedding_type
UpperCamelCase__ :Any = use_cache
UpperCamelCase__ :Any = classifier_dropout
class lowercase ( A__ ):
"""simple docstring"""
@property
def lowerCAmelCase__ ( self ):
'''simple docstring'''
if self.task == "multiple-choice":
UpperCamelCase__ :List[str] = {0: '''batch''', 1: '''choice''', 2: '''sequence'''}
else:
UpperCamelCase__ :Tuple = {0: '''batch''', 1: '''sequence'''}
return OrderedDict(
[
('''input_ids''', dynamic_axis),
('''attention_mask''', dynamic_axis),
] ) | 97 | 0 |
'''simple docstring'''
import io
import json
import unittest
from parameterized import parameterized
from transformers import FSMTForConditionalGeneration, FSMTTokenizer
from transformers.testing_utils import get_tests_dir, require_torch, slow, torch_device
from utils import calculate_bleu
_SCREAMING_SNAKE_CASE : int = get_tests_dir() + "/test_data/fsmt/fsmt_val_data.json"
with io.open(filename, "r", encoding="utf-8") as f:
_SCREAMING_SNAKE_CASE : List[Any] = json.load(f)
@require_torch
class _snake_case ( unittest.TestCase ):
def lowerCAmelCase__ ( self , a__ ) -> Dict:
'''simple docstring'''
return FSMTTokenizer.from_pretrained(a__ )
def lowerCAmelCase__ ( self , a__ ) -> Tuple:
'''simple docstring'''
snake_case_ = FSMTForConditionalGeneration.from_pretrained(a__ ).to(a__ )
if torch_device == "cuda":
model.half()
return model
@parameterized.expand(
[
["en-ru", 2_6.0],
["ru-en", 2_2.0],
["en-de", 2_2.0],
["de-en", 2_9.0],
] )
@slow
def lowerCAmelCase__ ( self , a__ , a__ ) -> Optional[int]:
'''simple docstring'''
snake_case_ = F'facebook/wmt19-{pair}'
snake_case_ = self.get_tokenizer(a__ )
snake_case_ = self.get_model(a__ )
snake_case_ = bleu_data[pair]["src"]
snake_case_ = bleu_data[pair]["tgt"]
snake_case_ = tokenizer(a__ , return_tensors="pt" , truncation=a__ , padding="longest" ).to(a__ )
snake_case_ = model.generate(
input_ids=batch.input_ids , num_beams=8 , )
snake_case_ = tokenizer.batch_decode(
a__ , skip_special_tokens=a__ , clean_up_tokenization_spaces=a__ )
snake_case_ = calculate_bleu(a__ , a__ )
print(a__ )
self.assertGreaterEqual(scores["bleu"] , a__ )
| 85 |
'''simple docstring'''
import json
import pathlib
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_available():
import torch
if is_vision_available():
from PIL import Image
from transformers import ConditionalDetrImageProcessor
class lowercase ( unittest.TestCase ):
"""simple docstring"""
def __init__( self , UpperCamelCase_ , UpperCamelCase_=7 , UpperCamelCase_=3 , UpperCamelCase_=30 , UpperCamelCase_=400 , UpperCamelCase_=True , UpperCamelCase_=None , UpperCamelCase_=True , UpperCamelCase_=[0.5, 0.5, 0.5] , UpperCamelCase_=[0.5, 0.5, 0.5] , UpperCamelCase_=True , UpperCamelCase_=1 / 255 , UpperCamelCase_=True , ):
'''simple docstring'''
UpperCamelCase__ :Dict = size if size is not None else {'''shortest_edge''': 18, '''longest_edge''': 1333}
UpperCamelCase__ :str = parent
UpperCamelCase__ :List[Any] = batch_size
UpperCamelCase__ :Dict = num_channels
UpperCamelCase__ :str = min_resolution
UpperCamelCase__ :Optional[Any] = max_resolution
UpperCamelCase__ :int = do_resize
UpperCamelCase__ :Optional[Any] = size
UpperCamelCase__ :Tuple = do_normalize
UpperCamelCase__ :List[Any] = image_mean
UpperCamelCase__ :Dict = image_std
UpperCamelCase__ :Union[str, Any] = do_rescale
UpperCamelCase__ :Union[str, Any] = rescale_factor
UpperCamelCase__ :Union[str, Any] = do_pad
def lowerCAmelCase__ ( self ):
'''simple docstring'''
return {
"do_resize": self.do_resize,
"size": self.size,
"do_normalize": self.do_normalize,
"image_mean": self.image_mean,
"image_std": self.image_std,
"do_rescale": self.do_rescale,
"rescale_factor": self.rescale_factor,
"do_pad": self.do_pad,
}
def lowerCAmelCase__ ( self , UpperCamelCase_ , UpperCamelCase_=False ):
'''simple docstring'''
if not batched:
UpperCamelCase__ :List[str] = image_inputs[0]
if isinstance(UpperCamelCase_ , Image.Image ):
UpperCamelCase__ , UpperCamelCase__ :List[str] = image.size
else:
UpperCamelCase__ , UpperCamelCase__ :List[Any] = image.shape[1], image.shape[2]
if w < h:
UpperCamelCase__ :int = int(self.size['''shortest_edge'''] * h / w )
UpperCamelCase__ :Dict = self.size['''shortest_edge''']
elif w > h:
UpperCamelCase__ :int = self.size['''shortest_edge''']
UpperCamelCase__ :Tuple = int(self.size['''shortest_edge'''] * w / h )
else:
UpperCamelCase__ :str = self.size['''shortest_edge''']
UpperCamelCase__ :str = self.size['''shortest_edge''']
else:
UpperCamelCase__ :Any = []
for image in image_inputs:
UpperCamelCase__ , UpperCamelCase__ :Dict = self.get_expected_values([image] )
expected_values.append((expected_height, expected_width) )
UpperCamelCase__ :List[Any] = max(UpperCamelCase_ , key=lambda UpperCamelCase_ : item[0] )[0]
UpperCamelCase__ :Optional[int] = max(UpperCamelCase_ , key=lambda UpperCamelCase_ : item[1] )[1]
return expected_height, expected_width
@require_torch
@require_vision
class lowercase ( A__ , unittest.TestCase ):
"""simple docstring"""
_a = ConditionalDetrImageProcessor if is_vision_available() else None
def lowerCAmelCase__ ( self ):
'''simple docstring'''
UpperCamelCase__ :List[Any] = ConditionalDetrImageProcessingTester(self )
@property
def lowerCAmelCase__ ( self ):
'''simple docstring'''
return self.image_processor_tester.prepare_image_processor_dict()
def lowerCAmelCase__ ( self ):
'''simple docstring'''
UpperCamelCase__ :Tuple = 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_ , '''size''' ) )
def lowerCAmelCase__ ( self ):
'''simple docstring'''
UpperCamelCase__ :Optional[int] = self.image_processing_class.from_dict(self.image_processor_dict )
self.assertEqual(image_processor.size , {'''shortest_edge''': 18, '''longest_edge''': 1333} )
self.assertEqual(image_processor.do_pad , UpperCamelCase_ )
UpperCamelCase__ :List[str] = self.image_processing_class.from_dict(
self.image_processor_dict , size=42 , max_size=84 , pad_and_return_pixel_mask=UpperCamelCase_ )
self.assertEqual(image_processor.size , {'''shortest_edge''': 42, '''longest_edge''': 84} )
self.assertEqual(image_processor.do_pad , UpperCamelCase_ )
def lowerCAmelCase__ ( self ):
'''simple docstring'''
pass
def lowerCAmelCase__ ( self ):
'''simple docstring'''
UpperCamelCase__ :Dict = self.image_processing_class(**self.image_processor_dict )
# create random PIL images
UpperCamelCase__ :List[str] = prepare_image_inputs(self.image_processor_tester , equal_resolution=UpperCamelCase_ )
for image in image_inputs:
self.assertIsInstance(UpperCamelCase_ , Image.Image )
# Test not batched input
UpperCamelCase__ :Any = image_processing(image_inputs[0] , return_tensors='''pt''' ).pixel_values
UpperCamelCase__ , UpperCamelCase__ :str = self.image_processor_tester.get_expected_values(UpperCamelCase_ )
self.assertEqual(
encoded_images.shape , (1, self.image_processor_tester.num_channels, expected_height, expected_width) , )
# Test batched
UpperCamelCase__ , UpperCamelCase__ :str = self.image_processor_tester.get_expected_values(UpperCamelCase_ , batched=UpperCamelCase_ )
UpperCamelCase__ :List[str] = 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,
expected_height,
expected_width,
) , )
def lowerCAmelCase__ ( self ):
'''simple docstring'''
UpperCamelCase__ :List[Any] = self.image_processing_class(**self.image_processor_dict )
# create random numpy tensors
UpperCamelCase__ :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
UpperCamelCase__ :Union[str, Any] = image_processing(image_inputs[0] , return_tensors='''pt''' ).pixel_values
UpperCamelCase__ , UpperCamelCase__ :List[Any] = self.image_processor_tester.get_expected_values(UpperCamelCase_ )
self.assertEqual(
encoded_images.shape , (1, self.image_processor_tester.num_channels, expected_height, expected_width) , )
# Test batched
UpperCamelCase__ :Union[str, Any] = image_processing(UpperCamelCase_ , return_tensors='''pt''' ).pixel_values
UpperCamelCase__ , UpperCamelCase__ :str = self.image_processor_tester.get_expected_values(UpperCamelCase_ , batched=UpperCamelCase_ )
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
expected_height,
expected_width,
) , )
def lowerCAmelCase__ ( self ):
'''simple docstring'''
UpperCamelCase__ :List[str] = self.image_processing_class(**self.image_processor_dict )
# create random PyTorch tensors
UpperCamelCase__ :Any = 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
UpperCamelCase__ :str = image_processing(image_inputs[0] , return_tensors='''pt''' ).pixel_values
UpperCamelCase__ , UpperCamelCase__ :Dict = self.image_processor_tester.get_expected_values(UpperCamelCase_ )
self.assertEqual(
encoded_images.shape , (1, self.image_processor_tester.num_channels, expected_height, expected_width) , )
# Test batched
UpperCamelCase__ :List[str] = image_processing(UpperCamelCase_ , return_tensors='''pt''' ).pixel_values
UpperCamelCase__ , UpperCamelCase__ :Optional[int] = self.image_processor_tester.get_expected_values(UpperCamelCase_ , batched=UpperCamelCase_ )
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
expected_height,
expected_width,
) , )
@slow
def lowerCAmelCase__ ( self ):
'''simple docstring'''
UpperCamelCase__ :Optional[Any] = Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
with open('''./tests/fixtures/tests_samples/COCO/coco_annotations.txt''' , '''r''' ) as f:
UpperCamelCase__ :Optional[int] = json.loads(f.read() )
UpperCamelCase__ :Any = {'''image_id''': 39769, '''annotations''': target}
# encode them
UpperCamelCase__ :str = ConditionalDetrImageProcessor.from_pretrained('''microsoft/conditional-detr-resnet-50''' )
UpperCamelCase__ :List[Any] = image_processing(images=UpperCamelCase_ , annotations=UpperCamelCase_ , return_tensors='''pt''' )
# verify pixel values
UpperCamelCase__ :List[str] = torch.Size([1, 3, 800, 1066] )
self.assertEqual(encoding['''pixel_values'''].shape , UpperCamelCase_ )
UpperCamelCase__ :str = torch.tensor([0.2796, 0.3138, 0.3481] )
self.assertTrue(torch.allclose(encoding['''pixel_values'''][0, 0, 0, :3] , UpperCamelCase_ , atol=1e-4 ) )
# verify area
UpperCamelCase__ :str = torch.tensor([5887.9600, 11250.2061, 489353.8438, 837122.7500, 147967.5156, 165732.3438] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''area'''] , UpperCamelCase_ ) )
# verify boxes
UpperCamelCase__ :Optional[Any] = torch.Size([6, 4] )
self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape , UpperCamelCase_ )
UpperCamelCase__ :Optional[Any] = torch.tensor([0.5503, 0.2765, 0.0604, 0.2215] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''boxes'''][0] , UpperCamelCase_ , atol=1e-3 ) )
# verify image_id
UpperCamelCase__ :List[Any] = torch.tensor([39769] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id'''] , UpperCamelCase_ ) )
# verify is_crowd
UpperCamelCase__ :int = torch.tensor([0, 0, 0, 0, 0, 0] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd'''] , UpperCamelCase_ ) )
# verify class_labels
UpperCamelCase__ :List[str] = torch.tensor([75, 75, 63, 65, 17, 17] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels'''] , UpperCamelCase_ ) )
# verify orig_size
UpperCamelCase__ :Tuple = torch.tensor([480, 640] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size'''] , UpperCamelCase_ ) )
# verify size
UpperCamelCase__ :Union[str, Any] = torch.tensor([800, 1066] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size'''] , UpperCamelCase_ ) )
@slow
def lowerCAmelCase__ ( self ):
'''simple docstring'''
UpperCamelCase__ :List[str] = Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
with open('''./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt''' , '''r''' ) as f:
UpperCamelCase__ :Tuple = json.loads(f.read() )
UpperCamelCase__ :List[str] = {'''file_name''': '''000000039769.png''', '''image_id''': 39769, '''segments_info''': target}
UpperCamelCase__ :Any = pathlib.Path('''./tests/fixtures/tests_samples/COCO/coco_panoptic''' )
# encode them
UpperCamelCase__ :List[Any] = ConditionalDetrImageProcessor(format='''coco_panoptic''' )
UpperCamelCase__ :Dict = image_processing(images=UpperCamelCase_ , annotations=UpperCamelCase_ , masks_path=UpperCamelCase_ , return_tensors='''pt''' )
# verify pixel values
UpperCamelCase__ :str = torch.Size([1, 3, 800, 1066] )
self.assertEqual(encoding['''pixel_values'''].shape , UpperCamelCase_ )
UpperCamelCase__ :Optional[int] = torch.tensor([0.2796, 0.3138, 0.3481] )
self.assertTrue(torch.allclose(encoding['''pixel_values'''][0, 0, 0, :3] , UpperCamelCase_ , atol=1e-4 ) )
# verify area
UpperCamelCase__ :Tuple = torch.tensor([147979.6875, 165527.0469, 484638.5938, 11292.9375, 5879.6562, 7634.1147] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''area'''] , UpperCamelCase_ ) )
# verify boxes
UpperCamelCase__ :Any = torch.Size([6, 4] )
self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape , UpperCamelCase_ )
UpperCamelCase__ :List[Any] = torch.tensor([0.2625, 0.5437, 0.4688, 0.8625] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''boxes'''][0] , UpperCamelCase_ , atol=1e-3 ) )
# verify image_id
UpperCamelCase__ :List[str] = torch.tensor([39769] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id'''] , UpperCamelCase_ ) )
# verify is_crowd
UpperCamelCase__ :Union[str, Any] = torch.tensor([0, 0, 0, 0, 0, 0] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd'''] , UpperCamelCase_ ) )
# verify class_labels
UpperCamelCase__ :str = torch.tensor([17, 17, 63, 75, 75, 93] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels'''] , UpperCamelCase_ ) )
# verify masks
UpperCamelCase__ :Optional[Any] = 822873
self.assertEqual(encoding['''labels'''][0]['''masks'''].sum().item() , UpperCamelCase_ )
# verify orig_size
UpperCamelCase__ :List[str] = torch.tensor([480, 640] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size'''] , UpperCamelCase_ ) )
# verify size
UpperCamelCase__ :List[Any] = torch.tensor([800, 1066] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size'''] , UpperCamelCase_ ) ) | 97 | 0 |
"""simple docstring"""
# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
lowerCamelCase__ = {"""configuration_mra""": ["""MRA_PRETRAINED_CONFIG_ARCHIVE_MAP""", """MraConfig"""]}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCamelCase__ = [
"""MRA_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""MraForMaskedLM""",
"""MraForMultipleChoice""",
"""MraForQuestionAnswering""",
"""MraForSequenceClassification""",
"""MraForTokenClassification""",
"""MraLayer""",
"""MraModel""",
"""MraPreTrainedModel""",
]
if TYPE_CHECKING:
from .configuration_mra import MRA_PRETRAINED_CONFIG_ARCHIVE_MAP, MraConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_mra import (
MRA_PRETRAINED_MODEL_ARCHIVE_LIST,
MraForMaskedLM,
MraForMultipleChoice,
MraForQuestionAnswering,
MraForSequenceClassification,
MraForTokenClassification,
MraLayer,
MraModel,
MraPreTrainedModel,
)
else:
import sys
lowerCamelCase__ = _LazyModule(__name__, globals()["""__file__"""], _import_structure) | 86 |
'''simple docstring'''
from collections import defaultdict
class lowercase :
"""simple docstring"""
def __init__( self , UpperCamelCase_ , UpperCamelCase_ ):
'''simple docstring'''
UpperCamelCase__ :List[Any] = total # total no of tasks (N)
# DP table will have a dimension of (2^M)*N
# initially all values are set to -1
UpperCamelCase__ :Union[str, Any] = [
[-1 for i in range(total + 1 )] for j in range(2 ** len(UpperCamelCase_ ) )
]
UpperCamelCase__ :str = defaultdict(UpperCamelCase_ ) # stores the list of persons for each task
# final_mask is used to check if all persons are included by setting all bits
# to 1
UpperCamelCase__ :Optional[int] = (1 << len(UpperCamelCase_ )) - 1
def lowerCAmelCase__ ( self , UpperCamelCase_ , UpperCamelCase_ ):
'''simple docstring'''
if mask == self.final_mask:
return 1
# if not everyone gets the task and no more tasks are available, return 0
if task_no > self.total_tasks:
return 0
# if case already considered
if self.dp[mask][task_no] != -1:
return self.dp[mask][task_no]
# Number of ways when we don't this task in the arrangement
UpperCamelCase__ :str = self.count_ways_until(UpperCamelCase_ , task_no + 1 )
# now assign the tasks one by one to all possible persons and recursively
# assign for the remaining tasks.
if task_no in self.task:
for p in self.task[task_no]:
# if p is already given a task
if mask & (1 << p):
continue
# assign this task to p and change the mask value. And recursively
# assign tasks with the new mask value.
total_ways_util += self.count_ways_until(mask | (1 << p) , task_no + 1 )
# save the value.
UpperCamelCase__ :Optional[int] = total_ways_util
return self.dp[mask][task_no]
def lowerCAmelCase__ ( self , UpperCamelCase_ ):
'''simple docstring'''
for i in range(len(UpperCamelCase_ ) ):
for j in task_performed[i]:
self.task[j].append(UpperCamelCase_ )
# call the function to fill the DP table, final answer is stored in dp[0][1]
return self.count_ways_until(0 , 1 )
if __name__ == "__main__":
__snake_case = 5 # total no of tasks (the value of N)
# the list of tasks that can be done by M persons.
__snake_case = [[1, 3, 4], [1, 2, 5], [3, 4]]
print(
AssignmentUsingBitmask(task_performed, total_tasks).count_no_of_ways(
task_performed
)
) | 97 | 0 |
UpperCamelCase = [0, 2, 4, 6, 8]
UpperCamelCase = [1, 3, 5, 7, 9]
def lowercase_ ( _lowerCamelCase : int , _lowerCamelCase : int , _lowerCamelCase : list[int] , _lowerCamelCase : int):
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
lowercase__ : str = 0
for digit in range(10):
lowercase__ : str = digit
result += reversible_numbers(
0 , (remainder + 2 * digit) // 10 , _lowerCamelCase , _lowerCamelCase)
return result
lowercase__ : Dict = 0
for digita in range(10):
lowercase__ : int = digita
if (remainder + digita) % 2 == 0:
lowercase__ : Optional[Any] = ODD_DIGITS
else:
lowercase__ : str = EVEN_DIGITS
for digita in other_parity_digits:
lowercase__ : List[str] = digita
result += reversible_numbers(
remaining_length - 2 , (remainder + digita + digita) // 10 , _lowerCamelCase , _lowerCamelCase , )
return result
def lowercase_ ( _lowerCamelCase : int = 9):
lowercase__ : Tuple = 0
for length in range(1 , max_power + 1):
result += reversible_numbers(_lowerCamelCase , 0 , [0] * length , _lowerCamelCase)
return result
if __name__ == "__main__":
print(f"{solution() = }")
| 87 |
'''simple docstring'''
import csv
import tweepy
# Twitter API credentials
__snake_case = ''''''
__snake_case = ''''''
__snake_case = ''''''
__snake_case = ''''''
def a ( __a ) -> None:
'''simple docstring'''
UpperCamelCase__ :List[Any] = tweepy.OAuthHandler(__a , __a )
auth.set_access_token(__a , __a )
UpperCamelCase__ :List[str] = tweepy.API(__a )
# initialize a list to hold all the tweepy Tweets
UpperCamelCase__ :Dict = []
# make initial request for most recent tweets (200 is the maximum allowed count)
UpperCamelCase__ :Tuple = api.user_timeline(screen_name=__a , count=200 )
# save most recent tweets
alltweets.extend(__a )
# save the id of the oldest tweet less one
UpperCamelCase__ :Union[str, Any] = alltweets[-1].id - 1
# keep grabbing tweets until there are no tweets left to grab
while len(__a ) > 0:
print(f'''getting tweets before {oldest}''' )
# all subsequent requests use the max_id param to prevent duplicates
UpperCamelCase__ :Union[str, Any] = api.user_timeline(
screen_name=__a , count=200 , max_id=__a )
# save most recent tweets
alltweets.extend(__a )
# update the id of the oldest tweet less one
UpperCamelCase__ :Tuple = alltweets[-1].id - 1
print(f'''...{len(__a )} tweets downloaded so far''' )
# transform the tweepy tweets into a 2D array that will populate the csv
UpperCamelCase__ :int = [[tweet.id_str, tweet.created_at, tweet.text] for tweet in alltweets]
# write the csv
with open(f'''new_{screen_name}_tweets.csv''' , '''w''' ) as f:
UpperCamelCase__ :Tuple = csv.writer(__a )
writer.writerow(['''id''', '''created_at''', '''text'''] )
writer.writerows(__a )
if __name__ == "__main__":
# pass in the username of the account you want to download
get_all_tweets('''FirePing32''') | 97 | 0 |
def a__ ( A_ ):
'''simple docstring'''
if collection == []:
return []
# get some information about the collection
__magic_name__ = len(A_ )
__magic_name__ = max(A_ )
__magic_name__ = min(A_ )
# create the counting array
__magic_name__ = coll_max + 1 - coll_min
__magic_name__ = [0] * counting_arr_length
# count how much a number appears in the collection
for number in collection:
counting_arr[number - coll_min] += 1
# sum each position with it's predecessors. now, counting_arr[i] tells
# us how many elements <= i has in the collection
for i in range(1, A_ ):
__magic_name__ = counting_arr[i] + counting_arr[i - 1]
# create the output collection
__magic_name__ = [0] * coll_len
# place the elements in the output, respecting the original order (stable
# sort) from end to begin, updating counting_arr
for i in reversed(range(0, A_ ) ):
__magic_name__ = collection[i]
counting_arr[collection[i] - coll_min] -= 1
return ordered
def a__ ( A_ ):
'''simple docstring'''
return "".join([chr(A_ ) for i in counting_sort([ord(A_ ) for c in string] )] )
if __name__ == "__main__":
# Test string sort
assert counting_sort_string('thisisthestring') == "eghhiiinrsssttt"
__lowerCAmelCase : Tuple = input('Enter numbers separated by a comma:\n').strip()
__lowerCAmelCase : Dict = [int(item) for item in user_input.split(',')]
print(counting_sort(unsorted))
| 88 |
'''simple docstring'''
import argparse
from collections import OrderedDict
from pathlib import Path
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from torchvision.transforms import functional as F
from transformers import DetrImageProcessor, TableTransformerConfig, TableTransformerForObjectDetection
from transformers.utils import logging
logging.set_verbosity_info()
__snake_case = logging.get_logger(__name__)
# here we list all keys to be renamed (original name on the left, our name on the right)
__snake_case = []
for i in range(6):
# 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 encoder + 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.encoder.norm.weight''', '''encoder.layernorm.weight'''),
('''transformer.encoder.norm.bias''', '''encoder.layernorm.bias'''),
('''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'''),
]
)
def a ( __a , __a , __a ) -> List[str]:
'''simple docstring'''
UpperCamelCase__ :List[Any] = state_dict.pop(__a )
UpperCamelCase__ :int = val
def a ( __a ) -> Any:
'''simple docstring'''
UpperCamelCase__ :Tuple = OrderedDict()
for key, value in state_dict.items():
if "backbone.0.body" in key:
UpperCamelCase__ :Dict = key.replace('''backbone.0.body''' , '''backbone.conv_encoder.model''' )
UpperCamelCase__ :List[str] = value
else:
UpperCamelCase__ :Dict = value
return new_state_dict
def a ( __a ) -> Optional[Any]:
'''simple docstring'''
UpperCamelCase__ :Optional[Any] = ''''''
# 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)
UpperCamelCase__ :Optional[Any] = state_dict.pop(f'''{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight''' )
UpperCamelCase__ :str = 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
UpperCamelCase__ :Any = in_proj_weight[:256, :]
UpperCamelCase__ :Tuple = in_proj_bias[:256]
UpperCamelCase__ :Optional[int] = in_proj_weight[256:512, :]
UpperCamelCase__ :Optional[Any] = in_proj_bias[256:512]
UpperCamelCase__ :Tuple = in_proj_weight[-256:, :]
UpperCamelCase__ :Dict = 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
UpperCamelCase__ :List[str] = state_dict.pop(f'''{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_weight''' )
UpperCamelCase__ :Optional[Any] = 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
UpperCamelCase__ :Any = in_proj_weight[:256, :]
UpperCamelCase__ :Optional[int] = in_proj_bias[:256]
UpperCamelCase__ :Tuple = in_proj_weight[256:512, :]
UpperCamelCase__ :Dict = in_proj_bias[256:512]
UpperCamelCase__ :Any = in_proj_weight[-256:, :]
UpperCamelCase__ :Dict = in_proj_bias[-256:]
# read in weights + bias of input projection layer of cross-attention
UpperCamelCase__ :List[str] = state_dict.pop(
f'''{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_weight''' )
UpperCamelCase__ :Any = 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
UpperCamelCase__ :Optional[Any] = in_proj_weight_cross_attn[:256, :]
UpperCamelCase__ :Any = in_proj_bias_cross_attn[:256]
UpperCamelCase__ :Any = in_proj_weight_cross_attn[256:512, :]
UpperCamelCase__ :Dict = in_proj_bias_cross_attn[256:512]
UpperCamelCase__ :str = in_proj_weight_cross_attn[-256:, :]
UpperCamelCase__ :Tuple = in_proj_bias_cross_attn[-256:]
def a ( __a , __a ) -> Optional[int]:
'''simple docstring'''
UpperCamelCase__ , UpperCamelCase__ :str = image.size
UpperCamelCase__ :Optional[Any] = max(__a , __a )
UpperCamelCase__ :List[Any] = 800 if '''detection''' in checkpoint_url else 1000
UpperCamelCase__ :Dict = target_max_size / current_max_size
UpperCamelCase__ :Any = image.resize((int(round(scale * width ) ), int(round(scale * height ) )) )
return resized_image
def a ( __a ) -> int:
'''simple docstring'''
UpperCamelCase__ :Any = F.to_tensor(__a )
UpperCamelCase__ :int = F.normalize(__a , mean=[0.4_8_5, 0.4_5_6, 0.4_0_6] , std=[0.2_2_9, 0.2_2_4, 0.2_2_5] )
return image
@torch.no_grad()
def a ( __a , __a , __a ) -> Dict:
'''simple docstring'''
logger.info('''Converting model...''' )
# load original state dict
UpperCamelCase__ :Optional[Any] = torch.hub.load_state_dict_from_url(__a , map_location='''cpu''' )
# rename keys
for src, dest in rename_keys:
rename_key(__a , __a , __a )
UpperCamelCase__ :Any = rename_backbone_keys(__a )
# query, key and value matrices need special treatment
read_in_q_k_v(__a )
# important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
UpperCamelCase__ :Dict = '''model.'''
for key in state_dict.copy().keys():
if not key.startswith('''class_labels_classifier''' ) and not key.startswith('''bbox_predictor''' ):
UpperCamelCase__ :Optional[Any] = state_dict.pop(__a )
UpperCamelCase__ :int = val
# create HuggingFace model and load state dict
UpperCamelCase__ :str = TableTransformerConfig(
backbone='''resnet18''' , mask_loss_coefficient=1 , dice_loss_coefficient=1 , ce_loss_coefficient=1 , bbox_loss_coefficient=5 , giou_loss_coefficient=2 , eos_coefficient=0.4 , class_cost=1 , bbox_cost=5 , giou_cost=2 , )
if "detection" in checkpoint_url:
UpperCamelCase__ :List[str] = 15
UpperCamelCase__ :int = 2
UpperCamelCase__ :Tuple = {0: '''table''', 1: '''table rotated'''}
UpperCamelCase__ :int = idalabel
UpperCamelCase__ :Dict = {v: k for k, v in idalabel.items()}
else:
UpperCamelCase__ :int = 125
UpperCamelCase__ :List[str] = 6
UpperCamelCase__ :Optional[Any] = {
0: '''table''',
1: '''table column''',
2: '''table row''',
3: '''table column header''',
4: '''table projected row header''',
5: '''table spanning cell''',
}
UpperCamelCase__ :Dict = idalabel
UpperCamelCase__ :Optional[Any] = {v: k for k, v in idalabel.items()}
UpperCamelCase__ :List[Any] = DetrImageProcessor(
format='''coco_detection''' , max_size=800 if '''detection''' in checkpoint_url else 1000 )
UpperCamelCase__ :int = TableTransformerForObjectDetection(__a )
model.load_state_dict(__a )
model.eval()
# verify our conversion
UpperCamelCase__ :Dict = '''example_pdf.png''' if '''detection''' in checkpoint_url else '''example_table.png'''
UpperCamelCase__ :Optional[Any] = hf_hub_download(repo_id='''nielsr/example-pdf''' , repo_type='''dataset''' , filename=__a )
UpperCamelCase__ :Tuple = Image.open(__a ).convert('''RGB''' )
UpperCamelCase__ :int = normalize(resize(__a , __a ) ).unsqueeze(0 )
UpperCamelCase__ :Optional[int] = model(__a )
if "detection" in checkpoint_url:
UpperCamelCase__ :Dict = (1, 15, 3)
UpperCamelCase__ :List[Any] = torch.tensor(
[[-6.7_8_9_7, -1_6.9_9_8_5, 6.7_9_3_7], [-8.0_1_8_6, -2_2.2_1_9_2, 6.9_6_7_7], [-7.3_1_1_7, -2_1.0_7_0_8, 7.4_0_5_5]] )
UpperCamelCase__ :Tuple = torch.tensor([[0.4_8_6_7, 0.1_7_6_7, 0.6_7_3_2], [0.6_7_1_8, 0.4_4_7_9, 0.3_8_3_0], [0.4_7_1_6, 0.1_7_6_0, 0.6_3_6_4]] )
else:
UpperCamelCase__ :Optional[Any] = (1, 125, 7)
UpperCamelCase__ :Dict = torch.tensor(
[[-1_8.1_4_3_0, -8.3_2_1_4, 4.8_2_7_4], [-1_8.4_6_8_5, -7.1_3_6_1, -4.2_6_6_7], [-2_6.3_6_9_3, -9.3_4_2_9, -4.9_9_6_2]] )
UpperCamelCase__ :List[Any] = torch.tensor([[0.4_9_8_3, 0.5_5_9_5, 0.9_4_4_0], [0.4_9_1_6, 0.6_3_1_5, 0.5_9_5_4], [0.6_1_0_8, 0.8_6_3_7, 0.1_1_3_5]] )
assert outputs.logits.shape == expected_shape
assert torch.allclose(outputs.logits[0, :3, :3] , __a , atol=1e-4 )
assert torch.allclose(outputs.pred_boxes[0, :3, :3] , __a , 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(__a ).mkdir(exist_ok=__a )
model.save_pretrained(__a )
image_processor.save_pretrained(__a )
if push_to_hub:
# Push model to HF hub
logger.info('''Pushing model to the hub...''' )
UpperCamelCase__ :Union[str, Any] = (
'''microsoft/table-transformer-detection'''
if '''detection''' in checkpoint_url
else '''microsoft/table-transformer-structure-recognition'''
)
model.push_to_hub(__a )
image_processor.push_to_hub(__a )
if __name__ == "__main__":
__snake_case = argparse.ArgumentParser()
parser.add_argument(
'''--checkpoint_url''',
default='''https://pubtables1m.blob.core.windows.net/model/pubtables1m_detection_detr_r18.pth''',
type=str,
choices=[
'''https://pubtables1m.blob.core.windows.net/model/pubtables1m_detection_detr_r18.pth''',
'''https://pubtables1m.blob.core.windows.net/model/pubtables1m_structure_detr_r18.pth''',
],
help='''URL of the Table Transformer checkpoint 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 or not to push the converted model to the 🤗 hub.'''
)
__snake_case = parser.parse_args()
convert_table_transformer_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub) | 97 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
__lowerCAmelCase = {
'''configuration_swiftformer''': [
'''SWIFTFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''SwiftFormerConfig''',
'''SwiftFormerOnnxConfig''',
]
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__lowerCAmelCase = [
'''SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''SwiftFormerForImageClassification''',
'''SwiftFormerModel''',
'''SwiftFormerPreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_swiftformer import (
SWIFTFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
SwiftFormerConfig,
SwiftFormerOnnxConfig,
)
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_swiftformer import (
SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
SwiftFormerForImageClassification,
SwiftFormerModel,
SwiftFormerPreTrainedModel,
)
else:
import sys
__lowerCAmelCase = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 89 |
'''simple docstring'''
from __future__ import annotations
from fractions import Fraction
from math import gcd, sqrt
def a ( __a ) -> bool:
'''simple docstring'''
UpperCamelCase__ :int = int(number**0.5 )
return number == sq * sq
def a ( __a , __a , __a , __a , __a , __a ) -> tuple[int, int]:
'''simple docstring'''
UpperCamelCase__ :int = x_num * y_den * z_den + y_num * x_den * z_den + z_num * x_den * y_den
UpperCamelCase__ :int = x_den * y_den * z_den
UpperCamelCase__ :int = gcd(__a , __a )
top //= hcf
bottom //= hcf
return top, bottom
def a ( __a = 35 ) -> int:
'''simple docstring'''
UpperCamelCase__ :set = set()
UpperCamelCase__ :int
UpperCamelCase__ :Fraction = Fraction(0 )
UpperCamelCase__ :tuple[int, int]
for x_num in range(1 , order + 1 ):
for x_den in range(x_num + 1 , order + 1 ):
for y_num in range(1 , order + 1 ):
for y_den in range(y_num + 1 , order + 1 ):
# n=1
UpperCamelCase__ :int = x_num * y_den + x_den * y_num
UpperCamelCase__ :Any = x_den * y_den
UpperCamelCase__ :Tuple = gcd(__a , __a )
z_num //= hcf
z_den //= hcf
if 0 < z_num < z_den <= order:
UpperCamelCase__ :Tuple = add_three(
__a , __a , __a , __a , __a , __a )
unique_s.add(__a )
# n=2
UpperCamelCase__ :List[str] = (
x_num * x_num * y_den * y_den + x_den * x_den * y_num * y_num
)
UpperCamelCase__ :Dict = x_den * x_den * y_den * y_den
if is_sq(__a ) and is_sq(__a ):
UpperCamelCase__ :Any = int(sqrt(__a ) )
UpperCamelCase__ :Optional[int] = int(sqrt(__a ) )
UpperCamelCase__ :int = gcd(__a , __a )
z_num //= hcf
z_den //= hcf
if 0 < z_num < z_den <= order:
UpperCamelCase__ :Tuple = add_three(
__a , __a , __a , __a , __a , __a )
unique_s.add(__a )
# n=-1
UpperCamelCase__ :Tuple = x_num * y_num
UpperCamelCase__ :Union[str, Any] = x_den * y_num + x_num * y_den
UpperCamelCase__ :List[str] = gcd(__a , __a )
z_num //= hcf
z_den //= hcf
if 0 < z_num < z_den <= order:
UpperCamelCase__ :Union[str, Any] = add_three(
__a , __a , __a , __a , __a , __a )
unique_s.add(__a )
# n=2
UpperCamelCase__ :Optional[Any] = x_num * x_num * y_num * y_num
UpperCamelCase__ :Tuple = (
x_den * x_den * y_num * y_num + x_num * x_num * y_den * y_den
)
if is_sq(__a ) and is_sq(__a ):
UpperCamelCase__ :str = int(sqrt(__a ) )
UpperCamelCase__ :Any = int(sqrt(__a ) )
UpperCamelCase__ :Dict = gcd(__a , __a )
z_num //= hcf
z_den //= hcf
if 0 < z_num < z_den <= order:
UpperCamelCase__ :int = add_three(
__a , __a , __a , __a , __a , __a )
unique_s.add(__a )
for num, den in unique_s:
total += Fraction(__a , __a )
return total.denominator + total.numerator
if __name__ == "__main__":
print(F"""{solution() = }""") | 97 | 0 |
import gc
import tempfile
import unittest
import numpy as np
import torch
from diffusers import VersatileDiffusionTextToImagePipeline
from diffusers.utils.testing_utils import nightly, require_torch_gpu, torch_device
__A = False
class __lowerCAmelCase ( unittest.TestCase ):
"""simple docstring"""
pass
@nightly
@require_torch_gpu
class __lowerCAmelCase ( unittest.TestCase ):
"""simple docstring"""
def lowercase_ ( self ) -> int:
'''simple docstring'''
# clean up the VRAM after each test
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def lowercase_ ( self ) -> str:
'''simple docstring'''
__lowerCamelCase = VersatileDiffusionTextToImagePipeline.from_pretrained('shi-labs/versatile-diffusion' )
# remove text_unet
pipe.remove_unused_weights()
pipe.to(lowerCamelCase__ )
pipe.set_progress_bar_config(disable=lowerCamelCase__ )
__lowerCamelCase = 'A painting of a squirrel eating a burger '
__lowerCamelCase = torch.manual_seed(0 )
__lowerCamelCase = pipe(
prompt=lowerCamelCase__ , generator=lowerCamelCase__ , guidance_scale=7.5 , num_inference_steps=2 , output_type='numpy' ).images
with tempfile.TemporaryDirectory() as tmpdirname:
pipe.save_pretrained(lowerCamelCase__ )
__lowerCamelCase = VersatileDiffusionTextToImagePipeline.from_pretrained(lowerCamelCase__ )
pipe.to(lowerCamelCase__ )
pipe.set_progress_bar_config(disable=lowerCamelCase__ )
__lowerCamelCase = generator.manual_seed(0 )
__lowerCamelCase = pipe(
prompt=lowerCamelCase__ , generator=lowerCamelCase__ , guidance_scale=7.5 , num_inference_steps=2 , output_type='numpy' ).images
assert np.abs(image - new_image ).sum() < 1e-5, "Models don't have the same forward pass"
def lowercase_ ( self ) -> Optional[Any]:
'''simple docstring'''
__lowerCamelCase = VersatileDiffusionTextToImagePipeline.from_pretrained(
'shi-labs/versatile-diffusion' , torch_dtype=torch.floataa )
pipe.to(lowerCamelCase__ )
pipe.set_progress_bar_config(disable=lowerCamelCase__ )
__lowerCamelCase = 'A painting of a squirrel eating a burger '
__lowerCamelCase = torch.manual_seed(0 )
__lowerCamelCase = pipe(
prompt=lowerCamelCase__ , generator=lowerCamelCase__ , guidance_scale=7.5 , num_inference_steps=50 , output_type='numpy' ).images
__lowerCamelCase = image[0, 253:256, 253:256, -1]
assert image.shape == (1, 512, 512, 3)
__lowerCamelCase = np.array([0.33_67, 0.31_69, 0.26_56, 0.38_70, 0.47_90, 0.37_96, 0.40_09, 0.48_78, 0.47_78] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
| 90 |
'''simple docstring'''
def a ( ) -> Union[str, Any]:
'''simple docstring'''
UpperCamelCase__ :Optional[int] = []
UpperCamelCase__ :int = 1
while len(__a ) < 1e6:
constant.append(str(__a ) )
i += 1
UpperCamelCase__ :Union[str, Any] = ''''''.join(__a )
return (
int(constant[0] )
* int(constant[9] )
* int(constant[99] )
* int(constant[999] )
* int(constant[9999] )
* int(constant[99999] )
* int(constant[999999] )
)
if __name__ == "__main__":
print(solution()) | 97 | 0 |
"""simple docstring"""
from manim import *
class lowerCAmelCase__ ( UpperCAmelCase__ ):
'''simple docstring'''
def _SCREAMING_SNAKE_CASE ( self : Optional[int]):
'''simple docstring'''
SCREAMING_SNAKE_CASE_ : List[str] = Rectangle(height=0.5 , width=0.5)
SCREAMING_SNAKE_CASE_ : Dict = Rectangle(height=0.46 , width=0.46).set_stroke(width=0)
SCREAMING_SNAKE_CASE_ : Any = [mem.copy() for i in range(6)]
SCREAMING_SNAKE_CASE_ : Optional[int] = [mem.copy() for i in range(6)]
SCREAMING_SNAKE_CASE_ : Optional[int] = VGroup(*lowercase_).arrange(lowercase_ , buff=0)
SCREAMING_SNAKE_CASE_ : Optional[int] = VGroup(*lowercase_).arrange(lowercase_ , buff=0)
SCREAMING_SNAKE_CASE_ : List[Any] = VGroup(lowercase_ , lowercase_).arrange(lowercase_ , buff=0)
SCREAMING_SNAKE_CASE_ : List[str] = Text('''CPU''' , font_size=24)
SCREAMING_SNAKE_CASE_ : Optional[Any] = Group(lowercase_ , lowercase_).arrange(lowercase_ , buff=0.5 , aligned_edge=lowercase_)
cpu.move_to([-2.5, -0.5, 0])
self.add(lowercase_)
SCREAMING_SNAKE_CASE_ : List[str] = [mem.copy() for i in range(1)]
SCREAMING_SNAKE_CASE_ : Union[str, Any] = VGroup(*lowercase_).arrange(lowercase_ , buff=0)
SCREAMING_SNAKE_CASE_ : List[str] = Text('''GPU''' , font_size=24)
SCREAMING_SNAKE_CASE_ : Optional[int] = Group(lowercase_ , lowercase_).arrange(lowercase_ , buff=0.5 , aligned_edge=lowercase_)
gpu.align_to(lowercase_ , lowercase_)
gpu.set_x(gpu.get_x() - 1)
self.add(lowercase_)
SCREAMING_SNAKE_CASE_ : Optional[Any] = [mem.copy() for i in range(6)]
SCREAMING_SNAKE_CASE_ : Tuple = VGroup(*lowercase_).arrange(lowercase_ , buff=0)
SCREAMING_SNAKE_CASE_ : Optional[int] = Text('''Model''' , font_size=24)
SCREAMING_SNAKE_CASE_ : Tuple = Group(lowercase_ , lowercase_).arrange(lowercase_ , buff=0.5 , aligned_edge=lowercase_)
model.move_to([3, -1.0, 0])
self.play(
Create(lowercase_ , run_time=1) , Create(lowercase_ , run_time=1) , Create(lowercase_ , run_time=1) , )
SCREAMING_SNAKE_CASE_ : Optional[Any] = MarkupText(
F'First, an empty model skeleton is loaded\ninto <span fgcolor=\'{YELLOW}\'>memory</span> without using much RAM.' , font_size=24 , )
SCREAMING_SNAKE_CASE_ : Dict = Square(side_length=2.2)
key.move_to([-5, 2, 0])
SCREAMING_SNAKE_CASE_ : Union[str, Any] = MarkupText(
F'<b>Key:</b>\n\n<span fgcolor=\'{YELLOW}\'>●</span> Empty Model' , font_size=18 , )
key_text.move_to([-5, 2.4, 0])
step_a.move_to([2, 2, 0])
self.play(Write(lowercase_ , run_time=2.5) , Write(lowercase_) , Write(lowercase_))
self.add(lowercase_)
SCREAMING_SNAKE_CASE_ : Any = []
SCREAMING_SNAKE_CASE_ : Any = []
SCREAMING_SNAKE_CASE_ : List[str] = []
for i, rect in enumerate(lowercase_):
SCREAMING_SNAKE_CASE_ : Any = Rectangle(height=0.46 , width=0.46).set_stroke(width=0.0).set_fill(lowercase_ , opacity=0.7)
cpu_target.move_to(lowercase_)
cpu_target.generate_target()
SCREAMING_SNAKE_CASE_ : Optional[int] = 0.46 / 4
SCREAMING_SNAKE_CASE_ : str = 0.46 / 3
if i == 0:
cpu_target.target.next_to(cpu_left_col_base[0].get_corner(DOWN + LEFT) , buff=0.02 , direction=lowercase_)
cpu_target.target.set_x(cpu_target.target.get_x() + 0.1)
elif i == 3:
cpu_target.target.next_to(cpu_targs[0].target , direction=lowercase_ , buff=0.0)
else:
cpu_target.target.next_to(cpu_targs[i - 1].target , direction=lowercase_ , buff=0.0)
cpu_targs.append(lowercase_)
first_animations.append(rect.animate(run_time=0.5).set_stroke(lowercase_))
second_animations.append(MoveToTarget(lowercase_ , run_time=1.5))
self.play(*lowercase_)
self.play(*lowercase_)
self.wait()
| 91 |
'''simple docstring'''
from PIL import Image
def a ( __a , __a ) -> Image:
'''simple docstring'''
def brightness(__a ) -> float:
return 128 + level + (c - 128)
if not -2_5_5.0 <= level <= 2_5_5.0:
raise ValueError('''level must be between -255.0 (black) and 255.0 (white)''' )
return img.point(__a )
if __name__ == "__main__":
# Load image
with Image.open('''image_data/lena.jpg''') as img:
# Change brightness to 100
__snake_case = change_brightness(img, 100)
brigt_img.save('''image_data/lena_brightness.png''', format='''png''') | 97 | 0 |
import unittest
from transformers import DebertaVaTokenizer, DebertaVaTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin
UpperCamelCase__ = get_tests_dir("""fixtures/spiece.model""")
@require_sentencepiece
@require_tokenizers
class a__ ( snake_case__ , unittest.TestCase ):
_a : Optional[Any] = DebertaVaTokenizer
_a : Optional[Any] = DebertaVaTokenizerFast
_a : List[str] = True
_a : Optional[Any] = True
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
super().setUp()
# We have a SentencePiece fixture for testing
__lowerCAmelCase = DebertaVaTokenizer(_A , unk_token="<unk>" )
tokenizer.save_pretrained(self.tmpdirname )
def __SCREAMING_SNAKE_CASE( self , _A ):
"""simple docstring"""
__lowerCAmelCase = "this is a test"
__lowerCAmelCase = "this is a test"
return input_text, output_text
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
__lowerCAmelCase = "<pad>"
__lowerCAmelCase = 0
self.assertEqual(self.get_tokenizer()._convert_token_to_id(_A ) , _A )
self.assertEqual(self.get_tokenizer()._convert_id_to_token(_A ) , _A )
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
__lowerCAmelCase = list(self.get_tokenizer().get_vocab().keys() )
self.assertEqual(vocab_keys[0] , "<pad>" )
self.assertEqual(vocab_keys[1] , "<unk>" )
self.assertEqual(vocab_keys[-1] , "[PAD]" )
self.assertEqual(len(_A ) , 3_0_0_0_1 )
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
self.assertEqual(self.get_tokenizer().vocab_size , 3_0_0_0_0 )
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
__lowerCAmelCase = " \tHeLLo!how \n Are yoU? "
__lowerCAmelCase = ["▁hello", "!", "how", "▁are", "▁you", "?"]
# fmt: on
__lowerCAmelCase = DebertaVaTokenizer(_A , do_lower_case=_A )
__lowerCAmelCase = tokenizer.convert_ids_to_tokens(tokenizer.encode(_A , add_special_tokens=_A ) )
self.assertListEqual(_A , _A )
__lowerCAmelCase = DebertaVaTokenizerFast(_A , do_lower_case=_A )
__lowerCAmelCase = rust_tokenizer.convert_ids_to_tokens(rust_tokenizer.encode(_A , add_special_tokens=_A ) )
self.assertListEqual(_A , _A )
@unittest.skip("There is an inconsistency between slow and fast tokenizer due to a bug in the fast one." )
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
pass
@unittest.skip("There is an inconsistency between slow and fast tokenizer due to a bug in the fast one." )
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
pass
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
__lowerCAmelCase = "I was born in 92000, and this is falsé."
__lowerCAmelCase = ["▁", "<unk>", "▁was", "▁born", "▁in", "▁9", "2000", "▁", ",", "▁and", "▁this", "▁is", "▁fal", "s", "<unk>", "▁", ".", ]
# fmt: on
__lowerCAmelCase = DebertaVaTokenizer(_A , split_by_punct=_A )
__lowerCAmelCase = tokenizer.convert_ids_to_tokens(tokenizer.encode(_A , add_special_tokens=_A ) )
self.assertListEqual(_A , _A )
__lowerCAmelCase = DebertaVaTokenizerFast(_A , split_by_punct=_A )
__lowerCAmelCase = rust_tokenizer.convert_ids_to_tokens(rust_tokenizer.encode(_A , add_special_tokens=_A ) )
self.assertListEqual(_A , _A )
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
__lowerCAmelCase = "I was born in 92000, and this is falsé."
__lowerCAmelCase = ["▁i", "▁was", "▁born", "▁in", "▁9", "2000", "▁", ",", "▁and", "▁this", "▁is", "▁fal", "s", "<unk>", "▁", ".", ]
# fmt: on
__lowerCAmelCase = DebertaVaTokenizer(_A , do_lower_case=_A , split_by_punct=_A )
__lowerCAmelCase = tokenizer.convert_ids_to_tokens(tokenizer.encode(_A , add_special_tokens=_A ) )
self.assertListEqual(_A , _A )
__lowerCAmelCase = DebertaVaTokenizerFast(_A , do_lower_case=_A , split_by_punct=_A )
__lowerCAmelCase = rust_tokenizer.convert_ids_to_tokens(rust_tokenizer.encode(_A , add_special_tokens=_A ) )
self.assertListEqual(_A , _A )
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
__lowerCAmelCase = "I was born in 92000, and this is falsé."
__lowerCAmelCase = ["▁i", "▁was", "▁born", "▁in", "▁9", "2000", ",", "▁and", "▁this", "▁is", "▁fal", "s", "<unk>", ".", ]
# fmt: on
__lowerCAmelCase = DebertaVaTokenizer(_A , do_lower_case=_A , split_by_punct=_A )
__lowerCAmelCase = tokenizer.convert_ids_to_tokens(tokenizer.encode(_A , add_special_tokens=_A ) )
self.assertListEqual(_A , _A )
__lowerCAmelCase = DebertaVaTokenizerFast(_A , do_lower_case=_A , split_by_punct=_A )
__lowerCAmelCase = rust_tokenizer.convert_ids_to_tokens(rust_tokenizer.encode(_A , add_special_tokens=_A ) )
self.assertListEqual(_A , _A )
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
__lowerCAmelCase = "I was born in 92000, and this is falsé."
__lowerCAmelCase = ["▁", "<unk>", "▁was", "▁born", "▁in", "▁9", "2000", "▁", ",", "▁and", "▁this", "▁is", "▁fal", "s", "<unk>", "▁", ".", ]
# fmt: on
__lowerCAmelCase = DebertaVaTokenizer(_A , do_lower_case=_A , split_by_punct=_A )
__lowerCAmelCase = tokenizer.convert_ids_to_tokens(tokenizer.encode(_A , add_special_tokens=_A ) )
self.assertListEqual(_A , _A )
__lowerCAmelCase = DebertaVaTokenizerFast(_A , do_lower_case=_A , split_by_punct=_A )
__lowerCAmelCase = rust_tokenizer.convert_ids_to_tokens(rust_tokenizer.encode(_A , add_special_tokens=_A ) )
self.assertListEqual(_A , _A )
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
__lowerCAmelCase = " \tHeLLo!how \n Are yoU? "
__lowerCAmelCase = ["▁", "<unk>", "e", "<unk>", "o", "!", "how", "▁", "<unk>", "re", "▁yo", "<unk>", "?"]
# fmt: on
__lowerCAmelCase = DebertaVaTokenizer(_A , do_lower_case=_A , split_by_punct=_A )
__lowerCAmelCase = tokenizer.convert_ids_to_tokens(tokenizer.encode(_A , add_special_tokens=_A ) )
self.assertListEqual(_A , _A )
__lowerCAmelCase = DebertaVaTokenizerFast(_A , do_lower_case=_A , split_by_punct=_A )
__lowerCAmelCase = rust_tokenizer.convert_ids_to_tokens(rust_tokenizer.encode(_A , add_special_tokens=_A ) )
self.assertListEqual(_A , _A )
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
__lowerCAmelCase = self.get_tokenizer()
__lowerCAmelCase = self.get_rust_tokenizer()
__lowerCAmelCase = "I was born in 92000, and this is falsé."
__lowerCAmelCase = tokenizer.convert_ids_to_tokens(tokenizer.encode(_A , add_special_tokens=_A ) )
__lowerCAmelCase = rust_tokenizer.convert_ids_to_tokens(rust_tokenizer.encode(_A , add_special_tokens=_A ) )
self.assertListEqual(_A , _A )
__lowerCAmelCase = tokenizer.encode(_A , add_special_tokens=_A )
__lowerCAmelCase = rust_tokenizer.encode(_A , add_special_tokens=_A )
self.assertListEqual(_A , _A )
__lowerCAmelCase = self.get_rust_tokenizer()
__lowerCAmelCase = tokenizer.encode(_A )
__lowerCAmelCase = rust_tokenizer.encode(_A )
self.assertListEqual(_A , _A )
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
__lowerCAmelCase = "This is a test"
__lowerCAmelCase = [1_3, 1, 4_3_9_8, 2_5, 2_1, 1_2_8_9]
__lowerCAmelCase = ["▁", "T", "his", "▁is", "▁a", "▁test"]
__lowerCAmelCase = ["▁", "<unk>", "his", "▁is", "▁a", "▁test"]
__lowerCAmelCase = DebertaVaTokenizer(_A , keep_accents=_A )
__lowerCAmelCase = DebertaVaTokenizerFast(_A , keep_accents=_A )
__lowerCAmelCase = tokenizer.encode(_A , add_special_tokens=_A )
self.assertListEqual(_A , _A )
__lowerCAmelCase = tokenizer.tokenize(_A )
self.assertListEqual(_A , _A )
__lowerCAmelCase = tokenizer.convert_ids_to_tokens(_A )
self.assertListEqual(_A , _A )
__lowerCAmelCase = rust_tokenizer.encode(_A , add_special_tokens=_A )
self.assertListEqual(_A , _A )
__lowerCAmelCase = rust_tokenizer.tokenize(_A )
self.assertListEqual(_A , _A )
__lowerCAmelCase = rust_tokenizer.convert_ids_to_tokens(_A )
self.assertListEqual(_A , _A )
# fmt: off
__lowerCAmelCase = "I was born in 92000, and this is falsé."
__lowerCAmelCase = [1_3, 1, 2_3, 3_8_6, 1_9, 5_6_1, 3_0_5_0, 1_5, 1_7, 4_8, 2_5, 8_2_5_6, 1_8, 1, 9]
__lowerCAmelCase = ["▁", "I", "▁was", "▁born", "▁in", "▁9", "2000", ",", "▁and", "▁this", "▁is", "▁fal", "s", "é", ".", ]
__lowerCAmelCase = ["▁", "<unk>", "▁was", "▁born", "▁in", "▁9", "2000", ",", "▁and", "▁this", "▁is", "▁fal", "s", "<unk>", ".", ]
# fmt: on
__lowerCAmelCase = tokenizer.encode(_A , add_special_tokens=_A )
self.assertListEqual(_A , _A )
__lowerCAmelCase = tokenizer.tokenize(_A )
self.assertListEqual(_A , _A )
__lowerCAmelCase = tokenizer.convert_ids_to_tokens(_A )
self.assertListEqual(_A , _A )
__lowerCAmelCase = rust_tokenizer.encode(_A , add_special_tokens=_A )
self.assertListEqual(_A , _A )
__lowerCAmelCase = rust_tokenizer.tokenize(_A )
self.assertListEqual(_A , _A )
__lowerCAmelCase = rust_tokenizer.convert_ids_to_tokens(_A )
self.assertListEqual(_A , _A )
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
__lowerCAmelCase = DebertaVaTokenizer(_A )
__lowerCAmelCase = tokenizer.encode("sequence builders" )
__lowerCAmelCase = tokenizer.encode("multi-sequence build" )
__lowerCAmelCase = tokenizer.build_inputs_with_special_tokens(_A )
__lowerCAmelCase = tokenizer.build_inputs_with_special_tokens(_A , _A )
self.assertEqual([tokenizer.cls_token_id] + text + [tokenizer.sep_token_id] , _A )
self.assertEqual(
[tokenizer.cls_token_id] + text + [tokenizer.sep_token_id] + text_a + [tokenizer.sep_token_id] , _A , )
@slow
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
__lowerCAmelCase = {"input_ids": [[1, 3_9_8_6_7, 3_6, 1_9_3_9_0, 4_8_6, 2_7, 3_5_0_5_2, 8_1_4_3_6, 1_8, 6_0_6_8_5, 1_2_2_5, 7, 3_5_0_5_2, 8_1_4_3_6, 1_8, 9_3_6_7, 1_6_8_9_9, 1_8, 1_5_9_3_7, 5_3, 5_9_4, 7_7_3, 1_8, 1_6_2_8_7, 3_0_4_6_5, 3_6, 1_5_9_3_7, 6, 4_1_1_3_9, 3_8, 3_6_9_7_9, 6_0_7_6_3, 1_9_1, 6, 3_4_1_3_2, 9_9, 6, 5_0_5_3_8, 3_9_0, 4_3_2_3_0, 6, 3_4_1_3_2, 2_7_7_9, 2_0_8_5_0, 1_4, 6_9_9, 1_0_7_2, 1_1_9_4, 3_6, 3_8_2, 1_0_9_0_1, 5_3, 7, 6_9_9, 1_0_7_2, 2_0_8_4, 3_6, 2_0_4_2_2, 6_3_0, 5_3, 1_9, 1_0_5, 3_0_4_9, 1_8_9_6, 1_0_5_3, 1_6_8_9_9, 1_5_0_6, 1_1, 3_7_9_7_8, 4_2_4_3, 7, 1_2_3_7, 3_1_8_6_9, 2_0_0, 1_6_5_6_6, 6_5_4, 6, 3_5_0_5_2, 8_1_4_3_6, 7, 5_5_6_3_0, 1_3_5_9_3, 4, 2], [1, 2_6, 1_5_0_1_1, 1_3, 6_6_7, 8, 1_0_5_3, 1_8, 2_3_6_1_1, 1_2_3_7, 7_2_3_5_6, 1_2_8_2_0, 3_4, 1_0_4_1_3_4, 1_2_0_9, 3_5, 1_3_3_1_3, 6_6_2_7, 2_1, 2_0_2, 3_4_7, 7, 1_6_4, 2_3_9_9, 1_1, 4_6, 4_4_8_5, 4, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 5, 1_2_3_2, 2_8_6_4, 1_5_7_8_5, 1_4_9_5_1, 1_0_5, 5, 8_5_8_1, 1_2_5_0, 4, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], "token_type_ids": [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], "attention_mask": [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]} # noqa: E501
# fmt: on
self.tokenizer_integration_test_util(
expected_encoding=_A , model_name="microsoft/deberta-v2-xlarge" , revision="ad6e42c1532ddf3a15c39246b63f5559d558b670" , )
| 92 |
'''simple docstring'''
from datetime import datetime as dt
import os
from github import Github
__snake_case = [
'''good first issue''',
'''good second issue''',
'''good difficult issue''',
'''feature request''',
'''new model''',
'''wip''',
]
def a ( ) -> List[str]:
'''simple docstring'''
UpperCamelCase__ :List[str] = Github(os.environ['''GITHUB_TOKEN'''] )
UpperCamelCase__ :Tuple = g.get_repo('''huggingface/transformers''' )
UpperCamelCase__ :Union[str, Any] = repo.get_issues(state='''open''' )
for issue in open_issues:
UpperCamelCase__ :List[Any] = sorted([comment for comment in issue.get_comments()] , key=lambda __a : i.created_at , reverse=__a )
UpperCamelCase__ :List[Any] = comments[0] if len(__a ) > 0 else None
if (
last_comment is not None
and last_comment.user.login == "github-actions[bot]"
and (dt.utcnow() - issue.updated_at).days > 7
and (dt.utcnow() - issue.created_at).days >= 30
and not any(label.name.lower() in LABELS_TO_EXEMPT for label in issue.get_labels() )
):
# print(f"Would close issue {issue.number} since it has been 7 days of inactivity since bot mention.")
issue.edit(state='''closed''' )
elif (
(dt.utcnow() - issue.updated_at).days > 23
and (dt.utcnow() - issue.created_at).days >= 30
and not any(label.name.lower() in LABELS_TO_EXEMPT for label in issue.get_labels() )
):
# print(f"Would add stale comment to {issue.number}")
issue.create_comment(
'''This issue has been automatically marked as stale because it has not had '''
'''recent activity. If you think this still needs to be addressed '''
'''please comment on this thread.\n\nPlease note that issues that do not follow the '''
'''[contributing guidelines](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md) '''
'''are likely to be ignored.''' )
if __name__ == "__main__":
main() | 97 | 0 |
'''simple docstring'''
import json
from typing import TYPE_CHECKING, List, Optional, Tuple
from tokenizers import pre_tokenizers
from ...tokenization_utils_base import BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_gpta import GPTaTokenizer
if TYPE_CHECKING:
from transformers.pipelines.conversational import Conversation
_lowercase : Union[str, Any] = logging.get_logger(__name__)
_lowercase : Optional[Any] = {"vocab_file": "vocab.json", "merges_file": "merges.txt", "tokenizer_file": "tokenizer.json"}
_lowercase : str = {
"vocab_file": {
"gpt2": "https://huggingface.co/gpt2/resolve/main/vocab.json",
"gpt2-medium": "https://huggingface.co/gpt2-medium/resolve/main/vocab.json",
"gpt2-large": "https://huggingface.co/gpt2-large/resolve/main/vocab.json",
"gpt2-xl": "https://huggingface.co/gpt2-xl/resolve/main/vocab.json",
"distilgpt2": "https://huggingface.co/distilgpt2/resolve/main/vocab.json",
},
"merges_file": {
"gpt2": "https://huggingface.co/gpt2/resolve/main/merges.txt",
"gpt2-medium": "https://huggingface.co/gpt2-medium/resolve/main/merges.txt",
"gpt2-large": "https://huggingface.co/gpt2-large/resolve/main/merges.txt",
"gpt2-xl": "https://huggingface.co/gpt2-xl/resolve/main/merges.txt",
"distilgpt2": "https://huggingface.co/distilgpt2/resolve/main/merges.txt",
},
"tokenizer_file": {
"gpt2": "https://huggingface.co/gpt2/resolve/main/tokenizer.json",
"gpt2-medium": "https://huggingface.co/gpt2-medium/resolve/main/tokenizer.json",
"gpt2-large": "https://huggingface.co/gpt2-large/resolve/main/tokenizer.json",
"gpt2-xl": "https://huggingface.co/gpt2-xl/resolve/main/tokenizer.json",
"distilgpt2": "https://huggingface.co/distilgpt2/resolve/main/tokenizer.json",
},
}
_lowercase : Dict = {
"gpt2": 1_0_2_4,
"gpt2-medium": 1_0_2_4,
"gpt2-large": 1_0_2_4,
"gpt2-xl": 1_0_2_4,
"distilgpt2": 1_0_2_4,
}
class lowerCAmelCase__ ( lowerCamelCase_ ):
lowerCAmelCase_ = VOCAB_FILES_NAMES
lowerCAmelCase_ = PRETRAINED_VOCAB_FILES_MAP
lowerCAmelCase_ = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
lowerCAmelCase_ = ['''input_ids''', '''attention_mask''']
lowerCAmelCase_ = GPTaTokenizer
def __init__( self , __SCREAMING_SNAKE_CASE=None , __SCREAMING_SNAKE_CASE=None , __SCREAMING_SNAKE_CASE=None , __SCREAMING_SNAKE_CASE="<|endoftext|>" , __SCREAMING_SNAKE_CASE="<|endoftext|>" , __SCREAMING_SNAKE_CASE="<|endoftext|>" , __SCREAMING_SNAKE_CASE=False , **__SCREAMING_SNAKE_CASE , ):
"""simple docstring"""
super().__init__(
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , tokenizer_file=__SCREAMING_SNAKE_CASE , unk_token=__SCREAMING_SNAKE_CASE , bos_token=__SCREAMING_SNAKE_CASE , eos_token=__SCREAMING_SNAKE_CASE , add_prefix_space=__SCREAMING_SNAKE_CASE , **__SCREAMING_SNAKE_CASE , )
lowercase_ : int = kwargs.pop('''add_bos_token''' , __SCREAMING_SNAKE_CASE )
lowercase_ : Union[str, Any] = json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__() )
if pre_tok_state.get('''add_prefix_space''' , __SCREAMING_SNAKE_CASE ) != add_prefix_space:
lowercase_ : Optional[Any] = getattr(__SCREAMING_SNAKE_CASE , pre_tok_state.pop('''type''' ) )
lowercase_ : List[Any] = add_prefix_space
lowercase_ : Optional[int] = pre_tok_class(**__SCREAMING_SNAKE_CASE )
lowercase_ : List[str] = add_prefix_space
def _snake_case ( self , *__SCREAMING_SNAKE_CASE , **__SCREAMING_SNAKE_CASE ):
"""simple docstring"""
lowercase_ : List[str] = kwargs.get('''is_split_into_words''' , __SCREAMING_SNAKE_CASE )
assert self.add_prefix_space or not is_split_into_words, (
F'''You need to instantiate {self.__class__.__name__} with add_prefix_space=True '''
"to use it with pretokenized inputs."
)
return super()._batch_encode_plus(*__SCREAMING_SNAKE_CASE , **__SCREAMING_SNAKE_CASE )
def _snake_case ( self , *__SCREAMING_SNAKE_CASE , **__SCREAMING_SNAKE_CASE ):
"""simple docstring"""
lowercase_ : str = kwargs.get('''is_split_into_words''' , __SCREAMING_SNAKE_CASE )
assert self.add_prefix_space or not is_split_into_words, (
F'''You need to instantiate {self.__class__.__name__} with add_prefix_space=True '''
"to use it with pretokenized inputs."
)
return super()._encode_plus(*__SCREAMING_SNAKE_CASE , **__SCREAMING_SNAKE_CASE )
def _snake_case ( self , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE = None ):
"""simple docstring"""
lowercase_ : str = self._tokenizer.model.save(__SCREAMING_SNAKE_CASE , name=__SCREAMING_SNAKE_CASE )
return tuple(__SCREAMING_SNAKE_CASE )
def _snake_case ( self , __SCREAMING_SNAKE_CASE ):
"""simple docstring"""
lowercase_ : Any = []
for is_user, text in conversation.iter_texts():
input_ids.extend(self.encode(__SCREAMING_SNAKE_CASE , add_special_tokens=__SCREAMING_SNAKE_CASE ) + [self.eos_token_id] )
if len(__SCREAMING_SNAKE_CASE ) > self.model_max_length:
lowercase_ : Union[str, Any] = input_ids[-self.model_max_length :]
return input_ids
| 93 |
'''simple docstring'''
import re
from filelock import FileLock
try:
import nltk
__snake_case = True
except (ImportError, ModuleNotFoundError):
__snake_case = False
if NLTK_AVAILABLE:
with FileLock('''.lock''') as lock:
nltk.download('''punkt''', quiet=True)
def a ( __a ) -> str:
'''simple docstring'''
re.sub('''<n>''' , '''''' , __a ) # 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(__a ) ) | 97 | 0 |
def __lowerCamelCase ( UpperCAmelCase_ : int , UpperCAmelCase_ : int ):
"""simple docstring"""
return int((input_a, input_a).count(1 ) != 0 )
def __lowerCamelCase ( ):
"""simple docstring"""
assert or_gate(0 , 0 ) == 0
assert or_gate(0 , 1 ) == 1
assert or_gate(1 , 0 ) == 1
assert or_gate(1 , 1 ) == 1
if __name__ == "__main__":
print(or_gate(0, 1))
print(or_gate(1, 0))
print(or_gate(0, 0))
print(or_gate(1, 1))
| 94 |
'''simple docstring'''
from pathlib import Path
import fire
from tqdm import tqdm
def a ( __a="ro" , __a="en" , __a="wmt16" , __a=None ) -> None:
'''simple docstring'''
try:
import datasets
except (ModuleNotFoundError, ImportError):
raise ImportError('''run pip install datasets''' )
UpperCamelCase__ :int = f'''{src_lang}-{tgt_lang}'''
print(f'''Converting {dataset}-{pair}''' )
UpperCamelCase__ :Tuple = datasets.load_dataset(__a , __a )
if save_dir is None:
UpperCamelCase__ :Any = f'''{dataset}-{pair}'''
UpperCamelCase__ :Dict = Path(__a )
save_dir.mkdir(exist_ok=__a )
for split in ds.keys():
print(f'''Splitting {split} with {ds[split].num_rows} records''' )
# to save to val.source, val.target like summary datasets
UpperCamelCase__ :Dict = '''val''' if split == '''validation''' else split
UpperCamelCase__ :List[Any] = save_dir.joinpath(f'''{fn}.source''' )
UpperCamelCase__ :int = save_dir.joinpath(f'''{fn}.target''' )
UpperCamelCase__ :Union[str, Any] = src_path.open('''w+''' )
UpperCamelCase__ :Tuple = tgt_path.open('''w+''' )
# reader is the bottleneck so writing one record at a time doesn't slow things down
for x in tqdm(ds[split] ):
UpperCamelCase__ :Union[str, Any] = x['''translation''']
src_fp.write(ex[src_lang] + '''\n''' )
tgt_fp.write(ex[tgt_lang] + '''\n''' )
print(f'''Saved {dataset} dataset to {save_dir}''' )
if __name__ == "__main__":
fire.Fire(download_wmt_dataset) | 97 | 0 |
def _A ( SCREAMING_SNAKE_CASE : list ):
"""simple docstring"""
a__ : Tuple =len(SCREAMING_SNAKE_CASE )
for _ in range(SCREAMING_SNAKE_CASE ):
for i in range(_ % 2 , arr_size - 1 , 2 ):
if arr[i + 1] < arr[i]:
a__ , a__ : Tuple =arr[i + 1], arr[i]
return arr
if __name__ == "__main__":
UpperCAmelCase : Dict = list(range(10, 0, -1))
print(F"""Original: {arr}. Sorted: {odd_even_transposition(arr)}""")
| 95 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...file_utils import _LazyModule, is_tokenizers_available, is_torch_available, is_vision_available
from ...utils import OptionalDependencyNotAvailable
__snake_case = {'''configuration_dpt''': ['''DPT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''DPTConfig''']}
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__snake_case = ['''DPTFeatureExtractor''']
__snake_case = ['''DPTImageProcessor''']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__snake_case = [
'''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
__snake_case = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__) | 97 | 0 |
"""simple docstring"""
import fire
from utils import calculate_rouge, save_json
def _snake_case ( lowercase__ , lowercase__ , lowercase__=None , **lowercase__ ):
_lowerCamelCase : Dict = [x.strip() for x in open(lowercase__ ).readlines()]
_lowerCamelCase : int = [x.strip() for x in open(lowercase__ ).readlines()][: len(lowercase__ )]
_lowerCamelCase : int = calculate_rouge(lowercase__ , lowercase__ , **lowercase__ )
if save_path is not None:
save_json(lowercase__ , lowercase__ , indent=lowercase__ )
return metrics # these print nicely
if __name__ == "__main__":
fire.Fire(calculate_rouge_path) | 96 |
'''simple docstring'''
def a ( __a , __a ) -> int:
'''simple docstring'''
if len(__a ) != len(__a ):
raise ValueError('''String lengths must match!''' )
UpperCamelCase__ :Union[str, Any] = 0
for chara, chara in zip(__a , __a ):
if chara != chara:
count += 1
return count
if __name__ == "__main__":
import doctest
doctest.testmod() | 97 | 0 |
"""simple docstring"""
import unittest
from transformers import (
MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
TextaTextGenerationPipeline,
pipeline,
)
from transformers.testing_utils import is_pipeline_test, require_tf, require_torch
from transformers.utils import is_torch_available
from .test_pipelines_common import ANY
if is_torch_available():
import torch
@is_pipeline_test
class snake_case ( unittest.TestCase ):
"""simple docstring"""
snake_case__ = MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
snake_case__ = TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
def __lowerCAmelCase ( self : Dict ,lowerCamelCase__ : Optional[Any] ,lowerCamelCase__ : Any ,lowerCamelCase__ : Any ):
UpperCAmelCase__ = TextaTextGenerationPipeline(model=lowerCamelCase__ ,tokenizer=lowerCamelCase__ )
return generator, ["Something to write", "Something else"]
def __lowerCAmelCase ( self : Dict ,lowerCamelCase__ : Optional[Any] ,lowerCamelCase__ : Optional[Any] ):
UpperCAmelCase__ = generator('Something there' )
self.assertEqual(lowerCamelCase__ ,[{'generated_text': ANY(lowerCamelCase__ )}] )
# These are encoder decoder, they don't just append to incoming string
self.assertFalse(outputs[0]['generated_text'].startswith('Something there' ) )
UpperCAmelCase__ = generator(['This is great !', 'Something else'] ,num_return_sequences=2 ,do_sample=lowerCamelCase__ )
self.assertEqual(
lowerCamelCase__ ,[
[{'generated_text': ANY(lowerCamelCase__ )}, {'generated_text': ANY(lowerCamelCase__ )}],
[{'generated_text': ANY(lowerCamelCase__ )}, {'generated_text': ANY(lowerCamelCase__ )}],
] ,)
UpperCAmelCase__ = generator(
['This is great !', 'Something else'] ,num_return_sequences=2 ,batch_size=2 ,do_sample=lowerCamelCase__ )
self.assertEqual(
lowerCamelCase__ ,[
[{'generated_text': ANY(lowerCamelCase__ )}, {'generated_text': ANY(lowerCamelCase__ )}],
[{'generated_text': ANY(lowerCamelCase__ )}, {'generated_text': ANY(lowerCamelCase__ )}],
] ,)
with self.assertRaises(lowerCamelCase__ ):
generator(4 )
@require_torch
def __lowerCAmelCase ( self : Optional[Any] ):
UpperCAmelCase__ = pipeline('text2text-generation' ,model='patrickvonplaten/t5-tiny-random' ,framework='pt' )
# do_sample=False necessary for reproducibility
UpperCAmelCase__ = generator('Something there' ,do_sample=lowerCamelCase__ )
self.assertEqual(lowerCamelCase__ ,[{'generated_text': ''}] )
UpperCAmelCase__ = 3
UpperCAmelCase__ = generator(
'Something there' ,num_return_sequences=lowerCamelCase__ ,num_beams=lowerCamelCase__ ,)
UpperCAmelCase__ = [
{'generated_text': 'Beide Beide Beide Beide Beide Beide Beide Beide Beide'},
{'generated_text': 'Beide Beide Beide Beide Beide Beide Beide Beide'},
{'generated_text': ''},
]
self.assertEqual(lowerCamelCase__ ,lowerCamelCase__ )
UpperCAmelCase__ = generator('This is a test' ,do_sample=lowerCamelCase__ ,num_return_sequences=2 ,return_tensors=lowerCamelCase__ )
self.assertEqual(
lowerCamelCase__ ,[
{'generated_token_ids': ANY(torch.Tensor )},
{'generated_token_ids': ANY(torch.Tensor )},
] ,)
UpperCAmelCase__ = generator.model.config.eos_token_id
UpperCAmelCase__ = '<pad>'
UpperCAmelCase__ = generator(
['This is a test', 'This is a second test'] ,do_sample=lowerCamelCase__ ,num_return_sequences=2 ,batch_size=2 ,return_tensors=lowerCamelCase__ ,)
self.assertEqual(
lowerCamelCase__ ,[
[
{'generated_token_ids': ANY(torch.Tensor )},
{'generated_token_ids': ANY(torch.Tensor )},
],
[
{'generated_token_ids': ANY(torch.Tensor )},
{'generated_token_ids': ANY(torch.Tensor )},
],
] ,)
@require_tf
def __lowerCAmelCase ( self : Any ):
UpperCAmelCase__ = pipeline('text2text-generation' ,model='patrickvonplaten/t5-tiny-random' ,framework='tf' )
# do_sample=False necessary for reproducibility
UpperCAmelCase__ = generator('Something there' ,do_sample=lowerCamelCase__ )
self.assertEqual(lowerCamelCase__ ,[{'generated_text': ''}] )
| 98 |
'''simple docstring'''
def a ( __a ) -> "list[int]":
'''simple docstring'''
if upper_limit < 0:
raise ValueError('''Limit for the Catalan sequence must be ≥ 0''' )
UpperCamelCase__ :Optional[Any] = [0] * (upper_limit + 1)
# Base case: C(0) = C(1) = 1
UpperCamelCase__ :int = 1
if upper_limit > 0:
UpperCamelCase__ :int = 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(__a ):
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:
__snake_case = 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() | 97 | 0 |
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available
if is_vision_available():
from PIL import Image
from transformers import OwlViTImageProcessor, OwlViTProcessor
@require_vision
class A__ ( unittest.TestCase ):
"""simple docstring"""
def __lowercase ( self) -> Optional[int]:
'''simple docstring'''
a__ : Optional[int] = tempfile.mkdtemp()
# fmt: off
a__ : Optional[Any] = ['', '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
a__ : List[Any] = dict(zip(lowercase , range(len(lowercase))))
a__ : int = ['#version: 0.2', 'l o', 'lo w</w>', 'e r</w>', '']
a__ : Dict = {'unk_token': '<unk>'}
a__ : Optional[int] = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['vocab_file'])
a__ : List[Any] = 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))
a__ : List[str] = {
'do_resize': True,
'size': 20,
'do_center_crop': True,
'crop_size': 18,
'do_normalize': True,
'image_mean': [0.48_14_54_66, 0.4_57_82_75, 0.40_82_10_73],
'image_std': [0.26_86_29_54, 0.26_13_02_58, 0.27_57_77_11],
}
a__ : Dict = os.path.join(self.tmpdirname , lowercase)
with open(self.image_processor_file , 'w' , encoding='utf-8') as fp:
json.dump(lowercase , lowercase)
def __lowercase ( self , **lowercase) -> Tuple:
'''simple docstring'''
return CLIPTokenizer.from_pretrained(self.tmpdirname , pad_token='!' , **lowercase)
def __lowercase ( self , **lowercase) -> Optional[int]:
'''simple docstring'''
return CLIPTokenizerFast.from_pretrained(self.tmpdirname , pad_token='!' , **lowercase)
def __lowercase ( self , **lowercase) -> List[Any]:
'''simple docstring'''
return OwlViTImageProcessor.from_pretrained(self.tmpdirname , **lowercase)
def __lowercase ( self) -> str:
'''simple docstring'''
shutil.rmtree(self.tmpdirname)
def __lowercase ( self) -> str:
'''simple docstring'''
a__ : List[Any] = [np.random.randint(255 , size=(3, 30, 400) , dtype=np.uinta)]
a__ : List[Any] = [Image.fromarray(np.moveaxis(lowercase , 0 , -1)) for x in image_inputs]
return image_inputs
def __lowercase ( self) -> List[str]:
'''simple docstring'''
a__ : List[Any] = self.get_tokenizer()
a__ : Tuple = self.get_rust_tokenizer()
a__ : Any = self.get_image_processor()
a__ : Optional[int] = OwlViTProcessor(tokenizer=lowercase , image_processor=lowercase)
processor_slow.save_pretrained(self.tmpdirname)
a__ : List[str] = OwlViTProcessor.from_pretrained(self.tmpdirname , use_fast=lowercase)
a__ : Optional[int] = OwlViTProcessor(tokenizer=lowercase , image_processor=lowercase)
processor_fast.save_pretrained(self.tmpdirname)
a__ : List[str] = OwlViTProcessor.from_pretrained(self.tmpdirname)
self.assertEqual(processor_slow.tokenizer.get_vocab() , tokenizer_slow.get_vocab())
self.assertEqual(processor_fast.tokenizer.get_vocab() , tokenizer_fast.get_vocab())
self.assertEqual(tokenizer_slow.get_vocab() , tokenizer_fast.get_vocab())
self.assertIsInstance(processor_slow.tokenizer , lowercase)
self.assertIsInstance(processor_fast.tokenizer , lowercase)
self.assertEqual(processor_slow.image_processor.to_json_string() , image_processor.to_json_string())
self.assertEqual(processor_fast.image_processor.to_json_string() , image_processor.to_json_string())
self.assertIsInstance(processor_slow.image_processor , lowercase)
self.assertIsInstance(processor_fast.image_processor , lowercase)
def __lowercase ( self) -> List[str]:
'''simple docstring'''
a__ : int = OwlViTProcessor(tokenizer=self.get_tokenizer() , image_processor=self.get_image_processor())
processor.save_pretrained(self.tmpdirname)
a__ : List[Any] = self.get_tokenizer(bos_token='(BOS)' , eos_token='(EOS)')
a__ : List[Any] = self.get_image_processor(do_normalize=lowercase)
a__ : Optional[Any] = OwlViTProcessor.from_pretrained(
self.tmpdirname , bos_token='(BOS)' , eos_token='(EOS)' , do_normalize=lowercase)
self.assertEqual(processor.tokenizer.get_vocab() , tokenizer_add_kwargs.get_vocab())
self.assertIsInstance(processor.tokenizer , lowercase)
self.assertEqual(processor.image_processor.to_json_string() , image_processor_add_kwargs.to_json_string())
self.assertIsInstance(processor.image_processor , lowercase)
def __lowercase ( self) -> int:
'''simple docstring'''
a__ : Dict = self.get_image_processor()
a__ : Any = self.get_tokenizer()
a__ : Union[str, Any] = OwlViTProcessor(tokenizer=lowercase , image_processor=lowercase)
a__ : Any = self.prepare_image_inputs()
a__ : int = image_processor(lowercase , return_tensors='np')
a__ : Optional[Any] = processor(images=lowercase , return_tensors='np')
for key in input_image_proc.keys():
self.assertAlmostEqual(input_image_proc[key].sum() , input_processor[key].sum() , delta=1e-2)
def __lowercase ( self) -> Union[str, Any]:
'''simple docstring'''
a__ : List[Any] = self.get_image_processor()
a__ : Optional[Any] = self.get_tokenizer()
a__ : str = OwlViTProcessor(tokenizer=lowercase , image_processor=lowercase)
a__ : List[Any] = 'lower newer'
a__ : str = processor(text=lowercase , return_tensors='np')
a__ : Dict = tokenizer(lowercase , return_tensors='np')
for key in encoded_tok.keys():
self.assertListEqual(encoded_tok[key][0].tolist() , encoded_processor[key][0].tolist())
def __lowercase ( self) -> Union[str, Any]:
'''simple docstring'''
a__ : Any = self.get_image_processor()
a__ : str = self.get_tokenizer()
a__ : Tuple = OwlViTProcessor(tokenizer=lowercase , image_processor=lowercase)
a__ : List[str] = 'lower newer'
a__ : Union[str, Any] = self.prepare_image_inputs()
a__ : Tuple = processor(text=lowercase , images=lowercase)
self.assertListEqual(list(inputs.keys()) , ['input_ids', 'attention_mask', 'pixel_values'])
# test if it raises when no input is passed
with pytest.raises(lowercase):
processor()
def __lowercase ( self) -> Optional[int]:
'''simple docstring'''
a__ : Any = 'google/owlvit-base-patch32'
a__ : int = OwlViTProcessor.from_pretrained(lowercase)
a__ : Dict = ['cat', 'nasa badge']
a__ : int = processor(text=lowercase)
a__ : Optional[int] = 16
self.assertListEqual(list(inputs.keys()) , ['input_ids', 'attention_mask'])
self.assertEqual(inputs['input_ids'].shape , (2, seq_length))
# test if it raises when no input is passed
with pytest.raises(lowercase):
processor()
def __lowercase ( self) -> int:
'''simple docstring'''
a__ : str = 'google/owlvit-base-patch32'
a__ : Any = OwlViTProcessor.from_pretrained(lowercase)
a__ : Optional[int] = [['cat', 'nasa badge'], ['person']]
a__ : Optional[int] = processor(text=lowercase)
a__ : Tuple = 16
a__ : List[str] = len(lowercase)
a__ : Union[str, Any] = max([len(lowercase) for texts in input_texts])
self.assertListEqual(list(inputs.keys()) , ['input_ids', 'attention_mask'])
self.assertEqual(inputs['input_ids'].shape , (batch_size * num_max_text_queries, seq_length))
# test if it raises when no input is passed
with pytest.raises(lowercase):
processor()
def __lowercase ( self) -> str:
'''simple docstring'''
a__ : Dict = 'google/owlvit-base-patch32'
a__ : int = OwlViTProcessor.from_pretrained(lowercase)
a__ : Optional[int] = ['cat', 'nasa badge']
a__ : str = processor(text=lowercase)
a__ : Optional[Any] = 16
a__ : Any = inputs['input_ids']
a__ : Optional[Any] = [
[4_9406, 2368, 4_9407, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[4_9406, 6841, 1_1301, 4_9407, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
]
self.assertListEqual(list(inputs.keys()) , ['input_ids', 'attention_mask'])
self.assertEqual(inputs['input_ids'].shape , (2, seq_length))
self.assertListEqual(list(input_ids[0]) , predicted_ids[0])
self.assertListEqual(list(input_ids[1]) , predicted_ids[1])
def __lowercase ( self) -> Union[str, Any]:
'''simple docstring'''
a__ : Optional[Any] = self.get_image_processor()
a__ : str = self.get_tokenizer()
a__ : List[Any] = OwlViTProcessor(tokenizer=lowercase , image_processor=lowercase)
a__ : Optional[Any] = self.prepare_image_inputs()
a__ : int = self.prepare_image_inputs()
a__ : int = processor(images=lowercase , query_images=lowercase)
self.assertListEqual(list(inputs.keys()) , ['query_pixel_values', 'pixel_values'])
# test if it raises when no input is passed
with pytest.raises(lowercase):
processor()
def __lowercase ( self) -> List[str]:
'''simple docstring'''
a__ : Optional[Any] = self.get_image_processor()
a__ : str = self.get_tokenizer()
a__ : List[str] = OwlViTProcessor(tokenizer=lowercase , image_processor=lowercase)
a__ : Any = [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]
a__ : int = processor.batch_decode(lowercase)
a__ : str = tokenizer.batch_decode(lowercase)
self.assertListEqual(lowercase , lowercase)
| 99 |
'''simple docstring'''
import io
import json
import fsspec
import pytest
from datasets import Dataset, DatasetDict, Features, NamedSplit, Value
from datasets.io.json import JsonDatasetReader, JsonDatasetWriter
from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases
def a ( __a , __a ) -> Optional[int]:
'''simple docstring'''
assert isinstance(__a , __a )
assert dataset.num_rows == 4
assert dataset.num_columns == 3
assert dataset.column_names == ["col_1", "col_2", "col_3"]
for feature, expected_dtype in expected_features.items():
assert dataset.features[feature].dtype == expected_dtype
@pytest.mark.parametrize('''keep_in_memory''' , [False, True] )
def a ( __a , __a , __a ) -> Optional[Any]:
'''simple docstring'''
UpperCamelCase__ :Union[str, Any] = tmp_path / '''cache'''
UpperCamelCase__ :Dict = {'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
UpperCamelCase__ :Tuple = JsonDatasetReader(__a , cache_dir=__a , keep_in_memory=__a ).read()
_check_json_dataset(__a , __a )
@pytest.mark.parametrize(
'''features''' , [
None,
{'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''},
{'''col_1''': '''string''', '''col_2''': '''string''', '''col_3''': '''string'''},
{'''col_1''': '''int32''', '''col_2''': '''int32''', '''col_3''': '''int32'''},
{'''col_1''': '''float32''', '''col_2''': '''float32''', '''col_3''': '''float32'''},
] , )
def a ( __a , __a , __a ) -> Any:
'''simple docstring'''
UpperCamelCase__ :Union[str, Any] = tmp_path / '''cache'''
UpperCamelCase__ :Optional[Any] = {'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
UpperCamelCase__ :Optional[Any] = features.copy() if features else default_expected_features
UpperCamelCase__ :Tuple = (
Features({feature: Value(__a ) for feature, dtype in features.items()} ) if features is not None else None
)
UpperCamelCase__ :int = JsonDatasetReader(__a , features=__a , cache_dir=__a ).read()
_check_json_dataset(__a , __a )
@pytest.mark.parametrize(
'''features''' , [
None,
{'''col_3''': '''float64''', '''col_1''': '''string''', '''col_2''': '''int64'''},
] , )
def a ( __a , __a , __a ) -> Tuple:
'''simple docstring'''
UpperCamelCase__ :int = tmp_path / '''cache'''
UpperCamelCase__ :str = {'''col_3''': '''float64''', '''col_1''': '''string''', '''col_2''': '''int64'''}
UpperCamelCase__ :Any = features.copy() if features else default_expected_features
UpperCamelCase__ :Union[str, Any] = (
Features({feature: Value(__a ) for feature, dtype in features.items()} ) if features is not None else None
)
UpperCamelCase__ :Any = JsonDatasetReader(__a , features=__a , cache_dir=__a ).read()
assert isinstance(__a , __a )
assert dataset.num_rows == 2
assert dataset.num_columns == 3
assert dataset.column_names == ["col_3", "col_1", "col_2"]
for feature, expected_dtype in expected_features.items():
assert dataset.features[feature].dtype == expected_dtype
def a ( __a , __a ) -> List[Any]:
'''simple docstring'''
UpperCamelCase__ :Any = {'''col_2''': '''int64''', '''col_3''': '''float64''', '''col_1''': '''string'''}
UpperCamelCase__ :int = features.copy()
UpperCamelCase__ :List[Any] = (
Features({feature: Value(__a ) for feature, dtype in features.items()} ) if features is not None else None
)
UpperCamelCase__ :Optional[int] = tmp_path / '''cache'''
UpperCamelCase__ :Dict = JsonDatasetReader(__a , features=__a , cache_dir=__a ).read()
assert isinstance(__a , __a )
assert dataset.num_rows == 2
assert dataset.num_columns == 3
assert dataset.column_names == ["col_2", "col_3", "col_1"]
for feature, expected_dtype in expected_features.items():
assert dataset.features[feature].dtype == expected_dtype
@pytest.mark.parametrize('''split''' , [None, NamedSplit('''train''' ), '''train''', '''test'''] )
def a ( __a , __a , __a ) -> List[Any]:
'''simple docstring'''
UpperCamelCase__ :Union[str, Any] = tmp_path / '''cache'''
UpperCamelCase__ :Optional[Any] = {'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
UpperCamelCase__ :List[Any] = JsonDatasetReader(__a , cache_dir=__a , split=__a ).read()
_check_json_dataset(__a , __a )
assert dataset.split == split if split else "train"
@pytest.mark.parametrize('''path_type''' , [str, list] )
def a ( __a , __a , __a ) -> Any:
'''simple docstring'''
if issubclass(__a , __a ):
UpperCamelCase__ :Union[str, Any] = jsonl_path
elif issubclass(__a , __a ):
UpperCamelCase__ :int = [jsonl_path]
UpperCamelCase__ :Dict = tmp_path / '''cache'''
UpperCamelCase__ :Any = {'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
UpperCamelCase__ :List[str] = JsonDatasetReader(__a , cache_dir=__a ).read()
_check_json_dataset(__a , __a )
def a ( __a , __a , __a=("train",) ) -> Optional[Any]:
'''simple docstring'''
assert isinstance(__a , __a )
for split in splits:
UpperCamelCase__ :Optional[int] = dataset_dict[split]
assert dataset.num_rows == 4
assert dataset.num_columns == 3
assert dataset.column_names == ["col_1", "col_2", "col_3"]
for feature, expected_dtype in expected_features.items():
assert dataset.features[feature].dtype == expected_dtype
@pytest.mark.parametrize('''keep_in_memory''' , [False, True] )
def a ( __a , __a , __a ) -> List[str]:
'''simple docstring'''
UpperCamelCase__ :List[str] = tmp_path / '''cache'''
UpperCamelCase__ :Dict = {'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
UpperCamelCase__ :str = JsonDatasetReader({'''train''': jsonl_path} , cache_dir=__a , keep_in_memory=__a ).read()
_check_json_datasetdict(__a , __a )
@pytest.mark.parametrize(
'''features''' , [
None,
{'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''},
{'''col_1''': '''string''', '''col_2''': '''string''', '''col_3''': '''string'''},
{'''col_1''': '''int32''', '''col_2''': '''int32''', '''col_3''': '''int32'''},
{'''col_1''': '''float32''', '''col_2''': '''float32''', '''col_3''': '''float32'''},
] , )
def a ( __a , __a , __a ) -> int:
'''simple docstring'''
UpperCamelCase__ :Tuple = tmp_path / '''cache'''
UpperCamelCase__ :Any = {'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
UpperCamelCase__ :Optional[int] = features.copy() if features else default_expected_features
UpperCamelCase__ :str = (
Features({feature: Value(__a ) for feature, dtype in features.items()} ) if features is not None else None
)
UpperCamelCase__ :Dict = JsonDatasetReader({'''train''': jsonl_path} , features=__a , cache_dir=__a ).read()
_check_json_datasetdict(__a , __a )
@pytest.mark.parametrize('''split''' , [None, NamedSplit('''train''' ), '''train''', '''test'''] )
def a ( __a , __a , __a ) -> str:
'''simple docstring'''
if split:
UpperCamelCase__ :List[str] = {split: jsonl_path}
else:
UpperCamelCase__ :int = '''train'''
UpperCamelCase__ :int = {'''train''': jsonl_path, '''test''': jsonl_path}
UpperCamelCase__ :Any = tmp_path / '''cache'''
UpperCamelCase__ :Union[str, Any] = {'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
UpperCamelCase__ :Any = JsonDatasetReader(__a , cache_dir=__a ).read()
_check_json_datasetdict(__a , __a , splits=list(path.keys() ) )
assert all(dataset[split].split == split for split in path.keys() )
def a ( __a ) -> Union[str, Any]:
'''simple docstring'''
return json.load(__a )
def a ( __a ) -> int:
'''simple docstring'''
return [json.loads(__a ) for line in buffer]
class lowercase :
"""simple docstring"""
@pytest.mark.parametrize('''lines, load_json_function''' , [(True, load_json_lines), (False, load_json)] )
def lowerCAmelCase__ ( self , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ ):
'''simple docstring'''
with io.BytesIO() as buffer:
JsonDatasetWriter(UpperCamelCase_ , UpperCamelCase_ , lines=UpperCamelCase_ ).write()
buffer.seek(0 )
UpperCamelCase__ :List[Any] = load_json_function(UpperCamelCase_ )
assert isinstance(UpperCamelCase_ , UpperCamelCase_ )
assert isinstance(exported_content[0] , UpperCamelCase_ )
assert len(UpperCamelCase_ ) == 10
@pytest.mark.parametrize(
'''orient, container, keys, len_at''' , [
('''records''', list, {'''tokens''', '''labels''', '''answers''', '''id'''}, None),
('''split''', dict, {'''columns''', '''data'''}, '''data'''),
('''index''', dict, set('''0123456789''' ), None),
('''columns''', dict, {'''tokens''', '''labels''', '''answers''', '''id'''}, '''tokens'''),
('''values''', list, None, None),
('''table''', dict, {'''schema''', '''data'''}, '''data'''),
] , )
def lowerCAmelCase__ ( self , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ ):
'''simple docstring'''
with io.BytesIO() as buffer:
JsonDatasetWriter(UpperCamelCase_ , UpperCamelCase_ , lines=UpperCamelCase_ , orient=UpperCamelCase_ ).write()
buffer.seek(0 )
UpperCamelCase__ :Optional[int] = load_json(UpperCamelCase_ )
assert isinstance(UpperCamelCase_ , UpperCamelCase_ )
if keys:
if container is dict:
assert exported_content.keys() == keys
else:
assert exported_content[0].keys() == keys
else:
assert not hasattr(UpperCamelCase_ , '''keys''' ) and not hasattr(exported_content[0] , '''keys''' )
if len_at:
assert len(exported_content[len_at] ) == 10
else:
assert len(UpperCamelCase_ ) == 10
@pytest.mark.parametrize('''lines, load_json_function''' , [(True, load_json_lines), (False, load_json)] )
def lowerCAmelCase__ ( self , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ ):
'''simple docstring'''
with io.BytesIO() as buffer:
JsonDatasetWriter(UpperCamelCase_ , UpperCamelCase_ , lines=UpperCamelCase_ , num_proc=2 ).write()
buffer.seek(0 )
UpperCamelCase__ :Union[str, Any] = load_json_function(UpperCamelCase_ )
assert isinstance(UpperCamelCase_ , UpperCamelCase_ )
assert isinstance(exported_content[0] , UpperCamelCase_ )
assert len(UpperCamelCase_ ) == 10
@pytest.mark.parametrize(
'''orient, container, keys, len_at''' , [
('''records''', list, {'''tokens''', '''labels''', '''answers''', '''id'''}, None),
('''split''', dict, {'''columns''', '''data'''}, '''data'''),
('''index''', dict, set('''0123456789''' ), None),
('''columns''', dict, {'''tokens''', '''labels''', '''answers''', '''id'''}, '''tokens'''),
('''values''', list, None, None),
('''table''', dict, {'''schema''', '''data'''}, '''data'''),
] , )
def lowerCAmelCase__ ( self , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ ):
'''simple docstring'''
with io.BytesIO() as buffer:
JsonDatasetWriter(UpperCamelCase_ , UpperCamelCase_ , lines=UpperCamelCase_ , orient=UpperCamelCase_ , num_proc=2 ).write()
buffer.seek(0 )
UpperCamelCase__ :int = load_json(UpperCamelCase_ )
assert isinstance(UpperCamelCase_ , UpperCamelCase_ )
if keys:
if container is dict:
assert exported_content.keys() == keys
else:
assert exported_content[0].keys() == keys
else:
assert not hasattr(UpperCamelCase_ , '''keys''' ) and not hasattr(exported_content[0] , '''keys''' )
if len_at:
assert len(exported_content[len_at] ) == 10
else:
assert len(UpperCamelCase_ ) == 10
def lowerCAmelCase__ ( self , UpperCamelCase_ ):
'''simple docstring'''
with pytest.raises(UpperCamelCase_ ):
with io.BytesIO() as buffer:
JsonDatasetWriter(UpperCamelCase_ , UpperCamelCase_ , num_proc=0 )
@pytest.mark.parametrize('''compression, extension''' , [('''gzip''', '''gz'''), ('''bz2''', '''bz2'''), ('''xz''', '''xz''')] )
def lowerCAmelCase__ ( self , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ ):
'''simple docstring'''
UpperCamelCase__ :Tuple = tmp_path_factory.mktemp('''data''' ) / F'''test.json.{extension}'''
UpperCamelCase__ :Union[str, Any] = str(shared_datadir / F'''test_file.json.{extension}''' )
JsonDatasetWriter(UpperCamelCase_ , UpperCamelCase_ , compression=UpperCamelCase_ ).write()
with fsspec.open(UpperCamelCase_ , '''rb''' , compression='''infer''' ) as f:
UpperCamelCase__ :Dict = f.read()
with fsspec.open(UpperCamelCase_ , '''rb''' , compression='''infer''' ) as f:
UpperCamelCase__ :int = f.read()
assert exported_content == original_content | 97 | 0 |
"""simple docstring"""
import requests
__magic_name__ = "https://newsapi.org/v1/articles?source=bbc-news&sortBy=top&apiKey="
def _lowerCAmelCase ( UpperCamelCase_ ):
# fetching a list of articles in json format
__SCREAMING_SNAKE_CASE = 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>")
| 100 |
'''simple docstring'''
import unittest
from dataclasses import dataclass
import pytest
from accelerate.commands.config.config_args import SageMakerConfig
from accelerate.utils import ComputeEnvironment
from accelerate.utils.launch import _convert_nargs_to_dict
@dataclass
class lowercase ( A__ ):
"""simple docstring"""
_a = ComputeEnvironment.AMAZON_SAGEMAKER
_a = True
_a = 'ml.p3.2xlarge'
_a = 'accelerate_sagemaker_execution_role'
_a = 'hf-sm'
_a = 'us-east-1'
_a = 1
_a = 'accelerate-sagemaker-1'
_a = '1.6'
_a = '4.4'
_a = 'train.py'
_a = [
'--model_name_or_path',
'bert',
'--do_train',
'False',
'--epochs',
'3',
'--learning_rate',
'5e-5',
'--max_steps',
'50.5',
]
_a = [
'--model_name_or_path',
'bert',
'--do_train',
'--do_test',
'False',
'--do_predict',
'--epochs',
'3',
'--learning_rate',
'5e-5',
'--max_steps',
'50.5',
]
class lowercase ( unittest.TestCase ):
"""simple docstring"""
def lowerCAmelCase__ ( self ):
'''simple docstring'''
UpperCamelCase__ :Union[str, Any] = _convert_nargs_to_dict(MockLaunchConfig.success_training_script_args )
assert isinstance(converted_args['''model_name_or_path'''] , UpperCamelCase_ )
assert isinstance(converted_args['''do_train'''] , UpperCamelCase_ )
assert isinstance(converted_args['''epochs'''] , UpperCamelCase_ )
assert isinstance(converted_args['''learning_rate'''] , UpperCamelCase_ )
assert isinstance(converted_args['''max_steps'''] , UpperCamelCase_ )
with pytest.raises(UpperCamelCase_ ):
_convert_nargs_to_dict(MockLaunchConfig.fail_training_script_args ) | 97 | 0 |
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