code stringlengths 82 54.1k | code_codestyle int64 0 699 | style_context stringlengths 111 35.6k | style_context_codestyle int64 0 699 | label int64 0 1 |
|---|---|---|---|---|
def lowerCamelCase_ ( _UpperCamelCase ) -> list[int]:
"""simple docstring"""
snake_case_ : Optional[int] = [0 for i in range(len(_UpperCamelCase ) )]
# initialize interval's left pointer and right pointer
snake_case_ , snake_case_ : Tuple = 0, 0
for i in range(1 , len(_UpperCamelCase ) ):
# case when current index is inside the interval
if i <= right_pointer:
snake_case_ : Any = min(right_pointer - i + 1 , z_result[i - left_pointer] )
snake_case_ : Tuple = min_edge
while go_next(_UpperCamelCase , _UpperCamelCase , _UpperCamelCase ):
z_result[i] += 1
# if new index's result gives us more right interval,
# we've to update left_pointer and right_pointer
if i + z_result[i] - 1 > right_pointer:
snake_case_ , snake_case_ : int = i, i + z_result[i] - 1
return z_result
def lowerCamelCase_ ( _UpperCamelCase , _UpperCamelCase , _UpperCamelCase ) -> bool:
"""simple docstring"""
return i + z_result[i] < len(_UpperCamelCase ) and s[z_result[i]] == s[i + z_result[i]]
def lowerCamelCase_ ( _UpperCamelCase , _UpperCamelCase ) -> int:
"""simple docstring"""
snake_case_ : Tuple = 0
# concatenate 'pattern' and 'input_str' and call z_function
# with concatenated string
snake_case_ : Union[str, Any] = z_function(pattern + input_str )
for val in z_result:
# if value is greater then length of the pattern string
# that means this index is starting position of substring
# which is equal to pattern string
if val >= len(_UpperCamelCase ):
answer += 1
return answer
if __name__ == "__main__":
import doctest
doctest.testmod()
| 60 |
'''simple docstring'''
from collections.abc import Iterator, MutableMapping
from dataclasses import dataclass
from typing import Generic, TypeVar
__lowerCamelCase : Dict = TypeVar('''KEY''')
__lowerCamelCase : int = TypeVar('''VAL''')
@dataclass(frozen=a_ , slots=a_ )
class A_ (Generic[KEY, VAL] ):
"""simple docstring"""
a__ = 42
a__ = 42
class A_ (_Item ):
"""simple docstring"""
def __init__( self :List[Any] ) -> None:
'''simple docstring'''
super().__init__(lowerCAmelCase__ , lowerCAmelCase__ )
def __bool__( self :Optional[int] ) -> bool:
'''simple docstring'''
return False
__lowerCamelCase : Dict = _DeletedItem()
class A_ (MutableMapping[KEY, VAL] ):
"""simple docstring"""
def __init__( self :Dict , lowerCAmelCase__ :int = 8 , lowerCAmelCase__ :float = 0.7_5 ) -> None:
'''simple docstring'''
snake_case_ : Any = initial_block_size
snake_case_ : list[_Item | None] = [None] * initial_block_size
assert 0.0 < capacity_factor < 1.0
snake_case_ : Tuple = capacity_factor
snake_case_ : List[Any] = 0
def _A ( self :Tuple , lowerCAmelCase__ :KEY ) -> int:
'''simple docstring'''
return hash(lowerCAmelCase__ ) % len(self._buckets )
def _A ( self :Any , lowerCAmelCase__ :int ) -> int:
'''simple docstring'''
return (ind + 1) % len(self._buckets )
def _A ( self :str , lowerCAmelCase__ :int , lowerCAmelCase__ :KEY , lowerCAmelCase__ :VAL ) -> bool:
'''simple docstring'''
snake_case_ : Optional[int] = self._buckets[ind]
if not stored:
snake_case_ : int = _Item(lowerCAmelCase__ , lowerCAmelCase__ )
self._len += 1
return True
elif stored.key == key:
snake_case_ : Optional[int] = _Item(lowerCAmelCase__ , lowerCAmelCase__ )
return True
else:
return False
def _A ( self :int ) -> bool:
'''simple docstring'''
snake_case_ : Any = len(self._buckets ) * self._capacity_factor
return len(self ) >= int(lowerCAmelCase__ )
def _A ( self :Any ) -> bool:
'''simple docstring'''
if len(self._buckets ) <= self._initial_block_size:
return False
snake_case_ : Optional[int] = len(self._buckets ) * self._capacity_factor / 2
return len(self ) < limit
def _A ( self :Tuple , lowerCAmelCase__ :int ) -> None:
'''simple docstring'''
snake_case_ : Tuple = self._buckets
snake_case_ : int = [None] * new_size
snake_case_ : Any = 0
for item in old_buckets:
if item:
self._add_item(item.key , item.val )
def _A ( self :Optional[int] ) -> None:
'''simple docstring'''
self._resize(len(self._buckets ) * 2 )
def _A ( self :str ) -> None:
'''simple docstring'''
self._resize(len(self._buckets ) // 2 )
def _A ( self :Optional[int] , lowerCAmelCase__ :KEY ) -> Iterator[int]:
'''simple docstring'''
snake_case_ : str = self._get_bucket_index(lowerCAmelCase__ )
for _ in range(len(self._buckets ) ):
yield ind
snake_case_ : List[Any] = self._get_next_ind(lowerCAmelCase__ )
def _A ( self :Union[str, Any] , lowerCAmelCase__ :KEY , lowerCAmelCase__ :VAL ) -> None:
'''simple docstring'''
for ind in self._iterate_buckets(lowerCAmelCase__ ):
if self._try_set(lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ ):
break
def __setitem__( self :Optional[int] , lowerCAmelCase__ :KEY , lowerCAmelCase__ :VAL ) -> None:
'''simple docstring'''
if self._is_full():
self._size_up()
self._add_item(lowerCAmelCase__ , lowerCAmelCase__ )
def __delitem__( self :List[Any] , lowerCAmelCase__ :KEY ) -> None:
'''simple docstring'''
for ind in self._iterate_buckets(lowerCAmelCase__ ):
snake_case_ : int = self._buckets[ind]
if item is None:
raise KeyError(lowerCAmelCase__ )
if item is _deleted:
continue
if item.key == key:
snake_case_ : List[str] = _deleted
self._len -= 1
break
if self._is_sparse():
self._size_down()
def __getitem__( self :List[str] , lowerCAmelCase__ :KEY ) -> VAL:
'''simple docstring'''
for ind in self._iterate_buckets(lowerCAmelCase__ ):
snake_case_ : Optional[Any] = self._buckets[ind]
if item is None:
break
if item is _deleted:
continue
if item.key == key:
return item.val
raise KeyError(lowerCAmelCase__ )
def __len__( self :Optional[Any] ) -> int:
'''simple docstring'''
return self._len
def __iter__( self :List[Any] ) -> Iterator[KEY]:
'''simple docstring'''
yield from (item.key for item in self._buckets if item)
def __repr__( self :Any ) -> str:
'''simple docstring'''
snake_case_ : Dict = " ,".join(
F'''{item.key}: {item.val}''' for item in self._buckets if item )
return F'''HashMap({val_string})'''
| 653 | 0 |
import os
# Precomputes a list of the 100 first triangular numbers
UpperCamelCase = [int(0.5 * n * (n + 1)) for n in range(1, 101)]
def _A ( ):
"""simple docstring"""
lowerCAmelCase__ = os.path.dirname(os.path.realpath(lowerCAmelCase_ ) )
lowerCAmelCase__ = os.path.join(lowerCAmelCase_ , "words.txt" )
lowerCAmelCase__ = ""
with open(lowerCAmelCase_ ) as f:
lowerCAmelCase__ = f.readline()
lowerCAmelCase__ = [word.strip("\"" ) for word in words.strip("\r\n" ).split("," )]
lowerCAmelCase__ = [
word
for word in [sum(ord(lowerCAmelCase_ ) - 64 for x in word ) for word in words]
if word in TRIANGULAR_NUMBERS
]
return len(lowerCAmelCase_ )
if __name__ == "__main__":
print(solution())
| 61 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__lowerCamelCase : str = logging.get_logger(__name__)
__lowerCamelCase : Tuple = {
'''bigcode/gpt_bigcode-santacoder''': '''https://huggingface.co/bigcode/gpt_bigcode-santacoder/resolve/main/config.json''',
}
class A_ (a_ ):
"""simple docstring"""
a__ = '''gpt_bigcode'''
a__ = ['''past_key_values''']
a__ = {
'''hidden_size''': '''n_embd''',
'''max_position_embeddings''': '''n_positions''',
'''num_attention_heads''': '''n_head''',
'''num_hidden_layers''': '''n_layer''',
}
def __init__( self :List[Any] , lowerCAmelCase__ :Any=50_257 , lowerCAmelCase__ :Dict=1_024 , lowerCAmelCase__ :Optional[int]=768 , lowerCAmelCase__ :Tuple=12 , lowerCAmelCase__ :int=12 , lowerCAmelCase__ :Optional[Any]=None , lowerCAmelCase__ :List[str]="gelu_pytorch_tanh" , lowerCAmelCase__ :Tuple=0.1 , lowerCAmelCase__ :Tuple=0.1 , lowerCAmelCase__ :str=0.1 , lowerCAmelCase__ :Any=1E-5 , lowerCAmelCase__ :Union[str, Any]=0.0_2 , lowerCAmelCase__ :Union[str, Any]=True , lowerCAmelCase__ :Optional[Any]=True , lowerCAmelCase__ :int=50_256 , lowerCAmelCase__ :List[str]=50_256 , lowerCAmelCase__ :List[Any]=True , lowerCAmelCase__ :str=True , lowerCAmelCase__ :int=True , **lowerCAmelCase__ :Union[str, Any] , ) -> Any:
'''simple docstring'''
snake_case_ : List[Any] = vocab_size
snake_case_ : Any = n_positions
snake_case_ : Any = n_embd
snake_case_ : Optional[Any] = n_layer
snake_case_ : List[Any] = n_head
snake_case_ : Tuple = n_inner
snake_case_ : str = activation_function
snake_case_ : Union[str, Any] = resid_pdrop
snake_case_ : Optional[Any] = embd_pdrop
snake_case_ : Any = attn_pdrop
snake_case_ : List[Any] = layer_norm_epsilon
snake_case_ : Tuple = initializer_range
snake_case_ : int = scale_attn_weights
snake_case_ : Union[str, Any] = use_cache
snake_case_ : Dict = attention_softmax_in_fpaa
snake_case_ : Any = scale_attention_softmax_in_fpaa
snake_case_ : List[str] = multi_query
snake_case_ : List[str] = bos_token_id
snake_case_ : Any = eos_token_id
super().__init__(bos_token_id=lowerCAmelCase__ , eos_token_id=lowerCAmelCase__ , **lowerCAmelCase__ )
| 653 | 0 |
from typing import Optional
import numpy as np
import torch
from torch import nn
from transformers import GPTaConfig, GPTaLMHeadModel
from transformers.modeling_utils import ModuleUtilsMixin
from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin
class SCREAMING_SNAKE_CASE ( lowerCAmelCase , lowerCAmelCase , lowerCAmelCase ):
'''simple docstring'''
UpperCamelCase_ : Optional[int] = [r'''h\.\d+\.attn\.bias''', r'''h\.\d+\.attn\.masked_bias''']
@register_to_config
def __init__( self : Union[str, Any] , UpperCAmelCase_ : int , UpperCAmelCase_ : int , UpperCAmelCase_ : Optional[int] = None , UpperCAmelCase_ : int = 5_0257 , UpperCAmelCase_ : int = 1024 , UpperCAmelCase_ : int = 768 , UpperCAmelCase_ : int = 12 , UpperCAmelCase_ : int = 12 , UpperCAmelCase_ : Optional[int] = None , UpperCAmelCase_ : str = "gelu_new" , UpperCAmelCase_ : float = 0.1 , UpperCAmelCase_ : float = 0.1 , UpperCAmelCase_ : float = 0.1 , UpperCAmelCase_ : float = 1E-5 , UpperCAmelCase_ : float = 0.02 , UpperCAmelCase_ : bool = True , UpperCAmelCase_ : bool = True , UpperCAmelCase_ : bool = False , UpperCAmelCase_ : bool = False , ):
super().__init__()
SCREAMING_SNAKE_CASE : Tuple = prefix_length
if prefix_inner_dim != n_embd and prefix_hidden_dim is None:
raise ValueError(
f'''`prefix_hidden_dim` cannot be `None` when `prefix_inner_dim`: {prefix_hidden_dim} and'''
f''' `n_embd`: {n_embd} are not equal.''' )
SCREAMING_SNAKE_CASE : Optional[int] = prefix_inner_dim
SCREAMING_SNAKE_CASE : Optional[int] = prefix_hidden_dim
SCREAMING_SNAKE_CASE : Dict = (
nn.Linear(self.prefix_inner_dim , self.prefix_hidden_dim )
if self.prefix_hidden_dim is not None
else nn.Identity()
)
SCREAMING_SNAKE_CASE : List[Any] = (
nn.Linear(self.prefix_hidden_dim , UpperCAmelCase_ ) if self.prefix_hidden_dim is not None else nn.Identity()
)
SCREAMING_SNAKE_CASE : Optional[int] = GPTaConfig(
vocab_size=UpperCAmelCase_ , n_positions=UpperCAmelCase_ , n_embd=UpperCAmelCase_ , n_layer=UpperCAmelCase_ , n_head=UpperCAmelCase_ , n_inner=UpperCAmelCase_ , activation_function=UpperCAmelCase_ , resid_pdrop=UpperCAmelCase_ , embd_pdrop=UpperCAmelCase_ , attn_pdrop=UpperCAmelCase_ , layer_norm_epsilon=UpperCAmelCase_ , initializer_range=UpperCAmelCase_ , scale_attn_weights=UpperCAmelCase_ , use_cache=UpperCAmelCase_ , scale_attn_by_inverse_layer_idx=UpperCAmelCase_ , reorder_and_upcast_attn=UpperCAmelCase_ , )
SCREAMING_SNAKE_CASE : List[Any] = GPTaLMHeadModel(UpperCAmelCase_ )
def _A ( self : Optional[Any] , UpperCAmelCase_ : torch.Tensor , UpperCAmelCase_ : torch.Tensor , UpperCAmelCase_ : Optional[torch.Tensor] = None , UpperCAmelCase_ : Optional[torch.Tensor] = None , ):
SCREAMING_SNAKE_CASE : List[str] = self.transformer.transformer.wte(UpperCAmelCase_ )
SCREAMING_SNAKE_CASE : int = self.encode_prefix(UpperCAmelCase_ )
SCREAMING_SNAKE_CASE : List[Any] = self.decode_prefix(UpperCAmelCase_ )
SCREAMING_SNAKE_CASE : Optional[Any] = torch.cat((prefix_embeds, embedding_text) , dim=1 )
if labels is not None:
SCREAMING_SNAKE_CASE : Any = self.get_dummy_token(input_ids.shape[0] , input_ids.device )
SCREAMING_SNAKE_CASE : Any = torch.cat((dummy_token, input_ids) , dim=1 )
SCREAMING_SNAKE_CASE : Optional[Any] = self.transformer(inputs_embeds=UpperCAmelCase_ , labels=UpperCAmelCase_ , attention_mask=UpperCAmelCase_ )
if self.prefix_hidden_dim is not None:
return out, hidden
else:
return out
def _A ( self : List[Any] , UpperCAmelCase_ : int , UpperCAmelCase_ : torch.device ):
return torch.zeros(UpperCAmelCase_ , self.prefix_length , dtype=torch.intaa , device=UpperCAmelCase_ )
def _A ( self : Optional[int] , UpperCAmelCase_ : Union[str, Any] ):
return self.encode_prefix(UpperCAmelCase_ )
@torch.no_grad()
def _A ( self : List[Any] , UpperCAmelCase_ : int , UpperCAmelCase_ : str , UpperCAmelCase_ : List[Any] ):
SCREAMING_SNAKE_CASE : Union[str, Any] = torch.split(UpperCAmelCase_ , 1 , dim=0 )
SCREAMING_SNAKE_CASE : List[str] = []
SCREAMING_SNAKE_CASE : Dict = []
for feature in features:
SCREAMING_SNAKE_CASE : List[Any] = self.decode_prefix(feature.to(UpperCAmelCase_ ) ) # back to the clip feature
# Only support beam search for now
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE : Dict = self.generate_beam(
input_embeds=UpperCAmelCase_ , device=UpperCAmelCase_ , eos_token_id=UpperCAmelCase_ )
generated_tokens.append(output_tokens[0] )
generated_seq_lengths.append(seq_lengths[0] )
SCREAMING_SNAKE_CASE : int = torch.stack(UpperCAmelCase_ )
SCREAMING_SNAKE_CASE : Dict = torch.stack(UpperCAmelCase_ )
return generated_tokens, generated_seq_lengths
@torch.no_grad()
def _A ( self : Any , UpperCAmelCase_ : Dict=None , UpperCAmelCase_ : Union[str, Any]=None , UpperCAmelCase_ : Optional[int]=None , UpperCAmelCase_ : int = 5 , UpperCAmelCase_ : int = 67 , UpperCAmelCase_ : float = 1.0 , UpperCAmelCase_ : Optional[int] = None , ):
SCREAMING_SNAKE_CASE : Tuple = eos_token_id
SCREAMING_SNAKE_CASE : Union[str, Any] = None
SCREAMING_SNAKE_CASE : int = None
SCREAMING_SNAKE_CASE : str = torch.ones(UpperCAmelCase_ , device=UpperCAmelCase_ , dtype=torch.int )
SCREAMING_SNAKE_CASE : Any = torch.zeros(UpperCAmelCase_ , device=UpperCAmelCase_ , dtype=torch.bool )
if input_embeds is not None:
SCREAMING_SNAKE_CASE : Tuple = input_embeds
else:
SCREAMING_SNAKE_CASE : int = self.transformer.transformer.wte(UpperCAmelCase_ )
for i in range(UpperCAmelCase_ ):
SCREAMING_SNAKE_CASE : Union[str, Any] = self.transformer(inputs_embeds=UpperCAmelCase_ )
SCREAMING_SNAKE_CASE : Tuple = outputs.logits
SCREAMING_SNAKE_CASE : int = logits[:, -1, :] / (temperature if temperature > 0 else 1.0)
SCREAMING_SNAKE_CASE : Dict = logits.softmax(-1 ).log()
if scores is None:
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE : Any = logits.topk(UpperCAmelCase_ , -1 )
SCREAMING_SNAKE_CASE : Tuple = generated.expand(UpperCAmelCase_ , *generated.shape[1:] )
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE : Dict = next_tokens.permute(1 , 0 ), scores.squeeze(0 )
if tokens is None:
SCREAMING_SNAKE_CASE : str = next_tokens
else:
SCREAMING_SNAKE_CASE : Optional[int] = tokens.expand(UpperCAmelCase_ , *tokens.shape[1:] )
SCREAMING_SNAKE_CASE : Optional[Any] = torch.cat((tokens, next_tokens) , dim=1 )
else:
SCREAMING_SNAKE_CASE : Tuple = -float(np.inf )
SCREAMING_SNAKE_CASE : List[str] = 0
SCREAMING_SNAKE_CASE : Dict = scores[:, None] + logits
seq_lengths[~is_stopped] += 1
SCREAMING_SNAKE_CASE : Any = scores_sum / seq_lengths[:, None]
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE : Tuple = scores_sum_average.view(-1 ).topk(UpperCAmelCase_ , -1 )
SCREAMING_SNAKE_CASE : str = next_tokens // scores_sum.shape[1]
SCREAMING_SNAKE_CASE : Union[str, Any] = seq_lengths[next_tokens_source]
SCREAMING_SNAKE_CASE : List[str] = next_tokens % scores_sum.shape[1]
SCREAMING_SNAKE_CASE : Dict = next_tokens.unsqueeze(1 )
SCREAMING_SNAKE_CASE : Optional[Any] = tokens[next_tokens_source]
SCREAMING_SNAKE_CASE : Optional[int] = torch.cat((tokens, next_tokens) , dim=1 )
SCREAMING_SNAKE_CASE : Any = generated[next_tokens_source]
SCREAMING_SNAKE_CASE : Optional[int] = scores_sum_average * seq_lengths
SCREAMING_SNAKE_CASE : Tuple = is_stopped[next_tokens_source]
SCREAMING_SNAKE_CASE : int = self.transformer.transformer.wte(next_tokens.squeeze() ).view(generated.shape[0] , 1 , -1 )
SCREAMING_SNAKE_CASE : Union[str, Any] = torch.cat((generated, next_token_embed) , dim=1 )
SCREAMING_SNAKE_CASE : Tuple = is_stopped + next_tokens.eq(UpperCAmelCase_ ).squeeze()
if is_stopped.all():
break
SCREAMING_SNAKE_CASE : List[str] = scores / seq_lengths
SCREAMING_SNAKE_CASE : Tuple = scores.argsort(descending=UpperCAmelCase_ )
# tokens tensors are already padded to max_seq_length
SCREAMING_SNAKE_CASE : int = [tokens[i] for i in order]
SCREAMING_SNAKE_CASE : List[str] = torch.stack(UpperCAmelCase_ , dim=0 )
SCREAMING_SNAKE_CASE : List[Any] = torch.tensor([seq_lengths[i] for i in order] , dtype=seq_lengths.dtype )
return output_texts, seq_lengths
| 62 |
'''simple docstring'''
import json
import logging
import os
import socket
import git
import numpy as np
import torch
logging.basicConfig(
format='''%(asctime)s - %(levelname)s - %(name)s - PID: %(process)d - %(message)s''',
datefmt='''%m/%d/%Y %H:%M:%S''',
level=logging.INFO,
)
__lowerCamelCase : Union[str, Any] = logging.getLogger(__name__)
def __UpperCAmelCase ( __magic_name__ )-> str:
"""simple docstring"""
snake_case_ : Dict = git.Repo(search_parent_directories=__magic_name__ )
snake_case_ : Optional[int] = {
"repo_id": str(__magic_name__ ),
"repo_sha": str(repo.head.object.hexsha ),
"repo_branch": str(repo.active_branch ),
}
with open(os.path.join(__magic_name__ ,"git_log.json" ) ,"w" ) as f:
json.dump(__magic_name__ ,__magic_name__ ,indent=4 )
def __UpperCAmelCase ( __magic_name__ )-> Tuple:
"""simple docstring"""
if params.n_gpu <= 0:
snake_case_ : Any = 0
snake_case_ : Any = -1
snake_case_ : Tuple = True
snake_case_ : List[str] = False
return
assert torch.cuda.is_available()
logger.info("Initializing GPUs" )
if params.n_gpu > 1:
assert params.local_rank != -1
snake_case_ : Optional[int] = int(os.environ["WORLD_SIZE"] )
snake_case_ : int = int(os.environ["N_GPU_NODE"] )
snake_case_ : Any = int(os.environ["RANK"] )
# number of nodes / node ID
snake_case_ : Dict = params.world_size // params.n_gpu_per_node
snake_case_ : Optional[int] = params.global_rank // params.n_gpu_per_node
snake_case_ : Tuple = True
assert params.n_nodes == int(os.environ["N_NODES"] )
assert params.node_id == int(os.environ["NODE_RANK"] )
# local job (single GPU)
else:
assert params.local_rank == -1
snake_case_ : Optional[int] = 1
snake_case_ : str = 0
snake_case_ : List[Any] = 0
snake_case_ : int = 0
snake_case_ : Dict = 1
snake_case_ : Optional[Any] = 1
snake_case_ : str = False
# sanity checks
assert params.n_nodes >= 1
assert 0 <= params.node_id < params.n_nodes
assert 0 <= params.local_rank <= params.global_rank < params.world_size
assert params.world_size == params.n_nodes * params.n_gpu_per_node
# define whether this is the master process / if we are in multi-node distributed mode
snake_case_ : str = params.node_id == 0 and params.local_rank == 0
snake_case_ : str = params.n_nodes > 1
# summary
snake_case_ : str = F'''--- Global rank: {params.global_rank} - '''
logger.info(PREFIX + "Number of nodes: %i" % params.n_nodes )
logger.info(PREFIX + "Node ID : %i" % params.node_id )
logger.info(PREFIX + "Local rank : %i" % params.local_rank )
logger.info(PREFIX + "World size : %i" % params.world_size )
logger.info(PREFIX + "GPUs per node : %i" % params.n_gpu_per_node )
logger.info(PREFIX + "Master : %s" % str(params.is_master ) )
logger.info(PREFIX + "Multi-node : %s" % str(params.multi_node ) )
logger.info(PREFIX + "Multi-GPU : %s" % str(params.multi_gpu ) )
logger.info(PREFIX + "Hostname : %s" % socket.gethostname() )
# set GPU device
torch.cuda.set_device(params.local_rank )
# initialize multi-GPU
if params.multi_gpu:
logger.info("Initializing PyTorch distributed" )
torch.distributed.init_process_group(
init_method="env://" ,backend="nccl" ,)
def __UpperCAmelCase ( __magic_name__ )-> Dict:
"""simple docstring"""
np.random.seed(args.seed )
torch.manual_seed(args.seed )
if args.n_gpu > 0:
torch.cuda.manual_seed_all(args.seed )
| 653 | 0 |
from collections import namedtuple
a : List[Any] = namedtuple("from_to", "from_ to")
a : Tuple = {
"cubicmeter": from_to(1, 1),
"litre": from_to(0.0_01, 1_000),
"kilolitre": from_to(1, 1),
"gallon": from_to(0.0_04_54, 2_64.1_72),
"cubicyard": from_to(0.7_64_55, 1.3_07_95),
"cubicfoot": from_to(0.0_28, 35.31_47),
"cup": from_to(0.0_00_23_65_88, 42_26.75),
}
def lowerCamelCase__ ( __lowerCamelCase : float , __lowerCamelCase : str , __lowerCamelCase : str ):
if from_type not in METRIC_CONVERSION:
raise ValueError(
f"""Invalid 'from_type' value: {from_type!r} Supported values are:\n"""
+ """, """.join(__lowerCamelCase ) )
if to_type not in METRIC_CONVERSION:
raise ValueError(
f"""Invalid 'to_type' value: {to_type!r}. Supported values are:\n"""
+ """, """.join(__lowerCamelCase ) )
return value * METRIC_CONVERSION[from_type].from_ * METRIC_CONVERSION[to_type].to
if __name__ == "__main__":
import doctest
doctest.testmod()
| 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 YolosImageProcessor
class A_ (unittest.TestCase ):
"""simple docstring"""
def __init__( self :Any , lowerCAmelCase__ :Dict , lowerCAmelCase__ :Dict=7 , lowerCAmelCase__ :Union[str, Any]=3 , lowerCAmelCase__ :List[str]=30 , lowerCAmelCase__ :List[str]=400 , lowerCAmelCase__ :Optional[Any]=True , lowerCAmelCase__ :Dict=None , lowerCAmelCase__ :str=True , lowerCAmelCase__ :Optional[int]=[0.5, 0.5, 0.5] , lowerCAmelCase__ :Optional[int]=[0.5, 0.5, 0.5] , lowerCAmelCase__ :str=True , lowerCAmelCase__ :int=1 / 255 , lowerCAmelCase__ :int=True , ) -> str:
'''simple docstring'''
snake_case_ : List[Any] = size if size is not None else {"shortest_edge": 18, "longest_edge": 1_333}
snake_case_ : Dict = parent
snake_case_ : Union[str, Any] = batch_size
snake_case_ : Optional[Any] = num_channels
snake_case_ : str = min_resolution
snake_case_ : Dict = max_resolution
snake_case_ : Optional[Any] = do_resize
snake_case_ : str = size
snake_case_ : Optional[int] = do_normalize
snake_case_ : Dict = image_mean
snake_case_ : Optional[int] = image_std
snake_case_ : List[str] = do_rescale
snake_case_ : Dict = rescale_factor
snake_case_ : str = do_pad
def _A ( self :List[Any] ) -> Dict:
'''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 _A ( self :Dict , lowerCAmelCase__ :List[str] , lowerCAmelCase__ :List[str]=False ) -> str:
'''simple docstring'''
if not batched:
snake_case_ : List[str] = image_inputs[0]
if isinstance(lowerCAmelCase__ , Image.Image ):
snake_case_, snake_case_ : int = image.size
else:
snake_case_, snake_case_ : Any = image.shape[1], image.shape[2]
if w < h:
snake_case_ : int = int(self.size["shortest_edge"] * h / w )
snake_case_ : List[Any] = self.size["shortest_edge"]
elif w > h:
snake_case_ : Optional[int] = self.size["shortest_edge"]
snake_case_ : str = int(self.size["shortest_edge"] * w / h )
else:
snake_case_ : Tuple = self.size["shortest_edge"]
snake_case_ : Dict = self.size["shortest_edge"]
else:
snake_case_ : List[str] = []
for image in image_inputs:
snake_case_, snake_case_ : Any = self.get_expected_values([image] )
expected_values.append((expected_height, expected_width) )
snake_case_ : str = max(lowerCAmelCase__ , key=lambda lowerCAmelCase__ : item[0] )[0]
snake_case_ : int = max(lowerCAmelCase__ , key=lambda lowerCAmelCase__ : item[1] )[1]
return expected_height, expected_width
@require_torch
@require_vision
class A_ (a_ , unittest.TestCase ):
"""simple docstring"""
a__ = YolosImageProcessor if is_vision_available() else None
def _A ( self :Optional[Any] ) -> str:
'''simple docstring'''
snake_case_ : int = YolosImageProcessingTester(self )
@property
def _A ( self :List[str] ) -> Any:
'''simple docstring'''
return self.image_processor_tester.prepare_image_processor_dict()
def _A ( self :List[str] ) -> Union[str, Any]:
'''simple docstring'''
snake_case_ : Tuple = self.image_processing_class(**self.image_processor_dict )
self.assertTrue(hasattr(lowerCAmelCase__ , "image_mean" ) )
self.assertTrue(hasattr(lowerCAmelCase__ , "image_std" ) )
self.assertTrue(hasattr(lowerCAmelCase__ , "do_normalize" ) )
self.assertTrue(hasattr(lowerCAmelCase__ , "do_resize" ) )
self.assertTrue(hasattr(lowerCAmelCase__ , "size" ) )
def _A ( self :List[Any] ) -> Any:
'''simple docstring'''
snake_case_ : List[Any] = self.image_processing_class.from_dict(self.image_processor_dict )
self.assertEqual(image_processor.size , {"shortest_edge": 18, "longest_edge": 1_333} )
self.assertEqual(image_processor.do_pad , lowerCAmelCase__ )
snake_case_ : Optional[int] = self.image_processing_class.from_dict(
self.image_processor_dict , size=42 , max_size=84 , pad_and_return_pixel_mask=lowerCAmelCase__ )
self.assertEqual(image_processor.size , {"shortest_edge": 42, "longest_edge": 84} )
self.assertEqual(image_processor.do_pad , lowerCAmelCase__ )
def _A ( self :List[str] ) -> int:
'''simple docstring'''
pass
def _A ( self :Optional[Any] ) -> Optional[Any]:
'''simple docstring'''
snake_case_ : Optional[int] = self.image_processing_class(**self.image_processor_dict )
# create random PIL images
snake_case_ : List[Any] = prepare_image_inputs(self.image_processor_tester , equal_resolution=lowerCAmelCase__ )
for image in image_inputs:
self.assertIsInstance(lowerCAmelCase__ , Image.Image )
# Test not batched input
snake_case_ : Optional[Any] = image_processing(image_inputs[0] , return_tensors="pt" ).pixel_values
snake_case_, snake_case_ : int = self.image_processor_tester.get_expected_values(lowerCAmelCase__ )
self.assertEqual(
encoded_images.shape , (1, self.image_processor_tester.num_channels, expected_height, expected_width) , )
# Test batched
snake_case_, snake_case_ : Any = self.image_processor_tester.get_expected_values(lowerCAmelCase__ , batched=lowerCAmelCase__ )
snake_case_ : Any = image_processing(lowerCAmelCase__ , return_tensors="pt" ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
expected_height,
expected_width,
) , )
def _A ( self :Dict ) -> Dict:
'''simple docstring'''
snake_case_ : str = self.image_processing_class(**self.image_processor_dict )
# create random numpy tensors
snake_case_ : Any = prepare_image_inputs(self.image_processor_tester , equal_resolution=lowerCAmelCase__ , numpify=lowerCAmelCase__ )
for image in image_inputs:
self.assertIsInstance(lowerCAmelCase__ , np.ndarray )
# Test not batched input
snake_case_ : Union[str, Any] = image_processing(image_inputs[0] , return_tensors="pt" ).pixel_values
snake_case_, snake_case_ : List[Any] = self.image_processor_tester.get_expected_values(lowerCAmelCase__ )
self.assertEqual(
encoded_images.shape , (1, self.image_processor_tester.num_channels, expected_height, expected_width) , )
# Test batched
snake_case_ : Tuple = image_processing(lowerCAmelCase__ , return_tensors="pt" ).pixel_values
snake_case_, snake_case_ : Dict = self.image_processor_tester.get_expected_values(lowerCAmelCase__ , batched=lowerCAmelCase__ )
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
expected_height,
expected_width,
) , )
def _A ( self :Tuple ) -> Tuple:
'''simple docstring'''
snake_case_ : List[str] = self.image_processing_class(**self.image_processor_dict )
# create random PyTorch tensors
snake_case_ : str = prepare_image_inputs(self.image_processor_tester , equal_resolution=lowerCAmelCase__ , torchify=lowerCAmelCase__ )
for image in image_inputs:
self.assertIsInstance(lowerCAmelCase__ , torch.Tensor )
# Test not batched input
snake_case_ : List[str] = image_processing(image_inputs[0] , return_tensors="pt" ).pixel_values
snake_case_, snake_case_ : Any = self.image_processor_tester.get_expected_values(lowerCAmelCase__ )
self.assertEqual(
encoded_images.shape , (1, self.image_processor_tester.num_channels, expected_height, expected_width) , )
# Test batched
snake_case_ : List[Any] = image_processing(lowerCAmelCase__ , return_tensors="pt" ).pixel_values
snake_case_, snake_case_ : Any = self.image_processor_tester.get_expected_values(lowerCAmelCase__ , batched=lowerCAmelCase__ )
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
expected_height,
expected_width,
) , )
def _A ( self :Tuple ) -> Dict:
'''simple docstring'''
snake_case_ : str = self.image_processing_class(**self.image_processor_dict )
snake_case_ : List[Any] = self.image_processing_class(do_resize=lowerCAmelCase__ , do_normalize=lowerCAmelCase__ , do_rescale=lowerCAmelCase__ )
# create random PyTorch tensors
snake_case_ : Tuple = prepare_image_inputs(self.image_processor_tester , equal_resolution=lowerCAmelCase__ , torchify=lowerCAmelCase__ )
for image in image_inputs:
self.assertIsInstance(lowerCAmelCase__ , torch.Tensor )
# Test whether the method "pad" and calling the image processor return the same tensors
snake_case_ : Tuple = image_processing_a.pad(lowerCAmelCase__ , return_tensors="pt" )
snake_case_ : Union[str, Any] = image_processing_a(lowerCAmelCase__ , return_tensors="pt" )
self.assertTrue(
torch.allclose(encoded_images_with_method["pixel_values"] , encoded_images["pixel_values"] , atol=1E-4 ) )
@slow
def _A ( self :str ) -> Any:
'''simple docstring'''
snake_case_ : List[str] = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png" )
with open("./tests/fixtures/tests_samples/COCO/coco_annotations.txt" , "r" ) as f:
snake_case_ : int = json.loads(f.read() )
snake_case_ : Optional[int] = {"image_id": 39_769, "annotations": target}
# encode them
snake_case_ : Tuple = YolosImageProcessor.from_pretrained("hustvl/yolos-small" )
snake_case_ : Dict = image_processing(images=lowerCAmelCase__ , annotations=lowerCAmelCase__ , return_tensors="pt" )
# verify pixel values
snake_case_ : Optional[int] = torch.Size([1, 3, 800, 1_066] )
self.assertEqual(encoding["pixel_values"].shape , lowerCAmelCase__ )
snake_case_ : str = torch.tensor([0.2_7_9_6, 0.3_1_3_8, 0.3_4_8_1] )
self.assertTrue(torch.allclose(encoding["pixel_values"][0, 0, 0, :3] , lowerCAmelCase__ , atol=1E-4 ) )
# verify area
snake_case_ : Dict = torch.tensor([5_8_8_7.9_6_0_0, 1_1_2_5_0.2_0_6_1, 4_8_9_3_5_3.8_4_3_8, 8_3_7_1_2_2.7_5_0_0, 1_4_7_9_6_7.5_1_5_6, 1_6_5_7_3_2.3_4_3_8] )
self.assertTrue(torch.allclose(encoding["labels"][0]["area"] , lowerCAmelCase__ ) )
# verify boxes
snake_case_ : Optional[int] = torch.Size([6, 4] )
self.assertEqual(encoding["labels"][0]["boxes"].shape , lowerCAmelCase__ )
snake_case_ : Any = torch.tensor([0.5_5_0_3, 0.2_7_6_5, 0.0_6_0_4, 0.2_2_1_5] )
self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"][0] , lowerCAmelCase__ , atol=1E-3 ) )
# verify image_id
snake_case_ : Dict = torch.tensor([39_769] )
self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"] , lowerCAmelCase__ ) )
# verify is_crowd
snake_case_ : int = torch.tensor([0, 0, 0, 0, 0, 0] )
self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"] , lowerCAmelCase__ ) )
# verify class_labels
snake_case_ : List[str] = torch.tensor([75, 75, 63, 65, 17, 17] )
self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"] , lowerCAmelCase__ ) )
# verify orig_size
snake_case_ : Any = torch.tensor([480, 640] )
self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"] , lowerCAmelCase__ ) )
# verify size
snake_case_ : List[Any] = torch.tensor([800, 1_066] )
self.assertTrue(torch.allclose(encoding["labels"][0]["size"] , lowerCAmelCase__ ) )
@slow
def _A ( self :Dict ) -> int:
'''simple docstring'''
snake_case_ : Tuple = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png" )
with open("./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt" , "r" ) as f:
snake_case_ : Optional[int] = json.loads(f.read() )
snake_case_ : Tuple = {"file_name": "000000039769.png", "image_id": 39_769, "segments_info": target}
snake_case_ : Any = pathlib.Path("./tests/fixtures/tests_samples/COCO/coco_panoptic" )
# encode them
snake_case_ : int = YolosImageProcessor(format="coco_panoptic" )
snake_case_ : Union[str, Any] = image_processing(images=lowerCAmelCase__ , annotations=lowerCAmelCase__ , masks_path=lowerCAmelCase__ , return_tensors="pt" )
# verify pixel values
snake_case_ : Optional[int] = torch.Size([1, 3, 800, 1_066] )
self.assertEqual(encoding["pixel_values"].shape , lowerCAmelCase__ )
snake_case_ : List[str] = torch.tensor([0.2_7_9_6, 0.3_1_3_8, 0.3_4_8_1] )
self.assertTrue(torch.allclose(encoding["pixel_values"][0, 0, 0, :3] , lowerCAmelCase__ , atol=1E-4 ) )
# verify area
snake_case_ : int = torch.tensor([1_4_7_9_7_9.6_8_7_5, 1_6_5_5_2_7.0_4_6_9, 4_8_4_6_3_8.5_9_3_8, 1_1_2_9_2.9_3_7_5, 5_8_7_9.6_5_6_2, 7_6_3_4.1_1_4_7] )
self.assertTrue(torch.allclose(encoding["labels"][0]["area"] , lowerCAmelCase__ ) )
# verify boxes
snake_case_ : Optional[int] = torch.Size([6, 4] )
self.assertEqual(encoding["labels"][0]["boxes"].shape , lowerCAmelCase__ )
snake_case_ : List[str] = torch.tensor([0.2_6_2_5, 0.5_4_3_7, 0.4_6_8_8, 0.8_6_2_5] )
self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"][0] , lowerCAmelCase__ , atol=1E-3 ) )
# verify image_id
snake_case_ : List[str] = torch.tensor([39_769] )
self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"] , lowerCAmelCase__ ) )
# verify is_crowd
snake_case_ : Dict = torch.tensor([0, 0, 0, 0, 0, 0] )
self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"] , lowerCAmelCase__ ) )
# verify class_labels
snake_case_ : str = torch.tensor([17, 17, 63, 75, 75, 93] )
self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"] , lowerCAmelCase__ ) )
# verify masks
snake_case_ : Any = 822_873
self.assertEqual(encoding["labels"][0]["masks"].sum().item() , lowerCAmelCase__ )
# verify orig_size
snake_case_ : int = torch.tensor([480, 640] )
self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"] , lowerCAmelCase__ ) )
# verify size
snake_case_ : Union[str, Any] = torch.tensor([800, 1_066] )
self.assertTrue(torch.allclose(encoding["labels"][0]["size"] , lowerCAmelCase__ ) )
| 653 | 0 |
import json
import logging
import os
import re
import sys
from dataclasses import dataclass, field
from typing import Any, Dict, List, Optional, Union
import datasets
import numpy as np
import torch
import torchaudio
from packaging import version
from torch import nn
import transformers
from transformers import (
HfArgumentParser,
Trainer,
TrainingArguments,
WavaVecaCTCTokenizer,
WavaVecaFeatureExtractor,
WavaVecaForCTC,
WavaVecaProcessor,
is_apex_available,
set_seed,
)
from transformers.trainer_utils import get_last_checkpoint, is_main_process
if is_apex_available():
from apex import amp
if version.parse(version.parse(torch.__version__).base_version) >= version.parse('1.6'):
lowercase_ : Any = True
from torch.cuda.amp import autocast
lowercase_ : Tuple = logging.getLogger(__name__)
def A__ ( snake_case_ : Optional[int]=None , snake_case_ : Optional[int]=None ):
return field(default_factory=lambda: default , metadata=snake_case_ )
@dataclass
class _lowerCamelCase :
__a = field(
metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"} )
__a = field(
default=UpperCamelCase_ , metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"} , )
__a = field(
default=UpperCamelCase_ , metadata={"help": "Whether to freeze the feature extractor layers of the model."} )
__a = field(
default=0.1 , metadata={"help": "The dropout ratio for the attention probabilities."} )
__a = field(
default=0.1 , metadata={"help": "The dropout ratio for activations inside the fully connected layer."} )
__a = field(
default=0.1 , metadata={
"help": "The dropout probabilitiy for all fully connected layers in the embeddings, encoder, and pooler."
} , )
__a = field(
default=0.1 , metadata={"help": "The dropout probabilitiy for all 1D convolutional layers in feature extractor."} , )
__a = field(
default=0.05 , metadata={
"help": (
"Propability of each feature vector along the time axis to be chosen as the start of the vector"
"span to be masked. Approximately ``mask_time_prob * sequence_length // mask_time_length`` feature"
"vectors will be masked along the time axis. This is only relevant if ``apply_spec_augment is True``."
)
} , )
__a = field(default=0.0 , metadata={"help": "The LayerDrop probability."} )
@dataclass
class _lowerCamelCase :
__a = field(
default=UpperCamelCase_ , metadata={"help": "The configuration name of the dataset to use (via the datasets library)."} )
__a = field(
default="train+validation" , metadata={
"help": "The name of the training data set split to use (via the datasets library). Defaults to 'train'"
} , )
__a = field(
default=UpperCamelCase_ , metadata={"help": "Overwrite the cached preprocessed datasets or not."} )
__a = field(
default=UpperCamelCase_ , metadata={"help": "The number of processes to use for the preprocessing."} , )
__a = field(
default=UpperCamelCase_ , metadata={
"help": (
"For debugging purposes or quicker training, truncate the number of training examples to this "
"value if set."
)
} , )
__a = field(
default=UpperCamelCase_ , metadata={
"help": (
"For debugging purposes or quicker training, truncate the number of validation examples to this "
"value if set."
)
} , )
__a = list_field(
default=[",", "?", ".", "!", "-", ";", ":", "\"\"", "%", "'", "\"", "�"] , metadata={"help": "A list of characters to remove from the transcripts."} , )
@dataclass
class _lowerCamelCase :
__a = 42
__a = True
__a = None
__a = None
__a = None
__a = None
def __call__( self , lowerCAmelCase ) -> Dict[str, torch.Tensor]:
# split inputs and labels since they have to be of different lenghts and need
# different padding methods
SCREAMING_SNAKE_CASE__: Optional[int]= [{'''input_values''': feature['''input_values''']} for feature in features]
SCREAMING_SNAKE_CASE__: Dict= [{'''input_ids''': feature['''labels''']} for feature in features]
SCREAMING_SNAKE_CASE__: Dict= self.processor.pad(
lowerCAmelCase , padding=self.padding , max_length=self.max_length , pad_to_multiple_of=self.pad_to_multiple_of , return_tensors='''pt''' , )
SCREAMING_SNAKE_CASE__: int= self.processor.pad(
labels=lowerCAmelCase , padding=self.padding , max_length=self.max_length_labels , pad_to_multiple_of=self.pad_to_multiple_of_labels , return_tensors='''pt''' , )
# replace padding with -100 to ignore loss correctly
SCREAMING_SNAKE_CASE__: Optional[int]= labels_batch['''input_ids'''].masked_fill(labels_batch.attention_mask.ne(1 ) , -100 )
SCREAMING_SNAKE_CASE__: List[Any]= labels
return batch
class _lowerCamelCase ( UpperCamelCase_ ):
def UpperCamelCase_ ( self , lowerCAmelCase , lowerCAmelCase ) -> torch.Tensor:
model.train()
SCREAMING_SNAKE_CASE__: Any= self._prepare_inputs(lowerCAmelCase )
if self.use_amp:
with autocast():
SCREAMING_SNAKE_CASE__: Tuple= self.compute_loss(lowerCAmelCase , lowerCAmelCase )
else:
SCREAMING_SNAKE_CASE__: List[Any]= self.compute_loss(lowerCAmelCase , lowerCAmelCase )
if self.args.n_gpu > 1:
if model.module.config.ctc_loss_reduction == "mean":
SCREAMING_SNAKE_CASE__: Dict= loss.mean()
elif model.module.config.ctc_loss_reduction == "sum":
SCREAMING_SNAKE_CASE__: str= loss.sum() / (inputs['''labels'''] >= 0).sum()
else:
raise ValueError(f'{model.config.ctc_loss_reduction} is not valid. Choose one of [\'mean\', \'sum\']' )
if self.args.gradient_accumulation_steps > 1:
SCREAMING_SNAKE_CASE__: Union[str, Any]= loss / self.args.gradient_accumulation_steps
if self.use_amp:
self.scaler.scale(lowerCAmelCase ).backward()
elif self.use_apex:
with amp.scale_loss(lowerCAmelCase , self.optimizer ) as scaled_loss:
scaled_loss.backward()
elif self.deepspeed:
self.deepspeed.backward(lowerCAmelCase )
else:
loss.backward()
return loss.detach()
def A__ ( ):
# See all possible arguments in src/transformers/training_args.py
# or by passing the --help flag to this script.
# We now keep distinct sets of args, for a cleaner separation of concerns.
SCREAMING_SNAKE_CASE__: Optional[int]= HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments) )
if len(sys.argv ) == 2 and sys.argv[1].endswith('''.json''' ):
# If we pass only one argument to the script and it's the path to a json file,
# let's parse it to get our arguments.
SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__: Optional[Any]= parser.parse_json_file(json_file=os.path.abspath(sys.argv[1] ) )
else:
SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__: str= parser.parse_args_into_dataclasses()
# Detecting last checkpoint.
SCREAMING_SNAKE_CASE__: int= None
if os.path.isdir(training_args.output_dir ) and training_args.do_train and not training_args.overwrite_output_dir:
SCREAMING_SNAKE_CASE__: Optional[Any]= get_last_checkpoint(training_args.output_dir )
if last_checkpoint is None and len(os.listdir(training_args.output_dir ) ) > 0:
raise ValueError(
F'Output directory ({training_args.output_dir}) already exists and is not empty. '
'''Use --overwrite_output_dir to overcome.''' )
elif last_checkpoint is not None:
logger.info(
F'Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change '
'''the `--output_dir` or add `--overwrite_output_dir` to train from scratch.''' )
# Setup logging
logging.basicConfig(
format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s''' , datefmt='''%m/%d/%Y %H:%M:%S''' , handlers=[logging.StreamHandler(sys.stdout )] , )
logger.setLevel(logging.INFO if is_main_process(training_args.local_rank ) else logging.WARN )
# Log on each process the small summary:
logger.warning(
F'Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}'
+ F'distributed training: {bool(training_args.local_rank != -1 )}, 16-bits training: {training_args.fpaa}' )
# Set the verbosity to info of the Transformers logger (on main process only):
if is_main_process(training_args.local_rank ):
transformers.utils.logging.set_verbosity_info()
logger.info('''Training/evaluation parameters %s''' , snake_case_ )
# Set seed before initializing model.
set_seed(training_args.seed )
# Get the datasets:
SCREAMING_SNAKE_CASE__: Optional[int]= datasets.load_dataset(
'''common_voice''' , data_args.dataset_config_name , split=data_args.train_split_name )
SCREAMING_SNAKE_CASE__: str= datasets.load_dataset('''common_voice''' , data_args.dataset_config_name , split='''test''' )
# Create and save tokenizer
SCREAMING_SNAKE_CASE__: Optional[int]= F'[{"".join(data_args.chars_to_ignore )}]'
def remove_special_characters(snake_case_ : Union[str, Any] ):
SCREAMING_SNAKE_CASE__: Any= re.sub(snake_case_ , '''''' , batch['''sentence'''] ).lower() + ''' '''
return batch
SCREAMING_SNAKE_CASE__: int= train_dataset.map(snake_case_ , remove_columns=['''sentence'''] )
SCREAMING_SNAKE_CASE__: List[str]= eval_dataset.map(snake_case_ , remove_columns=['''sentence'''] )
def extract_all_chars(snake_case_ : Optional[int] ):
SCREAMING_SNAKE_CASE__: int= ''' '''.join(batch['''text'''] )
SCREAMING_SNAKE_CASE__: List[Any]= list(set(snake_case_ ) )
return {"vocab": [vocab], "all_text": [all_text]}
SCREAMING_SNAKE_CASE__: Tuple= train_dataset.map(
snake_case_ , batched=snake_case_ , batch_size=-1 , keep_in_memory=snake_case_ , remove_columns=train_dataset.column_names , )
SCREAMING_SNAKE_CASE__: int= train_dataset.map(
snake_case_ , batched=snake_case_ , batch_size=-1 , keep_in_memory=snake_case_ , remove_columns=eval_dataset.column_names , )
SCREAMING_SNAKE_CASE__: Optional[Any]= list(set(vocab_train['''vocab'''][0] ) | set(vocab_test['''vocab'''][0] ) )
SCREAMING_SNAKE_CASE__: Optional[Any]= {v: k for k, v in enumerate(snake_case_ )}
SCREAMING_SNAKE_CASE__: List[str]= vocab_dict[''' ''']
del vocab_dict[" "]
SCREAMING_SNAKE_CASE__: Optional[Any]= len(snake_case_ )
SCREAMING_SNAKE_CASE__: Dict= len(snake_case_ )
with open('''vocab.json''' , '''w''' ) as vocab_file:
json.dump(snake_case_ , snake_case_ )
# Load pretrained model and tokenizer
#
# Distributed training:
# The .from_pretrained methods guarantee that only one local process can concurrently
# download model & vocab.
SCREAMING_SNAKE_CASE__: str= WavaVecaCTCTokenizer(
'''vocab.json''' , unk_token='''[UNK]''' , pad_token='''[PAD]''' , word_delimiter_token='''|''' , )
SCREAMING_SNAKE_CASE__: Dict= WavaVecaFeatureExtractor(
feature_size=1 , sampling_rate=16_000 , padding_value=0.0 , do_normalize=snake_case_ , return_attention_mask=snake_case_ )
SCREAMING_SNAKE_CASE__: int= WavaVecaProcessor(feature_extractor=snake_case_ , tokenizer=snake_case_ )
SCREAMING_SNAKE_CASE__: str= WavaVecaForCTC.from_pretrained(
model_args.model_name_or_path , cache_dir=model_args.cache_dir , activation_dropout=model_args.activation_dropout , attention_dropout=model_args.attention_dropout , hidden_dropout=model_args.hidden_dropout , feat_proj_dropout=model_args.feat_proj_dropout , mask_time_prob=model_args.mask_time_prob , gradient_checkpointing=training_args.gradient_checkpointing , layerdrop=model_args.layerdrop , ctc_loss_reduction='''mean''' , pad_token_id=processor.tokenizer.pad_token_id , vocab_size=len(processor.tokenizer ) , )
if data_args.max_train_samples is not None:
SCREAMING_SNAKE_CASE__: Any= min(len(snake_case_ ) , data_args.max_train_samples )
SCREAMING_SNAKE_CASE__: List[Any]= train_dataset.select(range(snake_case_ ) )
if data_args.max_val_samples is not None:
SCREAMING_SNAKE_CASE__: Dict= eval_dataset.select(range(data_args.max_val_samples ) )
SCREAMING_SNAKE_CASE__: Tuple= torchaudio.transforms.Resample(48_000 , 16_000 )
# Preprocessing the datasets.
# We need to read the aduio files as arrays and tokenize the targets.
def speech_file_to_array_fn(snake_case_ : Optional[Any] ):
SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__: Any= torchaudio.load(batch['''path'''] )
SCREAMING_SNAKE_CASE__: Dict= resampler(snake_case_ ).squeeze().numpy()
SCREAMING_SNAKE_CASE__: Any= 16_000
SCREAMING_SNAKE_CASE__: str= batch['''text''']
return batch
SCREAMING_SNAKE_CASE__: Optional[int]= train_dataset.map(
snake_case_ , remove_columns=train_dataset.column_names , num_proc=data_args.preprocessing_num_workers , )
SCREAMING_SNAKE_CASE__: Tuple= eval_dataset.map(
snake_case_ , remove_columns=eval_dataset.column_names , num_proc=data_args.preprocessing_num_workers , )
def prepare_dataset(snake_case_ : Dict ):
# check that all files have the correct sampling rate
assert (
len(set(batch['''sampling_rate'''] ) ) == 1
), F'Make sure all inputs have the same sampling rate of {processor.feature_extractor.sampling_rate}.'
SCREAMING_SNAKE_CASE__: Tuple= processor(
audio=batch['''speech'''] , text=batch['''target_text'''] , sampling_rate=batch['''sampling_rate'''][0] )
batch.update(snake_case_ )
return batch
SCREAMING_SNAKE_CASE__: Union[str, Any]= train_dataset.map(
snake_case_ , remove_columns=train_dataset.column_names , batch_size=training_args.per_device_train_batch_size , batched=snake_case_ , num_proc=data_args.preprocessing_num_workers , )
SCREAMING_SNAKE_CASE__: str= eval_dataset.map(
snake_case_ , remove_columns=eval_dataset.column_names , batch_size=training_args.per_device_train_batch_size , batched=snake_case_ , num_proc=data_args.preprocessing_num_workers , )
# Metric
SCREAMING_SNAKE_CASE__: str= datasets.load_metric('''wer''' )
def compute_metrics(snake_case_ : Any ):
SCREAMING_SNAKE_CASE__: Optional[Any]= pred.predictions
SCREAMING_SNAKE_CASE__: Any= np.argmax(snake_case_ , axis=-1 )
SCREAMING_SNAKE_CASE__: Any= processor.tokenizer.pad_token_id
SCREAMING_SNAKE_CASE__: Optional[Any]= processor.batch_decode(snake_case_ )
# we do not want to group tokens when computing the metrics
SCREAMING_SNAKE_CASE__: Dict= processor.batch_decode(pred.label_ids , group_tokens=snake_case_ )
SCREAMING_SNAKE_CASE__: List[Any]= wer_metric.compute(predictions=snake_case_ , references=snake_case_ )
return {"wer": wer}
if model_args.freeze_feature_extractor:
model.freeze_feature_extractor()
# Data collator
SCREAMING_SNAKE_CASE__: List[Any]= DataCollatorCTCWithPadding(processor=snake_case_ , padding=snake_case_ )
# Initialize our Trainer
SCREAMING_SNAKE_CASE__: Optional[Any]= CTCTrainer(
model=snake_case_ , data_collator=snake_case_ , args=snake_case_ , compute_metrics=snake_case_ , train_dataset=train_dataset if training_args.do_train else None , eval_dataset=eval_dataset if training_args.do_eval else None , tokenizer=processor.feature_extractor , )
# Training
if training_args.do_train:
if last_checkpoint is not None:
SCREAMING_SNAKE_CASE__: Dict= last_checkpoint
elif os.path.isdir(model_args.model_name_or_path ):
SCREAMING_SNAKE_CASE__: Any= model_args.model_name_or_path
else:
SCREAMING_SNAKE_CASE__: Optional[Any]= None
# Save the feature_extractor and the tokenizer
if is_main_process(training_args.local_rank ):
processor.save_pretrained(training_args.output_dir )
SCREAMING_SNAKE_CASE__: Union[str, Any]= trainer.train(resume_from_checkpoint=snake_case_ )
trainer.save_model()
SCREAMING_SNAKE_CASE__: List[str]= train_result.metrics
SCREAMING_SNAKE_CASE__: Optional[int]= (
data_args.max_train_samples if data_args.max_train_samples is not None else len(snake_case_ )
)
SCREAMING_SNAKE_CASE__: Union[str, Any]= min(snake_case_ , len(snake_case_ ) )
trainer.log_metrics('''train''' , snake_case_ )
trainer.save_metrics('''train''' , snake_case_ )
trainer.save_state()
# Evaluation
SCREAMING_SNAKE_CASE__: Union[str, Any]= {}
if training_args.do_eval:
logger.info('''*** Evaluate ***''' )
SCREAMING_SNAKE_CASE__: Optional[int]= trainer.evaluate()
SCREAMING_SNAKE_CASE__: Dict= data_args.max_val_samples if data_args.max_val_samples is not None else len(snake_case_ )
SCREAMING_SNAKE_CASE__: Tuple= min(snake_case_ , len(snake_case_ ) )
trainer.log_metrics('''eval''' , snake_case_ )
trainer.save_metrics('''eval''' , snake_case_ )
return results
if __name__ == "__main__":
main()
| 64 |
'''simple docstring'''
def __UpperCAmelCase ( __magic_name__ ,__magic_name__ )-> str:
"""simple docstring"""
if not isinstance(__magic_name__ ,__magic_name__ ):
raise ValueError("iterations must be defined as integers" )
if not isinstance(__magic_name__ ,__magic_name__ ) or not number >= 1:
raise ValueError(
"starting number must be\n and integer and be more than 0" )
if not iterations >= 1:
raise ValueError("Iterations must be done more than 0 times to play FizzBuzz" )
snake_case_ : Dict = ""
while number <= iterations:
if number % 3 == 0:
out += "Fizz"
if number % 5 == 0:
out += "Buzz"
if 0 not in (number % 3, number % 5):
out += str(__magic_name__ )
# print(out)
number += 1
out += " "
return out
if __name__ == "__main__":
import doctest
doctest.testmod()
| 653 | 0 |
"""simple docstring"""
from __future__ import annotations
from typing import Any
class __lowercase :
def __init__( self : int ,A : int ):
'''simple docstring'''
UpperCAmelCase__ : List[str] = num_of_nodes
UpperCAmelCase__ : list[list[int]] = []
UpperCAmelCase__ : dict[int, int] = {}
def __lowercase ( self : Any ,A : int ,A : int ,A : int ):
'''simple docstring'''
self.m_edges.append([u_node, v_node, weight] )
def __lowercase ( self : Tuple ,A : int ):
'''simple docstring'''
if self.m_component[u_node] == u_node:
return u_node
return self.find_component(self.m_component[u_node] )
def __lowercase ( self : List[Any] ,A : int ):
'''simple docstring'''
if self.m_component[u_node] != u_node:
for k in self.m_component:
UpperCAmelCase__ : List[Any] = self.find_component(A )
def __lowercase ( self : List[str] ,A : list[int] ,A : int ,A : int ):
'''simple docstring'''
if component_size[u_node] <= component_size[v_node]:
UpperCAmelCase__ : Any = v_node
component_size[v_node] += component_size[u_node]
self.set_component(A )
elif component_size[u_node] >= component_size[v_node]:
UpperCAmelCase__ : List[str] = self.find_component(A )
component_size[u_node] += component_size[v_node]
self.set_component(A )
def __lowercase ( self : int ):
'''simple docstring'''
UpperCAmelCase__ : Dict = []
UpperCAmelCase__ : str = 0
UpperCAmelCase__ : list[Any] = [-1] * self.m_num_of_nodes
# A list of components (initialized to all of the nodes)
for node in range(self.m_num_of_nodes ):
self.m_component.update({node: node} )
component_size.append(1 )
UpperCAmelCase__ : Tuple = self.m_num_of_nodes
while num_of_components > 1:
for edge in self.m_edges:
UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ : Optional[int] = edge
UpperCAmelCase__ : Tuple = self.m_component[u]
UpperCAmelCase__ : Optional[int] = self.m_component[v]
if u_component != v_component:
for component in (u_component, v_component):
if (
minimum_weight_edge[component] == -1
or minimum_weight_edge[component][2] > w
):
UpperCAmelCase__ : Optional[int] = [u, v, w]
for edge in minimum_weight_edge:
if isinstance(A ,A ):
UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ : Tuple = edge
UpperCAmelCase__ : str = self.m_component[u]
UpperCAmelCase__ : List[str] = self.m_component[v]
if u_component != v_component:
mst_weight += w
self.union(A ,A ,A )
print(f"Added edge [{u} - {v}]\nAdded weight: {w}\n" )
num_of_components -= 1
UpperCAmelCase__ : Union[str, Any] = [-1] * self.m_num_of_nodes
print(f"The total weight of the minimal spanning tree is: {mst_weight}" )
def lowerCAmelCase ( ):
'''simple docstring'''
if __name__ == "__main__":
import doctest
doctest.testmod()
| 65 |
'''simple docstring'''
import argparse
import os
# New Code #
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from accelerate import Accelerator, DistributedType
from accelerate.utils import find_executable_batch_size
########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing how to ensure out-of-memory errors never
# interrupt training, and builds off the `nlp_example.py` script.
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
# - single CPU or single GPU
# - multi GPUS (using PyTorch distributed mode)
# - (multi) TPUs
# - fp16 (mixed-precision) or fp32 (normal precision)
#
# New additions from the base script can be found quickly by
# looking for the # New Code # tags
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################
__lowerCamelCase : Tuple = 16
__lowerCamelCase : Optional[int] = 32
def __UpperCAmelCase ( __magic_name__ ,__magic_name__ = 16 )-> int:
"""simple docstring"""
snake_case_ : Optional[int] = AutoTokenizer.from_pretrained("bert-base-cased" )
snake_case_ : str = load_dataset("glue" ,"mrpc" )
def tokenize_function(__magic_name__ ):
# max_length=None => use the model max length (it's actually the default)
snake_case_ : Dict = tokenizer(examples["sentence1"] ,examples["sentence2"] ,truncation=__magic_name__ ,max_length=__magic_name__ )
return outputs
# Apply the method we just defined to all the examples in all the splits of the dataset
# starting with the main process first:
with accelerator.main_process_first():
snake_case_ : Any = datasets.map(
__magic_name__ ,batched=__magic_name__ ,remove_columns=["idx", "sentence1", "sentence2"] ,)
# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
# transformers library
snake_case_ : List[Any] = tokenized_datasets.rename_column("label" ,"labels" )
def collate_fn(__magic_name__ ):
# On TPU it's best to pad everything to the same length or training will be very slow.
snake_case_ : int = 128 if accelerator.distributed_type == DistributedType.TPU else None
# When using mixed precision we want round multiples of 8/16
if accelerator.mixed_precision == "fp8":
snake_case_ : Tuple = 16
elif accelerator.mixed_precision != "no":
snake_case_ : str = 8
else:
snake_case_ : Optional[Any] = None
return tokenizer.pad(
__magic_name__ ,padding="longest" ,max_length=__magic_name__ ,pad_to_multiple_of=__magic_name__ ,return_tensors="pt" ,)
# Instantiate dataloaders.
snake_case_ : str = DataLoader(
tokenized_datasets["train"] ,shuffle=__magic_name__ ,collate_fn=__magic_name__ ,batch_size=__magic_name__ )
snake_case_ : Optional[Any] = DataLoader(
tokenized_datasets["validation"] ,shuffle=__magic_name__ ,collate_fn=__magic_name__ ,batch_size=__magic_name__ )
return train_dataloader, eval_dataloader
# For testing only
if os.environ.get('''TESTING_MOCKED_DATALOADERS''', None) == "1":
from accelerate.test_utils.training import mocked_dataloaders
__lowerCamelCase : Optional[Any] = mocked_dataloaders # noqa: F811
def __UpperCAmelCase ( __magic_name__ ,__magic_name__ )-> Dict:
"""simple docstring"""
if os.environ.get("TESTING_MOCKED_DATALOADERS" ,__magic_name__ ) == "1":
snake_case_ : List[str] = 2
# Initialize accelerator
snake_case_ : Union[str, Any] = Accelerator(cpu=args.cpu ,mixed_precision=args.mixed_precision )
# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
snake_case_ : List[str] = config["lr"]
snake_case_ : Dict = int(config["num_epochs"] )
snake_case_ : Dict = int(config["seed"] )
snake_case_ : Optional[int] = int(config["batch_size"] )
snake_case_ : Dict = evaluate.load("glue" ,"mrpc" )
# New Code #
# We now can define an inner training loop function. It should take a batch size as the only parameter,
# and build the dataloaders in there.
# It also gets our decorator
@find_executable_batch_size(starting_batch_size=__magic_name__ )
def inner_training_loop(__magic_name__ ):
# And now just move everything below under this function
# We need to bring in the Accelerator object from earlier
nonlocal accelerator
# And reset all of its attributes that could hold onto any memory:
accelerator.free_memory()
# Then we can declare the model, optimizer, and everything else:
set_seed(__magic_name__ )
# Instantiate the model (we build the model here so that the seed also control new weights initialization)
snake_case_ : Optional[int] = AutoModelForSequenceClassification.from_pretrained("bert-base-cased" ,return_dict=__magic_name__ )
# We could avoid this line since the accelerator is set with `device_placement=True` (default value).
# Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
# creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
snake_case_ : Optional[int] = model.to(accelerator.device )
# Instantiate optimizer
snake_case_ : List[Any] = AdamW(params=model.parameters() ,lr=__magic_name__ )
snake_case_, snake_case_ : int = get_dataloaders(__magic_name__ ,__magic_name__ )
# Instantiate scheduler
snake_case_ : Tuple = get_linear_schedule_with_warmup(
optimizer=__magic_name__ ,num_warmup_steps=100 ,num_training_steps=(len(__magic_name__ ) * num_epochs) ,)
# Prepare everything
# There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
# prepare method.
snake_case_, snake_case_, snake_case_, snake_case_, snake_case_ : Tuple = accelerator.prepare(
__magic_name__ ,__magic_name__ ,__magic_name__ ,__magic_name__ ,__magic_name__ )
# Now we train the model
for epoch in range(__magic_name__ ):
model.train()
for step, batch in enumerate(__magic_name__ ):
# We could avoid this line since we set the accelerator with `device_placement=True`.
batch.to(accelerator.device )
snake_case_ : int = model(**__magic_name__ )
snake_case_ : Any = outputs.loss
accelerator.backward(__magic_name__ )
optimizer.step()
lr_scheduler.step()
optimizer.zero_grad()
model.eval()
for step, batch in enumerate(__magic_name__ ):
# We could avoid this line since we set the accelerator with `device_placement=True`.
batch.to(accelerator.device )
with torch.no_grad():
snake_case_ : Union[str, Any] = model(**__magic_name__ )
snake_case_ : List[str] = outputs.logits.argmax(dim=-1 )
snake_case_, snake_case_ : Union[str, Any] = accelerator.gather_for_metrics((predictions, batch["labels"]) )
metric.add_batch(
predictions=__magic_name__ ,references=__magic_name__ ,)
snake_case_ : Tuple = metric.compute()
# Use accelerator.print to print only on the main process.
accelerator.print(F'''epoch {epoch}:''' ,__magic_name__ )
# New Code #
# And call it at the end with no arguments
# Note: You could also refactor this outside of your training loop function
inner_training_loop()
def __UpperCAmelCase ( )-> List[str]:
"""simple docstring"""
snake_case_ : List[Any] = argparse.ArgumentParser(description="Simple example of training script." )
parser.add_argument(
"--mixed_precision" ,type=__magic_name__ ,default=__magic_name__ ,choices=["no", "fp16", "bf16", "fp8"] ,help="Whether to use mixed precision. Choose"
"between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10."
"and an Nvidia Ampere GPU." ,)
parser.add_argument("--cpu" ,action="store_true" ,help="If passed, will train on the CPU." )
snake_case_ : str = parser.parse_args()
snake_case_ : Optional[int] = {"lr": 2E-5, "num_epochs": 3, "seed": 42, "batch_size": 16}
training_function(__magic_name__ ,__magic_name__ )
if __name__ == "__main__":
main()
| 653 | 0 |
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
UpperCamelCase = logging.get_logger(__name__)
UpperCamelCase = {"vocab_file": "spiece.model"}
UpperCamelCase = {
"vocab_file": {
"bert_for_seq_generation": (
"https://huggingface.co/google/bert_for_seq_generation_L-24_bbc_encoder/resolve/main/spiece.model"
),
}
}
UpperCamelCase = {"bert_for_seq_generation": 512}
class lowerCAmelCase_ ( __snake_case ):
_UpperCamelCase : Optional[int] = VOCAB_FILES_NAMES
_UpperCamelCase : List[str] = PRETRAINED_VOCAB_FILES_MAP
_UpperCamelCase : Tuple = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
_UpperCamelCase : List[int] = []
_UpperCamelCase : Union[str, Any] = ["input_ids", "attention_mask"]
def __init__( self , _lowerCAmelCase , _lowerCAmelCase="<s>" , _lowerCAmelCase="</s>" , _lowerCAmelCase="<unk>" , _lowerCAmelCase="<pad>" , _lowerCAmelCase="<::::>" , _lowerCAmelCase = None , **_lowerCAmelCase , ):
_lowercase : Any = {} if sp_model_kwargs is None else sp_model_kwargs
# Add extra_ids to the special token list
super().__init__(
bos_token=_lowerCAmelCase , eos_token=_lowerCAmelCase , unk_token=_lowerCAmelCase , pad_token=_lowerCAmelCase , sep_token=_lowerCAmelCase , sp_model_kwargs=self.sp_model_kwargs , **_lowerCAmelCase , )
_lowercase : Tuple = vocab_file
_lowercase : List[str] = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(_lowerCAmelCase )
@property
def __a ( self ):
return self.sp_model.get_piece_size()
def __a ( self ):
_lowercase : Union[str, Any] = {self.convert_ids_to_tokens(_lowerCAmelCase ): i for i in range(self.vocab_size )}
vocab.update(self.added_tokens_encoder )
return vocab
def __getstate__( self ):
_lowercase : Optional[int] = self.__dict__.copy()
_lowercase : Optional[int] = None
return state
def __setstate__( self , _lowerCAmelCase ):
_lowercase : int = d
# for backward compatibility
if not hasattr(self , 'sp_model_kwargs' ):
_lowercase : List[str] = {}
_lowercase : Dict = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(self.vocab_file )
def __a ( self , _lowerCAmelCase ):
return self.sp_model.encode(_lowerCAmelCase , out_type=_lowerCAmelCase )
def __a ( self , _lowerCAmelCase ):
return self.sp_model.piece_to_id(_lowerCAmelCase )
def __a ( self , _lowerCAmelCase ):
_lowercase : int = self.sp_model.IdToPiece(_lowerCAmelCase )
return token
def __a ( self , _lowerCAmelCase ):
_lowercase : str = []
_lowercase : Union[str, Any] = ''
for token in tokens:
# make sure that special tokens are not decoded using sentencepiece model
if token in self.all_special_tokens:
out_string += self.sp_model.decode(_lowerCAmelCase ) + token
_lowercase : Dict = []
else:
current_sub_tokens.append(_lowerCAmelCase )
out_string += self.sp_model.decode(_lowerCAmelCase )
return out_string.strip()
def __a ( self , _lowerCAmelCase , _lowerCAmelCase = None ):
if not os.path.isdir(_lowerCAmelCase ):
logger.error(F"""Vocabulary path ({save_directory}) should be a directory""" )
return
_lowercase : str = os.path.join(
_lowerCAmelCase , (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file'] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(_lowerCAmelCase ) and os.path.isfile(self.vocab_file ):
copyfile(self.vocab_file , _lowerCAmelCase )
elif not os.path.isfile(self.vocab_file ):
with open(_lowerCAmelCase , 'wb' ) as fi:
_lowercase : Tuple = self.sp_model.serialized_model_proto()
fi.write(_lowerCAmelCase )
return (out_vocab_file,)
| 66 |
'''simple docstring'''
# Copyright 2023 The HuggingFace Inc. 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 torch
from ..models.auto import AutoModelForSequenceClassification, AutoTokenizer
from .base import PipelineTool
class A_ (a_ ):
"""simple docstring"""
a__ = '''facebook/bart-large-mnli'''
a__ = (
'''This is a tool that classifies an English text using provided labels. It takes two inputs: `text`, which '''
'''should be the text to classify, and `labels`, which should be the list of labels to use for classification. '''
'''It returns the most likely label in the list of provided `labels` for the input text.'''
)
a__ = '''text_classifier'''
a__ = AutoTokenizer
a__ = AutoModelForSequenceClassification
a__ = ['''text''', ['''text''']]
a__ = ['''text''']
def _A ( self :List[str] ) -> Union[str, Any]:
'''simple docstring'''
super().setup()
snake_case_ : Optional[int] = self.model.config
snake_case_ : Any = -1
for idx, label in config.idalabel.items():
if label.lower().startswith("entail" ):
snake_case_ : Union[str, Any] = int(lowerCAmelCase__ )
if self.entailment_id == -1:
raise ValueError("Could not determine the entailment ID from the model config, please pass it at init." )
def _A ( self :Dict , lowerCAmelCase__ :int , lowerCAmelCase__ :Tuple ) -> int:
'''simple docstring'''
snake_case_ : Tuple = labels
return self.pre_processor(
[text] * len(lowerCAmelCase__ ) , [F'''This example is {label}''' for label in labels] , return_tensors="pt" , padding="max_length" , )
def _A ( self :Any , lowerCAmelCase__ :str ) -> List[str]:
'''simple docstring'''
snake_case_ : Optional[int] = outputs.logits
snake_case_ : Tuple = torch.argmax(logits[:, 2] ).item()
return self._labels[label_id]
| 653 | 0 |
from math import factorial, pi
def SCREAMING_SNAKE_CASE__ ( snake_case__ :float , snake_case__ :int = 30 ) -> float:
if not isinstance(snake_case__ , (int, float) ):
raise ValueError('maclaurin_sin() requires either an int or float for theta' )
if not isinstance(snake_case__ , snake_case__ ) or accuracy <= 0:
raise ValueError('maclaurin_sin() requires a positive int for accuracy' )
_lowercase = float(snake_case__ )
_lowercase = theta // (2 * pi)
theta -= 2 * div * pi
return sum(
(-1) ** r * theta ** (2 * r + 1) / factorial(2 * r + 1 ) for r in range(snake_case__ ) )
def SCREAMING_SNAKE_CASE__ ( snake_case__ :float , snake_case__ :int = 30 ) -> float:
if not isinstance(snake_case__ , (int, float) ):
raise ValueError('maclaurin_cos() requires either an int or float for theta' )
if not isinstance(snake_case__ , snake_case__ ) or accuracy <= 0:
raise ValueError('maclaurin_cos() requires a positive int for accuracy' )
_lowercase = float(snake_case__ )
_lowercase = theta // (2 * pi)
theta -= 2 * div * pi
return sum((-1) ** r * theta ** (2 * r) / factorial(2 * r ) for r in range(snake_case__ ) )
if __name__ == "__main__":
import doctest
doctest.testmod()
print(maclaurin_sin(1_0))
print(maclaurin_sin(-1_0))
print(maclaurin_sin(1_0, 1_5))
print(maclaurin_sin(-1_0, 1_5))
print(maclaurin_cos(5))
print(maclaurin_cos(-5))
print(maclaurin_cos(1_0, 1_5))
print(maclaurin_cos(-1_0, 1_5)) | 67 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
is_vision_available,
)
__lowerCamelCase : Any = {'''configuration_vit''': ['''VIT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''ViTConfig''', '''ViTOnnxConfig''']}
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__lowerCamelCase : Any = ['''ViTFeatureExtractor''']
__lowerCamelCase : Any = ['''ViTImageProcessor''']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__lowerCamelCase : Optional[Any] = [
'''VIT_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''ViTForImageClassification''',
'''ViTForMaskedImageModeling''',
'''ViTModel''',
'''ViTPreTrainedModel''',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__lowerCamelCase : Union[str, Any] = [
'''TFViTForImageClassification''',
'''TFViTModel''',
'''TFViTPreTrainedModel''',
]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__lowerCamelCase : Tuple = [
'''FlaxViTForImageClassification''',
'''FlaxViTModel''',
'''FlaxViTPreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_vit import VIT_PRETRAINED_CONFIG_ARCHIVE_MAP, ViTConfig, ViTOnnxConfig
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .feature_extraction_vit import ViTFeatureExtractor
from .image_processing_vit import ViTImageProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_vit import (
VIT_PRETRAINED_MODEL_ARCHIVE_LIST,
ViTForImageClassification,
ViTForMaskedImageModeling,
ViTModel,
ViTPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_vit import TFViTForImageClassification, TFViTModel, TFViTPreTrainedModel
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_vit import FlaxViTForImageClassification, FlaxViTModel, FlaxViTPreTrainedModel
else:
import sys
__lowerCamelCase : List[str] = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 653 | 0 |
from __future__ import annotations
import pandas as pd
def lowercase__ ( A_: list[int] , A_: list[int] , A_: int ) -> list[int]:
"""simple docstring"""
__UpperCAmelCase =[0] * no_of_processes
__UpperCAmelCase =[0] * no_of_processes
# Copy the burst time into remaining_time[]
for i in range(A_ ):
__UpperCAmelCase =burst_time[i]
__UpperCAmelCase =0
__UpperCAmelCase =0
__UpperCAmelCase =999999999
__UpperCAmelCase =0
__UpperCAmelCase =False
# Process until all processes are completed
while complete != no_of_processes:
for j in range(A_ ):
if arrival_time[j] <= increment_time and remaining_time[j] > 0:
if remaining_time[j] < minm:
__UpperCAmelCase =remaining_time[j]
__UpperCAmelCase =j
__UpperCAmelCase =True
if not check:
increment_time += 1
continue
remaining_time[short] -= 1
__UpperCAmelCase =remaining_time[short]
if minm == 0:
__UpperCAmelCase =999999999
if remaining_time[short] == 0:
complete += 1
__UpperCAmelCase =False
# Find finish time of current process
__UpperCAmelCase =increment_time + 1
# Calculate waiting time
__UpperCAmelCase =finish_time - arrival_time[short]
__UpperCAmelCase =finar - burst_time[short]
if waiting_time[short] < 0:
__UpperCAmelCase =0
# Increment time
increment_time += 1
return waiting_time
def lowercase__ ( A_: list[int] , A_: int , A_: list[int] ) -> list[int]:
"""simple docstring"""
__UpperCAmelCase =[0] * no_of_processes
for i in range(A_ ):
__UpperCAmelCase =burst_time[i] + waiting_time[i]
return turn_around_time
def lowercase__ ( A_: list[int] , A_: list[int] , A_: int ) -> None:
"""simple docstring"""
__UpperCAmelCase =0
__UpperCAmelCase =0
for i in range(A_ ):
__UpperCAmelCase =total_waiting_time + waiting_time[i]
__UpperCAmelCase =total_turn_around_time + turn_around_time[i]
print(F'''Average waiting time = {total_waiting_time / no_of_processes:.5f}''' )
print("""Average turn around time =""" , total_turn_around_time / no_of_processes )
if __name__ == "__main__":
print("Enter how many process you want to analyze")
__A = int(input())
__A = [0] * no_of_processes
__A = [0] * no_of_processes
__A = list(range(1, no_of_processes + 1))
for i in range(no_of_processes):
print("Enter the arrival time and burst time for process:--" + str(i + 1))
__A , __A = map(int, input().split())
__A = calculate_waitingtime(arrival_time, burst_time, no_of_processes)
__A = burst_time
__A = no_of_processes
__A = waiting_time
__A = calculate_turnaroundtime(bt, n, wt)
calculate_average_times(waiting_time, turn_around_time, no_of_processes)
__A = pd.DataFrame(
list(zip(processes, burst_time, arrival_time, waiting_time, turn_around_time)),
columns=[
"Process",
"BurstTime",
"ArrivalTime",
"WaitingTime",
"TurnAroundTime",
],
)
# Printing the dataFrame
pd.set_option("display.max_rows", fcfs.shape[0] + 1)
print(fcfs)
| 68 |
'''simple docstring'''
import copy
import unittest
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
MODEL_FOR_MULTIPLE_CHOICE_MAPPING,
MODEL_FOR_QUESTION_ANSWERING_MAPPING,
MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
LayoutLMvaConfig,
LayoutLMvaForQuestionAnswering,
LayoutLMvaForSequenceClassification,
LayoutLMvaForTokenClassification,
LayoutLMvaModel,
)
from transformers.models.layoutlmva.modeling_layoutlmva import LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import LayoutLMvaImageProcessor
class A_ :
"""simple docstring"""
def __init__( self :Optional[Any] , lowerCAmelCase__ :List[str] , lowerCAmelCase__ :List[str]=2 , lowerCAmelCase__ :List[Any]=3 , lowerCAmelCase__ :Any=4 , lowerCAmelCase__ :List[Any]=2 , lowerCAmelCase__ :List[str]=7 , lowerCAmelCase__ :Any=True , lowerCAmelCase__ :Optional[int]=True , lowerCAmelCase__ :Optional[Any]=True , lowerCAmelCase__ :Optional[int]=True , lowerCAmelCase__ :List[str]=99 , lowerCAmelCase__ :Union[str, Any]=36 , lowerCAmelCase__ :Dict=3 , lowerCAmelCase__ :str=4 , lowerCAmelCase__ :Optional[int]=37 , lowerCAmelCase__ :Dict="gelu" , lowerCAmelCase__ :Optional[Any]=0.1 , lowerCAmelCase__ :Dict=0.1 , lowerCAmelCase__ :Optional[int]=512 , lowerCAmelCase__ :Union[str, Any]=16 , lowerCAmelCase__ :List[Any]=2 , lowerCAmelCase__ :Any=0.0_2 , lowerCAmelCase__ :Dict=6 , lowerCAmelCase__ :Optional[int]=6 , lowerCAmelCase__ :Any=3 , lowerCAmelCase__ :int=4 , lowerCAmelCase__ :int=None , lowerCAmelCase__ :Any=1_000 , ) -> Any:
'''simple docstring'''
snake_case_ : Optional[int] = parent
snake_case_ : Union[str, Any] = batch_size
snake_case_ : Optional[int] = num_channels
snake_case_ : List[Any] = image_size
snake_case_ : Optional[int] = patch_size
snake_case_ : Union[str, Any] = text_seq_length
snake_case_ : Dict = is_training
snake_case_ : Optional[Any] = use_input_mask
snake_case_ : Union[str, Any] = use_token_type_ids
snake_case_ : Dict = use_labels
snake_case_ : List[str] = vocab_size
snake_case_ : Optional[Any] = hidden_size
snake_case_ : List[str] = num_hidden_layers
snake_case_ : int = num_attention_heads
snake_case_ : List[str] = intermediate_size
snake_case_ : str = hidden_act
snake_case_ : Optional[Any] = hidden_dropout_prob
snake_case_ : Optional[int] = attention_probs_dropout_prob
snake_case_ : Union[str, Any] = max_position_embeddings
snake_case_ : List[Any] = type_vocab_size
snake_case_ : Union[str, Any] = type_sequence_label_size
snake_case_ : List[Any] = initializer_range
snake_case_ : Union[str, Any] = coordinate_size
snake_case_ : int = shape_size
snake_case_ : Tuple = num_labels
snake_case_ : List[Any] = num_choices
snake_case_ : List[str] = scope
snake_case_ : Dict = range_bbox
# LayoutLMv3's sequence length equals the number of text tokens + number of patches + 1 (we add 1 for the CLS token)
snake_case_ : str = text_seq_length
snake_case_ : Optional[int] = (image_size // patch_size) ** 2 + 1
snake_case_ : str = self.text_seq_length + self.image_seq_length
def _A ( self :Union[str, Any] ) -> Tuple:
'''simple docstring'''
snake_case_ : Dict = ids_tensor([self.batch_size, self.text_seq_length] , self.vocab_size )
snake_case_ : str = ids_tensor([self.batch_size, self.text_seq_length, 4] , self.range_bbox )
# Ensure that bbox is legal
for i in range(bbox.shape[0] ):
for j in range(bbox.shape[1] ):
if bbox[i, j, 3] < bbox[i, j, 1]:
snake_case_ : Optional[Any] = bbox[i, j, 3]
snake_case_ : Any = bbox[i, j, 1]
snake_case_ : Tuple = t
if bbox[i, j, 2] < bbox[i, j, 0]:
snake_case_ : str = bbox[i, j, 2]
snake_case_ : Dict = bbox[i, j, 0]
snake_case_ : Union[str, Any] = t
snake_case_ : int = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
snake_case_ : Dict = None
if self.use_input_mask:
snake_case_ : str = random_attention_mask([self.batch_size, self.text_seq_length] )
snake_case_ : Any = None
if self.use_token_type_ids:
snake_case_ : List[str] = ids_tensor([self.batch_size, self.text_seq_length] , self.type_vocab_size )
snake_case_ : Union[str, Any] = None
snake_case_ : str = None
if self.use_labels:
snake_case_ : List[Any] = ids_tensor([self.batch_size] , self.type_sequence_label_size )
snake_case_ : List[Any] = ids_tensor([self.batch_size, self.text_seq_length] , self.num_labels )
snake_case_ : str = LayoutLMvaConfig(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , initializer_range=self.initializer_range , coordinate_size=self.coordinate_size , shape_size=self.shape_size , input_size=self.image_size , patch_size=self.patch_size , )
return config, input_ids, bbox, pixel_values, token_type_ids, input_mask, sequence_labels, token_labels
def _A ( self :Dict , lowerCAmelCase__ :Dict , lowerCAmelCase__ :List[str] , lowerCAmelCase__ :str , lowerCAmelCase__ :List[Any] , lowerCAmelCase__ :Optional[Any] , lowerCAmelCase__ :List[str] , lowerCAmelCase__ :Dict , lowerCAmelCase__ :List[str] ) -> Optional[Any]:
'''simple docstring'''
snake_case_ : Tuple = LayoutLMvaModel(config=lowerCAmelCase__ )
model.to(lowerCAmelCase__ )
model.eval()
# text + image
snake_case_ : Tuple = model(lowerCAmelCase__ , pixel_values=lowerCAmelCase__ )
snake_case_ : Optional[int] = model(
lowerCAmelCase__ , bbox=lowerCAmelCase__ , pixel_values=lowerCAmelCase__ , attention_mask=lowerCAmelCase__ , token_type_ids=lowerCAmelCase__ )
snake_case_ : Optional[int] = model(lowerCAmelCase__ , bbox=lowerCAmelCase__ , pixel_values=lowerCAmelCase__ , token_type_ids=lowerCAmelCase__ )
snake_case_ : int = model(lowerCAmelCase__ , bbox=lowerCAmelCase__ , pixel_values=lowerCAmelCase__ )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
# text only
snake_case_ : List[Any] = model(lowerCAmelCase__ )
self.parent.assertEqual(
result.last_hidden_state.shape , (self.batch_size, self.text_seq_length, self.hidden_size) )
# image only
snake_case_ : Union[str, Any] = model(pixel_values=lowerCAmelCase__ )
self.parent.assertEqual(
result.last_hidden_state.shape , (self.batch_size, self.image_seq_length, self.hidden_size) )
def _A ( self :str , lowerCAmelCase__ :str , lowerCAmelCase__ :Tuple , lowerCAmelCase__ :Union[str, Any] , lowerCAmelCase__ :Union[str, Any] , lowerCAmelCase__ :Optional[Any] , lowerCAmelCase__ :List[str] , lowerCAmelCase__ :List[str] , lowerCAmelCase__ :Tuple ) -> List[Any]:
'''simple docstring'''
snake_case_ : str = self.num_labels
snake_case_ : List[Any] = LayoutLMvaForSequenceClassification(lowerCAmelCase__ )
model.to(lowerCAmelCase__ )
model.eval()
snake_case_ : Optional[int] = model(
lowerCAmelCase__ , bbox=lowerCAmelCase__ , pixel_values=lowerCAmelCase__ , attention_mask=lowerCAmelCase__ , token_type_ids=lowerCAmelCase__ , labels=lowerCAmelCase__ , )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def _A ( self :Union[str, Any] , lowerCAmelCase__ :List[Any] , lowerCAmelCase__ :int , lowerCAmelCase__ :List[str] , lowerCAmelCase__ :Tuple , lowerCAmelCase__ :str , lowerCAmelCase__ :Optional[int] , lowerCAmelCase__ :Any , lowerCAmelCase__ :Union[str, Any] ) -> str:
'''simple docstring'''
snake_case_ : Optional[int] = self.num_labels
snake_case_ : str = LayoutLMvaForTokenClassification(config=lowerCAmelCase__ )
model.to(lowerCAmelCase__ )
model.eval()
snake_case_ : List[Any] = model(
lowerCAmelCase__ , bbox=lowerCAmelCase__ , pixel_values=lowerCAmelCase__ , attention_mask=lowerCAmelCase__ , token_type_ids=lowerCAmelCase__ , labels=lowerCAmelCase__ , )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.text_seq_length, self.num_labels) )
def _A ( self :Optional[int] , lowerCAmelCase__ :Dict , lowerCAmelCase__ :str , lowerCAmelCase__ :Dict , lowerCAmelCase__ :Optional[int] , lowerCAmelCase__ :str , lowerCAmelCase__ :int , lowerCAmelCase__ :Union[str, Any] , lowerCAmelCase__ :str ) -> Tuple:
'''simple docstring'''
snake_case_ : List[str] = LayoutLMvaForQuestionAnswering(config=lowerCAmelCase__ )
model.to(lowerCAmelCase__ )
model.eval()
snake_case_ : List[Any] = model(
lowerCAmelCase__ , bbox=lowerCAmelCase__ , pixel_values=lowerCAmelCase__ , attention_mask=lowerCAmelCase__ , token_type_ids=lowerCAmelCase__ , start_positions=lowerCAmelCase__ , end_positions=lowerCAmelCase__ , )
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 :int ) -> Union[str, Any]:
'''simple docstring'''
snake_case_ : Dict = self.prepare_config_and_inputs()
(
(
snake_case_
), (
snake_case_
), (
snake_case_
), (
snake_case_
), (
snake_case_
), (
snake_case_
), (
snake_case_
), (
snake_case_
),
) : Optional[Any] = config_and_inputs
snake_case_ : Tuple = {
"input_ids": input_ids,
"bbox": bbox,
"pixel_values": pixel_values,
"token_type_ids": token_type_ids,
"attention_mask": input_mask,
}
return config, inputs_dict
@require_torch
class A_ (a_ , a_ , unittest.TestCase ):
"""simple docstring"""
a__ = False
a__ = False
a__ = False
a__ = (
(
LayoutLMvaModel,
LayoutLMvaForSequenceClassification,
LayoutLMvaForTokenClassification,
LayoutLMvaForQuestionAnswering,
)
if is_torch_available()
else ()
)
a__ = (
{'''document-question-answering''': LayoutLMvaForQuestionAnswering, '''feature-extraction''': LayoutLMvaModel}
if is_torch_available()
else {}
)
def _A ( self :Optional[Any] , lowerCAmelCase__ :Tuple , lowerCAmelCase__ :Tuple , lowerCAmelCase__ :List[Any] , lowerCAmelCase__ :Optional[Any] , lowerCAmelCase__ :List[Any] ) -> List[str]:
'''simple docstring'''
return True
def _A ( self :List[Any] ) -> str:
'''simple docstring'''
snake_case_ : Tuple = LayoutLMvaModelTester(self )
snake_case_ : Optional[int] = ConfigTester(self , config_class=lowerCAmelCase__ , hidden_size=37 )
def _A ( self :Tuple , lowerCAmelCase__ :Optional[int] , lowerCAmelCase__ :Dict , lowerCAmelCase__ :Union[str, Any]=False ) -> Any:
'''simple docstring'''
snake_case_ : List[str] = copy.deepcopy(lowerCAmelCase__ )
if model_class in get_values(lowerCAmelCase__ ):
snake_case_ : Optional[Any] = {
k: v.unsqueeze(1 ).expand(-1 , self.model_tester.num_choices , -1 ).contiguous()
if isinstance(lowerCAmelCase__ , torch.Tensor ) and v.ndim > 1
else v
for k, v in inputs_dict.items()
}
if return_labels:
if model_class in get_values(lowerCAmelCase__ ):
snake_case_ : Union[str, Any] = torch.ones(self.model_tester.batch_size , dtype=torch.long , device=lowerCAmelCase__ )
elif model_class in get_values(lowerCAmelCase__ ):
snake_case_ : List[Any] = torch.zeros(
self.model_tester.batch_size , dtype=torch.long , device=lowerCAmelCase__ )
snake_case_ : str = torch.zeros(
self.model_tester.batch_size , dtype=torch.long , device=lowerCAmelCase__ )
elif model_class in [
*get_values(lowerCAmelCase__ ),
]:
snake_case_ : Union[str, Any] = torch.zeros(
self.model_tester.batch_size , dtype=torch.long , device=lowerCAmelCase__ )
elif model_class in [
*get_values(lowerCAmelCase__ ),
]:
snake_case_ : List[str] = torch.zeros(
(self.model_tester.batch_size, self.model_tester.text_seq_length) , dtype=torch.long , device=lowerCAmelCase__ , )
return inputs_dict
def _A ( self :Any ) -> Any:
'''simple docstring'''
self.config_tester.run_common_tests()
def _A ( self :int ) -> int:
'''simple docstring'''
snake_case_ : Tuple = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*lowerCAmelCase__ )
def _A ( self :Any ) -> Dict:
'''simple docstring'''
snake_case_ : Dict = self.model_tester.prepare_config_and_inputs()
for type in ["absolute", "relative_key", "relative_key_query"]:
snake_case_ : int = type
self.model_tester.create_and_check_model(*lowerCAmelCase__ )
def _A ( self :int ) -> str:
'''simple docstring'''
snake_case_ : Optional[int] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_sequence_classification(*lowerCAmelCase__ )
def _A ( self :List[Any] ) -> Optional[Any]:
'''simple docstring'''
snake_case_ : List[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_token_classification(*lowerCAmelCase__ )
def _A ( self :int ) -> Union[str, Any]:
'''simple docstring'''
snake_case_ : Optional[int] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_question_answering(*lowerCAmelCase__ )
@slow
def _A ( self :Tuple ) -> List[Any]:
'''simple docstring'''
for model_name in LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
snake_case_ : str = LayoutLMvaModel.from_pretrained(lowerCAmelCase__ )
self.assertIsNotNone(lowerCAmelCase__ )
def __UpperCAmelCase ( )-> List[str]:
"""simple docstring"""
snake_case_ : List[str] = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png" )
return image
@require_torch
class A_ (unittest.TestCase ):
"""simple docstring"""
@cached_property
def _A ( self :Union[str, Any] ) -> Optional[Any]:
'''simple docstring'''
return LayoutLMvaImageProcessor(apply_ocr=lowerCAmelCase__ ) if is_vision_available() else None
@slow
def _A ( self :Union[str, Any] ) -> Union[str, Any]:
'''simple docstring'''
snake_case_ : Optional[int] = LayoutLMvaModel.from_pretrained("microsoft/layoutlmv3-base" ).to(lowerCAmelCase__ )
snake_case_ : Optional[Any] = self.default_image_processor
snake_case_ : Optional[int] = prepare_img()
snake_case_ : Union[str, Any] = image_processor(images=lowerCAmelCase__ , return_tensors="pt" ).pixel_values.to(lowerCAmelCase__ )
snake_case_ : List[str] = torch.tensor([[1, 2]] )
snake_case_ : Any = torch.tensor([[1, 2, 3, 4], [5, 6, 7, 8]] ).unsqueeze(0 )
# forward pass
snake_case_ : Any = model(
input_ids=input_ids.to(lowerCAmelCase__ ) , bbox=bbox.to(lowerCAmelCase__ ) , pixel_values=pixel_values.to(lowerCAmelCase__ ) , )
# verify the logits
snake_case_ : Optional[Any] = torch.Size((1, 199, 768) )
self.assertEqual(outputs.last_hidden_state.shape , lowerCAmelCase__ )
snake_case_ : str = torch.tensor(
[[-0.0_5_2_9, 0.3_6_1_8, 0.1_6_3_2], [-0.1_5_8_7, -0.1_6_6_7, -0.0_4_0_0], [-0.1_5_5_7, -0.1_6_7_1, -0.0_5_0_5]] ).to(lowerCAmelCase__ )
self.assertTrue(torch.allclose(outputs.last_hidden_state[0, :3, :3] , lowerCAmelCase__ , atol=1E-4 ) )
| 653 | 0 |
'''simple docstring'''
import json
import os
import torch
from diffusers import UNetaDModel
os.makedirs('''hub/hopper-medium-v2/unet/hor32''', exist_ok=True)
os.makedirs('''hub/hopper-medium-v2/unet/hor128''', exist_ok=True)
os.makedirs('''hub/hopper-medium-v2/value_function''', exist_ok=True)
def __UpperCAmelCase ( _UpperCAmelCase : List[str] ) -> str:
if hor == 1_28:
__snake_case = ("DownResnetBlock1D", "DownResnetBlock1D", "DownResnetBlock1D")
__snake_case = (32, 1_28, 2_56)
__snake_case = ("UpResnetBlock1D", "UpResnetBlock1D")
elif hor == 32:
__snake_case = ("DownResnetBlock1D", "DownResnetBlock1D", "DownResnetBlock1D", "DownResnetBlock1D")
__snake_case = (32, 64, 1_28, 2_56)
__snake_case = ("UpResnetBlock1D", "UpResnetBlock1D", "UpResnetBlock1D")
__snake_case = torch.load(F'''/Users/bglickenhaus/Documents/diffuser/temporal_unet-hopper-mediumv2-hor{hor}.torch''' )
__snake_case = model.state_dict()
__snake_case = {
"down_block_types": down_block_types,
"block_out_channels": block_out_channels,
"up_block_types": up_block_types,
"layers_per_block": 1,
"use_timestep_embedding": True,
"out_block_type": "OutConv1DBlock",
"norm_num_groups": 8,
"downsample_each_block": False,
"in_channels": 14,
"out_channels": 14,
"extra_in_channels": 0,
"time_embedding_type": "positional",
"flip_sin_to_cos": False,
"freq_shift": 1,
"sample_size": 6_55_36,
"mid_block_type": "MidResTemporalBlock1D",
"act_fn": "mish",
}
__snake_case = UNetaDModel(**_UpperCAmelCase )
print(F'''length of state dict: {len(state_dict.keys() )}''' )
print(F'''length of value function dict: {len(hf_value_function.state_dict().keys() )}''' )
__snake_case = dict(zip(model.state_dict().keys() , hf_value_function.state_dict().keys() ) )
for k, v in mapping.items():
__snake_case = state_dict.pop(_UpperCAmelCase )
hf_value_function.load_state_dict(_UpperCAmelCase )
torch.save(hf_value_function.state_dict() , F'''hub/hopper-medium-v2/unet/hor{hor}/diffusion_pytorch_model.bin''' )
with open(F'''hub/hopper-medium-v2/unet/hor{hor}/config.json''' , "w" ) as f:
json.dump(_UpperCAmelCase , _UpperCAmelCase )
def __UpperCAmelCase ( ) -> List[Any]:
__snake_case = {
"in_channels": 14,
"down_block_types": ("DownResnetBlock1D", "DownResnetBlock1D", "DownResnetBlock1D", "DownResnetBlock1D"),
"up_block_types": (),
"out_block_type": "ValueFunction",
"mid_block_type": "ValueFunctionMidBlock1D",
"block_out_channels": (32, 64, 1_28, 2_56),
"layers_per_block": 1,
"downsample_each_block": True,
"sample_size": 6_55_36,
"out_channels": 14,
"extra_in_channels": 0,
"time_embedding_type": "positional",
"use_timestep_embedding": True,
"flip_sin_to_cos": False,
"freq_shift": 1,
"norm_num_groups": 8,
"act_fn": "mish",
}
__snake_case = torch.load("/Users/bglickenhaus/Documents/diffuser/value_function-hopper-mediumv2-hor32.torch" )
__snake_case = model
__snake_case = UNetaDModel(**_UpperCAmelCase )
print(F'''length of state dict: {len(state_dict.keys() )}''' )
print(F'''length of value function dict: {len(hf_value_function.state_dict().keys() )}''' )
__snake_case = dict(zip(state_dict.keys() , hf_value_function.state_dict().keys() ) )
for k, v in mapping.items():
__snake_case = state_dict.pop(_UpperCAmelCase )
hf_value_function.load_state_dict(_UpperCAmelCase )
torch.save(hf_value_function.state_dict() , "hub/hopper-medium-v2/value_function/diffusion_pytorch_model.bin" )
with open("hub/hopper-medium-v2/value_function/config.json" , "w" ) as f:
json.dump(_UpperCAmelCase , _UpperCAmelCase )
if __name__ == "__main__":
unet(32)
# unet(128)
value_function()
| 69 |
'''simple docstring'''
import pytest
from datasets.parallel import ParallelBackendConfig, parallel_backend
from datasets.utils.py_utils import map_nested
from .utils import require_dill_gt_0_3_2, require_joblibspark, require_not_windows
def __UpperCAmelCase ( __magic_name__ )-> int: # picklable for multiprocessing
"""simple docstring"""
return i + 1
@require_dill_gt_0_3_2
@require_joblibspark
@require_not_windows
def __UpperCAmelCase ( )-> List[str]:
"""simple docstring"""
with parallel_backend("spark" ):
assert ParallelBackendConfig.backend_name == "spark"
snake_case_ : str = [1, 2, 3]
with pytest.raises(__magic_name__ ):
with parallel_backend("unsupported backend" ):
map_nested(__magic_name__ ,__magic_name__ ,num_proc=2 )
with pytest.raises(__magic_name__ ):
with parallel_backend("unsupported backend" ):
map_nested(__magic_name__ ,__magic_name__ ,num_proc=-1 )
@require_dill_gt_0_3_2
@require_joblibspark
@require_not_windows
@pytest.mark.parametrize("num_proc" ,[2, -1] )
def __UpperCAmelCase ( __magic_name__ )-> List[Any]:
"""simple docstring"""
snake_case_ : Optional[Any] = [1, 2]
snake_case_ : Union[str, Any] = {"a": 1, "b": 2}
snake_case_ : str = {"a": [1, 2], "b": [3, 4]}
snake_case_ : List[str] = {"a": {"1": 1}, "b": 2}
snake_case_ : Optional[int] = {"a": 1, "b": 2, "c": 3, "d": 4}
snake_case_ : Tuple = [2, 3]
snake_case_ : str = {"a": 2, "b": 3}
snake_case_ : Dict = {"a": [2, 3], "b": [4, 5]}
snake_case_ : List[Any] = {"a": {"1": 2}, "b": 3}
snake_case_ : str = {"a": 2, "b": 3, "c": 4, "d": 5}
with parallel_backend("spark" ):
assert map_nested(__magic_name__ ,__magic_name__ ,num_proc=__magic_name__ ) == expected_map_nested_sa
assert map_nested(__magic_name__ ,__magic_name__ ,num_proc=__magic_name__ ) == expected_map_nested_sa
assert map_nested(__magic_name__ ,__magic_name__ ,num_proc=__magic_name__ ) == expected_map_nested_sa
assert map_nested(__magic_name__ ,__magic_name__ ,num_proc=__magic_name__ ) == expected_map_nested_sa
assert map_nested(__magic_name__ ,__magic_name__ ,num_proc=__magic_name__ ) == expected_map_nested_sa
| 653 | 0 |
from unittest.mock import Mock, patch
from file_transfer.send_file import send_file
@patch('socket.socket' )
@patch('builtins.open' )
def _SCREAMING_SNAKE_CASE ( lowercase : Optional[Any] , lowercase : Optional[int] ):
'''simple docstring'''
lowerCamelCase_ = Mock()
lowerCamelCase_ = conn, Mock()
lowerCamelCase_ = iter([1, None] )
lowerCamelCase_ = lambda lowercase : next(lowercase )
# ===== invoke =====
send_file(filename='mytext.txt' , testing=lowercase )
# ===== ensurance =====
sock.assert_called_once()
sock.return_value.bind.assert_called_once()
sock.return_value.listen.assert_called_once()
sock.return_value.accept.assert_called_once()
conn.recv.assert_called_once()
file.return_value.__enter__.assert_called_once()
file.return_value.__enter__.return_value.read.assert_called()
conn.send.assert_called_once()
conn.close.assert_called_once()
sock.return_value.shutdown.assert_called_once()
sock.return_value.close.assert_called_once()
| 70 |
'''simple docstring'''
from dataclasses import asdict, dataclass
from typing import Optional
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__lowerCamelCase : Dict = logging.get_logger(__name__)
# TODO Update this
__lowerCamelCase : int = {
'''facebook/esm-1b''': '''https://huggingface.co/facebook/esm-1b/resolve/main/config.json''',
# See all ESM models at https://huggingface.co/models?filter=esm
}
class A_ (a_ ):
"""simple docstring"""
a__ = '''esm'''
def __init__( self :Dict , lowerCAmelCase__ :List[Any]=None , lowerCAmelCase__ :Optional[int]=None , lowerCAmelCase__ :str=None , lowerCAmelCase__ :int=768 , lowerCAmelCase__ :Tuple=12 , lowerCAmelCase__ :Dict=12 , lowerCAmelCase__ :Union[str, Any]=3_072 , lowerCAmelCase__ :int=0.1 , lowerCAmelCase__ :Optional[Any]=0.1 , lowerCAmelCase__ :List[Any]=1_026 , lowerCAmelCase__ :int=0.0_2 , lowerCAmelCase__ :Optional[int]=1E-1_2 , lowerCAmelCase__ :List[str]="absolute" , lowerCAmelCase__ :List[Any]=True , lowerCAmelCase__ :Dict=None , lowerCAmelCase__ :List[str]=False , lowerCAmelCase__ :List[Any]=False , lowerCAmelCase__ :Dict=None , lowerCAmelCase__ :str=None , **lowerCAmelCase__ :Union[str, Any] , ) -> Any:
'''simple docstring'''
super().__init__(pad_token_id=lowerCAmelCase__ , mask_token_id=lowerCAmelCase__ , **lowerCAmelCase__ )
snake_case_ : str = vocab_size
snake_case_ : str = hidden_size
snake_case_ : List[str] = num_hidden_layers
snake_case_ : List[str] = num_attention_heads
snake_case_ : Any = intermediate_size
snake_case_ : Optional[Any] = hidden_dropout_prob
snake_case_ : Tuple = attention_probs_dropout_prob
snake_case_ : List[Any] = max_position_embeddings
snake_case_ : str = initializer_range
snake_case_ : List[Any] = layer_norm_eps
snake_case_ : str = position_embedding_type
snake_case_ : Optional[int] = use_cache
snake_case_ : str = emb_layer_norm_before
snake_case_ : List[Any] = token_dropout
snake_case_ : str = is_folding_model
if is_folding_model:
if esmfold_config is None:
logger.info("No esmfold_config supplied for folding model, using default values." )
snake_case_ : Optional[Any] = EsmFoldConfig()
elif isinstance(lowerCAmelCase__ , lowerCAmelCase__ ):
snake_case_ : Union[str, Any] = EsmFoldConfig(**lowerCAmelCase__ )
snake_case_ : Optional[Any] = esmfold_config
if vocab_list is None:
logger.warning("No vocab_list supplied for folding model, assuming the ESM-2 vocabulary!" )
snake_case_ : List[str] = get_default_vocab_list()
else:
snake_case_ : List[str] = vocab_list
else:
snake_case_ : List[Any] = None
snake_case_ : int = None
if self.esmfold_config is not None and getattr(self.esmfold_config , "use_esm_attn_map" , lowerCAmelCase__ ):
raise ValueError("The HuggingFace port of ESMFold does not support use_esm_attn_map at this time!" )
def _A ( self :Optional[int] ) -> List[Any]:
'''simple docstring'''
snake_case_ : Any = super().to_dict()
if isinstance(self.esmfold_config , lowerCAmelCase__ ):
snake_case_ : Optional[int] = self.esmfold_config.to_dict()
return output
@dataclass
class A_ :
"""simple docstring"""
a__ = None
a__ = True
a__ = False
a__ = False
a__ = False
a__ = 0
a__ = True
a__ = False
a__ = 128
a__ = None
def _A ( self :Dict ) -> int:
'''simple docstring'''
if self.trunk is None:
snake_case_ : Dict = TrunkConfig()
elif isinstance(self.trunk , lowerCAmelCase__ ):
snake_case_ : int = TrunkConfig(**self.trunk )
def _A ( self :Optional[int] ) -> Union[str, Any]:
'''simple docstring'''
snake_case_ : Tuple = asdict(self )
snake_case_ : Optional[int] = self.trunk.to_dict()
return output
@dataclass
class A_ :
"""simple docstring"""
a__ = 48
a__ = 1024
a__ = 128
a__ = 32
a__ = 32
a__ = 32
a__ = 0
a__ = 0
a__ = False
a__ = 4
a__ = 128
a__ = None
def _A ( self :List[Any] ) -> Union[str, Any]:
'''simple docstring'''
if self.structure_module is None:
snake_case_ : Optional[int] = StructureModuleConfig()
elif isinstance(self.structure_module , lowerCAmelCase__ ):
snake_case_ : List[str] = StructureModuleConfig(**self.structure_module )
if self.max_recycles <= 0:
raise ValueError(F'''`max_recycles` should be positive, got {self.max_recycles}.''' )
if self.sequence_state_dim % self.sequence_state_dim != 0:
raise ValueError(
"`sequence_state_dim` should be a round multiple of `sequence_state_dim`, got"
F''' {self.sequence_state_dim} and {self.sequence_state_dim}.''' )
if self.pairwise_state_dim % self.pairwise_state_dim != 0:
raise ValueError(
"`pairwise_state_dim` should be a round multiple of `pairwise_state_dim`, got"
F''' {self.pairwise_state_dim} and {self.pairwise_state_dim}.''' )
snake_case_ : Dict = self.sequence_state_dim // self.sequence_head_width
snake_case_ : Optional[int] = self.pairwise_state_dim // self.pairwise_head_width
if self.sequence_state_dim != sequence_num_heads * self.sequence_head_width:
raise ValueError(
"`sequence_state_dim` should be equal to `sequence_num_heads * sequence_head_width, got"
F''' {self.sequence_state_dim} != {sequence_num_heads} * {self.sequence_head_width}.''' )
if self.pairwise_state_dim != pairwise_num_heads * self.pairwise_head_width:
raise ValueError(
"`pairwise_state_dim` should be equal to `pairwise_num_heads * pairwise_head_width, got"
F''' {self.pairwise_state_dim} != {pairwise_num_heads} * {self.pairwise_head_width}.''' )
if self.pairwise_state_dim % 2 != 0:
raise ValueError(F'''`pairwise_state_dim` should be even, got {self.pairwise_state_dim}.''' )
if self.dropout >= 0.4:
raise ValueError(F'''`dropout` should not be greater than 0.4, got {self.dropout}.''' )
def _A ( self :Tuple ) -> List[str]:
'''simple docstring'''
snake_case_ : int = asdict(self )
snake_case_ : Dict = self.structure_module.to_dict()
return output
@dataclass
class A_ :
"""simple docstring"""
a__ = 384
a__ = 128
a__ = 16
a__ = 128
a__ = 12
a__ = 4
a__ = 8
a__ = 0.1
a__ = 8
a__ = 1
a__ = 2
a__ = 7
a__ = 10
a__ = 1E-8
a__ = 1E5
def _A ( self :Dict ) -> Dict:
'''simple docstring'''
return asdict(self )
def __UpperCAmelCase ( )-> int:
"""simple docstring"""
return (
"<cls>",
"<pad>",
"<eos>",
"<unk>",
"L",
"A",
"G",
"V",
"S",
"E",
"R",
"T",
"I",
"D",
"P",
"K",
"Q",
"N",
"F",
"Y",
"M",
"H",
"W",
"C",
"X",
"B",
"U",
"Z",
"O",
".",
"-",
"<null_1>",
"<mask>",
)
| 653 | 0 |
'''simple docstring'''
from unittest.mock import patch
import pyspark
from datasets.packaged_modules.spark.spark import (
Spark,
SparkExamplesIterable,
_generate_iterable_examples,
)
from ..utils import (
require_dill_gt_0_3_2,
require_not_windows,
)
def a__ ( _SCREAMING_SNAKE_CASE : Any , _SCREAMING_SNAKE_CASE : List[Any] ) -> List[Any]:
"""simple docstring"""
UpperCAmelCase_ : Any = []
for part_id in partition_order:
UpperCAmelCase_ : str = df.where(F'''SPARK_PARTITION_ID() = {part_id}''' ).collect()
for row_idx, row in enumerate(_SCREAMING_SNAKE_CASE ):
expected_row_ids_and_row_dicts.append((F'''{part_id}_{row_idx}''', row.asDict()) )
return expected_row_ids_and_row_dicts
@require_not_windows
@require_dill_gt_0_3_2
def a__ ( ) -> Dict:
"""simple docstring"""
UpperCAmelCase_ : List[Any] = pyspark.sql.SparkSession.builder.master("local[*]" ).appName("pyspark" ).getOrCreate()
UpperCAmelCase_ : str = spark.range(1_00 ).repartition(1 )
UpperCAmelCase_ : str = Spark(_SCREAMING_SNAKE_CASE )
# The id ints will be converted to Pyarrow int64s, so each row will be 8 bytes. Setting a max_shard_size of 16 means
# that each partition can hold 2 rows.
spark_builder._repartition_df_if_needed(max_shard_size=16 )
# Given that the dataframe has 100 rows and each partition has 2 rows, we expect 50 partitions.
assert spark_builder.df.rdd.getNumPartitions() == 50
@require_not_windows
@require_dill_gt_0_3_2
def a__ ( ) -> Optional[int]:
"""simple docstring"""
UpperCAmelCase_ : str = pyspark.sql.SparkSession.builder.master("local[*]" ).appName("pyspark" ).getOrCreate()
UpperCAmelCase_ : Any = spark.range(10 ).repartition(2 )
UpperCAmelCase_ : Optional[int] = [1, 0]
UpperCAmelCase_ : Union[str, Any] = _generate_iterable_examples(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) # Reverse the partitions.
UpperCAmelCase_ : List[str] = _get_expected_row_ids_and_row_dicts_for_partition_order(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
for i, (row_id, row_dict) in enumerate(generate_fn() ):
UpperCAmelCase_ , UpperCAmelCase_ : List[Any] = expected_row_ids_and_row_dicts[i]
assert row_id == expected_row_id
assert row_dict == expected_row_dict
@require_not_windows
@require_dill_gt_0_3_2
def a__ ( ) -> Dict:
"""simple docstring"""
UpperCAmelCase_ : Optional[Any] = pyspark.sql.SparkSession.builder.master("local[*]" ).appName("pyspark" ).getOrCreate()
UpperCAmelCase_ : Any = spark.range(10 ).repartition(1 )
UpperCAmelCase_ : Any = SparkExamplesIterable(_SCREAMING_SNAKE_CASE )
assert it.n_shards == 1
for i, (row_id, row_dict) in enumerate(_SCREAMING_SNAKE_CASE ):
assert row_id == F'''0_{i}'''
assert row_dict == {"id": i}
@require_not_windows
@require_dill_gt_0_3_2
def a__ ( ) -> Optional[Any]:
"""simple docstring"""
UpperCAmelCase_ : Dict = pyspark.sql.SparkSession.builder.master("local[*]" ).appName("pyspark" ).getOrCreate()
UpperCAmelCase_ : Union[str, Any] = spark.range(30 ).repartition(3 )
# Mock the generator so that shuffle reverses the partition indices.
with patch("numpy.random.Generator" ) as generator_mock:
UpperCAmelCase_ : Any = lambda _SCREAMING_SNAKE_CASE : x.reverse()
UpperCAmelCase_ : Optional[Any] = _get_expected_row_ids_and_row_dicts_for_partition_order(_SCREAMING_SNAKE_CASE , [2, 1, 0] )
UpperCAmelCase_ : List[str] = SparkExamplesIterable(_SCREAMING_SNAKE_CASE ).shuffle_data_sources(_SCREAMING_SNAKE_CASE )
assert shuffled_it.n_shards == 3
for i, (row_id, row_dict) in enumerate(_SCREAMING_SNAKE_CASE ):
UpperCAmelCase_ , UpperCAmelCase_ : List[str] = expected_row_ids_and_row_dicts[i]
assert row_id == expected_row_id
assert row_dict == expected_row_dict
@require_not_windows
@require_dill_gt_0_3_2
def a__ ( ) -> int:
"""simple docstring"""
UpperCAmelCase_ : List[str] = pyspark.sql.SparkSession.builder.master("local[*]" ).appName("pyspark" ).getOrCreate()
UpperCAmelCase_ : Union[str, Any] = spark.range(20 ).repartition(4 )
# Partitions 0 and 2
UpperCAmelCase_ : Any = SparkExamplesIterable(_SCREAMING_SNAKE_CASE ).shard_data_sources(worker_id=0 , num_workers=2 )
assert shard_it_a.n_shards == 2
UpperCAmelCase_ : List[str] = _get_expected_row_ids_and_row_dicts_for_partition_order(_SCREAMING_SNAKE_CASE , [0, 2] )
for i, (row_id, row_dict) in enumerate(_SCREAMING_SNAKE_CASE ):
UpperCAmelCase_ , UpperCAmelCase_ : Any = expected_row_ids_and_row_dicts_a[i]
assert row_id == expected_row_id
assert row_dict == expected_row_dict
# Partitions 1 and 3
UpperCAmelCase_ : List[str] = SparkExamplesIterable(_SCREAMING_SNAKE_CASE ).shard_data_sources(worker_id=1 , num_workers=2 )
assert shard_it_a.n_shards == 2
UpperCAmelCase_ : Tuple = _get_expected_row_ids_and_row_dicts_for_partition_order(_SCREAMING_SNAKE_CASE , [1, 3] )
for i, (row_id, row_dict) in enumerate(_SCREAMING_SNAKE_CASE ):
UpperCAmelCase_ , UpperCAmelCase_ : List[str] = expected_row_ids_and_row_dicts_a[i]
assert row_id == expected_row_id
assert row_dict == expected_row_dict
@require_not_windows
@require_dill_gt_0_3_2
def a__ ( ) -> Tuple:
"""simple docstring"""
UpperCAmelCase_ : List[Any] = pyspark.sql.SparkSession.builder.master("local[*]" ).appName("pyspark" ).getOrCreate()
UpperCAmelCase_ : List[Any] = spark.range(1_00 ).repartition(1 )
UpperCAmelCase_ : List[str] = Spark(_SCREAMING_SNAKE_CASE )
# Choose a small max_shard_size for maximum partitioning.
spark_builder._repartition_df_if_needed(max_shard_size=1 )
# The new number of partitions should not be greater than the number of rows.
assert spark_builder.df.rdd.getNumPartitions() == 1_00
| 71 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__lowerCamelCase : Any = {
'''configuration_longformer''': [
'''LONGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''LongformerConfig''',
'''LongformerOnnxConfig''',
],
'''tokenization_longformer''': ['''LongformerTokenizer'''],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__lowerCamelCase : Any = ['''LongformerTokenizerFast''']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__lowerCamelCase : Dict = [
'''LONGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''LongformerForMaskedLM''',
'''LongformerForMultipleChoice''',
'''LongformerForQuestionAnswering''',
'''LongformerForSequenceClassification''',
'''LongformerForTokenClassification''',
'''LongformerModel''',
'''LongformerPreTrainedModel''',
'''LongformerSelfAttention''',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__lowerCamelCase : Any = [
'''TF_LONGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''TFLongformerForMaskedLM''',
'''TFLongformerForMultipleChoice''',
'''TFLongformerForQuestionAnswering''',
'''TFLongformerForSequenceClassification''',
'''TFLongformerForTokenClassification''',
'''TFLongformerModel''',
'''TFLongformerPreTrainedModel''',
'''TFLongformerSelfAttention''',
]
if TYPE_CHECKING:
from .configuration_longformer import (
LONGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
LongformerConfig,
LongformerOnnxConfig,
)
from .tokenization_longformer import LongformerTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_longformer_fast import LongformerTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_longformer import (
LONGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
LongformerForMaskedLM,
LongformerForMultipleChoice,
LongformerForQuestionAnswering,
LongformerForSequenceClassification,
LongformerForTokenClassification,
LongformerModel,
LongformerPreTrainedModel,
LongformerSelfAttention,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_longformer import (
TF_LONGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
TFLongformerForMaskedLM,
TFLongformerForMultipleChoice,
TFLongformerForQuestionAnswering,
TFLongformerForSequenceClassification,
TFLongformerForTokenClassification,
TFLongformerModel,
TFLongformerPreTrainedModel,
TFLongformerSelfAttention,
)
else:
import sys
__lowerCamelCase : Optional[Any] = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 653 | 0 |
'''simple docstring'''
from numpy import exp, pi, sqrt
def UpperCamelCase ( lowercase_ : Union[str, Any] , lowercase_ : float = 0.0 , lowercase_ : float = 1.0 ) -> int:
'''simple docstring'''
return 1 / sqrt(2 * pi * sigma**2 ) * exp(-((x - mu) ** 2) / (2 * sigma**2) )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 72 |
'''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
__lowerCamelCase : Optional[int] = 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 A_ :
"""simple docstring"""
def __init__( self :Tuple , lowerCAmelCase__ :List[str] , lowerCAmelCase__ :List[Any]=16 , lowerCAmelCase__ :Any=13 , lowerCAmelCase__ :Optional[Any]=7 , lowerCAmelCase__ :str=14 , lowerCAmelCase__ :Union[str, Any]=10 , lowerCAmelCase__ :Tuple=19 , lowerCAmelCase__ :Optional[Any]=5 , lowerCAmelCase__ :Dict=4 , lowerCAmelCase__ :Union[str, Any]=True , lowerCAmelCase__ :Any=16 , lowerCAmelCase__ :str=2 , lowerCAmelCase__ :List[Any]=4 , lowerCAmelCase__ :Any=4 , lowerCAmelCase__ :str="gelu" , lowerCAmelCase__ :Tuple=0.1 , lowerCAmelCase__ :Dict=0.1 , lowerCAmelCase__ :Optional[int]=[1, 2, 3, 4, 5] , lowerCAmelCase__ :str=25 , lowerCAmelCase__ :Optional[Any]=5 , ) -> Dict:
'''simple docstring'''
snake_case_ : List[str] = d_model
snake_case_ : Dict = parent
snake_case_ : Optional[Any] = batch_size
snake_case_ : Optional[Any] = prediction_length
snake_case_ : str = context_length
snake_case_ : Tuple = cardinality
snake_case_ : List[str] = num_time_features
snake_case_ : Optional[Any] = lags_sequence
snake_case_ : Union[str, Any] = embedding_dimension
snake_case_ : Optional[Any] = is_training
snake_case_ : Optional[Any] = hidden_size
snake_case_ : Any = num_hidden_layers
snake_case_ : Optional[Any] = num_attention_heads
snake_case_ : int = intermediate_size
snake_case_ : Any = hidden_act
snake_case_ : Union[str, Any] = hidden_dropout_prob
snake_case_ : Dict = attention_probs_dropout_prob
snake_case_ : List[str] = context_length
snake_case_ : Any = prediction_length + label_length
snake_case_ : Union[str, Any] = label_length
snake_case_ : List[Any] = moving_average
snake_case_ : str = autocorrelation_factor
def _A ( self :List[Any] ) -> Any:
'''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 _A ( self :Union[str, Any] , lowerCAmelCase__ :Optional[Any] ) -> Dict:
'''simple docstring'''
snake_case_ : Any = config.context_length + max(config.lags_sequence )
snake_case_ : Union[str, Any] = ids_tensor([self.batch_size, 1] , config.cardinality[0] )
snake_case_ : Optional[int] = floats_tensor([self.batch_size, _past_length, config.num_time_features] )
snake_case_ : List[Any] = floats_tensor([self.batch_size, _past_length] )
snake_case_ : Dict = floats_tensor([self.batch_size, _past_length] ) > 0.5
# decoder inputs
snake_case_ : List[Any] = floats_tensor([self.batch_size, config.prediction_length, config.num_time_features] )
snake_case_ : List[Any] = floats_tensor([self.batch_size, config.prediction_length] )
snake_case_ : int = {
"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 _A ( self :Dict ) -> Tuple:
'''simple docstring'''
snake_case_ : str = self.get_config()
snake_case_ : int = self.prepare_autoformer_inputs_dict(lowerCAmelCase__ )
return config, inputs_dict
def _A ( self :Optional[int] ) -> Dict:
'''simple docstring'''
snake_case_, snake_case_ : Union[str, Any] = self.prepare_config_and_inputs()
return config, inputs_dict
def _A ( self :Tuple , lowerCAmelCase__ :int , lowerCAmelCase__ :Optional[int] ) -> List[str]:
'''simple docstring'''
snake_case_ : Dict = AutoformerModel(config=lowerCAmelCase__ ).to(lowerCAmelCase__ ).eval()
snake_case_ : Optional[int] = model(**lowerCAmelCase__ )
snake_case_ : Any = outputs.encoder_last_hidden_state
snake_case_ : Dict = outputs.last_hidden_state
with tempfile.TemporaryDirectory() as tmpdirname:
snake_case_ : Optional[Any] = model.get_encoder()
encoder.save_pretrained(lowerCAmelCase__ )
snake_case_ : Tuple = AutoformerEncoder.from_pretrained(lowerCAmelCase__ ).to(lowerCAmelCase__ )
snake_case_, snake_case_, snake_case_, snake_case_, snake_case_ : List[str] = model.create_network_inputs(**lowerCAmelCase__ )
snake_case_, snake_case_ : Optional[int] = model.decomposition_layer(transformer_inputs[:, : config.context_length, ...] )
snake_case_ : List[Any] = torch.cat(
(transformer_inputs[:, : config.context_length, ...], feature[:, : config.context_length, ...]) , dim=-1 , )
snake_case_ : Optional[int] = encoder(inputs_embeds=lowerCAmelCase__ )[0]
self.parent.assertTrue((encoder_last_hidden_state_a - encoder_last_hidden_state).abs().max().item() < 1E-3 )
snake_case_ : Any = (
torch.mean(transformer_inputs[:, : config.context_length, ...] , dim=1 )
.unsqueeze(1 )
.repeat(1 , config.prediction_length , 1 )
)
snake_case_ : List[str] = torch.zeros(
[transformer_inputs.shape[0], config.prediction_length, transformer_inputs.shape[2]] , device=enc_input.device , )
snake_case_ : Optional[Any] = torch.cat(
(
torch.cat((seasonal_input[:, -config.label_length :, ...], zeros) , dim=1 ),
feature[:, config.context_length - config.label_length :, ...],
) , dim=-1 , )
snake_case_ : Any = 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:
snake_case_ : List[Any] = model.get_decoder()
decoder.save_pretrained(lowerCAmelCase__ )
snake_case_ : int = AutoformerDecoder.from_pretrained(lowerCAmelCase__ ).to(lowerCAmelCase__ )
snake_case_ : Tuple = decoder(
trend=lowerCAmelCase__ , inputs_embeds=lowerCAmelCase__ , encoder_hidden_states=lowerCAmelCase__ , )[0]
self.parent.assertTrue((last_hidden_state_a - last_hidden_state).abs().max().item() < 1E-3 )
@require_torch
class A_ (a_ , a_ , unittest.TestCase ):
"""simple docstring"""
a__ = (AutoformerModel, AutoformerForPrediction) if is_torch_available() else ()
a__ = (AutoformerForPrediction,) if is_torch_available() else ()
a__ = {'''feature-extraction''': AutoformerModel} if is_torch_available() else {}
a__ = False
a__ = False
a__ = False
a__ = False
a__ = False
a__ = False
def _A ( self :Dict ) -> int:
'''simple docstring'''
snake_case_ : Tuple = AutoformerModelTester(self )
snake_case_ : str = ConfigTester(self , config_class=lowerCAmelCase__ , has_text_modality=lowerCAmelCase__ )
def _A ( self :List[str] ) -> Tuple:
'''simple docstring'''
self.config_tester.run_common_tests()
def _A ( self :List[Any] ) -> Union[str, Any]:
'''simple docstring'''
snake_case_, snake_case_ : List[Any] = self.model_tester.prepare_config_and_inputs()
for model_class in self.all_model_classes:
snake_case_ : List[Any] = model_class(lowerCAmelCase__ )
with tempfile.TemporaryDirectory() as tmpdirname:
model.save_pretrained(lowerCAmelCase__ )
snake_case_, snake_case_ : str = model_class.from_pretrained(lowerCAmelCase__ , output_loading_info=lowerCAmelCase__ )
self.assertEqual(info["missing_keys"] , [] )
def _A ( self :Optional[int] ) -> Tuple:
'''simple docstring'''
snake_case_ : str = self.model_tester.prepare_config_and_inputs_for_common()
self.model_tester.check_encoder_decoder_model_standalone(*lowerCAmelCase__ )
@unittest.skip(reason="Model has no tokens embeddings" )
def _A ( self :str ) -> str:
'''simple docstring'''
pass
def _A ( self :Optional[Any] ) -> Union[str, Any]:
'''simple docstring'''
snake_case_ : List[Any] = inspect.signature(getattr(lowerCAmelCase__ , "forward" ) )
# The main input is the name of the argument after `self`
snake_case_ : Dict = list(model_signature.parameters.keys() )[1]
self.assertEqual(AutoformerModel.main_input_name , lowerCAmelCase__ )
def _A ( self :Optional[Any] ) -> Optional[int]:
'''simple docstring'''
snake_case_, snake_case_ : Tuple = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
snake_case_ : Tuple = model_class(lowerCAmelCase__ )
snake_case_ : int = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
snake_case_ : Optional[Any] = [*signature.parameters.keys()]
snake_case_ : Dict = [
"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(lowerCAmelCase__ )] , lowerCAmelCase__ )
def _A ( self :int ) -> Any:
'''simple docstring'''
snake_case_, snake_case_ : Tuple = self.model_tester.prepare_config_and_inputs_for_common()
snake_case_ : Union[str, Any] = True
snake_case_ : List[str] = getattr(self.model_tester , "seq_length" , lowerCAmelCase__ )
snake_case_ : Dict = getattr(self.model_tester , "decoder_seq_length" , lowerCAmelCase__ )
snake_case_ : Union[str, Any] = getattr(self.model_tester , "encoder_seq_length" , lowerCAmelCase__ )
snake_case_ : Union[str, Any] = getattr(self.model_tester , "d_model" , lowerCAmelCase__ )
snake_case_ : Dict = getattr(self.model_tester , "num_attention_heads" , lowerCAmelCase__ )
snake_case_ : Optional[int] = d_model // num_attention_heads
for model_class in self.all_model_classes:
snake_case_ : Any = True
snake_case_ : Any = False
snake_case_ : Dict = True
snake_case_ : List[str] = model_class(lowerCAmelCase__ )
model.to(lowerCAmelCase__ )
model.eval()
with torch.no_grad():
snake_case_ : Tuple = model(**self._prepare_for_class(lowerCAmelCase__ , lowerCAmelCase__ ) )
snake_case_ : Union[str, Any] = outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
self.assertEqual(len(lowerCAmelCase__ ) , self.model_tester.num_hidden_layers )
# check that output_attentions also work using config
del inputs_dict["output_attentions"]
snake_case_ : Optional[int] = True
snake_case_ : Any = model_class(lowerCAmelCase__ )
model.to(lowerCAmelCase__ )
model.eval()
with torch.no_grad():
snake_case_ : List[str] = model(**self._prepare_for_class(lowerCAmelCase__ , lowerCAmelCase__ ) )
snake_case_ : str = outputs.encoder_attentions
self.assertEqual(len(lowerCAmelCase__ ) , self.model_tester.num_hidden_layers )
self.assertListEqual(
list(attentions[0].shape[-3:] ) , [self.model_tester.num_attention_heads, encoder_seq_length, dim] , )
snake_case_ : Tuple = len(lowerCAmelCase__ )
snake_case_ : List[str] = 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(lowerCAmelCase__ , lowerCAmelCase__ )
# decoder attentions
snake_case_ : Optional[int] = outputs.decoder_attentions
self.assertIsInstance(lowerCAmelCase__ , (list, tuple) )
self.assertEqual(len(lowerCAmelCase__ ) , 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
snake_case_ : List[Any] = outputs.cross_attentions
self.assertIsInstance(lowerCAmelCase__ , (list, tuple) )
self.assertEqual(len(lowerCAmelCase__ ) , 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
snake_case_ : Optional[int] = True
snake_case_ : List[Any] = True
snake_case_ : Union[str, Any] = model_class(lowerCAmelCase__ )
model.to(lowerCAmelCase__ )
model.eval()
with torch.no_grad():
snake_case_ : List[Any] = model(**self._prepare_for_class(lowerCAmelCase__ , lowerCAmelCase__ ) )
self.assertEqual(out_len + 2 , len(lowerCAmelCase__ ) )
snake_case_ : Tuple = outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
self.assertEqual(len(lowerCAmelCase__ ) , 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 _A ( self :Any ) -> Optional[Any]:
'''simple docstring'''
super().test_retain_grad_hidden_states_attentions()
def __UpperCAmelCase ( __magic_name__="train-batch.pt" )-> int:
"""simple docstring"""
snake_case_ : List[str] = hf_hub_download(repo_id="hf-internal-testing/tourism-monthly-batch" ,filename=__magic_name__ ,repo_type="dataset" )
snake_case_ : List[str] = torch.load(__magic_name__ ,map_location=__magic_name__ )
return batch
@require_torch
@slow
class A_ (unittest.TestCase ):
"""simple docstring"""
def _A ( self :str ) -> Any:
'''simple docstring'''
snake_case_ : Optional[int] = AutoformerModel.from_pretrained("huggingface/autoformer-tourism-monthly" ).to(lowerCAmelCase__ )
snake_case_ : List[str] = prepare_batch()
with torch.no_grad():
snake_case_ : int = 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]
snake_case_ : Optional[int] = torch.Size(
(64, model.config.prediction_length + model.config.label_length, model.config.feature_size) )
self.assertEqual(output.shape , lowerCAmelCase__ )
snake_case_ : Optional[Any] = torch.tensor(
[[0.3_5_9_3, -1.3_3_9_8, 0.6_3_3_0], [0.2_2_7_9, 1.5_3_9_6, -0.1_7_9_2], [0.0_4_5_0, 1.3_2_2_5, -0.2_3_3_5]] , device=lowerCAmelCase__ )
self.assertTrue(torch.allclose(output[0, :3, :3] , lowerCAmelCase__ , atol=lowerCAmelCase__ ) )
def _A ( self :Any ) -> str:
'''simple docstring'''
snake_case_ : str = AutoformerForPrediction.from_pretrained("huggingface/autoformer-tourism-monthly" ).to(lowerCAmelCase__ )
snake_case_ : Optional[Any] = prepare_batch("val-batch.pt" )
with torch.no_grad():
snake_case_ : Tuple = 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
snake_case_ : Dict = torch.Size((64, model.config.context_length, model.config.d_model) )
self.assertEqual(output.shape , lowerCAmelCase__ )
snake_case_ : Any = torch.tensor(
[[-0.0_7_3_4, -0.9_0_3_6, 0.8_3_5_8], [4.7_1_8_6, 2.4_1_1_3, 1.9_5_8_1], [1.7_9_5_3, 2.3_5_5_8, 1.2_9_7_0]] , device=lowerCAmelCase__ )
self.assertTrue(torch.allclose(output[0, :3, :3] , lowerCAmelCase__ , atol=lowerCAmelCase__ ) )
def _A ( self :List[str] ) -> Any:
'''simple docstring'''
snake_case_ : List[Any] = AutoformerForPrediction.from_pretrained("huggingface/autoformer-tourism-monthly" ).to(lowerCAmelCase__ )
snake_case_ : str = prepare_batch("val-batch.pt" )
with torch.no_grad():
snake_case_ : Optional[Any] = 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"] , )
snake_case_ : List[Any] = torch.Size((64, model.config.num_parallel_samples, model.config.prediction_length) )
self.assertEqual(outputs.sequences.shape , lowerCAmelCase__ )
snake_case_ : Dict = torch.tensor([3_1_3_0.6_7_6_3, 4_0_5_6.5_2_9_3, 7_0_5_3.0_7_8_6] , device=lowerCAmelCase__ )
snake_case_ : Optional[Any] = outputs.sequences.mean(dim=1 )
self.assertTrue(torch.allclose(mean_prediction[0, -3:] , lowerCAmelCase__ , rtol=1E-1 ) )
| 653 | 0 |
import os
import unittest
from transformers import BertTokenizerFast
from transformers.models.bert.tokenization_bert import (
VOCAB_FILES_NAMES,
BasicTokenizer,
BertTokenizer,
WordpieceTokenizer,
_is_control,
_is_punctuation,
_is_whitespace,
)
from transformers.testing_utils import require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin, filter_non_english
@require_tokenizers
class _snake_case ( A__ , unittest.TestCase ):
_lowercase : int = BertTokenizer
_lowercase : str = BertTokenizerFast
_lowercase : List[Any] = True
_lowercase : List[Any] = True
_lowercase : Dict = filter_non_english
def SCREAMING_SNAKE_CASE__ ( self) -> Union[str, Any]:
super().setUp()
SCREAMING_SNAKE_CASE = [
'[UNK]',
'[CLS]',
'[SEP]',
'[PAD]',
'[MASK]',
'want',
'##want',
'##ed',
'wa',
'un',
'runn',
'##ing',
',',
'low',
'lowest',
]
SCREAMING_SNAKE_CASE = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['vocab_file'])
with open(self.vocab_file , 'w' , encoding='utf-8') as vocab_writer:
vocab_writer.write(''.join([x + '\n' for x in vocab_tokens]))
def SCREAMING_SNAKE_CASE__ ( self , a) -> Union[str, Any]:
SCREAMING_SNAKE_CASE = 'UNwant\u00E9d,running'
SCREAMING_SNAKE_CASE = 'unwanted, running'
return input_text, output_text
def SCREAMING_SNAKE_CASE__ ( self) -> Optional[Any]:
SCREAMING_SNAKE_CASE = self.tokenizer_class(self.vocab_file)
SCREAMING_SNAKE_CASE = tokenizer.tokenize('UNwant\u00E9d,running')
self.assertListEqual(a , ['un', '##want', '##ed', ',', 'runn', '##ing'])
self.assertListEqual(tokenizer.convert_tokens_to_ids(a) , [9, 6, 7, 12, 10, 11])
def SCREAMING_SNAKE_CASE__ ( self) -> Any:
if not self.test_rust_tokenizer:
return
SCREAMING_SNAKE_CASE = self.get_tokenizer()
SCREAMING_SNAKE_CASE = self.get_rust_tokenizer()
SCREAMING_SNAKE_CASE = 'UNwant\u00E9d,running'
SCREAMING_SNAKE_CASE = tokenizer.tokenize(a)
SCREAMING_SNAKE_CASE = rust_tokenizer.tokenize(a)
self.assertListEqual(a , a)
SCREAMING_SNAKE_CASE = tokenizer.encode(a , add_special_tokens=a)
SCREAMING_SNAKE_CASE = rust_tokenizer.encode(a , add_special_tokens=a)
self.assertListEqual(a , a)
SCREAMING_SNAKE_CASE = self.get_rust_tokenizer()
SCREAMING_SNAKE_CASE = tokenizer.encode(a)
SCREAMING_SNAKE_CASE = rust_tokenizer.encode(a)
self.assertListEqual(a , a)
# With lower casing
SCREAMING_SNAKE_CASE = self.get_tokenizer(do_lower_case=a)
SCREAMING_SNAKE_CASE = self.get_rust_tokenizer(do_lower_case=a)
SCREAMING_SNAKE_CASE = 'UNwant\u00E9d,running'
SCREAMING_SNAKE_CASE = tokenizer.tokenize(a)
SCREAMING_SNAKE_CASE = rust_tokenizer.tokenize(a)
self.assertListEqual(a , a)
SCREAMING_SNAKE_CASE = tokenizer.encode(a , add_special_tokens=a)
SCREAMING_SNAKE_CASE = rust_tokenizer.encode(a , add_special_tokens=a)
self.assertListEqual(a , a)
SCREAMING_SNAKE_CASE = self.get_rust_tokenizer()
SCREAMING_SNAKE_CASE = tokenizer.encode(a)
SCREAMING_SNAKE_CASE = rust_tokenizer.encode(a)
self.assertListEqual(a , a)
def SCREAMING_SNAKE_CASE__ ( self) -> List[Any]:
SCREAMING_SNAKE_CASE = BasicTokenizer()
self.assertListEqual(tokenizer.tokenize('ah\u535A\u63A8zz') , ['ah', '\u535A', '\u63A8', 'zz'])
def SCREAMING_SNAKE_CASE__ ( self) -> Tuple:
SCREAMING_SNAKE_CASE = BasicTokenizer(do_lower_case=a)
self.assertListEqual(
tokenizer.tokenize(' \tHeLLo!how \n Are yoU? ') , ['hello', '!', 'how', 'are', 'you', '?'])
self.assertListEqual(tokenizer.tokenize('H\u00E9llo') , ['hello'])
def SCREAMING_SNAKE_CASE__ ( self) -> List[Any]:
SCREAMING_SNAKE_CASE = BasicTokenizer(do_lower_case=a , strip_accents=a)
self.assertListEqual(
tokenizer.tokenize(' \tHäLLo!how \n Are yoU? ') , ['hällo', '!', 'how', 'are', 'you', '?'])
self.assertListEqual(tokenizer.tokenize('H\u00E9llo') , ['h\u00E9llo'])
def SCREAMING_SNAKE_CASE__ ( self) -> Union[str, Any]:
SCREAMING_SNAKE_CASE = BasicTokenizer(do_lower_case=a , strip_accents=a)
self.assertListEqual(
tokenizer.tokenize(' \tHäLLo!how \n Are yoU? ') , ['hallo', '!', 'how', 'are', 'you', '?'])
self.assertListEqual(tokenizer.tokenize('H\u00E9llo') , ['hello'])
def SCREAMING_SNAKE_CASE__ ( self) -> Optional[Any]:
SCREAMING_SNAKE_CASE = BasicTokenizer(do_lower_case=a)
self.assertListEqual(
tokenizer.tokenize(' \tHäLLo!how \n Are yoU? ') , ['hallo', '!', 'how', 'are', 'you', '?'])
self.assertListEqual(tokenizer.tokenize('H\u00E9llo') , ['hello'])
def SCREAMING_SNAKE_CASE__ ( self) -> str:
SCREAMING_SNAKE_CASE = BasicTokenizer(do_lower_case=a)
self.assertListEqual(
tokenizer.tokenize(' \tHeLLo!how \n Are yoU? ') , ['HeLLo', '!', 'how', 'Are', 'yoU', '?'])
def SCREAMING_SNAKE_CASE__ ( self) -> int:
SCREAMING_SNAKE_CASE = BasicTokenizer(do_lower_case=a , strip_accents=a)
self.assertListEqual(
tokenizer.tokenize(' \tHäLLo!how \n Are yoU? ') , ['HäLLo', '!', 'how', 'Are', 'yoU', '?'])
def SCREAMING_SNAKE_CASE__ ( self) -> Dict:
SCREAMING_SNAKE_CASE = BasicTokenizer(do_lower_case=a , strip_accents=a)
self.assertListEqual(
tokenizer.tokenize(' \tHäLLo!how \n Are yoU? ') , ['HaLLo', '!', 'how', 'Are', 'yoU', '?'])
def SCREAMING_SNAKE_CASE__ ( self) -> Optional[Any]:
SCREAMING_SNAKE_CASE = BasicTokenizer(do_lower_case=a , never_split=['[UNK]'])
self.assertListEqual(
tokenizer.tokenize(' \tHeLLo!how \n Are yoU? [UNK]') , ['HeLLo', '!', 'how', 'Are', 'yoU', '?', '[UNK]'])
def SCREAMING_SNAKE_CASE__ ( self) -> str:
SCREAMING_SNAKE_CASE = BasicTokenizer()
SCREAMING_SNAKE_CASE = 'a\n\'ll !!to?\'d of, can\'t.'
SCREAMING_SNAKE_CASE = ['a', '\'', 'll', '!', '!', 'to', '?', '\'', 'd', 'of', ',', 'can', '\'', 't', '.']
self.assertListEqual(tokenizer.tokenize(a) , a)
def SCREAMING_SNAKE_CASE__ ( self) -> str:
SCREAMING_SNAKE_CASE = ['[UNK]', '[CLS]', '[SEP]', 'want', '##want', '##ed', 'wa', 'un', 'runn', '##ing']
SCREAMING_SNAKE_CASE = {}
for i, token in enumerate(a):
SCREAMING_SNAKE_CASE = i
SCREAMING_SNAKE_CASE = WordpieceTokenizer(vocab=a , unk_token='[UNK]')
self.assertListEqual(tokenizer.tokenize('') , [])
self.assertListEqual(tokenizer.tokenize('unwanted running') , ['un', '##want', '##ed', 'runn', '##ing'])
self.assertListEqual(tokenizer.tokenize('unwantedX running') , ['[UNK]', 'runn', '##ing'])
def SCREAMING_SNAKE_CASE__ ( self) -> List[Any]:
self.assertTrue(_is_whitespace(' '))
self.assertTrue(_is_whitespace('\t'))
self.assertTrue(_is_whitespace('\r'))
self.assertTrue(_is_whitespace('\n'))
self.assertTrue(_is_whitespace('\u00A0'))
self.assertFalse(_is_whitespace('A'))
self.assertFalse(_is_whitespace('-'))
def SCREAMING_SNAKE_CASE__ ( self) -> List[Any]:
self.assertTrue(_is_control('\u0005'))
self.assertFalse(_is_control('A'))
self.assertFalse(_is_control(' '))
self.assertFalse(_is_control('\t'))
self.assertFalse(_is_control('\r'))
def SCREAMING_SNAKE_CASE__ ( self) -> Optional[int]:
self.assertTrue(_is_punctuation('-'))
self.assertTrue(_is_punctuation('$'))
self.assertTrue(_is_punctuation('`'))
self.assertTrue(_is_punctuation('.'))
self.assertFalse(_is_punctuation('A'))
self.assertFalse(_is_punctuation(' '))
def SCREAMING_SNAKE_CASE__ ( self) -> Optional[Any]:
SCREAMING_SNAKE_CASE = self.get_tokenizer()
SCREAMING_SNAKE_CASE = self.get_rust_tokenizer()
# Example taken from the issue https://github.com/huggingface/tokenizers/issues/340
self.assertListEqual([tokenizer.tokenize(a) for t in ['Test', '\xad', 'test']] , [['[UNK]'], [], ['[UNK]']])
self.assertListEqual(
[rust_tokenizer.tokenize(a) for t in ['Test', '\xad', 'test']] , [['[UNK]'], [], ['[UNK]']])
@slow
def SCREAMING_SNAKE_CASE__ ( self) -> Any:
SCREAMING_SNAKE_CASE = self.tokenizer_class.from_pretrained('bert-base-uncased')
SCREAMING_SNAKE_CASE = tokenizer.encode('sequence builders' , add_special_tokens=a)
SCREAMING_SNAKE_CASE = tokenizer.encode('multi-sequence build' , add_special_tokens=a)
SCREAMING_SNAKE_CASE = tokenizer.build_inputs_with_special_tokens(a)
SCREAMING_SNAKE_CASE = tokenizer.build_inputs_with_special_tokens(a , a)
assert encoded_sentence == [101] + text + [102]
assert encoded_pair == [101] + text + [102] + text_a + [102]
def SCREAMING_SNAKE_CASE__ ( self) -> Dict:
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(f'''{tokenizer.__class__.__name__} ({pretrained_name})'''):
SCREAMING_SNAKE_CASE = self.rust_tokenizer_class.from_pretrained(a , **a)
SCREAMING_SNAKE_CASE = f'''A, naïve {tokenizer_r.mask_token} AllenNLP sentence.'''
SCREAMING_SNAKE_CASE = tokenizer_r.encode_plus(
a , return_attention_mask=a , return_token_type_ids=a , return_offsets_mapping=a , add_special_tokens=a , )
SCREAMING_SNAKE_CASE = tokenizer_r.do_lower_case if hasattr(a , 'do_lower_case') else False
SCREAMING_SNAKE_CASE = (
[
((0, 0), tokenizer_r.cls_token),
((0, 1), 'A'),
((1, 2), ','),
((3, 5), 'na'),
((5, 6), '##ï'),
((6, 8), '##ve'),
((9, 15), tokenizer_r.mask_token),
((16, 21), 'Allen'),
((21, 23), '##NL'),
((23, 24), '##P'),
((25, 33), 'sentence'),
((33, 34), '.'),
((0, 0), tokenizer_r.sep_token),
]
if not do_lower_case
else [
((0, 0), tokenizer_r.cls_token),
((0, 1), 'a'),
((1, 2), ','),
((3, 8), 'naive'),
((9, 15), tokenizer_r.mask_token),
((16, 21), 'allen'),
((21, 23), '##nl'),
((23, 24), '##p'),
((25, 33), 'sentence'),
((33, 34), '.'),
((0, 0), tokenizer_r.sep_token),
]
)
self.assertEqual(
[e[1] for e in expected_results] , tokenizer_r.convert_ids_to_tokens(tokens['input_ids']))
self.assertEqual([e[0] for e in expected_results] , tokens['offset_mapping'])
def SCREAMING_SNAKE_CASE__ ( self) -> Optional[int]:
SCREAMING_SNAKE_CASE = ['的', '人', '有']
SCREAMING_SNAKE_CASE = ''.join(a)
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(f'''{tokenizer.__class__.__name__} ({pretrained_name})'''):
SCREAMING_SNAKE_CASE = True
SCREAMING_SNAKE_CASE = self.tokenizer_class.from_pretrained(a , **a)
SCREAMING_SNAKE_CASE = self.rust_tokenizer_class.from_pretrained(a , **a)
SCREAMING_SNAKE_CASE = tokenizer_p.encode(a , add_special_tokens=a)
SCREAMING_SNAKE_CASE = tokenizer_r.encode(a , add_special_tokens=a)
SCREAMING_SNAKE_CASE = tokenizer_r.convert_ids_to_tokens(a)
SCREAMING_SNAKE_CASE = tokenizer_p.convert_ids_to_tokens(a)
# it is expected that each Chinese character is not preceded by "##"
self.assertListEqual(a , a)
self.assertListEqual(a , a)
SCREAMING_SNAKE_CASE = False
SCREAMING_SNAKE_CASE = self.rust_tokenizer_class.from_pretrained(a , **a)
SCREAMING_SNAKE_CASE = self.tokenizer_class.from_pretrained(a , **a)
SCREAMING_SNAKE_CASE = tokenizer_r.encode(a , add_special_tokens=a)
SCREAMING_SNAKE_CASE = tokenizer_p.encode(a , add_special_tokens=a)
SCREAMING_SNAKE_CASE = tokenizer_r.convert_ids_to_tokens(a)
SCREAMING_SNAKE_CASE = tokenizer_p.convert_ids_to_tokens(a)
# it is expected that only the first Chinese character is not preceded by "##".
SCREAMING_SNAKE_CASE = [
f'''##{token}''' if idx != 0 else token for idx, token in enumerate(a)
]
self.assertListEqual(a , a)
self.assertListEqual(a , a)
| 73 |
'''simple docstring'''
import itertools
import json
import os
import unittest
from transformers import AddedToken, RobertaTokenizer, RobertaTokenizerFast
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class A_ (a_ , unittest.TestCase ):
"""simple docstring"""
a__ = RobertaTokenizer
a__ = RobertaTokenizerFast
a__ = True
a__ = {'''cls_token''': '''<s>'''}
def _A ( self :Optional[int] ) -> List[Any]:
'''simple docstring'''
super().setUp()
# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
snake_case_ : List[Any] = [
"l",
"o",
"w",
"e",
"r",
"s",
"t",
"i",
"d",
"n",
"\u0120",
"\u0120l",
"\u0120n",
"\u0120lo",
"\u0120low",
"er",
"\u0120lowest",
"\u0120newer",
"\u0120wider",
"<unk>",
]
snake_case_ : Tuple = dict(zip(lowerCAmelCase__ , range(len(lowerCAmelCase__ ) ) ) )
snake_case_ : List[Any] = ["#version: 0.2", "\u0120 l", "\u0120l o", "\u0120lo w", "e r", ""]
snake_case_ : int = {"unk_token": "<unk>"}
snake_case_ : List[str] = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES["vocab_file"] )
snake_case_ : Tuple = 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(lowerCAmelCase__ ) + "\n" )
with open(self.merges_file , "w" , encoding="utf-8" ) as fp:
fp.write("\n".join(lowerCAmelCase__ ) )
def _A ( self :Optional[Any] , **lowerCAmelCase__ :str ) -> str:
'''simple docstring'''
kwargs.update(self.special_tokens_map )
return self.tokenizer_class.from_pretrained(self.tmpdirname , **lowerCAmelCase__ )
def _A ( self :Any , **lowerCAmelCase__ :Tuple ) -> Optional[int]:
'''simple docstring'''
kwargs.update(self.special_tokens_map )
return RobertaTokenizerFast.from_pretrained(self.tmpdirname , **lowerCAmelCase__ )
def _A ( self :Optional[int] , lowerCAmelCase__ :str ) -> Optional[int]:
'''simple docstring'''
snake_case_ : int = "lower newer"
snake_case_ : Tuple = "lower newer"
return input_text, output_text
def _A ( self :Tuple ) -> Union[str, Any]:
'''simple docstring'''
snake_case_ : str = self.tokenizer_class(self.vocab_file , self.merges_file , **self.special_tokens_map )
snake_case_ : Dict = "lower newer"
snake_case_ : int = ["l", "o", "w", "er", "\u0120", "n", "e", "w", "er"]
snake_case_ : str = tokenizer.tokenize(lowerCAmelCase__ ) # , add_prefix_space=True)
self.assertListEqual(lowerCAmelCase__ , lowerCAmelCase__ )
snake_case_ : List[str] = tokens + [tokenizer.unk_token]
snake_case_ : Optional[int] = [0, 1, 2, 15, 10, 9, 3, 2, 15, 19]
self.assertListEqual(tokenizer.convert_tokens_to_ids(lowerCAmelCase__ ) , lowerCAmelCase__ )
def _A ( self :Any ) -> str:
'''simple docstring'''
snake_case_ : List[str] = self.get_tokenizer()
self.assertListEqual(tokenizer.encode("Hello world!" , add_special_tokens=lowerCAmelCase__ ) , [0, 31_414, 232, 328, 2] )
self.assertListEqual(
tokenizer.encode("Hello world! cécé herlolip 418" , add_special_tokens=lowerCAmelCase__ ) , [0, 31_414, 232, 328, 740, 1_140, 12_695, 69, 46_078, 1_588, 2] , )
@slow
def _A ( self :str ) -> List[str]:
'''simple docstring'''
snake_case_ : Tuple = self.tokenizer_class.from_pretrained("roberta-base" )
snake_case_ : List[str] = tokenizer.encode("sequence builders" , add_special_tokens=lowerCAmelCase__ )
snake_case_ : List[Any] = tokenizer.encode("multi-sequence build" , add_special_tokens=lowerCAmelCase__ )
snake_case_ : List[str] = tokenizer.encode(
"sequence builders" , add_special_tokens=lowerCAmelCase__ , add_prefix_space=lowerCAmelCase__ )
snake_case_ : Union[str, Any] = tokenizer.encode(
"sequence builders" , "multi-sequence build" , add_special_tokens=lowerCAmelCase__ , add_prefix_space=lowerCAmelCase__ )
snake_case_ : Optional[Any] = tokenizer.build_inputs_with_special_tokens(lowerCAmelCase__ )
snake_case_ : Any = tokenizer.build_inputs_with_special_tokens(lowerCAmelCase__ , lowerCAmelCase__ )
assert encoded_sentence == encoded_text_from_decode
assert encoded_pair == encoded_pair_from_decode
def _A ( self :List[Any] ) -> Any:
'''simple docstring'''
snake_case_ : Optional[Any] = self.get_tokenizer()
snake_case_ : Tuple = "Encode this sequence."
snake_case_ : Optional[Any] = tokenizer.byte_encoder[" ".encode("utf-8" )[0]]
# Testing encoder arguments
snake_case_ : str = tokenizer.encode(lowerCAmelCase__ , add_special_tokens=lowerCAmelCase__ , add_prefix_space=lowerCAmelCase__ )
snake_case_ : List[Any] = tokenizer.convert_ids_to_tokens(encoded[0] )[0]
self.assertNotEqual(lowerCAmelCase__ , lowerCAmelCase__ )
snake_case_ : List[Any] = tokenizer.encode(lowerCAmelCase__ , add_special_tokens=lowerCAmelCase__ , add_prefix_space=lowerCAmelCase__ )
snake_case_ : str = tokenizer.convert_ids_to_tokens(encoded[0] )[0]
self.assertEqual(lowerCAmelCase__ , lowerCAmelCase__ )
tokenizer.add_special_tokens({"bos_token": "<s>"} )
snake_case_ : str = tokenizer.encode(lowerCAmelCase__ , add_special_tokens=lowerCAmelCase__ )
snake_case_ : int = tokenizer.convert_ids_to_tokens(encoded[1] )[0]
self.assertNotEqual(lowerCAmelCase__ , lowerCAmelCase__ )
# Testing spaces after special tokens
snake_case_ : List[Any] = "<mask>"
tokenizer.add_special_tokens(
{"mask_token": AddedToken(lowerCAmelCase__ , lstrip=lowerCAmelCase__ , rstrip=lowerCAmelCase__ )} ) # mask token has a left space
snake_case_ : str = tokenizer.convert_tokens_to_ids(lowerCAmelCase__ )
snake_case_ : List[str] = "Encode <mask> sequence"
snake_case_ : List[Any] = "Encode <mask>sequence"
snake_case_ : Tuple = tokenizer.encode(lowerCAmelCase__ )
snake_case_ : int = encoded.index(lowerCAmelCase__ )
snake_case_ : Optional[Any] = tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1] )[0]
self.assertEqual(lowerCAmelCase__ , lowerCAmelCase__ )
snake_case_ : List[str] = tokenizer.encode(lowerCAmelCase__ )
snake_case_ : Union[str, Any] = encoded.index(lowerCAmelCase__ )
snake_case_ : int = tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1] )[0]
self.assertNotEqual(lowerCAmelCase__ , lowerCAmelCase__ )
def _A ( self :Tuple ) -> Tuple:
'''simple docstring'''
pass
def _A ( self :int ) -> Optional[Any]:
'''simple docstring'''
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(F'''{tokenizer.__class__.__name__} ({pretrained_name})''' ):
snake_case_ : List[Any] = self.rust_tokenizer_class.from_pretrained(lowerCAmelCase__ , **lowerCAmelCase__ )
snake_case_ : List[Any] = self.tokenizer_class.from_pretrained(lowerCAmelCase__ , **lowerCAmelCase__ )
snake_case_ : Any = "A, <mask> AllenNLP sentence."
snake_case_ : str = tokenizer_r.encode_plus(lowerCAmelCase__ , add_special_tokens=lowerCAmelCase__ , return_token_type_ids=lowerCAmelCase__ )
snake_case_ : int = tokenizer_p.encode_plus(lowerCAmelCase__ , add_special_tokens=lowerCAmelCase__ , return_token_type_ids=lowerCAmelCase__ )
# token_type_ids should put 0 everywhere
self.assertEqual(sum(tokens_r["token_type_ids"] ) , sum(tokens_p["token_type_ids"] ) )
# attention_mask should put 1 everywhere, so sum over length should be 1
self.assertEqual(
sum(tokens_r["attention_mask"] ) / len(tokens_r["attention_mask"] ) , sum(tokens_p["attention_mask"] ) / len(tokens_p["attention_mask"] ) , )
snake_case_ : List[Any] = tokenizer_r.convert_ids_to_tokens(tokens_r["input_ids"] )
snake_case_ : str = tokenizer_p.convert_ids_to_tokens(tokens_p["input_ids"] )
# Rust correctly handles the space before the mask while python doesnt
self.assertSequenceEqual(tokens_p["input_ids"] , [0, 250, 6, 50_264, 3_823, 487, 21_992, 3_645, 4, 2] )
self.assertSequenceEqual(tokens_r["input_ids"] , [0, 250, 6, 50_264, 3_823, 487, 21_992, 3_645, 4, 2] )
self.assertSequenceEqual(
lowerCAmelCase__ , ["<s>", "A", ",", "<mask>", "ĠAllen", "N", "LP", "Ġsentence", ".", "</s>"] )
self.assertSequenceEqual(
lowerCAmelCase__ , ["<s>", "A", ",", "<mask>", "ĠAllen", "N", "LP", "Ġsentence", ".", "</s>"] )
def _A ( self :int ) -> Tuple:
'''simple docstring'''
for trim_offsets, add_prefix_space in itertools.product([True, False] , repeat=2 ):
snake_case_ : str = self.rust_tokenizer_class.from_pretrained(
self.tmpdirname , use_fast=lowerCAmelCase__ , add_prefix_space=lowerCAmelCase__ , trim_offsets=lowerCAmelCase__ )
snake_case_ : Optional[Any] = json.loads(tokenizer_r.backend_tokenizer.pre_tokenizer.__getstate__() )
snake_case_ : Any = json.loads(tokenizer_r.backend_tokenizer.post_processor.__getstate__() )
self.assertEqual(pre_tokenizer_state["add_prefix_space"] , lowerCAmelCase__ )
self.assertEqual(post_processor_state["add_prefix_space"] , lowerCAmelCase__ )
self.assertEqual(post_processor_state["trim_offsets"] , lowerCAmelCase__ )
def _A ( self :List[str] ) -> List[Any]:
'''simple docstring'''
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(F'''{tokenizer.__class__.__name__} ({pretrained_name})''' ):
snake_case_ : str = "hello" # `hello` is a token in the vocabulary of `pretrained_name`
snake_case_ : Tuple = F'''{text_of_1_token} {text_of_1_token}'''
snake_case_ : Any = self.rust_tokenizer_class.from_pretrained(
lowerCAmelCase__ , use_fast=lowerCAmelCase__ , add_prefix_space=lowerCAmelCase__ , trim_offsets=lowerCAmelCase__ )
snake_case_ : Union[str, Any] = tokenizer_r(lowerCAmelCase__ , return_offsets_mapping=lowerCAmelCase__ , add_special_tokens=lowerCAmelCase__ )
self.assertEqual(encoding.offset_mapping[0] , (0, len(lowerCAmelCase__ )) )
self.assertEqual(
encoding.offset_mapping[1] , (len(lowerCAmelCase__ ) + 1, len(lowerCAmelCase__ ) + 1 + len(lowerCAmelCase__ )) , )
snake_case_ : List[str] = self.rust_tokenizer_class.from_pretrained(
lowerCAmelCase__ , use_fast=lowerCAmelCase__ , add_prefix_space=lowerCAmelCase__ , trim_offsets=lowerCAmelCase__ )
snake_case_ : Tuple = tokenizer_r(lowerCAmelCase__ , return_offsets_mapping=lowerCAmelCase__ , add_special_tokens=lowerCAmelCase__ )
self.assertEqual(encoding.offset_mapping[0] , (0, len(lowerCAmelCase__ )) )
self.assertEqual(
encoding.offset_mapping[1] , (len(lowerCAmelCase__ ) + 1, len(lowerCAmelCase__ ) + 1 + len(lowerCAmelCase__ )) , )
snake_case_ : Union[str, Any] = self.rust_tokenizer_class.from_pretrained(
lowerCAmelCase__ , use_fast=lowerCAmelCase__ , add_prefix_space=lowerCAmelCase__ , trim_offsets=lowerCAmelCase__ )
snake_case_ : Union[str, Any] = tokenizer_r(lowerCAmelCase__ , return_offsets_mapping=lowerCAmelCase__ , add_special_tokens=lowerCAmelCase__ )
self.assertEqual(encoding.offset_mapping[0] , (0, len(lowerCAmelCase__ )) )
self.assertEqual(
encoding.offset_mapping[1] , (len(lowerCAmelCase__ ), len(lowerCAmelCase__ ) + 1 + len(lowerCAmelCase__ )) , )
snake_case_ : Dict = self.rust_tokenizer_class.from_pretrained(
lowerCAmelCase__ , use_fast=lowerCAmelCase__ , add_prefix_space=lowerCAmelCase__ , trim_offsets=lowerCAmelCase__ )
snake_case_ : str = tokenizer_r(lowerCAmelCase__ , return_offsets_mapping=lowerCAmelCase__ , add_special_tokens=lowerCAmelCase__ )
self.assertEqual(encoding.offset_mapping[0] , (0, len(lowerCAmelCase__ )) )
self.assertEqual(
encoding.offset_mapping[1] , (len(lowerCAmelCase__ ), len(lowerCAmelCase__ ) + 1 + len(lowerCAmelCase__ )) , )
snake_case_ : Tuple = F''' {text}'''
# tokenizer_r = self.rust_tokenizer_class.from_pretrained(
# pretrained_name, use_fast=True, add_prefix_space=True, trim_offsets=True
# )
# encoding = tokenizer_r(text, return_offsets_mapping=True, add_special_tokens=False)
# self.assertEqual(encoding.offset_mapping[0], (1, 1 + len(text_of_1_token)))
# self.assertEqual(
# encoding.offset_mapping[1],
# (1 + len(text_of_1_token) + 1, 1 + len(text_of_1_token) + 1 + len(text_of_1_token)),
# )
snake_case_ : Dict = self.rust_tokenizer_class.from_pretrained(
lowerCAmelCase__ , use_fast=lowerCAmelCase__ , add_prefix_space=lowerCAmelCase__ , trim_offsets=lowerCAmelCase__ )
snake_case_ : Union[str, Any] = tokenizer_r(lowerCAmelCase__ , return_offsets_mapping=lowerCAmelCase__ , add_special_tokens=lowerCAmelCase__ )
self.assertEqual(encoding.offset_mapping[0] , (1, 1 + len(lowerCAmelCase__ )) )
self.assertEqual(
encoding.offset_mapping[1] , (1 + len(lowerCAmelCase__ ) + 1, 1 + len(lowerCAmelCase__ ) + 1 + len(lowerCAmelCase__ )) , )
snake_case_ : Any = self.rust_tokenizer_class.from_pretrained(
lowerCAmelCase__ , use_fast=lowerCAmelCase__ , add_prefix_space=lowerCAmelCase__ , trim_offsets=lowerCAmelCase__ )
snake_case_ : Any = tokenizer_r(lowerCAmelCase__ , return_offsets_mapping=lowerCAmelCase__ , add_special_tokens=lowerCAmelCase__ )
self.assertEqual(encoding.offset_mapping[0] , (0, 1 + len(lowerCAmelCase__ )) )
self.assertEqual(
encoding.offset_mapping[1] , (1 + len(lowerCAmelCase__ ), 1 + len(lowerCAmelCase__ ) + 1 + len(lowerCAmelCase__ )) , )
snake_case_ : Optional[Any] = self.rust_tokenizer_class.from_pretrained(
lowerCAmelCase__ , use_fast=lowerCAmelCase__ , add_prefix_space=lowerCAmelCase__ , trim_offsets=lowerCAmelCase__ )
snake_case_ : Optional[int] = tokenizer_r(lowerCAmelCase__ , return_offsets_mapping=lowerCAmelCase__ , add_special_tokens=lowerCAmelCase__ )
self.assertEqual(encoding.offset_mapping[0] , (0, 1 + len(lowerCAmelCase__ )) )
self.assertEqual(
encoding.offset_mapping[1] , (1 + len(lowerCAmelCase__ ), 1 + len(lowerCAmelCase__ ) + 1 + len(lowerCAmelCase__ )) , )
| 653 | 0 |
class __UpperCamelCase :
"""simple docstring"""
def __init__( self : str ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE : dict[str, TrieNode] = {} # Mapping from char to TrieNode
__SCREAMING_SNAKE_CASE : List[str] = False
def UpperCAmelCase__ ( self : Dict , _A : list[str] ):
"""simple docstring"""
for word in words:
self.insert(_A )
def UpperCAmelCase__ ( self : Optional[Any] , _A : str ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE : Union[str, Any] = self
for char in word:
if char not in curr.nodes:
__SCREAMING_SNAKE_CASE : str = TrieNode()
__SCREAMING_SNAKE_CASE : Optional[int] = curr.nodes[char]
__SCREAMING_SNAKE_CASE : Optional[int] = True
def UpperCAmelCase__ ( self : int , _A : str ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE : Dict = self
for char in word:
if char not in curr.nodes:
return False
__SCREAMING_SNAKE_CASE : int = curr.nodes[char]
return curr.is_leaf
def UpperCAmelCase__ ( self : List[str] , _A : str ):
"""simple docstring"""
def _delete(_A : TrieNode , _A : str , _A : int ) -> bool:
if index == len(_A ):
# If word does not exist
if not curr.is_leaf:
return False
__SCREAMING_SNAKE_CASE : Dict = False
return len(curr.nodes ) == 0
__SCREAMING_SNAKE_CASE : Optional[int] = word[index]
__SCREAMING_SNAKE_CASE : Optional[int] = curr.nodes.get(_A )
# If char not in current trie node
if not char_node:
return False
# Flag to check if node can be deleted
__SCREAMING_SNAKE_CASE : List[str] = _delete(_A , _A , index + 1 )
if delete_curr:
del curr.nodes[char]
return len(curr.nodes ) == 0
return delete_curr
_delete(self , _A , 0 )
def a__ ( snake_case , snake_case ):
"""simple docstring"""
if node.is_leaf:
print(snake_case , end=''' ''' )
for key, value in node.nodes.items():
print_words(snake_case , word + key )
def a__ ( ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE : str = '''banana bananas bandana band apple all beast'''.split()
__SCREAMING_SNAKE_CASE : Any = TrieNode()
root.insert_many(snake_case )
# print_words(root, "")
assert all(root.find(snake_case ) for word in words )
assert root.find('''banana''' )
assert not root.find('''bandanas''' )
assert not root.find('''apps''' )
assert root.find('''apple''' )
assert root.find('''all''' )
root.delete('''all''' )
assert not root.find('''all''' )
root.delete('''banana''' )
assert not root.find('''banana''' )
assert root.find('''bananas''' )
return True
def a__ ( snake_case , snake_case ):
"""simple docstring"""
print(str(snake_case ) , '''works!''' if passes else '''doesn\'t work :(''' )
def a__ ( ):
"""simple docstring"""
assert test_trie()
def a__ ( ):
"""simple docstring"""
print_results('''Testing trie functionality''' , test_trie() )
if __name__ == "__main__":
main()
| 74 |
'''simple docstring'''
import math
def __UpperCAmelCase ( __magic_name__ )-> bool:
"""simple docstring"""
snake_case_ : Optional[int] = math.loga(math.sqrt(4 * positive_integer + 1 ) / 2 + 1 / 2 )
return exponent == int(__magic_name__ )
def __UpperCAmelCase ( __magic_name__ = 1 / 1_2345 )-> int:
"""simple docstring"""
snake_case_ : Any = 0
snake_case_ : int = 0
snake_case_ : Union[str, Any] = 3
while True:
snake_case_ : Any = (integer**2 - 1) / 4
# if candidate is an integer, then there is a partition for k
if partition_candidate == int(__magic_name__ ):
snake_case_ : Optional[Any] = int(__magic_name__ )
total_partitions += 1
if check_partition_perfect(__magic_name__ ):
perfect_partitions += 1
if perfect_partitions > 0:
if perfect_partitions / total_partitions < max_proportion:
return int(__magic_name__ )
integer += 1
if __name__ == "__main__":
print(f'''{solution() = }''')
| 653 | 0 |
'''simple docstring'''
import unittest
from transformers import EsmConfig, is_torch_available
from transformers.testing_utils import TestCasePlus, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers.models.esm.modeling_esmfold import EsmForProteinFolding
class lowerCamelCase_ :
def __init__( self : Union[str, Any] , _A : List[Any] , _A : Any=13 , _A : Optional[int]=7 , _A : Optional[Any]=False , _A : Tuple=True , _A : Tuple=False , _A : Union[str, Any]=False , _A : Optional[Any]=19 , _A : str=32 , _A : str=5 , _A : Any=4 , _A : Optional[int]=37 , _A : int="gelu" , _A : Tuple=0.1 , _A : Dict=0.1 , _A : List[str]=512 , _A : Union[str, Any]=16 , _A : Union[str, Any]=2 , _A : Dict=0.0_2 , _A : Union[str, Any]=3 , _A : str=4 , _A : Tuple=None , ):
'''simple docstring'''
UpperCAmelCase__ : Optional[int] = parent
UpperCAmelCase__ : Tuple = batch_size
UpperCAmelCase__ : List[Any] = seq_length
UpperCAmelCase__ : Optional[int] = is_training
UpperCAmelCase__ : Union[str, Any] = use_input_mask
UpperCAmelCase__ : Optional[Any] = use_token_type_ids
UpperCAmelCase__ : List[Any] = use_labels
UpperCAmelCase__ : Any = vocab_size
UpperCAmelCase__ : Optional[int] = hidden_size
UpperCAmelCase__ : Dict = num_hidden_layers
UpperCAmelCase__ : List[str] = num_attention_heads
UpperCAmelCase__ : Optional[int] = intermediate_size
UpperCAmelCase__ : Tuple = hidden_act
UpperCAmelCase__ : List[str] = hidden_dropout_prob
UpperCAmelCase__ : List[Any] = attention_probs_dropout_prob
UpperCAmelCase__ : List[Any] = max_position_embeddings
UpperCAmelCase__ : List[Any] = type_vocab_size
UpperCAmelCase__ : Tuple = type_sequence_label_size
UpperCAmelCase__ : str = initializer_range
UpperCAmelCase__ : List[Any] = num_labels
UpperCAmelCase__ : Optional[int] = num_choices
UpperCAmelCase__ : Optional[Any] = scope
def lowercase_ ( self : List[Any] ):
'''simple docstring'''
UpperCAmelCase__ : Optional[Any] = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
UpperCAmelCase__ : Union[str, Any] = None
if self.use_input_mask:
UpperCAmelCase__ : str = random_attention_mask([self.batch_size, self.seq_length] )
UpperCAmelCase__ : int = None
UpperCAmelCase__ : int = None
UpperCAmelCase__ : Optional[Any] = None
if self.use_labels:
UpperCAmelCase__ : List[str] = ids_tensor([self.batch_size] , self.type_sequence_label_size )
UpperCAmelCase__ : Dict = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
UpperCAmelCase__ : Dict = ids_tensor([self.batch_size] , self.num_choices )
UpperCAmelCase__ : Tuple = self.get_config()
return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels
def lowercase_ ( self : str ):
'''simple docstring'''
UpperCAmelCase__ : int = EsmConfig(
vocab_size=33 , hidden_size=self.hidden_size , pad_token_id=1 , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , initializer_range=self.initializer_range , is_folding_model=_A , esmfold_config={'''trunk''': {'''num_blocks''': 2}, '''fp16_esm''': False} , )
return config
def lowercase_ ( self : Optional[int] , _A : List[Any] , _A : str , _A : Optional[int] , _A : Dict , _A : Optional[Any] , _A : Union[str, Any] ):
'''simple docstring'''
UpperCAmelCase__ : str = EsmForProteinFolding(config=_A ).float()
model.to(_A )
model.eval()
UpperCAmelCase__ : Optional[int] = model(_A , attention_mask=_A )
UpperCAmelCase__ : Optional[int] = model(_A )
UpperCAmelCase__ : Any = model(_A )
self.parent.assertEqual(result.positions.shape , (8, self.batch_size, self.seq_length, 14, 3) )
self.parent.assertEqual(result.angles.shape , (8, self.batch_size, self.seq_length, 7, 2) )
def lowercase_ ( self : Optional[Any] ):
'''simple docstring'''
UpperCAmelCase__ : List[str] = self.prepare_config_and_inputs()
(
(
UpperCAmelCase__
) , (
UpperCAmelCase__
) , (
UpperCAmelCase__
) , (
UpperCAmelCase__
) , (
UpperCAmelCase__
) , (
UpperCAmelCase__
) ,
) : str = config_and_inputs
UpperCAmelCase__ : Dict = {'''input_ids''': input_ids, '''attention_mask''': input_mask}
return config, inputs_dict
@require_torch
class lowerCamelCase_ ( __a , __a , unittest.TestCase ):
lowerCAmelCase__ = False
lowerCAmelCase__ = (EsmForProteinFolding,) if is_torch_available() else ()
lowerCAmelCase__ = ()
lowerCAmelCase__ = {} if is_torch_available() else {}
lowerCAmelCase__ = False
def lowercase_ ( self : Optional[Any] ):
'''simple docstring'''
UpperCAmelCase__ : int = EsmFoldModelTester(self )
UpperCAmelCase__ : Dict = ConfigTester(self , config_class=_A , hidden_size=37 )
def lowercase_ ( self : Any ):
'''simple docstring'''
self.config_tester.run_common_tests()
def lowercase_ ( self : Any ):
'''simple docstring'''
UpperCAmelCase__ : int = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*_A )
@unittest.skip('''Does not support attention outputs''' )
def lowercase_ ( self : List[Any] ):
'''simple docstring'''
pass
@unittest.skip
def lowercase_ ( self : List[Any] ):
'''simple docstring'''
pass
@unittest.skip('''Esm does not support embedding resizing''' )
def lowercase_ ( self : List[Any] ):
'''simple docstring'''
pass
@unittest.skip('''Esm does not support embedding resizing''' )
def lowercase_ ( self : Dict ):
'''simple docstring'''
pass
@unittest.skip('''ESMFold does not support passing input embeds!''' )
def lowercase_ ( self : Optional[int] ):
'''simple docstring'''
pass
@unittest.skip('''ESMFold does not support head pruning.''' )
def lowercase_ ( self : List[Any] ):
'''simple docstring'''
pass
@unittest.skip('''ESMFold does not support head pruning.''' )
def lowercase_ ( self : Tuple ):
'''simple docstring'''
pass
@unittest.skip('''ESMFold does not support head pruning.''' )
def lowercase_ ( self : int ):
'''simple docstring'''
pass
@unittest.skip('''ESMFold does not support head pruning.''' )
def lowercase_ ( self : Union[str, Any] ):
'''simple docstring'''
pass
@unittest.skip('''ESMFold does not support head pruning.''' )
def lowercase_ ( self : List[Any] ):
'''simple docstring'''
pass
@unittest.skip('''ESMFold does not output hidden states in the normal way.''' )
def lowercase_ ( self : Any ):
'''simple docstring'''
pass
@unittest.skip('''ESMfold does not output hidden states in the normal way.''' )
def lowercase_ ( self : List[str] ):
'''simple docstring'''
pass
@unittest.skip('''ESMFold only has one output format.''' )
def lowercase_ ( self : Optional[int] ):
'''simple docstring'''
pass
@unittest.skip('''This test doesn\'t work for ESMFold and doesn\'t test core functionality''' )
def lowercase_ ( self : Optional[Any] ):
'''simple docstring'''
pass
@unittest.skip('''ESMFold does not support input chunking.''' )
def lowercase_ ( self : Union[str, Any] ):
'''simple docstring'''
pass
@unittest.skip('''ESMFold doesn\'t respect you and it certainly doesn\'t respect your initialization arguments.''' )
def lowercase_ ( self : Dict ):
'''simple docstring'''
pass
@unittest.skip('''ESMFold doesn\'t support torchscript compilation.''' )
def lowercase_ ( self : int ):
'''simple docstring'''
pass
@unittest.skip('''ESMFold doesn\'t support torchscript compilation.''' )
def lowercase_ ( self : List[str] ):
'''simple docstring'''
pass
@unittest.skip('''ESMFold doesn\'t support torchscript compilation.''' )
def lowercase_ ( self : str ):
'''simple docstring'''
pass
@unittest.skip('''ESMFold doesn\'t support data parallel.''' )
def lowercase_ ( self : str ):
'''simple docstring'''
pass
@unittest.skip('''Will be fixed soon by reducing the size of the model used for common tests.''' )
def lowercase_ ( self : str ):
'''simple docstring'''
pass
@require_torch
class lowerCamelCase_ ( __a ):
@slow
def lowercase_ ( self : Optional[Any] ):
'''simple docstring'''
UpperCAmelCase__ : List[str] = EsmForProteinFolding.from_pretrained('''facebook/esmfold_v1''' ).float()
model.eval()
UpperCAmelCase__ : Optional[Any] = torch.tensor([[0, 6, 4, 13, 5, 4, 16, 12, 11, 7, 2]] )
UpperCAmelCase__ : Optional[int] = model(_A )['''positions''']
UpperCAmelCase__ : Union[str, Any] = torch.tensor([2.5_8_2_8, 0.7_9_9_3, -1_0.9_3_3_4] , dtype=torch.floataa )
self.assertTrue(torch.allclose(position_outputs[0, 0, 0, 0] , _A , atol=1e-4 ) )
| 75 |
'''simple docstring'''
import argparse
import json
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from typing import List
import timm
import torch
import torch.nn as nn
from huggingface_hub import hf_hub_download
from torch import Tensor
from transformers import AutoImageProcessor, ResNetConfig, ResNetForImageClassification
from transformers.utils import logging
logging.set_verbosity_info()
__lowerCamelCase : int = logging.get_logger()
@dataclass
class A_ :
"""simple docstring"""
a__ = 42
a__ = field(default_factory=a_ )
a__ = field(default_factory=a_ )
def _A ( self :List[Any] , lowerCAmelCase__ :Union[str, Any] , lowerCAmelCase__ :Tensor , lowerCAmelCase__ :Tensor ) -> int:
'''simple docstring'''
snake_case_ : int = len(list(m.modules() ) ) == 1 or isinstance(lowerCAmelCase__ , nn.Convad ) or isinstance(lowerCAmelCase__ , nn.BatchNormad )
if has_not_submodules:
self.traced.append(lowerCAmelCase__ )
def __call__( self :List[Any] , lowerCAmelCase__ :Tensor ) -> Union[str, Any]:
'''simple docstring'''
for m in self.module.modules():
self.handles.append(m.register_forward_hook(self._forward_hook ) )
self.module(lowerCAmelCase__ )
[x.remove() for x in self.handles]
return self
@property
def _A ( self :int ) -> List[Any]:
'''simple docstring'''
return list(filter(lambda lowerCAmelCase__ : len(list(x.state_dict().keys() ) ) > 0 , self.traced ) )
@dataclass
class A_ :
"""simple docstring"""
a__ = 42
a__ = 42
a__ = 0
a__ = field(default_factory=a_ )
a__ = field(default_factory=a_ )
def __call__( self :Tuple , lowerCAmelCase__ :Tensor ) -> Tuple:
'''simple docstring'''
snake_case_ : List[Any] = Tracker(self.dest )(lowerCAmelCase__ ).parametrized
snake_case_ : Tuple = Tracker(self.src )(lowerCAmelCase__ ).parametrized
snake_case_ : List[str] = list(filter(lambda lowerCAmelCase__ : type(lowerCAmelCase__ ) not in self.src_skip , lowerCAmelCase__ ) )
snake_case_ : Tuple = list(filter(lambda lowerCAmelCase__ : type(lowerCAmelCase__ ) not in self.dest_skip , lowerCAmelCase__ ) )
if len(lowerCAmelCase__ ) != len(lowerCAmelCase__ ):
raise Exception(
F'''Numbers of operations are different. Source module has {len(lowerCAmelCase__ )} operations while'''
F''' destination module has {len(lowerCAmelCase__ )}.''' )
for dest_m, src_m in zip(lowerCAmelCase__ , lowerCAmelCase__ ):
dest_m.load_state_dict(src_m.state_dict() )
if self.verbose == 1:
print(F'''Transfered from={src_m} to={dest_m}''' )
def __UpperCAmelCase ( __magic_name__ ,__magic_name__ ,__magic_name__ ,__magic_name__ = True )-> Optional[int]:
"""simple docstring"""
print(F'''Converting {name}...''' )
with torch.no_grad():
snake_case_ : List[str] = timm.create_model(__magic_name__ ,pretrained=__magic_name__ ).eval()
snake_case_ : Optional[int] = ResNetForImageClassification(__magic_name__ ).eval()
snake_case_ : Dict = ModuleTransfer(src=__magic_name__ ,dest=__magic_name__ )
snake_case_ : Optional[int] = torch.randn((1, 3, 224, 224) )
module_transfer(__magic_name__ )
assert torch.allclose(from_model(__magic_name__ ) ,our_model(__magic_name__ ).logits ), "The model logits don't match the original one."
snake_case_ : str = F'''resnet{'-'.join(name.split('resnet' ) )}'''
print(__magic_name__ )
if push_to_hub:
our_model.push_to_hub(
repo_path_or_name=save_directory / checkpoint_name ,commit_message="Add model" ,use_temp_dir=__magic_name__ ,)
# we can use the convnext one
snake_case_ : Optional[Any] = AutoImageProcessor.from_pretrained("facebook/convnext-base-224-22k-1k" )
image_processor.push_to_hub(
repo_path_or_name=save_directory / checkpoint_name ,commit_message="Add image processor" ,use_temp_dir=__magic_name__ ,)
print(F'''Pushed {checkpoint_name}''' )
def __UpperCAmelCase ( __magic_name__ ,__magic_name__ = None ,__magic_name__ = True )-> Tuple:
"""simple docstring"""
snake_case_ : List[str] = "imagenet-1k-id2label.json"
snake_case_ : Optional[Any] = 1000
snake_case_ : List[Any] = (1, num_labels)
snake_case_ : Optional[Any] = "huggingface/label-files"
snake_case_ : Dict = num_labels
snake_case_ : List[Any] = json.load(open(hf_hub_download(__magic_name__ ,__magic_name__ ,repo_type="dataset" ) ,"r" ) )
snake_case_ : List[str] = {int(__magic_name__ ): v for k, v in idalabel.items()}
snake_case_ : Any = idalabel
snake_case_ : List[Any] = {v: k for k, v in idalabel.items()}
snake_case_ : Optional[int] = partial(__magic_name__ ,num_labels=__magic_name__ ,idalabel=__magic_name__ ,labelaid=__magic_name__ )
snake_case_ : Optional[int] = {
"resnet18": ImageNetPreTrainedConfig(
depths=[2, 2, 2, 2] ,hidden_sizes=[64, 128, 256, 512] ,layer_type="basic" ),
"resnet26": ImageNetPreTrainedConfig(
depths=[2, 2, 2, 2] ,hidden_sizes=[256, 512, 1024, 2048] ,layer_type="bottleneck" ),
"resnet34": ImageNetPreTrainedConfig(
depths=[3, 4, 6, 3] ,hidden_sizes=[64, 128, 256, 512] ,layer_type="basic" ),
"resnet50": ImageNetPreTrainedConfig(
depths=[3, 4, 6, 3] ,hidden_sizes=[256, 512, 1024, 2048] ,layer_type="bottleneck" ),
"resnet101": ImageNetPreTrainedConfig(
depths=[3, 4, 23, 3] ,hidden_sizes=[256, 512, 1024, 2048] ,layer_type="bottleneck" ),
"resnet152": ImageNetPreTrainedConfig(
depths=[3, 8, 36, 3] ,hidden_sizes=[256, 512, 1024, 2048] ,layer_type="bottleneck" ),
}
if model_name:
convert_weight_and_push(__magic_name__ ,names_to_config[model_name] ,__magic_name__ ,__magic_name__ )
else:
for model_name, config in names_to_config.items():
convert_weight_and_push(__magic_name__ ,__magic_name__ ,__magic_name__ ,__magic_name__ )
return config, expected_shape
if __name__ == "__main__":
__lowerCamelCase : Optional[int] = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'''--model_name''',
default=None,
type=str,
help=(
'''The name of the model you wish to convert, it must be one of the supported resnet* architecture,'''
''' currently: resnet18,26,34,50,101,152. If `None`, all of them will the converted.'''
),
)
parser.add_argument(
'''--pytorch_dump_folder_path''',
default=None,
type=Path,
required=True,
help='''Path to the output PyTorch model directory.''',
)
parser.add_argument(
'''--push_to_hub''',
default=True,
type=bool,
required=False,
help='''If True, push model and image processor to the hub.''',
)
__lowerCamelCase : Tuple = parser.parse_args()
__lowerCamelCase : Path = args.pytorch_dump_folder_path
pytorch_dump_folder_path.mkdir(exist_ok=True, parents=True)
convert_weights_and_push(pytorch_dump_folder_path, args.model_name, args.push_to_hub)
| 653 | 0 |
"""simple docstring"""
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_dimension_format,
)
from ...image_utils import (
IMAGENET_STANDARD_MEAN,
IMAGENET_STANDARD_STD,
ChannelDimension,
ImageInput,
PILImageResampling,
make_list_of_images,
to_numpy_array,
valid_images,
)
from ...utils import TensorType, logging
a_ = logging.get_logger(__name__)
class UpperCAmelCase_ ( snake_case ):
UpperCamelCase =["pixel_values"]
def __init__( self , UpperCamelCase_ = True , UpperCamelCase_ = None , UpperCamelCase_ = PILImageResampling.BILINEAR , UpperCamelCase_ = True , UpperCamelCase_ = None , UpperCamelCase_ = True , UpperCamelCase_ = 1 / 2_55 , UpperCamelCase_ = True , UpperCamelCase_ = None , UpperCamelCase_ = None , **UpperCamelCase_ , ) -> None:
super().__init__(**UpperCamelCase_ )
__lowercase : List[str] = size if size is not None else {'''shortest_edge''': 2_56}
__lowercase : Dict = get_size_dict(UpperCamelCase_ , default_to_square=UpperCamelCase_ )
__lowercase : Optional[Any] = crop_size if crop_size is not None else {'''height''': 2_24, '''width''': 2_24}
__lowercase : Dict = get_size_dict(UpperCamelCase_ )
__lowercase : Dict = do_resize
__lowercase : Optional[Any] = size
__lowercase : List[Any] = resample
__lowercase : Dict = do_center_crop
__lowercase : Any = crop_size
__lowercase : List[str] = do_rescale
__lowercase : List[str] = rescale_factor
__lowercase : Optional[Any] = do_normalize
__lowercase : Any = image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
__lowercase : List[str] = image_std if image_std is not None else IMAGENET_STANDARD_STD
def _lowerCamelCase ( self , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ = PILImageResampling.BICUBIC , UpperCamelCase_ = None , **UpperCamelCase_ , ) -> np.ndarray:
__lowercase : List[Any] = get_size_dict(UpperCamelCase_ , default_to_square=UpperCamelCase_ )
if "shortest_edge" not in size:
raise ValueError(F"""The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}""" )
__lowercase : List[Any] = get_resize_output_image_size(UpperCamelCase_ , size=size['''shortest_edge'''] , default_to_square=UpperCamelCase_ )
return resize(UpperCamelCase_ , size=UpperCamelCase_ , resample=UpperCamelCase_ , data_format=UpperCamelCase_ , **UpperCamelCase_ )
def _lowerCamelCase ( self , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ = None , **UpperCamelCase_ , ) -> np.ndarray:
__lowercase : Union[str, Any] = get_size_dict(UpperCamelCase_ )
return center_crop(UpperCamelCase_ , size=(size['''height'''], size['''width''']) , data_format=UpperCamelCase_ , **UpperCamelCase_ )
def _lowerCamelCase ( self , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ = None , **UpperCamelCase_ ) -> np.ndarray:
return rescale(UpperCamelCase_ , scale=UpperCamelCase_ , data_format=UpperCamelCase_ , **UpperCamelCase_ )
def _lowerCamelCase ( self , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ = None , **UpperCamelCase_ , ) -> np.ndarray:
return normalize(UpperCamelCase_ , mean=UpperCamelCase_ , std=UpperCamelCase_ , data_format=UpperCamelCase_ , **UpperCamelCase_ )
def _lowerCamelCase ( self , UpperCamelCase_ , UpperCamelCase_ = None , UpperCamelCase_ = None , UpperCamelCase_ = None , UpperCamelCase_ = None , UpperCamelCase_ = None , UpperCamelCase_ = None , UpperCamelCase_ = None , UpperCamelCase_ = None , UpperCamelCase_ = None , UpperCamelCase_ = None , UpperCamelCase_ = None , UpperCamelCase_ = ChannelDimension.FIRST , **UpperCamelCase_ , ) -> Optional[Any]:
__lowercase : Union[str, Any] = do_resize if do_resize is not None else self.do_resize
__lowercase : Tuple = size if size is not None else self.size
__lowercase : Optional[Any] = get_size_dict(UpperCamelCase_ , default_to_square=UpperCamelCase_ )
__lowercase : int = resample if resample is not None else self.resample
__lowercase : Tuple = do_center_crop if do_center_crop is not None else self.do_center_crop
__lowercase : List[str] = crop_size if crop_size is not None else self.crop_size
__lowercase : List[str] = get_size_dict(UpperCamelCase_ )
__lowercase : Union[str, Any] = do_rescale if do_rescale is not None else self.do_rescale
__lowercase : Union[str, Any] = rescale_factor if rescale_factor is not None else self.rescale_factor
__lowercase : Dict = do_normalize if do_normalize is not None else self.do_normalize
__lowercase : Tuple = image_mean if image_mean is not None else self.image_mean
__lowercase : Any = image_std if image_std is not None else self.image_std
__lowercase : Any = make_list_of_images(UpperCamelCase_ )
if not valid_images(UpperCamelCase_ ):
raise ValueError(
'''Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '''
'''torch.Tensor, tf.Tensor or jax.ndarray.''' )
if do_resize and size is None:
raise ValueError('''Size must be specified if do_resize is True.''' )
if do_center_crop and crop_size is None:
raise ValueError('''Crop size must be specified if do_center_crop is True.''' )
if do_rescale and rescale_factor is None:
raise ValueError('''Rescale factor must be specified if do_rescale is True.''' )
if do_normalize and (image_mean is None or image_std is None):
raise ValueError('''Image mean and std must be specified if do_normalize is True.''' )
# All transformations expect numpy arrays.
__lowercase : Optional[int] = [to_numpy_array(UpperCamelCase_ ) for image in images]
if do_resize:
__lowercase : Tuple = [self.resize(image=UpperCamelCase_ , size=UpperCamelCase_ , resample=UpperCamelCase_ ) for image in images]
if do_center_crop:
__lowercase : Any = [self.center_crop(image=UpperCamelCase_ , size=UpperCamelCase_ ) for image in images]
if do_rescale:
__lowercase : str = [self.rescale(image=UpperCamelCase_ , scale=UpperCamelCase_ ) for image in images]
if do_normalize:
__lowercase : Optional[int] = [self.normalize(image=UpperCamelCase_ , mean=UpperCamelCase_ , std=UpperCamelCase_ ) for image in images]
__lowercase : str = [to_channel_dimension_format(UpperCamelCase_ , UpperCamelCase_ ) for image in images]
__lowercase : Optional[Any] = {'''pixel_values''': images}
return BatchFeature(data=UpperCamelCase_ , tensor_type=UpperCamelCase_ )
| 76 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__lowerCamelCase : List[Any] = logging.get_logger(__name__)
__lowerCamelCase : Dict = {
'''weiweishi/roc-bert-base-zh''': '''https://huggingface.co/weiweishi/roc-bert-base-zh/resolve/main/config.json''',
}
class A_ (a_ ):
"""simple docstring"""
a__ = '''roc_bert'''
def __init__( self :Dict , lowerCAmelCase__ :Optional[Any]=30_522 , lowerCAmelCase__ :Dict=768 , lowerCAmelCase__ :str=12 , lowerCAmelCase__ :Optional[int]=12 , lowerCAmelCase__ :Optional[Any]=3_072 , lowerCAmelCase__ :Any="gelu" , lowerCAmelCase__ :int=0.1 , lowerCAmelCase__ :Tuple=0.1 , lowerCAmelCase__ :List[str]=512 , lowerCAmelCase__ :int=2 , lowerCAmelCase__ :Optional[int]=0.0_2 , lowerCAmelCase__ :Tuple=1E-1_2 , lowerCAmelCase__ :Tuple=True , lowerCAmelCase__ :List[str]=0 , lowerCAmelCase__ :Optional[Any]="absolute" , lowerCAmelCase__ :Tuple=None , lowerCAmelCase__ :List[str]=True , lowerCAmelCase__ :Optional[Any]=True , lowerCAmelCase__ :List[str]=768 , lowerCAmelCase__ :Optional[Any]=910 , lowerCAmelCase__ :str=512 , lowerCAmelCase__ :int=24_858 , lowerCAmelCase__ :List[Any]=True , **lowerCAmelCase__ :int , ) -> List[str]:
'''simple docstring'''
snake_case_ : int = vocab_size
snake_case_ : Dict = max_position_embeddings
snake_case_ : int = hidden_size
snake_case_ : str = num_hidden_layers
snake_case_ : Union[str, Any] = num_attention_heads
snake_case_ : int = intermediate_size
snake_case_ : Optional[Any] = hidden_act
snake_case_ : Optional[int] = hidden_dropout_prob
snake_case_ : List[Any] = attention_probs_dropout_prob
snake_case_ : Dict = initializer_range
snake_case_ : str = type_vocab_size
snake_case_ : Tuple = layer_norm_eps
snake_case_ : Optional[Any] = use_cache
snake_case_ : Optional[Any] = enable_pronunciation
snake_case_ : List[Any] = enable_shape
snake_case_ : Optional[int] = pronunciation_embed_dim
snake_case_ : Dict = pronunciation_vocab_size
snake_case_ : int = shape_embed_dim
snake_case_ : Any = shape_vocab_size
snake_case_ : Optional[int] = concat_input
snake_case_ : List[Any] = position_embedding_type
snake_case_ : Any = classifier_dropout
super().__init__(pad_token_id=lowerCAmelCase__ , **lowerCAmelCase__ )
| 653 | 0 |
"""simple docstring"""
# This code is adapted from OpenAI's release
# https://github.com/openai/human-eval/blob/master/human_eval/execution.py
import contextlib
import faulthandler
import io
import multiprocessing
import os
import platform
import signal
import tempfile
def _UpperCamelCase ( UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase ) -> Optional[Any]:
"""simple docstring"""
__UpperCAmelCase : Any = multiprocessing.Manager()
__UpperCAmelCase : Dict = manager.list()
__UpperCAmelCase : Union[str, Any] = multiprocessing.Process(target=UpperCamelCase , args=(check_program, result, timeout) )
p.start()
p.join(timeout=timeout + 1 )
if p.is_alive():
p.kill()
if not result:
result.append("timed out" )
return {
"task_id": task_id,
"passed": result[0] == "passed",
"result": result[0],
"completion_id": completion_id,
}
def _UpperCamelCase ( UpperCamelCase , UpperCamelCase , UpperCamelCase ) -> Any:
"""simple docstring"""
with create_tempdir():
# These system calls are needed when cleaning up tempdir.
import os
import shutil
__UpperCAmelCase : Dict = shutil.rmtree
__UpperCAmelCase : Optional[Any] = os.rmdir
__UpperCAmelCase : int = os.chdir
# Disable functionalities that can make destructive changes to the test.
reliability_guard()
# Run program.
try:
__UpperCAmelCase : Any = {}
with swallow_io():
with time_limit(UpperCamelCase ):
exec(UpperCamelCase , UpperCamelCase )
result.append("passed" )
except TimeoutException:
result.append("timed out" )
except BaseException as e:
result.append(f"failed: {e}" )
# Needed for cleaning up.
__UpperCAmelCase : Union[str, Any] = rmtree
__UpperCAmelCase : Union[str, Any] = rmdir
__UpperCAmelCase : int = chdir
@contextlib.contextmanager
def _UpperCamelCase ( UpperCamelCase ) -> str:
"""simple docstring"""
def signal_handler(UpperCamelCase , UpperCamelCase ):
raise TimeoutException("Timed out!" )
signal.setitimer(signal.ITIMER_REAL , UpperCamelCase )
signal.signal(signal.SIGALRM , UpperCamelCase )
try:
yield
finally:
signal.setitimer(signal.ITIMER_REAL , 0 )
@contextlib.contextmanager
def _UpperCamelCase ( ) -> Optional[Any]:
"""simple docstring"""
__UpperCAmelCase : List[Any] = WriteOnlyStringIO()
with contextlib.redirect_stdout(UpperCamelCase ):
with contextlib.redirect_stderr(UpperCamelCase ):
with redirect_stdin(UpperCamelCase ):
yield
@contextlib.contextmanager
def _UpperCamelCase ( ) -> int:
"""simple docstring"""
with tempfile.TemporaryDirectory() as dirname:
with chdir(UpperCamelCase ):
yield dirname
class a__ ( __magic_name__ ):
pass
class a__ ( io.StringIO ):
def a_ ( self : Union[str, Any] , *UpperCamelCase_ : List[str] , **UpperCamelCase_ : List[str]):
"""simple docstring"""
raise OSError
def a_ ( self : str , *UpperCamelCase_ : Tuple , **UpperCamelCase_ : List[Any]):
"""simple docstring"""
raise OSError
def a_ ( self : Any , *UpperCamelCase_ : Dict , **UpperCamelCase_ : Optional[Any]):
"""simple docstring"""
raise OSError
def a_ ( self : Optional[int] , *UpperCamelCase_ : Dict , **UpperCamelCase_ : Tuple):
"""simple docstring"""
return False
class a__ ( contextlib._RedirectStream ): # type: ignore
lowercase_ = "stdin"
@contextlib.contextmanager
def _UpperCamelCase ( UpperCamelCase ) -> Dict:
"""simple docstring"""
if root == ".":
yield
return
__UpperCAmelCase : Tuple = os.getcwd()
os.chdir(UpperCamelCase )
try:
yield
except BaseException as exc:
raise exc
finally:
os.chdir(UpperCamelCase )
def _UpperCamelCase ( UpperCamelCase=None ) -> int:
"""simple docstring"""
if maximum_memory_bytes is not None:
import resource
resource.setrlimit(resource.RLIMIT_AS , (maximum_memory_bytes, maximum_memory_bytes) )
resource.setrlimit(resource.RLIMIT_DATA , (maximum_memory_bytes, maximum_memory_bytes) )
if not platform.uname().system == "Darwin":
resource.setrlimit(resource.RLIMIT_STACK , (maximum_memory_bytes, maximum_memory_bytes) )
faulthandler.disable()
import builtins
__UpperCAmelCase : Any = None
__UpperCAmelCase : int = None
import os
__UpperCAmelCase : Tuple = "1"
__UpperCAmelCase : List[Any] = None
__UpperCAmelCase : List[Any] = None
__UpperCAmelCase : Optional[int] = None
__UpperCAmelCase : Union[str, Any] = None
__UpperCAmelCase : Tuple = None
__UpperCAmelCase : List[str] = None
__UpperCAmelCase : Any = None
__UpperCAmelCase : Optional[int] = None
__UpperCAmelCase : Tuple = None
__UpperCAmelCase : Any = None
__UpperCAmelCase : List[str] = None
__UpperCAmelCase : Optional[Any] = None
__UpperCAmelCase : Union[str, Any] = None
__UpperCAmelCase : Optional[Any] = None
__UpperCAmelCase : Optional[int] = None
__UpperCAmelCase : Union[str, Any] = None
__UpperCAmelCase : Any = None
__UpperCAmelCase : Optional[int] = None
__UpperCAmelCase : Optional[Any] = None
__UpperCAmelCase : Union[str, Any] = None
__UpperCAmelCase : str = None
__UpperCAmelCase : List[str] = None
__UpperCAmelCase : Union[str, Any] = None
__UpperCAmelCase : Tuple = None
__UpperCAmelCase : Dict = None
__UpperCAmelCase : int = None
__UpperCAmelCase : Dict = None
import shutil
__UpperCAmelCase : str = None
__UpperCAmelCase : Union[str, Any] = None
__UpperCAmelCase : List[str] = None
import subprocess
__UpperCAmelCase : Optional[int] = None # type: ignore
__UpperCAmelCase : str = None
import sys
__UpperCAmelCase : Optional[Any] = None
__UpperCAmelCase : List[Any] = None
__UpperCAmelCase : Dict = None
__UpperCAmelCase : Optional[int] = None
__UpperCAmelCase : Optional[int] = None
| 77 |
'''simple docstring'''
def __UpperCAmelCase ( __magic_name__ ,__magic_name__ ,__magic_name__ )-> int:
"""simple docstring"""
def update_area_of_max_square(__magic_name__ ,__magic_name__ ) -> int:
# BASE CASE
if row >= rows or col >= cols:
return 0
snake_case_ : str = update_area_of_max_square(__magic_name__ ,col + 1 )
snake_case_ : Dict = update_area_of_max_square(row + 1 ,col + 1 )
snake_case_ : int = update_area_of_max_square(row + 1 ,__magic_name__ )
if mat[row][col]:
snake_case_ : str = 1 + min([right, diagonal, down] )
snake_case_ : Tuple = max(largest_square_area[0] ,__magic_name__ )
return sub_problem_sol
else:
return 0
snake_case_ : Union[str, Any] = [0]
update_area_of_max_square(0 ,0 )
return largest_square_area[0]
def __UpperCAmelCase ( __magic_name__ ,__magic_name__ ,__magic_name__ )-> int:
"""simple docstring"""
def update_area_of_max_square_using_dp_array(
__magic_name__ ,__magic_name__ ,__magic_name__ ) -> int:
if row >= rows or col >= cols:
return 0
if dp_array[row][col] != -1:
return dp_array[row][col]
snake_case_ : Dict = update_area_of_max_square_using_dp_array(__magic_name__ ,col + 1 ,__magic_name__ )
snake_case_ : List[Any] = update_area_of_max_square_using_dp_array(row + 1 ,col + 1 ,__magic_name__ )
snake_case_ : Any = update_area_of_max_square_using_dp_array(row + 1 ,__magic_name__ ,__magic_name__ )
if mat[row][col]:
snake_case_ : int = 1 + min([right, diagonal, down] )
snake_case_ : Tuple = max(largest_square_area[0] ,__magic_name__ )
snake_case_ : Optional[Any] = sub_problem_sol
return sub_problem_sol
else:
return 0
snake_case_ : List[Any] = [0]
snake_case_ : Optional[int] = [[-1] * cols for _ in range(__magic_name__ )]
update_area_of_max_square_using_dp_array(0 ,0 ,__magic_name__ )
return largest_square_area[0]
def __UpperCAmelCase ( __magic_name__ ,__magic_name__ ,__magic_name__ )-> int:
"""simple docstring"""
snake_case_ : Dict = [[0] * (cols + 1) for _ in range(rows + 1 )]
snake_case_ : Dict = 0
for row in range(rows - 1 ,-1 ,-1 ):
for col in range(cols - 1 ,-1 ,-1 ):
snake_case_ : List[str] = dp_array[row][col + 1]
snake_case_ : Any = dp_array[row + 1][col + 1]
snake_case_ : Any = dp_array[row + 1][col]
if mat[row][col] == 1:
snake_case_ : Any = 1 + min(__magic_name__ ,__magic_name__ ,__magic_name__ )
snake_case_ : str = max(dp_array[row][col] ,__magic_name__ )
else:
snake_case_ : Optional[Any] = 0
return largest_square_area
def __UpperCAmelCase ( __magic_name__ ,__magic_name__ ,__magic_name__ )-> int:
"""simple docstring"""
snake_case_ : str = [0] * (cols + 1)
snake_case_ : Tuple = [0] * (cols + 1)
snake_case_ : List[str] = 0
for row in range(rows - 1 ,-1 ,-1 ):
for col in range(cols - 1 ,-1 ,-1 ):
snake_case_ : Optional[Any] = current_row[col + 1]
snake_case_ : Optional[int] = next_row[col + 1]
snake_case_ : Dict = next_row[col]
if mat[row][col] == 1:
snake_case_ : Union[str, Any] = 1 + min(__magic_name__ ,__magic_name__ ,__magic_name__ )
snake_case_ : Any = max(current_row[col] ,__magic_name__ )
else:
snake_case_ : Dict = 0
snake_case_ : Optional[Any] = current_row
return largest_square_area
if __name__ == "__main__":
import doctest
doctest.testmod()
print(largest_square_area_in_matrix_bottom_up(2, 2, [[1, 1], [1, 1]]))
| 653 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
SCREAMING_SNAKE_CASE_: Tuple ={'configuration_fnet': ['FNET_PRETRAINED_CONFIG_ARCHIVE_MAP', 'FNetConfig']}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
SCREAMING_SNAKE_CASE_: Optional[Any] =['FNetTokenizer']
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
SCREAMING_SNAKE_CASE_: List[Any] =['FNetTokenizerFast']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
SCREAMING_SNAKE_CASE_: str =[
'FNET_PRETRAINED_MODEL_ARCHIVE_LIST',
'FNetForMaskedLM',
'FNetForMultipleChoice',
'FNetForNextSentencePrediction',
'FNetForPreTraining',
'FNetForQuestionAnswering',
'FNetForSequenceClassification',
'FNetForTokenClassification',
'FNetLayer',
'FNetModel',
'FNetPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_fnet import FNET_PRETRAINED_CONFIG_ARCHIVE_MAP, FNetConfig
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_fnet import FNetTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_fnet_fast import FNetTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_fnet import (
FNET_PRETRAINED_MODEL_ARCHIVE_LIST,
FNetForMaskedLM,
FNetForMultipleChoice,
FNetForNextSentencePrediction,
FNetForPreTraining,
FNetForQuestionAnswering,
FNetForSequenceClassification,
FNetForTokenClassification,
FNetLayer,
FNetModel,
FNetPreTrainedModel,
)
else:
import sys
SCREAMING_SNAKE_CASE_: str =_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 78 |
'''simple docstring'''
import os
import zipfile
import requests
from get_ci_error_statistics import download_artifact, get_artifacts_links
def __UpperCAmelCase ( __magic_name__ ,__magic_name__=7 )-> Tuple:
"""simple docstring"""
snake_case_ : List[str] = None
if token is not None:
snake_case_ : List[str] = {"Accept": "application/vnd.github+json", "Authorization": F'''Bearer {token}'''}
# The id of a workflow (not of a workflow run)
snake_case_ : Dict = "636036"
snake_case_ : List[str] = F'''https://api.github.com/repos/huggingface/transformers/actions/workflows/{workflow_id}/runs'''
# On `main` branch + event being `schedule` + not returning PRs + only `num_runs` results
url += F'''?branch=main&event=schedule&exclude_pull_requests=true&per_page={num_runs}'''
snake_case_ : Optional[Any] = requests.get(__magic_name__ ,headers=__magic_name__ ).json()
return result["workflow_runs"]
def __UpperCAmelCase ( __magic_name__ )-> Union[str, Any]:
"""simple docstring"""
snake_case_ : str = get_daily_ci_runs(__magic_name__ )
snake_case_ : Optional[int] = None
for workflow_run in workflow_runs:
if workflow_run["status"] == "completed":
snake_case_ : Dict = workflow_run["id"]
break
return workflow_run_id
def __UpperCAmelCase ( __magic_name__ ,__magic_name__ ,__magic_name__ )-> List[Any]:
"""simple docstring"""
snake_case_ : Optional[Any] = get_last_daily_ci_runs(__magic_name__ )
if workflow_run_id is not None:
snake_case_ : Union[str, Any] = get_artifacts_links(worflow_run_id=__magic_name__ ,token=__magic_name__ )
for artifact_name in artifact_names:
if artifact_name in artifacts_links:
snake_case_ : Union[str, Any] = artifacts_links[artifact_name]
download_artifact(
artifact_name=__magic_name__ ,artifact_url=__magic_name__ ,output_dir=__magic_name__ ,token=__magic_name__ )
def __UpperCAmelCase ( __magic_name__ ,__magic_name__ ,__magic_name__ )-> List[Any]:
"""simple docstring"""
get_last_daily_ci_artifacts(__magic_name__ ,__magic_name__ ,__magic_name__ )
snake_case_ : Union[str, Any] = {}
for artifact_name in artifact_names:
snake_case_ : Any = os.path.join(__magic_name__ ,F'''{artifact_name}.zip''' )
if os.path.isfile(__magic_name__ ):
snake_case_ : Tuple = {}
with zipfile.ZipFile(__magic_name__ ) as z:
for filename in z.namelist():
if not os.path.isdir(__magic_name__ ):
# read the file
with z.open(__magic_name__ ) as f:
snake_case_ : Optional[Any] = f.read().decode("UTF-8" )
return results
| 653 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
SCREAMING_SNAKE_CASE__ : Union[str, Any] = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE__ : List[Any] = {
"""google/vivit-b-16x2-kinetics400""": (
"""https://huggingface.co/google/vivit-b-16x2-kinetics400/resolve/main/config.json"""
),
# See all Vivit models at https://huggingface.co/models?filter=vivit
}
class UpperCAmelCase_ ( __lowerCamelCase ):
__lowerCamelCase = 'vivit'
def __init__( self , _lowerCAmelCase=224 , _lowerCAmelCase=32 , _lowerCAmelCase=[2, 16, 16] , _lowerCAmelCase=3 , _lowerCAmelCase=768 , _lowerCAmelCase=12 , _lowerCAmelCase=12 , _lowerCAmelCase=3072 , _lowerCAmelCase="gelu_fast" , _lowerCAmelCase=0.0 , _lowerCAmelCase=0.0 , _lowerCAmelCase=0.0_2 , _lowerCAmelCase=1e-06 , _lowerCAmelCase=True , **_lowerCAmelCase , ):
UpperCAmelCase__ : List[str] = hidden_size
UpperCAmelCase__ : int = num_hidden_layers
UpperCAmelCase__ : str = num_attention_heads
UpperCAmelCase__ : List[str] = intermediate_size
UpperCAmelCase__ : Optional[int] = hidden_act
UpperCAmelCase__ : List[str] = hidden_dropout_prob
UpperCAmelCase__ : Any = attention_probs_dropout_prob
UpperCAmelCase__ : List[Any] = initializer_range
UpperCAmelCase__ : Dict = layer_norm_eps
UpperCAmelCase__ : List[Any] = image_size
UpperCAmelCase__ : Dict = num_frames
UpperCAmelCase__ : Optional[Any] = tubelet_size
UpperCAmelCase__ : List[Any] = num_channels
UpperCAmelCase__ : Tuple = qkv_bias
super().__init__(**_lowerCAmelCase )
| 79 |
'''simple docstring'''
from string import ascii_uppercase
__lowerCamelCase : Optional[Any] = {char: i for i, char in enumerate(ascii_uppercase)}
__lowerCamelCase : List[str] = dict(enumerate(ascii_uppercase))
def __UpperCAmelCase ( __magic_name__ ,__magic_name__ )-> str:
"""simple docstring"""
snake_case_ : Tuple = len(__magic_name__ )
snake_case_ : str = 0
while True:
if x == i:
snake_case_ : List[str] = 0
if len(__magic_name__ ) == len(__magic_name__ ):
break
key += key[i]
i += 1
return key
def __UpperCAmelCase ( __magic_name__ ,__magic_name__ )-> str:
"""simple docstring"""
snake_case_ : str = ""
snake_case_ : List[Any] = 0
for letter in message:
if letter == " ":
cipher_text += " "
else:
snake_case_ : Optional[Any] = (dicta[letter] - dicta[key_new[i]]) % 26
i += 1
cipher_text += dicta[x]
return cipher_text
def __UpperCAmelCase ( __magic_name__ ,__magic_name__ )-> str:
"""simple docstring"""
snake_case_ : Dict = ""
snake_case_ : Dict = 0
for letter in cipher_text:
if letter == " ":
or_txt += " "
else:
snake_case_ : str = (dicta[letter] + dicta[key_new[i]] + 26) % 26
i += 1
or_txt += dicta[x]
return or_txt
def __UpperCAmelCase ( )-> None:
"""simple docstring"""
snake_case_ : List[str] = "THE GERMAN ATTACK"
snake_case_ : List[str] = "SECRET"
snake_case_ : Optional[int] = generate_key(__magic_name__ ,__magic_name__ )
snake_case_ : Any = cipher_text(__magic_name__ ,__magic_name__ )
print(F'''Encrypted Text = {s}''' )
print(F'''Original Text = {original_text(__magic_name__ ,__magic_name__ )}''' )
if __name__ == "__main__":
import doctest
doctest.testmod()
main()
| 653 | 0 |
# XXX: we want transformers master here - in the absense of conftest manipulating sys.path:
# hack it in for now:
import sys
from pathlib import Path
__UpperCamelCase : Optional[Any] = Path(__file__).resolve().parents[3] / """src"""
sys.path.insert(1, str(git_repo_path))
import dataclasses # noqa
import io # noqa
import itertools # noqa
import json # noqa
import os # noqa
import unittest # noqa
from copy import deepcopy # noqa
from parameterized import parameterized # noqa
from transformers import TrainingArguments, is_torch_available # noqa
from transformers.deepspeed import is_deepspeed_available # noqa
from transformers.file_utils import WEIGHTS_NAME # noqa
from transformers.testing_utils import ( # noqa
CaptureLogger,
ExtendSysPath,
TestCasePlus,
execute_subprocess_async,
get_gpu_count,
mockenv_context,
require_deepspeed,
require_torch_gpu,
require_torch_multi_gpu,
slow,
)
from transformers.trainer_utils import set_seed # noqa
set_seed(42)
__UpperCamelCase : str = {"""base""": """patrickvonplaten/wav2vec2_tiny_random""", """robust""": """patrickvonplaten/wav2vec2_tiny_random_robust"""}
__UpperCamelCase : List[str] = """zero2"""
__UpperCamelCase : List[Any] = """zero3"""
__UpperCamelCase : Optional[int] = [ZEROa, ZEROa]
def snake_case ( lowerCamelCase , lowerCamelCase , lowerCamelCase ):
'''simple docstring'''
__lowercase = parameterized.to_safe_name("""_""".join(str(lowerCamelCase ) for x in param.args ) )
return F'{func.__name__}_{param_based_name}'
# Cartesian-product of zero stages with models to test
__UpperCamelCase : Union[str, Any] = list(itertools.product(stages, models.keys()))
@slow
@require_deepspeed
@require_torch_gpu
class __UpperCamelCase ( _lowerCAmelCase ):
@parameterized.expand(_lowerCAmelCase , name_func=_lowerCAmelCase )
def _a ( self : Dict , _lowerCAmelCase : int , _lowerCAmelCase : Dict ) -> Union[str, Any]:
"""simple docstring"""
self.run_and_check(
stage=_lowerCAmelCase , model=_lowerCAmelCase , distributed=_lowerCAmelCase , fpaa=_lowerCAmelCase , )
@require_torch_multi_gpu
@parameterized.expand(_lowerCAmelCase , name_func=_lowerCAmelCase )
def _a ( self : Optional[int] , _lowerCAmelCase : Dict , _lowerCAmelCase : List[Any] ) -> Tuple:
"""simple docstring"""
self.run_and_check(
stage=_lowerCAmelCase , model=_lowerCAmelCase , distributed=_lowerCAmelCase , fpaa=_lowerCAmelCase , )
@parameterized.expand(_lowerCAmelCase , name_func=_lowerCAmelCase )
def _a ( self : Tuple , _lowerCAmelCase : Dict , _lowerCAmelCase : Tuple ) -> Optional[Any]:
"""simple docstring"""
self.run_and_check(
stage=_lowerCAmelCase , model=_lowerCAmelCase , distributed=_lowerCAmelCase , fpaa=_lowerCAmelCase , )
@require_torch_multi_gpu
@parameterized.expand(_lowerCAmelCase , name_func=_lowerCAmelCase )
def _a ( self : str , _lowerCAmelCase : Optional[Any] , _lowerCAmelCase : Dict ) -> str:
"""simple docstring"""
self.run_and_check(
stage=_lowerCAmelCase , model=_lowerCAmelCase , distributed=_lowerCAmelCase , fpaa=_lowerCAmelCase , )
def _a ( self : Union[str, Any] , _lowerCAmelCase : Optional[Any] ) -> Any:
"""simple docstring"""
pass
def _a ( self : str , _lowerCAmelCase : str , _lowerCAmelCase : str , _lowerCAmelCase : int = 10 , _lowerCAmelCase : bool = True , _lowerCAmelCase : bool = True , _lowerCAmelCase : bool = True , ) -> str:
"""simple docstring"""
__lowercase = models[model]
__lowercase = self.run_trainer(
stage=_lowerCAmelCase , model_name=_lowerCAmelCase , eval_steps=_lowerCAmelCase , num_train_epochs=1 , distributed=_lowerCAmelCase , fpaa=_lowerCAmelCase , )
self.do_checks(_lowerCAmelCase )
return output_dir
def _a ( self : List[str] , _lowerCAmelCase : str , _lowerCAmelCase : str , _lowerCAmelCase : int = 10 , _lowerCAmelCase : int = 1 , _lowerCAmelCase : bool = True , _lowerCAmelCase : bool = True , ) -> Dict:
"""simple docstring"""
__lowercase = self.get_auto_remove_tmp_dir("""./xxx""" , after=_lowerCAmelCase )
__lowercase = F'\n --model_name_or_path {model_name}\n --dataset_name hf-internal-testing/librispeech_asr_dummy\n --dataset_config_name clean\n --train_split_name validation\n --validation_split_name validation\n --output_dir {output_dir}\n --num_train_epochs {str(_lowerCAmelCase )}\n --per_device_train_batch_size 2\n --per_device_eval_batch_size 2\n --evaluation_strategy steps\n --learning_rate 5e-4\n --warmup_steps 8\n --orthography timit\n --preprocessing_num_workers 1\n --group_by_length\n --freeze_feature_extractor\n --report_to none\n --save_steps 0\n --eval_steps {eval_steps}\n --report_to none\n '.split()
if fpaa:
args.extend(["""--fp16"""] )
# currently ds_config_wav2vec2_zero.json requires "zero_optimization.find_unused_parameters": true,
# hence the separate config files
__lowercase = F'--deepspeed {self.test_file_dir_str}/ds_config_wav2vec2_{stage}.json'.split()
__lowercase = [F'{self.examples_dir_str}/research_projects/wav2vec2/run_asr.py']
__lowercase = self.get_launcher(_lowerCAmelCase )
__lowercase = launcher + script + args + ds_args
# keep for quick debug
# print(" ".join([f"\nPYTHONPATH={self.src_dir_str}"] +cmd)); die
execute_subprocess_async(_lowerCAmelCase , env=self.get_env() )
return output_dir
def _a ( self : List[str] , _lowerCAmelCase : str=False ) -> Dict:
"""simple docstring"""
__lowercase = min(2 , get_gpu_count() ) if distributed else 1
return F'deepspeed --num_nodes 1 --num_gpus {num_gpus}'.split()
| 80 |
'''simple docstring'''
import argparse
import os
import numpy as np
import tensorflow as tf
import torch
from transformers import BertModel
def __UpperCAmelCase ( __magic_name__ ,__magic_name__ ,__magic_name__ )-> Dict:
"""simple docstring"""
snake_case_ : Tuple = ("dense.weight", "attention.self.query", "attention.self.key", "attention.self.value")
snake_case_ : Union[str, Any] = (
("layer.", "layer_"),
("word_embeddings.weight", "word_embeddings"),
("position_embeddings.weight", "position_embeddings"),
("token_type_embeddings.weight", "token_type_embeddings"),
(".", "/"),
("LayerNorm/weight", "LayerNorm/gamma"),
("LayerNorm/bias", "LayerNorm/beta"),
("weight", "kernel"),
)
if not os.path.isdir(__magic_name__ ):
os.makedirs(__magic_name__ )
snake_case_ : str = model.state_dict()
def to_tf_var_name(__magic_name__ ):
for patt, repl in iter(__magic_name__ ):
snake_case_ : List[str] = name.replace(__magic_name__ ,__magic_name__ )
return F'''bert/{name}'''
def create_tf_var(__magic_name__ ,__magic_name__ ,__magic_name__ ):
snake_case_ : List[Any] = tf.dtypes.as_dtype(tensor.dtype )
snake_case_ : Union[str, Any] = tf.get_variable(dtype=__magic_name__ ,shape=tensor.shape ,name=__magic_name__ ,initializer=tf.zeros_initializer() )
session.run(tf.variables_initializer([tf_var] ) )
session.run(__magic_name__ )
return tf_var
tf.reset_default_graph()
with tf.Session() as session:
for var_name in state_dict:
snake_case_ : Optional[int] = to_tf_var_name(__magic_name__ )
snake_case_ : Dict = state_dict[var_name].numpy()
if any(x in var_name for x in tensors_to_transpose ):
snake_case_ : List[Any] = torch_tensor.T
snake_case_ : Union[str, Any] = create_tf_var(tensor=__magic_name__ ,name=__magic_name__ ,session=__magic_name__ )
tf.keras.backend.set_value(__magic_name__ ,__magic_name__ )
snake_case_ : List[str] = session.run(__magic_name__ )
print(F'''Successfully created {tf_name}: {np.allclose(__magic_name__ ,__magic_name__ )}''' )
snake_case_ : Any = tf.train.Saver(tf.trainable_variables() )
saver.save(__magic_name__ ,os.path.join(__magic_name__ ,model_name.replace("-" ,"_" ) + ".ckpt" ) )
def __UpperCAmelCase ( __magic_name__=None )-> Optional[Any]:
"""simple docstring"""
snake_case_ : Any = argparse.ArgumentParser()
parser.add_argument("--model_name" ,type=__magic_name__ ,required=__magic_name__ ,help="model name e.g. bert-base-uncased" )
parser.add_argument(
"--cache_dir" ,type=__magic_name__ ,default=__magic_name__ ,required=__magic_name__ ,help="Directory containing pytorch model" )
parser.add_argument("--pytorch_model_path" ,type=__magic_name__ ,required=__magic_name__ ,help="/path/to/<pytorch-model-name>.bin" )
parser.add_argument("--tf_cache_dir" ,type=__magic_name__ ,required=__magic_name__ ,help="Directory in which to save tensorflow model" )
snake_case_ : Optional[int] = parser.parse_args(__magic_name__ )
snake_case_ : Optional[int] = BertModel.from_pretrained(
pretrained_model_name_or_path=args.model_name ,state_dict=torch.load(args.pytorch_model_path ) ,cache_dir=args.cache_dir ,)
convert_pytorch_checkpoint_to_tf(model=__magic_name__ ,ckpt_dir=args.tf_cache_dir ,model_name=args.model_name )
if __name__ == "__main__":
main()
| 653 | 0 |
def lowerCAmelCase_ ( __lowerCamelCase , __lowerCamelCase ):
__snake_case : List[str] = len(__lowerCamelCase )
__snake_case : Union[str, Any] = len(__lowerCamelCase )
__snake_case : Optional[Any] = [[False for _ in range(m + 1 )] for _ in range(n + 1 )]
__snake_case : int = True
for i in range(__lowerCamelCase ):
for j in range(m + 1 ):
if dp[i][j]:
if j < m and a[i].upper() == b[j]:
__snake_case : Any = True
if a[i].islower():
__snake_case : List[str] = True
return dp[n][m]
if __name__ == "__main__":
import doctest
doctest.testmod()
| 81 |
'''simple docstring'''
from collections import deque
from .hash_table import HashTable
class A_ (a_ ):
"""simple docstring"""
def __init__( self :List[str] , *lowerCAmelCase__ :Optional[Any] , **lowerCAmelCase__ :Dict ) -> Union[str, Any]:
'''simple docstring'''
super().__init__(*lowerCAmelCase__ , **lowerCAmelCase__ )
def _A ( self :Optional[int] , lowerCAmelCase__ :int , lowerCAmelCase__ :Optional[Any] ) -> Union[str, Any]:
'''simple docstring'''
snake_case_ : Optional[int] = deque([] ) if self.values[key] is None else self.values[key]
self.values[key].appendleft(lowerCAmelCase__ )
snake_case_ : Tuple = self.values[key]
def _A ( self :int ) -> Dict:
'''simple docstring'''
return (
sum(self.charge_factor - len(lowerCAmelCase__ ) for slot in self.values )
/ self.size_table
* self.charge_factor
)
def _A ( self :str , lowerCAmelCase__ :Optional[Any] , lowerCAmelCase__ :Tuple=None ) -> Any:
'''simple docstring'''
if not (
len(self.values[key] ) == self.charge_factor and self.values.count(lowerCAmelCase__ ) == 0
):
return key
return super()._collision_resolution(lowerCAmelCase__ , lowerCAmelCase__ )
| 653 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowerCamelCase = {
"""configuration_trajectory_transformer""": [
"""TRAJECTORY_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""TrajectoryTransformerConfig""",
],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCamelCase = [
"""TRAJECTORY_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""TrajectoryTransformerModel""",
"""TrajectoryTransformerPreTrainedModel""",
"""load_tf_weights_in_trajectory_transformer""",
]
if TYPE_CHECKING:
from .configuration_trajectory_transformer import (
TRAJECTORY_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
TrajectoryTransformerConfig,
)
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_trajectory_transformer import (
TRAJECTORY_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
TrajectoryTransformerModel,
TrajectoryTransformerPreTrainedModel,
load_tf_weights_in_trajectory_transformer,
)
else:
import sys
lowerCamelCase = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 82 |
'''simple docstring'''
from collections.abc import Iterator, MutableMapping
from dataclasses import dataclass
from typing import Generic, TypeVar
__lowerCamelCase : Dict = TypeVar('''KEY''')
__lowerCamelCase : int = TypeVar('''VAL''')
@dataclass(frozen=a_ , slots=a_ )
class A_ (Generic[KEY, VAL] ):
"""simple docstring"""
a__ = 42
a__ = 42
class A_ (_Item ):
"""simple docstring"""
def __init__( self :List[Any] ) -> None:
'''simple docstring'''
super().__init__(lowerCAmelCase__ , lowerCAmelCase__ )
def __bool__( self :Optional[int] ) -> bool:
'''simple docstring'''
return False
__lowerCamelCase : Dict = _DeletedItem()
class A_ (MutableMapping[KEY, VAL] ):
"""simple docstring"""
def __init__( self :Dict , lowerCAmelCase__ :int = 8 , lowerCAmelCase__ :float = 0.7_5 ) -> None:
'''simple docstring'''
snake_case_ : Any = initial_block_size
snake_case_ : list[_Item | None] = [None] * initial_block_size
assert 0.0 < capacity_factor < 1.0
snake_case_ : Tuple = capacity_factor
snake_case_ : List[Any] = 0
def _A ( self :Tuple , lowerCAmelCase__ :KEY ) -> int:
'''simple docstring'''
return hash(lowerCAmelCase__ ) % len(self._buckets )
def _A ( self :Any , lowerCAmelCase__ :int ) -> int:
'''simple docstring'''
return (ind + 1) % len(self._buckets )
def _A ( self :str , lowerCAmelCase__ :int , lowerCAmelCase__ :KEY , lowerCAmelCase__ :VAL ) -> bool:
'''simple docstring'''
snake_case_ : Optional[int] = self._buckets[ind]
if not stored:
snake_case_ : int = _Item(lowerCAmelCase__ , lowerCAmelCase__ )
self._len += 1
return True
elif stored.key == key:
snake_case_ : Optional[int] = _Item(lowerCAmelCase__ , lowerCAmelCase__ )
return True
else:
return False
def _A ( self :int ) -> bool:
'''simple docstring'''
snake_case_ : Any = len(self._buckets ) * self._capacity_factor
return len(self ) >= int(lowerCAmelCase__ )
def _A ( self :Any ) -> bool:
'''simple docstring'''
if len(self._buckets ) <= self._initial_block_size:
return False
snake_case_ : Optional[int] = len(self._buckets ) * self._capacity_factor / 2
return len(self ) < limit
def _A ( self :Tuple , lowerCAmelCase__ :int ) -> None:
'''simple docstring'''
snake_case_ : Tuple = self._buckets
snake_case_ : int = [None] * new_size
snake_case_ : Any = 0
for item in old_buckets:
if item:
self._add_item(item.key , item.val )
def _A ( self :Optional[int] ) -> None:
'''simple docstring'''
self._resize(len(self._buckets ) * 2 )
def _A ( self :str ) -> None:
'''simple docstring'''
self._resize(len(self._buckets ) // 2 )
def _A ( self :Optional[int] , lowerCAmelCase__ :KEY ) -> Iterator[int]:
'''simple docstring'''
snake_case_ : str = self._get_bucket_index(lowerCAmelCase__ )
for _ in range(len(self._buckets ) ):
yield ind
snake_case_ : List[Any] = self._get_next_ind(lowerCAmelCase__ )
def _A ( self :Union[str, Any] , lowerCAmelCase__ :KEY , lowerCAmelCase__ :VAL ) -> None:
'''simple docstring'''
for ind in self._iterate_buckets(lowerCAmelCase__ ):
if self._try_set(lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ ):
break
def __setitem__( self :Optional[int] , lowerCAmelCase__ :KEY , lowerCAmelCase__ :VAL ) -> None:
'''simple docstring'''
if self._is_full():
self._size_up()
self._add_item(lowerCAmelCase__ , lowerCAmelCase__ )
def __delitem__( self :List[Any] , lowerCAmelCase__ :KEY ) -> None:
'''simple docstring'''
for ind in self._iterate_buckets(lowerCAmelCase__ ):
snake_case_ : int = self._buckets[ind]
if item is None:
raise KeyError(lowerCAmelCase__ )
if item is _deleted:
continue
if item.key == key:
snake_case_ : List[str] = _deleted
self._len -= 1
break
if self._is_sparse():
self._size_down()
def __getitem__( self :List[str] , lowerCAmelCase__ :KEY ) -> VAL:
'''simple docstring'''
for ind in self._iterate_buckets(lowerCAmelCase__ ):
snake_case_ : Optional[Any] = self._buckets[ind]
if item is None:
break
if item is _deleted:
continue
if item.key == key:
return item.val
raise KeyError(lowerCAmelCase__ )
def __len__( self :Optional[Any] ) -> int:
'''simple docstring'''
return self._len
def __iter__( self :List[Any] ) -> Iterator[KEY]:
'''simple docstring'''
yield from (item.key for item in self._buckets if item)
def __repr__( self :Any ) -> str:
'''simple docstring'''
snake_case_ : Dict = " ,".join(
F'''{item.key}: {item.val}''' for item in self._buckets if item )
return F'''HashMap({val_string})'''
| 653 | 0 |
"""simple docstring"""
import gc
import unittest
import torch
from parameterized import parameterized
from diffusers import AutoencoderKL
from diffusers.utils import floats_tensor, load_hf_numpy, require_torch_gpu, slow, torch_all_close, torch_device
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import enable_full_determinism
from .test_modeling_common import ModelTesterMixin, UNetTesterMixin
enable_full_determinism()
class __snake_case ( _lowercase , _lowercase , unittest.TestCase):
snake_case__ : Tuple = AutoencoderKL
snake_case__ : Optional[int] = "sample"
snake_case__ : Optional[Any] = 1e-2
@property
def SCREAMING_SNAKE_CASE ( self : Union[str, Any] ):
"""simple docstring"""
_lowerCamelCase : Optional[Any] = 4
_lowerCamelCase : Optional[Any] = 3
_lowerCamelCase : List[str] = (3_2, 3_2)
_lowerCamelCase : Any = floats_tensor((batch_size, num_channels) + sizes ).to(__lowerCAmelCase )
return {"sample": image}
@property
def SCREAMING_SNAKE_CASE ( self : List[Any] ):
"""simple docstring"""
return (3, 3_2, 3_2)
@property
def SCREAMING_SNAKE_CASE ( self : Dict ):
"""simple docstring"""
return (3, 3_2, 3_2)
def SCREAMING_SNAKE_CASE ( self : Optional[int] ):
"""simple docstring"""
_lowerCamelCase : Any = {
'''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,
}
_lowerCamelCase : Tuple = self.dummy_input
return init_dict, inputs_dict
def SCREAMING_SNAKE_CASE ( self : int ):
"""simple docstring"""
pass
def SCREAMING_SNAKE_CASE ( self : Optional[int] ):
"""simple docstring"""
pass
@unittest.skipIf(torch_device == '''mps''' , '''Gradient checkpointing skipped on MPS''' )
def SCREAMING_SNAKE_CASE ( self : List[Any] ):
"""simple docstring"""
_lowerCamelCase , _lowerCamelCase : Union[str, Any] = self.prepare_init_args_and_inputs_for_common()
_lowerCamelCase : List[str] = self.model_class(**__lowerCAmelCase )
model.to(__lowerCAmelCase )
assert not model.is_gradient_checkpointing and model.training
_lowerCamelCase : Union[str, Any] = model(**__lowerCAmelCase ).sample
# run the backwards pass on the model. For backwards pass, for simplicity purpose,
# we won't calculate the loss and rather backprop on out.sum()
model.zero_grad()
_lowerCamelCase : List[Any] = torch.randn_like(__lowerCAmelCase )
_lowerCamelCase : Tuple = (out - labels).mean()
loss.backward()
# re-instantiate the model now enabling gradient checkpointing
_lowerCamelCase : List[Any] = self.model_class(**__lowerCAmelCase )
# clone model
model_a.load_state_dict(model.state_dict() )
model_a.to(__lowerCAmelCase )
model_a.enable_gradient_checkpointing()
assert model_a.is_gradient_checkpointing and model_a.training
_lowerCamelCase : List[Any] = model_a(**__lowerCAmelCase ).sample
# run the backwards pass on the model. For backwards pass, for simplicity purpose,
# we won't calculate the loss and rather backprop on out.sum()
model_a.zero_grad()
_lowerCamelCase : Any = (out_a - labels).mean()
loss_a.backward()
# compare the output and parameters gradients
self.assertTrue((loss - loss_a).abs() < 1E-5 )
_lowerCamelCase : Optional[int] = dict(model.named_parameters() )
_lowerCamelCase : Any = dict(model_a.named_parameters() )
for name, param in named_params.items():
self.assertTrue(torch_all_close(param.grad.data , named_params_a[name].grad.data , atol=5E-5 ) )
def SCREAMING_SNAKE_CASE ( self : str ):
"""simple docstring"""
_lowerCamelCase , _lowerCamelCase : List[Any] = AutoencoderKL.from_pretrained('''fusing/autoencoder-kl-dummy''' , output_loading_info=__lowerCAmelCase )
self.assertIsNotNone(__lowerCAmelCase )
self.assertEqual(len(loading_info['''missing_keys'''] ) , 0 )
model.to(__lowerCAmelCase )
_lowerCamelCase : Union[str, Any] = model(**self.dummy_input )
assert image is not None, "Make sure output is not None"
def SCREAMING_SNAKE_CASE ( self : List[Any] ):
"""simple docstring"""
_lowerCamelCase : List[Any] = AutoencoderKL.from_pretrained('''fusing/autoencoder-kl-dummy''' )
_lowerCamelCase : int = model.to(__lowerCAmelCase )
model.eval()
if torch_device == "mps":
_lowerCamelCase : int = torch.manual_seed(0 )
else:
_lowerCamelCase : str = torch.Generator(device=__lowerCAmelCase ).manual_seed(0 )
_lowerCamelCase : int = torch.randn(
1 , model.config.in_channels , model.config.sample_size , model.config.sample_size , generator=torch.manual_seed(0 ) , )
_lowerCamelCase : Optional[int] = image.to(__lowerCAmelCase )
with torch.no_grad():
_lowerCamelCase : str = model(__lowerCAmelCase , sample_posterior=__lowerCAmelCase , generator=__lowerCAmelCase ).sample
_lowerCamelCase : List[str] = output[0, -1, -3:, -3:].flatten().cpu()
# Since the VAE Gaussian prior's generator is seeded on the appropriate device,
# the expected output slices are not the same for CPU and GPU.
if torch_device == "mps":
_lowerCamelCase : str = torch.tensor(
[
-4.0078E-01,
-3.8323E-04,
-1.2681E-01,
-1.1462E-01,
2.0095E-01,
1.0893E-01,
-8.8247E-02,
-3.0361E-01,
-9.8644E-03,
] )
elif torch_device == "cpu":
_lowerCamelCase : Optional[int] = torch.tensor(
[-0.13_52, 0.08_78, 0.04_19, -0.08_18, -0.10_69, 0.06_88, -0.14_58, -0.44_46, -0.00_26] )
else:
_lowerCamelCase : List[str] = torch.tensor(
[-0.24_21, 0.46_42, 0.25_07, -0.04_38, 0.06_82, 0.31_60, -0.20_18, -0.07_27, 0.24_85] )
self.assertTrue(torch_all_close(__lowerCAmelCase , __lowerCAmelCase , rtol=1E-2 ) )
@slow
class __snake_case ( unittest.TestCase):
def SCREAMING_SNAKE_CASE ( self : Any , __lowerCAmelCase : Tuple , __lowerCAmelCase : Any ):
"""simple docstring"""
return f'''gaussian_noise_s={seed}_shape={"_".join([str(__lowerCAmelCase ) for s in shape] )}.npy'''
def SCREAMING_SNAKE_CASE ( self : str ):
"""simple docstring"""
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def SCREAMING_SNAKE_CASE ( self : int , __lowerCAmelCase : Dict=0 , __lowerCAmelCase : Tuple=(4, 3, 5_1_2, 5_1_2) , __lowerCAmelCase : Tuple=False ):
"""simple docstring"""
_lowerCamelCase : str = torch.floataa if fpaa else torch.floataa
_lowerCamelCase : List[Any] = torch.from_numpy(load_hf_numpy(self.get_file_format(__lowerCAmelCase , __lowerCAmelCase ) ) ).to(__lowerCAmelCase ).to(__lowerCAmelCase )
return image
def SCREAMING_SNAKE_CASE ( self : Dict , __lowerCAmelCase : List[str]="CompVis/stable-diffusion-v1-4" , __lowerCAmelCase : List[str]=False ):
"""simple docstring"""
_lowerCamelCase : Any = '''fp16''' if fpaa else None
_lowerCamelCase : str = torch.floataa if fpaa else torch.floataa
_lowerCamelCase : Any = AutoencoderKL.from_pretrained(
__lowerCAmelCase , subfolder='''vae''' , torch_dtype=__lowerCAmelCase , revision=__lowerCAmelCase , )
model.to(__lowerCAmelCase ).eval()
return model
def SCREAMING_SNAKE_CASE ( self : int , __lowerCAmelCase : int=0 ):
"""simple docstring"""
if torch_device == "mps":
return torch.manual_seed(__lowerCAmelCase )
return torch.Generator(device=__lowerCAmelCase ).manual_seed(__lowerCAmelCase )
@parameterized.expand(
[
# fmt: off
[3_3, [-0.16_03, 0.98_78, -0.04_95, -0.07_90, -0.27_09, 0.83_75, -0.20_60, -0.08_24], [-0.23_95, 0.00_98, 0.01_02, -0.07_09, -0.28_40, -0.02_74, -0.07_18, -0.18_24]],
[4_7, [-0.23_76, 0.11_68, 0.13_32, -0.48_40, -0.25_08, -0.07_91, -0.04_93, -0.40_89], [0.03_50, 0.08_47, 0.04_67, 0.03_44, -0.08_42, -0.05_47, -0.06_33, -0.11_31]],
# fmt: on
] )
def SCREAMING_SNAKE_CASE ( self : str , __lowerCAmelCase : Tuple , __lowerCAmelCase : List[Any] , __lowerCAmelCase : Union[str, Any] ):
"""simple docstring"""
_lowerCamelCase : Dict = self.get_sd_vae_model()
_lowerCamelCase : List[str] = self.get_sd_image(__lowerCAmelCase )
_lowerCamelCase : Optional[int] = self.get_generator(__lowerCAmelCase )
with torch.no_grad():
_lowerCamelCase : Any = model(__lowerCAmelCase , generator=__lowerCAmelCase , sample_posterior=__lowerCAmelCase ).sample
assert sample.shape == image.shape
_lowerCamelCase : Optional[int] = sample[-1, -2:, -2:, :2].flatten().float().cpu()
_lowerCamelCase : Tuple = torch.tensor(expected_slice_mps if torch_device == '''mps''' else expected_slice )
assert torch_all_close(__lowerCAmelCase , __lowerCAmelCase , atol=3E-3 )
@parameterized.expand(
[
# fmt: off
[3_3, [-0.05_13, 0.02_89, 1.37_99, 0.21_66, -0.25_73, -0.08_71, 0.51_03, -0.09_99]],
[4_7, [-0.41_28, -0.13_20, -0.37_04, 0.19_65, -0.41_16, -0.23_32, -0.33_40, 0.22_47]],
# fmt: on
] )
@require_torch_gpu
def SCREAMING_SNAKE_CASE ( self : Union[str, Any] , __lowerCAmelCase : Dict , __lowerCAmelCase : Tuple ):
"""simple docstring"""
_lowerCamelCase : List[str] = self.get_sd_vae_model(fpaa=__lowerCAmelCase )
_lowerCamelCase : int = self.get_sd_image(__lowerCAmelCase , fpaa=__lowerCAmelCase )
_lowerCamelCase : Optional[int] = self.get_generator(__lowerCAmelCase )
with torch.no_grad():
_lowerCamelCase : Optional[Any] = model(__lowerCAmelCase , generator=__lowerCAmelCase , sample_posterior=__lowerCAmelCase ).sample
assert sample.shape == image.shape
_lowerCamelCase : Union[str, Any] = sample[-1, -2:, :2, -2:].flatten().float().cpu()
_lowerCamelCase : Optional[Any] = torch.tensor(__lowerCAmelCase )
assert torch_all_close(__lowerCAmelCase , __lowerCAmelCase , atol=1E-2 )
@parameterized.expand(
[
# fmt: off
[3_3, [-0.16_09, 0.98_66, -0.04_87, -0.07_77, -0.27_16, 0.83_68, -0.20_55, -0.08_14], [-0.23_95, 0.00_98, 0.01_02, -0.07_09, -0.28_40, -0.02_74, -0.07_18, -0.18_24]],
[4_7, [-0.23_77, 0.11_47, 0.13_33, -0.48_41, -0.25_06, -0.08_05, -0.04_91, -0.40_85], [0.03_50, 0.08_47, 0.04_67, 0.03_44, -0.08_42, -0.05_47, -0.06_33, -0.11_31]],
# fmt: on
] )
def SCREAMING_SNAKE_CASE ( self : Optional[Any] , __lowerCAmelCase : Any , __lowerCAmelCase : Tuple , __lowerCAmelCase : Union[str, Any] ):
"""simple docstring"""
_lowerCamelCase : Union[str, Any] = self.get_sd_vae_model()
_lowerCamelCase : int = self.get_sd_image(__lowerCAmelCase )
with torch.no_grad():
_lowerCamelCase : List[str] = model(__lowerCAmelCase ).sample
assert sample.shape == image.shape
_lowerCamelCase : Optional[int] = sample[-1, -2:, -2:, :2].flatten().float().cpu()
_lowerCamelCase : Tuple = torch.tensor(expected_slice_mps if torch_device == '''mps''' else expected_slice )
assert torch_all_close(__lowerCAmelCase , __lowerCAmelCase , atol=3E-3 )
@parameterized.expand(
[
# fmt: off
[1_3, [-0.20_51, -0.18_03, -0.23_11, -0.21_14, -0.32_92, -0.35_74, -0.29_53, -0.33_23]],
[3_7, [-0.26_32, -0.26_25, -0.21_99, -0.27_41, -0.45_39, -0.49_90, -0.37_20, -0.49_25]],
# fmt: on
] )
@require_torch_gpu
def SCREAMING_SNAKE_CASE ( self : Optional[Any] , __lowerCAmelCase : Union[str, Any] , __lowerCAmelCase : Optional[Any] ):
"""simple docstring"""
_lowerCamelCase : Dict = self.get_sd_vae_model()
_lowerCamelCase : Any = self.get_sd_image(__lowerCAmelCase , shape=(3, 4, 6_4, 6_4) )
with torch.no_grad():
_lowerCamelCase : List[Any] = model.decode(__lowerCAmelCase ).sample
assert list(sample.shape ) == [3, 3, 5_1_2, 5_1_2]
_lowerCamelCase : Tuple = sample[-1, -2:, :2, -2:].flatten().cpu()
_lowerCamelCase : List[Any] = torch.tensor(__lowerCAmelCase )
assert torch_all_close(__lowerCAmelCase , __lowerCAmelCase , atol=1E-3 )
@parameterized.expand(
[
# fmt: off
[2_7, [-0.03_69, 0.02_07, -0.07_76, -0.06_82, -0.17_47, -0.19_30, -0.14_65, -0.20_39]],
[1_6, [-0.16_28, -0.21_34, -0.27_47, -0.26_42, -0.37_74, -0.44_04, -0.36_87, -0.42_77]],
# fmt: on
] )
@require_torch_gpu
def SCREAMING_SNAKE_CASE ( self : Optional[Any] , __lowerCAmelCase : Optional[int] , __lowerCAmelCase : Any ):
"""simple docstring"""
_lowerCamelCase : str = self.get_sd_vae_model(fpaa=__lowerCAmelCase )
_lowerCamelCase : int = self.get_sd_image(__lowerCAmelCase , shape=(3, 4, 6_4, 6_4) , fpaa=__lowerCAmelCase )
with torch.no_grad():
_lowerCamelCase : Dict = model.decode(__lowerCAmelCase ).sample
assert list(sample.shape ) == [3, 3, 5_1_2, 5_1_2]
_lowerCamelCase : Dict = sample[-1, -2:, :2, -2:].flatten().float().cpu()
_lowerCamelCase : Union[str, Any] = torch.tensor(__lowerCAmelCase )
assert torch_all_close(__lowerCAmelCase , __lowerCAmelCase , atol=5E-3 )
@parameterized.expand([(1_3,), (1_6,), (2_7,)] )
@require_torch_gpu
@unittest.skipIf(not is_xformers_available() , reason='''xformers is not required when using PyTorch 2.0.''' )
def SCREAMING_SNAKE_CASE ( self : Any , __lowerCAmelCase : Optional[Any] ):
"""simple docstring"""
_lowerCamelCase : str = self.get_sd_vae_model(fpaa=__lowerCAmelCase )
_lowerCamelCase : Union[str, Any] = self.get_sd_image(__lowerCAmelCase , shape=(3, 4, 6_4, 6_4) , fpaa=__lowerCAmelCase )
with torch.no_grad():
_lowerCamelCase : List[str] = model.decode(__lowerCAmelCase ).sample
model.enable_xformers_memory_efficient_attention()
with torch.no_grad():
_lowerCamelCase : Optional[int] = model.decode(__lowerCAmelCase ).sample
assert list(sample.shape ) == [3, 3, 5_1_2, 5_1_2]
assert torch_all_close(__lowerCAmelCase , __lowerCAmelCase , atol=1E-1 )
@parameterized.expand([(1_3,), (1_6,), (3_7,)] )
@require_torch_gpu
@unittest.skipIf(not is_xformers_available() , reason='''xformers is not required when using PyTorch 2.0.''' )
def SCREAMING_SNAKE_CASE ( self : Optional[int] , __lowerCAmelCase : List[Any] ):
"""simple docstring"""
_lowerCamelCase : Dict = self.get_sd_vae_model()
_lowerCamelCase : List[str] = self.get_sd_image(__lowerCAmelCase , shape=(3, 4, 6_4, 6_4) )
with torch.no_grad():
_lowerCamelCase : Optional[int] = model.decode(__lowerCAmelCase ).sample
model.enable_xformers_memory_efficient_attention()
with torch.no_grad():
_lowerCamelCase : Any = model.decode(__lowerCAmelCase ).sample
assert list(sample.shape ) == [3, 3, 5_1_2, 5_1_2]
assert torch_all_close(__lowerCAmelCase , __lowerCAmelCase , atol=1E-2 )
@parameterized.expand(
[
# fmt: off
[3_3, [-0.30_01, 0.09_18, -2.69_84, -3.97_20, -3.20_99, -5.03_53, 1.73_38, -0.20_65, 3.42_67]],
[4_7, [-1.50_30, -4.38_71, -6.03_55, -9.11_57, -1.66_61, -2.78_53, 2.16_07, -5.08_23, 2.56_33]],
# fmt: on
] )
def SCREAMING_SNAKE_CASE ( self : Any , __lowerCAmelCase : int , __lowerCAmelCase : Optional[Any] ):
"""simple docstring"""
_lowerCamelCase : Optional[int] = self.get_sd_vae_model()
_lowerCamelCase : Optional[Any] = self.get_sd_image(__lowerCAmelCase )
_lowerCamelCase : List[str] = self.get_generator(__lowerCAmelCase )
with torch.no_grad():
_lowerCamelCase : Tuple = model.encode(__lowerCAmelCase ).latent_dist
_lowerCamelCase : Tuple = dist.sample(generator=__lowerCAmelCase )
assert list(sample.shape ) == [image.shape[0], 4] + [i // 8 for i in image.shape[2:]]
_lowerCamelCase : Optional[Any] = sample[0, -1, -3:, -3:].flatten().cpu()
_lowerCamelCase : str = torch.tensor(__lowerCAmelCase )
_lowerCamelCase : Optional[int] = 3E-3 if torch_device != '''mps''' else 1E-2
assert torch_all_close(__lowerCAmelCase , __lowerCAmelCase , atol=__lowerCAmelCase )
| 83 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__lowerCamelCase : str = logging.get_logger(__name__)
__lowerCamelCase : Tuple = {
'''bigcode/gpt_bigcode-santacoder''': '''https://huggingface.co/bigcode/gpt_bigcode-santacoder/resolve/main/config.json''',
}
class A_ (a_ ):
"""simple docstring"""
a__ = '''gpt_bigcode'''
a__ = ['''past_key_values''']
a__ = {
'''hidden_size''': '''n_embd''',
'''max_position_embeddings''': '''n_positions''',
'''num_attention_heads''': '''n_head''',
'''num_hidden_layers''': '''n_layer''',
}
def __init__( self :List[Any] , lowerCAmelCase__ :Any=50_257 , lowerCAmelCase__ :Dict=1_024 , lowerCAmelCase__ :Optional[int]=768 , lowerCAmelCase__ :Tuple=12 , lowerCAmelCase__ :int=12 , lowerCAmelCase__ :Optional[Any]=None , lowerCAmelCase__ :List[str]="gelu_pytorch_tanh" , lowerCAmelCase__ :Tuple=0.1 , lowerCAmelCase__ :Tuple=0.1 , lowerCAmelCase__ :str=0.1 , lowerCAmelCase__ :Any=1E-5 , lowerCAmelCase__ :Union[str, Any]=0.0_2 , lowerCAmelCase__ :Union[str, Any]=True , lowerCAmelCase__ :Optional[Any]=True , lowerCAmelCase__ :int=50_256 , lowerCAmelCase__ :List[str]=50_256 , lowerCAmelCase__ :List[Any]=True , lowerCAmelCase__ :str=True , lowerCAmelCase__ :int=True , **lowerCAmelCase__ :Union[str, Any] , ) -> Any:
'''simple docstring'''
snake_case_ : List[Any] = vocab_size
snake_case_ : Any = n_positions
snake_case_ : Any = n_embd
snake_case_ : Optional[Any] = n_layer
snake_case_ : List[Any] = n_head
snake_case_ : Tuple = n_inner
snake_case_ : str = activation_function
snake_case_ : Union[str, Any] = resid_pdrop
snake_case_ : Optional[Any] = embd_pdrop
snake_case_ : Any = attn_pdrop
snake_case_ : List[Any] = layer_norm_epsilon
snake_case_ : Tuple = initializer_range
snake_case_ : int = scale_attn_weights
snake_case_ : Union[str, Any] = use_cache
snake_case_ : Dict = attention_softmax_in_fpaa
snake_case_ : Any = scale_attention_softmax_in_fpaa
snake_case_ : List[str] = multi_query
snake_case_ : List[str] = bos_token_id
snake_case_ : Any = eos_token_id
super().__init__(bos_token_id=lowerCAmelCase__ , eos_token_id=lowerCAmelCase__ , **lowerCAmelCase__ )
| 653 | 0 |
import argparse
import hashlib # hashlib is only used inside the Test class
import struct
class A_ :
'''simple docstring'''
def __init__( self , snake_case ):
lowercase = data
lowercase = [0X6_7_4_5_2_3_0_1, 0Xe_f_c_d_a_b_8_9, 0X9_8_b_a_d_c_f_e, 0X1_0_3_2_5_4_7_6, 0Xc_3_d_2_e_1_f_0]
@staticmethod
def SCREAMING_SNAKE_CASE__ ( snake_case , snake_case ):
return ((n << b) | (n >> (32 - b))) & 0Xf_f_f_f_f_f_f_f
def SCREAMING_SNAKE_CASE__ ( self ):
lowercase = B'\x80' + B'\x00' * (63 - (len(self.data ) + 8) % 64)
lowercase = self.data + padding + struct.pack('>Q' , 8 * len(self.data ) )
return padded_data
def SCREAMING_SNAKE_CASE__ ( self ):
return [
self.padded_data[i : i + 64] for i in range(0 , len(self.padded_data ) , 64 )
]
def SCREAMING_SNAKE_CASE__ ( self , snake_case ):
lowercase = list(struct.unpack('>16L' , snake_case ) ) + [0] * 64
for i in range(16 , 80 ):
lowercase = self.rotate((w[i - 3] ^ w[i - 8] ^ w[i - 14] ^ w[i - 16]) , 1 )
return w
def SCREAMING_SNAKE_CASE__ ( self ):
lowercase = self.padding()
lowercase = self.split_blocks()
for block in self.blocks:
lowercase = self.expand_block(snake_case )
lowercase , lowercase , lowercase , lowercase , lowercase = self.h
for i in range(0 , 80 ):
if 0 <= i < 20:
lowercase = (b & c) | ((~b) & d)
lowercase = 0X5_a_8_2_7_9_9_9
elif 20 <= i < 40:
lowercase = b ^ c ^ d
lowercase = 0X6_e_d_9_e_b_a_1
elif 40 <= i < 60:
lowercase = (b & c) | (b & d) | (c & d)
lowercase = 0X8_f_1_b_b_c_d_c
elif 60 <= i < 80:
lowercase = b ^ c ^ d
lowercase = 0Xc_a_6_2_c_1_d_6
lowercase , lowercase , lowercase , lowercase , lowercase = (
self.rotate(snake_case , 5 ) + f + e + k + expanded_block[i] & 0Xf_f_f_f_f_f_f_f,
a,
self.rotate(snake_case , 30 ),
c,
d,
)
lowercase = (
self.h[0] + a & 0Xf_f_f_f_f_f_f_f,
self.h[1] + b & 0Xf_f_f_f_f_f_f_f,
self.h[2] + c & 0Xf_f_f_f_f_f_f_f,
self.h[3] + d & 0Xf_f_f_f_f_f_f_f,
self.h[4] + e & 0Xf_f_f_f_f_f_f_f,
)
return ("{:08x}" * 5).format(*self.h )
def UpperCAmelCase_ ( ):
lowercase = b'Test String'
assert SHAaHash(__SCREAMING_SNAKE_CASE ).final_hash() == hashlib.shaa(__SCREAMING_SNAKE_CASE ).hexdigest() # noqa: S324
def UpperCAmelCase_ ( ):
lowercase = argparse.ArgumentParser(description='Process some strings or files' )
parser.add_argument(
'--string' , dest='input_string' , default='Hello World!! Welcome to Cryptography' , help='Hash the string' , )
parser.add_argument('--file' , dest='input_file' , help='Hash contents of a file' )
lowercase = parser.parse_args()
lowercase = args.input_string
# In any case hash input should be a bytestring
if args.input_file:
with open(args.input_file , 'rb' ) as f:
lowercase = f.read()
else:
lowercase = bytes(__SCREAMING_SNAKE_CASE , 'utf-8' )
print(SHAaHash(__SCREAMING_SNAKE_CASE ).final_hash() )
if __name__ == "__main__":
main()
import doctest
doctest.testmod()
| 84 |
'''simple docstring'''
import json
import logging
import os
import socket
import git
import numpy as np
import torch
logging.basicConfig(
format='''%(asctime)s - %(levelname)s - %(name)s - PID: %(process)d - %(message)s''',
datefmt='''%m/%d/%Y %H:%M:%S''',
level=logging.INFO,
)
__lowerCamelCase : Union[str, Any] = logging.getLogger(__name__)
def __UpperCAmelCase ( __magic_name__ )-> str:
"""simple docstring"""
snake_case_ : Dict = git.Repo(search_parent_directories=__magic_name__ )
snake_case_ : Optional[int] = {
"repo_id": str(__magic_name__ ),
"repo_sha": str(repo.head.object.hexsha ),
"repo_branch": str(repo.active_branch ),
}
with open(os.path.join(__magic_name__ ,"git_log.json" ) ,"w" ) as f:
json.dump(__magic_name__ ,__magic_name__ ,indent=4 )
def __UpperCAmelCase ( __magic_name__ )-> Tuple:
"""simple docstring"""
if params.n_gpu <= 0:
snake_case_ : Any = 0
snake_case_ : Any = -1
snake_case_ : Tuple = True
snake_case_ : List[str] = False
return
assert torch.cuda.is_available()
logger.info("Initializing GPUs" )
if params.n_gpu > 1:
assert params.local_rank != -1
snake_case_ : Optional[int] = int(os.environ["WORLD_SIZE"] )
snake_case_ : int = int(os.environ["N_GPU_NODE"] )
snake_case_ : Any = int(os.environ["RANK"] )
# number of nodes / node ID
snake_case_ : Dict = params.world_size // params.n_gpu_per_node
snake_case_ : Optional[int] = params.global_rank // params.n_gpu_per_node
snake_case_ : Tuple = True
assert params.n_nodes == int(os.environ["N_NODES"] )
assert params.node_id == int(os.environ["NODE_RANK"] )
# local job (single GPU)
else:
assert params.local_rank == -1
snake_case_ : Optional[int] = 1
snake_case_ : str = 0
snake_case_ : List[Any] = 0
snake_case_ : int = 0
snake_case_ : Dict = 1
snake_case_ : Optional[Any] = 1
snake_case_ : str = False
# sanity checks
assert params.n_nodes >= 1
assert 0 <= params.node_id < params.n_nodes
assert 0 <= params.local_rank <= params.global_rank < params.world_size
assert params.world_size == params.n_nodes * params.n_gpu_per_node
# define whether this is the master process / if we are in multi-node distributed mode
snake_case_ : str = params.node_id == 0 and params.local_rank == 0
snake_case_ : str = params.n_nodes > 1
# summary
snake_case_ : str = F'''--- Global rank: {params.global_rank} - '''
logger.info(PREFIX + "Number of nodes: %i" % params.n_nodes )
logger.info(PREFIX + "Node ID : %i" % params.node_id )
logger.info(PREFIX + "Local rank : %i" % params.local_rank )
logger.info(PREFIX + "World size : %i" % params.world_size )
logger.info(PREFIX + "GPUs per node : %i" % params.n_gpu_per_node )
logger.info(PREFIX + "Master : %s" % str(params.is_master ) )
logger.info(PREFIX + "Multi-node : %s" % str(params.multi_node ) )
logger.info(PREFIX + "Multi-GPU : %s" % str(params.multi_gpu ) )
logger.info(PREFIX + "Hostname : %s" % socket.gethostname() )
# set GPU device
torch.cuda.set_device(params.local_rank )
# initialize multi-GPU
if params.multi_gpu:
logger.info("Initializing PyTorch distributed" )
torch.distributed.init_process_group(
init_method="env://" ,backend="nccl" ,)
def __UpperCAmelCase ( __magic_name__ )-> Dict:
"""simple docstring"""
np.random.seed(args.seed )
torch.manual_seed(args.seed )
if args.n_gpu > 0:
torch.cuda.manual_seed_all(args.seed )
| 653 | 0 |
from __future__ import annotations
import unittest
from transformers import BlenderbotSmallConfig, BlenderbotSmallTokenizer, is_tf_available
from transformers.testing_utils import require_tf, require_tokenizers, slow
from transformers.utils import cached_property
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import tensorflow as tf
from transformers import TFAutoModelForSeqaSeqLM, TFBlenderbotSmallForConditionalGeneration, TFBlenderbotSmallModel
@require_tf
class snake_case :
lowercase_ = BlenderbotSmallConfig
lowercase_ = {}
lowercase_ = 'gelu'
def __init__( self : List[Any] , a_ : int , a_ : Any=13 , a_ : List[str]=7 , a_ : Optional[int]=True , a_ : Tuple=False , a_ : Optional[int]=99 , a_ : Tuple=32 , a_ : Union[str, Any]=2 , a_ : Union[str, Any]=4 , a_ : str=37 , a_ : List[Any]=0.1 , a_ : int=0.1 , a_ : List[Any]=20 , a_ : Optional[Any]=2 , a_ : List[str]=1 , a_ : Tuple=0 , )-> int:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : int = parent
SCREAMING_SNAKE_CASE__ : Tuple = batch_size
SCREAMING_SNAKE_CASE__ : Optional[int] = seq_length
SCREAMING_SNAKE_CASE__ : Optional[Any] = is_training
SCREAMING_SNAKE_CASE__ : Tuple = use_labels
SCREAMING_SNAKE_CASE__ : List[Any] = vocab_size
SCREAMING_SNAKE_CASE__ : List[Any] = hidden_size
SCREAMING_SNAKE_CASE__ : List[Any] = num_hidden_layers
SCREAMING_SNAKE_CASE__ : int = num_attention_heads
SCREAMING_SNAKE_CASE__ : List[Any] = intermediate_size
SCREAMING_SNAKE_CASE__ : Tuple = hidden_dropout_prob
SCREAMING_SNAKE_CASE__ : Union[str, Any] = attention_probs_dropout_prob
SCREAMING_SNAKE_CASE__ : List[Any] = max_position_embeddings
SCREAMING_SNAKE_CASE__ : Optional[int] = eos_token_id
SCREAMING_SNAKE_CASE__ : Any = pad_token_id
SCREAMING_SNAKE_CASE__ : Optional[int] = bos_token_id
def __lowercase( self : Optional[int] )-> Union[str, Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Dict = ids_tensor([self.batch_size, self.seq_length - 1] , self.vocab_size )
SCREAMING_SNAKE_CASE__ : Tuple = tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size ) , 1 )
SCREAMING_SNAKE_CASE__ : Optional[Any] = tf.concat([input_ids, eos_tensor] , axis=1 )
SCREAMING_SNAKE_CASE__ : List[Any] = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
SCREAMING_SNAKE_CASE__ : List[Any] = self.config_cls(
vocab_size=self.vocab_size , d_model=self.hidden_size , encoder_layers=self.num_hidden_layers , decoder_layers=self.num_hidden_layers , encoder_attention_heads=self.num_attention_heads , decoder_attention_heads=self.num_attention_heads , encoder_ffn_dim=self.intermediate_size , decoder_ffn_dim=self.intermediate_size , dropout=self.hidden_dropout_prob , attention_dropout=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , eos_token_ids=[2] , bos_token_id=self.bos_token_id , pad_token_id=self.pad_token_id , decoder_start_token_id=self.pad_token_id , **self.config_updates , )
SCREAMING_SNAKE_CASE__ : List[Any] = prepare_blenderbot_small_inputs_dict(a_ , a_ , a_ )
return config, inputs_dict
def __lowercase( self : Dict , a_ : Dict , a_ : str )-> Union[str, Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : List[Any] = TFBlenderbotSmallModel(config=a_ ).get_decoder()
SCREAMING_SNAKE_CASE__ : Optional[int] = inputs_dict['input_ids']
SCREAMING_SNAKE_CASE__ : Union[str, Any] = input_ids[:1, :]
SCREAMING_SNAKE_CASE__ : List[str] = inputs_dict['attention_mask'][:1, :]
SCREAMING_SNAKE_CASE__ : Any = inputs_dict['head_mask']
SCREAMING_SNAKE_CASE__ : str = 1
# first forward pass
SCREAMING_SNAKE_CASE__ : Optional[int] = model(a_ , attention_mask=a_ , head_mask=a_ , use_cache=a_ )
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : List[Any] = outputs.to_tuple()
# create hypothetical next token and extent to next_input_ids
SCREAMING_SNAKE_CASE__ : Optional[Any] = ids_tensor((self.batch_size, 3) , config.vocab_size )
SCREAMING_SNAKE_CASE__ : List[Any] = tf.cast(ids_tensor((self.batch_size, 3) , 2 ) , tf.inta )
# append to next input_ids and
SCREAMING_SNAKE_CASE__ : List[str] = tf.concat([input_ids, next_tokens] , axis=-1 )
SCREAMING_SNAKE_CASE__ : Optional[int] = tf.concat([attention_mask, next_attn_mask] , axis=-1 )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = model(a_ , attention_mask=a_ )[0]
SCREAMING_SNAKE_CASE__ : int = model(a_ , attention_mask=a_ , past_key_values=a_ )[0]
self.parent.assertEqual(next_tokens.shape[1] , output_from_past.shape[1] )
# select random slice
SCREAMING_SNAKE_CASE__ : Optional[Any] = int(ids_tensor((1,) , output_from_past.shape[-1] ) )
SCREAMING_SNAKE_CASE__ : Any = output_from_no_past[:, -3:, random_slice_idx]
SCREAMING_SNAKE_CASE__ : Union[str, Any] = output_from_past[:, :, random_slice_idx]
# test that outputs are equal for slice
tf.debugging.assert_near(a_ , a_ , rtol=1e-3 )
def _a ( lowercase__ : Any , lowercase__ : Any , lowercase__ : List[str] , lowercase__ : Any=None , lowercase__ : str=None , lowercase__ : int=None , lowercase__ : str=None , lowercase__ : Tuple=None , ):
'''simple docstring'''
if attention_mask is None:
SCREAMING_SNAKE_CASE__ : Union[str, Any] = tf.cast(tf.math.not_equal(lowercase__ , config.pad_token_id ) , tf.inta )
if decoder_attention_mask is None:
SCREAMING_SNAKE_CASE__ : List[Any] = tf.concat(
[
tf.ones(decoder_input_ids[:, :1].shape , dtype=tf.inta ),
tf.cast(tf.math.not_equal(decoder_input_ids[:, 1:] , config.pad_token_id ) , tf.inta ),
] , axis=-1 , )
if head_mask is None:
SCREAMING_SNAKE_CASE__ : Tuple = tf.ones((config.encoder_layers, config.encoder_attention_heads) )
if decoder_head_mask is None:
SCREAMING_SNAKE_CASE__ : List[str] = tf.ones((config.decoder_layers, config.decoder_attention_heads) )
if cross_attn_head_mask is None:
SCREAMING_SNAKE_CASE__ : Tuple = tf.ones((config.decoder_layers, config.decoder_attention_heads) )
return {
"input_ids": input_ids,
"decoder_input_ids": decoder_input_ids,
"attention_mask": attention_mask,
"decoder_attention_mask": decoder_attention_mask,
"head_mask": head_mask,
"decoder_head_mask": decoder_head_mask,
"cross_attn_head_mask": cross_attn_head_mask,
}
@require_tf
class snake_case ( UpperCamelCase_ , UpperCamelCase_ , unittest.TestCase ):
lowercase_ = (
(TFBlenderbotSmallForConditionalGeneration, TFBlenderbotSmallModel) if is_tf_available() else ()
)
lowercase_ = (TFBlenderbotSmallForConditionalGeneration,) if is_tf_available() else ()
lowercase_ = (
{
'conversational': TFBlenderbotSmallForConditionalGeneration,
'feature-extraction': TFBlenderbotSmallModel,
'summarization': TFBlenderbotSmallForConditionalGeneration,
'text2text-generation': TFBlenderbotSmallForConditionalGeneration,
'translation': TFBlenderbotSmallForConditionalGeneration,
}
if is_tf_available()
else {}
)
lowercase_ = True
lowercase_ = False
lowercase_ = False
def __lowercase( self : Any )-> Optional[int]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Optional[Any] = TFBlenderbotSmallModelTester(self )
SCREAMING_SNAKE_CASE__ : Dict = ConfigTester(self , config_class=a_ )
def __lowercase( self : Optional[Any] )-> Dict:
"""simple docstring"""
self.config_tester.run_common_tests()
def __lowercase( self : List[str] )-> Optional[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Tuple = self.model_tester.prepare_config_and_inputs_for_common()
self.model_tester.check_decoder_model_past_large_inputs(*a_ )
@require_tokenizers
@require_tf
class snake_case ( unittest.TestCase ):
lowercase_ = [
'Social anxiety\nWow, I am never shy. Do you have anxiety?\nYes. I end up sweating and blushing and feel like '
' i\'m going to throw up.\nand why is that?'
]
lowercase_ = 'facebook/blenderbot_small-90M'
@cached_property
def __lowercase( self : int )-> Any:
"""simple docstring"""
# use "old" tokenizer here because of bug when downloading new tokenizer
return BlenderbotSmallTokenizer.from_pretrained('facebook/blenderbot-90M' )
@cached_property
def __lowercase( self : Optional[int] )-> Optional[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : List[Any] = TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name )
return model
@slow
def __lowercase( self : str )-> List[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Any = self.tokenizer(self.src_text , return_tensors='tf' )
SCREAMING_SNAKE_CASE__ : int = self.model.generate(
model_inputs.input_ids , attention_mask=model_inputs.attention_mask , num_beams=2 , use_cache=a_ , )
SCREAMING_SNAKE_CASE__ : Dict = self.tokenizer.batch_decode(generated_ids.numpy() , skip_special_tokens=a_ )[0]
assert generated_words in (
"i don't know. i just feel like i'm going to throw up. it's not fun.",
"i'm not sure. i just feel like i've been feeling like i have to be in a certain place",
"i'm not sure. i just feel like i've been in a bad situation.",
)
| 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 YolosImageProcessor
class A_ (unittest.TestCase ):
"""simple docstring"""
def __init__( self :Any , lowerCAmelCase__ :Dict , lowerCAmelCase__ :Dict=7 , lowerCAmelCase__ :Union[str, Any]=3 , lowerCAmelCase__ :List[str]=30 , lowerCAmelCase__ :List[str]=400 , lowerCAmelCase__ :Optional[Any]=True , lowerCAmelCase__ :Dict=None , lowerCAmelCase__ :str=True , lowerCAmelCase__ :Optional[int]=[0.5, 0.5, 0.5] , lowerCAmelCase__ :Optional[int]=[0.5, 0.5, 0.5] , lowerCAmelCase__ :str=True , lowerCAmelCase__ :int=1 / 255 , lowerCAmelCase__ :int=True , ) -> str:
'''simple docstring'''
snake_case_ : List[Any] = size if size is not None else {"shortest_edge": 18, "longest_edge": 1_333}
snake_case_ : Dict = parent
snake_case_ : Union[str, Any] = batch_size
snake_case_ : Optional[Any] = num_channels
snake_case_ : str = min_resolution
snake_case_ : Dict = max_resolution
snake_case_ : Optional[Any] = do_resize
snake_case_ : str = size
snake_case_ : Optional[int] = do_normalize
snake_case_ : Dict = image_mean
snake_case_ : Optional[int] = image_std
snake_case_ : List[str] = do_rescale
snake_case_ : Dict = rescale_factor
snake_case_ : str = do_pad
def _A ( self :List[Any] ) -> Dict:
'''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 _A ( self :Dict , lowerCAmelCase__ :List[str] , lowerCAmelCase__ :List[str]=False ) -> str:
'''simple docstring'''
if not batched:
snake_case_ : List[str] = image_inputs[0]
if isinstance(lowerCAmelCase__ , Image.Image ):
snake_case_, snake_case_ : int = image.size
else:
snake_case_, snake_case_ : Any = image.shape[1], image.shape[2]
if w < h:
snake_case_ : int = int(self.size["shortest_edge"] * h / w )
snake_case_ : List[Any] = self.size["shortest_edge"]
elif w > h:
snake_case_ : Optional[int] = self.size["shortest_edge"]
snake_case_ : str = int(self.size["shortest_edge"] * w / h )
else:
snake_case_ : Tuple = self.size["shortest_edge"]
snake_case_ : Dict = self.size["shortest_edge"]
else:
snake_case_ : List[str] = []
for image in image_inputs:
snake_case_, snake_case_ : Any = self.get_expected_values([image] )
expected_values.append((expected_height, expected_width) )
snake_case_ : str = max(lowerCAmelCase__ , key=lambda lowerCAmelCase__ : item[0] )[0]
snake_case_ : int = max(lowerCAmelCase__ , key=lambda lowerCAmelCase__ : item[1] )[1]
return expected_height, expected_width
@require_torch
@require_vision
class A_ (a_ , unittest.TestCase ):
"""simple docstring"""
a__ = YolosImageProcessor if is_vision_available() else None
def _A ( self :Optional[Any] ) -> str:
'''simple docstring'''
snake_case_ : int = YolosImageProcessingTester(self )
@property
def _A ( self :List[str] ) -> Any:
'''simple docstring'''
return self.image_processor_tester.prepare_image_processor_dict()
def _A ( self :List[str] ) -> Union[str, Any]:
'''simple docstring'''
snake_case_ : Tuple = self.image_processing_class(**self.image_processor_dict )
self.assertTrue(hasattr(lowerCAmelCase__ , "image_mean" ) )
self.assertTrue(hasattr(lowerCAmelCase__ , "image_std" ) )
self.assertTrue(hasattr(lowerCAmelCase__ , "do_normalize" ) )
self.assertTrue(hasattr(lowerCAmelCase__ , "do_resize" ) )
self.assertTrue(hasattr(lowerCAmelCase__ , "size" ) )
def _A ( self :List[Any] ) -> Any:
'''simple docstring'''
snake_case_ : List[Any] = self.image_processing_class.from_dict(self.image_processor_dict )
self.assertEqual(image_processor.size , {"shortest_edge": 18, "longest_edge": 1_333} )
self.assertEqual(image_processor.do_pad , lowerCAmelCase__ )
snake_case_ : Optional[int] = self.image_processing_class.from_dict(
self.image_processor_dict , size=42 , max_size=84 , pad_and_return_pixel_mask=lowerCAmelCase__ )
self.assertEqual(image_processor.size , {"shortest_edge": 42, "longest_edge": 84} )
self.assertEqual(image_processor.do_pad , lowerCAmelCase__ )
def _A ( self :List[str] ) -> int:
'''simple docstring'''
pass
def _A ( self :Optional[Any] ) -> Optional[Any]:
'''simple docstring'''
snake_case_ : Optional[int] = self.image_processing_class(**self.image_processor_dict )
# create random PIL images
snake_case_ : List[Any] = prepare_image_inputs(self.image_processor_tester , equal_resolution=lowerCAmelCase__ )
for image in image_inputs:
self.assertIsInstance(lowerCAmelCase__ , Image.Image )
# Test not batched input
snake_case_ : Optional[Any] = image_processing(image_inputs[0] , return_tensors="pt" ).pixel_values
snake_case_, snake_case_ : int = self.image_processor_tester.get_expected_values(lowerCAmelCase__ )
self.assertEqual(
encoded_images.shape , (1, self.image_processor_tester.num_channels, expected_height, expected_width) , )
# Test batched
snake_case_, snake_case_ : Any = self.image_processor_tester.get_expected_values(lowerCAmelCase__ , batched=lowerCAmelCase__ )
snake_case_ : Any = image_processing(lowerCAmelCase__ , return_tensors="pt" ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
expected_height,
expected_width,
) , )
def _A ( self :Dict ) -> Dict:
'''simple docstring'''
snake_case_ : str = self.image_processing_class(**self.image_processor_dict )
# create random numpy tensors
snake_case_ : Any = prepare_image_inputs(self.image_processor_tester , equal_resolution=lowerCAmelCase__ , numpify=lowerCAmelCase__ )
for image in image_inputs:
self.assertIsInstance(lowerCAmelCase__ , np.ndarray )
# Test not batched input
snake_case_ : Union[str, Any] = image_processing(image_inputs[0] , return_tensors="pt" ).pixel_values
snake_case_, snake_case_ : List[Any] = self.image_processor_tester.get_expected_values(lowerCAmelCase__ )
self.assertEqual(
encoded_images.shape , (1, self.image_processor_tester.num_channels, expected_height, expected_width) , )
# Test batched
snake_case_ : Tuple = image_processing(lowerCAmelCase__ , return_tensors="pt" ).pixel_values
snake_case_, snake_case_ : Dict = self.image_processor_tester.get_expected_values(lowerCAmelCase__ , batched=lowerCAmelCase__ )
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
expected_height,
expected_width,
) , )
def _A ( self :Tuple ) -> Tuple:
'''simple docstring'''
snake_case_ : List[str] = self.image_processing_class(**self.image_processor_dict )
# create random PyTorch tensors
snake_case_ : str = prepare_image_inputs(self.image_processor_tester , equal_resolution=lowerCAmelCase__ , torchify=lowerCAmelCase__ )
for image in image_inputs:
self.assertIsInstance(lowerCAmelCase__ , torch.Tensor )
# Test not batched input
snake_case_ : List[str] = image_processing(image_inputs[0] , return_tensors="pt" ).pixel_values
snake_case_, snake_case_ : Any = self.image_processor_tester.get_expected_values(lowerCAmelCase__ )
self.assertEqual(
encoded_images.shape , (1, self.image_processor_tester.num_channels, expected_height, expected_width) , )
# Test batched
snake_case_ : List[Any] = image_processing(lowerCAmelCase__ , return_tensors="pt" ).pixel_values
snake_case_, snake_case_ : Any = self.image_processor_tester.get_expected_values(lowerCAmelCase__ , batched=lowerCAmelCase__ )
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
expected_height,
expected_width,
) , )
def _A ( self :Tuple ) -> Dict:
'''simple docstring'''
snake_case_ : str = self.image_processing_class(**self.image_processor_dict )
snake_case_ : List[Any] = self.image_processing_class(do_resize=lowerCAmelCase__ , do_normalize=lowerCAmelCase__ , do_rescale=lowerCAmelCase__ )
# create random PyTorch tensors
snake_case_ : Tuple = prepare_image_inputs(self.image_processor_tester , equal_resolution=lowerCAmelCase__ , torchify=lowerCAmelCase__ )
for image in image_inputs:
self.assertIsInstance(lowerCAmelCase__ , torch.Tensor )
# Test whether the method "pad" and calling the image processor return the same tensors
snake_case_ : Tuple = image_processing_a.pad(lowerCAmelCase__ , return_tensors="pt" )
snake_case_ : Union[str, Any] = image_processing_a(lowerCAmelCase__ , return_tensors="pt" )
self.assertTrue(
torch.allclose(encoded_images_with_method["pixel_values"] , encoded_images["pixel_values"] , atol=1E-4 ) )
@slow
def _A ( self :str ) -> Any:
'''simple docstring'''
snake_case_ : List[str] = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png" )
with open("./tests/fixtures/tests_samples/COCO/coco_annotations.txt" , "r" ) as f:
snake_case_ : int = json.loads(f.read() )
snake_case_ : Optional[int] = {"image_id": 39_769, "annotations": target}
# encode them
snake_case_ : Tuple = YolosImageProcessor.from_pretrained("hustvl/yolos-small" )
snake_case_ : Dict = image_processing(images=lowerCAmelCase__ , annotations=lowerCAmelCase__ , return_tensors="pt" )
# verify pixel values
snake_case_ : Optional[int] = torch.Size([1, 3, 800, 1_066] )
self.assertEqual(encoding["pixel_values"].shape , lowerCAmelCase__ )
snake_case_ : str = torch.tensor([0.2_7_9_6, 0.3_1_3_8, 0.3_4_8_1] )
self.assertTrue(torch.allclose(encoding["pixel_values"][0, 0, 0, :3] , lowerCAmelCase__ , atol=1E-4 ) )
# verify area
snake_case_ : Dict = torch.tensor([5_8_8_7.9_6_0_0, 1_1_2_5_0.2_0_6_1, 4_8_9_3_5_3.8_4_3_8, 8_3_7_1_2_2.7_5_0_0, 1_4_7_9_6_7.5_1_5_6, 1_6_5_7_3_2.3_4_3_8] )
self.assertTrue(torch.allclose(encoding["labels"][0]["area"] , lowerCAmelCase__ ) )
# verify boxes
snake_case_ : Optional[int] = torch.Size([6, 4] )
self.assertEqual(encoding["labels"][0]["boxes"].shape , lowerCAmelCase__ )
snake_case_ : Any = torch.tensor([0.5_5_0_3, 0.2_7_6_5, 0.0_6_0_4, 0.2_2_1_5] )
self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"][0] , lowerCAmelCase__ , atol=1E-3 ) )
# verify image_id
snake_case_ : Dict = torch.tensor([39_769] )
self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"] , lowerCAmelCase__ ) )
# verify is_crowd
snake_case_ : int = torch.tensor([0, 0, 0, 0, 0, 0] )
self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"] , lowerCAmelCase__ ) )
# verify class_labels
snake_case_ : List[str] = torch.tensor([75, 75, 63, 65, 17, 17] )
self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"] , lowerCAmelCase__ ) )
# verify orig_size
snake_case_ : Any = torch.tensor([480, 640] )
self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"] , lowerCAmelCase__ ) )
# verify size
snake_case_ : List[Any] = torch.tensor([800, 1_066] )
self.assertTrue(torch.allclose(encoding["labels"][0]["size"] , lowerCAmelCase__ ) )
@slow
def _A ( self :Dict ) -> int:
'''simple docstring'''
snake_case_ : Tuple = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png" )
with open("./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt" , "r" ) as f:
snake_case_ : Optional[int] = json.loads(f.read() )
snake_case_ : Tuple = {"file_name": "000000039769.png", "image_id": 39_769, "segments_info": target}
snake_case_ : Any = pathlib.Path("./tests/fixtures/tests_samples/COCO/coco_panoptic" )
# encode them
snake_case_ : int = YolosImageProcessor(format="coco_panoptic" )
snake_case_ : Union[str, Any] = image_processing(images=lowerCAmelCase__ , annotations=lowerCAmelCase__ , masks_path=lowerCAmelCase__ , return_tensors="pt" )
# verify pixel values
snake_case_ : Optional[int] = torch.Size([1, 3, 800, 1_066] )
self.assertEqual(encoding["pixel_values"].shape , lowerCAmelCase__ )
snake_case_ : List[str] = torch.tensor([0.2_7_9_6, 0.3_1_3_8, 0.3_4_8_1] )
self.assertTrue(torch.allclose(encoding["pixel_values"][0, 0, 0, :3] , lowerCAmelCase__ , atol=1E-4 ) )
# verify area
snake_case_ : int = torch.tensor([1_4_7_9_7_9.6_8_7_5, 1_6_5_5_2_7.0_4_6_9, 4_8_4_6_3_8.5_9_3_8, 1_1_2_9_2.9_3_7_5, 5_8_7_9.6_5_6_2, 7_6_3_4.1_1_4_7] )
self.assertTrue(torch.allclose(encoding["labels"][0]["area"] , lowerCAmelCase__ ) )
# verify boxes
snake_case_ : Optional[int] = torch.Size([6, 4] )
self.assertEqual(encoding["labels"][0]["boxes"].shape , lowerCAmelCase__ )
snake_case_ : List[str] = torch.tensor([0.2_6_2_5, 0.5_4_3_7, 0.4_6_8_8, 0.8_6_2_5] )
self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"][0] , lowerCAmelCase__ , atol=1E-3 ) )
# verify image_id
snake_case_ : List[str] = torch.tensor([39_769] )
self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"] , lowerCAmelCase__ ) )
# verify is_crowd
snake_case_ : Dict = torch.tensor([0, 0, 0, 0, 0, 0] )
self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"] , lowerCAmelCase__ ) )
# verify class_labels
snake_case_ : str = torch.tensor([17, 17, 63, 75, 75, 93] )
self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"] , lowerCAmelCase__ ) )
# verify masks
snake_case_ : Any = 822_873
self.assertEqual(encoding["labels"][0]["masks"].sum().item() , lowerCAmelCase__ )
# verify orig_size
snake_case_ : int = torch.tensor([480, 640] )
self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"] , lowerCAmelCase__ ) )
# verify size
snake_case_ : Union[str, Any] = torch.tensor([800, 1_066] )
self.assertTrue(torch.allclose(encoding["labels"][0]["size"] , lowerCAmelCase__ ) )
| 653 | 0 |
import os
import re
import sys
import traceback
import warnings
from pathlib import Path
from typing import Dict, Optional, Union
from uuid import uuida
from huggingface_hub import HfFolder, ModelCard, ModelCardData, hf_hub_download, whoami
from huggingface_hub.file_download import REGEX_COMMIT_HASH
from huggingface_hub.utils import (
EntryNotFoundError,
RepositoryNotFoundError,
RevisionNotFoundError,
is_jinja_available,
)
from packaging import version
from requests import HTTPError
from .. import __version__
from .constants import (
DEPRECATED_REVISION_ARGS,
DIFFUSERS_CACHE,
HUGGINGFACE_CO_RESOLVE_ENDPOINT,
SAFETENSORS_WEIGHTS_NAME,
WEIGHTS_NAME,
)
from .import_utils import (
ENV_VARS_TRUE_VALUES,
_flax_version,
_jax_version,
_onnxruntime_version,
_torch_version,
is_flax_available,
is_onnx_available,
is_torch_available,
)
from .logging import get_logger
__a :Dict = get_logger(__name__)
__a :int = Path(__file__).parent / 'model_card_template.md'
__a :int = uuida().hex
__a :Optional[Any] = os.getenv('HF_HUB_OFFLINE', '').upper() in ENV_VARS_TRUE_VALUES
__a :List[Any] = os.getenv('DISABLE_TELEMETRY', '').upper() in ENV_VARS_TRUE_VALUES
__a :List[Any] = HUGGINGFACE_CO_RESOLVE_ENDPOINT + '/api/telemetry/'
def __snake_case ( __UpperCamelCase : Union[Dict, str, None] = None ):
"""simple docstring"""
A_ = f'''diffusers/{__version__}; python/{sys.version.split()[0]}; session_id/{SESSION_ID}'''
if DISABLE_TELEMETRY or HF_HUB_OFFLINE:
return ua + "; telemetry/off"
if is_torch_available():
ua += f'''; torch/{_torch_version}'''
if is_flax_available():
ua += f'''; jax/{_jax_version}'''
ua += f'''; flax/{_flax_version}'''
if is_onnx_available():
ua += f'''; onnxruntime/{_onnxruntime_version}'''
# CI will set this value to True
if os.environ.get("DIFFUSERS_IS_CI" ,"" ).upper() in ENV_VARS_TRUE_VALUES:
ua += "; is_ci/true"
if isinstance(__UpperCamelCase ,__UpperCamelCase ):
ua += "; " + "; ".join(f'''{k}/{v}''' for k, v in user_agent.items() )
elif isinstance(__UpperCamelCase ,__UpperCamelCase ):
ua += "; " + user_agent
return ua
def __snake_case ( __UpperCamelCase : str ,__UpperCamelCase : Optional[str] = None ,__UpperCamelCase : Optional[str] = None ):
"""simple docstring"""
if token is None:
A_ = HfFolder.get_token()
if organization is None:
A_ = whoami(__UpperCamelCase )["name"]
return f'''{username}/{model_id}'''
else:
return f'''{organization}/{model_id}'''
def __snake_case ( __UpperCamelCase : Any ,__UpperCamelCase : Dict ):
"""simple docstring"""
if not is_jinja_available():
raise ValueError(
"Modelcard rendering is based on Jinja templates."
" Please make sure to have `jinja` installed before using `create_model_card`."
" To install it, please run `pip install Jinja2`." )
if hasattr(__UpperCamelCase ,"local_rank" ) and args.local_rank not in [-1, 0]:
return
A_ = args.hub_token if hasattr(__UpperCamelCase ,"hub_token" ) else None
A_ = get_full_repo_name(__UpperCamelCase ,token=__UpperCamelCase )
A_ = ModelCard.from_template(
card_data=ModelCardData( # Card metadata object that will be converted to YAML block
language="en" ,license="apache-2.0" ,library_name="diffusers" ,tags=[] ,datasets=args.dataset_name ,metrics=[] ,) ,template_path=__UpperCamelCase ,model_name=__UpperCamelCase ,repo_name=__UpperCamelCase ,dataset_name=args.dataset_name if hasattr(__UpperCamelCase ,"dataset_name" ) else None ,learning_rate=args.learning_rate ,train_batch_size=args.train_batch_size ,eval_batch_size=args.eval_batch_size ,gradient_accumulation_steps=(
args.gradient_accumulation_steps if hasattr(__UpperCamelCase ,"gradient_accumulation_steps" ) else None
) ,adam_betaa=args.adam_betaa if hasattr(__UpperCamelCase ,"adam_beta1" ) else None ,adam_betaa=args.adam_betaa if hasattr(__UpperCamelCase ,"adam_beta2" ) else None ,adam_weight_decay=args.adam_weight_decay if hasattr(__UpperCamelCase ,"adam_weight_decay" ) else None ,adam_epsilon=args.adam_epsilon if hasattr(__UpperCamelCase ,"adam_epsilon" ) else None ,lr_scheduler=args.lr_scheduler if hasattr(__UpperCamelCase ,"lr_scheduler" ) else None ,lr_warmup_steps=args.lr_warmup_steps if hasattr(__UpperCamelCase ,"lr_warmup_steps" ) else None ,ema_inv_gamma=args.ema_inv_gamma if hasattr(__UpperCamelCase ,"ema_inv_gamma" ) else None ,ema_power=args.ema_power if hasattr(__UpperCamelCase ,"ema_power" ) else None ,ema_max_decay=args.ema_max_decay if hasattr(__UpperCamelCase ,"ema_max_decay" ) else None ,mixed_precision=args.mixed_precision ,)
A_ = os.path.join(args.output_dir ,"README.md" )
model_card.save(__UpperCamelCase )
def __snake_case ( __UpperCamelCase : Optional[str] ,__UpperCamelCase : Optional[str] = None ):
"""simple docstring"""
if resolved_file is None or commit_hash is not None:
return commit_hash
A_ = str(Path(__UpperCamelCase ).as_posix() )
A_ = re.search(R"snapshots/([^/]+)/" ,__UpperCamelCase )
if search is None:
return None
A_ = search.groups()[0]
return commit_hash if REGEX_COMMIT_HASH.match(__UpperCamelCase ) else None
# Old default cache path, potentially to be migrated.
# This logic was more or less taken from `transformers`, with the following differences:
# - Diffusers doesn't use custom environment variables to specify the cache path.
# - There is no need to migrate the cache format, just move the files to the new location.
__a :Optional[int] = os.path.expanduser(
os.getenv('HF_HOME', os.path.join(os.getenv('XDG_CACHE_HOME', '~/.cache'), 'huggingface'))
)
__a :Union[str, Any] = os.path.join(hf_cache_home, 'diffusers')
def __snake_case ( __UpperCamelCase : Optional[str] = None ,__UpperCamelCase : Optional[str] = None ):
"""simple docstring"""
if new_cache_dir is None:
A_ = DIFFUSERS_CACHE
if old_cache_dir is None:
A_ = old_diffusers_cache
A_ = Path(__UpperCamelCase ).expanduser()
A_ = Path(__UpperCamelCase ).expanduser()
for old_blob_path in old_cache_dir.glob("**/blobs/*" ):
if old_blob_path.is_file() and not old_blob_path.is_symlink():
A_ = new_cache_dir / old_blob_path.relative_to(__UpperCamelCase )
new_blob_path.parent.mkdir(parents=__UpperCamelCase ,exist_ok=__UpperCamelCase )
os.replace(__UpperCamelCase ,__UpperCamelCase )
try:
os.symlink(__UpperCamelCase ,__UpperCamelCase )
except OSError:
logger.warning(
"Could not create symlink between old cache and new cache. If you use an older version of diffusers again, files will be re-downloaded." )
# At this point, old_cache_dir contains symlinks to the new cache (it can still be used).
__a :str = os.path.join(DIFFUSERS_CACHE, 'version_diffusers_cache.txt')
if not os.path.isfile(cache_version_file):
__a :Dict = 0
else:
with open(cache_version_file) as f:
try:
__a :str = int(f.read())
except ValueError:
__a :List[str] = 0
if cache_version < 1:
__a :Tuple = os.path.isdir(old_diffusers_cache) and len(os.listdir(old_diffusers_cache)) > 0
if old_cache_is_not_empty:
logger.warning(
'The cache for model files in Diffusers v0.14.0 has moved to a new location. Moving your '
'existing cached models. This is a one-time operation, you can interrupt it or run it '
'later by calling `diffusers.utils.hub_utils.move_cache()`.'
)
try:
move_cache()
except Exception as e:
__a :List[Any] = '\n'.join(traceback.format_tb(e.__traceback__))
logger.error(
F"There was a problem when trying to move your cache:\n\n{trace}\n{e.__class__.__name__}: {e}\n\nPlease "
'file an issue at https://github.com/huggingface/diffusers/issues/new/choose, copy paste this whole '
'message and we will do our best to help.'
)
if cache_version < 1:
try:
os.makedirs(DIFFUSERS_CACHE, exist_ok=True)
with open(cache_version_file, 'w') as f:
f.write('1')
except Exception:
logger.warning(
F"There was a problem when trying to write in your cache folder ({DIFFUSERS_CACHE}). Please, ensure "
'the directory exists and can be written to.'
)
def __snake_case ( __UpperCamelCase : str ,__UpperCamelCase : Optional[str] = None ):
"""simple docstring"""
if variant is not None:
A_ = weights_name.split("." )
A_ = splits[:-1] + [variant] + splits[-1:]
A_ = ".".join(__UpperCamelCase )
return weights_name
def __snake_case ( __UpperCamelCase : Any ,*,
__UpperCamelCase : List[Any] ,__UpperCamelCase : Tuple ,__UpperCamelCase : Union[str, Any] ,__UpperCamelCase : Tuple ,__UpperCamelCase : Any ,__UpperCamelCase : Optional[int] ,__UpperCamelCase : Union[str, Any] ,__UpperCamelCase : Optional[Any] ,__UpperCamelCase : Union[str, Any] ,__UpperCamelCase : Dict ,__UpperCamelCase : Tuple=None ,):
"""simple docstring"""
A_ = str(__UpperCamelCase )
if os.path.isfile(__UpperCamelCase ):
return pretrained_model_name_or_path
elif os.path.isdir(__UpperCamelCase ):
if os.path.isfile(os.path.join(__UpperCamelCase ,__UpperCamelCase ) ):
# Load from a PyTorch checkpoint
A_ = os.path.join(__UpperCamelCase ,__UpperCamelCase )
return model_file
elif subfolder is not None and os.path.isfile(
os.path.join(__UpperCamelCase ,__UpperCamelCase ,__UpperCamelCase ) ):
A_ = os.path.join(__UpperCamelCase ,__UpperCamelCase ,__UpperCamelCase )
return model_file
else:
raise EnvironmentError(
f'''Error no file named {weights_name} found in directory {pretrained_model_name_or_path}.''' )
else:
# 1. First check if deprecated way of loading from branches is used
if (
revision in DEPRECATED_REVISION_ARGS
and (weights_name == WEIGHTS_NAME or weights_name == SAFETENSORS_WEIGHTS_NAME)
and version.parse(version.parse(__UpperCamelCase ).base_version ) >= version.parse("0.20.0" )
):
try:
A_ = hf_hub_download(
__UpperCamelCase ,filename=_add_variant(__UpperCamelCase ,__UpperCamelCase ) ,cache_dir=__UpperCamelCase ,force_download=__UpperCamelCase ,proxies=__UpperCamelCase ,resume_download=__UpperCamelCase ,local_files_only=__UpperCamelCase ,use_auth_token=__UpperCamelCase ,user_agent=__UpperCamelCase ,subfolder=__UpperCamelCase ,revision=revision or commit_hash ,)
warnings.warn(
f'''Loading the variant {revision} from {pretrained_model_name_or_path} via `revision=\'{revision}\'` is deprecated. Loading instead from `revision=\'main\'` with `variant={revision}`. Loading model variants via `revision=\'{revision}\'` will be removed in diffusers v1. Please use `variant=\'{revision}\'` instead.''' ,__UpperCamelCase ,)
return model_file
except: # noqa: E722
warnings.warn(
f'''You are loading the variant {revision} from {pretrained_model_name_or_path} via `revision=\'{revision}\'`. This behavior is deprecated and will be removed in diffusers v1. One should use `variant=\'{revision}\'` instead. However, it appears that {pretrained_model_name_or_path} currently does not have a {_add_variant(__UpperCamelCase ,__UpperCamelCase )} file in the \'main\' branch of {pretrained_model_name_or_path}. \n The Diffusers team and community would be very grateful if you could open an issue: https://github.com/huggingface/diffusers/issues/new with the title \'{pretrained_model_name_or_path} is missing {_add_variant(__UpperCamelCase ,__UpperCamelCase )}\' so that the correct variant file can be added.''' ,__UpperCamelCase ,)
try:
# 2. Load model file as usual
A_ = hf_hub_download(
__UpperCamelCase ,filename=__UpperCamelCase ,cache_dir=__UpperCamelCase ,force_download=__UpperCamelCase ,proxies=__UpperCamelCase ,resume_download=__UpperCamelCase ,local_files_only=__UpperCamelCase ,use_auth_token=__UpperCamelCase ,user_agent=__UpperCamelCase ,subfolder=__UpperCamelCase ,revision=revision or commit_hash ,)
return model_file
except RepositoryNotFoundError:
raise EnvironmentError(
f'''{pretrained_model_name_or_path} is not a local folder and is not a valid model identifier '''
"listed on 'https://huggingface.co/models'\nIf this is a private repository, make sure to pass a "
"token having permission to this repo with `use_auth_token` or log in with `huggingface-cli "
"login`." )
except RevisionNotFoundError:
raise EnvironmentError(
f'''{revision} is not a valid git identifier (branch name, tag name or commit id) that exists for '''
"this model name. Check the model page at "
f'''\'https://huggingface.co/{pretrained_model_name_or_path}\' for available revisions.''' )
except EntryNotFoundError:
raise EnvironmentError(
f'''{pretrained_model_name_or_path} does not appear to have a file named {weights_name}.''' )
except HTTPError as err:
raise EnvironmentError(
f'''There was a specific connection error when trying to load {pretrained_model_name_or_path}:\n{err}''' )
except ValueError:
raise EnvironmentError(
f'''We couldn\'t connect to \'{HUGGINGFACE_CO_RESOLVE_ENDPOINT}\' to load this model, couldn\'t find it'''
f''' in the cached files and it looks like {pretrained_model_name_or_path} is not the path to a'''
f''' directory containing a file named {weights_name} or'''
" \nCheckout your internet connection or see how to run the library in"
" offline mode at 'https://huggingface.co/docs/diffusers/installation#offline-mode'." )
except EnvironmentError:
raise EnvironmentError(
f'''Can\'t load the model for \'{pretrained_model_name_or_path}\'. If you were trying to load it from '''
"'https://huggingface.co/models', make sure you don't have a local directory with the same name. "
f'''Otherwise, make sure \'{pretrained_model_name_or_path}\' is the correct path to a directory '''
f'''containing a file named {weights_name}''' ) | 86 |
'''simple docstring'''
def __UpperCAmelCase ( __magic_name__ ,__magic_name__ )-> str:
"""simple docstring"""
if not isinstance(__magic_name__ ,__magic_name__ ):
raise ValueError("iterations must be defined as integers" )
if not isinstance(__magic_name__ ,__magic_name__ ) or not number >= 1:
raise ValueError(
"starting number must be\n and integer and be more than 0" )
if not iterations >= 1:
raise ValueError("Iterations must be done more than 0 times to play FizzBuzz" )
snake_case_ : Dict = ""
while number <= iterations:
if number % 3 == 0:
out += "Fizz"
if number % 5 == 0:
out += "Buzz"
if 0 not in (number % 3, number % 5):
out += str(__magic_name__ )
# print(out)
number += 1
out += " "
return out
if __name__ == "__main__":
import doctest
doctest.testmod()
| 653 | 0 |
import argparse
import numpy as np
import torch
from transformers import SpeechTaHifiGan, SpeechTaHifiGanConfig, logging
logging.set_verbosity_info()
_lowerCamelCase : int = logging.get_logger("""transformers.models.speecht5""")
def SCREAMING_SNAKE_CASE ( lowercase_ , lowercase_ , lowercase_ ) -> Tuple:
"""simple docstring"""
hf_model.apply_weight_norm()
A__ = checkpoint['''input_conv.weight_g''']
A__ = checkpoint['''input_conv.weight_v''']
A__ = checkpoint['''input_conv.bias''']
for i in range(len(config.upsample_rates ) ):
A__ = checkpoint[f"""upsamples.{i}.1.weight_g"""]
A__ = checkpoint[f"""upsamples.{i}.1.weight_v"""]
A__ = checkpoint[f"""upsamples.{i}.1.bias"""]
for i in range(len(config.upsample_rates ) * len(config.resblock_kernel_sizes ) ):
for j in range(len(config.resblock_dilation_sizes ) ):
A__ = checkpoint[f"""blocks.{i}.convs1.{j}.1.weight_g"""]
A__ = checkpoint[f"""blocks.{i}.convs1.{j}.1.weight_v"""]
A__ = checkpoint[f"""blocks.{i}.convs1.{j}.1.bias"""]
A__ = checkpoint[f"""blocks.{i}.convs2.{j}.1.weight_g"""]
A__ = checkpoint[f"""blocks.{i}.convs2.{j}.1.weight_v"""]
A__ = checkpoint[f"""blocks.{i}.convs2.{j}.1.bias"""]
A__ = checkpoint['''output_conv.1.weight_g''']
A__ = checkpoint['''output_conv.1.weight_v''']
A__ = checkpoint['''output_conv.1.bias''']
hf_model.remove_weight_norm()
@torch.no_grad()
def SCREAMING_SNAKE_CASE ( lowercase_ , lowercase_ , lowercase_ , lowercase_=None , lowercase_=None , ) -> str:
"""simple docstring"""
if config_path is not None:
A__ = SpeechTaHifiGanConfig.from_pretrained(lowercase_ )
else:
A__ = SpeechTaHifiGanConfig()
A__ = SpeechTaHifiGan(lowercase_ )
A__ = torch.load(lowercase_ )
load_weights(orig_checkpoint['''model''']['''generator'''] , lowercase_ , lowercase_ )
A__ = np.load(lowercase_ )
A__ = stats[0].reshape(-1 )
A__ = stats[1].reshape(-1 )
A__ = torch.from_numpy(lowercase_ ).float()
A__ = torch.from_numpy(lowercase_ ).float()
model.save_pretrained(lowercase_ )
if repo_id:
print('''Pushing to the hub...''' )
model.push_to_hub(lowercase_ )
if __name__ == "__main__":
_lowerCamelCase : Any = argparse.ArgumentParser()
parser.add_argument("""--checkpoint_path""", required=True, default=None, type=str, help="""Path to original checkpoint""")
parser.add_argument("""--stats_path""", required=True, default=None, type=str, help="""Path to stats.npy file""")
parser.add_argument("""--config_path""", default=None, type=str, help="""Path to hf config.json of model to convert""")
parser.add_argument(
"""--pytorch_dump_folder_path""", required=True, default=None, type=str, help="""Path to the output PyTorch model."""
)
parser.add_argument(
"""--push_to_hub""", default=None, type=str, help="""Where to upload the converted model on the 🤗 hub."""
)
_lowerCamelCase : List[str] = parser.parse_args()
convert_hifigan_checkpoint(
args.checkpoint_path,
args.stats_path,
args.pytorch_dump_folder_path,
args.config_path,
args.push_to_hub,
)
| 87 |
'''simple docstring'''
import argparse
import os
# New Code #
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from accelerate import Accelerator, DistributedType
from accelerate.utils import find_executable_batch_size
########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing how to ensure out-of-memory errors never
# interrupt training, and builds off the `nlp_example.py` script.
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
# - single CPU or single GPU
# - multi GPUS (using PyTorch distributed mode)
# - (multi) TPUs
# - fp16 (mixed-precision) or fp32 (normal precision)
#
# New additions from the base script can be found quickly by
# looking for the # New Code # tags
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################
__lowerCamelCase : Tuple = 16
__lowerCamelCase : Optional[int] = 32
def __UpperCAmelCase ( __magic_name__ ,__magic_name__ = 16 )-> int:
"""simple docstring"""
snake_case_ : Optional[int] = AutoTokenizer.from_pretrained("bert-base-cased" )
snake_case_ : str = load_dataset("glue" ,"mrpc" )
def tokenize_function(__magic_name__ ):
# max_length=None => use the model max length (it's actually the default)
snake_case_ : Dict = tokenizer(examples["sentence1"] ,examples["sentence2"] ,truncation=__magic_name__ ,max_length=__magic_name__ )
return outputs
# Apply the method we just defined to all the examples in all the splits of the dataset
# starting with the main process first:
with accelerator.main_process_first():
snake_case_ : Any = datasets.map(
__magic_name__ ,batched=__magic_name__ ,remove_columns=["idx", "sentence1", "sentence2"] ,)
# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
# transformers library
snake_case_ : List[Any] = tokenized_datasets.rename_column("label" ,"labels" )
def collate_fn(__magic_name__ ):
# On TPU it's best to pad everything to the same length or training will be very slow.
snake_case_ : int = 128 if accelerator.distributed_type == DistributedType.TPU else None
# When using mixed precision we want round multiples of 8/16
if accelerator.mixed_precision == "fp8":
snake_case_ : Tuple = 16
elif accelerator.mixed_precision != "no":
snake_case_ : str = 8
else:
snake_case_ : Optional[Any] = None
return tokenizer.pad(
__magic_name__ ,padding="longest" ,max_length=__magic_name__ ,pad_to_multiple_of=__magic_name__ ,return_tensors="pt" ,)
# Instantiate dataloaders.
snake_case_ : str = DataLoader(
tokenized_datasets["train"] ,shuffle=__magic_name__ ,collate_fn=__magic_name__ ,batch_size=__magic_name__ )
snake_case_ : Optional[Any] = DataLoader(
tokenized_datasets["validation"] ,shuffle=__magic_name__ ,collate_fn=__magic_name__ ,batch_size=__magic_name__ )
return train_dataloader, eval_dataloader
# For testing only
if os.environ.get('''TESTING_MOCKED_DATALOADERS''', None) == "1":
from accelerate.test_utils.training import mocked_dataloaders
__lowerCamelCase : Optional[Any] = mocked_dataloaders # noqa: F811
def __UpperCAmelCase ( __magic_name__ ,__magic_name__ )-> Dict:
"""simple docstring"""
if os.environ.get("TESTING_MOCKED_DATALOADERS" ,__magic_name__ ) == "1":
snake_case_ : List[str] = 2
# Initialize accelerator
snake_case_ : Union[str, Any] = Accelerator(cpu=args.cpu ,mixed_precision=args.mixed_precision )
# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
snake_case_ : List[str] = config["lr"]
snake_case_ : Dict = int(config["num_epochs"] )
snake_case_ : Dict = int(config["seed"] )
snake_case_ : Optional[int] = int(config["batch_size"] )
snake_case_ : Dict = evaluate.load("glue" ,"mrpc" )
# New Code #
# We now can define an inner training loop function. It should take a batch size as the only parameter,
# and build the dataloaders in there.
# It also gets our decorator
@find_executable_batch_size(starting_batch_size=__magic_name__ )
def inner_training_loop(__magic_name__ ):
# And now just move everything below under this function
# We need to bring in the Accelerator object from earlier
nonlocal accelerator
# And reset all of its attributes that could hold onto any memory:
accelerator.free_memory()
# Then we can declare the model, optimizer, and everything else:
set_seed(__magic_name__ )
# Instantiate the model (we build the model here so that the seed also control new weights initialization)
snake_case_ : Optional[int] = AutoModelForSequenceClassification.from_pretrained("bert-base-cased" ,return_dict=__magic_name__ )
# We could avoid this line since the accelerator is set with `device_placement=True` (default value).
# Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
# creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
snake_case_ : Optional[int] = model.to(accelerator.device )
# Instantiate optimizer
snake_case_ : List[Any] = AdamW(params=model.parameters() ,lr=__magic_name__ )
snake_case_, snake_case_ : int = get_dataloaders(__magic_name__ ,__magic_name__ )
# Instantiate scheduler
snake_case_ : Tuple = get_linear_schedule_with_warmup(
optimizer=__magic_name__ ,num_warmup_steps=100 ,num_training_steps=(len(__magic_name__ ) * num_epochs) ,)
# Prepare everything
# There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
# prepare method.
snake_case_, snake_case_, snake_case_, snake_case_, snake_case_ : Tuple = accelerator.prepare(
__magic_name__ ,__magic_name__ ,__magic_name__ ,__magic_name__ ,__magic_name__ )
# Now we train the model
for epoch in range(__magic_name__ ):
model.train()
for step, batch in enumerate(__magic_name__ ):
# We could avoid this line since we set the accelerator with `device_placement=True`.
batch.to(accelerator.device )
snake_case_ : int = model(**__magic_name__ )
snake_case_ : Any = outputs.loss
accelerator.backward(__magic_name__ )
optimizer.step()
lr_scheduler.step()
optimizer.zero_grad()
model.eval()
for step, batch in enumerate(__magic_name__ ):
# We could avoid this line since we set the accelerator with `device_placement=True`.
batch.to(accelerator.device )
with torch.no_grad():
snake_case_ : Union[str, Any] = model(**__magic_name__ )
snake_case_ : List[str] = outputs.logits.argmax(dim=-1 )
snake_case_, snake_case_ : Union[str, Any] = accelerator.gather_for_metrics((predictions, batch["labels"]) )
metric.add_batch(
predictions=__magic_name__ ,references=__magic_name__ ,)
snake_case_ : Tuple = metric.compute()
# Use accelerator.print to print only on the main process.
accelerator.print(F'''epoch {epoch}:''' ,__magic_name__ )
# New Code #
# And call it at the end with no arguments
# Note: You could also refactor this outside of your training loop function
inner_training_loop()
def __UpperCAmelCase ( )-> List[str]:
"""simple docstring"""
snake_case_ : List[Any] = argparse.ArgumentParser(description="Simple example of training script." )
parser.add_argument(
"--mixed_precision" ,type=__magic_name__ ,default=__magic_name__ ,choices=["no", "fp16", "bf16", "fp8"] ,help="Whether to use mixed precision. Choose"
"between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10."
"and an Nvidia Ampere GPU." ,)
parser.add_argument("--cpu" ,action="store_true" ,help="If passed, will train on the CPU." )
snake_case_ : str = parser.parse_args()
snake_case_ : Optional[int] = {"lr": 2E-5, "num_epochs": 3, "seed": 42, "batch_size": 16}
training_function(__magic_name__ ,__magic_name__ )
if __name__ == "__main__":
main()
| 653 | 0 |
"""simple docstring"""
from __future__ import annotations
from dataclasses import dataclass
@dataclass
class lowercase__ :
__UpperCAmelCase = 42
__UpperCAmelCase = None
__UpperCAmelCase = None
def _snake_case ( __snake_case : TreeNode | None ):
"""simple docstring"""
def is_valid_tree(__snake_case : TreeNode | None ) -> bool:
if node is None:
return True
if not isinstance(__snake_case , __snake_case ):
return False
try:
float(node.data )
except (TypeError, ValueError):
return False
return is_valid_tree(node.left ) and is_valid_tree(node.right )
if not is_valid_tree(__snake_case ):
raise ValueError(
"""Each node should be type of TreeNode and data should be float.""" )
def is_binary_search_tree_recursive_check(
__snake_case : TreeNode | None , __snake_case : float , __snake_case : float ) -> bool:
if node is None:
return True
return (
left_bound < node.data < right_bound
and is_binary_search_tree_recursive_check(node.left , __snake_case , node.data )
and is_binary_search_tree_recursive_check(
node.right , node.data , __snake_case )
)
return is_binary_search_tree_recursive_check(__snake_case , -float("""inf""" ) , float("""inf""" ) )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 88 |
'''simple docstring'''
# Copyright 2023 The HuggingFace Inc. 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 torch
from ..models.auto import AutoModelForSequenceClassification, AutoTokenizer
from .base import PipelineTool
class A_ (a_ ):
"""simple docstring"""
a__ = '''facebook/bart-large-mnli'''
a__ = (
'''This is a tool that classifies an English text using provided labels. It takes two inputs: `text`, which '''
'''should be the text to classify, and `labels`, which should be the list of labels to use for classification. '''
'''It returns the most likely label in the list of provided `labels` for the input text.'''
)
a__ = '''text_classifier'''
a__ = AutoTokenizer
a__ = AutoModelForSequenceClassification
a__ = ['''text''', ['''text''']]
a__ = ['''text''']
def _A ( self :List[str] ) -> Union[str, Any]:
'''simple docstring'''
super().setup()
snake_case_ : Optional[int] = self.model.config
snake_case_ : Any = -1
for idx, label in config.idalabel.items():
if label.lower().startswith("entail" ):
snake_case_ : Union[str, Any] = int(lowerCAmelCase__ )
if self.entailment_id == -1:
raise ValueError("Could not determine the entailment ID from the model config, please pass it at init." )
def _A ( self :Dict , lowerCAmelCase__ :int , lowerCAmelCase__ :Tuple ) -> int:
'''simple docstring'''
snake_case_ : Tuple = labels
return self.pre_processor(
[text] * len(lowerCAmelCase__ ) , [F'''This example is {label}''' for label in labels] , return_tensors="pt" , padding="max_length" , )
def _A ( self :Any , lowerCAmelCase__ :str ) -> List[str]:
'''simple docstring'''
snake_case_ : Optional[int] = outputs.logits
snake_case_ : Tuple = torch.argmax(logits[:, 2] ).item()
return self._labels[label_id]
| 653 | 0 |
import os
import shutil
from pathlib import Path
from typing import Optional, Union
import numpy as np
from huggingface_hub import hf_hub_download
from ..utils import ONNX_EXTERNAL_WEIGHTS_NAME, ONNX_WEIGHTS_NAME, is_onnx_available, logging
if is_onnx_available():
import onnxruntime as ort
SCREAMING_SNAKE_CASE : Dict = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE : Optional[int] = {
"tensor(bool)": np.bool_,
"tensor(int8)": np.inta,
"tensor(uint8)": np.uinta,
"tensor(int16)": np.intaa,
"tensor(uint16)": np.uintaa,
"tensor(int32)": np.intaa,
"tensor(uint32)": np.uintaa,
"tensor(int64)": np.intaa,
"tensor(uint64)": np.uintaa,
"tensor(float16)": np.floataa,
"tensor(float)": np.floataa,
"tensor(double)": np.floataa,
}
class _lowerCamelCase:
def __init__( self, lowerCamelCase=None, **lowerCamelCase) -> int:
"""simple docstring"""
logger.info('`diffusers.OnnxRuntimeModel` is experimental and might change in the future.')
_lowercase : Any = model
_lowercase : Dict = kwargs.get('model_save_dir', lowerCamelCase)
_lowercase : Any = kwargs.get('latest_model_name', lowerCamelCase)
def __call__( self, **lowerCamelCase) -> List[Any]:
"""simple docstring"""
_lowercase : Dict = {k: np.array(lowerCamelCase) for k, v in kwargs.items()}
return self.model.run(lowerCamelCase, lowerCamelCase)
@staticmethod
def UpperCamelCase ( lowerCamelCase, lowerCamelCase=None, lowerCamelCase=None) -> List[str]:
"""simple docstring"""
if provider is None:
logger.info('No onnxruntime provider specified, using CPUExecutionProvider')
_lowercase : Tuple = 'CPUExecutionProvider'
return ort.InferenceSession(lowerCamelCase, providers=[provider], sess_options=lowerCamelCase)
def UpperCamelCase ( self, lowerCamelCase, lowerCamelCase = None, **lowerCamelCase) -> str:
"""simple docstring"""
_lowercase : Union[str, Any] = file_name if file_name is not None else ONNX_WEIGHTS_NAME
_lowercase : List[Any] = self.model_save_dir.joinpath(self.latest_model_name)
_lowercase : Union[str, Any] = Path(lowerCamelCase).joinpath(lowerCamelCase)
try:
shutil.copyfile(lowerCamelCase, lowerCamelCase)
except shutil.SameFileError:
pass
# copy external weights (for models >2GB)
_lowercase : Union[str, Any] = self.model_save_dir.joinpath(lowerCamelCase)
if src_path.exists():
_lowercase : Any = Path(lowerCamelCase).joinpath(lowerCamelCase)
try:
shutil.copyfile(lowerCamelCase, lowerCamelCase)
except shutil.SameFileError:
pass
def UpperCamelCase ( self, lowerCamelCase, **lowerCamelCase, ) -> Tuple:
"""simple docstring"""
if os.path.isfile(lowerCamelCase):
logger.error(F'''Provided path ({save_directory}) should be a directory, not a file''')
return
os.makedirs(lowerCamelCase, exist_ok=lowerCamelCase)
# saving model weights/files
self._save_pretrained(lowerCamelCase, **lowerCamelCase)
@classmethod
def UpperCamelCase ( cls, lowerCamelCase, lowerCamelCase = None, lowerCamelCase = None, lowerCamelCase = False, lowerCamelCase = None, lowerCamelCase = None, lowerCamelCase = None, lowerCamelCase = None, **lowerCamelCase, ) -> Tuple:
"""simple docstring"""
_lowercase : Optional[Any] = file_name if file_name is not None else ONNX_WEIGHTS_NAME
# load model from local directory
if os.path.isdir(lowerCamelCase):
_lowercase : str = OnnxRuntimeModel.load_model(
os.path.join(lowerCamelCase, lowerCamelCase), provider=lowerCamelCase, sess_options=lowerCamelCase)
_lowercase : str = Path(lowerCamelCase)
# load model from hub
else:
# download model
_lowercase : List[Any] = hf_hub_download(
repo_id=lowerCamelCase, filename=lowerCamelCase, use_auth_token=lowerCamelCase, revision=lowerCamelCase, cache_dir=lowerCamelCase, force_download=lowerCamelCase, )
_lowercase : List[str] = Path(lowerCamelCase).parent
_lowercase : Any = Path(lowerCamelCase).name
_lowercase : Tuple = OnnxRuntimeModel.load_model(lowerCamelCase, provider=lowerCamelCase, sess_options=lowerCamelCase)
return cls(model=lowerCamelCase, **lowerCamelCase)
@classmethod
def UpperCamelCase ( cls, lowerCamelCase, lowerCamelCase = True, lowerCamelCase = None, lowerCamelCase = None, **lowerCamelCase, ) -> Any:
"""simple docstring"""
_lowercase : Any = None
if len(str(lowerCamelCase).split('@')) == 2:
_lowercase , _lowercase : int = model_id.split('@')
return cls._from_pretrained(
model_id=lowerCamelCase, revision=lowerCamelCase, cache_dir=lowerCamelCase, force_download=lowerCamelCase, use_auth_token=lowerCamelCase, **lowerCamelCase, )
| 89 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
is_vision_available,
)
__lowerCamelCase : Any = {'''configuration_vit''': ['''VIT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''ViTConfig''', '''ViTOnnxConfig''']}
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__lowerCamelCase : Any = ['''ViTFeatureExtractor''']
__lowerCamelCase : Any = ['''ViTImageProcessor''']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__lowerCamelCase : Optional[Any] = [
'''VIT_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''ViTForImageClassification''',
'''ViTForMaskedImageModeling''',
'''ViTModel''',
'''ViTPreTrainedModel''',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__lowerCamelCase : Union[str, Any] = [
'''TFViTForImageClassification''',
'''TFViTModel''',
'''TFViTPreTrainedModel''',
]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__lowerCamelCase : Tuple = [
'''FlaxViTForImageClassification''',
'''FlaxViTModel''',
'''FlaxViTPreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_vit import VIT_PRETRAINED_CONFIG_ARCHIVE_MAP, ViTConfig, ViTOnnxConfig
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .feature_extraction_vit import ViTFeatureExtractor
from .image_processing_vit import ViTImageProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_vit import (
VIT_PRETRAINED_MODEL_ARCHIVE_LIST,
ViTForImageClassification,
ViTForMaskedImageModeling,
ViTModel,
ViTPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_vit import TFViTForImageClassification, TFViTModel, TFViTPreTrainedModel
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_vit import FlaxViTForImageClassification, FlaxViTModel, FlaxViTPreTrainedModel
else:
import sys
__lowerCamelCase : List[str] = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 653 | 0 |
'''simple docstring'''
from __future__ import annotations
from collections.abc import Iterable, Iterator
from dataclasses import dataclass
__UpperCAmelCase = (3, 9, -11, 0, 7, 5, 1, -1)
__UpperCAmelCase = (4, 6, 2, 0, 8, 10, 3, -2)
@dataclass
class a__ :
'''simple docstring'''
lowercase__ : int
lowercase__ : Node | None
class a__ :
'''simple docstring'''
def __init__( self , lowerCamelCase_ ) -> None:
lowerCAmelCase__ = None
for i in sorted(lowerCamelCase_ , reverse=lowerCamelCase_ ):
lowerCAmelCase__ = Node(lowerCamelCase_ , self.head )
def __iter__( self ) -> Iterator[int]:
lowerCAmelCase__ = self.head
while node:
yield node.data
lowerCAmelCase__ = node.next_node
def __len__( self ) -> int:
return sum(1 for _ in self )
def __str__( self ) -> str:
return " -> ".join([str(lowerCamelCase_ ) for node in self] )
def _snake_case ( A , A ) -> SortedLinkedList:
return SortedLinkedList(list(A ) + list(A ) )
if __name__ == "__main__":
import doctest
doctest.testmod()
__UpperCAmelCase = SortedLinkedList
print(merge_lists(SSL(test_data_odd), SSL(test_data_even))) | 90 |
'''simple docstring'''
import copy
import unittest
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
MODEL_FOR_MULTIPLE_CHOICE_MAPPING,
MODEL_FOR_QUESTION_ANSWERING_MAPPING,
MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
LayoutLMvaConfig,
LayoutLMvaForQuestionAnswering,
LayoutLMvaForSequenceClassification,
LayoutLMvaForTokenClassification,
LayoutLMvaModel,
)
from transformers.models.layoutlmva.modeling_layoutlmva import LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import LayoutLMvaImageProcessor
class A_ :
"""simple docstring"""
def __init__( self :Optional[Any] , lowerCAmelCase__ :List[str] , lowerCAmelCase__ :List[str]=2 , lowerCAmelCase__ :List[Any]=3 , lowerCAmelCase__ :Any=4 , lowerCAmelCase__ :List[Any]=2 , lowerCAmelCase__ :List[str]=7 , lowerCAmelCase__ :Any=True , lowerCAmelCase__ :Optional[int]=True , lowerCAmelCase__ :Optional[Any]=True , lowerCAmelCase__ :Optional[int]=True , lowerCAmelCase__ :List[str]=99 , lowerCAmelCase__ :Union[str, Any]=36 , lowerCAmelCase__ :Dict=3 , lowerCAmelCase__ :str=4 , lowerCAmelCase__ :Optional[int]=37 , lowerCAmelCase__ :Dict="gelu" , lowerCAmelCase__ :Optional[Any]=0.1 , lowerCAmelCase__ :Dict=0.1 , lowerCAmelCase__ :Optional[int]=512 , lowerCAmelCase__ :Union[str, Any]=16 , lowerCAmelCase__ :List[Any]=2 , lowerCAmelCase__ :Any=0.0_2 , lowerCAmelCase__ :Dict=6 , lowerCAmelCase__ :Optional[int]=6 , lowerCAmelCase__ :Any=3 , lowerCAmelCase__ :int=4 , lowerCAmelCase__ :int=None , lowerCAmelCase__ :Any=1_000 , ) -> Any:
'''simple docstring'''
snake_case_ : Optional[int] = parent
snake_case_ : Union[str, Any] = batch_size
snake_case_ : Optional[int] = num_channels
snake_case_ : List[Any] = image_size
snake_case_ : Optional[int] = patch_size
snake_case_ : Union[str, Any] = text_seq_length
snake_case_ : Dict = is_training
snake_case_ : Optional[Any] = use_input_mask
snake_case_ : Union[str, Any] = use_token_type_ids
snake_case_ : Dict = use_labels
snake_case_ : List[str] = vocab_size
snake_case_ : Optional[Any] = hidden_size
snake_case_ : List[str] = num_hidden_layers
snake_case_ : int = num_attention_heads
snake_case_ : List[str] = intermediate_size
snake_case_ : str = hidden_act
snake_case_ : Optional[Any] = hidden_dropout_prob
snake_case_ : Optional[int] = attention_probs_dropout_prob
snake_case_ : Union[str, Any] = max_position_embeddings
snake_case_ : List[Any] = type_vocab_size
snake_case_ : Union[str, Any] = type_sequence_label_size
snake_case_ : List[Any] = initializer_range
snake_case_ : Union[str, Any] = coordinate_size
snake_case_ : int = shape_size
snake_case_ : Tuple = num_labels
snake_case_ : List[Any] = num_choices
snake_case_ : List[str] = scope
snake_case_ : Dict = range_bbox
# LayoutLMv3's sequence length equals the number of text tokens + number of patches + 1 (we add 1 for the CLS token)
snake_case_ : str = text_seq_length
snake_case_ : Optional[int] = (image_size // patch_size) ** 2 + 1
snake_case_ : str = self.text_seq_length + self.image_seq_length
def _A ( self :Union[str, Any] ) -> Tuple:
'''simple docstring'''
snake_case_ : Dict = ids_tensor([self.batch_size, self.text_seq_length] , self.vocab_size )
snake_case_ : str = ids_tensor([self.batch_size, self.text_seq_length, 4] , self.range_bbox )
# Ensure that bbox is legal
for i in range(bbox.shape[0] ):
for j in range(bbox.shape[1] ):
if bbox[i, j, 3] < bbox[i, j, 1]:
snake_case_ : Optional[Any] = bbox[i, j, 3]
snake_case_ : Any = bbox[i, j, 1]
snake_case_ : Tuple = t
if bbox[i, j, 2] < bbox[i, j, 0]:
snake_case_ : str = bbox[i, j, 2]
snake_case_ : Dict = bbox[i, j, 0]
snake_case_ : Union[str, Any] = t
snake_case_ : int = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
snake_case_ : Dict = None
if self.use_input_mask:
snake_case_ : str = random_attention_mask([self.batch_size, self.text_seq_length] )
snake_case_ : Any = None
if self.use_token_type_ids:
snake_case_ : List[str] = ids_tensor([self.batch_size, self.text_seq_length] , self.type_vocab_size )
snake_case_ : Union[str, Any] = None
snake_case_ : str = None
if self.use_labels:
snake_case_ : List[Any] = ids_tensor([self.batch_size] , self.type_sequence_label_size )
snake_case_ : List[Any] = ids_tensor([self.batch_size, self.text_seq_length] , self.num_labels )
snake_case_ : str = LayoutLMvaConfig(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , initializer_range=self.initializer_range , coordinate_size=self.coordinate_size , shape_size=self.shape_size , input_size=self.image_size , patch_size=self.patch_size , )
return config, input_ids, bbox, pixel_values, token_type_ids, input_mask, sequence_labels, token_labels
def _A ( self :Dict , lowerCAmelCase__ :Dict , lowerCAmelCase__ :List[str] , lowerCAmelCase__ :str , lowerCAmelCase__ :List[Any] , lowerCAmelCase__ :Optional[Any] , lowerCAmelCase__ :List[str] , lowerCAmelCase__ :Dict , lowerCAmelCase__ :List[str] ) -> Optional[Any]:
'''simple docstring'''
snake_case_ : Tuple = LayoutLMvaModel(config=lowerCAmelCase__ )
model.to(lowerCAmelCase__ )
model.eval()
# text + image
snake_case_ : Tuple = model(lowerCAmelCase__ , pixel_values=lowerCAmelCase__ )
snake_case_ : Optional[int] = model(
lowerCAmelCase__ , bbox=lowerCAmelCase__ , pixel_values=lowerCAmelCase__ , attention_mask=lowerCAmelCase__ , token_type_ids=lowerCAmelCase__ )
snake_case_ : Optional[int] = model(lowerCAmelCase__ , bbox=lowerCAmelCase__ , pixel_values=lowerCAmelCase__ , token_type_ids=lowerCAmelCase__ )
snake_case_ : int = model(lowerCAmelCase__ , bbox=lowerCAmelCase__ , pixel_values=lowerCAmelCase__ )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
# text only
snake_case_ : List[Any] = model(lowerCAmelCase__ )
self.parent.assertEqual(
result.last_hidden_state.shape , (self.batch_size, self.text_seq_length, self.hidden_size) )
# image only
snake_case_ : Union[str, Any] = model(pixel_values=lowerCAmelCase__ )
self.parent.assertEqual(
result.last_hidden_state.shape , (self.batch_size, self.image_seq_length, self.hidden_size) )
def _A ( self :str , lowerCAmelCase__ :str , lowerCAmelCase__ :Tuple , lowerCAmelCase__ :Union[str, Any] , lowerCAmelCase__ :Union[str, Any] , lowerCAmelCase__ :Optional[Any] , lowerCAmelCase__ :List[str] , lowerCAmelCase__ :List[str] , lowerCAmelCase__ :Tuple ) -> List[Any]:
'''simple docstring'''
snake_case_ : str = self.num_labels
snake_case_ : List[Any] = LayoutLMvaForSequenceClassification(lowerCAmelCase__ )
model.to(lowerCAmelCase__ )
model.eval()
snake_case_ : Optional[int] = model(
lowerCAmelCase__ , bbox=lowerCAmelCase__ , pixel_values=lowerCAmelCase__ , attention_mask=lowerCAmelCase__ , token_type_ids=lowerCAmelCase__ , labels=lowerCAmelCase__ , )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def _A ( self :Union[str, Any] , lowerCAmelCase__ :List[Any] , lowerCAmelCase__ :int , lowerCAmelCase__ :List[str] , lowerCAmelCase__ :Tuple , lowerCAmelCase__ :str , lowerCAmelCase__ :Optional[int] , lowerCAmelCase__ :Any , lowerCAmelCase__ :Union[str, Any] ) -> str:
'''simple docstring'''
snake_case_ : Optional[int] = self.num_labels
snake_case_ : str = LayoutLMvaForTokenClassification(config=lowerCAmelCase__ )
model.to(lowerCAmelCase__ )
model.eval()
snake_case_ : List[Any] = model(
lowerCAmelCase__ , bbox=lowerCAmelCase__ , pixel_values=lowerCAmelCase__ , attention_mask=lowerCAmelCase__ , token_type_ids=lowerCAmelCase__ , labels=lowerCAmelCase__ , )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.text_seq_length, self.num_labels) )
def _A ( self :Optional[int] , lowerCAmelCase__ :Dict , lowerCAmelCase__ :str , lowerCAmelCase__ :Dict , lowerCAmelCase__ :Optional[int] , lowerCAmelCase__ :str , lowerCAmelCase__ :int , lowerCAmelCase__ :Union[str, Any] , lowerCAmelCase__ :str ) -> Tuple:
'''simple docstring'''
snake_case_ : List[str] = LayoutLMvaForQuestionAnswering(config=lowerCAmelCase__ )
model.to(lowerCAmelCase__ )
model.eval()
snake_case_ : List[Any] = model(
lowerCAmelCase__ , bbox=lowerCAmelCase__ , pixel_values=lowerCAmelCase__ , attention_mask=lowerCAmelCase__ , token_type_ids=lowerCAmelCase__ , start_positions=lowerCAmelCase__ , end_positions=lowerCAmelCase__ , )
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 :int ) -> Union[str, Any]:
'''simple docstring'''
snake_case_ : Dict = self.prepare_config_and_inputs()
(
(
snake_case_
), (
snake_case_
), (
snake_case_
), (
snake_case_
), (
snake_case_
), (
snake_case_
), (
snake_case_
), (
snake_case_
),
) : Optional[Any] = config_and_inputs
snake_case_ : Tuple = {
"input_ids": input_ids,
"bbox": bbox,
"pixel_values": pixel_values,
"token_type_ids": token_type_ids,
"attention_mask": input_mask,
}
return config, inputs_dict
@require_torch
class A_ (a_ , a_ , unittest.TestCase ):
"""simple docstring"""
a__ = False
a__ = False
a__ = False
a__ = (
(
LayoutLMvaModel,
LayoutLMvaForSequenceClassification,
LayoutLMvaForTokenClassification,
LayoutLMvaForQuestionAnswering,
)
if is_torch_available()
else ()
)
a__ = (
{'''document-question-answering''': LayoutLMvaForQuestionAnswering, '''feature-extraction''': LayoutLMvaModel}
if is_torch_available()
else {}
)
def _A ( self :Optional[Any] , lowerCAmelCase__ :Tuple , lowerCAmelCase__ :Tuple , lowerCAmelCase__ :List[Any] , lowerCAmelCase__ :Optional[Any] , lowerCAmelCase__ :List[Any] ) -> List[str]:
'''simple docstring'''
return True
def _A ( self :List[Any] ) -> str:
'''simple docstring'''
snake_case_ : Tuple = LayoutLMvaModelTester(self )
snake_case_ : Optional[int] = ConfigTester(self , config_class=lowerCAmelCase__ , hidden_size=37 )
def _A ( self :Tuple , lowerCAmelCase__ :Optional[int] , lowerCAmelCase__ :Dict , lowerCAmelCase__ :Union[str, Any]=False ) -> Any:
'''simple docstring'''
snake_case_ : List[str] = copy.deepcopy(lowerCAmelCase__ )
if model_class in get_values(lowerCAmelCase__ ):
snake_case_ : Optional[Any] = {
k: v.unsqueeze(1 ).expand(-1 , self.model_tester.num_choices , -1 ).contiguous()
if isinstance(lowerCAmelCase__ , torch.Tensor ) and v.ndim > 1
else v
for k, v in inputs_dict.items()
}
if return_labels:
if model_class in get_values(lowerCAmelCase__ ):
snake_case_ : Union[str, Any] = torch.ones(self.model_tester.batch_size , dtype=torch.long , device=lowerCAmelCase__ )
elif model_class in get_values(lowerCAmelCase__ ):
snake_case_ : List[Any] = torch.zeros(
self.model_tester.batch_size , dtype=torch.long , device=lowerCAmelCase__ )
snake_case_ : str = torch.zeros(
self.model_tester.batch_size , dtype=torch.long , device=lowerCAmelCase__ )
elif model_class in [
*get_values(lowerCAmelCase__ ),
]:
snake_case_ : Union[str, Any] = torch.zeros(
self.model_tester.batch_size , dtype=torch.long , device=lowerCAmelCase__ )
elif model_class in [
*get_values(lowerCAmelCase__ ),
]:
snake_case_ : List[str] = torch.zeros(
(self.model_tester.batch_size, self.model_tester.text_seq_length) , dtype=torch.long , device=lowerCAmelCase__ , )
return inputs_dict
def _A ( self :Any ) -> Any:
'''simple docstring'''
self.config_tester.run_common_tests()
def _A ( self :int ) -> int:
'''simple docstring'''
snake_case_ : Tuple = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*lowerCAmelCase__ )
def _A ( self :Any ) -> Dict:
'''simple docstring'''
snake_case_ : Dict = self.model_tester.prepare_config_and_inputs()
for type in ["absolute", "relative_key", "relative_key_query"]:
snake_case_ : int = type
self.model_tester.create_and_check_model(*lowerCAmelCase__ )
def _A ( self :int ) -> str:
'''simple docstring'''
snake_case_ : Optional[int] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_sequence_classification(*lowerCAmelCase__ )
def _A ( self :List[Any] ) -> Optional[Any]:
'''simple docstring'''
snake_case_ : List[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_token_classification(*lowerCAmelCase__ )
def _A ( self :int ) -> Union[str, Any]:
'''simple docstring'''
snake_case_ : Optional[int] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_question_answering(*lowerCAmelCase__ )
@slow
def _A ( self :Tuple ) -> List[Any]:
'''simple docstring'''
for model_name in LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
snake_case_ : str = LayoutLMvaModel.from_pretrained(lowerCAmelCase__ )
self.assertIsNotNone(lowerCAmelCase__ )
def __UpperCAmelCase ( )-> List[str]:
"""simple docstring"""
snake_case_ : List[str] = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png" )
return image
@require_torch
class A_ (unittest.TestCase ):
"""simple docstring"""
@cached_property
def _A ( self :Union[str, Any] ) -> Optional[Any]:
'''simple docstring'''
return LayoutLMvaImageProcessor(apply_ocr=lowerCAmelCase__ ) if is_vision_available() else None
@slow
def _A ( self :Union[str, Any] ) -> Union[str, Any]:
'''simple docstring'''
snake_case_ : Optional[int] = LayoutLMvaModel.from_pretrained("microsoft/layoutlmv3-base" ).to(lowerCAmelCase__ )
snake_case_ : Optional[Any] = self.default_image_processor
snake_case_ : Optional[int] = prepare_img()
snake_case_ : Union[str, Any] = image_processor(images=lowerCAmelCase__ , return_tensors="pt" ).pixel_values.to(lowerCAmelCase__ )
snake_case_ : List[str] = torch.tensor([[1, 2]] )
snake_case_ : Any = torch.tensor([[1, 2, 3, 4], [5, 6, 7, 8]] ).unsqueeze(0 )
# forward pass
snake_case_ : Any = model(
input_ids=input_ids.to(lowerCAmelCase__ ) , bbox=bbox.to(lowerCAmelCase__ ) , pixel_values=pixel_values.to(lowerCAmelCase__ ) , )
# verify the logits
snake_case_ : Optional[Any] = torch.Size((1, 199, 768) )
self.assertEqual(outputs.last_hidden_state.shape , lowerCAmelCase__ )
snake_case_ : str = torch.tensor(
[[-0.0_5_2_9, 0.3_6_1_8, 0.1_6_3_2], [-0.1_5_8_7, -0.1_6_6_7, -0.0_4_0_0], [-0.1_5_5_7, -0.1_6_7_1, -0.0_5_0_5]] ).to(lowerCAmelCase__ )
self.assertTrue(torch.allclose(outputs.last_hidden_state[0, :3, :3] , lowerCAmelCase__ , atol=1E-4 ) )
| 653 | 0 |
"""simple docstring"""
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow
if is_torch_available():
import torch
from transformers import XLMRobertaModel
@require_sentencepiece
@require_tokenizers
@require_torch
class lowerCAmelCase_ ( unittest.TestCase ):
'''simple docstring'''
@slow
def _SCREAMING_SNAKE_CASE ( self : Dict ) -> str:
A = XLMRobertaModel.from_pretrained('xlm-roberta-base' )
A = torch.tensor([[0, 581, 1_0269, 83, 9_9942, 136, 6_0742, 23, 70, 8_0583, 1_8276, 2]] )
# The dog is cute and lives in the garden house
A = torch.Size((1, 12, 768) ) # batch_size, sequence_length, embedding_vector_dim
A = torch.tensor(
[[-0.01_01, 0.12_18, -0.08_03, 0.08_01, 0.13_27, 0.07_76, -0.12_15, 0.23_83, 0.33_38, 0.31_06, 0.03_00, 0.02_52]] )
# xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.base')
# xlmr.eval()
# expected_output_values_last_dim = xlmr.extract_features(input_ids[0])[:, :, -1]
with torch.no_grad():
A = model(A_ )['last_hidden_state'].detach()
self.assertEqual(output.shape ,A_ )
# compare the actual values for a slice of last dim
self.assertTrue(torch.allclose(output[:, :, -1] ,A_ ,atol=1e-3 ) )
@slow
def _SCREAMING_SNAKE_CASE ( self : Dict ) -> Dict:
A = XLMRobertaModel.from_pretrained('xlm-roberta-large' )
A = torch.tensor([[0, 581, 1_0269, 83, 9_9942, 136, 6_0742, 23, 70, 8_0583, 1_8276, 2]] )
# The dog is cute and lives in the garden house
A = torch.Size((1, 12, 1024) ) # batch_size, sequence_length, embedding_vector_dim
A = torch.tensor(
[[-0.06_99, -0.03_18, 0.07_05, -0.12_41, 0.09_99, -0.05_20, 0.10_04, -0.18_38, -0.47_04, 0.14_37, 0.08_21, 0.01_26]] )
# xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.large')
# xlmr.eval()
# expected_output_values_last_dim = xlmr.extract_features(input_ids[0])[:, :, -1]
with torch.no_grad():
A = model(A_ )['last_hidden_state'].detach()
self.assertEqual(output.shape ,A_ )
# compare the actual values for a slice of last dim
self.assertTrue(torch.allclose(output[:, :, -1] ,A_ ,atol=1e-3 ) ) | 91 |
'''simple docstring'''
import pytest
from datasets.parallel import ParallelBackendConfig, parallel_backend
from datasets.utils.py_utils import map_nested
from .utils import require_dill_gt_0_3_2, require_joblibspark, require_not_windows
def __UpperCAmelCase ( __magic_name__ )-> int: # picklable for multiprocessing
"""simple docstring"""
return i + 1
@require_dill_gt_0_3_2
@require_joblibspark
@require_not_windows
def __UpperCAmelCase ( )-> List[str]:
"""simple docstring"""
with parallel_backend("spark" ):
assert ParallelBackendConfig.backend_name == "spark"
snake_case_ : str = [1, 2, 3]
with pytest.raises(__magic_name__ ):
with parallel_backend("unsupported backend" ):
map_nested(__magic_name__ ,__magic_name__ ,num_proc=2 )
with pytest.raises(__magic_name__ ):
with parallel_backend("unsupported backend" ):
map_nested(__magic_name__ ,__magic_name__ ,num_proc=-1 )
@require_dill_gt_0_3_2
@require_joblibspark
@require_not_windows
@pytest.mark.parametrize("num_proc" ,[2, -1] )
def __UpperCAmelCase ( __magic_name__ )-> List[Any]:
"""simple docstring"""
snake_case_ : Optional[Any] = [1, 2]
snake_case_ : Union[str, Any] = {"a": 1, "b": 2}
snake_case_ : str = {"a": [1, 2], "b": [3, 4]}
snake_case_ : List[str] = {"a": {"1": 1}, "b": 2}
snake_case_ : Optional[int] = {"a": 1, "b": 2, "c": 3, "d": 4}
snake_case_ : Tuple = [2, 3]
snake_case_ : str = {"a": 2, "b": 3}
snake_case_ : Dict = {"a": [2, 3], "b": [4, 5]}
snake_case_ : List[Any] = {"a": {"1": 2}, "b": 3}
snake_case_ : str = {"a": 2, "b": 3, "c": 4, "d": 5}
with parallel_backend("spark" ):
assert map_nested(__magic_name__ ,__magic_name__ ,num_proc=__magic_name__ ) == expected_map_nested_sa
assert map_nested(__magic_name__ ,__magic_name__ ,num_proc=__magic_name__ ) == expected_map_nested_sa
assert map_nested(__magic_name__ ,__magic_name__ ,num_proc=__magic_name__ ) == expected_map_nested_sa
assert map_nested(__magic_name__ ,__magic_name__ ,num_proc=__magic_name__ ) == expected_map_nested_sa
assert map_nested(__magic_name__ ,__magic_name__ ,num_proc=__magic_name__ ) == expected_map_nested_sa
| 653 | 0 |
'''simple docstring'''
import numpy as np
from sklearn.datasets import fetch_california_housing
from sklearn.metrics import mean_absolute_error, mean_squared_error
from sklearn.model_selection import train_test_split
from xgboost import XGBRegressor
def _lowerCAmelCase ( __magic_name__ : dict ) -> tuple:
return (data["data"], data["target"])
def _lowerCAmelCase ( __magic_name__ : np.ndarray , __magic_name__ : np.ndarray , __magic_name__ : np.ndarray ) -> np.ndarray:
lowercase : Union[str, Any] =XGBRegressor(verbosity=0 , random_state=42 )
xgb.fit(__magic_name__ , __magic_name__ )
# Predict target for test data
lowercase : int =xgb.predict(__magic_name__ )
lowercase : Tuple =predictions.reshape(len(__magic_name__ ) , 1 )
return predictions
def _lowerCAmelCase ( ) -> None:
lowercase : Tuple =fetch_california_housing()
lowercase , lowercase : int =data_handling(__magic_name__ )
lowercase , lowercase , lowercase , lowercase : Optional[Any] =train_test_split(
__magic_name__ , __magic_name__ , test_size=0.2_5 , random_state=1 )
lowercase : List[str] =xgboost(__magic_name__ , __magic_name__ , __magic_name__ )
# Error printing
print(f'''Mean Absolute Error : {mean_absolute_error(__magic_name__ , __magic_name__ )}''' )
print(f'''Mean Square Error : {mean_squared_error(__magic_name__ , __magic_name__ )}''' )
if __name__ == "__main__":
import doctest
doctest.testmod(verbose=True)
main()
| 92 |
'''simple docstring'''
from dataclasses import asdict, dataclass
from typing import Optional
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__lowerCamelCase : Dict = logging.get_logger(__name__)
# TODO Update this
__lowerCamelCase : int = {
'''facebook/esm-1b''': '''https://huggingface.co/facebook/esm-1b/resolve/main/config.json''',
# See all ESM models at https://huggingface.co/models?filter=esm
}
class A_ (a_ ):
"""simple docstring"""
a__ = '''esm'''
def __init__( self :Dict , lowerCAmelCase__ :List[Any]=None , lowerCAmelCase__ :Optional[int]=None , lowerCAmelCase__ :str=None , lowerCAmelCase__ :int=768 , lowerCAmelCase__ :Tuple=12 , lowerCAmelCase__ :Dict=12 , lowerCAmelCase__ :Union[str, Any]=3_072 , lowerCAmelCase__ :int=0.1 , lowerCAmelCase__ :Optional[Any]=0.1 , lowerCAmelCase__ :List[Any]=1_026 , lowerCAmelCase__ :int=0.0_2 , lowerCAmelCase__ :Optional[int]=1E-1_2 , lowerCAmelCase__ :List[str]="absolute" , lowerCAmelCase__ :List[Any]=True , lowerCAmelCase__ :Dict=None , lowerCAmelCase__ :List[str]=False , lowerCAmelCase__ :List[Any]=False , lowerCAmelCase__ :Dict=None , lowerCAmelCase__ :str=None , **lowerCAmelCase__ :Union[str, Any] , ) -> Any:
'''simple docstring'''
super().__init__(pad_token_id=lowerCAmelCase__ , mask_token_id=lowerCAmelCase__ , **lowerCAmelCase__ )
snake_case_ : str = vocab_size
snake_case_ : str = hidden_size
snake_case_ : List[str] = num_hidden_layers
snake_case_ : List[str] = num_attention_heads
snake_case_ : Any = intermediate_size
snake_case_ : Optional[Any] = hidden_dropout_prob
snake_case_ : Tuple = attention_probs_dropout_prob
snake_case_ : List[Any] = max_position_embeddings
snake_case_ : str = initializer_range
snake_case_ : List[Any] = layer_norm_eps
snake_case_ : str = position_embedding_type
snake_case_ : Optional[int] = use_cache
snake_case_ : str = emb_layer_norm_before
snake_case_ : List[Any] = token_dropout
snake_case_ : str = is_folding_model
if is_folding_model:
if esmfold_config is None:
logger.info("No esmfold_config supplied for folding model, using default values." )
snake_case_ : Optional[Any] = EsmFoldConfig()
elif isinstance(lowerCAmelCase__ , lowerCAmelCase__ ):
snake_case_ : Union[str, Any] = EsmFoldConfig(**lowerCAmelCase__ )
snake_case_ : Optional[Any] = esmfold_config
if vocab_list is None:
logger.warning("No vocab_list supplied for folding model, assuming the ESM-2 vocabulary!" )
snake_case_ : List[str] = get_default_vocab_list()
else:
snake_case_ : List[str] = vocab_list
else:
snake_case_ : List[Any] = None
snake_case_ : int = None
if self.esmfold_config is not None and getattr(self.esmfold_config , "use_esm_attn_map" , lowerCAmelCase__ ):
raise ValueError("The HuggingFace port of ESMFold does not support use_esm_attn_map at this time!" )
def _A ( self :Optional[int] ) -> List[Any]:
'''simple docstring'''
snake_case_ : Any = super().to_dict()
if isinstance(self.esmfold_config , lowerCAmelCase__ ):
snake_case_ : Optional[int] = self.esmfold_config.to_dict()
return output
@dataclass
class A_ :
"""simple docstring"""
a__ = None
a__ = True
a__ = False
a__ = False
a__ = False
a__ = 0
a__ = True
a__ = False
a__ = 128
a__ = None
def _A ( self :Dict ) -> int:
'''simple docstring'''
if self.trunk is None:
snake_case_ : Dict = TrunkConfig()
elif isinstance(self.trunk , lowerCAmelCase__ ):
snake_case_ : int = TrunkConfig(**self.trunk )
def _A ( self :Optional[int] ) -> Union[str, Any]:
'''simple docstring'''
snake_case_ : Tuple = asdict(self )
snake_case_ : Optional[int] = self.trunk.to_dict()
return output
@dataclass
class A_ :
"""simple docstring"""
a__ = 48
a__ = 1024
a__ = 128
a__ = 32
a__ = 32
a__ = 32
a__ = 0
a__ = 0
a__ = False
a__ = 4
a__ = 128
a__ = None
def _A ( self :List[Any] ) -> Union[str, Any]:
'''simple docstring'''
if self.structure_module is None:
snake_case_ : Optional[int] = StructureModuleConfig()
elif isinstance(self.structure_module , lowerCAmelCase__ ):
snake_case_ : List[str] = StructureModuleConfig(**self.structure_module )
if self.max_recycles <= 0:
raise ValueError(F'''`max_recycles` should be positive, got {self.max_recycles}.''' )
if self.sequence_state_dim % self.sequence_state_dim != 0:
raise ValueError(
"`sequence_state_dim` should be a round multiple of `sequence_state_dim`, got"
F''' {self.sequence_state_dim} and {self.sequence_state_dim}.''' )
if self.pairwise_state_dim % self.pairwise_state_dim != 0:
raise ValueError(
"`pairwise_state_dim` should be a round multiple of `pairwise_state_dim`, got"
F''' {self.pairwise_state_dim} and {self.pairwise_state_dim}.''' )
snake_case_ : Dict = self.sequence_state_dim // self.sequence_head_width
snake_case_ : Optional[int] = self.pairwise_state_dim // self.pairwise_head_width
if self.sequence_state_dim != sequence_num_heads * self.sequence_head_width:
raise ValueError(
"`sequence_state_dim` should be equal to `sequence_num_heads * sequence_head_width, got"
F''' {self.sequence_state_dim} != {sequence_num_heads} * {self.sequence_head_width}.''' )
if self.pairwise_state_dim != pairwise_num_heads * self.pairwise_head_width:
raise ValueError(
"`pairwise_state_dim` should be equal to `pairwise_num_heads * pairwise_head_width, got"
F''' {self.pairwise_state_dim} != {pairwise_num_heads} * {self.pairwise_head_width}.''' )
if self.pairwise_state_dim % 2 != 0:
raise ValueError(F'''`pairwise_state_dim` should be even, got {self.pairwise_state_dim}.''' )
if self.dropout >= 0.4:
raise ValueError(F'''`dropout` should not be greater than 0.4, got {self.dropout}.''' )
def _A ( self :Tuple ) -> List[str]:
'''simple docstring'''
snake_case_ : int = asdict(self )
snake_case_ : Dict = self.structure_module.to_dict()
return output
@dataclass
class A_ :
"""simple docstring"""
a__ = 384
a__ = 128
a__ = 16
a__ = 128
a__ = 12
a__ = 4
a__ = 8
a__ = 0.1
a__ = 8
a__ = 1
a__ = 2
a__ = 7
a__ = 10
a__ = 1E-8
a__ = 1E5
def _A ( self :Dict ) -> Dict:
'''simple docstring'''
return asdict(self )
def __UpperCAmelCase ( )-> int:
"""simple docstring"""
return (
"<cls>",
"<pad>",
"<eos>",
"<unk>",
"L",
"A",
"G",
"V",
"S",
"E",
"R",
"T",
"I",
"D",
"P",
"K",
"Q",
"N",
"F",
"Y",
"M",
"H",
"W",
"C",
"X",
"B",
"U",
"Z",
"O",
".",
"-",
"<null_1>",
"<mask>",
)
| 653 | 0 |
"""simple docstring"""
import unittest
import numpy as np
from transformers import RoFormerConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask
if is_flax_available():
import jax.numpy as jnp
from transformers.models.roformer.modeling_flax_roformer import (
FlaxRoFormerForMaskedLM,
FlaxRoFormerForMultipleChoice,
FlaxRoFormerForQuestionAnswering,
FlaxRoFormerForSequenceClassification,
FlaxRoFormerForTokenClassification,
FlaxRoFormerModel,
)
class _lowerCAmelCase ( unittest.TestCase ):
"""simple docstring"""
def __init__( self , __UpperCAmelCase , __UpperCAmelCase=1_3 , __UpperCAmelCase=7 , __UpperCAmelCase=True , __UpperCAmelCase=True , __UpperCAmelCase=True , __UpperCAmelCase=True , __UpperCAmelCase=9_9 , __UpperCAmelCase=3_2 , __UpperCAmelCase=5 , __UpperCAmelCase=4 , __UpperCAmelCase=3_7 , __UpperCAmelCase="gelu" , __UpperCAmelCase=0.1 , __UpperCAmelCase=0.1 , __UpperCAmelCase=5_1_2 , __UpperCAmelCase=1_6 , __UpperCAmelCase=2 , __UpperCAmelCase=0.02 , __UpperCAmelCase=4 , ):
'''simple docstring'''
lowerCAmelCase__ :int = parent
lowerCAmelCase__ :Dict = batch_size
lowerCAmelCase__ :Union[str, Any] = seq_length
lowerCAmelCase__ :Union[str, Any] = is_training
lowerCAmelCase__ :int = use_attention_mask
lowerCAmelCase__ :Optional[int] = use_token_type_ids
lowerCAmelCase__ :Any = use_labels
lowerCAmelCase__ :Dict = vocab_size
lowerCAmelCase__ :Optional[Any] = hidden_size
lowerCAmelCase__ :Tuple = num_hidden_layers
lowerCAmelCase__ :int = num_attention_heads
lowerCAmelCase__ :List[Any] = intermediate_size
lowerCAmelCase__ :List[Any] = hidden_act
lowerCAmelCase__ :Optional[Any] = hidden_dropout_prob
lowerCAmelCase__ :Tuple = attention_probs_dropout_prob
lowerCAmelCase__ :Any = max_position_embeddings
lowerCAmelCase__ :Optional[Any] = type_vocab_size
lowerCAmelCase__ :int = type_sequence_label_size
lowerCAmelCase__ :Union[str, Any] = initializer_range
lowerCAmelCase__ :str = num_choices
def snake_case ( self ):
'''simple docstring'''
lowerCAmelCase__ :Optional[Any] = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
lowerCAmelCase__ :str = None
if self.use_attention_mask:
lowerCAmelCase__ :List[str] = random_attention_mask([self.batch_size, self.seq_length] )
lowerCAmelCase__ :List[str] = None
if self.use_token_type_ids:
lowerCAmelCase__ :Dict = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
lowerCAmelCase__ :Any = RoFormerConfig(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , is_decoder=__UpperCAmelCase , initializer_range=self.initializer_range , )
return config, input_ids, token_type_ids, attention_mask
def snake_case ( self ):
'''simple docstring'''
lowerCAmelCase__ :List[str] = self.prepare_config_and_inputs()
lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ :Any = config_and_inputs
lowerCAmelCase__ :Dict = {'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': attention_mask}
return config, inputs_dict
@require_flax
class _lowerCAmelCase ( a , unittest.TestCase ):
"""simple docstring"""
__magic_name__ :Any = True
__magic_name__ :List[Any] = (
(
FlaxRoFormerModel,
FlaxRoFormerForMaskedLM,
FlaxRoFormerForSequenceClassification,
FlaxRoFormerForTokenClassification,
FlaxRoFormerForMultipleChoice,
FlaxRoFormerForQuestionAnswering,
)
if is_flax_available()
else ()
)
def snake_case ( self ):
'''simple docstring'''
lowerCAmelCase__ :Dict = FlaxRoFormerModelTester(self )
@slow
def snake_case ( self ):
'''simple docstring'''
for model_class_name in self.all_model_classes:
lowerCAmelCase__ :Dict = model_class_name.from_pretrained('junnyu/roformer_chinese_small' , from_pt=__UpperCAmelCase )
lowerCAmelCase__ :Union[str, Any] = model(np.ones((1, 1) ) )
self.assertIsNotNone(__UpperCAmelCase )
@require_flax
class _lowerCAmelCase ( unittest.TestCase ):
"""simple docstring"""
@slow
def snake_case ( self ):
'''simple docstring'''
lowerCAmelCase__ :Any = FlaxRoFormerForMaskedLM.from_pretrained('junnyu/roformer_chinese_base' )
lowerCAmelCase__ :Union[str, Any] = jnp.array([[0, 1, 2, 3, 4, 5]] )
lowerCAmelCase__ :Tuple = model(__UpperCAmelCase )[0]
lowerCAmelCase__ :Any = 5_0_0_0_0
lowerCAmelCase__ :int = (1, 6, vocab_size)
self.assertEqual(output.shape , __UpperCAmelCase )
lowerCAmelCase__ :List[Any] = jnp.array(
[[[-0.12_05, -1.02_65, 0.29_22], [-1.51_34, 0.19_74, 0.15_19], [-5.01_35, -3.90_03, -0.84_04]]] )
self.assertTrue(jnp.allclose(output[:, :3, :3] , __UpperCAmelCase , atol=1E-4 ) )
| 93 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__lowerCamelCase : Any = {
'''configuration_longformer''': [
'''LONGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''LongformerConfig''',
'''LongformerOnnxConfig''',
],
'''tokenization_longformer''': ['''LongformerTokenizer'''],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__lowerCamelCase : Any = ['''LongformerTokenizerFast''']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__lowerCamelCase : Dict = [
'''LONGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''LongformerForMaskedLM''',
'''LongformerForMultipleChoice''',
'''LongformerForQuestionAnswering''',
'''LongformerForSequenceClassification''',
'''LongformerForTokenClassification''',
'''LongformerModel''',
'''LongformerPreTrainedModel''',
'''LongformerSelfAttention''',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__lowerCamelCase : Any = [
'''TF_LONGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''TFLongformerForMaskedLM''',
'''TFLongformerForMultipleChoice''',
'''TFLongformerForQuestionAnswering''',
'''TFLongformerForSequenceClassification''',
'''TFLongformerForTokenClassification''',
'''TFLongformerModel''',
'''TFLongformerPreTrainedModel''',
'''TFLongformerSelfAttention''',
]
if TYPE_CHECKING:
from .configuration_longformer import (
LONGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
LongformerConfig,
LongformerOnnxConfig,
)
from .tokenization_longformer import LongformerTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_longformer_fast import LongformerTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_longformer import (
LONGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
LongformerForMaskedLM,
LongformerForMultipleChoice,
LongformerForQuestionAnswering,
LongformerForSequenceClassification,
LongformerForTokenClassification,
LongformerModel,
LongformerPreTrainedModel,
LongformerSelfAttention,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_longformer import (
TF_LONGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
TFLongformerForMaskedLM,
TFLongformerForMultipleChoice,
TFLongformerForQuestionAnswering,
TFLongformerForSequenceClassification,
TFLongformerForTokenClassification,
TFLongformerModel,
TFLongformerPreTrainedModel,
TFLongformerSelfAttention,
)
else:
import sys
__lowerCamelCase : Optional[Any] = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 653 | 0 |
'''simple docstring'''
def lowercase_ ( __A : int , __A : list ) -> Tuple:
"""simple docstring"""
_enforce_args(__A , __A )
if n == 0:
return 0
lowercase : Optional[int] =float('''-inf''' )
for i in range(1 , n + 1 ):
lowercase : Any =max(
__A , prices[i - 1] + naive_cut_rod_recursive(n - i , __A ) )
return max_revue
def lowercase_ ( __A : int , __A : list ) -> Tuple:
"""simple docstring"""
_enforce_args(__A , __A )
lowercase : int =[float('''-inf''' ) for _ in range(n + 1 )]
return _top_down_cut_rod_recursive(__A , __A , __A )
def lowercase_ ( __A : int , __A : list , __A : list ) -> str:
"""simple docstring"""
if max_rev[n] >= 0:
return max_rev[n]
elif n == 0:
return 0
else:
lowercase : Dict =float('''-inf''' )
for i in range(1 , n + 1 ):
lowercase : Optional[int] =max(
__A , prices[i - 1] + _top_down_cut_rod_recursive(n - i , __A , __A ) , )
lowercase : Union[str, Any] =max_revenue
return max_rev[n]
def lowercase_ ( __A : int , __A : list ) -> List[str]:
"""simple docstring"""
_enforce_args(__A , __A )
# length(max_rev) = n + 1, to accommodate for the revenue obtainable from a rod of
# length 0.
lowercase : int =[float('''-inf''' ) for _ in range(n + 1 )]
lowercase : int =0
for i in range(1 , n + 1 ):
lowercase : List[Any] =max_rev[i]
for j in range(1 , i + 1 ):
lowercase : Union[str, Any] =max(__A , prices[j - 1] + max_rev[i - j] )
lowercase : str =max_revenue_i
return max_rev[n]
def lowercase_ ( __A : int , __A : list ) -> Dict:
"""simple docstring"""
if n < 0:
lowercase : List[Any] =F'n must be greater than or equal to 0. Got n = {n}'
raise ValueError(__A )
if n > len(__A ):
lowercase : Optional[int] =(
'''Each integral piece of rod must have a corresponding price. '''
F'Got n = {n} but length of prices = {len(__A )}'
)
raise ValueError(__A )
def lowercase_ ( ) -> Optional[int]:
"""simple docstring"""
lowercase : Optional[Any] =[6, 1_0, 1_2, 1_5, 2_0, 2_3]
lowercase : Union[str, Any] =len(__A )
# the best revenue comes from cutting the rod into 6 pieces, each
# of length 1 resulting in a revenue of 6 * 6 = 36.
lowercase : str =3_6
lowercase : Dict =top_down_cut_rod(__A , __A )
lowercase : Dict =bottom_up_cut_rod(__A , __A )
lowercase : Union[str, Any] =naive_cut_rod_recursive(__A , __A )
assert expected_max_revenue == max_rev_top_down
assert max_rev_top_down == max_rev_bottom_up
assert max_rev_bottom_up == max_rev_naive
if __name__ == "__main__":
main()
| 94 |
'''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
__lowerCamelCase : Optional[int] = 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 A_ :
"""simple docstring"""
def __init__( self :Tuple , lowerCAmelCase__ :List[str] , lowerCAmelCase__ :List[Any]=16 , lowerCAmelCase__ :Any=13 , lowerCAmelCase__ :Optional[Any]=7 , lowerCAmelCase__ :str=14 , lowerCAmelCase__ :Union[str, Any]=10 , lowerCAmelCase__ :Tuple=19 , lowerCAmelCase__ :Optional[Any]=5 , lowerCAmelCase__ :Dict=4 , lowerCAmelCase__ :Union[str, Any]=True , lowerCAmelCase__ :Any=16 , lowerCAmelCase__ :str=2 , lowerCAmelCase__ :List[Any]=4 , lowerCAmelCase__ :Any=4 , lowerCAmelCase__ :str="gelu" , lowerCAmelCase__ :Tuple=0.1 , lowerCAmelCase__ :Dict=0.1 , lowerCAmelCase__ :Optional[int]=[1, 2, 3, 4, 5] , lowerCAmelCase__ :str=25 , lowerCAmelCase__ :Optional[Any]=5 , ) -> Dict:
'''simple docstring'''
snake_case_ : List[str] = d_model
snake_case_ : Dict = parent
snake_case_ : Optional[Any] = batch_size
snake_case_ : Optional[Any] = prediction_length
snake_case_ : str = context_length
snake_case_ : Tuple = cardinality
snake_case_ : List[str] = num_time_features
snake_case_ : Optional[Any] = lags_sequence
snake_case_ : Union[str, Any] = embedding_dimension
snake_case_ : Optional[Any] = is_training
snake_case_ : Optional[Any] = hidden_size
snake_case_ : Any = num_hidden_layers
snake_case_ : Optional[Any] = num_attention_heads
snake_case_ : int = intermediate_size
snake_case_ : Any = hidden_act
snake_case_ : Union[str, Any] = hidden_dropout_prob
snake_case_ : Dict = attention_probs_dropout_prob
snake_case_ : List[str] = context_length
snake_case_ : Any = prediction_length + label_length
snake_case_ : Union[str, Any] = label_length
snake_case_ : List[Any] = moving_average
snake_case_ : str = autocorrelation_factor
def _A ( self :List[Any] ) -> Any:
'''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 _A ( self :Union[str, Any] , lowerCAmelCase__ :Optional[Any] ) -> Dict:
'''simple docstring'''
snake_case_ : Any = config.context_length + max(config.lags_sequence )
snake_case_ : Union[str, Any] = ids_tensor([self.batch_size, 1] , config.cardinality[0] )
snake_case_ : Optional[int] = floats_tensor([self.batch_size, _past_length, config.num_time_features] )
snake_case_ : List[Any] = floats_tensor([self.batch_size, _past_length] )
snake_case_ : Dict = floats_tensor([self.batch_size, _past_length] ) > 0.5
# decoder inputs
snake_case_ : List[Any] = floats_tensor([self.batch_size, config.prediction_length, config.num_time_features] )
snake_case_ : List[Any] = floats_tensor([self.batch_size, config.prediction_length] )
snake_case_ : int = {
"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 _A ( self :Dict ) -> Tuple:
'''simple docstring'''
snake_case_ : str = self.get_config()
snake_case_ : int = self.prepare_autoformer_inputs_dict(lowerCAmelCase__ )
return config, inputs_dict
def _A ( self :Optional[int] ) -> Dict:
'''simple docstring'''
snake_case_, snake_case_ : Union[str, Any] = self.prepare_config_and_inputs()
return config, inputs_dict
def _A ( self :Tuple , lowerCAmelCase__ :int , lowerCAmelCase__ :Optional[int] ) -> List[str]:
'''simple docstring'''
snake_case_ : Dict = AutoformerModel(config=lowerCAmelCase__ ).to(lowerCAmelCase__ ).eval()
snake_case_ : Optional[int] = model(**lowerCAmelCase__ )
snake_case_ : Any = outputs.encoder_last_hidden_state
snake_case_ : Dict = outputs.last_hidden_state
with tempfile.TemporaryDirectory() as tmpdirname:
snake_case_ : Optional[Any] = model.get_encoder()
encoder.save_pretrained(lowerCAmelCase__ )
snake_case_ : Tuple = AutoformerEncoder.from_pretrained(lowerCAmelCase__ ).to(lowerCAmelCase__ )
snake_case_, snake_case_, snake_case_, snake_case_, snake_case_ : List[str] = model.create_network_inputs(**lowerCAmelCase__ )
snake_case_, snake_case_ : Optional[int] = model.decomposition_layer(transformer_inputs[:, : config.context_length, ...] )
snake_case_ : List[Any] = torch.cat(
(transformer_inputs[:, : config.context_length, ...], feature[:, : config.context_length, ...]) , dim=-1 , )
snake_case_ : Optional[int] = encoder(inputs_embeds=lowerCAmelCase__ )[0]
self.parent.assertTrue((encoder_last_hidden_state_a - encoder_last_hidden_state).abs().max().item() < 1E-3 )
snake_case_ : Any = (
torch.mean(transformer_inputs[:, : config.context_length, ...] , dim=1 )
.unsqueeze(1 )
.repeat(1 , config.prediction_length , 1 )
)
snake_case_ : List[str] = torch.zeros(
[transformer_inputs.shape[0], config.prediction_length, transformer_inputs.shape[2]] , device=enc_input.device , )
snake_case_ : Optional[Any] = torch.cat(
(
torch.cat((seasonal_input[:, -config.label_length :, ...], zeros) , dim=1 ),
feature[:, config.context_length - config.label_length :, ...],
) , dim=-1 , )
snake_case_ : Any = 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:
snake_case_ : List[Any] = model.get_decoder()
decoder.save_pretrained(lowerCAmelCase__ )
snake_case_ : int = AutoformerDecoder.from_pretrained(lowerCAmelCase__ ).to(lowerCAmelCase__ )
snake_case_ : Tuple = decoder(
trend=lowerCAmelCase__ , inputs_embeds=lowerCAmelCase__ , encoder_hidden_states=lowerCAmelCase__ , )[0]
self.parent.assertTrue((last_hidden_state_a - last_hidden_state).abs().max().item() < 1E-3 )
@require_torch
class A_ (a_ , a_ , unittest.TestCase ):
"""simple docstring"""
a__ = (AutoformerModel, AutoformerForPrediction) if is_torch_available() else ()
a__ = (AutoformerForPrediction,) if is_torch_available() else ()
a__ = {'''feature-extraction''': AutoformerModel} if is_torch_available() else {}
a__ = False
a__ = False
a__ = False
a__ = False
a__ = False
a__ = False
def _A ( self :Dict ) -> int:
'''simple docstring'''
snake_case_ : Tuple = AutoformerModelTester(self )
snake_case_ : str = ConfigTester(self , config_class=lowerCAmelCase__ , has_text_modality=lowerCAmelCase__ )
def _A ( self :List[str] ) -> Tuple:
'''simple docstring'''
self.config_tester.run_common_tests()
def _A ( self :List[Any] ) -> Union[str, Any]:
'''simple docstring'''
snake_case_, snake_case_ : List[Any] = self.model_tester.prepare_config_and_inputs()
for model_class in self.all_model_classes:
snake_case_ : List[Any] = model_class(lowerCAmelCase__ )
with tempfile.TemporaryDirectory() as tmpdirname:
model.save_pretrained(lowerCAmelCase__ )
snake_case_, snake_case_ : str = model_class.from_pretrained(lowerCAmelCase__ , output_loading_info=lowerCAmelCase__ )
self.assertEqual(info["missing_keys"] , [] )
def _A ( self :Optional[int] ) -> Tuple:
'''simple docstring'''
snake_case_ : str = self.model_tester.prepare_config_and_inputs_for_common()
self.model_tester.check_encoder_decoder_model_standalone(*lowerCAmelCase__ )
@unittest.skip(reason="Model has no tokens embeddings" )
def _A ( self :str ) -> str:
'''simple docstring'''
pass
def _A ( self :Optional[Any] ) -> Union[str, Any]:
'''simple docstring'''
snake_case_ : List[Any] = inspect.signature(getattr(lowerCAmelCase__ , "forward" ) )
# The main input is the name of the argument after `self`
snake_case_ : Dict = list(model_signature.parameters.keys() )[1]
self.assertEqual(AutoformerModel.main_input_name , lowerCAmelCase__ )
def _A ( self :Optional[Any] ) -> Optional[int]:
'''simple docstring'''
snake_case_, snake_case_ : Tuple = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
snake_case_ : Tuple = model_class(lowerCAmelCase__ )
snake_case_ : int = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
snake_case_ : Optional[Any] = [*signature.parameters.keys()]
snake_case_ : Dict = [
"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(lowerCAmelCase__ )] , lowerCAmelCase__ )
def _A ( self :int ) -> Any:
'''simple docstring'''
snake_case_, snake_case_ : Tuple = self.model_tester.prepare_config_and_inputs_for_common()
snake_case_ : Union[str, Any] = True
snake_case_ : List[str] = getattr(self.model_tester , "seq_length" , lowerCAmelCase__ )
snake_case_ : Dict = getattr(self.model_tester , "decoder_seq_length" , lowerCAmelCase__ )
snake_case_ : Union[str, Any] = getattr(self.model_tester , "encoder_seq_length" , lowerCAmelCase__ )
snake_case_ : Union[str, Any] = getattr(self.model_tester , "d_model" , lowerCAmelCase__ )
snake_case_ : Dict = getattr(self.model_tester , "num_attention_heads" , lowerCAmelCase__ )
snake_case_ : Optional[int] = d_model // num_attention_heads
for model_class in self.all_model_classes:
snake_case_ : Any = True
snake_case_ : Any = False
snake_case_ : Dict = True
snake_case_ : List[str] = model_class(lowerCAmelCase__ )
model.to(lowerCAmelCase__ )
model.eval()
with torch.no_grad():
snake_case_ : Tuple = model(**self._prepare_for_class(lowerCAmelCase__ , lowerCAmelCase__ ) )
snake_case_ : Union[str, Any] = outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
self.assertEqual(len(lowerCAmelCase__ ) , self.model_tester.num_hidden_layers )
# check that output_attentions also work using config
del inputs_dict["output_attentions"]
snake_case_ : Optional[int] = True
snake_case_ : Any = model_class(lowerCAmelCase__ )
model.to(lowerCAmelCase__ )
model.eval()
with torch.no_grad():
snake_case_ : List[str] = model(**self._prepare_for_class(lowerCAmelCase__ , lowerCAmelCase__ ) )
snake_case_ : str = outputs.encoder_attentions
self.assertEqual(len(lowerCAmelCase__ ) , self.model_tester.num_hidden_layers )
self.assertListEqual(
list(attentions[0].shape[-3:] ) , [self.model_tester.num_attention_heads, encoder_seq_length, dim] , )
snake_case_ : Tuple = len(lowerCAmelCase__ )
snake_case_ : List[str] = 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(lowerCAmelCase__ , lowerCAmelCase__ )
# decoder attentions
snake_case_ : Optional[int] = outputs.decoder_attentions
self.assertIsInstance(lowerCAmelCase__ , (list, tuple) )
self.assertEqual(len(lowerCAmelCase__ ) , 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
snake_case_ : List[Any] = outputs.cross_attentions
self.assertIsInstance(lowerCAmelCase__ , (list, tuple) )
self.assertEqual(len(lowerCAmelCase__ ) , 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
snake_case_ : Optional[int] = True
snake_case_ : List[Any] = True
snake_case_ : Union[str, Any] = model_class(lowerCAmelCase__ )
model.to(lowerCAmelCase__ )
model.eval()
with torch.no_grad():
snake_case_ : List[Any] = model(**self._prepare_for_class(lowerCAmelCase__ , lowerCAmelCase__ ) )
self.assertEqual(out_len + 2 , len(lowerCAmelCase__ ) )
snake_case_ : Tuple = outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
self.assertEqual(len(lowerCAmelCase__ ) , 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 _A ( self :Any ) -> Optional[Any]:
'''simple docstring'''
super().test_retain_grad_hidden_states_attentions()
def __UpperCAmelCase ( __magic_name__="train-batch.pt" )-> int:
"""simple docstring"""
snake_case_ : List[str] = hf_hub_download(repo_id="hf-internal-testing/tourism-monthly-batch" ,filename=__magic_name__ ,repo_type="dataset" )
snake_case_ : List[str] = torch.load(__magic_name__ ,map_location=__magic_name__ )
return batch
@require_torch
@slow
class A_ (unittest.TestCase ):
"""simple docstring"""
def _A ( self :str ) -> Any:
'''simple docstring'''
snake_case_ : Optional[int] = AutoformerModel.from_pretrained("huggingface/autoformer-tourism-monthly" ).to(lowerCAmelCase__ )
snake_case_ : List[str] = prepare_batch()
with torch.no_grad():
snake_case_ : int = 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]
snake_case_ : Optional[int] = torch.Size(
(64, model.config.prediction_length + model.config.label_length, model.config.feature_size) )
self.assertEqual(output.shape , lowerCAmelCase__ )
snake_case_ : Optional[Any] = torch.tensor(
[[0.3_5_9_3, -1.3_3_9_8, 0.6_3_3_0], [0.2_2_7_9, 1.5_3_9_6, -0.1_7_9_2], [0.0_4_5_0, 1.3_2_2_5, -0.2_3_3_5]] , device=lowerCAmelCase__ )
self.assertTrue(torch.allclose(output[0, :3, :3] , lowerCAmelCase__ , atol=lowerCAmelCase__ ) )
def _A ( self :Any ) -> str:
'''simple docstring'''
snake_case_ : str = AutoformerForPrediction.from_pretrained("huggingface/autoformer-tourism-monthly" ).to(lowerCAmelCase__ )
snake_case_ : Optional[Any] = prepare_batch("val-batch.pt" )
with torch.no_grad():
snake_case_ : Tuple = 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
snake_case_ : Dict = torch.Size((64, model.config.context_length, model.config.d_model) )
self.assertEqual(output.shape , lowerCAmelCase__ )
snake_case_ : Any = torch.tensor(
[[-0.0_7_3_4, -0.9_0_3_6, 0.8_3_5_8], [4.7_1_8_6, 2.4_1_1_3, 1.9_5_8_1], [1.7_9_5_3, 2.3_5_5_8, 1.2_9_7_0]] , device=lowerCAmelCase__ )
self.assertTrue(torch.allclose(output[0, :3, :3] , lowerCAmelCase__ , atol=lowerCAmelCase__ ) )
def _A ( self :List[str] ) -> Any:
'''simple docstring'''
snake_case_ : List[Any] = AutoformerForPrediction.from_pretrained("huggingface/autoformer-tourism-monthly" ).to(lowerCAmelCase__ )
snake_case_ : str = prepare_batch("val-batch.pt" )
with torch.no_grad():
snake_case_ : Optional[Any] = 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"] , )
snake_case_ : List[Any] = torch.Size((64, model.config.num_parallel_samples, model.config.prediction_length) )
self.assertEqual(outputs.sequences.shape , lowerCAmelCase__ )
snake_case_ : Dict = torch.tensor([3_1_3_0.6_7_6_3, 4_0_5_6.5_2_9_3, 7_0_5_3.0_7_8_6] , device=lowerCAmelCase__ )
snake_case_ : Optional[Any] = outputs.sequences.mean(dim=1 )
self.assertTrue(torch.allclose(mean_prediction[0, -3:] , lowerCAmelCase__ , rtol=1E-1 ) )
| 653 | 0 |
"""simple docstring"""
import os
from typing import Optional
import fsspec
from fsspec.archive import AbstractArchiveFileSystem
from fsspec.utils import DEFAULT_BLOCK_SIZE
class UpperCamelCase_ (__A ):
__magic_name__ = ''''''
__magic_name__ = (
None # protocol passed in prefix to the url. ex: "gzip", for gzip://file.txt::http://foo.bar/file.txt.gz
)
__magic_name__ = None # compression type in fsspec. ex: "gzip"
__magic_name__ = None # extension of the filename to strip. ex: "".gz" to get file.txt from file.txt.gz
def __init__( self : List[Any] , lowerCAmelCase_ : str = "" , lowerCAmelCase_ : Optional[str] = None , lowerCAmelCase_ : Optional[dict] = None , **lowerCAmelCase_ : List[str] ) -> List[Any]:
super().__init__(self , **lowerCAmelCase_ )
# always open as "rb" since fsspec can then use the TextIOWrapper to make it work for "r" mode
UpperCAmelCase_ : Dict = fsspec.open(
lowerCAmelCase_ , mode="rb" , protocol=lowerCAmelCase_ , compression=self.compression , client_kwargs={
"requote_redirect_url": False, # see https://github.com/huggingface/datasets/pull/5459
"trust_env": True, # Enable reading proxy env variables.
**(target_options or {}).pop("client_kwargs" , {} ), # To avoid issues if it was already passed.
} , **(target_options or {}) , )
UpperCAmelCase_ : Optional[int] = os.path.basename(self.file.path.split("::" )[0] )
UpperCAmelCase_ : Any = (
self.compressed_name[: self.compressed_name.rindex("." )]
if "." in self.compressed_name
else self.compressed_name
)
UpperCAmelCase_ : Any = None
@classmethod
def _SCREAMING_SNAKE_CASE ( cls : Optional[Any] , lowerCAmelCase_ : List[str] ) -> Any:
# compressed file paths are always relative to the archive root
return super()._strip_protocol(lowerCAmelCase_ ).lstrip("/" )
def _SCREAMING_SNAKE_CASE ( self : Union[str, Any] ) -> List[str]:
if self.dir_cache is None:
UpperCAmelCase_ : Optional[Any] = {**self.file.fs.info(self.file.path ), "name": self.uncompressed_name}
UpperCAmelCase_ : str = {f["name"]: f}
def _SCREAMING_SNAKE_CASE ( self : int , lowerCAmelCase_ : str ) -> Any:
return self.file.open().read()
def _SCREAMING_SNAKE_CASE ( self : Optional[int] , lowerCAmelCase_ : str , lowerCAmelCase_ : str = "rb" , lowerCAmelCase_ : List[str]=None , lowerCAmelCase_ : List[str]=True , lowerCAmelCase_ : int=None , **lowerCAmelCase_ : Tuple , ) -> Tuple:
UpperCAmelCase_ : List[str] = self._strip_protocol(lowerCAmelCase_ )
if mode != "rb":
raise ValueError(f"""Tried to read with mode {mode} on file {self.file.path} opened with mode 'rb'""" )
return self.file.open()
class UpperCamelCase_ (__A ):
__magic_name__ = '''bz2'''
__magic_name__ = '''bz2'''
__magic_name__ = '''.bz2'''
class UpperCamelCase_ (__A ):
__magic_name__ = '''gzip'''
__magic_name__ = '''gzip'''
__magic_name__ = '''.gz'''
class UpperCamelCase_ (__A ):
__magic_name__ = '''lz4'''
__magic_name__ = '''lz4'''
__magic_name__ = '''.lz4'''
class UpperCamelCase_ (__A ):
__magic_name__ = '''xz'''
__magic_name__ = '''xz'''
__magic_name__ = '''.xz'''
class UpperCamelCase_ (__A ):
__magic_name__ = '''zstd'''
__magic_name__ = '''zstd'''
__magic_name__ = '''.zst'''
def __init__( self : Tuple , lowerCAmelCase_ : str , lowerCAmelCase_ : str = "rb" , lowerCAmelCase_ : Optional[str] = None , lowerCAmelCase_ : Optional[dict] = None , lowerCAmelCase_ : int = DEFAULT_BLOCK_SIZE , **lowerCAmelCase_ : List[Any] , ) -> Dict:
super().__init__(
fo=lowerCAmelCase_ , mode=lowerCAmelCase_ , target_protocol=lowerCAmelCase_ , target_options=lowerCAmelCase_ , block_size=lowerCAmelCase_ , **lowerCAmelCase_ , )
# We need to wrap the zstd decompressor to avoid this error in fsspec==2021.7.0 and zstandard==0.15.2:
#
# File "/Users/user/.virtualenvs/hf-datasets/lib/python3.7/site-packages/fsspec/core.py", line 145, in open
# out.close = close
# AttributeError: 'zstd.ZstdDecompressionReader' object attribute 'close' is read-only
#
# see https://github.com/intake/filesystem_spec/issues/725
UpperCAmelCase_ : Optional[Any] = self.file.__enter__
class UpperCamelCase_ :
def __init__( self : Tuple , lowerCAmelCase_ : List[Any] ) -> List[Any]:
UpperCAmelCase_ : Optional[int] = file_
def __enter__( self : Tuple ) -> List[Any]:
self._file.__enter__()
return self
def __exit__( self : Union[str, Any] , *lowerCAmelCase_ : int , **lowerCAmelCase_ : int ) -> Optional[int]:
self._file.__exit__(*lowerCAmelCase_ , **lowerCAmelCase_ )
def __iter__( self : Optional[int] ) -> int:
return iter(self._file )
def _SCREAMING_SNAKE_CASE ( self : Optional[int] ) -> int:
return next(self._file )
def __getattr__( self : Optional[int] , lowerCAmelCase_ : Optional[Any] ) -> List[Any]:
return getattr(self._file , lowerCAmelCase_ )
def fixed_enter(*lowerCAmelCase_ : int , **lowerCAmelCase_ : Optional[Any] ):
return WrappedFile(_enter(*lowerCAmelCase_ , **lowerCAmelCase_ ) )
UpperCAmelCase_ : List[Any] = fixed_enter
| 95 |
'''simple docstring'''
import itertools
import json
import os
import unittest
from transformers import AddedToken, RobertaTokenizer, RobertaTokenizerFast
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class A_ (a_ , unittest.TestCase ):
"""simple docstring"""
a__ = RobertaTokenizer
a__ = RobertaTokenizerFast
a__ = True
a__ = {'''cls_token''': '''<s>'''}
def _A ( self :Optional[int] ) -> List[Any]:
'''simple docstring'''
super().setUp()
# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
snake_case_ : List[Any] = [
"l",
"o",
"w",
"e",
"r",
"s",
"t",
"i",
"d",
"n",
"\u0120",
"\u0120l",
"\u0120n",
"\u0120lo",
"\u0120low",
"er",
"\u0120lowest",
"\u0120newer",
"\u0120wider",
"<unk>",
]
snake_case_ : Tuple = dict(zip(lowerCAmelCase__ , range(len(lowerCAmelCase__ ) ) ) )
snake_case_ : List[Any] = ["#version: 0.2", "\u0120 l", "\u0120l o", "\u0120lo w", "e r", ""]
snake_case_ : int = {"unk_token": "<unk>"}
snake_case_ : List[str] = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES["vocab_file"] )
snake_case_ : Tuple = 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(lowerCAmelCase__ ) + "\n" )
with open(self.merges_file , "w" , encoding="utf-8" ) as fp:
fp.write("\n".join(lowerCAmelCase__ ) )
def _A ( self :Optional[Any] , **lowerCAmelCase__ :str ) -> str:
'''simple docstring'''
kwargs.update(self.special_tokens_map )
return self.tokenizer_class.from_pretrained(self.tmpdirname , **lowerCAmelCase__ )
def _A ( self :Any , **lowerCAmelCase__ :Tuple ) -> Optional[int]:
'''simple docstring'''
kwargs.update(self.special_tokens_map )
return RobertaTokenizerFast.from_pretrained(self.tmpdirname , **lowerCAmelCase__ )
def _A ( self :Optional[int] , lowerCAmelCase__ :str ) -> Optional[int]:
'''simple docstring'''
snake_case_ : int = "lower newer"
snake_case_ : Tuple = "lower newer"
return input_text, output_text
def _A ( self :Tuple ) -> Union[str, Any]:
'''simple docstring'''
snake_case_ : str = self.tokenizer_class(self.vocab_file , self.merges_file , **self.special_tokens_map )
snake_case_ : Dict = "lower newer"
snake_case_ : int = ["l", "o", "w", "er", "\u0120", "n", "e", "w", "er"]
snake_case_ : str = tokenizer.tokenize(lowerCAmelCase__ ) # , add_prefix_space=True)
self.assertListEqual(lowerCAmelCase__ , lowerCAmelCase__ )
snake_case_ : List[str] = tokens + [tokenizer.unk_token]
snake_case_ : Optional[int] = [0, 1, 2, 15, 10, 9, 3, 2, 15, 19]
self.assertListEqual(tokenizer.convert_tokens_to_ids(lowerCAmelCase__ ) , lowerCAmelCase__ )
def _A ( self :Any ) -> str:
'''simple docstring'''
snake_case_ : List[str] = self.get_tokenizer()
self.assertListEqual(tokenizer.encode("Hello world!" , add_special_tokens=lowerCAmelCase__ ) , [0, 31_414, 232, 328, 2] )
self.assertListEqual(
tokenizer.encode("Hello world! cécé herlolip 418" , add_special_tokens=lowerCAmelCase__ ) , [0, 31_414, 232, 328, 740, 1_140, 12_695, 69, 46_078, 1_588, 2] , )
@slow
def _A ( self :str ) -> List[str]:
'''simple docstring'''
snake_case_ : Tuple = self.tokenizer_class.from_pretrained("roberta-base" )
snake_case_ : List[str] = tokenizer.encode("sequence builders" , add_special_tokens=lowerCAmelCase__ )
snake_case_ : List[Any] = tokenizer.encode("multi-sequence build" , add_special_tokens=lowerCAmelCase__ )
snake_case_ : List[str] = tokenizer.encode(
"sequence builders" , add_special_tokens=lowerCAmelCase__ , add_prefix_space=lowerCAmelCase__ )
snake_case_ : Union[str, Any] = tokenizer.encode(
"sequence builders" , "multi-sequence build" , add_special_tokens=lowerCAmelCase__ , add_prefix_space=lowerCAmelCase__ )
snake_case_ : Optional[Any] = tokenizer.build_inputs_with_special_tokens(lowerCAmelCase__ )
snake_case_ : Any = tokenizer.build_inputs_with_special_tokens(lowerCAmelCase__ , lowerCAmelCase__ )
assert encoded_sentence == encoded_text_from_decode
assert encoded_pair == encoded_pair_from_decode
def _A ( self :List[Any] ) -> Any:
'''simple docstring'''
snake_case_ : Optional[Any] = self.get_tokenizer()
snake_case_ : Tuple = "Encode this sequence."
snake_case_ : Optional[Any] = tokenizer.byte_encoder[" ".encode("utf-8" )[0]]
# Testing encoder arguments
snake_case_ : str = tokenizer.encode(lowerCAmelCase__ , add_special_tokens=lowerCAmelCase__ , add_prefix_space=lowerCAmelCase__ )
snake_case_ : List[Any] = tokenizer.convert_ids_to_tokens(encoded[0] )[0]
self.assertNotEqual(lowerCAmelCase__ , lowerCAmelCase__ )
snake_case_ : List[Any] = tokenizer.encode(lowerCAmelCase__ , add_special_tokens=lowerCAmelCase__ , add_prefix_space=lowerCAmelCase__ )
snake_case_ : str = tokenizer.convert_ids_to_tokens(encoded[0] )[0]
self.assertEqual(lowerCAmelCase__ , lowerCAmelCase__ )
tokenizer.add_special_tokens({"bos_token": "<s>"} )
snake_case_ : str = tokenizer.encode(lowerCAmelCase__ , add_special_tokens=lowerCAmelCase__ )
snake_case_ : int = tokenizer.convert_ids_to_tokens(encoded[1] )[0]
self.assertNotEqual(lowerCAmelCase__ , lowerCAmelCase__ )
# Testing spaces after special tokens
snake_case_ : List[Any] = "<mask>"
tokenizer.add_special_tokens(
{"mask_token": AddedToken(lowerCAmelCase__ , lstrip=lowerCAmelCase__ , rstrip=lowerCAmelCase__ )} ) # mask token has a left space
snake_case_ : str = tokenizer.convert_tokens_to_ids(lowerCAmelCase__ )
snake_case_ : List[str] = "Encode <mask> sequence"
snake_case_ : List[Any] = "Encode <mask>sequence"
snake_case_ : Tuple = tokenizer.encode(lowerCAmelCase__ )
snake_case_ : int = encoded.index(lowerCAmelCase__ )
snake_case_ : Optional[Any] = tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1] )[0]
self.assertEqual(lowerCAmelCase__ , lowerCAmelCase__ )
snake_case_ : List[str] = tokenizer.encode(lowerCAmelCase__ )
snake_case_ : Union[str, Any] = encoded.index(lowerCAmelCase__ )
snake_case_ : int = tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1] )[0]
self.assertNotEqual(lowerCAmelCase__ , lowerCAmelCase__ )
def _A ( self :Tuple ) -> Tuple:
'''simple docstring'''
pass
def _A ( self :int ) -> Optional[Any]:
'''simple docstring'''
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(F'''{tokenizer.__class__.__name__} ({pretrained_name})''' ):
snake_case_ : List[Any] = self.rust_tokenizer_class.from_pretrained(lowerCAmelCase__ , **lowerCAmelCase__ )
snake_case_ : List[Any] = self.tokenizer_class.from_pretrained(lowerCAmelCase__ , **lowerCAmelCase__ )
snake_case_ : Any = "A, <mask> AllenNLP sentence."
snake_case_ : str = tokenizer_r.encode_plus(lowerCAmelCase__ , add_special_tokens=lowerCAmelCase__ , return_token_type_ids=lowerCAmelCase__ )
snake_case_ : int = tokenizer_p.encode_plus(lowerCAmelCase__ , add_special_tokens=lowerCAmelCase__ , return_token_type_ids=lowerCAmelCase__ )
# token_type_ids should put 0 everywhere
self.assertEqual(sum(tokens_r["token_type_ids"] ) , sum(tokens_p["token_type_ids"] ) )
# attention_mask should put 1 everywhere, so sum over length should be 1
self.assertEqual(
sum(tokens_r["attention_mask"] ) / len(tokens_r["attention_mask"] ) , sum(tokens_p["attention_mask"] ) / len(tokens_p["attention_mask"] ) , )
snake_case_ : List[Any] = tokenizer_r.convert_ids_to_tokens(tokens_r["input_ids"] )
snake_case_ : str = tokenizer_p.convert_ids_to_tokens(tokens_p["input_ids"] )
# Rust correctly handles the space before the mask while python doesnt
self.assertSequenceEqual(tokens_p["input_ids"] , [0, 250, 6, 50_264, 3_823, 487, 21_992, 3_645, 4, 2] )
self.assertSequenceEqual(tokens_r["input_ids"] , [0, 250, 6, 50_264, 3_823, 487, 21_992, 3_645, 4, 2] )
self.assertSequenceEqual(
lowerCAmelCase__ , ["<s>", "A", ",", "<mask>", "ĠAllen", "N", "LP", "Ġsentence", ".", "</s>"] )
self.assertSequenceEqual(
lowerCAmelCase__ , ["<s>", "A", ",", "<mask>", "ĠAllen", "N", "LP", "Ġsentence", ".", "</s>"] )
def _A ( self :int ) -> Tuple:
'''simple docstring'''
for trim_offsets, add_prefix_space in itertools.product([True, False] , repeat=2 ):
snake_case_ : str = self.rust_tokenizer_class.from_pretrained(
self.tmpdirname , use_fast=lowerCAmelCase__ , add_prefix_space=lowerCAmelCase__ , trim_offsets=lowerCAmelCase__ )
snake_case_ : Optional[Any] = json.loads(tokenizer_r.backend_tokenizer.pre_tokenizer.__getstate__() )
snake_case_ : Any = json.loads(tokenizer_r.backend_tokenizer.post_processor.__getstate__() )
self.assertEqual(pre_tokenizer_state["add_prefix_space"] , lowerCAmelCase__ )
self.assertEqual(post_processor_state["add_prefix_space"] , lowerCAmelCase__ )
self.assertEqual(post_processor_state["trim_offsets"] , lowerCAmelCase__ )
def _A ( self :List[str] ) -> List[Any]:
'''simple docstring'''
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(F'''{tokenizer.__class__.__name__} ({pretrained_name})''' ):
snake_case_ : str = "hello" # `hello` is a token in the vocabulary of `pretrained_name`
snake_case_ : Tuple = F'''{text_of_1_token} {text_of_1_token}'''
snake_case_ : Any = self.rust_tokenizer_class.from_pretrained(
lowerCAmelCase__ , use_fast=lowerCAmelCase__ , add_prefix_space=lowerCAmelCase__ , trim_offsets=lowerCAmelCase__ )
snake_case_ : Union[str, Any] = tokenizer_r(lowerCAmelCase__ , return_offsets_mapping=lowerCAmelCase__ , add_special_tokens=lowerCAmelCase__ )
self.assertEqual(encoding.offset_mapping[0] , (0, len(lowerCAmelCase__ )) )
self.assertEqual(
encoding.offset_mapping[1] , (len(lowerCAmelCase__ ) + 1, len(lowerCAmelCase__ ) + 1 + len(lowerCAmelCase__ )) , )
snake_case_ : List[str] = self.rust_tokenizer_class.from_pretrained(
lowerCAmelCase__ , use_fast=lowerCAmelCase__ , add_prefix_space=lowerCAmelCase__ , trim_offsets=lowerCAmelCase__ )
snake_case_ : Tuple = tokenizer_r(lowerCAmelCase__ , return_offsets_mapping=lowerCAmelCase__ , add_special_tokens=lowerCAmelCase__ )
self.assertEqual(encoding.offset_mapping[0] , (0, len(lowerCAmelCase__ )) )
self.assertEqual(
encoding.offset_mapping[1] , (len(lowerCAmelCase__ ) + 1, len(lowerCAmelCase__ ) + 1 + len(lowerCAmelCase__ )) , )
snake_case_ : Union[str, Any] = self.rust_tokenizer_class.from_pretrained(
lowerCAmelCase__ , use_fast=lowerCAmelCase__ , add_prefix_space=lowerCAmelCase__ , trim_offsets=lowerCAmelCase__ )
snake_case_ : Union[str, Any] = tokenizer_r(lowerCAmelCase__ , return_offsets_mapping=lowerCAmelCase__ , add_special_tokens=lowerCAmelCase__ )
self.assertEqual(encoding.offset_mapping[0] , (0, len(lowerCAmelCase__ )) )
self.assertEqual(
encoding.offset_mapping[1] , (len(lowerCAmelCase__ ), len(lowerCAmelCase__ ) + 1 + len(lowerCAmelCase__ )) , )
snake_case_ : Dict = self.rust_tokenizer_class.from_pretrained(
lowerCAmelCase__ , use_fast=lowerCAmelCase__ , add_prefix_space=lowerCAmelCase__ , trim_offsets=lowerCAmelCase__ )
snake_case_ : str = tokenizer_r(lowerCAmelCase__ , return_offsets_mapping=lowerCAmelCase__ , add_special_tokens=lowerCAmelCase__ )
self.assertEqual(encoding.offset_mapping[0] , (0, len(lowerCAmelCase__ )) )
self.assertEqual(
encoding.offset_mapping[1] , (len(lowerCAmelCase__ ), len(lowerCAmelCase__ ) + 1 + len(lowerCAmelCase__ )) , )
snake_case_ : Tuple = F''' {text}'''
# tokenizer_r = self.rust_tokenizer_class.from_pretrained(
# pretrained_name, use_fast=True, add_prefix_space=True, trim_offsets=True
# )
# encoding = tokenizer_r(text, return_offsets_mapping=True, add_special_tokens=False)
# self.assertEqual(encoding.offset_mapping[0], (1, 1 + len(text_of_1_token)))
# self.assertEqual(
# encoding.offset_mapping[1],
# (1 + len(text_of_1_token) + 1, 1 + len(text_of_1_token) + 1 + len(text_of_1_token)),
# )
snake_case_ : Dict = self.rust_tokenizer_class.from_pretrained(
lowerCAmelCase__ , use_fast=lowerCAmelCase__ , add_prefix_space=lowerCAmelCase__ , trim_offsets=lowerCAmelCase__ )
snake_case_ : Union[str, Any] = tokenizer_r(lowerCAmelCase__ , return_offsets_mapping=lowerCAmelCase__ , add_special_tokens=lowerCAmelCase__ )
self.assertEqual(encoding.offset_mapping[0] , (1, 1 + len(lowerCAmelCase__ )) )
self.assertEqual(
encoding.offset_mapping[1] , (1 + len(lowerCAmelCase__ ) + 1, 1 + len(lowerCAmelCase__ ) + 1 + len(lowerCAmelCase__ )) , )
snake_case_ : Any = self.rust_tokenizer_class.from_pretrained(
lowerCAmelCase__ , use_fast=lowerCAmelCase__ , add_prefix_space=lowerCAmelCase__ , trim_offsets=lowerCAmelCase__ )
snake_case_ : Any = tokenizer_r(lowerCAmelCase__ , return_offsets_mapping=lowerCAmelCase__ , add_special_tokens=lowerCAmelCase__ )
self.assertEqual(encoding.offset_mapping[0] , (0, 1 + len(lowerCAmelCase__ )) )
self.assertEqual(
encoding.offset_mapping[1] , (1 + len(lowerCAmelCase__ ), 1 + len(lowerCAmelCase__ ) + 1 + len(lowerCAmelCase__ )) , )
snake_case_ : Optional[Any] = self.rust_tokenizer_class.from_pretrained(
lowerCAmelCase__ , use_fast=lowerCAmelCase__ , add_prefix_space=lowerCAmelCase__ , trim_offsets=lowerCAmelCase__ )
snake_case_ : Optional[int] = tokenizer_r(lowerCAmelCase__ , return_offsets_mapping=lowerCAmelCase__ , add_special_tokens=lowerCAmelCase__ )
self.assertEqual(encoding.offset_mapping[0] , (0, 1 + len(lowerCAmelCase__ )) )
self.assertEqual(
encoding.offset_mapping[1] , (1 + len(lowerCAmelCase__ ), 1 + len(lowerCAmelCase__ ) + 1 + len(lowerCAmelCase__ )) , )
| 653 | 0 |
"""simple docstring"""
import argparse
import re
import numpy as np
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
SamConfig,
SamImageProcessor,
SamModel,
SamProcessor,
SamVisionConfig,
)
__lowerCamelCase = {
'iou_prediction_head.layers.0': 'iou_prediction_head.proj_in',
'iou_prediction_head.layers.1': 'iou_prediction_head.layers.0',
'iou_prediction_head.layers.2': 'iou_prediction_head.proj_out',
'mask_decoder.output_upscaling.0': 'mask_decoder.upscale_conv1',
'mask_decoder.output_upscaling.1': 'mask_decoder.upscale_layer_norm',
'mask_decoder.output_upscaling.3': 'mask_decoder.upscale_conv2',
'mask_downscaling.0': 'mask_embed.conv1',
'mask_downscaling.1': 'mask_embed.layer_norm1',
'mask_downscaling.3': 'mask_embed.conv2',
'mask_downscaling.4': 'mask_embed.layer_norm2',
'mask_downscaling.6': 'mask_embed.conv3',
'point_embeddings': 'point_embed',
'pe_layer.positional_encoding_gaussian_matrix': 'shared_embedding.positional_embedding',
'image_encoder': 'vision_encoder',
'neck.0': 'neck.conv1',
'neck.1': 'neck.layer_norm1',
'neck.2': 'neck.conv2',
'neck.3': 'neck.layer_norm2',
'patch_embed.proj': 'patch_embed.projection',
'.norm': '.layer_norm',
'blocks': 'layers',
}
def a ( __UpperCAmelCase : Union[str, Any] ) -> List[str]:
__magic_name__: List[str] = {}
state_dict.pop("""pixel_mean""" , __UpperCAmelCase )
state_dict.pop("""pixel_std""" , __UpperCAmelCase )
__magic_name__: List[Any] = R""".*.output_hypernetworks_mlps.(\d+).layers.(\d+).*"""
for key, value in state_dict.items():
for key_to_modify, new_key in KEYS_TO_MODIFY_MAPPING.items():
if key_to_modify in key:
__magic_name__: List[Any] = key.replace(__UpperCAmelCase , __UpperCAmelCase )
if re.match(__UpperCAmelCase , __UpperCAmelCase ):
__magic_name__: str = int(re.match(__UpperCAmelCase , __UpperCAmelCase ).group(2 ) )
if layer_nb == 0:
__magic_name__: Dict = key.replace("""layers.0""" , """proj_in""" )
elif layer_nb == 1:
__magic_name__: List[str] = key.replace("""layers.1""" , """layers.0""" )
elif layer_nb == 2:
__magic_name__: Dict = key.replace("""layers.2""" , """proj_out""" )
__magic_name__: Dict = value
__magic_name__: Optional[Any] = model_state_dict[
"""prompt_encoder.shared_embedding.positional_embedding"""
]
return model_state_dict
def a ( __UpperCAmelCase : Optional[int] , __UpperCAmelCase : List[Any] , __UpperCAmelCase : Optional[int] , __UpperCAmelCase : int="ybelkada/segment-anything" ) -> Dict:
__magic_name__: List[Any] = hf_hub_download(__UpperCAmelCase , f'checkpoints/{model_name}.pth' )
if "sam_vit_b" in model_name:
__magic_name__: Tuple = SamConfig()
elif "sam_vit_l" in model_name:
__magic_name__: str = SamVisionConfig(
hidden_size=1_0_2_4 , num_hidden_layers=2_4 , num_attention_heads=1_6 , global_attn_indexes=[5, 1_1, 1_7, 2_3] , )
__magic_name__: Union[str, Any] = SamConfig(
vision_config=__UpperCAmelCase , )
elif "sam_vit_h" in model_name:
__magic_name__: int = SamVisionConfig(
hidden_size=1_2_8_0 , num_hidden_layers=3_2 , num_attention_heads=1_6 , global_attn_indexes=[7, 1_5, 2_3, 3_1] , )
__magic_name__: int = SamConfig(
vision_config=__UpperCAmelCase , )
__magic_name__: List[str] = torch.load(__UpperCAmelCase , map_location="""cpu""" )
__magic_name__: Dict = replace_keys(__UpperCAmelCase )
__magic_name__: Optional[Any] = SamImageProcessor()
__magic_name__: Any = SamProcessor(image_processor=__UpperCAmelCase )
__magic_name__: str = SamModel(__UpperCAmelCase )
hf_model.load_state_dict(__UpperCAmelCase )
__magic_name__: Optional[int] = hf_model.to("""cuda""" )
__magic_name__: int = """https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"""
__magic_name__: int = Image.open(requests.get(__UpperCAmelCase , stream=__UpperCAmelCase ).raw ).convert("""RGB""" )
__magic_name__: int = [[[4_0_0, 6_5_0]]]
__magic_name__: Optional[Any] = [[1]]
__magic_name__: Optional[int] = processor(images=np.array(__UpperCAmelCase ) , return_tensors="""pt""" ).to("""cuda""" )
with torch.no_grad():
__magic_name__: str = hf_model(**__UpperCAmelCase )
__magic_name__: Any = output.iou_scores.squeeze()
if model_name == "sam_vit_h_4b8939":
assert scores[-1].item() == 0.5_79_89_02_51_15_96_68
__magic_name__: List[Any] = processor(
images=np.array(__UpperCAmelCase ) , input_points=__UpperCAmelCase , input_labels=__UpperCAmelCase , return_tensors="""pt""" ).to("""cuda""" )
with torch.no_grad():
__magic_name__: Tuple = hf_model(**__UpperCAmelCase )
__magic_name__: str = output.iou_scores.squeeze()
assert scores[-1].item() == 0.97_12_60_30_92_19_36_04
__magic_name__: Any = ((7_5, 2_7_5, 1_7_2_5, 8_5_0),)
__magic_name__: List[str] = processor(images=np.array(__UpperCAmelCase ) , input_boxes=__UpperCAmelCase , return_tensors="""pt""" ).to("""cuda""" )
with torch.no_grad():
__magic_name__: Optional[int] = hf_model(**__UpperCAmelCase )
__magic_name__: Dict = output.iou_scores.squeeze()
assert scores[-1].item() == 0.86_86_01_56_05_92_65_14
# Test with 2 points and 1 image.
__magic_name__: Optional[Any] = [[[4_0_0, 6_5_0], [8_0_0, 6_5_0]]]
__magic_name__: List[str] = [[1, 1]]
__magic_name__: Optional[Any] = processor(
images=np.array(__UpperCAmelCase ) , input_points=__UpperCAmelCase , input_labels=__UpperCAmelCase , return_tensors="""pt""" ).to("""cuda""" )
with torch.no_grad():
__magic_name__: int = hf_model(**__UpperCAmelCase )
__magic_name__: List[str] = output.iou_scores.squeeze()
assert scores[-1].item() == 0.99_36_04_77_92_43_46_92
if __name__ == "__main__":
__lowerCamelCase = argparse.ArgumentParser()
__lowerCamelCase = ['sam_vit_b_01ec64', 'sam_vit_h_4b8939', 'sam_vit_l_0b3195']
parser.add_argument(
'--model_name',
default='sam_vit_h_4b8939',
choices=choices,
type=str,
help='Path to hf config.json of model to convert',
)
parser.add_argument('--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.')
parser.add_argument(
'--push_to_hub',
action='store_true',
help='Whether to push the model and processor to the hub after converting',
)
parser.add_argument(
'--model_hub_id',
default='ybelkada/segment-anything',
choices=choices,
type=str,
help='Path to hf config.json of model to convert',
)
__lowerCamelCase = parser.parse_args()
convert_sam_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub, args.model_hub_id)
| 96 |
'''simple docstring'''
import math
def __UpperCAmelCase ( __magic_name__ )-> bool:
"""simple docstring"""
snake_case_ : Optional[int] = math.loga(math.sqrt(4 * positive_integer + 1 ) / 2 + 1 / 2 )
return exponent == int(__magic_name__ )
def __UpperCAmelCase ( __magic_name__ = 1 / 1_2345 )-> int:
"""simple docstring"""
snake_case_ : Any = 0
snake_case_ : int = 0
snake_case_ : Union[str, Any] = 3
while True:
snake_case_ : Any = (integer**2 - 1) / 4
# if candidate is an integer, then there is a partition for k
if partition_candidate == int(__magic_name__ ):
snake_case_ : Optional[Any] = int(__magic_name__ )
total_partitions += 1
if check_partition_perfect(__magic_name__ ):
perfect_partitions += 1
if perfect_partitions > 0:
if perfect_partitions / total_partitions < max_proportion:
return int(__magic_name__ )
integer += 1
if __name__ == "__main__":
print(f'''{solution() = }''')
| 653 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
__a = logging.get_logger(__name__)
__a = {
'shi-labs/nat-mini-in1k-224': 'https://huggingface.co/shi-labs/nat-mini-in1k-224/resolve/main/config.json',
# See all Nat models at https://huggingface.co/models?filter=nat
}
class lowercase__( UpperCAmelCase , UpperCAmelCase ):
"""simple docstring"""
a :List[Any] = 'nat'
a :List[str] = {
'num_attention_heads': 'num_heads',
'num_hidden_layers': 'num_layers',
}
def __init__( self : List[Any] , SCREAMING_SNAKE_CASE_ : int=4 , SCREAMING_SNAKE_CASE_ : Optional[int]=3 , SCREAMING_SNAKE_CASE_ : int=6_4 , SCREAMING_SNAKE_CASE_ : Dict=[3, 4, 6, 5] , SCREAMING_SNAKE_CASE_ : List[str]=[2, 4, 8, 1_6] , SCREAMING_SNAKE_CASE_ : List[str]=7 , SCREAMING_SNAKE_CASE_ : List[Any]=3.0 , SCREAMING_SNAKE_CASE_ : Optional[int]=True , SCREAMING_SNAKE_CASE_ : List[Any]=0.0 , SCREAMING_SNAKE_CASE_ : Any=0.0 , SCREAMING_SNAKE_CASE_ : str=0.1 , SCREAMING_SNAKE_CASE_ : Any="gelu" , SCREAMING_SNAKE_CASE_ : Tuple=0.02 , SCREAMING_SNAKE_CASE_ : Optional[int]=1e-5 , SCREAMING_SNAKE_CASE_ : Tuple=0.0 , SCREAMING_SNAKE_CASE_ : str=None , SCREAMING_SNAKE_CASE_ : Optional[int]=None , **SCREAMING_SNAKE_CASE_ : Optional[int] , ) -> Optional[Any]:
super().__init__(**SCREAMING_SNAKE_CASE_ )
lowercase_ = patch_size
lowercase_ = num_channels
lowercase_ = embed_dim
lowercase_ = depths
lowercase_ = len(SCREAMING_SNAKE_CASE_ )
lowercase_ = num_heads
lowercase_ = kernel_size
lowercase_ = mlp_ratio
lowercase_ = qkv_bias
lowercase_ = hidden_dropout_prob
lowercase_ = attention_probs_dropout_prob
lowercase_ = drop_path_rate
lowercase_ = hidden_act
lowercase_ = layer_norm_eps
lowercase_ = initializer_range
# we set the hidden_size attribute in order to make Nat work with VisionEncoderDecoderModel
# this indicates the channel dimension after the last stage of the model
lowercase_ = int(embed_dim * 2 ** (len(SCREAMING_SNAKE_CASE_ ) - 1) )
lowercase_ = layer_scale_init_value
lowercase_ = ['''stem'''] + [f'''stage{idx}''' for idx in range(1 , len(SCREAMING_SNAKE_CASE_ ) + 1 )]
lowercase_ , lowercase_ = get_aligned_output_features_output_indices(
out_features=SCREAMING_SNAKE_CASE_ , out_indices=SCREAMING_SNAKE_CASE_ , stage_names=self.stage_names )
| 97 |
'''simple docstring'''
import argparse
import json
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from typing import List
import timm
import torch
import torch.nn as nn
from huggingface_hub import hf_hub_download
from torch import Tensor
from transformers import AutoImageProcessor, ResNetConfig, ResNetForImageClassification
from transformers.utils import logging
logging.set_verbosity_info()
__lowerCamelCase : int = logging.get_logger()
@dataclass
class A_ :
"""simple docstring"""
a__ = 42
a__ = field(default_factory=a_ )
a__ = field(default_factory=a_ )
def _A ( self :List[Any] , lowerCAmelCase__ :Union[str, Any] , lowerCAmelCase__ :Tensor , lowerCAmelCase__ :Tensor ) -> int:
'''simple docstring'''
snake_case_ : int = len(list(m.modules() ) ) == 1 or isinstance(lowerCAmelCase__ , nn.Convad ) or isinstance(lowerCAmelCase__ , nn.BatchNormad )
if has_not_submodules:
self.traced.append(lowerCAmelCase__ )
def __call__( self :List[Any] , lowerCAmelCase__ :Tensor ) -> Union[str, Any]:
'''simple docstring'''
for m in self.module.modules():
self.handles.append(m.register_forward_hook(self._forward_hook ) )
self.module(lowerCAmelCase__ )
[x.remove() for x in self.handles]
return self
@property
def _A ( self :int ) -> List[Any]:
'''simple docstring'''
return list(filter(lambda lowerCAmelCase__ : len(list(x.state_dict().keys() ) ) > 0 , self.traced ) )
@dataclass
class A_ :
"""simple docstring"""
a__ = 42
a__ = 42
a__ = 0
a__ = field(default_factory=a_ )
a__ = field(default_factory=a_ )
def __call__( self :Tuple , lowerCAmelCase__ :Tensor ) -> Tuple:
'''simple docstring'''
snake_case_ : List[Any] = Tracker(self.dest )(lowerCAmelCase__ ).parametrized
snake_case_ : Tuple = Tracker(self.src )(lowerCAmelCase__ ).parametrized
snake_case_ : List[str] = list(filter(lambda lowerCAmelCase__ : type(lowerCAmelCase__ ) not in self.src_skip , lowerCAmelCase__ ) )
snake_case_ : Tuple = list(filter(lambda lowerCAmelCase__ : type(lowerCAmelCase__ ) not in self.dest_skip , lowerCAmelCase__ ) )
if len(lowerCAmelCase__ ) != len(lowerCAmelCase__ ):
raise Exception(
F'''Numbers of operations are different. Source module has {len(lowerCAmelCase__ )} operations while'''
F''' destination module has {len(lowerCAmelCase__ )}.''' )
for dest_m, src_m in zip(lowerCAmelCase__ , lowerCAmelCase__ ):
dest_m.load_state_dict(src_m.state_dict() )
if self.verbose == 1:
print(F'''Transfered from={src_m} to={dest_m}''' )
def __UpperCAmelCase ( __magic_name__ ,__magic_name__ ,__magic_name__ ,__magic_name__ = True )-> Optional[int]:
"""simple docstring"""
print(F'''Converting {name}...''' )
with torch.no_grad():
snake_case_ : List[str] = timm.create_model(__magic_name__ ,pretrained=__magic_name__ ).eval()
snake_case_ : Optional[int] = ResNetForImageClassification(__magic_name__ ).eval()
snake_case_ : Dict = ModuleTransfer(src=__magic_name__ ,dest=__magic_name__ )
snake_case_ : Optional[int] = torch.randn((1, 3, 224, 224) )
module_transfer(__magic_name__ )
assert torch.allclose(from_model(__magic_name__ ) ,our_model(__magic_name__ ).logits ), "The model logits don't match the original one."
snake_case_ : str = F'''resnet{'-'.join(name.split('resnet' ) )}'''
print(__magic_name__ )
if push_to_hub:
our_model.push_to_hub(
repo_path_or_name=save_directory / checkpoint_name ,commit_message="Add model" ,use_temp_dir=__magic_name__ ,)
# we can use the convnext one
snake_case_ : Optional[Any] = AutoImageProcessor.from_pretrained("facebook/convnext-base-224-22k-1k" )
image_processor.push_to_hub(
repo_path_or_name=save_directory / checkpoint_name ,commit_message="Add image processor" ,use_temp_dir=__magic_name__ ,)
print(F'''Pushed {checkpoint_name}''' )
def __UpperCAmelCase ( __magic_name__ ,__magic_name__ = None ,__magic_name__ = True )-> Tuple:
"""simple docstring"""
snake_case_ : List[str] = "imagenet-1k-id2label.json"
snake_case_ : Optional[Any] = 1000
snake_case_ : List[Any] = (1, num_labels)
snake_case_ : Optional[Any] = "huggingface/label-files"
snake_case_ : Dict = num_labels
snake_case_ : List[Any] = json.load(open(hf_hub_download(__magic_name__ ,__magic_name__ ,repo_type="dataset" ) ,"r" ) )
snake_case_ : List[str] = {int(__magic_name__ ): v for k, v in idalabel.items()}
snake_case_ : Any = idalabel
snake_case_ : List[Any] = {v: k for k, v in idalabel.items()}
snake_case_ : Optional[int] = partial(__magic_name__ ,num_labels=__magic_name__ ,idalabel=__magic_name__ ,labelaid=__magic_name__ )
snake_case_ : Optional[int] = {
"resnet18": ImageNetPreTrainedConfig(
depths=[2, 2, 2, 2] ,hidden_sizes=[64, 128, 256, 512] ,layer_type="basic" ),
"resnet26": ImageNetPreTrainedConfig(
depths=[2, 2, 2, 2] ,hidden_sizes=[256, 512, 1024, 2048] ,layer_type="bottleneck" ),
"resnet34": ImageNetPreTrainedConfig(
depths=[3, 4, 6, 3] ,hidden_sizes=[64, 128, 256, 512] ,layer_type="basic" ),
"resnet50": ImageNetPreTrainedConfig(
depths=[3, 4, 6, 3] ,hidden_sizes=[256, 512, 1024, 2048] ,layer_type="bottleneck" ),
"resnet101": ImageNetPreTrainedConfig(
depths=[3, 4, 23, 3] ,hidden_sizes=[256, 512, 1024, 2048] ,layer_type="bottleneck" ),
"resnet152": ImageNetPreTrainedConfig(
depths=[3, 8, 36, 3] ,hidden_sizes=[256, 512, 1024, 2048] ,layer_type="bottleneck" ),
}
if model_name:
convert_weight_and_push(__magic_name__ ,names_to_config[model_name] ,__magic_name__ ,__magic_name__ )
else:
for model_name, config in names_to_config.items():
convert_weight_and_push(__magic_name__ ,__magic_name__ ,__magic_name__ ,__magic_name__ )
return config, expected_shape
if __name__ == "__main__":
__lowerCamelCase : Optional[int] = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'''--model_name''',
default=None,
type=str,
help=(
'''The name of the model you wish to convert, it must be one of the supported resnet* architecture,'''
''' currently: resnet18,26,34,50,101,152. If `None`, all of them will the converted.'''
),
)
parser.add_argument(
'''--pytorch_dump_folder_path''',
default=None,
type=Path,
required=True,
help='''Path to the output PyTorch model directory.''',
)
parser.add_argument(
'''--push_to_hub''',
default=True,
type=bool,
required=False,
help='''If True, push model and image processor to the hub.''',
)
__lowerCamelCase : Tuple = parser.parse_args()
__lowerCamelCase : Path = args.pytorch_dump_folder_path
pytorch_dump_folder_path.mkdir(exist_ok=True, parents=True)
convert_weights_and_push(pytorch_dump_folder_path, args.model_name, args.push_to_hub)
| 653 | 0 |
'''simple docstring'''
import unittest
from transformers import RoFormerTokenizer, RoFormerTokenizerFast
from transformers.testing_utils import require_rjieba, require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@require_rjieba
@require_tokenizers
class __lowerCAmelCase ( __magic_name__ , unittest.TestCase ):
"""simple docstring"""
_snake_case : Tuple = RoFormerTokenizer
_snake_case : Union[str, Any] = RoFormerTokenizerFast
_snake_case : Optional[Any] = True
_snake_case : Dict = True
def snake_case__ ( self : Any ) -> str:
'''simple docstring'''
super().setUp()
def snake_case__ ( self : List[str] , **lowerCAmelCase__ : Dict ) -> List[Any]:
'''simple docstring'''
return self.tokenizer_class.from_pretrained('''junnyu/roformer_chinese_base''' , **lowerCAmelCase__ )
def snake_case__ ( self : int , **lowerCAmelCase__ : Optional[int] ) -> List[Any]:
'''simple docstring'''
return self.rust_tokenizer_class.from_pretrained('''junnyu/roformer_chinese_base''' , **lowerCAmelCase__ )
def snake_case__ ( self : List[str] ) -> Union[str, Any]:
'''simple docstring'''
_UpperCamelCase = '''永和服装饰品有限公司,今天天气非常好'''
_UpperCamelCase = '''永和 服装 饰品 有限公司 , 今 天 天 气 非常 好'''
return input_text, output_text
def snake_case__ ( self : Union[str, Any] ) -> Optional[Any]:
'''simple docstring'''
_UpperCamelCase = self.get_tokenizer()
_UpperCamelCase , _UpperCamelCase = self.get_chinese_input_output_texts()
_UpperCamelCase = tokenizer.tokenize(lowerCAmelCase__ )
self.assertListEqual(lowerCAmelCase__ , output_text.split() )
_UpperCamelCase = tokens + [tokenizer.unk_token]
_UpperCamelCase = [22943, 21332, 34431, 45904, 117, 306, 1231, 1231, 2653, 33994, 1266, 100]
self.assertListEqual(tokenizer.convert_tokens_to_ids(lowerCAmelCase__ ) , lowerCAmelCase__ )
def snake_case__ ( self : int ) -> str:
'''simple docstring'''
_UpperCamelCase = self.get_rust_tokenizer()
_UpperCamelCase , _UpperCamelCase = self.get_chinese_input_output_texts()
_UpperCamelCase = tokenizer.tokenize(lowerCAmelCase__ )
self.assertListEqual(lowerCAmelCase__ , output_text.split() )
_UpperCamelCase = tokens + [tokenizer.unk_token]
_UpperCamelCase = [22943, 21332, 34431, 45904, 117, 306, 1231, 1231, 2653, 33994, 1266, 100]
self.assertListEqual(tokenizer.convert_tokens_to_ids(lowerCAmelCase__ ) , lowerCAmelCase__ )
def snake_case__ ( self : str ) -> int:
'''simple docstring'''
pass
def snake_case__ ( self : Optional[int] ) -> List[Any]:
'''simple docstring'''
pass
def snake_case__ ( self : Optional[int] ) -> Any:
'''simple docstring'''
pass
| 98 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__lowerCamelCase : List[Any] = logging.get_logger(__name__)
__lowerCamelCase : Dict = {
'''weiweishi/roc-bert-base-zh''': '''https://huggingface.co/weiweishi/roc-bert-base-zh/resolve/main/config.json''',
}
class A_ (a_ ):
"""simple docstring"""
a__ = '''roc_bert'''
def __init__( self :Dict , lowerCAmelCase__ :Optional[Any]=30_522 , lowerCAmelCase__ :Dict=768 , lowerCAmelCase__ :str=12 , lowerCAmelCase__ :Optional[int]=12 , lowerCAmelCase__ :Optional[Any]=3_072 , lowerCAmelCase__ :Any="gelu" , lowerCAmelCase__ :int=0.1 , lowerCAmelCase__ :Tuple=0.1 , lowerCAmelCase__ :List[str]=512 , lowerCAmelCase__ :int=2 , lowerCAmelCase__ :Optional[int]=0.0_2 , lowerCAmelCase__ :Tuple=1E-1_2 , lowerCAmelCase__ :Tuple=True , lowerCAmelCase__ :List[str]=0 , lowerCAmelCase__ :Optional[Any]="absolute" , lowerCAmelCase__ :Tuple=None , lowerCAmelCase__ :List[str]=True , lowerCAmelCase__ :Optional[Any]=True , lowerCAmelCase__ :List[str]=768 , lowerCAmelCase__ :Optional[Any]=910 , lowerCAmelCase__ :str=512 , lowerCAmelCase__ :int=24_858 , lowerCAmelCase__ :List[Any]=True , **lowerCAmelCase__ :int , ) -> List[str]:
'''simple docstring'''
snake_case_ : int = vocab_size
snake_case_ : Dict = max_position_embeddings
snake_case_ : int = hidden_size
snake_case_ : str = num_hidden_layers
snake_case_ : Union[str, Any] = num_attention_heads
snake_case_ : int = intermediate_size
snake_case_ : Optional[Any] = hidden_act
snake_case_ : Optional[int] = hidden_dropout_prob
snake_case_ : List[Any] = attention_probs_dropout_prob
snake_case_ : Dict = initializer_range
snake_case_ : str = type_vocab_size
snake_case_ : Tuple = layer_norm_eps
snake_case_ : Optional[Any] = use_cache
snake_case_ : Optional[Any] = enable_pronunciation
snake_case_ : List[Any] = enable_shape
snake_case_ : Optional[int] = pronunciation_embed_dim
snake_case_ : Dict = pronunciation_vocab_size
snake_case_ : int = shape_embed_dim
snake_case_ : Any = shape_vocab_size
snake_case_ : Optional[int] = concat_input
snake_case_ : List[Any] = position_embedding_type
snake_case_ : Any = classifier_dropout
super().__init__(pad_token_id=lowerCAmelCase__ , **lowerCAmelCase__ )
| 653 | 0 |
import argparse
import os
import re
SCREAMING_SNAKE_CASE = 'src/transformers'
# Pattern that looks at the indentation in a line.
SCREAMING_SNAKE_CASE = re.compile(r'^(\s*)\S')
# Pattern that matches `"key":" and puts `key` in group 0.
SCREAMING_SNAKE_CASE = re.compile(r'^\s*"([^"]+)":')
# Pattern that matches `_import_structure["key"]` and puts `key` in group 0.
SCREAMING_SNAKE_CASE = re.compile(r'^\s*_import_structure\["([^"]+)"\]')
# Pattern that matches `"key",` and puts `key` in group 0.
SCREAMING_SNAKE_CASE = re.compile(r'^\s*"([^"]+)",\s*$')
# Pattern that matches any `[stuff]` and puts `stuff` in group 0.
SCREAMING_SNAKE_CASE = re.compile(r'\[([^\]]+)\]')
def a (lowerCAmelCase__ ):
__a = _re_indent.search(lowerCAmelCase__ )
return "" if search is None else search.groups()[0]
def a (lowerCAmelCase__ , lowerCAmelCase__="" , lowerCAmelCase__=None , lowerCAmelCase__=None ):
__a = 0
__a = code.split("""\n""" )
if start_prompt is not None:
while not lines[index].startswith(lowerCAmelCase__ ):
index += 1
__a = ["""\n""".join(lines[:index] )]
else:
__a = []
# We split into blocks until we get to the `end_prompt` (or the end of the block).
__a = [lines[index]]
index += 1
while index < len(lowerCAmelCase__ ) and (end_prompt is None or not lines[index].startswith(lowerCAmelCase__ )):
if len(lines[index] ) > 0 and get_indent(lines[index] ) == indent_level:
if len(lowerCAmelCase__ ) > 0 and get_indent(current_block[-1] ).startswith(indent_level + """ """ ):
current_block.append(lines[index] )
blocks.append("""\n""".join(lowerCAmelCase__ ) )
if index < len(lowerCAmelCase__ ) - 1:
__a = [lines[index + 1]]
index += 1
else:
__a = []
else:
blocks.append("""\n""".join(lowerCAmelCase__ ) )
__a = [lines[index]]
else:
current_block.append(lines[index] )
index += 1
# Adds current block if it's nonempty.
if len(lowerCAmelCase__ ) > 0:
blocks.append("""\n""".join(lowerCAmelCase__ ) )
# Add final block after end_prompt if provided.
if end_prompt is not None and index < len(lowerCAmelCase__ ):
blocks.append("""\n""".join(lines[index:] ) )
return blocks
def a (lowerCAmelCase__ ):
def _inner(lowerCAmelCase__ ):
return key(lowerCAmelCase__ ).lower().replace("""_""" , """""" )
return _inner
def a (lowerCAmelCase__ , lowerCAmelCase__=None ):
# If no key is provided, we use a noop.
def noop(lowerCAmelCase__ ):
return x
if key is None:
__a = noop
# Constants are all uppercase, they go first.
__a = [obj for obj in objects if key(lowerCAmelCase__ ).isupper()]
# Classes are not all uppercase but start with a capital, they go second.
__a = [obj for obj in objects if key(lowerCAmelCase__ )[0].isupper() and not key(lowerCAmelCase__ ).isupper()]
# Functions begin with a lowercase, they go last.
__a = [obj for obj in objects if not key(lowerCAmelCase__ )[0].isupper()]
__a = ignore_underscore(lowerCAmelCase__ )
return sorted(lowerCAmelCase__ , key=lowerCAmelCase__ ) + sorted(lowerCAmelCase__ , key=lowerCAmelCase__ ) + sorted(lowerCAmelCase__ , key=lowerCAmelCase__ )
def a (lowerCAmelCase__ ):
# This inner function sort imports between [ ].
def _replace(lowerCAmelCase__ ):
__a = match.groups()[0]
if "," not in imports:
return f'''[{imports}]'''
__a = [part.strip().replace("""\"""" , """""" ) for part in imports.split(""",""" )]
# We will have a final empty element if the line finished with a comma.
if len(keys[-1] ) == 0:
__a = keys[:-1]
return "[" + ", ".join([f'''"{k}"''' for k in sort_objects(lowerCAmelCase__ )] ) + "]"
__a = import_statement.split("""\n""" )
if len(lowerCAmelCase__ ) > 3:
# Here we have to sort internal imports that are on several lines (one per name):
# key: [
# "object1",
# "object2",
# ...
# ]
# We may have to ignore one or two lines on each side.
__a = 2 if lines[1].strip() == """[""" else 1
__a = [(i, _re_strip_line.search(lowerCAmelCase__ ).groups()[0]) for i, line in enumerate(lines[idx:-idx] )]
__a = sort_objects(lowerCAmelCase__ , key=lambda lowerCAmelCase__ : x[1] )
__a = [lines[x[0] + idx] for x in sorted_indices]
return "\n".join(lines[:idx] + sorted_lines + lines[-idx:] )
elif len(lowerCAmelCase__ ) == 3:
# Here we have to sort internal imports that are on one separate line:
# key: [
# "object1", "object2", ...
# ]
if _re_bracket_content.search(lines[1] ) is not None:
__a = _re_bracket_content.sub(_replace , lines[1] )
else:
__a = [part.strip().replace("""\"""" , """""" ) for part in lines[1].split(""",""" )]
# We will have a final empty element if the line finished with a comma.
if len(keys[-1] ) == 0:
__a = keys[:-1]
__a = get_indent(lines[1] ) + """, """.join([f'''"{k}"''' for k in sort_objects(lowerCAmelCase__ )] )
return "\n".join(lowerCAmelCase__ )
else:
# Finally we have to deal with imports fitting on one line
__a = _re_bracket_content.sub(_replace , lowerCAmelCase__ )
return import_statement
def a (lowerCAmelCase__ , lowerCAmelCase__=True ):
with open(lowerCAmelCase__ , encoding="""utf-8""" ) as f:
__a = f.read()
if "_import_structure" not in code:
return
# Blocks of indent level 0
__a = split_code_in_indented_blocks(
lowerCAmelCase__ , start_prompt="""_import_structure = {""" , end_prompt="""if TYPE_CHECKING:""" )
# We ignore block 0 (everything untils start_prompt) and the last block (everything after end_prompt).
for block_idx in range(1 , len(lowerCAmelCase__ ) - 1 ):
# Check if the block contains some `_import_structure`s thingy to sort.
__a = main_blocks[block_idx]
__a = block.split("""\n""" )
# Get to the start of the imports.
__a = 0
while line_idx < len(lowerCAmelCase__ ) and "_import_structure" not in block_lines[line_idx]:
# Skip dummy import blocks
if "import dummy" in block_lines[line_idx]:
__a = len(lowerCAmelCase__ )
else:
line_idx += 1
if line_idx >= len(lowerCAmelCase__ ):
continue
# Ignore beginning and last line: they don't contain anything.
__a = """\n""".join(block_lines[line_idx:-1] )
__a = get_indent(block_lines[1] )
# Slit the internal block into blocks of indent level 1.
__a = split_code_in_indented_blocks(lowerCAmelCase__ , indent_level=lowerCAmelCase__ )
# We have two categories of import key: list or _import_structure[key].append/extend
__a = _re_direct_key if """_import_structure = {""" in block_lines[0] else _re_indirect_key
# Grab the keys, but there is a trap: some lines are empty or just comments.
__a = [(pattern.search(lowerCAmelCase__ ).groups()[0] if pattern.search(lowerCAmelCase__ ) is not None else None) for b in internal_blocks]
# We only sort the lines with a key.
__a = [(i, key) for i, key in enumerate(lowerCAmelCase__ ) if key is not None]
__a = [x[0] for x in sorted(lowerCAmelCase__ , key=lambda lowerCAmelCase__ : x[1] )]
# We reorder the blocks by leaving empty lines/comments as they were and reorder the rest.
__a = 0
__a = []
for i in range(len(lowerCAmelCase__ ) ):
if keys[i] is None:
reorderded_blocks.append(internal_blocks[i] )
else:
__a = sort_objects_in_import(internal_blocks[sorted_indices[count]] )
reorderded_blocks.append(lowerCAmelCase__ )
count += 1
# And we put our main block back together with its first and last line.
__a = """\n""".join(block_lines[:line_idx] + reorderded_blocks + [block_lines[-1]] )
if code != "\n".join(lowerCAmelCase__ ):
if check_only:
return True
else:
print(f'''Overwriting {file}.''' )
with open(lowerCAmelCase__ , """w""" , encoding="""utf-8""" ) as f:
f.write("""\n""".join(lowerCAmelCase__ ) )
def a (lowerCAmelCase__=True ):
__a = []
for root, _, files in os.walk(lowerCAmelCase__ ):
if "__init__.py" in files:
__a = sort_imports(os.path.join(lowerCAmelCase__ , """__init__.py""" ) , check_only=lowerCAmelCase__ )
if result:
__a = [os.path.join(lowerCAmelCase__ , """__init__.py""" )]
if len(lowerCAmelCase__ ) > 0:
raise ValueError(f'''Would overwrite {len(lowerCAmelCase__ )} files, run `make style`.''' )
if __name__ == "__main__":
SCREAMING_SNAKE_CASE = argparse.ArgumentParser()
parser.add_argument('--check_only', action='store_true', help='Whether to only check or fix style.')
SCREAMING_SNAKE_CASE = parser.parse_args()
sort_imports_in_all_inits(check_only=args.check_only)
| 99 |
'''simple docstring'''
def __UpperCAmelCase ( __magic_name__ ,__magic_name__ ,__magic_name__ )-> int:
"""simple docstring"""
def update_area_of_max_square(__magic_name__ ,__magic_name__ ) -> int:
# BASE CASE
if row >= rows or col >= cols:
return 0
snake_case_ : str = update_area_of_max_square(__magic_name__ ,col + 1 )
snake_case_ : Dict = update_area_of_max_square(row + 1 ,col + 1 )
snake_case_ : int = update_area_of_max_square(row + 1 ,__magic_name__ )
if mat[row][col]:
snake_case_ : str = 1 + min([right, diagonal, down] )
snake_case_ : Tuple = max(largest_square_area[0] ,__magic_name__ )
return sub_problem_sol
else:
return 0
snake_case_ : Union[str, Any] = [0]
update_area_of_max_square(0 ,0 )
return largest_square_area[0]
def __UpperCAmelCase ( __magic_name__ ,__magic_name__ ,__magic_name__ )-> int:
"""simple docstring"""
def update_area_of_max_square_using_dp_array(
__magic_name__ ,__magic_name__ ,__magic_name__ ) -> int:
if row >= rows or col >= cols:
return 0
if dp_array[row][col] != -1:
return dp_array[row][col]
snake_case_ : Dict = update_area_of_max_square_using_dp_array(__magic_name__ ,col + 1 ,__magic_name__ )
snake_case_ : List[Any] = update_area_of_max_square_using_dp_array(row + 1 ,col + 1 ,__magic_name__ )
snake_case_ : Any = update_area_of_max_square_using_dp_array(row + 1 ,__magic_name__ ,__magic_name__ )
if mat[row][col]:
snake_case_ : int = 1 + min([right, diagonal, down] )
snake_case_ : Tuple = max(largest_square_area[0] ,__magic_name__ )
snake_case_ : Optional[Any] = sub_problem_sol
return sub_problem_sol
else:
return 0
snake_case_ : List[Any] = [0]
snake_case_ : Optional[int] = [[-1] * cols for _ in range(__magic_name__ )]
update_area_of_max_square_using_dp_array(0 ,0 ,__magic_name__ )
return largest_square_area[0]
def __UpperCAmelCase ( __magic_name__ ,__magic_name__ ,__magic_name__ )-> int:
"""simple docstring"""
snake_case_ : Dict = [[0] * (cols + 1) for _ in range(rows + 1 )]
snake_case_ : Dict = 0
for row in range(rows - 1 ,-1 ,-1 ):
for col in range(cols - 1 ,-1 ,-1 ):
snake_case_ : List[str] = dp_array[row][col + 1]
snake_case_ : Any = dp_array[row + 1][col + 1]
snake_case_ : Any = dp_array[row + 1][col]
if mat[row][col] == 1:
snake_case_ : Any = 1 + min(__magic_name__ ,__magic_name__ ,__magic_name__ )
snake_case_ : str = max(dp_array[row][col] ,__magic_name__ )
else:
snake_case_ : Optional[Any] = 0
return largest_square_area
def __UpperCAmelCase ( __magic_name__ ,__magic_name__ ,__magic_name__ )-> int:
"""simple docstring"""
snake_case_ : str = [0] * (cols + 1)
snake_case_ : Tuple = [0] * (cols + 1)
snake_case_ : List[str] = 0
for row in range(rows - 1 ,-1 ,-1 ):
for col in range(cols - 1 ,-1 ,-1 ):
snake_case_ : Optional[Any] = current_row[col + 1]
snake_case_ : Optional[int] = next_row[col + 1]
snake_case_ : Dict = next_row[col]
if mat[row][col] == 1:
snake_case_ : Union[str, Any] = 1 + min(__magic_name__ ,__magic_name__ ,__magic_name__ )
snake_case_ : Any = max(current_row[col] ,__magic_name__ )
else:
snake_case_ : Dict = 0
snake_case_ : Optional[Any] = current_row
return largest_square_area
if __name__ == "__main__":
import doctest
doctest.testmod()
print(largest_square_area_in_matrix_bottom_up(2, 2, [[1, 1], [1, 1]]))
| 653 | 0 |
from __future__ import annotations
def __snake_case ( lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ ) -> int | float:
if len(lowerCAmelCase_ ) == 0:
raise ValueError('''find_max() arg is an empty sequence''' )
if (
left >= len(lowerCAmelCase_ )
or left < -len(lowerCAmelCase_ )
or right >= len(lowerCAmelCase_ )
or right < -len(lowerCAmelCase_ )
):
raise IndexError('''list index out of range''' )
if left == right:
return nums[left]
SCREAMING_SNAKE_CASE__ = (left + right) >> 1 # the middle
SCREAMING_SNAKE_CASE__ = find_max(lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ ) # find max in range[left, mid]
SCREAMING_SNAKE_CASE__ = find_max(lowerCAmelCase_ , mid + 1 , lowerCAmelCase_ ) # find max in range[mid + 1, right]
return left_max if left_max >= right_max else right_max
if __name__ == "__main__":
import doctest
doctest.testmod(verbose=True)
| 100 |
'''simple docstring'''
import os
import zipfile
import requests
from get_ci_error_statistics import download_artifact, get_artifacts_links
def __UpperCAmelCase ( __magic_name__ ,__magic_name__=7 )-> Tuple:
"""simple docstring"""
snake_case_ : List[str] = None
if token is not None:
snake_case_ : List[str] = {"Accept": "application/vnd.github+json", "Authorization": F'''Bearer {token}'''}
# The id of a workflow (not of a workflow run)
snake_case_ : Dict = "636036"
snake_case_ : List[str] = F'''https://api.github.com/repos/huggingface/transformers/actions/workflows/{workflow_id}/runs'''
# On `main` branch + event being `schedule` + not returning PRs + only `num_runs` results
url += F'''?branch=main&event=schedule&exclude_pull_requests=true&per_page={num_runs}'''
snake_case_ : Optional[Any] = requests.get(__magic_name__ ,headers=__magic_name__ ).json()
return result["workflow_runs"]
def __UpperCAmelCase ( __magic_name__ )-> Union[str, Any]:
"""simple docstring"""
snake_case_ : str = get_daily_ci_runs(__magic_name__ )
snake_case_ : Optional[int] = None
for workflow_run in workflow_runs:
if workflow_run["status"] == "completed":
snake_case_ : Dict = workflow_run["id"]
break
return workflow_run_id
def __UpperCAmelCase ( __magic_name__ ,__magic_name__ ,__magic_name__ )-> List[Any]:
"""simple docstring"""
snake_case_ : Optional[Any] = get_last_daily_ci_runs(__magic_name__ )
if workflow_run_id is not None:
snake_case_ : Union[str, Any] = get_artifacts_links(worflow_run_id=__magic_name__ ,token=__magic_name__ )
for artifact_name in artifact_names:
if artifact_name in artifacts_links:
snake_case_ : Union[str, Any] = artifacts_links[artifact_name]
download_artifact(
artifact_name=__magic_name__ ,artifact_url=__magic_name__ ,output_dir=__magic_name__ ,token=__magic_name__ )
def __UpperCAmelCase ( __magic_name__ ,__magic_name__ ,__magic_name__ )-> List[Any]:
"""simple docstring"""
get_last_daily_ci_artifacts(__magic_name__ ,__magic_name__ ,__magic_name__ )
snake_case_ : Union[str, Any] = {}
for artifact_name in artifact_names:
snake_case_ : Any = os.path.join(__magic_name__ ,F'''{artifact_name}.zip''' )
if os.path.isfile(__magic_name__ ):
snake_case_ : Tuple = {}
with zipfile.ZipFile(__magic_name__ ) as z:
for filename in z.namelist():
if not os.path.isdir(__magic_name__ ):
# read the file
with z.open(__magic_name__ ) as f:
snake_case_ : Optional[Any] = f.read().decode("UTF-8" )
return results
| 653 | 0 |
from typing import TYPE_CHECKING
from ...utils import _LazyModule
lowerCAmelCase__ : Dict ={'processing_wav2vec2_with_lm': ['Wav2Vec2ProcessorWithLM']}
if TYPE_CHECKING:
from .processing_wavaveca_with_lm import WavaVecaProcessorWithLM
else:
import sys
lowerCAmelCase__ : Optional[Any] =_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 101 |
'''simple docstring'''
from string import ascii_uppercase
__lowerCamelCase : Optional[Any] = {char: i for i, char in enumerate(ascii_uppercase)}
__lowerCamelCase : List[str] = dict(enumerate(ascii_uppercase))
def __UpperCAmelCase ( __magic_name__ ,__magic_name__ )-> str:
"""simple docstring"""
snake_case_ : Tuple = len(__magic_name__ )
snake_case_ : str = 0
while True:
if x == i:
snake_case_ : List[str] = 0
if len(__magic_name__ ) == len(__magic_name__ ):
break
key += key[i]
i += 1
return key
def __UpperCAmelCase ( __magic_name__ ,__magic_name__ )-> str:
"""simple docstring"""
snake_case_ : str = ""
snake_case_ : List[Any] = 0
for letter in message:
if letter == " ":
cipher_text += " "
else:
snake_case_ : Optional[Any] = (dicta[letter] - dicta[key_new[i]]) % 26
i += 1
cipher_text += dicta[x]
return cipher_text
def __UpperCAmelCase ( __magic_name__ ,__magic_name__ )-> str:
"""simple docstring"""
snake_case_ : Dict = ""
snake_case_ : Dict = 0
for letter in cipher_text:
if letter == " ":
or_txt += " "
else:
snake_case_ : str = (dicta[letter] + dicta[key_new[i]] + 26) % 26
i += 1
or_txt += dicta[x]
return or_txt
def __UpperCAmelCase ( )-> None:
"""simple docstring"""
snake_case_ : List[str] = "THE GERMAN ATTACK"
snake_case_ : List[str] = "SECRET"
snake_case_ : Optional[int] = generate_key(__magic_name__ ,__magic_name__ )
snake_case_ : Any = cipher_text(__magic_name__ ,__magic_name__ )
print(F'''Encrypted Text = {s}''' )
print(F'''Original Text = {original_text(__magic_name__ ,__magic_name__ )}''' )
if __name__ == "__main__":
import doctest
doctest.testmod()
main()
| 653 | 0 |
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
__magic_name__ : Any = logging.get_logger(__name__)
__magic_name__ : Tuple = {
"""microsoft/resnet-50""": """https://huggingface.co/microsoft/resnet-50/blob/main/config.json""",
}
class lowercase__ ( __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ):
"""simple docstring"""
__lowerCAmelCase : List[str] = """resnet"""
__lowerCAmelCase : List[Any] = ["""basic""", """bottleneck"""]
def __init__( self , _A=3 , _A=6_4 , _A=[2_5_6, 5_1_2, 1_0_2_4, 2_0_4_8] , _A=[3, 4, 6, 3] , _A="bottleneck" , _A="relu" , _A=False , _A=None , _A=None , **_A , ):
'''simple docstring'''
super().__init__(**_A )
if layer_type not in self.layer_types:
raise ValueError(f"""layer_type={layer_type} is not one of {",".join(self.layer_types )}""" )
UpperCamelCase : List[Any] = num_channels
UpperCamelCase : str = embedding_size
UpperCamelCase : List[Any] = hidden_sizes
UpperCamelCase : str = depths
UpperCamelCase : Tuple = layer_type
UpperCamelCase : Tuple = hidden_act
UpperCamelCase : str = downsample_in_first_stage
UpperCamelCase : str = ["""stem"""] + [f"""stage{idx}""" for idx in range(1 , len(_A ) + 1 )]
UpperCamelCase , UpperCamelCase : Union[str, Any] = get_aligned_output_features_output_indices(
out_features=_A , out_indices=_A , stage_names=self.stage_names )
class lowercase__ ( __SCREAMING_SNAKE_CASE ):
"""simple docstring"""
__lowerCAmelCase : Any = version.parse("""1.11""" )
@property
def _a ( self ):
'''simple docstring'''
return OrderedDict(
[
("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""}),
] )
@property
def _a ( self ):
'''simple docstring'''
return 1e-3
| 102 |
'''simple docstring'''
import argparse
import os
import numpy as np
import tensorflow as tf
import torch
from transformers import BertModel
def __UpperCAmelCase ( __magic_name__ ,__magic_name__ ,__magic_name__ )-> Dict:
"""simple docstring"""
snake_case_ : Tuple = ("dense.weight", "attention.self.query", "attention.self.key", "attention.self.value")
snake_case_ : Union[str, Any] = (
("layer.", "layer_"),
("word_embeddings.weight", "word_embeddings"),
("position_embeddings.weight", "position_embeddings"),
("token_type_embeddings.weight", "token_type_embeddings"),
(".", "/"),
("LayerNorm/weight", "LayerNorm/gamma"),
("LayerNorm/bias", "LayerNorm/beta"),
("weight", "kernel"),
)
if not os.path.isdir(__magic_name__ ):
os.makedirs(__magic_name__ )
snake_case_ : str = model.state_dict()
def to_tf_var_name(__magic_name__ ):
for patt, repl in iter(__magic_name__ ):
snake_case_ : List[str] = name.replace(__magic_name__ ,__magic_name__ )
return F'''bert/{name}'''
def create_tf_var(__magic_name__ ,__magic_name__ ,__magic_name__ ):
snake_case_ : List[Any] = tf.dtypes.as_dtype(tensor.dtype )
snake_case_ : Union[str, Any] = tf.get_variable(dtype=__magic_name__ ,shape=tensor.shape ,name=__magic_name__ ,initializer=tf.zeros_initializer() )
session.run(tf.variables_initializer([tf_var] ) )
session.run(__magic_name__ )
return tf_var
tf.reset_default_graph()
with tf.Session() as session:
for var_name in state_dict:
snake_case_ : Optional[int] = to_tf_var_name(__magic_name__ )
snake_case_ : Dict = state_dict[var_name].numpy()
if any(x in var_name for x in tensors_to_transpose ):
snake_case_ : List[Any] = torch_tensor.T
snake_case_ : Union[str, Any] = create_tf_var(tensor=__magic_name__ ,name=__magic_name__ ,session=__magic_name__ )
tf.keras.backend.set_value(__magic_name__ ,__magic_name__ )
snake_case_ : List[str] = session.run(__magic_name__ )
print(F'''Successfully created {tf_name}: {np.allclose(__magic_name__ ,__magic_name__ )}''' )
snake_case_ : Any = tf.train.Saver(tf.trainable_variables() )
saver.save(__magic_name__ ,os.path.join(__magic_name__ ,model_name.replace("-" ,"_" ) + ".ckpt" ) )
def __UpperCAmelCase ( __magic_name__=None )-> Optional[Any]:
"""simple docstring"""
snake_case_ : Any = argparse.ArgumentParser()
parser.add_argument("--model_name" ,type=__magic_name__ ,required=__magic_name__ ,help="model name e.g. bert-base-uncased" )
parser.add_argument(
"--cache_dir" ,type=__magic_name__ ,default=__magic_name__ ,required=__magic_name__ ,help="Directory containing pytorch model" )
parser.add_argument("--pytorch_model_path" ,type=__magic_name__ ,required=__magic_name__ ,help="/path/to/<pytorch-model-name>.bin" )
parser.add_argument("--tf_cache_dir" ,type=__magic_name__ ,required=__magic_name__ ,help="Directory in which to save tensorflow model" )
snake_case_ : Optional[int] = parser.parse_args(__magic_name__ )
snake_case_ : Optional[int] = BertModel.from_pretrained(
pretrained_model_name_or_path=args.model_name ,state_dict=torch.load(args.pytorch_model_path ) ,cache_dir=args.cache_dir ,)
convert_pytorch_checkpoint_to_tf(model=__magic_name__ ,ckpt_dir=args.tf_cache_dir ,model_name=args.model_name )
if __name__ == "__main__":
main()
| 653 | 0 |
"""simple docstring"""
def snake_case ( lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ ) -> int:
if len(lowerCAmelCase_ ) != len(lowerCAmelCase_ ):
raise ValueError('''The length of profit and weight must be same.''' )
if max_weight <= 0:
raise ValueError('''max_weight must greater than zero.''' )
if any(p < 0 for p in profit ):
raise ValueError('''Profit can not be negative.''' )
if any(w < 0 for w in weight ):
raise ValueError('''Weight can not be negative.''' )
# List created to store profit gained for the 1kg in case of each weight
# respectively. Calculate and append profit/weight for each element.
_snake_case = [p / w for p, w in zip(lowerCAmelCase_ , lowerCAmelCase_ )]
# Creating a copy of the list and sorting profit/weight in ascending order
_snake_case = sorted(lowerCAmelCase_ )
# declaring useful variables
_snake_case = len(lowerCAmelCase_ )
_snake_case = 0
_snake_case = 0
_snake_case = 0
# loop till the total weight do not reach max limit e.g. 15 kg and till i<length
while limit <= max_weight and i < length:
# flag value for encountered greatest element in sorted_profit_by_weight
_snake_case = sorted_profit_by_weight[length - i - 1]
_snake_case = profit_by_weight.index(lowerCAmelCase_ )
_snake_case = -1
# check if the weight encountered is less than the total weight
# encountered before.
if max_weight - limit >= weight[index]:
limit += weight[index]
# Adding profit gained for the given weight 1 ===
# weight[index]/weight[index]
gain += 1 * profit[index]
else:
# Since the weight encountered is greater than limit, therefore take the
# required number of remaining kgs and calculate profit for it.
# weight remaining / weight[index]
gain += (max_weight - limit) / weight[index] * profit[index]
break
i += 1
return gain
if __name__ == "__main__":
print(
'''Input profits, weights, and then max_weight (all positive ints) separated by '''
'''spaces.'''
)
snake_case = [int(x) for x in input('''Input profits separated by spaces: ''').split()]
snake_case = [int(x) for x in input('''Input weights separated by spaces: ''').split()]
snake_case = int(input('''Max weight allowed: '''))
# Function Call
calc_profit(profit, weight, max_weight)
| 103 |
'''simple docstring'''
from collections import deque
from .hash_table import HashTable
class A_ (a_ ):
"""simple docstring"""
def __init__( self :List[str] , *lowerCAmelCase__ :Optional[Any] , **lowerCAmelCase__ :Dict ) -> Union[str, Any]:
'''simple docstring'''
super().__init__(*lowerCAmelCase__ , **lowerCAmelCase__ )
def _A ( self :Optional[int] , lowerCAmelCase__ :int , lowerCAmelCase__ :Optional[Any] ) -> Union[str, Any]:
'''simple docstring'''
snake_case_ : Optional[int] = deque([] ) if self.values[key] is None else self.values[key]
self.values[key].appendleft(lowerCAmelCase__ )
snake_case_ : Tuple = self.values[key]
def _A ( self :int ) -> Dict:
'''simple docstring'''
return (
sum(self.charge_factor - len(lowerCAmelCase__ ) for slot in self.values )
/ self.size_table
* self.charge_factor
)
def _A ( self :str , lowerCAmelCase__ :Optional[Any] , lowerCAmelCase__ :Tuple=None ) -> Any:
'''simple docstring'''
if not (
len(self.values[key] ) == self.charge_factor and self.values.count(lowerCAmelCase__ ) == 0
):
return key
return super()._collision_resolution(lowerCAmelCase__ , lowerCAmelCase__ )
| 653 | 0 |
"""simple docstring"""
def _lowerCamelCase ( UpperCAmelCase_ : int = 100 ) -> int:
"""simple docstring"""
A__ = (n * (n + 1) // 2) ** 2
A__ = n * (n + 1) * (2 * n + 1) // 6
return sum_cubes - sum_squares
if __name__ == "__main__":
print(f'{solution() = }')
| 104 |
'''simple docstring'''
from collections.abc import Iterator, MutableMapping
from dataclasses import dataclass
from typing import Generic, TypeVar
__lowerCamelCase : Dict = TypeVar('''KEY''')
__lowerCamelCase : int = TypeVar('''VAL''')
@dataclass(frozen=a_ , slots=a_ )
class A_ (Generic[KEY, VAL] ):
"""simple docstring"""
a__ = 42
a__ = 42
class A_ (_Item ):
"""simple docstring"""
def __init__( self :List[Any] ) -> None:
'''simple docstring'''
super().__init__(lowerCAmelCase__ , lowerCAmelCase__ )
def __bool__( self :Optional[int] ) -> bool:
'''simple docstring'''
return False
__lowerCamelCase : Dict = _DeletedItem()
class A_ (MutableMapping[KEY, VAL] ):
"""simple docstring"""
def __init__( self :Dict , lowerCAmelCase__ :int = 8 , lowerCAmelCase__ :float = 0.7_5 ) -> None:
'''simple docstring'''
snake_case_ : Any = initial_block_size
snake_case_ : list[_Item | None] = [None] * initial_block_size
assert 0.0 < capacity_factor < 1.0
snake_case_ : Tuple = capacity_factor
snake_case_ : List[Any] = 0
def _A ( self :Tuple , lowerCAmelCase__ :KEY ) -> int:
'''simple docstring'''
return hash(lowerCAmelCase__ ) % len(self._buckets )
def _A ( self :Any , lowerCAmelCase__ :int ) -> int:
'''simple docstring'''
return (ind + 1) % len(self._buckets )
def _A ( self :str , lowerCAmelCase__ :int , lowerCAmelCase__ :KEY , lowerCAmelCase__ :VAL ) -> bool:
'''simple docstring'''
snake_case_ : Optional[int] = self._buckets[ind]
if not stored:
snake_case_ : int = _Item(lowerCAmelCase__ , lowerCAmelCase__ )
self._len += 1
return True
elif stored.key == key:
snake_case_ : Optional[int] = _Item(lowerCAmelCase__ , lowerCAmelCase__ )
return True
else:
return False
def _A ( self :int ) -> bool:
'''simple docstring'''
snake_case_ : Any = len(self._buckets ) * self._capacity_factor
return len(self ) >= int(lowerCAmelCase__ )
def _A ( self :Any ) -> bool:
'''simple docstring'''
if len(self._buckets ) <= self._initial_block_size:
return False
snake_case_ : Optional[int] = len(self._buckets ) * self._capacity_factor / 2
return len(self ) < limit
def _A ( self :Tuple , lowerCAmelCase__ :int ) -> None:
'''simple docstring'''
snake_case_ : Tuple = self._buckets
snake_case_ : int = [None] * new_size
snake_case_ : Any = 0
for item in old_buckets:
if item:
self._add_item(item.key , item.val )
def _A ( self :Optional[int] ) -> None:
'''simple docstring'''
self._resize(len(self._buckets ) * 2 )
def _A ( self :str ) -> None:
'''simple docstring'''
self._resize(len(self._buckets ) // 2 )
def _A ( self :Optional[int] , lowerCAmelCase__ :KEY ) -> Iterator[int]:
'''simple docstring'''
snake_case_ : str = self._get_bucket_index(lowerCAmelCase__ )
for _ in range(len(self._buckets ) ):
yield ind
snake_case_ : List[Any] = self._get_next_ind(lowerCAmelCase__ )
def _A ( self :Union[str, Any] , lowerCAmelCase__ :KEY , lowerCAmelCase__ :VAL ) -> None:
'''simple docstring'''
for ind in self._iterate_buckets(lowerCAmelCase__ ):
if self._try_set(lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ ):
break
def __setitem__( self :Optional[int] , lowerCAmelCase__ :KEY , lowerCAmelCase__ :VAL ) -> None:
'''simple docstring'''
if self._is_full():
self._size_up()
self._add_item(lowerCAmelCase__ , lowerCAmelCase__ )
def __delitem__( self :List[Any] , lowerCAmelCase__ :KEY ) -> None:
'''simple docstring'''
for ind in self._iterate_buckets(lowerCAmelCase__ ):
snake_case_ : int = self._buckets[ind]
if item is None:
raise KeyError(lowerCAmelCase__ )
if item is _deleted:
continue
if item.key == key:
snake_case_ : List[str] = _deleted
self._len -= 1
break
if self._is_sparse():
self._size_down()
def __getitem__( self :List[str] , lowerCAmelCase__ :KEY ) -> VAL:
'''simple docstring'''
for ind in self._iterate_buckets(lowerCAmelCase__ ):
snake_case_ : Optional[Any] = self._buckets[ind]
if item is None:
break
if item is _deleted:
continue
if item.key == key:
return item.val
raise KeyError(lowerCAmelCase__ )
def __len__( self :Optional[Any] ) -> int:
'''simple docstring'''
return self._len
def __iter__( self :List[Any] ) -> Iterator[KEY]:
'''simple docstring'''
yield from (item.key for item in self._buckets if item)
def __repr__( self :Any ) -> str:
'''simple docstring'''
snake_case_ : Dict = " ,".join(
F'''{item.key}: {item.val}''' for item in self._buckets if item )
return F'''HashMap({val_string})'''
| 653 | 0 |
import argparse
import torch
from ...utils import logging
from . import AlbertConfig, AlbertForPreTraining, load_tf_weights_in_albert
logging.set_verbosity_info()
def __UpperCAmelCase ( lowerCamelCase_ : int , lowerCamelCase_ : Optional[int] , lowerCamelCase_ : List[str] ) -> Optional[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Dict = AlbertConfig.from_json_file(lowerCamelCase_ )
print(F'Building PyTorch model from configuration: {config}' )
SCREAMING_SNAKE_CASE_ : Any = AlbertForPreTraining(lowerCamelCase_ )
# Load weights from tf checkpoint
load_tf_weights_in_albert(lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ )
# Save pytorch-model
print(F'Save PyTorch model to {pytorch_dump_path}' )
torch.save(model.state_dict() , lowerCamelCase_ )
if __name__ == "__main__":
UpperCamelCase__ : str = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'''--tf_checkpoint_path''', default=None, type=str, required=True, help='''Path to the TensorFlow checkpoint path.'''
)
parser.add_argument(
'''--albert_config_file''',
default=None,
type=str,
required=True,
help=(
'''The config json file corresponding to the pre-trained ALBERT model. \n'''
'''This specifies the model architecture.'''
),
)
parser.add_argument(
'''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
)
UpperCamelCase__ : Optional[Any] = parser.parse_args()
convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.albert_config_file, args.pytorch_dump_path)
| 105 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__lowerCamelCase : str = logging.get_logger(__name__)
__lowerCamelCase : Tuple = {
'''bigcode/gpt_bigcode-santacoder''': '''https://huggingface.co/bigcode/gpt_bigcode-santacoder/resolve/main/config.json''',
}
class A_ (a_ ):
"""simple docstring"""
a__ = '''gpt_bigcode'''
a__ = ['''past_key_values''']
a__ = {
'''hidden_size''': '''n_embd''',
'''max_position_embeddings''': '''n_positions''',
'''num_attention_heads''': '''n_head''',
'''num_hidden_layers''': '''n_layer''',
}
def __init__( self :List[Any] , lowerCAmelCase__ :Any=50_257 , lowerCAmelCase__ :Dict=1_024 , lowerCAmelCase__ :Optional[int]=768 , lowerCAmelCase__ :Tuple=12 , lowerCAmelCase__ :int=12 , lowerCAmelCase__ :Optional[Any]=None , lowerCAmelCase__ :List[str]="gelu_pytorch_tanh" , lowerCAmelCase__ :Tuple=0.1 , lowerCAmelCase__ :Tuple=0.1 , lowerCAmelCase__ :str=0.1 , lowerCAmelCase__ :Any=1E-5 , lowerCAmelCase__ :Union[str, Any]=0.0_2 , lowerCAmelCase__ :Union[str, Any]=True , lowerCAmelCase__ :Optional[Any]=True , lowerCAmelCase__ :int=50_256 , lowerCAmelCase__ :List[str]=50_256 , lowerCAmelCase__ :List[Any]=True , lowerCAmelCase__ :str=True , lowerCAmelCase__ :int=True , **lowerCAmelCase__ :Union[str, Any] , ) -> Any:
'''simple docstring'''
snake_case_ : List[Any] = vocab_size
snake_case_ : Any = n_positions
snake_case_ : Any = n_embd
snake_case_ : Optional[Any] = n_layer
snake_case_ : List[Any] = n_head
snake_case_ : Tuple = n_inner
snake_case_ : str = activation_function
snake_case_ : Union[str, Any] = resid_pdrop
snake_case_ : Optional[Any] = embd_pdrop
snake_case_ : Any = attn_pdrop
snake_case_ : List[Any] = layer_norm_epsilon
snake_case_ : Tuple = initializer_range
snake_case_ : int = scale_attn_weights
snake_case_ : Union[str, Any] = use_cache
snake_case_ : Dict = attention_softmax_in_fpaa
snake_case_ : Any = scale_attention_softmax_in_fpaa
snake_case_ : List[str] = multi_query
snake_case_ : List[str] = bos_token_id
snake_case_ : Any = eos_token_id
super().__init__(bos_token_id=lowerCAmelCase__ , eos_token_id=lowerCAmelCase__ , **lowerCAmelCase__ )
| 653 | 0 |
import os
from datetime import datetime as dt
from github import Github
__snake_case :int =[
'good first issue',
'feature request',
'wip',
]
def lowerCamelCase_ ( ) -> Any:
'''simple docstring'''
A = Github(os.environ['GITHUB_TOKEN'] )
A = g.get_repo('huggingface/accelerate' )
A = repo.get_issues(state='open' )
for issue in open_issues:
A = sorted([comment for comment in issue.get_comments()] , key=lambda lowerCAmelCase__ : i.created_at , reverse=lowerCAmelCase__ )
A = comments[0] if len(lowerCAmelCase__ ) > 0 else None
A = dt.utcnow()
A = (current_time - issue.updated_at).days
A = (current_time - issue.created_at).days
if (
last_comment is not None
and last_comment.user.login == "github-actions[bot]"
and days_since_updated > 7
and days_since_creation >= 30
and not any(label.name.lower() in LABELS_TO_EXEMPT for label in issue.get_labels() )
):
# Close issue since it has been 7 days of inactivity since bot mention.
issue.edit(state='closed' )
elif (
days_since_updated > 23
and days_since_creation >= 30
and not any(label.name.lower() in LABELS_TO_EXEMPT for label in issue.get_labels() )
):
# Add stale comment
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/accelerate/blob/main/CONTRIBUTING.md) '
'are likely to be ignored.' )
if __name__ == "__main__":
main() | 106 |
'''simple docstring'''
import json
import logging
import os
import socket
import git
import numpy as np
import torch
logging.basicConfig(
format='''%(asctime)s - %(levelname)s - %(name)s - PID: %(process)d - %(message)s''',
datefmt='''%m/%d/%Y %H:%M:%S''',
level=logging.INFO,
)
__lowerCamelCase : Union[str, Any] = logging.getLogger(__name__)
def __UpperCAmelCase ( __magic_name__ )-> str:
"""simple docstring"""
snake_case_ : Dict = git.Repo(search_parent_directories=__magic_name__ )
snake_case_ : Optional[int] = {
"repo_id": str(__magic_name__ ),
"repo_sha": str(repo.head.object.hexsha ),
"repo_branch": str(repo.active_branch ),
}
with open(os.path.join(__magic_name__ ,"git_log.json" ) ,"w" ) as f:
json.dump(__magic_name__ ,__magic_name__ ,indent=4 )
def __UpperCAmelCase ( __magic_name__ )-> Tuple:
"""simple docstring"""
if params.n_gpu <= 0:
snake_case_ : Any = 0
snake_case_ : Any = -1
snake_case_ : Tuple = True
snake_case_ : List[str] = False
return
assert torch.cuda.is_available()
logger.info("Initializing GPUs" )
if params.n_gpu > 1:
assert params.local_rank != -1
snake_case_ : Optional[int] = int(os.environ["WORLD_SIZE"] )
snake_case_ : int = int(os.environ["N_GPU_NODE"] )
snake_case_ : Any = int(os.environ["RANK"] )
# number of nodes / node ID
snake_case_ : Dict = params.world_size // params.n_gpu_per_node
snake_case_ : Optional[int] = params.global_rank // params.n_gpu_per_node
snake_case_ : Tuple = True
assert params.n_nodes == int(os.environ["N_NODES"] )
assert params.node_id == int(os.environ["NODE_RANK"] )
# local job (single GPU)
else:
assert params.local_rank == -1
snake_case_ : Optional[int] = 1
snake_case_ : str = 0
snake_case_ : List[Any] = 0
snake_case_ : int = 0
snake_case_ : Dict = 1
snake_case_ : Optional[Any] = 1
snake_case_ : str = False
# sanity checks
assert params.n_nodes >= 1
assert 0 <= params.node_id < params.n_nodes
assert 0 <= params.local_rank <= params.global_rank < params.world_size
assert params.world_size == params.n_nodes * params.n_gpu_per_node
# define whether this is the master process / if we are in multi-node distributed mode
snake_case_ : str = params.node_id == 0 and params.local_rank == 0
snake_case_ : str = params.n_nodes > 1
# summary
snake_case_ : str = F'''--- Global rank: {params.global_rank} - '''
logger.info(PREFIX + "Number of nodes: %i" % params.n_nodes )
logger.info(PREFIX + "Node ID : %i" % params.node_id )
logger.info(PREFIX + "Local rank : %i" % params.local_rank )
logger.info(PREFIX + "World size : %i" % params.world_size )
logger.info(PREFIX + "GPUs per node : %i" % params.n_gpu_per_node )
logger.info(PREFIX + "Master : %s" % str(params.is_master ) )
logger.info(PREFIX + "Multi-node : %s" % str(params.multi_node ) )
logger.info(PREFIX + "Multi-GPU : %s" % str(params.multi_gpu ) )
logger.info(PREFIX + "Hostname : %s" % socket.gethostname() )
# set GPU device
torch.cuda.set_device(params.local_rank )
# initialize multi-GPU
if params.multi_gpu:
logger.info("Initializing PyTorch distributed" )
torch.distributed.init_process_group(
init_method="env://" ,backend="nccl" ,)
def __UpperCAmelCase ( __magic_name__ )-> Dict:
"""simple docstring"""
np.random.seed(args.seed )
torch.manual_seed(args.seed )
if args.n_gpu > 0:
torch.cuda.manual_seed_all(args.seed )
| 653 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_UpperCAmelCase : Optional[Any] = {
'''configuration_clipseg''': [
'''CLIPSEG_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''CLIPSegConfig''',
'''CLIPSegTextConfig''',
'''CLIPSegVisionConfig''',
],
'''processing_clipseg''': ['''CLIPSegProcessor'''],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_UpperCAmelCase : Optional[Any] = [
'''CLIPSEG_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''CLIPSegModel''',
'''CLIPSegPreTrainedModel''',
'''CLIPSegTextModel''',
'''CLIPSegVisionModel''',
'''CLIPSegForImageSegmentation''',
]
if TYPE_CHECKING:
from .configuration_clipseg import (
CLIPSEG_PRETRAINED_CONFIG_ARCHIVE_MAP,
CLIPSegConfig,
CLIPSegTextConfig,
CLIPSegVisionConfig,
)
from .processing_clipseg import CLIPSegProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_clipseg import (
CLIPSEG_PRETRAINED_MODEL_ARCHIVE_LIST,
CLIPSegForImageSegmentation,
CLIPSegModel,
CLIPSegPreTrainedModel,
CLIPSegTextModel,
CLIPSegVisionModel,
)
else:
import sys
_UpperCAmelCase : Dict = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 107 |
'''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 YolosImageProcessor
class A_ (unittest.TestCase ):
"""simple docstring"""
def __init__( self :Any , lowerCAmelCase__ :Dict , lowerCAmelCase__ :Dict=7 , lowerCAmelCase__ :Union[str, Any]=3 , lowerCAmelCase__ :List[str]=30 , lowerCAmelCase__ :List[str]=400 , lowerCAmelCase__ :Optional[Any]=True , lowerCAmelCase__ :Dict=None , lowerCAmelCase__ :str=True , lowerCAmelCase__ :Optional[int]=[0.5, 0.5, 0.5] , lowerCAmelCase__ :Optional[int]=[0.5, 0.5, 0.5] , lowerCAmelCase__ :str=True , lowerCAmelCase__ :int=1 / 255 , lowerCAmelCase__ :int=True , ) -> str:
'''simple docstring'''
snake_case_ : List[Any] = size if size is not None else {"shortest_edge": 18, "longest_edge": 1_333}
snake_case_ : Dict = parent
snake_case_ : Union[str, Any] = batch_size
snake_case_ : Optional[Any] = num_channels
snake_case_ : str = min_resolution
snake_case_ : Dict = max_resolution
snake_case_ : Optional[Any] = do_resize
snake_case_ : str = size
snake_case_ : Optional[int] = do_normalize
snake_case_ : Dict = image_mean
snake_case_ : Optional[int] = image_std
snake_case_ : List[str] = do_rescale
snake_case_ : Dict = rescale_factor
snake_case_ : str = do_pad
def _A ( self :List[Any] ) -> Dict:
'''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 _A ( self :Dict , lowerCAmelCase__ :List[str] , lowerCAmelCase__ :List[str]=False ) -> str:
'''simple docstring'''
if not batched:
snake_case_ : List[str] = image_inputs[0]
if isinstance(lowerCAmelCase__ , Image.Image ):
snake_case_, snake_case_ : int = image.size
else:
snake_case_, snake_case_ : Any = image.shape[1], image.shape[2]
if w < h:
snake_case_ : int = int(self.size["shortest_edge"] * h / w )
snake_case_ : List[Any] = self.size["shortest_edge"]
elif w > h:
snake_case_ : Optional[int] = self.size["shortest_edge"]
snake_case_ : str = int(self.size["shortest_edge"] * w / h )
else:
snake_case_ : Tuple = self.size["shortest_edge"]
snake_case_ : Dict = self.size["shortest_edge"]
else:
snake_case_ : List[str] = []
for image in image_inputs:
snake_case_, snake_case_ : Any = self.get_expected_values([image] )
expected_values.append((expected_height, expected_width) )
snake_case_ : str = max(lowerCAmelCase__ , key=lambda lowerCAmelCase__ : item[0] )[0]
snake_case_ : int = max(lowerCAmelCase__ , key=lambda lowerCAmelCase__ : item[1] )[1]
return expected_height, expected_width
@require_torch
@require_vision
class A_ (a_ , unittest.TestCase ):
"""simple docstring"""
a__ = YolosImageProcessor if is_vision_available() else None
def _A ( self :Optional[Any] ) -> str:
'''simple docstring'''
snake_case_ : int = YolosImageProcessingTester(self )
@property
def _A ( self :List[str] ) -> Any:
'''simple docstring'''
return self.image_processor_tester.prepare_image_processor_dict()
def _A ( self :List[str] ) -> Union[str, Any]:
'''simple docstring'''
snake_case_ : Tuple = self.image_processing_class(**self.image_processor_dict )
self.assertTrue(hasattr(lowerCAmelCase__ , "image_mean" ) )
self.assertTrue(hasattr(lowerCAmelCase__ , "image_std" ) )
self.assertTrue(hasattr(lowerCAmelCase__ , "do_normalize" ) )
self.assertTrue(hasattr(lowerCAmelCase__ , "do_resize" ) )
self.assertTrue(hasattr(lowerCAmelCase__ , "size" ) )
def _A ( self :List[Any] ) -> Any:
'''simple docstring'''
snake_case_ : List[Any] = self.image_processing_class.from_dict(self.image_processor_dict )
self.assertEqual(image_processor.size , {"shortest_edge": 18, "longest_edge": 1_333} )
self.assertEqual(image_processor.do_pad , lowerCAmelCase__ )
snake_case_ : Optional[int] = self.image_processing_class.from_dict(
self.image_processor_dict , size=42 , max_size=84 , pad_and_return_pixel_mask=lowerCAmelCase__ )
self.assertEqual(image_processor.size , {"shortest_edge": 42, "longest_edge": 84} )
self.assertEqual(image_processor.do_pad , lowerCAmelCase__ )
def _A ( self :List[str] ) -> int:
'''simple docstring'''
pass
def _A ( self :Optional[Any] ) -> Optional[Any]:
'''simple docstring'''
snake_case_ : Optional[int] = self.image_processing_class(**self.image_processor_dict )
# create random PIL images
snake_case_ : List[Any] = prepare_image_inputs(self.image_processor_tester , equal_resolution=lowerCAmelCase__ )
for image in image_inputs:
self.assertIsInstance(lowerCAmelCase__ , Image.Image )
# Test not batched input
snake_case_ : Optional[Any] = image_processing(image_inputs[0] , return_tensors="pt" ).pixel_values
snake_case_, snake_case_ : int = self.image_processor_tester.get_expected_values(lowerCAmelCase__ )
self.assertEqual(
encoded_images.shape , (1, self.image_processor_tester.num_channels, expected_height, expected_width) , )
# Test batched
snake_case_, snake_case_ : Any = self.image_processor_tester.get_expected_values(lowerCAmelCase__ , batched=lowerCAmelCase__ )
snake_case_ : Any = image_processing(lowerCAmelCase__ , return_tensors="pt" ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
expected_height,
expected_width,
) , )
def _A ( self :Dict ) -> Dict:
'''simple docstring'''
snake_case_ : str = self.image_processing_class(**self.image_processor_dict )
# create random numpy tensors
snake_case_ : Any = prepare_image_inputs(self.image_processor_tester , equal_resolution=lowerCAmelCase__ , numpify=lowerCAmelCase__ )
for image in image_inputs:
self.assertIsInstance(lowerCAmelCase__ , np.ndarray )
# Test not batched input
snake_case_ : Union[str, Any] = image_processing(image_inputs[0] , return_tensors="pt" ).pixel_values
snake_case_, snake_case_ : List[Any] = self.image_processor_tester.get_expected_values(lowerCAmelCase__ )
self.assertEqual(
encoded_images.shape , (1, self.image_processor_tester.num_channels, expected_height, expected_width) , )
# Test batched
snake_case_ : Tuple = image_processing(lowerCAmelCase__ , return_tensors="pt" ).pixel_values
snake_case_, snake_case_ : Dict = self.image_processor_tester.get_expected_values(lowerCAmelCase__ , batched=lowerCAmelCase__ )
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
expected_height,
expected_width,
) , )
def _A ( self :Tuple ) -> Tuple:
'''simple docstring'''
snake_case_ : List[str] = self.image_processing_class(**self.image_processor_dict )
# create random PyTorch tensors
snake_case_ : str = prepare_image_inputs(self.image_processor_tester , equal_resolution=lowerCAmelCase__ , torchify=lowerCAmelCase__ )
for image in image_inputs:
self.assertIsInstance(lowerCAmelCase__ , torch.Tensor )
# Test not batched input
snake_case_ : List[str] = image_processing(image_inputs[0] , return_tensors="pt" ).pixel_values
snake_case_, snake_case_ : Any = self.image_processor_tester.get_expected_values(lowerCAmelCase__ )
self.assertEqual(
encoded_images.shape , (1, self.image_processor_tester.num_channels, expected_height, expected_width) , )
# Test batched
snake_case_ : List[Any] = image_processing(lowerCAmelCase__ , return_tensors="pt" ).pixel_values
snake_case_, snake_case_ : Any = self.image_processor_tester.get_expected_values(lowerCAmelCase__ , batched=lowerCAmelCase__ )
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
expected_height,
expected_width,
) , )
def _A ( self :Tuple ) -> Dict:
'''simple docstring'''
snake_case_ : str = self.image_processing_class(**self.image_processor_dict )
snake_case_ : List[Any] = self.image_processing_class(do_resize=lowerCAmelCase__ , do_normalize=lowerCAmelCase__ , do_rescale=lowerCAmelCase__ )
# create random PyTorch tensors
snake_case_ : Tuple = prepare_image_inputs(self.image_processor_tester , equal_resolution=lowerCAmelCase__ , torchify=lowerCAmelCase__ )
for image in image_inputs:
self.assertIsInstance(lowerCAmelCase__ , torch.Tensor )
# Test whether the method "pad" and calling the image processor return the same tensors
snake_case_ : Tuple = image_processing_a.pad(lowerCAmelCase__ , return_tensors="pt" )
snake_case_ : Union[str, Any] = image_processing_a(lowerCAmelCase__ , return_tensors="pt" )
self.assertTrue(
torch.allclose(encoded_images_with_method["pixel_values"] , encoded_images["pixel_values"] , atol=1E-4 ) )
@slow
def _A ( self :str ) -> Any:
'''simple docstring'''
snake_case_ : List[str] = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png" )
with open("./tests/fixtures/tests_samples/COCO/coco_annotations.txt" , "r" ) as f:
snake_case_ : int = json.loads(f.read() )
snake_case_ : Optional[int] = {"image_id": 39_769, "annotations": target}
# encode them
snake_case_ : Tuple = YolosImageProcessor.from_pretrained("hustvl/yolos-small" )
snake_case_ : Dict = image_processing(images=lowerCAmelCase__ , annotations=lowerCAmelCase__ , return_tensors="pt" )
# verify pixel values
snake_case_ : Optional[int] = torch.Size([1, 3, 800, 1_066] )
self.assertEqual(encoding["pixel_values"].shape , lowerCAmelCase__ )
snake_case_ : str = torch.tensor([0.2_7_9_6, 0.3_1_3_8, 0.3_4_8_1] )
self.assertTrue(torch.allclose(encoding["pixel_values"][0, 0, 0, :3] , lowerCAmelCase__ , atol=1E-4 ) )
# verify area
snake_case_ : Dict = torch.tensor([5_8_8_7.9_6_0_0, 1_1_2_5_0.2_0_6_1, 4_8_9_3_5_3.8_4_3_8, 8_3_7_1_2_2.7_5_0_0, 1_4_7_9_6_7.5_1_5_6, 1_6_5_7_3_2.3_4_3_8] )
self.assertTrue(torch.allclose(encoding["labels"][0]["area"] , lowerCAmelCase__ ) )
# verify boxes
snake_case_ : Optional[int] = torch.Size([6, 4] )
self.assertEqual(encoding["labels"][0]["boxes"].shape , lowerCAmelCase__ )
snake_case_ : Any = torch.tensor([0.5_5_0_3, 0.2_7_6_5, 0.0_6_0_4, 0.2_2_1_5] )
self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"][0] , lowerCAmelCase__ , atol=1E-3 ) )
# verify image_id
snake_case_ : Dict = torch.tensor([39_769] )
self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"] , lowerCAmelCase__ ) )
# verify is_crowd
snake_case_ : int = torch.tensor([0, 0, 0, 0, 0, 0] )
self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"] , lowerCAmelCase__ ) )
# verify class_labels
snake_case_ : List[str] = torch.tensor([75, 75, 63, 65, 17, 17] )
self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"] , lowerCAmelCase__ ) )
# verify orig_size
snake_case_ : Any = torch.tensor([480, 640] )
self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"] , lowerCAmelCase__ ) )
# verify size
snake_case_ : List[Any] = torch.tensor([800, 1_066] )
self.assertTrue(torch.allclose(encoding["labels"][0]["size"] , lowerCAmelCase__ ) )
@slow
def _A ( self :Dict ) -> int:
'''simple docstring'''
snake_case_ : Tuple = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png" )
with open("./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt" , "r" ) as f:
snake_case_ : Optional[int] = json.loads(f.read() )
snake_case_ : Tuple = {"file_name": "000000039769.png", "image_id": 39_769, "segments_info": target}
snake_case_ : Any = pathlib.Path("./tests/fixtures/tests_samples/COCO/coco_panoptic" )
# encode them
snake_case_ : int = YolosImageProcessor(format="coco_panoptic" )
snake_case_ : Union[str, Any] = image_processing(images=lowerCAmelCase__ , annotations=lowerCAmelCase__ , masks_path=lowerCAmelCase__ , return_tensors="pt" )
# verify pixel values
snake_case_ : Optional[int] = torch.Size([1, 3, 800, 1_066] )
self.assertEqual(encoding["pixel_values"].shape , lowerCAmelCase__ )
snake_case_ : List[str] = torch.tensor([0.2_7_9_6, 0.3_1_3_8, 0.3_4_8_1] )
self.assertTrue(torch.allclose(encoding["pixel_values"][0, 0, 0, :3] , lowerCAmelCase__ , atol=1E-4 ) )
# verify area
snake_case_ : int = torch.tensor([1_4_7_9_7_9.6_8_7_5, 1_6_5_5_2_7.0_4_6_9, 4_8_4_6_3_8.5_9_3_8, 1_1_2_9_2.9_3_7_5, 5_8_7_9.6_5_6_2, 7_6_3_4.1_1_4_7] )
self.assertTrue(torch.allclose(encoding["labels"][0]["area"] , lowerCAmelCase__ ) )
# verify boxes
snake_case_ : Optional[int] = torch.Size([6, 4] )
self.assertEqual(encoding["labels"][0]["boxes"].shape , lowerCAmelCase__ )
snake_case_ : List[str] = torch.tensor([0.2_6_2_5, 0.5_4_3_7, 0.4_6_8_8, 0.8_6_2_5] )
self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"][0] , lowerCAmelCase__ , atol=1E-3 ) )
# verify image_id
snake_case_ : List[str] = torch.tensor([39_769] )
self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"] , lowerCAmelCase__ ) )
# verify is_crowd
snake_case_ : Dict = torch.tensor([0, 0, 0, 0, 0, 0] )
self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"] , lowerCAmelCase__ ) )
# verify class_labels
snake_case_ : str = torch.tensor([17, 17, 63, 75, 75, 93] )
self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"] , lowerCAmelCase__ ) )
# verify masks
snake_case_ : Any = 822_873
self.assertEqual(encoding["labels"][0]["masks"].sum().item() , lowerCAmelCase__ )
# verify orig_size
snake_case_ : int = torch.tensor([480, 640] )
self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"] , lowerCAmelCase__ ) )
# verify size
snake_case_ : Union[str, Any] = torch.tensor([800, 1_066] )
self.assertTrue(torch.allclose(encoding["labels"][0]["size"] , lowerCAmelCase__ ) )
| 653 | 0 |
# limitations under the License.
from typing import Optional, Tuple, Union
import torch
from diffusers import DiffusionPipeline, ImagePipelineOutput
class SCREAMING_SNAKE_CASE__ ( UpperCAmelCase ):
'''simple docstring'''
def __init__( self : int , lowerCamelCase : Any , lowerCamelCase : Tuple ) -> Tuple:
"""simple docstring"""
super().__init__()
self.register_modules(unet=lowerCamelCase , scheduler=lowerCamelCase )
@torch.no_grad()
def __call__( self : Dict , lowerCamelCase : int = 1 , lowerCamelCase : Optional[torch.Generator] = None , lowerCamelCase : int = 50 , lowerCamelCase : Optional[str] = "pil" , lowerCamelCase : bool = True , **lowerCamelCase : List[Any] , ) -> Union[ImagePipelineOutput, Tuple]:
"""simple docstring"""
_UpperCAmelCase = torch.randn(
(batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size) , generator=lowerCamelCase , )
_UpperCAmelCase = image.to(self.device )
# set step values
self.scheduler.set_timesteps(lowerCamelCase )
for t in self.progress_bar(self.scheduler.timesteps ):
# 1. predict noise model_output
_UpperCAmelCase = self.unet(lowerCamelCase , lowerCamelCase ).sample
# 2. predict previous mean of image x_t-1 and add variance depending on eta
# eta corresponds to η in paper and should be between [0, 1]
# do x_t -> x_t-1
_UpperCAmelCase = self.scheduler.step(lowerCamelCase , lowerCamelCase , lowerCamelCase ).prev_sample
_UpperCAmelCase = (image / 2 + 0.5).clamp(0 , 1 )
_UpperCAmelCase = image.cpu().permute(0 , 2 , 3 , 1 ).numpy()
if output_type == "pil":
_UpperCAmelCase = self.numpy_to_pil(lowerCamelCase )
if not return_dict:
return (image,), "This is a local test"
return ImagePipelineOutput(images=lowerCamelCase ), "This is a local test" | 108 |
'''simple docstring'''
def __UpperCAmelCase ( __magic_name__ ,__magic_name__ )-> str:
"""simple docstring"""
if not isinstance(__magic_name__ ,__magic_name__ ):
raise ValueError("iterations must be defined as integers" )
if not isinstance(__magic_name__ ,__magic_name__ ) or not number >= 1:
raise ValueError(
"starting number must be\n and integer and be more than 0" )
if not iterations >= 1:
raise ValueError("Iterations must be done more than 0 times to play FizzBuzz" )
snake_case_ : Dict = ""
while number <= iterations:
if number % 3 == 0:
out += "Fizz"
if number % 5 == 0:
out += "Buzz"
if 0 not in (number % 3, number % 5):
out += str(__magic_name__ )
# print(out)
number += 1
out += " "
return out
if __name__ == "__main__":
import doctest
doctest.testmod()
| 653 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
a = {
"configuration_swinv2": ["SWINV2_PRETRAINED_CONFIG_ARCHIVE_MAP", "Swinv2Config"],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a = [
"SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST",
"Swinv2ForImageClassification",
"Swinv2ForMaskedImageModeling",
"Swinv2Model",
"Swinv2PreTrainedModel",
]
if TYPE_CHECKING:
from .configuration_swinva import SWINV2_PRETRAINED_CONFIG_ARCHIVE_MAP, SwinvaConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_swinva import (
SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST,
SwinvaForImageClassification,
SwinvaForMaskedImageModeling,
SwinvaModel,
SwinvaPreTrainedModel,
)
else:
import sys
a = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 109 |
'''simple docstring'''
import argparse
import os
# New Code #
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from accelerate import Accelerator, DistributedType
from accelerate.utils import find_executable_batch_size
########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing how to ensure out-of-memory errors never
# interrupt training, and builds off the `nlp_example.py` script.
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
# - single CPU or single GPU
# - multi GPUS (using PyTorch distributed mode)
# - (multi) TPUs
# - fp16 (mixed-precision) or fp32 (normal precision)
#
# New additions from the base script can be found quickly by
# looking for the # New Code # tags
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################
__lowerCamelCase : Tuple = 16
__lowerCamelCase : Optional[int] = 32
def __UpperCAmelCase ( __magic_name__ ,__magic_name__ = 16 )-> int:
"""simple docstring"""
snake_case_ : Optional[int] = AutoTokenizer.from_pretrained("bert-base-cased" )
snake_case_ : str = load_dataset("glue" ,"mrpc" )
def tokenize_function(__magic_name__ ):
# max_length=None => use the model max length (it's actually the default)
snake_case_ : Dict = tokenizer(examples["sentence1"] ,examples["sentence2"] ,truncation=__magic_name__ ,max_length=__magic_name__ )
return outputs
# Apply the method we just defined to all the examples in all the splits of the dataset
# starting with the main process first:
with accelerator.main_process_first():
snake_case_ : Any = datasets.map(
__magic_name__ ,batched=__magic_name__ ,remove_columns=["idx", "sentence1", "sentence2"] ,)
# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
# transformers library
snake_case_ : List[Any] = tokenized_datasets.rename_column("label" ,"labels" )
def collate_fn(__magic_name__ ):
# On TPU it's best to pad everything to the same length or training will be very slow.
snake_case_ : int = 128 if accelerator.distributed_type == DistributedType.TPU else None
# When using mixed precision we want round multiples of 8/16
if accelerator.mixed_precision == "fp8":
snake_case_ : Tuple = 16
elif accelerator.mixed_precision != "no":
snake_case_ : str = 8
else:
snake_case_ : Optional[Any] = None
return tokenizer.pad(
__magic_name__ ,padding="longest" ,max_length=__magic_name__ ,pad_to_multiple_of=__magic_name__ ,return_tensors="pt" ,)
# Instantiate dataloaders.
snake_case_ : str = DataLoader(
tokenized_datasets["train"] ,shuffle=__magic_name__ ,collate_fn=__magic_name__ ,batch_size=__magic_name__ )
snake_case_ : Optional[Any] = DataLoader(
tokenized_datasets["validation"] ,shuffle=__magic_name__ ,collate_fn=__magic_name__ ,batch_size=__magic_name__ )
return train_dataloader, eval_dataloader
# For testing only
if os.environ.get('''TESTING_MOCKED_DATALOADERS''', None) == "1":
from accelerate.test_utils.training import mocked_dataloaders
__lowerCamelCase : Optional[Any] = mocked_dataloaders # noqa: F811
def __UpperCAmelCase ( __magic_name__ ,__magic_name__ )-> Dict:
"""simple docstring"""
if os.environ.get("TESTING_MOCKED_DATALOADERS" ,__magic_name__ ) == "1":
snake_case_ : List[str] = 2
# Initialize accelerator
snake_case_ : Union[str, Any] = Accelerator(cpu=args.cpu ,mixed_precision=args.mixed_precision )
# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
snake_case_ : List[str] = config["lr"]
snake_case_ : Dict = int(config["num_epochs"] )
snake_case_ : Dict = int(config["seed"] )
snake_case_ : Optional[int] = int(config["batch_size"] )
snake_case_ : Dict = evaluate.load("glue" ,"mrpc" )
# New Code #
# We now can define an inner training loop function. It should take a batch size as the only parameter,
# and build the dataloaders in there.
# It also gets our decorator
@find_executable_batch_size(starting_batch_size=__magic_name__ )
def inner_training_loop(__magic_name__ ):
# And now just move everything below under this function
# We need to bring in the Accelerator object from earlier
nonlocal accelerator
# And reset all of its attributes that could hold onto any memory:
accelerator.free_memory()
# Then we can declare the model, optimizer, and everything else:
set_seed(__magic_name__ )
# Instantiate the model (we build the model here so that the seed also control new weights initialization)
snake_case_ : Optional[int] = AutoModelForSequenceClassification.from_pretrained("bert-base-cased" ,return_dict=__magic_name__ )
# We could avoid this line since the accelerator is set with `device_placement=True` (default value).
# Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
# creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
snake_case_ : Optional[int] = model.to(accelerator.device )
# Instantiate optimizer
snake_case_ : List[Any] = AdamW(params=model.parameters() ,lr=__magic_name__ )
snake_case_, snake_case_ : int = get_dataloaders(__magic_name__ ,__magic_name__ )
# Instantiate scheduler
snake_case_ : Tuple = get_linear_schedule_with_warmup(
optimizer=__magic_name__ ,num_warmup_steps=100 ,num_training_steps=(len(__magic_name__ ) * num_epochs) ,)
# Prepare everything
# There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
# prepare method.
snake_case_, snake_case_, snake_case_, snake_case_, snake_case_ : Tuple = accelerator.prepare(
__magic_name__ ,__magic_name__ ,__magic_name__ ,__magic_name__ ,__magic_name__ )
# Now we train the model
for epoch in range(__magic_name__ ):
model.train()
for step, batch in enumerate(__magic_name__ ):
# We could avoid this line since we set the accelerator with `device_placement=True`.
batch.to(accelerator.device )
snake_case_ : int = model(**__magic_name__ )
snake_case_ : Any = outputs.loss
accelerator.backward(__magic_name__ )
optimizer.step()
lr_scheduler.step()
optimizer.zero_grad()
model.eval()
for step, batch in enumerate(__magic_name__ ):
# We could avoid this line since we set the accelerator with `device_placement=True`.
batch.to(accelerator.device )
with torch.no_grad():
snake_case_ : Union[str, Any] = model(**__magic_name__ )
snake_case_ : List[str] = outputs.logits.argmax(dim=-1 )
snake_case_, snake_case_ : Union[str, Any] = accelerator.gather_for_metrics((predictions, batch["labels"]) )
metric.add_batch(
predictions=__magic_name__ ,references=__magic_name__ ,)
snake_case_ : Tuple = metric.compute()
# Use accelerator.print to print only on the main process.
accelerator.print(F'''epoch {epoch}:''' ,__magic_name__ )
# New Code #
# And call it at the end with no arguments
# Note: You could also refactor this outside of your training loop function
inner_training_loop()
def __UpperCAmelCase ( )-> List[str]:
"""simple docstring"""
snake_case_ : List[Any] = argparse.ArgumentParser(description="Simple example of training script." )
parser.add_argument(
"--mixed_precision" ,type=__magic_name__ ,default=__magic_name__ ,choices=["no", "fp16", "bf16", "fp8"] ,help="Whether to use mixed precision. Choose"
"between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10."
"and an Nvidia Ampere GPU." ,)
parser.add_argument("--cpu" ,action="store_true" ,help="If passed, will train on the CPU." )
snake_case_ : str = parser.parse_args()
snake_case_ : Optional[int] = {"lr": 2E-5, "num_epochs": 3, "seed": 42, "batch_size": 16}
training_function(__magic_name__ ,__magic_name__ )
if __name__ == "__main__":
main()
| 653 | 0 |
'''simple docstring'''
import requests
def _UpperCamelCase ( UpperCamelCase__ , UpperCamelCase__ ):
"""simple docstring"""
__magic_name__ : Union[str, Any] = {"Content-Type": "application/json"}
__magic_name__ : str = requests.post(UpperCamelCase__ , json={"text": message_body} , headers=UpperCamelCase__ )
if response.status_code != 200:
__magic_name__ : List[str] = (
"Request to slack returned an error "
F"""{response.status_code}, the response is:\n{response.text}"""
)
raise ValueError(UpperCamelCase__ )
if __name__ == "__main__":
# Set the slack url to the one provided by Slack when you create the webhook at
# https://my.slack.com/services/new/incoming-webhook/
send_slack_message("<YOUR MESSAGE BODY>", "<SLACK CHANNEL URL>") | 436 |
'''simple docstring'''
# Copyright 2023 The HuggingFace Inc. 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 torch
from ..models.auto import AutoModelForSequenceClassification, AutoTokenizer
from .base import PipelineTool
class A_ (a_ ):
"""simple docstring"""
a__ = '''facebook/bart-large-mnli'''
a__ = (
'''This is a tool that classifies an English text using provided labels. It takes two inputs: `text`, which '''
'''should be the text to classify, and `labels`, which should be the list of labels to use for classification. '''
'''It returns the most likely label in the list of provided `labels` for the input text.'''
)
a__ = '''text_classifier'''
a__ = AutoTokenizer
a__ = AutoModelForSequenceClassification
a__ = ['''text''', ['''text''']]
a__ = ['''text''']
def _A ( self :List[str] ) -> Union[str, Any]:
'''simple docstring'''
super().setup()
snake_case_ : Optional[int] = self.model.config
snake_case_ : Any = -1
for idx, label in config.idalabel.items():
if label.lower().startswith("entail" ):
snake_case_ : Union[str, Any] = int(lowerCAmelCase__ )
if self.entailment_id == -1:
raise ValueError("Could not determine the entailment ID from the model config, please pass it at init." )
def _A ( self :Dict , lowerCAmelCase__ :int , lowerCAmelCase__ :Tuple ) -> int:
'''simple docstring'''
snake_case_ : Tuple = labels
return self.pre_processor(
[text] * len(lowerCAmelCase__ ) , [F'''This example is {label}''' for label in labels] , return_tensors="pt" , padding="max_length" , )
def _A ( self :Any , lowerCAmelCase__ :str ) -> List[str]:
'''simple docstring'''
snake_case_ : Optional[int] = outputs.logits
snake_case_ : Tuple = torch.argmax(logits[:, 2] ).item()
return self._labels[label_id]
| 653 | 0 |
import numpy as np
def _lowerCamelCase( lowercase__ ) -> np.ndarray:
'''simple docstring'''
return 1 / (1 + np.exp(-vector ))
def _lowerCamelCase( lowercase__ ) -> np.ndarray:
'''simple docstring'''
return vector * sigmoid(lowercase__ )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 230 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
is_vision_available,
)
__lowerCamelCase : Any = {'''configuration_vit''': ['''VIT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''ViTConfig''', '''ViTOnnxConfig''']}
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__lowerCamelCase : Any = ['''ViTFeatureExtractor''']
__lowerCamelCase : Any = ['''ViTImageProcessor''']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__lowerCamelCase : Optional[Any] = [
'''VIT_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''ViTForImageClassification''',
'''ViTForMaskedImageModeling''',
'''ViTModel''',
'''ViTPreTrainedModel''',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__lowerCamelCase : Union[str, Any] = [
'''TFViTForImageClassification''',
'''TFViTModel''',
'''TFViTPreTrainedModel''',
]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__lowerCamelCase : Tuple = [
'''FlaxViTForImageClassification''',
'''FlaxViTModel''',
'''FlaxViTPreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_vit import VIT_PRETRAINED_CONFIG_ARCHIVE_MAP, ViTConfig, ViTOnnxConfig
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .feature_extraction_vit import ViTFeatureExtractor
from .image_processing_vit import ViTImageProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_vit import (
VIT_PRETRAINED_MODEL_ARCHIVE_LIST,
ViTForImageClassification,
ViTForMaskedImageModeling,
ViTModel,
ViTPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_vit import TFViTForImageClassification, TFViTModel, TFViTPreTrainedModel
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_vit import FlaxViTForImageClassification, FlaxViTModel, FlaxViTPreTrainedModel
else:
import sys
__lowerCamelCase : List[str] = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 653 | 0 |
'''simple docstring'''
import gzip
import hashlib
import json
import multiprocessing
import os
import re
import shutil
import time
from pathlib import Path
import numpy as np
from arguments import PreprocessingArguments
from datasets import load_dataset
from minhash_deduplication import deduplicate_dataset
from transformers import AutoTokenizer, HfArgumentParser
lowerCAmelCase : str = re.compile(r'\s+')
def A_( A : List[Any]):
return {"hash": hashlib.mda(re.sub(A , '' , example['content']).encode('utf-8')).hexdigest()}
def A_( A : List[str]):
UpperCamelCase = [len(A) for line in example["content"].splitlines()]
return {"line_mean": np.mean(A), "line_max": max(A)}
def A_( A : Optional[Any]):
UpperCamelCase = np.mean([c.isalnum() for c in example['content']])
return {"alpha_frac": alpha_frac}
def A_( A : Dict , A : List[Any]):
if example["hash"] in uniques:
uniques.remove(example['hash'])
return True
else:
return False
def A_( A : Any , A : List[Any]=5):
UpperCamelCase = ["auto-generated", "autogenerated", "automatically generated"]
UpperCamelCase = example["content"].splitlines()
for _, line in zip(range(A) , A):
for keyword in keywords:
if keyword in line.lower():
return {"autogenerated": True}
else:
return {"autogenerated": False}
def A_( A : List[Any] , A : List[str]=5 , A : Tuple=0.05):
UpperCamelCase = ["unit tests", "test file", "configuration file"]
UpperCamelCase = example["content"].splitlines()
UpperCamelCase = 0
UpperCamelCase = 0
# first test
for _, line in zip(range(A) , A):
for keyword in keywords:
if keyword in line.lower():
return {"config_or_test": True}
# second test
UpperCamelCase = example["content"].count('\n')
UpperCamelCase = int(coeff * nlines)
for line in lines:
count_config += line.lower().count('config')
count_test += line.lower().count('test')
if count_config > threshold or count_test > threshold:
return {"config_or_test": True}
return {"config_or_test": False}
def A_( A : Dict):
UpperCamelCase = ["def ", "class ", "for ", "while "]
UpperCamelCase = example["content"].splitlines()
for line in lines:
for keyword in keywords:
if keyword in line.lower():
return {"has_no_keywords": False}
return {"has_no_keywords": True}
def A_( A : List[str] , A : Optional[int]=4):
UpperCamelCase = example["content"].splitlines()
UpperCamelCase = 0
for line in lines:
counter += line.lower().count('=')
if counter > minimum:
return {"has_few_assignments": False}
return {"has_few_assignments": True}
def A_( A : str):
UpperCamelCase = tokenizer(example['content'] , truncation=A)["input_ids"]
UpperCamelCase = len(example['content']) / len(A)
return {"ratio": ratio}
def A_( A : List[Any]):
UpperCamelCase = {}
results.update(get_hash(A))
results.update(line_stats(A))
results.update(alpha_stats(A))
results.update(char_token_ratio(A))
results.update(is_autogenerated(A))
results.update(is_config_or_test(A))
results.update(has_no_keywords(A))
results.update(has_few_assignments(A))
return results
def A_( A : Any , A : Optional[Any] , A : Any):
if not check_uniques(A , A):
return False
elif example["autogenerated"]:
return False
elif example["line_max"] > args.line_max:
return False
elif example["line_mean"] > args.line_mean:
return False
elif example["alpha_frac"] < args.alpha_frac:
return False
elif example["ratio"] < args.min_token_ratio:
return False
elif example["config_or_test"] and np.random.rand() <= args.filter_proba:
return False
elif example["has_no_keywords"] and np.random.rand() <= args.filter_proba:
return False
elif example["has_few_assignments"]:
return False
else:
return True
def A_( A : Union[str, Any]):
with open(A , 'rb') as f_in:
with gzip.open(str(A) + '.gz' , 'wb' , compresslevel=6) as f_out:
shutil.copyfileobj(A , A)
os.unlink(A)
# Settings
lowerCAmelCase : List[str] = HfArgumentParser(PreprocessingArguments)
lowerCAmelCase : List[Any] = parser.parse_args()
if args.num_workers is None:
lowerCAmelCase : List[Any] = multiprocessing.cpu_count()
lowerCAmelCase : Tuple = AutoTokenizer.from_pretrained(args.tokenizer_dir)
# Load dataset
lowerCAmelCase : Union[str, Any] = time.time()
lowerCAmelCase : int = load_dataset(args.dataset_name, split='train')
print(f"""Time to load dataset: {time.time()-t_start:.2f}""")
# Run preprocessing
lowerCAmelCase : int = time.time()
lowerCAmelCase : Optional[int] = ds.map(preprocess, num_proc=args.num_workers)
print(f"""Time to preprocess dataset: {time.time()-t_start:.2f}""")
# Deduplicate hashes
lowerCAmelCase : Tuple = set(ds.unique('hash'))
lowerCAmelCase : Optional[Any] = len(uniques) / len(ds)
print(f"""Fraction of duplicates: {1-frac:.2%}""")
# Deduplicate data and apply heuristics
lowerCAmelCase : str = time.time()
lowerCAmelCase : Any = ds.filter(filter, fn_kwargs={'uniques': uniques, 'args': args})
print(f"""Time to filter dataset: {time.time()-t_start:.2f}""")
print(f"""Size of filtered dataset: {len(ds_filter)}""")
# Deduplicate with minhash and jaccard similarity
if args.near_deduplication:
lowerCAmelCase : List[Any] = time.time()
lowerCAmelCase : Union[str, Any] = deduplicate_dataset(ds_filter, args.jaccard_threshold)
print(f"""Time to deduplicate dataset: {time.time()-t_start:.2f}""")
print(f"""Size of deduplicate dataset: {len(ds_filter)}""")
# Save data in batches of samples_per_file
lowerCAmelCase : List[Any] = Path(args.output_dir)
output_dir.mkdir(exist_ok=True)
# save duplicate_clusters in the output_dir as artifacts
# not sure it is the right place the save it
if args.near_deduplication:
with open(output_dir / 'duplicate_clusters.json', 'w') as f:
json.dump(duplicate_clusters, f)
lowerCAmelCase : str = output_dir / '''data'''
data_dir.mkdir(exist_ok=True)
lowerCAmelCase : int = time.time()
for file_number, index in enumerate(range(0, len(ds_filter), args.samples_per_file)):
lowerCAmelCase : Optional[int] = str(data_dir / f"""file-{file_number+1:012}.json""")
lowerCAmelCase : Tuple = min(len(ds_filter), index + args.samples_per_file)
ds_filter.select(list(range(index, end_index))).to_json(file_path)
compress_file(file_path)
print(f"""Time to save dataset: {time.time()-t_start:.2f}""")
| 3 |
'''simple docstring'''
import copy
import unittest
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
MODEL_FOR_MULTIPLE_CHOICE_MAPPING,
MODEL_FOR_QUESTION_ANSWERING_MAPPING,
MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
LayoutLMvaConfig,
LayoutLMvaForQuestionAnswering,
LayoutLMvaForSequenceClassification,
LayoutLMvaForTokenClassification,
LayoutLMvaModel,
)
from transformers.models.layoutlmva.modeling_layoutlmva import LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import LayoutLMvaImageProcessor
class A_ :
"""simple docstring"""
def __init__( self :Optional[Any] , lowerCAmelCase__ :List[str] , lowerCAmelCase__ :List[str]=2 , lowerCAmelCase__ :List[Any]=3 , lowerCAmelCase__ :Any=4 , lowerCAmelCase__ :List[Any]=2 , lowerCAmelCase__ :List[str]=7 , lowerCAmelCase__ :Any=True , lowerCAmelCase__ :Optional[int]=True , lowerCAmelCase__ :Optional[Any]=True , lowerCAmelCase__ :Optional[int]=True , lowerCAmelCase__ :List[str]=99 , lowerCAmelCase__ :Union[str, Any]=36 , lowerCAmelCase__ :Dict=3 , lowerCAmelCase__ :str=4 , lowerCAmelCase__ :Optional[int]=37 , lowerCAmelCase__ :Dict="gelu" , lowerCAmelCase__ :Optional[Any]=0.1 , lowerCAmelCase__ :Dict=0.1 , lowerCAmelCase__ :Optional[int]=512 , lowerCAmelCase__ :Union[str, Any]=16 , lowerCAmelCase__ :List[Any]=2 , lowerCAmelCase__ :Any=0.0_2 , lowerCAmelCase__ :Dict=6 , lowerCAmelCase__ :Optional[int]=6 , lowerCAmelCase__ :Any=3 , lowerCAmelCase__ :int=4 , lowerCAmelCase__ :int=None , lowerCAmelCase__ :Any=1_000 , ) -> Any:
'''simple docstring'''
snake_case_ : Optional[int] = parent
snake_case_ : Union[str, Any] = batch_size
snake_case_ : Optional[int] = num_channels
snake_case_ : List[Any] = image_size
snake_case_ : Optional[int] = patch_size
snake_case_ : Union[str, Any] = text_seq_length
snake_case_ : Dict = is_training
snake_case_ : Optional[Any] = use_input_mask
snake_case_ : Union[str, Any] = use_token_type_ids
snake_case_ : Dict = use_labels
snake_case_ : List[str] = vocab_size
snake_case_ : Optional[Any] = hidden_size
snake_case_ : List[str] = num_hidden_layers
snake_case_ : int = num_attention_heads
snake_case_ : List[str] = intermediate_size
snake_case_ : str = hidden_act
snake_case_ : Optional[Any] = hidden_dropout_prob
snake_case_ : Optional[int] = attention_probs_dropout_prob
snake_case_ : Union[str, Any] = max_position_embeddings
snake_case_ : List[Any] = type_vocab_size
snake_case_ : Union[str, Any] = type_sequence_label_size
snake_case_ : List[Any] = initializer_range
snake_case_ : Union[str, Any] = coordinate_size
snake_case_ : int = shape_size
snake_case_ : Tuple = num_labels
snake_case_ : List[Any] = num_choices
snake_case_ : List[str] = scope
snake_case_ : Dict = range_bbox
# LayoutLMv3's sequence length equals the number of text tokens + number of patches + 1 (we add 1 for the CLS token)
snake_case_ : str = text_seq_length
snake_case_ : Optional[int] = (image_size // patch_size) ** 2 + 1
snake_case_ : str = self.text_seq_length + self.image_seq_length
def _A ( self :Union[str, Any] ) -> Tuple:
'''simple docstring'''
snake_case_ : Dict = ids_tensor([self.batch_size, self.text_seq_length] , self.vocab_size )
snake_case_ : str = ids_tensor([self.batch_size, self.text_seq_length, 4] , self.range_bbox )
# Ensure that bbox is legal
for i in range(bbox.shape[0] ):
for j in range(bbox.shape[1] ):
if bbox[i, j, 3] < bbox[i, j, 1]:
snake_case_ : Optional[Any] = bbox[i, j, 3]
snake_case_ : Any = bbox[i, j, 1]
snake_case_ : Tuple = t
if bbox[i, j, 2] < bbox[i, j, 0]:
snake_case_ : str = bbox[i, j, 2]
snake_case_ : Dict = bbox[i, j, 0]
snake_case_ : Union[str, Any] = t
snake_case_ : int = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
snake_case_ : Dict = None
if self.use_input_mask:
snake_case_ : str = random_attention_mask([self.batch_size, self.text_seq_length] )
snake_case_ : Any = None
if self.use_token_type_ids:
snake_case_ : List[str] = ids_tensor([self.batch_size, self.text_seq_length] , self.type_vocab_size )
snake_case_ : Union[str, Any] = None
snake_case_ : str = None
if self.use_labels:
snake_case_ : List[Any] = ids_tensor([self.batch_size] , self.type_sequence_label_size )
snake_case_ : List[Any] = ids_tensor([self.batch_size, self.text_seq_length] , self.num_labels )
snake_case_ : str = LayoutLMvaConfig(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , initializer_range=self.initializer_range , coordinate_size=self.coordinate_size , shape_size=self.shape_size , input_size=self.image_size , patch_size=self.patch_size , )
return config, input_ids, bbox, pixel_values, token_type_ids, input_mask, sequence_labels, token_labels
def _A ( self :Dict , lowerCAmelCase__ :Dict , lowerCAmelCase__ :List[str] , lowerCAmelCase__ :str , lowerCAmelCase__ :List[Any] , lowerCAmelCase__ :Optional[Any] , lowerCAmelCase__ :List[str] , lowerCAmelCase__ :Dict , lowerCAmelCase__ :List[str] ) -> Optional[Any]:
'''simple docstring'''
snake_case_ : Tuple = LayoutLMvaModel(config=lowerCAmelCase__ )
model.to(lowerCAmelCase__ )
model.eval()
# text + image
snake_case_ : Tuple = model(lowerCAmelCase__ , pixel_values=lowerCAmelCase__ )
snake_case_ : Optional[int] = model(
lowerCAmelCase__ , bbox=lowerCAmelCase__ , pixel_values=lowerCAmelCase__ , attention_mask=lowerCAmelCase__ , token_type_ids=lowerCAmelCase__ )
snake_case_ : Optional[int] = model(lowerCAmelCase__ , bbox=lowerCAmelCase__ , pixel_values=lowerCAmelCase__ , token_type_ids=lowerCAmelCase__ )
snake_case_ : int = model(lowerCAmelCase__ , bbox=lowerCAmelCase__ , pixel_values=lowerCAmelCase__ )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
# text only
snake_case_ : List[Any] = model(lowerCAmelCase__ )
self.parent.assertEqual(
result.last_hidden_state.shape , (self.batch_size, self.text_seq_length, self.hidden_size) )
# image only
snake_case_ : Union[str, Any] = model(pixel_values=lowerCAmelCase__ )
self.parent.assertEqual(
result.last_hidden_state.shape , (self.batch_size, self.image_seq_length, self.hidden_size) )
def _A ( self :str , lowerCAmelCase__ :str , lowerCAmelCase__ :Tuple , lowerCAmelCase__ :Union[str, Any] , lowerCAmelCase__ :Union[str, Any] , lowerCAmelCase__ :Optional[Any] , lowerCAmelCase__ :List[str] , lowerCAmelCase__ :List[str] , lowerCAmelCase__ :Tuple ) -> List[Any]:
'''simple docstring'''
snake_case_ : str = self.num_labels
snake_case_ : List[Any] = LayoutLMvaForSequenceClassification(lowerCAmelCase__ )
model.to(lowerCAmelCase__ )
model.eval()
snake_case_ : Optional[int] = model(
lowerCAmelCase__ , bbox=lowerCAmelCase__ , pixel_values=lowerCAmelCase__ , attention_mask=lowerCAmelCase__ , token_type_ids=lowerCAmelCase__ , labels=lowerCAmelCase__ , )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def _A ( self :Union[str, Any] , lowerCAmelCase__ :List[Any] , lowerCAmelCase__ :int , lowerCAmelCase__ :List[str] , lowerCAmelCase__ :Tuple , lowerCAmelCase__ :str , lowerCAmelCase__ :Optional[int] , lowerCAmelCase__ :Any , lowerCAmelCase__ :Union[str, Any] ) -> str:
'''simple docstring'''
snake_case_ : Optional[int] = self.num_labels
snake_case_ : str = LayoutLMvaForTokenClassification(config=lowerCAmelCase__ )
model.to(lowerCAmelCase__ )
model.eval()
snake_case_ : List[Any] = model(
lowerCAmelCase__ , bbox=lowerCAmelCase__ , pixel_values=lowerCAmelCase__ , attention_mask=lowerCAmelCase__ , token_type_ids=lowerCAmelCase__ , labels=lowerCAmelCase__ , )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.text_seq_length, self.num_labels) )
def _A ( self :Optional[int] , lowerCAmelCase__ :Dict , lowerCAmelCase__ :str , lowerCAmelCase__ :Dict , lowerCAmelCase__ :Optional[int] , lowerCAmelCase__ :str , lowerCAmelCase__ :int , lowerCAmelCase__ :Union[str, Any] , lowerCAmelCase__ :str ) -> Tuple:
'''simple docstring'''
snake_case_ : List[str] = LayoutLMvaForQuestionAnswering(config=lowerCAmelCase__ )
model.to(lowerCAmelCase__ )
model.eval()
snake_case_ : List[Any] = model(
lowerCAmelCase__ , bbox=lowerCAmelCase__ , pixel_values=lowerCAmelCase__ , attention_mask=lowerCAmelCase__ , token_type_ids=lowerCAmelCase__ , start_positions=lowerCAmelCase__ , end_positions=lowerCAmelCase__ , )
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 :int ) -> Union[str, Any]:
'''simple docstring'''
snake_case_ : Dict = self.prepare_config_and_inputs()
(
(
snake_case_
), (
snake_case_
), (
snake_case_
), (
snake_case_
), (
snake_case_
), (
snake_case_
), (
snake_case_
), (
snake_case_
),
) : Optional[Any] = config_and_inputs
snake_case_ : Tuple = {
"input_ids": input_ids,
"bbox": bbox,
"pixel_values": pixel_values,
"token_type_ids": token_type_ids,
"attention_mask": input_mask,
}
return config, inputs_dict
@require_torch
class A_ (a_ , a_ , unittest.TestCase ):
"""simple docstring"""
a__ = False
a__ = False
a__ = False
a__ = (
(
LayoutLMvaModel,
LayoutLMvaForSequenceClassification,
LayoutLMvaForTokenClassification,
LayoutLMvaForQuestionAnswering,
)
if is_torch_available()
else ()
)
a__ = (
{'''document-question-answering''': LayoutLMvaForQuestionAnswering, '''feature-extraction''': LayoutLMvaModel}
if is_torch_available()
else {}
)
def _A ( self :Optional[Any] , lowerCAmelCase__ :Tuple , lowerCAmelCase__ :Tuple , lowerCAmelCase__ :List[Any] , lowerCAmelCase__ :Optional[Any] , lowerCAmelCase__ :List[Any] ) -> List[str]:
'''simple docstring'''
return True
def _A ( self :List[Any] ) -> str:
'''simple docstring'''
snake_case_ : Tuple = LayoutLMvaModelTester(self )
snake_case_ : Optional[int] = ConfigTester(self , config_class=lowerCAmelCase__ , hidden_size=37 )
def _A ( self :Tuple , lowerCAmelCase__ :Optional[int] , lowerCAmelCase__ :Dict , lowerCAmelCase__ :Union[str, Any]=False ) -> Any:
'''simple docstring'''
snake_case_ : List[str] = copy.deepcopy(lowerCAmelCase__ )
if model_class in get_values(lowerCAmelCase__ ):
snake_case_ : Optional[Any] = {
k: v.unsqueeze(1 ).expand(-1 , self.model_tester.num_choices , -1 ).contiguous()
if isinstance(lowerCAmelCase__ , torch.Tensor ) and v.ndim > 1
else v
for k, v in inputs_dict.items()
}
if return_labels:
if model_class in get_values(lowerCAmelCase__ ):
snake_case_ : Union[str, Any] = torch.ones(self.model_tester.batch_size , dtype=torch.long , device=lowerCAmelCase__ )
elif model_class in get_values(lowerCAmelCase__ ):
snake_case_ : List[Any] = torch.zeros(
self.model_tester.batch_size , dtype=torch.long , device=lowerCAmelCase__ )
snake_case_ : str = torch.zeros(
self.model_tester.batch_size , dtype=torch.long , device=lowerCAmelCase__ )
elif model_class in [
*get_values(lowerCAmelCase__ ),
]:
snake_case_ : Union[str, Any] = torch.zeros(
self.model_tester.batch_size , dtype=torch.long , device=lowerCAmelCase__ )
elif model_class in [
*get_values(lowerCAmelCase__ ),
]:
snake_case_ : List[str] = torch.zeros(
(self.model_tester.batch_size, self.model_tester.text_seq_length) , dtype=torch.long , device=lowerCAmelCase__ , )
return inputs_dict
def _A ( self :Any ) -> Any:
'''simple docstring'''
self.config_tester.run_common_tests()
def _A ( self :int ) -> int:
'''simple docstring'''
snake_case_ : Tuple = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*lowerCAmelCase__ )
def _A ( self :Any ) -> Dict:
'''simple docstring'''
snake_case_ : Dict = self.model_tester.prepare_config_and_inputs()
for type in ["absolute", "relative_key", "relative_key_query"]:
snake_case_ : int = type
self.model_tester.create_and_check_model(*lowerCAmelCase__ )
def _A ( self :int ) -> str:
'''simple docstring'''
snake_case_ : Optional[int] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_sequence_classification(*lowerCAmelCase__ )
def _A ( self :List[Any] ) -> Optional[Any]:
'''simple docstring'''
snake_case_ : List[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_token_classification(*lowerCAmelCase__ )
def _A ( self :int ) -> Union[str, Any]:
'''simple docstring'''
snake_case_ : Optional[int] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_question_answering(*lowerCAmelCase__ )
@slow
def _A ( self :Tuple ) -> List[Any]:
'''simple docstring'''
for model_name in LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
snake_case_ : str = LayoutLMvaModel.from_pretrained(lowerCAmelCase__ )
self.assertIsNotNone(lowerCAmelCase__ )
def __UpperCAmelCase ( )-> List[str]:
"""simple docstring"""
snake_case_ : List[str] = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png" )
return image
@require_torch
class A_ (unittest.TestCase ):
"""simple docstring"""
@cached_property
def _A ( self :Union[str, Any] ) -> Optional[Any]:
'''simple docstring'''
return LayoutLMvaImageProcessor(apply_ocr=lowerCAmelCase__ ) if is_vision_available() else None
@slow
def _A ( self :Union[str, Any] ) -> Union[str, Any]:
'''simple docstring'''
snake_case_ : Optional[int] = LayoutLMvaModel.from_pretrained("microsoft/layoutlmv3-base" ).to(lowerCAmelCase__ )
snake_case_ : Optional[Any] = self.default_image_processor
snake_case_ : Optional[int] = prepare_img()
snake_case_ : Union[str, Any] = image_processor(images=lowerCAmelCase__ , return_tensors="pt" ).pixel_values.to(lowerCAmelCase__ )
snake_case_ : List[str] = torch.tensor([[1, 2]] )
snake_case_ : Any = torch.tensor([[1, 2, 3, 4], [5, 6, 7, 8]] ).unsqueeze(0 )
# forward pass
snake_case_ : Any = model(
input_ids=input_ids.to(lowerCAmelCase__ ) , bbox=bbox.to(lowerCAmelCase__ ) , pixel_values=pixel_values.to(lowerCAmelCase__ ) , )
# verify the logits
snake_case_ : Optional[Any] = torch.Size((1, 199, 768) )
self.assertEqual(outputs.last_hidden_state.shape , lowerCAmelCase__ )
snake_case_ : str = torch.tensor(
[[-0.0_5_2_9, 0.3_6_1_8, 0.1_6_3_2], [-0.1_5_8_7, -0.1_6_6_7, -0.0_4_0_0], [-0.1_5_5_7, -0.1_6_7_1, -0.0_5_0_5]] ).to(lowerCAmelCase__ )
self.assertTrue(torch.allclose(outputs.last_hidden_state[0, :3, :3] , lowerCAmelCase__ , atol=1E-4 ) )
| 653 | 0 |
'''simple docstring'''
import json
import os
import tempfile
import transformers
import datasets
from utils import generate_example_dataset, get_duration
_UpperCAmelCase : Union[str, Any] = 500000
_UpperCAmelCase : Union[str, Any] = os.path.split(__file__)
_UpperCAmelCase : Union[str, Any] = os.path.join(RESULTS_BASEPATH, """results""", RESULTS_FILENAME.replace(""".py""", """.json"""))
@get_duration
def snake_case__ ( UpperCamelCase ,**UpperCamelCase ) -> Optional[Any]:
_UpperCamelCase : str = dataset.map(**UpperCamelCase )
@get_duration
def snake_case__ ( UpperCamelCase ,**UpperCamelCase ) -> Union[str, Any]:
_UpperCamelCase : Dict = dataset.filter(**UpperCamelCase )
def snake_case__ ( ) -> int:
_UpperCamelCase : Any = {"num examples": SPEED_TEST_N_EXAMPLES}
with tempfile.TemporaryDirectory() as tmp_dir:
_UpperCamelCase : List[str] = datasets.Features({'''text''': datasets.Value('''string''' ), '''numbers''': datasets.Value('''float32''' )} )
_UpperCamelCase : int = generate_example_dataset(
os.path.join(UpperCamelCase ,'''dataset.arrow''' ) ,UpperCamelCase ,num_examples=UpperCamelCase )
_UpperCamelCase : Tuple = transformers.AutoTokenizer.from_pretrained('''bert-base-cased''' ,use_fast=UpperCamelCase )
def tokenize(UpperCamelCase ):
return tokenizer(examples['''text'''] )
_UpperCamelCase : List[str] = map(UpperCamelCase )
_UpperCamelCase : Tuple = map(UpperCamelCase ,batched=UpperCamelCase )
_UpperCamelCase : Union[str, Any] = map(UpperCamelCase ,function=lambda UpperCamelCase : None ,batched=UpperCamelCase )
with dataset.formatted_as(type='''numpy''' ):
_UpperCamelCase : str = map(UpperCamelCase ,function=lambda UpperCamelCase : None ,batched=UpperCamelCase )
with dataset.formatted_as(type='''pandas''' ):
_UpperCamelCase : int = map(UpperCamelCase ,function=lambda UpperCamelCase : None ,batched=UpperCamelCase )
with dataset.formatted_as(type='''torch''' ,columns='''numbers''' ):
_UpperCamelCase : Union[str, Any] = map(UpperCamelCase ,function=lambda UpperCamelCase : None ,batched=UpperCamelCase )
with dataset.formatted_as(type='''tensorflow''' ,columns='''numbers''' ):
_UpperCamelCase : str = map(UpperCamelCase ,function=lambda UpperCamelCase : None ,batched=UpperCamelCase )
_UpperCamelCase : Dict = map(UpperCamelCase ,function=UpperCamelCase ,batched=UpperCamelCase )
_UpperCamelCase : List[str] = filter(UpperCamelCase )
# Activate later when tokenizer support batched inputs
# with dataset.formatted_as(type='numpy'):
# times[func.__name__ + " fast-tokenizer batched numpy"] = func(dataset, function=tokenize, batched=True)
with open(UpperCamelCase ,'''wb''' ) as f:
f.write(json.dumps(UpperCamelCase ).encode('''utf-8''' ) )
if __name__ == "__main__": # useful to run the profiler
benchmark_map_filter()
| 683 |
'''simple docstring'''
import pytest
from datasets.parallel import ParallelBackendConfig, parallel_backend
from datasets.utils.py_utils import map_nested
from .utils import require_dill_gt_0_3_2, require_joblibspark, require_not_windows
def __UpperCAmelCase ( __magic_name__ )-> int: # picklable for multiprocessing
"""simple docstring"""
return i + 1
@require_dill_gt_0_3_2
@require_joblibspark
@require_not_windows
def __UpperCAmelCase ( )-> List[str]:
"""simple docstring"""
with parallel_backend("spark" ):
assert ParallelBackendConfig.backend_name == "spark"
snake_case_ : str = [1, 2, 3]
with pytest.raises(__magic_name__ ):
with parallel_backend("unsupported backend" ):
map_nested(__magic_name__ ,__magic_name__ ,num_proc=2 )
with pytest.raises(__magic_name__ ):
with parallel_backend("unsupported backend" ):
map_nested(__magic_name__ ,__magic_name__ ,num_proc=-1 )
@require_dill_gt_0_3_2
@require_joblibspark
@require_not_windows
@pytest.mark.parametrize("num_proc" ,[2, -1] )
def __UpperCAmelCase ( __magic_name__ )-> List[Any]:
"""simple docstring"""
snake_case_ : Optional[Any] = [1, 2]
snake_case_ : Union[str, Any] = {"a": 1, "b": 2}
snake_case_ : str = {"a": [1, 2], "b": [3, 4]}
snake_case_ : List[str] = {"a": {"1": 1}, "b": 2}
snake_case_ : Optional[int] = {"a": 1, "b": 2, "c": 3, "d": 4}
snake_case_ : Tuple = [2, 3]
snake_case_ : str = {"a": 2, "b": 3}
snake_case_ : Dict = {"a": [2, 3], "b": [4, 5]}
snake_case_ : List[Any] = {"a": {"1": 2}, "b": 3}
snake_case_ : str = {"a": 2, "b": 3, "c": 4, "d": 5}
with parallel_backend("spark" ):
assert map_nested(__magic_name__ ,__magic_name__ ,num_proc=__magic_name__ ) == expected_map_nested_sa
assert map_nested(__magic_name__ ,__magic_name__ ,num_proc=__magic_name__ ) == expected_map_nested_sa
assert map_nested(__magic_name__ ,__magic_name__ ,num_proc=__magic_name__ ) == expected_map_nested_sa
assert map_nested(__magic_name__ ,__magic_name__ ,num_proc=__magic_name__ ) == expected_map_nested_sa
assert map_nested(__magic_name__ ,__magic_name__ ,num_proc=__magic_name__ ) == expected_map_nested_sa
| 653 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
A : str = {
'''configuration_conditional_detr''': [
'''CONDITIONAL_DETR_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''ConditionalDetrConfig''',
'''ConditionalDetrOnnxConfig''',
]
}
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A : Tuple = ['''ConditionalDetrFeatureExtractor''']
A : Union[str, Any] = ['''ConditionalDetrImageProcessor''']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A : List[Any] = [
'''CONDITIONAL_DETR_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''ConditionalDetrForObjectDetection''',
'''ConditionalDetrForSegmentation''',
'''ConditionalDetrModel''',
'''ConditionalDetrPreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_conditional_detr import (
CONDITIONAL_DETR_PRETRAINED_CONFIG_ARCHIVE_MAP,
ConditionalDetrConfig,
ConditionalDetrOnnxConfig,
)
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .feature_extraction_conditional_detr import ConditionalDetrFeatureExtractor
from .image_processing_conditional_detr import ConditionalDetrImageProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_conditional_detr import (
CONDITIONAL_DETR_PRETRAINED_MODEL_ARCHIVE_LIST,
ConditionalDetrForObjectDetection,
ConditionalDetrForSegmentation,
ConditionalDetrModel,
ConditionalDetrPreTrainedModel,
)
else:
import sys
A : Union[str, Any] = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 176 |
'''simple docstring'''
from dataclasses import asdict, dataclass
from typing import Optional
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__lowerCamelCase : Dict = logging.get_logger(__name__)
# TODO Update this
__lowerCamelCase : int = {
'''facebook/esm-1b''': '''https://huggingface.co/facebook/esm-1b/resolve/main/config.json''',
# See all ESM models at https://huggingface.co/models?filter=esm
}
class A_ (a_ ):
"""simple docstring"""
a__ = '''esm'''
def __init__( self :Dict , lowerCAmelCase__ :List[Any]=None , lowerCAmelCase__ :Optional[int]=None , lowerCAmelCase__ :str=None , lowerCAmelCase__ :int=768 , lowerCAmelCase__ :Tuple=12 , lowerCAmelCase__ :Dict=12 , lowerCAmelCase__ :Union[str, Any]=3_072 , lowerCAmelCase__ :int=0.1 , lowerCAmelCase__ :Optional[Any]=0.1 , lowerCAmelCase__ :List[Any]=1_026 , lowerCAmelCase__ :int=0.0_2 , lowerCAmelCase__ :Optional[int]=1E-1_2 , lowerCAmelCase__ :List[str]="absolute" , lowerCAmelCase__ :List[Any]=True , lowerCAmelCase__ :Dict=None , lowerCAmelCase__ :List[str]=False , lowerCAmelCase__ :List[Any]=False , lowerCAmelCase__ :Dict=None , lowerCAmelCase__ :str=None , **lowerCAmelCase__ :Union[str, Any] , ) -> Any:
'''simple docstring'''
super().__init__(pad_token_id=lowerCAmelCase__ , mask_token_id=lowerCAmelCase__ , **lowerCAmelCase__ )
snake_case_ : str = vocab_size
snake_case_ : str = hidden_size
snake_case_ : List[str] = num_hidden_layers
snake_case_ : List[str] = num_attention_heads
snake_case_ : Any = intermediate_size
snake_case_ : Optional[Any] = hidden_dropout_prob
snake_case_ : Tuple = attention_probs_dropout_prob
snake_case_ : List[Any] = max_position_embeddings
snake_case_ : str = initializer_range
snake_case_ : List[Any] = layer_norm_eps
snake_case_ : str = position_embedding_type
snake_case_ : Optional[int] = use_cache
snake_case_ : str = emb_layer_norm_before
snake_case_ : List[Any] = token_dropout
snake_case_ : str = is_folding_model
if is_folding_model:
if esmfold_config is None:
logger.info("No esmfold_config supplied for folding model, using default values." )
snake_case_ : Optional[Any] = EsmFoldConfig()
elif isinstance(lowerCAmelCase__ , lowerCAmelCase__ ):
snake_case_ : Union[str, Any] = EsmFoldConfig(**lowerCAmelCase__ )
snake_case_ : Optional[Any] = esmfold_config
if vocab_list is None:
logger.warning("No vocab_list supplied for folding model, assuming the ESM-2 vocabulary!" )
snake_case_ : List[str] = get_default_vocab_list()
else:
snake_case_ : List[str] = vocab_list
else:
snake_case_ : List[Any] = None
snake_case_ : int = None
if self.esmfold_config is not None and getattr(self.esmfold_config , "use_esm_attn_map" , lowerCAmelCase__ ):
raise ValueError("The HuggingFace port of ESMFold does not support use_esm_attn_map at this time!" )
def _A ( self :Optional[int] ) -> List[Any]:
'''simple docstring'''
snake_case_ : Any = super().to_dict()
if isinstance(self.esmfold_config , lowerCAmelCase__ ):
snake_case_ : Optional[int] = self.esmfold_config.to_dict()
return output
@dataclass
class A_ :
"""simple docstring"""
a__ = None
a__ = True
a__ = False
a__ = False
a__ = False
a__ = 0
a__ = True
a__ = False
a__ = 128
a__ = None
def _A ( self :Dict ) -> int:
'''simple docstring'''
if self.trunk is None:
snake_case_ : Dict = TrunkConfig()
elif isinstance(self.trunk , lowerCAmelCase__ ):
snake_case_ : int = TrunkConfig(**self.trunk )
def _A ( self :Optional[int] ) -> Union[str, Any]:
'''simple docstring'''
snake_case_ : Tuple = asdict(self )
snake_case_ : Optional[int] = self.trunk.to_dict()
return output
@dataclass
class A_ :
"""simple docstring"""
a__ = 48
a__ = 1024
a__ = 128
a__ = 32
a__ = 32
a__ = 32
a__ = 0
a__ = 0
a__ = False
a__ = 4
a__ = 128
a__ = None
def _A ( self :List[Any] ) -> Union[str, Any]:
'''simple docstring'''
if self.structure_module is None:
snake_case_ : Optional[int] = StructureModuleConfig()
elif isinstance(self.structure_module , lowerCAmelCase__ ):
snake_case_ : List[str] = StructureModuleConfig(**self.structure_module )
if self.max_recycles <= 0:
raise ValueError(F'''`max_recycles` should be positive, got {self.max_recycles}.''' )
if self.sequence_state_dim % self.sequence_state_dim != 0:
raise ValueError(
"`sequence_state_dim` should be a round multiple of `sequence_state_dim`, got"
F''' {self.sequence_state_dim} and {self.sequence_state_dim}.''' )
if self.pairwise_state_dim % self.pairwise_state_dim != 0:
raise ValueError(
"`pairwise_state_dim` should be a round multiple of `pairwise_state_dim`, got"
F''' {self.pairwise_state_dim} and {self.pairwise_state_dim}.''' )
snake_case_ : Dict = self.sequence_state_dim // self.sequence_head_width
snake_case_ : Optional[int] = self.pairwise_state_dim // self.pairwise_head_width
if self.sequence_state_dim != sequence_num_heads * self.sequence_head_width:
raise ValueError(
"`sequence_state_dim` should be equal to `sequence_num_heads * sequence_head_width, got"
F''' {self.sequence_state_dim} != {sequence_num_heads} * {self.sequence_head_width}.''' )
if self.pairwise_state_dim != pairwise_num_heads * self.pairwise_head_width:
raise ValueError(
"`pairwise_state_dim` should be equal to `pairwise_num_heads * pairwise_head_width, got"
F''' {self.pairwise_state_dim} != {pairwise_num_heads} * {self.pairwise_head_width}.''' )
if self.pairwise_state_dim % 2 != 0:
raise ValueError(F'''`pairwise_state_dim` should be even, got {self.pairwise_state_dim}.''' )
if self.dropout >= 0.4:
raise ValueError(F'''`dropout` should not be greater than 0.4, got {self.dropout}.''' )
def _A ( self :Tuple ) -> List[str]:
'''simple docstring'''
snake_case_ : int = asdict(self )
snake_case_ : Dict = self.structure_module.to_dict()
return output
@dataclass
class A_ :
"""simple docstring"""
a__ = 384
a__ = 128
a__ = 16
a__ = 128
a__ = 12
a__ = 4
a__ = 8
a__ = 0.1
a__ = 8
a__ = 1
a__ = 2
a__ = 7
a__ = 10
a__ = 1E-8
a__ = 1E5
def _A ( self :Dict ) -> Dict:
'''simple docstring'''
return asdict(self )
def __UpperCAmelCase ( )-> int:
"""simple docstring"""
return (
"<cls>",
"<pad>",
"<eos>",
"<unk>",
"L",
"A",
"G",
"V",
"S",
"E",
"R",
"T",
"I",
"D",
"P",
"K",
"Q",
"N",
"F",
"Y",
"M",
"H",
"W",
"C",
"X",
"B",
"U",
"Z",
"O",
".",
"-",
"<null_1>",
"<mask>",
)
| 653 | 0 |
"""simple docstring"""
from transformers import DistilBertTokenizer, DistilBertTokenizerFast
from transformers.testing_utils import require_tokenizers, slow
from ..bert.test_tokenization_bert import BertTokenizationTest
@require_tokenizers
class _lowerCAmelCase ( a_ ):
"""simple docstring"""
__magic_name__ :List[str] = DistilBertTokenizer
__magic_name__ :Tuple = DistilBertTokenizerFast
__magic_name__ :Tuple = True
@slow
def snake_case ( self ):
'''simple docstring'''
lowerCAmelCase__ :List[str] = DistilBertTokenizer.from_pretrained('distilbert-base-uncased' )
lowerCAmelCase__ :List[str] = tokenizer.encode('sequence builders' , add_special_tokens=lowerCAmelCase__ )
lowerCAmelCase__ :Any = tokenizer.encode('multi-sequence build' , add_special_tokens=lowerCAmelCase__ )
lowerCAmelCase__ :Union[str, Any] = tokenizer.build_inputs_with_special_tokens(lowerCAmelCase__ )
lowerCAmelCase__ :Union[str, Any] = tokenizer.build_inputs_with_special_tokens(lowerCAmelCase__ , lowerCAmelCase__ )
assert encoded_sentence == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id]
assert encoded_pair == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id] + text_a + [
tokenizer.sep_token_id
]
| 93 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__lowerCamelCase : Any = {
'''configuration_longformer''': [
'''LONGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''LongformerConfig''',
'''LongformerOnnxConfig''',
],
'''tokenization_longformer''': ['''LongformerTokenizer'''],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__lowerCamelCase : Any = ['''LongformerTokenizerFast''']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__lowerCamelCase : Dict = [
'''LONGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''LongformerForMaskedLM''',
'''LongformerForMultipleChoice''',
'''LongformerForQuestionAnswering''',
'''LongformerForSequenceClassification''',
'''LongformerForTokenClassification''',
'''LongformerModel''',
'''LongformerPreTrainedModel''',
'''LongformerSelfAttention''',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__lowerCamelCase : Any = [
'''TF_LONGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''TFLongformerForMaskedLM''',
'''TFLongformerForMultipleChoice''',
'''TFLongformerForQuestionAnswering''',
'''TFLongformerForSequenceClassification''',
'''TFLongformerForTokenClassification''',
'''TFLongformerModel''',
'''TFLongformerPreTrainedModel''',
'''TFLongformerSelfAttention''',
]
if TYPE_CHECKING:
from .configuration_longformer import (
LONGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
LongformerConfig,
LongformerOnnxConfig,
)
from .tokenization_longformer import LongformerTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_longformer_fast import LongformerTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_longformer import (
LONGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
LongformerForMaskedLM,
LongformerForMultipleChoice,
LongformerForQuestionAnswering,
LongformerForSequenceClassification,
LongformerForTokenClassification,
LongformerModel,
LongformerPreTrainedModel,
LongformerSelfAttention,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_longformer import (
TF_LONGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
TFLongformerForMaskedLM,
TFLongformerForMultipleChoice,
TFLongformerForQuestionAnswering,
TFLongformerForSequenceClassification,
TFLongformerForTokenClassification,
TFLongformerModel,
TFLongformerPreTrainedModel,
TFLongformerSelfAttention,
)
else:
import sys
__lowerCamelCase : Optional[Any] = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 653 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
is_vision_available,
)
_a : Dict = {
'''configuration_layoutlmv3''': [
'''LAYOUTLMV3_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''LayoutLMv3Config''',
'''LayoutLMv3OnnxConfig''',
],
'''processing_layoutlmv3''': ['''LayoutLMv3Processor'''],
'''tokenization_layoutlmv3''': ['''LayoutLMv3Tokenizer'''],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_a : Tuple = ['''LayoutLMv3TokenizerFast''']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_a : List[Any] = [
'''LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''LayoutLMv3ForQuestionAnswering''',
'''LayoutLMv3ForSequenceClassification''',
'''LayoutLMv3ForTokenClassification''',
'''LayoutLMv3Model''',
'''LayoutLMv3PreTrainedModel''',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_a : int = [
'''TF_LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''TFLayoutLMv3ForQuestionAnswering''',
'''TFLayoutLMv3ForSequenceClassification''',
'''TFLayoutLMv3ForTokenClassification''',
'''TFLayoutLMv3Model''',
'''TFLayoutLMv3PreTrainedModel''',
]
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_a : List[str] = ['''LayoutLMv3FeatureExtractor''']
_a : Union[str, Any] = ['''LayoutLMv3ImageProcessor''']
if TYPE_CHECKING:
from .configuration_layoutlmva import (
LAYOUTLMV3_PRETRAINED_CONFIG_ARCHIVE_MAP,
LayoutLMvaConfig,
LayoutLMvaOnnxConfig,
)
from .processing_layoutlmva import LayoutLMvaProcessor
from .tokenization_layoutlmva import LayoutLMvaTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_layoutlmva_fast import LayoutLMvaTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_layoutlmva import (
LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST,
LayoutLMvaForQuestionAnswering,
LayoutLMvaForSequenceClassification,
LayoutLMvaForTokenClassification,
LayoutLMvaModel,
LayoutLMvaPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_layoutlmva import (
TF_LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST,
TFLayoutLMvaForQuestionAnswering,
TFLayoutLMvaForSequenceClassification,
TFLayoutLMvaForTokenClassification,
TFLayoutLMvaModel,
TFLayoutLMvaPreTrainedModel,
)
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .feature_extraction_layoutlmva import LayoutLMvaFeatureExtractor
from .image_processing_layoutlmva import LayoutLMvaImageProcessor
else:
import sys
_a : List[Any] = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 447 |
'''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
__lowerCamelCase : Optional[int] = 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 A_ :
"""simple docstring"""
def __init__( self :Tuple , lowerCAmelCase__ :List[str] , lowerCAmelCase__ :List[Any]=16 , lowerCAmelCase__ :Any=13 , lowerCAmelCase__ :Optional[Any]=7 , lowerCAmelCase__ :str=14 , lowerCAmelCase__ :Union[str, Any]=10 , lowerCAmelCase__ :Tuple=19 , lowerCAmelCase__ :Optional[Any]=5 , lowerCAmelCase__ :Dict=4 , lowerCAmelCase__ :Union[str, Any]=True , lowerCAmelCase__ :Any=16 , lowerCAmelCase__ :str=2 , lowerCAmelCase__ :List[Any]=4 , lowerCAmelCase__ :Any=4 , lowerCAmelCase__ :str="gelu" , lowerCAmelCase__ :Tuple=0.1 , lowerCAmelCase__ :Dict=0.1 , lowerCAmelCase__ :Optional[int]=[1, 2, 3, 4, 5] , lowerCAmelCase__ :str=25 , lowerCAmelCase__ :Optional[Any]=5 , ) -> Dict:
'''simple docstring'''
snake_case_ : List[str] = d_model
snake_case_ : Dict = parent
snake_case_ : Optional[Any] = batch_size
snake_case_ : Optional[Any] = prediction_length
snake_case_ : str = context_length
snake_case_ : Tuple = cardinality
snake_case_ : List[str] = num_time_features
snake_case_ : Optional[Any] = lags_sequence
snake_case_ : Union[str, Any] = embedding_dimension
snake_case_ : Optional[Any] = is_training
snake_case_ : Optional[Any] = hidden_size
snake_case_ : Any = num_hidden_layers
snake_case_ : Optional[Any] = num_attention_heads
snake_case_ : int = intermediate_size
snake_case_ : Any = hidden_act
snake_case_ : Union[str, Any] = hidden_dropout_prob
snake_case_ : Dict = attention_probs_dropout_prob
snake_case_ : List[str] = context_length
snake_case_ : Any = prediction_length + label_length
snake_case_ : Union[str, Any] = label_length
snake_case_ : List[Any] = moving_average
snake_case_ : str = autocorrelation_factor
def _A ( self :List[Any] ) -> Any:
'''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 _A ( self :Union[str, Any] , lowerCAmelCase__ :Optional[Any] ) -> Dict:
'''simple docstring'''
snake_case_ : Any = config.context_length + max(config.lags_sequence )
snake_case_ : Union[str, Any] = ids_tensor([self.batch_size, 1] , config.cardinality[0] )
snake_case_ : Optional[int] = floats_tensor([self.batch_size, _past_length, config.num_time_features] )
snake_case_ : List[Any] = floats_tensor([self.batch_size, _past_length] )
snake_case_ : Dict = floats_tensor([self.batch_size, _past_length] ) > 0.5
# decoder inputs
snake_case_ : List[Any] = floats_tensor([self.batch_size, config.prediction_length, config.num_time_features] )
snake_case_ : List[Any] = floats_tensor([self.batch_size, config.prediction_length] )
snake_case_ : int = {
"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 _A ( self :Dict ) -> Tuple:
'''simple docstring'''
snake_case_ : str = self.get_config()
snake_case_ : int = self.prepare_autoformer_inputs_dict(lowerCAmelCase__ )
return config, inputs_dict
def _A ( self :Optional[int] ) -> Dict:
'''simple docstring'''
snake_case_, snake_case_ : Union[str, Any] = self.prepare_config_and_inputs()
return config, inputs_dict
def _A ( self :Tuple , lowerCAmelCase__ :int , lowerCAmelCase__ :Optional[int] ) -> List[str]:
'''simple docstring'''
snake_case_ : Dict = AutoformerModel(config=lowerCAmelCase__ ).to(lowerCAmelCase__ ).eval()
snake_case_ : Optional[int] = model(**lowerCAmelCase__ )
snake_case_ : Any = outputs.encoder_last_hidden_state
snake_case_ : Dict = outputs.last_hidden_state
with tempfile.TemporaryDirectory() as tmpdirname:
snake_case_ : Optional[Any] = model.get_encoder()
encoder.save_pretrained(lowerCAmelCase__ )
snake_case_ : Tuple = AutoformerEncoder.from_pretrained(lowerCAmelCase__ ).to(lowerCAmelCase__ )
snake_case_, snake_case_, snake_case_, snake_case_, snake_case_ : List[str] = model.create_network_inputs(**lowerCAmelCase__ )
snake_case_, snake_case_ : Optional[int] = model.decomposition_layer(transformer_inputs[:, : config.context_length, ...] )
snake_case_ : List[Any] = torch.cat(
(transformer_inputs[:, : config.context_length, ...], feature[:, : config.context_length, ...]) , dim=-1 , )
snake_case_ : Optional[int] = encoder(inputs_embeds=lowerCAmelCase__ )[0]
self.parent.assertTrue((encoder_last_hidden_state_a - encoder_last_hidden_state).abs().max().item() < 1E-3 )
snake_case_ : Any = (
torch.mean(transformer_inputs[:, : config.context_length, ...] , dim=1 )
.unsqueeze(1 )
.repeat(1 , config.prediction_length , 1 )
)
snake_case_ : List[str] = torch.zeros(
[transformer_inputs.shape[0], config.prediction_length, transformer_inputs.shape[2]] , device=enc_input.device , )
snake_case_ : Optional[Any] = torch.cat(
(
torch.cat((seasonal_input[:, -config.label_length :, ...], zeros) , dim=1 ),
feature[:, config.context_length - config.label_length :, ...],
) , dim=-1 , )
snake_case_ : Any = 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:
snake_case_ : List[Any] = model.get_decoder()
decoder.save_pretrained(lowerCAmelCase__ )
snake_case_ : int = AutoformerDecoder.from_pretrained(lowerCAmelCase__ ).to(lowerCAmelCase__ )
snake_case_ : Tuple = decoder(
trend=lowerCAmelCase__ , inputs_embeds=lowerCAmelCase__ , encoder_hidden_states=lowerCAmelCase__ , )[0]
self.parent.assertTrue((last_hidden_state_a - last_hidden_state).abs().max().item() < 1E-3 )
@require_torch
class A_ (a_ , a_ , unittest.TestCase ):
"""simple docstring"""
a__ = (AutoformerModel, AutoformerForPrediction) if is_torch_available() else ()
a__ = (AutoformerForPrediction,) if is_torch_available() else ()
a__ = {'''feature-extraction''': AutoformerModel} if is_torch_available() else {}
a__ = False
a__ = False
a__ = False
a__ = False
a__ = False
a__ = False
def _A ( self :Dict ) -> int:
'''simple docstring'''
snake_case_ : Tuple = AutoformerModelTester(self )
snake_case_ : str = ConfigTester(self , config_class=lowerCAmelCase__ , has_text_modality=lowerCAmelCase__ )
def _A ( self :List[str] ) -> Tuple:
'''simple docstring'''
self.config_tester.run_common_tests()
def _A ( self :List[Any] ) -> Union[str, Any]:
'''simple docstring'''
snake_case_, snake_case_ : List[Any] = self.model_tester.prepare_config_and_inputs()
for model_class in self.all_model_classes:
snake_case_ : List[Any] = model_class(lowerCAmelCase__ )
with tempfile.TemporaryDirectory() as tmpdirname:
model.save_pretrained(lowerCAmelCase__ )
snake_case_, snake_case_ : str = model_class.from_pretrained(lowerCAmelCase__ , output_loading_info=lowerCAmelCase__ )
self.assertEqual(info["missing_keys"] , [] )
def _A ( self :Optional[int] ) -> Tuple:
'''simple docstring'''
snake_case_ : str = self.model_tester.prepare_config_and_inputs_for_common()
self.model_tester.check_encoder_decoder_model_standalone(*lowerCAmelCase__ )
@unittest.skip(reason="Model has no tokens embeddings" )
def _A ( self :str ) -> str:
'''simple docstring'''
pass
def _A ( self :Optional[Any] ) -> Union[str, Any]:
'''simple docstring'''
snake_case_ : List[Any] = inspect.signature(getattr(lowerCAmelCase__ , "forward" ) )
# The main input is the name of the argument after `self`
snake_case_ : Dict = list(model_signature.parameters.keys() )[1]
self.assertEqual(AutoformerModel.main_input_name , lowerCAmelCase__ )
def _A ( self :Optional[Any] ) -> Optional[int]:
'''simple docstring'''
snake_case_, snake_case_ : Tuple = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
snake_case_ : Tuple = model_class(lowerCAmelCase__ )
snake_case_ : int = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
snake_case_ : Optional[Any] = [*signature.parameters.keys()]
snake_case_ : Dict = [
"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(lowerCAmelCase__ )] , lowerCAmelCase__ )
def _A ( self :int ) -> Any:
'''simple docstring'''
snake_case_, snake_case_ : Tuple = self.model_tester.prepare_config_and_inputs_for_common()
snake_case_ : Union[str, Any] = True
snake_case_ : List[str] = getattr(self.model_tester , "seq_length" , lowerCAmelCase__ )
snake_case_ : Dict = getattr(self.model_tester , "decoder_seq_length" , lowerCAmelCase__ )
snake_case_ : Union[str, Any] = getattr(self.model_tester , "encoder_seq_length" , lowerCAmelCase__ )
snake_case_ : Union[str, Any] = getattr(self.model_tester , "d_model" , lowerCAmelCase__ )
snake_case_ : Dict = getattr(self.model_tester , "num_attention_heads" , lowerCAmelCase__ )
snake_case_ : Optional[int] = d_model // num_attention_heads
for model_class in self.all_model_classes:
snake_case_ : Any = True
snake_case_ : Any = False
snake_case_ : Dict = True
snake_case_ : List[str] = model_class(lowerCAmelCase__ )
model.to(lowerCAmelCase__ )
model.eval()
with torch.no_grad():
snake_case_ : Tuple = model(**self._prepare_for_class(lowerCAmelCase__ , lowerCAmelCase__ ) )
snake_case_ : Union[str, Any] = outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
self.assertEqual(len(lowerCAmelCase__ ) , self.model_tester.num_hidden_layers )
# check that output_attentions also work using config
del inputs_dict["output_attentions"]
snake_case_ : Optional[int] = True
snake_case_ : Any = model_class(lowerCAmelCase__ )
model.to(lowerCAmelCase__ )
model.eval()
with torch.no_grad():
snake_case_ : List[str] = model(**self._prepare_for_class(lowerCAmelCase__ , lowerCAmelCase__ ) )
snake_case_ : str = outputs.encoder_attentions
self.assertEqual(len(lowerCAmelCase__ ) , self.model_tester.num_hidden_layers )
self.assertListEqual(
list(attentions[0].shape[-3:] ) , [self.model_tester.num_attention_heads, encoder_seq_length, dim] , )
snake_case_ : Tuple = len(lowerCAmelCase__ )
snake_case_ : List[str] = 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(lowerCAmelCase__ , lowerCAmelCase__ )
# decoder attentions
snake_case_ : Optional[int] = outputs.decoder_attentions
self.assertIsInstance(lowerCAmelCase__ , (list, tuple) )
self.assertEqual(len(lowerCAmelCase__ ) , 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
snake_case_ : List[Any] = outputs.cross_attentions
self.assertIsInstance(lowerCAmelCase__ , (list, tuple) )
self.assertEqual(len(lowerCAmelCase__ ) , 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
snake_case_ : Optional[int] = True
snake_case_ : List[Any] = True
snake_case_ : Union[str, Any] = model_class(lowerCAmelCase__ )
model.to(lowerCAmelCase__ )
model.eval()
with torch.no_grad():
snake_case_ : List[Any] = model(**self._prepare_for_class(lowerCAmelCase__ , lowerCAmelCase__ ) )
self.assertEqual(out_len + 2 , len(lowerCAmelCase__ ) )
snake_case_ : Tuple = outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
self.assertEqual(len(lowerCAmelCase__ ) , 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 _A ( self :Any ) -> Optional[Any]:
'''simple docstring'''
super().test_retain_grad_hidden_states_attentions()
def __UpperCAmelCase ( __magic_name__="train-batch.pt" )-> int:
"""simple docstring"""
snake_case_ : List[str] = hf_hub_download(repo_id="hf-internal-testing/tourism-monthly-batch" ,filename=__magic_name__ ,repo_type="dataset" )
snake_case_ : List[str] = torch.load(__magic_name__ ,map_location=__magic_name__ )
return batch
@require_torch
@slow
class A_ (unittest.TestCase ):
"""simple docstring"""
def _A ( self :str ) -> Any:
'''simple docstring'''
snake_case_ : Optional[int] = AutoformerModel.from_pretrained("huggingface/autoformer-tourism-monthly" ).to(lowerCAmelCase__ )
snake_case_ : List[str] = prepare_batch()
with torch.no_grad():
snake_case_ : int = 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]
snake_case_ : Optional[int] = torch.Size(
(64, model.config.prediction_length + model.config.label_length, model.config.feature_size) )
self.assertEqual(output.shape , lowerCAmelCase__ )
snake_case_ : Optional[Any] = torch.tensor(
[[0.3_5_9_3, -1.3_3_9_8, 0.6_3_3_0], [0.2_2_7_9, 1.5_3_9_6, -0.1_7_9_2], [0.0_4_5_0, 1.3_2_2_5, -0.2_3_3_5]] , device=lowerCAmelCase__ )
self.assertTrue(torch.allclose(output[0, :3, :3] , lowerCAmelCase__ , atol=lowerCAmelCase__ ) )
def _A ( self :Any ) -> str:
'''simple docstring'''
snake_case_ : str = AutoformerForPrediction.from_pretrained("huggingface/autoformer-tourism-monthly" ).to(lowerCAmelCase__ )
snake_case_ : Optional[Any] = prepare_batch("val-batch.pt" )
with torch.no_grad():
snake_case_ : Tuple = 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
snake_case_ : Dict = torch.Size((64, model.config.context_length, model.config.d_model) )
self.assertEqual(output.shape , lowerCAmelCase__ )
snake_case_ : Any = torch.tensor(
[[-0.0_7_3_4, -0.9_0_3_6, 0.8_3_5_8], [4.7_1_8_6, 2.4_1_1_3, 1.9_5_8_1], [1.7_9_5_3, 2.3_5_5_8, 1.2_9_7_0]] , device=lowerCAmelCase__ )
self.assertTrue(torch.allclose(output[0, :3, :3] , lowerCAmelCase__ , atol=lowerCAmelCase__ ) )
def _A ( self :List[str] ) -> Any:
'''simple docstring'''
snake_case_ : List[Any] = AutoformerForPrediction.from_pretrained("huggingface/autoformer-tourism-monthly" ).to(lowerCAmelCase__ )
snake_case_ : str = prepare_batch("val-batch.pt" )
with torch.no_grad():
snake_case_ : Optional[Any] = 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"] , )
snake_case_ : List[Any] = torch.Size((64, model.config.num_parallel_samples, model.config.prediction_length) )
self.assertEqual(outputs.sequences.shape , lowerCAmelCase__ )
snake_case_ : Dict = torch.tensor([3_1_3_0.6_7_6_3, 4_0_5_6.5_2_9_3, 7_0_5_3.0_7_8_6] , device=lowerCAmelCase__ )
snake_case_ : Optional[Any] = outputs.sequences.mean(dim=1 )
self.assertTrue(torch.allclose(mean_prediction[0, -3:] , lowerCAmelCase__ , rtol=1E-1 ) )
| 653 | 0 |
def A ( _lowercase , _lowercase ):
return number | (1 << position)
def A ( _lowercase , _lowercase ):
return number & ~(1 << position)
def A ( _lowercase , _lowercase ):
return number ^ (1 << position)
def A ( _lowercase , _lowercase ):
return ((number >> position) & 1) == 1
def A ( _lowercase , _lowercase ):
return int((number & (1 << position)) != 0 )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 248 |
'''simple docstring'''
import itertools
import json
import os
import unittest
from transformers import AddedToken, RobertaTokenizer, RobertaTokenizerFast
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class A_ (a_ , unittest.TestCase ):
"""simple docstring"""
a__ = RobertaTokenizer
a__ = RobertaTokenizerFast
a__ = True
a__ = {'''cls_token''': '''<s>'''}
def _A ( self :Optional[int] ) -> List[Any]:
'''simple docstring'''
super().setUp()
# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
snake_case_ : List[Any] = [
"l",
"o",
"w",
"e",
"r",
"s",
"t",
"i",
"d",
"n",
"\u0120",
"\u0120l",
"\u0120n",
"\u0120lo",
"\u0120low",
"er",
"\u0120lowest",
"\u0120newer",
"\u0120wider",
"<unk>",
]
snake_case_ : Tuple = dict(zip(lowerCAmelCase__ , range(len(lowerCAmelCase__ ) ) ) )
snake_case_ : List[Any] = ["#version: 0.2", "\u0120 l", "\u0120l o", "\u0120lo w", "e r", ""]
snake_case_ : int = {"unk_token": "<unk>"}
snake_case_ : List[str] = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES["vocab_file"] )
snake_case_ : Tuple = 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(lowerCAmelCase__ ) + "\n" )
with open(self.merges_file , "w" , encoding="utf-8" ) as fp:
fp.write("\n".join(lowerCAmelCase__ ) )
def _A ( self :Optional[Any] , **lowerCAmelCase__ :str ) -> str:
'''simple docstring'''
kwargs.update(self.special_tokens_map )
return self.tokenizer_class.from_pretrained(self.tmpdirname , **lowerCAmelCase__ )
def _A ( self :Any , **lowerCAmelCase__ :Tuple ) -> Optional[int]:
'''simple docstring'''
kwargs.update(self.special_tokens_map )
return RobertaTokenizerFast.from_pretrained(self.tmpdirname , **lowerCAmelCase__ )
def _A ( self :Optional[int] , lowerCAmelCase__ :str ) -> Optional[int]:
'''simple docstring'''
snake_case_ : int = "lower newer"
snake_case_ : Tuple = "lower newer"
return input_text, output_text
def _A ( self :Tuple ) -> Union[str, Any]:
'''simple docstring'''
snake_case_ : str = self.tokenizer_class(self.vocab_file , self.merges_file , **self.special_tokens_map )
snake_case_ : Dict = "lower newer"
snake_case_ : int = ["l", "o", "w", "er", "\u0120", "n", "e", "w", "er"]
snake_case_ : str = tokenizer.tokenize(lowerCAmelCase__ ) # , add_prefix_space=True)
self.assertListEqual(lowerCAmelCase__ , lowerCAmelCase__ )
snake_case_ : List[str] = tokens + [tokenizer.unk_token]
snake_case_ : Optional[int] = [0, 1, 2, 15, 10, 9, 3, 2, 15, 19]
self.assertListEqual(tokenizer.convert_tokens_to_ids(lowerCAmelCase__ ) , lowerCAmelCase__ )
def _A ( self :Any ) -> str:
'''simple docstring'''
snake_case_ : List[str] = self.get_tokenizer()
self.assertListEqual(tokenizer.encode("Hello world!" , add_special_tokens=lowerCAmelCase__ ) , [0, 31_414, 232, 328, 2] )
self.assertListEqual(
tokenizer.encode("Hello world! cécé herlolip 418" , add_special_tokens=lowerCAmelCase__ ) , [0, 31_414, 232, 328, 740, 1_140, 12_695, 69, 46_078, 1_588, 2] , )
@slow
def _A ( self :str ) -> List[str]:
'''simple docstring'''
snake_case_ : Tuple = self.tokenizer_class.from_pretrained("roberta-base" )
snake_case_ : List[str] = tokenizer.encode("sequence builders" , add_special_tokens=lowerCAmelCase__ )
snake_case_ : List[Any] = tokenizer.encode("multi-sequence build" , add_special_tokens=lowerCAmelCase__ )
snake_case_ : List[str] = tokenizer.encode(
"sequence builders" , add_special_tokens=lowerCAmelCase__ , add_prefix_space=lowerCAmelCase__ )
snake_case_ : Union[str, Any] = tokenizer.encode(
"sequence builders" , "multi-sequence build" , add_special_tokens=lowerCAmelCase__ , add_prefix_space=lowerCAmelCase__ )
snake_case_ : Optional[Any] = tokenizer.build_inputs_with_special_tokens(lowerCAmelCase__ )
snake_case_ : Any = tokenizer.build_inputs_with_special_tokens(lowerCAmelCase__ , lowerCAmelCase__ )
assert encoded_sentence == encoded_text_from_decode
assert encoded_pair == encoded_pair_from_decode
def _A ( self :List[Any] ) -> Any:
'''simple docstring'''
snake_case_ : Optional[Any] = self.get_tokenizer()
snake_case_ : Tuple = "Encode this sequence."
snake_case_ : Optional[Any] = tokenizer.byte_encoder[" ".encode("utf-8" )[0]]
# Testing encoder arguments
snake_case_ : str = tokenizer.encode(lowerCAmelCase__ , add_special_tokens=lowerCAmelCase__ , add_prefix_space=lowerCAmelCase__ )
snake_case_ : List[Any] = tokenizer.convert_ids_to_tokens(encoded[0] )[0]
self.assertNotEqual(lowerCAmelCase__ , lowerCAmelCase__ )
snake_case_ : List[Any] = tokenizer.encode(lowerCAmelCase__ , add_special_tokens=lowerCAmelCase__ , add_prefix_space=lowerCAmelCase__ )
snake_case_ : str = tokenizer.convert_ids_to_tokens(encoded[0] )[0]
self.assertEqual(lowerCAmelCase__ , lowerCAmelCase__ )
tokenizer.add_special_tokens({"bos_token": "<s>"} )
snake_case_ : str = tokenizer.encode(lowerCAmelCase__ , add_special_tokens=lowerCAmelCase__ )
snake_case_ : int = tokenizer.convert_ids_to_tokens(encoded[1] )[0]
self.assertNotEqual(lowerCAmelCase__ , lowerCAmelCase__ )
# Testing spaces after special tokens
snake_case_ : List[Any] = "<mask>"
tokenizer.add_special_tokens(
{"mask_token": AddedToken(lowerCAmelCase__ , lstrip=lowerCAmelCase__ , rstrip=lowerCAmelCase__ )} ) # mask token has a left space
snake_case_ : str = tokenizer.convert_tokens_to_ids(lowerCAmelCase__ )
snake_case_ : List[str] = "Encode <mask> sequence"
snake_case_ : List[Any] = "Encode <mask>sequence"
snake_case_ : Tuple = tokenizer.encode(lowerCAmelCase__ )
snake_case_ : int = encoded.index(lowerCAmelCase__ )
snake_case_ : Optional[Any] = tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1] )[0]
self.assertEqual(lowerCAmelCase__ , lowerCAmelCase__ )
snake_case_ : List[str] = tokenizer.encode(lowerCAmelCase__ )
snake_case_ : Union[str, Any] = encoded.index(lowerCAmelCase__ )
snake_case_ : int = tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1] )[0]
self.assertNotEqual(lowerCAmelCase__ , lowerCAmelCase__ )
def _A ( self :Tuple ) -> Tuple:
'''simple docstring'''
pass
def _A ( self :int ) -> Optional[Any]:
'''simple docstring'''
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(F'''{tokenizer.__class__.__name__} ({pretrained_name})''' ):
snake_case_ : List[Any] = self.rust_tokenizer_class.from_pretrained(lowerCAmelCase__ , **lowerCAmelCase__ )
snake_case_ : List[Any] = self.tokenizer_class.from_pretrained(lowerCAmelCase__ , **lowerCAmelCase__ )
snake_case_ : Any = "A, <mask> AllenNLP sentence."
snake_case_ : str = tokenizer_r.encode_plus(lowerCAmelCase__ , add_special_tokens=lowerCAmelCase__ , return_token_type_ids=lowerCAmelCase__ )
snake_case_ : int = tokenizer_p.encode_plus(lowerCAmelCase__ , add_special_tokens=lowerCAmelCase__ , return_token_type_ids=lowerCAmelCase__ )
# token_type_ids should put 0 everywhere
self.assertEqual(sum(tokens_r["token_type_ids"] ) , sum(tokens_p["token_type_ids"] ) )
# attention_mask should put 1 everywhere, so sum over length should be 1
self.assertEqual(
sum(tokens_r["attention_mask"] ) / len(tokens_r["attention_mask"] ) , sum(tokens_p["attention_mask"] ) / len(tokens_p["attention_mask"] ) , )
snake_case_ : List[Any] = tokenizer_r.convert_ids_to_tokens(tokens_r["input_ids"] )
snake_case_ : str = tokenizer_p.convert_ids_to_tokens(tokens_p["input_ids"] )
# Rust correctly handles the space before the mask while python doesnt
self.assertSequenceEqual(tokens_p["input_ids"] , [0, 250, 6, 50_264, 3_823, 487, 21_992, 3_645, 4, 2] )
self.assertSequenceEqual(tokens_r["input_ids"] , [0, 250, 6, 50_264, 3_823, 487, 21_992, 3_645, 4, 2] )
self.assertSequenceEqual(
lowerCAmelCase__ , ["<s>", "A", ",", "<mask>", "ĠAllen", "N", "LP", "Ġsentence", ".", "</s>"] )
self.assertSequenceEqual(
lowerCAmelCase__ , ["<s>", "A", ",", "<mask>", "ĠAllen", "N", "LP", "Ġsentence", ".", "</s>"] )
def _A ( self :int ) -> Tuple:
'''simple docstring'''
for trim_offsets, add_prefix_space in itertools.product([True, False] , repeat=2 ):
snake_case_ : str = self.rust_tokenizer_class.from_pretrained(
self.tmpdirname , use_fast=lowerCAmelCase__ , add_prefix_space=lowerCAmelCase__ , trim_offsets=lowerCAmelCase__ )
snake_case_ : Optional[Any] = json.loads(tokenizer_r.backend_tokenizer.pre_tokenizer.__getstate__() )
snake_case_ : Any = json.loads(tokenizer_r.backend_tokenizer.post_processor.__getstate__() )
self.assertEqual(pre_tokenizer_state["add_prefix_space"] , lowerCAmelCase__ )
self.assertEqual(post_processor_state["add_prefix_space"] , lowerCAmelCase__ )
self.assertEqual(post_processor_state["trim_offsets"] , lowerCAmelCase__ )
def _A ( self :List[str] ) -> List[Any]:
'''simple docstring'''
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(F'''{tokenizer.__class__.__name__} ({pretrained_name})''' ):
snake_case_ : str = "hello" # `hello` is a token in the vocabulary of `pretrained_name`
snake_case_ : Tuple = F'''{text_of_1_token} {text_of_1_token}'''
snake_case_ : Any = self.rust_tokenizer_class.from_pretrained(
lowerCAmelCase__ , use_fast=lowerCAmelCase__ , add_prefix_space=lowerCAmelCase__ , trim_offsets=lowerCAmelCase__ )
snake_case_ : Union[str, Any] = tokenizer_r(lowerCAmelCase__ , return_offsets_mapping=lowerCAmelCase__ , add_special_tokens=lowerCAmelCase__ )
self.assertEqual(encoding.offset_mapping[0] , (0, len(lowerCAmelCase__ )) )
self.assertEqual(
encoding.offset_mapping[1] , (len(lowerCAmelCase__ ) + 1, len(lowerCAmelCase__ ) + 1 + len(lowerCAmelCase__ )) , )
snake_case_ : List[str] = self.rust_tokenizer_class.from_pretrained(
lowerCAmelCase__ , use_fast=lowerCAmelCase__ , add_prefix_space=lowerCAmelCase__ , trim_offsets=lowerCAmelCase__ )
snake_case_ : Tuple = tokenizer_r(lowerCAmelCase__ , return_offsets_mapping=lowerCAmelCase__ , add_special_tokens=lowerCAmelCase__ )
self.assertEqual(encoding.offset_mapping[0] , (0, len(lowerCAmelCase__ )) )
self.assertEqual(
encoding.offset_mapping[1] , (len(lowerCAmelCase__ ) + 1, len(lowerCAmelCase__ ) + 1 + len(lowerCAmelCase__ )) , )
snake_case_ : Union[str, Any] = self.rust_tokenizer_class.from_pretrained(
lowerCAmelCase__ , use_fast=lowerCAmelCase__ , add_prefix_space=lowerCAmelCase__ , trim_offsets=lowerCAmelCase__ )
snake_case_ : Union[str, Any] = tokenizer_r(lowerCAmelCase__ , return_offsets_mapping=lowerCAmelCase__ , add_special_tokens=lowerCAmelCase__ )
self.assertEqual(encoding.offset_mapping[0] , (0, len(lowerCAmelCase__ )) )
self.assertEqual(
encoding.offset_mapping[1] , (len(lowerCAmelCase__ ), len(lowerCAmelCase__ ) + 1 + len(lowerCAmelCase__ )) , )
snake_case_ : Dict = self.rust_tokenizer_class.from_pretrained(
lowerCAmelCase__ , use_fast=lowerCAmelCase__ , add_prefix_space=lowerCAmelCase__ , trim_offsets=lowerCAmelCase__ )
snake_case_ : str = tokenizer_r(lowerCAmelCase__ , return_offsets_mapping=lowerCAmelCase__ , add_special_tokens=lowerCAmelCase__ )
self.assertEqual(encoding.offset_mapping[0] , (0, len(lowerCAmelCase__ )) )
self.assertEqual(
encoding.offset_mapping[1] , (len(lowerCAmelCase__ ), len(lowerCAmelCase__ ) + 1 + len(lowerCAmelCase__ )) , )
snake_case_ : Tuple = F''' {text}'''
# tokenizer_r = self.rust_tokenizer_class.from_pretrained(
# pretrained_name, use_fast=True, add_prefix_space=True, trim_offsets=True
# )
# encoding = tokenizer_r(text, return_offsets_mapping=True, add_special_tokens=False)
# self.assertEqual(encoding.offset_mapping[0], (1, 1 + len(text_of_1_token)))
# self.assertEqual(
# encoding.offset_mapping[1],
# (1 + len(text_of_1_token) + 1, 1 + len(text_of_1_token) + 1 + len(text_of_1_token)),
# )
snake_case_ : Dict = self.rust_tokenizer_class.from_pretrained(
lowerCAmelCase__ , use_fast=lowerCAmelCase__ , add_prefix_space=lowerCAmelCase__ , trim_offsets=lowerCAmelCase__ )
snake_case_ : Union[str, Any] = tokenizer_r(lowerCAmelCase__ , return_offsets_mapping=lowerCAmelCase__ , add_special_tokens=lowerCAmelCase__ )
self.assertEqual(encoding.offset_mapping[0] , (1, 1 + len(lowerCAmelCase__ )) )
self.assertEqual(
encoding.offset_mapping[1] , (1 + len(lowerCAmelCase__ ) + 1, 1 + len(lowerCAmelCase__ ) + 1 + len(lowerCAmelCase__ )) , )
snake_case_ : Any = self.rust_tokenizer_class.from_pretrained(
lowerCAmelCase__ , use_fast=lowerCAmelCase__ , add_prefix_space=lowerCAmelCase__ , trim_offsets=lowerCAmelCase__ )
snake_case_ : Any = tokenizer_r(lowerCAmelCase__ , return_offsets_mapping=lowerCAmelCase__ , add_special_tokens=lowerCAmelCase__ )
self.assertEqual(encoding.offset_mapping[0] , (0, 1 + len(lowerCAmelCase__ )) )
self.assertEqual(
encoding.offset_mapping[1] , (1 + len(lowerCAmelCase__ ), 1 + len(lowerCAmelCase__ ) + 1 + len(lowerCAmelCase__ )) , )
snake_case_ : Optional[Any] = self.rust_tokenizer_class.from_pretrained(
lowerCAmelCase__ , use_fast=lowerCAmelCase__ , add_prefix_space=lowerCAmelCase__ , trim_offsets=lowerCAmelCase__ )
snake_case_ : Optional[int] = tokenizer_r(lowerCAmelCase__ , return_offsets_mapping=lowerCAmelCase__ , add_special_tokens=lowerCAmelCase__ )
self.assertEqual(encoding.offset_mapping[0] , (0, 1 + len(lowerCAmelCase__ )) )
self.assertEqual(
encoding.offset_mapping[1] , (1 + len(lowerCAmelCase__ ), 1 + len(lowerCAmelCase__ ) + 1 + len(lowerCAmelCase__ )) , )
| 653 | 0 |
'''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 YolosImageProcessor
class _UpperCamelCase ( unittest.TestCase ):
'''simple docstring'''
def __init__( self : Any , _lowerCAmelCase : Dict , _lowerCAmelCase : Dict=7 , _lowerCAmelCase : Union[str, Any]=3 , _lowerCAmelCase : List[str]=3_0 , _lowerCAmelCase : List[str]=4_0_0 , _lowerCAmelCase : Optional[Any]=True , _lowerCAmelCase : Dict=None , _lowerCAmelCase : str=True , _lowerCAmelCase : Optional[int]=[0.5, 0.5, 0.5] , _lowerCAmelCase : Optional[int]=[0.5, 0.5, 0.5] , _lowerCAmelCase : str=True , _lowerCAmelCase : int=1 / 2_5_5 , _lowerCAmelCase : int=True , ):
'''simple docstring'''
__lowercase =size if size is not None else {"shortest_edge": 1_8, "longest_edge": 1_3_3_3}
__lowercase =parent
__lowercase =batch_size
__lowercase =num_channels
__lowercase =min_resolution
__lowercase =max_resolution
__lowercase =do_resize
__lowercase =size
__lowercase =do_normalize
__lowercase =image_mean
__lowercase =image_std
__lowercase =do_rescale
__lowercase =rescale_factor
__lowercase =do_pad
def __lowerCamelCase ( self : List[Any]):
'''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 : Dict , _lowerCAmelCase : List[str] , _lowerCAmelCase : List[str]=False):
'''simple docstring'''
if not batched:
__lowercase =image_inputs[0]
if isinstance(lowerCAmelCase__ , Image.Image):
__lowercase =image.size
else:
__lowercase =image.shape[1], image.shape[2]
if w < h:
__lowercase =int(self.size['shortest_edge'] * h / w)
__lowercase =self.size["shortest_edge"]
elif w > h:
__lowercase =self.size["shortest_edge"]
__lowercase =int(self.size['shortest_edge'] * w / h)
else:
__lowercase =self.size["shortest_edge"]
__lowercase =self.size["shortest_edge"]
else:
__lowercase =[]
for image in image_inputs:
__lowercase =self.get_expected_values([image])
expected_values.append((expected_height, expected_width))
__lowercase =max(lowerCAmelCase__ , key=lambda _lowerCAmelCase: item[0])[0]
__lowercase =max(lowerCAmelCase__ , key=lambda _lowerCAmelCase: item[1])[1]
return expected_height, expected_width
@require_torch
@require_vision
class _UpperCamelCase ( a_ , unittest.TestCase ):
'''simple docstring'''
lowerCAmelCase__ = YolosImageProcessor if is_vision_available() else None
def __lowerCamelCase ( self : Optional[Any]):
'''simple docstring'''
__lowercase =YolosImageProcessingTester(self)
@property
def __lowerCamelCase ( self : List[str]):
'''simple docstring'''
return self.image_processor_tester.prepare_image_processor_dict()
def __lowerCamelCase ( self : List[str]):
'''simple docstring'''
__lowercase =self.image_processing_class(**self.image_processor_dict)
self.assertTrue(hasattr(lowerCAmelCase__ , 'image_mean'))
self.assertTrue(hasattr(lowerCAmelCase__ , 'image_std'))
self.assertTrue(hasattr(lowerCAmelCase__ , 'do_normalize'))
self.assertTrue(hasattr(lowerCAmelCase__ , 'do_resize'))
self.assertTrue(hasattr(lowerCAmelCase__ , 'size'))
def __lowerCamelCase ( self : List[Any]):
'''simple docstring'''
__lowercase =self.image_processing_class.from_dict(self.image_processor_dict)
self.assertEqual(image_processor.size , {'shortest_edge': 1_8, 'longest_edge': 1_3_3_3})
self.assertEqual(image_processor.do_pad , lowerCAmelCase__)
__lowercase =self.image_processing_class.from_dict(
self.image_processor_dict , size=4_2 , max_size=8_4 , pad_and_return_pixel_mask=lowerCAmelCase__)
self.assertEqual(image_processor.size , {'shortest_edge': 4_2, 'longest_edge': 8_4})
self.assertEqual(image_processor.do_pad , lowerCAmelCase__)
def __lowerCamelCase ( self : List[str]):
'''simple docstring'''
pass
def __lowerCamelCase ( self : Optional[Any]):
'''simple docstring'''
__lowercase =self.image_processing_class(**self.image_processor_dict)
# create random PIL images
__lowercase =prepare_image_inputs(self.image_processor_tester , equal_resolution=lowerCAmelCase__)
for image in image_inputs:
self.assertIsInstance(lowerCAmelCase__ , Image.Image)
# Test not batched input
__lowercase =image_processing(image_inputs[0] , return_tensors='pt').pixel_values
__lowercase =self.image_processor_tester.get_expected_values(lowerCAmelCase__)
self.assertEqual(
encoded_images.shape , (1, self.image_processor_tester.num_channels, expected_height, expected_width) , )
# Test batched
__lowercase =self.image_processor_tester.get_expected_values(lowerCAmelCase__ , batched=lowerCAmelCase__)
__lowercase =image_processing(lowerCAmelCase__ , return_tensors='pt').pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
expected_height,
expected_width,
) , )
def __lowerCamelCase ( self : Dict):
'''simple docstring'''
__lowercase =self.image_processing_class(**self.image_processor_dict)
# create random numpy tensors
__lowercase =prepare_image_inputs(self.image_processor_tester , equal_resolution=lowerCAmelCase__ , numpify=lowerCAmelCase__)
for image in image_inputs:
self.assertIsInstance(lowerCAmelCase__ , np.ndarray)
# Test not batched input
__lowercase =image_processing(image_inputs[0] , return_tensors='pt').pixel_values
__lowercase =self.image_processor_tester.get_expected_values(lowerCAmelCase__)
self.assertEqual(
encoded_images.shape , (1, self.image_processor_tester.num_channels, expected_height, expected_width) , )
# Test batched
__lowercase =image_processing(lowerCAmelCase__ , return_tensors='pt').pixel_values
__lowercase =self.image_processor_tester.get_expected_values(lowerCAmelCase__ , batched=lowerCAmelCase__)
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
expected_height,
expected_width,
) , )
def __lowerCamelCase ( self : Tuple):
'''simple docstring'''
__lowercase =self.image_processing_class(**self.image_processor_dict)
# create random PyTorch tensors
__lowercase =prepare_image_inputs(self.image_processor_tester , equal_resolution=lowerCAmelCase__ , torchify=lowerCAmelCase__)
for image in image_inputs:
self.assertIsInstance(lowerCAmelCase__ , torch.Tensor)
# Test not batched input
__lowercase =image_processing(image_inputs[0] , return_tensors='pt').pixel_values
__lowercase =self.image_processor_tester.get_expected_values(lowerCAmelCase__)
self.assertEqual(
encoded_images.shape , (1, self.image_processor_tester.num_channels, expected_height, expected_width) , )
# Test batched
__lowercase =image_processing(lowerCAmelCase__ , return_tensors='pt').pixel_values
__lowercase =self.image_processor_tester.get_expected_values(lowerCAmelCase__ , batched=lowerCAmelCase__)
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
expected_height,
expected_width,
) , )
def __lowerCamelCase ( self : Tuple):
'''simple docstring'''
__lowercase =self.image_processing_class(**self.image_processor_dict)
__lowercase =self.image_processing_class(do_resize=lowerCAmelCase__ , do_normalize=lowerCAmelCase__ , do_rescale=lowerCAmelCase__)
# create random PyTorch tensors
__lowercase =prepare_image_inputs(self.image_processor_tester , equal_resolution=lowerCAmelCase__ , torchify=lowerCAmelCase__)
for image in image_inputs:
self.assertIsInstance(lowerCAmelCase__ , torch.Tensor)
# Test whether the method "pad" and calling the image processor return the same tensors
__lowercase =image_processing_a.pad(lowerCAmelCase__ , return_tensors='pt')
__lowercase =image_processing_a(lowerCAmelCase__ , return_tensors='pt')
self.assertTrue(
torch.allclose(encoded_images_with_method['pixel_values'] , encoded_images['pixel_values'] , atol=1e-4))
@slow
def __lowerCamelCase ( self : str):
'''simple docstring'''
__lowercase =Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png')
with open('./tests/fixtures/tests_samples/COCO/coco_annotations.txt' , 'r') as f:
__lowercase =json.loads(f.read())
__lowercase ={"image_id": 3_9_7_6_9, "annotations": target}
# encode them
__lowercase =YolosImageProcessor.from_pretrained('hustvl/yolos-small')
__lowercase =image_processing(images=lowerCAmelCase__ , annotations=lowerCAmelCase__ , return_tensors='pt')
# verify pixel values
__lowercase =torch.Size([1, 3, 8_0_0, 1_0_6_6])
self.assertEqual(encoding['pixel_values'].shape , lowerCAmelCase__)
__lowercase =torch.tensor([0.2796, 0.3138, 0.3481])
self.assertTrue(torch.allclose(encoding['pixel_values'][0, 0, 0, :3] , lowerCAmelCase__ , atol=1e-4))
# verify area
__lowercase =torch.tensor([5_8_8_7.9_6_0_0, 1_1_2_5_0.2_0_6_1, 4_8_9_3_5_3.8_4_3_8, 8_3_7_1_2_2.7_5_0_0, 1_4_7_9_6_7.5_1_5_6, 1_6_5_7_3_2.3_4_3_8])
self.assertTrue(torch.allclose(encoding['labels'][0]['area'] , lowerCAmelCase__))
# verify boxes
__lowercase =torch.Size([6, 4])
self.assertEqual(encoding['labels'][0]['boxes'].shape , lowerCAmelCase__)
__lowercase =torch.tensor([0.5503, 0.2765, 0.0604, 0.2215])
self.assertTrue(torch.allclose(encoding['labels'][0]['boxes'][0] , lowerCAmelCase__ , atol=1e-3))
# verify image_id
__lowercase =torch.tensor([3_9_7_6_9])
self.assertTrue(torch.allclose(encoding['labels'][0]['image_id'] , lowerCAmelCase__))
# verify is_crowd
__lowercase =torch.tensor([0, 0, 0, 0, 0, 0])
self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd'] , lowerCAmelCase__))
# verify class_labels
__lowercase =torch.tensor([7_5, 7_5, 6_3, 6_5, 1_7, 1_7])
self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels'] , lowerCAmelCase__))
# verify orig_size
__lowercase =torch.tensor([4_8_0, 6_4_0])
self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size'] , lowerCAmelCase__))
# verify size
__lowercase =torch.tensor([8_0_0, 1_0_6_6])
self.assertTrue(torch.allclose(encoding['labels'][0]['size'] , lowerCAmelCase__))
@slow
def __lowerCamelCase ( self : Dict):
'''simple docstring'''
__lowercase =Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png')
with open('./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt' , 'r') as f:
__lowercase =json.loads(f.read())
__lowercase ={"file_name": "000000039769.png", "image_id": 3_9_7_6_9, "segments_info": target}
__lowercase =pathlib.Path('./tests/fixtures/tests_samples/COCO/coco_panoptic')
# encode them
__lowercase =YolosImageProcessor(format='coco_panoptic')
__lowercase =image_processing(images=lowerCAmelCase__ , annotations=lowerCAmelCase__ , masks_path=lowerCAmelCase__ , return_tensors='pt')
# verify pixel values
__lowercase =torch.Size([1, 3, 8_0_0, 1_0_6_6])
self.assertEqual(encoding['pixel_values'].shape , lowerCAmelCase__)
__lowercase =torch.tensor([0.2796, 0.3138, 0.3481])
self.assertTrue(torch.allclose(encoding['pixel_values'][0, 0, 0, :3] , lowerCAmelCase__ , atol=1e-4))
# verify area
__lowercase =torch.tensor([1_4_7_9_7_9.6_8_7_5, 1_6_5_5_2_7.0_4_6_9, 4_8_4_6_3_8.5_9_3_8, 1_1_2_9_2.9_3_7_5, 5_8_7_9.6_5_6_2, 7_6_3_4.1_1_4_7])
self.assertTrue(torch.allclose(encoding['labels'][0]['area'] , lowerCAmelCase__))
# verify boxes
__lowercase =torch.Size([6, 4])
self.assertEqual(encoding['labels'][0]['boxes'].shape , lowerCAmelCase__)
__lowercase =torch.tensor([0.2625, 0.5437, 0.4688, 0.8625])
self.assertTrue(torch.allclose(encoding['labels'][0]['boxes'][0] , lowerCAmelCase__ , atol=1e-3))
# verify image_id
__lowercase =torch.tensor([3_9_7_6_9])
self.assertTrue(torch.allclose(encoding['labels'][0]['image_id'] , lowerCAmelCase__))
# verify is_crowd
__lowercase =torch.tensor([0, 0, 0, 0, 0, 0])
self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd'] , lowerCAmelCase__))
# verify class_labels
__lowercase =torch.tensor([1_7, 1_7, 6_3, 7_5, 7_5, 9_3])
self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels'] , lowerCAmelCase__))
# verify masks
__lowercase =8_2_2_8_7_3
self.assertEqual(encoding['labels'][0]['masks'].sum().item() , lowerCAmelCase__)
# verify orig_size
__lowercase =torch.tensor([4_8_0, 6_4_0])
self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size'] , lowerCAmelCase__))
# verify size
__lowercase =torch.tensor([8_0_0, 1_0_6_6])
self.assertTrue(torch.allclose(encoding['labels'][0]['size'] , lowerCAmelCase__))
| 474 |
'''simple docstring'''
import math
def __UpperCAmelCase ( __magic_name__ )-> bool:
"""simple docstring"""
snake_case_ : Optional[int] = math.loga(math.sqrt(4 * positive_integer + 1 ) / 2 + 1 / 2 )
return exponent == int(__magic_name__ )
def __UpperCAmelCase ( __magic_name__ = 1 / 1_2345 )-> int:
"""simple docstring"""
snake_case_ : Any = 0
snake_case_ : int = 0
snake_case_ : Union[str, Any] = 3
while True:
snake_case_ : Any = (integer**2 - 1) / 4
# if candidate is an integer, then there is a partition for k
if partition_candidate == int(__magic_name__ ):
snake_case_ : Optional[Any] = int(__magic_name__ )
total_partitions += 1
if check_partition_perfect(__magic_name__ ):
perfect_partitions += 1
if perfect_partitions > 0:
if perfect_partitions / total_partitions < max_proportion:
return int(__magic_name__ )
integer += 1
if __name__ == "__main__":
print(f'''{solution() = }''')
| 653 | 0 |
'''simple docstring'''
import collections.abc
from typing import Optional, Tuple, Union
import torch
import torch.utils.checkpoint
from torch import nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from ...activations import ACTaFN
from ...modeling_outputs import BaseModelOutputWithNoAttention, ImageClassifierOutputWithNoAttention
from ...modeling_utils import PreTrainedModel
from ...utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward, logging
from .configuration_poolformer import PoolFormerConfig
lowercase_ = logging.get_logger(__name__)
# General docstring
lowercase_ = '''PoolFormerConfig'''
# Base docstring
lowercase_ = '''sail/poolformer_s12'''
lowercase_ = [1, 512, 7, 7]
# Image classification docstring
lowercase_ = '''sail/poolformer_s12'''
lowercase_ = '''tabby, tabby cat'''
lowercase_ = [
'''sail/poolformer_s12''',
# See all PoolFormer models at https://huggingface.co/models?filter=poolformer
]
def lowerCAmelCase (__A , __A = 0.0 , __A = False):
"""simple docstring"""
if drop_prob == 0.0 or not training:
return input
_a = 1 - drop_prob
_a = (input.shape[0],) + (1,) * (input.ndim - 1) # work with diff dim tensors, not just 2D ConvNets
_a = keep_prob + torch.rand(__A , dtype=input.dtype , device=input.device)
random_tensor.floor_() # binarize
_a = input.div(__A) * random_tensor
return output
class __A ( nn.Module ):
'''simple docstring'''
def __init__(self , A = None ) -> None:
"""simple docstring"""
super().__init__()
_a = drop_prob
def a__ (self , A ) -> torch.Tensor:
"""simple docstring"""
return drop_path(lowerCAmelCase__ , self.drop_prob , self.training )
def a__ (self ) -> str:
"""simple docstring"""
return "p={}".format(self.drop_prob )
class __A ( nn.Module ):
'''simple docstring'''
def __init__(self , A , A , A , A , A , A=None ) -> List[Any]:
"""simple docstring"""
super().__init__()
_a = patch_size if isinstance(lowerCAmelCase__ , collections.abc.Iterable ) else (patch_size, patch_size)
_a = stride if isinstance(lowerCAmelCase__ , collections.abc.Iterable ) else (stride, stride)
_a = padding if isinstance(lowerCAmelCase__ , collections.abc.Iterable ) else (padding, padding)
_a = nn.Convad(lowerCAmelCase__ , lowerCAmelCase__ , kernel_size=lowerCAmelCase__ , stride=lowerCAmelCase__ , padding=lowerCAmelCase__ )
_a = norm_layer(lowerCAmelCase__ ) if norm_layer else nn.Identity()
def a__ (self , A ) -> List[str]:
"""simple docstring"""
_a = self.projection(lowerCAmelCase__ )
_a = self.norm(lowerCAmelCase__ )
return embeddings
class __A ( nn.GroupNorm ):
'''simple docstring'''
def __init__(self , A , **A ) -> Dict:
"""simple docstring"""
super().__init__(1 , lowerCAmelCase__ , **lowerCAmelCase__ )
class __A ( nn.Module ):
'''simple docstring'''
def __init__(self , A ) -> Optional[int]:
"""simple docstring"""
super().__init__()
_a = nn.AvgPoolad(lowerCAmelCase__ , stride=1 , padding=pool_size // 2 , count_include_pad=lowerCAmelCase__ )
def a__ (self , A ) -> Optional[int]:
"""simple docstring"""
return self.pool(lowerCAmelCase__ ) - hidden_states
class __A ( nn.Module ):
'''simple docstring'''
def __init__(self , A , A , A , A ) -> Optional[Any]:
"""simple docstring"""
super().__init__()
_a = nn.Convad(lowerCAmelCase__ , lowerCAmelCase__ , 1 )
_a = nn.Convad(lowerCAmelCase__ , lowerCAmelCase__ , 1 )
_a = PoolFormerDropPath(lowerCAmelCase__ )
if isinstance(config.hidden_act , lowerCAmelCase__ ):
_a = ACTaFN[config.hidden_act]
else:
_a = config.hidden_act
def a__ (self , A ) -> Union[str, Any]:
"""simple docstring"""
_a = self.conva(lowerCAmelCase__ )
_a = self.act_fn(lowerCAmelCase__ )
_a = self.drop(lowerCAmelCase__ )
_a = self.conva(lowerCAmelCase__ )
_a = self.drop(lowerCAmelCase__ )
return hidden_states
class __A ( nn.Module ):
'''simple docstring'''
def __init__(self , A , A , A , A , A , A ) -> Tuple:
"""simple docstring"""
super().__init__()
_a = PoolFormerPooling(lowerCAmelCase__ )
_a = PoolFormerOutput(lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ )
_a = PoolFormerGroupNorm(lowerCAmelCase__ )
_a = PoolFormerGroupNorm(lowerCAmelCase__ )
# Useful for training neural nets
_a = PoolFormerDropPath(lowerCAmelCase__ ) if drop_path > 0.0 else nn.Identity()
_a = config.use_layer_scale
if config.use_layer_scale:
_a = nn.Parameter(
config.layer_scale_init_value * torch.ones((lowerCAmelCase__) ) , requires_grad=lowerCAmelCase__ )
_a = nn.Parameter(
config.layer_scale_init_value * torch.ones((lowerCAmelCase__) ) , requires_grad=lowerCAmelCase__ )
def a__ (self , A ) -> Dict:
"""simple docstring"""
if self.use_layer_scale:
_a = self.pooling(self.before_norm(lowerCAmelCase__ ) )
_a = self.layer_scale_a.unsqueeze(-1 ).unsqueeze(-1 ) * pooling_output
# First residual connection
_a = hidden_states + self.drop_path(lowerCAmelCase__ )
_a = ()
_a = self.output(self.after_norm(lowerCAmelCase__ ) )
_a = self.layer_scale_a.unsqueeze(-1 ).unsqueeze(-1 ) * layer_output
# Second residual connection
_a = hidden_states + self.drop_path(lowerCAmelCase__ )
_a = (output,) + outputs
return outputs
else:
_a = self.drop_path(self.pooling(self.before_norm(lowerCAmelCase__ ) ) )
# First residual connection
_a = pooling_output + hidden_states
_a = ()
# Second residual connection inside the PoolFormerOutput block
_a = self.drop_path(self.output(self.after_norm(lowerCAmelCase__ ) ) )
_a = hidden_states + layer_output
_a = (output,) + outputs
return outputs
class __A ( nn.Module ):
'''simple docstring'''
def __init__(self , A ) -> Optional[Any]:
"""simple docstring"""
super().__init__()
_a = config
# stochastic depth decay rule
_a = [x.item() for x in torch.linspace(0 , config.drop_path_rate , sum(config.depths ) )]
# patch embeddings
_a = []
for i in range(config.num_encoder_blocks ):
embeddings.append(
PoolFormerEmbeddings(
patch_size=config.patch_sizes[i] , stride=config.strides[i] , padding=config.padding[i] , num_channels=config.num_channels if i == 0 else config.hidden_sizes[i - 1] , hidden_size=config.hidden_sizes[i] , ) )
_a = nn.ModuleList(lowerCAmelCase__ )
# Transformer blocks
_a = []
_a = 0
for i in range(config.num_encoder_blocks ):
# each block consists of layers
_a = []
if i != 0:
cur += config.depths[i - 1]
for j in range(config.depths[i] ):
layers.append(
PoolFormerLayer(
lowerCAmelCase__ , num_channels=config.hidden_sizes[i] , pool_size=config.pool_size , hidden_size=config.hidden_sizes[i] , intermediate_size=int(config.hidden_sizes[i] * config.mlp_ratio ) , drop_path=dpr[cur + j] , ) )
blocks.append(nn.ModuleList(lowerCAmelCase__ ) )
_a = nn.ModuleList(lowerCAmelCase__ )
def a__ (self , A , A=False , A=True ) -> List[str]:
"""simple docstring"""
_a = () if output_hidden_states else None
_a = pixel_values
for idx, layers in enumerate(zip(self.patch_embeddings , self.block ) ):
_a = layers
# Get patch embeddings from hidden_states
_a = embedding_layer(lowerCAmelCase__ )
# Send the embeddings through the blocks
for _, blk in enumerate(lowerCAmelCase__ ):
_a = blk(lowerCAmelCase__ )
_a = layer_outputs[0]
if output_hidden_states:
_a = all_hidden_states + (hidden_states,)
if not return_dict:
return tuple(v for v in [hidden_states, all_hidden_states] if v is not None )
return BaseModelOutputWithNoAttention(last_hidden_state=lowerCAmelCase__ , hidden_states=lowerCAmelCase__ )
class __A ( a_ ):
'''simple docstring'''
__lowerCamelCase : str = PoolFormerConfig
__lowerCamelCase : List[Any] = 'poolformer'
__lowerCamelCase : str = 'pixel_values'
__lowerCamelCase : Tuple = True
def a__ (self , A ) -> Optional[Any]:
"""simple docstring"""
if isinstance(lowerCAmelCase__ , (nn.Linear, nn.Convad) ):
module.weight.data.normal_(mean=0.0 , std=self.config.initializer_range )
if module.bias is not None:
module.bias.data.zero_()
elif isinstance(lowerCAmelCase__ , nn.LayerNorm ):
module.bias.data.zero_()
module.weight.data.fill_(1.0 )
def a__ (self , A , A=False ) -> List[Any]:
"""simple docstring"""
if isinstance(lowerCAmelCase__ , lowerCAmelCase__ ):
_a = value
lowercase_ = R'''
This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) sub-class. Use
it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and
behavior.
Parameters:
config ([`PoolFormerConfig`]): Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.
'''
lowercase_ = R'''
Args:
pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):
Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. See
[`PoolFormerImageProcessor.__call__`] for details.
'''
@add_start_docstrings(
'The bare PoolFormer Model transformer outputting raw hidden-states without any specific head on top.' , a_ , )
class __A ( a_ ):
'''simple docstring'''
def __init__(self , A ) -> Optional[int]:
"""simple docstring"""
super().__init__(lowerCAmelCase__ )
_a = config
_a = PoolFormerEncoder(lowerCAmelCase__ )
# Initialize weights and apply final processing
self.post_init()
def a__ (self ) -> List[Any]:
"""simple docstring"""
return self.embeddings.patch_embeddings
@add_start_docstrings_to_model_forward(lowerCAmelCase__ )
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_DOC , output_type=lowerCAmelCase__ , config_class=_CONFIG_FOR_DOC , modality='''vision''' , expected_output=_EXPECTED_OUTPUT_SHAPE , )
def a__ (self , A = None , A = None , A = None , ) -> Union[Tuple, BaseModelOutputWithNoAttention]:
"""simple docstring"""
_a = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
_a = return_dict if return_dict is not None else self.config.use_return_dict
if pixel_values is None:
raise ValueError('''You have to specify pixel_values''' )
_a = self.encoder(
lowerCAmelCase__ , output_hidden_states=lowerCAmelCase__ , return_dict=lowerCAmelCase__ , )
_a = encoder_outputs[0]
if not return_dict:
return (sequence_output, None) + encoder_outputs[1:]
return BaseModelOutputWithNoAttention(
last_hidden_state=lowerCAmelCase__ , hidden_states=encoder_outputs.hidden_states , )
class __A ( nn.Module ):
'''simple docstring'''
def __init__(self , A ) -> List[str]:
"""simple docstring"""
super().__init__()
_a = nn.Linear(config.hidden_size , config.hidden_size )
def a__ (self , A ) -> Optional[Any]:
"""simple docstring"""
_a = self.dense(lowerCAmelCase__ )
return output
@add_start_docstrings(
'\n PoolFormer Model transformer with an image classification head on top\n ' , a_ , )
class __A ( a_ ):
'''simple docstring'''
def __init__(self , A ) -> Tuple:
"""simple docstring"""
super().__init__(lowerCAmelCase__ )
_a = config.num_labels
_a = PoolFormerModel(lowerCAmelCase__ )
# Final norm
_a = PoolFormerGroupNorm(config.hidden_sizes[-1] )
# Classifier head
_a = (
nn.Linear(config.hidden_sizes[-1] , config.num_labels ) if config.num_labels > 0 else nn.Identity()
)
# Initialize weights and apply final processing
self.post_init()
@add_start_docstrings_to_model_forward(lowerCAmelCase__ )
@add_code_sample_docstrings(
checkpoint=_IMAGE_CLASS_CHECKPOINT , output_type=lowerCAmelCase__ , config_class=_CONFIG_FOR_DOC , expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT , )
def a__ (self , A = None , A = None , A = None , A = None , ) -> Union[Tuple, ImageClassifierOutputWithNoAttention]:
"""simple docstring"""
_a = return_dict if return_dict is not None else self.config.use_return_dict
_a = self.poolformer(
lowerCAmelCase__ , output_hidden_states=lowerCAmelCase__ , return_dict=lowerCAmelCase__ , )
_a = outputs[0]
_a = self.classifier(self.norm(lowerCAmelCase__ ).mean([-2, -1] ) )
_a = None
if labels is not None:
if self.config.problem_type is None:
if self.num_labels == 1:
_a = "regression"
elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
_a = "single_label_classification"
else:
_a = "multi_label_classification"
if self.config.problem_type == "regression":
_a = MSELoss()
if self.num_labels == 1:
_a = loss_fct(logits.squeeze() , labels.squeeze() )
else:
_a = loss_fct(lowerCAmelCase__ , lowerCAmelCase__ )
elif self.config.problem_type == "single_label_classification":
_a = CrossEntropyLoss()
_a = loss_fct(logits.view(-1 , self.num_labels ) , labels.view(-1 ) )
elif self.config.problem_type == "multi_label_classification":
_a = BCEWithLogitsLoss()
_a = loss_fct(lowerCAmelCase__ , lowerCAmelCase__ )
if not return_dict:
_a = (logits,) + outputs[2:]
return ((loss,) + output) if loss is not None else output
return ImageClassifierOutputWithNoAttention(loss=lowerCAmelCase__ , logits=lowerCAmelCase__ , hidden_states=outputs.hidden_states )
| 11 |
'''simple docstring'''
import argparse
import json
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from typing import List
import timm
import torch
import torch.nn as nn
from huggingface_hub import hf_hub_download
from torch import Tensor
from transformers import AutoImageProcessor, ResNetConfig, ResNetForImageClassification
from transformers.utils import logging
logging.set_verbosity_info()
__lowerCamelCase : int = logging.get_logger()
@dataclass
class A_ :
"""simple docstring"""
a__ = 42
a__ = field(default_factory=a_ )
a__ = field(default_factory=a_ )
def _A ( self :List[Any] , lowerCAmelCase__ :Union[str, Any] , lowerCAmelCase__ :Tensor , lowerCAmelCase__ :Tensor ) -> int:
'''simple docstring'''
snake_case_ : int = len(list(m.modules() ) ) == 1 or isinstance(lowerCAmelCase__ , nn.Convad ) or isinstance(lowerCAmelCase__ , nn.BatchNormad )
if has_not_submodules:
self.traced.append(lowerCAmelCase__ )
def __call__( self :List[Any] , lowerCAmelCase__ :Tensor ) -> Union[str, Any]:
'''simple docstring'''
for m in self.module.modules():
self.handles.append(m.register_forward_hook(self._forward_hook ) )
self.module(lowerCAmelCase__ )
[x.remove() for x in self.handles]
return self
@property
def _A ( self :int ) -> List[Any]:
'''simple docstring'''
return list(filter(lambda lowerCAmelCase__ : len(list(x.state_dict().keys() ) ) > 0 , self.traced ) )
@dataclass
class A_ :
"""simple docstring"""
a__ = 42
a__ = 42
a__ = 0
a__ = field(default_factory=a_ )
a__ = field(default_factory=a_ )
def __call__( self :Tuple , lowerCAmelCase__ :Tensor ) -> Tuple:
'''simple docstring'''
snake_case_ : List[Any] = Tracker(self.dest )(lowerCAmelCase__ ).parametrized
snake_case_ : Tuple = Tracker(self.src )(lowerCAmelCase__ ).parametrized
snake_case_ : List[str] = list(filter(lambda lowerCAmelCase__ : type(lowerCAmelCase__ ) not in self.src_skip , lowerCAmelCase__ ) )
snake_case_ : Tuple = list(filter(lambda lowerCAmelCase__ : type(lowerCAmelCase__ ) not in self.dest_skip , lowerCAmelCase__ ) )
if len(lowerCAmelCase__ ) != len(lowerCAmelCase__ ):
raise Exception(
F'''Numbers of operations are different. Source module has {len(lowerCAmelCase__ )} operations while'''
F''' destination module has {len(lowerCAmelCase__ )}.''' )
for dest_m, src_m in zip(lowerCAmelCase__ , lowerCAmelCase__ ):
dest_m.load_state_dict(src_m.state_dict() )
if self.verbose == 1:
print(F'''Transfered from={src_m} to={dest_m}''' )
def __UpperCAmelCase ( __magic_name__ ,__magic_name__ ,__magic_name__ ,__magic_name__ = True )-> Optional[int]:
"""simple docstring"""
print(F'''Converting {name}...''' )
with torch.no_grad():
snake_case_ : List[str] = timm.create_model(__magic_name__ ,pretrained=__magic_name__ ).eval()
snake_case_ : Optional[int] = ResNetForImageClassification(__magic_name__ ).eval()
snake_case_ : Dict = ModuleTransfer(src=__magic_name__ ,dest=__magic_name__ )
snake_case_ : Optional[int] = torch.randn((1, 3, 224, 224) )
module_transfer(__magic_name__ )
assert torch.allclose(from_model(__magic_name__ ) ,our_model(__magic_name__ ).logits ), "The model logits don't match the original one."
snake_case_ : str = F'''resnet{'-'.join(name.split('resnet' ) )}'''
print(__magic_name__ )
if push_to_hub:
our_model.push_to_hub(
repo_path_or_name=save_directory / checkpoint_name ,commit_message="Add model" ,use_temp_dir=__magic_name__ ,)
# we can use the convnext one
snake_case_ : Optional[Any] = AutoImageProcessor.from_pretrained("facebook/convnext-base-224-22k-1k" )
image_processor.push_to_hub(
repo_path_or_name=save_directory / checkpoint_name ,commit_message="Add image processor" ,use_temp_dir=__magic_name__ ,)
print(F'''Pushed {checkpoint_name}''' )
def __UpperCAmelCase ( __magic_name__ ,__magic_name__ = None ,__magic_name__ = True )-> Tuple:
"""simple docstring"""
snake_case_ : List[str] = "imagenet-1k-id2label.json"
snake_case_ : Optional[Any] = 1000
snake_case_ : List[Any] = (1, num_labels)
snake_case_ : Optional[Any] = "huggingface/label-files"
snake_case_ : Dict = num_labels
snake_case_ : List[Any] = json.load(open(hf_hub_download(__magic_name__ ,__magic_name__ ,repo_type="dataset" ) ,"r" ) )
snake_case_ : List[str] = {int(__magic_name__ ): v for k, v in idalabel.items()}
snake_case_ : Any = idalabel
snake_case_ : List[Any] = {v: k for k, v in idalabel.items()}
snake_case_ : Optional[int] = partial(__magic_name__ ,num_labels=__magic_name__ ,idalabel=__magic_name__ ,labelaid=__magic_name__ )
snake_case_ : Optional[int] = {
"resnet18": ImageNetPreTrainedConfig(
depths=[2, 2, 2, 2] ,hidden_sizes=[64, 128, 256, 512] ,layer_type="basic" ),
"resnet26": ImageNetPreTrainedConfig(
depths=[2, 2, 2, 2] ,hidden_sizes=[256, 512, 1024, 2048] ,layer_type="bottleneck" ),
"resnet34": ImageNetPreTrainedConfig(
depths=[3, 4, 6, 3] ,hidden_sizes=[64, 128, 256, 512] ,layer_type="basic" ),
"resnet50": ImageNetPreTrainedConfig(
depths=[3, 4, 6, 3] ,hidden_sizes=[256, 512, 1024, 2048] ,layer_type="bottleneck" ),
"resnet101": ImageNetPreTrainedConfig(
depths=[3, 4, 23, 3] ,hidden_sizes=[256, 512, 1024, 2048] ,layer_type="bottleneck" ),
"resnet152": ImageNetPreTrainedConfig(
depths=[3, 8, 36, 3] ,hidden_sizes=[256, 512, 1024, 2048] ,layer_type="bottleneck" ),
}
if model_name:
convert_weight_and_push(__magic_name__ ,names_to_config[model_name] ,__magic_name__ ,__magic_name__ )
else:
for model_name, config in names_to_config.items():
convert_weight_and_push(__magic_name__ ,__magic_name__ ,__magic_name__ ,__magic_name__ )
return config, expected_shape
if __name__ == "__main__":
__lowerCamelCase : Optional[int] = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'''--model_name''',
default=None,
type=str,
help=(
'''The name of the model you wish to convert, it must be one of the supported resnet* architecture,'''
''' currently: resnet18,26,34,50,101,152. If `None`, all of them will the converted.'''
),
)
parser.add_argument(
'''--pytorch_dump_folder_path''',
default=None,
type=Path,
required=True,
help='''Path to the output PyTorch model directory.''',
)
parser.add_argument(
'''--push_to_hub''',
default=True,
type=bool,
required=False,
help='''If True, push model and image processor to the hub.''',
)
__lowerCamelCase : Tuple = parser.parse_args()
__lowerCamelCase : Path = args.pytorch_dump_folder_path
pytorch_dump_folder_path.mkdir(exist_ok=True, parents=True)
convert_weights_and_push(pytorch_dump_folder_path, args.model_name, args.push_to_hub)
| 653 | 0 |
import warnings
from ...utils import logging
from .image_processing_videomae import VideoMAEImageProcessor
__A : Dict = logging.get_logger(__name__)
class _SCREAMING_SNAKE_CASE ( a_ ):
'''simple docstring'''
def __init__( self : Optional[int] , *__lowerCamelCase : Optional[Any] , **__lowerCamelCase : Optional[int] ):
warnings.warn(
"The class VideoMAEFeatureExtractor is deprecated and will be removed in version 5 of Transformers."
" Please use VideoMAEImageProcessor instead." , lowerCAmelCase__ , )
super().__init__(*lowerCAmelCase__ , **lowerCAmelCase__ ) | 16 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__lowerCamelCase : List[Any] = logging.get_logger(__name__)
__lowerCamelCase : Dict = {
'''weiweishi/roc-bert-base-zh''': '''https://huggingface.co/weiweishi/roc-bert-base-zh/resolve/main/config.json''',
}
class A_ (a_ ):
"""simple docstring"""
a__ = '''roc_bert'''
def __init__( self :Dict , lowerCAmelCase__ :Optional[Any]=30_522 , lowerCAmelCase__ :Dict=768 , lowerCAmelCase__ :str=12 , lowerCAmelCase__ :Optional[int]=12 , lowerCAmelCase__ :Optional[Any]=3_072 , lowerCAmelCase__ :Any="gelu" , lowerCAmelCase__ :int=0.1 , lowerCAmelCase__ :Tuple=0.1 , lowerCAmelCase__ :List[str]=512 , lowerCAmelCase__ :int=2 , lowerCAmelCase__ :Optional[int]=0.0_2 , lowerCAmelCase__ :Tuple=1E-1_2 , lowerCAmelCase__ :Tuple=True , lowerCAmelCase__ :List[str]=0 , lowerCAmelCase__ :Optional[Any]="absolute" , lowerCAmelCase__ :Tuple=None , lowerCAmelCase__ :List[str]=True , lowerCAmelCase__ :Optional[Any]=True , lowerCAmelCase__ :List[str]=768 , lowerCAmelCase__ :Optional[Any]=910 , lowerCAmelCase__ :str=512 , lowerCAmelCase__ :int=24_858 , lowerCAmelCase__ :List[Any]=True , **lowerCAmelCase__ :int , ) -> List[str]:
'''simple docstring'''
snake_case_ : int = vocab_size
snake_case_ : Dict = max_position_embeddings
snake_case_ : int = hidden_size
snake_case_ : str = num_hidden_layers
snake_case_ : Union[str, Any] = num_attention_heads
snake_case_ : int = intermediate_size
snake_case_ : Optional[Any] = hidden_act
snake_case_ : Optional[int] = hidden_dropout_prob
snake_case_ : List[Any] = attention_probs_dropout_prob
snake_case_ : Dict = initializer_range
snake_case_ : str = type_vocab_size
snake_case_ : Tuple = layer_norm_eps
snake_case_ : Optional[Any] = use_cache
snake_case_ : Optional[Any] = enable_pronunciation
snake_case_ : List[Any] = enable_shape
snake_case_ : Optional[int] = pronunciation_embed_dim
snake_case_ : Dict = pronunciation_vocab_size
snake_case_ : int = shape_embed_dim
snake_case_ : Any = shape_vocab_size
snake_case_ : Optional[int] = concat_input
snake_case_ : List[Any] = position_embedding_type
snake_case_ : Any = classifier_dropout
super().__init__(pad_token_id=lowerCAmelCase__ , **lowerCAmelCase__ )
| 653 | 0 |
'''simple docstring'''
from math import factorial
class _snake_case :
'''simple docstring'''
def __init__( self: Tuple , __UpperCamelCase: List[Any] , __UpperCamelCase: int ) -> Dict:
__magic_name__ : str = real
if isinstance(lowerCAmelCase__ , lowerCAmelCase__ ):
__magic_name__ : str = [1] * rank
else:
__magic_name__ : Any = rank
def __repr__( self: int ) -> Optional[Any]:
return (
f"""{self.real}+"""
f"""{"+".join(str(lowerCAmelCase__ )+"E"+str(n+1 )for n,dual in enumerate(self.duals ) )}"""
)
def lowerCAmelCase__ ( self: Optional[Any] ) -> int:
__magic_name__ : Optional[int] = self.duals.copy()
while cur[-1] == 0:
cur.pop(-1 )
return Dual(self.real , lowerCAmelCase__ )
def __add__( self: List[str] , __UpperCamelCase: str ) -> Optional[Any]:
if not isinstance(lowerCAmelCase__ , lowerCAmelCase__ ):
return Dual(self.real + other , self.duals )
__magic_name__ : Optional[Any] = self.duals.copy()
__magic_name__ : Any = other.duals.copy()
if len(lowerCAmelCase__ ) > len(lowerCAmelCase__ ):
o_dual.extend([1] * (len(lowerCAmelCase__ ) - len(lowerCAmelCase__ )) )
elif len(lowerCAmelCase__ ) < len(lowerCAmelCase__ ):
s_dual.extend([1] * (len(lowerCAmelCase__ ) - len(lowerCAmelCase__ )) )
__magic_name__ : Optional[int] = []
for i in range(len(lowerCAmelCase__ ) ):
new_duals.append(s_dual[i] + o_dual[i] )
return Dual(self.real + other.real , lowerCAmelCase__ )
__snake_case = __add__
def __sub__( self: Dict , __UpperCamelCase: Tuple ) -> List[str]:
return self + other * -1
def __mul__( self: Union[str, Any] , __UpperCamelCase: str ) -> Tuple:
if not isinstance(lowerCAmelCase__ , lowerCAmelCase__ ):
__magic_name__ : Any = []
for i in self.duals:
new_duals.append(i * other )
return Dual(self.real * other , lowerCAmelCase__ )
__magic_name__ : Optional[int] = [0] * (len(self.duals ) + len(other.duals ) + 1)
for i, item in enumerate(self.duals ):
for j, jtem in enumerate(other.duals ):
new_duals[i + j + 1] += item * jtem
for k in range(len(self.duals ) ):
new_duals[k] += self.duals[k] * other.real
for index in range(len(other.duals ) ):
new_duals[index] += other.duals[index] * self.real
return Dual(self.real * other.real , lowerCAmelCase__ )
__snake_case = __mul__
def __truediv__( self: int , __UpperCamelCase: Any ) -> Tuple:
if not isinstance(lowerCAmelCase__ , lowerCAmelCase__ ):
__magic_name__ : Optional[Any] = []
for i in self.duals:
new_duals.append(i / other )
return Dual(self.real / other , lowerCAmelCase__ )
raise ValueError
def __floordiv__( self: List[Any] , __UpperCamelCase: List[str] ) -> Optional[int]:
if not isinstance(lowerCAmelCase__ , lowerCAmelCase__ ):
__magic_name__ : int = []
for i in self.duals:
new_duals.append(i // other )
return Dual(self.real // other , lowerCAmelCase__ )
raise ValueError
def __pow__( self: Optional[Any] , __UpperCamelCase: List[str] ) -> Optional[int]:
if n < 0 or isinstance(lowerCAmelCase__ , lowerCAmelCase__ ):
raise ValueError("power must be a positive integer" )
if n == 0:
return 1
if n == 1:
return self
__magic_name__ : Tuple = self
for _ in range(n - 1 ):
x *= self
return x
def _UpperCamelCase ( UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ):
"""simple docstring"""
if not callable(UpperCamelCase__ ):
raise ValueError("differentiate() requires a function as input for func" )
if not isinstance(UpperCamelCase__ , (float, int) ):
raise ValueError("differentiate() requires a float as input for position" )
if not isinstance(UpperCamelCase__ , UpperCamelCase__ ):
raise ValueError("differentiate() requires an int as input for order" )
__magic_name__ : Optional[Any] = Dual(UpperCamelCase__ , 1 )
__magic_name__ : Union[str, Any] = func(UpperCamelCase__ )
if order == 0:
return result.real
return result.duals[order - 1] * factorial(UpperCamelCase__ )
if __name__ == "__main__":
import doctest
doctest.testmod()
def _UpperCamelCase ( UpperCamelCase__ ):
"""simple docstring"""
return y**2 * y**4
print(differentiate(f, 9, 2)) | 436 |
'''simple docstring'''
def __UpperCAmelCase ( __magic_name__ ,__magic_name__ ,__magic_name__ )-> int:
"""simple docstring"""
def update_area_of_max_square(__magic_name__ ,__magic_name__ ) -> int:
# BASE CASE
if row >= rows or col >= cols:
return 0
snake_case_ : str = update_area_of_max_square(__magic_name__ ,col + 1 )
snake_case_ : Dict = update_area_of_max_square(row + 1 ,col + 1 )
snake_case_ : int = update_area_of_max_square(row + 1 ,__magic_name__ )
if mat[row][col]:
snake_case_ : str = 1 + min([right, diagonal, down] )
snake_case_ : Tuple = max(largest_square_area[0] ,__magic_name__ )
return sub_problem_sol
else:
return 0
snake_case_ : Union[str, Any] = [0]
update_area_of_max_square(0 ,0 )
return largest_square_area[0]
def __UpperCAmelCase ( __magic_name__ ,__magic_name__ ,__magic_name__ )-> int:
"""simple docstring"""
def update_area_of_max_square_using_dp_array(
__magic_name__ ,__magic_name__ ,__magic_name__ ) -> int:
if row >= rows or col >= cols:
return 0
if dp_array[row][col] != -1:
return dp_array[row][col]
snake_case_ : Dict = update_area_of_max_square_using_dp_array(__magic_name__ ,col + 1 ,__magic_name__ )
snake_case_ : List[Any] = update_area_of_max_square_using_dp_array(row + 1 ,col + 1 ,__magic_name__ )
snake_case_ : Any = update_area_of_max_square_using_dp_array(row + 1 ,__magic_name__ ,__magic_name__ )
if mat[row][col]:
snake_case_ : int = 1 + min([right, diagonal, down] )
snake_case_ : Tuple = max(largest_square_area[0] ,__magic_name__ )
snake_case_ : Optional[Any] = sub_problem_sol
return sub_problem_sol
else:
return 0
snake_case_ : List[Any] = [0]
snake_case_ : Optional[int] = [[-1] * cols for _ in range(__magic_name__ )]
update_area_of_max_square_using_dp_array(0 ,0 ,__magic_name__ )
return largest_square_area[0]
def __UpperCAmelCase ( __magic_name__ ,__magic_name__ ,__magic_name__ )-> int:
"""simple docstring"""
snake_case_ : Dict = [[0] * (cols + 1) for _ in range(rows + 1 )]
snake_case_ : Dict = 0
for row in range(rows - 1 ,-1 ,-1 ):
for col in range(cols - 1 ,-1 ,-1 ):
snake_case_ : List[str] = dp_array[row][col + 1]
snake_case_ : Any = dp_array[row + 1][col + 1]
snake_case_ : Any = dp_array[row + 1][col]
if mat[row][col] == 1:
snake_case_ : Any = 1 + min(__magic_name__ ,__magic_name__ ,__magic_name__ )
snake_case_ : str = max(dp_array[row][col] ,__magic_name__ )
else:
snake_case_ : Optional[Any] = 0
return largest_square_area
def __UpperCAmelCase ( __magic_name__ ,__magic_name__ ,__magic_name__ )-> int:
"""simple docstring"""
snake_case_ : str = [0] * (cols + 1)
snake_case_ : Tuple = [0] * (cols + 1)
snake_case_ : List[str] = 0
for row in range(rows - 1 ,-1 ,-1 ):
for col in range(cols - 1 ,-1 ,-1 ):
snake_case_ : Optional[Any] = current_row[col + 1]
snake_case_ : Optional[int] = next_row[col + 1]
snake_case_ : Dict = next_row[col]
if mat[row][col] == 1:
snake_case_ : Union[str, Any] = 1 + min(__magic_name__ ,__magic_name__ ,__magic_name__ )
snake_case_ : Any = max(current_row[col] ,__magic_name__ )
else:
snake_case_ : Dict = 0
snake_case_ : Optional[Any] = current_row
return largest_square_area
if __name__ == "__main__":
import doctest
doctest.testmod()
print(largest_square_area_in_matrix_bottom_up(2, 2, [[1, 1], [1, 1]]))
| 653 | 0 |
import argparse
from typing import List
import evaluate
import numpy as np
import torch
from datasets import DatasetDict, load_dataset
# New Code #
# We'll be using StratifiedKFold for this example
from sklearn.model_selection import StratifiedKFold
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from accelerate import Accelerator, DistributedType
########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing how to perform Cross Validation,
# and builds off the `nlp_example.py` script.
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
# - single CPU or single GPU
# - multi GPUS (using PyTorch distributed mode)
# - (multi) TPUs
# - fp16 (mixed-precision) or fp32 (normal precision)
#
# To help focus on the differences in the code, building `DataLoaders`
# was refactored into its own function.
# New additions from the base script can be found quickly by
# looking for the # New Code # tags
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################
lowerCAmelCase = 1_6
lowerCAmelCase = 3_2
def _lowerCamelCase( lowercase__ , lowercase__ , lowercase__ , lowercase__ , lowercase__ = 1_6 ) -> Tuple:
'''simple docstring'''
__lowercase= AutoTokenizer.from_pretrained('bert-base-cased' )
__lowercase= DatasetDict(
{
'train': dataset['train'].select(lowercase__ ),
'validation': dataset['train'].select(lowercase__ ),
'test': dataset['validation'],
} )
def tokenize_function(lowercase__ ):
# max_length=None => use the model max length (it's actually the default)
__lowercase= tokenizer(examples['sentence1'] , examples['sentence2'] , truncation=lowercase__ , max_length=lowercase__ )
return outputs
# Apply the method we just defined to all the examples in all the splits of the dataset
# starting with the main process first:
with accelerator.main_process_first():
__lowercase= datasets.map(
lowercase__ , batched=lowercase__ , remove_columns=['idx', 'sentence1', 'sentence2'] , )
# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
# transformers library
__lowercase= tokenized_datasets.rename_column('label' , 'labels' )
def collate_fn(lowercase__ ):
# On TPU it's best to pad everything to the same length or training will be very slow.
__lowercase= 1_2_8 if accelerator.distributed_type == DistributedType.TPU else None
# When using mixed precision we want round multiples of 8/16
if accelerator.mixed_precision == "fp8":
__lowercase= 1_6
elif accelerator.mixed_precision != "no":
__lowercase= 8
else:
__lowercase= None
return tokenizer.pad(
lowercase__ , padding='longest' , max_length=lowercase__ , pad_to_multiple_of=lowercase__ , return_tensors='pt' , )
# Instantiate dataloaders.
__lowercase= DataLoader(
tokenized_datasets['train'] , shuffle=lowercase__ , collate_fn=lowercase__ , batch_size=lowercase__ )
__lowercase= DataLoader(
tokenized_datasets['validation'] , shuffle=lowercase__ , collate_fn=lowercase__ , batch_size=lowercase__ )
__lowercase= DataLoader(
tokenized_datasets['test'] , shuffle=lowercase__ , collate_fn=lowercase__ , batch_size=lowercase__ )
return train_dataloader, eval_dataloader, test_dataloader
def _lowerCamelCase( lowercase__ , lowercase__ ) -> Dict:
'''simple docstring'''
__lowercase= []
# Download the dataset
__lowercase= load_dataset('glue' , 'mrpc' )
# Create our splits
__lowercase= StratifiedKFold(n_splits=int(args.num_folds ) )
# Initialize accelerator
__lowercase= Accelerator(cpu=args.cpu , mixed_precision=args.mixed_precision )
# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
__lowercase= config["lr"]
__lowercase= int(config['num_epochs'] )
__lowercase= int(config['seed'] )
__lowercase= int(config['batch_size'] )
__lowercase= evaluate.load('glue' , 'mrpc' )
# If the batch size is too big we use gradient accumulation
__lowercase= 1
if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
__lowercase= batch_size // MAX_GPU_BATCH_SIZE
__lowercase= MAX_GPU_BATCH_SIZE
set_seed(lowercase__ )
# New Code #
# Create our folds:
__lowercase= kfold.split(np.zeros(datasets['train'].num_rows ) , datasets['train']['label'] )
__lowercase= []
# Iterate over them
for i, (train_idxs, valid_idxs) in enumerate(lowercase__ ):
__lowercase= get_fold_dataloaders(
lowercase__ , lowercase__ , lowercase__ , lowercase__ , )
# Instantiate the model (we build the model here so that the seed also control new weights initialization)
__lowercase= AutoModelForSequenceClassification.from_pretrained('bert-base-cased' , return_dict=lowercase__ )
# We could avoid this line since the accelerator is set with `device_placement=True` (default value).
# Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
# creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
__lowercase= model.to(accelerator.device )
# Instantiate optimizer
__lowercase= AdamW(params=model.parameters() , lr=lowercase__ )
# Instantiate scheduler
__lowercase= get_linear_schedule_with_warmup(
optimizer=lowercase__ , num_warmup_steps=1_0_0 , num_training_steps=(len(lowercase__ ) * num_epochs) // gradient_accumulation_steps , )
# Prepare everything
# There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
# prepare method.
__lowercase= accelerator.prepare(
lowercase__ , lowercase__ , lowercase__ , lowercase__ , lowercase__ )
# Now we train the model
for epoch in range(lowercase__ ):
model.train()
for step, batch in enumerate(lowercase__ ):
# We could avoid this line since we set the accelerator with `device_placement=True`.
batch.to(accelerator.device )
__lowercase= model(**lowercase__ )
__lowercase= outputs.loss
__lowercase= loss / gradient_accumulation_steps
accelerator.backward(lowercase__ )
if step % gradient_accumulation_steps == 0:
optimizer.step()
lr_scheduler.step()
optimizer.zero_grad()
model.eval()
for step, batch in enumerate(lowercase__ ):
# We could avoid this line since we set the accelerator with `device_placement=True`.
batch.to(accelerator.device )
with torch.no_grad():
__lowercase= model(**lowercase__ )
__lowercase= outputs.logits.argmax(dim=-1 )
__lowercase= accelerator.gather_for_metrics((predictions, batch['labels']) )
metric.add_batch(
predictions=lowercase__ , references=lowercase__ , )
__lowercase= metric.compute()
# Use accelerator.print to print only on the main process.
accelerator.print(F'epoch {epoch}:' , lowercase__ )
# New Code #
# We also run predictions on the test set at the very end
__lowercase= []
for step, batch in enumerate(lowercase__ ):
# We could avoid this line since we set the accelerator with `device_placement=True`.
batch.to(accelerator.device )
with torch.no_grad():
__lowercase= model(**lowercase__ )
__lowercase= outputs.logits
__lowercase= accelerator.gather_for_metrics((predictions, batch['labels']) )
fold_predictions.append(predictions.cpu() )
if i == 0:
# We need all of the test predictions
test_references.append(references.cpu() )
# Use accelerator.print to print only on the main process.
test_predictions.append(torch.cat(lowercase__ , dim=0 ) )
# We now need to release all our memory and get rid of the current model, optimizer, etc
accelerator.free_memory()
# New Code #
# Finally we check the accuracy of our folded results:
__lowercase= torch.cat(lowercase__ , dim=0 )
__lowercase= torch.stack(lowercase__ , dim=0 ).sum(dim=0 ).div(int(args.num_folds ) ).argmax(dim=-1 )
__lowercase= metric.compute(predictions=lowercase__ , references=lowercase__ )
accelerator.print('Average test metrics from all folds:' , lowercase__ )
def _lowerCamelCase( ) -> List[Any]:
'''simple docstring'''
__lowercase= argparse.ArgumentParser(description='Simple example of training script.' )
parser.add_argument(
'--mixed_precision' , type=lowercase__ , default=lowercase__ , choices=['no', 'fp16', 'bf16', 'fp8'] , help='Whether to use mixed precision. Choose'
'between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10.'
'and an Nvidia Ampere GPU.' , )
parser.add_argument('--cpu' , action='store_true' , help='If passed, will train on the CPU.' )
# New Code #
parser.add_argument('--num_folds' , type=lowercase__ , default=3 , help='The number of splits to perform across the dataset' )
__lowercase= parser.parse_args()
__lowercase= {"lr": 2E-5, "num_epochs": 3, "seed": 4_2, "batch_size": 1_6}
training_function(lowercase__ , lowercase__ )
if __name__ == "__main__":
main()
| 230 |
'''simple docstring'''
import os
import zipfile
import requests
from get_ci_error_statistics import download_artifact, get_artifacts_links
def __UpperCAmelCase ( __magic_name__ ,__magic_name__=7 )-> Tuple:
"""simple docstring"""
snake_case_ : List[str] = None
if token is not None:
snake_case_ : List[str] = {"Accept": "application/vnd.github+json", "Authorization": F'''Bearer {token}'''}
# The id of a workflow (not of a workflow run)
snake_case_ : Dict = "636036"
snake_case_ : List[str] = F'''https://api.github.com/repos/huggingface/transformers/actions/workflows/{workflow_id}/runs'''
# On `main` branch + event being `schedule` + not returning PRs + only `num_runs` results
url += F'''?branch=main&event=schedule&exclude_pull_requests=true&per_page={num_runs}'''
snake_case_ : Optional[Any] = requests.get(__magic_name__ ,headers=__magic_name__ ).json()
return result["workflow_runs"]
def __UpperCAmelCase ( __magic_name__ )-> Union[str, Any]:
"""simple docstring"""
snake_case_ : str = get_daily_ci_runs(__magic_name__ )
snake_case_ : Optional[int] = None
for workflow_run in workflow_runs:
if workflow_run["status"] == "completed":
snake_case_ : Dict = workflow_run["id"]
break
return workflow_run_id
def __UpperCAmelCase ( __magic_name__ ,__magic_name__ ,__magic_name__ )-> List[Any]:
"""simple docstring"""
snake_case_ : Optional[Any] = get_last_daily_ci_runs(__magic_name__ )
if workflow_run_id is not None:
snake_case_ : Union[str, Any] = get_artifacts_links(worflow_run_id=__magic_name__ ,token=__magic_name__ )
for artifact_name in artifact_names:
if artifact_name in artifacts_links:
snake_case_ : Union[str, Any] = artifacts_links[artifact_name]
download_artifact(
artifact_name=__magic_name__ ,artifact_url=__magic_name__ ,output_dir=__magic_name__ ,token=__magic_name__ )
def __UpperCAmelCase ( __magic_name__ ,__magic_name__ ,__magic_name__ )-> List[Any]:
"""simple docstring"""
get_last_daily_ci_artifacts(__magic_name__ ,__magic_name__ ,__magic_name__ )
snake_case_ : Union[str, Any] = {}
for artifact_name in artifact_names:
snake_case_ : Any = os.path.join(__magic_name__ ,F'''{artifact_name}.zip''' )
if os.path.isfile(__magic_name__ ):
snake_case_ : Tuple = {}
with zipfile.ZipFile(__magic_name__ ) as z:
for filename in z.namelist():
if not os.path.isdir(__magic_name__ ):
# read the file
with z.open(__magic_name__ ) as f:
snake_case_ : Optional[Any] = f.read().decode("UTF-8" )
return results
| 653 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import _LazyModule
lowerCAmelCase : int = {'''tokenization_bertweet''': ['''BertweetTokenizer''']}
if TYPE_CHECKING:
from .tokenization_bertweet import BertweetTokenizer
else:
import sys
lowerCAmelCase : str = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 3 |
'''simple docstring'''
from string import ascii_uppercase
__lowerCamelCase : Optional[Any] = {char: i for i, char in enumerate(ascii_uppercase)}
__lowerCamelCase : List[str] = dict(enumerate(ascii_uppercase))
def __UpperCAmelCase ( __magic_name__ ,__magic_name__ )-> str:
"""simple docstring"""
snake_case_ : Tuple = len(__magic_name__ )
snake_case_ : str = 0
while True:
if x == i:
snake_case_ : List[str] = 0
if len(__magic_name__ ) == len(__magic_name__ ):
break
key += key[i]
i += 1
return key
def __UpperCAmelCase ( __magic_name__ ,__magic_name__ )-> str:
"""simple docstring"""
snake_case_ : str = ""
snake_case_ : List[Any] = 0
for letter in message:
if letter == " ":
cipher_text += " "
else:
snake_case_ : Optional[Any] = (dicta[letter] - dicta[key_new[i]]) % 26
i += 1
cipher_text += dicta[x]
return cipher_text
def __UpperCAmelCase ( __magic_name__ ,__magic_name__ )-> str:
"""simple docstring"""
snake_case_ : Dict = ""
snake_case_ : Dict = 0
for letter in cipher_text:
if letter == " ":
or_txt += " "
else:
snake_case_ : str = (dicta[letter] + dicta[key_new[i]] + 26) % 26
i += 1
or_txt += dicta[x]
return or_txt
def __UpperCAmelCase ( )-> None:
"""simple docstring"""
snake_case_ : List[str] = "THE GERMAN ATTACK"
snake_case_ : List[str] = "SECRET"
snake_case_ : Optional[int] = generate_key(__magic_name__ ,__magic_name__ )
snake_case_ : Any = cipher_text(__magic_name__ ,__magic_name__ )
print(F'''Encrypted Text = {s}''' )
print(F'''Original Text = {original_text(__magic_name__ ,__magic_name__ )}''' )
if __name__ == "__main__":
import doctest
doctest.testmod()
main()
| 653 | 0 |
'''simple docstring'''
import sys
from typing import Tuple
import numpy as np
import torch
from PIL import Image
from torch import nn
from transformers.image_utils import PILImageResampling
from utils import img_tensorize
class UpperCAmelCase :
"""simple docstring"""
def __init__( self , _snake_case , _snake_case=sys.maxsize ) -> str:
_UpperCamelCase : str = "bilinear"
_UpperCamelCase : Any = max_size
_UpperCamelCase : Dict = short_edge_length
def __call__( self , _snake_case ) -> Tuple:
_UpperCamelCase : str = []
for img in imgs:
_UpperCamelCase : Dict = img.shape[:2]
# later: provide list and randomly choose index for resize
_UpperCamelCase : str = np.random.randint(self.short_edge_length[0] , self.short_edge_length[1] + 1 )
if size == 0:
return img
_UpperCamelCase : Optional[int] = size * 1.0 / min(lowerCAmelCase__ , lowerCAmelCase__ )
if h < w:
_UpperCamelCase : Tuple = size, scale * w
else:
_UpperCamelCase : Dict = scale * h, size
if max(lowerCAmelCase__ , lowerCAmelCase__ ) > self.max_size:
_UpperCamelCase : Any = self.max_size * 1.0 / max(lowerCAmelCase__ , lowerCAmelCase__ )
_UpperCamelCase : Tuple = newh * scale
_UpperCamelCase : Optional[Any] = neww * scale
_UpperCamelCase : Dict = int(neww + 0.5 )
_UpperCamelCase : Union[str, Any] = int(newh + 0.5 )
if img.dtype == np.uinta:
_UpperCamelCase : str = Image.fromarray(lowerCAmelCase__ )
_UpperCamelCase : int = pil_image.resize((neww, newh) , PILImageResampling.BILINEAR )
_UpperCamelCase : Union[str, Any] = np.asarray(lowerCAmelCase__ )
else:
_UpperCamelCase : int = img.permute(2 , 0 , 1 ).unsqueeze(0 ) # 3, 0, 1) # hw(c) -> nchw
_UpperCamelCase : int = nn.functional.interpolate(
lowerCAmelCase__ , (newh, neww) , mode=self.interp_method , align_corners=lowerCAmelCase__ ).squeeze(0 )
img_augs.append(lowerCAmelCase__ )
return img_augs
class UpperCAmelCase :
"""simple docstring"""
def __init__( self , _snake_case ) -> Optional[Any]:
_UpperCamelCase : Dict = ResizeShortestEdge([cfg.INPUT.MIN_SIZE_TEST, cfg.INPUT.MIN_SIZE_TEST] , cfg.INPUT.MAX_SIZE_TEST )
_UpperCamelCase : List[Any] = cfg.INPUT.FORMAT
_UpperCamelCase : Union[str, Any] = cfg.SIZE_DIVISIBILITY
_UpperCamelCase : List[str] = cfg.PAD_VALUE
_UpperCamelCase : List[Any] = cfg.INPUT.MAX_SIZE_TEST
_UpperCamelCase : List[Any] = cfg.MODEL.DEVICE
_UpperCamelCase : Tuple = torch.tensor(cfg.MODEL.PIXEL_STD ).to(self.device ).view(len(cfg.MODEL.PIXEL_STD ) , 1 , 1 )
_UpperCamelCase : str = torch.tensor(cfg.MODEL.PIXEL_MEAN ).to(self.device ).view(len(cfg.MODEL.PIXEL_STD ) , 1 , 1 )
_UpperCamelCase : Dict = lambda _snake_case : (x - self.pixel_mean) / self.pixel_std
def _lowercase ( self , _snake_case ) -> int:
_UpperCamelCase : Dict = tuple(max(lowerCAmelCase__ ) for s in zip(*[img.shape for img in images] ) )
_UpperCamelCase : Any = [im.shape[-2:] for im in images]
_UpperCamelCase : Optional[int] = [
nn.functional.pad(
lowerCAmelCase__ , [0, max_size[-1] - size[1], 0, max_size[-2] - size[0]] , value=self.pad_value , )
for size, im in zip(lowerCAmelCase__ , lowerCAmelCase__ )
]
return torch.stack(lowerCAmelCase__ ), torch.tensor(lowerCAmelCase__ )
def __call__( self , _snake_case , _snake_case=False ) -> List[str]:
with torch.no_grad():
if not isinstance(lowerCAmelCase__ , lowerCAmelCase__ ):
_UpperCamelCase : Dict = [images]
if single_image:
assert len(lowerCAmelCase__ ) == 1
for i in range(len(lowerCAmelCase__ ) ):
if isinstance(images[i] , torch.Tensor ):
images.insert(lowerCAmelCase__ , images.pop(lowerCAmelCase__ ).to(self.device ).float() )
elif not isinstance(images[i] , torch.Tensor ):
images.insert(
lowerCAmelCase__ , torch.as_tensor(img_tensorize(images.pop(lowerCAmelCase__ ) , input_format=self.input_format ) )
.to(self.device )
.float() , )
# resize smallest edge
_UpperCamelCase : List[Any] = torch.tensor([im.shape[:2] for im in images] )
_UpperCamelCase : List[Any] = self.aug(lowerCAmelCase__ )
# transpose images and convert to torch tensors
# images = [torch.as_tensor(i.astype("float32")).permute(2, 0, 1).to(self.device) for i in images]
# now normalize before pad to avoid useless arithmetic
_UpperCamelCase : Any = [self.normalizer(lowerCAmelCase__ ) for x in images]
# now pad them to do the following operations
_UpperCamelCase : int = self.pad(lowerCAmelCase__ )
# Normalize
if self.size_divisibility > 0:
raise NotImplementedError()
# pad
_UpperCamelCase : Optional[Any] = torch.true_divide(lowerCAmelCase__ , lowerCAmelCase__ )
if single_image:
return images[0], sizes[0], scales_yx[0]
else:
return images, sizes, scales_yx
def snake_case__ ( UpperCamelCase ,UpperCamelCase ) -> Union[str, Any]:
boxes[:, 0::2] *= scale_yx[:, 1]
boxes[:, 1::2] *= scale_yx[:, 0]
return boxes
def snake_case__ ( UpperCamelCase ,UpperCamelCase ) -> List[str]:
assert torch.isfinite(UpperCamelCase ).all(), "Box tensor contains infinite or NaN!"
_UpperCamelCase : Optional[int] = box_size
tensor[:, 0].clamp_(min=0 ,max=UpperCamelCase )
tensor[:, 1].clamp_(min=0 ,max=UpperCamelCase )
tensor[:, 2].clamp_(min=0 ,max=UpperCamelCase )
tensor[:, 3].clamp_(min=0 ,max=UpperCamelCase )
| 683 |
'''simple docstring'''
import argparse
import os
import numpy as np
import tensorflow as tf
import torch
from transformers import BertModel
def __UpperCAmelCase ( __magic_name__ ,__magic_name__ ,__magic_name__ )-> Dict:
"""simple docstring"""
snake_case_ : Tuple = ("dense.weight", "attention.self.query", "attention.self.key", "attention.self.value")
snake_case_ : Union[str, Any] = (
("layer.", "layer_"),
("word_embeddings.weight", "word_embeddings"),
("position_embeddings.weight", "position_embeddings"),
("token_type_embeddings.weight", "token_type_embeddings"),
(".", "/"),
("LayerNorm/weight", "LayerNorm/gamma"),
("LayerNorm/bias", "LayerNorm/beta"),
("weight", "kernel"),
)
if not os.path.isdir(__magic_name__ ):
os.makedirs(__magic_name__ )
snake_case_ : str = model.state_dict()
def to_tf_var_name(__magic_name__ ):
for patt, repl in iter(__magic_name__ ):
snake_case_ : List[str] = name.replace(__magic_name__ ,__magic_name__ )
return F'''bert/{name}'''
def create_tf_var(__magic_name__ ,__magic_name__ ,__magic_name__ ):
snake_case_ : List[Any] = tf.dtypes.as_dtype(tensor.dtype )
snake_case_ : Union[str, Any] = tf.get_variable(dtype=__magic_name__ ,shape=tensor.shape ,name=__magic_name__ ,initializer=tf.zeros_initializer() )
session.run(tf.variables_initializer([tf_var] ) )
session.run(__magic_name__ )
return tf_var
tf.reset_default_graph()
with tf.Session() as session:
for var_name in state_dict:
snake_case_ : Optional[int] = to_tf_var_name(__magic_name__ )
snake_case_ : Dict = state_dict[var_name].numpy()
if any(x in var_name for x in tensors_to_transpose ):
snake_case_ : List[Any] = torch_tensor.T
snake_case_ : Union[str, Any] = create_tf_var(tensor=__magic_name__ ,name=__magic_name__ ,session=__magic_name__ )
tf.keras.backend.set_value(__magic_name__ ,__magic_name__ )
snake_case_ : List[str] = session.run(__magic_name__ )
print(F'''Successfully created {tf_name}: {np.allclose(__magic_name__ ,__magic_name__ )}''' )
snake_case_ : Any = tf.train.Saver(tf.trainable_variables() )
saver.save(__magic_name__ ,os.path.join(__magic_name__ ,model_name.replace("-" ,"_" ) + ".ckpt" ) )
def __UpperCAmelCase ( __magic_name__=None )-> Optional[Any]:
"""simple docstring"""
snake_case_ : Any = argparse.ArgumentParser()
parser.add_argument("--model_name" ,type=__magic_name__ ,required=__magic_name__ ,help="model name e.g. bert-base-uncased" )
parser.add_argument(
"--cache_dir" ,type=__magic_name__ ,default=__magic_name__ ,required=__magic_name__ ,help="Directory containing pytorch model" )
parser.add_argument("--pytorch_model_path" ,type=__magic_name__ ,required=__magic_name__ ,help="/path/to/<pytorch-model-name>.bin" )
parser.add_argument("--tf_cache_dir" ,type=__magic_name__ ,required=__magic_name__ ,help="Directory in which to save tensorflow model" )
snake_case_ : Optional[int] = parser.parse_args(__magic_name__ )
snake_case_ : Optional[int] = BertModel.from_pretrained(
pretrained_model_name_or_path=args.model_name ,state_dict=torch.load(args.pytorch_model_path ) ,cache_dir=args.cache_dir ,)
convert_pytorch_checkpoint_to_tf(model=__magic_name__ ,ckpt_dir=args.tf_cache_dir ,model_name=args.model_name )
if __name__ == "__main__":
main()
| 653 | 0 |
import unittest
from transformers import MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING, AutoTokenizer, is_vision_available
from transformers.pipelines import pipeline
from transformers.pipelines.document_question_answering import apply_tesseract
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
require_detectrona,
require_pytesseract,
require_tf,
require_torch,
require_vision,
slow,
)
from .test_pipelines_common import ANY
if is_vision_available():
from PIL import Image
from transformers.image_utils import load_image
else:
class A :
'''simple docstring'''
@staticmethod
def a_ ( *__lowerCAmelCase : List[Any] , **__lowerCAmelCase : Any ) -> Any:
"""simple docstring"""
pass
def __lowerCamelCase ( __a :int ) -> str:
"""simple docstring"""
return None
# This is a pinned image from a specific revision of a document question answering space, hosted by HuggingFace,
# so we can expect it to be available.
A : Optional[int] = (
'''https://huggingface.co/spaces/impira/docquery/resolve/2f6c96314dc84dfda62d40de9da55f2f5165d403/invoice.png'''
)
@is_pipeline_test
@require_torch
@require_vision
class A (unittest.TestCase ):
'''simple docstring'''
__lowerCamelCase : Optional[int] = MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING
@require_pytesseract
@require_vision
def a_ ( self : Optional[int] , __lowerCAmelCase : int , __lowerCAmelCase : Tuple , __lowerCAmelCase : str ) -> Tuple:
"""simple docstring"""
A__ = pipeline(
"""document-question-answering""" , model=lowerCAmelCase__ , tokenizer=lowerCAmelCase__ , image_processor=lowerCAmelCase__ )
A__ = INVOICE_URL
A__ = list(zip(*apply_tesseract(load_image(lowerCAmelCase__ ) , lowerCAmelCase__ , """""" ) ) )
A__ = "What is the placebo?"
A__ = [
{
"image": load_image(lowerCAmelCase__ ),
"question": question,
},
{
"image": image,
"question": question,
},
{
"image": image,
"question": question,
"word_boxes": word_boxes,
},
]
return dqa_pipeline, examples
def a_ ( self : Union[str, Any] , __lowerCAmelCase : Union[str, Any] , __lowerCAmelCase : Optional[int] ) -> str:
"""simple docstring"""
A__ = dqa_pipeline(lowerCAmelCase__ , top_k=2 )
self.assertEqual(
lowerCAmelCase__ , [
[
{"""score""": ANY(lowerCAmelCase__ ), """answer""": ANY(lowerCAmelCase__ ), """start""": ANY(lowerCAmelCase__ ), """end""": ANY(lowerCAmelCase__ )},
{"""score""": ANY(lowerCAmelCase__ ), """answer""": ANY(lowerCAmelCase__ ), """start""": ANY(lowerCAmelCase__ ), """end""": ANY(lowerCAmelCase__ )},
]
]
* 3 , )
@require_torch
@require_detectrona
@require_pytesseract
def a_ ( self : str ) -> List[Any]:
"""simple docstring"""
A__ = pipeline("""document-question-answering""" , model="""hf-internal-testing/tiny-random-layoutlmv2""" )
A__ = INVOICE_URL
A__ = "How many cats are there?"
A__ = [
{"score": 0.0_0_0_1, "answer": "oy 2312/2019", "start": 38, "end": 39},
{"score": 0.0_0_0_1, "answer": "oy 2312/2019 DUE", "start": 38, "end": 40},
]
A__ = dqa_pipeline(image=lowerCAmelCase__ , question=lowerCAmelCase__ , top_k=2 )
self.assertEqual(nested_simplify(lowerCAmelCase__ , decimals=4 ) , lowerCAmelCase__ )
A__ = dqa_pipeline({"""image""": image, """question""": question} , top_k=2 )
self.assertEqual(nested_simplify(lowerCAmelCase__ , decimals=4 ) , lowerCAmelCase__ )
# This image does not detect ANY text in it, meaning layoutlmv2 should fail.
# Empty answer probably
A__ = "./tests/fixtures/tests_samples/COCO/000000039769.png"
A__ = dqa_pipeline(image=lowerCAmelCase__ , question=lowerCAmelCase__ , top_k=2 )
self.assertEqual(lowerCAmelCase__ , [] )
# We can optionnally pass directly the words and bounding boxes
A__ = "./tests/fixtures/tests_samples/COCO/000000039769.png"
A__ = []
A__ = []
A__ = dqa_pipeline(image=lowerCAmelCase__ , question=lowerCAmelCase__ , words=lowerCAmelCase__ , boxes=lowerCAmelCase__ , top_k=2 )
self.assertEqual(lowerCAmelCase__ , [] )
@slow
@require_torch
@require_detectrona
@require_pytesseract
def a_ ( self : Any ) -> List[Any]:
"""simple docstring"""
A__ = pipeline(
"""document-question-answering""" , model="""tiennvcs/layoutlmv2-base-uncased-finetuned-docvqa""" , revision="""9977165""" , )
A__ = INVOICE_URL
A__ = "What is the invoice number?"
A__ = dqa_pipeline(image=lowerCAmelCase__ , question=lowerCAmelCase__ , top_k=2 )
self.assertEqual(
nested_simplify(lowerCAmelCase__ , decimals=4 ) , [
{"""score""": 0.9_9_4_4, """answer""": """us-001""", """start""": 16, """end""": 16},
{"""score""": 0.0_0_0_9, """answer""": """us-001""", """start""": 16, """end""": 16},
] , )
A__ = dqa_pipeline({"""image""": image, """question""": question} , top_k=2 )
self.assertEqual(
nested_simplify(lowerCAmelCase__ , decimals=4 ) , [
{"""score""": 0.9_9_4_4, """answer""": """us-001""", """start""": 16, """end""": 16},
{"""score""": 0.0_0_0_9, """answer""": """us-001""", """start""": 16, """end""": 16},
] , )
A__ = dqa_pipeline(
[{"""image""": image, """question""": question}, {"""image""": image, """question""": question}] , top_k=2 )
self.assertEqual(
nested_simplify(lowerCAmelCase__ , decimals=4 ) , [
[
{"""score""": 0.9_9_4_4, """answer""": """us-001""", """start""": 16, """end""": 16},
{"""score""": 0.0_0_0_9, """answer""": """us-001""", """start""": 16, """end""": 16},
],
]
* 2 , )
@slow
@require_torch
@require_detectrona
@require_pytesseract
def a_ ( self : str ) -> Any:
"""simple docstring"""
A__ = pipeline(
"""document-question-answering""" , model="""tiennvcs/layoutlmv2-base-uncased-finetuned-docvqa""" , revision="""9977165""" , max_seq_len=50 , )
A__ = INVOICE_URL
A__ = "What is the invoice number?"
A__ = dqa_pipeline(image=lowerCAmelCase__ , question=lowerCAmelCase__ , top_k=2 )
self.assertEqual(
nested_simplify(lowerCAmelCase__ , decimals=4 ) , [
{"""score""": 0.9_9_7_4, """answer""": """1110212019""", """start""": 23, """end""": 23},
{"""score""": 0.9_9_4_8, """answer""": """us-001""", """start""": 16, """end""": 16},
] , )
A__ = dqa_pipeline({"""image""": image, """question""": question} , top_k=2 )
self.assertEqual(
nested_simplify(lowerCAmelCase__ , decimals=4 ) , [
{"""score""": 0.9_9_7_4, """answer""": """1110212019""", """start""": 23, """end""": 23},
{"""score""": 0.9_9_4_8, """answer""": """us-001""", """start""": 16, """end""": 16},
] , )
A__ = dqa_pipeline(
[{"""image""": image, """question""": question}, {"""image""": image, """question""": question}] , top_k=2 )
self.assertEqual(
nested_simplify(lowerCAmelCase__ , decimals=4 ) , [
[
{"""score""": 0.9_9_7_4, """answer""": """1110212019""", """start""": 23, """end""": 23},
{"""score""": 0.9_9_4_8, """answer""": """us-001""", """start""": 16, """end""": 16},
]
]
* 2 , )
@slow
@require_torch
@require_pytesseract
@require_vision
def a_ ( self : Optional[Any] ) -> str:
"""simple docstring"""
A__ = AutoTokenizer.from_pretrained(
"""impira/layoutlm-document-qa""" , revision="""3dc6de3""" , add_prefix_space=lowerCAmelCase__ )
A__ = pipeline(
"""document-question-answering""" , model="""impira/layoutlm-document-qa""" , tokenizer=lowerCAmelCase__ , revision="""3dc6de3""" , )
A__ = INVOICE_URL
A__ = "What is the invoice number?"
A__ = dqa_pipeline(image=lowerCAmelCase__ , question=lowerCAmelCase__ , top_k=2 )
self.assertEqual(
nested_simplify(lowerCAmelCase__ , decimals=4 ) , [
{"""score""": 0.4_2_5_1, """answer""": """us-001""", """start""": 16, """end""": 16},
{"""score""": 0.0_8_1_9, """answer""": """1110212019""", """start""": 23, """end""": 23},
] , )
A__ = dqa_pipeline({"""image""": image, """question""": question} , top_k=2 )
self.assertEqual(
nested_simplify(lowerCAmelCase__ , decimals=4 ) , [
{"""score""": 0.4_2_5_1, """answer""": """us-001""", """start""": 16, """end""": 16},
{"""score""": 0.0_8_1_9, """answer""": """1110212019""", """start""": 23, """end""": 23},
] , )
A__ = dqa_pipeline(
[{"""image""": image, """question""": question}, {"""image""": image, """question""": question}] , top_k=2 )
self.assertEqual(
nested_simplify(lowerCAmelCase__ , decimals=4 ) , [
[
{"""score""": 0.4_2_5_1, """answer""": """us-001""", """start""": 16, """end""": 16},
{"""score""": 0.0_8_1_9, """answer""": """1110212019""", """start""": 23, """end""": 23},
]
]
* 2 , )
A__ = list(zip(*apply_tesseract(load_image(lowerCAmelCase__ ) , lowerCAmelCase__ , """""" ) ) )
# This model should also work if `image` is set to None
A__ = dqa_pipeline({"""image""": None, """word_boxes""": word_boxes, """question""": question} , top_k=2 )
self.assertEqual(
nested_simplify(lowerCAmelCase__ , decimals=4 ) , [
{"""score""": 0.4_2_5_1, """answer""": """us-001""", """start""": 16, """end""": 16},
{"""score""": 0.0_8_1_9, """answer""": """1110212019""", """start""": 23, """end""": 23},
] , )
@slow
@require_torch
@require_pytesseract
@require_vision
def a_ ( self : Any ) -> Dict:
"""simple docstring"""
A__ = AutoTokenizer.from_pretrained(
"""impira/layoutlm-document-qa""" , revision="""3dc6de3""" , add_prefix_space=lowerCAmelCase__ )
A__ = pipeline(
"""document-question-answering""" , model="""impira/layoutlm-document-qa""" , tokenizer=lowerCAmelCase__ , revision="""3dc6de3""" , max_seq_len=50 , )
A__ = INVOICE_URL
A__ = "What is the invoice number?"
A__ = dqa_pipeline(image=lowerCAmelCase__ , question=lowerCAmelCase__ , top_k=2 )
self.assertEqual(
nested_simplify(lowerCAmelCase__ , decimals=4 ) , [
{"""score""": 0.9_9_9_9, """answer""": """us-001""", """start""": 16, """end""": 16},
{"""score""": 0.9_9_9_8, """answer""": """us-001""", """start""": 16, """end""": 16},
] , )
A__ = dqa_pipeline(
[{"""image""": image, """question""": question}, {"""image""": image, """question""": question}] , top_k=2 )
self.assertEqual(
nested_simplify(lowerCAmelCase__ , decimals=4 ) , [
[
{"""score""": 0.9_9_9_9, """answer""": """us-001""", """start""": 16, """end""": 16},
{"""score""": 0.9_9_9_8, """answer""": """us-001""", """start""": 16, """end""": 16},
]
]
* 2 , )
A__ = list(zip(*apply_tesseract(load_image(lowerCAmelCase__ ) , lowerCAmelCase__ , """""" ) ) )
# This model should also work if `image` is set to None
A__ = dqa_pipeline({"""image""": None, """word_boxes""": word_boxes, """question""": question} , top_k=2 )
self.assertEqual(
nested_simplify(lowerCAmelCase__ , decimals=4 ) , [
{"""score""": 0.9_9_9_9, """answer""": """us-001""", """start""": 16, """end""": 16},
{"""score""": 0.9_9_9_8, """answer""": """us-001""", """start""": 16, """end""": 16},
] , )
@slow
@require_torch
def a_ ( self : int ) -> Dict:
"""simple docstring"""
A__ = pipeline(
"""document-question-answering""" , model="""naver-clova-ix/donut-base-finetuned-docvqa""" , tokenizer=AutoTokenizer.from_pretrained("""naver-clova-ix/donut-base-finetuned-docvqa""" ) , feature_extractor="""naver-clova-ix/donut-base-finetuned-docvqa""" , )
A__ = INVOICE_URL
A__ = "What is the invoice number?"
A__ = dqa_pipeline(image=lowerCAmelCase__ , question=lowerCAmelCase__ , top_k=2 )
self.assertEqual(nested_simplify(lowerCAmelCase__ , decimals=4 ) , [{"""answer""": """us-001"""}] )
@require_tf
@unittest.skip("""Document question answering not implemented in TF""" )
def a_ ( self : Optional[Any] ) -> List[str]:
"""simple docstring"""
pass
| 176 |
'''simple docstring'''
from collections import deque
from .hash_table import HashTable
class A_ (a_ ):
"""simple docstring"""
def __init__( self :List[str] , *lowerCAmelCase__ :Optional[Any] , **lowerCAmelCase__ :Dict ) -> Union[str, Any]:
'''simple docstring'''
super().__init__(*lowerCAmelCase__ , **lowerCAmelCase__ )
def _A ( self :Optional[int] , lowerCAmelCase__ :int , lowerCAmelCase__ :Optional[Any] ) -> Union[str, Any]:
'''simple docstring'''
snake_case_ : Optional[int] = deque([] ) if self.values[key] is None else self.values[key]
self.values[key].appendleft(lowerCAmelCase__ )
snake_case_ : Tuple = self.values[key]
def _A ( self :int ) -> Dict:
'''simple docstring'''
return (
sum(self.charge_factor - len(lowerCAmelCase__ ) for slot in self.values )
/ self.size_table
* self.charge_factor
)
def _A ( self :str , lowerCAmelCase__ :Optional[Any] , lowerCAmelCase__ :Tuple=None ) -> Any:
'''simple docstring'''
if not (
len(self.values[key] ) == self.charge_factor and self.values.count(lowerCAmelCase__ ) == 0
):
return key
return super()._collision_resolution(lowerCAmelCase__ , lowerCAmelCase__ )
| 653 | 0 |
"""simple docstring"""
from typing import List, Optional, Union
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class _lowerCAmelCase ( a_ ):
"""simple docstring"""
__magic_name__ :int = ["""image_processor""", """tokenizer"""]
__magic_name__ :Optional[int] = """Pix2StructImageProcessor"""
__magic_name__ :Union[str, Any] = ("""T5Tokenizer""", """T5TokenizerFast""")
def __init__( self , __UpperCAmelCase , __UpperCAmelCase ):
'''simple docstring'''
lowerCAmelCase__ :Union[str, Any] = False
super().__init__(lowerCAmelCase__ , lowerCAmelCase__ )
def __call__( self , __UpperCAmelCase=None , __UpperCAmelCase = None , __UpperCAmelCase = True , __UpperCAmelCase = False , __UpperCAmelCase = None , __UpperCAmelCase = None , __UpperCAmelCase = 2_0_4_8 , __UpperCAmelCase = 0 , __UpperCAmelCase = None , __UpperCAmelCase = None , __UpperCAmelCase = False , __UpperCAmelCase = False , __UpperCAmelCase = False , __UpperCAmelCase = False , __UpperCAmelCase = False , __UpperCAmelCase = True , __UpperCAmelCase = None , **__UpperCAmelCase , ):
'''simple docstring'''
if images is None and text is None:
raise ValueError('You have to specify either images or text.' )
# Get only text
if images is None and not self.image_processor.is_vqa:
lowerCAmelCase__ :Tuple = self.tokenizer
lowerCAmelCase__ :Any = self.tokenizer(
text=lowerCAmelCase__ , add_special_tokens=lowerCAmelCase__ , padding=lowerCAmelCase__ , truncation=lowerCAmelCase__ , max_length=lowerCAmelCase__ , stride=lowerCAmelCase__ , pad_to_multiple_of=lowerCAmelCase__ , return_attention_mask=lowerCAmelCase__ , return_overflowing_tokens=lowerCAmelCase__ , return_special_tokens_mask=lowerCAmelCase__ , return_offsets_mapping=lowerCAmelCase__ , return_token_type_ids=lowerCAmelCase__ , return_length=lowerCAmelCase__ , verbose=lowerCAmelCase__ , return_tensors=lowerCAmelCase__ , **lowerCAmelCase__ , )
return text_encoding
if not self.image_processor.is_vqa:
# add pixel_values
lowerCAmelCase__ :Optional[int] = self.image_processor(
lowerCAmelCase__ , return_tensors=lowerCAmelCase__ , max_patches=lowerCAmelCase__ , **lowerCAmelCase__ )
else:
# add pixel_values and bbox
lowerCAmelCase__ :Dict = self.image_processor(
lowerCAmelCase__ , return_tensors=lowerCAmelCase__ , max_patches=lowerCAmelCase__ , header_text=lowerCAmelCase__ , **lowerCAmelCase__ )
if text is not None and not self.image_processor.is_vqa:
lowerCAmelCase__ :Union[str, Any] = self.tokenizer(
text=lowerCAmelCase__ , add_special_tokens=lowerCAmelCase__ , padding=lowerCAmelCase__ , truncation=lowerCAmelCase__ , max_length=lowerCAmelCase__ , stride=lowerCAmelCase__ , pad_to_multiple_of=lowerCAmelCase__ , return_attention_mask=lowerCAmelCase__ , return_overflowing_tokens=lowerCAmelCase__ , return_special_tokens_mask=lowerCAmelCase__ , return_offsets_mapping=lowerCAmelCase__ , return_token_type_ids=lowerCAmelCase__ , return_length=lowerCAmelCase__ , verbose=lowerCAmelCase__ , return_tensors=lowerCAmelCase__ , **lowerCAmelCase__ , )
if "attention_mask" in text_encoding:
lowerCAmelCase__ :List[str] = text_encoding.pop('attention_mask' )
if "input_ids" in text_encoding:
lowerCAmelCase__ :Optional[int] = text_encoding.pop('input_ids' )
else:
lowerCAmelCase__ :List[Any] = None
if text_encoding is not None:
encoding_image_processor.update(lowerCAmelCase__ )
return encoding_image_processor
def snake_case ( self , *__UpperCAmelCase , **__UpperCAmelCase ):
'''simple docstring'''
return self.tokenizer.batch_decode(*lowerCAmelCase__ , **lowerCAmelCase__ )
def snake_case ( self , *__UpperCAmelCase , **__UpperCAmelCase ):
'''simple docstring'''
return self.tokenizer.decode(*lowerCAmelCase__ , **lowerCAmelCase__ )
@property
def snake_case ( self ):
'''simple docstring'''
lowerCAmelCase__ :Optional[int] = self.tokenizer.model_input_names
lowerCAmelCase__ :List[Any] = self.image_processor.model_input_names
return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names ) )
| 93 |
'''simple docstring'''
from collections.abc import Iterator, MutableMapping
from dataclasses import dataclass
from typing import Generic, TypeVar
__lowerCamelCase : Dict = TypeVar('''KEY''')
__lowerCamelCase : int = TypeVar('''VAL''')
@dataclass(frozen=a_ , slots=a_ )
class A_ (Generic[KEY, VAL] ):
"""simple docstring"""
a__ = 42
a__ = 42
class A_ (_Item ):
"""simple docstring"""
def __init__( self :List[Any] ) -> None:
'''simple docstring'''
super().__init__(lowerCAmelCase__ , lowerCAmelCase__ )
def __bool__( self :Optional[int] ) -> bool:
'''simple docstring'''
return False
__lowerCamelCase : Dict = _DeletedItem()
class A_ (MutableMapping[KEY, VAL] ):
"""simple docstring"""
def __init__( self :Dict , lowerCAmelCase__ :int = 8 , lowerCAmelCase__ :float = 0.7_5 ) -> None:
'''simple docstring'''
snake_case_ : Any = initial_block_size
snake_case_ : list[_Item | None] = [None] * initial_block_size
assert 0.0 < capacity_factor < 1.0
snake_case_ : Tuple = capacity_factor
snake_case_ : List[Any] = 0
def _A ( self :Tuple , lowerCAmelCase__ :KEY ) -> int:
'''simple docstring'''
return hash(lowerCAmelCase__ ) % len(self._buckets )
def _A ( self :Any , lowerCAmelCase__ :int ) -> int:
'''simple docstring'''
return (ind + 1) % len(self._buckets )
def _A ( self :str , lowerCAmelCase__ :int , lowerCAmelCase__ :KEY , lowerCAmelCase__ :VAL ) -> bool:
'''simple docstring'''
snake_case_ : Optional[int] = self._buckets[ind]
if not stored:
snake_case_ : int = _Item(lowerCAmelCase__ , lowerCAmelCase__ )
self._len += 1
return True
elif stored.key == key:
snake_case_ : Optional[int] = _Item(lowerCAmelCase__ , lowerCAmelCase__ )
return True
else:
return False
def _A ( self :int ) -> bool:
'''simple docstring'''
snake_case_ : Any = len(self._buckets ) * self._capacity_factor
return len(self ) >= int(lowerCAmelCase__ )
def _A ( self :Any ) -> bool:
'''simple docstring'''
if len(self._buckets ) <= self._initial_block_size:
return False
snake_case_ : Optional[int] = len(self._buckets ) * self._capacity_factor / 2
return len(self ) < limit
def _A ( self :Tuple , lowerCAmelCase__ :int ) -> None:
'''simple docstring'''
snake_case_ : Tuple = self._buckets
snake_case_ : int = [None] * new_size
snake_case_ : Any = 0
for item in old_buckets:
if item:
self._add_item(item.key , item.val )
def _A ( self :Optional[int] ) -> None:
'''simple docstring'''
self._resize(len(self._buckets ) * 2 )
def _A ( self :str ) -> None:
'''simple docstring'''
self._resize(len(self._buckets ) // 2 )
def _A ( self :Optional[int] , lowerCAmelCase__ :KEY ) -> Iterator[int]:
'''simple docstring'''
snake_case_ : str = self._get_bucket_index(lowerCAmelCase__ )
for _ in range(len(self._buckets ) ):
yield ind
snake_case_ : List[Any] = self._get_next_ind(lowerCAmelCase__ )
def _A ( self :Union[str, Any] , lowerCAmelCase__ :KEY , lowerCAmelCase__ :VAL ) -> None:
'''simple docstring'''
for ind in self._iterate_buckets(lowerCAmelCase__ ):
if self._try_set(lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ ):
break
def __setitem__( self :Optional[int] , lowerCAmelCase__ :KEY , lowerCAmelCase__ :VAL ) -> None:
'''simple docstring'''
if self._is_full():
self._size_up()
self._add_item(lowerCAmelCase__ , lowerCAmelCase__ )
def __delitem__( self :List[Any] , lowerCAmelCase__ :KEY ) -> None:
'''simple docstring'''
for ind in self._iterate_buckets(lowerCAmelCase__ ):
snake_case_ : int = self._buckets[ind]
if item is None:
raise KeyError(lowerCAmelCase__ )
if item is _deleted:
continue
if item.key == key:
snake_case_ : List[str] = _deleted
self._len -= 1
break
if self._is_sparse():
self._size_down()
def __getitem__( self :List[str] , lowerCAmelCase__ :KEY ) -> VAL:
'''simple docstring'''
for ind in self._iterate_buckets(lowerCAmelCase__ ):
snake_case_ : Optional[Any] = self._buckets[ind]
if item is None:
break
if item is _deleted:
continue
if item.key == key:
return item.val
raise KeyError(lowerCAmelCase__ )
def __len__( self :Optional[Any] ) -> int:
'''simple docstring'''
return self._len
def __iter__( self :List[Any] ) -> Iterator[KEY]:
'''simple docstring'''
yield from (item.key for item in self._buckets if item)
def __repr__( self :Any ) -> str:
'''simple docstring'''
snake_case_ : Dict = " ,".join(
F'''{item.key}: {item.val}''' for item in self._buckets if item )
return F'''HashMap({val_string})'''
| 653 | 0 |
'''simple docstring'''
import importlib
import json
import os
import sys
import tempfile
import unittest
from pathlib import Path
import transformers
import transformers.models.auto
from transformers.models.auto.configuration_auto import CONFIG_MAPPING, AutoConfig
from transformers.models.bert.configuration_bert import BertConfig
from transformers.models.roberta.configuration_roberta import RobertaConfig
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER, get_tests_dir
sys.path.append(str(Path(__file__).parent.parent.parent.parent / 'utils'))
from test_module.custom_configuration import CustomConfig # noqa E402
_a : Any = get_tests_dir('fixtures/dummy-config.json')
class lowercase_ ( unittest.TestCase ):
'''simple docstring'''
def snake_case_ ( self ) -> Tuple:
"""simple docstring"""
UpperCAmelCase = 0
def snake_case_ ( self ) -> int:
"""simple docstring"""
self.assertIsNotNone(transformers.models.auto.__spec__ )
self.assertIsNotNone(importlib.util.find_spec('transformers.models.auto' ) )
def snake_case_ ( self ) -> Optional[Any]:
"""simple docstring"""
UpperCAmelCase = AutoConfig.from_pretrained('bert-base-uncased' )
self.assertIsInstance(lowerCAmelCase__ , lowerCAmelCase__ )
def snake_case_ ( self ) -> Optional[Any]:
"""simple docstring"""
UpperCAmelCase = AutoConfig.from_pretrained(lowerCAmelCase__ )
self.assertIsInstance(lowerCAmelCase__ , lowerCAmelCase__ )
def snake_case_ ( self ) -> Tuple:
"""simple docstring"""
UpperCAmelCase = AutoConfig.from_pretrained(lowerCAmelCase__ )
self.assertIsInstance(lowerCAmelCase__ , lowerCAmelCase__ )
def snake_case_ ( self ) -> str:
"""simple docstring"""
UpperCAmelCase = AutoConfig.for_model('roberta' )
self.assertIsInstance(lowerCAmelCase__ , lowerCAmelCase__ )
def snake_case_ ( self ) -> List[Any]:
"""simple docstring"""
with tempfile.TemporaryDirectory() as tmp_dir:
# This model name contains bert and roberta, but roberta ends up being picked.
UpperCAmelCase = os.path.join(lowerCAmelCase__ , 'fake-roberta' )
os.makedirs(lowerCAmelCase__ , exist_ok=lowerCAmelCase__ )
with open(os.path.join(lowerCAmelCase__ , 'config.json' ) , 'w' ) as f:
f.write(json.dumps({} ) )
UpperCAmelCase = AutoConfig.from_pretrained(lowerCAmelCase__ )
self.assertEqual(type(lowerCAmelCase__ ) , lowerCAmelCase__ )
def snake_case_ ( self ) -> List[Any]:
"""simple docstring"""
try:
AutoConfig.register('custom' , lowerCAmelCase__ )
# Wrong model type will raise an error
with self.assertRaises(lowerCAmelCase__ ):
AutoConfig.register('model' , lowerCAmelCase__ )
# Trying to register something existing in the Transformers library will raise an error
with self.assertRaises(lowerCAmelCase__ ):
AutoConfig.register('bert' , lowerCAmelCase__ )
# Now that the config is registered, it can be used as any other config with the auto-API
UpperCAmelCase = CustomConfig()
with tempfile.TemporaryDirectory() as tmp_dir:
config.save_pretrained(lowerCAmelCase__ )
UpperCAmelCase = AutoConfig.from_pretrained(lowerCAmelCase__ )
self.assertIsInstance(lowerCAmelCase__ , lowerCAmelCase__ )
finally:
if "custom" in CONFIG_MAPPING._extra_content:
del CONFIG_MAPPING._extra_content["custom"]
def snake_case_ ( self ) -> Dict:
"""simple docstring"""
with self.assertRaisesRegex(
lowerCAmelCase__ , 'bert-base is not a local folder and is not a valid model identifier' ):
UpperCAmelCase = AutoConfig.from_pretrained('bert-base' )
def snake_case_ ( self ) -> Union[str, Any]:
"""simple docstring"""
with self.assertRaisesRegex(
lowerCAmelCase__ , r'aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)' ):
UpperCAmelCase = AutoConfig.from_pretrained(lowerCAmelCase__ , revision='aaaaaa' )
def snake_case_ ( self ) -> Any:
"""simple docstring"""
with self.assertRaisesRegex(
lowerCAmelCase__ , 'hf-internal-testing/no-config-test-repo does not appear to have a file named config.json.' , ):
UpperCAmelCase = AutoConfig.from_pretrained('hf-internal-testing/no-config-test-repo' )
def snake_case_ ( self ) -> Union[str, Any]:
"""simple docstring"""
with self.assertRaises(lowerCAmelCase__ ):
UpperCAmelCase = AutoConfig.from_pretrained('hf-internal-testing/test_dynamic_model' )
# If remote code is disabled, we can't load this config.
with self.assertRaises(lowerCAmelCase__ ):
UpperCAmelCase = AutoConfig.from_pretrained('hf-internal-testing/test_dynamic_model' , trust_remote_code=lowerCAmelCase__ )
UpperCAmelCase = AutoConfig.from_pretrained('hf-internal-testing/test_dynamic_model' , trust_remote_code=lowerCAmelCase__ )
self.assertEqual(config.__class__.__name__ , 'NewModelConfig' )
# Test config can be reloaded.
with tempfile.TemporaryDirectory() as tmp_dir:
config.save_pretrained(lowerCAmelCase__ )
UpperCAmelCase = AutoConfig.from_pretrained(lowerCAmelCase__ , trust_remote_code=lowerCAmelCase__ )
self.assertEqual(reloaded_config.__class__.__name__ , 'NewModelConfig' )
def snake_case_ ( self ) -> Union[str, Any]:
"""simple docstring"""
class lowercase_ ( a_ ):
'''simple docstring'''
__lowerCAmelCase : Dict = "new-model"
try:
AutoConfig.register('new-model' , lowerCAmelCase__ )
# If remote code is not set, the default is to use local
UpperCAmelCase = AutoConfig.from_pretrained('hf-internal-testing/test_dynamic_model' )
self.assertEqual(config.__class__.__name__ , 'NewModelConfigLocal' )
# If remote code is disabled, we load the local one.
UpperCAmelCase = AutoConfig.from_pretrained('hf-internal-testing/test_dynamic_model' , trust_remote_code=lowerCAmelCase__ )
self.assertEqual(config.__class__.__name__ , 'NewModelConfigLocal' )
# If remote is enabled, we load from the Hub
UpperCAmelCase = AutoConfig.from_pretrained('hf-internal-testing/test_dynamic_model' , trust_remote_code=lowerCAmelCase__ )
self.assertEqual(config.__class__.__name__ , 'NewModelConfig' )
finally:
if "new-model" in CONFIG_MAPPING._extra_content:
del CONFIG_MAPPING._extra_content["new-model"]
| 447 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__lowerCamelCase : str = logging.get_logger(__name__)
__lowerCamelCase : Tuple = {
'''bigcode/gpt_bigcode-santacoder''': '''https://huggingface.co/bigcode/gpt_bigcode-santacoder/resolve/main/config.json''',
}
class A_ (a_ ):
"""simple docstring"""
a__ = '''gpt_bigcode'''
a__ = ['''past_key_values''']
a__ = {
'''hidden_size''': '''n_embd''',
'''max_position_embeddings''': '''n_positions''',
'''num_attention_heads''': '''n_head''',
'''num_hidden_layers''': '''n_layer''',
}
def __init__( self :List[Any] , lowerCAmelCase__ :Any=50_257 , lowerCAmelCase__ :Dict=1_024 , lowerCAmelCase__ :Optional[int]=768 , lowerCAmelCase__ :Tuple=12 , lowerCAmelCase__ :int=12 , lowerCAmelCase__ :Optional[Any]=None , lowerCAmelCase__ :List[str]="gelu_pytorch_tanh" , lowerCAmelCase__ :Tuple=0.1 , lowerCAmelCase__ :Tuple=0.1 , lowerCAmelCase__ :str=0.1 , lowerCAmelCase__ :Any=1E-5 , lowerCAmelCase__ :Union[str, Any]=0.0_2 , lowerCAmelCase__ :Union[str, Any]=True , lowerCAmelCase__ :Optional[Any]=True , lowerCAmelCase__ :int=50_256 , lowerCAmelCase__ :List[str]=50_256 , lowerCAmelCase__ :List[Any]=True , lowerCAmelCase__ :str=True , lowerCAmelCase__ :int=True , **lowerCAmelCase__ :Union[str, Any] , ) -> Any:
'''simple docstring'''
snake_case_ : List[Any] = vocab_size
snake_case_ : Any = n_positions
snake_case_ : Any = n_embd
snake_case_ : Optional[Any] = n_layer
snake_case_ : List[Any] = n_head
snake_case_ : Tuple = n_inner
snake_case_ : str = activation_function
snake_case_ : Union[str, Any] = resid_pdrop
snake_case_ : Optional[Any] = embd_pdrop
snake_case_ : Any = attn_pdrop
snake_case_ : List[Any] = layer_norm_epsilon
snake_case_ : Tuple = initializer_range
snake_case_ : int = scale_attn_weights
snake_case_ : Union[str, Any] = use_cache
snake_case_ : Dict = attention_softmax_in_fpaa
snake_case_ : Any = scale_attention_softmax_in_fpaa
snake_case_ : List[str] = multi_query
snake_case_ : List[str] = bos_token_id
snake_case_ : Any = eos_token_id
super().__init__(bos_token_id=lowerCAmelCase__ , eos_token_id=lowerCAmelCase__ , **lowerCAmelCase__ )
| 653 | 0 |
from typing import TYPE_CHECKING
from ..utils import _LazyModule
__UpperCamelCase : Tuple = {
'''config''': [
'''EXTERNAL_DATA_FORMAT_SIZE_LIMIT''',
'''OnnxConfig''',
'''OnnxConfigWithPast''',
'''OnnxSeq2SeqConfigWithPast''',
'''PatchingSpec''',
],
'''convert''': ['''export''', '''validate_model_outputs'''],
'''features''': ['''FeaturesManager'''],
'''utils''': ['''ParameterFormat''', '''compute_serialized_parameters_size'''],
}
if TYPE_CHECKING:
from .config import (
EXTERNAL_DATA_FORMAT_SIZE_LIMIT,
OnnxConfig,
OnnxConfigWithPast,
OnnxSeqaSeqConfigWithPast,
PatchingSpec,
)
from .convert import export, validate_model_outputs
from .features import FeaturesManager
from .utils import ParameterFormat, compute_serialized_parameters_size
else:
import sys
__UpperCamelCase : Any = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 248 |
'''simple docstring'''
import json
import logging
import os
import socket
import git
import numpy as np
import torch
logging.basicConfig(
format='''%(asctime)s - %(levelname)s - %(name)s - PID: %(process)d - %(message)s''',
datefmt='''%m/%d/%Y %H:%M:%S''',
level=logging.INFO,
)
__lowerCamelCase : Union[str, Any] = logging.getLogger(__name__)
def __UpperCAmelCase ( __magic_name__ )-> str:
"""simple docstring"""
snake_case_ : Dict = git.Repo(search_parent_directories=__magic_name__ )
snake_case_ : Optional[int] = {
"repo_id": str(__magic_name__ ),
"repo_sha": str(repo.head.object.hexsha ),
"repo_branch": str(repo.active_branch ),
}
with open(os.path.join(__magic_name__ ,"git_log.json" ) ,"w" ) as f:
json.dump(__magic_name__ ,__magic_name__ ,indent=4 )
def __UpperCAmelCase ( __magic_name__ )-> Tuple:
"""simple docstring"""
if params.n_gpu <= 0:
snake_case_ : Any = 0
snake_case_ : Any = -1
snake_case_ : Tuple = True
snake_case_ : List[str] = False
return
assert torch.cuda.is_available()
logger.info("Initializing GPUs" )
if params.n_gpu > 1:
assert params.local_rank != -1
snake_case_ : Optional[int] = int(os.environ["WORLD_SIZE"] )
snake_case_ : int = int(os.environ["N_GPU_NODE"] )
snake_case_ : Any = int(os.environ["RANK"] )
# number of nodes / node ID
snake_case_ : Dict = params.world_size // params.n_gpu_per_node
snake_case_ : Optional[int] = params.global_rank // params.n_gpu_per_node
snake_case_ : Tuple = True
assert params.n_nodes == int(os.environ["N_NODES"] )
assert params.node_id == int(os.environ["NODE_RANK"] )
# local job (single GPU)
else:
assert params.local_rank == -1
snake_case_ : Optional[int] = 1
snake_case_ : str = 0
snake_case_ : List[Any] = 0
snake_case_ : int = 0
snake_case_ : Dict = 1
snake_case_ : Optional[Any] = 1
snake_case_ : str = False
# sanity checks
assert params.n_nodes >= 1
assert 0 <= params.node_id < params.n_nodes
assert 0 <= params.local_rank <= params.global_rank < params.world_size
assert params.world_size == params.n_nodes * params.n_gpu_per_node
# define whether this is the master process / if we are in multi-node distributed mode
snake_case_ : str = params.node_id == 0 and params.local_rank == 0
snake_case_ : str = params.n_nodes > 1
# summary
snake_case_ : str = F'''--- Global rank: {params.global_rank} - '''
logger.info(PREFIX + "Number of nodes: %i" % params.n_nodes )
logger.info(PREFIX + "Node ID : %i" % params.node_id )
logger.info(PREFIX + "Local rank : %i" % params.local_rank )
logger.info(PREFIX + "World size : %i" % params.world_size )
logger.info(PREFIX + "GPUs per node : %i" % params.n_gpu_per_node )
logger.info(PREFIX + "Master : %s" % str(params.is_master ) )
logger.info(PREFIX + "Multi-node : %s" % str(params.multi_node ) )
logger.info(PREFIX + "Multi-GPU : %s" % str(params.multi_gpu ) )
logger.info(PREFIX + "Hostname : %s" % socket.gethostname() )
# set GPU device
torch.cuda.set_device(params.local_rank )
# initialize multi-GPU
if params.multi_gpu:
logger.info("Initializing PyTorch distributed" )
torch.distributed.init_process_group(
init_method="env://" ,backend="nccl" ,)
def __UpperCAmelCase ( __magic_name__ )-> Dict:
"""simple docstring"""
np.random.seed(args.seed )
torch.manual_seed(args.seed )
if args.n_gpu > 0:
torch.cuda.manual_seed_all(args.seed )
| 653 | 0 |
'''simple docstring'''
import math
def _A ( _lowerCAmelCase ):
"""simple docstring"""
assert isinstance(_lowerCAmelCase , _lowerCAmelCase ) and (
number >= 0
), "'number' must been an int and positive"
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or not number % 2:
# Negatives, 0, 1 and all even numbers are not primes
return False
__lowercase =range(3 , int(math.sqrt(_lowerCAmelCase ) + 1 ) , 2 )
return not any(not number % i for i in odd_numbers )
def _A ( _lowerCAmelCase , _lowerCAmelCase=1 , **_lowerCAmelCase ):
"""simple docstring"""
__lowercase =factor * value
__lowercase =value
while not is_prime(_lowerCAmelCase ):
value += 1 if not ("desc" in kwargs and kwargs["desc"] is True) else -1
if value == first_value_val:
return next_prime(value + 1 , **_lowerCAmelCase )
return value
| 474 |
'''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 YolosImageProcessor
class A_ (unittest.TestCase ):
"""simple docstring"""
def __init__( self :Any , lowerCAmelCase__ :Dict , lowerCAmelCase__ :Dict=7 , lowerCAmelCase__ :Union[str, Any]=3 , lowerCAmelCase__ :List[str]=30 , lowerCAmelCase__ :List[str]=400 , lowerCAmelCase__ :Optional[Any]=True , lowerCAmelCase__ :Dict=None , lowerCAmelCase__ :str=True , lowerCAmelCase__ :Optional[int]=[0.5, 0.5, 0.5] , lowerCAmelCase__ :Optional[int]=[0.5, 0.5, 0.5] , lowerCAmelCase__ :str=True , lowerCAmelCase__ :int=1 / 255 , lowerCAmelCase__ :int=True , ) -> str:
'''simple docstring'''
snake_case_ : List[Any] = size if size is not None else {"shortest_edge": 18, "longest_edge": 1_333}
snake_case_ : Dict = parent
snake_case_ : Union[str, Any] = batch_size
snake_case_ : Optional[Any] = num_channels
snake_case_ : str = min_resolution
snake_case_ : Dict = max_resolution
snake_case_ : Optional[Any] = do_resize
snake_case_ : str = size
snake_case_ : Optional[int] = do_normalize
snake_case_ : Dict = image_mean
snake_case_ : Optional[int] = image_std
snake_case_ : List[str] = do_rescale
snake_case_ : Dict = rescale_factor
snake_case_ : str = do_pad
def _A ( self :List[Any] ) -> Dict:
'''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 _A ( self :Dict , lowerCAmelCase__ :List[str] , lowerCAmelCase__ :List[str]=False ) -> str:
'''simple docstring'''
if not batched:
snake_case_ : List[str] = image_inputs[0]
if isinstance(lowerCAmelCase__ , Image.Image ):
snake_case_, snake_case_ : int = image.size
else:
snake_case_, snake_case_ : Any = image.shape[1], image.shape[2]
if w < h:
snake_case_ : int = int(self.size["shortest_edge"] * h / w )
snake_case_ : List[Any] = self.size["shortest_edge"]
elif w > h:
snake_case_ : Optional[int] = self.size["shortest_edge"]
snake_case_ : str = int(self.size["shortest_edge"] * w / h )
else:
snake_case_ : Tuple = self.size["shortest_edge"]
snake_case_ : Dict = self.size["shortest_edge"]
else:
snake_case_ : List[str] = []
for image in image_inputs:
snake_case_, snake_case_ : Any = self.get_expected_values([image] )
expected_values.append((expected_height, expected_width) )
snake_case_ : str = max(lowerCAmelCase__ , key=lambda lowerCAmelCase__ : item[0] )[0]
snake_case_ : int = max(lowerCAmelCase__ , key=lambda lowerCAmelCase__ : item[1] )[1]
return expected_height, expected_width
@require_torch
@require_vision
class A_ (a_ , unittest.TestCase ):
"""simple docstring"""
a__ = YolosImageProcessor if is_vision_available() else None
def _A ( self :Optional[Any] ) -> str:
'''simple docstring'''
snake_case_ : int = YolosImageProcessingTester(self )
@property
def _A ( self :List[str] ) -> Any:
'''simple docstring'''
return self.image_processor_tester.prepare_image_processor_dict()
def _A ( self :List[str] ) -> Union[str, Any]:
'''simple docstring'''
snake_case_ : Tuple = self.image_processing_class(**self.image_processor_dict )
self.assertTrue(hasattr(lowerCAmelCase__ , "image_mean" ) )
self.assertTrue(hasattr(lowerCAmelCase__ , "image_std" ) )
self.assertTrue(hasattr(lowerCAmelCase__ , "do_normalize" ) )
self.assertTrue(hasattr(lowerCAmelCase__ , "do_resize" ) )
self.assertTrue(hasattr(lowerCAmelCase__ , "size" ) )
def _A ( self :List[Any] ) -> Any:
'''simple docstring'''
snake_case_ : List[Any] = self.image_processing_class.from_dict(self.image_processor_dict )
self.assertEqual(image_processor.size , {"shortest_edge": 18, "longest_edge": 1_333} )
self.assertEqual(image_processor.do_pad , lowerCAmelCase__ )
snake_case_ : Optional[int] = self.image_processing_class.from_dict(
self.image_processor_dict , size=42 , max_size=84 , pad_and_return_pixel_mask=lowerCAmelCase__ )
self.assertEqual(image_processor.size , {"shortest_edge": 42, "longest_edge": 84} )
self.assertEqual(image_processor.do_pad , lowerCAmelCase__ )
def _A ( self :List[str] ) -> int:
'''simple docstring'''
pass
def _A ( self :Optional[Any] ) -> Optional[Any]:
'''simple docstring'''
snake_case_ : Optional[int] = self.image_processing_class(**self.image_processor_dict )
# create random PIL images
snake_case_ : List[Any] = prepare_image_inputs(self.image_processor_tester , equal_resolution=lowerCAmelCase__ )
for image in image_inputs:
self.assertIsInstance(lowerCAmelCase__ , Image.Image )
# Test not batched input
snake_case_ : Optional[Any] = image_processing(image_inputs[0] , return_tensors="pt" ).pixel_values
snake_case_, snake_case_ : int = self.image_processor_tester.get_expected_values(lowerCAmelCase__ )
self.assertEqual(
encoded_images.shape , (1, self.image_processor_tester.num_channels, expected_height, expected_width) , )
# Test batched
snake_case_, snake_case_ : Any = self.image_processor_tester.get_expected_values(lowerCAmelCase__ , batched=lowerCAmelCase__ )
snake_case_ : Any = image_processing(lowerCAmelCase__ , return_tensors="pt" ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
expected_height,
expected_width,
) , )
def _A ( self :Dict ) -> Dict:
'''simple docstring'''
snake_case_ : str = self.image_processing_class(**self.image_processor_dict )
# create random numpy tensors
snake_case_ : Any = prepare_image_inputs(self.image_processor_tester , equal_resolution=lowerCAmelCase__ , numpify=lowerCAmelCase__ )
for image in image_inputs:
self.assertIsInstance(lowerCAmelCase__ , np.ndarray )
# Test not batched input
snake_case_ : Union[str, Any] = image_processing(image_inputs[0] , return_tensors="pt" ).pixel_values
snake_case_, snake_case_ : List[Any] = self.image_processor_tester.get_expected_values(lowerCAmelCase__ )
self.assertEqual(
encoded_images.shape , (1, self.image_processor_tester.num_channels, expected_height, expected_width) , )
# Test batched
snake_case_ : Tuple = image_processing(lowerCAmelCase__ , return_tensors="pt" ).pixel_values
snake_case_, snake_case_ : Dict = self.image_processor_tester.get_expected_values(lowerCAmelCase__ , batched=lowerCAmelCase__ )
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
expected_height,
expected_width,
) , )
def _A ( self :Tuple ) -> Tuple:
'''simple docstring'''
snake_case_ : List[str] = self.image_processing_class(**self.image_processor_dict )
# create random PyTorch tensors
snake_case_ : str = prepare_image_inputs(self.image_processor_tester , equal_resolution=lowerCAmelCase__ , torchify=lowerCAmelCase__ )
for image in image_inputs:
self.assertIsInstance(lowerCAmelCase__ , torch.Tensor )
# Test not batched input
snake_case_ : List[str] = image_processing(image_inputs[0] , return_tensors="pt" ).pixel_values
snake_case_, snake_case_ : Any = self.image_processor_tester.get_expected_values(lowerCAmelCase__ )
self.assertEqual(
encoded_images.shape , (1, self.image_processor_tester.num_channels, expected_height, expected_width) , )
# Test batched
snake_case_ : List[Any] = image_processing(lowerCAmelCase__ , return_tensors="pt" ).pixel_values
snake_case_, snake_case_ : Any = self.image_processor_tester.get_expected_values(lowerCAmelCase__ , batched=lowerCAmelCase__ )
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
expected_height,
expected_width,
) , )
def _A ( self :Tuple ) -> Dict:
'''simple docstring'''
snake_case_ : str = self.image_processing_class(**self.image_processor_dict )
snake_case_ : List[Any] = self.image_processing_class(do_resize=lowerCAmelCase__ , do_normalize=lowerCAmelCase__ , do_rescale=lowerCAmelCase__ )
# create random PyTorch tensors
snake_case_ : Tuple = prepare_image_inputs(self.image_processor_tester , equal_resolution=lowerCAmelCase__ , torchify=lowerCAmelCase__ )
for image in image_inputs:
self.assertIsInstance(lowerCAmelCase__ , torch.Tensor )
# Test whether the method "pad" and calling the image processor return the same tensors
snake_case_ : Tuple = image_processing_a.pad(lowerCAmelCase__ , return_tensors="pt" )
snake_case_ : Union[str, Any] = image_processing_a(lowerCAmelCase__ , return_tensors="pt" )
self.assertTrue(
torch.allclose(encoded_images_with_method["pixel_values"] , encoded_images["pixel_values"] , atol=1E-4 ) )
@slow
def _A ( self :str ) -> Any:
'''simple docstring'''
snake_case_ : List[str] = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png" )
with open("./tests/fixtures/tests_samples/COCO/coco_annotations.txt" , "r" ) as f:
snake_case_ : int = json.loads(f.read() )
snake_case_ : Optional[int] = {"image_id": 39_769, "annotations": target}
# encode them
snake_case_ : Tuple = YolosImageProcessor.from_pretrained("hustvl/yolos-small" )
snake_case_ : Dict = image_processing(images=lowerCAmelCase__ , annotations=lowerCAmelCase__ , return_tensors="pt" )
# verify pixel values
snake_case_ : Optional[int] = torch.Size([1, 3, 800, 1_066] )
self.assertEqual(encoding["pixel_values"].shape , lowerCAmelCase__ )
snake_case_ : str = torch.tensor([0.2_7_9_6, 0.3_1_3_8, 0.3_4_8_1] )
self.assertTrue(torch.allclose(encoding["pixel_values"][0, 0, 0, :3] , lowerCAmelCase__ , atol=1E-4 ) )
# verify area
snake_case_ : Dict = torch.tensor([5_8_8_7.9_6_0_0, 1_1_2_5_0.2_0_6_1, 4_8_9_3_5_3.8_4_3_8, 8_3_7_1_2_2.7_5_0_0, 1_4_7_9_6_7.5_1_5_6, 1_6_5_7_3_2.3_4_3_8] )
self.assertTrue(torch.allclose(encoding["labels"][0]["area"] , lowerCAmelCase__ ) )
# verify boxes
snake_case_ : Optional[int] = torch.Size([6, 4] )
self.assertEqual(encoding["labels"][0]["boxes"].shape , lowerCAmelCase__ )
snake_case_ : Any = torch.tensor([0.5_5_0_3, 0.2_7_6_5, 0.0_6_0_4, 0.2_2_1_5] )
self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"][0] , lowerCAmelCase__ , atol=1E-3 ) )
# verify image_id
snake_case_ : Dict = torch.tensor([39_769] )
self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"] , lowerCAmelCase__ ) )
# verify is_crowd
snake_case_ : int = torch.tensor([0, 0, 0, 0, 0, 0] )
self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"] , lowerCAmelCase__ ) )
# verify class_labels
snake_case_ : List[str] = torch.tensor([75, 75, 63, 65, 17, 17] )
self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"] , lowerCAmelCase__ ) )
# verify orig_size
snake_case_ : Any = torch.tensor([480, 640] )
self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"] , lowerCAmelCase__ ) )
# verify size
snake_case_ : List[Any] = torch.tensor([800, 1_066] )
self.assertTrue(torch.allclose(encoding["labels"][0]["size"] , lowerCAmelCase__ ) )
@slow
def _A ( self :Dict ) -> int:
'''simple docstring'''
snake_case_ : Tuple = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png" )
with open("./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt" , "r" ) as f:
snake_case_ : Optional[int] = json.loads(f.read() )
snake_case_ : Tuple = {"file_name": "000000039769.png", "image_id": 39_769, "segments_info": target}
snake_case_ : Any = pathlib.Path("./tests/fixtures/tests_samples/COCO/coco_panoptic" )
# encode them
snake_case_ : int = YolosImageProcessor(format="coco_panoptic" )
snake_case_ : Union[str, Any] = image_processing(images=lowerCAmelCase__ , annotations=lowerCAmelCase__ , masks_path=lowerCAmelCase__ , return_tensors="pt" )
# verify pixel values
snake_case_ : Optional[int] = torch.Size([1, 3, 800, 1_066] )
self.assertEqual(encoding["pixel_values"].shape , lowerCAmelCase__ )
snake_case_ : List[str] = torch.tensor([0.2_7_9_6, 0.3_1_3_8, 0.3_4_8_1] )
self.assertTrue(torch.allclose(encoding["pixel_values"][0, 0, 0, :3] , lowerCAmelCase__ , atol=1E-4 ) )
# verify area
snake_case_ : int = torch.tensor([1_4_7_9_7_9.6_8_7_5, 1_6_5_5_2_7.0_4_6_9, 4_8_4_6_3_8.5_9_3_8, 1_1_2_9_2.9_3_7_5, 5_8_7_9.6_5_6_2, 7_6_3_4.1_1_4_7] )
self.assertTrue(torch.allclose(encoding["labels"][0]["area"] , lowerCAmelCase__ ) )
# verify boxes
snake_case_ : Optional[int] = torch.Size([6, 4] )
self.assertEqual(encoding["labels"][0]["boxes"].shape , lowerCAmelCase__ )
snake_case_ : List[str] = torch.tensor([0.2_6_2_5, 0.5_4_3_7, 0.4_6_8_8, 0.8_6_2_5] )
self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"][0] , lowerCAmelCase__ , atol=1E-3 ) )
# verify image_id
snake_case_ : List[str] = torch.tensor([39_769] )
self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"] , lowerCAmelCase__ ) )
# verify is_crowd
snake_case_ : Dict = torch.tensor([0, 0, 0, 0, 0, 0] )
self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"] , lowerCAmelCase__ ) )
# verify class_labels
snake_case_ : str = torch.tensor([17, 17, 63, 75, 75, 93] )
self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"] , lowerCAmelCase__ ) )
# verify masks
snake_case_ : Any = 822_873
self.assertEqual(encoding["labels"][0]["masks"].sum().item() , lowerCAmelCase__ )
# verify orig_size
snake_case_ : int = torch.tensor([480, 640] )
self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"] , lowerCAmelCase__ ) )
# verify size
snake_case_ : Union[str, Any] = torch.tensor([800, 1_066] )
self.assertTrue(torch.allclose(encoding["labels"][0]["size"] , lowerCAmelCase__ ) )
| 653 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
lowercase_ = {
'''configuration_rag''': ['''RagConfig'''],
'''retrieval_rag''': ['''RagRetriever'''],
'''tokenization_rag''': ['''RagTokenizer'''],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowercase_ = [
'''RagModel''',
'''RagPreTrainedModel''',
'''RagSequenceForGeneration''',
'''RagTokenForGeneration''',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowercase_ = [
'''TFRagModel''',
'''TFRagPreTrainedModel''',
'''TFRagSequenceForGeneration''',
'''TFRagTokenForGeneration''',
]
if TYPE_CHECKING:
from .configuration_rag import RagConfig
from .retrieval_rag import RagRetriever
from .tokenization_rag import RagTokenizer
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_rag import RagModel, RagPreTrainedModel, RagSequenceForGeneration, RagTokenForGeneration
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_rag import (
TFRagModel,
TFRagPreTrainedModel,
TFRagSequenceForGeneration,
TFRagTokenForGeneration,
)
else:
import sys
lowercase_ = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 11 |
'''simple docstring'''
def __UpperCAmelCase ( __magic_name__ ,__magic_name__ )-> str:
"""simple docstring"""
if not isinstance(__magic_name__ ,__magic_name__ ):
raise ValueError("iterations must be defined as integers" )
if not isinstance(__magic_name__ ,__magic_name__ ) or not number >= 1:
raise ValueError(
"starting number must be\n and integer and be more than 0" )
if not iterations >= 1:
raise ValueError("Iterations must be done more than 0 times to play FizzBuzz" )
snake_case_ : Dict = ""
while number <= iterations:
if number % 3 == 0:
out += "Fizz"
if number % 5 == 0:
out += "Buzz"
if 0 not in (number % 3, number % 5):
out += str(__magic_name__ )
# print(out)
number += 1
out += " "
return out
if __name__ == "__main__":
import doctest
doctest.testmod()
| 653 | 0 |
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
EulerAncestralDiscreteScheduler,
LMSDiscreteScheduler,
PNDMScheduler,
StableDiffusionInstructPixaPixPipeline,
UNetaDConditionModel,
)
from diffusers.image_processor import VaeImageProcessor
from diffusers.utils import floats_tensor, load_image, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu
from ..pipeline_params import (
IMAGE_TO_IMAGE_IMAGE_PARAMS,
TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS,
TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin
enable_full_determinism()
class _SCREAMING_SNAKE_CASE ( a_ , a_ , a_ , unittest.TestCase ):
'''simple docstring'''
lowerCamelCase__ = StableDiffusionInstructPixaPixPipeline
lowerCamelCase__ = TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"height", "width", "cross_attention_kwargs"}
lowerCamelCase__ = TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
lowerCamelCase__ = IMAGE_TO_IMAGE_IMAGE_PARAMS
lowerCamelCase__ = IMAGE_TO_IMAGE_IMAGE_PARAMS
def _snake_case ( self : Dict ):
torch.manual_seed(0 )
SCREAMING_SNAKE_CASE = UNetaDConditionModel(
block_out_channels=(32, 64) , layers_per_block=2 , sample_size=32 , in_channels=8 , out_channels=4 , down_block_types=("DownBlock2D", "CrossAttnDownBlock2D") , up_block_types=("CrossAttnUpBlock2D", "UpBlock2D") , cross_attention_dim=32 , )
SCREAMING_SNAKE_CASE = PNDMScheduler(skip_prk_steps=lowerCAmelCase__ )
torch.manual_seed(0 )
SCREAMING_SNAKE_CASE = AutoencoderKL(
block_out_channels=[32, 64] , in_channels=3 , out_channels=3 , down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"] , up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"] , latent_channels=4 , )
torch.manual_seed(0 )
SCREAMING_SNAKE_CASE = CLIPTextConfig(
bos_token_id=0 , eos_token_id=2 , hidden_size=32 , intermediate_size=37 , layer_norm_eps=1e-05 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=1000 , )
SCREAMING_SNAKE_CASE = CLIPTextModel(lowerCAmelCase__ )
SCREAMING_SNAKE_CASE = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip" )
SCREAMING_SNAKE_CASE = {
"unet": unet,
"scheduler": scheduler,
"vae": vae,
"text_encoder": text_encoder,
"tokenizer": tokenizer,
"safety_checker": None,
"feature_extractor": None,
}
return components
def _snake_case ( self : Tuple , __lowerCamelCase : Optional[int] , __lowerCamelCase : Tuple=0 ):
SCREAMING_SNAKE_CASE = floats_tensor((1, 3, 32, 32) , rng=random.Random(lowerCAmelCase__ ) ).to(lowerCAmelCase__ )
SCREAMING_SNAKE_CASE = image.cpu().permute(0 , 2 , 3 , 1 )[0]
SCREAMING_SNAKE_CASE = Image.fromarray(np.uinta(lowerCAmelCase__ ) ).convert("RGB" )
if str(lowerCAmelCase__ ).startswith("mps" ):
SCREAMING_SNAKE_CASE = torch.manual_seed(lowerCAmelCase__ )
else:
SCREAMING_SNAKE_CASE = torch.Generator(device=lowerCAmelCase__ ).manual_seed(lowerCAmelCase__ )
SCREAMING_SNAKE_CASE = {
"prompt": "A painting of a squirrel eating a burger",
"image": image,
"generator": generator,
"num_inference_steps": 2,
"guidance_scale": 6.0,
"image_guidance_scale": 1,
"output_type": "numpy",
}
return inputs
def _snake_case ( self : Any ):
SCREAMING_SNAKE_CASE = "cpu" # ensure determinism for the device-dependent torch.Generator
SCREAMING_SNAKE_CASE = self.get_dummy_components()
SCREAMING_SNAKE_CASE = StableDiffusionInstructPixaPixPipeline(**lowerCAmelCase__ )
SCREAMING_SNAKE_CASE = sd_pipe.to(lowerCAmelCase__ )
sd_pipe.set_progress_bar_config(disable=lowerCAmelCase__ )
SCREAMING_SNAKE_CASE = self.get_dummy_inputs(lowerCAmelCase__ )
SCREAMING_SNAKE_CASE = sd_pipe(**lowerCAmelCase__ ).images
SCREAMING_SNAKE_CASE = image[0, -3:, -3:, -1]
assert image.shape == (1, 32, 32, 3)
SCREAMING_SNAKE_CASE = np.array([0.7_526, 0.3_750, 0.4_547, 0.6_117, 0.5_866, 0.5_016, 0.4_327, 0.5_642, 0.4_815] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-3
def _snake_case ( self : Optional[int] ):
SCREAMING_SNAKE_CASE = "cpu" # ensure determinism for the device-dependent torch.Generator
SCREAMING_SNAKE_CASE = self.get_dummy_components()
SCREAMING_SNAKE_CASE = StableDiffusionInstructPixaPixPipeline(**lowerCAmelCase__ )
SCREAMING_SNAKE_CASE = sd_pipe.to(lowerCAmelCase__ )
sd_pipe.set_progress_bar_config(disable=lowerCAmelCase__ )
SCREAMING_SNAKE_CASE = self.get_dummy_inputs(lowerCAmelCase__ )
SCREAMING_SNAKE_CASE = "french fries"
SCREAMING_SNAKE_CASE = sd_pipe(**lowerCAmelCase__ , negative_prompt=lowerCAmelCase__ )
SCREAMING_SNAKE_CASE = output.images
SCREAMING_SNAKE_CASE = image[0, -3:, -3:, -1]
assert image.shape == (1, 32, 32, 3)
SCREAMING_SNAKE_CASE = np.array([0.7_511, 0.3_642, 0.4_553, 0.6_236, 0.5_797, 0.5_013, 0.4_343, 0.5_611, 0.4_831] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-3
def _snake_case ( self : Tuple ):
SCREAMING_SNAKE_CASE = "cpu" # ensure determinism for the device-dependent torch.Generator
SCREAMING_SNAKE_CASE = self.get_dummy_components()
SCREAMING_SNAKE_CASE = StableDiffusionInstructPixaPixPipeline(**lowerCAmelCase__ )
SCREAMING_SNAKE_CASE = sd_pipe.to(lowerCAmelCase__ )
sd_pipe.set_progress_bar_config(disable=lowerCAmelCase__ )
SCREAMING_SNAKE_CASE = self.get_dummy_inputs(lowerCAmelCase__ )
SCREAMING_SNAKE_CASE = [inputs["prompt"]] * 2
SCREAMING_SNAKE_CASE = np.array(inputs["image"] ).astype(np.floataa ) / 255.0
SCREAMING_SNAKE_CASE = torch.from_numpy(lowerCAmelCase__ ).unsqueeze(0 ).to(lowerCAmelCase__ )
SCREAMING_SNAKE_CASE = image / 2 + 0.5
SCREAMING_SNAKE_CASE = image.permute(0 , 3 , 1 , 2 )
SCREAMING_SNAKE_CASE = image.repeat(2 , 1 , 1 , 1 )
SCREAMING_SNAKE_CASE = sd_pipe(**lowerCAmelCase__ ).images
SCREAMING_SNAKE_CASE = image[-1, -3:, -3:, -1]
assert image.shape == (2, 32, 32, 3)
SCREAMING_SNAKE_CASE = np.array([0.5_812, 0.5_748, 0.5_222, 0.5_908, 0.5_695, 0.7_174, 0.6_804, 0.5_523, 0.5_579] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-3
def _snake_case ( self : Any ):
SCREAMING_SNAKE_CASE = "cpu" # ensure determinism for the device-dependent torch.Generator
SCREAMING_SNAKE_CASE = self.get_dummy_components()
SCREAMING_SNAKE_CASE = EulerAncestralDiscreteScheduler(
beta_start=0.00_085 , beta_end=0.012 , beta_schedule="scaled_linear" )
SCREAMING_SNAKE_CASE = StableDiffusionInstructPixaPixPipeline(**lowerCAmelCase__ )
SCREAMING_SNAKE_CASE = sd_pipe.to(lowerCAmelCase__ )
sd_pipe.set_progress_bar_config(disable=lowerCAmelCase__ )
SCREAMING_SNAKE_CASE = self.get_dummy_inputs(lowerCAmelCase__ )
SCREAMING_SNAKE_CASE = sd_pipe(**lowerCAmelCase__ ).images
SCREAMING_SNAKE_CASE = image[0, -3:, -3:, -1]
SCREAMING_SNAKE_CASE = [round(lowerCAmelCase__ , 4 ) for x in image_slice.flatten().tolist()]
print(",".join([str(lowerCAmelCase__ ) for x in slice] ) )
assert image.shape == (1, 32, 32, 3)
SCREAMING_SNAKE_CASE = np.array([0.7_417, 0.3_842, 0.4_732, 0.5_776, 0.5_891, 0.5_139, 0.4_052, 0.5_673, 0.4_986] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-3
def _snake_case ( self : Tuple ):
super().test_inference_batch_single_identical(expected_max_diff=3e-3 )
def _snake_case ( self : List[Any] ):
SCREAMING_SNAKE_CASE = self.get_dummy_components()
SCREAMING_SNAKE_CASE = StableDiffusionInstructPixaPixPipeline(**lowerCAmelCase__ )
SCREAMING_SNAKE_CASE = VaeImageProcessor(do_resize=lowerCAmelCase__ , do_normalize=lowerCAmelCase__ )
SCREAMING_SNAKE_CASE = pipe.to(lowerCAmelCase__ )
pipe.set_progress_bar_config(disable=lowerCAmelCase__ )
SCREAMING_SNAKE_CASE = pipe(**self.get_dummy_inputs_by_type(lowerCAmelCase__ , input_image_type="pt" ) )[0]
SCREAMING_SNAKE_CASE = components["vae"]
SCREAMING_SNAKE_CASE = self.get_dummy_inputs_by_type(lowerCAmelCase__ , input_image_type="pt" )
for image_param in self.image_latents_params:
if image_param in inputs.keys():
SCREAMING_SNAKE_CASE = vae.encode(inputs[image_param] ).latent_dist.mode()
SCREAMING_SNAKE_CASE = pipe(**lowerCAmelCase__ )[0]
SCREAMING_SNAKE_CASE = np.abs(out - out_latents_inputs ).max()
self.assertLess(lowerCAmelCase__ , 1e-4 , "passing latents as image input generate different result from passing image" )
@slow
@require_torch_gpu
class _SCREAMING_SNAKE_CASE ( unittest.TestCase ):
'''simple docstring'''
def _snake_case ( self : int ):
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def _snake_case ( self : Optional[Any] , __lowerCamelCase : List[str]=0 ):
SCREAMING_SNAKE_CASE = torch.manual_seed(lowerCAmelCase__ )
SCREAMING_SNAKE_CASE = load_image(
"https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/stable_diffusion_pix2pix/example.jpg" )
SCREAMING_SNAKE_CASE = {
"prompt": "turn him into a cyborg",
"image": image,
"generator": generator,
"num_inference_steps": 3,
"guidance_scale": 7.5,
"image_guidance_scale": 1.0,
"output_type": "numpy",
}
return inputs
def _snake_case ( self : List[str] ):
SCREAMING_SNAKE_CASE = StableDiffusionInstructPixaPixPipeline.from_pretrained(
"timbrooks/instruct-pix2pix" , safety_checker=lowerCAmelCase__ )
pipe.to(lowerCAmelCase__ )
pipe.set_progress_bar_config(disable=lowerCAmelCase__ )
pipe.enable_attention_slicing()
SCREAMING_SNAKE_CASE = self.get_inputs()
SCREAMING_SNAKE_CASE = pipe(**lowerCAmelCase__ ).images
SCREAMING_SNAKE_CASE = image[0, -3:, -3:, -1].flatten()
assert image.shape == (1, 512, 512, 3)
SCREAMING_SNAKE_CASE = np.array([0.5_902, 0.6_015, 0.6_027, 0.5_983, 0.6_092, 0.6_061, 0.5_765, 0.5_785, 0.5_555] )
assert np.abs(expected_slice - image_slice ).max() < 1e-3
def _snake_case ( self : Optional[Any] ):
SCREAMING_SNAKE_CASE = StableDiffusionInstructPixaPixPipeline.from_pretrained(
"timbrooks/instruct-pix2pix" , safety_checker=lowerCAmelCase__ )
SCREAMING_SNAKE_CASE = LMSDiscreteScheduler.from_config(pipe.scheduler.config )
pipe.to(lowerCAmelCase__ )
pipe.set_progress_bar_config(disable=lowerCAmelCase__ )
pipe.enable_attention_slicing()
SCREAMING_SNAKE_CASE = self.get_inputs()
SCREAMING_SNAKE_CASE = pipe(**lowerCAmelCase__ ).images
SCREAMING_SNAKE_CASE = image[0, -3:, -3:, -1].flatten()
assert image.shape == (1, 512, 512, 3)
SCREAMING_SNAKE_CASE = np.array([0.6_578, 0.6_817, 0.6_972, 0.6_761, 0.6_856, 0.6_916, 0.6_428, 0.6_516, 0.6_301] )
assert np.abs(expected_slice - image_slice ).max() < 1e-3
def _snake_case ( self : Dict ):
SCREAMING_SNAKE_CASE = StableDiffusionInstructPixaPixPipeline.from_pretrained(
"timbrooks/instruct-pix2pix" , safety_checker=lowerCAmelCase__ )
SCREAMING_SNAKE_CASE = DDIMScheduler.from_config(pipe.scheduler.config )
pipe.to(lowerCAmelCase__ )
pipe.set_progress_bar_config(disable=lowerCAmelCase__ )
pipe.enable_attention_slicing()
SCREAMING_SNAKE_CASE = self.get_inputs()
SCREAMING_SNAKE_CASE = pipe(**lowerCAmelCase__ ).images
SCREAMING_SNAKE_CASE = image[0, -3:, -3:, -1].flatten()
assert image.shape == (1, 512, 512, 3)
SCREAMING_SNAKE_CASE = np.array([0.3_828, 0.3_834, 0.3_818, 0.3_792, 0.3_865, 0.3_752, 0.3_792, 0.3_847, 0.3_753] )
assert np.abs(expected_slice - image_slice ).max() < 1e-3
def _snake_case ( self : Optional[int] ):
SCREAMING_SNAKE_CASE = 0
def callback_fn(__lowerCamelCase : int , __lowerCamelCase : int , __lowerCamelCase : torch.FloatTensor ) -> None:
SCREAMING_SNAKE_CASE = True
nonlocal number_of_steps
number_of_steps += 1
if step == 1:
SCREAMING_SNAKE_CASE = latents.detach().cpu().numpy()
assert latents.shape == (1, 4, 64, 64)
SCREAMING_SNAKE_CASE = latents[0, -3:, -3:, -1]
SCREAMING_SNAKE_CASE = np.array([-0.2_463, -0.4_644, -0.9_756, 1.5_176, 1.4_414, 0.7_866, 0.9_897, 0.8_521, 0.7_983] )
assert np.abs(latents_slice.flatten() - expected_slice ).max() < 5e-2
elif step == 2:
SCREAMING_SNAKE_CASE = latents.detach().cpu().numpy()
assert latents.shape == (1, 4, 64, 64)
SCREAMING_SNAKE_CASE = latents[0, -3:, -3:, -1]
SCREAMING_SNAKE_CASE = np.array([-0.2_644, -0.4_626, -0.9_653, 1.5_176, 1.4_551, 0.7_686, 0.9_805, 0.8_452, 0.8_115] )
assert np.abs(latents_slice.flatten() - expected_slice ).max() < 5e-2
SCREAMING_SNAKE_CASE = False
SCREAMING_SNAKE_CASE = StableDiffusionInstructPixaPixPipeline.from_pretrained(
"timbrooks/instruct-pix2pix" , safety_checker=lowerCAmelCase__ , torch_dtype=torch.floataa )
SCREAMING_SNAKE_CASE = pipe.to(lowerCAmelCase__ )
pipe.set_progress_bar_config(disable=lowerCAmelCase__ )
pipe.enable_attention_slicing()
SCREAMING_SNAKE_CASE = self.get_inputs()
pipe(**lowerCAmelCase__ , callback=lowerCAmelCase__ , callback_steps=1 )
assert callback_fn.has_been_called
assert number_of_steps == 3
def _snake_case ( self : Union[str, Any] ):
torch.cuda.empty_cache()
torch.cuda.reset_max_memory_allocated()
torch.cuda.reset_peak_memory_stats()
SCREAMING_SNAKE_CASE = StableDiffusionInstructPixaPixPipeline.from_pretrained(
"timbrooks/instruct-pix2pix" , safety_checker=lowerCAmelCase__ , torch_dtype=torch.floataa )
SCREAMING_SNAKE_CASE = pipe.to(lowerCAmelCase__ )
pipe.set_progress_bar_config(disable=lowerCAmelCase__ )
pipe.enable_attention_slicing(1 )
pipe.enable_sequential_cpu_offload()
SCREAMING_SNAKE_CASE = self.get_inputs()
SCREAMING_SNAKE_CASE = pipe(**lowerCAmelCase__ )
SCREAMING_SNAKE_CASE = torch.cuda.max_memory_allocated()
# make sure that less than 2.2 GB is allocated
assert mem_bytes < 2.2 * 10**9
def _snake_case ( self : List[str] ):
SCREAMING_SNAKE_CASE = self.get_inputs()
# resize to resolution that is divisible by 8 but not 16 or 32
SCREAMING_SNAKE_CASE = inputs["image"].resize((504, 504) )
SCREAMING_SNAKE_CASE = "timbrooks/instruct-pix2pix"
SCREAMING_SNAKE_CASE = StableDiffusionInstructPixaPixPipeline.from_pretrained(
lowerCAmelCase__ , safety_checker=lowerCAmelCase__ , )
pipe.to(lowerCAmelCase__ )
pipe.set_progress_bar_config(disable=lowerCAmelCase__ )
pipe.enable_attention_slicing()
SCREAMING_SNAKE_CASE = pipe(**lowerCAmelCase__ )
SCREAMING_SNAKE_CASE = output.images[0]
SCREAMING_SNAKE_CASE = image[255:258, 383:386, -1]
assert image.shape == (504, 504, 3)
SCREAMING_SNAKE_CASE = np.array([0.2_726, 0.2_529, 0.2_664, 0.2_655, 0.2_641, 0.2_642, 0.2_591, 0.2_649, 0.2_590] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 5e-3 | 16 |
'''simple docstring'''
import argparse
import os
# New Code #
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from accelerate import Accelerator, DistributedType
from accelerate.utils import find_executable_batch_size
########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing how to ensure out-of-memory errors never
# interrupt training, and builds off the `nlp_example.py` script.
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
# - single CPU or single GPU
# - multi GPUS (using PyTorch distributed mode)
# - (multi) TPUs
# - fp16 (mixed-precision) or fp32 (normal precision)
#
# New additions from the base script can be found quickly by
# looking for the # New Code # tags
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################
__lowerCamelCase : Tuple = 16
__lowerCamelCase : Optional[int] = 32
def __UpperCAmelCase ( __magic_name__ ,__magic_name__ = 16 )-> int:
"""simple docstring"""
snake_case_ : Optional[int] = AutoTokenizer.from_pretrained("bert-base-cased" )
snake_case_ : str = load_dataset("glue" ,"mrpc" )
def tokenize_function(__magic_name__ ):
# max_length=None => use the model max length (it's actually the default)
snake_case_ : Dict = tokenizer(examples["sentence1"] ,examples["sentence2"] ,truncation=__magic_name__ ,max_length=__magic_name__ )
return outputs
# Apply the method we just defined to all the examples in all the splits of the dataset
# starting with the main process first:
with accelerator.main_process_first():
snake_case_ : Any = datasets.map(
__magic_name__ ,batched=__magic_name__ ,remove_columns=["idx", "sentence1", "sentence2"] ,)
# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
# transformers library
snake_case_ : List[Any] = tokenized_datasets.rename_column("label" ,"labels" )
def collate_fn(__magic_name__ ):
# On TPU it's best to pad everything to the same length or training will be very slow.
snake_case_ : int = 128 if accelerator.distributed_type == DistributedType.TPU else None
# When using mixed precision we want round multiples of 8/16
if accelerator.mixed_precision == "fp8":
snake_case_ : Tuple = 16
elif accelerator.mixed_precision != "no":
snake_case_ : str = 8
else:
snake_case_ : Optional[Any] = None
return tokenizer.pad(
__magic_name__ ,padding="longest" ,max_length=__magic_name__ ,pad_to_multiple_of=__magic_name__ ,return_tensors="pt" ,)
# Instantiate dataloaders.
snake_case_ : str = DataLoader(
tokenized_datasets["train"] ,shuffle=__magic_name__ ,collate_fn=__magic_name__ ,batch_size=__magic_name__ )
snake_case_ : Optional[Any] = DataLoader(
tokenized_datasets["validation"] ,shuffle=__magic_name__ ,collate_fn=__magic_name__ ,batch_size=__magic_name__ )
return train_dataloader, eval_dataloader
# For testing only
if os.environ.get('''TESTING_MOCKED_DATALOADERS''', None) == "1":
from accelerate.test_utils.training import mocked_dataloaders
__lowerCamelCase : Optional[Any] = mocked_dataloaders # noqa: F811
def __UpperCAmelCase ( __magic_name__ ,__magic_name__ )-> Dict:
"""simple docstring"""
if os.environ.get("TESTING_MOCKED_DATALOADERS" ,__magic_name__ ) == "1":
snake_case_ : List[str] = 2
# Initialize accelerator
snake_case_ : Union[str, Any] = Accelerator(cpu=args.cpu ,mixed_precision=args.mixed_precision )
# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
snake_case_ : List[str] = config["lr"]
snake_case_ : Dict = int(config["num_epochs"] )
snake_case_ : Dict = int(config["seed"] )
snake_case_ : Optional[int] = int(config["batch_size"] )
snake_case_ : Dict = evaluate.load("glue" ,"mrpc" )
# New Code #
# We now can define an inner training loop function. It should take a batch size as the only parameter,
# and build the dataloaders in there.
# It also gets our decorator
@find_executable_batch_size(starting_batch_size=__magic_name__ )
def inner_training_loop(__magic_name__ ):
# And now just move everything below under this function
# We need to bring in the Accelerator object from earlier
nonlocal accelerator
# And reset all of its attributes that could hold onto any memory:
accelerator.free_memory()
# Then we can declare the model, optimizer, and everything else:
set_seed(__magic_name__ )
# Instantiate the model (we build the model here so that the seed also control new weights initialization)
snake_case_ : Optional[int] = AutoModelForSequenceClassification.from_pretrained("bert-base-cased" ,return_dict=__magic_name__ )
# We could avoid this line since the accelerator is set with `device_placement=True` (default value).
# Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
# creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
snake_case_ : Optional[int] = model.to(accelerator.device )
# Instantiate optimizer
snake_case_ : List[Any] = AdamW(params=model.parameters() ,lr=__magic_name__ )
snake_case_, snake_case_ : int = get_dataloaders(__magic_name__ ,__magic_name__ )
# Instantiate scheduler
snake_case_ : Tuple = get_linear_schedule_with_warmup(
optimizer=__magic_name__ ,num_warmup_steps=100 ,num_training_steps=(len(__magic_name__ ) * num_epochs) ,)
# Prepare everything
# There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
# prepare method.
snake_case_, snake_case_, snake_case_, snake_case_, snake_case_ : Tuple = accelerator.prepare(
__magic_name__ ,__magic_name__ ,__magic_name__ ,__magic_name__ ,__magic_name__ )
# Now we train the model
for epoch in range(__magic_name__ ):
model.train()
for step, batch in enumerate(__magic_name__ ):
# We could avoid this line since we set the accelerator with `device_placement=True`.
batch.to(accelerator.device )
snake_case_ : int = model(**__magic_name__ )
snake_case_ : Any = outputs.loss
accelerator.backward(__magic_name__ )
optimizer.step()
lr_scheduler.step()
optimizer.zero_grad()
model.eval()
for step, batch in enumerate(__magic_name__ ):
# We could avoid this line since we set the accelerator with `device_placement=True`.
batch.to(accelerator.device )
with torch.no_grad():
snake_case_ : Union[str, Any] = model(**__magic_name__ )
snake_case_ : List[str] = outputs.logits.argmax(dim=-1 )
snake_case_, snake_case_ : Union[str, Any] = accelerator.gather_for_metrics((predictions, batch["labels"]) )
metric.add_batch(
predictions=__magic_name__ ,references=__magic_name__ ,)
snake_case_ : Tuple = metric.compute()
# Use accelerator.print to print only on the main process.
accelerator.print(F'''epoch {epoch}:''' ,__magic_name__ )
# New Code #
# And call it at the end with no arguments
# Note: You could also refactor this outside of your training loop function
inner_training_loop()
def __UpperCAmelCase ( )-> List[str]:
"""simple docstring"""
snake_case_ : List[Any] = argparse.ArgumentParser(description="Simple example of training script." )
parser.add_argument(
"--mixed_precision" ,type=__magic_name__ ,default=__magic_name__ ,choices=["no", "fp16", "bf16", "fp8"] ,help="Whether to use mixed precision. Choose"
"between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10."
"and an Nvidia Ampere GPU." ,)
parser.add_argument("--cpu" ,action="store_true" ,help="If passed, will train on the CPU." )
snake_case_ : str = parser.parse_args()
snake_case_ : Optional[int] = {"lr": 2E-5, "num_epochs": 3, "seed": 42, "batch_size": 16}
training_function(__magic_name__ ,__magic_name__ )
if __name__ == "__main__":
main()
| 653 | 0 |
'''simple docstring'''
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
EulerAncestralDiscreteScheduler,
LMSDiscreteScheduler,
PNDMScheduler,
StableDiffusionPanoramaPipeline,
UNetaDConditionModel,
)
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, skip_mps
from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin
enable_full_determinism()
@skip_mps
class _snake_case ( a_ , a_ , unittest.TestCase ):
'''simple docstring'''
__snake_case = StableDiffusionPanoramaPipeline
__snake_case = TEXT_TO_IMAGE_PARAMS
__snake_case = TEXT_TO_IMAGE_BATCH_PARAMS
__snake_case = TEXT_TO_IMAGE_IMAGE_PARAMS
__snake_case = TEXT_TO_IMAGE_IMAGE_PARAMS
def lowerCAmelCase__ ( self: List[str] ) -> List[Any]:
torch.manual_seed(0 )
__magic_name__ : Any = UNetaDConditionModel(
block_out_channels=(32, 64) , layers_per_block=1 , sample_size=32 , in_channels=4 , out_channels=4 , down_block_types=("DownBlock2D", "CrossAttnDownBlock2D") , up_block_types=("CrossAttnUpBlock2D", "UpBlock2D") , cross_attention_dim=32 , )
__magic_name__ : Optional[Any] = DDIMScheduler()
torch.manual_seed(0 )
__magic_name__ : int = AutoencoderKL(
block_out_channels=[32, 64] , in_channels=3 , out_channels=3 , down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"] , up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"] , latent_channels=4 , )
torch.manual_seed(0 )
__magic_name__ : str = CLIPTextConfig(
bos_token_id=0 , eos_token_id=2 , hidden_size=32 , intermediate_size=37 , layer_norm_eps=1E-05 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=1000 , )
__magic_name__ : Tuple = CLIPTextModel(lowerCAmelCase__ )
__magic_name__ : List[str] = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip" )
__magic_name__ : Optional[Any] = {
"unet": unet,
"scheduler": scheduler,
"vae": vae,
"text_encoder": text_encoder,
"tokenizer": tokenizer,
"safety_checker": None,
"feature_extractor": None,
}
return components
def lowerCAmelCase__ ( self: str , __UpperCamelCase: int , __UpperCamelCase: Optional[Any]=0 ) -> Optional[int]:
__magic_name__ : Optional[Any] = torch.manual_seed(lowerCAmelCase__ )
__magic_name__ : Dict = {
"prompt": "a photo of the dolomites",
"generator": generator,
# Setting height and width to None to prevent OOMs on CPU.
"height": None,
"width": None,
"num_inference_steps": 1,
"guidance_scale": 6.0,
"output_type": "numpy",
}
return inputs
def lowerCAmelCase__ ( self: Optional[int] ) -> str:
__magic_name__ : str = "cpu" # ensure determinism for the device-dependent torch.Generator
__magic_name__ : Tuple = self.get_dummy_components()
__magic_name__ : Dict = StableDiffusionPanoramaPipeline(**lowerCAmelCase__ )
__magic_name__ : List[Any] = sd_pipe.to(lowerCAmelCase__ )
sd_pipe.set_progress_bar_config(disable=lowerCAmelCase__ )
__magic_name__ : Optional[int] = self.get_dummy_inputs(lowerCAmelCase__ )
__magic_name__ : int = sd_pipe(**lowerCAmelCase__ ).images
__magic_name__ : Dict = image[0, -3:, -3:, -1]
assert image.shape == (1, 64, 64, 3)
__magic_name__ : int = np.array([0.6_1_8_6, 0.5_3_7_4, 0.4_9_1_5, 0.4_1_3_5, 0.4_1_1_4, 0.4_5_6_3, 0.5_1_2_8, 0.4_9_7_7, 0.4_7_5_7] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
def lowerCAmelCase__ ( self: Any ) -> Optional[Any]:
super().test_inference_batch_consistent(batch_sizes=[1, 2] )
def lowerCAmelCase__ ( self: List[str] ) -> Union[str, Any]:
super().test_inference_batch_single_identical(batch_size=2 , expected_max_diff=3.25E-3 )
def lowerCAmelCase__ ( self: List[str] ) -> List[str]:
__magic_name__ : Dict = "cpu" # ensure determinism for the device-dependent torch.Generator
__magic_name__ : int = self.get_dummy_components()
__magic_name__ : Tuple = StableDiffusionPanoramaPipeline(**lowerCAmelCase__ )
__magic_name__ : List[str] = sd_pipe.to(lowerCAmelCase__ )
sd_pipe.set_progress_bar_config(disable=lowerCAmelCase__ )
__magic_name__ : Optional[int] = self.get_dummy_inputs(lowerCAmelCase__ )
__magic_name__ : Optional[int] = "french fries"
__magic_name__ : Optional[Any] = sd_pipe(**lowerCAmelCase__ , negative_prompt=lowerCAmelCase__ )
__magic_name__ : List[str] = output.images
__magic_name__ : Optional[Any] = image[0, -3:, -3:, -1]
assert image.shape == (1, 64, 64, 3)
__magic_name__ : str = np.array([0.6_1_8_7, 0.5_3_7_5, 0.4_9_1_5, 0.4_1_3_6, 0.4_1_1_4, 0.4_5_6_3, 0.5_1_2_8, 0.4_9_7_6, 0.4_7_5_7] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
def lowerCAmelCase__ ( self: List[str] ) -> List[Any]:
__magic_name__ : List[Any] = "cpu" # ensure determinism for the device-dependent torch.Generator
__magic_name__ : List[Any] = self.get_dummy_components()
__magic_name__ : List[Any] = StableDiffusionPanoramaPipeline(**lowerCAmelCase__ )
__magic_name__ : int = sd_pipe.to(lowerCAmelCase__ )
sd_pipe.set_progress_bar_config(disable=lowerCAmelCase__ )
__magic_name__ : str = self.get_dummy_inputs(lowerCAmelCase__ )
__magic_name__ : Optional[int] = sd_pipe(**lowerCAmelCase__ , view_batch_size=2 )
__magic_name__ : Optional[Any] = output.images
__magic_name__ : Any = image[0, -3:, -3:, -1]
assert image.shape == (1, 64, 64, 3)
__magic_name__ : Tuple = np.array([0.6_1_8_7, 0.5_3_7_5, 0.4_9_1_5, 0.4_1_3_6, 0.4_1_1_4, 0.4_5_6_3, 0.5_1_2_8, 0.4_9_7_6, 0.4_7_5_7] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
def lowerCAmelCase__ ( self: Tuple ) -> List[str]:
__magic_name__ : Optional[int] = "cpu" # ensure determinism for the device-dependent torch.Generator
__magic_name__ : List[str] = self.get_dummy_components()
__magic_name__ : Any = EulerAncestralDiscreteScheduler(
beta_start=0.0_0_0_8_5 , beta_end=0.0_1_2 , beta_schedule="scaled_linear" )
__magic_name__ : Optional[int] = StableDiffusionPanoramaPipeline(**lowerCAmelCase__ )
__magic_name__ : Optional[int] = sd_pipe.to(lowerCAmelCase__ )
sd_pipe.set_progress_bar_config(disable=lowerCAmelCase__ )
__magic_name__ : Optional[Any] = self.get_dummy_inputs(lowerCAmelCase__ )
__magic_name__ : Union[str, Any] = sd_pipe(**lowerCAmelCase__ ).images
__magic_name__ : List[str] = image[0, -3:, -3:, -1]
assert image.shape == (1, 64, 64, 3)
__magic_name__ : int = np.array([0.4_0_2_4, 0.6_5_1_0, 0.4_9_0_1, 0.5_3_7_8, 0.5_8_1_3, 0.5_6_2_2, 0.4_7_9_5, 0.4_4_6_7, 0.4_9_5_2] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
def lowerCAmelCase__ ( self: Tuple ) -> List[str]:
__magic_name__ : Dict = "cpu" # ensure determinism for the device-dependent torch.Generator
__magic_name__ : Optional[int] = self.get_dummy_components()
__magic_name__ : Tuple = PNDMScheduler(
beta_start=0.0_0_0_8_5 , beta_end=0.0_1_2 , beta_schedule="scaled_linear" , skip_prk_steps=lowerCAmelCase__ )
__magic_name__ : str = StableDiffusionPanoramaPipeline(**lowerCAmelCase__ )
__magic_name__ : Optional[Any] = sd_pipe.to(lowerCAmelCase__ )
sd_pipe.set_progress_bar_config(disable=lowerCAmelCase__ )
__magic_name__ : Any = self.get_dummy_inputs(lowerCAmelCase__ )
__magic_name__ : Optional[int] = sd_pipe(**lowerCAmelCase__ ).images
__magic_name__ : Tuple = image[0, -3:, -3:, -1]
assert image.shape == (1, 64, 64, 3)
__magic_name__ : str = np.array([0.6_3_9_1, 0.6_2_9_1, 0.4_8_6_1, 0.5_1_3_4, 0.5_5_5_2, 0.4_5_7_8, 0.5_0_3_2, 0.5_0_2_3, 0.4_5_3_9] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
@slow
@require_torch_gpu
class _snake_case ( unittest.TestCase ):
'''simple docstring'''
def lowerCAmelCase__ ( self: List[str] ) -> Tuple:
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def lowerCAmelCase__ ( self: Tuple , __UpperCamelCase: str=0 ) -> List[Any]:
__magic_name__ : Optional[int] = torch.manual_seed(lowerCAmelCase__ )
__magic_name__ : Dict = {
"prompt": "a photo of the dolomites",
"generator": generator,
"num_inference_steps": 3,
"guidance_scale": 7.5,
"output_type": "numpy",
}
return inputs
def lowerCAmelCase__ ( self: Tuple ) -> str:
__magic_name__ : Union[str, Any] = "stabilityai/stable-diffusion-2-base"
__magic_name__ : List[Any] = DDIMScheduler.from_pretrained(lowerCAmelCase__ , subfolder="scheduler" )
__magic_name__ : Dict = StableDiffusionPanoramaPipeline.from_pretrained(lowerCAmelCase__ , scheduler=lowerCAmelCase__ , safety_checker=lowerCAmelCase__ )
pipe.to(lowerCAmelCase__ )
pipe.set_progress_bar_config(disable=lowerCAmelCase__ )
pipe.enable_attention_slicing()
__magic_name__ : Tuple = self.get_inputs()
__magic_name__ : Tuple = pipe(**lowerCAmelCase__ ).images
__magic_name__ : Any = image[0, -3:, -3:, -1].flatten()
assert image.shape == (1, 512, 2048, 3)
__magic_name__ : Any = np.array(
[
0.3_6_9_6_8_3_9_2,
0.2_7_0_2_5_3_7_2,
0.3_2_4_4_6_7_6_6,
0.2_8_3_7_9_3_8_7,
0.3_6_3_6_3_2_7_4,
0.3_0_7_3_3_3_4_7,
0.2_7_1_0_0_0_2_7,
0.2_7_0_5_4_1_2_5,
0.2_5_5_3_6_0_9_6,
] )
assert np.abs(expected_slice - image_slice ).max() < 1E-2
def lowerCAmelCase__ ( self: List[Any] ) -> Tuple:
__magic_name__ : List[Any] = StableDiffusionPanoramaPipeline.from_pretrained(
"stabilityai/stable-diffusion-2-base" , safety_checker=lowerCAmelCase__ )
__magic_name__ : Tuple = LMSDiscreteScheduler.from_config(pipe.scheduler.config )
pipe.to(lowerCAmelCase__ )
pipe.set_progress_bar_config(disable=lowerCAmelCase__ )
pipe.enable_attention_slicing()
__magic_name__ : str = self.get_inputs()
__magic_name__ : Any = pipe(**lowerCAmelCase__ ).images
__magic_name__ : int = image[0, -3:, -3:, -1].flatten()
assert image.shape == (1, 512, 2048, 3)
__magic_name__ : Tuple = np.array(
[
[
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
]
] )
assert np.abs(expected_slice - image_slice ).max() < 1E-3
def lowerCAmelCase__ ( self: Optional[Any] ) -> str:
__magic_name__ : Optional[int] = 0
def callback_fn(__UpperCamelCase: int , __UpperCamelCase: int , __UpperCamelCase: torch.FloatTensor ) -> None:
__magic_name__ : Dict = True
nonlocal number_of_steps
number_of_steps += 1
if step == 1:
__magic_name__ : Optional[int] = latents.detach().cpu().numpy()
assert latents.shape == (1, 4, 64, 256)
__magic_name__ : Optional[Any] = latents[0, -3:, -3:, -1]
__magic_name__ : int = np.array(
[
0.1_8_6_8_1_8_6_9,
0.3_3_9_0_7_8_1_6,
0.5_3_6_1_2_7_6,
0.1_4_4_3_2_8_6_5,
-0.0_2_8_5_6_6_1_1,
-0.7_3_9_4_1_1_2_3,
0.2_3_3_9_7_9_8_7,
0.4_7_3_2_2_6_8_2,
-0.3_7_8_2_3_1_6_4,
] )
assert np.abs(latents_slice.flatten() - expected_slice ).max() < 5E-2
elif step == 2:
__magic_name__ : int = latents.detach().cpu().numpy()
assert latents.shape == (1, 4, 64, 256)
__magic_name__ : Tuple = latents[0, -3:, -3:, -1]
__magic_name__ : Dict = np.array(
[
0.1_8_5_3_9_6_4_5,
0.3_3_9_8_7_2_4_8,
0.5_3_7_8_5_5_9,
0.1_4_4_3_7_1_4_2,
-0.0_2_4_5_5_2_6_1,
-0.7_3_3_8_3_1_7,
0.2_3_9_9_0_7_5_5,
0.4_7_3_5_6_2_7_2,
-0.3_7_8_6_5_0_5,
] )
assert np.abs(latents_slice.flatten() - expected_slice ).max() < 5E-2
__magic_name__ : Tuple = False
__magic_name__ : str = "stabilityai/stable-diffusion-2-base"
__magic_name__ : Any = DDIMScheduler.from_pretrained(lowerCAmelCase__ , subfolder="scheduler" )
__magic_name__ : int = StableDiffusionPanoramaPipeline.from_pretrained(lowerCAmelCase__ , scheduler=lowerCAmelCase__ , safety_checker=lowerCAmelCase__ )
__magic_name__ : Tuple = pipe.to(lowerCAmelCase__ )
pipe.set_progress_bar_config(disable=lowerCAmelCase__ )
pipe.enable_attention_slicing()
__magic_name__ : int = self.get_inputs()
pipe(**lowerCAmelCase__ , callback=lowerCAmelCase__ , callback_steps=1 )
assert callback_fn.has_been_called
assert number_of_steps == 3
def lowerCAmelCase__ ( self: List[str] ) -> Union[str, Any]:
torch.cuda.empty_cache()
torch.cuda.reset_max_memory_allocated()
torch.cuda.reset_peak_memory_stats()
__magic_name__ : Optional[int] = "stabilityai/stable-diffusion-2-base"
__magic_name__ : Dict = DDIMScheduler.from_pretrained(lowerCAmelCase__ , subfolder="scheduler" )
__magic_name__ : Any = StableDiffusionPanoramaPipeline.from_pretrained(lowerCAmelCase__ , scheduler=lowerCAmelCase__ , safety_checker=lowerCAmelCase__ )
__magic_name__ : Union[str, Any] = pipe.to(lowerCAmelCase__ )
pipe.set_progress_bar_config(disable=lowerCAmelCase__ )
pipe.enable_attention_slicing(1 )
pipe.enable_sequential_cpu_offload()
__magic_name__ : Dict = self.get_inputs()
__magic_name__ : int = pipe(**lowerCAmelCase__ )
__magic_name__ : Tuple = torch.cuda.max_memory_allocated()
# make sure that less than 5.2 GB is allocated
assert mem_bytes < 5.5 * 10**9 | 436 |
'''simple docstring'''
# Copyright 2023 The HuggingFace Inc. 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 torch
from ..models.auto import AutoModelForSequenceClassification, AutoTokenizer
from .base import PipelineTool
class A_ (a_ ):
"""simple docstring"""
a__ = '''facebook/bart-large-mnli'''
a__ = (
'''This is a tool that classifies an English text using provided labels. It takes two inputs: `text`, which '''
'''should be the text to classify, and `labels`, which should be the list of labels to use for classification. '''
'''It returns the most likely label in the list of provided `labels` for the input text.'''
)
a__ = '''text_classifier'''
a__ = AutoTokenizer
a__ = AutoModelForSequenceClassification
a__ = ['''text''', ['''text''']]
a__ = ['''text''']
def _A ( self :List[str] ) -> Union[str, Any]:
'''simple docstring'''
super().setup()
snake_case_ : Optional[int] = self.model.config
snake_case_ : Any = -1
for idx, label in config.idalabel.items():
if label.lower().startswith("entail" ):
snake_case_ : Union[str, Any] = int(lowerCAmelCase__ )
if self.entailment_id == -1:
raise ValueError("Could not determine the entailment ID from the model config, please pass it at init." )
def _A ( self :Dict , lowerCAmelCase__ :int , lowerCAmelCase__ :Tuple ) -> int:
'''simple docstring'''
snake_case_ : Tuple = labels
return self.pre_processor(
[text] * len(lowerCAmelCase__ ) , [F'''This example is {label}''' for label in labels] , return_tensors="pt" , padding="max_length" , )
def _A ( self :Any , lowerCAmelCase__ :str ) -> List[str]:
'''simple docstring'''
snake_case_ : Optional[int] = outputs.logits
snake_case_ : Tuple = torch.argmax(logits[:, 2] ).item()
return self._labels[label_id]
| 653 | 0 |
from transformers import HfArgumentParser, TensorFlowBenchmark, TensorFlowBenchmarkArguments
def _lowerCamelCase( ) -> Any:
'''simple docstring'''
__lowercase= HfArgumentParser(lowercase__ )
__lowercase= parser.parse_args_into_dataclasses()[0]
__lowercase= TensorFlowBenchmark(args=lowercase__ )
try:
__lowercase= parser.parse_args_into_dataclasses()[0]
except ValueError as e:
__lowercase= "Arg --no_{0} is no longer used, please use --no-{0} instead."
__lowercase= " ".join(str(lowercase__ ).split(' ' )[:-1] )
__lowercase= ""
__lowercase= eval(str(lowercase__ ).split(' ' )[-1] )
__lowercase= []
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(lowercase__ )
if len(lowercase__ ) > 0:
__lowercase= full_error_msg + begin_error_msg + str(lowercase__ )
raise ValueError(lowercase__ )
benchmark.run()
if __name__ == "__main__":
main()
| 230 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
is_vision_available,
)
__lowerCamelCase : Any = {'''configuration_vit''': ['''VIT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''ViTConfig''', '''ViTOnnxConfig''']}
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__lowerCamelCase : Any = ['''ViTFeatureExtractor''']
__lowerCamelCase : Any = ['''ViTImageProcessor''']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__lowerCamelCase : Optional[Any] = [
'''VIT_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''ViTForImageClassification''',
'''ViTForMaskedImageModeling''',
'''ViTModel''',
'''ViTPreTrainedModel''',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__lowerCamelCase : Union[str, Any] = [
'''TFViTForImageClassification''',
'''TFViTModel''',
'''TFViTPreTrainedModel''',
]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__lowerCamelCase : Tuple = [
'''FlaxViTForImageClassification''',
'''FlaxViTModel''',
'''FlaxViTPreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_vit import VIT_PRETRAINED_CONFIG_ARCHIVE_MAP, ViTConfig, ViTOnnxConfig
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .feature_extraction_vit import ViTFeatureExtractor
from .image_processing_vit import ViTImageProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_vit import (
VIT_PRETRAINED_MODEL_ARCHIVE_LIST,
ViTForImageClassification,
ViTForMaskedImageModeling,
ViTModel,
ViTPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_vit import TFViTForImageClassification, TFViTModel, TFViTPreTrainedModel
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_vit import FlaxViTForImageClassification, FlaxViTModel, FlaxViTPreTrainedModel
else:
import sys
__lowerCamelCase : List[str] = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 653 | 0 |
'''simple docstring'''
import inspect
import re
from hashlib import shaaaa
from typing import Dict, List
from .arrow import arrow
from .audiofolder import audiofolder
from .csv import csv
from .imagefolder import imagefolder
from .json import json
from .pandas import pandas
from .parquet import parquet
from .sql import sql # noqa F401
from .text import text
def A_( A : str):
UpperCamelCase = []
for line in lines:
UpperCamelCase = re.sub(r'#.*' , '' , A) # remove comments
if line:
filtered_lines.append(A)
UpperCamelCase = "\n".join(A)
# Make a hash from all this code
UpperCamelCase = full_str.encode('utf-8')
return shaaaa(A).hexdigest()
# get importable module names and hash for caching
lowerCAmelCase : Dict = {
'''csv''': (csv.__name__, _hash_python_lines(inspect.getsource(csv).splitlines())),
'''json''': (json.__name__, _hash_python_lines(inspect.getsource(json).splitlines())),
'''pandas''': (pandas.__name__, _hash_python_lines(inspect.getsource(pandas).splitlines())),
'''parquet''': (parquet.__name__, _hash_python_lines(inspect.getsource(parquet).splitlines())),
'''arrow''': (arrow.__name__, _hash_python_lines(inspect.getsource(arrow).splitlines())),
'''text''': (text.__name__, _hash_python_lines(inspect.getsource(text).splitlines())),
'''imagefolder''': (imagefolder.__name__, _hash_python_lines(inspect.getsource(imagefolder).splitlines())),
'''audiofolder''': (audiofolder.__name__, _hash_python_lines(inspect.getsource(audiofolder).splitlines())),
}
# Used to infer the module to use based on the data files extensions
lowerCAmelCase : Union[str, Any] = {
'''.csv''': ('''csv''', {}),
'''.tsv''': ('''csv''', {'''sep''': '''\t'''}),
'''.json''': ('''json''', {}),
'''.jsonl''': ('''json''', {}),
'''.parquet''': ('''parquet''', {}),
'''.arrow''': ('''arrow''', {}),
'''.txt''': ('''text''', {}),
}
_EXTENSION_TO_MODULE.update({ext: ('imagefolder', {}) for ext in imagefolder.ImageFolder.EXTENSIONS})
_EXTENSION_TO_MODULE.update({ext.upper(): ('imagefolder', {}) for ext in imagefolder.ImageFolder.EXTENSIONS})
_EXTENSION_TO_MODULE.update({ext: ('audiofolder', {}) for ext in audiofolder.AudioFolder.EXTENSIONS})
_EXTENSION_TO_MODULE.update({ext.upper(): ('audiofolder', {}) for ext in audiofolder.AudioFolder.EXTENSIONS})
lowerCAmelCase : str = {'''imagefolder''', '''audiofolder'''}
# Used to filter data files based on extensions given a module name
lowerCAmelCase : Dict[str, List[str]] = {}
for _ext, (_module, _) in _EXTENSION_TO_MODULE.items():
_MODULE_TO_EXTENSIONS.setdefault(_module, []).append(_ext)
_MODULE_TO_EXTENSIONS["imagefolder"].append('.zip')
_MODULE_TO_EXTENSIONS["audiofolder"].append('.zip')
| 3 |
'''simple docstring'''
import copy
import unittest
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
MODEL_FOR_MULTIPLE_CHOICE_MAPPING,
MODEL_FOR_QUESTION_ANSWERING_MAPPING,
MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
LayoutLMvaConfig,
LayoutLMvaForQuestionAnswering,
LayoutLMvaForSequenceClassification,
LayoutLMvaForTokenClassification,
LayoutLMvaModel,
)
from transformers.models.layoutlmva.modeling_layoutlmva import LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import LayoutLMvaImageProcessor
class A_ :
"""simple docstring"""
def __init__( self :Optional[Any] , lowerCAmelCase__ :List[str] , lowerCAmelCase__ :List[str]=2 , lowerCAmelCase__ :List[Any]=3 , lowerCAmelCase__ :Any=4 , lowerCAmelCase__ :List[Any]=2 , lowerCAmelCase__ :List[str]=7 , lowerCAmelCase__ :Any=True , lowerCAmelCase__ :Optional[int]=True , lowerCAmelCase__ :Optional[Any]=True , lowerCAmelCase__ :Optional[int]=True , lowerCAmelCase__ :List[str]=99 , lowerCAmelCase__ :Union[str, Any]=36 , lowerCAmelCase__ :Dict=3 , lowerCAmelCase__ :str=4 , lowerCAmelCase__ :Optional[int]=37 , lowerCAmelCase__ :Dict="gelu" , lowerCAmelCase__ :Optional[Any]=0.1 , lowerCAmelCase__ :Dict=0.1 , lowerCAmelCase__ :Optional[int]=512 , lowerCAmelCase__ :Union[str, Any]=16 , lowerCAmelCase__ :List[Any]=2 , lowerCAmelCase__ :Any=0.0_2 , lowerCAmelCase__ :Dict=6 , lowerCAmelCase__ :Optional[int]=6 , lowerCAmelCase__ :Any=3 , lowerCAmelCase__ :int=4 , lowerCAmelCase__ :int=None , lowerCAmelCase__ :Any=1_000 , ) -> Any:
'''simple docstring'''
snake_case_ : Optional[int] = parent
snake_case_ : Union[str, Any] = batch_size
snake_case_ : Optional[int] = num_channels
snake_case_ : List[Any] = image_size
snake_case_ : Optional[int] = patch_size
snake_case_ : Union[str, Any] = text_seq_length
snake_case_ : Dict = is_training
snake_case_ : Optional[Any] = use_input_mask
snake_case_ : Union[str, Any] = use_token_type_ids
snake_case_ : Dict = use_labels
snake_case_ : List[str] = vocab_size
snake_case_ : Optional[Any] = hidden_size
snake_case_ : List[str] = num_hidden_layers
snake_case_ : int = num_attention_heads
snake_case_ : List[str] = intermediate_size
snake_case_ : str = hidden_act
snake_case_ : Optional[Any] = hidden_dropout_prob
snake_case_ : Optional[int] = attention_probs_dropout_prob
snake_case_ : Union[str, Any] = max_position_embeddings
snake_case_ : List[Any] = type_vocab_size
snake_case_ : Union[str, Any] = type_sequence_label_size
snake_case_ : List[Any] = initializer_range
snake_case_ : Union[str, Any] = coordinate_size
snake_case_ : int = shape_size
snake_case_ : Tuple = num_labels
snake_case_ : List[Any] = num_choices
snake_case_ : List[str] = scope
snake_case_ : Dict = range_bbox
# LayoutLMv3's sequence length equals the number of text tokens + number of patches + 1 (we add 1 for the CLS token)
snake_case_ : str = text_seq_length
snake_case_ : Optional[int] = (image_size // patch_size) ** 2 + 1
snake_case_ : str = self.text_seq_length + self.image_seq_length
def _A ( self :Union[str, Any] ) -> Tuple:
'''simple docstring'''
snake_case_ : Dict = ids_tensor([self.batch_size, self.text_seq_length] , self.vocab_size )
snake_case_ : str = ids_tensor([self.batch_size, self.text_seq_length, 4] , self.range_bbox )
# Ensure that bbox is legal
for i in range(bbox.shape[0] ):
for j in range(bbox.shape[1] ):
if bbox[i, j, 3] < bbox[i, j, 1]:
snake_case_ : Optional[Any] = bbox[i, j, 3]
snake_case_ : Any = bbox[i, j, 1]
snake_case_ : Tuple = t
if bbox[i, j, 2] < bbox[i, j, 0]:
snake_case_ : str = bbox[i, j, 2]
snake_case_ : Dict = bbox[i, j, 0]
snake_case_ : Union[str, Any] = t
snake_case_ : int = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
snake_case_ : Dict = None
if self.use_input_mask:
snake_case_ : str = random_attention_mask([self.batch_size, self.text_seq_length] )
snake_case_ : Any = None
if self.use_token_type_ids:
snake_case_ : List[str] = ids_tensor([self.batch_size, self.text_seq_length] , self.type_vocab_size )
snake_case_ : Union[str, Any] = None
snake_case_ : str = None
if self.use_labels:
snake_case_ : List[Any] = ids_tensor([self.batch_size] , self.type_sequence_label_size )
snake_case_ : List[Any] = ids_tensor([self.batch_size, self.text_seq_length] , self.num_labels )
snake_case_ : str = LayoutLMvaConfig(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , initializer_range=self.initializer_range , coordinate_size=self.coordinate_size , shape_size=self.shape_size , input_size=self.image_size , patch_size=self.patch_size , )
return config, input_ids, bbox, pixel_values, token_type_ids, input_mask, sequence_labels, token_labels
def _A ( self :Dict , lowerCAmelCase__ :Dict , lowerCAmelCase__ :List[str] , lowerCAmelCase__ :str , lowerCAmelCase__ :List[Any] , lowerCAmelCase__ :Optional[Any] , lowerCAmelCase__ :List[str] , lowerCAmelCase__ :Dict , lowerCAmelCase__ :List[str] ) -> Optional[Any]:
'''simple docstring'''
snake_case_ : Tuple = LayoutLMvaModel(config=lowerCAmelCase__ )
model.to(lowerCAmelCase__ )
model.eval()
# text + image
snake_case_ : Tuple = model(lowerCAmelCase__ , pixel_values=lowerCAmelCase__ )
snake_case_ : Optional[int] = model(
lowerCAmelCase__ , bbox=lowerCAmelCase__ , pixel_values=lowerCAmelCase__ , attention_mask=lowerCAmelCase__ , token_type_ids=lowerCAmelCase__ )
snake_case_ : Optional[int] = model(lowerCAmelCase__ , bbox=lowerCAmelCase__ , pixel_values=lowerCAmelCase__ , token_type_ids=lowerCAmelCase__ )
snake_case_ : int = model(lowerCAmelCase__ , bbox=lowerCAmelCase__ , pixel_values=lowerCAmelCase__ )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
# text only
snake_case_ : List[Any] = model(lowerCAmelCase__ )
self.parent.assertEqual(
result.last_hidden_state.shape , (self.batch_size, self.text_seq_length, self.hidden_size) )
# image only
snake_case_ : Union[str, Any] = model(pixel_values=lowerCAmelCase__ )
self.parent.assertEqual(
result.last_hidden_state.shape , (self.batch_size, self.image_seq_length, self.hidden_size) )
def _A ( self :str , lowerCAmelCase__ :str , lowerCAmelCase__ :Tuple , lowerCAmelCase__ :Union[str, Any] , lowerCAmelCase__ :Union[str, Any] , lowerCAmelCase__ :Optional[Any] , lowerCAmelCase__ :List[str] , lowerCAmelCase__ :List[str] , lowerCAmelCase__ :Tuple ) -> List[Any]:
'''simple docstring'''
snake_case_ : str = self.num_labels
snake_case_ : List[Any] = LayoutLMvaForSequenceClassification(lowerCAmelCase__ )
model.to(lowerCAmelCase__ )
model.eval()
snake_case_ : Optional[int] = model(
lowerCAmelCase__ , bbox=lowerCAmelCase__ , pixel_values=lowerCAmelCase__ , attention_mask=lowerCAmelCase__ , token_type_ids=lowerCAmelCase__ , labels=lowerCAmelCase__ , )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def _A ( self :Union[str, Any] , lowerCAmelCase__ :List[Any] , lowerCAmelCase__ :int , lowerCAmelCase__ :List[str] , lowerCAmelCase__ :Tuple , lowerCAmelCase__ :str , lowerCAmelCase__ :Optional[int] , lowerCAmelCase__ :Any , lowerCAmelCase__ :Union[str, Any] ) -> str:
'''simple docstring'''
snake_case_ : Optional[int] = self.num_labels
snake_case_ : str = LayoutLMvaForTokenClassification(config=lowerCAmelCase__ )
model.to(lowerCAmelCase__ )
model.eval()
snake_case_ : List[Any] = model(
lowerCAmelCase__ , bbox=lowerCAmelCase__ , pixel_values=lowerCAmelCase__ , attention_mask=lowerCAmelCase__ , token_type_ids=lowerCAmelCase__ , labels=lowerCAmelCase__ , )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.text_seq_length, self.num_labels) )
def _A ( self :Optional[int] , lowerCAmelCase__ :Dict , lowerCAmelCase__ :str , lowerCAmelCase__ :Dict , lowerCAmelCase__ :Optional[int] , lowerCAmelCase__ :str , lowerCAmelCase__ :int , lowerCAmelCase__ :Union[str, Any] , lowerCAmelCase__ :str ) -> Tuple:
'''simple docstring'''
snake_case_ : List[str] = LayoutLMvaForQuestionAnswering(config=lowerCAmelCase__ )
model.to(lowerCAmelCase__ )
model.eval()
snake_case_ : List[Any] = model(
lowerCAmelCase__ , bbox=lowerCAmelCase__ , pixel_values=lowerCAmelCase__ , attention_mask=lowerCAmelCase__ , token_type_ids=lowerCAmelCase__ , start_positions=lowerCAmelCase__ , end_positions=lowerCAmelCase__ , )
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 :int ) -> Union[str, Any]:
'''simple docstring'''
snake_case_ : Dict = self.prepare_config_and_inputs()
(
(
snake_case_
), (
snake_case_
), (
snake_case_
), (
snake_case_
), (
snake_case_
), (
snake_case_
), (
snake_case_
), (
snake_case_
),
) : Optional[Any] = config_and_inputs
snake_case_ : Tuple = {
"input_ids": input_ids,
"bbox": bbox,
"pixel_values": pixel_values,
"token_type_ids": token_type_ids,
"attention_mask": input_mask,
}
return config, inputs_dict
@require_torch
class A_ (a_ , a_ , unittest.TestCase ):
"""simple docstring"""
a__ = False
a__ = False
a__ = False
a__ = (
(
LayoutLMvaModel,
LayoutLMvaForSequenceClassification,
LayoutLMvaForTokenClassification,
LayoutLMvaForQuestionAnswering,
)
if is_torch_available()
else ()
)
a__ = (
{'''document-question-answering''': LayoutLMvaForQuestionAnswering, '''feature-extraction''': LayoutLMvaModel}
if is_torch_available()
else {}
)
def _A ( self :Optional[Any] , lowerCAmelCase__ :Tuple , lowerCAmelCase__ :Tuple , lowerCAmelCase__ :List[Any] , lowerCAmelCase__ :Optional[Any] , lowerCAmelCase__ :List[Any] ) -> List[str]:
'''simple docstring'''
return True
def _A ( self :List[Any] ) -> str:
'''simple docstring'''
snake_case_ : Tuple = LayoutLMvaModelTester(self )
snake_case_ : Optional[int] = ConfigTester(self , config_class=lowerCAmelCase__ , hidden_size=37 )
def _A ( self :Tuple , lowerCAmelCase__ :Optional[int] , lowerCAmelCase__ :Dict , lowerCAmelCase__ :Union[str, Any]=False ) -> Any:
'''simple docstring'''
snake_case_ : List[str] = copy.deepcopy(lowerCAmelCase__ )
if model_class in get_values(lowerCAmelCase__ ):
snake_case_ : Optional[Any] = {
k: v.unsqueeze(1 ).expand(-1 , self.model_tester.num_choices , -1 ).contiguous()
if isinstance(lowerCAmelCase__ , torch.Tensor ) and v.ndim > 1
else v
for k, v in inputs_dict.items()
}
if return_labels:
if model_class in get_values(lowerCAmelCase__ ):
snake_case_ : Union[str, Any] = torch.ones(self.model_tester.batch_size , dtype=torch.long , device=lowerCAmelCase__ )
elif model_class in get_values(lowerCAmelCase__ ):
snake_case_ : List[Any] = torch.zeros(
self.model_tester.batch_size , dtype=torch.long , device=lowerCAmelCase__ )
snake_case_ : str = torch.zeros(
self.model_tester.batch_size , dtype=torch.long , device=lowerCAmelCase__ )
elif model_class in [
*get_values(lowerCAmelCase__ ),
]:
snake_case_ : Union[str, Any] = torch.zeros(
self.model_tester.batch_size , dtype=torch.long , device=lowerCAmelCase__ )
elif model_class in [
*get_values(lowerCAmelCase__ ),
]:
snake_case_ : List[str] = torch.zeros(
(self.model_tester.batch_size, self.model_tester.text_seq_length) , dtype=torch.long , device=lowerCAmelCase__ , )
return inputs_dict
def _A ( self :Any ) -> Any:
'''simple docstring'''
self.config_tester.run_common_tests()
def _A ( self :int ) -> int:
'''simple docstring'''
snake_case_ : Tuple = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*lowerCAmelCase__ )
def _A ( self :Any ) -> Dict:
'''simple docstring'''
snake_case_ : Dict = self.model_tester.prepare_config_and_inputs()
for type in ["absolute", "relative_key", "relative_key_query"]:
snake_case_ : int = type
self.model_tester.create_and_check_model(*lowerCAmelCase__ )
def _A ( self :int ) -> str:
'''simple docstring'''
snake_case_ : Optional[int] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_sequence_classification(*lowerCAmelCase__ )
def _A ( self :List[Any] ) -> Optional[Any]:
'''simple docstring'''
snake_case_ : List[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_token_classification(*lowerCAmelCase__ )
def _A ( self :int ) -> Union[str, Any]:
'''simple docstring'''
snake_case_ : Optional[int] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_question_answering(*lowerCAmelCase__ )
@slow
def _A ( self :Tuple ) -> List[Any]:
'''simple docstring'''
for model_name in LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
snake_case_ : str = LayoutLMvaModel.from_pretrained(lowerCAmelCase__ )
self.assertIsNotNone(lowerCAmelCase__ )
def __UpperCAmelCase ( )-> List[str]:
"""simple docstring"""
snake_case_ : List[str] = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png" )
return image
@require_torch
class A_ (unittest.TestCase ):
"""simple docstring"""
@cached_property
def _A ( self :Union[str, Any] ) -> Optional[Any]:
'''simple docstring'''
return LayoutLMvaImageProcessor(apply_ocr=lowerCAmelCase__ ) if is_vision_available() else None
@slow
def _A ( self :Union[str, Any] ) -> Union[str, Any]:
'''simple docstring'''
snake_case_ : Optional[int] = LayoutLMvaModel.from_pretrained("microsoft/layoutlmv3-base" ).to(lowerCAmelCase__ )
snake_case_ : Optional[Any] = self.default_image_processor
snake_case_ : Optional[int] = prepare_img()
snake_case_ : Union[str, Any] = image_processor(images=lowerCAmelCase__ , return_tensors="pt" ).pixel_values.to(lowerCAmelCase__ )
snake_case_ : List[str] = torch.tensor([[1, 2]] )
snake_case_ : Any = torch.tensor([[1, 2, 3, 4], [5, 6, 7, 8]] ).unsqueeze(0 )
# forward pass
snake_case_ : Any = model(
input_ids=input_ids.to(lowerCAmelCase__ ) , bbox=bbox.to(lowerCAmelCase__ ) , pixel_values=pixel_values.to(lowerCAmelCase__ ) , )
# verify the logits
snake_case_ : Optional[Any] = torch.Size((1, 199, 768) )
self.assertEqual(outputs.last_hidden_state.shape , lowerCAmelCase__ )
snake_case_ : str = torch.tensor(
[[-0.0_5_2_9, 0.3_6_1_8, 0.1_6_3_2], [-0.1_5_8_7, -0.1_6_6_7, -0.0_4_0_0], [-0.1_5_5_7, -0.1_6_7_1, -0.0_5_0_5]] ).to(lowerCAmelCase__ )
self.assertTrue(torch.allclose(outputs.last_hidden_state[0, :3, :3] , lowerCAmelCase__ , atol=1E-4 ) )
| 653 | 0 |
'''simple docstring'''
def snake_case__ ( ) -> str:
_UpperCamelCase : str = []
_UpperCamelCase : List[Any] = 1
while len(UpperCamelCase ) < 1e6:
constant.append(str(UpperCamelCase ) )
i += 1
_UpperCamelCase : str = "".join(UpperCamelCase )
return (
int(constant[0] )
* int(constant[9] )
* int(constant[99] )
* int(constant[9_99] )
* int(constant[99_99] )
* int(constant[9_99_99] )
* int(constant[99_99_99] )
)
if __name__ == "__main__":
print(solution())
| 683 |
'''simple docstring'''
import pytest
from datasets.parallel import ParallelBackendConfig, parallel_backend
from datasets.utils.py_utils import map_nested
from .utils import require_dill_gt_0_3_2, require_joblibspark, require_not_windows
def __UpperCAmelCase ( __magic_name__ )-> int: # picklable for multiprocessing
"""simple docstring"""
return i + 1
@require_dill_gt_0_3_2
@require_joblibspark
@require_not_windows
def __UpperCAmelCase ( )-> List[str]:
"""simple docstring"""
with parallel_backend("spark" ):
assert ParallelBackendConfig.backend_name == "spark"
snake_case_ : str = [1, 2, 3]
with pytest.raises(__magic_name__ ):
with parallel_backend("unsupported backend" ):
map_nested(__magic_name__ ,__magic_name__ ,num_proc=2 )
with pytest.raises(__magic_name__ ):
with parallel_backend("unsupported backend" ):
map_nested(__magic_name__ ,__magic_name__ ,num_proc=-1 )
@require_dill_gt_0_3_2
@require_joblibspark
@require_not_windows
@pytest.mark.parametrize("num_proc" ,[2, -1] )
def __UpperCAmelCase ( __magic_name__ )-> List[Any]:
"""simple docstring"""
snake_case_ : Optional[Any] = [1, 2]
snake_case_ : Union[str, Any] = {"a": 1, "b": 2}
snake_case_ : str = {"a": [1, 2], "b": [3, 4]}
snake_case_ : List[str] = {"a": {"1": 1}, "b": 2}
snake_case_ : Optional[int] = {"a": 1, "b": 2, "c": 3, "d": 4}
snake_case_ : Tuple = [2, 3]
snake_case_ : str = {"a": 2, "b": 3}
snake_case_ : Dict = {"a": [2, 3], "b": [4, 5]}
snake_case_ : List[Any] = {"a": {"1": 2}, "b": 3}
snake_case_ : str = {"a": 2, "b": 3, "c": 4, "d": 5}
with parallel_backend("spark" ):
assert map_nested(__magic_name__ ,__magic_name__ ,num_proc=__magic_name__ ) == expected_map_nested_sa
assert map_nested(__magic_name__ ,__magic_name__ ,num_proc=__magic_name__ ) == expected_map_nested_sa
assert map_nested(__magic_name__ ,__magic_name__ ,num_proc=__magic_name__ ) == expected_map_nested_sa
assert map_nested(__magic_name__ ,__magic_name__ ,num_proc=__magic_name__ ) == expected_map_nested_sa
assert map_nested(__magic_name__ ,__magic_name__ ,num_proc=__magic_name__ ) == expected_map_nested_sa
| 653 | 0 |
from typing import List, Optional, Union
from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class A (a_ ):
'''simple docstring'''
__lowerCamelCase : Dict = ['''image_processor''', '''tokenizer''']
__lowerCamelCase : Optional[Any] = '''BlipImageProcessor'''
__lowerCamelCase : List[str] = ('''BertTokenizer''', '''BertTokenizerFast''')
def __init__( self : Any , __lowerCAmelCase : List[Any] , __lowerCAmelCase : Tuple ) -> int:
"""simple docstring"""
A__ = False
super().__init__(lowerCAmelCase__ , lowerCAmelCase__ )
A__ = self.image_processor
def __call__( self : Dict , __lowerCAmelCase : ImageInput = None , __lowerCAmelCase : Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None , __lowerCAmelCase : bool = True , __lowerCAmelCase : Union[bool, str, PaddingStrategy] = False , __lowerCAmelCase : Union[bool, str, TruncationStrategy] = None , __lowerCAmelCase : Optional[int] = None , __lowerCAmelCase : int = 0 , __lowerCAmelCase : Optional[int] = None , __lowerCAmelCase : Optional[bool] = None , __lowerCAmelCase : bool = False , __lowerCAmelCase : bool = False , __lowerCAmelCase : bool = False , __lowerCAmelCase : bool = False , __lowerCAmelCase : bool = False , __lowerCAmelCase : bool = True , __lowerCAmelCase : Optional[Union[str, TensorType]] = None , **__lowerCAmelCase : Optional[int] , ) -> BatchEncoding:
"""simple docstring"""
if images is None and text is None:
raise ValueError("""You have to specify either images or text.""" )
# Get only text
if images is None:
A__ = self.tokenizer
A__ = self.tokenizer(
text=lowerCAmelCase__ , add_special_tokens=lowerCAmelCase__ , padding=lowerCAmelCase__ , truncation=lowerCAmelCase__ , max_length=lowerCAmelCase__ , stride=lowerCAmelCase__ , pad_to_multiple_of=lowerCAmelCase__ , return_attention_mask=lowerCAmelCase__ , return_overflowing_tokens=lowerCAmelCase__ , return_special_tokens_mask=lowerCAmelCase__ , return_offsets_mapping=lowerCAmelCase__ , return_token_type_ids=lowerCAmelCase__ , return_length=lowerCAmelCase__ , verbose=lowerCAmelCase__ , return_tensors=lowerCAmelCase__ , **lowerCAmelCase__ , )
return text_encoding
# add pixel_values
A__ = self.image_processor(lowerCAmelCase__ , return_tensors=lowerCAmelCase__ )
if text is not None:
A__ = self.tokenizer(
text=lowerCAmelCase__ , add_special_tokens=lowerCAmelCase__ , padding=lowerCAmelCase__ , truncation=lowerCAmelCase__ , max_length=lowerCAmelCase__ , stride=lowerCAmelCase__ , pad_to_multiple_of=lowerCAmelCase__ , return_attention_mask=lowerCAmelCase__ , return_overflowing_tokens=lowerCAmelCase__ , return_special_tokens_mask=lowerCAmelCase__ , return_offsets_mapping=lowerCAmelCase__ , return_token_type_ids=lowerCAmelCase__ , return_length=lowerCAmelCase__ , verbose=lowerCAmelCase__ , return_tensors=lowerCAmelCase__ , **lowerCAmelCase__ , )
else:
A__ = None
if text_encoding is not None:
encoding_image_processor.update(lowerCAmelCase__ )
return encoding_image_processor
def a_ ( self : Tuple , *__lowerCAmelCase : List[str] , **__lowerCAmelCase : Dict ) -> List[Any]:
"""simple docstring"""
return self.tokenizer.batch_decode(*lowerCAmelCase__ , **lowerCAmelCase__ )
def a_ ( self : Dict , *__lowerCAmelCase : Any , **__lowerCAmelCase : Tuple ) -> Union[str, Any]:
"""simple docstring"""
return self.tokenizer.decode(*lowerCAmelCase__ , **lowerCAmelCase__ )
@property
def a_ ( self : Dict ) -> Union[str, Any]:
"""simple docstring"""
A__ = self.tokenizer.model_input_names
A__ = self.image_processor.model_input_names
return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names ) )
| 176 |
'''simple docstring'''
from dataclasses import asdict, dataclass
from typing import Optional
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__lowerCamelCase : Dict = logging.get_logger(__name__)
# TODO Update this
__lowerCamelCase : int = {
'''facebook/esm-1b''': '''https://huggingface.co/facebook/esm-1b/resolve/main/config.json''',
# See all ESM models at https://huggingface.co/models?filter=esm
}
class A_ (a_ ):
"""simple docstring"""
a__ = '''esm'''
def __init__( self :Dict , lowerCAmelCase__ :List[Any]=None , lowerCAmelCase__ :Optional[int]=None , lowerCAmelCase__ :str=None , lowerCAmelCase__ :int=768 , lowerCAmelCase__ :Tuple=12 , lowerCAmelCase__ :Dict=12 , lowerCAmelCase__ :Union[str, Any]=3_072 , lowerCAmelCase__ :int=0.1 , lowerCAmelCase__ :Optional[Any]=0.1 , lowerCAmelCase__ :List[Any]=1_026 , lowerCAmelCase__ :int=0.0_2 , lowerCAmelCase__ :Optional[int]=1E-1_2 , lowerCAmelCase__ :List[str]="absolute" , lowerCAmelCase__ :List[Any]=True , lowerCAmelCase__ :Dict=None , lowerCAmelCase__ :List[str]=False , lowerCAmelCase__ :List[Any]=False , lowerCAmelCase__ :Dict=None , lowerCAmelCase__ :str=None , **lowerCAmelCase__ :Union[str, Any] , ) -> Any:
'''simple docstring'''
super().__init__(pad_token_id=lowerCAmelCase__ , mask_token_id=lowerCAmelCase__ , **lowerCAmelCase__ )
snake_case_ : str = vocab_size
snake_case_ : str = hidden_size
snake_case_ : List[str] = num_hidden_layers
snake_case_ : List[str] = num_attention_heads
snake_case_ : Any = intermediate_size
snake_case_ : Optional[Any] = hidden_dropout_prob
snake_case_ : Tuple = attention_probs_dropout_prob
snake_case_ : List[Any] = max_position_embeddings
snake_case_ : str = initializer_range
snake_case_ : List[Any] = layer_norm_eps
snake_case_ : str = position_embedding_type
snake_case_ : Optional[int] = use_cache
snake_case_ : str = emb_layer_norm_before
snake_case_ : List[Any] = token_dropout
snake_case_ : str = is_folding_model
if is_folding_model:
if esmfold_config is None:
logger.info("No esmfold_config supplied for folding model, using default values." )
snake_case_ : Optional[Any] = EsmFoldConfig()
elif isinstance(lowerCAmelCase__ , lowerCAmelCase__ ):
snake_case_ : Union[str, Any] = EsmFoldConfig(**lowerCAmelCase__ )
snake_case_ : Optional[Any] = esmfold_config
if vocab_list is None:
logger.warning("No vocab_list supplied for folding model, assuming the ESM-2 vocabulary!" )
snake_case_ : List[str] = get_default_vocab_list()
else:
snake_case_ : List[str] = vocab_list
else:
snake_case_ : List[Any] = None
snake_case_ : int = None
if self.esmfold_config is not None and getattr(self.esmfold_config , "use_esm_attn_map" , lowerCAmelCase__ ):
raise ValueError("The HuggingFace port of ESMFold does not support use_esm_attn_map at this time!" )
def _A ( self :Optional[int] ) -> List[Any]:
'''simple docstring'''
snake_case_ : Any = super().to_dict()
if isinstance(self.esmfold_config , lowerCAmelCase__ ):
snake_case_ : Optional[int] = self.esmfold_config.to_dict()
return output
@dataclass
class A_ :
"""simple docstring"""
a__ = None
a__ = True
a__ = False
a__ = False
a__ = False
a__ = 0
a__ = True
a__ = False
a__ = 128
a__ = None
def _A ( self :Dict ) -> int:
'''simple docstring'''
if self.trunk is None:
snake_case_ : Dict = TrunkConfig()
elif isinstance(self.trunk , lowerCAmelCase__ ):
snake_case_ : int = TrunkConfig(**self.trunk )
def _A ( self :Optional[int] ) -> Union[str, Any]:
'''simple docstring'''
snake_case_ : Tuple = asdict(self )
snake_case_ : Optional[int] = self.trunk.to_dict()
return output
@dataclass
class A_ :
"""simple docstring"""
a__ = 48
a__ = 1024
a__ = 128
a__ = 32
a__ = 32
a__ = 32
a__ = 0
a__ = 0
a__ = False
a__ = 4
a__ = 128
a__ = None
def _A ( self :List[Any] ) -> Union[str, Any]:
'''simple docstring'''
if self.structure_module is None:
snake_case_ : Optional[int] = StructureModuleConfig()
elif isinstance(self.structure_module , lowerCAmelCase__ ):
snake_case_ : List[str] = StructureModuleConfig(**self.structure_module )
if self.max_recycles <= 0:
raise ValueError(F'''`max_recycles` should be positive, got {self.max_recycles}.''' )
if self.sequence_state_dim % self.sequence_state_dim != 0:
raise ValueError(
"`sequence_state_dim` should be a round multiple of `sequence_state_dim`, got"
F''' {self.sequence_state_dim} and {self.sequence_state_dim}.''' )
if self.pairwise_state_dim % self.pairwise_state_dim != 0:
raise ValueError(
"`pairwise_state_dim` should be a round multiple of `pairwise_state_dim`, got"
F''' {self.pairwise_state_dim} and {self.pairwise_state_dim}.''' )
snake_case_ : Dict = self.sequence_state_dim // self.sequence_head_width
snake_case_ : Optional[int] = self.pairwise_state_dim // self.pairwise_head_width
if self.sequence_state_dim != sequence_num_heads * self.sequence_head_width:
raise ValueError(
"`sequence_state_dim` should be equal to `sequence_num_heads * sequence_head_width, got"
F''' {self.sequence_state_dim} != {sequence_num_heads} * {self.sequence_head_width}.''' )
if self.pairwise_state_dim != pairwise_num_heads * self.pairwise_head_width:
raise ValueError(
"`pairwise_state_dim` should be equal to `pairwise_num_heads * pairwise_head_width, got"
F''' {self.pairwise_state_dim} != {pairwise_num_heads} * {self.pairwise_head_width}.''' )
if self.pairwise_state_dim % 2 != 0:
raise ValueError(F'''`pairwise_state_dim` should be even, got {self.pairwise_state_dim}.''' )
if self.dropout >= 0.4:
raise ValueError(F'''`dropout` should not be greater than 0.4, got {self.dropout}.''' )
def _A ( self :Tuple ) -> List[str]:
'''simple docstring'''
snake_case_ : int = asdict(self )
snake_case_ : Dict = self.structure_module.to_dict()
return output
@dataclass
class A_ :
"""simple docstring"""
a__ = 384
a__ = 128
a__ = 16
a__ = 128
a__ = 12
a__ = 4
a__ = 8
a__ = 0.1
a__ = 8
a__ = 1
a__ = 2
a__ = 7
a__ = 10
a__ = 1E-8
a__ = 1E5
def _A ( self :Dict ) -> Dict:
'''simple docstring'''
return asdict(self )
def __UpperCAmelCase ( )-> int:
"""simple docstring"""
return (
"<cls>",
"<pad>",
"<eos>",
"<unk>",
"L",
"A",
"G",
"V",
"S",
"E",
"R",
"T",
"I",
"D",
"P",
"K",
"Q",
"N",
"F",
"Y",
"M",
"H",
"W",
"C",
"X",
"B",
"U",
"Z",
"O",
".",
"-",
"<null_1>",
"<mask>",
)
| 653 | 0 |
"""simple docstring"""
import numpy as np
def __A (_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) ->str:
"""simple docstring"""
lowerCAmelCase__ :int = int(np.ceil((x_end - xa) / h ) )
lowerCAmelCase__ :Any = np.zeros((n + 1,) )
lowerCAmelCase__ :List[Any] = ya
lowerCAmelCase__ :List[Any] = xa
for k in range(_SCREAMING_SNAKE_CASE ):
lowerCAmelCase__ :Optional[int] = f(_SCREAMING_SNAKE_CASE , y[k] )
lowerCAmelCase__ :Union[str, Any] = f(x + 0.5 * h , y[k] + 0.5 * h * ka )
lowerCAmelCase__ :int = f(x + 0.5 * h , y[k] + 0.5 * h * ka )
lowerCAmelCase__ :Optional[int] = f(x + h , y[k] + h * ka )
lowerCAmelCase__ :Dict = y[k] + (1 / 6) * h * (ka + 2 * ka + 2 * ka + ka)
x += h
return y
if __name__ == "__main__":
import doctest
doctest.testmod()
| 93 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__lowerCamelCase : Any = {
'''configuration_longformer''': [
'''LONGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''LongformerConfig''',
'''LongformerOnnxConfig''',
],
'''tokenization_longformer''': ['''LongformerTokenizer'''],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__lowerCamelCase : Any = ['''LongformerTokenizerFast''']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__lowerCamelCase : Dict = [
'''LONGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''LongformerForMaskedLM''',
'''LongformerForMultipleChoice''',
'''LongformerForQuestionAnswering''',
'''LongformerForSequenceClassification''',
'''LongformerForTokenClassification''',
'''LongformerModel''',
'''LongformerPreTrainedModel''',
'''LongformerSelfAttention''',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__lowerCamelCase : Any = [
'''TF_LONGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''TFLongformerForMaskedLM''',
'''TFLongformerForMultipleChoice''',
'''TFLongformerForQuestionAnswering''',
'''TFLongformerForSequenceClassification''',
'''TFLongformerForTokenClassification''',
'''TFLongformerModel''',
'''TFLongformerPreTrainedModel''',
'''TFLongformerSelfAttention''',
]
if TYPE_CHECKING:
from .configuration_longformer import (
LONGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
LongformerConfig,
LongformerOnnxConfig,
)
from .tokenization_longformer import LongformerTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_longformer_fast import LongformerTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_longformer import (
LONGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
LongformerForMaskedLM,
LongformerForMultipleChoice,
LongformerForQuestionAnswering,
LongformerForSequenceClassification,
LongformerForTokenClassification,
LongformerModel,
LongformerPreTrainedModel,
LongformerSelfAttention,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_longformer import (
TF_LONGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
TFLongformerForMaskedLM,
TFLongformerForMultipleChoice,
TFLongformerForQuestionAnswering,
TFLongformerForSequenceClassification,
TFLongformerForTokenClassification,
TFLongformerModel,
TFLongformerPreTrainedModel,
TFLongformerSelfAttention,
)
else:
import sys
__lowerCamelCase : Optional[Any] = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 653 | 0 |
'''simple docstring'''
from importlib import import_module
from .logging import get_logger
_a : Any = get_logger(__name__)
class lowercase_ :
'''simple docstring'''
def __init__( self , a_ , a_=None ) -> Optional[int]:
"""simple docstring"""
UpperCAmelCase = attrs or []
if module is not None:
for key in module.__dict__:
if key in attrs or not key.startswith('__' ):
setattr(self , lowerCAmelCase__ , getattr(lowerCAmelCase__ , lowerCAmelCase__ ) )
UpperCAmelCase = module._original_module if isinstance(lowerCAmelCase__ , _PatchedModuleObj ) else module
class lowercase_ :
'''simple docstring'''
__lowerCAmelCase : List[str] = []
def __init__( self , a_ , a_ , a_ , a_=None ) -> List[str]:
"""simple docstring"""
UpperCAmelCase = obj
UpperCAmelCase = target
UpperCAmelCase = new
UpperCAmelCase = target.split('.' )[0]
UpperCAmelCase = {}
UpperCAmelCase = attrs or []
def __enter__( self ) -> List[Any]:
"""simple docstring"""
UpperCAmelCase = self.target.split('.' )
# Patch modules:
# it's used to patch attributes of submodules like "os.path.join";
# in this case we need to patch "os" and "os.path"
for i in range(len(lowerCAmelCase__ ) ):
try:
UpperCAmelCase = import_module('.'.join(submodules[: i + 1] ) )
except ModuleNotFoundError:
continue
# We iterate over all the globals in self.obj in case we find "os" or "os.path"
for attr in self.obj.__dir__():
UpperCAmelCase = getattr(self.obj , lowerCAmelCase__ )
# We don't check for the name of the global, but rather if its value *is* "os" or "os.path".
# This allows to patch renamed modules like "from os import path as ospath".
if obj_attr is submodule or (
(isinstance(lowerCAmelCase__ , _PatchedModuleObj ) and obj_attr._original_module is submodule)
):
UpperCAmelCase = obj_attr
# patch at top level
setattr(self.obj , lowerCAmelCase__ , _PatchedModuleObj(lowerCAmelCase__ , attrs=self.attrs ) )
UpperCAmelCase = getattr(self.obj , lowerCAmelCase__ )
# construct lower levels patches
for key in submodules[i + 1 :]:
setattr(lowerCAmelCase__ , lowerCAmelCase__ , _PatchedModuleObj(getattr(lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ ) , attrs=self.attrs ) )
UpperCAmelCase = getattr(lowerCAmelCase__ , lowerCAmelCase__ )
# finally set the target attribute
setattr(lowerCAmelCase__ , lowerCAmelCase__ , self.new )
# Patch attribute itself:
# it's used for builtins like "open",
# and also to patch "os.path.join" we may also need to patch "join"
# itself if it was imported as "from os.path import join".
if submodules: # if it's an attribute of a submodule like "os.path.join"
try:
UpperCAmelCase = getattr(import_module('.'.join(lowerCAmelCase__ ) ) , lowerCAmelCase__ )
except (AttributeError, ModuleNotFoundError):
return
# We iterate over all the globals in self.obj in case we find "os.path.join"
for attr in self.obj.__dir__():
# We don't check for the name of the global, but rather if its value *is* "os.path.join".
# This allows to patch renamed attributes like "from os.path import join as pjoin".
if getattr(self.obj , lowerCAmelCase__ ) is attr_value:
UpperCAmelCase = getattr(self.obj , lowerCAmelCase__ )
setattr(self.obj , lowerCAmelCase__ , self.new )
elif target_attr in globals()["__builtins__"]: # if it'a s builtin like "open"
UpperCAmelCase = globals()["__builtins__"][target_attr]
setattr(self.obj , lowerCAmelCase__ , self.new )
else:
raise RuntimeError(F'''Tried to patch attribute {target_attr} instead of a submodule.''' )
def __exit__( self , *a_ ) -> Dict:
"""simple docstring"""
for attr in list(self.original ):
setattr(self.obj , lowerCAmelCase__ , self.original.pop(lowerCAmelCase__ ) )
def snake_case_ ( self ) -> Tuple:
"""simple docstring"""
self.__enter__()
self._active_patches.append(self )
def snake_case_ ( self ) -> Optional[Any]:
"""simple docstring"""
try:
self._active_patches.remove(self )
except ValueError:
# If the patch hasn't been started this will fail
return None
return self.__exit__()
| 447 |
'''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
__lowerCamelCase : Optional[int] = 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 A_ :
"""simple docstring"""
def __init__( self :Tuple , lowerCAmelCase__ :List[str] , lowerCAmelCase__ :List[Any]=16 , lowerCAmelCase__ :Any=13 , lowerCAmelCase__ :Optional[Any]=7 , lowerCAmelCase__ :str=14 , lowerCAmelCase__ :Union[str, Any]=10 , lowerCAmelCase__ :Tuple=19 , lowerCAmelCase__ :Optional[Any]=5 , lowerCAmelCase__ :Dict=4 , lowerCAmelCase__ :Union[str, Any]=True , lowerCAmelCase__ :Any=16 , lowerCAmelCase__ :str=2 , lowerCAmelCase__ :List[Any]=4 , lowerCAmelCase__ :Any=4 , lowerCAmelCase__ :str="gelu" , lowerCAmelCase__ :Tuple=0.1 , lowerCAmelCase__ :Dict=0.1 , lowerCAmelCase__ :Optional[int]=[1, 2, 3, 4, 5] , lowerCAmelCase__ :str=25 , lowerCAmelCase__ :Optional[Any]=5 , ) -> Dict:
'''simple docstring'''
snake_case_ : List[str] = d_model
snake_case_ : Dict = parent
snake_case_ : Optional[Any] = batch_size
snake_case_ : Optional[Any] = prediction_length
snake_case_ : str = context_length
snake_case_ : Tuple = cardinality
snake_case_ : List[str] = num_time_features
snake_case_ : Optional[Any] = lags_sequence
snake_case_ : Union[str, Any] = embedding_dimension
snake_case_ : Optional[Any] = is_training
snake_case_ : Optional[Any] = hidden_size
snake_case_ : Any = num_hidden_layers
snake_case_ : Optional[Any] = num_attention_heads
snake_case_ : int = intermediate_size
snake_case_ : Any = hidden_act
snake_case_ : Union[str, Any] = hidden_dropout_prob
snake_case_ : Dict = attention_probs_dropout_prob
snake_case_ : List[str] = context_length
snake_case_ : Any = prediction_length + label_length
snake_case_ : Union[str, Any] = label_length
snake_case_ : List[Any] = moving_average
snake_case_ : str = autocorrelation_factor
def _A ( self :List[Any] ) -> Any:
'''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 _A ( self :Union[str, Any] , lowerCAmelCase__ :Optional[Any] ) -> Dict:
'''simple docstring'''
snake_case_ : Any = config.context_length + max(config.lags_sequence )
snake_case_ : Union[str, Any] = ids_tensor([self.batch_size, 1] , config.cardinality[0] )
snake_case_ : Optional[int] = floats_tensor([self.batch_size, _past_length, config.num_time_features] )
snake_case_ : List[Any] = floats_tensor([self.batch_size, _past_length] )
snake_case_ : Dict = floats_tensor([self.batch_size, _past_length] ) > 0.5
# decoder inputs
snake_case_ : List[Any] = floats_tensor([self.batch_size, config.prediction_length, config.num_time_features] )
snake_case_ : List[Any] = floats_tensor([self.batch_size, config.prediction_length] )
snake_case_ : int = {
"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 _A ( self :Dict ) -> Tuple:
'''simple docstring'''
snake_case_ : str = self.get_config()
snake_case_ : int = self.prepare_autoformer_inputs_dict(lowerCAmelCase__ )
return config, inputs_dict
def _A ( self :Optional[int] ) -> Dict:
'''simple docstring'''
snake_case_, snake_case_ : Union[str, Any] = self.prepare_config_and_inputs()
return config, inputs_dict
def _A ( self :Tuple , lowerCAmelCase__ :int , lowerCAmelCase__ :Optional[int] ) -> List[str]:
'''simple docstring'''
snake_case_ : Dict = AutoformerModel(config=lowerCAmelCase__ ).to(lowerCAmelCase__ ).eval()
snake_case_ : Optional[int] = model(**lowerCAmelCase__ )
snake_case_ : Any = outputs.encoder_last_hidden_state
snake_case_ : Dict = outputs.last_hidden_state
with tempfile.TemporaryDirectory() as tmpdirname:
snake_case_ : Optional[Any] = model.get_encoder()
encoder.save_pretrained(lowerCAmelCase__ )
snake_case_ : Tuple = AutoformerEncoder.from_pretrained(lowerCAmelCase__ ).to(lowerCAmelCase__ )
snake_case_, snake_case_, snake_case_, snake_case_, snake_case_ : List[str] = model.create_network_inputs(**lowerCAmelCase__ )
snake_case_, snake_case_ : Optional[int] = model.decomposition_layer(transformer_inputs[:, : config.context_length, ...] )
snake_case_ : List[Any] = torch.cat(
(transformer_inputs[:, : config.context_length, ...], feature[:, : config.context_length, ...]) , dim=-1 , )
snake_case_ : Optional[int] = encoder(inputs_embeds=lowerCAmelCase__ )[0]
self.parent.assertTrue((encoder_last_hidden_state_a - encoder_last_hidden_state).abs().max().item() < 1E-3 )
snake_case_ : Any = (
torch.mean(transformer_inputs[:, : config.context_length, ...] , dim=1 )
.unsqueeze(1 )
.repeat(1 , config.prediction_length , 1 )
)
snake_case_ : List[str] = torch.zeros(
[transformer_inputs.shape[0], config.prediction_length, transformer_inputs.shape[2]] , device=enc_input.device , )
snake_case_ : Optional[Any] = torch.cat(
(
torch.cat((seasonal_input[:, -config.label_length :, ...], zeros) , dim=1 ),
feature[:, config.context_length - config.label_length :, ...],
) , dim=-1 , )
snake_case_ : Any = 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:
snake_case_ : List[Any] = model.get_decoder()
decoder.save_pretrained(lowerCAmelCase__ )
snake_case_ : int = AutoformerDecoder.from_pretrained(lowerCAmelCase__ ).to(lowerCAmelCase__ )
snake_case_ : Tuple = decoder(
trend=lowerCAmelCase__ , inputs_embeds=lowerCAmelCase__ , encoder_hidden_states=lowerCAmelCase__ , )[0]
self.parent.assertTrue((last_hidden_state_a - last_hidden_state).abs().max().item() < 1E-3 )
@require_torch
class A_ (a_ , a_ , unittest.TestCase ):
"""simple docstring"""
a__ = (AutoformerModel, AutoformerForPrediction) if is_torch_available() else ()
a__ = (AutoformerForPrediction,) if is_torch_available() else ()
a__ = {'''feature-extraction''': AutoformerModel} if is_torch_available() else {}
a__ = False
a__ = False
a__ = False
a__ = False
a__ = False
a__ = False
def _A ( self :Dict ) -> int:
'''simple docstring'''
snake_case_ : Tuple = AutoformerModelTester(self )
snake_case_ : str = ConfigTester(self , config_class=lowerCAmelCase__ , has_text_modality=lowerCAmelCase__ )
def _A ( self :List[str] ) -> Tuple:
'''simple docstring'''
self.config_tester.run_common_tests()
def _A ( self :List[Any] ) -> Union[str, Any]:
'''simple docstring'''
snake_case_, snake_case_ : List[Any] = self.model_tester.prepare_config_and_inputs()
for model_class in self.all_model_classes:
snake_case_ : List[Any] = model_class(lowerCAmelCase__ )
with tempfile.TemporaryDirectory() as tmpdirname:
model.save_pretrained(lowerCAmelCase__ )
snake_case_, snake_case_ : str = model_class.from_pretrained(lowerCAmelCase__ , output_loading_info=lowerCAmelCase__ )
self.assertEqual(info["missing_keys"] , [] )
def _A ( self :Optional[int] ) -> Tuple:
'''simple docstring'''
snake_case_ : str = self.model_tester.prepare_config_and_inputs_for_common()
self.model_tester.check_encoder_decoder_model_standalone(*lowerCAmelCase__ )
@unittest.skip(reason="Model has no tokens embeddings" )
def _A ( self :str ) -> str:
'''simple docstring'''
pass
def _A ( self :Optional[Any] ) -> Union[str, Any]:
'''simple docstring'''
snake_case_ : List[Any] = inspect.signature(getattr(lowerCAmelCase__ , "forward" ) )
# The main input is the name of the argument after `self`
snake_case_ : Dict = list(model_signature.parameters.keys() )[1]
self.assertEqual(AutoformerModel.main_input_name , lowerCAmelCase__ )
def _A ( self :Optional[Any] ) -> Optional[int]:
'''simple docstring'''
snake_case_, snake_case_ : Tuple = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
snake_case_ : Tuple = model_class(lowerCAmelCase__ )
snake_case_ : int = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
snake_case_ : Optional[Any] = [*signature.parameters.keys()]
snake_case_ : Dict = [
"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(lowerCAmelCase__ )] , lowerCAmelCase__ )
def _A ( self :int ) -> Any:
'''simple docstring'''
snake_case_, snake_case_ : Tuple = self.model_tester.prepare_config_and_inputs_for_common()
snake_case_ : Union[str, Any] = True
snake_case_ : List[str] = getattr(self.model_tester , "seq_length" , lowerCAmelCase__ )
snake_case_ : Dict = getattr(self.model_tester , "decoder_seq_length" , lowerCAmelCase__ )
snake_case_ : Union[str, Any] = getattr(self.model_tester , "encoder_seq_length" , lowerCAmelCase__ )
snake_case_ : Union[str, Any] = getattr(self.model_tester , "d_model" , lowerCAmelCase__ )
snake_case_ : Dict = getattr(self.model_tester , "num_attention_heads" , lowerCAmelCase__ )
snake_case_ : Optional[int] = d_model // num_attention_heads
for model_class in self.all_model_classes:
snake_case_ : Any = True
snake_case_ : Any = False
snake_case_ : Dict = True
snake_case_ : List[str] = model_class(lowerCAmelCase__ )
model.to(lowerCAmelCase__ )
model.eval()
with torch.no_grad():
snake_case_ : Tuple = model(**self._prepare_for_class(lowerCAmelCase__ , lowerCAmelCase__ ) )
snake_case_ : Union[str, Any] = outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
self.assertEqual(len(lowerCAmelCase__ ) , self.model_tester.num_hidden_layers )
# check that output_attentions also work using config
del inputs_dict["output_attentions"]
snake_case_ : Optional[int] = True
snake_case_ : Any = model_class(lowerCAmelCase__ )
model.to(lowerCAmelCase__ )
model.eval()
with torch.no_grad():
snake_case_ : List[str] = model(**self._prepare_for_class(lowerCAmelCase__ , lowerCAmelCase__ ) )
snake_case_ : str = outputs.encoder_attentions
self.assertEqual(len(lowerCAmelCase__ ) , self.model_tester.num_hidden_layers )
self.assertListEqual(
list(attentions[0].shape[-3:] ) , [self.model_tester.num_attention_heads, encoder_seq_length, dim] , )
snake_case_ : Tuple = len(lowerCAmelCase__ )
snake_case_ : List[str] = 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(lowerCAmelCase__ , lowerCAmelCase__ )
# decoder attentions
snake_case_ : Optional[int] = outputs.decoder_attentions
self.assertIsInstance(lowerCAmelCase__ , (list, tuple) )
self.assertEqual(len(lowerCAmelCase__ ) , 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
snake_case_ : List[Any] = outputs.cross_attentions
self.assertIsInstance(lowerCAmelCase__ , (list, tuple) )
self.assertEqual(len(lowerCAmelCase__ ) , 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
snake_case_ : Optional[int] = True
snake_case_ : List[Any] = True
snake_case_ : Union[str, Any] = model_class(lowerCAmelCase__ )
model.to(lowerCAmelCase__ )
model.eval()
with torch.no_grad():
snake_case_ : List[Any] = model(**self._prepare_for_class(lowerCAmelCase__ , lowerCAmelCase__ ) )
self.assertEqual(out_len + 2 , len(lowerCAmelCase__ ) )
snake_case_ : Tuple = outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
self.assertEqual(len(lowerCAmelCase__ ) , 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 _A ( self :Any ) -> Optional[Any]:
'''simple docstring'''
super().test_retain_grad_hidden_states_attentions()
def __UpperCAmelCase ( __magic_name__="train-batch.pt" )-> int:
"""simple docstring"""
snake_case_ : List[str] = hf_hub_download(repo_id="hf-internal-testing/tourism-monthly-batch" ,filename=__magic_name__ ,repo_type="dataset" )
snake_case_ : List[str] = torch.load(__magic_name__ ,map_location=__magic_name__ )
return batch
@require_torch
@slow
class A_ (unittest.TestCase ):
"""simple docstring"""
def _A ( self :str ) -> Any:
'''simple docstring'''
snake_case_ : Optional[int] = AutoformerModel.from_pretrained("huggingface/autoformer-tourism-monthly" ).to(lowerCAmelCase__ )
snake_case_ : List[str] = prepare_batch()
with torch.no_grad():
snake_case_ : int = 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]
snake_case_ : Optional[int] = torch.Size(
(64, model.config.prediction_length + model.config.label_length, model.config.feature_size) )
self.assertEqual(output.shape , lowerCAmelCase__ )
snake_case_ : Optional[Any] = torch.tensor(
[[0.3_5_9_3, -1.3_3_9_8, 0.6_3_3_0], [0.2_2_7_9, 1.5_3_9_6, -0.1_7_9_2], [0.0_4_5_0, 1.3_2_2_5, -0.2_3_3_5]] , device=lowerCAmelCase__ )
self.assertTrue(torch.allclose(output[0, :3, :3] , lowerCAmelCase__ , atol=lowerCAmelCase__ ) )
def _A ( self :Any ) -> str:
'''simple docstring'''
snake_case_ : str = AutoformerForPrediction.from_pretrained("huggingface/autoformer-tourism-monthly" ).to(lowerCAmelCase__ )
snake_case_ : Optional[Any] = prepare_batch("val-batch.pt" )
with torch.no_grad():
snake_case_ : Tuple = 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
snake_case_ : Dict = torch.Size((64, model.config.context_length, model.config.d_model) )
self.assertEqual(output.shape , lowerCAmelCase__ )
snake_case_ : Any = torch.tensor(
[[-0.0_7_3_4, -0.9_0_3_6, 0.8_3_5_8], [4.7_1_8_6, 2.4_1_1_3, 1.9_5_8_1], [1.7_9_5_3, 2.3_5_5_8, 1.2_9_7_0]] , device=lowerCAmelCase__ )
self.assertTrue(torch.allclose(output[0, :3, :3] , lowerCAmelCase__ , atol=lowerCAmelCase__ ) )
def _A ( self :List[str] ) -> Any:
'''simple docstring'''
snake_case_ : List[Any] = AutoformerForPrediction.from_pretrained("huggingface/autoformer-tourism-monthly" ).to(lowerCAmelCase__ )
snake_case_ : str = prepare_batch("val-batch.pt" )
with torch.no_grad():
snake_case_ : Optional[Any] = 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"] , )
snake_case_ : List[Any] = torch.Size((64, model.config.num_parallel_samples, model.config.prediction_length) )
self.assertEqual(outputs.sequences.shape , lowerCAmelCase__ )
snake_case_ : Dict = torch.tensor([3_1_3_0.6_7_6_3, 4_0_5_6.5_2_9_3, 7_0_5_3.0_7_8_6] , device=lowerCAmelCase__ )
snake_case_ : Optional[Any] = outputs.sequences.mean(dim=1 )
self.assertTrue(torch.allclose(mean_prediction[0, -3:] , lowerCAmelCase__ , rtol=1E-1 ) )
| 653 | 0 |
import argparse
import torch
from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_from_original_stable_diffusion_ckpt
if __name__ == "__main__":
__UpperCamelCase : Optional[int] = argparse.ArgumentParser()
parser.add_argument(
'--checkpoint_path', default=None, type=str, required=True, help='Path to the checkpoint to convert.'
)
# !wget https://raw.githubusercontent.com/CompVis/stable-diffusion/main/configs/stable-diffusion/v1-inference.yaml
parser.add_argument(
'--original_config_file',
default=None,
type=str,
help='The YAML config file corresponding to the original architecture.',
)
parser.add_argument(
'--num_in_channels',
default=None,
type=int,
help='The number of input channels. If `None` number of input channels will be automatically inferred.',
)
parser.add_argument(
'--scheduler_type',
default='pndm',
type=str,
help='Type of scheduler to use. Should be one of [\'pndm\', \'lms\', \'ddim\', \'euler\', \'euler-ancestral\', \'dpm\']',
)
parser.add_argument(
'--pipeline_type',
default=None,
type=str,
help=(
'The pipeline type. One of \'FrozenOpenCLIPEmbedder\', \'FrozenCLIPEmbedder\', \'PaintByExample\''
'. If `None` pipeline will be automatically inferred.'
),
)
parser.add_argument(
'--image_size',
default=None,
type=int,
help=(
'The image size that the model was trained on. Use 512 for Stable Diffusion v1.X and Stable Siffusion v2'
' Base. Use 768 for Stable Diffusion v2.'
),
)
parser.add_argument(
'--prediction_type',
default=None,
type=str,
help=(
'The prediction type that the model was trained on. Use \'epsilon\' for Stable Diffusion v1.X and Stable'
' Diffusion v2 Base. Use \'v_prediction\' for Stable Diffusion v2.'
),
)
parser.add_argument(
'--extract_ema',
action='store_true',
help=(
'Only relevant for checkpoints that have both EMA and non-EMA weights. Whether to extract the EMA weights'
' or not. Defaults to `False`. Add `--extract_ema` to extract the EMA weights. EMA weights usually yield'
' higher quality images for inference. Non-EMA weights are usually better to continue fine-tuning.'
),
)
parser.add_argument(
'--upcast_attention',
action='store_true',
help=(
'Whether the attention computation should always be upcasted. This is necessary when running stable'
' diffusion 2.1.'
),
)
parser.add_argument(
'--from_safetensors',
action='store_true',
help='If `--checkpoint_path` is in `safetensors` format, load checkpoint with safetensors instead of PyTorch.',
)
parser.add_argument(
'--to_safetensors',
action='store_true',
help='Whether to store pipeline in safetensors format or not.',
)
parser.add_argument('--dump_path', default=None, type=str, required=True, help='Path to the output model.')
parser.add_argument('--device', type=str, help='Device to use (e.g. cpu, cuda:0, cuda:1, etc.)')
parser.add_argument(
'--stable_unclip',
type=str,
default=None,
required=False,
help='Set if this is a stable unCLIP model. One of \'txt2img\' or \'img2img\'.',
)
parser.add_argument(
'--stable_unclip_prior',
type=str,
default=None,
required=False,
help='Set if this is a stable unCLIP txt2img model. Selects which prior to use. If `--stable_unclip` is set to `txt2img`, the karlo prior (https://huggingface.co/kakaobrain/karlo-v1-alpha/tree/main/prior) is selected by default.',
)
parser.add_argument(
'--clip_stats_path',
type=str,
help='Path to the clip stats file. Only required if the stable unclip model\'s config specifies `model.params.noise_aug_config.params.clip_stats_path`.',
required=False,
)
parser.add_argument(
'--controlnet', action='store_true', default=None, help='Set flag if this is a controlnet checkpoint.'
)
parser.add_argument('--half', action='store_true', help='Save weights in half precision.')
parser.add_argument(
'--vae_path',
type=str,
default=None,
required=False,
help='Set to a path, hub id to an already converted vae to not convert it again.',
)
__UpperCamelCase : Tuple = parser.parse_args()
__UpperCamelCase : Union[str, Any] = download_from_original_stable_diffusion_ckpt(
checkpoint_path=args.checkpoint_path,
original_config_file=args.original_config_file,
image_size=args.image_size,
prediction_type=args.prediction_type,
model_type=args.pipeline_type,
extract_ema=args.extract_ema,
scheduler_type=args.scheduler_type,
num_in_channels=args.num_in_channels,
upcast_attention=args.upcast_attention,
from_safetensors=args.from_safetensors,
device=args.device,
stable_unclip=args.stable_unclip,
stable_unclip_prior=args.stable_unclip_prior,
clip_stats_path=args.clip_stats_path,
controlnet=args.controlnet,
vae_path=args.vae_path,
)
if args.half:
pipe.to(torch_dtype=torch.floataa)
if args.controlnet:
# only save the controlnet model
pipe.controlnet.save_pretrained(args.dump_path, safe_serialization=args.to_safetensors)
else:
pipe.save_pretrained(args.dump_path, safe_serialization=args.to_safetensors)
| 248 |
'''simple docstring'''
import itertools
import json
import os
import unittest
from transformers import AddedToken, RobertaTokenizer, RobertaTokenizerFast
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class A_ (a_ , unittest.TestCase ):
"""simple docstring"""
a__ = RobertaTokenizer
a__ = RobertaTokenizerFast
a__ = True
a__ = {'''cls_token''': '''<s>'''}
def _A ( self :Optional[int] ) -> List[Any]:
'''simple docstring'''
super().setUp()
# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
snake_case_ : List[Any] = [
"l",
"o",
"w",
"e",
"r",
"s",
"t",
"i",
"d",
"n",
"\u0120",
"\u0120l",
"\u0120n",
"\u0120lo",
"\u0120low",
"er",
"\u0120lowest",
"\u0120newer",
"\u0120wider",
"<unk>",
]
snake_case_ : Tuple = dict(zip(lowerCAmelCase__ , range(len(lowerCAmelCase__ ) ) ) )
snake_case_ : List[Any] = ["#version: 0.2", "\u0120 l", "\u0120l o", "\u0120lo w", "e r", ""]
snake_case_ : int = {"unk_token": "<unk>"}
snake_case_ : List[str] = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES["vocab_file"] )
snake_case_ : Tuple = 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(lowerCAmelCase__ ) + "\n" )
with open(self.merges_file , "w" , encoding="utf-8" ) as fp:
fp.write("\n".join(lowerCAmelCase__ ) )
def _A ( self :Optional[Any] , **lowerCAmelCase__ :str ) -> str:
'''simple docstring'''
kwargs.update(self.special_tokens_map )
return self.tokenizer_class.from_pretrained(self.tmpdirname , **lowerCAmelCase__ )
def _A ( self :Any , **lowerCAmelCase__ :Tuple ) -> Optional[int]:
'''simple docstring'''
kwargs.update(self.special_tokens_map )
return RobertaTokenizerFast.from_pretrained(self.tmpdirname , **lowerCAmelCase__ )
def _A ( self :Optional[int] , lowerCAmelCase__ :str ) -> Optional[int]:
'''simple docstring'''
snake_case_ : int = "lower newer"
snake_case_ : Tuple = "lower newer"
return input_text, output_text
def _A ( self :Tuple ) -> Union[str, Any]:
'''simple docstring'''
snake_case_ : str = self.tokenizer_class(self.vocab_file , self.merges_file , **self.special_tokens_map )
snake_case_ : Dict = "lower newer"
snake_case_ : int = ["l", "o", "w", "er", "\u0120", "n", "e", "w", "er"]
snake_case_ : str = tokenizer.tokenize(lowerCAmelCase__ ) # , add_prefix_space=True)
self.assertListEqual(lowerCAmelCase__ , lowerCAmelCase__ )
snake_case_ : List[str] = tokens + [tokenizer.unk_token]
snake_case_ : Optional[int] = [0, 1, 2, 15, 10, 9, 3, 2, 15, 19]
self.assertListEqual(tokenizer.convert_tokens_to_ids(lowerCAmelCase__ ) , lowerCAmelCase__ )
def _A ( self :Any ) -> str:
'''simple docstring'''
snake_case_ : List[str] = self.get_tokenizer()
self.assertListEqual(tokenizer.encode("Hello world!" , add_special_tokens=lowerCAmelCase__ ) , [0, 31_414, 232, 328, 2] )
self.assertListEqual(
tokenizer.encode("Hello world! cécé herlolip 418" , add_special_tokens=lowerCAmelCase__ ) , [0, 31_414, 232, 328, 740, 1_140, 12_695, 69, 46_078, 1_588, 2] , )
@slow
def _A ( self :str ) -> List[str]:
'''simple docstring'''
snake_case_ : Tuple = self.tokenizer_class.from_pretrained("roberta-base" )
snake_case_ : List[str] = tokenizer.encode("sequence builders" , add_special_tokens=lowerCAmelCase__ )
snake_case_ : List[Any] = tokenizer.encode("multi-sequence build" , add_special_tokens=lowerCAmelCase__ )
snake_case_ : List[str] = tokenizer.encode(
"sequence builders" , add_special_tokens=lowerCAmelCase__ , add_prefix_space=lowerCAmelCase__ )
snake_case_ : Union[str, Any] = tokenizer.encode(
"sequence builders" , "multi-sequence build" , add_special_tokens=lowerCAmelCase__ , add_prefix_space=lowerCAmelCase__ )
snake_case_ : Optional[Any] = tokenizer.build_inputs_with_special_tokens(lowerCAmelCase__ )
snake_case_ : Any = tokenizer.build_inputs_with_special_tokens(lowerCAmelCase__ , lowerCAmelCase__ )
assert encoded_sentence == encoded_text_from_decode
assert encoded_pair == encoded_pair_from_decode
def _A ( self :List[Any] ) -> Any:
'''simple docstring'''
snake_case_ : Optional[Any] = self.get_tokenizer()
snake_case_ : Tuple = "Encode this sequence."
snake_case_ : Optional[Any] = tokenizer.byte_encoder[" ".encode("utf-8" )[0]]
# Testing encoder arguments
snake_case_ : str = tokenizer.encode(lowerCAmelCase__ , add_special_tokens=lowerCAmelCase__ , add_prefix_space=lowerCAmelCase__ )
snake_case_ : List[Any] = tokenizer.convert_ids_to_tokens(encoded[0] )[0]
self.assertNotEqual(lowerCAmelCase__ , lowerCAmelCase__ )
snake_case_ : List[Any] = tokenizer.encode(lowerCAmelCase__ , add_special_tokens=lowerCAmelCase__ , add_prefix_space=lowerCAmelCase__ )
snake_case_ : str = tokenizer.convert_ids_to_tokens(encoded[0] )[0]
self.assertEqual(lowerCAmelCase__ , lowerCAmelCase__ )
tokenizer.add_special_tokens({"bos_token": "<s>"} )
snake_case_ : str = tokenizer.encode(lowerCAmelCase__ , add_special_tokens=lowerCAmelCase__ )
snake_case_ : int = tokenizer.convert_ids_to_tokens(encoded[1] )[0]
self.assertNotEqual(lowerCAmelCase__ , lowerCAmelCase__ )
# Testing spaces after special tokens
snake_case_ : List[Any] = "<mask>"
tokenizer.add_special_tokens(
{"mask_token": AddedToken(lowerCAmelCase__ , lstrip=lowerCAmelCase__ , rstrip=lowerCAmelCase__ )} ) # mask token has a left space
snake_case_ : str = tokenizer.convert_tokens_to_ids(lowerCAmelCase__ )
snake_case_ : List[str] = "Encode <mask> sequence"
snake_case_ : List[Any] = "Encode <mask>sequence"
snake_case_ : Tuple = tokenizer.encode(lowerCAmelCase__ )
snake_case_ : int = encoded.index(lowerCAmelCase__ )
snake_case_ : Optional[Any] = tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1] )[0]
self.assertEqual(lowerCAmelCase__ , lowerCAmelCase__ )
snake_case_ : List[str] = tokenizer.encode(lowerCAmelCase__ )
snake_case_ : Union[str, Any] = encoded.index(lowerCAmelCase__ )
snake_case_ : int = tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1] )[0]
self.assertNotEqual(lowerCAmelCase__ , lowerCAmelCase__ )
def _A ( self :Tuple ) -> Tuple:
'''simple docstring'''
pass
def _A ( self :int ) -> Optional[Any]:
'''simple docstring'''
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(F'''{tokenizer.__class__.__name__} ({pretrained_name})''' ):
snake_case_ : List[Any] = self.rust_tokenizer_class.from_pretrained(lowerCAmelCase__ , **lowerCAmelCase__ )
snake_case_ : List[Any] = self.tokenizer_class.from_pretrained(lowerCAmelCase__ , **lowerCAmelCase__ )
snake_case_ : Any = "A, <mask> AllenNLP sentence."
snake_case_ : str = tokenizer_r.encode_plus(lowerCAmelCase__ , add_special_tokens=lowerCAmelCase__ , return_token_type_ids=lowerCAmelCase__ )
snake_case_ : int = tokenizer_p.encode_plus(lowerCAmelCase__ , add_special_tokens=lowerCAmelCase__ , return_token_type_ids=lowerCAmelCase__ )
# token_type_ids should put 0 everywhere
self.assertEqual(sum(tokens_r["token_type_ids"] ) , sum(tokens_p["token_type_ids"] ) )
# attention_mask should put 1 everywhere, so sum over length should be 1
self.assertEqual(
sum(tokens_r["attention_mask"] ) / len(tokens_r["attention_mask"] ) , sum(tokens_p["attention_mask"] ) / len(tokens_p["attention_mask"] ) , )
snake_case_ : List[Any] = tokenizer_r.convert_ids_to_tokens(tokens_r["input_ids"] )
snake_case_ : str = tokenizer_p.convert_ids_to_tokens(tokens_p["input_ids"] )
# Rust correctly handles the space before the mask while python doesnt
self.assertSequenceEqual(tokens_p["input_ids"] , [0, 250, 6, 50_264, 3_823, 487, 21_992, 3_645, 4, 2] )
self.assertSequenceEqual(tokens_r["input_ids"] , [0, 250, 6, 50_264, 3_823, 487, 21_992, 3_645, 4, 2] )
self.assertSequenceEqual(
lowerCAmelCase__ , ["<s>", "A", ",", "<mask>", "ĠAllen", "N", "LP", "Ġsentence", ".", "</s>"] )
self.assertSequenceEqual(
lowerCAmelCase__ , ["<s>", "A", ",", "<mask>", "ĠAllen", "N", "LP", "Ġsentence", ".", "</s>"] )
def _A ( self :int ) -> Tuple:
'''simple docstring'''
for trim_offsets, add_prefix_space in itertools.product([True, False] , repeat=2 ):
snake_case_ : str = self.rust_tokenizer_class.from_pretrained(
self.tmpdirname , use_fast=lowerCAmelCase__ , add_prefix_space=lowerCAmelCase__ , trim_offsets=lowerCAmelCase__ )
snake_case_ : Optional[Any] = json.loads(tokenizer_r.backend_tokenizer.pre_tokenizer.__getstate__() )
snake_case_ : Any = json.loads(tokenizer_r.backend_tokenizer.post_processor.__getstate__() )
self.assertEqual(pre_tokenizer_state["add_prefix_space"] , lowerCAmelCase__ )
self.assertEqual(post_processor_state["add_prefix_space"] , lowerCAmelCase__ )
self.assertEqual(post_processor_state["trim_offsets"] , lowerCAmelCase__ )
def _A ( self :List[str] ) -> List[Any]:
'''simple docstring'''
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(F'''{tokenizer.__class__.__name__} ({pretrained_name})''' ):
snake_case_ : str = "hello" # `hello` is a token in the vocabulary of `pretrained_name`
snake_case_ : Tuple = F'''{text_of_1_token} {text_of_1_token}'''
snake_case_ : Any = self.rust_tokenizer_class.from_pretrained(
lowerCAmelCase__ , use_fast=lowerCAmelCase__ , add_prefix_space=lowerCAmelCase__ , trim_offsets=lowerCAmelCase__ )
snake_case_ : Union[str, Any] = tokenizer_r(lowerCAmelCase__ , return_offsets_mapping=lowerCAmelCase__ , add_special_tokens=lowerCAmelCase__ )
self.assertEqual(encoding.offset_mapping[0] , (0, len(lowerCAmelCase__ )) )
self.assertEqual(
encoding.offset_mapping[1] , (len(lowerCAmelCase__ ) + 1, len(lowerCAmelCase__ ) + 1 + len(lowerCAmelCase__ )) , )
snake_case_ : List[str] = self.rust_tokenizer_class.from_pretrained(
lowerCAmelCase__ , use_fast=lowerCAmelCase__ , add_prefix_space=lowerCAmelCase__ , trim_offsets=lowerCAmelCase__ )
snake_case_ : Tuple = tokenizer_r(lowerCAmelCase__ , return_offsets_mapping=lowerCAmelCase__ , add_special_tokens=lowerCAmelCase__ )
self.assertEqual(encoding.offset_mapping[0] , (0, len(lowerCAmelCase__ )) )
self.assertEqual(
encoding.offset_mapping[1] , (len(lowerCAmelCase__ ) + 1, len(lowerCAmelCase__ ) + 1 + len(lowerCAmelCase__ )) , )
snake_case_ : Union[str, Any] = self.rust_tokenizer_class.from_pretrained(
lowerCAmelCase__ , use_fast=lowerCAmelCase__ , add_prefix_space=lowerCAmelCase__ , trim_offsets=lowerCAmelCase__ )
snake_case_ : Union[str, Any] = tokenizer_r(lowerCAmelCase__ , return_offsets_mapping=lowerCAmelCase__ , add_special_tokens=lowerCAmelCase__ )
self.assertEqual(encoding.offset_mapping[0] , (0, len(lowerCAmelCase__ )) )
self.assertEqual(
encoding.offset_mapping[1] , (len(lowerCAmelCase__ ), len(lowerCAmelCase__ ) + 1 + len(lowerCAmelCase__ )) , )
snake_case_ : Dict = self.rust_tokenizer_class.from_pretrained(
lowerCAmelCase__ , use_fast=lowerCAmelCase__ , add_prefix_space=lowerCAmelCase__ , trim_offsets=lowerCAmelCase__ )
snake_case_ : str = tokenizer_r(lowerCAmelCase__ , return_offsets_mapping=lowerCAmelCase__ , add_special_tokens=lowerCAmelCase__ )
self.assertEqual(encoding.offset_mapping[0] , (0, len(lowerCAmelCase__ )) )
self.assertEqual(
encoding.offset_mapping[1] , (len(lowerCAmelCase__ ), len(lowerCAmelCase__ ) + 1 + len(lowerCAmelCase__ )) , )
snake_case_ : Tuple = F''' {text}'''
# tokenizer_r = self.rust_tokenizer_class.from_pretrained(
# pretrained_name, use_fast=True, add_prefix_space=True, trim_offsets=True
# )
# encoding = tokenizer_r(text, return_offsets_mapping=True, add_special_tokens=False)
# self.assertEqual(encoding.offset_mapping[0], (1, 1 + len(text_of_1_token)))
# self.assertEqual(
# encoding.offset_mapping[1],
# (1 + len(text_of_1_token) + 1, 1 + len(text_of_1_token) + 1 + len(text_of_1_token)),
# )
snake_case_ : Dict = self.rust_tokenizer_class.from_pretrained(
lowerCAmelCase__ , use_fast=lowerCAmelCase__ , add_prefix_space=lowerCAmelCase__ , trim_offsets=lowerCAmelCase__ )
snake_case_ : Union[str, Any] = tokenizer_r(lowerCAmelCase__ , return_offsets_mapping=lowerCAmelCase__ , add_special_tokens=lowerCAmelCase__ )
self.assertEqual(encoding.offset_mapping[0] , (1, 1 + len(lowerCAmelCase__ )) )
self.assertEqual(
encoding.offset_mapping[1] , (1 + len(lowerCAmelCase__ ) + 1, 1 + len(lowerCAmelCase__ ) + 1 + len(lowerCAmelCase__ )) , )
snake_case_ : Any = self.rust_tokenizer_class.from_pretrained(
lowerCAmelCase__ , use_fast=lowerCAmelCase__ , add_prefix_space=lowerCAmelCase__ , trim_offsets=lowerCAmelCase__ )
snake_case_ : Any = tokenizer_r(lowerCAmelCase__ , return_offsets_mapping=lowerCAmelCase__ , add_special_tokens=lowerCAmelCase__ )
self.assertEqual(encoding.offset_mapping[0] , (0, 1 + len(lowerCAmelCase__ )) )
self.assertEqual(
encoding.offset_mapping[1] , (1 + len(lowerCAmelCase__ ), 1 + len(lowerCAmelCase__ ) + 1 + len(lowerCAmelCase__ )) , )
snake_case_ : Optional[Any] = self.rust_tokenizer_class.from_pretrained(
lowerCAmelCase__ , use_fast=lowerCAmelCase__ , add_prefix_space=lowerCAmelCase__ , trim_offsets=lowerCAmelCase__ )
snake_case_ : Optional[int] = tokenizer_r(lowerCAmelCase__ , return_offsets_mapping=lowerCAmelCase__ , add_special_tokens=lowerCAmelCase__ )
self.assertEqual(encoding.offset_mapping[0] , (0, 1 + len(lowerCAmelCase__ )) )
self.assertEqual(
encoding.offset_mapping[1] , (1 + len(lowerCAmelCase__ ), 1 + len(lowerCAmelCase__ ) + 1 + len(lowerCAmelCase__ )) , )
| 653 | 0 |
'''simple docstring'''
import json
from typing import Dict, List, Optional, Tuple, Union
from tokenizers import pre_tokenizers, processors
from ...tokenization_utils_base import AddedToken, BatchEncoding, EncodedInput
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import PaddingStrategy, logging
from .tokenization_led import LEDTokenizer
lowerCamelCase = logging.get_logger(__name__)
lowerCamelCase = {'''vocab_file''': '''vocab.json''', '''merges_file''': '''merges.txt''', '''tokenizer_file''': '''tokenizer.json'''}
lowerCamelCase = {
'''vocab_file''': {
'''allenai/led-base-16384''': '''https://huggingface.co/allenai/led-base-16384/resolve/main/vocab.json''',
},
'''merges_file''': {
'''allenai/led-base-16384''': '''https://huggingface.co/allenai/led-base-16384/resolve/main/merges.txt''',
},
'''tokenizer_file''': {
'''allenai/led-base-16384''': '''https://huggingface.co/allenai/led-base-16384/resolve/main/tokenizer.json''',
},
}
lowerCamelCase = {
'''allenai/led-base-16384''': 1_6384,
}
class _UpperCamelCase ( a_ ):
'''simple docstring'''
lowerCAmelCase__ = VOCAB_FILES_NAMES
lowerCAmelCase__ = PRETRAINED_VOCAB_FILES_MAP
lowerCAmelCase__ = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
lowerCAmelCase__ = LEDTokenizer
lowerCAmelCase__ = ["""input_ids""", """attention_mask"""]
def __init__( self : str , _lowerCAmelCase : Dict=None , _lowerCAmelCase : Union[str, Any]=None , _lowerCAmelCase : List[str]=None , _lowerCAmelCase : Dict="replace" , _lowerCAmelCase : Dict="<s>" , _lowerCAmelCase : List[str]="</s>" , _lowerCAmelCase : List[Any]="</s>" , _lowerCAmelCase : str="<s>" , _lowerCAmelCase : str="<unk>" , _lowerCAmelCase : Optional[int]="<pad>" , _lowerCAmelCase : Dict="<mask>" , _lowerCAmelCase : int=False , _lowerCAmelCase : List[str]=True , **_lowerCAmelCase : List[str] , ):
'''simple docstring'''
super().__init__(
lowerCAmelCase__ , lowerCAmelCase__ , tokenizer_file=lowerCAmelCase__ , errors=lowerCAmelCase__ , bos_token=lowerCAmelCase__ , eos_token=lowerCAmelCase__ , sep_token=lowerCAmelCase__ , cls_token=lowerCAmelCase__ , unk_token=lowerCAmelCase__ , pad_token=lowerCAmelCase__ , mask_token=lowerCAmelCase__ , add_prefix_space=lowerCAmelCase__ , trim_offsets=lowerCAmelCase__ , **lowerCAmelCase__ , )
__lowercase =json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__())
if pre_tok_state.get('add_prefix_space' , lowerCAmelCase__) != add_prefix_space:
__lowercase =getattr(lowerCAmelCase__ , pre_tok_state.pop('type'))
__lowercase =add_prefix_space
__lowercase =pre_tok_class(**lowerCAmelCase__)
__lowercase =add_prefix_space
# the pre_tokenizer is already updated in the GPT2TokenizerFast `__init__`
__lowercase ="post_processor"
__lowercase =getattr(self.backend_tokenizer , lowerCAmelCase__ , lowerCAmelCase__)
if tokenizer_component_instance:
__lowercase =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:
__lowercase =tuple(state['sep'])
if "cls" in state:
__lowercase =tuple(state['cls'])
__lowercase =False
if state.get('add_prefix_space' , lowerCAmelCase__) != add_prefix_space:
__lowercase =add_prefix_space
__lowercase =True
if state.get('trim_offsets' , lowerCAmelCase__) != trim_offsets:
__lowercase =trim_offsets
__lowercase =True
if changes_to_apply:
__lowercase =getattr(lowerCAmelCase__ , state.pop('type'))
__lowercase =component_class(**lowerCAmelCase__)
setattr(self.backend_tokenizer , lowerCAmelCase__ , lowerCAmelCase__)
@property
# Copied from transformers.models.bart.tokenization_bart_fast.BartTokenizerFast.mask_token with BART->LED
def __lowerCamelCase ( self : List[Any]):
'''simple docstring'''
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 __lowerCamelCase ( self : Any , _lowerCAmelCase : Optional[Any]):
'''simple docstring'''
__lowercase =AddedToken(lowerCAmelCase__ , lstrip=lowerCAmelCase__ , rstrip=lowerCAmelCase__) if isinstance(lowerCAmelCase__ , lowerCAmelCase__) else value
__lowercase =value
def __lowerCamelCase ( self : List[str] , *_lowerCAmelCase : Optional[int] , **_lowerCAmelCase : str):
'''simple docstring'''
__lowercase =kwargs.get('is_split_into_words' , lowerCAmelCase__)
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(*lowerCAmelCase__ , **lowerCAmelCase__)
def __lowerCamelCase ( self : str , *_lowerCAmelCase : Any , **_lowerCAmelCase : Tuple):
'''simple docstring'''
__lowercase =kwargs.get('is_split_into_words' , lowerCAmelCase__)
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(*lowerCAmelCase__ , **lowerCAmelCase__)
def __lowerCamelCase ( self : Dict , _lowerCAmelCase : str , _lowerCAmelCase : Optional[str] = None):
'''simple docstring'''
__lowercase =self._tokenizer.model.save(lowerCAmelCase__ , name=lowerCAmelCase__)
return tuple(lowerCAmelCase__)
def __lowerCamelCase ( self : Optional[Any] , _lowerCAmelCase : Any , _lowerCAmelCase : List[Any]=None):
'''simple docstring'''
__lowercase =[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 : Union[str, Any] , _lowerCAmelCase : List[int] , _lowerCAmelCase : Optional[List[int]] = None):
'''simple docstring'''
__lowercase =[self.sep_token_id]
__lowercase =[self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep) * [0]
return len(cls + token_ids_a + sep + sep + token_ids_a + sep) * [0]
def __lowerCamelCase ( self : Dict , _lowerCAmelCase : Union[Dict[str, EncodedInput], BatchEncoding] , _lowerCAmelCase : Optional[int] = None , _lowerCAmelCase : PaddingStrategy = PaddingStrategy.DO_NOT_PAD , _lowerCAmelCase : Optional[int] = None , _lowerCAmelCase : Optional[bool] = None , ):
'''simple docstring'''
__lowercase =super()._pad(
encoded_inputs=lowerCAmelCase__ , max_length=lowerCAmelCase__ , padding_strategy=lowerCAmelCase__ , pad_to_multiple_of=lowerCAmelCase__ , return_attention_mask=lowerCAmelCase__ , )
# Load from model defaults
if return_attention_mask is None:
__lowercase ="attention_mask" in self.model_input_names
if return_attention_mask and "global_attention_mask" in encoded_inputs:
__lowercase =encoded_inputs[self.model_input_names[0]]
# `global_attention_mask` need to have the same length as other (sequential) inputs.
__lowercase =len(encoded_inputs['global_attention_mask']) != len(lowerCAmelCase__)
if needs_to_be_padded:
__lowercase =len(lowerCAmelCase__) - len(encoded_inputs['global_attention_mask'])
if self.padding_side == "right":
# Use `-1` since `0` in `global_attention_mask` means `local attention` instead of `not to attend`
__lowercase =(
encoded_inputs["global_attention_mask"] + [-1] * difference
)
elif self.padding_side == "left":
__lowercase =[-1] * difference + encoded_inputs[
"global_attention_mask"
]
else:
raise ValueError('Invalid padding strategy:' + str(self.padding_side))
return encoded_inputs
| 474 |
'''simple docstring'''
import math
def __UpperCAmelCase ( __magic_name__ )-> bool:
"""simple docstring"""
snake_case_ : Optional[int] = math.loga(math.sqrt(4 * positive_integer + 1 ) / 2 + 1 / 2 )
return exponent == int(__magic_name__ )
def __UpperCAmelCase ( __magic_name__ = 1 / 1_2345 )-> int:
"""simple docstring"""
snake_case_ : Any = 0
snake_case_ : int = 0
snake_case_ : Union[str, Any] = 3
while True:
snake_case_ : Any = (integer**2 - 1) / 4
# if candidate is an integer, then there is a partition for k
if partition_candidate == int(__magic_name__ ):
snake_case_ : Optional[Any] = int(__magic_name__ )
total_partitions += 1
if check_partition_perfect(__magic_name__ ):
perfect_partitions += 1
if perfect_partitions > 0:
if perfect_partitions / total_partitions < max_proportion:
return int(__magic_name__ )
integer += 1
if __name__ == "__main__":
print(f'''{solution() = }''')
| 653 | 0 |
'''simple docstring'''
from torch import nn
def lowerCAmelCase (__A):
"""simple docstring"""
if act_fn in ["swish", "silu"]:
return nn.SiLU()
elif act_fn == "mish":
return nn.Mish()
elif act_fn == "gelu":
return nn.GELU()
else:
raise ValueError(F'''Unsupported activation function: {act_fn}''')
| 11 |
'''simple docstring'''
import argparse
import json
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from typing import List
import timm
import torch
import torch.nn as nn
from huggingface_hub import hf_hub_download
from torch import Tensor
from transformers import AutoImageProcessor, ResNetConfig, ResNetForImageClassification
from transformers.utils import logging
logging.set_verbosity_info()
__lowerCamelCase : int = logging.get_logger()
@dataclass
class A_ :
"""simple docstring"""
a__ = 42
a__ = field(default_factory=a_ )
a__ = field(default_factory=a_ )
def _A ( self :List[Any] , lowerCAmelCase__ :Union[str, Any] , lowerCAmelCase__ :Tensor , lowerCAmelCase__ :Tensor ) -> int:
'''simple docstring'''
snake_case_ : int = len(list(m.modules() ) ) == 1 or isinstance(lowerCAmelCase__ , nn.Convad ) or isinstance(lowerCAmelCase__ , nn.BatchNormad )
if has_not_submodules:
self.traced.append(lowerCAmelCase__ )
def __call__( self :List[Any] , lowerCAmelCase__ :Tensor ) -> Union[str, Any]:
'''simple docstring'''
for m in self.module.modules():
self.handles.append(m.register_forward_hook(self._forward_hook ) )
self.module(lowerCAmelCase__ )
[x.remove() for x in self.handles]
return self
@property
def _A ( self :int ) -> List[Any]:
'''simple docstring'''
return list(filter(lambda lowerCAmelCase__ : len(list(x.state_dict().keys() ) ) > 0 , self.traced ) )
@dataclass
class A_ :
"""simple docstring"""
a__ = 42
a__ = 42
a__ = 0
a__ = field(default_factory=a_ )
a__ = field(default_factory=a_ )
def __call__( self :Tuple , lowerCAmelCase__ :Tensor ) -> Tuple:
'''simple docstring'''
snake_case_ : List[Any] = Tracker(self.dest )(lowerCAmelCase__ ).parametrized
snake_case_ : Tuple = Tracker(self.src )(lowerCAmelCase__ ).parametrized
snake_case_ : List[str] = list(filter(lambda lowerCAmelCase__ : type(lowerCAmelCase__ ) not in self.src_skip , lowerCAmelCase__ ) )
snake_case_ : Tuple = list(filter(lambda lowerCAmelCase__ : type(lowerCAmelCase__ ) not in self.dest_skip , lowerCAmelCase__ ) )
if len(lowerCAmelCase__ ) != len(lowerCAmelCase__ ):
raise Exception(
F'''Numbers of operations are different. Source module has {len(lowerCAmelCase__ )} operations while'''
F''' destination module has {len(lowerCAmelCase__ )}.''' )
for dest_m, src_m in zip(lowerCAmelCase__ , lowerCAmelCase__ ):
dest_m.load_state_dict(src_m.state_dict() )
if self.verbose == 1:
print(F'''Transfered from={src_m} to={dest_m}''' )
def __UpperCAmelCase ( __magic_name__ ,__magic_name__ ,__magic_name__ ,__magic_name__ = True )-> Optional[int]:
"""simple docstring"""
print(F'''Converting {name}...''' )
with torch.no_grad():
snake_case_ : List[str] = timm.create_model(__magic_name__ ,pretrained=__magic_name__ ).eval()
snake_case_ : Optional[int] = ResNetForImageClassification(__magic_name__ ).eval()
snake_case_ : Dict = ModuleTransfer(src=__magic_name__ ,dest=__magic_name__ )
snake_case_ : Optional[int] = torch.randn((1, 3, 224, 224) )
module_transfer(__magic_name__ )
assert torch.allclose(from_model(__magic_name__ ) ,our_model(__magic_name__ ).logits ), "The model logits don't match the original one."
snake_case_ : str = F'''resnet{'-'.join(name.split('resnet' ) )}'''
print(__magic_name__ )
if push_to_hub:
our_model.push_to_hub(
repo_path_or_name=save_directory / checkpoint_name ,commit_message="Add model" ,use_temp_dir=__magic_name__ ,)
# we can use the convnext one
snake_case_ : Optional[Any] = AutoImageProcessor.from_pretrained("facebook/convnext-base-224-22k-1k" )
image_processor.push_to_hub(
repo_path_or_name=save_directory / checkpoint_name ,commit_message="Add image processor" ,use_temp_dir=__magic_name__ ,)
print(F'''Pushed {checkpoint_name}''' )
def __UpperCAmelCase ( __magic_name__ ,__magic_name__ = None ,__magic_name__ = True )-> Tuple:
"""simple docstring"""
snake_case_ : List[str] = "imagenet-1k-id2label.json"
snake_case_ : Optional[Any] = 1000
snake_case_ : List[Any] = (1, num_labels)
snake_case_ : Optional[Any] = "huggingface/label-files"
snake_case_ : Dict = num_labels
snake_case_ : List[Any] = json.load(open(hf_hub_download(__magic_name__ ,__magic_name__ ,repo_type="dataset" ) ,"r" ) )
snake_case_ : List[str] = {int(__magic_name__ ): v for k, v in idalabel.items()}
snake_case_ : Any = idalabel
snake_case_ : List[Any] = {v: k for k, v in idalabel.items()}
snake_case_ : Optional[int] = partial(__magic_name__ ,num_labels=__magic_name__ ,idalabel=__magic_name__ ,labelaid=__magic_name__ )
snake_case_ : Optional[int] = {
"resnet18": ImageNetPreTrainedConfig(
depths=[2, 2, 2, 2] ,hidden_sizes=[64, 128, 256, 512] ,layer_type="basic" ),
"resnet26": ImageNetPreTrainedConfig(
depths=[2, 2, 2, 2] ,hidden_sizes=[256, 512, 1024, 2048] ,layer_type="bottleneck" ),
"resnet34": ImageNetPreTrainedConfig(
depths=[3, 4, 6, 3] ,hidden_sizes=[64, 128, 256, 512] ,layer_type="basic" ),
"resnet50": ImageNetPreTrainedConfig(
depths=[3, 4, 6, 3] ,hidden_sizes=[256, 512, 1024, 2048] ,layer_type="bottleneck" ),
"resnet101": ImageNetPreTrainedConfig(
depths=[3, 4, 23, 3] ,hidden_sizes=[256, 512, 1024, 2048] ,layer_type="bottleneck" ),
"resnet152": ImageNetPreTrainedConfig(
depths=[3, 8, 36, 3] ,hidden_sizes=[256, 512, 1024, 2048] ,layer_type="bottleneck" ),
}
if model_name:
convert_weight_and_push(__magic_name__ ,names_to_config[model_name] ,__magic_name__ ,__magic_name__ )
else:
for model_name, config in names_to_config.items():
convert_weight_and_push(__magic_name__ ,__magic_name__ ,__magic_name__ ,__magic_name__ )
return config, expected_shape
if __name__ == "__main__":
__lowerCamelCase : Optional[int] = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'''--model_name''',
default=None,
type=str,
help=(
'''The name of the model you wish to convert, it must be one of the supported resnet* architecture,'''
''' currently: resnet18,26,34,50,101,152. If `None`, all of them will the converted.'''
),
)
parser.add_argument(
'''--pytorch_dump_folder_path''',
default=None,
type=Path,
required=True,
help='''Path to the output PyTorch model directory.''',
)
parser.add_argument(
'''--push_to_hub''',
default=True,
type=bool,
required=False,
help='''If True, push model and image processor to the hub.''',
)
__lowerCamelCase : Tuple = parser.parse_args()
__lowerCamelCase : Path = args.pytorch_dump_folder_path
pytorch_dump_folder_path.mkdir(exist_ok=True, parents=True)
convert_weights_and_push(pytorch_dump_folder_path, args.model_name, args.push_to_hub)
| 653 | 0 |
import argparse
import json
import os
import fairseq
import torch
from fairseq.data import Dictionary
from transformers import (
WavaVecaConfig,
WavaVecaCTCTokenizer,
WavaVecaFeatureExtractor,
WavaVecaForCTC,
WavaVecaForPreTraining,
WavaVecaProcessor,
logging,
)
from transformers.models.wavaveca.modeling_wavaveca import WavaVecaForSequenceClassification
logging.set_verbosity_info()
__A : Tuple = logging.get_logger(__name__)
__A : str = {
'''post_extract_proj''': '''feature_projection.projection''',
'''encoder.pos_conv.0''': '''encoder.pos_conv_embed.conv''',
'''self_attn.k_proj''': '''encoder.layers.*.attention.k_proj''',
'''self_attn.v_proj''': '''encoder.layers.*.attention.v_proj''',
'''self_attn.q_proj''': '''encoder.layers.*.attention.q_proj''',
'''self_attn.out_proj''': '''encoder.layers.*.attention.out_proj''',
'''self_attn_layer_norm''': '''encoder.layers.*.layer_norm''',
'''fc1''': '''encoder.layers.*.feed_forward.intermediate_dense''',
'''fc2''': '''encoder.layers.*.feed_forward.output_dense''',
'''final_layer_norm''': '''encoder.layers.*.final_layer_norm''',
'''encoder.layer_norm''': '''encoder.layer_norm''',
'''adapter_layer''': '''encoder.layers.*.adapter_layer''',
'''w2v_model.layer_norm''': '''feature_projection.layer_norm''',
'''quantizer.weight_proj''': '''quantizer.weight_proj''',
'''quantizer.vars''': '''quantizer.codevectors''',
'''project_q''': '''project_q''',
'''final_proj''': '''project_hid''',
'''w2v_encoder.proj''': '''lm_head''',
'''mask_emb''': '''masked_spec_embed''',
'''pooling_layer.linear''': '''projector''',
'''pooling_layer.projection''': '''classifier''',
}
__A : List[str] = [
'''lm_head''',
'''quantizer.weight_proj''',
'''quantizer.codevectors''',
'''project_q''',
'''project_hid''',
'''projector''',
'''classifier''',
]
def __a ( A__ : List[str] ):
SCREAMING_SNAKE_CASE = {}
with open(A__ , "r" ) as file:
for line_number, line in enumerate(A__ ):
SCREAMING_SNAKE_CASE = line.strip()
if line:
SCREAMING_SNAKE_CASE = line.split()
SCREAMING_SNAKE_CASE = line_number
SCREAMING_SNAKE_CASE = words[0]
SCREAMING_SNAKE_CASE = value
return result
def __a ( A__ : Optional[Any] , A__ : Tuple , A__ : str , A__ : List[str] , A__ : Union[str, Any] ):
for attribute in key.split("." ):
SCREAMING_SNAKE_CASE = getattr(A__ , A__ )
SCREAMING_SNAKE_CASE = None
for param_key in PARAM_MAPPING.keys():
if full_name.endswith(A__ ):
SCREAMING_SNAKE_CASE = PARAM_MAPPING[full_name.split("." )[-1]]
SCREAMING_SNAKE_CASE = "param"
if weight_type is not None and weight_type != "param":
SCREAMING_SNAKE_CASE = getattr(A__ , A__ ).shape
elif weight_type is not None and weight_type == "param":
SCREAMING_SNAKE_CASE = hf_pointer
for attribute in hf_param_name.split("." ):
SCREAMING_SNAKE_CASE = getattr(A__ , A__ )
SCREAMING_SNAKE_CASE = shape_pointer.shape
# let's reduce dimension
SCREAMING_SNAKE_CASE = value[0]
else:
SCREAMING_SNAKE_CASE = hf_pointer.shape
if hf_shape != value.shape:
raise ValueError(
F"Shape of hf {key + '.' + weight_type if weight_type is not None else ''} is {hf_shape}, but should be"
F" {value.shape} for {full_name}" )
if weight_type == "weight":
SCREAMING_SNAKE_CASE = value
elif weight_type == "weight_g":
SCREAMING_SNAKE_CASE = value
elif weight_type == "weight_v":
SCREAMING_SNAKE_CASE = value
elif weight_type == "bias":
SCREAMING_SNAKE_CASE = value
elif weight_type == "param":
for attribute in hf_param_name.split("." ):
SCREAMING_SNAKE_CASE = getattr(A__ , A__ )
SCREAMING_SNAKE_CASE = value
else:
SCREAMING_SNAKE_CASE = value
logger.info(F"{key + '.' + weight_type if weight_type is not None else ''} was initialized from {full_name}." )
def __a ( A__ : Optional[Any] , A__ : Tuple , A__ : List[Any] , A__ : List[str] , A__ : int ):
SCREAMING_SNAKE_CASE = None
for param_key in PARAM_MAPPING.keys():
if full_name.endswith(A__ ):
SCREAMING_SNAKE_CASE = PARAM_MAPPING[full_name.split("." )[-1]]
SCREAMING_SNAKE_CASE = "param"
if weight_type is not None and weight_type != "param":
SCREAMING_SNAKE_CASE = ".".join([key, weight_type] )
elif weight_type is not None and weight_type == "param":
SCREAMING_SNAKE_CASE = ".".join([key, hf_param_name] )
else:
SCREAMING_SNAKE_CASE = key
SCREAMING_SNAKE_CASE = value if "lm_head" in full_key else value[0]
__A : Optional[int] = {
'''W_a''': '''linear_1.weight''',
'''W_b''': '''linear_2.weight''',
'''b_a''': '''linear_1.bias''',
'''b_b''': '''linear_2.bias''',
'''ln_W''': '''norm.weight''',
'''ln_b''': '''norm.bias''',
}
def __a ( A__ : Dict , A__ : Optional[int] , A__ : Optional[int]=None , A__ : int=None ):
SCREAMING_SNAKE_CASE = False
for key, mapped_key in MAPPING.items():
SCREAMING_SNAKE_CASE = "wav2vec2." + mapped_key if mapped_key not in TOP_LEVEL_KEYS else mapped_key
if key in name or key.split("w2v_model." )[-1] == name.split("." )[0]:
SCREAMING_SNAKE_CASE = True
if "*" in mapped_key:
SCREAMING_SNAKE_CASE = name.split(A__ )[0].split("." )[-2]
SCREAMING_SNAKE_CASE = mapped_key.replace("*" , A__ )
if "weight_g" in name:
SCREAMING_SNAKE_CASE = "weight_g"
elif "weight_v" in name:
SCREAMING_SNAKE_CASE = "weight_v"
elif "bias" in name:
SCREAMING_SNAKE_CASE = "bias"
elif "weight" in name:
# TODO: don't match quantizer.weight_proj
SCREAMING_SNAKE_CASE = "weight"
else:
SCREAMING_SNAKE_CASE = None
if hf_dict is not None:
rename_dict(A__ , A__ , A__ , A__ , A__ )
else:
set_recursively(A__ , A__ , A__ , A__ , A__ )
return is_used
return is_used
def __a ( A__ : Any , A__ : List[str] , A__ : str ):
SCREAMING_SNAKE_CASE = []
SCREAMING_SNAKE_CASE = fairseq_model.state_dict()
SCREAMING_SNAKE_CASE = hf_model.wavaveca.feature_extractor
for name, value in fairseq_dict.items():
SCREAMING_SNAKE_CASE = False
if "conv_layers" in name:
load_conv_layer(
A__ , A__ , A__ , A__ , hf_model.config.feat_extract_norm == "group" , )
SCREAMING_SNAKE_CASE = True
else:
SCREAMING_SNAKE_CASE = load_wavaveca_layer(A__ , A__ , A__ )
if not is_used:
unused_weights.append(A__ )
logger.warning(F"Unused weights: {unused_weights}" )
def __a ( A__ : Optional[int] , A__ : Optional[int] , A__ : Union[str, Any] , A__ : int , A__ : str ):
SCREAMING_SNAKE_CASE = full_name.split("conv_layers." )[-1]
SCREAMING_SNAKE_CASE = name.split("." )
SCREAMING_SNAKE_CASE = int(items[0] )
SCREAMING_SNAKE_CASE = int(items[1] )
if type_id == 0:
if "bias" in name:
if value.shape != feature_extractor.conv_layers[layer_id].conv.bias.data.shape:
raise ValueError(
F"{full_name} has size {value.shape}, but"
F" {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found." )
SCREAMING_SNAKE_CASE = value
logger.info(F"Feat extract conv layer {layer_id} was initialized from {full_name}." )
elif "weight" in name:
if value.shape != feature_extractor.conv_layers[layer_id].conv.weight.data.shape:
raise ValueError(
F"{full_name} has size {value.shape}, but"
F" {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found." )
SCREAMING_SNAKE_CASE = value
logger.info(F"Feat extract conv layer {layer_id} was initialized from {full_name}." )
elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
if "bias" in name:
if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape:
raise ValueError(
F"{full_name} has size {value.shape}, but"
F" {feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape} was found." )
SCREAMING_SNAKE_CASE = value
logger.info(F"Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}." )
elif "weight" in name:
if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape:
raise ValueError(
F"{full_name} has size {value.shape}, but"
F" {feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape} was found." )
SCREAMING_SNAKE_CASE = value
logger.info(F"Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}." )
else:
unused_weights.append(A__ )
@torch.no_grad()
def __a ( A__ : Dict , A__ : List[Any] , A__ : Optional[Any]=None , A__ : List[Any]=None , A__ : Dict=True , A__ : Tuple=False ):
if config_path is not None:
SCREAMING_SNAKE_CASE = WavaVecaConfig.from_pretrained(A__ )
else:
SCREAMING_SNAKE_CASE = WavaVecaConfig()
if is_seq_class:
SCREAMING_SNAKE_CASE = read_txt_into_dict(A__ )
SCREAMING_SNAKE_CASE = idalabel
SCREAMING_SNAKE_CASE = WavaVecaForSequenceClassification(A__ )
SCREAMING_SNAKE_CASE = WavaVecaFeatureExtractor(
feature_size=1 , sampling_rate=16000 , padding_value=0 , do_normalize=A__ , return_attention_mask=A__ , )
feature_extractor.save_pretrained(A__ )
elif is_finetuned:
if dict_path:
SCREAMING_SNAKE_CASE = Dictionary.load(A__ )
# important change bos & pad token id since CTC symbol is <pad> and
# not <s> as in fairseq
SCREAMING_SNAKE_CASE = target_dict.pad_index
SCREAMING_SNAKE_CASE = target_dict.bos_index
SCREAMING_SNAKE_CASE = target_dict.eos_index
SCREAMING_SNAKE_CASE = len(target_dict.symbols )
SCREAMING_SNAKE_CASE = os.path.join(A__ , "vocab.json" )
if not os.path.isdir(A__ ):
logger.error("--pytorch_dump_folder_path ({}) should be a directory".format(A__ ) )
return
os.makedirs(A__ , exist_ok=A__ )
SCREAMING_SNAKE_CASE = target_dict.indices
# fairseq has the <pad> and <s> switched
SCREAMING_SNAKE_CASE = 0
SCREAMING_SNAKE_CASE = 1
with open(A__ , "w" , encoding="utf-8" ) as vocab_handle:
json.dump(A__ , A__ )
SCREAMING_SNAKE_CASE = WavaVecaCTCTokenizer(
A__ , unk_token=target_dict.unk_word , pad_token=target_dict.pad_word , bos_token=target_dict.bos_word , eos_token=target_dict.eos_word , word_delimiter_token="|" , do_lower_case=A__ , )
SCREAMING_SNAKE_CASE = True if config.feat_extract_norm == "layer" else False
SCREAMING_SNAKE_CASE = WavaVecaFeatureExtractor(
feature_size=1 , sampling_rate=16000 , padding_value=0 , do_normalize=A__ , return_attention_mask=A__ , )
SCREAMING_SNAKE_CASE = WavaVecaProcessor(feature_extractor=A__ , tokenizer=A__ )
processor.save_pretrained(A__ )
SCREAMING_SNAKE_CASE = WavaVecaForCTC(A__ )
else:
SCREAMING_SNAKE_CASE = WavaVecaForPreTraining(A__ )
if is_finetuned or is_seq_class:
SCREAMING_SNAKE_CASE = fairseq.checkpoint_utils.load_model_ensemble_and_task(
[checkpoint_path] , arg_overrides={"data": "/".join(dict_path.split("/" )[:-1] )} )
else:
SCREAMING_SNAKE_CASE = argparse.Namespace(task="audio_pretraining" )
SCREAMING_SNAKE_CASE = fairseq.tasks.setup_task(A__ )
SCREAMING_SNAKE_CASE = fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path] , task=A__ )
SCREAMING_SNAKE_CASE = model[0].eval()
recursively_load_weights(A__ , A__ , not is_finetuned )
hf_wavavec.save_pretrained(A__ )
if __name__ == "__main__":
__A : Any = argparse.ArgumentParser()
parser.add_argument('--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.')
parser.add_argument('--checkpoint_path', default=None, type=str, help='Path to fairseq checkpoint')
parser.add_argument('--dict_path', default=None, type=str, help='Path to dict of fine-tuned model')
parser.add_argument('--config_path', default=None, type=str, help='Path to hf config.json of model to convert')
parser.add_argument(
'--not_finetuned', action='store_true', help='Whether the model to convert is a fine-tuned model or not'
)
parser.add_argument(
'--is_seq_class',
action='store_true',
help='Whether the model to convert is a fine-tuned sequence classification model or not',
)
__A : List[str] = parser.parse_args()
__A : Optional[Any] = not args.not_finetuned and not args.is_seq_class
convert_wavaveca_checkpoint(
args.checkpoint_path,
args.pytorch_dump_folder_path,
args.config_path,
args.dict_path,
is_finetuned,
args.is_seq_class,
) | 16 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__lowerCamelCase : List[Any] = logging.get_logger(__name__)
__lowerCamelCase : Dict = {
'''weiweishi/roc-bert-base-zh''': '''https://huggingface.co/weiweishi/roc-bert-base-zh/resolve/main/config.json''',
}
class A_ (a_ ):
"""simple docstring"""
a__ = '''roc_bert'''
def __init__( self :Dict , lowerCAmelCase__ :Optional[Any]=30_522 , lowerCAmelCase__ :Dict=768 , lowerCAmelCase__ :str=12 , lowerCAmelCase__ :Optional[int]=12 , lowerCAmelCase__ :Optional[Any]=3_072 , lowerCAmelCase__ :Any="gelu" , lowerCAmelCase__ :int=0.1 , lowerCAmelCase__ :Tuple=0.1 , lowerCAmelCase__ :List[str]=512 , lowerCAmelCase__ :int=2 , lowerCAmelCase__ :Optional[int]=0.0_2 , lowerCAmelCase__ :Tuple=1E-1_2 , lowerCAmelCase__ :Tuple=True , lowerCAmelCase__ :List[str]=0 , lowerCAmelCase__ :Optional[Any]="absolute" , lowerCAmelCase__ :Tuple=None , lowerCAmelCase__ :List[str]=True , lowerCAmelCase__ :Optional[Any]=True , lowerCAmelCase__ :List[str]=768 , lowerCAmelCase__ :Optional[Any]=910 , lowerCAmelCase__ :str=512 , lowerCAmelCase__ :int=24_858 , lowerCAmelCase__ :List[Any]=True , **lowerCAmelCase__ :int , ) -> List[str]:
'''simple docstring'''
snake_case_ : int = vocab_size
snake_case_ : Dict = max_position_embeddings
snake_case_ : int = hidden_size
snake_case_ : str = num_hidden_layers
snake_case_ : Union[str, Any] = num_attention_heads
snake_case_ : int = intermediate_size
snake_case_ : Optional[Any] = hidden_act
snake_case_ : Optional[int] = hidden_dropout_prob
snake_case_ : List[Any] = attention_probs_dropout_prob
snake_case_ : Dict = initializer_range
snake_case_ : str = type_vocab_size
snake_case_ : Tuple = layer_norm_eps
snake_case_ : Optional[Any] = use_cache
snake_case_ : Optional[Any] = enable_pronunciation
snake_case_ : List[Any] = enable_shape
snake_case_ : Optional[int] = pronunciation_embed_dim
snake_case_ : Dict = pronunciation_vocab_size
snake_case_ : int = shape_embed_dim
snake_case_ : Any = shape_vocab_size
snake_case_ : Optional[int] = concat_input
snake_case_ : List[Any] = position_embedding_type
snake_case_ : Any = classifier_dropout
super().__init__(pad_token_id=lowerCAmelCase__ , **lowerCAmelCase__ )
| 653 | 0 |
'''simple docstring'''
from __future__ import annotations
import os
import tempfile
import unittest
import numpy as np
from huggingface_hub import hf_hub_download
from transformers import is_tensorflow_text_available, is_tf_available
from transformers.testing_utils import require_tensorflow_text, require_tf, slow
from ..test_modeling_tf_common import floats_tensor
from .test_framework_agnostic import GenerationIntegrationTestsMixin
if is_tf_available():
import tensorflow as tf
from transformers import (
AutoTokenizer,
TFAutoModelForCausalLM,
TFAutoModelForSeqaSeqLM,
TFAutoModelForSpeechSeqaSeq,
TFAutoModelForVisionaSeq,
TFBartForConditionalGeneration,
TFLogitsProcessorList,
TFMinLengthLogitsProcessor,
tf_top_k_top_p_filtering,
)
if is_tensorflow_text_available():
import tensorflow_text as text
@require_tf
class _snake_case ( unittest.TestCase ):
'''simple docstring'''
def lowerCAmelCase__ ( self: Union[str, Any] ) -> str:
__magic_name__ : Optional[int] = tf.convert_to_tensor(
[
[
8.2_2_2_0_9_9_1, # 3rd highest value; idx. 0
-0.5_6_2_0_0_4_4,
5.2_3_2_2_9_7_5_2,
4.0_3_8_6_3_9_3,
-6.8_7_9_8_3_7_8,
-0.5_4_7_8_5_8_0_2,
-3.2_0_1_2_1_5_3,
2.9_2_7_7_7_1_7_6,
1.8_8_1_7_1_9_5_3,
7.3_5_3_4_1_2_7_6, # 5th highest value; idx. 9
8.4_3_2_0_7_8_3_3, # 2nd highest value; idx. 10
-9.8_5_7_1_1_8_3_6,
-5.9_6_2_0_9_2_3_6,
-1.1_3_0_3_9_1_6_1,
-7.1_1_1_5_2_9_4,
-0.8_3_6_9_6_3_3,
-5.3_1_8_6_4_0_8,
7.0_6_4_2_7_4_0_7,
0.8_1_3_6_9_3_4_4,
-0.8_2_0_2_3_8_1_7,
-5.9_1_7_9_7_9_6,
0.5_8_8_1_3_4_4_3,
-6.9_9_7_7_8_4_3_8,
4.7_1_5_5_1_1_8_9,
-0.1_8_7_7_1_6_3_7,
7.4_4_0_2_0_7_5_9, # 4th highest value; idx. 25
9.3_8_4_5_0_9_8_7, # 1st highest value; idx. 26
2.1_2_6_6_2_9_4_1,
-9.3_2_5_6_2_0_3_8,
2.3_5_6_5_2_5_2_2,
], # cummulative prob of 5 highest values <= 0.6
[
0.5_8_4_2_5_5_1_8,
4.5_3_1_3_9_2_3_8,
-5.5_7_5_1_0_4_6_4,
-6.2_8_0_3_0_6_9_9,
-7.1_9_5_2_9_5_0_3,
-4.0_2_1_2_2_5_5_1,
1.3_9_3_3_7_0_3_7,
-6.0_6_7_0_7_0_5_7,
1.5_9_4_8_0_5_1_7,
-9.6_4_3_1_1_9,
0.0_3_9_0_7_7_9_9,
0.6_7_2_3_1_7_6_2,
-8.8_8_2_0_6_7_2_6,
6.2_7_1_1_5_9_2_2, # 4th highest value; idx. 13
2.2_8_5_2_0_7_2_3,
4.8_2_7_6_7_5_0_6,
4.3_0_4_2_1_3_6_8,
8.8_2_7_5_3_1_3, # 2nd highest value; idx. 17
5.4_4_0_2_9_9_5_8, # 5th highest value; idx. 18
-4.4_7_3_5_7_9_4,
7.3_8_5_7_9_5_3_6, # 3rd highest value; idx. 20
-2.9_1_0_5_1_6_6_3,
2.6_1_9_4_6_0_7_7,
-2.5_6_7_4_7_6_2,
-9.4_8_9_5_9_3_0_2,
-4.0_2_9_2_2_6_4_5,
-1.3_5_4_1_6_9_1_8,
9.6_7_7_0_2_3_2_3, # 1st highest value; idx. 27
-5.8_9_4_7_8_5_5_3,
1.8_5_3_7_0_4_6_7,
], # cummulative prob of 5 highest values <= 0.6
] , dtype=tf.floataa , )
__magic_name__ : Optional[Any] = tf.convert_to_tensor(
[[0, 0], [0, 9], [0, 10], [0, 25], [0, 26], [1, 13], [1, 17], [1, 18], [1, 20], [1, 27]] , dtype=tf.intaa , ) # expected non filtered idx as noted above
__magic_name__ : List[str] = tf.convert_to_tensor(
[8.2_2_2_0_9_9, 7.3_5_3_4_1_2_6, 8.4_3_2_0_7_8, 7.4_4_0_2_0_7_5, 9.3_8_4_5_1, 6.2_7_1_1_5_9, 8.8_2_7_5_3_1, 5.4_4_0_2_9_9_5, 7.3_8_5_7_9_5_6, 9.6_7_7_0_2_3] , dtype=tf.floataa , ) # expected non filtered values as noted above
__magic_name__ : Tuple = tf_top_k_top_p_filtering(lowerCAmelCase__ , top_k=10 , top_p=0.6 , min_tokens_to_keep=4 )
__magic_name__ : Dict = output[output != -float("inf" )]
__magic_name__ : List[str] = tf.cast(
tf.where(tf.not_equal(lowerCAmelCase__ , tf.constant(-float("inf" ) , dtype=tf.floataa ) ) ) , dtype=tf.intaa , )
tf.debugging.assert_near(lowerCAmelCase__ , lowerCAmelCase__ , rtol=1E-12 )
tf.debugging.assert_equal(lowerCAmelCase__ , lowerCAmelCase__ )
@require_tf
class _snake_case ( unittest.TestCase , a_ ):
'''simple docstring'''
if is_tf_available():
__snake_case = {
"AutoModelForCausalLM": TFAutoModelForCausalLM,
"AutoModelForSpeechSeq2Seq": TFAutoModelForSpeechSeqaSeq,
"AutoModelForSeq2SeqLM": TFAutoModelForSeqaSeqLM,
"AutoModelForVision2Seq": TFAutoModelForVisionaSeq,
"LogitsProcessorList": TFLogitsProcessorList,
"MinLengthLogitsProcessor": TFMinLengthLogitsProcessor,
"create_tensor_fn": tf.convert_to_tensor,
"floats_tensor": floats_tensor,
"return_tensors": "tf",
}
@slow
def lowerCAmelCase__ ( self: Any ) -> List[Any]:
__magic_name__ : Tuple = TFAutoModelForCausalLM.from_pretrained("hf-internal-testing/tiny-random-gpt2" )
__magic_name__ : int = 2
__magic_name__ : Optional[int] = 2
class _snake_case ( tf.Module ):
'''simple docstring'''
def __init__( self: Optional[Any] , __UpperCamelCase: Union[str, Any] ) -> Any:
super(lowerCAmelCase__ , self ).__init__()
__magic_name__ : Any = model
@tf.function(
input_signature=(
tf.TensorSpec((None, input_length) , tf.intaa , name="input_ids" ),
tf.TensorSpec((None, input_length) , tf.intaa , name="attention_mask" ),
) , jit_compile=lowerCAmelCase__ , )
def lowerCAmelCase__ ( self: Dict , __UpperCamelCase: List[str] , __UpperCamelCase: Optional[int] ) -> Optional[Any]:
__magic_name__ : Union[str, Any] = self.model.generate(
input_ids=lowerCAmelCase__ , attention_mask=lowerCAmelCase__ , max_new_tokens=lowerCAmelCase__ , return_dict_in_generate=lowerCAmelCase__ , )
return {"sequences": outputs["sequences"]}
__magic_name__ : Optional[Any] = [[2, 0], [102, 103]]
__magic_name__ : Union[str, Any] = [[1, 0], [1, 1]]
__magic_name__ : int = DummyModel(model=lowerCAmelCase__ )
with tempfile.TemporaryDirectory() as tmp_dir:
tf.saved_model.save(lowerCAmelCase__ , lowerCAmelCase__ , signatures={"serving_default": dummy_model.serving} )
__magic_name__ : Optional[Any] = tf.saved_model.load(lowerCAmelCase__ ).signatures["serving_default"]
for batch_size in range(1 , len(lowerCAmelCase__ ) + 1 ):
__magic_name__ : Dict = {
"input_ids": tf.constant(dummy_input_ids[:batch_size] ),
"attention_mask": tf.constant(dummy_attention_masks[:batch_size] ),
}
__magic_name__ : Dict = serving_func(**lowerCAmelCase__ )["sequences"]
__magic_name__ : Optional[Any] = test_model.generate(**lowerCAmelCase__ , max_new_tokens=lowerCAmelCase__ )
tf.debugging.assert_equal(lowerCAmelCase__ , lowerCAmelCase__ )
@slow
def lowerCAmelCase__ ( self: Tuple ) -> int:
__magic_name__ : List[str] = TFAutoModelForCausalLM.from_pretrained("hf-internal-testing/tiny-random-gpt2" )
__magic_name__ : Dict = 1
__magic_name__ : Tuple = 2
class _snake_case ( tf.Module ):
'''simple docstring'''
def __init__( self: int , __UpperCamelCase: Tuple ) -> Dict:
super(lowerCAmelCase__ , self ).__init__()
__magic_name__ : Tuple = model
@tf.function(
input_signature=(
tf.TensorSpec((batch_size, None) , tf.intaa , name="input_ids" ),
tf.TensorSpec((batch_size, None) , tf.intaa , name="attention_mask" ),
) , jit_compile=lowerCAmelCase__ , )
def lowerCAmelCase__ ( self: Union[str, Any] , __UpperCamelCase: str , __UpperCamelCase: Optional[Any] ) -> List[str]:
__magic_name__ : Optional[Any] = self.model.generate(
input_ids=lowerCAmelCase__ , attention_mask=lowerCAmelCase__ , max_new_tokens=lowerCAmelCase__ , return_dict_in_generate=lowerCAmelCase__ , )
return {"sequences": outputs["sequences"]}
__magic_name__ : Any = [[2], [102, 103]]
__magic_name__ : Optional[int] = [[1], [1, 1]]
__magic_name__ : Tuple = DummyModel(model=lowerCAmelCase__ )
with tempfile.TemporaryDirectory() as tmp_dir:
tf.saved_model.save(lowerCAmelCase__ , lowerCAmelCase__ , signatures={"serving_default": dummy_model.serving} )
__magic_name__ : Tuple = tf.saved_model.load(lowerCAmelCase__ ).signatures["serving_default"]
for input_row in range(len(lowerCAmelCase__ ) ):
__magic_name__ : Optional[int] = {
"input_ids": tf.constant([dummy_input_ids[input_row]] ),
"attention_mask": tf.constant([dummy_attention_masks[input_row]] ),
}
__magic_name__ : str = serving_func(**lowerCAmelCase__ )["sequences"]
__magic_name__ : Optional[Any] = test_model.generate(**lowerCAmelCase__ , max_new_tokens=lowerCAmelCase__ )
tf.debugging.assert_equal(lowerCAmelCase__ , lowerCAmelCase__ )
@slow
@require_tensorflow_text
def lowerCAmelCase__ ( self: Dict ) -> Optional[int]:
with tempfile.TemporaryDirectory() as tmp_dir:
# file needed to load the TF tokenizer
hf_hub_download(repo_id="google/flan-t5-small" , filename="spiece.model" , local_dir=lowerCAmelCase__ )
class _snake_case ( tf.keras.layers.Layer ):
'''simple docstring'''
def __init__( self: Optional[Any] ) -> List[str]:
super().__init__()
__magic_name__ : Union[str, Any] = text.SentencepieceTokenizer(
model=tf.io.gfile.GFile(os.path.join(lowerCAmelCase__ , "spiece.model" ) , "rb" ).read() )
__magic_name__ : str = TFAutoModelForSeqaSeqLM.from_pretrained("hf-internal-testing/tiny-random-t5" )
def lowerCAmelCase__ ( self: Dict , __UpperCamelCase: Any , *__UpperCamelCase: Tuple , **__UpperCamelCase: Optional[int] ) -> Union[str, Any]:
__magic_name__ : Optional[Any] = self.tokenizer.tokenize(lowerCAmelCase__ )
__magic_name__ : List[str] = text.pad_model_inputs(
lowerCAmelCase__ , max_seq_length=64 , pad_value=self.model.config.pad_token_id )
__magic_name__ : List[Any] = self.model.generate(input_ids=lowerCAmelCase__ , attention_mask=lowerCAmelCase__ )
return self.tokenizer.detokenize(lowerCAmelCase__ )
__magic_name__ : int = CompleteSentenceTransformer()
__magic_name__ : int = tf.keras.layers.Input(shape=(1,) , dtype=tf.string , name="inputs" )
__magic_name__ : int = complete_model(lowerCAmelCase__ )
__magic_name__ : List[str] = tf.keras.Model(lowerCAmelCase__ , lowerCAmelCase__ )
keras_model.save(lowerCAmelCase__ )
def lowerCAmelCase__ ( self: Union[str, Any] ) -> Union[str, Any]:
__magic_name__ : List[Any] = {
"do_sample": True,
"num_beams": 1,
"top_p": 0.7,
"top_k": 10,
"temperature": 0.7,
}
__magic_name__ : Union[str, Any] = 14
__magic_name__ : Any = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-gpt2" )
__magic_name__ : List[Any] = "Hello, my dog is cute and"
__magic_name__ : Union[str, Any] = tokenizer(lowerCAmelCase__ , return_tensors="tf" )
__magic_name__ : Union[str, Any] = TFAutoModelForCausalLM.from_pretrained("hf-internal-testing/tiny-random-gpt2" )
__magic_name__ : int = 638
# forces the generation to happen on CPU, to avoid GPU-related quirks
with tf.device(":/CPU:0" ):
tf.random.set_seed(0 )
__magic_name__ : Any = model.generate(**lowerCAmelCase__ , eos_token_id=lowerCAmelCase__ , **lowerCAmelCase__ )
self.assertTrue(expectation == len(generated_tokens[0] ) )
__magic_name__ : Tuple = [638, 198]
with tf.device(":/CPU:0" ):
tf.random.set_seed(0 )
__magic_name__ : Any = model.generate(**lowerCAmelCase__ , eos_token_id=lowerCAmelCase__ , **lowerCAmelCase__ )
self.assertTrue(expectation == len(generated_tokens[0] ) )
def lowerCAmelCase__ ( self: int ) -> Any:
__magic_name__ : Any = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-bart" )
__magic_name__ : List[str] = "Hugging Face is a technology company based in New York and Paris."
__magic_name__ : List[Any] = bart_tokenizer(lowerCAmelCase__ , return_tensors="tf" ).input_ids
__magic_name__ : int = TFBartForConditionalGeneration.from_pretrained("hf-internal-testing/tiny-random-bart" )
__magic_name__ : Optional[Any] = bart_model.generate(lowerCAmelCase__ ).numpy()
class _snake_case ( a_ ):
'''simple docstring'''
def lowerCAmelCase__ ( self: Tuple , __UpperCamelCase: Any , __UpperCamelCase: List[Any]=None , **__UpperCamelCase: Any ) -> List[Any]:
return super().call(lowerCAmelCase__ , **lowerCAmelCase__ )
__magic_name__ : int = FakeBart.from_pretrained("hf-internal-testing/tiny-random-bart" )
__magic_name__ : List[Any] = bart_model.generate(lowerCAmelCase__ , foo="bar" ).numpy()
self.assertTrue(np.array_equal(lowerCAmelCase__ , lowerCAmelCase__ ) )
class _snake_case ( bart_model.model.encoder.__class__ ):
'''simple docstring'''
def lowerCAmelCase__ ( self: Optional[Any] , __UpperCamelCase: Union[str, Any] , **__UpperCamelCase: List[Any] ) -> List[Any]:
return super().call(lowerCAmelCase__ , **lowerCAmelCase__ )
__magic_name__ : int = FakeEncoder(bart_model.config , bart_model.model.shared )
__magic_name__ : Union[str, Any] = fake_encoder
# Normal generation still works (the output will be different because the encoder weights are different)
__magic_name__ : List[str] = bart_model.generate(lowerCAmelCase__ ).numpy()
with self.assertRaises(lowerCAmelCase__ ):
# FakeEncoder.call() accepts **kwargs -> no filtering -> value error due to unexpected input "foo"
bart_model.generate(lowerCAmelCase__ , foo="bar" ) | 436 |
'''simple docstring'''
def __UpperCAmelCase ( __magic_name__ ,__magic_name__ ,__magic_name__ )-> int:
"""simple docstring"""
def update_area_of_max_square(__magic_name__ ,__magic_name__ ) -> int:
# BASE CASE
if row >= rows or col >= cols:
return 0
snake_case_ : str = update_area_of_max_square(__magic_name__ ,col + 1 )
snake_case_ : Dict = update_area_of_max_square(row + 1 ,col + 1 )
snake_case_ : int = update_area_of_max_square(row + 1 ,__magic_name__ )
if mat[row][col]:
snake_case_ : str = 1 + min([right, diagonal, down] )
snake_case_ : Tuple = max(largest_square_area[0] ,__magic_name__ )
return sub_problem_sol
else:
return 0
snake_case_ : Union[str, Any] = [0]
update_area_of_max_square(0 ,0 )
return largest_square_area[0]
def __UpperCAmelCase ( __magic_name__ ,__magic_name__ ,__magic_name__ )-> int:
"""simple docstring"""
def update_area_of_max_square_using_dp_array(
__magic_name__ ,__magic_name__ ,__magic_name__ ) -> int:
if row >= rows or col >= cols:
return 0
if dp_array[row][col] != -1:
return dp_array[row][col]
snake_case_ : Dict = update_area_of_max_square_using_dp_array(__magic_name__ ,col + 1 ,__magic_name__ )
snake_case_ : List[Any] = update_area_of_max_square_using_dp_array(row + 1 ,col + 1 ,__magic_name__ )
snake_case_ : Any = update_area_of_max_square_using_dp_array(row + 1 ,__magic_name__ ,__magic_name__ )
if mat[row][col]:
snake_case_ : int = 1 + min([right, diagonal, down] )
snake_case_ : Tuple = max(largest_square_area[0] ,__magic_name__ )
snake_case_ : Optional[Any] = sub_problem_sol
return sub_problem_sol
else:
return 0
snake_case_ : List[Any] = [0]
snake_case_ : Optional[int] = [[-1] * cols for _ in range(__magic_name__ )]
update_area_of_max_square_using_dp_array(0 ,0 ,__magic_name__ )
return largest_square_area[0]
def __UpperCAmelCase ( __magic_name__ ,__magic_name__ ,__magic_name__ )-> int:
"""simple docstring"""
snake_case_ : Dict = [[0] * (cols + 1) for _ in range(rows + 1 )]
snake_case_ : Dict = 0
for row in range(rows - 1 ,-1 ,-1 ):
for col in range(cols - 1 ,-1 ,-1 ):
snake_case_ : List[str] = dp_array[row][col + 1]
snake_case_ : Any = dp_array[row + 1][col + 1]
snake_case_ : Any = dp_array[row + 1][col]
if mat[row][col] == 1:
snake_case_ : Any = 1 + min(__magic_name__ ,__magic_name__ ,__magic_name__ )
snake_case_ : str = max(dp_array[row][col] ,__magic_name__ )
else:
snake_case_ : Optional[Any] = 0
return largest_square_area
def __UpperCAmelCase ( __magic_name__ ,__magic_name__ ,__magic_name__ )-> int:
"""simple docstring"""
snake_case_ : str = [0] * (cols + 1)
snake_case_ : Tuple = [0] * (cols + 1)
snake_case_ : List[str] = 0
for row in range(rows - 1 ,-1 ,-1 ):
for col in range(cols - 1 ,-1 ,-1 ):
snake_case_ : Optional[Any] = current_row[col + 1]
snake_case_ : Optional[int] = next_row[col + 1]
snake_case_ : Dict = next_row[col]
if mat[row][col] == 1:
snake_case_ : Union[str, Any] = 1 + min(__magic_name__ ,__magic_name__ ,__magic_name__ )
snake_case_ : Any = max(current_row[col] ,__magic_name__ )
else:
snake_case_ : Dict = 0
snake_case_ : Optional[Any] = current_row
return largest_square_area
if __name__ == "__main__":
import doctest
doctest.testmod()
print(largest_square_area_in_matrix_bottom_up(2, 2, [[1, 1], [1, 1]]))
| 653 | 0 |
import torch
from diffusers import DiffusionPipeline
class A ( a_ ):
def __init__(self , lowerCAmelCase , lowerCAmelCase ):
super().__init__()
self.register_modules(unet=lowerCAmelCase__ , scheduler=lowerCAmelCase__ )
def __call__(self ):
__lowercase= torch.randn(
(1, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size) , )
__lowercase= 1
__lowercase= self.unet(lowerCAmelCase__ , lowerCAmelCase__ ).sample
__lowercase= self.scheduler.step(lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ ).prev_sample
__lowercase= scheduler_output - scheduler_output + torch.ones_like(lowerCAmelCase__ )
return result
| 230 |
'''simple docstring'''
import os
import zipfile
import requests
from get_ci_error_statistics import download_artifact, get_artifacts_links
def __UpperCAmelCase ( __magic_name__ ,__magic_name__=7 )-> Tuple:
"""simple docstring"""
snake_case_ : List[str] = None
if token is not None:
snake_case_ : List[str] = {"Accept": "application/vnd.github+json", "Authorization": F'''Bearer {token}'''}
# The id of a workflow (not of a workflow run)
snake_case_ : Dict = "636036"
snake_case_ : List[str] = F'''https://api.github.com/repos/huggingface/transformers/actions/workflows/{workflow_id}/runs'''
# On `main` branch + event being `schedule` + not returning PRs + only `num_runs` results
url += F'''?branch=main&event=schedule&exclude_pull_requests=true&per_page={num_runs}'''
snake_case_ : Optional[Any] = requests.get(__magic_name__ ,headers=__magic_name__ ).json()
return result["workflow_runs"]
def __UpperCAmelCase ( __magic_name__ )-> Union[str, Any]:
"""simple docstring"""
snake_case_ : str = get_daily_ci_runs(__magic_name__ )
snake_case_ : Optional[int] = None
for workflow_run in workflow_runs:
if workflow_run["status"] == "completed":
snake_case_ : Dict = workflow_run["id"]
break
return workflow_run_id
def __UpperCAmelCase ( __magic_name__ ,__magic_name__ ,__magic_name__ )-> List[Any]:
"""simple docstring"""
snake_case_ : Optional[Any] = get_last_daily_ci_runs(__magic_name__ )
if workflow_run_id is not None:
snake_case_ : Union[str, Any] = get_artifacts_links(worflow_run_id=__magic_name__ ,token=__magic_name__ )
for artifact_name in artifact_names:
if artifact_name in artifacts_links:
snake_case_ : Union[str, Any] = artifacts_links[artifact_name]
download_artifact(
artifact_name=__magic_name__ ,artifact_url=__magic_name__ ,output_dir=__magic_name__ ,token=__magic_name__ )
def __UpperCAmelCase ( __magic_name__ ,__magic_name__ ,__magic_name__ )-> List[Any]:
"""simple docstring"""
get_last_daily_ci_artifacts(__magic_name__ ,__magic_name__ ,__magic_name__ )
snake_case_ : Union[str, Any] = {}
for artifact_name in artifact_names:
snake_case_ : Any = os.path.join(__magic_name__ ,F'''{artifact_name}.zip''' )
if os.path.isfile(__magic_name__ ):
snake_case_ : Tuple = {}
with zipfile.ZipFile(__magic_name__ ) as z:
for filename in z.namelist():
if not os.path.isdir(__magic_name__ ):
# read the file
with z.open(__magic_name__ ) as f:
snake_case_ : Optional[Any] = f.read().decode("UTF-8" )
return results
| 653 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
lowerCAmelCase : Optional[Any] = {
'''configuration_transfo_xl''': ['''TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''TransfoXLConfig'''],
'''tokenization_transfo_xl''': ['''TransfoXLCorpus''', '''TransfoXLTokenizer'''],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase : str = [
'''TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''AdaptiveEmbedding''',
'''TransfoXLForSequenceClassification''',
'''TransfoXLLMHeadModel''',
'''TransfoXLModel''',
'''TransfoXLPreTrainedModel''',
'''load_tf_weights_in_transfo_xl''',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase : Any = [
'''TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''TFAdaptiveEmbedding''',
'''TFTransfoXLForSequenceClassification''',
'''TFTransfoXLLMHeadModel''',
'''TFTransfoXLMainLayer''',
'''TFTransfoXLModel''',
'''TFTransfoXLPreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_transfo_xl import TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP, TransfoXLConfig
from .tokenization_transfo_xl import TransfoXLCorpus, TransfoXLTokenizer
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_transfo_xl import (
TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST,
AdaptiveEmbedding,
TransfoXLForSequenceClassification,
TransfoXLLMHeadModel,
TransfoXLModel,
TransfoXLPreTrainedModel,
load_tf_weights_in_transfo_xl,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_transfo_xl import (
TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST,
TFAdaptiveEmbedding,
TFTransfoXLForSequenceClassification,
TFTransfoXLLMHeadModel,
TFTransfoXLMainLayer,
TFTransfoXLModel,
TFTransfoXLPreTrainedModel,
)
else:
import sys
lowerCAmelCase : Optional[Any] = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 3 |
'''simple docstring'''
from string import ascii_uppercase
__lowerCamelCase : Optional[Any] = {char: i for i, char in enumerate(ascii_uppercase)}
__lowerCamelCase : List[str] = dict(enumerate(ascii_uppercase))
def __UpperCAmelCase ( __magic_name__ ,__magic_name__ )-> str:
"""simple docstring"""
snake_case_ : Tuple = len(__magic_name__ )
snake_case_ : str = 0
while True:
if x == i:
snake_case_ : List[str] = 0
if len(__magic_name__ ) == len(__magic_name__ ):
break
key += key[i]
i += 1
return key
def __UpperCAmelCase ( __magic_name__ ,__magic_name__ )-> str:
"""simple docstring"""
snake_case_ : str = ""
snake_case_ : List[Any] = 0
for letter in message:
if letter == " ":
cipher_text += " "
else:
snake_case_ : Optional[Any] = (dicta[letter] - dicta[key_new[i]]) % 26
i += 1
cipher_text += dicta[x]
return cipher_text
def __UpperCAmelCase ( __magic_name__ ,__magic_name__ )-> str:
"""simple docstring"""
snake_case_ : Dict = ""
snake_case_ : Dict = 0
for letter in cipher_text:
if letter == " ":
or_txt += " "
else:
snake_case_ : str = (dicta[letter] + dicta[key_new[i]] + 26) % 26
i += 1
or_txt += dicta[x]
return or_txt
def __UpperCAmelCase ( )-> None:
"""simple docstring"""
snake_case_ : List[str] = "THE GERMAN ATTACK"
snake_case_ : List[str] = "SECRET"
snake_case_ : Optional[int] = generate_key(__magic_name__ ,__magic_name__ )
snake_case_ : Any = cipher_text(__magic_name__ ,__magic_name__ )
print(F'''Encrypted Text = {s}''' )
print(F'''Original Text = {original_text(__magic_name__ ,__magic_name__ )}''' )
if __name__ == "__main__":
import doctest
doctest.testmod()
main()
| 653 | 0 |
'''simple docstring'''
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from tokenizers import processors
from ...tokenization_utils import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_mbart import MBartTokenizer
else:
_UpperCAmelCase : Optional[Any] = None
_UpperCAmelCase : Dict = logging.get_logger(__name__)
_UpperCAmelCase : Tuple = {'''vocab_file''': '''sentencepiece.bpe.model''', '''tokenizer_file''': '''tokenizer.json'''}
_UpperCAmelCase : Optional[int] = {
'''vocab_file''': {
'''facebook/mbart-large-en-ro''': (
'''https://huggingface.co/facebook/mbart-large-en-ro/resolve/main/sentencepiece.bpe.model'''
),
'''facebook/mbart-large-cc25''': (
'''https://huggingface.co/facebook/mbart-large-cc25/resolve/main/sentencepiece.bpe.model'''
),
},
'''tokenizer_file''': {
'''facebook/mbart-large-en-ro''': '''https://huggingface.co/facebook/mbart-large-en-ro/resolve/main/tokenizer.json''',
'''facebook/mbart-large-cc25''': '''https://huggingface.co/facebook/mbart-large-cc25/resolve/main/tokenizer.json''',
},
}
_UpperCAmelCase : Optional[int] = {
'''facebook/mbart-large-en-ro''': 1024,
'''facebook/mbart-large-cc25''': 1024,
}
# fmt: off
_UpperCAmelCase : str = ['''ar_AR''', '''cs_CZ''', '''de_DE''', '''en_XX''', '''es_XX''', '''et_EE''', '''fi_FI''', '''fr_XX''', '''gu_IN''', '''hi_IN''', '''it_IT''', '''ja_XX''', '''kk_KZ''', '''ko_KR''', '''lt_LT''', '''lv_LV''', '''my_MM''', '''ne_NP''', '''nl_XX''', '''ro_RO''', '''ru_RU''', '''si_LK''', '''tr_TR''', '''vi_VN''', '''zh_CN''']
class UpperCAmelCase ( a_ ):
"""simple docstring"""
A__ : Dict = VOCAB_FILES_NAMES
A__ : int = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
A__ : int = PRETRAINED_VOCAB_FILES_MAP
A__ : Tuple = ['input_ids', 'attention_mask']
A__ : Optional[int] = MBartTokenizer
A__ : List[Any] = []
A__ : Optional[int] = []
def __init__( self , _snake_case=None , _snake_case=None , _snake_case="<s>" , _snake_case="</s>" , _snake_case="</s>" , _snake_case="<s>" , _snake_case="<unk>" , _snake_case="<pad>" , _snake_case="<mask>" , _snake_case=None , _snake_case=None , _snake_case=None , **_snake_case , ) -> Optional[Any]:
_UpperCamelCase : int = AddedToken(lowerCAmelCase__ , lstrip=lowerCAmelCase__ , rstrip=lowerCAmelCase__ ) if isinstance(lowerCAmelCase__ , lowerCAmelCase__ ) else mask_token
super().__init__(
vocab_file=lowerCAmelCase__ , tokenizer_file=lowerCAmelCase__ , bos_token=lowerCAmelCase__ , eos_token=lowerCAmelCase__ , sep_token=lowerCAmelCase__ , cls_token=lowerCAmelCase__ , unk_token=lowerCAmelCase__ , pad_token=lowerCAmelCase__ , mask_token=lowerCAmelCase__ , src_lang=lowerCAmelCase__ , tgt_lang=lowerCAmelCase__ , additional_special_tokens=lowerCAmelCase__ , **lowerCAmelCase__ , )
_UpperCamelCase : Optional[Any] = vocab_file
_UpperCamelCase : Optional[Any] = False if not self.vocab_file else True
_UpperCamelCase : Any = FAIRSEQ_LANGUAGE_CODES.copy()
if additional_special_tokens is not None:
# Only add those special tokens if they are not already there.
_additional_special_tokens.extend(
[t for t in additional_special_tokens if t not in _additional_special_tokens] )
self.add_special_tokens({'''additional_special_tokens''': _additional_special_tokens} )
_UpperCamelCase : Union[str, Any] = {
lang_code: self.convert_tokens_to_ids(lowerCAmelCase__ ) for lang_code in FAIRSEQ_LANGUAGE_CODES
}
_UpperCamelCase : Dict = src_lang if src_lang is not None else "en_XX"
_UpperCamelCase : Union[str, Any] = self.convert_tokens_to_ids(self._src_lang )
_UpperCamelCase : Tuple = tgt_lang
self.set_src_lang_special_tokens(self._src_lang )
@property
def _lowercase ( self ) -> str:
return self._src_lang
@src_lang.setter
def _lowercase ( self , _snake_case ) -> None:
_UpperCamelCase : Union[str, Any] = new_src_lang
self.set_src_lang_special_tokens(self._src_lang )
def _lowercase ( self , _snake_case , _snake_case = None ) -> List[int]:
if token_ids_a is None:
return self.prefix_tokens + token_ids_a + self.suffix_tokens
# We don't expect to process pairs, but leave the pair logic for API consistency
return self.prefix_tokens + token_ids_a + token_ids_a + self.suffix_tokens
def _lowercase ( self , _snake_case , _snake_case = None ) -> List[int]:
_UpperCamelCase : Union[str, Any] = [self.sep_token_id]
_UpperCamelCase : int = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep + sep + token_ids_a + sep ) * [0]
def _lowercase ( self , _snake_case , _snake_case , _snake_case , _snake_case , **_snake_case ) -> str:
if src_lang is None or tgt_lang is None:
raise ValueError('''Translation requires a `src_lang` and a `tgt_lang` for this model''' )
_UpperCamelCase : List[str] = src_lang
_UpperCamelCase : Optional[Any] = self(lowerCAmelCase__ , add_special_tokens=lowerCAmelCase__ , return_tensors=lowerCAmelCase__ , **lowerCAmelCase__ )
_UpperCamelCase : Optional[int] = self.convert_tokens_to_ids(lowerCAmelCase__ )
_UpperCamelCase : int = tgt_lang_id
return inputs
def _lowercase ( self , _snake_case , _snake_case = "en_XX" , _snake_case = None , _snake_case = "ro_RO" , **_snake_case , ) -> BatchEncoding:
_UpperCamelCase : Any = src_lang
_UpperCamelCase : Optional[int] = tgt_lang
return super().prepare_seqaseq_batch(lowerCAmelCase__ , lowerCAmelCase__ , **lowerCAmelCase__ )
def _lowercase ( self ) -> Optional[int]:
return self.set_src_lang_special_tokens(self.src_lang )
def _lowercase ( self ) -> Union[str, Any]:
return self.set_tgt_lang_special_tokens(self.tgt_lang )
def _lowercase ( self , _snake_case ) -> None:
_UpperCamelCase : Union[str, Any] = self.convert_tokens_to_ids(lowerCAmelCase__ )
_UpperCamelCase : Tuple = []
_UpperCamelCase : Union[str, Any] = [self.eos_token_id, self.cur_lang_code]
_UpperCamelCase : Dict = self.convert_ids_to_tokens(self.prefix_tokens )
_UpperCamelCase : int = self.convert_ids_to_tokens(self.suffix_tokens )
_UpperCamelCase : Any = processors.TemplateProcessing(
single=prefix_tokens_str + ['''$A'''] + suffix_tokens_str , pair=prefix_tokens_str + ['''$A''', '''$B'''] + suffix_tokens_str , special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str , self.prefix_tokens + self.suffix_tokens ) ) , )
def _lowercase ( self , _snake_case ) -> None:
_UpperCamelCase : Any = self.convert_tokens_to_ids(lowerCAmelCase__ )
_UpperCamelCase : List[Any] = []
_UpperCamelCase : List[str] = [self.eos_token_id, self.cur_lang_code]
_UpperCamelCase : Dict = self.convert_ids_to_tokens(self.prefix_tokens )
_UpperCamelCase : Union[str, Any] = self.convert_ids_to_tokens(self.suffix_tokens )
_UpperCamelCase : int = processors.TemplateProcessing(
single=prefix_tokens_str + ['''$A'''] + suffix_tokens_str , pair=prefix_tokens_str + ['''$A''', '''$B'''] + suffix_tokens_str , special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str , self.prefix_tokens + self.suffix_tokens ) ) , )
def _lowercase ( self , _snake_case , _snake_case = 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(lowerCAmelCase__ ):
logger.error(F'''Vocabulary path ({save_directory}) should be a directory.''' )
return
_UpperCamelCase : Union[str, Any] = os.path.join(
lowerCAmelCase__ , (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file'''] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(lowerCAmelCase__ ):
copyfile(self.vocab_file , lowerCAmelCase__ )
return (out_vocab_file,)
| 683 |
'''simple docstring'''
import argparse
import os
import numpy as np
import tensorflow as tf
import torch
from transformers import BertModel
def __UpperCAmelCase ( __magic_name__ ,__magic_name__ ,__magic_name__ )-> Dict:
"""simple docstring"""
snake_case_ : Tuple = ("dense.weight", "attention.self.query", "attention.self.key", "attention.self.value")
snake_case_ : Union[str, Any] = (
("layer.", "layer_"),
("word_embeddings.weight", "word_embeddings"),
("position_embeddings.weight", "position_embeddings"),
("token_type_embeddings.weight", "token_type_embeddings"),
(".", "/"),
("LayerNorm/weight", "LayerNorm/gamma"),
("LayerNorm/bias", "LayerNorm/beta"),
("weight", "kernel"),
)
if not os.path.isdir(__magic_name__ ):
os.makedirs(__magic_name__ )
snake_case_ : str = model.state_dict()
def to_tf_var_name(__magic_name__ ):
for patt, repl in iter(__magic_name__ ):
snake_case_ : List[str] = name.replace(__magic_name__ ,__magic_name__ )
return F'''bert/{name}'''
def create_tf_var(__magic_name__ ,__magic_name__ ,__magic_name__ ):
snake_case_ : List[Any] = tf.dtypes.as_dtype(tensor.dtype )
snake_case_ : Union[str, Any] = tf.get_variable(dtype=__magic_name__ ,shape=tensor.shape ,name=__magic_name__ ,initializer=tf.zeros_initializer() )
session.run(tf.variables_initializer([tf_var] ) )
session.run(__magic_name__ )
return tf_var
tf.reset_default_graph()
with tf.Session() as session:
for var_name in state_dict:
snake_case_ : Optional[int] = to_tf_var_name(__magic_name__ )
snake_case_ : Dict = state_dict[var_name].numpy()
if any(x in var_name for x in tensors_to_transpose ):
snake_case_ : List[Any] = torch_tensor.T
snake_case_ : Union[str, Any] = create_tf_var(tensor=__magic_name__ ,name=__magic_name__ ,session=__magic_name__ )
tf.keras.backend.set_value(__magic_name__ ,__magic_name__ )
snake_case_ : List[str] = session.run(__magic_name__ )
print(F'''Successfully created {tf_name}: {np.allclose(__magic_name__ ,__magic_name__ )}''' )
snake_case_ : Any = tf.train.Saver(tf.trainable_variables() )
saver.save(__magic_name__ ,os.path.join(__magic_name__ ,model_name.replace("-" ,"_" ) + ".ckpt" ) )
def __UpperCAmelCase ( __magic_name__=None )-> Optional[Any]:
"""simple docstring"""
snake_case_ : Any = argparse.ArgumentParser()
parser.add_argument("--model_name" ,type=__magic_name__ ,required=__magic_name__ ,help="model name e.g. bert-base-uncased" )
parser.add_argument(
"--cache_dir" ,type=__magic_name__ ,default=__magic_name__ ,required=__magic_name__ ,help="Directory containing pytorch model" )
parser.add_argument("--pytorch_model_path" ,type=__magic_name__ ,required=__magic_name__ ,help="/path/to/<pytorch-model-name>.bin" )
parser.add_argument("--tf_cache_dir" ,type=__magic_name__ ,required=__magic_name__ ,help="Directory in which to save tensorflow model" )
snake_case_ : Optional[int] = parser.parse_args(__magic_name__ )
snake_case_ : Optional[int] = BertModel.from_pretrained(
pretrained_model_name_or_path=args.model_name ,state_dict=torch.load(args.pytorch_model_path ) ,cache_dir=args.cache_dir ,)
convert_pytorch_checkpoint_to_tf(model=__magic_name__ ,ckpt_dir=args.tf_cache_dir ,model_name=args.model_name )
if __name__ == "__main__":
main()
| 653 | 0 |
from .constants import (
MODEL_NAME,
OPTIMIZER_NAME,
RNG_STATE_NAME,
SAFE_WEIGHTS_INDEX_NAME,
SAFE_WEIGHTS_NAME,
SCALER_NAME,
SCHEDULER_NAME,
TORCH_LAUNCH_PARAMS,
WEIGHTS_INDEX_NAME,
WEIGHTS_NAME,
)
from .dataclasses import (
BnbQuantizationConfig,
ComputeEnvironment,
CustomDtype,
DeepSpeedPlugin,
DistributedDataParallelKwargs,
DistributedType,
DynamoBackend,
FPaRecipeKwargs,
FullyShardedDataParallelPlugin,
GradientAccumulationPlugin,
GradScalerKwargs,
InitProcessGroupKwargs,
KwargsHandler,
LoggerType,
MegatronLMPlugin,
PrecisionType,
ProjectConfiguration,
RNGType,
SageMakerDistributedType,
TensorInformation,
TorchDynamoPlugin,
)
from .environment import get_int_from_env, parse_choice_from_env, parse_flag_from_env
from .imports import (
get_ccl_version,
is_abit_bnb_available,
is_abit_bnb_available,
is_aim_available,
is_bfaa_available,
is_bnb_available,
is_botoa_available,
is_ccl_available,
is_comet_ml_available,
is_datasets_available,
is_deepspeed_available,
is_fpa_available,
is_ipex_available,
is_megatron_lm_available,
is_mlflow_available,
is_mps_available,
is_npu_available,
is_rich_available,
is_safetensors_available,
is_sagemaker_available,
is_tensorboard_available,
is_tpu_available,
is_transformers_available,
is_wandb_available,
is_xpu_available,
)
from .modeling import (
check_device_map,
check_tied_parameters_in_config,
check_tied_parameters_on_same_device,
compute_module_sizes,
convert_file_size_to_int,
dtype_byte_size,
find_tied_parameters,
get_balanced_memory,
get_max_layer_size,
get_max_memory,
get_mixed_precision_context_manager,
id_tensor_storage,
infer_auto_device_map,
load_checkpoint_in_model,
load_offloaded_weights,
load_state_dict,
named_module_tensors,
retie_parameters,
set_module_tensor_to_device,
shard_checkpoint,
)
from .offload import (
OffloadedWeightsLoader,
PrefixedDataset,
extract_submodules_state_dict,
load_offloaded_weight,
offload_state_dict,
offload_weight,
save_offload_index,
)
from .operations import (
broadcast,
broadcast_object_list,
concatenate,
convert_outputs_to_fpaa,
convert_to_fpaa,
find_batch_size,
find_device,
gather,
gather_object,
get_data_structure,
honor_type,
initialize_tensors,
is_namedtuple,
is_tensor_information,
is_torch_tensor,
listify,
pad_across_processes,
recursively_apply,
reduce,
send_to_device,
slice_tensors,
)
from .versions import compare_versions, is_torch_version
if is_deepspeed_available():
from .deepspeed import (
DeepSpeedEngineWrapper,
DeepSpeedOptimizerWrapper,
DeepSpeedSchedulerWrapper,
DummyOptim,
DummyScheduler,
HfDeepSpeedConfig,
)
from .bnb import has_abit_bnb_layers, load_and_quantize_model
from .fsdp_utils import load_fsdp_model, load_fsdp_optimizer, save_fsdp_model, save_fsdp_optimizer
from .launch import (
PrepareForLaunch,
_filter_args,
prepare_deepspeed_cmd_env,
prepare_multi_gpu_env,
prepare_sagemager_args_inputs,
prepare_simple_launcher_cmd_env,
prepare_tpu,
)
from .megatron_lm import (
AbstractTrainStep,
BertTrainStep,
GPTTrainStep,
MegatronEngine,
MegatronLMDummyDataLoader,
MegatronLMDummyScheduler,
MegatronLMOptimizerWrapper,
MegatronLMSchedulerWrapper,
TaTrainStep,
avg_losses_across_data_parallel_group,
gather_across_data_parallel_groups,
)
from .megatron_lm import initialize as megatron_lm_initialize
from .megatron_lm import prepare_data_loader as megatron_lm_prepare_data_loader
from .megatron_lm import prepare_model as megatron_lm_prepare_model
from .megatron_lm import prepare_optimizer as megatron_lm_prepare_optimizer
from .megatron_lm import prepare_scheduler as megatron_lm_prepare_scheduler
from .memory import find_executable_batch_size, release_memory
from .other import (
extract_model_from_parallel,
get_pretty_name,
is_port_in_use,
merge_dicts,
patch_environment,
save,
wait_for_everyone,
write_basic_config,
)
from .random import set_seed, synchronize_rng_state, synchronize_rng_states
from .torch_xla import install_xla
from .tqdm import tqdm
from .transformer_engine import convert_model, has_transformer_engine_layers
| 176 |
'''simple docstring'''
from collections import deque
from .hash_table import HashTable
class A_ (a_ ):
"""simple docstring"""
def __init__( self :List[str] , *lowerCAmelCase__ :Optional[Any] , **lowerCAmelCase__ :Dict ) -> Union[str, Any]:
'''simple docstring'''
super().__init__(*lowerCAmelCase__ , **lowerCAmelCase__ )
def _A ( self :Optional[int] , lowerCAmelCase__ :int , lowerCAmelCase__ :Optional[Any] ) -> Union[str, Any]:
'''simple docstring'''
snake_case_ : Optional[int] = deque([] ) if self.values[key] is None else self.values[key]
self.values[key].appendleft(lowerCAmelCase__ )
snake_case_ : Tuple = self.values[key]
def _A ( self :int ) -> Dict:
'''simple docstring'''
return (
sum(self.charge_factor - len(lowerCAmelCase__ ) for slot in self.values )
/ self.size_table
* self.charge_factor
)
def _A ( self :str , lowerCAmelCase__ :Optional[Any] , lowerCAmelCase__ :Tuple=None ) -> Any:
'''simple docstring'''
if not (
len(self.values[key] ) == self.charge_factor and self.values.count(lowerCAmelCase__ ) == 0
):
return key
return super()._collision_resolution(lowerCAmelCase__ , lowerCAmelCase__ )
| 653 | 0 |
"""simple docstring"""
from jiwer import compute_measures
import datasets
__A = '''\
@inproceedings{inproceedings,
author = {Morris, Andrew and Maier, Viktoria and Green, Phil},
year = {2004},
month = {01},
pages = {},
title = {From WER and RIL to MER and WIL: improved evaluation measures for connected speech recognition.}
}
'''
__A = '''\
Word error rate (WER) is a common metric of the performance of an automatic speech recognition system.
The general difficulty of measuring performance lies in the fact that the recognized word sequence can have a different length from the reference word sequence (supposedly the correct one). The WER is derived from the Levenshtein distance, working at the word level instead of the phoneme level. The WER is a valuable tool for comparing different systems as well as for evaluating improvements within one system. This kind of measurement, however, provides no details on the nature of translation errors and further work is therefore required to identify the main source(s) of error and to focus any research effort.
This problem is solved by first aligning the recognized word sequence with the reference (spoken) word sequence using dynamic string alignment. Examination of this issue is seen through a theory called the power law that states the correlation between perplexity and word error rate.
Word error rate can then be computed as:
WER = (S + D + I) / N = (S + D + I) / (S + D + C)
where
S is the number of substitutions,
D is the number of deletions,
I is the number of insertions,
C is the number of correct words,
N is the number of words in the reference (N=S+D+C).
This value indicates the average number of errors per reference word. The lower the value, the better the
performance of the ASR system with a WER of 0 being a perfect score.
'''
__A = '''
Compute WER score of transcribed segments against references.
Args:
references: List of references for each speech input.
predictions: List of transcriptions to score.
concatenate_texts (bool, default=False): Whether to concatenate all input texts or compute WER iteratively.
Returns:
(float): the word error rate
Examples:
>>> predictions = ["this is the prediction", "there is an other sample"]
>>> references = ["this is the reference", "there is another one"]
>>> wer = datasets.load_metric("wer")
>>> wer_score = wer.compute(predictions=predictions, references=references)
>>> print(wer_score)
0.5
'''
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class _lowerCAmelCase ( datasets.Metric ):
"""simple docstring"""
def snake_case ( self ):
'''simple docstring'''
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
'predictions': datasets.Value('string' , id='sequence' ),
'references': datasets.Value('string' , id='sequence' ),
} ) , codebase_urls=['https://github.com/jitsi/jiwer/'] , reference_urls=[
'https://en.wikipedia.org/wiki/Word_error_rate',
] , )
def snake_case ( self , __UpperCAmelCase=None , __UpperCAmelCase=None , __UpperCAmelCase=False ):
'''simple docstring'''
if concatenate_texts:
return compute_measures(lowerCAmelCase__ , lowerCAmelCase__ )["wer"]
else:
lowerCAmelCase__ :int = 0
lowerCAmelCase__ :str = 0
for prediction, reference in zip(lowerCAmelCase__ , lowerCAmelCase__ ):
lowerCAmelCase__ :Union[str, Any] = compute_measures(lowerCAmelCase__ , lowerCAmelCase__ )
incorrect += measures["substitutions"] + measures["deletions"] + measures["insertions"]
total += measures["substitutions"] + measures["deletions"] + measures["hits"]
return incorrect / total
| 93 |
'''simple docstring'''
from collections.abc import Iterator, MutableMapping
from dataclasses import dataclass
from typing import Generic, TypeVar
__lowerCamelCase : Dict = TypeVar('''KEY''')
__lowerCamelCase : int = TypeVar('''VAL''')
@dataclass(frozen=a_ , slots=a_ )
class A_ (Generic[KEY, VAL] ):
"""simple docstring"""
a__ = 42
a__ = 42
class A_ (_Item ):
"""simple docstring"""
def __init__( self :List[Any] ) -> None:
'''simple docstring'''
super().__init__(lowerCAmelCase__ , lowerCAmelCase__ )
def __bool__( self :Optional[int] ) -> bool:
'''simple docstring'''
return False
__lowerCamelCase : Dict = _DeletedItem()
class A_ (MutableMapping[KEY, VAL] ):
"""simple docstring"""
def __init__( self :Dict , lowerCAmelCase__ :int = 8 , lowerCAmelCase__ :float = 0.7_5 ) -> None:
'''simple docstring'''
snake_case_ : Any = initial_block_size
snake_case_ : list[_Item | None] = [None] * initial_block_size
assert 0.0 < capacity_factor < 1.0
snake_case_ : Tuple = capacity_factor
snake_case_ : List[Any] = 0
def _A ( self :Tuple , lowerCAmelCase__ :KEY ) -> int:
'''simple docstring'''
return hash(lowerCAmelCase__ ) % len(self._buckets )
def _A ( self :Any , lowerCAmelCase__ :int ) -> int:
'''simple docstring'''
return (ind + 1) % len(self._buckets )
def _A ( self :str , lowerCAmelCase__ :int , lowerCAmelCase__ :KEY , lowerCAmelCase__ :VAL ) -> bool:
'''simple docstring'''
snake_case_ : Optional[int] = self._buckets[ind]
if not stored:
snake_case_ : int = _Item(lowerCAmelCase__ , lowerCAmelCase__ )
self._len += 1
return True
elif stored.key == key:
snake_case_ : Optional[int] = _Item(lowerCAmelCase__ , lowerCAmelCase__ )
return True
else:
return False
def _A ( self :int ) -> bool:
'''simple docstring'''
snake_case_ : Any = len(self._buckets ) * self._capacity_factor
return len(self ) >= int(lowerCAmelCase__ )
def _A ( self :Any ) -> bool:
'''simple docstring'''
if len(self._buckets ) <= self._initial_block_size:
return False
snake_case_ : Optional[int] = len(self._buckets ) * self._capacity_factor / 2
return len(self ) < limit
def _A ( self :Tuple , lowerCAmelCase__ :int ) -> None:
'''simple docstring'''
snake_case_ : Tuple = self._buckets
snake_case_ : int = [None] * new_size
snake_case_ : Any = 0
for item in old_buckets:
if item:
self._add_item(item.key , item.val )
def _A ( self :Optional[int] ) -> None:
'''simple docstring'''
self._resize(len(self._buckets ) * 2 )
def _A ( self :str ) -> None:
'''simple docstring'''
self._resize(len(self._buckets ) // 2 )
def _A ( self :Optional[int] , lowerCAmelCase__ :KEY ) -> Iterator[int]:
'''simple docstring'''
snake_case_ : str = self._get_bucket_index(lowerCAmelCase__ )
for _ in range(len(self._buckets ) ):
yield ind
snake_case_ : List[Any] = self._get_next_ind(lowerCAmelCase__ )
def _A ( self :Union[str, Any] , lowerCAmelCase__ :KEY , lowerCAmelCase__ :VAL ) -> None:
'''simple docstring'''
for ind in self._iterate_buckets(lowerCAmelCase__ ):
if self._try_set(lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ ):
break
def __setitem__( self :Optional[int] , lowerCAmelCase__ :KEY , lowerCAmelCase__ :VAL ) -> None:
'''simple docstring'''
if self._is_full():
self._size_up()
self._add_item(lowerCAmelCase__ , lowerCAmelCase__ )
def __delitem__( self :List[Any] , lowerCAmelCase__ :KEY ) -> None:
'''simple docstring'''
for ind in self._iterate_buckets(lowerCAmelCase__ ):
snake_case_ : int = self._buckets[ind]
if item is None:
raise KeyError(lowerCAmelCase__ )
if item is _deleted:
continue
if item.key == key:
snake_case_ : List[str] = _deleted
self._len -= 1
break
if self._is_sparse():
self._size_down()
def __getitem__( self :List[str] , lowerCAmelCase__ :KEY ) -> VAL:
'''simple docstring'''
for ind in self._iterate_buckets(lowerCAmelCase__ ):
snake_case_ : Optional[Any] = self._buckets[ind]
if item is None:
break
if item is _deleted:
continue
if item.key == key:
return item.val
raise KeyError(lowerCAmelCase__ )
def __len__( self :Optional[Any] ) -> int:
'''simple docstring'''
return self._len
def __iter__( self :List[Any] ) -> Iterator[KEY]:
'''simple docstring'''
yield from (item.key for item in self._buckets if item)
def __repr__( self :Any ) -> str:
'''simple docstring'''
snake_case_ : Dict = " ,".join(
F'''{item.key}: {item.val}''' for item in self._buckets if item )
return F'''HashMap({val_string})'''
| 653 | 0 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_a : List[Any] = logging.get_logger(__name__)
_a : Dict = {
'''weiweishi/roc-bert-base-zh''': '''https://huggingface.co/weiweishi/roc-bert-base-zh/resolve/main/config.json''',
}
class lowercase_ ( a_ ):
'''simple docstring'''
__lowerCAmelCase : str = "roc_bert"
def __init__( self , a_=3_0_5_2_2 , a_=7_6_8 , a_=1_2 , a_=1_2 , a_=3_0_7_2 , a_="gelu" , a_=0.1 , a_=0.1 , a_=5_1_2 , a_=2 , a_=0.02 , a_=1E-12 , a_=True , a_=0 , a_="absolute" , a_=None , a_=True , a_=True , a_=7_6_8 , a_=9_1_0 , a_=5_1_2 , a_=2_4_8_5_8 , a_=True , **a_ , ) -> List[str]:
"""simple docstring"""
UpperCAmelCase = vocab_size
UpperCAmelCase = max_position_embeddings
UpperCAmelCase = hidden_size
UpperCAmelCase = num_hidden_layers
UpperCAmelCase = num_attention_heads
UpperCAmelCase = intermediate_size
UpperCAmelCase = hidden_act
UpperCAmelCase = hidden_dropout_prob
UpperCAmelCase = attention_probs_dropout_prob
UpperCAmelCase = initializer_range
UpperCAmelCase = type_vocab_size
UpperCAmelCase = layer_norm_eps
UpperCAmelCase = use_cache
UpperCAmelCase = enable_pronunciation
UpperCAmelCase = enable_shape
UpperCAmelCase = pronunciation_embed_dim
UpperCAmelCase = pronunciation_vocab_size
UpperCAmelCase = shape_embed_dim
UpperCAmelCase = shape_vocab_size
UpperCAmelCase = concat_input
UpperCAmelCase = position_embedding_type
UpperCAmelCase = classifier_dropout
super().__init__(pad_token_id=lowerCAmelCase__ , **lowerCAmelCase__ )
| 447 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__lowerCamelCase : str = logging.get_logger(__name__)
__lowerCamelCase : Tuple = {
'''bigcode/gpt_bigcode-santacoder''': '''https://huggingface.co/bigcode/gpt_bigcode-santacoder/resolve/main/config.json''',
}
class A_ (a_ ):
"""simple docstring"""
a__ = '''gpt_bigcode'''
a__ = ['''past_key_values''']
a__ = {
'''hidden_size''': '''n_embd''',
'''max_position_embeddings''': '''n_positions''',
'''num_attention_heads''': '''n_head''',
'''num_hidden_layers''': '''n_layer''',
}
def __init__( self :List[Any] , lowerCAmelCase__ :Any=50_257 , lowerCAmelCase__ :Dict=1_024 , lowerCAmelCase__ :Optional[int]=768 , lowerCAmelCase__ :Tuple=12 , lowerCAmelCase__ :int=12 , lowerCAmelCase__ :Optional[Any]=None , lowerCAmelCase__ :List[str]="gelu_pytorch_tanh" , lowerCAmelCase__ :Tuple=0.1 , lowerCAmelCase__ :Tuple=0.1 , lowerCAmelCase__ :str=0.1 , lowerCAmelCase__ :Any=1E-5 , lowerCAmelCase__ :Union[str, Any]=0.0_2 , lowerCAmelCase__ :Union[str, Any]=True , lowerCAmelCase__ :Optional[Any]=True , lowerCAmelCase__ :int=50_256 , lowerCAmelCase__ :List[str]=50_256 , lowerCAmelCase__ :List[Any]=True , lowerCAmelCase__ :str=True , lowerCAmelCase__ :int=True , **lowerCAmelCase__ :Union[str, Any] , ) -> Any:
'''simple docstring'''
snake_case_ : List[Any] = vocab_size
snake_case_ : Any = n_positions
snake_case_ : Any = n_embd
snake_case_ : Optional[Any] = n_layer
snake_case_ : List[Any] = n_head
snake_case_ : Tuple = n_inner
snake_case_ : str = activation_function
snake_case_ : Union[str, Any] = resid_pdrop
snake_case_ : Optional[Any] = embd_pdrop
snake_case_ : Any = attn_pdrop
snake_case_ : List[Any] = layer_norm_epsilon
snake_case_ : Tuple = initializer_range
snake_case_ : int = scale_attn_weights
snake_case_ : Union[str, Any] = use_cache
snake_case_ : Dict = attention_softmax_in_fpaa
snake_case_ : Any = scale_attention_softmax_in_fpaa
snake_case_ : List[str] = multi_query
snake_case_ : List[str] = bos_token_id
snake_case_ : Any = eos_token_id
super().__init__(bos_token_id=lowerCAmelCase__ , eos_token_id=lowerCAmelCase__ , **lowerCAmelCase__ )
| 653 | 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_camembert import CamembertTokenizer
else:
__UpperCamelCase : str = None
__UpperCamelCase : List[Any] = logging.get_logger(__name__)
__UpperCamelCase : Dict = {'''vocab_file''': '''sentencepiece.bpe.model''', '''tokenizer_file''': '''tokenizer.json'''}
__UpperCamelCase : Any = {
'''vocab_file''': {
'''camembert-base''': '''https://huggingface.co/camembert-base/resolve/main/sentencepiece.bpe.model''',
},
'''tokenizer_file''': {
'''camembert-base''': '''https://huggingface.co/camembert-base/resolve/main/tokenizer.json''',
},
}
__UpperCamelCase : List[str] = {
'''camembert-base''': 512,
}
__UpperCamelCase : int = '''▁'''
class lowercase__ ( a_):
UpperCamelCase_ = VOCAB_FILES_NAMES
UpperCamelCase_ = PRETRAINED_VOCAB_FILES_MAP
UpperCamelCase_ = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
UpperCamelCase_ = ["""input_ids""", """attention_mask"""]
UpperCamelCase_ = CamembertTokenizer
def __init__( self : int , UpperCamelCase__ : Tuple=None , UpperCamelCase__ : List[str]=None , UpperCamelCase__ : Tuple="<s>" , UpperCamelCase__ : Tuple="</s>" , UpperCamelCase__ : Any="</s>" , UpperCamelCase__ : Optional[int]="<s>" , UpperCamelCase__ : Optional[Any]="<unk>" , UpperCamelCase__ : int="<pad>" , UpperCamelCase__ : List[str]="<mask>" , UpperCamelCase__ : Union[str, Any]=["<s>NOTUSED", "</s>NOTUSED"] , **UpperCamelCase__ : Tuple , ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : str = AddedToken(lowerCAmelCase__ , lstrip=lowerCAmelCase__ , rstrip=lowerCAmelCase__ ) if isinstance(lowerCAmelCase__ , lowerCAmelCase__ ) else mask_token
super().__init__(
lowerCAmelCase__ , tokenizer_file=lowerCAmelCase__ , bos_token=lowerCAmelCase__ , eos_token=lowerCAmelCase__ , sep_token=lowerCAmelCase__ , cls_token=lowerCAmelCase__ , unk_token=lowerCAmelCase__ , pad_token=lowerCAmelCase__ , mask_token=lowerCAmelCase__ , additional_special_tokens=lowerCAmelCase__ , **lowerCAmelCase__ , )
SCREAMING_SNAKE_CASE : int = vocab_file
SCREAMING_SNAKE_CASE : str = False if not self.vocab_file else True
def __A ( self : int , UpperCamelCase__ : List[int] , UpperCamelCase__ : Optional[List[int]] = None ):
'''simple docstring'''
if token_ids_a is None:
return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
SCREAMING_SNAKE_CASE : Dict = [self.cls_token_id]
SCREAMING_SNAKE_CASE : Dict = [self.sep_token_id]
return cls + token_ids_a + sep + sep + token_ids_a + sep
def __A ( self : int , UpperCamelCase__ : List[int] , UpperCamelCase__ : Optional[List[int]] = None ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : List[Any] = [self.sep_token_id]
SCREAMING_SNAKE_CASE : List[Any] = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep + sep + token_ids_a + sep ) * [0]
def __A ( self : Dict , UpperCamelCase__ : str , UpperCamelCase__ : Optional[str] = None ):
'''simple docstring'''
if not self.can_save_slow_tokenizer:
raise ValueError(
'''Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '''
'''tokenizer.''' )
if not os.path.isdir(lowerCAmelCase__ ):
logger.error(f"""Vocabulary path ({save_directory}) should be a directory""" )
return
SCREAMING_SNAKE_CASE : List[str] = os.path.join(
lowerCAmelCase__ , (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file'''] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(lowerCAmelCase__ ):
copyfile(self.vocab_file , lowerCAmelCase__ )
return (out_vocab_file,)
| 248 |
'''simple docstring'''
import json
import logging
import os
import socket
import git
import numpy as np
import torch
logging.basicConfig(
format='''%(asctime)s - %(levelname)s - %(name)s - PID: %(process)d - %(message)s''',
datefmt='''%m/%d/%Y %H:%M:%S''',
level=logging.INFO,
)
__lowerCamelCase : Union[str, Any] = logging.getLogger(__name__)
def __UpperCAmelCase ( __magic_name__ )-> str:
"""simple docstring"""
snake_case_ : Dict = git.Repo(search_parent_directories=__magic_name__ )
snake_case_ : Optional[int] = {
"repo_id": str(__magic_name__ ),
"repo_sha": str(repo.head.object.hexsha ),
"repo_branch": str(repo.active_branch ),
}
with open(os.path.join(__magic_name__ ,"git_log.json" ) ,"w" ) as f:
json.dump(__magic_name__ ,__magic_name__ ,indent=4 )
def __UpperCAmelCase ( __magic_name__ )-> Tuple:
"""simple docstring"""
if params.n_gpu <= 0:
snake_case_ : Any = 0
snake_case_ : Any = -1
snake_case_ : Tuple = True
snake_case_ : List[str] = False
return
assert torch.cuda.is_available()
logger.info("Initializing GPUs" )
if params.n_gpu > 1:
assert params.local_rank != -1
snake_case_ : Optional[int] = int(os.environ["WORLD_SIZE"] )
snake_case_ : int = int(os.environ["N_GPU_NODE"] )
snake_case_ : Any = int(os.environ["RANK"] )
# number of nodes / node ID
snake_case_ : Dict = params.world_size // params.n_gpu_per_node
snake_case_ : Optional[int] = params.global_rank // params.n_gpu_per_node
snake_case_ : Tuple = True
assert params.n_nodes == int(os.environ["N_NODES"] )
assert params.node_id == int(os.environ["NODE_RANK"] )
# local job (single GPU)
else:
assert params.local_rank == -1
snake_case_ : Optional[int] = 1
snake_case_ : str = 0
snake_case_ : List[Any] = 0
snake_case_ : int = 0
snake_case_ : Dict = 1
snake_case_ : Optional[Any] = 1
snake_case_ : str = False
# sanity checks
assert params.n_nodes >= 1
assert 0 <= params.node_id < params.n_nodes
assert 0 <= params.local_rank <= params.global_rank < params.world_size
assert params.world_size == params.n_nodes * params.n_gpu_per_node
# define whether this is the master process / if we are in multi-node distributed mode
snake_case_ : str = params.node_id == 0 and params.local_rank == 0
snake_case_ : str = params.n_nodes > 1
# summary
snake_case_ : str = F'''--- Global rank: {params.global_rank} - '''
logger.info(PREFIX + "Number of nodes: %i" % params.n_nodes )
logger.info(PREFIX + "Node ID : %i" % params.node_id )
logger.info(PREFIX + "Local rank : %i" % params.local_rank )
logger.info(PREFIX + "World size : %i" % params.world_size )
logger.info(PREFIX + "GPUs per node : %i" % params.n_gpu_per_node )
logger.info(PREFIX + "Master : %s" % str(params.is_master ) )
logger.info(PREFIX + "Multi-node : %s" % str(params.multi_node ) )
logger.info(PREFIX + "Multi-GPU : %s" % str(params.multi_gpu ) )
logger.info(PREFIX + "Hostname : %s" % socket.gethostname() )
# set GPU device
torch.cuda.set_device(params.local_rank )
# initialize multi-GPU
if params.multi_gpu:
logger.info("Initializing PyTorch distributed" )
torch.distributed.init_process_group(
init_method="env://" ,backend="nccl" ,)
def __UpperCAmelCase ( __magic_name__ )-> Dict:
"""simple docstring"""
np.random.seed(args.seed )
torch.manual_seed(args.seed )
if args.n_gpu > 0:
torch.cuda.manual_seed_all(args.seed )
| 653 | 0 |
'''simple docstring'''
import contextlib
from multiprocessing import Pool, RLock
from tqdm.auto import tqdm
from ..utils import experimental, logging
lowerCamelCase = logging.get_logger(__name__)
class _UpperCamelCase :
'''simple docstring'''
lowerCAmelCase__ = None
@experimental
def _A ( _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase ):
"""simple docstring"""
if ParallelBackendConfig.backend_name is None:
return _map_with_multiprocessing_pool(
_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase )
return _map_with_joblib(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase )
def _A ( _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase ):
"""simple docstring"""
__lowercase =num_proc if num_proc <= len(_lowerCAmelCase ) else len(_lowerCAmelCase )
__lowercase =[] # We organize the splits ourselve (contiguous splits)
for index in range(_lowerCAmelCase ):
__lowercase =len(_lowerCAmelCase ) // num_proc
__lowercase =len(_lowerCAmelCase ) % num_proc
__lowercase =div * index + min(_lowerCAmelCase , _lowerCAmelCase )
__lowercase =start + div + (1 if index < mod else 0)
split_kwds.append((function, iterable[start:end], types, index, disable_tqdm, desc) )
if len(_lowerCAmelCase ) != sum(len(i[1] ) for i in split_kwds ):
raise ValueError(
f"""Error dividing inputs iterable among processes. """
f"""Total number of objects {len(_lowerCAmelCase )}, """
f"""length: {sum(len(i[1] ) for i in split_kwds )}""" )
logger.info(
f"""Spawning {num_proc} processes for {len(_lowerCAmelCase )} objects in slices of {[len(i[1] ) for i in split_kwds]}""" )
__lowercase =None, None
if not disable_tqdm:
__lowercase =(RLock(),), tqdm.set_lock
with Pool(_lowerCAmelCase , initargs=_lowerCAmelCase , initializer=_lowerCAmelCase ) as pool:
__lowercase =pool.map(_lowerCAmelCase , _lowerCAmelCase )
logger.info(f"""Finished {num_proc} processes""" )
__lowercase =[obj for proc_res in mapped for obj in proc_res]
logger.info(f"""Unpacked {len(_lowerCAmelCase )} objects""" )
return mapped
def _A ( _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase ):
"""simple docstring"""
import joblib
with joblib.parallel_backend(ParallelBackendConfig.backend_name , n_jobs=_lowerCAmelCase ):
return joblib.Parallel()(
joblib.delayed(_lowerCAmelCase )((function, obj, types, None, True, None) ) for obj in iterable )
@experimental
@contextlib.contextmanager
def _A ( _lowerCAmelCase ):
"""simple docstring"""
__lowercase =backend_name
if backend_name == "spark":
from joblibspark import register_spark
register_spark()
# TODO: call create_cache_and_write_probe if "download" in steps
# TODO: raise NotImplementedError when Dataset.map etc is called
try:
yield
finally:
__lowercase =None
| 474 |
'''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 YolosImageProcessor
class A_ (unittest.TestCase ):
"""simple docstring"""
def __init__( self :Any , lowerCAmelCase__ :Dict , lowerCAmelCase__ :Dict=7 , lowerCAmelCase__ :Union[str, Any]=3 , lowerCAmelCase__ :List[str]=30 , lowerCAmelCase__ :List[str]=400 , lowerCAmelCase__ :Optional[Any]=True , lowerCAmelCase__ :Dict=None , lowerCAmelCase__ :str=True , lowerCAmelCase__ :Optional[int]=[0.5, 0.5, 0.5] , lowerCAmelCase__ :Optional[int]=[0.5, 0.5, 0.5] , lowerCAmelCase__ :str=True , lowerCAmelCase__ :int=1 / 255 , lowerCAmelCase__ :int=True , ) -> str:
'''simple docstring'''
snake_case_ : List[Any] = size if size is not None else {"shortest_edge": 18, "longest_edge": 1_333}
snake_case_ : Dict = parent
snake_case_ : Union[str, Any] = batch_size
snake_case_ : Optional[Any] = num_channels
snake_case_ : str = min_resolution
snake_case_ : Dict = max_resolution
snake_case_ : Optional[Any] = do_resize
snake_case_ : str = size
snake_case_ : Optional[int] = do_normalize
snake_case_ : Dict = image_mean
snake_case_ : Optional[int] = image_std
snake_case_ : List[str] = do_rescale
snake_case_ : Dict = rescale_factor
snake_case_ : str = do_pad
def _A ( self :List[Any] ) -> Dict:
'''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 _A ( self :Dict , lowerCAmelCase__ :List[str] , lowerCAmelCase__ :List[str]=False ) -> str:
'''simple docstring'''
if not batched:
snake_case_ : List[str] = image_inputs[0]
if isinstance(lowerCAmelCase__ , Image.Image ):
snake_case_, snake_case_ : int = image.size
else:
snake_case_, snake_case_ : Any = image.shape[1], image.shape[2]
if w < h:
snake_case_ : int = int(self.size["shortest_edge"] * h / w )
snake_case_ : List[Any] = self.size["shortest_edge"]
elif w > h:
snake_case_ : Optional[int] = self.size["shortest_edge"]
snake_case_ : str = int(self.size["shortest_edge"] * w / h )
else:
snake_case_ : Tuple = self.size["shortest_edge"]
snake_case_ : Dict = self.size["shortest_edge"]
else:
snake_case_ : List[str] = []
for image in image_inputs:
snake_case_, snake_case_ : Any = self.get_expected_values([image] )
expected_values.append((expected_height, expected_width) )
snake_case_ : str = max(lowerCAmelCase__ , key=lambda lowerCAmelCase__ : item[0] )[0]
snake_case_ : int = max(lowerCAmelCase__ , key=lambda lowerCAmelCase__ : item[1] )[1]
return expected_height, expected_width
@require_torch
@require_vision
class A_ (a_ , unittest.TestCase ):
"""simple docstring"""
a__ = YolosImageProcessor if is_vision_available() else None
def _A ( self :Optional[Any] ) -> str:
'''simple docstring'''
snake_case_ : int = YolosImageProcessingTester(self )
@property
def _A ( self :List[str] ) -> Any:
'''simple docstring'''
return self.image_processor_tester.prepare_image_processor_dict()
def _A ( self :List[str] ) -> Union[str, Any]:
'''simple docstring'''
snake_case_ : Tuple = self.image_processing_class(**self.image_processor_dict )
self.assertTrue(hasattr(lowerCAmelCase__ , "image_mean" ) )
self.assertTrue(hasattr(lowerCAmelCase__ , "image_std" ) )
self.assertTrue(hasattr(lowerCAmelCase__ , "do_normalize" ) )
self.assertTrue(hasattr(lowerCAmelCase__ , "do_resize" ) )
self.assertTrue(hasattr(lowerCAmelCase__ , "size" ) )
def _A ( self :List[Any] ) -> Any:
'''simple docstring'''
snake_case_ : List[Any] = self.image_processing_class.from_dict(self.image_processor_dict )
self.assertEqual(image_processor.size , {"shortest_edge": 18, "longest_edge": 1_333} )
self.assertEqual(image_processor.do_pad , lowerCAmelCase__ )
snake_case_ : Optional[int] = self.image_processing_class.from_dict(
self.image_processor_dict , size=42 , max_size=84 , pad_and_return_pixel_mask=lowerCAmelCase__ )
self.assertEqual(image_processor.size , {"shortest_edge": 42, "longest_edge": 84} )
self.assertEqual(image_processor.do_pad , lowerCAmelCase__ )
def _A ( self :List[str] ) -> int:
'''simple docstring'''
pass
def _A ( self :Optional[Any] ) -> Optional[Any]:
'''simple docstring'''
snake_case_ : Optional[int] = self.image_processing_class(**self.image_processor_dict )
# create random PIL images
snake_case_ : List[Any] = prepare_image_inputs(self.image_processor_tester , equal_resolution=lowerCAmelCase__ )
for image in image_inputs:
self.assertIsInstance(lowerCAmelCase__ , Image.Image )
# Test not batched input
snake_case_ : Optional[Any] = image_processing(image_inputs[0] , return_tensors="pt" ).pixel_values
snake_case_, snake_case_ : int = self.image_processor_tester.get_expected_values(lowerCAmelCase__ )
self.assertEqual(
encoded_images.shape , (1, self.image_processor_tester.num_channels, expected_height, expected_width) , )
# Test batched
snake_case_, snake_case_ : Any = self.image_processor_tester.get_expected_values(lowerCAmelCase__ , batched=lowerCAmelCase__ )
snake_case_ : Any = image_processing(lowerCAmelCase__ , return_tensors="pt" ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
expected_height,
expected_width,
) , )
def _A ( self :Dict ) -> Dict:
'''simple docstring'''
snake_case_ : str = self.image_processing_class(**self.image_processor_dict )
# create random numpy tensors
snake_case_ : Any = prepare_image_inputs(self.image_processor_tester , equal_resolution=lowerCAmelCase__ , numpify=lowerCAmelCase__ )
for image in image_inputs:
self.assertIsInstance(lowerCAmelCase__ , np.ndarray )
# Test not batched input
snake_case_ : Union[str, Any] = image_processing(image_inputs[0] , return_tensors="pt" ).pixel_values
snake_case_, snake_case_ : List[Any] = self.image_processor_tester.get_expected_values(lowerCAmelCase__ )
self.assertEqual(
encoded_images.shape , (1, self.image_processor_tester.num_channels, expected_height, expected_width) , )
# Test batched
snake_case_ : Tuple = image_processing(lowerCAmelCase__ , return_tensors="pt" ).pixel_values
snake_case_, snake_case_ : Dict = self.image_processor_tester.get_expected_values(lowerCAmelCase__ , batched=lowerCAmelCase__ )
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
expected_height,
expected_width,
) , )
def _A ( self :Tuple ) -> Tuple:
'''simple docstring'''
snake_case_ : List[str] = self.image_processing_class(**self.image_processor_dict )
# create random PyTorch tensors
snake_case_ : str = prepare_image_inputs(self.image_processor_tester , equal_resolution=lowerCAmelCase__ , torchify=lowerCAmelCase__ )
for image in image_inputs:
self.assertIsInstance(lowerCAmelCase__ , torch.Tensor )
# Test not batched input
snake_case_ : List[str] = image_processing(image_inputs[0] , return_tensors="pt" ).pixel_values
snake_case_, snake_case_ : Any = self.image_processor_tester.get_expected_values(lowerCAmelCase__ )
self.assertEqual(
encoded_images.shape , (1, self.image_processor_tester.num_channels, expected_height, expected_width) , )
# Test batched
snake_case_ : List[Any] = image_processing(lowerCAmelCase__ , return_tensors="pt" ).pixel_values
snake_case_, snake_case_ : Any = self.image_processor_tester.get_expected_values(lowerCAmelCase__ , batched=lowerCAmelCase__ )
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
expected_height,
expected_width,
) , )
def _A ( self :Tuple ) -> Dict:
'''simple docstring'''
snake_case_ : str = self.image_processing_class(**self.image_processor_dict )
snake_case_ : List[Any] = self.image_processing_class(do_resize=lowerCAmelCase__ , do_normalize=lowerCAmelCase__ , do_rescale=lowerCAmelCase__ )
# create random PyTorch tensors
snake_case_ : Tuple = prepare_image_inputs(self.image_processor_tester , equal_resolution=lowerCAmelCase__ , torchify=lowerCAmelCase__ )
for image in image_inputs:
self.assertIsInstance(lowerCAmelCase__ , torch.Tensor )
# Test whether the method "pad" and calling the image processor return the same tensors
snake_case_ : Tuple = image_processing_a.pad(lowerCAmelCase__ , return_tensors="pt" )
snake_case_ : Union[str, Any] = image_processing_a(lowerCAmelCase__ , return_tensors="pt" )
self.assertTrue(
torch.allclose(encoded_images_with_method["pixel_values"] , encoded_images["pixel_values"] , atol=1E-4 ) )
@slow
def _A ( self :str ) -> Any:
'''simple docstring'''
snake_case_ : List[str] = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png" )
with open("./tests/fixtures/tests_samples/COCO/coco_annotations.txt" , "r" ) as f:
snake_case_ : int = json.loads(f.read() )
snake_case_ : Optional[int] = {"image_id": 39_769, "annotations": target}
# encode them
snake_case_ : Tuple = YolosImageProcessor.from_pretrained("hustvl/yolos-small" )
snake_case_ : Dict = image_processing(images=lowerCAmelCase__ , annotations=lowerCAmelCase__ , return_tensors="pt" )
# verify pixel values
snake_case_ : Optional[int] = torch.Size([1, 3, 800, 1_066] )
self.assertEqual(encoding["pixel_values"].shape , lowerCAmelCase__ )
snake_case_ : str = torch.tensor([0.2_7_9_6, 0.3_1_3_8, 0.3_4_8_1] )
self.assertTrue(torch.allclose(encoding["pixel_values"][0, 0, 0, :3] , lowerCAmelCase__ , atol=1E-4 ) )
# verify area
snake_case_ : Dict = torch.tensor([5_8_8_7.9_6_0_0, 1_1_2_5_0.2_0_6_1, 4_8_9_3_5_3.8_4_3_8, 8_3_7_1_2_2.7_5_0_0, 1_4_7_9_6_7.5_1_5_6, 1_6_5_7_3_2.3_4_3_8] )
self.assertTrue(torch.allclose(encoding["labels"][0]["area"] , lowerCAmelCase__ ) )
# verify boxes
snake_case_ : Optional[int] = torch.Size([6, 4] )
self.assertEqual(encoding["labels"][0]["boxes"].shape , lowerCAmelCase__ )
snake_case_ : Any = torch.tensor([0.5_5_0_3, 0.2_7_6_5, 0.0_6_0_4, 0.2_2_1_5] )
self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"][0] , lowerCAmelCase__ , atol=1E-3 ) )
# verify image_id
snake_case_ : Dict = torch.tensor([39_769] )
self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"] , lowerCAmelCase__ ) )
# verify is_crowd
snake_case_ : int = torch.tensor([0, 0, 0, 0, 0, 0] )
self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"] , lowerCAmelCase__ ) )
# verify class_labels
snake_case_ : List[str] = torch.tensor([75, 75, 63, 65, 17, 17] )
self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"] , lowerCAmelCase__ ) )
# verify orig_size
snake_case_ : Any = torch.tensor([480, 640] )
self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"] , lowerCAmelCase__ ) )
# verify size
snake_case_ : List[Any] = torch.tensor([800, 1_066] )
self.assertTrue(torch.allclose(encoding["labels"][0]["size"] , lowerCAmelCase__ ) )
@slow
def _A ( self :Dict ) -> int:
'''simple docstring'''
snake_case_ : Tuple = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png" )
with open("./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt" , "r" ) as f:
snake_case_ : Optional[int] = json.loads(f.read() )
snake_case_ : Tuple = {"file_name": "000000039769.png", "image_id": 39_769, "segments_info": target}
snake_case_ : Any = pathlib.Path("./tests/fixtures/tests_samples/COCO/coco_panoptic" )
# encode them
snake_case_ : int = YolosImageProcessor(format="coco_panoptic" )
snake_case_ : Union[str, Any] = image_processing(images=lowerCAmelCase__ , annotations=lowerCAmelCase__ , masks_path=lowerCAmelCase__ , return_tensors="pt" )
# verify pixel values
snake_case_ : Optional[int] = torch.Size([1, 3, 800, 1_066] )
self.assertEqual(encoding["pixel_values"].shape , lowerCAmelCase__ )
snake_case_ : List[str] = torch.tensor([0.2_7_9_6, 0.3_1_3_8, 0.3_4_8_1] )
self.assertTrue(torch.allclose(encoding["pixel_values"][0, 0, 0, :3] , lowerCAmelCase__ , atol=1E-4 ) )
# verify area
snake_case_ : int = torch.tensor([1_4_7_9_7_9.6_8_7_5, 1_6_5_5_2_7.0_4_6_9, 4_8_4_6_3_8.5_9_3_8, 1_1_2_9_2.9_3_7_5, 5_8_7_9.6_5_6_2, 7_6_3_4.1_1_4_7] )
self.assertTrue(torch.allclose(encoding["labels"][0]["area"] , lowerCAmelCase__ ) )
# verify boxes
snake_case_ : Optional[int] = torch.Size([6, 4] )
self.assertEqual(encoding["labels"][0]["boxes"].shape , lowerCAmelCase__ )
snake_case_ : List[str] = torch.tensor([0.2_6_2_5, 0.5_4_3_7, 0.4_6_8_8, 0.8_6_2_5] )
self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"][0] , lowerCAmelCase__ , atol=1E-3 ) )
# verify image_id
snake_case_ : List[str] = torch.tensor([39_769] )
self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"] , lowerCAmelCase__ ) )
# verify is_crowd
snake_case_ : Dict = torch.tensor([0, 0, 0, 0, 0, 0] )
self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"] , lowerCAmelCase__ ) )
# verify class_labels
snake_case_ : str = torch.tensor([17, 17, 63, 75, 75, 93] )
self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"] , lowerCAmelCase__ ) )
# verify masks
snake_case_ : Any = 822_873
self.assertEqual(encoding["labels"][0]["masks"].sum().item() , lowerCAmelCase__ )
# verify orig_size
snake_case_ : int = torch.tensor([480, 640] )
self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"] , lowerCAmelCase__ ) )
# verify size
snake_case_ : Union[str, Any] = torch.tensor([800, 1_066] )
self.assertTrue(torch.allclose(encoding["labels"][0]["size"] , lowerCAmelCase__ ) )
| 653 | 0 |
'''simple docstring'''
import importlib
import shutil
import threading
import warnings
from typing import List
import fsspec
import fsspec.asyn
from . import compression
from .hffilesystem import HfFileSystem
lowercase_ = importlib.util.find_spec("s3fs") is not None
if _has_safs:
from .safilesystem import SaFileSystem # noqa: F401
lowercase_ = [
compression.BzaFileSystem,
compression.GzipFileSystem,
compression.LzaFileSystem,
compression.XzFileSystem,
compression.ZstdFileSystem,
]
# Register custom filesystems
for fs_class in COMPRESSION_FILESYSTEMS + [HfFileSystem]:
if fs_class.protocol in fsspec.registry and fsspec.registry[fs_class.protocol] is not fs_class:
warnings.warn(F"""A filesystem protocol was already set for {fs_class.protocol} and will be overwritten.""")
fsspec.register_implementation(fs_class.protocol, fs_class, clobber=True)
def lowerCAmelCase (__A):
"""simple docstring"""
if "://" in dataset_path:
_a = dataset_path.split('''://''')[1]
return dataset_path
def lowerCAmelCase (__A):
"""simple docstring"""
if fs is not None and fs.protocol != "file":
return True
else:
return False
def lowerCAmelCase (__A , __A , __A):
"""simple docstring"""
_a = not is_remote_filesystem(__A)
if is_local:
# LocalFileSystem.mv does copy + rm, it is more efficient to simply move a local directory
shutil.move(fs._strip_protocol(__A) , fs._strip_protocol(__A))
else:
fs.mv(__A , __A , recursive=__A)
def lowerCAmelCase ():
"""simple docstring"""
if hasattr(fsspec.asyn , '''reset_lock'''):
# for future fsspec>2022.05.0
fsspec.asyn.reset_lock()
else:
_a = None
_a = None
_a = threading.Lock()
| 11 |
'''simple docstring'''
def __UpperCAmelCase ( __magic_name__ ,__magic_name__ )-> str:
"""simple docstring"""
if not isinstance(__magic_name__ ,__magic_name__ ):
raise ValueError("iterations must be defined as integers" )
if not isinstance(__magic_name__ ,__magic_name__ ) or not number >= 1:
raise ValueError(
"starting number must be\n and integer and be more than 0" )
if not iterations >= 1:
raise ValueError("Iterations must be done more than 0 times to play FizzBuzz" )
snake_case_ : Dict = ""
while number <= iterations:
if number % 3 == 0:
out += "Fizz"
if number % 5 == 0:
out += "Buzz"
if 0 not in (number % 3, number % 5):
out += str(__magic_name__ )
# print(out)
number += 1
out += " "
return out
if __name__ == "__main__":
import doctest
doctest.testmod()
| 653 | 0 |
from collections.abc import Iterator, MutableMapping
from dataclasses import dataclass
from typing import Generic, TypeVar
__A : Dict = TypeVar('KEY')
__A : int = TypeVar('VAL')
@dataclass(frozen=a_ , slots=a_ )
class _SCREAMING_SNAKE_CASE ( Generic[KEY, VAL] ):
'''simple docstring'''
lowerCamelCase__ = 4_2
lowerCamelCase__ = 4_2
class _SCREAMING_SNAKE_CASE ( _Item ):
'''simple docstring'''
def __init__( self : List[Any] ):
super().__init__(lowerCAmelCase__ , lowerCAmelCase__ )
def __bool__( self : Optional[int] ):
return False
__A : Dict = _DeletedItem()
class _SCREAMING_SNAKE_CASE ( MutableMapping[KEY, VAL] ):
'''simple docstring'''
def __init__( self : Dict , __lowerCamelCase : int = 8 , __lowerCamelCase : float = 0.75 ):
SCREAMING_SNAKE_CASE = initial_block_size
SCREAMING_SNAKE_CASE = [None] * initial_block_size
assert 0.0 < capacity_factor < 1.0
SCREAMING_SNAKE_CASE = capacity_factor
SCREAMING_SNAKE_CASE = 0
def _snake_case ( self : Tuple , __lowerCamelCase : KEY ):
return hash(lowerCAmelCase__ ) % len(self._buckets )
def _snake_case ( self : Any , __lowerCamelCase : int ):
return (ind + 1) % len(self._buckets )
def _snake_case ( self : str , __lowerCamelCase : int , __lowerCamelCase : KEY , __lowerCamelCase : VAL ):
SCREAMING_SNAKE_CASE = self._buckets[ind]
if not stored:
SCREAMING_SNAKE_CASE = _Item(lowerCAmelCase__ , lowerCAmelCase__ )
self._len += 1
return True
elif stored.key == key:
SCREAMING_SNAKE_CASE = _Item(lowerCAmelCase__ , lowerCAmelCase__ )
return True
else:
return False
def _snake_case ( self : int ):
SCREAMING_SNAKE_CASE = len(self._buckets ) * self._capacity_factor
return len(self ) >= int(lowerCAmelCase__ )
def _snake_case ( self : Any ):
if len(self._buckets ) <= self._initial_block_size:
return False
SCREAMING_SNAKE_CASE = len(self._buckets ) * self._capacity_factor / 2
return len(self ) < limit
def _snake_case ( self : Tuple , __lowerCamelCase : int ):
SCREAMING_SNAKE_CASE = self._buckets
SCREAMING_SNAKE_CASE = [None] * new_size
SCREAMING_SNAKE_CASE = 0
for item in old_buckets:
if item:
self._add_item(item.key , item.val )
def _snake_case ( self : Optional[int] ):
self._resize(len(self._buckets ) * 2 )
def _snake_case ( self : str ):
self._resize(len(self._buckets ) // 2 )
def _snake_case ( self : Optional[int] , __lowerCamelCase : KEY ):
SCREAMING_SNAKE_CASE = self._get_bucket_index(lowerCAmelCase__ )
for _ in range(len(self._buckets ) ):
yield ind
SCREAMING_SNAKE_CASE = self._get_next_ind(lowerCAmelCase__ )
def _snake_case ( self : Union[str, Any] , __lowerCamelCase : KEY , __lowerCamelCase : VAL ):
for ind in self._iterate_buckets(lowerCAmelCase__ ):
if self._try_set(lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ ):
break
def __setitem__( self : Optional[int] , __lowerCamelCase : KEY , __lowerCamelCase : VAL ):
if self._is_full():
self._size_up()
self._add_item(lowerCAmelCase__ , lowerCAmelCase__ )
def __delitem__( self : List[Any] , __lowerCamelCase : KEY ):
for ind in self._iterate_buckets(lowerCAmelCase__ ):
SCREAMING_SNAKE_CASE = self._buckets[ind]
if item is None:
raise KeyError(lowerCAmelCase__ )
if item is _deleted:
continue
if item.key == key:
SCREAMING_SNAKE_CASE = _deleted
self._len -= 1
break
if self._is_sparse():
self._size_down()
def __getitem__( self : List[str] , __lowerCamelCase : KEY ):
for ind in self._iterate_buckets(lowerCAmelCase__ ):
SCREAMING_SNAKE_CASE = self._buckets[ind]
if item is None:
break
if item is _deleted:
continue
if item.key == key:
return item.val
raise KeyError(lowerCAmelCase__ )
def __len__( self : Optional[Any] ):
return self._len
def __iter__( self : List[Any] ):
yield from (item.key for item in self._buckets if item)
def __repr__( self : Any ):
SCREAMING_SNAKE_CASE = " ,".join(
f"{item.key}: {item.val}" for item in self._buckets if item )
return f"HashMap({val_string})" | 16 |
'''simple docstring'''
import argparse
import os
# New Code #
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from accelerate import Accelerator, DistributedType
from accelerate.utils import find_executable_batch_size
########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing how to ensure out-of-memory errors never
# interrupt training, and builds off the `nlp_example.py` script.
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
# - single CPU or single GPU
# - multi GPUS (using PyTorch distributed mode)
# - (multi) TPUs
# - fp16 (mixed-precision) or fp32 (normal precision)
#
# New additions from the base script can be found quickly by
# looking for the # New Code # tags
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################
__lowerCamelCase : Tuple = 16
__lowerCamelCase : Optional[int] = 32
def __UpperCAmelCase ( __magic_name__ ,__magic_name__ = 16 )-> int:
"""simple docstring"""
snake_case_ : Optional[int] = AutoTokenizer.from_pretrained("bert-base-cased" )
snake_case_ : str = load_dataset("glue" ,"mrpc" )
def tokenize_function(__magic_name__ ):
# max_length=None => use the model max length (it's actually the default)
snake_case_ : Dict = tokenizer(examples["sentence1"] ,examples["sentence2"] ,truncation=__magic_name__ ,max_length=__magic_name__ )
return outputs
# Apply the method we just defined to all the examples in all the splits of the dataset
# starting with the main process first:
with accelerator.main_process_first():
snake_case_ : Any = datasets.map(
__magic_name__ ,batched=__magic_name__ ,remove_columns=["idx", "sentence1", "sentence2"] ,)
# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
# transformers library
snake_case_ : List[Any] = tokenized_datasets.rename_column("label" ,"labels" )
def collate_fn(__magic_name__ ):
# On TPU it's best to pad everything to the same length or training will be very slow.
snake_case_ : int = 128 if accelerator.distributed_type == DistributedType.TPU else None
# When using mixed precision we want round multiples of 8/16
if accelerator.mixed_precision == "fp8":
snake_case_ : Tuple = 16
elif accelerator.mixed_precision != "no":
snake_case_ : str = 8
else:
snake_case_ : Optional[Any] = None
return tokenizer.pad(
__magic_name__ ,padding="longest" ,max_length=__magic_name__ ,pad_to_multiple_of=__magic_name__ ,return_tensors="pt" ,)
# Instantiate dataloaders.
snake_case_ : str = DataLoader(
tokenized_datasets["train"] ,shuffle=__magic_name__ ,collate_fn=__magic_name__ ,batch_size=__magic_name__ )
snake_case_ : Optional[Any] = DataLoader(
tokenized_datasets["validation"] ,shuffle=__magic_name__ ,collate_fn=__magic_name__ ,batch_size=__magic_name__ )
return train_dataloader, eval_dataloader
# For testing only
if os.environ.get('''TESTING_MOCKED_DATALOADERS''', None) == "1":
from accelerate.test_utils.training import mocked_dataloaders
__lowerCamelCase : Optional[Any] = mocked_dataloaders # noqa: F811
def __UpperCAmelCase ( __magic_name__ ,__magic_name__ )-> Dict:
"""simple docstring"""
if os.environ.get("TESTING_MOCKED_DATALOADERS" ,__magic_name__ ) == "1":
snake_case_ : List[str] = 2
# Initialize accelerator
snake_case_ : Union[str, Any] = Accelerator(cpu=args.cpu ,mixed_precision=args.mixed_precision )
# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
snake_case_ : List[str] = config["lr"]
snake_case_ : Dict = int(config["num_epochs"] )
snake_case_ : Dict = int(config["seed"] )
snake_case_ : Optional[int] = int(config["batch_size"] )
snake_case_ : Dict = evaluate.load("glue" ,"mrpc" )
# New Code #
# We now can define an inner training loop function. It should take a batch size as the only parameter,
# and build the dataloaders in there.
# It also gets our decorator
@find_executable_batch_size(starting_batch_size=__magic_name__ )
def inner_training_loop(__magic_name__ ):
# And now just move everything below under this function
# We need to bring in the Accelerator object from earlier
nonlocal accelerator
# And reset all of its attributes that could hold onto any memory:
accelerator.free_memory()
# Then we can declare the model, optimizer, and everything else:
set_seed(__magic_name__ )
# Instantiate the model (we build the model here so that the seed also control new weights initialization)
snake_case_ : Optional[int] = AutoModelForSequenceClassification.from_pretrained("bert-base-cased" ,return_dict=__magic_name__ )
# We could avoid this line since the accelerator is set with `device_placement=True` (default value).
# Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
# creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
snake_case_ : Optional[int] = model.to(accelerator.device )
# Instantiate optimizer
snake_case_ : List[Any] = AdamW(params=model.parameters() ,lr=__magic_name__ )
snake_case_, snake_case_ : int = get_dataloaders(__magic_name__ ,__magic_name__ )
# Instantiate scheduler
snake_case_ : Tuple = get_linear_schedule_with_warmup(
optimizer=__magic_name__ ,num_warmup_steps=100 ,num_training_steps=(len(__magic_name__ ) * num_epochs) ,)
# Prepare everything
# There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
# prepare method.
snake_case_, snake_case_, snake_case_, snake_case_, snake_case_ : Tuple = accelerator.prepare(
__magic_name__ ,__magic_name__ ,__magic_name__ ,__magic_name__ ,__magic_name__ )
# Now we train the model
for epoch in range(__magic_name__ ):
model.train()
for step, batch in enumerate(__magic_name__ ):
# We could avoid this line since we set the accelerator with `device_placement=True`.
batch.to(accelerator.device )
snake_case_ : int = model(**__magic_name__ )
snake_case_ : Any = outputs.loss
accelerator.backward(__magic_name__ )
optimizer.step()
lr_scheduler.step()
optimizer.zero_grad()
model.eval()
for step, batch in enumerate(__magic_name__ ):
# We could avoid this line since we set the accelerator with `device_placement=True`.
batch.to(accelerator.device )
with torch.no_grad():
snake_case_ : Union[str, Any] = model(**__magic_name__ )
snake_case_ : List[str] = outputs.logits.argmax(dim=-1 )
snake_case_, snake_case_ : Union[str, Any] = accelerator.gather_for_metrics((predictions, batch["labels"]) )
metric.add_batch(
predictions=__magic_name__ ,references=__magic_name__ ,)
snake_case_ : Tuple = metric.compute()
# Use accelerator.print to print only on the main process.
accelerator.print(F'''epoch {epoch}:''' ,__magic_name__ )
# New Code #
# And call it at the end with no arguments
# Note: You could also refactor this outside of your training loop function
inner_training_loop()
def __UpperCAmelCase ( )-> List[str]:
"""simple docstring"""
snake_case_ : List[Any] = argparse.ArgumentParser(description="Simple example of training script." )
parser.add_argument(
"--mixed_precision" ,type=__magic_name__ ,default=__magic_name__ ,choices=["no", "fp16", "bf16", "fp8"] ,help="Whether to use mixed precision. Choose"
"between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10."
"and an Nvidia Ampere GPU." ,)
parser.add_argument("--cpu" ,action="store_true" ,help="If passed, will train on the CPU." )
snake_case_ : str = parser.parse_args()
snake_case_ : Optional[int] = {"lr": 2E-5, "num_epochs": 3, "seed": 42, "batch_size": 16}
training_function(__magic_name__ ,__magic_name__ )
if __name__ == "__main__":
main()
| 653 | 0 |
'''simple docstring'''
import copy
import unittest
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
MODEL_FOR_MULTIPLE_CHOICE_MAPPING,
MODEL_FOR_QUESTION_ANSWERING_MAPPING,
MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
LayoutLMvaConfig,
LayoutLMvaForQuestionAnswering,
LayoutLMvaForSequenceClassification,
LayoutLMvaForTokenClassification,
LayoutLMvaModel,
)
from transformers.models.layoutlmva.modeling_layoutlmva import LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import LayoutLMvaImageProcessor
class _snake_case :
'''simple docstring'''
def __init__( self: Optional[Any] , __UpperCamelCase: List[str] , __UpperCamelCase: List[str]=2 , __UpperCamelCase: List[Any]=3 , __UpperCamelCase: Any=4 , __UpperCamelCase: List[Any]=2 , __UpperCamelCase: List[str]=7 , __UpperCamelCase: Any=True , __UpperCamelCase: Optional[int]=True , __UpperCamelCase: Optional[Any]=True , __UpperCamelCase: Optional[int]=True , __UpperCamelCase: List[str]=99 , __UpperCamelCase: Union[str, Any]=36 , __UpperCamelCase: Dict=3 , __UpperCamelCase: str=4 , __UpperCamelCase: Optional[int]=37 , __UpperCamelCase: Dict="gelu" , __UpperCamelCase: Optional[Any]=0.1 , __UpperCamelCase: Dict=0.1 , __UpperCamelCase: Optional[int]=512 , __UpperCamelCase: Union[str, Any]=16 , __UpperCamelCase: List[Any]=2 , __UpperCamelCase: Any=0.0_2 , __UpperCamelCase: Dict=6 , __UpperCamelCase: Optional[int]=6 , __UpperCamelCase: Any=3 , __UpperCamelCase: int=4 , __UpperCamelCase: int=None , __UpperCamelCase: Any=1000 , ) -> Any:
__magic_name__ : Optional[int] = parent
__magic_name__ : Union[str, Any] = batch_size
__magic_name__ : Optional[int] = num_channels
__magic_name__ : List[Any] = image_size
__magic_name__ : Optional[int] = patch_size
__magic_name__ : Union[str, Any] = text_seq_length
__magic_name__ : Dict = is_training
__magic_name__ : Optional[Any] = use_input_mask
__magic_name__ : Union[str, Any] = use_token_type_ids
__magic_name__ : Dict = use_labels
__magic_name__ : List[str] = vocab_size
__magic_name__ : Optional[Any] = hidden_size
__magic_name__ : List[str] = num_hidden_layers
__magic_name__ : int = num_attention_heads
__magic_name__ : List[str] = intermediate_size
__magic_name__ : str = hidden_act
__magic_name__ : Optional[Any] = hidden_dropout_prob
__magic_name__ : Optional[int] = attention_probs_dropout_prob
__magic_name__ : Union[str, Any] = max_position_embeddings
__magic_name__ : List[Any] = type_vocab_size
__magic_name__ : Union[str, Any] = type_sequence_label_size
__magic_name__ : List[Any] = initializer_range
__magic_name__ : Union[str, Any] = coordinate_size
__magic_name__ : int = shape_size
__magic_name__ : Tuple = num_labels
__magic_name__ : List[Any] = num_choices
__magic_name__ : List[str] = scope
__magic_name__ : Dict = range_bbox
# LayoutLMv3's sequence length equals the number of text tokens + number of patches + 1 (we add 1 for the CLS token)
__magic_name__ : str = text_seq_length
__magic_name__ : Optional[int] = (image_size // patch_size) ** 2 + 1
__magic_name__ : str = self.text_seq_length + self.image_seq_length
def lowerCAmelCase__ ( self: Union[str, Any] ) -> Tuple:
__magic_name__ : Dict = ids_tensor([self.batch_size, self.text_seq_length] , self.vocab_size )
__magic_name__ : str = ids_tensor([self.batch_size, self.text_seq_length, 4] , self.range_bbox )
# Ensure that bbox is legal
for i in range(bbox.shape[0] ):
for j in range(bbox.shape[1] ):
if bbox[i, j, 3] < bbox[i, j, 1]:
__magic_name__ : Optional[Any] = bbox[i, j, 3]
__magic_name__ : Any = bbox[i, j, 1]
__magic_name__ : Tuple = t
if bbox[i, j, 2] < bbox[i, j, 0]:
__magic_name__ : str = bbox[i, j, 2]
__magic_name__ : Dict = bbox[i, j, 0]
__magic_name__ : Union[str, Any] = t
__magic_name__ : int = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
__magic_name__ : Dict = None
if self.use_input_mask:
__magic_name__ : str = random_attention_mask([self.batch_size, self.text_seq_length] )
__magic_name__ : Any = None
if self.use_token_type_ids:
__magic_name__ : List[str] = ids_tensor([self.batch_size, self.text_seq_length] , self.type_vocab_size )
__magic_name__ : Union[str, Any] = None
__magic_name__ : str = None
if self.use_labels:
__magic_name__ : List[Any] = ids_tensor([self.batch_size] , self.type_sequence_label_size )
__magic_name__ : List[Any] = ids_tensor([self.batch_size, self.text_seq_length] , self.num_labels )
__magic_name__ : str = LayoutLMvaConfig(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , initializer_range=self.initializer_range , coordinate_size=self.coordinate_size , shape_size=self.shape_size , input_size=self.image_size , patch_size=self.patch_size , )
return config, input_ids, bbox, pixel_values, token_type_ids, input_mask, sequence_labels, token_labels
def lowerCAmelCase__ ( self: Dict , __UpperCamelCase: Dict , __UpperCamelCase: List[str] , __UpperCamelCase: str , __UpperCamelCase: List[Any] , __UpperCamelCase: Optional[Any] , __UpperCamelCase: List[str] , __UpperCamelCase: Dict , __UpperCamelCase: List[str] ) -> Optional[Any]:
__magic_name__ : Tuple = LayoutLMvaModel(config=lowerCAmelCase__ )
model.to(lowerCAmelCase__ )
model.eval()
# text + image
__magic_name__ : Tuple = model(lowerCAmelCase__ , pixel_values=lowerCAmelCase__ )
__magic_name__ : Optional[int] = model(
lowerCAmelCase__ , bbox=lowerCAmelCase__ , pixel_values=lowerCAmelCase__ , attention_mask=lowerCAmelCase__ , token_type_ids=lowerCAmelCase__ )
__magic_name__ : Optional[int] = model(lowerCAmelCase__ , bbox=lowerCAmelCase__ , pixel_values=lowerCAmelCase__ , token_type_ids=lowerCAmelCase__ )
__magic_name__ : int = model(lowerCAmelCase__ , bbox=lowerCAmelCase__ , pixel_values=lowerCAmelCase__ )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
# text only
__magic_name__ : List[Any] = model(lowerCAmelCase__ )
self.parent.assertEqual(
result.last_hidden_state.shape , (self.batch_size, self.text_seq_length, self.hidden_size) )
# image only
__magic_name__ : Union[str, Any] = model(pixel_values=lowerCAmelCase__ )
self.parent.assertEqual(
result.last_hidden_state.shape , (self.batch_size, self.image_seq_length, self.hidden_size) )
def lowerCAmelCase__ ( self: str , __UpperCamelCase: str , __UpperCamelCase: Tuple , __UpperCamelCase: Union[str, Any] , __UpperCamelCase: Union[str, Any] , __UpperCamelCase: Optional[Any] , __UpperCamelCase: List[str] , __UpperCamelCase: List[str] , __UpperCamelCase: Tuple ) -> List[Any]:
__magic_name__ : str = self.num_labels
__magic_name__ : List[Any] = LayoutLMvaForSequenceClassification(lowerCAmelCase__ )
model.to(lowerCAmelCase__ )
model.eval()
__magic_name__ : Optional[int] = model(
lowerCAmelCase__ , bbox=lowerCAmelCase__ , pixel_values=lowerCAmelCase__ , attention_mask=lowerCAmelCase__ , token_type_ids=lowerCAmelCase__ , labels=lowerCAmelCase__ , )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def lowerCAmelCase__ ( self: Union[str, Any] , __UpperCamelCase: List[Any] , __UpperCamelCase: int , __UpperCamelCase: List[str] , __UpperCamelCase: Tuple , __UpperCamelCase: str , __UpperCamelCase: Optional[int] , __UpperCamelCase: Any , __UpperCamelCase: Union[str, Any] ) -> str:
__magic_name__ : Optional[int] = self.num_labels
__magic_name__ : str = LayoutLMvaForTokenClassification(config=lowerCAmelCase__ )
model.to(lowerCAmelCase__ )
model.eval()
__magic_name__ : List[Any] = model(
lowerCAmelCase__ , bbox=lowerCAmelCase__ , pixel_values=lowerCAmelCase__ , attention_mask=lowerCAmelCase__ , token_type_ids=lowerCAmelCase__ , labels=lowerCAmelCase__ , )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.text_seq_length, self.num_labels) )
def lowerCAmelCase__ ( self: Optional[int] , __UpperCamelCase: Dict , __UpperCamelCase: str , __UpperCamelCase: Dict , __UpperCamelCase: Optional[int] , __UpperCamelCase: str , __UpperCamelCase: int , __UpperCamelCase: Union[str, Any] , __UpperCamelCase: str ) -> Tuple:
__magic_name__ : List[str] = LayoutLMvaForQuestionAnswering(config=lowerCAmelCase__ )
model.to(lowerCAmelCase__ )
model.eval()
__magic_name__ : List[Any] = model(
lowerCAmelCase__ , bbox=lowerCAmelCase__ , pixel_values=lowerCAmelCase__ , attention_mask=lowerCAmelCase__ , token_type_ids=lowerCAmelCase__ , start_positions=lowerCAmelCase__ , end_positions=lowerCAmelCase__ , )
self.parent.assertEqual(result.start_logits.shape , (self.batch_size, self.seq_length) )
self.parent.assertEqual(result.end_logits.shape , (self.batch_size, self.seq_length) )
def lowerCAmelCase__ ( self: int ) -> Union[str, Any]:
__magic_name__ : Dict = self.prepare_config_and_inputs()
(
__magic_name__
) : Optional[Any] = config_and_inputs
__magic_name__ : Tuple = {
"input_ids": input_ids,
"bbox": bbox,
"pixel_values": pixel_values,
"token_type_ids": token_type_ids,
"attention_mask": input_mask,
}
return config, inputs_dict
@require_torch
class _snake_case ( a_ , a_ , unittest.TestCase ):
'''simple docstring'''
__snake_case = False
__snake_case = False
__snake_case = False
__snake_case = (
(
LayoutLMvaModel,
LayoutLMvaForSequenceClassification,
LayoutLMvaForTokenClassification,
LayoutLMvaForQuestionAnswering,
)
if is_torch_available()
else ()
)
__snake_case = (
{"document-question-answering": LayoutLMvaForQuestionAnswering, "feature-extraction": LayoutLMvaModel}
if is_torch_available()
else {}
)
def lowerCAmelCase__ ( self: Optional[Any] , __UpperCamelCase: Tuple , __UpperCamelCase: Tuple , __UpperCamelCase: List[Any] , __UpperCamelCase: Optional[Any] , __UpperCamelCase: List[Any] ) -> List[str]:
return True
def lowerCAmelCase__ ( self: List[Any] ) -> str:
__magic_name__ : Tuple = LayoutLMvaModelTester(self )
__magic_name__ : Optional[int] = ConfigTester(self , config_class=lowerCAmelCase__ , hidden_size=37 )
def lowerCAmelCase__ ( self: Tuple , __UpperCamelCase: Optional[int] , __UpperCamelCase: Dict , __UpperCamelCase: Union[str, Any]=False ) -> Any:
__magic_name__ : List[str] = copy.deepcopy(lowerCAmelCase__ )
if model_class in get_values(lowerCAmelCase__ ):
__magic_name__ : Optional[Any] = {
k: v.unsqueeze(1 ).expand(-1 , self.model_tester.num_choices , -1 ).contiguous()
if isinstance(lowerCAmelCase__ , torch.Tensor ) and v.ndim > 1
else v
for k, v in inputs_dict.items()
}
if return_labels:
if model_class in get_values(lowerCAmelCase__ ):
__magic_name__ : Union[str, Any] = torch.ones(self.model_tester.batch_size , dtype=torch.long , device=lowerCAmelCase__ )
elif model_class in get_values(lowerCAmelCase__ ):
__magic_name__ : List[Any] = torch.zeros(
self.model_tester.batch_size , dtype=torch.long , device=lowerCAmelCase__ )
__magic_name__ : str = torch.zeros(
self.model_tester.batch_size , dtype=torch.long , device=lowerCAmelCase__ )
elif model_class in [
*get_values(lowerCAmelCase__ ),
]:
__magic_name__ : Union[str, Any] = torch.zeros(
self.model_tester.batch_size , dtype=torch.long , device=lowerCAmelCase__ )
elif model_class in [
*get_values(lowerCAmelCase__ ),
]:
__magic_name__ : List[str] = torch.zeros(
(self.model_tester.batch_size, self.model_tester.text_seq_length) , dtype=torch.long , device=lowerCAmelCase__ , )
return inputs_dict
def lowerCAmelCase__ ( self: Any ) -> Any:
self.config_tester.run_common_tests()
def lowerCAmelCase__ ( self: int ) -> int:
__magic_name__ : Tuple = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*lowerCAmelCase__ )
def lowerCAmelCase__ ( self: Any ) -> Dict:
__magic_name__ : Dict = self.model_tester.prepare_config_and_inputs()
for type in ["absolute", "relative_key", "relative_key_query"]:
__magic_name__ : int = type
self.model_tester.create_and_check_model(*lowerCAmelCase__ )
def lowerCAmelCase__ ( self: int ) -> str:
__magic_name__ : Optional[int] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_sequence_classification(*lowerCAmelCase__ )
def lowerCAmelCase__ ( self: List[Any] ) -> Optional[Any]:
__magic_name__ : List[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_token_classification(*lowerCAmelCase__ )
def lowerCAmelCase__ ( self: int ) -> Union[str, Any]:
__magic_name__ : Optional[int] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_question_answering(*lowerCAmelCase__ )
@slow
def lowerCAmelCase__ ( self: Tuple ) -> List[Any]:
for model_name in LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
__magic_name__ : str = LayoutLMvaModel.from_pretrained(lowerCAmelCase__ )
self.assertIsNotNone(lowerCAmelCase__ )
def _UpperCamelCase ( ):
"""simple docstring"""
__magic_name__ : List[str] = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png" )
return image
@require_torch
class _snake_case ( unittest.TestCase ):
'''simple docstring'''
@cached_property
def lowerCAmelCase__ ( self: Union[str, Any] ) -> Optional[Any]:
return LayoutLMvaImageProcessor(apply_ocr=lowerCAmelCase__ ) if is_vision_available() else None
@slow
def lowerCAmelCase__ ( self: Union[str, Any] ) -> Union[str, Any]:
__magic_name__ : Optional[int] = LayoutLMvaModel.from_pretrained("microsoft/layoutlmv3-base" ).to(lowerCAmelCase__ )
__magic_name__ : Optional[Any] = self.default_image_processor
__magic_name__ : Optional[int] = prepare_img()
__magic_name__ : Union[str, Any] = image_processor(images=lowerCAmelCase__ , return_tensors="pt" ).pixel_values.to(lowerCAmelCase__ )
__magic_name__ : List[str] = torch.tensor([[1, 2]] )
__magic_name__ : Any = torch.tensor([[1, 2, 3, 4], [5, 6, 7, 8]] ).unsqueeze(0 )
# forward pass
__magic_name__ : Any = model(
input_ids=input_ids.to(lowerCAmelCase__ ) , bbox=bbox.to(lowerCAmelCase__ ) , pixel_values=pixel_values.to(lowerCAmelCase__ ) , )
# verify the logits
__magic_name__ : Optional[Any] = torch.Size((1, 199, 768) )
self.assertEqual(outputs.last_hidden_state.shape , lowerCAmelCase__ )
__magic_name__ : str = torch.tensor(
[[-0.0_5_2_9, 0.3_6_1_8, 0.1_6_3_2], [-0.1_5_8_7, -0.1_6_6_7, -0.0_4_0_0], [-0.1_5_5_7, -0.1_6_7_1, -0.0_5_0_5]] ).to(lowerCAmelCase__ )
self.assertTrue(torch.allclose(outputs.last_hidden_state[0, :3, :3] , lowerCAmelCase__ , atol=1E-4 ) ) | 436 |
'''simple docstring'''
# Copyright 2023 The HuggingFace Inc. 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 torch
from ..models.auto import AutoModelForSequenceClassification, AutoTokenizer
from .base import PipelineTool
class A_ (a_ ):
"""simple docstring"""
a__ = '''facebook/bart-large-mnli'''
a__ = (
'''This is a tool that classifies an English text using provided labels. It takes two inputs: `text`, which '''
'''should be the text to classify, and `labels`, which should be the list of labels to use for classification. '''
'''It returns the most likely label in the list of provided `labels` for the input text.'''
)
a__ = '''text_classifier'''
a__ = AutoTokenizer
a__ = AutoModelForSequenceClassification
a__ = ['''text''', ['''text''']]
a__ = ['''text''']
def _A ( self :List[str] ) -> Union[str, Any]:
'''simple docstring'''
super().setup()
snake_case_ : Optional[int] = self.model.config
snake_case_ : Any = -1
for idx, label in config.idalabel.items():
if label.lower().startswith("entail" ):
snake_case_ : Union[str, Any] = int(lowerCAmelCase__ )
if self.entailment_id == -1:
raise ValueError("Could not determine the entailment ID from the model config, please pass it at init." )
def _A ( self :Dict , lowerCAmelCase__ :int , lowerCAmelCase__ :Tuple ) -> int:
'''simple docstring'''
snake_case_ : Tuple = labels
return self.pre_processor(
[text] * len(lowerCAmelCase__ ) , [F'''This example is {label}''' for label in labels] , return_tensors="pt" , padding="max_length" , )
def _A ( self :Any , lowerCAmelCase__ :str ) -> List[str]:
'''simple docstring'''
snake_case_ : Optional[int] = outputs.logits
snake_case_ : Tuple = torch.argmax(logits[:, 2] ).item()
return self._labels[label_id]
| 653 | 0 |
def _lowerCamelCase( lowercase__ ) -> list[list[float]]:
'''simple docstring'''
__lowercase= []
for data in source_data:
for i, el in enumerate(lowercase__ ):
if len(lowercase__ ) < i + 1:
data_lists.append([] )
data_lists[i].append(float(lowercase__ ) )
return data_lists
def _lowerCamelCase( lowercase__ , lowercase__ ) -> list[list[float]]:
'''simple docstring'''
__lowercase= []
for dlist, weight in zip(lowercase__ , lowercase__ ):
__lowercase= min(lowercase__ )
__lowercase= max(lowercase__ )
__lowercase= []
# for weight 0 score is 1 - actual score
if weight == 0:
for item in dlist:
try:
score.append(1 - ((item - mind) / (maxd - mind)) )
except ZeroDivisionError:
score.append(1 )
elif weight == 1:
for item in dlist:
try:
score.append((item - mind) / (maxd - mind) )
except ZeroDivisionError:
score.append(0 )
# weight not 0 or 1
else:
__lowercase= F'Invalid weight of {weight:f} provided'
raise ValueError(lowercase__ )
score_lists.append(lowercase__ )
return score_lists
def _lowerCamelCase( lowercase__ ) -> list[float]:
'''simple docstring'''
__lowercase= [0 for i in range(len(score_lists[0] ) )]
for slist in score_lists:
for j, ele in enumerate(lowercase__ ):
__lowercase= final_scores[j] + ele
return final_scores
def _lowerCamelCase( lowercase__ , lowercase__ ) -> list[list[float]]:
'''simple docstring'''
__lowercase= get_data(lowercase__ )
__lowercase= calculate_each_score(lowercase__ , lowercase__ )
__lowercase= generate_final_scores(lowercase__ )
# append scores to source data
for i, ele in enumerate(lowercase__ ):
source_data[i].append(lowercase__ )
return source_data
| 230 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
is_vision_available,
)
__lowerCamelCase : Any = {'''configuration_vit''': ['''VIT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''ViTConfig''', '''ViTOnnxConfig''']}
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__lowerCamelCase : Any = ['''ViTFeatureExtractor''']
__lowerCamelCase : Any = ['''ViTImageProcessor''']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__lowerCamelCase : Optional[Any] = [
'''VIT_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''ViTForImageClassification''',
'''ViTForMaskedImageModeling''',
'''ViTModel''',
'''ViTPreTrainedModel''',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__lowerCamelCase : Union[str, Any] = [
'''TFViTForImageClassification''',
'''TFViTModel''',
'''TFViTPreTrainedModel''',
]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__lowerCamelCase : Tuple = [
'''FlaxViTForImageClassification''',
'''FlaxViTModel''',
'''FlaxViTPreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_vit import VIT_PRETRAINED_CONFIG_ARCHIVE_MAP, ViTConfig, ViTOnnxConfig
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .feature_extraction_vit import ViTFeatureExtractor
from .image_processing_vit import ViTImageProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_vit import (
VIT_PRETRAINED_MODEL_ARCHIVE_LIST,
ViTForImageClassification,
ViTForMaskedImageModeling,
ViTModel,
ViTPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_vit import TFViTForImageClassification, TFViTModel, TFViTPreTrainedModel
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_vit import FlaxViTForImageClassification, FlaxViTModel, FlaxViTPreTrainedModel
else:
import sys
__lowerCamelCase : List[str] = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 653 | 0 |
'''simple docstring'''
from ... import PretrainedConfig
lowerCAmelCase : Any = {
'''sijunhe/nezha-cn-base''': '''https://huggingface.co/sijunhe/nezha-cn-base/resolve/main/config.json''',
}
class SCREAMING_SNAKE_CASE__ ( a_):
lowerCAmelCase_ = NEZHA_PRETRAINED_CONFIG_ARCHIVE_MAP
lowerCAmelCase_ = """nezha"""
def __init__( self , A_=21128 , A_=768 , A_=12 , A_=12 , A_=3072 , A_="gelu" , A_=0.1 , A_=0.1 , A_=512 , A_=64 , A_=2 , A_=0.02 , A_=1e-12 , A_=0.1 , A_=0 , A_=2 , A_=3 , A_=True , **A_ , )-> Optional[Any]:
'''simple docstring'''
super().__init__(pad_token_id=lowerCAmelCase__ , bos_token_id=lowerCAmelCase__ , eos_token_id=lowerCAmelCase__ , **lowerCAmelCase__ )
UpperCamelCase = vocab_size
UpperCamelCase = hidden_size
UpperCamelCase = num_hidden_layers
UpperCamelCase = num_attention_heads
UpperCamelCase = hidden_act
UpperCamelCase = intermediate_size
UpperCamelCase = hidden_dropout_prob
UpperCamelCase = attention_probs_dropout_prob
UpperCamelCase = max_position_embeddings
UpperCamelCase = max_relative_position
UpperCamelCase = type_vocab_size
UpperCamelCase = initializer_range
UpperCamelCase = layer_norm_eps
UpperCamelCase = classifier_dropout
UpperCamelCase = use_cache
| 3 |
'''simple docstring'''
import copy
import unittest
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
MODEL_FOR_MULTIPLE_CHOICE_MAPPING,
MODEL_FOR_QUESTION_ANSWERING_MAPPING,
MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
LayoutLMvaConfig,
LayoutLMvaForQuestionAnswering,
LayoutLMvaForSequenceClassification,
LayoutLMvaForTokenClassification,
LayoutLMvaModel,
)
from transformers.models.layoutlmva.modeling_layoutlmva import LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import LayoutLMvaImageProcessor
class A_ :
"""simple docstring"""
def __init__( self :Optional[Any] , lowerCAmelCase__ :List[str] , lowerCAmelCase__ :List[str]=2 , lowerCAmelCase__ :List[Any]=3 , lowerCAmelCase__ :Any=4 , lowerCAmelCase__ :List[Any]=2 , lowerCAmelCase__ :List[str]=7 , lowerCAmelCase__ :Any=True , lowerCAmelCase__ :Optional[int]=True , lowerCAmelCase__ :Optional[Any]=True , lowerCAmelCase__ :Optional[int]=True , lowerCAmelCase__ :List[str]=99 , lowerCAmelCase__ :Union[str, Any]=36 , lowerCAmelCase__ :Dict=3 , lowerCAmelCase__ :str=4 , lowerCAmelCase__ :Optional[int]=37 , lowerCAmelCase__ :Dict="gelu" , lowerCAmelCase__ :Optional[Any]=0.1 , lowerCAmelCase__ :Dict=0.1 , lowerCAmelCase__ :Optional[int]=512 , lowerCAmelCase__ :Union[str, Any]=16 , lowerCAmelCase__ :List[Any]=2 , lowerCAmelCase__ :Any=0.0_2 , lowerCAmelCase__ :Dict=6 , lowerCAmelCase__ :Optional[int]=6 , lowerCAmelCase__ :Any=3 , lowerCAmelCase__ :int=4 , lowerCAmelCase__ :int=None , lowerCAmelCase__ :Any=1_000 , ) -> Any:
'''simple docstring'''
snake_case_ : Optional[int] = parent
snake_case_ : Union[str, Any] = batch_size
snake_case_ : Optional[int] = num_channels
snake_case_ : List[Any] = image_size
snake_case_ : Optional[int] = patch_size
snake_case_ : Union[str, Any] = text_seq_length
snake_case_ : Dict = is_training
snake_case_ : Optional[Any] = use_input_mask
snake_case_ : Union[str, Any] = use_token_type_ids
snake_case_ : Dict = use_labels
snake_case_ : List[str] = vocab_size
snake_case_ : Optional[Any] = hidden_size
snake_case_ : List[str] = num_hidden_layers
snake_case_ : int = num_attention_heads
snake_case_ : List[str] = intermediate_size
snake_case_ : str = hidden_act
snake_case_ : Optional[Any] = hidden_dropout_prob
snake_case_ : Optional[int] = attention_probs_dropout_prob
snake_case_ : Union[str, Any] = max_position_embeddings
snake_case_ : List[Any] = type_vocab_size
snake_case_ : Union[str, Any] = type_sequence_label_size
snake_case_ : List[Any] = initializer_range
snake_case_ : Union[str, Any] = coordinate_size
snake_case_ : int = shape_size
snake_case_ : Tuple = num_labels
snake_case_ : List[Any] = num_choices
snake_case_ : List[str] = scope
snake_case_ : Dict = range_bbox
# LayoutLMv3's sequence length equals the number of text tokens + number of patches + 1 (we add 1 for the CLS token)
snake_case_ : str = text_seq_length
snake_case_ : Optional[int] = (image_size // patch_size) ** 2 + 1
snake_case_ : str = self.text_seq_length + self.image_seq_length
def _A ( self :Union[str, Any] ) -> Tuple:
'''simple docstring'''
snake_case_ : Dict = ids_tensor([self.batch_size, self.text_seq_length] , self.vocab_size )
snake_case_ : str = ids_tensor([self.batch_size, self.text_seq_length, 4] , self.range_bbox )
# Ensure that bbox is legal
for i in range(bbox.shape[0] ):
for j in range(bbox.shape[1] ):
if bbox[i, j, 3] < bbox[i, j, 1]:
snake_case_ : Optional[Any] = bbox[i, j, 3]
snake_case_ : Any = bbox[i, j, 1]
snake_case_ : Tuple = t
if bbox[i, j, 2] < bbox[i, j, 0]:
snake_case_ : str = bbox[i, j, 2]
snake_case_ : Dict = bbox[i, j, 0]
snake_case_ : Union[str, Any] = t
snake_case_ : int = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
snake_case_ : Dict = None
if self.use_input_mask:
snake_case_ : str = random_attention_mask([self.batch_size, self.text_seq_length] )
snake_case_ : Any = None
if self.use_token_type_ids:
snake_case_ : List[str] = ids_tensor([self.batch_size, self.text_seq_length] , self.type_vocab_size )
snake_case_ : Union[str, Any] = None
snake_case_ : str = None
if self.use_labels:
snake_case_ : List[Any] = ids_tensor([self.batch_size] , self.type_sequence_label_size )
snake_case_ : List[Any] = ids_tensor([self.batch_size, self.text_seq_length] , self.num_labels )
snake_case_ : str = LayoutLMvaConfig(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , initializer_range=self.initializer_range , coordinate_size=self.coordinate_size , shape_size=self.shape_size , input_size=self.image_size , patch_size=self.patch_size , )
return config, input_ids, bbox, pixel_values, token_type_ids, input_mask, sequence_labels, token_labels
def _A ( self :Dict , lowerCAmelCase__ :Dict , lowerCAmelCase__ :List[str] , lowerCAmelCase__ :str , lowerCAmelCase__ :List[Any] , lowerCAmelCase__ :Optional[Any] , lowerCAmelCase__ :List[str] , lowerCAmelCase__ :Dict , lowerCAmelCase__ :List[str] ) -> Optional[Any]:
'''simple docstring'''
snake_case_ : Tuple = LayoutLMvaModel(config=lowerCAmelCase__ )
model.to(lowerCAmelCase__ )
model.eval()
# text + image
snake_case_ : Tuple = model(lowerCAmelCase__ , pixel_values=lowerCAmelCase__ )
snake_case_ : Optional[int] = model(
lowerCAmelCase__ , bbox=lowerCAmelCase__ , pixel_values=lowerCAmelCase__ , attention_mask=lowerCAmelCase__ , token_type_ids=lowerCAmelCase__ )
snake_case_ : Optional[int] = model(lowerCAmelCase__ , bbox=lowerCAmelCase__ , pixel_values=lowerCAmelCase__ , token_type_ids=lowerCAmelCase__ )
snake_case_ : int = model(lowerCAmelCase__ , bbox=lowerCAmelCase__ , pixel_values=lowerCAmelCase__ )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
# text only
snake_case_ : List[Any] = model(lowerCAmelCase__ )
self.parent.assertEqual(
result.last_hidden_state.shape , (self.batch_size, self.text_seq_length, self.hidden_size) )
# image only
snake_case_ : Union[str, Any] = model(pixel_values=lowerCAmelCase__ )
self.parent.assertEqual(
result.last_hidden_state.shape , (self.batch_size, self.image_seq_length, self.hidden_size) )
def _A ( self :str , lowerCAmelCase__ :str , lowerCAmelCase__ :Tuple , lowerCAmelCase__ :Union[str, Any] , lowerCAmelCase__ :Union[str, Any] , lowerCAmelCase__ :Optional[Any] , lowerCAmelCase__ :List[str] , lowerCAmelCase__ :List[str] , lowerCAmelCase__ :Tuple ) -> List[Any]:
'''simple docstring'''
snake_case_ : str = self.num_labels
snake_case_ : List[Any] = LayoutLMvaForSequenceClassification(lowerCAmelCase__ )
model.to(lowerCAmelCase__ )
model.eval()
snake_case_ : Optional[int] = model(
lowerCAmelCase__ , bbox=lowerCAmelCase__ , pixel_values=lowerCAmelCase__ , attention_mask=lowerCAmelCase__ , token_type_ids=lowerCAmelCase__ , labels=lowerCAmelCase__ , )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def _A ( self :Union[str, Any] , lowerCAmelCase__ :List[Any] , lowerCAmelCase__ :int , lowerCAmelCase__ :List[str] , lowerCAmelCase__ :Tuple , lowerCAmelCase__ :str , lowerCAmelCase__ :Optional[int] , lowerCAmelCase__ :Any , lowerCAmelCase__ :Union[str, Any] ) -> str:
'''simple docstring'''
snake_case_ : Optional[int] = self.num_labels
snake_case_ : str = LayoutLMvaForTokenClassification(config=lowerCAmelCase__ )
model.to(lowerCAmelCase__ )
model.eval()
snake_case_ : List[Any] = model(
lowerCAmelCase__ , bbox=lowerCAmelCase__ , pixel_values=lowerCAmelCase__ , attention_mask=lowerCAmelCase__ , token_type_ids=lowerCAmelCase__ , labels=lowerCAmelCase__ , )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.text_seq_length, self.num_labels) )
def _A ( self :Optional[int] , lowerCAmelCase__ :Dict , lowerCAmelCase__ :str , lowerCAmelCase__ :Dict , lowerCAmelCase__ :Optional[int] , lowerCAmelCase__ :str , lowerCAmelCase__ :int , lowerCAmelCase__ :Union[str, Any] , lowerCAmelCase__ :str ) -> Tuple:
'''simple docstring'''
snake_case_ : List[str] = LayoutLMvaForQuestionAnswering(config=lowerCAmelCase__ )
model.to(lowerCAmelCase__ )
model.eval()
snake_case_ : List[Any] = model(
lowerCAmelCase__ , bbox=lowerCAmelCase__ , pixel_values=lowerCAmelCase__ , attention_mask=lowerCAmelCase__ , token_type_ids=lowerCAmelCase__ , start_positions=lowerCAmelCase__ , end_positions=lowerCAmelCase__ , )
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 :int ) -> Union[str, Any]:
'''simple docstring'''
snake_case_ : Dict = self.prepare_config_and_inputs()
(
(
snake_case_
), (
snake_case_
), (
snake_case_
), (
snake_case_
), (
snake_case_
), (
snake_case_
), (
snake_case_
), (
snake_case_
),
) : Optional[Any] = config_and_inputs
snake_case_ : Tuple = {
"input_ids": input_ids,
"bbox": bbox,
"pixel_values": pixel_values,
"token_type_ids": token_type_ids,
"attention_mask": input_mask,
}
return config, inputs_dict
@require_torch
class A_ (a_ , a_ , unittest.TestCase ):
"""simple docstring"""
a__ = False
a__ = False
a__ = False
a__ = (
(
LayoutLMvaModel,
LayoutLMvaForSequenceClassification,
LayoutLMvaForTokenClassification,
LayoutLMvaForQuestionAnswering,
)
if is_torch_available()
else ()
)
a__ = (
{'''document-question-answering''': LayoutLMvaForQuestionAnswering, '''feature-extraction''': LayoutLMvaModel}
if is_torch_available()
else {}
)
def _A ( self :Optional[Any] , lowerCAmelCase__ :Tuple , lowerCAmelCase__ :Tuple , lowerCAmelCase__ :List[Any] , lowerCAmelCase__ :Optional[Any] , lowerCAmelCase__ :List[Any] ) -> List[str]:
'''simple docstring'''
return True
def _A ( self :List[Any] ) -> str:
'''simple docstring'''
snake_case_ : Tuple = LayoutLMvaModelTester(self )
snake_case_ : Optional[int] = ConfigTester(self , config_class=lowerCAmelCase__ , hidden_size=37 )
def _A ( self :Tuple , lowerCAmelCase__ :Optional[int] , lowerCAmelCase__ :Dict , lowerCAmelCase__ :Union[str, Any]=False ) -> Any:
'''simple docstring'''
snake_case_ : List[str] = copy.deepcopy(lowerCAmelCase__ )
if model_class in get_values(lowerCAmelCase__ ):
snake_case_ : Optional[Any] = {
k: v.unsqueeze(1 ).expand(-1 , self.model_tester.num_choices , -1 ).contiguous()
if isinstance(lowerCAmelCase__ , torch.Tensor ) and v.ndim > 1
else v
for k, v in inputs_dict.items()
}
if return_labels:
if model_class in get_values(lowerCAmelCase__ ):
snake_case_ : Union[str, Any] = torch.ones(self.model_tester.batch_size , dtype=torch.long , device=lowerCAmelCase__ )
elif model_class in get_values(lowerCAmelCase__ ):
snake_case_ : List[Any] = torch.zeros(
self.model_tester.batch_size , dtype=torch.long , device=lowerCAmelCase__ )
snake_case_ : str = torch.zeros(
self.model_tester.batch_size , dtype=torch.long , device=lowerCAmelCase__ )
elif model_class in [
*get_values(lowerCAmelCase__ ),
]:
snake_case_ : Union[str, Any] = torch.zeros(
self.model_tester.batch_size , dtype=torch.long , device=lowerCAmelCase__ )
elif model_class in [
*get_values(lowerCAmelCase__ ),
]:
snake_case_ : List[str] = torch.zeros(
(self.model_tester.batch_size, self.model_tester.text_seq_length) , dtype=torch.long , device=lowerCAmelCase__ , )
return inputs_dict
def _A ( self :Any ) -> Any:
'''simple docstring'''
self.config_tester.run_common_tests()
def _A ( self :int ) -> int:
'''simple docstring'''
snake_case_ : Tuple = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*lowerCAmelCase__ )
def _A ( self :Any ) -> Dict:
'''simple docstring'''
snake_case_ : Dict = self.model_tester.prepare_config_and_inputs()
for type in ["absolute", "relative_key", "relative_key_query"]:
snake_case_ : int = type
self.model_tester.create_and_check_model(*lowerCAmelCase__ )
def _A ( self :int ) -> str:
'''simple docstring'''
snake_case_ : Optional[int] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_sequence_classification(*lowerCAmelCase__ )
def _A ( self :List[Any] ) -> Optional[Any]:
'''simple docstring'''
snake_case_ : List[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_token_classification(*lowerCAmelCase__ )
def _A ( self :int ) -> Union[str, Any]:
'''simple docstring'''
snake_case_ : Optional[int] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_question_answering(*lowerCAmelCase__ )
@slow
def _A ( self :Tuple ) -> List[Any]:
'''simple docstring'''
for model_name in LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
snake_case_ : str = LayoutLMvaModel.from_pretrained(lowerCAmelCase__ )
self.assertIsNotNone(lowerCAmelCase__ )
def __UpperCAmelCase ( )-> List[str]:
"""simple docstring"""
snake_case_ : List[str] = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png" )
return image
@require_torch
class A_ (unittest.TestCase ):
"""simple docstring"""
@cached_property
def _A ( self :Union[str, Any] ) -> Optional[Any]:
'''simple docstring'''
return LayoutLMvaImageProcessor(apply_ocr=lowerCAmelCase__ ) if is_vision_available() else None
@slow
def _A ( self :Union[str, Any] ) -> Union[str, Any]:
'''simple docstring'''
snake_case_ : Optional[int] = LayoutLMvaModel.from_pretrained("microsoft/layoutlmv3-base" ).to(lowerCAmelCase__ )
snake_case_ : Optional[Any] = self.default_image_processor
snake_case_ : Optional[int] = prepare_img()
snake_case_ : Union[str, Any] = image_processor(images=lowerCAmelCase__ , return_tensors="pt" ).pixel_values.to(lowerCAmelCase__ )
snake_case_ : List[str] = torch.tensor([[1, 2]] )
snake_case_ : Any = torch.tensor([[1, 2, 3, 4], [5, 6, 7, 8]] ).unsqueeze(0 )
# forward pass
snake_case_ : Any = model(
input_ids=input_ids.to(lowerCAmelCase__ ) , bbox=bbox.to(lowerCAmelCase__ ) , pixel_values=pixel_values.to(lowerCAmelCase__ ) , )
# verify the logits
snake_case_ : Optional[Any] = torch.Size((1, 199, 768) )
self.assertEqual(outputs.last_hidden_state.shape , lowerCAmelCase__ )
snake_case_ : str = torch.tensor(
[[-0.0_5_2_9, 0.3_6_1_8, 0.1_6_3_2], [-0.1_5_8_7, -0.1_6_6_7, -0.0_4_0_0], [-0.1_5_5_7, -0.1_6_7_1, -0.0_5_0_5]] ).to(lowerCAmelCase__ )
self.assertTrue(torch.allclose(outputs.last_hidden_state[0, :3, :3] , lowerCAmelCase__ , atol=1E-4 ) )
| 653 | 0 |
'''simple docstring'''
import tensorflow as tf
from ...tf_utils import shape_list
class UpperCAmelCase ( tf.keras.layers.Layer ):
"""simple docstring"""
def __init__( self , _snake_case , _snake_case , _snake_case , _snake_case , _snake_case=1 , _snake_case=False , **_snake_case ) -> Dict:
super().__init__(**lowerCAmelCase__ )
_UpperCamelCase : Optional[int] = vocab_size
_UpperCamelCase : Tuple = d_embed
_UpperCamelCase : Tuple = d_proj
_UpperCamelCase : Tuple = cutoffs + [vocab_size]
_UpperCamelCase : Optional[int] = [0] + self.cutoffs
_UpperCamelCase : List[str] = div_val
_UpperCamelCase : Tuple = self.cutoffs[0]
_UpperCamelCase : Union[str, Any] = len(self.cutoffs ) - 1
_UpperCamelCase : List[Any] = self.shortlist_size + self.n_clusters
_UpperCamelCase : Optional[Any] = keep_order
_UpperCamelCase : int = []
_UpperCamelCase : Dict = []
def _lowercase ( self , _snake_case ) -> str:
if self.n_clusters > 0:
_UpperCamelCase : Optional[int] = self.add_weight(
shape=(self.n_clusters, self.d_embed) , initializer='''zeros''' , trainable=lowerCAmelCase__ , name='''cluster_weight''' )
_UpperCamelCase : Dict = self.add_weight(
shape=(self.n_clusters,) , initializer='''zeros''' , trainable=lowerCAmelCase__ , name='''cluster_bias''' )
if self.div_val == 1:
for i in range(len(self.cutoffs ) ):
if self.d_proj != self.d_embed:
_UpperCamelCase : List[str] = self.add_weight(
shape=(self.d_embed, self.d_proj) , initializer='''zeros''' , trainable=lowerCAmelCase__ , name=F'''out_projs_._{i}''' , )
self.out_projs.append(lowerCAmelCase__ )
else:
self.out_projs.append(lowerCAmelCase__ )
_UpperCamelCase : Any = self.add_weight(
shape=(self.vocab_size, self.d_embed) , initializer='''zeros''' , trainable=lowerCAmelCase__ , name=F'''out_layers_._{i}_._weight''' , )
_UpperCamelCase : List[str] = self.add_weight(
shape=(self.vocab_size,) , initializer='''zeros''' , trainable=lowerCAmelCase__ , name=F'''out_layers_._{i}_._bias''' , )
self.out_layers.append((weight, bias) )
else:
for i in range(len(self.cutoffs ) ):
_UpperCamelCase : List[str] = self.cutoff_ends[i], self.cutoff_ends[i + 1]
_UpperCamelCase : Optional[int] = self.d_embed // (self.div_val**i)
_UpperCamelCase : int = self.add_weight(
shape=(d_emb_i, self.d_proj) , initializer='''zeros''' , trainable=lowerCAmelCase__ , name=F'''out_projs_._{i}''' )
self.out_projs.append(lowerCAmelCase__ )
_UpperCamelCase : List[str] = self.add_weight(
shape=(r_idx - l_idx, d_emb_i) , initializer='''zeros''' , trainable=lowerCAmelCase__ , name=F'''out_layers_._{i}_._weight''' , )
_UpperCamelCase : List[str] = self.add_weight(
shape=(r_idx - l_idx,) , initializer='''zeros''' , trainable=lowerCAmelCase__ , name=F'''out_layers_._{i}_._bias''' , )
self.out_layers.append((weight, bias) )
super().build(lowerCAmelCase__ )
@staticmethod
def _lowercase ( _snake_case , _snake_case , _snake_case , _snake_case=None ) -> str:
_UpperCamelCase : Tuple = x
if proj is not None:
_UpperCamelCase : List[str] = tf.einsum('''ibd,ed->ibe''' , lowerCAmelCase__ , lowerCAmelCase__ )
return tf.einsum('''ibd,nd->ibn''' , lowerCAmelCase__ , lowerCAmelCase__ ) + b
@staticmethod
def _lowercase ( _snake_case , _snake_case ) -> Dict:
_UpperCamelCase : Optional[int] = shape_list(lowerCAmelCase__ )
_UpperCamelCase : Dict = tf.range(lp_size[0] , dtype=target.dtype )
_UpperCamelCase : List[Any] = tf.stack([r, target] , 1 )
return tf.gather_nd(lowerCAmelCase__ , lowerCAmelCase__ )
def _lowercase ( self , _snake_case , _snake_case , _snake_case=True , _snake_case=False ) -> Tuple:
_UpperCamelCase : Optional[Any] = 0
if self.n_clusters == 0:
_UpperCamelCase : List[Any] = self._logit(lowerCAmelCase__ , self.out_layers[0][0] , self.out_layers[0][1] , self.out_projs[0] )
if target is not None:
_UpperCamelCase : Optional[Any] = tf.nn.sparse_softmax_cross_entropy_with_logits(labels=lowerCAmelCase__ , logits=lowerCAmelCase__ )
_UpperCamelCase : List[str] = tf.nn.log_softmax(lowerCAmelCase__ , axis=-1 )
else:
_UpperCamelCase : Optional[int] = shape_list(lowerCAmelCase__ )
_UpperCamelCase : int = []
_UpperCamelCase : Dict = tf.zeros(hidden_sizes[:2] )
for i in range(len(self.cutoffs ) ):
_UpperCamelCase : Optional[int] = self.cutoff_ends[i], self.cutoff_ends[i + 1]
if target is not None:
_UpperCamelCase : str = (target >= l_idx) & (target < r_idx)
_UpperCamelCase : Optional[Any] = tf.where(lowerCAmelCase__ )
_UpperCamelCase : Tuple = tf.boolean_mask(lowerCAmelCase__ , lowerCAmelCase__ ) - l_idx
if self.div_val == 1:
_UpperCamelCase : Optional[int] = self.out_layers[0][0][l_idx:r_idx]
_UpperCamelCase : Any = self.out_layers[0][1][l_idx:r_idx]
else:
_UpperCamelCase : str = self.out_layers[i][0]
_UpperCamelCase : List[str] = self.out_layers[i][1]
if i == 0:
_UpperCamelCase : str = tf.concat([cur_W, self.cluster_weight] , 0 )
_UpperCamelCase : Tuple = tf.concat([cur_b, self.cluster_bias] , 0 )
_UpperCamelCase : List[str] = self._logit(lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , self.out_projs[0] )
_UpperCamelCase : Tuple = tf.nn.log_softmax(lowerCAmelCase__ )
out.append(head_logprob[..., : self.cutoffs[0]] )
if target is not None:
_UpperCamelCase : Optional[Any] = tf.boolean_mask(lowerCAmelCase__ , lowerCAmelCase__ )
_UpperCamelCase : List[Any] = self._gather_logprob(lowerCAmelCase__ , lowerCAmelCase__ )
else:
_UpperCamelCase : Any = self._logit(lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , self.out_projs[i] )
_UpperCamelCase : Optional[Any] = tf.nn.log_softmax(lowerCAmelCase__ )
_UpperCamelCase : Any = self.cutoffs[0] + i - 1 # No probability for the head cluster
_UpperCamelCase : int = head_logprob[..., cluster_prob_idx, None] + tail_logprob
out.append(lowerCAmelCase__ )
if target is not None:
_UpperCamelCase : Optional[Any] = tf.boolean_mask(lowerCAmelCase__ , lowerCAmelCase__ )
_UpperCamelCase : Union[str, Any] = tf.boolean_mask(lowerCAmelCase__ , lowerCAmelCase__ )
_UpperCamelCase : Tuple = self._gather_logprob(lowerCAmelCase__ , lowerCAmelCase__ )
cur_logprob += cur_head_logprob[:, self.cutoff_ends[1] + i - 1]
if target is not None:
loss += tf.scatter_nd(lowerCAmelCase__ , -cur_logprob , shape_list(lowerCAmelCase__ ) )
_UpperCamelCase : Optional[Any] = tf.concat(lowerCAmelCase__ , axis=-1 )
if target is not None:
if return_mean:
_UpperCamelCase : Any = tf.reduce_mean(lowerCAmelCase__ )
# Add the training-time loss value to the layer using `self.add_loss()`.
self.add_loss(lowerCAmelCase__ )
# Log the loss as a metric (we could log arbitrary metrics,
# including different metrics for training and inference.
self.add_metric(lowerCAmelCase__ , name=self.name , aggregation='''mean''' if return_mean else '''''' )
return out
| 683 |
'''simple docstring'''
import pytest
from datasets.parallel import ParallelBackendConfig, parallel_backend
from datasets.utils.py_utils import map_nested
from .utils import require_dill_gt_0_3_2, require_joblibspark, require_not_windows
def __UpperCAmelCase ( __magic_name__ )-> int: # picklable for multiprocessing
"""simple docstring"""
return i + 1
@require_dill_gt_0_3_2
@require_joblibspark
@require_not_windows
def __UpperCAmelCase ( )-> List[str]:
"""simple docstring"""
with parallel_backend("spark" ):
assert ParallelBackendConfig.backend_name == "spark"
snake_case_ : str = [1, 2, 3]
with pytest.raises(__magic_name__ ):
with parallel_backend("unsupported backend" ):
map_nested(__magic_name__ ,__magic_name__ ,num_proc=2 )
with pytest.raises(__magic_name__ ):
with parallel_backend("unsupported backend" ):
map_nested(__magic_name__ ,__magic_name__ ,num_proc=-1 )
@require_dill_gt_0_3_2
@require_joblibspark
@require_not_windows
@pytest.mark.parametrize("num_proc" ,[2, -1] )
def __UpperCAmelCase ( __magic_name__ )-> List[Any]:
"""simple docstring"""
snake_case_ : Optional[Any] = [1, 2]
snake_case_ : Union[str, Any] = {"a": 1, "b": 2}
snake_case_ : str = {"a": [1, 2], "b": [3, 4]}
snake_case_ : List[str] = {"a": {"1": 1}, "b": 2}
snake_case_ : Optional[int] = {"a": 1, "b": 2, "c": 3, "d": 4}
snake_case_ : Tuple = [2, 3]
snake_case_ : str = {"a": 2, "b": 3}
snake_case_ : Dict = {"a": [2, 3], "b": [4, 5]}
snake_case_ : List[Any] = {"a": {"1": 2}, "b": 3}
snake_case_ : str = {"a": 2, "b": 3, "c": 4, "d": 5}
with parallel_backend("spark" ):
assert map_nested(__magic_name__ ,__magic_name__ ,num_proc=__magic_name__ ) == expected_map_nested_sa
assert map_nested(__magic_name__ ,__magic_name__ ,num_proc=__magic_name__ ) == expected_map_nested_sa
assert map_nested(__magic_name__ ,__magic_name__ ,num_proc=__magic_name__ ) == expected_map_nested_sa
assert map_nested(__magic_name__ ,__magic_name__ ,num_proc=__magic_name__ ) == expected_map_nested_sa
assert map_nested(__magic_name__ ,__magic_name__ ,num_proc=__magic_name__ ) == expected_map_nested_sa
| 653 | 0 |
import itertools
import json
import os
import unittest
from transformers import AddedToken, RobertaTokenizer, RobertaTokenizerFast
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class A (a_ , unittest.TestCase ):
'''simple docstring'''
__lowerCamelCase : Optional[int] = RobertaTokenizer
__lowerCamelCase : int = RobertaTokenizerFast
__lowerCamelCase : Union[str, Any] = True
__lowerCamelCase : Any = {'''cls_token''': '''<s>'''}
def a_ ( self : Optional[int] ) -> List[Any]:
"""simple docstring"""
super().setUp()
# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
A__ = [
"l",
"o",
"w",
"e",
"r",
"s",
"t",
"i",
"d",
"n",
"\u0120",
"\u0120l",
"\u0120n",
"\u0120lo",
"\u0120low",
"er",
"\u0120lowest",
"\u0120newer",
"\u0120wider",
"<unk>",
]
A__ = dict(zip(lowerCAmelCase__ , range(len(lowerCAmelCase__ ) ) ) )
A__ = ["#version: 0.2", "\u0120 l", "\u0120l o", "\u0120lo w", "e r", ""]
A__ = {"unk_token": "<unk>"}
A__ = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES["""vocab_file"""] )
A__ = 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(lowerCAmelCase__ ) + """\n""" )
with open(self.merges_file , """w""" , encoding="""utf-8""" ) as fp:
fp.write("""\n""".join(lowerCAmelCase__ ) )
def a_ ( self : Optional[Any] , **__lowerCAmelCase : str ) -> str:
"""simple docstring"""
kwargs.update(self.special_tokens_map )
return self.tokenizer_class.from_pretrained(self.tmpdirname , **lowerCAmelCase__ )
def a_ ( self : Any , **__lowerCAmelCase : Tuple ) -> Optional[int]:
"""simple docstring"""
kwargs.update(self.special_tokens_map )
return RobertaTokenizerFast.from_pretrained(self.tmpdirname , **lowerCAmelCase__ )
def a_ ( self : Optional[int] , __lowerCAmelCase : str ) -> Optional[int]:
"""simple docstring"""
A__ = "lower newer"
A__ = "lower newer"
return input_text, output_text
def a_ ( self : Tuple ) -> Union[str, Any]:
"""simple docstring"""
A__ = self.tokenizer_class(self.vocab_file , self.merges_file , **self.special_tokens_map )
A__ = "lower newer"
A__ = ["l", "o", "w", "er", "\u0120", "n", "e", "w", "er"]
A__ = tokenizer.tokenize(lowerCAmelCase__ ) # , add_prefix_space=True)
self.assertListEqual(lowerCAmelCase__ , lowerCAmelCase__ )
A__ = tokens + [tokenizer.unk_token]
A__ = [0, 1, 2, 15, 10, 9, 3, 2, 15, 19]
self.assertListEqual(tokenizer.convert_tokens_to_ids(lowerCAmelCase__ ) , lowerCAmelCase__ )
def a_ ( self : Any ) -> str:
"""simple docstring"""
A__ = self.get_tokenizer()
self.assertListEqual(tokenizer.encode("""Hello world!""" , add_special_tokens=lowerCAmelCase__ ) , [0, 3_14_14, 2_32, 3_28, 2] )
self.assertListEqual(
tokenizer.encode("""Hello world! cécé herlolip 418""" , add_special_tokens=lowerCAmelCase__ ) , [0, 3_14_14, 2_32, 3_28, 7_40, 11_40, 1_26_95, 69, 4_60_78, 15_88, 2] , )
@slow
def a_ ( self : str ) -> List[str]:
"""simple docstring"""
A__ = self.tokenizer_class.from_pretrained("""roberta-base""" )
A__ = tokenizer.encode("""sequence builders""" , add_special_tokens=lowerCAmelCase__ )
A__ = tokenizer.encode("""multi-sequence build""" , add_special_tokens=lowerCAmelCase__ )
A__ = tokenizer.encode(
"""sequence builders""" , add_special_tokens=lowerCAmelCase__ , add_prefix_space=lowerCAmelCase__ )
A__ = tokenizer.encode(
"""sequence builders""" , """multi-sequence build""" , add_special_tokens=lowerCAmelCase__ , add_prefix_space=lowerCAmelCase__ )
A__ = tokenizer.build_inputs_with_special_tokens(lowerCAmelCase__ )
A__ = tokenizer.build_inputs_with_special_tokens(lowerCAmelCase__ , lowerCAmelCase__ )
assert encoded_sentence == encoded_text_from_decode
assert encoded_pair == encoded_pair_from_decode
def a_ ( self : List[Any] ) -> Any:
"""simple docstring"""
A__ = self.get_tokenizer()
A__ = "Encode this sequence."
A__ = tokenizer.byte_encoder[" ".encode("""utf-8""" )[0]]
# Testing encoder arguments
A__ = tokenizer.encode(lowerCAmelCase__ , add_special_tokens=lowerCAmelCase__ , add_prefix_space=lowerCAmelCase__ )
A__ = tokenizer.convert_ids_to_tokens(encoded[0] )[0]
self.assertNotEqual(lowerCAmelCase__ , lowerCAmelCase__ )
A__ = tokenizer.encode(lowerCAmelCase__ , add_special_tokens=lowerCAmelCase__ , add_prefix_space=lowerCAmelCase__ )
A__ = tokenizer.convert_ids_to_tokens(encoded[0] )[0]
self.assertEqual(lowerCAmelCase__ , lowerCAmelCase__ )
tokenizer.add_special_tokens({"""bos_token""": """<s>"""} )
A__ = tokenizer.encode(lowerCAmelCase__ , add_special_tokens=lowerCAmelCase__ )
A__ = tokenizer.convert_ids_to_tokens(encoded[1] )[0]
self.assertNotEqual(lowerCAmelCase__ , lowerCAmelCase__ )
# Testing spaces after special tokens
A__ = "<mask>"
tokenizer.add_special_tokens(
{"""mask_token""": AddedToken(lowerCAmelCase__ , lstrip=lowerCAmelCase__ , rstrip=lowerCAmelCase__ )} ) # mask token has a left space
A__ = tokenizer.convert_tokens_to_ids(lowerCAmelCase__ )
A__ = "Encode <mask> sequence"
A__ = "Encode <mask>sequence"
A__ = tokenizer.encode(lowerCAmelCase__ )
A__ = encoded.index(lowerCAmelCase__ )
A__ = tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1] )[0]
self.assertEqual(lowerCAmelCase__ , lowerCAmelCase__ )
A__ = tokenizer.encode(lowerCAmelCase__ )
A__ = encoded.index(lowerCAmelCase__ )
A__ = tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1] )[0]
self.assertNotEqual(lowerCAmelCase__ , lowerCAmelCase__ )
def a_ ( self : Tuple ) -> Tuple:
"""simple docstring"""
pass
def a_ ( self : int ) -> Optional[Any]:
"""simple docstring"""
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(f'{tokenizer.__class__.__name__} ({pretrained_name})' ):
A__ = self.rust_tokenizer_class.from_pretrained(lowerCAmelCase__ , **lowerCAmelCase__ )
A__ = self.tokenizer_class.from_pretrained(lowerCAmelCase__ , **lowerCAmelCase__ )
A__ = "A, <mask> AllenNLP sentence."
A__ = tokenizer_r.encode_plus(lowerCAmelCase__ , add_special_tokens=lowerCAmelCase__ , return_token_type_ids=lowerCAmelCase__ )
A__ = tokenizer_p.encode_plus(lowerCAmelCase__ , add_special_tokens=lowerCAmelCase__ , return_token_type_ids=lowerCAmelCase__ )
# token_type_ids should put 0 everywhere
self.assertEqual(sum(tokens_r["""token_type_ids"""] ) , sum(tokens_p["""token_type_ids"""] ) )
# attention_mask should put 1 everywhere, so sum over length should be 1
self.assertEqual(
sum(tokens_r["""attention_mask"""] ) / len(tokens_r["""attention_mask"""] ) , sum(tokens_p["""attention_mask"""] ) / len(tokens_p["""attention_mask"""] ) , )
A__ = tokenizer_r.convert_ids_to_tokens(tokens_r["""input_ids"""] )
A__ = tokenizer_p.convert_ids_to_tokens(tokens_p["""input_ids"""] )
# Rust correctly handles the space before the mask while python doesnt
self.assertSequenceEqual(tokens_p["""input_ids"""] , [0, 2_50, 6, 5_02_64, 38_23, 4_87, 2_19_92, 36_45, 4, 2] )
self.assertSequenceEqual(tokens_r["""input_ids"""] , [0, 2_50, 6, 5_02_64, 38_23, 4_87, 2_19_92, 36_45, 4, 2] )
self.assertSequenceEqual(
lowerCAmelCase__ , ["""<s>""", """A""", """,""", """<mask>""", """ĠAllen""", """N""", """LP""", """Ġsentence""", """.""", """</s>"""] )
self.assertSequenceEqual(
lowerCAmelCase__ , ["""<s>""", """A""", """,""", """<mask>""", """ĠAllen""", """N""", """LP""", """Ġsentence""", """.""", """</s>"""] )
def a_ ( self : int ) -> Tuple:
"""simple docstring"""
for trim_offsets, add_prefix_space in itertools.product([True, False] , repeat=2 ):
A__ = self.rust_tokenizer_class.from_pretrained(
self.tmpdirname , use_fast=lowerCAmelCase__ , add_prefix_space=lowerCAmelCase__ , trim_offsets=lowerCAmelCase__ )
A__ = json.loads(tokenizer_r.backend_tokenizer.pre_tokenizer.__getstate__() )
A__ = json.loads(tokenizer_r.backend_tokenizer.post_processor.__getstate__() )
self.assertEqual(pre_tokenizer_state["""add_prefix_space"""] , lowerCAmelCase__ )
self.assertEqual(post_processor_state["""add_prefix_space"""] , lowerCAmelCase__ )
self.assertEqual(post_processor_state["""trim_offsets"""] , lowerCAmelCase__ )
def a_ ( self : List[str] ) -> List[Any]:
"""simple docstring"""
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(f'{tokenizer.__class__.__name__} ({pretrained_name})' ):
A__ = "hello" # `hello` is a token in the vocabulary of `pretrained_name`
A__ = f'{text_of_1_token} {text_of_1_token}'
A__ = self.rust_tokenizer_class.from_pretrained(
lowerCAmelCase__ , use_fast=lowerCAmelCase__ , add_prefix_space=lowerCAmelCase__ , trim_offsets=lowerCAmelCase__ )
A__ = tokenizer_r(lowerCAmelCase__ , return_offsets_mapping=lowerCAmelCase__ , add_special_tokens=lowerCAmelCase__ )
self.assertEqual(encoding.offset_mapping[0] , (0, len(lowerCAmelCase__ )) )
self.assertEqual(
encoding.offset_mapping[1] , (len(lowerCAmelCase__ ) + 1, len(lowerCAmelCase__ ) + 1 + len(lowerCAmelCase__ )) , )
A__ = self.rust_tokenizer_class.from_pretrained(
lowerCAmelCase__ , use_fast=lowerCAmelCase__ , add_prefix_space=lowerCAmelCase__ , trim_offsets=lowerCAmelCase__ )
A__ = tokenizer_r(lowerCAmelCase__ , return_offsets_mapping=lowerCAmelCase__ , add_special_tokens=lowerCAmelCase__ )
self.assertEqual(encoding.offset_mapping[0] , (0, len(lowerCAmelCase__ )) )
self.assertEqual(
encoding.offset_mapping[1] , (len(lowerCAmelCase__ ) + 1, len(lowerCAmelCase__ ) + 1 + len(lowerCAmelCase__ )) , )
A__ = self.rust_tokenizer_class.from_pretrained(
lowerCAmelCase__ , use_fast=lowerCAmelCase__ , add_prefix_space=lowerCAmelCase__ , trim_offsets=lowerCAmelCase__ )
A__ = tokenizer_r(lowerCAmelCase__ , return_offsets_mapping=lowerCAmelCase__ , add_special_tokens=lowerCAmelCase__ )
self.assertEqual(encoding.offset_mapping[0] , (0, len(lowerCAmelCase__ )) )
self.assertEqual(
encoding.offset_mapping[1] , (len(lowerCAmelCase__ ), len(lowerCAmelCase__ ) + 1 + len(lowerCAmelCase__ )) , )
A__ = self.rust_tokenizer_class.from_pretrained(
lowerCAmelCase__ , use_fast=lowerCAmelCase__ , add_prefix_space=lowerCAmelCase__ , trim_offsets=lowerCAmelCase__ )
A__ = tokenizer_r(lowerCAmelCase__ , return_offsets_mapping=lowerCAmelCase__ , add_special_tokens=lowerCAmelCase__ )
self.assertEqual(encoding.offset_mapping[0] , (0, len(lowerCAmelCase__ )) )
self.assertEqual(
encoding.offset_mapping[1] , (len(lowerCAmelCase__ ), len(lowerCAmelCase__ ) + 1 + len(lowerCAmelCase__ )) , )
A__ = f' {text}'
# tokenizer_r = self.rust_tokenizer_class.from_pretrained(
# pretrained_name, use_fast=True, add_prefix_space=True, trim_offsets=True
# )
# encoding = tokenizer_r(text, return_offsets_mapping=True, add_special_tokens=False)
# self.assertEqual(encoding.offset_mapping[0], (1, 1 + len(text_of_1_token)))
# self.assertEqual(
# encoding.offset_mapping[1],
# (1 + len(text_of_1_token) + 1, 1 + len(text_of_1_token) + 1 + len(text_of_1_token)),
# )
A__ = self.rust_tokenizer_class.from_pretrained(
lowerCAmelCase__ , use_fast=lowerCAmelCase__ , add_prefix_space=lowerCAmelCase__ , trim_offsets=lowerCAmelCase__ )
A__ = tokenizer_r(lowerCAmelCase__ , return_offsets_mapping=lowerCAmelCase__ , add_special_tokens=lowerCAmelCase__ )
self.assertEqual(encoding.offset_mapping[0] , (1, 1 + len(lowerCAmelCase__ )) )
self.assertEqual(
encoding.offset_mapping[1] , (1 + len(lowerCAmelCase__ ) + 1, 1 + len(lowerCAmelCase__ ) + 1 + len(lowerCAmelCase__ )) , )
A__ = self.rust_tokenizer_class.from_pretrained(
lowerCAmelCase__ , use_fast=lowerCAmelCase__ , add_prefix_space=lowerCAmelCase__ , trim_offsets=lowerCAmelCase__ )
A__ = tokenizer_r(lowerCAmelCase__ , return_offsets_mapping=lowerCAmelCase__ , add_special_tokens=lowerCAmelCase__ )
self.assertEqual(encoding.offset_mapping[0] , (0, 1 + len(lowerCAmelCase__ )) )
self.assertEqual(
encoding.offset_mapping[1] , (1 + len(lowerCAmelCase__ ), 1 + len(lowerCAmelCase__ ) + 1 + len(lowerCAmelCase__ )) , )
A__ = self.rust_tokenizer_class.from_pretrained(
lowerCAmelCase__ , use_fast=lowerCAmelCase__ , add_prefix_space=lowerCAmelCase__ , trim_offsets=lowerCAmelCase__ )
A__ = tokenizer_r(lowerCAmelCase__ , return_offsets_mapping=lowerCAmelCase__ , add_special_tokens=lowerCAmelCase__ )
self.assertEqual(encoding.offset_mapping[0] , (0, 1 + len(lowerCAmelCase__ )) )
self.assertEqual(
encoding.offset_mapping[1] , (1 + len(lowerCAmelCase__ ), 1 + len(lowerCAmelCase__ ) + 1 + len(lowerCAmelCase__ )) , )
| 176 |
'''simple docstring'''
from dataclasses import asdict, dataclass
from typing import Optional
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__lowerCamelCase : Dict = logging.get_logger(__name__)
# TODO Update this
__lowerCamelCase : int = {
'''facebook/esm-1b''': '''https://huggingface.co/facebook/esm-1b/resolve/main/config.json''',
# See all ESM models at https://huggingface.co/models?filter=esm
}
class A_ (a_ ):
"""simple docstring"""
a__ = '''esm'''
def __init__( self :Dict , lowerCAmelCase__ :List[Any]=None , lowerCAmelCase__ :Optional[int]=None , lowerCAmelCase__ :str=None , lowerCAmelCase__ :int=768 , lowerCAmelCase__ :Tuple=12 , lowerCAmelCase__ :Dict=12 , lowerCAmelCase__ :Union[str, Any]=3_072 , lowerCAmelCase__ :int=0.1 , lowerCAmelCase__ :Optional[Any]=0.1 , lowerCAmelCase__ :List[Any]=1_026 , lowerCAmelCase__ :int=0.0_2 , lowerCAmelCase__ :Optional[int]=1E-1_2 , lowerCAmelCase__ :List[str]="absolute" , lowerCAmelCase__ :List[Any]=True , lowerCAmelCase__ :Dict=None , lowerCAmelCase__ :List[str]=False , lowerCAmelCase__ :List[Any]=False , lowerCAmelCase__ :Dict=None , lowerCAmelCase__ :str=None , **lowerCAmelCase__ :Union[str, Any] , ) -> Any:
'''simple docstring'''
super().__init__(pad_token_id=lowerCAmelCase__ , mask_token_id=lowerCAmelCase__ , **lowerCAmelCase__ )
snake_case_ : str = vocab_size
snake_case_ : str = hidden_size
snake_case_ : List[str] = num_hidden_layers
snake_case_ : List[str] = num_attention_heads
snake_case_ : Any = intermediate_size
snake_case_ : Optional[Any] = hidden_dropout_prob
snake_case_ : Tuple = attention_probs_dropout_prob
snake_case_ : List[Any] = max_position_embeddings
snake_case_ : str = initializer_range
snake_case_ : List[Any] = layer_norm_eps
snake_case_ : str = position_embedding_type
snake_case_ : Optional[int] = use_cache
snake_case_ : str = emb_layer_norm_before
snake_case_ : List[Any] = token_dropout
snake_case_ : str = is_folding_model
if is_folding_model:
if esmfold_config is None:
logger.info("No esmfold_config supplied for folding model, using default values." )
snake_case_ : Optional[Any] = EsmFoldConfig()
elif isinstance(lowerCAmelCase__ , lowerCAmelCase__ ):
snake_case_ : Union[str, Any] = EsmFoldConfig(**lowerCAmelCase__ )
snake_case_ : Optional[Any] = esmfold_config
if vocab_list is None:
logger.warning("No vocab_list supplied for folding model, assuming the ESM-2 vocabulary!" )
snake_case_ : List[str] = get_default_vocab_list()
else:
snake_case_ : List[str] = vocab_list
else:
snake_case_ : List[Any] = None
snake_case_ : int = None
if self.esmfold_config is not None and getattr(self.esmfold_config , "use_esm_attn_map" , lowerCAmelCase__ ):
raise ValueError("The HuggingFace port of ESMFold does not support use_esm_attn_map at this time!" )
def _A ( self :Optional[int] ) -> List[Any]:
'''simple docstring'''
snake_case_ : Any = super().to_dict()
if isinstance(self.esmfold_config , lowerCAmelCase__ ):
snake_case_ : Optional[int] = self.esmfold_config.to_dict()
return output
@dataclass
class A_ :
"""simple docstring"""
a__ = None
a__ = True
a__ = False
a__ = False
a__ = False
a__ = 0
a__ = True
a__ = False
a__ = 128
a__ = None
def _A ( self :Dict ) -> int:
'''simple docstring'''
if self.trunk is None:
snake_case_ : Dict = TrunkConfig()
elif isinstance(self.trunk , lowerCAmelCase__ ):
snake_case_ : int = TrunkConfig(**self.trunk )
def _A ( self :Optional[int] ) -> Union[str, Any]:
'''simple docstring'''
snake_case_ : Tuple = asdict(self )
snake_case_ : Optional[int] = self.trunk.to_dict()
return output
@dataclass
class A_ :
"""simple docstring"""
a__ = 48
a__ = 1024
a__ = 128
a__ = 32
a__ = 32
a__ = 32
a__ = 0
a__ = 0
a__ = False
a__ = 4
a__ = 128
a__ = None
def _A ( self :List[Any] ) -> Union[str, Any]:
'''simple docstring'''
if self.structure_module is None:
snake_case_ : Optional[int] = StructureModuleConfig()
elif isinstance(self.structure_module , lowerCAmelCase__ ):
snake_case_ : List[str] = StructureModuleConfig(**self.structure_module )
if self.max_recycles <= 0:
raise ValueError(F'''`max_recycles` should be positive, got {self.max_recycles}.''' )
if self.sequence_state_dim % self.sequence_state_dim != 0:
raise ValueError(
"`sequence_state_dim` should be a round multiple of `sequence_state_dim`, got"
F''' {self.sequence_state_dim} and {self.sequence_state_dim}.''' )
if self.pairwise_state_dim % self.pairwise_state_dim != 0:
raise ValueError(
"`pairwise_state_dim` should be a round multiple of `pairwise_state_dim`, got"
F''' {self.pairwise_state_dim} and {self.pairwise_state_dim}.''' )
snake_case_ : Dict = self.sequence_state_dim // self.sequence_head_width
snake_case_ : Optional[int] = self.pairwise_state_dim // self.pairwise_head_width
if self.sequence_state_dim != sequence_num_heads * self.sequence_head_width:
raise ValueError(
"`sequence_state_dim` should be equal to `sequence_num_heads * sequence_head_width, got"
F''' {self.sequence_state_dim} != {sequence_num_heads} * {self.sequence_head_width}.''' )
if self.pairwise_state_dim != pairwise_num_heads * self.pairwise_head_width:
raise ValueError(
"`pairwise_state_dim` should be equal to `pairwise_num_heads * pairwise_head_width, got"
F''' {self.pairwise_state_dim} != {pairwise_num_heads} * {self.pairwise_head_width}.''' )
if self.pairwise_state_dim % 2 != 0:
raise ValueError(F'''`pairwise_state_dim` should be even, got {self.pairwise_state_dim}.''' )
if self.dropout >= 0.4:
raise ValueError(F'''`dropout` should not be greater than 0.4, got {self.dropout}.''' )
def _A ( self :Tuple ) -> List[str]:
'''simple docstring'''
snake_case_ : int = asdict(self )
snake_case_ : Dict = self.structure_module.to_dict()
return output
@dataclass
class A_ :
"""simple docstring"""
a__ = 384
a__ = 128
a__ = 16
a__ = 128
a__ = 12
a__ = 4
a__ = 8
a__ = 0.1
a__ = 8
a__ = 1
a__ = 2
a__ = 7
a__ = 10
a__ = 1E-8
a__ = 1E5
def _A ( self :Dict ) -> Dict:
'''simple docstring'''
return asdict(self )
def __UpperCAmelCase ( )-> int:
"""simple docstring"""
return (
"<cls>",
"<pad>",
"<eos>",
"<unk>",
"L",
"A",
"G",
"V",
"S",
"E",
"R",
"T",
"I",
"D",
"P",
"K",
"Q",
"N",
"F",
"Y",
"M",
"H",
"W",
"C",
"X",
"B",
"U",
"Z",
"O",
".",
"-",
"<null_1>",
"<mask>",
)
| 653 | 0 |
"""simple docstring"""
from collections import namedtuple
__A = namedtuple("""from_to""", """from_ to""")
__A = {
'''cubicmeter''': from_to(1, 1),
'''litre''': from_to(0.001, 1000),
'''kilolitre''': from_to(1, 1),
'''gallon''': from_to(0.0_0454, 264.172),
'''cubicyard''': from_to(0.7_6455, 1.3_0795),
'''cubicfoot''': from_to(0.028, 35.3147),
'''cup''': from_to(0.0_0023_6588, 4226.75),
}
def __A (_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) ->float:
"""simple docstring"""
if from_type not in METRIC_CONVERSION:
raise ValueError(
F"Invalid \'from_type\' value: {from_type!r} Supported values are:\n"
+ ', '.join(_SCREAMING_SNAKE_CASE ) )
if to_type not in METRIC_CONVERSION:
raise ValueError(
F"Invalid \'to_type\' value: {to_type!r}. Supported values are:\n"
+ ', '.join(_SCREAMING_SNAKE_CASE ) )
return value * METRIC_CONVERSION[from_type].from_ * METRIC_CONVERSION[to_type].to
if __name__ == "__main__":
import doctest
doctest.testmod()
| 93 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__lowerCamelCase : Any = {
'''configuration_longformer''': [
'''LONGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''LongformerConfig''',
'''LongformerOnnxConfig''',
],
'''tokenization_longformer''': ['''LongformerTokenizer'''],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__lowerCamelCase : Any = ['''LongformerTokenizerFast''']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__lowerCamelCase : Dict = [
'''LONGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''LongformerForMaskedLM''',
'''LongformerForMultipleChoice''',
'''LongformerForQuestionAnswering''',
'''LongformerForSequenceClassification''',
'''LongformerForTokenClassification''',
'''LongformerModel''',
'''LongformerPreTrainedModel''',
'''LongformerSelfAttention''',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__lowerCamelCase : Any = [
'''TF_LONGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''TFLongformerForMaskedLM''',
'''TFLongformerForMultipleChoice''',
'''TFLongformerForQuestionAnswering''',
'''TFLongformerForSequenceClassification''',
'''TFLongformerForTokenClassification''',
'''TFLongformerModel''',
'''TFLongformerPreTrainedModel''',
'''TFLongformerSelfAttention''',
]
if TYPE_CHECKING:
from .configuration_longformer import (
LONGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
LongformerConfig,
LongformerOnnxConfig,
)
from .tokenization_longformer import LongformerTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_longformer_fast import LongformerTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_longformer import (
LONGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
LongformerForMaskedLM,
LongformerForMultipleChoice,
LongformerForQuestionAnswering,
LongformerForSequenceClassification,
LongformerForTokenClassification,
LongformerModel,
LongformerPreTrainedModel,
LongformerSelfAttention,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_longformer import (
TF_LONGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
TFLongformerForMaskedLM,
TFLongformerForMultipleChoice,
TFLongformerForQuestionAnswering,
TFLongformerForSequenceClassification,
TFLongformerForTokenClassification,
TFLongformerModel,
TFLongformerPreTrainedModel,
TFLongformerSelfAttention,
)
else:
import sys
__lowerCamelCase : Optional[Any] = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 653 | 0 |
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