code stringlengths 81 54k | code_codestyle int64 0 721 | style_context stringlengths 91 41.9k | style_context_codestyle int64 0 699 | label int64 0 1 |
|---|---|---|---|---|
def __A ( lowerCamelCase_ , lowerCamelCase_ ):
"""simple docstring"""
print("""\nThe shortest path matrix using Floyd Warshall algorithm\n""" )
for i in range(__UpperCamelCase ):
for j in range(__UpperCamelCase ):
if dist[i][j] != float("""inf""" ):
print(int(dist[i][j] ) , end="""\t""" )
else:
print("""INF""" , end="""\t""" )
print()
def __A ( lowerCamelCase_ , lowerCamelCase_ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : Optional[Any] = [[float("""inf""" ) for _ in range(__UpperCamelCase )] for _ in range(__UpperCamelCase )]
for i in range(__UpperCamelCase ):
for j in range(__UpperCamelCase ):
SCREAMING_SNAKE_CASE : List[Any] = graph[i][j]
# check vertex k against all other vertices (i, j)
for k in range(__UpperCamelCase ):
# looping through rows of graph array
for i in range(__UpperCamelCase ):
# looping through columns of graph array
for j in range(__UpperCamelCase ):
if (
dist[i][k] != float("""inf""" )
and dist[k][j] != float("""inf""" )
and dist[i][k] + dist[k][j] < dist[i][j]
):
SCREAMING_SNAKE_CASE : Optional[Any] = dist[i][k] + dist[k][j]
_print_dist(__UpperCamelCase , __UpperCamelCase )
return dist, v
if __name__ == "__main__":
__UpperCAmelCase = int(input("""Enter number of vertices: """))
__UpperCAmelCase = int(input("""Enter number of edges: """))
__UpperCAmelCase = [[float("""inf""") for i in range(v)] for j in range(v)]
for i in range(v):
__UpperCAmelCase = 0.0
# src and dst are indices that must be within the array size graph[e][v]
# failure to follow this will result in an error
for i in range(e):
print("""\nEdge """, i + 1)
__UpperCAmelCase = int(input("""Enter source:"""))
__UpperCAmelCase = int(input("""Enter destination:"""))
__UpperCAmelCase = float(input("""Enter weight:"""))
__UpperCAmelCase = weight
floyd_warshall(graph, v)
# Example Input
# Enter number of vertices: 3
# Enter number of edges: 2
# # generated graph from vertex and edge inputs
# [[inf, inf, inf], [inf, inf, inf], [inf, inf, inf]]
# [[0.0, inf, inf], [inf, 0.0, inf], [inf, inf, 0.0]]
# specify source, destination and weight for edge #1
# Edge 1
# Enter source:1
# Enter destination:2
# Enter weight:2
# specify source, destination and weight for edge #2
# Edge 2
# Enter source:2
# Enter destination:1
# Enter weight:1
# # Expected Output from the vertice, edge and src, dst, weight inputs!!
# 0 INF INF
# INF 0 2
# INF 1 0
| 721 |
'''simple docstring'''
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
if TYPE_CHECKING:
from ... import FeatureExtractionMixin, PreTrainedTokenizerBase, TensorType
__UpperCAmelCase = logging.get_logger(__name__)
__UpperCAmelCase = {
"""microsoft/deberta-v2-xlarge""": """https://huggingface.co/microsoft/deberta-v2-xlarge/resolve/main/config.json""",
"""microsoft/deberta-v2-xxlarge""": """https://huggingface.co/microsoft/deberta-v2-xxlarge/resolve/main/config.json""",
"""microsoft/deberta-v2-xlarge-mnli""": (
"""https://huggingface.co/microsoft/deberta-v2-xlarge-mnli/resolve/main/config.json"""
),
"""microsoft/deberta-v2-xxlarge-mnli""": (
"""https://huggingface.co/microsoft/deberta-v2-xxlarge-mnli/resolve/main/config.json"""
),
}
class UpperCamelCase__ ( lowercase_ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE__ = '''deberta-v2'''
def __init__( self : int , lowerCamelCase_ : Optional[Any]=12_81_00 , lowerCamelCase_ : str=15_36 , lowerCamelCase_ : int=24 , lowerCamelCase_ : List[str]=24 , lowerCamelCase_ : List[Any]=61_44 , lowerCamelCase_ : List[Any]="gelu" , lowerCamelCase_ : Optional[Any]=0.1 , lowerCamelCase_ : List[Any]=0.1 , lowerCamelCase_ : str=5_12 , lowerCamelCase_ : str=0 , lowerCamelCase_ : Union[str, Any]=0.02 , lowerCamelCase_ : Dict=1e-7 , lowerCamelCase_ : Optional[int]=False , lowerCamelCase_ : Optional[int]=-1 , lowerCamelCase_ : List[str]=0 , lowerCamelCase_ : Tuple=True , lowerCamelCase_ : Optional[int]=None , lowerCamelCase_ : Optional[Any]=0 , lowerCamelCase_ : Dict="gelu" , **lowerCamelCase_ : Optional[int] , ):
'''simple docstring'''
super().__init__(**lowerCamelCase_ )
SCREAMING_SNAKE_CASE : Optional[int] = hidden_size
SCREAMING_SNAKE_CASE : Optional[Any] = num_hidden_layers
SCREAMING_SNAKE_CASE : int = num_attention_heads
SCREAMING_SNAKE_CASE : List[str] = intermediate_size
SCREAMING_SNAKE_CASE : int = hidden_act
SCREAMING_SNAKE_CASE : Tuple = hidden_dropout_prob
SCREAMING_SNAKE_CASE : int = attention_probs_dropout_prob
SCREAMING_SNAKE_CASE : str = max_position_embeddings
SCREAMING_SNAKE_CASE : Tuple = type_vocab_size
SCREAMING_SNAKE_CASE : Optional[int] = initializer_range
SCREAMING_SNAKE_CASE : List[Any] = relative_attention
SCREAMING_SNAKE_CASE : str = max_relative_positions
SCREAMING_SNAKE_CASE : int = pad_token_id
SCREAMING_SNAKE_CASE : List[str] = position_biased_input
# Backwards compatibility
if type(lowerCamelCase_ ) == str:
SCREAMING_SNAKE_CASE : Dict = [x.strip() for x in pos_att_type.lower().split("""|""" )]
SCREAMING_SNAKE_CASE : Any = pos_att_type
SCREAMING_SNAKE_CASE : Any = vocab_size
SCREAMING_SNAKE_CASE : Optional[Any] = layer_norm_eps
SCREAMING_SNAKE_CASE : str = kwargs.get("""pooler_hidden_size""" , lowerCamelCase_ )
SCREAMING_SNAKE_CASE : Dict = pooler_dropout
SCREAMING_SNAKE_CASE : Any = pooler_hidden_act
class UpperCamelCase__ ( lowercase_ ):
"""simple docstring"""
@property
def lowerCamelCase_ ( self : Optional[int] ):
'''simple docstring'''
if self.task == "multiple-choice":
SCREAMING_SNAKE_CASE : Optional[int] = {0: """batch""", 1: """choice""", 2: """sequence"""}
else:
SCREAMING_SNAKE_CASE : Union[str, Any] = {0: """batch""", 1: """sequence"""}
if self._config.type_vocab_size > 0:
return OrderedDict(
[("""input_ids""", dynamic_axis), ("""attention_mask""", dynamic_axis), ("""token_type_ids""", dynamic_axis)] )
else:
return OrderedDict([("""input_ids""", dynamic_axis), ("""attention_mask""", dynamic_axis)] )
@property
def lowerCamelCase_ ( self : Tuple ):
'''simple docstring'''
return 12
def lowerCamelCase_ ( self : Optional[Any] , lowerCamelCase_ : Union["PreTrainedTokenizerBase", "FeatureExtractionMixin"] , lowerCamelCase_ : int = -1 , lowerCamelCase_ : int = -1 , lowerCamelCase_ : int = -1 , lowerCamelCase_ : bool = False , lowerCamelCase_ : Optional["TensorType"] = None , lowerCamelCase_ : int = 3 , lowerCamelCase_ : int = 40 , lowerCamelCase_ : int = 40 , lowerCamelCase_ : "PreTrainedTokenizerBase" = None , ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Dict = super().generate_dummy_inputs(preprocessor=lowerCamelCase_ , framework=lowerCamelCase_ )
if self._config.type_vocab_size == 0 and "token_type_ids" in dummy_inputs:
del dummy_inputs["token_type_ids"]
return dummy_inputs
| 79 | 0 |
'''simple docstring'''
import inspect
import os
import unittest
import torch
import accelerate
from accelerate import Accelerator
from accelerate.test_utils import execute_subprocess_async, require_multi_gpu
from accelerate.utils import patch_environment
class UpperCamelCase__ ( unittest.TestCase ):
"""simple docstring"""
def lowerCamelCase_ ( self : List[Any] ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : List[Any] = inspect.getfile(accelerate.test_utils )
SCREAMING_SNAKE_CASE : str = os.path.sep.join(mod_file.split(os.path.sep )[:-1] + ["""scripts""", """test_script.py"""] )
SCREAMING_SNAKE_CASE : Union[str, Any] = os.path.sep.join(
mod_file.split(os.path.sep )[:-1] + ["""scripts""", """test_distributed_data_loop.py"""] )
SCREAMING_SNAKE_CASE : int = os.path.sep.join(mod_file.split(os.path.sep )[:-1] + ["""scripts""", """test_ops.py"""] )
@require_multi_gpu
def lowerCamelCase_ ( self : Optional[int] ):
'''simple docstring'''
print(f'''Found {torch.cuda.device_count()} devices.''' )
SCREAMING_SNAKE_CASE : Optional[int] = ["""torchrun""", f'''--nproc_per_node={torch.cuda.device_count()}''', self.test_file_path]
with patch_environment(omp_num_threads=1 ):
execute_subprocess_async(A_ , env=os.environ.copy() )
@require_multi_gpu
def lowerCamelCase_ ( self : Optional[int] ):
'''simple docstring'''
print(f'''Found {torch.cuda.device_count()} devices.''' )
SCREAMING_SNAKE_CASE : Dict = ["""torchrun""", f'''--nproc_per_node={torch.cuda.device_count()}''', self.operation_file_path]
print(f'''Command: {cmd}''' )
with patch_environment(omp_num_threads=1 ):
execute_subprocess_async(A_ , env=os.environ.copy() )
@require_multi_gpu
def lowerCamelCase_ ( self : Tuple ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : str = ["""torchrun""", f'''--nproc_per_node={torch.cuda.device_count()}''', inspect.getfile(self.__class__ )]
with patch_environment(omp_num_threads=1 ):
execute_subprocess_async(A_ , env=os.environ.copy() )
@require_multi_gpu
def lowerCamelCase_ ( self : str ):
'''simple docstring'''
print(f'''Found {torch.cuda.device_count()} devices, using 2 devices only''' )
SCREAMING_SNAKE_CASE : Optional[Any] = ["""torchrun""", f'''--nproc_per_node={torch.cuda.device_count()}''', self.data_loop_file_path]
with patch_environment(omp_num_threads=1 , cuda_visible_devices="""0,1""" ):
execute_subprocess_async(A_ , env=os.environ.copy() )
if __name__ == "__main__":
__UpperCAmelCase = Accelerator()
__UpperCAmelCase = (accelerator.state.process_index + 2, 10)
__UpperCAmelCase = torch.randint(0, 10, shape).to(accelerator.device)
__UpperCAmelCase = """"""
__UpperCAmelCase = accelerator.pad_across_processes(tensor)
if tensora.shape[0] != accelerator.state.num_processes + 1:
error_msg += f"Found shape {tensora.shape} but should have {accelerator.state.num_processes + 1} at dim 0."
if not torch.equal(tensora[: accelerator.state.process_index + 2], tensor):
error_msg += "Tensors have different values."
if not torch.all(tensora[accelerator.state.process_index + 2 :] == 0):
error_msg += "Padding was not done with the right value (0)."
__UpperCAmelCase = accelerator.pad_across_processes(tensor, pad_first=True)
if tensora.shape[0] != accelerator.state.num_processes + 1:
error_msg += f"Found shape {tensora.shape} but should have {accelerator.state.num_processes + 1} at dim 0."
__UpperCAmelCase = accelerator.state.num_processes - accelerator.state.process_index - 1
if not torch.equal(tensora[index:], tensor):
error_msg += "Tensors have different values."
if not torch.all(tensora[:index] == 0):
error_msg += "Padding was not done with the right value (0)."
# Raise error at the end to make sure we don't stop at the first failure.
if len(error_msg) > 0:
raise ValueError(error_msg)
| 700 |
'''simple docstring'''
from collections import deque
from math import floor
from random import random
from time import time
class UpperCamelCase__ :
"""simple docstring"""
def __init__( self : Optional[Any] ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : str = {}
def lowerCamelCase_ ( self : Union[str, Any] , lowerCamelCase_ : Optional[Any] , lowerCamelCase_ : Any , lowerCamelCase_ : Optional[int]=1 ):
'''simple docstring'''
if self.graph.get(lowerCamelCase_ ):
if self.graph[u].count([w, v] ) == 0:
self.graph[u].append([w, v] )
else:
SCREAMING_SNAKE_CASE : str = [[w, v]]
if not self.graph.get(lowerCamelCase_ ):
SCREAMING_SNAKE_CASE : Tuple = []
def lowerCamelCase_ ( self : Optional[Any] ):
'''simple docstring'''
return list(self.graph )
def lowerCamelCase_ ( self : Optional[int] , lowerCamelCase_ : Any , lowerCamelCase_ : str ):
'''simple docstring'''
if self.graph.get(lowerCamelCase_ ):
for _ in self.graph[u]:
if _[1] == v:
self.graph[u].remove(lowerCamelCase_ )
def lowerCamelCase_ ( self : List[str] , lowerCamelCase_ : Union[str, Any]=-2 , lowerCamelCase_ : str=-1 ):
'''simple docstring'''
if s == d:
return []
SCREAMING_SNAKE_CASE : List[str] = []
SCREAMING_SNAKE_CASE : List[str] = []
if s == -2:
SCREAMING_SNAKE_CASE : Tuple = list(self.graph )[0]
stack.append(lowerCamelCase_ )
visited.append(lowerCamelCase_ )
SCREAMING_SNAKE_CASE : Any = s
while True:
# check if there is any non isolated nodes
if len(self.graph[s] ) != 0:
SCREAMING_SNAKE_CASE : Optional[Any] = s
for node in self.graph[s]:
if visited.count(node[1] ) < 1:
if node[1] == d:
visited.append(lowerCamelCase_ )
return visited
else:
stack.append(node[1] )
visited.append(node[1] )
SCREAMING_SNAKE_CASE : Tuple = node[1]
break
# check if all the children are visited
if s == ss:
stack.pop()
if len(lowerCamelCase_ ) != 0:
SCREAMING_SNAKE_CASE : int = stack[len(lowerCamelCase_ ) - 1]
else:
SCREAMING_SNAKE_CASE : Any = ss
# check if se have reached the starting point
if len(lowerCamelCase_ ) == 0:
return visited
def lowerCamelCase_ ( self : Dict , lowerCamelCase_ : Optional[int]=-1 ):
'''simple docstring'''
if c == -1:
SCREAMING_SNAKE_CASE : str = floor(random() * 1_00_00 ) + 10
for i in range(lowerCamelCase_ ):
# every vertex has max 100 edges
for _ in range(floor(random() * 1_02 ) + 1 ):
SCREAMING_SNAKE_CASE : Union[str, Any] = floor(random() * c ) + 1
if n != i:
self.add_pair(lowerCamelCase_ , lowerCamelCase_ , 1 )
def lowerCamelCase_ ( self : List[Any] , lowerCamelCase_ : Any=-2 ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : str = deque()
SCREAMING_SNAKE_CASE : List[str] = []
if s == -2:
SCREAMING_SNAKE_CASE : int = list(self.graph )[0]
d.append(lowerCamelCase_ )
visited.append(lowerCamelCase_ )
while d:
SCREAMING_SNAKE_CASE : Dict = d.popleft()
if len(self.graph[s] ) != 0:
for node in self.graph[s]:
if visited.count(node[1] ) < 1:
d.append(node[1] )
visited.append(node[1] )
return visited
def lowerCamelCase_ ( self : Dict , lowerCamelCase_ : Optional[Any] ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Dict = 0
for x in self.graph:
for y in self.graph[x]:
if y[1] == u:
count += 1
return count
def lowerCamelCase_ ( self : Optional[Any] , lowerCamelCase_ : Tuple ):
'''simple docstring'''
return len(self.graph[u] )
def lowerCamelCase_ ( self : str , lowerCamelCase_ : Optional[Any]=-2 ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : List[Any] = []
SCREAMING_SNAKE_CASE : List[str] = []
if s == -2:
SCREAMING_SNAKE_CASE : Union[str, Any] = list(self.graph )[0]
stack.append(lowerCamelCase_ )
visited.append(lowerCamelCase_ )
SCREAMING_SNAKE_CASE : Union[str, Any] = s
SCREAMING_SNAKE_CASE : List[str] = []
while True:
# check if there is any non isolated nodes
if len(self.graph[s] ) != 0:
SCREAMING_SNAKE_CASE : Optional[Any] = s
for node in self.graph[s]:
if visited.count(node[1] ) < 1:
stack.append(node[1] )
visited.append(node[1] )
SCREAMING_SNAKE_CASE : int = node[1]
break
# check if all the children are visited
if s == ss:
sorted_nodes.append(stack.pop() )
if len(lowerCamelCase_ ) != 0:
SCREAMING_SNAKE_CASE : List[Any] = stack[len(lowerCamelCase_ ) - 1]
else:
SCREAMING_SNAKE_CASE : int = ss
# check if se have reached the starting point
if len(lowerCamelCase_ ) == 0:
return sorted_nodes
def lowerCamelCase_ ( self : Optional[int] ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : List[Any] = []
SCREAMING_SNAKE_CASE : Any = []
SCREAMING_SNAKE_CASE : List[Any] = list(self.graph )[0]
stack.append(lowerCamelCase_ )
visited.append(lowerCamelCase_ )
SCREAMING_SNAKE_CASE : Any = -2
SCREAMING_SNAKE_CASE : int = []
SCREAMING_SNAKE_CASE : Union[str, Any] = s
SCREAMING_SNAKE_CASE : Tuple = False
SCREAMING_SNAKE_CASE : Union[str, Any] = set()
while True:
# check if there is any non isolated nodes
if len(self.graph[s] ) != 0:
SCREAMING_SNAKE_CASE : Union[str, Any] = s
for node in self.graph[s]:
if (
visited.count(node[1] ) > 0
and node[1] != parent
and indirect_parents.count(node[1] ) > 0
and not on_the_way_back
):
SCREAMING_SNAKE_CASE : Any = len(lowerCamelCase_ ) - 1
while len_stack >= 0:
if stack[len_stack] == node[1]:
anticipating_nodes.add(node[1] )
break
else:
anticipating_nodes.add(stack[len_stack] )
len_stack -= 1
if visited.count(node[1] ) < 1:
stack.append(node[1] )
visited.append(node[1] )
SCREAMING_SNAKE_CASE : int = node[1]
break
# check if all the children are visited
if s == ss:
stack.pop()
SCREAMING_SNAKE_CASE : int = True
if len(lowerCamelCase_ ) != 0:
SCREAMING_SNAKE_CASE : Any = stack[len(lowerCamelCase_ ) - 1]
else:
SCREAMING_SNAKE_CASE : List[str] = False
indirect_parents.append(lowerCamelCase_ )
SCREAMING_SNAKE_CASE : List[Any] = s
SCREAMING_SNAKE_CASE : List[Any] = ss
# check if se have reached the starting point
if len(lowerCamelCase_ ) == 0:
return list(lowerCamelCase_ )
def lowerCamelCase_ ( self : Dict ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Tuple = []
SCREAMING_SNAKE_CASE : int = []
SCREAMING_SNAKE_CASE : Any = list(self.graph )[0]
stack.append(lowerCamelCase_ )
visited.append(lowerCamelCase_ )
SCREAMING_SNAKE_CASE : Union[str, Any] = -2
SCREAMING_SNAKE_CASE : List[Any] = []
SCREAMING_SNAKE_CASE : Tuple = s
SCREAMING_SNAKE_CASE : Dict = False
SCREAMING_SNAKE_CASE : Optional[Any] = set()
while True:
# check if there is any non isolated nodes
if len(self.graph[s] ) != 0:
SCREAMING_SNAKE_CASE : str = s
for node in self.graph[s]:
if (
visited.count(node[1] ) > 0
and node[1] != parent
and indirect_parents.count(node[1] ) > 0
and not on_the_way_back
):
SCREAMING_SNAKE_CASE : str = len(lowerCamelCase_ ) - 1
while len_stack_minus_one >= 0:
if stack[len_stack_minus_one] == node[1]:
anticipating_nodes.add(node[1] )
break
else:
return True
if visited.count(node[1] ) < 1:
stack.append(node[1] )
visited.append(node[1] )
SCREAMING_SNAKE_CASE : Optional[Any] = node[1]
break
# check if all the children are visited
if s == ss:
stack.pop()
SCREAMING_SNAKE_CASE : Dict = True
if len(lowerCamelCase_ ) != 0:
SCREAMING_SNAKE_CASE : List[str] = stack[len(lowerCamelCase_ ) - 1]
else:
SCREAMING_SNAKE_CASE : List[Any] = False
indirect_parents.append(lowerCamelCase_ )
SCREAMING_SNAKE_CASE : Optional[int] = s
SCREAMING_SNAKE_CASE : Optional[int] = ss
# check if se have reached the starting point
if len(lowerCamelCase_ ) == 0:
return False
def lowerCamelCase_ ( self : Any , lowerCamelCase_ : str=-2 , lowerCamelCase_ : int=-1 ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Optional[Any] = time()
self.dfs(lowerCamelCase_ , lowerCamelCase_ )
SCREAMING_SNAKE_CASE : List[str] = time()
return end - begin
def lowerCamelCase_ ( self : int , lowerCamelCase_ : Tuple=-2 ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Optional[int] = time()
self.bfs(lowerCamelCase_ )
SCREAMING_SNAKE_CASE : List[str] = time()
return end - begin
class UpperCamelCase__ :
"""simple docstring"""
def __init__( self : Optional[int] ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Tuple = {}
def lowerCamelCase_ ( self : List[str] , lowerCamelCase_ : Optional[Any] , lowerCamelCase_ : Dict , lowerCamelCase_ : Union[str, Any]=1 ):
'''simple docstring'''
if self.graph.get(lowerCamelCase_ ):
# if there already is a edge
if self.graph[u].count([w, v] ) == 0:
self.graph[u].append([w, v] )
else:
# if u does not exist
SCREAMING_SNAKE_CASE : Any = [[w, v]]
# add the other way
if self.graph.get(lowerCamelCase_ ):
# if there already is a edge
if self.graph[v].count([w, u] ) == 0:
self.graph[v].append([w, u] )
else:
# if u does not exist
SCREAMING_SNAKE_CASE : Any = [[w, u]]
def lowerCamelCase_ ( self : Tuple , lowerCamelCase_ : Tuple , lowerCamelCase_ : Any ):
'''simple docstring'''
if self.graph.get(lowerCamelCase_ ):
for _ in self.graph[u]:
if _[1] == v:
self.graph[u].remove(lowerCamelCase_ )
# the other way round
if self.graph.get(lowerCamelCase_ ):
for _ in self.graph[v]:
if _[1] == u:
self.graph[v].remove(lowerCamelCase_ )
def lowerCamelCase_ ( self : int , lowerCamelCase_ : str=-2 , lowerCamelCase_ : List[str]=-1 ):
'''simple docstring'''
if s == d:
return []
SCREAMING_SNAKE_CASE : List[str] = []
SCREAMING_SNAKE_CASE : Any = []
if s == -2:
SCREAMING_SNAKE_CASE : List[Any] = list(self.graph )[0]
stack.append(lowerCamelCase_ )
visited.append(lowerCamelCase_ )
SCREAMING_SNAKE_CASE : List[str] = s
while True:
# check if there is any non isolated nodes
if len(self.graph[s] ) != 0:
SCREAMING_SNAKE_CASE : Union[str, Any] = s
for node in self.graph[s]:
if visited.count(node[1] ) < 1:
if node[1] == d:
visited.append(lowerCamelCase_ )
return visited
else:
stack.append(node[1] )
visited.append(node[1] )
SCREAMING_SNAKE_CASE : Any = node[1]
break
# check if all the children are visited
if s == ss:
stack.pop()
if len(lowerCamelCase_ ) != 0:
SCREAMING_SNAKE_CASE : Any = stack[len(lowerCamelCase_ ) - 1]
else:
SCREAMING_SNAKE_CASE : List[str] = ss
# check if se have reached the starting point
if len(lowerCamelCase_ ) == 0:
return visited
def lowerCamelCase_ ( self : Any , lowerCamelCase_ : List[str]=-1 ):
'''simple docstring'''
if c == -1:
SCREAMING_SNAKE_CASE : Any = floor(random() * 1_00_00 ) + 10
for i in range(lowerCamelCase_ ):
# every vertex has max 100 edges
for _ in range(floor(random() * 1_02 ) + 1 ):
SCREAMING_SNAKE_CASE : List[str] = floor(random() * c ) + 1
if n != i:
self.add_pair(lowerCamelCase_ , lowerCamelCase_ , 1 )
def lowerCamelCase_ ( self : List[str] , lowerCamelCase_ : List[Any]=-2 ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : List[Any] = deque()
SCREAMING_SNAKE_CASE : Tuple = []
if s == -2:
SCREAMING_SNAKE_CASE : Any = list(self.graph )[0]
d.append(lowerCamelCase_ )
visited.append(lowerCamelCase_ )
while d:
SCREAMING_SNAKE_CASE : List[Any] = d.popleft()
if len(self.graph[s] ) != 0:
for node in self.graph[s]:
if visited.count(node[1] ) < 1:
d.append(node[1] )
visited.append(node[1] )
return visited
def lowerCamelCase_ ( self : Tuple , lowerCamelCase_ : str ):
'''simple docstring'''
return len(self.graph[u] )
def lowerCamelCase_ ( self : List[str] ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : List[str] = []
SCREAMING_SNAKE_CASE : Any = []
SCREAMING_SNAKE_CASE : Optional[Any] = list(self.graph )[0]
stack.append(lowerCamelCase_ )
visited.append(lowerCamelCase_ )
SCREAMING_SNAKE_CASE : Optional[int] = -2
SCREAMING_SNAKE_CASE : Tuple = []
SCREAMING_SNAKE_CASE : Any = s
SCREAMING_SNAKE_CASE : Optional[Any] = False
SCREAMING_SNAKE_CASE : Optional[Any] = set()
while True:
# check if there is any non isolated nodes
if len(self.graph[s] ) != 0:
SCREAMING_SNAKE_CASE : Optional[Any] = s
for node in self.graph[s]:
if (
visited.count(node[1] ) > 0
and node[1] != parent
and indirect_parents.count(node[1] ) > 0
and not on_the_way_back
):
SCREAMING_SNAKE_CASE : Optional[Any] = len(lowerCamelCase_ ) - 1
while len_stack >= 0:
if stack[len_stack] == node[1]:
anticipating_nodes.add(node[1] )
break
else:
anticipating_nodes.add(stack[len_stack] )
len_stack -= 1
if visited.count(node[1] ) < 1:
stack.append(node[1] )
visited.append(node[1] )
SCREAMING_SNAKE_CASE : str = node[1]
break
# check if all the children are visited
if s == ss:
stack.pop()
SCREAMING_SNAKE_CASE : Optional[int] = True
if len(lowerCamelCase_ ) != 0:
SCREAMING_SNAKE_CASE : int = stack[len(lowerCamelCase_ ) - 1]
else:
SCREAMING_SNAKE_CASE : Union[str, Any] = False
indirect_parents.append(lowerCamelCase_ )
SCREAMING_SNAKE_CASE : List[str] = s
SCREAMING_SNAKE_CASE : str = ss
# check if se have reached the starting point
if len(lowerCamelCase_ ) == 0:
return list(lowerCamelCase_ )
def lowerCamelCase_ ( self : Union[str, Any] ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Optional[Any] = []
SCREAMING_SNAKE_CASE : int = []
SCREAMING_SNAKE_CASE : Any = list(self.graph )[0]
stack.append(lowerCamelCase_ )
visited.append(lowerCamelCase_ )
SCREAMING_SNAKE_CASE : Dict = -2
SCREAMING_SNAKE_CASE : int = []
SCREAMING_SNAKE_CASE : int = s
SCREAMING_SNAKE_CASE : Union[str, Any] = False
SCREAMING_SNAKE_CASE : Tuple = set()
while True:
# check if there is any non isolated nodes
if len(self.graph[s] ) != 0:
SCREAMING_SNAKE_CASE : Any = s
for node in self.graph[s]:
if (
visited.count(node[1] ) > 0
and node[1] != parent
and indirect_parents.count(node[1] ) > 0
and not on_the_way_back
):
SCREAMING_SNAKE_CASE : Optional[Any] = len(lowerCamelCase_ ) - 1
while len_stack_minus_one >= 0:
if stack[len_stack_minus_one] == node[1]:
anticipating_nodes.add(node[1] )
break
else:
return True
if visited.count(node[1] ) < 1:
stack.append(node[1] )
visited.append(node[1] )
SCREAMING_SNAKE_CASE : Tuple = node[1]
break
# check if all the children are visited
if s == ss:
stack.pop()
SCREAMING_SNAKE_CASE : Any = True
if len(lowerCamelCase_ ) != 0:
SCREAMING_SNAKE_CASE : str = stack[len(lowerCamelCase_ ) - 1]
else:
SCREAMING_SNAKE_CASE : Optional[Any] = False
indirect_parents.append(lowerCamelCase_ )
SCREAMING_SNAKE_CASE : Any = s
SCREAMING_SNAKE_CASE : Tuple = ss
# check if se have reached the starting point
if len(lowerCamelCase_ ) == 0:
return False
def lowerCamelCase_ ( self : List[Any] ):
'''simple docstring'''
return list(self.graph )
def lowerCamelCase_ ( self : str , lowerCamelCase_ : List[str]=-2 , lowerCamelCase_ : str=-1 ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : str = time()
self.dfs(lowerCamelCase_ , lowerCamelCase_ )
SCREAMING_SNAKE_CASE : Tuple = time()
return end - begin
def lowerCamelCase_ ( self : Tuple , lowerCamelCase_ : Dict=-2 ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Optional[Any] = time()
self.bfs(lowerCamelCase_ )
SCREAMING_SNAKE_CASE : Dict = time()
return end - begin
| 79 | 0 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__UpperCAmelCase = logging.get_logger(__name__)
__UpperCAmelCase = {
"""uw-madison/mra-base-512-4""": """https://huggingface.co/uw-madison/mra-base-512-4/resolve/main/config.json""",
}
class UpperCamelCase__ ( lowercase__ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE__ = 'mra'
def __init__( self : Union[str, Any] , lowerCamelCase_ : Any=5_02_65 , lowerCamelCase_ : str=7_68 , lowerCamelCase_ : int=12 , lowerCamelCase_ : int=12 , lowerCamelCase_ : int=30_72 , lowerCamelCase_ : List[str]="gelu" , lowerCamelCase_ : List[str]=0.1 , lowerCamelCase_ : Any=0.1 , lowerCamelCase_ : Any=5_12 , lowerCamelCase_ : int=1 , lowerCamelCase_ : List[str]=0.02 , lowerCamelCase_ : int=1e-5 , lowerCamelCase_ : Dict="absolute" , lowerCamelCase_ : List[Any]=4 , lowerCamelCase_ : Any="full" , lowerCamelCase_ : List[Any]=0 , lowerCamelCase_ : str=0 , lowerCamelCase_ : int=1 , lowerCamelCase_ : Optional[int]=0 , lowerCamelCase_ : Optional[int]=2 , **lowerCamelCase_ : str , ):
'''simple docstring'''
super().__init__(pad_token_id=lowerCamelCase_ , bos_token_id=lowerCamelCase_ , eos_token_id=lowerCamelCase_ , **lowerCamelCase_ )
SCREAMING_SNAKE_CASE : Optional[Any] = vocab_size
SCREAMING_SNAKE_CASE : Optional[int] = max_position_embeddings
SCREAMING_SNAKE_CASE : List[str] = hidden_size
SCREAMING_SNAKE_CASE : Optional[int] = num_hidden_layers
SCREAMING_SNAKE_CASE : Tuple = num_attention_heads
SCREAMING_SNAKE_CASE : Tuple = intermediate_size
SCREAMING_SNAKE_CASE : int = hidden_act
SCREAMING_SNAKE_CASE : Optional[int] = hidden_dropout_prob
SCREAMING_SNAKE_CASE : str = attention_probs_dropout_prob
SCREAMING_SNAKE_CASE : str = initializer_range
SCREAMING_SNAKE_CASE : Union[str, Any] = type_vocab_size
SCREAMING_SNAKE_CASE : str = layer_norm_eps
SCREAMING_SNAKE_CASE : Optional[Any] = position_embedding_type
SCREAMING_SNAKE_CASE : Any = block_per_row
SCREAMING_SNAKE_CASE : Union[str, Any] = approx_mode
SCREAMING_SNAKE_CASE : Optional[Any] = initial_prior_first_n_blocks
SCREAMING_SNAKE_CASE : str = initial_prior_diagonal_n_blocks
| 701 |
'''simple docstring'''
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
__UpperCAmelCase = logging.get_logger(__name__)
__UpperCAmelCase = {"""vocab_file""": """sentencepiece.bpe.model"""}
__UpperCAmelCase = {
"""vocab_file""": {
"""moussaKam/mbarthez""": """https://huggingface.co/moussaKam/mbarthez/resolve/main/sentencepiece.bpe.model""",
"""moussaKam/barthez""": """https://huggingface.co/moussaKam/barthez/resolve/main/sentencepiece.bpe.model""",
"""moussaKam/barthez-orangesum-title""": (
"""https://huggingface.co/moussaKam/barthez-orangesum-title/resolve/main/sentencepiece.bpe.model"""
),
},
}
__UpperCAmelCase = {
"""moussaKam/mbarthez""": 1024,
"""moussaKam/barthez""": 1024,
"""moussaKam/barthez-orangesum-title""": 1024,
}
__UpperCAmelCase = """▁"""
class UpperCamelCase__ ( lowercase_ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE__ = VOCAB_FILES_NAMES
SCREAMING_SNAKE_CASE__ = PRETRAINED_VOCAB_FILES_MAP
SCREAMING_SNAKE_CASE__ = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
SCREAMING_SNAKE_CASE__ = ['''input_ids''', '''attention_mask''']
def __init__( self : Optional[Any] , lowerCamelCase_ : List[str] , lowerCamelCase_ : Tuple="<s>" , lowerCamelCase_ : Union[str, Any]="</s>" , lowerCamelCase_ : Tuple="</s>" , lowerCamelCase_ : str="<s>" , lowerCamelCase_ : Optional[int]="<unk>" , lowerCamelCase_ : List[Any]="<pad>" , lowerCamelCase_ : Optional[Any]="<mask>" , lowerCamelCase_ : Optional[Dict[str, Any]] = None , **lowerCamelCase_ : List[str] , ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : List[str] = AddedToken(lowerCamelCase_ , lstrip=lowerCamelCase_ , rstrip=lowerCamelCase_ ) if isinstance(lowerCamelCase_ , lowerCamelCase_ ) else mask_token
SCREAMING_SNAKE_CASE : Union[str, Any] = {} if sp_model_kwargs is None else sp_model_kwargs
super().__init__(
bos_token=lowerCamelCase_ , eos_token=lowerCamelCase_ , unk_token=lowerCamelCase_ , sep_token=lowerCamelCase_ , cls_token=lowerCamelCase_ , pad_token=lowerCamelCase_ , mask_token=lowerCamelCase_ , sp_model_kwargs=self.sp_model_kwargs , **lowerCamelCase_ , )
SCREAMING_SNAKE_CASE : Dict = vocab_file
SCREAMING_SNAKE_CASE : Dict = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(str(lowerCamelCase_ ) )
SCREAMING_SNAKE_CASE : Optional[int] = {"""<s>""": 0, """<pad>""": 1, """</s>""": 2, """<unk>""": 3}
SCREAMING_SNAKE_CASE : str = len(self.sp_model ) - 1
SCREAMING_SNAKE_CASE : Optional[int] = {v: k for k, v in self.fairseq_tokens_to_ids.items()}
def lowerCamelCase_ ( self : List[str] , lowerCamelCase_ : List[int] , lowerCamelCase_ : 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 : List[Any] = [self.cls_token_id]
SCREAMING_SNAKE_CASE : int = [self.sep_token_id]
return cls + token_ids_a + sep + sep + token_ids_a + sep
def lowerCamelCase_ ( self : Any , lowerCamelCase_ : List[int] , lowerCamelCase_ : Optional[List[int]] = None , lowerCamelCase_ : bool = False ):
'''simple docstring'''
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=lowerCamelCase_ , token_ids_a=lowerCamelCase_ , already_has_special_tokens=lowerCamelCase_ )
if token_ids_a is None:
return [1] + ([0] * len(lowerCamelCase_ )) + [1]
return [1] + ([0] * len(lowerCamelCase_ )) + [1, 1] + ([0] * len(lowerCamelCase_ )) + [1]
def lowerCamelCase_ ( self : str , lowerCamelCase_ : List[int] , lowerCamelCase_ : Optional[List[int]] = None ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : List[str] = [self.sep_token_id]
SCREAMING_SNAKE_CASE : Dict = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep + sep + token_ids_a + sep ) * [0]
@property
def lowerCamelCase_ ( self : List[str] ):
'''simple docstring'''
return len(self.sp_model )
def lowerCamelCase_ ( self : Optional[int] ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : List[str] = {self.convert_ids_to_tokens(lowerCamelCase_ ): i for i in range(self.vocab_size )}
vocab.update(self.added_tokens_encoder )
return vocab
def lowerCamelCase_ ( self : Union[str, Any] , lowerCamelCase_ : str ):
'''simple docstring'''
return self.sp_model.encode(lowerCamelCase_ , out_type=lowerCamelCase_ )
def lowerCamelCase_ ( self : Tuple , lowerCamelCase_ : str ):
'''simple docstring'''
if token in self.fairseq_tokens_to_ids:
return self.fairseq_tokens_to_ids[token]
SCREAMING_SNAKE_CASE : List[str] = self.sp_model.PieceToId(lowerCamelCase_ )
return spm_id if spm_id else self.unk_token_id
def lowerCamelCase_ ( self : int , lowerCamelCase_ : List[str] ):
'''simple docstring'''
if index in self.fairseq_ids_to_tokens:
return self.fairseq_ids_to_tokens[index]
return self.sp_model.IdToPiece(lowerCamelCase_ )
def lowerCamelCase_ ( self : Optional[Any] , lowerCamelCase_ : Optional[Any] ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : str = []
SCREAMING_SNAKE_CASE : Tuple = """"""
SCREAMING_SNAKE_CASE : Dict = False
for token in tokens:
# make sure that special tokens are not decoded using sentencepiece model
if token in self.all_special_tokens:
if not prev_is_special:
out_string += " "
out_string += self.sp_model.decode(lowerCamelCase_ ) + token
SCREAMING_SNAKE_CASE : str = True
SCREAMING_SNAKE_CASE : Optional[Any] = []
else:
current_sub_tokens.append(lowerCamelCase_ )
SCREAMING_SNAKE_CASE : Optional[int] = False
out_string += self.sp_model.decode(lowerCamelCase_ )
return out_string.strip()
def __getstate__( self : Tuple ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Dict = self.__dict__.copy()
SCREAMING_SNAKE_CASE : List[Any] = None
return state
def __setstate__( self : Optional[Any] , lowerCamelCase_ : Tuple ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : str = d
# for backward compatibility
if not hasattr(self , """sp_model_kwargs""" ):
SCREAMING_SNAKE_CASE : int = {}
SCREAMING_SNAKE_CASE : Any = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(self.vocab_file )
def lowerCamelCase_ ( self : Any , lowerCamelCase_ : str , lowerCamelCase_ : Optional[str] = None ):
'''simple docstring'''
if not os.path.isdir(lowerCamelCase_ ):
logger.error(f'''Vocabulary path ({save_directory}) should be a directory''' )
return
SCREAMING_SNAKE_CASE : Dict = os.path.join(
lowerCamelCase_ , (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(lowerCamelCase_ ) and os.path.isfile(self.vocab_file ):
copyfile(self.vocab_file , lowerCamelCase_ )
elif not os.path.isfile(self.vocab_file ):
with open(lowerCamelCase_ , """wb""" ) as fi:
SCREAMING_SNAKE_CASE : Any = self.sp_model.serialized_model_proto()
fi.write(lowerCamelCase_ )
return (out_vocab_file,)
| 79 | 0 |
'''simple docstring'''
from math import acos, sin
from typing import List, Tuple, Union
import numpy as np
import torch
from PIL import Image
from ...models import AutoencoderKL, UNetaDConditionModel
from ...schedulers import DDIMScheduler, DDPMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import AudioPipelineOutput, BaseOutput, DiffusionPipeline, ImagePipelineOutput
from .mel import Mel
class UpperCamelCase__ ( lowercase_ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE__ = ['''vqvae''']
def __init__( self : Optional[int] , lowerCamelCase_ : Any , lowerCamelCase_ : int , lowerCamelCase_ : Dict , lowerCamelCase_ : List[str] , ):
'''simple docstring'''
super().__init__()
self.register_modules(unet=lowerCamelCase_ , scheduler=lowerCamelCase_ , mel=lowerCamelCase_ , vqvae=lowerCamelCase_ )
def lowerCamelCase_ ( self : Optional[int] ):
'''simple docstring'''
return 50 if isinstance(self.scheduler , lowerCamelCase_ ) else 10_00
@torch.no_grad()
def __call__( self : str , lowerCamelCase_ : Union[str, Any] = 1 , lowerCamelCase_ : Any = None , lowerCamelCase_ : Union[str, Any] = None , lowerCamelCase_ : Any = 0 , lowerCamelCase_ : Dict = 0 , lowerCamelCase_ : Optional[int] = None , lowerCamelCase_ : str = None , lowerCamelCase_ : str = 0 , lowerCamelCase_ : str = 0 , lowerCamelCase_ : Union[str, Any] = None , lowerCamelCase_ : List[str] = 0 , lowerCamelCase_ : Any = None , lowerCamelCase_ : List[Any] = None , lowerCamelCase_ : Any=True , ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Dict = steps or self.get_default_steps()
self.scheduler.set_timesteps(lowerCamelCase_ )
SCREAMING_SNAKE_CASE : Optional[Any] = step_generator or generator
# For backwards compatibility
if type(self.unet.config.sample_size ) == int:
SCREAMING_SNAKE_CASE : Union[str, Any] = (self.unet.config.sample_size, self.unet.config.sample_size)
if noise is None:
SCREAMING_SNAKE_CASE : List[Any] = randn_tensor(
(
batch_size,
self.unet.config.in_channels,
self.unet.config.sample_size[0],
self.unet.config.sample_size[1],
) , generator=lowerCamelCase_ , device=self.device , )
SCREAMING_SNAKE_CASE : Optional[int] = noise
SCREAMING_SNAKE_CASE : Optional[int] = None
if audio_file is not None or raw_audio is not None:
self.mel.load_audio(lowerCamelCase_ , lowerCamelCase_ )
SCREAMING_SNAKE_CASE : Dict = self.mel.audio_slice_to_image(lowerCamelCase_ )
SCREAMING_SNAKE_CASE : Tuple = np.frombuffer(input_image.tobytes() , dtype="""uint8""" ).reshape(
(input_image.height, input_image.width) )
SCREAMING_SNAKE_CASE : Any = (input_image / 2_55) * 2 - 1
SCREAMING_SNAKE_CASE : Union[str, Any] = torch.tensor(input_image[np.newaxis, :, :] , dtype=torch.float ).to(self.device )
if self.vqvae is not None:
SCREAMING_SNAKE_CASE : List[Any] = self.vqvae.encode(torch.unsqueeze(lowerCamelCase_ , 0 ) ).latent_dist.sample(
generator=lowerCamelCase_ )[0]
SCREAMING_SNAKE_CASE : List[str] = self.vqvae.config.scaling_factor * input_images
if start_step > 0:
SCREAMING_SNAKE_CASE : Any = self.scheduler.add_noise(lowerCamelCase_ , lowerCamelCase_ , self.scheduler.timesteps[start_step - 1] )
SCREAMING_SNAKE_CASE : int = (
self.unet.config.sample_size[1] * self.mel.get_sample_rate() / self.mel.x_res / self.mel.hop_length
)
SCREAMING_SNAKE_CASE : List[Any] = int(mask_start_secs * pixels_per_second )
SCREAMING_SNAKE_CASE : Dict = int(mask_end_secs * pixels_per_second )
SCREAMING_SNAKE_CASE : Optional[int] = self.scheduler.add_noise(lowerCamelCase_ , lowerCamelCase_ , torch.tensor(self.scheduler.timesteps[start_step:] ) )
for step, t in enumerate(self.progress_bar(self.scheduler.timesteps[start_step:] ) ):
if isinstance(self.unet , lowerCamelCase_ ):
SCREAMING_SNAKE_CASE : Optional[Any] = self.unet(lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ )["""sample"""]
else:
SCREAMING_SNAKE_CASE : int = self.unet(lowerCamelCase_ , lowerCamelCase_ )["""sample"""]
if isinstance(self.scheduler , lowerCamelCase_ ):
SCREAMING_SNAKE_CASE : Any = self.scheduler.step(
model_output=lowerCamelCase_ , timestep=lowerCamelCase_ , sample=lowerCamelCase_ , eta=lowerCamelCase_ , generator=lowerCamelCase_ , )["""prev_sample"""]
else:
SCREAMING_SNAKE_CASE : Any = self.scheduler.step(
model_output=lowerCamelCase_ , timestep=lowerCamelCase_ , sample=lowerCamelCase_ , generator=lowerCamelCase_ , )["""prev_sample"""]
if mask is not None:
if mask_start > 0:
SCREAMING_SNAKE_CASE : List[str] = mask[:, step, :, :mask_start]
if mask_end > 0:
SCREAMING_SNAKE_CASE : List[Any] = mask[:, step, :, -mask_end:]
if self.vqvae is not None:
# 0.18215 was scaling factor used in training to ensure unit variance
SCREAMING_SNAKE_CASE : Any = 1 / self.vqvae.config.scaling_factor * images
SCREAMING_SNAKE_CASE : Any = self.vqvae.decode(lowerCamelCase_ )["""sample"""]
SCREAMING_SNAKE_CASE : List[Any] = (images / 2 + 0.5).clamp(0 , 1 )
SCREAMING_SNAKE_CASE : Dict = images.cpu().permute(0 , 2 , 3 , 1 ).numpy()
SCREAMING_SNAKE_CASE : str = (images * 2_55).round().astype("""uint8""" )
SCREAMING_SNAKE_CASE : List[str] = list(
(Image.fromarray(_[:, :, 0] ) for _ in images)
if images.shape[3] == 1
else (Image.fromarray(lowerCamelCase_ , mode="""RGB""" ).convert("""L""" ) for _ in images) )
SCREAMING_SNAKE_CASE : Optional[Any] = [self.mel.image_to_audio(lowerCamelCase_ ) for _ in images]
if not return_dict:
return images, (self.mel.get_sample_rate(), audios)
return BaseOutput(**AudioPipelineOutput(np.array(lowerCamelCase_ )[:, np.newaxis, :] ) , **ImagePipelineOutput(lowerCamelCase_ ) )
@torch.no_grad()
def lowerCamelCase_ ( self : Union[str, Any] , lowerCamelCase_ : List[str] , lowerCamelCase_ : Union[str, Any] = 50 ):
'''simple docstring'''
assert isinstance(self.scheduler , lowerCamelCase_ )
self.scheduler.set_timesteps(lowerCamelCase_ )
SCREAMING_SNAKE_CASE : Tuple = np.array(
[np.frombuffer(image.tobytes() , dtype="""uint8""" ).reshape((1, image.height, image.width) ) for image in images] )
SCREAMING_SNAKE_CASE : int = (sample / 2_55) * 2 - 1
SCREAMING_SNAKE_CASE : Union[str, Any] = torch.Tensor(lowerCamelCase_ ).to(self.device )
for t in self.progress_bar(torch.flip(self.scheduler.timesteps , (0,) ) ):
SCREAMING_SNAKE_CASE : List[str] = t - self.scheduler.config.num_train_timesteps // self.scheduler.num_inference_steps
SCREAMING_SNAKE_CASE : Tuple = self.scheduler.alphas_cumprod[t]
SCREAMING_SNAKE_CASE : Optional[int] = (
self.scheduler.alphas_cumprod[prev_timestep]
if prev_timestep >= 0
else self.scheduler.final_alpha_cumprod
)
SCREAMING_SNAKE_CASE : List[Any] = 1 - alpha_prod_t
SCREAMING_SNAKE_CASE : int = self.unet(lowerCamelCase_ , lowerCamelCase_ )["""sample"""]
SCREAMING_SNAKE_CASE : List[str] = (1 - alpha_prod_t_prev) ** 0.5 * model_output
SCREAMING_SNAKE_CASE : Optional[Any] = (sample - pred_sample_direction) * alpha_prod_t_prev ** (-0.5)
SCREAMING_SNAKE_CASE : List[str] = sample * alpha_prod_t ** 0.5 + beta_prod_t ** 0.5 * model_output
return sample
@staticmethod
def lowerCamelCase_ ( lowerCamelCase_ : Dict , lowerCamelCase_ : Dict , lowerCamelCase_ : str ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Dict = acos(torch.dot(torch.flatten(lowerCamelCase_ ) , torch.flatten(lowerCamelCase_ ) ) / torch.norm(lowerCamelCase_ ) / torch.norm(lowerCamelCase_ ) )
return sin((1 - alpha) * theta ) * xa / sin(lowerCamelCase_ ) + sin(alpha * theta ) * xa / sin(lowerCamelCase_ )
| 702 |
'''simple docstring'''
from __future__ import annotations
import unittest
from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
if is_tf_available():
import numpy as np
import tensorflow as tf
from transformers import TFXLMRobertaModel
@require_tf
@require_sentencepiece
@require_tokenizers
class UpperCamelCase__ ( unittest.TestCase ):
"""simple docstring"""
@slow
def lowerCamelCase_ ( self : Tuple ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Union[str, Any] = TFXLMRobertaModel.from_pretrained("""jplu/tf-xlm-roberta-base""" )
SCREAMING_SNAKE_CASE : Dict = {
"""input_ids""": tf.convert_to_tensor([[0, 26_46, 1_02_69, 83, 9_99_42, 2]] , dtype=tf.intaa ), # "My dog is cute"
"""attention_mask""": tf.convert_to_tensor([[1, 1, 1, 1, 1, 1]] , dtype=tf.intaa ),
}
SCREAMING_SNAKE_CASE : List[str] = model(lowerCamelCase_ )["""last_hidden_state"""]
SCREAMING_SNAKE_CASE : Union[str, Any] = tf.TensorShape((1, 6, 7_68) )
self.assertEqual(output.shape , lowerCamelCase_ )
# compare the actual values for a slice.
SCREAMING_SNAKE_CASE : Optional[int] = tf.convert_to_tensor(
[
[
[0.0_681_762, 0.10_894_451, 0.06_772_504],
[-0.06_423_668, 0.02_366_615, 0.04_329_344],
[-0.06_057_295, 0.09_974_135, -0.00_070_584],
]
] , dtype=tf.floataa , )
self.assertTrue(np.allclose(output[:, :3, :3].numpy() , expected_slice.numpy() , atol=1e-4 ) )
| 79 | 0 |
'''simple docstring'''
def __A ( lowerCamelCase_ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : Tuple = 0
for ch in input_str:
SCREAMING_SNAKE_CASE : Optional[int] = ord(snake_case_ )
SCREAMING_SNAKE_CASE : List[Any] = pow(2 , snake_case_ )
# If we already turned on bit for current character's unicode
if bitmap >> ch_unicode & 1 == 1:
return False
bitmap |= ch_bit_index_on
return True
if __name__ == "__main__":
import doctest
doctest.testmod()
| 703 |
'''simple docstring'''
from dataclasses import dataclass, field
from typing import TYPE_CHECKING, Any, ClassVar, Dict, List, Optional, Union
import pyarrow as pa
if TYPE_CHECKING:
from .features import FeatureType
@dataclass
class UpperCamelCase__ :
"""simple docstring"""
SCREAMING_SNAKE_CASE__ = 42
SCREAMING_SNAKE_CASE__ = None
# Automatically constructed
SCREAMING_SNAKE_CASE__ = "dict"
SCREAMING_SNAKE_CASE__ = None
SCREAMING_SNAKE_CASE__ = field(default='''Translation''' , init=lowercase_ , repr=lowercase_ )
def __call__( self : int ):
'''simple docstring'''
return pa.struct({lang: pa.string() for lang in sorted(self.languages )} )
def lowerCamelCase_ ( self : Tuple ):
'''simple docstring'''
from .features import Value
return {k: Value("""string""" ) for k in sorted(self.languages )}
@dataclass
class UpperCamelCase__ :
"""simple docstring"""
SCREAMING_SNAKE_CASE__ = None
SCREAMING_SNAKE_CASE__ = None
SCREAMING_SNAKE_CASE__ = None
# Automatically constructed
SCREAMING_SNAKE_CASE__ = "dict"
SCREAMING_SNAKE_CASE__ = None
SCREAMING_SNAKE_CASE__ = field(default='''TranslationVariableLanguages''' , init=lowercase_ , repr=lowercase_ )
def lowerCamelCase_ ( self : Optional[int] ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Optional[Any] = sorted(set(self.languages ) ) if self.languages else None
SCREAMING_SNAKE_CASE : str = len(self.languages ) if self.languages else None
def __call__( self : Tuple ):
'''simple docstring'''
return pa.struct({"""language""": pa.list_(pa.string() ), """translation""": pa.list_(pa.string() )} )
def lowerCamelCase_ ( self : Optional[int] , lowerCamelCase_ : Optional[Any] ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Optional[Any] = set(self.languages )
if self.languages and set(lowerCamelCase_ ) - lang_set:
raise ValueError(
f'''Some languages in example ({", ".join(sorted(set(lowerCamelCase_ ) - lang_set ) )}) are not in valid set ({", ".join(lowerCamelCase_ )}).''' )
# Convert dictionary into tuples, splitting out cases where there are
# multiple translations for a single language.
SCREAMING_SNAKE_CASE : List[Any] = []
for lang, text in translation_dict.items():
if isinstance(lowerCamelCase_ , lowerCamelCase_ ):
translation_tuples.append((lang, text) )
else:
translation_tuples.extend([(lang, el) for el in text] )
# Ensure translations are in ascending order by language code.
SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE : Optional[Any] = zip(*sorted(lowerCamelCase_ ) )
return {"language": languages, "translation": translations}
def lowerCamelCase_ ( self : List[str] ):
'''simple docstring'''
from .features import Sequence, Value
return {
"language": Sequence(Value("""string""" ) ),
"translation": Sequence(Value("""string""" ) ),
}
| 79 | 0 |
'''simple docstring'''
from __future__ import annotations
import numpy as np
def __A ( lowerCamelCase_ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : int = np.shape(_lowerCamelCase )
if rows != columns:
SCREAMING_SNAKE_CASE : List[str] = (
"'table' has to be of square shaped array but got a "
f'''{rows}x{columns} array:\n{table}'''
)
raise ValueError(_lowerCamelCase )
SCREAMING_SNAKE_CASE : Dict = np.zeros((rows, columns) )
SCREAMING_SNAKE_CASE : Union[str, Any] = np.zeros((rows, columns) )
for i in range(_lowerCamelCase ):
for j in range(_lowerCamelCase ):
SCREAMING_SNAKE_CASE : Dict = sum(lower[i][k] * upper[k][j] for k in range(_lowerCamelCase ) )
if upper[j][j] == 0:
raise ArithmeticError("""No LU decomposition exists""" )
SCREAMING_SNAKE_CASE : Any = (table[i][j] - total) / upper[j][j]
SCREAMING_SNAKE_CASE : Tuple = 1
for j in range(_lowerCamelCase , _lowerCamelCase ):
SCREAMING_SNAKE_CASE : Optional[Any] = sum(lower[i][k] * upper[k][j] for k in range(_lowerCamelCase ) )
SCREAMING_SNAKE_CASE : Any = table[i][j] - total
return lower, upper
if __name__ == "__main__":
import doctest
doctest.testmod()
| 704 |
'''simple docstring'''
import warnings
from ...utils import logging
from .image_processing_flava import FlavaImageProcessor
__UpperCAmelCase = logging.get_logger(__name__)
class UpperCamelCase__ ( lowercase_ ):
"""simple docstring"""
def __init__( self : Dict , *lowerCamelCase_ : List[str] , **lowerCamelCase_ : Dict ):
'''simple docstring'''
warnings.warn(
"""The class FlavaFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please"""
""" use FlavaImageProcessor instead.""" , lowerCamelCase_ , )
super().__init__(*lowerCamelCase_ , **lowerCamelCase_ )
| 79 | 0 |
'''simple docstring'''
def __A ( lowerCamelCase_ = 1_00 ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : Any = n * (n + 1) * (2 * n + 1) / 6
SCREAMING_SNAKE_CASE : str = (n * (n + 1) / 2) ** 2
return int(square_of_sum - sum_of_squares )
if __name__ == "__main__":
print(f'''{solution() = }''')
| 705 |
'''simple docstring'''
from .integrations import (
is_optuna_available,
is_ray_available,
is_sigopt_available,
is_wandb_available,
run_hp_search_optuna,
run_hp_search_ray,
run_hp_search_sigopt,
run_hp_search_wandb,
)
from .trainer_utils import (
HPSearchBackend,
default_hp_space_optuna,
default_hp_space_ray,
default_hp_space_sigopt,
default_hp_space_wandb,
)
from .utils import logging
__UpperCAmelCase = logging.get_logger(__name__)
class UpperCamelCase__ :
"""simple docstring"""
SCREAMING_SNAKE_CASE__ = 42
SCREAMING_SNAKE_CASE__ = None
@staticmethod
def lowerCamelCase_ ( ):
'''simple docstring'''
raise NotImplementedError
def lowerCamelCase_ ( self : Tuple , lowerCamelCase_ : Optional[int] , lowerCamelCase_ : int , lowerCamelCase_ : str , **lowerCamelCase_ : str ):
'''simple docstring'''
raise NotImplementedError
def lowerCamelCase_ ( self : int , lowerCamelCase_ : Dict ):
'''simple docstring'''
raise NotImplementedError
def lowerCamelCase_ ( self : Optional[int] ):
'''simple docstring'''
if not self.is_available():
raise RuntimeError(
f'''You picked the {self.name} backend, but it is not installed. Run {self.pip_install()}.''' )
@classmethod
def lowerCamelCase_ ( cls : Any ):
'''simple docstring'''
return f'''`pip install {cls.pip_package or cls.name}`'''
class UpperCamelCase__ ( lowercase_ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE__ = '''optuna'''
@staticmethod
def lowerCamelCase_ ( ):
'''simple docstring'''
return is_optuna_available()
def lowerCamelCase_ ( self : Any , lowerCamelCase_ : Union[str, Any] , lowerCamelCase_ : int , lowerCamelCase_ : str , **lowerCamelCase_ : Dict ):
'''simple docstring'''
return run_hp_search_optuna(lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ , **lowerCamelCase_ )
def lowerCamelCase_ ( self : str , lowerCamelCase_ : Any ):
'''simple docstring'''
return default_hp_space_optuna(lowerCamelCase_ )
class UpperCamelCase__ ( lowercase_ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE__ = '''ray'''
SCREAMING_SNAKE_CASE__ = '''\'ray[tune]\''''
@staticmethod
def lowerCamelCase_ ( ):
'''simple docstring'''
return is_ray_available()
def lowerCamelCase_ ( self : Any , lowerCamelCase_ : Any , lowerCamelCase_ : int , lowerCamelCase_ : str , **lowerCamelCase_ : int ):
'''simple docstring'''
return run_hp_search_ray(lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ , **lowerCamelCase_ )
def lowerCamelCase_ ( self : List[str] , lowerCamelCase_ : Optional[int] ):
'''simple docstring'''
return default_hp_space_ray(lowerCamelCase_ )
class UpperCamelCase__ ( lowercase_ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE__ = '''sigopt'''
@staticmethod
def lowerCamelCase_ ( ):
'''simple docstring'''
return is_sigopt_available()
def lowerCamelCase_ ( self : Optional[Any] , lowerCamelCase_ : Any , lowerCamelCase_ : int , lowerCamelCase_ : str , **lowerCamelCase_ : int ):
'''simple docstring'''
return run_hp_search_sigopt(lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ , **lowerCamelCase_ )
def lowerCamelCase_ ( self : List[Any] , lowerCamelCase_ : Optional[Any] ):
'''simple docstring'''
return default_hp_space_sigopt(lowerCamelCase_ )
class UpperCamelCase__ ( lowercase_ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE__ = '''wandb'''
@staticmethod
def lowerCamelCase_ ( ):
'''simple docstring'''
return is_wandb_available()
def lowerCamelCase_ ( self : Union[str, Any] , lowerCamelCase_ : str , lowerCamelCase_ : int , lowerCamelCase_ : str , **lowerCamelCase_ : Optional[Any] ):
'''simple docstring'''
return run_hp_search_wandb(lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ , **lowerCamelCase_ )
def lowerCamelCase_ ( self : Union[str, Any] , lowerCamelCase_ : Tuple ):
'''simple docstring'''
return default_hp_space_wandb(lowerCamelCase_ )
__UpperCAmelCase = {
HPSearchBackend(backend.name): backend for backend in [OptunaBackend, RayTuneBackend, SigOptBackend, WandbBackend]
}
def __A ( ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : List[Any] = [backend for backend in ALL_HYPERPARAMETER_SEARCH_BACKENDS.values() if backend.is_available()]
if len(lowerCamelCase_ ) > 0:
SCREAMING_SNAKE_CASE : List[Any] = available_backends[0].name
if len(lowerCamelCase_ ) > 1:
logger.info(
f'''{len(lowerCamelCase_ )} hyperparameter search backends available. Using {name} as the default.''' )
return name
raise RuntimeError(
"""No hyperparameter search backend available.\n"""
+ """\n""".join(
f''' - To install {backend.name} run {backend.pip_install()}'''
for backend in ALL_HYPERPARAMETER_SEARCH_BACKENDS.values() ) )
| 79 | 0 |
'''simple docstring'''
import unittest
from transformers import load_tool
from .test_tools_common import ToolTesterMixin
__UpperCAmelCase = """
Hugging Face was founded in 2016 by French entrepreneurs Clément Delangue, Julien Chaumond, and Thomas Wolf originally as a company that developed a chatbot app targeted at teenagers.[2] After open-sourcing the model behind the chatbot, the company pivoted to focus on being a platform for machine learning.
In March 2021, Hugging Face raised $40 million in a Series B funding round.[3]
On April 28, 2021, the company launched the BigScience Research Workshop in collaboration with several other research groups to release an open large language model.[4] In 2022, the workshop concluded with the announcement of BLOOM, a multilingual large language model with 176 billion parameters.[5]
"""
class UpperCamelCase__ ( unittest.TestCase , __a ):
"""simple docstring"""
def lowerCamelCase_ ( self : List[str] ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Tuple = load_tool("""text-question-answering""" )
self.tool.setup()
SCREAMING_SNAKE_CASE : Any = load_tool("""text-question-answering""" , remote=a_ )
def lowerCamelCase_ ( self : List[str] ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : str = self.tool(a_ , """What did Hugging Face do in April 2021?""" )
self.assertEqual(a_ , """launched the BigScience Research Workshop""" )
def lowerCamelCase_ ( self : List[str] ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Union[str, Any] = self.remote_tool(a_ , """What did Hugging Face do in April 2021?""" )
self.assertEqual(a_ , """launched the BigScience Research Workshop""" )
def lowerCamelCase_ ( self : Tuple ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : str = self.tool(text=a_ , question="""What did Hugging Face do in April 2021?""" )
self.assertEqual(a_ , """launched the BigScience Research Workshop""" )
def lowerCamelCase_ ( self : Union[str, Any] ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : str = self.remote_tool(text=a_ , question="""What did Hugging Face do in April 2021?""" )
self.assertEqual(a_ , """launched the BigScience Research Workshop""" )
| 706 |
'''simple docstring'''
import glob
import os
import random
from string import ascii_lowercase, digits
import cva
__UpperCAmelCase = """"""
__UpperCAmelCase = """"""
__UpperCAmelCase = """"""
__UpperCAmelCase = 1 # (0 is vertical, 1 is horizontal)
def __A ( ):
"""simple docstring"""
SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE : str = get_dataset(lowerCamelCase_ , lowerCamelCase_ )
print("""Processing...""" )
SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE : Any = update_image_and_anno(lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ )
for index, image in enumerate(lowerCamelCase_ ):
# Get random string code: '7b7ad245cdff75241935e4dd860f3bad'
SCREAMING_SNAKE_CASE : Optional[int] = random_chars(32 )
SCREAMING_SNAKE_CASE : Optional[Any] = paths[index].split(os.sep )[-1].rsplit(""".""" , 1 )[0]
SCREAMING_SNAKE_CASE : Dict = f'''{OUTPUT_DIR}/{file_name}_FLIP_{letter_code}'''
cva.imwrite(f'''/{file_root}.jpg''' , lowerCamelCase_ , [cva.IMWRITE_JPEG_QUALITY, 85] )
print(f'''Success {index+1}/{len(lowerCamelCase_ )} with {file_name}''' )
SCREAMING_SNAKE_CASE : Optional[Any] = []
for anno in new_annos[index]:
SCREAMING_SNAKE_CASE : Optional[Any] = f'''{anno[0]} {anno[1]} {anno[2]} {anno[3]} {anno[4]}'''
annos_list.append(lowerCamelCase_ )
with open(f'''/{file_root}.txt''' , """w""" ) as outfile:
outfile.write("""\n""".join(line for line in annos_list ) )
def __A ( lowerCamelCase_ , lowerCamelCase_ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : str = []
SCREAMING_SNAKE_CASE : Any = []
for label_file in glob.glob(os.path.join(lowerCamelCase_ , """*.txt""" ) ):
SCREAMING_SNAKE_CASE : str = label_file.split(os.sep )[-1].rsplit(""".""" , 1 )[0]
with open(lowerCamelCase_ ) as in_file:
SCREAMING_SNAKE_CASE : Any = in_file.readlines()
SCREAMING_SNAKE_CASE : List[Any] = os.path.join(lowerCamelCase_ , f'''{label_name}.jpg''' )
SCREAMING_SNAKE_CASE : Tuple = []
for obj_list in obj_lists:
SCREAMING_SNAKE_CASE : Union[str, Any] = obj_list.rstrip("""\n""" ).split(""" """ )
boxes.append(
[
int(obj[0] ),
float(obj[1] ),
float(obj[2] ),
float(obj[3] ),
float(obj[4] ),
] )
if not boxes:
continue
img_paths.append(lowerCamelCase_ )
labels.append(lowerCamelCase_ )
return img_paths, labels
def __A ( lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ = 1 ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : Tuple = []
SCREAMING_SNAKE_CASE : List[str] = []
SCREAMING_SNAKE_CASE : Optional[Any] = []
for idx in range(len(lowerCamelCase_ ) ):
SCREAMING_SNAKE_CASE : List[Any] = []
SCREAMING_SNAKE_CASE : Dict = img_list[idx]
path_list.append(lowerCamelCase_ )
SCREAMING_SNAKE_CASE : int = anno_list[idx]
SCREAMING_SNAKE_CASE : Optional[Any] = cva.imread(lowerCamelCase_ )
if flip_type == 1:
SCREAMING_SNAKE_CASE : List[str] = cva.flip(lowerCamelCase_ , lowerCamelCase_ )
for bbox in img_annos:
SCREAMING_SNAKE_CASE : List[Any] = 1 - bbox[1]
new_annos.append([bbox[0], x_center_new, bbox[2], bbox[3], bbox[4]] )
elif flip_type == 0:
SCREAMING_SNAKE_CASE : Any = cva.flip(lowerCamelCase_ , lowerCamelCase_ )
for bbox in img_annos:
SCREAMING_SNAKE_CASE : Optional[Any] = 1 - bbox[2]
new_annos.append([bbox[0], bbox[1], y_center_new, bbox[3], bbox[4]] )
new_annos_lists.append(lowerCamelCase_ )
new_imgs_list.append(lowerCamelCase_ )
return new_imgs_list, new_annos_lists, path_list
def __A ( lowerCamelCase_ = 32 ):
"""simple docstring"""
assert number_char > 1, "The number of character should greater than 1"
SCREAMING_SNAKE_CASE : Dict = ascii_lowercase + digits
return "".join(random.choice(lowerCamelCase_ ) for _ in range(lowerCamelCase_ ) )
if __name__ == "__main__":
main()
print("""DONE ✅""")
| 79 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available
__UpperCAmelCase = {"""tokenization_herbert""": ["""HerbertTokenizer"""]}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__UpperCAmelCase = ["""HerbertTokenizerFast"""]
if TYPE_CHECKING:
from .tokenization_herbert import HerbertTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_herbert_fast import HerbertTokenizerFast
else:
import sys
__UpperCAmelCase = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 707 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__UpperCAmelCase = logging.get_logger(__name__)
__UpperCAmelCase = {
"""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__ ( lowercase_ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE__ = '''vivit'''
def __init__( self : Tuple , lowerCamelCase_ : str=2_24 , lowerCamelCase_ : List[Any]=32 , lowerCamelCase_ : Tuple=[2, 16, 16] , lowerCamelCase_ : List[str]=3 , lowerCamelCase_ : Dict=7_68 , lowerCamelCase_ : Dict=12 , lowerCamelCase_ : Any=12 , lowerCamelCase_ : List[Any]=30_72 , lowerCamelCase_ : List[str]="gelu_fast" , lowerCamelCase_ : str=0.0 , lowerCamelCase_ : Any=0.0 , lowerCamelCase_ : Optional[int]=0.02 , lowerCamelCase_ : List[Any]=1e-06 , lowerCamelCase_ : Tuple=True , **lowerCamelCase_ : Tuple , ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : List[str] = hidden_size
SCREAMING_SNAKE_CASE : Union[str, Any] = num_hidden_layers
SCREAMING_SNAKE_CASE : List[str] = num_attention_heads
SCREAMING_SNAKE_CASE : str = intermediate_size
SCREAMING_SNAKE_CASE : List[Any] = hidden_act
SCREAMING_SNAKE_CASE : Optional[Any] = hidden_dropout_prob
SCREAMING_SNAKE_CASE : Tuple = attention_probs_dropout_prob
SCREAMING_SNAKE_CASE : Tuple = initializer_range
SCREAMING_SNAKE_CASE : str = layer_norm_eps
SCREAMING_SNAKE_CASE : str = image_size
SCREAMING_SNAKE_CASE : Dict = num_frames
SCREAMING_SNAKE_CASE : Optional[Any] = tubelet_size
SCREAMING_SNAKE_CASE : Dict = num_channels
SCREAMING_SNAKE_CASE : int = qkv_bias
super().__init__(**lowerCamelCase_ )
| 79 | 0 |
'''simple docstring'''
def __A ( lowerCamelCase_ ):
"""simple docstring"""
assert (
isinstance(UpperCAmelCase__ , UpperCAmelCase__ ) and number_of_steps > 0
), f'''number_of_steps needs to be positive integer, your input {number_of_steps}'''
if number_of_steps == 1:
return 1
SCREAMING_SNAKE_CASE : int = 1, 1
for _ in range(number_of_steps - 1 ):
SCREAMING_SNAKE_CASE : Optional[Any] = current + previous, current
return current
if __name__ == "__main__":
import doctest
doctest.testmod()
| 708 |
'''simple docstring'''
import math
class UpperCamelCase__ :
"""simple docstring"""
def __init__( self : List[str] , lowerCamelCase_ : Tuple=0 ): # a graph with Node 0,1,...,N-1
'''simple docstring'''
SCREAMING_SNAKE_CASE : Any = n
SCREAMING_SNAKE_CASE : Optional[int] = [
[math.inf for j in range(0 , lowerCamelCase_ )] for i in range(0 , lowerCamelCase_ )
] # adjacency matrix for weight
SCREAMING_SNAKE_CASE : Union[str, Any] = [
[math.inf for j in range(0 , lowerCamelCase_ )] for i in range(0 , lowerCamelCase_ )
] # dp[i][j] stores minimum distance from i to j
def lowerCamelCase_ ( self : Dict , lowerCamelCase_ : str , lowerCamelCase_ : List[str] , lowerCamelCase_ : int ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Union[str, Any] = w
def lowerCamelCase_ ( self : Optional[int] ):
'''simple docstring'''
for k in range(0 , self.n ):
for i in range(0 , self.n ):
for j in range(0 , self.n ):
SCREAMING_SNAKE_CASE : Union[str, Any] = min(self.dp[i][j] , self.dp[i][k] + self.dp[k][j] )
def lowerCamelCase_ ( self : Dict , lowerCamelCase_ : List[Any] , lowerCamelCase_ : Union[str, Any] ):
'''simple docstring'''
return self.dp[u][v]
if __name__ == "__main__":
__UpperCAmelCase = Graph(5)
graph.add_edge(0, 2, 9)
graph.add_edge(0, 4, 10)
graph.add_edge(1, 3, 5)
graph.add_edge(2, 3, 7)
graph.add_edge(3, 0, 10)
graph.add_edge(3, 1, 2)
graph.add_edge(3, 2, 1)
graph.add_edge(3, 4, 6)
graph.add_edge(4, 1, 3)
graph.add_edge(4, 2, 4)
graph.add_edge(4, 3, 9)
graph.floyd_warshall()
graph.show_min(1, 4)
graph.show_min(0, 3)
| 79 | 0 |
'''simple docstring'''
import argparse
import os
import subprocess
from packaging.version import Version, parse
from accelerate.commands.config.config_args import default_config_file, load_config_from_file
__UpperCAmelCase = '''Run commands across TPU VMs for initial setup before running `accelerate launch`.'''
def __A ( lowerCamelCase_=None ):
"""simple docstring"""
if subparsers is not None:
SCREAMING_SNAKE_CASE : int = subparsers.add_parser("""tpu-config""" , description=_description )
else:
SCREAMING_SNAKE_CASE : List[Any] = argparse.ArgumentParser("""Accelerate tpu-config command""" , description=_description )
# Core arguments
SCREAMING_SNAKE_CASE : Dict = parser.add_argument_group(
"""Config Arguments""" , """Arguments that can be configured through `accelerate config`.""" )
config_args.add_argument(
"""--config_file""" , type=_lowercase , default=_lowercase , help="""Path to the config file to use for accelerate.""" , )
config_args.add_argument(
"""--tpu_name""" , default=_lowercase , help="""The name of the TPU to use. If not specified, will use the TPU specified in the config file.""" , )
config_args.add_argument(
"""--tpu_zone""" , default=_lowercase , help="""The zone of the TPU to use. If not specified, will use the zone specified in the config file.""" , )
SCREAMING_SNAKE_CASE : Any = parser.add_argument_group("""TPU Arguments""" , """Arguments for options ran inside the TPU.""" )
pod_args.add_argument(
"""--use_alpha""" , action="""store_true""" , help="""Whether to use `gcloud alpha` when running the TPU training script instead of `gcloud`.""" , )
pod_args.add_argument(
"""--command_file""" , default=_lowercase , help="""The path to the file containing the commands to run on the pod on startup.""" , )
pod_args.add_argument(
"""--command""" , action="""append""" , nargs="""+""" , help="""A command to run on the pod. Can be passed multiple times.""" , )
pod_args.add_argument(
"""--install_accelerate""" , action="""store_true""" , help="""Whether to install accelerate on the pod. Defaults to False.""" , )
pod_args.add_argument(
"""--accelerate_version""" , default="""latest""" , help="""The version of accelerate to install on the pod. If not specified, will use the latest pypi version. Specify 'dev' to install from GitHub.""" , )
pod_args.add_argument(
"""--debug""" , action="""store_true""" , help="""If set, will print the command that would be run instead of running it.""" )
if subparsers is not None:
parser.set_defaults(func=_lowercase )
return parser
def __A ( lowerCamelCase_ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : Union[str, Any] = None
# Get the default from the config file if it exists.
if args.config_file is not None or os.path.isfile(_lowercase ):
SCREAMING_SNAKE_CASE : Optional[Any] = load_config_from_file(args.config_file )
if not args.command_file and defaults.command_file is not None and not args.command:
SCREAMING_SNAKE_CASE : int = defaults.command_file
if not args.command and defaults.commands is not None:
SCREAMING_SNAKE_CASE : Union[str, Any] = defaults.commands
if not args.tpu_name:
SCREAMING_SNAKE_CASE : int = defaults.tpu_name
if not args.tpu_zone:
SCREAMING_SNAKE_CASE : Union[str, Any] = defaults.tpu_zone
if args.accelerate_version == "dev":
SCREAMING_SNAKE_CASE : int = "git+https://github.com/huggingface/accelerate.git"
elif args.accelerate_version == "latest":
SCREAMING_SNAKE_CASE : Optional[Any] = "accelerate -U"
elif isinstance(parse(args.accelerate_version ) , _lowercase ):
SCREAMING_SNAKE_CASE : Optional[Any] = f'''accelerate=={args.accelerate_version}'''
if not args.command_file and not args.command:
raise ValueError("""You must specify either a command file or a command to run on the pod.""" )
if args.command_file:
with open(args.command_file , """r""" ) as f:
SCREAMING_SNAKE_CASE : int = [f.read().splitlines()]
# To turn list of lists into list of strings
if isinstance(args.command[0] , _lowercase ):
SCREAMING_SNAKE_CASE : Union[str, Any] = [line for cmd in args.command for line in cmd]
# Default to the shared folder and install accelerate
SCREAMING_SNAKE_CASE : Tuple = ["cd /usr/share"]
if args.install_accelerate:
new_cmd += [f'''pip install {args.accelerate_version}''']
new_cmd += args.command
SCREAMING_SNAKE_CASE : List[Any] = "; ".join(_lowercase )
# Then send it to gcloud
# Eventually try to use google-api-core to do this instead of subprocess
SCREAMING_SNAKE_CASE : str = ["gcloud"]
if args.use_alpha:
cmd += ["alpha"]
cmd += [
"compute",
"tpus",
"tpu-vm",
"ssh",
args.tpu_name,
"--zone",
args.tpu_zone,
"--command",
args.command,
"--worker",
"all",
]
if args.debug:
print(f'''Running {" ".join(_lowercase )}''' )
return
subprocess.run(_lowercase )
print("""Successfully setup pod.""" )
def __A ( ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : List[Any] = tpu_command_parser()
SCREAMING_SNAKE_CASE : Optional[int] = parser.parse_args()
tpu_command_launcher(_lowercase )
| 709 |
'''simple docstring'''
import math
def __A ( lowerCamelCase_ ):
"""simple docstring"""
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
return False
# All primes number are in format of 6k +/- 1
for i in range(5 , int(math.sqrt(lowerCamelCase_ ) + 1 ) , 6 ):
if number % i == 0 or number % (i + 2) == 0:
return False
return True
def __A ( lowerCamelCase_ = 1_00_01 ):
"""simple docstring"""
try:
SCREAMING_SNAKE_CASE : Tuple = int(lowerCamelCase_ )
except (TypeError, ValueError):
raise TypeError("""Parameter nth must be int or castable to int.""" ) from None
if nth <= 0:
raise ValueError("""Parameter nth must be greater than or equal to one.""" )
SCREAMING_SNAKE_CASE : list[int] = []
SCREAMING_SNAKE_CASE : Dict = 2
while len(lowerCamelCase_ ) < nth:
if is_prime(lowerCamelCase_ ):
primes.append(lowerCamelCase_ )
num += 1
else:
num += 1
return primes[len(lowerCamelCase_ ) - 1]
if __name__ == "__main__":
print(f'''{solution() = }''')
| 79 | 0 |
import numpy as np
import datasets
__UpperCAmelCase = '''
Compute the Mahalanobis Distance
Mahalonobis distance is the distance between a point and a distribution.
And not between two distinct points. It is effectively a multivariate equivalent of the Euclidean distance.
It was introduced by Prof. P. C. Mahalanobis in 1936
and has been used in various statistical applications ever since
[source: https://www.machinelearningplus.com/statistics/mahalanobis-distance/]
'''
__UpperCAmelCase = '''\
@article{de2000mahalanobis,
title={The mahalanobis distance},
author={De Maesschalck, Roy and Jouan-Rimbaud, Delphine and Massart, D{\'e}sir{\'e} L},
journal={Chemometrics and intelligent laboratory systems},
volume={50},
number={1},
pages={1--18},
year={2000},
publisher={Elsevier}
}
'''
__UpperCAmelCase = '''
Args:
X: List of datapoints to be compared with the `reference_distribution`.
reference_distribution: List of datapoints from the reference distribution we want to compare to.
Returns:
mahalanobis: The Mahalonobis distance for each datapoint in `X`.
Examples:
>>> mahalanobis_metric = datasets.load_metric("mahalanobis")
>>> results = mahalanobis_metric.compute(reference_distribution=[[0, 1], [1, 0]], X=[[0, 1]])
>>> print(results)
{\'mahalanobis\': array([0.5])}
'''
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class UpperCamelCase__ ( datasets.Metric ):
"""simple docstring"""
def lowerCamelCase_ ( self : List[Any] ):
'''simple docstring'''
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
"""X""": datasets.Sequence(datasets.Value("""float""" , id="""sequence""" ) , id="""X""" ),
} ) , )
def lowerCamelCase_ ( self : Any , lowerCamelCase_ : Dict , lowerCamelCase_ : Dict ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Tuple = np.array(__lowerCAmelCase )
SCREAMING_SNAKE_CASE : int = np.array(__lowerCAmelCase )
# Assert that arrays are 2D
if len(X.shape ) != 2:
raise ValueError("""Expected `X` to be a 2D vector""" )
if len(reference_distribution.shape ) != 2:
raise ValueError("""Expected `reference_distribution` to be a 2D vector""" )
if reference_distribution.shape[0] < 2:
raise ValueError(
"""Expected `reference_distribution` to be a 2D vector with more than one element in the first dimension""" )
# Get mahalanobis distance for each prediction
SCREAMING_SNAKE_CASE : str = X - np.mean(__lowerCAmelCase )
SCREAMING_SNAKE_CASE : Union[str, Any] = np.cov(reference_distribution.T )
try:
SCREAMING_SNAKE_CASE : Optional[int] = np.linalg.inv(__lowerCAmelCase )
except np.linalg.LinAlgError:
SCREAMING_SNAKE_CASE : Any = np.linalg.pinv(__lowerCAmelCase )
SCREAMING_SNAKE_CASE : List[Any] = np.dot(__lowerCAmelCase , __lowerCAmelCase )
SCREAMING_SNAKE_CASE : Union[str, Any] = np.dot(__lowerCAmelCase , X_minus_mu.T ).diagonal()
return {"mahalanobis": mahal_dist}
| 710 |
'''simple docstring'''
from __future__ import annotations
import json
import requests
from bsa import BeautifulSoup
from fake_useragent import UserAgent
__UpperCAmelCase = {"""UserAgent""": UserAgent().random}
def __A ( lowerCamelCase_ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : str = script.contents[0]
SCREAMING_SNAKE_CASE : int = json.loads(data[data.find("""{\"config\"""" ) : -1] )
return info["entry_data"]["ProfilePage"][0]["graphql"]["user"]
class UpperCamelCase__ :
"""simple docstring"""
def __init__( self : Dict , lowerCamelCase_ : str ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Any = f'''https://www.instagram.com/{username}/'''
SCREAMING_SNAKE_CASE : Any = self.get_json()
def lowerCamelCase_ ( self : Tuple ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Dict = requests.get(self.url , headers=lowerCamelCase_ ).text
SCREAMING_SNAKE_CASE : List[Any] = BeautifulSoup(lowerCamelCase_ , """html.parser""" ).find_all("""script""" )
try:
return extract_user_profile(scripts[4] )
except (json.decoder.JSONDecodeError, KeyError):
return extract_user_profile(scripts[3] )
def __repr__( self : Dict ):
'''simple docstring'''
return f'''{self.__class__.__name__}(\'{self.username}\')'''
def __str__( self : int ):
'''simple docstring'''
return f'''{self.fullname} ({self.username}) is {self.biography}'''
@property
def lowerCamelCase_ ( self : Tuple ):
'''simple docstring'''
return self.user_data["username"]
@property
def lowerCamelCase_ ( self : Optional[Any] ):
'''simple docstring'''
return self.user_data["full_name"]
@property
def lowerCamelCase_ ( self : int ):
'''simple docstring'''
return self.user_data["biography"]
@property
def lowerCamelCase_ ( self : str ):
'''simple docstring'''
return self.user_data["business_email"]
@property
def lowerCamelCase_ ( self : str ):
'''simple docstring'''
return self.user_data["external_url"]
@property
def lowerCamelCase_ ( self : List[Any] ):
'''simple docstring'''
return self.user_data["edge_followed_by"]["count"]
@property
def lowerCamelCase_ ( self : List[Any] ):
'''simple docstring'''
return self.user_data["edge_follow"]["count"]
@property
def lowerCamelCase_ ( self : List[Any] ):
'''simple docstring'''
return self.user_data["edge_owner_to_timeline_media"]["count"]
@property
def lowerCamelCase_ ( self : str ):
'''simple docstring'''
return self.user_data["profile_pic_url_hd"]
@property
def lowerCamelCase_ ( self : Dict ):
'''simple docstring'''
return self.user_data["is_verified"]
@property
def lowerCamelCase_ ( self : Optional[int] ):
'''simple docstring'''
return self.user_data["is_private"]
def __A ( lowerCamelCase_ = "github" ):
"""simple docstring"""
import os
if os.environ.get("""CI""" ):
return # test failing on GitHub Actions
SCREAMING_SNAKE_CASE : Any = InstagramUser(lowerCamelCase_ )
assert instagram_user.user_data
assert isinstance(instagram_user.user_data , lowerCamelCase_ )
assert instagram_user.username == username
if username != "github":
return
assert instagram_user.fullname == "GitHub"
assert instagram_user.biography == "Built for developers."
assert instagram_user.number_of_posts > 1_50
assert instagram_user.number_of_followers > 12_00_00
assert instagram_user.number_of_followings > 15
assert instagram_user.email == "support@github.com"
assert instagram_user.website == "https://github.com/readme"
assert instagram_user.profile_picture_url.startswith("""https://instagram.""" )
assert instagram_user.is_verified is True
assert instagram_user.is_private is False
if __name__ == "__main__":
import doctest
doctest.testmod()
__UpperCAmelCase = InstagramUser("""github""")
print(instagram_user)
print(f'''{instagram_user.number_of_posts = }''')
print(f'''{instagram_user.number_of_followers = }''')
print(f'''{instagram_user.number_of_followings = }''')
print(f'''{instagram_user.email = }''')
print(f'''{instagram_user.website = }''')
print(f'''{instagram_user.profile_picture_url = }''')
print(f'''{instagram_user.is_verified = }''')
print(f'''{instagram_user.is_private = }''')
| 79 | 0 |
'''simple docstring'''
import json
import sys
import tempfile
import unittest
from pathlib import Path
import transformers
from transformers import (
CONFIG_MAPPING,
IMAGE_PROCESSOR_MAPPING,
AutoConfig,
AutoImageProcessor,
CLIPConfig,
CLIPImageProcessor,
)
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER
sys.path.append(str(Path(__file__).parent.parent.parent.parent / """utils"""))
from test_module.custom_configuration import CustomConfig # noqa E402
from test_module.custom_image_processing import CustomImageProcessor # noqa E402
class UpperCamelCase__ ( unittest.TestCase ):
"""simple docstring"""
def lowerCamelCase_ ( self : List[Any] ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Any = 0
def lowerCamelCase_ ( self : Any ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : List[str] = AutoImageProcessor.from_pretrained("""openai/clip-vit-base-patch32""" )
self.assertIsInstance(UpperCAmelCase_ , UpperCAmelCase_ )
def lowerCamelCase_ ( self : Union[str, Any] ):
'''simple docstring'''
with tempfile.TemporaryDirectory() as tmpdirname:
SCREAMING_SNAKE_CASE : str = Path(UpperCAmelCase_ ) / """preprocessor_config.json"""
SCREAMING_SNAKE_CASE : int = Path(UpperCAmelCase_ ) / """config.json"""
json.dump(
{"""image_processor_type""": """CLIPImageProcessor""", """processor_class""": """CLIPProcessor"""} , open(UpperCAmelCase_ , """w""" ) , )
json.dump({"""model_type""": """clip"""} , open(UpperCAmelCase_ , """w""" ) )
SCREAMING_SNAKE_CASE : Union[str, Any] = AutoImageProcessor.from_pretrained(UpperCAmelCase_ )
self.assertIsInstance(UpperCAmelCase_ , UpperCAmelCase_ )
def lowerCamelCase_ ( self : int ):
'''simple docstring'''
with tempfile.TemporaryDirectory() as tmpdirname:
SCREAMING_SNAKE_CASE : List[str] = Path(UpperCAmelCase_ ) / """preprocessor_config.json"""
SCREAMING_SNAKE_CASE : str = Path(UpperCAmelCase_ ) / """config.json"""
json.dump(
{"""feature_extractor_type""": """CLIPFeatureExtractor""", """processor_class""": """CLIPProcessor"""} , open(UpperCAmelCase_ , """w""" ) , )
json.dump({"""model_type""": """clip"""} , open(UpperCAmelCase_ , """w""" ) )
SCREAMING_SNAKE_CASE : List[str] = AutoImageProcessor.from_pretrained(UpperCAmelCase_ )
self.assertIsInstance(UpperCAmelCase_ , UpperCAmelCase_ )
def lowerCamelCase_ ( self : Union[str, Any] ):
'''simple docstring'''
with tempfile.TemporaryDirectory() as tmpdirname:
SCREAMING_SNAKE_CASE : Optional[Any] = CLIPConfig()
# Create a dummy config file with image_proceesor_type
SCREAMING_SNAKE_CASE : List[Any] = Path(UpperCAmelCase_ ) / """preprocessor_config.json"""
SCREAMING_SNAKE_CASE : Any = Path(UpperCAmelCase_ ) / """config.json"""
json.dump(
{"""image_processor_type""": """CLIPImageProcessor""", """processor_class""": """CLIPProcessor"""} , open(UpperCAmelCase_ , """w""" ) , )
json.dump({"""model_type""": """clip"""} , open(UpperCAmelCase_ , """w""" ) )
# remove image_processor_type to make sure config.json alone is enough to load image processor locally
SCREAMING_SNAKE_CASE : Optional[int] = AutoImageProcessor.from_pretrained(UpperCAmelCase_ ).to_dict()
config_dict.pop("""image_processor_type""" )
SCREAMING_SNAKE_CASE : Dict = CLIPImageProcessor(**UpperCAmelCase_ )
# save in new folder
model_config.save_pretrained(UpperCAmelCase_ )
config.save_pretrained(UpperCAmelCase_ )
SCREAMING_SNAKE_CASE : str = AutoImageProcessor.from_pretrained(UpperCAmelCase_ )
# make sure private variable is not incorrectly saved
SCREAMING_SNAKE_CASE : List[str] = json.loads(config.to_json_string() )
self.assertTrue("""_processor_class""" not in dict_as_saved )
self.assertIsInstance(UpperCAmelCase_ , UpperCAmelCase_ )
def lowerCamelCase_ ( self : Optional[Any] ):
'''simple docstring'''
with tempfile.TemporaryDirectory() as tmpdirname:
SCREAMING_SNAKE_CASE : List[Any] = Path(UpperCAmelCase_ ) / """preprocessor_config.json"""
json.dump(
{"""image_processor_type""": """CLIPImageProcessor""", """processor_class""": """CLIPProcessor"""} , open(UpperCAmelCase_ , """w""" ) , )
SCREAMING_SNAKE_CASE : Dict = AutoImageProcessor.from_pretrained(UpperCAmelCase_ )
self.assertIsInstance(UpperCAmelCase_ , UpperCAmelCase_ )
def lowerCamelCase_ ( self : Tuple ):
'''simple docstring'''
with self.assertRaisesRegex(
UpperCAmelCase_ , """clip-base is not a local folder and is not a valid model identifier""" ):
SCREAMING_SNAKE_CASE : List[str] = AutoImageProcessor.from_pretrained("""clip-base""" )
def lowerCamelCase_ ( self : Union[str, Any] ):
'''simple docstring'''
with self.assertRaisesRegex(
UpperCAmelCase_ , R"""aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)""" ):
SCREAMING_SNAKE_CASE : Any = AutoImageProcessor.from_pretrained(UpperCAmelCase_ , revision="""aaaaaa""" )
def lowerCamelCase_ ( self : Union[str, Any] ):
'''simple docstring'''
with self.assertRaisesRegex(
UpperCAmelCase_ , """hf-internal-testing/config-no-model does not appear to have a file named preprocessor_config.json.""" , ):
SCREAMING_SNAKE_CASE : Any = AutoImageProcessor.from_pretrained("""hf-internal-testing/config-no-model""" )
def lowerCamelCase_ ( self : Union[str, Any] ):
'''simple docstring'''
with self.assertRaises(UpperCAmelCase_ ):
SCREAMING_SNAKE_CASE : Tuple = AutoImageProcessor.from_pretrained("""hf-internal-testing/test_dynamic_image_processor""" )
# If remote code is disabled, we can't load this config.
with self.assertRaises(UpperCAmelCase_ ):
SCREAMING_SNAKE_CASE : Tuple = AutoImageProcessor.from_pretrained(
"""hf-internal-testing/test_dynamic_image_processor""" , trust_remote_code=UpperCAmelCase_ )
SCREAMING_SNAKE_CASE : Tuple = AutoImageProcessor.from_pretrained(
"""hf-internal-testing/test_dynamic_image_processor""" , trust_remote_code=UpperCAmelCase_ )
self.assertEqual(image_processor.__class__.__name__ , """NewImageProcessor""" )
# Test image processor can be reloaded.
with tempfile.TemporaryDirectory() as tmp_dir:
image_processor.save_pretrained(UpperCAmelCase_ )
SCREAMING_SNAKE_CASE : Optional[int] = AutoImageProcessor.from_pretrained(UpperCAmelCase_ , trust_remote_code=UpperCAmelCase_ )
self.assertEqual(reloaded_image_processor.__class__.__name__ , """NewImageProcessor""" )
def lowerCamelCase_ ( self : str ):
'''simple docstring'''
try:
AutoConfig.register("""custom""" , UpperCAmelCase_ )
AutoImageProcessor.register(UpperCAmelCase_ , UpperCAmelCase_ )
# Trying to register something existing in the Transformers library will raise an error
with self.assertRaises(UpperCAmelCase_ ):
AutoImageProcessor.register(UpperCAmelCase_ , UpperCAmelCase_ )
with tempfile.TemporaryDirectory() as tmpdirname:
SCREAMING_SNAKE_CASE : List[Any] = Path(UpperCAmelCase_ ) / """preprocessor_config.json"""
SCREAMING_SNAKE_CASE : Union[str, Any] = Path(UpperCAmelCase_ ) / """config.json"""
json.dump(
{"""feature_extractor_type""": """CLIPFeatureExtractor""", """processor_class""": """CLIPProcessor"""} , open(UpperCAmelCase_ , """w""" ) , )
json.dump({"""model_type""": """clip"""} , open(UpperCAmelCase_ , """w""" ) )
SCREAMING_SNAKE_CASE : Dict = CustomImageProcessor.from_pretrained(UpperCAmelCase_ )
# Now that the config is registered, it can be used as any other config with the auto-API
with tempfile.TemporaryDirectory() as tmp_dir:
image_processor.save_pretrained(UpperCAmelCase_ )
SCREAMING_SNAKE_CASE : Union[str, Any] = AutoImageProcessor.from_pretrained(UpperCAmelCase_ )
self.assertIsInstance(UpperCAmelCase_ , UpperCAmelCase_ )
finally:
if "custom" in CONFIG_MAPPING._extra_content:
del CONFIG_MAPPING._extra_content["custom"]
if CustomConfig in IMAGE_PROCESSOR_MAPPING._extra_content:
del IMAGE_PROCESSOR_MAPPING._extra_content[CustomConfig]
def lowerCamelCase_ ( self : int ):
'''simple docstring'''
class UpperCamelCase__ ( __UpperCAmelCase ):
"""simple docstring"""
SCREAMING_SNAKE_CASE__ = True
try:
AutoConfig.register("""custom""" , UpperCAmelCase_ )
AutoImageProcessor.register(UpperCAmelCase_ , UpperCAmelCase_ )
# If remote code is not set, the default is to use local
SCREAMING_SNAKE_CASE : Any = AutoImageProcessor.from_pretrained("""hf-internal-testing/test_dynamic_image_processor""" )
self.assertEqual(image_processor.__class__.__name__ , """NewImageProcessor""" )
self.assertTrue(image_processor.is_local )
# If remote code is disabled, we load the local one.
SCREAMING_SNAKE_CASE : str = AutoImageProcessor.from_pretrained(
"""hf-internal-testing/test_dynamic_image_processor""" , trust_remote_code=UpperCAmelCase_ )
self.assertEqual(image_processor.__class__.__name__ , """NewImageProcessor""" )
self.assertTrue(image_processor.is_local )
# If remote is enabled, we load from the Hub
SCREAMING_SNAKE_CASE : Any = AutoImageProcessor.from_pretrained(
"""hf-internal-testing/test_dynamic_image_processor""" , trust_remote_code=UpperCAmelCase_ )
self.assertEqual(image_processor.__class__.__name__ , """NewImageProcessor""" )
self.assertTrue(not hasattr(UpperCAmelCase_ , """is_local""" ) )
finally:
if "custom" in CONFIG_MAPPING._extra_content:
del CONFIG_MAPPING._extra_content["custom"]
if CustomConfig in IMAGE_PROCESSOR_MAPPING._extra_content:
del IMAGE_PROCESSOR_MAPPING._extra_content[CustomConfig]
| 711 |
'''simple docstring'''
import argparse
import logging
from collections import namedtuple
import torch
from model_bertabs import BertAbsSummarizer
from models.model_builder import AbsSummarizer # The authors' implementation
from transformers import BertTokenizer
logging.basicConfig(level=logging.INFO)
__UpperCAmelCase = logging.getLogger(__name__)
__UpperCAmelCase = """Hello world! cécé herlolip"""
__UpperCAmelCase = namedtuple(
"""BertAbsConfig""",
[
"""temp_dir""",
"""large""",
"""use_bert_emb""",
"""finetune_bert""",
"""encoder""",
"""share_emb""",
"""max_pos""",
"""enc_layers""",
"""enc_hidden_size""",
"""enc_heads""",
"""enc_ff_size""",
"""enc_dropout""",
"""dec_layers""",
"""dec_hidden_size""",
"""dec_heads""",
"""dec_ff_size""",
"""dec_dropout""",
],
)
def __A ( lowerCamelCase_ , lowerCamelCase_ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : Tuple = BertAbsConfig(
temp_dir=""".""" , finetune_bert=lowerCamelCase_ , large=lowerCamelCase_ , share_emb=lowerCamelCase_ , use_bert_emb=lowerCamelCase_ , encoder="""bert""" , max_pos=5_12 , enc_layers=6 , enc_hidden_size=5_12 , enc_heads=8 , enc_ff_size=5_12 , enc_dropout=0.2 , dec_layers=6 , dec_hidden_size=7_68 , dec_heads=8 , dec_ff_size=20_48 , dec_dropout=0.2 , )
SCREAMING_SNAKE_CASE : int = torch.load(lowerCamelCase_ , lambda lowerCamelCase_ , lowerCamelCase_ : storage )
SCREAMING_SNAKE_CASE : List[str] = AbsSummarizer(lowerCamelCase_ , torch.device("""cpu""" ) , lowerCamelCase_ )
original.eval()
SCREAMING_SNAKE_CASE : Optional[int] = BertAbsSummarizer(lowerCamelCase_ , torch.device("""cpu""" ) )
new_model.eval()
# -------------------
# Convert the weights
# -------------------
logging.info("""convert the model""" )
new_model.bert.load_state_dict(original.bert.state_dict() )
new_model.decoder.load_state_dict(original.decoder.state_dict() )
new_model.generator.load_state_dict(original.generator.state_dict() )
# ----------------------------------
# Make sure the outpus are identical
# ----------------------------------
logging.info("""Make sure that the models' outputs are identical""" )
SCREAMING_SNAKE_CASE : int = BertTokenizer.from_pretrained("""bert-base-uncased""" )
# prepare the model inputs
SCREAMING_SNAKE_CASE : Optional[Any] = tokenizer.encode("""This is sample éàalj'-.""" )
encoder_input_ids.extend([tokenizer.pad_token_id] * (5_12 - len(lowerCamelCase_ )) )
SCREAMING_SNAKE_CASE : List[str] = torch.tensor(lowerCamelCase_ ).unsqueeze(0 )
SCREAMING_SNAKE_CASE : Optional[int] = tokenizer.encode("""This is sample 3 éàalj'-.""" )
decoder_input_ids.extend([tokenizer.pad_token_id] * (5_12 - len(lowerCamelCase_ )) )
SCREAMING_SNAKE_CASE : Optional[Any] = torch.tensor(lowerCamelCase_ ).unsqueeze(0 )
# failsafe to make sure the weights reset does not affect the
# loaded weights.
assert torch.max(torch.abs(original.generator[0].weight - new_model.generator[0].weight ) ) == 0
# forward pass
SCREAMING_SNAKE_CASE : Optional[int] = encoder_input_ids
SCREAMING_SNAKE_CASE : Optional[Any] = decoder_input_ids
SCREAMING_SNAKE_CASE : List[str] = None
SCREAMING_SNAKE_CASE : Tuple = None
SCREAMING_SNAKE_CASE : int = None
SCREAMING_SNAKE_CASE : List[Any] = None
SCREAMING_SNAKE_CASE : Optional[int] = None
# The original model does not apply the geneator layer immediatly but rather in
# the beam search (where it combines softmax + linear layer). Since we already
# apply the softmax in our generation process we only apply the linear layer here.
# We make sure that the outputs of the full stack are identical
SCREAMING_SNAKE_CASE : str = original(lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ )[0]
SCREAMING_SNAKE_CASE : Optional[Any] = original.generator(lowerCamelCase_ )
SCREAMING_SNAKE_CASE : List[Any] = new_model(
lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ )[0]
SCREAMING_SNAKE_CASE : str = new_model.generator(lowerCamelCase_ )
SCREAMING_SNAKE_CASE : int = torch.max(torch.abs(output_converted_model - output_original_model ) ).item()
print("""Maximum absolute difference beween weights: {:.2f}""".format(lowerCamelCase_ ) )
SCREAMING_SNAKE_CASE : Optional[int] = torch.max(torch.abs(output_converted_generator - output_original_generator ) ).item()
print("""Maximum absolute difference beween weights: {:.2f}""".format(lowerCamelCase_ ) )
SCREAMING_SNAKE_CASE : Any = torch.allclose(lowerCamelCase_ , lowerCamelCase_ , atol=1E-3 )
if are_identical:
logging.info("""all weights are equal up to 1e-3""" )
else:
raise ValueError("""the weights are different. The new model is likely different from the original one.""" )
# The model has been saved with torch.save(model) and this is bound to the exact
# directory structure. We save the state_dict instead.
logging.info("""saving the model's state dictionary""" )
torch.save(
new_model.state_dict() , """./bertabs-finetuned-cnndm-extractive-abstractive-summarization/pytorch_model.bin""" )
if __name__ == "__main__":
__UpperCAmelCase = argparse.ArgumentParser()
parser.add_argument(
"""--bertabs_checkpoint_path""",
default=None,
type=str,
required=True,
help="""Path the official PyTorch dump.""",
)
parser.add_argument(
"""--pytorch_dump_folder_path""",
default=None,
type=str,
required=True,
help="""Path to the output PyTorch model.""",
)
__UpperCAmelCase = parser.parse_args()
convert_bertabs_checkpoints(
args.bertabs_checkpoint_path,
args.pytorch_dump_folder_path,
)
| 79 | 0 |
'''simple docstring'''
import collections
import os
from typing import List, Optional, Tuple
from transformers.utils import is_jieba_available, requires_backends
if is_jieba_available():
import jieba
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
__UpperCAmelCase = logging.get_logger(__name__)
__UpperCAmelCase = {'vocab_file': 'vocab.txt'}
__UpperCAmelCase = {
'vocab_file': {
'openbmb/cpm-ant-10b': 'https://huggingface.co/openbmb/cpm-ant-10b/blob/main/vocab.txt',
},
}
__UpperCAmelCase = {
'openbmb/cpm-ant-10b': 1024,
}
def __A ( lowerCamelCase_ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : Optional[int] = collections.OrderedDict()
with open(_lowercase , """r""" , encoding="""utf-8""" ) as reader:
SCREAMING_SNAKE_CASE : List[str] = reader.readlines()
for index, token in enumerate(_lowercase ):
SCREAMING_SNAKE_CASE : Union[str, Any] = token.rstrip("""\n""" )
SCREAMING_SNAKE_CASE : Optional[int] = index
return vocab
class UpperCamelCase__ ( UpperCamelCase_ ):
"""simple docstring"""
def __init__( self : Tuple , lowerCamelCase_ : Optional[Any] , lowerCamelCase_ : Union[str, Any]="<unk>" , lowerCamelCase_ : List[Any]=2_00 ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Optional[Any] = vocab
SCREAMING_SNAKE_CASE : str = unk_token
SCREAMING_SNAKE_CASE : Any = max_input_chars_per_word
def lowerCamelCase_ ( self : Any , lowerCamelCase_ : Dict ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : List[Any] = list(UpperCamelCase__ )
if len(UpperCamelCase__ ) > self.max_input_chars_per_word:
return [self.unk_token]
SCREAMING_SNAKE_CASE : Optional[Any] = 0
SCREAMING_SNAKE_CASE : Optional[Any] = []
while start < len(UpperCamelCase__ ):
SCREAMING_SNAKE_CASE : List[Any] = len(UpperCamelCase__ )
SCREAMING_SNAKE_CASE : Any = None
while start < end:
SCREAMING_SNAKE_CASE : int = ''''''.join(chars[start:end] )
if substr in self.vocab:
SCREAMING_SNAKE_CASE : List[Any] = substr
break
end -= 1
if cur_substr is None:
sub_tokens.append(self.unk_token )
start += 1
else:
sub_tokens.append(UpperCamelCase__ )
SCREAMING_SNAKE_CASE : List[str] = end
return sub_tokens
class UpperCamelCase__ ( UpperCamelCase_ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE__ = VOCAB_FILES_NAMES
SCREAMING_SNAKE_CASE__ = PRETRAINED_VOCAB_FILES_MAP
SCREAMING_SNAKE_CASE__ = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
SCREAMING_SNAKE_CASE__ = ['''input_ids''', '''attention_mask''']
SCREAMING_SNAKE_CASE__ = False
def __init__( self : List[Any] , lowerCamelCase_ : Optional[int] , lowerCamelCase_ : Union[str, Any]="<d>" , lowerCamelCase_ : Union[str, Any]="</d>" , lowerCamelCase_ : Dict="<s>" , lowerCamelCase_ : int="</s>" , lowerCamelCase_ : Optional[Any]="<pad>" , lowerCamelCase_ : Union[str, Any]="<unk>" , lowerCamelCase_ : int="</n>" , lowerCamelCase_ : List[str]="</_>" , lowerCamelCase_ : Tuple="left" , **lowerCamelCase_ : List[str] , ):
'''simple docstring'''
requires_backends(self , ["""jieba"""] )
super().__init__(
bod_token=UpperCamelCase__ , eod_token=UpperCamelCase__ , bos_token=UpperCamelCase__ , eos_token=UpperCamelCase__ , pad_token=UpperCamelCase__ , unk_token=UpperCamelCase__ , line_token=UpperCamelCase__ , space_token=UpperCamelCase__ , padding_side=UpperCamelCase__ , **UpperCamelCase__ , )
SCREAMING_SNAKE_CASE : List[str] = bod_token
SCREAMING_SNAKE_CASE : Optional[int] = eod_token
SCREAMING_SNAKE_CASE : Dict = load_vocab(UpperCamelCase__ )
SCREAMING_SNAKE_CASE : Any = self.encoder[space_token]
SCREAMING_SNAKE_CASE : Any = self.encoder[line_token]
del self.encoder[space_token]
del self.encoder[line_token]
SCREAMING_SNAKE_CASE : Union[str, Any] = collections.OrderedDict(sorted(self.encoder.items() , key=lambda lowerCamelCase_ : x[1] ) )
SCREAMING_SNAKE_CASE : str = {v: k for k, v in self.encoder.items()}
SCREAMING_SNAKE_CASE : Optional[int] = WordpieceTokenizer(vocab=self.encoder , unk_token=self.unk_token )
@property
def lowerCamelCase_ ( self : Union[str, Any] ):
'''simple docstring'''
return self.encoder[self.bod_token]
@property
def lowerCamelCase_ ( self : Tuple ):
'''simple docstring'''
return self.encoder[self.eod_token]
@property
def lowerCamelCase_ ( self : Optional[int] ):
'''simple docstring'''
return self.encoder["\n"]
@property
def lowerCamelCase_ ( self : Optional[Any] ):
'''simple docstring'''
return len(self.encoder )
def lowerCamelCase_ ( self : int ):
'''simple docstring'''
return dict(self.encoder , **self.added_tokens_encoder )
def lowerCamelCase_ ( self : Optional[int] , lowerCamelCase_ : List[str] ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : str = []
for x in jieba.cut(UpperCamelCase__ , cut_all=UpperCamelCase__ ):
output_tokens.extend(self.wordpiece_tokenizer.tokenize(UpperCamelCase__ ) )
return output_tokens
def lowerCamelCase_ ( self : List[Any] , lowerCamelCase_ : Union[str, Any] , **lowerCamelCase_ : Dict ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Union[str, Any] = [i for i in token_ids if i >= 0]
SCREAMING_SNAKE_CASE : int = [
x for x in token_ids if x != self.pad_token_id and x != self.eos_token_id and x != self.bos_token_id
]
return super()._decode(UpperCamelCase__ , **UpperCamelCase__ )
def lowerCamelCase_ ( self : Dict , lowerCamelCase_ : Any ):
'''simple docstring'''
return token in self.encoder
def lowerCamelCase_ ( self : int , lowerCamelCase_ : List[str] ):
'''simple docstring'''
return "".join(UpperCamelCase__ )
def lowerCamelCase_ ( self : Any , lowerCamelCase_ : Optional[Any] ):
'''simple docstring'''
return self.encoder.get(UpperCamelCase__ , self.encoder.get(self.unk_token ) )
def lowerCamelCase_ ( self : str , lowerCamelCase_ : str ):
'''simple docstring'''
return self.decoder.get(UpperCamelCase__ , self.unk_token )
def lowerCamelCase_ ( self : int , lowerCamelCase_ : str , lowerCamelCase_ : Optional[str] = None ):
'''simple docstring'''
if os.path.isdir(UpperCamelCase__ ):
SCREAMING_SNAKE_CASE : List[str] = os.path.join(
UpperCamelCase__ , (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""] )
else:
SCREAMING_SNAKE_CASE : int = (filename_prefix + '''-''' if filename_prefix else '''''') + save_directory
SCREAMING_SNAKE_CASE : List[Any] = 0
if " " in self.encoder:
SCREAMING_SNAKE_CASE : Optional[Any] = self.encoder[''' ''']
del self.encoder[" "]
if "\n" in self.encoder:
SCREAMING_SNAKE_CASE : str = self.encoder['''\n''']
del self.encoder["\n"]
SCREAMING_SNAKE_CASE : str = collections.OrderedDict(sorted(self.encoder.items() , key=lambda lowerCamelCase_ : x[1] ) )
with open(UpperCamelCase__ , """w""" , encoding="""utf-8""" ) as writer:
for token, token_index in self.encoder.items():
if index != token_index:
logger.warning(
f'''Saving vocabulary to {vocab_file}: vocabulary indices are not consecutive.'''
""" Please check that the vocabulary is not corrupted!""" )
SCREAMING_SNAKE_CASE : Optional[Any] = token_index
writer.write(token + """\n""" )
index += 1
return (vocab_file,)
def lowerCamelCase_ ( self : Union[str, Any] , lowerCamelCase_ : List[int] , lowerCamelCase_ : List[int] = None ):
'''simple docstring'''
if token_ids_a is None:
return [self.bos_token_id] + token_ids_a
return [self.bos_token_id] + token_ids_a + [self.bos_token_id] + token_ids_a
def lowerCamelCase_ ( self : Optional[Any] , lowerCamelCase_ : List[int] , lowerCamelCase_ : Optional[List[int]] = None , lowerCamelCase_ : bool = False ):
'''simple docstring'''
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=UpperCamelCase__ , token_ids_a=UpperCamelCase__ , already_has_special_tokens=UpperCamelCase__ )
if token_ids_a is not None:
return [1] + ([0] * len(UpperCamelCase__ )) + [1] + ([0] * len(UpperCamelCase__ ))
return [1] + ([0] * len(UpperCamelCase__ ))
| 712 |
'''simple docstring'''
import itertools
import json
import linecache
import os
import pickle
import re
import socket
import string
from collections import Counter
from logging import getLogger
from pathlib import Path
from typing import Callable, Dict, Iterable, List
import git
import torch
from torch.utils.data import Dataset
from transformers import BartTokenizer, RagTokenizer, TaTokenizer
def __A ( lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_=True , lowerCamelCase_="pt" ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : Tuple = {"""add_prefix_space""": True} if isinstance(lowerCamelCase_ , lowerCamelCase_ ) and not line.startswith(""" """ ) else {}
SCREAMING_SNAKE_CASE : Optional[Any] = padding_side
return tokenizer(
[line] , max_length=lowerCamelCase_ , padding="""max_length""" if pad_to_max_length else None , truncation=lowerCamelCase_ , return_tensors=lowerCamelCase_ , add_special_tokens=lowerCamelCase_ , **lowerCamelCase_ , )
def __A ( lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_=None , ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : int = input_ids.ne(lowerCamelCase_ ).any(dim=0 )
if attention_mask is None:
return input_ids[:, keep_column_mask]
else:
return (input_ids[:, keep_column_mask], attention_mask[:, keep_column_mask])
class UpperCamelCase__ ( lowercase_ ):
"""simple docstring"""
def __init__( self : str , lowerCamelCase_ : Tuple , lowerCamelCase_ : Tuple , lowerCamelCase_ : List[str] , lowerCamelCase_ : List[str] , lowerCamelCase_ : List[str]="train" , lowerCamelCase_ : List[str]=None , lowerCamelCase_ : List[Any]=None , lowerCamelCase_ : int=None , lowerCamelCase_ : Union[str, Any]="" , ):
'''simple docstring'''
super().__init__()
SCREAMING_SNAKE_CASE : str = Path(lowerCamelCase_ ).joinpath(type_path + """.source""" )
SCREAMING_SNAKE_CASE : Optional[Any] = Path(lowerCamelCase_ ).joinpath(type_path + """.target""" )
SCREAMING_SNAKE_CASE : Union[str, Any] = self.get_char_lens(self.src_file )
SCREAMING_SNAKE_CASE : int = max_source_length
SCREAMING_SNAKE_CASE : str = max_target_length
assert min(self.src_lens ) > 0, f'''found empty line in {self.src_file}'''
SCREAMING_SNAKE_CASE : List[str] = tokenizer
SCREAMING_SNAKE_CASE : Dict = prefix
if n_obs is not None:
SCREAMING_SNAKE_CASE : List[Any] = self.src_lens[:n_obs]
SCREAMING_SNAKE_CASE : int = src_lang
SCREAMING_SNAKE_CASE : Optional[int] = tgt_lang
def __len__( self : List[Any] ):
'''simple docstring'''
return len(self.src_lens )
def __getitem__( self : Union[str, Any] , lowerCamelCase_ : Optional[Any] ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Any = index + 1 # linecache starts at 1
SCREAMING_SNAKE_CASE : Dict = self.prefix + linecache.getline(str(self.src_file ) , lowerCamelCase_ ).rstrip("""\n""" )
SCREAMING_SNAKE_CASE : Dict = linecache.getline(str(self.tgt_file ) , lowerCamelCase_ ).rstrip("""\n""" )
assert source_line, f'''empty source line for index {index}'''
assert tgt_line, f'''empty tgt line for index {index}'''
# Need to add eos token manually for T5
if isinstance(self.tokenizer , lowerCamelCase_ ):
source_line += self.tokenizer.eos_token
tgt_line += self.tokenizer.eos_token
# Pad source and target to the right
SCREAMING_SNAKE_CASE : Union[str, Any] = (
self.tokenizer.question_encoder if isinstance(self.tokenizer , lowerCamelCase_ ) else self.tokenizer
)
SCREAMING_SNAKE_CASE : Any = self.tokenizer.generator if isinstance(self.tokenizer , lowerCamelCase_ ) else self.tokenizer
SCREAMING_SNAKE_CASE : Optional[int] = encode_line(lowerCamelCase_ , lowerCamelCase_ , self.max_source_length , """right""" )
SCREAMING_SNAKE_CASE : Union[str, Any] = encode_line(lowerCamelCase_ , lowerCamelCase_ , self.max_target_length , """right""" )
SCREAMING_SNAKE_CASE : Tuple = source_inputs["""input_ids"""].squeeze()
SCREAMING_SNAKE_CASE : Tuple = target_inputs["""input_ids"""].squeeze()
SCREAMING_SNAKE_CASE : List[str] = source_inputs["""attention_mask"""].squeeze()
return {
"input_ids": source_ids,
"attention_mask": src_mask,
"decoder_input_ids": target_ids,
}
@staticmethod
def lowerCamelCase_ ( lowerCamelCase_ : Dict ):
'''simple docstring'''
return [len(lowerCamelCase_ ) for x in Path(lowerCamelCase_ ).open().readlines()]
def lowerCamelCase_ ( self : Tuple , lowerCamelCase_ : Any ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Tuple = torch.stack([x["""input_ids"""] for x in batch] )
SCREAMING_SNAKE_CASE : int = torch.stack([x["""attention_mask"""] for x in batch] )
SCREAMING_SNAKE_CASE : int = torch.stack([x["""decoder_input_ids"""] for x in batch] )
SCREAMING_SNAKE_CASE : int = (
self.tokenizer.generator.pad_token_id
if isinstance(self.tokenizer , lowerCamelCase_ )
else self.tokenizer.pad_token_id
)
SCREAMING_SNAKE_CASE : Dict = (
self.tokenizer.question_encoder.pad_token_id
if isinstance(self.tokenizer , lowerCamelCase_ )
else self.tokenizer.pad_token_id
)
SCREAMING_SNAKE_CASE : Optional[int] = trim_batch(lowerCamelCase_ , lowerCamelCase_ )
SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE : Optional[int] = trim_batch(lowerCamelCase_ , lowerCamelCase_ , attention_mask=lowerCamelCase_ )
SCREAMING_SNAKE_CASE : int = {
"""input_ids""": source_ids,
"""attention_mask""": source_mask,
"""decoder_input_ids""": y,
}
return batch
__UpperCAmelCase = getLogger(__name__)
def __A ( lowerCamelCase_ ):
"""simple docstring"""
return list(itertools.chain.from_iterable(lowerCamelCase_ ) )
def __A ( lowerCamelCase_ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : str = get_git_info()
save_json(lowerCamelCase_ , os.path.join(lowerCamelCase_ , """git_log.json""" ) )
def __A ( lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_=4 , **lowerCamelCase_ ):
"""simple docstring"""
with open(lowerCamelCase_ , """w""" ) as f:
json.dump(lowerCamelCase_ , lowerCamelCase_ , indent=lowerCamelCase_ , **lowerCamelCase_ )
def __A ( lowerCamelCase_ ):
"""simple docstring"""
with open(lowerCamelCase_ ) as f:
return json.load(lowerCamelCase_ )
def __A ( ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : List[Any] = git.Repo(search_parent_directories=lowerCamelCase_ )
SCREAMING_SNAKE_CASE : Tuple = {
"""repo_id""": str(lowerCamelCase_ ),
"""repo_sha""": str(repo.head.object.hexsha ),
"""repo_branch""": str(repo.active_branch ),
"""hostname""": str(socket.gethostname() ),
}
return repo_infos
def __A ( lowerCamelCase_ , lowerCamelCase_ ):
"""simple docstring"""
return list(map(lowerCamelCase_ , lowerCamelCase_ ) )
def __A ( lowerCamelCase_ , lowerCamelCase_ ):
"""simple docstring"""
with open(lowerCamelCase_ , """wb""" ) as f:
return pickle.dump(lowerCamelCase_ , lowerCamelCase_ )
def __A ( lowerCamelCase_ ):
"""simple docstring"""
def remove_articles(lowerCamelCase_ ):
return re.sub(R"""\b(a|an|the)\b""" , """ """ , lowerCamelCase_ )
def white_space_fix(lowerCamelCase_ ):
return " ".join(text.split() )
def remove_punc(lowerCamelCase_ ):
SCREAMING_SNAKE_CASE : int = set(string.punctuation )
return "".join(ch for ch in text if ch not in exclude )
def lower(lowerCamelCase_ ):
return text.lower()
return white_space_fix(remove_articles(remove_punc(lower(lowerCamelCase_ ) ) ) )
def __A ( lowerCamelCase_ , lowerCamelCase_ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : Tuple = normalize_answer(lowerCamelCase_ ).split()
SCREAMING_SNAKE_CASE : Optional[int] = normalize_answer(lowerCamelCase_ ).split()
SCREAMING_SNAKE_CASE : Tuple = Counter(lowerCamelCase_ ) & Counter(lowerCamelCase_ )
SCREAMING_SNAKE_CASE : Any = sum(common.values() )
if num_same == 0:
return 0
SCREAMING_SNAKE_CASE : Optional[int] = 1.0 * num_same / len(lowerCamelCase_ )
SCREAMING_SNAKE_CASE : str = 1.0 * num_same / len(lowerCamelCase_ )
SCREAMING_SNAKE_CASE : List[str] = (2 * precision * recall) / (precision + recall)
return fa
def __A ( lowerCamelCase_ , lowerCamelCase_ ):
"""simple docstring"""
return normalize_answer(lowerCamelCase_ ) == normalize_answer(lowerCamelCase_ )
def __A ( lowerCamelCase_ , lowerCamelCase_ ):
"""simple docstring"""
assert len(lowerCamelCase_ ) == len(lowerCamelCase_ )
SCREAMING_SNAKE_CASE : Union[str, Any] = 0
for hypo, pred in zip(lowerCamelCase_ , lowerCamelCase_ ):
em += exact_match_score(lowerCamelCase_ , lowerCamelCase_ )
if len(lowerCamelCase_ ) > 0:
em /= len(lowerCamelCase_ )
return {"em": em}
def __A ( lowerCamelCase_ ):
"""simple docstring"""
return model_prefix.startswith("""rag""" )
def __A ( lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : Any = {p: p for p in extra_params}
# T5 models don't have `dropout` param, they have `dropout_rate` instead
SCREAMING_SNAKE_CASE : Dict = """dropout_rate"""
for p in extra_params:
if getattr(lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ ):
if not hasattr(lowerCamelCase_ , lowerCamelCase_ ) and not hasattr(lowerCamelCase_ , equivalent_param[p] ):
logger.info("""config doesn't have a `{}` attribute""".format(lowerCamelCase_ ) )
delattr(lowerCamelCase_ , lowerCamelCase_ )
continue
SCREAMING_SNAKE_CASE : Dict = p if hasattr(lowerCamelCase_ , lowerCamelCase_ ) else equivalent_param[p]
setattr(lowerCamelCase_ , lowerCamelCase_ , getattr(lowerCamelCase_ , lowerCamelCase_ ) )
delattr(lowerCamelCase_ , lowerCamelCase_ )
return hparams, config
| 79 | 0 |
'''simple docstring'''
import warnings
from ...utils import logging
from .image_processing_flava import FlavaImageProcessor
__UpperCAmelCase = logging.get_logger(__name__)
class UpperCamelCase__ ( lowercase_ ):
"""simple docstring"""
def __init__( self : List[Any] , *lowerCamelCase_ : Any , **lowerCamelCase_ : Union[str, Any] ):
'''simple docstring'''
warnings.warn(
"""The class FlavaFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please"""
""" use FlavaImageProcessor instead.""" , _SCREAMING_SNAKE_CASE , )
super().__init__(*_SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE )
| 713 |
'''simple docstring'''
def __A ( lowerCamelCase_ ):
"""simple docstring"""
return 1 if digit in (0, 1) else (digit * factorial(digit - 1 ))
def __A ( lowerCamelCase_ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : Optional[Any] = 0
SCREAMING_SNAKE_CASE : List[str] = number
while duplicate > 0:
SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE : Dict = divmod(lowerCamelCase_ , 10 )
fact_sum += factorial(lowerCamelCase_ )
return fact_sum == number
if __name__ == "__main__":
print("""Program to check whether a number is a Krisnamurthy Number or not.""")
__UpperCAmelCase = int(input("""Enter number: """).strip())
print(
f'''{number} is {'' if krishnamurthy(number) else 'not '}a Krishnamurthy Number.'''
)
| 79 | 0 |
import argparse
import json
import os
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from accelerate import Accelerator, DistributedType
from accelerate.utils.deepspeed import DummyOptim, DummyScheduler
__UpperCAmelCase = 16
__UpperCAmelCase = 32
def __A ( lowerCamelCase_ , lowerCamelCase_ = 16 , lowerCamelCase_ = "bert-base-cased" ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : Dict = AutoTokenizer.from_pretrained(lowerCamelCase_ )
SCREAMING_SNAKE_CASE : Optional[int] = load_dataset("""glue""" , """mrpc""" )
def tokenize_function(lowerCamelCase_ ):
# max_length=None => use the model max length (it's actually the default)
SCREAMING_SNAKE_CASE : List[str] = tokenizer(examples["""sentence1"""] , examples["""sentence2"""] , truncation=lowerCamelCase_ , max_length=lowerCamelCase_ )
return outputs
# Apply the method we just defined to all the examples in all the splits of the dataset
SCREAMING_SNAKE_CASE : List[str] = datasets.map(
lowerCamelCase_ , batched=lowerCamelCase_ , remove_columns=["""idx""", """sentence1""", """sentence2"""] , load_from_cache_file=lowerCamelCase_ )
# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
# transformers library
SCREAMING_SNAKE_CASE : Optional[Any] = tokenized_datasets.rename_column("""label""" , """labels""" )
def collate_fn(lowerCamelCase_ ):
# On TPU it's best to pad everything to the same length or training will be very slow.
if accelerator.distributed_type == DistributedType.TPU:
return tokenizer.pad(lowerCamelCase_ , padding="""max_length""" , max_length=1_28 , return_tensors="""pt""" )
return tokenizer.pad(lowerCamelCase_ , padding="""longest""" , return_tensors="""pt""" )
# Instantiate dataloaders.
SCREAMING_SNAKE_CASE : Any = DataLoader(
tokenized_datasets["""train"""] , shuffle=lowerCamelCase_ , collate_fn=lowerCamelCase_ , batch_size=lowerCamelCase_ )
SCREAMING_SNAKE_CASE : int = DataLoader(
tokenized_datasets["""validation"""] , shuffle=lowerCamelCase_ , collate_fn=lowerCamelCase_ , batch_size=lowerCamelCase_ )
return train_dataloader, eval_dataloader
def __A ( lowerCamelCase_ , lowerCamelCase_ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : List[Any] = Accelerator()
# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
SCREAMING_SNAKE_CASE : Any = config["""lr"""]
SCREAMING_SNAKE_CASE : Dict = int(config["""num_epochs"""] )
SCREAMING_SNAKE_CASE : List[Any] = int(config["""seed"""] )
SCREAMING_SNAKE_CASE : List[str] = int(config["""batch_size"""] )
SCREAMING_SNAKE_CASE : List[Any] = args.model_name_or_path
set_seed(lowerCamelCase_ )
SCREAMING_SNAKE_CASE : List[str] = get_dataloaders(lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ )
# Instantiate the model (we build the model here so that the seed also control new weights initialization)
SCREAMING_SNAKE_CASE : Tuple = AutoModelForSequenceClassification.from_pretrained(lowerCamelCase_ , return_dict=lowerCamelCase_ )
# Instantiate optimizer
SCREAMING_SNAKE_CASE : Tuple = (
AdamW
if accelerator.state.deepspeed_plugin is None
or """optimizer""" not in accelerator.state.deepspeed_plugin.deepspeed_config
else DummyOptim
)
SCREAMING_SNAKE_CASE : List[str] = optimizer_cls(params=model.parameters() , lr=lowerCamelCase_ )
if accelerator.state.deepspeed_plugin is not None:
SCREAMING_SNAKE_CASE : Any = accelerator.state.deepspeed_plugin.deepspeed_config[
"""gradient_accumulation_steps"""
]
else:
SCREAMING_SNAKE_CASE : Any = 1
SCREAMING_SNAKE_CASE : str = (len(lowerCamelCase_ ) * num_epochs) // gradient_accumulation_steps
# Instantiate scheduler
if (
accelerator.state.deepspeed_plugin is None
or "scheduler" not in accelerator.state.deepspeed_plugin.deepspeed_config
):
SCREAMING_SNAKE_CASE : List[Any] = get_linear_schedule_with_warmup(
optimizer=lowerCamelCase_ , num_warmup_steps=0 , num_training_steps=lowerCamelCase_ , )
else:
SCREAMING_SNAKE_CASE : Dict = DummyScheduler(lowerCamelCase_ , total_num_steps=lowerCamelCase_ , warmup_num_steps=0 )
# 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.
SCREAMING_SNAKE_CASE : Dict = accelerator.prepare(
lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ )
# We need to keep track of how many total steps we have iterated over
SCREAMING_SNAKE_CASE : str = 0
# We also need to keep track of the stating epoch so files are named properly
SCREAMING_SNAKE_CASE : Tuple = 0
# Now we train the model
SCREAMING_SNAKE_CASE : str = evaluate.load("""glue""" , """mrpc""" )
SCREAMING_SNAKE_CASE : int = 0
SCREAMING_SNAKE_CASE : Tuple = {}
for epoch in range(lowerCamelCase_ , lowerCamelCase_ ):
model.train()
for step, batch in enumerate(lowerCamelCase_ ):
SCREAMING_SNAKE_CASE : List[str] = model(**lowerCamelCase_ )
SCREAMING_SNAKE_CASE : int = outputs.loss
SCREAMING_SNAKE_CASE : List[Any] = loss / gradient_accumulation_steps
accelerator.backward(lowerCamelCase_ )
if step % gradient_accumulation_steps == 0:
optimizer.step()
lr_scheduler.step()
optimizer.zero_grad()
overall_step += 1
model.eval()
SCREAMING_SNAKE_CASE : List[Any] = 0
for step, batch in enumerate(lowerCamelCase_ ):
# We could avoid this line since we set the accelerator with `device_placement=True`.
batch.to(accelerator.device )
with torch.no_grad():
SCREAMING_SNAKE_CASE : Optional[int] = model(**lowerCamelCase_ )
SCREAMING_SNAKE_CASE : Dict = outputs.logits.argmax(dim=-1 )
# It is slightly faster to call this once, than multiple times
SCREAMING_SNAKE_CASE : str = accelerator.gather(
(predictions, batch["""labels"""]) ) # If we are in a multiprocess environment, the last batch has duplicates
if accelerator.use_distributed:
if step == len(lowerCamelCase_ ) - 1:
SCREAMING_SNAKE_CASE : int = predictions[: len(eval_dataloader.dataset ) - samples_seen]
SCREAMING_SNAKE_CASE : Union[str, Any] = references[: len(eval_dataloader.dataset ) - samples_seen]
else:
samples_seen += references.shape[0]
metric.add_batch(
predictions=lowerCamelCase_ , references=lowerCamelCase_ , )
SCREAMING_SNAKE_CASE : List[str] = metric.compute()
# Use accelerator.print to print only on the main process.
accelerator.print(f'''epoch {epoch}:''' , lowerCamelCase_ )
SCREAMING_SNAKE_CASE : str = eval_metric["""accuracy"""]
if best_performance < eval_metric["accuracy"]:
SCREAMING_SNAKE_CASE : Dict = eval_metric["""accuracy"""]
if args.performance_lower_bound is not None:
assert (
args.performance_lower_bound <= best_performance
), f'''Best performance metric {best_performance} is lower than the lower bound {args.performance_lower_bound}'''
accelerator.wait_for_everyone()
if accelerator.is_main_process:
with open(os.path.join(args.output_dir , """all_results.json""" ) , """w""" ) as f:
json.dump(lowerCamelCase_ , lowerCamelCase_ )
def __A ( ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : int = argparse.ArgumentParser(description="""Simple example of training script tracking peak GPU memory usage.""" )
parser.add_argument(
"""--model_name_or_path""" , type=lowerCamelCase_ , default="""bert-base-cased""" , help="""Path to pretrained model or model identifier from huggingface.co/models.""" , required=lowerCamelCase_ , )
parser.add_argument(
"""--output_dir""" , type=lowerCamelCase_ , default=""".""" , help="""Optional save directory where all checkpoint folders will be stored. Default is the current working directory.""" , )
parser.add_argument(
"""--performance_lower_bound""" , type=lowerCamelCase_ , default=lowerCamelCase_ , help="""Optional lower bound for the performance metric. If set, the training will throw error when the performance metric drops below this value.""" , )
parser.add_argument(
"""--num_epochs""" , type=lowerCamelCase_ , default=3 , help="""Number of train epochs.""" , )
SCREAMING_SNAKE_CASE : Optional[int] = parser.parse_args()
SCREAMING_SNAKE_CASE : Any = {"""lr""": 2E-5, """num_epochs""": args.num_epochs, """seed""": 42, """batch_size""": 16}
training_function(lowerCamelCase_ , lowerCamelCase_ )
if __name__ == "__main__":
main()
| 714 |
'''simple docstring'''
# Lint as: python3
import sys
from collections.abc import Mapping
from typing import TYPE_CHECKING
import numpy as np
import pyarrow as pa
from .. import config
from ..utils.py_utils import map_nested
from .formatting import TensorFormatter
if TYPE_CHECKING:
import torch
class UpperCamelCase__ ( TensorFormatter[Mapping, '''torch.Tensor''', Mapping] ):
"""simple docstring"""
def __init__( self : Tuple , lowerCamelCase_ : str=None , **lowerCamelCase_ : Dict ):
'''simple docstring'''
super().__init__(features=lowerCamelCase_ )
SCREAMING_SNAKE_CASE : Tuple = torch_tensor_kwargs
import torch # noqa import torch at initialization
def lowerCamelCase_ ( self : Dict , lowerCamelCase_ : Union[str, Any] ):
'''simple docstring'''
import torch
if isinstance(lowerCamelCase_ , lowerCamelCase_ ) and column:
if all(
isinstance(lowerCamelCase_ , torch.Tensor ) and x.shape == column[0].shape and x.dtype == column[0].dtype
for x in column ):
return torch.stack(lowerCamelCase_ )
return column
def lowerCamelCase_ ( self : List[str] , lowerCamelCase_ : int ):
'''simple docstring'''
import torch
if isinstance(lowerCamelCase_ , (str, bytes, type(lowerCamelCase_ )) ):
return value
elif isinstance(lowerCamelCase_ , (np.character, np.ndarray) ) and np.issubdtype(value.dtype , np.character ):
return value.tolist()
SCREAMING_SNAKE_CASE : str = {}
if isinstance(lowerCamelCase_ , (np.number, np.ndarray) ) and np.issubdtype(value.dtype , np.integer ):
SCREAMING_SNAKE_CASE : Any = {"""dtype""": torch.intaa}
elif isinstance(lowerCamelCase_ , (np.number, np.ndarray) ) and np.issubdtype(value.dtype , np.floating ):
SCREAMING_SNAKE_CASE : int = {"""dtype""": torch.floataa}
elif config.PIL_AVAILABLE and "PIL" in sys.modules:
import PIL.Image
if isinstance(lowerCamelCase_ , PIL.Image.Image ):
SCREAMING_SNAKE_CASE : List[Any] = np.asarray(lowerCamelCase_ )
return torch.tensor(lowerCamelCase_ , **{**default_dtype, **self.torch_tensor_kwargs} )
def lowerCamelCase_ ( self : Optional[int] , lowerCamelCase_ : Optional[Any] ):
'''simple docstring'''
import torch
# support for torch, tf, jax etc.
if hasattr(lowerCamelCase_ , """__array__""" ) and not isinstance(lowerCamelCase_ , torch.Tensor ):
SCREAMING_SNAKE_CASE : Dict = data_struct.__array__()
# support for nested types like struct of list of struct
if isinstance(lowerCamelCase_ , np.ndarray ):
if data_struct.dtype == object: # torch tensors cannot be instantied from an array of objects
return self._consolidate([self.recursive_tensorize(lowerCamelCase_ ) for substruct in data_struct] )
elif isinstance(lowerCamelCase_ , (list, tuple) ):
return self._consolidate([self.recursive_tensorize(lowerCamelCase_ ) for substruct in data_struct] )
return self._tensorize(lowerCamelCase_ )
def lowerCamelCase_ ( self : int , lowerCamelCase_ : dict ):
'''simple docstring'''
return map_nested(self._recursive_tensorize , lowerCamelCase_ , map_list=lowerCamelCase_ )
def lowerCamelCase_ ( self : Optional[int] , lowerCamelCase_ : pa.Table ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : str = self.numpy_arrow_extractor().extract_row(lowerCamelCase_ )
SCREAMING_SNAKE_CASE : Dict = self.python_features_decoder.decode_row(lowerCamelCase_ )
return self.recursive_tensorize(lowerCamelCase_ )
def lowerCamelCase_ ( self : Tuple , lowerCamelCase_ : pa.Table ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : List[str] = self.numpy_arrow_extractor().extract_column(lowerCamelCase_ )
SCREAMING_SNAKE_CASE : Tuple = self.python_features_decoder.decode_column(lowerCamelCase_ , pa_table.column_names[0] )
SCREAMING_SNAKE_CASE : List[str] = self.recursive_tensorize(lowerCamelCase_ )
SCREAMING_SNAKE_CASE : List[str] = self._consolidate(lowerCamelCase_ )
return column
def lowerCamelCase_ ( self : Dict , lowerCamelCase_ : pa.Table ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Optional[Any] = self.numpy_arrow_extractor().extract_batch(lowerCamelCase_ )
SCREAMING_SNAKE_CASE : Dict = self.python_features_decoder.decode_batch(lowerCamelCase_ )
SCREAMING_SNAKE_CASE : Optional[Any] = self.recursive_tensorize(lowerCamelCase_ )
for column_name in batch:
SCREAMING_SNAKE_CASE : Tuple = self._consolidate(batch[column_name] )
return batch
| 79 | 0 |
'''simple docstring'''
from ...utils import (
OptionalDependencyNotAvailable,
is_torch_available,
is_transformers_available,
is_transformers_version,
)
try:
if not (is_transformers_available() and is_torch_available() and is_transformers_version(""">=""", """4.25.0""")):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ...utils.dummy_torch_and_transformers_objects import UnCLIPImageVariationPipeline, UnCLIPPipeline
else:
from .pipeline_unclip import UnCLIPPipeline
from .pipeline_unclip_image_variation import UnCLIPImageVariationPipeline
from .text_proj import UnCLIPTextProjModel
| 715 |
'''simple docstring'''
import itertools
import os
import random
import tempfile
import unittest
import numpy as np
from transformers import TvltFeatureExtractor, is_datasets_available
from transformers.testing_utils import check_json_file_has_correct_format, require_torch, require_torchaudio
from transformers.utils.import_utils import is_torch_available
from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin
if is_torch_available():
import torch
if is_datasets_available():
from datasets import load_dataset
__UpperCAmelCase = random.Random()
def __A ( lowerCamelCase_ , lowerCamelCase_=1.0 , lowerCamelCase_=None , lowerCamelCase_=None ):
"""simple docstring"""
if rng is None:
SCREAMING_SNAKE_CASE : Optional[Any] = global_rng
SCREAMING_SNAKE_CASE : Optional[int] = []
for batch_idx in range(shape[0] ):
values.append([] )
for _ in range(shape[1] ):
values[-1].append(rng.random() * scale )
return values
class UpperCamelCase__ ( unittest.TestCase ):
"""simple docstring"""
def __init__( self : List[str] , lowerCamelCase_ : List[Any] , lowerCamelCase_ : Optional[int]=7 , lowerCamelCase_ : Optional[int]=4_00 , lowerCamelCase_ : int=20_00 , lowerCamelCase_ : List[str]=20_48 , lowerCamelCase_ : Optional[Any]=1_28 , lowerCamelCase_ : Optional[Any]=1 , lowerCamelCase_ : str=5_12 , lowerCamelCase_ : Dict=30 , lowerCamelCase_ : Dict=4_41_00 , ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Tuple = parent
SCREAMING_SNAKE_CASE : Union[str, Any] = batch_size
SCREAMING_SNAKE_CASE : List[str] = min_seq_length
SCREAMING_SNAKE_CASE : Any = max_seq_length
SCREAMING_SNAKE_CASE : Optional[int] = (self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
SCREAMING_SNAKE_CASE : int = spectrogram_length
SCREAMING_SNAKE_CASE : List[Any] = feature_size
SCREAMING_SNAKE_CASE : Any = num_audio_channels
SCREAMING_SNAKE_CASE : Tuple = hop_length
SCREAMING_SNAKE_CASE : str = chunk_length
SCREAMING_SNAKE_CASE : Dict = sampling_rate
def lowerCamelCase_ ( self : List[Any] ):
'''simple docstring'''
return {
"spectrogram_length": self.spectrogram_length,
"feature_size": self.feature_size,
"num_audio_channels": self.num_audio_channels,
"hop_length": self.hop_length,
"chunk_length": self.chunk_length,
"sampling_rate": self.sampling_rate,
}
def lowerCamelCase_ ( self : Dict , lowerCamelCase_ : Tuple=False , lowerCamelCase_ : Any=False ):
'''simple docstring'''
def _flatten(lowerCamelCase_ : Dict ):
return list(itertools.chain(*lowerCamelCase_ ) )
if equal_length:
SCREAMING_SNAKE_CASE : Optional[Any] = [floats_list((self.max_seq_length, self.feature_size) ) for _ in range(self.batch_size )]
else:
# make sure that inputs increase in size
SCREAMING_SNAKE_CASE : Dict = [
floats_list((x, self.feature_size) )
for x in range(self.min_seq_length , self.max_seq_length , self.seq_length_diff )
]
if numpify:
SCREAMING_SNAKE_CASE : Optional[int] = [np.asarray(lowerCamelCase_ ) for x in speech_inputs]
return speech_inputs
@require_torch
@require_torchaudio
class UpperCamelCase__ ( lowercase_ , unittest.TestCase ):
"""simple docstring"""
SCREAMING_SNAKE_CASE__ = TvltFeatureExtractor
def lowerCamelCase_ ( self : List[Any] ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : List[str] = TvltFeatureExtractionTester(self )
def lowerCamelCase_ ( self : Optional[int] ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Dict = self.feature_extraction_class(**self.feat_extract_dict )
self.assertTrue(hasattr(lowerCamelCase_ , """spectrogram_length""" ) )
self.assertTrue(hasattr(lowerCamelCase_ , """feature_size""" ) )
self.assertTrue(hasattr(lowerCamelCase_ , """num_audio_channels""" ) )
self.assertTrue(hasattr(lowerCamelCase_ , """hop_length""" ) )
self.assertTrue(hasattr(lowerCamelCase_ , """chunk_length""" ) )
self.assertTrue(hasattr(lowerCamelCase_ , """sampling_rate""" ) )
def lowerCamelCase_ ( self : List[str] ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Optional[Any] = self.feature_extraction_class(**self.feat_extract_dict )
with tempfile.TemporaryDirectory() as tmpdirname:
SCREAMING_SNAKE_CASE : Any = feat_extract_first.save_pretrained(lowerCamelCase_ )[0]
check_json_file_has_correct_format(lowerCamelCase_ )
SCREAMING_SNAKE_CASE : Union[str, Any] = self.feature_extraction_class.from_pretrained(lowerCamelCase_ )
SCREAMING_SNAKE_CASE : List[str] = feat_extract_first.to_dict()
SCREAMING_SNAKE_CASE : List[Any] = feat_extract_second.to_dict()
SCREAMING_SNAKE_CASE : Optional[int] = dict_first.pop("""mel_filters""" )
SCREAMING_SNAKE_CASE : Union[str, Any] = dict_second.pop("""mel_filters""" )
self.assertTrue(np.allclose(lowerCamelCase_ , lowerCamelCase_ ) )
self.assertEqual(lowerCamelCase_ , lowerCamelCase_ )
def lowerCamelCase_ ( self : Optional[int] ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Dict = self.feature_extraction_class(**self.feat_extract_dict )
with tempfile.TemporaryDirectory() as tmpdirname:
SCREAMING_SNAKE_CASE : int = os.path.join(lowerCamelCase_ , """feat_extract.json""" )
feat_extract_first.to_json_file(lowerCamelCase_ )
SCREAMING_SNAKE_CASE : Optional[Any] = self.feature_extraction_class.from_json_file(lowerCamelCase_ )
SCREAMING_SNAKE_CASE : List[str] = feat_extract_first.to_dict()
SCREAMING_SNAKE_CASE : int = feat_extract_second.to_dict()
SCREAMING_SNAKE_CASE : List[str] = dict_first.pop("""mel_filters""" )
SCREAMING_SNAKE_CASE : Optional[Any] = dict_second.pop("""mel_filters""" )
self.assertTrue(np.allclose(lowerCamelCase_ , lowerCamelCase_ ) )
self.assertEqual(lowerCamelCase_ , lowerCamelCase_ )
def lowerCamelCase_ ( self : Any ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : int = self.feature_extraction_class(**self.feat_extract_dict )
# create three inputs of length 800, 1000, and 1200
SCREAMING_SNAKE_CASE : Optional[int] = [floats_list((1, x) )[0] for x in range(8_00 , 14_00 , 2_00 )]
SCREAMING_SNAKE_CASE : int = [np.asarray(lowerCamelCase_ ) for speech_input in speech_inputs]
# Test not batched input
SCREAMING_SNAKE_CASE : Union[str, Any] = feature_extractor(np_speech_inputs[0] , return_tensors="""np""" , sampling_rate=4_41_00 ).audio_values
self.assertTrue(encoded_audios.ndim == 4 )
self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size )
self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length )
self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels )
# Test batched
SCREAMING_SNAKE_CASE : Optional[int] = feature_extractor(lowerCamelCase_ , return_tensors="""np""" , sampling_rate=4_41_00 ).audio_values
self.assertTrue(encoded_audios.ndim == 4 )
self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size )
self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length )
self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels )
# Test audio masking
SCREAMING_SNAKE_CASE : List[str] = feature_extractor(
lowerCamelCase_ , return_tensors="""np""" , sampling_rate=4_41_00 , mask_audio=lowerCamelCase_ ).audio_values
self.assertTrue(encoded_audios.ndim == 4 )
self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size )
self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length )
self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels )
# Test 2-D numpy arrays are batched.
SCREAMING_SNAKE_CASE : Optional[int] = [floats_list((1, x) )[0] for x in (8_00, 8_00, 8_00)]
SCREAMING_SNAKE_CASE : int = np.asarray(lowerCamelCase_ )
SCREAMING_SNAKE_CASE : Tuple = feature_extractor(lowerCamelCase_ , return_tensors="""np""" , sampling_rate=4_41_00 ).audio_values
self.assertTrue(encoded_audios.ndim == 4 )
self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size )
self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length )
self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels )
def lowerCamelCase_ ( self : Any , lowerCamelCase_ : int ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : List[Any] = load_dataset("""hf-internal-testing/librispeech_asr_dummy""" , """clean""" , split="""validation""" )
# automatic decoding with librispeech
SCREAMING_SNAKE_CASE : Union[str, Any] = ds.sort("""id""" ).select(range(lowerCamelCase_ ) )[:num_samples]["""audio"""]
return [x["array"] for x in speech_samples]
def lowerCamelCase_ ( self : Dict ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : str = self._load_datasamples(1 )
SCREAMING_SNAKE_CASE : Tuple = TvltFeatureExtractor()
SCREAMING_SNAKE_CASE : List[Any] = feature_extractor(lowerCamelCase_ , return_tensors="""pt""" ).audio_values
self.assertEquals(audio_values.shape , (1, 1, 1_92, 1_28) )
SCREAMING_SNAKE_CASE : List[Any] = torch.tensor([[-0.3_032, -0.2_708], [-0.4_434, -0.4_007]] )
self.assertTrue(torch.allclose(audio_values[0, 0, :2, :2] , lowerCamelCase_ , atol=1e-4 ) )
| 79 | 0 |
def __A ( lowerCamelCase_ , lowerCamelCase_ ):
"""simple docstring"""
if a < 0 or b < 0:
raise ValueError("""the value of both inputs must be positive""" )
SCREAMING_SNAKE_CASE : Any = str(bin(_lowerCAmelCase ) )[2:] # remove the leading "0b"
SCREAMING_SNAKE_CASE : List[str] = str(bin(_lowerCAmelCase ) )[2:] # remove the leading "0b"
SCREAMING_SNAKE_CASE : List[str] = max(len(_lowerCAmelCase ) , len(_lowerCAmelCase ) )
return "0b" + "".join(
str(int(char_a != char_b ) )
for char_a, char_b in zip(a_binary.zfill(_lowerCAmelCase ) , b_binary.zfill(_lowerCAmelCase ) ) )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 716 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
__UpperCAmelCase = {
"""configuration_mvp""": ["""MVP_PRETRAINED_CONFIG_ARCHIVE_MAP""", """MvpConfig""", """MvpOnnxConfig"""],
"""tokenization_mvp""": ["""MvpTokenizer"""],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__UpperCAmelCase = ["""MvpTokenizerFast"""]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__UpperCAmelCase = [
"""MVP_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""MvpForCausalLM""",
"""MvpForConditionalGeneration""",
"""MvpForQuestionAnswering""",
"""MvpForSequenceClassification""",
"""MvpModel""",
"""MvpPreTrainedModel""",
]
if TYPE_CHECKING:
from .configuration_mvp import MVP_PRETRAINED_CONFIG_ARCHIVE_MAP, MvpConfig, MvpOnnxConfig
from .tokenization_mvp import MvpTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_mvp_fast import MvpTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_mvp import (
MVP_PRETRAINED_MODEL_ARCHIVE_LIST,
MvpForCausalLM,
MvpForConditionalGeneration,
MvpForQuestionAnswering,
MvpForSequenceClassification,
MvpModel,
MvpPreTrainedModel,
)
else:
import sys
__UpperCAmelCase = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 79 | 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 ( lowerCamelCase_ , lowerCamelCase_ ) -> Dict:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Tuple = []
for part_id in partition_order:
SCREAMING_SNAKE_CASE : List[Any] = df.where(f'''SPARK_PARTITION_ID() = {part_id}''' ).collect()
for row_idx, row in enumerate(lowerCamelCase_ ):
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 ( ) -> List[str]:
"""simple docstring"""
SCREAMING_SNAKE_CASE : int = pyspark.sql.SparkSession.builder.master("""local[*]""" ).appName("""pyspark""" ).getOrCreate()
SCREAMING_SNAKE_CASE : Tuple = spark.range(1_00 ).repartition(1 )
SCREAMING_SNAKE_CASE : Optional[Any] = Spark(lowerCamelCase_ )
# 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[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Any = pyspark.sql.SparkSession.builder.master("""local[*]""" ).appName("""pyspark""" ).getOrCreate()
SCREAMING_SNAKE_CASE : str = spark.range(10 ).repartition(2 )
SCREAMING_SNAKE_CASE : int = [1, 0]
SCREAMING_SNAKE_CASE : Optional[Any] = _generate_iterable_examples(lowerCamelCase_ , lowerCamelCase_ ) # Reverse the partitions.
SCREAMING_SNAKE_CASE : Any = _get_expected_row_ids_and_row_dicts_for_partition_order(lowerCamelCase_ , lowerCamelCase_ )
for i, (row_id, row_dict) in enumerate(generate_fn() ):
SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE : 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 ( ) -> Optional[int]:
"""simple docstring"""
SCREAMING_SNAKE_CASE : int = pyspark.sql.SparkSession.builder.master("""local[*]""" ).appName("""pyspark""" ).getOrCreate()
SCREAMING_SNAKE_CASE : str = spark.range(10 ).repartition(1 )
SCREAMING_SNAKE_CASE : Tuple = SparkExamplesIterable(lowerCamelCase_ )
assert it.n_shards == 1
for i, (row_id, row_dict) in enumerate(lowerCamelCase_ ):
assert row_id == f'''0_{i}'''
assert row_dict == {"id": i}
@require_not_windows
@require_dill_gt_0_3_2
def __A ( ) -> int:
"""simple docstring"""
SCREAMING_SNAKE_CASE : List[Any] = pyspark.sql.SparkSession.builder.master("""local[*]""" ).appName("""pyspark""" ).getOrCreate()
SCREAMING_SNAKE_CASE : Optional[int] = spark.range(30 ).repartition(3 )
# Mock the generator so that shuffle reverses the partition indices.
with patch("""numpy.random.Generator""" ) as generator_mock:
SCREAMING_SNAKE_CASE : List[str] = lambda lowerCamelCase_ : x.reverse()
SCREAMING_SNAKE_CASE : List[Any] = _get_expected_row_ids_and_row_dicts_for_partition_order(lowerCamelCase_ , [2, 1, 0] )
SCREAMING_SNAKE_CASE : Optional[int] = SparkExamplesIterable(lowerCamelCase_ ).shuffle_data_sources(lowerCamelCase_ )
assert shuffled_it.n_shards == 3
for i, (row_id, row_dict) in enumerate(lowerCamelCase_ ):
SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE : 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 ( ) -> Optional[int]:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Tuple = pyspark.sql.SparkSession.builder.master("""local[*]""" ).appName("""pyspark""" ).getOrCreate()
SCREAMING_SNAKE_CASE : List[Any] = spark.range(20 ).repartition(4 )
# Partitions 0 and 2
SCREAMING_SNAKE_CASE : Optional[int] = SparkExamplesIterable(lowerCamelCase_ ).shard_data_sources(worker_id=0 , num_workers=2 )
assert shard_it_a.n_shards == 2
SCREAMING_SNAKE_CASE : Optional[int] = _get_expected_row_ids_and_row_dicts_for_partition_order(lowerCamelCase_ , [0, 2] )
for i, (row_id, row_dict) in enumerate(lowerCamelCase_ ):
SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE : Dict = expected_row_ids_and_row_dicts_a[i]
assert row_id == expected_row_id
assert row_dict == expected_row_dict
# Partitions 1 and 3
SCREAMING_SNAKE_CASE : int = SparkExamplesIterable(lowerCamelCase_ ).shard_data_sources(worker_id=1 , num_workers=2 )
assert shard_it_a.n_shards == 2
SCREAMING_SNAKE_CASE : Optional[int] = _get_expected_row_ids_and_row_dicts_for_partition_order(lowerCamelCase_ , [1, 3] )
for i, (row_id, row_dict) in enumerate(lowerCamelCase_ ):
SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE : 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 ( ) -> Optional[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE : int = pyspark.sql.SparkSession.builder.master("""local[*]""" ).appName("""pyspark""" ).getOrCreate()
SCREAMING_SNAKE_CASE : Optional[Any] = spark.range(1_00 ).repartition(1 )
SCREAMING_SNAKE_CASE : int = Spark(lowerCamelCase_ )
# 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
| 717 |
'''simple docstring'''
__UpperCAmelCase = [
"""Audio""",
"""Array2D""",
"""Array3D""",
"""Array4D""",
"""Array5D""",
"""ClassLabel""",
"""Features""",
"""Sequence""",
"""Value""",
"""Image""",
"""Translation""",
"""TranslationVariableLanguages""",
]
from .audio import Audio
from .features import ArrayaD, ArrayaD, ArrayaD, ArrayaD, ClassLabel, Features, Sequence, Value
from .image import Image
from .translation import Translation, TranslationVariableLanguages
| 79 | 0 |
'''simple docstring'''
from __future__ import annotations
import math
from collections import Counter
from string import ascii_lowercase
def __A ( lowerCamelCase_ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : str = analyze_text(__lowerCAmelCase )
SCREAMING_SNAKE_CASE : Dict = list(""" """ + ascii_lowercase )
# what is our total sum of probabilities.
SCREAMING_SNAKE_CASE : Dict = sum(single_char_strings.values() )
# one length string
SCREAMING_SNAKE_CASE : Any = 0
# for each alpha we go in our dict and if it is in it we calculate entropy
for ch in my_alphas:
if ch in single_char_strings:
SCREAMING_SNAKE_CASE : Any = single_char_strings[ch]
SCREAMING_SNAKE_CASE : Any = my_str / all_sum
my_fir_sum += prob * math.loga(__lowerCAmelCase ) # entropy formula.
# print entropy
print(f'''{round(-1 * my_fir_sum ):.1f}''' )
# two len string
SCREAMING_SNAKE_CASE : Tuple = sum(two_char_strings.values() )
SCREAMING_SNAKE_CASE : Any = 0
# for each alpha (two in size) calculate entropy.
for cha in my_alphas:
for cha in my_alphas:
SCREAMING_SNAKE_CASE : List[Any] = cha + cha
if sequence in two_char_strings:
SCREAMING_SNAKE_CASE : Tuple = two_char_strings[sequence]
SCREAMING_SNAKE_CASE : str = int(__lowerCAmelCase ) / all_sum
my_sec_sum += prob * math.loga(__lowerCAmelCase )
# print second entropy
print(f'''{round(-1 * my_sec_sum ):.1f}''' )
# print the difference between them
print(f'''{round((-1 * my_sec_sum) - (-1 * my_fir_sum) ):.1f}''' )
def __A ( lowerCamelCase_ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : Optional[int] = Counter() # type: ignore
SCREAMING_SNAKE_CASE : Tuple = Counter() # type: ignore
single_char_strings[text[-1]] += 1
# first case when we have space at start.
two_char_strings[" " + text[0]] += 1
for i in range(0 , len(__lowerCAmelCase ) - 1 ):
single_char_strings[text[i]] += 1
two_char_strings[text[i : i + 2]] += 1
return single_char_strings, two_char_strings
def __A ( ):
"""simple docstring"""
import doctest
doctest.testmod()
# text = (
# "Had repulsive dashwoods suspicion sincerity but advantage now him. Remark "
# "easily garret nor nay. Civil those mrs enjoy shy fat merry. You greatest "
# "jointure saw horrible. He private he on be imagine suppose. Fertile "
# "beloved evident through no service elderly is. Blind there if every no so "
# "at. Own neglected you preferred way sincerity delivered his attempted. To "
# "of message cottage windows do besides against uncivil. Delightful "
# "unreserved impossible few estimating men favourable see entreaties. She "
# "propriety immediate was improving. He or entrance humoured likewise "
# "moderate. Much nor game son say feel. Fat make met can must form into "
# "gate. Me we offending prevailed discovery. "
# )
# calculate_prob(text)
if __name__ == "__main__":
main()
| 718 |
'''simple docstring'''
from typing import List, Optional, Union
import torch
from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDPMScheduler
from ...utils import (
is_accelerate_available,
is_accelerate_version,
logging,
randn_tensor,
replace_example_docstring,
)
__UpperCAmelCase = logging.get_logger(__name__) # pylint: disable=invalid-name
__UpperCAmelCase = """
Examples:
```py
>>> import torch
>>> import numpy as np
>>> from diffusers import KandinskyV22PriorPipeline, KandinskyV22ControlnetPipeline
>>> from transformers import pipeline
>>> from diffusers.utils import load_image
>>> def make_hint(image, depth_estimator):
... image = depth_estimator(image)[\"depth\"]
... image = np.array(image)
... image = image[:, :, None]
... image = np.concatenate([image, image, image], axis=2)
... detected_map = torch.from_numpy(image).float() / 255.0
... hint = detected_map.permute(2, 0, 1)
... return hint
>>> depth_estimator = pipeline(\"depth-estimation\")
>>> pipe_prior = KandinskyV22PriorPipeline.from_pretrained(
... \"kandinsky-community/kandinsky-2-2-prior\", torch_dtype=torch.float16
... )
>>> pipe_prior = pipe_prior.to(\"cuda\")
>>> pipe = KandinskyV22ControlnetPipeline.from_pretrained(
... \"kandinsky-community/kandinsky-2-2-controlnet-depth\", torch_dtype=torch.float16
... )
>>> pipe = pipe.to(\"cuda\")
>>> img = load_image(
... \"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main\"
... \"/kandinsky/cat.png\"
... ).resize((768, 768))
>>> hint = make_hint(img, depth_estimator).unsqueeze(0).half().to(\"cuda\")
>>> prompt = \"A robot, 4k photo\"
>>> negative_prior_prompt = \"lowres, text, error, cropped, worst quality, low quality, jpeg artifacts, ugly, duplicate, morbid, mutilated, out of frame, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, mutation, deformed, blurry, dehydrated, bad anatomy, bad proportions, extra limbs, cloned face, disfigured, gross proportions, malformed limbs, missing arms, missing legs, extra arms, extra legs, fused fingers, too many fingers, long neck, username, watermark, signature\"
>>> generator = torch.Generator(device=\"cuda\").manual_seed(43)
>>> image_emb, zero_image_emb = pipe_prior(
... prompt=prompt, negative_prompt=negative_prior_prompt, generator=generator
... ).to_tuple()
>>> images = pipe(
... image_embeds=image_emb,
... negative_image_embeds=zero_image_emb,
... hint=hint,
... num_inference_steps=50,
... generator=generator,
... height=768,
... width=768,
... ).images
>>> images[0].save(\"robot_cat.png\")
```
"""
def __A ( lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_=8 ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : Optional[Any] = height // scale_factor**2
if height % scale_factor**2 != 0:
new_height += 1
SCREAMING_SNAKE_CASE : List[str] = width // scale_factor**2
if width % scale_factor**2 != 0:
new_width += 1
return new_height * scale_factor, new_width * scale_factor
class UpperCamelCase__ ( lowercase_ ):
"""simple docstring"""
def __init__( self : Union[str, Any] , lowerCamelCase_ : UNetaDConditionModel , lowerCamelCase_ : DDPMScheduler , lowerCamelCase_ : VQModel , ):
'''simple docstring'''
super().__init__()
self.register_modules(
unet=lowerCamelCase_ , scheduler=lowerCamelCase_ , movq=lowerCamelCase_ , )
SCREAMING_SNAKE_CASE : str = 2 ** (len(self.movq.config.block_out_channels ) - 1)
def lowerCamelCase_ ( self : int , lowerCamelCase_ : int , lowerCamelCase_ : Optional[int] , lowerCamelCase_ : List[str] , lowerCamelCase_ : Any , lowerCamelCase_ : Tuple , lowerCamelCase_ : int ):
'''simple docstring'''
if latents is None:
SCREAMING_SNAKE_CASE : Tuple = randn_tensor(lowerCamelCase_ , generator=lowerCamelCase_ , device=lowerCamelCase_ , dtype=lowerCamelCase_ )
else:
if latents.shape != shape:
raise ValueError(f'''Unexpected latents shape, got {latents.shape}, expected {shape}''' )
SCREAMING_SNAKE_CASE : Dict = latents.to(lowerCamelCase_ )
SCREAMING_SNAKE_CASE : Any = latents * scheduler.init_noise_sigma
return latents
def lowerCamelCase_ ( self : Union[str, Any] , lowerCamelCase_ : Dict=0 ):
'''simple docstring'''
if is_accelerate_available():
from accelerate import cpu_offload
else:
raise ImportError("""Please install accelerate via `pip install accelerate`""" )
SCREAMING_SNAKE_CASE : List[Any] = torch.device(f'''cuda:{gpu_id}''' )
SCREAMING_SNAKE_CASE : Union[str, Any] = [
self.unet,
self.movq,
]
for cpu_offloaded_model in models:
if cpu_offloaded_model is not None:
cpu_offload(lowerCamelCase_ , lowerCamelCase_ )
def lowerCamelCase_ ( self : List[Any] , lowerCamelCase_ : Tuple=0 ):
'''simple docstring'''
if is_accelerate_available() and is_accelerate_version(""">=""" , """0.17.0.dev0""" ):
from accelerate import cpu_offload_with_hook
else:
raise ImportError("""`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher.""" )
SCREAMING_SNAKE_CASE : Any = torch.device(f'''cuda:{gpu_id}''' )
if self.device.type != "cpu":
self.to("""cpu""" , silence_dtype_warnings=lowerCamelCase_ )
torch.cuda.empty_cache() # otherwise we don't see the memory savings (but they probably exist)
SCREAMING_SNAKE_CASE : Union[str, Any] = None
for cpu_offloaded_model in [self.unet, self.movq]:
SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE : List[Any] = cpu_offload_with_hook(lowerCamelCase_ , lowerCamelCase_ , prev_module_hook=lowerCamelCase_ )
# We'll offload the last model manually.
SCREAMING_SNAKE_CASE : str = hook
@property
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
def lowerCamelCase_ ( self : str ):
'''simple docstring'''
if not hasattr(self.unet , """_hf_hook""" ):
return self.device
for module in self.unet.modules():
if (
hasattr(lowerCamelCase_ , """_hf_hook""" )
and hasattr(module._hf_hook , """execution_device""" )
and module._hf_hook.execution_device is not None
):
return torch.device(module._hf_hook.execution_device )
return self.device
@torch.no_grad()
@replace_example_docstring(lowerCamelCase_ )
def __call__( self : Optional[Any] , lowerCamelCase_ : Union[torch.FloatTensor, List[torch.FloatTensor]] , lowerCamelCase_ : Union[torch.FloatTensor, List[torch.FloatTensor]] , lowerCamelCase_ : torch.FloatTensor , lowerCamelCase_ : int = 5_12 , lowerCamelCase_ : int = 5_12 , lowerCamelCase_ : int = 1_00 , lowerCamelCase_ : float = 4.0 , lowerCamelCase_ : int = 1 , lowerCamelCase_ : Optional[Union[torch.Generator, List[torch.Generator]]] = None , lowerCamelCase_ : Optional[torch.FloatTensor] = None , lowerCamelCase_ : Optional[str] = "pil" , lowerCamelCase_ : bool = True , ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : str = self._execution_device
SCREAMING_SNAKE_CASE : Optional[int] = guidance_scale > 1.0
if isinstance(lowerCamelCase_ , lowerCamelCase_ ):
SCREAMING_SNAKE_CASE : int = torch.cat(lowerCamelCase_ , dim=0 )
if isinstance(lowerCamelCase_ , lowerCamelCase_ ):
SCREAMING_SNAKE_CASE : Dict = torch.cat(lowerCamelCase_ , dim=0 )
if isinstance(lowerCamelCase_ , lowerCamelCase_ ):
SCREAMING_SNAKE_CASE : Optional[Any] = torch.cat(lowerCamelCase_ , dim=0 )
SCREAMING_SNAKE_CASE : Any = image_embeds.shape[0] * num_images_per_prompt
if do_classifier_free_guidance:
SCREAMING_SNAKE_CASE : List[Any] = image_embeds.repeat_interleave(lowerCamelCase_ , dim=0 )
SCREAMING_SNAKE_CASE : Optional[int] = negative_image_embeds.repeat_interleave(lowerCamelCase_ , dim=0 )
SCREAMING_SNAKE_CASE : Dict = hint.repeat_interleave(lowerCamelCase_ , dim=0 )
SCREAMING_SNAKE_CASE : List[Any] = torch.cat([negative_image_embeds, image_embeds] , dim=0 ).to(dtype=self.unet.dtype , device=lowerCamelCase_ )
SCREAMING_SNAKE_CASE : Optional[int] = torch.cat([hint, hint] , dim=0 ).to(dtype=self.unet.dtype , device=lowerCamelCase_ )
self.scheduler.set_timesteps(lowerCamelCase_ , device=lowerCamelCase_ )
SCREAMING_SNAKE_CASE : Union[str, Any] = self.scheduler.timesteps
SCREAMING_SNAKE_CASE : Any = self.movq.config.latent_channels
SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE : Optional[Any] = downscale_height_and_width(lowerCamelCase_ , lowerCamelCase_ , self.movq_scale_factor )
# create initial latent
SCREAMING_SNAKE_CASE : str = self.prepare_latents(
(batch_size, num_channels_latents, height, width) , image_embeds.dtype , lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ , self.scheduler , )
for i, t in enumerate(self.progress_bar(lowerCamelCase_ ) ):
# expand the latents if we are doing classifier free guidance
SCREAMING_SNAKE_CASE : Dict = torch.cat([latents] * 2 ) if do_classifier_free_guidance else latents
SCREAMING_SNAKE_CASE : Union[str, Any] = {"""image_embeds""": image_embeds, """hint""": hint}
SCREAMING_SNAKE_CASE : Dict = self.unet(
sample=lowerCamelCase_ , timestep=lowerCamelCase_ , encoder_hidden_states=lowerCamelCase_ , added_cond_kwargs=lowerCamelCase_ , return_dict=lowerCamelCase_ , )[0]
if do_classifier_free_guidance:
SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE : str = noise_pred.split(latents.shape[1] , dim=1 )
SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE : Optional[Any] = noise_pred.chunk(2 )
SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE : Union[str, Any] = variance_pred.chunk(2 )
SCREAMING_SNAKE_CASE : List[str] = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
SCREAMING_SNAKE_CASE : str = torch.cat([noise_pred, variance_pred_text] , dim=1 )
if not (
hasattr(self.scheduler.config , """variance_type""" )
and self.scheduler.config.variance_type in ["learned", "learned_range"]
):
SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE : Optional[Any] = noise_pred.split(latents.shape[1] , dim=1 )
# compute the previous noisy sample x_t -> x_t-1
SCREAMING_SNAKE_CASE : str = self.scheduler.step(
lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ , generator=lowerCamelCase_ , )[0]
# post-processing
SCREAMING_SNAKE_CASE : List[str] = self.movq.decode(lowerCamelCase_ , force_not_quantize=lowerCamelCase_ )["""sample"""]
if output_type not in ["pt", "np", "pil"]:
raise ValueError(f'''Only the output types `pt`, `pil` and `np` are supported not output_type={output_type}''' )
if output_type in ["np", "pil"]:
SCREAMING_SNAKE_CASE : Optional[int] = image * 0.5 + 0.5
SCREAMING_SNAKE_CASE : List[Any] = image.clamp(0 , 1 )
SCREAMING_SNAKE_CASE : List[Any] = image.cpu().permute(0 , 2 , 3 , 1 ).float().numpy()
if output_type == "pil":
SCREAMING_SNAKE_CASE : Any = self.numpy_to_pil(lowerCamelCase_ )
if not return_dict:
return (image,)
return ImagePipelineOutput(images=lowerCamelCase_ )
| 79 | 0 |
'''simple docstring'''
import importlib
import sys
from argparse import REMAINDER, ArgumentParser
from pathlib import Path
import torch_xla.distributed.xla_multiprocessing as xmp
def __A ( ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : List[Any] = ArgumentParser(
description=(
"""PyTorch TPU distributed training launch helper utility that will spawn up multiple distributed processes"""
) )
# Optional arguments for the launch helper
parser.add_argument("""--num_cores""" , type=lowercase__ , default=1 , help="""Number of TPU cores to use (1 or 8).""" )
# positional
parser.add_argument(
"""training_script""" , type=lowercase__ , help=(
"""The full path to the single TPU training """
"""program/script to be launched in parallel, """
"""followed by all the arguments for the """
"""training script"""
) , )
# rest from the training program
parser.add_argument("""training_script_args""" , nargs=lowercase__ )
return parser.parse_args()
def __A ( ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : Optional[int] = parse_args()
# Import training_script as a module.
SCREAMING_SNAKE_CASE : Union[str, Any] = Path(args.training_script )
sys.path.append(str(script_fpath.parent.resolve() ) )
SCREAMING_SNAKE_CASE : Optional[int] = script_fpath.stem
SCREAMING_SNAKE_CASE : Tuple = importlib.import_module(lowercase__ )
# Patch sys.argv
SCREAMING_SNAKE_CASE : int = [args.training_script] + args.training_script_args + ["""--tpu_num_cores""", str(args.num_cores )]
xmp.spawn(mod._mp_fn , args=() , nprocs=args.num_cores )
if __name__ == "__main__":
main()
| 719 |
'''simple docstring'''
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_big_bird import BigBirdTokenizer
else:
__UpperCAmelCase = None
__UpperCAmelCase = logging.get_logger(__name__)
__UpperCAmelCase = {"""vocab_file""": """spiece.model""", """tokenizer_file""": """tokenizer.json"""}
__UpperCAmelCase = {
"""vocab_file""": {
"""google/bigbird-roberta-base""": """https://huggingface.co/google/bigbird-roberta-base/resolve/main/spiece.model""",
"""google/bigbird-roberta-large""": (
"""https://huggingface.co/google/bigbird-roberta-large/resolve/main/spiece.model"""
),
"""google/bigbird-base-trivia-itc""": (
"""https://huggingface.co/google/bigbird-base-trivia-itc/resolve/main/spiece.model"""
),
},
"""tokenizer_file""": {
"""google/bigbird-roberta-base""": (
"""https://huggingface.co/google/bigbird-roberta-base/resolve/main/tokenizer.json"""
),
"""google/bigbird-roberta-large""": (
"""https://huggingface.co/google/bigbird-roberta-large/resolve/main/tokenizer.json"""
),
"""google/bigbird-base-trivia-itc""": (
"""https://huggingface.co/google/bigbird-base-trivia-itc/resolve/main/tokenizer.json"""
),
},
}
__UpperCAmelCase = {
"""google/bigbird-roberta-base""": 4096,
"""google/bigbird-roberta-large""": 4096,
"""google/bigbird-base-trivia-itc""": 4096,
}
__UpperCAmelCase = """▁"""
class UpperCamelCase__ ( lowercase_ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE__ = VOCAB_FILES_NAMES
SCREAMING_SNAKE_CASE__ = PRETRAINED_VOCAB_FILES_MAP
SCREAMING_SNAKE_CASE__ = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
SCREAMING_SNAKE_CASE__ = BigBirdTokenizer
SCREAMING_SNAKE_CASE__ = ['''input_ids''', '''attention_mask''']
SCREAMING_SNAKE_CASE__ = []
def __init__( self : Any , lowerCamelCase_ : str=None , lowerCamelCase_ : List[Any]=None , lowerCamelCase_ : Dict="<unk>" , lowerCamelCase_ : int="<s>" , lowerCamelCase_ : Optional[Any]="</s>" , lowerCamelCase_ : Dict="<pad>" , lowerCamelCase_ : Tuple="[SEP]" , lowerCamelCase_ : Dict="[MASK]" , lowerCamelCase_ : Union[str, Any]="[CLS]" , **lowerCamelCase_ : Dict , ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Any = AddedToken(lowerCamelCase_ , lstrip=lowerCamelCase_ , rstrip=lowerCamelCase_ ) if isinstance(lowerCamelCase_ , lowerCamelCase_ ) else bos_token
SCREAMING_SNAKE_CASE : Dict = AddedToken(lowerCamelCase_ , lstrip=lowerCamelCase_ , rstrip=lowerCamelCase_ ) if isinstance(lowerCamelCase_ , lowerCamelCase_ ) else eos_token
SCREAMING_SNAKE_CASE : Optional[Any] = AddedToken(lowerCamelCase_ , lstrip=lowerCamelCase_ , rstrip=lowerCamelCase_ ) if isinstance(lowerCamelCase_ , lowerCamelCase_ ) else unk_token
SCREAMING_SNAKE_CASE : int = AddedToken(lowerCamelCase_ , lstrip=lowerCamelCase_ , rstrip=lowerCamelCase_ ) if isinstance(lowerCamelCase_ , lowerCamelCase_ ) else pad_token
SCREAMING_SNAKE_CASE : Any = AddedToken(lowerCamelCase_ , lstrip=lowerCamelCase_ , rstrip=lowerCamelCase_ ) if isinstance(lowerCamelCase_ , lowerCamelCase_ ) else cls_token
SCREAMING_SNAKE_CASE : Any = AddedToken(lowerCamelCase_ , lstrip=lowerCamelCase_ , rstrip=lowerCamelCase_ ) if isinstance(lowerCamelCase_ , lowerCamelCase_ ) else sep_token
# Mask token behave like a normal word, i.e. include the space before it
SCREAMING_SNAKE_CASE : int = 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_ , unk_token=lowerCamelCase_ , sep_token=lowerCamelCase_ , pad_token=lowerCamelCase_ , cls_token=lowerCamelCase_ , mask_token=lowerCamelCase_ , **lowerCamelCase_ , )
SCREAMING_SNAKE_CASE : List[Any] = vocab_file
SCREAMING_SNAKE_CASE : Optional[Any] = False if not self.vocab_file else True
def lowerCamelCase_ ( self : Union[str, Any] , lowerCamelCase_ : List[int] , lowerCamelCase_ : Optional[List[int]] = None ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Optional[Any] = [self.sep_token_id]
SCREAMING_SNAKE_CASE : int = [self.cls_token_id]
if token_ids_a is None:
return cls + token_ids_a + sep
return cls + token_ids_a + sep + token_ids_a + sep
def lowerCamelCase_ ( self : List[Any] , lowerCamelCase_ : List[int] , lowerCamelCase_ : Optional[List[int]] = None , lowerCamelCase_ : bool = False ):
'''simple docstring'''
if already_has_special_tokens:
if token_ids_a is not None:
raise ValueError(
"""You should not supply a second sequence if the provided sequence of """
"""ids is already formatted with special tokens for the model.""" )
return [1 if x in [self.sep_token_id, self.cls_token_id] else 0 for x in token_ids_a]
if token_ids_a is None:
return [1] + ([0] * len(lowerCamelCase_ )) + [1]
return [1] + ([0] * len(lowerCamelCase_ )) + [1] + ([0] * len(lowerCamelCase_ )) + [1]
def lowerCamelCase_ ( self : Optional[int] , lowerCamelCase_ : List[int] , lowerCamelCase_ : Optional[List[int]] = None ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Any = [self.sep_token_id]
SCREAMING_SNAKE_CASE : Optional[int] = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep ) * [0] + len(token_ids_a + sep ) * [1]
def lowerCamelCase_ ( self : str , lowerCamelCase_ : str , lowerCamelCase_ : 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 : Tuple = 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,)
| 79 | 0 |
'''simple docstring'''
from typing import List, Optional, Tuple, Union
import torch
from ...models import UNetaDModel
from ...schedulers import ScoreSdeVeScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class UpperCamelCase__ ( __lowercase ):
"""simple docstring"""
SCREAMING_SNAKE_CASE__ = 42
SCREAMING_SNAKE_CASE__ = 42
def __init__( self : Any , lowerCamelCase_ : UNetaDModel , lowerCamelCase_ : ScoreSdeVeScheduler ):
'''simple docstring'''
super().__init__()
self.register_modules(unet=__a , scheduler=__a )
@torch.no_grad()
def __call__( self : List[str] , lowerCamelCase_ : int = 1 , lowerCamelCase_ : int = 20_00 , lowerCamelCase_ : Optional[Union[torch.Generator, List[torch.Generator]]] = None , lowerCamelCase_ : Optional[str] = "pil" , lowerCamelCase_ : bool = True , **lowerCamelCase_ : Any , ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : str = self.unet.config.sample_size
SCREAMING_SNAKE_CASE : List[Any] = (batch_size, 3, img_size, img_size)
SCREAMING_SNAKE_CASE : str = self.unet
SCREAMING_SNAKE_CASE : Any = randn_tensor(__a , generator=__a ) * self.scheduler.init_noise_sigma
SCREAMING_SNAKE_CASE : Any = sample.to(self.device )
self.scheduler.set_timesteps(__a )
self.scheduler.set_sigmas(__a )
for i, t in enumerate(self.progress_bar(self.scheduler.timesteps ) ):
SCREAMING_SNAKE_CASE : int = self.scheduler.sigmas[i] * torch.ones(shape[0] , device=self.device )
# correction step
for _ in range(self.scheduler.config.correct_steps ):
SCREAMING_SNAKE_CASE : Optional[Any] = self.unet(__a , __a ).sample
SCREAMING_SNAKE_CASE : Any = self.scheduler.step_correct(__a , __a , generator=__a ).prev_sample
# prediction step
SCREAMING_SNAKE_CASE : Any = model(__a , __a ).sample
SCREAMING_SNAKE_CASE : int = self.scheduler.step_pred(__a , __a , __a , generator=__a )
SCREAMING_SNAKE_CASE : List[str] = output.prev_sample, output.prev_sample_mean
SCREAMING_SNAKE_CASE : Tuple = sample_mean.clamp(0 , 1 )
SCREAMING_SNAKE_CASE : Dict = sample.cpu().permute(0 , 2 , 3 , 1 ).numpy()
if output_type == "pil":
SCREAMING_SNAKE_CASE : List[Any] = self.numpy_to_pil(__a )
if not return_dict:
return (sample,)
return ImagePipelineOutput(images=__a )
| 720 |
'''simple docstring'''
import unittest
import torch
from torch import nn
from diffusers.models.activations import get_activation
class UpperCamelCase__ ( unittest.TestCase ):
"""simple docstring"""
def lowerCamelCase_ ( self : List[str] ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : List[Any] = get_activation("""swish""" )
self.assertIsInstance(lowerCamelCase_ , nn.SiLU )
self.assertEqual(act(torch.tensor(-1_00 , dtype=torch.floataa ) ).item() , 0 )
self.assertNotEqual(act(torch.tensor(-1 , dtype=torch.floataa ) ).item() , 0 )
self.assertEqual(act(torch.tensor(0 , dtype=torch.floataa ) ).item() , 0 )
self.assertEqual(act(torch.tensor(20 , dtype=torch.floataa ) ).item() , 20 )
def lowerCamelCase_ ( self : int ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Any = get_activation("""silu""" )
self.assertIsInstance(lowerCamelCase_ , nn.SiLU )
self.assertEqual(act(torch.tensor(-1_00 , dtype=torch.floataa ) ).item() , 0 )
self.assertNotEqual(act(torch.tensor(-1 , dtype=torch.floataa ) ).item() , 0 )
self.assertEqual(act(torch.tensor(0 , dtype=torch.floataa ) ).item() , 0 )
self.assertEqual(act(torch.tensor(20 , dtype=torch.floataa ) ).item() , 20 )
def lowerCamelCase_ ( self : Optional[int] ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : List[Any] = get_activation("""mish""" )
self.assertIsInstance(lowerCamelCase_ , nn.Mish )
self.assertEqual(act(torch.tensor(-2_00 , dtype=torch.floataa ) ).item() , 0 )
self.assertNotEqual(act(torch.tensor(-1 , dtype=torch.floataa ) ).item() , 0 )
self.assertEqual(act(torch.tensor(0 , dtype=torch.floataa ) ).item() , 0 )
self.assertEqual(act(torch.tensor(20 , dtype=torch.floataa ) ).item() , 20 )
def lowerCamelCase_ ( self : List[str] ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : str = get_activation("""gelu""" )
self.assertIsInstance(lowerCamelCase_ , nn.GELU )
self.assertEqual(act(torch.tensor(-1_00 , dtype=torch.floataa ) ).item() , 0 )
self.assertNotEqual(act(torch.tensor(-1 , dtype=torch.floataa ) ).item() , 0 )
self.assertEqual(act(torch.tensor(0 , dtype=torch.floataa ) ).item() , 0 )
self.assertEqual(act(torch.tensor(20 , dtype=torch.floataa ) ).item() , 20 )
| 79 | 0 |
import itertools
from dataclasses import dataclass
from typing import Optional
import pandas as pd
import pyarrow as pa
import datasets
from datasets.table import table_cast
@dataclass
class UpperCamelCase__ ( datasets.BuilderConfig ):
"""simple docstring"""
SCREAMING_SNAKE_CASE__ = None
class UpperCamelCase__ ( datasets.ArrowBasedBuilder ):
"""simple docstring"""
SCREAMING_SNAKE_CASE__ = PandasConfig
def lowerCamelCase_ ( self : Tuple ):
'''simple docstring'''
return datasets.DatasetInfo(features=self.config.features )
def lowerCamelCase_ ( self : List[str] , lowerCamelCase_ : List[str] ):
'''simple docstring'''
if not self.config.data_files:
raise ValueError(f'''At least one data file must be specified, but got data_files={self.config.data_files}''' )
SCREAMING_SNAKE_CASE : str = dl_manager.download_and_extract(self.config.data_files )
if isinstance(UpperCAmelCase__ , (str, list, tuple) ):
SCREAMING_SNAKE_CASE : Union[str, Any] = data_files
if isinstance(UpperCAmelCase__ , UpperCAmelCase__ ):
SCREAMING_SNAKE_CASE : Dict = [files]
# Use `dl_manager.iter_files` to skip hidden files in an extracted archive
SCREAMING_SNAKE_CASE : Tuple = [dl_manager.iter_files(UpperCAmelCase__ ) for file in files]
return [datasets.SplitGenerator(name=datasets.Split.TRAIN , gen_kwargs={"""files""": files} )]
SCREAMING_SNAKE_CASE : List[Any] = []
for split_name, files in data_files.items():
if isinstance(UpperCAmelCase__ , UpperCAmelCase__ ):
SCREAMING_SNAKE_CASE : Tuple = [files]
# Use `dl_manager.iter_files` to skip hidden files in an extracted archive
SCREAMING_SNAKE_CASE : Any = [dl_manager.iter_files(UpperCAmelCase__ ) for file in files]
splits.append(datasets.SplitGenerator(name=UpperCAmelCase__ , gen_kwargs={"""files""": files} ) )
return splits
def lowerCamelCase_ ( self : Tuple , lowerCamelCase_ : pa.Table ):
'''simple docstring'''
if self.config.features is not None:
# more expensive cast to support nested features with keys in a different order
# allows str <-> int/float or str to Audio for example
SCREAMING_SNAKE_CASE : List[Any] = table_cast(UpperCAmelCase__ , self.config.features.arrow_schema )
return pa_table
def lowerCamelCase_ ( self : List[Any] , lowerCamelCase_ : Optional[Any] ):
'''simple docstring'''
for i, file in enumerate(itertools.chain.from_iterable(UpperCAmelCase__ ) ):
with open(UpperCAmelCase__ , """rb""" ) as f:
SCREAMING_SNAKE_CASE : Union[str, Any] = pa.Table.from_pandas(pd.read_pickle(UpperCAmelCase__ ) )
yield i, self._cast_table(UpperCAmelCase__ )
| 721 |
'''simple docstring'''
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
if TYPE_CHECKING:
from ... import FeatureExtractionMixin, PreTrainedTokenizerBase, TensorType
__UpperCAmelCase = logging.get_logger(__name__)
__UpperCAmelCase = {
"""microsoft/deberta-v2-xlarge""": """https://huggingface.co/microsoft/deberta-v2-xlarge/resolve/main/config.json""",
"""microsoft/deberta-v2-xxlarge""": """https://huggingface.co/microsoft/deberta-v2-xxlarge/resolve/main/config.json""",
"""microsoft/deberta-v2-xlarge-mnli""": (
"""https://huggingface.co/microsoft/deberta-v2-xlarge-mnli/resolve/main/config.json"""
),
"""microsoft/deberta-v2-xxlarge-mnli""": (
"""https://huggingface.co/microsoft/deberta-v2-xxlarge-mnli/resolve/main/config.json"""
),
}
class UpperCamelCase__ ( lowercase_ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE__ = '''deberta-v2'''
def __init__( self : int , lowerCamelCase_ : Optional[Any]=12_81_00 , lowerCamelCase_ : str=15_36 , lowerCamelCase_ : int=24 , lowerCamelCase_ : List[str]=24 , lowerCamelCase_ : List[Any]=61_44 , lowerCamelCase_ : List[Any]="gelu" , lowerCamelCase_ : Optional[Any]=0.1 , lowerCamelCase_ : List[Any]=0.1 , lowerCamelCase_ : str=5_12 , lowerCamelCase_ : str=0 , lowerCamelCase_ : Union[str, Any]=0.02 , lowerCamelCase_ : Dict=1e-7 , lowerCamelCase_ : Optional[int]=False , lowerCamelCase_ : Optional[int]=-1 , lowerCamelCase_ : List[str]=0 , lowerCamelCase_ : Tuple=True , lowerCamelCase_ : Optional[int]=None , lowerCamelCase_ : Optional[Any]=0 , lowerCamelCase_ : Dict="gelu" , **lowerCamelCase_ : Optional[int] , ):
'''simple docstring'''
super().__init__(**lowerCamelCase_ )
SCREAMING_SNAKE_CASE : Optional[int] = hidden_size
SCREAMING_SNAKE_CASE : Optional[Any] = num_hidden_layers
SCREAMING_SNAKE_CASE : int = num_attention_heads
SCREAMING_SNAKE_CASE : List[str] = intermediate_size
SCREAMING_SNAKE_CASE : int = hidden_act
SCREAMING_SNAKE_CASE : Tuple = hidden_dropout_prob
SCREAMING_SNAKE_CASE : int = attention_probs_dropout_prob
SCREAMING_SNAKE_CASE : str = max_position_embeddings
SCREAMING_SNAKE_CASE : Tuple = type_vocab_size
SCREAMING_SNAKE_CASE : Optional[int] = initializer_range
SCREAMING_SNAKE_CASE : List[Any] = relative_attention
SCREAMING_SNAKE_CASE : str = max_relative_positions
SCREAMING_SNAKE_CASE : int = pad_token_id
SCREAMING_SNAKE_CASE : List[str] = position_biased_input
# Backwards compatibility
if type(lowerCamelCase_ ) == str:
SCREAMING_SNAKE_CASE : Dict = [x.strip() for x in pos_att_type.lower().split("""|""" )]
SCREAMING_SNAKE_CASE : Any = pos_att_type
SCREAMING_SNAKE_CASE : Any = vocab_size
SCREAMING_SNAKE_CASE : Optional[Any] = layer_norm_eps
SCREAMING_SNAKE_CASE : str = kwargs.get("""pooler_hidden_size""" , lowerCamelCase_ )
SCREAMING_SNAKE_CASE : Dict = pooler_dropout
SCREAMING_SNAKE_CASE : Any = pooler_hidden_act
class UpperCamelCase__ ( lowercase_ ):
"""simple docstring"""
@property
def lowerCamelCase_ ( self : Optional[int] ):
'''simple docstring'''
if self.task == "multiple-choice":
SCREAMING_SNAKE_CASE : Optional[int] = {0: """batch""", 1: """choice""", 2: """sequence"""}
else:
SCREAMING_SNAKE_CASE : Union[str, Any] = {0: """batch""", 1: """sequence"""}
if self._config.type_vocab_size > 0:
return OrderedDict(
[("""input_ids""", dynamic_axis), ("""attention_mask""", dynamic_axis), ("""token_type_ids""", dynamic_axis)] )
else:
return OrderedDict([("""input_ids""", dynamic_axis), ("""attention_mask""", dynamic_axis)] )
@property
def lowerCamelCase_ ( self : Tuple ):
'''simple docstring'''
return 12
def lowerCamelCase_ ( self : Optional[Any] , lowerCamelCase_ : Union["PreTrainedTokenizerBase", "FeatureExtractionMixin"] , lowerCamelCase_ : int = -1 , lowerCamelCase_ : int = -1 , lowerCamelCase_ : int = -1 , lowerCamelCase_ : bool = False , lowerCamelCase_ : Optional["TensorType"] = None , lowerCamelCase_ : int = 3 , lowerCamelCase_ : int = 40 , lowerCamelCase_ : int = 40 , lowerCamelCase_ : "PreTrainedTokenizerBase" = None , ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Dict = super().generate_dummy_inputs(preprocessor=lowerCamelCase_ , framework=lowerCamelCase_ )
if self._config.type_vocab_size == 0 and "token_type_ids" in dummy_inputs:
del dummy_inputs["token_type_ids"]
return dummy_inputs
| 79 | 0 |
'''simple docstring'''
import itertools
import os
import random
import tempfile
import unittest
import numpy as np
from datasets import load_dataset
from transformers import is_speech_available
from transformers.testing_utils import check_json_file_has_correct_format, require_torch, require_torchaudio
from transformers.utils.import_utils import is_torch_available
from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin
if is_speech_available():
from transformers import WhisperFeatureExtractor
if is_torch_available():
import torch
__UpperCAmelCase = random.Random()
def __A ( lowerCamelCase_ , lowerCamelCase_=1.0 , lowerCamelCase_=None , lowerCamelCase_=None ):
"""simple docstring"""
if rng is None:
SCREAMING_SNAKE_CASE : Optional[int] = global_rng
SCREAMING_SNAKE_CASE : Union[str, Any] = []
for batch_idx in range(shape[0] ):
values.append([] )
for _ in range(shape[1] ):
values[-1].append(rng.random() * scale )
return values
@require_torch
@require_torchaudio
class UpperCamelCase__ ( unittest.TestCase ):
"""simple docstring"""
def __init__( self : Any , lowerCamelCase_ : Optional[Any] , lowerCamelCase_ : List[str]=7 , lowerCamelCase_ : str=4_00 , lowerCamelCase_ : str=20_00 , lowerCamelCase_ : str=10 , lowerCamelCase_ : Optional[int]=1_60 , lowerCamelCase_ : Optional[int]=8 , lowerCamelCase_ : int=0.0 , lowerCamelCase_ : List[str]=40_00 , lowerCamelCase_ : str=False , lowerCamelCase_ : Tuple=True , ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : int = parent
SCREAMING_SNAKE_CASE : List[Any] = batch_size
SCREAMING_SNAKE_CASE : Union[str, Any] = min_seq_length
SCREAMING_SNAKE_CASE : Tuple = max_seq_length
SCREAMING_SNAKE_CASE : Dict = (self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
SCREAMING_SNAKE_CASE : Tuple = padding_value
SCREAMING_SNAKE_CASE : Tuple = sampling_rate
SCREAMING_SNAKE_CASE : Any = return_attention_mask
SCREAMING_SNAKE_CASE : Tuple = do_normalize
SCREAMING_SNAKE_CASE : List[str] = feature_size
SCREAMING_SNAKE_CASE : Any = chunk_length
SCREAMING_SNAKE_CASE : Optional[int] = hop_length
def lowerCamelCase_ ( self : Dict ):
'''simple docstring'''
return {
"feature_size": self.feature_size,
"hop_length": self.hop_length,
"chunk_length": self.chunk_length,
"padding_value": self.padding_value,
"sampling_rate": self.sampling_rate,
"return_attention_mask": self.return_attention_mask,
"do_normalize": self.do_normalize,
}
def lowerCamelCase_ ( self : int , lowerCamelCase_ : int=False , lowerCamelCase_ : Optional[int]=False ):
'''simple docstring'''
def _flatten(lowerCamelCase_ : str ):
return list(itertools.chain(*A__ ) )
if equal_length:
SCREAMING_SNAKE_CASE : int = [floats_list((self.max_seq_length, self.feature_size) ) for _ in range(self.batch_size )]
else:
# make sure that inputs increase in size
SCREAMING_SNAKE_CASE : int = [
floats_list((x, self.feature_size) )
for x in range(self.min_seq_length , self.max_seq_length , self.seq_length_diff )
]
if numpify:
SCREAMING_SNAKE_CASE : Dict = [np.asarray(A__ ) for x in speech_inputs]
return speech_inputs
@require_torch
@require_torchaudio
class UpperCamelCase__ ( __a , unittest.TestCase ):
"""simple docstring"""
SCREAMING_SNAKE_CASE__ = WhisperFeatureExtractor if is_speech_available() else None
def lowerCamelCase_ ( self : Tuple ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : int = WhisperFeatureExtractionTester(self )
def lowerCamelCase_ ( self : List[str] ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Any = self.feature_extraction_class(**self.feat_extract_dict )
with tempfile.TemporaryDirectory() as tmpdirname:
SCREAMING_SNAKE_CASE : Tuple = feat_extract_first.save_pretrained(A__ )[0]
check_json_file_has_correct_format(A__ )
SCREAMING_SNAKE_CASE : Tuple = self.feature_extraction_class.from_pretrained(A__ )
SCREAMING_SNAKE_CASE : Union[str, Any] = feat_extract_first.to_dict()
SCREAMING_SNAKE_CASE : int = feat_extract_second.to_dict()
SCREAMING_SNAKE_CASE : str = feat_extract_first.mel_filters
SCREAMING_SNAKE_CASE : str = feat_extract_second.mel_filters
self.assertTrue(np.allclose(A__ , A__ ) )
self.assertEqual(A__ , A__ )
def lowerCamelCase_ ( self : Optional[Any] ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Tuple = self.feature_extraction_class(**self.feat_extract_dict )
with tempfile.TemporaryDirectory() as tmpdirname:
SCREAMING_SNAKE_CASE : Optional[int] = os.path.join(A__ , """feat_extract.json""" )
feat_extract_first.to_json_file(A__ )
SCREAMING_SNAKE_CASE : Union[str, Any] = self.feature_extraction_class.from_json_file(A__ )
SCREAMING_SNAKE_CASE : str = feat_extract_first.to_dict()
SCREAMING_SNAKE_CASE : Optional[int] = feat_extract_second.to_dict()
SCREAMING_SNAKE_CASE : Optional[Any] = feat_extract_first.mel_filters
SCREAMING_SNAKE_CASE : Optional[int] = feat_extract_second.mel_filters
self.assertTrue(np.allclose(A__ , A__ ) )
self.assertEqual(A__ , A__ )
def lowerCamelCase_ ( self : Tuple ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Any = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
# create three inputs of length 800, 1000, and 1200
SCREAMING_SNAKE_CASE : Dict = [floats_list((1, x) )[0] for x in range(8_00 , 14_00 , 2_00 )]
SCREAMING_SNAKE_CASE : Dict = [np.asarray(A__ ) for speech_input in speech_inputs]
# Test feature size
SCREAMING_SNAKE_CASE : Any = feature_extractor(A__ , padding="""max_length""" , return_tensors="""np""" ).input_features
self.assertTrue(input_features.ndim == 3 )
self.assertTrue(input_features.shape[-1] == feature_extractor.nb_max_frames )
self.assertTrue(input_features.shape[-2] == feature_extractor.feature_size )
# Test not batched input
SCREAMING_SNAKE_CASE : List[Any] = feature_extractor(speech_inputs[0] , return_tensors="""np""" ).input_features
SCREAMING_SNAKE_CASE : Optional[Any] = feature_extractor(np_speech_inputs[0] , return_tensors="""np""" ).input_features
self.assertTrue(np.allclose(A__ , A__ , atol=1e-3 ) )
# Test batched
SCREAMING_SNAKE_CASE : str = feature_extractor(A__ , return_tensors="""np""" ).input_features
SCREAMING_SNAKE_CASE : int = feature_extractor(A__ , return_tensors="""np""" ).input_features
for enc_seq_a, enc_seq_a in zip(A__ , A__ ):
self.assertTrue(np.allclose(A__ , A__ , atol=1e-3 ) )
# Test 2-D numpy arrays are batched.
SCREAMING_SNAKE_CASE : str = [floats_list((1, x) )[0] for x in (8_00, 8_00, 8_00)]
SCREAMING_SNAKE_CASE : str = np.asarray(A__ )
SCREAMING_SNAKE_CASE : Optional[int] = feature_extractor(A__ , return_tensors="""np""" ).input_features
SCREAMING_SNAKE_CASE : List[Any] = feature_extractor(A__ , return_tensors="""np""" ).input_features
for enc_seq_a, enc_seq_a in zip(A__ , A__ ):
self.assertTrue(np.allclose(A__ , A__ , atol=1e-3 ) )
# Test truncation required
SCREAMING_SNAKE_CASE : Optional[Any] = [floats_list((1, x) )[0] for x in range(2_00 , (feature_extractor.n_samples + 5_00) , 2_00 )]
SCREAMING_SNAKE_CASE : int = [np.asarray(A__ ) for speech_input in speech_inputs]
SCREAMING_SNAKE_CASE : str = [x[: feature_extractor.n_samples] for x in speech_inputs]
SCREAMING_SNAKE_CASE : Optional[int] = [np.asarray(A__ ) for speech_input in speech_inputs_truncated]
SCREAMING_SNAKE_CASE : Optional[Any] = feature_extractor(A__ , return_tensors="""np""" ).input_features
SCREAMING_SNAKE_CASE : Tuple = feature_extractor(A__ , return_tensors="""np""" ).input_features
for enc_seq_a, enc_seq_a in zip(A__ , A__ ):
self.assertTrue(np.allclose(A__ , A__ , atol=1e-3 ) )
def lowerCamelCase_ ( self : Optional[Any] ):
'''simple docstring'''
import torch
SCREAMING_SNAKE_CASE : Any = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
SCREAMING_SNAKE_CASE : Union[str, Any] = np.random.rand(1_00 , 32 ).astype(np.floataa )
SCREAMING_SNAKE_CASE : int = np_speech_inputs.tolist()
for inputs in [py_speech_inputs, np_speech_inputs]:
SCREAMING_SNAKE_CASE : Optional[int] = feature_extractor.pad([{"""input_features""": inputs}] , return_tensors="""np""" )
self.assertTrue(np_processed.input_features.dtype == np.floataa )
SCREAMING_SNAKE_CASE : Dict = feature_extractor.pad([{"""input_features""": inputs}] , return_tensors="""pt""" )
self.assertTrue(pt_processed.input_features.dtype == torch.floataa )
def lowerCamelCase_ ( self : Optional[int] , lowerCamelCase_ : List[Any] ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Dict = load_dataset("""hf-internal-testing/librispeech_asr_dummy""" , """clean""" , split="""validation""" )
# automatic decoding with librispeech
SCREAMING_SNAKE_CASE : str = ds.sort("""id""" ).select(range(A__ ) )[:num_samples]["""audio"""]
return [x["array"] for x in speech_samples]
def lowerCamelCase_ ( self : List[str] ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Optional[int] = torch.tensor(
[
0.1_193, -0.0_946, -0.1_098, -0.0_196, 0.0_225, -0.0_690, -0.1_736, 0.0_951,
0.0_971, -0.0_817, -0.0_702, 0.0_162, 0.0_260, 0.0_017, -0.0_192, -0.1_678,
0.0_709, -0.1_867, -0.0_655, -0.0_274, -0.0_234, -0.1_884, -0.0_516, -0.0_554,
-0.0_274, -0.1_425, -0.1_423, 0.0_837, 0.0_377, -0.0_854
] )
# fmt: on
SCREAMING_SNAKE_CASE : Tuple = self._load_datasamples(1 )
SCREAMING_SNAKE_CASE : List[Any] = WhisperFeatureExtractor()
SCREAMING_SNAKE_CASE : Tuple = feature_extractor(A__ , return_tensors="""pt""" ).input_features
self.assertEqual(input_features.shape , (1, 80, 30_00) )
self.assertTrue(torch.allclose(input_features[0, 0, :30] , A__ , atol=1e-4 ) )
def lowerCamelCase_ ( self : Union[str, Any] ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Any = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
SCREAMING_SNAKE_CASE : Any = self._load_datasamples(1 )[0]
SCREAMING_SNAKE_CASE : int = ((audio - audio.min()) / (audio.max() - audio.min())) * 6_55_35 # Rescale to [0, 65535] to show issue
SCREAMING_SNAKE_CASE : int = feat_extract.zero_mean_unit_var_norm([audio] , attention_mask=A__ )[0]
self.assertTrue(np.all(np.mean(A__ ) < 1e-3 ) )
self.assertTrue(np.all(np.abs(np.var(A__ ) - 1 ) < 1e-3 ) )
| 700 |
'''simple docstring'''
from collections import deque
from math import floor
from random import random
from time import time
class UpperCamelCase__ :
"""simple docstring"""
def __init__( self : Optional[Any] ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : str = {}
def lowerCamelCase_ ( self : Union[str, Any] , lowerCamelCase_ : Optional[Any] , lowerCamelCase_ : Any , lowerCamelCase_ : Optional[int]=1 ):
'''simple docstring'''
if self.graph.get(lowerCamelCase_ ):
if self.graph[u].count([w, v] ) == 0:
self.graph[u].append([w, v] )
else:
SCREAMING_SNAKE_CASE : str = [[w, v]]
if not self.graph.get(lowerCamelCase_ ):
SCREAMING_SNAKE_CASE : Tuple = []
def lowerCamelCase_ ( self : Optional[Any] ):
'''simple docstring'''
return list(self.graph )
def lowerCamelCase_ ( self : Optional[int] , lowerCamelCase_ : Any , lowerCamelCase_ : str ):
'''simple docstring'''
if self.graph.get(lowerCamelCase_ ):
for _ in self.graph[u]:
if _[1] == v:
self.graph[u].remove(lowerCamelCase_ )
def lowerCamelCase_ ( self : List[str] , lowerCamelCase_ : Union[str, Any]=-2 , lowerCamelCase_ : str=-1 ):
'''simple docstring'''
if s == d:
return []
SCREAMING_SNAKE_CASE : List[str] = []
SCREAMING_SNAKE_CASE : List[str] = []
if s == -2:
SCREAMING_SNAKE_CASE : Tuple = list(self.graph )[0]
stack.append(lowerCamelCase_ )
visited.append(lowerCamelCase_ )
SCREAMING_SNAKE_CASE : Any = s
while True:
# check if there is any non isolated nodes
if len(self.graph[s] ) != 0:
SCREAMING_SNAKE_CASE : Optional[Any] = s
for node in self.graph[s]:
if visited.count(node[1] ) < 1:
if node[1] == d:
visited.append(lowerCamelCase_ )
return visited
else:
stack.append(node[1] )
visited.append(node[1] )
SCREAMING_SNAKE_CASE : Tuple = node[1]
break
# check if all the children are visited
if s == ss:
stack.pop()
if len(lowerCamelCase_ ) != 0:
SCREAMING_SNAKE_CASE : int = stack[len(lowerCamelCase_ ) - 1]
else:
SCREAMING_SNAKE_CASE : Any = ss
# check if se have reached the starting point
if len(lowerCamelCase_ ) == 0:
return visited
def lowerCamelCase_ ( self : Dict , lowerCamelCase_ : Optional[int]=-1 ):
'''simple docstring'''
if c == -1:
SCREAMING_SNAKE_CASE : str = floor(random() * 1_00_00 ) + 10
for i in range(lowerCamelCase_ ):
# every vertex has max 100 edges
for _ in range(floor(random() * 1_02 ) + 1 ):
SCREAMING_SNAKE_CASE : Union[str, Any] = floor(random() * c ) + 1
if n != i:
self.add_pair(lowerCamelCase_ , lowerCamelCase_ , 1 )
def lowerCamelCase_ ( self : List[Any] , lowerCamelCase_ : Any=-2 ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : str = deque()
SCREAMING_SNAKE_CASE : List[str] = []
if s == -2:
SCREAMING_SNAKE_CASE : int = list(self.graph )[0]
d.append(lowerCamelCase_ )
visited.append(lowerCamelCase_ )
while d:
SCREAMING_SNAKE_CASE : Dict = d.popleft()
if len(self.graph[s] ) != 0:
for node in self.graph[s]:
if visited.count(node[1] ) < 1:
d.append(node[1] )
visited.append(node[1] )
return visited
def lowerCamelCase_ ( self : Dict , lowerCamelCase_ : Optional[Any] ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Dict = 0
for x in self.graph:
for y in self.graph[x]:
if y[1] == u:
count += 1
return count
def lowerCamelCase_ ( self : Optional[Any] , lowerCamelCase_ : Tuple ):
'''simple docstring'''
return len(self.graph[u] )
def lowerCamelCase_ ( self : str , lowerCamelCase_ : Optional[Any]=-2 ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : List[Any] = []
SCREAMING_SNAKE_CASE : List[str] = []
if s == -2:
SCREAMING_SNAKE_CASE : Union[str, Any] = list(self.graph )[0]
stack.append(lowerCamelCase_ )
visited.append(lowerCamelCase_ )
SCREAMING_SNAKE_CASE : Union[str, Any] = s
SCREAMING_SNAKE_CASE : List[str] = []
while True:
# check if there is any non isolated nodes
if len(self.graph[s] ) != 0:
SCREAMING_SNAKE_CASE : Optional[Any] = s
for node in self.graph[s]:
if visited.count(node[1] ) < 1:
stack.append(node[1] )
visited.append(node[1] )
SCREAMING_SNAKE_CASE : int = node[1]
break
# check if all the children are visited
if s == ss:
sorted_nodes.append(stack.pop() )
if len(lowerCamelCase_ ) != 0:
SCREAMING_SNAKE_CASE : List[Any] = stack[len(lowerCamelCase_ ) - 1]
else:
SCREAMING_SNAKE_CASE : int = ss
# check if se have reached the starting point
if len(lowerCamelCase_ ) == 0:
return sorted_nodes
def lowerCamelCase_ ( self : Optional[int] ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : List[Any] = []
SCREAMING_SNAKE_CASE : Any = []
SCREAMING_SNAKE_CASE : List[Any] = list(self.graph )[0]
stack.append(lowerCamelCase_ )
visited.append(lowerCamelCase_ )
SCREAMING_SNAKE_CASE : Any = -2
SCREAMING_SNAKE_CASE : int = []
SCREAMING_SNAKE_CASE : Union[str, Any] = s
SCREAMING_SNAKE_CASE : Tuple = False
SCREAMING_SNAKE_CASE : Union[str, Any] = set()
while True:
# check if there is any non isolated nodes
if len(self.graph[s] ) != 0:
SCREAMING_SNAKE_CASE : Union[str, Any] = s
for node in self.graph[s]:
if (
visited.count(node[1] ) > 0
and node[1] != parent
and indirect_parents.count(node[1] ) > 0
and not on_the_way_back
):
SCREAMING_SNAKE_CASE : Any = len(lowerCamelCase_ ) - 1
while len_stack >= 0:
if stack[len_stack] == node[1]:
anticipating_nodes.add(node[1] )
break
else:
anticipating_nodes.add(stack[len_stack] )
len_stack -= 1
if visited.count(node[1] ) < 1:
stack.append(node[1] )
visited.append(node[1] )
SCREAMING_SNAKE_CASE : int = node[1]
break
# check if all the children are visited
if s == ss:
stack.pop()
SCREAMING_SNAKE_CASE : int = True
if len(lowerCamelCase_ ) != 0:
SCREAMING_SNAKE_CASE : Any = stack[len(lowerCamelCase_ ) - 1]
else:
SCREAMING_SNAKE_CASE : List[str] = False
indirect_parents.append(lowerCamelCase_ )
SCREAMING_SNAKE_CASE : List[Any] = s
SCREAMING_SNAKE_CASE : List[Any] = ss
# check if se have reached the starting point
if len(lowerCamelCase_ ) == 0:
return list(lowerCamelCase_ )
def lowerCamelCase_ ( self : Dict ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Tuple = []
SCREAMING_SNAKE_CASE : int = []
SCREAMING_SNAKE_CASE : Any = list(self.graph )[0]
stack.append(lowerCamelCase_ )
visited.append(lowerCamelCase_ )
SCREAMING_SNAKE_CASE : Union[str, Any] = -2
SCREAMING_SNAKE_CASE : List[Any] = []
SCREAMING_SNAKE_CASE : Tuple = s
SCREAMING_SNAKE_CASE : Dict = False
SCREAMING_SNAKE_CASE : Optional[Any] = set()
while True:
# check if there is any non isolated nodes
if len(self.graph[s] ) != 0:
SCREAMING_SNAKE_CASE : str = s
for node in self.graph[s]:
if (
visited.count(node[1] ) > 0
and node[1] != parent
and indirect_parents.count(node[1] ) > 0
and not on_the_way_back
):
SCREAMING_SNAKE_CASE : str = len(lowerCamelCase_ ) - 1
while len_stack_minus_one >= 0:
if stack[len_stack_minus_one] == node[1]:
anticipating_nodes.add(node[1] )
break
else:
return True
if visited.count(node[1] ) < 1:
stack.append(node[1] )
visited.append(node[1] )
SCREAMING_SNAKE_CASE : Optional[Any] = node[1]
break
# check if all the children are visited
if s == ss:
stack.pop()
SCREAMING_SNAKE_CASE : Dict = True
if len(lowerCamelCase_ ) != 0:
SCREAMING_SNAKE_CASE : List[str] = stack[len(lowerCamelCase_ ) - 1]
else:
SCREAMING_SNAKE_CASE : List[Any] = False
indirect_parents.append(lowerCamelCase_ )
SCREAMING_SNAKE_CASE : Optional[int] = s
SCREAMING_SNAKE_CASE : Optional[int] = ss
# check if se have reached the starting point
if len(lowerCamelCase_ ) == 0:
return False
def lowerCamelCase_ ( self : Any , lowerCamelCase_ : str=-2 , lowerCamelCase_ : int=-1 ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Optional[Any] = time()
self.dfs(lowerCamelCase_ , lowerCamelCase_ )
SCREAMING_SNAKE_CASE : List[str] = time()
return end - begin
def lowerCamelCase_ ( self : int , lowerCamelCase_ : Tuple=-2 ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Optional[int] = time()
self.bfs(lowerCamelCase_ )
SCREAMING_SNAKE_CASE : List[str] = time()
return end - begin
class UpperCamelCase__ :
"""simple docstring"""
def __init__( self : Optional[int] ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Tuple = {}
def lowerCamelCase_ ( self : List[str] , lowerCamelCase_ : Optional[Any] , lowerCamelCase_ : Dict , lowerCamelCase_ : Union[str, Any]=1 ):
'''simple docstring'''
if self.graph.get(lowerCamelCase_ ):
# if there already is a edge
if self.graph[u].count([w, v] ) == 0:
self.graph[u].append([w, v] )
else:
# if u does not exist
SCREAMING_SNAKE_CASE : Any = [[w, v]]
# add the other way
if self.graph.get(lowerCamelCase_ ):
# if there already is a edge
if self.graph[v].count([w, u] ) == 0:
self.graph[v].append([w, u] )
else:
# if u does not exist
SCREAMING_SNAKE_CASE : Any = [[w, u]]
def lowerCamelCase_ ( self : Tuple , lowerCamelCase_ : Tuple , lowerCamelCase_ : Any ):
'''simple docstring'''
if self.graph.get(lowerCamelCase_ ):
for _ in self.graph[u]:
if _[1] == v:
self.graph[u].remove(lowerCamelCase_ )
# the other way round
if self.graph.get(lowerCamelCase_ ):
for _ in self.graph[v]:
if _[1] == u:
self.graph[v].remove(lowerCamelCase_ )
def lowerCamelCase_ ( self : int , lowerCamelCase_ : str=-2 , lowerCamelCase_ : List[str]=-1 ):
'''simple docstring'''
if s == d:
return []
SCREAMING_SNAKE_CASE : List[str] = []
SCREAMING_SNAKE_CASE : Any = []
if s == -2:
SCREAMING_SNAKE_CASE : List[Any] = list(self.graph )[0]
stack.append(lowerCamelCase_ )
visited.append(lowerCamelCase_ )
SCREAMING_SNAKE_CASE : List[str] = s
while True:
# check if there is any non isolated nodes
if len(self.graph[s] ) != 0:
SCREAMING_SNAKE_CASE : Union[str, Any] = s
for node in self.graph[s]:
if visited.count(node[1] ) < 1:
if node[1] == d:
visited.append(lowerCamelCase_ )
return visited
else:
stack.append(node[1] )
visited.append(node[1] )
SCREAMING_SNAKE_CASE : Any = node[1]
break
# check if all the children are visited
if s == ss:
stack.pop()
if len(lowerCamelCase_ ) != 0:
SCREAMING_SNAKE_CASE : Any = stack[len(lowerCamelCase_ ) - 1]
else:
SCREAMING_SNAKE_CASE : List[str] = ss
# check if se have reached the starting point
if len(lowerCamelCase_ ) == 0:
return visited
def lowerCamelCase_ ( self : Any , lowerCamelCase_ : List[str]=-1 ):
'''simple docstring'''
if c == -1:
SCREAMING_SNAKE_CASE : Any = floor(random() * 1_00_00 ) + 10
for i in range(lowerCamelCase_ ):
# every vertex has max 100 edges
for _ in range(floor(random() * 1_02 ) + 1 ):
SCREAMING_SNAKE_CASE : List[str] = floor(random() * c ) + 1
if n != i:
self.add_pair(lowerCamelCase_ , lowerCamelCase_ , 1 )
def lowerCamelCase_ ( self : List[str] , lowerCamelCase_ : List[Any]=-2 ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : List[Any] = deque()
SCREAMING_SNAKE_CASE : Tuple = []
if s == -2:
SCREAMING_SNAKE_CASE : Any = list(self.graph )[0]
d.append(lowerCamelCase_ )
visited.append(lowerCamelCase_ )
while d:
SCREAMING_SNAKE_CASE : List[Any] = d.popleft()
if len(self.graph[s] ) != 0:
for node in self.graph[s]:
if visited.count(node[1] ) < 1:
d.append(node[1] )
visited.append(node[1] )
return visited
def lowerCamelCase_ ( self : Tuple , lowerCamelCase_ : str ):
'''simple docstring'''
return len(self.graph[u] )
def lowerCamelCase_ ( self : List[str] ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : List[str] = []
SCREAMING_SNAKE_CASE : Any = []
SCREAMING_SNAKE_CASE : Optional[Any] = list(self.graph )[0]
stack.append(lowerCamelCase_ )
visited.append(lowerCamelCase_ )
SCREAMING_SNAKE_CASE : Optional[int] = -2
SCREAMING_SNAKE_CASE : Tuple = []
SCREAMING_SNAKE_CASE : Any = s
SCREAMING_SNAKE_CASE : Optional[Any] = False
SCREAMING_SNAKE_CASE : Optional[Any] = set()
while True:
# check if there is any non isolated nodes
if len(self.graph[s] ) != 0:
SCREAMING_SNAKE_CASE : Optional[Any] = s
for node in self.graph[s]:
if (
visited.count(node[1] ) > 0
and node[1] != parent
and indirect_parents.count(node[1] ) > 0
and not on_the_way_back
):
SCREAMING_SNAKE_CASE : Optional[Any] = len(lowerCamelCase_ ) - 1
while len_stack >= 0:
if stack[len_stack] == node[1]:
anticipating_nodes.add(node[1] )
break
else:
anticipating_nodes.add(stack[len_stack] )
len_stack -= 1
if visited.count(node[1] ) < 1:
stack.append(node[1] )
visited.append(node[1] )
SCREAMING_SNAKE_CASE : str = node[1]
break
# check if all the children are visited
if s == ss:
stack.pop()
SCREAMING_SNAKE_CASE : Optional[int] = True
if len(lowerCamelCase_ ) != 0:
SCREAMING_SNAKE_CASE : int = stack[len(lowerCamelCase_ ) - 1]
else:
SCREAMING_SNAKE_CASE : Union[str, Any] = False
indirect_parents.append(lowerCamelCase_ )
SCREAMING_SNAKE_CASE : List[str] = s
SCREAMING_SNAKE_CASE : str = ss
# check if se have reached the starting point
if len(lowerCamelCase_ ) == 0:
return list(lowerCamelCase_ )
def lowerCamelCase_ ( self : Union[str, Any] ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Optional[Any] = []
SCREAMING_SNAKE_CASE : int = []
SCREAMING_SNAKE_CASE : Any = list(self.graph )[0]
stack.append(lowerCamelCase_ )
visited.append(lowerCamelCase_ )
SCREAMING_SNAKE_CASE : Dict = -2
SCREAMING_SNAKE_CASE : int = []
SCREAMING_SNAKE_CASE : int = s
SCREAMING_SNAKE_CASE : Union[str, Any] = False
SCREAMING_SNAKE_CASE : Tuple = set()
while True:
# check if there is any non isolated nodes
if len(self.graph[s] ) != 0:
SCREAMING_SNAKE_CASE : Any = s
for node in self.graph[s]:
if (
visited.count(node[1] ) > 0
and node[1] != parent
and indirect_parents.count(node[1] ) > 0
and not on_the_way_back
):
SCREAMING_SNAKE_CASE : Optional[Any] = len(lowerCamelCase_ ) - 1
while len_stack_minus_one >= 0:
if stack[len_stack_minus_one] == node[1]:
anticipating_nodes.add(node[1] )
break
else:
return True
if visited.count(node[1] ) < 1:
stack.append(node[1] )
visited.append(node[1] )
SCREAMING_SNAKE_CASE : Tuple = node[1]
break
# check if all the children are visited
if s == ss:
stack.pop()
SCREAMING_SNAKE_CASE : Any = True
if len(lowerCamelCase_ ) != 0:
SCREAMING_SNAKE_CASE : str = stack[len(lowerCamelCase_ ) - 1]
else:
SCREAMING_SNAKE_CASE : Optional[Any] = False
indirect_parents.append(lowerCamelCase_ )
SCREAMING_SNAKE_CASE : Any = s
SCREAMING_SNAKE_CASE : Tuple = ss
# check if se have reached the starting point
if len(lowerCamelCase_ ) == 0:
return False
def lowerCamelCase_ ( self : List[Any] ):
'''simple docstring'''
return list(self.graph )
def lowerCamelCase_ ( self : str , lowerCamelCase_ : List[str]=-2 , lowerCamelCase_ : str=-1 ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : str = time()
self.dfs(lowerCamelCase_ , lowerCamelCase_ )
SCREAMING_SNAKE_CASE : Tuple = time()
return end - begin
def lowerCamelCase_ ( self : Tuple , lowerCamelCase_ : Dict=-2 ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Optional[Any] = time()
self.bfs(lowerCamelCase_ )
SCREAMING_SNAKE_CASE : Dict = time()
return end - begin
| 79 | 0 |
'''simple docstring'''
import unittest
from transformers import GPTSwaTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin
__UpperCAmelCase = get_tests_dir("""fixtures/test_sentencepiece_with_bytefallback.model""")
@require_sentencepiece
@require_tokenizers
class UpperCamelCase__ ( lowercase_ , unittest.TestCase ):
"""simple docstring"""
SCREAMING_SNAKE_CASE__ = GPTSwaTokenizer
SCREAMING_SNAKE_CASE__ = False
SCREAMING_SNAKE_CASE__ = True
SCREAMING_SNAKE_CASE__ = False
def lowerCamelCase_ ( self : Optional[Any] ):
'''simple docstring'''
super().setUp()
# We have a SentencePiece fixture for testing
SCREAMING_SNAKE_CASE : List[Any] = GPTSwaTokenizer(lowerCamelCase_ , eos_token="""<unk>""" , bos_token="""<unk>""" , pad_token="""<unk>""" )
tokenizer.save_pretrained(self.tmpdirname )
def lowerCamelCase_ ( self : Optional[int] , lowerCamelCase_ : str ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Optional[int] = """This is a test"""
SCREAMING_SNAKE_CASE : List[Any] = """This is a test"""
return input_text, output_text
def lowerCamelCase_ ( self : str ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Optional[int] = """<s>"""
SCREAMING_SNAKE_CASE : Dict = 1
self.assertEqual(self.get_tokenizer()._convert_token_to_id(lowerCamelCase_ ) , lowerCamelCase_ )
self.assertEqual(self.get_tokenizer()._convert_id_to_token(lowerCamelCase_ ) , lowerCamelCase_ )
def lowerCamelCase_ ( self : Union[str, Any] ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : str = list(self.get_tokenizer().get_vocab().keys() )
self.assertEqual(vocab_keys[0] , """<unk>""" )
self.assertEqual(vocab_keys[1] , """<s>""" )
self.assertEqual(vocab_keys[-1] , """j""" )
self.assertEqual(len(lowerCamelCase_ ) , 20_00 )
def lowerCamelCase_ ( self : Dict ):
'''simple docstring'''
self.assertEqual(self.get_tokenizer().vocab_size , 20_00 )
def lowerCamelCase_ ( self : Tuple ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Optional[Any] = GPTSwaTokenizer(lowerCamelCase_ )
SCREAMING_SNAKE_CASE : List[Any] = tokenizer.tokenize("""This is a test""" )
self.assertListEqual(lowerCamelCase_ , ["""▁This""", """▁is""", """▁a""", """▁t""", """est"""] )
self.assertListEqual(tokenizer.convert_tokens_to_ids(lowerCamelCase_ ) , [4_65, 2_87, 2_65, 6_31, 8_42] )
SCREAMING_SNAKE_CASE : Optional[int] = tokenizer.tokenize("""I was born in 92000, and this is falsé.""" )
# fmt: off
self.assertListEqual(
lowerCamelCase_ , ["""▁I""", """▁was""", """▁bor""", """n""", """▁in""", """▁""", """<0x39>""", """2""", """0""", """0""", """0""", """,""", """▁and""", """▁this""", """▁is""", """▁f""", """al""", """s""", """<0xC3>""", """<0xA9>""", """."""] , )
# fmt: on
SCREAMING_SNAKE_CASE : Tuple = tokenizer.convert_tokens_to_ids(lowerCamelCase_ )
self.assertListEqual(
lowerCamelCase_ , [2_62, 2_72, 15_25, 2_86, 2_71, 2_68, 60, 9_16, 6_33, 6_33, 6_33, 2_59, 2_66, 3_01, 2_87, 3_84, 3_67, 2_63, 1_98, 1_72, 2_60] , )
SCREAMING_SNAKE_CASE : int = tokenizer.convert_ids_to_tokens(lowerCamelCase_ )
# fmt: off
self.assertListEqual(
lowerCamelCase_ , ["""▁I""", """▁was""", """▁bor""", """n""", """▁in""", """▁""", """<0x39>""", """2""", """0""", """0""", """0""", """,""", """▁and""", """▁this""", """▁is""", """▁f""", """al""", """s""", """<0xC3>""", """<0xA9>""", """."""] )
# fmt: on
def lowerCamelCase_ ( self : Optional[Any] ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : str = GPTSwaTokenizer(lowerCamelCase_ )
SCREAMING_SNAKE_CASE : Any = ["""This is a test""", """I was born in 92000, and this is falsé."""]
SCREAMING_SNAKE_CASE : Optional[Any] = [
[4_65, 2_87, 2_65, 6_31, 8_42],
[2_62, 2_72, 15_25, 2_86, 2_71, 2_68, 60, 9_16, 6_33, 6_33, 6_33, 2_59, 2_66, 3_01, 2_87, 3_84, 3_67, 2_63, 1_98, 1_72, 2_60],
]
# Test that encode_fast returns the same as tokenize + convert_tokens_to_ids
for text, expected_ids in zip(lowerCamelCase_ , lowerCamelCase_ ):
self.assertListEqual(tokenizer.encode_fast(lowerCamelCase_ ) , lowerCamelCase_ )
# Test that decode_fast returns the input text
for text, token_ids in zip(lowerCamelCase_ , lowerCamelCase_ ):
self.assertEqual(tokenizer.decode_fast(lowerCamelCase_ ) , lowerCamelCase_ )
@slow
def lowerCamelCase_ ( self : Tuple ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : List[str] = [
"""<|python|>def fibonacci(n)\n if n < 0:\n print(\'Incorrect input\')""",
"""Hey there, how are you doing this fine day?""",
"""This is a text with a trailing spaces followed by a dot .""",
"""Häj sväjs lillebrör! =)""",
"""Det är inget fel på Mr. Cool""",
]
# fmt: off
SCREAMING_SNAKE_CASE : List[str] = {"""input_ids""": [[6_34_23, 5, 68_11, 1_49_54, 2_82, 8_16, 38_21, 6_34_66, 6_34_25, 6_34_62, 18, 6_39_78, 6_78, 3_01, 13_20, 6_34_23, 6_34_55, 6_34_58, 18, 6_39_82, 42_46, 39_40, 19_01, 4_77_89, 55_47, 1_89_94], [1_96_30, 11_00, 6_34_46, 13_42, 6_33, 5_44, 44_88, 5_93, 51_02, 24_16, 6_34_95, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [16_52, 4_28, 2_68, 19_36, 5_15, 2_68, 5_85_93, 2_24_13, 91_06, 5_46, 2_68, 3_32_13, 6_39_79, 6_98, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [5_51_30, 6_34_50, 9_24, 6_34_49, 22_49, 40_62, 15_58, 3_18, 6_35_04, 2_14_98, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [5_09, 3_77, 28_27, 25_59, 3_32, 65_75, 6_34_43, 2_68_01, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], """token_type_ids""": [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], """attention_mask""": [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}
# fmt: on
self.tokenizer_integration_test_util(
expected_encoding=lowerCamelCase_ , model_name="""AI-Sweden/gpt-sw3-126m""" , sequences=lowerCamelCase_ , )
| 701 |
'''simple docstring'''
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
__UpperCAmelCase = logging.get_logger(__name__)
__UpperCAmelCase = {"""vocab_file""": """sentencepiece.bpe.model"""}
__UpperCAmelCase = {
"""vocab_file""": {
"""moussaKam/mbarthez""": """https://huggingface.co/moussaKam/mbarthez/resolve/main/sentencepiece.bpe.model""",
"""moussaKam/barthez""": """https://huggingface.co/moussaKam/barthez/resolve/main/sentencepiece.bpe.model""",
"""moussaKam/barthez-orangesum-title""": (
"""https://huggingface.co/moussaKam/barthez-orangesum-title/resolve/main/sentencepiece.bpe.model"""
),
},
}
__UpperCAmelCase = {
"""moussaKam/mbarthez""": 1024,
"""moussaKam/barthez""": 1024,
"""moussaKam/barthez-orangesum-title""": 1024,
}
__UpperCAmelCase = """▁"""
class UpperCamelCase__ ( lowercase_ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE__ = VOCAB_FILES_NAMES
SCREAMING_SNAKE_CASE__ = PRETRAINED_VOCAB_FILES_MAP
SCREAMING_SNAKE_CASE__ = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
SCREAMING_SNAKE_CASE__ = ['''input_ids''', '''attention_mask''']
def __init__( self : Optional[Any] , lowerCamelCase_ : List[str] , lowerCamelCase_ : Tuple="<s>" , lowerCamelCase_ : Union[str, Any]="</s>" , lowerCamelCase_ : Tuple="</s>" , lowerCamelCase_ : str="<s>" , lowerCamelCase_ : Optional[int]="<unk>" , lowerCamelCase_ : List[Any]="<pad>" , lowerCamelCase_ : Optional[Any]="<mask>" , lowerCamelCase_ : Optional[Dict[str, Any]] = None , **lowerCamelCase_ : List[str] , ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : List[str] = AddedToken(lowerCamelCase_ , lstrip=lowerCamelCase_ , rstrip=lowerCamelCase_ ) if isinstance(lowerCamelCase_ , lowerCamelCase_ ) else mask_token
SCREAMING_SNAKE_CASE : Union[str, Any] = {} if sp_model_kwargs is None else sp_model_kwargs
super().__init__(
bos_token=lowerCamelCase_ , eos_token=lowerCamelCase_ , unk_token=lowerCamelCase_ , sep_token=lowerCamelCase_ , cls_token=lowerCamelCase_ , pad_token=lowerCamelCase_ , mask_token=lowerCamelCase_ , sp_model_kwargs=self.sp_model_kwargs , **lowerCamelCase_ , )
SCREAMING_SNAKE_CASE : Dict = vocab_file
SCREAMING_SNAKE_CASE : Dict = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(str(lowerCamelCase_ ) )
SCREAMING_SNAKE_CASE : Optional[int] = {"""<s>""": 0, """<pad>""": 1, """</s>""": 2, """<unk>""": 3}
SCREAMING_SNAKE_CASE : str = len(self.sp_model ) - 1
SCREAMING_SNAKE_CASE : Optional[int] = {v: k for k, v in self.fairseq_tokens_to_ids.items()}
def lowerCamelCase_ ( self : List[str] , lowerCamelCase_ : List[int] , lowerCamelCase_ : 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 : List[Any] = [self.cls_token_id]
SCREAMING_SNAKE_CASE : int = [self.sep_token_id]
return cls + token_ids_a + sep + sep + token_ids_a + sep
def lowerCamelCase_ ( self : Any , lowerCamelCase_ : List[int] , lowerCamelCase_ : Optional[List[int]] = None , lowerCamelCase_ : bool = False ):
'''simple docstring'''
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=lowerCamelCase_ , token_ids_a=lowerCamelCase_ , already_has_special_tokens=lowerCamelCase_ )
if token_ids_a is None:
return [1] + ([0] * len(lowerCamelCase_ )) + [1]
return [1] + ([0] * len(lowerCamelCase_ )) + [1, 1] + ([0] * len(lowerCamelCase_ )) + [1]
def lowerCamelCase_ ( self : str , lowerCamelCase_ : List[int] , lowerCamelCase_ : Optional[List[int]] = None ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : List[str] = [self.sep_token_id]
SCREAMING_SNAKE_CASE : Dict = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep + sep + token_ids_a + sep ) * [0]
@property
def lowerCamelCase_ ( self : List[str] ):
'''simple docstring'''
return len(self.sp_model )
def lowerCamelCase_ ( self : Optional[int] ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : List[str] = {self.convert_ids_to_tokens(lowerCamelCase_ ): i for i in range(self.vocab_size )}
vocab.update(self.added_tokens_encoder )
return vocab
def lowerCamelCase_ ( self : Union[str, Any] , lowerCamelCase_ : str ):
'''simple docstring'''
return self.sp_model.encode(lowerCamelCase_ , out_type=lowerCamelCase_ )
def lowerCamelCase_ ( self : Tuple , lowerCamelCase_ : str ):
'''simple docstring'''
if token in self.fairseq_tokens_to_ids:
return self.fairseq_tokens_to_ids[token]
SCREAMING_SNAKE_CASE : List[str] = self.sp_model.PieceToId(lowerCamelCase_ )
return spm_id if spm_id else self.unk_token_id
def lowerCamelCase_ ( self : int , lowerCamelCase_ : List[str] ):
'''simple docstring'''
if index in self.fairseq_ids_to_tokens:
return self.fairseq_ids_to_tokens[index]
return self.sp_model.IdToPiece(lowerCamelCase_ )
def lowerCamelCase_ ( self : Optional[Any] , lowerCamelCase_ : Optional[Any] ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : str = []
SCREAMING_SNAKE_CASE : Tuple = """"""
SCREAMING_SNAKE_CASE : Dict = False
for token in tokens:
# make sure that special tokens are not decoded using sentencepiece model
if token in self.all_special_tokens:
if not prev_is_special:
out_string += " "
out_string += self.sp_model.decode(lowerCamelCase_ ) + token
SCREAMING_SNAKE_CASE : str = True
SCREAMING_SNAKE_CASE : Optional[Any] = []
else:
current_sub_tokens.append(lowerCamelCase_ )
SCREAMING_SNAKE_CASE : Optional[int] = False
out_string += self.sp_model.decode(lowerCamelCase_ )
return out_string.strip()
def __getstate__( self : Tuple ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Dict = self.__dict__.copy()
SCREAMING_SNAKE_CASE : List[Any] = None
return state
def __setstate__( self : Optional[Any] , lowerCamelCase_ : Tuple ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : str = d
# for backward compatibility
if not hasattr(self , """sp_model_kwargs""" ):
SCREAMING_SNAKE_CASE : int = {}
SCREAMING_SNAKE_CASE : Any = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(self.vocab_file )
def lowerCamelCase_ ( self : Any , lowerCamelCase_ : str , lowerCamelCase_ : Optional[str] = None ):
'''simple docstring'''
if not os.path.isdir(lowerCamelCase_ ):
logger.error(f'''Vocabulary path ({save_directory}) should be a directory''' )
return
SCREAMING_SNAKE_CASE : Dict = os.path.join(
lowerCamelCase_ , (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(lowerCamelCase_ ) and os.path.isfile(self.vocab_file ):
copyfile(self.vocab_file , lowerCamelCase_ )
elif not os.path.isfile(self.vocab_file ):
with open(lowerCamelCase_ , """wb""" ) as fi:
SCREAMING_SNAKE_CASE : Any = self.sp_model.serialized_model_proto()
fi.write(lowerCamelCase_ )
return (out_vocab_file,)
| 79 | 0 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__UpperCAmelCase = logging.get_logger(__name__)
__UpperCAmelCase = {
"""abeja/gpt-neox-japanese-2.7b""": """https://huggingface.co/abeja/gpt-neox-japanese-2.7b/resolve/main/config.json""",
}
class UpperCamelCase__ ( __snake_case ):
"""simple docstring"""
SCREAMING_SNAKE_CASE__ = '''gpt_neox_japanese'''
def __init__( self : Optional[Any] , lowerCamelCase_ : str=3_20_00 , lowerCamelCase_ : List[Any]=25_60 , lowerCamelCase_ : Optional[Any]=32 , lowerCamelCase_ : List[Any]=32 , lowerCamelCase_ : List[str]=4 , lowerCamelCase_ : Any="gelu" , lowerCamelCase_ : Tuple=1.00 , lowerCamelCase_ : List[str]=1_00_00 , lowerCamelCase_ : Dict=20_48 , lowerCamelCase_ : Tuple=0.02 , lowerCamelCase_ : Optional[Any]=1e-5 , lowerCamelCase_ : Union[str, Any]=True , lowerCamelCase_ : Tuple=3_19_96 , lowerCamelCase_ : Optional[Any]=3_19_99 , lowerCamelCase_ : Dict=0.1 , lowerCamelCase_ : List[str]=0.0 , **lowerCamelCase_ : Optional[Any] , ):
'''simple docstring'''
super().__init__(bos_token_id=__UpperCamelCase , eos_token_id=__UpperCamelCase , **__UpperCamelCase )
SCREAMING_SNAKE_CASE : List[Any] = vocab_size
SCREAMING_SNAKE_CASE : Tuple = max_position_embeddings
SCREAMING_SNAKE_CASE : Union[str, Any] = hidden_size
SCREAMING_SNAKE_CASE : str = num_hidden_layers
SCREAMING_SNAKE_CASE : Any = num_attention_heads
SCREAMING_SNAKE_CASE : Dict = intermediate_multiple_size
SCREAMING_SNAKE_CASE : Optional[Any] = hidden_act
SCREAMING_SNAKE_CASE : int = rotary_pct
SCREAMING_SNAKE_CASE : Dict = rotary_emb_base
SCREAMING_SNAKE_CASE : int = initializer_range
SCREAMING_SNAKE_CASE : int = layer_norm_eps
SCREAMING_SNAKE_CASE : List[Any] = use_cache
SCREAMING_SNAKE_CASE : Any = attention_dropout
SCREAMING_SNAKE_CASE : Optional[Any] = hidden_dropout
| 702 |
'''simple docstring'''
from __future__ import annotations
import unittest
from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
if is_tf_available():
import numpy as np
import tensorflow as tf
from transformers import TFXLMRobertaModel
@require_tf
@require_sentencepiece
@require_tokenizers
class UpperCamelCase__ ( unittest.TestCase ):
"""simple docstring"""
@slow
def lowerCamelCase_ ( self : Tuple ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Union[str, Any] = TFXLMRobertaModel.from_pretrained("""jplu/tf-xlm-roberta-base""" )
SCREAMING_SNAKE_CASE : Dict = {
"""input_ids""": tf.convert_to_tensor([[0, 26_46, 1_02_69, 83, 9_99_42, 2]] , dtype=tf.intaa ), # "My dog is cute"
"""attention_mask""": tf.convert_to_tensor([[1, 1, 1, 1, 1, 1]] , dtype=tf.intaa ),
}
SCREAMING_SNAKE_CASE : List[str] = model(lowerCamelCase_ )["""last_hidden_state"""]
SCREAMING_SNAKE_CASE : Union[str, Any] = tf.TensorShape((1, 6, 7_68) )
self.assertEqual(output.shape , lowerCamelCase_ )
# compare the actual values for a slice.
SCREAMING_SNAKE_CASE : Optional[int] = tf.convert_to_tensor(
[
[
[0.0_681_762, 0.10_894_451, 0.06_772_504],
[-0.06_423_668, 0.02_366_615, 0.04_329_344],
[-0.06_057_295, 0.09_974_135, -0.00_070_584],
]
] , dtype=tf.floataa , )
self.assertTrue(np.allclose(output[:, :3, :3].numpy() , expected_slice.numpy() , atol=1e-4 ) )
| 79 | 0 |
'''simple docstring'''
from . import __version__
# Backward compatibility imports, to make sure all those objects can be found in file_utils
from .utils import (
CLOUDFRONT_DISTRIB_PREFIX,
CONFIG_NAME,
DISABLE_TELEMETRY,
DUMMY_INPUTS,
DUMMY_MASK,
ENV_VARS_TRUE_AND_AUTO_VALUES,
ENV_VARS_TRUE_VALUES,
FEATURE_EXTRACTOR_NAME,
FLAX_WEIGHTS_NAME,
HF_MODULES_CACHE,
HUGGINGFACE_CO_PREFIX,
HUGGINGFACE_CO_RESOLVE_ENDPOINT,
MODEL_CARD_NAME,
MULTIPLE_CHOICE_DUMMY_INPUTS,
PYTORCH_PRETRAINED_BERT_CACHE,
PYTORCH_TRANSFORMERS_CACHE,
S3_BUCKET_PREFIX,
SENTENCEPIECE_UNDERLINE,
SPIECE_UNDERLINE,
TF2_WEIGHTS_NAME,
TF_WEIGHTS_NAME,
TORCH_FX_REQUIRED_VERSION,
TRANSFORMERS_CACHE,
TRANSFORMERS_DYNAMIC_MODULE_NAME,
USE_JAX,
USE_TF,
USE_TORCH,
WEIGHTS_INDEX_NAME,
WEIGHTS_NAME,
ContextManagers,
DummyObject,
EntryNotFoundError,
ExplicitEnum,
ModelOutput,
PaddingStrategy,
PushToHubMixin,
RepositoryNotFoundError,
RevisionNotFoundError,
TensorType,
_LazyModule,
add_code_sample_docstrings,
add_end_docstrings,
add_start_docstrings,
add_start_docstrings_to_model_forward,
cached_property,
copy_func,
default_cache_path,
define_sagemaker_information,
get_cached_models,
get_file_from_repo,
get_full_repo_name,
get_torch_version,
has_file,
http_user_agent,
is_apex_available,
is_bsa_available,
is_coloredlogs_available,
is_datasets_available,
is_detectrona_available,
is_faiss_available,
is_flax_available,
is_ftfy_available,
is_in_notebook,
is_ipex_available,
is_librosa_available,
is_offline_mode,
is_onnx_available,
is_pandas_available,
is_phonemizer_available,
is_protobuf_available,
is_psutil_available,
is_pyanvml_available,
is_pyctcdecode_available,
is_pytesseract_available,
is_pytorch_quantization_available,
is_rjieba_available,
is_sagemaker_dp_enabled,
is_sagemaker_mp_enabled,
is_scipy_available,
is_sentencepiece_available,
is_seqio_available,
is_sklearn_available,
is_soundfile_availble,
is_spacy_available,
is_speech_available,
is_tensor,
is_tensorflow_probability_available,
is_tfaonnx_available,
is_tf_available,
is_timm_available,
is_tokenizers_available,
is_torch_available,
is_torch_bfaa_available,
is_torch_cuda_available,
is_torch_fx_available,
is_torch_fx_proxy,
is_torch_mps_available,
is_torch_tfaa_available,
is_torch_tpu_available,
is_torchaudio_available,
is_training_run_on_sagemaker,
is_vision_available,
replace_return_docstrings,
requires_backends,
to_numpy,
to_py_obj,
torch_only_method,
)
| 703 |
'''simple docstring'''
from dataclasses import dataclass, field
from typing import TYPE_CHECKING, Any, ClassVar, Dict, List, Optional, Union
import pyarrow as pa
if TYPE_CHECKING:
from .features import FeatureType
@dataclass
class UpperCamelCase__ :
"""simple docstring"""
SCREAMING_SNAKE_CASE__ = 42
SCREAMING_SNAKE_CASE__ = None
# Automatically constructed
SCREAMING_SNAKE_CASE__ = "dict"
SCREAMING_SNAKE_CASE__ = None
SCREAMING_SNAKE_CASE__ = field(default='''Translation''' , init=lowercase_ , repr=lowercase_ )
def __call__( self : int ):
'''simple docstring'''
return pa.struct({lang: pa.string() for lang in sorted(self.languages )} )
def lowerCamelCase_ ( self : Tuple ):
'''simple docstring'''
from .features import Value
return {k: Value("""string""" ) for k in sorted(self.languages )}
@dataclass
class UpperCamelCase__ :
"""simple docstring"""
SCREAMING_SNAKE_CASE__ = None
SCREAMING_SNAKE_CASE__ = None
SCREAMING_SNAKE_CASE__ = None
# Automatically constructed
SCREAMING_SNAKE_CASE__ = "dict"
SCREAMING_SNAKE_CASE__ = None
SCREAMING_SNAKE_CASE__ = field(default='''TranslationVariableLanguages''' , init=lowercase_ , repr=lowercase_ )
def lowerCamelCase_ ( self : Optional[int] ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Optional[Any] = sorted(set(self.languages ) ) if self.languages else None
SCREAMING_SNAKE_CASE : str = len(self.languages ) if self.languages else None
def __call__( self : Tuple ):
'''simple docstring'''
return pa.struct({"""language""": pa.list_(pa.string() ), """translation""": pa.list_(pa.string() )} )
def lowerCamelCase_ ( self : Optional[int] , lowerCamelCase_ : Optional[Any] ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Optional[Any] = set(self.languages )
if self.languages and set(lowerCamelCase_ ) - lang_set:
raise ValueError(
f'''Some languages in example ({", ".join(sorted(set(lowerCamelCase_ ) - lang_set ) )}) are not in valid set ({", ".join(lowerCamelCase_ )}).''' )
# Convert dictionary into tuples, splitting out cases where there are
# multiple translations for a single language.
SCREAMING_SNAKE_CASE : List[Any] = []
for lang, text in translation_dict.items():
if isinstance(lowerCamelCase_ , lowerCamelCase_ ):
translation_tuples.append((lang, text) )
else:
translation_tuples.extend([(lang, el) for el in text] )
# Ensure translations are in ascending order by language code.
SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE : Optional[Any] = zip(*sorted(lowerCamelCase_ ) )
return {"language": languages, "translation": translations}
def lowerCamelCase_ ( self : List[str] ):
'''simple docstring'''
from .features import Sequence, Value
return {
"language": Sequence(Value("""string""" ) ),
"translation": Sequence(Value("""string""" ) ),
}
| 79 | 0 |
'''simple docstring'''
def __A ( lowerCamelCase_ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : Optional[int] = len(SCREAMING_SNAKE_CASE__ )
for i in range(1 , SCREAMING_SNAKE_CASE__ ):
SCREAMING_SNAKE_CASE : Tuple = collection[i]
SCREAMING_SNAKE_CASE : Tuple = 0
SCREAMING_SNAKE_CASE : str = i - 1
while low <= high:
SCREAMING_SNAKE_CASE : Optional[int] = (low + high) // 2
if val < collection[mid]:
SCREAMING_SNAKE_CASE : List[str] = mid - 1
else:
SCREAMING_SNAKE_CASE : str = mid + 1
for j in range(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , -1 ):
SCREAMING_SNAKE_CASE : List[str] = collection[j - 1]
SCREAMING_SNAKE_CASE : Any = val
return collection
if __name__ == "__main__":
__UpperCAmelCase = input("""Enter numbers separated by a comma:\n""").strip()
__UpperCAmelCase = [int(item) for item in user_input.split(""",""")]
print(binary_insertion_sort(unsorted))
| 704 |
'''simple docstring'''
import warnings
from ...utils import logging
from .image_processing_flava import FlavaImageProcessor
__UpperCAmelCase = logging.get_logger(__name__)
class UpperCamelCase__ ( lowercase_ ):
"""simple docstring"""
def __init__( self : Dict , *lowerCamelCase_ : List[str] , **lowerCamelCase_ : Dict ):
'''simple docstring'''
warnings.warn(
"""The class FlavaFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please"""
""" use FlavaImageProcessor instead.""" , lowerCamelCase_ , )
super().__init__(*lowerCamelCase_ , **lowerCamelCase_ )
| 79 | 0 |
'''simple docstring'''
from typing import Dict, Iterable, 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_DEFAULT_MEAN,
IMAGENET_DEFAULT_STD,
ChannelDimension,
ImageInput,
PILImageResampling,
make_list_of_images,
to_numpy_array,
valid_images,
)
from ...utils import TensorType, logging
__UpperCAmelCase = logging.get_logger(__name__)
class UpperCamelCase__ ( __SCREAMING_SNAKE_CASE ):
"""simple docstring"""
SCREAMING_SNAKE_CASE__ = ['''pixel_values''']
def __init__( self : str , lowerCamelCase_ : bool = True , lowerCamelCase_ : Dict[str, int] = None , lowerCamelCase_ : PILImageResampling = PILImageResampling.BICUBIC , lowerCamelCase_ : bool = True , lowerCamelCase_ : Dict[str, int] = None , lowerCamelCase_ : bool = True , lowerCamelCase_ : Union[int, float] = 1 / 2_55 , lowerCamelCase_ : bool = True , lowerCamelCase_ : Optional[Union[float, Iterable[float]]] = IMAGENET_DEFAULT_MEAN , lowerCamelCase_ : Optional[Union[float, Iterable[float]]] = IMAGENET_DEFAULT_STD , **lowerCamelCase_ : Tuple , ):
'''simple docstring'''
super().__init__(**__snake_case )
SCREAMING_SNAKE_CASE : Optional[int] = size if size is not None else {'''shortest_edge''': 2_24}
SCREAMING_SNAKE_CASE : str = get_size_dict(__snake_case , default_to_square=__snake_case )
SCREAMING_SNAKE_CASE : Optional[int] = crop_size if crop_size is not None else {'''height''': 2_24, '''width''': 2_24}
SCREAMING_SNAKE_CASE : List[str] = get_size_dict(__snake_case , param_name="""crop_size""" )
SCREAMING_SNAKE_CASE : Tuple = do_resize
SCREAMING_SNAKE_CASE : Dict = size
SCREAMING_SNAKE_CASE : Optional[Any] = resample
SCREAMING_SNAKE_CASE : List[str] = do_center_crop
SCREAMING_SNAKE_CASE : List[Any] = crop_size
SCREAMING_SNAKE_CASE : Dict = do_rescale
SCREAMING_SNAKE_CASE : str = rescale_factor
SCREAMING_SNAKE_CASE : str = do_normalize
SCREAMING_SNAKE_CASE : Dict = image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
SCREAMING_SNAKE_CASE : Union[str, Any] = image_std if image_std is not None else IMAGENET_DEFAULT_STD
def lowerCamelCase_ ( self : Tuple , lowerCamelCase_ : np.ndarray , lowerCamelCase_ : Dict[str, int] , lowerCamelCase_ : PILImageResampling = PILImageResampling.BICUBIC , lowerCamelCase_ : Optional[Union[str, ChannelDimension]] = None , **lowerCamelCase_ : int , ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Union[str, Any] = get_size_dict(__snake_case , default_to_square=__snake_case )
# size_dict is a dict with either keys "height" and "width" or "shortest_edge"
if "shortest_edge" in size:
SCREAMING_SNAKE_CASE : List[str] = int((2_56 / 2_24) * size["""shortest_edge"""] )
SCREAMING_SNAKE_CASE : Union[str, Any] = get_resize_output_image_size(__snake_case , size=__snake_case , default_to_square=__snake_case )
SCREAMING_SNAKE_CASE : List[str] = {'''height''': output_size[0], '''width''': output_size[1]}
if "height" not in size_dict or "width" not in size_dict:
raise ValueError(
f'''Size dict must have keys \'height\' and \'width\' or \'shortest_edge\'. Got {size_dict.keys()}''' )
return resize(
__snake_case , size=(size_dict["""height"""], size_dict["""width"""]) , resample=__snake_case , data_format=__snake_case , **__snake_case )
def lowerCamelCase_ ( self : Union[str, Any] , lowerCamelCase_ : np.ndarray , lowerCamelCase_ : Dict[str, int] , lowerCamelCase_ : Optional[Union[str, ChannelDimension]] = None , **lowerCamelCase_ : Optional[Any] , ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Optional[Any] = get_size_dict(__snake_case )
if "height" not in size or "width" not in size:
raise ValueError(f'''Size dict must have keys \'height\' and \'width\'. Got {size.keys()}''' )
return center_crop(__snake_case , size=(size["""height"""], size["""width"""]) , data_format=__snake_case , **__snake_case )
def lowerCamelCase_ ( self : Union[str, Any] , lowerCamelCase_ : np.ndarray , lowerCamelCase_ : Union[int, float] , lowerCamelCase_ : Optional[Union[str, ChannelDimension]] = None , **lowerCamelCase_ : Any , ):
'''simple docstring'''
return rescale(__snake_case , scale=__snake_case , data_format=__snake_case , **__snake_case )
def lowerCamelCase_ ( self : int , lowerCamelCase_ : np.ndarray , lowerCamelCase_ : Union[float, List[float]] , lowerCamelCase_ : Union[float, List[float]] , lowerCamelCase_ : Optional[Union[str, ChannelDimension]] = None , **lowerCamelCase_ : Tuple , ):
'''simple docstring'''
return normalize(__snake_case , mean=__snake_case , std=__snake_case , data_format=__snake_case , **__snake_case )
def lowerCamelCase_ ( self : List[Any] , lowerCamelCase_ : ImageInput , lowerCamelCase_ : Optional[bool] = None , lowerCamelCase_ : Optional[Dict[str, int]] = None , lowerCamelCase_ : PILImageResampling = None , lowerCamelCase_ : Optional[bool] = None , lowerCamelCase_ : Optional[Dict[str, int]] = None , lowerCamelCase_ : Optional[bool] = None , lowerCamelCase_ : Optional[float] = None , lowerCamelCase_ : Optional[bool] = None , lowerCamelCase_ : Optional[Union[float, Iterable[float]]] = None , lowerCamelCase_ : Optional[Union[float, Iterable[float]]] = None , lowerCamelCase_ : Optional[TensorType] = None , lowerCamelCase_ : ChannelDimension = ChannelDimension.FIRST , **lowerCamelCase_ : Dict , ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Optional[Any] = do_resize if do_resize is not None else self.do_resize
SCREAMING_SNAKE_CASE : Optional[Any] = resample if resample is not None else self.resample
SCREAMING_SNAKE_CASE : List[str] = do_center_crop if do_center_crop is not None else self.do_center_crop
SCREAMING_SNAKE_CASE : Union[str, Any] = do_rescale if do_rescale is not None else self.do_rescale
SCREAMING_SNAKE_CASE : List[Any] = rescale_factor if rescale_factor is not None else self.rescale_factor
SCREAMING_SNAKE_CASE : Optional[int] = do_normalize if do_normalize is not None else self.do_normalize
SCREAMING_SNAKE_CASE : Dict = image_mean if image_mean is not None else self.image_mean
SCREAMING_SNAKE_CASE : Tuple = image_std if image_std is not None else self.image_std
SCREAMING_SNAKE_CASE : Any = size if size is not None else self.size
SCREAMING_SNAKE_CASE : int = get_size_dict(__snake_case , default_to_square=__snake_case )
SCREAMING_SNAKE_CASE : Optional[int] = crop_size if crop_size is not None else self.crop_size
SCREAMING_SNAKE_CASE : Tuple = get_size_dict(__snake_case , param_name="""crop_size""" )
SCREAMING_SNAKE_CASE : List[Any] = make_list_of_images(__snake_case )
if not valid_images(__snake_case ):
raise ValueError(
"""Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, """
"""torch.Tensor, tf.Tensor or jax.ndarray.""" )
if do_resize and size is None:
raise ValueError("""Size must be specified if do_resize is True.""" )
if do_center_crop and crop_size is None:
raise ValueError("""Crop size must be specified if do_center_crop is True.""" )
if do_rescale and rescale_factor is None:
raise ValueError("""Rescale factor must be specified if do_rescale is True.""" )
if do_normalize and (image_mean is None or image_std is None):
raise ValueError("""Image mean and std must be specified if do_normalize is True.""" )
# All transformations expect numpy arrays.
SCREAMING_SNAKE_CASE : Tuple = [to_numpy_array(__snake_case ) for image in images]
if do_resize:
SCREAMING_SNAKE_CASE : Optional[int] = [self.resize(__snake_case , __snake_case , __snake_case ) for image in images]
if do_center_crop:
SCREAMING_SNAKE_CASE : Any = [self.center_crop(__snake_case , __snake_case ) for image in images]
if do_rescale:
SCREAMING_SNAKE_CASE : int = [self.rescale(__snake_case , __snake_case ) for image in images]
if do_normalize:
SCREAMING_SNAKE_CASE : Optional[Any] = [self.normalize(__snake_case , __snake_case , __snake_case ) for image in images]
SCREAMING_SNAKE_CASE : int = [to_channel_dimension_format(__snake_case , __snake_case ) for image in images]
SCREAMING_SNAKE_CASE : List[str] = {'''pixel_values''': images}
return BatchFeature(data=__snake_case , tensor_type=__snake_case )
| 705 |
'''simple docstring'''
from .integrations import (
is_optuna_available,
is_ray_available,
is_sigopt_available,
is_wandb_available,
run_hp_search_optuna,
run_hp_search_ray,
run_hp_search_sigopt,
run_hp_search_wandb,
)
from .trainer_utils import (
HPSearchBackend,
default_hp_space_optuna,
default_hp_space_ray,
default_hp_space_sigopt,
default_hp_space_wandb,
)
from .utils import logging
__UpperCAmelCase = logging.get_logger(__name__)
class UpperCamelCase__ :
"""simple docstring"""
SCREAMING_SNAKE_CASE__ = 42
SCREAMING_SNAKE_CASE__ = None
@staticmethod
def lowerCamelCase_ ( ):
'''simple docstring'''
raise NotImplementedError
def lowerCamelCase_ ( self : Tuple , lowerCamelCase_ : Optional[int] , lowerCamelCase_ : int , lowerCamelCase_ : str , **lowerCamelCase_ : str ):
'''simple docstring'''
raise NotImplementedError
def lowerCamelCase_ ( self : int , lowerCamelCase_ : Dict ):
'''simple docstring'''
raise NotImplementedError
def lowerCamelCase_ ( self : Optional[int] ):
'''simple docstring'''
if not self.is_available():
raise RuntimeError(
f'''You picked the {self.name} backend, but it is not installed. Run {self.pip_install()}.''' )
@classmethod
def lowerCamelCase_ ( cls : Any ):
'''simple docstring'''
return f'''`pip install {cls.pip_package or cls.name}`'''
class UpperCamelCase__ ( lowercase_ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE__ = '''optuna'''
@staticmethod
def lowerCamelCase_ ( ):
'''simple docstring'''
return is_optuna_available()
def lowerCamelCase_ ( self : Any , lowerCamelCase_ : Union[str, Any] , lowerCamelCase_ : int , lowerCamelCase_ : str , **lowerCamelCase_ : Dict ):
'''simple docstring'''
return run_hp_search_optuna(lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ , **lowerCamelCase_ )
def lowerCamelCase_ ( self : str , lowerCamelCase_ : Any ):
'''simple docstring'''
return default_hp_space_optuna(lowerCamelCase_ )
class UpperCamelCase__ ( lowercase_ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE__ = '''ray'''
SCREAMING_SNAKE_CASE__ = '''\'ray[tune]\''''
@staticmethod
def lowerCamelCase_ ( ):
'''simple docstring'''
return is_ray_available()
def lowerCamelCase_ ( self : Any , lowerCamelCase_ : Any , lowerCamelCase_ : int , lowerCamelCase_ : str , **lowerCamelCase_ : int ):
'''simple docstring'''
return run_hp_search_ray(lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ , **lowerCamelCase_ )
def lowerCamelCase_ ( self : List[str] , lowerCamelCase_ : Optional[int] ):
'''simple docstring'''
return default_hp_space_ray(lowerCamelCase_ )
class UpperCamelCase__ ( lowercase_ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE__ = '''sigopt'''
@staticmethod
def lowerCamelCase_ ( ):
'''simple docstring'''
return is_sigopt_available()
def lowerCamelCase_ ( self : Optional[Any] , lowerCamelCase_ : Any , lowerCamelCase_ : int , lowerCamelCase_ : str , **lowerCamelCase_ : int ):
'''simple docstring'''
return run_hp_search_sigopt(lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ , **lowerCamelCase_ )
def lowerCamelCase_ ( self : List[Any] , lowerCamelCase_ : Optional[Any] ):
'''simple docstring'''
return default_hp_space_sigopt(lowerCamelCase_ )
class UpperCamelCase__ ( lowercase_ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE__ = '''wandb'''
@staticmethod
def lowerCamelCase_ ( ):
'''simple docstring'''
return is_wandb_available()
def lowerCamelCase_ ( self : Union[str, Any] , lowerCamelCase_ : str , lowerCamelCase_ : int , lowerCamelCase_ : str , **lowerCamelCase_ : Optional[Any] ):
'''simple docstring'''
return run_hp_search_wandb(lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ , **lowerCamelCase_ )
def lowerCamelCase_ ( self : Union[str, Any] , lowerCamelCase_ : Tuple ):
'''simple docstring'''
return default_hp_space_wandb(lowerCamelCase_ )
__UpperCAmelCase = {
HPSearchBackend(backend.name): backend for backend in [OptunaBackend, RayTuneBackend, SigOptBackend, WandbBackend]
}
def __A ( ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : List[Any] = [backend for backend in ALL_HYPERPARAMETER_SEARCH_BACKENDS.values() if backend.is_available()]
if len(lowerCamelCase_ ) > 0:
SCREAMING_SNAKE_CASE : List[Any] = available_backends[0].name
if len(lowerCamelCase_ ) > 1:
logger.info(
f'''{len(lowerCamelCase_ )} hyperparameter search backends available. Using {name} as the default.''' )
return name
raise RuntimeError(
"""No hyperparameter search backend available.\n"""
+ """\n""".join(
f''' - To install {backend.name} run {backend.pip_install()}'''
for backend in ALL_HYPERPARAMETER_SEARCH_BACKENDS.values() ) )
| 79 | 0 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__UpperCAmelCase = logging.get_logger(__name__)
__UpperCAmelCase = {
"""microsoft/cvt-13""": """https://huggingface.co/microsoft/cvt-13/resolve/main/config.json""",
# See all Cvt models at https://huggingface.co/models?filter=cvt
}
class UpperCamelCase__ ( lowercase_ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE__ = "cvt"
def __init__( self : Optional[Any] , lowerCamelCase_ : Optional[Any]=3 , lowerCamelCase_ : int=[7, 3, 3] , lowerCamelCase_ : Optional[Any]=[4, 2, 2] , lowerCamelCase_ : int=[2, 1, 1] , lowerCamelCase_ : str=[64, 1_92, 3_84] , lowerCamelCase_ : Union[str, Any]=[1, 3, 6] , lowerCamelCase_ : Dict=[1, 2, 10] , lowerCamelCase_ : List[str]=[4.0, 4.0, 4.0] , lowerCamelCase_ : int=[0.0, 0.0, 0.0] , lowerCamelCase_ : Optional[Any]=[0.0, 0.0, 0.0] , lowerCamelCase_ : Any=[0.0, 0.0, 0.1] , lowerCamelCase_ : int=[True, True, True] , lowerCamelCase_ : Any=[False, False, True] , lowerCamelCase_ : Union[str, Any]=["dw_bn", "dw_bn", "dw_bn"] , lowerCamelCase_ : int=[3, 3, 3] , lowerCamelCase_ : List[Any]=[1, 1, 1] , lowerCamelCase_ : List[Any]=[2, 2, 2] , lowerCamelCase_ : Any=[1, 1, 1] , lowerCamelCase_ : List[str]=[1, 1, 1] , lowerCamelCase_ : Optional[int]=0.02 , lowerCamelCase_ : Optional[int]=1e-12 , **lowerCamelCase_ : str , ):
'''simple docstring'''
super().__init__(**lowerCamelCase_ )
SCREAMING_SNAKE_CASE : Optional[Any] = num_channels
SCREAMING_SNAKE_CASE : List[Any] = patch_sizes
SCREAMING_SNAKE_CASE : Optional[int] = patch_stride
SCREAMING_SNAKE_CASE : Optional[int] = patch_padding
SCREAMING_SNAKE_CASE : List[str] = embed_dim
SCREAMING_SNAKE_CASE : List[str] = num_heads
SCREAMING_SNAKE_CASE : Optional[Any] = depth
SCREAMING_SNAKE_CASE : str = mlp_ratio
SCREAMING_SNAKE_CASE : Optional[int] = attention_drop_rate
SCREAMING_SNAKE_CASE : Optional[int] = drop_rate
SCREAMING_SNAKE_CASE : List[str] = drop_path_rate
SCREAMING_SNAKE_CASE : str = qkv_bias
SCREAMING_SNAKE_CASE : Any = cls_token
SCREAMING_SNAKE_CASE : Any = qkv_projection_method
SCREAMING_SNAKE_CASE : int = kernel_qkv
SCREAMING_SNAKE_CASE : Any = padding_kv
SCREAMING_SNAKE_CASE : Any = stride_kv
SCREAMING_SNAKE_CASE : Optional[Any] = padding_q
SCREAMING_SNAKE_CASE : Tuple = stride_q
SCREAMING_SNAKE_CASE : Tuple = initializer_range
SCREAMING_SNAKE_CASE : Optional[Any] = layer_norm_eps
| 706 |
'''simple docstring'''
import glob
import os
import random
from string import ascii_lowercase, digits
import cva
__UpperCAmelCase = """"""
__UpperCAmelCase = """"""
__UpperCAmelCase = """"""
__UpperCAmelCase = 1 # (0 is vertical, 1 is horizontal)
def __A ( ):
"""simple docstring"""
SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE : str = get_dataset(lowerCamelCase_ , lowerCamelCase_ )
print("""Processing...""" )
SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE : Any = update_image_and_anno(lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ )
for index, image in enumerate(lowerCamelCase_ ):
# Get random string code: '7b7ad245cdff75241935e4dd860f3bad'
SCREAMING_SNAKE_CASE : Optional[int] = random_chars(32 )
SCREAMING_SNAKE_CASE : Optional[Any] = paths[index].split(os.sep )[-1].rsplit(""".""" , 1 )[0]
SCREAMING_SNAKE_CASE : Dict = f'''{OUTPUT_DIR}/{file_name}_FLIP_{letter_code}'''
cva.imwrite(f'''/{file_root}.jpg''' , lowerCamelCase_ , [cva.IMWRITE_JPEG_QUALITY, 85] )
print(f'''Success {index+1}/{len(lowerCamelCase_ )} with {file_name}''' )
SCREAMING_SNAKE_CASE : Optional[Any] = []
for anno in new_annos[index]:
SCREAMING_SNAKE_CASE : Optional[Any] = f'''{anno[0]} {anno[1]} {anno[2]} {anno[3]} {anno[4]}'''
annos_list.append(lowerCamelCase_ )
with open(f'''/{file_root}.txt''' , """w""" ) as outfile:
outfile.write("""\n""".join(line for line in annos_list ) )
def __A ( lowerCamelCase_ , lowerCamelCase_ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : str = []
SCREAMING_SNAKE_CASE : Any = []
for label_file in glob.glob(os.path.join(lowerCamelCase_ , """*.txt""" ) ):
SCREAMING_SNAKE_CASE : str = label_file.split(os.sep )[-1].rsplit(""".""" , 1 )[0]
with open(lowerCamelCase_ ) as in_file:
SCREAMING_SNAKE_CASE : Any = in_file.readlines()
SCREAMING_SNAKE_CASE : List[Any] = os.path.join(lowerCamelCase_ , f'''{label_name}.jpg''' )
SCREAMING_SNAKE_CASE : Tuple = []
for obj_list in obj_lists:
SCREAMING_SNAKE_CASE : Union[str, Any] = obj_list.rstrip("""\n""" ).split(""" """ )
boxes.append(
[
int(obj[0] ),
float(obj[1] ),
float(obj[2] ),
float(obj[3] ),
float(obj[4] ),
] )
if not boxes:
continue
img_paths.append(lowerCamelCase_ )
labels.append(lowerCamelCase_ )
return img_paths, labels
def __A ( lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ = 1 ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : Tuple = []
SCREAMING_SNAKE_CASE : List[str] = []
SCREAMING_SNAKE_CASE : Optional[Any] = []
for idx in range(len(lowerCamelCase_ ) ):
SCREAMING_SNAKE_CASE : List[Any] = []
SCREAMING_SNAKE_CASE : Dict = img_list[idx]
path_list.append(lowerCamelCase_ )
SCREAMING_SNAKE_CASE : int = anno_list[idx]
SCREAMING_SNAKE_CASE : Optional[Any] = cva.imread(lowerCamelCase_ )
if flip_type == 1:
SCREAMING_SNAKE_CASE : List[str] = cva.flip(lowerCamelCase_ , lowerCamelCase_ )
for bbox in img_annos:
SCREAMING_SNAKE_CASE : List[Any] = 1 - bbox[1]
new_annos.append([bbox[0], x_center_new, bbox[2], bbox[3], bbox[4]] )
elif flip_type == 0:
SCREAMING_SNAKE_CASE : Any = cva.flip(lowerCamelCase_ , lowerCamelCase_ )
for bbox in img_annos:
SCREAMING_SNAKE_CASE : Optional[Any] = 1 - bbox[2]
new_annos.append([bbox[0], bbox[1], y_center_new, bbox[3], bbox[4]] )
new_annos_lists.append(lowerCamelCase_ )
new_imgs_list.append(lowerCamelCase_ )
return new_imgs_list, new_annos_lists, path_list
def __A ( lowerCamelCase_ = 32 ):
"""simple docstring"""
assert number_char > 1, "The number of character should greater than 1"
SCREAMING_SNAKE_CASE : Dict = ascii_lowercase + digits
return "".join(random.choice(lowerCamelCase_ ) for _ in range(lowerCamelCase_ ) )
if __name__ == "__main__":
main()
print("""DONE ✅""")
| 79 | 0 |
'''simple docstring'''
from collections import defaultdict
from math import ceil, sqrt
def __A ( lowerCamelCase_ = 1_00_00_00 , lowerCamelCase_ = 10 ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : defaultdict = defaultdict(lowercase_ )
for outer_width in range(3 , (t_limit // 4) + 2 ):
if outer_width * outer_width > t_limit:
SCREAMING_SNAKE_CASE : Any = max(
ceil(sqrt(outer_width * outer_width - t_limit ) ) , 1 )
else:
SCREAMING_SNAKE_CASE : str = 1
hole_width_lower_bound += (outer_width - hole_width_lower_bound) % 2
for hole_width in range(lowercase_ , outer_width - 1 , 2 ):
count[outer_width * outer_width - hole_width * hole_width] += 1
return sum(1 for n in count.values() if 1 <= n <= 10 )
if __name__ == "__main__":
print(f'''{solution() = }''')
| 707 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__UpperCAmelCase = logging.get_logger(__name__)
__UpperCAmelCase = {
"""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__ ( lowercase_ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE__ = '''vivit'''
def __init__( self : Tuple , lowerCamelCase_ : str=2_24 , lowerCamelCase_ : List[Any]=32 , lowerCamelCase_ : Tuple=[2, 16, 16] , lowerCamelCase_ : List[str]=3 , lowerCamelCase_ : Dict=7_68 , lowerCamelCase_ : Dict=12 , lowerCamelCase_ : Any=12 , lowerCamelCase_ : List[Any]=30_72 , lowerCamelCase_ : List[str]="gelu_fast" , lowerCamelCase_ : str=0.0 , lowerCamelCase_ : Any=0.0 , lowerCamelCase_ : Optional[int]=0.02 , lowerCamelCase_ : List[Any]=1e-06 , lowerCamelCase_ : Tuple=True , **lowerCamelCase_ : Tuple , ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : List[str] = hidden_size
SCREAMING_SNAKE_CASE : Union[str, Any] = num_hidden_layers
SCREAMING_SNAKE_CASE : List[str] = num_attention_heads
SCREAMING_SNAKE_CASE : str = intermediate_size
SCREAMING_SNAKE_CASE : List[Any] = hidden_act
SCREAMING_SNAKE_CASE : Optional[Any] = hidden_dropout_prob
SCREAMING_SNAKE_CASE : Tuple = attention_probs_dropout_prob
SCREAMING_SNAKE_CASE : Tuple = initializer_range
SCREAMING_SNAKE_CASE : str = layer_norm_eps
SCREAMING_SNAKE_CASE : str = image_size
SCREAMING_SNAKE_CASE : Dict = num_frames
SCREAMING_SNAKE_CASE : Optional[Any] = tubelet_size
SCREAMING_SNAKE_CASE : Dict = num_channels
SCREAMING_SNAKE_CASE : int = qkv_bias
super().__init__(**lowerCamelCase_ )
| 79 | 0 |
'''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 re
from ..models.auto import AutoProcessor
from ..models.vision_encoder_decoder import VisionEncoderDecoderModel
from ..utils import is_vision_available
from .base import PipelineTool
if is_vision_available():
from PIL import Image
class UpperCamelCase__ ( UpperCAmelCase_ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE__ = "naver-clova-ix/donut-base-finetuned-docvqa"
SCREAMING_SNAKE_CASE__ = (
"This is a tool that answers a question about an document (pdf). It takes an input named `document` which "
"should be the document containing the information, as well as a `question` that is the question about the "
"document. It returns a text that contains the answer to the question."
)
SCREAMING_SNAKE_CASE__ = "document_qa"
SCREAMING_SNAKE_CASE__ = AutoProcessor
SCREAMING_SNAKE_CASE__ = VisionEncoderDecoderModel
SCREAMING_SNAKE_CASE__ = ["image", "text"]
SCREAMING_SNAKE_CASE__ = ["text"]
def __init__( self : Optional[int] , *lowerCamelCase_ : Tuple , **lowerCamelCase_ : Tuple ):
'''simple docstring'''
if not is_vision_available():
raise ValueError("""Pillow must be installed to use the DocumentQuestionAnsweringTool.""" )
super().__init__(*_snake_case , **_snake_case )
def lowerCamelCase_ ( self : Union[str, Any] , lowerCamelCase_ : "Image" , lowerCamelCase_ : str ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Dict = """<s_docvqa><s_question>{user_input}</s_question><s_answer>"""
SCREAMING_SNAKE_CASE : Any = task_prompt.replace("""{user_input}""" , _snake_case )
SCREAMING_SNAKE_CASE : List[Any] = self.pre_processor.tokenizer(
_snake_case , add_special_tokens=_snake_case , return_tensors="""pt""" ).input_ids
SCREAMING_SNAKE_CASE : Tuple = self.pre_processor(_snake_case , return_tensors="""pt""" ).pixel_values
return {"decoder_input_ids": decoder_input_ids, "pixel_values": pixel_values}
def lowerCamelCase_ ( self : int , lowerCamelCase_ : Optional[Any] ):
'''simple docstring'''
return self.model.generate(
inputs["""pixel_values"""].to(self.device ) , decoder_input_ids=inputs["""decoder_input_ids"""].to(self.device ) , max_length=self.model.decoder.config.max_position_embeddings , early_stopping=_snake_case , pad_token_id=self.pre_processor.tokenizer.pad_token_id , eos_token_id=self.pre_processor.tokenizer.eos_token_id , use_cache=_snake_case , num_beams=1 , bad_words_ids=[[self.pre_processor.tokenizer.unk_token_id]] , return_dict_in_generate=_snake_case , ).sequences
def lowerCamelCase_ ( self : List[Any] , lowerCamelCase_ : List[str] ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Tuple = self.pre_processor.batch_decode(_snake_case )[0]
SCREAMING_SNAKE_CASE : Any = sequence.replace(self.pre_processor.tokenizer.eos_token , """""" )
SCREAMING_SNAKE_CASE : str = sequence.replace(self.pre_processor.tokenizer.pad_token , """""" )
SCREAMING_SNAKE_CASE : Optional[int] = re.sub(R"""<.*?>""" , """""" , _snake_case , count=1 ).strip() # remove first task start token
SCREAMING_SNAKE_CASE : Optional[int] = self.pre_processor.tokenajson(_snake_case )
return sequence["answer"]
| 708 |
'''simple docstring'''
import math
class UpperCamelCase__ :
"""simple docstring"""
def __init__( self : List[str] , lowerCamelCase_ : Tuple=0 ): # a graph with Node 0,1,...,N-1
'''simple docstring'''
SCREAMING_SNAKE_CASE : Any = n
SCREAMING_SNAKE_CASE : Optional[int] = [
[math.inf for j in range(0 , lowerCamelCase_ )] for i in range(0 , lowerCamelCase_ )
] # adjacency matrix for weight
SCREAMING_SNAKE_CASE : Union[str, Any] = [
[math.inf for j in range(0 , lowerCamelCase_ )] for i in range(0 , lowerCamelCase_ )
] # dp[i][j] stores minimum distance from i to j
def lowerCamelCase_ ( self : Dict , lowerCamelCase_ : str , lowerCamelCase_ : List[str] , lowerCamelCase_ : int ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Union[str, Any] = w
def lowerCamelCase_ ( self : Optional[int] ):
'''simple docstring'''
for k in range(0 , self.n ):
for i in range(0 , self.n ):
for j in range(0 , self.n ):
SCREAMING_SNAKE_CASE : Union[str, Any] = min(self.dp[i][j] , self.dp[i][k] + self.dp[k][j] )
def lowerCamelCase_ ( self : Dict , lowerCamelCase_ : List[Any] , lowerCamelCase_ : Union[str, Any] ):
'''simple docstring'''
return self.dp[u][v]
if __name__ == "__main__":
__UpperCAmelCase = Graph(5)
graph.add_edge(0, 2, 9)
graph.add_edge(0, 4, 10)
graph.add_edge(1, 3, 5)
graph.add_edge(2, 3, 7)
graph.add_edge(3, 0, 10)
graph.add_edge(3, 1, 2)
graph.add_edge(3, 2, 1)
graph.add_edge(3, 4, 6)
graph.add_edge(4, 1, 3)
graph.add_edge(4, 2, 4)
graph.add_edge(4, 3, 9)
graph.floyd_warshall()
graph.show_min(1, 4)
graph.show_min(0, 3)
| 79 | 0 |
'''simple docstring'''
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import Features, Sequence, Value
from .base import TaskTemplate
@dataclass(frozen=_lowerCAmelCase )
class UpperCamelCase__ ( _lowerCAmelCase ):
"""simple docstring"""
# `task` is not a ClassVar since we want it to be part of the `asdict` output for JSON serialization
SCREAMING_SNAKE_CASE__ = field(default='''question-answering-extractive''' , metadata={'''include_in_asdict_even_if_is_default''': True} )
SCREAMING_SNAKE_CASE__ = Features({'''question''': Value('''string''' ), '''context''': Value('''string''' )} )
SCREAMING_SNAKE_CASE__ = Features(
{
'''answers''': Sequence(
{
'''text''': Value('''string''' ),
'''answer_start''': Value('''int32''' ),
} )
} )
SCREAMING_SNAKE_CASE__ = "question"
SCREAMING_SNAKE_CASE__ = "context"
SCREAMING_SNAKE_CASE__ = "answers"
@property
def lowerCamelCase_ ( self : Optional[int] ):
'''simple docstring'''
return {self.question_column: "question", self.context_column: "context", self.answers_column: "answers"}
| 709 |
'''simple docstring'''
import math
def __A ( lowerCamelCase_ ):
"""simple docstring"""
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
return False
# All primes number are in format of 6k +/- 1
for i in range(5 , int(math.sqrt(lowerCamelCase_ ) + 1 ) , 6 ):
if number % i == 0 or number % (i + 2) == 0:
return False
return True
def __A ( lowerCamelCase_ = 1_00_01 ):
"""simple docstring"""
try:
SCREAMING_SNAKE_CASE : Tuple = int(lowerCamelCase_ )
except (TypeError, ValueError):
raise TypeError("""Parameter nth must be int or castable to int.""" ) from None
if nth <= 0:
raise ValueError("""Parameter nth must be greater than or equal to one.""" )
SCREAMING_SNAKE_CASE : list[int] = []
SCREAMING_SNAKE_CASE : Dict = 2
while len(lowerCamelCase_ ) < nth:
if is_prime(lowerCamelCase_ ):
primes.append(lowerCamelCase_ )
num += 1
else:
num += 1
return primes[len(lowerCamelCase_ ) - 1]
if __name__ == "__main__":
print(f'''{solution() = }''')
| 79 | 0 |
import unittest
import numpy as np
import torch
from diffusers import KarrasVePipeline, KarrasVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device
enable_full_determinism()
class UpperCamelCase__ ( unittest.TestCase ):
"""simple docstring"""
@property
def lowerCamelCase_ ( self : List[str] ):
'''simple docstring'''
torch.manual_seed(0 )
SCREAMING_SNAKE_CASE : int = UNetaDModel(
block_out_channels=(32, 64) , layers_per_block=2 , sample_size=32 , in_channels=3 , out_channels=3 , down_block_types=("""DownBlock2D""", """AttnDownBlock2D""") , up_block_types=("""AttnUpBlock2D""", """UpBlock2D""") , )
return model
def lowerCamelCase_ ( self : Union[str, Any] ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : List[str] = self.dummy_uncond_unet
SCREAMING_SNAKE_CASE : Tuple = KarrasVeScheduler()
SCREAMING_SNAKE_CASE : Any = KarrasVePipeline(unet=_a , scheduler=_a )
pipe.to(_a )
pipe.set_progress_bar_config(disable=_a )
SCREAMING_SNAKE_CASE : Dict = torch.manual_seed(0 )
SCREAMING_SNAKE_CASE : Optional[Any] = pipe(num_inference_steps=2 , generator=_a , output_type="""numpy""" ).images
SCREAMING_SNAKE_CASE : Optional[Any] = torch.manual_seed(0 )
SCREAMING_SNAKE_CASE : Tuple = pipe(num_inference_steps=2 , generator=_a , output_type="""numpy""" , return_dict=_a )[0]
SCREAMING_SNAKE_CASE : Tuple = image[0, -3:, -3:, -1]
SCREAMING_SNAKE_CASE : Union[str, Any] = image_from_tuple[0, -3:, -3:, -1]
assert image.shape == (1, 32, 32, 3)
SCREAMING_SNAKE_CASE : Dict = np.array([0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
assert np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 1e-2
@slow
@require_torch
class UpperCamelCase__ ( unittest.TestCase ):
"""simple docstring"""
def lowerCamelCase_ ( self : int ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : int = """google/ncsnpp-celebahq-256"""
SCREAMING_SNAKE_CASE : str = UNetaDModel.from_pretrained(_a )
SCREAMING_SNAKE_CASE : int = KarrasVeScheduler()
SCREAMING_SNAKE_CASE : str = KarrasVePipeline(unet=_a , scheduler=_a )
pipe.to(_a )
pipe.set_progress_bar_config(disable=_a )
SCREAMING_SNAKE_CASE : int = torch.manual_seed(0 )
SCREAMING_SNAKE_CASE : Tuple = pipe(num_inference_steps=20 , generator=_a , output_type="""numpy""" ).images
SCREAMING_SNAKE_CASE : List[Any] = image[0, -3:, -3:, -1]
assert image.shape == (1, 2_56, 2_56, 3)
SCREAMING_SNAKE_CASE : Optional[Any] = np.array([0.578, 0.5_811, 0.5_924, 0.5_809, 0.587, 0.5_886, 0.5_861, 0.5_802, 0.586] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
| 710 |
'''simple docstring'''
from __future__ import annotations
import json
import requests
from bsa import BeautifulSoup
from fake_useragent import UserAgent
__UpperCAmelCase = {"""UserAgent""": UserAgent().random}
def __A ( lowerCamelCase_ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : str = script.contents[0]
SCREAMING_SNAKE_CASE : int = json.loads(data[data.find("""{\"config\"""" ) : -1] )
return info["entry_data"]["ProfilePage"][0]["graphql"]["user"]
class UpperCamelCase__ :
"""simple docstring"""
def __init__( self : Dict , lowerCamelCase_ : str ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Any = f'''https://www.instagram.com/{username}/'''
SCREAMING_SNAKE_CASE : Any = self.get_json()
def lowerCamelCase_ ( self : Tuple ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Dict = requests.get(self.url , headers=lowerCamelCase_ ).text
SCREAMING_SNAKE_CASE : List[Any] = BeautifulSoup(lowerCamelCase_ , """html.parser""" ).find_all("""script""" )
try:
return extract_user_profile(scripts[4] )
except (json.decoder.JSONDecodeError, KeyError):
return extract_user_profile(scripts[3] )
def __repr__( self : Dict ):
'''simple docstring'''
return f'''{self.__class__.__name__}(\'{self.username}\')'''
def __str__( self : int ):
'''simple docstring'''
return f'''{self.fullname} ({self.username}) is {self.biography}'''
@property
def lowerCamelCase_ ( self : Tuple ):
'''simple docstring'''
return self.user_data["username"]
@property
def lowerCamelCase_ ( self : Optional[Any] ):
'''simple docstring'''
return self.user_data["full_name"]
@property
def lowerCamelCase_ ( self : int ):
'''simple docstring'''
return self.user_data["biography"]
@property
def lowerCamelCase_ ( self : str ):
'''simple docstring'''
return self.user_data["business_email"]
@property
def lowerCamelCase_ ( self : str ):
'''simple docstring'''
return self.user_data["external_url"]
@property
def lowerCamelCase_ ( self : List[Any] ):
'''simple docstring'''
return self.user_data["edge_followed_by"]["count"]
@property
def lowerCamelCase_ ( self : List[Any] ):
'''simple docstring'''
return self.user_data["edge_follow"]["count"]
@property
def lowerCamelCase_ ( self : List[Any] ):
'''simple docstring'''
return self.user_data["edge_owner_to_timeline_media"]["count"]
@property
def lowerCamelCase_ ( self : str ):
'''simple docstring'''
return self.user_data["profile_pic_url_hd"]
@property
def lowerCamelCase_ ( self : Dict ):
'''simple docstring'''
return self.user_data["is_verified"]
@property
def lowerCamelCase_ ( self : Optional[int] ):
'''simple docstring'''
return self.user_data["is_private"]
def __A ( lowerCamelCase_ = "github" ):
"""simple docstring"""
import os
if os.environ.get("""CI""" ):
return # test failing on GitHub Actions
SCREAMING_SNAKE_CASE : Any = InstagramUser(lowerCamelCase_ )
assert instagram_user.user_data
assert isinstance(instagram_user.user_data , lowerCamelCase_ )
assert instagram_user.username == username
if username != "github":
return
assert instagram_user.fullname == "GitHub"
assert instagram_user.biography == "Built for developers."
assert instagram_user.number_of_posts > 1_50
assert instagram_user.number_of_followers > 12_00_00
assert instagram_user.number_of_followings > 15
assert instagram_user.email == "support@github.com"
assert instagram_user.website == "https://github.com/readme"
assert instagram_user.profile_picture_url.startswith("""https://instagram.""" )
assert instagram_user.is_verified is True
assert instagram_user.is_private is False
if __name__ == "__main__":
import doctest
doctest.testmod()
__UpperCAmelCase = InstagramUser("""github""")
print(instagram_user)
print(f'''{instagram_user.number_of_posts = }''')
print(f'''{instagram_user.number_of_followers = }''')
print(f'''{instagram_user.number_of_followings = }''')
print(f'''{instagram_user.email = }''')
print(f'''{instagram_user.website = }''')
print(f'''{instagram_user.profile_picture_url = }''')
print(f'''{instagram_user.is_verified = }''')
print(f'''{instagram_user.is_private = }''')
| 79 | 0 |
'''simple docstring'''
import contextlib
import importlib
import io
import unittest
import transformers
# Try to import everything from transformers to ensure every object can be loaded.
from transformers import * # noqa F406
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER, require_flax, require_tf, require_torch
from transformers.utils import ContextManagers, find_labels, is_flax_available, is_tf_available, is_torch_available
if is_torch_available():
from transformers import BertForPreTraining, BertForQuestionAnswering, BertForSequenceClassification
if is_tf_available():
from transformers import TFBertForPreTraining, TFBertForQuestionAnswering, TFBertForSequenceClassification
if is_flax_available():
from transformers import FlaxBertForPreTraining, FlaxBertForQuestionAnswering, FlaxBertForSequenceClassification
__UpperCAmelCase = DUMMY_UNKNOWN_IDENTIFIER
# An actual model hosted on huggingface.co
__UpperCAmelCase = 'main'
# Default branch name
__UpperCAmelCase = 'f2c752cfc5c0ab6f4bdec59acea69eefbee381c2'
# One particular commit (not the top of `main`)
__UpperCAmelCase = 'aaaaaaa'
# This commit does not exist, so we should 404.
__UpperCAmelCase = 'd9e9f15bc825e4b2c9249e9578f884bbcb5e3684'
# Sha-1 of config.json on the top of `main`, for checking purposes
__UpperCAmelCase = '4b243c475af8d0a7754e87d7d096c92e5199ec2fe168a2ee7998e3b8e9bcb1d3'
@contextlib.contextmanager
def __A ( ):
"""simple docstring"""
print("""Welcome!""" )
yield
print("""Bye!""" )
@contextlib.contextmanager
def __A ( ):
"""simple docstring"""
print("""Bonjour!""" )
yield
print("""Au revoir!""" )
class UpperCamelCase__ ( unittest.TestCase ):
"""simple docstring"""
def lowerCamelCase_ ( self : Any ):
'''simple docstring'''
assert transformers.__spec__ is not None
assert importlib.util.find_spec("""transformers""" ) is not None
class UpperCamelCase__ ( unittest.TestCase ):
"""simple docstring"""
@unittest.mock.patch("""sys.stdout""" , new_callable=io.StringIO )
def lowerCamelCase_ ( self : int , lowerCamelCase_ : List[str] ):
'''simple docstring'''
with ContextManagers([] ):
print("""Transformers are awesome!""" )
# The print statement adds a new line at the end of the output
self.assertEqual(mock_stdout.getvalue() , """Transformers are awesome!\n""" )
@unittest.mock.patch("""sys.stdout""" , new_callable=io.StringIO )
def lowerCamelCase_ ( self : Dict , lowerCamelCase_ : int ):
'''simple docstring'''
with ContextManagers([context_en()] ):
print("""Transformers are awesome!""" )
# The output should be wrapped with an English welcome and goodbye
self.assertEqual(mock_stdout.getvalue() , """Welcome!\nTransformers are awesome!\nBye!\n""" )
@unittest.mock.patch("""sys.stdout""" , new_callable=io.StringIO )
def lowerCamelCase_ ( self : List[str] , lowerCamelCase_ : List[Any] ):
'''simple docstring'''
with ContextManagers([context_fr(), context_en()] ):
print("""Transformers are awesome!""" )
# The output should be wrapped with an English and French welcome and goodbye
self.assertEqual(mock_stdout.getvalue() , """Bonjour!\nWelcome!\nTransformers are awesome!\nBye!\nAu revoir!\n""" )
@require_torch
def lowerCamelCase_ ( self : Optional[Any] ):
'''simple docstring'''
self.assertEqual(find_labels(UpperCamelCase__ ) , ["""labels"""] )
self.assertEqual(find_labels(UpperCamelCase__ ) , ["""labels""", """next_sentence_label"""] )
self.assertEqual(find_labels(UpperCamelCase__ ) , ["""start_positions""", """end_positions"""] )
class UpperCamelCase__ ( SCREAMING_SNAKE_CASE_ ):
"""simple docstring"""
pass
self.assertEqual(find_labels(UpperCamelCase__ ) , ["""labels"""] )
@require_tf
def lowerCamelCase_ ( self : Optional[int] ):
'''simple docstring'''
self.assertEqual(find_labels(UpperCamelCase__ ) , ["""labels"""] )
self.assertEqual(find_labels(UpperCamelCase__ ) , ["""labels""", """next_sentence_label"""] )
self.assertEqual(find_labels(UpperCamelCase__ ) , ["""start_positions""", """end_positions"""] )
class UpperCamelCase__ ( SCREAMING_SNAKE_CASE_ ):
"""simple docstring"""
pass
self.assertEqual(find_labels(UpperCamelCase__ ) , ["""labels"""] )
@require_flax
def lowerCamelCase_ ( self : List[str] ):
'''simple docstring'''
self.assertEqual(find_labels(UpperCamelCase__ ) , [] )
self.assertEqual(find_labels(UpperCamelCase__ ) , [] )
self.assertEqual(find_labels(UpperCamelCase__ ) , [] )
class UpperCamelCase__ ( SCREAMING_SNAKE_CASE_ ):
"""simple docstring"""
pass
self.assertEqual(find_labels(UpperCamelCase__ ) , [] )
| 711 |
'''simple docstring'''
import argparse
import logging
from collections import namedtuple
import torch
from model_bertabs import BertAbsSummarizer
from models.model_builder import AbsSummarizer # The authors' implementation
from transformers import BertTokenizer
logging.basicConfig(level=logging.INFO)
__UpperCAmelCase = logging.getLogger(__name__)
__UpperCAmelCase = """Hello world! cécé herlolip"""
__UpperCAmelCase = namedtuple(
"""BertAbsConfig""",
[
"""temp_dir""",
"""large""",
"""use_bert_emb""",
"""finetune_bert""",
"""encoder""",
"""share_emb""",
"""max_pos""",
"""enc_layers""",
"""enc_hidden_size""",
"""enc_heads""",
"""enc_ff_size""",
"""enc_dropout""",
"""dec_layers""",
"""dec_hidden_size""",
"""dec_heads""",
"""dec_ff_size""",
"""dec_dropout""",
],
)
def __A ( lowerCamelCase_ , lowerCamelCase_ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : Tuple = BertAbsConfig(
temp_dir=""".""" , finetune_bert=lowerCamelCase_ , large=lowerCamelCase_ , share_emb=lowerCamelCase_ , use_bert_emb=lowerCamelCase_ , encoder="""bert""" , max_pos=5_12 , enc_layers=6 , enc_hidden_size=5_12 , enc_heads=8 , enc_ff_size=5_12 , enc_dropout=0.2 , dec_layers=6 , dec_hidden_size=7_68 , dec_heads=8 , dec_ff_size=20_48 , dec_dropout=0.2 , )
SCREAMING_SNAKE_CASE : int = torch.load(lowerCamelCase_ , lambda lowerCamelCase_ , lowerCamelCase_ : storage )
SCREAMING_SNAKE_CASE : List[str] = AbsSummarizer(lowerCamelCase_ , torch.device("""cpu""" ) , lowerCamelCase_ )
original.eval()
SCREAMING_SNAKE_CASE : Optional[int] = BertAbsSummarizer(lowerCamelCase_ , torch.device("""cpu""" ) )
new_model.eval()
# -------------------
# Convert the weights
# -------------------
logging.info("""convert the model""" )
new_model.bert.load_state_dict(original.bert.state_dict() )
new_model.decoder.load_state_dict(original.decoder.state_dict() )
new_model.generator.load_state_dict(original.generator.state_dict() )
# ----------------------------------
# Make sure the outpus are identical
# ----------------------------------
logging.info("""Make sure that the models' outputs are identical""" )
SCREAMING_SNAKE_CASE : int = BertTokenizer.from_pretrained("""bert-base-uncased""" )
# prepare the model inputs
SCREAMING_SNAKE_CASE : Optional[Any] = tokenizer.encode("""This is sample éàalj'-.""" )
encoder_input_ids.extend([tokenizer.pad_token_id] * (5_12 - len(lowerCamelCase_ )) )
SCREAMING_SNAKE_CASE : List[str] = torch.tensor(lowerCamelCase_ ).unsqueeze(0 )
SCREAMING_SNAKE_CASE : Optional[int] = tokenizer.encode("""This is sample 3 éàalj'-.""" )
decoder_input_ids.extend([tokenizer.pad_token_id] * (5_12 - len(lowerCamelCase_ )) )
SCREAMING_SNAKE_CASE : Optional[Any] = torch.tensor(lowerCamelCase_ ).unsqueeze(0 )
# failsafe to make sure the weights reset does not affect the
# loaded weights.
assert torch.max(torch.abs(original.generator[0].weight - new_model.generator[0].weight ) ) == 0
# forward pass
SCREAMING_SNAKE_CASE : Optional[int] = encoder_input_ids
SCREAMING_SNAKE_CASE : Optional[Any] = decoder_input_ids
SCREAMING_SNAKE_CASE : List[str] = None
SCREAMING_SNAKE_CASE : Tuple = None
SCREAMING_SNAKE_CASE : int = None
SCREAMING_SNAKE_CASE : List[Any] = None
SCREAMING_SNAKE_CASE : Optional[int] = None
# The original model does not apply the geneator layer immediatly but rather in
# the beam search (where it combines softmax + linear layer). Since we already
# apply the softmax in our generation process we only apply the linear layer here.
# We make sure that the outputs of the full stack are identical
SCREAMING_SNAKE_CASE : str = original(lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ )[0]
SCREAMING_SNAKE_CASE : Optional[Any] = original.generator(lowerCamelCase_ )
SCREAMING_SNAKE_CASE : List[Any] = new_model(
lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ )[0]
SCREAMING_SNAKE_CASE : str = new_model.generator(lowerCamelCase_ )
SCREAMING_SNAKE_CASE : int = torch.max(torch.abs(output_converted_model - output_original_model ) ).item()
print("""Maximum absolute difference beween weights: {:.2f}""".format(lowerCamelCase_ ) )
SCREAMING_SNAKE_CASE : Optional[int] = torch.max(torch.abs(output_converted_generator - output_original_generator ) ).item()
print("""Maximum absolute difference beween weights: {:.2f}""".format(lowerCamelCase_ ) )
SCREAMING_SNAKE_CASE : Any = torch.allclose(lowerCamelCase_ , lowerCamelCase_ , atol=1E-3 )
if are_identical:
logging.info("""all weights are equal up to 1e-3""" )
else:
raise ValueError("""the weights are different. The new model is likely different from the original one.""" )
# The model has been saved with torch.save(model) and this is bound to the exact
# directory structure. We save the state_dict instead.
logging.info("""saving the model's state dictionary""" )
torch.save(
new_model.state_dict() , """./bertabs-finetuned-cnndm-extractive-abstractive-summarization/pytorch_model.bin""" )
if __name__ == "__main__":
__UpperCAmelCase = argparse.ArgumentParser()
parser.add_argument(
"""--bertabs_checkpoint_path""",
default=None,
type=str,
required=True,
help="""Path the official PyTorch dump.""",
)
parser.add_argument(
"""--pytorch_dump_folder_path""",
default=None,
type=str,
required=True,
help="""Path to the output PyTorch model.""",
)
__UpperCAmelCase = parser.parse_args()
convert_bertabs_checkpoints(
args.bertabs_checkpoint_path,
args.pytorch_dump_folder_path,
)
| 79 | 0 |
'''simple docstring'''
def __A ( lowerCamelCase_ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : Dict = 0
for ch in input_str:
SCREAMING_SNAKE_CASE : int = ord(lowerCamelCase_ )
SCREAMING_SNAKE_CASE : Optional[Any] = pow(2 , lowerCamelCase_ )
# If we already turned on bit for current character's unicode
if bitmap >> ch_unicode & 1 == 1:
return False
bitmap |= ch_bit_index_on
return True
if __name__ == "__main__":
import doctest
doctest.testmod()
| 712 |
'''simple docstring'''
import itertools
import json
import linecache
import os
import pickle
import re
import socket
import string
from collections import Counter
from logging import getLogger
from pathlib import Path
from typing import Callable, Dict, Iterable, List
import git
import torch
from torch.utils.data import Dataset
from transformers import BartTokenizer, RagTokenizer, TaTokenizer
def __A ( lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_=True , lowerCamelCase_="pt" ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : Tuple = {"""add_prefix_space""": True} if isinstance(lowerCamelCase_ , lowerCamelCase_ ) and not line.startswith(""" """ ) else {}
SCREAMING_SNAKE_CASE : Optional[Any] = padding_side
return tokenizer(
[line] , max_length=lowerCamelCase_ , padding="""max_length""" if pad_to_max_length else None , truncation=lowerCamelCase_ , return_tensors=lowerCamelCase_ , add_special_tokens=lowerCamelCase_ , **lowerCamelCase_ , )
def __A ( lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_=None , ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : int = input_ids.ne(lowerCamelCase_ ).any(dim=0 )
if attention_mask is None:
return input_ids[:, keep_column_mask]
else:
return (input_ids[:, keep_column_mask], attention_mask[:, keep_column_mask])
class UpperCamelCase__ ( lowercase_ ):
"""simple docstring"""
def __init__( self : str , lowerCamelCase_ : Tuple , lowerCamelCase_ : Tuple , lowerCamelCase_ : List[str] , lowerCamelCase_ : List[str] , lowerCamelCase_ : List[str]="train" , lowerCamelCase_ : List[str]=None , lowerCamelCase_ : List[Any]=None , lowerCamelCase_ : int=None , lowerCamelCase_ : Union[str, Any]="" , ):
'''simple docstring'''
super().__init__()
SCREAMING_SNAKE_CASE : str = Path(lowerCamelCase_ ).joinpath(type_path + """.source""" )
SCREAMING_SNAKE_CASE : Optional[Any] = Path(lowerCamelCase_ ).joinpath(type_path + """.target""" )
SCREAMING_SNAKE_CASE : Union[str, Any] = self.get_char_lens(self.src_file )
SCREAMING_SNAKE_CASE : int = max_source_length
SCREAMING_SNAKE_CASE : str = max_target_length
assert min(self.src_lens ) > 0, f'''found empty line in {self.src_file}'''
SCREAMING_SNAKE_CASE : List[str] = tokenizer
SCREAMING_SNAKE_CASE : Dict = prefix
if n_obs is not None:
SCREAMING_SNAKE_CASE : List[Any] = self.src_lens[:n_obs]
SCREAMING_SNAKE_CASE : int = src_lang
SCREAMING_SNAKE_CASE : Optional[int] = tgt_lang
def __len__( self : List[Any] ):
'''simple docstring'''
return len(self.src_lens )
def __getitem__( self : Union[str, Any] , lowerCamelCase_ : Optional[Any] ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Any = index + 1 # linecache starts at 1
SCREAMING_SNAKE_CASE : Dict = self.prefix + linecache.getline(str(self.src_file ) , lowerCamelCase_ ).rstrip("""\n""" )
SCREAMING_SNAKE_CASE : Dict = linecache.getline(str(self.tgt_file ) , lowerCamelCase_ ).rstrip("""\n""" )
assert source_line, f'''empty source line for index {index}'''
assert tgt_line, f'''empty tgt line for index {index}'''
# Need to add eos token manually for T5
if isinstance(self.tokenizer , lowerCamelCase_ ):
source_line += self.tokenizer.eos_token
tgt_line += self.tokenizer.eos_token
# Pad source and target to the right
SCREAMING_SNAKE_CASE : Union[str, Any] = (
self.tokenizer.question_encoder if isinstance(self.tokenizer , lowerCamelCase_ ) else self.tokenizer
)
SCREAMING_SNAKE_CASE : Any = self.tokenizer.generator if isinstance(self.tokenizer , lowerCamelCase_ ) else self.tokenizer
SCREAMING_SNAKE_CASE : Optional[int] = encode_line(lowerCamelCase_ , lowerCamelCase_ , self.max_source_length , """right""" )
SCREAMING_SNAKE_CASE : Union[str, Any] = encode_line(lowerCamelCase_ , lowerCamelCase_ , self.max_target_length , """right""" )
SCREAMING_SNAKE_CASE : Tuple = source_inputs["""input_ids"""].squeeze()
SCREAMING_SNAKE_CASE : Tuple = target_inputs["""input_ids"""].squeeze()
SCREAMING_SNAKE_CASE : List[str] = source_inputs["""attention_mask"""].squeeze()
return {
"input_ids": source_ids,
"attention_mask": src_mask,
"decoder_input_ids": target_ids,
}
@staticmethod
def lowerCamelCase_ ( lowerCamelCase_ : Dict ):
'''simple docstring'''
return [len(lowerCamelCase_ ) for x in Path(lowerCamelCase_ ).open().readlines()]
def lowerCamelCase_ ( self : Tuple , lowerCamelCase_ : Any ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Tuple = torch.stack([x["""input_ids"""] for x in batch] )
SCREAMING_SNAKE_CASE : int = torch.stack([x["""attention_mask"""] for x in batch] )
SCREAMING_SNAKE_CASE : int = torch.stack([x["""decoder_input_ids"""] for x in batch] )
SCREAMING_SNAKE_CASE : int = (
self.tokenizer.generator.pad_token_id
if isinstance(self.tokenizer , lowerCamelCase_ )
else self.tokenizer.pad_token_id
)
SCREAMING_SNAKE_CASE : Dict = (
self.tokenizer.question_encoder.pad_token_id
if isinstance(self.tokenizer , lowerCamelCase_ )
else self.tokenizer.pad_token_id
)
SCREAMING_SNAKE_CASE : Optional[int] = trim_batch(lowerCamelCase_ , lowerCamelCase_ )
SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE : Optional[int] = trim_batch(lowerCamelCase_ , lowerCamelCase_ , attention_mask=lowerCamelCase_ )
SCREAMING_SNAKE_CASE : int = {
"""input_ids""": source_ids,
"""attention_mask""": source_mask,
"""decoder_input_ids""": y,
}
return batch
__UpperCAmelCase = getLogger(__name__)
def __A ( lowerCamelCase_ ):
"""simple docstring"""
return list(itertools.chain.from_iterable(lowerCamelCase_ ) )
def __A ( lowerCamelCase_ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : str = get_git_info()
save_json(lowerCamelCase_ , os.path.join(lowerCamelCase_ , """git_log.json""" ) )
def __A ( lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_=4 , **lowerCamelCase_ ):
"""simple docstring"""
with open(lowerCamelCase_ , """w""" ) as f:
json.dump(lowerCamelCase_ , lowerCamelCase_ , indent=lowerCamelCase_ , **lowerCamelCase_ )
def __A ( lowerCamelCase_ ):
"""simple docstring"""
with open(lowerCamelCase_ ) as f:
return json.load(lowerCamelCase_ )
def __A ( ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : List[Any] = git.Repo(search_parent_directories=lowerCamelCase_ )
SCREAMING_SNAKE_CASE : Tuple = {
"""repo_id""": str(lowerCamelCase_ ),
"""repo_sha""": str(repo.head.object.hexsha ),
"""repo_branch""": str(repo.active_branch ),
"""hostname""": str(socket.gethostname() ),
}
return repo_infos
def __A ( lowerCamelCase_ , lowerCamelCase_ ):
"""simple docstring"""
return list(map(lowerCamelCase_ , lowerCamelCase_ ) )
def __A ( lowerCamelCase_ , lowerCamelCase_ ):
"""simple docstring"""
with open(lowerCamelCase_ , """wb""" ) as f:
return pickle.dump(lowerCamelCase_ , lowerCamelCase_ )
def __A ( lowerCamelCase_ ):
"""simple docstring"""
def remove_articles(lowerCamelCase_ ):
return re.sub(R"""\b(a|an|the)\b""" , """ """ , lowerCamelCase_ )
def white_space_fix(lowerCamelCase_ ):
return " ".join(text.split() )
def remove_punc(lowerCamelCase_ ):
SCREAMING_SNAKE_CASE : int = set(string.punctuation )
return "".join(ch for ch in text if ch not in exclude )
def lower(lowerCamelCase_ ):
return text.lower()
return white_space_fix(remove_articles(remove_punc(lower(lowerCamelCase_ ) ) ) )
def __A ( lowerCamelCase_ , lowerCamelCase_ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : Tuple = normalize_answer(lowerCamelCase_ ).split()
SCREAMING_SNAKE_CASE : Optional[int] = normalize_answer(lowerCamelCase_ ).split()
SCREAMING_SNAKE_CASE : Tuple = Counter(lowerCamelCase_ ) & Counter(lowerCamelCase_ )
SCREAMING_SNAKE_CASE : Any = sum(common.values() )
if num_same == 0:
return 0
SCREAMING_SNAKE_CASE : Optional[int] = 1.0 * num_same / len(lowerCamelCase_ )
SCREAMING_SNAKE_CASE : str = 1.0 * num_same / len(lowerCamelCase_ )
SCREAMING_SNAKE_CASE : List[str] = (2 * precision * recall) / (precision + recall)
return fa
def __A ( lowerCamelCase_ , lowerCamelCase_ ):
"""simple docstring"""
return normalize_answer(lowerCamelCase_ ) == normalize_answer(lowerCamelCase_ )
def __A ( lowerCamelCase_ , lowerCamelCase_ ):
"""simple docstring"""
assert len(lowerCamelCase_ ) == len(lowerCamelCase_ )
SCREAMING_SNAKE_CASE : Union[str, Any] = 0
for hypo, pred in zip(lowerCamelCase_ , lowerCamelCase_ ):
em += exact_match_score(lowerCamelCase_ , lowerCamelCase_ )
if len(lowerCamelCase_ ) > 0:
em /= len(lowerCamelCase_ )
return {"em": em}
def __A ( lowerCamelCase_ ):
"""simple docstring"""
return model_prefix.startswith("""rag""" )
def __A ( lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : Any = {p: p for p in extra_params}
# T5 models don't have `dropout` param, they have `dropout_rate` instead
SCREAMING_SNAKE_CASE : Dict = """dropout_rate"""
for p in extra_params:
if getattr(lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ ):
if not hasattr(lowerCamelCase_ , lowerCamelCase_ ) and not hasattr(lowerCamelCase_ , equivalent_param[p] ):
logger.info("""config doesn't have a `{}` attribute""".format(lowerCamelCase_ ) )
delattr(lowerCamelCase_ , lowerCamelCase_ )
continue
SCREAMING_SNAKE_CASE : Dict = p if hasattr(lowerCamelCase_ , lowerCamelCase_ ) else equivalent_param[p]
setattr(lowerCamelCase_ , lowerCamelCase_ , getattr(lowerCamelCase_ , lowerCamelCase_ ) )
delattr(lowerCamelCase_ , lowerCamelCase_ )
return hparams, config
| 79 | 0 |
'''simple docstring'''
from abc import ABC, abstractmethod
from typing import List, Optional
class UpperCamelCase__ ( _A ):
"""simple docstring"""
def __init__( self : str ):
'''simple docstring'''
self.test()
def lowerCamelCase_ ( self : Tuple ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : List[str] = 0
SCREAMING_SNAKE_CASE : str = False
while not completed:
if counter == 1:
self.reset()
SCREAMING_SNAKE_CASE : Optional[int] = self.advance()
if not self.does_advance(lowerCamelCase_ ):
raise Exception(
"""Custom Constraint is not defined correctly. self.does_advance(self.advance()) must be true.""" )
SCREAMING_SNAKE_CASE : Union[str, Any] = self.update(lowerCamelCase_ )
counter += 1
if counter > 1_00_00:
raise Exception("""update() does not fulfill the constraint.""" )
if self.remaining() != 0:
raise Exception("""Custom Constraint is not defined correctly.""" )
@abstractmethod
def lowerCamelCase_ ( self : Union[str, Any] ):
'''simple docstring'''
raise NotImplementedError(
f'''{self.__class__} is an abstract class. Only classes inheriting this class can be called.''' )
@abstractmethod
def lowerCamelCase_ ( self : Optional[Any] , lowerCamelCase_ : Tuple ):
'''simple docstring'''
raise NotImplementedError(
f'''{self.__class__} is an abstract class. Only classes inheriting this class can be called.''' )
@abstractmethod
def lowerCamelCase_ ( self : Dict , lowerCamelCase_ : Any ):
'''simple docstring'''
raise NotImplementedError(
f'''{self.__class__} is an abstract class. Only classes inheriting this class can be called.''' )
@abstractmethod
def lowerCamelCase_ ( self : Tuple ):
'''simple docstring'''
raise NotImplementedError(
f'''{self.__class__} is an abstract class. Only classes inheriting this class can be called.''' )
@abstractmethod
def lowerCamelCase_ ( self : List[Any] ):
'''simple docstring'''
raise NotImplementedError(
f'''{self.__class__} is an abstract class. Only classes inheriting this class can be called.''' )
@abstractmethod
def lowerCamelCase_ ( self : int , lowerCamelCase_ : int=False ):
'''simple docstring'''
raise NotImplementedError(
f'''{self.__class__} is an abstract class. Only classes inheriting this class can be called.''' )
class UpperCamelCase__ ( _A ):
"""simple docstring"""
def __init__( self : Optional[int] , lowerCamelCase_ : str ):
'''simple docstring'''
super(lowerCamelCase_ , self ).__init__()
if not isinstance(lowerCamelCase_ , lowerCamelCase_ ) or len(lowerCamelCase_ ) == 0:
raise ValueError(f'''`token_ids` has to be a non-empty list, but is {token_ids}.''' )
if any((not isinstance(lowerCamelCase_ , lowerCamelCase_ ) or token_id < 0) for token_id in token_ids ):
raise ValueError(f'''Each list in `token_ids` has to be a list of positive integers, but is {token_ids}.''' )
SCREAMING_SNAKE_CASE : List[Any] = token_ids
SCREAMING_SNAKE_CASE : List[str] = len(self.token_ids )
SCREAMING_SNAKE_CASE : Optional[Any] = -1 # the index of the currently fulfilled step
SCREAMING_SNAKE_CASE : Dict = False
def lowerCamelCase_ ( self : int ):
'''simple docstring'''
if self.completed:
return None
return self.token_ids[self.fulfilled_idx + 1]
def lowerCamelCase_ ( self : Tuple , lowerCamelCase_ : Union[str, Any] ):
'''simple docstring'''
if not isinstance(lowerCamelCase_ , lowerCamelCase_ ):
raise ValueError(f'''`token_id` has to be an `int`, but is {token_id} of type {type(lowerCamelCase_ )}''' )
if self.completed:
return False
return token_id == self.token_ids[self.fulfilled_idx + 1]
def lowerCamelCase_ ( self : Dict , lowerCamelCase_ : Optional[Any] ):
'''simple docstring'''
if not isinstance(lowerCamelCase_ , lowerCamelCase_ ):
raise ValueError(f'''`token_id` has to be an `int`, but is {token_id} of type {type(lowerCamelCase_ )}''' )
SCREAMING_SNAKE_CASE : Tuple = False
SCREAMING_SNAKE_CASE : str = False
SCREAMING_SNAKE_CASE : Tuple = False
if self.does_advance(lowerCamelCase_ ):
self.fulfilled_idx += 1
SCREAMING_SNAKE_CASE : int = True
if self.fulfilled_idx == (self.seqlen - 1):
SCREAMING_SNAKE_CASE : List[Any] = True
SCREAMING_SNAKE_CASE : Tuple = completed
else:
# failed to make progress.
SCREAMING_SNAKE_CASE : List[Any] = True
self.reset()
return stepped, completed, reset
def lowerCamelCase_ ( self : Any ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Union[str, Any] = False
SCREAMING_SNAKE_CASE : Union[str, Any] = 0
def lowerCamelCase_ ( self : List[str] ):
'''simple docstring'''
return self.seqlen - (self.fulfilled_idx + 1)
def lowerCamelCase_ ( self : Union[str, Any] , lowerCamelCase_ : List[str]=False ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Tuple = PhrasalConstraint(self.token_ids )
if stateful:
SCREAMING_SNAKE_CASE : str = self.seqlen
SCREAMING_SNAKE_CASE : Optional[Any] = self.fulfilled_idx
SCREAMING_SNAKE_CASE : List[str] = self.completed
return new_constraint
class UpperCamelCase__ :
"""simple docstring"""
def __init__( self : Any , lowerCamelCase_ : Union[str, Any] , lowerCamelCase_ : List[str]=True ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Optional[int] = max([len(lowerCamelCase_ ) for one in nested_token_ids] )
SCREAMING_SNAKE_CASE : Union[str, Any] = {}
for token_ids in nested_token_ids:
SCREAMING_SNAKE_CASE : Tuple = root
for tidx, token_id in enumerate(lowerCamelCase_ ):
if token_id not in level:
SCREAMING_SNAKE_CASE : Tuple = {}
SCREAMING_SNAKE_CASE : List[str] = level[token_id]
if no_subsets and self.has_subsets(lowerCamelCase_ , lowerCamelCase_ ):
raise ValueError(
"""Each list in `nested_token_ids` can't be a complete subset of another list, but is"""
f''' {nested_token_ids}.''' )
SCREAMING_SNAKE_CASE : List[Any] = root
def lowerCamelCase_ ( self : int , lowerCamelCase_ : Optional[Any] ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : str = self.trie
for current_token in current_seq:
SCREAMING_SNAKE_CASE : Any = start[current_token]
SCREAMING_SNAKE_CASE : Union[str, Any] = list(start.keys() )
return next_tokens
def lowerCamelCase_ ( self : int , lowerCamelCase_ : Any ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : str = self.next_tokens(lowerCamelCase_ )
return len(lowerCamelCase_ ) == 0
def lowerCamelCase_ ( self : Union[str, Any] , lowerCamelCase_ : int ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : int = list(root.values() )
if len(lowerCamelCase_ ) == 0:
return 1
else:
return sum([self.count_leaves(lowerCamelCase_ ) for nn in next_nodes] )
def lowerCamelCase_ ( self : Tuple , lowerCamelCase_ : List[Any] , lowerCamelCase_ : Dict ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Any = self.count_leaves(lowerCamelCase_ )
return len(lowerCamelCase_ ) != leaf_count
class UpperCamelCase__ ( _A ):
"""simple docstring"""
def __init__( self : str , lowerCamelCase_ : Any ):
'''simple docstring'''
super(lowerCamelCase_ , self ).__init__()
if not isinstance(lowerCamelCase_ , lowerCamelCase_ ) or len(lowerCamelCase_ ) == 0:
raise ValueError(f'''`nested_token_ids` has to be a non-empty list, but is {nested_token_ids}.''' )
if any(not isinstance(lowerCamelCase_ , lowerCamelCase_ ) for token_ids in nested_token_ids ):
raise ValueError(f'''`nested_token_ids` has to be a list of lists, but is {nested_token_ids}.''' )
if any(
any((not isinstance(lowerCamelCase_ , lowerCamelCase_ ) or token_id < 0) for token_id in token_ids )
for token_ids in nested_token_ids ):
raise ValueError(
f'''Each list in `nested_token_ids` has to be a list of positive integers, but is {nested_token_ids}.''' )
SCREAMING_SNAKE_CASE : Dict = DisjunctiveTrie(lowerCamelCase_ )
SCREAMING_SNAKE_CASE : List[Any] = nested_token_ids
SCREAMING_SNAKE_CASE : Tuple = self.trie.max_height
SCREAMING_SNAKE_CASE : Tuple = []
SCREAMING_SNAKE_CASE : str = False
def lowerCamelCase_ ( self : List[Any] ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : List[Any] = self.trie.next_tokens(self.current_seq )
if len(lowerCamelCase_ ) == 0:
return None
else:
return token_list
def lowerCamelCase_ ( self : int , lowerCamelCase_ : List[Any] ):
'''simple docstring'''
if not isinstance(lowerCamelCase_ , lowerCamelCase_ ):
raise ValueError(f'''`token_id` is supposed to be type `int`, but is {token_id} of type {type(lowerCamelCase_ )}''' )
SCREAMING_SNAKE_CASE : Optional[Any] = self.trie.next_tokens(self.current_seq )
return token_id in next_tokens
def lowerCamelCase_ ( self : Tuple , lowerCamelCase_ : int ):
'''simple docstring'''
if not isinstance(lowerCamelCase_ , lowerCamelCase_ ):
raise ValueError(f'''`token_id` is supposed to be type `int`, but is {token_id} of type {type(lowerCamelCase_ )}''' )
SCREAMING_SNAKE_CASE : Optional[int] = False
SCREAMING_SNAKE_CASE : Tuple = False
SCREAMING_SNAKE_CASE : Dict = False
if self.does_advance(lowerCamelCase_ ):
self.current_seq.append(lowerCamelCase_ )
SCREAMING_SNAKE_CASE : Optional[Any] = True
else:
SCREAMING_SNAKE_CASE : Optional[int] = True
self.reset()
SCREAMING_SNAKE_CASE : Dict = self.trie.reached_leaf(self.current_seq )
SCREAMING_SNAKE_CASE : Any = completed
return stepped, completed, reset
def lowerCamelCase_ ( self : Optional[int] ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Optional[Any] = False
SCREAMING_SNAKE_CASE : Optional[int] = []
def lowerCamelCase_ ( self : str ):
'''simple docstring'''
if self.completed:
# since this can be completed without reaching max height
return 0
else:
return self.seqlen - len(self.current_seq )
def lowerCamelCase_ ( self : Tuple , lowerCamelCase_ : Tuple=False ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Tuple = DisjunctiveConstraint(self.token_ids )
if stateful:
SCREAMING_SNAKE_CASE : str = self.seqlen
SCREAMING_SNAKE_CASE : Tuple = self.current_seq
SCREAMING_SNAKE_CASE : Optional[Any] = self.completed
return new_constraint
class UpperCamelCase__ :
"""simple docstring"""
def __init__( self : str , lowerCamelCase_ : Any ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Dict = constraints
# max # of steps required to fulfill a given constraint
SCREAMING_SNAKE_CASE : Any = max([c.seqlen for c in constraints] )
SCREAMING_SNAKE_CASE : Dict = len(lowerCamelCase_ )
SCREAMING_SNAKE_CASE : int = False
self.init_state()
def lowerCamelCase_ ( self : Tuple ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Optional[int] = []
SCREAMING_SNAKE_CASE : Tuple = None
SCREAMING_SNAKE_CASE : Any = [constraint.copy(stateful=lowerCamelCase_ ) for constraint in self.constraints]
def lowerCamelCase_ ( self : Dict ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : List[str] = 0
if self.inprogress_constraint:
# extra points for having a constraint mid-fulfilled
add += self.max_seqlen - self.inprogress_constraint.remaining()
return (len(self.complete_constraints ) * self.max_seqlen) + add
def lowerCamelCase_ ( self : Union[str, Any] ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Any = []
if self.inprogress_constraint is None:
for constraint in self.pending_constraints: # "pending" == "unfulfilled yet"
SCREAMING_SNAKE_CASE : int = constraint.advance()
if isinstance(lowerCamelCase_ , lowerCamelCase_ ):
token_list.append(lowerCamelCase_ )
elif isinstance(lowerCamelCase_ , lowerCamelCase_ ):
token_list.extend(lowerCamelCase_ )
else:
SCREAMING_SNAKE_CASE : List[Any] = self.inprogress_constraint.advance()
if isinstance(lowerCamelCase_ , lowerCamelCase_ ):
token_list.append(lowerCamelCase_ )
elif isinstance(lowerCamelCase_ , lowerCamelCase_ ):
token_list.extend(lowerCamelCase_ )
if len(lowerCamelCase_ ) == 0:
return None
else:
return token_list
def lowerCamelCase_ ( self : Optional[Any] , lowerCamelCase_ : str ):
'''simple docstring'''
self.init_state()
if token_ids is not None:
for token in token_ids:
# completes or steps **one** constraint
SCREAMING_SNAKE_CASE : Optional[Any] = self.add(lowerCamelCase_ )
# the entire list of constraints are fulfilled
if self.completed:
break
def lowerCamelCase_ ( self : Dict , lowerCamelCase_ : Tuple ):
'''simple docstring'''
if not isinstance(lowerCamelCase_ , lowerCamelCase_ ):
raise ValueError(f'''`token_id` should be an `int`, but is `{token_id}`.''' )
SCREAMING_SNAKE_CASE : Optional[Any] = False, False
if self.completed:
SCREAMING_SNAKE_CASE : List[Any] = True
SCREAMING_SNAKE_CASE : str = False
return complete, stepped
if self.inprogress_constraint is not None:
# In the middle of fulfilling a constraint. If the `token_id` *does* makes an incremental progress to current
# job, simply update the state
SCREAMING_SNAKE_CASE : Dict = self.inprogress_constraint.update(lowerCamelCase_ )
if reset:
# 1. If the next token breaks the progress, then we must restart.
# e.g. constraint = "I love pies" and sequence so far is "I love" but `token_id` == "books".
# But that doesn't mean we self.init_state(), since we only reset the state for this particular
# constraint, not the full list of constraints.
self.pending_constraints.append(self.inprogress_constraint.copy(stateful=lowerCamelCase_ ) )
SCREAMING_SNAKE_CASE : Any = None
if complete:
# 2. If the next token completes the constraint, move it to completed list, set
# inprogress to None. If there are no pending constraints either, then this full list of constraints
# is complete.
self.complete_constraints.append(self.inprogress_constraint )
SCREAMING_SNAKE_CASE : str = None
if len(self.pending_constraints ) == 0:
# we're done!
SCREAMING_SNAKE_CASE : int = True
else:
# Not in the middle of fulfilling a constraint. So does this `token_id` helps us step towards any of our list
# of constraints?
for cidx, pending_constraint in enumerate(self.pending_constraints ):
if pending_constraint.does_advance(lowerCamelCase_ ):
SCREAMING_SNAKE_CASE : Union[str, Any] = pending_constraint.update(lowerCamelCase_ )
if not stepped:
raise Exception(
"""`constraint.update(token_id)` is not yielding incremental progress, """
"""even though `constraint.does_advance(token_id)` is true.""" )
if complete:
self.complete_constraints.append(lowerCamelCase_ )
SCREAMING_SNAKE_CASE : Union[str, Any] = None
if not complete and stepped:
SCREAMING_SNAKE_CASE : Union[str, Any] = pending_constraint
if complete or stepped:
# If we made any progress at all, then it's at least not a "pending constraint".
SCREAMING_SNAKE_CASE : List[str] = (
self.pending_constraints[:cidx] + self.pending_constraints[cidx + 1 :]
)
if len(self.pending_constraints ) == 0 and self.inprogress_constraint is None:
# If there's no longer any pending after this and no inprogress either, then we must be
# complete.
SCREAMING_SNAKE_CASE : int = True
break # prevent accidentally stepping through multiple constraints with just one token.
return complete, stepped
def lowerCamelCase_ ( self : Any , lowerCamelCase_ : Union[str, Any]=True ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : List[str] = ConstraintListState(self.constraints ) # we actually never though self.constraints objects
# throughout this process. So it's at initialization state.
if stateful:
SCREAMING_SNAKE_CASE : Optional[Any] = [
constraint.copy(stateful=lowerCamelCase_ ) for constraint in self.complete_constraints
]
if self.inprogress_constraint is not None:
SCREAMING_SNAKE_CASE : List[Any] = self.inprogress_constraint.copy(stateful=lowerCamelCase_ )
SCREAMING_SNAKE_CASE : str = [constraint.copy() for constraint in self.pending_constraints]
return new_state
| 713 |
'''simple docstring'''
def __A ( lowerCamelCase_ ):
"""simple docstring"""
return 1 if digit in (0, 1) else (digit * factorial(digit - 1 ))
def __A ( lowerCamelCase_ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : Optional[Any] = 0
SCREAMING_SNAKE_CASE : List[str] = number
while duplicate > 0:
SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE : Dict = divmod(lowerCamelCase_ , 10 )
fact_sum += factorial(lowerCamelCase_ )
return fact_sum == number
if __name__ == "__main__":
print("""Program to check whether a number is a Krisnamurthy Number or not.""")
__UpperCAmelCase = int(input("""Enter number: """).strip())
print(
f'''{number} is {'' if krishnamurthy(number) else 'not '}a Krishnamurthy Number.'''
)
| 79 | 0 |
import argparse
import json
import subprocess
def __A ( lowerCamelCase_ , lowerCamelCase_ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : Union[str, Any] = []
SCREAMING_SNAKE_CASE : Dict = (
f'''curl -H "Accept: application/vnd.github+json" -H "Authorization: Bearer {token}"'''
""" https://api.github.com/repos/huggingface/transformers/actions/runners"""
)
SCREAMING_SNAKE_CASE : int = subprocess.run(a__ , shell=a__ , stdout=subprocess.PIPE )
SCREAMING_SNAKE_CASE : Optional[int] = output.stdout.decode("""utf-8""" )
SCREAMING_SNAKE_CASE : Tuple = json.loads(a__ )
SCREAMING_SNAKE_CASE : Any = status["""runners"""]
for runner in runners:
if runner["name"] in target_runners:
if runner["status"] == "offline":
offline_runners.append(a__ )
# save the result so we can report them on Slack
with open("""offline_runners.txt""" , """w""" ) as fp:
fp.write(json.dumps(a__ ) )
if len(a__ ) > 0:
SCREAMING_SNAKE_CASE : Tuple = """\n""".join([x["""name"""] for x in offline_runners] )
raise ValueError(f'''The following runners are offline:\n{failed}''' )
if __name__ == "__main__":
def __A ( lowerCamelCase_ ):
"""simple docstring"""
return values.split(""",""" )
__UpperCAmelCase = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"""--target_runners""",
default=None,
type=list_str,
required=True,
help="""Comma-separated list of runners to check status.""",
)
parser.add_argument(
"""--token""", default=None, type=str, required=True, help="""A token that has actions:read permission."""
)
__UpperCAmelCase = parser.parse_args()
get_runner_status(args.target_runners, args.token)
| 714 |
'''simple docstring'''
# Lint as: python3
import sys
from collections.abc import Mapping
from typing import TYPE_CHECKING
import numpy as np
import pyarrow as pa
from .. import config
from ..utils.py_utils import map_nested
from .formatting import TensorFormatter
if TYPE_CHECKING:
import torch
class UpperCamelCase__ ( TensorFormatter[Mapping, '''torch.Tensor''', Mapping] ):
"""simple docstring"""
def __init__( self : Tuple , lowerCamelCase_ : str=None , **lowerCamelCase_ : Dict ):
'''simple docstring'''
super().__init__(features=lowerCamelCase_ )
SCREAMING_SNAKE_CASE : Tuple = torch_tensor_kwargs
import torch # noqa import torch at initialization
def lowerCamelCase_ ( self : Dict , lowerCamelCase_ : Union[str, Any] ):
'''simple docstring'''
import torch
if isinstance(lowerCamelCase_ , lowerCamelCase_ ) and column:
if all(
isinstance(lowerCamelCase_ , torch.Tensor ) and x.shape == column[0].shape and x.dtype == column[0].dtype
for x in column ):
return torch.stack(lowerCamelCase_ )
return column
def lowerCamelCase_ ( self : List[str] , lowerCamelCase_ : int ):
'''simple docstring'''
import torch
if isinstance(lowerCamelCase_ , (str, bytes, type(lowerCamelCase_ )) ):
return value
elif isinstance(lowerCamelCase_ , (np.character, np.ndarray) ) and np.issubdtype(value.dtype , np.character ):
return value.tolist()
SCREAMING_SNAKE_CASE : str = {}
if isinstance(lowerCamelCase_ , (np.number, np.ndarray) ) and np.issubdtype(value.dtype , np.integer ):
SCREAMING_SNAKE_CASE : Any = {"""dtype""": torch.intaa}
elif isinstance(lowerCamelCase_ , (np.number, np.ndarray) ) and np.issubdtype(value.dtype , np.floating ):
SCREAMING_SNAKE_CASE : int = {"""dtype""": torch.floataa}
elif config.PIL_AVAILABLE and "PIL" in sys.modules:
import PIL.Image
if isinstance(lowerCamelCase_ , PIL.Image.Image ):
SCREAMING_SNAKE_CASE : List[Any] = np.asarray(lowerCamelCase_ )
return torch.tensor(lowerCamelCase_ , **{**default_dtype, **self.torch_tensor_kwargs} )
def lowerCamelCase_ ( self : Optional[int] , lowerCamelCase_ : Optional[Any] ):
'''simple docstring'''
import torch
# support for torch, tf, jax etc.
if hasattr(lowerCamelCase_ , """__array__""" ) and not isinstance(lowerCamelCase_ , torch.Tensor ):
SCREAMING_SNAKE_CASE : Dict = data_struct.__array__()
# support for nested types like struct of list of struct
if isinstance(lowerCamelCase_ , np.ndarray ):
if data_struct.dtype == object: # torch tensors cannot be instantied from an array of objects
return self._consolidate([self.recursive_tensorize(lowerCamelCase_ ) for substruct in data_struct] )
elif isinstance(lowerCamelCase_ , (list, tuple) ):
return self._consolidate([self.recursive_tensorize(lowerCamelCase_ ) for substruct in data_struct] )
return self._tensorize(lowerCamelCase_ )
def lowerCamelCase_ ( self : int , lowerCamelCase_ : dict ):
'''simple docstring'''
return map_nested(self._recursive_tensorize , lowerCamelCase_ , map_list=lowerCamelCase_ )
def lowerCamelCase_ ( self : Optional[int] , lowerCamelCase_ : pa.Table ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : str = self.numpy_arrow_extractor().extract_row(lowerCamelCase_ )
SCREAMING_SNAKE_CASE : Dict = self.python_features_decoder.decode_row(lowerCamelCase_ )
return self.recursive_tensorize(lowerCamelCase_ )
def lowerCamelCase_ ( self : Tuple , lowerCamelCase_ : pa.Table ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : List[str] = self.numpy_arrow_extractor().extract_column(lowerCamelCase_ )
SCREAMING_SNAKE_CASE : Tuple = self.python_features_decoder.decode_column(lowerCamelCase_ , pa_table.column_names[0] )
SCREAMING_SNAKE_CASE : List[str] = self.recursive_tensorize(lowerCamelCase_ )
SCREAMING_SNAKE_CASE : List[str] = self._consolidate(lowerCamelCase_ )
return column
def lowerCamelCase_ ( self : Dict , lowerCamelCase_ : pa.Table ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Optional[Any] = self.numpy_arrow_extractor().extract_batch(lowerCamelCase_ )
SCREAMING_SNAKE_CASE : Dict = self.python_features_decoder.decode_batch(lowerCamelCase_ )
SCREAMING_SNAKE_CASE : Optional[Any] = self.recursive_tensorize(lowerCamelCase_ )
for column_name in batch:
SCREAMING_SNAKE_CASE : Tuple = self._consolidate(batch[column_name] )
return batch
| 79 | 0 |
'''simple docstring'''
import argparse
import random
import joblib
import numpy as np
import torch
from igf.igf import (
SecondaryLearner,
collect_objective_set,
compute_perplexity,
generate_datasets,
load_gpta,
recopy_gpta,
set_seed,
train_secondary_learner,
)
from torch.utils.data import DataLoader, RandomSampler
from transformers import GPTaLMHeadModel
def __A ( lowerCamelCase_=32 , lowerCamelCase_=10 , lowerCamelCase_=1_00 , lowerCamelCase_=10_26 , lowerCamelCase_=True , lowerCamelCase_="data/tokenized_stories_train_wikitext103.jbl" , lowerCamelCase_="igf_context_pairs.jbl" , ):
"""simple docstring"""
set_seed(3 )
# generate train_data and objective_set
SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE : Optional[Any] = generate_datasets(
UpperCamelCase__ , UpperCamelCase__ , number=UpperCamelCase__ , min_len=10_26 , trim=UpperCamelCase__ )
# keeps model same across runs
set_seed(4 )
# model, lm_optimizer, lm_scheduler = recopy_gpt2(model, device, max_steps) # store original model weights
# can we train on GPU?
SCREAMING_SNAKE_CASE : List[Any] = torch.device("""cuda:0""" if torch.cuda.is_available() else """cpu""" )
# load pretrained model
SCREAMING_SNAKE_CASE : int = load_gpta("""gpt2""" ).to(UpperCamelCase__ )
print("""computing perplexity on objective set""" )
SCREAMING_SNAKE_CASE : Tuple = compute_perplexity(UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ).item()
print("""perplexity on objective set:""" , UpperCamelCase__ )
# collect igf pairs and save to file demo.jbl
collect_objective_set(UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ )
# clean up, delete model and data we don't need anymore
del model, train_data, objective_set
torch.cuda.empty_cache()
def __A ( lowerCamelCase_ , lowerCamelCase_=15 , lowerCamelCase_=1_28 , lowerCamelCase_=1_00 , lowerCamelCase_="igf_model.pt" , ):
"""simple docstring"""
set_seed(42 )
# Load pre-trained model
SCREAMING_SNAKE_CASE : Optional[int] = GPTaLMHeadModel.from_pretrained("""gpt2""" )
# Initialize secondary learner to use embedding weights of model
SCREAMING_SNAKE_CASE : List[str] = SecondaryLearner(UpperCamelCase__ )
# Train secondary learner
SCREAMING_SNAKE_CASE : Union[str, Any] = train_secondary_learner(
UpperCamelCase__ , UpperCamelCase__ , max_epochs=UpperCamelCase__ , batch_size=UpperCamelCase__ , eval_freq=1_00 , igf_model_path=UpperCamelCase__ , )
del model, secondary_learner_train_data
torch.cuda.empty_cache()
return secondary_learner
def __A ( lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_=32 , lowerCamelCase_=10_00 , lowerCamelCase_=16 , lowerCamelCase_=1.0 , lowerCamelCase_=recopy_gpta , lowerCamelCase_=None , lowerCamelCase_=10 , lowerCamelCase_="gpt2_finetuned.pt" , ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : str = torch.device("""cuda:0""" if torch.cuda.is_available() else """cpu""" )
SCREAMING_SNAKE_CASE : Dict = RandomSampler(UpperCamelCase__ )
SCREAMING_SNAKE_CASE : Optional[int] = DataLoader(UpperCamelCase__ , sampler=UpperCamelCase__ )
SCREAMING_SNAKE_CASE : str = max_steps // (len(UpperCamelCase__ )) + 1
SCREAMING_SNAKE_CASE : List[Any] = 0
SCREAMING_SNAKE_CASE : int = torch.zeros((1, context_len) , dtype=torch.long , device=UpperCamelCase__ )
SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE : Optional[Any] = recopy_model(UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ )
model.train()
if secondary_learner is not None:
secondary_learner.to(UpperCamelCase__ )
secondary_learner.eval()
SCREAMING_SNAKE_CASE : str = []
SCREAMING_SNAKE_CASE : Tuple = 0
SCREAMING_SNAKE_CASE : str = []
SCREAMING_SNAKE_CASE : List[Any] = []
# Compute the performance of the transformer model at the beginning
SCREAMING_SNAKE_CASE : Dict = compute_perplexity(UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ )
test_perps.append(UpperCamelCase__ )
print("""Test perplexity, step""" , UpperCamelCase__ , """:""" , UpperCamelCase__ )
for epoch in range(int(UpperCamelCase__ ) ):
for step, example in enumerate(UpperCamelCase__ ):
torch.cuda.empty_cache()
SCREAMING_SNAKE_CASE : List[str] = random.randint(0 , example.size(2 ) - context_len - 1 )
SCREAMING_SNAKE_CASE : Optional[int] = example[0, 0, start : start + context_len]
lm_optimizer.zero_grad()
SCREAMING_SNAKE_CASE : List[str] = model(UpperCamelCase__ , labels=UpperCamelCase__ )
SCREAMING_SNAKE_CASE : List[str] = True
if secondary_learner is not None:
SCREAMING_SNAKE_CASE : Optional[int] = secondary_learner.forward(
torch.tensor(UpperCamelCase__ , dtype=torch.long , device=UpperCamelCase__ ).unsqueeze(0 ) )[0].item()
observed_qs.append(float(UpperCamelCase__ ) )
# Here we implement the simple non-constant threshold for the predicted IG(X) value
# We will decay the selectivity of our secondary learner filter from
# 1 standard deviation above average to 1 below average after 10 batches.
if global_step == 10:
SCREAMING_SNAKE_CASE : List[str] = -1
if predicted_q < threshold:
SCREAMING_SNAKE_CASE : List[Any] = False
# If we passed the filter, add the context to the batch!
if do_backprop:
contexts.append(np.array(context.cpu() ) )
SCREAMING_SNAKE_CASE : Dict = outputs[0]
lm_loss.backward()
examples += 1
del outputs
# Once the batch is filled with enough contexts, backprop on the batch.
if examples == batch_size:
torch.cuda.empty_cache()
SCREAMING_SNAKE_CASE : Dict = 0
# Do LM backprop
torch.nn.utils.clip_grad_norm_(model.parameters() , 3.0 )
lm_optimizer.step()
lm_scheduler.step() # Update learning rate schedule
global_step += 1
# Compute the performance of the transformer model at this batch
if global_step % eval_interval == 0:
SCREAMING_SNAKE_CASE : int = compute_perplexity(UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ )
test_perps.append(UpperCamelCase__ )
print("""Test perplexity, step""" , UpperCamelCase__ , """:""" , UpperCamelCase__ )
# Break out of the loop after 60 batches
if max_steps > 0 and global_step > 60:
break
if max_steps > 0 and global_step > 60:
break
# save finetuned transformer model
torch.save(model.state_dict() , UpperCamelCase__ )
torch.cuda.empty_cache()
# Do some cleaning up so we can reinitialize for the next run of this function
del lm_optimizer
del lm_scheduler
return model
def __A ( ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : str = argparse.ArgumentParser(description="""Fine-tune a transformer model with IGF on a language modeling task""" )
# Required parameters
parser.add_argument(
"""--data_dir""" , default=UpperCamelCase__ , type=UpperCamelCase__ , required=UpperCamelCase__ , help="""The input data dir. Should contain data files for WikiText.""" , )
parser.add_argument(
"""--model_name_or_path""" , default=UpperCamelCase__ , type=UpperCamelCase__ , required=UpperCamelCase__ , help="""Path to pretrained model or model identifier from huggingface.co/models""" , )
parser.add_argument(
"""--data_file""" , type=UpperCamelCase__ , default=UpperCamelCase__ , help=(
"""A jbl file containing tokenized data which can be split as objective dataset, """
"""train_dataset and test_dataset."""
) , )
parser.add_argument(
"""--igf_data_file""" , type=UpperCamelCase__ , default=UpperCamelCase__ , help="""A jbl file containing the context and information gain pairs to train secondary learner.""" , )
parser.add_argument(
"""--output_dir""" , default=UpperCamelCase__ , type=UpperCamelCase__ , required=UpperCamelCase__ , help="""The output directory where the final fine-tuned model is stored.""" , )
parser.add_argument(
"""--tokenizer_name""" , default=UpperCamelCase__ , type=UpperCamelCase__ , help="""Pretrained tokenizer name or path if not the same as model_name""" , )
parser.add_argument("""--seed""" , type=UpperCamelCase__ , default=UpperCamelCase__ , help="""A seed for reproducible training.""" )
parser.add_argument(
"""--context_len""" , default=32 , type=UpperCamelCase__ , help=(
"""The maximum total input sequence length after tokenization. Sequences longer """
"""than this will be truncated, sequences shorter will be padded."""
) , )
parser.add_argument(
"""--size_objective_set""" , default=1_00 , type=UpperCamelCase__ , help="""number of articles that are long enough to be used as our objective set""" , )
parser.add_argument(
"""--eval_freq""" , default=1_00 , type=UpperCamelCase__ , help="""secondary model evaluation is triggered at eval_freq""" )
parser.add_argument("""--max_steps""" , default=10_00 , type=UpperCamelCase__ , help="""To calculate training epochs""" )
parser.add_argument(
"""--secondary_learner_batch_size""" , default=1_28 , type=UpperCamelCase__ , help="""batch size of training data for secondary learner""" , )
parser.add_argument(
"""--batch_size""" , default=16 , type=UpperCamelCase__ , help="""batch size of training data of language model(gpt2) """ )
parser.add_argument(
"""--eval_interval""" , default=10 , type=UpperCamelCase__ , help=(
"""decay the selectivity of our secondary learner filter from"""
"""1 standard deviation above average to 1 below average after 10 batches"""
) , )
parser.add_argument(
"""--number""" , default=1_00 , type=UpperCamelCase__ , help="""The number of examples split to be used as objective_set/test_data""" )
parser.add_argument(
"""--min_len""" , default=10_26 , type=UpperCamelCase__ , help="""The minimum length of the article to be used as objective set""" )
parser.add_argument(
"""--secondary_learner_max_epochs""" , default=15 , type=UpperCamelCase__ , help="""number of epochs to train secondary learner""" )
parser.add_argument("""--trim""" , default=UpperCamelCase__ , type=UpperCamelCase__ , help="""truncate the example if it exceeds context length""" )
parser.add_argument(
"""--threshold""" , default=1.0 , type=UpperCamelCase__ , help=(
"""The threshold value used by secondary learner to filter the train_data and allow only"""
""" informative data as input to the model"""
) , )
parser.add_argument("""--finetuned_model_name""" , default="""gpt2_finetuned.pt""" , type=UpperCamelCase__ , help="""finetuned_model_name""" )
parser.add_argument(
"""--recopy_model""" , default=UpperCamelCase__ , type=UpperCamelCase__ , help="""Reset the model to the original pretrained GPT-2 weights after each iteration""" , )
# function calls
# Collecting *n* pairs of context and information gain(X, IG(X)) for training the secondary learner
generate_n_pairs(
context_len=32 , max_steps=10 , size_objective_set=1_00 , min_len=10_26 , trim=UpperCamelCase__ , data_file="""data/tokenized_stories_train_wikitext103.jbl""" , igf_data_file="""igf_context_pairs.jbl""" , )
# Load train data for secondary learner
SCREAMING_SNAKE_CASE : Union[str, Any] = joblib.load("""data/IGF_values.jbl""" )
# Train secondary learner
SCREAMING_SNAKE_CASE : List[str] = training_secondary_learner(
UpperCamelCase__ , secondary_learner_max_epochs=15 , secondary_learner_batch_size=1_28 , eval_freq=1_00 , igf_model_path="""igf_model.pt""" , )
# load pretrained gpt2 model
SCREAMING_SNAKE_CASE : str = GPTaLMHeadModel.from_pretrained("""gpt2""" )
set_seed(42 )
# Generate train and test data to train and evaluate gpt2 model
SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE : Dict = generate_datasets(
context_len=32 , file="""data/tokenized_stories_train_wikitext103.jbl""" , number=1_00 , min_len=10_26 , trim=UpperCamelCase__ )
# fine-tuning of the gpt2 model using igf (Information Gain Filtration)
finetune(
UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , context_len=32 , max_steps=10_00 , batch_size=16 , threshold=1.0 , recopy_model=UpperCamelCase__ , secondary_learner=UpperCamelCase__ , eval_interval=10 , finetuned_model_name="""gpt2_finetuned.pt""" , )
if __name__ == "__main__":
main()
| 715 |
'''simple docstring'''
import itertools
import os
import random
import tempfile
import unittest
import numpy as np
from transformers import TvltFeatureExtractor, is_datasets_available
from transformers.testing_utils import check_json_file_has_correct_format, require_torch, require_torchaudio
from transformers.utils.import_utils import is_torch_available
from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin
if is_torch_available():
import torch
if is_datasets_available():
from datasets import load_dataset
__UpperCAmelCase = random.Random()
def __A ( lowerCamelCase_ , lowerCamelCase_=1.0 , lowerCamelCase_=None , lowerCamelCase_=None ):
"""simple docstring"""
if rng is None:
SCREAMING_SNAKE_CASE : Optional[Any] = global_rng
SCREAMING_SNAKE_CASE : Optional[int] = []
for batch_idx in range(shape[0] ):
values.append([] )
for _ in range(shape[1] ):
values[-1].append(rng.random() * scale )
return values
class UpperCamelCase__ ( unittest.TestCase ):
"""simple docstring"""
def __init__( self : List[str] , lowerCamelCase_ : List[Any] , lowerCamelCase_ : Optional[int]=7 , lowerCamelCase_ : Optional[int]=4_00 , lowerCamelCase_ : int=20_00 , lowerCamelCase_ : List[str]=20_48 , lowerCamelCase_ : Optional[Any]=1_28 , lowerCamelCase_ : Optional[Any]=1 , lowerCamelCase_ : str=5_12 , lowerCamelCase_ : Dict=30 , lowerCamelCase_ : Dict=4_41_00 , ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Tuple = parent
SCREAMING_SNAKE_CASE : Union[str, Any] = batch_size
SCREAMING_SNAKE_CASE : List[str] = min_seq_length
SCREAMING_SNAKE_CASE : Any = max_seq_length
SCREAMING_SNAKE_CASE : Optional[int] = (self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
SCREAMING_SNAKE_CASE : int = spectrogram_length
SCREAMING_SNAKE_CASE : List[Any] = feature_size
SCREAMING_SNAKE_CASE : Any = num_audio_channels
SCREAMING_SNAKE_CASE : Tuple = hop_length
SCREAMING_SNAKE_CASE : str = chunk_length
SCREAMING_SNAKE_CASE : Dict = sampling_rate
def lowerCamelCase_ ( self : List[Any] ):
'''simple docstring'''
return {
"spectrogram_length": self.spectrogram_length,
"feature_size": self.feature_size,
"num_audio_channels": self.num_audio_channels,
"hop_length": self.hop_length,
"chunk_length": self.chunk_length,
"sampling_rate": self.sampling_rate,
}
def lowerCamelCase_ ( self : Dict , lowerCamelCase_ : Tuple=False , lowerCamelCase_ : Any=False ):
'''simple docstring'''
def _flatten(lowerCamelCase_ : Dict ):
return list(itertools.chain(*lowerCamelCase_ ) )
if equal_length:
SCREAMING_SNAKE_CASE : Optional[Any] = [floats_list((self.max_seq_length, self.feature_size) ) for _ in range(self.batch_size )]
else:
# make sure that inputs increase in size
SCREAMING_SNAKE_CASE : Dict = [
floats_list((x, self.feature_size) )
for x in range(self.min_seq_length , self.max_seq_length , self.seq_length_diff )
]
if numpify:
SCREAMING_SNAKE_CASE : Optional[int] = [np.asarray(lowerCamelCase_ ) for x in speech_inputs]
return speech_inputs
@require_torch
@require_torchaudio
class UpperCamelCase__ ( lowercase_ , unittest.TestCase ):
"""simple docstring"""
SCREAMING_SNAKE_CASE__ = TvltFeatureExtractor
def lowerCamelCase_ ( self : List[Any] ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : List[str] = TvltFeatureExtractionTester(self )
def lowerCamelCase_ ( self : Optional[int] ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Dict = self.feature_extraction_class(**self.feat_extract_dict )
self.assertTrue(hasattr(lowerCamelCase_ , """spectrogram_length""" ) )
self.assertTrue(hasattr(lowerCamelCase_ , """feature_size""" ) )
self.assertTrue(hasattr(lowerCamelCase_ , """num_audio_channels""" ) )
self.assertTrue(hasattr(lowerCamelCase_ , """hop_length""" ) )
self.assertTrue(hasattr(lowerCamelCase_ , """chunk_length""" ) )
self.assertTrue(hasattr(lowerCamelCase_ , """sampling_rate""" ) )
def lowerCamelCase_ ( self : List[str] ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Optional[Any] = self.feature_extraction_class(**self.feat_extract_dict )
with tempfile.TemporaryDirectory() as tmpdirname:
SCREAMING_SNAKE_CASE : Any = feat_extract_first.save_pretrained(lowerCamelCase_ )[0]
check_json_file_has_correct_format(lowerCamelCase_ )
SCREAMING_SNAKE_CASE : Union[str, Any] = self.feature_extraction_class.from_pretrained(lowerCamelCase_ )
SCREAMING_SNAKE_CASE : List[str] = feat_extract_first.to_dict()
SCREAMING_SNAKE_CASE : List[Any] = feat_extract_second.to_dict()
SCREAMING_SNAKE_CASE : Optional[int] = dict_first.pop("""mel_filters""" )
SCREAMING_SNAKE_CASE : Union[str, Any] = dict_second.pop("""mel_filters""" )
self.assertTrue(np.allclose(lowerCamelCase_ , lowerCamelCase_ ) )
self.assertEqual(lowerCamelCase_ , lowerCamelCase_ )
def lowerCamelCase_ ( self : Optional[int] ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Dict = self.feature_extraction_class(**self.feat_extract_dict )
with tempfile.TemporaryDirectory() as tmpdirname:
SCREAMING_SNAKE_CASE : int = os.path.join(lowerCamelCase_ , """feat_extract.json""" )
feat_extract_first.to_json_file(lowerCamelCase_ )
SCREAMING_SNAKE_CASE : Optional[Any] = self.feature_extraction_class.from_json_file(lowerCamelCase_ )
SCREAMING_SNAKE_CASE : List[str] = feat_extract_first.to_dict()
SCREAMING_SNAKE_CASE : int = feat_extract_second.to_dict()
SCREAMING_SNAKE_CASE : List[str] = dict_first.pop("""mel_filters""" )
SCREAMING_SNAKE_CASE : Optional[Any] = dict_second.pop("""mel_filters""" )
self.assertTrue(np.allclose(lowerCamelCase_ , lowerCamelCase_ ) )
self.assertEqual(lowerCamelCase_ , lowerCamelCase_ )
def lowerCamelCase_ ( self : Any ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : int = self.feature_extraction_class(**self.feat_extract_dict )
# create three inputs of length 800, 1000, and 1200
SCREAMING_SNAKE_CASE : Optional[int] = [floats_list((1, x) )[0] for x in range(8_00 , 14_00 , 2_00 )]
SCREAMING_SNAKE_CASE : int = [np.asarray(lowerCamelCase_ ) for speech_input in speech_inputs]
# Test not batched input
SCREAMING_SNAKE_CASE : Union[str, Any] = feature_extractor(np_speech_inputs[0] , return_tensors="""np""" , sampling_rate=4_41_00 ).audio_values
self.assertTrue(encoded_audios.ndim == 4 )
self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size )
self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length )
self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels )
# Test batched
SCREAMING_SNAKE_CASE : Optional[int] = feature_extractor(lowerCamelCase_ , return_tensors="""np""" , sampling_rate=4_41_00 ).audio_values
self.assertTrue(encoded_audios.ndim == 4 )
self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size )
self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length )
self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels )
# Test audio masking
SCREAMING_SNAKE_CASE : List[str] = feature_extractor(
lowerCamelCase_ , return_tensors="""np""" , sampling_rate=4_41_00 , mask_audio=lowerCamelCase_ ).audio_values
self.assertTrue(encoded_audios.ndim == 4 )
self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size )
self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length )
self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels )
# Test 2-D numpy arrays are batched.
SCREAMING_SNAKE_CASE : Optional[int] = [floats_list((1, x) )[0] for x in (8_00, 8_00, 8_00)]
SCREAMING_SNAKE_CASE : int = np.asarray(lowerCamelCase_ )
SCREAMING_SNAKE_CASE : Tuple = feature_extractor(lowerCamelCase_ , return_tensors="""np""" , sampling_rate=4_41_00 ).audio_values
self.assertTrue(encoded_audios.ndim == 4 )
self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size )
self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length )
self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels )
def lowerCamelCase_ ( self : Any , lowerCamelCase_ : int ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : List[Any] = load_dataset("""hf-internal-testing/librispeech_asr_dummy""" , """clean""" , split="""validation""" )
# automatic decoding with librispeech
SCREAMING_SNAKE_CASE : Union[str, Any] = ds.sort("""id""" ).select(range(lowerCamelCase_ ) )[:num_samples]["""audio"""]
return [x["array"] for x in speech_samples]
def lowerCamelCase_ ( self : Dict ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : str = self._load_datasamples(1 )
SCREAMING_SNAKE_CASE : Tuple = TvltFeatureExtractor()
SCREAMING_SNAKE_CASE : List[Any] = feature_extractor(lowerCamelCase_ , return_tensors="""pt""" ).audio_values
self.assertEquals(audio_values.shape , (1, 1, 1_92, 1_28) )
SCREAMING_SNAKE_CASE : List[Any] = torch.tensor([[-0.3_032, -0.2_708], [-0.4_434, -0.4_007]] )
self.assertTrue(torch.allclose(audio_values[0, 0, :2, :2] , lowerCamelCase_ , atol=1e-4 ) )
| 79 | 0 |
from typing import List, Optional, Union
import torch
from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDPMScheduler
from ...utils import (
is_accelerate_available,
is_accelerate_version,
logging,
randn_tensor,
replace_example_docstring,
)
__UpperCAmelCase = logging.get_logger(__name__) # pylint: disable=invalid-name
__UpperCAmelCase = '''
Examples:
```py
>>> from diffusers import KandinskyV22Pipeline, KandinskyV22PriorPipeline
>>> import torch
>>> pipe_prior = KandinskyV22PriorPipeline.from_pretrained("kandinsky-community/kandinsky-2-2-prior")
>>> pipe_prior.to("cuda")
>>> prompt = "red cat, 4k photo"
>>> out = pipe_prior(prompt)
>>> image_emb = out.image_embeds
>>> zero_image_emb = out.negative_image_embeds
>>> pipe = KandinskyV22Pipeline.from_pretrained("kandinsky-community/kandinsky-2-2-decoder")
>>> pipe.to("cuda")
>>> image = pipe(
... image_embeds=image_emb,
... negative_image_embeds=zero_image_emb,
... height=768,
... width=768,
... num_inference_steps=50,
... ).images
>>> image[0].save("cat.png")
```
'''
def __A ( lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_=8 ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : int = height // scale_factor**2
if height % scale_factor**2 != 0:
new_height += 1
SCREAMING_SNAKE_CASE : Dict = width // scale_factor**2
if width % scale_factor**2 != 0:
new_width += 1
return new_height * scale_factor, new_width * scale_factor
class UpperCamelCase__ ( __UpperCAmelCase ):
"""simple docstring"""
def __init__( self : Dict , lowerCamelCase_ : UNetaDConditionModel , lowerCamelCase_ : DDPMScheduler , lowerCamelCase_ : VQModel , ):
'''simple docstring'''
super().__init__()
self.register_modules(
unet=__SCREAMING_SNAKE_CASE , scheduler=__SCREAMING_SNAKE_CASE , movq=__SCREAMING_SNAKE_CASE , )
SCREAMING_SNAKE_CASE : Union[str, Any] = 2 ** (len(self.movq.config.block_out_channels ) - 1)
def lowerCamelCase_ ( self : Any , lowerCamelCase_ : Tuple , lowerCamelCase_ : Optional[Any] , lowerCamelCase_ : Tuple , lowerCamelCase_ : Tuple , lowerCamelCase_ : List[str] , lowerCamelCase_ : List[str] ):
'''simple docstring'''
if latents is None:
SCREAMING_SNAKE_CASE : int = randn_tensor(__SCREAMING_SNAKE_CASE , generator=__SCREAMING_SNAKE_CASE , device=__SCREAMING_SNAKE_CASE , dtype=__SCREAMING_SNAKE_CASE )
else:
if latents.shape != shape:
raise ValueError(f'''Unexpected latents shape, got {latents.shape}, expected {shape}''' )
SCREAMING_SNAKE_CASE : Tuple = latents.to(__SCREAMING_SNAKE_CASE )
SCREAMING_SNAKE_CASE : Optional[int] = latents * scheduler.init_noise_sigma
return latents
def lowerCamelCase_ ( self : List[str] , lowerCamelCase_ : Optional[Any]=0 ):
'''simple docstring'''
if is_accelerate_available():
from accelerate import cpu_offload
else:
raise ImportError("""Please install accelerate via `pip install accelerate`""" )
SCREAMING_SNAKE_CASE : Any = torch.device(f'''cuda:{gpu_id}''' )
SCREAMING_SNAKE_CASE : Tuple = [
self.unet,
self.movq,
]
for cpu_offloaded_model in models:
if cpu_offloaded_model is not None:
cpu_offload(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
def lowerCamelCase_ ( self : int , lowerCamelCase_ : str=0 ):
'''simple docstring'''
if is_accelerate_available() and is_accelerate_version(""">=""" , """0.17.0.dev0""" ):
from accelerate import cpu_offload_with_hook
else:
raise ImportError("""`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher.""" )
SCREAMING_SNAKE_CASE : Tuple = torch.device(f'''cuda:{gpu_id}''' )
if self.device.type != "cpu":
self.to("""cpu""" , silence_dtype_warnings=__SCREAMING_SNAKE_CASE )
torch.cuda.empty_cache() # otherwise we don't see the memory savings (but they probably exist)
SCREAMING_SNAKE_CASE : Tuple = None
for cpu_offloaded_model in [self.unet, self.movq]:
SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE : int = cpu_offload_with_hook(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , prev_module_hook=__SCREAMING_SNAKE_CASE )
# We'll offload the last model manually.
SCREAMING_SNAKE_CASE : Dict = hook
@property
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
def lowerCamelCase_ ( self : Optional[Any] ):
'''simple docstring'''
if not hasattr(self.unet , """_hf_hook""" ):
return self.device
for module in self.unet.modules():
if (
hasattr(__SCREAMING_SNAKE_CASE , """_hf_hook""" )
and hasattr(module._hf_hook , """execution_device""" )
and module._hf_hook.execution_device is not None
):
return torch.device(module._hf_hook.execution_device )
return self.device
@torch.no_grad()
@replace_example_docstring(__SCREAMING_SNAKE_CASE )
def __call__( self : str , lowerCamelCase_ : Union[torch.FloatTensor, List[torch.FloatTensor]] , lowerCamelCase_ : Union[torch.FloatTensor, List[torch.FloatTensor]] , lowerCamelCase_ : int = 5_12 , lowerCamelCase_ : int = 5_12 , lowerCamelCase_ : int = 1_00 , lowerCamelCase_ : float = 4.0 , lowerCamelCase_ : int = 1 , lowerCamelCase_ : Optional[Union[torch.Generator, List[torch.Generator]]] = None , lowerCamelCase_ : Optional[torch.FloatTensor] = None , lowerCamelCase_ : Optional[str] = "pil" , lowerCamelCase_ : bool = True , ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Optional[int] = self._execution_device
SCREAMING_SNAKE_CASE : Dict = guidance_scale > 1.0
if isinstance(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ):
SCREAMING_SNAKE_CASE : Optional[Any] = torch.cat(__SCREAMING_SNAKE_CASE , dim=0 )
SCREAMING_SNAKE_CASE : Any = image_embeds.shape[0] * num_images_per_prompt
if isinstance(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ):
SCREAMING_SNAKE_CASE : Optional[Any] = torch.cat(__SCREAMING_SNAKE_CASE , dim=0 )
if do_classifier_free_guidance:
SCREAMING_SNAKE_CASE : Any = image_embeds.repeat_interleave(__SCREAMING_SNAKE_CASE , dim=0 )
SCREAMING_SNAKE_CASE : str = negative_image_embeds.repeat_interleave(__SCREAMING_SNAKE_CASE , dim=0 )
SCREAMING_SNAKE_CASE : Dict = torch.cat([negative_image_embeds, image_embeds] , dim=0 ).to(dtype=self.unet.dtype , device=__SCREAMING_SNAKE_CASE )
self.scheduler.set_timesteps(__SCREAMING_SNAKE_CASE , device=__SCREAMING_SNAKE_CASE )
SCREAMING_SNAKE_CASE : Union[str, Any] = self.scheduler.timesteps
SCREAMING_SNAKE_CASE : Union[str, Any] = self.unet.config.in_channels
SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE : List[str] = downscale_height_and_width(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , self.movq_scale_factor )
# create initial latent
SCREAMING_SNAKE_CASE : Any = self.prepare_latents(
(batch_size, num_channels_latents, height, width) , image_embeds.dtype , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , self.scheduler , )
for i, t in enumerate(self.progress_bar(__SCREAMING_SNAKE_CASE ) ):
# expand the latents if we are doing classifier free guidance
SCREAMING_SNAKE_CASE : Optional[Any] = torch.cat([latents] * 2 ) if do_classifier_free_guidance else latents
SCREAMING_SNAKE_CASE : int = {"""image_embeds""": image_embeds}
SCREAMING_SNAKE_CASE : Union[str, Any] = self.unet(
sample=__SCREAMING_SNAKE_CASE , timestep=__SCREAMING_SNAKE_CASE , encoder_hidden_states=__SCREAMING_SNAKE_CASE , added_cond_kwargs=__SCREAMING_SNAKE_CASE , return_dict=__SCREAMING_SNAKE_CASE , )[0]
if do_classifier_free_guidance:
SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE : Optional[int] = noise_pred.split(latents.shape[1] , dim=1 )
SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE : Any = noise_pred.chunk(2 )
SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE : List[Any] = variance_pred.chunk(2 )
SCREAMING_SNAKE_CASE : Optional[Any] = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
SCREAMING_SNAKE_CASE : List[str] = torch.cat([noise_pred, variance_pred_text] , dim=1 )
if not (
hasattr(self.scheduler.config , """variance_type""" )
and self.scheduler.config.variance_type in ["learned", "learned_range"]
):
SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE : Tuple = noise_pred.split(latents.shape[1] , dim=1 )
# compute the previous noisy sample x_t -> x_t-1
SCREAMING_SNAKE_CASE : List[str] = self.scheduler.step(
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , generator=__SCREAMING_SNAKE_CASE , )[0]
# post-processing
SCREAMING_SNAKE_CASE : Tuple = self.movq.decode(__SCREAMING_SNAKE_CASE , force_not_quantize=__SCREAMING_SNAKE_CASE )["""sample"""]
if output_type not in ["pt", "np", "pil"]:
raise ValueError(f'''Only the output types `pt`, `pil` and `np` are supported not output_type={output_type}''' )
if output_type in ["np", "pil"]:
SCREAMING_SNAKE_CASE : Union[str, Any] = image * 0.5 + 0.5
SCREAMING_SNAKE_CASE : Any = image.clamp(0 , 1 )
SCREAMING_SNAKE_CASE : Tuple = image.cpu().permute(0 , 2 , 3 , 1 ).float().numpy()
if output_type == "pil":
SCREAMING_SNAKE_CASE : Dict = self.numpy_to_pil(__SCREAMING_SNAKE_CASE )
if not return_dict:
return (image,)
return ImagePipelineOutput(images=__SCREAMING_SNAKE_CASE )
| 716 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
__UpperCAmelCase = {
"""configuration_mvp""": ["""MVP_PRETRAINED_CONFIG_ARCHIVE_MAP""", """MvpConfig""", """MvpOnnxConfig"""],
"""tokenization_mvp""": ["""MvpTokenizer"""],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__UpperCAmelCase = ["""MvpTokenizerFast"""]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__UpperCAmelCase = [
"""MVP_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""MvpForCausalLM""",
"""MvpForConditionalGeneration""",
"""MvpForQuestionAnswering""",
"""MvpForSequenceClassification""",
"""MvpModel""",
"""MvpPreTrainedModel""",
]
if TYPE_CHECKING:
from .configuration_mvp import MVP_PRETRAINED_CONFIG_ARCHIVE_MAP, MvpConfig, MvpOnnxConfig
from .tokenization_mvp import MvpTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_mvp_fast import MvpTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_mvp import (
MVP_PRETRAINED_MODEL_ARCHIVE_LIST,
MvpForCausalLM,
MvpForConditionalGeneration,
MvpForQuestionAnswering,
MvpForSequenceClassification,
MvpModel,
MvpPreTrainedModel,
)
else:
import sys
__UpperCAmelCase = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 79 | 0 |
'''simple docstring'''
import cva
import numpy as np
class UpperCamelCase__ :
"""simple docstring"""
def __init__( self : List[Any] , lowerCamelCase_ : Optional[int] , lowerCamelCase_ : Dict ):
'''simple docstring'''
if k in (0.04, 0.06):
SCREAMING_SNAKE_CASE : List[str] = k
SCREAMING_SNAKE_CASE : str = window_size
else:
raise ValueError("""invalid k value""" )
def __str__( self : int ):
'''simple docstring'''
return str(self.k )
def lowerCamelCase_ ( self : Any , lowerCamelCase_ : Union[str, Any] ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Any = cva.imread(__a , 0 )
SCREAMING_SNAKE_CASE : Optional[Any] = img.shape
SCREAMING_SNAKE_CASE : list[list[int]] = []
SCREAMING_SNAKE_CASE : str = img.copy()
SCREAMING_SNAKE_CASE : Optional[int] = cva.cvtColor(__a , cva.COLOR_GRAY2RGB )
SCREAMING_SNAKE_CASE : List[str] = np.gradient(__a )
SCREAMING_SNAKE_CASE : Optional[Any] = dx**2
SCREAMING_SNAKE_CASE : int = dy**2
SCREAMING_SNAKE_CASE : List[str] = dx * dy
SCREAMING_SNAKE_CASE : Any = 0.04
SCREAMING_SNAKE_CASE : List[Any] = self.window_size // 2
for y in range(__a , h - offset ):
for x in range(__a , w - offset ):
SCREAMING_SNAKE_CASE : Union[str, Any] = ixx[
y - offset : y + offset + 1, x - offset : x + offset + 1
].sum()
SCREAMING_SNAKE_CASE : int = iyy[
y - offset : y + offset + 1, x - offset : x + offset + 1
].sum()
SCREAMING_SNAKE_CASE : List[Any] = ixy[
y - offset : y + offset + 1, x - offset : x + offset + 1
].sum()
SCREAMING_SNAKE_CASE : Dict = (wxx * wyy) - (wxy**2)
SCREAMING_SNAKE_CASE : List[str] = wxx + wyy
SCREAMING_SNAKE_CASE : Tuple = det - k * (trace**2)
# Can change the value
if r > 0.5:
corner_list.append([x, y, r] )
color_img.itemset((y, x, 0) , 0 )
color_img.itemset((y, x, 1) , 0 )
color_img.itemset((y, x, 2) , 2_55 )
return color_img, corner_list
if __name__ == "__main__":
__UpperCAmelCase = HarrisCorner(0.04, 3)
__UpperCAmelCase , __UpperCAmelCase = edge_detect.detect("""path_to_image""")
cva.imwrite("""detect.png""", color_img)
| 717 |
'''simple docstring'''
__UpperCAmelCase = [
"""Audio""",
"""Array2D""",
"""Array3D""",
"""Array4D""",
"""Array5D""",
"""ClassLabel""",
"""Features""",
"""Sequence""",
"""Value""",
"""Image""",
"""Translation""",
"""TranslationVariableLanguages""",
]
from .audio import Audio
from .features import ArrayaD, ArrayaD, ArrayaD, ArrayaD, ClassLabel, Features, Sequence, Value
from .image import Image
from .translation import Translation, TranslationVariableLanguages
| 79 | 0 |
'''simple docstring'''
from typing import Dict, List
from nltk.translate import gleu_score
import datasets
from datasets import MetricInfo
__UpperCAmelCase = """\\n@misc{wu2016googles,\n title={Google\'s Neural Machine Translation System: Bridging the Gap between Human and Machine Translation},\n author={Yonghui Wu and Mike Schuster and Zhifeng Chen and Quoc V. Le and Mohammad Norouzi and Wolfgang Macherey\n and Maxim Krikun and Yuan Cao and Qin Gao and Klaus Macherey and Jeff Klingner and Apurva Shah and Melvin\n Johnson and Xiaobing Liu and Łukasz Kaiser and Stephan Gouws and Yoshikiyo Kato and Taku Kudo and Hideto\n Kazawa and Keith Stevens and George Kurian and Nishant Patil and Wei Wang and Cliff Young and\n Jason Smith and Jason Riesa and Alex Rudnick and Oriol Vinyals and Greg Corrado and Macduff Hughes\n and Jeffrey Dean},\n year={2016},\n eprint={1609.08144},\n archivePrefix={arXiv},\n primaryClass={cs.CL}\n}\n"""
__UpperCAmelCase = """\\nThe BLEU score has some undesirable properties when used for single\nsentences, as it was designed to be a corpus measure. We therefore\nuse a slightly different score for our RL experiments which we call\nthe \'GLEU score\'. For the GLEU score, we record all sub-sequences of\n1, 2, 3 or 4 tokens in output and target sequence (n-grams). We then\ncompute a recall, which is the ratio of the number of matching n-grams\nto the number of total n-grams in the target (ground truth) sequence,\nand a precision, which is the ratio of the number of matching n-grams\nto the number of total n-grams in the generated output sequence. Then\nGLEU score is simply the minimum of recall and precision. This GLEU\nscore\'s range is always between 0 (no matches) and 1 (all match) and\nit is symmetrical when switching output and target. According to\nour experiments, GLEU score correlates quite well with the BLEU\nmetric on a corpus level but does not have its drawbacks for our per\nsentence reward objective.\n"""
__UpperCAmelCase = """\\nComputes corpus-level Google BLEU (GLEU) score of translated segments against one or more references.\nInstead of averaging the sentence level GLEU scores (i.e. macro-average precision), Wu et al. (2016) sum up the matching\ntokens and the max of hypothesis and reference tokens for each sentence, then compute using the aggregate values.\n\nArgs:\n predictions (list of str): list of translations to score.\n Each translation should be tokenized into a list of tokens.\n references (list of list of str): list of lists of references for each translation.\n Each reference should be tokenized into a list of tokens.\n min_len (int): The minimum order of n-gram this function should extract. Defaults to 1.\n max_len (int): The maximum order of n-gram this function should extract. Defaults to 4.\n\nReturns:\n \'google_bleu\': google_bleu score\n\nExamples:\n Example 1:\n >>> hyp1 = [\'It\', \'is\', \'a\', \'guide\', \'to\', \'action\', \'which\',\n ... \'ensures\', \'that\', \'the\', \'rubber\', \'duck\', \'always\',\n ... \'disobeys\', \'the\', \'commands\', \'of\', \'the\', \'cat\']\n >>> ref1a = [\'It\', \'is\', \'the\', \'guiding\', \'principle\', \'which\',\n ... \'guarantees\', \'the\', \'rubber\', \'duck\', \'forces\', \'never\',\n ... \'being\', \'under\', \'the\', \'command\', \'of\', \'the\', \'cat\']\n\n >>> hyp2 = [\'he\', \'read\', \'the\', \'book\', \'because\', \'he\', \'was\',\n ... \'interested\', \'in\', \'world\', \'history\']\n >>> ref2a = [\'he\', \'was\', \'interested\', \'in\', \'world\', \'history\',\n ... \'because\', \'he\', \'read\', \'the\', \'book\']\n\n >>> list_of_references = [[ref1a], [ref2a]]\n >>> hypotheses = [hyp1, hyp2]\n >>> google_bleu = datasets.load_metric(\"google_bleu\")\n >>> results = google_bleu.compute(predictions=hypotheses, references=list_of_references)\n >>> print(round(results[\"google_bleu\"], 2))\n 0.44\n\n Example 2:\n >>> hyp1 = [\'It\', \'is\', \'a\', \'guide\', \'to\', \'action\', \'which\',\n ... \'ensures\', \'that\', \'the\', \'rubber\', \'duck\', \'always\',\n ... \'disobeys\', \'the\', \'commands\', \'of\', \'the\', \'cat\']\n >>> ref1a = [\'It\', \'is\', \'the\', \'guiding\', \'principle\', \'which\',\n ... \'guarantees\', \'the\', \'rubber\', \'duck\', \'forces\', \'never\',\n ... \'being\', \'under\', \'the\', \'command\', \'of\', \'the\', \'cat\']\n >>> ref1b = [\'It\', \'is\', \'a\', \'guide\', \'to\', \'action\', \'that\',\n ... \'ensures\', \'that\', \'the\', \'rubber\', \'duck\', \'will\', \'never\',\n ... \'heed\', \'the\', \'cat\', \'commands\']\n >>> ref1c = [\'It\', \'is\', \'the\', \'practical\', \'guide\', \'for\', \'the\',\n ... \'rubber\', \'duck\', \'army\', \'never\', \'to\', \'heed\', \'the\', \'directions\',\n ... \'of\', \'the\', \'cat\']\n\n >>> hyp2 = [\'he\', \'read\', \'the\', \'book\', \'because\', \'he\', \'was\',\n ... \'interested\', \'in\', \'world\', \'history\']\n >>> ref2a = [\'he\', \'was\', \'interested\', \'in\', \'world\', \'history\',\n ... \'because\', \'he\', \'read\', \'the\', \'book\']\n\n >>> list_of_references = [[ref1a, ref1b, ref1c], [ref2a]]\n >>> hypotheses = [hyp1, hyp2]\n >>> google_bleu = datasets.load_metric(\"google_bleu\")\n >>> results = google_bleu.compute(predictions=hypotheses, references=list_of_references)\n >>> print(round(results[\"google_bleu\"], 2))\n 0.61\n\n Example 3:\n >>> hyp1 = [\'It\', \'is\', \'a\', \'guide\', \'to\', \'action\', \'which\',\n ... \'ensures\', \'that\', \'the\', \'rubber\', \'duck\', \'always\',\n ... \'disobeys\', \'the\', \'commands\', \'of\', \'the\', \'cat\']\n >>> ref1a = [\'It\', \'is\', \'the\', \'guiding\', \'principle\', \'which\',\n ... \'guarantees\', \'the\', \'rubber\', \'duck\', \'forces\', \'never\',\n ... \'being\', \'under\', \'the\', \'command\', \'of\', \'the\', \'cat\']\n >>> ref1b = [\'It\', \'is\', \'a\', \'guide\', \'to\', \'action\', \'that\',\n ... \'ensures\', \'that\', \'the\', \'rubber\', \'duck\', \'will\', \'never\',\n ... \'heed\', \'the\', \'cat\', \'commands\']\n >>> ref1c = [\'It\', \'is\', \'the\', \'practical\', \'guide\', \'for\', \'the\',\n ... \'rubber\', \'duck\', \'army\', \'never\', \'to\', \'heed\', \'the\', \'directions\',\n ... \'of\', \'the\', \'cat\']\n\n >>> hyp2 = [\'he\', \'read\', \'the\', \'book\', \'because\', \'he\', \'was\',\n ... \'interested\', \'in\', \'world\', \'history\']\n >>> ref2a = [\'he\', \'was\', \'interested\', \'in\', \'world\', \'history\',\n ... \'because\', \'he\', \'read\', \'the\', \'book\']\n\n >>> list_of_references = [[ref1a, ref1b, ref1c], [ref2a]]\n >>> hypotheses = [hyp1, hyp2]\n >>> google_bleu = datasets.load_metric(\"google_bleu\")\n >>> results = google_bleu.compute(predictions=hypotheses, references=list_of_references, min_len=2)\n >>> print(round(results[\"google_bleu\"], 2))\n 0.53\n\n Example 4:\n >>> hyp1 = [\'It\', \'is\', \'a\', \'guide\', \'to\', \'action\', \'which\',\n ... \'ensures\', \'that\', \'the\', \'rubber\', \'duck\', \'always\',\n ... \'disobeys\', \'the\', \'commands\', \'of\', \'the\', \'cat\']\n >>> ref1a = [\'It\', \'is\', \'the\', \'guiding\', \'principle\', \'which\',\n ... \'guarantees\', \'the\', \'rubber\', \'duck\', \'forces\', \'never\',\n ... \'being\', \'under\', \'the\', \'command\', \'of\', \'the\', \'cat\']\n >>> ref1b = [\'It\', \'is\', \'a\', \'guide\', \'to\', \'action\', \'that\',\n ... \'ensures\', \'that\', \'the\', \'rubber\', \'duck\', \'will\', \'never\',\n ... \'heed\', \'the\', \'cat\', \'commands\']\n >>> ref1c = [\'It\', \'is\', \'the\', \'practical\', \'guide\', \'for\', \'the\',\n ... \'rubber\', \'duck\', \'army\', \'never\', \'to\', \'heed\', \'the\', \'directions\',\n ... \'of\', \'the\', \'cat\']\n\n >>> hyp2 = [\'he\', \'read\', \'the\', \'book\', \'because\', \'he\', \'was\',\n ... \'interested\', \'in\', \'world\', \'history\']\n >>> ref2a = [\'he\', \'was\', \'interested\', \'in\', \'world\', \'history\',\n ... \'because\', \'he\', \'read\', \'the\', \'book\']\n\n >>> list_of_references = [[ref1a, ref1b, ref1c], [ref2a]]\n >>> hypotheses = [hyp1, hyp2]\n >>> google_bleu = datasets.load_metric(\"google_bleu\")\n >>> results = google_bleu.compute(predictions=hypotheses,references=list_of_references, min_len=2, max_len=6)\n >>> print(round(results[\"google_bleu\"], 2))\n 0.4\n"""
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class UpperCamelCase__ ( datasets.Metric ):
"""simple docstring"""
def lowerCamelCase_ ( self : Union[str, Any] ):
'''simple docstring'''
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
"""predictions""": datasets.Sequence(datasets.Value("""string""" , id="""token""" ) , id="""sequence""" ),
"""references""": datasets.Sequence(
datasets.Sequence(datasets.Value("""string""" , id="""token""" ) , id="""sequence""" ) , id="""references""" ),
} ) , )
def lowerCamelCase_ ( self : Optional[int] , lowerCamelCase_ : List[List[List[str]]] , lowerCamelCase_ : List[List[str]] , lowerCamelCase_ : int = 1 , lowerCamelCase_ : int = 4 , ):
'''simple docstring'''
return {
"google_bleu": gleu_score.corpus_gleu(
list_of_references=__A , hypotheses=__A , min_len=__A , max_len=__A )
}
| 718 |
'''simple docstring'''
from typing import List, Optional, Union
import torch
from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDPMScheduler
from ...utils import (
is_accelerate_available,
is_accelerate_version,
logging,
randn_tensor,
replace_example_docstring,
)
__UpperCAmelCase = logging.get_logger(__name__) # pylint: disable=invalid-name
__UpperCAmelCase = """
Examples:
```py
>>> import torch
>>> import numpy as np
>>> from diffusers import KandinskyV22PriorPipeline, KandinskyV22ControlnetPipeline
>>> from transformers import pipeline
>>> from diffusers.utils import load_image
>>> def make_hint(image, depth_estimator):
... image = depth_estimator(image)[\"depth\"]
... image = np.array(image)
... image = image[:, :, None]
... image = np.concatenate([image, image, image], axis=2)
... detected_map = torch.from_numpy(image).float() / 255.0
... hint = detected_map.permute(2, 0, 1)
... return hint
>>> depth_estimator = pipeline(\"depth-estimation\")
>>> pipe_prior = KandinskyV22PriorPipeline.from_pretrained(
... \"kandinsky-community/kandinsky-2-2-prior\", torch_dtype=torch.float16
... )
>>> pipe_prior = pipe_prior.to(\"cuda\")
>>> pipe = KandinskyV22ControlnetPipeline.from_pretrained(
... \"kandinsky-community/kandinsky-2-2-controlnet-depth\", torch_dtype=torch.float16
... )
>>> pipe = pipe.to(\"cuda\")
>>> img = load_image(
... \"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main\"
... \"/kandinsky/cat.png\"
... ).resize((768, 768))
>>> hint = make_hint(img, depth_estimator).unsqueeze(0).half().to(\"cuda\")
>>> prompt = \"A robot, 4k photo\"
>>> negative_prior_prompt = \"lowres, text, error, cropped, worst quality, low quality, jpeg artifacts, ugly, duplicate, morbid, mutilated, out of frame, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, mutation, deformed, blurry, dehydrated, bad anatomy, bad proportions, extra limbs, cloned face, disfigured, gross proportions, malformed limbs, missing arms, missing legs, extra arms, extra legs, fused fingers, too many fingers, long neck, username, watermark, signature\"
>>> generator = torch.Generator(device=\"cuda\").manual_seed(43)
>>> image_emb, zero_image_emb = pipe_prior(
... prompt=prompt, negative_prompt=negative_prior_prompt, generator=generator
... ).to_tuple()
>>> images = pipe(
... image_embeds=image_emb,
... negative_image_embeds=zero_image_emb,
... hint=hint,
... num_inference_steps=50,
... generator=generator,
... height=768,
... width=768,
... ).images
>>> images[0].save(\"robot_cat.png\")
```
"""
def __A ( lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_=8 ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : Optional[Any] = height // scale_factor**2
if height % scale_factor**2 != 0:
new_height += 1
SCREAMING_SNAKE_CASE : List[str] = width // scale_factor**2
if width % scale_factor**2 != 0:
new_width += 1
return new_height * scale_factor, new_width * scale_factor
class UpperCamelCase__ ( lowercase_ ):
"""simple docstring"""
def __init__( self : Union[str, Any] , lowerCamelCase_ : UNetaDConditionModel , lowerCamelCase_ : DDPMScheduler , lowerCamelCase_ : VQModel , ):
'''simple docstring'''
super().__init__()
self.register_modules(
unet=lowerCamelCase_ , scheduler=lowerCamelCase_ , movq=lowerCamelCase_ , )
SCREAMING_SNAKE_CASE : str = 2 ** (len(self.movq.config.block_out_channels ) - 1)
def lowerCamelCase_ ( self : int , lowerCamelCase_ : int , lowerCamelCase_ : Optional[int] , lowerCamelCase_ : List[str] , lowerCamelCase_ : Any , lowerCamelCase_ : Tuple , lowerCamelCase_ : int ):
'''simple docstring'''
if latents is None:
SCREAMING_SNAKE_CASE : Tuple = randn_tensor(lowerCamelCase_ , generator=lowerCamelCase_ , device=lowerCamelCase_ , dtype=lowerCamelCase_ )
else:
if latents.shape != shape:
raise ValueError(f'''Unexpected latents shape, got {latents.shape}, expected {shape}''' )
SCREAMING_SNAKE_CASE : Dict = latents.to(lowerCamelCase_ )
SCREAMING_SNAKE_CASE : Any = latents * scheduler.init_noise_sigma
return latents
def lowerCamelCase_ ( self : Union[str, Any] , lowerCamelCase_ : Dict=0 ):
'''simple docstring'''
if is_accelerate_available():
from accelerate import cpu_offload
else:
raise ImportError("""Please install accelerate via `pip install accelerate`""" )
SCREAMING_SNAKE_CASE : List[Any] = torch.device(f'''cuda:{gpu_id}''' )
SCREAMING_SNAKE_CASE : Union[str, Any] = [
self.unet,
self.movq,
]
for cpu_offloaded_model in models:
if cpu_offloaded_model is not None:
cpu_offload(lowerCamelCase_ , lowerCamelCase_ )
def lowerCamelCase_ ( self : List[Any] , lowerCamelCase_ : Tuple=0 ):
'''simple docstring'''
if is_accelerate_available() and is_accelerate_version(""">=""" , """0.17.0.dev0""" ):
from accelerate import cpu_offload_with_hook
else:
raise ImportError("""`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher.""" )
SCREAMING_SNAKE_CASE : Any = torch.device(f'''cuda:{gpu_id}''' )
if self.device.type != "cpu":
self.to("""cpu""" , silence_dtype_warnings=lowerCamelCase_ )
torch.cuda.empty_cache() # otherwise we don't see the memory savings (but they probably exist)
SCREAMING_SNAKE_CASE : Union[str, Any] = None
for cpu_offloaded_model in [self.unet, self.movq]:
SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE : List[Any] = cpu_offload_with_hook(lowerCamelCase_ , lowerCamelCase_ , prev_module_hook=lowerCamelCase_ )
# We'll offload the last model manually.
SCREAMING_SNAKE_CASE : str = hook
@property
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
def lowerCamelCase_ ( self : str ):
'''simple docstring'''
if not hasattr(self.unet , """_hf_hook""" ):
return self.device
for module in self.unet.modules():
if (
hasattr(lowerCamelCase_ , """_hf_hook""" )
and hasattr(module._hf_hook , """execution_device""" )
and module._hf_hook.execution_device is not None
):
return torch.device(module._hf_hook.execution_device )
return self.device
@torch.no_grad()
@replace_example_docstring(lowerCamelCase_ )
def __call__( self : Optional[Any] , lowerCamelCase_ : Union[torch.FloatTensor, List[torch.FloatTensor]] , lowerCamelCase_ : Union[torch.FloatTensor, List[torch.FloatTensor]] , lowerCamelCase_ : torch.FloatTensor , lowerCamelCase_ : int = 5_12 , lowerCamelCase_ : int = 5_12 , lowerCamelCase_ : int = 1_00 , lowerCamelCase_ : float = 4.0 , lowerCamelCase_ : int = 1 , lowerCamelCase_ : Optional[Union[torch.Generator, List[torch.Generator]]] = None , lowerCamelCase_ : Optional[torch.FloatTensor] = None , lowerCamelCase_ : Optional[str] = "pil" , lowerCamelCase_ : bool = True , ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : str = self._execution_device
SCREAMING_SNAKE_CASE : Optional[int] = guidance_scale > 1.0
if isinstance(lowerCamelCase_ , lowerCamelCase_ ):
SCREAMING_SNAKE_CASE : int = torch.cat(lowerCamelCase_ , dim=0 )
if isinstance(lowerCamelCase_ , lowerCamelCase_ ):
SCREAMING_SNAKE_CASE : Dict = torch.cat(lowerCamelCase_ , dim=0 )
if isinstance(lowerCamelCase_ , lowerCamelCase_ ):
SCREAMING_SNAKE_CASE : Optional[Any] = torch.cat(lowerCamelCase_ , dim=0 )
SCREAMING_SNAKE_CASE : Any = image_embeds.shape[0] * num_images_per_prompt
if do_classifier_free_guidance:
SCREAMING_SNAKE_CASE : List[Any] = image_embeds.repeat_interleave(lowerCamelCase_ , dim=0 )
SCREAMING_SNAKE_CASE : Optional[int] = negative_image_embeds.repeat_interleave(lowerCamelCase_ , dim=0 )
SCREAMING_SNAKE_CASE : Dict = hint.repeat_interleave(lowerCamelCase_ , dim=0 )
SCREAMING_SNAKE_CASE : List[Any] = torch.cat([negative_image_embeds, image_embeds] , dim=0 ).to(dtype=self.unet.dtype , device=lowerCamelCase_ )
SCREAMING_SNAKE_CASE : Optional[int] = torch.cat([hint, hint] , dim=0 ).to(dtype=self.unet.dtype , device=lowerCamelCase_ )
self.scheduler.set_timesteps(lowerCamelCase_ , device=lowerCamelCase_ )
SCREAMING_SNAKE_CASE : Union[str, Any] = self.scheduler.timesteps
SCREAMING_SNAKE_CASE : Any = self.movq.config.latent_channels
SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE : Optional[Any] = downscale_height_and_width(lowerCamelCase_ , lowerCamelCase_ , self.movq_scale_factor )
# create initial latent
SCREAMING_SNAKE_CASE : str = self.prepare_latents(
(batch_size, num_channels_latents, height, width) , image_embeds.dtype , lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ , self.scheduler , )
for i, t in enumerate(self.progress_bar(lowerCamelCase_ ) ):
# expand the latents if we are doing classifier free guidance
SCREAMING_SNAKE_CASE : Dict = torch.cat([latents] * 2 ) if do_classifier_free_guidance else latents
SCREAMING_SNAKE_CASE : Union[str, Any] = {"""image_embeds""": image_embeds, """hint""": hint}
SCREAMING_SNAKE_CASE : Dict = self.unet(
sample=lowerCamelCase_ , timestep=lowerCamelCase_ , encoder_hidden_states=lowerCamelCase_ , added_cond_kwargs=lowerCamelCase_ , return_dict=lowerCamelCase_ , )[0]
if do_classifier_free_guidance:
SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE : str = noise_pred.split(latents.shape[1] , dim=1 )
SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE : Optional[Any] = noise_pred.chunk(2 )
SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE : Union[str, Any] = variance_pred.chunk(2 )
SCREAMING_SNAKE_CASE : List[str] = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
SCREAMING_SNAKE_CASE : str = torch.cat([noise_pred, variance_pred_text] , dim=1 )
if not (
hasattr(self.scheduler.config , """variance_type""" )
and self.scheduler.config.variance_type in ["learned", "learned_range"]
):
SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE : Optional[Any] = noise_pred.split(latents.shape[1] , dim=1 )
# compute the previous noisy sample x_t -> x_t-1
SCREAMING_SNAKE_CASE : str = self.scheduler.step(
lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ , generator=lowerCamelCase_ , )[0]
# post-processing
SCREAMING_SNAKE_CASE : List[str] = self.movq.decode(lowerCamelCase_ , force_not_quantize=lowerCamelCase_ )["""sample"""]
if output_type not in ["pt", "np", "pil"]:
raise ValueError(f'''Only the output types `pt`, `pil` and `np` are supported not output_type={output_type}''' )
if output_type in ["np", "pil"]:
SCREAMING_SNAKE_CASE : Optional[int] = image * 0.5 + 0.5
SCREAMING_SNAKE_CASE : List[Any] = image.clamp(0 , 1 )
SCREAMING_SNAKE_CASE : List[Any] = image.cpu().permute(0 , 2 , 3 , 1 ).float().numpy()
if output_type == "pil":
SCREAMING_SNAKE_CASE : Any = self.numpy_to_pil(lowerCamelCase_ )
if not return_dict:
return (image,)
return ImagePipelineOutput(images=lowerCamelCase_ )
| 79 | 0 |
'''simple docstring'''
import argparse
import json
from typing import List
from ltp import LTP
from transformers.models.bert.tokenization_bert import BertTokenizer
def __A ( lowerCamelCase_ ):
"""simple docstring"""
if (
(cp >= 0x4e00 and cp <= 0x9fff)
or (cp >= 0x3400 and cp <= 0x4dbf) #
or (cp >= 0x20000 and cp <= 0x2a6df) #
or (cp >= 0x2a700 and cp <= 0x2b73f) #
or (cp >= 0x2b740 and cp <= 0x2b81f) #
or (cp >= 0x2b820 and cp <= 0x2ceaf) #
or (cp >= 0xf900 and cp <= 0xfaff)
or (cp >= 0x2f800 and cp <= 0x2fa1f) #
): #
return True
return False
def __A ( lowerCamelCase_ ):
"""simple docstring"""
for char in word:
SCREAMING_SNAKE_CASE : List[str] = ord(_lowerCamelCase )
if not _is_chinese_char(_lowerCamelCase ):
return 0
return 1
def __A ( lowerCamelCase_ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : Tuple = set()
for token in tokens:
SCREAMING_SNAKE_CASE : Optional[int] = len(_lowerCamelCase ) > 1 and is_chinese(_lowerCamelCase )
if chinese_word:
word_set.add(_lowerCamelCase )
SCREAMING_SNAKE_CASE : Any = list(_lowerCamelCase )
return word_list
def __A ( lowerCamelCase_ , lowerCamelCase_ ):
"""simple docstring"""
if not chinese_word_set:
return bert_tokens
SCREAMING_SNAKE_CASE : Any = max([len(_lowerCamelCase ) for w in chinese_word_set] )
SCREAMING_SNAKE_CASE : Union[str, Any] = bert_tokens
SCREAMING_SNAKE_CASE : List[Any] = 0, len(_lowerCamelCase )
while start < end:
SCREAMING_SNAKE_CASE : str = True
if is_chinese(bert_word[start] ):
SCREAMING_SNAKE_CASE : List[Any] = min(end - start , _lowerCamelCase )
for i in range(_lowerCamelCase , 1 , -1 ):
SCREAMING_SNAKE_CASE : Optional[int] = """""".join(bert_word[start : start + i] )
if whole_word in chinese_word_set:
for j in range(start + 1 , start + i ):
SCREAMING_SNAKE_CASE : List[Any] = """##""" + bert_word[j]
SCREAMING_SNAKE_CASE : List[Any] = start + i
SCREAMING_SNAKE_CASE : int = False
break
if single_word:
start += 1
return bert_word
def __A ( lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : Optional[int] = []
for i in range(0 , len(_lowerCamelCase ) , 1_00 ):
SCREAMING_SNAKE_CASE : Tuple = ltp_tokenizer.pipeline(lines[i : i + 1_00] , tasks=["""cws"""] ).cws
SCREAMING_SNAKE_CASE : Union[str, Any] = [get_chinese_word(_lowerCamelCase ) for r in res]
ltp_res.extend(_lowerCamelCase )
assert len(_lowerCamelCase ) == len(_lowerCamelCase )
SCREAMING_SNAKE_CASE : Tuple = []
for i in range(0 , len(_lowerCamelCase ) , 1_00 ):
SCREAMING_SNAKE_CASE : Optional[int] = bert_tokenizer(lines[i : i + 1_00] , add_special_tokens=_lowerCamelCase , truncation=_lowerCamelCase , max_length=5_12 )
bert_res.extend(res["""input_ids"""] )
assert len(_lowerCamelCase ) == len(_lowerCamelCase )
SCREAMING_SNAKE_CASE : List[Any] = []
for input_ids, chinese_word in zip(_lowerCamelCase , _lowerCamelCase ):
SCREAMING_SNAKE_CASE : str = []
for id in input_ids:
SCREAMING_SNAKE_CASE : Union[str, Any] = bert_tokenizer._convert_id_to_token(_lowerCamelCase )
input_tokens.append(_lowerCamelCase )
SCREAMING_SNAKE_CASE : Optional[int] = add_sub_symbol(_lowerCamelCase , _lowerCamelCase )
SCREAMING_SNAKE_CASE : Union[str, Any] = []
# We only save pos of chinese subwords start with ##, which mean is part of a whole word.
for i, token in enumerate(_lowerCamelCase ):
if token[:2] == "##":
SCREAMING_SNAKE_CASE : Optional[Any] = token[2:]
# save chinese tokens' pos
if len(_lowerCamelCase ) == 1 and _is_chinese_char(ord(_lowerCamelCase ) ):
ref_id.append(_lowerCamelCase )
ref_ids.append(_lowerCamelCase )
assert len(_lowerCamelCase ) == len(_lowerCamelCase )
return ref_ids
def __A ( lowerCamelCase_ ):
"""simple docstring"""
with open(args.file_name , """r""" , encoding="""utf-8""" ) as f:
SCREAMING_SNAKE_CASE : Tuple = f.readlines()
SCREAMING_SNAKE_CASE : List[Any] = [line.strip() for line in data if len(_lowerCamelCase ) > 0 and not line.isspace()] # avoid delimiter like '\u2029'
SCREAMING_SNAKE_CASE : List[Any] = LTP(args.ltp ) # faster in GPU device
SCREAMING_SNAKE_CASE : Dict = BertTokenizer.from_pretrained(args.bert )
SCREAMING_SNAKE_CASE : Optional[int] = prepare_ref(_lowerCamelCase , _lowerCamelCase , _lowerCamelCase )
with open(args.save_path , """w""" , encoding="""utf-8""" ) as f:
SCREAMING_SNAKE_CASE : int = [json.dumps(_lowerCamelCase ) + """\n""" for ref in ref_ids]
f.writelines(_lowerCamelCase )
if __name__ == "__main__":
__UpperCAmelCase = argparse.ArgumentParser(description="""prepare_chinese_ref""")
parser.add_argument(
"""--file_name""",
required=False,
type=str,
default="""./resources/chinese-demo.txt""",
help="""file need process, same as training data in lm""",
)
parser.add_argument(
"""--ltp""",
required=False,
type=str,
default="""./resources/ltp""",
help="""resources for LTP tokenizer, usually a path""",
)
parser.add_argument(
"""--bert""",
required=False,
type=str,
default="""./resources/robert""",
help="""resources for Bert tokenizer""",
)
parser.add_argument(
"""--save_path""",
required=False,
type=str,
default="""./resources/ref.txt""",
help="""path to save res""",
)
__UpperCAmelCase = parser.parse_args()
main(args)
| 719 |
'''simple docstring'''
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_big_bird import BigBirdTokenizer
else:
__UpperCAmelCase = None
__UpperCAmelCase = logging.get_logger(__name__)
__UpperCAmelCase = {"""vocab_file""": """spiece.model""", """tokenizer_file""": """tokenizer.json"""}
__UpperCAmelCase = {
"""vocab_file""": {
"""google/bigbird-roberta-base""": """https://huggingface.co/google/bigbird-roberta-base/resolve/main/spiece.model""",
"""google/bigbird-roberta-large""": (
"""https://huggingface.co/google/bigbird-roberta-large/resolve/main/spiece.model"""
),
"""google/bigbird-base-trivia-itc""": (
"""https://huggingface.co/google/bigbird-base-trivia-itc/resolve/main/spiece.model"""
),
},
"""tokenizer_file""": {
"""google/bigbird-roberta-base""": (
"""https://huggingface.co/google/bigbird-roberta-base/resolve/main/tokenizer.json"""
),
"""google/bigbird-roberta-large""": (
"""https://huggingface.co/google/bigbird-roberta-large/resolve/main/tokenizer.json"""
),
"""google/bigbird-base-trivia-itc""": (
"""https://huggingface.co/google/bigbird-base-trivia-itc/resolve/main/tokenizer.json"""
),
},
}
__UpperCAmelCase = {
"""google/bigbird-roberta-base""": 4096,
"""google/bigbird-roberta-large""": 4096,
"""google/bigbird-base-trivia-itc""": 4096,
}
__UpperCAmelCase = """▁"""
class UpperCamelCase__ ( lowercase_ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE__ = VOCAB_FILES_NAMES
SCREAMING_SNAKE_CASE__ = PRETRAINED_VOCAB_FILES_MAP
SCREAMING_SNAKE_CASE__ = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
SCREAMING_SNAKE_CASE__ = BigBirdTokenizer
SCREAMING_SNAKE_CASE__ = ['''input_ids''', '''attention_mask''']
SCREAMING_SNAKE_CASE__ = []
def __init__( self : Any , lowerCamelCase_ : str=None , lowerCamelCase_ : List[Any]=None , lowerCamelCase_ : Dict="<unk>" , lowerCamelCase_ : int="<s>" , lowerCamelCase_ : Optional[Any]="</s>" , lowerCamelCase_ : Dict="<pad>" , lowerCamelCase_ : Tuple="[SEP]" , lowerCamelCase_ : Dict="[MASK]" , lowerCamelCase_ : Union[str, Any]="[CLS]" , **lowerCamelCase_ : Dict , ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Any = AddedToken(lowerCamelCase_ , lstrip=lowerCamelCase_ , rstrip=lowerCamelCase_ ) if isinstance(lowerCamelCase_ , lowerCamelCase_ ) else bos_token
SCREAMING_SNAKE_CASE : Dict = AddedToken(lowerCamelCase_ , lstrip=lowerCamelCase_ , rstrip=lowerCamelCase_ ) if isinstance(lowerCamelCase_ , lowerCamelCase_ ) else eos_token
SCREAMING_SNAKE_CASE : Optional[Any] = AddedToken(lowerCamelCase_ , lstrip=lowerCamelCase_ , rstrip=lowerCamelCase_ ) if isinstance(lowerCamelCase_ , lowerCamelCase_ ) else unk_token
SCREAMING_SNAKE_CASE : int = AddedToken(lowerCamelCase_ , lstrip=lowerCamelCase_ , rstrip=lowerCamelCase_ ) if isinstance(lowerCamelCase_ , lowerCamelCase_ ) else pad_token
SCREAMING_SNAKE_CASE : Any = AddedToken(lowerCamelCase_ , lstrip=lowerCamelCase_ , rstrip=lowerCamelCase_ ) if isinstance(lowerCamelCase_ , lowerCamelCase_ ) else cls_token
SCREAMING_SNAKE_CASE : Any = AddedToken(lowerCamelCase_ , lstrip=lowerCamelCase_ , rstrip=lowerCamelCase_ ) if isinstance(lowerCamelCase_ , lowerCamelCase_ ) else sep_token
# Mask token behave like a normal word, i.e. include the space before it
SCREAMING_SNAKE_CASE : int = 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_ , unk_token=lowerCamelCase_ , sep_token=lowerCamelCase_ , pad_token=lowerCamelCase_ , cls_token=lowerCamelCase_ , mask_token=lowerCamelCase_ , **lowerCamelCase_ , )
SCREAMING_SNAKE_CASE : List[Any] = vocab_file
SCREAMING_SNAKE_CASE : Optional[Any] = False if not self.vocab_file else True
def lowerCamelCase_ ( self : Union[str, Any] , lowerCamelCase_ : List[int] , lowerCamelCase_ : Optional[List[int]] = None ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Optional[Any] = [self.sep_token_id]
SCREAMING_SNAKE_CASE : int = [self.cls_token_id]
if token_ids_a is None:
return cls + token_ids_a + sep
return cls + token_ids_a + sep + token_ids_a + sep
def lowerCamelCase_ ( self : List[Any] , lowerCamelCase_ : List[int] , lowerCamelCase_ : Optional[List[int]] = None , lowerCamelCase_ : bool = False ):
'''simple docstring'''
if already_has_special_tokens:
if token_ids_a is not None:
raise ValueError(
"""You should not supply a second sequence if the provided sequence of """
"""ids is already formatted with special tokens for the model.""" )
return [1 if x in [self.sep_token_id, self.cls_token_id] else 0 for x in token_ids_a]
if token_ids_a is None:
return [1] + ([0] * len(lowerCamelCase_ )) + [1]
return [1] + ([0] * len(lowerCamelCase_ )) + [1] + ([0] * len(lowerCamelCase_ )) + [1]
def lowerCamelCase_ ( self : Optional[int] , lowerCamelCase_ : List[int] , lowerCamelCase_ : Optional[List[int]] = None ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Any = [self.sep_token_id]
SCREAMING_SNAKE_CASE : Optional[int] = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep ) * [0] + len(token_ids_a + sep ) * [1]
def lowerCamelCase_ ( self : str , lowerCamelCase_ : str , lowerCamelCase_ : 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 : Tuple = 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,)
| 79 | 0 |
'''simple docstring'''
from __future__ import annotations
import typing
from collections.abc import Iterable
import numpy as np
__UpperCAmelCase = typing.Union[Iterable[float], Iterable[int], np.ndarray] # noqa: UP007
__UpperCAmelCase = typing.Union[np.floataa, int, float] # noqa: UP007
def __A ( lowerCamelCase_ , lowerCamelCase_ ):
"""simple docstring"""
return np.sqrt(np.sum((np.asarray(lowercase_ ) - np.asarray(lowercase_ )) ** 2 ) )
def __A ( lowerCamelCase_ , lowerCamelCase_ ):
"""simple docstring"""
return sum((va - va) ** 2 for va, va in zip(lowercase_ , lowercase_ ) ) ** (1 / 2)
if __name__ == "__main__":
def __A ( ):
"""simple docstring"""
from timeit import timeit
print("""Without Numpy""" )
print(
timeit(
"""euclidean_distance_no_np([1, 2, 3], [4, 5, 6])""" , number=1_00_00 , globals=globals() , ) )
print("""With Numpy""" )
print(
timeit(
"""euclidean_distance([1, 2, 3], [4, 5, 6])""" , number=1_00_00 , globals=globals() , ) )
benchmark()
| 720 |
'''simple docstring'''
import unittest
import torch
from torch import nn
from diffusers.models.activations import get_activation
class UpperCamelCase__ ( unittest.TestCase ):
"""simple docstring"""
def lowerCamelCase_ ( self : List[str] ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : List[Any] = get_activation("""swish""" )
self.assertIsInstance(lowerCamelCase_ , nn.SiLU )
self.assertEqual(act(torch.tensor(-1_00 , dtype=torch.floataa ) ).item() , 0 )
self.assertNotEqual(act(torch.tensor(-1 , dtype=torch.floataa ) ).item() , 0 )
self.assertEqual(act(torch.tensor(0 , dtype=torch.floataa ) ).item() , 0 )
self.assertEqual(act(torch.tensor(20 , dtype=torch.floataa ) ).item() , 20 )
def lowerCamelCase_ ( self : int ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Any = get_activation("""silu""" )
self.assertIsInstance(lowerCamelCase_ , nn.SiLU )
self.assertEqual(act(torch.tensor(-1_00 , dtype=torch.floataa ) ).item() , 0 )
self.assertNotEqual(act(torch.tensor(-1 , dtype=torch.floataa ) ).item() , 0 )
self.assertEqual(act(torch.tensor(0 , dtype=torch.floataa ) ).item() , 0 )
self.assertEqual(act(torch.tensor(20 , dtype=torch.floataa ) ).item() , 20 )
def lowerCamelCase_ ( self : Optional[int] ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : List[Any] = get_activation("""mish""" )
self.assertIsInstance(lowerCamelCase_ , nn.Mish )
self.assertEqual(act(torch.tensor(-2_00 , dtype=torch.floataa ) ).item() , 0 )
self.assertNotEqual(act(torch.tensor(-1 , dtype=torch.floataa ) ).item() , 0 )
self.assertEqual(act(torch.tensor(0 , dtype=torch.floataa ) ).item() , 0 )
self.assertEqual(act(torch.tensor(20 , dtype=torch.floataa ) ).item() , 20 )
def lowerCamelCase_ ( self : List[str] ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : str = get_activation("""gelu""" )
self.assertIsInstance(lowerCamelCase_ , nn.GELU )
self.assertEqual(act(torch.tensor(-1_00 , dtype=torch.floataa ) ).item() , 0 )
self.assertNotEqual(act(torch.tensor(-1 , dtype=torch.floataa ) ).item() , 0 )
self.assertEqual(act(torch.tensor(0 , dtype=torch.floataa ) ).item() , 0 )
self.assertEqual(act(torch.tensor(20 , dtype=torch.floataa ) ).item() , 20 )
| 79 | 0 |
from __future__ import annotations
def __A ( lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ , ):
"""simple docstring"""
if (stress, tangential_force, area).count(0 ) != 1:
raise ValueError("""You cannot supply more or less than 2 values""" )
elif stress < 0:
raise ValueError("""Stress cannot be negative""" )
elif tangential_force < 0:
raise ValueError("""Tangential Force cannot be negative""" )
elif area < 0:
raise ValueError("""Area cannot be negative""" )
elif stress == 0:
return (
"stress",
tangential_force / area,
)
elif tangential_force == 0:
return (
"tangential_force",
stress * area,
)
else:
return (
"area",
tangential_force / stress,
)
if __name__ == "__main__":
import doctest
doctest.testmod()
| 721 |
'''simple docstring'''
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
if TYPE_CHECKING:
from ... import FeatureExtractionMixin, PreTrainedTokenizerBase, TensorType
__UpperCAmelCase = logging.get_logger(__name__)
__UpperCAmelCase = {
"""microsoft/deberta-v2-xlarge""": """https://huggingface.co/microsoft/deberta-v2-xlarge/resolve/main/config.json""",
"""microsoft/deberta-v2-xxlarge""": """https://huggingface.co/microsoft/deberta-v2-xxlarge/resolve/main/config.json""",
"""microsoft/deberta-v2-xlarge-mnli""": (
"""https://huggingface.co/microsoft/deberta-v2-xlarge-mnli/resolve/main/config.json"""
),
"""microsoft/deberta-v2-xxlarge-mnli""": (
"""https://huggingface.co/microsoft/deberta-v2-xxlarge-mnli/resolve/main/config.json"""
),
}
class UpperCamelCase__ ( lowercase_ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE__ = '''deberta-v2'''
def __init__( self : int , lowerCamelCase_ : Optional[Any]=12_81_00 , lowerCamelCase_ : str=15_36 , lowerCamelCase_ : int=24 , lowerCamelCase_ : List[str]=24 , lowerCamelCase_ : List[Any]=61_44 , lowerCamelCase_ : List[Any]="gelu" , lowerCamelCase_ : Optional[Any]=0.1 , lowerCamelCase_ : List[Any]=0.1 , lowerCamelCase_ : str=5_12 , lowerCamelCase_ : str=0 , lowerCamelCase_ : Union[str, Any]=0.02 , lowerCamelCase_ : Dict=1e-7 , lowerCamelCase_ : Optional[int]=False , lowerCamelCase_ : Optional[int]=-1 , lowerCamelCase_ : List[str]=0 , lowerCamelCase_ : Tuple=True , lowerCamelCase_ : Optional[int]=None , lowerCamelCase_ : Optional[Any]=0 , lowerCamelCase_ : Dict="gelu" , **lowerCamelCase_ : Optional[int] , ):
'''simple docstring'''
super().__init__(**lowerCamelCase_ )
SCREAMING_SNAKE_CASE : Optional[int] = hidden_size
SCREAMING_SNAKE_CASE : Optional[Any] = num_hidden_layers
SCREAMING_SNAKE_CASE : int = num_attention_heads
SCREAMING_SNAKE_CASE : List[str] = intermediate_size
SCREAMING_SNAKE_CASE : int = hidden_act
SCREAMING_SNAKE_CASE : Tuple = hidden_dropout_prob
SCREAMING_SNAKE_CASE : int = attention_probs_dropout_prob
SCREAMING_SNAKE_CASE : str = max_position_embeddings
SCREAMING_SNAKE_CASE : Tuple = type_vocab_size
SCREAMING_SNAKE_CASE : Optional[int] = initializer_range
SCREAMING_SNAKE_CASE : List[Any] = relative_attention
SCREAMING_SNAKE_CASE : str = max_relative_positions
SCREAMING_SNAKE_CASE : int = pad_token_id
SCREAMING_SNAKE_CASE : List[str] = position_biased_input
# Backwards compatibility
if type(lowerCamelCase_ ) == str:
SCREAMING_SNAKE_CASE : Dict = [x.strip() for x in pos_att_type.lower().split("""|""" )]
SCREAMING_SNAKE_CASE : Any = pos_att_type
SCREAMING_SNAKE_CASE : Any = vocab_size
SCREAMING_SNAKE_CASE : Optional[Any] = layer_norm_eps
SCREAMING_SNAKE_CASE : str = kwargs.get("""pooler_hidden_size""" , lowerCamelCase_ )
SCREAMING_SNAKE_CASE : Dict = pooler_dropout
SCREAMING_SNAKE_CASE : Any = pooler_hidden_act
class UpperCamelCase__ ( lowercase_ ):
"""simple docstring"""
@property
def lowerCamelCase_ ( self : Optional[int] ):
'''simple docstring'''
if self.task == "multiple-choice":
SCREAMING_SNAKE_CASE : Optional[int] = {0: """batch""", 1: """choice""", 2: """sequence"""}
else:
SCREAMING_SNAKE_CASE : Union[str, Any] = {0: """batch""", 1: """sequence"""}
if self._config.type_vocab_size > 0:
return OrderedDict(
[("""input_ids""", dynamic_axis), ("""attention_mask""", dynamic_axis), ("""token_type_ids""", dynamic_axis)] )
else:
return OrderedDict([("""input_ids""", dynamic_axis), ("""attention_mask""", dynamic_axis)] )
@property
def lowerCamelCase_ ( self : Tuple ):
'''simple docstring'''
return 12
def lowerCamelCase_ ( self : Optional[Any] , lowerCamelCase_ : Union["PreTrainedTokenizerBase", "FeatureExtractionMixin"] , lowerCamelCase_ : int = -1 , lowerCamelCase_ : int = -1 , lowerCamelCase_ : int = -1 , lowerCamelCase_ : bool = False , lowerCamelCase_ : Optional["TensorType"] = None , lowerCamelCase_ : int = 3 , lowerCamelCase_ : int = 40 , lowerCamelCase_ : int = 40 , lowerCamelCase_ : "PreTrainedTokenizerBase" = None , ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Dict = super().generate_dummy_inputs(preprocessor=lowerCamelCase_ , framework=lowerCamelCase_ )
if self._config.type_vocab_size == 0 and "token_type_ids" in dummy_inputs:
del dummy_inputs["token_type_ids"]
return dummy_inputs
| 79 | 0 |
'''simple docstring'''
from typing import Any
def __A ( lowerCamelCase_ ):
"""simple docstring"""
if not input_list:
return []
SCREAMING_SNAKE_CASE : Dict = [input_list.count(_lowerCamelCase ) for value in input_list]
SCREAMING_SNAKE_CASE : Dict = max(_lowerCamelCase ) # Gets the maximum count in the input list.
# Gets values of modes
return sorted({input_list[i] for i, value in enumerate(_lowerCamelCase ) if value == y} )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 700 |
'''simple docstring'''
from collections import deque
from math import floor
from random import random
from time import time
class UpperCamelCase__ :
"""simple docstring"""
def __init__( self : Optional[Any] ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : str = {}
def lowerCamelCase_ ( self : Union[str, Any] , lowerCamelCase_ : Optional[Any] , lowerCamelCase_ : Any , lowerCamelCase_ : Optional[int]=1 ):
'''simple docstring'''
if self.graph.get(lowerCamelCase_ ):
if self.graph[u].count([w, v] ) == 0:
self.graph[u].append([w, v] )
else:
SCREAMING_SNAKE_CASE : str = [[w, v]]
if not self.graph.get(lowerCamelCase_ ):
SCREAMING_SNAKE_CASE : Tuple = []
def lowerCamelCase_ ( self : Optional[Any] ):
'''simple docstring'''
return list(self.graph )
def lowerCamelCase_ ( self : Optional[int] , lowerCamelCase_ : Any , lowerCamelCase_ : str ):
'''simple docstring'''
if self.graph.get(lowerCamelCase_ ):
for _ in self.graph[u]:
if _[1] == v:
self.graph[u].remove(lowerCamelCase_ )
def lowerCamelCase_ ( self : List[str] , lowerCamelCase_ : Union[str, Any]=-2 , lowerCamelCase_ : str=-1 ):
'''simple docstring'''
if s == d:
return []
SCREAMING_SNAKE_CASE : List[str] = []
SCREAMING_SNAKE_CASE : List[str] = []
if s == -2:
SCREAMING_SNAKE_CASE : Tuple = list(self.graph )[0]
stack.append(lowerCamelCase_ )
visited.append(lowerCamelCase_ )
SCREAMING_SNAKE_CASE : Any = s
while True:
# check if there is any non isolated nodes
if len(self.graph[s] ) != 0:
SCREAMING_SNAKE_CASE : Optional[Any] = s
for node in self.graph[s]:
if visited.count(node[1] ) < 1:
if node[1] == d:
visited.append(lowerCamelCase_ )
return visited
else:
stack.append(node[1] )
visited.append(node[1] )
SCREAMING_SNAKE_CASE : Tuple = node[1]
break
# check if all the children are visited
if s == ss:
stack.pop()
if len(lowerCamelCase_ ) != 0:
SCREAMING_SNAKE_CASE : int = stack[len(lowerCamelCase_ ) - 1]
else:
SCREAMING_SNAKE_CASE : Any = ss
# check if se have reached the starting point
if len(lowerCamelCase_ ) == 0:
return visited
def lowerCamelCase_ ( self : Dict , lowerCamelCase_ : Optional[int]=-1 ):
'''simple docstring'''
if c == -1:
SCREAMING_SNAKE_CASE : str = floor(random() * 1_00_00 ) + 10
for i in range(lowerCamelCase_ ):
# every vertex has max 100 edges
for _ in range(floor(random() * 1_02 ) + 1 ):
SCREAMING_SNAKE_CASE : Union[str, Any] = floor(random() * c ) + 1
if n != i:
self.add_pair(lowerCamelCase_ , lowerCamelCase_ , 1 )
def lowerCamelCase_ ( self : List[Any] , lowerCamelCase_ : Any=-2 ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : str = deque()
SCREAMING_SNAKE_CASE : List[str] = []
if s == -2:
SCREAMING_SNAKE_CASE : int = list(self.graph )[0]
d.append(lowerCamelCase_ )
visited.append(lowerCamelCase_ )
while d:
SCREAMING_SNAKE_CASE : Dict = d.popleft()
if len(self.graph[s] ) != 0:
for node in self.graph[s]:
if visited.count(node[1] ) < 1:
d.append(node[1] )
visited.append(node[1] )
return visited
def lowerCamelCase_ ( self : Dict , lowerCamelCase_ : Optional[Any] ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Dict = 0
for x in self.graph:
for y in self.graph[x]:
if y[1] == u:
count += 1
return count
def lowerCamelCase_ ( self : Optional[Any] , lowerCamelCase_ : Tuple ):
'''simple docstring'''
return len(self.graph[u] )
def lowerCamelCase_ ( self : str , lowerCamelCase_ : Optional[Any]=-2 ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : List[Any] = []
SCREAMING_SNAKE_CASE : List[str] = []
if s == -2:
SCREAMING_SNAKE_CASE : Union[str, Any] = list(self.graph )[0]
stack.append(lowerCamelCase_ )
visited.append(lowerCamelCase_ )
SCREAMING_SNAKE_CASE : Union[str, Any] = s
SCREAMING_SNAKE_CASE : List[str] = []
while True:
# check if there is any non isolated nodes
if len(self.graph[s] ) != 0:
SCREAMING_SNAKE_CASE : Optional[Any] = s
for node in self.graph[s]:
if visited.count(node[1] ) < 1:
stack.append(node[1] )
visited.append(node[1] )
SCREAMING_SNAKE_CASE : int = node[1]
break
# check if all the children are visited
if s == ss:
sorted_nodes.append(stack.pop() )
if len(lowerCamelCase_ ) != 0:
SCREAMING_SNAKE_CASE : List[Any] = stack[len(lowerCamelCase_ ) - 1]
else:
SCREAMING_SNAKE_CASE : int = ss
# check if se have reached the starting point
if len(lowerCamelCase_ ) == 0:
return sorted_nodes
def lowerCamelCase_ ( self : Optional[int] ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : List[Any] = []
SCREAMING_SNAKE_CASE : Any = []
SCREAMING_SNAKE_CASE : List[Any] = list(self.graph )[0]
stack.append(lowerCamelCase_ )
visited.append(lowerCamelCase_ )
SCREAMING_SNAKE_CASE : Any = -2
SCREAMING_SNAKE_CASE : int = []
SCREAMING_SNAKE_CASE : Union[str, Any] = s
SCREAMING_SNAKE_CASE : Tuple = False
SCREAMING_SNAKE_CASE : Union[str, Any] = set()
while True:
# check if there is any non isolated nodes
if len(self.graph[s] ) != 0:
SCREAMING_SNAKE_CASE : Union[str, Any] = s
for node in self.graph[s]:
if (
visited.count(node[1] ) > 0
and node[1] != parent
and indirect_parents.count(node[1] ) > 0
and not on_the_way_back
):
SCREAMING_SNAKE_CASE : Any = len(lowerCamelCase_ ) - 1
while len_stack >= 0:
if stack[len_stack] == node[1]:
anticipating_nodes.add(node[1] )
break
else:
anticipating_nodes.add(stack[len_stack] )
len_stack -= 1
if visited.count(node[1] ) < 1:
stack.append(node[1] )
visited.append(node[1] )
SCREAMING_SNAKE_CASE : int = node[1]
break
# check if all the children are visited
if s == ss:
stack.pop()
SCREAMING_SNAKE_CASE : int = True
if len(lowerCamelCase_ ) != 0:
SCREAMING_SNAKE_CASE : Any = stack[len(lowerCamelCase_ ) - 1]
else:
SCREAMING_SNAKE_CASE : List[str] = False
indirect_parents.append(lowerCamelCase_ )
SCREAMING_SNAKE_CASE : List[Any] = s
SCREAMING_SNAKE_CASE : List[Any] = ss
# check if se have reached the starting point
if len(lowerCamelCase_ ) == 0:
return list(lowerCamelCase_ )
def lowerCamelCase_ ( self : Dict ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Tuple = []
SCREAMING_SNAKE_CASE : int = []
SCREAMING_SNAKE_CASE : Any = list(self.graph )[0]
stack.append(lowerCamelCase_ )
visited.append(lowerCamelCase_ )
SCREAMING_SNAKE_CASE : Union[str, Any] = -2
SCREAMING_SNAKE_CASE : List[Any] = []
SCREAMING_SNAKE_CASE : Tuple = s
SCREAMING_SNAKE_CASE : Dict = False
SCREAMING_SNAKE_CASE : Optional[Any] = set()
while True:
# check if there is any non isolated nodes
if len(self.graph[s] ) != 0:
SCREAMING_SNAKE_CASE : str = s
for node in self.graph[s]:
if (
visited.count(node[1] ) > 0
and node[1] != parent
and indirect_parents.count(node[1] ) > 0
and not on_the_way_back
):
SCREAMING_SNAKE_CASE : str = len(lowerCamelCase_ ) - 1
while len_stack_minus_one >= 0:
if stack[len_stack_minus_one] == node[1]:
anticipating_nodes.add(node[1] )
break
else:
return True
if visited.count(node[1] ) < 1:
stack.append(node[1] )
visited.append(node[1] )
SCREAMING_SNAKE_CASE : Optional[Any] = node[1]
break
# check if all the children are visited
if s == ss:
stack.pop()
SCREAMING_SNAKE_CASE : Dict = True
if len(lowerCamelCase_ ) != 0:
SCREAMING_SNAKE_CASE : List[str] = stack[len(lowerCamelCase_ ) - 1]
else:
SCREAMING_SNAKE_CASE : List[Any] = False
indirect_parents.append(lowerCamelCase_ )
SCREAMING_SNAKE_CASE : Optional[int] = s
SCREAMING_SNAKE_CASE : Optional[int] = ss
# check if se have reached the starting point
if len(lowerCamelCase_ ) == 0:
return False
def lowerCamelCase_ ( self : Any , lowerCamelCase_ : str=-2 , lowerCamelCase_ : int=-1 ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Optional[Any] = time()
self.dfs(lowerCamelCase_ , lowerCamelCase_ )
SCREAMING_SNAKE_CASE : List[str] = time()
return end - begin
def lowerCamelCase_ ( self : int , lowerCamelCase_ : Tuple=-2 ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Optional[int] = time()
self.bfs(lowerCamelCase_ )
SCREAMING_SNAKE_CASE : List[str] = time()
return end - begin
class UpperCamelCase__ :
"""simple docstring"""
def __init__( self : Optional[int] ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Tuple = {}
def lowerCamelCase_ ( self : List[str] , lowerCamelCase_ : Optional[Any] , lowerCamelCase_ : Dict , lowerCamelCase_ : Union[str, Any]=1 ):
'''simple docstring'''
if self.graph.get(lowerCamelCase_ ):
# if there already is a edge
if self.graph[u].count([w, v] ) == 0:
self.graph[u].append([w, v] )
else:
# if u does not exist
SCREAMING_SNAKE_CASE : Any = [[w, v]]
# add the other way
if self.graph.get(lowerCamelCase_ ):
# if there already is a edge
if self.graph[v].count([w, u] ) == 0:
self.graph[v].append([w, u] )
else:
# if u does not exist
SCREAMING_SNAKE_CASE : Any = [[w, u]]
def lowerCamelCase_ ( self : Tuple , lowerCamelCase_ : Tuple , lowerCamelCase_ : Any ):
'''simple docstring'''
if self.graph.get(lowerCamelCase_ ):
for _ in self.graph[u]:
if _[1] == v:
self.graph[u].remove(lowerCamelCase_ )
# the other way round
if self.graph.get(lowerCamelCase_ ):
for _ in self.graph[v]:
if _[1] == u:
self.graph[v].remove(lowerCamelCase_ )
def lowerCamelCase_ ( self : int , lowerCamelCase_ : str=-2 , lowerCamelCase_ : List[str]=-1 ):
'''simple docstring'''
if s == d:
return []
SCREAMING_SNAKE_CASE : List[str] = []
SCREAMING_SNAKE_CASE : Any = []
if s == -2:
SCREAMING_SNAKE_CASE : List[Any] = list(self.graph )[0]
stack.append(lowerCamelCase_ )
visited.append(lowerCamelCase_ )
SCREAMING_SNAKE_CASE : List[str] = s
while True:
# check if there is any non isolated nodes
if len(self.graph[s] ) != 0:
SCREAMING_SNAKE_CASE : Union[str, Any] = s
for node in self.graph[s]:
if visited.count(node[1] ) < 1:
if node[1] == d:
visited.append(lowerCamelCase_ )
return visited
else:
stack.append(node[1] )
visited.append(node[1] )
SCREAMING_SNAKE_CASE : Any = node[1]
break
# check if all the children are visited
if s == ss:
stack.pop()
if len(lowerCamelCase_ ) != 0:
SCREAMING_SNAKE_CASE : Any = stack[len(lowerCamelCase_ ) - 1]
else:
SCREAMING_SNAKE_CASE : List[str] = ss
# check if se have reached the starting point
if len(lowerCamelCase_ ) == 0:
return visited
def lowerCamelCase_ ( self : Any , lowerCamelCase_ : List[str]=-1 ):
'''simple docstring'''
if c == -1:
SCREAMING_SNAKE_CASE : Any = floor(random() * 1_00_00 ) + 10
for i in range(lowerCamelCase_ ):
# every vertex has max 100 edges
for _ in range(floor(random() * 1_02 ) + 1 ):
SCREAMING_SNAKE_CASE : List[str] = floor(random() * c ) + 1
if n != i:
self.add_pair(lowerCamelCase_ , lowerCamelCase_ , 1 )
def lowerCamelCase_ ( self : List[str] , lowerCamelCase_ : List[Any]=-2 ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : List[Any] = deque()
SCREAMING_SNAKE_CASE : Tuple = []
if s == -2:
SCREAMING_SNAKE_CASE : Any = list(self.graph )[0]
d.append(lowerCamelCase_ )
visited.append(lowerCamelCase_ )
while d:
SCREAMING_SNAKE_CASE : List[Any] = d.popleft()
if len(self.graph[s] ) != 0:
for node in self.graph[s]:
if visited.count(node[1] ) < 1:
d.append(node[1] )
visited.append(node[1] )
return visited
def lowerCamelCase_ ( self : Tuple , lowerCamelCase_ : str ):
'''simple docstring'''
return len(self.graph[u] )
def lowerCamelCase_ ( self : List[str] ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : List[str] = []
SCREAMING_SNAKE_CASE : Any = []
SCREAMING_SNAKE_CASE : Optional[Any] = list(self.graph )[0]
stack.append(lowerCamelCase_ )
visited.append(lowerCamelCase_ )
SCREAMING_SNAKE_CASE : Optional[int] = -2
SCREAMING_SNAKE_CASE : Tuple = []
SCREAMING_SNAKE_CASE : Any = s
SCREAMING_SNAKE_CASE : Optional[Any] = False
SCREAMING_SNAKE_CASE : Optional[Any] = set()
while True:
# check if there is any non isolated nodes
if len(self.graph[s] ) != 0:
SCREAMING_SNAKE_CASE : Optional[Any] = s
for node in self.graph[s]:
if (
visited.count(node[1] ) > 0
and node[1] != parent
and indirect_parents.count(node[1] ) > 0
and not on_the_way_back
):
SCREAMING_SNAKE_CASE : Optional[Any] = len(lowerCamelCase_ ) - 1
while len_stack >= 0:
if stack[len_stack] == node[1]:
anticipating_nodes.add(node[1] )
break
else:
anticipating_nodes.add(stack[len_stack] )
len_stack -= 1
if visited.count(node[1] ) < 1:
stack.append(node[1] )
visited.append(node[1] )
SCREAMING_SNAKE_CASE : str = node[1]
break
# check if all the children are visited
if s == ss:
stack.pop()
SCREAMING_SNAKE_CASE : Optional[int] = True
if len(lowerCamelCase_ ) != 0:
SCREAMING_SNAKE_CASE : int = stack[len(lowerCamelCase_ ) - 1]
else:
SCREAMING_SNAKE_CASE : Union[str, Any] = False
indirect_parents.append(lowerCamelCase_ )
SCREAMING_SNAKE_CASE : List[str] = s
SCREAMING_SNAKE_CASE : str = ss
# check if se have reached the starting point
if len(lowerCamelCase_ ) == 0:
return list(lowerCamelCase_ )
def lowerCamelCase_ ( self : Union[str, Any] ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Optional[Any] = []
SCREAMING_SNAKE_CASE : int = []
SCREAMING_SNAKE_CASE : Any = list(self.graph )[0]
stack.append(lowerCamelCase_ )
visited.append(lowerCamelCase_ )
SCREAMING_SNAKE_CASE : Dict = -2
SCREAMING_SNAKE_CASE : int = []
SCREAMING_SNAKE_CASE : int = s
SCREAMING_SNAKE_CASE : Union[str, Any] = False
SCREAMING_SNAKE_CASE : Tuple = set()
while True:
# check if there is any non isolated nodes
if len(self.graph[s] ) != 0:
SCREAMING_SNAKE_CASE : Any = s
for node in self.graph[s]:
if (
visited.count(node[1] ) > 0
and node[1] != parent
and indirect_parents.count(node[1] ) > 0
and not on_the_way_back
):
SCREAMING_SNAKE_CASE : Optional[Any] = len(lowerCamelCase_ ) - 1
while len_stack_minus_one >= 0:
if stack[len_stack_minus_one] == node[1]:
anticipating_nodes.add(node[1] )
break
else:
return True
if visited.count(node[1] ) < 1:
stack.append(node[1] )
visited.append(node[1] )
SCREAMING_SNAKE_CASE : Tuple = node[1]
break
# check if all the children are visited
if s == ss:
stack.pop()
SCREAMING_SNAKE_CASE : Any = True
if len(lowerCamelCase_ ) != 0:
SCREAMING_SNAKE_CASE : str = stack[len(lowerCamelCase_ ) - 1]
else:
SCREAMING_SNAKE_CASE : Optional[Any] = False
indirect_parents.append(lowerCamelCase_ )
SCREAMING_SNAKE_CASE : Any = s
SCREAMING_SNAKE_CASE : Tuple = ss
# check if se have reached the starting point
if len(lowerCamelCase_ ) == 0:
return False
def lowerCamelCase_ ( self : List[Any] ):
'''simple docstring'''
return list(self.graph )
def lowerCamelCase_ ( self : str , lowerCamelCase_ : List[str]=-2 , lowerCamelCase_ : str=-1 ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : str = time()
self.dfs(lowerCamelCase_ , lowerCamelCase_ )
SCREAMING_SNAKE_CASE : Tuple = time()
return end - begin
def lowerCamelCase_ ( self : Tuple , lowerCamelCase_ : Dict=-2 ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Optional[Any] = time()
self.bfs(lowerCamelCase_ )
SCREAMING_SNAKE_CASE : Dict = time()
return end - begin
| 79 | 0 |
'''simple docstring'''
from .data_collator import (
DataCollatorForLanguageModeling,
DataCollatorForPermutationLanguageModeling,
DataCollatorForSeqaSeq,
DataCollatorForSOP,
DataCollatorForTokenClassification,
DataCollatorForWholeWordMask,
DataCollatorWithPadding,
DefaultDataCollator,
default_data_collator,
)
from .metrics import glue_compute_metrics, xnli_compute_metrics
from .processors import (
DataProcessor,
InputExample,
InputFeatures,
SingleSentenceClassificationProcessor,
SquadExample,
SquadFeatures,
SquadVaProcessor,
SquadVaProcessor,
glue_convert_examples_to_features,
glue_output_modes,
glue_processors,
glue_tasks_num_labels,
squad_convert_examples_to_features,
xnli_output_modes,
xnli_processors,
xnli_tasks_num_labels,
)
| 701 |
'''simple docstring'''
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
__UpperCAmelCase = logging.get_logger(__name__)
__UpperCAmelCase = {"""vocab_file""": """sentencepiece.bpe.model"""}
__UpperCAmelCase = {
"""vocab_file""": {
"""moussaKam/mbarthez""": """https://huggingface.co/moussaKam/mbarthez/resolve/main/sentencepiece.bpe.model""",
"""moussaKam/barthez""": """https://huggingface.co/moussaKam/barthez/resolve/main/sentencepiece.bpe.model""",
"""moussaKam/barthez-orangesum-title""": (
"""https://huggingface.co/moussaKam/barthez-orangesum-title/resolve/main/sentencepiece.bpe.model"""
),
},
}
__UpperCAmelCase = {
"""moussaKam/mbarthez""": 1024,
"""moussaKam/barthez""": 1024,
"""moussaKam/barthez-orangesum-title""": 1024,
}
__UpperCAmelCase = """▁"""
class UpperCamelCase__ ( lowercase_ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE__ = VOCAB_FILES_NAMES
SCREAMING_SNAKE_CASE__ = PRETRAINED_VOCAB_FILES_MAP
SCREAMING_SNAKE_CASE__ = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
SCREAMING_SNAKE_CASE__ = ['''input_ids''', '''attention_mask''']
def __init__( self : Optional[Any] , lowerCamelCase_ : List[str] , lowerCamelCase_ : Tuple="<s>" , lowerCamelCase_ : Union[str, Any]="</s>" , lowerCamelCase_ : Tuple="</s>" , lowerCamelCase_ : str="<s>" , lowerCamelCase_ : Optional[int]="<unk>" , lowerCamelCase_ : List[Any]="<pad>" , lowerCamelCase_ : Optional[Any]="<mask>" , lowerCamelCase_ : Optional[Dict[str, Any]] = None , **lowerCamelCase_ : List[str] , ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : List[str] = AddedToken(lowerCamelCase_ , lstrip=lowerCamelCase_ , rstrip=lowerCamelCase_ ) if isinstance(lowerCamelCase_ , lowerCamelCase_ ) else mask_token
SCREAMING_SNAKE_CASE : Union[str, Any] = {} if sp_model_kwargs is None else sp_model_kwargs
super().__init__(
bos_token=lowerCamelCase_ , eos_token=lowerCamelCase_ , unk_token=lowerCamelCase_ , sep_token=lowerCamelCase_ , cls_token=lowerCamelCase_ , pad_token=lowerCamelCase_ , mask_token=lowerCamelCase_ , sp_model_kwargs=self.sp_model_kwargs , **lowerCamelCase_ , )
SCREAMING_SNAKE_CASE : Dict = vocab_file
SCREAMING_SNAKE_CASE : Dict = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(str(lowerCamelCase_ ) )
SCREAMING_SNAKE_CASE : Optional[int] = {"""<s>""": 0, """<pad>""": 1, """</s>""": 2, """<unk>""": 3}
SCREAMING_SNAKE_CASE : str = len(self.sp_model ) - 1
SCREAMING_SNAKE_CASE : Optional[int] = {v: k for k, v in self.fairseq_tokens_to_ids.items()}
def lowerCamelCase_ ( self : List[str] , lowerCamelCase_ : List[int] , lowerCamelCase_ : 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 : List[Any] = [self.cls_token_id]
SCREAMING_SNAKE_CASE : int = [self.sep_token_id]
return cls + token_ids_a + sep + sep + token_ids_a + sep
def lowerCamelCase_ ( self : Any , lowerCamelCase_ : List[int] , lowerCamelCase_ : Optional[List[int]] = None , lowerCamelCase_ : bool = False ):
'''simple docstring'''
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=lowerCamelCase_ , token_ids_a=lowerCamelCase_ , already_has_special_tokens=lowerCamelCase_ )
if token_ids_a is None:
return [1] + ([0] * len(lowerCamelCase_ )) + [1]
return [1] + ([0] * len(lowerCamelCase_ )) + [1, 1] + ([0] * len(lowerCamelCase_ )) + [1]
def lowerCamelCase_ ( self : str , lowerCamelCase_ : List[int] , lowerCamelCase_ : Optional[List[int]] = None ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : List[str] = [self.sep_token_id]
SCREAMING_SNAKE_CASE : Dict = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep + sep + token_ids_a + sep ) * [0]
@property
def lowerCamelCase_ ( self : List[str] ):
'''simple docstring'''
return len(self.sp_model )
def lowerCamelCase_ ( self : Optional[int] ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : List[str] = {self.convert_ids_to_tokens(lowerCamelCase_ ): i for i in range(self.vocab_size )}
vocab.update(self.added_tokens_encoder )
return vocab
def lowerCamelCase_ ( self : Union[str, Any] , lowerCamelCase_ : str ):
'''simple docstring'''
return self.sp_model.encode(lowerCamelCase_ , out_type=lowerCamelCase_ )
def lowerCamelCase_ ( self : Tuple , lowerCamelCase_ : str ):
'''simple docstring'''
if token in self.fairseq_tokens_to_ids:
return self.fairseq_tokens_to_ids[token]
SCREAMING_SNAKE_CASE : List[str] = self.sp_model.PieceToId(lowerCamelCase_ )
return spm_id if spm_id else self.unk_token_id
def lowerCamelCase_ ( self : int , lowerCamelCase_ : List[str] ):
'''simple docstring'''
if index in self.fairseq_ids_to_tokens:
return self.fairseq_ids_to_tokens[index]
return self.sp_model.IdToPiece(lowerCamelCase_ )
def lowerCamelCase_ ( self : Optional[Any] , lowerCamelCase_ : Optional[Any] ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : str = []
SCREAMING_SNAKE_CASE : Tuple = """"""
SCREAMING_SNAKE_CASE : Dict = False
for token in tokens:
# make sure that special tokens are not decoded using sentencepiece model
if token in self.all_special_tokens:
if not prev_is_special:
out_string += " "
out_string += self.sp_model.decode(lowerCamelCase_ ) + token
SCREAMING_SNAKE_CASE : str = True
SCREAMING_SNAKE_CASE : Optional[Any] = []
else:
current_sub_tokens.append(lowerCamelCase_ )
SCREAMING_SNAKE_CASE : Optional[int] = False
out_string += self.sp_model.decode(lowerCamelCase_ )
return out_string.strip()
def __getstate__( self : Tuple ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Dict = self.__dict__.copy()
SCREAMING_SNAKE_CASE : List[Any] = None
return state
def __setstate__( self : Optional[Any] , lowerCamelCase_ : Tuple ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : str = d
# for backward compatibility
if not hasattr(self , """sp_model_kwargs""" ):
SCREAMING_SNAKE_CASE : int = {}
SCREAMING_SNAKE_CASE : Any = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(self.vocab_file )
def lowerCamelCase_ ( self : Any , lowerCamelCase_ : str , lowerCamelCase_ : Optional[str] = None ):
'''simple docstring'''
if not os.path.isdir(lowerCamelCase_ ):
logger.error(f'''Vocabulary path ({save_directory}) should be a directory''' )
return
SCREAMING_SNAKE_CASE : Dict = os.path.join(
lowerCamelCase_ , (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(lowerCamelCase_ ) and os.path.isfile(self.vocab_file ):
copyfile(self.vocab_file , lowerCamelCase_ )
elif not os.path.isfile(self.vocab_file ):
with open(lowerCamelCase_ , """wb""" ) as fi:
SCREAMING_SNAKE_CASE : Any = self.sp_model.serialized_model_proto()
fi.write(lowerCamelCase_ )
return (out_vocab_file,)
| 79 | 0 |
'''simple docstring'''
import unittest
from diffusers.pipelines.pipeline_utils import is_safetensors_compatible
class UpperCamelCase__ ( unittest.TestCase ):
"""simple docstring"""
def lowerCamelCase_ ( self : Union[str, Any] ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : List[Any] = [
"safety_checker/pytorch_model.bin",
"safety_checker/model.safetensors",
"vae/diffusion_pytorch_model.bin",
"vae/diffusion_pytorch_model.safetensors",
"text_encoder/pytorch_model.bin",
"text_encoder/model.safetensors",
"unet/diffusion_pytorch_model.bin",
"unet/diffusion_pytorch_model.safetensors",
]
self.assertTrue(is_safetensors_compatible(lowercase__ ) )
def lowerCamelCase_ ( self : Tuple ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : int = [
"unet/diffusion_pytorch_model.bin",
"unet/diffusion_pytorch_model.safetensors",
]
self.assertTrue(is_safetensors_compatible(lowercase__ ) )
def lowerCamelCase_ ( self : List[Any] ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : str = [
"safety_checker/pytorch_model.bin",
"safety_checker/model.safetensors",
"vae/diffusion_pytorch_model.bin",
"vae/diffusion_pytorch_model.safetensors",
"text_encoder/pytorch_model.bin",
"text_encoder/model.safetensors",
"unet/diffusion_pytorch_model.bin",
# Removed: 'unet/diffusion_pytorch_model.safetensors',
]
self.assertFalse(is_safetensors_compatible(lowercase__ ) )
def lowerCamelCase_ ( self : Dict ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Optional[int] = [
"text_encoder/pytorch_model.bin",
"text_encoder/model.safetensors",
]
self.assertTrue(is_safetensors_compatible(lowercase__ ) )
def lowerCamelCase_ ( self : Tuple ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Dict = [
"safety_checker/pytorch_model.bin",
"safety_checker/model.safetensors",
"vae/diffusion_pytorch_model.bin",
"vae/diffusion_pytorch_model.safetensors",
"text_encoder/pytorch_model.bin",
# Removed: 'text_encoder/model.safetensors',
"unet/diffusion_pytorch_model.bin",
"unet/diffusion_pytorch_model.safetensors",
]
self.assertFalse(is_safetensors_compatible(lowercase__ ) )
def lowerCamelCase_ ( self : int ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : List[str] = [
"safety_checker/pytorch_model.fp16.bin",
"safety_checker/model.fp16.safetensors",
"vae/diffusion_pytorch_model.fp16.bin",
"vae/diffusion_pytorch_model.fp16.safetensors",
"text_encoder/pytorch_model.fp16.bin",
"text_encoder/model.fp16.safetensors",
"unet/diffusion_pytorch_model.fp16.bin",
"unet/diffusion_pytorch_model.fp16.safetensors",
]
SCREAMING_SNAKE_CASE : Dict = "fp16"
self.assertTrue(is_safetensors_compatible(lowercase__ , variant=lowercase__ ) )
def lowerCamelCase_ ( self : Any ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Tuple = [
"unet/diffusion_pytorch_model.fp16.bin",
"unet/diffusion_pytorch_model.fp16.safetensors",
]
SCREAMING_SNAKE_CASE : str = "fp16"
self.assertTrue(is_safetensors_compatible(lowercase__ , variant=lowercase__ ) )
def lowerCamelCase_ ( self : Tuple ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Optional[Any] = [
"unet/diffusion_pytorch_model.bin",
"unet/diffusion_pytorch_model.safetensors",
]
SCREAMING_SNAKE_CASE : Optional[Any] = "fp16"
self.assertTrue(is_safetensors_compatible(lowercase__ , variant=lowercase__ ) )
def lowerCamelCase_ ( self : int ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : List[str] = [
"safety_checker/pytorch_model.fp16.bin",
"safety_checker/model.fp16.safetensors",
"vae/diffusion_pytorch_model.fp16.bin",
"vae/diffusion_pytorch_model.fp16.safetensors",
"text_encoder/pytorch_model.fp16.bin",
"text_encoder/model.fp16.safetensors",
"unet/diffusion_pytorch_model.fp16.bin",
# Removed: 'unet/diffusion_pytorch_model.fp16.safetensors',
]
SCREAMING_SNAKE_CASE : Union[str, Any] = "fp16"
self.assertFalse(is_safetensors_compatible(lowercase__ , variant=lowercase__ ) )
def lowerCamelCase_ ( self : List[Any] ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Dict = [
"text_encoder/pytorch_model.fp16.bin",
"text_encoder/model.fp16.safetensors",
]
SCREAMING_SNAKE_CASE : Optional[Any] = "fp16"
self.assertTrue(is_safetensors_compatible(lowercase__ , variant=lowercase__ ) )
def lowerCamelCase_ ( self : Dict ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Any = [
"text_encoder/pytorch_model.bin",
"text_encoder/model.safetensors",
]
SCREAMING_SNAKE_CASE : str = "fp16"
self.assertTrue(is_safetensors_compatible(lowercase__ , variant=lowercase__ ) )
def lowerCamelCase_ ( self : str ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Optional[int] = [
"safety_checker/pytorch_model.fp16.bin",
"safety_checker/model.fp16.safetensors",
"vae/diffusion_pytorch_model.fp16.bin",
"vae/diffusion_pytorch_model.fp16.safetensors",
"text_encoder/pytorch_model.fp16.bin",
# 'text_encoder/model.fp16.safetensors',
"unet/diffusion_pytorch_model.fp16.bin",
"unet/diffusion_pytorch_model.fp16.safetensors",
]
SCREAMING_SNAKE_CASE : Dict = "fp16"
self.assertFalse(is_safetensors_compatible(lowercase__ , variant=lowercase__ ) )
| 702 |
'''simple docstring'''
from __future__ import annotations
import unittest
from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
if is_tf_available():
import numpy as np
import tensorflow as tf
from transformers import TFXLMRobertaModel
@require_tf
@require_sentencepiece
@require_tokenizers
class UpperCamelCase__ ( unittest.TestCase ):
"""simple docstring"""
@slow
def lowerCamelCase_ ( self : Tuple ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Union[str, Any] = TFXLMRobertaModel.from_pretrained("""jplu/tf-xlm-roberta-base""" )
SCREAMING_SNAKE_CASE : Dict = {
"""input_ids""": tf.convert_to_tensor([[0, 26_46, 1_02_69, 83, 9_99_42, 2]] , dtype=tf.intaa ), # "My dog is cute"
"""attention_mask""": tf.convert_to_tensor([[1, 1, 1, 1, 1, 1]] , dtype=tf.intaa ),
}
SCREAMING_SNAKE_CASE : List[str] = model(lowerCamelCase_ )["""last_hidden_state"""]
SCREAMING_SNAKE_CASE : Union[str, Any] = tf.TensorShape((1, 6, 7_68) )
self.assertEqual(output.shape , lowerCamelCase_ )
# compare the actual values for a slice.
SCREAMING_SNAKE_CASE : Optional[int] = tf.convert_to_tensor(
[
[
[0.0_681_762, 0.10_894_451, 0.06_772_504],
[-0.06_423_668, 0.02_366_615, 0.04_329_344],
[-0.06_057_295, 0.09_974_135, -0.00_070_584],
]
] , dtype=tf.floataa , )
self.assertTrue(np.allclose(output[:, :3, :3].numpy() , expected_slice.numpy() , atol=1e-4 ) )
| 79 | 0 |
'''simple docstring'''
import logging
import os
import sys
from dataclasses import dataclass, field
from itertools import chain
from typing import Optional, Union
import datasets
import numpy as np
import torch
from datasets import load_dataset
import transformers
from transformers import (
AutoConfig,
AutoModelForMultipleChoice,
AutoTokenizer,
HfArgumentParser,
Trainer,
TrainingArguments,
default_data_collator,
set_seed,
)
from transformers.tokenization_utils_base import PreTrainedTokenizerBase
from transformers.trainer_utils import get_last_checkpoint
from transformers.utils import PaddingStrategy, check_min_version, send_example_telemetry
# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
check_min_version("""4.31.0""")
__UpperCAmelCase = logging.getLogger(__name__)
@dataclass
class UpperCamelCase__ :
"""simple docstring"""
SCREAMING_SNAKE_CASE__ = field(
metadata={'''help''': '''Path to pretrained model or model identifier from huggingface.co/models'''} )
SCREAMING_SNAKE_CASE__ = field(
default=lowercase_ , metadata={'''help''': '''Pretrained config name or path if not the same as model_name'''} )
SCREAMING_SNAKE_CASE__ = field(
default=lowercase_ , metadata={'''help''': '''Pretrained tokenizer name or path if not the same as model_name'''} )
SCREAMING_SNAKE_CASE__ = field(
default=lowercase_ , metadata={'''help''': '''Where do you want to store the pretrained models downloaded from huggingface.co'''} , )
SCREAMING_SNAKE_CASE__ = field(
default=lowercase_ , metadata={'''help''': '''Whether to use one of the fast tokenizer (backed by the tokenizers library) or not.'''} , )
SCREAMING_SNAKE_CASE__ = field(
default='''main''' , metadata={'''help''': '''The specific model version to use (can be a branch name, tag name or commit id).'''} , )
SCREAMING_SNAKE_CASE__ = field(
default=lowercase_ , metadata={
'''help''': (
'''Will use the token generated when running `huggingface-cli login` (necessary to use this script '''
'''with private models).'''
)
} , )
@dataclass
class UpperCamelCase__ :
"""simple docstring"""
SCREAMING_SNAKE_CASE__ = field(default=lowercase_ , metadata={'''help''': '''The input training data file (a text file).'''} )
SCREAMING_SNAKE_CASE__ = field(
default=lowercase_ , metadata={'''help''': '''An optional input evaluation data file to evaluate the perplexity on (a text file).'''} , )
SCREAMING_SNAKE_CASE__ = field(
default=lowercase_ , metadata={'''help''': '''Overwrite the cached training and evaluation sets'''} )
SCREAMING_SNAKE_CASE__ = field(
default=lowercase_ , metadata={'''help''': '''The number of processes to use for the preprocessing.'''} , )
SCREAMING_SNAKE_CASE__ = field(
default=lowercase_ , metadata={
'''help''': (
'''The maximum total input sequence length after tokenization. If passed, sequences longer '''
'''than this will be truncated, sequences shorter will be padded.'''
)
} , )
SCREAMING_SNAKE_CASE__ = field(
default=lowercase_ , metadata={
'''help''': (
'''Whether to pad all samples to the maximum sentence length. '''
'''If False, will pad the samples dynamically when batching to the maximum length in the batch. More '''
'''efficient on GPU but very bad for TPU.'''
)
} , )
SCREAMING_SNAKE_CASE__ = field(
default=lowercase_ , metadata={
'''help''': (
'''For debugging purposes or quicker training, truncate the number of training examples to this '''
'''value if set.'''
)
} , )
SCREAMING_SNAKE_CASE__ = field(
default=lowercase_ , metadata={
'''help''': (
'''For debugging purposes or quicker training, truncate the number of evaluation examples to this '''
'''value if set.'''
)
} , )
def lowerCamelCase_ ( self : Optional[Any] ):
'''simple docstring'''
if self.train_file is not None:
SCREAMING_SNAKE_CASE : Tuple = self.train_file.split(""".""" )[-1]
assert extension in ["csv", "json"], "`train_file` should be a csv or a json file."
if self.validation_file is not None:
SCREAMING_SNAKE_CASE : List[str] = self.validation_file.split(""".""" )[-1]
assert extension in ["csv", "json"], "`validation_file` should be a csv or a json file."
@dataclass
class UpperCamelCase__ :
"""simple docstring"""
SCREAMING_SNAKE_CASE__ = 42
SCREAMING_SNAKE_CASE__ = True
SCREAMING_SNAKE_CASE__ = None
SCREAMING_SNAKE_CASE__ = None
def __call__( self : List[str] , lowerCamelCase_ : Optional[int] ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Any = 'label' if 'label' in features[0].keys() else 'labels'
SCREAMING_SNAKE_CASE : str = [feature.pop(lowerCamelCase_ ) for feature in features]
SCREAMING_SNAKE_CASE : Union[str, Any] = len(lowerCamelCase_ )
SCREAMING_SNAKE_CASE : Optional[Any] = len(features[0]["""input_ids"""] )
SCREAMING_SNAKE_CASE : str = [
[{k: v[i] for k, v in feature.items()} for i in range(lowerCamelCase_ )] for feature in features
]
SCREAMING_SNAKE_CASE : Optional[int] = list(chain(*lowerCamelCase_ ) )
SCREAMING_SNAKE_CASE : str = self.tokenizer.pad(
lowerCamelCase_ , padding=self.padding , max_length=self.max_length , pad_to_multiple_of=self.pad_to_multiple_of , return_tensors="""pt""" , )
# Un-flatten
SCREAMING_SNAKE_CASE : Optional[int] = {k: v.view(lowerCamelCase_ , lowerCamelCase_ , -1 ) for k, v in batch.items()}
# Add back labels
SCREAMING_SNAKE_CASE : int = torch.tensor(lowerCamelCase_ , dtype=torch.intaa )
return batch
def __A ( ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : Dict = 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 : Any = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1] ) )
else:
SCREAMING_SNAKE_CASE : List[Any] = parser.parse_args_into_dataclasses()
# Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The
# information sent is the one passed as arguments along with your Python/PyTorch versions.
send_example_telemetry("""run_swag""" , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
# Setup logging
logging.basicConfig(
format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s""" , datefmt="""%m/%d/%Y %H:%M:%S""" , handlers=[logging.StreamHandler(sys.stdout )] , )
if training_args.should_log:
# The default of training_args.log_level is passive, so we set log level at info here to have that default.
transformers.utils.logging.set_verbosity_info()
SCREAMING_SNAKE_CASE : Dict = training_args.get_process_log_level()
logger.setLevel(SCREAMING_SNAKE_CASE_ )
datasets.utils.logging.set_verbosity(SCREAMING_SNAKE_CASE_ )
transformers.utils.logging.set_verbosity(SCREAMING_SNAKE_CASE_ )
transformers.utils.logging.enable_default_handler()
transformers.utils.logging.enable_explicit_format()
# Log on each process the small summary:
logger.warning(
f'''Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}'''
+ f'''distributed training: {bool(training_args.local_rank != -1 )}, 16-bits training: {training_args.fpaa}''' )
logger.info(f'''Training/evaluation parameters {training_args}''' )
# Detecting last checkpoint.
SCREAMING_SNAKE_CASE : Tuple = None
if os.path.isdir(training_args.output_dir ) and training_args.do_train and not training_args.overwrite_output_dir:
SCREAMING_SNAKE_CASE : int = get_last_checkpoint(training_args.output_dir )
if last_checkpoint is None and len(os.listdir(training_args.output_dir ) ) > 0:
raise ValueError(
f'''Output directory ({training_args.output_dir}) already exists and is not empty. '''
"""Use --overwrite_output_dir to overcome.""" )
elif last_checkpoint is not None and training_args.resume_from_checkpoint is None:
logger.info(
f'''Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change '''
"""the `--output_dir` or add `--overwrite_output_dir` to train from scratch.""" )
# Set seed before initializing model.
set_seed(training_args.seed )
# Get the datasets: you can either provide your own CSV/JSON/TXT training and evaluation files (see below)
# or just provide the name of one of the public datasets available on the hub at https://huggingface.co/datasets/
# (the dataset will be downloaded automatically from the datasets Hub).
# For CSV/JSON files, this script will use the column called 'text' or the first column if no column called
# 'text' is found. You can easily tweak this behavior (see below).
# In distributed training, the load_dataset function guarantee that only one local process can concurrently
# download the dataset.
if data_args.train_file is not None or data_args.validation_file is not None:
SCREAMING_SNAKE_CASE : Union[str, Any] = {}
if data_args.train_file is not None:
SCREAMING_SNAKE_CASE : Union[str, Any] = data_args.train_file
if data_args.validation_file is not None:
SCREAMING_SNAKE_CASE : str = data_args.validation_file
SCREAMING_SNAKE_CASE : Union[str, Any] = data_args.train_file.split(""".""" )[-1]
SCREAMING_SNAKE_CASE : List[Any] = load_dataset(
SCREAMING_SNAKE_CASE_ , data_files=SCREAMING_SNAKE_CASE_ , cache_dir=model_args.cache_dir , use_auth_token=True if model_args.use_auth_token else None , )
else:
# Downloading and loading the swag dataset from the hub.
SCREAMING_SNAKE_CASE : Union[str, Any] = load_dataset(
"""swag""" , """regular""" , cache_dir=model_args.cache_dir , use_auth_token=True if model_args.use_auth_token else None , )
# See more about loading any type of standard or custom dataset (from files, python dict, pandas DataFrame, etc) at
# https://huggingface.co/docs/datasets/loading_datasets.html.
# 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 : List[str] = AutoConfig.from_pretrained(
model_args.config_name if model_args.config_name else model_args.model_name_or_path , cache_dir=model_args.cache_dir , revision=model_args.model_revision , use_auth_token=True if model_args.use_auth_token else None , )
SCREAMING_SNAKE_CASE : Optional[Any] = AutoTokenizer.from_pretrained(
model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path , cache_dir=model_args.cache_dir , use_fast=model_args.use_fast_tokenizer , revision=model_args.model_revision , use_auth_token=True if model_args.use_auth_token else None , )
SCREAMING_SNAKE_CASE : Tuple = AutoModelForMultipleChoice.from_pretrained(
model_args.model_name_or_path , from_tf=bool(""".ckpt""" in model_args.model_name_or_path ) , config=SCREAMING_SNAKE_CASE_ , cache_dir=model_args.cache_dir , revision=model_args.model_revision , use_auth_token=True if model_args.use_auth_token else None , )
# When using your own dataset or a different dataset from swag, you will probably need to change this.
SCREAMING_SNAKE_CASE : Dict = [f'''ending{i}''' for i in range(4 )]
SCREAMING_SNAKE_CASE : Any = 'sent1'
SCREAMING_SNAKE_CASE : Dict = 'sent2'
if data_args.max_seq_length is None:
SCREAMING_SNAKE_CASE : List[str] = tokenizer.model_max_length
if max_seq_length > 10_24:
logger.warning(
"""The chosen tokenizer supports a `model_max_length` that is longer than the default `block_size` value"""
""" of 1024. If you would like to use a longer `block_size` up to `tokenizer.model_max_length` you can"""
""" override this default with `--block_size xxx`.""" )
SCREAMING_SNAKE_CASE : Tuple = 10_24
else:
if data_args.max_seq_length > tokenizer.model_max_length:
logger.warning(
f'''The max_seq_length passed ({data_args.max_seq_length}) is larger than the maximum length for the'''
f'''model ({tokenizer.model_max_length}). Using max_seq_length={tokenizer.model_max_length}.''' )
SCREAMING_SNAKE_CASE : List[Any] = min(data_args.max_seq_length , tokenizer.model_max_length )
# Preprocessing the datasets.
def preprocess_function(lowerCamelCase_ ):
SCREAMING_SNAKE_CASE : List[str] = [[context] * 4 for context in examples[context_name]]
SCREAMING_SNAKE_CASE : int = examples[question_header_name]
SCREAMING_SNAKE_CASE : List[str] = [
[f'''{header} {examples[end][i]}''' for end in ending_names] for i, header in enumerate(SCREAMING_SNAKE_CASE_ )
]
# Flatten out
SCREAMING_SNAKE_CASE : Union[str, Any] = list(chain(*SCREAMING_SNAKE_CASE_ ) )
SCREAMING_SNAKE_CASE : Optional[Any] = list(chain(*SCREAMING_SNAKE_CASE_ ) )
# Tokenize
SCREAMING_SNAKE_CASE : Union[str, Any] = tokenizer(
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , truncation=SCREAMING_SNAKE_CASE_ , max_length=SCREAMING_SNAKE_CASE_ , padding="""max_length""" if data_args.pad_to_max_length else False , )
# Un-flatten
return {k: [v[i : i + 4] for i in range(0 , len(SCREAMING_SNAKE_CASE_ ) , 4 )] for k, v in tokenized_examples.items()}
if training_args.do_train:
if "train" not in raw_datasets:
raise ValueError("""--do_train requires a train dataset""" )
SCREAMING_SNAKE_CASE : Dict = raw_datasets['train']
if data_args.max_train_samples is not None:
SCREAMING_SNAKE_CASE : List[str] = min(len(SCREAMING_SNAKE_CASE_ ) , data_args.max_train_samples )
SCREAMING_SNAKE_CASE : str = train_dataset.select(range(SCREAMING_SNAKE_CASE_ ) )
with training_args.main_process_first(desc="""train dataset map pre-processing""" ):
SCREAMING_SNAKE_CASE : int = train_dataset.map(
SCREAMING_SNAKE_CASE_ , batched=SCREAMING_SNAKE_CASE_ , num_proc=data_args.preprocessing_num_workers , load_from_cache_file=not data_args.overwrite_cache , )
if training_args.do_eval:
if "validation" not in raw_datasets:
raise ValueError("""--do_eval requires a validation dataset""" )
SCREAMING_SNAKE_CASE : Any = raw_datasets['validation']
if data_args.max_eval_samples is not None:
SCREAMING_SNAKE_CASE : Optional[int] = min(len(SCREAMING_SNAKE_CASE_ ) , data_args.max_eval_samples )
SCREAMING_SNAKE_CASE : Dict = eval_dataset.select(range(SCREAMING_SNAKE_CASE_ ) )
with training_args.main_process_first(desc="""validation dataset map pre-processing""" ):
SCREAMING_SNAKE_CASE : str = eval_dataset.map(
SCREAMING_SNAKE_CASE_ , batched=SCREAMING_SNAKE_CASE_ , num_proc=data_args.preprocessing_num_workers , load_from_cache_file=not data_args.overwrite_cache , )
# Data collator
SCREAMING_SNAKE_CASE : List[str] = (
default_data_collator
if data_args.pad_to_max_length
else DataCollatorForMultipleChoice(tokenizer=SCREAMING_SNAKE_CASE_ , pad_to_multiple_of=8 if training_args.fpaa else None )
)
# Metric
def compute_metrics(lowerCamelCase_ ):
SCREAMING_SNAKE_CASE : Optional[Any] = eval_predictions
SCREAMING_SNAKE_CASE : Tuple = np.argmax(SCREAMING_SNAKE_CASE_ , axis=1 )
return {"accuracy": (preds == label_ids).astype(np.floataa ).mean().item()}
# Initialize our Trainer
SCREAMING_SNAKE_CASE : Optional[int] = Trainer(
model=SCREAMING_SNAKE_CASE_ , args=SCREAMING_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=SCREAMING_SNAKE_CASE_ , data_collator=SCREAMING_SNAKE_CASE_ , compute_metrics=SCREAMING_SNAKE_CASE_ , )
# Training
if training_args.do_train:
SCREAMING_SNAKE_CASE : str = None
if training_args.resume_from_checkpoint is not None:
SCREAMING_SNAKE_CASE : str = training_args.resume_from_checkpoint
elif last_checkpoint is not None:
SCREAMING_SNAKE_CASE : Union[str, Any] = last_checkpoint
SCREAMING_SNAKE_CASE : Optional[Any] = trainer.train(resume_from_checkpoint=SCREAMING_SNAKE_CASE_ )
trainer.save_model() # Saves the tokenizer too for easy upload
SCREAMING_SNAKE_CASE : Tuple = train_result.metrics
SCREAMING_SNAKE_CASE : List[str] = (
data_args.max_train_samples if data_args.max_train_samples is not None else len(SCREAMING_SNAKE_CASE_ )
)
SCREAMING_SNAKE_CASE : Optional[Any] = min(SCREAMING_SNAKE_CASE_ , len(SCREAMING_SNAKE_CASE_ ) )
trainer.log_metrics("""train""" , SCREAMING_SNAKE_CASE_ )
trainer.save_metrics("""train""" , SCREAMING_SNAKE_CASE_ )
trainer.save_state()
# Evaluation
if training_args.do_eval:
logger.info("""*** Evaluate ***""" )
SCREAMING_SNAKE_CASE : List[str] = trainer.evaluate()
SCREAMING_SNAKE_CASE : Optional[int] = data_args.max_eval_samples if data_args.max_eval_samples is not None else len(SCREAMING_SNAKE_CASE_ )
SCREAMING_SNAKE_CASE : Optional[int] = min(SCREAMING_SNAKE_CASE_ , len(SCREAMING_SNAKE_CASE_ ) )
trainer.log_metrics("""eval""" , SCREAMING_SNAKE_CASE_ )
trainer.save_metrics("""eval""" , SCREAMING_SNAKE_CASE_ )
SCREAMING_SNAKE_CASE : Any = {
'finetuned_from': model_args.model_name_or_path,
'tasks': 'multiple-choice',
'dataset_tags': 'swag',
'dataset_args': 'regular',
'dataset': 'SWAG',
'language': 'en',
}
if training_args.push_to_hub:
trainer.push_to_hub(**SCREAMING_SNAKE_CASE_ )
else:
trainer.create_model_card(**SCREAMING_SNAKE_CASE_ )
def __A ( lowerCamelCase_ ):
"""simple docstring"""
main()
if __name__ == "__main__":
main()
| 703 |
'''simple docstring'''
from dataclasses import dataclass, field
from typing import TYPE_CHECKING, Any, ClassVar, Dict, List, Optional, Union
import pyarrow as pa
if TYPE_CHECKING:
from .features import FeatureType
@dataclass
class UpperCamelCase__ :
"""simple docstring"""
SCREAMING_SNAKE_CASE__ = 42
SCREAMING_SNAKE_CASE__ = None
# Automatically constructed
SCREAMING_SNAKE_CASE__ = "dict"
SCREAMING_SNAKE_CASE__ = None
SCREAMING_SNAKE_CASE__ = field(default='''Translation''' , init=lowercase_ , repr=lowercase_ )
def __call__( self : int ):
'''simple docstring'''
return pa.struct({lang: pa.string() for lang in sorted(self.languages )} )
def lowerCamelCase_ ( self : Tuple ):
'''simple docstring'''
from .features import Value
return {k: Value("""string""" ) for k in sorted(self.languages )}
@dataclass
class UpperCamelCase__ :
"""simple docstring"""
SCREAMING_SNAKE_CASE__ = None
SCREAMING_SNAKE_CASE__ = None
SCREAMING_SNAKE_CASE__ = None
# Automatically constructed
SCREAMING_SNAKE_CASE__ = "dict"
SCREAMING_SNAKE_CASE__ = None
SCREAMING_SNAKE_CASE__ = field(default='''TranslationVariableLanguages''' , init=lowercase_ , repr=lowercase_ )
def lowerCamelCase_ ( self : Optional[int] ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Optional[Any] = sorted(set(self.languages ) ) if self.languages else None
SCREAMING_SNAKE_CASE : str = len(self.languages ) if self.languages else None
def __call__( self : Tuple ):
'''simple docstring'''
return pa.struct({"""language""": pa.list_(pa.string() ), """translation""": pa.list_(pa.string() )} )
def lowerCamelCase_ ( self : Optional[int] , lowerCamelCase_ : Optional[Any] ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Optional[Any] = set(self.languages )
if self.languages and set(lowerCamelCase_ ) - lang_set:
raise ValueError(
f'''Some languages in example ({", ".join(sorted(set(lowerCamelCase_ ) - lang_set ) )}) are not in valid set ({", ".join(lowerCamelCase_ )}).''' )
# Convert dictionary into tuples, splitting out cases where there are
# multiple translations for a single language.
SCREAMING_SNAKE_CASE : List[Any] = []
for lang, text in translation_dict.items():
if isinstance(lowerCamelCase_ , lowerCamelCase_ ):
translation_tuples.append((lang, text) )
else:
translation_tuples.extend([(lang, el) for el in text] )
# Ensure translations are in ascending order by language code.
SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE : Optional[Any] = zip(*sorted(lowerCamelCase_ ) )
return {"language": languages, "translation": translations}
def lowerCamelCase_ ( self : List[str] ):
'''simple docstring'''
from .features import Sequence, Value
return {
"language": Sequence(Value("""string""" ) ),
"translation": Sequence(Value("""string""" ) ),
}
| 79 | 0 |
'''simple docstring'''
from __future__ import annotations
from typing import Any
class UpperCamelCase__ ( __A ):
"""simple docstring"""
pass
class UpperCamelCase__ :
"""simple docstring"""
def __init__( self : Tuple , lowerCamelCase_ : Any ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Any = data
SCREAMING_SNAKE_CASE : Node | None = None
def __iter__( self : str ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : str = self
SCREAMING_SNAKE_CASE : str = []
while node:
if node in visited:
raise ContainsLoopError
visited.append(lowerCamelCase_ )
yield node.data
SCREAMING_SNAKE_CASE : int = node.next_node
@property
def lowerCamelCase_ ( self : Tuple ):
'''simple docstring'''
try:
list(self )
return False
except ContainsLoopError:
return True
if __name__ == "__main__":
__UpperCAmelCase = Node(1)
__UpperCAmelCase = Node(2)
__UpperCAmelCase = Node(3)
__UpperCAmelCase = Node(4)
print(root_node.has_loop) # False
__UpperCAmelCase = root_node.next_node
print(root_node.has_loop) # True
__UpperCAmelCase = Node(5)
__UpperCAmelCase = Node(6)
__UpperCAmelCase = Node(5)
__UpperCAmelCase = Node(6)
print(root_node.has_loop) # False
__UpperCAmelCase = Node(1)
print(root_node.has_loop) # False
| 704 |
'''simple docstring'''
import warnings
from ...utils import logging
from .image_processing_flava import FlavaImageProcessor
__UpperCAmelCase = logging.get_logger(__name__)
class UpperCamelCase__ ( lowercase_ ):
"""simple docstring"""
def __init__( self : Dict , *lowerCamelCase_ : List[str] , **lowerCamelCase_ : Dict ):
'''simple docstring'''
warnings.warn(
"""The class FlavaFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please"""
""" use FlavaImageProcessor instead.""" , lowerCamelCase_ , )
super().__init__(*lowerCamelCase_ , **lowerCamelCase_ )
| 79 | 0 |
'''simple docstring'''
import math
def __A ( lowerCamelCase_ = 1_00 ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : List[Any] = sum(i * i for i in range(1 , n + 1 ) )
SCREAMING_SNAKE_CASE : Optional[Any] = int(math.pow(sum(range(1 , n + 1 ) ) , 2 ) )
return square_of_sum - sum_of_squares
if __name__ == "__main__":
print(f'''{solution() = }''')
| 705 |
'''simple docstring'''
from .integrations import (
is_optuna_available,
is_ray_available,
is_sigopt_available,
is_wandb_available,
run_hp_search_optuna,
run_hp_search_ray,
run_hp_search_sigopt,
run_hp_search_wandb,
)
from .trainer_utils import (
HPSearchBackend,
default_hp_space_optuna,
default_hp_space_ray,
default_hp_space_sigopt,
default_hp_space_wandb,
)
from .utils import logging
__UpperCAmelCase = logging.get_logger(__name__)
class UpperCamelCase__ :
"""simple docstring"""
SCREAMING_SNAKE_CASE__ = 42
SCREAMING_SNAKE_CASE__ = None
@staticmethod
def lowerCamelCase_ ( ):
'''simple docstring'''
raise NotImplementedError
def lowerCamelCase_ ( self : Tuple , lowerCamelCase_ : Optional[int] , lowerCamelCase_ : int , lowerCamelCase_ : str , **lowerCamelCase_ : str ):
'''simple docstring'''
raise NotImplementedError
def lowerCamelCase_ ( self : int , lowerCamelCase_ : Dict ):
'''simple docstring'''
raise NotImplementedError
def lowerCamelCase_ ( self : Optional[int] ):
'''simple docstring'''
if not self.is_available():
raise RuntimeError(
f'''You picked the {self.name} backend, but it is not installed. Run {self.pip_install()}.''' )
@classmethod
def lowerCamelCase_ ( cls : Any ):
'''simple docstring'''
return f'''`pip install {cls.pip_package or cls.name}`'''
class UpperCamelCase__ ( lowercase_ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE__ = '''optuna'''
@staticmethod
def lowerCamelCase_ ( ):
'''simple docstring'''
return is_optuna_available()
def lowerCamelCase_ ( self : Any , lowerCamelCase_ : Union[str, Any] , lowerCamelCase_ : int , lowerCamelCase_ : str , **lowerCamelCase_ : Dict ):
'''simple docstring'''
return run_hp_search_optuna(lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ , **lowerCamelCase_ )
def lowerCamelCase_ ( self : str , lowerCamelCase_ : Any ):
'''simple docstring'''
return default_hp_space_optuna(lowerCamelCase_ )
class UpperCamelCase__ ( lowercase_ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE__ = '''ray'''
SCREAMING_SNAKE_CASE__ = '''\'ray[tune]\''''
@staticmethod
def lowerCamelCase_ ( ):
'''simple docstring'''
return is_ray_available()
def lowerCamelCase_ ( self : Any , lowerCamelCase_ : Any , lowerCamelCase_ : int , lowerCamelCase_ : str , **lowerCamelCase_ : int ):
'''simple docstring'''
return run_hp_search_ray(lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ , **lowerCamelCase_ )
def lowerCamelCase_ ( self : List[str] , lowerCamelCase_ : Optional[int] ):
'''simple docstring'''
return default_hp_space_ray(lowerCamelCase_ )
class UpperCamelCase__ ( lowercase_ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE__ = '''sigopt'''
@staticmethod
def lowerCamelCase_ ( ):
'''simple docstring'''
return is_sigopt_available()
def lowerCamelCase_ ( self : Optional[Any] , lowerCamelCase_ : Any , lowerCamelCase_ : int , lowerCamelCase_ : str , **lowerCamelCase_ : int ):
'''simple docstring'''
return run_hp_search_sigopt(lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ , **lowerCamelCase_ )
def lowerCamelCase_ ( self : List[Any] , lowerCamelCase_ : Optional[Any] ):
'''simple docstring'''
return default_hp_space_sigopt(lowerCamelCase_ )
class UpperCamelCase__ ( lowercase_ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE__ = '''wandb'''
@staticmethod
def lowerCamelCase_ ( ):
'''simple docstring'''
return is_wandb_available()
def lowerCamelCase_ ( self : Union[str, Any] , lowerCamelCase_ : str , lowerCamelCase_ : int , lowerCamelCase_ : str , **lowerCamelCase_ : Optional[Any] ):
'''simple docstring'''
return run_hp_search_wandb(lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ , **lowerCamelCase_ )
def lowerCamelCase_ ( self : Union[str, Any] , lowerCamelCase_ : Tuple ):
'''simple docstring'''
return default_hp_space_wandb(lowerCamelCase_ )
__UpperCAmelCase = {
HPSearchBackend(backend.name): backend for backend in [OptunaBackend, RayTuneBackend, SigOptBackend, WandbBackend]
}
def __A ( ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : List[Any] = [backend for backend in ALL_HYPERPARAMETER_SEARCH_BACKENDS.values() if backend.is_available()]
if len(lowerCamelCase_ ) > 0:
SCREAMING_SNAKE_CASE : List[Any] = available_backends[0].name
if len(lowerCamelCase_ ) > 1:
logger.info(
f'''{len(lowerCamelCase_ )} hyperparameter search backends available. Using {name} as the default.''' )
return name
raise RuntimeError(
"""No hyperparameter search backend available.\n"""
+ """\n""".join(
f''' - To install {backend.name} run {backend.pip_install()}'''
for backend in ALL_HYPERPARAMETER_SEARCH_BACKENDS.values() ) )
| 79 | 0 |
'''simple docstring'''
import unittest
from diffusers.models.unet_ad_blocks import * # noqa F403
from diffusers.utils import torch_device
from .test_unet_blocks_common import UNetBlockTesterMixin
class UpperCamelCase__ ( __a , unittest.TestCase ):
"""simple docstring"""
SCREAMING_SNAKE_CASE__ = DownBlockaD # noqa F405
SCREAMING_SNAKE_CASE__ = '''down'''
def lowerCamelCase_ ( self : Dict ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Tuple = [-0.0_232, -0.9_869, 0.8_054, -0.0_637, -0.1_688, -1.4_264, 0.4_470, -1.3_394, 0.0_904]
super().test_output(snake_case__ )
class UpperCamelCase__ ( __a , unittest.TestCase ):
"""simple docstring"""
SCREAMING_SNAKE_CASE__ = ResnetDownsampleBlockaD # noqa F405
SCREAMING_SNAKE_CASE__ = '''down'''
def lowerCamelCase_ ( self : Dict ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Dict = [0.0_710, 0.2_410, -0.7_320, -1.0_757, -1.1_343, 0.3_540, -0.0_133, -0.2_576, 0.0_948]
super().test_output(snake_case__ )
class UpperCamelCase__ ( __a , unittest.TestCase ):
"""simple docstring"""
SCREAMING_SNAKE_CASE__ = AttnDownBlockaD # noqa F405
SCREAMING_SNAKE_CASE__ = '''down'''
def lowerCamelCase_ ( self : Dict ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Union[str, Any] = [0.0_636, 0.8_964, -0.6_234, -1.0_131, 0.0_844, 0.4_935, 0.3_437, 0.0_911, -0.2_957]
super().test_output(snake_case__ )
class UpperCamelCase__ ( __a , unittest.TestCase ):
"""simple docstring"""
SCREAMING_SNAKE_CASE__ = CrossAttnDownBlockaD # noqa F405
SCREAMING_SNAKE_CASE__ = '''down'''
def lowerCamelCase_ ( self : List[Any] ):
'''simple docstring'''
SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE : str = super().prepare_init_args_and_inputs_for_common()
SCREAMING_SNAKE_CASE : List[str] = 32
return init_dict, inputs_dict
def lowerCamelCase_ ( self : int ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : str = [0.2_238, -0.7_396, -0.2_255, -0.3_829, 0.1_925, 1.1_665, 0.0_603, -0.7_295, 0.1_983]
super().test_output(snake_case__ )
class UpperCamelCase__ ( __a , unittest.TestCase ):
"""simple docstring"""
SCREAMING_SNAKE_CASE__ = SimpleCrossAttnDownBlockaD # noqa F405
SCREAMING_SNAKE_CASE__ = '''down'''
@property
def lowerCamelCase_ ( self : Dict ):
'''simple docstring'''
return super().get_dummy_input(include_encoder_hidden_states=snake_case__ )
def lowerCamelCase_ ( self : Optional[Any] ):
'''simple docstring'''
SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE : Union[str, Any] = super().prepare_init_args_and_inputs_for_common()
SCREAMING_SNAKE_CASE : Tuple = 32
return init_dict, inputs_dict
@unittest.skipIf(torch_device == """mps""" , """MPS result is not consistent""" )
def lowerCamelCase_ ( self : Dict ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Optional[int] = [0.7_921, -0.0_992, -0.1_962, -0.7_695, -0.4_242, 0.7_804, 0.4_737, 0.2_765, 0.3_338]
super().test_output(snake_case__ )
class UpperCamelCase__ ( __a , unittest.TestCase ):
"""simple docstring"""
SCREAMING_SNAKE_CASE__ = SkipDownBlockaD # noqa F405
SCREAMING_SNAKE_CASE__ = '''down'''
@property
def lowerCamelCase_ ( self : Optional[Any] ):
'''simple docstring'''
return super().get_dummy_input(include_skip_sample=snake_case__ )
def lowerCamelCase_ ( self : Union[str, Any] ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Optional[Any] = [-0.0_845, -0.2_087, -0.2_465, 0.0_971, 0.1_900, -0.0_484, 0.2_664, 0.4_179, 0.5_069]
super().test_output(snake_case__ )
class UpperCamelCase__ ( __a , unittest.TestCase ):
"""simple docstring"""
SCREAMING_SNAKE_CASE__ = AttnSkipDownBlockaD # noqa F405
SCREAMING_SNAKE_CASE__ = '''down'''
@property
def lowerCamelCase_ ( self : Optional[int] ):
'''simple docstring'''
return super().get_dummy_input(include_skip_sample=snake_case__ )
def lowerCamelCase_ ( self : Optional[int] ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Union[str, Any] = [0.5_539, 0.1_609, 0.4_924, 0.0_537, -0.1_995, 0.4_050, 0.0_979, -0.2_721, -0.0_642]
super().test_output(snake_case__ )
class UpperCamelCase__ ( __a , unittest.TestCase ):
"""simple docstring"""
SCREAMING_SNAKE_CASE__ = DownEncoderBlockaD # noqa F405
SCREAMING_SNAKE_CASE__ = '''down'''
@property
def lowerCamelCase_ ( self : Optional[Any] ):
'''simple docstring'''
return super().get_dummy_input(include_temb=snake_case__ )
def lowerCamelCase_ ( self : Any ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Union[str, Any] = {
"""in_channels""": 32,
"""out_channels""": 32,
}
SCREAMING_SNAKE_CASE : str = self.dummy_input
return init_dict, inputs_dict
def lowerCamelCase_ ( self : Union[str, Any] ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : List[str] = [1.1_102, 0.5_302, 0.4_872, -0.0_023, -0.8_042, 0.0_483, -0.3_489, -0.5_632, 0.7_626]
super().test_output(snake_case__ )
class UpperCamelCase__ ( __a , unittest.TestCase ):
"""simple docstring"""
SCREAMING_SNAKE_CASE__ = AttnDownEncoderBlockaD # noqa F405
SCREAMING_SNAKE_CASE__ = '''down'''
@property
def lowerCamelCase_ ( self : Any ):
'''simple docstring'''
return super().get_dummy_input(include_temb=snake_case__ )
def lowerCamelCase_ ( self : Union[str, Any] ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Union[str, Any] = {
"""in_channels""": 32,
"""out_channels""": 32,
}
SCREAMING_SNAKE_CASE : Dict = self.dummy_input
return init_dict, inputs_dict
def lowerCamelCase_ ( self : Dict ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Union[str, Any] = [0.8_966, -0.1_486, 0.8_568, 0.8_141, -0.9_046, -0.1_342, -0.0_972, -0.7_417, 0.1_538]
super().test_output(snake_case__ )
class UpperCamelCase__ ( __a , unittest.TestCase ):
"""simple docstring"""
SCREAMING_SNAKE_CASE__ = UNetMidBlockaD # noqa F405
SCREAMING_SNAKE_CASE__ = '''mid'''
def lowerCamelCase_ ( self : List[str] ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : str = {
"""in_channels""": 32,
"""temb_channels""": 1_28,
}
SCREAMING_SNAKE_CASE : Optional[int] = self.dummy_input
return init_dict, inputs_dict
def lowerCamelCase_ ( self : int ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Optional[Any] = [-0.1_062, 1.7_248, 0.3_494, 1.4_569, -0.0_910, -1.2_421, -0.9_984, 0.6_736, 1.0_028]
super().test_output(snake_case__ )
class UpperCamelCase__ ( __a , unittest.TestCase ):
"""simple docstring"""
SCREAMING_SNAKE_CASE__ = UNetMidBlockaDCrossAttn # noqa F405
SCREAMING_SNAKE_CASE__ = '''mid'''
def lowerCamelCase_ ( self : int ):
'''simple docstring'''
SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE : Any = super().prepare_init_args_and_inputs_for_common()
SCREAMING_SNAKE_CASE : Union[str, Any] = 32
return init_dict, inputs_dict
def lowerCamelCase_ ( self : Tuple ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Union[str, Any] = [0.0_187, 2.4_220, 0.4_484, 1.1_203, -0.6_121, -1.5_122, -0.8_270, 0.7_851, 1.8_335]
super().test_output(snake_case__ )
class UpperCamelCase__ ( __a , unittest.TestCase ):
"""simple docstring"""
SCREAMING_SNAKE_CASE__ = UNetMidBlockaDSimpleCrossAttn # noqa F405
SCREAMING_SNAKE_CASE__ = '''mid'''
@property
def lowerCamelCase_ ( self : Optional[Any] ):
'''simple docstring'''
return super().get_dummy_input(include_encoder_hidden_states=snake_case__ )
def lowerCamelCase_ ( self : Dict ):
'''simple docstring'''
SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE : str = super().prepare_init_args_and_inputs_for_common()
SCREAMING_SNAKE_CASE : Optional[Any] = 32
return init_dict, inputs_dict
def lowerCamelCase_ ( self : str ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : int = [0.7_143, 1.9_974, 0.5_448, 1.3_977, 0.1_282, -1.1_237, -1.4_238, 0.5_530, 0.8_880]
super().test_output(snake_case__ )
class UpperCamelCase__ ( __a , unittest.TestCase ):
"""simple docstring"""
SCREAMING_SNAKE_CASE__ = UpBlockaD # noqa F405
SCREAMING_SNAKE_CASE__ = '''up'''
@property
def lowerCamelCase_ ( self : List[Any] ):
'''simple docstring'''
return super().get_dummy_input(include_res_hidden_states_tuple=snake_case__ )
def lowerCamelCase_ ( self : Optional[int] ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Union[str, Any] = [-0.2_041, -0.4_165, -0.3_022, 0.0_041, -0.6_628, -0.7_053, 0.1_928, -0.0_325, 0.0_523]
super().test_output(snake_case__ )
class UpperCamelCase__ ( __a , unittest.TestCase ):
"""simple docstring"""
SCREAMING_SNAKE_CASE__ = ResnetUpsampleBlockaD # noqa F405
SCREAMING_SNAKE_CASE__ = '''up'''
@property
def lowerCamelCase_ ( self : Optional[int] ):
'''simple docstring'''
return super().get_dummy_input(include_res_hidden_states_tuple=snake_case__ )
def lowerCamelCase_ ( self : List[Any] ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : int = [0.2_287, 0.3_549, -0.1_346, 0.4_797, -0.1_715, -0.9_649, 0.7_305, -0.5_864, -0.6_244]
super().test_output(snake_case__ )
class UpperCamelCase__ ( __a , unittest.TestCase ):
"""simple docstring"""
SCREAMING_SNAKE_CASE__ = CrossAttnUpBlockaD # noqa F405
SCREAMING_SNAKE_CASE__ = '''up'''
@property
def lowerCamelCase_ ( self : int ):
'''simple docstring'''
return super().get_dummy_input(include_res_hidden_states_tuple=snake_case__ )
def lowerCamelCase_ ( self : Any ):
'''simple docstring'''
SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE : str = super().prepare_init_args_and_inputs_for_common()
SCREAMING_SNAKE_CASE : Dict = 32
return init_dict, inputs_dict
def lowerCamelCase_ ( self : Dict ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Optional[Any] = [-0.1_403, -0.3_515, -0.0_420, -0.1_425, 0.3_167, 0.5_094, -0.2_181, 0.5_931, 0.5_582]
super().test_output(snake_case__ )
class UpperCamelCase__ ( __a , unittest.TestCase ):
"""simple docstring"""
SCREAMING_SNAKE_CASE__ = SimpleCrossAttnUpBlockaD # noqa F405
SCREAMING_SNAKE_CASE__ = '''up'''
@property
def lowerCamelCase_ ( self : List[str] ):
'''simple docstring'''
return super().get_dummy_input(include_res_hidden_states_tuple=snake_case__ , include_encoder_hidden_states=snake_case__ )
def lowerCamelCase_ ( self : Optional[Any] ):
'''simple docstring'''
SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE : str = super().prepare_init_args_and_inputs_for_common()
SCREAMING_SNAKE_CASE : Dict = 32
return init_dict, inputs_dict
def lowerCamelCase_ ( self : Optional[int] ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Union[str, Any] = [0.2_645, 0.1_480, 0.0_909, 0.8_044, -0.9_758, -0.9_083, 0.0_994, -1.1_453, -0.7_402]
super().test_output(snake_case__ )
class UpperCamelCase__ ( __a , unittest.TestCase ):
"""simple docstring"""
SCREAMING_SNAKE_CASE__ = AttnUpBlockaD # noqa F405
SCREAMING_SNAKE_CASE__ = '''up'''
@property
def lowerCamelCase_ ( self : Dict ):
'''simple docstring'''
return super().get_dummy_input(include_res_hidden_states_tuple=snake_case__ )
@unittest.skipIf(torch_device == """mps""" , """MPS result is not consistent""" )
def lowerCamelCase_ ( self : str ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : List[str] = [0.0_979, 0.1_326, 0.0_021, 0.0_659, 0.2_249, 0.0_059, 0.1_132, 0.5_952, 0.1_033]
super().test_output(snake_case__ )
class UpperCamelCase__ ( __a , unittest.TestCase ):
"""simple docstring"""
SCREAMING_SNAKE_CASE__ = SkipUpBlockaD # noqa F405
SCREAMING_SNAKE_CASE__ = '''up'''
@property
def lowerCamelCase_ ( self : Tuple ):
'''simple docstring'''
return super().get_dummy_input(include_res_hidden_states_tuple=snake_case__ )
def lowerCamelCase_ ( self : Optional[Any] ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : List[str] = [-0.0_893, -0.1_234, -0.1_506, -0.0_332, 0.0_123, -0.0_211, 0.0_566, 0.0_143, 0.0_362]
super().test_output(snake_case__ )
class UpperCamelCase__ ( __a , unittest.TestCase ):
"""simple docstring"""
SCREAMING_SNAKE_CASE__ = AttnSkipUpBlockaD # noqa F405
SCREAMING_SNAKE_CASE__ = '''up'''
@property
def lowerCamelCase_ ( self : List[str] ):
'''simple docstring'''
return super().get_dummy_input(include_res_hidden_states_tuple=snake_case__ )
def lowerCamelCase_ ( self : Optional[Any] ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Dict = [0.0_361, 0.0_617, 0.2_787, -0.0_350, 0.0_342, 0.3_421, -0.0_843, 0.0_913, 0.3_015]
super().test_output(snake_case__ )
class UpperCamelCase__ ( __a , unittest.TestCase ):
"""simple docstring"""
SCREAMING_SNAKE_CASE__ = UpDecoderBlockaD # noqa F405
SCREAMING_SNAKE_CASE__ = '''up'''
@property
def lowerCamelCase_ ( self : Any ):
'''simple docstring'''
return super().get_dummy_input(include_temb=snake_case__ )
def lowerCamelCase_ ( self : Dict ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : str = {"""in_channels""": 32, """out_channels""": 32}
SCREAMING_SNAKE_CASE : Dict = self.dummy_input
return init_dict, inputs_dict
def lowerCamelCase_ ( self : str ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : List[Any] = [0.4_404, 0.1_998, -0.9_886, -0.3_320, -0.3_128, -0.7_034, -0.6_955, -0.2_338, -0.3_137]
super().test_output(snake_case__ )
class UpperCamelCase__ ( __a , unittest.TestCase ):
"""simple docstring"""
SCREAMING_SNAKE_CASE__ = AttnUpDecoderBlockaD # noqa F405
SCREAMING_SNAKE_CASE__ = '''up'''
@property
def lowerCamelCase_ ( self : Dict ):
'''simple docstring'''
return super().get_dummy_input(include_temb=snake_case__ )
def lowerCamelCase_ ( self : Optional[int] ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Dict = {"""in_channels""": 32, """out_channels""": 32}
SCREAMING_SNAKE_CASE : Any = self.dummy_input
return init_dict, inputs_dict
def lowerCamelCase_ ( self : Any ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Optional[Any] = [0.6_738, 0.4_491, 0.1_055, 1.0_710, 0.7_316, 0.3_339, 0.3_352, 0.1_023, 0.3_568]
super().test_output(snake_case__ )
| 706 |
'''simple docstring'''
import glob
import os
import random
from string import ascii_lowercase, digits
import cva
__UpperCAmelCase = """"""
__UpperCAmelCase = """"""
__UpperCAmelCase = """"""
__UpperCAmelCase = 1 # (0 is vertical, 1 is horizontal)
def __A ( ):
"""simple docstring"""
SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE : str = get_dataset(lowerCamelCase_ , lowerCamelCase_ )
print("""Processing...""" )
SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE : Any = update_image_and_anno(lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ )
for index, image in enumerate(lowerCamelCase_ ):
# Get random string code: '7b7ad245cdff75241935e4dd860f3bad'
SCREAMING_SNAKE_CASE : Optional[int] = random_chars(32 )
SCREAMING_SNAKE_CASE : Optional[Any] = paths[index].split(os.sep )[-1].rsplit(""".""" , 1 )[0]
SCREAMING_SNAKE_CASE : Dict = f'''{OUTPUT_DIR}/{file_name}_FLIP_{letter_code}'''
cva.imwrite(f'''/{file_root}.jpg''' , lowerCamelCase_ , [cva.IMWRITE_JPEG_QUALITY, 85] )
print(f'''Success {index+1}/{len(lowerCamelCase_ )} with {file_name}''' )
SCREAMING_SNAKE_CASE : Optional[Any] = []
for anno in new_annos[index]:
SCREAMING_SNAKE_CASE : Optional[Any] = f'''{anno[0]} {anno[1]} {anno[2]} {anno[3]} {anno[4]}'''
annos_list.append(lowerCamelCase_ )
with open(f'''/{file_root}.txt''' , """w""" ) as outfile:
outfile.write("""\n""".join(line for line in annos_list ) )
def __A ( lowerCamelCase_ , lowerCamelCase_ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : str = []
SCREAMING_SNAKE_CASE : Any = []
for label_file in glob.glob(os.path.join(lowerCamelCase_ , """*.txt""" ) ):
SCREAMING_SNAKE_CASE : str = label_file.split(os.sep )[-1].rsplit(""".""" , 1 )[0]
with open(lowerCamelCase_ ) as in_file:
SCREAMING_SNAKE_CASE : Any = in_file.readlines()
SCREAMING_SNAKE_CASE : List[Any] = os.path.join(lowerCamelCase_ , f'''{label_name}.jpg''' )
SCREAMING_SNAKE_CASE : Tuple = []
for obj_list in obj_lists:
SCREAMING_SNAKE_CASE : Union[str, Any] = obj_list.rstrip("""\n""" ).split(""" """ )
boxes.append(
[
int(obj[0] ),
float(obj[1] ),
float(obj[2] ),
float(obj[3] ),
float(obj[4] ),
] )
if not boxes:
continue
img_paths.append(lowerCamelCase_ )
labels.append(lowerCamelCase_ )
return img_paths, labels
def __A ( lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ = 1 ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : Tuple = []
SCREAMING_SNAKE_CASE : List[str] = []
SCREAMING_SNAKE_CASE : Optional[Any] = []
for idx in range(len(lowerCamelCase_ ) ):
SCREAMING_SNAKE_CASE : List[Any] = []
SCREAMING_SNAKE_CASE : Dict = img_list[idx]
path_list.append(lowerCamelCase_ )
SCREAMING_SNAKE_CASE : int = anno_list[idx]
SCREAMING_SNAKE_CASE : Optional[Any] = cva.imread(lowerCamelCase_ )
if flip_type == 1:
SCREAMING_SNAKE_CASE : List[str] = cva.flip(lowerCamelCase_ , lowerCamelCase_ )
for bbox in img_annos:
SCREAMING_SNAKE_CASE : List[Any] = 1 - bbox[1]
new_annos.append([bbox[0], x_center_new, bbox[2], bbox[3], bbox[4]] )
elif flip_type == 0:
SCREAMING_SNAKE_CASE : Any = cva.flip(lowerCamelCase_ , lowerCamelCase_ )
for bbox in img_annos:
SCREAMING_SNAKE_CASE : Optional[Any] = 1 - bbox[2]
new_annos.append([bbox[0], bbox[1], y_center_new, bbox[3], bbox[4]] )
new_annos_lists.append(lowerCamelCase_ )
new_imgs_list.append(lowerCamelCase_ )
return new_imgs_list, new_annos_lists, path_list
def __A ( lowerCamelCase_ = 32 ):
"""simple docstring"""
assert number_char > 1, "The number of character should greater than 1"
SCREAMING_SNAKE_CASE : Dict = ascii_lowercase + digits
return "".join(random.choice(lowerCamelCase_ ) for _ in range(lowerCamelCase_ ) )
if __name__ == "__main__":
main()
print("""DONE ✅""")
| 79 | 0 |
'''simple docstring'''
from __future__ import annotations
# This is the precision for this function which can be altered.
# It is recommended for users to keep this number greater than or equal to 10.
__UpperCAmelCase = 10
def __A ( lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ ):
"""simple docstring"""
for i in range(lowerCAmelCase_ , lowerCAmelCase_ ):
if array[i] == target:
return i
return -1
def __A ( lowerCamelCase_ , lowerCamelCase_ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : str = 0
SCREAMING_SNAKE_CASE : List[Any] = len(lowerCAmelCase_ )
while left <= right:
if right - left < precision:
return lin_search(lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ )
SCREAMING_SNAKE_CASE : Union[str, Any] = (left + right) // 3 + 1
SCREAMING_SNAKE_CASE : Optional[int] = 2 * (left + right) // 3 + 1
if array[one_third] == target:
return one_third
elif array[two_third] == target:
return two_third
elif target < array[one_third]:
SCREAMING_SNAKE_CASE : int = one_third - 1
elif array[two_third] < target:
SCREAMING_SNAKE_CASE : Any = two_third + 1
else:
SCREAMING_SNAKE_CASE : int = one_third + 1
SCREAMING_SNAKE_CASE : Dict = two_third - 1
else:
return -1
def __A ( lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ ):
"""simple docstring"""
if left < right:
if right - left < precision:
return lin_search(lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ )
SCREAMING_SNAKE_CASE : Union[str, Any] = (left + right) // 3 + 1
SCREAMING_SNAKE_CASE : Any = 2 * (left + right) // 3 + 1
if array[one_third] == target:
return one_third
elif array[two_third] == target:
return two_third
elif target < array[one_third]:
return rec_ternary_search(lowerCAmelCase_ , one_third - 1 , lowerCAmelCase_ , lowerCAmelCase_ )
elif array[two_third] < target:
return rec_ternary_search(two_third + 1 , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ )
else:
return rec_ternary_search(one_third + 1 , two_third - 1 , lowerCAmelCase_ , lowerCAmelCase_ )
else:
return -1
if __name__ == "__main__":
import doctest
doctest.testmod()
__UpperCAmelCase = input("""Enter numbers separated by comma:\n""").strip()
__UpperCAmelCase = [int(item.strip()) for item in user_input.split(""",""")]
assert collection == sorted(collection), f"List must be ordered.\n{collection}."
__UpperCAmelCase = int(input("""Enter the number to be found in the list:\n""").strip())
__UpperCAmelCase = ite_ternary_search(collection, target)
__UpperCAmelCase = rec_ternary_search(0, len(collection) - 1, collection, target)
if resulta != -1:
print(f'''Iterative search: {target} found at positions: {resulta}''')
print(f'''Recursive search: {target} found at positions: {resulta}''')
else:
print("""Not found""")
| 707 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__UpperCAmelCase = logging.get_logger(__name__)
__UpperCAmelCase = {
"""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__ ( lowercase_ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE__ = '''vivit'''
def __init__( self : Tuple , lowerCamelCase_ : str=2_24 , lowerCamelCase_ : List[Any]=32 , lowerCamelCase_ : Tuple=[2, 16, 16] , lowerCamelCase_ : List[str]=3 , lowerCamelCase_ : Dict=7_68 , lowerCamelCase_ : Dict=12 , lowerCamelCase_ : Any=12 , lowerCamelCase_ : List[Any]=30_72 , lowerCamelCase_ : List[str]="gelu_fast" , lowerCamelCase_ : str=0.0 , lowerCamelCase_ : Any=0.0 , lowerCamelCase_ : Optional[int]=0.02 , lowerCamelCase_ : List[Any]=1e-06 , lowerCamelCase_ : Tuple=True , **lowerCamelCase_ : Tuple , ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : List[str] = hidden_size
SCREAMING_SNAKE_CASE : Union[str, Any] = num_hidden_layers
SCREAMING_SNAKE_CASE : List[str] = num_attention_heads
SCREAMING_SNAKE_CASE : str = intermediate_size
SCREAMING_SNAKE_CASE : List[Any] = hidden_act
SCREAMING_SNAKE_CASE : Optional[Any] = hidden_dropout_prob
SCREAMING_SNAKE_CASE : Tuple = attention_probs_dropout_prob
SCREAMING_SNAKE_CASE : Tuple = initializer_range
SCREAMING_SNAKE_CASE : str = layer_norm_eps
SCREAMING_SNAKE_CASE : str = image_size
SCREAMING_SNAKE_CASE : Dict = num_frames
SCREAMING_SNAKE_CASE : Optional[Any] = tubelet_size
SCREAMING_SNAKE_CASE : Dict = num_channels
SCREAMING_SNAKE_CASE : int = qkv_bias
super().__init__(**lowerCamelCase_ )
| 79 | 0 |
'''simple docstring'''
def __A ( lowerCamelCase_ , lowerCamelCase_ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : Tuple = len(_snake_case ) + 1
SCREAMING_SNAKE_CASE : List[str] = len(_snake_case ) + 1
# dp is a 2d matrix where dp[i][j] denotes whether prefix string of
# length i of input_string matches with prefix string of length j of
# given pattern.
# "dp" stands for dynamic programming.
SCREAMING_SNAKE_CASE : Optional[Any] = [[0 for i in range(_snake_case )] for j in range(_snake_case )]
# since string of zero length match pattern of zero length
SCREAMING_SNAKE_CASE : str = 1
# since pattern of zero length will never match with string of non-zero length
for i in range(1 , _snake_case ):
SCREAMING_SNAKE_CASE : Optional[Any] = 0
# since string of zero length will match with pattern where there
# is at least one * alternatively
for j in range(1 , _snake_case ):
SCREAMING_SNAKE_CASE : List[Any] = dp[0][j - 2] if pattern[j - 1] == """*""" else 0
# now using bottom-up approach to find for all remaining lengths
for i in range(1 , _snake_case ):
for j in range(1 , _snake_case ):
if input_string[i - 1] == pattern[j - 1] or pattern[j - 1] == ".":
SCREAMING_SNAKE_CASE : Dict = dp[i - 1][j - 1]
elif pattern[j - 1] == "*":
if dp[i][j - 2] == 1:
SCREAMING_SNAKE_CASE : Dict = 1
elif pattern[j - 2] in (input_string[i - 1], "."):
SCREAMING_SNAKE_CASE : Any = dp[i - 1][j]
else:
SCREAMING_SNAKE_CASE : Dict = 0
else:
SCREAMING_SNAKE_CASE : Tuple = 0
return bool(dp[-1][-1] )
if __name__ == "__main__":
import doctest
doctest.testmod()
# inputing the strings
# input_string = input("input a string :")
# pattern = input("input a pattern :")
__UpperCAmelCase = """aab"""
__UpperCAmelCase = """c*a*b"""
# using function to check whether given string matches the given pattern
if match_pattern(input_string, pattern):
print(f'''{input_string} matches the given pattern {pattern}''')
else:
print(f'''{input_string} does not match with the given pattern {pattern}''')
| 708 |
'''simple docstring'''
import math
class UpperCamelCase__ :
"""simple docstring"""
def __init__( self : List[str] , lowerCamelCase_ : Tuple=0 ): # a graph with Node 0,1,...,N-1
'''simple docstring'''
SCREAMING_SNAKE_CASE : Any = n
SCREAMING_SNAKE_CASE : Optional[int] = [
[math.inf for j in range(0 , lowerCamelCase_ )] for i in range(0 , lowerCamelCase_ )
] # adjacency matrix for weight
SCREAMING_SNAKE_CASE : Union[str, Any] = [
[math.inf for j in range(0 , lowerCamelCase_ )] for i in range(0 , lowerCamelCase_ )
] # dp[i][j] stores minimum distance from i to j
def lowerCamelCase_ ( self : Dict , lowerCamelCase_ : str , lowerCamelCase_ : List[str] , lowerCamelCase_ : int ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Union[str, Any] = w
def lowerCamelCase_ ( self : Optional[int] ):
'''simple docstring'''
for k in range(0 , self.n ):
for i in range(0 , self.n ):
for j in range(0 , self.n ):
SCREAMING_SNAKE_CASE : Union[str, Any] = min(self.dp[i][j] , self.dp[i][k] + self.dp[k][j] )
def lowerCamelCase_ ( self : Dict , lowerCamelCase_ : List[Any] , lowerCamelCase_ : Union[str, Any] ):
'''simple docstring'''
return self.dp[u][v]
if __name__ == "__main__":
__UpperCAmelCase = Graph(5)
graph.add_edge(0, 2, 9)
graph.add_edge(0, 4, 10)
graph.add_edge(1, 3, 5)
graph.add_edge(2, 3, 7)
graph.add_edge(3, 0, 10)
graph.add_edge(3, 1, 2)
graph.add_edge(3, 2, 1)
graph.add_edge(3, 4, 6)
graph.add_edge(4, 1, 3)
graph.add_edge(4, 2, 4)
graph.add_edge(4, 3, 9)
graph.floyd_warshall()
graph.show_min(1, 4)
graph.show_min(0, 3)
| 79 | 0 |
'''simple docstring'''
import numpy as np
from nltk.translate import meteor_score
import datasets
from datasets.config import importlib_metadata, version
__UpperCAmelCase = version.parse(importlib_metadata.version("""nltk"""))
if NLTK_VERSION >= version.Version("""3.6.4"""):
from nltk import word_tokenize
__UpperCAmelCase = '\\n@inproceedings{banarjee2005,\n title = {{METEOR}: An Automatic Metric for {MT} Evaluation with Improved Correlation with Human Judgments},\n author = {Banerjee, Satanjeev and Lavie, Alon},\n booktitle = {Proceedings of the {ACL} Workshop on Intrinsic and Extrinsic Evaluation Measures for Machine Translation and/or Summarization},\n month = jun,\n year = {2005},\n address = {Ann Arbor, Michigan},\n publisher = {Association for Computational Linguistics},\n url = {https://www.aclweb.org/anthology/W05-0909},\n pages = {65--72},\n}\n'
__UpperCAmelCase = '\\nMETEOR, an automatic metric for machine translation evaluation\nthat is based on a generalized concept of unigram matching between the\nmachine-produced translation and human-produced reference translations.\nUnigrams can be matched based on their surface forms, stemmed forms,\nand meanings; furthermore, METEOR can be easily extended to include more\nadvanced matching strategies. Once all generalized unigram matches\nbetween the two strings have been found, METEOR computes a score for\nthis matching using a combination of unigram-precision, unigram-recall, and\na measure of fragmentation that is designed to directly capture how\nwell-ordered the matched words in the machine translation are in relation\nto the reference.\n\nMETEOR gets an R correlation value of 0.347 with human evaluation on the Arabic\ndata and 0.331 on the Chinese data. This is shown to be an improvement on\nusing simply unigram-precision, unigram-recall and their harmonic F1\ncombination.\n'
__UpperCAmelCase = '\nComputes METEOR score of translated segments against one or more references.\nArgs:\n predictions: list of predictions to score. Each prediction\n should be a string with tokens separated by spaces.\n references: list of reference for each prediction. Each\n reference should be a string with tokens separated by spaces.\n alpha: Parameter for controlling relative weights of precision and recall. default: 0.9\n beta: Parameter for controlling shape of penalty as a function of fragmentation. default: 3\n gamma: Relative weight assigned to fragmentation penalty. default: 0.5\nReturns:\n \'meteor\': meteor score.\nExamples:\n\n >>> meteor = datasets.load_metric(\'meteor\')\n >>> predictions = ["It is a guide to action which ensures that the military always obeys the commands of the party"]\n >>> references = ["It is a guide to action that ensures that the military will forever heed Party commands"]\n >>> results = meteor.compute(predictions=predictions, references=references)\n >>> print(round(results["meteor"], 4))\n 0.6944\n'
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class UpperCamelCase__ ( datasets.Metric ):
"""simple docstring"""
def lowerCamelCase_ ( self : Optional[int] ):
'''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/nltk/nltk/blob/develop/nltk/translate/meteor_score.py"""] , reference_urls=[
"""https://www.nltk.org/api/nltk.translate.html#module-nltk.translate.meteor_score""",
"""https://en.wikipedia.org/wiki/METEOR""",
] , )
def lowerCamelCase_ ( self : Dict , lowerCamelCase_ : List[Any] ):
'''simple docstring'''
import nltk
nltk.download("""wordnet""" )
if NLTK_VERSION >= version.Version("""3.6.5""" ):
nltk.download("""punkt""" )
if NLTK_VERSION >= version.Version("""3.6.6""" ):
nltk.download("""omw-1.4""" )
def lowerCamelCase_ ( self : str , lowerCamelCase_ : List[Any] , lowerCamelCase_ : Tuple , lowerCamelCase_ : Optional[int]=0.9 , lowerCamelCase_ : int=3 , lowerCamelCase_ : Optional[Any]=0.5 ):
'''simple docstring'''
if NLTK_VERSION >= version.Version("""3.6.5""" ):
SCREAMING_SNAKE_CASE : Union[str, Any] = [
meteor_score.single_meteor_score(
word_tokenize(_SCREAMING_SNAKE_CASE ) , word_tokenize(_SCREAMING_SNAKE_CASE ) , alpha=_SCREAMING_SNAKE_CASE , beta=_SCREAMING_SNAKE_CASE , gamma=_SCREAMING_SNAKE_CASE )
for ref, pred in zip(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
]
else:
SCREAMING_SNAKE_CASE : Tuple = [
meteor_score.single_meteor_score(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , alpha=_SCREAMING_SNAKE_CASE , beta=_SCREAMING_SNAKE_CASE , gamma=_SCREAMING_SNAKE_CASE )
for ref, pred in zip(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
]
return {"meteor": np.mean(_SCREAMING_SNAKE_CASE )}
| 709 |
'''simple docstring'''
import math
def __A ( lowerCamelCase_ ):
"""simple docstring"""
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
return False
# All primes number are in format of 6k +/- 1
for i in range(5 , int(math.sqrt(lowerCamelCase_ ) + 1 ) , 6 ):
if number % i == 0 or number % (i + 2) == 0:
return False
return True
def __A ( lowerCamelCase_ = 1_00_01 ):
"""simple docstring"""
try:
SCREAMING_SNAKE_CASE : Tuple = int(lowerCamelCase_ )
except (TypeError, ValueError):
raise TypeError("""Parameter nth must be int or castable to int.""" ) from None
if nth <= 0:
raise ValueError("""Parameter nth must be greater than or equal to one.""" )
SCREAMING_SNAKE_CASE : list[int] = []
SCREAMING_SNAKE_CASE : Dict = 2
while len(lowerCamelCase_ ) < nth:
if is_prime(lowerCamelCase_ ):
primes.append(lowerCamelCase_ )
num += 1
else:
num += 1
return primes[len(lowerCamelCase_ ) - 1]
if __name__ == "__main__":
print(f'''{solution() = }''')
| 79 | 0 |
import math
import tensorflow as tf
from packaging import version
def __A ( lowerCamelCase_ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : Dict = tf.convert_to_tensor(A_ )
SCREAMING_SNAKE_CASE : Tuple = 0.5 * (1.0 + tf.math.erf(x / tf.cast(tf.sqrt(2.0 ) , x.dtype ) ))
return x * cdf
def __A ( lowerCamelCase_ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : int = tf.convert_to_tensor(A_ )
SCREAMING_SNAKE_CASE : Tuple = tf.cast(math.pi , x.dtype )
SCREAMING_SNAKE_CASE : List[str] = tf.cast(0.044_715 , x.dtype )
SCREAMING_SNAKE_CASE : Union[str, Any] = 0.5 * (1.0 + tf.tanh(tf.sqrt(2.0 / pi ) * (x + coeff * tf.pow(A_ , 3 )) ))
return x * cdf
def __A ( lowerCamelCase_ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : Dict = tf.convert_to_tensor(A_ )
return x * tf.tanh(tf.math.softplus(A_ ) )
def __A ( lowerCamelCase_ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : int = tf.convert_to_tensor(A_ )
SCREAMING_SNAKE_CASE : Dict = tf.cast(0.044_715 , x.dtype )
SCREAMING_SNAKE_CASE : Optional[int] = tf.cast(0.7_978_845_608 , x.dtype )
return 0.5 * x * (1.0 + tf.tanh(x * coeffa * (1.0 + coeffa * x * x) ))
def __A ( lowerCamelCase_ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : Optional[Any] = tf.convert_to_tensor(A_ )
SCREAMING_SNAKE_CASE : str = tf.cast(1.702 , x.dtype )
return x * tf.math.sigmoid(coeff * x )
def __A ( lowerCamelCase_ ):
"""simple docstring"""
return tf.clip_by_value(_gelu(A_ ) , -10 , 10 )
def __A ( lowerCamelCase_ , lowerCamelCase_=-1 ):
"""simple docstring"""
SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE : Optional[Any] = tf.split(A_ , 2 , axis=A_ )
return a * tf.math.sigmoid(A_ )
if version.parse(tf.version.VERSION) >= version.parse("""2.4"""):
def __A ( lowerCamelCase_ ):
"""simple docstring"""
return tf.keras.activations.gelu(A_ , approximate=A_ )
__UpperCAmelCase = tf.keras.activations.gelu
__UpperCAmelCase = approximate_gelu_wrap
else:
__UpperCAmelCase = _gelu
__UpperCAmelCase = _gelu_new
__UpperCAmelCase = {
'gelu': gelu,
'gelu_10': gelu_aa,
'gelu_fast': gelu_fast,
'gelu_new': gelu_new,
'glu': glu,
'mish': mish,
'quick_gelu': quick_gelu,
'relu': tf.keras.activations.relu,
'sigmoid': tf.keras.activations.sigmoid,
'silu': tf.keras.activations.swish,
'swish': tf.keras.activations.swish,
'tanh': tf.keras.activations.tanh,
}
def __A ( lowerCamelCase_ ):
"""simple docstring"""
if activation_string in ACTaFN:
return ACTaFN[activation_string]
else:
raise KeyError(f'''function {activation_string} not found in ACT2FN mapping {list(ACTaFN.keys() )}''' )
| 710 |
'''simple docstring'''
from __future__ import annotations
import json
import requests
from bsa import BeautifulSoup
from fake_useragent import UserAgent
__UpperCAmelCase = {"""UserAgent""": UserAgent().random}
def __A ( lowerCamelCase_ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : str = script.contents[0]
SCREAMING_SNAKE_CASE : int = json.loads(data[data.find("""{\"config\"""" ) : -1] )
return info["entry_data"]["ProfilePage"][0]["graphql"]["user"]
class UpperCamelCase__ :
"""simple docstring"""
def __init__( self : Dict , lowerCamelCase_ : str ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Any = f'''https://www.instagram.com/{username}/'''
SCREAMING_SNAKE_CASE : Any = self.get_json()
def lowerCamelCase_ ( self : Tuple ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Dict = requests.get(self.url , headers=lowerCamelCase_ ).text
SCREAMING_SNAKE_CASE : List[Any] = BeautifulSoup(lowerCamelCase_ , """html.parser""" ).find_all("""script""" )
try:
return extract_user_profile(scripts[4] )
except (json.decoder.JSONDecodeError, KeyError):
return extract_user_profile(scripts[3] )
def __repr__( self : Dict ):
'''simple docstring'''
return f'''{self.__class__.__name__}(\'{self.username}\')'''
def __str__( self : int ):
'''simple docstring'''
return f'''{self.fullname} ({self.username}) is {self.biography}'''
@property
def lowerCamelCase_ ( self : Tuple ):
'''simple docstring'''
return self.user_data["username"]
@property
def lowerCamelCase_ ( self : Optional[Any] ):
'''simple docstring'''
return self.user_data["full_name"]
@property
def lowerCamelCase_ ( self : int ):
'''simple docstring'''
return self.user_data["biography"]
@property
def lowerCamelCase_ ( self : str ):
'''simple docstring'''
return self.user_data["business_email"]
@property
def lowerCamelCase_ ( self : str ):
'''simple docstring'''
return self.user_data["external_url"]
@property
def lowerCamelCase_ ( self : List[Any] ):
'''simple docstring'''
return self.user_data["edge_followed_by"]["count"]
@property
def lowerCamelCase_ ( self : List[Any] ):
'''simple docstring'''
return self.user_data["edge_follow"]["count"]
@property
def lowerCamelCase_ ( self : List[Any] ):
'''simple docstring'''
return self.user_data["edge_owner_to_timeline_media"]["count"]
@property
def lowerCamelCase_ ( self : str ):
'''simple docstring'''
return self.user_data["profile_pic_url_hd"]
@property
def lowerCamelCase_ ( self : Dict ):
'''simple docstring'''
return self.user_data["is_verified"]
@property
def lowerCamelCase_ ( self : Optional[int] ):
'''simple docstring'''
return self.user_data["is_private"]
def __A ( lowerCamelCase_ = "github" ):
"""simple docstring"""
import os
if os.environ.get("""CI""" ):
return # test failing on GitHub Actions
SCREAMING_SNAKE_CASE : Any = InstagramUser(lowerCamelCase_ )
assert instagram_user.user_data
assert isinstance(instagram_user.user_data , lowerCamelCase_ )
assert instagram_user.username == username
if username != "github":
return
assert instagram_user.fullname == "GitHub"
assert instagram_user.biography == "Built for developers."
assert instagram_user.number_of_posts > 1_50
assert instagram_user.number_of_followers > 12_00_00
assert instagram_user.number_of_followings > 15
assert instagram_user.email == "support@github.com"
assert instagram_user.website == "https://github.com/readme"
assert instagram_user.profile_picture_url.startswith("""https://instagram.""" )
assert instagram_user.is_verified is True
assert instagram_user.is_private is False
if __name__ == "__main__":
import doctest
doctest.testmod()
__UpperCAmelCase = InstagramUser("""github""")
print(instagram_user)
print(f'''{instagram_user.number_of_posts = }''')
print(f'''{instagram_user.number_of_followers = }''')
print(f'''{instagram_user.number_of_followings = }''')
print(f'''{instagram_user.email = }''')
print(f'''{instagram_user.website = }''')
print(f'''{instagram_user.profile_picture_url = }''')
print(f'''{instagram_user.is_verified = }''')
print(f'''{instagram_user.is_private = }''')
| 79 | 0 |
'''simple docstring'''
import argparse
import torch
# Step 1. clone https://github.com/microsoft/unilm
# Step 2. git checkout to https://github.com/microsoft/unilm/commit/b94ec76c36f02fb2b0bf0dcb0b8554a2185173cd
# Step 3. cd unilm
# Step 4. ln -s $(realpath wavlm/modules.py) ./ # create simlink
# import classes
from unilm.wavlm.WavLM import WavLM as WavLMOrig
from unilm.wavlm.WavLM import WavLMConfig as WavLMConfigOrig
from transformers import WavLMConfig, WavLMModel, logging
logging.set_verbosity_info()
__UpperCAmelCase = logging.get_logger(__name__)
__UpperCAmelCase = {
'''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.grep_linear''': '''encoder.layers.*.attention.gru_rel_pos_linear''',
'''self_attn.relative_attention_bias''': '''encoder.layers.*.attention.rel_attn_embed''',
'''self_attn.grep_a''': '''encoder.layers.*.attention.gru_rel_pos_const''',
'''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''',
'''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''': '''ctc_proj''',
'''mask_emb''': '''masked_spec_embed''',
}
__UpperCAmelCase = [
'''ctc_proj''',
'''quantizer.weight_proj''',
'''quantizer.codevectors''',
'''project_q''',
'''project_hid''',
]
def __A ( lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ ):
"""simple docstring"""
for attribute in key.split(""".""" ):
SCREAMING_SNAKE_CASE : List[str] = getattr(lowerCamelCase_ , lowerCamelCase_ )
if weight_type is not None:
SCREAMING_SNAKE_CASE : Dict = getattr(lowerCamelCase_ , lowerCamelCase_ ).shape
else:
SCREAMING_SNAKE_CASE : List[Any] = hf_pointer.shape
assert hf_shape == value.shape, (
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 : Dict = value
elif weight_type == "weight_g":
SCREAMING_SNAKE_CASE : List[Any] = value
elif weight_type == "weight_v":
SCREAMING_SNAKE_CASE : Any = value
elif weight_type == "bias":
SCREAMING_SNAKE_CASE : int = value
else:
SCREAMING_SNAKE_CASE : int = value
logger.info(f'''{key + "." + weight_type if weight_type is not None else ""} was initialized from {full_name}.''' )
def __A ( lowerCamelCase_ , lowerCamelCase_ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : Optional[int] = []
SCREAMING_SNAKE_CASE : Optional[int] = fairseq_model.state_dict()
SCREAMING_SNAKE_CASE : Optional[int] = hf_model.feature_extractor
for name, value in fairseq_dict.items():
SCREAMING_SNAKE_CASE : List[str] = False
if "conv_layers" in name:
load_conv_layer(
lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ , hf_model.config.feat_extract_norm == """group""" , )
SCREAMING_SNAKE_CASE : Union[str, Any] = True
else:
for key, mapped_key in MAPPING.items():
if key in name or key.split("""w2v_model.""" )[-1] == name.split(""".""" )[0]:
SCREAMING_SNAKE_CASE : List[Any] = True
if "*" in mapped_key:
SCREAMING_SNAKE_CASE : str = name.split(lowerCamelCase_ )[0].split(""".""" )[-2]
SCREAMING_SNAKE_CASE : Any = mapped_key.replace("""*""" , lowerCamelCase_ )
if "weight_g" in name:
SCREAMING_SNAKE_CASE : Dict = 'weight_g'
elif "weight_v" in name:
SCREAMING_SNAKE_CASE : Optional[int] = 'weight_v'
elif "bias" in name and "relative_attention_bias" not in name:
SCREAMING_SNAKE_CASE : Dict = 'bias'
elif "weight" in name:
# TODO: don't match quantizer.weight_proj
SCREAMING_SNAKE_CASE : Union[str, Any] = 'weight'
else:
SCREAMING_SNAKE_CASE : List[Any] = None
set_recursively(lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ )
continue
if not is_used:
unused_weights.append(lowerCamelCase_ )
logger.warning(f'''Unused weights: {unused_weights}''' )
def __A ( lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : Union[str, Any] = full_name.split("""conv_layers.""" )[-1]
SCREAMING_SNAKE_CASE : int = name.split(""".""" )
SCREAMING_SNAKE_CASE : Tuple = int(items[0] )
SCREAMING_SNAKE_CASE : Union[str, Any] = int(items[1] )
if type_id == 0:
if "bias" in name:
assert value.shape == feature_extractor.conv_layers[layer_id].conv.bias.data.shape, (
f'''{full_name} has size {value.shape}, but'''
f''' {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found.'''
)
SCREAMING_SNAKE_CASE : int = value
logger.info(f'''Feat extract conv layer {layer_id} was initialized from {full_name}.''' )
elif "weight" in name:
assert value.shape == feature_extractor.conv_layers[layer_id].conv.weight.data.shape, (
f'''{full_name} has size {value.shape}, but'''
f''' {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found.'''
)
SCREAMING_SNAKE_CASE : Tuple = 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:
assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape, (
f'''{full_name} has size {value.shape}, but {feature_extractor[layer_id].layer_norm.bias.data.shape} was'''
" found."
)
SCREAMING_SNAKE_CASE : Tuple = value
logger.info(f'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.''' )
elif "weight" in name:
assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape, (
f'''{full_name} has size {value.shape}, but'''
f''' {feature_extractor[layer_id].layer_norm.weight.data.shape} was found.'''
)
SCREAMING_SNAKE_CASE : int = value
logger.info(f'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.''' )
else:
unused_weights.append(lowerCamelCase_ )
@torch.no_grad()
def __A ( lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_=None ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : Optional[int] = torch.load(lowerCamelCase_ )
SCREAMING_SNAKE_CASE : str = WavLMConfigOrig(checkpoint["""cfg"""] )
SCREAMING_SNAKE_CASE : Optional[int] = WavLMOrig(lowerCamelCase_ )
model.load_state_dict(checkpoint["""model"""] )
model.eval()
if config_path is not None:
SCREAMING_SNAKE_CASE : Tuple = WavLMConfig.from_pretrained(lowerCamelCase_ )
else:
SCREAMING_SNAKE_CASE : Any = WavLMConfig()
SCREAMING_SNAKE_CASE : Optional[int] = WavLMModel(lowerCamelCase_ )
recursively_load_weights(lowerCamelCase_ , lowerCamelCase_ )
hf_wavlm.save_pretrained(lowerCamelCase_ )
if __name__ == "__main__":
__UpperCAmelCase = 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("""--config_path""", default=None, type=str, help="""Path to hf config.json of model to convert""")
__UpperCAmelCase = parser.parse_args()
convert_wavlm_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path)
| 711 |
'''simple docstring'''
import argparse
import logging
from collections import namedtuple
import torch
from model_bertabs import BertAbsSummarizer
from models.model_builder import AbsSummarizer # The authors' implementation
from transformers import BertTokenizer
logging.basicConfig(level=logging.INFO)
__UpperCAmelCase = logging.getLogger(__name__)
__UpperCAmelCase = """Hello world! cécé herlolip"""
__UpperCAmelCase = namedtuple(
"""BertAbsConfig""",
[
"""temp_dir""",
"""large""",
"""use_bert_emb""",
"""finetune_bert""",
"""encoder""",
"""share_emb""",
"""max_pos""",
"""enc_layers""",
"""enc_hidden_size""",
"""enc_heads""",
"""enc_ff_size""",
"""enc_dropout""",
"""dec_layers""",
"""dec_hidden_size""",
"""dec_heads""",
"""dec_ff_size""",
"""dec_dropout""",
],
)
def __A ( lowerCamelCase_ , lowerCamelCase_ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : Tuple = BertAbsConfig(
temp_dir=""".""" , finetune_bert=lowerCamelCase_ , large=lowerCamelCase_ , share_emb=lowerCamelCase_ , use_bert_emb=lowerCamelCase_ , encoder="""bert""" , max_pos=5_12 , enc_layers=6 , enc_hidden_size=5_12 , enc_heads=8 , enc_ff_size=5_12 , enc_dropout=0.2 , dec_layers=6 , dec_hidden_size=7_68 , dec_heads=8 , dec_ff_size=20_48 , dec_dropout=0.2 , )
SCREAMING_SNAKE_CASE : int = torch.load(lowerCamelCase_ , lambda lowerCamelCase_ , lowerCamelCase_ : storage )
SCREAMING_SNAKE_CASE : List[str] = AbsSummarizer(lowerCamelCase_ , torch.device("""cpu""" ) , lowerCamelCase_ )
original.eval()
SCREAMING_SNAKE_CASE : Optional[int] = BertAbsSummarizer(lowerCamelCase_ , torch.device("""cpu""" ) )
new_model.eval()
# -------------------
# Convert the weights
# -------------------
logging.info("""convert the model""" )
new_model.bert.load_state_dict(original.bert.state_dict() )
new_model.decoder.load_state_dict(original.decoder.state_dict() )
new_model.generator.load_state_dict(original.generator.state_dict() )
# ----------------------------------
# Make sure the outpus are identical
# ----------------------------------
logging.info("""Make sure that the models' outputs are identical""" )
SCREAMING_SNAKE_CASE : int = BertTokenizer.from_pretrained("""bert-base-uncased""" )
# prepare the model inputs
SCREAMING_SNAKE_CASE : Optional[Any] = tokenizer.encode("""This is sample éàalj'-.""" )
encoder_input_ids.extend([tokenizer.pad_token_id] * (5_12 - len(lowerCamelCase_ )) )
SCREAMING_SNAKE_CASE : List[str] = torch.tensor(lowerCamelCase_ ).unsqueeze(0 )
SCREAMING_SNAKE_CASE : Optional[int] = tokenizer.encode("""This is sample 3 éàalj'-.""" )
decoder_input_ids.extend([tokenizer.pad_token_id] * (5_12 - len(lowerCamelCase_ )) )
SCREAMING_SNAKE_CASE : Optional[Any] = torch.tensor(lowerCamelCase_ ).unsqueeze(0 )
# failsafe to make sure the weights reset does not affect the
# loaded weights.
assert torch.max(torch.abs(original.generator[0].weight - new_model.generator[0].weight ) ) == 0
# forward pass
SCREAMING_SNAKE_CASE : Optional[int] = encoder_input_ids
SCREAMING_SNAKE_CASE : Optional[Any] = decoder_input_ids
SCREAMING_SNAKE_CASE : List[str] = None
SCREAMING_SNAKE_CASE : Tuple = None
SCREAMING_SNAKE_CASE : int = None
SCREAMING_SNAKE_CASE : List[Any] = None
SCREAMING_SNAKE_CASE : Optional[int] = None
# The original model does not apply the geneator layer immediatly but rather in
# the beam search (where it combines softmax + linear layer). Since we already
# apply the softmax in our generation process we only apply the linear layer here.
# We make sure that the outputs of the full stack are identical
SCREAMING_SNAKE_CASE : str = original(lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ )[0]
SCREAMING_SNAKE_CASE : Optional[Any] = original.generator(lowerCamelCase_ )
SCREAMING_SNAKE_CASE : List[Any] = new_model(
lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ )[0]
SCREAMING_SNAKE_CASE : str = new_model.generator(lowerCamelCase_ )
SCREAMING_SNAKE_CASE : int = torch.max(torch.abs(output_converted_model - output_original_model ) ).item()
print("""Maximum absolute difference beween weights: {:.2f}""".format(lowerCamelCase_ ) )
SCREAMING_SNAKE_CASE : Optional[int] = torch.max(torch.abs(output_converted_generator - output_original_generator ) ).item()
print("""Maximum absolute difference beween weights: {:.2f}""".format(lowerCamelCase_ ) )
SCREAMING_SNAKE_CASE : Any = torch.allclose(lowerCamelCase_ , lowerCamelCase_ , atol=1E-3 )
if are_identical:
logging.info("""all weights are equal up to 1e-3""" )
else:
raise ValueError("""the weights are different. The new model is likely different from the original one.""" )
# The model has been saved with torch.save(model) and this is bound to the exact
# directory structure. We save the state_dict instead.
logging.info("""saving the model's state dictionary""" )
torch.save(
new_model.state_dict() , """./bertabs-finetuned-cnndm-extractive-abstractive-summarization/pytorch_model.bin""" )
if __name__ == "__main__":
__UpperCAmelCase = argparse.ArgumentParser()
parser.add_argument(
"""--bertabs_checkpoint_path""",
default=None,
type=str,
required=True,
help="""Path the official PyTorch dump.""",
)
parser.add_argument(
"""--pytorch_dump_folder_path""",
default=None,
type=str,
required=True,
help="""Path to the output PyTorch model.""",
)
__UpperCAmelCase = parser.parse_args()
convert_bertabs_checkpoints(
args.bertabs_checkpoint_path,
args.pytorch_dump_folder_path,
)
| 79 | 0 |
'''simple docstring'''
from __future__ import annotations
from collections import namedtuple
def __A ( lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : List[str] = namedtuple("""result""" , """name value""" )
if (voltage, current, power).count(0 ) != 1:
raise ValueError("""Only one argument must be 0""" )
elif power < 0:
raise ValueError(
"""Power cannot be negative in any electrical/electronics system""" )
elif voltage == 0:
return result("""voltage""" , power / current )
elif current == 0:
return result("""current""" , power / voltage )
elif power == 0:
return result("""power""" , float(round(abs(voltage * current ) , 2 ) ) )
else:
raise ValueError("""Exactly one argument must be 0""" )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 712 |
'''simple docstring'''
import itertools
import json
import linecache
import os
import pickle
import re
import socket
import string
from collections import Counter
from logging import getLogger
from pathlib import Path
from typing import Callable, Dict, Iterable, List
import git
import torch
from torch.utils.data import Dataset
from transformers import BartTokenizer, RagTokenizer, TaTokenizer
def __A ( lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_=True , lowerCamelCase_="pt" ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : Tuple = {"""add_prefix_space""": True} if isinstance(lowerCamelCase_ , lowerCamelCase_ ) and not line.startswith(""" """ ) else {}
SCREAMING_SNAKE_CASE : Optional[Any] = padding_side
return tokenizer(
[line] , max_length=lowerCamelCase_ , padding="""max_length""" if pad_to_max_length else None , truncation=lowerCamelCase_ , return_tensors=lowerCamelCase_ , add_special_tokens=lowerCamelCase_ , **lowerCamelCase_ , )
def __A ( lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_=None , ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : int = input_ids.ne(lowerCamelCase_ ).any(dim=0 )
if attention_mask is None:
return input_ids[:, keep_column_mask]
else:
return (input_ids[:, keep_column_mask], attention_mask[:, keep_column_mask])
class UpperCamelCase__ ( lowercase_ ):
"""simple docstring"""
def __init__( self : str , lowerCamelCase_ : Tuple , lowerCamelCase_ : Tuple , lowerCamelCase_ : List[str] , lowerCamelCase_ : List[str] , lowerCamelCase_ : List[str]="train" , lowerCamelCase_ : List[str]=None , lowerCamelCase_ : List[Any]=None , lowerCamelCase_ : int=None , lowerCamelCase_ : Union[str, Any]="" , ):
'''simple docstring'''
super().__init__()
SCREAMING_SNAKE_CASE : str = Path(lowerCamelCase_ ).joinpath(type_path + """.source""" )
SCREAMING_SNAKE_CASE : Optional[Any] = Path(lowerCamelCase_ ).joinpath(type_path + """.target""" )
SCREAMING_SNAKE_CASE : Union[str, Any] = self.get_char_lens(self.src_file )
SCREAMING_SNAKE_CASE : int = max_source_length
SCREAMING_SNAKE_CASE : str = max_target_length
assert min(self.src_lens ) > 0, f'''found empty line in {self.src_file}'''
SCREAMING_SNAKE_CASE : List[str] = tokenizer
SCREAMING_SNAKE_CASE : Dict = prefix
if n_obs is not None:
SCREAMING_SNAKE_CASE : List[Any] = self.src_lens[:n_obs]
SCREAMING_SNAKE_CASE : int = src_lang
SCREAMING_SNAKE_CASE : Optional[int] = tgt_lang
def __len__( self : List[Any] ):
'''simple docstring'''
return len(self.src_lens )
def __getitem__( self : Union[str, Any] , lowerCamelCase_ : Optional[Any] ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Any = index + 1 # linecache starts at 1
SCREAMING_SNAKE_CASE : Dict = self.prefix + linecache.getline(str(self.src_file ) , lowerCamelCase_ ).rstrip("""\n""" )
SCREAMING_SNAKE_CASE : Dict = linecache.getline(str(self.tgt_file ) , lowerCamelCase_ ).rstrip("""\n""" )
assert source_line, f'''empty source line for index {index}'''
assert tgt_line, f'''empty tgt line for index {index}'''
# Need to add eos token manually for T5
if isinstance(self.tokenizer , lowerCamelCase_ ):
source_line += self.tokenizer.eos_token
tgt_line += self.tokenizer.eos_token
# Pad source and target to the right
SCREAMING_SNAKE_CASE : Union[str, Any] = (
self.tokenizer.question_encoder if isinstance(self.tokenizer , lowerCamelCase_ ) else self.tokenizer
)
SCREAMING_SNAKE_CASE : Any = self.tokenizer.generator if isinstance(self.tokenizer , lowerCamelCase_ ) else self.tokenizer
SCREAMING_SNAKE_CASE : Optional[int] = encode_line(lowerCamelCase_ , lowerCamelCase_ , self.max_source_length , """right""" )
SCREAMING_SNAKE_CASE : Union[str, Any] = encode_line(lowerCamelCase_ , lowerCamelCase_ , self.max_target_length , """right""" )
SCREAMING_SNAKE_CASE : Tuple = source_inputs["""input_ids"""].squeeze()
SCREAMING_SNAKE_CASE : Tuple = target_inputs["""input_ids"""].squeeze()
SCREAMING_SNAKE_CASE : List[str] = source_inputs["""attention_mask"""].squeeze()
return {
"input_ids": source_ids,
"attention_mask": src_mask,
"decoder_input_ids": target_ids,
}
@staticmethod
def lowerCamelCase_ ( lowerCamelCase_ : Dict ):
'''simple docstring'''
return [len(lowerCamelCase_ ) for x in Path(lowerCamelCase_ ).open().readlines()]
def lowerCamelCase_ ( self : Tuple , lowerCamelCase_ : Any ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Tuple = torch.stack([x["""input_ids"""] for x in batch] )
SCREAMING_SNAKE_CASE : int = torch.stack([x["""attention_mask"""] for x in batch] )
SCREAMING_SNAKE_CASE : int = torch.stack([x["""decoder_input_ids"""] for x in batch] )
SCREAMING_SNAKE_CASE : int = (
self.tokenizer.generator.pad_token_id
if isinstance(self.tokenizer , lowerCamelCase_ )
else self.tokenizer.pad_token_id
)
SCREAMING_SNAKE_CASE : Dict = (
self.tokenizer.question_encoder.pad_token_id
if isinstance(self.tokenizer , lowerCamelCase_ )
else self.tokenizer.pad_token_id
)
SCREAMING_SNAKE_CASE : Optional[int] = trim_batch(lowerCamelCase_ , lowerCamelCase_ )
SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE : Optional[int] = trim_batch(lowerCamelCase_ , lowerCamelCase_ , attention_mask=lowerCamelCase_ )
SCREAMING_SNAKE_CASE : int = {
"""input_ids""": source_ids,
"""attention_mask""": source_mask,
"""decoder_input_ids""": y,
}
return batch
__UpperCAmelCase = getLogger(__name__)
def __A ( lowerCamelCase_ ):
"""simple docstring"""
return list(itertools.chain.from_iterable(lowerCamelCase_ ) )
def __A ( lowerCamelCase_ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : str = get_git_info()
save_json(lowerCamelCase_ , os.path.join(lowerCamelCase_ , """git_log.json""" ) )
def __A ( lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_=4 , **lowerCamelCase_ ):
"""simple docstring"""
with open(lowerCamelCase_ , """w""" ) as f:
json.dump(lowerCamelCase_ , lowerCamelCase_ , indent=lowerCamelCase_ , **lowerCamelCase_ )
def __A ( lowerCamelCase_ ):
"""simple docstring"""
with open(lowerCamelCase_ ) as f:
return json.load(lowerCamelCase_ )
def __A ( ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : List[Any] = git.Repo(search_parent_directories=lowerCamelCase_ )
SCREAMING_SNAKE_CASE : Tuple = {
"""repo_id""": str(lowerCamelCase_ ),
"""repo_sha""": str(repo.head.object.hexsha ),
"""repo_branch""": str(repo.active_branch ),
"""hostname""": str(socket.gethostname() ),
}
return repo_infos
def __A ( lowerCamelCase_ , lowerCamelCase_ ):
"""simple docstring"""
return list(map(lowerCamelCase_ , lowerCamelCase_ ) )
def __A ( lowerCamelCase_ , lowerCamelCase_ ):
"""simple docstring"""
with open(lowerCamelCase_ , """wb""" ) as f:
return pickle.dump(lowerCamelCase_ , lowerCamelCase_ )
def __A ( lowerCamelCase_ ):
"""simple docstring"""
def remove_articles(lowerCamelCase_ ):
return re.sub(R"""\b(a|an|the)\b""" , """ """ , lowerCamelCase_ )
def white_space_fix(lowerCamelCase_ ):
return " ".join(text.split() )
def remove_punc(lowerCamelCase_ ):
SCREAMING_SNAKE_CASE : int = set(string.punctuation )
return "".join(ch for ch in text if ch not in exclude )
def lower(lowerCamelCase_ ):
return text.lower()
return white_space_fix(remove_articles(remove_punc(lower(lowerCamelCase_ ) ) ) )
def __A ( lowerCamelCase_ , lowerCamelCase_ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : Tuple = normalize_answer(lowerCamelCase_ ).split()
SCREAMING_SNAKE_CASE : Optional[int] = normalize_answer(lowerCamelCase_ ).split()
SCREAMING_SNAKE_CASE : Tuple = Counter(lowerCamelCase_ ) & Counter(lowerCamelCase_ )
SCREAMING_SNAKE_CASE : Any = sum(common.values() )
if num_same == 0:
return 0
SCREAMING_SNAKE_CASE : Optional[int] = 1.0 * num_same / len(lowerCamelCase_ )
SCREAMING_SNAKE_CASE : str = 1.0 * num_same / len(lowerCamelCase_ )
SCREAMING_SNAKE_CASE : List[str] = (2 * precision * recall) / (precision + recall)
return fa
def __A ( lowerCamelCase_ , lowerCamelCase_ ):
"""simple docstring"""
return normalize_answer(lowerCamelCase_ ) == normalize_answer(lowerCamelCase_ )
def __A ( lowerCamelCase_ , lowerCamelCase_ ):
"""simple docstring"""
assert len(lowerCamelCase_ ) == len(lowerCamelCase_ )
SCREAMING_SNAKE_CASE : Union[str, Any] = 0
for hypo, pred in zip(lowerCamelCase_ , lowerCamelCase_ ):
em += exact_match_score(lowerCamelCase_ , lowerCamelCase_ )
if len(lowerCamelCase_ ) > 0:
em /= len(lowerCamelCase_ )
return {"em": em}
def __A ( lowerCamelCase_ ):
"""simple docstring"""
return model_prefix.startswith("""rag""" )
def __A ( lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : Any = {p: p for p in extra_params}
# T5 models don't have `dropout` param, they have `dropout_rate` instead
SCREAMING_SNAKE_CASE : Dict = """dropout_rate"""
for p in extra_params:
if getattr(lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ ):
if not hasattr(lowerCamelCase_ , lowerCamelCase_ ) and not hasattr(lowerCamelCase_ , equivalent_param[p] ):
logger.info("""config doesn't have a `{}` attribute""".format(lowerCamelCase_ ) )
delattr(lowerCamelCase_ , lowerCamelCase_ )
continue
SCREAMING_SNAKE_CASE : Dict = p if hasattr(lowerCamelCase_ , lowerCamelCase_ ) else equivalent_param[p]
setattr(lowerCamelCase_ , lowerCamelCase_ , getattr(lowerCamelCase_ , lowerCamelCase_ ) )
delattr(lowerCamelCase_ , lowerCamelCase_ )
return hparams, config
| 79 | 0 |
'''simple docstring'''
from __future__ import annotations
from itertools import permutations
from random import randint
from timeit import repeat
def __A ( ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : Any = [randint(-10_00 , 10_00 ) for i in range(10 )]
SCREAMING_SNAKE_CASE : Union[str, Any] = randint(-50_00 , 50_00 )
return (arr, r)
__UpperCAmelCase = make_dataset()
def __A ( lowerCamelCase_ , lowerCamelCase_ ):
"""simple docstring"""
for triplet in permutations(A__ , 3 ):
if sum(A__ ) == target:
return tuple(sorted(A__ ) )
return (0, 0, 0)
def __A ( lowerCamelCase_ , lowerCamelCase_ ):
"""simple docstring"""
arr.sort()
SCREAMING_SNAKE_CASE : Dict = len(A__ )
for i in range(n - 1 ):
SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE : int = i + 1, n - 1
while left < right:
if arr[i] + arr[left] + arr[right] == target:
return (arr[i], arr[left], arr[right])
elif arr[i] + arr[left] + arr[right] < target:
left += 1
elif arr[i] + arr[left] + arr[right] > target:
right -= 1
return (0, 0, 0)
def __A ( ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : str = """
from __main__ import dataset, triplet_sum1, triplet_sum2
"""
SCREAMING_SNAKE_CASE : Union[str, Any] = """
triplet_sum1(*dataset)
"""
SCREAMING_SNAKE_CASE : Optional[int] = """
triplet_sum2(*dataset)
"""
SCREAMING_SNAKE_CASE : Tuple = repeat(setup=A__ , stmt=A__ , repeat=5 , number=1_00_00 )
SCREAMING_SNAKE_CASE : Optional[int] = repeat(setup=A__ , stmt=A__ , repeat=5 , number=1_00_00 )
return (min(A__ ), min(A__ ))
if __name__ == "__main__":
from doctest import testmod
testmod()
__UpperCAmelCase = solution_times()
print(f'''The time for naive implementation is {times[0]}.''')
print(f'''The time for optimized implementation is {times[1]}.''')
| 713 |
'''simple docstring'''
def __A ( lowerCamelCase_ ):
"""simple docstring"""
return 1 if digit in (0, 1) else (digit * factorial(digit - 1 ))
def __A ( lowerCamelCase_ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : Optional[Any] = 0
SCREAMING_SNAKE_CASE : List[str] = number
while duplicate > 0:
SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE : Dict = divmod(lowerCamelCase_ , 10 )
fact_sum += factorial(lowerCamelCase_ )
return fact_sum == number
if __name__ == "__main__":
print("""Program to check whether a number is a Krisnamurthy Number or not.""")
__UpperCAmelCase = int(input("""Enter number: """).strip())
print(
f'''{number} is {'' if krishnamurthy(number) else 'not '}a Krishnamurthy Number.'''
)
| 79 | 0 |
import unittest
from transformers import is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, require_torch, slow
if is_flax_available():
import optax
from flax.training.common_utils import onehot
from transformers import AutoTokenizer, FlaxMTaForConditionalGeneration
from transformers.models.ta.modeling_flax_ta import shift_tokens_right
@require_torch
@require_sentencepiece
@require_tokenizers
@require_flax
class UpperCamelCase__ ( unittest.TestCase ):
"""simple docstring"""
@slow
def lowerCamelCase_ ( self : Tuple ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : str = FlaxMTaForConditionalGeneration.from_pretrained("""google/mt5-small""" )
SCREAMING_SNAKE_CASE : List[Any] = AutoTokenizer.from_pretrained("""google/mt5-small""" )
SCREAMING_SNAKE_CASE : Dict = tokenizer("""Hello there""" , return_tensors="""np""" ).input_ids
SCREAMING_SNAKE_CASE : Dict = tokenizer("""Hi I am""" , return_tensors="""np""" ).input_ids
SCREAMING_SNAKE_CASE : List[Any] = shift_tokens_right(lowerCamelCase_ , model.config.pad_token_id , model.config.decoder_start_token_id )
SCREAMING_SNAKE_CASE : str = model(lowerCamelCase_ , decoder_input_ids=lowerCamelCase_ ).logits
SCREAMING_SNAKE_CASE : Optional[int] = optax.softmax_cross_entropy(lowerCamelCase_ , onehot(lowerCamelCase_ , logits.shape[-1] ) ).mean()
SCREAMING_SNAKE_CASE : List[Any] = -(labels.shape[-1] * loss.item())
SCREAMING_SNAKE_CASE : int = -84.9_127
self.assertTrue(abs(mtf_score - EXPECTED_SCORE ) < 1e-4 )
| 714 |
'''simple docstring'''
# Lint as: python3
import sys
from collections.abc import Mapping
from typing import TYPE_CHECKING
import numpy as np
import pyarrow as pa
from .. import config
from ..utils.py_utils import map_nested
from .formatting import TensorFormatter
if TYPE_CHECKING:
import torch
class UpperCamelCase__ ( TensorFormatter[Mapping, '''torch.Tensor''', Mapping] ):
"""simple docstring"""
def __init__( self : Tuple , lowerCamelCase_ : str=None , **lowerCamelCase_ : Dict ):
'''simple docstring'''
super().__init__(features=lowerCamelCase_ )
SCREAMING_SNAKE_CASE : Tuple = torch_tensor_kwargs
import torch # noqa import torch at initialization
def lowerCamelCase_ ( self : Dict , lowerCamelCase_ : Union[str, Any] ):
'''simple docstring'''
import torch
if isinstance(lowerCamelCase_ , lowerCamelCase_ ) and column:
if all(
isinstance(lowerCamelCase_ , torch.Tensor ) and x.shape == column[0].shape and x.dtype == column[0].dtype
for x in column ):
return torch.stack(lowerCamelCase_ )
return column
def lowerCamelCase_ ( self : List[str] , lowerCamelCase_ : int ):
'''simple docstring'''
import torch
if isinstance(lowerCamelCase_ , (str, bytes, type(lowerCamelCase_ )) ):
return value
elif isinstance(lowerCamelCase_ , (np.character, np.ndarray) ) and np.issubdtype(value.dtype , np.character ):
return value.tolist()
SCREAMING_SNAKE_CASE : str = {}
if isinstance(lowerCamelCase_ , (np.number, np.ndarray) ) and np.issubdtype(value.dtype , np.integer ):
SCREAMING_SNAKE_CASE : Any = {"""dtype""": torch.intaa}
elif isinstance(lowerCamelCase_ , (np.number, np.ndarray) ) and np.issubdtype(value.dtype , np.floating ):
SCREAMING_SNAKE_CASE : int = {"""dtype""": torch.floataa}
elif config.PIL_AVAILABLE and "PIL" in sys.modules:
import PIL.Image
if isinstance(lowerCamelCase_ , PIL.Image.Image ):
SCREAMING_SNAKE_CASE : List[Any] = np.asarray(lowerCamelCase_ )
return torch.tensor(lowerCamelCase_ , **{**default_dtype, **self.torch_tensor_kwargs} )
def lowerCamelCase_ ( self : Optional[int] , lowerCamelCase_ : Optional[Any] ):
'''simple docstring'''
import torch
# support for torch, tf, jax etc.
if hasattr(lowerCamelCase_ , """__array__""" ) and not isinstance(lowerCamelCase_ , torch.Tensor ):
SCREAMING_SNAKE_CASE : Dict = data_struct.__array__()
# support for nested types like struct of list of struct
if isinstance(lowerCamelCase_ , np.ndarray ):
if data_struct.dtype == object: # torch tensors cannot be instantied from an array of objects
return self._consolidate([self.recursive_tensorize(lowerCamelCase_ ) for substruct in data_struct] )
elif isinstance(lowerCamelCase_ , (list, tuple) ):
return self._consolidate([self.recursive_tensorize(lowerCamelCase_ ) for substruct in data_struct] )
return self._tensorize(lowerCamelCase_ )
def lowerCamelCase_ ( self : int , lowerCamelCase_ : dict ):
'''simple docstring'''
return map_nested(self._recursive_tensorize , lowerCamelCase_ , map_list=lowerCamelCase_ )
def lowerCamelCase_ ( self : Optional[int] , lowerCamelCase_ : pa.Table ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : str = self.numpy_arrow_extractor().extract_row(lowerCamelCase_ )
SCREAMING_SNAKE_CASE : Dict = self.python_features_decoder.decode_row(lowerCamelCase_ )
return self.recursive_tensorize(lowerCamelCase_ )
def lowerCamelCase_ ( self : Tuple , lowerCamelCase_ : pa.Table ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : List[str] = self.numpy_arrow_extractor().extract_column(lowerCamelCase_ )
SCREAMING_SNAKE_CASE : Tuple = self.python_features_decoder.decode_column(lowerCamelCase_ , pa_table.column_names[0] )
SCREAMING_SNAKE_CASE : List[str] = self.recursive_tensorize(lowerCamelCase_ )
SCREAMING_SNAKE_CASE : List[str] = self._consolidate(lowerCamelCase_ )
return column
def lowerCamelCase_ ( self : Dict , lowerCamelCase_ : pa.Table ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Optional[Any] = self.numpy_arrow_extractor().extract_batch(lowerCamelCase_ )
SCREAMING_SNAKE_CASE : Dict = self.python_features_decoder.decode_batch(lowerCamelCase_ )
SCREAMING_SNAKE_CASE : Optional[Any] = self.recursive_tensorize(lowerCamelCase_ )
for column_name in batch:
SCREAMING_SNAKE_CASE : Tuple = self._consolidate(batch[column_name] )
return batch
| 79 | 0 |
'''simple docstring'''
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, List, Mapping, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import TensorType, logging
if TYPE_CHECKING:
from ...onnx.config import PatchingSpec
from ...tokenization_utils_base import PreTrainedTokenizerBase
__UpperCAmelCase = logging.get_logger(__name__)
__UpperCAmelCase = {
"""allenai/longformer-base-4096""": """https://huggingface.co/allenai/longformer-base-4096/resolve/main/config.json""",
"""allenai/longformer-large-4096""": """https://huggingface.co/allenai/longformer-large-4096/resolve/main/config.json""",
"""allenai/longformer-large-4096-finetuned-triviaqa""": (
"""https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/config.json"""
),
"""allenai/longformer-base-4096-extra.pos.embd.only""": (
"""https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/config.json"""
),
"""allenai/longformer-large-4096-extra.pos.embd.only""": (
"""https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/config.json"""
),
}
class UpperCamelCase__ ( lowercase_ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE__ = '''longformer'''
def __init__( self : Dict , lowerCamelCase_ : Union[List[int], int] = 5_12 , lowerCamelCase_ : int = 2 , lowerCamelCase_ : int = 1 , lowerCamelCase_ : int = 0 , lowerCamelCase_ : int = 2 , lowerCamelCase_ : int = 3_05_22 , lowerCamelCase_ : int = 7_68 , lowerCamelCase_ : int = 12 , lowerCamelCase_ : int = 12 , lowerCamelCase_ : int = 30_72 , lowerCamelCase_ : str = "gelu" , lowerCamelCase_ : float = 0.1 , lowerCamelCase_ : float = 0.1 , lowerCamelCase_ : int = 5_12 , lowerCamelCase_ : int = 2 , lowerCamelCase_ : float = 0.02 , lowerCamelCase_ : float = 1e-12 , lowerCamelCase_ : bool = False , **lowerCamelCase_ : Optional[Any] , ):
'''simple docstring'''
super().__init__(pad_token_id=lowercase_ , **lowercase_ )
SCREAMING_SNAKE_CASE : str = attention_window
SCREAMING_SNAKE_CASE : List[Any] = sep_token_id
SCREAMING_SNAKE_CASE : List[str] = bos_token_id
SCREAMING_SNAKE_CASE : Union[str, Any] = eos_token_id
SCREAMING_SNAKE_CASE : Tuple = vocab_size
SCREAMING_SNAKE_CASE : Optional[int] = hidden_size
SCREAMING_SNAKE_CASE : Any = num_hidden_layers
SCREAMING_SNAKE_CASE : Union[str, Any] = num_attention_heads
SCREAMING_SNAKE_CASE : List[str] = hidden_act
SCREAMING_SNAKE_CASE : int = intermediate_size
SCREAMING_SNAKE_CASE : Optional[int] = hidden_dropout_prob
SCREAMING_SNAKE_CASE : Dict = attention_probs_dropout_prob
SCREAMING_SNAKE_CASE : Any = max_position_embeddings
SCREAMING_SNAKE_CASE : Union[str, Any] = type_vocab_size
SCREAMING_SNAKE_CASE : Dict = initializer_range
SCREAMING_SNAKE_CASE : Dict = layer_norm_eps
SCREAMING_SNAKE_CASE : Dict = onnx_export
class UpperCamelCase__ ( lowercase_ ):
"""simple docstring"""
def __init__( self : str , lowerCamelCase_ : "PretrainedConfig" , lowerCamelCase_ : str = "default" , lowerCamelCase_ : "List[PatchingSpec]" = None ):
'''simple docstring'''
super().__init__(lowercase_ , lowercase_ , lowercase_ )
SCREAMING_SNAKE_CASE : Dict = True
@property
def lowerCamelCase_ ( self : Optional[Any] ):
'''simple docstring'''
if self.task == "multiple-choice":
SCREAMING_SNAKE_CASE : Union[str, Any] = {0: """batch""", 1: """choice""", 2: """sequence"""}
else:
SCREAMING_SNAKE_CASE : List[str] = {0: """batch""", 1: """sequence"""}
return OrderedDict(
[
("""input_ids""", dynamic_axis),
("""attention_mask""", dynamic_axis),
("""global_attention_mask""", dynamic_axis),
] )
@property
def lowerCamelCase_ ( self : Optional[int] ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Optional[Any] = super().outputs
if self.task == "default":
SCREAMING_SNAKE_CASE : str = {0: """batch"""}
return outputs
@property
def lowerCamelCase_ ( self : Optional[Any] ):
'''simple docstring'''
return 1e-4
@property
def lowerCamelCase_ ( self : Any ):
'''simple docstring'''
return max(super().default_onnx_opset , 14 )
def lowerCamelCase_ ( self : Dict , lowerCamelCase_ : "PreTrainedTokenizerBase" , lowerCamelCase_ : int = -1 , lowerCamelCase_ : int = -1 , lowerCamelCase_ : bool = False , lowerCamelCase_ : Optional[TensorType] = None , ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Any = super().generate_dummy_inputs(
preprocessor=lowercase_ , batch_size=lowercase_ , seq_length=lowercase_ , is_pair=lowercase_ , framework=lowercase_ )
import torch
# for some reason, replacing this code by inputs["global_attention_mask"] = torch.randint(2, inputs["input_ids"].shape, dtype=torch.int64)
# makes the export fail randomly
SCREAMING_SNAKE_CASE : Dict = torch.zeros_like(inputs["""input_ids"""] )
# make every second token global
SCREAMING_SNAKE_CASE : Any = 1
return inputs
| 715 |
'''simple docstring'''
import itertools
import os
import random
import tempfile
import unittest
import numpy as np
from transformers import TvltFeatureExtractor, is_datasets_available
from transformers.testing_utils import check_json_file_has_correct_format, require_torch, require_torchaudio
from transformers.utils.import_utils import is_torch_available
from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin
if is_torch_available():
import torch
if is_datasets_available():
from datasets import load_dataset
__UpperCAmelCase = random.Random()
def __A ( lowerCamelCase_ , lowerCamelCase_=1.0 , lowerCamelCase_=None , lowerCamelCase_=None ):
"""simple docstring"""
if rng is None:
SCREAMING_SNAKE_CASE : Optional[Any] = global_rng
SCREAMING_SNAKE_CASE : Optional[int] = []
for batch_idx in range(shape[0] ):
values.append([] )
for _ in range(shape[1] ):
values[-1].append(rng.random() * scale )
return values
class UpperCamelCase__ ( unittest.TestCase ):
"""simple docstring"""
def __init__( self : List[str] , lowerCamelCase_ : List[Any] , lowerCamelCase_ : Optional[int]=7 , lowerCamelCase_ : Optional[int]=4_00 , lowerCamelCase_ : int=20_00 , lowerCamelCase_ : List[str]=20_48 , lowerCamelCase_ : Optional[Any]=1_28 , lowerCamelCase_ : Optional[Any]=1 , lowerCamelCase_ : str=5_12 , lowerCamelCase_ : Dict=30 , lowerCamelCase_ : Dict=4_41_00 , ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Tuple = parent
SCREAMING_SNAKE_CASE : Union[str, Any] = batch_size
SCREAMING_SNAKE_CASE : List[str] = min_seq_length
SCREAMING_SNAKE_CASE : Any = max_seq_length
SCREAMING_SNAKE_CASE : Optional[int] = (self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
SCREAMING_SNAKE_CASE : int = spectrogram_length
SCREAMING_SNAKE_CASE : List[Any] = feature_size
SCREAMING_SNAKE_CASE : Any = num_audio_channels
SCREAMING_SNAKE_CASE : Tuple = hop_length
SCREAMING_SNAKE_CASE : str = chunk_length
SCREAMING_SNAKE_CASE : Dict = sampling_rate
def lowerCamelCase_ ( self : List[Any] ):
'''simple docstring'''
return {
"spectrogram_length": self.spectrogram_length,
"feature_size": self.feature_size,
"num_audio_channels": self.num_audio_channels,
"hop_length": self.hop_length,
"chunk_length": self.chunk_length,
"sampling_rate": self.sampling_rate,
}
def lowerCamelCase_ ( self : Dict , lowerCamelCase_ : Tuple=False , lowerCamelCase_ : Any=False ):
'''simple docstring'''
def _flatten(lowerCamelCase_ : Dict ):
return list(itertools.chain(*lowerCamelCase_ ) )
if equal_length:
SCREAMING_SNAKE_CASE : Optional[Any] = [floats_list((self.max_seq_length, self.feature_size) ) for _ in range(self.batch_size )]
else:
# make sure that inputs increase in size
SCREAMING_SNAKE_CASE : Dict = [
floats_list((x, self.feature_size) )
for x in range(self.min_seq_length , self.max_seq_length , self.seq_length_diff )
]
if numpify:
SCREAMING_SNAKE_CASE : Optional[int] = [np.asarray(lowerCamelCase_ ) for x in speech_inputs]
return speech_inputs
@require_torch
@require_torchaudio
class UpperCamelCase__ ( lowercase_ , unittest.TestCase ):
"""simple docstring"""
SCREAMING_SNAKE_CASE__ = TvltFeatureExtractor
def lowerCamelCase_ ( self : List[Any] ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : List[str] = TvltFeatureExtractionTester(self )
def lowerCamelCase_ ( self : Optional[int] ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Dict = self.feature_extraction_class(**self.feat_extract_dict )
self.assertTrue(hasattr(lowerCamelCase_ , """spectrogram_length""" ) )
self.assertTrue(hasattr(lowerCamelCase_ , """feature_size""" ) )
self.assertTrue(hasattr(lowerCamelCase_ , """num_audio_channels""" ) )
self.assertTrue(hasattr(lowerCamelCase_ , """hop_length""" ) )
self.assertTrue(hasattr(lowerCamelCase_ , """chunk_length""" ) )
self.assertTrue(hasattr(lowerCamelCase_ , """sampling_rate""" ) )
def lowerCamelCase_ ( self : List[str] ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Optional[Any] = self.feature_extraction_class(**self.feat_extract_dict )
with tempfile.TemporaryDirectory() as tmpdirname:
SCREAMING_SNAKE_CASE : Any = feat_extract_first.save_pretrained(lowerCamelCase_ )[0]
check_json_file_has_correct_format(lowerCamelCase_ )
SCREAMING_SNAKE_CASE : Union[str, Any] = self.feature_extraction_class.from_pretrained(lowerCamelCase_ )
SCREAMING_SNAKE_CASE : List[str] = feat_extract_first.to_dict()
SCREAMING_SNAKE_CASE : List[Any] = feat_extract_second.to_dict()
SCREAMING_SNAKE_CASE : Optional[int] = dict_first.pop("""mel_filters""" )
SCREAMING_SNAKE_CASE : Union[str, Any] = dict_second.pop("""mel_filters""" )
self.assertTrue(np.allclose(lowerCamelCase_ , lowerCamelCase_ ) )
self.assertEqual(lowerCamelCase_ , lowerCamelCase_ )
def lowerCamelCase_ ( self : Optional[int] ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Dict = self.feature_extraction_class(**self.feat_extract_dict )
with tempfile.TemporaryDirectory() as tmpdirname:
SCREAMING_SNAKE_CASE : int = os.path.join(lowerCamelCase_ , """feat_extract.json""" )
feat_extract_first.to_json_file(lowerCamelCase_ )
SCREAMING_SNAKE_CASE : Optional[Any] = self.feature_extraction_class.from_json_file(lowerCamelCase_ )
SCREAMING_SNAKE_CASE : List[str] = feat_extract_first.to_dict()
SCREAMING_SNAKE_CASE : int = feat_extract_second.to_dict()
SCREAMING_SNAKE_CASE : List[str] = dict_first.pop("""mel_filters""" )
SCREAMING_SNAKE_CASE : Optional[Any] = dict_second.pop("""mel_filters""" )
self.assertTrue(np.allclose(lowerCamelCase_ , lowerCamelCase_ ) )
self.assertEqual(lowerCamelCase_ , lowerCamelCase_ )
def lowerCamelCase_ ( self : Any ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : int = self.feature_extraction_class(**self.feat_extract_dict )
# create three inputs of length 800, 1000, and 1200
SCREAMING_SNAKE_CASE : Optional[int] = [floats_list((1, x) )[0] for x in range(8_00 , 14_00 , 2_00 )]
SCREAMING_SNAKE_CASE : int = [np.asarray(lowerCamelCase_ ) for speech_input in speech_inputs]
# Test not batched input
SCREAMING_SNAKE_CASE : Union[str, Any] = feature_extractor(np_speech_inputs[0] , return_tensors="""np""" , sampling_rate=4_41_00 ).audio_values
self.assertTrue(encoded_audios.ndim == 4 )
self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size )
self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length )
self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels )
# Test batched
SCREAMING_SNAKE_CASE : Optional[int] = feature_extractor(lowerCamelCase_ , return_tensors="""np""" , sampling_rate=4_41_00 ).audio_values
self.assertTrue(encoded_audios.ndim == 4 )
self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size )
self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length )
self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels )
# Test audio masking
SCREAMING_SNAKE_CASE : List[str] = feature_extractor(
lowerCamelCase_ , return_tensors="""np""" , sampling_rate=4_41_00 , mask_audio=lowerCamelCase_ ).audio_values
self.assertTrue(encoded_audios.ndim == 4 )
self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size )
self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length )
self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels )
# Test 2-D numpy arrays are batched.
SCREAMING_SNAKE_CASE : Optional[int] = [floats_list((1, x) )[0] for x in (8_00, 8_00, 8_00)]
SCREAMING_SNAKE_CASE : int = np.asarray(lowerCamelCase_ )
SCREAMING_SNAKE_CASE : Tuple = feature_extractor(lowerCamelCase_ , return_tensors="""np""" , sampling_rate=4_41_00 ).audio_values
self.assertTrue(encoded_audios.ndim == 4 )
self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size )
self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length )
self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels )
def lowerCamelCase_ ( self : Any , lowerCamelCase_ : int ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : List[Any] = load_dataset("""hf-internal-testing/librispeech_asr_dummy""" , """clean""" , split="""validation""" )
# automatic decoding with librispeech
SCREAMING_SNAKE_CASE : Union[str, Any] = ds.sort("""id""" ).select(range(lowerCamelCase_ ) )[:num_samples]["""audio"""]
return [x["array"] for x in speech_samples]
def lowerCamelCase_ ( self : Dict ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : str = self._load_datasamples(1 )
SCREAMING_SNAKE_CASE : Tuple = TvltFeatureExtractor()
SCREAMING_SNAKE_CASE : List[Any] = feature_extractor(lowerCamelCase_ , return_tensors="""pt""" ).audio_values
self.assertEquals(audio_values.shape , (1, 1, 1_92, 1_28) )
SCREAMING_SNAKE_CASE : List[Any] = torch.tensor([[-0.3_032, -0.2_708], [-0.4_434, -0.4_007]] )
self.assertTrue(torch.allclose(audio_values[0, 0, :2, :2] , lowerCamelCase_ , atol=1e-4 ) )
| 79 | 0 |
import tempfile
import unittest
from transformers import SPIECE_UNDERLINE, BatchEncoding, PLBartTokenizer, is_torch_available
from transformers.testing_utils import (
get_tests_dir,
nested_simplify,
require_sentencepiece,
require_tokenizers,
require_torch,
)
from ...test_tokenization_common import TokenizerTesterMixin
__UpperCAmelCase = get_tests_dir("""fixtures/test_sentencepiece.model""")
if is_torch_available():
from transformers.models.plbart.modeling_plbart import shift_tokens_right
__UpperCAmelCase = 50003
__UpperCAmelCase = 50002
@require_sentencepiece
@require_tokenizers
class UpperCamelCase__ ( __lowerCAmelCase , unittest.TestCase ):
"""simple docstring"""
SCREAMING_SNAKE_CASE__ = PLBartTokenizer
SCREAMING_SNAKE_CASE__ = None
SCREAMING_SNAKE_CASE__ = False
def lowerCamelCase_ ( self : Dict ):
'''simple docstring'''
super().setUp()
# We have a SentencePiece fixture for testing
SCREAMING_SNAKE_CASE : List[Any] = PLBartTokenizer(lowerCAmelCase_ , language_codes="""base""" , keep_accents=lowerCAmelCase_ )
tokenizer.save_pretrained(self.tmpdirname )
def lowerCamelCase_ ( self : Union[str, Any] ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : List[str] = PLBartTokenizer(lowerCAmelCase_ , language_codes="""base""" , keep_accents=lowerCAmelCase_ )
SCREAMING_SNAKE_CASE : Dict = tokenizer.tokenize("""This is a test""" )
self.assertListEqual(lowerCAmelCase_ , ["""▁This""", """▁is""", """▁a""", """▁t""", """est"""] )
self.assertListEqual(
tokenizer.convert_tokens_to_ids(lowerCAmelCase_ ) , [value + tokenizer.fairseq_offset for value in [2_85, 46, 10, 1_70, 3_82]] , )
SCREAMING_SNAKE_CASE : Any = tokenizer.tokenize("""I was born in 92000, and this is falsé.""" )
self.assertListEqual(
lowerCAmelCase_ , [
SPIECE_UNDERLINE + """I""",
SPIECE_UNDERLINE + """was""",
SPIECE_UNDERLINE + """b""",
"""or""",
"""n""",
SPIECE_UNDERLINE + """in""",
SPIECE_UNDERLINE + """""",
"""9""",
"""2""",
"""0""",
"""0""",
"""0""",
""",""",
SPIECE_UNDERLINE + """and""",
SPIECE_UNDERLINE + """this""",
SPIECE_UNDERLINE + """is""",
SPIECE_UNDERLINE + """f""",
"""al""",
"""s""",
"""é""",
""".""",
] , )
SCREAMING_SNAKE_CASE : List[Any] = tokenizer.convert_tokens_to_ids(lowerCAmelCase_ )
self.assertListEqual(
lowerCAmelCase_ , [
value + tokenizer.fairseq_offset
for value in [8, 21, 84, 55, 24, 19, 7, 2, 6_02, 3_47, 3_47, 3_47, 3, 12, 66, 46, 72, 80, 6, 2, 4]
] , )
SCREAMING_SNAKE_CASE : Optional[Any] = tokenizer.convert_ids_to_tokens(lowerCAmelCase_ )
self.assertListEqual(
lowerCAmelCase_ , [
SPIECE_UNDERLINE + """I""",
SPIECE_UNDERLINE + """was""",
SPIECE_UNDERLINE + """b""",
"""or""",
"""n""",
SPIECE_UNDERLINE + """in""",
SPIECE_UNDERLINE + """""",
"""<unk>""",
"""2""",
"""0""",
"""0""",
"""0""",
""",""",
SPIECE_UNDERLINE + """and""",
SPIECE_UNDERLINE + """this""",
SPIECE_UNDERLINE + """is""",
SPIECE_UNDERLINE + """f""",
"""al""",
"""s""",
"""<unk>""",
""".""",
] , )
SCREAMING_SNAKE_CASE : Optional[int] = tokenizer.vocab_size
SCREAMING_SNAKE_CASE : Union[str, Any] = [tokenizer.convert_ids_to_tokens(lowerCAmelCase_ ) for x in range(end - 4 , lowerCAmelCase_ )]
self.assertListEqual(lowerCAmelCase_ , ["""__java__""", """__python__""", """__en_XX__""", """<mask>"""] )
SCREAMING_SNAKE_CASE : Tuple = """java.lang.Exception, python.lang.Exception, javascript, php, ruby, go"""
SCREAMING_SNAKE_CASE : Optional[int] = tokenizer(lowerCAmelCase_ ).input_ids
self.assertEqual(
tokenizer.decode(lowerCAmelCase_ , skip_special_tokens=lowerCAmelCase_ , clean_up_tokenization_spaces=lowerCAmelCase_ ) , lowerCAmelCase_ , )
def lowerCamelCase_ ( self : List[Any] ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : str = PLBartTokenizer(lowerCAmelCase_ , language_codes="""multi""" , keep_accents=lowerCAmelCase_ )
SCREAMING_SNAKE_CASE : List[Any] = tokenizer.tokenize("""This is a test""" )
self.assertListEqual(lowerCAmelCase_ , ["""▁This""", """▁is""", """▁a""", """▁t""", """est"""] )
self.assertListEqual(
tokenizer.convert_tokens_to_ids(lowerCAmelCase_ ) , [value + tokenizer.fairseq_offset for value in [2_85, 46, 10, 1_70, 3_82]] , )
SCREAMING_SNAKE_CASE : Dict = tokenizer.tokenize("""I was born in 92000, and this is falsé.""" )
self.assertListEqual(
lowerCAmelCase_ , [
SPIECE_UNDERLINE + """I""",
SPIECE_UNDERLINE + """was""",
SPIECE_UNDERLINE + """b""",
"""or""",
"""n""",
SPIECE_UNDERLINE + """in""",
SPIECE_UNDERLINE + """""",
"""9""",
"""2""",
"""0""",
"""0""",
"""0""",
""",""",
SPIECE_UNDERLINE + """and""",
SPIECE_UNDERLINE + """this""",
SPIECE_UNDERLINE + """is""",
SPIECE_UNDERLINE + """f""",
"""al""",
"""s""",
"""é""",
""".""",
] , )
SCREAMING_SNAKE_CASE : List[str] = tokenizer.convert_tokens_to_ids(lowerCAmelCase_ )
self.assertListEqual(
lowerCAmelCase_ , [
value + tokenizer.fairseq_offset
for value in [8, 21, 84, 55, 24, 19, 7, 2, 6_02, 3_47, 3_47, 3_47, 3, 12, 66, 46, 72, 80, 6, 2, 4]
] , )
SCREAMING_SNAKE_CASE : Optional[int] = tokenizer.convert_ids_to_tokens(lowerCAmelCase_ )
self.assertListEqual(
lowerCAmelCase_ , [
SPIECE_UNDERLINE + """I""",
SPIECE_UNDERLINE + """was""",
SPIECE_UNDERLINE + """b""",
"""or""",
"""n""",
SPIECE_UNDERLINE + """in""",
SPIECE_UNDERLINE + """""",
"""<unk>""",
"""2""",
"""0""",
"""0""",
"""0""",
""",""",
SPIECE_UNDERLINE + """and""",
SPIECE_UNDERLINE + """this""",
SPIECE_UNDERLINE + """is""",
SPIECE_UNDERLINE + """f""",
"""al""",
"""s""",
"""<unk>""",
""".""",
] , )
SCREAMING_SNAKE_CASE : Optional[int] = tokenizer.vocab_size
SCREAMING_SNAKE_CASE : Any = [tokenizer.convert_ids_to_tokens(lowerCAmelCase_ ) for x in range(end - 7 , lowerCAmelCase_ )]
self.assertListEqual(
lowerCAmelCase_ , ["""__java__""", """__python__""", """__en_XX__""", """__javascript__""", """__php__""", """__ruby__""", """__go__"""] )
SCREAMING_SNAKE_CASE : List[Any] = """java.lang.Exception, python.lang.Exception, javascript, php, ruby, go"""
SCREAMING_SNAKE_CASE : Dict = tokenizer(lowerCAmelCase_ ).input_ids
self.assertEqual(
tokenizer.decode(lowerCAmelCase_ , skip_special_tokens=lowerCAmelCase_ , clean_up_tokenization_spaces=lowerCAmelCase_ ) , lowerCAmelCase_ , )
@require_torch
@require_sentencepiece
@require_tokenizers
class UpperCamelCase__ ( unittest.TestCase ):
"""simple docstring"""
SCREAMING_SNAKE_CASE__ = '''uclanlp/plbart-python-en_XX'''
SCREAMING_SNAKE_CASE__ = [
'''def maximum(a,b,c):NEW_LINE_INDENTreturn max([a,b,c])''',
'''def sum(a,b,c):NEW_LINE_INDENTreturn sum([a,b,c])''',
]
SCREAMING_SNAKE_CASE__ = [
'''Returns the maximum value of a b c.''',
'''Sums the values of a b c.''',
]
SCREAMING_SNAKE_CASE__ = [
134,
5452,
3_3460,
3_3441,
3_3463,
3_3465,
3_3463,
3_3449,
988,
20,
3_3456,
19,
3_3456,
771,
39,
4258,
889,
3318,
3_3441,
3_3463,
3_3465,
3_3463,
3_3449,
2471,
2,
PYTHON_CODE,
]
@classmethod
def lowerCamelCase_ ( cls : Optional[int] ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Optional[int] = PLBartTokenizer.from_pretrained(
cls.checkpoint_name , language_codes="""base""" , src_lang="""python""" , tgt_lang="""en_XX""" )
SCREAMING_SNAKE_CASE : Optional[Any] = 1
return cls
def lowerCamelCase_ ( self : List[Any] ):
'''simple docstring'''
self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["""__java__"""] , 5_00_01 )
self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["""__python__"""] , 5_00_02 )
self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["""__en_XX__"""] , 5_00_03 )
def lowerCamelCase_ ( self : Optional[int] ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : List[Any] = self.tokenizer.batch_encode_plus(self.src_text ).input_ids[0]
self.assertListEqual(self.expected_src_tokens , lowerCAmelCase_ )
def lowerCamelCase_ ( self : int ):
'''simple docstring'''
self.assertIn(lowerCAmelCase_ , self.tokenizer.all_special_ids )
SCREAMING_SNAKE_CASE : Union[str, Any] = [EN_CODE, 90_37, 3_34_42, 57, 7_52, 1_53, 14, 56, 18, 9, 2]
SCREAMING_SNAKE_CASE : List[Any] = self.tokenizer.decode(lowerCAmelCase_ , skip_special_tokens=lowerCAmelCase_ )
SCREAMING_SNAKE_CASE : Optional[int] = self.tokenizer.decode(generated_ids[1:] , skip_special_tokens=lowerCAmelCase_ )
self.assertEqual(lowerCAmelCase_ , lowerCAmelCase_ )
self.assertNotIn(self.tokenizer.eos_token , lowerCAmelCase_ )
def lowerCamelCase_ ( self : Tuple ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Tuple = ["""def sum(a,b,c):NEW_LINE_INDENTreturn sum([a,b,c])""" * 20]
self.assertIsInstance(src_text[0] , lowerCAmelCase_ )
SCREAMING_SNAKE_CASE : Optional[int] = 10
SCREAMING_SNAKE_CASE : str = self.tokenizer(lowerCAmelCase_ , max_length=lowerCAmelCase_ , truncation=lowerCAmelCase_ ).input_ids[0]
self.assertEqual(ids[-2] , 2 )
self.assertEqual(ids[-1] , lowerCAmelCase_ )
self.assertEqual(len(lowerCAmelCase_ ) , lowerCAmelCase_ )
def lowerCamelCase_ ( self : List[Any] ):
'''simple docstring'''
self.assertListEqual(self.tokenizer.convert_tokens_to_ids(["""<mask>""", """__java__"""] ) , [5_00_04, 5_00_01] )
def lowerCamelCase_ ( self : Any ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : int = tempfile.mkdtemp()
SCREAMING_SNAKE_CASE : Tuple = self.tokenizer.fairseq_tokens_to_ids
self.tokenizer.save_pretrained(lowerCAmelCase_ )
SCREAMING_SNAKE_CASE : Tuple = PLBartTokenizer.from_pretrained(lowerCAmelCase_ )
self.assertDictEqual(new_tok.fairseq_tokens_to_ids , lowerCAmelCase_ )
@require_torch
def lowerCamelCase_ ( self : Any ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : List[str] = self.tokenizer(self.src_text , text_target=self.tgt_text , padding=lowerCAmelCase_ , return_tensors="""pt""" )
SCREAMING_SNAKE_CASE : str = shift_tokens_right(batch["""labels"""] , self.tokenizer.pad_token_id )
# fairseq batch: https://gist.github.com/sshleifer/cba08bc2109361a74ac3760a7e30e4f4
self.assertEqual(batch.input_ids[1][-2:].tolist() , [2, PYTHON_CODE] )
self.assertEqual(batch.decoder_input_ids[1][0] , lowerCAmelCase_ )
self.assertEqual(batch.decoder_input_ids[1][-1] , 2 )
self.assertEqual(batch.labels[1][-2:].tolist() , [2, EN_CODE] )
@require_torch
def lowerCamelCase_ ( self : str ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Union[str, Any] = self.tokenizer(
self.src_text , text_target=self.tgt_text , padding=lowerCAmelCase_ , truncation=lowerCAmelCase_ , max_length=len(self.expected_src_tokens ) , return_tensors="""pt""" , )
SCREAMING_SNAKE_CASE : int = shift_tokens_right(batch["""labels"""] , self.tokenizer.pad_token_id )
self.assertIsInstance(lowerCAmelCase_ , lowerCAmelCase_ )
self.assertEqual((2, 26) , batch.input_ids.shape )
self.assertEqual((2, 26) , batch.attention_mask.shape )
SCREAMING_SNAKE_CASE : Any = batch.input_ids.tolist()[0]
self.assertListEqual(self.expected_src_tokens , lowerCAmelCase_ )
self.assertEqual(2 , batch.decoder_input_ids[0, -1] ) # EOS
# Test that special tokens are reset
self.assertEqual(self.tokenizer.prefix_tokens , [] )
self.assertEqual(self.tokenizer.suffix_tokens , [self.tokenizer.eos_token_id, PYTHON_CODE] )
def lowerCamelCase_ ( self : Dict ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Optional[Any] = self.tokenizer(self.src_text , padding=lowerCAmelCase_ , truncation=lowerCAmelCase_ , max_length=3 , return_tensors="""pt""" )
SCREAMING_SNAKE_CASE : Union[str, Any] = self.tokenizer(
text_target=self.tgt_text , padding=lowerCAmelCase_ , truncation=lowerCAmelCase_ , max_length=10 , return_tensors="""pt""" )
SCREAMING_SNAKE_CASE : List[str] = targets["""input_ids"""]
SCREAMING_SNAKE_CASE : List[str] = shift_tokens_right(lowerCAmelCase_ , self.tokenizer.pad_token_id )
self.assertEqual(batch.input_ids.shape[1] , 3 )
self.assertEqual(batch.decoder_input_ids.shape[1] , 10 )
@require_torch
def lowerCamelCase_ ( self : Optional[int] ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Tuple = self.tokenizer._build_translation_inputs(
"""A test""" , return_tensors="""pt""" , src_lang="""en_XX""" , tgt_lang="""java""" )
self.assertEqual(
nested_simplify(lowerCAmelCase_ ) , {
# A, test, EOS, en_XX
"""input_ids""": [[1_50, 2_42, 2, 5_00_03]],
"""attention_mask""": [[1, 1, 1, 1]],
# java
"""forced_bos_token_id""": 5_00_01,
} , )
| 716 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
__UpperCAmelCase = {
"""configuration_mvp""": ["""MVP_PRETRAINED_CONFIG_ARCHIVE_MAP""", """MvpConfig""", """MvpOnnxConfig"""],
"""tokenization_mvp""": ["""MvpTokenizer"""],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__UpperCAmelCase = ["""MvpTokenizerFast"""]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__UpperCAmelCase = [
"""MVP_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""MvpForCausalLM""",
"""MvpForConditionalGeneration""",
"""MvpForQuestionAnswering""",
"""MvpForSequenceClassification""",
"""MvpModel""",
"""MvpPreTrainedModel""",
]
if TYPE_CHECKING:
from .configuration_mvp import MVP_PRETRAINED_CONFIG_ARCHIVE_MAP, MvpConfig, MvpOnnxConfig
from .tokenization_mvp import MvpTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_mvp_fast import MvpTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_mvp import (
MVP_PRETRAINED_MODEL_ARCHIVE_LIST,
MvpForCausalLM,
MvpForConditionalGeneration,
MvpForQuestionAnswering,
MvpForSequenceClassification,
MvpModel,
MvpPreTrainedModel,
)
else:
import sys
__UpperCAmelCase = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 79 | 0 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__UpperCAmelCase = logging.get_logger(__name__)
__UpperCAmelCase = {
'facebook/nllb-moe-54B': 'https://huggingface.co/facebook/nllb-moe-54b/resolve/main/config.json',
}
class UpperCamelCase__ ( lowercase_ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE__ = 'nllb-moe'
SCREAMING_SNAKE_CASE__ = ['past_key_values']
SCREAMING_SNAKE_CASE__ = {'num_attention_heads': 'encoder_attention_heads', 'hidden_size': 'd_model'}
def __init__( self : List[str] , lowerCamelCase_ : Dict=12_81_12 , lowerCamelCase_ : Any=10_24 , lowerCamelCase_ : str=12 , lowerCamelCase_ : Union[str, Any]=40_96 , lowerCamelCase_ : Tuple=16 , lowerCamelCase_ : Union[str, Any]=12 , lowerCamelCase_ : int=40_96 , lowerCamelCase_ : Optional[Any]=16 , lowerCamelCase_ : List[Any]=0.05 , lowerCamelCase_ : Dict=0.05 , lowerCamelCase_ : Optional[Any]=True , lowerCamelCase_ : Optional[Any]=True , lowerCamelCase_ : List[Any]="relu" , lowerCamelCase_ : Union[str, Any]=10_24 , lowerCamelCase_ : int=0.1 , lowerCamelCase_ : Tuple=0.1 , lowerCamelCase_ : Tuple=0.0 , lowerCamelCase_ : str=0.02 , lowerCamelCase_ : Dict=2 , lowerCamelCase_ : List[str]=True , lowerCamelCase_ : Any=False , lowerCamelCase_ : Union[str, Any]="float32" , lowerCamelCase_ : int=False , lowerCamelCase_ : List[Any]=1_28 , lowerCamelCase_ : Optional[int]=64 , lowerCamelCase_ : Optional[Any]=4 , lowerCamelCase_ : List[str]=4 , lowerCamelCase_ : Optional[Any]=0.001 , lowerCamelCase_ : Tuple=0.001 , lowerCamelCase_ : Tuple="all" , lowerCamelCase_ : Optional[Any]=False , lowerCamelCase_ : int=False , lowerCamelCase_ : List[Any]=1.0 , lowerCamelCase_ : List[str]=0.2 , lowerCamelCase_ : List[str]=1 , lowerCamelCase_ : Tuple=0 , lowerCamelCase_ : List[str]=2 , lowerCamelCase_ : Tuple=False , **lowerCamelCase_ : Dict , ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : int = vocab_size
SCREAMING_SNAKE_CASE : List[str] = max_position_embeddings
SCREAMING_SNAKE_CASE : List[Any] = d_model
SCREAMING_SNAKE_CASE : List[Any] = encoder_ffn_dim
SCREAMING_SNAKE_CASE : Optional[Any] = encoder_layers
SCREAMING_SNAKE_CASE : List[Any] = encoder_attention_heads
SCREAMING_SNAKE_CASE : Union[str, Any] = decoder_ffn_dim
SCREAMING_SNAKE_CASE : str = decoder_layers
SCREAMING_SNAKE_CASE : Optional[int] = decoder_attention_heads
SCREAMING_SNAKE_CASE : str = dropout
SCREAMING_SNAKE_CASE : Any = attention_dropout
SCREAMING_SNAKE_CASE : Any = activation_dropout
SCREAMING_SNAKE_CASE : List[Any] = activation_function
SCREAMING_SNAKE_CASE : Any = init_std
SCREAMING_SNAKE_CASE : Optional[Any] = encoder_layerdrop
SCREAMING_SNAKE_CASE : Dict = decoder_layerdrop
SCREAMING_SNAKE_CASE : Any = use_cache
SCREAMING_SNAKE_CASE : Optional[int] = encoder_layers
SCREAMING_SNAKE_CASE : Union[str, Any] = scale_embedding # scale factor will be sqrt(d_model) if True
SCREAMING_SNAKE_CASE : List[str] = router_z_loss_coef
SCREAMING_SNAKE_CASE : str = router_aux_loss_coef
SCREAMING_SNAKE_CASE : Union[str, Any] = decoder_sparse_step
SCREAMING_SNAKE_CASE : Optional[int] = encoder_sparse_step
SCREAMING_SNAKE_CASE : int = num_experts
SCREAMING_SNAKE_CASE : int = expert_capacity
SCREAMING_SNAKE_CASE : int = router_bias
if router_dtype not in ["float32", "float16", "bfloat16"]:
raise ValueError(f'''`router_dtype` must be one of \'float32\', \'float16\' or \'bfloat16\', got {router_dtype}''' )
SCREAMING_SNAKE_CASE : Union[str, Any] = router_dtype
SCREAMING_SNAKE_CASE : Optional[Any] = router_ignore_padding_tokens
SCREAMING_SNAKE_CASE : str = batch_prioritized_routing
SCREAMING_SNAKE_CASE : Optional[Any] = second_expert_policy
SCREAMING_SNAKE_CASE : Dict = normalize_router_prob_before_dropping
SCREAMING_SNAKE_CASE : Optional[int] = moe_eval_capacity_token_fraction
SCREAMING_SNAKE_CASE : Tuple = moe_token_dropout
SCREAMING_SNAKE_CASE : List[Any] = output_router_logits
super().__init__(
pad_token_id=UpperCAmelCase__ , bos_token_id=UpperCAmelCase__ , eos_token_id=UpperCAmelCase__ , is_encoder_decoder=UpperCAmelCase__ , decoder_start_token_id=UpperCAmelCase__ , **UpperCAmelCase__ , )
| 717 |
'''simple docstring'''
__UpperCAmelCase = [
"""Audio""",
"""Array2D""",
"""Array3D""",
"""Array4D""",
"""Array5D""",
"""ClassLabel""",
"""Features""",
"""Sequence""",
"""Value""",
"""Image""",
"""Translation""",
"""TranslationVariableLanguages""",
]
from .audio import Audio
from .features import ArrayaD, ArrayaD, ArrayaD, ArrayaD, ClassLabel, Features, Sequence, Value
from .image import Image
from .translation import Translation, TranslationVariableLanguages
| 79 | 0 |
'''simple docstring'''
import requests
__UpperCAmelCase = """YOUR API KEY"""
def __A ( lowerCamelCase_ , lowerCamelCase_ = giphy_api_key ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : str = "+".join(query.split() )
SCREAMING_SNAKE_CASE : Optional[int] = f'''https://api.giphy.com/v1/gifs/search?q={formatted_query}&api_key={api_key}'''
SCREAMING_SNAKE_CASE : Dict = requests.get(lowerCamelCase_ ).json()["data"]
return [gif["url"] for gif in gifs]
if __name__ == "__main__":
print("""\n""".join(get_gifs("""space ship""")))
| 718 |
'''simple docstring'''
from typing import List, Optional, Union
import torch
from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDPMScheduler
from ...utils import (
is_accelerate_available,
is_accelerate_version,
logging,
randn_tensor,
replace_example_docstring,
)
__UpperCAmelCase = logging.get_logger(__name__) # pylint: disable=invalid-name
__UpperCAmelCase = """
Examples:
```py
>>> import torch
>>> import numpy as np
>>> from diffusers import KandinskyV22PriorPipeline, KandinskyV22ControlnetPipeline
>>> from transformers import pipeline
>>> from diffusers.utils import load_image
>>> def make_hint(image, depth_estimator):
... image = depth_estimator(image)[\"depth\"]
... image = np.array(image)
... image = image[:, :, None]
... image = np.concatenate([image, image, image], axis=2)
... detected_map = torch.from_numpy(image).float() / 255.0
... hint = detected_map.permute(2, 0, 1)
... return hint
>>> depth_estimator = pipeline(\"depth-estimation\")
>>> pipe_prior = KandinskyV22PriorPipeline.from_pretrained(
... \"kandinsky-community/kandinsky-2-2-prior\", torch_dtype=torch.float16
... )
>>> pipe_prior = pipe_prior.to(\"cuda\")
>>> pipe = KandinskyV22ControlnetPipeline.from_pretrained(
... \"kandinsky-community/kandinsky-2-2-controlnet-depth\", torch_dtype=torch.float16
... )
>>> pipe = pipe.to(\"cuda\")
>>> img = load_image(
... \"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main\"
... \"/kandinsky/cat.png\"
... ).resize((768, 768))
>>> hint = make_hint(img, depth_estimator).unsqueeze(0).half().to(\"cuda\")
>>> prompt = \"A robot, 4k photo\"
>>> negative_prior_prompt = \"lowres, text, error, cropped, worst quality, low quality, jpeg artifacts, ugly, duplicate, morbid, mutilated, out of frame, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, mutation, deformed, blurry, dehydrated, bad anatomy, bad proportions, extra limbs, cloned face, disfigured, gross proportions, malformed limbs, missing arms, missing legs, extra arms, extra legs, fused fingers, too many fingers, long neck, username, watermark, signature\"
>>> generator = torch.Generator(device=\"cuda\").manual_seed(43)
>>> image_emb, zero_image_emb = pipe_prior(
... prompt=prompt, negative_prompt=negative_prior_prompt, generator=generator
... ).to_tuple()
>>> images = pipe(
... image_embeds=image_emb,
... negative_image_embeds=zero_image_emb,
... hint=hint,
... num_inference_steps=50,
... generator=generator,
... height=768,
... width=768,
... ).images
>>> images[0].save(\"robot_cat.png\")
```
"""
def __A ( lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_=8 ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : Optional[Any] = height // scale_factor**2
if height % scale_factor**2 != 0:
new_height += 1
SCREAMING_SNAKE_CASE : List[str] = width // scale_factor**2
if width % scale_factor**2 != 0:
new_width += 1
return new_height * scale_factor, new_width * scale_factor
class UpperCamelCase__ ( lowercase_ ):
"""simple docstring"""
def __init__( self : Union[str, Any] , lowerCamelCase_ : UNetaDConditionModel , lowerCamelCase_ : DDPMScheduler , lowerCamelCase_ : VQModel , ):
'''simple docstring'''
super().__init__()
self.register_modules(
unet=lowerCamelCase_ , scheduler=lowerCamelCase_ , movq=lowerCamelCase_ , )
SCREAMING_SNAKE_CASE : str = 2 ** (len(self.movq.config.block_out_channels ) - 1)
def lowerCamelCase_ ( self : int , lowerCamelCase_ : int , lowerCamelCase_ : Optional[int] , lowerCamelCase_ : List[str] , lowerCamelCase_ : Any , lowerCamelCase_ : Tuple , lowerCamelCase_ : int ):
'''simple docstring'''
if latents is None:
SCREAMING_SNAKE_CASE : Tuple = randn_tensor(lowerCamelCase_ , generator=lowerCamelCase_ , device=lowerCamelCase_ , dtype=lowerCamelCase_ )
else:
if latents.shape != shape:
raise ValueError(f'''Unexpected latents shape, got {latents.shape}, expected {shape}''' )
SCREAMING_SNAKE_CASE : Dict = latents.to(lowerCamelCase_ )
SCREAMING_SNAKE_CASE : Any = latents * scheduler.init_noise_sigma
return latents
def lowerCamelCase_ ( self : Union[str, Any] , lowerCamelCase_ : Dict=0 ):
'''simple docstring'''
if is_accelerate_available():
from accelerate import cpu_offload
else:
raise ImportError("""Please install accelerate via `pip install accelerate`""" )
SCREAMING_SNAKE_CASE : List[Any] = torch.device(f'''cuda:{gpu_id}''' )
SCREAMING_SNAKE_CASE : Union[str, Any] = [
self.unet,
self.movq,
]
for cpu_offloaded_model in models:
if cpu_offloaded_model is not None:
cpu_offload(lowerCamelCase_ , lowerCamelCase_ )
def lowerCamelCase_ ( self : List[Any] , lowerCamelCase_ : Tuple=0 ):
'''simple docstring'''
if is_accelerate_available() and is_accelerate_version(""">=""" , """0.17.0.dev0""" ):
from accelerate import cpu_offload_with_hook
else:
raise ImportError("""`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher.""" )
SCREAMING_SNAKE_CASE : Any = torch.device(f'''cuda:{gpu_id}''' )
if self.device.type != "cpu":
self.to("""cpu""" , silence_dtype_warnings=lowerCamelCase_ )
torch.cuda.empty_cache() # otherwise we don't see the memory savings (but they probably exist)
SCREAMING_SNAKE_CASE : Union[str, Any] = None
for cpu_offloaded_model in [self.unet, self.movq]:
SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE : List[Any] = cpu_offload_with_hook(lowerCamelCase_ , lowerCamelCase_ , prev_module_hook=lowerCamelCase_ )
# We'll offload the last model manually.
SCREAMING_SNAKE_CASE : str = hook
@property
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
def lowerCamelCase_ ( self : str ):
'''simple docstring'''
if not hasattr(self.unet , """_hf_hook""" ):
return self.device
for module in self.unet.modules():
if (
hasattr(lowerCamelCase_ , """_hf_hook""" )
and hasattr(module._hf_hook , """execution_device""" )
and module._hf_hook.execution_device is not None
):
return torch.device(module._hf_hook.execution_device )
return self.device
@torch.no_grad()
@replace_example_docstring(lowerCamelCase_ )
def __call__( self : Optional[Any] , lowerCamelCase_ : Union[torch.FloatTensor, List[torch.FloatTensor]] , lowerCamelCase_ : Union[torch.FloatTensor, List[torch.FloatTensor]] , lowerCamelCase_ : torch.FloatTensor , lowerCamelCase_ : int = 5_12 , lowerCamelCase_ : int = 5_12 , lowerCamelCase_ : int = 1_00 , lowerCamelCase_ : float = 4.0 , lowerCamelCase_ : int = 1 , lowerCamelCase_ : Optional[Union[torch.Generator, List[torch.Generator]]] = None , lowerCamelCase_ : Optional[torch.FloatTensor] = None , lowerCamelCase_ : Optional[str] = "pil" , lowerCamelCase_ : bool = True , ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : str = self._execution_device
SCREAMING_SNAKE_CASE : Optional[int] = guidance_scale > 1.0
if isinstance(lowerCamelCase_ , lowerCamelCase_ ):
SCREAMING_SNAKE_CASE : int = torch.cat(lowerCamelCase_ , dim=0 )
if isinstance(lowerCamelCase_ , lowerCamelCase_ ):
SCREAMING_SNAKE_CASE : Dict = torch.cat(lowerCamelCase_ , dim=0 )
if isinstance(lowerCamelCase_ , lowerCamelCase_ ):
SCREAMING_SNAKE_CASE : Optional[Any] = torch.cat(lowerCamelCase_ , dim=0 )
SCREAMING_SNAKE_CASE : Any = image_embeds.shape[0] * num_images_per_prompt
if do_classifier_free_guidance:
SCREAMING_SNAKE_CASE : List[Any] = image_embeds.repeat_interleave(lowerCamelCase_ , dim=0 )
SCREAMING_SNAKE_CASE : Optional[int] = negative_image_embeds.repeat_interleave(lowerCamelCase_ , dim=0 )
SCREAMING_SNAKE_CASE : Dict = hint.repeat_interleave(lowerCamelCase_ , dim=0 )
SCREAMING_SNAKE_CASE : List[Any] = torch.cat([negative_image_embeds, image_embeds] , dim=0 ).to(dtype=self.unet.dtype , device=lowerCamelCase_ )
SCREAMING_SNAKE_CASE : Optional[int] = torch.cat([hint, hint] , dim=0 ).to(dtype=self.unet.dtype , device=lowerCamelCase_ )
self.scheduler.set_timesteps(lowerCamelCase_ , device=lowerCamelCase_ )
SCREAMING_SNAKE_CASE : Union[str, Any] = self.scheduler.timesteps
SCREAMING_SNAKE_CASE : Any = self.movq.config.latent_channels
SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE : Optional[Any] = downscale_height_and_width(lowerCamelCase_ , lowerCamelCase_ , self.movq_scale_factor )
# create initial latent
SCREAMING_SNAKE_CASE : str = self.prepare_latents(
(batch_size, num_channels_latents, height, width) , image_embeds.dtype , lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ , self.scheduler , )
for i, t in enumerate(self.progress_bar(lowerCamelCase_ ) ):
# expand the latents if we are doing classifier free guidance
SCREAMING_SNAKE_CASE : Dict = torch.cat([latents] * 2 ) if do_classifier_free_guidance else latents
SCREAMING_SNAKE_CASE : Union[str, Any] = {"""image_embeds""": image_embeds, """hint""": hint}
SCREAMING_SNAKE_CASE : Dict = self.unet(
sample=lowerCamelCase_ , timestep=lowerCamelCase_ , encoder_hidden_states=lowerCamelCase_ , added_cond_kwargs=lowerCamelCase_ , return_dict=lowerCamelCase_ , )[0]
if do_classifier_free_guidance:
SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE : str = noise_pred.split(latents.shape[1] , dim=1 )
SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE : Optional[Any] = noise_pred.chunk(2 )
SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE : Union[str, Any] = variance_pred.chunk(2 )
SCREAMING_SNAKE_CASE : List[str] = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
SCREAMING_SNAKE_CASE : str = torch.cat([noise_pred, variance_pred_text] , dim=1 )
if not (
hasattr(self.scheduler.config , """variance_type""" )
and self.scheduler.config.variance_type in ["learned", "learned_range"]
):
SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE : Optional[Any] = noise_pred.split(latents.shape[1] , dim=1 )
# compute the previous noisy sample x_t -> x_t-1
SCREAMING_SNAKE_CASE : str = self.scheduler.step(
lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ , generator=lowerCamelCase_ , )[0]
# post-processing
SCREAMING_SNAKE_CASE : List[str] = self.movq.decode(lowerCamelCase_ , force_not_quantize=lowerCamelCase_ )["""sample"""]
if output_type not in ["pt", "np", "pil"]:
raise ValueError(f'''Only the output types `pt`, `pil` and `np` are supported not output_type={output_type}''' )
if output_type in ["np", "pil"]:
SCREAMING_SNAKE_CASE : Optional[int] = image * 0.5 + 0.5
SCREAMING_SNAKE_CASE : List[Any] = image.clamp(0 , 1 )
SCREAMING_SNAKE_CASE : List[Any] = image.cpu().permute(0 , 2 , 3 , 1 ).float().numpy()
if output_type == "pil":
SCREAMING_SNAKE_CASE : Any = self.numpy_to_pil(lowerCamelCase_ )
if not return_dict:
return (image,)
return ImagePipelineOutput(images=lowerCamelCase_ )
| 79 | 0 |
'''simple docstring'''
import logging
import os
from typing import List, Tuple
import numpy as np
import psutil
import torch
import torch.distributed as dist
from transformers import RagRetriever
__UpperCAmelCase = logging.getLogger(__name__)
class UpperCamelCase__ ( __lowerCamelCase ):
"""simple docstring"""
def __init__( self : int , lowerCamelCase_ : Union[str, Any] , lowerCamelCase_ : Optional[int] , lowerCamelCase_ : Dict , lowerCamelCase_ : str=None ):
'''simple docstring'''
super().__init__(
SCREAMING_SNAKE_CASE_ , question_encoder_tokenizer=SCREAMING_SNAKE_CASE_ , generator_tokenizer=SCREAMING_SNAKE_CASE_ , index=SCREAMING_SNAKE_CASE_ , init_retrieval=SCREAMING_SNAKE_CASE_ , )
SCREAMING_SNAKE_CASE : Optional[Any] = None
def lowerCamelCase_ ( self : Any , lowerCamelCase_ : int ):
'''simple docstring'''
logger.info("""initializing retrieval""" )
# initializing a separate process group for retrieval as the default
# nccl backend doesn't support gather/scatter operations while gloo
# is too slow to replace nccl for the core gpu communication
if dist.is_initialized():
logger.info("""dist initialized""" )
# needs to be set manually
SCREAMING_SNAKE_CASE : Optional[Any] = self._infer_socket_ifname()
# avoid clash with the NCCL port
SCREAMING_SNAKE_CASE : Any = str(distributed_port + 1 )
SCREAMING_SNAKE_CASE : Tuple = dist.new_group(ranks=SCREAMING_SNAKE_CASE_ , backend="""gloo""" )
# initialize retriever only on the main worker
if not dist.is_initialized() or self._is_main():
logger.info("""dist not initialized / main""" )
self.index.init_index()
# all processes wait untill the retriever is initialized by the main process
if dist.is_initialized():
torch.distributed.barrier(group=self.process_group )
def lowerCamelCase_ ( self : Optional[Any] ):
'''simple docstring'''
return dist.get_rank(group=self.process_group ) == 0
def lowerCamelCase_ ( self : Optional[int] , lowerCamelCase_ : List[Any] , lowerCamelCase_ : int , lowerCamelCase_ : str=torch.floataa ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Any = torch.empty(SCREAMING_SNAKE_CASE_ , dtype=SCREAMING_SNAKE_CASE_ )
dist.scatter(SCREAMING_SNAKE_CASE_ , src=0 , scatter_list=SCREAMING_SNAKE_CASE_ , group=self.process_group )
return target_tensor
def lowerCamelCase_ ( self : Dict ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Dict = psutil.net_if_addrs()
# a hacky way to deal with varying network interface names
SCREAMING_SNAKE_CASE : str = next((addr for addr in addrs if addr.startswith("""e""" )) , SCREAMING_SNAKE_CASE_ )
return ifname
def lowerCamelCase_ ( self : str , lowerCamelCase_ : Any , lowerCamelCase_ : int ):
'''simple docstring'''
if not dist.is_initialized():
SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE : str = self._main_retrieve(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
return retrieved_doc_embeds, doc_ids, self.index.get_doc_dicts(SCREAMING_SNAKE_CASE_ )
# distributed training
SCREAMING_SNAKE_CASE : str = dist.get_world_size(group=self.process_group )
# gather logic
SCREAMING_SNAKE_CASE : List[Any] = None
if self._is_main():
SCREAMING_SNAKE_CASE : Any = [torch.empty(question_hidden_states.shape , dtype=torch.floataa ) for _ in range(SCREAMING_SNAKE_CASE_ )]
dist.gather(torch.tensor(SCREAMING_SNAKE_CASE_ ) , dst=0 , gather_list=SCREAMING_SNAKE_CASE_ , group=self.process_group )
# scatter logic
SCREAMING_SNAKE_CASE : Union[str, Any] = question_hidden_states.shape[0]
SCREAMING_SNAKE_CASE : str = []
SCREAMING_SNAKE_CASE : List[Any] = []
if self._is_main():
assert len(SCREAMING_SNAKE_CASE_ ) == world_size
SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE : int = self._main_retrieve(torch.cat(SCREAMING_SNAKE_CASE_ ).numpy() , SCREAMING_SNAKE_CASE_ )
SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE : Union[str, Any] = torch.tensor(SCREAMING_SNAKE_CASE_ ), torch.tensor(SCREAMING_SNAKE_CASE_ )
SCREAMING_SNAKE_CASE : Any = self._chunk_tensor(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
SCREAMING_SNAKE_CASE : str = self._chunk_tensor(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
SCREAMING_SNAKE_CASE : str = self._scattered(SCREAMING_SNAKE_CASE_ , [n_queries, n_docs] , target_type=torch.intaa )
SCREAMING_SNAKE_CASE : Tuple = self._scattered(SCREAMING_SNAKE_CASE_ , [n_queries, n_docs, question_hidden_states.shape[1]] )
return retrieved_doc_embeds.numpy(), doc_ids.numpy(), self.index.get_doc_dicts(SCREAMING_SNAKE_CASE_ )
| 719 |
'''simple docstring'''
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_big_bird import BigBirdTokenizer
else:
__UpperCAmelCase = None
__UpperCAmelCase = logging.get_logger(__name__)
__UpperCAmelCase = {"""vocab_file""": """spiece.model""", """tokenizer_file""": """tokenizer.json"""}
__UpperCAmelCase = {
"""vocab_file""": {
"""google/bigbird-roberta-base""": """https://huggingface.co/google/bigbird-roberta-base/resolve/main/spiece.model""",
"""google/bigbird-roberta-large""": (
"""https://huggingface.co/google/bigbird-roberta-large/resolve/main/spiece.model"""
),
"""google/bigbird-base-trivia-itc""": (
"""https://huggingface.co/google/bigbird-base-trivia-itc/resolve/main/spiece.model"""
),
},
"""tokenizer_file""": {
"""google/bigbird-roberta-base""": (
"""https://huggingface.co/google/bigbird-roberta-base/resolve/main/tokenizer.json"""
),
"""google/bigbird-roberta-large""": (
"""https://huggingface.co/google/bigbird-roberta-large/resolve/main/tokenizer.json"""
),
"""google/bigbird-base-trivia-itc""": (
"""https://huggingface.co/google/bigbird-base-trivia-itc/resolve/main/tokenizer.json"""
),
},
}
__UpperCAmelCase = {
"""google/bigbird-roberta-base""": 4096,
"""google/bigbird-roberta-large""": 4096,
"""google/bigbird-base-trivia-itc""": 4096,
}
__UpperCAmelCase = """▁"""
class UpperCamelCase__ ( lowercase_ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE__ = VOCAB_FILES_NAMES
SCREAMING_SNAKE_CASE__ = PRETRAINED_VOCAB_FILES_MAP
SCREAMING_SNAKE_CASE__ = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
SCREAMING_SNAKE_CASE__ = BigBirdTokenizer
SCREAMING_SNAKE_CASE__ = ['''input_ids''', '''attention_mask''']
SCREAMING_SNAKE_CASE__ = []
def __init__( self : Any , lowerCamelCase_ : str=None , lowerCamelCase_ : List[Any]=None , lowerCamelCase_ : Dict="<unk>" , lowerCamelCase_ : int="<s>" , lowerCamelCase_ : Optional[Any]="</s>" , lowerCamelCase_ : Dict="<pad>" , lowerCamelCase_ : Tuple="[SEP]" , lowerCamelCase_ : Dict="[MASK]" , lowerCamelCase_ : Union[str, Any]="[CLS]" , **lowerCamelCase_ : Dict , ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Any = AddedToken(lowerCamelCase_ , lstrip=lowerCamelCase_ , rstrip=lowerCamelCase_ ) if isinstance(lowerCamelCase_ , lowerCamelCase_ ) else bos_token
SCREAMING_SNAKE_CASE : Dict = AddedToken(lowerCamelCase_ , lstrip=lowerCamelCase_ , rstrip=lowerCamelCase_ ) if isinstance(lowerCamelCase_ , lowerCamelCase_ ) else eos_token
SCREAMING_SNAKE_CASE : Optional[Any] = AddedToken(lowerCamelCase_ , lstrip=lowerCamelCase_ , rstrip=lowerCamelCase_ ) if isinstance(lowerCamelCase_ , lowerCamelCase_ ) else unk_token
SCREAMING_SNAKE_CASE : int = AddedToken(lowerCamelCase_ , lstrip=lowerCamelCase_ , rstrip=lowerCamelCase_ ) if isinstance(lowerCamelCase_ , lowerCamelCase_ ) else pad_token
SCREAMING_SNAKE_CASE : Any = AddedToken(lowerCamelCase_ , lstrip=lowerCamelCase_ , rstrip=lowerCamelCase_ ) if isinstance(lowerCamelCase_ , lowerCamelCase_ ) else cls_token
SCREAMING_SNAKE_CASE : Any = AddedToken(lowerCamelCase_ , lstrip=lowerCamelCase_ , rstrip=lowerCamelCase_ ) if isinstance(lowerCamelCase_ , lowerCamelCase_ ) else sep_token
# Mask token behave like a normal word, i.e. include the space before it
SCREAMING_SNAKE_CASE : int = 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_ , unk_token=lowerCamelCase_ , sep_token=lowerCamelCase_ , pad_token=lowerCamelCase_ , cls_token=lowerCamelCase_ , mask_token=lowerCamelCase_ , **lowerCamelCase_ , )
SCREAMING_SNAKE_CASE : List[Any] = vocab_file
SCREAMING_SNAKE_CASE : Optional[Any] = False if not self.vocab_file else True
def lowerCamelCase_ ( self : Union[str, Any] , lowerCamelCase_ : List[int] , lowerCamelCase_ : Optional[List[int]] = None ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Optional[Any] = [self.sep_token_id]
SCREAMING_SNAKE_CASE : int = [self.cls_token_id]
if token_ids_a is None:
return cls + token_ids_a + sep
return cls + token_ids_a + sep + token_ids_a + sep
def lowerCamelCase_ ( self : List[Any] , lowerCamelCase_ : List[int] , lowerCamelCase_ : Optional[List[int]] = None , lowerCamelCase_ : bool = False ):
'''simple docstring'''
if already_has_special_tokens:
if token_ids_a is not None:
raise ValueError(
"""You should not supply a second sequence if the provided sequence of """
"""ids is already formatted with special tokens for the model.""" )
return [1 if x in [self.sep_token_id, self.cls_token_id] else 0 for x in token_ids_a]
if token_ids_a is None:
return [1] + ([0] * len(lowerCamelCase_ )) + [1]
return [1] + ([0] * len(lowerCamelCase_ )) + [1] + ([0] * len(lowerCamelCase_ )) + [1]
def lowerCamelCase_ ( self : Optional[int] , lowerCamelCase_ : List[int] , lowerCamelCase_ : Optional[List[int]] = None ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Any = [self.sep_token_id]
SCREAMING_SNAKE_CASE : Optional[int] = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep ) * [0] + len(token_ids_a + sep ) * [1]
def lowerCamelCase_ ( self : str , lowerCamelCase_ : str , lowerCamelCase_ : 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 : Tuple = 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,)
| 79 | 0 |
'''simple docstring'''
import gc
import unittest
import numpy as np
import torch
from diffusers import AutoencoderKL, DDIMScheduler, DiTPipeline, DPMSolverMultistepScheduler, TransformeraDModel
from diffusers.utils import is_xformers_available, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu
from ..pipeline_params import (
CLASS_CONDITIONED_IMAGE_GENERATION_BATCH_PARAMS,
CLASS_CONDITIONED_IMAGE_GENERATION_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin
enable_full_determinism()
class UpperCamelCase__ ( _snake_case , unittest.TestCase ):
"""simple docstring"""
SCREAMING_SNAKE_CASE__ = DiTPipeline
SCREAMING_SNAKE_CASE__ = CLASS_CONDITIONED_IMAGE_GENERATION_PARAMS
SCREAMING_SNAKE_CASE__ = PipelineTesterMixin.required_optional_params - {
"""latents""",
"""num_images_per_prompt""",
"""callback""",
"""callback_steps""",
}
SCREAMING_SNAKE_CASE__ = CLASS_CONDITIONED_IMAGE_GENERATION_BATCH_PARAMS
SCREAMING_SNAKE_CASE__ = False
def lowerCamelCase_ ( self : Optional[Any] ):
'''simple docstring'''
torch.manual_seed(0 )
SCREAMING_SNAKE_CASE : Union[str, Any] = TransformeraDModel(
sample_size=16 , num_layers=2 , patch_size=4 , attention_head_dim=8 , num_attention_heads=2 , in_channels=4 , out_channels=8 , attention_bias=snake_case_ , activation_fn="""gelu-approximate""" , num_embeds_ada_norm=10_00 , norm_type="""ada_norm_zero""" , norm_elementwise_affine=snake_case_ , )
SCREAMING_SNAKE_CASE : int = AutoencoderKL()
SCREAMING_SNAKE_CASE : List[str] = DDIMScheduler()
SCREAMING_SNAKE_CASE : Dict = {"transformer": transformer.eval(), "vae": vae.eval(), "scheduler": scheduler}
return components
def lowerCamelCase_ ( self : str , lowerCamelCase_ : Optional[Any] , lowerCamelCase_ : List[str]=0 ):
'''simple docstring'''
if str(snake_case_ ).startswith("""mps""" ):
SCREAMING_SNAKE_CASE : Dict = torch.manual_seed(snake_case_ )
else:
SCREAMING_SNAKE_CASE : List[Any] = torch.Generator(device=snake_case_ ).manual_seed(snake_case_ )
SCREAMING_SNAKE_CASE : List[Any] = {
"class_labels": [1],
"generator": generator,
"num_inference_steps": 2,
"output_type": "numpy",
}
return inputs
def lowerCamelCase_ ( self : List[Any] ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Any = "cpu"
SCREAMING_SNAKE_CASE : str = self.get_dummy_components()
SCREAMING_SNAKE_CASE : Union[str, Any] = self.pipeline_class(**snake_case_ )
pipe.to(snake_case_ )
pipe.set_progress_bar_config(disable=snake_case_ )
SCREAMING_SNAKE_CASE : Tuple = self.get_dummy_inputs(snake_case_ )
SCREAMING_SNAKE_CASE : List[str] = pipe(**snake_case_ ).images
SCREAMING_SNAKE_CASE : Optional[int] = image[0, -3:, -3:, -1]
self.assertEqual(image.shape , (1, 16, 16, 3) )
SCREAMING_SNAKE_CASE : List[str] = np.array([0.2_946, 0.6_601, 0.4_329, 0.3_296, 0.4_144, 0.5_319, 0.7_273, 0.5_013, 0.4_457] )
SCREAMING_SNAKE_CASE : Optional[Any] = np.abs(image_slice.flatten() - expected_slice ).max()
self.assertLessEqual(snake_case_ , 1e-3 )
def lowerCamelCase_ ( self : Optional[Any] ):
'''simple docstring'''
self._test_inference_batch_single_identical(relax_max_difference=snake_case_ , expected_max_diff=1e-3 )
@unittest.skipIf(
torch_device != """cuda""" or not is_xformers_available() , reason="""XFormers attention is only available with CUDA and `xformers` installed""" , )
def lowerCamelCase_ ( self : Optional[int] ):
'''simple docstring'''
self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3 )
@require_torch_gpu
@slow
class UpperCamelCase__ ( unittest.TestCase ):
"""simple docstring"""
def lowerCamelCase_ ( self : List[str] ):
'''simple docstring'''
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def lowerCamelCase_ ( self : Union[str, Any] ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : List[str] = torch.manual_seed(0 )
SCREAMING_SNAKE_CASE : Dict = DiTPipeline.from_pretrained("""facebook/DiT-XL-2-256""" )
pipe.to("""cuda""" )
SCREAMING_SNAKE_CASE : Union[str, Any] = ["vase", "umbrella", "white shark", "white wolf"]
SCREAMING_SNAKE_CASE : Optional[Any] = pipe.get_label_ids(snake_case_ )
SCREAMING_SNAKE_CASE : Tuple = pipe(snake_case_ , generator=snake_case_ , num_inference_steps=40 , output_type="""np""" ).images
for word, image in zip(snake_case_ , snake_case_ ):
SCREAMING_SNAKE_CASE : str = load_numpy(
f'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/dit/{word}.npy''' )
assert np.abs((expected_image - image).max() ) < 1e-2
def lowerCamelCase_ ( self : Dict ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : int = DiTPipeline.from_pretrained("""facebook/DiT-XL-2-512""" )
SCREAMING_SNAKE_CASE : Optional[Any] = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config )
pipe.to("""cuda""" )
SCREAMING_SNAKE_CASE : str = ["vase", "umbrella"]
SCREAMING_SNAKE_CASE : Any = pipe.get_label_ids(snake_case_ )
SCREAMING_SNAKE_CASE : List[str] = torch.manual_seed(0 )
SCREAMING_SNAKE_CASE : Tuple = pipe(snake_case_ , generator=snake_case_ , num_inference_steps=25 , output_type="""np""" ).images
for word, image in zip(snake_case_ , snake_case_ ):
SCREAMING_SNAKE_CASE : Any = load_numpy(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
f'''/dit/{word}_512.npy''' )
assert np.abs((expected_image - image).max() ) < 1e-1
| 720 |
'''simple docstring'''
import unittest
import torch
from torch import nn
from diffusers.models.activations import get_activation
class UpperCamelCase__ ( unittest.TestCase ):
"""simple docstring"""
def lowerCamelCase_ ( self : List[str] ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : List[Any] = get_activation("""swish""" )
self.assertIsInstance(lowerCamelCase_ , nn.SiLU )
self.assertEqual(act(torch.tensor(-1_00 , dtype=torch.floataa ) ).item() , 0 )
self.assertNotEqual(act(torch.tensor(-1 , dtype=torch.floataa ) ).item() , 0 )
self.assertEqual(act(torch.tensor(0 , dtype=torch.floataa ) ).item() , 0 )
self.assertEqual(act(torch.tensor(20 , dtype=torch.floataa ) ).item() , 20 )
def lowerCamelCase_ ( self : int ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Any = get_activation("""silu""" )
self.assertIsInstance(lowerCamelCase_ , nn.SiLU )
self.assertEqual(act(torch.tensor(-1_00 , dtype=torch.floataa ) ).item() , 0 )
self.assertNotEqual(act(torch.tensor(-1 , dtype=torch.floataa ) ).item() , 0 )
self.assertEqual(act(torch.tensor(0 , dtype=torch.floataa ) ).item() , 0 )
self.assertEqual(act(torch.tensor(20 , dtype=torch.floataa ) ).item() , 20 )
def lowerCamelCase_ ( self : Optional[int] ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : List[Any] = get_activation("""mish""" )
self.assertIsInstance(lowerCamelCase_ , nn.Mish )
self.assertEqual(act(torch.tensor(-2_00 , dtype=torch.floataa ) ).item() , 0 )
self.assertNotEqual(act(torch.tensor(-1 , dtype=torch.floataa ) ).item() , 0 )
self.assertEqual(act(torch.tensor(0 , dtype=torch.floataa ) ).item() , 0 )
self.assertEqual(act(torch.tensor(20 , dtype=torch.floataa ) ).item() , 20 )
def lowerCamelCase_ ( self : List[str] ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : str = get_activation("""gelu""" )
self.assertIsInstance(lowerCamelCase_ , nn.GELU )
self.assertEqual(act(torch.tensor(-1_00 , dtype=torch.floataa ) ).item() , 0 )
self.assertNotEqual(act(torch.tensor(-1 , dtype=torch.floataa ) ).item() , 0 )
self.assertEqual(act(torch.tensor(0 , dtype=torch.floataa ) ).item() , 0 )
self.assertEqual(act(torch.tensor(20 , dtype=torch.floataa ) ).item() , 20 )
| 79 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
__UpperCAmelCase = {
"""configuration_squeezebert""": [
"""SQUEEZEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""SqueezeBertConfig""",
"""SqueezeBertOnnxConfig""",
],
"""tokenization_squeezebert""": ["""SqueezeBertTokenizer"""],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__UpperCAmelCase = ["""SqueezeBertTokenizerFast"""]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__UpperCAmelCase = [
"""SQUEEZEBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""SqueezeBertForMaskedLM""",
"""SqueezeBertForMultipleChoice""",
"""SqueezeBertForQuestionAnswering""",
"""SqueezeBertForSequenceClassification""",
"""SqueezeBertForTokenClassification""",
"""SqueezeBertModel""",
"""SqueezeBertModule""",
"""SqueezeBertPreTrainedModel""",
]
if TYPE_CHECKING:
from .configuration_squeezebert import (
SQUEEZEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
SqueezeBertConfig,
SqueezeBertOnnxConfig,
)
from .tokenization_squeezebert import SqueezeBertTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_squeezebert_fast import SqueezeBertTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_squeezebert import (
SQUEEZEBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
SqueezeBertForMaskedLM,
SqueezeBertForMultipleChoice,
SqueezeBertForQuestionAnswering,
SqueezeBertForSequenceClassification,
SqueezeBertForTokenClassification,
SqueezeBertModel,
SqueezeBertModule,
SqueezeBertPreTrainedModel,
)
else:
import sys
__UpperCAmelCase = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 721 |
'''simple docstring'''
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
if TYPE_CHECKING:
from ... import FeatureExtractionMixin, PreTrainedTokenizerBase, TensorType
__UpperCAmelCase = logging.get_logger(__name__)
__UpperCAmelCase = {
"""microsoft/deberta-v2-xlarge""": """https://huggingface.co/microsoft/deberta-v2-xlarge/resolve/main/config.json""",
"""microsoft/deberta-v2-xxlarge""": """https://huggingface.co/microsoft/deberta-v2-xxlarge/resolve/main/config.json""",
"""microsoft/deberta-v2-xlarge-mnli""": (
"""https://huggingface.co/microsoft/deberta-v2-xlarge-mnli/resolve/main/config.json"""
),
"""microsoft/deberta-v2-xxlarge-mnli""": (
"""https://huggingface.co/microsoft/deberta-v2-xxlarge-mnli/resolve/main/config.json"""
),
}
class UpperCamelCase__ ( lowercase_ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE__ = '''deberta-v2'''
def __init__( self : int , lowerCamelCase_ : Optional[Any]=12_81_00 , lowerCamelCase_ : str=15_36 , lowerCamelCase_ : int=24 , lowerCamelCase_ : List[str]=24 , lowerCamelCase_ : List[Any]=61_44 , lowerCamelCase_ : List[Any]="gelu" , lowerCamelCase_ : Optional[Any]=0.1 , lowerCamelCase_ : List[Any]=0.1 , lowerCamelCase_ : str=5_12 , lowerCamelCase_ : str=0 , lowerCamelCase_ : Union[str, Any]=0.02 , lowerCamelCase_ : Dict=1e-7 , lowerCamelCase_ : Optional[int]=False , lowerCamelCase_ : Optional[int]=-1 , lowerCamelCase_ : List[str]=0 , lowerCamelCase_ : Tuple=True , lowerCamelCase_ : Optional[int]=None , lowerCamelCase_ : Optional[Any]=0 , lowerCamelCase_ : Dict="gelu" , **lowerCamelCase_ : Optional[int] , ):
'''simple docstring'''
super().__init__(**lowerCamelCase_ )
SCREAMING_SNAKE_CASE : Optional[int] = hidden_size
SCREAMING_SNAKE_CASE : Optional[Any] = num_hidden_layers
SCREAMING_SNAKE_CASE : int = num_attention_heads
SCREAMING_SNAKE_CASE : List[str] = intermediate_size
SCREAMING_SNAKE_CASE : int = hidden_act
SCREAMING_SNAKE_CASE : Tuple = hidden_dropout_prob
SCREAMING_SNAKE_CASE : int = attention_probs_dropout_prob
SCREAMING_SNAKE_CASE : str = max_position_embeddings
SCREAMING_SNAKE_CASE : Tuple = type_vocab_size
SCREAMING_SNAKE_CASE : Optional[int] = initializer_range
SCREAMING_SNAKE_CASE : List[Any] = relative_attention
SCREAMING_SNAKE_CASE : str = max_relative_positions
SCREAMING_SNAKE_CASE : int = pad_token_id
SCREAMING_SNAKE_CASE : List[str] = position_biased_input
# Backwards compatibility
if type(lowerCamelCase_ ) == str:
SCREAMING_SNAKE_CASE : Dict = [x.strip() for x in pos_att_type.lower().split("""|""" )]
SCREAMING_SNAKE_CASE : Any = pos_att_type
SCREAMING_SNAKE_CASE : Any = vocab_size
SCREAMING_SNAKE_CASE : Optional[Any] = layer_norm_eps
SCREAMING_SNAKE_CASE : str = kwargs.get("""pooler_hidden_size""" , lowerCamelCase_ )
SCREAMING_SNAKE_CASE : Dict = pooler_dropout
SCREAMING_SNAKE_CASE : Any = pooler_hidden_act
class UpperCamelCase__ ( lowercase_ ):
"""simple docstring"""
@property
def lowerCamelCase_ ( self : Optional[int] ):
'''simple docstring'''
if self.task == "multiple-choice":
SCREAMING_SNAKE_CASE : Optional[int] = {0: """batch""", 1: """choice""", 2: """sequence"""}
else:
SCREAMING_SNAKE_CASE : Union[str, Any] = {0: """batch""", 1: """sequence"""}
if self._config.type_vocab_size > 0:
return OrderedDict(
[("""input_ids""", dynamic_axis), ("""attention_mask""", dynamic_axis), ("""token_type_ids""", dynamic_axis)] )
else:
return OrderedDict([("""input_ids""", dynamic_axis), ("""attention_mask""", dynamic_axis)] )
@property
def lowerCamelCase_ ( self : Tuple ):
'''simple docstring'''
return 12
def lowerCamelCase_ ( self : Optional[Any] , lowerCamelCase_ : Union["PreTrainedTokenizerBase", "FeatureExtractionMixin"] , lowerCamelCase_ : int = -1 , lowerCamelCase_ : int = -1 , lowerCamelCase_ : int = -1 , lowerCamelCase_ : bool = False , lowerCamelCase_ : Optional["TensorType"] = None , lowerCamelCase_ : int = 3 , lowerCamelCase_ : int = 40 , lowerCamelCase_ : int = 40 , lowerCamelCase_ : "PreTrainedTokenizerBase" = None , ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Dict = super().generate_dummy_inputs(preprocessor=lowerCamelCase_ , framework=lowerCamelCase_ )
if self._config.type_vocab_size == 0 and "token_type_ids" in dummy_inputs:
del dummy_inputs["token_type_ids"]
return dummy_inputs
| 79 | 0 |
'''simple docstring'''
import argparse
import torch
from transformers import RemBertConfig, RemBertModel, load_tf_weights_in_rembert
from transformers.utils import logging
logging.set_verbosity_info()
def __A ( lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : List[Any] = RemBertConfig.from_json_file(_lowerCAmelCase )
print("""Building PyTorch model from configuration: {}""".format(str(_lowerCAmelCase ) ) )
SCREAMING_SNAKE_CASE : Optional[int] = RemBertModel(_lowerCAmelCase )
# Load weights from tf checkpoint
load_tf_weights_in_rembert(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase )
# Save pytorch-model
print("""Save PyTorch model to {}""".format(_lowerCAmelCase ) )
torch.save(model.state_dict() , _lowerCAmelCase )
if __name__ == "__main__":
__UpperCAmelCase = 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(
"""--rembert_config_file""",
default=None,
type=str,
required=True,
help=(
"""The config json file corresponding to the pre-trained RemBERT 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 = parser.parse_args()
convert_rembert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.rembert_config_file, args.pytorch_dump_path)
| 700 |
'''simple docstring'''
from collections import deque
from math import floor
from random import random
from time import time
class UpperCamelCase__ :
"""simple docstring"""
def __init__( self : Optional[Any] ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : str = {}
def lowerCamelCase_ ( self : Union[str, Any] , lowerCamelCase_ : Optional[Any] , lowerCamelCase_ : Any , lowerCamelCase_ : Optional[int]=1 ):
'''simple docstring'''
if self.graph.get(lowerCamelCase_ ):
if self.graph[u].count([w, v] ) == 0:
self.graph[u].append([w, v] )
else:
SCREAMING_SNAKE_CASE : str = [[w, v]]
if not self.graph.get(lowerCamelCase_ ):
SCREAMING_SNAKE_CASE : Tuple = []
def lowerCamelCase_ ( self : Optional[Any] ):
'''simple docstring'''
return list(self.graph )
def lowerCamelCase_ ( self : Optional[int] , lowerCamelCase_ : Any , lowerCamelCase_ : str ):
'''simple docstring'''
if self.graph.get(lowerCamelCase_ ):
for _ in self.graph[u]:
if _[1] == v:
self.graph[u].remove(lowerCamelCase_ )
def lowerCamelCase_ ( self : List[str] , lowerCamelCase_ : Union[str, Any]=-2 , lowerCamelCase_ : str=-1 ):
'''simple docstring'''
if s == d:
return []
SCREAMING_SNAKE_CASE : List[str] = []
SCREAMING_SNAKE_CASE : List[str] = []
if s == -2:
SCREAMING_SNAKE_CASE : Tuple = list(self.graph )[0]
stack.append(lowerCamelCase_ )
visited.append(lowerCamelCase_ )
SCREAMING_SNAKE_CASE : Any = s
while True:
# check if there is any non isolated nodes
if len(self.graph[s] ) != 0:
SCREAMING_SNAKE_CASE : Optional[Any] = s
for node in self.graph[s]:
if visited.count(node[1] ) < 1:
if node[1] == d:
visited.append(lowerCamelCase_ )
return visited
else:
stack.append(node[1] )
visited.append(node[1] )
SCREAMING_SNAKE_CASE : Tuple = node[1]
break
# check if all the children are visited
if s == ss:
stack.pop()
if len(lowerCamelCase_ ) != 0:
SCREAMING_SNAKE_CASE : int = stack[len(lowerCamelCase_ ) - 1]
else:
SCREAMING_SNAKE_CASE : Any = ss
# check if se have reached the starting point
if len(lowerCamelCase_ ) == 0:
return visited
def lowerCamelCase_ ( self : Dict , lowerCamelCase_ : Optional[int]=-1 ):
'''simple docstring'''
if c == -1:
SCREAMING_SNAKE_CASE : str = floor(random() * 1_00_00 ) + 10
for i in range(lowerCamelCase_ ):
# every vertex has max 100 edges
for _ in range(floor(random() * 1_02 ) + 1 ):
SCREAMING_SNAKE_CASE : Union[str, Any] = floor(random() * c ) + 1
if n != i:
self.add_pair(lowerCamelCase_ , lowerCamelCase_ , 1 )
def lowerCamelCase_ ( self : List[Any] , lowerCamelCase_ : Any=-2 ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : str = deque()
SCREAMING_SNAKE_CASE : List[str] = []
if s == -2:
SCREAMING_SNAKE_CASE : int = list(self.graph )[0]
d.append(lowerCamelCase_ )
visited.append(lowerCamelCase_ )
while d:
SCREAMING_SNAKE_CASE : Dict = d.popleft()
if len(self.graph[s] ) != 0:
for node in self.graph[s]:
if visited.count(node[1] ) < 1:
d.append(node[1] )
visited.append(node[1] )
return visited
def lowerCamelCase_ ( self : Dict , lowerCamelCase_ : Optional[Any] ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Dict = 0
for x in self.graph:
for y in self.graph[x]:
if y[1] == u:
count += 1
return count
def lowerCamelCase_ ( self : Optional[Any] , lowerCamelCase_ : Tuple ):
'''simple docstring'''
return len(self.graph[u] )
def lowerCamelCase_ ( self : str , lowerCamelCase_ : Optional[Any]=-2 ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : List[Any] = []
SCREAMING_SNAKE_CASE : List[str] = []
if s == -2:
SCREAMING_SNAKE_CASE : Union[str, Any] = list(self.graph )[0]
stack.append(lowerCamelCase_ )
visited.append(lowerCamelCase_ )
SCREAMING_SNAKE_CASE : Union[str, Any] = s
SCREAMING_SNAKE_CASE : List[str] = []
while True:
# check if there is any non isolated nodes
if len(self.graph[s] ) != 0:
SCREAMING_SNAKE_CASE : Optional[Any] = s
for node in self.graph[s]:
if visited.count(node[1] ) < 1:
stack.append(node[1] )
visited.append(node[1] )
SCREAMING_SNAKE_CASE : int = node[1]
break
# check if all the children are visited
if s == ss:
sorted_nodes.append(stack.pop() )
if len(lowerCamelCase_ ) != 0:
SCREAMING_SNAKE_CASE : List[Any] = stack[len(lowerCamelCase_ ) - 1]
else:
SCREAMING_SNAKE_CASE : int = ss
# check if se have reached the starting point
if len(lowerCamelCase_ ) == 0:
return sorted_nodes
def lowerCamelCase_ ( self : Optional[int] ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : List[Any] = []
SCREAMING_SNAKE_CASE : Any = []
SCREAMING_SNAKE_CASE : List[Any] = list(self.graph )[0]
stack.append(lowerCamelCase_ )
visited.append(lowerCamelCase_ )
SCREAMING_SNAKE_CASE : Any = -2
SCREAMING_SNAKE_CASE : int = []
SCREAMING_SNAKE_CASE : Union[str, Any] = s
SCREAMING_SNAKE_CASE : Tuple = False
SCREAMING_SNAKE_CASE : Union[str, Any] = set()
while True:
# check if there is any non isolated nodes
if len(self.graph[s] ) != 0:
SCREAMING_SNAKE_CASE : Union[str, Any] = s
for node in self.graph[s]:
if (
visited.count(node[1] ) > 0
and node[1] != parent
and indirect_parents.count(node[1] ) > 0
and not on_the_way_back
):
SCREAMING_SNAKE_CASE : Any = len(lowerCamelCase_ ) - 1
while len_stack >= 0:
if stack[len_stack] == node[1]:
anticipating_nodes.add(node[1] )
break
else:
anticipating_nodes.add(stack[len_stack] )
len_stack -= 1
if visited.count(node[1] ) < 1:
stack.append(node[1] )
visited.append(node[1] )
SCREAMING_SNAKE_CASE : int = node[1]
break
# check if all the children are visited
if s == ss:
stack.pop()
SCREAMING_SNAKE_CASE : int = True
if len(lowerCamelCase_ ) != 0:
SCREAMING_SNAKE_CASE : Any = stack[len(lowerCamelCase_ ) - 1]
else:
SCREAMING_SNAKE_CASE : List[str] = False
indirect_parents.append(lowerCamelCase_ )
SCREAMING_SNAKE_CASE : List[Any] = s
SCREAMING_SNAKE_CASE : List[Any] = ss
# check if se have reached the starting point
if len(lowerCamelCase_ ) == 0:
return list(lowerCamelCase_ )
def lowerCamelCase_ ( self : Dict ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Tuple = []
SCREAMING_SNAKE_CASE : int = []
SCREAMING_SNAKE_CASE : Any = list(self.graph )[0]
stack.append(lowerCamelCase_ )
visited.append(lowerCamelCase_ )
SCREAMING_SNAKE_CASE : Union[str, Any] = -2
SCREAMING_SNAKE_CASE : List[Any] = []
SCREAMING_SNAKE_CASE : Tuple = s
SCREAMING_SNAKE_CASE : Dict = False
SCREAMING_SNAKE_CASE : Optional[Any] = set()
while True:
# check if there is any non isolated nodes
if len(self.graph[s] ) != 0:
SCREAMING_SNAKE_CASE : str = s
for node in self.graph[s]:
if (
visited.count(node[1] ) > 0
and node[1] != parent
and indirect_parents.count(node[1] ) > 0
and not on_the_way_back
):
SCREAMING_SNAKE_CASE : str = len(lowerCamelCase_ ) - 1
while len_stack_minus_one >= 0:
if stack[len_stack_minus_one] == node[1]:
anticipating_nodes.add(node[1] )
break
else:
return True
if visited.count(node[1] ) < 1:
stack.append(node[1] )
visited.append(node[1] )
SCREAMING_SNAKE_CASE : Optional[Any] = node[1]
break
# check if all the children are visited
if s == ss:
stack.pop()
SCREAMING_SNAKE_CASE : Dict = True
if len(lowerCamelCase_ ) != 0:
SCREAMING_SNAKE_CASE : List[str] = stack[len(lowerCamelCase_ ) - 1]
else:
SCREAMING_SNAKE_CASE : List[Any] = False
indirect_parents.append(lowerCamelCase_ )
SCREAMING_SNAKE_CASE : Optional[int] = s
SCREAMING_SNAKE_CASE : Optional[int] = ss
# check if se have reached the starting point
if len(lowerCamelCase_ ) == 0:
return False
def lowerCamelCase_ ( self : Any , lowerCamelCase_ : str=-2 , lowerCamelCase_ : int=-1 ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Optional[Any] = time()
self.dfs(lowerCamelCase_ , lowerCamelCase_ )
SCREAMING_SNAKE_CASE : List[str] = time()
return end - begin
def lowerCamelCase_ ( self : int , lowerCamelCase_ : Tuple=-2 ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Optional[int] = time()
self.bfs(lowerCamelCase_ )
SCREAMING_SNAKE_CASE : List[str] = time()
return end - begin
class UpperCamelCase__ :
"""simple docstring"""
def __init__( self : Optional[int] ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Tuple = {}
def lowerCamelCase_ ( self : List[str] , lowerCamelCase_ : Optional[Any] , lowerCamelCase_ : Dict , lowerCamelCase_ : Union[str, Any]=1 ):
'''simple docstring'''
if self.graph.get(lowerCamelCase_ ):
# if there already is a edge
if self.graph[u].count([w, v] ) == 0:
self.graph[u].append([w, v] )
else:
# if u does not exist
SCREAMING_SNAKE_CASE : Any = [[w, v]]
# add the other way
if self.graph.get(lowerCamelCase_ ):
# if there already is a edge
if self.graph[v].count([w, u] ) == 0:
self.graph[v].append([w, u] )
else:
# if u does not exist
SCREAMING_SNAKE_CASE : Any = [[w, u]]
def lowerCamelCase_ ( self : Tuple , lowerCamelCase_ : Tuple , lowerCamelCase_ : Any ):
'''simple docstring'''
if self.graph.get(lowerCamelCase_ ):
for _ in self.graph[u]:
if _[1] == v:
self.graph[u].remove(lowerCamelCase_ )
# the other way round
if self.graph.get(lowerCamelCase_ ):
for _ in self.graph[v]:
if _[1] == u:
self.graph[v].remove(lowerCamelCase_ )
def lowerCamelCase_ ( self : int , lowerCamelCase_ : str=-2 , lowerCamelCase_ : List[str]=-1 ):
'''simple docstring'''
if s == d:
return []
SCREAMING_SNAKE_CASE : List[str] = []
SCREAMING_SNAKE_CASE : Any = []
if s == -2:
SCREAMING_SNAKE_CASE : List[Any] = list(self.graph )[0]
stack.append(lowerCamelCase_ )
visited.append(lowerCamelCase_ )
SCREAMING_SNAKE_CASE : List[str] = s
while True:
# check if there is any non isolated nodes
if len(self.graph[s] ) != 0:
SCREAMING_SNAKE_CASE : Union[str, Any] = s
for node in self.graph[s]:
if visited.count(node[1] ) < 1:
if node[1] == d:
visited.append(lowerCamelCase_ )
return visited
else:
stack.append(node[1] )
visited.append(node[1] )
SCREAMING_SNAKE_CASE : Any = node[1]
break
# check if all the children are visited
if s == ss:
stack.pop()
if len(lowerCamelCase_ ) != 0:
SCREAMING_SNAKE_CASE : Any = stack[len(lowerCamelCase_ ) - 1]
else:
SCREAMING_SNAKE_CASE : List[str] = ss
# check if se have reached the starting point
if len(lowerCamelCase_ ) == 0:
return visited
def lowerCamelCase_ ( self : Any , lowerCamelCase_ : List[str]=-1 ):
'''simple docstring'''
if c == -1:
SCREAMING_SNAKE_CASE : Any = floor(random() * 1_00_00 ) + 10
for i in range(lowerCamelCase_ ):
# every vertex has max 100 edges
for _ in range(floor(random() * 1_02 ) + 1 ):
SCREAMING_SNAKE_CASE : List[str] = floor(random() * c ) + 1
if n != i:
self.add_pair(lowerCamelCase_ , lowerCamelCase_ , 1 )
def lowerCamelCase_ ( self : List[str] , lowerCamelCase_ : List[Any]=-2 ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : List[Any] = deque()
SCREAMING_SNAKE_CASE : Tuple = []
if s == -2:
SCREAMING_SNAKE_CASE : Any = list(self.graph )[0]
d.append(lowerCamelCase_ )
visited.append(lowerCamelCase_ )
while d:
SCREAMING_SNAKE_CASE : List[Any] = d.popleft()
if len(self.graph[s] ) != 0:
for node in self.graph[s]:
if visited.count(node[1] ) < 1:
d.append(node[1] )
visited.append(node[1] )
return visited
def lowerCamelCase_ ( self : Tuple , lowerCamelCase_ : str ):
'''simple docstring'''
return len(self.graph[u] )
def lowerCamelCase_ ( self : List[str] ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : List[str] = []
SCREAMING_SNAKE_CASE : Any = []
SCREAMING_SNAKE_CASE : Optional[Any] = list(self.graph )[0]
stack.append(lowerCamelCase_ )
visited.append(lowerCamelCase_ )
SCREAMING_SNAKE_CASE : Optional[int] = -2
SCREAMING_SNAKE_CASE : Tuple = []
SCREAMING_SNAKE_CASE : Any = s
SCREAMING_SNAKE_CASE : Optional[Any] = False
SCREAMING_SNAKE_CASE : Optional[Any] = set()
while True:
# check if there is any non isolated nodes
if len(self.graph[s] ) != 0:
SCREAMING_SNAKE_CASE : Optional[Any] = s
for node in self.graph[s]:
if (
visited.count(node[1] ) > 0
and node[1] != parent
and indirect_parents.count(node[1] ) > 0
and not on_the_way_back
):
SCREAMING_SNAKE_CASE : Optional[Any] = len(lowerCamelCase_ ) - 1
while len_stack >= 0:
if stack[len_stack] == node[1]:
anticipating_nodes.add(node[1] )
break
else:
anticipating_nodes.add(stack[len_stack] )
len_stack -= 1
if visited.count(node[1] ) < 1:
stack.append(node[1] )
visited.append(node[1] )
SCREAMING_SNAKE_CASE : str = node[1]
break
# check if all the children are visited
if s == ss:
stack.pop()
SCREAMING_SNAKE_CASE : Optional[int] = True
if len(lowerCamelCase_ ) != 0:
SCREAMING_SNAKE_CASE : int = stack[len(lowerCamelCase_ ) - 1]
else:
SCREAMING_SNAKE_CASE : Union[str, Any] = False
indirect_parents.append(lowerCamelCase_ )
SCREAMING_SNAKE_CASE : List[str] = s
SCREAMING_SNAKE_CASE : str = ss
# check if se have reached the starting point
if len(lowerCamelCase_ ) == 0:
return list(lowerCamelCase_ )
def lowerCamelCase_ ( self : Union[str, Any] ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Optional[Any] = []
SCREAMING_SNAKE_CASE : int = []
SCREAMING_SNAKE_CASE : Any = list(self.graph )[0]
stack.append(lowerCamelCase_ )
visited.append(lowerCamelCase_ )
SCREAMING_SNAKE_CASE : Dict = -2
SCREAMING_SNAKE_CASE : int = []
SCREAMING_SNAKE_CASE : int = s
SCREAMING_SNAKE_CASE : Union[str, Any] = False
SCREAMING_SNAKE_CASE : Tuple = set()
while True:
# check if there is any non isolated nodes
if len(self.graph[s] ) != 0:
SCREAMING_SNAKE_CASE : Any = s
for node in self.graph[s]:
if (
visited.count(node[1] ) > 0
and node[1] != parent
and indirect_parents.count(node[1] ) > 0
and not on_the_way_back
):
SCREAMING_SNAKE_CASE : Optional[Any] = len(lowerCamelCase_ ) - 1
while len_stack_minus_one >= 0:
if stack[len_stack_minus_one] == node[1]:
anticipating_nodes.add(node[1] )
break
else:
return True
if visited.count(node[1] ) < 1:
stack.append(node[1] )
visited.append(node[1] )
SCREAMING_SNAKE_CASE : Tuple = node[1]
break
# check if all the children are visited
if s == ss:
stack.pop()
SCREAMING_SNAKE_CASE : Any = True
if len(lowerCamelCase_ ) != 0:
SCREAMING_SNAKE_CASE : str = stack[len(lowerCamelCase_ ) - 1]
else:
SCREAMING_SNAKE_CASE : Optional[Any] = False
indirect_parents.append(lowerCamelCase_ )
SCREAMING_SNAKE_CASE : Any = s
SCREAMING_SNAKE_CASE : Tuple = ss
# check if se have reached the starting point
if len(lowerCamelCase_ ) == 0:
return False
def lowerCamelCase_ ( self : List[Any] ):
'''simple docstring'''
return list(self.graph )
def lowerCamelCase_ ( self : str , lowerCamelCase_ : List[str]=-2 , lowerCamelCase_ : str=-1 ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : str = time()
self.dfs(lowerCamelCase_ , lowerCamelCase_ )
SCREAMING_SNAKE_CASE : Tuple = time()
return end - begin
def lowerCamelCase_ ( self : Tuple , lowerCamelCase_ : Dict=-2 ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Optional[Any] = time()
self.bfs(lowerCamelCase_ )
SCREAMING_SNAKE_CASE : Dict = time()
return end - begin
| 79 | 0 |
'''simple docstring'''
from __future__ import annotations
from math import pi
def __A ( lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ ):
"""simple docstring"""
if (inductance, frequency, reactance).count(0 ) != 1:
raise ValueError("""One and only one argument must be 0""" )
if inductance < 0:
raise ValueError("""Inductance cannot be negative""" )
if frequency < 0:
raise ValueError("""Frequency cannot be negative""" )
if reactance < 0:
raise ValueError("""Inductive reactance cannot be negative""" )
if inductance == 0:
return {"inductance": reactance / (2 * pi * frequency)}
elif frequency == 0:
return {"frequency": reactance / (2 * pi * inductance)}
elif reactance == 0:
return {"reactance": 2 * pi * frequency * inductance}
else:
raise ValueError("""Exactly one argument must be 0""" )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 701 |
'''simple docstring'''
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
__UpperCAmelCase = logging.get_logger(__name__)
__UpperCAmelCase = {"""vocab_file""": """sentencepiece.bpe.model"""}
__UpperCAmelCase = {
"""vocab_file""": {
"""moussaKam/mbarthez""": """https://huggingface.co/moussaKam/mbarthez/resolve/main/sentencepiece.bpe.model""",
"""moussaKam/barthez""": """https://huggingface.co/moussaKam/barthez/resolve/main/sentencepiece.bpe.model""",
"""moussaKam/barthez-orangesum-title""": (
"""https://huggingface.co/moussaKam/barthez-orangesum-title/resolve/main/sentencepiece.bpe.model"""
),
},
}
__UpperCAmelCase = {
"""moussaKam/mbarthez""": 1024,
"""moussaKam/barthez""": 1024,
"""moussaKam/barthez-orangesum-title""": 1024,
}
__UpperCAmelCase = """▁"""
class UpperCamelCase__ ( lowercase_ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE__ = VOCAB_FILES_NAMES
SCREAMING_SNAKE_CASE__ = PRETRAINED_VOCAB_FILES_MAP
SCREAMING_SNAKE_CASE__ = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
SCREAMING_SNAKE_CASE__ = ['''input_ids''', '''attention_mask''']
def __init__( self : Optional[Any] , lowerCamelCase_ : List[str] , lowerCamelCase_ : Tuple="<s>" , lowerCamelCase_ : Union[str, Any]="</s>" , lowerCamelCase_ : Tuple="</s>" , lowerCamelCase_ : str="<s>" , lowerCamelCase_ : Optional[int]="<unk>" , lowerCamelCase_ : List[Any]="<pad>" , lowerCamelCase_ : Optional[Any]="<mask>" , lowerCamelCase_ : Optional[Dict[str, Any]] = None , **lowerCamelCase_ : List[str] , ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : List[str] = AddedToken(lowerCamelCase_ , lstrip=lowerCamelCase_ , rstrip=lowerCamelCase_ ) if isinstance(lowerCamelCase_ , lowerCamelCase_ ) else mask_token
SCREAMING_SNAKE_CASE : Union[str, Any] = {} if sp_model_kwargs is None else sp_model_kwargs
super().__init__(
bos_token=lowerCamelCase_ , eos_token=lowerCamelCase_ , unk_token=lowerCamelCase_ , sep_token=lowerCamelCase_ , cls_token=lowerCamelCase_ , pad_token=lowerCamelCase_ , mask_token=lowerCamelCase_ , sp_model_kwargs=self.sp_model_kwargs , **lowerCamelCase_ , )
SCREAMING_SNAKE_CASE : Dict = vocab_file
SCREAMING_SNAKE_CASE : Dict = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(str(lowerCamelCase_ ) )
SCREAMING_SNAKE_CASE : Optional[int] = {"""<s>""": 0, """<pad>""": 1, """</s>""": 2, """<unk>""": 3}
SCREAMING_SNAKE_CASE : str = len(self.sp_model ) - 1
SCREAMING_SNAKE_CASE : Optional[int] = {v: k for k, v in self.fairseq_tokens_to_ids.items()}
def lowerCamelCase_ ( self : List[str] , lowerCamelCase_ : List[int] , lowerCamelCase_ : 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 : List[Any] = [self.cls_token_id]
SCREAMING_SNAKE_CASE : int = [self.sep_token_id]
return cls + token_ids_a + sep + sep + token_ids_a + sep
def lowerCamelCase_ ( self : Any , lowerCamelCase_ : List[int] , lowerCamelCase_ : Optional[List[int]] = None , lowerCamelCase_ : bool = False ):
'''simple docstring'''
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=lowerCamelCase_ , token_ids_a=lowerCamelCase_ , already_has_special_tokens=lowerCamelCase_ )
if token_ids_a is None:
return [1] + ([0] * len(lowerCamelCase_ )) + [1]
return [1] + ([0] * len(lowerCamelCase_ )) + [1, 1] + ([0] * len(lowerCamelCase_ )) + [1]
def lowerCamelCase_ ( self : str , lowerCamelCase_ : List[int] , lowerCamelCase_ : Optional[List[int]] = None ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : List[str] = [self.sep_token_id]
SCREAMING_SNAKE_CASE : Dict = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep + sep + token_ids_a + sep ) * [0]
@property
def lowerCamelCase_ ( self : List[str] ):
'''simple docstring'''
return len(self.sp_model )
def lowerCamelCase_ ( self : Optional[int] ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : List[str] = {self.convert_ids_to_tokens(lowerCamelCase_ ): i for i in range(self.vocab_size )}
vocab.update(self.added_tokens_encoder )
return vocab
def lowerCamelCase_ ( self : Union[str, Any] , lowerCamelCase_ : str ):
'''simple docstring'''
return self.sp_model.encode(lowerCamelCase_ , out_type=lowerCamelCase_ )
def lowerCamelCase_ ( self : Tuple , lowerCamelCase_ : str ):
'''simple docstring'''
if token in self.fairseq_tokens_to_ids:
return self.fairseq_tokens_to_ids[token]
SCREAMING_SNAKE_CASE : List[str] = self.sp_model.PieceToId(lowerCamelCase_ )
return spm_id if spm_id else self.unk_token_id
def lowerCamelCase_ ( self : int , lowerCamelCase_ : List[str] ):
'''simple docstring'''
if index in self.fairseq_ids_to_tokens:
return self.fairseq_ids_to_tokens[index]
return self.sp_model.IdToPiece(lowerCamelCase_ )
def lowerCamelCase_ ( self : Optional[Any] , lowerCamelCase_ : Optional[Any] ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : str = []
SCREAMING_SNAKE_CASE : Tuple = """"""
SCREAMING_SNAKE_CASE : Dict = False
for token in tokens:
# make sure that special tokens are not decoded using sentencepiece model
if token in self.all_special_tokens:
if not prev_is_special:
out_string += " "
out_string += self.sp_model.decode(lowerCamelCase_ ) + token
SCREAMING_SNAKE_CASE : str = True
SCREAMING_SNAKE_CASE : Optional[Any] = []
else:
current_sub_tokens.append(lowerCamelCase_ )
SCREAMING_SNAKE_CASE : Optional[int] = False
out_string += self.sp_model.decode(lowerCamelCase_ )
return out_string.strip()
def __getstate__( self : Tuple ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Dict = self.__dict__.copy()
SCREAMING_SNAKE_CASE : List[Any] = None
return state
def __setstate__( self : Optional[Any] , lowerCamelCase_ : Tuple ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : str = d
# for backward compatibility
if not hasattr(self , """sp_model_kwargs""" ):
SCREAMING_SNAKE_CASE : int = {}
SCREAMING_SNAKE_CASE : Any = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(self.vocab_file )
def lowerCamelCase_ ( self : Any , lowerCamelCase_ : str , lowerCamelCase_ : Optional[str] = None ):
'''simple docstring'''
if not os.path.isdir(lowerCamelCase_ ):
logger.error(f'''Vocabulary path ({save_directory}) should be a directory''' )
return
SCREAMING_SNAKE_CASE : Dict = os.path.join(
lowerCamelCase_ , (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(lowerCamelCase_ ) and os.path.isfile(self.vocab_file ):
copyfile(self.vocab_file , lowerCamelCase_ )
elif not os.path.isfile(self.vocab_file ):
with open(lowerCamelCase_ , """wb""" ) as fi:
SCREAMING_SNAKE_CASE : Any = self.sp_model.serialized_model_proto()
fi.write(lowerCamelCase_ )
return (out_vocab_file,)
| 79 | 0 |
'''simple docstring'''
import numpy as np
from matplotlib import pyplot as plt
from sklearn.datasets import load_iris
from sklearn.metrics import ConfusionMatrixDisplay
from sklearn.model_selection import train_test_split
from xgboost import XGBClassifier
def __A ( lowerCamelCase_ ):
"""simple docstring"""
return (data["data"], data["target"])
def __A ( lowerCamelCase_ , lowerCamelCase_ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : Dict = XGBClassifier()
classifier.fit(UpperCamelCase__ , UpperCamelCase__ )
return classifier
def __A ( ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : int = load_iris()
SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE : Optional[Any] = data_handling(UpperCamelCase__ )
SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE : str = train_test_split(
UpperCamelCase__ , UpperCamelCase__ , test_size=0.25 )
SCREAMING_SNAKE_CASE : Tuple = iris["""target_names"""]
# Create an XGBoost Classifier from the training data
SCREAMING_SNAKE_CASE : Union[str, Any] = xgboost(UpperCamelCase__ , UpperCamelCase__ )
# Display the confusion matrix of the classifier with both training and test sets
ConfusionMatrixDisplay.from_estimator(
UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , display_labels=UpperCamelCase__ , cmap="""Blues""" , normalize="""true""" , )
plt.title("""Normalized Confusion Matrix - IRIS Dataset""" )
plt.show()
if __name__ == "__main__":
import doctest
doctest.testmod(verbose=True)
main()
| 702 |
'''simple docstring'''
from __future__ import annotations
import unittest
from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
if is_tf_available():
import numpy as np
import tensorflow as tf
from transformers import TFXLMRobertaModel
@require_tf
@require_sentencepiece
@require_tokenizers
class UpperCamelCase__ ( unittest.TestCase ):
"""simple docstring"""
@slow
def lowerCamelCase_ ( self : Tuple ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Union[str, Any] = TFXLMRobertaModel.from_pretrained("""jplu/tf-xlm-roberta-base""" )
SCREAMING_SNAKE_CASE : Dict = {
"""input_ids""": tf.convert_to_tensor([[0, 26_46, 1_02_69, 83, 9_99_42, 2]] , dtype=tf.intaa ), # "My dog is cute"
"""attention_mask""": tf.convert_to_tensor([[1, 1, 1, 1, 1, 1]] , dtype=tf.intaa ),
}
SCREAMING_SNAKE_CASE : List[str] = model(lowerCamelCase_ )["""last_hidden_state"""]
SCREAMING_SNAKE_CASE : Union[str, Any] = tf.TensorShape((1, 6, 7_68) )
self.assertEqual(output.shape , lowerCamelCase_ )
# compare the actual values for a slice.
SCREAMING_SNAKE_CASE : Optional[int] = tf.convert_to_tensor(
[
[
[0.0_681_762, 0.10_894_451, 0.06_772_504],
[-0.06_423_668, 0.02_366_615, 0.04_329_344],
[-0.06_057_295, 0.09_974_135, -0.00_070_584],
]
] , dtype=tf.floataa , )
self.assertTrue(np.allclose(output[:, :3, :3].numpy() , expected_slice.numpy() , atol=1e-4 ) )
| 79 | 0 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
__UpperCAmelCase = logging.get_logger(__name__)
class UpperCamelCase__ ( snake_case__ , snake_case__ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE__ = '''maskformer-swin'''
SCREAMING_SNAKE_CASE__ = {
'''num_attention_heads''': '''num_heads''',
'''num_hidden_layers''': '''num_layers''',
}
def __init__( self : Dict , lowerCamelCase_ : int=2_24 , lowerCamelCase_ : str=4 , lowerCamelCase_ : int=3 , lowerCamelCase_ : str=96 , lowerCamelCase_ : Any=[2, 2, 6, 2] , lowerCamelCase_ : Any=[3, 6, 12, 24] , lowerCamelCase_ : List[Any]=7 , lowerCamelCase_ : Union[str, Any]=4.0 , lowerCamelCase_ : str=True , lowerCamelCase_ : Optional[Any]=0.0 , lowerCamelCase_ : int=0.0 , lowerCamelCase_ : int=0.1 , lowerCamelCase_ : List[Any]="gelu" , lowerCamelCase_ : Optional[Any]=False , lowerCamelCase_ : str=0.02 , lowerCamelCase_ : Optional[Any]=1e-5 , lowerCamelCase_ : Any=None , lowerCamelCase_ : int=None , **lowerCamelCase_ : Any , ):
'''simple docstring'''
super().__init__(**lowercase_ )
SCREAMING_SNAKE_CASE : List[str] = image_size
SCREAMING_SNAKE_CASE : Any = patch_size
SCREAMING_SNAKE_CASE : List[Any] = num_channels
SCREAMING_SNAKE_CASE : str = embed_dim
SCREAMING_SNAKE_CASE : Union[str, Any] = depths
SCREAMING_SNAKE_CASE : Optional[Any] = len(lowercase_ )
SCREAMING_SNAKE_CASE : Union[str, Any] = num_heads
SCREAMING_SNAKE_CASE : Union[str, Any] = window_size
SCREAMING_SNAKE_CASE : str = mlp_ratio
SCREAMING_SNAKE_CASE : List[str] = qkv_bias
SCREAMING_SNAKE_CASE : Any = hidden_dropout_prob
SCREAMING_SNAKE_CASE : int = attention_probs_dropout_prob
SCREAMING_SNAKE_CASE : int = drop_path_rate
SCREAMING_SNAKE_CASE : str = hidden_act
SCREAMING_SNAKE_CASE : int = use_absolute_embeddings
SCREAMING_SNAKE_CASE : List[Any] = layer_norm_eps
SCREAMING_SNAKE_CASE : List[Any] = initializer_range
# we set the hidden_size attribute in order to make Swin work with VisionEncoderDecoderModel
# this indicates the channel dimension after the last stage of the model
SCREAMING_SNAKE_CASE : int = int(embed_dim * 2 ** (len(lowercase_ ) - 1) )
SCREAMING_SNAKE_CASE : Any = ["stem"] + [f'''stage{idx}''' for idx in range(1 , len(lowercase_ ) + 1 )]
SCREAMING_SNAKE_CASE : Tuple = get_aligned_output_features_output_indices(
out_features=lowercase_ , out_indices=lowercase_ , stage_names=self.stage_names )
| 703 |
'''simple docstring'''
from dataclasses import dataclass, field
from typing import TYPE_CHECKING, Any, ClassVar, Dict, List, Optional, Union
import pyarrow as pa
if TYPE_CHECKING:
from .features import FeatureType
@dataclass
class UpperCamelCase__ :
"""simple docstring"""
SCREAMING_SNAKE_CASE__ = 42
SCREAMING_SNAKE_CASE__ = None
# Automatically constructed
SCREAMING_SNAKE_CASE__ = "dict"
SCREAMING_SNAKE_CASE__ = None
SCREAMING_SNAKE_CASE__ = field(default='''Translation''' , init=lowercase_ , repr=lowercase_ )
def __call__( self : int ):
'''simple docstring'''
return pa.struct({lang: pa.string() for lang in sorted(self.languages )} )
def lowerCamelCase_ ( self : Tuple ):
'''simple docstring'''
from .features import Value
return {k: Value("""string""" ) for k in sorted(self.languages )}
@dataclass
class UpperCamelCase__ :
"""simple docstring"""
SCREAMING_SNAKE_CASE__ = None
SCREAMING_SNAKE_CASE__ = None
SCREAMING_SNAKE_CASE__ = None
# Automatically constructed
SCREAMING_SNAKE_CASE__ = "dict"
SCREAMING_SNAKE_CASE__ = None
SCREAMING_SNAKE_CASE__ = field(default='''TranslationVariableLanguages''' , init=lowercase_ , repr=lowercase_ )
def lowerCamelCase_ ( self : Optional[int] ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Optional[Any] = sorted(set(self.languages ) ) if self.languages else None
SCREAMING_SNAKE_CASE : str = len(self.languages ) if self.languages else None
def __call__( self : Tuple ):
'''simple docstring'''
return pa.struct({"""language""": pa.list_(pa.string() ), """translation""": pa.list_(pa.string() )} )
def lowerCamelCase_ ( self : Optional[int] , lowerCamelCase_ : Optional[Any] ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Optional[Any] = set(self.languages )
if self.languages and set(lowerCamelCase_ ) - lang_set:
raise ValueError(
f'''Some languages in example ({", ".join(sorted(set(lowerCamelCase_ ) - lang_set ) )}) are not in valid set ({", ".join(lowerCamelCase_ )}).''' )
# Convert dictionary into tuples, splitting out cases where there are
# multiple translations for a single language.
SCREAMING_SNAKE_CASE : List[Any] = []
for lang, text in translation_dict.items():
if isinstance(lowerCamelCase_ , lowerCamelCase_ ):
translation_tuples.append((lang, text) )
else:
translation_tuples.extend([(lang, el) for el in text] )
# Ensure translations are in ascending order by language code.
SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE : Optional[Any] = zip(*sorted(lowerCamelCase_ ) )
return {"language": languages, "translation": translations}
def lowerCamelCase_ ( self : List[str] ):
'''simple docstring'''
from .features import Sequence, Value
return {
"language": Sequence(Value("""string""" ) ),
"translation": Sequence(Value("""string""" ) ),
}
| 79 | 0 |
'''simple docstring'''
def __A ( lowerCamelCase_ ):
"""simple docstring"""
if not grid or not grid[0]:
raise TypeError("""The grid does not contain the appropriate information""" )
for cell_n in range(1 , len(grid[0] ) ):
grid[0][cell_n] += grid[0][cell_n - 1]
SCREAMING_SNAKE_CASE : int = grid[0]
for row_n in range(1 , len(lowerCamelCase_ ) ):
SCREAMING_SNAKE_CASE : List[str] = grid[row_n]
SCREAMING_SNAKE_CASE : int = fill_row(lowerCamelCase_ , lowerCamelCase_ )
SCREAMING_SNAKE_CASE : Dict = grid[row_n]
return grid[-1][-1]
def __A ( lowerCamelCase_ , lowerCamelCase_ ):
"""simple docstring"""
current_row[0] += row_above[0]
for cell_n in range(1 , len(lowerCamelCase_ ) ):
current_row[cell_n] += min(current_row[cell_n - 1] , row_above[cell_n] )
return current_row
if __name__ == "__main__":
import doctest
doctest.testmod()
| 704 |
'''simple docstring'''
import warnings
from ...utils import logging
from .image_processing_flava import FlavaImageProcessor
__UpperCAmelCase = logging.get_logger(__name__)
class UpperCamelCase__ ( lowercase_ ):
"""simple docstring"""
def __init__( self : Dict , *lowerCamelCase_ : List[str] , **lowerCamelCase_ : Dict ):
'''simple docstring'''
warnings.warn(
"""The class FlavaFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please"""
""" use FlavaImageProcessor instead.""" , lowerCamelCase_ , )
super().__init__(*lowerCamelCase_ , **lowerCamelCase_ )
| 79 | 0 |
'''simple docstring'''
import warnings
from typing import List, Optional, Tuple, Union
import numpy as np
import PIL
import torch
from ...models import UNetaDModel
from ...schedulers import RePaintScheduler
from ...utils import PIL_INTERPOLATION, logging, randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
__UpperCAmelCase = logging.get_logger(__name__) # pylint: disable=invalid-name
def __A ( lowerCamelCase_ ):
"""simple docstring"""
warnings.warn(
"""The preprocess method is deprecated and will be removed in a future version. Please"""
""" use VaeImageProcessor.preprocess instead""" , _lowerCamelCase , )
if isinstance(_lowerCamelCase , torch.Tensor ):
return image
elif isinstance(_lowerCamelCase , PIL.Image.Image ):
SCREAMING_SNAKE_CASE : int = [image]
if isinstance(image[0] , PIL.Image.Image ):
SCREAMING_SNAKE_CASE : Union[str, Any] = image[0].size
SCREAMING_SNAKE_CASE : List[Any] = (x - x % 8 for x in (w, h)) # resize to integer multiple of 8
SCREAMING_SNAKE_CASE : Any = [np.array(i.resize((w, h) , resample=PIL_INTERPOLATION["""lanczos"""] ) )[None, :] for i in image]
SCREAMING_SNAKE_CASE : Dict = np.concatenate(_lowerCamelCase , axis=0 )
SCREAMING_SNAKE_CASE : Any = np.array(_lowerCamelCase ).astype(np.floataa ) / 2_55.0
SCREAMING_SNAKE_CASE : Union[str, Any] = image.transpose(0 , 3 , 1 , 2 )
SCREAMING_SNAKE_CASE : Optional[int] = 2.0 * image - 1.0
SCREAMING_SNAKE_CASE : List[str] = torch.from_numpy(_lowerCamelCase )
elif isinstance(image[0] , torch.Tensor ):
SCREAMING_SNAKE_CASE : Dict = torch.cat(_lowerCamelCase , dim=0 )
return image
def __A ( lowerCamelCase_ ):
"""simple docstring"""
if isinstance(_lowerCamelCase , torch.Tensor ):
return mask
elif isinstance(_lowerCamelCase , PIL.Image.Image ):
SCREAMING_SNAKE_CASE : Any = [mask]
if isinstance(mask[0] , PIL.Image.Image ):
SCREAMING_SNAKE_CASE : Optional[int] = mask[0].size
SCREAMING_SNAKE_CASE : List[Any] = (x - x % 32 for x in (w, h)) # resize to integer multiple of 32
SCREAMING_SNAKE_CASE : List[str] = [np.array(m.convert("""L""" ).resize((w, h) , resample=PIL_INTERPOLATION["""nearest"""] ) )[None, :] for m in mask]
SCREAMING_SNAKE_CASE : Dict = np.concatenate(_lowerCamelCase , axis=0 )
SCREAMING_SNAKE_CASE : Any = mask.astype(np.floataa ) / 2_55.0
SCREAMING_SNAKE_CASE : List[str] = 0
SCREAMING_SNAKE_CASE : Dict = 1
SCREAMING_SNAKE_CASE : List[Any] = torch.from_numpy(_lowerCamelCase )
elif isinstance(mask[0] , torch.Tensor ):
SCREAMING_SNAKE_CASE : Optional[Any] = torch.cat(_lowerCamelCase , dim=0 )
return mask
class UpperCamelCase__ ( __lowercase ):
"""simple docstring"""
SCREAMING_SNAKE_CASE__ = 42
SCREAMING_SNAKE_CASE__ = 42
def __init__( self : Optional[Any] , lowerCamelCase_ : str , lowerCamelCase_ : List[str] ):
'''simple docstring'''
super().__init__()
self.register_modules(unet=lowerCamelCase_ , scheduler=lowerCamelCase_ )
@torch.no_grad()
def __call__( self : Union[str, Any] , lowerCamelCase_ : Union[torch.Tensor, PIL.Image.Image] , lowerCamelCase_ : Union[torch.Tensor, PIL.Image.Image] , lowerCamelCase_ : int = 2_50 , lowerCamelCase_ : float = 0.0 , lowerCamelCase_ : int = 10 , lowerCamelCase_ : int = 10 , lowerCamelCase_ : Optional[Union[torch.Generator, List[torch.Generator]]] = None , lowerCamelCase_ : Optional[str] = "pil" , lowerCamelCase_ : bool = True , ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Tuple = image
SCREAMING_SNAKE_CASE : Optional[Any] = _preprocess_image(lowerCamelCase_ )
SCREAMING_SNAKE_CASE : Optional[int] = original_image.to(device=self.device , dtype=self.unet.dtype )
SCREAMING_SNAKE_CASE : Dict = _preprocess_mask(lowerCamelCase_ )
SCREAMING_SNAKE_CASE : Any = mask_image.to(device=self.device , dtype=self.unet.dtype )
SCREAMING_SNAKE_CASE : str = original_image.shape[0]
# sample gaussian noise to begin the loop
if isinstance(lowerCamelCase_ , lowerCamelCase_ ) and len(lowerCamelCase_ ) != batch_size:
raise ValueError(
f'''You have passed a list of generators of length {len(lowerCamelCase_ )}, but requested an effective batch'''
f''' size of {batch_size}. Make sure the batch size matches the length of the generators.''' )
SCREAMING_SNAKE_CASE : List[str] = original_image.shape
SCREAMING_SNAKE_CASE : List[Any] = randn_tensor(lowerCamelCase_ , generator=lowerCamelCase_ , device=self.device , dtype=self.unet.dtype )
# set step values
self.scheduler.set_timesteps(lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ , self.device )
SCREAMING_SNAKE_CASE : Tuple = eta
SCREAMING_SNAKE_CASE : List[str] = self.scheduler.timesteps[0] + 1
SCREAMING_SNAKE_CASE : str = generator[0] if isinstance(lowerCamelCase_ , lowerCamelCase_ ) else generator
for i, t in enumerate(self.progress_bar(self.scheduler.timesteps ) ):
if t < t_last:
# predict the noise residual
SCREAMING_SNAKE_CASE : Union[str, Any] = self.unet(lowerCamelCase_ , lowerCamelCase_ ).sample
# compute previous image: x_t -> x_t-1
SCREAMING_SNAKE_CASE : Tuple = self.scheduler.step(lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ ).prev_sample
else:
# compute the reverse: x_t-1 -> x_t
SCREAMING_SNAKE_CASE : List[str] = self.scheduler.undo_step(lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ )
SCREAMING_SNAKE_CASE : Dict = t
SCREAMING_SNAKE_CASE : Union[str, Any] = (image / 2 + 0.5).clamp(0 , 1 )
SCREAMING_SNAKE_CASE : Tuple = image.cpu().permute(0 , 2 , 3 , 1 ).numpy()
if output_type == "pil":
SCREAMING_SNAKE_CASE : str = self.numpy_to_pil(lowerCamelCase_ )
if not return_dict:
return (image,)
return ImagePipelineOutput(images=lowerCamelCase_ )
| 705 |
'''simple docstring'''
from .integrations import (
is_optuna_available,
is_ray_available,
is_sigopt_available,
is_wandb_available,
run_hp_search_optuna,
run_hp_search_ray,
run_hp_search_sigopt,
run_hp_search_wandb,
)
from .trainer_utils import (
HPSearchBackend,
default_hp_space_optuna,
default_hp_space_ray,
default_hp_space_sigopt,
default_hp_space_wandb,
)
from .utils import logging
__UpperCAmelCase = logging.get_logger(__name__)
class UpperCamelCase__ :
"""simple docstring"""
SCREAMING_SNAKE_CASE__ = 42
SCREAMING_SNAKE_CASE__ = None
@staticmethod
def lowerCamelCase_ ( ):
'''simple docstring'''
raise NotImplementedError
def lowerCamelCase_ ( self : Tuple , lowerCamelCase_ : Optional[int] , lowerCamelCase_ : int , lowerCamelCase_ : str , **lowerCamelCase_ : str ):
'''simple docstring'''
raise NotImplementedError
def lowerCamelCase_ ( self : int , lowerCamelCase_ : Dict ):
'''simple docstring'''
raise NotImplementedError
def lowerCamelCase_ ( self : Optional[int] ):
'''simple docstring'''
if not self.is_available():
raise RuntimeError(
f'''You picked the {self.name} backend, but it is not installed. Run {self.pip_install()}.''' )
@classmethod
def lowerCamelCase_ ( cls : Any ):
'''simple docstring'''
return f'''`pip install {cls.pip_package or cls.name}`'''
class UpperCamelCase__ ( lowercase_ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE__ = '''optuna'''
@staticmethod
def lowerCamelCase_ ( ):
'''simple docstring'''
return is_optuna_available()
def lowerCamelCase_ ( self : Any , lowerCamelCase_ : Union[str, Any] , lowerCamelCase_ : int , lowerCamelCase_ : str , **lowerCamelCase_ : Dict ):
'''simple docstring'''
return run_hp_search_optuna(lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ , **lowerCamelCase_ )
def lowerCamelCase_ ( self : str , lowerCamelCase_ : Any ):
'''simple docstring'''
return default_hp_space_optuna(lowerCamelCase_ )
class UpperCamelCase__ ( lowercase_ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE__ = '''ray'''
SCREAMING_SNAKE_CASE__ = '''\'ray[tune]\''''
@staticmethod
def lowerCamelCase_ ( ):
'''simple docstring'''
return is_ray_available()
def lowerCamelCase_ ( self : Any , lowerCamelCase_ : Any , lowerCamelCase_ : int , lowerCamelCase_ : str , **lowerCamelCase_ : int ):
'''simple docstring'''
return run_hp_search_ray(lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ , **lowerCamelCase_ )
def lowerCamelCase_ ( self : List[str] , lowerCamelCase_ : Optional[int] ):
'''simple docstring'''
return default_hp_space_ray(lowerCamelCase_ )
class UpperCamelCase__ ( lowercase_ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE__ = '''sigopt'''
@staticmethod
def lowerCamelCase_ ( ):
'''simple docstring'''
return is_sigopt_available()
def lowerCamelCase_ ( self : Optional[Any] , lowerCamelCase_ : Any , lowerCamelCase_ : int , lowerCamelCase_ : str , **lowerCamelCase_ : int ):
'''simple docstring'''
return run_hp_search_sigopt(lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ , **lowerCamelCase_ )
def lowerCamelCase_ ( self : List[Any] , lowerCamelCase_ : Optional[Any] ):
'''simple docstring'''
return default_hp_space_sigopt(lowerCamelCase_ )
class UpperCamelCase__ ( lowercase_ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE__ = '''wandb'''
@staticmethod
def lowerCamelCase_ ( ):
'''simple docstring'''
return is_wandb_available()
def lowerCamelCase_ ( self : Union[str, Any] , lowerCamelCase_ : str , lowerCamelCase_ : int , lowerCamelCase_ : str , **lowerCamelCase_ : Optional[Any] ):
'''simple docstring'''
return run_hp_search_wandb(lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ , **lowerCamelCase_ )
def lowerCamelCase_ ( self : Union[str, Any] , lowerCamelCase_ : Tuple ):
'''simple docstring'''
return default_hp_space_wandb(lowerCamelCase_ )
__UpperCAmelCase = {
HPSearchBackend(backend.name): backend for backend in [OptunaBackend, RayTuneBackend, SigOptBackend, WandbBackend]
}
def __A ( ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : List[Any] = [backend for backend in ALL_HYPERPARAMETER_SEARCH_BACKENDS.values() if backend.is_available()]
if len(lowerCamelCase_ ) > 0:
SCREAMING_SNAKE_CASE : List[Any] = available_backends[0].name
if len(lowerCamelCase_ ) > 1:
logger.info(
f'''{len(lowerCamelCase_ )} hyperparameter search backends available. Using {name} as the default.''' )
return name
raise RuntimeError(
"""No hyperparameter search backend available.\n"""
+ """\n""".join(
f''' - To install {backend.name} run {backend.pip_install()}'''
for backend in ALL_HYPERPARAMETER_SEARCH_BACKENDS.values() ) )
| 79 | 0 |
'''simple docstring'''
from argparse import ArgumentParser, Namespace
from ..utils import logging
from . import BaseTransformersCLICommand
def __A ( lowerCamelCase_ ):
"""simple docstring"""
return ConvertCommand(
args.model_type , args.tf_checkpoint , args.pytorch_dump_output , args.config , args.finetuning_task_name )
__UpperCAmelCase = "\ntransformers can only be used from the commandline to convert TensorFlow models in PyTorch, In that case, it requires\nTensorFlow to be installed. Please see https://www.tensorflow.org/install/ for installation instructions.\n"
class UpperCamelCase__ ( lowercase_ ):
"""simple docstring"""
@staticmethod
def lowerCamelCase_ ( lowerCamelCase_ : ArgumentParser ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Tuple = parser.add_parser(
"""convert""" , help="""CLI tool to run convert model from original author checkpoints to Transformers PyTorch checkpoints.""" , )
train_parser.add_argument("""--model_type""" , type=__UpperCamelCase , required=__UpperCamelCase , help="""Model\'s type.""" )
train_parser.add_argument(
"""--tf_checkpoint""" , type=__UpperCamelCase , required=__UpperCamelCase , help="""TensorFlow checkpoint path or folder.""" )
train_parser.add_argument(
"""--pytorch_dump_output""" , type=__UpperCamelCase , required=__UpperCamelCase , help="""Path to the PyTorch saved model output.""" )
train_parser.add_argument("""--config""" , type=__UpperCamelCase , default="""""" , help="""Configuration file path or folder.""" )
train_parser.add_argument(
"""--finetuning_task_name""" , type=__UpperCamelCase , default=__UpperCamelCase , help="""Optional fine-tuning task name if the TF model was a finetuned model.""" , )
train_parser.set_defaults(func=__UpperCamelCase )
def __init__( self : Any , lowerCamelCase_ : str , lowerCamelCase_ : str , lowerCamelCase_ : str , lowerCamelCase_ : str , lowerCamelCase_ : str , *lowerCamelCase_ : str , ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Optional[Any] = logging.get_logger("""transformers-cli/converting""" )
self._logger.info(f'''Loading model {model_type}''' )
SCREAMING_SNAKE_CASE : Tuple = model_type
SCREAMING_SNAKE_CASE : Any = tf_checkpoint
SCREAMING_SNAKE_CASE : Any = pytorch_dump_output
SCREAMING_SNAKE_CASE : Optional[int] = config
SCREAMING_SNAKE_CASE : List[Any] = finetuning_task_name
def lowerCamelCase_ ( self : List[Any] ):
'''simple docstring'''
if self._model_type == "albert":
try:
from ..models.albert.convert_albert_original_tf_checkpoint_to_pytorch import (
convert_tf_checkpoint_to_pytorch,
)
except ImportError:
raise ImportError(__UpperCamelCase )
convert_tf_checkpoint_to_pytorch(self._tf_checkpoint , self._config , self._pytorch_dump_output )
elif self._model_type == "bert":
try:
from ..models.bert.convert_bert_original_tf_checkpoint_to_pytorch import (
convert_tf_checkpoint_to_pytorch,
)
except ImportError:
raise ImportError(__UpperCamelCase )
convert_tf_checkpoint_to_pytorch(self._tf_checkpoint , self._config , self._pytorch_dump_output )
elif self._model_type == "funnel":
try:
from ..models.funnel.convert_funnel_original_tf_checkpoint_to_pytorch import (
convert_tf_checkpoint_to_pytorch,
)
except ImportError:
raise ImportError(__UpperCamelCase )
convert_tf_checkpoint_to_pytorch(self._tf_checkpoint , self._config , self._pytorch_dump_output )
elif self._model_type == "t5":
try:
from ..models.ta.convert_ta_original_tf_checkpoint_to_pytorch import convert_tf_checkpoint_to_pytorch
except ImportError:
raise ImportError(__UpperCamelCase )
convert_tf_checkpoint_to_pytorch(self._tf_checkpoint , self._config , self._pytorch_dump_output )
elif self._model_type == "gpt":
from ..models.openai.convert_openai_original_tf_checkpoint_to_pytorch import (
convert_openai_checkpoint_to_pytorch,
)
convert_openai_checkpoint_to_pytorch(self._tf_checkpoint , self._config , self._pytorch_dump_output )
elif self._model_type == "transfo_xl":
try:
from ..models.transfo_xl.convert_transfo_xl_original_tf_checkpoint_to_pytorch import (
convert_transfo_xl_checkpoint_to_pytorch,
)
except ImportError:
raise ImportError(__UpperCamelCase )
if "ckpt" in self._tf_checkpoint.lower():
SCREAMING_SNAKE_CASE : str = self._tf_checkpoint
SCREAMING_SNAKE_CASE : List[str] = """"""
else:
SCREAMING_SNAKE_CASE : Any = self._tf_checkpoint
SCREAMING_SNAKE_CASE : Union[str, Any] = """"""
convert_transfo_xl_checkpoint_to_pytorch(
__UpperCamelCase , self._config , self._pytorch_dump_output , __UpperCamelCase )
elif self._model_type == "gpt2":
try:
from ..models.gpta.convert_gpta_original_tf_checkpoint_to_pytorch import (
convert_gpta_checkpoint_to_pytorch,
)
except ImportError:
raise ImportError(__UpperCamelCase )
convert_gpta_checkpoint_to_pytorch(self._tf_checkpoint , self._config , self._pytorch_dump_output )
elif self._model_type == "xlnet":
try:
from ..models.xlnet.convert_xlnet_original_tf_checkpoint_to_pytorch import (
convert_xlnet_checkpoint_to_pytorch,
)
except ImportError:
raise ImportError(__UpperCamelCase )
convert_xlnet_checkpoint_to_pytorch(
self._tf_checkpoint , self._config , self._pytorch_dump_output , self._finetuning_task_name )
elif self._model_type == "xlm":
from ..models.xlm.convert_xlm_original_pytorch_checkpoint_to_pytorch import (
convert_xlm_checkpoint_to_pytorch,
)
convert_xlm_checkpoint_to_pytorch(self._tf_checkpoint , self._pytorch_dump_output )
elif self._model_type == "lxmert":
from ..models.lxmert.convert_lxmert_original_tf_checkpoint_to_pytorch import (
convert_lxmert_checkpoint_to_pytorch,
)
convert_lxmert_checkpoint_to_pytorch(self._tf_checkpoint , self._pytorch_dump_output )
elif self._model_type == "rembert":
from ..models.rembert.convert_rembert_tf_checkpoint_to_pytorch import (
convert_rembert_tf_checkpoint_to_pytorch,
)
convert_rembert_tf_checkpoint_to_pytorch(self._tf_checkpoint , self._config , self._pytorch_dump_output )
else:
raise ValueError(
"""--model_type should be selected in the list [bert, gpt, gpt2, t5, transfo_xl, xlnet, xlm, lxmert]""" )
| 706 |
'''simple docstring'''
import glob
import os
import random
from string import ascii_lowercase, digits
import cva
__UpperCAmelCase = """"""
__UpperCAmelCase = """"""
__UpperCAmelCase = """"""
__UpperCAmelCase = 1 # (0 is vertical, 1 is horizontal)
def __A ( ):
"""simple docstring"""
SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE : str = get_dataset(lowerCamelCase_ , lowerCamelCase_ )
print("""Processing...""" )
SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE : Any = update_image_and_anno(lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ )
for index, image in enumerate(lowerCamelCase_ ):
# Get random string code: '7b7ad245cdff75241935e4dd860f3bad'
SCREAMING_SNAKE_CASE : Optional[int] = random_chars(32 )
SCREAMING_SNAKE_CASE : Optional[Any] = paths[index].split(os.sep )[-1].rsplit(""".""" , 1 )[0]
SCREAMING_SNAKE_CASE : Dict = f'''{OUTPUT_DIR}/{file_name}_FLIP_{letter_code}'''
cva.imwrite(f'''/{file_root}.jpg''' , lowerCamelCase_ , [cva.IMWRITE_JPEG_QUALITY, 85] )
print(f'''Success {index+1}/{len(lowerCamelCase_ )} with {file_name}''' )
SCREAMING_SNAKE_CASE : Optional[Any] = []
for anno in new_annos[index]:
SCREAMING_SNAKE_CASE : Optional[Any] = f'''{anno[0]} {anno[1]} {anno[2]} {anno[3]} {anno[4]}'''
annos_list.append(lowerCamelCase_ )
with open(f'''/{file_root}.txt''' , """w""" ) as outfile:
outfile.write("""\n""".join(line for line in annos_list ) )
def __A ( lowerCamelCase_ , lowerCamelCase_ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : str = []
SCREAMING_SNAKE_CASE : Any = []
for label_file in glob.glob(os.path.join(lowerCamelCase_ , """*.txt""" ) ):
SCREAMING_SNAKE_CASE : str = label_file.split(os.sep )[-1].rsplit(""".""" , 1 )[0]
with open(lowerCamelCase_ ) as in_file:
SCREAMING_SNAKE_CASE : Any = in_file.readlines()
SCREAMING_SNAKE_CASE : List[Any] = os.path.join(lowerCamelCase_ , f'''{label_name}.jpg''' )
SCREAMING_SNAKE_CASE : Tuple = []
for obj_list in obj_lists:
SCREAMING_SNAKE_CASE : Union[str, Any] = obj_list.rstrip("""\n""" ).split(""" """ )
boxes.append(
[
int(obj[0] ),
float(obj[1] ),
float(obj[2] ),
float(obj[3] ),
float(obj[4] ),
] )
if not boxes:
continue
img_paths.append(lowerCamelCase_ )
labels.append(lowerCamelCase_ )
return img_paths, labels
def __A ( lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ = 1 ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : Tuple = []
SCREAMING_SNAKE_CASE : List[str] = []
SCREAMING_SNAKE_CASE : Optional[Any] = []
for idx in range(len(lowerCamelCase_ ) ):
SCREAMING_SNAKE_CASE : List[Any] = []
SCREAMING_SNAKE_CASE : Dict = img_list[idx]
path_list.append(lowerCamelCase_ )
SCREAMING_SNAKE_CASE : int = anno_list[idx]
SCREAMING_SNAKE_CASE : Optional[Any] = cva.imread(lowerCamelCase_ )
if flip_type == 1:
SCREAMING_SNAKE_CASE : List[str] = cva.flip(lowerCamelCase_ , lowerCamelCase_ )
for bbox in img_annos:
SCREAMING_SNAKE_CASE : List[Any] = 1 - bbox[1]
new_annos.append([bbox[0], x_center_new, bbox[2], bbox[3], bbox[4]] )
elif flip_type == 0:
SCREAMING_SNAKE_CASE : Any = cva.flip(lowerCamelCase_ , lowerCamelCase_ )
for bbox in img_annos:
SCREAMING_SNAKE_CASE : Optional[Any] = 1 - bbox[2]
new_annos.append([bbox[0], bbox[1], y_center_new, bbox[3], bbox[4]] )
new_annos_lists.append(lowerCamelCase_ )
new_imgs_list.append(lowerCamelCase_ )
return new_imgs_list, new_annos_lists, path_list
def __A ( lowerCamelCase_ = 32 ):
"""simple docstring"""
assert number_char > 1, "The number of character should greater than 1"
SCREAMING_SNAKE_CASE : Dict = ascii_lowercase + digits
return "".join(random.choice(lowerCamelCase_ ) for _ in range(lowerCamelCase_ ) )
if __name__ == "__main__":
main()
print("""DONE ✅""")
| 79 | 0 |
'''simple docstring'''
import torch
from torch import nn
from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin
class UpperCamelCase__ ( A_ , A_ ):
"""simple docstring"""
@register_to_config
def __init__( self : Optional[int] , *,
lowerCamelCase_ : Union[str, Any] = 4 , lowerCamelCase_ : Optional[Any] = 7_68 , lowerCamelCase_ : Optional[int] , lowerCamelCase_ : Dict , ):
'''simple docstring'''
super().__init__()
SCREAMING_SNAKE_CASE : str = nn.Parameter(torch.zeros(lowerCamelCase_ ) )
# parameters for additional clip time embeddings
SCREAMING_SNAKE_CASE : Tuple = nn.Linear(lowerCamelCase_ , lowerCamelCase_ )
SCREAMING_SNAKE_CASE : Optional[Any] = nn.Linear(lowerCamelCase_ , lowerCamelCase_ )
# parameters for encoder hidden states
SCREAMING_SNAKE_CASE : Any = clip_extra_context_tokens
SCREAMING_SNAKE_CASE : List[str] = nn.Linear(
lowerCamelCase_ , self.clip_extra_context_tokens * cross_attention_dim )
SCREAMING_SNAKE_CASE : str = nn.Linear(lowerCamelCase_ , lowerCamelCase_ )
SCREAMING_SNAKE_CASE : str = nn.LayerNorm(lowerCamelCase_ )
def lowerCamelCase_ ( self : Dict , *, lowerCamelCase_ : Optional[int] , lowerCamelCase_ : List[Any] , lowerCamelCase_ : Optional[Any] , lowerCamelCase_ : Tuple ):
'''simple docstring'''
if do_classifier_free_guidance:
# Add the classifier free guidance embeddings to the image embeddings
SCREAMING_SNAKE_CASE : Tuple = image_embeddings.shape[0]
SCREAMING_SNAKE_CASE : Tuple = self.learned_classifier_free_guidance_embeddings.unsqueeze(0 )
SCREAMING_SNAKE_CASE : int = classifier_free_guidance_embeddings.expand(
lowerCamelCase_ , -1 )
SCREAMING_SNAKE_CASE : Optional[int] = torch.cat([classifier_free_guidance_embeddings, image_embeddings] , dim=0 )
# The image embeddings batch size and the text embeddings batch size are equal
assert image_embeddings.shape[0] == prompt_embeds.shape[0]
SCREAMING_SNAKE_CASE : Dict = prompt_embeds.shape[0]
# "Specifically, we modify the architecture described in Nichol et al. (2021) by projecting and
# adding CLIP embeddings to the existing timestep embedding, ...
SCREAMING_SNAKE_CASE : Union[str, Any] = self.embedding_proj(lowerCamelCase_ )
SCREAMING_SNAKE_CASE : Dict = self.clip_image_embeddings_project_to_time_embeddings(lowerCamelCase_ )
SCREAMING_SNAKE_CASE : Tuple = time_projected_image_embeddings + time_projected_prompt_embeds
# ... and by projecting CLIP embeddings into four
# extra tokens of context that are concatenated to the sequence of outputs from the GLIDE text encoder"
SCREAMING_SNAKE_CASE : List[Any] = self.clip_extra_context_tokens_proj(lowerCamelCase_ )
SCREAMING_SNAKE_CASE : List[Any] = clip_extra_context_tokens.reshape(lowerCamelCase_ , -1 , self.clip_extra_context_tokens )
SCREAMING_SNAKE_CASE : Dict = clip_extra_context_tokens.permute(0 , 2 , 1 )
SCREAMING_SNAKE_CASE : List[Any] = self.encoder_hidden_states_proj(lowerCamelCase_ )
SCREAMING_SNAKE_CASE : Optional[Any] = self.text_encoder_hidden_states_norm(lowerCamelCase_ )
SCREAMING_SNAKE_CASE : Tuple = torch.cat([clip_extra_context_tokens, text_encoder_hidden_states] , dim=1 )
return text_encoder_hidden_states, additive_clip_time_embeddings
| 707 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__UpperCAmelCase = logging.get_logger(__name__)
__UpperCAmelCase = {
"""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__ ( lowercase_ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE__ = '''vivit'''
def __init__( self : Tuple , lowerCamelCase_ : str=2_24 , lowerCamelCase_ : List[Any]=32 , lowerCamelCase_ : Tuple=[2, 16, 16] , lowerCamelCase_ : List[str]=3 , lowerCamelCase_ : Dict=7_68 , lowerCamelCase_ : Dict=12 , lowerCamelCase_ : Any=12 , lowerCamelCase_ : List[Any]=30_72 , lowerCamelCase_ : List[str]="gelu_fast" , lowerCamelCase_ : str=0.0 , lowerCamelCase_ : Any=0.0 , lowerCamelCase_ : Optional[int]=0.02 , lowerCamelCase_ : List[Any]=1e-06 , lowerCamelCase_ : Tuple=True , **lowerCamelCase_ : Tuple , ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : List[str] = hidden_size
SCREAMING_SNAKE_CASE : Union[str, Any] = num_hidden_layers
SCREAMING_SNAKE_CASE : List[str] = num_attention_heads
SCREAMING_SNAKE_CASE : str = intermediate_size
SCREAMING_SNAKE_CASE : List[Any] = hidden_act
SCREAMING_SNAKE_CASE : Optional[Any] = hidden_dropout_prob
SCREAMING_SNAKE_CASE : Tuple = attention_probs_dropout_prob
SCREAMING_SNAKE_CASE : Tuple = initializer_range
SCREAMING_SNAKE_CASE : str = layer_norm_eps
SCREAMING_SNAKE_CASE : str = image_size
SCREAMING_SNAKE_CASE : Dict = num_frames
SCREAMING_SNAKE_CASE : Optional[Any] = tubelet_size
SCREAMING_SNAKE_CASE : Dict = num_channels
SCREAMING_SNAKE_CASE : int = qkv_bias
super().__init__(**lowerCamelCase_ )
| 79 | 0 |
'''simple docstring'''
import os
from bleurt import score # From: git+https://github.com/google-research/bleurt.git
import datasets
__UpperCAmelCase = datasets.logging.get_logger(__name__)
__UpperCAmelCase = """\\n@inproceedings{bleurt,\n title={BLEURT: Learning Robust Metrics for Text Generation},\n author={Thibault Sellam and Dipanjan Das and Ankur P. Parikh},\n booktitle={ACL},\n year={2020},\n url={https://arxiv.org/abs/2004.04696}\n}\n"""
__UpperCAmelCase = """\\nBLEURT a learnt evaluation metric for Natural Language Generation. It is built using multiple phases of transfer learning starting from a pretrained BERT model (Devlin et al. 2018)\nand then employing another pre-training phrase using synthetic data. Finally it is trained on WMT human annotations. You may run BLEURT out-of-the-box or fine-tune\nit for your specific application (the latter is expected to perform better).\n\nSee the project\'s README at https://github.com/google-research/bleurt#readme for more information.\n"""
__UpperCAmelCase = """\nBLEURT score.\n\nArgs:\n `predictions` (list of str): prediction/candidate sentences\n `references` (list of str): reference sentences\n `checkpoint` BLEURT checkpoint. Will default to BLEURT-tiny if None.\n\nReturns:\n \'scores\': List of scores.\nExamples:\n\n >>> predictions = [\"hello there\", \"general kenobi\"]\n >>> references = [\"hello there\", \"general kenobi\"]\n >>> bleurt = datasets.load_metric(\"bleurt\")\n >>> results = bleurt.compute(predictions=predictions, references=references)\n >>> print([round(v, 2) for v in results[\"scores\"]])\n [1.03, 1.04]\n"""
__UpperCAmelCase = {
"""bleurt-tiny-128""": """https://storage.googleapis.com/bleurt-oss/bleurt-tiny-128.zip""",
"""bleurt-tiny-512""": """https://storage.googleapis.com/bleurt-oss/bleurt-tiny-512.zip""",
"""bleurt-base-128""": """https://storage.googleapis.com/bleurt-oss/bleurt-base-128.zip""",
"""bleurt-base-512""": """https://storage.googleapis.com/bleurt-oss/bleurt-base-512.zip""",
"""bleurt-large-128""": """https://storage.googleapis.com/bleurt-oss/bleurt-large-128.zip""",
"""bleurt-large-512""": """https://storage.googleapis.com/bleurt-oss/bleurt-large-512.zip""",
"""BLEURT-20-D3""": """https://storage.googleapis.com/bleurt-oss-21/BLEURT-20-D3.zip""",
"""BLEURT-20-D6""": """https://storage.googleapis.com/bleurt-oss-21/BLEURT-20-D6.zip""",
"""BLEURT-20-D12""": """https://storage.googleapis.com/bleurt-oss-21/BLEURT-20-D12.zip""",
"""BLEURT-20""": """https://storage.googleapis.com/bleurt-oss-21/BLEURT-20.zip""",
}
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class UpperCamelCase__ ( datasets.Metric ):
"""simple docstring"""
def lowerCamelCase_ ( self : Any ):
'''simple docstring'''
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , homepage="""https://github.com/google-research/bleurt""" , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
"""predictions""": datasets.Value("""string""" , id="""sequence""" ),
"""references""": datasets.Value("""string""" , id="""sequence""" ),
} ) , codebase_urls=["""https://github.com/google-research/bleurt"""] , reference_urls=["""https://github.com/google-research/bleurt""", """https://arxiv.org/abs/2004.04696"""] , )
def lowerCamelCase_ ( self : Union[str, Any] , lowerCamelCase_ : Optional[Any] ):
'''simple docstring'''
if self.config_name == "default":
logger.warning(
"""Using default BLEURT-Base checkpoint for sequence maximum length 128. """
"""You can use a bigger model for better results with e.g.: datasets.load_metric(\'bleurt\', \'bleurt-large-512\').""" )
SCREAMING_SNAKE_CASE : List[Any] = 'bleurt-base-128'
if self.config_name.lower() in CHECKPOINT_URLS:
SCREAMING_SNAKE_CASE : Tuple = self.config_name.lower()
elif self.config_name.upper() in CHECKPOINT_URLS:
SCREAMING_SNAKE_CASE : Any = self.config_name.upper()
else:
raise KeyError(
f'''{self.config_name} model not found. You should supply the name of a model checkpoint for bleurt in {CHECKPOINT_URLS.keys()}''' )
# download the model checkpoint specified by self.config_name and set up the scorer
SCREAMING_SNAKE_CASE : List[Any] = dl_manager.download_and_extract(CHECKPOINT_URLS[checkpoint_name] )
SCREAMING_SNAKE_CASE : int = score.BleurtScorer(os.path.join(UpperCamelCase__ , UpperCamelCase__ ) )
def lowerCamelCase_ ( self : Dict , lowerCamelCase_ : int , lowerCamelCase_ : Optional[Any] ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Tuple = self.scorer.score(references=UpperCamelCase__ , candidates=UpperCamelCase__ )
return {"scores": scores}
| 708 |
'''simple docstring'''
import math
class UpperCamelCase__ :
"""simple docstring"""
def __init__( self : List[str] , lowerCamelCase_ : Tuple=0 ): # a graph with Node 0,1,...,N-1
'''simple docstring'''
SCREAMING_SNAKE_CASE : Any = n
SCREAMING_SNAKE_CASE : Optional[int] = [
[math.inf for j in range(0 , lowerCamelCase_ )] for i in range(0 , lowerCamelCase_ )
] # adjacency matrix for weight
SCREAMING_SNAKE_CASE : Union[str, Any] = [
[math.inf for j in range(0 , lowerCamelCase_ )] for i in range(0 , lowerCamelCase_ )
] # dp[i][j] stores minimum distance from i to j
def lowerCamelCase_ ( self : Dict , lowerCamelCase_ : str , lowerCamelCase_ : List[str] , lowerCamelCase_ : int ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Union[str, Any] = w
def lowerCamelCase_ ( self : Optional[int] ):
'''simple docstring'''
for k in range(0 , self.n ):
for i in range(0 , self.n ):
for j in range(0 , self.n ):
SCREAMING_SNAKE_CASE : Union[str, Any] = min(self.dp[i][j] , self.dp[i][k] + self.dp[k][j] )
def lowerCamelCase_ ( self : Dict , lowerCamelCase_ : List[Any] , lowerCamelCase_ : Union[str, Any] ):
'''simple docstring'''
return self.dp[u][v]
if __name__ == "__main__":
__UpperCAmelCase = Graph(5)
graph.add_edge(0, 2, 9)
graph.add_edge(0, 4, 10)
graph.add_edge(1, 3, 5)
graph.add_edge(2, 3, 7)
graph.add_edge(3, 0, 10)
graph.add_edge(3, 1, 2)
graph.add_edge(3, 2, 1)
graph.add_edge(3, 4, 6)
graph.add_edge(4, 1, 3)
graph.add_edge(4, 2, 4)
graph.add_edge(4, 3, 9)
graph.floyd_warshall()
graph.show_min(1, 4)
graph.show_min(0, 3)
| 79 | 0 |
'''simple docstring'''
from .configuration_bert_masked import MaskedBertConfig
from .modeling_bert_masked import (
MaskedBertForMultipleChoice,
MaskedBertForQuestionAnswering,
MaskedBertForSequenceClassification,
MaskedBertForTokenClassification,
MaskedBertModel,
)
from .modules import *
| 709 |
'''simple docstring'''
import math
def __A ( lowerCamelCase_ ):
"""simple docstring"""
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
return False
# All primes number are in format of 6k +/- 1
for i in range(5 , int(math.sqrt(lowerCamelCase_ ) + 1 ) , 6 ):
if number % i == 0 or number % (i + 2) == 0:
return False
return True
def __A ( lowerCamelCase_ = 1_00_01 ):
"""simple docstring"""
try:
SCREAMING_SNAKE_CASE : Tuple = int(lowerCamelCase_ )
except (TypeError, ValueError):
raise TypeError("""Parameter nth must be int or castable to int.""" ) from None
if nth <= 0:
raise ValueError("""Parameter nth must be greater than or equal to one.""" )
SCREAMING_SNAKE_CASE : list[int] = []
SCREAMING_SNAKE_CASE : Dict = 2
while len(lowerCamelCase_ ) < nth:
if is_prime(lowerCamelCase_ ):
primes.append(lowerCamelCase_ )
num += 1
else:
num += 1
return primes[len(lowerCamelCase_ ) - 1]
if __name__ == "__main__":
print(f'''{solution() = }''')
| 79 | 0 |
import pprint
import requests
__UpperCAmelCase = """https://zenquotes.io/api"""
def __A ( ):
"""simple docstring"""
return requests.get(API_ENDPOINT_URL + """/today""" ).json()
def __A ( ):
"""simple docstring"""
return requests.get(API_ENDPOINT_URL + """/random""" ).json()
if __name__ == "__main__":
__UpperCAmelCase = random_quotes()
pprint.pprint(response)
| 710 |
'''simple docstring'''
from __future__ import annotations
import json
import requests
from bsa import BeautifulSoup
from fake_useragent import UserAgent
__UpperCAmelCase = {"""UserAgent""": UserAgent().random}
def __A ( lowerCamelCase_ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : str = script.contents[0]
SCREAMING_SNAKE_CASE : int = json.loads(data[data.find("""{\"config\"""" ) : -1] )
return info["entry_data"]["ProfilePage"][0]["graphql"]["user"]
class UpperCamelCase__ :
"""simple docstring"""
def __init__( self : Dict , lowerCamelCase_ : str ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Any = f'''https://www.instagram.com/{username}/'''
SCREAMING_SNAKE_CASE : Any = self.get_json()
def lowerCamelCase_ ( self : Tuple ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Dict = requests.get(self.url , headers=lowerCamelCase_ ).text
SCREAMING_SNAKE_CASE : List[Any] = BeautifulSoup(lowerCamelCase_ , """html.parser""" ).find_all("""script""" )
try:
return extract_user_profile(scripts[4] )
except (json.decoder.JSONDecodeError, KeyError):
return extract_user_profile(scripts[3] )
def __repr__( self : Dict ):
'''simple docstring'''
return f'''{self.__class__.__name__}(\'{self.username}\')'''
def __str__( self : int ):
'''simple docstring'''
return f'''{self.fullname} ({self.username}) is {self.biography}'''
@property
def lowerCamelCase_ ( self : Tuple ):
'''simple docstring'''
return self.user_data["username"]
@property
def lowerCamelCase_ ( self : Optional[Any] ):
'''simple docstring'''
return self.user_data["full_name"]
@property
def lowerCamelCase_ ( self : int ):
'''simple docstring'''
return self.user_data["biography"]
@property
def lowerCamelCase_ ( self : str ):
'''simple docstring'''
return self.user_data["business_email"]
@property
def lowerCamelCase_ ( self : str ):
'''simple docstring'''
return self.user_data["external_url"]
@property
def lowerCamelCase_ ( self : List[Any] ):
'''simple docstring'''
return self.user_data["edge_followed_by"]["count"]
@property
def lowerCamelCase_ ( self : List[Any] ):
'''simple docstring'''
return self.user_data["edge_follow"]["count"]
@property
def lowerCamelCase_ ( self : List[Any] ):
'''simple docstring'''
return self.user_data["edge_owner_to_timeline_media"]["count"]
@property
def lowerCamelCase_ ( self : str ):
'''simple docstring'''
return self.user_data["profile_pic_url_hd"]
@property
def lowerCamelCase_ ( self : Dict ):
'''simple docstring'''
return self.user_data["is_verified"]
@property
def lowerCamelCase_ ( self : Optional[int] ):
'''simple docstring'''
return self.user_data["is_private"]
def __A ( lowerCamelCase_ = "github" ):
"""simple docstring"""
import os
if os.environ.get("""CI""" ):
return # test failing on GitHub Actions
SCREAMING_SNAKE_CASE : Any = InstagramUser(lowerCamelCase_ )
assert instagram_user.user_data
assert isinstance(instagram_user.user_data , lowerCamelCase_ )
assert instagram_user.username == username
if username != "github":
return
assert instagram_user.fullname == "GitHub"
assert instagram_user.biography == "Built for developers."
assert instagram_user.number_of_posts > 1_50
assert instagram_user.number_of_followers > 12_00_00
assert instagram_user.number_of_followings > 15
assert instagram_user.email == "support@github.com"
assert instagram_user.website == "https://github.com/readme"
assert instagram_user.profile_picture_url.startswith("""https://instagram.""" )
assert instagram_user.is_verified is True
assert instagram_user.is_private is False
if __name__ == "__main__":
import doctest
doctest.testmod()
__UpperCAmelCase = InstagramUser("""github""")
print(instagram_user)
print(f'''{instagram_user.number_of_posts = }''')
print(f'''{instagram_user.number_of_followers = }''')
print(f'''{instagram_user.number_of_followings = }''')
print(f'''{instagram_user.email = }''')
print(f'''{instagram_user.website = }''')
print(f'''{instagram_user.profile_picture_url = }''')
print(f'''{instagram_user.is_verified = }''')
print(f'''{instagram_user.is_private = }''')
| 79 | 0 |
'''simple docstring'''
import argparse
import os
import platform
import numpy as np
import psutil
import torch
from accelerate import __version__ as version
from accelerate.commands.config import default_config_file, load_config_from_file
from ..utils import is_npu_available, is_xpu_available
def __A ( lowerCamelCase_=None ):
"""simple docstring"""
if subparsers is not None:
SCREAMING_SNAKE_CASE : int = subparsers.add_parser("""env""" )
else:
SCREAMING_SNAKE_CASE : Dict = argparse.ArgumentParser("""Accelerate env command""" )
parser.add_argument(
"""--config_file""" , default=__lowerCAmelCase , help="""The config file to use for the default values in the launching script.""" )
if subparsers is not None:
parser.set_defaults(func=__lowerCAmelCase )
return parser
def __A ( lowerCamelCase_ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : str = torch.__version__
SCREAMING_SNAKE_CASE : Union[str, Any] = torch.cuda.is_available()
SCREAMING_SNAKE_CASE : str = is_xpu_available()
SCREAMING_SNAKE_CASE : int = is_npu_available()
SCREAMING_SNAKE_CASE : List[Any] = """Not found"""
# Get the default from the config file.
if args.config_file is not None or os.path.isfile(__lowerCAmelCase ):
SCREAMING_SNAKE_CASE : int = load_config_from_file(args.config_file ).to_dict()
SCREAMING_SNAKE_CASE : Union[str, Any] = {
"""`Accelerate` version""": version,
"""Platform""": platform.platform(),
"""Python version""": platform.python_version(),
"""Numpy version""": np.__version__,
"""PyTorch version (GPU?)""": f'''{pt_version} ({pt_cuda_available})''',
"""PyTorch XPU available""": str(__lowerCAmelCase ),
"""PyTorch NPU available""": str(__lowerCAmelCase ),
"""System RAM""": f'''{psutil.virtual_memory().total / 10_24 ** 3:.2f} GB''',
}
if pt_cuda_available:
SCREAMING_SNAKE_CASE : List[str] = torch.cuda.get_device_name()
print("""\nCopy-and-paste the text below in your GitHub issue\n""" )
print("""\n""".join([f'''- {prop}: {val}''' for prop, val in info.items()] ) )
print("""- `Accelerate` default config:""" if args.config_file is None else """- `Accelerate` config passed:""" )
SCREAMING_SNAKE_CASE : List[Any] = (
"""\n""".join([f'''\t- {prop}: {val}''' for prop, val in accelerate_config.items()] )
if isinstance(__lowerCAmelCase , __lowerCAmelCase )
else f'''\t{accelerate_config}'''
)
print(__lowerCAmelCase )
SCREAMING_SNAKE_CASE : Optional[Any] = accelerate_config
return info
def __A ( ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : Tuple = env_command_parser()
SCREAMING_SNAKE_CASE : Any = parser.parse_args()
env_command(__lowerCAmelCase )
return 0
if __name__ == "__main__":
raise SystemExit(main())
| 711 |
'''simple docstring'''
import argparse
import logging
from collections import namedtuple
import torch
from model_bertabs import BertAbsSummarizer
from models.model_builder import AbsSummarizer # The authors' implementation
from transformers import BertTokenizer
logging.basicConfig(level=logging.INFO)
__UpperCAmelCase = logging.getLogger(__name__)
__UpperCAmelCase = """Hello world! cécé herlolip"""
__UpperCAmelCase = namedtuple(
"""BertAbsConfig""",
[
"""temp_dir""",
"""large""",
"""use_bert_emb""",
"""finetune_bert""",
"""encoder""",
"""share_emb""",
"""max_pos""",
"""enc_layers""",
"""enc_hidden_size""",
"""enc_heads""",
"""enc_ff_size""",
"""enc_dropout""",
"""dec_layers""",
"""dec_hidden_size""",
"""dec_heads""",
"""dec_ff_size""",
"""dec_dropout""",
],
)
def __A ( lowerCamelCase_ , lowerCamelCase_ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : Tuple = BertAbsConfig(
temp_dir=""".""" , finetune_bert=lowerCamelCase_ , large=lowerCamelCase_ , share_emb=lowerCamelCase_ , use_bert_emb=lowerCamelCase_ , encoder="""bert""" , max_pos=5_12 , enc_layers=6 , enc_hidden_size=5_12 , enc_heads=8 , enc_ff_size=5_12 , enc_dropout=0.2 , dec_layers=6 , dec_hidden_size=7_68 , dec_heads=8 , dec_ff_size=20_48 , dec_dropout=0.2 , )
SCREAMING_SNAKE_CASE : int = torch.load(lowerCamelCase_ , lambda lowerCamelCase_ , lowerCamelCase_ : storage )
SCREAMING_SNAKE_CASE : List[str] = AbsSummarizer(lowerCamelCase_ , torch.device("""cpu""" ) , lowerCamelCase_ )
original.eval()
SCREAMING_SNAKE_CASE : Optional[int] = BertAbsSummarizer(lowerCamelCase_ , torch.device("""cpu""" ) )
new_model.eval()
# -------------------
# Convert the weights
# -------------------
logging.info("""convert the model""" )
new_model.bert.load_state_dict(original.bert.state_dict() )
new_model.decoder.load_state_dict(original.decoder.state_dict() )
new_model.generator.load_state_dict(original.generator.state_dict() )
# ----------------------------------
# Make sure the outpus are identical
# ----------------------------------
logging.info("""Make sure that the models' outputs are identical""" )
SCREAMING_SNAKE_CASE : int = BertTokenizer.from_pretrained("""bert-base-uncased""" )
# prepare the model inputs
SCREAMING_SNAKE_CASE : Optional[Any] = tokenizer.encode("""This is sample éàalj'-.""" )
encoder_input_ids.extend([tokenizer.pad_token_id] * (5_12 - len(lowerCamelCase_ )) )
SCREAMING_SNAKE_CASE : List[str] = torch.tensor(lowerCamelCase_ ).unsqueeze(0 )
SCREAMING_SNAKE_CASE : Optional[int] = tokenizer.encode("""This is sample 3 éàalj'-.""" )
decoder_input_ids.extend([tokenizer.pad_token_id] * (5_12 - len(lowerCamelCase_ )) )
SCREAMING_SNAKE_CASE : Optional[Any] = torch.tensor(lowerCamelCase_ ).unsqueeze(0 )
# failsafe to make sure the weights reset does not affect the
# loaded weights.
assert torch.max(torch.abs(original.generator[0].weight - new_model.generator[0].weight ) ) == 0
# forward pass
SCREAMING_SNAKE_CASE : Optional[int] = encoder_input_ids
SCREAMING_SNAKE_CASE : Optional[Any] = decoder_input_ids
SCREAMING_SNAKE_CASE : List[str] = None
SCREAMING_SNAKE_CASE : Tuple = None
SCREAMING_SNAKE_CASE : int = None
SCREAMING_SNAKE_CASE : List[Any] = None
SCREAMING_SNAKE_CASE : Optional[int] = None
# The original model does not apply the geneator layer immediatly but rather in
# the beam search (where it combines softmax + linear layer). Since we already
# apply the softmax in our generation process we only apply the linear layer here.
# We make sure that the outputs of the full stack are identical
SCREAMING_SNAKE_CASE : str = original(lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ )[0]
SCREAMING_SNAKE_CASE : Optional[Any] = original.generator(lowerCamelCase_ )
SCREAMING_SNAKE_CASE : List[Any] = new_model(
lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ )[0]
SCREAMING_SNAKE_CASE : str = new_model.generator(lowerCamelCase_ )
SCREAMING_SNAKE_CASE : int = torch.max(torch.abs(output_converted_model - output_original_model ) ).item()
print("""Maximum absolute difference beween weights: {:.2f}""".format(lowerCamelCase_ ) )
SCREAMING_SNAKE_CASE : Optional[int] = torch.max(torch.abs(output_converted_generator - output_original_generator ) ).item()
print("""Maximum absolute difference beween weights: {:.2f}""".format(lowerCamelCase_ ) )
SCREAMING_SNAKE_CASE : Any = torch.allclose(lowerCamelCase_ , lowerCamelCase_ , atol=1E-3 )
if are_identical:
logging.info("""all weights are equal up to 1e-3""" )
else:
raise ValueError("""the weights are different. The new model is likely different from the original one.""" )
# The model has been saved with torch.save(model) and this is bound to the exact
# directory structure. We save the state_dict instead.
logging.info("""saving the model's state dictionary""" )
torch.save(
new_model.state_dict() , """./bertabs-finetuned-cnndm-extractive-abstractive-summarization/pytorch_model.bin""" )
if __name__ == "__main__":
__UpperCAmelCase = argparse.ArgumentParser()
parser.add_argument(
"""--bertabs_checkpoint_path""",
default=None,
type=str,
required=True,
help="""Path the official PyTorch dump.""",
)
parser.add_argument(
"""--pytorch_dump_folder_path""",
default=None,
type=str,
required=True,
help="""Path to the output PyTorch model.""",
)
__UpperCAmelCase = parser.parse_args()
convert_bertabs_checkpoints(
args.bertabs_checkpoint_path,
args.pytorch_dump_folder_path,
)
| 79 | 0 |
'''simple docstring'''
import pytest
from datasets.utils.sharding import _distribute_shards, _number_of_shards_in_gen_kwargs, _split_gen_kwargs
@pytest.mark.parametrize(
"""kwargs, expected""" , [
({"""num_shards""": 0, """max_num_jobs""": 1}, []),
({"""num_shards""": 10, """max_num_jobs""": 1}, [range(10 )]),
({"""num_shards""": 10, """max_num_jobs""": 10}, [range(a_ , i + 1 ) for i in range(10 )]),
({"""num_shards""": 1, """max_num_jobs""": 10}, [range(1 )]),
({"""num_shards""": 10, """max_num_jobs""": 3}, [range(0 , 4 ), range(4 , 7 ), range(7 , 10 )]),
({"""num_shards""": 3, """max_num_jobs""": 10}, [range(0 , 1 ), range(1 , 2 ), range(2 , 3 )]),
] , )
def __A ( lowerCamelCase_ , lowerCamelCase_ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : Union[str, Any] = _distribute_shards(**a_ )
assert out == expected
@pytest.mark.parametrize(
"""gen_kwargs, max_num_jobs, expected""" , [
({"""foo""": 0}, 10, [{"""foo""": 0}]),
({"""shards""": [0, 1, 2, 3]}, 1, [{"""shards""": [0, 1, 2, 3]}]),
({"""shards""": [0, 1, 2, 3]}, 4, [{"""shards""": [0]}, {"""shards""": [1]}, {"""shards""": [2]}, {"""shards""": [3]}]),
({"""shards""": [0, 1]}, 4, [{"""shards""": [0]}, {"""shards""": [1]}]),
({"""shards""": [0, 1, 2, 3]}, 2, [{"""shards""": [0, 1]}, {"""shards""": [2, 3]}]),
] , )
def __A ( lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : Any = _split_gen_kwargs(a_ , a_ )
assert out == expected
@pytest.mark.parametrize(
"""gen_kwargs, expected""" , [
({"""foo""": 0}, 1),
({"""shards""": [0]}, 1),
({"""shards""": [0, 1, 2, 3]}, 4),
({"""shards""": [0, 1, 2, 3], """foo""": 0}, 4),
({"""shards""": [0, 1, 2, 3], """other""": (0, 1)}, 4),
({"""shards""": [0, 1, 2, 3], """shards2""": [0, 1]}, RuntimeError),
] , )
def __A ( lowerCamelCase_ , lowerCamelCase_ ):
"""simple docstring"""
if expected is RuntimeError:
with pytest.raises(a_ ):
_number_of_shards_in_gen_kwargs(a_ )
else:
SCREAMING_SNAKE_CASE : Dict = _number_of_shards_in_gen_kwargs(a_ )
assert out == expected
| 712 |
'''simple docstring'''
import itertools
import json
import linecache
import os
import pickle
import re
import socket
import string
from collections import Counter
from logging import getLogger
from pathlib import Path
from typing import Callable, Dict, Iterable, List
import git
import torch
from torch.utils.data import Dataset
from transformers import BartTokenizer, RagTokenizer, TaTokenizer
def __A ( lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_=True , lowerCamelCase_="pt" ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : Tuple = {"""add_prefix_space""": True} if isinstance(lowerCamelCase_ , lowerCamelCase_ ) and not line.startswith(""" """ ) else {}
SCREAMING_SNAKE_CASE : Optional[Any] = padding_side
return tokenizer(
[line] , max_length=lowerCamelCase_ , padding="""max_length""" if pad_to_max_length else None , truncation=lowerCamelCase_ , return_tensors=lowerCamelCase_ , add_special_tokens=lowerCamelCase_ , **lowerCamelCase_ , )
def __A ( lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_=None , ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : int = input_ids.ne(lowerCamelCase_ ).any(dim=0 )
if attention_mask is None:
return input_ids[:, keep_column_mask]
else:
return (input_ids[:, keep_column_mask], attention_mask[:, keep_column_mask])
class UpperCamelCase__ ( lowercase_ ):
"""simple docstring"""
def __init__( self : str , lowerCamelCase_ : Tuple , lowerCamelCase_ : Tuple , lowerCamelCase_ : List[str] , lowerCamelCase_ : List[str] , lowerCamelCase_ : List[str]="train" , lowerCamelCase_ : List[str]=None , lowerCamelCase_ : List[Any]=None , lowerCamelCase_ : int=None , lowerCamelCase_ : Union[str, Any]="" , ):
'''simple docstring'''
super().__init__()
SCREAMING_SNAKE_CASE : str = Path(lowerCamelCase_ ).joinpath(type_path + """.source""" )
SCREAMING_SNAKE_CASE : Optional[Any] = Path(lowerCamelCase_ ).joinpath(type_path + """.target""" )
SCREAMING_SNAKE_CASE : Union[str, Any] = self.get_char_lens(self.src_file )
SCREAMING_SNAKE_CASE : int = max_source_length
SCREAMING_SNAKE_CASE : str = max_target_length
assert min(self.src_lens ) > 0, f'''found empty line in {self.src_file}'''
SCREAMING_SNAKE_CASE : List[str] = tokenizer
SCREAMING_SNAKE_CASE : Dict = prefix
if n_obs is not None:
SCREAMING_SNAKE_CASE : List[Any] = self.src_lens[:n_obs]
SCREAMING_SNAKE_CASE : int = src_lang
SCREAMING_SNAKE_CASE : Optional[int] = tgt_lang
def __len__( self : List[Any] ):
'''simple docstring'''
return len(self.src_lens )
def __getitem__( self : Union[str, Any] , lowerCamelCase_ : Optional[Any] ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Any = index + 1 # linecache starts at 1
SCREAMING_SNAKE_CASE : Dict = self.prefix + linecache.getline(str(self.src_file ) , lowerCamelCase_ ).rstrip("""\n""" )
SCREAMING_SNAKE_CASE : Dict = linecache.getline(str(self.tgt_file ) , lowerCamelCase_ ).rstrip("""\n""" )
assert source_line, f'''empty source line for index {index}'''
assert tgt_line, f'''empty tgt line for index {index}'''
# Need to add eos token manually for T5
if isinstance(self.tokenizer , lowerCamelCase_ ):
source_line += self.tokenizer.eos_token
tgt_line += self.tokenizer.eos_token
# Pad source and target to the right
SCREAMING_SNAKE_CASE : Union[str, Any] = (
self.tokenizer.question_encoder if isinstance(self.tokenizer , lowerCamelCase_ ) else self.tokenizer
)
SCREAMING_SNAKE_CASE : Any = self.tokenizer.generator if isinstance(self.tokenizer , lowerCamelCase_ ) else self.tokenizer
SCREAMING_SNAKE_CASE : Optional[int] = encode_line(lowerCamelCase_ , lowerCamelCase_ , self.max_source_length , """right""" )
SCREAMING_SNAKE_CASE : Union[str, Any] = encode_line(lowerCamelCase_ , lowerCamelCase_ , self.max_target_length , """right""" )
SCREAMING_SNAKE_CASE : Tuple = source_inputs["""input_ids"""].squeeze()
SCREAMING_SNAKE_CASE : Tuple = target_inputs["""input_ids"""].squeeze()
SCREAMING_SNAKE_CASE : List[str] = source_inputs["""attention_mask"""].squeeze()
return {
"input_ids": source_ids,
"attention_mask": src_mask,
"decoder_input_ids": target_ids,
}
@staticmethod
def lowerCamelCase_ ( lowerCamelCase_ : Dict ):
'''simple docstring'''
return [len(lowerCamelCase_ ) for x in Path(lowerCamelCase_ ).open().readlines()]
def lowerCamelCase_ ( self : Tuple , lowerCamelCase_ : Any ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Tuple = torch.stack([x["""input_ids"""] for x in batch] )
SCREAMING_SNAKE_CASE : int = torch.stack([x["""attention_mask"""] for x in batch] )
SCREAMING_SNAKE_CASE : int = torch.stack([x["""decoder_input_ids"""] for x in batch] )
SCREAMING_SNAKE_CASE : int = (
self.tokenizer.generator.pad_token_id
if isinstance(self.tokenizer , lowerCamelCase_ )
else self.tokenizer.pad_token_id
)
SCREAMING_SNAKE_CASE : Dict = (
self.tokenizer.question_encoder.pad_token_id
if isinstance(self.tokenizer , lowerCamelCase_ )
else self.tokenizer.pad_token_id
)
SCREAMING_SNAKE_CASE : Optional[int] = trim_batch(lowerCamelCase_ , lowerCamelCase_ )
SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE : Optional[int] = trim_batch(lowerCamelCase_ , lowerCamelCase_ , attention_mask=lowerCamelCase_ )
SCREAMING_SNAKE_CASE : int = {
"""input_ids""": source_ids,
"""attention_mask""": source_mask,
"""decoder_input_ids""": y,
}
return batch
__UpperCAmelCase = getLogger(__name__)
def __A ( lowerCamelCase_ ):
"""simple docstring"""
return list(itertools.chain.from_iterable(lowerCamelCase_ ) )
def __A ( lowerCamelCase_ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : str = get_git_info()
save_json(lowerCamelCase_ , os.path.join(lowerCamelCase_ , """git_log.json""" ) )
def __A ( lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_=4 , **lowerCamelCase_ ):
"""simple docstring"""
with open(lowerCamelCase_ , """w""" ) as f:
json.dump(lowerCamelCase_ , lowerCamelCase_ , indent=lowerCamelCase_ , **lowerCamelCase_ )
def __A ( lowerCamelCase_ ):
"""simple docstring"""
with open(lowerCamelCase_ ) as f:
return json.load(lowerCamelCase_ )
def __A ( ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : List[Any] = git.Repo(search_parent_directories=lowerCamelCase_ )
SCREAMING_SNAKE_CASE : Tuple = {
"""repo_id""": str(lowerCamelCase_ ),
"""repo_sha""": str(repo.head.object.hexsha ),
"""repo_branch""": str(repo.active_branch ),
"""hostname""": str(socket.gethostname() ),
}
return repo_infos
def __A ( lowerCamelCase_ , lowerCamelCase_ ):
"""simple docstring"""
return list(map(lowerCamelCase_ , lowerCamelCase_ ) )
def __A ( lowerCamelCase_ , lowerCamelCase_ ):
"""simple docstring"""
with open(lowerCamelCase_ , """wb""" ) as f:
return pickle.dump(lowerCamelCase_ , lowerCamelCase_ )
def __A ( lowerCamelCase_ ):
"""simple docstring"""
def remove_articles(lowerCamelCase_ ):
return re.sub(R"""\b(a|an|the)\b""" , """ """ , lowerCamelCase_ )
def white_space_fix(lowerCamelCase_ ):
return " ".join(text.split() )
def remove_punc(lowerCamelCase_ ):
SCREAMING_SNAKE_CASE : int = set(string.punctuation )
return "".join(ch for ch in text if ch not in exclude )
def lower(lowerCamelCase_ ):
return text.lower()
return white_space_fix(remove_articles(remove_punc(lower(lowerCamelCase_ ) ) ) )
def __A ( lowerCamelCase_ , lowerCamelCase_ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : Tuple = normalize_answer(lowerCamelCase_ ).split()
SCREAMING_SNAKE_CASE : Optional[int] = normalize_answer(lowerCamelCase_ ).split()
SCREAMING_SNAKE_CASE : Tuple = Counter(lowerCamelCase_ ) & Counter(lowerCamelCase_ )
SCREAMING_SNAKE_CASE : Any = sum(common.values() )
if num_same == 0:
return 0
SCREAMING_SNAKE_CASE : Optional[int] = 1.0 * num_same / len(lowerCamelCase_ )
SCREAMING_SNAKE_CASE : str = 1.0 * num_same / len(lowerCamelCase_ )
SCREAMING_SNAKE_CASE : List[str] = (2 * precision * recall) / (precision + recall)
return fa
def __A ( lowerCamelCase_ , lowerCamelCase_ ):
"""simple docstring"""
return normalize_answer(lowerCamelCase_ ) == normalize_answer(lowerCamelCase_ )
def __A ( lowerCamelCase_ , lowerCamelCase_ ):
"""simple docstring"""
assert len(lowerCamelCase_ ) == len(lowerCamelCase_ )
SCREAMING_SNAKE_CASE : Union[str, Any] = 0
for hypo, pred in zip(lowerCamelCase_ , lowerCamelCase_ ):
em += exact_match_score(lowerCamelCase_ , lowerCamelCase_ )
if len(lowerCamelCase_ ) > 0:
em /= len(lowerCamelCase_ )
return {"em": em}
def __A ( lowerCamelCase_ ):
"""simple docstring"""
return model_prefix.startswith("""rag""" )
def __A ( lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : Any = {p: p for p in extra_params}
# T5 models don't have `dropout` param, they have `dropout_rate` instead
SCREAMING_SNAKE_CASE : Dict = """dropout_rate"""
for p in extra_params:
if getattr(lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ ):
if not hasattr(lowerCamelCase_ , lowerCamelCase_ ) and not hasattr(lowerCamelCase_ , equivalent_param[p] ):
logger.info("""config doesn't have a `{}` attribute""".format(lowerCamelCase_ ) )
delattr(lowerCamelCase_ , lowerCamelCase_ )
continue
SCREAMING_SNAKE_CASE : Dict = p if hasattr(lowerCamelCase_ , lowerCamelCase_ ) else equivalent_param[p]
setattr(lowerCamelCase_ , lowerCamelCase_ , getattr(lowerCamelCase_ , lowerCamelCase_ ) )
delattr(lowerCamelCase_ , lowerCamelCase_ )
return hparams, config
| 79 | 0 |
'''simple docstring'''
def __A ( lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : Optional[int] = [False] * len(UpperCAmelCase__ )
SCREAMING_SNAKE_CASE : Any = []
queue.append(UpperCAmelCase__ )
SCREAMING_SNAKE_CASE : Optional[Any] = True
while queue:
SCREAMING_SNAKE_CASE : int = queue.pop(0 )
for ind in range(len(graph[u] ) ):
if visited[ind] is False and graph[u][ind] > 0:
queue.append(UpperCAmelCase__ )
SCREAMING_SNAKE_CASE : Optional[Any] = True
SCREAMING_SNAKE_CASE : Optional[Any] = u
return visited[t]
def __A ( lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : List[str] = [-1] * (len(UpperCAmelCase__ ))
SCREAMING_SNAKE_CASE : int = 0
while bfs(UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ ):
SCREAMING_SNAKE_CASE : Optional[Any] = float("""Inf""" )
SCREAMING_SNAKE_CASE : Optional[Any] = sink
while s != source:
# Find the minimum value in select path
SCREAMING_SNAKE_CASE : Any = min(UpperCAmelCase__ , graph[parent[s]][s] )
SCREAMING_SNAKE_CASE : List[str] = parent[s]
max_flow += path_flow
SCREAMING_SNAKE_CASE : List[str] = sink
while v != source:
SCREAMING_SNAKE_CASE : int = parent[v]
graph[u][v] -= path_flow
graph[v][u] += path_flow
SCREAMING_SNAKE_CASE : List[Any] = parent[v]
return max_flow
__UpperCAmelCase = [
[0, 16, 13, 0, 0, 0],
[0, 0, 10, 12, 0, 0],
[0, 4, 0, 0, 14, 0],
[0, 0, 9, 0, 0, 20],
[0, 0, 0, 7, 0, 4],
[0, 0, 0, 0, 0, 0],
]
__UpperCAmelCase , __UpperCAmelCase = 0, 5
print(ford_fulkerson(graph, source, sink))
| 713 |
'''simple docstring'''
def __A ( lowerCamelCase_ ):
"""simple docstring"""
return 1 if digit in (0, 1) else (digit * factorial(digit - 1 ))
def __A ( lowerCamelCase_ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : Optional[Any] = 0
SCREAMING_SNAKE_CASE : List[str] = number
while duplicate > 0:
SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE : Dict = divmod(lowerCamelCase_ , 10 )
fact_sum += factorial(lowerCamelCase_ )
return fact_sum == number
if __name__ == "__main__":
print("""Program to check whether a number is a Krisnamurthy Number or not.""")
__UpperCAmelCase = int(input("""Enter number: """).strip())
print(
f'''{number} is {'' if krishnamurthy(number) else 'not '}a Krishnamurthy Number.'''
)
| 79 | 0 |
import random
import unittest
import numpy as np
from diffusers import (
DPMSolverMultistepScheduler,
EulerAncestralDiscreteScheduler,
EulerDiscreteScheduler,
LMSDiscreteScheduler,
OnnxStableDiffusionImgaImgPipeline,
PNDMScheduler,
)
from diffusers.utils import floats_tensor
from diffusers.utils.testing_utils import (
is_onnx_available,
load_image,
nightly,
require_onnxruntime,
require_torch_gpu,
)
from ..test_pipelines_onnx_common import OnnxPipelineTesterMixin
if is_onnx_available():
import onnxruntime as ort
class UpperCamelCase__ ( lowercase_ , unittest.TestCase ):
"""simple docstring"""
SCREAMING_SNAKE_CASE__ = '''hf-internal-testing/tiny-random-OnnxStableDiffusionPipeline'''
def lowerCamelCase_ ( self : Tuple , lowerCamelCase_ : List[str]=0 ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : List[str] = floats_tensor((1, 3, 1_28, 1_28) , rng=random.Random(__UpperCamelCase ) )
SCREAMING_SNAKE_CASE : Any = np.random.RandomState(__UpperCamelCase )
SCREAMING_SNAKE_CASE : Any = {
"""prompt""": """A painting of a squirrel eating a burger""",
"""image""": image,
"""generator""": generator,
"""num_inference_steps""": 3,
"""strength""": 0.75,
"""guidance_scale""": 7.5,
"""output_type""": """numpy""",
}
return inputs
def lowerCamelCase_ ( self : Optional[Any] ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : List[str] = OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint , provider="""CPUExecutionProvider""" )
pipe.set_progress_bar_config(disable=__UpperCamelCase )
SCREAMING_SNAKE_CASE : Tuple = self.get_dummy_inputs()
SCREAMING_SNAKE_CASE : List[str] = pipe(**__UpperCamelCase ).images
SCREAMING_SNAKE_CASE : Union[str, Any] = image[0, -3:, -3:, -1].flatten()
assert image.shape == (1, 1_28, 1_28, 3)
SCREAMING_SNAKE_CASE : List[Any] = np.array([0.69_643, 0.58_484, 0.50_314, 0.58_760, 0.55_368, 0.59_643, 0.51_529, 0.41_217, 0.49_087] )
assert np.abs(image_slice - expected_slice ).max() < 1e-1
def lowerCamelCase_ ( self : Optional[Any] ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Tuple = OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint , provider="""CPUExecutionProvider""" )
SCREAMING_SNAKE_CASE : Any = PNDMScheduler.from_config(pipe.scheduler.config , skip_prk_steps=__UpperCamelCase )
pipe.set_progress_bar_config(disable=__UpperCamelCase )
SCREAMING_SNAKE_CASE : int = self.get_dummy_inputs()
SCREAMING_SNAKE_CASE : Optional[int] = pipe(**__UpperCamelCase ).images
SCREAMING_SNAKE_CASE : Tuple = image[0, -3:, -3:, -1]
assert image.shape == (1, 1_28, 1_28, 3)
SCREAMING_SNAKE_CASE : Any = np.array([0.61_737, 0.54_642, 0.53_183, 0.54_465, 0.52_742, 0.60_525, 0.49_969, 0.40_655, 0.48_154] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-1
def lowerCamelCase_ ( self : Dict ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : str = OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint , provider="""CPUExecutionProvider""" )
SCREAMING_SNAKE_CASE : List[Any] = LMSDiscreteScheduler.from_config(pipe.scheduler.config )
pipe.set_progress_bar_config(disable=__UpperCamelCase )
# warmup pass to apply optimizations
SCREAMING_SNAKE_CASE : str = pipe(**self.get_dummy_inputs() )
SCREAMING_SNAKE_CASE : int = self.get_dummy_inputs()
SCREAMING_SNAKE_CASE : Union[str, Any] = pipe(**__UpperCamelCase ).images
SCREAMING_SNAKE_CASE : Optional[Any] = image[0, -3:, -3:, -1]
assert image.shape == (1, 1_28, 1_28, 3)
SCREAMING_SNAKE_CASE : Optional[Any] = np.array([0.52_761, 0.59_977, 0.49_033, 0.49_619, 0.54_282, 0.50_311, 0.47_600, 0.40_918, 0.45_203] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-1
def lowerCamelCase_ ( self : Any ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Any = OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint , provider="""CPUExecutionProvider""" )
SCREAMING_SNAKE_CASE : Any = EulerDiscreteScheduler.from_config(pipe.scheduler.config )
pipe.set_progress_bar_config(disable=__UpperCamelCase )
SCREAMING_SNAKE_CASE : str = self.get_dummy_inputs()
SCREAMING_SNAKE_CASE : Any = pipe(**__UpperCamelCase ).images
SCREAMING_SNAKE_CASE : Dict = image[0, -3:, -3:, -1]
assert image.shape == (1, 1_28, 1_28, 3)
SCREAMING_SNAKE_CASE : Optional[Any] = np.array([0.52_911, 0.60_004, 0.49_229, 0.49_805, 0.54_502, 0.50_680, 0.47_777, 0.41_028, 0.45_304] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-1
def lowerCamelCase_ ( self : str ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : List[str] = OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint , provider="""CPUExecutionProvider""" )
SCREAMING_SNAKE_CASE : str = EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config )
pipe.set_progress_bar_config(disable=__UpperCamelCase )
SCREAMING_SNAKE_CASE : Any = self.get_dummy_inputs()
SCREAMING_SNAKE_CASE : Optional[int] = pipe(**__UpperCamelCase ).images
SCREAMING_SNAKE_CASE : Union[str, Any] = image[0, -3:, -3:, -1]
assert image.shape == (1, 1_28, 1_28, 3)
SCREAMING_SNAKE_CASE : str = np.array([0.52_911, 0.60_004, 0.49_229, 0.49_805, 0.54_502, 0.50_680, 0.47_777, 0.41_028, 0.45_304] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-1
def lowerCamelCase_ ( self : Dict ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Union[str, Any] = OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint , provider="""CPUExecutionProvider""" )
SCREAMING_SNAKE_CASE : List[str] = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config )
pipe.set_progress_bar_config(disable=__UpperCamelCase )
SCREAMING_SNAKE_CASE : Union[str, Any] = self.get_dummy_inputs()
SCREAMING_SNAKE_CASE : Any = pipe(**__UpperCamelCase ).images
SCREAMING_SNAKE_CASE : Optional[int] = image[0, -3:, -3:, -1]
assert image.shape == (1, 1_28, 1_28, 3)
SCREAMING_SNAKE_CASE : str = np.array([0.65_331, 0.58_277, 0.48_204, 0.56_059, 0.53_665, 0.56_235, 0.50_969, 0.40_009, 0.46_552] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-1
@nightly
@require_onnxruntime
@require_torch_gpu
class UpperCamelCase__ ( unittest.TestCase ):
"""simple docstring"""
@property
def lowerCamelCase_ ( self : Tuple ):
'''simple docstring'''
return (
"CUDAExecutionProvider",
{
"gpu_mem_limit": "15000000000", # 15GB
"arena_extend_strategy": "kSameAsRequested",
},
)
@property
def lowerCamelCase_ ( self : int ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Optional[Any] = ort.SessionOptions()
SCREAMING_SNAKE_CASE : Any = False
return options
def lowerCamelCase_ ( self : str ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : int = load_image(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
"""/img2img/sketch-mountains-input.jpg""" )
SCREAMING_SNAKE_CASE : Optional[Any] = init_image.resize((7_68, 5_12) )
# using the PNDM scheduler by default
SCREAMING_SNAKE_CASE : str = OnnxStableDiffusionImgaImgPipeline.from_pretrained(
"""CompVis/stable-diffusion-v1-4""" , revision="""onnx""" , safety_checker=__UpperCamelCase , feature_extractor=__UpperCamelCase , provider=self.gpu_provider , sess_options=self.gpu_options , )
pipe.set_progress_bar_config(disable=__UpperCamelCase )
SCREAMING_SNAKE_CASE : Union[str, Any] = """A fantasy landscape, trending on artstation"""
SCREAMING_SNAKE_CASE : Tuple = np.random.RandomState(0 )
SCREAMING_SNAKE_CASE : int = pipe(
prompt=__UpperCamelCase , image=__UpperCamelCase , strength=0.75 , guidance_scale=7.5 , num_inference_steps=10 , generator=__UpperCamelCase , output_type="""np""" , )
SCREAMING_SNAKE_CASE : int = output.images
SCREAMING_SNAKE_CASE : Any = images[0, 2_55:2_58, 3_83:3_86, -1]
assert images.shape == (1, 5_12, 7_68, 3)
SCREAMING_SNAKE_CASE : int = np.array([0.4_909, 0.5_059, 0.5_372, 0.4_623, 0.4_876, 0.5_049, 0.4_820, 0.4_956, 0.5_019] )
# TODO: lower the tolerance after finding the cause of onnxruntime reproducibility issues
assert np.abs(image_slice.flatten() - expected_slice ).max() < 2e-2
def lowerCamelCase_ ( self : Union[str, Any] ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Union[str, Any] = load_image(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
"""/img2img/sketch-mountains-input.jpg""" )
SCREAMING_SNAKE_CASE : str = init_image.resize((7_68, 5_12) )
SCREAMING_SNAKE_CASE : Optional[int] = LMSDiscreteScheduler.from_pretrained(
"""runwayml/stable-diffusion-v1-5""" , subfolder="""scheduler""" , revision="""onnx""" )
SCREAMING_SNAKE_CASE : int = OnnxStableDiffusionImgaImgPipeline.from_pretrained(
"""runwayml/stable-diffusion-v1-5""" , revision="""onnx""" , scheduler=__UpperCamelCase , safety_checker=__UpperCamelCase , feature_extractor=__UpperCamelCase , provider=self.gpu_provider , sess_options=self.gpu_options , )
pipe.set_progress_bar_config(disable=__UpperCamelCase )
SCREAMING_SNAKE_CASE : List[Any] = """A fantasy landscape, trending on artstation"""
SCREAMING_SNAKE_CASE : str = np.random.RandomState(0 )
SCREAMING_SNAKE_CASE : Union[str, Any] = pipe(
prompt=__UpperCamelCase , image=__UpperCamelCase , strength=0.75 , guidance_scale=7.5 , num_inference_steps=20 , generator=__UpperCamelCase , output_type="""np""" , )
SCREAMING_SNAKE_CASE : Any = output.images
SCREAMING_SNAKE_CASE : List[Any] = images[0, 2_55:2_58, 3_83:3_86, -1]
assert images.shape == (1, 5_12, 7_68, 3)
SCREAMING_SNAKE_CASE : Tuple = np.array([0.8_043, 0.926, 0.9_581, 0.8_119, 0.8_954, 0.913, 0.7_209, 0.7_463, 0.7_431] )
# TODO: lower the tolerance after finding the cause of onnxruntime reproducibility issues
assert np.abs(image_slice.flatten() - expected_slice ).max() < 2e-2
| 714 |
'''simple docstring'''
# Lint as: python3
import sys
from collections.abc import Mapping
from typing import TYPE_CHECKING
import numpy as np
import pyarrow as pa
from .. import config
from ..utils.py_utils import map_nested
from .formatting import TensorFormatter
if TYPE_CHECKING:
import torch
class UpperCamelCase__ ( TensorFormatter[Mapping, '''torch.Tensor''', Mapping] ):
"""simple docstring"""
def __init__( self : Tuple , lowerCamelCase_ : str=None , **lowerCamelCase_ : Dict ):
'''simple docstring'''
super().__init__(features=lowerCamelCase_ )
SCREAMING_SNAKE_CASE : Tuple = torch_tensor_kwargs
import torch # noqa import torch at initialization
def lowerCamelCase_ ( self : Dict , lowerCamelCase_ : Union[str, Any] ):
'''simple docstring'''
import torch
if isinstance(lowerCamelCase_ , lowerCamelCase_ ) and column:
if all(
isinstance(lowerCamelCase_ , torch.Tensor ) and x.shape == column[0].shape and x.dtype == column[0].dtype
for x in column ):
return torch.stack(lowerCamelCase_ )
return column
def lowerCamelCase_ ( self : List[str] , lowerCamelCase_ : int ):
'''simple docstring'''
import torch
if isinstance(lowerCamelCase_ , (str, bytes, type(lowerCamelCase_ )) ):
return value
elif isinstance(lowerCamelCase_ , (np.character, np.ndarray) ) and np.issubdtype(value.dtype , np.character ):
return value.tolist()
SCREAMING_SNAKE_CASE : str = {}
if isinstance(lowerCamelCase_ , (np.number, np.ndarray) ) and np.issubdtype(value.dtype , np.integer ):
SCREAMING_SNAKE_CASE : Any = {"""dtype""": torch.intaa}
elif isinstance(lowerCamelCase_ , (np.number, np.ndarray) ) and np.issubdtype(value.dtype , np.floating ):
SCREAMING_SNAKE_CASE : int = {"""dtype""": torch.floataa}
elif config.PIL_AVAILABLE and "PIL" in sys.modules:
import PIL.Image
if isinstance(lowerCamelCase_ , PIL.Image.Image ):
SCREAMING_SNAKE_CASE : List[Any] = np.asarray(lowerCamelCase_ )
return torch.tensor(lowerCamelCase_ , **{**default_dtype, **self.torch_tensor_kwargs} )
def lowerCamelCase_ ( self : Optional[int] , lowerCamelCase_ : Optional[Any] ):
'''simple docstring'''
import torch
# support for torch, tf, jax etc.
if hasattr(lowerCamelCase_ , """__array__""" ) and not isinstance(lowerCamelCase_ , torch.Tensor ):
SCREAMING_SNAKE_CASE : Dict = data_struct.__array__()
# support for nested types like struct of list of struct
if isinstance(lowerCamelCase_ , np.ndarray ):
if data_struct.dtype == object: # torch tensors cannot be instantied from an array of objects
return self._consolidate([self.recursive_tensorize(lowerCamelCase_ ) for substruct in data_struct] )
elif isinstance(lowerCamelCase_ , (list, tuple) ):
return self._consolidate([self.recursive_tensorize(lowerCamelCase_ ) for substruct in data_struct] )
return self._tensorize(lowerCamelCase_ )
def lowerCamelCase_ ( self : int , lowerCamelCase_ : dict ):
'''simple docstring'''
return map_nested(self._recursive_tensorize , lowerCamelCase_ , map_list=lowerCamelCase_ )
def lowerCamelCase_ ( self : Optional[int] , lowerCamelCase_ : pa.Table ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : str = self.numpy_arrow_extractor().extract_row(lowerCamelCase_ )
SCREAMING_SNAKE_CASE : Dict = self.python_features_decoder.decode_row(lowerCamelCase_ )
return self.recursive_tensorize(lowerCamelCase_ )
def lowerCamelCase_ ( self : Tuple , lowerCamelCase_ : pa.Table ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : List[str] = self.numpy_arrow_extractor().extract_column(lowerCamelCase_ )
SCREAMING_SNAKE_CASE : Tuple = self.python_features_decoder.decode_column(lowerCamelCase_ , pa_table.column_names[0] )
SCREAMING_SNAKE_CASE : List[str] = self.recursive_tensorize(lowerCamelCase_ )
SCREAMING_SNAKE_CASE : List[str] = self._consolidate(lowerCamelCase_ )
return column
def lowerCamelCase_ ( self : Dict , lowerCamelCase_ : pa.Table ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Optional[Any] = self.numpy_arrow_extractor().extract_batch(lowerCamelCase_ )
SCREAMING_SNAKE_CASE : Dict = self.python_features_decoder.decode_batch(lowerCamelCase_ )
SCREAMING_SNAKE_CASE : Optional[Any] = self.recursive_tensorize(lowerCamelCase_ )
for column_name in batch:
SCREAMING_SNAKE_CASE : Tuple = self._consolidate(batch[column_name] )
return batch
| 79 | 0 |
'''simple docstring'''
class UpperCamelCase__ :
"""simple docstring"""
def __init__( self : List[Any] , lowerCamelCase_ : int = "" , lowerCamelCase_ : str = False ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Optional[int] = {}
# A node will be a leaf if the tree contains its word
SCREAMING_SNAKE_CASE : Optional[Any] = is_leaf
SCREAMING_SNAKE_CASE : Tuple = prefix
def lowerCamelCase_ ( self : Dict , lowerCamelCase_ : List[Any] ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Optional[Any] = 0
for q, w in zip(self.prefix , lowerCamelCase_ ):
if q != w:
break
x += 1
return self.prefix[:x], self.prefix[x:], word[x:]
def lowerCamelCase_ ( self : List[Any] , lowerCamelCase_ : List[str] ):
'''simple docstring'''
for word in words:
self.insert(lowerCamelCase_ )
def lowerCamelCase_ ( self : Union[str, Any] , lowerCamelCase_ : Optional[Any] ):
'''simple docstring'''
if self.prefix == word:
SCREAMING_SNAKE_CASE : Any = True
# Case 2: The node has no edges that have a prefix to the word
# Solution: We create an edge from the current node to a new one
# containing the word
elif word[0] not in self.nodes:
SCREAMING_SNAKE_CASE : List[Any] = RadixNode(prefix=lowerCamelCase_ , is_leaf=lowerCamelCase_ )
else:
SCREAMING_SNAKE_CASE : Union[str, Any] = self.nodes[word[0]]
SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE : Optional[Any] = incoming_node.match(
lowerCamelCase_ )
# Case 3: The node prefix is equal to the matching
# Solution: We insert remaining word on the next node
if remaining_prefix == "":
self.nodes[matching_string[0]].insert(lowerCamelCase_ )
# Case 4: The word is greater equal to the matching
# Solution: Create a node in between both nodes, change
# prefixes and add the new node for the remaining word
else:
SCREAMING_SNAKE_CASE : int = remaining_prefix
SCREAMING_SNAKE_CASE : Optional[int] = self.nodes[matching_string[0]]
SCREAMING_SNAKE_CASE : Union[str, Any] = RadixNode(lowerCamelCase_ , lowerCamelCase_ )
SCREAMING_SNAKE_CASE : List[str] = aux_node
if remaining_word == "":
SCREAMING_SNAKE_CASE : Optional[Any] = True
else:
self.nodes[matching_string[0]].insert(lowerCamelCase_ )
def lowerCamelCase_ ( self : List[Any] , lowerCamelCase_ : Optional[Any] ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Any = self.nodes.get(word[0] , lowerCamelCase_ )
if not incoming_node:
return False
else:
SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE : List[Any] = incoming_node.match(
lowerCamelCase_ )
# If there is remaining prefix, the word can't be on the tree
if remaining_prefix != "":
return False
# This applies when the word and the prefix are equal
elif remaining_word == "":
return incoming_node.is_leaf
# We have word remaining so we check the next node
else:
return incoming_node.find(lowerCamelCase_ )
def lowerCamelCase_ ( self : Tuple , lowerCamelCase_ : Optional[Any] ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Any = self.nodes.get(word[0] , lowerCamelCase_ )
if not incoming_node:
return False
else:
SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE : Dict = incoming_node.match(
lowerCamelCase_ )
# If there is remaining prefix, the word can't be on the tree
if remaining_prefix != "":
return False
# We have word remaining so we check the next node
elif remaining_word != "":
return incoming_node.delete(lowerCamelCase_ )
else:
# If it is not a leaf, we don't have to delete
if not incoming_node.is_leaf:
return False
else:
# We delete the nodes if no edges go from it
if len(incoming_node.nodes ) == 0:
del self.nodes[word[0]]
# We merge the current node with its only child
if len(self.nodes ) == 1 and not self.is_leaf:
SCREAMING_SNAKE_CASE : Optional[Any] = list(self.nodes.values() )[0]
SCREAMING_SNAKE_CASE : Dict = merging_node.is_leaf
self.prefix += merging_node.prefix
SCREAMING_SNAKE_CASE : Tuple = merging_node.nodes
# If there is more than 1 edge, we just mark it as non-leaf
elif len(incoming_node.nodes ) > 1:
SCREAMING_SNAKE_CASE : List[Any] = False
# If there is 1 edge, we merge it with its child
else:
SCREAMING_SNAKE_CASE : int = list(incoming_node.nodes.values() )[0]
SCREAMING_SNAKE_CASE : Union[str, Any] = merging_node.is_leaf
incoming_node.prefix += merging_node.prefix
SCREAMING_SNAKE_CASE : Any = merging_node.nodes
return True
def lowerCamelCase_ ( self : Union[str, Any] , lowerCamelCase_ : Tuple = 0 ):
'''simple docstring'''
if self.prefix != "":
print("""-""" * height , self.prefix , """ (leaf)""" if self.is_leaf else """""" )
for value in self.nodes.values():
value.print_tree(height + 1 )
def __A ( ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : List[str] = """banana bananas bandana band apple all beast""".split()
SCREAMING_SNAKE_CASE : List[str] = RadixNode()
root.insert_many(_UpperCamelCase )
assert all(root.find(_UpperCamelCase ) for word in words )
assert not root.find("""bandanas""" )
assert not root.find("""apps""" )
root.delete("""all""" )
assert not root.find("""all""" )
root.delete("""banana""" )
assert not root.find("""banana""" )
assert root.find("""bananas""" )
return True
def __A ( ):
"""simple docstring"""
assert test_trie()
def __A ( ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : Optional[Any] = RadixNode()
SCREAMING_SNAKE_CASE : List[Any] = """banana bananas bandanas bandana band apple all beast""".split()
root.insert_many(_UpperCamelCase )
print("""Words:""" , _UpperCamelCase )
print("""Tree:""" )
root.print_tree()
if __name__ == "__main__":
main()
| 715 |
'''simple docstring'''
import itertools
import os
import random
import tempfile
import unittest
import numpy as np
from transformers import TvltFeatureExtractor, is_datasets_available
from transformers.testing_utils import check_json_file_has_correct_format, require_torch, require_torchaudio
from transformers.utils.import_utils import is_torch_available
from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin
if is_torch_available():
import torch
if is_datasets_available():
from datasets import load_dataset
__UpperCAmelCase = random.Random()
def __A ( lowerCamelCase_ , lowerCamelCase_=1.0 , lowerCamelCase_=None , lowerCamelCase_=None ):
"""simple docstring"""
if rng is None:
SCREAMING_SNAKE_CASE : Optional[Any] = global_rng
SCREAMING_SNAKE_CASE : Optional[int] = []
for batch_idx in range(shape[0] ):
values.append([] )
for _ in range(shape[1] ):
values[-1].append(rng.random() * scale )
return values
class UpperCamelCase__ ( unittest.TestCase ):
"""simple docstring"""
def __init__( self : List[str] , lowerCamelCase_ : List[Any] , lowerCamelCase_ : Optional[int]=7 , lowerCamelCase_ : Optional[int]=4_00 , lowerCamelCase_ : int=20_00 , lowerCamelCase_ : List[str]=20_48 , lowerCamelCase_ : Optional[Any]=1_28 , lowerCamelCase_ : Optional[Any]=1 , lowerCamelCase_ : str=5_12 , lowerCamelCase_ : Dict=30 , lowerCamelCase_ : Dict=4_41_00 , ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Tuple = parent
SCREAMING_SNAKE_CASE : Union[str, Any] = batch_size
SCREAMING_SNAKE_CASE : List[str] = min_seq_length
SCREAMING_SNAKE_CASE : Any = max_seq_length
SCREAMING_SNAKE_CASE : Optional[int] = (self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
SCREAMING_SNAKE_CASE : int = spectrogram_length
SCREAMING_SNAKE_CASE : List[Any] = feature_size
SCREAMING_SNAKE_CASE : Any = num_audio_channels
SCREAMING_SNAKE_CASE : Tuple = hop_length
SCREAMING_SNAKE_CASE : str = chunk_length
SCREAMING_SNAKE_CASE : Dict = sampling_rate
def lowerCamelCase_ ( self : List[Any] ):
'''simple docstring'''
return {
"spectrogram_length": self.spectrogram_length,
"feature_size": self.feature_size,
"num_audio_channels": self.num_audio_channels,
"hop_length": self.hop_length,
"chunk_length": self.chunk_length,
"sampling_rate": self.sampling_rate,
}
def lowerCamelCase_ ( self : Dict , lowerCamelCase_ : Tuple=False , lowerCamelCase_ : Any=False ):
'''simple docstring'''
def _flatten(lowerCamelCase_ : Dict ):
return list(itertools.chain(*lowerCamelCase_ ) )
if equal_length:
SCREAMING_SNAKE_CASE : Optional[Any] = [floats_list((self.max_seq_length, self.feature_size) ) for _ in range(self.batch_size )]
else:
# make sure that inputs increase in size
SCREAMING_SNAKE_CASE : Dict = [
floats_list((x, self.feature_size) )
for x in range(self.min_seq_length , self.max_seq_length , self.seq_length_diff )
]
if numpify:
SCREAMING_SNAKE_CASE : Optional[int] = [np.asarray(lowerCamelCase_ ) for x in speech_inputs]
return speech_inputs
@require_torch
@require_torchaudio
class UpperCamelCase__ ( lowercase_ , unittest.TestCase ):
"""simple docstring"""
SCREAMING_SNAKE_CASE__ = TvltFeatureExtractor
def lowerCamelCase_ ( self : List[Any] ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : List[str] = TvltFeatureExtractionTester(self )
def lowerCamelCase_ ( self : Optional[int] ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Dict = self.feature_extraction_class(**self.feat_extract_dict )
self.assertTrue(hasattr(lowerCamelCase_ , """spectrogram_length""" ) )
self.assertTrue(hasattr(lowerCamelCase_ , """feature_size""" ) )
self.assertTrue(hasattr(lowerCamelCase_ , """num_audio_channels""" ) )
self.assertTrue(hasattr(lowerCamelCase_ , """hop_length""" ) )
self.assertTrue(hasattr(lowerCamelCase_ , """chunk_length""" ) )
self.assertTrue(hasattr(lowerCamelCase_ , """sampling_rate""" ) )
def lowerCamelCase_ ( self : List[str] ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Optional[Any] = self.feature_extraction_class(**self.feat_extract_dict )
with tempfile.TemporaryDirectory() as tmpdirname:
SCREAMING_SNAKE_CASE : Any = feat_extract_first.save_pretrained(lowerCamelCase_ )[0]
check_json_file_has_correct_format(lowerCamelCase_ )
SCREAMING_SNAKE_CASE : Union[str, Any] = self.feature_extraction_class.from_pretrained(lowerCamelCase_ )
SCREAMING_SNAKE_CASE : List[str] = feat_extract_first.to_dict()
SCREAMING_SNAKE_CASE : List[Any] = feat_extract_second.to_dict()
SCREAMING_SNAKE_CASE : Optional[int] = dict_first.pop("""mel_filters""" )
SCREAMING_SNAKE_CASE : Union[str, Any] = dict_second.pop("""mel_filters""" )
self.assertTrue(np.allclose(lowerCamelCase_ , lowerCamelCase_ ) )
self.assertEqual(lowerCamelCase_ , lowerCamelCase_ )
def lowerCamelCase_ ( self : Optional[int] ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Dict = self.feature_extraction_class(**self.feat_extract_dict )
with tempfile.TemporaryDirectory() as tmpdirname:
SCREAMING_SNAKE_CASE : int = os.path.join(lowerCamelCase_ , """feat_extract.json""" )
feat_extract_first.to_json_file(lowerCamelCase_ )
SCREAMING_SNAKE_CASE : Optional[Any] = self.feature_extraction_class.from_json_file(lowerCamelCase_ )
SCREAMING_SNAKE_CASE : List[str] = feat_extract_first.to_dict()
SCREAMING_SNAKE_CASE : int = feat_extract_second.to_dict()
SCREAMING_SNAKE_CASE : List[str] = dict_first.pop("""mel_filters""" )
SCREAMING_SNAKE_CASE : Optional[Any] = dict_second.pop("""mel_filters""" )
self.assertTrue(np.allclose(lowerCamelCase_ , lowerCamelCase_ ) )
self.assertEqual(lowerCamelCase_ , lowerCamelCase_ )
def lowerCamelCase_ ( self : Any ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : int = self.feature_extraction_class(**self.feat_extract_dict )
# create three inputs of length 800, 1000, and 1200
SCREAMING_SNAKE_CASE : Optional[int] = [floats_list((1, x) )[0] for x in range(8_00 , 14_00 , 2_00 )]
SCREAMING_SNAKE_CASE : int = [np.asarray(lowerCamelCase_ ) for speech_input in speech_inputs]
# Test not batched input
SCREAMING_SNAKE_CASE : Union[str, Any] = feature_extractor(np_speech_inputs[0] , return_tensors="""np""" , sampling_rate=4_41_00 ).audio_values
self.assertTrue(encoded_audios.ndim == 4 )
self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size )
self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length )
self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels )
# Test batched
SCREAMING_SNAKE_CASE : Optional[int] = feature_extractor(lowerCamelCase_ , return_tensors="""np""" , sampling_rate=4_41_00 ).audio_values
self.assertTrue(encoded_audios.ndim == 4 )
self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size )
self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length )
self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels )
# Test audio masking
SCREAMING_SNAKE_CASE : List[str] = feature_extractor(
lowerCamelCase_ , return_tensors="""np""" , sampling_rate=4_41_00 , mask_audio=lowerCamelCase_ ).audio_values
self.assertTrue(encoded_audios.ndim == 4 )
self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size )
self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length )
self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels )
# Test 2-D numpy arrays are batched.
SCREAMING_SNAKE_CASE : Optional[int] = [floats_list((1, x) )[0] for x in (8_00, 8_00, 8_00)]
SCREAMING_SNAKE_CASE : int = np.asarray(lowerCamelCase_ )
SCREAMING_SNAKE_CASE : Tuple = feature_extractor(lowerCamelCase_ , return_tensors="""np""" , sampling_rate=4_41_00 ).audio_values
self.assertTrue(encoded_audios.ndim == 4 )
self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size )
self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length )
self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels )
def lowerCamelCase_ ( self : Any , lowerCamelCase_ : int ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : List[Any] = load_dataset("""hf-internal-testing/librispeech_asr_dummy""" , """clean""" , split="""validation""" )
# automatic decoding with librispeech
SCREAMING_SNAKE_CASE : Union[str, Any] = ds.sort("""id""" ).select(range(lowerCamelCase_ ) )[:num_samples]["""audio"""]
return [x["array"] for x in speech_samples]
def lowerCamelCase_ ( self : Dict ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : str = self._load_datasamples(1 )
SCREAMING_SNAKE_CASE : Tuple = TvltFeatureExtractor()
SCREAMING_SNAKE_CASE : List[Any] = feature_extractor(lowerCamelCase_ , return_tensors="""pt""" ).audio_values
self.assertEquals(audio_values.shape , (1, 1, 1_92, 1_28) )
SCREAMING_SNAKE_CASE : List[Any] = torch.tensor([[-0.3_032, -0.2_708], [-0.4_434, -0.4_007]] )
self.assertTrue(torch.allclose(audio_values[0, 0, :2, :2] , lowerCamelCase_ , atol=1e-4 ) )
| 79 | 0 |
def __A ( lowerCamelCase_ = 1_00_00_00 ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : int = 1
SCREAMING_SNAKE_CASE : Any = 1
SCREAMING_SNAKE_CASE : List[Any] = {1: 1}
for inputa in range(2 , lowerCamelCase_ ):
SCREAMING_SNAKE_CASE : str = 0
SCREAMING_SNAKE_CASE : List[str] = inputa
while True:
if number in counters:
counter += counters[number]
break
if number % 2 == 0:
number //= 2
counter += 1
else:
SCREAMING_SNAKE_CASE : Optional[int] = (3 * number) + 1
counter += 1
if inputa not in counters:
SCREAMING_SNAKE_CASE : Optional[int] = counter
if counter > pre_counter:
SCREAMING_SNAKE_CASE : Union[str, Any] = inputa
SCREAMING_SNAKE_CASE : List[str] = counter
return largest_number
if __name__ == "__main__":
print(solution(int(input().strip())))
| 716 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
__UpperCAmelCase = {
"""configuration_mvp""": ["""MVP_PRETRAINED_CONFIG_ARCHIVE_MAP""", """MvpConfig""", """MvpOnnxConfig"""],
"""tokenization_mvp""": ["""MvpTokenizer"""],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__UpperCAmelCase = ["""MvpTokenizerFast"""]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__UpperCAmelCase = [
"""MVP_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""MvpForCausalLM""",
"""MvpForConditionalGeneration""",
"""MvpForQuestionAnswering""",
"""MvpForSequenceClassification""",
"""MvpModel""",
"""MvpPreTrainedModel""",
]
if TYPE_CHECKING:
from .configuration_mvp import MVP_PRETRAINED_CONFIG_ARCHIVE_MAP, MvpConfig, MvpOnnxConfig
from .tokenization_mvp import MvpTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_mvp_fast import MvpTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_mvp import (
MVP_PRETRAINED_MODEL_ARCHIVE_LIST,
MvpForCausalLM,
MvpForConditionalGeneration,
MvpForQuestionAnswering,
MvpForSequenceClassification,
MvpModel,
MvpPreTrainedModel,
)
else:
import sys
__UpperCAmelCase = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 79 | 0 |
'''simple docstring'''
import collections
from typing import List, Optional, Union
from ...tokenization_utils_base import BatchEncoding
from ...utils import TensorType, add_end_docstrings, add_start_docstrings, logging
from ..bert.tokenization_bert_fast import BertTokenizerFast
from .tokenization_dpr import DPRContextEncoderTokenizer, DPRQuestionEncoderTokenizer, DPRReaderTokenizer
__UpperCAmelCase = logging.get_logger(__name__)
__UpperCAmelCase = {"vocab_file": "vocab.txt", "tokenizer_file": "tokenizer.json"}
__UpperCAmelCase = {
"vocab_file": {
"facebook/dpr-ctx_encoder-single-nq-base": (
"https://huggingface.co/facebook/dpr-ctx_encoder-single-nq-base/resolve/main/vocab.txt"
),
"facebook/dpr-ctx_encoder-multiset-base": (
"https://huggingface.co/facebook/dpr-ctx_encoder-multiset-base/resolve/main/vocab.txt"
),
},
"tokenizer_file": {
"facebook/dpr-ctx_encoder-single-nq-base": (
"https://huggingface.co/facebook/dpr-ctx_encoder-single-nq-base/resolve/main/tokenizer.json"
),
"facebook/dpr-ctx_encoder-multiset-base": (
"https://huggingface.co/facebook/dpr-ctx_encoder-multiset-base/resolve/main/tokenizer.json"
),
},
}
__UpperCAmelCase = {
"vocab_file": {
"facebook/dpr-question_encoder-single-nq-base": (
"https://huggingface.co/facebook/dpr-question_encoder-single-nq-base/resolve/main/vocab.txt"
),
"facebook/dpr-question_encoder-multiset-base": (
"https://huggingface.co/facebook/dpr-question_encoder-multiset-base/resolve/main/vocab.txt"
),
},
"tokenizer_file": {
"facebook/dpr-question_encoder-single-nq-base": (
"https://huggingface.co/facebook/dpr-question_encoder-single-nq-base/resolve/main/tokenizer.json"
),
"facebook/dpr-question_encoder-multiset-base": (
"https://huggingface.co/facebook/dpr-question_encoder-multiset-base/resolve/main/tokenizer.json"
),
},
}
__UpperCAmelCase = {
"vocab_file": {
"facebook/dpr-reader-single-nq-base": (
"https://huggingface.co/facebook/dpr-reader-single-nq-base/resolve/main/vocab.txt"
),
"facebook/dpr-reader-multiset-base": (
"https://huggingface.co/facebook/dpr-reader-multiset-base/resolve/main/vocab.txt"
),
},
"tokenizer_file": {
"facebook/dpr-reader-single-nq-base": (
"https://huggingface.co/facebook/dpr-reader-single-nq-base/resolve/main/tokenizer.json"
),
"facebook/dpr-reader-multiset-base": (
"https://huggingface.co/facebook/dpr-reader-multiset-base/resolve/main/tokenizer.json"
),
},
}
__UpperCAmelCase = {
"facebook/dpr-ctx_encoder-single-nq-base": 512,
"facebook/dpr-ctx_encoder-multiset-base": 512,
}
__UpperCAmelCase = {
"facebook/dpr-question_encoder-single-nq-base": 512,
"facebook/dpr-question_encoder-multiset-base": 512,
}
__UpperCAmelCase = {
"facebook/dpr-reader-single-nq-base": 512,
"facebook/dpr-reader-multiset-base": 512,
}
__UpperCAmelCase = {
"facebook/dpr-ctx_encoder-single-nq-base": {"do_lower_case": True},
"facebook/dpr-ctx_encoder-multiset-base": {"do_lower_case": True},
}
__UpperCAmelCase = {
"facebook/dpr-question_encoder-single-nq-base": {"do_lower_case": True},
"facebook/dpr-question_encoder-multiset-base": {"do_lower_case": True},
}
__UpperCAmelCase = {
"facebook/dpr-reader-single-nq-base": {"do_lower_case": True},
"facebook/dpr-reader-multiset-base": {"do_lower_case": True},
}
class UpperCamelCase__ ( __lowerCAmelCase ):
"""simple docstring"""
SCREAMING_SNAKE_CASE__ = VOCAB_FILES_NAMES
SCREAMING_SNAKE_CASE__ = CONTEXT_ENCODER_PRETRAINED_VOCAB_FILES_MAP
SCREAMING_SNAKE_CASE__ = CONTEXT_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
SCREAMING_SNAKE_CASE__ = CONTEXT_ENCODER_PRETRAINED_INIT_CONFIGURATION
SCREAMING_SNAKE_CASE__ = DPRContextEncoderTokenizer
class UpperCamelCase__ ( __lowerCAmelCase ):
"""simple docstring"""
SCREAMING_SNAKE_CASE__ = VOCAB_FILES_NAMES
SCREAMING_SNAKE_CASE__ = QUESTION_ENCODER_PRETRAINED_VOCAB_FILES_MAP
SCREAMING_SNAKE_CASE__ = QUESTION_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
SCREAMING_SNAKE_CASE__ = QUESTION_ENCODER_PRETRAINED_INIT_CONFIGURATION
SCREAMING_SNAKE_CASE__ = DPRQuestionEncoderTokenizer
__UpperCAmelCase = collections.namedtuple(
"""DPRSpanPrediction""", ["""span_score""", """relevance_score""", """doc_id""", """start_index""", """end_index""", """text"""]
)
__UpperCAmelCase = collections.namedtuple("""DPRReaderOutput""", ["""start_logits""", """end_logits""", """relevance_logits"""])
__UpperCAmelCase = r"\n Return a dictionary with the token ids of the input strings and other information to give to `.decode_best_spans`.\n It converts the strings of a question and different passages (title and text) in a sequence of IDs (integers),\n using the tokenizer and vocabulary. The resulting `input_ids` is a matrix of size `(n_passages, sequence_length)`\n with the format:\n\n [CLS] <question token ids> [SEP] <titles ids> [SEP] <texts ids>\n\n Args:\n questions (`str` or `List[str]`):\n The questions to be encoded. You can specify one question for many passages. In this case, the question\n will be duplicated like `[questions] * n_passages`. Otherwise you have to specify as many questions as in\n `titles` or `texts`.\n titles (`str` or `List[str]`):\n The passages titles to be encoded. This can be a string or a list of strings if there are several passages.\n texts (`str` or `List[str]`):\n The passages texts to be encoded. This can be a string or a list of strings if there are several passages.\n padding (`bool`, `str` or [`~utils.PaddingStrategy`], *optional*, defaults to `False`):\n Activates and controls padding. Accepts the following values:\n\n - `True` or `'longest'`: Pad to the longest sequence in the batch (or no padding if only a single sequence\n if provided).\n - `'max_length'`: Pad to a maximum length specified with the argument `max_length` or to the maximum\n acceptable input length for the model if that argument is not provided.\n - `False` or `'do_not_pad'` (default): No padding (i.e., can output a batch with sequences of different\n lengths).\n truncation (`bool`, `str` or [`~tokenization_utils_base.TruncationStrategy`], *optional*, defaults to `False`):\n Activates and controls truncation. Accepts the following values:\n\n - `True` or `'longest_first'`: Truncate to a maximum length specified with the argument `max_length` or to\n the maximum acceptable input length for the model if that argument is not provided. This will truncate\n token by token, removing a token from the longest sequence in the pair if a pair of sequences (or a batch\n of pairs) is provided.\n - `'only_first'`: Truncate to a maximum length specified with the argument `max_length` or to the maximum\n acceptable input length for the model if that argument is not provided. This will only truncate the first\n sequence of a pair if a pair of sequences (or a batch of pairs) is provided.\n - `'only_second'`: Truncate to a maximum length specified with the argument `max_length` or to the maximum\n acceptable input length for the model if that argument is not provided. This will only truncate the\n second sequence of a pair if a pair of sequences (or a batch of pairs) is provided.\n - `False` or `'do_not_truncate'` (default): No truncation (i.e., can output batch with sequence lengths\n greater than the model maximum admissible input size).\n max_length (`int`, *optional*):\n Controls the maximum length to use by one of the truncation/padding parameters.\n\n If left unset or set to `None`, this will use the predefined model maximum length if a maximum length\n is required by one of the truncation/padding parameters. If the model has no specific maximum input\n length (like XLNet) truncation/padding to a maximum length will be deactivated.\n return_tensors (`str` or [`~utils.TensorType`], *optional*):\n If set, will return tensors instead of list of python integers. Acceptable values are:\n\n - `'tf'`: Return TensorFlow `tf.constant` objects.\n - `'pt'`: Return PyTorch `torch.Tensor` objects.\n - `'np'`: Return Numpy `np.ndarray` objects.\n return_attention_mask (`bool`, *optional*):\n Whether or not to return the attention mask. If not set, will return the attention mask according to the\n specific tokenizer's default, defined by the `return_outputs` attribute.\n\n [What are attention masks?](../glossary#attention-mask)\n\n Return:\n `Dict[str, List[List[int]]]`: A dictionary with the following keys:\n\n - `input_ids`: List of token ids to be fed to a model.\n - `attention_mask`: List of indices specifying which tokens should be attended to by the model.\n "
@add_start_docstrings(__lowerCAmelCase )
class UpperCamelCase__ :
"""simple docstring"""
def __call__( self : Any , lowerCamelCase_ : Union[str, Any] , lowerCamelCase_ : Optional[str] = None , lowerCamelCase_ : Optional[str] = None , lowerCamelCase_ : Union[bool, str] = False , lowerCamelCase_ : Union[bool, str] = False , lowerCamelCase_ : Optional[int] = None , lowerCamelCase_ : Optional[Union[str, TensorType]] = None , lowerCamelCase_ : Optional[bool] = None , **lowerCamelCase_ : Optional[Any] , ):
'''simple docstring'''
if titles is None and texts is None:
return super().__call__(
lowerCamelCase__ , padding=lowerCamelCase__ , truncation=lowerCamelCase__ , max_length=lowerCamelCase__ , return_tensors=lowerCamelCase__ , return_attention_mask=lowerCamelCase__ , **lowerCamelCase__ , )
elif titles is None or texts is None:
SCREAMING_SNAKE_CASE : int = titles if texts is None else texts
return super().__call__(
lowerCamelCase__ , lowerCamelCase__ , padding=lowerCamelCase__ , truncation=lowerCamelCase__ , max_length=lowerCamelCase__ , return_tensors=lowerCamelCase__ , return_attention_mask=lowerCamelCase__ , **lowerCamelCase__ , )
SCREAMING_SNAKE_CASE : Union[str, Any] = titles if not isinstance(lowerCamelCase__ , lowerCamelCase__ ) else [titles]
SCREAMING_SNAKE_CASE : Optional[int] = texts if not isinstance(lowerCamelCase__ , lowerCamelCase__ ) else [texts]
SCREAMING_SNAKE_CASE : Optional[Any] = len(lowerCamelCase__ )
SCREAMING_SNAKE_CASE : str = questions if not isinstance(lowerCamelCase__ , lowerCamelCase__ ) else [questions] * n_passages
assert len(lowerCamelCase__ ) == len(
lowerCamelCase__ ), f'''There should be as many titles than texts but got {len(lowerCamelCase__ )} titles and {len(lowerCamelCase__ )} texts.'''
SCREAMING_SNAKE_CASE : Any = super().__call__(lowerCamelCase__ , lowerCamelCase__ , padding=lowerCamelCase__ , truncation=lowerCamelCase__ )['''input_ids''']
SCREAMING_SNAKE_CASE : Tuple = super().__call__(lowerCamelCase__ , add_special_tokens=lowerCamelCase__ , padding=lowerCamelCase__ , truncation=lowerCamelCase__ )['''input_ids''']
SCREAMING_SNAKE_CASE : str = {
'''input_ids''': [
(encoded_question_and_title + encoded_text)[:max_length]
if max_length is not None and truncation
else encoded_question_and_title + encoded_text
for encoded_question_and_title, encoded_text in zip(lowerCamelCase__ , lowerCamelCase__ )
]
}
if return_attention_mask is not False:
SCREAMING_SNAKE_CASE : int = []
for input_ids in encoded_inputs["input_ids"]:
attention_mask.append([int(input_id != self.pad_token_id ) for input_id in input_ids] )
SCREAMING_SNAKE_CASE : Optional[int] = attention_mask
return self.pad(lowerCamelCase__ , padding=lowerCamelCase__ , max_length=lowerCamelCase__ , return_tensors=lowerCamelCase__ )
def lowerCamelCase_ ( self : List[str] , lowerCamelCase_ : BatchEncoding , lowerCamelCase_ : DPRReaderOutput , lowerCamelCase_ : int = 16 , lowerCamelCase_ : int = 64 , lowerCamelCase_ : int = 4 , ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Any = reader_input['''input_ids''']
SCREAMING_SNAKE_CASE : Union[str, Any] = reader_output[:3]
SCREAMING_SNAKE_CASE : Optional[Any] = len(lowerCamelCase__ )
SCREAMING_SNAKE_CASE : Optional[Any] = sorted(range(lowerCamelCase__ ) , reverse=lowerCamelCase__ , key=relevance_logits.__getitem__ )
SCREAMING_SNAKE_CASE : List[DPRReaderOutput] = []
for doc_id in sorted_docs:
SCREAMING_SNAKE_CASE : int = list(input_ids[doc_id] )
# assuming question & title information is at the beginning of the sequence
SCREAMING_SNAKE_CASE : Tuple = sequence_ids.index(self.sep_token_id , 2 ) + 1 # second sep id
if sequence_ids[-1] == self.pad_token_id:
SCREAMING_SNAKE_CASE : Tuple = sequence_ids.index(self.pad_token_id )
else:
SCREAMING_SNAKE_CASE : Union[str, Any] = len(lowerCamelCase__ )
SCREAMING_SNAKE_CASE : Optional[Any] = self._get_best_spans(
start_logits=start_logits[doc_id][passage_offset:sequence_len] , end_logits=end_logits[doc_id][passage_offset:sequence_len] , max_answer_length=lowerCamelCase__ , top_spans=lowerCamelCase__ , )
for start_index, end_index in best_spans:
start_index += passage_offset
end_index += passage_offset
nbest_spans_predictions.append(
DPRSpanPrediction(
span_score=start_logits[doc_id][start_index] + end_logits[doc_id][end_index] , relevance_score=relevance_logits[doc_id] , doc_id=lowerCamelCase__ , start_index=lowerCamelCase__ , end_index=lowerCamelCase__ , text=self.decode(sequence_ids[start_index : end_index + 1] ) , ) )
if len(lowerCamelCase__ ) >= num_spans:
break
return nbest_spans_predictions[:num_spans]
def lowerCamelCase_ ( self : List[Any] , lowerCamelCase_ : List[int] , lowerCamelCase_ : List[int] , lowerCamelCase_ : int , lowerCamelCase_ : int , ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : List[Any] = []
for start_index, start_score in enumerate(lowerCamelCase__ ):
for answer_length, end_score in enumerate(end_logits[start_index : start_index + max_answer_length] ):
scores.append(((start_index, start_index + answer_length), start_score + end_score) )
SCREAMING_SNAKE_CASE : Dict = sorted(lowerCamelCase__ , key=lambda lowerCamelCase_ : x[1] , reverse=lowerCamelCase__ )
SCREAMING_SNAKE_CASE : Union[str, Any] = []
for (start_index, end_index), score in scores:
assert start_index <= end_index, f'''Wrong span indices: [{start_index}:{end_index}]'''
SCREAMING_SNAKE_CASE : List[str] = end_index - start_index + 1
assert length <= max_answer_length, f'''Span is too long: {length} > {max_answer_length}'''
if any(
start_index <= prev_start_index <= prev_end_index <= end_index
or prev_start_index <= start_index <= end_index <= prev_end_index
for (prev_start_index, prev_end_index) in chosen_span_intervals ):
continue
chosen_span_intervals.append((start_index, end_index) )
if len(lowerCamelCase__ ) == top_spans:
break
return chosen_span_intervals
@add_end_docstrings(__lowerCAmelCase )
class UpperCamelCase__ ( __lowerCAmelCase , __lowerCAmelCase ):
"""simple docstring"""
SCREAMING_SNAKE_CASE__ = VOCAB_FILES_NAMES
SCREAMING_SNAKE_CASE__ = READER_PRETRAINED_VOCAB_FILES_MAP
SCREAMING_SNAKE_CASE__ = READER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
SCREAMING_SNAKE_CASE__ = READER_PRETRAINED_INIT_CONFIGURATION
SCREAMING_SNAKE_CASE__ = ["input_ids", "attention_mask"]
SCREAMING_SNAKE_CASE__ = DPRReaderTokenizer
| 717 |
'''simple docstring'''
__UpperCAmelCase = [
"""Audio""",
"""Array2D""",
"""Array3D""",
"""Array4D""",
"""Array5D""",
"""ClassLabel""",
"""Features""",
"""Sequence""",
"""Value""",
"""Image""",
"""Translation""",
"""TranslationVariableLanguages""",
]
from .audio import Audio
from .features import ArrayaD, ArrayaD, ArrayaD, ArrayaD, ClassLabel, Features, Sequence, Value
from .image import Image
from .translation import Translation, TranslationVariableLanguages
| 79 | 0 |
'''simple docstring'''
def __A ( lowerCamelCase_ , lowerCamelCase_ ):
"""simple docstring"""
return numa ^ numa < 0
if __name__ == "__main__":
import doctest
doctest.testmod()
| 718 |
'''simple docstring'''
from typing import List, Optional, Union
import torch
from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDPMScheduler
from ...utils import (
is_accelerate_available,
is_accelerate_version,
logging,
randn_tensor,
replace_example_docstring,
)
__UpperCAmelCase = logging.get_logger(__name__) # pylint: disable=invalid-name
__UpperCAmelCase = """
Examples:
```py
>>> import torch
>>> import numpy as np
>>> from diffusers import KandinskyV22PriorPipeline, KandinskyV22ControlnetPipeline
>>> from transformers import pipeline
>>> from diffusers.utils import load_image
>>> def make_hint(image, depth_estimator):
... image = depth_estimator(image)[\"depth\"]
... image = np.array(image)
... image = image[:, :, None]
... image = np.concatenate([image, image, image], axis=2)
... detected_map = torch.from_numpy(image).float() / 255.0
... hint = detected_map.permute(2, 0, 1)
... return hint
>>> depth_estimator = pipeline(\"depth-estimation\")
>>> pipe_prior = KandinskyV22PriorPipeline.from_pretrained(
... \"kandinsky-community/kandinsky-2-2-prior\", torch_dtype=torch.float16
... )
>>> pipe_prior = pipe_prior.to(\"cuda\")
>>> pipe = KandinskyV22ControlnetPipeline.from_pretrained(
... \"kandinsky-community/kandinsky-2-2-controlnet-depth\", torch_dtype=torch.float16
... )
>>> pipe = pipe.to(\"cuda\")
>>> img = load_image(
... \"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main\"
... \"/kandinsky/cat.png\"
... ).resize((768, 768))
>>> hint = make_hint(img, depth_estimator).unsqueeze(0).half().to(\"cuda\")
>>> prompt = \"A robot, 4k photo\"
>>> negative_prior_prompt = \"lowres, text, error, cropped, worst quality, low quality, jpeg artifacts, ugly, duplicate, morbid, mutilated, out of frame, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, mutation, deformed, blurry, dehydrated, bad anatomy, bad proportions, extra limbs, cloned face, disfigured, gross proportions, malformed limbs, missing arms, missing legs, extra arms, extra legs, fused fingers, too many fingers, long neck, username, watermark, signature\"
>>> generator = torch.Generator(device=\"cuda\").manual_seed(43)
>>> image_emb, zero_image_emb = pipe_prior(
... prompt=prompt, negative_prompt=negative_prior_prompt, generator=generator
... ).to_tuple()
>>> images = pipe(
... image_embeds=image_emb,
... negative_image_embeds=zero_image_emb,
... hint=hint,
... num_inference_steps=50,
... generator=generator,
... height=768,
... width=768,
... ).images
>>> images[0].save(\"robot_cat.png\")
```
"""
def __A ( lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_=8 ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : Optional[Any] = height // scale_factor**2
if height % scale_factor**2 != 0:
new_height += 1
SCREAMING_SNAKE_CASE : List[str] = width // scale_factor**2
if width % scale_factor**2 != 0:
new_width += 1
return new_height * scale_factor, new_width * scale_factor
class UpperCamelCase__ ( lowercase_ ):
"""simple docstring"""
def __init__( self : Union[str, Any] , lowerCamelCase_ : UNetaDConditionModel , lowerCamelCase_ : DDPMScheduler , lowerCamelCase_ : VQModel , ):
'''simple docstring'''
super().__init__()
self.register_modules(
unet=lowerCamelCase_ , scheduler=lowerCamelCase_ , movq=lowerCamelCase_ , )
SCREAMING_SNAKE_CASE : str = 2 ** (len(self.movq.config.block_out_channels ) - 1)
def lowerCamelCase_ ( self : int , lowerCamelCase_ : int , lowerCamelCase_ : Optional[int] , lowerCamelCase_ : List[str] , lowerCamelCase_ : Any , lowerCamelCase_ : Tuple , lowerCamelCase_ : int ):
'''simple docstring'''
if latents is None:
SCREAMING_SNAKE_CASE : Tuple = randn_tensor(lowerCamelCase_ , generator=lowerCamelCase_ , device=lowerCamelCase_ , dtype=lowerCamelCase_ )
else:
if latents.shape != shape:
raise ValueError(f'''Unexpected latents shape, got {latents.shape}, expected {shape}''' )
SCREAMING_SNAKE_CASE : Dict = latents.to(lowerCamelCase_ )
SCREAMING_SNAKE_CASE : Any = latents * scheduler.init_noise_sigma
return latents
def lowerCamelCase_ ( self : Union[str, Any] , lowerCamelCase_ : Dict=0 ):
'''simple docstring'''
if is_accelerate_available():
from accelerate import cpu_offload
else:
raise ImportError("""Please install accelerate via `pip install accelerate`""" )
SCREAMING_SNAKE_CASE : List[Any] = torch.device(f'''cuda:{gpu_id}''' )
SCREAMING_SNAKE_CASE : Union[str, Any] = [
self.unet,
self.movq,
]
for cpu_offloaded_model in models:
if cpu_offloaded_model is not None:
cpu_offload(lowerCamelCase_ , lowerCamelCase_ )
def lowerCamelCase_ ( self : List[Any] , lowerCamelCase_ : Tuple=0 ):
'''simple docstring'''
if is_accelerate_available() and is_accelerate_version(""">=""" , """0.17.0.dev0""" ):
from accelerate import cpu_offload_with_hook
else:
raise ImportError("""`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher.""" )
SCREAMING_SNAKE_CASE : Any = torch.device(f'''cuda:{gpu_id}''' )
if self.device.type != "cpu":
self.to("""cpu""" , silence_dtype_warnings=lowerCamelCase_ )
torch.cuda.empty_cache() # otherwise we don't see the memory savings (but they probably exist)
SCREAMING_SNAKE_CASE : Union[str, Any] = None
for cpu_offloaded_model in [self.unet, self.movq]:
SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE : List[Any] = cpu_offload_with_hook(lowerCamelCase_ , lowerCamelCase_ , prev_module_hook=lowerCamelCase_ )
# We'll offload the last model manually.
SCREAMING_SNAKE_CASE : str = hook
@property
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
def lowerCamelCase_ ( self : str ):
'''simple docstring'''
if not hasattr(self.unet , """_hf_hook""" ):
return self.device
for module in self.unet.modules():
if (
hasattr(lowerCamelCase_ , """_hf_hook""" )
and hasattr(module._hf_hook , """execution_device""" )
and module._hf_hook.execution_device is not None
):
return torch.device(module._hf_hook.execution_device )
return self.device
@torch.no_grad()
@replace_example_docstring(lowerCamelCase_ )
def __call__( self : Optional[Any] , lowerCamelCase_ : Union[torch.FloatTensor, List[torch.FloatTensor]] , lowerCamelCase_ : Union[torch.FloatTensor, List[torch.FloatTensor]] , lowerCamelCase_ : torch.FloatTensor , lowerCamelCase_ : int = 5_12 , lowerCamelCase_ : int = 5_12 , lowerCamelCase_ : int = 1_00 , lowerCamelCase_ : float = 4.0 , lowerCamelCase_ : int = 1 , lowerCamelCase_ : Optional[Union[torch.Generator, List[torch.Generator]]] = None , lowerCamelCase_ : Optional[torch.FloatTensor] = None , lowerCamelCase_ : Optional[str] = "pil" , lowerCamelCase_ : bool = True , ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : str = self._execution_device
SCREAMING_SNAKE_CASE : Optional[int] = guidance_scale > 1.0
if isinstance(lowerCamelCase_ , lowerCamelCase_ ):
SCREAMING_SNAKE_CASE : int = torch.cat(lowerCamelCase_ , dim=0 )
if isinstance(lowerCamelCase_ , lowerCamelCase_ ):
SCREAMING_SNAKE_CASE : Dict = torch.cat(lowerCamelCase_ , dim=0 )
if isinstance(lowerCamelCase_ , lowerCamelCase_ ):
SCREAMING_SNAKE_CASE : Optional[Any] = torch.cat(lowerCamelCase_ , dim=0 )
SCREAMING_SNAKE_CASE : Any = image_embeds.shape[0] * num_images_per_prompt
if do_classifier_free_guidance:
SCREAMING_SNAKE_CASE : List[Any] = image_embeds.repeat_interleave(lowerCamelCase_ , dim=0 )
SCREAMING_SNAKE_CASE : Optional[int] = negative_image_embeds.repeat_interleave(lowerCamelCase_ , dim=0 )
SCREAMING_SNAKE_CASE : Dict = hint.repeat_interleave(lowerCamelCase_ , dim=0 )
SCREAMING_SNAKE_CASE : List[Any] = torch.cat([negative_image_embeds, image_embeds] , dim=0 ).to(dtype=self.unet.dtype , device=lowerCamelCase_ )
SCREAMING_SNAKE_CASE : Optional[int] = torch.cat([hint, hint] , dim=0 ).to(dtype=self.unet.dtype , device=lowerCamelCase_ )
self.scheduler.set_timesteps(lowerCamelCase_ , device=lowerCamelCase_ )
SCREAMING_SNAKE_CASE : Union[str, Any] = self.scheduler.timesteps
SCREAMING_SNAKE_CASE : Any = self.movq.config.latent_channels
SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE : Optional[Any] = downscale_height_and_width(lowerCamelCase_ , lowerCamelCase_ , self.movq_scale_factor )
# create initial latent
SCREAMING_SNAKE_CASE : str = self.prepare_latents(
(batch_size, num_channels_latents, height, width) , image_embeds.dtype , lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ , self.scheduler , )
for i, t in enumerate(self.progress_bar(lowerCamelCase_ ) ):
# expand the latents if we are doing classifier free guidance
SCREAMING_SNAKE_CASE : Dict = torch.cat([latents] * 2 ) if do_classifier_free_guidance else latents
SCREAMING_SNAKE_CASE : Union[str, Any] = {"""image_embeds""": image_embeds, """hint""": hint}
SCREAMING_SNAKE_CASE : Dict = self.unet(
sample=lowerCamelCase_ , timestep=lowerCamelCase_ , encoder_hidden_states=lowerCamelCase_ , added_cond_kwargs=lowerCamelCase_ , return_dict=lowerCamelCase_ , )[0]
if do_classifier_free_guidance:
SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE : str = noise_pred.split(latents.shape[1] , dim=1 )
SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE : Optional[Any] = noise_pred.chunk(2 )
SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE : Union[str, Any] = variance_pred.chunk(2 )
SCREAMING_SNAKE_CASE : List[str] = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
SCREAMING_SNAKE_CASE : str = torch.cat([noise_pred, variance_pred_text] , dim=1 )
if not (
hasattr(self.scheduler.config , """variance_type""" )
and self.scheduler.config.variance_type in ["learned", "learned_range"]
):
SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE : Optional[Any] = noise_pred.split(latents.shape[1] , dim=1 )
# compute the previous noisy sample x_t -> x_t-1
SCREAMING_SNAKE_CASE : str = self.scheduler.step(
lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ , generator=lowerCamelCase_ , )[0]
# post-processing
SCREAMING_SNAKE_CASE : List[str] = self.movq.decode(lowerCamelCase_ , force_not_quantize=lowerCamelCase_ )["""sample"""]
if output_type not in ["pt", "np", "pil"]:
raise ValueError(f'''Only the output types `pt`, `pil` and `np` are supported not output_type={output_type}''' )
if output_type in ["np", "pil"]:
SCREAMING_SNAKE_CASE : Optional[int] = image * 0.5 + 0.5
SCREAMING_SNAKE_CASE : List[Any] = image.clamp(0 , 1 )
SCREAMING_SNAKE_CASE : List[Any] = image.cpu().permute(0 , 2 , 3 , 1 ).float().numpy()
if output_type == "pil":
SCREAMING_SNAKE_CASE : Any = self.numpy_to_pil(lowerCamelCase_ )
if not return_dict:
return (image,)
return ImagePipelineOutput(images=lowerCamelCase_ )
| 79 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_torch_available
__UpperCAmelCase = {
"""configuration_longt5""": ["""LONGT5_PRETRAINED_CONFIG_ARCHIVE_MAP""", """LongT5Config""", """LongT5OnnxConfig"""],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__UpperCAmelCase = [
"""LONGT5_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""LongT5EncoderModel""",
"""LongT5ForConditionalGeneration""",
"""LongT5Model""",
"""LongT5PreTrainedModel""",
]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__UpperCAmelCase = [
"""FlaxLongT5ForConditionalGeneration""",
"""FlaxLongT5Model""",
"""FlaxLongT5PreTrainedModel""",
]
if TYPE_CHECKING:
from .configuration_longta import LONGT5_PRETRAINED_CONFIG_ARCHIVE_MAP, LongTaConfig, LongTaOnnxConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_longta import (
LONGT5_PRETRAINED_MODEL_ARCHIVE_LIST,
LongTaEncoderModel,
LongTaForConditionalGeneration,
LongTaModel,
LongTaPreTrainedModel,
)
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_longta import (
FlaxLongTaForConditionalGeneration,
FlaxLongTaModel,
FlaxLongTaPreTrainedModel,
)
else:
import sys
__UpperCAmelCase = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 719 |
'''simple docstring'''
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_big_bird import BigBirdTokenizer
else:
__UpperCAmelCase = None
__UpperCAmelCase = logging.get_logger(__name__)
__UpperCAmelCase = {"""vocab_file""": """spiece.model""", """tokenizer_file""": """tokenizer.json"""}
__UpperCAmelCase = {
"""vocab_file""": {
"""google/bigbird-roberta-base""": """https://huggingface.co/google/bigbird-roberta-base/resolve/main/spiece.model""",
"""google/bigbird-roberta-large""": (
"""https://huggingface.co/google/bigbird-roberta-large/resolve/main/spiece.model"""
),
"""google/bigbird-base-trivia-itc""": (
"""https://huggingface.co/google/bigbird-base-trivia-itc/resolve/main/spiece.model"""
),
},
"""tokenizer_file""": {
"""google/bigbird-roberta-base""": (
"""https://huggingface.co/google/bigbird-roberta-base/resolve/main/tokenizer.json"""
),
"""google/bigbird-roberta-large""": (
"""https://huggingface.co/google/bigbird-roberta-large/resolve/main/tokenizer.json"""
),
"""google/bigbird-base-trivia-itc""": (
"""https://huggingface.co/google/bigbird-base-trivia-itc/resolve/main/tokenizer.json"""
),
},
}
__UpperCAmelCase = {
"""google/bigbird-roberta-base""": 4096,
"""google/bigbird-roberta-large""": 4096,
"""google/bigbird-base-trivia-itc""": 4096,
}
__UpperCAmelCase = """▁"""
class UpperCamelCase__ ( lowercase_ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE__ = VOCAB_FILES_NAMES
SCREAMING_SNAKE_CASE__ = PRETRAINED_VOCAB_FILES_MAP
SCREAMING_SNAKE_CASE__ = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
SCREAMING_SNAKE_CASE__ = BigBirdTokenizer
SCREAMING_SNAKE_CASE__ = ['''input_ids''', '''attention_mask''']
SCREAMING_SNAKE_CASE__ = []
def __init__( self : Any , lowerCamelCase_ : str=None , lowerCamelCase_ : List[Any]=None , lowerCamelCase_ : Dict="<unk>" , lowerCamelCase_ : int="<s>" , lowerCamelCase_ : Optional[Any]="</s>" , lowerCamelCase_ : Dict="<pad>" , lowerCamelCase_ : Tuple="[SEP]" , lowerCamelCase_ : Dict="[MASK]" , lowerCamelCase_ : Union[str, Any]="[CLS]" , **lowerCamelCase_ : Dict , ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Any = AddedToken(lowerCamelCase_ , lstrip=lowerCamelCase_ , rstrip=lowerCamelCase_ ) if isinstance(lowerCamelCase_ , lowerCamelCase_ ) else bos_token
SCREAMING_SNAKE_CASE : Dict = AddedToken(lowerCamelCase_ , lstrip=lowerCamelCase_ , rstrip=lowerCamelCase_ ) if isinstance(lowerCamelCase_ , lowerCamelCase_ ) else eos_token
SCREAMING_SNAKE_CASE : Optional[Any] = AddedToken(lowerCamelCase_ , lstrip=lowerCamelCase_ , rstrip=lowerCamelCase_ ) if isinstance(lowerCamelCase_ , lowerCamelCase_ ) else unk_token
SCREAMING_SNAKE_CASE : int = AddedToken(lowerCamelCase_ , lstrip=lowerCamelCase_ , rstrip=lowerCamelCase_ ) if isinstance(lowerCamelCase_ , lowerCamelCase_ ) else pad_token
SCREAMING_SNAKE_CASE : Any = AddedToken(lowerCamelCase_ , lstrip=lowerCamelCase_ , rstrip=lowerCamelCase_ ) if isinstance(lowerCamelCase_ , lowerCamelCase_ ) else cls_token
SCREAMING_SNAKE_CASE : Any = AddedToken(lowerCamelCase_ , lstrip=lowerCamelCase_ , rstrip=lowerCamelCase_ ) if isinstance(lowerCamelCase_ , lowerCamelCase_ ) else sep_token
# Mask token behave like a normal word, i.e. include the space before it
SCREAMING_SNAKE_CASE : int = 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_ , unk_token=lowerCamelCase_ , sep_token=lowerCamelCase_ , pad_token=lowerCamelCase_ , cls_token=lowerCamelCase_ , mask_token=lowerCamelCase_ , **lowerCamelCase_ , )
SCREAMING_SNAKE_CASE : List[Any] = vocab_file
SCREAMING_SNAKE_CASE : Optional[Any] = False if not self.vocab_file else True
def lowerCamelCase_ ( self : Union[str, Any] , lowerCamelCase_ : List[int] , lowerCamelCase_ : Optional[List[int]] = None ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Optional[Any] = [self.sep_token_id]
SCREAMING_SNAKE_CASE : int = [self.cls_token_id]
if token_ids_a is None:
return cls + token_ids_a + sep
return cls + token_ids_a + sep + token_ids_a + sep
def lowerCamelCase_ ( self : List[Any] , lowerCamelCase_ : List[int] , lowerCamelCase_ : Optional[List[int]] = None , lowerCamelCase_ : bool = False ):
'''simple docstring'''
if already_has_special_tokens:
if token_ids_a is not None:
raise ValueError(
"""You should not supply a second sequence if the provided sequence of """
"""ids is already formatted with special tokens for the model.""" )
return [1 if x in [self.sep_token_id, self.cls_token_id] else 0 for x in token_ids_a]
if token_ids_a is None:
return [1] + ([0] * len(lowerCamelCase_ )) + [1]
return [1] + ([0] * len(lowerCamelCase_ )) + [1] + ([0] * len(lowerCamelCase_ )) + [1]
def lowerCamelCase_ ( self : Optional[int] , lowerCamelCase_ : List[int] , lowerCamelCase_ : Optional[List[int]] = None ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Any = [self.sep_token_id]
SCREAMING_SNAKE_CASE : Optional[int] = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep ) * [0] + len(token_ids_a + sep ) * [1]
def lowerCamelCase_ ( self : str , lowerCamelCase_ : str , lowerCamelCase_ : 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 : Tuple = 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,)
| 79 | 0 |
'''simple docstring'''
import itertools
import json
import linecache
import os
import pickle
import re
import socket
import string
from collections import Counter
from logging import getLogger
from pathlib import Path
from typing import Callable, Dict, Iterable, List
import git
import torch
from torch.utils.data import Dataset
from transformers import BartTokenizer, RagTokenizer, TaTokenizer
def __A ( lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_=True , lowerCamelCase_="pt" ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : Tuple = {"""add_prefix_space""": True} if isinstance(lowerCamelCase__ , lowerCamelCase__ ) and not line.startswith(""" """ ) else {}
SCREAMING_SNAKE_CASE : List[str] = padding_side
return tokenizer(
[line] , max_length=lowerCamelCase__ , padding="""max_length""" if pad_to_max_length else None , truncation=lowerCamelCase__ , return_tensors=lowerCamelCase__ , add_special_tokens=lowerCamelCase__ , **lowerCamelCase__ , )
def __A ( lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_=None , ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : Optional[int] = input_ids.ne(lowerCamelCase__ ).any(dim=0 )
if attention_mask is None:
return input_ids[:, keep_column_mask]
else:
return (input_ids[:, keep_column_mask], attention_mask[:, keep_column_mask])
class UpperCamelCase__ ( lowercase_ ):
"""simple docstring"""
def __init__( self : Union[str, Any] , lowerCamelCase_ : List[str] , lowerCamelCase_ : int , lowerCamelCase_ : str , lowerCamelCase_ : Optional[Any] , lowerCamelCase_ : Dict="train" , lowerCamelCase_ : Dict=None , lowerCamelCase_ : str=None , lowerCamelCase_ : Union[str, Any]=None , lowerCamelCase_ : Dict="" , ):
'''simple docstring'''
super().__init__()
SCREAMING_SNAKE_CASE : Tuple = Path(lowerCamelCase_ ).joinpath(type_path + """.source""" )
SCREAMING_SNAKE_CASE : List[str] = Path(lowerCamelCase_ ).joinpath(type_path + """.target""" )
SCREAMING_SNAKE_CASE : str = self.get_char_lens(self.src_file )
SCREAMING_SNAKE_CASE : int = max_source_length
SCREAMING_SNAKE_CASE : str = max_target_length
assert min(self.src_lens ) > 0, f'''found empty line in {self.src_file}'''
SCREAMING_SNAKE_CASE : str = tokenizer
SCREAMING_SNAKE_CASE : str = prefix
if n_obs is not None:
SCREAMING_SNAKE_CASE : Tuple = self.src_lens[:n_obs]
SCREAMING_SNAKE_CASE : Dict = src_lang
SCREAMING_SNAKE_CASE : Dict = tgt_lang
def __len__( self : Dict ):
'''simple docstring'''
return len(self.src_lens )
def __getitem__( self : Union[str, Any] , lowerCamelCase_ : Optional[int] ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Dict = index + 1 # linecache starts at 1
SCREAMING_SNAKE_CASE : Any = self.prefix + linecache.getline(str(self.src_file ) , lowerCamelCase_ ).rstrip("""\n""" )
SCREAMING_SNAKE_CASE : Union[str, Any] = linecache.getline(str(self.tgt_file ) , lowerCamelCase_ ).rstrip("""\n""" )
assert source_line, f'''empty source line for index {index}'''
assert tgt_line, f'''empty tgt line for index {index}'''
# Need to add eos token manually for T5
if isinstance(self.tokenizer , lowerCamelCase_ ):
source_line += self.tokenizer.eos_token
tgt_line += self.tokenizer.eos_token
# Pad source and target to the right
SCREAMING_SNAKE_CASE : Dict = (
self.tokenizer.question_encoder if isinstance(self.tokenizer , lowerCamelCase_ ) else self.tokenizer
)
SCREAMING_SNAKE_CASE : Optional[Any] = self.tokenizer.generator if isinstance(self.tokenizer , lowerCamelCase_ ) else self.tokenizer
SCREAMING_SNAKE_CASE : Tuple = encode_line(lowerCamelCase_ , lowerCamelCase_ , self.max_source_length , """right""" )
SCREAMING_SNAKE_CASE : Tuple = encode_line(lowerCamelCase_ , lowerCamelCase_ , self.max_target_length , """right""" )
SCREAMING_SNAKE_CASE : Optional[Any] = source_inputs["""input_ids"""].squeeze()
SCREAMING_SNAKE_CASE : Optional[Any] = target_inputs["""input_ids"""].squeeze()
SCREAMING_SNAKE_CASE : List[Any] = source_inputs["""attention_mask"""].squeeze()
return {
"input_ids": source_ids,
"attention_mask": src_mask,
"decoder_input_ids": target_ids,
}
@staticmethod
def lowerCamelCase_ ( lowerCamelCase_ : List[str] ):
'''simple docstring'''
return [len(lowerCamelCase_ ) for x in Path(lowerCamelCase_ ).open().readlines()]
def lowerCamelCase_ ( self : Union[str, Any] , lowerCamelCase_ : List[str] ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Any = torch.stack([x["""input_ids"""] for x in batch] )
SCREAMING_SNAKE_CASE : Any = torch.stack([x["""attention_mask"""] for x in batch] )
SCREAMING_SNAKE_CASE : Optional[int] = torch.stack([x["""decoder_input_ids"""] for x in batch] )
SCREAMING_SNAKE_CASE : Tuple = (
self.tokenizer.generator.pad_token_id
if isinstance(self.tokenizer , lowerCamelCase_ )
else self.tokenizer.pad_token_id
)
SCREAMING_SNAKE_CASE : Tuple = (
self.tokenizer.question_encoder.pad_token_id
if isinstance(self.tokenizer , lowerCamelCase_ )
else self.tokenizer.pad_token_id
)
SCREAMING_SNAKE_CASE : List[Any] = trim_batch(lowerCamelCase_ , lowerCamelCase_ )
SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE : List[str] = trim_batch(lowerCamelCase_ , lowerCamelCase_ , attention_mask=lowerCamelCase_ )
SCREAMING_SNAKE_CASE : Tuple = {
"""input_ids""": source_ids,
"""attention_mask""": source_mask,
"""decoder_input_ids""": y,
}
return batch
__UpperCAmelCase = getLogger(__name__)
def __A ( lowerCamelCase_ ):
"""simple docstring"""
return list(itertools.chain.from_iterable(lowerCamelCase__ ) )
def __A ( lowerCamelCase_ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : Any = get_git_info()
save_json(lowerCamelCase__ , os.path.join(lowerCamelCase__ , """git_log.json""" ) )
def __A ( lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_=4 , **lowerCamelCase_ ):
"""simple docstring"""
with open(lowerCamelCase__ , """w""" ) as f:
json.dump(lowerCamelCase__ , lowerCamelCase__ , indent=lowerCamelCase__ , **lowerCamelCase__ )
def __A ( lowerCamelCase_ ):
"""simple docstring"""
with open(lowerCamelCase__ ) as f:
return json.load(lowerCamelCase__ )
def __A ( ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : int = git.Repo(search_parent_directories=lowerCamelCase__ )
SCREAMING_SNAKE_CASE : List[Any] = {
"""repo_id""": str(lowerCamelCase__ ),
"""repo_sha""": str(repo.head.object.hexsha ),
"""repo_branch""": str(repo.active_branch ),
"""hostname""": str(socket.gethostname() ),
}
return repo_infos
def __A ( lowerCamelCase_ , lowerCamelCase_ ):
"""simple docstring"""
return list(map(lowerCamelCase__ , lowerCamelCase__ ) )
def __A ( lowerCamelCase_ , lowerCamelCase_ ):
"""simple docstring"""
with open(lowerCamelCase__ , """wb""" ) as f:
return pickle.dump(lowerCamelCase__ , lowerCamelCase__ )
def __A ( lowerCamelCase_ ):
"""simple docstring"""
def remove_articles(lowerCamelCase_ ):
return re.sub(R"""\b(a|an|the)\b""" , """ """ , lowerCamelCase__ )
def white_space_fix(lowerCamelCase_ ):
return " ".join(text.split() )
def remove_punc(lowerCamelCase_ ):
SCREAMING_SNAKE_CASE : Optional[Any] = set(string.punctuation )
return "".join(ch for ch in text if ch not in exclude )
def lower(lowerCamelCase_ ):
return text.lower()
return white_space_fix(remove_articles(remove_punc(lower(lowerCamelCase__ ) ) ) )
def __A ( lowerCamelCase_ , lowerCamelCase_ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : Dict = normalize_answer(lowerCamelCase__ ).split()
SCREAMING_SNAKE_CASE : Optional[Any] = normalize_answer(lowerCamelCase__ ).split()
SCREAMING_SNAKE_CASE : int = Counter(lowerCamelCase__ ) & Counter(lowerCamelCase__ )
SCREAMING_SNAKE_CASE : Optional[Any] = sum(common.values() )
if num_same == 0:
return 0
SCREAMING_SNAKE_CASE : Dict = 1.0 * num_same / len(lowerCamelCase__ )
SCREAMING_SNAKE_CASE : List[str] = 1.0 * num_same / len(lowerCamelCase__ )
SCREAMING_SNAKE_CASE : Optional[Any] = (2 * precision * recall) / (precision + recall)
return fa
def __A ( lowerCamelCase_ , lowerCamelCase_ ):
"""simple docstring"""
return normalize_answer(lowerCamelCase__ ) == normalize_answer(lowerCamelCase__ )
def __A ( lowerCamelCase_ , lowerCamelCase_ ):
"""simple docstring"""
assert len(lowerCamelCase__ ) == len(lowerCamelCase__ )
SCREAMING_SNAKE_CASE : Dict = 0
for hypo, pred in zip(lowerCamelCase__ , lowerCamelCase__ ):
em += exact_match_score(lowerCamelCase__ , lowerCamelCase__ )
if len(lowerCamelCase__ ) > 0:
em /= len(lowerCamelCase__ )
return {"em": em}
def __A ( lowerCamelCase_ ):
"""simple docstring"""
return model_prefix.startswith("""rag""" )
def __A ( lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : int = {p: p for p in extra_params}
# T5 models don't have `dropout` param, they have `dropout_rate` instead
SCREAMING_SNAKE_CASE : Dict = """dropout_rate"""
for p in extra_params:
if getattr(lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ ):
if not hasattr(lowerCamelCase__ , lowerCamelCase__ ) and not hasattr(lowerCamelCase__ , equivalent_param[p] ):
logger.info("""config doesn\'t have a `{}` attribute""".format(lowerCamelCase__ ) )
delattr(lowerCamelCase__ , lowerCamelCase__ )
continue
SCREAMING_SNAKE_CASE : Optional[int] = p if hasattr(lowerCamelCase__ , lowerCamelCase__ ) else equivalent_param[p]
setattr(lowerCamelCase__ , lowerCamelCase__ , getattr(lowerCamelCase__ , lowerCamelCase__ ) )
delattr(lowerCamelCase__ , lowerCamelCase__ )
return hparams, config
| 720 |
'''simple docstring'''
import unittest
import torch
from torch import nn
from diffusers.models.activations import get_activation
class UpperCamelCase__ ( unittest.TestCase ):
"""simple docstring"""
def lowerCamelCase_ ( self : List[str] ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : List[Any] = get_activation("""swish""" )
self.assertIsInstance(lowerCamelCase_ , nn.SiLU )
self.assertEqual(act(torch.tensor(-1_00 , dtype=torch.floataa ) ).item() , 0 )
self.assertNotEqual(act(torch.tensor(-1 , dtype=torch.floataa ) ).item() , 0 )
self.assertEqual(act(torch.tensor(0 , dtype=torch.floataa ) ).item() , 0 )
self.assertEqual(act(torch.tensor(20 , dtype=torch.floataa ) ).item() , 20 )
def lowerCamelCase_ ( self : int ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Any = get_activation("""silu""" )
self.assertIsInstance(lowerCamelCase_ , nn.SiLU )
self.assertEqual(act(torch.tensor(-1_00 , dtype=torch.floataa ) ).item() , 0 )
self.assertNotEqual(act(torch.tensor(-1 , dtype=torch.floataa ) ).item() , 0 )
self.assertEqual(act(torch.tensor(0 , dtype=torch.floataa ) ).item() , 0 )
self.assertEqual(act(torch.tensor(20 , dtype=torch.floataa ) ).item() , 20 )
def lowerCamelCase_ ( self : Optional[int] ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : List[Any] = get_activation("""mish""" )
self.assertIsInstance(lowerCamelCase_ , nn.Mish )
self.assertEqual(act(torch.tensor(-2_00 , dtype=torch.floataa ) ).item() , 0 )
self.assertNotEqual(act(torch.tensor(-1 , dtype=torch.floataa ) ).item() , 0 )
self.assertEqual(act(torch.tensor(0 , dtype=torch.floataa ) ).item() , 0 )
self.assertEqual(act(torch.tensor(20 , dtype=torch.floataa ) ).item() , 20 )
def lowerCamelCase_ ( self : List[str] ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : str = get_activation("""gelu""" )
self.assertIsInstance(lowerCamelCase_ , nn.GELU )
self.assertEqual(act(torch.tensor(-1_00 , dtype=torch.floataa ) ).item() , 0 )
self.assertNotEqual(act(torch.tensor(-1 , dtype=torch.floataa ) ).item() , 0 )
self.assertEqual(act(torch.tensor(0 , dtype=torch.floataa ) ).item() , 0 )
self.assertEqual(act(torch.tensor(20 , dtype=torch.floataa ) ).item() , 20 )
| 79 | 0 |
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
__UpperCAmelCase = logging.get_logger(__name__)
__UpperCAmelCase = {
"""facebook/deit-base-distilled-patch16-224""": (
"""https://huggingface.co/facebook/deit-base-patch16-224/resolve/main/config.json"""
),
# See all DeiT models at https://huggingface.co/models?filter=deit
}
class UpperCamelCase__ ( snake_case__ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE__ = '''deit'''
def __init__( self : Dict , lowerCamelCase_ : Optional[int]=7_68 , lowerCamelCase_ : Any=12 , lowerCamelCase_ : Union[str, Any]=12 , lowerCamelCase_ : str=30_72 , lowerCamelCase_ : Tuple="gelu" , lowerCamelCase_ : Dict=0.0 , lowerCamelCase_ : Optional[Any]=0.0 , lowerCamelCase_ : Union[str, Any]=0.02 , lowerCamelCase_ : Tuple=1e-12 , lowerCamelCase_ : Optional[int]=2_24 , lowerCamelCase_ : Optional[int]=16 , lowerCamelCase_ : Optional[Any]=3 , lowerCamelCase_ : Union[str, Any]=True , lowerCamelCase_ : Optional[int]=16 , **lowerCamelCase_ : List[Any] , ):
'''simple docstring'''
super().__init__(**_SCREAMING_SNAKE_CASE )
SCREAMING_SNAKE_CASE : Optional[Any] = hidden_size
SCREAMING_SNAKE_CASE : List[Any] = num_hidden_layers
SCREAMING_SNAKE_CASE : int = num_attention_heads
SCREAMING_SNAKE_CASE : Union[str, Any] = intermediate_size
SCREAMING_SNAKE_CASE : Optional[int] = hidden_act
SCREAMING_SNAKE_CASE : List[Any] = hidden_dropout_prob
SCREAMING_SNAKE_CASE : Union[str, Any] = attention_probs_dropout_prob
SCREAMING_SNAKE_CASE : List[str] = initializer_range
SCREAMING_SNAKE_CASE : int = layer_norm_eps
SCREAMING_SNAKE_CASE : int = image_size
SCREAMING_SNAKE_CASE : Dict = patch_size
SCREAMING_SNAKE_CASE : List[Any] = num_channels
SCREAMING_SNAKE_CASE : int = qkv_bias
SCREAMING_SNAKE_CASE : Dict = encoder_stride
class UpperCamelCase__ ( snake_case__ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE__ = version.parse('''1.11''' )
@property
def lowerCamelCase_ ( self : Optional[Any] ):
'''simple docstring'''
return OrderedDict(
[
("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""}),
] )
@property
def lowerCamelCase_ ( self : Union[str, Any] ):
'''simple docstring'''
return 1e-4
| 721 |
'''simple docstring'''
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
if TYPE_CHECKING:
from ... import FeatureExtractionMixin, PreTrainedTokenizerBase, TensorType
__UpperCAmelCase = logging.get_logger(__name__)
__UpperCAmelCase = {
"""microsoft/deberta-v2-xlarge""": """https://huggingface.co/microsoft/deberta-v2-xlarge/resolve/main/config.json""",
"""microsoft/deberta-v2-xxlarge""": """https://huggingface.co/microsoft/deberta-v2-xxlarge/resolve/main/config.json""",
"""microsoft/deberta-v2-xlarge-mnli""": (
"""https://huggingface.co/microsoft/deberta-v2-xlarge-mnli/resolve/main/config.json"""
),
"""microsoft/deberta-v2-xxlarge-mnli""": (
"""https://huggingface.co/microsoft/deberta-v2-xxlarge-mnli/resolve/main/config.json"""
),
}
class UpperCamelCase__ ( lowercase_ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE__ = '''deberta-v2'''
def __init__( self : int , lowerCamelCase_ : Optional[Any]=12_81_00 , lowerCamelCase_ : str=15_36 , lowerCamelCase_ : int=24 , lowerCamelCase_ : List[str]=24 , lowerCamelCase_ : List[Any]=61_44 , lowerCamelCase_ : List[Any]="gelu" , lowerCamelCase_ : Optional[Any]=0.1 , lowerCamelCase_ : List[Any]=0.1 , lowerCamelCase_ : str=5_12 , lowerCamelCase_ : str=0 , lowerCamelCase_ : Union[str, Any]=0.02 , lowerCamelCase_ : Dict=1e-7 , lowerCamelCase_ : Optional[int]=False , lowerCamelCase_ : Optional[int]=-1 , lowerCamelCase_ : List[str]=0 , lowerCamelCase_ : Tuple=True , lowerCamelCase_ : Optional[int]=None , lowerCamelCase_ : Optional[Any]=0 , lowerCamelCase_ : Dict="gelu" , **lowerCamelCase_ : Optional[int] , ):
'''simple docstring'''
super().__init__(**lowerCamelCase_ )
SCREAMING_SNAKE_CASE : Optional[int] = hidden_size
SCREAMING_SNAKE_CASE : Optional[Any] = num_hidden_layers
SCREAMING_SNAKE_CASE : int = num_attention_heads
SCREAMING_SNAKE_CASE : List[str] = intermediate_size
SCREAMING_SNAKE_CASE : int = hidden_act
SCREAMING_SNAKE_CASE : Tuple = hidden_dropout_prob
SCREAMING_SNAKE_CASE : int = attention_probs_dropout_prob
SCREAMING_SNAKE_CASE : str = max_position_embeddings
SCREAMING_SNAKE_CASE : Tuple = type_vocab_size
SCREAMING_SNAKE_CASE : Optional[int] = initializer_range
SCREAMING_SNAKE_CASE : List[Any] = relative_attention
SCREAMING_SNAKE_CASE : str = max_relative_positions
SCREAMING_SNAKE_CASE : int = pad_token_id
SCREAMING_SNAKE_CASE : List[str] = position_biased_input
# Backwards compatibility
if type(lowerCamelCase_ ) == str:
SCREAMING_SNAKE_CASE : Dict = [x.strip() for x in pos_att_type.lower().split("""|""" )]
SCREAMING_SNAKE_CASE : Any = pos_att_type
SCREAMING_SNAKE_CASE : Any = vocab_size
SCREAMING_SNAKE_CASE : Optional[Any] = layer_norm_eps
SCREAMING_SNAKE_CASE : str = kwargs.get("""pooler_hidden_size""" , lowerCamelCase_ )
SCREAMING_SNAKE_CASE : Dict = pooler_dropout
SCREAMING_SNAKE_CASE : Any = pooler_hidden_act
class UpperCamelCase__ ( lowercase_ ):
"""simple docstring"""
@property
def lowerCamelCase_ ( self : Optional[int] ):
'''simple docstring'''
if self.task == "multiple-choice":
SCREAMING_SNAKE_CASE : Optional[int] = {0: """batch""", 1: """choice""", 2: """sequence"""}
else:
SCREAMING_SNAKE_CASE : Union[str, Any] = {0: """batch""", 1: """sequence"""}
if self._config.type_vocab_size > 0:
return OrderedDict(
[("""input_ids""", dynamic_axis), ("""attention_mask""", dynamic_axis), ("""token_type_ids""", dynamic_axis)] )
else:
return OrderedDict([("""input_ids""", dynamic_axis), ("""attention_mask""", dynamic_axis)] )
@property
def lowerCamelCase_ ( self : Tuple ):
'''simple docstring'''
return 12
def lowerCamelCase_ ( self : Optional[Any] , lowerCamelCase_ : Union["PreTrainedTokenizerBase", "FeatureExtractionMixin"] , lowerCamelCase_ : int = -1 , lowerCamelCase_ : int = -1 , lowerCamelCase_ : int = -1 , lowerCamelCase_ : bool = False , lowerCamelCase_ : Optional["TensorType"] = None , lowerCamelCase_ : int = 3 , lowerCamelCase_ : int = 40 , lowerCamelCase_ : int = 40 , lowerCamelCase_ : "PreTrainedTokenizerBase" = None , ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Dict = super().generate_dummy_inputs(preprocessor=lowerCamelCase_ , framework=lowerCamelCase_ )
if self._config.type_vocab_size == 0 and "token_type_ids" in dummy_inputs:
del dummy_inputs["token_type_ids"]
return dummy_inputs
| 79 | 0 |
'''simple docstring'''
import os
import tempfile
import unittest
import uuid
from pathlib import Path
from transformers.testing_utils import get_tests_dir, require_soundfile, require_torch, require_vision
from transformers.tools.agent_types import AgentAudio, AgentImage, AgentText
from transformers.utils import is_soundfile_availble, is_torch_available, is_vision_available
if is_torch_available():
import torch
if is_soundfile_availble():
import soundfile as sf
if is_vision_available():
from PIL import Image
def __A ( lowerCamelCase_="" ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : List[Any] = tempfile.mkdtemp()
return os.path.join(lowerCamelCase_ , str(uuid.uuida() ) + suffix )
@require_soundfile
@require_torch
class UpperCamelCase__ ( unittest.TestCase ):
"""simple docstring"""
def lowerCamelCase_ ( self : int ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : List[str] = torch.rand(12 , dtype=torch.floataa ) - 0.5
SCREAMING_SNAKE_CASE : Union[str, Any] = AgentAudio(lowerCamelCase_ )
SCREAMING_SNAKE_CASE : Any = str(agent_type.to_string() )
# Ensure that the tensor and the agent_type's tensor are the same
self.assertTrue(torch.allclose(lowerCamelCase_ , agent_type.to_raw() , atol=1e-4 ) )
del agent_type
# Ensure the path remains even after the object deletion
self.assertTrue(os.path.exists(lowerCamelCase_ ) )
# Ensure that the file contains the same value as the original tensor
SCREAMING_SNAKE_CASE : Union[str, Any] = sf.read(lowerCamelCase_ )
self.assertTrue(torch.allclose(lowerCamelCase_ , torch.tensor(lowerCamelCase_ ) , atol=1e-4 ) )
def lowerCamelCase_ ( self : List[Any] ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : str = torch.rand(12 , dtype=torch.floataa ) - 0.5
SCREAMING_SNAKE_CASE : List[str] = get_new_path(suffix=""".wav""" )
sf.write(lowerCamelCase_ , lowerCamelCase_ , 1_60_00 )
SCREAMING_SNAKE_CASE : Optional[int] = AgentAudio(lowerCamelCase_ )
self.assertTrue(torch.allclose(lowerCamelCase_ , agent_type.to_raw() , atol=1e-4 ) )
self.assertEqual(agent_type.to_string() , lowerCamelCase_ )
@require_vision
@require_torch
class UpperCamelCase__ ( unittest.TestCase ):
"""simple docstring"""
def lowerCamelCase_ ( self : List[str] ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Tuple = torch.randint(0 , 2_56 , (64, 64, 3) )
SCREAMING_SNAKE_CASE : Union[str, Any] = AgentImage(lowerCamelCase_ )
SCREAMING_SNAKE_CASE : Union[str, Any] = str(agent_type.to_string() )
# Ensure that the tensor and the agent_type's tensor are the same
self.assertTrue(torch.allclose(lowerCamelCase_ , agent_type._tensor , atol=1e-4 ) )
self.assertIsInstance(agent_type.to_raw() , Image.Image )
# Ensure the path remains even after the object deletion
del agent_type
self.assertTrue(os.path.exists(lowerCamelCase_ ) )
def lowerCamelCase_ ( self : List[str] ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : str = Path(get_tests_dir("""fixtures/tests_samples/COCO""" ) ) / """000000039769.png"""
SCREAMING_SNAKE_CASE : Optional[int] = Image.open(lowerCamelCase_ )
SCREAMING_SNAKE_CASE : str = AgentImage(lowerCamelCase_ )
self.assertTrue(path.samefile(agent_type.to_string() ) )
self.assertTrue(image == agent_type.to_raw() )
# Ensure the path remains even after the object deletion
del agent_type
self.assertTrue(os.path.exists(lowerCamelCase_ ) )
def lowerCamelCase_ ( self : Dict ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Optional[int] = Path(get_tests_dir("""fixtures/tests_samples/COCO""" ) ) / """000000039769.png"""
SCREAMING_SNAKE_CASE : List[str] = Image.open(lowerCamelCase_ )
SCREAMING_SNAKE_CASE : Tuple = AgentImage(lowerCamelCase_ )
self.assertFalse(path.samefile(agent_type.to_string() ) )
self.assertTrue(image == agent_type.to_raw() )
# Ensure the path remains even after the object deletion
del agent_type
self.assertTrue(os.path.exists(lowerCamelCase_ ) )
class UpperCamelCase__ ( unittest.TestCase ):
"""simple docstring"""
def lowerCamelCase_ ( self : Union[str, Any] ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Any = """Hey!"""
SCREAMING_SNAKE_CASE : Tuple = AgentText(lowerCamelCase_ )
self.assertEqual(lowerCamelCase_ , agent_type.to_string() )
self.assertEqual(lowerCamelCase_ , agent_type.to_raw() )
self.assertEqual(lowerCamelCase_ , lowerCamelCase_ )
| 700 |
'''simple docstring'''
from collections import deque
from math import floor
from random import random
from time import time
class UpperCamelCase__ :
"""simple docstring"""
def __init__( self : Optional[Any] ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : str = {}
def lowerCamelCase_ ( self : Union[str, Any] , lowerCamelCase_ : Optional[Any] , lowerCamelCase_ : Any , lowerCamelCase_ : Optional[int]=1 ):
'''simple docstring'''
if self.graph.get(lowerCamelCase_ ):
if self.graph[u].count([w, v] ) == 0:
self.graph[u].append([w, v] )
else:
SCREAMING_SNAKE_CASE : str = [[w, v]]
if not self.graph.get(lowerCamelCase_ ):
SCREAMING_SNAKE_CASE : Tuple = []
def lowerCamelCase_ ( self : Optional[Any] ):
'''simple docstring'''
return list(self.graph )
def lowerCamelCase_ ( self : Optional[int] , lowerCamelCase_ : Any , lowerCamelCase_ : str ):
'''simple docstring'''
if self.graph.get(lowerCamelCase_ ):
for _ in self.graph[u]:
if _[1] == v:
self.graph[u].remove(lowerCamelCase_ )
def lowerCamelCase_ ( self : List[str] , lowerCamelCase_ : Union[str, Any]=-2 , lowerCamelCase_ : str=-1 ):
'''simple docstring'''
if s == d:
return []
SCREAMING_SNAKE_CASE : List[str] = []
SCREAMING_SNAKE_CASE : List[str] = []
if s == -2:
SCREAMING_SNAKE_CASE : Tuple = list(self.graph )[0]
stack.append(lowerCamelCase_ )
visited.append(lowerCamelCase_ )
SCREAMING_SNAKE_CASE : Any = s
while True:
# check if there is any non isolated nodes
if len(self.graph[s] ) != 0:
SCREAMING_SNAKE_CASE : Optional[Any] = s
for node in self.graph[s]:
if visited.count(node[1] ) < 1:
if node[1] == d:
visited.append(lowerCamelCase_ )
return visited
else:
stack.append(node[1] )
visited.append(node[1] )
SCREAMING_SNAKE_CASE : Tuple = node[1]
break
# check if all the children are visited
if s == ss:
stack.pop()
if len(lowerCamelCase_ ) != 0:
SCREAMING_SNAKE_CASE : int = stack[len(lowerCamelCase_ ) - 1]
else:
SCREAMING_SNAKE_CASE : Any = ss
# check if se have reached the starting point
if len(lowerCamelCase_ ) == 0:
return visited
def lowerCamelCase_ ( self : Dict , lowerCamelCase_ : Optional[int]=-1 ):
'''simple docstring'''
if c == -1:
SCREAMING_SNAKE_CASE : str = floor(random() * 1_00_00 ) + 10
for i in range(lowerCamelCase_ ):
# every vertex has max 100 edges
for _ in range(floor(random() * 1_02 ) + 1 ):
SCREAMING_SNAKE_CASE : Union[str, Any] = floor(random() * c ) + 1
if n != i:
self.add_pair(lowerCamelCase_ , lowerCamelCase_ , 1 )
def lowerCamelCase_ ( self : List[Any] , lowerCamelCase_ : Any=-2 ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : str = deque()
SCREAMING_SNAKE_CASE : List[str] = []
if s == -2:
SCREAMING_SNAKE_CASE : int = list(self.graph )[0]
d.append(lowerCamelCase_ )
visited.append(lowerCamelCase_ )
while d:
SCREAMING_SNAKE_CASE : Dict = d.popleft()
if len(self.graph[s] ) != 0:
for node in self.graph[s]:
if visited.count(node[1] ) < 1:
d.append(node[1] )
visited.append(node[1] )
return visited
def lowerCamelCase_ ( self : Dict , lowerCamelCase_ : Optional[Any] ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Dict = 0
for x in self.graph:
for y in self.graph[x]:
if y[1] == u:
count += 1
return count
def lowerCamelCase_ ( self : Optional[Any] , lowerCamelCase_ : Tuple ):
'''simple docstring'''
return len(self.graph[u] )
def lowerCamelCase_ ( self : str , lowerCamelCase_ : Optional[Any]=-2 ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : List[Any] = []
SCREAMING_SNAKE_CASE : List[str] = []
if s == -2:
SCREAMING_SNAKE_CASE : Union[str, Any] = list(self.graph )[0]
stack.append(lowerCamelCase_ )
visited.append(lowerCamelCase_ )
SCREAMING_SNAKE_CASE : Union[str, Any] = s
SCREAMING_SNAKE_CASE : List[str] = []
while True:
# check if there is any non isolated nodes
if len(self.graph[s] ) != 0:
SCREAMING_SNAKE_CASE : Optional[Any] = s
for node in self.graph[s]:
if visited.count(node[1] ) < 1:
stack.append(node[1] )
visited.append(node[1] )
SCREAMING_SNAKE_CASE : int = node[1]
break
# check if all the children are visited
if s == ss:
sorted_nodes.append(stack.pop() )
if len(lowerCamelCase_ ) != 0:
SCREAMING_SNAKE_CASE : List[Any] = stack[len(lowerCamelCase_ ) - 1]
else:
SCREAMING_SNAKE_CASE : int = ss
# check if se have reached the starting point
if len(lowerCamelCase_ ) == 0:
return sorted_nodes
def lowerCamelCase_ ( self : Optional[int] ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : List[Any] = []
SCREAMING_SNAKE_CASE : Any = []
SCREAMING_SNAKE_CASE : List[Any] = list(self.graph )[0]
stack.append(lowerCamelCase_ )
visited.append(lowerCamelCase_ )
SCREAMING_SNAKE_CASE : Any = -2
SCREAMING_SNAKE_CASE : int = []
SCREAMING_SNAKE_CASE : Union[str, Any] = s
SCREAMING_SNAKE_CASE : Tuple = False
SCREAMING_SNAKE_CASE : Union[str, Any] = set()
while True:
# check if there is any non isolated nodes
if len(self.graph[s] ) != 0:
SCREAMING_SNAKE_CASE : Union[str, Any] = s
for node in self.graph[s]:
if (
visited.count(node[1] ) > 0
and node[1] != parent
and indirect_parents.count(node[1] ) > 0
and not on_the_way_back
):
SCREAMING_SNAKE_CASE : Any = len(lowerCamelCase_ ) - 1
while len_stack >= 0:
if stack[len_stack] == node[1]:
anticipating_nodes.add(node[1] )
break
else:
anticipating_nodes.add(stack[len_stack] )
len_stack -= 1
if visited.count(node[1] ) < 1:
stack.append(node[1] )
visited.append(node[1] )
SCREAMING_SNAKE_CASE : int = node[1]
break
# check if all the children are visited
if s == ss:
stack.pop()
SCREAMING_SNAKE_CASE : int = True
if len(lowerCamelCase_ ) != 0:
SCREAMING_SNAKE_CASE : Any = stack[len(lowerCamelCase_ ) - 1]
else:
SCREAMING_SNAKE_CASE : List[str] = False
indirect_parents.append(lowerCamelCase_ )
SCREAMING_SNAKE_CASE : List[Any] = s
SCREAMING_SNAKE_CASE : List[Any] = ss
# check if se have reached the starting point
if len(lowerCamelCase_ ) == 0:
return list(lowerCamelCase_ )
def lowerCamelCase_ ( self : Dict ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Tuple = []
SCREAMING_SNAKE_CASE : int = []
SCREAMING_SNAKE_CASE : Any = list(self.graph )[0]
stack.append(lowerCamelCase_ )
visited.append(lowerCamelCase_ )
SCREAMING_SNAKE_CASE : Union[str, Any] = -2
SCREAMING_SNAKE_CASE : List[Any] = []
SCREAMING_SNAKE_CASE : Tuple = s
SCREAMING_SNAKE_CASE : Dict = False
SCREAMING_SNAKE_CASE : Optional[Any] = set()
while True:
# check if there is any non isolated nodes
if len(self.graph[s] ) != 0:
SCREAMING_SNAKE_CASE : str = s
for node in self.graph[s]:
if (
visited.count(node[1] ) > 0
and node[1] != parent
and indirect_parents.count(node[1] ) > 0
and not on_the_way_back
):
SCREAMING_SNAKE_CASE : str = len(lowerCamelCase_ ) - 1
while len_stack_minus_one >= 0:
if stack[len_stack_minus_one] == node[1]:
anticipating_nodes.add(node[1] )
break
else:
return True
if visited.count(node[1] ) < 1:
stack.append(node[1] )
visited.append(node[1] )
SCREAMING_SNAKE_CASE : Optional[Any] = node[1]
break
# check if all the children are visited
if s == ss:
stack.pop()
SCREAMING_SNAKE_CASE : Dict = True
if len(lowerCamelCase_ ) != 0:
SCREAMING_SNAKE_CASE : List[str] = stack[len(lowerCamelCase_ ) - 1]
else:
SCREAMING_SNAKE_CASE : List[Any] = False
indirect_parents.append(lowerCamelCase_ )
SCREAMING_SNAKE_CASE : Optional[int] = s
SCREAMING_SNAKE_CASE : Optional[int] = ss
# check if se have reached the starting point
if len(lowerCamelCase_ ) == 0:
return False
def lowerCamelCase_ ( self : Any , lowerCamelCase_ : str=-2 , lowerCamelCase_ : int=-1 ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Optional[Any] = time()
self.dfs(lowerCamelCase_ , lowerCamelCase_ )
SCREAMING_SNAKE_CASE : List[str] = time()
return end - begin
def lowerCamelCase_ ( self : int , lowerCamelCase_ : Tuple=-2 ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Optional[int] = time()
self.bfs(lowerCamelCase_ )
SCREAMING_SNAKE_CASE : List[str] = time()
return end - begin
class UpperCamelCase__ :
"""simple docstring"""
def __init__( self : Optional[int] ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Tuple = {}
def lowerCamelCase_ ( self : List[str] , lowerCamelCase_ : Optional[Any] , lowerCamelCase_ : Dict , lowerCamelCase_ : Union[str, Any]=1 ):
'''simple docstring'''
if self.graph.get(lowerCamelCase_ ):
# if there already is a edge
if self.graph[u].count([w, v] ) == 0:
self.graph[u].append([w, v] )
else:
# if u does not exist
SCREAMING_SNAKE_CASE : Any = [[w, v]]
# add the other way
if self.graph.get(lowerCamelCase_ ):
# if there already is a edge
if self.graph[v].count([w, u] ) == 0:
self.graph[v].append([w, u] )
else:
# if u does not exist
SCREAMING_SNAKE_CASE : Any = [[w, u]]
def lowerCamelCase_ ( self : Tuple , lowerCamelCase_ : Tuple , lowerCamelCase_ : Any ):
'''simple docstring'''
if self.graph.get(lowerCamelCase_ ):
for _ in self.graph[u]:
if _[1] == v:
self.graph[u].remove(lowerCamelCase_ )
# the other way round
if self.graph.get(lowerCamelCase_ ):
for _ in self.graph[v]:
if _[1] == u:
self.graph[v].remove(lowerCamelCase_ )
def lowerCamelCase_ ( self : int , lowerCamelCase_ : str=-2 , lowerCamelCase_ : List[str]=-1 ):
'''simple docstring'''
if s == d:
return []
SCREAMING_SNAKE_CASE : List[str] = []
SCREAMING_SNAKE_CASE : Any = []
if s == -2:
SCREAMING_SNAKE_CASE : List[Any] = list(self.graph )[0]
stack.append(lowerCamelCase_ )
visited.append(lowerCamelCase_ )
SCREAMING_SNAKE_CASE : List[str] = s
while True:
# check if there is any non isolated nodes
if len(self.graph[s] ) != 0:
SCREAMING_SNAKE_CASE : Union[str, Any] = s
for node in self.graph[s]:
if visited.count(node[1] ) < 1:
if node[1] == d:
visited.append(lowerCamelCase_ )
return visited
else:
stack.append(node[1] )
visited.append(node[1] )
SCREAMING_SNAKE_CASE : Any = node[1]
break
# check if all the children are visited
if s == ss:
stack.pop()
if len(lowerCamelCase_ ) != 0:
SCREAMING_SNAKE_CASE : Any = stack[len(lowerCamelCase_ ) - 1]
else:
SCREAMING_SNAKE_CASE : List[str] = ss
# check if se have reached the starting point
if len(lowerCamelCase_ ) == 0:
return visited
def lowerCamelCase_ ( self : Any , lowerCamelCase_ : List[str]=-1 ):
'''simple docstring'''
if c == -1:
SCREAMING_SNAKE_CASE : Any = floor(random() * 1_00_00 ) + 10
for i in range(lowerCamelCase_ ):
# every vertex has max 100 edges
for _ in range(floor(random() * 1_02 ) + 1 ):
SCREAMING_SNAKE_CASE : List[str] = floor(random() * c ) + 1
if n != i:
self.add_pair(lowerCamelCase_ , lowerCamelCase_ , 1 )
def lowerCamelCase_ ( self : List[str] , lowerCamelCase_ : List[Any]=-2 ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : List[Any] = deque()
SCREAMING_SNAKE_CASE : Tuple = []
if s == -2:
SCREAMING_SNAKE_CASE : Any = list(self.graph )[0]
d.append(lowerCamelCase_ )
visited.append(lowerCamelCase_ )
while d:
SCREAMING_SNAKE_CASE : List[Any] = d.popleft()
if len(self.graph[s] ) != 0:
for node in self.graph[s]:
if visited.count(node[1] ) < 1:
d.append(node[1] )
visited.append(node[1] )
return visited
def lowerCamelCase_ ( self : Tuple , lowerCamelCase_ : str ):
'''simple docstring'''
return len(self.graph[u] )
def lowerCamelCase_ ( self : List[str] ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : List[str] = []
SCREAMING_SNAKE_CASE : Any = []
SCREAMING_SNAKE_CASE : Optional[Any] = list(self.graph )[0]
stack.append(lowerCamelCase_ )
visited.append(lowerCamelCase_ )
SCREAMING_SNAKE_CASE : Optional[int] = -2
SCREAMING_SNAKE_CASE : Tuple = []
SCREAMING_SNAKE_CASE : Any = s
SCREAMING_SNAKE_CASE : Optional[Any] = False
SCREAMING_SNAKE_CASE : Optional[Any] = set()
while True:
# check if there is any non isolated nodes
if len(self.graph[s] ) != 0:
SCREAMING_SNAKE_CASE : Optional[Any] = s
for node in self.graph[s]:
if (
visited.count(node[1] ) > 0
and node[1] != parent
and indirect_parents.count(node[1] ) > 0
and not on_the_way_back
):
SCREAMING_SNAKE_CASE : Optional[Any] = len(lowerCamelCase_ ) - 1
while len_stack >= 0:
if stack[len_stack] == node[1]:
anticipating_nodes.add(node[1] )
break
else:
anticipating_nodes.add(stack[len_stack] )
len_stack -= 1
if visited.count(node[1] ) < 1:
stack.append(node[1] )
visited.append(node[1] )
SCREAMING_SNAKE_CASE : str = node[1]
break
# check if all the children are visited
if s == ss:
stack.pop()
SCREAMING_SNAKE_CASE : Optional[int] = True
if len(lowerCamelCase_ ) != 0:
SCREAMING_SNAKE_CASE : int = stack[len(lowerCamelCase_ ) - 1]
else:
SCREAMING_SNAKE_CASE : Union[str, Any] = False
indirect_parents.append(lowerCamelCase_ )
SCREAMING_SNAKE_CASE : List[str] = s
SCREAMING_SNAKE_CASE : str = ss
# check if se have reached the starting point
if len(lowerCamelCase_ ) == 0:
return list(lowerCamelCase_ )
def lowerCamelCase_ ( self : Union[str, Any] ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Optional[Any] = []
SCREAMING_SNAKE_CASE : int = []
SCREAMING_SNAKE_CASE : Any = list(self.graph )[0]
stack.append(lowerCamelCase_ )
visited.append(lowerCamelCase_ )
SCREAMING_SNAKE_CASE : Dict = -2
SCREAMING_SNAKE_CASE : int = []
SCREAMING_SNAKE_CASE : int = s
SCREAMING_SNAKE_CASE : Union[str, Any] = False
SCREAMING_SNAKE_CASE : Tuple = set()
while True:
# check if there is any non isolated nodes
if len(self.graph[s] ) != 0:
SCREAMING_SNAKE_CASE : Any = s
for node in self.graph[s]:
if (
visited.count(node[1] ) > 0
and node[1] != parent
and indirect_parents.count(node[1] ) > 0
and not on_the_way_back
):
SCREAMING_SNAKE_CASE : Optional[Any] = len(lowerCamelCase_ ) - 1
while len_stack_minus_one >= 0:
if stack[len_stack_minus_one] == node[1]:
anticipating_nodes.add(node[1] )
break
else:
return True
if visited.count(node[1] ) < 1:
stack.append(node[1] )
visited.append(node[1] )
SCREAMING_SNAKE_CASE : Tuple = node[1]
break
# check if all the children are visited
if s == ss:
stack.pop()
SCREAMING_SNAKE_CASE : Any = True
if len(lowerCamelCase_ ) != 0:
SCREAMING_SNAKE_CASE : str = stack[len(lowerCamelCase_ ) - 1]
else:
SCREAMING_SNAKE_CASE : Optional[Any] = False
indirect_parents.append(lowerCamelCase_ )
SCREAMING_SNAKE_CASE : Any = s
SCREAMING_SNAKE_CASE : Tuple = ss
# check if se have reached the starting point
if len(lowerCamelCase_ ) == 0:
return False
def lowerCamelCase_ ( self : List[Any] ):
'''simple docstring'''
return list(self.graph )
def lowerCamelCase_ ( self : str , lowerCamelCase_ : List[str]=-2 , lowerCamelCase_ : str=-1 ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : str = time()
self.dfs(lowerCamelCase_ , lowerCamelCase_ )
SCREAMING_SNAKE_CASE : Tuple = time()
return end - begin
def lowerCamelCase_ ( self : Tuple , lowerCamelCase_ : Dict=-2 ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Optional[Any] = time()
self.bfs(lowerCamelCase_ )
SCREAMING_SNAKE_CASE : Dict = time()
return end - begin
| 79 | 0 |
'''simple docstring'''
import tempfile
import torch
from diffusers import (
DEISMultistepScheduler,
DPMSolverMultistepScheduler,
DPMSolverSinglestepScheduler,
UniPCMultistepScheduler,
)
from .test_schedulers import SchedulerCommonTest
class UpperCamelCase__ ( lowercase_ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE__ = (DEISMultistepScheduler,)
SCREAMING_SNAKE_CASE__ = (('''num_inference_steps''', 25),)
def lowerCamelCase_ ( self : List[str] , **lowerCamelCase_ : Any ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Dict = {
"""num_train_timesteps""": 10_00,
"""beta_start""": 0.0_001,
"""beta_end""": 0.02,
"""beta_schedule""": """linear""",
"""solver_order""": 2,
}
config.update(**lowerCamelCase_ )
return config
def lowerCamelCase_ ( self : List[Any] , lowerCamelCase_ : Any=0 , **lowerCamelCase_ : Optional[Any] ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Any = dict(self.forward_default_kwargs )
SCREAMING_SNAKE_CASE : List[str] = kwargs.pop("""num_inference_steps""" , lowerCamelCase_ )
SCREAMING_SNAKE_CASE : Dict = self.dummy_sample
SCREAMING_SNAKE_CASE : Any = 0.1 * sample
SCREAMING_SNAKE_CASE : str = [residual + 0.2, residual + 0.15, residual + 0.10]
for scheduler_class in self.scheduler_classes:
SCREAMING_SNAKE_CASE : int = self.get_scheduler_config(**lowerCamelCase_ )
SCREAMING_SNAKE_CASE : Optional[int] = scheduler_class(**lowerCamelCase_ )
scheduler.set_timesteps(lowerCamelCase_ )
# copy over dummy past residuals
SCREAMING_SNAKE_CASE : Optional[Any] = dummy_past_residuals[: scheduler.config.solver_order]
with tempfile.TemporaryDirectory() as tmpdirname:
scheduler.save_config(lowerCamelCase_ )
SCREAMING_SNAKE_CASE : Optional[int] = scheduler_class.from_pretrained(lowerCamelCase_ )
new_scheduler.set_timesteps(lowerCamelCase_ )
# copy over dummy past residuals
SCREAMING_SNAKE_CASE : List[str] = dummy_past_residuals[: new_scheduler.config.solver_order]
SCREAMING_SNAKE_CASE : str = sample, sample
for t in range(lowerCamelCase_ , time_step + scheduler.config.solver_order + 1 ):
SCREAMING_SNAKE_CASE : Tuple = scheduler.step(lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ , **lowerCamelCase_ ).prev_sample
SCREAMING_SNAKE_CASE : Optional[int] = new_scheduler.step(lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ , **lowerCamelCase_ ).prev_sample
assert torch.sum(torch.abs(output - new_output ) ) < 1e-5, "Scheduler outputs are not identical"
def lowerCamelCase_ ( self : Tuple ):
'''simple docstring'''
pass
def lowerCamelCase_ ( self : Tuple , lowerCamelCase_ : Union[str, Any]=0 , **lowerCamelCase_ : Any ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Tuple = dict(self.forward_default_kwargs )
SCREAMING_SNAKE_CASE : Optional[Any] = kwargs.pop("""num_inference_steps""" , lowerCamelCase_ )
SCREAMING_SNAKE_CASE : Optional[int] = self.dummy_sample
SCREAMING_SNAKE_CASE : Optional[Any] = 0.1 * sample
SCREAMING_SNAKE_CASE : Optional[Any] = [residual + 0.2, residual + 0.15, residual + 0.10]
for scheduler_class in self.scheduler_classes:
SCREAMING_SNAKE_CASE : str = self.get_scheduler_config()
SCREAMING_SNAKE_CASE : Any = scheduler_class(**lowerCamelCase_ )
scheduler.set_timesteps(lowerCamelCase_ )
# copy over dummy past residuals (must be after setting timesteps)
SCREAMING_SNAKE_CASE : List[str] = dummy_past_residuals[: scheduler.config.solver_order]
with tempfile.TemporaryDirectory() as tmpdirname:
scheduler.save_config(lowerCamelCase_ )
SCREAMING_SNAKE_CASE : List[str] = scheduler_class.from_pretrained(lowerCamelCase_ )
# copy over dummy past residuals
new_scheduler.set_timesteps(lowerCamelCase_ )
# copy over dummy past residual (must be after setting timesteps)
SCREAMING_SNAKE_CASE : Dict = dummy_past_residuals[: new_scheduler.config.solver_order]
SCREAMING_SNAKE_CASE : List[Any] = scheduler.step(lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ , **lowerCamelCase_ ).prev_sample
SCREAMING_SNAKE_CASE : Tuple = new_scheduler.step(lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ , **lowerCamelCase_ ).prev_sample
assert torch.sum(torch.abs(output - new_output ) ) < 1e-5, "Scheduler outputs are not identical"
def lowerCamelCase_ ( self : Dict , lowerCamelCase_ : Union[str, Any]=None , **lowerCamelCase_ : Optional[int] ):
'''simple docstring'''
if scheduler is None:
SCREAMING_SNAKE_CASE : Optional[int] = self.scheduler_classes[0]
SCREAMING_SNAKE_CASE : Optional[Any] = self.get_scheduler_config(**lowerCamelCase_ )
SCREAMING_SNAKE_CASE : int = scheduler_class(**lowerCamelCase_ )
SCREAMING_SNAKE_CASE : List[Any] = self.scheduler_classes[0]
SCREAMING_SNAKE_CASE : str = self.get_scheduler_config(**lowerCamelCase_ )
SCREAMING_SNAKE_CASE : str = scheduler_class(**lowerCamelCase_ )
SCREAMING_SNAKE_CASE : Tuple = 10
SCREAMING_SNAKE_CASE : Any = self.dummy_model()
SCREAMING_SNAKE_CASE : Union[str, Any] = self.dummy_sample_deter
scheduler.set_timesteps(lowerCamelCase_ )
for i, t in enumerate(scheduler.timesteps ):
SCREAMING_SNAKE_CASE : Dict = model(lowerCamelCase_ , lowerCamelCase_ )
SCREAMING_SNAKE_CASE : Optional[int] = scheduler.step(lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ ).prev_sample
return sample
def lowerCamelCase_ ( self : Dict ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Tuple = dict(self.forward_default_kwargs )
SCREAMING_SNAKE_CASE : int = kwargs.pop("""num_inference_steps""" , lowerCamelCase_ )
for scheduler_class in self.scheduler_classes:
SCREAMING_SNAKE_CASE : str = self.get_scheduler_config()
SCREAMING_SNAKE_CASE : Optional[Any] = scheduler_class(**lowerCamelCase_ )
SCREAMING_SNAKE_CASE : Optional[int] = self.dummy_sample
SCREAMING_SNAKE_CASE : Any = 0.1 * sample
if num_inference_steps is not None and hasattr(lowerCamelCase_ , """set_timesteps""" ):
scheduler.set_timesteps(lowerCamelCase_ )
elif num_inference_steps is not None and not hasattr(lowerCamelCase_ , """set_timesteps""" ):
SCREAMING_SNAKE_CASE : str = num_inference_steps
# copy over dummy past residuals (must be done after set_timesteps)
SCREAMING_SNAKE_CASE : Optional[Any] = [residual + 0.2, residual + 0.15, residual + 0.10]
SCREAMING_SNAKE_CASE : int = dummy_past_residuals[: scheduler.config.solver_order]
SCREAMING_SNAKE_CASE : int = scheduler.timesteps[5]
SCREAMING_SNAKE_CASE : int = scheduler.timesteps[6]
SCREAMING_SNAKE_CASE : List[Any] = scheduler.step(lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ , **lowerCamelCase_ ).prev_sample
SCREAMING_SNAKE_CASE : Tuple = scheduler.step(lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ , **lowerCamelCase_ ).prev_sample
self.assertEqual(output_a.shape , sample.shape )
self.assertEqual(output_a.shape , output_a.shape )
def lowerCamelCase_ ( self : List[Any] ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : str = DEISMultistepScheduler(**self.get_scheduler_config() )
SCREAMING_SNAKE_CASE : Union[str, Any] = self.full_loop(scheduler=lowerCamelCase_ )
SCREAMING_SNAKE_CASE : List[Any] = torch.mean(torch.abs(lowerCamelCase_ ) )
assert abs(result_mean.item() - 0.23_916 ) < 1e-3
SCREAMING_SNAKE_CASE : List[str] = DPMSolverSinglestepScheduler.from_config(scheduler.config )
SCREAMING_SNAKE_CASE : Tuple = DPMSolverMultistepScheduler.from_config(scheduler.config )
SCREAMING_SNAKE_CASE : Dict = UniPCMultistepScheduler.from_config(scheduler.config )
SCREAMING_SNAKE_CASE : int = DEISMultistepScheduler.from_config(scheduler.config )
SCREAMING_SNAKE_CASE : Optional[Any] = self.full_loop(scheduler=lowerCamelCase_ )
SCREAMING_SNAKE_CASE : List[str] = torch.mean(torch.abs(lowerCamelCase_ ) )
assert abs(result_mean.item() - 0.23_916 ) < 1e-3
def lowerCamelCase_ ( self : str ):
'''simple docstring'''
for timesteps in [25, 50, 1_00, 9_99, 10_00]:
self.check_over_configs(num_train_timesteps=lowerCamelCase_ )
def lowerCamelCase_ ( self : int ):
'''simple docstring'''
self.check_over_configs(thresholding=lowerCamelCase_ )
for order in [1, 2, 3]:
for solver_type in ["logrho"]:
for threshold in [0.5, 1.0, 2.0]:
for prediction_type in ["epsilon", "sample"]:
self.check_over_configs(
thresholding=lowerCamelCase_ , prediction_type=lowerCamelCase_ , sample_max_value=lowerCamelCase_ , algorithm_type="""deis""" , solver_order=lowerCamelCase_ , solver_type=lowerCamelCase_ , )
def lowerCamelCase_ ( self : Tuple ):
'''simple docstring'''
for prediction_type in ["epsilon", "v_prediction"]:
self.check_over_configs(prediction_type=lowerCamelCase_ )
def lowerCamelCase_ ( self : str ):
'''simple docstring'''
for algorithm_type in ["deis"]:
for solver_type in ["logrho"]:
for order in [1, 2, 3]:
for prediction_type in ["epsilon", "sample"]:
self.check_over_configs(
solver_order=lowerCamelCase_ , solver_type=lowerCamelCase_ , prediction_type=lowerCamelCase_ , algorithm_type=lowerCamelCase_ , )
SCREAMING_SNAKE_CASE : Dict = self.full_loop(
solver_order=lowerCamelCase_ , solver_type=lowerCamelCase_ , prediction_type=lowerCamelCase_ , algorithm_type=lowerCamelCase_ , )
assert not torch.isnan(lowerCamelCase_ ).any(), "Samples have nan numbers"
def lowerCamelCase_ ( self : int ):
'''simple docstring'''
self.check_over_configs(lower_order_final=lowerCamelCase_ )
self.check_over_configs(lower_order_final=lowerCamelCase_ )
def lowerCamelCase_ ( self : str ):
'''simple docstring'''
for num_inference_steps in [1, 2, 3, 5, 10, 50, 1_00, 9_99, 10_00]:
self.check_over_forward(num_inference_steps=lowerCamelCase_ , time_step=0 )
def lowerCamelCase_ ( self : int ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : List[Any] = self.full_loop()
SCREAMING_SNAKE_CASE : List[Any] = torch.mean(torch.abs(lowerCamelCase_ ) )
assert abs(result_mean.item() - 0.23_916 ) < 1e-3
def lowerCamelCase_ ( self : Dict ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : List[str] = self.full_loop(prediction_type="""v_prediction""" )
SCREAMING_SNAKE_CASE : Any = torch.mean(torch.abs(lowerCamelCase_ ) )
assert abs(result_mean.item() - 0.091 ) < 1e-3
def lowerCamelCase_ ( self : int ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Optional[int] = self.scheduler_classes[0]
SCREAMING_SNAKE_CASE : List[str] = self.get_scheduler_config(thresholding=lowerCamelCase_ , dynamic_thresholding_ratio=0 )
SCREAMING_SNAKE_CASE : Dict = scheduler_class(**lowerCamelCase_ )
SCREAMING_SNAKE_CASE : List[str] = 10
SCREAMING_SNAKE_CASE : Optional[int] = self.dummy_model()
SCREAMING_SNAKE_CASE : Dict = self.dummy_sample_deter.half()
scheduler.set_timesteps(lowerCamelCase_ )
for i, t in enumerate(scheduler.timesteps ):
SCREAMING_SNAKE_CASE : List[Any] = model(lowerCamelCase_ , lowerCamelCase_ )
SCREAMING_SNAKE_CASE : List[Any] = scheduler.step(lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ ).prev_sample
assert sample.dtype == torch.floataa
| 701 |
'''simple docstring'''
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
__UpperCAmelCase = logging.get_logger(__name__)
__UpperCAmelCase = {"""vocab_file""": """sentencepiece.bpe.model"""}
__UpperCAmelCase = {
"""vocab_file""": {
"""moussaKam/mbarthez""": """https://huggingface.co/moussaKam/mbarthez/resolve/main/sentencepiece.bpe.model""",
"""moussaKam/barthez""": """https://huggingface.co/moussaKam/barthez/resolve/main/sentencepiece.bpe.model""",
"""moussaKam/barthez-orangesum-title""": (
"""https://huggingface.co/moussaKam/barthez-orangesum-title/resolve/main/sentencepiece.bpe.model"""
),
},
}
__UpperCAmelCase = {
"""moussaKam/mbarthez""": 1024,
"""moussaKam/barthez""": 1024,
"""moussaKam/barthez-orangesum-title""": 1024,
}
__UpperCAmelCase = """▁"""
class UpperCamelCase__ ( lowercase_ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE__ = VOCAB_FILES_NAMES
SCREAMING_SNAKE_CASE__ = PRETRAINED_VOCAB_FILES_MAP
SCREAMING_SNAKE_CASE__ = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
SCREAMING_SNAKE_CASE__ = ['''input_ids''', '''attention_mask''']
def __init__( self : Optional[Any] , lowerCamelCase_ : List[str] , lowerCamelCase_ : Tuple="<s>" , lowerCamelCase_ : Union[str, Any]="</s>" , lowerCamelCase_ : Tuple="</s>" , lowerCamelCase_ : str="<s>" , lowerCamelCase_ : Optional[int]="<unk>" , lowerCamelCase_ : List[Any]="<pad>" , lowerCamelCase_ : Optional[Any]="<mask>" , lowerCamelCase_ : Optional[Dict[str, Any]] = None , **lowerCamelCase_ : List[str] , ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : List[str] = AddedToken(lowerCamelCase_ , lstrip=lowerCamelCase_ , rstrip=lowerCamelCase_ ) if isinstance(lowerCamelCase_ , lowerCamelCase_ ) else mask_token
SCREAMING_SNAKE_CASE : Union[str, Any] = {} if sp_model_kwargs is None else sp_model_kwargs
super().__init__(
bos_token=lowerCamelCase_ , eos_token=lowerCamelCase_ , unk_token=lowerCamelCase_ , sep_token=lowerCamelCase_ , cls_token=lowerCamelCase_ , pad_token=lowerCamelCase_ , mask_token=lowerCamelCase_ , sp_model_kwargs=self.sp_model_kwargs , **lowerCamelCase_ , )
SCREAMING_SNAKE_CASE : Dict = vocab_file
SCREAMING_SNAKE_CASE : Dict = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(str(lowerCamelCase_ ) )
SCREAMING_SNAKE_CASE : Optional[int] = {"""<s>""": 0, """<pad>""": 1, """</s>""": 2, """<unk>""": 3}
SCREAMING_SNAKE_CASE : str = len(self.sp_model ) - 1
SCREAMING_SNAKE_CASE : Optional[int] = {v: k for k, v in self.fairseq_tokens_to_ids.items()}
def lowerCamelCase_ ( self : List[str] , lowerCamelCase_ : List[int] , lowerCamelCase_ : 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 : List[Any] = [self.cls_token_id]
SCREAMING_SNAKE_CASE : int = [self.sep_token_id]
return cls + token_ids_a + sep + sep + token_ids_a + sep
def lowerCamelCase_ ( self : Any , lowerCamelCase_ : List[int] , lowerCamelCase_ : Optional[List[int]] = None , lowerCamelCase_ : bool = False ):
'''simple docstring'''
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=lowerCamelCase_ , token_ids_a=lowerCamelCase_ , already_has_special_tokens=lowerCamelCase_ )
if token_ids_a is None:
return [1] + ([0] * len(lowerCamelCase_ )) + [1]
return [1] + ([0] * len(lowerCamelCase_ )) + [1, 1] + ([0] * len(lowerCamelCase_ )) + [1]
def lowerCamelCase_ ( self : str , lowerCamelCase_ : List[int] , lowerCamelCase_ : Optional[List[int]] = None ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : List[str] = [self.sep_token_id]
SCREAMING_SNAKE_CASE : Dict = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep + sep + token_ids_a + sep ) * [0]
@property
def lowerCamelCase_ ( self : List[str] ):
'''simple docstring'''
return len(self.sp_model )
def lowerCamelCase_ ( self : Optional[int] ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : List[str] = {self.convert_ids_to_tokens(lowerCamelCase_ ): i for i in range(self.vocab_size )}
vocab.update(self.added_tokens_encoder )
return vocab
def lowerCamelCase_ ( self : Union[str, Any] , lowerCamelCase_ : str ):
'''simple docstring'''
return self.sp_model.encode(lowerCamelCase_ , out_type=lowerCamelCase_ )
def lowerCamelCase_ ( self : Tuple , lowerCamelCase_ : str ):
'''simple docstring'''
if token in self.fairseq_tokens_to_ids:
return self.fairseq_tokens_to_ids[token]
SCREAMING_SNAKE_CASE : List[str] = self.sp_model.PieceToId(lowerCamelCase_ )
return spm_id if spm_id else self.unk_token_id
def lowerCamelCase_ ( self : int , lowerCamelCase_ : List[str] ):
'''simple docstring'''
if index in self.fairseq_ids_to_tokens:
return self.fairseq_ids_to_tokens[index]
return self.sp_model.IdToPiece(lowerCamelCase_ )
def lowerCamelCase_ ( self : Optional[Any] , lowerCamelCase_ : Optional[Any] ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : str = []
SCREAMING_SNAKE_CASE : Tuple = """"""
SCREAMING_SNAKE_CASE : Dict = False
for token in tokens:
# make sure that special tokens are not decoded using sentencepiece model
if token in self.all_special_tokens:
if not prev_is_special:
out_string += " "
out_string += self.sp_model.decode(lowerCamelCase_ ) + token
SCREAMING_SNAKE_CASE : str = True
SCREAMING_SNAKE_CASE : Optional[Any] = []
else:
current_sub_tokens.append(lowerCamelCase_ )
SCREAMING_SNAKE_CASE : Optional[int] = False
out_string += self.sp_model.decode(lowerCamelCase_ )
return out_string.strip()
def __getstate__( self : Tuple ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Dict = self.__dict__.copy()
SCREAMING_SNAKE_CASE : List[Any] = None
return state
def __setstate__( self : Optional[Any] , lowerCamelCase_ : Tuple ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : str = d
# for backward compatibility
if not hasattr(self , """sp_model_kwargs""" ):
SCREAMING_SNAKE_CASE : int = {}
SCREAMING_SNAKE_CASE : Any = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(self.vocab_file )
def lowerCamelCase_ ( self : Any , lowerCamelCase_ : str , lowerCamelCase_ : Optional[str] = None ):
'''simple docstring'''
if not os.path.isdir(lowerCamelCase_ ):
logger.error(f'''Vocabulary path ({save_directory}) should be a directory''' )
return
SCREAMING_SNAKE_CASE : Dict = os.path.join(
lowerCamelCase_ , (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(lowerCamelCase_ ) and os.path.isfile(self.vocab_file ):
copyfile(self.vocab_file , lowerCamelCase_ )
elif not os.path.isfile(self.vocab_file ):
with open(lowerCamelCase_ , """wb""" ) as fi:
SCREAMING_SNAKE_CASE : Any = self.sp_model.serialized_model_proto()
fi.write(lowerCamelCase_ )
return (out_vocab_file,)
| 79 | 0 |
'''simple docstring'''
import warnings
from ...utils import logging
from .image_processing_glpn import GLPNImageProcessor
__UpperCAmelCase = logging.get_logger(__name__)
class UpperCamelCase__ ( lowercase_ ):
"""simple docstring"""
def __init__( self : Optional[int] , *lowerCamelCase_ : List[str] , **lowerCamelCase_ : Any ):
'''simple docstring'''
warnings.warn(
"""The class GLPNFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please"""
""" use GLPNImageProcessor instead.""" , lowerCamelCase_ , )
super().__init__(*lowerCamelCase_ , **lowerCamelCase_ )
| 702 |
'''simple docstring'''
from __future__ import annotations
import unittest
from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
if is_tf_available():
import numpy as np
import tensorflow as tf
from transformers import TFXLMRobertaModel
@require_tf
@require_sentencepiece
@require_tokenizers
class UpperCamelCase__ ( unittest.TestCase ):
"""simple docstring"""
@slow
def lowerCamelCase_ ( self : Tuple ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Union[str, Any] = TFXLMRobertaModel.from_pretrained("""jplu/tf-xlm-roberta-base""" )
SCREAMING_SNAKE_CASE : Dict = {
"""input_ids""": tf.convert_to_tensor([[0, 26_46, 1_02_69, 83, 9_99_42, 2]] , dtype=tf.intaa ), # "My dog is cute"
"""attention_mask""": tf.convert_to_tensor([[1, 1, 1, 1, 1, 1]] , dtype=tf.intaa ),
}
SCREAMING_SNAKE_CASE : List[str] = model(lowerCamelCase_ )["""last_hidden_state"""]
SCREAMING_SNAKE_CASE : Union[str, Any] = tf.TensorShape((1, 6, 7_68) )
self.assertEqual(output.shape , lowerCamelCase_ )
# compare the actual values for a slice.
SCREAMING_SNAKE_CASE : Optional[int] = tf.convert_to_tensor(
[
[
[0.0_681_762, 0.10_894_451, 0.06_772_504],
[-0.06_423_668, 0.02_366_615, 0.04_329_344],
[-0.06_057_295, 0.09_974_135, -0.00_070_584],
]
] , dtype=tf.floataa , )
self.assertTrue(np.allclose(output[:, :3, :3].numpy() , expected_slice.numpy() , atol=1e-4 ) )
| 79 | 0 |
'''simple docstring'''
import shutil
import tempfile
import unittest
from transformers import (
SPIECE_UNDERLINE,
AddedToken,
BatchEncoding,
NllbTokenizer,
NllbTokenizerFast,
is_torch_available,
)
from transformers.testing_utils import (
get_tests_dir,
nested_simplify,
require_sentencepiece,
require_tokenizers,
require_torch,
)
from ...test_tokenization_common import TokenizerTesterMixin
__UpperCAmelCase = get_tests_dir("""fixtures/test_sentencepiece.model""")
if is_torch_available():
from transformers.models.mam_aaa.modeling_mam_aaa import shift_tokens_right
__UpperCAmelCase = 256047
__UpperCAmelCase = 256145
@require_sentencepiece
@require_tokenizers
class UpperCamelCase__ ( lowercase_ , unittest.TestCase ):
"""simple docstring"""
SCREAMING_SNAKE_CASE__ = NllbTokenizer
SCREAMING_SNAKE_CASE__ = NllbTokenizerFast
SCREAMING_SNAKE_CASE__ = True
SCREAMING_SNAKE_CASE__ = True
SCREAMING_SNAKE_CASE__ = {}
def lowerCamelCase_ ( self : int ):
'''simple docstring'''
super().setUp()
# We have a SentencePiece fixture for testing
SCREAMING_SNAKE_CASE : Union[str, Any] = NllbTokenizer(lowerCamelCase_ , keep_accents=lowerCamelCase_ )
tokenizer.save_pretrained(self.tmpdirname )
def lowerCamelCase_ ( self : List[str] ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : List[str] = NllbTokenizer(lowerCamelCase_ , keep_accents=lowerCamelCase_ )
SCREAMING_SNAKE_CASE : str = tokenizer.tokenize("""This is a test""" )
self.assertListEqual(lowerCamelCase_ , ["""▁This""", """▁is""", """▁a""", """▁t""", """est"""] )
self.assertListEqual(
tokenizer.convert_tokens_to_ids(lowerCamelCase_ ) , [value + tokenizer.fairseq_offset for value in [2_85, 46, 10, 1_70, 3_82]] , )
SCREAMING_SNAKE_CASE : Optional[int] = tokenizer.tokenize("""I was born in 92000, and this is falsé.""" )
self.assertListEqual(
lowerCamelCase_ , [
SPIECE_UNDERLINE + """I""",
SPIECE_UNDERLINE + """was""",
SPIECE_UNDERLINE + """b""",
"""or""",
"""n""",
SPIECE_UNDERLINE + """in""",
SPIECE_UNDERLINE + """""",
"""9""",
"""2""",
"""0""",
"""0""",
"""0""",
""",""",
SPIECE_UNDERLINE + """and""",
SPIECE_UNDERLINE + """this""",
SPIECE_UNDERLINE + """is""",
SPIECE_UNDERLINE + """f""",
"""al""",
"""s""",
"""é""",
""".""",
] , )
SCREAMING_SNAKE_CASE : Union[str, Any] = tokenizer.convert_tokens_to_ids(lowerCamelCase_ )
self.assertListEqual(
lowerCamelCase_ , [
value + tokenizer.fairseq_offset
for value in [8, 21, 84, 55, 24, 19, 7, 2, 6_02, 3_47, 3_47, 3_47, 3, 12, 66, 46, 72, 80, 6, 2, 4]
] , )
SCREAMING_SNAKE_CASE : Optional[Any] = tokenizer.convert_ids_to_tokens(lowerCamelCase_ )
self.assertListEqual(
lowerCamelCase_ , [
SPIECE_UNDERLINE + """I""",
SPIECE_UNDERLINE + """was""",
SPIECE_UNDERLINE + """b""",
"""or""",
"""n""",
SPIECE_UNDERLINE + """in""",
SPIECE_UNDERLINE + """""",
"""<unk>""",
"""2""",
"""0""",
"""0""",
"""0""",
""",""",
SPIECE_UNDERLINE + """and""",
SPIECE_UNDERLINE + """this""",
SPIECE_UNDERLINE + """is""",
SPIECE_UNDERLINE + """f""",
"""al""",
"""s""",
"""<unk>""",
""".""",
] , )
def lowerCamelCase_ ( self : List[str] ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Any = (self.rust_tokenizer_class, """hf-internal-testing/tiny-random-nllb""", {})
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(f'''{tokenizer.__class__.__name__} ({pretrained_name})''' ):
SCREAMING_SNAKE_CASE : int = self.rust_tokenizer_class.from_pretrained(lowerCamelCase_ , **lowerCamelCase_ )
SCREAMING_SNAKE_CASE : Optional[Any] = self.tokenizer_class.from_pretrained(lowerCamelCase_ , **lowerCamelCase_ )
SCREAMING_SNAKE_CASE : Tuple = tempfile.mkdtemp()
SCREAMING_SNAKE_CASE : List[str] = tokenizer_r.save_pretrained(lowerCamelCase_ )
SCREAMING_SNAKE_CASE : Union[str, Any] = tokenizer_p.save_pretrained(lowerCamelCase_ )
# Checks it save with the same files + the tokenizer.json file for the fast one
self.assertTrue(any("""tokenizer.json""" in f for f in tokenizer_r_files ) )
SCREAMING_SNAKE_CASE : Any = tuple(f for f in tokenizer_r_files if """tokenizer.json""" not in f )
self.assertSequenceEqual(lowerCamelCase_ , lowerCamelCase_ )
# Checks everything loads correctly in the same way
SCREAMING_SNAKE_CASE : Dict = tokenizer_r.from_pretrained(lowerCamelCase_ )
SCREAMING_SNAKE_CASE : Dict = tokenizer_p.from_pretrained(lowerCamelCase_ )
# Check special tokens are set accordingly on Rust and Python
for key in tokenizer_pp.special_tokens_map:
self.assertTrue(hasattr(lowerCamelCase_ , lowerCamelCase_ ) )
shutil.rmtree(lowerCamelCase_ )
# Save tokenizer rust, legacy_format=True
SCREAMING_SNAKE_CASE : Tuple = tempfile.mkdtemp()
SCREAMING_SNAKE_CASE : str = tokenizer_r.save_pretrained(lowerCamelCase_ , legacy_format=lowerCamelCase_ )
SCREAMING_SNAKE_CASE : int = tokenizer_p.save_pretrained(lowerCamelCase_ )
# Checks it save with the same files
self.assertSequenceEqual(lowerCamelCase_ , lowerCamelCase_ )
# Checks everything loads correctly in the same way
SCREAMING_SNAKE_CASE : str = tokenizer_r.from_pretrained(lowerCamelCase_ )
SCREAMING_SNAKE_CASE : Dict = tokenizer_p.from_pretrained(lowerCamelCase_ )
# Check special tokens are set accordingly on Rust and Python
for key in tokenizer_pp.special_tokens_map:
self.assertTrue(hasattr(lowerCamelCase_ , lowerCamelCase_ ) )
shutil.rmtree(lowerCamelCase_ )
# Save tokenizer rust, legacy_format=False
SCREAMING_SNAKE_CASE : Tuple = tempfile.mkdtemp()
SCREAMING_SNAKE_CASE : Dict = tokenizer_r.save_pretrained(lowerCamelCase_ , legacy_format=lowerCamelCase_ )
SCREAMING_SNAKE_CASE : Any = tokenizer_p.save_pretrained(lowerCamelCase_ )
# Checks it saved the tokenizer.json file
self.assertTrue(any("""tokenizer.json""" in f for f in tokenizer_r_files ) )
# Checks everything loads correctly in the same way
SCREAMING_SNAKE_CASE : int = tokenizer_r.from_pretrained(lowerCamelCase_ )
SCREAMING_SNAKE_CASE : Optional[Any] = tokenizer_p.from_pretrained(lowerCamelCase_ )
# Check special tokens are set accordingly on Rust and Python
for key in tokenizer_pp.special_tokens_map:
self.assertTrue(hasattr(lowerCamelCase_ , lowerCamelCase_ ) )
shutil.rmtree(lowerCamelCase_ )
@require_torch
def lowerCamelCase_ ( self : Dict ):
'''simple docstring'''
if not self.test_seqaseq:
return
SCREAMING_SNAKE_CASE : int = self.get_tokenizers()
for tokenizer in tokenizers:
with self.subTest(f'''{tokenizer.__class__.__name__}''' ):
# Longer text that will definitely require truncation.
SCREAMING_SNAKE_CASE : Optional[int] = [
""" UN Chief Says There Is No Military Solution in Syria""",
""" Secretary-General Ban Ki-moon says his response to Russia's stepped up military support for"""
""" Syria is that 'there is no military solution' to the nearly five-year conflict and more weapons"""
""" will only worsen the violence and misery for millions of people.""",
]
SCREAMING_SNAKE_CASE : Tuple = [
"""Şeful ONU declară că nu există o soluţie militară în Siria""",
"""Secretarul General Ban Ki-moon declară că răspunsul său la intensificarea sprijinului militar al"""
""" Rusiei pentru Siria este că \"nu există o soluţie militară\" la conflictul de aproape cinci ani şi"""
""" că noi arme nu vor face decât să înrăutăţească violenţele şi mizeria pentru milioane de oameni.""",
]
try:
SCREAMING_SNAKE_CASE : Optional[Any] = tokenizer.prepare_seqaseq_batch(
src_texts=lowerCamelCase_ , tgt_texts=lowerCamelCase_ , max_length=3 , max_target_length=10 , return_tensors="""pt""" , src_lang="""eng_Latn""" , tgt_lang="""ron_Latn""" , )
except NotImplementedError:
return
self.assertEqual(batch.input_ids.shape[1] , 3 )
self.assertEqual(batch.labels.shape[1] , 10 )
# max_target_length will default to max_length if not specified
SCREAMING_SNAKE_CASE : List[str] = tokenizer.prepare_seqaseq_batch(
lowerCamelCase_ , tgt_texts=lowerCamelCase_ , max_length=3 , return_tensors="""pt""" )
self.assertEqual(batch.input_ids.shape[1] , 3 )
self.assertEqual(batch.labels.shape[1] , 3 )
SCREAMING_SNAKE_CASE : Optional[Any] = tokenizer.prepare_seqaseq_batch(
src_texts=lowerCamelCase_ , max_length=3 , max_target_length=10 , return_tensors="""pt""" )
self.assertEqual(batch_encoder_only.input_ids.shape[1] , 3 )
self.assertEqual(batch_encoder_only.attention_mask.shape[1] , 3 )
self.assertNotIn("""decoder_input_ids""" , lowerCamelCase_ )
@unittest.skip("""Unfortunately way too slow to build a BPE with SentencePiece.""" )
def lowerCamelCase_ ( self : str ):
'''simple docstring'''
pass
def lowerCamelCase_ ( self : Optional[int] ):
'''simple docstring'''
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(f'''{tokenizer.__class__.__name__} ({pretrained_name})''' ):
SCREAMING_SNAKE_CASE : int = [AddedToken("""<special>""" , lstrip=lowerCamelCase_ )]
SCREAMING_SNAKE_CASE : int = self.rust_tokenizer_class.from_pretrained(
lowerCamelCase_ , additional_special_tokens=lowerCamelCase_ , **lowerCamelCase_ )
SCREAMING_SNAKE_CASE : List[Any] = tokenizer_r.encode("""Hey this is a <special> token""" )
SCREAMING_SNAKE_CASE : Tuple = tokenizer_r.encode("""<special>""" , add_special_tokens=lowerCamelCase_ )[0]
self.assertTrue(special_token_id in r_output )
if self.test_slow_tokenizer:
SCREAMING_SNAKE_CASE : Dict = self.rust_tokenizer_class.from_pretrained(
lowerCamelCase_ , additional_special_tokens=lowerCamelCase_ , **lowerCamelCase_ , )
SCREAMING_SNAKE_CASE : List[Any] = self.tokenizer_class.from_pretrained(
lowerCamelCase_ , additional_special_tokens=lowerCamelCase_ , **lowerCamelCase_ )
SCREAMING_SNAKE_CASE : Union[str, Any] = tokenizer_p.encode("""Hey this is a <special> token""" )
SCREAMING_SNAKE_CASE : Any = tokenizer_cr.encode("""Hey this is a <special> token""" )
self.assertEqual(lowerCamelCase_ , lowerCamelCase_ )
self.assertEqual(lowerCamelCase_ , lowerCamelCase_ )
self.assertTrue(special_token_id in p_output )
self.assertTrue(special_token_id in cr_output )
@require_torch
@require_sentencepiece
@require_tokenizers
class UpperCamelCase__ ( unittest.TestCase ):
"""simple docstring"""
SCREAMING_SNAKE_CASE__ = '''facebook/nllb-200-distilled-600M'''
SCREAMING_SNAKE_CASE__ = [
''' UN Chief Says There Is No Military Solution in Syria''',
''' Secretary-General Ban Ki-moon says his response to Russia\'s stepped up military support for Syria is that "there is no military solution" to the nearly five-year conflict and more weapons will only worsen the violence and misery for millions of people.''',
]
SCREAMING_SNAKE_CASE__ = [
'''Şeful ONU declară că nu există o soluţie militară în Siria''',
'''Secretarul General Ban Ki-moon declară că răspunsul său la intensificarea sprijinului militar al Rusiei'''
''' pentru Siria este că "nu există o soluţie militară" la conflictul de aproape cinci ani şi că noi arme nu vor'''
''' face decât să înrăutăţească violenţele şi mizeria pentru milioane de oameni.''',
]
SCREAMING_SNAKE_CASE__ = [
25_6047,
1_6297,
13_4408,
8165,
24_8066,
1_4734,
950,
1135,
10_5721,
3573,
83,
2_7352,
108,
4_9486,
2,
]
@classmethod
def lowerCamelCase_ ( cls : Tuple ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : NllbTokenizer = NllbTokenizer.from_pretrained(
cls.checkpoint_name , src_lang="""eng_Latn""" , tgt_lang="""ron_Latn""" )
SCREAMING_SNAKE_CASE : List[Any] = 1
return cls
def lowerCamelCase_ ( self : Optional[Any] ):
'''simple docstring'''
self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["""ace_Arab"""] , 25_60_01 )
self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["""ace_Latn"""] , 25_60_02 )
self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["""fra_Latn"""] , 25_60_57 )
def lowerCamelCase_ ( self : int ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : List[Any] = self.tokenizer.batch_encode_plus(self.src_text ).input_ids[0]
self.assertListEqual(self.expected_src_tokens , lowerCamelCase_ )
def lowerCamelCase_ ( self : Tuple ):
'''simple docstring'''
self.assertIn(lowerCamelCase_ , self.tokenizer.all_special_ids )
# fmt: off
SCREAMING_SNAKE_CASE : Tuple = [RO_CODE, 42_54, 9_80_68, 11_29_23, 3_90_72, 39_09, 7_13, 10_27_67, 26, 1_73_14, 3_56_42, 1_46_83, 3_31_18, 20_22, 6_69_87, 2, 25_60_47]
# fmt: on
SCREAMING_SNAKE_CASE : Dict = self.tokenizer.decode(lowerCamelCase_ , skip_special_tokens=lowerCamelCase_ )
SCREAMING_SNAKE_CASE : List[Any] = self.tokenizer.decode(generated_ids[1:] , skip_special_tokens=lowerCamelCase_ )
self.assertEqual(lowerCamelCase_ , lowerCamelCase_ )
self.assertNotIn(self.tokenizer.eos_token , lowerCamelCase_ )
def lowerCamelCase_ ( self : Any ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : int = ["""this is gunna be a long sentence """ * 20]
assert isinstance(src_text[0] , lowerCamelCase_ )
SCREAMING_SNAKE_CASE : Dict = 10
SCREAMING_SNAKE_CASE : int = self.tokenizer(lowerCamelCase_ , max_length=lowerCamelCase_ , truncation=lowerCamelCase_ ).input_ids[0]
self.assertEqual(ids[-1] , 2 )
self.assertEqual(ids[0] , lowerCamelCase_ )
self.assertEqual(len(lowerCamelCase_ ) , lowerCamelCase_ )
def lowerCamelCase_ ( self : List[Any] ):
'''simple docstring'''
self.assertListEqual(self.tokenizer.convert_tokens_to_ids(["""<mask>""", """ar_AR"""] ) , [25_62_03, 3] )
def lowerCamelCase_ ( self : List[Any] ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Tuple = tempfile.mkdtemp()
SCREAMING_SNAKE_CASE : str = self.tokenizer.fairseq_tokens_to_ids
self.tokenizer.save_pretrained(lowerCamelCase_ )
SCREAMING_SNAKE_CASE : List[str] = NllbTokenizer.from_pretrained(lowerCamelCase_ )
self.assertDictEqual(new_tok.fairseq_tokens_to_ids , lowerCamelCase_ )
@require_torch
def lowerCamelCase_ ( self : Dict ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Dict = self.tokenizer(
self.src_text , text_target=self.tgt_text , padding=lowerCamelCase_ , truncation=lowerCamelCase_ , max_length=len(self.expected_src_tokens ) , return_tensors="""pt""" , )
SCREAMING_SNAKE_CASE : List[str] = shift_tokens_right(
batch["""labels"""] , self.tokenizer.pad_token_id , self.tokenizer.lang_code_to_id["""ron_Latn"""] )
self.assertIsInstance(lowerCamelCase_ , lowerCamelCase_ )
self.assertEqual((2, 15) , batch.input_ids.shape )
self.assertEqual((2, 15) , batch.attention_mask.shape )
SCREAMING_SNAKE_CASE : Union[str, Any] = batch.input_ids.tolist()[0]
self.assertListEqual(self.expected_src_tokens , lowerCamelCase_ )
self.assertEqual(lowerCamelCase_ , batch.decoder_input_ids[0, 0] ) # EOS
# Test that special tokens are reset
self.assertEqual(self.tokenizer.prefix_tokens , [EN_CODE] )
self.assertEqual(self.tokenizer.suffix_tokens , [self.tokenizer.eos_token_id] )
def lowerCamelCase_ ( self : int ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : int = self.tokenizer(self.src_text , padding=lowerCamelCase_ , truncation=lowerCamelCase_ , max_length=3 , return_tensors="""pt""" )
SCREAMING_SNAKE_CASE : Union[str, Any] = self.tokenizer(
text_target=self.tgt_text , padding=lowerCamelCase_ , truncation=lowerCamelCase_ , max_length=10 , return_tensors="""pt""" )
SCREAMING_SNAKE_CASE : str = targets["""input_ids"""]
SCREAMING_SNAKE_CASE : Optional[int] = shift_tokens_right(
lowerCamelCase_ , self.tokenizer.pad_token_id , decoder_start_token_id=self.tokenizer.lang_code_to_id[self.tokenizer.tgt_lang] , )
self.assertEqual(batch.input_ids.shape[1] , 3 )
self.assertEqual(batch.decoder_input_ids.shape[1] , 10 )
@require_torch
def lowerCamelCase_ ( self : Union[str, Any] ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : int = self.tokenizer._build_translation_inputs(
"""A test""" , return_tensors="""pt""" , src_lang="""eng_Latn""" , tgt_lang="""fra_Latn""" )
self.assertEqual(
nested_simplify(lowerCamelCase_ ) , {
# A, test, EOS, en_XX
"""input_ids""": [[25_60_47, 70, 73_56, 2]],
"""attention_mask""": [[1, 1, 1, 1]],
# ar_AR
"""forced_bos_token_id""": 25_60_57,
} , )
@require_torch
def lowerCamelCase_ ( self : Tuple ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : List[Any] = True
SCREAMING_SNAKE_CASE : Dict = self.tokenizer(
"""UN Chief says there is no military solution in Syria""" , src_lang="""eng_Latn""" , tgt_lang="""fra_Latn""" )
self.assertEqual(
inputs.input_ids , [1_62_97, 13_44_08, 2_56_53, 63_70, 2_48, 2_54, 10_39_29, 9_49_95, 1_08, 4_94_86, 2, 25_60_47] )
SCREAMING_SNAKE_CASE : List[str] = False
SCREAMING_SNAKE_CASE : int = self.tokenizer(
"""UN Chief says there is no military solution in Syria""" , src_lang="""eng_Latn""" , tgt_lang="""fra_Latn""" )
self.assertEqual(
inputs.input_ids , [25_60_47, 1_62_97, 13_44_08, 2_56_53, 63_70, 2_48, 2_54, 10_39_29, 9_49_95, 1_08, 4_94_86, 2] )
| 703 |
'''simple docstring'''
from dataclasses import dataclass, field
from typing import TYPE_CHECKING, Any, ClassVar, Dict, List, Optional, Union
import pyarrow as pa
if TYPE_CHECKING:
from .features import FeatureType
@dataclass
class UpperCamelCase__ :
"""simple docstring"""
SCREAMING_SNAKE_CASE__ = 42
SCREAMING_SNAKE_CASE__ = None
# Automatically constructed
SCREAMING_SNAKE_CASE__ = "dict"
SCREAMING_SNAKE_CASE__ = None
SCREAMING_SNAKE_CASE__ = field(default='''Translation''' , init=lowercase_ , repr=lowercase_ )
def __call__( self : int ):
'''simple docstring'''
return pa.struct({lang: pa.string() for lang in sorted(self.languages )} )
def lowerCamelCase_ ( self : Tuple ):
'''simple docstring'''
from .features import Value
return {k: Value("""string""" ) for k in sorted(self.languages )}
@dataclass
class UpperCamelCase__ :
"""simple docstring"""
SCREAMING_SNAKE_CASE__ = None
SCREAMING_SNAKE_CASE__ = None
SCREAMING_SNAKE_CASE__ = None
# Automatically constructed
SCREAMING_SNAKE_CASE__ = "dict"
SCREAMING_SNAKE_CASE__ = None
SCREAMING_SNAKE_CASE__ = field(default='''TranslationVariableLanguages''' , init=lowercase_ , repr=lowercase_ )
def lowerCamelCase_ ( self : Optional[int] ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Optional[Any] = sorted(set(self.languages ) ) if self.languages else None
SCREAMING_SNAKE_CASE : str = len(self.languages ) if self.languages else None
def __call__( self : Tuple ):
'''simple docstring'''
return pa.struct({"""language""": pa.list_(pa.string() ), """translation""": pa.list_(pa.string() )} )
def lowerCamelCase_ ( self : Optional[int] , lowerCamelCase_ : Optional[Any] ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Optional[Any] = set(self.languages )
if self.languages and set(lowerCamelCase_ ) - lang_set:
raise ValueError(
f'''Some languages in example ({", ".join(sorted(set(lowerCamelCase_ ) - lang_set ) )}) are not in valid set ({", ".join(lowerCamelCase_ )}).''' )
# Convert dictionary into tuples, splitting out cases where there are
# multiple translations for a single language.
SCREAMING_SNAKE_CASE : List[Any] = []
for lang, text in translation_dict.items():
if isinstance(lowerCamelCase_ , lowerCamelCase_ ):
translation_tuples.append((lang, text) )
else:
translation_tuples.extend([(lang, el) for el in text] )
# Ensure translations are in ascending order by language code.
SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE : Optional[Any] = zip(*sorted(lowerCamelCase_ ) )
return {"language": languages, "translation": translations}
def lowerCamelCase_ ( self : List[str] ):
'''simple docstring'''
from .features import Sequence, Value
return {
"language": Sequence(Value("""string""" ) ),
"translation": Sequence(Value("""string""" ) ),
}
| 79 | 0 |
'''simple docstring'''
import os
from typing import Dict, List, Tuple, TypeVar, Union
__UpperCAmelCase = TypeVar("""T""")
__UpperCAmelCase = Union[List[T], Tuple[T, ...]]
__UpperCAmelCase = Union[T, List[T], Dict[str, T]]
__UpperCAmelCase = Union[str, bytes, os.PathLike]
| 704 |
'''simple docstring'''
import warnings
from ...utils import logging
from .image_processing_flava import FlavaImageProcessor
__UpperCAmelCase = logging.get_logger(__name__)
class UpperCamelCase__ ( lowercase_ ):
"""simple docstring"""
def __init__( self : Dict , *lowerCamelCase_ : List[str] , **lowerCamelCase_ : Dict ):
'''simple docstring'''
warnings.warn(
"""The class FlavaFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please"""
""" use FlavaImageProcessor instead.""" , lowerCamelCase_ , )
super().__init__(*lowerCamelCase_ , **lowerCamelCase_ )
| 79 | 0 |
'''simple docstring'''
from typing import Any, Dict, List, Union
from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, ChunkPipeline
if is_vision_available():
from PIL import Image
from ..image_utils import load_image
if is_torch_available():
import torch
from transformers.modeling_outputs import BaseModelOutput
from ..models.auto.modeling_auto import MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING
__UpperCAmelCase = logging.get_logger(__name__)
@add_end_docstrings(lowercase_ )
class UpperCamelCase__ ( lowercase_ ):
"""simple docstring"""
def __init__( self : Tuple , **lowerCamelCase_ : Union[str, Any] ):
'''simple docstring'''
super().__init__(**lowerCamelCase_ )
if self.framework == "tf":
raise ValueError(f'''The {self.__class__} is only available in PyTorch.''' )
requires_backends(self , """vision""" )
self.check_model_type(lowerCamelCase_ )
def __call__( self : int , lowerCamelCase_ : Union[str, "Image.Image", List[Dict[str, Any]]] , lowerCamelCase_ : Union[str, List[str]] = None , **lowerCamelCase_ : Tuple , ):
'''simple docstring'''
if "text_queries" in kwargs:
SCREAMING_SNAKE_CASE : List[str] = kwargs.pop("""text_queries""" )
if isinstance(lowerCamelCase_ , (str, Image.Image) ):
SCREAMING_SNAKE_CASE : int = {"""image""": image, """candidate_labels""": candidate_labels}
else:
SCREAMING_SNAKE_CASE : Optional[Any] = image
SCREAMING_SNAKE_CASE : List[str] = super().__call__(lowerCamelCase_ , **lowerCamelCase_ )
return results
def lowerCamelCase_ ( self : str , **lowerCamelCase_ : Optional[Any] ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : List[str] = {}
if "threshold" in kwargs:
SCREAMING_SNAKE_CASE : Any = kwargs["""threshold"""]
if "top_k" in kwargs:
SCREAMING_SNAKE_CASE : Tuple = kwargs["""top_k"""]
return {}, {}, postprocess_params
def lowerCamelCase_ ( self : int , lowerCamelCase_ : Any ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : int = load_image(inputs["""image"""] )
SCREAMING_SNAKE_CASE : Optional[Any] = inputs["""candidate_labels"""]
if isinstance(lowerCamelCase_ , lowerCamelCase_ ):
SCREAMING_SNAKE_CASE : Dict = candidate_labels.split(""",""" )
SCREAMING_SNAKE_CASE : Union[str, Any] = torch.tensor([[image.height, image.width]] , dtype=torch.intaa )
for i, candidate_label in enumerate(lowerCamelCase_ ):
SCREAMING_SNAKE_CASE : Any = self.tokenizer(lowerCamelCase_ , return_tensors=self.framework )
SCREAMING_SNAKE_CASE : List[str] = self.image_processor(lowerCamelCase_ , return_tensors=self.framework )
yield {
"is_last": i == len(lowerCamelCase_ ) - 1,
"target_size": target_size,
"candidate_label": candidate_label,
**text_inputs,
**image_features,
}
def lowerCamelCase_ ( self : Any , lowerCamelCase_ : int ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Union[str, Any] = model_inputs.pop("""target_size""" )
SCREAMING_SNAKE_CASE : Any = model_inputs.pop("""candidate_label""" )
SCREAMING_SNAKE_CASE : str = model_inputs.pop("""is_last""" )
SCREAMING_SNAKE_CASE : List[Any] = self.model(**lowerCamelCase_ )
SCREAMING_SNAKE_CASE : List[str] = {"""target_size""": target_size, """candidate_label""": candidate_label, """is_last""": is_last, **outputs}
return model_outputs
def lowerCamelCase_ ( self : int , lowerCamelCase_ : str , lowerCamelCase_ : List[str]=0.1 , lowerCamelCase_ : Union[str, Any]=None ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Any = []
for model_output in model_outputs:
SCREAMING_SNAKE_CASE : List[str] = model_output["""candidate_label"""]
SCREAMING_SNAKE_CASE : Union[str, Any] = BaseModelOutput(lowerCamelCase_ )
SCREAMING_SNAKE_CASE : Any = self.image_processor.post_process_object_detection(
outputs=lowerCamelCase_ , threshold=lowerCamelCase_ , target_sizes=model_output["""target_size"""] )[0]
for index in outputs["scores"].nonzero():
SCREAMING_SNAKE_CASE : str = outputs["""scores"""][index].item()
SCREAMING_SNAKE_CASE : List[Any] = self._get_bounding_box(outputs["""boxes"""][index][0] )
SCREAMING_SNAKE_CASE : Any = {"""score""": score, """label""": label, """box""": box}
results.append(lowerCamelCase_ )
SCREAMING_SNAKE_CASE : List[str] = sorted(lowerCamelCase_ , key=lambda lowerCamelCase_ : x["score"] , reverse=lowerCamelCase_ )
if top_k:
SCREAMING_SNAKE_CASE : str = results[:top_k]
return results
def lowerCamelCase_ ( self : Optional[int] , lowerCamelCase_ : "torch.Tensor" ):
'''simple docstring'''
if self.framework != "pt":
raise ValueError("""The ZeroShotObjectDetectionPipeline is only available in PyTorch.""" )
SCREAMING_SNAKE_CASE : Union[str, Any] = box.int().tolist()
SCREAMING_SNAKE_CASE : Dict = {
"""xmin""": xmin,
"""ymin""": ymin,
"""xmax""": xmax,
"""ymax""": ymax,
}
return bbox
| 705 |
'''simple docstring'''
from .integrations import (
is_optuna_available,
is_ray_available,
is_sigopt_available,
is_wandb_available,
run_hp_search_optuna,
run_hp_search_ray,
run_hp_search_sigopt,
run_hp_search_wandb,
)
from .trainer_utils import (
HPSearchBackend,
default_hp_space_optuna,
default_hp_space_ray,
default_hp_space_sigopt,
default_hp_space_wandb,
)
from .utils import logging
__UpperCAmelCase = logging.get_logger(__name__)
class UpperCamelCase__ :
"""simple docstring"""
SCREAMING_SNAKE_CASE__ = 42
SCREAMING_SNAKE_CASE__ = None
@staticmethod
def lowerCamelCase_ ( ):
'''simple docstring'''
raise NotImplementedError
def lowerCamelCase_ ( self : Tuple , lowerCamelCase_ : Optional[int] , lowerCamelCase_ : int , lowerCamelCase_ : str , **lowerCamelCase_ : str ):
'''simple docstring'''
raise NotImplementedError
def lowerCamelCase_ ( self : int , lowerCamelCase_ : Dict ):
'''simple docstring'''
raise NotImplementedError
def lowerCamelCase_ ( self : Optional[int] ):
'''simple docstring'''
if not self.is_available():
raise RuntimeError(
f'''You picked the {self.name} backend, but it is not installed. Run {self.pip_install()}.''' )
@classmethod
def lowerCamelCase_ ( cls : Any ):
'''simple docstring'''
return f'''`pip install {cls.pip_package or cls.name}`'''
class UpperCamelCase__ ( lowercase_ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE__ = '''optuna'''
@staticmethod
def lowerCamelCase_ ( ):
'''simple docstring'''
return is_optuna_available()
def lowerCamelCase_ ( self : Any , lowerCamelCase_ : Union[str, Any] , lowerCamelCase_ : int , lowerCamelCase_ : str , **lowerCamelCase_ : Dict ):
'''simple docstring'''
return run_hp_search_optuna(lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ , **lowerCamelCase_ )
def lowerCamelCase_ ( self : str , lowerCamelCase_ : Any ):
'''simple docstring'''
return default_hp_space_optuna(lowerCamelCase_ )
class UpperCamelCase__ ( lowercase_ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE__ = '''ray'''
SCREAMING_SNAKE_CASE__ = '''\'ray[tune]\''''
@staticmethod
def lowerCamelCase_ ( ):
'''simple docstring'''
return is_ray_available()
def lowerCamelCase_ ( self : Any , lowerCamelCase_ : Any , lowerCamelCase_ : int , lowerCamelCase_ : str , **lowerCamelCase_ : int ):
'''simple docstring'''
return run_hp_search_ray(lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ , **lowerCamelCase_ )
def lowerCamelCase_ ( self : List[str] , lowerCamelCase_ : Optional[int] ):
'''simple docstring'''
return default_hp_space_ray(lowerCamelCase_ )
class UpperCamelCase__ ( lowercase_ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE__ = '''sigopt'''
@staticmethod
def lowerCamelCase_ ( ):
'''simple docstring'''
return is_sigopt_available()
def lowerCamelCase_ ( self : Optional[Any] , lowerCamelCase_ : Any , lowerCamelCase_ : int , lowerCamelCase_ : str , **lowerCamelCase_ : int ):
'''simple docstring'''
return run_hp_search_sigopt(lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ , **lowerCamelCase_ )
def lowerCamelCase_ ( self : List[Any] , lowerCamelCase_ : Optional[Any] ):
'''simple docstring'''
return default_hp_space_sigopt(lowerCamelCase_ )
class UpperCamelCase__ ( lowercase_ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE__ = '''wandb'''
@staticmethod
def lowerCamelCase_ ( ):
'''simple docstring'''
return is_wandb_available()
def lowerCamelCase_ ( self : Union[str, Any] , lowerCamelCase_ : str , lowerCamelCase_ : int , lowerCamelCase_ : str , **lowerCamelCase_ : Optional[Any] ):
'''simple docstring'''
return run_hp_search_wandb(lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ , **lowerCamelCase_ )
def lowerCamelCase_ ( self : Union[str, Any] , lowerCamelCase_ : Tuple ):
'''simple docstring'''
return default_hp_space_wandb(lowerCamelCase_ )
__UpperCAmelCase = {
HPSearchBackend(backend.name): backend for backend in [OptunaBackend, RayTuneBackend, SigOptBackend, WandbBackend]
}
def __A ( ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : List[Any] = [backend for backend in ALL_HYPERPARAMETER_SEARCH_BACKENDS.values() if backend.is_available()]
if len(lowerCamelCase_ ) > 0:
SCREAMING_SNAKE_CASE : List[Any] = available_backends[0].name
if len(lowerCamelCase_ ) > 1:
logger.info(
f'''{len(lowerCamelCase_ )} hyperparameter search backends available. Using {name} as the default.''' )
return name
raise RuntimeError(
"""No hyperparameter search backend available.\n"""
+ """\n""".join(
f''' - To install {backend.name} run {backend.pip_install()}'''
for backend in ALL_HYPERPARAMETER_SEARCH_BACKENDS.values() ) )
| 79 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
__UpperCAmelCase = {
"""configuration_vivit""": ["""VIVIT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """VivitConfig"""],
}
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__UpperCAmelCase = ["""VivitImageProcessor"""]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__UpperCAmelCase = [
"""VIVIT_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""VivitModel""",
"""VivitPreTrainedModel""",
"""VivitForVideoClassification""",
]
if TYPE_CHECKING:
from .configuration_vivit import VIVIT_PRETRAINED_CONFIG_ARCHIVE_MAP, VivitConfig
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .image_processing_vivit import VivitImageProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_vivit import (
VIVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
VivitForVideoClassification,
VivitModel,
VivitPreTrainedModel,
)
else:
import sys
__UpperCAmelCase = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 706 |
'''simple docstring'''
import glob
import os
import random
from string import ascii_lowercase, digits
import cva
__UpperCAmelCase = """"""
__UpperCAmelCase = """"""
__UpperCAmelCase = """"""
__UpperCAmelCase = 1 # (0 is vertical, 1 is horizontal)
def __A ( ):
"""simple docstring"""
SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE : str = get_dataset(lowerCamelCase_ , lowerCamelCase_ )
print("""Processing...""" )
SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE : Any = update_image_and_anno(lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ )
for index, image in enumerate(lowerCamelCase_ ):
# Get random string code: '7b7ad245cdff75241935e4dd860f3bad'
SCREAMING_SNAKE_CASE : Optional[int] = random_chars(32 )
SCREAMING_SNAKE_CASE : Optional[Any] = paths[index].split(os.sep )[-1].rsplit(""".""" , 1 )[0]
SCREAMING_SNAKE_CASE : Dict = f'''{OUTPUT_DIR}/{file_name}_FLIP_{letter_code}'''
cva.imwrite(f'''/{file_root}.jpg''' , lowerCamelCase_ , [cva.IMWRITE_JPEG_QUALITY, 85] )
print(f'''Success {index+1}/{len(lowerCamelCase_ )} with {file_name}''' )
SCREAMING_SNAKE_CASE : Optional[Any] = []
for anno in new_annos[index]:
SCREAMING_SNAKE_CASE : Optional[Any] = f'''{anno[0]} {anno[1]} {anno[2]} {anno[3]} {anno[4]}'''
annos_list.append(lowerCamelCase_ )
with open(f'''/{file_root}.txt''' , """w""" ) as outfile:
outfile.write("""\n""".join(line for line in annos_list ) )
def __A ( lowerCamelCase_ , lowerCamelCase_ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : str = []
SCREAMING_SNAKE_CASE : Any = []
for label_file in glob.glob(os.path.join(lowerCamelCase_ , """*.txt""" ) ):
SCREAMING_SNAKE_CASE : str = label_file.split(os.sep )[-1].rsplit(""".""" , 1 )[0]
with open(lowerCamelCase_ ) as in_file:
SCREAMING_SNAKE_CASE : Any = in_file.readlines()
SCREAMING_SNAKE_CASE : List[Any] = os.path.join(lowerCamelCase_ , f'''{label_name}.jpg''' )
SCREAMING_SNAKE_CASE : Tuple = []
for obj_list in obj_lists:
SCREAMING_SNAKE_CASE : Union[str, Any] = obj_list.rstrip("""\n""" ).split(""" """ )
boxes.append(
[
int(obj[0] ),
float(obj[1] ),
float(obj[2] ),
float(obj[3] ),
float(obj[4] ),
] )
if not boxes:
continue
img_paths.append(lowerCamelCase_ )
labels.append(lowerCamelCase_ )
return img_paths, labels
def __A ( lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ = 1 ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : Tuple = []
SCREAMING_SNAKE_CASE : List[str] = []
SCREAMING_SNAKE_CASE : Optional[Any] = []
for idx in range(len(lowerCamelCase_ ) ):
SCREAMING_SNAKE_CASE : List[Any] = []
SCREAMING_SNAKE_CASE : Dict = img_list[idx]
path_list.append(lowerCamelCase_ )
SCREAMING_SNAKE_CASE : int = anno_list[idx]
SCREAMING_SNAKE_CASE : Optional[Any] = cva.imread(lowerCamelCase_ )
if flip_type == 1:
SCREAMING_SNAKE_CASE : List[str] = cva.flip(lowerCamelCase_ , lowerCamelCase_ )
for bbox in img_annos:
SCREAMING_SNAKE_CASE : List[Any] = 1 - bbox[1]
new_annos.append([bbox[0], x_center_new, bbox[2], bbox[3], bbox[4]] )
elif flip_type == 0:
SCREAMING_SNAKE_CASE : Any = cva.flip(lowerCamelCase_ , lowerCamelCase_ )
for bbox in img_annos:
SCREAMING_SNAKE_CASE : Optional[Any] = 1 - bbox[2]
new_annos.append([bbox[0], bbox[1], y_center_new, bbox[3], bbox[4]] )
new_annos_lists.append(lowerCamelCase_ )
new_imgs_list.append(lowerCamelCase_ )
return new_imgs_list, new_annos_lists, path_list
def __A ( lowerCamelCase_ = 32 ):
"""simple docstring"""
assert number_char > 1, "The number of character should greater than 1"
SCREAMING_SNAKE_CASE : Dict = ascii_lowercase + digits
return "".join(random.choice(lowerCamelCase_ ) for _ in range(lowerCamelCase_ ) )
if __name__ == "__main__":
main()
print("""DONE ✅""")
| 79 | 0 |
'''simple docstring'''
def __A ( lowerCamelCase_ ):
"""simple docstring"""
if len(lowerCamelCase_ ) <= 1:
return lst
SCREAMING_SNAKE_CASE : Tuple = 1
while i < len(lowerCamelCase_ ):
if lst[i - 1] <= lst[i]:
i += 1
else:
SCREAMING_SNAKE_CASE : str = lst[i], lst[i - 1]
i -= 1
if i == 0:
SCREAMING_SNAKE_CASE : Dict = 1
return lst
if __name__ == "__main__":
__UpperCAmelCase = input("""Enter numbers separated by a comma:\n""").strip()
__UpperCAmelCase = [int(item) for item in user_input.split(""",""")]
print(gnome_sort(unsorted))
| 707 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__UpperCAmelCase = logging.get_logger(__name__)
__UpperCAmelCase = {
"""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__ ( lowercase_ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE__ = '''vivit'''
def __init__( self : Tuple , lowerCamelCase_ : str=2_24 , lowerCamelCase_ : List[Any]=32 , lowerCamelCase_ : Tuple=[2, 16, 16] , lowerCamelCase_ : List[str]=3 , lowerCamelCase_ : Dict=7_68 , lowerCamelCase_ : Dict=12 , lowerCamelCase_ : Any=12 , lowerCamelCase_ : List[Any]=30_72 , lowerCamelCase_ : List[str]="gelu_fast" , lowerCamelCase_ : str=0.0 , lowerCamelCase_ : Any=0.0 , lowerCamelCase_ : Optional[int]=0.02 , lowerCamelCase_ : List[Any]=1e-06 , lowerCamelCase_ : Tuple=True , **lowerCamelCase_ : Tuple , ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : List[str] = hidden_size
SCREAMING_SNAKE_CASE : Union[str, Any] = num_hidden_layers
SCREAMING_SNAKE_CASE : List[str] = num_attention_heads
SCREAMING_SNAKE_CASE : str = intermediate_size
SCREAMING_SNAKE_CASE : List[Any] = hidden_act
SCREAMING_SNAKE_CASE : Optional[Any] = hidden_dropout_prob
SCREAMING_SNAKE_CASE : Tuple = attention_probs_dropout_prob
SCREAMING_SNAKE_CASE : Tuple = initializer_range
SCREAMING_SNAKE_CASE : str = layer_norm_eps
SCREAMING_SNAKE_CASE : str = image_size
SCREAMING_SNAKE_CASE : Dict = num_frames
SCREAMING_SNAKE_CASE : Optional[Any] = tubelet_size
SCREAMING_SNAKE_CASE : Dict = num_channels
SCREAMING_SNAKE_CASE : int = qkv_bias
super().__init__(**lowerCamelCase_ )
| 79 | 0 |
'''simple docstring'''
import json
import unittest
import numpy as np
from huggingface_hub import hf_hub_download
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_available():
import torch
if is_vision_available():
from transformers import OneFormerImageProcessor
from transformers.models.oneformer.image_processing_oneformer import binary_mask_to_rle
from transformers.models.oneformer.modeling_oneformer import OneFormerForUniversalSegmentationOutput
if is_vision_available():
from PIL import Image
def __A ( lowerCamelCase_ , lowerCamelCase_="shi-labs/oneformer_demo" ):
"""simple docstring"""
with open(hf_hub_download(lowerCamelCase_ , lowerCamelCase_ , repo_type="""dataset""" ) , """r""" ) as f:
SCREAMING_SNAKE_CASE : Any = json.load(lowerCamelCase_ )
SCREAMING_SNAKE_CASE : str = {}
SCREAMING_SNAKE_CASE : Tuple = []
SCREAMING_SNAKE_CASE : Optional[int] = []
for key, info in class_info.items():
SCREAMING_SNAKE_CASE : str = info["""name"""]
class_names.append(info["""name"""] )
if info["isthing"]:
thing_ids.append(int(lowerCamelCase_ ) )
SCREAMING_SNAKE_CASE : List[str] = thing_ids
SCREAMING_SNAKE_CASE : List[str] = class_names
return metadata
class UpperCamelCase__ ( unittest.TestCase ):
"""simple docstring"""
def __init__( self : Dict , lowerCamelCase_ : Union[str, Any] , lowerCamelCase_ : List[Any]=7 , lowerCamelCase_ : Dict=3 , lowerCamelCase_ : str=30 , lowerCamelCase_ : List[str]=4_00 , lowerCamelCase_ : int=None , lowerCamelCase_ : int=True , lowerCamelCase_ : Dict=True , lowerCamelCase_ : Dict=[0.5, 0.5, 0.5] , lowerCamelCase_ : Any=[0.5, 0.5, 0.5] , lowerCamelCase_ : str=10 , lowerCamelCase_ : Any=False , lowerCamelCase_ : Union[str, Any]=2_55 , lowerCamelCase_ : Optional[Any]="shi-labs/oneformer_demo" , lowerCamelCase_ : Tuple="ade20k_panoptic.json" , lowerCamelCase_ : int=10 , ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Optional[int] = parent
SCREAMING_SNAKE_CASE : Any = batch_size
SCREAMING_SNAKE_CASE : Union[str, Any] = num_channels
SCREAMING_SNAKE_CASE : List[str] = min_resolution
SCREAMING_SNAKE_CASE : Union[str, Any] = max_resolution
SCREAMING_SNAKE_CASE : Dict = do_resize
SCREAMING_SNAKE_CASE : Tuple = {"""shortest_edge""": 32, """longest_edge""": 13_33} if size is None else size
SCREAMING_SNAKE_CASE : Any = do_normalize
SCREAMING_SNAKE_CASE : Optional[Any] = image_mean
SCREAMING_SNAKE_CASE : List[Any] = image_std
SCREAMING_SNAKE_CASE : List[Any] = class_info_file
SCREAMING_SNAKE_CASE : Tuple = prepare_metadata(lowerCamelCase_ , lowerCamelCase_ )
SCREAMING_SNAKE_CASE : Any = num_text
SCREAMING_SNAKE_CASE : int = repo_path
# for the post_process_functions
SCREAMING_SNAKE_CASE : List[Any] = 2
SCREAMING_SNAKE_CASE : Union[str, Any] = 10
SCREAMING_SNAKE_CASE : Tuple = 10
SCREAMING_SNAKE_CASE : List[str] = 3
SCREAMING_SNAKE_CASE : Optional[int] = 4
SCREAMING_SNAKE_CASE : Any = num_labels
SCREAMING_SNAKE_CASE : Tuple = do_reduce_labels
SCREAMING_SNAKE_CASE : Dict = ignore_index
def lowerCamelCase_ ( self : Union[str, 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,
"num_labels": self.num_labels,
"do_reduce_labels": self.do_reduce_labels,
"ignore_index": self.ignore_index,
"class_info_file": self.class_info_file,
"metadata": self.metadata,
"num_text": self.num_text,
}
def lowerCamelCase_ ( self : Any , lowerCamelCase_ : Tuple , lowerCamelCase_ : Optional[int]=False ):
'''simple docstring'''
if not batched:
SCREAMING_SNAKE_CASE : Union[str, Any] = image_inputs[0]
if isinstance(lowerCamelCase_ , Image.Image ):
SCREAMING_SNAKE_CASE : Dict = image.size
else:
SCREAMING_SNAKE_CASE : Tuple = image.shape[1], image.shape[2]
if w < h:
SCREAMING_SNAKE_CASE : List[Any] = int(self.size["""shortest_edge"""] * h / w )
SCREAMING_SNAKE_CASE : Optional[Any] = self.size["""shortest_edge"""]
elif w > h:
SCREAMING_SNAKE_CASE : Optional[Any] = self.size["""shortest_edge"""]
SCREAMING_SNAKE_CASE : Optional[int] = int(self.size["""shortest_edge"""] * w / h )
else:
SCREAMING_SNAKE_CASE : List[str] = self.size["""shortest_edge"""]
SCREAMING_SNAKE_CASE : List[str] = self.size["""shortest_edge"""]
else:
SCREAMING_SNAKE_CASE : Optional[int] = []
for image in image_inputs:
SCREAMING_SNAKE_CASE : List[str] = self.get_expected_values([image] )
expected_values.append((expected_height, expected_width) )
SCREAMING_SNAKE_CASE : Any = max(lowerCamelCase_ , key=lambda lowerCamelCase_ : item[0] )[0]
SCREAMING_SNAKE_CASE : Any = max(lowerCamelCase_ , key=lambda lowerCamelCase_ : item[1] )[1]
return expected_height, expected_width
def lowerCamelCase_ ( self : Dict ):
'''simple docstring'''
return OneFormerForUniversalSegmentationOutput(
# +1 for null class
class_queries_logits=torch.randn((self.batch_size, self.num_queries, self.num_classes + 1) ) , masks_queries_logits=torch.randn((self.batch_size, self.num_queries, self.height, self.width) ) , )
@require_torch
@require_vision
class UpperCamelCase__ ( lowercase_ , unittest.TestCase ):
"""simple docstring"""
SCREAMING_SNAKE_CASE__ = OneFormerImageProcessor if (is_vision_available() and is_torch_available()) else None
# only for test_image_processing_common.test_image_proc_to_json_string
SCREAMING_SNAKE_CASE__ = image_processing_class
def lowerCamelCase_ ( self : Union[str, Any] ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Optional[Any] = OneFormerImageProcessorTester(self )
@property
def lowerCamelCase_ ( self : List[Any] ):
'''simple docstring'''
return self.image_processing_tester.prepare_image_processor_dict()
def lowerCamelCase_ ( self : Dict ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : List[Any] = 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""" ) )
self.assertTrue(hasattr(lowerCamelCase_ , """ignore_index""" ) )
self.assertTrue(hasattr(lowerCamelCase_ , """class_info_file""" ) )
self.assertTrue(hasattr(lowerCamelCase_ , """num_text""" ) )
self.assertTrue(hasattr(lowerCamelCase_ , """repo_path""" ) )
self.assertTrue(hasattr(lowerCamelCase_ , """metadata""" ) )
self.assertTrue(hasattr(lowerCamelCase_ , """do_reduce_labels""" ) )
def lowerCamelCase_ ( self : Optional[int] ):
'''simple docstring'''
pass
def lowerCamelCase_ ( self : List[str] ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : List[str] = self.image_processing_class(**self.image_processor_dict )
# create random PIL images
SCREAMING_SNAKE_CASE : Optional[int] = prepare_image_inputs(self.image_processing_tester , equal_resolution=lowerCamelCase_ )
for image in image_inputs:
self.assertIsInstance(lowerCamelCase_ , Image.Image )
# Test not batched input
SCREAMING_SNAKE_CASE : int = image_processor(image_inputs[0] , ["""semantic"""] , return_tensors="""pt""" ).pixel_values
SCREAMING_SNAKE_CASE : Any = self.image_processing_tester.get_expected_values(lowerCamelCase_ )
self.assertEqual(
encoded_images.shape , (1, self.image_processing_tester.num_channels, expected_height, expected_width) , )
# Test batched
SCREAMING_SNAKE_CASE : Optional[Any] = self.image_processing_tester.get_expected_values(lowerCamelCase_ , batched=lowerCamelCase_ )
SCREAMING_SNAKE_CASE : Dict = image_processor(
lowerCamelCase_ , ["""semantic"""] * len(lowerCamelCase_ ) , return_tensors="""pt""" ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processing_tester.batch_size,
self.image_processing_tester.num_channels,
expected_height,
expected_width,
) , )
def lowerCamelCase_ ( self : str ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : List[str] = self.image_processing_class(**self.image_processor_dict )
# create random numpy tensors
SCREAMING_SNAKE_CASE : int = prepare_image_inputs(self.image_processing_tester , equal_resolution=lowerCamelCase_ , numpify=lowerCamelCase_ )
for image in image_inputs:
self.assertIsInstance(lowerCamelCase_ , np.ndarray )
# Test not batched input
SCREAMING_SNAKE_CASE : Dict = image_processor(image_inputs[0] , ["""semantic"""] , return_tensors="""pt""" ).pixel_values
SCREAMING_SNAKE_CASE : int = self.image_processing_tester.get_expected_values(lowerCamelCase_ )
self.assertEqual(
encoded_images.shape , (1, self.image_processing_tester.num_channels, expected_height, expected_width) , )
# Test batched
SCREAMING_SNAKE_CASE : Optional[int] = self.image_processing_tester.get_expected_values(lowerCamelCase_ , batched=lowerCamelCase_ )
SCREAMING_SNAKE_CASE : Any = image_processor(
lowerCamelCase_ , ["""semantic"""] * len(lowerCamelCase_ ) , return_tensors="""pt""" ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processing_tester.batch_size,
self.image_processing_tester.num_channels,
expected_height,
expected_width,
) , )
def lowerCamelCase_ ( self : int ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Tuple = self.image_processing_class(**self.image_processor_dict )
# create random PyTorch tensors
SCREAMING_SNAKE_CASE : int = prepare_image_inputs(self.image_processing_tester , equal_resolution=lowerCamelCase_ , torchify=lowerCamelCase_ )
for image in image_inputs:
self.assertIsInstance(lowerCamelCase_ , torch.Tensor )
# Test not batched input
SCREAMING_SNAKE_CASE : Optional[int] = image_processor(image_inputs[0] , ["""semantic"""] , return_tensors="""pt""" ).pixel_values
SCREAMING_SNAKE_CASE : int = self.image_processing_tester.get_expected_values(lowerCamelCase_ )
self.assertEqual(
encoded_images.shape , (1, self.image_processing_tester.num_channels, expected_height, expected_width) , )
# Test batched
SCREAMING_SNAKE_CASE : List[Any] = self.image_processing_tester.get_expected_values(lowerCamelCase_ , batched=lowerCamelCase_ )
SCREAMING_SNAKE_CASE : Union[str, Any] = image_processor(
lowerCamelCase_ , ["""semantic"""] * len(lowerCamelCase_ ) , return_tensors="""pt""" ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processing_tester.batch_size,
self.image_processing_tester.num_channels,
expected_height,
expected_width,
) , )
def lowerCamelCase_ ( self : int , lowerCamelCase_ : List[Any]=False , lowerCamelCase_ : Union[str, Any]=False , lowerCamelCase_ : Tuple="np" ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Optional[Any] = self.image_processing_class(**self.image_processor_dict )
# prepare image and target
SCREAMING_SNAKE_CASE : Any = self.image_processing_tester.num_labels
SCREAMING_SNAKE_CASE : List[str] = None
SCREAMING_SNAKE_CASE : Optional[int] = None
SCREAMING_SNAKE_CASE : str = prepare_image_inputs(self.image_processing_tester , equal_resolution=lowerCamelCase_ )
if with_segmentation_maps:
SCREAMING_SNAKE_CASE : str = num_labels
if is_instance_map:
SCREAMING_SNAKE_CASE : Dict = list(range(lowerCamelCase_ ) ) * 2
SCREAMING_SNAKE_CASE : List[Any] = dict(enumerate(lowerCamelCase_ ) )
SCREAMING_SNAKE_CASE : Tuple = [
np.random.randint(0 , high * 2 , (img.size[1], img.size[0]) ).astype(np.uinta ) for img in image_inputs
]
if segmentation_type == "pil":
SCREAMING_SNAKE_CASE : List[str] = [Image.fromarray(lowerCamelCase_ ) for annotation in annotations]
SCREAMING_SNAKE_CASE : str = image_processor(
lowerCamelCase_ , ["""semantic"""] * len(lowerCamelCase_ ) , lowerCamelCase_ , return_tensors="""pt""" , instance_id_to_semantic_id=lowerCamelCase_ , pad_and_return_pixel_mask=lowerCamelCase_ , )
return inputs
def lowerCamelCase_ ( self : Tuple ):
'''simple docstring'''
pass
def lowerCamelCase_ ( self : Tuple ):
'''simple docstring'''
def common(lowerCamelCase_ : List[str]=False , lowerCamelCase_ : int=None ):
SCREAMING_SNAKE_CASE : Optional[Any] = self.comm_get_image_processor_inputs(
with_segmentation_maps=lowerCamelCase_ , is_instance_map=lowerCamelCase_ , segmentation_type=lowerCamelCase_ )
SCREAMING_SNAKE_CASE : Tuple = inputs["""mask_labels"""]
SCREAMING_SNAKE_CASE : Optional[Any] = inputs["""class_labels"""]
SCREAMING_SNAKE_CASE : Tuple = inputs["""pixel_values"""]
SCREAMING_SNAKE_CASE : Optional[Any] = inputs["""text_inputs"""]
# check the batch_size
for mask_label, class_label, text_input in zip(lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ ):
self.assertEqual(mask_label.shape[0] , class_label.shape[0] )
# this ensure padding has happened
self.assertEqual(mask_label.shape[1:] , pixel_values.shape[2:] )
self.assertEqual(len(lowerCamelCase_ ) , self.image_processing_tester.num_text )
common()
common(is_instance_map=lowerCamelCase_ )
common(is_instance_map=lowerCamelCase_ , segmentation_type="""pil""" )
common(is_instance_map=lowerCamelCase_ , segmentation_type="""pil""" )
def lowerCamelCase_ ( self : Tuple ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Optional[int] = np.zeros((20, 50) )
SCREAMING_SNAKE_CASE : Dict = 1
SCREAMING_SNAKE_CASE : str = 1
SCREAMING_SNAKE_CASE : Tuple = 1
SCREAMING_SNAKE_CASE : Tuple = binary_mask_to_rle(lowerCamelCase_ )
self.assertEqual(len(lowerCamelCase_ ) , 4 )
self.assertEqual(rle[0] , 21 )
self.assertEqual(rle[1] , 45 )
def lowerCamelCase_ ( self : str ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : str = self.image_processing_class(
num_labels=self.image_processing_tester.num_classes , max_seq_length=77 , task_seq_length=77 , class_info_file="""ade20k_panoptic.json""" , num_text=self.image_processing_tester.num_text , repo_path="""shi-labs/oneformer_demo""" , )
SCREAMING_SNAKE_CASE : Optional[Any] = self.image_processing_tester.get_fake_oneformer_outputs()
SCREAMING_SNAKE_CASE : Optional[Any] = fature_extractor.post_process_semantic_segmentation(lowerCamelCase_ )
self.assertEqual(len(lowerCamelCase_ ) , self.image_processing_tester.batch_size )
self.assertEqual(
segmentation[0].shape , (
self.image_processing_tester.height,
self.image_processing_tester.width,
) , )
SCREAMING_SNAKE_CASE : Optional[Any] = [(1, 4) for i in range(self.image_processing_tester.batch_size )]
SCREAMING_SNAKE_CASE : Dict = fature_extractor.post_process_semantic_segmentation(lowerCamelCase_ , target_sizes=lowerCamelCase_ )
self.assertEqual(segmentation[0].shape , target_sizes[0] )
def lowerCamelCase_ ( self : Optional[Any] ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Optional[int] = self.image_processing_class(
num_labels=self.image_processing_tester.num_classes , max_seq_length=77 , task_seq_length=77 , class_info_file="""ade20k_panoptic.json""" , num_text=self.image_processing_tester.num_text , repo_path="""shi-labs/oneformer_demo""" , )
SCREAMING_SNAKE_CASE : List[str] = self.image_processing_tester.get_fake_oneformer_outputs()
SCREAMING_SNAKE_CASE : List[Any] = image_processor.post_process_instance_segmentation(lowerCamelCase_ , threshold=0 )
self.assertTrue(len(lowerCamelCase_ ) == self.image_processing_tester.batch_size )
for el in segmentation:
self.assertTrue("""segmentation""" in el )
self.assertTrue("""segments_info""" in el )
self.assertEqual(type(el["""segments_info"""] ) , lowerCamelCase_ )
self.assertEqual(
el["""segmentation"""].shape , (self.image_processing_tester.height, self.image_processing_tester.width) )
def lowerCamelCase_ ( self : List[Any] ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : List[str] = self.image_processing_class(
num_labels=self.image_processing_tester.num_classes , max_seq_length=77 , task_seq_length=77 , class_info_file="""ade20k_panoptic.json""" , num_text=self.image_processing_tester.num_text , repo_path="""shi-labs/oneformer_demo""" , )
SCREAMING_SNAKE_CASE : List[str] = self.image_processing_tester.get_fake_oneformer_outputs()
SCREAMING_SNAKE_CASE : Any = image_processor.post_process_panoptic_segmentation(lowerCamelCase_ , threshold=0 )
self.assertTrue(len(lowerCamelCase_ ) == self.image_processing_tester.batch_size )
for el in segmentation:
self.assertTrue("""segmentation""" in el )
self.assertTrue("""segments_info""" in el )
self.assertEqual(type(el["""segments_info"""] ) , lowerCamelCase_ )
self.assertEqual(
el["""segmentation"""].shape , (self.image_processing_tester.height, self.image_processing_tester.width) )
| 708 |
'''simple docstring'''
import math
class UpperCamelCase__ :
"""simple docstring"""
def __init__( self : List[str] , lowerCamelCase_ : Tuple=0 ): # a graph with Node 0,1,...,N-1
'''simple docstring'''
SCREAMING_SNAKE_CASE : Any = n
SCREAMING_SNAKE_CASE : Optional[int] = [
[math.inf for j in range(0 , lowerCamelCase_ )] for i in range(0 , lowerCamelCase_ )
] # adjacency matrix for weight
SCREAMING_SNAKE_CASE : Union[str, Any] = [
[math.inf for j in range(0 , lowerCamelCase_ )] for i in range(0 , lowerCamelCase_ )
] # dp[i][j] stores minimum distance from i to j
def lowerCamelCase_ ( self : Dict , lowerCamelCase_ : str , lowerCamelCase_ : List[str] , lowerCamelCase_ : int ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Union[str, Any] = w
def lowerCamelCase_ ( self : Optional[int] ):
'''simple docstring'''
for k in range(0 , self.n ):
for i in range(0 , self.n ):
for j in range(0 , self.n ):
SCREAMING_SNAKE_CASE : Union[str, Any] = min(self.dp[i][j] , self.dp[i][k] + self.dp[k][j] )
def lowerCamelCase_ ( self : Dict , lowerCamelCase_ : List[Any] , lowerCamelCase_ : Union[str, Any] ):
'''simple docstring'''
return self.dp[u][v]
if __name__ == "__main__":
__UpperCAmelCase = Graph(5)
graph.add_edge(0, 2, 9)
graph.add_edge(0, 4, 10)
graph.add_edge(1, 3, 5)
graph.add_edge(2, 3, 7)
graph.add_edge(3, 0, 10)
graph.add_edge(3, 1, 2)
graph.add_edge(3, 2, 1)
graph.add_edge(3, 4, 6)
graph.add_edge(4, 1, 3)
graph.add_edge(4, 2, 4)
graph.add_edge(4, 3, 9)
graph.floyd_warshall()
graph.show_min(1, 4)
graph.show_min(0, 3)
| 79 | 0 |
'''simple docstring'''
from __future__ import annotations
import inspect
import unittest
from transformers import ViTConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import tensorflow as tf
from transformers import TFViTForImageClassification, TFViTModel
if is_vision_available():
from PIL import Image
from transformers import ViTImageProcessor
class UpperCamelCase__ :
"""simple docstring"""
def __init__( self : Union[str, Any] , lowerCamelCase_ : Optional[Any] , lowerCamelCase_ : Dict=13 , lowerCamelCase_ : Union[str, Any]=30 , lowerCamelCase_ : Any=2 , lowerCamelCase_ : str=3 , lowerCamelCase_ : Optional[int]=True , lowerCamelCase_ : Optional[Any]=True , lowerCamelCase_ : List[Any]=32 , lowerCamelCase_ : Tuple=2 , lowerCamelCase_ : Any=4 , lowerCamelCase_ : Dict=37 , lowerCamelCase_ : List[Any]="gelu" , lowerCamelCase_ : List[Any]=0.1 , lowerCamelCase_ : Any=0.1 , lowerCamelCase_ : Any=10 , lowerCamelCase_ : str=0.02 , lowerCamelCase_ : int=3 , lowerCamelCase_ : Optional[int]=None , ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Dict = parent
SCREAMING_SNAKE_CASE : int = batch_size
SCREAMING_SNAKE_CASE : Optional[Any] = image_size
SCREAMING_SNAKE_CASE : int = patch_size
SCREAMING_SNAKE_CASE : Tuple = num_channels
SCREAMING_SNAKE_CASE : List[Any] = is_training
SCREAMING_SNAKE_CASE : Union[str, Any] = use_labels
SCREAMING_SNAKE_CASE : str = hidden_size
SCREAMING_SNAKE_CASE : List[Any] = num_hidden_layers
SCREAMING_SNAKE_CASE : Union[str, Any] = num_attention_heads
SCREAMING_SNAKE_CASE : List[Any] = intermediate_size
SCREAMING_SNAKE_CASE : List[Any] = hidden_act
SCREAMING_SNAKE_CASE : List[Any] = hidden_dropout_prob
SCREAMING_SNAKE_CASE : Any = attention_probs_dropout_prob
SCREAMING_SNAKE_CASE : int = type_sequence_label_size
SCREAMING_SNAKE_CASE : Tuple = initializer_range
SCREAMING_SNAKE_CASE : Union[str, Any] = scope
# in ViT, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
SCREAMING_SNAKE_CASE : Tuple = (image_size // patch_size) ** 2
SCREAMING_SNAKE_CASE : Union[str, Any] = num_patches + 1
def lowerCamelCase_ ( self : Optional[Any] ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : str = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
SCREAMING_SNAKE_CASE : List[str] = None
if self.use_labels:
SCREAMING_SNAKE_CASE : int = ids_tensor([self.batch_size] , self.type_sequence_label_size )
SCREAMING_SNAKE_CASE : Optional[int] = self.get_config()
return config, pixel_values, labels
def lowerCamelCase_ ( self : List[Any] ):
'''simple docstring'''
return ViTConfig(
image_size=self.image_size , patch_size=self.patch_size , num_channels=self.num_channels , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , is_decoder=lowerCamelCase_ , initializer_range=self.initializer_range , )
def lowerCamelCase_ ( self : Optional[Any] , lowerCamelCase_ : Dict , lowerCamelCase_ : Optional[int] , lowerCamelCase_ : Optional[Any] ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : int = TFViTModel(config=lowerCamelCase_ )
SCREAMING_SNAKE_CASE : List[str] = model(lowerCamelCase_ , training=lowerCamelCase_ )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
# Test with an image with different size than the one specified in config.
SCREAMING_SNAKE_CASE : Optional[Any] = self.image_size // 2
SCREAMING_SNAKE_CASE : Dict = pixel_values[:, :, :image_size, :image_size]
SCREAMING_SNAKE_CASE : Optional[int] = model(lowerCamelCase_ , interpolate_pos_encoding=lowerCamelCase_ , training=lowerCamelCase_ )
SCREAMING_SNAKE_CASE : Dict = (image_size // self.patch_size) ** 2 + 1
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, seq_length, self.hidden_size) )
def lowerCamelCase_ ( self : Union[str, Any] , lowerCamelCase_ : Dict , lowerCamelCase_ : List[Any] , lowerCamelCase_ : Union[str, Any] ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : str = self.type_sequence_label_size
SCREAMING_SNAKE_CASE : Any = TFViTForImageClassification(lowerCamelCase_ )
SCREAMING_SNAKE_CASE : List[Any] = model(lowerCamelCase_ , labels=lowerCamelCase_ , training=lowerCamelCase_ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size) )
# Test with an image with different size than the one specified in config.
SCREAMING_SNAKE_CASE : Optional[Any] = self.image_size // 2
SCREAMING_SNAKE_CASE : Any = pixel_values[:, :, :image_size, :image_size]
SCREAMING_SNAKE_CASE : Union[str, Any] = model(lowerCamelCase_ , interpolate_pos_encoding=lowerCamelCase_ , training=lowerCamelCase_ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size) )
# test greyscale images
SCREAMING_SNAKE_CASE : Union[str, Any] = 1
SCREAMING_SNAKE_CASE : str = TFViTForImageClassification(lowerCamelCase_ )
SCREAMING_SNAKE_CASE : List[str] = floats_tensor([self.batch_size, 1, self.image_size, self.image_size] )
SCREAMING_SNAKE_CASE : Union[str, Any] = model(lowerCamelCase_ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size) )
def lowerCamelCase_ ( self : int ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : List[str] = self.prepare_config_and_inputs()
SCREAMING_SNAKE_CASE : Optional[int] = config_and_inputs
SCREAMING_SNAKE_CASE : int = {"""pixel_values""": pixel_values}
return config, inputs_dict
@require_tf
class UpperCamelCase__ ( lowercase_ , lowercase_ , unittest.TestCase ):
"""simple docstring"""
SCREAMING_SNAKE_CASE__ = (TFViTModel, TFViTForImageClassification) if is_tf_available() else ()
SCREAMING_SNAKE_CASE__ = (
{'''feature-extraction''': TFViTModel, '''image-classification''': TFViTForImageClassification}
if is_tf_available()
else {}
)
SCREAMING_SNAKE_CASE__ = False
SCREAMING_SNAKE_CASE__ = False
SCREAMING_SNAKE_CASE__ = False
def lowerCamelCase_ ( self : List[str] ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : str = TFViTModelTester(self )
SCREAMING_SNAKE_CASE : Optional[Any] = ConfigTester(self , config_class=lowerCamelCase_ , has_text_modality=lowerCamelCase_ , hidden_size=37 )
def lowerCamelCase_ ( self : Optional[Any] ):
'''simple docstring'''
self.config_tester.run_common_tests()
@unittest.skip(reason="""ViT does not use inputs_embeds""" )
def lowerCamelCase_ ( self : Optional[int] ):
'''simple docstring'''
pass
@unittest.skip(reason="""ViT does not use inputs_embeds""" )
def lowerCamelCase_ ( self : int ):
'''simple docstring'''
pass
def lowerCamelCase_ ( self : Tuple ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Optional[Any] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
SCREAMING_SNAKE_CASE : Any = model_class(lowerCamelCase_ )
self.assertIsInstance(model.get_input_embeddings() , (tf.keras.layers.Layer) )
SCREAMING_SNAKE_CASE : Optional[int] = model.get_output_embeddings()
self.assertTrue(x is None or isinstance(lowerCamelCase_ , tf.keras.layers.Layer ) )
def lowerCamelCase_ ( self : int ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : int = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
SCREAMING_SNAKE_CASE : List[str] = model_class(lowerCamelCase_ )
SCREAMING_SNAKE_CASE : int = inspect.signature(model.call )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
SCREAMING_SNAKE_CASE : Union[str, Any] = [*signature.parameters.keys()]
SCREAMING_SNAKE_CASE : Tuple = ["""pixel_values"""]
self.assertListEqual(arg_names[:1] , lowerCamelCase_ )
def lowerCamelCase_ ( self : Union[str, Any] ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : List[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*lowerCamelCase_ )
def lowerCamelCase_ ( self : Tuple ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Dict = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*lowerCamelCase_ )
@slow
def lowerCamelCase_ ( self : Optional[Any] ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Optional[int] = TFViTModel.from_pretrained("""google/vit-base-patch16-224""" )
self.assertIsNotNone(lowerCamelCase_ )
def __A ( ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : Optional[int] = Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""" )
return image
@require_tf
@require_vision
class UpperCamelCase__ ( unittest.TestCase ):
"""simple docstring"""
@cached_property
def lowerCamelCase_ ( self : Union[str, Any] ):
'''simple docstring'''
return ViTImageProcessor.from_pretrained("""google/vit-base-patch16-224""" ) if is_vision_available() else None
@slow
def lowerCamelCase_ ( self : str ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : List[str] = TFViTForImageClassification.from_pretrained("""google/vit-base-patch16-224""" )
SCREAMING_SNAKE_CASE : str = self.default_image_processor
SCREAMING_SNAKE_CASE : int = prepare_img()
SCREAMING_SNAKE_CASE : Tuple = image_processor(images=lowerCamelCase_ , return_tensors="""tf""" )
# forward pass
SCREAMING_SNAKE_CASE : int = model(**lowerCamelCase_ )
# verify the logits
SCREAMING_SNAKE_CASE : Any = tf.TensorShape((1, 10_00) )
self.assertEqual(outputs.logits.shape , lowerCamelCase_ )
SCREAMING_SNAKE_CASE : Union[str, Any] = tf.constant([-0.2_744, 0.8_215, -0.0_836] )
tf.debugging.assert_near(outputs.logits[0, :3] , lowerCamelCase_ , atol=1e-4 )
| 709 |
'''simple docstring'''
import math
def __A ( lowerCamelCase_ ):
"""simple docstring"""
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
return False
# All primes number are in format of 6k +/- 1
for i in range(5 , int(math.sqrt(lowerCamelCase_ ) + 1 ) , 6 ):
if number % i == 0 or number % (i + 2) == 0:
return False
return True
def __A ( lowerCamelCase_ = 1_00_01 ):
"""simple docstring"""
try:
SCREAMING_SNAKE_CASE : Tuple = int(lowerCamelCase_ )
except (TypeError, ValueError):
raise TypeError("""Parameter nth must be int or castable to int.""" ) from None
if nth <= 0:
raise ValueError("""Parameter nth must be greater than or equal to one.""" )
SCREAMING_SNAKE_CASE : list[int] = []
SCREAMING_SNAKE_CASE : Dict = 2
while len(lowerCamelCase_ ) < nth:
if is_prime(lowerCamelCase_ ):
primes.append(lowerCamelCase_ )
num += 1
else:
num += 1
return primes[len(lowerCamelCase_ ) - 1]
if __name__ == "__main__":
print(f'''{solution() = }''')
| 79 | 0 |
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available
if is_vision_available():
from PIL import Image
from transformers import CLIPImageProcessor, CLIPProcessor
@require_vision
class UpperCamelCase__ ( unittest.TestCase ):
"""simple docstring"""
def lowerCamelCase_ ( self : Any ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Optional[int] = tempfile.mkdtemp()
# fmt: off
SCREAMING_SNAKE_CASE : Tuple = ["""l""", """o""", """w""", """e""", """r""", """s""", """t""", """i""", """d""", """n""", """lo""", """l</w>""", """w</w>""", """r</w>""", """t</w>""", """low</w>""", """er</w>""", """lowest</w>""", """newer</w>""", """wider""", """<unk>""", """<|startoftext|>""", """<|endoftext|>"""]
# fmt: on
SCREAMING_SNAKE_CASE : Optional[Any] = dict(zip(lowerCamelCase_ , range(len(lowerCamelCase_ ) ) ) )
SCREAMING_SNAKE_CASE : str = ["""#version: 0.2""", """l o""", """lo w</w>""", """e r</w>""", """"""]
SCREAMING_SNAKE_CASE : Any = {"""unk_token""": """<unk>"""}
SCREAMING_SNAKE_CASE : str = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES["""vocab_file"""] )
SCREAMING_SNAKE_CASE : List[Any] = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES["""merges_file"""] )
with open(self.vocab_file , """w""" , encoding="""utf-8""" ) as fp:
fp.write(json.dumps(lowerCamelCase_ ) + """\n""" )
with open(self.merges_file , """w""" , encoding="""utf-8""" ) as fp:
fp.write("""\n""".join(lowerCamelCase_ ) )
SCREAMING_SNAKE_CASE : str = {
"""do_resize""": True,
"""size""": 20,
"""do_center_crop""": True,
"""crop_size""": 18,
"""do_normalize""": True,
"""image_mean""": [0.48_145_466, 0.4_578_275, 0.40_821_073],
"""image_std""": [0.26_862_954, 0.26_130_258, 0.27_577_711],
}
SCREAMING_SNAKE_CASE : Tuple = os.path.join(self.tmpdirname , lowerCamelCase_ )
with open(self.image_processor_file , """w""" , encoding="""utf-8""" ) as fp:
json.dump(lowerCamelCase_ , lowerCamelCase_ )
def lowerCamelCase_ ( self : Optional[int] , **lowerCamelCase_ : str ):
'''simple docstring'''
return CLIPTokenizer.from_pretrained(self.tmpdirname , **lowerCamelCase_ )
def lowerCamelCase_ ( self : Any , **lowerCamelCase_ : Optional[int] ):
'''simple docstring'''
return CLIPTokenizerFast.from_pretrained(self.tmpdirname , **lowerCamelCase_ )
def lowerCamelCase_ ( self : Union[str, Any] , **lowerCamelCase_ : Union[str, Any] ):
'''simple docstring'''
return CLIPImageProcessor.from_pretrained(self.tmpdirname , **lowerCamelCase_ )
def lowerCamelCase_ ( self : List[str] ):
'''simple docstring'''
shutil.rmtree(self.tmpdirname )
def lowerCamelCase_ ( self : Optional[Any] ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Any = [np.random.randint(2_55 , size=(3, 30, 4_00) , dtype=np.uinta )]
SCREAMING_SNAKE_CASE : Tuple = [Image.fromarray(np.moveaxis(lowerCamelCase_ , 0 , -1 ) ) for x in image_inputs]
return image_inputs
def lowerCamelCase_ ( self : List[Any] ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Tuple = self.get_tokenizer()
SCREAMING_SNAKE_CASE : Optional[Any] = self.get_rust_tokenizer()
SCREAMING_SNAKE_CASE : str = self.get_image_processor()
SCREAMING_SNAKE_CASE : Optional[int] = CLIPProcessor(tokenizer=lowerCamelCase_ , image_processor=lowerCamelCase_ )
processor_slow.save_pretrained(self.tmpdirname )
SCREAMING_SNAKE_CASE : Optional[int] = CLIPProcessor.from_pretrained(self.tmpdirname , use_fast=lowerCamelCase_ )
SCREAMING_SNAKE_CASE : Optional[Any] = CLIPProcessor(tokenizer=lowerCamelCase_ , image_processor=lowerCamelCase_ )
processor_fast.save_pretrained(self.tmpdirname )
SCREAMING_SNAKE_CASE : Tuple = CLIPProcessor.from_pretrained(self.tmpdirname )
self.assertEqual(processor_slow.tokenizer.get_vocab() , tokenizer_slow.get_vocab() )
self.assertEqual(processor_fast.tokenizer.get_vocab() , tokenizer_fast.get_vocab() )
self.assertEqual(tokenizer_slow.get_vocab() , tokenizer_fast.get_vocab() )
self.assertIsInstance(processor_slow.tokenizer , lowerCamelCase_ )
self.assertIsInstance(processor_fast.tokenizer , lowerCamelCase_ )
self.assertEqual(processor_slow.image_processor.to_json_string() , image_processor.to_json_string() )
self.assertEqual(processor_fast.image_processor.to_json_string() , image_processor.to_json_string() )
self.assertIsInstance(processor_slow.image_processor , lowerCamelCase_ )
self.assertIsInstance(processor_fast.image_processor , lowerCamelCase_ )
def lowerCamelCase_ ( self : List[Any] ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : List[str] = CLIPProcessor(tokenizer=self.get_tokenizer() , image_processor=self.get_image_processor() )
processor.save_pretrained(self.tmpdirname )
SCREAMING_SNAKE_CASE : Dict = self.get_tokenizer(bos_token="""(BOS)""" , eos_token="""(EOS)""" )
SCREAMING_SNAKE_CASE : Union[str, Any] = self.get_image_processor(do_normalize=lowerCamelCase_ , padding_value=1.0 )
SCREAMING_SNAKE_CASE : Optional[Any] = CLIPProcessor.from_pretrained(
self.tmpdirname , bos_token="""(BOS)""" , eos_token="""(EOS)""" , do_normalize=lowerCamelCase_ , padding_value=1.0 )
self.assertEqual(processor.tokenizer.get_vocab() , tokenizer_add_kwargs.get_vocab() )
self.assertIsInstance(processor.tokenizer , lowerCamelCase_ )
self.assertEqual(processor.image_processor.to_json_string() , image_processor_add_kwargs.to_json_string() )
self.assertIsInstance(processor.image_processor , lowerCamelCase_ )
def lowerCamelCase_ ( self : Optional[Any] ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : List[Any] = self.get_image_processor()
SCREAMING_SNAKE_CASE : List[Any] = self.get_tokenizer()
SCREAMING_SNAKE_CASE : Optional[Any] = CLIPProcessor(tokenizer=lowerCamelCase_ , image_processor=lowerCamelCase_ )
SCREAMING_SNAKE_CASE : Optional[int] = self.prepare_image_inputs()
SCREAMING_SNAKE_CASE : Any = image_processor(lowerCamelCase_ , return_tensors="""np""" )
SCREAMING_SNAKE_CASE : Union[str, Any] = processor(images=lowerCamelCase_ , return_tensors="""np""" )
for key in input_image_proc.keys():
self.assertAlmostEqual(input_image_proc[key].sum() , input_processor[key].sum() , delta=1e-2 )
def lowerCamelCase_ ( self : Union[str, Any] ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Union[str, Any] = self.get_image_processor()
SCREAMING_SNAKE_CASE : Any = self.get_tokenizer()
SCREAMING_SNAKE_CASE : int = CLIPProcessor(tokenizer=lowerCamelCase_ , image_processor=lowerCamelCase_ )
SCREAMING_SNAKE_CASE : Any = """lower newer"""
SCREAMING_SNAKE_CASE : Dict = processor(text=lowerCamelCase_ )
SCREAMING_SNAKE_CASE : Optional[int] = tokenizer(lowerCamelCase_ )
for key in encoded_tok.keys():
self.assertListEqual(encoded_tok[key] , encoded_processor[key] )
def lowerCamelCase_ ( self : Dict ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Optional[Any] = self.get_image_processor()
SCREAMING_SNAKE_CASE : Tuple = self.get_tokenizer()
SCREAMING_SNAKE_CASE : Tuple = CLIPProcessor(tokenizer=lowerCamelCase_ , image_processor=lowerCamelCase_ )
SCREAMING_SNAKE_CASE : Union[str, Any] = """lower newer"""
SCREAMING_SNAKE_CASE : Dict = self.prepare_image_inputs()
SCREAMING_SNAKE_CASE : Dict = processor(text=lowerCamelCase_ , images=lowerCamelCase_ )
self.assertListEqual(list(inputs.keys() ) , ["""input_ids""", """attention_mask""", """pixel_values"""] )
# test if it raises when no input is passed
with pytest.raises(lowerCamelCase_ ):
processor()
def lowerCamelCase_ ( self : Dict ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Dict = self.get_image_processor()
SCREAMING_SNAKE_CASE : Tuple = self.get_tokenizer()
SCREAMING_SNAKE_CASE : List[str] = CLIPProcessor(tokenizer=lowerCamelCase_ , image_processor=lowerCamelCase_ )
SCREAMING_SNAKE_CASE : List[str] = [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]
SCREAMING_SNAKE_CASE : Optional[Any] = processor.batch_decode(lowerCamelCase_ )
SCREAMING_SNAKE_CASE : Union[str, Any] = tokenizer.batch_decode(lowerCamelCase_ )
self.assertListEqual(lowerCamelCase_ , lowerCamelCase_ )
def lowerCamelCase_ ( self : Optional[Any] ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Tuple = self.get_image_processor()
SCREAMING_SNAKE_CASE : Union[str, Any] = self.get_tokenizer()
SCREAMING_SNAKE_CASE : Optional[int] = CLIPProcessor(tokenizer=lowerCamelCase_ , image_processor=lowerCamelCase_ )
SCREAMING_SNAKE_CASE : Tuple = """lower newer"""
SCREAMING_SNAKE_CASE : Any = self.prepare_image_inputs()
SCREAMING_SNAKE_CASE : int = processor(text=lowerCamelCase_ , images=lowerCamelCase_ )
self.assertListEqual(list(inputs.keys() ) , processor.model_input_names )
| 710 |
'''simple docstring'''
from __future__ import annotations
import json
import requests
from bsa import BeautifulSoup
from fake_useragent import UserAgent
__UpperCAmelCase = {"""UserAgent""": UserAgent().random}
def __A ( lowerCamelCase_ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : str = script.contents[0]
SCREAMING_SNAKE_CASE : int = json.loads(data[data.find("""{\"config\"""" ) : -1] )
return info["entry_data"]["ProfilePage"][0]["graphql"]["user"]
class UpperCamelCase__ :
"""simple docstring"""
def __init__( self : Dict , lowerCamelCase_ : str ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Any = f'''https://www.instagram.com/{username}/'''
SCREAMING_SNAKE_CASE : Any = self.get_json()
def lowerCamelCase_ ( self : Tuple ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Dict = requests.get(self.url , headers=lowerCamelCase_ ).text
SCREAMING_SNAKE_CASE : List[Any] = BeautifulSoup(lowerCamelCase_ , """html.parser""" ).find_all("""script""" )
try:
return extract_user_profile(scripts[4] )
except (json.decoder.JSONDecodeError, KeyError):
return extract_user_profile(scripts[3] )
def __repr__( self : Dict ):
'''simple docstring'''
return f'''{self.__class__.__name__}(\'{self.username}\')'''
def __str__( self : int ):
'''simple docstring'''
return f'''{self.fullname} ({self.username}) is {self.biography}'''
@property
def lowerCamelCase_ ( self : Tuple ):
'''simple docstring'''
return self.user_data["username"]
@property
def lowerCamelCase_ ( self : Optional[Any] ):
'''simple docstring'''
return self.user_data["full_name"]
@property
def lowerCamelCase_ ( self : int ):
'''simple docstring'''
return self.user_data["biography"]
@property
def lowerCamelCase_ ( self : str ):
'''simple docstring'''
return self.user_data["business_email"]
@property
def lowerCamelCase_ ( self : str ):
'''simple docstring'''
return self.user_data["external_url"]
@property
def lowerCamelCase_ ( self : List[Any] ):
'''simple docstring'''
return self.user_data["edge_followed_by"]["count"]
@property
def lowerCamelCase_ ( self : List[Any] ):
'''simple docstring'''
return self.user_data["edge_follow"]["count"]
@property
def lowerCamelCase_ ( self : List[Any] ):
'''simple docstring'''
return self.user_data["edge_owner_to_timeline_media"]["count"]
@property
def lowerCamelCase_ ( self : str ):
'''simple docstring'''
return self.user_data["profile_pic_url_hd"]
@property
def lowerCamelCase_ ( self : Dict ):
'''simple docstring'''
return self.user_data["is_verified"]
@property
def lowerCamelCase_ ( self : Optional[int] ):
'''simple docstring'''
return self.user_data["is_private"]
def __A ( lowerCamelCase_ = "github" ):
"""simple docstring"""
import os
if os.environ.get("""CI""" ):
return # test failing on GitHub Actions
SCREAMING_SNAKE_CASE : Any = InstagramUser(lowerCamelCase_ )
assert instagram_user.user_data
assert isinstance(instagram_user.user_data , lowerCamelCase_ )
assert instagram_user.username == username
if username != "github":
return
assert instagram_user.fullname == "GitHub"
assert instagram_user.biography == "Built for developers."
assert instagram_user.number_of_posts > 1_50
assert instagram_user.number_of_followers > 12_00_00
assert instagram_user.number_of_followings > 15
assert instagram_user.email == "support@github.com"
assert instagram_user.website == "https://github.com/readme"
assert instagram_user.profile_picture_url.startswith("""https://instagram.""" )
assert instagram_user.is_verified is True
assert instagram_user.is_private is False
if __name__ == "__main__":
import doctest
doctest.testmod()
__UpperCAmelCase = InstagramUser("""github""")
print(instagram_user)
print(f'''{instagram_user.number_of_posts = }''')
print(f'''{instagram_user.number_of_followers = }''')
print(f'''{instagram_user.number_of_followings = }''')
print(f'''{instagram_user.email = }''')
print(f'''{instagram_user.website = }''')
print(f'''{instagram_user.profile_picture_url = }''')
print(f'''{instagram_user.is_verified = }''')
print(f'''{instagram_user.is_private = }''')
| 79 | 0 |
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