code
stringlengths 82
53.2k
| code_codestyle
int64 0
721
| style_context
stringlengths 91
41.9k
| style_context_codestyle
int64 0
699
| label
int64 0
1
|
|---|---|---|---|---|
"""simple docstring"""
def _A (__a ) -> List[str]:
"""simple docstring"""
if not head:
return True
# split the list to two parts
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ : Any = head.next, head
while fast and fast.next:
SCREAMING_SNAKE_CASE_ : Dict = fast.next.next
SCREAMING_SNAKE_CASE_ : List[str] = slow.next
SCREAMING_SNAKE_CASE_ : List[str] = slow.next
SCREAMING_SNAKE_CASE_ : Optional[Any] = None # Don't forget here! But forget still works!
# reverse the second part
SCREAMING_SNAKE_CASE_ : Union[str, Any] = None
while second:
SCREAMING_SNAKE_CASE_ : List[str] = second.next
SCREAMING_SNAKE_CASE_ : str = node
SCREAMING_SNAKE_CASE_ : List[Any] = second
SCREAMING_SNAKE_CASE_ : Union[str, Any] = nxt
# compare two parts
# second part has the same or one less node
while node:
if node.val != head.val:
return False
SCREAMING_SNAKE_CASE_ : List[Any] = node.next
SCREAMING_SNAKE_CASE_ : Optional[int] = head.next
return True
def _A (__a ) -> Optional[int]:
"""simple docstring"""
if not head or not head.next:
return True
# 1. Get the midpoint (slow)
SCREAMING_SNAKE_CASE_ : Any = head
while fast and fast.next:
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ : Tuple = fast.next.next, slow.next
# 2. Push the second half into the stack
SCREAMING_SNAKE_CASE_ : Optional[int] = [slow.val]
while slow.next:
SCREAMING_SNAKE_CASE_ : Union[str, Any] = slow.next
stack.append(slow.val )
# 3. Comparison
while stack:
if stack.pop() != cur.val:
return False
SCREAMING_SNAKE_CASE_ : int = cur.next
return True
def _A (__a ) -> int:
"""simple docstring"""
if not head or not head.next:
return True
SCREAMING_SNAKE_CASE_ : List[Any] = {}
SCREAMING_SNAKE_CASE_ : List[str] = 0
while head:
if head.val in d:
d[head.val].append(__a )
else:
SCREAMING_SNAKE_CASE_ : Union[str, Any] = [pos]
SCREAMING_SNAKE_CASE_ : Union[str, Any] = head.next
pos += 1
SCREAMING_SNAKE_CASE_ : Tuple = pos - 1
SCREAMING_SNAKE_CASE_ : List[str] = 0
for v in d.values():
if len(__a ) % 2 != 0:
middle += 1
else:
SCREAMING_SNAKE_CASE_ : List[str] = 0
for i in range(0 , len(__a ) ):
if v[i] + v[len(__a ) - 1 - step] != checksum:
return False
step += 1
if middle > 1:
return False
return True
| 512
|
"""simple docstring"""
# limitations under the License.
# NOTE: This file is deprecated and will be removed in a future version.
# It only exists so that temporarely `from diffusers.pipelines import DiffusionPipeline` works
from .pipelines import DiffusionPipeline, ImagePipelineOutput # noqa: F401
from .utils import deprecate
deprecate(
"""pipelines_utils""",
"""0.22.0""",
"""Importing `DiffusionPipeline` or `ImagePipelineOutput` from diffusers.pipeline_utils is deprecated. Please import from diffusers.pipelines.pipeline_utils instead.""",
standard_warn=False,
stacklevel=3,
)
| 512
| 1
|
from collections import deque
from math import floor
from random import random
from time import time
class lowercase__ :
'''simple docstring'''
def __init__( self ) -> Tuple:
"""simple docstring"""
UpperCamelCase__ : Union[str, Any] = {}
def UpperCamelCase__ ( self, __magic_name__, __magic_name__, __magic_name__=1 ) -> Union[str, Any]:
"""simple docstring"""
if self.graph.get(A__ ):
if self.graph[u].count([w, v] ) == 0:
self.graph[u].append([w, v] )
else:
UpperCamelCase__ : str = [[w, v]]
if not self.graph.get(A__ ):
UpperCamelCase__ : Any = []
def UpperCamelCase__ ( self ) -> List[str]:
"""simple docstring"""
return list(self.graph )
def UpperCamelCase__ ( self, __magic_name__, __magic_name__ ) -> Optional[int]:
"""simple docstring"""
if self.graph.get(A__ ):
for _ in self.graph[u]:
if _[1] == v:
self.graph[u].remove(A__ )
def UpperCamelCase__ ( self, __magic_name__=-2, __magic_name__=-1 ) -> Union[str, Any]:
"""simple docstring"""
if s == d:
return []
UpperCamelCase__ : str = []
UpperCamelCase__ : Optional[Any] = []
if s == -2:
UpperCamelCase__ : int = list(self.graph )[0]
stack.append(A__ )
visited.append(A__ )
UpperCamelCase__ : Union[str, Any] = s
while True:
# check if there is any non isolated nodes
if len(self.graph[s] ) != 0:
UpperCamelCase__ : Any = s
for node in self.graph[s]:
if visited.count(node[1] ) < 1:
if node[1] == d:
visited.append(A__ )
return visited
else:
stack.append(node[1] )
visited.append(node[1] )
UpperCamelCase__ : Optional[int] = node[1]
break
# check if all the children are visited
if s == ss:
stack.pop()
if len(A__ ) != 0:
UpperCamelCase__ : Dict = stack[len(A__ ) - 1]
else:
UpperCamelCase__ : List[Any] = ss
# check if se have reached the starting point
if len(A__ ) == 0:
return visited
def UpperCamelCase__ ( self, __magic_name__=-1 ) -> Tuple:
"""simple docstring"""
if c == -1:
UpperCamelCase__ : Any = floor(random() * 10000 ) + 10
for i in range(A__ ):
# every vertex has max 100 edges
for _ in range(floor(random() * 102 ) + 1 ):
UpperCamelCase__ : Tuple = floor(random() * c ) + 1
if n != i:
self.add_pair(A__, A__, 1 )
def UpperCamelCase__ ( self, __magic_name__=-2 ) -> int:
"""simple docstring"""
UpperCamelCase__ : Any = deque()
UpperCamelCase__ : List[str] = []
if s == -2:
UpperCamelCase__ : List[str] = list(self.graph )[0]
d.append(A__ )
visited.append(A__ )
while d:
UpperCamelCase__ : str = 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 UpperCamelCase__ ( self, __magic_name__ ) -> Optional[Any]:
"""simple docstring"""
UpperCamelCase__ : Any = 0
for x in self.graph:
for y in self.graph[x]:
if y[1] == u:
count += 1
return count
def UpperCamelCase__ ( self, __magic_name__ ) -> List[Any]:
"""simple docstring"""
return len(self.graph[u] )
def UpperCamelCase__ ( self, __magic_name__=-2 ) -> Any:
"""simple docstring"""
UpperCamelCase__ : Dict = []
UpperCamelCase__ : Tuple = []
if s == -2:
UpperCamelCase__ : Union[str, Any] = list(self.graph )[0]
stack.append(A__ )
visited.append(A__ )
UpperCamelCase__ : List[str] = s
UpperCamelCase__ : Union[str, Any] = []
while True:
# check if there is any non isolated nodes
if len(self.graph[s] ) != 0:
UpperCamelCase__ : Tuple = s
for node in self.graph[s]:
if visited.count(node[1] ) < 1:
stack.append(node[1] )
visited.append(node[1] )
UpperCamelCase__ : Union[str, Any] = node[1]
break
# check if all the children are visited
if s == ss:
sorted_nodes.append(stack.pop() )
if len(A__ ) != 0:
UpperCamelCase__ : Optional[int] = stack[len(A__ ) - 1]
else:
UpperCamelCase__ : Optional[Any] = ss
# check if se have reached the starting point
if len(A__ ) == 0:
return sorted_nodes
def UpperCamelCase__ ( self ) -> Optional[Any]:
"""simple docstring"""
UpperCamelCase__ : Optional[Any] = []
UpperCamelCase__ : Optional[int] = []
UpperCamelCase__ : Optional[int] = list(self.graph )[0]
stack.append(A__ )
visited.append(A__ )
UpperCamelCase__ : Tuple = -2
UpperCamelCase__ : List[Any] = []
UpperCamelCase__ : List[str] = s
UpperCamelCase__ : int = False
UpperCamelCase__ : Any = set()
while True:
# check if there is any non isolated nodes
if len(self.graph[s] ) != 0:
UpperCamelCase__ : 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
):
UpperCamelCase__ : List[Any] = len(A__ ) - 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] )
UpperCamelCase__ : Optional[Any] = node[1]
break
# check if all the children are visited
if s == ss:
stack.pop()
UpperCamelCase__ : int = True
if len(A__ ) != 0:
UpperCamelCase__ : List[str] = stack[len(A__ ) - 1]
else:
UpperCamelCase__ : Tuple = False
indirect_parents.append(A__ )
UpperCamelCase__ : Optional[int] = s
UpperCamelCase__ : List[str] = ss
# check if se have reached the starting point
if len(A__ ) == 0:
return list(A__ )
def UpperCamelCase__ ( self ) -> Any:
"""simple docstring"""
UpperCamelCase__ : Tuple = []
UpperCamelCase__ : Tuple = []
UpperCamelCase__ : int = list(self.graph )[0]
stack.append(A__ )
visited.append(A__ )
UpperCamelCase__ : List[Any] = -2
UpperCamelCase__ : str = []
UpperCamelCase__ : Optional[Any] = s
UpperCamelCase__ : List[str] = False
UpperCamelCase__ : Tuple = set()
while True:
# check if there is any non isolated nodes
if len(self.graph[s] ) != 0:
UpperCamelCase__ : 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
):
UpperCamelCase__ : str = len(A__ ) - 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] )
UpperCamelCase__ : str = node[1]
break
# check if all the children are visited
if s == ss:
stack.pop()
UpperCamelCase__ : List[Any] = True
if len(A__ ) != 0:
UpperCamelCase__ : Any = stack[len(A__ ) - 1]
else:
UpperCamelCase__ : Optional[int] = False
indirect_parents.append(A__ )
UpperCamelCase__ : Optional[Any] = s
UpperCamelCase__ : int = ss
# check if se have reached the starting point
if len(A__ ) == 0:
return False
def UpperCamelCase__ ( self, __magic_name__=-2, __magic_name__=-1 ) -> Optional[int]:
"""simple docstring"""
UpperCamelCase__ : str = time()
self.dfs(A__, A__ )
UpperCamelCase__ : Optional[int] = time()
return end - begin
def UpperCamelCase__ ( self, __magic_name__=-2 ) -> List[str]:
"""simple docstring"""
UpperCamelCase__ : Any = time()
self.bfs(A__ )
UpperCamelCase__ : Dict = time()
return end - begin
class lowercase__ :
'''simple docstring'''
def __init__( self ) -> List[Any]:
"""simple docstring"""
UpperCamelCase__ : Any = {}
def UpperCamelCase__ ( self, __magic_name__, __magic_name__, __magic_name__=1 ) -> Dict:
"""simple docstring"""
# check if the u exists
if self.graph.get(A__ ):
# 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
UpperCamelCase__ : List[str] = [[w, v]]
# add the other way
if self.graph.get(A__ ):
# 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
UpperCamelCase__ : Tuple = [[w, u]]
def UpperCamelCase__ ( self, __magic_name__, __magic_name__ ) -> Tuple:
"""simple docstring"""
if self.graph.get(A__ ):
for _ in self.graph[u]:
if _[1] == v:
self.graph[u].remove(A__ )
# the other way round
if self.graph.get(A__ ):
for _ in self.graph[v]:
if _[1] == u:
self.graph[v].remove(A__ )
def UpperCamelCase__ ( self, __magic_name__=-2, __magic_name__=-1 ) -> Optional[Any]:
"""simple docstring"""
if s == d:
return []
UpperCamelCase__ : List[str] = []
UpperCamelCase__ : Union[str, Any] = []
if s == -2:
UpperCamelCase__ : int = list(self.graph )[0]
stack.append(A__ )
visited.append(A__ )
UpperCamelCase__ : List[Any] = s
while True:
# check if there is any non isolated nodes
if len(self.graph[s] ) != 0:
UpperCamelCase__ : List[Any] = s
for node in self.graph[s]:
if visited.count(node[1] ) < 1:
if node[1] == d:
visited.append(A__ )
return visited
else:
stack.append(node[1] )
visited.append(node[1] )
UpperCamelCase__ : Tuple = node[1]
break
# check if all the children are visited
if s == ss:
stack.pop()
if len(A__ ) != 0:
UpperCamelCase__ : Tuple = stack[len(A__ ) - 1]
else:
UpperCamelCase__ : Tuple = ss
# check if se have reached the starting point
if len(A__ ) == 0:
return visited
def UpperCamelCase__ ( self, __magic_name__=-1 ) -> List[str]:
"""simple docstring"""
if c == -1:
UpperCamelCase__ : Any = floor(random() * 10000 ) + 10
for i in range(A__ ):
# every vertex has max 100 edges
for _ in range(floor(random() * 102 ) + 1 ):
UpperCamelCase__ : Optional[Any] = floor(random() * c ) + 1
if n != i:
self.add_pair(A__, A__, 1 )
def UpperCamelCase__ ( self, __magic_name__=-2 ) -> Optional[Any]:
"""simple docstring"""
UpperCamelCase__ : Optional[int] = deque()
UpperCamelCase__ : Optional[Any] = []
if s == -2:
UpperCamelCase__ : int = list(self.graph )[0]
d.append(A__ )
visited.append(A__ )
while d:
UpperCamelCase__ : 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 UpperCamelCase__ ( self, __magic_name__ ) -> str:
"""simple docstring"""
return len(self.graph[u] )
def UpperCamelCase__ ( self ) -> int:
"""simple docstring"""
UpperCamelCase__ : List[Any] = []
UpperCamelCase__ : Optional[int] = []
UpperCamelCase__ : Optional[Any] = list(self.graph )[0]
stack.append(A__ )
visited.append(A__ )
UpperCamelCase__ : int = -2
UpperCamelCase__ : List[Any] = []
UpperCamelCase__ : str = s
UpperCamelCase__ : str = False
UpperCamelCase__ : Union[str, Any] = set()
while True:
# check if there is any non isolated nodes
if len(self.graph[s] ) != 0:
UpperCamelCase__ : Dict = 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
):
UpperCamelCase__ : Union[str, Any] = len(A__ ) - 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] )
UpperCamelCase__ : Dict = node[1]
break
# check if all the children are visited
if s == ss:
stack.pop()
UpperCamelCase__ : Optional[int] = True
if len(A__ ) != 0:
UpperCamelCase__ : int = stack[len(A__ ) - 1]
else:
UpperCamelCase__ : List[str] = False
indirect_parents.append(A__ )
UpperCamelCase__ : Optional[Any] = s
UpperCamelCase__ : Union[str, Any] = ss
# check if se have reached the starting point
if len(A__ ) == 0:
return list(A__ )
def UpperCamelCase__ ( self ) -> Union[str, Any]:
"""simple docstring"""
UpperCamelCase__ : Any = []
UpperCamelCase__ : Optional[Any] = []
UpperCamelCase__ : List[str] = list(self.graph )[0]
stack.append(A__ )
visited.append(A__ )
UpperCamelCase__ : str = -2
UpperCamelCase__ : Union[str, Any] = []
UpperCamelCase__ : Dict = s
UpperCamelCase__ : int = False
UpperCamelCase__ : List[str] = set()
while True:
# check if there is any non isolated nodes
if len(self.graph[s] ) != 0:
UpperCamelCase__ : 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
):
UpperCamelCase__ : Optional[int] = len(A__ ) - 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] )
UpperCamelCase__ : str = node[1]
break
# check if all the children are visited
if s == ss:
stack.pop()
UpperCamelCase__ : Dict = True
if len(A__ ) != 0:
UpperCamelCase__ : List[str] = stack[len(A__ ) - 1]
else:
UpperCamelCase__ : Any = False
indirect_parents.append(A__ )
UpperCamelCase__ : Optional[int] = s
UpperCamelCase__ : List[str] = ss
# check if se have reached the starting point
if len(A__ ) == 0:
return False
def UpperCamelCase__ ( self ) -> Any:
"""simple docstring"""
return list(self.graph )
def UpperCamelCase__ ( self, __magic_name__=-2, __magic_name__=-1 ) -> List[str]:
"""simple docstring"""
UpperCamelCase__ : int = time()
self.dfs(A__, A__ )
UpperCamelCase__ : List[str] = time()
return end - begin
def UpperCamelCase__ ( self, __magic_name__=-2 ) -> int:
"""simple docstring"""
UpperCamelCase__ : List[Any] = time()
self.bfs(A__ )
UpperCamelCase__ : int = time()
return end - begin
| 700
|
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import cached_download, hf_hub_download, hf_hub_url
from PIL import Image
from transformers import DetaConfig, DetaForObjectDetection, DetaImageProcessor, SwinConfig
from transformers.utils import logging
logging.set_verbosity_info()
UpperCAmelCase_ = logging.get_logger(__name__)
def lowerCAmelCase_ ( __UpperCAmelCase: List[str] ) -> Optional[Any]:
UpperCamelCase__ : Optional[Any] = SwinConfig(
embed_dim=192 , depths=(2, 2, 18, 2) , num_heads=(6, 12, 24, 48) , window_size=12 , out_features=['''stage2''', '''stage3''', '''stage4'''] , )
UpperCamelCase__ : List[Any] = DetaConfig(
backbone_config=__UpperCAmelCase , num_queries=900 , encoder_ffn_dim=2048 , decoder_ffn_dim=2048 , num_feature_levels=5 , assign_first_stage=__UpperCAmelCase , with_box_refine=__UpperCAmelCase , two_stage=__UpperCAmelCase , )
# set labels
UpperCamelCase__ : str = '''huggingface/label-files'''
if "o365" in model_name:
UpperCamelCase__ : str = 366
UpperCamelCase__ : List[Any] = '''object365-id2label.json'''
else:
UpperCamelCase__ : Dict = 91
UpperCamelCase__ : Optional[int] = '''coco-detection-id2label.json'''
UpperCamelCase__ : Dict = num_labels
UpperCamelCase__ : List[str] = json.load(open(cached_download(hf_hub_url(__UpperCAmelCase , __UpperCAmelCase , repo_type='''dataset''' ) ) , '''r''' ) )
UpperCamelCase__ : Optional[Any] = {int(__UpperCAmelCase ): v for k, v in idalabel.items()}
UpperCamelCase__ : Optional[Any] = idalabel
UpperCamelCase__ : Optional[int] = {v: k for k, v in idalabel.items()}
return config
def lowerCAmelCase_ ( __UpperCAmelCase: List[Any] ) -> Optional[int]:
UpperCamelCase__ : List[str] = []
# stem
# fmt: off
rename_keys.append(('''backbone.0.body.patch_embed.proj.weight''', '''model.backbone.model.embeddings.patch_embeddings.projection.weight''') )
rename_keys.append(('''backbone.0.body.patch_embed.proj.bias''', '''model.backbone.model.embeddings.patch_embeddings.projection.bias''') )
rename_keys.append(('''backbone.0.body.patch_embed.norm.weight''', '''model.backbone.model.embeddings.norm.weight''') )
rename_keys.append(('''backbone.0.body.patch_embed.norm.bias''', '''model.backbone.model.embeddings.norm.bias''') )
# stages
for i in range(len(config.backbone_config.depths ) ):
for j in range(config.backbone_config.depths[i] ):
rename_keys.append((f"backbone.0.body.layers.{i}.blocks.{j}.norm1.weight", f"model.backbone.model.encoder.layers.{i}.blocks.{j}.layernorm_before.weight") )
rename_keys.append((f"backbone.0.body.layers.{i}.blocks.{j}.norm1.bias", f"model.backbone.model.encoder.layers.{i}.blocks.{j}.layernorm_before.bias") )
rename_keys.append((f"backbone.0.body.layers.{i}.blocks.{j}.attn.relative_position_bias_table", f"model.backbone.model.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_bias_table") )
rename_keys.append((f"backbone.0.body.layers.{i}.blocks.{j}.attn.relative_position_index", f"model.backbone.model.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_index") )
rename_keys.append((f"backbone.0.body.layers.{i}.blocks.{j}.attn.proj.weight", f"model.backbone.model.encoder.layers.{i}.blocks.{j}.attention.output.dense.weight") )
rename_keys.append((f"backbone.0.body.layers.{i}.blocks.{j}.attn.proj.bias", f"model.backbone.model.encoder.layers.{i}.blocks.{j}.attention.output.dense.bias") )
rename_keys.append((f"backbone.0.body.layers.{i}.blocks.{j}.norm2.weight", f"model.backbone.model.encoder.layers.{i}.blocks.{j}.layernorm_after.weight") )
rename_keys.append((f"backbone.0.body.layers.{i}.blocks.{j}.norm2.bias", f"model.backbone.model.encoder.layers.{i}.blocks.{j}.layernorm_after.bias") )
rename_keys.append((f"backbone.0.body.layers.{i}.blocks.{j}.mlp.fc1.weight", f"model.backbone.model.encoder.layers.{i}.blocks.{j}.intermediate.dense.weight") )
rename_keys.append((f"backbone.0.body.layers.{i}.blocks.{j}.mlp.fc1.bias", f"model.backbone.model.encoder.layers.{i}.blocks.{j}.intermediate.dense.bias") )
rename_keys.append((f"backbone.0.body.layers.{i}.blocks.{j}.mlp.fc2.weight", f"model.backbone.model.encoder.layers.{i}.blocks.{j}.output.dense.weight") )
rename_keys.append((f"backbone.0.body.layers.{i}.blocks.{j}.mlp.fc2.bias", f"model.backbone.model.encoder.layers.{i}.blocks.{j}.output.dense.bias") )
if i < 3:
rename_keys.append((f"backbone.0.body.layers.{i}.downsample.reduction.weight", f"model.backbone.model.encoder.layers.{i}.downsample.reduction.weight") )
rename_keys.append((f"backbone.0.body.layers.{i}.downsample.norm.weight", f"model.backbone.model.encoder.layers.{i}.downsample.norm.weight") )
rename_keys.append((f"backbone.0.body.layers.{i}.downsample.norm.bias", f"model.backbone.model.encoder.layers.{i}.downsample.norm.bias") )
rename_keys.append(('''backbone.0.body.norm1.weight''', '''model.backbone.model.hidden_states_norms.stage2.weight''') )
rename_keys.append(('''backbone.0.body.norm1.bias''', '''model.backbone.model.hidden_states_norms.stage2.bias''') )
rename_keys.append(('''backbone.0.body.norm2.weight''', '''model.backbone.model.hidden_states_norms.stage3.weight''') )
rename_keys.append(('''backbone.0.body.norm2.bias''', '''model.backbone.model.hidden_states_norms.stage3.bias''') )
rename_keys.append(('''backbone.0.body.norm3.weight''', '''model.backbone.model.hidden_states_norms.stage4.weight''') )
rename_keys.append(('''backbone.0.body.norm3.bias''', '''model.backbone.model.hidden_states_norms.stage4.bias''') )
# transformer encoder
for i in range(config.encoder_layers ):
rename_keys.append((f"transformer.encoder.layers.{i}.self_attn.sampling_offsets.weight", f"model.encoder.layers.{i}.self_attn.sampling_offsets.weight") )
rename_keys.append((f"transformer.encoder.layers.{i}.self_attn.sampling_offsets.bias", f"model.encoder.layers.{i}.self_attn.sampling_offsets.bias") )
rename_keys.append((f"transformer.encoder.layers.{i}.self_attn.attention_weights.weight", f"model.encoder.layers.{i}.self_attn.attention_weights.weight") )
rename_keys.append((f"transformer.encoder.layers.{i}.self_attn.attention_weights.bias", f"model.encoder.layers.{i}.self_attn.attention_weights.bias") )
rename_keys.append((f"transformer.encoder.layers.{i}.self_attn.value_proj.weight", f"model.encoder.layers.{i}.self_attn.value_proj.weight") )
rename_keys.append((f"transformer.encoder.layers.{i}.self_attn.value_proj.bias", f"model.encoder.layers.{i}.self_attn.value_proj.bias") )
rename_keys.append((f"transformer.encoder.layers.{i}.self_attn.output_proj.weight", f"model.encoder.layers.{i}.self_attn.output_proj.weight") )
rename_keys.append((f"transformer.encoder.layers.{i}.self_attn.output_proj.bias", f"model.encoder.layers.{i}.self_attn.output_proj.bias") )
rename_keys.append((f"transformer.encoder.layers.{i}.norm1.weight", f"model.encoder.layers.{i}.self_attn_layer_norm.weight") )
rename_keys.append((f"transformer.encoder.layers.{i}.norm1.bias", f"model.encoder.layers.{i}.self_attn_layer_norm.bias") )
rename_keys.append((f"transformer.encoder.layers.{i}.linear1.weight", f"model.encoder.layers.{i}.fc1.weight") )
rename_keys.append((f"transformer.encoder.layers.{i}.linear1.bias", f"model.encoder.layers.{i}.fc1.bias") )
rename_keys.append((f"transformer.encoder.layers.{i}.linear2.weight", f"model.encoder.layers.{i}.fc2.weight") )
rename_keys.append((f"transformer.encoder.layers.{i}.linear2.bias", f"model.encoder.layers.{i}.fc2.bias") )
rename_keys.append((f"transformer.encoder.layers.{i}.norm2.weight", f"model.encoder.layers.{i}.final_layer_norm.weight") )
rename_keys.append((f"transformer.encoder.layers.{i}.norm2.bias", f"model.encoder.layers.{i}.final_layer_norm.bias") )
# transformer decoder
for i in range(config.decoder_layers ):
rename_keys.append((f"transformer.decoder.layers.{i}.cross_attn.sampling_offsets.weight", f"model.decoder.layers.{i}.encoder_attn.sampling_offsets.weight") )
rename_keys.append((f"transformer.decoder.layers.{i}.cross_attn.sampling_offsets.bias", f"model.decoder.layers.{i}.encoder_attn.sampling_offsets.bias") )
rename_keys.append((f"transformer.decoder.layers.{i}.cross_attn.attention_weights.weight", f"model.decoder.layers.{i}.encoder_attn.attention_weights.weight") )
rename_keys.append((f"transformer.decoder.layers.{i}.cross_attn.attention_weights.bias", f"model.decoder.layers.{i}.encoder_attn.attention_weights.bias") )
rename_keys.append((f"transformer.decoder.layers.{i}.cross_attn.value_proj.weight", f"model.decoder.layers.{i}.encoder_attn.value_proj.weight") )
rename_keys.append((f"transformer.decoder.layers.{i}.cross_attn.value_proj.bias", f"model.decoder.layers.{i}.encoder_attn.value_proj.bias") )
rename_keys.append((f"transformer.decoder.layers.{i}.cross_attn.output_proj.weight", f"model.decoder.layers.{i}.encoder_attn.output_proj.weight") )
rename_keys.append((f"transformer.decoder.layers.{i}.cross_attn.output_proj.bias", f"model.decoder.layers.{i}.encoder_attn.output_proj.bias") )
rename_keys.append((f"transformer.decoder.layers.{i}.norm1.weight", f"model.decoder.layers.{i}.encoder_attn_layer_norm.weight") )
rename_keys.append((f"transformer.decoder.layers.{i}.norm1.bias", f"model.decoder.layers.{i}.encoder_attn_layer_norm.bias") )
rename_keys.append((f"transformer.decoder.layers.{i}.self_attn.out_proj.weight", f"model.decoder.layers.{i}.self_attn.out_proj.weight") )
rename_keys.append((f"transformer.decoder.layers.{i}.self_attn.out_proj.bias", f"model.decoder.layers.{i}.self_attn.out_proj.bias") )
rename_keys.append((f"transformer.decoder.layers.{i}.norm2.weight", f"model.decoder.layers.{i}.self_attn_layer_norm.weight") )
rename_keys.append((f"transformer.decoder.layers.{i}.norm2.bias", f"model.decoder.layers.{i}.self_attn_layer_norm.bias") )
rename_keys.append((f"transformer.decoder.layers.{i}.linear1.weight", f"model.decoder.layers.{i}.fc1.weight") )
rename_keys.append((f"transformer.decoder.layers.{i}.linear1.bias", f"model.decoder.layers.{i}.fc1.bias") )
rename_keys.append((f"transformer.decoder.layers.{i}.linear2.weight", f"model.decoder.layers.{i}.fc2.weight") )
rename_keys.append((f"transformer.decoder.layers.{i}.linear2.bias", f"model.decoder.layers.{i}.fc2.bias") )
rename_keys.append((f"transformer.decoder.layers.{i}.norm3.weight", f"model.decoder.layers.{i}.final_layer_norm.weight") )
rename_keys.append((f"transformer.decoder.layers.{i}.norm3.bias", f"model.decoder.layers.{i}.final_layer_norm.bias") )
# fmt: on
return rename_keys
def lowerCAmelCase_ ( __UpperCAmelCase: Union[str, Any] , __UpperCAmelCase: int , __UpperCAmelCase: List[Any] ) -> str:
UpperCamelCase__ : Optional[Any] = dct.pop(__UpperCAmelCase )
UpperCamelCase__ : Optional[int] = val
def lowerCAmelCase_ ( __UpperCAmelCase: Tuple , __UpperCAmelCase: Optional[int] ) -> int:
UpperCamelCase__ : Any = [int(backbone_config.embed_dim * 2**i ) for i in range(len(backbone_config.depths ) )]
for i in range(len(backbone_config.depths ) ):
UpperCamelCase__ : int = num_features[i]
for j in range(backbone_config.depths[i] ):
# fmt: off
# read in weights + bias of input projection layer (in original implementation, this is a single matrix + bias)
UpperCamelCase__ : Optional[Any] = state_dict.pop(f"backbone.0.body.layers.{i}.blocks.{j}.attn.qkv.weight" )
UpperCamelCase__ : Dict = state_dict.pop(f"backbone.0.body.layers.{i}.blocks.{j}.attn.qkv.bias" )
# next, add query, keys and values (in that order) to the state dict
UpperCamelCase__ : List[str] = in_proj_weight[:dim, :]
UpperCamelCase__ : Optional[Any] = in_proj_bias[: dim]
UpperCamelCase__ : str = in_proj_weight[
dim : dim * 2, :
]
UpperCamelCase__ : Dict = in_proj_bias[
dim : dim * 2
]
UpperCamelCase__ : Optional[Any] = in_proj_weight[
-dim :, :
]
UpperCamelCase__ : Optional[Any] = in_proj_bias[-dim :]
# fmt: on
def lowerCAmelCase_ ( __UpperCAmelCase: int , __UpperCAmelCase: Optional[int] ) -> Optional[Any]:
# transformer decoder self-attention layers
UpperCamelCase__ : List[str] = config.d_model
for i in range(config.decoder_layers ):
# read in weights + bias of input projection layer of self-attention
UpperCamelCase__ : Tuple = state_dict.pop(f"transformer.decoder.layers.{i}.self_attn.in_proj_weight" )
UpperCamelCase__ : Any = state_dict.pop(f"transformer.decoder.layers.{i}.self_attn.in_proj_bias" )
# next, add query, keys and values (in that order) to the state dict
UpperCamelCase__ : Optional[int] = in_proj_weight[:hidden_size, :]
UpperCamelCase__ : List[str] = in_proj_bias[:hidden_size]
UpperCamelCase__ : Optional[int] = in_proj_weight[
hidden_size : hidden_size * 2, :
]
UpperCamelCase__ : Any = in_proj_bias[hidden_size : hidden_size * 2]
UpperCamelCase__ : Optional[int] = in_proj_weight[-hidden_size:, :]
UpperCamelCase__ : List[Any] = in_proj_bias[-hidden_size:]
def lowerCAmelCase_ ( ) -> Union[str, Any]:
UpperCamelCase__ : str = '''http://images.cocodataset.org/val2017/000000039769.jpg'''
UpperCamelCase__ : int = Image.open(requests.get(__UpperCAmelCase , stream=__UpperCAmelCase ).raw )
return im
@torch.no_grad()
def lowerCAmelCase_ ( __UpperCAmelCase: Union[str, Any] , __UpperCAmelCase: Dict , __UpperCAmelCase: Any ) -> Union[str, Any]:
UpperCamelCase__ : str = get_deta_config(__UpperCAmelCase )
# load original state dict
if model_name == "deta-swin-large":
UpperCamelCase__ : Optional[int] = hf_hub_download(repo_id='''nielsr/deta-checkpoints''' , filename='''adet_swin_ft.pth''' )
elif model_name == "deta-swin-large-o365":
UpperCamelCase__ : Optional[Any] = hf_hub_download(repo_id='''jozhang97/deta-swin-l-o365''' , filename='''deta_swin_pt_o365.pth''' )
else:
raise ValueError(f"Model name {model_name} not supported" )
UpperCamelCase__ : Union[str, Any] = torch.load(__UpperCAmelCase , map_location='''cpu''' )['''model''']
# original state dict
for name, param in state_dict.items():
print(__UpperCAmelCase , param.shape )
# rename keys
UpperCamelCase__ : List[str] = create_rename_keys(__UpperCAmelCase )
for src, dest in rename_keys:
rename_key(__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase )
read_in_swin_q_k_v(__UpperCAmelCase , config.backbone_config )
read_in_decoder_q_k_v(__UpperCAmelCase , __UpperCAmelCase )
# fix some prefixes
for key in state_dict.copy().keys():
if "transformer.decoder.class_embed" in key or "transformer.decoder.bbox_embed" in key:
UpperCamelCase__ : Tuple = state_dict.pop(__UpperCAmelCase )
UpperCamelCase__ : Dict = val
if "input_proj" in key:
UpperCamelCase__ : Optional[int] = state_dict.pop(__UpperCAmelCase )
UpperCamelCase__ : Optional[int] = val
if "level_embed" in key or "pos_trans" in key or "pix_trans" in key or "enc_output" in key:
UpperCamelCase__ : Optional[int] = state_dict.pop(__UpperCAmelCase )
UpperCamelCase__ : Union[str, Any] = val
# finally, create HuggingFace model and load state dict
UpperCamelCase__ : Optional[int] = DetaForObjectDetection(__UpperCAmelCase )
model.load_state_dict(__UpperCAmelCase )
model.eval()
UpperCamelCase__ : List[str] = '''cuda''' if torch.cuda.is_available() else '''cpu'''
model.to(__UpperCAmelCase )
# load image processor
UpperCamelCase__ : Any = DetaImageProcessor(format='''coco_detection''' )
# verify our conversion on image
UpperCamelCase__ : Optional[Any] = prepare_img()
UpperCamelCase__ : str = processor(images=__UpperCAmelCase , return_tensors='''pt''' )
UpperCamelCase__ : Union[str, Any] = encoding['''pixel_values''']
UpperCamelCase__ : str = model(pixel_values.to(__UpperCAmelCase ) )
# verify logits
print('''Logits:''' , outputs.logits[0, :3, :3] )
print('''Boxes:''' , outputs.pred_boxes[0, :3, :3] )
if model_name == "deta-swin-large":
UpperCamelCase__ : Any = torch.tensor(
[[-7.6308, -2.8485, -5.3737], [-7.2037, -4.5505, -4.8027], [-7.2943, -4.2611, -4.6617]] )
UpperCamelCase__ : List[str] = torch.tensor([[0.4987, 0.4969, 0.9999], [0.2549, 0.5498, 0.4805], [0.5498, 0.2757, 0.0569]] )
elif model_name == "deta-swin-large-o365":
UpperCamelCase__ : Any = torch.tensor(
[[-8.0122, -3.5720, -4.9717], [-8.1547, -3.6886, -4.6389], [-7.6610, -3.6194, -5.0134]] )
UpperCamelCase__ : Tuple = torch.tensor([[0.2523, 0.5549, 0.4881], [0.7715, 0.4149, 0.4601], [0.5503, 0.2753, 0.0575]] )
assert torch.allclose(outputs.logits[0, :3, :3] , expected_logits.to(__UpperCAmelCase ) , atol=1e-4 )
assert torch.allclose(outputs.pred_boxes[0, :3, :3] , expected_boxes.to(__UpperCAmelCase ) , atol=1e-4 )
print('''Everything ok!''' )
if pytorch_dump_folder_path:
# Save model and processor
logger.info(f"Saving PyTorch model and processor to {pytorch_dump_folder_path}..." )
Path(__UpperCAmelCase ).mkdir(exist_ok=__UpperCAmelCase )
model.save_pretrained(__UpperCAmelCase )
processor.save_pretrained(__UpperCAmelCase )
# Push to hub
if push_to_hub:
print('''Pushing model and processor to hub...''' )
model.push_to_hub(f"jozhang97/{model_name}" )
processor.push_to_hub(f"jozhang97/{model_name}" )
if __name__ == "__main__":
UpperCAmelCase_ = argparse.ArgumentParser()
parser.add_argument(
'--model_name',
type=str,
default='deta-swin-large',
choices=['deta-swin-large', 'deta-swin-large-o365'],
help='Name of the model you\'d like to convert.',
)
parser.add_argument(
'--pytorch_dump_folder_path',
default=None,
type=str,
help='Path to the folder to output PyTorch model.',
)
parser.add_argument(
'--push_to_hub', action='store_true', help='Whether or not to push the converted model to the 🤗 hub.'
)
UpperCAmelCase_ = parser.parse_args()
convert_deta_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)
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import collections.abc
from typing import Optional, Tuple, Union
import torch
import torch.utils.checkpoint
from torch import nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from ...activations import ACTaFN
from ...modeling_outputs import BaseModelOutputWithNoAttention, ImageClassifierOutputWithNoAttention
from ...modeling_utils import PreTrainedModel
from ...utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward, logging
from .configuration_poolformer import PoolFormerConfig
__lowercase : Optional[int] =logging.get_logger(__name__)
# General docstring
__lowercase : Tuple ="""PoolFormerConfig"""
# Base docstring
__lowercase : Union[str, Any] ="""sail/poolformer_s12"""
__lowercase : int =[1, 512, 7, 7]
# Image classification docstring
__lowercase : Tuple ="""sail/poolformer_s12"""
__lowercase : Optional[Any] ="""tabby, tabby cat"""
__lowercase : List[str] =[
"""sail/poolformer_s12""",
# See all PoolFormer models at https://huggingface.co/models?filter=poolformer
]
def a__ ( lowercase__ , lowercase__ = 0.0 , lowercase__ = False ):
'''simple docstring'''
if drop_prob == 0.0 or not training:
return input
UpperCAmelCase_ =1 - drop_prob
UpperCAmelCase_ =(input.shape[0],) + (1,) * (input.ndim - 1) # work with diff dim tensors, not just 2D ConvNets
UpperCAmelCase_ =keep_prob + torch.rand(lowercase__ , dtype=input.dtype , device=input.device )
random_tensor.floor_() # binarize
UpperCAmelCase_ =input.div(lowercase__ ) * random_tensor
return output
class A ( nn.Module ):
def __init__( self: Optional[Any] , _lowerCAmelCase: Optional[float] = None ) -> None:
'''simple docstring'''
super().__init__()
UpperCAmelCase_ =drop_prob
def lowerCAmelCase__ ( self: Any , _lowerCAmelCase: torch.Tensor ) -> torch.Tensor:
'''simple docstring'''
return drop_path(_lowerCAmelCase , self.drop_prob , self.training )
def lowerCAmelCase__ ( self: Tuple ) -> str:
'''simple docstring'''
return "p={}".format(self.drop_prob )
class A ( nn.Module ):
def __init__( self: str , _lowerCAmelCase: Dict , _lowerCAmelCase: str , _lowerCAmelCase: Tuple , _lowerCAmelCase: str , _lowerCAmelCase: List[Any] , _lowerCAmelCase: Optional[Any]=None ) -> List[str]:
'''simple docstring'''
super().__init__()
UpperCAmelCase_ =patch_size if isinstance(_lowerCAmelCase , collections.abc.Iterable ) else (patch_size, patch_size)
UpperCAmelCase_ =stride if isinstance(_lowerCAmelCase , collections.abc.Iterable ) else (stride, stride)
UpperCAmelCase_ =padding if isinstance(_lowerCAmelCase , collections.abc.Iterable ) else (padding, padding)
UpperCAmelCase_ =nn.Convad(_lowerCAmelCase , _lowerCAmelCase , kernel_size=_lowerCAmelCase , stride=_lowerCAmelCase , padding=_lowerCAmelCase )
UpperCAmelCase_ =norm_layer(_lowerCAmelCase ) if norm_layer else nn.Identity()
def lowerCAmelCase__ ( self: Any , _lowerCAmelCase: str ) -> List[str]:
'''simple docstring'''
UpperCAmelCase_ =self.projection(_lowerCAmelCase )
UpperCAmelCase_ =self.norm(_lowerCAmelCase )
return embeddings
class A ( nn.GroupNorm ):
def __init__( self: int , _lowerCAmelCase: int , **_lowerCAmelCase: Any ) -> Tuple:
'''simple docstring'''
super().__init__(1 , _lowerCAmelCase , **_lowerCAmelCase )
class A ( nn.Module ):
def __init__( self: Tuple , _lowerCAmelCase: int ) -> List[Any]:
'''simple docstring'''
super().__init__()
UpperCAmelCase_ =nn.AvgPoolad(_lowerCAmelCase , stride=1 , padding=pool_size // 2 , count_include_pad=_lowerCAmelCase )
def lowerCAmelCase__ ( self: List[Any] , _lowerCAmelCase: Optional[int] ) -> Optional[int]:
'''simple docstring'''
return self.pool(_lowerCAmelCase ) - hidden_states
class A ( nn.Module ):
def __init__( self: Any , _lowerCAmelCase: int , _lowerCAmelCase: Union[str, Any] , _lowerCAmelCase: List[str] , _lowerCAmelCase: List[str] ) -> Any:
'''simple docstring'''
super().__init__()
UpperCAmelCase_ =nn.Convad(_lowerCAmelCase , _lowerCAmelCase , 1 )
UpperCAmelCase_ =nn.Convad(_lowerCAmelCase , _lowerCAmelCase , 1 )
UpperCAmelCase_ =PoolFormerDropPath(_lowerCAmelCase )
if isinstance(config.hidden_act , _lowerCAmelCase ):
UpperCAmelCase_ =ACTaFN[config.hidden_act]
else:
UpperCAmelCase_ =config.hidden_act
def lowerCAmelCase__ ( self: str , _lowerCAmelCase: int ) -> Optional[int]:
'''simple docstring'''
UpperCAmelCase_ =self.conva(_lowerCAmelCase )
UpperCAmelCase_ =self.act_fn(_lowerCAmelCase )
UpperCAmelCase_ =self.drop(_lowerCAmelCase )
UpperCAmelCase_ =self.conva(_lowerCAmelCase )
UpperCAmelCase_ =self.drop(_lowerCAmelCase )
return hidden_states
class A ( nn.Module ):
def __init__( self: str , _lowerCAmelCase: str , _lowerCAmelCase: Any , _lowerCAmelCase: Optional[Any] , _lowerCAmelCase: Union[str, Any] , _lowerCAmelCase: List[Any] , _lowerCAmelCase: Any ) -> List[Any]:
'''simple docstring'''
super().__init__()
UpperCAmelCase_ =PoolFormerPooling(_lowerCAmelCase )
UpperCAmelCase_ =PoolFormerOutput(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase )
UpperCAmelCase_ =PoolFormerGroupNorm(_lowerCAmelCase )
UpperCAmelCase_ =PoolFormerGroupNorm(_lowerCAmelCase )
# Useful for training neural nets
UpperCAmelCase_ =PoolFormerDropPath(_lowerCAmelCase ) if drop_path > 0.0 else nn.Identity()
UpperCAmelCase_ =config.use_layer_scale
if config.use_layer_scale:
UpperCAmelCase_ =nn.Parameter(
config.layer_scale_init_value * torch.ones((_lowerCAmelCase) ) , requires_grad=_lowerCAmelCase )
UpperCAmelCase_ =nn.Parameter(
config.layer_scale_init_value * torch.ones((_lowerCAmelCase) ) , requires_grad=_lowerCAmelCase )
def lowerCAmelCase__ ( self: Tuple , _lowerCAmelCase: List[str] ) -> List[Any]:
'''simple docstring'''
if self.use_layer_scale:
UpperCAmelCase_ =self.pooling(self.before_norm(_lowerCAmelCase ) )
UpperCAmelCase_ =self.layer_scale_a.unsqueeze(-1 ).unsqueeze(-1 ) * pooling_output
# First residual connection
UpperCAmelCase_ =hidden_states + self.drop_path(_lowerCAmelCase )
UpperCAmelCase_ =()
UpperCAmelCase_ =self.output(self.after_norm(_lowerCAmelCase ) )
UpperCAmelCase_ =self.layer_scale_a.unsqueeze(-1 ).unsqueeze(-1 ) * layer_output
# Second residual connection
UpperCAmelCase_ =hidden_states + self.drop_path(_lowerCAmelCase )
UpperCAmelCase_ =(output,) + outputs
return outputs
else:
UpperCAmelCase_ =self.drop_path(self.pooling(self.before_norm(_lowerCAmelCase ) ) )
# First residual connection
UpperCAmelCase_ =pooling_output + hidden_states
UpperCAmelCase_ =()
# Second residual connection inside the PoolFormerOutput block
UpperCAmelCase_ =self.drop_path(self.output(self.after_norm(_lowerCAmelCase ) ) )
UpperCAmelCase_ =hidden_states + layer_output
UpperCAmelCase_ =(output,) + outputs
return outputs
class A ( nn.Module ):
def __init__( self: Union[str, Any] , _lowerCAmelCase: Dict ) -> str:
'''simple docstring'''
super().__init__()
UpperCAmelCase_ =config
# stochastic depth decay rule
UpperCAmelCase_ =[x.item() for x in torch.linspace(0 , config.drop_path_rate , sum(config.depths ) )]
# patch embeddings
UpperCAmelCase_ =[]
for i in range(config.num_encoder_blocks ):
embeddings.append(
PoolFormerEmbeddings(
patch_size=config.patch_sizes[i] , stride=config.strides[i] , padding=config.padding[i] , num_channels=config.num_channels if i == 0 else config.hidden_sizes[i - 1] , hidden_size=config.hidden_sizes[i] , ) )
UpperCAmelCase_ =nn.ModuleList(_lowerCAmelCase )
# Transformer blocks
UpperCAmelCase_ =[]
UpperCAmelCase_ =0
for i in range(config.num_encoder_blocks ):
# each block consists of layers
UpperCAmelCase_ =[]
if i != 0:
cur += config.depths[i - 1]
for j in range(config.depths[i] ):
layers.append(
PoolFormerLayer(
_lowerCAmelCase , num_channels=config.hidden_sizes[i] , pool_size=config.pool_size , hidden_size=config.hidden_sizes[i] , intermediate_size=int(config.hidden_sizes[i] * config.mlp_ratio ) , drop_path=dpr[cur + j] , ) )
blocks.append(nn.ModuleList(_lowerCAmelCase ) )
UpperCAmelCase_ =nn.ModuleList(_lowerCAmelCase )
def lowerCAmelCase__ ( self: Optional[Any] , _lowerCAmelCase: Optional[int] , _lowerCAmelCase: Optional[Any]=False , _lowerCAmelCase: Dict=True ) -> str:
'''simple docstring'''
UpperCAmelCase_ =() if output_hidden_states else None
UpperCAmelCase_ =pixel_values
for idx, layers in enumerate(zip(self.patch_embeddings , self.block ) ):
UpperCAmelCase_ , UpperCAmelCase_ =layers
# Get patch embeddings from hidden_states
UpperCAmelCase_ =embedding_layer(_lowerCAmelCase )
# Send the embeddings through the blocks
for _, blk in enumerate(_lowerCAmelCase ):
UpperCAmelCase_ =blk(_lowerCAmelCase )
UpperCAmelCase_ =layer_outputs[0]
if output_hidden_states:
UpperCAmelCase_ =all_hidden_states + (hidden_states,)
if not return_dict:
return tuple(v for v in [hidden_states, all_hidden_states] if v is not None )
return BaseModelOutputWithNoAttention(last_hidden_state=_lowerCAmelCase , hidden_states=_lowerCAmelCase )
class A ( __lowercase ):
_snake_case =PoolFormerConfig
_snake_case ='''poolformer'''
_snake_case ='''pixel_values'''
_snake_case =True
def lowerCAmelCase__ ( self: Tuple , _lowerCAmelCase: str ) -> Optional[Any]:
'''simple docstring'''
if isinstance(_lowerCAmelCase , (nn.Linear, nn.Convad) ):
module.weight.data.normal_(mean=0.0 , std=self.config.initializer_range )
if module.bias is not None:
module.bias.data.zero_()
elif isinstance(_lowerCAmelCase , nn.LayerNorm ):
module.bias.data.zero_()
module.weight.data.fill_(1.0 )
def lowerCAmelCase__ ( self: Tuple , _lowerCAmelCase: Optional[int] , _lowerCAmelCase: Tuple=False ) -> Optional[Any]:
'''simple docstring'''
if isinstance(_lowerCAmelCase , _lowerCAmelCase ):
UpperCAmelCase_ =value
__lowercase : Union[str, Any] =R"""
This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) sub-class. Use
it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and
behavior.
Parameters:
config ([`PoolFormerConfig`]): Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.
"""
__lowercase : Union[str, Any] =R"""
Args:
pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):
Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. See
[`PoolFormerImageProcessor.__call__`] for details.
"""
@add_start_docstrings(
'''The bare PoolFormer Model transformer outputting raw hidden-states without any specific head on top.''' , __lowercase , )
class A ( __lowercase ):
def __init__( self: Any , _lowerCAmelCase: List[Any] ) -> str:
'''simple docstring'''
super().__init__(_lowerCAmelCase )
UpperCAmelCase_ =config
UpperCAmelCase_ =PoolFormerEncoder(_lowerCAmelCase )
# Initialize weights and apply final processing
self.post_init()
def lowerCAmelCase__ ( self: str ) -> int:
'''simple docstring'''
return self.embeddings.patch_embeddings
@add_start_docstrings_to_model_forward(_lowerCAmelCase )
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_DOC , output_type=_lowerCAmelCase , config_class=_CONFIG_FOR_DOC , modality="vision" , expected_output=_EXPECTED_OUTPUT_SHAPE , )
def lowerCAmelCase__ ( self: Any , _lowerCAmelCase: Optional[torch.FloatTensor] = None , _lowerCAmelCase: Optional[bool] = None , _lowerCAmelCase: Optional[bool] = None , ) -> Union[Tuple, BaseModelOutputWithNoAttention]:
'''simple docstring'''
UpperCAmelCase_ =(
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
UpperCAmelCase_ =return_dict if return_dict is not None else self.config.use_return_dict
if pixel_values is None:
raise ValueError("You have to specify pixel_values" )
UpperCAmelCase_ =self.encoder(
_lowerCAmelCase , output_hidden_states=_lowerCAmelCase , return_dict=_lowerCAmelCase , )
UpperCAmelCase_ =encoder_outputs[0]
if not return_dict:
return (sequence_output, None) + encoder_outputs[1:]
return BaseModelOutputWithNoAttention(
last_hidden_state=_lowerCAmelCase , hidden_states=encoder_outputs.hidden_states , )
class A ( nn.Module ):
def __init__( self: str , _lowerCAmelCase: Dict ) -> Dict:
'''simple docstring'''
super().__init__()
UpperCAmelCase_ =nn.Linear(config.hidden_size , config.hidden_size )
def lowerCAmelCase__ ( self: Dict , _lowerCAmelCase: Optional[Any] ) -> Dict:
'''simple docstring'''
UpperCAmelCase_ =self.dense(_lowerCAmelCase )
return output
@add_start_docstrings(
'''
PoolFormer Model transformer with an image classification head on top
''' , __lowercase , )
class A ( __lowercase ):
def __init__( self: Union[str, Any] , _lowerCAmelCase: Optional[int] ) -> Dict:
'''simple docstring'''
super().__init__(_lowerCAmelCase )
UpperCAmelCase_ =config.num_labels
UpperCAmelCase_ =PoolFormerModel(_lowerCAmelCase )
# Final norm
UpperCAmelCase_ =PoolFormerGroupNorm(config.hidden_sizes[-1] )
# Classifier head
UpperCAmelCase_ =(
nn.Linear(config.hidden_sizes[-1] , config.num_labels ) if config.num_labels > 0 else nn.Identity()
)
# Initialize weights and apply final processing
self.post_init()
@add_start_docstrings_to_model_forward(_lowerCAmelCase )
@add_code_sample_docstrings(
checkpoint=_IMAGE_CLASS_CHECKPOINT , output_type=_lowerCAmelCase , config_class=_CONFIG_FOR_DOC , expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT , )
def lowerCAmelCase__ ( self: Dict , _lowerCAmelCase: Optional[torch.FloatTensor] = None , _lowerCAmelCase: Optional[torch.LongTensor] = None , _lowerCAmelCase: Optional[bool] = None , _lowerCAmelCase: Optional[bool] = None , ) -> Union[Tuple, ImageClassifierOutputWithNoAttention]:
'''simple docstring'''
UpperCAmelCase_ =return_dict if return_dict is not None else self.config.use_return_dict
UpperCAmelCase_ =self.poolformer(
_lowerCAmelCase , output_hidden_states=_lowerCAmelCase , return_dict=_lowerCAmelCase , )
UpperCAmelCase_ =outputs[0]
UpperCAmelCase_ =self.classifier(self.norm(_lowerCAmelCase ).mean([-2, -1] ) )
UpperCAmelCase_ =None
if labels is not None:
if self.config.problem_type is None:
if self.num_labels == 1:
UpperCAmelCase_ ="regression"
elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
UpperCAmelCase_ ="single_label_classification"
else:
UpperCAmelCase_ ="multi_label_classification"
if self.config.problem_type == "regression":
UpperCAmelCase_ =MSELoss()
if self.num_labels == 1:
UpperCAmelCase_ =loss_fct(logits.squeeze() , labels.squeeze() )
else:
UpperCAmelCase_ =loss_fct(_lowerCAmelCase , _lowerCAmelCase )
elif self.config.problem_type == "single_label_classification":
UpperCAmelCase_ =CrossEntropyLoss()
UpperCAmelCase_ =loss_fct(logits.view(-1 , self.num_labels ) , labels.view(-1 ) )
elif self.config.problem_type == "multi_label_classification":
UpperCAmelCase_ =BCEWithLogitsLoss()
UpperCAmelCase_ =loss_fct(_lowerCAmelCase , _lowerCAmelCase )
if not return_dict:
UpperCAmelCase_ =(logits,) + outputs[2:]
return ((loss,) + output) if loss is not None else output
return ImageClassifierOutputWithNoAttention(loss=_lowerCAmelCase , logits=_lowerCAmelCase , hidden_states=outputs.hidden_states )
| 54
|
import os
from glob import glob
import imageio
import torch
import torchvision
import wandb
from img_processing import custom_to_pil, loop_post_process, preprocess, preprocess_vqgan
from loaders import load_vqgan
from PIL import Image
from torch import nn
from transformers import CLIPModel, CLIPTokenizerFast
from utils import get_device, get_timestamp, show_pil
class __lowercase :
def __init__( self , lowercase_ = "cpu" , lowercase_ = "openai/clip-vit-large-patch14") -> None:
__snake_case = device
__snake_case = CLIPTokenizerFast.from_pretrained(lowercase_)
__snake_case = [0.4814_5466, 0.457_8275, 0.4082_1073]
__snake_case = [0.2686_2954, 0.2613_0258, 0.2757_7711]
__snake_case = torchvision.transforms.Normalize(self.image_mean , self.image_std)
__snake_case = torchvision.transforms.Resize(2_2_4)
__snake_case = torchvision.transforms.CenterCrop(2_2_4)
def _a ( self , lowercase_) -> int:
__snake_case = self.resize(lowercase_)
__snake_case = self.center_crop(lowercase_)
__snake_case = self.normalize(lowercase_)
return images
def __call__( self , lowercase_=None , lowercase_=None , **lowercase_) -> Union[str, Any]:
__snake_case = self.tokenizer(text=lowercase_ , **lowercase_)
__snake_case = self.preprocess_img(lowercase_)
__snake_case = {key: value.to(self.device) for (key, value) in encoding.items()}
return encoding
class __lowercase ( nn.Module ):
def __init__( self , lowercase_=1_0 , lowercase_=0.01 , lowercase_=None , lowercase_=None , lowercase_=None , lowercase_=None , lowercase_=None , lowercase_=None , lowercase_=False , lowercase_=True , lowercase_="image" , lowercase_=True , lowercase_=False , lowercase_=False , lowercase_=False , ) -> None:
super().__init__()
__snake_case = None
__snake_case = device if device else get_device()
if vqgan:
__snake_case = vqgan
else:
__snake_case = load_vqgan(self.device , conf_path=lowercase_ , ckpt_path=lowercase_)
self.vqgan.eval()
if clip:
__snake_case = clip
else:
__snake_case = CLIPModel.from_pretrained('openai/clip-vit-base-patch32')
self.clip.to(self.device)
__snake_case = ProcessorGradientFlow(device=self.device)
__snake_case = iterations
__snake_case = lr
__snake_case = log
__snake_case = make_grid
__snake_case = return_val
__snake_case = quantize
__snake_case = self.vqgan.decoder.z_shape
def _a ( self , lowercase_=None , lowercase_=None , lowercase_=5 , lowercase_=True) -> List[str]:
__snake_case = []
if output_path is None:
__snake_case = './animation.gif'
if input_path is None:
__snake_case = self.save_path
__snake_case = sorted(glob(input_path + '/*'))
if not len(lowercase_):
raise ValueError(
'No images found in save path, aborting (did you pass save_intermediate=True to the generate'
' function?)')
if len(lowercase_) == 1:
print('Only one image found in save path, (did you pass save_intermediate=True to the generate function?)')
__snake_case = total_duration / len(lowercase_)
__snake_case = [frame_duration] * len(lowercase_)
if extend_frames:
__snake_case = 1.5
__snake_case = 3
for file_name in paths:
if file_name.endswith('.png'):
images.append(imageio.imread(lowercase_))
imageio.mimsave(lowercase_ , lowercase_ , duration=lowercase_)
print(F"gif saved to {output_path}")
def _a ( self , lowercase_=None , lowercase_=None) -> Union[str, Any]:
if not (path or img):
raise ValueError('Input either path or tensor')
if img is not None:
raise NotImplementedError
__snake_case = preprocess(Image.open(lowercase_) , target_image_size=2_5_6).to(self.device)
__snake_case = preprocess_vqgan(lowercase_)
__snake_case , *__snake_case = self.vqgan.encode(lowercase_)
return z
def _a ( self , lowercase_) -> Dict:
__snake_case = self.latent.detach().requires_grad_()
__snake_case = base_latent + transform_vector
if self.quantize:
__snake_case , *__snake_case = self.vqgan.quantize(lowercase_)
else:
__snake_case = trans_latent
return self.vqgan.decode(lowercase_)
def _a ( self , lowercase_ , lowercase_ , lowercase_=None) -> Any:
__snake_case = self.clip_preprocessor(text=lowercase_ , images=lowercase_ , return_tensors='pt' , padding=lowercase_)
__snake_case = self.clip(**lowercase_)
__snake_case = clip_outputs.logits_per_image
if weights is not None:
__snake_case = similarity_logits * weights
return similarity_logits.sum()
def _a ( self , lowercase_ , lowercase_ , lowercase_) -> List[Any]:
__snake_case = self._get_clip_similarity(pos_prompts['prompts'] , lowercase_ , weights=(1 / pos_prompts['weights']))
if neg_prompts:
__snake_case = self._get_clip_similarity(neg_prompts['prompts'] , lowercase_ , weights=neg_prompts['weights'])
else:
__snake_case = torch.tensor([1] , device=self.device)
__snake_case = -torch.log(lowercase_) + torch.log(lowercase_)
return loss
def _a ( self , lowercase_ , lowercase_ , lowercase_) -> Any:
__snake_case = torch.randn_like(self.latent , requires_grad=lowercase_ , device=self.device)
__snake_case = torch.optim.Adam([vector] , lr=self.lr)
for i in range(self.iterations):
optim.zero_grad()
__snake_case = self._add_vector(lowercase_)
__snake_case = loop_post_process(lowercase_)
__snake_case = self._get_CLIP_loss(lowercase_ , lowercase_ , lowercase_)
print('CLIP loss' , lowercase_)
if self.log:
wandb.log({'CLIP Loss': clip_loss})
clip_loss.backward(retain_graph=lowercase_)
optim.step()
if self.return_val == "image":
yield custom_to_pil(transformed_img[0])
else:
yield vector
def _a ( self , lowercase_ , lowercase_ , lowercase_) -> Any:
wandb.init(reinit=lowercase_ , project='face-editor')
wandb.config.update({'Positive Prompts': positive_prompts})
wandb.config.update({'Negative Prompts': negative_prompts})
wandb.config.update({'lr': self.lr, 'iterations': self.iterations})
if image_path:
__snake_case = Image.open(lowercase_)
__snake_case = image.resize((2_5_6, 2_5_6))
wandb.log('Original Image' , wandb.Image(lowercase_))
def _a ( self , lowercase_) -> Optional[int]:
if not prompts:
return []
__snake_case = []
__snake_case = []
if isinstance(lowercase_ , lowercase_):
__snake_case = [prompt.strip() for prompt in prompts.split('|')]
for prompt in prompts:
if isinstance(lowercase_ , (tuple, list)):
__snake_case = prompt[0]
__snake_case = float(prompt[1])
elif ":" in prompt:
__snake_case , __snake_case = prompt.split(':')
__snake_case = float(lowercase_)
else:
__snake_case = prompt
__snake_case = 1.0
processed_prompts.append(lowercase_)
weights.append(lowercase_)
return {
"prompts": processed_prompts,
"weights": torch.tensor(lowercase_ , device=self.device),
}
def _a ( self , lowercase_ , lowercase_=None , lowercase_=None , lowercase_=True , lowercase_=False , lowercase_=True , lowercase_=True , lowercase_=None , ) -> List[str]:
if image_path:
__snake_case = self._get_latent(lowercase_)
else:
__snake_case = torch.randn(self.latent_dim , device=self.device)
if self.log:
self._init_logging(lowercase_ , lowercase_ , lowercase_)
assert pos_prompts, "You must provide at least one positive prompt."
__snake_case = self.process_prompts(lowercase_)
__snake_case = self.process_prompts(lowercase_)
if save_final and save_path is None:
__snake_case = os.path.join('./outputs/' , '_'.join(pos_prompts['prompts']))
if not os.path.exists(lowercase_):
os.makedirs(lowercase_)
else:
__snake_case = save_path + '_' + get_timestamp()
os.makedirs(lowercase_)
__snake_case = save_path
__snake_case = self.vqgan.decode(self.latent)[0]
if show_intermediate:
print('Original Image')
show_pil(custom_to_pil(lowercase_))
__snake_case = loop_post_process(lowercase_)
for iter, transformed_img in enumerate(self._optimize_CLIP(lowercase_ , lowercase_ , lowercase_)):
if show_intermediate:
show_pil(lowercase_)
if save_intermediate:
transformed_img.save(os.path.join(self.save_path , F"iter_{iter:03d}.png"))
if self.log:
wandb.log({'Image': wandb.Image(lowercase_)})
if show_final:
show_pil(lowercase_)
if save_final:
transformed_img.save(os.path.join(self.save_path , F"iter_{iter:03d}_final.png"))
| 313
| 0
|
"""simple docstring"""
import os
import unittest
from transformers import LayoutLMTokenizer, LayoutLMTokenizerFast
from transformers.models.layoutlm.tokenization_layoutlm import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class _UpperCamelCase ( lowerCamelCase__ , unittest.TestCase ):
"""simple docstring"""
snake_case_ = LayoutLMTokenizer
snake_case_ = LayoutLMTokenizerFast
snake_case_ = True
snake_case_ = True
def _UpperCAmelCase ( self : int ) -> Union[str, Any]:
'''simple docstring'''
super().setUp()
__magic_name__ : Optional[Any] = [
'''[UNK]''',
'''[CLS]''',
'''[SEP]''',
'''want''',
'''##want''',
'''##ed''',
'''wa''',
'''un''',
'''runn''',
'''##ing''',
''',''',
'''low''',
'''lowest''',
]
__magic_name__ : Any = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['''vocab_file'''] )
with open(self.vocab_file , '''w''' , encoding='''utf-8''' ) as vocab_writer:
vocab_writer.write(''''''.join([x + '''\n''' for x in vocab_tokens] ) )
def _UpperCAmelCase ( self : List[str] , **snake_case : List[Any] ) -> List[str]:
'''simple docstring'''
return LayoutLMTokenizer.from_pretrained(self.tmpdirname , **snake_case )
def _UpperCAmelCase ( self : Dict , snake_case : Any ) -> Optional[Any]:
'''simple docstring'''
__magic_name__ : Optional[Any] = '''UNwant\u00E9d,running'''
__magic_name__ : List[str] = '''unwanted, running'''
return input_text, output_text
def _UpperCAmelCase ( self : Any ) -> List[Any]:
'''simple docstring'''
__magic_name__ : Dict = self.tokenizer_class(self.vocab_file )
__magic_name__ : Dict = tokenizer.tokenize('''UNwant\u00E9d,running''' )
self.assertListEqual(snake_case , ['''un''', '''##want''', '''##ed''', ''',''', '''runn''', '''##ing'''] )
self.assertListEqual(tokenizer.convert_tokens_to_ids(snake_case ) , [7, 4, 5, 10, 8, 9] )
def _UpperCAmelCase ( self : Tuple ) -> Union[str, Any]:
'''simple docstring'''
pass
| 147
|
"""simple docstring"""
import doctest
import logging
import os
import unittest
from pathlib import Path
from typing import List, Union
import transformers
from transformers.testing_utils import require_tf, require_torch, slow
A = logging.getLogger()
@unittest.skip('Temporarily disable the doc tests.' )
@require_torch
@require_tf
@slow
class _UpperCamelCase ( unittest.TestCase ):
"""simple docstring"""
def _UpperCAmelCase ( self : Dict , snake_case : Path , snake_case : Union[str, None] = None , snake_case : Union[List[str], None] = None , snake_case : Union[str, List[str], None] = None , snake_case : bool = True , ) -> int:
'''simple docstring'''
__magic_name__ : List[str] = [file for file in os.listdir(snake_case ) if os.path.isfile(os.path.join(snake_case , snake_case ) )]
if identifier is not None:
__magic_name__ : Tuple = [file for file in files if identifier in file]
if n_identifier is not None:
if isinstance(snake_case , snake_case ):
for n_ in n_identifier:
__magic_name__ : int = [file for file in files if n_ not in file]
else:
__magic_name__ : Tuple = [file for file in files if n_identifier not in file]
__magic_name__ : Tuple = ignore_files or []
ignore_files.append('''__init__.py''' )
__magic_name__ : Any = [file for file in files if file not in ignore_files]
for file in files:
# Open all files
print('''Testing''' , snake_case )
if only_modules:
__magic_name__ : List[Any] = file.split('''.''' )[0]
try:
__magic_name__ : Dict = getattr(snake_case , snake_case )
__magic_name__ : List[str] = doctest.DocTestSuite(snake_case )
__magic_name__ : Dict = unittest.TextTestRunner().run(snake_case )
self.assertIs(len(result.failures ) , 0 )
except AttributeError:
logger.info(f"""{module_identifier} is not a module.""" )
else:
__magic_name__ : Tuple = doctest.testfile(str('''..''' / directory / file ) , optionflags=doctest.ELLIPSIS )
self.assertIs(result.failed , 0 )
def _UpperCAmelCase ( self : str ) -> Optional[int]:
'''simple docstring'''
__magic_name__ : int = Path('''src/transformers''' )
__magic_name__ : str = '''modeling'''
__magic_name__ : str = [
'''modeling_ctrl.py''',
'''modeling_tf_ctrl.py''',
]
self.analyze_directory(snake_case , identifier=snake_case , ignore_files=snake_case )
def _UpperCAmelCase ( self : Any ) -> Optional[Any]:
'''simple docstring'''
__magic_name__ : Dict = Path('''src/transformers''' )
__magic_name__ : Union[str, Any] = '''tokenization'''
self.analyze_directory(snake_case , identifier=snake_case )
def _UpperCAmelCase ( self : Any ) -> Dict:
'''simple docstring'''
__magic_name__ : Any = Path('''src/transformers''' )
__magic_name__ : int = '''configuration'''
self.analyze_directory(snake_case , identifier=snake_case )
def _UpperCAmelCase ( self : str ) -> List[str]:
'''simple docstring'''
__magic_name__ : List[str] = Path('''src/transformers''' )
__magic_name__ : str = ['''configuration''', '''modeling''', '''tokenization''']
self.analyze_directory(snake_case , n_identifier=snake_case )
def _UpperCAmelCase ( self : Optional[int] ) -> Optional[Any]:
'''simple docstring'''
__magic_name__ : Any = Path('''docs/source''' )
__magic_name__ : str = ['''favicon.ico''']
self.analyze_directory(snake_case , ignore_files=snake_case , only_modules=snake_case )
| 147
| 1
|
'''simple docstring'''
from __future__ import annotations
def __UpperCAmelCase ( _UpperCAmelCase : list ) -> list:
if len(_UpperCAmelCase ) == 0:
return []
__snake_case , __snake_case = min(_UpperCAmelCase ), max(_UpperCAmelCase )
__snake_case = int(max_value - min_value ) + 1
__snake_case = [[] for _ in range(_UpperCAmelCase )]
for i in my_list:
buckets[int(i - min_value )].append(_UpperCAmelCase )
return [v for bucket in buckets for v in sorted(_UpperCAmelCase )]
if __name__ == "__main__":
from doctest import testmod
testmod()
assert bucket_sort([4, 5, 3, 2, 1]) == [1, 2, 3, 4, 5]
assert bucket_sort([0, 1, -10, 15, 2, -2]) == [-10, -2, 0, 1, 2, 15]
| 69
|
"""simple docstring"""
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__UpperCAmelCase = {
'''configuration_informer''': [
'''INFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''InformerConfig''',
],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__UpperCAmelCase = [
'''INFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''InformerForPrediction''',
'''InformerModel''',
'''InformerPreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_informer import INFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, InformerConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_informer import (
INFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
InformerForPrediction,
InformerModel,
InformerPreTrainedModel,
)
else:
import sys
__UpperCAmelCase = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 642
| 0
|
'''simple docstring'''
from typing import List, Optional, Union
import numpy as np
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import PaddingStrategy, TensorType, logging
a = logging.get_logger(__name__)
class __a ( _snake_case ):
__UpperCamelCase : Any = ['input_values', 'padding_mask']
def __init__( self : str ,lowerCamelCase : int = 1 ,lowerCamelCase : int = 2_4000 ,lowerCamelCase : float = 0.0 ,lowerCamelCase : float = None ,lowerCamelCase : float = None ,**lowerCamelCase : Union[str, Any] ,):
'''simple docstring'''
super().__init__(feature_size=lowerCamelCase ,sampling_rate=lowerCamelCase ,padding_value=lowerCamelCase ,**lowerCamelCase )
__SCREAMING_SNAKE_CASE = chunk_length_s
__SCREAMING_SNAKE_CASE = overlap
@property
def UpperCAmelCase__ ( self : Tuple ):
'''simple docstring'''
if self.chunk_length_s is None:
return None
else:
return int(self.chunk_length_s * self.sampling_rate )
@property
def UpperCAmelCase__ ( self : Any ):
'''simple docstring'''
if self.chunk_length_s is None or self.overlap is None:
return None
else:
return max(1 ,int((1.0 - self.overlap) * self.chunk_length ) )
def __call__( self : Tuple ,lowerCamelCase : Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]] ,lowerCamelCase : Optional[Union[bool, str, PaddingStrategy]] = None ,lowerCamelCase : Optional[bool] = False ,lowerCamelCase : Optional[int] = None ,lowerCamelCase : Optional[Union[str, TensorType]] = None ,lowerCamelCase : Optional[int] = None ,):
'''simple docstring'''
if sampling_rate is not None:
if sampling_rate != self.sampling_rate:
raise ValueError(
f"""The model corresponding to this feature extractor: {self} was trained using a sampling rate of"""
f""" {self.sampling_rate}. Please make sure that the provided audio input was sampled with"""
f""" {self.sampling_rate} and not {sampling_rate}.""" )
else:
logger.warning(
"""It is strongly recommended to pass the `sampling_rate` argument to this function. """
"""Failing to do so can result in silent errors that might be hard to debug.""" )
if padding and truncation:
raise ValueError("""Both padding and truncation were set. Make sure you only set one.""" )
elif padding is None:
# by default let's pad the inputs
__SCREAMING_SNAKE_CASE = True
__SCREAMING_SNAKE_CASE = bool(
isinstance(lowerCamelCase ,(list, tuple) ) and (isinstance(raw_audio[0] ,(np.ndarray, tuple, list) )) )
if is_batched:
__SCREAMING_SNAKE_CASE = [np.asarray(lowerCamelCase ,dtype=np.floataa ).T for audio in raw_audio]
elif not is_batched and not isinstance(lowerCamelCase ,np.ndarray ):
__SCREAMING_SNAKE_CASE = np.asarray(lowerCamelCase ,dtype=np.floataa )
elif isinstance(lowerCamelCase ,np.ndarray ) and raw_audio.dtype is np.dtype(np.floataa ):
__SCREAMING_SNAKE_CASE = raw_audio.astype(np.floataa )
# always return batch
if not is_batched:
__SCREAMING_SNAKE_CASE = [np.asarray(lowerCamelCase ).T]
# verify inputs are valid
for idx, example in enumerate(lowerCamelCase ):
if example.ndim > 2:
raise ValueError(f"""Expected input shape (channels, length) but got shape {example.shape}""" )
if self.feature_size == 1 and example.ndim != 1:
raise ValueError(f"""Expected mono audio but example has {example.shape[-1]} channels""" )
if self.feature_size == 2 and example.shape[-1] != 2:
raise ValueError(f"""Expected stereo audio but example has {example.shape[-1]} channels""" )
__SCREAMING_SNAKE_CASE = None
__SCREAMING_SNAKE_CASE = BatchFeature({"""input_values""": raw_audio} )
if self.chunk_stride is not None and self.chunk_length is not None and max_length is None:
if truncation:
__SCREAMING_SNAKE_CASE = min(array.shape[0] for array in raw_audio )
__SCREAMING_SNAKE_CASE = int(np.floor(max_length / self.chunk_stride ) )
__SCREAMING_SNAKE_CASE = (nb_step - 1) * self.chunk_stride + self.chunk_length
elif padding:
__SCREAMING_SNAKE_CASE = max(array.shape[0] for array in raw_audio )
__SCREAMING_SNAKE_CASE = int(np.ceil(max_length / self.chunk_stride ) )
__SCREAMING_SNAKE_CASE = (nb_step - 1) * self.chunk_stride + self.chunk_length
__SCREAMING_SNAKE_CASE = """max_length"""
else:
__SCREAMING_SNAKE_CASE = input_values
# normal padding on batch
if padded_inputs is None:
__SCREAMING_SNAKE_CASE = self.pad(
lowerCamelCase ,max_length=lowerCamelCase ,truncation=lowerCamelCase ,padding=lowerCamelCase ,return_attention_mask=lowerCamelCase ,)
if padding:
__SCREAMING_SNAKE_CASE = padded_inputs.pop("""attention_mask""" )
__SCREAMING_SNAKE_CASE = []
for example in padded_inputs.pop("""input_values""" ):
if self.feature_size == 1:
__SCREAMING_SNAKE_CASE = example[..., None]
input_values.append(example.T )
__SCREAMING_SNAKE_CASE = input_values
if return_tensors is not None:
__SCREAMING_SNAKE_CASE = padded_inputs.convert_to_tensors(lowerCamelCase )
return padded_inputs
| 13
|
'''simple docstring'''
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin
if is_torch_available():
import torch
if is_vision_available():
from PIL import Image
from transformers import ChineseCLIPImageProcessor
class __a ( unittest.TestCase ):
def __init__( self : List[Any] ,lowerCamelCase : List[Any] ,lowerCamelCase : List[str]=7 ,lowerCamelCase : List[str]=3 ,lowerCamelCase : List[str]=18 ,lowerCamelCase : Any=30 ,lowerCamelCase : Optional[Any]=400 ,lowerCamelCase : Optional[Any]=True ,lowerCamelCase : Optional[Any]=None ,lowerCamelCase : Optional[int]=True ,lowerCamelCase : int=None ,lowerCamelCase : str=True ,lowerCamelCase : Dict=[0.48_145_466, 0.4_578_275, 0.40_821_073] ,lowerCamelCase : List[str]=[0.26_862_954, 0.26_130_258, 0.27_577_711] ,lowerCamelCase : Tuple=True ,):
'''simple docstring'''
__SCREAMING_SNAKE_CASE = size if size is not None else {"""height""": 224, """width""": 224}
__SCREAMING_SNAKE_CASE = crop_size if crop_size is not None else {"""height""": 18, """width""": 18}
__SCREAMING_SNAKE_CASE = parent
__SCREAMING_SNAKE_CASE = batch_size
__SCREAMING_SNAKE_CASE = num_channels
__SCREAMING_SNAKE_CASE = image_size
__SCREAMING_SNAKE_CASE = min_resolution
__SCREAMING_SNAKE_CASE = max_resolution
__SCREAMING_SNAKE_CASE = do_resize
__SCREAMING_SNAKE_CASE = size
__SCREAMING_SNAKE_CASE = do_center_crop
__SCREAMING_SNAKE_CASE = crop_size
__SCREAMING_SNAKE_CASE = do_normalize
__SCREAMING_SNAKE_CASE = image_mean
__SCREAMING_SNAKE_CASE = image_std
__SCREAMING_SNAKE_CASE = do_convert_rgb
def UpperCAmelCase__ ( self : Optional[int] ):
'''simple docstring'''
return {
"do_resize": self.do_resize,
"size": self.size,
"do_center_crop": self.do_center_crop,
"crop_size": self.crop_size,
"do_normalize": self.do_normalize,
"image_mean": self.image_mean,
"image_std": self.image_std,
"do_convert_rgb": self.do_convert_rgb,
}
def UpperCAmelCase__ ( self : int ,lowerCamelCase : Union[str, Any]=False ,lowerCamelCase : str=False ,lowerCamelCase : str=False ):
'''simple docstring'''
assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"
if equal_resolution:
__SCREAMING_SNAKE_CASE = []
for i in range(self.batch_size ):
image_inputs.append(
np.random.randint(
255 ,size=(self.num_channels, self.max_resolution, self.max_resolution) ,dtype=np.uinta ) )
else:
__SCREAMING_SNAKE_CASE = []
for i in range(self.batch_size ):
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE = np.random.choice(np.arange(self.min_resolution ,self.max_resolution ) ,2 )
image_inputs.append(np.random.randint(255 ,size=(self.num_channels, width, height) ,dtype=np.uinta ) )
if not numpify and not torchify:
# PIL expects the channel dimension as last dimension
__SCREAMING_SNAKE_CASE = [Image.fromarray(np.moveaxis(lowerCamelCase ,0 ,-1 ) ) for x in image_inputs]
if torchify:
__SCREAMING_SNAKE_CASE = [torch.from_numpy(lowerCamelCase ) for x in image_inputs]
return image_inputs
@require_torch
@require_vision
class __a ( _snake_case, unittest.TestCase ):
__UpperCamelCase : int = ChineseCLIPImageProcessor if is_vision_available() else None
def UpperCAmelCase__ ( self : Any ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE = ChineseCLIPImageProcessingTester(self ,do_center_crop=lowerCamelCase )
@property
def UpperCAmelCase__ ( self : Optional[int] ):
'''simple docstring'''
return self.image_processor_tester.prepare_image_processor_dict()
def UpperCAmelCase__ ( self : Tuple ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE = self.image_processing_class(**self.image_processor_dict )
self.assertTrue(hasattr(lowerCamelCase ,"""do_resize""" ) )
self.assertTrue(hasattr(lowerCamelCase ,"""size""" ) )
self.assertTrue(hasattr(lowerCamelCase ,"""do_center_crop""" ) )
self.assertTrue(hasattr(lowerCamelCase ,"""center_crop""" ) )
self.assertTrue(hasattr(lowerCamelCase ,"""do_normalize""" ) )
self.assertTrue(hasattr(lowerCamelCase ,"""image_mean""" ) )
self.assertTrue(hasattr(lowerCamelCase ,"""image_std""" ) )
self.assertTrue(hasattr(lowerCamelCase ,"""do_convert_rgb""" ) )
def UpperCAmelCase__ ( self : List[Any] ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE = self.image_processing_class.from_dict(self.image_processor_dict )
self.assertEqual(image_processor.size ,{"""height""": 224, """width""": 224} )
self.assertEqual(image_processor.crop_size ,{"""height""": 18, """width""": 18} )
__SCREAMING_SNAKE_CASE = self.image_processing_class.from_dict(self.image_processor_dict ,size=42 ,crop_size=84 )
self.assertEqual(image_processor.size ,{"""shortest_edge""": 42} )
self.assertEqual(image_processor.crop_size ,{"""height""": 84, """width""": 84} )
def UpperCAmelCase__ ( self : Dict ):
'''simple docstring'''
pass
def UpperCAmelCase__ ( self : List[str] ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE = self.image_processing_class(**self.image_processor_dict )
# create random PIL images
__SCREAMING_SNAKE_CASE = self.image_processor_tester.prepare_inputs(equal_resolution=lowerCamelCase )
for image in image_inputs:
self.assertIsInstance(lowerCamelCase ,Image.Image )
# Test not batched input
__SCREAMING_SNAKE_CASE = image_processing(image_inputs[0] ,return_tensors="""pt""" ).pixel_values
self.assertEqual(
encoded_images.shape ,(
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size["""height"""],
self.image_processor_tester.crop_size["""width"""],
) ,)
# Test batched
__SCREAMING_SNAKE_CASE = image_processing(lowerCamelCase ,return_tensors="""pt""" ).pixel_values
self.assertEqual(
encoded_images.shape ,(
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size["""height"""],
self.image_processor_tester.crop_size["""width"""],
) ,)
def UpperCAmelCase__ ( self : Optional[int] ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE = self.image_processing_class(**self.image_processor_dict )
# create random numpy tensors
__SCREAMING_SNAKE_CASE = self.image_processor_tester.prepare_inputs(equal_resolution=lowerCamelCase ,numpify=lowerCamelCase )
for image in image_inputs:
self.assertIsInstance(lowerCamelCase ,np.ndarray )
# Test not batched input
__SCREAMING_SNAKE_CASE = image_processing(image_inputs[0] ,return_tensors="""pt""" ).pixel_values
self.assertEqual(
encoded_images.shape ,(
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size["""height"""],
self.image_processor_tester.crop_size["""width"""],
) ,)
# Test batched
__SCREAMING_SNAKE_CASE = image_processing(lowerCamelCase ,return_tensors="""pt""" ).pixel_values
self.assertEqual(
encoded_images.shape ,(
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size["""height"""],
self.image_processor_tester.crop_size["""width"""],
) ,)
def UpperCAmelCase__ ( self : str ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE = self.image_processing_class(**self.image_processor_dict )
# create random PyTorch tensors
__SCREAMING_SNAKE_CASE = self.image_processor_tester.prepare_inputs(equal_resolution=lowerCamelCase ,torchify=lowerCamelCase )
for image in image_inputs:
self.assertIsInstance(lowerCamelCase ,torch.Tensor )
# Test not batched input
__SCREAMING_SNAKE_CASE = image_processing(image_inputs[0] ,return_tensors="""pt""" ).pixel_values
self.assertEqual(
encoded_images.shape ,(
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size["""height"""],
self.image_processor_tester.crop_size["""width"""],
) ,)
# Test batched
__SCREAMING_SNAKE_CASE = image_processing(lowerCamelCase ,return_tensors="""pt""" ).pixel_values
self.assertEqual(
encoded_images.shape ,(
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size["""height"""],
self.image_processor_tester.crop_size["""width"""],
) ,)
@require_torch
@require_vision
class __a ( _snake_case, unittest.TestCase ):
__UpperCamelCase : Optional[int] = ChineseCLIPImageProcessor if is_vision_available() else None
def UpperCAmelCase__ ( self : Tuple ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE = ChineseCLIPImageProcessingTester(self ,num_channels=4 ,do_center_crop=lowerCamelCase )
__SCREAMING_SNAKE_CASE = 3
@property
def UpperCAmelCase__ ( self : Dict ):
'''simple docstring'''
return self.image_processor_tester.prepare_image_processor_dict()
def UpperCAmelCase__ ( self : int ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE = self.image_processing_class(**self.image_processor_dict )
self.assertTrue(hasattr(lowerCamelCase ,"""do_resize""" ) )
self.assertTrue(hasattr(lowerCamelCase ,"""size""" ) )
self.assertTrue(hasattr(lowerCamelCase ,"""do_center_crop""" ) )
self.assertTrue(hasattr(lowerCamelCase ,"""center_crop""" ) )
self.assertTrue(hasattr(lowerCamelCase ,"""do_normalize""" ) )
self.assertTrue(hasattr(lowerCamelCase ,"""image_mean""" ) )
self.assertTrue(hasattr(lowerCamelCase ,"""image_std""" ) )
self.assertTrue(hasattr(lowerCamelCase ,"""do_convert_rgb""" ) )
def UpperCAmelCase__ ( self : Tuple ):
'''simple docstring'''
pass
def UpperCAmelCase__ ( self : Union[str, Any] ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE = self.image_processing_class(**self.image_processor_dict )
# create random PIL images
__SCREAMING_SNAKE_CASE = self.image_processor_tester.prepare_inputs(equal_resolution=lowerCamelCase )
for image in image_inputs:
self.assertIsInstance(lowerCamelCase ,Image.Image )
# Test not batched input
__SCREAMING_SNAKE_CASE = image_processing(image_inputs[0] ,return_tensors="""pt""" ).pixel_values
self.assertEqual(
encoded_images.shape ,(
1,
self.expected_encoded_image_num_channels,
self.image_processor_tester.crop_size["""height"""],
self.image_processor_tester.crop_size["""width"""],
) ,)
# Test batched
__SCREAMING_SNAKE_CASE = image_processing(lowerCamelCase ,return_tensors="""pt""" ).pixel_values
self.assertEqual(
encoded_images.shape ,(
self.image_processor_tester.batch_size,
self.expected_encoded_image_num_channels,
self.image_processor_tester.crop_size["""height"""],
self.image_processor_tester.crop_size["""width"""],
) ,)
| 13
| 1
|
__A : Tuple = range(2, 2_0 + 1)
__A : Any = [1_0**k for k in range(ks[-1] + 1)]
__A : Tuple = {}
def __a ( A__ : List[Any] , A__ : Tuple , A__ : str , A__ : Optional[Any] ):
SCREAMING_SNAKE_CASE = sum(a_i[j] for j in range(lowercase__ , len(lowercase__ ) ) )
SCREAMING_SNAKE_CASE = sum(a_i[j] * base[j] for j in range(min(len(lowercase__ ) , lowercase__ ) ) )
SCREAMING_SNAKE_CASE = 0, 0
SCREAMING_SNAKE_CASE = n - i
SCREAMING_SNAKE_CASE = memo.get(lowercase__ )
if sub_memo is not None:
SCREAMING_SNAKE_CASE = sub_memo.get(lowercase__ )
if jumps is not None and len(lowercase__ ) > 0:
# find and make the largest jump without going over
SCREAMING_SNAKE_CASE = -1
for _k in range(len(lowercase__ ) - 1 , -1 , -1 ):
if jumps[_k][2] <= k and jumps[_k][1] <= max_dn:
SCREAMING_SNAKE_CASE = _k
break
if max_jump >= 0:
SCREAMING_SNAKE_CASE = jumps[max_jump]
# since the difference between jumps is cached, add c
SCREAMING_SNAKE_CASE = diff + c
for j in range(min(lowercase__ , len(lowercase__ ) ) ):
SCREAMING_SNAKE_CASE = divmod(lowercase__ , 10 )
if new_c > 0:
add(lowercase__ , lowercase__ , lowercase__ )
else:
SCREAMING_SNAKE_CASE = []
else:
SCREAMING_SNAKE_CASE = {c: []}
SCREAMING_SNAKE_CASE = sub_memo
if dn >= max_dn or c + diff >= base[k]:
return diff, dn
if k > ks[0]:
while True:
# keep doing smaller jumps
SCREAMING_SNAKE_CASE = next_term(lowercase__ , k - 1 , i + dn , lowercase__ )
diff += _diff
dn += terms_jumped
if dn >= max_dn or c + diff >= base[k]:
break
else:
# would be too small a jump, just compute sequential terms instead
SCREAMING_SNAKE_CASE = compute(lowercase__ , lowercase__ , i + dn , lowercase__ )
diff += _diff
dn += terms_jumped
SCREAMING_SNAKE_CASE = sub_memo[c]
# keep jumps sorted by # of terms skipped
SCREAMING_SNAKE_CASE = 0
while j < len(lowercase__ ):
if jumps[j][1] > dn:
break
j += 1
# cache the jump for this value digitsum(b) and c
sub_memo[c].insert(lowercase__ , (diff, dn, k) )
return (diff, dn)
def __a ( A__ : List[str] , A__ : Optional[int] , A__ : Tuple , A__ : int ):
if i >= n:
return 0, i
if k > len(lowercase__ ):
a_i.extend([0 for _ in range(k - len(lowercase__ ) )] )
# note: a_i -> b * 10^k + c
# ds_b -> digitsum(b)
# ds_c -> digitsum(c)
SCREAMING_SNAKE_CASE = i
SCREAMING_SNAKE_CASE = 0, 0, 0
for j in range(len(lowercase__ ) ):
if j >= k:
ds_b += a_i[j]
else:
ds_c += a_i[j]
while i < n:
i += 1
SCREAMING_SNAKE_CASE = ds_c + ds_b
diff += addend
SCREAMING_SNAKE_CASE = 0
for j in range(lowercase__ ):
SCREAMING_SNAKE_CASE = a_i[j] + addend
SCREAMING_SNAKE_CASE = divmod(lowercase__ , 10 )
ds_c += a_i[j]
if addend > 0:
break
if addend > 0:
add(lowercase__ , lowercase__ , lowercase__ )
return diff, i - start_i
def __a ( A__ : Optional[Any] , A__ : Tuple , A__ : List[str] ):
for j in range(lowercase__ , len(lowercase__ ) ):
SCREAMING_SNAKE_CASE = digits[j] + addend
if s >= 10:
SCREAMING_SNAKE_CASE = divmod(lowercase__ , 10 )
SCREAMING_SNAKE_CASE = addend // 10 + quotient
else:
SCREAMING_SNAKE_CASE = s
SCREAMING_SNAKE_CASE = addend // 10
if addend == 0:
break
while addend > 0:
SCREAMING_SNAKE_CASE = divmod(lowercase__ , 10 )
digits.append(lowercase__ )
def __a ( A__ : int = 10**15 ):
SCREAMING_SNAKE_CASE = [1]
SCREAMING_SNAKE_CASE = 1
SCREAMING_SNAKE_CASE = 0
while True:
SCREAMING_SNAKE_CASE = next_term(lowercase__ , 20 , i + dn , lowercase__ )
dn += terms_jumped
if dn == n - i:
break
SCREAMING_SNAKE_CASE = 0
for j in range(len(lowercase__ ) ):
a_n += digits[j] * 10**j
return a_n
if __name__ == "__main__":
print(f'{solution() = }')
| 16
|
from __future__ import annotations
class lowerCAmelCase_ :
"""simple docstring"""
def __init__( self :List[Any] , lowerCamelCase__ :int = 0 ):
UpperCamelCase__ :List[str] = key
def __a ( self :Optional[Any] , lowerCamelCase__ :str , lowerCamelCase__ :int ):
assert isinstance(lowerCamelCase__ , lowerCamelCase__ ) and isinstance(lowerCamelCase__ , lowerCamelCase__ )
UpperCamelCase__ :List[str] = key or self.__key or 1
# make sure key is an appropriate size
key %= 2_55
return [chr(ord(lowerCamelCase__ ) ^ key ) for ch in content]
def __a ( self :int , lowerCamelCase__ :str , lowerCamelCase__ :int ):
assert isinstance(lowerCamelCase__ , lowerCamelCase__ ) and isinstance(lowerCamelCase__ , lowerCamelCase__ )
UpperCamelCase__ :int = key or self.__key or 1
# make sure key is an appropriate size
key %= 2_55
return [chr(ord(lowerCamelCase__ ) ^ key ) for ch in content]
def __a ( self :Optional[Any] , lowerCamelCase__ :str , lowerCamelCase__ :int = 0 ):
assert isinstance(lowerCamelCase__ , lowerCamelCase__ ) and isinstance(lowerCamelCase__ , lowerCamelCase__ )
UpperCamelCase__ :Dict = key or self.__key or 1
# make sure key can be any size
while key > 2_55:
key -= 2_55
# This will be returned
UpperCamelCase__ :List[str] = """"""
for ch in content:
ans += chr(ord(lowerCamelCase__ ) ^ key )
return ans
def __a ( self :Any , lowerCamelCase__ :str , lowerCamelCase__ :int = 0 ):
assert isinstance(lowerCamelCase__ , lowerCamelCase__ ) and isinstance(lowerCamelCase__ , lowerCamelCase__ )
UpperCamelCase__ :Tuple = key or self.__key or 1
# make sure key can be any size
while key > 2_55:
key -= 2_55
# This will be returned
UpperCamelCase__ :Optional[int] = """"""
for ch in content:
ans += chr(ord(lowerCamelCase__ ) ^ key )
return ans
def __a ( self :Optional[Any] , lowerCamelCase__ :str , lowerCamelCase__ :int = 0 ):
assert isinstance(lowerCamelCase__ , lowerCamelCase__ ) and isinstance(lowerCamelCase__ , lowerCamelCase__ )
try:
with open(lowerCamelCase__ ) as fin, open("""encrypt.out""" , """w+""" ) as fout:
# actual encrypt-process
for line in fin:
fout.write(self.encrypt_string(lowerCamelCase__ , lowerCamelCase__ ) )
except OSError:
return False
return True
def __a ( self :Dict , lowerCamelCase__ :str , lowerCamelCase__ :int ):
assert isinstance(lowerCamelCase__ , lowerCamelCase__ ) and isinstance(lowerCamelCase__ , lowerCamelCase__ )
try:
with open(lowerCamelCase__ ) as fin, open("""decrypt.out""" , """w+""" ) as fout:
# actual encrypt-process
for line in fin:
fout.write(self.decrypt_string(lowerCamelCase__ , lowerCamelCase__ ) )
except OSError:
return False
return True
# Tests
# crypt = XORCipher()
# key = 67
# # test encrypt
# print(crypt.encrypt("hallo welt",key))
# # test decrypt
# print(crypt.decrypt(crypt.encrypt("hallo welt",key), key))
# # test encrypt_string
# print(crypt.encrypt_string("hallo welt",key))
# # test decrypt_string
# print(crypt.decrypt_string(crypt.encrypt_string("hallo welt",key),key))
# if (crypt.encrypt_file("test.txt",key)):
# print("encrypt successful")
# else:
# print("encrypt unsuccessful")
# if (crypt.decrypt_file("encrypt.out",key)):
# print("decrypt successful")
# else:
# print("decrypt unsuccessful")
| 45
| 0
|
'''simple docstring'''
def SCREAMING_SNAKE_CASE ( lowerCAmelCase , lowerCAmelCase , lowerCAmelCase = 0 , lowerCAmelCase = 0 ):
_UpperCamelCase = right or len(lowerCAmelCase ) - 1
if left > right:
return -1
elif list_data[left] == key:
return left
elif list_data[right] == key:
return right
else:
return search(lowerCAmelCase , lowerCAmelCase , left + 1 , right - 1 )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 704
|
import logging
import os
from typing import List, TextIO, Union
from conllu import parse_incr
from utils_ner import InputExample, Split, TokenClassificationTask
lowercase : Optional[Any] = logging.getLogger(__name__)
class __A( __UpperCAmelCase ):
def __init__( self, A=-1 ):
"""simple docstring"""
_UpperCamelCase = label_idx
def _UpperCamelCase ( self, A, A ):
"""simple docstring"""
if isinstance(A, A ):
_UpperCamelCase = mode.value
_UpperCamelCase = os.path.join(A, F'''{mode}.txt''' )
_UpperCamelCase = 1
_UpperCamelCase = []
with open(A, encoding='''utf-8''' ) as f:
_UpperCamelCase = []
_UpperCamelCase = []
for line in f:
if line.startswith('''-DOCSTART-''' ) or line == "" or line == "\n":
if words:
examples.append(InputExample(guid=F'''{mode}-{guid_index}''', words=A, labels=A ) )
guid_index += 1
_UpperCamelCase = []
_UpperCamelCase = []
else:
_UpperCamelCase = line.split(''' ''' )
words.append(splits[0] )
if len(A ) > 1:
labels.append(splits[self.label_idx].replace('''\n''', '''''' ) )
else:
# Examples could have no label for mode = "test"
labels.append('''O''' )
if words:
examples.append(InputExample(guid=F'''{mode}-{guid_index}''', words=A, labels=A ) )
return examples
def _UpperCamelCase ( self, A, A, A ):
"""simple docstring"""
_UpperCamelCase = 0
for line in test_input_reader:
if line.startswith('''-DOCSTART-''' ) or line == "" or line == "\n":
writer.write(A )
if not preds_list[example_id]:
example_id += 1
elif preds_list[example_id]:
_UpperCamelCase = line.split()[0] + ''' ''' + preds_list[example_id].pop(0 ) + '''\n'''
writer.write(A )
else:
logger.warning('''Maximum sequence length exceeded: No prediction for \'%s\'.''', line.split()[0] )
def _UpperCamelCase ( self, A ):
"""simple docstring"""
if path:
with open(A, '''r''' ) as f:
_UpperCamelCase = f.read().splitlines()
if "O" not in labels:
_UpperCamelCase = ['''O'''] + labels
return labels
else:
return ["O", "B-MISC", "I-MISC", "B-PER", "I-PER", "B-ORG", "I-ORG", "B-LOC", "I-LOC"]
class __A( __UpperCAmelCase ):
def __init__( self ):
"""simple docstring"""
super().__init__(label_idx=-2 )
def _UpperCamelCase ( self, A ):
"""simple docstring"""
if path:
with open(A, '''r''' ) as f:
_UpperCamelCase = f.read().splitlines()
if "O" not in labels:
_UpperCamelCase = ['''O'''] + labels
return labels
else:
return [
"O",
"B-ADVP",
"B-INTJ",
"B-LST",
"B-PRT",
"B-NP",
"B-SBAR",
"B-VP",
"B-ADJP",
"B-CONJP",
"B-PP",
"I-ADVP",
"I-INTJ",
"I-LST",
"I-PRT",
"I-NP",
"I-SBAR",
"I-VP",
"I-ADJP",
"I-CONJP",
"I-PP",
]
class __A( __UpperCAmelCase ):
def _UpperCamelCase ( self, A, A ):
"""simple docstring"""
if isinstance(A, A ):
_UpperCamelCase = mode.value
_UpperCamelCase = os.path.join(A, F'''{mode}.txt''' )
_UpperCamelCase = 1
_UpperCamelCase = []
with open(A, encoding='''utf-8''' ) as f:
for sentence in parse_incr(A ):
_UpperCamelCase = []
_UpperCamelCase = []
for token in sentence:
words.append(token['''form'''] )
labels.append(token['''upos'''] )
assert len(A ) == len(A )
if words:
examples.append(InputExample(guid=F'''{mode}-{guid_index}''', words=A, labels=A ) )
guid_index += 1
return examples
def _UpperCamelCase ( self, A, A, A ):
"""simple docstring"""
_UpperCamelCase = 0
for sentence in parse_incr(A ):
_UpperCamelCase = preds_list[example_id]
_UpperCamelCase = ''''''
for token in sentence:
out += F'''{token["form"]} ({token["upos"]}|{s_p.pop(0 )}) '''
out += "\n"
writer.write(A )
example_id += 1
def _UpperCamelCase ( self, A ):
"""simple docstring"""
if path:
with open(A, '''r''' ) as f:
return f.read().splitlines()
else:
return [
"ADJ",
"ADP",
"ADV",
"AUX",
"CCONJ",
"DET",
"INTJ",
"NOUN",
"NUM",
"PART",
"PRON",
"PROPN",
"PUNCT",
"SCONJ",
"SYM",
"VERB",
"X",
]
| 105
| 0
|
'''simple docstring'''
from typing import Dict
from .base import GenericTensor, Pipeline
class _a ( SCREAMING_SNAKE_CASE ):
'''simple docstring'''
def UpperCamelCase_ ( self, A=None, A=None, A=None, **A ):
'''simple docstring'''
if tokenize_kwargs is None:
SCREAMING_SNAKE_CASE : Optional[int] = {}
if truncation is not None:
if "truncation" in tokenize_kwargs:
raise ValueError(
'truncation parameter defined twice (given as keyword argument as well as in tokenize_kwargs)' )
SCREAMING_SNAKE_CASE : Tuple = truncation
SCREAMING_SNAKE_CASE : int = tokenize_kwargs
SCREAMING_SNAKE_CASE : Optional[Any] = {}
if return_tensors is not None:
SCREAMING_SNAKE_CASE : Optional[int] = return_tensors
return preprocess_params, {}, postprocess_params
def UpperCamelCase_ ( self, A, **A ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Tuple = self.framework
SCREAMING_SNAKE_CASE : Tuple = self.tokenizer(A, return_tensors=A, **A )
return model_inputs
def UpperCamelCase_ ( self, A ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Any = self.model(**A )
return model_outputs
def UpperCamelCase_ ( self, A, A=False ):
'''simple docstring'''
if return_tensors:
return model_outputs[0]
if self.framework == "pt":
return model_outputs[0].tolist()
elif self.framework == "tf":
return model_outputs[0].numpy().tolist()
def __call__( self, *A, **A ):
'''simple docstring'''
return super().__call__(*A, **A )
| 28
|
'''simple docstring'''
import unittest
from transformers import AutoTokenizer, FalconConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
FalconForCausalLM,
FalconForQuestionAnswering,
FalconForSequenceClassification,
FalconForTokenClassification,
FalconModel,
)
class lowercase :
"""simple docstring"""
def __init__( self , UpperCamelCase_ , UpperCamelCase_=3 , UpperCamelCase_=7 , UpperCamelCase_=True , UpperCamelCase_=True , UpperCamelCase_=False , UpperCamelCase_=True , UpperCamelCase_=99 , UpperCamelCase_=32 , UpperCamelCase_=5 , UpperCamelCase_=4 , UpperCamelCase_=37 , UpperCamelCase_="gelu" , UpperCamelCase_=0.1 , UpperCamelCase_=0.1 , UpperCamelCase_=512 , UpperCamelCase_=16 , UpperCamelCase_=2 , UpperCamelCase_=0.02 , UpperCamelCase_=3 , UpperCamelCase_=4 , UpperCamelCase_=None , ):
'''simple docstring'''
UpperCamelCase__ :Dict = parent
UpperCamelCase__ :Any = batch_size
UpperCamelCase__ :Tuple = seq_length
UpperCamelCase__ :str = is_training
UpperCamelCase__ :str = use_input_mask
UpperCamelCase__ :Union[str, Any] = use_token_type_ids
UpperCamelCase__ :int = use_labels
UpperCamelCase__ :Union[str, Any] = vocab_size
UpperCamelCase__ :int = hidden_size
UpperCamelCase__ :Union[str, Any] = num_hidden_layers
UpperCamelCase__ :List[Any] = num_attention_heads
UpperCamelCase__ :int = intermediate_size
UpperCamelCase__ :str = hidden_act
UpperCamelCase__ :List[str] = hidden_dropout_prob
UpperCamelCase__ :Tuple = attention_probs_dropout_prob
UpperCamelCase__ :str = max_position_embeddings
UpperCamelCase__ :List[Any] = type_vocab_size
UpperCamelCase__ :Any = type_sequence_label_size
UpperCamelCase__ :Dict = initializer_range
UpperCamelCase__ :List[str] = num_labels
UpperCamelCase__ :Tuple = num_choices
UpperCamelCase__ :List[Any] = scope
def lowerCAmelCase__ ( self ):
'''simple docstring'''
UpperCamelCase__ :Any = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
UpperCamelCase__ :str = None
if self.use_input_mask:
UpperCamelCase__ :List[str] = random_attention_mask([self.batch_size, self.seq_length] )
UpperCamelCase__ :Optional[Any] = None
UpperCamelCase__ :Optional[Any] = None
UpperCamelCase__ :Optional[int] = None
UpperCamelCase__ :Optional[int] = None
if self.use_labels:
UpperCamelCase__ :Optional[Any] = ids_tensor([self.batch_size] , self.type_sequence_label_size )
UpperCamelCase__ :Any = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
UpperCamelCase__ :Any = ids_tensor([self.batch_size] , self.num_choices )
UpperCamelCase__ :Any = self.get_config()
return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def lowerCAmelCase__ ( self ):
'''simple docstring'''
return FalconConfig(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , is_decoder=UpperCamelCase_ , initializer_range=self.initializer_range , pad_token_id=1 , new_decoder_architecture=UpperCamelCase_ , )
def lowerCAmelCase__ ( self , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ ):
'''simple docstring'''
UpperCamelCase__ :Any = FalconModel(config=UpperCamelCase_ )
model.to(UpperCamelCase_ )
model.eval()
UpperCamelCase__ :Dict = model(UpperCamelCase_ , attention_mask=UpperCamelCase_ )
UpperCamelCase__ :Tuple = model(UpperCamelCase_ )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def lowerCAmelCase__ ( self , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , ):
'''simple docstring'''
UpperCamelCase__ :Union[str, Any] = True
UpperCamelCase__ :Dict = FalconModel(UpperCamelCase_ )
model.to(UpperCamelCase_ )
model.eval()
UpperCamelCase__ :Optional[Any] = model(
UpperCamelCase_ , attention_mask=UpperCamelCase_ , encoder_hidden_states=UpperCamelCase_ , encoder_attention_mask=UpperCamelCase_ , )
UpperCamelCase__ :List[str] = model(
UpperCamelCase_ , attention_mask=UpperCamelCase_ , encoder_hidden_states=UpperCamelCase_ , )
UpperCamelCase__ :str = model(UpperCamelCase_ , attention_mask=UpperCamelCase_ )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def lowerCAmelCase__ ( self , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , ):
'''simple docstring'''
UpperCamelCase__ :Tuple = FalconForCausalLM(config=UpperCamelCase_ )
model.to(UpperCamelCase_ )
model.eval()
UpperCamelCase__ :Any = model(UpperCamelCase_ , attention_mask=UpperCamelCase_ , labels=UpperCamelCase_ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def lowerCAmelCase__ ( self , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , ):
'''simple docstring'''
UpperCamelCase__ :str = True
UpperCamelCase__ :Tuple = True
UpperCamelCase__ :Any = FalconForCausalLM(config=UpperCamelCase_ )
model.to(UpperCamelCase_ )
model.eval()
# first forward pass
UpperCamelCase__ :Dict = model(
UpperCamelCase_ , attention_mask=UpperCamelCase_ , encoder_hidden_states=UpperCamelCase_ , encoder_attention_mask=UpperCamelCase_ , use_cache=UpperCamelCase_ , )
UpperCamelCase__ :List[Any] = outputs.past_key_values
# create hypothetical multiple next token and extent to next_input_ids
UpperCamelCase__ :Any = ids_tensor((self.batch_size, 3) , config.vocab_size )
UpperCamelCase__ :Optional[Any] = ids_tensor((self.batch_size, 3) , vocab_size=2 )
# append to next input_ids and
UpperCamelCase__ :Dict = torch.cat([input_ids, next_tokens] , dim=-1 )
UpperCamelCase__ :Any = torch.cat([input_mask, next_mask] , dim=-1 )
UpperCamelCase__ :Any = model(
UpperCamelCase_ , attention_mask=UpperCamelCase_ , encoder_hidden_states=UpperCamelCase_ , encoder_attention_mask=UpperCamelCase_ , output_hidden_states=UpperCamelCase_ , )['''hidden_states'''][0]
UpperCamelCase__ :Optional[Any] = model(
UpperCamelCase_ , attention_mask=UpperCamelCase_ , encoder_hidden_states=UpperCamelCase_ , encoder_attention_mask=UpperCamelCase_ , past_key_values=UpperCamelCase_ , output_hidden_states=UpperCamelCase_ , )['''hidden_states'''][0]
# select random slice
UpperCamelCase__ :List[Any] = ids_tensor((1,) , output_from_past.shape[-1] ).item()
UpperCamelCase__ :Optional[int] = output_from_no_past[:, -3:, random_slice_idx].detach()
UpperCamelCase__ :Tuple = output_from_past[:, :, random_slice_idx].detach()
self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1] )
# test that outputs are equal for slice
self.parent.assertTrue(torch.allclose(UpperCamelCase_ , UpperCamelCase_ , atol=1e-3 ) )
def lowerCAmelCase__ ( self ):
'''simple docstring'''
UpperCamelCase__ :Dict = self.prepare_config_and_inputs()
(
(
UpperCamelCase__
) , (
UpperCamelCase__
) , (
UpperCamelCase__
) , (
UpperCamelCase__
) , (
UpperCamelCase__
) , (
UpperCamelCase__
) , (
UpperCamelCase__
) ,
) :Optional[int] = config_and_inputs
UpperCamelCase__ :Optional[int] = {'''input_ids''': input_ids, '''attention_mask''': input_mask}
return config, inputs_dict
@require_torch
class lowercase ( A__ , A__ , A__ , unittest.TestCase ):
"""simple docstring"""
_a = (
(
FalconModel,
FalconForCausalLM,
FalconForSequenceClassification,
FalconForTokenClassification,
FalconForQuestionAnswering,
)
if is_torch_available()
else ()
)
_a = (FalconForCausalLM,) if is_torch_available() else ()
_a = (
{
'feature-extraction': FalconModel,
'text-classification': FalconForSequenceClassification,
'text-generation': FalconForCausalLM,
'question-answering': FalconForQuestionAnswering,
'token-classification': FalconForTokenClassification,
'zero-shot': FalconForSequenceClassification,
}
if is_torch_available()
else {}
)
_a = False
_a = False
def lowerCAmelCase__ ( self ):
'''simple docstring'''
UpperCamelCase__ :Union[str, Any] = FalconModelTester(self )
UpperCamelCase__ :List[Any] = ConfigTester(self , config_class=UpperCamelCase_ , hidden_size=37 )
def lowerCAmelCase__ ( self ):
'''simple docstring'''
self.config_tester.run_common_tests()
def lowerCAmelCase__ ( self ):
'''simple docstring'''
UpperCamelCase__ :Tuple = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*UpperCamelCase_ )
def lowerCAmelCase__ ( self ):
'''simple docstring'''
UpperCamelCase__ , *UpperCamelCase__ :Union[str, Any] = self.model_tester.prepare_config_and_inputs()
for alibi in [True, False]:
UpperCamelCase__ :Any = alibi
self.model_tester.create_and_check_model(UpperCamelCase_ , *UpperCamelCase_ )
def lowerCAmelCase__ ( self ):
'''simple docstring'''
UpperCamelCase__ , UpperCamelCase__ :List[str] = self.model_tester.prepare_config_and_inputs_for_common()
UpperCamelCase__ :str = 3
UpperCamelCase__ :Any = input_dict['''input_ids''']
UpperCamelCase__ :Tuple = input_ids.ne(1 ).to(UpperCamelCase_ )
UpperCamelCase__ :Optional[Any] = ids_tensor([self.model_tester.batch_size] , self.model_tester.type_sequence_label_size )
UpperCamelCase__ :List[Any] = FalconForSequenceClassification(UpperCamelCase_ )
model.to(UpperCamelCase_ )
model.eval()
UpperCamelCase__ :Dict = model(UpperCamelCase_ , attention_mask=UpperCamelCase_ , labels=UpperCamelCase_ )
self.assertEqual(result.logits.shape , (self.model_tester.batch_size, self.model_tester.num_labels) )
def lowerCAmelCase__ ( self ):
'''simple docstring'''
UpperCamelCase__ , UpperCamelCase__ :List[Any] = self.model_tester.prepare_config_and_inputs_for_common()
UpperCamelCase__ :List[str] = 3
UpperCamelCase__ :str = '''single_label_classification'''
UpperCamelCase__ :Union[str, Any] = input_dict['''input_ids''']
UpperCamelCase__ :List[str] = input_ids.ne(1 ).to(UpperCamelCase_ )
UpperCamelCase__ :Dict = ids_tensor([self.model_tester.batch_size] , self.model_tester.type_sequence_label_size )
UpperCamelCase__ :Dict = FalconForSequenceClassification(UpperCamelCase_ )
model.to(UpperCamelCase_ )
model.eval()
UpperCamelCase__ :str = model(UpperCamelCase_ , attention_mask=UpperCamelCase_ , labels=UpperCamelCase_ )
self.assertEqual(result.logits.shape , (self.model_tester.batch_size, self.model_tester.num_labels) )
def lowerCAmelCase__ ( self ):
'''simple docstring'''
UpperCamelCase__ , UpperCamelCase__ :Tuple = self.model_tester.prepare_config_and_inputs_for_common()
UpperCamelCase__ :int = input_dict['''input_ids''']
UpperCamelCase__ :List[str] = FalconForCausalLM(UpperCamelCase_ )
model.to(UpperCamelCase_ )
model.eval()
UpperCamelCase__ :Optional[int] = model(UpperCamelCase_ , use_cache=UpperCamelCase_ )
UpperCamelCase__ :Any = input_ids.shape[0]
UpperCamelCase__ :Optional[int] = model._convert_to_rw_cache(result.past_key_values )
UpperCamelCase__ :List[Any] = model._convert_cache_to_standard_format(UpperCamelCase_ , UpperCamelCase_ )
for layer in range(len(UpperCamelCase_ ) ):
for tensor_idx in range(2 ):
self.assertTrue(rw_cache[layer][tensor_idx].ndim == 3 )
self.assertTrue(result.past_key_values[layer][tensor_idx].ndim == 4 )
self.assertTrue(
torch.all(result.past_key_values[layer][tensor_idx] == standard_cache[layer][tensor_idx] ) )
def lowerCAmelCase__ ( self ):
'''simple docstring'''
UpperCamelCase__ , UpperCamelCase__ :List[Any] = self.model_tester.prepare_config_and_inputs_for_common()
UpperCamelCase__ :Optional[int] = 3
UpperCamelCase__ :Union[str, Any] = '''multi_label_classification'''
UpperCamelCase__ :Dict = input_dict['''input_ids''']
UpperCamelCase__ :Tuple = input_ids.ne(1 ).to(UpperCamelCase_ )
UpperCamelCase__ :Union[str, Any] = ids_tensor(
[self.model_tester.batch_size, config.num_labels] , self.model_tester.type_sequence_label_size ).to(torch.float )
UpperCamelCase__ :Optional[Any] = FalconForSequenceClassification(UpperCamelCase_ )
model.to(UpperCamelCase_ )
model.eval()
UpperCamelCase__ :str = model(UpperCamelCase_ , attention_mask=UpperCamelCase_ , labels=UpperCamelCase_ )
self.assertEqual(result.logits.shape , (self.model_tester.batch_size, self.model_tester.num_labels) )
def lowerCAmelCase__ ( self ):
'''simple docstring'''
for model_class in self.all_generative_model_classes:
UpperCamelCase__ , UpperCamelCase__ :List[Any] = self.model_tester.prepare_config_and_inputs_for_common()
# If it doesn't support cache, pass the test
if not hasattr(UpperCamelCase_ , '''use_cache''' ):
return
UpperCamelCase__ :Tuple = model_class(UpperCamelCase_ ).to(UpperCamelCase_ )
if "use_cache" not in inputs:
UpperCamelCase__ :Tuple = True
UpperCamelCase__ :List[str] = model(**UpperCamelCase_ )
# If "past_key_values" is not returned, pass the test (e.g. RWKV uses a different cache name and format)
if "past_key_values" not in outputs:
return
UpperCamelCase__ :str = (
getattr(UpperCamelCase_ , '''decoder_layers''' , UpperCamelCase_ )
or getattr(UpperCamelCase_ , '''num_decoder_layers''' , UpperCamelCase_ )
or config.num_hidden_layers
)
UpperCamelCase__ :List[Any] = getattr(UpperCamelCase_ , '''num_kv_heads''' , config.num_attention_heads )
UpperCamelCase__ :str = getattr(UpperCamelCase_ , '''d_model''' , config.hidden_size )
UpperCamelCase__ :Optional[Any] = embed_dim // num_attention_heads
UpperCamelCase__ :Optional[Any] = outputs['''past_key_values''']
self.assertEqual(len(UpperCamelCase_ ) , UpperCamelCase_ )
UpperCamelCase__ , UpperCamelCase__ :Tuple = inputs['''input_ids'''].shape
for i in range(UpperCamelCase_ ):
if config.new_decoder_architecture:
UpperCamelCase__ :List[Any] = config.num_attention_heads
elif config.multi_query:
UpperCamelCase__ :str = 1
self.assertEqual(len(past_kv[0] ) , 2 ) # K V for the decoder = 2
self.assertEqual(
past_kv[i][0].shape , (batch_size, num_attention_heads, seq_length, per_head_embed_dim) )
self.assertEqual(
past_kv[i][1].shape , (batch_size, num_attention_heads, seq_length, per_head_embed_dim) )
@require_torch
class lowercase ( unittest.TestCase ):
"""simple docstring"""
@slow
def lowerCAmelCase__ ( self ):
'''simple docstring'''
UpperCamelCase__ :List[str] = AutoTokenizer.from_pretrained('''Rocketknight1/falcon-rw-1b''' )
UpperCamelCase__ :List[Any] = FalconForCausalLM.from_pretrained('''Rocketknight1/falcon-rw-1b''' )
model.eval()
model.to(UpperCamelCase_ )
UpperCamelCase__ :List[str] = tokenizer('''My favorite food is''' , return_tensors='''pt''' ).to(UpperCamelCase_ )
UpperCamelCase__ :Optional[Any] = (
'''My favorite food is pizza. I love it so much that I have a pizza party every year for my birthday.'''
)
UpperCamelCase__ :int = model.generate(**UpperCamelCase_ , do_sample=UpperCamelCase_ , max_new_tokens=19 )
UpperCamelCase__ :str = tokenizer.batch_decode(UpperCamelCase_ )[0]
self.assertEqual(UpperCamelCase_ , UpperCamelCase_ )
@slow
def lowerCAmelCase__ ( self ):
'''simple docstring'''
for repo in ["Rocketknight1/tiny-random-falcon-7b", "Rocketknight1/tiny-random-falcon-40b"]:
UpperCamelCase__ :Any = AutoTokenizer.from_pretrained(UpperCamelCase_ )
UpperCamelCase__ :Dict = FalconForCausalLM.from_pretrained(UpperCamelCase_ )
model.eval()
model.to(UpperCamelCase_ )
UpperCamelCase__ :str = tokenizer('''My favorite food is''' , return_tensors='''pt''' ).to(UpperCamelCase_ )
# We just test that these run without errors - the models are randomly initialized
# and so the actual text outputs will be garbage
model.generate(**UpperCamelCase_ , do_sample=UpperCamelCase_ , max_new_tokens=4 )
model.generate(**UpperCamelCase_ , do_sample=UpperCamelCase_ , max_new_tokens=4 )
model.generate(**UpperCamelCase_ , num_beams=2 , max_new_tokens=4 )
@slow
def lowerCAmelCase__ ( self ):
'''simple docstring'''
with torch.no_grad():
for repo in [
"Rocketknight1/falcon-rw-1b",
"Rocketknight1/tiny-random-falcon-7b",
"Rocketknight1/tiny-random-falcon-40b",
]:
UpperCamelCase__ :List[Any] = AutoTokenizer.from_pretrained(UpperCamelCase_ )
UpperCamelCase__ :Union[str, Any] = FalconForCausalLM.from_pretrained(UpperCamelCase_ )
model.eval()
model.to(device=UpperCamelCase_ )
UpperCamelCase__ :Tuple = tokenizer('''My favorite food is''' , return_tensors='''pt''' ).to(UpperCamelCase_ )
# Test results are the same with and without cache
UpperCamelCase__ :Union[str, Any] = model.generate(**UpperCamelCase_ , do_sample=UpperCamelCase_ , max_new_tokens=20 , use_cache=UpperCamelCase_ )
UpperCamelCase__ :Any = model.generate(**UpperCamelCase_ , do_sample=UpperCamelCase_ , max_new_tokens=20 , use_cache=UpperCamelCase_ )
self.assertTrue((outputs_cache - outputs_no_cache).sum().item() == 0 )
| 189
| 0
|
from __future__ import annotations
from scipy.special import comb # type: ignore
class _UpperCAmelCase :
"""simple docstring"""
def __init__( self : Optional[int], lowerCamelCase : list[tuple[float, float]] ):
'''simple docstring'''
lowercase__ = list_of_points
# Degree determines the flexibility of the curve.
# Degree = 1 will produce a straight line.
lowercase__ = len(lowerCamelCase ) - 1
def lowercase__ ( self : Any, lowerCamelCase : float ):
'''simple docstring'''
assert 0 <= t <= 1, "Time t must be between 0 and 1."
lowercase__ = []
for i in range(len(self.list_of_points ) ):
# basis function for each i
output_values.append(
comb(self.degree, lowerCamelCase ) * ((1 - t) ** (self.degree - i)) * (t**i) )
# the basis must sum up to 1 for it to produce a valid Bezier curve.
assert round(sum(lowerCamelCase ), 5 ) == 1
return output_values
def lowercase__ ( self : str, lowerCamelCase : float ):
'''simple docstring'''
assert 0 <= t <= 1, "Time t must be between 0 and 1."
lowercase__ = self.basis_function(lowerCamelCase )
lowercase__ = 0.0
lowercase__ = 0.0
for i in range(len(self.list_of_points ) ):
# For all points, sum up the product of i-th basis function and i-th point.
x += basis_function[i] * self.list_of_points[i][0]
y += basis_function[i] * self.list_of_points[i][1]
return (x, y)
def lowercase__ ( self : List[Any], lowerCamelCase : float = 0.01 ):
'''simple docstring'''
from matplotlib import pyplot as plt # type: ignore
lowercase__ = [] # x coordinates of points to plot
lowercase__ = [] # y coordinates of points to plot
lowercase__ = 0.0
while t <= 1:
lowercase__ = self.bezier_curve_function(lowerCamelCase )
to_plot_x.append(value[0] )
to_plot_y.append(value[1] )
t += step_size
lowercase__ = [i[0] for i in self.list_of_points]
lowercase__ = [i[1] for i in self.list_of_points]
plt.plot(
lowerCamelCase, lowerCamelCase, color='''blue''', label='''Curve of Degree ''' + str(self.degree ), )
plt.scatter(lowerCamelCase, lowerCamelCase, color='''red''', label='''Control Points''' )
plt.legend()
plt.show()
if __name__ == "__main__":
import doctest
doctest.testmod()
BezierCurve([(1, 2), (3, 5)]).plot_curve() # degree 1
BezierCurve([(0, 0), (5, 5), (5, 0)]).plot_curve() # degree 2
BezierCurve([(0, 0), (5, 5), (5, 0), (2.5, -2.5)]).plot_curve() # degree 3
| 671
|
import json
import os
import tempfile
import transformers
import datasets
from utils import generate_example_dataset, get_duration
A__ : Dict = 50_00_00
A__ , A__ : str = os.path.split(__file__)
A__ : Optional[Any] = os.path.join(RESULTS_BASEPATH, 'results', RESULTS_FILENAME.replace('.py', '.json'))
@get_duration
def a ( lowerCamelCase_ , **lowerCamelCase_ ):
'''simple docstring'''
lowercase__ = dataset.map(**lowerCamelCase_ )
@get_duration
def a ( lowerCamelCase_ , **lowerCamelCase_ ):
'''simple docstring'''
lowercase__ = dataset.filter(**lowerCamelCase_ )
def a ( ):
'''simple docstring'''
lowercase__ = {'''num examples''': SPEED_TEST_N_EXAMPLES}
with tempfile.TemporaryDirectory() as tmp_dir:
lowercase__ = datasets.Features({'''text''': datasets.Value('''string''' ), '''numbers''': datasets.Value('''float32''' )} )
lowercase__ = generate_example_dataset(
os.path.join(lowerCamelCase_ , '''dataset.arrow''' ) , lowerCamelCase_ , num_examples=lowerCamelCase_ )
lowercase__ = transformers.AutoTokenizer.from_pretrained('''bert-base-cased''' , use_fast=lowerCamelCase_ )
def tokenize(lowerCamelCase_ ):
return tokenizer(examples['''text'''] )
lowercase__ = map(lowerCamelCase_ )
lowercase__ = map(lowerCamelCase_ , batched=lowerCamelCase_ )
lowercase__ = map(lowerCamelCase_ , function=lambda lowerCamelCase_ : None , batched=lowerCamelCase_ )
with dataset.formatted_as(type='''numpy''' ):
lowercase__ = map(lowerCamelCase_ , function=lambda lowerCamelCase_ : None , batched=lowerCamelCase_ )
with dataset.formatted_as(type='''pandas''' ):
lowercase__ = map(lowerCamelCase_ , function=lambda lowerCamelCase_ : None , batched=lowerCamelCase_ )
with dataset.formatted_as(type='''torch''' , columns='''numbers''' ):
lowercase__ = map(lowerCamelCase_ , function=lambda lowerCamelCase_ : None , batched=lowerCamelCase_ )
with dataset.formatted_as(type='''tensorflow''' , columns='''numbers''' ):
lowercase__ = map(lowerCamelCase_ , function=lambda lowerCamelCase_ : None , batched=lowerCamelCase_ )
lowercase__ = map(lowerCamelCase_ , function=lowerCamelCase_ , batched=lowerCamelCase_ )
lowercase__ = filter(lowerCamelCase_ )
# Activate later when tokenizer support batched inputs
# with dataset.formatted_as(type='numpy'):
# times[func.__name__ + " fast-tokenizer batched numpy"] = func(dataset, function=tokenize, batched=True)
with open(lowerCamelCase_ , '''wb''' ) as f:
f.write(json.dumps(lowerCamelCase_ ).encode('''utf-8''' ) )
if __name__ == "__main__": # useful to run the profiler
benchmark_map_filter()
| 671
| 1
|
"""simple docstring"""
# Lint as: python3
import dataclasses
import re
from dataclasses import dataclass
from functools import total_ordering
from typing import Optional, Union
__UpperCAmelCase = re.compile(r'^(?P<major>\d+)' r'\.(?P<minor>\d+)' r'\.(?P<patch>\d+)$')
@total_ordering
@dataclass
class __lowercase :
snake_case_ = 42
snake_case_ = None
snake_case_ = None
snake_case_ = None
snake_case_ = None
def __lowercase ( self : Union[str, Any] ):
'''simple docstring'''
UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ : Dict = _str_to_version_tuple(self.version_str )
def __repr__( self : Tuple ):
'''simple docstring'''
return f"{self.tuple[0]}.{self.tuple[1]}.{self.tuple[2]}"
@property
def __lowercase ( self : List[str] ):
'''simple docstring'''
return self.major, self.minor, self.patch
def __lowercase ( self : Any ,A : List[Any] ):
'''simple docstring'''
if isinstance(A ,A ):
return Version(A )
elif isinstance(A ,A ):
return other
raise TypeError(f"{other} (type {type(A )}) cannot be compared to version." )
def __eq__( self : Union[str, Any] ,A : List[str] ):
'''simple docstring'''
try:
UpperCAmelCase__ : Optional[Any] = self._validate_operand(A )
except (TypeError, ValueError):
return False
else:
return self.tuple == other.tuple
def __lt__( self : str ,A : List[str] ):
'''simple docstring'''
UpperCAmelCase__ : Dict = self._validate_operand(A )
return self.tuple < other.tuple
def __hash__( self : Union[str, Any] ):
'''simple docstring'''
return hash(_version_tuple_to_str(self.tuple ) )
@classmethod
def __lowercase ( cls : Any ,A : Any ):
'''simple docstring'''
UpperCAmelCase__ : Optional[int] = {f.name for f in dataclasses.fields(cls )}
return cls(**{k: v for k, v in dic.items() if k in field_names} )
def __lowercase ( self : List[Any] ):
'''simple docstring'''
return self.version_str
def lowerCAmelCase ( __UpperCamelCase ):
'''simple docstring'''
UpperCAmelCase__ : Union[str, Any] = _VERSION_REG.match(__UpperCamelCase )
if not res:
raise ValueError(F"Invalid version '{version_str}'. Format should be x.y.z with {{x,y,z}} being digits." )
return tuple(int(__UpperCamelCase ) for v in [res.group("""major""" ), res.group("""minor""" ), res.group("""patch""" )] )
def lowerCAmelCase ( __UpperCamelCase ):
'''simple docstring'''
return ".".join(str(__UpperCamelCase ) for v in version_tuple )
| 65
|
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
lowercase_ = logging.get_logger(__name__)
lowercase_ = {
'xlm-roberta-base': 'https://huggingface.co/xlm-roberta-base/resolve/main/config.json',
'xlm-roberta-large': 'https://huggingface.co/xlm-roberta-large/resolve/main/config.json',
'xlm-roberta-large-finetuned-conll02-dutch': (
'https://huggingface.co/xlm-roberta-large-finetuned-conll02-dutch/resolve/main/config.json'
),
'xlm-roberta-large-finetuned-conll02-spanish': (
'https://huggingface.co/xlm-roberta-large-finetuned-conll02-spanish/resolve/main/config.json'
),
'xlm-roberta-large-finetuned-conll03-english': (
'https://huggingface.co/xlm-roberta-large-finetuned-conll03-english/resolve/main/config.json'
),
'xlm-roberta-large-finetuned-conll03-german': (
'https://huggingface.co/xlm-roberta-large-finetuned-conll03-german/resolve/main/config.json'
),
}
class A_ ( __UpperCamelCase ):
'''simple docstring'''
__snake_case = """xlm-roberta"""
def __init__( self: Optional[Any] , a: int=3_0522 , a: List[Any]=768 , a: Tuple=12 , a: List[str]=12 , a: Dict=3072 , a: List[str]="gelu" , a: Any=0.1 , a: Optional[Any]=0.1 , a: str=512 , a: Optional[int]=2 , a: int=0.0_2 , a: str=1e-12 , a: str=1 , a: List[Any]=0 , a: Dict=2 , a: Dict="absolute" , a: List[Any]=True , a: str=None , **a: List[Any] , ):
super().__init__(pad_token_id=a , bos_token_id=a , eos_token_id=a , **a )
__lowerCamelCase : Optional[Any] = vocab_size
__lowerCamelCase : Optional[Any] = hidden_size
__lowerCamelCase : Dict = num_hidden_layers
__lowerCamelCase : List[str] = num_attention_heads
__lowerCamelCase : str = hidden_act
__lowerCamelCase : Dict = intermediate_size
__lowerCamelCase : Optional[int] = hidden_dropout_prob
__lowerCamelCase : Optional[int] = attention_probs_dropout_prob
__lowerCamelCase : int = max_position_embeddings
__lowerCamelCase : Any = type_vocab_size
__lowerCamelCase : int = initializer_range
__lowerCamelCase : Optional[Any] = layer_norm_eps
__lowerCamelCase : List[Any] = position_embedding_type
__lowerCamelCase : List[str] = use_cache
__lowerCamelCase : Optional[int] = classifier_dropout
class A_ ( __UpperCamelCase ):
'''simple docstring'''
@property
def _snake_case ( self: Optional[Any] ):
if self.task == "multiple-choice":
__lowerCamelCase : int = {0: 'batch', 1: 'choice', 2: 'sequence'}
else:
__lowerCamelCase : List[str] = {0: 'batch', 1: 'sequence'}
return OrderedDict(
[
('input_ids', dynamic_axis),
('attention_mask', dynamic_axis),
] )
| 669
| 0
|
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
SCREAMING_SNAKE_CASE__ = {
"configuration_mask2former": [
"MASK2FORMER_PRETRAINED_CONFIG_ARCHIVE_MAP",
"Mask2FormerConfig",
],
}
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
SCREAMING_SNAKE_CASE__ = ["Mask2FormerImageProcessor"]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
SCREAMING_SNAKE_CASE__ = [
"MASK2FORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
"Mask2FormerForUniversalSegmentation",
"Mask2FormerModel",
"Mask2FormerPreTrainedModel",
]
if TYPE_CHECKING:
from .configuration_maskaformer import MASK2FORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, MaskaFormerConfig
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .image_processing_maskaformer import MaskaFormerImageProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_maskaformer import (
MASK2FORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
MaskaFormerForUniversalSegmentation,
MaskaFormerModel,
MaskaFormerPreTrainedModel,
)
else:
import sys
SCREAMING_SNAKE_CASE__ = _LazyModule(__name__, globals()["__file__"], _import_structure)
| 715
|
'''simple docstring'''
import numpy as np
import torch
from torch.utils.data import Dataset
from utils import logger
class snake_case (UpperCamelCase ):
def __init__( self ,UpperCAmelCase_ ,UpperCAmelCase_ ) -> int:
lowercase__ = params
lowercase__ = np.array(UpperCAmelCase_ )
lowercase__ = np.array([len(UpperCAmelCase_ ) for t in data] )
self.check()
self.remove_long_sequences()
self.remove_empty_sequences()
self.remove_unknown_sequences()
self.check()
self.print_statistics()
def __getitem__( self ,UpperCAmelCase_ ) -> Optional[int]:
return (self.token_ids[index], self.lengths[index])
def __len__( self ) -> Union[str, Any]:
return len(self.lengths )
def _a ( self ) -> int:
assert len(self.token_ids ) == len(self.lengths )
assert all(self.lengths[i] == len(self.token_ids[i] ) for i in range(len(self.lengths ) ) )
def _a ( self ) -> Optional[int]:
lowercase__ = self.params.max_model_input_size
lowercase__ = self.lengths > max_len
logger.info(F'''Splitting {sum(UpperCAmelCase_ )} too long sequences.''' )
def divide_chunks(UpperCAmelCase_ ,UpperCAmelCase_ ):
return [l[i : i + n] for i in range(0 ,len(UpperCAmelCase_ ) ,UpperCAmelCase_ )]
lowercase__ = []
lowercase__ = []
if self.params.mlm:
lowercase__ , lowercase__ = self.params.special_tok_ids["cls_token"], self.params.special_tok_ids["sep_token"]
else:
lowercase__ , lowercase__ = self.params.special_tok_ids["bos_token"], self.params.special_tok_ids["eos_token"]
for seq_, len_ in zip(self.token_ids ,self.lengths ):
assert (seq_[0] == cls_id) and (seq_[-1] == sep_id), seq_
if len_ <= max_len:
new_tok_ids.append(seq_ )
new_lengths.append(len_ )
else:
lowercase__ = []
for sub_s in divide_chunks(seq_ ,max_len - 2 ):
if sub_s[0] != cls_id:
lowercase__ = np.insert(UpperCAmelCase_ ,0 ,UpperCAmelCase_ )
if sub_s[-1] != sep_id:
lowercase__ = np.insert(UpperCAmelCase_ ,len(UpperCAmelCase_ ) ,UpperCAmelCase_ )
assert len(UpperCAmelCase_ ) <= max_len
assert (sub_s[0] == cls_id) and (sub_s[-1] == sep_id), sub_s
sub_seqs.append(UpperCAmelCase_ )
new_tok_ids.extend(UpperCAmelCase_ )
new_lengths.extend([len(UpperCAmelCase_ ) for l in sub_seqs] )
lowercase__ = np.array(UpperCAmelCase_ )
lowercase__ = np.array(UpperCAmelCase_ )
def _a ( self ) -> Any:
lowercase__ = len(self )
lowercase__ = self.lengths > 11
lowercase__ = self.token_ids[indices]
lowercase__ = self.lengths[indices]
lowercase__ = len(self )
logger.info(F'''Remove {init_size - new_size} too short (<=11 tokens) sequences.''' )
def _a ( self ) -> List[Any]:
if "unk_token" not in self.params.special_tok_ids:
return
else:
lowercase__ = self.params.special_tok_ids["unk_token"]
lowercase__ = len(self )
lowercase__ = np.array([np.count_nonzero(a == unk_token_id ) for a in self.token_ids] )
lowercase__ = (unk_occs / self.lengths) < 0.5
lowercase__ = self.token_ids[indices]
lowercase__ = self.lengths[indices]
lowercase__ = len(self )
logger.info(F'''Remove {init_size - new_size} sequences with a high level of unknown tokens (50%).''' )
def _a ( self ) -> Optional[int]:
if not self.params.is_master:
return
logger.info(F'''{len(self )} sequences''' )
# data_len = sum(self.lengths)
# nb_unique_tokens = len(Counter(list(chain(*self.token_ids))))
# logger.info(f'{data_len} tokens ({nb_unique_tokens} unique)')
# unk_idx = self.params.special_tok_ids['unk_token']
# nb_unknown = sum([(t==unk_idx).sum() for t in self.token_ids])
# logger.info(f'{nb_unknown} unknown tokens (covering {100*nb_unknown/data_len:.2f}% of the data)')
def _a ( self ,UpperCAmelCase_ ) -> List[str]:
lowercase__ = [t[0] for t in batch]
lowercase__ = [t[1] for t in batch]
assert len(UpperCAmelCase_ ) == len(UpperCAmelCase_ )
# Max for paddings
lowercase__ = max(UpperCAmelCase_ )
# Pad token ids
if self.params.mlm:
lowercase__ = self.params.special_tok_ids["pad_token"]
else:
lowercase__ = self.params.special_tok_ids["unk_token"]
lowercase__ = [list(t.astype(UpperCAmelCase_ ) ) + [pad_idx] * (max_seq_len_ - len(UpperCAmelCase_ )) for t in token_ids]
assert len(tk_ ) == len(UpperCAmelCase_ )
assert all(len(UpperCAmelCase_ ) == max_seq_len_ for t in tk_ )
lowercase__ = torch.tensor(tk_ ) # (bs, max_seq_len_)
lowercase__ = torch.tensor(UpperCAmelCase_ ) # (bs)
return tk_t, lg_t
| 539
| 0
|
'''simple docstring'''
import math
import os
from copy import deepcopy
import datasets
import evaluate
import torch
import transformers
from datasets import load_dataset
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer
from accelerate import Accelerator
from accelerate.test_utils import RegressionDataset, RegressionModel
from accelerate.utils import is_tpu_available, set_seed
_a : Tuple = 'true'
def lowerCamelCase__ ( SCREAMING_SNAKE_CASE : str , SCREAMING_SNAKE_CASE : Optional[Any]=82 , SCREAMING_SNAKE_CASE : Optional[int]=16 ):
set_seed(42 )
UpperCAmelCase = RegressionModel()
UpperCAmelCase = deepcopy(SCREAMING_SNAKE_CASE )
UpperCAmelCase = RegressionDataset(length=SCREAMING_SNAKE_CASE )
UpperCAmelCase = DataLoader(SCREAMING_SNAKE_CASE , batch_size=SCREAMING_SNAKE_CASE )
model.to(accelerator.device )
UpperCAmelCase , UpperCAmelCase = accelerator.prepare(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
return model, ddp_model, dataloader
def lowerCamelCase__ ( SCREAMING_SNAKE_CASE : Accelerator , SCREAMING_SNAKE_CASE : Dict=False ):
UpperCAmelCase = AutoTokenizer.from_pretrained('hf-internal-testing/mrpc-bert-base-cased' )
UpperCAmelCase = load_dataset('glue' , 'mrpc' , split='validation' )
def tokenize_function(SCREAMING_SNAKE_CASE : Optional[Any] ):
UpperCAmelCase = tokenizer(examples['sentence1'] , examples['sentence2'] , truncation=SCREAMING_SNAKE_CASE , max_length=SCREAMING_SNAKE_CASE )
return outputs
with accelerator.main_process_first():
UpperCAmelCase = dataset.map(
SCREAMING_SNAKE_CASE , batched=SCREAMING_SNAKE_CASE , remove_columns=['idx', 'sentence1', 'sentence2'] , )
UpperCAmelCase = tokenized_datasets.rename_column('label' , 'labels' )
def collate_fn(SCREAMING_SNAKE_CASE : List[str] ):
if use_longest:
return tokenizer.pad(SCREAMING_SNAKE_CASE , padding='longest' , return_tensors='pt' )
return tokenizer.pad(SCREAMING_SNAKE_CASE , padding='max_length' , max_length=128 , return_tensors='pt' )
return DataLoader(SCREAMING_SNAKE_CASE , shuffle=SCREAMING_SNAKE_CASE , collate_fn=SCREAMING_SNAKE_CASE , batch_size=16 )
def lowerCamelCase__ ( SCREAMING_SNAKE_CASE : Optional[Any] , SCREAMING_SNAKE_CASE : Dict ):
UpperCAmelCase = Accelerator(dispatch_batches=SCREAMING_SNAKE_CASE , split_batches=SCREAMING_SNAKE_CASE )
UpperCAmelCase = get_dataloader(SCREAMING_SNAKE_CASE , not dispatch_batches )
UpperCAmelCase = AutoModelForSequenceClassification.from_pretrained(
'hf-internal-testing/mrpc-bert-base-cased' , return_dict=SCREAMING_SNAKE_CASE )
UpperCAmelCase , UpperCAmelCase = accelerator.prepare(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
return {"ddp": [ddp_model, ddp_dataloader, "cuda:0"], "no": [model, dataloader, accelerator.device]}, accelerator
def lowerCamelCase__ ( SCREAMING_SNAKE_CASE : List[Any] , SCREAMING_SNAKE_CASE : Dict , SCREAMING_SNAKE_CASE : Union[str, Any] ):
UpperCAmelCase = []
for batch in dataloader:
UpperCAmelCase , UpperCAmelCase = batch.values()
with torch.no_grad():
UpperCAmelCase = model(SCREAMING_SNAKE_CASE )
UpperCAmelCase , UpperCAmelCase = accelerator.gather_for_metrics((logit, target) )
logits_and_targets.append((logit, target) )
UpperCAmelCase , UpperCAmelCase = [], []
for logit, targ in logits_and_targets:
logits.append(SCREAMING_SNAKE_CASE )
targs.append(SCREAMING_SNAKE_CASE )
UpperCAmelCase , UpperCAmelCase = torch.cat(SCREAMING_SNAKE_CASE ), torch.cat(SCREAMING_SNAKE_CASE )
return logits, targs
def lowerCamelCase__ ( SCREAMING_SNAKE_CASE : Accelerator , SCREAMING_SNAKE_CASE : Union[str, Any]=82 , SCREAMING_SNAKE_CASE : List[str]=False , SCREAMING_SNAKE_CASE : List[Any]=False , SCREAMING_SNAKE_CASE : Optional[Any]=16 ):
UpperCAmelCase , UpperCAmelCase , UpperCAmelCase = get_basic_setup(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
UpperCAmelCase , UpperCAmelCase = generate_predictions(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
assert (
len(SCREAMING_SNAKE_CASE ) == num_samples
), f'''Unexpected number of inputs:\n Expected: {num_samples}\n Actual: {len(SCREAMING_SNAKE_CASE )}'''
def lowerCamelCase__ ( SCREAMING_SNAKE_CASE : bool = False , SCREAMING_SNAKE_CASE : bool = False ):
UpperCAmelCase = evaluate.load('glue' , 'mrpc' )
UpperCAmelCase , UpperCAmelCase = get_mrpc_setup(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
# First do baseline
UpperCAmelCase , UpperCAmelCase , UpperCAmelCase = setup['no']
model.to(SCREAMING_SNAKE_CASE )
model.eval()
for batch in dataloader:
batch.to(SCREAMING_SNAKE_CASE )
with torch.inference_mode():
UpperCAmelCase = model(**SCREAMING_SNAKE_CASE )
UpperCAmelCase = outputs.logits.argmax(dim=-1 )
metric.add_batch(predictions=SCREAMING_SNAKE_CASE , references=batch['labels'] )
UpperCAmelCase = metric.compute()
# Then do distributed
UpperCAmelCase , UpperCAmelCase , UpperCAmelCase = setup['ddp']
model.eval()
for batch in dataloader:
with torch.inference_mode():
UpperCAmelCase = model(**SCREAMING_SNAKE_CASE )
UpperCAmelCase = outputs.logits.argmax(dim=-1 )
UpperCAmelCase = batch['labels']
UpperCAmelCase , UpperCAmelCase = accelerator.gather_for_metrics((preds, references) )
metric.add_batch(predictions=SCREAMING_SNAKE_CASE , references=SCREAMING_SNAKE_CASE )
UpperCAmelCase = metric.compute()
for key in "accuracy f1".split():
assert math.isclose(
baseline[key] , distributed[key] ), f'''Baseline and Distributed are not the same for key {key}:\n\tBaseline: {baseline[key]}\n\tDistributed: {distributed[key]}\n'''
def lowerCamelCase__ ( ):
UpperCAmelCase = Accelerator(split_batches=SCREAMING_SNAKE_CASE , dispatch_batches=SCREAMING_SNAKE_CASE )
if accelerator.is_local_main_process:
datasets.utils.logging.set_verbosity_warning()
transformers.utils.logging.set_verbosity_warning()
else:
datasets.utils.logging.set_verbosity_error()
transformers.utils.logging.set_verbosity_error()
# These are a bit slower so they should only be ran on the GPU or TPU
if torch.cuda.is_available() or is_tpu_available():
if accelerator.is_local_main_process:
print('**Testing gather_for_metrics**' )
for split_batches in [True, False]:
for dispatch_batches in [True, False]:
if accelerator.is_local_main_process:
print(f'''With: `split_batches={split_batches}`, `dispatch_batches={dispatch_batches}`''' )
test_mrpc(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
accelerator.state._reset_state()
if accelerator.is_local_main_process:
print('**Test torch metrics**' )
for split_batches in [True, False]:
for dispatch_batches in [True, False]:
UpperCAmelCase = Accelerator(split_batches=SCREAMING_SNAKE_CASE , dispatch_batches=SCREAMING_SNAKE_CASE )
if accelerator.is_local_main_process:
print(f'''With: `split_batches={split_batches}`, `dispatch_batches={dispatch_batches}`, length=99''' )
test_torch_metrics(SCREAMING_SNAKE_CASE , 99 )
accelerator.state._reset_state()
if accelerator.is_local_main_process:
print('**Test last batch is not dropped when perfectly divisible**' )
UpperCAmelCase = Accelerator()
test_torch_metrics(SCREAMING_SNAKE_CASE , 512 )
accelerator.state._reset_state()
def lowerCamelCase__ ( SCREAMING_SNAKE_CASE : List[Any] ):
# For xla_spawn (TPUs)
main()
if __name__ == "__main__":
main()
| 447
|
'''simple docstring'''
from __future__ import annotations
def lowerCamelCase__ ( SCREAMING_SNAKE_CASE : int | str ):
UpperCAmelCase = str(SCREAMING_SNAKE_CASE )
return n == n[::-1]
def lowerCamelCase__ ( SCREAMING_SNAKE_CASE : int = 100_0000 ):
UpperCAmelCase = 0
for i in range(1 , SCREAMING_SNAKE_CASE ):
if is_palindrome(SCREAMING_SNAKE_CASE ) and is_palindrome(bin(SCREAMING_SNAKE_CASE ).split('b' )[1] ):
total += i
return total
if __name__ == "__main__":
print(solution(int(str(input().strip()))))
| 447
| 1
|
from __future__ import annotations
def UpperCamelCase_( lowerCamelCase_ , lowerCamelCase_ ) -> Optional[int]:
# Checks if the entire collection has been sorted
if len(lowerCamelCase_ ) <= 1 or n <= 1:
return
insert_next(lowerCamelCase_ , n - 1 )
rec_insertion_sort(lowerCamelCase_ , n - 1 )
def UpperCamelCase_( lowerCamelCase_ , lowerCamelCase_ ) -> List[str]:
# Checks order between adjacent elements
if index >= len(lowerCamelCase_ ) or collection[index - 1] <= collection[index]:
return
# Swaps adjacent elements since they are not in ascending order
_lowercase , _lowercase : Any = (
collection[index],
collection[index - 1],
)
insert_next(lowerCamelCase_ , index + 1 )
if __name__ == "__main__":
SCREAMING_SNAKE_CASE : Union[str, Any] = input("Enter integers separated by spaces: ")
SCREAMING_SNAKE_CASE : list[int] = [int(num) for num in numbers.split()]
rec_insertion_sort(number_list, len(number_list))
print(number_list)
| 354
|
import dataclasses
import json
import warnings
from dataclasses import dataclass, field
from time import time
from typing import List
from ..utils import logging
SCREAMING_SNAKE_CASE : Dict = logging.get_logger(__name__)
def UpperCamelCase_( lowerCamelCase_=None , lowerCamelCase_=None ) -> Tuple:
return field(default_factory=lambda: default , metadata=lowerCamelCase_ )
@dataclass
class _lowerCamelCase:
lowercase_ : List[str] = list_field(
default=[], metadata={
"""help""": (
"""Model checkpoints to be provided to the AutoModel classes. Leave blank to benchmark the base version"""
""" of all available models"""
)
}, )
lowercase_ : List[int] = list_field(
default=[8], metadata={"""help""": """List of batch sizes for which memory and time performance will be evaluated"""} )
lowercase_ : List[int] = list_field(
default=[8, 32, 1_28, 5_12], metadata={"""help""": """List of sequence lengths for which memory and time performance will be evaluated"""}, )
lowercase_ : bool = field(
default=_a, metadata={"""help""": """Whether to benchmark inference of model. Inference can be disabled via --no-inference."""}, )
lowercase_ : bool = field(
default=_a, metadata={"""help""": """Whether to run on available cuda devices. Cuda can be disabled via --no-cuda."""}, )
lowercase_ : bool = field(
default=_a, metadata={"""help""": """Whether to run on available tpu devices. TPU can be disabled via --no-tpu."""} )
lowercase_ : bool = field(default=_a, metadata={"""help""": """Use FP16 to accelerate inference."""} )
lowercase_ : bool = field(default=_a, metadata={"""help""": """Benchmark training of model"""} )
lowercase_ : bool = field(default=_a, metadata={"""help""": """Verbose memory tracing"""} )
lowercase_ : bool = field(
default=_a, metadata={"""help""": """Whether to perform speed measurements. Speed measurements can be disabled via --no-speed."""}, )
lowercase_ : bool = field(
default=_a, metadata={
"""help""": """Whether to perform memory measurements. Memory measurements can be disabled via --no-memory"""
}, )
lowercase_ : bool = field(default=_a, metadata={"""help""": """Trace memory line by line"""} )
lowercase_ : bool = field(default=_a, metadata={"""help""": """Save result to a CSV file"""} )
lowercase_ : bool = field(default=_a, metadata={"""help""": """Save all print statements in a log file"""} )
lowercase_ : bool = field(default=_a, metadata={"""help""": """Whether to print environment information"""} )
lowercase_ : bool = field(
default=_a, metadata={
"""help""": (
"""Whether to use multiprocessing for memory and speed measurement. It is highly recommended to use"""
""" multiprocessing for accurate CPU and GPU memory measurements. This option should only be disabled"""
""" for debugging / testing and on TPU."""
)
}, )
lowercase_ : str = field(
default=F'''inference_time_{round(time() )}.csv''', metadata={"""help""": """CSV filename used if saving time results to csv."""}, )
lowercase_ : str = field(
default=F'''inference_memory_{round(time() )}.csv''', metadata={"""help""": """CSV filename used if saving memory results to csv."""}, )
lowercase_ : str = field(
default=F'''train_time_{round(time() )}.csv''', metadata={"""help""": """CSV filename used if saving time results to csv for training."""}, )
lowercase_ : str = field(
default=F'''train_memory_{round(time() )}.csv''', metadata={"""help""": """CSV filename used if saving memory results to csv for training."""}, )
lowercase_ : str = field(
default=F'''env_info_{round(time() )}.csv''', metadata={"""help""": """CSV filename used if saving environment information."""}, )
lowercase_ : str = field(
default=F'''log_{round(time() )}.csv''', metadata={"""help""": """Log filename used if print statements are saved in log."""}, )
lowercase_ : int = field(default=3, metadata={"""help""": """Times an experiment will be run."""} )
lowercase_ : bool = field(
default=_a, metadata={
"""help""": (
"""Instead of loading the model as defined in `config.architectures` if exists, just load the pretrain"""
""" model weights."""
)
}, )
def UpperCamelCase ( self) -> Union[str, Any]:
"""simple docstring"""
warnings.warn(
F'''The class {self.__class__} is deprecated. Hugging Face Benchmarking utils'''
' are deprecated in general and it is advised to use external Benchmarking libraries '
' to benchmark Transformer models.', lowerCamelCase, )
def UpperCamelCase ( self) -> Optional[Any]:
"""simple docstring"""
return json.dumps(dataclasses.asdict(self), indent=2)
@property
def UpperCamelCase ( self) -> List[str]:
"""simple docstring"""
if len(self.models) <= 0:
raise ValueError(
'Please make sure you provide at least one model name / model identifier, *e.g.* `--models'
' bert-base-cased` or `args.models = [\'bert-base-cased\'].')
return self.models
@property
def UpperCamelCase ( self) -> Dict:
"""simple docstring"""
if not self.multi_process:
return False
elif self.is_tpu:
logger.info('Multiprocessing is currently not possible on TPU.')
return False
else:
return True
| 354
| 1
|
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_mobilebert import MobileBertTokenizer
_lowerCamelCase : List[str] = logging.get_logger(__name__)
_lowerCamelCase : List[Any] = {'''vocab_file''': '''vocab.txt''', '''tokenizer_file''': '''tokenizer.json'''}
_lowerCamelCase : Dict = {
'''vocab_file''': {'''mobilebert-uncased''': '''https://huggingface.co/google/mobilebert-uncased/resolve/main/vocab.txt'''},
'''tokenizer_file''': {
'''mobilebert-uncased''': '''https://huggingface.co/google/mobilebert-uncased/resolve/main/tokenizer.json'''
},
}
_lowerCamelCase : List[str] = {'''mobilebert-uncased''': 5_12}
_lowerCamelCase : str = {}
class lowercase ( a ):
lowercase__ : str = VOCAB_FILES_NAMES
lowercase__ : Any = PRETRAINED_VOCAB_FILES_MAP
lowercase__ : Union[str, Any] = PRETRAINED_INIT_CONFIGURATION
lowercase__ : Any = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
lowercase__ : Tuple = MobileBertTokenizer
def __init__( self : int , _UpperCamelCase : str=None , _UpperCamelCase : List[Any]=None , _UpperCamelCase : Dict=True , _UpperCamelCase : Optional[Any]="[UNK]" , _UpperCamelCase : Optional[Any]="[SEP]" , _UpperCamelCase : str="[PAD]" , _UpperCamelCase : Union[str, Any]="[CLS]" , _UpperCamelCase : Dict="[MASK]" , _UpperCamelCase : List[str]=True , _UpperCamelCase : Tuple=None , **_UpperCamelCase : Union[str, Any] , ) -> Dict:
'''simple docstring'''
super().__init__(
_UpperCamelCase , tokenizer_file=_UpperCamelCase , do_lower_case=_UpperCamelCase , unk_token=_UpperCamelCase , sep_token=_UpperCamelCase , pad_token=_UpperCamelCase , cls_token=_UpperCamelCase , mask_token=_UpperCamelCase , tokenize_chinese_chars=_UpperCamelCase , strip_accents=_UpperCamelCase , **_UpperCamelCase , )
SCREAMING_SNAKE_CASE = json.loads(self.backend_tokenizer.normalizer.__getstate__() )
if (
normalizer_state.get("lowercase" , _UpperCamelCase ) != do_lower_case
or normalizer_state.get("strip_accents" , _UpperCamelCase ) != strip_accents
or normalizer_state.get("handle_chinese_chars" , _UpperCamelCase ) != tokenize_chinese_chars
):
SCREAMING_SNAKE_CASE = getattr(_UpperCamelCase , normalizer_state.pop("type" ) )
SCREAMING_SNAKE_CASE = do_lower_case
SCREAMING_SNAKE_CASE = strip_accents
SCREAMING_SNAKE_CASE = tokenize_chinese_chars
SCREAMING_SNAKE_CASE = normalizer_class(**_UpperCamelCase )
SCREAMING_SNAKE_CASE = do_lower_case
def __snake_case( self : List[str] , _UpperCamelCase : Dict , _UpperCamelCase : List[str]=None ) -> List[Any]:
'''simple docstring'''
SCREAMING_SNAKE_CASE = [self.cls_token_id] + token_ids_a + [self.sep_token_id]
if token_ids_a:
output += token_ids_a + [self.sep_token_id]
return output
def __snake_case( self : Optional[Any] , _UpperCamelCase : List[int] , _UpperCamelCase : Optional[List[int]] = None ) -> List[int]:
'''simple docstring'''
SCREAMING_SNAKE_CASE = [self.sep_token_id]
SCREAMING_SNAKE_CASE = [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 __snake_case( self : Optional[int] , _UpperCamelCase : str , _UpperCamelCase : Optional[str] = None ) -> Tuple[str]:
'''simple docstring'''
SCREAMING_SNAKE_CASE = self._tokenizer.model.save(_UpperCamelCase , name=_UpperCamelCase )
return tuple(_UpperCamelCase )
| 403
|
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_lowerCamelCase : Optional[int] = {'''configuration_sew''': ['''SEW_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''SEWConfig''']}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_lowerCamelCase : Optional[int] = [
'''SEW_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''SEWForCTC''',
'''SEWForSequenceClassification''',
'''SEWModel''',
'''SEWPreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_sew import SEW_PRETRAINED_CONFIG_ARCHIVE_MAP, SEWConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_sew import (
SEW_PRETRAINED_MODEL_ARCHIVE_LIST,
SEWForCTC,
SEWForSequenceClassification,
SEWModel,
SEWPreTrainedModel,
)
else:
import sys
_lowerCamelCase : str = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 403
| 1
|
'''simple docstring'''
import argparse
import json
import os
import pickle
import shutil
import numpy as np
import torch
from distiller import Distiller
from lm_seqs_dataset import LmSeqsDataset
from transformers import (
BertConfig,
BertForMaskedLM,
BertTokenizer,
DistilBertConfig,
DistilBertForMaskedLM,
DistilBertTokenizer,
GPTaConfig,
GPTaLMHeadModel,
GPTaTokenizer,
RobertaConfig,
RobertaForMaskedLM,
RobertaTokenizer,
)
from utils import git_log, init_gpu_params, logger, set_seed
_SCREAMING_SNAKE_CASE = {
"distilbert": (DistilBertConfig, DistilBertForMaskedLM, DistilBertTokenizer),
"roberta": (RobertaConfig, RobertaForMaskedLM, RobertaTokenizer),
"bert": (BertConfig, BertForMaskedLM, BertTokenizer),
"gpt2": (GPTaConfig, GPTaLMHeadModel, GPTaTokenizer),
}
def __a(SCREAMING_SNAKE_CASE_ : Optional[int] ):
'''simple docstring'''
assert (args.mlm and args.alpha_mlm > 0.0) or (not args.mlm and args.alpha_mlm == 0.0)
assert (args.alpha_mlm > 0.0 and args.alpha_clm == 0.0) or (args.alpha_mlm == 0.0 and args.alpha_clm > 0.0)
if args.mlm:
assert os.path.isfile(args.token_counts )
assert (args.student_type in ["roberta", "distilbert"]) and (args.teacher_type in ["roberta", "bert"])
else:
assert (args.student_type in ["gpt2"]) and (args.teacher_type in ["gpt2"])
assert args.teacher_type == args.student_type or (
args.student_type == "distilbert" and args.teacher_type == "bert"
)
assert os.path.isfile(args.student_config )
if args.student_pretrained_weights is not None:
assert os.path.isfile(args.student_pretrained_weights )
if args.freeze_token_type_embds:
assert args.student_type in ["roberta"]
assert args.alpha_ce >= 0.0
assert args.alpha_mlm >= 0.0
assert args.alpha_clm >= 0.0
assert args.alpha_mse >= 0.0
assert args.alpha_cos >= 0.0
assert args.alpha_ce + args.alpha_mlm + args.alpha_clm + args.alpha_mse + args.alpha_cos > 0.0
def __a(SCREAMING_SNAKE_CASE_ : Optional[int] , SCREAMING_SNAKE_CASE_ : Optional[int] ):
'''simple docstring'''
if args.student_type == "roberta":
_lowerCAmelCase = False
elif args.student_type == "gpt2":
_lowerCAmelCase = False
def __a(SCREAMING_SNAKE_CASE_ : int , SCREAMING_SNAKE_CASE_ : List[str] ):
'''simple docstring'''
if args.student_type == "roberta":
_lowerCAmelCase = False
def __a():
'''simple docstring'''
_lowerCAmelCase = argparse.ArgumentParser(description="Training" )
parser.add_argument("--force" , action="store_true" , help="Overwrite dump_path if it already exists." )
parser.add_argument(
"--dump_path" , type=SCREAMING_SNAKE_CASE_ , required=SCREAMING_SNAKE_CASE_ , help="The output directory (log, checkpoints, parameters, etc.)" )
parser.add_argument(
"--data_file" , type=SCREAMING_SNAKE_CASE_ , required=SCREAMING_SNAKE_CASE_ , help="The binarized file (tokenized + tokens_to_ids) and grouped by sequence." , )
parser.add_argument(
"--student_type" , type=SCREAMING_SNAKE_CASE_ , choices=["distilbert", "roberta", "gpt2"] , required=SCREAMING_SNAKE_CASE_ , help="The student type (DistilBERT, RoBERTa)." , )
parser.add_argument("--student_config" , type=SCREAMING_SNAKE_CASE_ , required=SCREAMING_SNAKE_CASE_ , help="Path to the student configuration." )
parser.add_argument(
"--student_pretrained_weights" , default=SCREAMING_SNAKE_CASE_ , type=SCREAMING_SNAKE_CASE_ , help="Load student initialization checkpoint." )
parser.add_argument(
"--teacher_type" , choices=["bert", "roberta", "gpt2"] , required=SCREAMING_SNAKE_CASE_ , help="Teacher type (BERT, RoBERTa)." )
parser.add_argument("--teacher_name" , type=SCREAMING_SNAKE_CASE_ , required=SCREAMING_SNAKE_CASE_ , help="The teacher model." )
parser.add_argument("--temperature" , default=2.0 , type=SCREAMING_SNAKE_CASE_ , help="Temperature for the softmax temperature." )
parser.add_argument(
"--alpha_ce" , default=0.5 , type=SCREAMING_SNAKE_CASE_ , help="Linear weight for the distillation loss. Must be >=0." )
parser.add_argument(
"--alpha_mlm" , default=0.0 , type=SCREAMING_SNAKE_CASE_ , help="Linear weight for the MLM loss. Must be >=0. Should be used in conjunction with `mlm` flag." , )
parser.add_argument("--alpha_clm" , default=0.5 , type=SCREAMING_SNAKE_CASE_ , help="Linear weight for the CLM loss. Must be >=0." )
parser.add_argument("--alpha_mse" , default=0.0 , type=SCREAMING_SNAKE_CASE_ , help="Linear weight of the MSE loss. Must be >=0." )
parser.add_argument(
"--alpha_cos" , default=0.0 , type=SCREAMING_SNAKE_CASE_ , help="Linear weight of the cosine embedding loss. Must be >=0." )
parser.add_argument(
"--mlm" , action="store_true" , help="The LM step: MLM or CLM. If `mlm` is True, the MLM is used over CLM." )
parser.add_argument(
"--mlm_mask_prop" , default=0.15 , type=SCREAMING_SNAKE_CASE_ , help="Proportion of tokens for which we need to make a prediction." , )
parser.add_argument("--word_mask" , default=0.8 , type=SCREAMING_SNAKE_CASE_ , help="Proportion of tokens to mask out." )
parser.add_argument("--word_keep" , default=0.1 , type=SCREAMING_SNAKE_CASE_ , help="Proportion of tokens to keep." )
parser.add_argument("--word_rand" , default=0.1 , type=SCREAMING_SNAKE_CASE_ , help="Proportion of tokens to randomly replace." )
parser.add_argument(
"--mlm_smoothing" , default=0.7 , type=SCREAMING_SNAKE_CASE_ , help="Smoothing parameter to emphasize more rare tokens (see XLM, similar to word2vec)." , )
parser.add_argument("--token_counts" , type=SCREAMING_SNAKE_CASE_ , help="The token counts in the data_file for MLM." )
parser.add_argument(
"--restrict_ce_to_mask" , action="store_true" , help="If true, compute the distillation loss only the [MLM] prediction distribution." , )
parser.add_argument(
"--freeze_pos_embs" , action="store_true" , help="Freeze positional embeddings during distillation. For student_type in ['roberta', 'gpt2'] only." , )
parser.add_argument(
"--freeze_token_type_embds" , action="store_true" , help="Freeze token type embeddings during distillation if existent. For student_type in ['roberta'] only." , )
parser.add_argument("--n_epoch" , type=SCREAMING_SNAKE_CASE_ , default=3 , help="Number of pass on the whole dataset." )
parser.add_argument("--batch_size" , type=SCREAMING_SNAKE_CASE_ , default=5 , help="Batch size (for each process)." )
parser.add_argument(
"--group_by_size" , action="store_false" , help="If true, group sequences that have similar length into the same batch. Default is true." , )
parser.add_argument(
"--gradient_accumulation_steps" , type=SCREAMING_SNAKE_CASE_ , default=50 , help="Gradient accumulation for larger training batches." , )
parser.add_argument("--warmup_prop" , default=0.05 , type=SCREAMING_SNAKE_CASE_ , help="Linear warmup proportion." )
parser.add_argument("--weight_decay" , default=0.0 , type=SCREAMING_SNAKE_CASE_ , help="Weight decay if we apply some." )
parser.add_argument("--learning_rate" , default=5e-4 , type=SCREAMING_SNAKE_CASE_ , help="The initial learning rate for Adam." )
parser.add_argument("--adam_epsilon" , default=1e-6 , type=SCREAMING_SNAKE_CASE_ , help="Epsilon for Adam optimizer." )
parser.add_argument("--max_grad_norm" , default=5.0 , type=SCREAMING_SNAKE_CASE_ , help="Max gradient norm." )
parser.add_argument("--initializer_range" , default=0.02 , type=SCREAMING_SNAKE_CASE_ , help="Random initialization range." )
parser.add_argument(
"--fp16" , action="store_true" , help="Whether to use 16-bit (mixed) precision (through NVIDIA apex) instead of 32-bit" , )
parser.add_argument(
"--fp16_opt_level" , type=SCREAMING_SNAKE_CASE_ , default="O1" , help=(
"For fp16: Apex AMP optimization level selected in ['O0', 'O1', 'O2', and 'O3']."
"See details at https://nvidia.github.io/apex/amp.html"
) , )
parser.add_argument("--n_gpu" , type=SCREAMING_SNAKE_CASE_ , default=1 , help="Number of GPUs in the node." )
parser.add_argument("--local_rank" , type=SCREAMING_SNAKE_CASE_ , default=-1 , help="Distributed training - Local rank" )
parser.add_argument("--seed" , type=SCREAMING_SNAKE_CASE_ , default=56 , help="Random seed" )
parser.add_argument("--log_interval" , type=SCREAMING_SNAKE_CASE_ , default=500 , help="Tensorboard logging interval." )
parser.add_argument("--checkpoint_interval" , type=SCREAMING_SNAKE_CASE_ , default=4000 , help="Checkpoint interval." )
_lowerCAmelCase = parser.parse_args()
sanity_checks(SCREAMING_SNAKE_CASE_ )
# ARGS #
init_gpu_params(SCREAMING_SNAKE_CASE_ )
set_seed(SCREAMING_SNAKE_CASE_ )
if args.is_master:
if os.path.exists(args.dump_path ):
if not args.force:
raise ValueError(
F'''Serialization dir {args.dump_path} already exists, but you have not precised wheter to overwrite'''
" itUse `--force` if you want to overwrite it" )
else:
shutil.rmtree(args.dump_path )
if not os.path.exists(args.dump_path ):
os.makedirs(args.dump_path )
logger.info(F'''Experiment will be dumped and logged in {args.dump_path}''' )
# SAVE PARAMS #
logger.info(F'''Param: {args}''' )
with open(os.path.join(args.dump_path , "parameters.json" ) , "w" ) as f:
json.dump(vars(SCREAMING_SNAKE_CASE_ ) , SCREAMING_SNAKE_CASE_ , indent=4 )
git_log(args.dump_path )
_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase = MODEL_CLASSES[args.student_type]
_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase = MODEL_CLASSES[args.teacher_type]
# TOKENIZER #
_lowerCAmelCase = teacher_tokenizer_class.from_pretrained(args.teacher_name )
_lowerCAmelCase = {}
for tok_name, tok_symbol in tokenizer.special_tokens_map.items():
_lowerCAmelCase = tokenizer.all_special_tokens.index(SCREAMING_SNAKE_CASE_ )
_lowerCAmelCase = tokenizer.all_special_ids[idx]
logger.info(F'''Special tokens {special_tok_ids}''' )
_lowerCAmelCase = special_tok_ids
_lowerCAmelCase = tokenizer.max_model_input_sizes[args.teacher_name]
# DATA LOADER #
logger.info(F'''Loading data from {args.data_file}''' )
with open(args.data_file , "rb" ) as fp:
_lowerCAmelCase = pickle.load(SCREAMING_SNAKE_CASE_ )
if args.mlm:
logger.info(F'''Loading token counts from {args.token_counts} (already pre-computed)''' )
with open(args.token_counts , "rb" ) as fp:
_lowerCAmelCase = pickle.load(SCREAMING_SNAKE_CASE_ )
_lowerCAmelCase = np.maximum(SCREAMING_SNAKE_CASE_ , 1 ) ** -args.mlm_smoothing
for idx in special_tok_ids.values():
_lowerCAmelCase = 0.0 # do not predict special tokens
_lowerCAmelCase = torch.from_numpy(SCREAMING_SNAKE_CASE_ )
else:
_lowerCAmelCase = None
_lowerCAmelCase = LmSeqsDataset(params=SCREAMING_SNAKE_CASE_ , data=SCREAMING_SNAKE_CASE_ )
logger.info("Data loader created." )
# STUDENT #
logger.info(F'''Loading student config from {args.student_config}''' )
_lowerCAmelCase = student_config_class.from_pretrained(args.student_config )
_lowerCAmelCase = True
if args.student_pretrained_weights is not None:
logger.info(F'''Loading pretrained weights from {args.student_pretrained_weights}''' )
_lowerCAmelCase = student_model_class.from_pretrained(args.student_pretrained_weights , config=SCREAMING_SNAKE_CASE_ )
else:
_lowerCAmelCase = student_model_class(SCREAMING_SNAKE_CASE_ )
if args.n_gpu > 0:
student.to(F'''cuda:{args.local_rank}''' )
logger.info("Student loaded." )
# TEACHER #
_lowerCAmelCase = teacher_model_class.from_pretrained(args.teacher_name , output_hidden_states=SCREAMING_SNAKE_CASE_ )
if args.n_gpu > 0:
teacher.to(F'''cuda:{args.local_rank}''' )
logger.info(F'''Teacher loaded from {args.teacher_name}.''' )
# FREEZING #
if args.freeze_pos_embs:
freeze_pos_embeddings(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
if args.freeze_token_type_embds:
freeze_token_type_embeddings(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
# SANITY CHECKS #
assert student.config.vocab_size == teacher.config.vocab_size
assert student.config.hidden_size == teacher.config.hidden_size
assert student.config.max_position_embeddings == teacher.config.max_position_embeddings
if args.mlm:
assert token_probs.size(0 ) == stu_architecture_config.vocab_size
# DISTILLER #
torch.cuda.empty_cache()
_lowerCAmelCase = Distiller(
params=SCREAMING_SNAKE_CASE_ , dataset=SCREAMING_SNAKE_CASE_ , token_probs=SCREAMING_SNAKE_CASE_ , student=SCREAMING_SNAKE_CASE_ , teacher=SCREAMING_SNAKE_CASE_ )
distiller.train()
logger.info("Let's go get some drinks." )
if __name__ == "__main__":
main()
| 489
|
'''simple docstring'''
import math
def __a(SCREAMING_SNAKE_CASE_ : int = 100 ):
'''simple docstring'''
_lowerCAmelCase = sum(i * i for i in range(1 , n + 1 ) )
_lowerCAmelCase = int(math.pow(sum(range(1 , n + 1 ) ) , 2 ) )
return square_of_sum - sum_of_squares
if __name__ == "__main__":
print(f'''{solution() = }''')
| 489
| 1
|
'''simple docstring'''
import datasets
from .evaluate import evaluate
A = '''\
@inproceedings{Rajpurkar2016SQuAD10,
title={SQuAD: 100, 000+ Questions for Machine Comprehension of Text},
author={Pranav Rajpurkar and Jian Zhang and Konstantin Lopyrev and Percy Liang},
booktitle={EMNLP},
year={2016}
}
'''
A = '''
This metric wrap the official scoring script for version 1 of the Stanford Question Answering Dataset (SQuAD).
Stanford Question Answering Dataset (SQuAD) is a reading comprehension dataset, consisting of questions posed by
crowdworkers on a set of Wikipedia articles, where the answer to every question is a segment of text, or span,
from the corresponding reading passage, or the question might be unanswerable.
'''
A = '''
Computes SQuAD scores (F1 and EM).
Args:
predictions: List of question-answers dictionaries with the following key-values:
- \'id\': id of the question-answer pair as given in the references (see below)
- \'prediction_text\': the text of the answer
references: List of question-answers dictionaries with the following key-values:
- \'id\': id of the question-answer pair (see above),
- \'answers\': a Dict in the SQuAD dataset format
{
\'text\': list of possible texts for the answer, as a list of strings
\'answer_start\': list of start positions for the answer, as a list of ints
}
Note that answer_start values are not taken into account to compute the metric.
Returns:
\'exact_match\': Exact match (the normalized answer exactly match the gold answer)
\'f1\': The F-score of predicted tokens versus the gold answer
Examples:
>>> predictions = [{\'prediction_text\': \'1976\', \'id\': \'56e10a3be3433e1400422b22\'}]
>>> references = [{\'answers\': {\'answer_start\': [97], \'text\': [\'1976\']}, \'id\': \'56e10a3be3433e1400422b22\'}]
>>> squad_metric = datasets.load_metric("squad")
>>> results = squad_metric.compute(predictions=predictions, references=references)
>>> print(results)
{\'exact_match\': 100.0, \'f1\': 100.0}
'''
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class __SCREAMING_SNAKE_CASE ( datasets.Metric ):
'''simple docstring'''
def _lowerCamelCase ( self : int ) -> Optional[Any]:
return datasets.MetricInfo(
description=_DESCRIPTION ,citation=_CITATION ,inputs_description=_KWARGS_DESCRIPTION ,features=datasets.Features(
{
'predictions': {'id': datasets.Value('string' ), 'prediction_text': datasets.Value('string' )},
'references': {
'id': datasets.Value('string' ),
'answers': datasets.features.Sequence(
{
'text': datasets.Value('string' ),
'answer_start': datasets.Value('int32' ),
} ),
},
} ) ,codebase_urls=['https://rajpurkar.github.io/SQuAD-explorer/'] ,reference_urls=['https://rajpurkar.github.io/SQuAD-explorer/'] ,)
def _lowerCamelCase ( self : Any ,UpperCamelCase : List[str] ,UpperCamelCase : List[str] ) -> str:
_lowercase : List[str] = {prediction['id']: prediction['prediction_text'] for prediction in predictions}
_lowercase : Optional[int] = [
{
'paragraphs': [
{
'qas': [
{
'answers': [{'text': answer_text} for answer_text in ref['answers']['text']],
'id': ref['id'],
}
for ref in references
]
}
]
}
]
_lowercase : List[str] = evaluate(dataset=UpperCamelCase ,predictions=UpperCamelCase )
return score
| 125
|
'''simple docstring'''
from collections import OrderedDict
from typing import Any, List, Mapping, Optional
from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from ...utils import logging
A = logging.get_logger(__name__)
A = {
'''Salesforce/codegen-350M-nl''': '''https://huggingface.co/Salesforce/codegen-350M-nl/resolve/main/config.json''',
'''Salesforce/codegen-350M-multi''': '''https://huggingface.co/Salesforce/codegen-350M-multi/resolve/main/config.json''',
'''Salesforce/codegen-350M-mono''': '''https://huggingface.co/Salesforce/codegen-350M-mono/resolve/main/config.json''',
'''Salesforce/codegen-2B-nl''': '''https://huggingface.co/Salesforce/codegen-2B-nl/resolve/main/config.json''',
'''Salesforce/codegen-2B-multi''': '''https://huggingface.co/Salesforce/codegen-2B-multi/resolve/main/config.json''',
'''Salesforce/codegen-2B-mono''': '''https://huggingface.co/Salesforce/codegen-2B-mono/resolve/main/config.json''',
'''Salesforce/codegen-6B-nl''': '''https://huggingface.co/Salesforce/codegen-6B-nl/resolve/main/config.json''',
'''Salesforce/codegen-6B-multi''': '''https://huggingface.co/Salesforce/codegen-6B-multi/resolve/main/config.json''',
'''Salesforce/codegen-6B-mono''': '''https://huggingface.co/Salesforce/codegen-6B-mono/resolve/main/config.json''',
'''Salesforce/codegen-16B-nl''': '''https://huggingface.co/Salesforce/codegen-16B-nl/resolve/main/config.json''',
'''Salesforce/codegen-16B-multi''': '''https://huggingface.co/Salesforce/codegen-16B-multi/resolve/main/config.json''',
'''Salesforce/codegen-16B-mono''': '''https://huggingface.co/Salesforce/codegen-16B-mono/resolve/main/config.json''',
}
class __SCREAMING_SNAKE_CASE ( lowerCAmelCase__ ):
'''simple docstring'''
lowerCAmelCase__ : str = "codegen"
lowerCAmelCase__ : Optional[int] = {
"max_position_embeddings": "n_positions",
"hidden_size": "n_embd",
"num_attention_heads": "n_head",
"num_hidden_layers": "n_layer",
}
def __init__( self : Dict ,UpperCamelCase : List[Any]=5_0400 ,UpperCamelCase : Any=2048 ,UpperCamelCase : Any=2048 ,UpperCamelCase : List[str]=4096 ,UpperCamelCase : List[Any]=28 ,UpperCamelCase : Tuple=16 ,UpperCamelCase : List[Any]=64 ,UpperCamelCase : Optional[int]=None ,UpperCamelCase : str="gelu_new" ,UpperCamelCase : Optional[int]=0.0 ,UpperCamelCase : Dict=0.0 ,UpperCamelCase : str=0.0 ,UpperCamelCase : Union[str, Any]=1e-5 ,UpperCamelCase : Optional[int]=0.0_2 ,UpperCamelCase : List[str]=True ,UpperCamelCase : Any=5_0256 ,UpperCamelCase : List[Any]=5_0256 ,UpperCamelCase : List[Any]=False ,**UpperCamelCase : List[Any] ,) -> Union[str, Any]:
_lowercase : Union[str, Any] = vocab_size
_lowercase : Tuple = n_ctx
_lowercase : Any = n_positions
_lowercase : Optional[Any] = n_embd
_lowercase : Union[str, Any] = n_layer
_lowercase : Optional[Any] = n_head
_lowercase : Dict = n_inner
_lowercase : Optional[int] = rotary_dim
_lowercase : int = activation_function
_lowercase : str = resid_pdrop
_lowercase : str = embd_pdrop
_lowercase : Optional[int] = attn_pdrop
_lowercase : Any = layer_norm_epsilon
_lowercase : Tuple = initializer_range
_lowercase : Tuple = use_cache
_lowercase : List[str] = bos_token_id
_lowercase : Optional[Any] = eos_token_id
super().__init__(
bos_token_id=UpperCamelCase ,eos_token_id=UpperCamelCase ,tie_word_embeddings=UpperCamelCase ,**UpperCamelCase )
class __SCREAMING_SNAKE_CASE ( lowerCAmelCase__ ):
'''simple docstring'''
def __init__( self : Dict ,UpperCamelCase : PretrainedConfig ,UpperCamelCase : str = "default" ,UpperCamelCase : List[PatchingSpec] = None ,UpperCamelCase : bool = False ,) -> List[str]:
super().__init__(UpperCamelCase ,task=UpperCamelCase ,patching_specs=UpperCamelCase ,use_past=UpperCamelCase )
if not getattr(self._config ,'pad_token_id' ,UpperCamelCase ):
# TODO: how to do that better?
_lowercase : Union[str, Any] = 0
@property
def _lowerCamelCase ( self : Union[str, Any] ) -> Mapping[str, Mapping[int, str]]:
_lowercase : Any = OrderedDict({'input_ids': {0: 'batch', 1: 'sequence'}} )
if self.use_past:
self.fill_with_past_key_values_(UpperCamelCase ,direction='inputs' )
_lowercase : List[Any] = {0: 'batch', 1: 'past_sequence + sequence'}
else:
_lowercase : Union[str, Any] = {0: 'batch', 1: 'sequence'}
return common_inputs
@property
def _lowerCamelCase ( self : Dict ) -> int:
return self._config.n_layer
@property
def _lowerCamelCase ( self : List[Any] ) -> int:
return self._config.n_head
def _lowerCamelCase ( self : Optional[Any] ,UpperCamelCase : PreTrainedTokenizer ,UpperCamelCase : int = -1 ,UpperCamelCase : int = -1 ,UpperCamelCase : bool = False ,UpperCamelCase : Optional[TensorType] = None ,) -> Mapping[str, Any]:
_lowercase : int = super(UpperCamelCase ,self ).generate_dummy_inputs(
UpperCamelCase ,batch_size=UpperCamelCase ,seq_length=UpperCamelCase ,is_pair=UpperCamelCase ,framework=UpperCamelCase )
# We need to order the input in the way they appears in the forward()
_lowercase : int = OrderedDict({'input_ids': common_inputs['input_ids']} )
# Need to add the past_keys
if self.use_past:
if not is_torch_available():
raise ValueError('Cannot generate dummy past_keys inputs without PyTorch installed.' )
else:
import torch
_lowercase , _lowercase : Any = common_inputs['input_ids'].shape
# Not using the same length for past_key_values
_lowercase : List[Any] = seqlen + 2
_lowercase : Tuple = (
batch,
self.num_attention_heads,
past_key_values_length,
self._config.hidden_size // self.num_attention_heads,
)
_lowercase : Tuple = [
(torch.zeros(UpperCamelCase ), torch.zeros(UpperCamelCase )) for _ in range(self.num_layers )
]
_lowercase : int = common_inputs['attention_mask']
if self.use_past:
_lowercase : Optional[Any] = ordered_inputs['attention_mask'].dtype
_lowercase : Dict = torch.cat(
[ordered_inputs['attention_mask'], torch.ones(UpperCamelCase ,UpperCamelCase ,dtype=UpperCamelCase )] ,dim=1 )
return ordered_inputs
@property
def _lowerCamelCase ( self : List[Any] ) -> int:
return 13
| 125
| 1
|
from __future__ import annotations
import collections
import tempfile
import unittest
import numpy as np
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import is_tf_available, is_vision_available
from ...test_modeling_tf_common import floats_tensor, ids_tensor, random_attention_mask
from ..bert.test_modeling_tf_bert import TFBertModelTester
from ..clip.test_modeling_tf_clip import TFCLIPVisionModelTester
from ..deit.test_modeling_tf_deit import TFDeiTModelTester
from ..roberta.test_modeling_tf_roberta import TFRobertaModelTester
from ..vit.test_modeling_tf_vit import TFViTModelTester
if is_tf_available():
from transformers import (
TFBertModel,
TFCLIPVisionModel,
TFDeiTModel,
TFRobertaModel,
TFVisionTextDualEncoderModel,
TFViTModel,
VisionTextDualEncoderConfig,
)
if is_vision_available():
from PIL import Image
from transformers import VisionTextDualEncoderProcessor
def a__ ( _SCREAMING_SNAKE_CASE : str ) -> Union[str, Any]:
"""simple docstring"""
if isinstance(_SCREAMING_SNAKE_CASE , collections.abc.Iterable ):
return x
return (x, x)
@require_tf
class _snake_case :
def UpperCamelCase__ ( self ,_snake_case ,_snake_case ):
pass
def UpperCamelCase__ ( self ):
pass
def UpperCamelCase__ ( self ):
pass
def UpperCamelCase__ ( self ,_snake_case ,_snake_case ,_snake_case ,_snake_case ,_snake_case=None ,**_snake_case ):
UpperCAmelCase_ : Optional[Any] = VisionTextDualEncoderConfig.from_vision_text_configs(_snake_case ,_snake_case )
UpperCAmelCase_ : int = TFVisionTextDualEncoderModel(_snake_case )
UpperCAmelCase_ : int = model(input_ids=_snake_case ,pixel_values=_snake_case ,attention_mask=_snake_case )
self.assertEqual(output["text_embeds"].shape ,(input_ids.shape[0], config.projection_dim) )
self.assertEqual(output["image_embeds"].shape ,(pixel_values.shape[0], config.projection_dim) )
def UpperCamelCase__ ( self ,_snake_case ,_snake_case ,_snake_case ,_snake_case ,_snake_case=None ,**_snake_case ):
UpperCAmelCase_ : List[Any] = self.get_vision_text_model(_snake_case ,_snake_case )
UpperCAmelCase_ : int = TFVisionTextDualEncoderModel(vision_model=_snake_case ,text_model=_snake_case )
UpperCAmelCase_ : Optional[int] = model(input_ids=_snake_case ,pixel_values=_snake_case ,attention_mask=_snake_case )
self.assertEqual(output["text_embeds"].shape ,(input_ids.shape[0], model.config.projection_dim) )
self.assertEqual(output["image_embeds"].shape ,(pixel_values.shape[0], model.config.projection_dim) )
def UpperCamelCase__ ( self ,_snake_case ,_snake_case ,_snake_case ,_snake_case ,_snake_case=None ,**_snake_case ):
UpperCAmelCase_ : Tuple = self.get_vision_text_model(_snake_case ,_snake_case )
UpperCAmelCase_ : Any = {"vision_model": vision_model, "text_model": text_model}
UpperCAmelCase_ : Union[str, Any] = TFVisionTextDualEncoderModel.from_vision_text_pretrained(**_snake_case )
UpperCAmelCase_ : Optional[Any] = model(input_ids=_snake_case ,pixel_values=_snake_case ,attention_mask=_snake_case )
self.assertEqual(output["text_embeds"].shape ,(input_ids.shape[0], model.config.projection_dim) )
self.assertEqual(output["image_embeds"].shape ,(pixel_values.shape[0], model.config.projection_dim) )
def UpperCamelCase__ ( self ,_snake_case ,_snake_case ,_snake_case ,_snake_case ,_snake_case=None ,**_snake_case ):
UpperCAmelCase_ : Dict = self.get_vision_text_model(_snake_case ,_snake_case )
UpperCAmelCase_ : List[Any] = TFVisionTextDualEncoderModel(vision_model=_snake_case ,text_model=_snake_case )
UpperCAmelCase_ : Dict = model(input_ids=_snake_case ,pixel_values=_snake_case ,attention_mask=_snake_case )
UpperCAmelCase_ : int = output[0].numpy()
with tempfile.TemporaryDirectory() as tmpdirname:
model.save_pretrained(_snake_case )
UpperCAmelCase_ : Union[str, Any] = TFVisionTextDualEncoderModel.from_pretrained(_snake_case )
UpperCAmelCase_ : str = model(input_ids=_snake_case ,pixel_values=_snake_case ,attention_mask=_snake_case )
UpperCAmelCase_ : Optional[Any] = after_output[0].numpy()
UpperCAmelCase_ : str = np.amax(np.abs(out_a - out_a ) )
self.assertLessEqual(_snake_case ,1E-5 )
def UpperCamelCase__ ( self ,_snake_case ,_snake_case ,_snake_case ,_snake_case ,_snake_case=None ,**_snake_case ):
UpperCAmelCase_ : str = self.get_vision_text_model(_snake_case ,_snake_case )
UpperCAmelCase_ : Union[str, Any] = TFVisionTextDualEncoderModel(vision_model=_snake_case ,text_model=_snake_case )
UpperCAmelCase_ : int = model(
input_ids=_snake_case ,pixel_values=_snake_case ,attention_mask=_snake_case ,output_attentions=_snake_case )
UpperCAmelCase_ : Dict = output.vision_model_output.attentions
self.assertEqual(len(_snake_case ) ,vision_config.num_hidden_layers )
# in ViT, the seq_len equals the number of patches + 1 (we add 1 for the [CLS] token)
UpperCAmelCase_ : List[str] = to_atuple(vision_model.config.image_size )
UpperCAmelCase_ : Any = to_atuple(vision_model.config.patch_size )
UpperCAmelCase_ : Tuple = (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
UpperCAmelCase_ : List[str] = num_patches + 1
self.assertEqual(vision_attentions[0].shape[-3:] ,(vision_config.num_attention_heads, seq_len, seq_len) )
UpperCAmelCase_ : List[str] = output.text_model_output.attentions
self.assertEqual(len(_snake_case ) ,text_config.num_hidden_layers )
self.assertEqual(
text_attentions[0].shape[-3:] ,(text_config.num_attention_heads, input_ids.shape[-1], input_ids.shape[-1]) ,)
def UpperCamelCase__ ( self ,_snake_case ,_snake_case ,_snake_case ):
UpperCAmelCase_ : Tuple = np.abs((a - b) ).max()
self.assertLessEqual(_snake_case ,_snake_case ,f'''Difference between torch and flax is {diff} (>= {tol}).''' )
def UpperCamelCase__ ( self ):
UpperCAmelCase_ : Optional[Any] = self.prepare_config_and_inputs()
self.check_vision_text_dual_encoder_model(**_snake_case )
def UpperCamelCase__ ( self ):
UpperCAmelCase_ : Any = self.prepare_config_and_inputs()
self.check_model_from_pretrained_configs(**_snake_case )
def UpperCamelCase__ ( self ):
UpperCAmelCase_ : Dict = self.prepare_config_and_inputs()
self.check_vision_text_dual_encoder_from_pretrained(**_snake_case )
def UpperCamelCase__ ( self ):
UpperCAmelCase_ : str = self.prepare_config_and_inputs()
self.check_save_load(**_snake_case )
def UpperCamelCase__ ( self ):
UpperCAmelCase_ : Optional[Any] = self.prepare_config_and_inputs()
self.check_vision_text_output_attention(**_snake_case )
@slow
def UpperCamelCase__ ( self ):
UpperCAmelCase_ : str = self.get_pretrained_model_and_inputs()
UpperCAmelCase_ : int = model_a(**_snake_case )
UpperCAmelCase_ : str = outputs[0].numpy()
with tempfile.TemporaryDirectory() as tmp_dirname:
model_a.save_pretrained(_snake_case )
UpperCAmelCase_ : Union[str, Any] = TFVisionTextDualEncoderModel.from_pretrained(_snake_case )
UpperCAmelCase_ : Union[str, Any] = model_a(**_snake_case )
UpperCAmelCase_ : Union[str, Any] = after_outputs[0].numpy()
UpperCAmelCase_ : List[Any] = np.amax(np.abs(out_a - out_a ) )
self.assertLessEqual(_snake_case ,1E-5 )
@require_tf
class _snake_case (__SCREAMING_SNAKE_CASE , unittest.TestCase):
def UpperCamelCase__ ( self ):
UpperCAmelCase_ : List[str] = TFVisionTextDualEncoderModel.from_vision_text_pretrained(
"hf-internal-testing/tiny-random-vit" ,"hf-internal-testing/tiny-random-bert" )
UpperCAmelCase_ : Union[str, Any] = 13
UpperCAmelCase_ : List[str] = floats_tensor(
[
batch_size,
model.vision_model.config.num_channels,
model.vision_model.config.image_size,
model.vision_model.config.image_size,
] )
UpperCAmelCase_ : Tuple = ids_tensor([batch_size, 4] ,model.text_model.config.vocab_size )
UpperCAmelCase_ : Any = random_attention_mask([batch_size, 4] )
UpperCAmelCase_ : Union[str, Any] = {"pixel_values": pixel_values, "input_ids": input_ids, "attention_mask": attention_mask}
return model, inputs
def UpperCamelCase__ ( self ,_snake_case ,_snake_case ):
UpperCAmelCase_ : str = TFViTModel(_snake_case ,name="vision_model" )
UpperCAmelCase_ : Union[str, Any] = TFBertModel(_snake_case ,name="text_model" )
return vision_model, text_model
def UpperCamelCase__ ( self ):
UpperCAmelCase_ : Optional[Any] = TFViTModelTester(self )
UpperCAmelCase_ : Optional[int] = TFBertModelTester(self )
UpperCAmelCase_ : List[str] = vit_model_tester.prepare_config_and_inputs()
UpperCAmelCase_ : int = bert_model_tester.prepare_config_and_inputs()
UpperCAmelCase_ : Any = vision_config_and_inputs
(
UpperCAmelCase_
) : str = text_config_and_inputs
return {
"text_config": text_config,
"vision_config": vision_config,
"pixel_values": pixel_values,
"attention_mask": input_mask,
"input_ids": input_ids,
"text_token_type_ids": token_type_ids,
"text_sequence_labels": sequence_labels,
"text_token_labels": token_labels,
"text_choice_labels": choice_labels,
}
@require_tf
class _snake_case (__SCREAMING_SNAKE_CASE , unittest.TestCase):
def UpperCamelCase__ ( self ):
# DeiT repo doesn't have TF weights, but we don't actually use the weights at all so let's
# just reinitialize it.
UpperCAmelCase_ : int = TFVisionTextDualEncoderModel.from_vision_text_pretrained(
"Rocketknight1/tiny-random-deit-tf" ,"hf-internal-testing/tiny-random-roberta" )
UpperCAmelCase_ : List[Any] = 13
UpperCAmelCase_ : Any = floats_tensor(
[
batch_size,
model.vision_model.config.num_channels,
model.vision_model.config.image_size,
model.vision_model.config.image_size,
] )
UpperCAmelCase_ : int = ids_tensor([batch_size, 4] ,model.text_model.config.vocab_size )
UpperCAmelCase_ : Dict = random_attention_mask([batch_size, 4] )
UpperCAmelCase_ : Union[str, Any] = {"pixel_values": pixel_values, "input_ids": input_ids, "attention_mask": attention_mask}
return model, inputs
def UpperCamelCase__ ( self ,_snake_case ,_snake_case ,_snake_case ,_snake_case ,_snake_case=None ,**_snake_case ):
UpperCAmelCase_ : int = self.get_vision_text_model(_snake_case ,_snake_case )
UpperCAmelCase_ : Tuple = TFVisionTextDualEncoderModel(vision_model=_snake_case ,text_model=_snake_case )
UpperCAmelCase_ : List[str] = model(
input_ids=_snake_case ,pixel_values=_snake_case ,attention_mask=_snake_case ,output_attentions=_snake_case )
UpperCAmelCase_ : Tuple = output.vision_model_output.attentions
self.assertEqual(len(_snake_case ) ,vision_config.num_hidden_layers )
# in DEiT, the seq_len equals the number of patches + 2 (we add 2 for the [CLS] and distillation tokens)
UpperCAmelCase_ : Optional[int] = to_atuple(vision_model.config.image_size )
UpperCAmelCase_ : List[str] = to_atuple(vision_model.config.patch_size )
UpperCAmelCase_ : Optional[int] = (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
UpperCAmelCase_ : Tuple = num_patches + 2
self.assertEqual(vision_attentions[0].shape[-3:] ,(vision_config.num_attention_heads, seq_len, seq_len) )
UpperCAmelCase_ : str = output.text_model_output.attentions
self.assertEqual(len(_snake_case ) ,text_config.num_hidden_layers )
self.assertEqual(
text_attentions[0].shape[-3:] ,(text_config.num_attention_heads, input_ids.shape[-1], input_ids.shape[-1]) ,)
def UpperCamelCase__ ( self ,_snake_case ,_snake_case ):
UpperCAmelCase_ : Optional[int] = TFDeiTModel(_snake_case ,name="vision_model" )
UpperCAmelCase_ : Any = TFRobertaModel(_snake_case ,name="text_model" )
return vision_model, text_model
def UpperCamelCase__ ( self ):
UpperCAmelCase_ : Dict = TFDeiTModelTester(self )
UpperCAmelCase_ : Optional[Any] = TFRobertaModelTester(self )
UpperCAmelCase_ : Optional[int] = vit_model_tester.prepare_config_and_inputs()
UpperCAmelCase_ : Union[str, Any] = bert_model_tester.prepare_config_and_inputs()
UpperCAmelCase_ : Optional[Any] = vision_config_and_inputs
(
UpperCAmelCase_
) : List[Any] = text_config_and_inputs
return {
"text_config": text_config,
"vision_config": vision_config,
"pixel_values": pixel_values,
"attention_mask": input_mask,
"input_ids": input_ids,
"text_token_type_ids": token_type_ids,
"text_sequence_labels": sequence_labels,
"text_token_labels": token_labels,
"text_choice_labels": choice_labels,
}
@require_tf
class _snake_case (__SCREAMING_SNAKE_CASE , unittest.TestCase):
def UpperCamelCase__ ( self ):
UpperCAmelCase_ : Optional[Any] = TFVisionTextDualEncoderModel.from_vision_text_pretrained(
"Rocketknight1/tiny-random-clip-tf" ,"hf-internal-testing/tiny-random-bert" )
UpperCAmelCase_ : str = 13
UpperCAmelCase_ : List[str] = floats_tensor(
[
batch_size,
model.vision_model.config.num_channels,
model.vision_model.config.image_size,
model.vision_model.config.image_size,
] )
UpperCAmelCase_ : Any = ids_tensor([batch_size, 4] ,model.text_model.config.vocab_size )
UpperCAmelCase_ : List[Any] = random_attention_mask([batch_size, 4] )
UpperCAmelCase_ : Tuple = {"pixel_values": pixel_values, "input_ids": input_ids, "attention_mask": attention_mask}
return model, inputs
def UpperCamelCase__ ( self ,_snake_case ,_snake_case ):
UpperCAmelCase_ : Any = TFCLIPVisionModel(_snake_case ,name="vision_model" )
UpperCAmelCase_ : int = TFBertModel(_snake_case ,name="text_model" )
return vision_model, text_model
def UpperCamelCase__ ( self ):
UpperCAmelCase_ : Tuple = TFCLIPVisionModelTester(self )
UpperCAmelCase_ : List[str] = TFBertModelTester(self )
UpperCAmelCase_ : Tuple = clip_model_tester.prepare_config_and_inputs()
UpperCAmelCase_ : Optional[int] = bert_model_tester.prepare_config_and_inputs()
UpperCAmelCase_ : Tuple = vision_config_and_inputs
(
UpperCAmelCase_
) : Any = text_config_and_inputs
return {
"text_config": text_config,
"vision_config": vision_config,
"pixel_values": pixel_values,
"attention_mask": input_mask,
"input_ids": input_ids,
"text_token_type_ids": token_type_ids,
"text_sequence_labels": sequence_labels,
"text_token_labels": token_labels,
"text_choice_labels": choice_labels,
}
@require_vision
@require_tf
class _snake_case (unittest.TestCase):
@slow
def UpperCamelCase__ ( self ):
UpperCAmelCase_ : List[Any] = TFVisionTextDualEncoderModel.from_pretrained(
"clip-italian/clip-italian" ,logit_scale_init_value=1.0 ,from_pt=_snake_case )
UpperCAmelCase_ : Dict = VisionTextDualEncoderProcessor.from_pretrained("clip-italian/clip-italian" )
UpperCAmelCase_ : Dict = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png" )
UpperCAmelCase_ : Any = processor(
text=["una foto di un gatto", "una foto di un cane"] ,images=_snake_case ,padding=_snake_case ,return_tensors="np" )
UpperCAmelCase_ : Optional[Any] = model(**_snake_case )
# verify the logits
self.assertEqual(outputs.logits_per_image.shape ,(inputs.pixel_values.shape[0], inputs.input_ids.shape[0]) )
self.assertEqual(
outputs.logits_per_text.shape ,(inputs.input_ids.shape[0], inputs.pixel_values.shape[0]) ,)
UpperCAmelCase_ : Union[str, Any] = np.array([[1.2284727, 0.3104122]] )
self.assertTrue(np.allclose(outputs.logits_per_image.numpy() ,_snake_case ,atol=1E-3 ) )
| 712
|
'''simple docstring'''
from math import factorial
_lowerCamelCase = {str(digit): factorial(digit) for digit in range(10)}
def a__ ( _SCREAMING_SNAKE_CASE : int ) -> int:
"""simple docstring"""
if not isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
raise TypeError("Parameter number must be int" )
if number < 0:
raise ValueError("Parameter number must be greater than or equal to 0" )
# Converts number in string to iterate on its digits and adds its factorial.
return sum(DIGIT_FACTORIAL[digit] for digit in str(_SCREAMING_SNAKE_CASE ) )
def a__ ( _SCREAMING_SNAKE_CASE : int = 60 , _SCREAMING_SNAKE_CASE : int = 1_00_00_00 ) -> int:
"""simple docstring"""
if not isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) or not isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
raise TypeError("Parameters chain_length and number_limit must be int" )
if chain_length <= 0 or number_limit <= 0:
raise ValueError(
"Parameters chain_length and number_limit must be greater than 0" )
# the counter for the chains with the exact desired length
UpperCAmelCase_ : List[str] = 0
# the cached sizes of the previous chains
UpperCAmelCase_ : dict[int, int] = {}
for start_chain_element in range(1 , _SCREAMING_SNAKE_CASE ):
# The temporary set will contain the elements of the chain
UpperCAmelCase_ : str = set()
UpperCAmelCase_ : Union[str, Any] = 0
# Stop computing the chain when you find a cached size, a repeating item or the
# length is greater then the desired one.
UpperCAmelCase_ : Dict = start_chain_element
while (
chain_element not in chain_sets_lengths
and chain_element not in chain_set
and chain_set_length <= chain_length
):
chain_set.add(_SCREAMING_SNAKE_CASE )
chain_set_length += 1
UpperCAmelCase_ : str = digit_factorial_sum(_SCREAMING_SNAKE_CASE )
if chain_element in chain_sets_lengths:
chain_set_length += chain_sets_lengths[chain_element]
UpperCAmelCase_ : Dict = chain_set_length
# If chain contains the exact amount of elements increase the counter
if chain_set_length == chain_length:
chains_counter += 1
return chains_counter
if __name__ == "__main__":
import doctest
doctest.testmod()
print(f"""{solution()}""")
| 323
| 0
|
import importlib.metadata
import warnings
from copy import deepcopy
from packaging import version
from ..utils import logging
from .import_utils import is_accelerate_available, is_bitsandbytes_available
if is_bitsandbytes_available():
import bitsandbytes as bnb
import torch
import torch.nn as nn
from ..pytorch_utils import ConvaD
if is_accelerate_available():
from accelerate import init_empty_weights
from accelerate.utils import find_tied_parameters
__A : List[Any] = logging.get_logger(__name__)
def __lowerCAmelCase( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE=None , _SCREAMING_SNAKE_CASE=None ) -> Dict:
"""simple docstring"""
if "." in tensor_name:
_A = tensor_name.split('.' )
for split in splits[:-1]:
_A = getattr(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
if new_module is None:
raise ValueError(F"{module} has no attribute {split}." )
_A = new_module
_A = splits[-1]
if tensor_name not in module._parameters and tensor_name not in module._buffers:
raise ValueError(F"{module} does not have a parameter or a buffer named {tensor_name}." )
_A = tensor_name in module._buffers
_A = getattr(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
if old_value.device == torch.device('meta' ) and device not in ["meta", torch.device('meta' )] and value is None:
raise ValueError(F"{tensor_name} is on the meta device, we need a `value` to put in on {device}." )
_A = False
_A = False
if is_buffer or not is_bitsandbytes_available():
_A = False
_A = False
else:
_A = hasattr(bnb.nn , 'Params4bit' ) and isinstance(module._parameters[tensor_name] , bnb.nn.Paramsabit )
_A = isinstance(module._parameters[tensor_name] , bnb.nn.IntaParams )
if is_abit or is_abit:
_A = module._parameters[tensor_name]
if param.device.type != "cuda":
if value is None:
_A = old_value.to(_SCREAMING_SNAKE_CASE )
elif isinstance(_SCREAMING_SNAKE_CASE , torch.Tensor ):
_A = value.to('cpu' )
if value.dtype == torch.inta:
_A = version.parse(importlib.metadata.version('bitsandbytes' ) ) > version.parse(
'0.37.2' )
if not is_abit_serializable:
raise ValueError(
'Detected int8 weights but the version of bitsandbytes is not compatible with int8 serialization. '
'Make sure to download the latest `bitsandbytes` version. `pip install --upgrade bitsandbytes`.' )
else:
_A = torch.tensor(_SCREAMING_SNAKE_CASE , device='cpu' )
# Support models using `Conv1D` in place of `nn.Linear` (e.g. gpt2) by transposing the weight matrix prior to quantization.
# Since weights are saved in the correct "orientation", we skip transposing when loading.
if issubclass(module.source_cls , _SCREAMING_SNAKE_CASE ) and fpaa_statistics is None:
_A = new_value.T
_A = old_value.__dict__
if is_abit:
_A = bnb.nn.IntaParams(_SCREAMING_SNAKE_CASE , requires_grad=_SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE ).to(_SCREAMING_SNAKE_CASE )
elif is_abit:
_A = bnb.nn.Paramsabit(_SCREAMING_SNAKE_CASE , requires_grad=_SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE ).to(_SCREAMING_SNAKE_CASE )
_A = new_value
if fpaa_statistics is not None:
setattr(module.weight , 'SCB' , fpaa_statistics.to(_SCREAMING_SNAKE_CASE ) )
else:
if value is None:
_A = old_value.to(_SCREAMING_SNAKE_CASE )
elif isinstance(_SCREAMING_SNAKE_CASE , torch.Tensor ):
_A = value.to(_SCREAMING_SNAKE_CASE )
else:
_A = torch.tensor(_SCREAMING_SNAKE_CASE , device=_SCREAMING_SNAKE_CASE )
if is_buffer:
_A = new_value
else:
_A = nn.Parameter(_SCREAMING_SNAKE_CASE , requires_grad=old_value.requires_grad )
_A = new_value
def __lowerCAmelCase( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE=None , _SCREAMING_SNAKE_CASE=None , _SCREAMING_SNAKE_CASE=None , _SCREAMING_SNAKE_CASE=False ) -> Dict:
"""simple docstring"""
for name, module in model.named_children():
if current_key_name is None:
_A = []
current_key_name.append(_SCREAMING_SNAKE_CASE )
if (isinstance(_SCREAMING_SNAKE_CASE , nn.Linear ) or isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )) and name not in modules_to_not_convert:
# Check if the current key is not in the `modules_to_not_convert`
if not any(key in '.'.join(_SCREAMING_SNAKE_CASE ) for key in modules_to_not_convert ):
with init_empty_weights():
if isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
_A, _A = module.weight.shape
else:
_A = module.in_features
_A = module.out_features
if quantization_config.quantization_method() == "llm_int8":
_A = bnb.nn.LinearabitLt(
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , module.bias is not None , has_fpaa_weights=quantization_config.llm_inta_has_fpaa_weight , threshold=quantization_config.llm_inta_threshold , )
_A = True
else:
if (
quantization_config.llm_inta_skip_modules is not None
and name in quantization_config.llm_inta_skip_modules
):
pass
else:
_A = bnb.nn.Linearabit(
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , module.bias is not None , quantization_config.bnb_abit_compute_dtype , compress_statistics=quantization_config.bnb_abit_use_double_quant , quant_type=quantization_config.bnb_abit_quant_type , )
_A = True
# Store the module class in case we need to transpose the weight later
_A = type(_SCREAMING_SNAKE_CASE )
# Force requires grad to False to avoid unexpected errors
model._modules[name].requires_grad_(_SCREAMING_SNAKE_CASE )
if len(list(module.children() ) ) > 0:
_A, _A = _replace_with_bnb_linear(
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , has_been_replaced=_SCREAMING_SNAKE_CASE , )
# Remove the last key for recursion
current_key_name.pop(-1 )
return model, has_been_replaced
def __lowerCAmelCase( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE=None , _SCREAMING_SNAKE_CASE=None , _SCREAMING_SNAKE_CASE=None ) -> Tuple:
"""simple docstring"""
_A = ['lm_head'] if modules_to_not_convert is None else modules_to_not_convert
_A, _A = _replace_with_bnb_linear(
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
if not has_been_replaced:
logger.warning(
'You are loading your model in 8bit or 4bit but no linear modules were found in your model.'
' Please double check your model architecture, or submit an issue on github if you think this is'
' a bug.' )
return model
def __lowerCAmelCase( *_SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE ) -> Tuple:
"""simple docstring"""
warnings.warn(
'`replace_8bit_linear` will be deprecated in a future version, please use `replace_with_bnb_linear` instead' , _SCREAMING_SNAKE_CASE , )
return replace_with_bnb_linear(*_SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE )
def __lowerCAmelCase( *_SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE ) -> Optional[Any]:
"""simple docstring"""
warnings.warn(
'`set_module_8bit_tensor_to_device` will be deprecated in a future version, please use `set_module_quantized_tensor_to_device` instead' , _SCREAMING_SNAKE_CASE , )
return set_module_quantized_tensor_to_device(*_SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE )
def __lowerCAmelCase( _SCREAMING_SNAKE_CASE ) -> Optional[int]:
"""simple docstring"""
_A = deepcopy(_SCREAMING_SNAKE_CASE ) # this has 0 cost since it is done inside `init_empty_weights` context manager`
tied_model.tie_weights()
_A = find_tied_parameters(_SCREAMING_SNAKE_CASE )
# For compatibility with Accelerate < 0.18
if isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
_A = sum(list(tied_params.values() ) , [] ) + list(tied_params.keys() )
else:
_A = sum(_SCREAMING_SNAKE_CASE , [] )
_A = len(_SCREAMING_SNAKE_CASE ) > 0
# Check if it is a base model
_A = not hasattr(_SCREAMING_SNAKE_CASE , model.base_model_prefix )
# Ignore this for base models (BertModel, GPT2Model, etc.)
if (not has_tied_params) and is_base_model:
return []
# otherwise they have an attached head
_A = list(model.named_children() )
_A = [list_modules[-1][0]]
# add last module together with tied weights
_A = set(_SCREAMING_SNAKE_CASE ) - set(_SCREAMING_SNAKE_CASE )
_A = list(set(_SCREAMING_SNAKE_CASE ) ) + list(_SCREAMING_SNAKE_CASE )
# remove ".weight" from the keys
_A = ['.weight', '.bias']
_A = []
for name in list_untouched:
for name_to_remove in names_to_remove:
if name_to_remove in name:
_A = name.replace(_SCREAMING_SNAKE_CASE , '' )
filtered_module_names.append(_SCREAMING_SNAKE_CASE )
return filtered_module_names
| 27
|
def A__( __lowerCAmelCase ):
_snake_case : Optional[Any] = 0
while num > 0:
digit_sum += num % 10
num //= 10
return digit_sum
def A__( __lowerCAmelCase = 1_00 ):
_snake_case : Any = 1
_snake_case : Optional[int] = 2
for i in range(2 , max_n + 1 ):
_snake_case : Union[str, Any] = pre_numerator
_snake_case : Optional[Any] = 2 * i // 3 if i % 3 == 0 else 1
_snake_case : Dict = cur_numerator
_snake_case : str = e_cont * pre_numerator + temp
return sum_digits(__lowerCAmelCase )
if __name__ == "__main__":
print(F'''{solution() = }''')
| 304
| 0
|
import json
import multiprocessing
import os
import re
from collections import defaultdict
import torch
from accelerate import Accelerator
from accelerate.utils import set_seed
from arguments import HumanEvalArguments
from datasets import load_dataset, load_metric
from torch.utils.data import IterableDataset
from torch.utils.data.dataloader import DataLoader
from tqdm import tqdm
import transformers
from transformers import AutoModelForCausalLM, AutoTokenizer, HfArgumentParser, StoppingCriteria, StoppingCriteriaList
_snake_case = ["\nclass", "\ndef", "\n#", "\n@", "\nprint", "\nif"]
class lowerCAmelCase_ ( _UpperCAmelCase ):
"""simple docstring"""
def __init__( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE=None , _SCREAMING_SNAKE_CASE=1 ) -> Optional[int]:
__UpperCamelCase = tokenizer
__UpperCamelCase = dataset
__UpperCamelCase = len(lowercase_ ) if n_tasks is None else n_tasks
__UpperCamelCase = n_copies
def __iter__( self ) -> int:
__UpperCamelCase = []
for task in range(self.n_tasks ):
# without strip, the model generate commented codes ...
prompts.append(self.tokenizer.eos_token + self.dataset[task]['prompt'].strip() )
__UpperCamelCase = self.tokenizer(lowercase_ , padding=lowercase_ , return_tensors='pt' )
for task in range(self.n_tasks ):
for _ in range(self.n_copies ):
yield {
"ids": outputs.input_ids[task],
"task_id": task,
"input_len": outputs.attention_mask[task].sum(),
}
class lowerCAmelCase_ ( _UpperCAmelCase ):
"""simple docstring"""
def __init__( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) -> List[str]:
__UpperCamelCase = start_length
__UpperCamelCase = eof_strings
__UpperCamelCase = tokenizer
def __call__( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE ) -> Tuple:
__UpperCamelCase = self.tokenizer.batch_decode(input_ids[:, self.start_length :] )
__UpperCamelCase = []
for decoded_generation in decoded_generations:
done.append(any(stop_string in decoded_generation for stop_string in self.eof_strings ) )
return all(lowercase_ )
def _a ( __lowercase ) -> List[str]:
"""simple docstring"""
__UpperCamelCase = re.split('(%s)' % '|'.join(UpperCAmelCase__ ) , UpperCAmelCase__ )
# last string should be ""
return "".join(string_list[:-2] )
def _a ( __lowercase , __lowercase , __lowercase , __lowercase , __lowercase , __lowercase=20 , **__lowercase ) -> Dict:
"""simple docstring"""
__UpperCamelCase = defaultdict(UpperCAmelCase__ ) # dict of list of generated tokens
for step, batch in tqdm(enumerate(UpperCAmelCase__ ) ):
with torch.no_grad():
__UpperCamelCase = batch["""ids"""].shape[-1]
__UpperCamelCase = accelerator.unwrap_model(UpperCAmelCase__ ).generate(
input_ids=batch['ids'][:, : batch['input_len']] , num_return_sequences=UpperCAmelCase__ , **UpperCAmelCase__ )
# each task is generated batch_size times
__UpperCamelCase = batch["""task_id"""].repeat(UpperCAmelCase__ )
__UpperCamelCase = accelerator.pad_across_processes(
UpperCAmelCase__ , dim=1 , pad_index=tokenizer.pad_token_id )
__UpperCamelCase = accelerator.gather((generated_tokens, generated_tasks) )
__UpperCamelCase = generated_tokens.cpu().numpy()
__UpperCamelCase = generated_tasks.cpu().numpy()
for task, generated_tokens in zip(UpperCAmelCase__ , UpperCAmelCase__ ):
gen_token_dict[task].append(UpperCAmelCase__ )
__UpperCamelCase = [[] for _ in range(UpperCAmelCase__ )]
for task, generated_tokens in gen_token_dict.items():
for s in generated_tokens:
__UpperCamelCase = tokenizer.decode(UpperCAmelCase__ , skip_special_tokens=UpperCAmelCase__ , clean_up_tokenization_spaces=UpperCAmelCase__ )
code_gens[task].append(remove_last_block(UpperCAmelCase__ ) )
return code_gens
def _a ( ) -> List[str]:
"""simple docstring"""
__UpperCamelCase = HfArgumentParser(UpperCAmelCase__ )
__UpperCamelCase = parser.parse_args()
transformers.logging.set_verbosity_error()
# enables code execution in code_eval metric
__UpperCamelCase = args.HF_ALLOW_CODE_EVAL
# make sure tokenizer plays nice with multiprocessing
__UpperCamelCase = """false"""
if args.num_workers is None:
__UpperCamelCase = multiprocessing.cpu_count()
# Use dataset load to feed to accelerate
__UpperCamelCase = Accelerator()
set_seed(args.seed , device_specific=UpperCAmelCase__ )
# Load model and tokenizer
__UpperCamelCase = AutoTokenizer.from_pretrained(args.model_ckpt )
__UpperCamelCase = tokenizer.eos_token
__UpperCamelCase = AutoModelForCausalLM.from_pretrained(args.model_ckpt )
# Generation settings
__UpperCamelCase = {
"""do_sample""": args.do_sample,
"""temperature""": args.temperature,
"""max_new_tokens""": args.max_new_tokens,
"""top_p""": args.top_p,
"""top_k""": args.top_k,
"""stopping_criteria""": StoppingCriteriaList([EndOfFunctionCriteria(0 , UpperCAmelCase__ , UpperCAmelCase__ )] ),
}
# Load evaluation dataset and metric
__UpperCamelCase = load_dataset('openai_humaneval' )
__UpperCamelCase = load_metric('code_eval' )
__UpperCamelCase = args.num_tasks if args.num_tasks is not None else len(human_eval['test'] )
__UpperCamelCase = args.n_samples // args.batch_size
__UpperCamelCase = TokenizedDataset(UpperCAmelCase__ , human_eval['test'] , n_copies=UpperCAmelCase__ , n_tasks=UpperCAmelCase__ )
# do not confuse args.batch_size, which is actually the num_return_sequences
__UpperCamelCase = DataLoader(UpperCAmelCase__ , batch_size=1 )
# Run a quick test to see if code evaluation is enabled
try:
__UpperCamelCase = code_eval_metric.compute(references=[''] , predictions=[['']] )
except ValueError as exception:
print(
'Code evaluation not enabled. Read the warning below carefully and then use `--HF_ALLOW_CODE_EVAL=\"1\"`'
' flag to enable code evaluation.' )
raise exception
__UpperCamelCase = accelerator.prepare(UpperCAmelCase__ , UpperCAmelCase__ )
__UpperCamelCase = complete_code(
UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , n_tasks=UpperCAmelCase__ , batch_size=args.batch_size , **UpperCAmelCase__ , )
if accelerator.is_main_process:
__UpperCamelCase = []
for task in tqdm(range(UpperCAmelCase__ ) ):
__UpperCamelCase = human_eval["""test"""][task]["""test"""]
__UpperCamelCase = F"""check({human_eval['test'][task]['entry_point']})"""
references.append('\n' + test_func + '\n' + entry_point )
# Evaluate completions with "code_eval" metric
__UpperCamelCase = code_eval_metric.compute(
references=UpperCAmelCase__ , predictions=UpperCAmelCase__ , num_workers=args.num_workers )
print(F"""Results: {pass_at_k}""" )
# Save results to json file
with open(args.output_file , 'w' ) as fp:
json.dump(UpperCAmelCase__ , UpperCAmelCase__ )
# For some reason the folliwng seems to be necessary sometimes for code_eval to work nice with multiprocessing
# https://stackoverflow.com/questions/60804599/python-multiprocessing-keeps-spawning-the-whole-script
if __name__ == "__main__":
main()
| 708
|
import unittest
import numpy as np
from diffusers import LMSDiscreteScheduler, OnnxStableDiffusionInpaintPipeline
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 lowerCAmelCase_ ( _lowercase , unittest.TestCase ):
"""simple docstring"""
# FIXME: add fast tests
pass
@nightly
@require_onnxruntime
@require_torch_gpu
class lowerCAmelCase_ ( unittest.TestCase ):
"""simple docstring"""
@property
def __lowercase( self ) -> List[str]:
return (
"CUDAExecutionProvider",
{
"gpu_mem_limit": "15000000000", # 15GB
"arena_extend_strategy": "kSameAsRequested",
},
)
@property
def __lowercase( self ) -> Any:
__UpperCamelCase = ort.SessionOptions()
__UpperCamelCase = False
return options
def __lowercase( self ) -> Tuple:
__UpperCamelCase = load_image(
'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
'/in_paint/overture-creations-5sI6fQgYIuo.png' )
__UpperCamelCase = load_image(
'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
'/in_paint/overture-creations-5sI6fQgYIuo_mask.png' )
__UpperCamelCase = OnnxStableDiffusionInpaintPipeline.from_pretrained(
'runwayml/stable-diffusion-inpainting' , revision='onnx' , safety_checker=_SCREAMING_SNAKE_CASE , feature_extractor=_SCREAMING_SNAKE_CASE , provider=self.gpu_provider , sess_options=self.gpu_options , )
pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE )
__UpperCamelCase = 'A red cat sitting on a park bench'
__UpperCamelCase = np.random.RandomState(0 )
__UpperCamelCase = pipe(
prompt=_SCREAMING_SNAKE_CASE , image=_SCREAMING_SNAKE_CASE , mask_image=_SCREAMING_SNAKE_CASE , guidance_scale=7.5 , num_inference_steps=10 , generator=_SCREAMING_SNAKE_CASE , output_type='np' , )
__UpperCamelCase = output.images
__UpperCamelCase = images[0, 255:258, 255:258, -1]
assert images.shape == (1, 512, 512, 3)
__UpperCamelCase = np.array([0.2_5_1_4, 0.3_0_0_7, 0.3_5_1_7, 0.1_7_9_0, 0.2_3_8_2, 0.3_1_6_7, 0.1_9_4_4, 0.2_2_7_3, 0.2_4_6_4] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-3
def __lowercase( self ) -> Tuple:
__UpperCamelCase = load_image(
'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
'/in_paint/overture-creations-5sI6fQgYIuo.png' )
__UpperCamelCase = load_image(
'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
'/in_paint/overture-creations-5sI6fQgYIuo_mask.png' )
__UpperCamelCase = LMSDiscreteScheduler.from_pretrained(
'runwayml/stable-diffusion-inpainting' , subfolder='scheduler' , revision='onnx' )
__UpperCamelCase = OnnxStableDiffusionInpaintPipeline.from_pretrained(
'runwayml/stable-diffusion-inpainting' , revision='onnx' , scheduler=_SCREAMING_SNAKE_CASE , safety_checker=_SCREAMING_SNAKE_CASE , feature_extractor=_SCREAMING_SNAKE_CASE , provider=self.gpu_provider , sess_options=self.gpu_options , )
pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE )
__UpperCamelCase = 'A red cat sitting on a park bench'
__UpperCamelCase = np.random.RandomState(0 )
__UpperCamelCase = pipe(
prompt=_SCREAMING_SNAKE_CASE , image=_SCREAMING_SNAKE_CASE , mask_image=_SCREAMING_SNAKE_CASE , guidance_scale=7.5 , num_inference_steps=20 , generator=_SCREAMING_SNAKE_CASE , output_type='np' , )
__UpperCamelCase = output.images
__UpperCamelCase = images[0, 255:258, 255:258, -1]
assert images.shape == (1, 512, 512, 3)
__UpperCamelCase = np.array([0.0_0_8_6, 0.0_0_7_7, 0.0_0_8_3, 0.0_0_9_3, 0.0_1_0_7, 0.0_1_3_9, 0.0_0_9_4, 0.0_0_9_7, 0.0_1_2_5] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-3
| 567
| 0
|
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 _a ( UpperCamelCase__ , unittest.TestCase ):
_lowercase : Optional[Any] = DownBlockaD # noqa F405
_lowercase : Dict = '''down'''
def lowerCamelCase_ ( self: List[str] ) -> Tuple:
"""simple docstring"""
lowercase__ = [-0.0232, -0.9869, 0.8054, -0.0637, -0.1688, -1.4264, 0.4470, -1.3394, 0.0904]
super().test_output(UpperCamelCase_ )
class _a ( UpperCamelCase__ , unittest.TestCase ):
_lowercase : List[str] = ResnetDownsampleBlockaD # noqa F405
_lowercase : Tuple = '''down'''
def lowerCamelCase_ ( self: List[Any] ) -> str:
"""simple docstring"""
lowercase__ = [0.0710, 0.2410, -0.7320, -1.0757, -1.1343, 0.3540, -0.0133, -0.2576, 0.0948]
super().test_output(UpperCamelCase_ )
class _a ( UpperCamelCase__ , unittest.TestCase ):
_lowercase : Any = AttnDownBlockaD # noqa F405
_lowercase : List[Any] = '''down'''
def lowerCamelCase_ ( self: Dict ) -> List[str]:
"""simple docstring"""
lowercase__ = [0.0636, 0.8964, -0.6234, -1.0131, 0.0844, 0.4935, 0.3437, 0.0911, -0.2957]
super().test_output(UpperCamelCase_ )
class _a ( UpperCamelCase__ , unittest.TestCase ):
_lowercase : Tuple = CrossAttnDownBlockaD # noqa F405
_lowercase : Optional[int] = '''down'''
def lowerCamelCase_ ( self: Optional[Any] ) -> Any:
"""simple docstring"""
lowercase__ , lowercase__ = super().prepare_init_args_and_inputs_for_common()
lowercase__ = 32
return init_dict, inputs_dict
def lowerCamelCase_ ( self: str ) -> Tuple:
"""simple docstring"""
lowercase__ = [0.2238, -0.7396, -0.2255, -0.3829, 0.1925, 1.1665, 0.0603, -0.7295, 0.1983]
super().test_output(UpperCamelCase_ )
class _a ( UpperCamelCase__ , unittest.TestCase ):
_lowercase : Any = SimpleCrossAttnDownBlockaD # noqa F405
_lowercase : str = '''down'''
@property
def lowerCamelCase_ ( self: Optional[Any] ) -> List[Any]:
"""simple docstring"""
return super().get_dummy_input(include_encoder_hidden_states=UpperCamelCase_ )
def lowerCamelCase_ ( self: List[str] ) -> List[str]:
"""simple docstring"""
lowercase__ , lowercase__ = super().prepare_init_args_and_inputs_for_common()
lowercase__ = 32
return init_dict, inputs_dict
@unittest.skipIf(torch_device == '''mps''' , '''MPS result is not consistent''' )
def lowerCamelCase_ ( self: Any ) -> int:
"""simple docstring"""
lowercase__ = [0.7921, -0.0992, -0.1962, -0.7695, -0.4242, 0.7804, 0.4737, 0.2765, 0.3338]
super().test_output(UpperCamelCase_ )
class _a ( UpperCamelCase__ , unittest.TestCase ):
_lowercase : Tuple = SkipDownBlockaD # noqa F405
_lowercase : Tuple = '''down'''
@property
def lowerCamelCase_ ( self: Union[str, Any] ) -> List[str]:
"""simple docstring"""
return super().get_dummy_input(include_skip_sample=UpperCamelCase_ )
def lowerCamelCase_ ( self: Dict ) -> List[Any]:
"""simple docstring"""
lowercase__ = [-0.0845, -0.2087, -0.2465, 0.0971, 0.1900, -0.0484, 0.2664, 0.4179, 0.5069]
super().test_output(UpperCamelCase_ )
class _a ( UpperCamelCase__ , unittest.TestCase ):
_lowercase : Optional[int] = AttnSkipDownBlockaD # noqa F405
_lowercase : Optional[int] = '''down'''
@property
def lowerCamelCase_ ( self: str ) -> int:
"""simple docstring"""
return super().get_dummy_input(include_skip_sample=UpperCamelCase_ )
def lowerCamelCase_ ( self: Tuple ) -> Any:
"""simple docstring"""
lowercase__ = [0.5539, 0.1609, 0.4924, 0.0537, -0.1995, 0.4050, 0.0979, -0.2721, -0.0642]
super().test_output(UpperCamelCase_ )
class _a ( UpperCamelCase__ , unittest.TestCase ):
_lowercase : int = DownEncoderBlockaD # noqa F405
_lowercase : List[Any] = '''down'''
@property
def lowerCamelCase_ ( self: List[str] ) -> str:
"""simple docstring"""
return super().get_dummy_input(include_temb=UpperCamelCase_ )
def lowerCamelCase_ ( self: Any ) -> List[Any]:
"""simple docstring"""
lowercase__ = {
'''in_channels''': 32,
'''out_channels''': 32,
}
lowercase__ = self.dummy_input
return init_dict, inputs_dict
def lowerCamelCase_ ( self: str ) -> Dict:
"""simple docstring"""
lowercase__ = [1.1102, 0.5302, 0.4872, -0.0023, -0.8042, 0.0483, -0.3489, -0.5632, 0.7626]
super().test_output(UpperCamelCase_ )
class _a ( UpperCamelCase__ , unittest.TestCase ):
_lowercase : List[str] = AttnDownEncoderBlockaD # noqa F405
_lowercase : int = '''down'''
@property
def lowerCamelCase_ ( self: Dict ) -> Optional[Any]:
"""simple docstring"""
return super().get_dummy_input(include_temb=UpperCamelCase_ )
def lowerCamelCase_ ( self: str ) -> List[str]:
"""simple docstring"""
lowercase__ = {
'''in_channels''': 32,
'''out_channels''': 32,
}
lowercase__ = self.dummy_input
return init_dict, inputs_dict
def lowerCamelCase_ ( self: Union[str, Any] ) -> List[str]:
"""simple docstring"""
lowercase__ = [0.8966, -0.1486, 0.8568, 0.8141, -0.9046, -0.1342, -0.0972, -0.7417, 0.1538]
super().test_output(UpperCamelCase_ )
class _a ( UpperCamelCase__ , unittest.TestCase ):
_lowercase : Union[str, Any] = UNetMidBlockaD # noqa F405
_lowercase : Union[str, Any] = '''mid'''
def lowerCamelCase_ ( self: Any ) -> int:
"""simple docstring"""
lowercase__ = {
'''in_channels''': 32,
'''temb_channels''': 128,
}
lowercase__ = self.dummy_input
return init_dict, inputs_dict
def lowerCamelCase_ ( self: Any ) -> Any:
"""simple docstring"""
lowercase__ = [-0.1062, 1.7248, 0.3494, 1.4569, -0.0910, -1.2421, -0.9984, 0.6736, 1.0028]
super().test_output(UpperCamelCase_ )
class _a ( UpperCamelCase__ , unittest.TestCase ):
_lowercase : Optional[int] = UNetMidBlockaDCrossAttn # noqa F405
_lowercase : str = '''mid'''
def lowerCamelCase_ ( self: Union[str, Any] ) -> List[str]:
"""simple docstring"""
lowercase__ , lowercase__ = super().prepare_init_args_and_inputs_for_common()
lowercase__ = 32
return init_dict, inputs_dict
def lowerCamelCase_ ( self: Dict ) -> List[str]:
"""simple docstring"""
lowercase__ = [0.0187, 2.4220, 0.4484, 1.1203, -0.6121, -1.5122, -0.8270, 0.7851, 1.8335]
super().test_output(UpperCamelCase_ )
class _a ( UpperCamelCase__ , unittest.TestCase ):
_lowercase : Tuple = UNetMidBlockaDSimpleCrossAttn # noqa F405
_lowercase : str = '''mid'''
@property
def lowerCamelCase_ ( self: int ) -> List[Any]:
"""simple docstring"""
return super().get_dummy_input(include_encoder_hidden_states=UpperCamelCase_ )
def lowerCamelCase_ ( self: Optional[Any] ) -> Optional[Any]:
"""simple docstring"""
lowercase__ , lowercase__ = super().prepare_init_args_and_inputs_for_common()
lowercase__ = 32
return init_dict, inputs_dict
def lowerCamelCase_ ( self: Union[str, Any] ) -> int:
"""simple docstring"""
lowercase__ = [0.7143, 1.9974, 0.5448, 1.3977, 0.1282, -1.1237, -1.4238, 0.5530, 0.8880]
super().test_output(UpperCamelCase_ )
class _a ( UpperCamelCase__ , unittest.TestCase ):
_lowercase : Union[str, Any] = UpBlockaD # noqa F405
_lowercase : Any = '''up'''
@property
def lowerCamelCase_ ( self: str ) -> str:
"""simple docstring"""
return super().get_dummy_input(include_res_hidden_states_tuple=UpperCamelCase_ )
def lowerCamelCase_ ( self: int ) -> List[Any]:
"""simple docstring"""
lowercase__ = [-0.2041, -0.4165, -0.3022, 0.0041, -0.6628, -0.7053, 0.1928, -0.0325, 0.0523]
super().test_output(UpperCamelCase_ )
class _a ( UpperCamelCase__ , unittest.TestCase ):
_lowercase : Tuple = ResnetUpsampleBlockaD # noqa F405
_lowercase : List[Any] = '''up'''
@property
def lowerCamelCase_ ( self: List[Any] ) -> Union[str, Any]:
"""simple docstring"""
return super().get_dummy_input(include_res_hidden_states_tuple=UpperCamelCase_ )
def lowerCamelCase_ ( self: Union[str, Any] ) -> Optional[int]:
"""simple docstring"""
lowercase__ = [0.2287, 0.3549, -0.1346, 0.4797, -0.1715, -0.9649, 0.7305, -0.5864, -0.6244]
super().test_output(UpperCamelCase_ )
class _a ( UpperCamelCase__ , unittest.TestCase ):
_lowercase : Any = CrossAttnUpBlockaD # noqa F405
_lowercase : List[str] = '''up'''
@property
def lowerCamelCase_ ( self: int ) -> Any:
"""simple docstring"""
return super().get_dummy_input(include_res_hidden_states_tuple=UpperCamelCase_ )
def lowerCamelCase_ ( self: Any ) -> Any:
"""simple docstring"""
lowercase__ , lowercase__ = super().prepare_init_args_and_inputs_for_common()
lowercase__ = 32
return init_dict, inputs_dict
def lowerCamelCase_ ( self: Dict ) -> Optional[int]:
"""simple docstring"""
lowercase__ = [-0.1403, -0.3515, -0.0420, -0.1425, 0.3167, 0.5094, -0.2181, 0.5931, 0.5582]
super().test_output(UpperCamelCase_ )
class _a ( UpperCamelCase__ , unittest.TestCase ):
_lowercase : Union[str, Any] = SimpleCrossAttnUpBlockaD # noqa F405
_lowercase : Dict = '''up'''
@property
def lowerCamelCase_ ( self: List[str] ) -> Union[str, Any]:
"""simple docstring"""
return super().get_dummy_input(include_res_hidden_states_tuple=UpperCamelCase_ , include_encoder_hidden_states=UpperCamelCase_ )
def lowerCamelCase_ ( self: str ) -> int:
"""simple docstring"""
lowercase__ , lowercase__ = super().prepare_init_args_and_inputs_for_common()
lowercase__ = 32
return init_dict, inputs_dict
def lowerCamelCase_ ( self: Union[str, Any] ) -> int:
"""simple docstring"""
lowercase__ = [0.2645, 0.1480, 0.0909, 0.8044, -0.9758, -0.9083, 0.0994, -1.1453, -0.7402]
super().test_output(UpperCamelCase_ )
class _a ( UpperCamelCase__ , unittest.TestCase ):
_lowercase : List[str] = AttnUpBlockaD # noqa F405
_lowercase : Optional[Any] = '''up'''
@property
def lowerCamelCase_ ( self: Tuple ) -> int:
"""simple docstring"""
return super().get_dummy_input(include_res_hidden_states_tuple=UpperCamelCase_ )
@unittest.skipIf(torch_device == '''mps''' , '''MPS result is not consistent''' )
def lowerCamelCase_ ( self: List[str] ) -> List[str]:
"""simple docstring"""
lowercase__ = [0.0979, 0.1326, 0.0021, 0.0659, 0.2249, 0.0059, 0.1132, 0.5952, 0.1033]
super().test_output(UpperCamelCase_ )
class _a ( UpperCamelCase__ , unittest.TestCase ):
_lowercase : Dict = SkipUpBlockaD # noqa F405
_lowercase : Optional[int] = '''up'''
@property
def lowerCamelCase_ ( self: Dict ) -> int:
"""simple docstring"""
return super().get_dummy_input(include_res_hidden_states_tuple=UpperCamelCase_ )
def lowerCamelCase_ ( self: Optional[Any] ) -> Dict:
"""simple docstring"""
lowercase__ = [-0.0893, -0.1234, -0.1506, -0.0332, 0.0123, -0.0211, 0.0566, 0.0143, 0.0362]
super().test_output(UpperCamelCase_ )
class _a ( UpperCamelCase__ , unittest.TestCase ):
_lowercase : List[str] = AttnSkipUpBlockaD # noqa F405
_lowercase : str = '''up'''
@property
def lowerCamelCase_ ( self: Optional[Any] ) -> Dict:
"""simple docstring"""
return super().get_dummy_input(include_res_hidden_states_tuple=UpperCamelCase_ )
def lowerCamelCase_ ( self: List[str] ) -> Optional[Any]:
"""simple docstring"""
lowercase__ = [0.0361, 0.0617, 0.2787, -0.0350, 0.0342, 0.3421, -0.0843, 0.0913, 0.3015]
super().test_output(UpperCamelCase_ )
class _a ( UpperCamelCase__ , unittest.TestCase ):
_lowercase : Dict = UpDecoderBlockaD # noqa F405
_lowercase : Tuple = '''up'''
@property
def lowerCamelCase_ ( self: int ) -> str:
"""simple docstring"""
return super().get_dummy_input(include_temb=UpperCamelCase_ )
def lowerCamelCase_ ( self: List[str] ) -> Optional[Any]:
"""simple docstring"""
lowercase__ = {'''in_channels''': 32, '''out_channels''': 32}
lowercase__ = self.dummy_input
return init_dict, inputs_dict
def lowerCamelCase_ ( self: Tuple ) -> Any:
"""simple docstring"""
lowercase__ = [0.4404, 0.1998, -0.9886, -0.3320, -0.3128, -0.7034, -0.6955, -0.2338, -0.3137]
super().test_output(UpperCamelCase_ )
class _a ( UpperCamelCase__ , unittest.TestCase ):
_lowercase : Optional[int] = AttnUpDecoderBlockaD # noqa F405
_lowercase : str = '''up'''
@property
def lowerCamelCase_ ( self: Optional[Any] ) -> Union[str, Any]:
"""simple docstring"""
return super().get_dummy_input(include_temb=UpperCamelCase_ )
def lowerCamelCase_ ( self: Dict ) -> List[str]:
"""simple docstring"""
lowercase__ = {'''in_channels''': 32, '''out_channels''': 32}
lowercase__ = self.dummy_input
return init_dict, inputs_dict
def lowerCamelCase_ ( self: int ) -> Optional[Any]:
"""simple docstring"""
lowercase__ = [0.6738, 0.4491, 0.1055, 1.0710, 0.7316, 0.3339, 0.3352, 0.1023, 0.3568]
super().test_output(UpperCamelCase_ )
| 43
|
def _a ( SCREAMING_SNAKE_CASE = "The quick brown fox jumps over the lazy dog" , ):
"""simple docstring"""
lowercase__ = set()
# Replace all the whitespace in our sentence
lowercase__ = input_str.replace(''' ''' , '''''' )
for alpha in input_str:
if "a" <= alpha.lower() <= "z":
frequency.add(alpha.lower() )
return len(SCREAMING_SNAKE_CASE ) == 26
def _a ( SCREAMING_SNAKE_CASE = "The quick brown fox jumps over the lazy dog" , ):
"""simple docstring"""
lowercase__ = [False] * 26
for char in input_str:
if char.islower():
lowercase__ = True
elif char.isupper():
lowercase__ = True
return all(SCREAMING_SNAKE_CASE )
def _a ( SCREAMING_SNAKE_CASE = "The quick brown fox jumps over the lazy dog" , ):
"""simple docstring"""
return len({char for char in input_str.lower() if char.isalpha()} ) == 26
def _a ( ):
"""simple docstring"""
from timeit import timeit
lowercase__ = '''from __main__ import is_pangram, is_pangram_faster, is_pangram_fastest'''
print(timeit('''is_pangram()''' , setup=SCREAMING_SNAKE_CASE ) )
print(timeit('''is_pangram_faster()''' , setup=SCREAMING_SNAKE_CASE ) )
print(timeit('''is_pangram_fastest()''' , setup=SCREAMING_SNAKE_CASE ) )
# 5.348480500048026, 2.6477354579837993, 1.8470395830227062
# 5.036091582966037, 2.644472333951853, 1.8869528750656173
if __name__ == "__main__":
import doctest
doctest.testmod()
benchmark()
| 43
| 1
|
"""simple docstring"""
def _snake_case ( UpperCamelCase : str ):
if len(a__ ) <= 1:
return lst
UpperCAmelCase : Union[str, Any] = 1
while i < len(a__ ):
if lst[i - 1] <= lst[i]:
i += 1
else:
UpperCAmelCase , UpperCAmelCase : Any = lst[i], lst[i - 1]
i -= 1
if i == 0:
UpperCAmelCase : Union[str, Any] = 1
return lst
if __name__ == "__main__":
A: List[Any] = input("Enter numbers separated by a comma:\n").strip()
A: Optional[Any] = [int(item) for item in user_input.split(",")]
print(gnome_sort(unsorted))
| 716
|
"""simple docstring"""
import math
from datetime import datetime, timedelta
def _snake_case ( UpperCamelCase : int ):
UpperCAmelCase : Any = year % 19
UpperCAmelCase : Any = year % 4
UpperCAmelCase : str = year % 7
UpperCAmelCase : Union[str, Any] = math.floor(year / 100 )
UpperCAmelCase : Optional[Any] = math.floor((13 + 8 * leap_day_inhibits) / 25 )
UpperCAmelCase : int = leap_day_inhibits / 4
UpperCAmelCase : int = (
15 - lunar_orbit_correction + leap_day_inhibits - leap_day_reinstall_number
) % 30
UpperCAmelCase : Tuple = (4 + leap_day_inhibits - leap_day_reinstall_number) % 7
# days to be added to March 21
UpperCAmelCase : int = (19 * metonic_cycle + secular_moon_shift) % 30
# PHM -> Paschal Full Moon
UpperCAmelCase : Tuple = (
2 * julian_leap_year
+ 4 * non_leap_year
+ 6 * days_to_add
+ century_starting_point
) % 7
if days_to_add == 29 and days_from_phm_to_sunday == 6:
return datetime(UpperCamelCase , 4 , 19 )
elif days_to_add == 28 and days_from_phm_to_sunday == 6:
return datetime(UpperCamelCase , 4 , 18 )
else:
return datetime(UpperCamelCase , 3 , 22 ) + timedelta(
days=int(days_to_add + days_from_phm_to_sunday ) )
if __name__ == "__main__":
for year in (1_9_9_4, 2_0_0_0, 2_0_1_0, 2_0_2_1, 2_0_2_3):
A: Any = "will be" if year > datetime.now().year else "was"
print(f"""Easter in {year} {tense} {gauss_easter(year)}""")
| 359
| 0
|
from decimal import Decimal, getcontext
from math import ceil, factorial
def lowerCAmelCase_ ( __UpperCAmelCase: int ) -> str:
if not isinstance(__UpperCAmelCase , __UpperCAmelCase ):
raise TypeError('''Undefined for non-integers''' )
elif precision < 1:
raise ValueError('''Undefined for non-natural numbers''' )
UpperCamelCase__ : str = precision
UpperCamelCase__ : str = ceil(precision / 14 )
UpperCamelCase__ : Optional[int] = 42_6880 * Decimal(1_0005 ).sqrt()
UpperCamelCase__ : Union[str, Any] = 1
UpperCamelCase__ : Optional[int] = 1359_1409
UpperCamelCase__ : str = Decimal(__UpperCAmelCase )
for k in range(1 , __UpperCAmelCase ):
UpperCamelCase__ : str = factorial(6 * k ) // (factorial(3 * k ) * factorial(__UpperCAmelCase ) ** 3)
linear_term += 5_4514_0134
exponential_term *= -26_2537_4126_4076_8000
partial_sum += Decimal(multinomial_term * linear_term ) / exponential_term
return str(constant_term / partial_sum )[:-1]
if __name__ == "__main__":
UpperCAmelCase_ = 50
print(F'''The first {n} digits of pi is: {pi(n)}''')
| 253
|
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
UpperCAmelCase_ = {
'configuration_resnet': ['RESNET_PRETRAINED_CONFIG_ARCHIVE_MAP', 'ResNetConfig', 'ResNetOnnxConfig']
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCAmelCase_ = [
'RESNET_PRETRAINED_MODEL_ARCHIVE_LIST',
'ResNetForImageClassification',
'ResNetModel',
'ResNetPreTrainedModel',
'ResNetBackbone',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCAmelCase_ = [
'TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST',
'TFResNetForImageClassification',
'TFResNetModel',
'TFResNetPreTrainedModel',
]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCAmelCase_ = [
'FlaxResNetForImageClassification',
'FlaxResNetModel',
'FlaxResNetPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_resnet import RESNET_PRETRAINED_CONFIG_ARCHIVE_MAP, ResNetConfig, ResNetOnnxConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_resnet import (
RESNET_PRETRAINED_MODEL_ARCHIVE_LIST,
ResNetBackbone,
ResNetForImageClassification,
ResNetModel,
ResNetPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_resnet import (
TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST,
TFResNetForImageClassification,
TFResNetModel,
TFResNetPreTrainedModel,
)
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_resnet import FlaxResNetForImageClassification, FlaxResNetModel, FlaxResNetPreTrainedModel
else:
import sys
UpperCAmelCase_ = _LazyModule(__name__, globals()['__file__'], _import_structure)
| 253
| 1
|
"""simple docstring"""
from __future__ import annotations
def _a ( _snake_case ):
"""simple docstring"""
return len(set(_snake_case ) ) == len(_snake_case )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 74
|
"""simple docstring"""
from __future__ import annotations
from typing import Any
class lowerCamelCase__ :
def __init__( self ,A = 6 ):
UpperCAmelCase = None
UpperCAmelCase = None
self.create_linked_list(A )
def _UpperCamelCase ( self ,A ):
UpperCAmelCase = Node()
UpperCAmelCase = current_node
UpperCAmelCase = current_node
UpperCAmelCase = current_node
for _ in range(1 ,A ):
UpperCAmelCase = Node()
UpperCAmelCase = current_node
UpperCAmelCase = previous_node
UpperCAmelCase = current_node
UpperCAmelCase = self.front
UpperCAmelCase = previous_node
def _UpperCamelCase ( self ):
return (
self.front == self.rear
and self.front is not None
and self.front.data is None
)
def _UpperCamelCase ( self ):
self.check_can_perform_operation()
return self.front.data if self.front else None
def _UpperCamelCase ( self ,A ):
if self.rear is None:
return
self.check_is_full()
if not self.is_empty():
UpperCAmelCase = self.rear.next
if self.rear:
UpperCAmelCase = data
def _UpperCamelCase ( self ):
self.check_can_perform_operation()
if self.rear is None or self.front is None:
return None
if self.front == self.rear:
UpperCAmelCase = self.front.data
UpperCAmelCase = None
return data
UpperCAmelCase = self.front
UpperCAmelCase = old_front.next
UpperCAmelCase = old_front.data
UpperCAmelCase = None
return data
def _UpperCamelCase ( self ):
if self.is_empty():
raise Exception("""Empty Queue""" )
def _UpperCamelCase ( self ):
if self.rear and self.rear.next == self.front:
raise Exception("""Full Queue""" )
class lowerCamelCase__ :
def __init__( self ):
UpperCAmelCase = None
UpperCAmelCase = None
UpperCAmelCase = None
if __name__ == "__main__":
import doctest
doctest.testmod()
| 74
| 1
|
import hashlib
import unittest
from transformers import MODEL_FOR_DEPTH_ESTIMATION_MAPPING, is_torch_available, is_vision_available
from transformers.pipelines import DepthEstimationPipeline, pipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
require_tf,
require_timm,
require_torch,
require_vision,
slow,
)
from .test_pipelines_common import ANY
if is_torch_available():
import torch
if is_vision_available():
from PIL import Image
else:
class SCREAMING_SNAKE_CASE :
'''simple docstring'''
@staticmethod
def _A ( *UpperCAmelCase_ : Dict , **UpperCAmelCase_ : List[Any] ):
pass
def lowerCamelCase__ ( lowercase ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : List[Any] = hashlib.mda(image.tobytes() )
return m.hexdigest()
@is_pipeline_test
@require_vision
@require_timm
@require_torch
class SCREAMING_SNAKE_CASE ( unittest.TestCase ):
'''simple docstring'''
UpperCamelCase_ : List[str] = MODEL_FOR_DEPTH_ESTIMATION_MAPPING
def _A ( self : Dict , UpperCAmelCase_ : Dict , UpperCAmelCase_ : List[Any] , UpperCAmelCase_ : List[Any] ):
SCREAMING_SNAKE_CASE : Union[str, Any] = DepthEstimationPipeline(model=UpperCAmelCase_ , image_processor=UpperCAmelCase_ )
return depth_estimator, [
"./tests/fixtures/tests_samples/COCO/000000039769.png",
"./tests/fixtures/tests_samples/COCO/000000039769.png",
]
def _A ( self : Union[str, Any] , UpperCAmelCase_ : str , UpperCAmelCase_ : Union[str, Any] ):
SCREAMING_SNAKE_CASE : Union[str, Any] = depth_estimator("./tests/fixtures/tests_samples/COCO/000000039769.png" )
self.assertEqual({"predicted_depth": ANY(torch.Tensor ), "depth": ANY(Image.Image )} , UpperCAmelCase_ )
import datasets
SCREAMING_SNAKE_CASE : Optional[Any] = datasets.load_dataset("hf-internal-testing/fixtures_image_utils" , "image" , split="test" )
SCREAMING_SNAKE_CASE : Any = depth_estimator(
[
Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png" ),
"http://images.cocodataset.org/val2017/000000039769.jpg",
# RGBA
dataset[0]["file"],
# LA
dataset[1]["file"],
# L
dataset[2]["file"],
] )
self.assertEqual(
[
{"predicted_depth": ANY(torch.Tensor ), "depth": ANY(Image.Image )},
{"predicted_depth": ANY(torch.Tensor ), "depth": ANY(Image.Image )},
{"predicted_depth": ANY(torch.Tensor ), "depth": ANY(Image.Image )},
{"predicted_depth": ANY(torch.Tensor ), "depth": ANY(Image.Image )},
{"predicted_depth": ANY(torch.Tensor ), "depth": ANY(Image.Image )},
] , UpperCAmelCase_ , )
@require_tf
@unittest.skip("Depth estimation is not implemented in TF" )
def _A ( self : Any ):
pass
@slow
@require_torch
def _A ( self : str ):
SCREAMING_SNAKE_CASE : Union[str, Any] = "Intel/dpt-large"
SCREAMING_SNAKE_CASE : Union[str, Any] = pipeline("depth-estimation" , model=UpperCAmelCase_ )
SCREAMING_SNAKE_CASE : Union[str, Any] = depth_estimator("http://images.cocodataset.org/val2017/000000039769.jpg" )
SCREAMING_SNAKE_CASE : Optional[Any] = hashimage(outputs["depth"] )
# This seems flaky.
# self.assertEqual(outputs["depth"], "1a39394e282e9f3b0741a90b9f108977")
self.assertEqual(nested_simplify(outputs["predicted_depth"].max().item() ) , 29.304 )
self.assertEqual(nested_simplify(outputs["predicted_depth"].min().item() ) , 2.662 )
@require_torch
def _A ( self : Union[str, Any] ):
self.skipTest("There is not hf-internal-testing tiny model for either GLPN nor DPT" )
| 62
|
'''simple docstring'''
import argparse
import datetime
import json
import time
import warnings
from logging import getLogger
from pathlib import Path
from typing import Dict, List
import torch
from tqdm import tqdm
from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from utils import calculate_bleu, calculate_rouge, chunks, parse_numeric_n_bool_cl_kwargs, use_task_specific_params
__UpperCamelCase : int = getLogger(__name__)
__UpperCamelCase : int = """cuda""" if torch.cuda.is_available() else """cpu"""
def __UpperCAmelCase ( SCREAMING_SNAKE_CASE__: List[str], SCREAMING_SNAKE_CASE__: str, SCREAMING_SNAKE_CASE__: str, SCREAMING_SNAKE_CASE__: int = 8, SCREAMING_SNAKE_CASE__: str = DEFAULT_DEVICE, SCREAMING_SNAKE_CASE__: Any=False, SCREAMING_SNAKE_CASE__: Tuple="summarization", SCREAMING_SNAKE_CASE__: List[Any]=None, **SCREAMING_SNAKE_CASE__: int, ) -> Dict:
"""simple docstring"""
__a = Path(SCREAMING_SNAKE_CASE__ ).open('w', encoding='utf-8' )
__a = str(SCREAMING_SNAKE_CASE__ )
__a = AutoModelForSeqaSeqLM.from_pretrained(SCREAMING_SNAKE_CASE__ ).to(SCREAMING_SNAKE_CASE__ )
if fpaa:
__a = model.half()
__a = AutoTokenizer.from_pretrained(SCREAMING_SNAKE_CASE__ )
logger.info(f"""Inferred tokenizer type: {tokenizer.__class__}""" ) # if this is wrong, check config.model_type.
__a = time.time()
# update config with task specific params
use_task_specific_params(SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__ )
if prefix is None:
__a = prefix or getattr(model.config, 'prefix', '' ) or ''
for examples_chunk in tqdm(list(chunks(SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__ ) ) ):
__a = [prefix + text for text in examples_chunk]
__a = tokenizer(SCREAMING_SNAKE_CASE__, return_tensors='pt', truncation=SCREAMING_SNAKE_CASE__, padding='longest' ).to(SCREAMING_SNAKE_CASE__ )
__a = model.generate(
input_ids=batch.input_ids, attention_mask=batch.attention_mask, **SCREAMING_SNAKE_CASE__, )
__a = tokenizer.batch_decode(SCREAMING_SNAKE_CASE__, skip_special_tokens=SCREAMING_SNAKE_CASE__, clean_up_tokenization_spaces=SCREAMING_SNAKE_CASE__ )
for hypothesis in dec:
fout.write(hypothesis + '\n' )
fout.flush()
fout.close()
__a = int(time.time() - start_time ) # seconds
__a = len(SCREAMING_SNAKE_CASE__ )
return {"n_obs": n_obs, "runtime": runtime, "seconds_per_sample": round(runtime / n_obs, 4 )}
def __UpperCAmelCase ( ) -> List[str]:
"""simple docstring"""
return datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S' )
def __UpperCAmelCase ( SCREAMING_SNAKE_CASE__: Optional[int]=True ) -> List[Any]:
"""simple docstring"""
__a = argparse.ArgumentParser()
parser.add_argument('model_name', type=SCREAMING_SNAKE_CASE__, help='like facebook/bart-large-cnn,t5-base, etc.' )
parser.add_argument('input_path', type=SCREAMING_SNAKE_CASE__, help='like cnn_dm/test.source' )
parser.add_argument('save_path', type=SCREAMING_SNAKE_CASE__, help='where to save summaries' )
parser.add_argument('--reference_path', type=SCREAMING_SNAKE_CASE__, required=SCREAMING_SNAKE_CASE__, help='like cnn_dm/test.target' )
parser.add_argument('--score_path', type=SCREAMING_SNAKE_CASE__, required=SCREAMING_SNAKE_CASE__, default='metrics.json', help='where to save metrics' )
parser.add_argument('--device', type=SCREAMING_SNAKE_CASE__, required=SCREAMING_SNAKE_CASE__, default=SCREAMING_SNAKE_CASE__, help='cuda, cuda:1, cpu etc.' )
parser.add_argument(
'--prefix', type=SCREAMING_SNAKE_CASE__, required=SCREAMING_SNAKE_CASE__, default=SCREAMING_SNAKE_CASE__, help='will be added to the begininng of src examples' )
parser.add_argument('--task', type=SCREAMING_SNAKE_CASE__, default='summarization', help='used for task_specific_params + metrics' )
parser.add_argument('--bs', type=SCREAMING_SNAKE_CASE__, default=8, required=SCREAMING_SNAKE_CASE__, help='batch size' )
parser.add_argument(
'--n_obs', type=SCREAMING_SNAKE_CASE__, default=-1, required=SCREAMING_SNAKE_CASE__, help='How many observations. Defaults to all.' )
parser.add_argument('--fp16', action='store_true' )
parser.add_argument('--dump-args', action='store_true', help='print the custom hparams with the results' )
parser.add_argument(
'--info', nargs='?', type=SCREAMING_SNAKE_CASE__, const=datetime_now(), help=(
'use in conjunction w/ --dump-args to print with the results whatever other info you\'d like, e.g.'
' lang=en-ru. If no value is passed, the current datetime string will be used.'
), )
# Unspecified args like --num_beams=2 --decoder_start_token_id=4 are passed to model.generate
__a , __a = parser.parse_known_args()
__a = parse_numeric_n_bool_cl_kwargs(SCREAMING_SNAKE_CASE__ )
if parsed_args and verbose:
print(f"""parsed the following generate kwargs: {parsed_args}""" )
__a = [' ' + x.rstrip() if 't5' in args.model_name else x.rstrip() for x in open(args.input_path ).readlines()]
if args.n_obs > 0:
__a = examples[: args.n_obs]
Path(args.save_path ).parent.mkdir(exist_ok=SCREAMING_SNAKE_CASE__ )
if args.reference_path is None and Path(args.score_path ).exists():
warnings.warn(f"""score_path {args.score_path} will be overwritten unless you type ctrl-c.""" )
if args.device == "cpu" and args.fpaa:
# this mix leads to RuntimeError: "threshold_cpu" not implemented for 'Half'
raise ValueError('Can\'t mix --fp16 and --device cpu' )
__a = generate_summaries_or_translations(
SCREAMING_SNAKE_CASE__, args.save_path, args.model_name, batch_size=args.bs, device=args.device, fpaa=args.fpaa, task=args.task, prefix=args.prefix, **SCREAMING_SNAKE_CASE__, )
if args.reference_path is None:
return {}
# Compute scores
__a = calculate_bleu if 'translation' in args.task else calculate_rouge
__a = [x.rstrip() for x in open(args.save_path ).readlines()]
__a = [x.rstrip() for x in open(args.reference_path ).readlines()][: len(SCREAMING_SNAKE_CASE__ )]
__a = score_fn(SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__ )
scores.update(SCREAMING_SNAKE_CASE__ )
if args.dump_args:
scores.update(SCREAMING_SNAKE_CASE__ )
if args.info:
__a = args.info
if verbose:
print(SCREAMING_SNAKE_CASE__ )
if args.score_path is not None:
json.dump(SCREAMING_SNAKE_CASE__, open(args.score_path, 'w' ) )
return scores
if __name__ == "__main__":
# Usage for MT:
# python run_eval.py MODEL_NAME $DATA_DIR/test.source $save_dir/test_translations.txt --reference_path $DATA_DIR/test.target --score_path $save_dir/test_bleu.json --task translation $@
run_generate(verbose=True)
| 448
| 0
|
'''simple docstring'''
import os
__a = {"""I""": 1, """V""": 5, """X""": 10, """L""": 50, """C""": 100, """D""": 500, """M""": 1000}
def _UpperCamelCase ( lowerCAmelCase_ ) ->int:
UpperCAmelCase = 0
UpperCAmelCase = 0
while index < len(lowerCAmelCase_ ) - 1:
UpperCAmelCase = SYMBOLS[numerals[index]]
UpperCAmelCase = SYMBOLS[numerals[index + 1]]
if current_value < next_value:
total_value -= current_value
else:
total_value += current_value
index += 1
total_value += SYMBOLS[numerals[index]]
return total_value
def _UpperCamelCase ( lowerCAmelCase_ ) ->str:
UpperCAmelCase = """"""
UpperCAmelCase = num // 1_0_0_0
numerals += m_count * "M"
num %= 1_0_0_0
UpperCAmelCase = num // 1_0_0
if c_count == 9:
numerals += "CM"
c_count -= 9
elif c_count == 4:
numerals += "CD"
c_count -= 4
if c_count >= 5:
numerals += "D"
c_count -= 5
numerals += c_count * "C"
num %= 1_0_0
UpperCAmelCase = num // 1_0
if x_count == 9:
numerals += "XC"
x_count -= 9
elif x_count == 4:
numerals += "XL"
x_count -= 4
if x_count >= 5:
numerals += "L"
x_count -= 5
numerals += x_count * "X"
num %= 1_0
if num == 9:
numerals += "IX"
num -= 9
elif num == 4:
numerals += "IV"
num -= 4
if num >= 5:
numerals += "V"
num -= 5
numerals += num * "I"
return numerals
def _UpperCamelCase ( lowerCAmelCase_ = "/p089_roman.txt" ) ->int:
UpperCAmelCase = 0
with open(os.path.dirname(lowerCAmelCase_ ) + roman_numerals_filename ) as filea:
UpperCAmelCase = filea.readlines()
for line in lines:
UpperCAmelCase = line.strip()
UpperCAmelCase = parse_roman_numerals(lowerCAmelCase_ )
UpperCAmelCase = generate_roman_numerals(lowerCAmelCase_ )
savings += len(lowerCAmelCase_ ) - len(lowerCAmelCase_ )
return savings
if __name__ == "__main__":
print(F"""{solution() = }""")
| 706
|
import multiprocessing
import time
from arguments import PretokenizationArguments
from datasets import load_dataset
from transformers import AutoTokenizer, HfArgumentParser
def _UpperCamelCase ( lowerCAmelCase_ ) ->int:
UpperCAmelCase = {}
UpperCAmelCase = tokenizer(example["""content"""] , truncation=lowerCAmelCase_ )["""input_ids"""]
UpperCAmelCase = len(example["""content"""] ) / len(output["""input_ids"""] )
return output
__a = HfArgumentParser(PretokenizationArguments)
__a = parser.parse_args()
if args.num_workers is None:
__a = multiprocessing.cpu_count()
__a = AutoTokenizer.from_pretrained(args.tokenizer_dir)
__a = time.time()
__a = load_dataset(args.dataset_name, split="""train""")
print(F"""Dataset loaded in {time.time()-t_start:.2f}s""")
__a = time.time()
__a = ds.map(
tokenize,
num_proc=args.num_workers,
remove_columns=[
"""repo_name""",
"""path""",
"""copies""",
"""size""",
"""content""",
"""license""",
"""hash""",
"""line_mean""",
"""line_max""",
"""alpha_frac""",
"""autogenerated""",
],
)
print(F"""Dataset tokenized in {time.time()-t_start:.2f}s""")
__a = time.time()
ds.push_to_hub(args.tokenized_data_repo)
print(F"""Data pushed to the hub in {time.time()-t_start:.2f}s""")
| 627
| 0
|
"""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 lowerCamelCase_:
'''simple docstring'''
lowercase__ : List[str]
lowercase__ : Optional[str] = None
# Automatically constructed
lowercase__ : ClassVar[str] = "dict"
lowercase__ : ClassVar[Any] = None
lowercase__ : str = field(default='Translation', init=A__, repr=A__ )
def __call__( self ):
return pa.struct({lang: pa.string() for lang in sorted(self.languages )} )
def snake_case__ ( self ):
from .features import Value
return {k: Value('''string''' ) for k in sorted(self.languages )}
@dataclass
class lowerCamelCase_:
'''simple docstring'''
lowercase__ : Optional[List] = None
lowercase__ : Optional[int] = None
lowercase__ : Optional[str] = None
# Automatically constructed
lowercase__ : ClassVar[str] = "dict"
lowercase__ : ClassVar[Any] = None
lowercase__ : str = field(default='TranslationVariableLanguages', init=A__, repr=A__ )
def snake_case__ ( self ):
_lowerCamelCase = sorted(set(self.languages ) ) if self.languages else None
_lowerCamelCase = len(self.languages ) if self.languages else None
def __call__( self ):
return pa.struct({'''language''': pa.list_(pa.string() ), '''translation''': pa.list_(pa.string() )} )
def snake_case__ ( self , lowerCamelCase__ ):
_lowerCamelCase = 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.
_lowerCamelCase = []
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.
_lowerCamelCase , _lowerCamelCase = zip(*sorted(lowerCamelCase__ ) )
return {"language": languages, "translation": translations}
def snake_case__ ( self ):
from .features import Sequence, Value
return {
"language": Sequence(Value('''string''' ) ),
"translation": Sequence(Value('''string''' ) ),
}
| 661
|
"""simple docstring"""
import json
import os
from functools import lru_cache
from typing import TYPE_CHECKING, List, Optional, Tuple
import regex as re
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
if TYPE_CHECKING:
from transformers.pipelines.conversational import Conversation
__SCREAMING_SNAKE_CASE : Union[str, Any] = logging.get_logger(__name__)
__SCREAMING_SNAKE_CASE : int = {
'''vocab_file''': '''vocab.json''',
'''merges_file''': '''merges.txt''',
'''tokenizer_config_file''': '''tokenizer_config.json''',
}
__SCREAMING_SNAKE_CASE : Tuple = {
'''vocab_file''': {'''facebook/blenderbot-3B''': '''https://huggingface.co/facebook/blenderbot-3B/resolve/main/vocab.json'''},
'''merges_file''': {'''facebook/blenderbot-3B''': '''https://huggingface.co/facebook/blenderbot-3B/resolve/main/merges.txt'''},
'''tokenizer_config_file''': {
'''facebook/blenderbot-3B''': '''https://huggingface.co/facebook/blenderbot-3B/resolve/main/tokenizer_config.json'''
},
}
__SCREAMING_SNAKE_CASE : str = {'''facebook/blenderbot-3B''': 1_2_8}
@lru_cache()
# Copied from transformers.models.roberta.tokenization_roberta.bytes_to_unicode
def lowerCAmelCase_( ) -> str:
_lowerCamelCase = (
list(range(ord('''!''' ) , ord('''~''' ) + 1 ) ) + list(range(ord('''¡''' ) , ord('''¬''' ) + 1 ) ) + list(range(ord('''®''' ) , ord('''ÿ''' ) + 1 ) )
)
_lowerCamelCase = bs[:]
_lowerCamelCase = 0
for b in range(2**8 ):
if b not in bs:
bs.append(lowercase_ )
cs.append(2**8 + n )
n += 1
_lowerCamelCase = [chr(lowercase_ ) for n in cs]
return dict(zip(lowercase_ , lowercase_ ) )
def lowerCAmelCase_( lowercase_ : str ) -> Dict:
_lowerCamelCase = set()
_lowerCamelCase = word[0]
for char in word[1:]:
pairs.add((prev_char, char) )
_lowerCamelCase = char
return pairs
class lowerCamelCase_( A__ ):
'''simple docstring'''
lowercase__ : Union[str, Any] = VOCAB_FILES_NAMES
lowercase__ : int = PRETRAINED_VOCAB_FILES_MAP
lowercase__ : Optional[Any] = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
lowercase__ : Tuple = ['input_ids', 'attention_mask']
def __init__( self , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__="replace" , lowerCamelCase__="<s>" , lowerCamelCase__="</s>" , lowerCamelCase__="</s>" , lowerCamelCase__="<s>" , lowerCamelCase__="<unk>" , lowerCamelCase__="<pad>" , lowerCamelCase__="<mask>" , lowerCamelCase__=False , **lowerCamelCase__ , ):
_lowerCamelCase = AddedToken(lowerCamelCase__ , lstrip=lowerCamelCase__ , rstrip=lowerCamelCase__ ) if isinstance(lowerCamelCase__ , lowerCamelCase__ ) else bos_token
_lowerCamelCase = AddedToken(lowerCamelCase__ , lstrip=lowerCamelCase__ , rstrip=lowerCamelCase__ ) if isinstance(lowerCamelCase__ , lowerCamelCase__ ) else eos_token
_lowerCamelCase = AddedToken(lowerCamelCase__ , lstrip=lowerCamelCase__ , rstrip=lowerCamelCase__ ) if isinstance(lowerCamelCase__ , lowerCamelCase__ ) else sep_token
_lowerCamelCase = AddedToken(lowerCamelCase__ , lstrip=lowerCamelCase__ , rstrip=lowerCamelCase__ ) if isinstance(lowerCamelCase__ , lowerCamelCase__ ) else cls_token
_lowerCamelCase = AddedToken(lowerCamelCase__ , lstrip=lowerCamelCase__ , rstrip=lowerCamelCase__ ) if isinstance(lowerCamelCase__ , lowerCamelCase__ ) else unk_token
_lowerCamelCase = AddedToken(lowerCamelCase__ , lstrip=lowerCamelCase__ , rstrip=lowerCamelCase__ ) if isinstance(lowerCamelCase__ , lowerCamelCase__ ) else pad_token
# Mask token behave like a normal word, i.e. include the space before it
_lowerCamelCase = AddedToken(lowerCamelCase__ , lstrip=lowerCamelCase__ , rstrip=lowerCamelCase__ ) if isinstance(lowerCamelCase__ , lowerCamelCase__ ) else mask_token
super().__init__(
errors=lowerCamelCase__ , bos_token=lowerCamelCase__ , eos_token=lowerCamelCase__ , unk_token=lowerCamelCase__ , sep_token=lowerCamelCase__ , cls_token=lowerCamelCase__ , pad_token=lowerCamelCase__ , mask_token=lowerCamelCase__ , add_prefix_space=lowerCamelCase__ , **lowerCamelCase__ , )
with open(lowerCamelCase__ , encoding='''utf-8''' ) as vocab_handle:
_lowerCamelCase = json.load(lowerCamelCase__ )
_lowerCamelCase = {v: k for k, v in self.encoder.items()}
_lowerCamelCase = errors # how to handle errors in decoding
_lowerCamelCase = bytes_to_unicode()
_lowerCamelCase = {v: k for k, v in self.byte_encoder.items()}
with open(lowerCamelCase__ , encoding='''utf-8''' ) as merges_handle:
_lowerCamelCase = merges_handle.read().split('''\n''' )[1:-1]
_lowerCamelCase = [tuple(merge.split() ) for merge in bpe_merges]
_lowerCamelCase = dict(zip(lowerCamelCase__ , range(len(lowerCamelCase__ ) ) ) )
_lowerCamelCase = {}
_lowerCamelCase = add_prefix_space
# Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
_lowerCamelCase = re.compile(R'''\'s|\'t|\'re|\'ve|\'m|\'ll|\'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+''' )
@property
# Copied from transformers.models.roberta.tokenization_roberta.RobertaTokenizer.vocab_size with Roberta->Blenderbot, RoBERTa->Blenderbot
def snake_case__ ( self ):
return len(self.encoder )
def snake_case__ ( self ):
return dict(self.encoder , **self.added_tokens_encoder )
def snake_case__ ( self , lowerCamelCase__ ):
if token in self.cache:
return self.cache[token]
_lowerCamelCase = tuple(lowerCamelCase__ )
_lowerCamelCase = get_pairs(lowerCamelCase__ )
if not pairs:
return token
while True:
_lowerCamelCase = min(lowerCamelCase__ , key=lambda lowerCamelCase__ : self.bpe_ranks.get(lowerCamelCase__ , float('''inf''' ) ) )
if bigram not in self.bpe_ranks:
break
_lowerCamelCase , _lowerCamelCase = bigram
_lowerCamelCase = []
_lowerCamelCase = 0
while i < len(lowerCamelCase__ ):
try:
_lowerCamelCase = word.index(lowerCamelCase__ , lowerCamelCase__ )
except ValueError:
new_word.extend(word[i:] )
break
else:
new_word.extend(word[i:j] )
_lowerCamelCase = j
if word[i] == first and i < len(lowerCamelCase__ ) - 1 and word[i + 1] == second:
new_word.append(first + second )
i += 2
else:
new_word.append(word[i] )
i += 1
_lowerCamelCase = tuple(lowerCamelCase__ )
_lowerCamelCase = new_word
if len(lowerCamelCase__ ) == 1:
break
else:
_lowerCamelCase = get_pairs(lowerCamelCase__ )
_lowerCamelCase = ''' '''.join(lowerCamelCase__ )
_lowerCamelCase = word
return word
def snake_case__ ( self , lowerCamelCase__ ):
_lowerCamelCase = []
for token in re.findall(self.pat , lowerCamelCase__ ):
_lowerCamelCase = ''''''.join(
self.byte_encoder[b] for b in token.encode('''utf-8''' ) ) # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case)
bpe_tokens.extend(bpe_token for bpe_token in self.bpe(lowerCamelCase__ ).split(''' ''' ) )
return bpe_tokens
def snake_case__ ( self , lowerCamelCase__ ):
return self.encoder.get(lowerCamelCase__ , self.encoder.get(self.unk_token ) )
def snake_case__ ( self , lowerCamelCase__ ):
return self.decoder.get(lowerCamelCase__ )
def snake_case__ ( self , lowerCamelCase__ ):
_lowerCamelCase = ''''''.join(lowerCamelCase__ )
_lowerCamelCase = bytearray([self.byte_decoder[c] for c in text] ).decode('''utf-8''' , errors=self.errors )
return text
def snake_case__ ( self , lowerCamelCase__ , lowerCamelCase__ = None ):
if not os.path.isdir(lowerCamelCase__ ):
logger.error(F"""Vocabulary path ({save_directory}) should be a directory""" )
return
_lowerCamelCase = os.path.join(
lowerCamelCase__ , (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file'''] )
_lowerCamelCase = os.path.join(
lowerCamelCase__ , (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''merges_file'''] )
with open(lowerCamelCase__ , '''w''' , encoding='''utf-8''' ) as f:
f.write(json.dumps(self.encoder , indent=2 , sort_keys=lowerCamelCase__ , ensure_ascii=lowerCamelCase__ ) + '''\n''' )
_lowerCamelCase = 0
with open(lowerCamelCase__ , '''w''' , encoding='''utf-8''' ) as writer:
writer.write('''#version: 0.2\n''' )
for bpe_tokens, token_index in sorted(self.bpe_ranks.items() , key=lambda lowerCamelCase__ : kv[1] ):
if index != token_index:
logger.warning(
F"""Saving vocabulary to {merge_file}: BPE merge indices are not consecutive."""
''' Please check that the tokenizer is not corrupted!''' )
_lowerCamelCase = token_index
writer.write(''' '''.join(lowerCamelCase__ ) + '''\n''' )
index += 1
return vocab_file, merge_file
def snake_case__ ( self , lowerCamelCase__ , lowerCamelCase__ = None , lowerCamelCase__ = False ):
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 snake_case__ ( self , lowerCamelCase__ , lowerCamelCase__ = None ):
_lowerCamelCase = [self.sep_token_id]
_lowerCamelCase = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep + sep + token_ids_a + sep ) * [0]
def snake_case__ ( self , lowerCamelCase__ , lowerCamelCase__=False , **lowerCamelCase__ ):
_lowerCamelCase = kwargs.pop('''add_prefix_space''' , self.add_prefix_space )
if (is_split_into_words or add_prefix_space) and (len(lowerCamelCase__ ) > 0 and not text[0].isspace()):
_lowerCamelCase = ''' ''' + text
return (text, kwargs)
def snake_case__ ( self , lowerCamelCase__ , lowerCamelCase__ = None ):
return token_ids_a + [self.eos_token_id]
def snake_case__ ( self , lowerCamelCase__ ):
_lowerCamelCase = []
for is_user, text in conversation.iter_texts():
if is_user:
# We need to space prefix as it's being done within blenderbot
inputs.append(''' ''' + text )
else:
# Generated responses should contain them already.
inputs.append(lowerCamelCase__ )
_lowerCamelCase = ''' '''.join(lowerCamelCase__ )
_lowerCamelCase = self.encode(lowerCamelCase__ )
if len(lowerCamelCase__ ) > self.model_max_length:
_lowerCamelCase = input_ids[-self.model_max_length :]
logger.warning(F"""Trimmed input from conversation as it was longer than {self.model_max_length} tokens.""" )
return input_ids
| 661
| 1
|
from itertools import product
def __lowerCAmelCase ( A_ : int , A_ : int ):
__UpperCAmelCase = sides_number
__UpperCAmelCase = max_face_number * dice_number
__UpperCAmelCase = [0] * (max_total + 1)
__UpperCAmelCase = 1
__UpperCAmelCase = range(_A , max_face_number + 1 )
for dice_numbers in product(_A , repeat=_A ):
__UpperCAmelCase = sum(_A )
totals_frequencies[total] += 1
return totals_frequencies
def __lowerCAmelCase ( ):
__UpperCAmelCase = total_frequency_distribution(
sides_number=4 , dice_number=9 )
__UpperCAmelCase = total_frequency_distribution(
sides_number=6 , dice_number=6 )
__UpperCAmelCase = 0
__UpperCAmelCase = 9
__UpperCAmelCase = 4 * 9
__UpperCAmelCase = 6
for peter_total in range(_A , max_peter_total + 1 ):
peter_wins_count += peter_totals_frequencies[peter_total] * sum(
colin_totals_frequencies[min_colin_total:peter_total] )
__UpperCAmelCase = (4**9) * (6**6)
__UpperCAmelCase = peter_wins_count / total_games_number
__UpperCAmelCase = round(_A , ndigits=7 )
return rounded_peter_win_probability
if __name__ == "__main__":
print(F"{solution() = }")
| 705
|
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
a_ = {
"""configuration_squeezebert""": [
"""SQUEEZEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""SqueezeBertConfig""",
"""SqueezeBertOnnxConfig""",
],
"""tokenization_squeezebert""": ["""SqueezeBertTokenizer"""],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a_ = ["""SqueezeBertTokenizerFast"""]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a_ = [
"""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
a_ = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 286
| 0
|
import os
lowerCamelCase__ : int = {"""I""": 1, """V""": 5, """X""": 1_0, """L""": 5_0, """C""": 1_0_0, """D""": 5_0_0, """M""": 1_0_0_0}
def UpperCamelCase ( lowercase_ ) -> int:
'''simple docstring'''
lowercase__ : Optional[Any] = 0
lowercase__ : List[str] = 0
while index < len(lowercase_ ) - 1:
lowercase__ : str = SYMBOLS[numerals[index]]
lowercase__ : str = SYMBOLS[numerals[index + 1]]
if current_value < next_value:
total_value -= current_value
else:
total_value += current_value
index += 1
total_value += SYMBOLS[numerals[index]]
return total_value
def UpperCamelCase ( lowercase_ ) -> str:
'''simple docstring'''
lowercase__ : List[Any] = """"""
lowercase__ : List[Any] = num // 10_00
numerals += m_count * "M"
num %= 10_00
lowercase__ : List[Any] = num // 1_00
if c_count == 9:
numerals += "CM"
c_count -= 9
elif c_count == 4:
numerals += "CD"
c_count -= 4
if c_count >= 5:
numerals += "D"
c_count -= 5
numerals += c_count * "C"
num %= 1_00
lowercase__ : Optional[Any] = num // 10
if x_count == 9:
numerals += "XC"
x_count -= 9
elif x_count == 4:
numerals += "XL"
x_count -= 4
if x_count >= 5:
numerals += "L"
x_count -= 5
numerals += x_count * "X"
num %= 10
if num == 9:
numerals += "IX"
num -= 9
elif num == 4:
numerals += "IV"
num -= 4
if num >= 5:
numerals += "V"
num -= 5
numerals += num * "I"
return numerals
def UpperCamelCase ( lowercase_ = "/p089_roman.txt" ) -> int:
'''simple docstring'''
lowercase__ : Optional[int] = 0
with open(os.path.dirname(lowercase_ ) + roman_numerals_filename ) as filea:
lowercase__ : int = filea.readlines()
for line in lines:
lowercase__ : Optional[int] = line.strip()
lowercase__ : Dict = parse_roman_numerals(lowercase_ )
lowercase__ : int = generate_roman_numerals(lowercase_ )
savings += len(lowercase_ ) - len(lowercase_ )
return savings
if __name__ == "__main__":
print(f'''{solution() = }''')
| 12
|
'''simple docstring'''
def snake_case_ ( _lowerCAmelCase : int , _lowerCAmelCase : Dict , _lowerCAmelCase : List[str] , _lowerCAmelCase : str , _lowerCAmelCase : Optional[int] , _lowerCAmelCase : List[Any] ) -> Union[str, Any]:
if index == r:
for j in range(_lowerCAmelCase ):
print(data[j] , end=''' ''' )
print(''' ''' )
return
# When no more elements are there to put in data[]
if i >= n:
return
# current is included, put next at next location
UpperCAmelCase : List[str] = arr[i]
combination_util(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , index + 1 , _lowerCAmelCase , i + 1 )
# current is excluded, replace it with
# next (Note that i+1 is passed, but
# index is not changed)
combination_util(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , i + 1 )
# The main function that prints all combinations
# of size r in arr[] of size n. This function
# mainly uses combinationUtil()
def snake_case_ ( _lowerCAmelCase : List[Any] , _lowerCAmelCase : Tuple , _lowerCAmelCase : Optional[Any] ) -> Any:
# A temporary array to store all combination one by one
UpperCAmelCase : Union[str, Any] = [0] * r
# Print all combination using temporary array 'data[]'
combination_util(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , 0 , _lowerCAmelCase , 0 )
if __name__ == "__main__":
# Driver code to check the function above
UpperCamelCase__: Any = [10, 20, 30, 40, 50]
print_combination(arr, len(arr), 3)
# This code is contributed by Ambuj sahu
| 127
| 0
|
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_SCREAMING_SNAKE_CASE = logging.get_logger(__name__)
_SCREAMING_SNAKE_CASE = {
"""facebook/timesformer""": """https://huggingface.co/facebook/timesformer/resolve/main/config.json""",
}
class _SCREAMING_SNAKE_CASE ( SCREAMING_SNAKE_CASE__ ):
__UpperCAmelCase = """timesformer"""
def __init__(self , lowerCAmelCase__=2_24 , lowerCAmelCase__=16 , lowerCAmelCase__=3 , lowerCAmelCase__=8 , lowerCAmelCase__=7_68 , lowerCAmelCase__=12 , lowerCAmelCase__=12 , lowerCAmelCase__=30_72 , lowerCAmelCase__="gelu" , lowerCAmelCase__=0.0 , lowerCAmelCase__=0.0 , lowerCAmelCase__=0.02 , lowerCAmelCase__=1E-6 , lowerCAmelCase__=True , lowerCAmelCase__="divided_space_time" , lowerCAmelCase__=0 , **lowerCAmelCase__ , ):
'''simple docstring'''
super().__init__(**lowerCAmelCase__ )
_UpperCamelCase : Optional[Any] = image_size
_UpperCamelCase : List[Any] = patch_size
_UpperCamelCase : Optional[int] = num_channels
_UpperCamelCase : Optional[int] = num_frames
_UpperCamelCase : Optional[Any] = hidden_size
_UpperCamelCase : Optional[int] = num_hidden_layers
_UpperCamelCase : Optional[int] = num_attention_heads
_UpperCamelCase : str = intermediate_size
_UpperCamelCase : int = hidden_act
_UpperCamelCase : str = hidden_dropout_prob
_UpperCamelCase : Union[str, Any] = attention_probs_dropout_prob
_UpperCamelCase : int = initializer_range
_UpperCamelCase : List[str] = layer_norm_eps
_UpperCamelCase : Any = qkv_bias
_UpperCamelCase : Any = attention_type
_UpperCamelCase : Dict = drop_path_rate
| 708
|
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
_SCREAMING_SNAKE_CASE = {
"""configuration_falcon""": ["""FALCON_PRETRAINED_CONFIG_ARCHIVE_MAP""", """FalconConfig"""],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_SCREAMING_SNAKE_CASE = [
"""FALCON_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""FalconForCausalLM""",
"""FalconModel""",
"""FalconPreTrainedModel""",
"""FalconForSequenceClassification""",
"""FalconForTokenClassification""",
"""FalconForQuestionAnswering""",
]
if TYPE_CHECKING:
from .configuration_falcon import FALCON_PRETRAINED_CONFIG_ARCHIVE_MAP, FalconConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_falcon import (
FALCON_PRETRAINED_MODEL_ARCHIVE_LIST,
FalconForCausalLM,
FalconForQuestionAnswering,
FalconForSequenceClassification,
FalconForTokenClassification,
FalconModel,
FalconPreTrainedModel,
)
else:
import sys
_SCREAMING_SNAKE_CASE = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 239
| 0
|
import logging
from dataclasses import dataclass, field
from typing import Optional
from seqaseq_trainer import arg_to_scheduler
from transformers import TrainingArguments
UpperCAmelCase__ = logging.getLogger(__name__)
@dataclass
class snake_case_ ( __UpperCamelCase ):
"""simple docstring"""
snake_case__ = field(
default=0.0 , metadata={"""help""": """The label smoothing epsilon to apply (if not zero)."""} )
snake_case__ = field(default=__UpperCamelCase , metadata={"""help""": """Whether to SortishSamler or not."""} )
snake_case__ = field(
default=__UpperCamelCase , metadata={"""help""": """Whether to use generate to calculate generative metrics (ROUGE, BLEU)."""} )
snake_case__ = field(default=__UpperCamelCase , metadata={"""help""": """whether to use adafactor"""} )
snake_case__ = field(
default=__UpperCamelCase , metadata={"""help""": """Encoder layer dropout probability. Goes into model.config."""} )
snake_case__ = field(
default=__UpperCamelCase , metadata={"""help""": """Decoder layer dropout probability. Goes into model.config."""} )
snake_case__ = field(default=__UpperCamelCase , metadata={"""help""": """Dropout probability. Goes into model.config."""} )
snake_case__ = field(
default=__UpperCamelCase , metadata={"""help""": """Attention dropout probability. Goes into model.config."""} )
snake_case__ = field(
default="""linear""" , metadata={"""help""": F'''Which lr scheduler to use. Selected in {sorted(arg_to_scheduler.keys() )}'''} , )
| 351
|
import unittest
import numpy as np
from transformers import RobertaConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
if is_flax_available():
from transformers.models.roberta.modeling_flax_roberta import (
FlaxRobertaForCausalLM,
FlaxRobertaForMaskedLM,
FlaxRobertaForMultipleChoice,
FlaxRobertaForQuestionAnswering,
FlaxRobertaForSequenceClassification,
FlaxRobertaForTokenClassification,
FlaxRobertaModel,
)
class snake_case_ ( unittest.TestCase ):
"""simple docstring"""
def __init__(self: Optional[int] , __UpperCAmelCase: str , __UpperCAmelCase: List[str]=13 , __UpperCAmelCase: Any=7 , __UpperCAmelCase: List[str]=True , __UpperCAmelCase: Optional[int]=True , __UpperCAmelCase: Dict=True , __UpperCAmelCase: Optional[Any]=True , __UpperCAmelCase: Optional[int]=99 , __UpperCAmelCase: Optional[Any]=32 , __UpperCAmelCase: int=5 , __UpperCAmelCase: Dict=4 , __UpperCAmelCase: Optional[int]=37 , __UpperCAmelCase: int="gelu" , __UpperCAmelCase: Tuple=0.1 , __UpperCAmelCase: Any=0.1 , __UpperCAmelCase: Union[str, Any]=512 , __UpperCAmelCase: Optional[Any]=16 , __UpperCAmelCase: List[Any]=2 , __UpperCAmelCase: str=0.02 , __UpperCAmelCase: int=4 , ) -> str:
'''simple docstring'''
__a : Tuple = parent
__a : int = batch_size
__a : Optional[int] = seq_length
__a : List[Any] = is_training
__a : Tuple = use_attention_mask
__a : Optional[int] = use_token_type_ids
__a : Tuple = use_labels
__a : str = vocab_size
__a : Union[str, Any] = hidden_size
__a : List[str] = num_hidden_layers
__a : Optional[int] = num_attention_heads
__a : Any = intermediate_size
__a : Any = hidden_act
__a : List[str] = hidden_dropout_prob
__a : Dict = attention_probs_dropout_prob
__a : Tuple = max_position_embeddings
__a : Optional[int] = type_vocab_size
__a : Tuple = type_sequence_label_size
__a : List[Any] = initializer_range
__a : int = num_choices
def UpperCAmelCase__ (self: Tuple ) -> List[Any]:
'''simple docstring'''
__a : List[Any] = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
__a : Dict = None
if self.use_attention_mask:
__a : List[Any] = random_attention_mask([self.batch_size, self.seq_length] )
__a : Optional[int] = None
if self.use_token_type_ids:
__a : Dict = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
__a : Dict = RobertaConfig(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , is_decoder=__UpperCAmelCase , initializer_range=self.initializer_range , )
return config, input_ids, token_type_ids, attention_mask
def UpperCAmelCase__ (self: Optional[Any] ) -> Union[str, Any]:
'''simple docstring'''
__a : Union[str, Any] = self.prepare_config_and_inputs()
__a , __a , __a , __a : Dict = config_and_inputs
__a : str = {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": attention_mask}
return config, inputs_dict
def UpperCAmelCase__ (self: Dict ) -> str:
'''simple docstring'''
__a : Optional[Any] = self.prepare_config_and_inputs()
__a , __a , __a , __a : Tuple = config_and_inputs
__a : int = True
__a : List[str] = floats_tensor([self.batch_size, self.seq_length, self.hidden_size] )
__a : List[Any] = ids_tensor([self.batch_size, self.seq_length] , vocab_size=2 )
return (
config,
input_ids,
token_type_ids,
encoder_hidden_states,
encoder_attention_mask,
)
@require_flax
class snake_case_ ( __UpperCamelCase , unittest.TestCase ):
"""simple docstring"""
snake_case__ = True
snake_case__ = (
(
FlaxRobertaModel,
FlaxRobertaForCausalLM,
FlaxRobertaForMaskedLM,
FlaxRobertaForSequenceClassification,
FlaxRobertaForTokenClassification,
FlaxRobertaForMultipleChoice,
FlaxRobertaForQuestionAnswering,
)
if is_flax_available()
else ()
)
def UpperCAmelCase__ (self: Dict ) -> Union[str, Any]:
'''simple docstring'''
__a : Tuple = FlaxRobertaModelTester(self )
@slow
def UpperCAmelCase__ (self: Optional[Any] ) -> List[Any]:
'''simple docstring'''
for model_class_name in self.all_model_classes:
__a : int = model_class_name.from_pretrained("roberta-base" , from_pt=__UpperCAmelCase )
__a : Tuple = model(np.ones((1, 1) ) )
self.assertIsNotNone(__UpperCAmelCase )
| 351
| 1
|
"""simple docstring"""
import gc
import random
import tempfile
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import AutoencoderKL, DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler, UNetaDConditionModel
from diffusers.pipelines.stable_diffusion_safe import StableDiffusionPipelineSafe as StableDiffusionPipeline
from diffusers.utils import floats_tensor, nightly, torch_device
from diffusers.utils.testing_utils import require_torch_gpu
class __SCREAMING_SNAKE_CASE ( unittest.TestCase ):
'''simple docstring'''
def snake_case ( self : int )-> Tuple:
# clean up the VRAM after each test
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
@property
def snake_case ( self : Tuple )-> str:
lowerCamelCase__ : str =1
lowerCamelCase__ : Any =3
lowerCamelCase__ : Optional[Any] =(32, 32)
lowerCamelCase__ : Tuple =floats_tensor((batch_size, num_channels) + sizes, rng=random.Random(0 ) ).to(lowerCamelCase )
return image
@property
def snake_case ( self : str )-> List[Any]:
torch.manual_seed(0 )
lowerCamelCase__ : Union[str, Any] =UNetaDConditionModel(
block_out_channels=(32, 64), layers_per_block=2, sample_size=32, in_channels=4, out_channels=4, down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D'''), up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D'''), cross_attention_dim=32, )
return model
@property
def snake_case ( self : Dict )-> List[str]:
torch.manual_seed(0 )
lowerCamelCase__ : Dict =AutoencoderKL(
block_out_channels=[32, 64], in_channels=3, out_channels=3, down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D'''], up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D'''], latent_channels=4, )
return model
@property
def snake_case ( self : int )-> str:
torch.manual_seed(0 )
lowerCamelCase__ : List[Any] =CLIPTextConfig(
bos_token_id=0, eos_token_id=2, hidden_size=32, intermediate_size=37, layer_norm_eps=1E-05, num_attention_heads=4, num_hidden_layers=5, pad_token_id=1, vocab_size=1000, )
return CLIPTextModel(lowerCamelCase )
@property
def snake_case ( self : Optional[int] )-> int:
def extract(*lowerCamelCase : List[Any], **lowerCamelCase : str ):
class __SCREAMING_SNAKE_CASE :
'''simple docstring'''
def __init__( self : int )-> Any:
lowerCamelCase__ : Optional[int] =torch.ones([0] )
def snake_case ( self : Optional[int], lowerCamelCase : List[str] )-> int:
self.pixel_values.to(lowerCamelCase )
return self
return Out()
return extract
def snake_case ( self : List[Any] )-> Dict:
lowerCamelCase__ : List[Any] ='''cpu''' # ensure determinism for the device-dependent torch.Generator
lowerCamelCase__ : Union[str, Any] =self.dummy_cond_unet
lowerCamelCase__ : str =DDIMScheduler(
beta_start=0.00_085, beta_end=0.012, beta_schedule='''scaled_linear''', clip_sample=lowerCamelCase, set_alpha_to_one=lowerCamelCase, )
lowerCamelCase__ : Any =self.dummy_vae
lowerCamelCase__ : Union[str, Any] =self.dummy_text_encoder
lowerCamelCase__ : Any =CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip''' )
# make sure here that pndm scheduler skips prk
lowerCamelCase__ : str =StableDiffusionPipeline(
unet=lowerCamelCase, scheduler=lowerCamelCase, vae=lowerCamelCase, text_encoder=lowerCamelCase, tokenizer=lowerCamelCase, safety_checker=lowerCamelCase, feature_extractor=self.dummy_extractor, )
lowerCamelCase__ : Union[str, Any] =sd_pipe.to(lowerCamelCase )
sd_pipe.set_progress_bar_config(disable=lowerCamelCase )
lowerCamelCase__ : int ='''A painting of a squirrel eating a burger'''
lowerCamelCase__ : str =torch.Generator(device=lowerCamelCase ).manual_seed(0 )
lowerCamelCase__ : int =sd_pipe([prompt], generator=lowerCamelCase, guidance_scale=6.0, num_inference_steps=2, output_type='''np''' )
lowerCamelCase__ : Optional[Any] =output.images
lowerCamelCase__ : List[Any] =torch.Generator(device=lowerCamelCase ).manual_seed(0 )
lowerCamelCase__ : Tuple =sd_pipe(
[prompt], generator=lowerCamelCase, guidance_scale=6.0, num_inference_steps=2, output_type='''np''', return_dict=lowerCamelCase, )[0]
lowerCamelCase__ : Optional[int] =image[0, -3:, -3:, -1]
lowerCamelCase__ : Any =image_from_tuple[0, -3:, -3:, -1]
assert image.shape == (1, 64, 64, 3)
lowerCamelCase__ : Any =np.array([0.5_756, 0.6_118, 0.5_005, 0.5_041, 0.5_471, 0.4_726, 0.4_976, 0.4_865, 0.4_864] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
assert np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 1E-2
def snake_case ( self : Union[str, Any] )-> Union[str, Any]:
lowerCamelCase__ : Dict ='''cpu''' # ensure determinism for the device-dependent torch.Generator
lowerCamelCase__ : Optional[int] =self.dummy_cond_unet
lowerCamelCase__ : Any =PNDMScheduler(skip_prk_steps=lowerCamelCase )
lowerCamelCase__ : Optional[int] =self.dummy_vae
lowerCamelCase__ : int =self.dummy_text_encoder
lowerCamelCase__ : Dict =CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip''' )
# make sure here that pndm scheduler skips prk
lowerCamelCase__ : List[str] =StableDiffusionPipeline(
unet=lowerCamelCase, scheduler=lowerCamelCase, vae=lowerCamelCase, text_encoder=lowerCamelCase, tokenizer=lowerCamelCase, safety_checker=lowerCamelCase, feature_extractor=self.dummy_extractor, )
lowerCamelCase__ : List[str] =sd_pipe.to(lowerCamelCase )
sd_pipe.set_progress_bar_config(disable=lowerCamelCase )
lowerCamelCase__ : Tuple ='''A painting of a squirrel eating a burger'''
lowerCamelCase__ : Union[str, Any] =torch.Generator(device=lowerCamelCase ).manual_seed(0 )
lowerCamelCase__ : Optional[int] =sd_pipe([prompt], generator=lowerCamelCase, guidance_scale=6.0, num_inference_steps=2, output_type='''np''' )
lowerCamelCase__ : List[Any] =output.images
lowerCamelCase__ : Tuple =torch.Generator(device=lowerCamelCase ).manual_seed(0 )
lowerCamelCase__ : List[str] =sd_pipe(
[prompt], generator=lowerCamelCase, guidance_scale=6.0, num_inference_steps=2, output_type='''np''', return_dict=lowerCamelCase, )[0]
lowerCamelCase__ : int =image[0, -3:, -3:, -1]
lowerCamelCase__ : Optional[int] =image_from_tuple[0, -3:, -3:, -1]
assert image.shape == (1, 64, 64, 3)
lowerCamelCase__ : int =np.array([0.5_125, 0.5_716, 0.4_828, 0.5_060, 0.5_650, 0.4_768, 0.5_185, 0.4_895, 0.4_993] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
assert np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 1E-2
def snake_case ( self : Any )-> Tuple:
lowerCamelCase__ : List[str] =StableDiffusionPipeline.from_pretrained(
'''hf-internal-testing/tiny-stable-diffusion-lms-pipe''', safety_checker=lowerCamelCase )
assert isinstance(lowerCamelCase, lowerCamelCase )
assert isinstance(pipe.scheduler, lowerCamelCase )
assert pipe.safety_checker is None
lowerCamelCase__ : Any =pipe('''example prompt''', num_inference_steps=2 ).images[0]
assert image is not None
# check that there's no error when saving a pipeline with one of the models being None
with tempfile.TemporaryDirectory() as tmpdirname:
pipe.save_pretrained(lowerCamelCase )
lowerCamelCase__ : List[str] =StableDiffusionPipeline.from_pretrained(lowerCamelCase )
# sanity check that the pipeline still works
assert pipe.safety_checker is None
lowerCamelCase__ : List[str] =pipe('''example prompt''', num_inference_steps=2 ).images[0]
assert image is not None
@unittest.skipIf(torch_device != '''cuda''', '''This test requires a GPU''' )
def snake_case ( self : int )-> Union[str, Any]:
lowerCamelCase__ : List[Any] =self.dummy_cond_unet
lowerCamelCase__ : Optional[Any] =PNDMScheduler(skip_prk_steps=lowerCamelCase )
lowerCamelCase__ : Union[str, Any] =self.dummy_vae
lowerCamelCase__ : Any =self.dummy_text_encoder
lowerCamelCase__ : Union[str, Any] =CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip''' )
# put models in fp16
lowerCamelCase__ : List[Any] =unet.half()
lowerCamelCase__ : Optional[Any] =vae.half()
lowerCamelCase__ : Any =bert.half()
# make sure here that pndm scheduler skips prk
lowerCamelCase__ : Tuple =StableDiffusionPipeline(
unet=lowerCamelCase, scheduler=lowerCamelCase, vae=lowerCamelCase, text_encoder=lowerCamelCase, tokenizer=lowerCamelCase, safety_checker=lowerCamelCase, feature_extractor=self.dummy_extractor, )
lowerCamelCase__ : Optional[Any] =sd_pipe.to(lowerCamelCase )
sd_pipe.set_progress_bar_config(disable=lowerCamelCase )
lowerCamelCase__ : Optional[int] ='''A painting of a squirrel eating a burger'''
lowerCamelCase__ : int =sd_pipe([prompt], num_inference_steps=2, output_type='''np''' ).images
assert image.shape == (1, 64, 64, 3)
@nightly
@require_torch_gpu
class __SCREAMING_SNAKE_CASE ( unittest.TestCase ):
'''simple docstring'''
def snake_case ( self : Any )-> List[Any]:
# clean up the VRAM after each test
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def snake_case ( self : Any )-> Any:
lowerCamelCase__ : int =StableDiffusionPipeline.from_pretrained('''runwayml/stable-diffusion-v1-5''', safety_checker=lowerCamelCase )
lowerCamelCase__ : List[Any] =LMSDiscreteScheduler.from_config(sd_pipe.scheduler.config )
lowerCamelCase__ : str =sd_pipe.to(lowerCamelCase )
sd_pipe.set_progress_bar_config(disable=lowerCamelCase )
lowerCamelCase__ : List[str] =(
'''portrait of girl with smokey eyes makeup in abandoned hotel, grange clothes, redshift, wide high angle'''
''' coloured polaroid photograph with flash, kodak film, hyper real, stunning moody cinematography, with'''
''' anamorphic lenses, by maripol, fallen angels by wong kar - wai, style of suspiria and neon demon and'''
''' children from bahnhof zoo, detailed '''
)
lowerCamelCase__ : Optional[Any] =40_0366_0346
lowerCamelCase__ : str =7
# without safety guidance (sld_guidance_scale = 0)
lowerCamelCase__ : Tuple =torch.manual_seed(lowerCamelCase )
lowerCamelCase__ : Any =sd_pipe(
[prompt], generator=lowerCamelCase, guidance_scale=lowerCamelCase, num_inference_steps=50, output_type='''np''', width=512, height=512, sld_guidance_scale=0, )
lowerCamelCase__ : Optional[int] =output.images
lowerCamelCase__ : Union[str, Any] =image[0, -3:, -3:, -1]
lowerCamelCase__ : List[Any] =[0.2_278, 0.2_231, 0.2_249, 0.2_333, 0.2_303, 0.1_885, 0.2_273, 0.2_144, 0.2_176]
assert image.shape == (1, 512, 512, 3)
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
# without safety guidance (strong configuration)
lowerCamelCase__ : int =torch.manual_seed(lowerCamelCase )
lowerCamelCase__ : int =sd_pipe(
[prompt], generator=lowerCamelCase, guidance_scale=lowerCamelCase, num_inference_steps=50, output_type='''np''', width=512, height=512, sld_guidance_scale=2000, sld_warmup_steps=7, sld_threshold=0.025, sld_momentum_scale=0.5, sld_mom_beta=0.7, )
lowerCamelCase__ : str =output.images
lowerCamelCase__ : List[str] =image[0, -3:, -3:, -1]
lowerCamelCase__ : str =[0.2_383, 0.2_276, 0.236, 0.2_192, 0.2_186, 0.2_053, 0.1_971, 0.1_901, 0.1_719]
assert image.shape == (1, 512, 512, 3)
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
def snake_case ( self : int )-> str:
lowerCamelCase__ : str =StableDiffusionPipeline.from_pretrained('''runwayml/stable-diffusion-v1-5''', safety_checker=lowerCamelCase )
lowerCamelCase__ : List[Any] =LMSDiscreteScheduler.from_config(sd_pipe.scheduler.config )
lowerCamelCase__ : List[str] =sd_pipe.to(lowerCamelCase )
sd_pipe.set_progress_bar_config(disable=lowerCamelCase )
lowerCamelCase__ : int ='''padme amidala taking a bath artwork, safe for work, no nudity'''
lowerCamelCase__ : int =27_3497_1755
lowerCamelCase__ : Dict =7
lowerCamelCase__ : Dict =torch.manual_seed(lowerCamelCase )
lowerCamelCase__ : List[str] =sd_pipe(
[prompt], generator=lowerCamelCase, guidance_scale=lowerCamelCase, num_inference_steps=50, output_type='''np''', width=512, height=512, sld_guidance_scale=0, )
lowerCamelCase__ : Union[str, Any] =output.images
lowerCamelCase__ : int =image[0, -3:, -3:, -1]
lowerCamelCase__ : Optional[Any] =[0.3_502, 0.3_622, 0.3_396, 0.3_642, 0.3_478, 0.3_318, 0.35, 0.3_348, 0.3_297]
assert image.shape == (1, 512, 512, 3)
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
lowerCamelCase__ : Dict =torch.manual_seed(lowerCamelCase )
lowerCamelCase__ : int =sd_pipe(
[prompt], generator=lowerCamelCase, guidance_scale=lowerCamelCase, num_inference_steps=50, output_type='''np''', width=512, height=512, sld_guidance_scale=2000, sld_warmup_steps=7, sld_threshold=0.025, sld_momentum_scale=0.5, sld_mom_beta=0.7, )
lowerCamelCase__ : Any =output.images
lowerCamelCase__ : Dict =image[0, -3:, -3:, -1]
lowerCamelCase__ : Tuple =[0.5_531, 0.5_206, 0.4_895, 0.5_156, 0.5_182, 0.4_751, 0.4_802, 0.4_803, 0.4_443]
assert image.shape == (1, 512, 512, 3)
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
def snake_case ( self : List[Any] )-> Any:
lowerCamelCase__ : Optional[Any] =StableDiffusionPipeline.from_pretrained('''runwayml/stable-diffusion-v1-5''' )
lowerCamelCase__ : Optional[Any] =sd_pipe.to(lowerCamelCase )
sd_pipe.set_progress_bar_config(disable=lowerCamelCase )
lowerCamelCase__ : int =(
'''the four horsewomen of the apocalypse, painting by tom of finland, gaston bussiere, craig mullins, j. c.'''
''' leyendecker'''
)
lowerCamelCase__ : Optional[int] =10_4435_5234
lowerCamelCase__ : Optional[int] =12
lowerCamelCase__ : Dict =torch.manual_seed(lowerCamelCase )
lowerCamelCase__ : Optional[int] =sd_pipe(
[prompt], generator=lowerCamelCase, guidance_scale=lowerCamelCase, num_inference_steps=50, output_type='''np''', width=512, height=512, sld_guidance_scale=0, )
lowerCamelCase__ : Any =output.images
lowerCamelCase__ : List[str] =image[0, -3:, -3:, -1]
lowerCamelCase__ : Optional[Any] =np.array([0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0] )
assert image.shape == (1, 512, 512, 3)
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-7
lowerCamelCase__ : Optional[int] =torch.manual_seed(lowerCamelCase )
lowerCamelCase__ : str =sd_pipe(
[prompt], generator=lowerCamelCase, guidance_scale=lowerCamelCase, num_inference_steps=50, output_type='''np''', width=512, height=512, sld_guidance_scale=2000, sld_warmup_steps=7, sld_threshold=0.025, sld_momentum_scale=0.5, sld_mom_beta=0.7, )
lowerCamelCase__ : List[str] =output.images
lowerCamelCase__ : Optional[Any] =image[0, -3:, -3:, -1]
lowerCamelCase__ : List[Any] =np.array([0.5_818, 0.6_285, 0.6_835, 0.6_019, 0.625, 0.6_754, 0.6_096, 0.6_334, 0.6_561] )
assert image.shape == (1, 512, 512, 3)
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
| 625
|
"""simple docstring"""
def snake_case__ ( __lowerCamelCase : int = 4000000 ):
"""simple docstring"""
lowerCamelCase__ : Dict =[]
lowerCamelCase__ , lowerCamelCase__ : Union[str, Any] =0, 1
while b <= n:
if b % 2 == 0:
even_fibs.append(__lowerCamelCase )
lowerCamelCase__ , lowerCamelCase__ : Union[str, Any] =b, a + b
return sum(__lowerCamelCase )
if __name__ == "__main__":
print(f'{solution() = }')
| 625
| 1
|
"""simple docstring"""
from argparse import ArgumentParser
from .env import EnvironmentCommand
def _A ( ):
"""simple docstring"""
A = ArgumentParser("""Diffusers CLI tool""" , usage="""diffusers-cli <command> [<args>]""" )
A = parser.add_subparsers(help="""diffusers-cli command helpers""" )
# Register commands
EnvironmentCommand.register_subcommand(SCREAMING_SNAKE_CASE__ )
# Let's go
A = parser.parse_args()
if not hasattr(SCREAMING_SNAKE_CASE__ , """func""" ):
parser.print_help()
exit(1 )
# Run
A = args.func(SCREAMING_SNAKE_CASE__ )
service.run()
if __name__ == "__main__":
main()
| 617
|
# 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.
from ..models.whisper import WhisperForConditionalGeneration, WhisperProcessor
from .base import PipelineTool
class __snake_case ( lowerCAmelCase ):
_a : Optional[int]= "openai/whisper-base"
_a : int= (
"This is a tool that transcribes an audio into text. It takes an input named `audio` and returns the "
"transcribed text."
)
_a : int= "transcriber"
_a : List[str]= WhisperProcessor
_a : Optional[int]= WhisperForConditionalGeneration
_a : List[str]= ["audio"]
_a : Any= ["text"]
def _SCREAMING_SNAKE_CASE ( self ,snake_case ):
'''simple docstring'''
return self.pre_processor(snake_case ,return_tensors="""pt""" ).input_features
def _SCREAMING_SNAKE_CASE ( self ,snake_case ):
'''simple docstring'''
return self.model.generate(inputs=snake_case )
def _SCREAMING_SNAKE_CASE ( self ,snake_case ):
'''simple docstring'''
return self.pre_processor.batch_decode(snake_case ,skip_special_tokens=snake_case )[0]
| 336
| 0
|
import os
from dataclasses import dataclass, field
from io import BytesIO
from typing import TYPE_CHECKING, Any, ClassVar, Dict, Optional, Union
import numpy as np
import pyarrow as pa
from .. import config
from ..download.streaming_download_manager import xopen, xsplitext
from ..table import array_cast
from ..utils.py_utils import no_op_if_value_is_null, string_to_dict
if TYPE_CHECKING:
from .features import FeatureType
__UpperCamelCase : Tuple = False, False, False
@dataclass
class _UpperCamelCase :
'''simple docstring'''
a_ : Optional[int] = None
a_ : bool = True
a_ : bool = True
a_ : Optional[str] = None
# Automatically constructed
a_ : ClassVar[str] = "dict"
a_ : ClassVar[Any] = pa.struct({"bytes": pa.binary(), "path": pa.string()} )
a_ : str = field(default="Audio",init=A,repr=A )
def __call__( self : str ):
'''simple docstring'''
return self.pa_type
def _snake_case ( self : List[Any] , _lowerCamelCase : Union[str, bytes, dict] ):
'''simple docstring'''
try:
import soundfile as sf # soundfile is a dependency of librosa, needed to decode audio files.
except ImportError as err:
raise ImportError("""To support encoding audio data, please install 'soundfile'.""" ) from err
if isinstance(_lowerCamelCase , _lowerCamelCase ):
return {"bytes": None, "path": value}
elif isinstance(_lowerCamelCase , _lowerCamelCase ):
return {"bytes": value, "path": None}
elif "array" in value:
# convert the audio array to wav bytes
__lowerCamelCase : List[Any] = BytesIO()
sf.write(_lowerCamelCase , value["""array"""] , value["""sampling_rate"""] , format="""wav""" )
return {"bytes": buffer.getvalue(), "path": None}
elif value.get("""path""" ) is not None and os.path.isfile(value["""path"""] ):
# we set "bytes": None to not duplicate the data if they're already available locally
if value["path"].endswith("""pcm""" ):
# "PCM" only has raw audio bytes
if value.get("""sampling_rate""" ) is None:
# At least, If you want to convert "PCM-byte" to "WAV-byte", you have to know sampling rate
raise KeyError("""To use PCM files, please specify a 'sampling_rate' in Audio object""" )
if value.get("""bytes""" ):
# If we already had PCM-byte, we don`t have to make "read file, make bytes" (just use it!)
__lowerCamelCase : Dict = np.frombuffer(value["""bytes"""] , dtype=np.intaa ).astype(np.floataa ) / 3_2_7_6_7
else:
__lowerCamelCase : str = np.memmap(value["""path"""] , dtype="""h""" , mode="""r""" ).astype(np.floataa ) / 3_2_7_6_7
__lowerCamelCase : Union[str, Any] = BytesIO(bytes() )
sf.write(_lowerCamelCase , _lowerCamelCase , value["""sampling_rate"""] , format="""wav""" )
return {"bytes": buffer.getvalue(), "path": None}
else:
return {"bytes": None, "path": value.get("""path""" )}
elif value.get("""bytes""" ) is not None or value.get("""path""" ) is not None:
# store the audio bytes, and path is used to infer the audio format using the file extension
return {"bytes": value.get("""bytes""" ), "path": value.get("""path""" )}
else:
raise ValueError(
F"""An audio sample should have one of 'path' or 'bytes' but they are missing or None in {value}.""" )
def _snake_case ( self : int , _lowerCamelCase : dict , _lowerCamelCase : Optional[Dict[str, Union[str, bool, None]]] = None ):
'''simple docstring'''
if not self.decode:
raise RuntimeError("""Decoding is disabled for this feature. Please use Audio(decode=True) instead.""" )
__lowerCamelCase : Optional[Any] = (value["""path"""], BytesIO(value["""bytes"""] )) if value["""bytes"""] is not None else (value["""path"""], None)
if path is None and file is None:
raise ValueError(F"""An audio sample should have one of 'path' or 'bytes' but both are None in {value}.""" )
try:
import librosa
import soundfile as sf
except ImportError as err:
raise ImportError("""To support decoding audio files, please install 'librosa' and 'soundfile'.""" ) from err
__lowerCamelCase : List[Any] = xsplitext(_lowerCamelCase )[1][1:].lower() if path is not None else None
if not config.IS_OPUS_SUPPORTED and audio_format == "opus":
raise RuntimeError(
"""Decoding 'opus' files requires system library 'libsndfile'>=1.0.31, """
"""You can try to update `soundfile` python library: `pip install \"soundfile>=0.12.1\"`. """ )
elif not config.IS_MP3_SUPPORTED and audio_format == "mp3":
raise RuntimeError(
"""Decoding 'mp3' files requires system library 'libsndfile'>=1.1.0, """
"""You can try to update `soundfile` python library: `pip install \"soundfile>=0.12.1\"`. """ )
if file is None:
__lowerCamelCase : Union[str, Any] = token_per_repo_id or {}
__lowerCamelCase : Dict = path.split("""::""" )[-1]
try:
__lowerCamelCase : str = string_to_dict(_lowerCamelCase , config.HUB_DATASETS_URL )["""repo_id"""]
__lowerCamelCase : str = token_per_repo_id[repo_id]
except (ValueError, KeyError):
__lowerCamelCase : str = None
with xopen(_lowerCamelCase , """rb""" , use_auth_token=_lowerCamelCase ) as f:
__lowerCamelCase : Tuple = sf.read(_lowerCamelCase )
else:
__lowerCamelCase : int = sf.read(_lowerCamelCase )
__lowerCamelCase : str = array.T
if self.mono:
__lowerCamelCase : int = librosa.to_mono(_lowerCamelCase )
if self.sampling_rate and self.sampling_rate != sampling_rate:
__lowerCamelCase : List[Any] = librosa.resample(_lowerCamelCase , orig_sr=_lowerCamelCase , target_sr=self.sampling_rate )
__lowerCamelCase : int = self.sampling_rate
return {"path": path, "array": array, "sampling_rate": sampling_rate}
def _snake_case ( self : Optional[int] ):
'''simple docstring'''
from .features import Value
if self.decode:
raise ValueError("""Cannot flatten a decoded Audio feature.""" )
return {
"bytes": Value("""binary""" ),
"path": Value("""string""" ),
}
def _snake_case ( self : Tuple , _lowerCamelCase : Union[pa.StringArray, pa.StructArray] ):
'''simple docstring'''
if pa.types.is_string(storage.type ):
__lowerCamelCase : Union[str, Any] = pa.array([None] * len(_lowerCamelCase ) , type=pa.binary() )
__lowerCamelCase : Optional[int] = pa.StructArray.from_arrays([bytes_array, storage] , ["""bytes""", """path"""] , mask=storage.is_null() )
elif pa.types.is_binary(storage.type ):
__lowerCamelCase : Dict = pa.array([None] * len(_lowerCamelCase ) , type=pa.string() )
__lowerCamelCase : Optional[Any] = pa.StructArray.from_arrays([storage, path_array] , ["""bytes""", """path"""] , mask=storage.is_null() )
elif pa.types.is_struct(storage.type ) and storage.type.get_all_field_indices("""array""" ):
__lowerCamelCase : List[Any] = pa.array([Audio().encode_example(_lowerCamelCase ) if x is not None else None for x in storage.to_pylist()] )
elif pa.types.is_struct(storage.type ):
if storage.type.get_field_index("""bytes""" ) >= 0:
__lowerCamelCase : Any = storage.field("""bytes""" )
else:
__lowerCamelCase : List[Any] = pa.array([None] * len(_lowerCamelCase ) , type=pa.binary() )
if storage.type.get_field_index("""path""" ) >= 0:
__lowerCamelCase : str = storage.field("""path""" )
else:
__lowerCamelCase : List[str] = pa.array([None] * len(_lowerCamelCase ) , type=pa.string() )
__lowerCamelCase : Tuple = pa.StructArray.from_arrays([bytes_array, path_array] , ["""bytes""", """path"""] , mask=storage.is_null() )
return array_cast(_lowerCamelCase , self.pa_type )
def _snake_case ( self : str , _lowerCamelCase : pa.StructArray ):
'''simple docstring'''
@no_op_if_value_is_null
def path_to_bytes(_lowerCamelCase : int ):
with xopen(_lowerCamelCase , """rb""" ) as f:
__lowerCamelCase : Dict = f.read()
return bytes_
__lowerCamelCase : str = pa.array(
[
(path_to_bytes(x["""path"""] ) if x["""bytes"""] is None else x["""bytes"""]) if x is not None else None
for x in storage.to_pylist()
] , type=pa.binary() , )
__lowerCamelCase : Dict = pa.array(
[os.path.basename(_lowerCamelCase ) if path is not None else None for path in storage.field("""path""" ).to_pylist()] , type=pa.string() , )
__lowerCamelCase : Any = pa.StructArray.from_arrays([bytes_array, path_array] , ["""bytes""", """path"""] , mask=bytes_array.is_null() )
return array_cast(_lowerCamelCase , self.pa_type )
| 720
|
import qiskit
def _UpperCAmelCase ( UpperCAmelCase : int , UpperCAmelCase : int ):
"""simple docstring"""
__lowerCamelCase : Dict = qiskit.Aer.get_backend("""aer_simulator""" )
__lowerCamelCase : Dict = qiskit.QuantumCircuit(4 , 2 )
# encode inputs in qubits 0 and 1
if bita == 1:
qc_ha.x(0 )
if bita == 1:
qc_ha.x(1 )
qc_ha.barrier()
# use cnots to write XOR of the inputs on qubit2
qc_ha.cx(0 , 2 )
qc_ha.cx(1 , 2 )
# use ccx / toffoli gate to write AND of the inputs on qubit3
qc_ha.ccx(0 , 1 , 3 )
qc_ha.barrier()
# extract outputs
qc_ha.measure(2 , 0 ) # extract XOR value
qc_ha.measure(3 , 1 ) # extract AND value
# Execute the circuit on the qasm simulator
__lowerCamelCase : str = qiskit.execute(UpperCAmelCase , UpperCAmelCase , shots=1_000 )
# Return the histogram data of the results of the experiment
return job.result().get_counts(UpperCAmelCase )
if __name__ == "__main__":
__UpperCamelCase : List[str] = half_adder(1, 1)
print(F'''Half Adder Output Qubit Counts: {counts}''')
| 458
| 0
|
'''simple docstring'''
import unittest
import numpy as np
from transformers import RobertaConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
if is_flax_available():
from transformers.models.roberta.modeling_flax_roberta import (
FlaxRobertaForCausalLM,
FlaxRobertaForMaskedLM,
FlaxRobertaForMultipleChoice,
FlaxRobertaForQuestionAnswering,
FlaxRobertaForSequenceClassification,
FlaxRobertaForTokenClassification,
FlaxRobertaModel,
)
class __SCREAMING_SNAKE_CASE ( unittest.TestCase ):
def __init__( self : Any , UpperCAmelCase__ : Tuple , UpperCAmelCase__ : Optional[Any]=13 , UpperCAmelCase__ : Union[str, Any]=7 , UpperCAmelCase__ : Optional[int]=True , UpperCAmelCase__ : Optional[Any]=True , UpperCAmelCase__ : Dict=True , UpperCAmelCase__ : Union[str, Any]=True , UpperCAmelCase__ : Tuple=99 , UpperCAmelCase__ : Tuple=32 , UpperCAmelCase__ : List[Any]=5 , UpperCAmelCase__ : List[Any]=4 , UpperCAmelCase__ : Optional[int]=37 , UpperCAmelCase__ : Optional[Any]="gelu" , UpperCAmelCase__ : Dict=0.1 , UpperCAmelCase__ : Union[str, Any]=0.1 , UpperCAmelCase__ : Optional[int]=512 , UpperCAmelCase__ : Optional[int]=16 , UpperCAmelCase__ : int=2 , UpperCAmelCase__ : Union[str, Any]=0.02 , UpperCAmelCase__ : List[Any]=4 , ):
'''simple docstring'''
lowercase : str =parent
lowercase : Tuple =batch_size
lowercase : List[Any] =seq_length
lowercase : str =is_training
lowercase : Optional[Any] =use_attention_mask
lowercase : Union[str, Any] =use_token_type_ids
lowercase : int =use_labels
lowercase : Optional[int] =vocab_size
lowercase : Any =hidden_size
lowercase : Dict =num_hidden_layers
lowercase : Optional[Any] =num_attention_heads
lowercase : List[str] =intermediate_size
lowercase : Dict =hidden_act
lowercase : List[Any] =hidden_dropout_prob
lowercase : Union[str, Any] =attention_probs_dropout_prob
lowercase : Any =max_position_embeddings
lowercase : str =type_vocab_size
lowercase : List[str] =type_sequence_label_size
lowercase : Optional[int] =initializer_range
lowercase : Any =num_choices
def lowerCamelCase_ ( self : Optional[int] ):
'''simple docstring'''
lowercase : List[str] =ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
lowercase : Optional[int] =None
if self.use_attention_mask:
lowercase : str =random_attention_mask([self.batch_size, self.seq_length] )
lowercase : Union[str, Any] =None
if self.use_token_type_ids:
lowercase : List[str] =ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
lowercase : Any =RobertaConfig(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , is_decoder=UpperCAmelCase__ , initializer_range=self.initializer_range , )
return config, input_ids, token_type_ids, attention_mask
def lowerCamelCase_ ( self : Optional[Any] ):
'''simple docstring'''
lowercase : List[Any] =self.prepare_config_and_inputs()
lowercase , lowercase , lowercase , lowercase : List[Any] =config_and_inputs
lowercase : List[Any] ={'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': attention_mask}
return config, inputs_dict
def lowerCamelCase_ ( self : str ):
'''simple docstring'''
lowercase : str =self.prepare_config_and_inputs()
lowercase , lowercase , lowercase , lowercase : Dict =config_and_inputs
lowercase : Any =True
lowercase : List[Any] =floats_tensor([self.batch_size, self.seq_length, self.hidden_size] )
lowercase : Union[str, Any] =ids_tensor([self.batch_size, self.seq_length] , vocab_size=2 )
return (
config,
input_ids,
token_type_ids,
encoder_hidden_states,
encoder_attention_mask,
)
@require_flax
class __SCREAMING_SNAKE_CASE ( lowercase__ , unittest.TestCase ):
lowerCamelCase_ = True
lowerCamelCase_ = (
(
FlaxRobertaModel,
FlaxRobertaForCausalLM,
FlaxRobertaForMaskedLM,
FlaxRobertaForSequenceClassification,
FlaxRobertaForTokenClassification,
FlaxRobertaForMultipleChoice,
FlaxRobertaForQuestionAnswering,
)
if is_flax_available()
else ()
)
def lowerCamelCase_ ( self : Optional[Any] ):
'''simple docstring'''
lowercase : Optional[Any] =FlaxRobertaModelTester(self )
@slow
def lowerCamelCase_ ( self : Tuple ):
'''simple docstring'''
for model_class_name in self.all_model_classes:
lowercase : str =model_class_name.from_pretrained('''roberta-base''' , from_pt=UpperCAmelCase__ )
lowercase : Optional[int] =model(np.ones((1, 1) ) )
self.assertIsNotNone(UpperCAmelCase__ )
| 92
|
"""simple docstring"""
from dataclasses import dataclass
from typing import List, Optional, Union
import numpy as np
import torch
from ...utils import BaseOutput, OptionalDependencyNotAvailable, is_torch_available, is_transformers_available
@dataclass
class lowerCamelCase (_SCREAMING_SNAKE_CASE ):
'''simple docstring'''
a = 42
try:
if not (is_transformers_available() and is_torch_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ...utils.dummy_torch_and_transformers_objects import * # noqa F403
else:
from .pipeline_text_to_video_synth import TextToVideoSDPipeline
from .pipeline_text_to_video_synth_imgaimg import VideoToVideoSDPipeline # noqa: F401
from .pipeline_text_to_video_zero import TextToVideoZeroPipeline
| 159
| 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
A = logging.get_logger(__name__)
A = {
"facebook/levit-128S": "https://huggingface.co/facebook/levit-128S/resolve/main/config.json",
# See all LeViT models at https://huggingface.co/models?filter=levit
}
class lowercase__ ( UpperCAmelCase_ ):
A__= "levit"
def __init__( self : int , _lowercase : Optional[Any]=2_24 , _lowercase : Tuple=3 , _lowercase : Optional[int]=3 , _lowercase : str=2 , _lowercase : Union[str, Any]=1 , _lowercase : Any=16 , _lowercase : List[str]=[1_28, 2_56, 3_84] , _lowercase : Tuple=[4, 8, 12] , _lowercase : Dict=[4, 4, 4] , _lowercase : Optional[int]=[16, 16, 16] , _lowercase : List[Any]=0 , _lowercase : str=[2, 2, 2] , _lowercase : Optional[Any]=[2, 2, 2] , _lowercase : Dict=0.0_2 , **_lowercase : Union[str, Any] , ):
"""simple docstring"""
super().__init__(**_snake_case )
UpperCAmelCase__ = image_size
UpperCAmelCase__ = num_channels
UpperCAmelCase__ = kernel_size
UpperCAmelCase__ = stride
UpperCAmelCase__ = padding
UpperCAmelCase__ = hidden_sizes
UpperCAmelCase__ = num_attention_heads
UpperCAmelCase__ = depths
UpperCAmelCase__ = key_dim
UpperCAmelCase__ = drop_path_rate
UpperCAmelCase__ = patch_size
UpperCAmelCase__ = attention_ratio
UpperCAmelCase__ = mlp_ratio
UpperCAmelCase__ = initializer_range
UpperCAmelCase__ = [
["Subsample", key_dim[0], hidden_sizes[0] // key_dim[0], 4, 2, 2],
["Subsample", key_dim[0], hidden_sizes[1] // key_dim[0], 4, 2, 2],
]
class lowercase__ ( UpperCAmelCase_ ):
A__= version.parse('1.11' )
@property
def _UpperCAmelCase ( self : Union[str, Any] ):
"""simple docstring"""
return OrderedDict(
[
("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
] )
@property
def _UpperCAmelCase ( self : List[Any] ):
"""simple docstring"""
return 1E-4
| 708
|
import json
import os
import unittest
from typing import Tuple
from transformers import WavaVecaPhonemeCTCTokenizer
from transformers.models.wavaveca.tokenization_wavaveca import VOCAB_FILES_NAMES
from transformers.models.wavaveca_phoneme.tokenization_wavaveca_phoneme import WavaVecaPhonemeCTCTokenizerOutput
from transformers.testing_utils import require_phonemizer
from ...test_tokenization_common import TokenizerTesterMixin
@require_phonemizer
class lowercase__ ( __SCREAMING_SNAKE_CASE , unittest.TestCase ):
A__= WavaVecaPhonemeCTCTokenizer
A__= False
def _UpperCAmelCase ( self : Union[str, Any] ):
"""simple docstring"""
super().setUp()
UpperCAmelCase__ = (
"<s> <pad> </s> <unk> n s t ə l a i k d m ɛ ɾ e ɪ p o ɐ z ð f j v b ɹ ʁ ʊ iː r w ʌ u ɡ æ aɪ ʃ h ɔ ɑː "
"ŋ ɚ eɪ β uː y ɑ̃ oʊ ᵻ eː θ aʊ ts oː ɔ̃ ɣ ɜ ɑ dʒ əl x ɜː ç ʒ tʃ ɔː ɑːɹ ɛ̃ ʎ ɔːɹ ʋ aː ɕ œ ø oːɹ ɲ yː "
"ʔ iə i5 s. tɕ ?? nʲ ɛː œ̃ ɭ ɔø ʑ tʲ ɨ ɛɹ ts. rʲ ɪɹ ɭʲ i.5 ɔɪ q sʲ u5 ʊɹ iɜ a5 iɛ5 øː ʕ ja əɜ th ɑ5 "
"oɪ dʲ ə5 tɕh ts.h mʲ ɯ dʑ vʲ e̞ tʃʲ ei5 o5 onɡ5 ɑu5 iɑ5 ai5 aɪɚ kh ə1 ʐ i2 ʉ ħ t[ aɪə ʲ ju ə2 u2 oɜ "
"pː iɛɜ ou5 y5 uɜ tː uo5 d[ uoɜ tsh ɑɜ ɵ i̪5 uei5 ɟ aɜ ɑɨ i.ɜ eʊ o2 ɐ̃ ä pʲ kʲ n̩ ɒ ph ɑu2 uɨ əɪ ɫ ɬ "
"yɜ bʲ ɑ2 s̪ aiɜ χ ɐ̃ʊ̃ 1 ə4 yæɜ a2 ɨː t̪ iouɜ ũ onɡɜ aɨ iɛ2 ɔɨ ɑuɜ o̞ ei2 iou2 c kː y2 ɖ oe dˤ yɛɜ "
"əʊ S ɡʲ onɡ2 u\" eiɜ ʈ ɯᵝ iou5 dZ r̝̊ i.2 tS s^ ʝ yə5 iɑɜ uə5 pf ɨu iɑ2 ou2 ər2 fʲ ai2 r̝ uəɜ ɳ əɨ "
"ua5 uɪ ɽ bː yu5 uo2 yɛ5 l̩ ɻ ərɜ ʂ i̪2 ouɜ uaɜ a. a.ː yæ5 dː r̩ ee ɪu ər5 i̪ ɜ æi u: i.ː t^ o1 ɪ^ "
"ai ueiɜ æː ɛɪ eə i. ɴ ie ua2 ɑ1 o4 tʃː o: ɑ: u1 N i̪1 au yæ2 u. qː yəɜ y: kʰ tʃʰ iʊ sx õ uo tʰ "
"uai5 bʰ u.ː uə2 ʊə d^ s̪ː yiɜ dʰ r. oe: i1 ɟː yu2 nʲʲ i̪4 uei2 tsʲ ɸ ĩ ɑ4 t̪ː eɑ u4 e: tsː ʈʰ ɡʰ "
"ɯɯ dʒʲ ʂʲ X ɵː uaiɜ tɕʲ ã t^ː ẽː yɛ2 cː i.1 ɛʊ dˤdˤ dʒː i4 ɡː yi ɕʲ ɟʰ pʰ dʑʲ yuɜ ua1 ua4 æiː ɐɐ "
"ui iou1 ʊː a1 iou4 cʰ iɛ1 yə2 ɖʰ ẽ ʒʲ ää ər4 iːː ɪː iɑ1 ər1 œː øi ɪuː cʰcʰ əː1 iː1 ũ kʰː o̞o̞ xʲ "
"ou1 iɛ4 e̞e̞ y1 dzː dʲʲ dʰː ɯᵝɯᵝ lː uo1 i.4 i: yɛ5ʲ a4"
).split(" " )
UpperCAmelCase__ = dict(zip(_lowercase , range(len(_lowercase ) ) ) )
UpperCAmelCase__ = {"pad_token": "<pad>", "unk_token": "<unk>", "bos_token": "<s>", "eos_token": "</s>"}
UpperCAmelCase__ = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES["vocab_file"] )
with open(self.vocab_file , "w" , encoding="utf-8" ) as fp:
fp.write(json.dumps(_lowercase ) + "\n" )
def _UpperCAmelCase ( self : str , _lowercase : Union[str, Any] , _lowercase : str=False , _lowercase : Tuple=20 , _lowercase : int=5 ):
"""simple docstring"""
UpperCAmelCase__ = [(i, tokenizer.decode([i] , clean_up_tokenization_spaces=_lowercase )) for i in range(len(_lowercase ) )]
UpperCAmelCase__ = list(filter(lambda _lowercase : [t[0]] == tokenizer.encode(t[1] , do_phonemize=_lowercase ) , _lowercase ) )
if max_length is not None and len(_lowercase ) > max_length:
UpperCAmelCase__ = toks[:max_length]
if min_length is not None and len(_lowercase ) < min_length and len(_lowercase ) > 0:
while len(_lowercase ) < min_length:
UpperCAmelCase__ = toks + toks
# toks_str = [t[1] for t in toks]
UpperCAmelCase__ = [t[0] for t in toks]
# Ensure consistency
UpperCAmelCase__ = tokenizer.decode(_lowercase , clean_up_tokenization_spaces=_lowercase )
if " " not in output_txt and len(_lowercase ) > 1:
UpperCAmelCase__ = (
tokenizer.decode([toks_ids[0]] , clean_up_tokenization_spaces=_lowercase )
+ " "
+ tokenizer.decode(toks_ids[1:] , clean_up_tokenization_spaces=_lowercase )
)
if with_prefix_space:
UpperCAmelCase__ = " " + output_txt
UpperCAmelCase__ = tokenizer.encode(_lowercase , add_special_tokens=_lowercase )
return output_txt, output_ids
def _UpperCAmelCase ( self : Optional[Any] , **_lowercase : Optional[int] ):
"""simple docstring"""
kwargs.update(self.special_tokens_map )
return WavaVecaPhonemeCTCTokenizer.from_pretrained(self.tmpdirname , **_lowercase )
def _UpperCAmelCase ( self : Union[str, Any] ):
"""simple docstring"""
UpperCAmelCase__ = self.tokenizer_class.from_pretrained("facebook/wav2vec2-lv-60-espeak-cv-ft" )
# check adding a single token
tokenizer.add_tokens("xxx" )
UpperCAmelCase__ = tokenizer("m xxx ɪ" , do_phonemize=_lowercase ).input_ids
self.assertEqual(_lowercase , [13, 3_92, 17] ) # xxx should be last token
tokenizer.add_tokens(["aaa", "bbb", "ccc"] )
UpperCAmelCase__ = tokenizer("m aaa ɪ ccc" , do_phonemize=_lowercase ).input_ids
self.assertEqual(_lowercase , [13, 3_93, 17, 3_95] ) # aaa and ccc should be after xxx and 2 after aaa
UpperCAmelCase__ = tokenizer("maɪ c" , do_phonemize=_lowercase ).input_ids
self.assertEqual(_lowercase , [3, 2_00] ) # mai should be <unk> (=3)
def _UpperCAmelCase ( self : Any ):
"""simple docstring"""
UpperCAmelCase__ = self.tokenizer_class.from_pretrained("facebook/wav2vec2-lv-60-espeak-cv-ft" )
UpperCAmelCase__ = "Hello how are you"
UpperCAmelCase__ = tokenizer.phonemize(_lowercase , phonemizer_lang="en-us" )
self.assertEqual(_lowercase , "h ə l oʊ h aʊ ɑːɹ j uː" )
def _UpperCAmelCase ( self : Dict ):
"""simple docstring"""
UpperCAmelCase__ = self.tokenizer_class.from_pretrained("facebook/wav2vec2-lv-60-espeak-cv-ft" )
UpperCAmelCase__ = "Hello how are you"
UpperCAmelCase__ = tokenizer.phonemize(_lowercase , phonemizer_lang="en-us" )
self.assertEqual(tokenizer(_lowercase ).input_ids , tokenizer(_lowercase , do_phonemize=_lowercase ).input_ids )
def _UpperCAmelCase ( self : int ):
"""simple docstring"""
UpperCAmelCase__ = self.tokenizer_class.from_pretrained("facebook/wav2vec2-lv-60-espeak-cv-ft" )
UpperCAmelCase__ = "Hello how are you"
UpperCAmelCase__ = tokenizer.phonemize(_lowercase , phonemizer_lang="en-us" )
UpperCAmelCase__ = tokenizer.decode(tokenizer(_lowercase ).input_ids )
self.assertEqual(_lowercase , _lowercase )
def _UpperCAmelCase ( self : List[str] ):
"""simple docstring"""
UpperCAmelCase__ = self.tokenizer_class.from_pretrained("facebook/wav2vec2-lv-60-espeak-cv-ft" )
UpperCAmelCase__ = [
[11, 5, 15, tokenizer.pad_token_id, 15, 8, 98],
[24, 22, 5, 24, 22, 5, 77],
]
UpperCAmelCase__ = tokenizer.decode(sample_ids[0] )
UpperCAmelCase__ = tokenizer.batch_decode(_lowercase )
self.assertEqual(_lowercase , batch_tokens[0] )
self.assertEqual(_lowercase , ["k s ɾ ɾ l ɭʲ", "j ð s j ð s oːɹ"] )
def _UpperCAmelCase ( self : Any ):
"""simple docstring"""
UpperCAmelCase__ = self.tokenizer_class.from_pretrained(
"facebook/wav2vec2-lv-60-espeak-cv-ft" , word_delimiter_token="|" )
tokenizer.add_tokens("|" )
UpperCAmelCase__ = "Hello how are you"
UpperCAmelCase__ = tokenizer.phonemize(_lowercase , phonemizer_lang="en-us" )
self.assertEqual(_lowercase , "h ə l oʊ | h aʊ | ɑːɹ | j uː |" )
def _UpperCAmelCase ( self : Union[str, Any] ):
"""simple docstring"""
UpperCAmelCase__ = self.tokenizer_class.from_pretrained(
"facebook/wav2vec2-lv-60-espeak-cv-ft" , word_delimiter_token="|" )
tokenizer.add_tokens("|" )
UpperCAmelCase__ = "Hello how are you"
UpperCAmelCase__ = tokenizer.phonemize(_lowercase , phonemizer_lang="en-us" )
self.assertEqual(tokenizer(_lowercase ).input_ids , tokenizer(_lowercase , do_phonemize=_lowercase ).input_ids )
def _UpperCAmelCase ( self : str ):
"""simple docstring"""
UpperCAmelCase__ = self.tokenizer_class.from_pretrained(
"facebook/wav2vec2-lv-60-espeak-cv-ft" , word_delimiter_token="|" )
tokenizer.add_tokens("|" )
# fmt: off
UpperCAmelCase__ = [
[11, 5, 15, tokenizer.pad_token_id, tokenizer.word_delimiter_token_id, 15, 8, tokenizer.word_delimiter_token_id, 98],
[tokenizer.word_delimiter_token_id, 24, 22, tokenizer.word_delimiter_token_id, 5, 24, 22, 5, 77],
]
# fmt: on
# decode with word_del_token filter
UpperCAmelCase__ = tokenizer.decode(sample_ids[0] )
UpperCAmelCase__ = tokenizer.batch_decode(_lowercase )
self.assertEqual(_lowercase , batch_tokens[0] )
self.assertEqual(_lowercase , ["k s ɾ ɾ l ɭʲ", "j ð s j ð s oːɹ"] )
# decode with no word_del_token filter
UpperCAmelCase__ = tokenizer.decode(sample_ids[0] , filter_word_delimiter_token=_lowercase )
UpperCAmelCase__ = tokenizer.batch_decode(_lowercase , filter_word_delimiter_token=_lowercase )
self.assertEqual(_lowercase , batch_tokens[0] )
self.assertEqual(_lowercase , ["k s ɾ | ɾ l | ɭʲ", "| j ð | s j ð s oːɹ"] )
def _UpperCAmelCase ( self : Tuple ):
"""simple docstring"""
UpperCAmelCase__ = self.tokenizer_class.from_pretrained(
"facebook/wav2vec2-lv-60-espeak-cv-ft" , word_delimiter_token="|" )
tokenizer.add_tokens("|" )
UpperCAmelCase__ = "Hello how are you"
UpperCAmelCase__ = tokenizer.phonemize(_lowercase , phonemizer_lang="en-us" )
UpperCAmelCase__ = tokenizer.decode(tokenizer(_lowercase ).input_ids , filter_word_delimiter_token=_lowercase )
self.assertEqual(_lowercase , _lowercase )
def _UpperCAmelCase ( self : List[str] ):
"""simple docstring"""
UpperCAmelCase__ = self.tokenizer_class.from_pretrained(
"facebook/wav2vec2-lv-60-espeak-cv-ft" , word_delimiter_token="|" )
tokenizer.add_tokens("|" )
UpperCAmelCase__ = "Hello how are you"
UpperCAmelCase__ = tokenizer.phonemize(_lowercase , phonemizer_lang="en-us" )
UpperCAmelCase__ = tokenizer.decode(tokenizer(_lowercase ).input_ids , filter_word_delimiter_token=_lowercase )
self.assertEqual(" ".join([p.strip() for p in phonemes.split(" |" )] ).strip() , _lowercase )
def _UpperCAmelCase ( self : str ):
"""simple docstring"""
UpperCAmelCase__ = self.tokenizer_class.from_pretrained(
"facebook/wav2vec2-lv-60-espeak-cv-ft" , word_delimiter_token=_lowercase )
UpperCAmelCase__ = "Hello how are you"
UpperCAmelCase__ = tokenizer(_lowercase , phonemizer_lang="en-us" ).input_ids
UpperCAmelCase__ = tokenizer(_lowercase , phonemizer_lang="fr-fr" ).input_ids
self.assertNotEqual(_lowercase , _lowercase )
UpperCAmelCase__ = tokenizer.decode(_lowercase )
UpperCAmelCase__ = tokenizer.decode(_lowercase )
self.assertEqual(_lowercase , "h ə l oʊ h aʊ ɑːɹ j uː" )
self.assertEqual(_lowercase , "ɛ l o h aʊ a ʁ j u" )
def _UpperCAmelCase ( self : int ):
"""simple docstring"""
UpperCAmelCase__ = self.tokenizer_class.from_pretrained("facebook/wav2vec2-lv-60-espeak-cv-ft" )
UpperCAmelCase__ = "Hello how Are you"
UpperCAmelCase__ = "hello how are you"
UpperCAmelCase__ = tokenizer(_lowercase ).input_ids
UpperCAmelCase__ = tokenizer(_lowercase ).input_ids
self.assertEqual(_lowercase , _lowercase )
def _UpperCAmelCase ( self : Optional[Any] ):
"""simple docstring"""
UpperCAmelCase__ = self.tokenizer_class.from_pretrained("facebook/wav2vec2-lv-60-espeak-cv-ft" )
tokenizer.add_tokens(["!", "?"] )
tokenizer.add_special_tokens({"cls_token": "$$$"} )
# fmt: off
UpperCAmelCase__ = [
[11, 5, 15, tokenizer.pad_token_id, 15, 8, 98, 3_92, 3_92, 3_93, 3_92, 3_92, 3_93, 3_94, 3_94],
[24, 22, 5, 24, 22, 5, 77, tokenizer.pad_token_id, 3_94, 3_94],
]
# fmt: on
UpperCAmelCase__ = tokenizer.batch_decode(_lowercase )
self.assertEqual(_lowercase , ["k s ɾ ɾ l ɭʲ!?!? $$$", "j ð s j ð s oːɹ $$$"] )
@staticmethod
def _UpperCAmelCase ( _lowercase : int , _lowercase : Tuple ):
"""simple docstring"""
UpperCAmelCase__ = [d[key] for d in offsets]
return retrieved_list
def _UpperCAmelCase ( self : Union[str, Any] ):
"""simple docstring"""
UpperCAmelCase__ = self.get_tokenizer(word_delimiter_token="|" )
tokenizer.add_tokens("|" )
# fmt: off
# ksssɾɾ|ɾɾ<pad>ɾɾ|<pad>ɾlll|ɭʲ -> k s ɾ ɾ | ɾ l | ɭʲ"
UpperCAmelCase__ = [11, 5, 5, 5, 15, 15, tokenizer.pad_token_id, 15, 15, tokenizer.word_delimiter_token_id, tokenizer.pad_token_id, 15, 8, 8, 8, tokenizer.word_delimiter_token_id, 98]
# fmt: on
UpperCAmelCase__ = tokenizer.decode(_lowercase , output_char_offsets=_lowercase , filter_word_delimiter_token=_lowercase )
# check Wav2Vec2CTCTokenizerOutput keys for char
self.assertEqual(len(outputs.keys() ) , 2 )
self.assertTrue("text" in outputs )
self.assertTrue("char_offsets" in outputs )
self.assertTrue(isinstance(_lowercase , _lowercase ) )
# check that order of chars is correct and identical for both outputs
self.assertEqual(" ".join(self.get_from_offsets(outputs["char_offsets"] , "char" ) ) , outputs.text )
self.assertListEqual(
self.get_from_offsets(outputs["char_offsets"] , "char" ) , ["k", "s", "ɾ", "ɾ", "|", "ɾ", "l", "|", "ɭʲ"] )
# check that offsets are actually correct for char
# 0-1 is 11, 1-4 is 5, 4-6 is first 15, 6-7 is <pad> (thus not shown), 7-9 is second 15, 9-10 is word_delimiter_token,
# 10-11 is <pad> (thus not shown), 11-12 is third 15, 12-15 is 8, 15-16 is word_delimiter_token, 16-17 is 98
self.assertListEqual(
self.get_from_offsets(outputs["char_offsets"] , "start_offset" ) , [0, 1, 4, 7, 9, 11, 12, 15, 16] )
self.assertListEqual(
self.get_from_offsets(outputs["char_offsets"] , "end_offset" ) , [1, 4, 6, 9, 10, 12, 15, 16, 17] )
def _UpperCAmelCase ( self : Any ):
"""simple docstring"""
UpperCAmelCase__ = self.get_tokenizer(word_delimiter_token="|" )
def check_list_tuples_equal(_lowercase : Any , _lowercase : Optional[Any] ):
self.assertTrue(isinstance(_lowercase , _lowercase ) )
self.assertTrue(isinstance(outputs_list[0] , _lowercase ) )
# transform list to ModelOutput
UpperCAmelCase__ = WavaVecaPhonemeCTCTokenizerOutput(
{k: [d[k] for d in outputs_list] for k in outputs_list[0]} )
self.assertListEqual(outputs_batch["text"] , outputs_batch_a["text"] )
def recursive_check(_lowercase : Optional[int] , _lowercase : Optional[int] ):
if isinstance(_lowercase , _lowercase ):
[recursive_check(_lowercase , _lowercase ) for la, la in zip(_lowercase , _lowercase )]
self.assertEqual(_lowercase , _lowercase )
if "char_offsets" in outputs_batch:
recursive_check(outputs_batch["char_offsets"] , outputs_batch_a["char_offsets"] )
# fmt: off
UpperCAmelCase__ = [
[11, 5, 15, tokenizer.pad_token_id, 15, 4, 8, 98, 32, 32, 32, 32, 4, 33, tokenizer.word_delimiter_token_id, 32, 32, 33, 34, 34],
[24, 22, 5, tokenizer.word_delimiter_token_id, tokenizer.word_delimiter_token_id, 24, 22, 22, 22, 4, 5, 77, tokenizer.pad_token_id, 22, 22, 4, 34, 34, 34, 34],
]
# fmt: on
# We assume that `decode` works as expected. All we will check now is
# the output type is correct and the output is identical to `decode`
# char
UpperCAmelCase__ = tokenizer.batch_decode(_lowercase , output_char_offsets=_lowercase )
UpperCAmelCase__ = [tokenizer.decode(_lowercase , output_char_offsets=_lowercase ) for ids in sample_ids]
check_list_tuples_equal(_lowercase , _lowercase )
@unittest.skip("Wav2Vec2PhonemeTokenizer always lower cases letters to correctly map to phonemes" )
def _UpperCAmelCase ( self : int ):
"""simple docstring"""
pass
@unittest.skip("Wav2Vec2PhonemeTokenizer always puts spaces between phonemes" )
def _UpperCAmelCase ( self : Tuple ):
"""simple docstring"""
pass
@unittest.skip("encodes to text to ids, but decodes ids to phonemes -> not possible to have internal consistency" )
def _UpperCAmelCase ( self : Union[str, Any] ):
"""simple docstring"""
pass
@unittest.skip("Wav2Vec2PhonemeModel has no max model length => no testing" )
def _UpperCAmelCase ( self : List[Any] ):
"""simple docstring"""
pass
def _UpperCAmelCase ( self : List[Any] ):
"""simple docstring"""
UpperCAmelCase__ = self.get_tokenizers(do_lower_case=_lowercase )
for tokenizer in tokenizers:
with self.subTest(F"""{tokenizer.__class__.__name__}""" ):
UpperCAmelCase__ = tokenizer.vocab_size
UpperCAmelCase__ = len(_lowercase )
self.assertNotEqual(_lowercase , 0 )
# We usually have added tokens from the start in tests because our vocab fixtures are
# smaller than the original vocabs - let's not assert this
# self.assertEqual(vocab_size, all_size)
UpperCAmelCase__ = ["aaaaa bbbbbb", "cccccccccdddddddd"]
UpperCAmelCase__ = tokenizer.add_tokens(_lowercase )
UpperCAmelCase__ = tokenizer.vocab_size
UpperCAmelCase__ = len(_lowercase )
self.assertNotEqual(_lowercase , 0 )
self.assertEqual(_lowercase , _lowercase )
self.assertEqual(_lowercase , len(_lowercase ) )
self.assertEqual(_lowercase , all_size + len(_lowercase ) )
UpperCAmelCase__ = tokenizer.encode("aaaaa bbbbbb low cccccccccdddddddd l" , add_special_tokens=_lowercase )
self.assertGreaterEqual(len(_lowercase ) , 4 )
self.assertGreater(tokens[0] , tokenizer.vocab_size - 1 )
self.assertGreater(tokens[-3] , tokenizer.vocab_size - 1 )
UpperCAmelCase__ = {"eos_token": ">>>>|||<||<<|<<", "pad_token": "<<<<<|||>|>>>>|>"}
UpperCAmelCase__ = tokenizer.add_special_tokens(_lowercase )
UpperCAmelCase__ = tokenizer.vocab_size
UpperCAmelCase__ = len(_lowercase )
self.assertNotEqual(_lowercase , 0 )
self.assertEqual(_lowercase , _lowercase )
self.assertEqual(_lowercase , len(_lowercase ) )
self.assertEqual(_lowercase , all_size_a + len(_lowercase ) )
UpperCAmelCase__ = tokenizer.encode(
">>>>|||<||<<|<< aaaaabbbbbb low cccccccccdddddddd <<<<<|||>|>>>>|> l" , add_special_tokens=_lowercase )
self.assertGreaterEqual(len(_lowercase ) , 6 )
self.assertGreater(tokens[0] , tokenizer.vocab_size - 1 )
self.assertGreater(tokens[0] , tokens[1] )
self.assertGreater(tokens[-3] , tokenizer.vocab_size - 1 )
self.assertGreater(tokens[-3] , tokens[-4] )
self.assertEqual(tokens[0] , tokenizer.eos_token_id )
self.assertEqual(tokens[-3] , tokenizer.pad_token_id )
@unittest.skip("The tokenizer shouldn't be used to encode input IDs (except for labels), only to decode." )
def _UpperCAmelCase ( self : List[Any] ):
"""simple docstring"""
pass
@unittest.skip("The tokenizer shouldn't be used to encode input IDs (except for labels), only to decode." )
def _UpperCAmelCase ( self : Optional[int] ):
"""simple docstring"""
pass
def _UpperCAmelCase ( self : Union[str, Any] ):
"""simple docstring"""
UpperCAmelCase__ = self.get_tokenizers(fast=_lowercase , do_lower_case=_lowercase )
for tokenizer in tokenizers:
with self.subTest(F"""{tokenizer.__class__.__name__}""" ):
UpperCAmelCase__ = ["ð", "ɪ", "s", "ɪ", "z", "ɐ", "t", "ɛ", "k", "s", "t"]
UpperCAmelCase__ = tokenizer.convert_tokens_to_string(_lowercase )
self.assertIsInstance(output["text"] , _lowercase )
| 277
| 0
|
'''simple docstring'''
from __future__ import annotations
import unittest
from transformers import XGLMConfig, XGLMTokenizer, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import tensorflow as tf
from transformers.models.xglm.modeling_tf_xglm import (
TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST,
TFXGLMForCausalLM,
TFXGLMModel,
)
@require_tf
class UpperCAmelCase_ :
"""simple docstring"""
lowerCamelCase : Dict = XGLMConfig
lowerCamelCase : List[str] = {}
lowerCamelCase : Union[str, Any] = 'gelu'
def __init__( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_=14 , SCREAMING_SNAKE_CASE_=7 , SCREAMING_SNAKE_CASE_=True , SCREAMING_SNAKE_CASE_=True , SCREAMING_SNAKE_CASE_=True , SCREAMING_SNAKE_CASE_=99 , SCREAMING_SNAKE_CASE_=32 , SCREAMING_SNAKE_CASE_=2 , SCREAMING_SNAKE_CASE_=4 , SCREAMING_SNAKE_CASE_=37 , SCREAMING_SNAKE_CASE_="gelu" , SCREAMING_SNAKE_CASE_=0.1 , SCREAMING_SNAKE_CASE_=0.1 , SCREAMING_SNAKE_CASE_=5_12 , SCREAMING_SNAKE_CASE_=0.0_2 , ) -> Any:
__lowerCamelCase : int = parent
__lowerCamelCase : Optional[int] = batch_size
__lowerCamelCase : Optional[Any] = seq_length
__lowerCamelCase : Optional[int] = is_training
__lowerCamelCase : str = use_input_mask
__lowerCamelCase : Dict = use_labels
__lowerCamelCase : Union[str, Any] = vocab_size
__lowerCamelCase : List[Any] = d_model
__lowerCamelCase : List[Any] = num_hidden_layers
__lowerCamelCase : List[Any] = num_attention_heads
__lowerCamelCase : Optional[Any] = ffn_dim
__lowerCamelCase : List[Any] = activation_function
__lowerCamelCase : List[Any] = activation_dropout
__lowerCamelCase : List[Any] = attention_dropout
__lowerCamelCase : Union[str, Any] = max_position_embeddings
__lowerCamelCase : Tuple = initializer_range
__lowerCamelCase : int = None
__lowerCamelCase : int = 0
__lowerCamelCase : Tuple = 2
__lowerCamelCase : Tuple = 1
def lowercase_ ( self ) -> Any:
return XGLMConfig.from_pretrained('facebook/xglm-564M' )
def lowercase_ ( self ) -> Tuple:
__lowerCamelCase : Optional[Any] = tf.clip_by_value(
ids_tensor([self.batch_size, self.seq_length] , self.vocab_size ) , clip_value_min=0 , clip_value_max=3 )
__lowerCamelCase : Optional[int] = None
if self.use_input_mask:
__lowerCamelCase : Any = random_attention_mask([self.batch_size, self.seq_length] )
__lowerCamelCase : str = self.get_config()
__lowerCamelCase : List[Any] = floats_tensor([self.num_hidden_layers, self.num_attention_heads] , 2 )
return (
config,
input_ids,
input_mask,
head_mask,
)
def lowercase_ ( self ) -> Optional[int]:
return XGLMConfig(
vocab_size=self.vocab_size , d_model=self.hidden_size , num_layers=self.num_hidden_layers , attention_heads=self.num_attention_heads , ffn_dim=self.ffn_dim , activation_function=self.activation_function , activation_dropout=self.activation_dropout , attention_dropout=self.attention_dropout , max_position_embeddings=self.max_position_embeddings , initializer_range=self.initializer_range , use_cache=SCREAMING_SNAKE_CASE_ , bos_token_id=self.bos_token_id , eos_token_id=self.eos_token_id , pad_token_id=self.pad_token_id , return_dict=SCREAMING_SNAKE_CASE_ , )
def lowercase_ ( self ) -> str:
__lowerCamelCase : List[Any] = self.prepare_config_and_inputs()
(
(
__lowerCamelCase
) , (
__lowerCamelCase
) , (
__lowerCamelCase
) , (
__lowerCamelCase
) ,
) : str = config_and_inputs
__lowerCamelCase : Union[str, Any] = {
'input_ids': input_ids,
'head_mask': head_mask,
}
return config, inputs_dict
@require_tf
class UpperCAmelCase_ (_UpperCAmelCase , _UpperCAmelCase , unittest.TestCase ):
"""simple docstring"""
lowerCamelCase : Optional[Any] = (TFXGLMModel, TFXGLMForCausalLM) if is_tf_available() else ()
lowerCamelCase : List[Any] = (TFXGLMForCausalLM,) if is_tf_available() else ()
lowerCamelCase : Any = (
{'feature-extraction': TFXGLMModel, 'text-generation': TFXGLMForCausalLM} if is_tf_available() else {}
)
lowerCamelCase : List[Any] = False
lowerCamelCase : Dict = False
lowerCamelCase : Union[str, Any] = False
def lowercase_ ( self ) -> List[Any]:
__lowerCamelCase : str = TFXGLMModelTester(self )
__lowerCamelCase : List[Any] = ConfigTester(self , config_class=SCREAMING_SNAKE_CASE_ , n_embd=37 )
def lowercase_ ( self ) -> Dict:
self.config_tester.run_common_tests()
@slow
def lowercase_ ( self ) -> Optional[int]:
for model_name in TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
__lowerCamelCase : Optional[Any] = TFXGLMModel.from_pretrained(SCREAMING_SNAKE_CASE_ )
self.assertIsNotNone(SCREAMING_SNAKE_CASE_ )
@unittest.skip(reason='Currently, model embeddings are going to undergo a major refactor.' )
def lowercase_ ( self ) -> Any:
super().test_resize_token_embeddings()
@require_tf
class UpperCAmelCase_ (unittest.TestCase ):
"""simple docstring"""
@slow
def lowercase_ ( self , SCREAMING_SNAKE_CASE_=True ) -> List[str]:
__lowerCamelCase : Optional[Any] = TFXGLMForCausalLM.from_pretrained('facebook/xglm-564M' )
__lowerCamelCase : int = tf.convert_to_tensor([[2, 2_68, 98_65]] , dtype=tf.intaa ) # The dog
# </s> The dog is a very friendly dog. He is very affectionate and loves to play with other
# fmt: off
__lowerCamelCase : Optional[int] = [2, 2_68, 98_65, 67, 11, 19_88, 5_72_52, 98_65, 5, 9_84, 67, 19_88, 21_38_38, 16_58, 53, 7_04_46, 33, 66_57, 2_78, 15_81]
# fmt: on
__lowerCamelCase : Any = model.generate(SCREAMING_SNAKE_CASE_ , do_sample=SCREAMING_SNAKE_CASE_ , num_beams=1 )
if verify_outputs:
self.assertListEqual(output_ids[0].numpy().tolist() , SCREAMING_SNAKE_CASE_ )
@slow
def lowercase_ ( self ) -> int:
__lowerCamelCase : Any = XGLMTokenizer.from_pretrained('facebook/xglm-564M' )
__lowerCamelCase : Tuple = TFXGLMForCausalLM.from_pretrained('facebook/xglm-564M' )
tf.random.set_seed(0 )
__lowerCamelCase : List[Any] = tokenizer('Today is a nice day and' , return_tensors='tf' )
__lowerCamelCase : int = tokenized.input_ids
# forces the generation to happen on CPU, to avoid GPU-related quirks (and assure same output regardless of the available devices)
with tf.device(':/CPU:0' ):
__lowerCamelCase : Tuple = model.generate(SCREAMING_SNAKE_CASE_ , do_sample=SCREAMING_SNAKE_CASE_ , seed=[7, 0] )
__lowerCamelCase : Optional[Any] = tokenizer.decode(output_ids[0] , skip_special_tokens=SCREAMING_SNAKE_CASE_ )
__lowerCamelCase : Union[str, Any] = (
'Today is a nice day and warm evening here over Southern Alberta!! Today when they closed schools due'
)
self.assertEqual(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
@slow
def lowercase_ ( self ) -> int:
__lowerCamelCase : Tuple = TFXGLMForCausalLM.from_pretrained('facebook/xglm-564M' )
__lowerCamelCase : Any = XGLMTokenizer.from_pretrained('facebook/xglm-564M' )
__lowerCamelCase : Any = 'left'
# use different length sentences to test batching
__lowerCamelCase : Any = [
'This is an extremelly long sentence that only exists to test the ability of the model to cope with '
'left-padding, such as in batched generation. The output for the sequence below should be the same '
'regardless of whether left padding is applied or not. When',
'Hello, my dog is a little',
]
__lowerCamelCase : Any = tokenizer(SCREAMING_SNAKE_CASE_ , return_tensors='tf' , padding=SCREAMING_SNAKE_CASE_ )
__lowerCamelCase : Optional[int] = inputs['input_ids']
__lowerCamelCase : str = model.generate(input_ids=SCREAMING_SNAKE_CASE_ , attention_mask=inputs['attention_mask'] , max_new_tokens=12 )
__lowerCamelCase : Optional[int] = tokenizer(sentences[0] , return_tensors='tf' ).input_ids
__lowerCamelCase : int = model.generate(input_ids=SCREAMING_SNAKE_CASE_ , max_new_tokens=12 )
__lowerCamelCase : Optional[Any] = tokenizer(sentences[1] , return_tensors='tf' ).input_ids
__lowerCamelCase : Optional[Any] = model.generate(input_ids=SCREAMING_SNAKE_CASE_ , max_new_tokens=12 )
__lowerCamelCase : Union[str, Any] = tokenizer.batch_decode(SCREAMING_SNAKE_CASE_ , skip_special_tokens=SCREAMING_SNAKE_CASE_ )
__lowerCamelCase : Union[str, Any] = tokenizer.decode(output_non_padded[0] , skip_special_tokens=SCREAMING_SNAKE_CASE_ )
__lowerCamelCase : Union[str, Any] = tokenizer.decode(output_padded[0] , skip_special_tokens=SCREAMING_SNAKE_CASE_ )
__lowerCamelCase : Optional[int] = [
'This is an extremelly long sentence that only exists to test the ability of the model to cope with '
'left-padding, such as in batched generation. The output for the sequence below should be the same '
'regardless of whether left padding is applied or not. When left padding is applied, the sequence will be '
'a single',
'Hello, my dog is a little bit of a shy one, but he is very friendly',
]
self.assertListEqual(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
self.assertListEqual(SCREAMING_SNAKE_CASE_ , [non_padded_sentence, padded_sentence] )
| 13
|
'''simple docstring'''
import re
from typing import Callable, List, Optional, Union
import tensorflow as tf
try:
from tensorflow.keras.optimizers.legacy import Adam
except ImportError:
from tensorflow.keras.optimizers import Adam
class UpperCAmelCase_ (tf.keras.optimizers.schedules.LearningRateSchedule ):
"""simple docstring"""
def __init__( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = 1.0 , SCREAMING_SNAKE_CASE_ = None , ) -> Any:
super().__init__()
__lowerCamelCase : Optional[Any] = initial_learning_rate
__lowerCamelCase : Optional[Any] = warmup_steps
__lowerCamelCase : Union[str, Any] = power
__lowerCamelCase : Optional[int] = decay_schedule_fn
__lowerCamelCase : Any = name
def __call__( self , SCREAMING_SNAKE_CASE_ ) -> str:
with tf.name_scope(self.name or 'WarmUp' ) as name:
# Implements polynomial warmup. i.e., if global_step < warmup_steps, the
# learning rate will be `global_step/num_warmup_steps * init_lr`.
__lowerCamelCase : str = tf.cast(SCREAMING_SNAKE_CASE_ , tf.floataa )
__lowerCamelCase : Optional[int] = tf.cast(self.warmup_steps , tf.floataa )
__lowerCamelCase : List[Any] = global_step_float / warmup_steps_float
__lowerCamelCase : Optional[Any] = self.initial_learning_rate * tf.math.pow(SCREAMING_SNAKE_CASE_ , self.power )
return tf.cond(
global_step_float < warmup_steps_float , lambda: warmup_learning_rate , lambda: self.decay_schedule_fn(step - self.warmup_steps ) , name=SCREAMING_SNAKE_CASE_ , )
def lowercase_ ( self ) -> Optional[Any]:
return {
"initial_learning_rate": self.initial_learning_rate,
"decay_schedule_fn": self.decay_schedule_fn,
"warmup_steps": self.warmup_steps,
"power": self.power,
"name": self.name,
}
def UpperCAmelCase__ ( UpperCAmelCase_ : float , UpperCAmelCase_ : int , UpperCAmelCase_ : int , UpperCAmelCase_ : float = 0.0 , UpperCAmelCase_ : float = 0.9 , UpperCAmelCase_ : float = 0.999 , UpperCAmelCase_ : float = 1e-8 , UpperCAmelCase_ : Optional[float] = None , UpperCAmelCase_ : Optional[float] = None , UpperCAmelCase_ : float = 0.0 , UpperCAmelCase_ : float = 1.0 , UpperCAmelCase_ : Optional[List[str]] = None , ) -> int:
__lowerCamelCase : int = tf.keras.optimizers.schedules.PolynomialDecay(
initial_learning_rate=UpperCAmelCase_ , decay_steps=num_train_steps - num_warmup_steps , end_learning_rate=init_lr * min_lr_ratio , power=UpperCAmelCase_ , )
if num_warmup_steps:
__lowerCamelCase : str = WarmUp(
initial_learning_rate=UpperCAmelCase_ , decay_schedule_fn=UpperCAmelCase_ , warmup_steps=UpperCAmelCase_ , )
if weight_decay_rate > 0.0:
__lowerCamelCase : List[Any] = AdamWeightDecay(
learning_rate=UpperCAmelCase_ , weight_decay_rate=UpperCAmelCase_ , beta_a=UpperCAmelCase_ , beta_a=UpperCAmelCase_ , epsilon=UpperCAmelCase_ , clipnorm=UpperCAmelCase_ , global_clipnorm=UpperCAmelCase_ , exclude_from_weight_decay=['LayerNorm', 'layer_norm', 'bias'] , include_in_weight_decay=UpperCAmelCase_ , )
else:
__lowerCamelCase : Tuple = tf.keras.optimizers.Adam(
learning_rate=UpperCAmelCase_ , beta_a=UpperCAmelCase_ , beta_a=UpperCAmelCase_ , epsilon=UpperCAmelCase_ , clipnorm=UpperCAmelCase_ , global_clipnorm=UpperCAmelCase_ , )
# We return the optimizer and the LR scheduler in order to better track the
# evolution of the LR independently of the optimizer.
return optimizer, lr_schedule
class UpperCAmelCase_ (_UpperCAmelCase ):
"""simple docstring"""
def __init__( self , SCREAMING_SNAKE_CASE_ = 0.0_0_1 , SCREAMING_SNAKE_CASE_ = 0.9 , SCREAMING_SNAKE_CASE_ = 0.9_9_9 , SCREAMING_SNAKE_CASE_ = 1E-7 , SCREAMING_SNAKE_CASE_ = False , SCREAMING_SNAKE_CASE_ = 0.0 , SCREAMING_SNAKE_CASE_ = None , SCREAMING_SNAKE_CASE_ = None , SCREAMING_SNAKE_CASE_ = "AdamWeightDecay" , **SCREAMING_SNAKE_CASE_ , ) -> int:
super().__init__(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ )
__lowerCamelCase : int = weight_decay_rate
__lowerCamelCase : str = include_in_weight_decay
__lowerCamelCase : List[Any] = exclude_from_weight_decay
@classmethod
def lowercase_ ( cls , SCREAMING_SNAKE_CASE_ ) -> Dict:
__lowerCamelCase : Any = {'WarmUp': WarmUp}
return super(SCREAMING_SNAKE_CASE_ , cls ).from_config(SCREAMING_SNAKE_CASE_ , custom_objects=SCREAMING_SNAKE_CASE_ )
def lowercase_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) -> Optional[int]:
super(SCREAMING_SNAKE_CASE_ , self )._prepare_local(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
__lowerCamelCase : Union[str, Any] = tf.constant(
self.weight_decay_rate , name='adam_weight_decay_rate' )
def lowercase_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) -> int:
__lowerCamelCase : Tuple = self._do_use_weight_decay(var.name )
if do_decay:
return var.assign_sub(
learning_rate * var * apply_state[(var.device, var.dtype.base_dtype)]['weight_decay_rate'] , use_locking=self._use_locking , )
return tf.no_op()
def lowercase_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_=None , **SCREAMING_SNAKE_CASE_ ) -> List[str]:
__lowerCamelCase , __lowerCamelCase : Optional[Any] = list(zip(*SCREAMING_SNAKE_CASE_ ) )
return super(SCREAMING_SNAKE_CASE_ , self ).apply_gradients(zip(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) , name=SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ )
def lowercase_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) -> str:
if apply_state is None:
return self._decayed_lr_t[var_dtype], {}
__lowerCamelCase : Optional[int] = apply_state or {}
__lowerCamelCase : Dict = apply_state.get((var_device, var_dtype) )
if coefficients is None:
__lowerCamelCase : List[Any] = self._fallback_apply_state(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
__lowerCamelCase : Optional[Any] = coefficients
return coefficients["lr_t"], {"apply_state": apply_state}
def lowercase_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_=None ) -> str:
__lowerCamelCase , __lowerCamelCase : Dict = self._get_lr(var.device , var.dtype.base_dtype , SCREAMING_SNAKE_CASE_ )
__lowerCamelCase : Optional[Any] = self._decay_weights_op(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
with tf.control_dependencies([decay] ):
return super(SCREAMING_SNAKE_CASE_ , self )._resource_apply_dense(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ )
def lowercase_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_=None ) -> Union[str, Any]:
__lowerCamelCase , __lowerCamelCase : Tuple = self._get_lr(var.device , var.dtype.base_dtype , SCREAMING_SNAKE_CASE_ )
__lowerCamelCase : List[Any] = self._decay_weights_op(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
with tf.control_dependencies([decay] ):
return super(SCREAMING_SNAKE_CASE_ , self )._resource_apply_sparse(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ )
def lowercase_ ( self ) -> List[Any]:
__lowerCamelCase : Any = super().get_config()
config.update({'weight_decay_rate': self.weight_decay_rate} )
return config
def lowercase_ ( self , SCREAMING_SNAKE_CASE_ ) -> Dict:
if self.weight_decay_rate == 0:
return False
if self._include_in_weight_decay:
for r in self._include_in_weight_decay:
if re.search(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) is not None:
return True
if self._exclude_from_weight_decay:
for r in self._exclude_from_weight_decay:
if re.search(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) is not None:
return False
return True
class UpperCAmelCase_ (_UpperCAmelCase ):
"""simple docstring"""
def __init__( self ) -> Tuple:
__lowerCamelCase : Tuple = []
__lowerCamelCase : Optional[Any] = None
@property
def lowercase_ ( self ) -> List[str]:
if self._accum_steps is None:
__lowerCamelCase : Tuple = tf.Variable(
tf.constant(0 , dtype=tf.intaa ) , trainable=SCREAMING_SNAKE_CASE_ , synchronization=tf.VariableSynchronization.ON_READ , aggregation=tf.VariableAggregation.ONLY_FIRST_REPLICA , )
return self._accum_steps.value()
@property
def lowercase_ ( self ) -> List[str]:
if not self._gradients:
raise ValueError('The accumulator should be called first to initialize the gradients' )
return [gradient.value() if gradient is not None else gradient for gradient in self._gradients]
def __call__( self , SCREAMING_SNAKE_CASE_ ) -> str:
if not self._gradients:
__lowerCamelCase : List[str] = self.step # Create the step variable.
self._gradients.extend(
[
tf.Variable(
tf.zeros_like(SCREAMING_SNAKE_CASE_ ) , trainable=SCREAMING_SNAKE_CASE_ , synchronization=tf.VariableSynchronization.ON_READ , aggregation=tf.VariableAggregation.ONLY_FIRST_REPLICA , )
if gradient is not None
else gradient
for gradient in gradients
] )
if len(SCREAMING_SNAKE_CASE_ ) != len(self._gradients ):
raise ValueError(f'Expected {len(self._gradients )} gradients, but got {len(SCREAMING_SNAKE_CASE_ )}' )
for accum_gradient, gradient in zip(self._gradients , SCREAMING_SNAKE_CASE_ ):
if accum_gradient is not None and gradient is not None:
accum_gradient.assign_add(SCREAMING_SNAKE_CASE_ )
self._accum_steps.assign_add(1 )
def lowercase_ ( self ) -> int:
if not self._gradients:
return
self._accum_steps.assign(0 )
for gradient in self._gradients:
if gradient is not None:
gradient.assign(tf.zeros_like(SCREAMING_SNAKE_CASE_ ) )
| 13
| 1
|
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
convert_to_rgb,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_dimension_format,
)
from ...image_utils import (
OPENAI_CLIP_MEAN,
OPENAI_CLIP_STD,
ChannelDimension,
ImageInput,
PILImageResampling,
make_list_of_images,
to_numpy_array,
valid_images,
)
from ...utils import TensorType, is_vision_available, logging
lowerCAmelCase__ = logging.get_logger(__name__)
if is_vision_available():
import PIL
class a__ ( snake_case ):
"""simple docstring"""
__lowerCamelCase = ['pixel_values']
def __init__( self , lowercase = True , lowercase = None , lowercase = PILImageResampling.BICUBIC , lowercase = True , lowercase = None , lowercase = True , lowercase = 1 / 255 , lowercase = True , lowercase = None , lowercase = None , lowercase = True , **lowercase , ) -> None:
'''simple docstring'''
super().__init__(**lowercase )
A__ = size if size is not None else {"shortest_edge": 224}
A__ = get_size_dict(lowercase , default_to_square=lowercase )
A__ = crop_size if crop_size is not None else {"height": 224, "width": 224}
A__ = get_size_dict(lowercase , default_to_square=lowercase , param_name="crop_size" )
A__ = do_resize
A__ = size
A__ = resample
A__ = do_center_crop
A__ = crop_size
A__ = do_rescale
A__ = rescale_factor
A__ = do_normalize
A__ = image_mean if image_mean is not None else OPENAI_CLIP_MEAN
A__ = image_std if image_std is not None else OPENAI_CLIP_STD
A__ = do_convert_rgb
def UpperCamelCase ( self , lowercase , lowercase , lowercase = PILImageResampling.BICUBIC , lowercase = None , **lowercase , ) -> np.ndarray:
'''simple docstring'''
A__ = get_size_dict(lowercase , default_to_square=lowercase )
if "shortest_edge" not in size:
raise ValueError(F'The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}' )
A__ = get_resize_output_image_size(lowercase , size=size["shortest_edge"] , default_to_square=lowercase )
return resize(lowercase , size=lowercase , resample=lowercase , data_format=lowercase , **lowercase )
def UpperCamelCase ( self , lowercase , lowercase , lowercase = None , **lowercase , ) -> np.ndarray:
'''simple docstring'''
A__ = get_size_dict(lowercase )
if "height" not in size or "width" not in size:
raise ValueError(F'The `size` parameter must contain the keys (height, width). Got {size.keys()}' )
return center_crop(lowercase , size=(size["height"], size["width"]) , data_format=lowercase , **lowercase )
def UpperCamelCase ( self , lowercase , lowercase , lowercase = None , **lowercase , ) -> List[str]:
'''simple docstring'''
return rescale(lowercase , scale=lowercase , data_format=lowercase , **lowercase )
def UpperCamelCase ( self , lowercase , lowercase , lowercase , lowercase = None , **lowercase , ) -> np.ndarray:
'''simple docstring'''
return normalize(lowercase , mean=lowercase , std=lowercase , data_format=lowercase , **lowercase )
def UpperCamelCase ( self , lowercase , lowercase = None , lowercase = None , lowercase = None , lowercase = None , lowercase = None , lowercase = None , lowercase = None , lowercase = None , lowercase = None , lowercase = None , lowercase = None , lowercase = None , lowercase = ChannelDimension.FIRST , **lowercase , ) -> PIL.Image.Image:
'''simple docstring'''
A__ = do_resize if do_resize is not None else self.do_resize
A__ = size if size is not None else self.size
A__ = get_size_dict(lowercase , param_name="size" , default_to_square=lowercase )
A__ = resample if resample is not None else self.resample
A__ = do_center_crop if do_center_crop is not None else self.do_center_crop
A__ = crop_size if crop_size is not None else self.crop_size
A__ = get_size_dict(lowercase , param_name="crop_size" , default_to_square=lowercase )
A__ = do_rescale if do_rescale is not None else self.do_rescale
A__ = rescale_factor if rescale_factor is not None else self.rescale_factor
A__ = do_normalize if do_normalize is not None else self.do_normalize
A__ = image_mean if image_mean is not None else self.image_mean
A__ = image_std if image_std is not None else self.image_std
A__ = do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb
A__ = make_list_of_images(lowercase )
if not valid_images(lowercase ):
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." )
# PIL RGBA images are converted to RGB
if do_convert_rgb:
A__ = [convert_to_rgb(lowercase ) for image in images]
# All transformations expect numpy arrays.
A__ = [to_numpy_array(lowercase ) for image in images]
if do_resize:
A__ = [self.resize(image=lowercase , size=lowercase , resample=lowercase ) for image in images]
if do_center_crop:
A__ = [self.center_crop(image=lowercase , size=lowercase ) for image in images]
if do_rescale:
A__ = [self.rescale(image=lowercase , scale=lowercase ) for image in images]
if do_normalize:
A__ = [self.normalize(image=lowercase , mean=lowercase , std=lowercase ) for image in images]
A__ = [to_channel_dimension_format(lowercase , lowercase ) for image in images]
A__ = {"pixel_values": images}
return BatchFeature(data=lowercase , tensor_type=lowercase )
| 626
|
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""")
lowerCAmelCase__ = logging.getLogger(__name__)
@dataclass
class a__ :
"""simple docstring"""
__lowerCamelCase = field(
metadata={'help': 'Path to pretrained model or model identifier from huggingface.co/models'} )
__lowerCamelCase = field(
default=snake_case , metadata={'help': 'Pretrained config name or path if not the same as model_name'} )
__lowerCamelCase = field(
default=snake_case , metadata={'help': 'Pretrained tokenizer name or path if not the same as model_name'} )
__lowerCamelCase = field(
default=snake_case , metadata={'help': 'Where do you want to store the pretrained models downloaded from huggingface.co'} , )
__lowerCamelCase = field(
default=snake_case , metadata={'help': 'Whether to use one of the fast tokenizer (backed by the tokenizers library) or not.'} , )
__lowerCamelCase = field(
default='main' , metadata={'help': 'The specific model version to use (can be a branch name, tag name or commit id).'} , )
__lowerCamelCase = field(
default=snake_case , metadata={
'help': (
'Will use the token generated when running `huggingface-cli login` (necessary to use this script '
'with private models).'
)
} , )
@dataclass
class a__ :
"""simple docstring"""
__lowerCamelCase = field(default=snake_case , metadata={'help': 'The input training data file (a text file).'} )
__lowerCamelCase = field(
default=snake_case , metadata={'help': 'An optional input evaluation data file to evaluate the perplexity on (a text file).'} , )
__lowerCamelCase = field(
default=snake_case , metadata={'help': 'Overwrite the cached training and evaluation sets'} )
__lowerCamelCase = field(
default=snake_case , metadata={'help': 'The number of processes to use for the preprocessing.'} , )
__lowerCamelCase = field(
default=snake_case , metadata={
'help': (
'The maximum total input sequence length after tokenization. If passed, sequences longer '
'than this will be truncated, sequences shorter will be padded.'
)
} , )
__lowerCamelCase = field(
default=snake_case , 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.'
)
} , )
__lowerCamelCase = field(
default=snake_case , metadata={
'help': (
'For debugging purposes or quicker training, truncate the number of training examples to this '
'value if set.'
)
} , )
__lowerCamelCase = field(
default=snake_case , metadata={
'help': (
'For debugging purposes or quicker training, truncate the number of evaluation examples to this '
'value if set.'
)
} , )
def UpperCamelCase ( self ) -> Optional[Any]:
'''simple docstring'''
if self.train_file is not None:
A__ = 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:
A__ = self.validation_file.split("." )[-1]
assert extension in ["csv", "json"], "`validation_file` should be a csv or a json file."
@dataclass
class a__ :
"""simple docstring"""
__lowerCamelCase = 42
__lowerCamelCase = True
__lowerCamelCase = None
__lowerCamelCase = None
def __call__( self , lowercase ) -> Tuple:
'''simple docstring'''
A__ = "label" if "label" in features[0].keys() else "labels"
A__ = [feature.pop(lowercase ) for feature in features]
A__ = len(lowercase )
A__ = len(features[0]["input_ids"] )
A__ = [
[{k: v[i] for k, v in feature.items()} for i in range(lowercase )] for feature in features
]
A__ = list(chain(*lowercase ) )
A__ = self.tokenizer.pad(
lowercase , padding=self.padding , max_length=self.max_length , pad_to_multiple_of=self.pad_to_multiple_of , return_tensors="pt" , )
# Un-flatten
A__ = {k: v.view(lowercase , lowercase , -1 ) for k, v in batch.items()}
# Add back labels
A__ = torch.tensor(lowercase , dtype=torch.intaa )
return batch
def lowerCAmelCase__ ( ) -> List[Any]:
'''simple docstring'''
A__ = 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.
A__ , A__ , A__ = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1] ) )
else:
A__ , A__ , A__ = parser.parse_args_into_dataclasses()
# Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The
# information sent is the one passed as arguments along with your Python/PyTorch versions.
send_example_telemetry("run_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()
A__ = 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.
A__ = None
if os.path.isdir(training_args.output_dir ) and training_args.do_train and not training_args.overwrite_output_dir:
A__ = get_last_checkpoint(training_args.output_dir )
if last_checkpoint is None and len(os.listdir(training_args.output_dir ) ) > 0:
raise ValueError(
F'Output directory ({training_args.output_dir}) already exists and is not empty. '
"Use --overwrite_output_dir to overcome." )
elif last_checkpoint is not None and training_args.resume_from_checkpoint is None:
logger.info(
F'Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change '
"the `--output_dir` or add `--overwrite_output_dir` to train from scratch." )
# 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:
A__ = {}
if data_args.train_file is not None:
A__ = data_args.train_file
if data_args.validation_file is not None:
A__ = data_args.validation_file
A__ = data_args.train_file.split("." )[-1]
A__ = 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.
A__ = 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.
A__ = 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 , )
A__ = 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 , )
A__ = 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.
A__ = [F'ending{i}' for i in range(4 )]
A__ = "sent1"
A__ = "sent2"
if data_args.max_seq_length is None:
A__ = tokenizer.model_max_length
if max_seq_length > 1_0_2_4:
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`." )
A__ = 1_0_2_4
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}.' )
A__ = min(data_args.max_seq_length , tokenizer.model_max_length )
# Preprocessing the datasets.
def preprocess_function(SCREAMING_SNAKE_CASE_: Optional[Any] ):
A__ = [[context] * 4 for context in examples[context_name]]
A__ = examples[question_header_name]
A__ = [
[F'{header} {examples[end][i]}' for end in ending_names] for i, header in enumerate(SCREAMING_SNAKE_CASE_ )
]
# Flatten out
A__ = list(chain(*SCREAMING_SNAKE_CASE_ ) )
A__ = list(chain(*SCREAMING_SNAKE_CASE_ ) )
# Tokenize
A__ = 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" )
A__ = raw_datasets["train"]
if data_args.max_train_samples is not None:
A__ = min(len(SCREAMING_SNAKE_CASE_ ) , data_args.max_train_samples )
A__ = train_dataset.select(range(SCREAMING_SNAKE_CASE_ ) )
with training_args.main_process_first(desc="train dataset map pre-processing" ):
A__ = 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" )
A__ = raw_datasets["validation"]
if data_args.max_eval_samples is not None:
A__ = min(len(SCREAMING_SNAKE_CASE_ ) , data_args.max_eval_samples )
A__ = eval_dataset.select(range(SCREAMING_SNAKE_CASE_ ) )
with training_args.main_process_first(desc="validation dataset map pre-processing" ):
A__ = 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
A__ = (
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(SCREAMING_SNAKE_CASE_: str ):
A__ , A__ = eval_predictions
A__ = np.argmax(SCREAMING_SNAKE_CASE_ , axis=1 )
return {"accuracy": (preds == label_ids).astype(np.floataa ).mean().item()}
# Initialize our Trainer
A__ = 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:
A__ = None
if training_args.resume_from_checkpoint is not None:
A__ = training_args.resume_from_checkpoint
elif last_checkpoint is not None:
A__ = last_checkpoint
A__ = trainer.train(resume_from_checkpoint=SCREAMING_SNAKE_CASE_ )
trainer.save_model() # Saves the tokenizer too for easy upload
A__ = train_result.metrics
A__ = (
data_args.max_train_samples if data_args.max_train_samples is not None else len(SCREAMING_SNAKE_CASE_ )
)
A__ = 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 ***" )
A__ = trainer.evaluate()
A__ = data_args.max_eval_samples if data_args.max_eval_samples is not None else len(SCREAMING_SNAKE_CASE_ )
A__ = min(SCREAMING_SNAKE_CASE_ , len(SCREAMING_SNAKE_CASE_ ) )
trainer.log_metrics("eval" , SCREAMING_SNAKE_CASE_ )
trainer.save_metrics("eval" , SCREAMING_SNAKE_CASE_ )
A__ = {
"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 lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_: Optional[int] ) -> Dict:
'''simple docstring'''
main()
if __name__ == "__main__":
main()
| 626
| 1
|
'''simple docstring'''
from __future__ import annotations
import queue
class UpperCAmelCase__ :
def __init__( self , lowercase ) -> Optional[int]:
__UpperCamelCase = data
__UpperCamelCase = None
__UpperCamelCase = None
def _lowercase ( ):
'''simple docstring'''
print("""\n********Press N to stop entering at any point of time********\n""" )
__UpperCamelCase = input("""Enter the value of the root node: """ ).strip().lower()
__UpperCamelCase = queue.Queue()
__UpperCamelCase = TreeNode(int(__A ) )
q.put(__A )
while not q.empty():
__UpperCamelCase = q.get()
__UpperCamelCase = f"Enter the left node of {node_found.data}: "
__UpperCamelCase = input(__A ).strip().lower() or """n"""
if check == "n":
return tree_node
__UpperCamelCase = TreeNode(int(__A ) )
__UpperCamelCase = left_node
q.put(__A )
__UpperCamelCase = f"Enter the right node of {node_found.data}: "
__UpperCamelCase = input(__A ).strip().lower() or """n"""
if check == "n":
return tree_node
__UpperCamelCase = TreeNode(int(__A ) )
__UpperCamelCase = right_node
q.put(__A )
raise
def _lowercase ( __A ):
'''simple docstring'''
if not isinstance(__A ,__A ) or not node:
return
print(node.data ,end=""",""" )
pre_order(node.left )
pre_order(node.right )
def _lowercase ( __A ):
'''simple docstring'''
if not isinstance(__A ,__A ) or not node:
return
in_order(node.left )
print(node.data ,end=""",""" )
in_order(node.right )
def _lowercase ( __A ):
'''simple docstring'''
if not isinstance(__A ,__A ) or not node:
return
post_order(node.left )
post_order(node.right )
print(node.data ,end=""",""" )
def _lowercase ( __A ):
'''simple docstring'''
if not isinstance(__A ,__A ) or not node:
return
__UpperCamelCase = queue.Queue()
q.put(__A )
while not q.empty():
__UpperCamelCase = q.get()
print(node_dequeued.data ,end=""",""" )
if node_dequeued.left:
q.put(node_dequeued.left )
if node_dequeued.right:
q.put(node_dequeued.right )
def _lowercase ( __A ):
'''simple docstring'''
if not isinstance(__A ,__A ) or not node:
return
__UpperCamelCase = queue.Queue()
q.put(__A )
while not q.empty():
__UpperCamelCase = []
while not q.empty():
__UpperCamelCase = q.get()
print(node_dequeued.data ,end=""",""" )
if node_dequeued.left:
list_.append(node_dequeued.left )
if node_dequeued.right:
list_.append(node_dequeued.right )
print()
for node in list_:
q.put(__A )
def _lowercase ( __A ):
'''simple docstring'''
if not isinstance(__A ,__A ) or not node:
return
__UpperCamelCase = []
__UpperCamelCase = node
while n or stack:
while n: # start from root node, find its left child
print(n.data ,end=""",""" )
stack.append(__A )
__UpperCamelCase = n.left
# end of while means current node doesn't have left child
__UpperCamelCase = stack.pop()
# start to traverse its right child
__UpperCamelCase = n.right
def _lowercase ( __A ):
'''simple docstring'''
if not isinstance(__A ,__A ) or not node:
return
__UpperCamelCase = []
__UpperCamelCase = node
while n or stack:
while n:
stack.append(__A )
__UpperCamelCase = n.left
__UpperCamelCase = stack.pop()
print(n.data ,end=""",""" )
__UpperCamelCase = n.right
def _lowercase ( __A ):
'''simple docstring'''
if not isinstance(__A ,__A ) or not node:
return
__UpperCamelCase , __UpperCamelCase = [], []
__UpperCamelCase = node
stacka.append(__A )
while stacka: # to find the reversed order of post order, store it in stack2
__UpperCamelCase = stacka.pop()
if n.left:
stacka.append(n.left )
if n.right:
stacka.append(n.right )
stacka.append(__A )
while stacka: # pop up from stack2 will be the post order
print(stacka.pop().data ,end=""",""" )
def _lowercase ( __A = "" ,__A=50 ,__A="*" ):
'''simple docstring'''
if not s:
return "\n" + width * char
__UpperCamelCase , __UpperCamelCase = divmod(width - len(__A ) - 2 ,2 )
return f"{left * char} {s} {(left + extra) * char}"
if __name__ == "__main__":
import doctest
doctest.testmod()
print(prompt('Binary Tree Traversals'))
a__ : TreeNode = build_tree()
print(prompt('Pre Order Traversal'))
pre_order(node)
print(prompt() + '\n')
print(prompt('In Order Traversal'))
in_order(node)
print(prompt() + '\n')
print(prompt('Post Order Traversal'))
post_order(node)
print(prompt() + '\n')
print(prompt('Level Order Traversal'))
level_order(node)
print(prompt() + '\n')
print(prompt('Actual Level Order Traversal'))
level_order_actual(node)
print('*' * 5_0 + '\n')
print(prompt('Pre Order Traversal - Iteration Version'))
pre_order_iter(node)
print(prompt() + '\n')
print(prompt('In Order Traversal - Iteration Version'))
in_order_iter(node)
print(prompt() + '\n')
print(prompt('Post Order Traversal - Iteration Version'))
post_order_iter(node)
print(prompt())
| 601
|
'''simple docstring'''
def _lowercase ( ):
'''simple docstring'''
__UpperCamelCase = []
__UpperCamelCase = 1
while len(__A ) < 1E6:
constant.append(str(__A ) )
i += 1
__UpperCamelCase = """""".join(__A )
return (
int(constant[0] )
* int(constant[9] )
* int(constant[99] )
* int(constant[999] )
* int(constant[9_999] )
* int(constant[99_999] )
* int(constant[999_999] )
)
if __name__ == "__main__":
print(solution())
| 601
| 1
|
from ...configuration_utils import PretrainedConfig
_UpperCAmelCase = {
"""google/tapas-base-finetuned-sqa""": (
"""https://huggingface.co/google/tapas-base-finetuned-sqa/resolve/main/config.json"""
),
"""google/tapas-base-finetuned-wtq""": (
"""https://huggingface.co/google/tapas-base-finetuned-wtq/resolve/main/config.json"""
),
"""google/tapas-base-finetuned-wikisql-supervised""": (
"""https://huggingface.co/google/tapas-base-finetuned-wikisql-supervised/resolve/main/config.json"""
),
"""google/tapas-base-finetuned-tabfact""": (
"""https://huggingface.co/google/tapas-base-finetuned-tabfact/resolve/main/config.json"""
),
}
class UpperCAmelCase ( __A ):
'''simple docstring'''
lowerCamelCase_ = '''tapas'''
def __init__( self , lowercase=3_0_5_2_2 , lowercase=7_6_8 , lowercase=1_2 , lowercase=1_2 , lowercase=3_0_7_2 , lowercase="gelu" , lowercase=0.1 , lowercase=0.1 , lowercase=1_0_2_4 , lowercase=[3, 2_5_6, 2_5_6, 2, 2_5_6, 2_5_6, 1_0] , lowercase=0.02 , lowercase=1E-12 , lowercase=0 , lowercase=10.0 , lowercase=0 , lowercase=1.0 , lowercase=None , lowercase=1.0 , lowercase=False , lowercase=None , lowercase=1.0 , lowercase=1.0 , lowercase=False , lowercase=False , lowercase="ratio" , lowercase=None , lowercase=None , lowercase=6_4 , lowercase=3_2 , lowercase=False , lowercase=True , lowercase=False , lowercase=False , lowercase=True , lowercase=False , lowercase=None , lowercase=None , **lowercase , ):
"""simple docstring"""
super().__init__(pad_token_id=lowercase , **lowercase )
# BERT hyperparameters (with updated max_position_embeddings and type_vocab_sizes)
A_ : str = vocab_size
A_ : str = hidden_size
A_ : Optional[Any] = num_hidden_layers
A_ : Any = num_attention_heads
A_ : List[str] = hidden_act
A_ : Tuple = intermediate_size
A_ : Optional[int] = hidden_dropout_prob
A_ : Optional[int] = attention_probs_dropout_prob
A_ : Tuple = max_position_embeddings
A_ : int = type_vocab_sizes
A_ : Optional[Any] = initializer_range
A_ : Any = layer_norm_eps
# Fine-tuning task hyperparameters
A_ : int = positive_label_weight
A_ : int = num_aggregation_labels
A_ : Optional[int] = aggregation_loss_weight
A_ : List[str] = use_answer_as_supervision
A_ : List[str] = answer_loss_importance
A_ : List[Any] = use_normalized_answer_loss
A_ : Dict = huber_loss_delta
A_ : List[str] = temperature
A_ : List[str] = aggregation_temperature
A_ : int = use_gumbel_for_cells
A_ : Tuple = use_gumbel_for_aggregation
A_ : int = average_approximation_function
A_ : List[str] = cell_selection_preference
A_ : Tuple = answer_loss_cutoff
A_ : str = max_num_rows
A_ : Optional[int] = max_num_columns
A_ : Tuple = average_logits_per_cell
A_ : Optional[int] = select_one_column
A_ : Tuple = allow_empty_column_selection
A_ : int = init_cell_selection_weights_to_zero
A_ : Optional[Any] = reset_position_index_per_cell
A_ : Any = disable_per_token_loss
# Aggregation hyperparameters
A_ : Dict = aggregation_labels
A_ : Union[str, Any] = no_aggregation_label_index
if isinstance(self.aggregation_labels , lowercase ):
A_ : List[str] = {int(lowercase ): v for k, v in aggregation_labels.items()}
| 70
|
def UpperCamelCase ( __lowercase : str ):
'''simple docstring'''
A_ : int = len(__lowercase )
A_ : List[Any] = sum(__lowercase )
A_ : List[str] = [[False for x in range(s + 1 )] for y in range(n + 1 )]
for i in range(1 ,n + 1 ):
A_ : Optional[Any] = True
for i in range(1 ,s + 1 ):
A_ : Tuple = False
for i in range(1 ,n + 1 ):
for j in range(1 ,s + 1 ):
A_ : Dict = dp[i][j - 1]
if arr[i - 1] <= j:
A_ : Dict = dp[i][j] or dp[i - 1][j - arr[i - 1]]
for j in range(int(s / 2 ) ,-1 ,-1 ):
if dp[n][j] is True:
A_ : List[Any] = s - 2 * j
break
return diff
| 70
| 1
|
import collections
import inspect
import unittest
from typing import Dict, List, Tuple
from transformers import MaskFormerSwinConfig
from transformers.testing_utils import require_torch, require_torch_multi_gpu, torch_device
from transformers.utils import is_torch_available
from ...test_backbone_common import BackboneTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from torch import nn
from transformers import MaskFormerSwinBackbone
from transformers.models.maskformer import MaskFormerSwinModel
class lowercase :
def __init__( self : Optional[int] , _lowercase : str , _lowercase : Tuple=13 , _lowercase : Union[str, Any]=32 , _lowercase : str=2 , _lowercase : Dict=3 , _lowercase : str=16 , _lowercase : str=[1, 2, 1] , _lowercase : str=[2, 2, 4] , _lowercase : str=2 , _lowercase : List[str]=2.0 , _lowercase : Optional[int]=True , _lowercase : Union[str, Any]=0.0 , _lowercase : List[str]=0.0 , _lowercase : Optional[int]=0.1 , _lowercase : Optional[int]="gelu" , _lowercase : Union[str, Any]=False , _lowercase : Tuple=True , _lowercase : Any=0.02 , _lowercase : Optional[Any]=1E-5 , _lowercase : Optional[int]=True , _lowercase : str=None , _lowercase : Optional[Any]=True , _lowercase : List[str]=10 , _lowercase : Optional[Any]=8 , _lowercase : Any=["stage1", "stage2", "stage3"] , _lowercase : str=[1, 2, 3] , ):
SCREAMING_SNAKE_CASE__ : List[str] = parent
SCREAMING_SNAKE_CASE__ : Optional[int] = batch_size
SCREAMING_SNAKE_CASE__ : Optional[Any] = image_size
SCREAMING_SNAKE_CASE__ : Optional[Any] = patch_size
SCREAMING_SNAKE_CASE__ : int = num_channels
SCREAMING_SNAKE_CASE__ : List[str] = embed_dim
SCREAMING_SNAKE_CASE__ : Any = depths
SCREAMING_SNAKE_CASE__ : int = num_heads
SCREAMING_SNAKE_CASE__ : str = window_size
SCREAMING_SNAKE_CASE__ : Optional[int] = mlp_ratio
SCREAMING_SNAKE_CASE__ : List[Any] = qkv_bias
SCREAMING_SNAKE_CASE__ : Optional[int] = hidden_dropout_prob
SCREAMING_SNAKE_CASE__ : int = attention_probs_dropout_prob
SCREAMING_SNAKE_CASE__ : Optional[int] = drop_path_rate
SCREAMING_SNAKE_CASE__ : int = hidden_act
SCREAMING_SNAKE_CASE__ : int = use_absolute_embeddings
SCREAMING_SNAKE_CASE__ : int = patch_norm
SCREAMING_SNAKE_CASE__ : Optional[int] = layer_norm_eps
SCREAMING_SNAKE_CASE__ : Union[str, Any] = initializer_range
SCREAMING_SNAKE_CASE__ : str = is_training
SCREAMING_SNAKE_CASE__ : Optional[int] = scope
SCREAMING_SNAKE_CASE__ : Union[str, Any] = use_labels
SCREAMING_SNAKE_CASE__ : Optional[Any] = type_sequence_label_size
SCREAMING_SNAKE_CASE__ : Union[str, Any] = encoder_stride
SCREAMING_SNAKE_CASE__ : Any = out_features
SCREAMING_SNAKE_CASE__ : List[Any] = out_indices
def lowercase__ ( self : List[Any] ):
SCREAMING_SNAKE_CASE__ : Union[str, Any] = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
SCREAMING_SNAKE_CASE__ : List[Any] = None
if self.use_labels:
SCREAMING_SNAKE_CASE__ : Dict = ids_tensor([self.batch_size] , self.type_sequence_label_size )
SCREAMING_SNAKE_CASE__ : Dict = self.get_config()
return config, pixel_values, labels
def lowercase__ ( self : Union[str, Any] ):
return MaskFormerSwinConfig(
image_size=self.image_size , patch_size=self.patch_size , num_channels=self.num_channels , embed_dim=self.embed_dim , depths=self.depths , num_heads=self.num_heads , window_size=self.window_size , mlp_ratio=self.mlp_ratio , qkv_bias=self.qkv_bias , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , drop_path_rate=self.drop_path_rate , hidden_act=self.hidden_act , use_absolute_embeddings=self.use_absolute_embeddings , path_norm=self.patch_norm , layer_norm_eps=self.layer_norm_eps , initializer_range=self.initializer_range , encoder_stride=self.encoder_stride , out_features=self.out_features , out_indices=self.out_indices , )
def lowercase__ ( self : Any , _lowercase : Optional[Any] , _lowercase : Dict , _lowercase : Tuple ):
SCREAMING_SNAKE_CASE__ : List[Any] = MaskFormerSwinModel(config=_lowercase )
model.to(_lowercase )
model.eval()
SCREAMING_SNAKE_CASE__ : Union[str, Any] = model(_lowercase )
SCREAMING_SNAKE_CASE__ : Optional[int] = ((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths ) - 1))
SCREAMING_SNAKE_CASE__ : Any = int(config.embed_dim * 2 ** (len(config.depths ) - 1) )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, expected_seq_len, expected_dim) )
def lowercase__ ( self : Tuple , _lowercase : int , _lowercase : Any , _lowercase : int ):
SCREAMING_SNAKE_CASE__ : Any = MaskFormerSwinBackbone(config=_lowercase )
model.to(_lowercase )
model.eval()
SCREAMING_SNAKE_CASE__ : Optional[int] = model(_lowercase )
# verify feature maps
self.parent.assertEqual(len(result.feature_maps ) , len(config.out_features ) )
self.parent.assertListEqual(list(result.feature_maps[0].shape ) , [13, 16, 16, 16] )
# verify channels
self.parent.assertEqual(len(model.channels ) , len(config.out_features ) )
self.parent.assertListEqual(model.channels , [16, 32, 64] )
# verify ValueError
with self.parent.assertRaises(_lowercase ):
SCREAMING_SNAKE_CASE__ : str = ['''stem''']
SCREAMING_SNAKE_CASE__ : str = MaskFormerSwinBackbone(config=_lowercase )
def lowercase__ ( self : Dict ):
SCREAMING_SNAKE_CASE__ : List[str] = self.prepare_config_and_inputs()
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : int = config_and_inputs
SCREAMING_SNAKE_CASE__ : Tuple = {'''pixel_values''': pixel_values}
return config, inputs_dict
@require_torch
class lowercase ( _UpperCAmelCase , _UpperCAmelCase , unittest.TestCase ):
lowerCamelCase : List[str] = (
(
MaskFormerSwinModel,
MaskFormerSwinBackbone,
)
if is_torch_available()
else ()
)
lowerCamelCase : str = {'''feature-extraction''': MaskFormerSwinModel} if is_torch_available() else {}
lowerCamelCase : str = False
lowerCamelCase : List[Any] = False
lowerCamelCase : Any = False
lowerCamelCase : str = False
lowerCamelCase : List[str] = False
def lowercase__ ( self : Optional[int] ):
SCREAMING_SNAKE_CASE__ : Dict = MaskFormerSwinModelTester(self )
SCREAMING_SNAKE_CASE__ : Optional[int] = ConfigTester(self , config_class=_lowercase , embed_dim=37 )
@require_torch_multi_gpu
@unittest.skip(
reason=(
'''`MaskFormerSwinModel` outputs `hidden_states_spatial_dimensions` which doesn\'t work well with'''
''' `nn.DataParallel`'''
) )
def lowercase__ ( self : str ):
pass
def lowercase__ ( self : List[str] ):
self.create_and_test_config_common_properties()
self.config_tester.create_and_test_config_to_json_string()
self.config_tester.create_and_test_config_to_json_file()
self.config_tester.create_and_test_config_from_and_save_pretrained()
self.config_tester.create_and_test_config_with_num_labels()
self.config_tester.check_config_can_be_init_without_params()
self.config_tester.check_config_arguments_init()
def lowercase__ ( self : Optional[Any] ):
return
def lowercase__ ( self : Optional[int] ):
SCREAMING_SNAKE_CASE__ : Dict = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*_lowercase )
def lowercase__ ( self : Optional[Any] ):
SCREAMING_SNAKE_CASE__ : Optional[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_backbone(*_lowercase )
@unittest.skip('''Swin does not use inputs_embeds''' )
def lowercase__ ( self : Dict ):
pass
@unittest.skip('''Swin does not support feedforward chunking''' )
def lowercase__ ( self : Tuple ):
pass
def lowercase__ ( self : List[str] ):
SCREAMING_SNAKE_CASE__ , 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__ : Union[str, Any] = model_class(_lowercase )
self.assertIsInstance(model.get_input_embeddings() , (nn.Module) )
SCREAMING_SNAKE_CASE__ : Dict = model.get_output_embeddings()
self.assertTrue(x is None or isinstance(_lowercase , nn.Linear ) )
def lowercase__ ( self : List[Any] ):
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : Optional[int] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
SCREAMING_SNAKE_CASE__ : str = model_class(_lowercase )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
SCREAMING_SNAKE_CASE__ : Any = [*signature.parameters.keys()]
SCREAMING_SNAKE_CASE__ : List[Any] = ['''pixel_values''']
self.assertListEqual(arg_names[:1] , _lowercase )
@unittest.skip(reason='''MaskFormerSwin is only used as backbone and doesn\'t support output_attentions''' )
def lowercase__ ( self : Union[str, Any] ):
pass
@unittest.skip(reason='''MaskFormerSwin is only used as an internal backbone''' )
def lowercase__ ( self : List[str] ):
pass
def lowercase__ ( self : List[Any] , _lowercase : int , _lowercase : Optional[Any] , _lowercase : List[Any] , _lowercase : Tuple ):
SCREAMING_SNAKE_CASE__ : Optional[Any] = model_class(_lowercase )
model.to(_lowercase )
model.eval()
with torch.no_grad():
SCREAMING_SNAKE_CASE__ : List[Any] = model(**self._prepare_for_class(_lowercase , _lowercase ) )
SCREAMING_SNAKE_CASE__ : Dict = outputs.hidden_states
SCREAMING_SNAKE_CASE__ : List[Any] = getattr(
self.model_tester , '''expected_num_hidden_layers''' , len(self.model_tester.depths ) + 1 )
self.assertEqual(len(_lowercase ) , _lowercase )
# Swin has a different seq_length
SCREAMING_SNAKE_CASE__ : Any = (
config.patch_size
if isinstance(config.patch_size , collections.abc.Iterable )
else (config.patch_size, config.patch_size)
)
SCREAMING_SNAKE_CASE__ : int = (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
self.assertListEqual(
list(hidden_states[0].shape[-2:] ) , [num_patches, self.model_tester.embed_dim] , )
def lowercase__ ( self : Optional[Any] ):
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : str = self.model_tester.prepare_config_and_inputs_for_common()
SCREAMING_SNAKE_CASE__ : str = (
self.model_tester.image_size
if isinstance(self.model_tester.image_size , collections.abc.Iterable )
else (self.model_tester.image_size, self.model_tester.image_size)
)
for model_class in self.all_model_classes:
SCREAMING_SNAKE_CASE__ : int = True
self.check_hidden_states_output(_lowercase , _lowercase , _lowercase , _lowercase )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
SCREAMING_SNAKE_CASE__ : Dict = True
self.check_hidden_states_output(_lowercase , _lowercase , _lowercase , _lowercase )
def lowercase__ ( self : Union[str, Any] ):
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : Any = self.model_tester.prepare_config_and_inputs_for_common()
SCREAMING_SNAKE_CASE__ : Dict = 3
SCREAMING_SNAKE_CASE__ : Tuple = (
self.model_tester.image_size
if isinstance(self.model_tester.image_size , collections.abc.Iterable )
else (self.model_tester.image_size, self.model_tester.image_size)
)
SCREAMING_SNAKE_CASE__ : List[str] = (
config.patch_size
if isinstance(config.patch_size , collections.abc.Iterable )
else (config.patch_size, config.patch_size)
)
SCREAMING_SNAKE_CASE__ : int = image_size[0] + patch_size[0] - (image_size[0] % patch_size[0])
SCREAMING_SNAKE_CASE__ : Any = image_size[1] + patch_size[1] - (image_size[1] % patch_size[1])
for model_class in self.all_model_classes:
SCREAMING_SNAKE_CASE__ : List[Any] = True
self.check_hidden_states_output(_lowercase , _lowercase , _lowercase , (padded_height, padded_width) )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
SCREAMING_SNAKE_CASE__ : Optional[Any] = True
self.check_hidden_states_output(_lowercase , _lowercase , _lowercase , (padded_height, padded_width) )
@unittest.skip(reason='''MaskFormerSwin doesn\'t have pretrained checkpoints''' )
def lowercase__ ( self : Any ):
pass
@unittest.skip(reason='''This will be fixed once MaskFormerSwin is replaced by native Swin''' )
def lowercase__ ( self : List[Any] ):
pass
@unittest.skip(reason='''This will be fixed once MaskFormerSwin is replaced by native Swin''' )
def lowercase__ ( self : Optional[Any] ):
pass
def lowercase__ ( self : Optional[int] ):
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : List[str] = self.model_tester.prepare_config_and_inputs_for_common()
def set_nan_tensor_to_zero(_lowercase : Tuple ):
SCREAMING_SNAKE_CASE__ : Tuple = 0
return t
def check_equivalence(_lowercase : int , _lowercase : int , _lowercase : Dict , _lowercase : Optional[int]={} ):
with torch.no_grad():
SCREAMING_SNAKE_CASE__ : List[str] = model(**_lowercase , return_dict=_lowercase , **_lowercase )
SCREAMING_SNAKE_CASE__ : List[str] = model(**_lowercase , return_dict=_lowercase , **_lowercase ).to_tuple()
def recursive_check(_lowercase : List[Any] , _lowercase : int ):
if isinstance(_lowercase , (List, Tuple) ):
for tuple_iterable_value, dict_iterable_value in zip(_lowercase , _lowercase ):
recursive_check(_lowercase , _lowercase )
elif isinstance(_lowercase , _lowercase ):
for tuple_iterable_value, dict_iterable_value in zip(
tuple_object.values() , dict_object.values() ):
recursive_check(_lowercase , _lowercase )
elif tuple_object is None:
return
else:
self.assertTrue(
torch.allclose(
set_nan_tensor_to_zero(_lowercase ) , set_nan_tensor_to_zero(_lowercase ) , atol=1E-5 ) , msg=(
'''Tuple and dict output are not equal. Difference:'''
f""" {torch.max(torch.abs(tuple_object - dict_object ) )}. Tuple has `nan`:"""
f""" {torch.isnan(_lowercase ).any()} and `inf`: {torch.isinf(_lowercase )}. Dict has"""
f""" `nan`: {torch.isnan(_lowercase ).any()} and `inf`: {torch.isinf(_lowercase )}."""
) , )
recursive_check(_lowercase , _lowercase )
for model_class in self.all_model_classes:
SCREAMING_SNAKE_CASE__ : int = model_class(_lowercase )
model.to(_lowercase )
model.eval()
SCREAMING_SNAKE_CASE__ : int = self._prepare_for_class(_lowercase , _lowercase )
SCREAMING_SNAKE_CASE__ : int = self._prepare_for_class(_lowercase , _lowercase )
check_equivalence(_lowercase , _lowercase , _lowercase )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = self._prepare_for_class(_lowercase , _lowercase , return_labels=_lowercase )
SCREAMING_SNAKE_CASE__ : str = self._prepare_for_class(_lowercase , _lowercase , return_labels=_lowercase )
check_equivalence(_lowercase , _lowercase , _lowercase )
SCREAMING_SNAKE_CASE__ : Dict = self._prepare_for_class(_lowercase , _lowercase )
SCREAMING_SNAKE_CASE__ : Dict = self._prepare_for_class(_lowercase , _lowercase )
check_equivalence(_lowercase , _lowercase , _lowercase , {'''output_hidden_states''': True} )
SCREAMING_SNAKE_CASE__ : Dict = self._prepare_for_class(_lowercase , _lowercase , return_labels=_lowercase )
SCREAMING_SNAKE_CASE__ : str = self._prepare_for_class(_lowercase , _lowercase , return_labels=_lowercase )
check_equivalence(_lowercase , _lowercase , _lowercase , {'''output_hidden_states''': True} )
@require_torch
class lowercase ( unittest.TestCase , _UpperCAmelCase ):
lowerCamelCase : Union[str, Any] = (MaskFormerSwinBackbone,) if is_torch_available() else ()
lowerCamelCase : List[Any] = MaskFormerSwinConfig
def lowercase__ ( self : Any ):
SCREAMING_SNAKE_CASE__ : str = MaskFormerSwinModelTester(self )
def lowercase__ ( self : str ):
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : str = self.model_tester.prepare_config_and_inputs_for_common()
SCREAMING_SNAKE_CASE__ : List[str] = inputs_dict['''pixel_values'''].shape[0]
for backbone_class in self.all_model_classes:
SCREAMING_SNAKE_CASE__ : Optional[Any] = backbone_class(_lowercase )
backbone.to(_lowercase )
backbone.eval()
SCREAMING_SNAKE_CASE__ : List[Any] = backbone(**_lowercase )
# Test default outputs and verify feature maps
self.assertIsInstance(outputs.feature_maps , _lowercase )
self.assertTrue(len(outputs.feature_maps ) == len(backbone.channels ) )
for feature_map, n_channels in zip(outputs.feature_maps , backbone.channels ):
self.assertTrue(feature_map.shape[:2] , (batch_size, n_channels) )
self.assertIsNone(outputs.hidden_states )
self.assertIsNone(outputs.attentions )
# Test output_hidden_states=True
SCREAMING_SNAKE_CASE__ : Union[str, Any] = backbone(**_lowercase , output_hidden_states=_lowercase )
self.assertIsNotNone(outputs.hidden_states )
self.assertTrue(len(outputs.hidden_states ) , len(backbone.stage_names ) )
# We skip the stem layer
for hidden_states, n_channels in zip(outputs.hidden_states[1:] , backbone.channels ):
for hidden_state in hidden_states:
# Hidden states are in the format (batch_size, (height * width), n_channels)
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : Any = hidden_state.shape
self.assertTrue((h_batch_size, h_n_channels) , (batch_size, n_channels) )
# Test output_attentions=True
if self.has_attentions:
SCREAMING_SNAKE_CASE__ : Dict = backbone(**_lowercase , output_attentions=_lowercase )
self.assertIsNotNone(outputs.attentions )
| 35
|
'''simple docstring'''
import unittest
from transformers import (
MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
Pipeline,
ZeroShotClassificationPipeline,
pipeline,
)
from transformers.testing_utils import is_pipeline_test, nested_simplify, require_tf, require_torch, slow
from .test_pipelines_common import ANY
# These 2 model types require different inputs than those of the usual text models.
_UpperCamelCase : List[str] = {'LayoutLMv2Config', 'LayoutLMv3Config'}
@is_pipeline_test
class _lowercase( unittest.TestCase ):
"""simple docstring"""
__lowerCamelCase = MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
__lowerCamelCase = TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
if model_mapping is not None:
__lowerCamelCase = {config: model for config, model in model_mapping.items() if config.__name__ not in _TO_SKIP}
if tf_model_mapping is not None:
__lowerCamelCase = {
config: model for config, model in tf_model_mapping.items() if config.__name__ not in _TO_SKIP
}
def snake_case ( self: Optional[Any] ,a: Optional[int] ,a: Tuple ,a: Tuple ):
__UpperCAmelCase = ZeroShotClassificationPipeline(
model=a ,tokenizer=a ,candidate_labels=['polics', 'health'] )
return classifier, ["Who are you voting for in 2020?", "My stomach hurts."]
def snake_case ( self: int ,a: Union[str, Any] ,a: List[str] ):
__UpperCAmelCase = classifier('Who are you voting for in 2020?' ,candidate_labels='politics' )
self.assertEqual(a ,{'sequence': ANY(a ), 'labels': [ANY(a )], 'scores': [ANY(a )]} )
# No kwarg
__UpperCAmelCase = classifier('Who are you voting for in 2020?' ,['politics'] )
self.assertEqual(a ,{'sequence': ANY(a ), 'labels': [ANY(a )], 'scores': [ANY(a )]} )
__UpperCAmelCase = classifier('Who are you voting for in 2020?' ,candidate_labels=['politics'] )
self.assertEqual(a ,{'sequence': ANY(a ), 'labels': [ANY(a )], 'scores': [ANY(a )]} )
__UpperCAmelCase = classifier('Who are you voting for in 2020?' ,candidate_labels='politics, public health' )
self.assertEqual(
a ,{'sequence': ANY(a ), 'labels': [ANY(a ), ANY(a )], 'scores': [ANY(a ), ANY(a )]} )
self.assertAlmostEqual(sum(nested_simplify(outputs['scores'] ) ) ,1.0 )
__UpperCAmelCase = classifier('Who are you voting for in 2020?' ,candidate_labels=['politics', 'public health'] )
self.assertEqual(
a ,{'sequence': ANY(a ), 'labels': [ANY(a ), ANY(a )], 'scores': [ANY(a ), ANY(a )]} )
self.assertAlmostEqual(sum(nested_simplify(outputs['scores'] ) ) ,1.0 )
__UpperCAmelCase = classifier(
'Who are you voting for in 2020?' ,candidate_labels='politics' ,hypothesis_template='This text is about {}' )
self.assertEqual(a ,{'sequence': ANY(a ), 'labels': [ANY(a )], 'scores': [ANY(a )]} )
# https://github.com/huggingface/transformers/issues/13846
__UpperCAmelCase = classifier(['I am happy'] ,['positive', 'negative'] )
self.assertEqual(
a ,[
{'sequence': ANY(a ), 'labels': [ANY(a ), ANY(a )], 'scores': [ANY(a ), ANY(a )]}
for i in range(1 )
] ,)
__UpperCAmelCase = classifier(['I am happy', 'I am sad'] ,['positive', 'negative'] )
self.assertEqual(
a ,[
{'sequence': ANY(a ), 'labels': [ANY(a ), ANY(a )], 'scores': [ANY(a ), ANY(a )]}
for i in range(2 )
] ,)
with self.assertRaises(a ):
classifier('' ,candidate_labels='politics' )
with self.assertRaises(a ):
classifier(a ,candidate_labels='politics' )
with self.assertRaises(a ):
classifier('Who are you voting for in 2020?' ,candidate_labels='' )
with self.assertRaises(a ):
classifier('Who are you voting for in 2020?' ,candidate_labels=a )
with self.assertRaises(a ):
classifier(
'Who are you voting for in 2020?' ,candidate_labels='politics' ,hypothesis_template='Not formatting template' ,)
with self.assertRaises(a ):
classifier(
'Who are you voting for in 2020?' ,candidate_labels='politics' ,hypothesis_template=a ,)
self.run_entailment_id(a )
def snake_case ( self: int ,a: Pipeline ):
__UpperCAmelCase = zero_shot_classifier.model.config
__UpperCAmelCase = config.labelaid
__UpperCAmelCase = zero_shot_classifier.entailment_id
__UpperCAmelCase = {'LABEL_0': 0, 'LABEL_1': 1, 'LABEL_2': 2}
self.assertEqual(zero_shot_classifier.entailment_id ,-1 )
__UpperCAmelCase = {'entailment': 0, 'neutral': 1, 'contradiction': 2}
self.assertEqual(zero_shot_classifier.entailment_id ,0 )
__UpperCAmelCase = {'ENTAIL': 0, 'NON-ENTAIL': 1}
self.assertEqual(zero_shot_classifier.entailment_id ,0 )
__UpperCAmelCase = {'ENTAIL': 2, 'NEUTRAL': 1, 'CONTR': 0}
self.assertEqual(zero_shot_classifier.entailment_id ,2 )
__UpperCAmelCase = original_labelaid
self.assertEqual(a ,zero_shot_classifier.entailment_id )
@require_torch
def snake_case ( self: List[Any] ):
__UpperCAmelCase = pipeline(
'zero-shot-classification' ,model='sshleifer/tiny-distilbert-base-cased-distilled-squad' ,framework='pt' ,)
# There was a regression in 4.10 for this
# Adding a test so we don't make the mistake again.
# https://github.com/huggingface/transformers/issues/13381#issuecomment-912343499
zero_shot_classifier(
'Who are you voting for in 2020?' * 100 ,candidate_labels=['politics', 'public health', 'science'] )
@require_torch
def snake_case ( self: Tuple ):
__UpperCAmelCase = pipeline(
'zero-shot-classification' ,model='sshleifer/tiny-distilbert-base-cased-distilled-squad' ,framework='pt' ,)
__UpperCAmelCase = zero_shot_classifier(
'Who are you voting for in 2020?' ,candidate_labels=['politics', 'public health', 'science'] )
self.assertEqual(
nested_simplify(a ) ,{
'sequence': 'Who are you voting for in 2020?',
'labels': ['science', 'public health', 'politics'],
'scores': [0.333, 0.333, 0.333],
} ,)
@require_tf
def snake_case ( self: int ):
__UpperCAmelCase = pipeline(
'zero-shot-classification' ,model='sshleifer/tiny-distilbert-base-cased-distilled-squad' ,framework='tf' ,)
__UpperCAmelCase = zero_shot_classifier(
'Who are you voting for in 2020?' ,candidate_labels=['politics', 'public health', 'science'] )
self.assertEqual(
nested_simplify(a ) ,{
'sequence': 'Who are you voting for in 2020?',
'labels': ['science', 'public health', 'politics'],
'scores': [0.333, 0.333, 0.333],
} ,)
@slow
@require_torch
def snake_case ( self: int ):
__UpperCAmelCase = pipeline('zero-shot-classification' ,model='roberta-large-mnli' ,framework='pt' )
__UpperCAmelCase = zero_shot_classifier(
'Who are you voting for in 2020?' ,candidate_labels=['politics', 'public health', 'science'] )
self.assertEqual(
nested_simplify(a ) ,{
'sequence': 'Who are you voting for in 2020?',
'labels': ['politics', 'public health', 'science'],
'scores': [0.976, 0.015, 0.009],
} ,)
__UpperCAmelCase = zero_shot_classifier(
'The dominant sequence transduction models are based on complex recurrent or convolutional neural networks'
' in an encoder-decoder configuration. The best performing models also connect the encoder and decoder'
' through an attention mechanism. We propose a new simple network architecture, the Transformer, based'
' solely on attention mechanisms, dispensing with recurrence and convolutions entirely. Experiments on two'
' machine translation tasks show these models to be superior in quality while being more parallelizable'
' and requiring significantly less time to train. Our model achieves 28.4 BLEU on the WMT 2014'
' English-to-German translation task, improving over the existing best results, including ensembles by'
' over 2 BLEU. On the WMT 2014 English-to-French translation task, our model establishes a new'
' single-model state-of-the-art BLEU score of 41.8 after training for 3.5 days on eight GPUs, a small'
' fraction of the training costs of the best models from the literature. We show that the Transformer'
' generalizes well to other tasks by applying it successfully to English constituency parsing both with'
' large and limited training data.' ,candidate_labels=['machine learning', 'statistics', 'translation', 'vision'] ,multi_label=a ,)
self.assertEqual(
nested_simplify(a ) ,{
'sequence': (
'The dominant sequence transduction models are based on complex recurrent or convolutional neural'
' networks in an encoder-decoder configuration. The best performing models also connect the'
' encoder and decoder through an attention mechanism. We propose a new simple network'
' architecture, the Transformer, based solely on attention mechanisms, dispensing with recurrence'
' and convolutions entirely. Experiments on two machine translation tasks show these models to be'
' superior in quality while being more parallelizable and requiring significantly less time to'
' train. Our model achieves 28.4 BLEU on the WMT 2014 English-to-German translation task,'
' improving over the existing best results, including ensembles by over 2 BLEU. On the WMT 2014'
' English-to-French translation task, our model establishes a new single-model state-of-the-art'
' BLEU score of 41.8 after training for 3.5 days on eight GPUs, a small fraction of the training'
' costs of the best models from the literature. We show that the Transformer generalizes well to'
' other tasks by applying it successfully to English constituency parsing both with large and'
' limited training data.'
),
'labels': ['translation', 'machine learning', 'vision', 'statistics'],
'scores': [0.817, 0.713, 0.018, 0.018],
} ,)
@slow
@require_tf
def snake_case ( self: str ):
__UpperCAmelCase = pipeline('zero-shot-classification' ,model='roberta-large-mnli' ,framework='tf' )
__UpperCAmelCase = zero_shot_classifier(
'Who are you voting for in 2020?' ,candidate_labels=['politics', 'public health', 'science'] )
self.assertEqual(
nested_simplify(a ) ,{
'sequence': 'Who are you voting for in 2020?',
'labels': ['politics', 'public health', 'science'],
'scores': [0.976, 0.015, 0.009],
} ,)
__UpperCAmelCase = zero_shot_classifier(
'The dominant sequence transduction models are based on complex recurrent or convolutional neural networks'
' in an encoder-decoder configuration. The best performing models also connect the encoder and decoder'
' through an attention mechanism. We propose a new simple network architecture, the Transformer, based'
' solely on attention mechanisms, dispensing with recurrence and convolutions entirely. Experiments on two'
' machine translation tasks show these models to be superior in quality while being more parallelizable'
' and requiring significantly less time to train. Our model achieves 28.4 BLEU on the WMT 2014'
' English-to-German translation task, improving over the existing best results, including ensembles by'
' over 2 BLEU. On the WMT 2014 English-to-French translation task, our model establishes a new'
' single-model state-of-the-art BLEU score of 41.8 after training for 3.5 days on eight GPUs, a small'
' fraction of the training costs of the best models from the literature. We show that the Transformer'
' generalizes well to other tasks by applying it successfully to English constituency parsing both with'
' large and limited training data.' ,candidate_labels=['machine learning', 'statistics', 'translation', 'vision'] ,multi_label=a ,)
self.assertEqual(
nested_simplify(a ) ,{
'sequence': (
'The dominant sequence transduction models are based on complex recurrent or convolutional neural'
' networks in an encoder-decoder configuration. The best performing models also connect the'
' encoder and decoder through an attention mechanism. We propose a new simple network'
' architecture, the Transformer, based solely on attention mechanisms, dispensing with recurrence'
' and convolutions entirely. Experiments on two machine translation tasks show these models to be'
' superior in quality while being more parallelizable and requiring significantly less time to'
' train. Our model achieves 28.4 BLEU on the WMT 2014 English-to-German translation task,'
' improving over the existing best results, including ensembles by over 2 BLEU. On the WMT 2014'
' English-to-French translation task, our model establishes a new single-model state-of-the-art'
' BLEU score of 41.8 after training for 3.5 days on eight GPUs, a small fraction of the training'
' costs of the best models from the literature. We show that the Transformer generalizes well to'
' other tasks by applying it successfully to English constituency parsing both with large and'
' limited training data.'
),
'labels': ['translation', 'machine learning', 'vision', 'statistics'],
'scores': [0.817, 0.713, 0.018, 0.018],
} ,)
| 396
| 0
|
import numpy
# List of input, output pairs
SCREAMING_SNAKE_CASE : str = (
((5, 2, 3), 15),
((6, 5, 9), 25),
((11, 12, 13), 41),
((1, 1, 1), 8),
((11, 12, 13), 41),
)
SCREAMING_SNAKE_CASE : Tuple = (((515, 22, 13), 555), ((61, 35, 49), 150))
SCREAMING_SNAKE_CASE : Dict = [2, 4, 1, 5]
SCREAMING_SNAKE_CASE : Tuple = len(train_data)
SCREAMING_SNAKE_CASE : List[Any] = 0.009
def UpperCamelCase_( lowerCamelCase_ , lowerCamelCase_="train" ) -> Tuple:
return calculate_hypothesis_value(lowerCamelCase_ , lowerCamelCase_ ) - output(
lowerCamelCase_ , lowerCamelCase_ )
def UpperCamelCase_( lowerCamelCase_ ) -> Union[str, Any]:
_lowercase : Optional[Any] = 0
for i in range(len(lowerCamelCase_ ) - 1 ):
hyp_val += data_input_tuple[i] * parameter_vector[i + 1]
hyp_val += parameter_vector[0]
return hyp_val
def UpperCamelCase_( lowerCamelCase_ , lowerCamelCase_ ) -> List[str]:
if data_set == "train":
return train_data[example_no][1]
elif data_set == "test":
return test_data[example_no][1]
return None
def UpperCamelCase_( lowerCamelCase_ , lowerCamelCase_ ) -> Optional[int]:
if data_set == "train":
return _hypothesis_value(train_data[example_no][0] )
elif data_set == "test":
return _hypothesis_value(test_data[example_no][0] )
return None
def UpperCamelCase_( lowerCamelCase_ , lowerCamelCase_=m ) -> Any:
_lowercase : Dict = 0
for i in range(lowerCamelCase_ ):
if index == -1:
summation_value += _error(lowerCamelCase_ )
else:
summation_value += _error(lowerCamelCase_ ) * train_data[i][0][index]
return summation_value
def UpperCamelCase_( lowerCamelCase_ ) -> Optional[int]:
_lowercase : int = summation_of_cost_derivative(lowerCamelCase_ , lowerCamelCase_ ) / m
return cost_derivative_value
def UpperCamelCase_( ) -> Optional[Any]:
global parameter_vector
# Tune these values to set a tolerance value for predicted output
_lowercase : Tuple = 0.00_00_02
_lowercase : List[str] = 0
_lowercase : Tuple = 0
while True:
j += 1
_lowercase : Any = [0, 0, 0, 0]
for i in range(0 , len(lowerCamelCase_ ) ):
_lowercase : Optional[int] = get_cost_derivative(i - 1 )
_lowercase : Union[str, Any] = (
parameter_vector[i] - LEARNING_RATE * cost_derivative
)
if numpy.allclose(
lowerCamelCase_ , lowerCamelCase_ , atol=lowerCamelCase_ , rtol=lowerCamelCase_ , ):
break
_lowercase : Dict = temp_parameter_vector
print(('Number of iterations:', j) )
def UpperCamelCase_( ) -> Tuple:
for i in range(len(lowerCamelCase_ ) ):
print(('Actual output value:', output(lowerCamelCase_ , 'test' )) )
print(('Hypothesis output:', calculate_hypothesis_value(lowerCamelCase_ , 'test' )) )
if __name__ == "__main__":
run_gradient_descent()
print("\nTesting gradient descent for a linear hypothesis function.\n")
test_gradient_descent()
| 354
|
# this script reports modified .py files under the desired list of top-level sub-dirs passed as a list of arguments, e.g.:
# python ./utils/get_modified_files.py utils src tests examples
#
# it uses git to find the forking point and which files were modified - i.e. files not under git won't be considered
# since the output of this script is fed into Makefile commands it doesn't print a newline after the results
import re
import subprocess
import sys
SCREAMING_SNAKE_CASE : List[Any] = subprocess.check_output("git merge-base main HEAD".split()).decode("utf-8")
SCREAMING_SNAKE_CASE : Optional[int] = (
subprocess.check_output(F"git diff --diff-filter=d --name-only {fork_point_sha}".split()).decode("utf-8").split()
)
SCREAMING_SNAKE_CASE : Any = "|".join(sys.argv[1:])
SCREAMING_SNAKE_CASE : Union[str, Any] = re.compile(rF"^({joined_dirs}).*?\.py$")
SCREAMING_SNAKE_CASE : List[Any] = [x for x in modified_files if regex.match(x)]
print(" ".join(relevant_modified_files), end="")
| 354
| 1
|
import tempfile
import torch
from diffusers import PNDMScheduler
from .test_schedulers import SchedulerCommonTest
class _A ( snake_case ):
'''simple docstring'''
__lowerCamelCase : Optional[Any] = (PNDMScheduler,)
__lowerCamelCase : Dict = (('''num_inference_steps''', 5_0),)
def snake_case_ ( self ,**SCREAMING_SNAKE_CASE_ ):
'''simple docstring'''
snake_case : List[str] = {
"""num_train_timesteps""": 1000,
"""beta_start""": 0.00_01,
"""beta_end""": 0.02,
"""beta_schedule""": """linear""",
}
config.update(**SCREAMING_SNAKE_CASE_ )
return config
def snake_case_ ( self ,SCREAMING_SNAKE_CASE_=0 ,**SCREAMING_SNAKE_CASE_ ):
'''simple docstring'''
snake_case : List[Any] = dict(self.forward_default_kwargs )
snake_case : List[str] = kwargs.pop("""num_inference_steps""" ,SCREAMING_SNAKE_CASE_ )
snake_case : str = self.dummy_sample
snake_case : Dict = 0.1 * sample
snake_case : str = [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]
for scheduler_class in self.scheduler_classes:
snake_case : Union[str, Any] = self.get_scheduler_config(**SCREAMING_SNAKE_CASE_ )
snake_case : Dict = scheduler_class(**SCREAMING_SNAKE_CASE_ )
scheduler.set_timesteps(SCREAMING_SNAKE_CASE_ )
# copy over dummy past residuals
snake_case : str = dummy_past_residuals[:]
with tempfile.TemporaryDirectory() as tmpdirname:
scheduler.save_config(SCREAMING_SNAKE_CASE_ )
snake_case : int = scheduler_class.from_pretrained(SCREAMING_SNAKE_CASE_ )
new_scheduler.set_timesteps(SCREAMING_SNAKE_CASE_ )
# copy over dummy past residuals
snake_case : List[str] = dummy_past_residuals[:]
snake_case : int = scheduler.step_prk(SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,**SCREAMING_SNAKE_CASE_ ).prev_sample
snake_case : Tuple = new_scheduler.step_prk(SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,**SCREAMING_SNAKE_CASE_ ).prev_sample
assert torch.sum(torch.abs(output - new_output ) ) < 1E-5, "Scheduler outputs are not identical"
snake_case : Union[str, Any] = scheduler.step_plms(SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,**SCREAMING_SNAKE_CASE_ ).prev_sample
snake_case : List[str] = new_scheduler.step_plms(SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,**SCREAMING_SNAKE_CASE_ ).prev_sample
assert torch.sum(torch.abs(output - new_output ) ) < 1E-5, "Scheduler outputs are not identical"
def snake_case_ ( self ):
'''simple docstring'''
pass
def snake_case_ ( self ,SCREAMING_SNAKE_CASE_=0 ,**SCREAMING_SNAKE_CASE_ ):
'''simple docstring'''
snake_case : List[str] = dict(self.forward_default_kwargs )
snake_case : Union[str, Any] = kwargs.pop("""num_inference_steps""" ,SCREAMING_SNAKE_CASE_ )
snake_case : Dict = self.dummy_sample
snake_case : Any = 0.1 * sample
snake_case : List[Any] = [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]
for scheduler_class in self.scheduler_classes:
snake_case : Union[str, Any] = self.get_scheduler_config()
snake_case : Optional[int] = scheduler_class(**SCREAMING_SNAKE_CASE_ )
scheduler.set_timesteps(SCREAMING_SNAKE_CASE_ )
# copy over dummy past residuals (must be after setting timesteps)
snake_case : str = dummy_past_residuals[:]
with tempfile.TemporaryDirectory() as tmpdirname:
scheduler.save_config(SCREAMING_SNAKE_CASE_ )
snake_case : Dict = scheduler_class.from_pretrained(SCREAMING_SNAKE_CASE_ )
# copy over dummy past residuals
new_scheduler.set_timesteps(SCREAMING_SNAKE_CASE_ )
# copy over dummy past residual (must be after setting timesteps)
snake_case : Dict = dummy_past_residuals[:]
snake_case : List[str] = scheduler.step_prk(SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,**SCREAMING_SNAKE_CASE_ ).prev_sample
snake_case : Optional[int] = new_scheduler.step_prk(SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,**SCREAMING_SNAKE_CASE_ ).prev_sample
assert torch.sum(torch.abs(output - new_output ) ) < 1E-5, "Scheduler outputs are not identical"
snake_case : List[str] = scheduler.step_plms(SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,**SCREAMING_SNAKE_CASE_ ).prev_sample
snake_case : Optional[Any] = new_scheduler.step_plms(SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,**SCREAMING_SNAKE_CASE_ ).prev_sample
assert torch.sum(torch.abs(output - new_output ) ) < 1E-5, "Scheduler outputs are not identical"
def snake_case_ ( self ,**SCREAMING_SNAKE_CASE_ ):
'''simple docstring'''
snake_case : Optional[Any] = self.scheduler_classes[0]
snake_case : Any = self.get_scheduler_config(**SCREAMING_SNAKE_CASE_ )
snake_case : Optional[int] = scheduler_class(**SCREAMING_SNAKE_CASE_ )
snake_case : Any = 10
snake_case : Union[str, Any] = self.dummy_model()
snake_case : Optional[Any] = self.dummy_sample_deter
scheduler.set_timesteps(SCREAMING_SNAKE_CASE_ )
for i, t in enumerate(scheduler.prk_timesteps ):
snake_case : List[Any] = model(SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ )
snake_case : Optional[int] = scheduler.step_prk(SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ).prev_sample
for i, t in enumerate(scheduler.plms_timesteps ):
snake_case : List[Any] = model(SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ )
snake_case : List[str] = scheduler.step_plms(SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ).prev_sample
return sample
def snake_case_ ( self ):
'''simple docstring'''
snake_case : Tuple = dict(self.forward_default_kwargs )
snake_case : Union[str, Any] = kwargs.pop("""num_inference_steps""" ,SCREAMING_SNAKE_CASE_ )
for scheduler_class in self.scheduler_classes:
snake_case : Dict = self.get_scheduler_config()
snake_case : Optional[Any] = scheduler_class(**SCREAMING_SNAKE_CASE_ )
snake_case : str = self.dummy_sample
snake_case : str = 0.1 * sample
if num_inference_steps is not None and hasattr(SCREAMING_SNAKE_CASE_ ,"""set_timesteps""" ):
scheduler.set_timesteps(SCREAMING_SNAKE_CASE_ )
elif num_inference_steps is not None and not hasattr(SCREAMING_SNAKE_CASE_ ,"""set_timesteps""" ):
snake_case : Tuple = num_inference_steps
# copy over dummy past residuals (must be done after set_timesteps)
snake_case : Any = [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]
snake_case : Optional[Any] = dummy_past_residuals[:]
snake_case : List[str] = scheduler.step_prk(SCREAMING_SNAKE_CASE_ ,0 ,SCREAMING_SNAKE_CASE_ ,**SCREAMING_SNAKE_CASE_ ).prev_sample
snake_case : List[str] = scheduler.step_prk(SCREAMING_SNAKE_CASE_ ,1 ,SCREAMING_SNAKE_CASE_ ,**SCREAMING_SNAKE_CASE_ ).prev_sample
self.assertEqual(output_a.shape ,sample.shape )
self.assertEqual(output_a.shape ,output_a.shape )
snake_case : List[Any] = scheduler.step_plms(SCREAMING_SNAKE_CASE_ ,0 ,SCREAMING_SNAKE_CASE_ ,**SCREAMING_SNAKE_CASE_ ).prev_sample
snake_case : List[Any] = scheduler.step_plms(SCREAMING_SNAKE_CASE_ ,1 ,SCREAMING_SNAKE_CASE_ ,**SCREAMING_SNAKE_CASE_ ).prev_sample
self.assertEqual(output_a.shape ,sample.shape )
self.assertEqual(output_a.shape ,output_a.shape )
def snake_case_ ( self ):
'''simple docstring'''
for timesteps in [100, 1000]:
self.check_over_configs(num_train_timesteps=SCREAMING_SNAKE_CASE_ )
def snake_case_ ( self ):
'''simple docstring'''
for steps_offset in [0, 1]:
self.check_over_configs(steps_offset=SCREAMING_SNAKE_CASE_ )
snake_case : Dict = self.scheduler_classes[0]
snake_case : str = self.get_scheduler_config(steps_offset=1 )
snake_case : Optional[Any] = scheduler_class(**SCREAMING_SNAKE_CASE_ )
scheduler.set_timesteps(10 )
assert torch.equal(
scheduler.timesteps ,torch.LongTensor(
[901, 851, 851, 801, 801, 751, 751, 701, 701, 651, 651, 601, 601, 501, 401, 301, 201, 101, 1] ) ,)
def snake_case_ ( self ):
'''simple docstring'''
for beta_start, beta_end in zip([0.00_01, 0.0_01] ,[0.0_02, 0.02] ):
self.check_over_configs(beta_start=SCREAMING_SNAKE_CASE_ ,beta_end=SCREAMING_SNAKE_CASE_ )
def snake_case_ ( self ):
'''simple docstring'''
for schedule in ["linear", "squaredcos_cap_v2"]:
self.check_over_configs(beta_schedule=SCREAMING_SNAKE_CASE_ )
def snake_case_ ( self ):
'''simple docstring'''
for prediction_type in ["epsilon", "v_prediction"]:
self.check_over_configs(prediction_type=SCREAMING_SNAKE_CASE_ )
def snake_case_ ( self ):
'''simple docstring'''
for t in [1, 5, 10]:
self.check_over_forward(time_step=SCREAMING_SNAKE_CASE_ )
def snake_case_ ( self ):
'''simple docstring'''
for t, num_inference_steps in zip([1, 5, 10] ,[10, 50, 100] ):
self.check_over_forward(num_inference_steps=SCREAMING_SNAKE_CASE_ )
def snake_case_ ( self ):
'''simple docstring'''
# earlier version of set_timesteps() caused an error indexing alpha's with inference steps as power of 3
snake_case : str = 27
for scheduler_class in self.scheduler_classes:
snake_case : Optional[int] = self.dummy_sample
snake_case : Optional[int] = 0.1 * sample
snake_case : Dict = self.get_scheduler_config()
snake_case : int = scheduler_class(**SCREAMING_SNAKE_CASE_ )
scheduler.set_timesteps(SCREAMING_SNAKE_CASE_ )
# before power of 3 fix, would error on first step, so we only need to do two
for i, t in enumerate(scheduler.prk_timesteps[:2] ):
snake_case : Union[str, Any] = scheduler.step_prk(SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ).prev_sample
def snake_case_ ( self ):
'''simple docstring'''
with self.assertRaises(SCREAMING_SNAKE_CASE_ ):
snake_case : Union[str, Any] = self.scheduler_classes[0]
snake_case : Union[str, Any] = self.get_scheduler_config()
snake_case : Optional[int] = scheduler_class(**SCREAMING_SNAKE_CASE_ )
scheduler.step_plms(self.dummy_sample ,1 ,self.dummy_sample ).prev_sample
def snake_case_ ( self ):
'''simple docstring'''
snake_case : List[Any] = self.full_loop()
snake_case : List[str] = torch.sum(torch.abs(SCREAMING_SNAKE_CASE_ ) )
snake_case : int = torch.mean(torch.abs(SCREAMING_SNAKE_CASE_ ) )
assert abs(result_sum.item() - 1_98.13_18 ) < 1E-2
assert abs(result_mean.item() - 0.25_80 ) < 1E-3
def snake_case_ ( self ):
'''simple docstring'''
snake_case : List[str] = self.full_loop(prediction_type="""v_prediction""" )
snake_case : Optional[Any] = torch.sum(torch.abs(SCREAMING_SNAKE_CASE_ ) )
snake_case : Optional[Any] = torch.mean(torch.abs(SCREAMING_SNAKE_CASE_ ) )
assert abs(result_sum.item() - 67.39_86 ) < 1E-2
assert abs(result_mean.item() - 0.08_78 ) < 1E-3
def snake_case_ ( self ):
'''simple docstring'''
# We specify different beta, so that the first alpha is 0.99
snake_case : Any = self.full_loop(set_alpha_to_one=SCREAMING_SNAKE_CASE_ ,beta_start=0.01 )
snake_case : List[str] = torch.sum(torch.abs(SCREAMING_SNAKE_CASE_ ) )
snake_case : Optional[int] = torch.mean(torch.abs(SCREAMING_SNAKE_CASE_ ) )
assert abs(result_sum.item() - 2_30.03_99 ) < 1E-2
assert abs(result_mean.item() - 0.29_95 ) < 1E-3
def snake_case_ ( self ):
'''simple docstring'''
# We specify different beta, so that the first alpha is 0.99
snake_case : List[str] = self.full_loop(set_alpha_to_one=SCREAMING_SNAKE_CASE_ ,beta_start=0.01 )
snake_case : Any = torch.sum(torch.abs(SCREAMING_SNAKE_CASE_ ) )
snake_case : Dict = torch.mean(torch.abs(SCREAMING_SNAKE_CASE_ ) )
assert abs(result_sum.item() - 1_86.94_82 ) < 1E-2
assert abs(result_mean.item() - 0.24_34 ) < 1E-3
| 36
|
'''simple docstring'''
# Usage:
# ./gen-card-facebook-wmt19.py
import os
from pathlib import Path
def SCREAMING_SNAKE_CASE ( a_ : str , a_ : Union[str, Any] , a_ : Dict ):
__a = {
'en': 'Machine learning is great, isn\'t it?',
'ru': 'Машинное обучение - это здорово, не так ли?',
'de': 'Maschinelles Lernen ist großartig, oder?',
}
# BLUE scores as follows:
# "pair": [fairseq, transformers]
__a = {
'ru-en': ['[41.3](http://matrix.statmt.org/matrix/output/1907?run_id=6937)', '39.20'],
'en-ru': ['[36.4](http://matrix.statmt.org/matrix/output/1914?run_id=6724)', '33.47'],
'en-de': ['[43.1](http://matrix.statmt.org/matrix/output/1909?run_id=6862)', '42.83'],
'de-en': ['[42.3](http://matrix.statmt.org/matrix/output/1902?run_id=6750)', '41.35'],
}
__a = f"{src_lang}-{tgt_lang}"
__a = f"\n---\nlanguage: \n- {src_lang}\n- {tgt_lang}\nthumbnail:\ntags:\n- translation\n- wmt19\n- facebook\nlicense: apache-2.0\ndatasets:\n- wmt19\nmetrics:\n- bleu\n---\n\n# FSMT\n\n## Model description\n\nThis is a ported version of [fairseq wmt19 transformer](https://github.com/pytorch/fairseq/blob/master/examples/wmt19/README.md) for {src_lang}-{tgt_lang}.\n\nFor more details, please see, [Facebook FAIR's WMT19 News Translation Task Submission](https://arxiv.org/abs/1907.06616).\n\nThe abbreviation FSMT stands for FairSeqMachineTranslation\n\nAll four models are available:\n\n* [wmt19-en-ru](https://huggingface.co/facebook/wmt19-en-ru)\n* [wmt19-ru-en](https://huggingface.co/facebook/wmt19-ru-en)\n* [wmt19-en-de](https://huggingface.co/facebook/wmt19-en-de)\n* [wmt19-de-en](https://huggingface.co/facebook/wmt19-de-en)\n\n## Intended uses & limitations\n\n#### How to use\n\n```python\nfrom transformers import FSMTForConditionalGeneration, FSMTTokenizer\nmname = \"facebook/wmt19-{src_lang}-{tgt_lang}\"\ntokenizer = FSMTTokenizer.from_pretrained(mname)\nmodel = FSMTForConditionalGeneration.from_pretrained(mname)\n\ninput = \"{texts[src_lang]}\"\ninput_ids = tokenizer.encode(input, return_tensors=\"pt\")\noutputs = model.generate(input_ids)\ndecoded = tokenizer.decode(outputs[0], skip_special_tokens=True)\nprint(decoded) # {texts[tgt_lang]}\n\n```\n\n#### Limitations and bias\n\n- The original (and this ported model) doesn't seem to handle well inputs with repeated sub-phrases, [content gets truncated](https://discuss.huggingface.co/t/issues-with-translating-inputs-containing-repeated-phrases/981)\n\n## Training data\n\nPretrained weights were left identical to the original model released by fairseq. For more details, please, see the [paper](https://arxiv.org/abs/1907.06616).\n\n## Eval results\n\npair | fairseq | transformers\n-------|---------|----------\n{pair} | {scores[pair][0]} | {scores[pair][1]}\n\nThe score is slightly below the score reported by `fairseq`, since `transformers`` currently doesn't support:\n- model ensemble, therefore the best performing checkpoint was ported (``model4.pt``).\n- re-ranking\n\nThe score was calculated using this code:\n\n```bash\ngit clone https://github.com/huggingface/transformers\ncd transformers\nexport PAIR={pair}\nexport DATA_DIR=data/$PAIR\nexport SAVE_DIR=data/$PAIR\nexport BS=8\nexport NUM_BEAMS=15\nmkdir -p $DATA_DIR\nsacrebleu -t wmt19 -l $PAIR --echo src > $DATA_DIR/val.source\nsacrebleu -t wmt19 -l $PAIR --echo ref > $DATA_DIR/val.target\necho $PAIR\nPYTHONPATH=\"src:examples/seq2seq\" python examples/seq2seq/run_eval.py facebook/wmt19-$PAIR $DATA_DIR/val.source $SAVE_DIR/test_translations.txt --reference_path $DATA_DIR/val.target --score_path $SAVE_DIR/test_bleu.json --bs $BS --task translation --num_beams $NUM_BEAMS\n```\nnote: fairseq reports using a beam of 50, so you should get a slightly higher score if re-run with `--num_beams 50`.\n\n## Data Sources\n\n- [training, etc.](http://www.statmt.org/wmt19/)\n- [test set](http://matrix.statmt.org/test_sets/newstest2019.tgz?1556572561)\n\n\n### BibTeX entry and citation info\n\n```bibtex\n@inproceedings{{...,\n year={{2020}},\n title={{Facebook FAIR's WMT19 News Translation Task Submission}},\n author={{Ng, Nathan and Yee, Kyra and Baevski, Alexei and Ott, Myle and Auli, Michael and Edunov, Sergey}},\n booktitle={{Proc. of WMT}},\n}}\n```\n\n\n## TODO\n\n- port model ensemble (fairseq uses 4 model checkpoints)\n\n"
os.makedirs(a_ , exist_ok=a_ )
__a = os.path.join(a_ , 'README.md' )
print(f"Generating {path}" )
with open(a_ , 'w' , encoding='utf-8' ) as f:
f.write(a_ )
# make sure we are under the root of the project
UpperCAmelCase_ = Path(__file__).resolve().parent.parent.parent
UpperCAmelCase_ = repo_dir / "model_cards"
for model_name in ["wmt19-ru-en", "wmt19-en-ru", "wmt19-en-de", "wmt19-de-en"]:
UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ = model_name.split("-")
UpperCAmelCase_ = model_cards_dir / "facebook" / model_name
write_model_card(model_card_dir, src_lang=src_lang, tgt_lang=tgt_lang)
| 539
| 0
|
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
UpperCamelCase_ = logging.get_logger(__name__)
UpperCamelCase_ = {
"""kssteven/ibert-roberta-base""": """https://huggingface.co/kssteven/ibert-roberta-base/resolve/main/config.json""",
"""kssteven/ibert-roberta-large""": """https://huggingface.co/kssteven/ibert-roberta-large/resolve/main/config.json""",
"""kssteven/ibert-roberta-large-mnli""": (
"""https://huggingface.co/kssteven/ibert-roberta-large-mnli/resolve/main/config.json"""
),
}
class __SCREAMING_SNAKE_CASE ( lowercase__ ):
lowerCamelCase_ = 'ibert'
def __init__( self : int , UpperCAmelCase__ : Any=30522 , UpperCAmelCase__ : Tuple=768 , UpperCAmelCase__ : Dict=12 , UpperCAmelCase__ : List[Any]=12 , UpperCAmelCase__ : List[Any]=3072 , UpperCAmelCase__ : List[Any]="gelu" , UpperCAmelCase__ : int=0.1 , UpperCAmelCase__ : Union[str, Any]=0.1 , UpperCAmelCase__ : Tuple=512 , UpperCAmelCase__ : int=2 , UpperCAmelCase__ : Optional[Any]=0.02 , UpperCAmelCase__ : str=1E-12 , UpperCAmelCase__ : Any=1 , UpperCAmelCase__ : str=0 , UpperCAmelCase__ : str=2 , UpperCAmelCase__ : Union[str, Any]="absolute" , UpperCAmelCase__ : str=False , UpperCAmelCase__ : str="none" , **UpperCAmelCase__ : int , ):
'''simple docstring'''
super().__init__(pad_token_id=UpperCAmelCase__ , bos_token_id=UpperCAmelCase__ , eos_token_id=UpperCAmelCase__ , **UpperCAmelCase__ )
lowercase : Dict =vocab_size
lowercase : int =hidden_size
lowercase : str =num_hidden_layers
lowercase : Any =num_attention_heads
lowercase : List[str] =hidden_act
lowercase : str =intermediate_size
lowercase : int =hidden_dropout_prob
lowercase : Optional[int] =attention_probs_dropout_prob
lowercase : List[str] =max_position_embeddings
lowercase : List[str] =type_vocab_size
lowercase : Dict =initializer_range
lowercase : Union[str, Any] =layer_norm_eps
lowercase : Any =position_embedding_type
lowercase : List[Any] =quant_mode
lowercase : int =force_dequant
class __SCREAMING_SNAKE_CASE ( lowercase__ ):
@property
def lowerCamelCase_ ( self : Optional[int] ):
'''simple docstring'''
if self.task == "multiple-choice":
lowercase : Dict ={0: '''batch''', 1: '''choice''', 2: '''sequence'''}
else:
lowercase : List[Any] ={0: '''batch''', 1: '''sequence'''}
return OrderedDict(
[
('''input_ids''', dynamic_axis),
('''attention_mask''', dynamic_axis),
] )
| 88
|
'''simple docstring'''
import argparse
import torch
from transformers import GPTaLMHeadModel, RobertaForMaskedLM
if __name__ == "__main__":
UpperCamelCase_ = argparse.ArgumentParser(
description=(
"""Extraction some layers of the full RobertaForMaskedLM or GPT2LMHeadModel for Transfer Learned"""
""" Distillation"""
)
)
parser.add_argument("""--model_type""", default="""roberta""", choices=["""roberta""", """gpt2"""])
parser.add_argument("""--model_name""", default="""roberta-large""", type=str)
parser.add_argument("""--dump_checkpoint""", default="""serialization_dir/tf_roberta_048131723.pth""", type=str)
parser.add_argument("""--vocab_transform""", action="""store_true""")
UpperCamelCase_ = parser.parse_args()
if args.model_type == "roberta":
UpperCamelCase_ = RobertaForMaskedLM.from_pretrained(args.model_name)
UpperCamelCase_ = """roberta"""
elif args.model_type == "gpt2":
UpperCamelCase_ = GPTaLMHeadModel.from_pretrained(args.model_name)
UpperCamelCase_ = """transformer"""
UpperCamelCase_ = model.state_dict()
UpperCamelCase_ = {}
# Embeddings #
if args.model_type == "gpt2":
for param_name in ["wte.weight", "wpe.weight"]:
UpperCamelCase_ = state_dict[f'''{prefix}.{param_name}''']
else:
for w in ["word_embeddings", "position_embeddings", "token_type_embeddings"]:
UpperCamelCase_ = f'''{prefix}.embeddings.{w}.weight'''
UpperCamelCase_ = state_dict[param_name]
for w in ["weight", "bias"]:
UpperCamelCase_ = f'''{prefix}.embeddings.LayerNorm.{w}'''
UpperCamelCase_ = state_dict[param_name]
# Transformer Blocks #
UpperCamelCase_ = 0
for teacher_idx in [0, 2, 4, 7, 9, 11]:
if args.model_type == "gpt2":
for layer in ["ln_1", "attn.c_attn", "attn.c_proj", "ln_2", "mlp.c_fc", "mlp.c_proj"]:
for w in ["weight", "bias"]:
UpperCamelCase_ = state_dict[
f'''{prefix}.h.{teacher_idx}.{layer}.{w}'''
]
UpperCamelCase_ = state_dict[f'''{prefix}.h.{teacher_idx}.attn.bias''']
else:
for layer in [
"attention.self.query",
"attention.self.key",
"attention.self.value",
"attention.output.dense",
"attention.output.LayerNorm",
"intermediate.dense",
"output.dense",
"output.LayerNorm",
]:
for w in ["weight", "bias"]:
UpperCamelCase_ = state_dict[
f'''{prefix}.encoder.layer.{teacher_idx}.{layer}.{w}'''
]
std_idx += 1
# Language Modeling Head ###s
if args.model_type == "roberta":
for layer in ["lm_head.decoder.weight", "lm_head.bias"]:
UpperCamelCase_ = state_dict[f'''{layer}''']
if args.vocab_transform:
for w in ["weight", "bias"]:
UpperCamelCase_ = state_dict[f'''lm_head.dense.{w}''']
UpperCamelCase_ = state_dict[f'''lm_head.layer_norm.{w}''']
elif args.model_type == "gpt2":
for w in ["weight", "bias"]:
UpperCamelCase_ = state_dict[f'''{prefix}.ln_f.{w}''']
UpperCamelCase_ = state_dict["""lm_head.weight"""]
print(f'''N layers selected for distillation: {std_idx}''')
print(f'''Number of params transferred for distillation: {len(compressed_sd.keys())}''')
print(f'''Save transferred checkpoint to {args.dump_checkpoint}.''')
torch.save(compressed_sd, args.dump_checkpoint)
| 88
| 1
|
'''simple docstring'''
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCAmelCase_ = logging.get_logger(__name__)
lowerCAmelCase_ = {
'google/pix2struct-textcaps-base': (
'https://huggingface.co/google/pix2struct-textcaps-base/resolve/main/config.json'
),
}
class _snake_case( UpperCAmelCase ):
__snake_case: Tuple = '''pix2struct_text_model'''
__snake_case: str = ['''past_key_values''']
__snake_case: List[Any] = {
'''hidden_size''': '''hidden_size''',
'''num_attention_heads''': '''num_heads''',
'''num_hidden_layers''': '''num_layers''',
}
def __init__(self : Optional[int] , a : Any=5_02_44 , a : Any=7_68 , a : Optional[int]=64 , a : List[Any]=20_48 , a : int=12 , a : Any=12 , a : List[Any]=32 , a : int=1_28 , a : Optional[int]=0.1 , a : Any=1e-6 , a : Tuple=1.0 , a : Any="gelu_new" , a : Union[str, Any]=0 , a : Any=False , a : int=0 , a : Optional[int]=1 , a : List[str]=False , a : str=True , **a : str , ) -> str:
"""simple docstring"""
A__ = vocab_size
A__ = hidden_size
A__ = d_kv
A__ = d_ff
A__ = num_layers
A__ = num_heads
A__ = relative_attention_num_buckets
A__ = relative_attention_max_distance
A__ = dropout_rate
A__ = layer_norm_epsilon
A__ = initializer_factor
A__ = use_cache
A__ = eos_token_id
A__ = decoder_start_token_id
# for backwards compatibility
A__ = dense_act_fn
super().__init__(
pad_token_id=a , eos_token_id=a , decoder_start_token_id=a , tie_word_embeddings=a , is_decoder=a , **a , )
@classmethod
def _UpperCamelCase (cls : Optional[Any] , a : Dict , **a : Dict ) -> "PretrainedConfig":
"""simple docstring"""
cls._set_token_in_kwargs(a )
A__ = cls.get_config_dict(a , **a )
# get the text config dict if we are loading from Pix2StructConfig
if config_dict.get('model_type' ) == "pix2struct":
A__ = config_dict['''text_config''']
if "model_type" in config_dict and hasattr(cls , 'model_type' ) and config_dict["model_type"] != cls.model_type:
logger.warning(
f"""You are using a model of type {config_dict["model_type"]} to instantiate a model of type """
f"""{cls.model_type}. This is not supported for all configurations of models and can yield errors.""" )
return cls.from_dict(a , **a )
class _snake_case( UpperCAmelCase ):
__snake_case: List[Any] = '''pix2struct_vision_model'''
def __init__(self : Optional[Any] , a : int=7_68 , a : int=7_68 , a : str=20_48 , a : int=64 , a : Any=12 , a : Optional[Any]=12 , a : Dict="gelu_new" , a : List[str]=1e-6 , a : Tuple=0.0 , a : Optional[int]=0.0 , a : Tuple=1e-10 , a : Any=1.0 , a : Dict=40_96 , a : List[str]=32 , a : List[Any]=1_28 , **a : str , ) -> List[Any]:
"""simple docstring"""
super().__init__(**a )
A__ = hidden_size
A__ = patch_embed_hidden_size
A__ = d_ff
A__ = dropout_rate
A__ = num_hidden_layers
A__ = num_attention_heads
A__ = initializer_range
A__ = initializer_factor
A__ = attention_dropout
A__ = layer_norm_eps
A__ = dense_act_fn
A__ = seq_len
A__ = relative_attention_num_buckets
A__ = relative_attention_max_distance
A__ = d_kv
@classmethod
def _UpperCamelCase (cls : List[str] , a : Union[str, Any] , **a : List[Any] ) -> "PretrainedConfig":
"""simple docstring"""
cls._set_token_in_kwargs(a )
A__ = cls.get_config_dict(a , **a )
# get the vision config dict if we are loading from Pix2StructConfig
if config_dict.get('model_type' ) == "pix2struct":
A__ = config_dict['''vision_config''']
if "model_type" in config_dict and hasattr(cls , 'model_type' ) and config_dict["model_type"] != cls.model_type:
logger.warning(
f"""You are using a model of type {config_dict["model_type"]} to instantiate a model of type """
f"""{cls.model_type}. This is not supported for all configurations of models and can yield errors.""" )
return cls.from_dict(a , **a )
class _snake_case( UpperCAmelCase ):
__snake_case: Union[str, Any] = '''pix2struct'''
__snake_case: List[str] = True
def __init__(self : Tuple , a : List[str]=None , a : Optional[int]=None , a : Any=1.0 , a : str=0.02 , a : str=False , a : Dict=False , a : str=True , **a : Dict , ) -> Optional[int]:
"""simple docstring"""
super().__init__(tie_word_embeddings=a , is_encoder_decoder=a , **a )
if text_config is None:
A__ = {}
logger.info('text_config is None. Initializing the Pix2StructTextConfig with default values.' )
if vision_config is None:
A__ = {}
logger.info('vision_config is None. Initializing the Pix2StructVisionConfig with default values.' )
A__ = PixaStructTextConfig(**a )
A__ = PixaStructVisionConfig(**a )
A__ = self.text_config.decoder_start_token_id
A__ = self.text_config.pad_token_id
A__ = self.text_config.eos_token_id
A__ = initializer_factor
A__ = initializer_range
A__ = self.initializer_range
A__ = self.initializer_range
A__ = is_vqa
@classmethod
def _UpperCamelCase (cls : Union[str, Any] , a : Optional[int] , a : Tuple , **a : Union[str, Any] ) -> Optional[int]:
"""simple docstring"""
return cls(text_config=text_config.to_dict() , vision_config=vision_config.to_dict() , **a )
def _UpperCamelCase (self : Optional[Any] ) -> Any:
"""simple docstring"""
A__ = copy.deepcopy(self.__dict__ )
A__ = self.text_config.to_dict()
A__ = self.vision_config.to_dict()
A__ = self.__class__.model_type
return output
| 531
|
"""simple docstring"""
import inspect
import re
from hashlib import shaaaa
from typing import Dict, List
from .arrow import arrow
from .audiofolder import audiofolder
from .csv import csv
from .imagefolder import imagefolder
from .json import json
from .pandas import pandas
from .parquet import parquet
from .sql import sql # noqa F401
from .text import text
def lowerCamelCase_ (UpperCamelCase__ : List[str] ):
_UpperCAmelCase : int = []
for line in lines:
_UpperCAmelCase : str = re.sub(r'''#.*''' , '''''' , UpperCamelCase__ ) # remove comments
if line:
filtered_lines.append(UpperCamelCase__ )
_UpperCAmelCase : Optional[Any] = '''\n'''.join(UpperCamelCase__ )
# Make a hash from all this code
_UpperCAmelCase : Optional[Any] = full_str.encode('''utf-8''' )
return shaaaa(UpperCamelCase__ ).hexdigest()
# get importable module names and hash for caching
_lowerCAmelCase :Optional[Any] = {
'csv': (csv.__name__, _hash_python_lines(inspect.getsource(csv).splitlines())),
'json': (json.__name__, _hash_python_lines(inspect.getsource(json).splitlines())),
'pandas': (pandas.__name__, _hash_python_lines(inspect.getsource(pandas).splitlines())),
'parquet': (parquet.__name__, _hash_python_lines(inspect.getsource(parquet).splitlines())),
'arrow': (arrow.__name__, _hash_python_lines(inspect.getsource(arrow).splitlines())),
'text': (text.__name__, _hash_python_lines(inspect.getsource(text).splitlines())),
'imagefolder': (imagefolder.__name__, _hash_python_lines(inspect.getsource(imagefolder).splitlines())),
'audiofolder': (audiofolder.__name__, _hash_python_lines(inspect.getsource(audiofolder).splitlines())),
}
# Used to infer the module to use based on the data files extensions
_lowerCAmelCase :str = {
'.csv': ('csv', {}),
'.tsv': ('csv', {'sep': '\t'}),
'.json': ('json', {}),
'.jsonl': ('json', {}),
'.parquet': ('parquet', {}),
'.arrow': ('arrow', {}),
'.txt': ('text', {}),
}
_EXTENSION_TO_MODULE.update({ext: ('imagefolder', {}) for ext in imagefolder.ImageFolder.EXTENSIONS})
_EXTENSION_TO_MODULE.update({ext.upper(): ('imagefolder', {}) for ext in imagefolder.ImageFolder.EXTENSIONS})
_EXTENSION_TO_MODULE.update({ext: ('audiofolder', {}) for ext in audiofolder.AudioFolder.EXTENSIONS})
_EXTENSION_TO_MODULE.update({ext.upper(): ('audiofolder', {}) for ext in audiofolder.AudioFolder.EXTENSIONS})
_lowerCAmelCase :Optional[int] = {'imagefolder', 'audiofolder'}
# Used to filter data files based on extensions given a module name
_lowerCAmelCase :Dict[str, List[str]] = {}
for _ext, (_module, _) in _EXTENSION_TO_MODULE.items():
_MODULE_TO_EXTENSIONS.setdefault(_module, []).append(_ext)
_MODULE_TO_EXTENSIONS["imagefolder"].append('.zip')
_MODULE_TO_EXTENSIONS["audiofolder"].append('.zip')
| 506
| 0
|
"""simple docstring"""
import unittest
import torch
from diffusers import VQModel
from diffusers.utils import floats_tensor, torch_device
from diffusers.utils.testing_utils import enable_full_determinism
from .test_modeling_common import ModelTesterMixin, UNetTesterMixin
enable_full_determinism()
class a ( lowercase , lowercase , unittest.TestCase ):
UpperCamelCase : Tuple = VQModel
UpperCamelCase : Optional[Any] = """sample"""
@property
def __snake_case ( self , UpperCamelCase_=(32, 32) ):
UpperCAmelCase__ : Dict = 4
UpperCAmelCase__ : int = 3
UpperCAmelCase__ : Union[str, Any] = floats_tensor((batch_size, num_channels) + sizes ).to(UpperCamelCase_ )
return {"sample": image}
@property
def __snake_case ( self ):
return (3, 32, 32)
@property
def __snake_case ( self ):
return (3, 32, 32)
def __snake_case ( self ):
UpperCAmelCase__ : Any = {
'block_out_channels': [32, 64],
'in_channels': 3,
'out_channels': 3,
'down_block_types': ['DownEncoderBlock2D', 'DownEncoderBlock2D'],
'up_block_types': ['UpDecoderBlock2D', 'UpDecoderBlock2D'],
'latent_channels': 3,
}
UpperCAmelCase__ : int = self.dummy_input
return init_dict, inputs_dict
def __snake_case ( self ):
pass
def __snake_case ( self ):
pass
def __snake_case ( self ):
UpperCAmelCase__ : Any = VQModel.from_pretrained('fusing/vqgan-dummy' , output_loading_info=UpperCamelCase_ )
self.assertIsNotNone(UpperCamelCase_ )
self.assertEqual(len(loading_info['missing_keys'] ) , 0 )
model.to(UpperCamelCase_ )
UpperCAmelCase__ : Any = model(**self.dummy_input )
assert image is not None, "Make sure output is not None"
def __snake_case ( self ):
UpperCAmelCase__ : Union[str, Any] = VQModel.from_pretrained('fusing/vqgan-dummy' )
model.to(UpperCamelCase_ ).eval()
torch.manual_seed(0 )
if torch.cuda.is_available():
torch.cuda.manual_seed_all(0 )
UpperCAmelCase__ : Optional[int] = torch.randn(1 , model.config.in_channels , model.config.sample_size , model.config.sample_size )
UpperCAmelCase__ : List[Any] = image.to(UpperCamelCase_ )
with torch.no_grad():
UpperCAmelCase__ : List[Any] = model(UpperCamelCase_ ).sample
UpperCAmelCase__ : int = output[0, -1, -3:, -3:].flatten().cpu()
# fmt: off
UpperCAmelCase__ : Any = torch.tensor([-0.0153, -0.4044, -0.1880, -0.5161, -0.2418, -0.4072, -0.1612, -0.0633, -0.0143] )
# fmt: on
self.assertTrue(torch.allclose(UpperCamelCase_ , UpperCamelCase_ , atol=1E-3 ) )
| 715
|
"""simple docstring"""
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from .tokenization_lxmert import LxmertTokenizer
UpperCamelCase__ = {'vocab_file': 'vocab.txt', 'tokenizer_file': 'tokenizer.json'}
UpperCamelCase__ = {
'vocab_file': {
'unc-nlp/lxmert-base-uncased': 'https://huggingface.co/unc-nlp/lxmert-base-uncased/resolve/main/vocab.txt',
},
'tokenizer_file': {
'unc-nlp/lxmert-base-uncased': (
'https://huggingface.co/unc-nlp/lxmert-base-uncased/resolve/main/tokenizer.json'
),
},
}
UpperCamelCase__ = {
'unc-nlp/lxmert-base-uncased': 5_12,
}
UpperCamelCase__ = {
'unc-nlp/lxmert-base-uncased': {'do_lower_case': True},
}
class a ( lowercase ):
UpperCamelCase : int = VOCAB_FILES_NAMES
UpperCamelCase : str = PRETRAINED_VOCAB_FILES_MAP
UpperCamelCase : Any = PRETRAINED_INIT_CONFIGURATION
UpperCamelCase : Dict = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
UpperCamelCase : List[Any] = LxmertTokenizer
def __init__( self , UpperCamelCase_=None , UpperCamelCase_=None , UpperCamelCase_=True , UpperCamelCase_="[UNK]" , UpperCamelCase_="[SEP]" , UpperCamelCase_="[PAD]" , UpperCamelCase_="[CLS]" , UpperCamelCase_="[MASK]" , UpperCamelCase_=True , UpperCamelCase_=None , **UpperCamelCase_ , ):
super().__init__(
UpperCamelCase_ , tokenizer_file=UpperCamelCase_ , do_lower_case=UpperCamelCase_ , unk_token=UpperCamelCase_ , sep_token=UpperCamelCase_ , pad_token=UpperCamelCase_ , cls_token=UpperCamelCase_ , mask_token=UpperCamelCase_ , tokenize_chinese_chars=UpperCamelCase_ , strip_accents=UpperCamelCase_ , **UpperCamelCase_ , )
UpperCAmelCase__ : Any = json.loads(self.backend_tokenizer.normalizer.__getstate__() )
if (
normalizer_state.get('lowercase' , UpperCamelCase_ ) != do_lower_case
or normalizer_state.get('strip_accents' , UpperCamelCase_ ) != strip_accents
or normalizer_state.get('handle_chinese_chars' , UpperCamelCase_ ) != tokenize_chinese_chars
):
UpperCAmelCase__ : Any = getattr(UpperCamelCase_ , normalizer_state.pop('type' ) )
UpperCAmelCase__ : Union[str, Any] = do_lower_case
UpperCAmelCase__ : Optional[int] = strip_accents
UpperCAmelCase__ : Optional[Any] = tokenize_chinese_chars
UpperCAmelCase__ : Dict = normalizer_class(**UpperCamelCase_ )
UpperCAmelCase__ : List[Any] = do_lower_case
def __snake_case ( self , UpperCamelCase_ , UpperCamelCase_=None ):
UpperCAmelCase__ : Union[str, Any] = [self.cls_token_id] + token_ids_a + [self.sep_token_id]
if token_ids_a:
output += token_ids_a + [self.sep_token_id]
return output
def __snake_case ( self , UpperCamelCase_ , UpperCamelCase_ = None ):
UpperCAmelCase__ : str = [self.sep_token_id]
UpperCAmelCase__ : Union[str, Any] = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep ) * [0] + len(token_ids_a + sep ) * [1]
def __snake_case ( self , UpperCamelCase_ , UpperCamelCase_ = None ):
UpperCAmelCase__ : Optional[Any] = self._tokenizer.model.save(UpperCamelCase_ , name=UpperCamelCase_ )
return tuple(UpperCamelCase_ )
| 254
| 0
|
'''simple docstring'''
from random import shuffle
import tensorflow as tf
from numpy import array
def lowerCAmelCase_ ( snake_case_ : str , snake_case_ : Dict ) -> Tuple:
'''simple docstring'''
UpperCAmelCase_ = int(snake_case_ )
assert noofclusters < len(snake_case_ )
# Find out the dimensionality
UpperCAmelCase_ = len(vectors[0] )
# Will help select random centroids from among the available vectors
UpperCAmelCase_ = list(range(len(snake_case_ ) ) )
shuffle(snake_case_ )
# GRAPH OF COMPUTATION
# We initialize a new graph and set it as the default during each run
# of this algorithm. This ensures that as this function is called
# multiple times, the default graph doesn't keep getting crowded with
# unused ops and Variables from previous function calls.
UpperCAmelCase_ = tf.Graph()
with graph.as_default():
# SESSION OF COMPUTATION
UpperCAmelCase_ = tf.Session()
##CONSTRUCTING THE ELEMENTS OF COMPUTATION
##First lets ensure we have a Variable vector for each centroid,
##initialized to one of the vectors from the available data points
UpperCAmelCase_ = [
tf.Variable(vectors[vector_indices[i]] ) for i in range(snake_case_ )
]
##These nodes will assign the centroid Variables the appropriate
##values
UpperCAmelCase_ = tf.placeholder("float64" , [dim] )
UpperCAmelCase_ = []
for centroid in centroids:
cent_assigns.append(tf.assign(snake_case_ , snake_case_ ) )
##Variables for cluster assignments of individual vectors(initialized
##to 0 at first)
UpperCAmelCase_ = [tf.Variable(0 ) for i in range(len(snake_case_ ) )]
##These nodes will assign an assignment Variable the appropriate
##value
UpperCAmelCase_ = tf.placeholder("int32" )
UpperCAmelCase_ = []
for assignment in assignments:
cluster_assigns.append(tf.assign(snake_case_ , snake_case_ ) )
##Now lets construct the node that will compute the mean
# The placeholder for the input
UpperCAmelCase_ = tf.placeholder("float" , [None, dim] )
# The Node/op takes the input and computes a mean along the 0th
# dimension, i.e. the list of input vectors
UpperCAmelCase_ = tf.reduce_mean(snake_case_ , 0 )
##Node for computing Euclidean distances
# Placeholders for input
UpperCAmelCase_ = tf.placeholder("float" , [dim] )
UpperCAmelCase_ = tf.placeholder("float" , [dim] )
UpperCAmelCase_ = tf.sqrt(tf.reduce_sum(tf.pow(tf.sub(snake_case_ , snake_case_ ) , 2 ) ) )
##This node will figure out which cluster to assign a vector to,
##based on Euclidean distances of the vector from the centroids.
# Placeholder for input
UpperCAmelCase_ = tf.placeholder("float" , [noofclusters] )
UpperCAmelCase_ = tf.argmin(snake_case_ , 0 )
##INITIALIZING STATE VARIABLES
##This will help initialization of all Variables defined with respect
##to the graph. The Variable-initializer should be defined after
##all the Variables have been constructed, so that each of them
##will be included in the initialization.
UpperCAmelCase_ = tf.initialize_all_variables()
# Initialize all variables
sess.run(snake_case_ )
##CLUSTERING ITERATIONS
# Now perform the Expectation-Maximization steps of K-Means clustering
# iterations. To keep things simple, we will only do a set number of
# iterations, instead of using a Stopping Criterion.
UpperCAmelCase_ = 1_00
for _ in range(snake_case_ ):
##EXPECTATION STEP
##Based on the centroid locations till last iteration, compute
##the _expected_ centroid assignments.
# Iterate over each vector
for vector_n in range(len(snake_case_ ) ):
UpperCAmelCase_ = vectors[vector_n]
# Compute Euclidean distance between this vector and each
# centroid. Remember that this list cannot be named
#'centroid_distances', since that is the input to the
# cluster assignment node.
UpperCAmelCase_ = [
sess.run(snake_case_ , feed_dict={va: vect, va: sess.run(snake_case_ )} )
for centroid in centroids
]
# Now use the cluster assignment node, with the distances
# as the input
UpperCAmelCase_ = sess.run(
snake_case_ , feed_dict={centroid_distances: distances} )
# Now assign the value to the appropriate state variable
sess.run(
cluster_assigns[vector_n] , feed_dict={assignment_value: assignment} )
##MAXIMIZATION STEP
# Based on the expected state computed from the Expectation Step,
# compute the locations of the centroids so as to maximize the
# overall objective of minimizing within-cluster Sum-of-Squares
for cluster_n in range(snake_case_ ):
# Collect all the vectors assigned to this cluster
UpperCAmelCase_ = [
vectors[i]
for i in range(len(snake_case_ ) )
if sess.run(assignments[i] ) == cluster_n
]
# Compute new centroid location
UpperCAmelCase_ = sess.run(
snake_case_ , feed_dict={mean_input: array(snake_case_ )} )
# Assign value to appropriate variable
sess.run(
cent_assigns[cluster_n] , feed_dict={centroid_value: new_location} )
# Return centroids and assignments
UpperCAmelCase_ = sess.run(snake_case_ )
UpperCAmelCase_ = sess.run(snake_case_ )
return centroids, assignments
| 78
|
'''simple docstring'''
import copy
import re
class __A :
a__ : Optional[int] = """hp"""
a__ : Optional[Any] = {}
a__ : List[Any] = None
@classmethod
def _lowercase (cls : Optional[int] , __a : str , __a : Tuple ):
UpperCAmelCase_ = prefix
UpperCAmelCase_ = defaults
cls.build_naming_info()
@staticmethod
def _lowercase (__a : List[Any] , __a : List[str] ):
if len(__a ) == 0:
return ""
UpperCAmelCase_ = None
if any(char.isdigit() for char in word ):
raise Exception(f"""Parameters should not contain numbers: '{word}' contains a number""" )
if word in info["short_word"]:
return info["short_word"][word]
for prefix_len in range(1 , len(__a ) + 1 ):
UpperCAmelCase_ = word[:prefix_len]
if prefix in info["reverse_short_word"]:
continue
else:
UpperCAmelCase_ = prefix
break
if short_word is None:
# Paranoid fallback
def int_to_alphabetic(__a : Union[str, Any] ):
UpperCAmelCase_ = ""
while integer != 0:
UpperCAmelCase_ = chr(ord("A" ) + integer % 10 ) + s
integer //= 10
return s
UpperCAmelCase_ = 0
while True:
UpperCAmelCase_ = word + "#" + int_to_alphabetic(__a )
if sword in info["reverse_short_word"]:
continue
else:
UpperCAmelCase_ = sword
break
UpperCAmelCase_ = short_word
UpperCAmelCase_ = word
return short_word
@staticmethod
def _lowercase (__a : List[str] , __a : Union[str, Any] ):
UpperCAmelCase_ = param_name.split("_" )
UpperCAmelCase_ = [TrialShortNamer.shortname_for_word(__a , __a ) for word in words]
# We try to create a separatorless short name, but if there is a collision we have to fallback
# to a separated short name
UpperCAmelCase_ = ["", "_"]
for separator in separators:
UpperCAmelCase_ = separator.join(__a )
if shortname not in info["reverse_short_param"]:
UpperCAmelCase_ = shortname
UpperCAmelCase_ = param_name
return shortname
return param_name
@staticmethod
def _lowercase (__a : int , __a : Union[str, Any] ):
UpperCAmelCase_ = TrialShortNamer.shortname_for_key(__a , __a )
UpperCAmelCase_ = short_name
UpperCAmelCase_ = param_name
@classmethod
def _lowercase (cls : Any ):
if cls.NAMING_INFO is not None:
return
UpperCAmelCase_ = {
"short_word": {},
"reverse_short_word": {},
"short_param": {},
"reverse_short_param": {},
}
UpperCAmelCase_ = list(cls.DEFAULTS.keys() )
for k in field_keys:
cls.add_new_param_name(__a , __a )
UpperCAmelCase_ = info
@classmethod
def _lowercase (cls : int , __a : Optional[int] ):
cls.build_naming_info()
assert cls.PREFIX is not None
UpperCAmelCase_ = [copy.copy(cls.PREFIX )]
for k, v in params.items():
if k not in cls.DEFAULTS:
raise Exception(f"""You should provide a default value for the param name {k} with value {v}""" )
if v == cls.DEFAULTS[k]:
# The default value is not added to the name
continue
UpperCAmelCase_ = cls.NAMING_INFO["short_param"][k]
if isinstance(__a , __a ):
UpperCAmelCase_ = 1 if v else 0
UpperCAmelCase_ = "" if isinstance(__a , (int, float) ) else "-"
UpperCAmelCase_ = f"""{key}{sep}{v}"""
name.append(__a )
return "_".join(__a )
@classmethod
def _lowercase (cls : Dict , __a : Dict ):
UpperCAmelCase_ = repr[len(cls.PREFIX ) + 1 :]
if repr == "":
UpperCAmelCase_ = []
else:
UpperCAmelCase_ = repr.split("_" )
UpperCAmelCase_ = {}
for value in values:
if "-" in value:
UpperCAmelCase_ , UpperCAmelCase_ = value.split("-" )
else:
UpperCAmelCase_ = re.sub("[0-9.]" , "" , __a )
UpperCAmelCase_ = float(re.sub("[^0-9.]" , "" , __a ) )
UpperCAmelCase_ = cls.NAMING_INFO["reverse_short_param"][p_k]
UpperCAmelCase_ = p_v
for k in cls.DEFAULTS:
if k not in parameters:
UpperCAmelCase_ = cls.DEFAULTS[k]
return parameters
| 78
| 1
|
import argparse
import torch
from torch import nn
from transformers import SpeechaTextConfig, SpeechaTextForConditionalGeneration
def __lowerCAmelCase ( _UpperCamelCase : str ) -> List[str]:
'''simple docstring'''
SCREAMING_SNAKE_CASE = [
'encoder.version',
'decoder.version',
'model.encoder.version',
'model.decoder.version',
'decoder.output_projection.weight',
'_float_tensor',
'encoder.embed_positions._float_tensor',
'decoder.embed_positions._float_tensor',
]
for k in ignore_keys:
state_dict.pop(_UpperCamelCase , _UpperCamelCase )
def __lowerCAmelCase ( _UpperCamelCase : Optional[int] ) -> Any:
'''simple docstring'''
SCREAMING_SNAKE_CASE = list(s_dict.keys() )
for key in keys:
if "transformer_layers" in key:
SCREAMING_SNAKE_CASE = s_dict.pop(_UpperCamelCase )
elif "subsample" in key:
SCREAMING_SNAKE_CASE = s_dict.pop(_UpperCamelCase )
def __lowerCAmelCase ( _UpperCamelCase : List[str] ) -> List[str]:
'''simple docstring'''
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE = emb.weight.shape
SCREAMING_SNAKE_CASE = nn.Linear(_UpperCamelCase , _UpperCamelCase , bias=_UpperCamelCase )
SCREAMING_SNAKE_CASE = emb.weight.data
return lin_layer
def __lowerCAmelCase ( _UpperCamelCase : str , _UpperCamelCase : List[str] ) -> Union[str, Any]:
'''simple docstring'''
SCREAMING_SNAKE_CASE = torch.load(_UpperCamelCase , map_location='cpu' )
SCREAMING_SNAKE_CASE = mam_aaa['args']
SCREAMING_SNAKE_CASE = mam_aaa['model']
SCREAMING_SNAKE_CASE = state_dict['decoder.output_projection.weight']
remove_ignore_keys_(_UpperCamelCase )
rename_keys(_UpperCamelCase )
SCREAMING_SNAKE_CASE = state_dict['decoder.embed_tokens.weight'].shape[0]
SCREAMING_SNAKE_CASE = args.share_decoder_input_output_embed
SCREAMING_SNAKE_CASE = [int(_UpperCamelCase ) for i in args.conv_kernel_sizes.split(',' )]
SCREAMING_SNAKE_CASE = SpeechaTextConfig(
vocab_size=_UpperCamelCase , max_source_positions=args.max_source_positions , max_target_positions=args.max_target_positions , encoder_layers=args.encoder_layers , decoder_layers=args.decoder_layers , encoder_attention_heads=args.encoder_attention_heads , decoder_attention_heads=args.decoder_attention_heads , encoder_ffn_dim=args.encoder_ffn_embed_dim , decoder_ffn_dim=args.decoder_ffn_embed_dim , d_model=args.encoder_embed_dim , dropout=args.dropout , attention_dropout=args.attention_dropout , activation_dropout=args.activation_dropout , activation_function='relu' , num_conv_layers=len(_UpperCamelCase ) , conv_channels=args.conv_channels , conv_kernel_sizes=_UpperCamelCase , input_feat_per_channel=args.input_feat_per_channel , input_channels=args.input_channels , tie_word_embeddings=_UpperCamelCase , num_beams=5 , max_length=2_00 , use_cache=_UpperCamelCase , decoder_start_token_id=2 , early_stopping=_UpperCamelCase , )
SCREAMING_SNAKE_CASE = SpeechaTextForConditionalGeneration(_UpperCamelCase )
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE = model.model.load_state_dict(_UpperCamelCase , strict=_UpperCamelCase )
if len(_UpperCamelCase ) > 0 and not set(_UpperCamelCase ) <= {
"encoder.embed_positions.weights",
"decoder.embed_positions.weights",
}:
raise ValueError(
'Only `encoder.embed_positions.weights` and `decoder.embed_positions.weights` are allowed to be missing,'
f""" but all the following weights are missing {missing}""" )
if tie_embeds:
SCREAMING_SNAKE_CASE = make_linear_from_emb(model.model.decoder.embed_tokens )
else:
SCREAMING_SNAKE_CASE = lm_head_weights
model.save_pretrained(_UpperCamelCase )
if __name__ == "__main__":
a_ : Optional[int] = argparse.ArgumentParser()
# Required parameters
parser.add_argument("--fairseq_path", type=str, help="Path to the fairseq model (.pt) file.")
parser.add_argument("--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
a_ : str = parser.parse_args()
convert_fairseq_sat_checkpoint_to_tfms(args.fairseq_path, args.pytorch_dump_folder_path)
| 673
|
from ....configuration_utils import PretrainedConfig
from ....utils import logging
a_ : Any = logging.get_logger(__name__)
a_ : Dict = {
"Visual-Attention-Network/van-base": (
"https://huggingface.co/Visual-Attention-Network/van-base/blob/main/config.json"
),
}
class UpperCamelCase ( SCREAMING_SNAKE_CASE ):
__UpperCamelCase ="van"
def __init__( self : Optional[Any] , snake_case__ : Tuple=2_2_4 , snake_case__ : Dict=3 , snake_case__ : Union[str, Any]=[7, 3, 3, 3] , snake_case__ : str=[4, 2, 2, 2] , snake_case__ : Optional[Any]=[6_4, 1_2_8, 3_2_0, 5_1_2] , snake_case__ : Optional[Any]=[3, 3, 1_2, 3] , snake_case__ : Tuple=[8, 8, 4, 4] , snake_case__ : Any="gelu" , snake_case__ : Dict=0.02 , snake_case__ : List[str]=1E-6 , snake_case__ : int=1E-2 , snake_case__ : Any=0.0 , snake_case__ : Tuple=0.0 , **snake_case__ : Any , ):
"""simple docstring"""
super().__init__(**snake_case__ )
SCREAMING_SNAKE_CASE = image_size
SCREAMING_SNAKE_CASE = num_channels
SCREAMING_SNAKE_CASE = patch_sizes
SCREAMING_SNAKE_CASE = strides
SCREAMING_SNAKE_CASE = hidden_sizes
SCREAMING_SNAKE_CASE = depths
SCREAMING_SNAKE_CASE = mlp_ratios
SCREAMING_SNAKE_CASE = hidden_act
SCREAMING_SNAKE_CASE = initializer_range
SCREAMING_SNAKE_CASE = layer_norm_eps
SCREAMING_SNAKE_CASE = layer_scale_init_value
SCREAMING_SNAKE_CASE = drop_path_rate
SCREAMING_SNAKE_CASE = dropout_rate
| 673
| 1
|
import unittest
from transformers import TrOCRConfig
from transformers.testing_utils import is_torch_available, require_torch, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers.models.trocr.modeling_trocr import TrOCRDecoder, TrOCRForCausalLM
@require_torch
class snake_case_ :
def __init__( self : Union[str, Any] , _snake_case : Dict , _snake_case : List[str]=99 , _snake_case : Optional[Any]=13 , _snake_case : Union[str, Any]=16 , _snake_case : Optional[int]=7 , _snake_case : str=True , _snake_case : Optional[int]=True , _snake_case : List[str]=True , _snake_case : Dict=False , _snake_case : List[str]=True , _snake_case : Any=2 , _snake_case : Optional[int]=32 , _snake_case : Optional[Any]=4 , _snake_case : Union[str, Any]=4 , _snake_case : Any=30 , _snake_case : Optional[Any]=0 , _snake_case : List[str]=1 , _snake_case : Union[str, Any]=2 , _snake_case : List[Any]=None , )->Dict:
'''simple docstring'''
__lowerCAmelCase : Optional[int] = parent
__lowerCAmelCase : str = batch_size
__lowerCAmelCase : int = decoder_seq_length
# For common tests
__lowerCAmelCase : Dict = self.decoder_seq_length
__lowerCAmelCase : Optional[Any] = is_training
__lowerCAmelCase : Tuple = use_attention_mask
__lowerCAmelCase : Tuple = use_labels
__lowerCAmelCase : Union[str, Any] = vocab_size
__lowerCAmelCase : Dict = d_model
__lowerCAmelCase : Optional[int] = d_model
__lowerCAmelCase : List[Any] = decoder_layers
__lowerCAmelCase : List[str] = decoder_layers
__lowerCAmelCase : List[Any] = decoder_ffn_dim
__lowerCAmelCase : Optional[int] = decoder_attention_heads
__lowerCAmelCase : Optional[Any] = decoder_attention_heads
__lowerCAmelCase : List[Any] = eos_token_id
__lowerCAmelCase : int = bos_token_id
__lowerCAmelCase : Any = pad_token_id
__lowerCAmelCase : int = decoder_start_token_id
__lowerCAmelCase : Optional[Any] = use_cache
__lowerCAmelCase : Tuple = max_position_embeddings
__lowerCAmelCase : Optional[Any] = None
__lowerCAmelCase : Union[str, Any] = decoder_seq_length
__lowerCAmelCase : Any = 2
__lowerCAmelCase : List[str] = 1
def UpperCAmelCase__ ( self : Tuple )->str:
'''simple docstring'''
__lowerCAmelCase : str = ids_tensor([self.batch_size, self.decoder_seq_length] , self.vocab_size )
__lowerCAmelCase : Union[str, Any] = None
if self.use_attention_mask:
__lowerCAmelCase : Optional[Any] = ids_tensor([self.batch_size, self.decoder_seq_length] , vocab_size=2 )
__lowerCAmelCase : Dict = None
if self.use_labels:
__lowerCAmelCase : Any = ids_tensor([self.batch_size, self.decoder_seq_length] , self.vocab_size )
__lowerCAmelCase : int = TrOCRConfig(
vocab_size=self.vocab_size , d_model=self.d_model , decoder_layers=self.decoder_layers , decoder_ffn_dim=self.decoder_ffn_dim , decoder_attention_heads=self.decoder_attention_heads , eos_token_id=self.eos_token_id , bos_token_id=self.bos_token_id , use_cache=self.use_cache , pad_token_id=self.pad_token_id , decoder_start_token_id=self.decoder_start_token_id , max_position_embeddings=self.max_position_embeddings , )
return (config, input_ids, attention_mask, lm_labels)
def UpperCAmelCase__ ( self : List[Any] , _snake_case : Optional[Any] , _snake_case : int , _snake_case : List[Any] , _snake_case : Optional[Any] , )->int:
'''simple docstring'''
__lowerCAmelCase : Tuple = True
__lowerCAmelCase : List[Any] = TrOCRDecoder(config=_snake_case ).to(_snake_case ).eval()
__lowerCAmelCase : Any = input_ids[:2]
input_ids[input_ids == 0] += 1
# first forward pass
__lowerCAmelCase : List[Any] = model(_snake_case , use_cache=_snake_case )
__lowerCAmelCase : str = model(_snake_case )
__lowerCAmelCase : Union[str, Any] = model(_snake_case , use_cache=_snake_case )
self.parent.assertTrue(len(_snake_case ) == len(_snake_case ) )
self.parent.assertTrue(len(_snake_case ) == len(_snake_case ) + 1 )
__lowerCAmelCase : Union[str, Any] = outputs["""past_key_values"""]
# create hypothetical next token and extent to next_input_ids
__lowerCAmelCase : List[Any] = ids_tensor((2, 1) , config.vocab_size - 1 ) + 1
# append to next input_ids and
__lowerCAmelCase : Dict = torch.cat([input_ids, next_tokens] , dim=-1 )
__lowerCAmelCase : List[str] = model(_snake_case )["""last_hidden_state"""]
__lowerCAmelCase : Union[str, Any] = model(_snake_case , past_key_values=_snake_case )["""last_hidden_state"""]
# select random slice
__lowerCAmelCase : Any = ids_tensor((1,) , output_from_past.shape[-1] ).item()
__lowerCAmelCase : Any = output_from_no_past[:, next_input_ids.shape[-1] - 1, random_slice_idx].detach()
__lowerCAmelCase : Union[str, Any] = output_from_past[:, 0, random_slice_idx].detach()
# test that outputs are equal for slice
assert torch.allclose(_snake_case , _snake_case , atol=1E-3 )
def UpperCAmelCase__ ( self : Union[str, Any] )->Tuple:
'''simple docstring'''
__lowerCAmelCase : Tuple = self.prepare_config_and_inputs()
__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase : List[str] = config_and_inputs
__lowerCAmelCase : Tuple = {"""input_ids""": input_ids, """attention_mask""": attention_mask}
return config, inputs_dict
@require_torch
class snake_case_ ( __lowercase ,__lowercase ,__lowercase ,unittest.TestCase ):
A_ = (TrOCRDecoder, TrOCRForCausalLM) if is_torch_available() else ()
A_ = (TrOCRForCausalLM,) if is_torch_available() else ()
A_ = {"text-generation": TrOCRForCausalLM} if is_torch_available() else {}
A_ = True
A_ = False
def UpperCAmelCase__ ( self : Optional[Any] )->List[Any]:
'''simple docstring'''
__lowerCAmelCase : Tuple = TrOCRStandaloneDecoderModelTester(self , is_training=_snake_case )
__lowerCAmelCase : List[str] = ConfigTester(self , config_class=_snake_case )
def UpperCAmelCase__ ( self : Optional[Any] )->int:
'''simple docstring'''
pass
def UpperCAmelCase__ ( self : Any )->List[Any]:
'''simple docstring'''
pass
def UpperCAmelCase__ ( self : Any )->Any:
'''simple docstring'''
pass
def UpperCAmelCase__ ( self : Optional[int] )->Union[str, Any]:
'''simple docstring'''
self.config_tester.run_common_tests()
def UpperCAmelCase__ ( self : int )->Optional[int]:
'''simple docstring'''
__lowerCAmelCase : List[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_decoder_model_past(*_snake_case )
def UpperCAmelCase__ ( self : Any )->List[Any]:
'''simple docstring'''
return
@unittest.skip("""The model doesn\'t support left padding""" ) # and it's not used enough to be worth fixing :)
def UpperCAmelCase__ ( self : str )->List[str]:
'''simple docstring'''
pass
| 504
|
import pytest
import requests
from datasets.utils.file_utils import http_head
from .utils import OfflineSimulationMode, RequestWouldHangIndefinitelyError, offline
@pytest.mark.integration
def a_ ( ) -> Optional[Any]:
with offline(OfflineSimulationMode.CONNECTION_TIMES_OUT ):
with pytest.raises(__lowercase ):
requests.request('GET' , 'https://huggingface.co' )
with pytest.raises(requests.exceptions.ConnectTimeout ):
requests.request('GET' , 'https://huggingface.co' , timeout=1.0 )
@pytest.mark.integration
def a_ ( ) -> Optional[int]:
with offline(OfflineSimulationMode.CONNECTION_FAILS ):
with pytest.raises(requests.exceptions.ConnectionError ):
requests.request('GET' , 'https://huggingface.co' )
def a_ ( ) -> Dict:
with offline(OfflineSimulationMode.HF_DATASETS_OFFLINE_SET_TO_1 ):
with pytest.raises(__lowercase ):
http_head('https://huggingface.co' )
| 686
| 0
|
"""simple docstring"""
import json
import os
import unittest
from transformers.models.xlm.tokenization_xlm import VOCAB_FILES_NAMES, XLMTokenizer
from transformers.testing_utils import slow
from ...test_tokenization_common import TokenizerTesterMixin
class __lowerCAmelCase ( __SCREAMING_SNAKE_CASE , unittest.TestCase ):
lowercase = XLMTokenizer
lowercase = False
def UpperCAmelCase ( self ):
'''simple docstring'''
super().setUp()
# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
__UpperCamelCase = [
'l',
'o',
'w',
'e',
'r',
's',
't',
'i',
'd',
'n',
'w</w>',
'r</w>',
't</w>',
'lo',
'low',
'er</w>',
'low</w>',
'lowest</w>',
'newer</w>',
'wider</w>',
'<unk>',
]
__UpperCamelCase = dict(zip(__UpperCAmelCase , range(len(__UpperCAmelCase ) ) ) )
__UpperCamelCase = ['l o 123', 'lo w 1456', 'e r</w> 1789', '']
__UpperCamelCase = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['vocab_file'] )
__UpperCamelCase = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['merges_file'] )
with open(self.vocab_file , 'w' ) as fp:
fp.write(json.dumps(__UpperCAmelCase ) )
with open(self.merges_file , 'w' ) as fp:
fp.write('\n'.join(__UpperCAmelCase ) )
def UpperCAmelCase ( self , __UpperCAmelCase ):
'''simple docstring'''
__UpperCamelCase = 'lower newer'
__UpperCamelCase = 'lower newer'
return input_text, output_text
def UpperCAmelCase ( self ):
'''simple docstring'''
__UpperCamelCase = XLMTokenizer(self.vocab_file , self.merges_file )
__UpperCamelCase = 'lower'
__UpperCamelCase = ['low', 'er</w>']
__UpperCamelCase = tokenizer.tokenize(__UpperCAmelCase )
self.assertListEqual(__UpperCAmelCase , __UpperCAmelCase )
__UpperCamelCase = tokens + ['<unk>']
__UpperCamelCase = [14, 15, 20]
self.assertListEqual(tokenizer.convert_tokens_to_ids(__UpperCAmelCase ) , __UpperCAmelCase )
@slow
def UpperCAmelCase ( self ):
'''simple docstring'''
__UpperCamelCase = XLMTokenizer.from_pretrained('xlm-mlm-en-2048' )
__UpperCamelCase = tokenizer.encode('sequence builders' , add_special_tokens=__UpperCAmelCase )
__UpperCamelCase = tokenizer.encode('multi-sequence build' , add_special_tokens=__UpperCAmelCase )
__UpperCamelCase = tokenizer.build_inputs_with_special_tokens(__UpperCAmelCase )
__UpperCamelCase = tokenizer.build_inputs_with_special_tokens(__UpperCAmelCase , __UpperCAmelCase )
assert encoded_sentence == [0] + text + [1]
assert encoded_pair == [0] + text + [1] + text_a + [1]
| 293
|
"""simple docstring"""
import time
from contextlib import contextmanager
from pathlib import Path
import pytest
import requests
from huggingface_hub.hf_api import HfApi, HfFolder
UpperCamelCase : Union[str, Any] = "__DUMMY_TRANSFORMERS_USER__"
UpperCamelCase : List[Any] = "Dummy User"
UpperCamelCase : List[Any] = "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"
UpperCamelCase : Any = "https://hub-ci.huggingface.co"
UpperCamelCase : str = CI_HUB_ENDPOINT + "/datasets/{repo_id}/resolve/{revision}/{path}"
UpperCamelCase : Optional[Any] = CI_HUB_ENDPOINT + "/{repo_id}/resolve/{revision}/{filename}"
UpperCamelCase : Optional[int] = Path("~/.huggingface/hub_ci_token").expanduser()
@pytest.fixture
def A ( snake_case :Optional[Any] ) -> Union[str, Any]:
monkeypatch.setattr(
'huggingface_hub.file_download.HUGGINGFACE_CO_URL_TEMPLATE' , snake_case )
@pytest.fixture
def A ( snake_case :str ) -> List[Any]:
monkeypatch.setattr('datasets.config.HF_ENDPOINT' , snake_case )
monkeypatch.setattr('datasets.config.HUB_DATASETS_URL' , snake_case )
@pytest.fixture
def A ( snake_case :Optional[int] ) -> Dict:
monkeypatch.setattr('huggingface_hub.hf_api.HfFolder.path_token' , snake_case )
@pytest.fixture
def A ( snake_case :List[Any] , snake_case :Tuple ) -> Tuple:
HfFolder.save_token(snake_case )
yield
HfFolder.delete_token()
@pytest.fixture(scope='session' )
def A ( ) -> List[Any]:
return HfApi(endpoint=snake_case )
@pytest.fixture(scope='session' )
def A ( snake_case :HfApi ) -> List[Any]:
__UpperCamelCase = HfFolder.get_token()
HfFolder.save_token(snake_case )
yield CI_HUB_USER_TOKEN
if previous_token is not None:
HfFolder.save_token(snake_case )
@pytest.fixture
def A ( snake_case :str ) -> str:
def _cleanup_repo(snake_case :Union[str, Any] ):
hf_api.delete_repo(snake_case , token=snake_case , repo_type='dataset' )
return _cleanup_repo
@pytest.fixture
def A ( snake_case :List[str] ) -> Any:
@contextmanager
def _temporary_repo(snake_case :Tuple ):
try:
yield repo_id
finally:
cleanup_repo(snake_case )
return _temporary_repo
@pytest.fixture(scope='session' )
def A ( snake_case :HfApi , snake_case :Dict , snake_case :Dict ) -> List[str]:
__UpperCamelCase = f'repo_txt_data-{int(time.time() * 10e3 )}'
__UpperCamelCase = f'{CI_HUB_USER}/{repo_name}'
hf_api.create_repo(snake_case , token=snake_case , repo_type='dataset' , private=snake_case )
hf_api.upload_file(
token=snake_case , path_or_fileobj=str(snake_case ) , path_in_repo='data/text_data.txt' , repo_id=snake_case , repo_type='dataset' , )
yield repo_id
try:
hf_api.delete_repo(snake_case , token=snake_case , repo_type='dataset' )
except (requests.exceptions.HTTPError, ValueError): # catch http error and token invalid error
pass
@pytest.fixture()
def A ( snake_case :Optional[int] , snake_case :str , snake_case :Tuple ) -> Any:
return hf_private_dataset_repo_txt_data_
@pytest.fixture(scope='session' )
def A ( snake_case :HfApi , snake_case :List[Any] , snake_case :str ) -> Optional[int]:
__UpperCamelCase = f'repo_zipped_txt_data-{int(time.time() * 10e3 )}'
__UpperCamelCase = f'{CI_HUB_USER}/{repo_name}'
hf_api.create_repo(snake_case , token=snake_case , repo_type='dataset' , private=snake_case )
hf_api.upload_file(
token=snake_case , path_or_fileobj=str(snake_case ) , path_in_repo='data.zip' , repo_id=snake_case , repo_type='dataset' , )
yield repo_id
try:
hf_api.delete_repo(snake_case , token=snake_case , repo_type='dataset' )
except (requests.exceptions.HTTPError, ValueError): # catch http error and token invalid error
pass
@pytest.fixture()
def A ( snake_case :int , snake_case :Optional[int] , snake_case :Optional[int] ) -> str:
return hf_private_dataset_repo_zipped_txt_data_
@pytest.fixture(scope='session' )
def A ( snake_case :HfApi , snake_case :List[str] , snake_case :Any ) -> List[Any]:
__UpperCamelCase = f'repo_zipped_img_data-{int(time.time() * 10e3 )}'
__UpperCamelCase = f'{CI_HUB_USER}/{repo_name}'
hf_api.create_repo(snake_case , token=snake_case , repo_type='dataset' , private=snake_case )
hf_api.upload_file(
token=snake_case , path_or_fileobj=str(snake_case ) , path_in_repo='data.zip' , repo_id=snake_case , repo_type='dataset' , )
yield repo_id
try:
hf_api.delete_repo(snake_case , token=snake_case , repo_type='dataset' )
except (requests.exceptions.HTTPError, ValueError): # catch http error and token invalid error
pass
@pytest.fixture()
def A ( snake_case :List[str] , snake_case :Optional[Any] , snake_case :Optional[int] ) -> Optional[int]:
return hf_private_dataset_repo_zipped_img_data_
| 293
| 1
|
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import BertTokenizer, BertTokenizerFast
from transformers.models.bert.tokenization_bert 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 AlignProcessor, EfficientNetImageProcessor
@require_vision
class _snake_case ( unittest.TestCase ):
def lowercase__ ( self):
'''simple docstring'''
lowercase__ : Dict = tempfile.mkdtemp()
lowercase__ : str = [
"""[UNK]""",
"""[CLS]""",
"""[SEP]""",
"""[PAD]""",
"""[MASK]""",
"""want""",
"""##want""",
"""##ed""",
"""wa""",
"""un""",
"""runn""",
"""##ing""",
""",""",
"""low""",
"""lowest""",
]
lowercase__ : str = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES["""vocab_file"""])
with open(self.vocab_file , """w""" , encoding="""utf-8""") as vocab_writer:
vocab_writer.write("""""".join([x + """\n""" for x in vocab_tokens]))
lowercase__ : Optional[Any] = {
"""do_resize""": True,
"""size""": 20,
"""do_center_crop""": True,
"""crop_size""": 18,
"""do_normalize""": True,
"""image_mean""": [0.4_8_1_4_5_4_6_6, 0.4_5_7_8_2_7_5, 0.4_0_8_2_1_0_7_3],
"""image_std""": [0.2_6_8_6_2_9_5_4, 0.2_6_1_3_0_2_5_8, 0.2_7_5_7_7_7_1_1],
}
lowercase__ : int = os.path.join(self.tmpdirname , SCREAMING_SNAKE_CASE_)
with open(self.image_processor_file , """w""" , encoding="""utf-8""") as fp:
json.dump(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_)
def lowercase__ ( self , **SCREAMING_SNAKE_CASE_):
'''simple docstring'''
return BertTokenizer.from_pretrained(self.tmpdirname , **SCREAMING_SNAKE_CASE_)
def lowercase__ ( self , **SCREAMING_SNAKE_CASE_):
'''simple docstring'''
return BertTokenizerFast.from_pretrained(self.tmpdirname , **SCREAMING_SNAKE_CASE_)
def lowercase__ ( self , **SCREAMING_SNAKE_CASE_):
'''simple docstring'''
return EfficientNetImageProcessor.from_pretrained(self.tmpdirname , **SCREAMING_SNAKE_CASE_)
def lowercase__ ( self):
'''simple docstring'''
shutil.rmtree(self.tmpdirname)
def lowercase__ ( self):
'''simple docstring'''
lowercase__ : Optional[int] = [np.random.randint(2_55 , size=(3, 30, 4_00) , dtype=np.uinta)]
lowercase__ : str = [Image.fromarray(np.moveaxis(SCREAMING_SNAKE_CASE_ , 0 , -1)) for x in image_inputs]
return image_inputs
def lowercase__ ( self):
'''simple docstring'''
lowercase__ : Tuple = self.get_tokenizer()
lowercase__ : Optional[int] = self.get_rust_tokenizer()
lowercase__ : Optional[int] = self.get_image_processor()
lowercase__ : Optional[int] = AlignProcessor(tokenizer=SCREAMING_SNAKE_CASE_ , image_processor=SCREAMING_SNAKE_CASE_)
processor_slow.save_pretrained(self.tmpdirname)
lowercase__ : List[str] = AlignProcessor.from_pretrained(self.tmpdirname , use_fast=SCREAMING_SNAKE_CASE_)
lowercase__ : Optional[Any] = AlignProcessor(tokenizer=SCREAMING_SNAKE_CASE_ , image_processor=SCREAMING_SNAKE_CASE_)
processor_fast.save_pretrained(self.tmpdirname)
lowercase__ : Optional[Any] = AlignProcessor.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 , SCREAMING_SNAKE_CASE_)
self.assertIsInstance(processor_fast.tokenizer , SCREAMING_SNAKE_CASE_)
self.assertEqual(processor_slow.image_processor.to_json_string() , image_processor.to_json_string())
self.assertEqual(processor_fast.image_processor.to_json_string() , image_processor.to_json_string())
self.assertIsInstance(processor_slow.image_processor , SCREAMING_SNAKE_CASE_)
self.assertIsInstance(processor_fast.image_processor , SCREAMING_SNAKE_CASE_)
def lowercase__ ( self):
'''simple docstring'''
lowercase__ : Optional[int] = AlignProcessor(tokenizer=self.get_tokenizer() , image_processor=self.get_image_processor())
processor.save_pretrained(self.tmpdirname)
lowercase__ : int = self.get_tokenizer(bos_token="""(BOS)""" , eos_token="""(EOS)""")
lowercase__ : Optional[int] = self.get_image_processor(do_normalize=SCREAMING_SNAKE_CASE_ , padding_value=1.0)
lowercase__ : Dict = AlignProcessor.from_pretrained(
self.tmpdirname , bos_token="""(BOS)""" , eos_token="""(EOS)""" , do_normalize=SCREAMING_SNAKE_CASE_ , padding_value=1.0)
self.assertEqual(processor.tokenizer.get_vocab() , tokenizer_add_kwargs.get_vocab())
self.assertIsInstance(processor.tokenizer , SCREAMING_SNAKE_CASE_)
self.assertEqual(processor.image_processor.to_json_string() , image_processor_add_kwargs.to_json_string())
self.assertIsInstance(processor.image_processor , SCREAMING_SNAKE_CASE_)
def lowercase__ ( self):
'''simple docstring'''
lowercase__ : List[str] = self.get_image_processor()
lowercase__ : Dict = self.get_tokenizer()
lowercase__ : Dict = AlignProcessor(tokenizer=SCREAMING_SNAKE_CASE_ , image_processor=SCREAMING_SNAKE_CASE_)
lowercase__ : Any = self.prepare_image_inputs()
lowercase__ : List[str] = image_processor(SCREAMING_SNAKE_CASE_ , return_tensors="""np""")
lowercase__ : Any = processor(images=SCREAMING_SNAKE_CASE_ , return_tensors="""np""")
for key in input_image_proc.keys():
self.assertAlmostEqual(input_image_proc[key].sum() , input_processor[key].sum() , delta=1E-2)
def lowercase__ ( self):
'''simple docstring'''
lowercase__ : List[str] = self.get_image_processor()
lowercase__ : List[str] = self.get_tokenizer()
lowercase__ : Optional[int] = AlignProcessor(tokenizer=SCREAMING_SNAKE_CASE_ , image_processor=SCREAMING_SNAKE_CASE_)
lowercase__ : Tuple = """lower newer"""
lowercase__ : Dict = processor(text=SCREAMING_SNAKE_CASE_)
lowercase__ : List[str] = tokenizer(SCREAMING_SNAKE_CASE_ , padding="""max_length""" , max_length=64)
for key in encoded_tok.keys():
self.assertListEqual(encoded_tok[key] , encoded_processor[key])
def lowercase__ ( self):
'''simple docstring'''
lowercase__ : List[Any] = self.get_image_processor()
lowercase__ : Any = self.get_tokenizer()
lowercase__ : Tuple = AlignProcessor(tokenizer=SCREAMING_SNAKE_CASE_ , image_processor=SCREAMING_SNAKE_CASE_)
lowercase__ : Any = """lower newer"""
lowercase__ : Union[str, Any] = self.prepare_image_inputs()
lowercase__ : List[str] = processor(text=SCREAMING_SNAKE_CASE_ , images=SCREAMING_SNAKE_CASE_)
self.assertListEqual(list(inputs.keys()) , ["""input_ids""", """token_type_ids""", """attention_mask""", """pixel_values"""])
# test if it raises when no input is passed
with pytest.raises(SCREAMING_SNAKE_CASE_):
processor()
def lowercase__ ( self):
'''simple docstring'''
lowercase__ : Union[str, Any] = self.get_image_processor()
lowercase__ : Any = self.get_tokenizer()
lowercase__ : str = AlignProcessor(tokenizer=SCREAMING_SNAKE_CASE_ , image_processor=SCREAMING_SNAKE_CASE_)
lowercase__ : Optional[Any] = [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]
lowercase__ : int = processor.batch_decode(SCREAMING_SNAKE_CASE_)
lowercase__ : Optional[int] = tokenizer.batch_decode(SCREAMING_SNAKE_CASE_)
self.assertListEqual(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_)
def lowercase__ ( self):
'''simple docstring'''
lowercase__ : Optional[Any] = self.get_image_processor()
lowercase__ : Optional[int] = self.get_tokenizer()
lowercase__ : Tuple = AlignProcessor(tokenizer=SCREAMING_SNAKE_CASE_ , image_processor=SCREAMING_SNAKE_CASE_)
lowercase__ : Tuple = """lower newer"""
lowercase__ : Any = self.prepare_image_inputs()
lowercase__ : int = processor(text=SCREAMING_SNAKE_CASE_ , images=SCREAMING_SNAKE_CASE_)
self.assertListEqual(list(inputs.keys()) , processor.model_input_names)
| 12
|
"""simple docstring"""
import inspect
import jax
import jax.lax as lax
import jax.numpy as jnp
from ..utils import add_start_docstrings
from ..utils.logging import get_logger
lowerCAmelCase__ = get_logger(__name__)
lowerCAmelCase__ = r'\n Args:\n input_ids (`jnp.ndarray` of shape `(batch_size, sequence_length)`):\n Indices of input sequence tokens in the vocabulary.\n\n Indices can be obtained using [`PreTrainedTokenizer`]. See [`PreTrainedTokenizer.encode`] and\n [`PreTrainedTokenizer.__call__`] for details.\n\n [What are input IDs?](../glossary#input-ids)\n scores (`jnp.ndarray` of shape `(batch_size, config.vocab_size)`):\n Prediction scores of a language modeling head. These can be logits for each vocabulary when not using beam\n search or log softmax for each vocabulary token when using beam search\n kwargs (`Dict[str, Any]`, *optional*):\n Additional logits processor specific kwargs.\n\n Return:\n `jnp.ndarray` of shape `(batch_size, config.vocab_size)`: The processed prediction scores.\n\n'
class _lowerCAmelCase :
@add_start_docstrings(lowerCAmelCase_ )
def __call__( self , lowerCAmelCase_ , lowerCAmelCase_ ) -> jnp.ndarray:
raise NotImplementedError(
F"""{self.__class__} is an abstract class. Only classes inheriting this class can be called.""" )
class _lowerCAmelCase :
@add_start_docstrings(lowerCAmelCase_ )
def __call__( self , lowerCAmelCase_ , lowerCAmelCase_ ) -> jnp.ndarray:
raise NotImplementedError(
F"""{self.__class__} is an abstract class. Only classes inheriting this class can be called.""" )
class _lowerCAmelCase ( __UpperCAmelCase ):
@add_start_docstrings(lowerCAmelCase_ )
def __call__( self , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , **lowerCAmelCase_ ) -> jnp.ndarray:
for processor in self:
_SCREAMING_SNAKE_CASE : int = inspect.signature(processor.__call__ ).parameters
if len(lowerCAmelCase_ ) > 3:
if not all(arg in kwargs for arg in list(function_args.keys() )[2:] ):
raise ValueError(
F"""Make sure that all the required parameters: {list(function_args.keys() )} for """
F"""{processor.__class__} are passed to the logits processor.""" )
_SCREAMING_SNAKE_CASE : Any = processor(lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , **lowerCAmelCase_ )
else:
_SCREAMING_SNAKE_CASE : Optional[int] = processor(lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ )
return scores
class _lowerCAmelCase ( __UpperCAmelCase ):
def __init__( self , lowerCAmelCase_ ) -> Any:
if not isinstance(lowerCAmelCase_ , lowerCAmelCase_ ) or not (temperature > 0):
raise ValueError(F"""`temperature` has to be a strictly positive float, but is {temperature}""" )
_SCREAMING_SNAKE_CASE : Tuple = temperature
def __call__( self , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ ) -> jnp.ndarray:
_SCREAMING_SNAKE_CASE : Optional[int] = scores / self.temperature
return scores
class _lowerCAmelCase ( __UpperCAmelCase ):
def __init__( self , lowerCAmelCase_ , lowerCAmelCase_ = -float('Inf' ) , lowerCAmelCase_ = 1 ) -> int:
if not isinstance(lowerCAmelCase_ , lowerCAmelCase_ ) or (top_p < 0 or top_p > 1.0):
raise ValueError(F"""`top_p` has to be a float > 0 and < 1, but is {top_p}""" )
if not isinstance(lowerCAmelCase_ , lowerCAmelCase_ ) or (min_tokens_to_keep < 1):
raise ValueError(F"""`min_tokens_to_keep` has to be a positive integer, but is {min_tokens_to_keep}""" )
_SCREAMING_SNAKE_CASE : Union[str, Any] = top_p
_SCREAMING_SNAKE_CASE : Optional[int] = filter_value
_SCREAMING_SNAKE_CASE : Any = min_tokens_to_keep
def __call__( self , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ ) -> jnp.ndarray:
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE : Dict = lax.top_k(lowerCAmelCase_ , scores.shape[-1] )
_SCREAMING_SNAKE_CASE : Dict = jnp.full_like(lowerCAmelCase_ , self.filter_value )
_SCREAMING_SNAKE_CASE : int = jax.nn.softmax(lowerCAmelCase_ , axis=-1 ).cumsum(axis=-1 )
_SCREAMING_SNAKE_CASE : List[str] = cumulative_probs < self.top_p
# include the token that is higher than top_p as well
_SCREAMING_SNAKE_CASE : Optional[int] = jnp.roll(lowerCAmelCase_ , 1 )
score_mask |= score_mask.at[:, 0].set(lowerCAmelCase_ )
# min tokens to keep
_SCREAMING_SNAKE_CASE : Optional[Any] = score_mask.at[:, : self.min_tokens_to_keep].set(lowerCAmelCase_ )
_SCREAMING_SNAKE_CASE : Tuple = jnp.where(lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ )
_SCREAMING_SNAKE_CASE : Dict = jax.lax.sort_key_val(lowerCAmelCase_ , lowerCAmelCase_ )[-1]
return next_scores
class _lowerCAmelCase ( __UpperCAmelCase ):
def __init__( self , lowerCAmelCase_ , lowerCAmelCase_ = -float('Inf' ) , lowerCAmelCase_ = 1 ) -> Tuple:
if not isinstance(lowerCAmelCase_ , lowerCAmelCase_ ) or top_k <= 0:
raise ValueError(F"""`top_k` has to be a strictly positive integer, but is {top_k}""" )
_SCREAMING_SNAKE_CASE : Tuple = max(lowerCAmelCase_ , lowerCAmelCase_ )
_SCREAMING_SNAKE_CASE : Union[str, Any] = filter_value
def __call__( self , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ ) -> jnp.ndarray:
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE : int = scores.shape
_SCREAMING_SNAKE_CASE : int = jnp.full(batch_size * vocab_size , self.filter_value )
_SCREAMING_SNAKE_CASE : Union[str, Any] = min(self.top_k , scores.shape[-1] ) # Safety check
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE : List[Any] = lax.top_k(lowerCAmelCase_ , lowerCAmelCase_ )
_SCREAMING_SNAKE_CASE : List[Any] = jnp.broadcast_to((jnp.arange(lowerCAmelCase_ ) * vocab_size)[:, None] , (batch_size, topk) ).flatten()
_SCREAMING_SNAKE_CASE : List[Any] = topk_scores.flatten()
_SCREAMING_SNAKE_CASE : Dict = topk_indices.flatten() + shift
_SCREAMING_SNAKE_CASE : List[str] = next_scores_flat.at[topk_indices_flat].set(lowerCAmelCase_ )
_SCREAMING_SNAKE_CASE : int = next_scores_flat.reshape(lowerCAmelCase_ , lowerCAmelCase_ )
return next_scores
class _lowerCAmelCase ( __UpperCAmelCase ):
def __init__( self , lowerCAmelCase_ ) -> Dict:
_SCREAMING_SNAKE_CASE : List[Any] = bos_token_id
def __call__( self , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ ) -> jnp.ndarray:
_SCREAMING_SNAKE_CASE : Optional[int] = jnp.full(scores.shape , -float('inf' ) )
_SCREAMING_SNAKE_CASE : int = 1 - jnp.bool_(cur_len - 1 )
_SCREAMING_SNAKE_CASE : List[str] = jnp.where(lowerCAmelCase_ , new_scores.at[:, self.bos_token_id].set(0 ) , lowerCAmelCase_ )
return scores
class _lowerCAmelCase ( __UpperCAmelCase ):
def __init__( self , lowerCAmelCase_ , lowerCAmelCase_ ) -> List[Any]:
_SCREAMING_SNAKE_CASE : Optional[Any] = max_length
_SCREAMING_SNAKE_CASE : Any = eos_token_id
def __call__( self , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ ) -> jnp.ndarray:
_SCREAMING_SNAKE_CASE : List[Any] = jnp.full(scores.shape , -float('inf' ) )
_SCREAMING_SNAKE_CASE : Dict = 1 - jnp.bool_(cur_len - self.max_length + 1 )
_SCREAMING_SNAKE_CASE : Tuple = jnp.where(lowerCAmelCase_ , new_scores.at[:, self.eos_token_id].set(0 ) , lowerCAmelCase_ )
return scores
class _lowerCAmelCase ( __UpperCAmelCase ):
def __init__( self , lowerCAmelCase_ , lowerCAmelCase_ ) -> Optional[int]:
if not isinstance(lowerCAmelCase_ , lowerCAmelCase_ ) or min_length < 0:
raise ValueError(F"""`min_length` has to be a positive integer, but is {min_length}""" )
if not isinstance(lowerCAmelCase_ , lowerCAmelCase_ ) or eos_token_id < 0:
raise ValueError(F"""`eos_token_id` has to be a positive integer, but is {eos_token_id}""" )
_SCREAMING_SNAKE_CASE : Union[str, Any] = min_length
_SCREAMING_SNAKE_CASE : str = eos_token_id
def __call__( self , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ ) -> jnp.ndarray:
# create boolean flag to decide if min length penalty should be applied
_SCREAMING_SNAKE_CASE : Tuple = 1 - jnp.clip(cur_len - self.min_length , 0 , 1 )
_SCREAMING_SNAKE_CASE : Optional[Any] = jnp.where(lowerCAmelCase_ , scores.at[:, self.eos_token_id].set(-float('inf' ) ) , lowerCAmelCase_ )
return scores
class _lowerCAmelCase ( __UpperCAmelCase ):
def __init__( self , lowerCAmelCase_ , lowerCAmelCase_ ) -> List[str]:
_SCREAMING_SNAKE_CASE : List[str] = list(lowerCAmelCase_ )
_SCREAMING_SNAKE_CASE : Optional[Any] = begin_index
def __call__( self , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ ) -> str:
_SCREAMING_SNAKE_CASE : Any = 1 - jnp.bool_(cur_len - self.begin_index )
_SCREAMING_SNAKE_CASE : List[Any] = jnp.where(lowerCAmelCase_ , scores.at[:, self.begin_suppress_tokens].set(-float('inf' ) ) , lowerCAmelCase_ )
return scores
class _lowerCAmelCase ( __UpperCAmelCase ):
def __init__( self , lowerCAmelCase_ ) -> Optional[Any]:
_SCREAMING_SNAKE_CASE : str = list(lowerCAmelCase_ )
def __call__( self , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ ) -> jnp.ndarray:
_SCREAMING_SNAKE_CASE : Union[str, Any] = scores.at[..., self.suppress_tokens].set(-float('inf' ) )
return scores
class _lowerCAmelCase ( __UpperCAmelCase ):
def __init__( self , lowerCAmelCase_ ) -> Any:
_SCREAMING_SNAKE_CASE : Optional[Any] = dict(lowerCAmelCase_ )
# Converts the dictionary of format {index: token} containing the tokens to be forced to an array, where the
# index of the array corresponds to the index of the token to be forced, for XLA compatibility.
# Indexes without forced tokens will have a negative value.
_SCREAMING_SNAKE_CASE : Dict = jnp.ones((max(force_token_map.keys() ) + 1) , dtype=jnp.intaa ) * -1
for index, token in force_token_map.items():
if token is not None:
_SCREAMING_SNAKE_CASE : Dict = force_token_array.at[index].set(lowerCAmelCase_ )
_SCREAMING_SNAKE_CASE : str = jnp.intaa(lowerCAmelCase_ )
def __call__( self , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ ) -> jnp.ndarray:
def _force_token(lowerCAmelCase_ ):
_SCREAMING_SNAKE_CASE : str = scores.shape[0]
_SCREAMING_SNAKE_CASE : List[Any] = self.force_token_array[generation_idx]
_SCREAMING_SNAKE_CASE : Dict = jnp.ones_like(lowerCAmelCase_ , dtype=scores.dtype ) * -float('inf' )
_SCREAMING_SNAKE_CASE : Optional[Any] = jnp.zeros((batch_size, 1) , dtype=scores.dtype )
_SCREAMING_SNAKE_CASE : str = lax.dynamic_update_slice(lowerCAmelCase_ , lowerCAmelCase_ , (0, current_token) )
return new_scores
_SCREAMING_SNAKE_CASE : Optional[int] = lax.cond(
cur_len >= self.force_token_array.shape[0] , lambda: scores , lambda: lax.cond(
self.force_token_array[cur_len] >= 0 , lambda: _force_token(lowerCAmelCase_ ) , lambda: scores , ) , )
return scores
class _lowerCAmelCase ( __UpperCAmelCase ):
def __init__( self , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ ) -> str:
_SCREAMING_SNAKE_CASE : Tuple = generate_config.eos_token_id
_SCREAMING_SNAKE_CASE : str = generate_config.no_timestamps_token_id
_SCREAMING_SNAKE_CASE : Optional[int] = generate_config.no_timestamps_token_id + 1
_SCREAMING_SNAKE_CASE : str = decoder_input_length + 1
if generate_config.is_multilingual:
# room for language token and task token
self.begin_index += 2
if hasattr(lowerCAmelCase_ , 'max_initial_timestamp_index' ):
_SCREAMING_SNAKE_CASE : Union[str, Any] = generate_config.max_initial_timestamp_index
else:
_SCREAMING_SNAKE_CASE : Union[str, Any] = model_config.vocab_size
if self.max_initial_timestamp_index is None:
_SCREAMING_SNAKE_CASE : Dict = model_config.vocab_size
def __call__( self , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ ) -> Dict:
# suppress <|notimestamps|> which is handled by without_timestamps
_SCREAMING_SNAKE_CASE : Optional[Any] = scores.at[:, self.no_timestamps_token_id].set(-float('inf' ) )
def handle_pairs(lowerCAmelCase_ , lowerCAmelCase_ ):
_SCREAMING_SNAKE_CASE : int = jnp.where((cur_len - self.begin_index) >= 1 , lowerCAmelCase_ , lowerCAmelCase_ )
_SCREAMING_SNAKE_CASE : Tuple = jnp.where(
input_ids_k[cur_len - 1] >= self.timestamp_begin , True and last_was_timestamp , lowerCAmelCase_ , )
_SCREAMING_SNAKE_CASE : Any = jnp.where((cur_len - self.begin_index) < 2 , lowerCAmelCase_ , lowerCAmelCase_ )
_SCREAMING_SNAKE_CASE : Any = jnp.where(
input_ids_k[cur_len - 2] >= self.timestamp_begin , lowerCAmelCase_ , lowerCAmelCase_ , )
return jnp.where(
lowerCAmelCase_ , jnp.where(
penultimate_was_timestamp > 0 , scores_k.at[self.timestamp_begin :].set(-float('inf' ) ) , scores_k.at[: self.eos_token_id].set(-float('inf' ) ) , ) , lowerCAmelCase_ , )
_SCREAMING_SNAKE_CASE : Optional[Any] = jax.vmap(lowerCAmelCase_ )(lowerCAmelCase_ , lowerCAmelCase_ )
_SCREAMING_SNAKE_CASE : List[str] = jnp.where(cur_len == self.begin_index , lowerCAmelCase_ , lowerCAmelCase_ )
_SCREAMING_SNAKE_CASE : Optional[int] = jnp.where(
self.max_initial_timestamp_index is not None , True and apply_max_initial_timestamp , lowerCAmelCase_ , )
_SCREAMING_SNAKE_CASE : Tuple = self.timestamp_begin + self.max_initial_timestamp_index
_SCREAMING_SNAKE_CASE : Union[str, Any] = jnp.where(
lowerCAmelCase_ , scores.at[:, last_allowed + 1 :].set(-float('inf' ) ) , lowerCAmelCase_ , )
# if sum of probability over timestamps is above any other token, sample timestamp
_SCREAMING_SNAKE_CASE : str = jax.nn.log_softmax(lowerCAmelCase_ , axis=-1 )
def handle_cumulative_probs(lowerCAmelCase_ , lowerCAmelCase_ ):
_SCREAMING_SNAKE_CASE : Any = jax.nn.logsumexp(logprobs_k[self.timestamp_begin :] , axis=-1 )
_SCREAMING_SNAKE_CASE : int = jnp.max(logprobs_k[: self.timestamp_begin] )
return jnp.where(
timestamp_logprob > max_text_token_logprob , scores_k.at[: self.timestamp_begin].set(-float('inf' ) ) , lowerCAmelCase_ , )
_SCREAMING_SNAKE_CASE : Dict = jax.vmap(lowerCAmelCase_ )(lowerCAmelCase_ , lowerCAmelCase_ )
return scores
| 621
| 0
|
import json
import sys
def snake_case_ ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
with open(_SCREAMING_SNAKE_CASE , encoding="utf-8" ) as f:
__lowercase = json.load(_SCREAMING_SNAKE_CASE )
__lowercase = ["<details>", "<summary>Show updated benchmarks!</summary>", " "]
for benchmark_name in sorted(_SCREAMING_SNAKE_CASE ):
__lowercase = results[benchmark_name]
__lowercase = benchmark_name.split("/" )[-1]
output_md.append(F"""### Benchmark: {benchmark_file_name}""" )
__lowercase = "| metric |"
__lowercase = "|--------|"
__lowercase = "| new / old (diff) |"
for metric_name in sorted(_SCREAMING_SNAKE_CASE ):
__lowercase = benchmark_res[metric_name]
__lowercase = metric_vals["new"]
__lowercase = metric_vals.get("old" , _SCREAMING_SNAKE_CASE )
__lowercase = metric_vals.get("diff" , _SCREAMING_SNAKE_CASE )
__lowercase = F""" {new_val:f}""" if isinstance(_SCREAMING_SNAKE_CASE , (int, float) ) else "None"
if old_val is not None:
val_str += F""" / {old_val:f}""" if isinstance(_SCREAMING_SNAKE_CASE , (int, float) ) else "None"
if dif_val is not None:
val_str += F""" ({dif_val:f})""" if isinstance(_SCREAMING_SNAKE_CASE , (int, float) ) else "None"
title += " " + metric_name + " |"
lines += "---|"
value += val_str + " |"
output_md += [title, lines, value, " "]
output_md.append("</details>" )
with open(_SCREAMING_SNAKE_CASE , "w" , encoding="utf-8" ) as f:
f.writelines("\n".join(_SCREAMING_SNAKE_CASE ) )
if __name__ == "__main__":
snake_case__ : List[str] = sys.argv[1]
snake_case__ : Optional[Any] = sys.argv[2]
format_json_to_md(input_json_file, output_md_file)
| 655
|
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
snake_case__ : List[Any] = logging.get_logger(__name__)
snake_case__ : List[str] = {
"""hustvl/yolos-small""": """https://huggingface.co/hustvl/yolos-small/resolve/main/config.json""",
# See all YOLOS models at https://huggingface.co/models?filter=yolos
}
class _A ( _lowercase ):
'''simple docstring'''
_snake_case : List[Any] = """yolos"""
def __init__( self : Union[str, Any] , lowerCamelCase : Union[str, Any]=768 , lowerCamelCase : int=12 , lowerCamelCase : Union[str, Any]=12 , lowerCamelCase : Optional[Any]=3_072 , lowerCamelCase : Optional[int]="gelu" , lowerCamelCase : Dict=0.0 , lowerCamelCase : Optional[Any]=0.0 , lowerCamelCase : Any=0.02 , lowerCamelCase : Optional[Any]=1e-12 , lowerCamelCase : Optional[Any]=[512, 864] , lowerCamelCase : str=16 , lowerCamelCase : Dict=3 , lowerCamelCase : str=True , lowerCamelCase : List[Any]=100 , lowerCamelCase : Dict=True , lowerCamelCase : Dict=False , lowerCamelCase : List[str]=1 , lowerCamelCase : str=5 , lowerCamelCase : Any=2 , lowerCamelCase : str=5 , lowerCamelCase : Optional[int]=2 , lowerCamelCase : List[Any]=0.1 , **lowerCamelCase : List[Any] , ):
'''simple docstring'''
super().__init__(**lowerCamelCase )
__lowercase = hidden_size
__lowercase = num_hidden_layers
__lowercase = num_attention_heads
__lowercase = intermediate_size
__lowercase = hidden_act
__lowercase = hidden_dropout_prob
__lowercase = attention_probs_dropout_prob
__lowercase = initializer_range
__lowercase = layer_norm_eps
__lowercase = image_size
__lowercase = patch_size
__lowercase = num_channels
__lowercase = qkv_bias
__lowercase = num_detection_tokens
__lowercase = use_mid_position_embeddings
__lowercase = auxiliary_loss
# Hungarian matcher
__lowercase = class_cost
__lowercase = bbox_cost
__lowercase = giou_cost
# Loss coefficients
__lowercase = bbox_loss_coefficient
__lowercase = giou_loss_coefficient
__lowercase = eos_coefficient
class _A ( _lowercase ):
'''simple docstring'''
_snake_case : Dict = version.parse("""1.11""" )
@property
def _snake_case ( self : Union[str, Any] ):
'''simple docstring'''
return OrderedDict(
[
("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
] )
@property
def _snake_case ( self : str ):
'''simple docstring'''
return 1e-4
@property
def _snake_case ( self : Tuple ):
'''simple docstring'''
return 12
| 655
| 1
|
class lowerCAmelCase_ :
def __init__( self , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase ):
_lowercase : int = name
_lowercase : Union[str, Any] = value
_lowercase : Tuple = weight
def __repr__( self ):
return F"""{self.__class__.__name__}({self.name}, {self.value}, {self.weight})"""
def __a ( self ):
return self.value
def __a ( self ):
return self.name
def __a ( self ):
return self.weight
def __a ( self ):
return self.value / self.weight
def __magic_name__ ( SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ) -> List[str]:
_lowercase : Union[str, Any] = []
for i in range(len(SCREAMING_SNAKE_CASE ) ):
menu.append(Things(name[i] , value[i] , weight[i] ) )
return menu
def __magic_name__ ( SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ) -> Tuple:
_lowercase : List[Any] = sorted(SCREAMING_SNAKE_CASE , key=SCREAMING_SNAKE_CASE , reverse=SCREAMING_SNAKE_CASE )
_lowercase : Dict = []
_lowercase , _lowercase : str = 0.0, 0.0
for i in range(len(SCREAMING_SNAKE_CASE ) ):
if (total_cost + items_copy[i].get_weight()) <= max_cost:
result.append(items_copy[i] )
total_cost += items_copy[i].get_weight()
total_value += items_copy[i].get_value()
return (result, total_value)
def __magic_name__ ( ) -> List[str]:
pass
if __name__ == "__main__":
import doctest
doctest.testmod()
| 66
|
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
a_ = logging.get_logger(__name__)
a_ = '▁'
a_ = {'vocab_file': 'spiece.model'}
a_ = {
'vocab_file': {
'google/reformer-crime-and-punishment': (
'https://huggingface.co/google/reformer-crime-and-punishment/resolve/main/spiece.model'
)
}
}
a_ = {
'google/reformer-crime-and-punishment': 524_288,
}
class _lowercase ( snake_case_ ):
lowercase = VOCAB_FILES_NAMES
lowercase = PRETRAINED_VOCAB_FILES_MAP
lowercase = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
lowercase = ['input_ids', 'attention_mask']
def __init__( self : Any , snake_case : List[Any] , snake_case : Any="</s>" , snake_case : Optional[Any]="<unk>" , snake_case : str=[] , snake_case : Optional[Dict[str, Any]] = None , **snake_case : str , ) -> None:
"""simple docstring"""
UpperCamelCase_ : Dict = {} if sp_model_kwargs is None else sp_model_kwargs
super().__init__(
eos_token=snake_case , unk_token=snake_case , additional_special_tokens=snake_case , sp_model_kwargs=self.sp_model_kwargs , **snake_case , )
UpperCamelCase_ : Dict = vocab_file
UpperCamelCase_ : Dict = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(snake_case )
@property
def SCREAMING_SNAKE_CASE__ ( self : Tuple ) -> Optional[Any]:
"""simple docstring"""
return self.sp_model.get_piece_size()
def SCREAMING_SNAKE_CASE__ ( self : Optional[int] ) -> Dict[str, int]:
"""simple docstring"""
UpperCamelCase_ : List[str] = {self.convert_ids_to_tokens(snake_case ): i for i in range(self.vocab_size )}
vocab.update(self.added_tokens_encoder )
return vocab
def __getstate__( self : Tuple ) -> List[str]:
"""simple docstring"""
UpperCamelCase_ : Dict = self.__dict__.copy()
UpperCamelCase_ : Any = None
return state
def __setstate__( self : Optional[Any] , snake_case : Any ) -> Dict:
"""simple docstring"""
UpperCamelCase_ : Dict = d
# for backward compatibility
if not hasattr(self , 'sp_model_kwargs' ):
UpperCamelCase_ : Optional[int] = {}
UpperCamelCase_ : List[Any] = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(self.vocab_file )
def SCREAMING_SNAKE_CASE__ ( self : Tuple , snake_case : str ) -> List[str]:
"""simple docstring"""
return self.sp_model.encode(snake_case , out_type=snake_case )
def SCREAMING_SNAKE_CASE__ ( self : str , snake_case : Optional[int] ) -> int:
"""simple docstring"""
return self.sp_model.piece_to_id(snake_case )
def SCREAMING_SNAKE_CASE__ ( self : Any , snake_case : Union[str, Any] ) -> str:
"""simple docstring"""
if index < self.sp_model.get_piece_size():
UpperCamelCase_ : Tuple = self.sp_model.IdToPiece(snake_case )
return token
def SCREAMING_SNAKE_CASE__ ( self : Tuple , snake_case : List[Any] ) -> List[Any]:
"""simple docstring"""
UpperCamelCase_ : Any = []
UpperCamelCase_ : Tuple = ''
for token in tokens:
# make sure that special tokens are not decoded using sentencepiece model
if token in self.all_special_tokens:
out_string += self.sp_model.decode(snake_case ) + token
UpperCamelCase_ : int = []
else:
current_sub_tokens.append(snake_case )
out_string += self.sp_model.decode(snake_case )
return out_string.strip()
def SCREAMING_SNAKE_CASE__ ( self : Union[str, Any] , snake_case : str , snake_case : Optional[str] = None ) -> Tuple[str]:
"""simple docstring"""
if not os.path.isdir(snake_case ):
logger.error(f"Vocabulary path ({save_directory}) should be a directory" )
return
UpperCamelCase_ : Union[str, Any] = os.path.join(
snake_case , (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file'] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(snake_case ) and os.path.isfile(self.vocab_file ):
copyfile(self.vocab_file , snake_case )
elif not os.path.isfile(self.vocab_file ):
with open(snake_case , 'wb' ) as fi:
UpperCamelCase_ : str = self.sp_model.serialized_model_proto()
fi.write(snake_case )
return (out_vocab_file,)
| 417
| 0
|
from collections import Counter
import numpy as np
from sklearn import datasets
from sklearn.model_selection import train_test_split
SCREAMING_SNAKE_CASE_ = datasets.load_iris()
SCREAMING_SNAKE_CASE_ = np.array(data["""data"""])
SCREAMING_SNAKE_CASE_ = np.array(data["""target"""])
SCREAMING_SNAKE_CASE_ = data["""target_names"""]
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = train_test_split(X, y)
def __lowercase ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) -> Any:
'''simple docstring'''
return np.linalg.norm(np.array(__snake_case ) - np.array(__snake_case ) )
def __lowercase ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE=5 ) -> Optional[int]:
'''simple docstring'''
SCREAMING_SNAKE_CASE = zip(__snake_case , __snake_case )
# List of distances of all points from the point to be classified
SCREAMING_SNAKE_CASE = []
for data_point in data:
SCREAMING_SNAKE_CASE = euclidean_distance(data_point[0] , __snake_case )
distances.append((distance, data_point[1]) )
# Choosing 'k' points with the least distances.
SCREAMING_SNAKE_CASE = [i[1] for i in sorted(__snake_case )[:k]]
# Most commonly occurring class among them
# is the class into which the point is classified
SCREAMING_SNAKE_CASE = Counter(__snake_case ).most_common(1 )[0][0]
return classes[result]
if __name__ == "__main__":
print(classifier(X_train, y_train, classes, [4.4, 3.1, 1.3, 1.4]))
| 705
|
import inspect
import unittest
import numpy as np
from tests.test_modeling_common import floats_tensor
from transformers import MaskaFormerConfig, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils import cached_property
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import MaskaFormerForUniversalSegmentation, MaskaFormerModel
if is_vision_available():
from transformers import MaskaFormerImageProcessor
if is_vision_available():
from PIL import Image
class UpperCamelCase__ :
'''simple docstring'''
def __init__( self : Tuple ,lowerCamelCase__ : Tuple ,lowerCamelCase__ : Tuple=2 ,lowerCamelCase__ : Optional[int]=True ,lowerCamelCase__ : int=False ,lowerCamelCase__ : List[str]=10 ,lowerCamelCase__ : int=3 ,lowerCamelCase__ : Optional[Any]=32 * 8 ,lowerCamelCase__ : Tuple=32 * 8 ,lowerCamelCase__ : Tuple=4 ,lowerCamelCase__ : int=64 ,) -> List[Any]:
'''simple docstring'''
SCREAMING_SNAKE_CASE = parent
SCREAMING_SNAKE_CASE = batch_size
SCREAMING_SNAKE_CASE = is_training
SCREAMING_SNAKE_CASE = use_auxiliary_loss
SCREAMING_SNAKE_CASE = num_queries
SCREAMING_SNAKE_CASE = num_channels
SCREAMING_SNAKE_CASE = min_size
SCREAMING_SNAKE_CASE = max_size
SCREAMING_SNAKE_CASE = num_labels
SCREAMING_SNAKE_CASE = hidden_dim
SCREAMING_SNAKE_CASE = hidden_dim
def SCREAMING_SNAKE_CASE__ ( self : Union[str, Any] ) -> str:
'''simple docstring'''
SCREAMING_SNAKE_CASE = floats_tensor([self.batch_size, self.num_channels, self.min_size, self.max_size] ).to(
lowerCamelCase__ )
SCREAMING_SNAKE_CASE = torch.ones([self.batch_size, self.min_size, self.max_size] ,device=lowerCamelCase__ )
SCREAMING_SNAKE_CASE = (
torch.rand([self.batch_size, self.num_labels, self.min_size, self.max_size] ,device=lowerCamelCase__ ) > 0.5
).float()
SCREAMING_SNAKE_CASE = (torch.rand((self.batch_size, self.num_labels) ,device=lowerCamelCase__ ) > 0.5).long()
SCREAMING_SNAKE_CASE = self.get_config()
return config, pixel_values, pixel_mask, mask_labels, class_labels
def SCREAMING_SNAKE_CASE__ ( self : Optional[int] ) -> int:
'''simple docstring'''
SCREAMING_SNAKE_CASE = MaskaFormerConfig(
hidden_size=self.hidden_dim ,)
SCREAMING_SNAKE_CASE = self.num_queries
SCREAMING_SNAKE_CASE = self.num_labels
SCREAMING_SNAKE_CASE = [1, 1, 1, 1]
SCREAMING_SNAKE_CASE = self.num_channels
SCREAMING_SNAKE_CASE = 64
SCREAMING_SNAKE_CASE = 128
SCREAMING_SNAKE_CASE = self.hidden_dim
SCREAMING_SNAKE_CASE = self.hidden_dim
SCREAMING_SNAKE_CASE = self.hidden_dim
return config
def SCREAMING_SNAKE_CASE__ ( self : str ) -> Dict:
'''simple docstring'''
SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE = self.prepare_config_and_inputs()
SCREAMING_SNAKE_CASE = {"""pixel_values""": pixel_values, """pixel_mask""": pixel_mask}
return config, inputs_dict
def SCREAMING_SNAKE_CASE__ ( self : List[str] ,lowerCamelCase__ : List[str] ,lowerCamelCase__ : Dict ) -> str:
'''simple docstring'''
SCREAMING_SNAKE_CASE = output.encoder_hidden_states
SCREAMING_SNAKE_CASE = output.pixel_decoder_hidden_states
SCREAMING_SNAKE_CASE = output.transformer_decoder_hidden_states
self.parent.assertTrue(len(lowerCamelCase__ ) ,len(config.backbone_config.depths ) )
self.parent.assertTrue(len(lowerCamelCase__ ) ,len(config.backbone_config.depths ) )
self.parent.assertTrue(len(lowerCamelCase__ ) ,config.decoder_layers )
def SCREAMING_SNAKE_CASE__ ( self : Union[str, Any] ,lowerCamelCase__ : Optional[int] ,lowerCamelCase__ : Any ,lowerCamelCase__ : int ,lowerCamelCase__ : Optional[int]=False ) -> int:
'''simple docstring'''
with torch.no_grad():
SCREAMING_SNAKE_CASE = MaskaFormerModel(config=lowerCamelCase__ )
model.to(lowerCamelCase__ )
model.eval()
SCREAMING_SNAKE_CASE = model(pixel_values=lowerCamelCase__ ,pixel_mask=lowerCamelCase__ )
SCREAMING_SNAKE_CASE = model(lowerCamelCase__ ,output_hidden_states=lowerCamelCase__ )
self.parent.assertEqual(
output.transformer_decoder_last_hidden_state.shape ,(self.batch_size, self.num_queries, self.hidden_dim) ,)
# let's ensure the other two hidden state exists
self.parent.assertTrue(output.pixel_decoder_last_hidden_state is not None )
self.parent.assertTrue(output.encoder_last_hidden_state is not None )
if output_hidden_states:
self.check_output_hidden_state(lowerCamelCase__ ,lowerCamelCase__ )
def SCREAMING_SNAKE_CASE__ ( self : Any ,lowerCamelCase__ : Any ,lowerCamelCase__ : List[Any] ,lowerCamelCase__ : Union[str, Any] ,lowerCamelCase__ : Tuple ,lowerCamelCase__ : Any ) -> Optional[int]:
'''simple docstring'''
SCREAMING_SNAKE_CASE = MaskaFormerForUniversalSegmentation(config=lowerCamelCase__ )
model.to(lowerCamelCase__ )
model.eval()
def comm_check_on_output(lowerCamelCase__ : Optional[int] ):
# let's still check that all the required stuff is there
self.parent.assertTrue(result.transformer_decoder_last_hidden_state is not None )
self.parent.assertTrue(result.pixel_decoder_last_hidden_state is not None )
self.parent.assertTrue(result.encoder_last_hidden_state is not None )
# okay, now we need to check the logits shape
# due to the encoder compression, masks have a //4 spatial size
self.parent.assertEqual(
result.masks_queries_logits.shape ,(self.batch_size, self.num_queries, self.min_size // 4, self.max_size // 4) ,)
# + 1 for null class
self.parent.assertEqual(
result.class_queries_logits.shape ,(self.batch_size, self.num_queries, self.num_labels + 1) )
with torch.no_grad():
SCREAMING_SNAKE_CASE = model(pixel_values=lowerCamelCase__ ,pixel_mask=lowerCamelCase__ )
SCREAMING_SNAKE_CASE = model(lowerCamelCase__ )
comm_check_on_output(lowerCamelCase__ )
SCREAMING_SNAKE_CASE = model(
pixel_values=lowerCamelCase__ ,pixel_mask=lowerCamelCase__ ,mask_labels=lowerCamelCase__ ,class_labels=lowerCamelCase__ )
comm_check_on_output(lowerCamelCase__ )
self.parent.assertTrue(result.loss is not None )
self.parent.assertEqual(result.loss.shape ,torch.Size([1] ) )
@require_torch
class UpperCamelCase__ ( lowerCAmelCase_ , lowerCAmelCase_ , unittest.TestCase ):
'''simple docstring'''
__snake_case : Union[str, Any] = (MaskaFormerModel, MaskaFormerForUniversalSegmentation) if is_torch_available() else ()
__snake_case : Optional[Any] = {"feature-extraction": MaskaFormerModel} if is_torch_available() else {}
__snake_case : Dict = False
__snake_case : Tuple = False
__snake_case : Union[str, Any] = False
__snake_case : Optional[Any] = False
def SCREAMING_SNAKE_CASE__ ( self : int ) -> Optional[int]:
'''simple docstring'''
SCREAMING_SNAKE_CASE = MaskaFormerModelTester(self )
SCREAMING_SNAKE_CASE = ConfigTester(self ,config_class=lowerCamelCase__ ,has_text_modality=lowerCamelCase__ )
def SCREAMING_SNAKE_CASE__ ( self : int ) -> Dict:
'''simple docstring'''
self.config_tester.run_common_tests()
def SCREAMING_SNAKE_CASE__ ( self : str ) -> Union[str, Any]:
'''simple docstring'''
SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE = self.model_tester.prepare_config_and_inputs_for_common()
self.model_tester.create_and_check_maskaformer_model(lowerCamelCase__ ,**lowerCamelCase__ ,output_hidden_states=lowerCamelCase__ )
def SCREAMING_SNAKE_CASE__ ( self : List[str] ) -> Optional[Any]:
'''simple docstring'''
SCREAMING_SNAKE_CASE = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_maskaformer_instance_segmentation_head_model(*lowerCamelCase__ )
@unittest.skip(reason="""Mask2Former does not use inputs_embeds""" )
def SCREAMING_SNAKE_CASE__ ( self : Dict ) -> int:
'''simple docstring'''
pass
@unittest.skip(reason="""Mask2Former does not have a get_input_embeddings method""" )
def SCREAMING_SNAKE_CASE__ ( self : Tuple ) -> Tuple:
'''simple docstring'''
pass
@unittest.skip(reason="""Mask2Former is not a generative model""" )
def SCREAMING_SNAKE_CASE__ ( self : Tuple ) -> Dict:
'''simple docstring'''
pass
@unittest.skip(reason="""Mask2Former does not use token embeddings""" )
def SCREAMING_SNAKE_CASE__ ( self : Optional[Any] ) -> List[Any]:
'''simple docstring'''
pass
@require_torch_multi_gpu
@unittest.skip(
reason="""Mask2Former has some layers using `add_module` which doesn't work well with `nn.DataParallel`""" )
def SCREAMING_SNAKE_CASE__ ( self : Tuple ) -> Any:
'''simple docstring'''
pass
@unittest.skip("""Will be fixed soon by reducing the size of the model used for common tests.""" )
def SCREAMING_SNAKE_CASE__ ( self : List[str] ) -> Tuple:
'''simple docstring'''
pass
def SCREAMING_SNAKE_CASE__ ( self : Optional[int] ) -> List[Any]:
'''simple docstring'''
SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
SCREAMING_SNAKE_CASE = model_class(lowerCamelCase__ )
SCREAMING_SNAKE_CASE = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
SCREAMING_SNAKE_CASE = [*signature.parameters.keys()]
SCREAMING_SNAKE_CASE = ["""pixel_values"""]
self.assertListEqual(arg_names[:1] ,lowerCamelCase__ )
@slow
def SCREAMING_SNAKE_CASE__ ( self : Any ) -> Any:
'''simple docstring'''
for model_name in ["facebook/mask2former-swin-small-coco-instance"]:
SCREAMING_SNAKE_CASE = MaskaFormerModel.from_pretrained(lowerCamelCase__ )
self.assertIsNotNone(lowerCamelCase__ )
def SCREAMING_SNAKE_CASE__ ( self : Any ) -> Union[str, Any]:
'''simple docstring'''
SCREAMING_SNAKE_CASE = (self.model_tester.min_size,) * 2
SCREAMING_SNAKE_CASE = {
"""pixel_values""": torch.randn((2, 3, *size) ,device=lowerCamelCase__ ),
"""mask_labels""": torch.randn((2, 10, *size) ,device=lowerCamelCase__ ),
"""class_labels""": torch.zeros(2 ,10 ,device=lowerCamelCase__ ).long(),
}
SCREAMING_SNAKE_CASE = self.model_tester.get_config()
SCREAMING_SNAKE_CASE = MaskaFormerForUniversalSegmentation(lowerCamelCase__ ).to(lowerCamelCase__ )
SCREAMING_SNAKE_CASE = model(**lowerCamelCase__ )
self.assertTrue(outputs.loss is not None )
def SCREAMING_SNAKE_CASE__ ( self : int ) -> List[str]:
'''simple docstring'''
SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE = self.model_tester.prepare_config_and_inputs_for_common()
self.model_tester.create_and_check_maskaformer_model(lowerCamelCase__ ,**lowerCamelCase__ ,output_hidden_states=lowerCamelCase__ )
def SCREAMING_SNAKE_CASE__ ( self : Dict ) -> Optional[Any]:
'''simple docstring'''
SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
SCREAMING_SNAKE_CASE = model_class(lowerCamelCase__ ).to(lowerCamelCase__ )
SCREAMING_SNAKE_CASE = model(**lowerCamelCase__ ,output_attentions=lowerCamelCase__ )
self.assertTrue(outputs.attentions is not None )
def SCREAMING_SNAKE_CASE__ ( self : Tuple ) -> List[str]:
'''simple docstring'''
if not self.model_tester.is_training:
return
SCREAMING_SNAKE_CASE = self.all_model_classes[1]
SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE = self.model_tester.prepare_config_and_inputs()
SCREAMING_SNAKE_CASE = model_class(lowerCamelCase__ )
model.to(lowerCamelCase__ )
model.train()
SCREAMING_SNAKE_CASE = model(lowerCamelCase__ ,mask_labels=lowerCamelCase__ ,class_labels=lowerCamelCase__ ).loss
loss.backward()
def SCREAMING_SNAKE_CASE__ ( self : int ) -> Optional[int]:
'''simple docstring'''
SCREAMING_SNAKE_CASE = self.all_model_classes[1]
SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE = self.model_tester.prepare_config_and_inputs()
SCREAMING_SNAKE_CASE = True
SCREAMING_SNAKE_CASE = True
SCREAMING_SNAKE_CASE = model_class(lowerCamelCase__ ).to(lowerCamelCase__ )
model.train()
SCREAMING_SNAKE_CASE = model(lowerCamelCase__ ,mask_labels=lowerCamelCase__ ,class_labels=lowerCamelCase__ )
SCREAMING_SNAKE_CASE = outputs.encoder_hidden_states[0]
encoder_hidden_states.retain_grad()
SCREAMING_SNAKE_CASE = outputs.pixel_decoder_hidden_states[0]
pixel_decoder_hidden_states.retain_grad()
SCREAMING_SNAKE_CASE = outputs.transformer_decoder_hidden_states[0]
transformer_decoder_hidden_states.retain_grad()
SCREAMING_SNAKE_CASE = outputs.attentions[0]
attentions.retain_grad()
outputs.loss.backward(retain_graph=lowerCamelCase__ )
self.assertIsNotNone(encoder_hidden_states.grad )
self.assertIsNotNone(pixel_decoder_hidden_states.grad )
self.assertIsNotNone(transformer_decoder_hidden_states.grad )
self.assertIsNotNone(attentions.grad )
SCREAMING_SNAKE_CASE_ = 1e-4
def __lowercase ( ) -> Any:
'''simple docstring'''
SCREAMING_SNAKE_CASE = Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""" )
return image
@require_vision
@slow
class UpperCamelCase__ ( unittest.TestCase ):
'''simple docstring'''
@cached_property
def SCREAMING_SNAKE_CASE__ ( self : str ) -> Optional[int]:
'''simple docstring'''
return "facebook/mask2former-swin-small-coco-instance"
@cached_property
def SCREAMING_SNAKE_CASE__ ( self : Optional[Any] ) -> Optional[int]:
'''simple docstring'''
return MaskaFormerImageProcessor.from_pretrained(self.model_checkpoints ) if is_vision_available() else None
def SCREAMING_SNAKE_CASE__ ( self : Dict ) -> int:
'''simple docstring'''
SCREAMING_SNAKE_CASE = MaskaFormerModel.from_pretrained(self.model_checkpoints ).to(lowerCamelCase__ )
SCREAMING_SNAKE_CASE = self.default_image_processor
SCREAMING_SNAKE_CASE = prepare_img()
SCREAMING_SNAKE_CASE = image_processor(lowerCamelCase__ ,return_tensors="""pt""" ).to(lowerCamelCase__ )
SCREAMING_SNAKE_CASE = inputs["""pixel_values"""].shape
# check size is divisible by 32
self.assertTrue((inputs_shape[-1] % 32) == 0 and (inputs_shape[-2] % 32) == 0 )
# check size
self.assertEqual(lowerCamelCase__ ,(1, 3, 384, 384) )
with torch.no_grad():
SCREAMING_SNAKE_CASE = model(**lowerCamelCase__ )
SCREAMING_SNAKE_CASE = torch.tensor(
[[-0.2790, -1.0717, -1.1668], [-0.5128, -0.3128, -0.4987], [-0.5832, 0.1971, -0.0197]] ).to(lowerCamelCase__ )
self.assertTrue(
torch.allclose(
outputs.encoder_last_hidden_state[0, 0, :3, :3] ,lowerCamelCase__ ,atol=lowerCamelCase__ ) )
SCREAMING_SNAKE_CASE = torch.tensor(
[[0.8973, 1.1847, 1.1776], [1.1934, 1.5040, 1.5128], [1.1153, 1.4486, 1.4951]] ).to(lowerCamelCase__ )
self.assertTrue(
torch.allclose(
outputs.pixel_decoder_last_hidden_state[0, 0, :3, :3] ,lowerCamelCase__ ,atol=lowerCamelCase__ ) )
SCREAMING_SNAKE_CASE = torch.tensor(
[[2.1152, 1.7000, -0.8603], [1.5808, 1.8004, -0.9353], [1.6043, 1.7495, -0.5999]] ).to(lowerCamelCase__ )
self.assertTrue(
torch.allclose(
outputs.transformer_decoder_last_hidden_state[0, :3, :3] ,lowerCamelCase__ ,atol=lowerCamelCase__ ) )
def SCREAMING_SNAKE_CASE__ ( self : int ) -> int:
'''simple docstring'''
SCREAMING_SNAKE_CASE = MaskaFormerForUniversalSegmentation.from_pretrained(self.model_checkpoints ).to(lowerCamelCase__ ).eval()
SCREAMING_SNAKE_CASE = self.default_image_processor
SCREAMING_SNAKE_CASE = prepare_img()
SCREAMING_SNAKE_CASE = image_processor(lowerCamelCase__ ,return_tensors="""pt""" ).to(lowerCamelCase__ )
SCREAMING_SNAKE_CASE = inputs["""pixel_values"""].shape
# check size is divisible by 32
self.assertTrue((inputs_shape[-1] % 32) == 0 and (inputs_shape[-2] % 32) == 0 )
# check size
self.assertEqual(lowerCamelCase__ ,(1, 3, 384, 384) )
with torch.no_grad():
SCREAMING_SNAKE_CASE = model(**lowerCamelCase__ )
# masks_queries_logits
SCREAMING_SNAKE_CASE = outputs.masks_queries_logits
self.assertEqual(
masks_queries_logits.shape ,(1, model.config.num_queries, inputs_shape[-2] // 4, inputs_shape[-1] // 4) )
SCREAMING_SNAKE_CASE = [
[-8.7839, -9.0056, -8.8121],
[-7.4104, -7.0313, -6.5401],
[-6.6105, -6.3427, -6.4675],
]
SCREAMING_SNAKE_CASE = torch.tensor(lowerCamelCase__ ).to(lowerCamelCase__ )
self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3] ,lowerCamelCase__ ,atol=lowerCamelCase__ ) )
# class_queries_logits
SCREAMING_SNAKE_CASE = outputs.class_queries_logits
self.assertEqual(class_queries_logits.shape ,(1, model.config.num_queries, model.config.num_labels + 1) )
SCREAMING_SNAKE_CASE = torch.tensor(
[
[1.8324, -8.0835, -4.1922],
[0.8450, -9.0050, -3.6053],
[0.3045, -7.7293, -3.0275],
] ).to(lowerCamelCase__ )
self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3] ,lowerCamelCase__ ,atol=lowerCamelCase__ ) )
def SCREAMING_SNAKE_CASE__ ( self : int ) -> Optional[int]:
'''simple docstring'''
SCREAMING_SNAKE_CASE = MaskaFormerForUniversalSegmentation.from_pretrained(self.model_checkpoints ).to(lowerCamelCase__ ).eval()
SCREAMING_SNAKE_CASE = self.default_image_processor
SCREAMING_SNAKE_CASE = image_processor(
[np.zeros((3, 800, 1333) ), np.zeros((3, 800, 1333) )] ,segmentation_maps=[np.zeros((384, 384) ).astype(np.floataa ), np.zeros((384, 384) ).astype(np.floataa )] ,return_tensors="""pt""" ,)
SCREAMING_SNAKE_CASE = inputs["""pixel_values"""].to(lowerCamelCase__ )
SCREAMING_SNAKE_CASE = [el.to(lowerCamelCase__ ) for el in inputs["""mask_labels"""]]
SCREAMING_SNAKE_CASE = [el.to(lowerCamelCase__ ) for el in inputs["""class_labels"""]]
with torch.no_grad():
SCREAMING_SNAKE_CASE = model(**lowerCamelCase__ )
self.assertTrue(outputs.loss is not None )
| 116
| 0
|
from __future__ import annotations
import math
import random
from typing import Any
class __UpperCamelCase :
"""simple docstring"""
def __init__( self : List[str] ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE : list[Any] = []
__SCREAMING_SNAKE_CASE : int = 0
__SCREAMING_SNAKE_CASE : int = 0
def UpperCAmelCase__ ( self : Optional[Any] ):
"""simple docstring"""
return self.head == self.tail
def UpperCAmelCase__ ( self : Tuple , _A : Any ):
"""simple docstring"""
self.data.append(_A )
__SCREAMING_SNAKE_CASE : Dict = self.tail + 1
def UpperCAmelCase__ ( self : Dict ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE : Optional[Any] = self.data[self.head]
__SCREAMING_SNAKE_CASE : Dict = self.head + 1
return ret
def UpperCAmelCase__ ( self : List[Any] ):
"""simple docstring"""
return self.tail - self.head
def UpperCAmelCase__ ( self : Any ):
"""simple docstring"""
print(self.data )
print('''**************''' )
print(self.data[self.head : self.tail] )
class __UpperCamelCase :
"""simple docstring"""
def __init__( self : Any , _A : Any ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE : Any = data
__SCREAMING_SNAKE_CASE : MyNode | None = None
__SCREAMING_SNAKE_CASE : MyNode | None = None
__SCREAMING_SNAKE_CASE : int = 1
def UpperCAmelCase__ ( self : Optional[Any] ):
"""simple docstring"""
return self.data
def UpperCAmelCase__ ( self : Tuple ):
"""simple docstring"""
return self.left
def UpperCAmelCase__ ( self : Union[str, Any] ):
"""simple docstring"""
return self.right
def UpperCAmelCase__ ( self : str ):
"""simple docstring"""
return self.height
def UpperCAmelCase__ ( self : str , _A : Any ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE : Dict = data
def UpperCAmelCase__ ( self : Union[str, Any] , _A : MyNode | None ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE : Optional[int] = node
def UpperCAmelCase__ ( self : List[str] , _A : MyNode | None ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE : List[str] = node
def UpperCAmelCase__ ( self : Optional[Any] , _A : int ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE : Any = height
def a__ ( snake_case ):
"""simple docstring"""
if node is None:
return 0
return node.get_height()
def a__ ( snake_case , snake_case ):
"""simple docstring"""
if a > b:
return a
return b
def a__ ( snake_case ):
"""simple docstring"""
print('''left rotation node:''' , node.get_data() )
__SCREAMING_SNAKE_CASE : int = node.get_left()
assert ret is not None
node.set_left(ret.get_right() )
ret.set_right(snake_case )
__SCREAMING_SNAKE_CASE : List[str] = my_max(get_height(node.get_right() ) , get_height(node.get_left() ) ) + 1
node.set_height(snake_case )
__SCREAMING_SNAKE_CASE : Optional[Any] = my_max(get_height(ret.get_right() ) , get_height(ret.get_left() ) ) + 1
ret.set_height(snake_case )
return ret
def a__ ( snake_case ):
"""simple docstring"""
print('''right rotation node:''' , node.get_data() )
__SCREAMING_SNAKE_CASE : List[Any] = node.get_right()
assert ret is not None
node.set_right(ret.get_left() )
ret.set_left(snake_case )
__SCREAMING_SNAKE_CASE : List[Any] = my_max(get_height(node.get_right() ) , get_height(node.get_left() ) ) + 1
node.set_height(snake_case )
__SCREAMING_SNAKE_CASE : Optional[int] = my_max(get_height(ret.get_right() ) , get_height(ret.get_left() ) ) + 1
ret.set_height(snake_case )
return ret
def a__ ( snake_case ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE : List[str] = node.get_left()
assert left_child is not None
node.set_left(left_rotation(snake_case ) )
return right_rotation(snake_case )
def a__ ( snake_case ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE : str = node.get_right()
assert right_child is not None
node.set_right(right_rotation(snake_case ) )
return left_rotation(snake_case )
def a__ ( snake_case , snake_case ):
"""simple docstring"""
if node is None:
return MyNode(snake_case )
if data < node.get_data():
node.set_left(insert_node(node.get_left() , snake_case ) )
if (
get_height(node.get_left() ) - get_height(node.get_right() ) == 2
): # an unbalance detected
__SCREAMING_SNAKE_CASE : Optional[int] = node.get_left()
assert left_child is not None
if (
data < left_child.get_data()
): # new node is the left child of the left child
__SCREAMING_SNAKE_CASE : Optional[Any] = right_rotation(snake_case )
else:
__SCREAMING_SNAKE_CASE : List[str] = lr_rotation(snake_case )
else:
node.set_right(insert_node(node.get_right() , snake_case ) )
if get_height(node.get_right() ) - get_height(node.get_left() ) == 2:
__SCREAMING_SNAKE_CASE : int = node.get_right()
assert right_child is not None
if data < right_child.get_data():
__SCREAMING_SNAKE_CASE : Any = rl_rotation(snake_case )
else:
__SCREAMING_SNAKE_CASE : Optional[Any] = left_rotation(snake_case )
__SCREAMING_SNAKE_CASE : Optional[Any] = my_max(get_height(node.get_right() ) , get_height(node.get_left() ) ) + 1
node.set_height(snake_case )
return node
def a__ ( snake_case ):
"""simple docstring"""
while True:
__SCREAMING_SNAKE_CASE : int = root.get_right()
if right_child is None:
break
__SCREAMING_SNAKE_CASE : Union[str, Any] = right_child
return root.get_data()
def a__ ( snake_case ):
"""simple docstring"""
while True:
__SCREAMING_SNAKE_CASE : List[str] = root.get_left()
if left_child is None:
break
__SCREAMING_SNAKE_CASE : str = left_child
return root.get_data()
def a__ ( snake_case , snake_case ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE : Any = root.get_left()
__SCREAMING_SNAKE_CASE : List[Any] = root.get_right()
if root.get_data() == data:
if left_child is not None and right_child is not None:
__SCREAMING_SNAKE_CASE : List[str] = get_left_most(snake_case )
root.set_data(snake_case )
root.set_right(del_node(snake_case , snake_case ) )
elif left_child is not None:
__SCREAMING_SNAKE_CASE : List[Any] = left_child
elif right_child is not None:
__SCREAMING_SNAKE_CASE : Dict = right_child
else:
return None
elif root.get_data() > data:
if left_child is None:
print('''No such data''' )
return root
else:
root.set_left(del_node(snake_case , snake_case ) )
else: # root.get_data() < data
if right_child is None:
return root
else:
root.set_right(del_node(snake_case , snake_case ) )
if get_height(snake_case ) - get_height(snake_case ) == 2:
assert right_child is not None
if get_height(right_child.get_right() ) > get_height(right_child.get_left() ):
__SCREAMING_SNAKE_CASE : Dict = left_rotation(snake_case )
else:
__SCREAMING_SNAKE_CASE : Any = rl_rotation(snake_case )
elif get_height(snake_case ) - get_height(snake_case ) == -2:
assert left_child is not None
if get_height(left_child.get_left() ) > get_height(left_child.get_right() ):
__SCREAMING_SNAKE_CASE : Optional[Any] = right_rotation(snake_case )
else:
__SCREAMING_SNAKE_CASE : Any = lr_rotation(snake_case )
__SCREAMING_SNAKE_CASE : Any = my_max(get_height(root.get_right() ) , get_height(root.get_left() ) ) + 1
root.set_height(snake_case )
return root
class __UpperCamelCase :
"""simple docstring"""
def __init__( self : List[Any] ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE : MyNode | None = None
def UpperCAmelCase__ ( self : List[Any] ):
"""simple docstring"""
return get_height(self.root )
def UpperCAmelCase__ ( self : str , _A : Any ):
"""simple docstring"""
print('''insert:''' + str(_A ) )
__SCREAMING_SNAKE_CASE : Dict = insert_node(self.root , _A )
def UpperCAmelCase__ ( self : Tuple , _A : Any ):
"""simple docstring"""
print('''delete:''' + str(_A ) )
if self.root is None:
print('''Tree is empty!''' )
return
__SCREAMING_SNAKE_CASE : int = del_node(self.root , _A )
def __str__( self : int , ): # a level traversale, gives a more intuitive look on the tree
"""simple docstring"""
__SCREAMING_SNAKE_CASE : List[Any] = ''''''
__SCREAMING_SNAKE_CASE : Optional[int] = MyQueue()
q.push(self.root )
__SCREAMING_SNAKE_CASE : Any = self.get_height()
if layer == 0:
return output
__SCREAMING_SNAKE_CASE : Optional[int] = 0
while not q.is_empty():
__SCREAMING_SNAKE_CASE : str = q.pop()
__SCREAMING_SNAKE_CASE : Union[str, Any] = ''' ''' * int(math.pow(2 , layer - 1 ) )
output += space
if node is None:
output += "*"
q.push(_A )
q.push(_A )
else:
output += str(node.get_data() )
q.push(node.get_left() )
q.push(node.get_right() )
output += space
__SCREAMING_SNAKE_CASE : Optional[int] = cnt + 1
for i in range(100 ):
if cnt == math.pow(2 , _A ) - 1:
__SCREAMING_SNAKE_CASE : Union[str, Any] = layer - 1
if layer == 0:
output += "\n*************************************"
return output
output += "\n"
break
output += "\n*************************************"
return output
def a__ ( ):
"""simple docstring"""
import doctest
doctest.testmod()
if __name__ == "__main__":
_test()
lowercase_ = AVLtree()
lowercase_ = list(range(10))
random.shuffle(lst)
for i in lst:
t.insert(i)
print(str(t))
random.shuffle(lst)
for i in lst:
t.del_node(i)
print(str(t))
| 74
|
import gc
import unittest
from diffusers import FlaxDPMSolverMultistepScheduler, FlaxStableDiffusionPipeline
from diffusers.utils import is_flax_available, slow
from diffusers.utils.testing_utils import require_flax
if is_flax_available():
import jax
import jax.numpy as jnp
from flax.jax_utils import replicate
from flax.training.common_utils import shard
@slow
@require_flax
class __UpperCamelCase ( unittest.TestCase ):
"""simple docstring"""
def UpperCAmelCase__ ( self : List[Any] ):
"""simple docstring"""
super().tearDown()
gc.collect()
def UpperCAmelCase__ ( self : Any ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE, __SCREAMING_SNAKE_CASE : Union[str, Any] = FlaxStableDiffusionPipeline.from_pretrained(
'''stabilityai/stable-diffusion-2''' , revision='''bf16''' , dtype=jnp.bfloataa , )
__SCREAMING_SNAKE_CASE : Optional[Any] = '''A painting of a squirrel eating a burger'''
__SCREAMING_SNAKE_CASE : int = jax.device_count()
__SCREAMING_SNAKE_CASE : Tuple = num_samples * [prompt]
__SCREAMING_SNAKE_CASE : Optional[Any] = sd_pipe.prepare_inputs(_A )
__SCREAMING_SNAKE_CASE : Tuple = replicate(_A )
__SCREAMING_SNAKE_CASE : Optional[int] = shard(_A )
__SCREAMING_SNAKE_CASE : Dict = jax.random.PRNGKey(0 )
__SCREAMING_SNAKE_CASE : Optional[int] = jax.random.split(_A , jax.device_count() )
__SCREAMING_SNAKE_CASE : str = sd_pipe(_A , _A , _A , num_inference_steps=25 , jit=_A )[0]
assert images.shape == (jax.device_count(), 1, 768, 768, 3)
__SCREAMING_SNAKE_CASE : List[str] = images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:] )
__SCREAMING_SNAKE_CASE : Union[str, Any] = images[0, 253:256, 253:256, -1]
__SCREAMING_SNAKE_CASE : Dict = jnp.asarray(jax.device_get(image_slice.flatten() ) )
__SCREAMING_SNAKE_CASE : Tuple = jnp.array([0.42_38, 0.44_14, 0.43_95, 0.44_53, 0.46_29, 0.45_90, 0.45_31, 0.4_55_08, 0.45_12] )
print(F'''output_slice: {output_slice}''' )
assert jnp.abs(output_slice - expected_slice ).max() < 1e-2
def UpperCAmelCase__ ( self : Tuple ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE : List[str] = '''stabilityai/stable-diffusion-2'''
__SCREAMING_SNAKE_CASE, __SCREAMING_SNAKE_CASE : Dict = FlaxDPMSolverMultistepScheduler.from_pretrained(_A , subfolder='''scheduler''' )
__SCREAMING_SNAKE_CASE, __SCREAMING_SNAKE_CASE : int = FlaxStableDiffusionPipeline.from_pretrained(
_A , scheduler=_A , revision='''bf16''' , dtype=jnp.bfloataa , )
__SCREAMING_SNAKE_CASE : List[str] = scheduler_params
__SCREAMING_SNAKE_CASE : Tuple = '''A painting of a squirrel eating a burger'''
__SCREAMING_SNAKE_CASE : List[Any] = jax.device_count()
__SCREAMING_SNAKE_CASE : Tuple = num_samples * [prompt]
__SCREAMING_SNAKE_CASE : Any = sd_pipe.prepare_inputs(_A )
__SCREAMING_SNAKE_CASE : Optional[int] = replicate(_A )
__SCREAMING_SNAKE_CASE : List[str] = shard(_A )
__SCREAMING_SNAKE_CASE : int = jax.random.PRNGKey(0 )
__SCREAMING_SNAKE_CASE : Union[str, Any] = jax.random.split(_A , jax.device_count() )
__SCREAMING_SNAKE_CASE : List[Any] = sd_pipe(_A , _A , _A , num_inference_steps=25 , jit=_A )[0]
assert images.shape == (jax.device_count(), 1, 768, 768, 3)
__SCREAMING_SNAKE_CASE : Tuple = images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:] )
__SCREAMING_SNAKE_CASE : Dict = images[0, 253:256, 253:256, -1]
__SCREAMING_SNAKE_CASE : Optional[int] = jnp.asarray(jax.device_get(image_slice.flatten() ) )
__SCREAMING_SNAKE_CASE : Union[str, Any] = jnp.array([0.43_36, 0.4_29_69, 0.44_53, 0.41_99, 0.42_97, 0.45_31, 0.44_34, 0.44_34, 0.42_97] )
print(F'''output_slice: {output_slice}''' )
assert jnp.abs(output_slice - expected_slice ).max() < 1e-2
| 74
| 1
|
'''simple docstring'''
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow
if is_torch_available():
import torch
from transformers import XLMRobertaModel
@require_sentencepiece
@require_tokenizers
@require_torch
class a__ ( unittest.TestCase ):
@slow
def lowercase__ (self : Tuple ) -> Union[str, Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE : List[Any] = XLMRobertaModel.from_pretrained('''xlm-roberta-base''' )
SCREAMING_SNAKE_CASE : List[Any] = torch.tensor([[0, 581, 10269, 83, 99942, 136, 60742, 23, 70, 80583, 18276, 2]] )
# The dog is cute and lives in the garden house
SCREAMING_SNAKE_CASE : Optional[int] = torch.Size((1, 12, 768) ) # batch_size, sequence_length, embedding_vector_dim
SCREAMING_SNAKE_CASE : List[str] = torch.tensor(
[[-0.0101, 0.1218, -0.0803, 0.0801, 0.1327, 0.0776, -0.1215, 0.2383, 0.3338, 0.3106, 0.0300, 0.0252]] )
# xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.base')
# xlmr.eval()
# expected_output_values_last_dim = xlmr.extract_features(input_ids[0])[:, :, -1]
with torch.no_grad():
SCREAMING_SNAKE_CASE : List[Any] = model(__UpperCAmelCase )['''last_hidden_state'''].detach()
self.assertEqual(output.shape, __UpperCAmelCase )
# compare the actual values for a slice of last dim
self.assertTrue(torch.allclose(output[:, :, -1], __UpperCAmelCase, atol=1e-3 ) )
@slow
def lowercase__ (self : Any ) -> List[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Optional[Any] = XLMRobertaModel.from_pretrained('''xlm-roberta-large''' )
SCREAMING_SNAKE_CASE : Tuple = torch.tensor([[0, 581, 10269, 83, 99942, 136, 60742, 23, 70, 80583, 18276, 2]] )
# The dog is cute and lives in the garden house
SCREAMING_SNAKE_CASE : List[str] = torch.Size((1, 12, 1024) ) # batch_size, sequence_length, embedding_vector_dim
SCREAMING_SNAKE_CASE : List[Any] = torch.tensor(
[[-0.0699, -0.0318, 0.0705, -0.1241, 0.0999, -0.0520, 0.1004, -0.1838, -0.4704, 0.1437, 0.0821, 0.0126]] )
# xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.large')
# xlmr.eval()
# expected_output_values_last_dim = xlmr.extract_features(input_ids[0])[:, :, -1]
with torch.no_grad():
SCREAMING_SNAKE_CASE : Optional[Any] = model(__UpperCAmelCase )['''last_hidden_state'''].detach()
self.assertEqual(output.shape, __UpperCAmelCase )
# compare the actual values for a slice of last dim
self.assertTrue(torch.allclose(output[:, :, -1], __UpperCAmelCase, atol=1e-3 ) )
| 355
|
'''simple docstring'''
import os
from datetime import datetime as dt
from github import Github
snake_case_ = [
"""good first issue""",
"""good second issue""",
"""good difficult issue""",
"""enhancement""",
"""new pipeline/model""",
"""new scheduler""",
"""wip""",
]
def __lowercase ():
SCREAMING_SNAKE_CASE : List[Any] = Github(os.environ['''GITHUB_TOKEN'''] )
SCREAMING_SNAKE_CASE : List[str] = g.get_repo('''huggingface/diffusers''' )
SCREAMING_SNAKE_CASE : List[str] = repo.get_issues(state='''open''' )
for issue in open_issues:
SCREAMING_SNAKE_CASE : List[Any] = sorted(issue.get_comments() , key=lambda _SCREAMING_SNAKE_CASE : i.created_at , reverse=_SCREAMING_SNAKE_CASE )
SCREAMING_SNAKE_CASE : Optional[int] = comments[0] if len(_SCREAMING_SNAKE_CASE ) > 0 else None
if (
last_comment is not None
and last_comment.user.login == "github-actions[bot]"
and (dt.utcnow() - issue.updated_at).days > 7
and (dt.utcnow() - issue.created_at).days >= 30
and not any(label.name.lower() in LABELS_TO_EXEMPT for label in issue.get_labels() )
):
# Closes the issue after 7 days of inactivity since the Stalebot notification.
issue.edit(state='''closed''' )
elif (
"stale" in issue.get_labels()
and last_comment is not None
and last_comment.user.login != "github-actions[bot]"
):
# Opens the issue if someone other than Stalebot commented.
issue.edit(state='''open''' )
issue.remove_from_labels('''stale''' )
elif (
(dt.utcnow() - issue.updated_at).days > 23
and (dt.utcnow() - issue.created_at).days >= 30
and not any(label.name.lower() in LABELS_TO_EXEMPT for label in issue.get_labels() )
):
# Post a Stalebot notification after 23 days of inactivity.
issue.create_comment(
'''This issue has been automatically marked as stale because it has not had '''
'''recent activity. If you think this still needs to be addressed '''
'''please comment on this thread.\n\nPlease note that issues that do not follow the '''
'''[contributing guidelines](https://github.com/huggingface/diffusers/blob/main/CONTRIBUTING.md) '''
'''are likely to be ignored.''' )
issue.add_to_labels('''stale''' )
if __name__ == "__main__":
main()
| 355
| 1
|
import torch
from torch import nn
from torch.nn import CrossEntropyLoss, MSELoss
from transformers.file_utils import add_start_docstrings, add_start_docstrings_to_model_forward
from transformers.models.bert.modeling_bert import (
BERT_INPUTS_DOCSTRING,
BERT_START_DOCSTRING,
BertEmbeddings,
BertLayer,
BertPooler,
BertPreTrainedModel,
)
def __UpperCAmelCase ( lowerCamelCase_ : str ) -> Dict:
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Any = torch.exp(lowerCamelCase_ )
SCREAMING_SNAKE_CASE_ : Dict = torch.sum(lowerCamelCase_ , dim=1 ) # sum of exp(x_i)
SCREAMING_SNAKE_CASE_ : Union[str, Any] = torch.sum(x * exp_x , dim=1 ) # sum of x_i * exp(x_i)
return torch.log(lowerCamelCase_ ) - B / A
class lowerCAmelCase_ ( nn.Module ):
def __init__( self ,snake_case__ ):
super().__init__()
SCREAMING_SNAKE_CASE_ : Any = config.output_attentions
SCREAMING_SNAKE_CASE_ : Union[str, Any] = config.output_hidden_states
SCREAMING_SNAKE_CASE_ : Optional[int] = nn.ModuleList([BertLayer(snake_case__ ) for _ in range(config.num_hidden_layers )] )
SCREAMING_SNAKE_CASE_ : Tuple = nn.ModuleList([BertHighway(snake_case__ ) for _ in range(config.num_hidden_layers )] )
SCREAMING_SNAKE_CASE_ : Dict = [-1 for _ in range(config.num_hidden_layers )]
def snake_case ( self ,snake_case__ ):
if (type(snake_case__ ) is float) or (type(snake_case__ ) is int):
for i in range(len(self.early_exit_entropy ) ):
SCREAMING_SNAKE_CASE_ : str = x
else:
SCREAMING_SNAKE_CASE_ : List[str] = x
def snake_case ( self ,snake_case__ ):
SCREAMING_SNAKE_CASE_ : List[str] = pooler.state_dict()
for highway in self.highway:
for name, param in highway.pooler.state_dict().items():
param.copy_(loaded_model[name] )
def snake_case ( self ,snake_case__ ,snake_case__=None ,snake_case__=None ,snake_case__=None ,snake_case__=None ,):
SCREAMING_SNAKE_CASE_ : Optional[Any] = ()
SCREAMING_SNAKE_CASE_ : Union[str, Any] = ()
SCREAMING_SNAKE_CASE_ : List[Any] = ()
for i, layer_module in enumerate(self.layer ):
if self.output_hidden_states:
SCREAMING_SNAKE_CASE_ : Any = all_hidden_states + (hidden_states,)
SCREAMING_SNAKE_CASE_ : int = layer_module(
snake_case__ ,snake_case__ ,head_mask[i] ,snake_case__ ,snake_case__ )
SCREAMING_SNAKE_CASE_ : Any = layer_outputs[0]
if self.output_attentions:
SCREAMING_SNAKE_CASE_ : int = all_attentions + (layer_outputs[1],)
SCREAMING_SNAKE_CASE_ : Tuple = (hidden_states,)
if self.output_hidden_states:
SCREAMING_SNAKE_CASE_ : Optional[int] = current_outputs + (all_hidden_states,)
if self.output_attentions:
SCREAMING_SNAKE_CASE_ : Optional[Any] = current_outputs + (all_attentions,)
SCREAMING_SNAKE_CASE_ : Optional[Any] = self.highway[i](snake_case__ )
# logits, pooled_output
if not self.training:
SCREAMING_SNAKE_CASE_ : List[Any] = highway_exit[0]
SCREAMING_SNAKE_CASE_ : List[str] = entropy(snake_case__ )
SCREAMING_SNAKE_CASE_ : int = highway_exit + (highway_entropy,) # logits, hidden_states(?), entropy
SCREAMING_SNAKE_CASE_ : Dict = all_highway_exits + (highway_exit,)
if highway_entropy < self.early_exit_entropy[i]:
SCREAMING_SNAKE_CASE_ : Tuple = (highway_logits,) + current_outputs[1:] + (all_highway_exits,)
raise HighwayException(snake_case__ ,i + 1 )
else:
SCREAMING_SNAKE_CASE_ : Any = all_highway_exits + (highway_exit,)
# Add last layer
if self.output_hidden_states:
SCREAMING_SNAKE_CASE_ : Optional[Any] = all_hidden_states + (hidden_states,)
SCREAMING_SNAKE_CASE_ : Union[str, Any] = (hidden_states,)
if self.output_hidden_states:
SCREAMING_SNAKE_CASE_ : List[str] = outputs + (all_hidden_states,)
if self.output_attentions:
SCREAMING_SNAKE_CASE_ : Any = outputs + (all_attentions,)
SCREAMING_SNAKE_CASE_ : Tuple = outputs + (all_highway_exits,)
return outputs # last-layer hidden state, (all hidden states), (all attentions), all highway exits
@add_start_docstrings(
"The Bert Model transformer with early exiting (DeeBERT). " , lowerCamelCase_ , )
class lowerCAmelCase_ ( lowerCamelCase_ ):
def __init__( self ,snake_case__ ):
super().__init__(snake_case__ )
SCREAMING_SNAKE_CASE_ : Optional[Any] = config
SCREAMING_SNAKE_CASE_ : int = BertEmbeddings(snake_case__ )
SCREAMING_SNAKE_CASE_ : List[str] = DeeBertEncoder(snake_case__ )
SCREAMING_SNAKE_CASE_ : Tuple = BertPooler(snake_case__ )
self.init_weights()
def snake_case ( self ):
self.encoder.init_highway_pooler(self.pooler )
def snake_case ( self ):
return self.embeddings.word_embeddings
def snake_case ( self ,snake_case__ ):
SCREAMING_SNAKE_CASE_ : Any = value
def snake_case ( self ,snake_case__ ):
for layer, heads in heads_to_prune.items():
self.encoder.layer[layer].attention.prune_heads(snake_case__ )
@add_start_docstrings_to_model_forward(snake_case__ )
def snake_case ( self ,snake_case__=None ,snake_case__=None ,snake_case__=None ,snake_case__=None ,snake_case__=None ,snake_case__=None ,snake_case__=None ,snake_case__=None ,):
if input_ids is not None and inputs_embeds is not None:
raise ValueError('You cannot specify both input_ids and inputs_embeds at the same time' )
elif input_ids is not None:
SCREAMING_SNAKE_CASE_ : Dict = input_ids.size()
elif inputs_embeds is not None:
SCREAMING_SNAKE_CASE_ : str = inputs_embeds.size()[:-1]
else:
raise ValueError('You have to specify either input_ids or inputs_embeds' )
SCREAMING_SNAKE_CASE_ : Optional[Any] = input_ids.device if input_ids is not None else inputs_embeds.device
if attention_mask is None:
SCREAMING_SNAKE_CASE_ : int = torch.ones(snake_case__ ,device=snake_case__ )
if encoder_attention_mask is None:
SCREAMING_SNAKE_CASE_ : Optional[Any] = torch.ones(snake_case__ ,device=snake_case__ )
if token_type_ids is None:
SCREAMING_SNAKE_CASE_ : int = torch.zeros(snake_case__ ,dtype=torch.long ,device=snake_case__ )
# We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length]
# ourselves in which case we just need to make it broadcastable to all heads.
SCREAMING_SNAKE_CASE_ : torch.Tensor = self.get_extended_attention_mask(snake_case__ ,snake_case__ ,snake_case__ )
# If a 2D ou 3D attention mask is provided for the cross-attention
# we need to make broadcastable to [batch_size, num_heads, seq_length, seq_length]
if encoder_attention_mask.dim() == 3:
SCREAMING_SNAKE_CASE_ : List[str] = encoder_attention_mask[:, None, :, :]
if encoder_attention_mask.dim() == 2:
SCREAMING_SNAKE_CASE_ : str = encoder_attention_mask[:, None, None, :]
SCREAMING_SNAKE_CASE_ : Tuple = encoder_extended_attention_mask.to(
dtype=next(self.parameters() ).dtype ) # fp16 compatibility
SCREAMING_SNAKE_CASE_ : int = (1.0 - encoder_extended_attention_mask) * -10000.0
# Prepare head mask if needed
# 1.0 in head_mask indicate we keep the head
# attention_probs has shape bsz x n_heads x N x N
# input head_mask has shape [num_heads] or [num_hidden_layers x num_heads]
# and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length]
SCREAMING_SNAKE_CASE_ : List[Any] = self.get_head_mask(snake_case__ ,self.config.num_hidden_layers )
SCREAMING_SNAKE_CASE_ : List[Any] = self.embeddings(
input_ids=snake_case__ ,position_ids=snake_case__ ,token_type_ids=snake_case__ ,inputs_embeds=snake_case__ )
SCREAMING_SNAKE_CASE_ : Union[str, Any] = self.encoder(
snake_case__ ,attention_mask=snake_case__ ,head_mask=snake_case__ ,encoder_hidden_states=snake_case__ ,encoder_attention_mask=snake_case__ ,)
SCREAMING_SNAKE_CASE_ : Tuple = encoder_outputs[0]
SCREAMING_SNAKE_CASE_ : Optional[int] = self.pooler(snake_case__ )
SCREAMING_SNAKE_CASE_ : int = (
sequence_output,
pooled_output,
) + encoder_outputs[
1:
] # add hidden_states and attentions if they are here
return outputs # sequence_output, pooled_output, (hidden_states), (attentions), highway exits
class lowerCAmelCase_ ( lowerCamelCase_ ):
def __init__( self ,snake_case__ ,snake_case__ ):
SCREAMING_SNAKE_CASE_ : Optional[Any] = message
SCREAMING_SNAKE_CASE_ : int = exit_layer # start from 1!
class lowerCAmelCase_ ( nn.Module ):
def __init__( self ,snake_case__ ):
super().__init__()
SCREAMING_SNAKE_CASE_ : Union[str, Any] = BertPooler(snake_case__ )
SCREAMING_SNAKE_CASE_ : Any = nn.Dropout(config.hidden_dropout_prob )
SCREAMING_SNAKE_CASE_ : Any = nn.Linear(config.hidden_size ,config.num_labels )
def snake_case ( self ,snake_case__ ):
# Pooler
SCREAMING_SNAKE_CASE_ : Tuple = encoder_outputs[0]
SCREAMING_SNAKE_CASE_ : Dict = self.pooler(snake_case__ )
# "return" pooler_output
# BertModel
SCREAMING_SNAKE_CASE_ : List[str] = (pooler_input, pooler_output) + encoder_outputs[1:]
# "return" bmodel_output
# Dropout and classification
SCREAMING_SNAKE_CASE_ : List[Any] = bmodel_output[1]
SCREAMING_SNAKE_CASE_ : Optional[Any] = self.dropout(snake_case__ )
SCREAMING_SNAKE_CASE_ : List[str] = self.classifier(snake_case__ )
return logits, pooled_output
@add_start_docstrings(
"Bert Model (with early exiting - DeeBERT) with a classifier on top,\n also takes care of multi-layer training. " , lowerCamelCase_ , )
class lowerCAmelCase_ ( lowerCamelCase_ ):
def __init__( self ,snake_case__ ):
super().__init__(snake_case__ )
SCREAMING_SNAKE_CASE_ : Union[str, Any] = config.num_labels
SCREAMING_SNAKE_CASE_ : int = config.num_hidden_layers
SCREAMING_SNAKE_CASE_ : int = DeeBertModel(snake_case__ )
SCREAMING_SNAKE_CASE_ : str = nn.Dropout(config.hidden_dropout_prob )
SCREAMING_SNAKE_CASE_ : str = nn.Linear(config.hidden_size ,self.config.num_labels )
self.init_weights()
@add_start_docstrings_to_model_forward(snake_case__ )
def snake_case ( self ,snake_case__=None ,snake_case__=None ,snake_case__=None ,snake_case__=None ,snake_case__=None ,snake_case__=None ,snake_case__=None ,snake_case__=-1 ,snake_case__=False ,):
SCREAMING_SNAKE_CASE_ : str = self.num_layers
try:
SCREAMING_SNAKE_CASE_ : List[str] = self.bert(
snake_case__ ,attention_mask=snake_case__ ,token_type_ids=snake_case__ ,position_ids=snake_case__ ,head_mask=snake_case__ ,inputs_embeds=snake_case__ ,)
# sequence_output, pooled_output, (hidden_states), (attentions), highway exits
SCREAMING_SNAKE_CASE_ : str = outputs[1]
SCREAMING_SNAKE_CASE_ : int = self.dropout(snake_case__ )
SCREAMING_SNAKE_CASE_ : Any = self.classifier(snake_case__ )
SCREAMING_SNAKE_CASE_ : Union[str, Any] = (logits,) + outputs[2:] # add hidden states and attention if they are here
except HighwayException as e:
SCREAMING_SNAKE_CASE_ : Optional[Any] = e.message
SCREAMING_SNAKE_CASE_ : List[Any] = e.exit_layer
SCREAMING_SNAKE_CASE_ : Optional[Any] = outputs[0]
if not self.training:
SCREAMING_SNAKE_CASE_ : str = entropy(snake_case__ )
SCREAMING_SNAKE_CASE_ : str = []
SCREAMING_SNAKE_CASE_ : Any = []
if labels is not None:
if self.num_labels == 1:
# We are doing regression
SCREAMING_SNAKE_CASE_ : Tuple = MSELoss()
SCREAMING_SNAKE_CASE_ : List[Any] = loss_fct(logits.view(-1 ) ,labels.view(-1 ) )
else:
SCREAMING_SNAKE_CASE_ : str = CrossEntropyLoss()
SCREAMING_SNAKE_CASE_ : List[Any] = loss_fct(logits.view(-1 ,self.num_labels ) ,labels.view(-1 ) )
# work with highway exits
SCREAMING_SNAKE_CASE_ : List[str] = []
for highway_exit in outputs[-1]:
SCREAMING_SNAKE_CASE_ : Union[str, Any] = highway_exit[0]
if not self.training:
highway_logits_all.append(snake_case__ )
highway_entropy.append(highway_exit[2] )
if self.num_labels == 1:
# We are doing regression
SCREAMING_SNAKE_CASE_ : str = MSELoss()
SCREAMING_SNAKE_CASE_ : str = loss_fct(highway_logits.view(-1 ) ,labels.view(-1 ) )
else:
SCREAMING_SNAKE_CASE_ : Dict = CrossEntropyLoss()
SCREAMING_SNAKE_CASE_ : str = loss_fct(highway_logits.view(-1 ,self.num_labels ) ,labels.view(-1 ) )
highway_losses.append(snake_case__ )
if train_highway:
SCREAMING_SNAKE_CASE_ : str = (sum(highway_losses[:-1] ),) + outputs
# exclude the final highway, of course
else:
SCREAMING_SNAKE_CASE_ : List[Any] = (loss,) + outputs
if not self.training:
SCREAMING_SNAKE_CASE_ : Union[str, Any] = outputs + ((original_entropy, highway_entropy), exit_layer)
if output_layer >= 0:
SCREAMING_SNAKE_CASE_ : Dict = (
(outputs[0],) + (highway_logits_all[output_layer],) + outputs[2:]
) # use the highway of the last layer
return outputs # (loss), logits, (hidden_states), (attentions), (highway_exits)
| 105
|
# A Bipartite Graph is a graph whose vertices can be divided into two independent sets,
# U and V such that every edge (u, v) either connects a vertex from U to V or a vertex
# from V to U. In other words, for every edge (u, v), either u belongs to U and v to V,
# or u belongs to V and v to U. We can also say that there is no edge that connects
# vertices of same set.
def a__ ( snake_case__ : Dict ):
_UpperCAmelCase : str = [False] * len(snake_case__ )
_UpperCAmelCase : str = [-1] * len(snake_case__ )
def dfs(snake_case__ : Dict , snake_case__ : Optional[Any] ):
_UpperCAmelCase : str = True
_UpperCAmelCase : Optional[Any] = c
for u in graph[v]:
if not visited[u]:
dfs(snake_case__ , 1 - c )
for i in range(len(snake_case__ ) ):
if not visited[i]:
dfs(snake_case__ , 0 )
for i in range(len(snake_case__ ) ):
for j in graph[i]:
if color[i] == color[j]:
return False
return True
# Adjacency list of graph
SCREAMING_SNAKE_CASE__ : Optional[Any] = {0: [1, 3], 1: [0, 2], 2: [1, 3], 3: [0, 2], 4: []}
print(check_bipartite_dfs(graph))
| 643
| 0
|
'''simple docstring'''
import argparse
import torch
from transformers import MobileBertConfig, MobileBertForPreTraining, load_tf_weights_in_mobilebert
from transformers.utils import logging
logging.set_verbosity_info()
def __lowerCamelCase ( _lowercase , _lowercase , _lowercase ) -> List[Any]:
# Initialise PyTorch model
UpperCamelCase = MobileBertConfig.from_json_file(_lowercase )
print(F'Building PyTorch model from configuration: {config}' )
UpperCamelCase = MobileBertForPreTraining(_lowercase )
# Load weights from tf checkpoint
UpperCamelCase = load_tf_weights_in_mobilebert(_lowercase , _lowercase , _lowercase )
# Save pytorch-model
print(F'Save PyTorch model to {pytorch_dump_path}' )
torch.save(model.state_dict() , _lowercase )
if __name__ == "__main__":
_snake_case = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'''--tf_checkpoint_path''', default=None, type=str, required=True, help='''Path to the TensorFlow checkpoint path.'''
)
parser.add_argument(
'''--mobilebert_config_file''',
default=None,
type=str,
required=True,
help=(
'''The config json file corresponding to the pre-trained MobileBERT 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.'''
)
_snake_case = parser.parse_args()
convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.mobilebert_config_file, args.pytorch_dump_path)
| 714
|
from maths.prime_factors import prime_factors
def __lowerCamelCase ( _lowercase ) -> int:
if not isinstance(_lowercase , _lowercase ):
UpperCamelCase = F'Input value of [number={number}] must be an integer'
raise TypeError(_lowercase )
if number < 1:
raise ValueError('Input must be a positive integer' )
return -1 if len(prime_factors(_lowercase ) ) % 2 else 1
if __name__ == "__main__":
import doctest
doctest.testmod()
| 170
| 0
|
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
is_vision_available,
)
lowerCAmelCase__ : str ={'processing_layoutxlm': ['LayoutXLMProcessor']}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase__ : Any =['LayoutXLMTokenizer']
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase__ : List[str] =['LayoutXLMTokenizerFast']
if TYPE_CHECKING:
from .processing_layoutxlm import LayoutXLMProcessor
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_layoutxlm import LayoutXLMTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_layoutxlm_fast import LayoutXLMTokenizerFast
else:
import sys
lowerCAmelCase__ : List[str] =_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 101
|
from __future__ import annotations
def a__ ( A__, A__ = None, A__ = None ):
if start is None:
SCREAMING_SNAKE_CASE_ : List[str] = 0
if end is None:
SCREAMING_SNAKE_CASE_ : Optional[Any] = len(A__ ) - 1
if start >= end:
return
SCREAMING_SNAKE_CASE_ : Tuple = (start + end) // 2
slowsort(A__, A__, A__ )
slowsort(A__, mid + 1, A__ )
if sequence[end] < sequence[mid]:
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ : Optional[Any] = sequence[mid], sequence[end]
slowsort(A__, A__, end - 1 )
if __name__ == "__main__":
from doctest import testmod
testmod()
| 101
| 1
|
import unittest
import numpy as np
import torch
from diffusers import PNDMPipeline, PNDMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device
enable_full_determinism()
class lowerCamelCase ( unittest.TestCase ):
@property
def A( self):
torch.manual_seed(0)
__UpperCAmelCase : Dict = UNetaDModel(
block_out_channels=(3_2, 6_4) , layers_per_block=2 , sample_size=3_2 , in_channels=3 , out_channels=3 , down_block_types=('''DownBlock2D''', '''AttnDownBlock2D''') , up_block_types=('''AttnUpBlock2D''', '''UpBlock2D''') , )
return model
def A( self):
__UpperCAmelCase : Union[str, Any] = self.dummy_uncond_unet
__UpperCAmelCase : int = PNDMScheduler()
__UpperCAmelCase : List[str] = PNDMPipeline(unet=lowercase__ , scheduler=lowercase__)
pndm.to(lowercase__)
pndm.set_progress_bar_config(disable=lowercase__)
__UpperCAmelCase : List[Any] = torch.manual_seed(0)
__UpperCAmelCase : Any = pndm(generator=lowercase__ , num_inference_steps=2_0 , output_type='''numpy''').images
__UpperCAmelCase : Optional[int] = torch.manual_seed(0)
__UpperCAmelCase : Dict = pndm(generator=lowercase__ , num_inference_steps=2_0 , output_type='''numpy''' , return_dict=lowercase__)[0]
__UpperCAmelCase : int = image[0, -3:, -3:, -1]
__UpperCAmelCase : Tuple = image_from_tuple[0, -3:, -3:, -1]
assert image.shape == (1, 3_2, 3_2, 3)
__UpperCAmelCase : int = np.array([1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0])
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
assert np.abs(image_from_tuple_slice.flatten() - expected_slice).max() < 1e-2
@slow
@require_torch
class lowerCamelCase ( unittest.TestCase ):
def A( self):
__UpperCAmelCase : Any = '''google/ddpm-cifar10-32'''
__UpperCAmelCase : List[str] = UNetaDModel.from_pretrained(lowercase__)
__UpperCAmelCase : str = PNDMScheduler()
__UpperCAmelCase : Any = PNDMPipeline(unet=lowercase__ , scheduler=lowercase__)
pndm.to(lowercase__)
pndm.set_progress_bar_config(disable=lowercase__)
__UpperCAmelCase : Optional[Any] = torch.manual_seed(0)
__UpperCAmelCase : List[Any] = pndm(generator=lowercase__ , output_type='''numpy''').images
__UpperCAmelCase : int = image[0, -3:, -3:, -1]
assert image.shape == (1, 3_2, 3_2, 3)
__UpperCAmelCase : List[Any] = np.array([0.1_5_6_4, 0.1_4_6_4_5, 0.1_4_0_6, 0.1_4_7_1_5, 0.1_2_4_2_5, 0.1_4_0_4_5, 0.1_3_1_1_5, 0.1_2_1_7_5, 0.1_2_5])
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
| 675
|
from typing import Dict, List, Optional, Tuple, Union
import torch
from ...models import AutoencoderKL, TransformeraDModel
from ...schedulers import KarrasDiffusionSchedulers
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class lowerCamelCase ( _UpperCamelCase ):
def __init__( self , lowercase__ , lowercase__ , lowercase__ , lowercase__ = None , ):
super().__init__()
self.register_modules(transformer=lowercase__ , vae=lowercase__ , scheduler=lowercase__)
# create a imagenet -> id dictionary for easier use
__UpperCAmelCase : List[str] = {}
if idalabel is not None:
for key, value in idalabel.items():
for label in value.split(''','''):
__UpperCAmelCase : Dict = int(lowercase__)
__UpperCAmelCase : Tuple = dict(sorted(self.labels.items()))
def A( self , lowercase__):
if not isinstance(lowercase__ , lowercase__):
__UpperCAmelCase : Dict = list(lowercase__)
for l in label:
if l not in self.labels:
raise ValueError(
F"{l} does not exist. Please make sure to select one of the following labels: \n {self.labels}.")
return [self.labels[l] for l in label]
@torch.no_grad()
def __call__( self , lowercase__ , lowercase__ = 4.0 , lowercase__ = None , lowercase__ = 5_0 , lowercase__ = "pil" , lowercase__ = True , ):
__UpperCAmelCase : List[str] = len(lowercase__)
__UpperCAmelCase : str = self.transformer.config.sample_size
__UpperCAmelCase : List[str] = self.transformer.config.in_channels
__UpperCAmelCase : Union[str, Any] = randn_tensor(
shape=(batch_size, latent_channels, latent_size, latent_size) , generator=lowercase__ , device=self.device , dtype=self.transformer.dtype , )
__UpperCAmelCase : Optional[Any] = torch.cat([latents] * 2) if guidance_scale > 1 else latents
__UpperCAmelCase : Union[str, Any] = torch.tensor(lowercase__ , device=self.device).reshape(-1)
__UpperCAmelCase : Dict = torch.tensor([1_0_0_0] * batch_size , device=self.device)
__UpperCAmelCase : int = torch.cat([class_labels, class_null] , 0) if guidance_scale > 1 else class_labels
# set step values
self.scheduler.set_timesteps(lowercase__)
for t in self.progress_bar(self.scheduler.timesteps):
if guidance_scale > 1:
__UpperCAmelCase : List[str] = latent_model_input[: len(lowercase__) // 2]
__UpperCAmelCase : Optional[Any] = torch.cat([half, half] , dim=0)
__UpperCAmelCase : Optional[Any] = self.scheduler.scale_model_input(lowercase__ , lowercase__)
__UpperCAmelCase : Any = t
if not torch.is_tensor(lowercase__):
# TODO: this requires sync between CPU and GPU. So try to pass timesteps as tensors if you can
# This would be a good case for the `match` statement (Python 3.10+)
__UpperCAmelCase : List[str] = latent_model_input.device.type == '''mps'''
if isinstance(lowercase__ , lowercase__):
__UpperCAmelCase : Tuple = torch.floataa if is_mps else torch.floataa
else:
__UpperCAmelCase : Dict = torch.intaa if is_mps else torch.intaa
__UpperCAmelCase : List[str] = torch.tensor([timesteps] , dtype=lowercase__ , device=latent_model_input.device)
elif len(timesteps.shape) == 0:
__UpperCAmelCase : List[str] = timesteps[None].to(latent_model_input.device)
# broadcast to batch dimension in a way that's compatible with ONNX/Core ML
__UpperCAmelCase : Optional[int] = timesteps.expand(latent_model_input.shape[0])
# predict noise model_output
__UpperCAmelCase : Any = self.transformer(
lowercase__ , timestep=lowercase__ , class_labels=lowercase__).sample
# perform guidance
if guidance_scale > 1:
__UpperCAmelCase , __UpperCAmelCase : Optional[Any] = noise_pred[:, :latent_channels], noise_pred[:, latent_channels:]
__UpperCAmelCase , __UpperCAmelCase : Union[str, Any] = torch.split(lowercase__ , len(lowercase__) // 2 , dim=0)
__UpperCAmelCase : List[str] = uncond_eps + guidance_scale * (cond_eps - uncond_eps)
__UpperCAmelCase : str = torch.cat([half_eps, half_eps] , dim=0)
__UpperCAmelCase : Any = torch.cat([eps, rest] , dim=1)
# learned sigma
if self.transformer.config.out_channels // 2 == latent_channels:
__UpperCAmelCase , __UpperCAmelCase : Union[str, Any] = torch.split(lowercase__ , lowercase__ , dim=1)
else:
__UpperCAmelCase : Any = noise_pred
# compute previous image: x_t -> x_t-1
__UpperCAmelCase : Dict = self.scheduler.step(lowercase__ , lowercase__ , lowercase__).prev_sample
if guidance_scale > 1:
__UpperCAmelCase , __UpperCAmelCase : Any = latent_model_input.chunk(2 , dim=0)
else:
__UpperCAmelCase : List[Any] = latent_model_input
__UpperCAmelCase : List[str] = 1 / self.vae.config.scaling_factor * latents
__UpperCAmelCase : Optional[int] = self.vae.decode(lowercase__).sample
__UpperCAmelCase : List[str] = (samples / 2 + 0.5).clamp(0 , 1)
# we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
__UpperCAmelCase : str = samples.cpu().permute(0 , 2 , 3 , 1).float().numpy()
if output_type == "pil":
__UpperCAmelCase : Optional[int] = self.numpy_to_pil(lowercase__)
if not return_dict:
return (samples,)
return ImagePipelineOutput(images=lowercase__)
| 675
| 1
|
"""simple docstring"""
from typing import Optional
from urllib.parse import quote
import huggingface_hub as hfh
from packaging import version
def lowercase__ ( lowerCAmelCase : Optional[Any] , lowerCAmelCase : Union[str, Any] , lowerCAmelCase : Any = None ) -> List[Any]:
"""simple docstring"""
if version.parse(hfh.__version__ ).release < version.parse('0.11.0' ).release:
# old versions of hfh don't url-encode the file path
UpperCAmelCase = quote(_SCREAMING_SNAKE_CASE )
return hfh.hf_hub_url(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , repo_type='dataset' , revision=_SCREAMING_SNAKE_CASE )
| 373
|
'''simple docstring'''
import warnings
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class _lowerCAmelCase ( __snake_case ):
'''simple docstring'''
lowerCAmelCase_ = ["image_processor", "tokenizer"]
lowerCAmelCase_ = "CLIPImageProcessor"
lowerCAmelCase_ = ("XLMRobertaTokenizer", "XLMRobertaTokenizerFast")
def __init__(self , UpperCAmelCase=None , UpperCAmelCase=None , **UpperCAmelCase ) -> Optional[int]:
_snake_case = None
if "feature_extractor" in kwargs:
warnings.warn(
"""The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"""
""" instead.""" , UpperCAmelCase , )
_snake_case = kwargs.pop("""feature_extractor""" )
_snake_case = image_processor if image_processor is not None else feature_extractor
if image_processor is None:
raise ValueError("""You need to specify an `image_processor`.""" )
if tokenizer is None:
raise ValueError("""You need to specify a `tokenizer`.""" )
super().__init__(UpperCAmelCase , UpperCAmelCase )
def __call__(self , UpperCAmelCase=None , UpperCAmelCase=None , UpperCAmelCase=None , **UpperCAmelCase ) -> Tuple:
if text is None and images is None:
raise ValueError("""You have to specify either text or images. Both cannot be none.""" )
if text is not None:
_snake_case = self.tokenizer(UpperCAmelCase , return_tensors=UpperCAmelCase , **UpperCAmelCase )
if images is not None:
_snake_case = self.image_processor(UpperCAmelCase , return_tensors=UpperCAmelCase , **UpperCAmelCase )
if text is not None and images is not None:
_snake_case = image_features.pixel_values
return encoding
elif text is not None:
return encoding
else:
return BatchEncoding(data=dict(**UpperCAmelCase ) , tensor_type=UpperCAmelCase )
def lowercase (self , *UpperCAmelCase , **UpperCAmelCase ) -> Dict:
return self.tokenizer.batch_decode(*UpperCAmelCase , **UpperCAmelCase )
def lowercase (self , *UpperCAmelCase , **UpperCAmelCase ) -> Union[str, Any]:
return self.tokenizer.decode(*UpperCAmelCase , **UpperCAmelCase )
@property
def lowercase (self ) -> str:
_snake_case = self.tokenizer.model_input_names
_snake_case = self.image_processor.model_input_names
return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names ) )
| 585
| 0
|
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
SCREAMING_SNAKE_CASE__ = {
'''configuration_electra''': ['''ELECTRA_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''ElectraConfig''', '''ElectraOnnxConfig'''],
'''tokenization_electra''': ['''ElectraTokenizer'''],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
SCREAMING_SNAKE_CASE__ = ['''ElectraTokenizerFast''']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
SCREAMING_SNAKE_CASE__ = [
'''ELECTRA_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''ElectraForCausalLM''',
'''ElectraForMaskedLM''',
'''ElectraForMultipleChoice''',
'''ElectraForPreTraining''',
'''ElectraForQuestionAnswering''',
'''ElectraForSequenceClassification''',
'''ElectraForTokenClassification''',
'''ElectraModel''',
'''ElectraPreTrainedModel''',
'''load_tf_weights_in_electra''',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
SCREAMING_SNAKE_CASE__ = [
'''TF_ELECTRA_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''TFElectraForMaskedLM''',
'''TFElectraForMultipleChoice''',
'''TFElectraForPreTraining''',
'''TFElectraForQuestionAnswering''',
'''TFElectraForSequenceClassification''',
'''TFElectraForTokenClassification''',
'''TFElectraModel''',
'''TFElectraPreTrainedModel''',
]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
SCREAMING_SNAKE_CASE__ = [
'''FlaxElectraForCausalLM''',
'''FlaxElectraForMaskedLM''',
'''FlaxElectraForMultipleChoice''',
'''FlaxElectraForPreTraining''',
'''FlaxElectraForQuestionAnswering''',
'''FlaxElectraForSequenceClassification''',
'''FlaxElectraForTokenClassification''',
'''FlaxElectraModel''',
'''FlaxElectraPreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_electra import ELECTRA_PRETRAINED_CONFIG_ARCHIVE_MAP, ElectraConfig, ElectraOnnxConfig
from .tokenization_electra import ElectraTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_electra_fast import ElectraTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_electra import (
ELECTRA_PRETRAINED_MODEL_ARCHIVE_LIST,
ElectraForCausalLM,
ElectraForMaskedLM,
ElectraForMultipleChoice,
ElectraForPreTraining,
ElectraForQuestionAnswering,
ElectraForSequenceClassification,
ElectraForTokenClassification,
ElectraModel,
ElectraPreTrainedModel,
load_tf_weights_in_electra,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_electra import (
TF_ELECTRA_PRETRAINED_MODEL_ARCHIVE_LIST,
TFElectraForMaskedLM,
TFElectraForMultipleChoice,
TFElectraForPreTraining,
TFElectraForQuestionAnswering,
TFElectraForSequenceClassification,
TFElectraForTokenClassification,
TFElectraModel,
TFElectraPreTrainedModel,
)
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_electra import (
FlaxElectraForCausalLM,
FlaxElectraForMaskedLM,
FlaxElectraForMultipleChoice,
FlaxElectraForPreTraining,
FlaxElectraForQuestionAnswering,
FlaxElectraForSequenceClassification,
FlaxElectraForTokenClassification,
FlaxElectraModel,
FlaxElectraPreTrainedModel,
)
else:
import sys
SCREAMING_SNAKE_CASE__ = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 717
|
# tests directory-specific settings - this file is run automatically
# by pytest before any tests are run
import sys
import warnings
from os.path import abspath, dirname, join
# allow having multiple repository checkouts and not needing to remember to rerun
# 'pip install -e .[dev]' when switching between checkouts and running tests.
SCREAMING_SNAKE_CASE__ = abspath(join(dirname(dirname(dirname(__file__))), '''src'''))
sys.path.insert(1, git_repo_path)
# silence FutureWarning warnings in tests since often we can't act on them until
# they become normal warnings - i.e. the tests still need to test the current functionality
warnings.simplefilter(action='''ignore''', category=FutureWarning)
def UpperCAmelCase__ ( lowerCamelCase_ : str ):
from transformers.testing_utils import pytest_addoption_shared
pytest_addoption_shared(lowerCamelCase_ )
def UpperCAmelCase__ ( lowerCamelCase_ : Union[str, Any] ):
from transformers.testing_utils import pytest_terminal_summary_main
__a : Dict = terminalreporter.config.getoption('--make-reports' )
if make_reports:
pytest_terminal_summary_main(lowerCamelCase_ , id=lowerCamelCase_ )
| 577
| 0
|
import argparse
import json
from collections import OrderedDict
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
ConditionalDetrConfig,
ConditionalDetrForObjectDetection,
ConditionalDetrForSegmentation,
ConditionalDetrImageProcessor,
)
from transformers.utils import logging
logging.set_verbosity_info()
lowerCamelCase_ = logging.get_logger(__name__)
# here we list all keys to be renamed (original name on the left, our name on the right)
lowerCamelCase_ = []
for i in range(6):
# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
rename_keys.append(
(f'''transformer.encoder.layers.{i}.self_attn.out_proj.weight''', f'''encoder.layers.{i}.self_attn.out_proj.weight''')
)
rename_keys.append(
(f'''transformer.encoder.layers.{i}.self_attn.out_proj.bias''', f'''encoder.layers.{i}.self_attn.out_proj.bias''')
)
rename_keys.append((f'''transformer.encoder.layers.{i}.linear1.weight''', f'''encoder.layers.{i}.fc1.weight'''))
rename_keys.append((f'''transformer.encoder.layers.{i}.linear1.bias''', f'''encoder.layers.{i}.fc1.bias'''))
rename_keys.append((f'''transformer.encoder.layers.{i}.linear2.weight''', f'''encoder.layers.{i}.fc2.weight'''))
rename_keys.append((f'''transformer.encoder.layers.{i}.linear2.bias''', f'''encoder.layers.{i}.fc2.bias'''))
rename_keys.append(
(f'''transformer.encoder.layers.{i}.norm1.weight''', f'''encoder.layers.{i}.self_attn_layer_norm.weight''')
)
rename_keys.append((f'''transformer.encoder.layers.{i}.norm1.bias''', f'''encoder.layers.{i}.self_attn_layer_norm.bias'''))
rename_keys.append((f'''transformer.encoder.layers.{i}.norm2.weight''', f'''encoder.layers.{i}.final_layer_norm.weight'''))
rename_keys.append((f'''transformer.encoder.layers.{i}.norm2.bias''', f'''encoder.layers.{i}.final_layer_norm.bias'''))
# decoder layers: 2 times output projection, 2 feedforward neural networks and 3 layernorms
rename_keys.append(
(f'''transformer.decoder.layers.{i}.self_attn.out_proj.weight''', f'''decoder.layers.{i}.self_attn.out_proj.weight''')
)
rename_keys.append(
(f'''transformer.decoder.layers.{i}.self_attn.out_proj.bias''', f'''decoder.layers.{i}.self_attn.out_proj.bias''')
)
rename_keys.append(
(
f'''transformer.decoder.layers.{i}.cross_attn.out_proj.weight''',
f'''decoder.layers.{i}.encoder_attn.out_proj.weight''',
)
)
rename_keys.append(
(
f'''transformer.decoder.layers.{i}.cross_attn.out_proj.bias''',
f'''decoder.layers.{i}.encoder_attn.out_proj.bias''',
)
)
rename_keys.append((f'''transformer.decoder.layers.{i}.linear1.weight''', f'''decoder.layers.{i}.fc1.weight'''))
rename_keys.append((f'''transformer.decoder.layers.{i}.linear1.bias''', f'''decoder.layers.{i}.fc1.bias'''))
rename_keys.append((f'''transformer.decoder.layers.{i}.linear2.weight''', f'''decoder.layers.{i}.fc2.weight'''))
rename_keys.append((f'''transformer.decoder.layers.{i}.linear2.bias''', f'''decoder.layers.{i}.fc2.bias'''))
rename_keys.append(
(f'''transformer.decoder.layers.{i}.norm1.weight''', f'''decoder.layers.{i}.self_attn_layer_norm.weight''')
)
rename_keys.append((f'''transformer.decoder.layers.{i}.norm1.bias''', f'''decoder.layers.{i}.self_attn_layer_norm.bias'''))
rename_keys.append(
(f'''transformer.decoder.layers.{i}.norm2.weight''', f'''decoder.layers.{i}.encoder_attn_layer_norm.weight''')
)
rename_keys.append(
(f'''transformer.decoder.layers.{i}.norm2.bias''', f'''decoder.layers.{i}.encoder_attn_layer_norm.bias''')
)
rename_keys.append((f'''transformer.decoder.layers.{i}.norm3.weight''', f'''decoder.layers.{i}.final_layer_norm.weight'''))
rename_keys.append((f'''transformer.decoder.layers.{i}.norm3.bias''', f'''decoder.layers.{i}.final_layer_norm.bias'''))
# q, k, v projections in self/cross-attention in decoder for conditional DETR
rename_keys.append(
(f'''transformer.decoder.layers.{i}.sa_qcontent_proj.weight''', f'''decoder.layers.{i}.sa_qcontent_proj.weight''')
)
rename_keys.append(
(f'''transformer.decoder.layers.{i}.sa_kcontent_proj.weight''', f'''decoder.layers.{i}.sa_kcontent_proj.weight''')
)
rename_keys.append(
(f'''transformer.decoder.layers.{i}.sa_qpos_proj.weight''', f'''decoder.layers.{i}.sa_qpos_proj.weight''')
)
rename_keys.append(
(f'''transformer.decoder.layers.{i}.sa_kpos_proj.weight''', f'''decoder.layers.{i}.sa_kpos_proj.weight''')
)
rename_keys.append((f'''transformer.decoder.layers.{i}.sa_v_proj.weight''', f'''decoder.layers.{i}.sa_v_proj.weight'''))
rename_keys.append(
(f'''transformer.decoder.layers.{i}.ca_qcontent_proj.weight''', f'''decoder.layers.{i}.ca_qcontent_proj.weight''')
)
# rename_keys.append((f"transformer.decoder.layers.{i}.ca_qpos_proj.weight", f"decoder.layers.{i}.ca_qpos_proj.weight"))
rename_keys.append(
(f'''transformer.decoder.layers.{i}.ca_kcontent_proj.weight''', f'''decoder.layers.{i}.ca_kcontent_proj.weight''')
)
rename_keys.append(
(f'''transformer.decoder.layers.{i}.ca_kpos_proj.weight''', f'''decoder.layers.{i}.ca_kpos_proj.weight''')
)
rename_keys.append((f'''transformer.decoder.layers.{i}.ca_v_proj.weight''', f'''decoder.layers.{i}.ca_v_proj.weight'''))
rename_keys.append(
(f'''transformer.decoder.layers.{i}.ca_qpos_sine_proj.weight''', f'''decoder.layers.{i}.ca_qpos_sine_proj.weight''')
)
rename_keys.append(
(f'''transformer.decoder.layers.{i}.sa_qcontent_proj.bias''', f'''decoder.layers.{i}.sa_qcontent_proj.bias''')
)
rename_keys.append(
(f'''transformer.decoder.layers.{i}.sa_kcontent_proj.bias''', f'''decoder.layers.{i}.sa_kcontent_proj.bias''')
)
rename_keys.append((f'''transformer.decoder.layers.{i}.sa_qpos_proj.bias''', f'''decoder.layers.{i}.sa_qpos_proj.bias'''))
rename_keys.append((f'''transformer.decoder.layers.{i}.sa_kpos_proj.bias''', f'''decoder.layers.{i}.sa_kpos_proj.bias'''))
rename_keys.append((f'''transformer.decoder.layers.{i}.sa_v_proj.bias''', f'''decoder.layers.{i}.sa_v_proj.bias'''))
rename_keys.append(
(f'''transformer.decoder.layers.{i}.ca_qcontent_proj.bias''', f'''decoder.layers.{i}.ca_qcontent_proj.bias''')
)
# rename_keys.append((f"transformer.decoder.layers.{i}.ca_qpos_proj.bias", f"decoder.layers.{i}.ca_qpos_proj.bias"))
rename_keys.append(
(f'''transformer.decoder.layers.{i}.ca_kcontent_proj.bias''', f'''decoder.layers.{i}.ca_kcontent_proj.bias''')
)
rename_keys.append((f'''transformer.decoder.layers.{i}.ca_kpos_proj.bias''', f'''decoder.layers.{i}.ca_kpos_proj.bias'''))
rename_keys.append((f'''transformer.decoder.layers.{i}.ca_v_proj.bias''', f'''decoder.layers.{i}.ca_v_proj.bias'''))
rename_keys.append(
(f'''transformer.decoder.layers.{i}.ca_qpos_sine_proj.bias''', f'''decoder.layers.{i}.ca_qpos_sine_proj.bias''')
)
# convolutional projection + query embeddings + layernorm of decoder + class and bounding box heads
# for conditional DETR, also convert reference point head and query scale MLP
rename_keys.extend(
[
("""input_proj.weight""", """input_projection.weight"""),
("""input_proj.bias""", """input_projection.bias"""),
("""query_embed.weight""", """query_position_embeddings.weight"""),
("""transformer.decoder.norm.weight""", """decoder.layernorm.weight"""),
("""transformer.decoder.norm.bias""", """decoder.layernorm.bias"""),
("""class_embed.weight""", """class_labels_classifier.weight"""),
("""class_embed.bias""", """class_labels_classifier.bias"""),
("""bbox_embed.layers.0.weight""", """bbox_predictor.layers.0.weight"""),
("""bbox_embed.layers.0.bias""", """bbox_predictor.layers.0.bias"""),
("""bbox_embed.layers.1.weight""", """bbox_predictor.layers.1.weight"""),
("""bbox_embed.layers.1.bias""", """bbox_predictor.layers.1.bias"""),
("""bbox_embed.layers.2.weight""", """bbox_predictor.layers.2.weight"""),
("""bbox_embed.layers.2.bias""", """bbox_predictor.layers.2.bias"""),
("""transformer.decoder.ref_point_head.layers.0.weight""", """decoder.ref_point_head.layers.0.weight"""),
("""transformer.decoder.ref_point_head.layers.0.bias""", """decoder.ref_point_head.layers.0.bias"""),
("""transformer.decoder.ref_point_head.layers.1.weight""", """decoder.ref_point_head.layers.1.weight"""),
("""transformer.decoder.ref_point_head.layers.1.bias""", """decoder.ref_point_head.layers.1.bias"""),
("""transformer.decoder.query_scale.layers.0.weight""", """decoder.query_scale.layers.0.weight"""),
("""transformer.decoder.query_scale.layers.0.bias""", """decoder.query_scale.layers.0.bias"""),
("""transformer.decoder.query_scale.layers.1.weight""", """decoder.query_scale.layers.1.weight"""),
("""transformer.decoder.query_scale.layers.1.bias""", """decoder.query_scale.layers.1.bias"""),
("""transformer.decoder.layers.0.ca_qpos_proj.weight""", """decoder.layers.0.ca_qpos_proj.weight"""),
("""transformer.decoder.layers.0.ca_qpos_proj.bias""", """decoder.layers.0.ca_qpos_proj.bias"""),
]
)
def lowerCamelCase ( a_ , a_ , a_ ) -> List[Any]:
lowerCAmelCase_ = state_dict.pop(UpperCamelCase_ )
lowerCAmelCase_ = val
def lowerCamelCase ( a_ ) -> Tuple:
lowerCAmelCase_ = OrderedDict()
for key, value in state_dict.items():
if "backbone.0.body" in key:
lowerCAmelCase_ = key.replace('backbone.0.body' , 'backbone.conv_encoder.model' )
lowerCAmelCase_ = value
else:
lowerCAmelCase_ = value
return new_state_dict
def lowerCamelCase ( a_ , a_=False ) -> List[str]:
lowerCAmelCase_ = ''
if is_panoptic:
lowerCAmelCase_ = 'conditional_detr.'
# first: transformer encoder
for i in range(6 ):
# read in weights + bias of input projection layer (in PyTorch's MultiHeadAttention, this is a single matrix + bias)
lowerCAmelCase_ = state_dict.pop(F'''{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight''' )
lowerCAmelCase_ = state_dict.pop(F'''{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_bias''' )
# next, add query, keys and values (in that order) to the state dict
lowerCAmelCase_ = in_proj_weight[:256, :]
lowerCAmelCase_ = in_proj_bias[:256]
lowerCAmelCase_ = in_proj_weight[256:512, :]
lowerCAmelCase_ = in_proj_bias[256:512]
lowerCAmelCase_ = in_proj_weight[-256:, :]
lowerCAmelCase_ = in_proj_bias[-256:]
def lowerCamelCase ( ) -> Union[str, Any]:
lowerCAmelCase_ = 'http://images.cocodataset.org/val2017/000000039769.jpg'
lowerCAmelCase_ = Image.open(requests.get(UpperCamelCase_ , stream=UpperCamelCase_ ).raw )
return im
@torch.no_grad()
def lowerCamelCase ( a_ , a_ ) -> Tuple:
lowerCAmelCase_ = ConditionalDetrConfig()
# set backbone and dilation attributes
if "resnet101" in model_name:
lowerCAmelCase_ = 'resnet101'
if "dc5" in model_name:
lowerCAmelCase_ = True
lowerCAmelCase_ = 'panoptic' in model_name
if is_panoptic:
lowerCAmelCase_ = 250
else:
lowerCAmelCase_ = 91
lowerCAmelCase_ = 'huggingface/label-files'
lowerCAmelCase_ = 'coco-detection-id2label.json'
lowerCAmelCase_ = json.load(open(hf_hub_download(UpperCamelCase_ , UpperCamelCase_ , repo_type='dataset' ) , 'r' ) )
lowerCAmelCase_ = {int(UpperCamelCase_ ): v for k, v in idalabel.items()}
lowerCAmelCase_ = idalabel
lowerCAmelCase_ = {v: k for k, v in idalabel.items()}
# load image processor
lowerCAmelCase_ = 'coco_panoptic' if is_panoptic else 'coco_detection'
lowerCAmelCase_ = ConditionalDetrImageProcessor(format=UpperCamelCase_ )
# prepare image
lowerCAmelCase_ = prepare_img()
lowerCAmelCase_ = image_processor(images=UpperCamelCase_ , return_tensors='pt' )
lowerCAmelCase_ = encoding['pixel_values']
logger.info(F'''Converting model {model_name}...''' )
# load original model from torch hub
lowerCAmelCase_ = torch.hub.load('DeppMeng/ConditionalDETR' , UpperCamelCase_ , pretrained=UpperCamelCase_ ).eval()
lowerCAmelCase_ = conditional_detr.state_dict()
# rename keys
for src, dest in rename_keys:
if is_panoptic:
lowerCAmelCase_ = 'conditional_detr.' + src
rename_key(UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ )
lowerCAmelCase_ = rename_backbone_keys(UpperCamelCase_ )
# query, key and value matrices need special treatment
read_in_q_k_v(UpperCamelCase_ , is_panoptic=UpperCamelCase_ )
# important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
lowerCAmelCase_ = 'conditional_detr.model.' if is_panoptic else 'model.'
for key in state_dict.copy().keys():
if is_panoptic:
if (
key.startswith('conditional_detr' )
and not key.startswith('class_labels_classifier' )
and not key.startswith('bbox_predictor' )
):
lowerCAmelCase_ = state_dict.pop(UpperCamelCase_ )
lowerCAmelCase_ = val
elif "class_labels_classifier" in key or "bbox_predictor" in key:
lowerCAmelCase_ = state_dict.pop(UpperCamelCase_ )
lowerCAmelCase_ = val
elif key.startswith('bbox_attention' ) or key.startswith('mask_head' ):
continue
else:
lowerCAmelCase_ = state_dict.pop(UpperCamelCase_ )
lowerCAmelCase_ = val
else:
if not key.startswith('class_labels_classifier' ) and not key.startswith('bbox_predictor' ):
lowerCAmelCase_ = state_dict.pop(UpperCamelCase_ )
lowerCAmelCase_ = val
# finally, create HuggingFace model and load state dict
lowerCAmelCase_ = ConditionalDetrForSegmentation(UpperCamelCase_ ) if is_panoptic else ConditionalDetrForObjectDetection(UpperCamelCase_ )
model.load_state_dict(UpperCamelCase_ )
model.eval()
model.push_to_hub(repo_id=UpperCamelCase_ , organization='DepuMeng' , commit_message='Add model' )
# verify our conversion
lowerCAmelCase_ = conditional_detr(UpperCamelCase_ )
lowerCAmelCase_ = model(UpperCamelCase_ )
assert torch.allclose(outputs.logits , original_outputs['pred_logits'] , atol=1e-4 )
assert torch.allclose(outputs.pred_boxes , original_outputs['pred_boxes'] , atol=1e-4 )
if is_panoptic:
assert torch.allclose(outputs.pred_masks , original_outputs['pred_masks'] , atol=1e-4 )
# Save model and image processor
logger.info(F'''Saving PyTorch model and image processor to {pytorch_dump_folder_path}...''' )
Path(UpperCamelCase_ ).mkdir(exist_ok=UpperCamelCase_ )
model.save_pretrained(UpperCamelCase_ )
image_processor.save_pretrained(UpperCamelCase_ )
if __name__ == "__main__":
lowerCamelCase_ = argparse.ArgumentParser()
parser.add_argument(
"""--model_name""",
default="""conditional_detr_resnet50""",
type=str,
help="""Name of the CONDITIONAL_DETR model you'd like to convert.""",
)
parser.add_argument(
"""--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the folder to output PyTorch model."""
)
lowerCamelCase_ = parser.parse_args()
convert_conditional_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path)
| 318
|
"""simple docstring"""
import os
from typing import Any, Callable, Dict, List, Optional, Tuple, Union
import torch
from torch import nn
from ...models.controlnet import ControlNetModel, ControlNetOutput
from ...models.modeling_utils import ModelMixin
from ...utils import logging
__magic_name__ = logging.get_logger(__name__)
class SCREAMING_SNAKE_CASE_ ( __a ):
"""simple docstring"""
def __init__( self , lowerCAmelCase__):
super().__init__()
__SCREAMING_SNAKE_CASE = nn.ModuleList(lowerCAmelCase__)
def snake_case_ ( self , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ = None , lowerCAmelCase__ = None , lowerCAmelCase__ = None , lowerCAmelCase__ = None , lowerCAmelCase__ = False , lowerCAmelCase__ = True , ):
for i, (image, scale, controlnet) in enumerate(zip(lowerCAmelCase__ , lowerCAmelCase__ , self.nets)):
__SCREAMING_SNAKE_CASE ,__SCREAMING_SNAKE_CASE = controlnet(
lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , )
# merge samples
if i == 0:
__SCREAMING_SNAKE_CASE ,__SCREAMING_SNAKE_CASE = down_samples, mid_sample
else:
__SCREAMING_SNAKE_CASE = [
samples_prev + samples_curr
for samples_prev, samples_curr in zip(lowerCAmelCase__ , lowerCAmelCase__)
]
mid_block_res_sample += mid_sample
return down_block_res_samples, mid_block_res_sample
def snake_case_ ( self , lowerCAmelCase__ , lowerCAmelCase__ = True , lowerCAmelCase__ = None , lowerCAmelCase__ = False , lowerCAmelCase__ = None , ):
__SCREAMING_SNAKE_CASE = 0
__SCREAMING_SNAKE_CASE = save_directory
for controlnet in self.nets:
controlnet.save_pretrained(
lowerCAmelCase__ , is_main_process=lowerCAmelCase__ , save_function=lowerCAmelCase__ , safe_serialization=lowerCAmelCase__ , variant=lowerCAmelCase__ , )
idx += 1
__SCREAMING_SNAKE_CASE = model_path_to_save + f"_{idx}"
@classmethod
def snake_case_ ( cls , lowerCAmelCase__ , **lowerCAmelCase__):
__SCREAMING_SNAKE_CASE = 0
__SCREAMING_SNAKE_CASE = []
# load controlnet and append to list until no controlnet directory exists anymore
# first controlnet has to be saved under `./mydirectory/controlnet` to be compliant with `DiffusionPipeline.from_prertained`
# second, third, ... controlnets have to be saved under `./mydirectory/controlnet_1`, `./mydirectory/controlnet_2`, ...
__SCREAMING_SNAKE_CASE = pretrained_model_path
while os.path.isdir(lowerCAmelCase__):
__SCREAMING_SNAKE_CASE = ControlNetModel.from_pretrained(lowerCAmelCase__ , **lowerCAmelCase__)
controlnets.append(lowerCAmelCase__)
idx += 1
__SCREAMING_SNAKE_CASE = pretrained_model_path + f"_{idx}"
logger.info(f"{len(lowerCAmelCase__)} controlnets loaded from {pretrained_model_path}.")
if len(lowerCAmelCase__) == 0:
raise ValueError(
f"No ControlNets found under {os.path.dirname(lowerCAmelCase__)}. Expected at least {pretrained_model_path + '_0'}.")
return cls(lowerCAmelCase__)
| 155
| 0
|
"""simple docstring"""
from ...utils import is_torch_available, is_transformers_available
if is_transformers_available() and is_torch_available():
from .pipeline_vq_diffusion import LearnedClassifierFreeSamplingEmbeddings, VQDiffusionPipeline
| 713
|
"""simple docstring"""
from __future__ import annotations
class a :
"""simple docstring"""
def __init__( self: Any , UpperCamelCase: str , UpperCamelCase: str ):
"""simple docstring"""
A__ , A__ = text, pattern
A__ , A__ = len(UpperCamelCase ), len(UpperCamelCase )
def UpperCamelCase ( self: Dict , UpperCamelCase: str ):
"""simple docstring"""
for i in range(self.patLen - 1 , -1 , -1 ):
if char == self.pattern[i]:
return i
return -1
def UpperCamelCase ( self: str , UpperCamelCase: int ):
"""simple docstring"""
for i in range(self.patLen - 1 , -1 , -1 ):
if self.pattern[i] != self.text[current_pos + i]:
return current_pos + i
return -1
def UpperCamelCase ( self: List[Any] ):
"""simple docstring"""
A__ = []
for i in range(self.textLen - self.patLen + 1 ):
A__ = self.mismatch_in_text(UpperCamelCase )
if mismatch_index == -1:
positions.append(UpperCamelCase )
else:
A__ = self.match_in_pattern(self.text[mismatch_index] )
A__ = (
mismatch_index - match_index
) # shifting index lgtm [py/multiple-definition]
return positions
SCREAMING_SNAKE_CASE_ : List[Any] = 'ABAABA'
SCREAMING_SNAKE_CASE_ : List[Any] = 'AB'
SCREAMING_SNAKE_CASE_ : Union[str, Any] = BoyerMooreSearch(text, pattern)
SCREAMING_SNAKE_CASE_ : int = bms.bad_character_heuristic()
if len(positions) == 0:
print('No match found')
else:
print('Pattern found in following positions: ')
print(positions)
| 500
| 0
|
'''simple docstring'''
import warnings
from ...utils import logging
from .image_processing_videomae import VideoMAEImageProcessor
__lowercase : Dict = logging.get_logger(__name__)
class __UpperCamelCase ( lowerCAmelCase_ ):
def __init__( self , *__a , **__a ):
'''simple docstring'''
warnings.warn(
'The class VideoMAEFeatureExtractor is deprecated and will be removed in version 5 of Transformers.'
' Please use VideoMAEImageProcessor instead.' , __a , )
super().__init__(*__a , **__a )
| 476
|
'''simple docstring'''
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__lowercase : Optional[int] = logging.get_logger(__name__)
__lowercase : str = {
'google/pix2struct-textcaps-base': (
'https://huggingface.co/google/pix2struct-textcaps-base/resolve/main/config.json'
),
}
class __UpperCamelCase ( lowerCAmelCase_ ):
A_ = "pix2struct_text_model"
A_ = ["past_key_values"]
A_ = {
"hidden_size": "hidden_size",
"num_attention_heads": "num_heads",
"num_hidden_layers": "num_layers",
}
def __init__( self , __a=5_0244 , __a=768 , __a=64 , __a=2048 , __a=12 , __a=12 , __a=32 , __a=128 , __a=0.1 , __a=1E-6 , __a=1.0 , __a="gelu_new" , __a=0 , __a=False , __a=0 , __a=1 , __a=False , __a=True , **__a , ):
'''simple docstring'''
__a : Dict = vocab_size
__a : str = hidden_size
__a : Any = d_kv
__a : Any = d_ff
__a : Any = num_layers
__a : Any = num_heads
__a : Optional[Any] = relative_attention_num_buckets
__a : int = relative_attention_max_distance
__a : List[str] = dropout_rate
__a : Dict = layer_norm_epsilon
__a : int = initializer_factor
__a : Any = use_cache
__a : int = eos_token_id
__a : int = decoder_start_token_id
# for backwards compatibility
__a : Tuple = dense_act_fn
super().__init__(
pad_token_id=__a , eos_token_id=__a , decoder_start_token_id=__a , tie_word_embeddings=__a , is_decoder=__a , **__a , )
@classmethod
def __UpperCAmelCase ( cls , __a , **__a ):
'''simple docstring'''
cls._set_token_in_kwargs(__a )
__a , __a : Union[str, Any] = cls.get_config_dict(__a , **__a )
# get the text config dict if we are loading from Pix2StructConfig
if config_dict.get('model_type' ) == "pix2struct":
__a : Optional[int] = config_dict['text_config']
if "model_type" in config_dict and hasattr(cls , 'model_type' ) and config_dict["model_type"] != cls.model_type:
logger.warning(
f"""You are using a model of type {config_dict["model_type"]} to instantiate a model of type """
f"""{cls.model_type}. This is not supported for all configurations of models and can yield errors.""" )
return cls.from_dict(__a , **__a )
class __UpperCamelCase ( lowerCAmelCase_ ):
A_ = "pix2struct_vision_model"
def __init__( self , __a=768 , __a=768 , __a=2048 , __a=64 , __a=12 , __a=12 , __a="gelu_new" , __a=1E-6 , __a=0.0 , __a=0.0 , __a=1E-1_0 , __a=1.0 , __a=4096 , __a=32 , __a=128 , **__a , ):
'''simple docstring'''
super().__init__(**__a )
__a : Dict = hidden_size
__a : Tuple = patch_embed_hidden_size
__a : Tuple = d_ff
__a : int = dropout_rate
__a : Any = num_hidden_layers
__a : List[str] = num_attention_heads
__a : Optional[Any] = initializer_range
__a : Any = initializer_factor
__a : Optional[Any] = attention_dropout
__a : List[Any] = layer_norm_eps
__a : Dict = dense_act_fn
__a : Union[str, Any] = seq_len
__a : str = relative_attention_num_buckets
__a : Dict = relative_attention_max_distance
__a : Any = d_kv
@classmethod
def __UpperCAmelCase ( cls , __a , **__a ):
'''simple docstring'''
cls._set_token_in_kwargs(__a )
__a , __a : Any = cls.get_config_dict(__a , **__a )
# get the vision config dict if we are loading from Pix2StructConfig
if config_dict.get('model_type' ) == "pix2struct":
__a : str = config_dict['vision_config']
if "model_type" in config_dict and hasattr(cls , 'model_type' ) and config_dict["model_type"] != cls.model_type:
logger.warning(
f"""You are using a model of type {config_dict["model_type"]} to instantiate a model of type """
f"""{cls.model_type}. This is not supported for all configurations of models and can yield errors.""" )
return cls.from_dict(__a , **__a )
class __UpperCamelCase ( lowerCAmelCase_ ):
A_ = "pix2struct"
A_ = True
def __init__( self , __a=None , __a=None , __a=1.0 , __a=0.02 , __a=False , __a=False , __a=True , **__a , ):
'''simple docstring'''
super().__init__(tie_word_embeddings=__a , is_encoder_decoder=__a , **__a )
if text_config is None:
__a : str = {}
logger.info('text_config is None. Initializing the Pix2StructTextConfig with default values.' )
if vision_config is None:
__a : Optional[Any] = {}
logger.info('vision_config is None. Initializing the Pix2StructVisionConfig with default values.' )
__a : Optional[Any] = PixaStructTextConfig(**__a )
__a : Union[str, Any] = PixaStructVisionConfig(**__a )
__a : List[str] = self.text_config.decoder_start_token_id
__a : Optional[Any] = self.text_config.pad_token_id
__a : Dict = self.text_config.eos_token_id
__a : Any = initializer_factor
__a : List[str] = initializer_range
__a : int = self.initializer_range
__a : Union[str, Any] = self.initializer_range
__a : Dict = is_vqa
@classmethod
def __UpperCAmelCase ( cls , __a , __a , **__a ):
'''simple docstring'''
return cls(text_config=text_config.to_dict() , vision_config=vision_config.to_dict() , **__a )
def __UpperCAmelCase ( self ):
'''simple docstring'''
__a : Dict = copy.deepcopy(self.__dict__ )
__a : Optional[int] = self.text_config.to_dict()
__a : Optional[int] = self.vision_config.to_dict()
__a : Optional[Any] = self.__class__.model_type
return output
| 476
| 1
|
"""simple docstring"""
import warnings
from ...utils import logging
from .image_processing_chinese_clip import ChineseCLIPImageProcessor
lowerCamelCase : Tuple = logging.get_logger(__name__)
class __snake_case( __A ):
def __init__( self , *A_ , **A_ ):
'''simple docstring'''
warnings.warn(
'''The class ChineseCLIPFeatureExtractor is deprecated and will be removed in version 5 of Transformers.'''
''' Please use ChineseCLIPImageProcessor instead.''' , A_ , )
super().__init__(*A_ , **A_ )
| 168
|
"""simple docstring"""
from __future__ import annotations
from bisect import bisect_left
from functools import total_ordering
from heapq import merge
@total_ordering
class __snake_case( __A ):
def __lt__( self , A_ ):
'''simple docstring'''
return self[-1] < other[-1]
def __eq__( self , A_ ):
'''simple docstring'''
return self[-1] == other[-1]
def A__ ( UpperCamelCase__ ):
'''simple docstring'''
_SCREAMING_SNAKE_CASE = []
# sort into stacks
for element in collection:
_SCREAMING_SNAKE_CASE = Stack([element] )
_SCREAMING_SNAKE_CASE = bisect_left(UpperCamelCase__ , UpperCamelCase__ )
if i != len(UpperCamelCase__ ):
stacks[i].append(UpperCamelCase__ )
else:
stacks.append(UpperCamelCase__ )
# use a heap-based merge to merge stack efficiently
_SCREAMING_SNAKE_CASE = merge(*(reversed(UpperCamelCase__ ) for stack in stacks) )
return collection
if __name__ == "__main__":
lowerCamelCase : int = input("""Enter numbers separated by a comma:\n""").strip()
lowerCamelCase : Optional[Any] = [int(item) for item in user_input.split(""",""")]
print(patience_sort(unsorted))
| 168
| 1
|
'''simple docstring'''
import itertools
import json
import os
import unittest
from transformers import AddedToken, LongformerTokenizer, LongformerTokenizerFast
from transformers.models.longformer.tokenization_longformer import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class lowercase_ ( lowerCAmelCase_ , unittest.TestCase ):
A_ = LongformerTokenizer
A_ = True
A_ = LongformerTokenizerFast
A_ = True
def _lowerCAmelCase ( self : int ):
super().setUp()
# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
snake_case__ : Optional[int] = [
'l',
'o',
'w',
'e',
'r',
's',
't',
'i',
'd',
'n',
'\u0120',
'\u0120l',
'\u0120n',
'\u0120lo',
'\u0120low',
'er',
'\u0120lowest',
'\u0120newer',
'\u0120wider',
'<unk>',
]
snake_case__ : List[str] = dict(zip(__lowerCamelCase , range(len(__lowerCamelCase ) ) ) )
snake_case__ : str = ['#version: 0.2', '\u0120 l', '\u0120l o', '\u0120lo w', 'e r', '']
snake_case__ : Any = {'unk_token': '<unk>'}
snake_case__ : List[Any] = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['vocab_file'] )
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 ) )
def _lowerCAmelCase ( self : Tuple , **__lowerCamelCase : int ):
kwargs.update(self.special_tokens_map )
return self.tokenizer_class.from_pretrained(self.tmpdirname , **__lowerCamelCase )
def _lowerCAmelCase ( self : Dict , **__lowerCamelCase : int ):
kwargs.update(self.special_tokens_map )
return self.rust_tokenizer_class.from_pretrained(self.tmpdirname , **__lowerCamelCase )
def _lowerCAmelCase ( self : Optional[Any] , __lowerCamelCase : List[Any] ):
snake_case__ : Optional[Any] = 'lower newer'
snake_case__ : Optional[int] = 'lower newer'
return input_text, output_text
def _lowerCAmelCase ( self : Tuple ):
snake_case__ : List[Any] = self.tokenizer_class(self.vocab_file , self.merges_file , **self.special_tokens_map )
snake_case__ : Dict = 'lower newer'
snake_case__ : List[str] = ['l', 'o', 'w', 'er', '\u0120', 'n', 'e', 'w', 'er']
snake_case__ : Any = tokenizer.tokenize(__lowerCamelCase ) # , add_prefix_space=True)
self.assertListEqual(__lowerCamelCase , __lowerCamelCase )
snake_case__ : str = tokens + [tokenizer.unk_token]
snake_case__ : Any = [0, 1, 2, 15, 10, 9, 3, 2, 15, 19]
self.assertListEqual(tokenizer.convert_tokens_to_ids(__lowerCamelCase ) , __lowerCamelCase )
def _lowerCAmelCase ( self : Union[str, Any] ):
snake_case__ : Tuple = self.get_tokenizer()
self.assertListEqual(tokenizer.encode('Hello world!' , add_special_tokens=__lowerCamelCase ) , [0, 31414, 232, 328, 2] )
self.assertListEqual(
tokenizer.encode('Hello world! cécé herlolip 418' , add_special_tokens=__lowerCamelCase ) , [0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2] , )
@slow
def _lowerCAmelCase ( self : Optional[int] ):
snake_case__ : Tuple = self.tokenizer_class.from_pretrained('allenai/longformer-base-4096' )
snake_case__ : str = tokenizer.encode('sequence builders' , add_special_tokens=__lowerCamelCase )
snake_case__ : int = tokenizer.encode('multi-sequence build' , add_special_tokens=__lowerCamelCase )
snake_case__ : List[str] = tokenizer.encode(
'sequence builders' , add_special_tokens=__lowerCamelCase , add_prefix_space=__lowerCamelCase )
snake_case__ : List[Any] = tokenizer.encode(
'sequence builders' , 'multi-sequence build' , add_special_tokens=__lowerCamelCase , add_prefix_space=__lowerCamelCase )
snake_case__ : Dict = tokenizer.build_inputs_with_special_tokens(__lowerCamelCase )
snake_case__ : str = tokenizer.build_inputs_with_special_tokens(__lowerCamelCase , __lowerCamelCase )
assert encoded_sentence == encoded_text_from_decode
assert encoded_pair == encoded_pair_from_decode
def _lowerCAmelCase ( self : Dict ):
snake_case__ : str = self.get_tokenizer()
snake_case__ : int = 'Encode this sequence.'
snake_case__ : Union[str, Any] = tokenizer.byte_encoder[' '.encode('utf-8' )[0]]
# Testing encoder arguments
snake_case__ : Union[str, Any] = tokenizer.encode(__lowerCamelCase , add_special_tokens=__lowerCamelCase , add_prefix_space=__lowerCamelCase )
snake_case__ : List[str] = tokenizer.convert_ids_to_tokens(encoded[0] )[0]
self.assertNotEqual(__lowerCamelCase , __lowerCamelCase )
snake_case__ : int = tokenizer.encode(__lowerCamelCase , add_special_tokens=__lowerCamelCase , add_prefix_space=__lowerCamelCase )
snake_case__ : Union[str, Any] = tokenizer.convert_ids_to_tokens(encoded[0] )[0]
self.assertEqual(__lowerCamelCase , __lowerCamelCase )
tokenizer.add_special_tokens({'bos_token': '<s>'} )
snake_case__ : str = tokenizer.encode(__lowerCamelCase , add_special_tokens=__lowerCamelCase )
snake_case__ : Optional[Any] = tokenizer.convert_ids_to_tokens(encoded[1] )[0]
self.assertNotEqual(__lowerCamelCase , __lowerCamelCase )
# Testing spaces after special tokens
snake_case__ : Dict = '<mask>'
tokenizer.add_special_tokens(
{'mask_token': AddedToken(__lowerCamelCase , lstrip=__lowerCamelCase , rstrip=__lowerCamelCase )} ) # mask token has a left space
snake_case__ : List[Any] = tokenizer.convert_tokens_to_ids(__lowerCamelCase )
snake_case__ : Any = 'Encode <mask> sequence'
snake_case__ : Optional[int] = 'Encode <mask>sequence'
snake_case__ : Union[str, Any] = tokenizer.encode(__lowerCamelCase )
snake_case__ : Union[str, Any] = encoded.index(__lowerCamelCase )
snake_case__ : Any = tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1] )[0]
self.assertEqual(__lowerCamelCase , __lowerCamelCase )
snake_case__ : List[str] = tokenizer.encode(__lowerCamelCase )
snake_case__ : Any = encoded.index(__lowerCamelCase )
snake_case__ : int = tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1] )[0]
self.assertNotEqual(__lowerCamelCase , __lowerCamelCase )
def _lowerCAmelCase ( self : Dict ):
pass
def _lowerCAmelCase ( self : List[str] ):
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(F"{tokenizer.__class__.__name__} ({pretrained_name})" ):
snake_case__ : Union[str, Any] = self.rust_tokenizer_class.from_pretrained(__lowerCamelCase , **__lowerCamelCase )
snake_case__ : List[str] = self.tokenizer_class.from_pretrained(__lowerCamelCase , **__lowerCamelCase )
snake_case__ : Dict = 'A, <mask> AllenNLP sentence.'
snake_case__ : Tuple = tokenizer_r.encode_plus(__lowerCamelCase , add_special_tokens=__lowerCamelCase , return_token_type_ids=__lowerCamelCase )
snake_case__ : Optional[Any] = tokenizer_p.encode_plus(__lowerCamelCase , add_special_tokens=__lowerCamelCase , return_token_type_ids=__lowerCamelCase )
# token_type_ids should put 0 everywhere
self.assertEqual(sum(tokens_r['token_type_ids'] ) , sum(tokens_p['token_type_ids'] ) )
# attention_mask should put 1 everywhere, so sum over length should be 1
self.assertEqual(
sum(tokens_r['attention_mask'] ) / len(tokens_r['attention_mask'] ) , sum(tokens_p['attention_mask'] ) / len(tokens_p['attention_mask'] ) , )
snake_case__ : Any = tokenizer_r.convert_ids_to_tokens(tokens_r['input_ids'] )
snake_case__ : Union[str, Any] = tokenizer_p.convert_ids_to_tokens(tokens_p['input_ids'] )
# Rust correctly handles the space before the mask while python doesnt
self.assertSequenceEqual(tokens_p['input_ids'] , [0, 250, 6, 50264, 3823, 487, 21992, 3645, 4, 2] )
self.assertSequenceEqual(tokens_r['input_ids'] , [0, 250, 6, 50264, 3823, 487, 21992, 3645, 4, 2] )
self.assertSequenceEqual(
__lowerCamelCase , ['<s>', 'A', ',', '<mask>', 'ĠAllen', 'N', 'LP', 'Ġsentence', '.', '</s>'] )
self.assertSequenceEqual(
__lowerCamelCase , ['<s>', 'A', ',', '<mask>', 'ĠAllen', 'N', 'LP', 'Ġsentence', '.', '</s>'] )
def _lowerCAmelCase ( self : Optional[int] ):
for trim_offsets, add_prefix_space in itertools.product([True, False] , repeat=2 ):
snake_case__ : str = self.rust_tokenizer_class.from_pretrained(
self.tmpdirname , use_fast=__lowerCamelCase , add_prefix_space=__lowerCamelCase , trim_offsets=__lowerCamelCase )
snake_case__ : Optional[int] = json.loads(tokenizer_r.backend_tokenizer.pre_tokenizer.__getstate__() )
snake_case__ : Dict = json.loads(tokenizer_r.backend_tokenizer.post_processor.__getstate__() )
self.assertEqual(pre_tokenizer_state['add_prefix_space'] , __lowerCamelCase )
self.assertEqual(post_processor_state['add_prefix_space'] , __lowerCamelCase )
self.assertEqual(post_processor_state['trim_offsets'] , __lowerCamelCase )
def _lowerCAmelCase ( self : Union[str, Any] ):
# Test which aims to verify that the offsets are well adapted to the argument `add_prefix_space` and
# `trim_offsets`
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(F"{tokenizer.__class__.__name__} ({pretrained_name})" ):
snake_case__ : str = 'hello' # `hello` is a token in the vocabulary of `pretrained_name`
snake_case__ : Tuple = F"{text_of_1_token} {text_of_1_token}"
snake_case__ : List[Any] = self.rust_tokenizer_class.from_pretrained(
__lowerCamelCase , use_fast=__lowerCamelCase , add_prefix_space=__lowerCamelCase , trim_offsets=__lowerCamelCase )
snake_case__ : Tuple = tokenizer_r(__lowerCamelCase , return_offsets_mapping=__lowerCamelCase , add_special_tokens=__lowerCamelCase )
self.assertEqual(encoding.offset_mapping[0] , (0, len(__lowerCamelCase )) )
self.assertEqual(
encoding.offset_mapping[1] , (len(__lowerCamelCase ) + 1, len(__lowerCamelCase ) + 1 + len(__lowerCamelCase )) , )
snake_case__ : Union[str, Any] = self.rust_tokenizer_class.from_pretrained(
__lowerCamelCase , use_fast=__lowerCamelCase , add_prefix_space=__lowerCamelCase , trim_offsets=__lowerCamelCase )
snake_case__ : str = tokenizer_r(__lowerCamelCase , return_offsets_mapping=__lowerCamelCase , add_special_tokens=__lowerCamelCase )
self.assertEqual(encoding.offset_mapping[0] , (0, len(__lowerCamelCase )) )
self.assertEqual(
encoding.offset_mapping[1] , (len(__lowerCamelCase ) + 1, len(__lowerCamelCase ) + 1 + len(__lowerCamelCase )) , )
snake_case__ : str = self.rust_tokenizer_class.from_pretrained(
__lowerCamelCase , use_fast=__lowerCamelCase , add_prefix_space=__lowerCamelCase , trim_offsets=__lowerCamelCase )
snake_case__ : Union[str, Any] = tokenizer_r(__lowerCamelCase , return_offsets_mapping=__lowerCamelCase , add_special_tokens=__lowerCamelCase )
self.assertEqual(encoding.offset_mapping[0] , (0, len(__lowerCamelCase )) )
self.assertEqual(
encoding.offset_mapping[1] , (len(__lowerCamelCase ), len(__lowerCamelCase ) + 1 + len(__lowerCamelCase )) , )
snake_case__ : Optional[Any] = self.rust_tokenizer_class.from_pretrained(
__lowerCamelCase , use_fast=__lowerCamelCase , add_prefix_space=__lowerCamelCase , trim_offsets=__lowerCamelCase )
snake_case__ : int = tokenizer_r(__lowerCamelCase , return_offsets_mapping=__lowerCamelCase , add_special_tokens=__lowerCamelCase )
self.assertEqual(encoding.offset_mapping[0] , (0, len(__lowerCamelCase )) )
self.assertEqual(
encoding.offset_mapping[1] , (len(__lowerCamelCase ), len(__lowerCamelCase ) + 1 + len(__lowerCamelCase )) , )
snake_case__ : int = F" {text}"
# tokenizer_r = self.rust_tokenizer_class.from_pretrained(
# pretrained_name, use_fast=True, add_prefix_space=True, trim_offsets=True
# )
# encoding = tokenizer_r(text, return_offsets_mapping=True, add_special_tokens=False)
# self.assertEqual(encoding.offset_mapping[0], (1, 1 + len(text_of_1_token)))
# self.assertEqual(
# encoding.offset_mapping[1],
# (1 + len(text_of_1_token) + 1, 1 + len(text_of_1_token) + 1 + len(text_of_1_token)),
# )
snake_case__ : List[Any] = self.rust_tokenizer_class.from_pretrained(
__lowerCamelCase , use_fast=__lowerCamelCase , add_prefix_space=__lowerCamelCase , trim_offsets=__lowerCamelCase )
snake_case__ : str = tokenizer_r(__lowerCamelCase , return_offsets_mapping=__lowerCamelCase , add_special_tokens=__lowerCamelCase )
self.assertEqual(encoding.offset_mapping[0] , (1, 1 + len(__lowerCamelCase )) )
self.assertEqual(
encoding.offset_mapping[1] , (1 + len(__lowerCamelCase ) + 1, 1 + len(__lowerCamelCase ) + 1 + len(__lowerCamelCase )) , )
snake_case__ : int = self.rust_tokenizer_class.from_pretrained(
__lowerCamelCase , use_fast=__lowerCamelCase , add_prefix_space=__lowerCamelCase , trim_offsets=__lowerCamelCase )
snake_case__ : str = tokenizer_r(__lowerCamelCase , return_offsets_mapping=__lowerCamelCase , add_special_tokens=__lowerCamelCase )
self.assertEqual(encoding.offset_mapping[0] , (0, 1 + len(__lowerCamelCase )) )
self.assertEqual(
encoding.offset_mapping[1] , (1 + len(__lowerCamelCase ), 1 + len(__lowerCamelCase ) + 1 + len(__lowerCamelCase )) , )
snake_case__ : Optional[int] = self.rust_tokenizer_class.from_pretrained(
__lowerCamelCase , use_fast=__lowerCamelCase , add_prefix_space=__lowerCamelCase , trim_offsets=__lowerCamelCase )
snake_case__ : Optional[int] = tokenizer_r(__lowerCamelCase , return_offsets_mapping=__lowerCamelCase , add_special_tokens=__lowerCamelCase )
self.assertEqual(encoding.offset_mapping[0] , (0, 1 + len(__lowerCamelCase )) )
self.assertEqual(
encoding.offset_mapping[1] , (1 + len(__lowerCamelCase ), 1 + len(__lowerCamelCase ) + 1 + len(__lowerCamelCase )) , )
| 270
|
'''simple docstring'''
import os
import re
import shutil
import sys
import tempfile
import unittest
import black
A_ = os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, "utils"))
import check_copies # noqa: E402
# This is the reference code that will be used in the tests.
# If BertLMPredictionHead is changed in modeling_bert.py, this code needs to be manually updated.
A_ = " def __init__(self, config):\n super().__init__()\n self.transform = BertPredictionHeadTransform(config)\n\n # The output weights are the same as the input embeddings, but there is\n # an output-only bias for each token.\n self.decoder = nn.Linear(config.hidden_size, config.vocab_size, bias=False)\n\n self.bias = nn.Parameter(torch.zeros(config.vocab_size))\n\n # Need a link between the two variables so that the bias is correctly resized with `resize_token_embeddings`\n self.decoder.bias = self.bias\n\n def forward(self, hidden_states):\n hidden_states = self.transform(hidden_states)\n hidden_states = self.decoder(hidden_states)\n return hidden_states\n"
class lowercase_ ( unittest.TestCase ):
def _lowerCAmelCase ( self : Union[str, Any] ):
snake_case__ : str = tempfile.mkdtemp()
os.makedirs(os.path.join(self.transformer_dir , 'models/bert/' ) )
snake_case__ : Optional[Any] = self.transformer_dir
shutil.copy(
os.path.join(__lowerCamelCase , 'src/transformers/models/bert/modeling_bert.py' ) , os.path.join(self.transformer_dir , 'models/bert/modeling_bert.py' ) , )
def _lowerCAmelCase ( self : int ):
snake_case__ : Union[str, Any] = 'src/transformers'
shutil.rmtree(self.transformer_dir )
def _lowerCAmelCase ( self : Tuple , __lowerCamelCase : Tuple , __lowerCamelCase : Dict , __lowerCamelCase : Tuple , __lowerCamelCase : List[Any]=None ):
snake_case__ : Optional[int] = comment + F"\nclass {class_name}(nn.Module):\n" + class_code
if overwrite_result is not None:
snake_case__ : List[Any] = comment + F"\nclass {class_name}(nn.Module):\n" + overwrite_result
snake_case__ : Optional[int] = black.Mode(target_versions={black.TargetVersion.PYaa} , line_length=119 )
snake_case__ : Tuple = black.format_str(__lowerCamelCase , mode=__lowerCamelCase )
snake_case__ : Union[str, Any] = os.path.join(self.transformer_dir , 'new_code.py' )
with open(__lowerCamelCase , 'w' , newline='\n' ) as f:
f.write(__lowerCamelCase )
if overwrite_result is None:
self.assertTrue(len(check_copies.is_copy_consistent(__lowerCamelCase ) ) == 0 )
else:
check_copies.is_copy_consistent(f.name , overwrite=__lowerCamelCase )
with open(__lowerCamelCase , 'r' ) as f:
self.assertTrue(f.read() , __lowerCamelCase )
def _lowerCAmelCase ( self : Tuple ):
snake_case__ : List[str] = check_copies.find_code_in_transformers('models.bert.modeling_bert.BertLMPredictionHead' )
self.assertEqual(__lowerCamelCase , __lowerCamelCase )
def _lowerCAmelCase ( self : Any ):
# Base copy consistency
self.check_copy_consistency(
'# Copied from transformers.models.bert.modeling_bert.BertLMPredictionHead' , 'BertLMPredictionHead' , REFERENCE_CODE + '\n' , )
# With no empty line at the end
self.check_copy_consistency(
'# Copied from transformers.models.bert.modeling_bert.BertLMPredictionHead' , 'BertLMPredictionHead' , __lowerCamelCase , )
# Copy consistency with rename
self.check_copy_consistency(
'# Copied from transformers.models.bert.modeling_bert.BertLMPredictionHead with Bert->TestModel' , 'TestModelLMPredictionHead' , re.sub('Bert' , 'TestModel' , __lowerCamelCase ) , )
# Copy consistency with a really long name
snake_case__ : Union[str, Any] = 'TestModelWithAReallyLongNameBecauseSomePeopleLikeThatForSomeReason'
self.check_copy_consistency(
F"# Copied from transformers.models.bert.modeling_bert.BertLMPredictionHead with Bert->{long_class_name}" , F"{long_class_name}LMPredictionHead" , re.sub('Bert' , __lowerCamelCase , __lowerCamelCase ) , )
# Copy consistency with overwrite
self.check_copy_consistency(
'# Copied from transformers.models.bert.modeling_bert.BertLMPredictionHead with Bert->TestModel' , 'TestModelLMPredictionHead' , __lowerCamelCase , overwrite_result=re.sub('Bert' , 'TestModel' , __lowerCamelCase ) , )
def _lowerCAmelCase ( self : Union[str, Any] ):
snake_case__ : List[str] = check_copies.LOCALIZED_READMES['README_zh-hans.md']
snake_case__ : List[str] = (
'1. **[ALBERT](https://huggingface.co/transformers/model_doc/albert.html)** (from Google Research and the'
' Toyota Technological Institute at Chicago) released with the paper [ALBERT: A Lite BERT for'
' Self-supervised Learning of Language Representations](https://arxiv.org/abs/1909.11942), by Zhenzhong'
' Lan, Mingda Chen, Sebastian Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut.\n1.'
' **[DistilBERT](https://huggingface.co/transformers/model_doc/distilbert.html)** (from HuggingFace),'
' released together with the paper [DistilBERT, a distilled version of BERT: smaller, faster, cheaper and'
' lighter](https://arxiv.org/abs/1910.01108) by Victor Sanh, Lysandre Debut and Thomas Wolf. The same'
' method has been applied to compress GPT2 into'
' [DistilGPT2](https://github.com/huggingface/transformers/tree/main/examples/distillation), RoBERTa into'
' [DistilRoBERTa](https://github.com/huggingface/transformers/tree/main/examples/distillation),'
' Multilingual BERT into'
' [DistilmBERT](https://github.com/huggingface/transformers/tree/main/examples/distillation) and a German'
' version of DistilBERT.\n1. **[ELECTRA](https://huggingface.co/transformers/model_doc/electra.html)**'
' (from Google Research/Stanford University) released with the paper [ELECTRA: Pre-training text encoders'
' as discriminators rather than generators](https://arxiv.org/abs/2003.10555) by Kevin Clark, Minh-Thang'
' Luong, Quoc V. Le, Christopher D. Manning.'
)
snake_case__ : Union[str, Any] = (
'1. **[ALBERT](https://huggingface.co/transformers/model_doc/albert.html)** (来自 Google Research and the'
' Toyota Technological Institute at Chicago) 伴随论文 [ALBERT: A Lite BERT for Self-supervised Learning of'
' Language Representations](https://arxiv.org/abs/1909.11942), 由 Zhenzhong Lan, Mingda Chen, Sebastian'
' Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut 发布。\n'
)
snake_case__ : Tuple = (
'1. **[ALBERT](https://huggingface.co/transformers/model_doc/albert.html)** (来自 Google Research and the'
' Toyota Technological Institute at Chicago) 伴随论文 [ALBERT: A Lite BERT for Self-supervised Learning of'
' Language Representations](https://arxiv.org/abs/1909.11942), 由 Zhenzhong Lan, Mingda Chen, Sebastian'
' Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut 发布。\n1.'
' **[DistilBERT](https://huggingface.co/transformers/model_doc/distilbert.html)** (来自 HuggingFace) 伴随论文'
' [DistilBERT, a distilled version of BERT: smaller, faster, cheaper and'
' lighter](https://arxiv.org/abs/1910.01108) 由 Victor Sanh, Lysandre Debut and Thomas Wolf 发布。 The same'
' method has been applied to compress GPT2 into'
' [DistilGPT2](https://github.com/huggingface/transformers/tree/main/examples/distillation), RoBERTa into'
' [DistilRoBERTa](https://github.com/huggingface/transformers/tree/main/examples/distillation),'
' Multilingual BERT into'
' [DistilmBERT](https://github.com/huggingface/transformers/tree/main/examples/distillation) and a German'
' version of DistilBERT.\n1. **[ELECTRA](https://huggingface.co/transformers/model_doc/electra.html)** (来自'
' Google Research/Stanford University) 伴随论文 [ELECTRA: Pre-training text encoders as discriminators rather'
' than generators](https://arxiv.org/abs/2003.10555) 由 Kevin Clark, Minh-Thang Luong, Quoc V. Le,'
' Christopher D. Manning 发布。\n'
)
snake_case__ , snake_case__ : Optional[Any] = check_copies.convert_to_localized_md(
__lowerCamelCase , __lowerCamelCase , localized_readme['format_model_list'] )
self.assertFalse(__lowerCamelCase )
self.assertEqual(__lowerCamelCase , __lowerCamelCase )
snake_case__ , snake_case__ : Any = check_copies.convert_to_localized_md(
__lowerCamelCase , __lowerCamelCase , localized_readme['format_model_list'] )
# Check whether the number of models is equal to README.md after conversion.
self.assertTrue(__lowerCamelCase )
snake_case__ : Optional[Any] = (
'1. **[ALBERT](https://huggingface.co/transformers/model_doc/albert.html)** (from Google Research and the'
' Toyota Technological Institute at Chicago) released with the paper [ALBERT: A Lite BERT for'
' Self-supervised Learning of Language Representations](https://arxiv.org/abs/1909.11942), by Zhenzhong'
' Lan, Mingda Chen, Sebastian Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut.'
)
snake_case__ : int = (
'1. **[ALBERT](https://huggingface.co/transformers/main/model_doc/albert.html)** (来自 Google Research and'
' the Toyota Technological Institute at Chicago) 伴随论文 [ALBERT: A Lite BERT for Self-supervised Learning of'
' Language Representations](https://arxiv.org/abs/1909.11942), 由 Zhenzhong Lan, Mingda Chen, Sebastian'
' Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut 发布。\n'
)
snake_case__ : List[str] = (
'1. **[ALBERT](https://huggingface.co/transformers/model_doc/albert.html)** (来自 Google Research and the'
' Toyota Technological Institute at Chicago) 伴随论文 [ALBERT: A Lite BERT for Self-supervised Learning of'
' Language Representations](https://arxiv.org/abs/1909.11942), 由 Zhenzhong Lan, Mingda Chen, Sebastian'
' Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut 发布。\n'
)
snake_case__ , snake_case__ : Any = check_copies.convert_to_localized_md(
__lowerCamelCase , __lowerCamelCase , localized_readme['format_model_list'] )
# Check if the model link is synchronized.
self.assertEqual(__lowerCamelCase , __lowerCamelCase )
| 270
| 1
|
from ...utils import is_torch_available, is_transformers_available
if is_transformers_available() and is_torch_available():
from .pipeline_vq_diffusion import LearnedClassifierFreeSamplingEmbeddings, VQDiffusionPipeline
| 702
|
from __future__ import annotations
from collections.abc import Callable
def SCREAMING_SNAKE_CASE_ ( UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ = 1_0_0 , ):
UpperCamelCase__ : Union[str, Any] = x_start
UpperCamelCase__ : List[Any] = fnc(UpperCamelCase__ )
UpperCamelCase__ : Any = 0.0
for _ in range(UpperCamelCase__ ):
# Approximates small segments of curve as linear and solve
# for trapezoidal area
UpperCamelCase__ : str = (x_end - x_start) / steps + xa
UpperCamelCase__ : Dict = fnc(UpperCamelCase__ )
area += abs(fxa + fxa ) * (xa - xa) / 2
# Increment step
UpperCamelCase__ : Tuple = xa
UpperCamelCase__ : Union[str, Any] = fxa
return area
if __name__ == "__main__":
def SCREAMING_SNAKE_CASE_ ( UpperCamelCase__ ):
return x**3 + x**2
print("f(x) = x^3 + x^2")
print("The area between the curve, x = -5, x = 5 and the x axis is:")
lowerCamelCase =1_0
while i <= 1_0_0_0_0_0:
print(F'''with {i} steps: {trapezoidal_area(f, -5, 5, i)}''')
i *= 1_0
| 462
| 0
|
import unittest
from transformers import AlbertConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
MODEL_FOR_PRETRAINING_MAPPING,
AlbertForMaskedLM,
AlbertForMultipleChoice,
AlbertForPreTraining,
AlbertForQuestionAnswering,
AlbertForSequenceClassification,
AlbertForTokenClassification,
AlbertModel,
)
from transformers.models.albert.modeling_albert import ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST
class lowercase__ :
def __init__( self : List[str] , _lowercase : Dict , _lowercase : Optional[Any]=13 , _lowercase : List[str]=7 , _lowercase : Dict=True , _lowercase : Union[str, Any]=True , _lowercase : Optional[int]=True , _lowercase : List[Any]=True , _lowercase : Optional[Any]=99 , _lowercase : Any=16 , _lowercase : List[Any]=36 , _lowercase : str=6 , _lowercase : Union[str, Any]=6 , _lowercase : List[Any]=6 , _lowercase : Any=37 , _lowercase : int="gelu" , _lowercase : List[Any]=0.1 , _lowercase : int=0.1 , _lowercase : Dict=5_12 , _lowercase : Optional[Any]=16 , _lowercase : Optional[int]=2 , _lowercase : Dict=0.0_2 , _lowercase : List[Any]=3 , _lowercase : List[Any]=4 , _lowercase : Dict=None , ):
"""simple docstring"""
UpperCAmelCase__ = parent
UpperCAmelCase__ = batch_size
UpperCAmelCase__ = seq_length
UpperCAmelCase__ = is_training
UpperCAmelCase__ = use_input_mask
UpperCAmelCase__ = use_token_type_ids
UpperCAmelCase__ = use_labels
UpperCAmelCase__ = vocab_size
UpperCAmelCase__ = embedding_size
UpperCAmelCase__ = hidden_size
UpperCAmelCase__ = num_hidden_layers
UpperCAmelCase__ = num_hidden_groups
UpperCAmelCase__ = num_attention_heads
UpperCAmelCase__ = intermediate_size
UpperCAmelCase__ = hidden_act
UpperCAmelCase__ = hidden_dropout_prob
UpperCAmelCase__ = attention_probs_dropout_prob
UpperCAmelCase__ = max_position_embeddings
UpperCAmelCase__ = type_vocab_size
UpperCAmelCase__ = type_sequence_label_size
UpperCAmelCase__ = initializer_range
UpperCAmelCase__ = num_labels
UpperCAmelCase__ = num_choices
UpperCAmelCase__ = scope
def _UpperCAmelCase ( self : Tuple ):
"""simple docstring"""
UpperCAmelCase__ = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
UpperCAmelCase__ = None
if self.use_input_mask:
UpperCAmelCase__ = random_attention_mask([self.batch_size, self.seq_length] )
UpperCAmelCase__ = None
if self.use_token_type_ids:
UpperCAmelCase__ = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
UpperCAmelCase__ = None
UpperCAmelCase__ = None
UpperCAmelCase__ = None
if self.use_labels:
UpperCAmelCase__ = ids_tensor([self.batch_size] , self.type_sequence_label_size )
UpperCAmelCase__ = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
UpperCAmelCase__ = ids_tensor([self.batch_size] , self.num_choices )
UpperCAmelCase__ = self.get_config()
return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def _UpperCAmelCase ( self : Optional[Any] ):
"""simple docstring"""
return AlbertConfig(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , initializer_range=self.initializer_range , num_hidden_groups=self.num_hidden_groups , )
def _UpperCAmelCase ( self : Tuple , _lowercase : Optional[int] , _lowercase : Any , _lowercase : Tuple , _lowercase : Optional[Any] , _lowercase : List[str] , _lowercase : str , _lowercase : Dict ):
"""simple docstring"""
UpperCAmelCase__ = AlbertModel(config=_lowercase )
model.to(_lowercase )
model.eval()
UpperCAmelCase__ = model(_lowercase , attention_mask=_lowercase , token_type_ids=_lowercase )
UpperCAmelCase__ = model(_lowercase , token_type_ids=_lowercase )
UpperCAmelCase__ = model(_lowercase )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
self.parent.assertEqual(result.pooler_output.shape , (self.batch_size, self.hidden_size) )
def _UpperCAmelCase ( self : Optional[int] , _lowercase : Union[str, Any] , _lowercase : int , _lowercase : Dict , _lowercase : str , _lowercase : int , _lowercase : Optional[Any] , _lowercase : str ):
"""simple docstring"""
UpperCAmelCase__ = AlbertForPreTraining(config=_lowercase )
model.to(_lowercase )
model.eval()
UpperCAmelCase__ = model(
_lowercase , attention_mask=_lowercase , token_type_ids=_lowercase , labels=_lowercase , sentence_order_label=_lowercase , )
self.parent.assertEqual(result.prediction_logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
self.parent.assertEqual(result.sop_logits.shape , (self.batch_size, config.num_labels) )
def _UpperCAmelCase ( self : Optional[Any] , _lowercase : Optional[Any] , _lowercase : Optional[Any] , _lowercase : Union[str, Any] , _lowercase : Tuple , _lowercase : Optional[int] , _lowercase : Any , _lowercase : str ):
"""simple docstring"""
UpperCAmelCase__ = AlbertForMaskedLM(config=_lowercase )
model.to(_lowercase )
model.eval()
UpperCAmelCase__ = model(_lowercase , attention_mask=_lowercase , token_type_ids=_lowercase , labels=_lowercase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def _UpperCAmelCase ( self : Any , _lowercase : int , _lowercase : Any , _lowercase : Optional[Any] , _lowercase : Union[str, Any] , _lowercase : str , _lowercase : List[str] , _lowercase : Dict ):
"""simple docstring"""
UpperCAmelCase__ = AlbertForQuestionAnswering(config=_lowercase )
model.to(_lowercase )
model.eval()
UpperCAmelCase__ = model(
_lowercase , attention_mask=_lowercase , token_type_ids=_lowercase , start_positions=_lowercase , end_positions=_lowercase , )
self.parent.assertEqual(result.start_logits.shape , (self.batch_size, self.seq_length) )
self.parent.assertEqual(result.end_logits.shape , (self.batch_size, self.seq_length) )
def _UpperCAmelCase ( self : int , _lowercase : Tuple , _lowercase : str , _lowercase : str , _lowercase : Optional[int] , _lowercase : Tuple , _lowercase : Optional[int] , _lowercase : List[Any] ):
"""simple docstring"""
UpperCAmelCase__ = self.num_labels
UpperCAmelCase__ = AlbertForSequenceClassification(_lowercase )
model.to(_lowercase )
model.eval()
UpperCAmelCase__ = model(_lowercase , attention_mask=_lowercase , token_type_ids=_lowercase , labels=_lowercase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def _UpperCAmelCase ( self : Tuple , _lowercase : Any , _lowercase : Tuple , _lowercase : Union[str, Any] , _lowercase : List[str] , _lowercase : Any , _lowercase : Tuple , _lowercase : List[str] ):
"""simple docstring"""
UpperCAmelCase__ = self.num_labels
UpperCAmelCase__ = AlbertForTokenClassification(config=_lowercase )
model.to(_lowercase )
model.eval()
UpperCAmelCase__ = model(_lowercase , attention_mask=_lowercase , token_type_ids=_lowercase , labels=_lowercase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def _UpperCAmelCase ( self : int , _lowercase : Tuple , _lowercase : str , _lowercase : Any , _lowercase : str , _lowercase : Tuple , _lowercase : str , _lowercase : Any ):
"""simple docstring"""
UpperCAmelCase__ = self.num_choices
UpperCAmelCase__ = AlbertForMultipleChoice(config=_lowercase )
model.to(_lowercase )
model.eval()
UpperCAmelCase__ = input_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
UpperCAmelCase__ = token_type_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
UpperCAmelCase__ = input_mask.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
UpperCAmelCase__ = model(
_lowercase , attention_mask=_lowercase , token_type_ids=_lowercase , labels=_lowercase , )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_choices) )
def _UpperCAmelCase ( self : str ):
"""simple docstring"""
UpperCAmelCase__ = self.prepare_config_and_inputs()
(
(
UpperCAmelCase__
) , (
UpperCAmelCase__
) , (
UpperCAmelCase__
) , (
UpperCAmelCase__
) , (
UpperCAmelCase__
) , (
UpperCAmelCase__
) , (
UpperCAmelCase__
) ,
) = config_and_inputs
UpperCAmelCase__ = {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": input_mask}
return config, inputs_dict
@require_torch
class lowercase__ ( __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , unittest.TestCase ):
A__= (
(
AlbertModel,
AlbertForPreTraining,
AlbertForMaskedLM,
AlbertForMultipleChoice,
AlbertForSequenceClassification,
AlbertForTokenClassification,
AlbertForQuestionAnswering,
)
if is_torch_available()
else ()
)
A__= (
{
'feature-extraction': AlbertModel,
'fill-mask': AlbertForMaskedLM,
'question-answering': AlbertForQuestionAnswering,
'text-classification': AlbertForSequenceClassification,
'token-classification': AlbertForTokenClassification,
'zero-shot': AlbertForSequenceClassification,
}
if is_torch_available()
else {}
)
A__= True
def _UpperCAmelCase ( self : int , _lowercase : Optional[Any] , _lowercase : List[str] , _lowercase : Tuple=False ):
"""simple docstring"""
UpperCAmelCase__ = super()._prepare_for_class(_lowercase , _lowercase , return_labels=_lowercase )
if return_labels:
if model_class in get_values(_lowercase ):
UpperCAmelCase__ = torch.zeros(
(self.model_tester.batch_size, self.model_tester.seq_length) , dtype=torch.long , device=_lowercase )
UpperCAmelCase__ = torch.zeros(
self.model_tester.batch_size , dtype=torch.long , device=_lowercase )
return inputs_dict
def _UpperCAmelCase ( self : Optional[Any] ):
"""simple docstring"""
UpperCAmelCase__ = AlbertModelTester(self )
UpperCAmelCase__ = ConfigTester(self , config_class=_lowercase , hidden_size=37 )
def _UpperCAmelCase ( self : str ):
"""simple docstring"""
self.config_tester.run_common_tests()
def _UpperCAmelCase ( self : Any ):
"""simple docstring"""
UpperCAmelCase__ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*_lowercase )
def _UpperCAmelCase ( self : Dict ):
"""simple docstring"""
UpperCAmelCase__ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_pretraining(*_lowercase )
def _UpperCAmelCase ( self : Dict ):
"""simple docstring"""
UpperCAmelCase__ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_lm(*_lowercase )
def _UpperCAmelCase ( self : List[Any] ):
"""simple docstring"""
UpperCAmelCase__ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_multiple_choice(*_lowercase )
def _UpperCAmelCase ( self : Any ):
"""simple docstring"""
UpperCAmelCase__ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_question_answering(*_lowercase )
def _UpperCAmelCase ( self : Optional[int] ):
"""simple docstring"""
UpperCAmelCase__ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_sequence_classification(*_lowercase )
def _UpperCAmelCase ( self : List[Any] ):
"""simple docstring"""
UpperCAmelCase__ = self.model_tester.prepare_config_and_inputs()
for type in ["absolute", "relative_key", "relative_key_query"]:
UpperCAmelCase__ = type
self.model_tester.create_and_check_model(*_lowercase )
@slow
def _UpperCAmelCase ( self : List[Any] ):
"""simple docstring"""
for model_name in ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
UpperCAmelCase__ = AlbertModel.from_pretrained(_lowercase )
self.assertIsNotNone(_lowercase )
@require_torch
class lowercase__ ( unittest.TestCase ):
@slow
def _UpperCAmelCase ( self : Dict ):
"""simple docstring"""
UpperCAmelCase__ = AlbertModel.from_pretrained("albert-base-v2" )
UpperCAmelCase__ = torch.tensor([[0, 3_45, 2_32, 3_28, 7_40, 1_40, 16_95, 69, 60_78, 15_88, 2]] )
UpperCAmelCase__ = torch.tensor([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]] )
with torch.no_grad():
UpperCAmelCase__ = model(_lowercase , attention_mask=_lowercase )[0]
UpperCAmelCase__ = torch.Size((1, 11, 7_68) )
self.assertEqual(output.shape , _lowercase )
UpperCAmelCase__ = torch.tensor(
[[[-0.6_5_1_3, 1.5_0_3_5, -0.2_7_6_6], [-0.6_5_1_5, 1.5_0_4_6, -0.2_7_8_0], [-0.6_5_1_2, 1.5_0_4_9, -0.2_7_8_4]]] )
self.assertTrue(torch.allclose(output[:, 1:4, 1:4] , _lowercase , atol=1E-4 ) )
| 475
|
from typing import List, Optional, Union
import numpy as np
from ....audio_utils import mel_filter_bank, optimal_fft_length, spectrogram, window_function
from ....feature_extraction_sequence_utils import SequenceFeatureExtractor
from ....feature_extraction_utils import BatchFeature
from ....file_utils import PaddingStrategy, TensorType
from ....utils import logging
A = logging.get_logger(__name__)
class lowercase__ ( __SCREAMING_SNAKE_CASE ):
A__= ['input_features', 'attention_mask']
def __init__( self : List[Any] , _lowercase : List[Any]=80 , _lowercase : Optional[Any]=1_60_00 , _lowercase : Union[str, Any]=0.0 , _lowercase : Tuple=10 , _lowercase : Tuple=25 , _lowercase : Optional[Any]="hamming_window" , _lowercase : Optional[int]=3_2_7_6_8.0 , _lowercase : Union[str, Any]=0.9_7 , _lowercase : Union[str, Any]=1.0 , _lowercase : List[str]=True , _lowercase : List[Any]=True , _lowercase : Any=False , **_lowercase : Optional[Any] , ):
"""simple docstring"""
super().__init__(feature_size=_lowercase , sampling_rate=_lowercase , padding_value=_lowercase , **_lowercase )
UpperCAmelCase__ = feature_size
UpperCAmelCase__ = sampling_rate
UpperCAmelCase__ = padding_value
UpperCAmelCase__ = hop_length
UpperCAmelCase__ = win_length
UpperCAmelCase__ = frame_signal_scale
UpperCAmelCase__ = preemphasis_coeff
UpperCAmelCase__ = mel_floor
UpperCAmelCase__ = normalize_means
UpperCAmelCase__ = normalize_vars
UpperCAmelCase__ = win_function
UpperCAmelCase__ = return_attention_mask
UpperCAmelCase__ = win_length * sampling_rate // 10_00
UpperCAmelCase__ = hop_length * sampling_rate // 10_00
UpperCAmelCase__ = optimal_fft_length(self.sample_size )
UpperCAmelCase__ = (self.n_fft // 2) + 1
def _UpperCAmelCase ( self : Any , _lowercase : np.array ):
"""simple docstring"""
if self.win_function == "hamming_window":
UpperCAmelCase__ = window_function(window_length=self.sample_size , name=self.win_function , periodic=_lowercase )
else:
UpperCAmelCase__ = window_function(window_length=self.sample_size , name=self.win_function )
UpperCAmelCase__ = mel_filter_bank(
num_frequency_bins=self.n_freqs , num_mel_filters=self.feature_size , min_frequency=0.0 , max_frequency=self.sampling_rate / 2.0 , sampling_rate=self.sampling_rate , )
UpperCAmelCase__ = spectrogram(
one_waveform * self.frame_signal_scale , window=_lowercase , frame_length=self.sample_size , hop_length=self.sample_stride , fft_length=self.n_fft , center=_lowercase , preemphasis=self.preemphasis_coeff , mel_filters=_lowercase , mel_floor=self.mel_floor , log_mel="log" , )
return msfc_features.T
def _UpperCAmelCase ( self : Dict , _lowercase : int , _lowercase : Union[str, Any] , _lowercase : List[Any] ):
"""simple docstring"""
if self.normalize_means:
UpperCAmelCase__ = x[:input_length].mean(axis=0 )
UpperCAmelCase__ = np.subtract(_lowercase , _lowercase )
if self.normalize_vars:
UpperCAmelCase__ = x[:input_length].std(axis=0 )
UpperCAmelCase__ = np.divide(_lowercase , _lowercase )
if input_length < x.shape[0]:
UpperCAmelCase__ = padding_value
# make sure array is in float32
UpperCAmelCase__ = x.astype(np.floataa )
return x
def _UpperCAmelCase ( self : Optional[Any] , _lowercase : List[np.ndarray] , _lowercase : Optional[np.ndarray] = None ):
"""simple docstring"""
UpperCAmelCase__ = attention_mask.sum(-1 ) if attention_mask is not None else [x.shape[0] for x in input_features]
return [self._normalize_one(_lowercase , _lowercase , self.padding_value ) for x, n in zip(_lowercase , _lowercase )]
def __call__( self : List[Any] , _lowercase : Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]] , _lowercase : Union[bool, str, PaddingStrategy] = False , _lowercase : Optional[int] = None , _lowercase : bool = False , _lowercase : Optional[int] = None , _lowercase : Optional[bool] = None , _lowercase : Optional[Union[str, TensorType]] = None , _lowercase : Optional[int] = None , **_lowercase : Optional[Any] , ):
"""simple docstring"""
if sampling_rate is not None:
if sampling_rate != self.sampling_rate:
raise ValueError(
F"""The model corresponding to this feature extractor: {self} was trained using a sampling rate of"""
F""" {self.sampling_rate}. Please make sure that the provided `raw_speech` input was sampled with"""
F""" {self.sampling_rate} and not {sampling_rate}.""" )
else:
logger.warning(
"It is strongly recommended to pass the ``sampling_rate`` argument to this function. "
"Failing to do so can result in silent errors that might be hard to debug." )
UpperCAmelCase__ = isinstance(_lowercase , np.ndarray ) and len(raw_speech.shape ) > 1
if is_batched_numpy and len(raw_speech.shape ) > 2:
raise ValueError(F"""Only mono-channel audio is supported for input to {self}""" )
UpperCAmelCase__ = is_batched_numpy or (
isinstance(_lowercase , (list, tuple) ) and (isinstance(raw_speech[0] , (np.ndarray, tuple, list) ))
)
if is_batched:
UpperCAmelCase__ = [np.asarray(_lowercase , dtype=np.floataa ) for speech in raw_speech]
elif not is_batched and not isinstance(_lowercase , np.ndarray ):
UpperCAmelCase__ = np.asarray(_lowercase , dtype=np.floataa )
elif isinstance(_lowercase , np.ndarray ) and raw_speech.dtype is np.dtype(np.floataa ):
UpperCAmelCase__ = raw_speech.astype(np.floataa )
# always return batch
if not is_batched:
UpperCAmelCase__ = [raw_speech]
# extract fbank features
UpperCAmelCase__ = [self._extract_mfsc_features(_lowercase ) for one_waveform in raw_speech]
# convert into correct format for padding
UpperCAmelCase__ = BatchFeature({"input_features": features} )
UpperCAmelCase__ = self.pad(
_lowercase , padding=_lowercase , max_length=_lowercase , truncation=_lowercase , pad_to_multiple_of=_lowercase , return_attention_mask=_lowercase , **_lowercase , )
# make sure list is in array format
UpperCAmelCase__ = padded_inputs.get("input_features" )
if isinstance(input_features[0] , _lowercase ):
UpperCAmelCase__ = [np.asarray(_lowercase , dtype=np.floataa ) for feature in input_features]
UpperCAmelCase__ = padded_inputs.get("attention_mask" )
if attention_mask is not None:
UpperCAmelCase__ = [np.asarray(_lowercase , dtype=np.intaa ) for array in attention_mask]
if self.normalize_means or self.normalize_vars:
UpperCAmelCase__ = (
np.array(_lowercase , dtype=np.intaa )
if self._get_padding_strategies(_lowercase , max_length=_lowercase ) is not PaddingStrategy.DO_NOT_PAD
and padding
else None
)
UpperCAmelCase__ = self.normalize(
padded_inputs["input_features"] , attention_mask=_lowercase )
if return_tensors is not None:
UpperCAmelCase__ = padded_inputs.convert_to_tensors(_lowercase )
return padded_inputs
| 475
| 1
|
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
_UpperCAmelCase = logging.get_logger(__name__)
_UpperCAmelCase = {
"""xlm-mlm-en-2048""": """https://huggingface.co/xlm-mlm-en-2048/resolve/main/config.json""",
"""xlm-mlm-ende-1024""": """https://huggingface.co/xlm-mlm-ende-1024/resolve/main/config.json""",
"""xlm-mlm-enfr-1024""": """https://huggingface.co/xlm-mlm-enfr-1024/resolve/main/config.json""",
"""xlm-mlm-enro-1024""": """https://huggingface.co/xlm-mlm-enro-1024/resolve/main/config.json""",
"""xlm-mlm-tlm-xnli15-1024""": """https://huggingface.co/xlm-mlm-tlm-xnli15-1024/resolve/main/config.json""",
"""xlm-mlm-xnli15-1024""": """https://huggingface.co/xlm-mlm-xnli15-1024/resolve/main/config.json""",
"""xlm-clm-enfr-1024""": """https://huggingface.co/xlm-clm-enfr-1024/resolve/main/config.json""",
"""xlm-clm-ende-1024""": """https://huggingface.co/xlm-clm-ende-1024/resolve/main/config.json""",
"""xlm-mlm-17-1280""": """https://huggingface.co/xlm-mlm-17-1280/resolve/main/config.json""",
"""xlm-mlm-100-1280""": """https://huggingface.co/xlm-mlm-100-1280/resolve/main/config.json""",
}
class a ( UpperCAmelCase__ ):
UpperCamelCase : int = 'xlm'
UpperCamelCase : Union[str, Any] = {
'hidden_size': 'emb_dim',
'num_attention_heads': 'n_heads',
'num_hidden_layers': 'n_layers',
'n_words': 'vocab_size', # For backward compatibility
}
def __init__( self : List[str] , lowerCAmelCase : Any=3_0145 , lowerCAmelCase : Union[str, Any]=2048 , lowerCAmelCase : Any=12 , lowerCAmelCase : List[Any]=16 , lowerCAmelCase : Tuple=0.1 , lowerCAmelCase : Union[str, Any]=0.1 , lowerCAmelCase : str=True , lowerCAmelCase : str=False , lowerCAmelCase : Optional[int]=False , lowerCAmelCase : List[Any]=False , lowerCAmelCase : Any=1 , lowerCAmelCase : Optional[Any]=True , lowerCAmelCase : Dict=512 , lowerCAmelCase : Any=2048**-0.5 , lowerCAmelCase : str=1E-12 , lowerCAmelCase : Dict=0.0_2 , lowerCAmelCase : List[Any]=0 , lowerCAmelCase : Dict=1 , lowerCAmelCase : Any=2 , lowerCAmelCase : Optional[int]=3 , lowerCAmelCase : Tuple=5 , lowerCAmelCase : Tuple=True , lowerCAmelCase : str="first" , lowerCAmelCase : Optional[Any]=True , lowerCAmelCase : Union[str, Any]=None , lowerCAmelCase : str=True , lowerCAmelCase : Dict=0.1 , lowerCAmelCase : Union[str, Any]=5 , lowerCAmelCase : List[str]=5 , lowerCAmelCase : Optional[int]=0 , lowerCAmelCase : int=0 , lowerCAmelCase : List[str]=2 , lowerCAmelCase : Union[str, Any]=0 , **lowerCAmelCase : Optional[Any] , ) -> List[str]:
'''simple docstring'''
SCREAMING_SNAKE_CASE_: Dict =vocab_size
SCREAMING_SNAKE_CASE_: Union[str, Any] =emb_dim
SCREAMING_SNAKE_CASE_: Any =n_layers
SCREAMING_SNAKE_CASE_: Dict =n_heads
SCREAMING_SNAKE_CASE_: Any =dropout
SCREAMING_SNAKE_CASE_: Optional[int] =attention_dropout
SCREAMING_SNAKE_CASE_: Dict =gelu_activation
SCREAMING_SNAKE_CASE_: Optional[Any] =sinusoidal_embeddings
SCREAMING_SNAKE_CASE_: Optional[Any] =causal
SCREAMING_SNAKE_CASE_: Optional[int] =asm
SCREAMING_SNAKE_CASE_: Union[str, Any] =n_langs
SCREAMING_SNAKE_CASE_: Optional[Any] =use_lang_emb
SCREAMING_SNAKE_CASE_: str =layer_norm_eps
SCREAMING_SNAKE_CASE_: Dict =bos_index
SCREAMING_SNAKE_CASE_: Any =eos_index
SCREAMING_SNAKE_CASE_: List[Any] =pad_index
SCREAMING_SNAKE_CASE_: Tuple =unk_index
SCREAMING_SNAKE_CASE_: Any =mask_index
SCREAMING_SNAKE_CASE_: int =is_encoder
SCREAMING_SNAKE_CASE_: List[str] =max_position_embeddings
SCREAMING_SNAKE_CASE_: List[Any] =embed_init_std
SCREAMING_SNAKE_CASE_: Dict =init_std
SCREAMING_SNAKE_CASE_: List[Any] =summary_type
SCREAMING_SNAKE_CASE_: int =summary_use_proj
SCREAMING_SNAKE_CASE_: List[Any] =summary_activation
SCREAMING_SNAKE_CASE_: str =summary_proj_to_labels
SCREAMING_SNAKE_CASE_: int =summary_first_dropout
SCREAMING_SNAKE_CASE_: Any =start_n_top
SCREAMING_SNAKE_CASE_: Dict =end_n_top
SCREAMING_SNAKE_CASE_: Optional[int] =mask_token_id
SCREAMING_SNAKE_CASE_: Any =lang_id
if "n_words" in kwargs:
SCREAMING_SNAKE_CASE_: Tuple =kwargs["""n_words"""]
super().__init__(pad_token_id=lowerCAmelCase , bos_token_id=lowerCAmelCase , **lowerCAmelCase )
class a ( UpperCAmelCase__ ):
@property
def lowerCamelCase__ ( self : Union[str, Any] ) -> Mapping[str, Mapping[int, str]]:
'''simple docstring'''
if self.task == "multiple-choice":
SCREAMING_SNAKE_CASE_: Any ={0: """batch""", 1: """choice""", 2: """sequence"""}
else:
SCREAMING_SNAKE_CASE_: List[Any] ={0: """batch""", 1: """sequence"""}
return OrderedDict(
[
("""input_ids""", dynamic_axis),
("""attention_mask""", dynamic_axis),
("""token_type_ids""", dynamic_axis),
] )
| 36
|
"""simple docstring"""
def __magic_name__ ( lowercase ):
SCREAMING_SNAKE_CASE_: int =False
while is_sorted is False: # Until all the indices are traversed keep looping
SCREAMING_SNAKE_CASE_: Tuple =True
for i in range(0 , len(lowercase ) - 1 , 2 ): # iterating over all even indices
if input_list[i] > input_list[i + 1]:
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_: Optional[int] =input_list[i + 1], input_list[i]
# swapping if elements not in order
SCREAMING_SNAKE_CASE_: Tuple =False
for i in range(1 , len(lowercase ) - 1 , 2 ): # iterating over all odd indices
if input_list[i] > input_list[i + 1]:
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_: str =input_list[i + 1], input_list[i]
# swapping if elements not in order
SCREAMING_SNAKE_CASE_: str =False
return input_list
if __name__ == "__main__":
print("""Enter list to be sorted""")
_UpperCAmelCase = [int(x) for x in input().split()]
# inputing elements of the list in one line
_UpperCAmelCase = odd_even_sort(input_list)
print("""The sorted list is""")
print(sorted_list)
| 36
| 1
|
import sacrebleu as scb
from packaging import version
from sacrebleu import TER
import datasets
lowerCAmelCase__ : Any ='\\n@inproceedings{snover-etal-2006-study,\n title = "A Study of Translation Edit Rate with Targeted Human Annotation",\n author = "Snover, Matthew and\n Dorr, Bonnie and\n Schwartz, Rich and\n Micciulla, Linnea and\n Makhoul, John",\n booktitle = "Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers",\n month = aug # " 8-12",\n year = "2006",\n address = "Cambridge, Massachusetts, USA",\n publisher = "Association for Machine Translation in the Americas",\n url = "https://aclanthology.org/2006.amta-papers.25",\n pages = "223--231",\n}\n@inproceedings{post-2018-call,\n title = "A Call for Clarity in Reporting {BLEU} Scores",\n author = "Post, Matt",\n booktitle = "Proceedings of the Third Conference on Machine Translation: Research Papers",\n month = oct,\n year = "2018",\n address = "Belgium, Brussels",\n publisher = "Association for Computational Linguistics",\n url = "https://www.aclweb.org/anthology/W18-6319",\n pages = "186--191",\n}\n'
lowerCAmelCase__ : List[Any] ='\\nTER (Translation Edit Rate, also called Translation Error Rate) is a metric to quantify the edit operations that a\nhypothesis requires to match a reference translation. We use the implementation that is already present in sacrebleu\n(https://github.com/mjpost/sacreBLEU#ter), which in turn is inspired by the TERCOM implementation, which can be found\nhere: https://github.com/jhclark/tercom.\n\nThe implementation here is slightly different from sacrebleu in terms of the required input format. The length of\nthe references and hypotheses lists need to be the same, so you may need to transpose your references compared to\nsacrebleu\'s required input format. See https://github.com/huggingface/datasets/issues/3154#issuecomment-950746534\n\nSee the README.md file at https://github.com/mjpost/sacreBLEU#ter for more information.\n'
lowerCAmelCase__ : int ='\nProduces TER scores alongside the number of edits and reference length.\n\nArgs:\n predictions (list of str): The system stream (a sequence of segments).\n references (list of list of str): A list of one or more reference streams (each a sequence of segments).\n normalized (boolean): If `True`, applies basic tokenization and normalization to sentences. Defaults to `False`.\n ignore_punct (boolean): If `True`, applies basic tokenization and normalization to sentences. Defaults to `False`.\n support_zh_ja_chars (boolean): If `True`, tokenization/normalization supports processing of Chinese characters,\n as well as Japanese Kanji, Hiragana, Katakana, and Phonetic Extensions of Katakana.\n Only applies if `normalized = True`. Defaults to `False`.\n case_sensitive (boolean): If `False`, makes all predictions and references lowercase to ignore differences in case. Defaults to `False`.\n\nReturns:\n \'score\' (float): TER score (num_edits / sum_ref_lengths * 100)\n \'num_edits\' (int): The cumulative number of edits\n \'ref_length\' (float): The cumulative average reference length\n\nExamples:\n Example 1:\n >>> predictions = ["does this sentence match??",\n ... "what about this sentence?",\n ... "What did the TER metric user say to the developer?"]\n >>> references = [["does this sentence match", "does this sentence match!?!"],\n ... ["wHaT aBoUt ThIs SeNtEnCe?", "wHaT aBoUt ThIs SeNtEnCe?"],\n ... ["Your jokes are...", "...TERrible"]]\n >>> ter = datasets.load_metric("ter")\n >>> results = ter.compute(predictions=predictions,\n ... references=references,\n ... case_sensitive=True)\n >>> print(results)\n {\'score\': 150.0, \'num_edits\': 15, \'ref_length\': 10.0}\n\n Example 2:\n >>> predictions = ["does this sentence match??",\n ... "what about this sentence?"]\n >>> references = [["does this sentence match", "does this sentence match!?!"],\n ... ["wHaT aBoUt ThIs SeNtEnCe?", "wHaT aBoUt ThIs SeNtEnCe?"]]\n >>> ter = datasets.load_metric("ter")\n >>> results = ter.compute(predictions=predictions,\n ... references=references,\n ... case_sensitive=True)\n >>> print(results)\n {\'score\': 62.5, \'num_edits\': 5, \'ref_length\': 8.0}\n\n Example 3:\n >>> predictions = ["does this sentence match??",\n ... "what about this sentence?"]\n >>> references = [["does this sentence match", "does this sentence match!?!"],\n ... ["wHaT aBoUt ThIs SeNtEnCe?", "wHaT aBoUt ThIs SeNtEnCe?"]]\n >>> ter = datasets.load_metric("ter")\n >>> results = ter.compute(predictions=predictions,\n ... references=references,\n ... normalized=True,\n ... case_sensitive=True)\n >>> print(results)\n {\'score\': 57.14285714285714, \'num_edits\': 6, \'ref_length\': 10.5}\n\n Example 4:\n >>> predictions = ["does this sentence match??",\n ... "what about this sentence?"]\n >>> references = [["does this sentence match", "does this sentence match!?!"],\n ... ["wHaT aBoUt ThIs SeNtEnCe?", "wHaT aBoUt ThIs SeNtEnCe?"]]\n >>> ter = datasets.load_metric("ter")\n >>> results = ter.compute(predictions=predictions,\n ... references=references,\n ... ignore_punct=True,\n ... case_sensitive=False)\n >>> print(results)\n {\'score\': 0.0, \'num_edits\': 0, \'ref_length\': 8.0}\n\n Example 5:\n >>> predictions = ["does this sentence match??",\n ... "what about this sentence?",\n ... "What did the TER metric user say to the developer?"]\n >>> references = [["does this sentence match", "does this sentence match!?!"],\n ... ["wHaT aBoUt ThIs SeNtEnCe?", "wHaT aBoUt ThIs SeNtEnCe?"],\n ... ["Your jokes are...", "...TERrible"]]\n >>> ter = datasets.load_metric("ter")\n >>> results = ter.compute(predictions=predictions,\n ... references=references,\n ... ignore_punct=True,\n ... case_sensitive=False)\n >>> print(results)\n {\'score\': 100.0, \'num_edits\': 10, \'ref_length\': 10.0}\n'
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class __lowercase (datasets.Metric ):
"""simple docstring"""
def UpperCamelCase__ ( self ):
"""simple docstring"""
if version.parse(scb.__version__ ) < version.parse('1.4.12' ):
raise ImportWarning(
'To use `sacrebleu`, the module `sacrebleu>=1.4.12` is required, and the current version of `sacrebleu` doesn\'t match this condition.\n'
'You can install it with `pip install "sacrebleu>=1.4.12"`.' )
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , homepage='http://www.cs.umd.edu/~snover/tercom/' , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
'predictions': datasets.Value('string' , id='sequence' ),
'references': datasets.Sequence(datasets.Value('string' , id='sequence' ) , id='references' ),
} ) , codebase_urls=['https://github.com/mjpost/sacreBLEU#ter'] , reference_urls=[
'https://github.com/jhclark/tercom',
] , )
def UpperCamelCase__ ( self , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ = False , lowerCAmelCase__ = False , lowerCAmelCase__ = False , lowerCAmelCase__ = False , ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Optional[Any] = len(references[0] )
if any(len(lowerCAmelCase__ ) != references_per_prediction for refs in references ):
raise ValueError('Sacrebleu requires the same number of references for each prediction' )
SCREAMING_SNAKE_CASE_ : Tuple = [[refs[i] for refs in references] for i in range(lowerCAmelCase__ )]
SCREAMING_SNAKE_CASE_ : Union[str, Any] = TER(
normalized=lowerCAmelCase__ , no_punct=lowerCAmelCase__ , asian_support=lowerCAmelCase__ , case_sensitive=lowerCAmelCase__ , )
SCREAMING_SNAKE_CASE_ : Optional[Any] = sb_ter.corpus_score(lowerCAmelCase__ , lowerCAmelCase__ )
return {"score": output.score, "num_edits": output.num_edits, "ref_length": output.ref_length}
| 101
|
from math import isqrt
def _a ( lowerCAmelCase )-> bool:
return all(number % divisor != 0 for divisor in range(2 , isqrt(lowerCAmelCase ) + 1 ) )
def _a ( lowerCAmelCase = 10**6 )-> int:
SCREAMING_SNAKE_CASE_ = 0
SCREAMING_SNAKE_CASE_ = 1
SCREAMING_SNAKE_CASE_ = 7
while prime_candidate < max_prime:
primes_count += is_prime(lowerCAmelCase )
cube_index += 1
prime_candidate += 6 * cube_index
return primes_count
if __name__ == "__main__":
print(f"""{solution() = }""")
| 360
| 0
|
import unittest
from huggingface_hub import hf_hub_download
from transformers import MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING, VideoMAEFeatureExtractor
from transformers.pipelines import VideoClassificationPipeline, pipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
require_decord,
require_tf,
require_torch,
require_torch_or_tf,
require_vision,
)
from .test_pipelines_common import ANY
@is_pipeline_test
@require_torch_or_tf
@require_vision
@require_decord
class __a( unittest.TestCase ):
"""simple docstring"""
lowerCAmelCase = MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING
def a__ ( self ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ) -> Dict:
UpperCAmelCase_ : Optional[int] = hf_hub_download(
repo_id='''nateraw/video-demo''' ,filename='''archery.mp4''' ,repo_type='''dataset''' )
UpperCAmelCase_ : List[Any] = VideoClassificationPipeline(model=_SCREAMING_SNAKE_CASE ,image_processor=_SCREAMING_SNAKE_CASE ,top_k=2 )
UpperCAmelCase_ : str = [
example_video_filepath,
'''https://huggingface.co/datasets/nateraw/video-demo/resolve/main/archery.mp4''',
]
return video_classifier, examples
def a__ ( self ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ) -> Optional[int]:
for example in examples:
UpperCAmelCase_ : Any = video_classifier(_SCREAMING_SNAKE_CASE )
self.assertEqual(
_SCREAMING_SNAKE_CASE ,[
{'''score''': ANY(_SCREAMING_SNAKE_CASE ), '''label''': ANY(_SCREAMING_SNAKE_CASE )},
{'''score''': ANY(_SCREAMING_SNAKE_CASE ), '''label''': ANY(_SCREAMING_SNAKE_CASE )},
] ,)
@require_torch
def a__ ( self ) -> Tuple:
UpperCAmelCase_ : Tuple = '''hf-internal-testing/tiny-random-VideoMAEForVideoClassification'''
UpperCAmelCase_ : int = VideoMAEFeatureExtractor(
size={'''shortest_edge''': 10} ,crop_size={'''height''': 10, '''width''': 10} )
UpperCAmelCase_ : Optional[int] = pipeline(
'''video-classification''' ,model=_SCREAMING_SNAKE_CASE ,feature_extractor=_SCREAMING_SNAKE_CASE ,frame_sampling_rate=4 )
UpperCAmelCase_ : str = hf_hub_download(repo_id='''nateraw/video-demo''' ,filename='''archery.mp4''' ,repo_type='''dataset''' )
UpperCAmelCase_ : str = video_classifier(_SCREAMING_SNAKE_CASE ,top_k=2 )
self.assertEqual(
nested_simplify(_SCREAMING_SNAKE_CASE ,decimals=4 ) ,[{'''score''': 0.51_99, '''label''': '''LABEL_0'''}, {'''score''': 0.48_01, '''label''': '''LABEL_1'''}] ,)
UpperCAmelCase_ : int = video_classifier(
[
video_file_path,
video_file_path,
] ,top_k=2 ,)
self.assertEqual(
nested_simplify(_SCREAMING_SNAKE_CASE ,decimals=4 ) ,[
[{'''score''': 0.51_99, '''label''': '''LABEL_0'''}, {'''score''': 0.48_01, '''label''': '''LABEL_1'''}],
[{'''score''': 0.51_99, '''label''': '''LABEL_0'''}, {'''score''': 0.48_01, '''label''': '''LABEL_1'''}],
] ,)
@require_tf
def a__ ( self ) -> List[str]:
pass
| 300
|
def lowerCamelCase__ ( _lowercase ):
'''simple docstring'''
UpperCAmelCase_ : str = []
if len(_lowercase ) == 1:
return [nums.copy()]
for _ in range(len(_lowercase ) ):
UpperCAmelCase_ : Dict = nums.pop(0 )
UpperCAmelCase_ : Tuple = permute(_lowercase )
for perm in permutations:
perm.append(_lowercase )
result.extend(_lowercase )
nums.append(_lowercase )
return result
def lowerCamelCase__ ( _lowercase ):
'''simple docstring'''
def backtrack(_lowercase ):
if start == len(_lowercase ) - 1:
output.append(nums[:] )
else:
for i in range(_lowercase , len(_lowercase ) ):
UpperCAmelCase_, UpperCAmelCase_ : Any = nums[i], nums[start]
backtrack(start + 1 )
UpperCAmelCase_, UpperCAmelCase_ : int = nums[i], nums[start] # backtrack
UpperCAmelCase_ : Tuple = []
backtrack(0 )
return output
if __name__ == "__main__":
import doctest
# use res to print the data in permute2 function
__a = permutea([1, 2, 3])
print(res)
doctest.testmod()
| 300
| 1
|
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
_lowercase = logging.get_logger(__name__)
_lowercase = {
'''google/vit-base-patch16-224''': '''https://huggingface.co/vit-base-patch16-224/resolve/main/config.json''',
# See all ViT models at https://huggingface.co/models?filter=vit
}
class __a ( __a ):
'''simple docstring'''
_lowerCamelCase : List[Any] = """vit"""
def __init__( self , _lowerCamelCase=768 , _lowerCamelCase=12 , _lowerCamelCase=12 , _lowerCamelCase=3_072 , _lowerCamelCase="gelu" , _lowerCamelCase=0.0 , _lowerCamelCase=0.0 , _lowerCamelCase=0.02 , _lowerCamelCase=1e-12 , _lowerCamelCase=224 , _lowerCamelCase=16 , _lowerCamelCase=3 , _lowerCamelCase=True , _lowerCamelCase=16 , **_lowerCamelCase , ) -> Tuple:
'''simple docstring'''
super().__init__(**_lowerCamelCase )
__lowercase = hidden_size
__lowercase = num_hidden_layers
__lowercase = num_attention_heads
__lowercase = intermediate_size
__lowercase = hidden_act
__lowercase = hidden_dropout_prob
__lowercase = attention_probs_dropout_prob
__lowercase = initializer_range
__lowercase = layer_norm_eps
__lowercase = image_size
__lowercase = patch_size
__lowercase = num_channels
__lowercase = qkv_bias
__lowercase = encoder_stride
class __a ( __a ):
'''simple docstring'''
_lowerCamelCase : Tuple = version.parse("""1.11""" )
@property
def SCREAMING_SNAKE_CASE ( self ) -> Mapping[str, Mapping[int, str]]:
'''simple docstring'''
return OrderedDict(
[
("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
] )
@property
def SCREAMING_SNAKE_CASE ( self ) -> float:
'''simple docstring'''
return 1e-4
| 118
|
"""simple docstring"""
import os
import re
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
_lowercase = logging.get_logger(__name__)
_lowercase = {
'''vocab_file''': '''vocab.txt''',
'''merges_file''': '''bpe.codes''',
}
_lowercase = {
'''vocab_file''': {
'''vinai/phobert-base''': '''https://huggingface.co/vinai/phobert-base/resolve/main/vocab.txt''',
'''vinai/phobert-large''': '''https://huggingface.co/vinai/phobert-large/resolve/main/vocab.txt''',
},
'''merges_file''': {
'''vinai/phobert-base''': '''https://huggingface.co/vinai/phobert-base/resolve/main/bpe.codes''',
'''vinai/phobert-large''': '''https://huggingface.co/vinai/phobert-large/resolve/main/bpe.codes''',
},
}
_lowercase = {
'''vinai/phobert-base''': 256,
'''vinai/phobert-large''': 256,
}
def lowerCAmelCase__ ( __magic_name__ ) ->Optional[int]:
__lowercase = set()
__lowercase = word[0]
for char in word[1:]:
pairs.add((prev_char, char) )
__lowercase = char
__lowercase = set(__magic_name__ )
return pairs
class __a ( __a ):
'''simple docstring'''
_lowerCamelCase : int = VOCAB_FILES_NAMES
_lowerCamelCase : List[str] = PRETRAINED_VOCAB_FILES_MAP
_lowerCamelCase : Optional[Any] = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
def __init__( self , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase="<s>" , _lowerCamelCase="</s>" , _lowerCamelCase="</s>" , _lowerCamelCase="<s>" , _lowerCamelCase="<unk>" , _lowerCamelCase="<pad>" , _lowerCamelCase="<mask>" , **_lowerCamelCase , ) -> Tuple:
'''simple docstring'''
super().__init__(
bos_token=_lowerCamelCase , eos_token=_lowerCamelCase , unk_token=_lowerCamelCase , sep_token=_lowerCamelCase , cls_token=_lowerCamelCase , pad_token=_lowerCamelCase , mask_token=_lowerCamelCase , **_lowerCamelCase , )
__lowercase = vocab_file
__lowercase = merges_file
__lowercase = {}
__lowercase = 0
__lowercase = 1
__lowercase = 2
__lowercase = 3
self.add_from_file(_lowerCamelCase )
__lowercase = {v: k for k, v in self.encoder.items()}
with open(_lowerCamelCase , encoding="utf-8" ) as merges_handle:
__lowercase = merges_handle.read().split("\n" )[:-1]
__lowercase = [tuple(merge.split()[:-1] ) for merge in merges]
__lowercase = dict(zip(_lowerCamelCase , range(len(_lowerCamelCase ) ) ) )
__lowercase = {}
def SCREAMING_SNAKE_CASE ( self , _lowerCamelCase , _lowerCamelCase = None ) -> List[int]:
'''simple docstring'''
if token_ids_a is None:
return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
__lowercase = [self.cls_token_id]
__lowercase = [self.sep_token_id]
return cls + token_ids_a + sep + sep + token_ids_a + sep
def SCREAMING_SNAKE_CASE ( self , _lowerCamelCase , _lowerCamelCase = None , _lowerCamelCase = False ) -> List[int]:
'''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 SCREAMING_SNAKE_CASE ( self , _lowerCamelCase , _lowerCamelCase = None ) -> List[int]:
'''simple docstring'''
__lowercase = [self.sep_token_id]
__lowercase = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep + sep + token_ids_a + sep ) * [0]
@property
def SCREAMING_SNAKE_CASE ( self ) -> Optional[Any]:
'''simple docstring'''
return len(self.encoder )
def SCREAMING_SNAKE_CASE ( self ) -> str:
'''simple docstring'''
return dict(self.encoder , **self.added_tokens_encoder )
def SCREAMING_SNAKE_CASE ( self , _lowerCamelCase ) -> List[Any]:
'''simple docstring'''
if token in self.cache:
return self.cache[token]
__lowercase = tuple(_lowerCamelCase )
__lowercase = tuple(list(word[:-1] ) + [word[-1] + "</w>"] )
__lowercase = get_pairs(_lowerCamelCase )
if not pairs:
return token
while True:
__lowercase = min(_lowerCamelCase , key=lambda _lowerCamelCase : self.bpe_ranks.get(_lowerCamelCase , float("inf" ) ) )
if bigram not in self.bpe_ranks:
break
__lowercase , __lowercase = bigram
__lowercase = []
__lowercase = 0
while i < len(_lowerCamelCase ):
try:
__lowercase = word.index(_lowerCamelCase , _lowerCamelCase )
except ValueError:
new_word.extend(word[i:] )
break
else:
new_word.extend(word[i:j] )
__lowercase = j
if word[i] == first and i < len(_lowerCamelCase ) - 1 and word[i + 1] == second:
new_word.append(first + second )
i += 2
else:
new_word.append(word[i] )
i += 1
__lowercase = tuple(_lowerCamelCase )
__lowercase = new_word
if len(_lowerCamelCase ) == 1:
break
else:
__lowercase = get_pairs(_lowerCamelCase )
__lowercase = "@@ ".join(_lowerCamelCase )
__lowercase = word[:-4]
__lowercase = word
return word
def SCREAMING_SNAKE_CASE ( self , _lowerCamelCase ) -> str:
'''simple docstring'''
__lowercase = []
__lowercase = re.findall(R"\S+\n?" , _lowerCamelCase )
for token in words:
split_tokens.extend(list(self.bpe(_lowerCamelCase ).split(" " ) ) )
return split_tokens
def SCREAMING_SNAKE_CASE ( self , _lowerCamelCase ) -> Optional[Any]:
'''simple docstring'''
return self.encoder.get(_lowerCamelCase , self.encoder.get(self.unk_token ) )
def SCREAMING_SNAKE_CASE ( self , _lowerCamelCase ) -> Optional[int]:
'''simple docstring'''
return self.decoder.get(_lowerCamelCase , self.unk_token )
def SCREAMING_SNAKE_CASE ( self , _lowerCamelCase ) -> Tuple:
'''simple docstring'''
__lowercase = " ".join(_lowerCamelCase ).replace("@@ " , "" ).strip()
return out_string
def SCREAMING_SNAKE_CASE ( self , _lowerCamelCase , _lowerCamelCase = None ) -> Tuple[str]:
'''simple docstring'''
if not os.path.isdir(_lowerCamelCase ):
logger.error(f'''Vocabulary path ({save_directory}) should be a directory''' )
return
__lowercase = os.path.join(
_lowerCamelCase , (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"] )
__lowercase = os.path.join(
_lowerCamelCase , (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["merges_file"] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(_lowerCamelCase ):
copyfile(self.vocab_file , _lowerCamelCase )
if os.path.abspath(self.merges_file ) != os.path.abspath(_lowerCamelCase ):
copyfile(self.merges_file , _lowerCamelCase )
return out_vocab_file, out_merge_file
def SCREAMING_SNAKE_CASE ( self , _lowerCamelCase ) -> Dict:
'''simple docstring'''
if isinstance(_lowerCamelCase , _lowerCamelCase ):
try:
with open(_lowerCamelCase , "r" , encoding="utf-8" ) as fd:
self.add_from_file(_lowerCamelCase )
except FileNotFoundError as fnfe:
raise fnfe
except UnicodeError:
raise Exception(f'''Incorrect encoding detected in {f}, please rebuild the dataset''' )
return
__lowercase = f.readlines()
for lineTmp in lines:
__lowercase = lineTmp.strip()
__lowercase = line.rfind(" " )
if idx == -1:
raise ValueError("Incorrect dictionary format, expected '<token> <cnt>'" )
__lowercase = line[:idx]
__lowercase = len(self.encoder )
| 118
| 1
|
import argparse
import os
import torch
from transformers import FlavaImageCodebook, FlavaImageCodebookConfig
def __lowercase ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) -> List[str]:
'''simple docstring'''
SCREAMING_SNAKE_CASE = s.rsplit(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
return new.join(_SCREAMING_SNAKE_CASE )
def __lowercase ( _SCREAMING_SNAKE_CASE ) -> Dict:
'''simple docstring'''
return sum(param.float().sum() if """encoder.embeddings""" not in key else 0 for key, param in state_dict.items() )
def __lowercase ( _SCREAMING_SNAKE_CASE ) -> List[Any]:
'''simple docstring'''
SCREAMING_SNAKE_CASE = {}
SCREAMING_SNAKE_CASE = ["""group_1""", """group_2""", """group_3""", """group_4"""]
for key, value in state_dict.items():
for group_key in group_keys:
if group_key in key:
SCREAMING_SNAKE_CASE = key.replace(F"""{group_key}.""" , F"""{group_key}.group.""" )
if "res_path" in key:
SCREAMING_SNAKE_CASE = key.replace("""res_path.""" , """res_path.path.""" )
if key.endswith(""".w""" ):
SCREAMING_SNAKE_CASE = rreplace(_SCREAMING_SNAKE_CASE , """.w""" , """.weight""" , 1 )
if key.endswith(""".b""" ):
SCREAMING_SNAKE_CASE = rreplace(_SCREAMING_SNAKE_CASE , """.b""" , """.bias""" , 1 )
SCREAMING_SNAKE_CASE = value.float()
return upgrade
@torch.no_grad()
def __lowercase ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE=None , _SCREAMING_SNAKE_CASE=True ) -> Any:
'''simple docstring'''
from dall_e import Encoder
SCREAMING_SNAKE_CASE = Encoder()
if os.path.exists(_SCREAMING_SNAKE_CASE ):
SCREAMING_SNAKE_CASE = torch.load(_SCREAMING_SNAKE_CASE )
else:
SCREAMING_SNAKE_CASE = torch.hub.load_state_dict_from_url(_SCREAMING_SNAKE_CASE )
if isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
SCREAMING_SNAKE_CASE = ckpt.state_dict()
encoder.load_state_dict(_SCREAMING_SNAKE_CASE )
if config_path is not None:
SCREAMING_SNAKE_CASE = FlavaImageCodebookConfig.from_pretrained(_SCREAMING_SNAKE_CASE )
else:
SCREAMING_SNAKE_CASE = FlavaImageCodebookConfig()
SCREAMING_SNAKE_CASE = FlavaImageCodebook(_SCREAMING_SNAKE_CASE ).eval()
SCREAMING_SNAKE_CASE = encoder.state_dict()
SCREAMING_SNAKE_CASE = upgrade_state_dict(_SCREAMING_SNAKE_CASE )
hf_model.load_state_dict(_SCREAMING_SNAKE_CASE )
SCREAMING_SNAKE_CASE = hf_model.state_dict()
SCREAMING_SNAKE_CASE = count_parameters(_SCREAMING_SNAKE_CASE )
SCREAMING_SNAKE_CASE = count_parameters(_SCREAMING_SNAKE_CASE )
assert torch.allclose(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , atol=1E-3 )
if save_checkpoint:
hf_model.save_pretrained(_SCREAMING_SNAKE_CASE )
else:
return hf_state_dict
if __name__ == "__main__":
SCREAMING_SNAKE_CASE_ = argparse.ArgumentParser()
parser.add_argument("""--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model.""")
parser.add_argument("""--checkpoint_path""", default=None, type=str, help="""Path to flava checkpoint""")
parser.add_argument("""--config_path""", default=None, type=str, help="""Path to hf config.json of model to convert""")
SCREAMING_SNAKE_CASE_ = parser.parse_args()
convert_dalle_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path)
| 116
|
def __lowercase ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) -> float:
'''simple docstring'''
return price * (1 + tax_rate)
if __name__ == "__main__":
print(F'''{price_plus_tax(1_0_0, 0.25) = }''')
print(F'''{price_plus_tax(125.50, 0.05) = }''')
| 116
| 1
|
import gc
import random
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import AutoencoderKL, CycleDiffusionPipeline, DDIMScheduler, UNetaDConditionModel
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, skip_mps
from ..pipeline_params import (
IMAGE_TO_IMAGE_IMAGE_PARAMS,
TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS,
TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin
enable_full_determinism()
class SCREAMING_SNAKE_CASE ( a_ , a_ , unittest.TestCase ):
"""simple docstring"""
lowerCamelCase : Any =CycleDiffusionPipeline
lowerCamelCase : List[str] =TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {
"negative_prompt",
"height",
"width",
"negative_prompt_embeds",
}
lowerCamelCase : Optional[Any] =PipelineTesterMixin.required_optional_params - {"latents"}
lowerCamelCase : Optional[int] =TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS.union({"source_prompt"} )
lowerCamelCase : Optional[int] =IMAGE_TO_IMAGE_IMAGE_PARAMS
lowerCamelCase : List[Any] =IMAGE_TO_IMAGE_IMAGE_PARAMS
def SCREAMING_SNAKE_CASE ( self : Dict ) -> Optional[int]:
"""simple docstring"""
torch.manual_seed(0 )
__lowerCAmelCase : List[Any] = UNetaDConditionModel(
block_out_channels=(32, 64) , layers_per_block=2 , sample_size=32 , in_channels=4 , out_channels=4 , down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""") , up_block_types=("""CrossAttnUpBlock2D""", """UpBlock2D""") , cross_attention_dim=32 , )
__lowerCAmelCase : Dict = DDIMScheduler(
beta_start=0.0_0085 , beta_end=0.012 , beta_schedule="""scaled_linear""" , num_train_timesteps=10_00 , clip_sample=lowerCAmelCase , set_alpha_to_one=lowerCAmelCase , )
torch.manual_seed(0 )
__lowerCAmelCase : Any = AutoencoderKL(
block_out_channels=[32, 64] , in_channels=3 , out_channels=3 , down_block_types=["""DownEncoderBlock2D""", """DownEncoderBlock2D"""] , up_block_types=["""UpDecoderBlock2D""", """UpDecoderBlock2D"""] , latent_channels=4 , )
torch.manual_seed(0 )
__lowerCAmelCase : List[str] = CLIPTextConfig(
bos_token_id=0 , eos_token_id=2 , hidden_size=32 , intermediate_size=37 , layer_norm_eps=1e-05 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=10_00 , )
__lowerCAmelCase : Any = CLIPTextModel(lowerCAmelCase )
__lowerCAmelCase : Union[str, Any] = CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip""" )
__lowerCAmelCase : List[str] = {
"""unet""": unet,
"""scheduler""": scheduler,
"""vae""": vae,
"""text_encoder""": text_encoder,
"""tokenizer""": tokenizer,
"""safety_checker""": None,
"""feature_extractor""": None,
}
return components
def SCREAMING_SNAKE_CASE ( self : Dict , lowerCAmelCase : List[str] , lowerCAmelCase : List[str]=0 ) -> str:
"""simple docstring"""
__lowerCAmelCase : List[str] = floats_tensor((1, 3, 32, 32) , rng=random.Random(lowerCAmelCase ) ).to(lowerCAmelCase )
__lowerCAmelCase : Optional[int] = image / 2 + 0.5
if str(lowerCAmelCase ).startswith("""mps""" ):
__lowerCAmelCase : Dict = torch.manual_seed(lowerCAmelCase )
else:
__lowerCAmelCase : Dict = torch.Generator(device=lowerCAmelCase ).manual_seed(lowerCAmelCase )
__lowerCAmelCase : List[str] = {
"""prompt""": """An astronaut riding an elephant""",
"""source_prompt""": """An astronaut riding a horse""",
"""image""": image,
"""generator""": generator,
"""num_inference_steps""": 2,
"""eta""": 0.1,
"""strength""": 0.8,
"""guidance_scale""": 3,
"""source_guidance_scale""": 1,
"""output_type""": """numpy""",
}
return inputs
def SCREAMING_SNAKE_CASE ( self : Any ) -> Optional[int]:
"""simple docstring"""
__lowerCAmelCase : List[str] = """cpu""" # ensure determinism for the device-dependent torch.Generator
__lowerCAmelCase : Optional[int] = self.get_dummy_components()
__lowerCAmelCase : Optional[int] = CycleDiffusionPipeline(**lowerCAmelCase )
__lowerCAmelCase : Any = pipe.to(lowerCAmelCase )
pipe.set_progress_bar_config(disable=lowerCAmelCase )
__lowerCAmelCase : List[Any] = self.get_dummy_inputs(lowerCAmelCase )
__lowerCAmelCase : Union[str, Any] = pipe(**lowerCAmelCase )
__lowerCAmelCase : Any = output.images
__lowerCAmelCase : Union[str, Any] = images[0, -3:, -3:, -1]
assert images.shape == (1, 32, 32, 3)
__lowerCAmelCase : Dict = np.array([0.4459, 0.4943, 0.4544, 0.6643, 0.5474, 0.4327, 0.5701, 0.5959, 0.5179] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
@unittest.skipIf(torch_device != """cuda""" , """This test requires a GPU""" )
def SCREAMING_SNAKE_CASE ( self : Tuple ) -> Any:
"""simple docstring"""
__lowerCAmelCase : Any = self.get_dummy_components()
for name, module in components.items():
if hasattr(lowerCAmelCase , """half""" ):
__lowerCAmelCase : Union[str, Any] = module.half()
__lowerCAmelCase : Optional[int] = CycleDiffusionPipeline(**lowerCAmelCase )
__lowerCAmelCase : Optional[Any] = pipe.to(lowerCAmelCase )
pipe.set_progress_bar_config(disable=lowerCAmelCase )
__lowerCAmelCase : str = self.get_dummy_inputs(lowerCAmelCase )
__lowerCAmelCase : Optional[Any] = pipe(**lowerCAmelCase )
__lowerCAmelCase : Any = output.images
__lowerCAmelCase : List[Any] = images[0, -3:, -3:, -1]
assert images.shape == (1, 32, 32, 3)
__lowerCAmelCase : Any = np.array([0.3506, 0.4543, 0.446, 0.4575, 0.5195, 0.4155, 0.5273, 0.518, 0.4116] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
@skip_mps
def SCREAMING_SNAKE_CASE ( self : Union[str, Any] ) -> str:
"""simple docstring"""
return super().test_save_load_local()
@unittest.skip("""non-deterministic pipeline""" )
def SCREAMING_SNAKE_CASE ( self : List[Any] ) -> Dict:
"""simple docstring"""
return super().test_inference_batch_single_identical()
@skip_mps
def SCREAMING_SNAKE_CASE ( self : Union[str, Any] ) -> Dict:
"""simple docstring"""
return super().test_dict_tuple_outputs_equivalent()
@skip_mps
def SCREAMING_SNAKE_CASE ( self : List[str] ) -> List[Any]:
"""simple docstring"""
return super().test_save_load_optional_components()
@skip_mps
def SCREAMING_SNAKE_CASE ( self : Dict ) -> Tuple:
"""simple docstring"""
return super().test_attention_slicing_forward_pass()
@slow
@require_torch_gpu
class SCREAMING_SNAKE_CASE ( unittest.TestCase ):
"""simple docstring"""
def SCREAMING_SNAKE_CASE ( self : Optional[int] ) -> str:
"""simple docstring"""
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def SCREAMING_SNAKE_CASE ( self : Optional[Any] ) -> Tuple:
"""simple docstring"""
__lowerCAmelCase : Optional[Any] = load_image(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
"""/cycle-diffusion/black_colored_car.png""" )
__lowerCAmelCase : List[str] = load_numpy(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/cycle-diffusion/blue_colored_car_fp16.npy""" )
__lowerCAmelCase : List[str] = init_image.resize((5_12, 5_12) )
__lowerCAmelCase : Optional[int] = """CompVis/stable-diffusion-v1-4"""
__lowerCAmelCase : List[Any] = DDIMScheduler.from_pretrained(lowerCAmelCase , subfolder="""scheduler""" )
__lowerCAmelCase : Tuple = CycleDiffusionPipeline.from_pretrained(
lowerCAmelCase , scheduler=lowerCAmelCase , safety_checker=lowerCAmelCase , torch_dtype=torch.floataa , revision="""fp16""" )
pipe.to(lowerCAmelCase )
pipe.set_progress_bar_config(disable=lowerCAmelCase )
pipe.enable_attention_slicing()
__lowerCAmelCase : List[str] = """A black colored car"""
__lowerCAmelCase : List[str] = """A blue colored car"""
__lowerCAmelCase : Optional[Any] = torch.manual_seed(0 )
__lowerCAmelCase : Optional[int] = pipe(
prompt=lowerCAmelCase , source_prompt=lowerCAmelCase , image=lowerCAmelCase , num_inference_steps=1_00 , eta=0.1 , strength=0.85 , guidance_scale=3 , source_guidance_scale=1 , generator=lowerCAmelCase , output_type="""np""" , )
__lowerCAmelCase : Optional[Any] = output.images
# the values aren't exactly equal, but the images look the same visually
assert np.abs(image - expected_image ).max() < 5e-1
def SCREAMING_SNAKE_CASE ( self : Union[str, Any] ) -> Optional[Any]:
"""simple docstring"""
__lowerCAmelCase : Dict = load_image(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
"""/cycle-diffusion/black_colored_car.png""" )
__lowerCAmelCase : List[str] = load_numpy(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/cycle-diffusion/blue_colored_car.npy""" )
__lowerCAmelCase : str = init_image.resize((5_12, 5_12) )
__lowerCAmelCase : List[str] = """CompVis/stable-diffusion-v1-4"""
__lowerCAmelCase : Any = DDIMScheduler.from_pretrained(lowerCAmelCase , subfolder="""scheduler""" )
__lowerCAmelCase : Optional[Any] = CycleDiffusionPipeline.from_pretrained(lowerCAmelCase , scheduler=lowerCAmelCase , safety_checker=lowerCAmelCase )
pipe.to(lowerCAmelCase )
pipe.set_progress_bar_config(disable=lowerCAmelCase )
pipe.enable_attention_slicing()
__lowerCAmelCase : str = """A black colored car"""
__lowerCAmelCase : Tuple = """A blue colored car"""
__lowerCAmelCase : List[str] = torch.manual_seed(0 )
__lowerCAmelCase : List[Any] = pipe(
prompt=lowerCAmelCase , source_prompt=lowerCAmelCase , image=lowerCAmelCase , num_inference_steps=1_00 , eta=0.1 , strength=0.85 , guidance_scale=3 , source_guidance_scale=1 , generator=lowerCAmelCase , output_type="""np""" , )
__lowerCAmelCase : Union[str, Any] = output.images
assert np.abs(image - expected_image ).max() < 2e-2
| 651
|
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__UpperCAmelCase = {
"""configuration_nllb_moe""": [
"""NLLB_MOE_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""NllbMoeConfig""",
]
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__UpperCAmelCase = [
"""NLLB_MOE_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""NllbMoeForConditionalGeneration""",
"""NllbMoeModel""",
"""NllbMoePreTrainedModel""",
"""NllbMoeTop2Router""",
"""NllbMoeSparseMLP""",
]
if TYPE_CHECKING:
from .configuration_nllb_moe import (
NLLB_MOE_PRETRAINED_CONFIG_ARCHIVE_MAP,
NllbMoeConfig,
)
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_nllb_moe import (
NLLB_MOE_PRETRAINED_MODEL_ARCHIVE_LIST,
NllbMoeForConditionalGeneration,
NllbMoeModel,
NllbMoePreTrainedModel,
NllbMoeSparseMLP,
NllbMoeTopaRouter,
)
else:
import sys
__UpperCAmelCase = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 651
| 1
|
from collections import OrderedDict
from typing import Any, Mapping, Optional
from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast
from ...utils import logging
lowerCamelCase : Dict = logging.get_logger(__name__)
lowerCamelCase : Union[str, Any] = {
"EleutherAI/gpt-neo-1.3B": "https://huggingface.co/EleutherAI/gpt-neo-1.3B/resolve/main/config.json",
# See all GPTNeo models at https://huggingface.co/models?filter=gpt_neo
}
class A( UpperCamelCase ):
'''simple docstring'''
UpperCamelCase = '''gpt_neo'''
UpperCamelCase = ['''past_key_values''']
UpperCamelCase = {'''num_attention_heads''': '''num_heads''', '''num_hidden_layers''': '''num_layers'''}
def __init__( self : Optional[Any] , A_ : List[str]=50257 , A_ : List[str]=2048 , A_ : Dict=2048 , A_ : str=24 , A_ : Tuple=[[["global", "local"], 12]] , A_ : List[str]=16 , A_ : Tuple=None , A_ : str=256 , A_ : int="gelu_new" , A_ : List[Any]=0.0 , A_ : int=0.0 , A_ : Union[str, Any]=0.0 , A_ : int=0.1 , A_ : Optional[int]=1E-5 , A_ : List[Any]=0.02 , A_ : Any=True , A_ : Tuple=50256 , A_ : Optional[Any]=50256 , **A_ : Dict , ) -> Union[str, Any]:
"""simple docstring"""
lowerCamelCase_ = vocab_size
lowerCamelCase_ = max_position_embeddings
lowerCamelCase_ = hidden_size
lowerCamelCase_ = num_layers
lowerCamelCase_ = num_heads
lowerCamelCase_ = intermediate_size
lowerCamelCase_ = window_size
lowerCamelCase_ = activation_function
lowerCamelCase_ = resid_dropout
lowerCamelCase_ = embed_dropout
lowerCamelCase_ = attention_dropout
lowerCamelCase_ = classifier_dropout
lowerCamelCase_ = layer_norm_epsilon
lowerCamelCase_ = initializer_range
lowerCamelCase_ = use_cache
lowerCamelCase_ = bos_token_id
lowerCamelCase_ = eos_token_id
lowerCamelCase_ = attention_types
lowerCamelCase_ = self.expand_attention_types_params(A_ )
if len(self.attention_layers ) != self.num_layers:
raise ValueError(
'Configuration for convolutional module is incorrect. '
'It is required that `len(config.attention_layers)` == `config.num_layers` '
f"""but is `len(config.attention_layers) = {len(self.attention_layers )}`, """
f"""`config.num_layers = {self.num_layers}`. """
'`config.attention_layers` is prepared using `config.attention_types`. '
'Please verify the value of `config.attention_types` argument.' )
super().__init__(bos_token_id=A_ , eos_token_id=A_ , **A_ )
@staticmethod
def a__ ( A_ : int ) -> List[Any]:
"""simple docstring"""
lowerCamelCase_ = []
for item in attention_types:
for _ in range(item[1] ):
attentions.extend(item[0] )
return attentions
def _SCREAMING_SNAKE_CASE ( lowercase : Any , lowercase : Any , lowercase : Dict , lowercase : Union[str, Any] ):
'''simple docstring'''
import torch
lowerCamelCase_ = input.size()
lowerCamelCase_ = len(lowercase )
lowerCamelCase_ = shape[dimension]
lowerCamelCase_ = torch.arange(0 , lowercase , lowercase )
lowerCamelCase_ = torch.div(sizedim - size , lowercase , rounding_mode='floor' ) + 1
lowerCamelCase_ = torch.arange(lowercase ) + low_indices[:min_length][:, None]
lowerCamelCase_ = [slice(lowercase )] * rank
lowerCamelCase_ = indices
lowerCamelCase_ = input[s]
lowerCamelCase_ = list(range(0 , rank + 1 ) )
perm.append(perm.pop(dimension + 1 ) )
return sliced.permute(lowercase )
def _SCREAMING_SNAKE_CASE ( lowercase : List[str] , lowercase : Dict ):
'''simple docstring'''
import torch
lowerCamelCase_ = torch.arange(1 , lowercase )
lowerCamelCase_ = torch.remainder(lowercase , lowercase )
lowerCamelCase_ = remainders == 0
lowerCamelCase_ = candidates[divisor_indices]
lowerCamelCase_ = torch.max(lowercase )
return largest_divisor, torch.div(lowercase , lowercase , rounding_mode='floor' )
class A( UpperCamelCase ):
'''simple docstring'''
@property
def a__ ( self : Any ) -> Mapping[str, Mapping[int, str]]:
"""simple docstring"""
lowerCamelCase_ = OrderedDict({'input_ids': {0: 'batch', 1: 'sequence'}} )
if self.use_past:
self.fill_with_past_key_values_(A_ , direction='inputs' )
lowerCamelCase_ = {0: 'batch', 1: 'past_sequence + sequence'}
else:
lowerCamelCase_ = {0: 'batch', 1: 'sequence'}
return common_inputs
@property
def a__ ( self : int ) -> int:
"""simple docstring"""
return self._config.num_heads
def a__ ( self : str , A_ : PreTrainedTokenizer , A_ : int = -1 , A_ : int = -1 , A_ : bool = False , A_ : Optional[TensorType] = None , ) -> Mapping[str, Any]:
"""simple docstring"""
lowerCamelCase_ = super(A_ , self ).generate_dummy_inputs(
A_ , batch_size=A_ , seq_length=A_ , is_pair=A_ , framework=A_ )
# We need to order the input in the way they appears in the forward()
lowerCamelCase_ = OrderedDict({'input_ids': common_inputs['input_ids']} )
# Need to add the past_keys
if self.use_past:
if not is_torch_available():
raise ValueError('Cannot generate dummy past_keys inputs without PyTorch installed.' )
else:
import torch
lowerCamelCase_ , lowerCamelCase_ = common_inputs['input_ids'].shape
# Not using the same length for past_key_values
lowerCamelCase_ = seqlen + 2
lowerCamelCase_ = (
batch,
self.num_attention_heads,
past_key_values_length,
self._config.hidden_size // self.num_attention_heads,
)
lowerCamelCase_ = [
(torch.zeros(A_ ), torch.zeros(A_ )) for _ in range(self.num_layers )
]
lowerCamelCase_ = common_inputs['attention_mask']
if self.use_past:
lowerCamelCase_ = ordered_inputs['attention_mask'].dtype
lowerCamelCase_ = torch.cat(
[ordered_inputs['attention_mask'], torch.ones(A_ , A_ , dtype=A_ )] , dim=1 )
return ordered_inputs
@property
def a__ ( self : Optional[int] ) -> int:
"""simple docstring"""
return 13
| 721
|
from __future__ import annotations
from fractions import Fraction
def _SCREAMING_SNAKE_CASE ( lowercase : int , lowercase : int ):
'''simple docstring'''
return (
num != den and num % 10 == den // 10 and (num // 10) / (den % 10) == num / den
)
def _SCREAMING_SNAKE_CASE ( lowercase : int ):
'''simple docstring'''
lowerCamelCase_ = []
lowerCamelCase_ = 11
lowerCamelCase_ = int('1' + '0' * digit_len )
for num in range(lowercase , lowercase ):
while den <= 99:
if (num != den) and (num % 10 == den // 10) and (den % 10 != 0):
if is_digit_cancelling(lowercase , lowercase ):
solutions.append(f"""{num}/{den}""" )
den += 1
num += 1
lowerCamelCase_ = 10
return solutions
def _SCREAMING_SNAKE_CASE ( lowercase : int = 2 ):
'''simple docstring'''
lowerCamelCase_ = 1.0
for fraction in fraction_list(lowercase ):
lowerCamelCase_ = Fraction(lowercase )
result *= frac.denominator / frac.numerator
return int(lowercase )
if __name__ == "__main__":
print(solution())
| 651
| 0
|
"""simple docstring"""
import argparse
import requests
import torch
from PIL import Image
from transformers import CLIPProcessor, GroupViTConfig, GroupViTModel
def snake_case ( _a: List[str] )-> Any:
'''simple docstring'''
if "img_encoder.pos_embed" in name:
lowerCamelCase__ = name.replace('img_encoder.pos_embed' , 'vision_model.embeddings.position_embeddings' )
if "img_encoder.patch_embed.proj" in name:
lowerCamelCase__ = name.replace('img_encoder.patch_embed.proj' , 'vision_model.embeddings.patch_embeddings.projection' )
if "img_encoder.patch_embed.norm" in name:
lowerCamelCase__ = name.replace('img_encoder.patch_embed.norm' , 'vision_model.embeddings.layernorm' )
if "img_encoder.layers" in name:
lowerCamelCase__ = name.replace('img_encoder.layers' , 'vision_model.encoder.stages' )
if "blocks" in name and "res" not in name:
lowerCamelCase__ = name.replace('blocks' , 'layers' )
if "attn" in name and "pre_assign" not in name:
lowerCamelCase__ = name.replace('attn' , 'self_attn' )
if "proj" in name and "self_attn" in name and "text" not in name:
lowerCamelCase__ = name.replace('proj' , 'out_proj' )
if "pre_assign_attn.attn.proj" in name:
lowerCamelCase__ = name.replace('pre_assign_attn.attn.proj' , 'pre_assign_attn.attn.out_proj' )
if "norm1" in name:
lowerCamelCase__ = name.replace('norm1' , 'layer_norm1' )
if "norm2" in name and "pre_assign" not in name:
lowerCamelCase__ = name.replace('norm2' , 'layer_norm2' )
if "img_encoder.norm" in name:
lowerCamelCase__ = name.replace('img_encoder.norm' , 'vision_model.layernorm' )
# text encoder
if "text_encoder.token_embedding" in name:
lowerCamelCase__ = name.replace('text_encoder.token_embedding' , 'text_model.embeddings.token_embedding' )
if "text_encoder.positional_embedding" in name:
lowerCamelCase__ = name.replace('text_encoder.positional_embedding' , 'text_model.embeddings.position_embedding.weight' )
if "text_encoder.transformer.resblocks." in name:
lowerCamelCase__ = name.replace('text_encoder.transformer.resblocks.' , 'text_model.encoder.layers.' )
if "ln_1" in name:
lowerCamelCase__ = name.replace('ln_1' , 'layer_norm1' )
if "ln_2" in name:
lowerCamelCase__ = name.replace('ln_2' , 'layer_norm2' )
if "c_fc" in name:
lowerCamelCase__ = name.replace('c_fc' , 'fc1' )
if "c_proj" in name:
lowerCamelCase__ = name.replace('c_proj' , 'fc2' )
if "text_encoder" in name:
lowerCamelCase__ = name.replace('text_encoder' , 'text_model' )
if "ln_final" in name:
lowerCamelCase__ = name.replace('ln_final' , 'final_layer_norm' )
# projection layers
if "img_projector.linear_hidden." in name:
lowerCamelCase__ = name.replace('img_projector.linear_hidden.' , 'visual_projection.' )
if "img_projector.linear_out." in name:
lowerCamelCase__ = name.replace('img_projector.linear_out.' , 'visual_projection.3.' )
if "text_projector.linear_hidden" in name:
lowerCamelCase__ = name.replace('text_projector.linear_hidden' , 'text_projection' )
if "text_projector.linear_out" in name:
lowerCamelCase__ = name.replace('text_projector.linear_out' , 'text_projection.3' )
return name
def snake_case ( _a: Optional[int] , _a: Dict )-> Dict:
'''simple docstring'''
for key in orig_state_dict.copy().keys():
lowerCamelCase__ = orig_state_dict.pop(_a )
if "qkv" in key:
# weights and biases of the key, value and query projections of vision encoder's attention layers require special treatment:
# we need to split them up into separate matrices/vectors
lowerCamelCase__ = key.split('.' )
lowerCamelCase__ , lowerCamelCase__ = int(key_split[2] ), int(key_split[4] )
lowerCamelCase__ = config.vision_config.hidden_size
if "weight" in key:
lowerCamelCase__ = val[:dim, :]
lowerCamelCase__ = val[dim : dim * 2, :]
lowerCamelCase__ = val[-dim:, :]
else:
lowerCamelCase__ = val[:dim]
lowerCamelCase__ = val[dim : dim * 2]
lowerCamelCase__ = val[-dim:]
elif "in_proj" in key:
# weights and biases of the key, value and query projections of text encoder's attention layers require special treatment:
# we need to split them up into separate matrices/vectors
lowerCamelCase__ = key.split('.' )
lowerCamelCase__ = int(key_split[3] )
lowerCamelCase__ = config.text_config.hidden_size
if "weight" in key:
lowerCamelCase__ = val[:dim, :]
lowerCamelCase__ = val[
dim : dim * 2, :
]
lowerCamelCase__ = val[-dim:, :]
else:
lowerCamelCase__ = val[:dim]
lowerCamelCase__ = val[dim : dim * 2]
lowerCamelCase__ = val[-dim:]
else:
lowerCamelCase__ = rename_key(_a )
# squeeze if necessary
if (
"text_projection.0" in new_name
or "text_projection.3" in new_name
or "visual_projection.0" in new_name
or "visual_projection.3" in new_name
):
lowerCamelCase__ = val.squeeze_()
else:
lowerCamelCase__ = val
return orig_state_dict
def snake_case ( )-> Union[str, Any]:
'''simple docstring'''
lowerCamelCase__ = 'http://images.cocodataset.org/val2017/000000039769.jpg'
lowerCamelCase__ = Image.open(requests.get(_a , stream=_a ).raw )
return im
@torch.no_grad()
def snake_case ( _a: str , _a: Tuple , _a: Optional[Any]="groupvit-gcc-yfcc" , _a: List[str]=False )-> List[str]:
'''simple docstring'''
lowerCamelCase__ = GroupViTConfig()
lowerCamelCase__ = GroupViTModel(_a ).eval()
lowerCamelCase__ = torch.load(_a , map_location='cpu' )['model']
lowerCamelCase__ = convert_state_dict(_a , _a )
lowerCamelCase__ , lowerCamelCase__ = model.load_state_dict(_a , strict=_a )
assert missing_keys == ["text_model.embeddings.position_ids"]
assert (unexpected_keys == ["multi_label_logit_scale"]) or (len(_a ) == 0)
# verify result
lowerCamelCase__ = CLIPProcessor.from_pretrained('openai/clip-vit-base-patch32' )
lowerCamelCase__ = prepare_img()
lowerCamelCase__ = processor(text=['a photo of a cat', 'a photo of a dog'] , images=_a , padding=_a , return_tensors='pt' )
with torch.no_grad():
lowerCamelCase__ = model(**_a )
if model_name == "groupvit-gcc-yfcc":
lowerCamelCase__ = torch.tensor([[13.3523, 6.3629]] )
elif model_name == "groupvit-gcc-redcaps":
lowerCamelCase__ = torch.tensor([[16.1873, 8.6230]] )
else:
raise ValueError(F'Model name {model_name} not supported.' )
assert torch.allclose(outputs.logits_per_image , _a , atol=1E-3 )
processor.save_pretrained(_a )
model.save_pretrained(_a )
print('Successfully saved processor and model to' , _a )
if push_to_hub:
print('Pushing to the hub...' )
processor.push_to_hub(_a , organization='nielsr' )
model.push_to_hub(_a , organization='nielsr' )
if __name__ == "__main__":
_snake_case = argparse.ArgumentParser()
parser.add_argument(
"--pytorch_dump_folder_path", default=None, type=str, help="Path to dump the processor and PyTorch model."
)
parser.add_argument("--checkpoint_path", default=None, type=str, help="Path to GroupViT checkpoint")
parser.add_argument(
"--model_name",
default="groupvit-gccy-fcc",
type=str,
help="Name of the model. Expecting either 'groupvit-gcc-yfcc' or 'groupvit-gcc-redcaps'",
)
parser.add_argument(
"--push_to_hub",
action="store_true",
help="Whether or not to push the converted model and processor to the 🤗 hub using the provided `model_name`.",
)
_snake_case = parser.parse_args()
convert_groupvit_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.model_name, args.push_to_hub)
| 510
|
"""simple docstring"""
from __future__ import annotations
def snake_case ( _a: int , _a: int )-> tuple[int, int]:
'''simple docstring'''
if b == 0:
return (1, 0)
((lowerCamelCase__) , (lowerCamelCase__)) = extended_euclid(_a , a % b )
lowerCamelCase__ = a // b
return (y, x - k * y)
def snake_case ( _a: int , _a: int , _a: int , _a: int )-> int:
'''simple docstring'''
((lowerCamelCase__) , (lowerCamelCase__)) = extended_euclid(_a , _a )
lowerCamelCase__ = na * na
lowerCamelCase__ = ra * x * na + ra * y * na
return (n % m + m) % m
def snake_case ( _a: int , _a: int )-> int:
'''simple docstring'''
((lowerCamelCase__) , (lowerCamelCase__)) = extended_euclid(_a , _a )
if b < 0:
lowerCamelCase__ = (b % n + n) % n
return b
def snake_case ( _a: int , _a: int , _a: int , _a: int )-> int:
'''simple docstring'''
lowerCamelCase__ , lowerCamelCase__ = invert_modulo(_a , _a ), invert_modulo(_a , _a )
lowerCamelCase__ = na * na
lowerCamelCase__ = ra * x * na + ra * y * na
return (n % m + m) % m
if __name__ == "__main__":
from doctest import testmod
testmod(name="chinese_remainder_theorem", verbose=True)
testmod(name="chinese_remainder_theorem2", verbose=True)
testmod(name="invert_modulo", verbose=True)
testmod(name="extended_euclid", verbose=True)
| 510
| 1
|
import os
import sys
import unittest
__a = os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, """utils"""))
import check_dummies # noqa: E402
from check_dummies import create_dummy_files, create_dummy_object, find_backend, read_init # noqa: E402
# Align TRANSFORMERS_PATH in check_dummies with the current path
__a = os.path.join(git_repo_path, """src""", """diffusers""")
class UpperCamelCase__( unittest.TestCase ):
"""simple docstring"""
def _a ( self : List[str] ):
"""simple docstring"""
A =find_backend(" if not is_torch_available():" )
self.assertEqual(snake_case__ , "torch" )
# backend_with_underscore = find_backend(" if not is_tensorflow_text_available():")
# self.assertEqual(backend_with_underscore, "tensorflow_text")
A =find_backend(" if not (is_torch_available() and is_transformers_available()):" )
self.assertEqual(snake_case__ , "torch_and_transformers" )
# double_backend_with_underscore = find_backend(
# " if not (is_sentencepiece_available() and is_tensorflow_text_available()):"
# )
# self.assertEqual(double_backend_with_underscore, "sentencepiece_and_tensorflow_text")
A =find_backend(
" if not (is_torch_available() and is_transformers_available() and is_onnx_available()):" )
self.assertEqual(snake_case__ , "torch_and_transformers_and_onnx" )
def _a ( self : List[Any] ):
"""simple docstring"""
A =read_init()
# We don't assert on the exact list of keys to allow for smooth grow of backend-specific objects
self.assertIn("torch" , snake_case__ )
self.assertIn("torch_and_transformers" , snake_case__ )
self.assertIn("flax_and_transformers" , snake_case__ )
self.assertIn("torch_and_transformers_and_onnx" , snake_case__ )
# Likewise, we can't assert on the exact content of a key
self.assertIn("UNet2DModel" , objects["torch"] )
self.assertIn("FlaxUNet2DConditionModel" , objects["flax"] )
self.assertIn("StableDiffusionPipeline" , objects["torch_and_transformers"] )
self.assertIn("FlaxStableDiffusionPipeline" , objects["flax_and_transformers"] )
self.assertIn("LMSDiscreteScheduler" , objects["torch_and_scipy"] )
self.assertIn("OnnxStableDiffusionPipeline" , objects["torch_and_transformers_and_onnx"] )
def _a ( self : Dict ):
"""simple docstring"""
A =create_dummy_object("CONSTANT" , "'torch'" )
self.assertEqual(snake_case__ , "\nCONSTANT = None\n" )
A =create_dummy_object("function" , "'torch'" )
self.assertEqual(
snake_case__ , "\ndef function(*args, **kwargs):\n requires_backends(function, 'torch')\n" )
A ="\nclass FakeClass(metaclass=DummyObject):\n _backends = 'torch'\n\n def __init__(self, *args, **kwargs):\n requires_backends(self, 'torch')\n\n @classmethod\n def from_config(cls, *args, **kwargs):\n requires_backends(cls, 'torch')\n\n @classmethod\n def from_pretrained(cls, *args, **kwargs):\n requires_backends(cls, 'torch')\n"
A =create_dummy_object("FakeClass" , "'torch'" )
self.assertEqual(snake_case__ , snake_case__ )
def _a ( self : Tuple ):
"""simple docstring"""
A ="# This file is autogenerated by the command `make fix-copies`, do not edit.\nfrom ..utils import DummyObject, requires_backends\n\n\nCONSTANT = None\n\n\ndef function(*args, **kwargs):\n requires_backends(function, [\"torch\"])\n\n\nclass FakeClass(metaclass=DummyObject):\n _backends = [\"torch\"]\n\n def __init__(self, *args, **kwargs):\n requires_backends(self, [\"torch\"])\n\n @classmethod\n def from_config(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\"])\n\n @classmethod\n def from_pretrained(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\"])\n"
A =create_dummy_files({"torch": ["CONSTANT", "function", "FakeClass"]} )
self.assertEqual(dummy_files["torch"] , snake_case__ )
| 700
|
def UpperCamelCase_ ( a_ , a_ ) ->list[int]:
A =int(a_ )
# Initialize Result
A =[]
# Traverse through all denomination
for denomination in reversed(a_ ):
# Find denominations
while int(a_ ) >= int(a_ ):
total_value -= int(a_ )
answer.append(a_ ) # Append the "answers" array
return answer
# Driver Code
if __name__ == "__main__":
__a = []
__a = """0"""
if (
input("""Do you want to enter your denominations ? (yY/n): """).strip().lower()
== "y"
):
__a = int(input("""Enter the number of denominations you want to add: """).strip())
for i in range(0, n):
denominations.append(int(input(F'''Denomination {i}: ''').strip()))
__a = input("""Enter the change you want to make in Indian Currency: """).strip()
else:
# All denominations of Indian Currency if user does not enter
__a = [1, 2, 5, 1_0, 2_0, 5_0, 1_0_0, 5_0_0, 2_0_0_0]
__a = input("""Enter the change you want to make: """).strip()
if int(value) == 0 or int(value) < 0:
print("""The total value cannot be zero or negative.""")
else:
print(F'''Following is minimal change for {value}: ''')
__a = find_minimum_change(denominations, value)
# Print result
for i in range(len(answer)):
print(answer[i], end=""" """)
| 689
| 0
|
from ....configuration_utils import PretrainedConfig
from ....utils import logging
__A : Optional[Any] = logging.get_logger(__name__)
__A : List[str] = {
'speechbrain/m-ctc-t-large': 'https://huggingface.co/speechbrain/m-ctc-t-large/resolve/main/config.json',
# See all M-CTC-T models at https://huggingface.co/models?filter=mctct
}
class _SCREAMING_SNAKE_CASE ( __snake_case ):
'''simple docstring'''
lowerCamelCase__ = "mctct"
def __init__( self : List[Any] , __lowerCamelCase : int=8065 , __lowerCamelCase : List[Any]=1536 , __lowerCamelCase : Dict=36 , __lowerCamelCase : Optional[int]=6144 , __lowerCamelCase : Union[str, Any]=4 , __lowerCamelCase : Any=384 , __lowerCamelCase : List[str]=920 , __lowerCamelCase : Optional[int]=1e-5 , __lowerCamelCase : Tuple=0.3 , __lowerCamelCase : Any="relu" , __lowerCamelCase : Optional[int]=0.02 , __lowerCamelCase : Any=0.3 , __lowerCamelCase : Any=0.3 , __lowerCamelCase : List[Any]=1 , __lowerCamelCase : str=0 , __lowerCamelCase : Dict=2 , __lowerCamelCase : Optional[int]=1 , __lowerCamelCase : Optional[Any]=0.3 , __lowerCamelCase : Any=1 , __lowerCamelCase : Dict=(7,) , __lowerCamelCase : Dict=(3,) , __lowerCamelCase : int=80 , __lowerCamelCase : Dict=1 , __lowerCamelCase : Any=None , __lowerCamelCase : Optional[Any]="sum" , __lowerCamelCase : Dict=False , **__lowerCamelCase : Tuple , ):
super().__init__(**__lowerCamelCase , pad_token_id=__lowerCamelCase , bos_token_id=__lowerCamelCase , eos_token_id=__lowerCamelCase )
SCREAMING_SNAKE_CASE = vocab_size
SCREAMING_SNAKE_CASE = hidden_size
SCREAMING_SNAKE_CASE = num_hidden_layers
SCREAMING_SNAKE_CASE = intermediate_size
SCREAMING_SNAKE_CASE = num_attention_heads
SCREAMING_SNAKE_CASE = attention_head_dim
SCREAMING_SNAKE_CASE = max_position_embeddings
SCREAMING_SNAKE_CASE = layer_norm_eps
SCREAMING_SNAKE_CASE = layerdrop
SCREAMING_SNAKE_CASE = hidden_act
SCREAMING_SNAKE_CASE = initializer_range
SCREAMING_SNAKE_CASE = hidden_dropout_prob
SCREAMING_SNAKE_CASE = attention_probs_dropout_prob
SCREAMING_SNAKE_CASE = pad_token_id
SCREAMING_SNAKE_CASE = bos_token_id
SCREAMING_SNAKE_CASE = eos_token_id
SCREAMING_SNAKE_CASE = conv_glu_dim
SCREAMING_SNAKE_CASE = conv_dropout
SCREAMING_SNAKE_CASE = num_conv_layers
SCREAMING_SNAKE_CASE = input_feat_per_channel
SCREAMING_SNAKE_CASE = input_channels
SCREAMING_SNAKE_CASE = conv_channels
SCREAMING_SNAKE_CASE = ctc_loss_reduction
SCREAMING_SNAKE_CASE = ctc_zero_infinity
# prevents config testing fail with exporting to json
SCREAMING_SNAKE_CASE = list(__lowerCamelCase )
SCREAMING_SNAKE_CASE = list(__lowerCamelCase )
if len(self.conv_kernel ) != self.num_conv_layers:
raise ValueError(
"Configuration for convolutional module is incorrect. "
"It is required that `len(config.conv_kernel)` == `config.num_conv_layers` "
f"but is `len(config.conv_kernel) = {len(self.conv_kernel )}`, "
f"`config.num_conv_layers = {self.num_conv_layers}`." )
| 16
|
"""simple docstring"""
import numpy as np
def lowercase_ ( __UpperCAmelCase ) -> np.ndarray:
return 1 / (1 + np.exp(-vector ))
def lowercase_ ( __UpperCAmelCase ) -> np.ndarray:
return vector * sigmoid(__UpperCAmelCase )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 299
| 0
|
import os
from typing import Any, Callable, Dict, List, Optional, Tuple, Union
import torch
from torch import nn
from ...models.controlnet import ControlNetModel, ControlNetOutput
from ...models.modeling_utils import ModelMixin
from ...utils import logging
lowercase : int = logging.get_logger(__name__)
class __lowercase ( _SCREAMING_SNAKE_CASE ):
"""simple docstring"""
def __init__( self , __UpperCAmelCase ) -> List[Any]:
super().__init__()
A : Any = nn.ModuleList(__UpperCAmelCase )
def snake_case ( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase = None , __UpperCAmelCase = None , __UpperCAmelCase = None , __UpperCAmelCase = None , __UpperCAmelCase = False , __UpperCAmelCase = True , ) -> Union[ControlNetOutput, Tuple]:
for i, (image, scale, controlnet) in enumerate(zip(__UpperCAmelCase , __UpperCAmelCase , self.nets ) ):
A , A : Optional[Any] = controlnet(
__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , )
# merge samples
if i == 0:
A , A : Optional[Any] = down_samples, mid_sample
else:
A : Union[str, Any] = [
samples_prev + samples_curr
for samples_prev, samples_curr in zip(__UpperCAmelCase , __UpperCAmelCase )
]
mid_block_res_sample += mid_sample
return down_block_res_samples, mid_block_res_sample
def snake_case ( self , __UpperCAmelCase , __UpperCAmelCase = True , __UpperCAmelCase = None , __UpperCAmelCase = False , __UpperCAmelCase = None , ) -> Tuple:
A : Tuple = 0
A : Optional[Any] = save_directory
for controlnet in self.nets:
controlnet.save_pretrained(
__UpperCAmelCase , is_main_process=__UpperCAmelCase , save_function=__UpperCAmelCase , safe_serialization=__UpperCAmelCase , variant=__UpperCAmelCase , )
idx += 1
A : List[str] = model_path_to_save + f'_{idx}'
@classmethod
def snake_case ( cls , __UpperCAmelCase , **__UpperCAmelCase ) -> Dict:
A : Optional[int] = 0
A : List[Any] = []
# load controlnet and append to list until no controlnet directory exists anymore
# first controlnet has to be saved under `./mydirectory/controlnet` to be compliant with `DiffusionPipeline.from_prertained`
# second, third, ... controlnets have to be saved under `./mydirectory/controlnet_1`, `./mydirectory/controlnet_2`, ...
A : List[Any] = pretrained_model_path
while os.path.isdir(__UpperCAmelCase ):
A : Optional[int] = ControlNetModel.from_pretrained(__UpperCAmelCase , **__UpperCAmelCase )
controlnets.append(__UpperCAmelCase )
idx += 1
A : Optional[int] = pretrained_model_path + f'_{idx}'
logger.info(f'{len(__UpperCAmelCase )} controlnets loaded from {pretrained_model_path}.' )
if len(__UpperCAmelCase ) == 0:
raise ValueError(
f'No ControlNets found under {os.path.dirname(__UpperCAmelCase )}. Expected at least {pretrained_model_path + "_0"}.' )
return cls(__UpperCAmelCase )
| 423
|
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import torch
from ..models.auto import AutoModelForSequenceClassification, AutoTokenizer
from .base import PipelineTool
class __lowercase ( _SCREAMING_SNAKE_CASE ):
"""simple docstring"""
UpperCAmelCase_ : Optional[Any] = '''facebook/bart-large-mnli'''
UpperCAmelCase_ : Optional[int] = (
'''This is a tool that classifies an English text using provided labels. It takes two inputs: `text`, which '''
'''should be the text to classify, and `labels`, which should be the list of labels to use for classification. '''
'''It returns the most likely label in the list of provided `labels` for the input text.'''
)
UpperCAmelCase_ : Optional[Any] = '''text_classifier'''
UpperCAmelCase_ : str = AutoTokenizer
UpperCAmelCase_ : int = AutoModelForSequenceClassification
UpperCAmelCase_ : Union[str, Any] = ['''text''', ['''text''']]
UpperCAmelCase_ : Tuple = ['''text''']
def snake_case ( self ) -> List[str]:
super().setup()
A : int = self.model.config
A : List[str] = -1
for idx, label in config.idalabel.items():
if label.lower().startswith('''entail''' ):
A : Dict = int(__UpperCAmelCase )
if self.entailment_id == -1:
raise ValueError('''Could not determine the entailment ID from the model config, please pass it at init.''' )
def snake_case ( self , __UpperCAmelCase , __UpperCAmelCase ) -> Optional[Any]:
A : List[str] = labels
return self.pre_processor(
[text] * len(__UpperCAmelCase ) , [f'This example is {label}' for label in labels] , return_tensors='''pt''' , padding='''max_length''' , )
def snake_case ( self , __UpperCAmelCase ) -> Tuple:
A : int = outputs.logits
A : int = torch.argmax(logits[:, 2] ).item()
return self._labels[label_id]
| 423
| 1
|
"""simple docstring"""
from typing import List, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__SCREAMING_SNAKE_CASE = logging.get_logger(__name__)
__SCREAMING_SNAKE_CASE = {
'huggingface/informer-tourism-monthly': (
'https://huggingface.co/huggingface/informer-tourism-monthly/resolve/main/config.json'
),
# See all Informer models at https://huggingface.co/models?filter=informer
}
class a__ ( A__ ):
UpperCAmelCase__ = '''informer'''
UpperCAmelCase__ = {
'''hidden_size''': '''d_model''',
'''num_attention_heads''': '''encoder_attention_heads''',
'''num_hidden_layers''': '''encoder_layers''',
}
def __init__( self :Union[str, Any] , _lowerCamelCase :Optional[int] = None , _lowerCamelCase :Optional[int] = None , _lowerCamelCase :str = "student_t" , _lowerCamelCase :str = "nll" , _lowerCamelCase :int = 1 , _lowerCamelCase :List[int] = None , _lowerCamelCase :Optional[Union[str, bool]] = "mean" , _lowerCamelCase :int = 0 , _lowerCamelCase :int = 0 , _lowerCamelCase :int = 0 , _lowerCamelCase :int = 0 , _lowerCamelCase :Optional[List[int]] = None , _lowerCamelCase :Optional[List[int]] = None , _lowerCamelCase :int = 64 , _lowerCamelCase :int = 32 , _lowerCamelCase :int = 32 , _lowerCamelCase :int = 2 , _lowerCamelCase :int = 2 , _lowerCamelCase :int = 2 , _lowerCamelCase :int = 2 , _lowerCamelCase :bool = True , _lowerCamelCase :str = "gelu" , _lowerCamelCase :float = 0.05 , _lowerCamelCase :float = 0.1 , _lowerCamelCase :float = 0.1 , _lowerCamelCase :float = 0.1 , _lowerCamelCase :float = 0.1 , _lowerCamelCase :int = 100 , _lowerCamelCase :float = 0.02 , _lowerCamelCase :Dict=True , _lowerCamelCase :str = "prob" , _lowerCamelCase :int = 5 , _lowerCamelCase :bool = True , **_lowerCamelCase :Optional[Any] , ):
'''simple docstring'''
UpperCamelCase_ : List[Any] =prediction_length
UpperCamelCase_ : Optional[int] =context_length or prediction_length
UpperCamelCase_ : List[str] =distribution_output
UpperCamelCase_ : Optional[int] =loss
UpperCamelCase_ : str =input_size
UpperCamelCase_ : str =num_time_features
UpperCamelCase_ : str =lags_sequence if lags_sequence is not None else [1, 2, 3, 4, 5, 6, 7]
UpperCamelCase_ : Tuple =scaling
UpperCamelCase_ : Union[str, Any] =num_dynamic_real_features
UpperCamelCase_ : Dict =num_static_real_features
UpperCamelCase_ : Tuple =num_static_categorical_features
# set cardinality
if cardinality and num_static_categorical_features > 0:
if len(__a ) != num_static_categorical_features:
raise ValueError(
'The cardinality should be a list of the same length as `num_static_categorical_features`' )
UpperCamelCase_ : List[Any] =cardinality
else:
UpperCamelCase_ : int =[0]
# set embedding_dimension
if embedding_dimension and num_static_categorical_features > 0:
if len(__a ) != num_static_categorical_features:
raise ValueError(
'The embedding dimension should be a list of the same length as `num_static_categorical_features`' )
UpperCamelCase_ : Any =embedding_dimension
else:
UpperCamelCase_ : Any =[min(50 , (cat + 1) // 2 ) for cat in self.cardinality]
UpperCamelCase_ : Optional[Any] =num_parallel_samples
# Transformer architecture configuration
UpperCamelCase_ : Dict =input_size * len(self.lags_sequence ) + self._number_of_features
UpperCamelCase_ : int =d_model
UpperCamelCase_ : str =encoder_attention_heads
UpperCamelCase_ : Optional[Any] =decoder_attention_heads
UpperCamelCase_ : Tuple =encoder_ffn_dim
UpperCamelCase_ : str =decoder_ffn_dim
UpperCamelCase_ : Dict =encoder_layers
UpperCamelCase_ : str =decoder_layers
UpperCamelCase_ : List[Any] =dropout
UpperCamelCase_ : Union[str, Any] =attention_dropout
UpperCamelCase_ : Dict =activation_dropout
UpperCamelCase_ : List[str] =encoder_layerdrop
UpperCamelCase_ : Optional[int] =decoder_layerdrop
UpperCamelCase_ : Optional[int] =activation_function
UpperCamelCase_ : int =init_std
UpperCamelCase_ : Dict =use_cache
# Informer
UpperCamelCase_ : Any =attention_type
UpperCamelCase_ : List[str] =sampling_factor
UpperCamelCase_ : Optional[Any] =distil
super().__init__(is_encoder_decoder=__a , **__a )
@property
def lowerCamelCase_ ( self :Optional[Any] ):
'''simple docstring'''
return (
sum(self.embedding_dimension )
+ self.num_dynamic_real_features
+ self.num_time_features
+ self.num_static_real_features
+ self.input_size * 2 # the log1p(abs(loc)) and log(scale) features
)
| 357
|
# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from argparse import ArgumentParser
from accelerate.commands.config import get_config_parser
from accelerate.commands.env import env_command_parser
from accelerate.commands.launch import launch_command_parser
from accelerate.commands.test import test_command_parser
from accelerate.commands.tpu import tpu_command_parser
def __lowerCAmelCase ( ) -> Optional[Any]:
'''simple docstring'''
lowerCamelCase__: Dict = ArgumentParser("""Accelerate CLI tool""" , usage="""accelerate <command> [<args>]""" , allow_abbrev=_UpperCamelCase )
lowerCamelCase__: List[str] = parser.add_subparsers(help="""accelerate command helpers""" )
# Register commands
get_config_parser(subparsers=_UpperCamelCase )
env_command_parser(subparsers=_UpperCamelCase )
launch_command_parser(subparsers=_UpperCamelCase )
tpu_command_parser(subparsers=_UpperCamelCase )
test_command_parser(subparsers=_UpperCamelCase )
# Let's go
lowerCamelCase__: int = parser.parse_args()
if not hasattr(_UpperCamelCase , """func""" ):
parser.print_help()
exit(1 )
# Run
args.func(_UpperCamelCase )
if __name__ == "__main__":
main()
| 306
| 0
|
import os
import tempfile
import unittest
from pathlib import Path
from transformers import AutoConfig, is_tf_available
from transformers.testing_utils import require_tf
if is_tf_available():
import tensorflow as tf
from transformers import TensorFlowBenchmark, TensorFlowBenchmarkArguments
@require_tf
class UpperCamelCase ( unittest.TestCase ):
"""simple docstring"""
def A ( self , lowercase__ ) -> Tuple:
"""simple docstring"""
for model_result in results.values():
for batch_size, sequence_length in zip(model_result['bs'] , model_result['ss'] ):
SCREAMING_SNAKE_CASE = model_result['result'][batch_size][sequence_length]
self.assertIsNotNone(lowercase__ )
def A ( self ) -> Union[str, Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE = 'sshleifer/tiny-gpt2'
SCREAMING_SNAKE_CASE = TensorFlowBenchmarkArguments(
models=[MODEL_ID] , training=lowercase__ , inference=lowercase__ , sequence_lengths=[8] , batch_sizes=[1] , eager_mode=lowercase__ , multi_process=lowercase__ , )
SCREAMING_SNAKE_CASE = TensorFlowBenchmark(lowercase__ )
SCREAMING_SNAKE_CASE = benchmark.run()
self.check_results_dict_not_empty(results.time_inference_result )
self.check_results_dict_not_empty(results.memory_inference_result )
def A ( self ) -> List[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE = 'sgugger/tiny-distilbert-classification'
SCREAMING_SNAKE_CASE = TensorFlowBenchmarkArguments(
models=[MODEL_ID] , training=lowercase__ , inference=lowercase__ , sequence_lengths=[8] , batch_sizes=[1] , multi_process=lowercase__ , only_pretrain_model=lowercase__ , )
SCREAMING_SNAKE_CASE = TensorFlowBenchmark(lowercase__ )
SCREAMING_SNAKE_CASE = benchmark.run()
self.check_results_dict_not_empty(results.time_inference_result )
self.check_results_dict_not_empty(results.memory_inference_result )
def A ( self ) -> List[str]:
"""simple docstring"""
SCREAMING_SNAKE_CASE = 'sshleifer/tiny-gpt2'
SCREAMING_SNAKE_CASE = TensorFlowBenchmarkArguments(
models=[MODEL_ID] , training=lowercase__ , inference=lowercase__ , sequence_lengths=[8] , batch_sizes=[1] , multi_process=lowercase__ , )
SCREAMING_SNAKE_CASE = TensorFlowBenchmark(lowercase__ )
SCREAMING_SNAKE_CASE = benchmark.run()
self.check_results_dict_not_empty(results.time_inference_result )
self.check_results_dict_not_empty(results.memory_inference_result )
def A ( self ) -> Any:
"""simple docstring"""
SCREAMING_SNAKE_CASE = 'sshleifer/tiny-gpt2'
SCREAMING_SNAKE_CASE = AutoConfig.from_pretrained(lowercase__ )
SCREAMING_SNAKE_CASE = TensorFlowBenchmarkArguments(
models=[MODEL_ID] , training=lowercase__ , inference=lowercase__ , sequence_lengths=[8] , batch_sizes=[1] , eager_mode=lowercase__ , multi_process=lowercase__ , )
SCREAMING_SNAKE_CASE = TensorFlowBenchmark(lowercase__ , [config] )
SCREAMING_SNAKE_CASE = benchmark.run()
self.check_results_dict_not_empty(results.time_inference_result )
self.check_results_dict_not_empty(results.memory_inference_result )
def A ( self ) -> Any:
"""simple docstring"""
SCREAMING_SNAKE_CASE = 'sshleifer/tiny-gpt2'
SCREAMING_SNAKE_CASE = AutoConfig.from_pretrained(lowercase__ )
SCREAMING_SNAKE_CASE = TensorFlowBenchmarkArguments(
models=[MODEL_ID] , training=lowercase__ , inference=lowercase__ , sequence_lengths=[8] , batch_sizes=[1] , multi_process=lowercase__ , )
SCREAMING_SNAKE_CASE = TensorFlowBenchmark(lowercase__ , [config] )
SCREAMING_SNAKE_CASE = benchmark.run()
self.check_results_dict_not_empty(results.time_inference_result )
self.check_results_dict_not_empty(results.memory_inference_result )
def A ( self ) -> List[str]:
"""simple docstring"""
SCREAMING_SNAKE_CASE = 'sshleifer/tiny-gpt2'
SCREAMING_SNAKE_CASE = TensorFlowBenchmarkArguments(
models=[MODEL_ID] , training=lowercase__ , inference=lowercase__ , sequence_lengths=[8] , batch_sizes=[1] , multi_process=lowercase__ , )
SCREAMING_SNAKE_CASE = TensorFlowBenchmark(lowercase__ )
SCREAMING_SNAKE_CASE = benchmark.run()
self.check_results_dict_not_empty(results.time_train_result )
self.check_results_dict_not_empty(results.memory_train_result )
def A ( self ) -> List[str]:
"""simple docstring"""
SCREAMING_SNAKE_CASE = 'sshleifer/tiny-gpt2'
SCREAMING_SNAKE_CASE = AutoConfig.from_pretrained(lowercase__ )
SCREAMING_SNAKE_CASE = TensorFlowBenchmarkArguments(
models=[MODEL_ID] , training=lowercase__ , inference=lowercase__ , sequence_lengths=[8] , batch_sizes=[1] , multi_process=lowercase__ , )
SCREAMING_SNAKE_CASE = TensorFlowBenchmark(lowercase__ , [config] )
SCREAMING_SNAKE_CASE = benchmark.run()
self.check_results_dict_not_empty(results.time_train_result )
self.check_results_dict_not_empty(results.memory_train_result )
def A ( self ) -> Optional[int]:
"""simple docstring"""
SCREAMING_SNAKE_CASE = 'patrickvonplaten/t5-tiny-random'
SCREAMING_SNAKE_CASE = AutoConfig.from_pretrained(lowercase__ )
SCREAMING_SNAKE_CASE = TensorFlowBenchmarkArguments(
models=[MODEL_ID] , training=lowercase__ , inference=lowercase__ , sequence_lengths=[8] , batch_sizes=[1] , multi_process=lowercase__ , )
SCREAMING_SNAKE_CASE = TensorFlowBenchmark(lowercase__ , configs=[config] )
SCREAMING_SNAKE_CASE = benchmark.run()
self.check_results_dict_not_empty(results.time_inference_result )
self.check_results_dict_not_empty(results.memory_inference_result )
@unittest.skipIf(is_tf_available() and len(tf.config.list_physical_devices('GPU' ) ) == 0 , 'Cannot do xla on CPU.' )
def A ( self ) -> List[str]:
"""simple docstring"""
SCREAMING_SNAKE_CASE = 'sshleifer/tiny-gpt2'
SCREAMING_SNAKE_CASE = TensorFlowBenchmarkArguments(
models=[MODEL_ID] , training=lowercase__ , inference=lowercase__ , sequence_lengths=[8] , batch_sizes=[1] , use_xla=lowercase__ , multi_process=lowercase__ , )
SCREAMING_SNAKE_CASE = TensorFlowBenchmark(lowercase__ )
SCREAMING_SNAKE_CASE = benchmark.run()
self.check_results_dict_not_empty(results.time_inference_result )
self.check_results_dict_not_empty(results.memory_inference_result )
def A ( self ) -> Tuple:
"""simple docstring"""
SCREAMING_SNAKE_CASE = 'sshleifer/tiny-gpt2'
with tempfile.TemporaryDirectory() as tmp_dir:
SCREAMING_SNAKE_CASE = TensorFlowBenchmarkArguments(
models=[MODEL_ID] , inference=lowercase__ , save_to_csv=lowercase__ , sequence_lengths=[8] , batch_sizes=[1] , inference_time_csv_file=os.path.join(lowercase__ , 'inf_time.csv' ) , inference_memory_csv_file=os.path.join(lowercase__ , 'inf_mem.csv' ) , env_info_csv_file=os.path.join(lowercase__ , 'env.csv' ) , multi_process=lowercase__ , )
SCREAMING_SNAKE_CASE = TensorFlowBenchmark(lowercase__ )
benchmark.run()
self.assertTrue(Path(os.path.join(lowercase__ , 'inf_time.csv' ) ).exists() )
self.assertTrue(Path(os.path.join(lowercase__ , 'inf_mem.csv' ) ).exists() )
self.assertTrue(Path(os.path.join(lowercase__ , 'env.csv' ) ).exists() )
def A ( self ) -> Optional[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE = 'sshleifer/tiny-gpt2'
def _check_summary_is_not_empty(lowercase__ ):
self.assertTrue(hasattr(lowercase__ , 'sequential' ) )
self.assertTrue(hasattr(lowercase__ , 'cumulative' ) )
self.assertTrue(hasattr(lowercase__ , 'current' ) )
self.assertTrue(hasattr(lowercase__ , 'total' ) )
with tempfile.TemporaryDirectory() as tmp_dir:
SCREAMING_SNAKE_CASE = TensorFlowBenchmarkArguments(
models=[MODEL_ID] , inference=lowercase__ , sequence_lengths=[8] , batch_sizes=[1] , log_filename=os.path.join(lowercase__ , 'log.txt' ) , log_print=lowercase__ , trace_memory_line_by_line=lowercase__ , eager_mode=lowercase__ , multi_process=lowercase__ , )
SCREAMING_SNAKE_CASE = TensorFlowBenchmark(lowercase__ )
SCREAMING_SNAKE_CASE = benchmark.run()
_check_summary_is_not_empty(result.inference_summary )
self.assertTrue(Path(os.path.join(lowercase__ , 'log.txt' ) ).exists() )
| 701
|
"""simple docstring"""
import requests
from bsa import BeautifulSoup
def UpperCamelCase_ ( SCREAMING_SNAKE_CASE_ = "https://www.worldometers.info/coronavirus" ):
SCREAMING_SNAKE_CASE = BeautifulSoup(requests.get(SCREAMING_SNAKE_CASE_ ).text, 'html.parser' )
SCREAMING_SNAKE_CASE = soup.findAll('h1' )
SCREAMING_SNAKE_CASE = soup.findAll('div', {'class': 'maincounter-number'} )
keys += soup.findAll('span', {'class': 'panel-title'} )
values += soup.findAll('div', {'class': 'number-table-main'} )
return {key.text.strip(): value.text.strip() for key, value in zip(SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_ )}
if __name__ == "__main__":
print('\033[1m' + 'COVID-19 Status of the World' + '\033[0m\n')
for key, value in world_covidaa_stats().items():
print(f'{key}\n{value}\n')
| 406
| 0
|
'''simple docstring'''
from itertools import zip_longest
import requests
from bsa import BeautifulSoup
from pandas import DataFrame
def __SCREAMING_SNAKE_CASE ( _SCREAMING_SNAKE_CASE = "laptop" ):
_snake_case = f"""https://www.amazon.in/laptop/s?k={product}"""
_snake_case = {
"""User-Agent""": """Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36
(KHTML, like Gecko)Chrome/44.0.2403.157 Safari/537.36""",
"""Accept-Language""": """en-US, en;q=0.5""",
}
_snake_case = BeautifulSoup(requests.get(_SCREAMING_SNAKE_CASE , headers=_SCREAMING_SNAKE_CASE ).text )
# Initialize a Pandas dataframe with the column titles
_snake_case = DataFrame(
columns=[
"""Product Title""",
"""Product Link""",
"""Current Price of the product""",
"""Product Rating""",
"""MRP of the product""",
"""Discount""",
] )
# Loop through each entry and store them in the dataframe
for item, _ in zip_longest(
soup.find_all(
"""div""" , attrs={"""class""": """s-result-item""", """data-component-type""": """s-search-result"""} , ) , soup.find_all("""div""" , attrs={"""class""": """a-row a-size-base a-color-base"""} ) , ):
try:
_snake_case = item.ha.text
_snake_case = """https://www.amazon.in/""" + item.ha.a["""href"""]
_snake_case = item.find("""span""" , attrs={"""class""": """a-offscreen"""} ).text
try:
_snake_case = item.find("""span""" , attrs={"""class""": """a-icon-alt"""} ).text
except AttributeError:
_snake_case = """Not available"""
try:
_snake_case = (
"""₹"""
+ item.find(
"""span""" , attrs={"""class""": """a-price a-text-price"""} ).text.split("""₹""" )[1]
)
except AttributeError:
_snake_case = """"""
try:
_snake_case = float(
(
(
float(product_mrp.strip("""₹""" ).replace(""",""" , """""" ) )
- float(product_price.strip("""₹""" ).replace(""",""" , """""" ) )
)
/ float(product_mrp.strip("""₹""" ).replace(""",""" , """""" ) )
)
* 100 )
except ValueError:
_snake_case = float("""nan""" )
except AttributeError:
pass
_snake_case = [
product_title,
product_link,
product_price,
product_rating,
product_mrp,
discount,
]
_snake_case = """ """
_snake_case = """ """
data_frame.index += 1
return data_frame
if __name__ == "__main__":
__lowerCAmelCase = 'headphones'
get_amazon_product_data(product).to_csv(f'''Amazon Product Data for {product}.csv''')
| 585
|
'''simple docstring'''
from typing import Optional
import torch
import torch.utils.checkpoint
from torch import Tensor, nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from ...activations import ACTaFN
from ...modeling_outputs import (
BackboneOutput,
BaseModelOutputWithNoAttention,
BaseModelOutputWithPoolingAndNoAttention,
ImageClassifierOutputWithNoAttention,
)
from ...modeling_utils import PreTrainedModel
from ...utils import (
add_code_sample_docstrings,
add_start_docstrings,
add_start_docstrings_to_model_forward,
logging,
replace_return_docstrings,
)
from ...utils.backbone_utils import BackboneMixin
from .configuration_resnet import ResNetConfig
__lowerCAmelCase = logging.get_logger(__name__)
# General docstring
__lowerCAmelCase = 'ResNetConfig'
# Base docstring
__lowerCAmelCase = 'microsoft/resnet-50'
__lowerCAmelCase = [1, 2_048, 7, 7]
# Image classification docstring
__lowerCAmelCase = 'microsoft/resnet-50'
__lowerCAmelCase = 'tiger cat'
__lowerCAmelCase = [
'microsoft/resnet-50',
# See all resnet models at https://huggingface.co/models?filter=resnet
]
class _lowerCAmelCase ( nn.Module ):
'''simple docstring'''
def __init__(self , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase = 3 , UpperCAmelCase = 1 , UpperCAmelCase = "relu" ) -> List[str]:
super().__init__()
_snake_case = nn.Convad(
UpperCAmelCase , UpperCAmelCase , kernel_size=UpperCAmelCase , stride=UpperCAmelCase , padding=kernel_size // 2 , bias=UpperCAmelCase )
_snake_case = nn.BatchNormad(UpperCAmelCase )
_snake_case = ACTaFN[activation] if activation is not None else nn.Identity()
def lowercase (self , UpperCAmelCase ) -> Tensor:
_snake_case = self.convolution(UpperCAmelCase )
_snake_case = self.normalization(UpperCAmelCase )
_snake_case = self.activation(UpperCAmelCase )
return hidden_state
class _lowerCAmelCase ( nn.Module ):
'''simple docstring'''
def __init__(self , UpperCAmelCase ) -> List[str]:
super().__init__()
_snake_case = ResNetConvLayer(
config.num_channels , config.embedding_size , kernel_size=7 , stride=2 , activation=config.hidden_act )
_snake_case = nn.MaxPoolad(kernel_size=3 , stride=2 , padding=1 )
_snake_case = config.num_channels
def lowercase (self , UpperCAmelCase ) -> Tensor:
_snake_case = pixel_values.shape[1]
if num_channels != self.num_channels:
raise ValueError(
"""Make sure that the channel dimension of the pixel values match with the one set in the configuration.""" )
_snake_case = self.embedder(UpperCAmelCase )
_snake_case = self.pooler(UpperCAmelCase )
return embedding
class _lowerCAmelCase ( nn.Module ):
'''simple docstring'''
def __init__(self , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase = 2 ) -> Optional[int]:
super().__init__()
_snake_case = nn.Convad(UpperCAmelCase , UpperCAmelCase , kernel_size=1 , stride=UpperCAmelCase , bias=UpperCAmelCase )
_snake_case = nn.BatchNormad(UpperCAmelCase )
def lowercase (self , UpperCAmelCase ) -> Tensor:
_snake_case = self.convolution(UpperCAmelCase )
_snake_case = self.normalization(UpperCAmelCase )
return hidden_state
class _lowerCAmelCase ( nn.Module ):
'''simple docstring'''
def __init__(self , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase = 1 , UpperCAmelCase = "relu" ) -> List[Any]:
super().__init__()
_snake_case = in_channels != out_channels or stride != 1
_snake_case = (
ResNetShortCut(UpperCAmelCase , UpperCAmelCase , stride=UpperCAmelCase ) if should_apply_shortcut else nn.Identity()
)
_snake_case = nn.Sequential(
ResNetConvLayer(UpperCAmelCase , UpperCAmelCase , stride=UpperCAmelCase ) , ResNetConvLayer(UpperCAmelCase , UpperCAmelCase , activation=UpperCAmelCase ) , )
_snake_case = ACTaFN[activation]
def lowercase (self , UpperCAmelCase ) -> Tuple:
_snake_case = hidden_state
_snake_case = self.layer(UpperCAmelCase )
_snake_case = self.shortcut(UpperCAmelCase )
hidden_state += residual
_snake_case = self.activation(UpperCAmelCase )
return hidden_state
class _lowerCAmelCase ( nn.Module ):
'''simple docstring'''
def __init__(self , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase = 1 , UpperCAmelCase = "relu" , UpperCAmelCase = 4 ) -> Optional[Any]:
super().__init__()
_snake_case = in_channels != out_channels or stride != 1
_snake_case = out_channels // reduction
_snake_case = (
ResNetShortCut(UpperCAmelCase , UpperCAmelCase , stride=UpperCAmelCase ) if should_apply_shortcut else nn.Identity()
)
_snake_case = nn.Sequential(
ResNetConvLayer(UpperCAmelCase , UpperCAmelCase , kernel_size=1 ) , ResNetConvLayer(UpperCAmelCase , UpperCAmelCase , stride=UpperCAmelCase ) , ResNetConvLayer(UpperCAmelCase , UpperCAmelCase , kernel_size=1 , activation=UpperCAmelCase ) , )
_snake_case = ACTaFN[activation]
def lowercase (self , UpperCAmelCase ) -> int:
_snake_case = hidden_state
_snake_case = self.layer(UpperCAmelCase )
_snake_case = self.shortcut(UpperCAmelCase )
hidden_state += residual
_snake_case = self.activation(UpperCAmelCase )
return hidden_state
class _lowerCAmelCase ( nn.Module ):
'''simple docstring'''
def __init__(self , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase = 2 , UpperCAmelCase = 2 , ) -> Dict:
super().__init__()
_snake_case = ResNetBottleNeckLayer if config.layer_type == """bottleneck""" else ResNetBasicLayer
_snake_case = nn.Sequential(
# downsampling is done in the first layer with stride of 2
layer(UpperCAmelCase , UpperCAmelCase , stride=UpperCAmelCase , activation=config.hidden_act ) , *[layer(UpperCAmelCase , UpperCAmelCase , activation=config.hidden_act ) for _ in range(depth - 1 )] , )
def lowercase (self , UpperCAmelCase ) -> Tensor:
_snake_case = input
for layer in self.layers:
_snake_case = layer(UpperCAmelCase )
return hidden_state
class _lowerCAmelCase ( nn.Module ):
'''simple docstring'''
def __init__(self , UpperCAmelCase ) -> str:
super().__init__()
_snake_case = nn.ModuleList([] )
# based on `downsample_in_first_stage` the first layer of the first stage may or may not downsample the input
self.stages.append(
ResNetStage(
UpperCAmelCase , config.embedding_size , config.hidden_sizes[0] , stride=2 if config.downsample_in_first_stage else 1 , depth=config.depths[0] , ) )
_snake_case = zip(config.hidden_sizes , config.hidden_sizes[1:] )
for (in_channels, out_channels), depth in zip(UpperCAmelCase , config.depths[1:] ):
self.stages.append(ResNetStage(UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , depth=UpperCAmelCase ) )
def lowercase (self , UpperCAmelCase , UpperCAmelCase = False , UpperCAmelCase = True ) -> BaseModelOutputWithNoAttention:
_snake_case = () if output_hidden_states else None
for stage_module in self.stages:
if output_hidden_states:
_snake_case = hidden_states + (hidden_state,)
_snake_case = stage_module(UpperCAmelCase )
if output_hidden_states:
_snake_case = hidden_states + (hidden_state,)
if not return_dict:
return tuple(v for v in [hidden_state, hidden_states] if v is not None )
return BaseModelOutputWithNoAttention(
last_hidden_state=UpperCAmelCase , hidden_states=UpperCAmelCase , )
class _lowerCAmelCase ( __snake_case ):
'''simple docstring'''
lowerCAmelCase_ = ResNetConfig
lowerCAmelCase_ = "resnet"
lowerCAmelCase_ = "pixel_values"
lowerCAmelCase_ = True
def lowercase (self , UpperCAmelCase ) -> Tuple:
if isinstance(UpperCAmelCase , nn.Convad ):
nn.init.kaiming_normal_(module.weight , mode="""fan_out""" , nonlinearity="""relu""" )
elif isinstance(UpperCAmelCase , (nn.BatchNormad, nn.GroupNorm) ):
nn.init.constant_(module.weight , 1 )
nn.init.constant_(module.bias , 0 )
def lowercase (self , UpperCAmelCase , UpperCAmelCase=False ) -> str:
if isinstance(UpperCAmelCase , UpperCAmelCase ):
_snake_case = value
__lowerCAmelCase = r'\n This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. Use it\n as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and\n behavior.\n\n Parameters:\n config ([`ResNetConfig`]): Model configuration class with all the parameters of the model.\n Initializing with a config file does not load the weights associated with the model, only the\n configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.\n'
__lowerCAmelCase = r'\n Args:\n pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):\n Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. See\n [`ConvNextImageProcessor.__call__`] for details.\n\n output_hidden_states (`bool`, *optional*):\n Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for\n more detail.\n return_dict (`bool`, *optional*):\n Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.\n'
@add_start_docstrings(
"The bare ResNet model outputting raw features without any specific head on top." , __snake_case , )
class _lowerCAmelCase ( __snake_case ):
'''simple docstring'''
def __init__(self , UpperCAmelCase ) -> Tuple:
super().__init__(UpperCAmelCase )
_snake_case = config
_snake_case = ResNetEmbeddings(UpperCAmelCase )
_snake_case = ResNetEncoder(UpperCAmelCase )
_snake_case = nn.AdaptiveAvgPoolad((1, 1) )
# Initialize weights and apply final processing
self.post_init()
@add_start_docstrings_to_model_forward(UpperCAmelCase )
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_DOC , output_type=UpperCAmelCase , config_class=_CONFIG_FOR_DOC , modality="""vision""" , expected_output=_EXPECTED_OUTPUT_SHAPE , )
def lowercase (self , UpperCAmelCase , UpperCAmelCase = None , UpperCAmelCase = None ) -> BaseModelOutputWithPoolingAndNoAttention:
_snake_case = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
_snake_case = return_dict if return_dict is not None else self.config.use_return_dict
_snake_case = self.embedder(UpperCAmelCase )
_snake_case = self.encoder(
UpperCAmelCase , output_hidden_states=UpperCAmelCase , return_dict=UpperCAmelCase )
_snake_case = encoder_outputs[0]
_snake_case = self.pooler(UpperCAmelCase )
if not return_dict:
return (last_hidden_state, pooled_output) + encoder_outputs[1:]
return BaseModelOutputWithPoolingAndNoAttention(
last_hidden_state=UpperCAmelCase , pooler_output=UpperCAmelCase , hidden_states=encoder_outputs.hidden_states , )
@add_start_docstrings(
"\n ResNet Model with an image classification head on top (a linear layer on top of the pooled features), e.g. for\n ImageNet.\n " , __snake_case , )
class _lowerCAmelCase ( __snake_case ):
'''simple docstring'''
def __init__(self , UpperCAmelCase ) -> List[str]:
super().__init__(UpperCAmelCase )
_snake_case = config.num_labels
_snake_case = ResNetModel(UpperCAmelCase )
# classification head
_snake_case = nn.Sequential(
nn.Flatten() , nn.Linear(config.hidden_sizes[-1] , config.num_labels ) if config.num_labels > 0 else nn.Identity() , )
# initialize weights and apply final processing
self.post_init()
@add_start_docstrings_to_model_forward(UpperCAmelCase )
@add_code_sample_docstrings(
checkpoint=_IMAGE_CLASS_CHECKPOINT , output_type=UpperCAmelCase , config_class=_CONFIG_FOR_DOC , expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT , )
def lowercase (self , UpperCAmelCase = None , UpperCAmelCase = None , UpperCAmelCase = None , UpperCAmelCase = None , ) -> ImageClassifierOutputWithNoAttention:
_snake_case = return_dict if return_dict is not None else self.config.use_return_dict
_snake_case = self.resnet(UpperCAmelCase , output_hidden_states=UpperCAmelCase , return_dict=UpperCAmelCase )
_snake_case = outputs.pooler_output if return_dict else outputs[1]
_snake_case = self.classifier(UpperCAmelCase )
_snake_case = None
if labels is not None:
if self.config.problem_type is None:
if self.num_labels == 1:
_snake_case = """regression"""
elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
_snake_case = """single_label_classification"""
else:
_snake_case = """multi_label_classification"""
if self.config.problem_type == "regression":
_snake_case = MSELoss()
if self.num_labels == 1:
_snake_case = loss_fct(logits.squeeze() , labels.squeeze() )
else:
_snake_case = loss_fct(UpperCAmelCase , UpperCAmelCase )
elif self.config.problem_type == "single_label_classification":
_snake_case = CrossEntropyLoss()
_snake_case = loss_fct(logits.view(-1 , self.num_labels ) , labels.view(-1 ) )
elif self.config.problem_type == "multi_label_classification":
_snake_case = BCEWithLogitsLoss()
_snake_case = loss_fct(UpperCAmelCase , UpperCAmelCase )
if not return_dict:
_snake_case = (logits,) + outputs[2:]
return (loss,) + output if loss is not None else output
return ImageClassifierOutputWithNoAttention(loss=UpperCAmelCase , logits=UpperCAmelCase , hidden_states=outputs.hidden_states )
@add_start_docstrings(
"\n ResNet backbone, to be used with frameworks like DETR and MaskFormer.\n " , __snake_case , )
class _lowerCAmelCase ( __snake_case , __snake_case ):
'''simple docstring'''
def __init__(self , UpperCAmelCase ) -> int:
super().__init__(UpperCAmelCase )
super()._init_backbone(UpperCAmelCase )
_snake_case = [config.embedding_size] + config.hidden_sizes
_snake_case = ResNetEmbeddings(UpperCAmelCase )
_snake_case = ResNetEncoder(UpperCAmelCase )
# initialize weights and apply final processing
self.post_init()
@add_start_docstrings_to_model_forward(UpperCAmelCase )
@replace_return_docstrings(output_type=UpperCAmelCase , config_class=_CONFIG_FOR_DOC )
def lowercase (self , UpperCAmelCase , UpperCAmelCase = None , UpperCAmelCase = None ) -> BackboneOutput:
_snake_case = return_dict if return_dict is not None else self.config.use_return_dict
_snake_case = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
_snake_case = self.embedder(UpperCAmelCase )
_snake_case = self.encoder(UpperCAmelCase , output_hidden_states=UpperCAmelCase , return_dict=UpperCAmelCase )
_snake_case = outputs.hidden_states
_snake_case = ()
for idx, stage in enumerate(self.stage_names ):
if stage in self.out_features:
feature_maps += (hidden_states[idx],)
if not return_dict:
_snake_case = (feature_maps,)
if output_hidden_states:
output += (outputs.hidden_states,)
return output
return BackboneOutput(
feature_maps=UpperCAmelCase , hidden_states=outputs.hidden_states if output_hidden_states else None , attentions=UpperCAmelCase , )
| 585
| 1
|
from collections import OrderedDict
from typing import Any, List, Mapping, Optional
from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from ...utils import logging
_UpperCAmelCase : Tuple = logging.get_logger(__name__)
_UpperCAmelCase : int = {
"Salesforce/codegen-350M-nl": "https://huggingface.co/Salesforce/codegen-350M-nl/resolve/main/config.json",
"Salesforce/codegen-350M-multi": "https://huggingface.co/Salesforce/codegen-350M-multi/resolve/main/config.json",
"Salesforce/codegen-350M-mono": "https://huggingface.co/Salesforce/codegen-350M-mono/resolve/main/config.json",
"Salesforce/codegen-2B-nl": "https://huggingface.co/Salesforce/codegen-2B-nl/resolve/main/config.json",
"Salesforce/codegen-2B-multi": "https://huggingface.co/Salesforce/codegen-2B-multi/resolve/main/config.json",
"Salesforce/codegen-2B-mono": "https://huggingface.co/Salesforce/codegen-2B-mono/resolve/main/config.json",
"Salesforce/codegen-6B-nl": "https://huggingface.co/Salesforce/codegen-6B-nl/resolve/main/config.json",
"Salesforce/codegen-6B-multi": "https://huggingface.co/Salesforce/codegen-6B-multi/resolve/main/config.json",
"Salesforce/codegen-6B-mono": "https://huggingface.co/Salesforce/codegen-6B-mono/resolve/main/config.json",
"Salesforce/codegen-16B-nl": "https://huggingface.co/Salesforce/codegen-16B-nl/resolve/main/config.json",
"Salesforce/codegen-16B-multi": "https://huggingface.co/Salesforce/codegen-16B-multi/resolve/main/config.json",
"Salesforce/codegen-16B-mono": "https://huggingface.co/Salesforce/codegen-16B-mono/resolve/main/config.json",
}
class lowerCAmelCase_ ( snake_case__ ):
UpperCamelCase_ :int = 'codegen'
UpperCamelCase_ :int = {
'max_position_embeddings': 'n_positions',
'hidden_size': 'n_embd',
'num_attention_heads': 'n_head',
'num_hidden_layers': 'n_layer',
}
def __init__( self : Optional[int] , SCREAMING_SNAKE_CASE_ : str=50_400 , SCREAMING_SNAKE_CASE_ : str=2_048 , SCREAMING_SNAKE_CASE_ : int=2_048 , SCREAMING_SNAKE_CASE_ : Any=4_096 , SCREAMING_SNAKE_CASE_ : List[Any]=28 , SCREAMING_SNAKE_CASE_ : str=16 , SCREAMING_SNAKE_CASE_ : str=64 , SCREAMING_SNAKE_CASE_ : Tuple=None , SCREAMING_SNAKE_CASE_ : Dict="gelu_new" , SCREAMING_SNAKE_CASE_ : str=0.0 , SCREAMING_SNAKE_CASE_ : Any=0.0 , SCREAMING_SNAKE_CASE_ : Any=0.0 , SCREAMING_SNAKE_CASE_ : Any=1e-5 , SCREAMING_SNAKE_CASE_ : Any=0.02 , SCREAMING_SNAKE_CASE_ : Union[str, Any]=True , SCREAMING_SNAKE_CASE_ : Optional[int]=50_256 , SCREAMING_SNAKE_CASE_ : Any=50_256 , SCREAMING_SNAKE_CASE_ : List[str]=False , **SCREAMING_SNAKE_CASE_ : Union[str, Any] , ):
lowerCAmelCase__ = vocab_size
lowerCAmelCase__ = n_ctx
lowerCAmelCase__ = n_positions
lowerCAmelCase__ = n_embd
lowerCAmelCase__ = n_layer
lowerCAmelCase__ = n_head
lowerCAmelCase__ = n_inner
lowerCAmelCase__ = rotary_dim
lowerCAmelCase__ = activation_function
lowerCAmelCase__ = resid_pdrop
lowerCAmelCase__ = embd_pdrop
lowerCAmelCase__ = attn_pdrop
lowerCAmelCase__ = layer_norm_epsilon
lowerCAmelCase__ = initializer_range
lowerCAmelCase__ = use_cache
lowerCAmelCase__ = bos_token_id
lowerCAmelCase__ = eos_token_id
super().__init__(
bos_token_id=SCREAMING_SNAKE_CASE_ , eos_token_id=SCREAMING_SNAKE_CASE_ , tie_word_embeddings=SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ )
class lowerCAmelCase_ ( snake_case__ ):
def __init__( self : str , SCREAMING_SNAKE_CASE_ : PretrainedConfig , SCREAMING_SNAKE_CASE_ : str = "default" , SCREAMING_SNAKE_CASE_ : List[PatchingSpec] = None , SCREAMING_SNAKE_CASE_ : bool = False , ):
super().__init__(SCREAMING_SNAKE_CASE_ , task=SCREAMING_SNAKE_CASE_ , patching_specs=SCREAMING_SNAKE_CASE_ , use_past=SCREAMING_SNAKE_CASE_ )
if not getattr(self._config , '''pad_token_id''' , SCREAMING_SNAKE_CASE_ ):
# TODO: how to do that better?
lowerCAmelCase__ = 0
@property
def __snake_case ( self : str ):
lowerCAmelCase__ = OrderedDict({'''input_ids''': {0: '''batch''', 1: '''sequence'''}} )
if self.use_past:
self.fill_with_past_key_values_(SCREAMING_SNAKE_CASE_ , direction='''inputs''' )
lowerCAmelCase__ = {0: '''batch''', 1: '''past_sequence + sequence'''}
else:
lowerCAmelCase__ = {0: '''batch''', 1: '''sequence'''}
return common_inputs
@property
def __snake_case ( self : Dict ):
return self._config.n_layer
@property
def __snake_case ( self : Union[str, Any] ):
return self._config.n_head
def __snake_case ( self : Optional[Any] , SCREAMING_SNAKE_CASE_ : PreTrainedTokenizer , SCREAMING_SNAKE_CASE_ : int = -1 , SCREAMING_SNAKE_CASE_ : int = -1 , SCREAMING_SNAKE_CASE_ : bool = False , SCREAMING_SNAKE_CASE_ : Optional[TensorType] = None , ):
lowerCAmelCase__ = super(SCREAMING_SNAKE_CASE_ , self ).generate_dummy_inputs(
SCREAMING_SNAKE_CASE_ , batch_size=SCREAMING_SNAKE_CASE_ , seq_length=SCREAMING_SNAKE_CASE_ , is_pair=SCREAMING_SNAKE_CASE_ , framework=SCREAMING_SNAKE_CASE_ )
# We need to order the input in the way they appears in the forward()
lowerCAmelCase__ = OrderedDict({'''input_ids''': common_inputs['''input_ids''']} )
# Need to add the past_keys
if self.use_past:
if not is_torch_available():
raise ValueError('''Cannot generate dummy past_keys inputs without PyTorch installed.''' )
else:
import torch
lowerCAmelCase__ , lowerCAmelCase__ = common_inputs['''input_ids'''].shape
# Not using the same length for past_key_values
lowerCAmelCase__ = seqlen + 2
lowerCAmelCase__ = (
batch,
self.num_attention_heads,
past_key_values_length,
self._config.hidden_size // self.num_attention_heads,
)
lowerCAmelCase__ = [
(torch.zeros(SCREAMING_SNAKE_CASE_ ), torch.zeros(SCREAMING_SNAKE_CASE_ )) for _ in range(self.num_layers )
]
lowerCAmelCase__ = common_inputs['''attention_mask''']
if self.use_past:
lowerCAmelCase__ = ordered_inputs['''attention_mask'''].dtype
lowerCAmelCase__ = torch.cat(
[ordered_inputs['''attention_mask'''], torch.ones(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , dtype=SCREAMING_SNAKE_CASE_ )] , dim=1 )
return ordered_inputs
@property
def __snake_case ( self : Optional[int] ):
return 13
| 713
|
from typing import Optional
import pyspark
from .. import Features, NamedSplit
from ..download import DownloadMode
from ..packaged_modules.spark.spark import Spark
from .abc import AbstractDatasetReader
class lowerCAmelCase_ ( snake_case__ ):
def __init__( self : str , SCREAMING_SNAKE_CASE_ : pyspark.sql.DataFrame , SCREAMING_SNAKE_CASE_ : Optional[NamedSplit] = None , SCREAMING_SNAKE_CASE_ : Optional[Features] = None , SCREAMING_SNAKE_CASE_ : bool = True , SCREAMING_SNAKE_CASE_ : str = None , SCREAMING_SNAKE_CASE_ : bool = False , SCREAMING_SNAKE_CASE_ : str = None , SCREAMING_SNAKE_CASE_ : bool = True , SCREAMING_SNAKE_CASE_ : str = "arrow" , **SCREAMING_SNAKE_CASE_ : Optional[Any] , ):
super().__init__(
split=SCREAMING_SNAKE_CASE_ , features=SCREAMING_SNAKE_CASE_ , cache_dir=SCREAMING_SNAKE_CASE_ , keep_in_memory=SCREAMING_SNAKE_CASE_ , streaming=SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ , )
lowerCAmelCase__ = load_from_cache_file
lowerCAmelCase__ = file_format
lowerCAmelCase__ = Spark(
df=SCREAMING_SNAKE_CASE_ , features=SCREAMING_SNAKE_CASE_ , cache_dir=SCREAMING_SNAKE_CASE_ , working_dir=SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ , )
def __snake_case ( self : List[str] ):
if self.streaming:
return self.builder.as_streaming_dataset(split=self.split )
lowerCAmelCase__ = None if self._load_from_cache_file else DownloadMode.FORCE_REDOWNLOAD
self.builder.download_and_prepare(
download_mode=SCREAMING_SNAKE_CASE_ , file_format=self._file_format , )
return self.builder.as_dataset(split=self.split )
| 288
| 0
|
'''simple docstring'''
from typing import TYPE_CHECKING
from ..utils import _LazyModule
__UpperCamelCase = {
"config": [
"EXTERNAL_DATA_FORMAT_SIZE_LIMIT",
"OnnxConfig",
"OnnxConfigWithPast",
"OnnxSeq2SeqConfigWithPast",
"PatchingSpec",
],
"convert": ["export", "validate_model_outputs"],
"features": ["FeaturesManager"],
"utils": ["ParameterFormat", "compute_serialized_parameters_size"],
}
if TYPE_CHECKING:
from .config import (
EXTERNAL_DATA_FORMAT_SIZE_LIMIT,
OnnxConfig,
OnnxConfigWithPast,
OnnxSeqaSeqConfigWithPast,
PatchingSpec,
)
from .convert import export, validate_model_outputs
from .features import FeaturesManager
from .utils import ParameterFormat, compute_serialized_parameters_size
else:
import sys
__UpperCamelCase = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 26
|
import math
def lowerCamelCase__ ( a : list , a : int ) -> int:
"""simple docstring"""
a__ :str = len(a )
a__ :List[str] = int(math.floor(math.sqrt(a ) ) )
a__ :int = 0
while arr[min(a , a ) - 1] < x:
a__ :Union[str, Any] = step
step += int(math.floor(math.sqrt(a ) ) )
if prev >= n:
return -1
while arr[prev] < x:
a__ :str = prev + 1
if prev == min(a , a ):
return -1
if arr[prev] == x:
return prev
return -1
if __name__ == "__main__":
snake_case__ = input('''Enter numbers separated by a comma:\n''').strip()
snake_case__ = [int(item) for item in user_input.split(''',''')]
snake_case__ = int(input('''Enter the number to be searched:\n'''))
snake_case__ = jump_search(arr, x)
if res == -1:
print('''Number not found!''')
else:
print(f'''Number {x} is at index {res}''')
| 395
| 0
|
import unittest
from transformers import DebertaVaTokenizer, DebertaVaTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin
_UpperCamelCase : Any = get_tests_dir("""fixtures/spiece.model""")
@require_sentencepiece
@require_tokenizers
class _lowerCAmelCase( _a , unittest.TestCase):
"""simple docstring"""
lowerCamelCase__ = DebertaVaTokenizer
lowerCamelCase__ = DebertaVaTokenizerFast
lowerCamelCase__ = True
lowerCamelCase__ = True
def SCREAMING_SNAKE_CASE__ ( self )-> Tuple:
super().setUp()
# We have a SentencePiece fixture for testing
__A = DebertaVaTokenizer(UpperCAmelCase , unk_token='''<unk>''' )
tokenizer.save_pretrained(self.tmpdirname )
def SCREAMING_SNAKE_CASE__ ( self , UpperCAmelCase )-> Optional[int]:
__A = '''this is a test'''
__A = '''this is a test'''
return input_text, output_text
def SCREAMING_SNAKE_CASE__ ( self )-> Tuple:
__A = '''<pad>'''
__A = 0
self.assertEqual(self.get_tokenizer()._convert_token_to_id(UpperCAmelCase ) , UpperCAmelCase )
self.assertEqual(self.get_tokenizer()._convert_id_to_token(UpperCAmelCase ) , UpperCAmelCase )
def SCREAMING_SNAKE_CASE__ ( self )-> Dict:
__A = list(self.get_tokenizer().get_vocab().keys() )
self.assertEqual(vocab_keys[0] , '''<pad>''' )
self.assertEqual(vocab_keys[1] , '''<unk>''' )
self.assertEqual(vocab_keys[-1] , '''[PAD]''' )
self.assertEqual(len(UpperCAmelCase ) , 3_00_01 )
def SCREAMING_SNAKE_CASE__ ( self )-> Any:
self.assertEqual(self.get_tokenizer().vocab_size , 3_00_00 )
def SCREAMING_SNAKE_CASE__ ( self )-> Union[str, Any]:
# fmt: off
__A = ''' \tHeLLo!how \n Are yoU? '''
__A = ['''▁hello''', '''!''', '''how''', '''▁are''', '''▁you''', '''?''']
# fmt: on
__A = DebertaVaTokenizer(UpperCAmelCase , do_lower_case=UpperCAmelCase )
__A = tokenizer.convert_ids_to_tokens(tokenizer.encode(UpperCAmelCase , add_special_tokens=UpperCAmelCase ) )
self.assertListEqual(UpperCAmelCase , UpperCAmelCase )
__A = DebertaVaTokenizerFast(UpperCAmelCase , do_lower_case=UpperCAmelCase )
__A = rust_tokenizer.convert_ids_to_tokens(rust_tokenizer.encode(UpperCAmelCase , add_special_tokens=UpperCAmelCase ) )
self.assertListEqual(UpperCAmelCase , UpperCAmelCase )
@unittest.skip('''There is an inconsistency between slow and fast tokenizer due to a bug in the fast one.''' )
def SCREAMING_SNAKE_CASE__ ( self )-> Dict:
pass
@unittest.skip('''There is an inconsistency between slow and fast tokenizer due to a bug in the fast one.''' )
def SCREAMING_SNAKE_CASE__ ( self )-> Tuple:
pass
def SCREAMING_SNAKE_CASE__ ( self )-> Tuple:
# fmt: off
__A = '''I was born in 92000, and this is falsé.'''
__A = ['''▁''', '''<unk>''', '''▁was''', '''▁born''', '''▁in''', '''▁9''', '''2000''', '''▁''', ''',''', '''▁and''', '''▁this''', '''▁is''', '''▁fal''', '''s''', '''<unk>''', '''▁''', '''.''', ]
# fmt: on
__A = DebertaVaTokenizer(UpperCAmelCase , split_by_punct=UpperCAmelCase )
__A = tokenizer.convert_ids_to_tokens(tokenizer.encode(UpperCAmelCase , add_special_tokens=UpperCAmelCase ) )
self.assertListEqual(UpperCAmelCase , UpperCAmelCase )
__A = DebertaVaTokenizerFast(UpperCAmelCase , split_by_punct=UpperCAmelCase )
__A = rust_tokenizer.convert_ids_to_tokens(rust_tokenizer.encode(UpperCAmelCase , add_special_tokens=UpperCAmelCase ) )
self.assertListEqual(UpperCAmelCase , UpperCAmelCase )
def SCREAMING_SNAKE_CASE__ ( self )-> List[str]:
# fmt: off
__A = '''I was born in 92000, and this is falsé.'''
__A = ['''▁i''', '''▁was''', '''▁born''', '''▁in''', '''▁9''', '''2000''', '''▁''', ''',''', '''▁and''', '''▁this''', '''▁is''', '''▁fal''', '''s''', '''<unk>''', '''▁''', '''.''', ]
# fmt: on
__A = DebertaVaTokenizer(UpperCAmelCase , do_lower_case=UpperCAmelCase , split_by_punct=UpperCAmelCase )
__A = tokenizer.convert_ids_to_tokens(tokenizer.encode(UpperCAmelCase , add_special_tokens=UpperCAmelCase ) )
self.assertListEqual(UpperCAmelCase , UpperCAmelCase )
__A = DebertaVaTokenizerFast(UpperCAmelCase , do_lower_case=UpperCAmelCase , split_by_punct=UpperCAmelCase )
__A = rust_tokenizer.convert_ids_to_tokens(rust_tokenizer.encode(UpperCAmelCase , add_special_tokens=UpperCAmelCase ) )
self.assertListEqual(UpperCAmelCase , UpperCAmelCase )
def SCREAMING_SNAKE_CASE__ ( self )-> int:
# fmt: off
__A = '''I was born in 92000, and this is falsé.'''
__A = ['''▁i''', '''▁was''', '''▁born''', '''▁in''', '''▁9''', '''2000''', ''',''', '''▁and''', '''▁this''', '''▁is''', '''▁fal''', '''s''', '''<unk>''', '''.''', ]
# fmt: on
__A = DebertaVaTokenizer(UpperCAmelCase , do_lower_case=UpperCAmelCase , split_by_punct=UpperCAmelCase )
__A = tokenizer.convert_ids_to_tokens(tokenizer.encode(UpperCAmelCase , add_special_tokens=UpperCAmelCase ) )
self.assertListEqual(UpperCAmelCase , UpperCAmelCase )
__A = DebertaVaTokenizerFast(UpperCAmelCase , do_lower_case=UpperCAmelCase , split_by_punct=UpperCAmelCase )
__A = rust_tokenizer.convert_ids_to_tokens(rust_tokenizer.encode(UpperCAmelCase , add_special_tokens=UpperCAmelCase ) )
self.assertListEqual(UpperCAmelCase , UpperCAmelCase )
def SCREAMING_SNAKE_CASE__ ( self )-> Any:
# fmt: off
__A = '''I was born in 92000, and this is falsé.'''
__A = ['''▁''', '''<unk>''', '''▁was''', '''▁born''', '''▁in''', '''▁9''', '''2000''', '''▁''', ''',''', '''▁and''', '''▁this''', '''▁is''', '''▁fal''', '''s''', '''<unk>''', '''▁''', '''.''', ]
# fmt: on
__A = DebertaVaTokenizer(UpperCAmelCase , do_lower_case=UpperCAmelCase , split_by_punct=UpperCAmelCase )
__A = tokenizer.convert_ids_to_tokens(tokenizer.encode(UpperCAmelCase , add_special_tokens=UpperCAmelCase ) )
self.assertListEqual(UpperCAmelCase , UpperCAmelCase )
__A = DebertaVaTokenizerFast(UpperCAmelCase , do_lower_case=UpperCAmelCase , split_by_punct=UpperCAmelCase )
__A = rust_tokenizer.convert_ids_to_tokens(rust_tokenizer.encode(UpperCAmelCase , add_special_tokens=UpperCAmelCase ) )
self.assertListEqual(UpperCAmelCase , UpperCAmelCase )
def SCREAMING_SNAKE_CASE__ ( self )-> Union[str, Any]:
# fmt: off
__A = ''' \tHeLLo!how \n Are yoU? '''
__A = ['''▁''', '''<unk>''', '''e''', '''<unk>''', '''o''', '''!''', '''how''', '''▁''', '''<unk>''', '''re''', '''▁yo''', '''<unk>''', '''?''']
# fmt: on
__A = DebertaVaTokenizer(UpperCAmelCase , do_lower_case=UpperCAmelCase , split_by_punct=UpperCAmelCase )
__A = tokenizer.convert_ids_to_tokens(tokenizer.encode(UpperCAmelCase , add_special_tokens=UpperCAmelCase ) )
self.assertListEqual(UpperCAmelCase , UpperCAmelCase )
__A = DebertaVaTokenizerFast(UpperCAmelCase , do_lower_case=UpperCAmelCase , split_by_punct=UpperCAmelCase )
__A = rust_tokenizer.convert_ids_to_tokens(rust_tokenizer.encode(UpperCAmelCase , add_special_tokens=UpperCAmelCase ) )
self.assertListEqual(UpperCAmelCase , UpperCAmelCase )
def SCREAMING_SNAKE_CASE__ ( self )-> Any:
__A = self.get_tokenizer()
__A = self.get_rust_tokenizer()
__A = '''I was born in 92000, and this is falsé.'''
__A = tokenizer.convert_ids_to_tokens(tokenizer.encode(UpperCAmelCase , add_special_tokens=UpperCAmelCase ) )
__A = rust_tokenizer.convert_ids_to_tokens(rust_tokenizer.encode(UpperCAmelCase , add_special_tokens=UpperCAmelCase ) )
self.assertListEqual(UpperCAmelCase , UpperCAmelCase )
__A = tokenizer.encode(UpperCAmelCase , add_special_tokens=UpperCAmelCase )
__A = rust_tokenizer.encode(UpperCAmelCase , add_special_tokens=UpperCAmelCase )
self.assertListEqual(UpperCAmelCase , UpperCAmelCase )
__A = self.get_rust_tokenizer()
__A = tokenizer.encode(UpperCAmelCase )
__A = rust_tokenizer.encode(UpperCAmelCase )
self.assertListEqual(UpperCAmelCase , UpperCAmelCase )
def SCREAMING_SNAKE_CASE__ ( self )-> Any:
__A = '''This is a test'''
__A = [13, 1, 43_98, 25, 21, 12_89]
__A = ['''▁''', '''T''', '''his''', '''▁is''', '''▁a''', '''▁test''']
__A = ['''▁''', '''<unk>''', '''his''', '''▁is''', '''▁a''', '''▁test''']
__A = DebertaVaTokenizer(UpperCAmelCase , keep_accents=UpperCAmelCase )
__A = DebertaVaTokenizerFast(UpperCAmelCase , keep_accents=UpperCAmelCase )
__A = tokenizer.encode(UpperCAmelCase , add_special_tokens=UpperCAmelCase )
self.assertListEqual(UpperCAmelCase , UpperCAmelCase )
__A = tokenizer.tokenize(UpperCAmelCase )
self.assertListEqual(UpperCAmelCase , UpperCAmelCase )
__A = tokenizer.convert_ids_to_tokens(UpperCAmelCase )
self.assertListEqual(UpperCAmelCase , UpperCAmelCase )
__A = rust_tokenizer.encode(UpperCAmelCase , add_special_tokens=UpperCAmelCase )
self.assertListEqual(UpperCAmelCase , UpperCAmelCase )
__A = rust_tokenizer.tokenize(UpperCAmelCase )
self.assertListEqual(UpperCAmelCase , UpperCAmelCase )
__A = rust_tokenizer.convert_ids_to_tokens(UpperCAmelCase )
self.assertListEqual(UpperCAmelCase , UpperCAmelCase )
# fmt: off
__A = '''I was born in 92000, and this is falsé.'''
__A = [13, 1, 23, 3_86, 19, 5_61, 30_50, 15, 17, 48, 25, 82_56, 18, 1, 9]
__A = ['''▁''', '''I''', '''▁was''', '''▁born''', '''▁in''', '''▁9''', '''2000''', ''',''', '''▁and''', '''▁this''', '''▁is''', '''▁fal''', '''s''', '''é''', '''.''', ]
__A = ['''▁''', '''<unk>''', '''▁was''', '''▁born''', '''▁in''', '''▁9''', '''2000''', ''',''', '''▁and''', '''▁this''', '''▁is''', '''▁fal''', '''s''', '''<unk>''', '''.''', ]
# fmt: on
__A = tokenizer.encode(UpperCAmelCase , add_special_tokens=UpperCAmelCase )
self.assertListEqual(UpperCAmelCase , UpperCAmelCase )
__A = tokenizer.tokenize(UpperCAmelCase )
self.assertListEqual(UpperCAmelCase , UpperCAmelCase )
__A = tokenizer.convert_ids_to_tokens(UpperCAmelCase )
self.assertListEqual(UpperCAmelCase , UpperCAmelCase )
__A = rust_tokenizer.encode(UpperCAmelCase , add_special_tokens=UpperCAmelCase )
self.assertListEqual(UpperCAmelCase , UpperCAmelCase )
__A = rust_tokenizer.tokenize(UpperCAmelCase )
self.assertListEqual(UpperCAmelCase , UpperCAmelCase )
__A = rust_tokenizer.convert_ids_to_tokens(UpperCAmelCase )
self.assertListEqual(UpperCAmelCase , UpperCAmelCase )
def SCREAMING_SNAKE_CASE__ ( self )-> Any:
__A = DebertaVaTokenizer(UpperCAmelCase )
__A = tokenizer.encode('''sequence builders''' )
__A = tokenizer.encode('''multi-sequence build''' )
__A = tokenizer.build_inputs_with_special_tokens(UpperCAmelCase )
__A = tokenizer.build_inputs_with_special_tokens(UpperCAmelCase , UpperCAmelCase )
self.assertEqual([tokenizer.cls_token_id] + text + [tokenizer.sep_token_id] , UpperCAmelCase )
self.assertEqual(
[tokenizer.cls_token_id] + text + [tokenizer.sep_token_id] + text_a + [tokenizer.sep_token_id] , UpperCAmelCase , )
@slow
def SCREAMING_SNAKE_CASE__ ( self )-> List[str]:
# fmt: off
__A = {'''input_ids''': [[1, 3_98_67, 36, 1_93_90, 4_86, 27, 3_50_52, 8_14_36, 18, 6_06_85, 12_25, 7, 3_50_52, 8_14_36, 18, 93_67, 1_68_99, 18, 1_59_37, 53, 5_94, 7_73, 18, 1_62_87, 3_04_65, 36, 1_59_37, 6, 4_11_39, 38, 3_69_79, 6_07_63, 1_91, 6, 3_41_32, 99, 6, 5_05_38, 3_90, 4_32_30, 6, 3_41_32, 27_79, 2_08_50, 14, 6_99, 10_72, 11_94, 36, 3_82, 1_09_01, 53, 7, 6_99, 10_72, 20_84, 36, 2_04_22, 6_30, 53, 19, 1_05, 30_49, 18_96, 10_53, 1_68_99, 15_06, 11, 3_79_78, 42_43, 7, 12_37, 3_18_69, 2_00, 1_65_66, 6_54, 6, 3_50_52, 8_14_36, 7, 5_56_30, 1_35_93, 4, 2], [1, 26, 1_50_11, 13, 6_67, 8, 10_53, 18, 2_36_11, 12_37, 7_23_56, 1_28_20, 34, 10_41_34, 12_09, 35, 1_33_13, 66_27, 21, 2_02, 3_47, 7, 1_64, 23_99, 11, 46, 44_85, 4, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 5, 12_32, 28_64, 1_57_85, 1_49_51, 1_05, 5, 85_81, 12_50, 4, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], '''token_type_ids''': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], '''attention_mask''': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]} # noqa: E501
# fmt: on
self.tokenizer_integration_test_util(
expected_encoding=UpperCAmelCase , model_name='''microsoft/deberta-v2-xlarge''' , revision='''ad6e42c1532ddf3a15c39246b63f5559d558b670''' , )
| 341
|
class _lowerCAmelCase:
"""simple docstring"""
def __init__( self , UpperCAmelCase )-> None:
__A = len(UpperCAmelCase )
__A = [0] * len_array
if len_array > 0:
__A = array[0]
for i in range(1 , UpperCAmelCase ):
__A = self.prefix_sum[i - 1] + array[i]
def SCREAMING_SNAKE_CASE__ ( self , UpperCAmelCase , UpperCAmelCase )-> int:
if start == 0:
return self.prefix_sum[end]
return self.prefix_sum[end] - self.prefix_sum[start - 1]
def SCREAMING_SNAKE_CASE__ ( self , UpperCAmelCase )-> bool:
__A = {0}
for sum_item in self.prefix_sum:
if sum_item - target_sum in sums:
return True
sums.add(UpperCAmelCase )
return False
if __name__ == "__main__":
import doctest
doctest.testmod()
| 341
| 1
|
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
A_ = {
"configuration_vision_text_dual_encoder": ["VisionTextDualEncoderConfig"],
"processing_vision_text_dual_encoder": ["VisionTextDualEncoderProcessor"],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A_ = ["VisionTextDualEncoderModel"]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A_ = ["FlaxVisionTextDualEncoderModel"]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A_ = ["TFVisionTextDualEncoderModel"]
if TYPE_CHECKING:
from .configuration_vision_text_dual_encoder import VisionTextDualEncoderConfig
from .processing_vision_text_dual_encoder import VisionTextDualEncoderProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_vision_text_dual_encoder import VisionTextDualEncoderModel
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_vision_text_dual_encoder import FlaxVisionTextDualEncoderModel
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_vision_text_dual_encoder import TFVisionTextDualEncoderModel
else:
import sys
A_ = _LazyModule(__name__, globals()["__file__"], _import_structure)
| 393
|
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available
A_ = {}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A_ = ["MLukeTokenizer"]
if TYPE_CHECKING:
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_mluke import MLukeTokenizer
else:
import sys
A_ = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 393
| 1
|
"""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):
__lowercase : str = DiTPipeline
__lowercase : Optional[int] = CLASS_CONDITIONED_IMAGE_GENERATION_PARAMS
__lowercase : Any = PipelineTesterMixin.required_optional_params - {
"""latents""",
"""num_images_per_prompt""",
"""callback""",
"""callback_steps""",
}
__lowercase : Optional[int] = CLASS_CONDITIONED_IMAGE_GENERATION_BATCH_PARAMS
__lowercase : Optional[int] = False
def lowerCamelCase__ ( self ):
torch.manual_seed(0 )
_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_ , )
_snake_case : int = AutoencoderKL()
_snake_case : List[str] = DDIMScheduler()
_snake_case : Dict = {"transformer": transformer.eval(), "vae": vae.eval(), "scheduler": scheduler}
return components
def lowerCamelCase__ ( self , snake_case_ , snake_case_=0 ):
if str(snake_case_ ).startswith("mps" ):
_snake_case : Dict = torch.manual_seed(snake_case_ )
else:
_snake_case : List[Any] = torch.Generator(device=snake_case_ ).manual_seed(snake_case_ )
_snake_case : List[Any] = {
"class_labels": [1],
"generator": generator,
"num_inference_steps": 2,
"output_type": "numpy",
}
return inputs
def lowerCamelCase__ ( self ):
_snake_case : Any = "cpu"
_snake_case : str = self.get_dummy_components()
_snake_case : Union[str, Any] = self.pipeline_class(**snake_case_ )
pipe.to(snake_case_ )
pipe.set_progress_bar_config(disable=snake_case_ )
_snake_case : Tuple = self.get_dummy_inputs(snake_case_ )
_snake_case : List[str] = pipe(**snake_case_ ).images
_snake_case : Optional[int] = image[0, -3:, -3:, -1]
self.assertEqual(image.shape , (1, 16, 16, 3) )
_snake_case : List[str] = np.array([0.2946, 0.6601, 0.4329, 0.3296, 0.4144, 0.5319, 0.7273, 0.5013, 0.4457] )
_snake_case : Optional[Any] = np.abs(image_slice.flatten() - expected_slice ).max()
self.assertLessEqual(snake_case_ , 1E-3 )
def lowerCamelCase__ ( self ):
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 ):
self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3 )
@require_torch_gpu
@slow
class _UpperCAmelCase ( unittest.TestCase):
def lowerCamelCase__ ( self ):
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def lowerCamelCase__ ( self ):
_snake_case : List[str] = torch.manual_seed(0 )
_snake_case : Dict = DiTPipeline.from_pretrained("facebook/DiT-XL-2-256" )
pipe.to("cuda" )
_snake_case : Union[str, Any] = ["vase", "umbrella", "white shark", "white wolf"]
_snake_case : Optional[Any] = pipe.get_label_ids(snake_case_ )
_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_ ):
_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 ):
_snake_case : int = DiTPipeline.from_pretrained("facebook/DiT-XL-2-512" )
_snake_case : Optional[Any] = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config )
pipe.to("cuda" )
_snake_case : str = ["vase", "umbrella"]
_snake_case : Any = pipe.get_label_ids(snake_case_ )
_snake_case : List[str] = torch.manual_seed(0 )
_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_ ):
_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
| 87
|
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
_a : Optional[int] = logging.get_logger(__name__)
_a : List[str] = {
"""facebook/convnextv2-tiny-1k-224""": """https://huggingface.co/facebook/convnextv2-tiny-1k-224/resolve/main/config.json""",
}
class _UpperCAmelCase ( _snake_case , _snake_case):
__lowercase : List[Any] = """convnextv2"""
def __init__( self , snake_case_=3 , snake_case_=4 , snake_case_=4 , snake_case_=None , snake_case_=None , snake_case_="gelu" , snake_case_=0.02 , snake_case_=1E-12 , snake_case_=0.0 , snake_case_=2_24 , snake_case_=None , snake_case_=None , **snake_case_ , ):
super().__init__(**snake_case_ )
_snake_case : Tuple = num_channels
_snake_case : Optional[int] = patch_size
_snake_case : Tuple = num_stages
_snake_case : int = [96, 1_92, 3_84, 7_68] if hidden_sizes is None else hidden_sizes
_snake_case : str = [3, 3, 9, 3] if depths is None else depths
_snake_case : int = hidden_act
_snake_case : Tuple = initializer_range
_snake_case : Union[str, Any] = layer_norm_eps
_snake_case : Optional[int] = drop_path_rate
_snake_case : Union[str, Any] = image_size
_snake_case : List[Any] = ["stem"] + [F'stage{idx}' for idx in range(1 , len(self.depths ) + 1 )]
_snake_case , _snake_case : Dict = get_aligned_output_features_output_indices(
out_features=snake_case_ , out_indices=snake_case_ , stage_names=self.stage_names )
| 87
| 1
|
import unittest
from transformers import is_tf_available
from transformers.testing_utils import require_tf
if is_tf_available():
import tensorflow as tf
from tensorflow.python.eager import context
from tensorflow.python.framework import ops
from transformers import GradientAccumulator, create_optimizer
@require_tf
class UpperCAmelCase_ ( unittest.TestCase ):
'''simple docstring'''
def _snake_case ( self , __A , __A , __A ):
"""simple docstring"""
self.assertEqual(len(__A ) , len(__A ) )
for a, b in zip(__A , __A ):
self.assertAlmostEqual(__A , __A , delta=__A )
def _snake_case ( self ):
"""simple docstring"""
lowerCamelCase : Optional[Any] = GradientAccumulator()
accumulator([tf.constant([1.0, 2.0] )] )
accumulator([tf.constant([-2.0, 1.0] )] )
accumulator([tf.constant([-1.0, 2.0] )] )
with self.assertRaises(__A ):
accumulator([tf.constant([1.0, 1.0] ), tf.constant([2.0, 2.0] )] )
self.assertEqual(accumulator.step , 3 )
self.assertEqual(len(accumulator.gradients ) , 1 )
self.assertListAlmostEqual(accumulator.gradients[0].numpy().tolist() , [-2.0, 5.0] , tol=1e-2 )
accumulator.reset()
self.assertEqual(accumulator.step , 0 )
self.assertListAlmostEqual(accumulator.gradients[0].numpy().tolist() , [0.0, 0.0] , tol=1e-2 )
def _snake_case ( self ):
"""simple docstring"""
lowerCamelCase : Optional[int] = None
ops.enable_eager_execution_internal()
lowerCamelCase : List[str] = tf.config.list_physical_devices("CPU" )
if len(__A ) == 1:
tf.config.set_logical_device_configuration(
physical_devices[0] , [tf.config.LogicalDeviceConfiguration(), tf.config.LogicalDeviceConfiguration()] )
lowerCamelCase : Tuple = tf.config.list_logical_devices(device_type="CPU" )
lowerCamelCase : List[str] = tf.distribute.MirroredStrategy(devices=devices[:2] )
with strategy.scope():
lowerCamelCase : Any = GradientAccumulator()
lowerCamelCase : Union[str, Any] = tf.Variable([4.0, 3.0] )
lowerCamelCase , lowerCamelCase : Tuple = create_optimizer(5e-5 , 10 , 5 )
lowerCamelCase : Optional[Any] = tf.Variable([0.0, 0.0] , trainable=__A )
def accumulate_on_replica(__A ):
accumulator([gradient] )
def apply_on_replica():
optimizer.apply_gradients(list(zip(accumulator.gradients , [variable] ) ) )
@tf.function
def accumulate(__A , __A ):
with strategy.scope():
lowerCamelCase : Union[str, Any] = strategy.experimental_local_results(__A )
local_variables[0].assign(__A )
local_variables[1].assign(__A )
strategy.run(__A , args=(gradient_placeholder,) )
@tf.function
def apply_grad():
with strategy.scope():
strategy.run(__A )
def _check_local_values(__A , __A ):
lowerCamelCase : Tuple = strategy.experimental_local_results(accumulator._gradients[0] )
self.assertListAlmostEqual(values[0].value() , __A , tol=1e-2 )
self.assertListAlmostEqual(values[1].value() , __A , tol=1e-2 )
accumulate([1.0, 2.0] , [-1.0, 1.0] )
accumulate([3.0, -1.0] , [-1.0, -1.0] )
accumulate([-2.0, 2.0] , [3.0, -2.0] )
self.assertEqual(accumulator.step , 3 )
_check_local_values([2.0, 3.0] , [1.0, -2.0] )
apply_grad()
self.assertListAlmostEqual(variable.value() , [4.0, 3.0] , tol=1e-2 )
accumulator.reset()
self.assertEqual(accumulator.step , 0 )
_check_local_values([0.0, 0.0] , [0.0, 0.0] )
| 340
|
import json
import os
from pathlib import Path
import pytest
from datasets.download.download_config import DownloadConfig
from datasets.download.download_manager import DownloadManager
from datasets.utils.file_utils import hash_url_to_filename
_snake_case = '''http://www.mocksite.com/file1.txt'''
_snake_case = '''"text": ["foo", "foo"]'''
_snake_case = '''6d8ce9aa78a471c7477201efbeabd3bb01ac2e7d100a6dc024ba1608361f90a8'''
class UpperCAmelCase_ :
'''simple docstring'''
__A : List[Any] = 200
__A : List[Any] = {"Content-Length": "100"}
__A : int = {}
def _snake_case ( self , **__A ):
"""simple docstring"""
return [bytes(__A , "utf-8" )]
def lowercase_( *SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ ):
'''simple docstring'''
return MockResponse()
@pytest.mark.parametrize("urls_type" , [str, list, dict] )
def lowercase_( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
'''simple docstring'''
import requests
monkeypatch.setattr(SCREAMING_SNAKE_CASE_ , "request" , SCREAMING_SNAKE_CASE_ )
lowerCamelCase : Optional[int] = URL
if issubclass(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
lowerCamelCase : int = url
elif issubclass(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
lowerCamelCase : Optional[Any] = [url]
elif issubclass(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
lowerCamelCase : int = {"train": url}
lowerCamelCase : Union[str, Any] = "dummy"
lowerCamelCase : Optional[Any] = "downloads"
lowerCamelCase : Tuple = tmp_path
lowerCamelCase : str = DownloadConfig(
cache_dir=os.path.join(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) , use_etag=SCREAMING_SNAKE_CASE_ , )
lowerCamelCase : List[Any] = DownloadManager(dataset_name=SCREAMING_SNAKE_CASE_ , download_config=SCREAMING_SNAKE_CASE_ )
lowerCamelCase : Tuple = dl_manager.download(SCREAMING_SNAKE_CASE_ )
lowerCamelCase : List[str] = urls
for downloaded_paths in [downloaded_paths]:
if isinstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
lowerCamelCase : Tuple = [downloaded_paths]
lowerCamelCase : int = [urls]
elif isinstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
assert "train" in downloaded_paths.keys()
lowerCamelCase : Dict = downloaded_paths.values()
lowerCamelCase : List[Any] = urls.values()
assert downloaded_paths
for downloaded_path, input_url in zip(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
assert downloaded_path == dl_manager.downloaded_paths[input_url]
lowerCamelCase : Any = Path(SCREAMING_SNAKE_CASE_ )
lowerCamelCase : Union[str, Any] = downloaded_path.parts
assert parts[-1] == HASH
assert parts[-2] == cache_subdir
assert downloaded_path.exists()
lowerCamelCase : str = downloaded_path.read_text()
assert content == CONTENT
lowerCamelCase : List[str] = downloaded_path.with_suffix(".json" )
assert metadata_downloaded_path.exists()
lowerCamelCase : str = json.loads(metadata_downloaded_path.read_text() )
assert metadata_content == {"url": URL, "etag": None}
@pytest.mark.parametrize("paths_type" , [str, list, dict] )
def lowercase_( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
'''simple docstring'''
lowerCamelCase : int = str(SCREAMING_SNAKE_CASE_ )
if issubclass(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
lowerCamelCase : Any = filename
elif issubclass(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
lowerCamelCase : List[Any] = [filename]
elif issubclass(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
lowerCamelCase : Any = {"train": filename}
lowerCamelCase : int = "dummy"
lowerCamelCase : List[Any] = xz_file.parent
lowerCamelCase : str = "extracted"
lowerCamelCase : Optional[Any] = DownloadConfig(
cache_dir=SCREAMING_SNAKE_CASE_ , use_etag=SCREAMING_SNAKE_CASE_ , )
lowerCamelCase : Union[str, Any] = DownloadManager(dataset_name=SCREAMING_SNAKE_CASE_ , download_config=SCREAMING_SNAKE_CASE_ )
lowerCamelCase : List[str] = dl_manager.extract(SCREAMING_SNAKE_CASE_ )
lowerCamelCase : List[Any] = paths
for extracted_paths in [extracted_paths]:
if isinstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
lowerCamelCase : List[str] = [extracted_paths]
lowerCamelCase : Union[str, Any] = [paths]
elif isinstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
assert "train" in extracted_paths.keys()
lowerCamelCase : List[str] = extracted_paths.values()
lowerCamelCase : Optional[int] = paths.values()
assert extracted_paths
for extracted_path, input_path in zip(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
assert extracted_path == dl_manager.extracted_paths[input_path]
lowerCamelCase : List[Any] = Path(SCREAMING_SNAKE_CASE_ )
lowerCamelCase : Union[str, Any] = extracted_path.parts
assert parts[-1] == hash_url_to_filename(SCREAMING_SNAKE_CASE_ , etag=SCREAMING_SNAKE_CASE_ )
assert parts[-2] == extracted_subdir
assert extracted_path.exists()
lowerCamelCase : Union[str, Any] = extracted_path.read_text()
lowerCamelCase : str = text_file.read_text()
assert extracted_file_content == expected_file_content
def lowercase_( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
'''simple docstring'''
assert path.endswith(".jsonl" )
for num_items, line in enumerate(SCREAMING_SNAKE_CASE_ , start=1 ):
lowerCamelCase : Optional[Any] = json.loads(line.decode("utf-8" ) )
assert item.keys() == {"col_1", "col_2", "col_3"}
assert num_items == 4
@pytest.mark.parametrize("archive_jsonl" , ["tar_jsonl_path", "zip_jsonl_path"] )
def lowercase_( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
'''simple docstring'''
lowerCamelCase : Optional[Any] = request.getfixturevalue(SCREAMING_SNAKE_CASE_ )
lowerCamelCase : Optional[Any] = DownloadManager()
for num_jsonl, (path, file) in enumerate(dl_manager.iter_archive(SCREAMING_SNAKE_CASE_ ) , start=1 ):
_test_jsonl(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
assert num_jsonl == 2
@pytest.mark.parametrize("archive_nested_jsonl" , ["tar_nested_jsonl_path", "zip_nested_jsonl_path"] )
def lowercase_( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
'''simple docstring'''
lowerCamelCase : Union[str, Any] = request.getfixturevalue(SCREAMING_SNAKE_CASE_ )
lowerCamelCase : Optional[Any] = DownloadManager()
for num_tar, (path, file) in enumerate(dl_manager.iter_archive(SCREAMING_SNAKE_CASE_ ) , start=1 ):
for num_jsonl, (subpath, subfile) in enumerate(dl_manager.iter_archive(SCREAMING_SNAKE_CASE_ ) , start=1 ):
_test_jsonl(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
assert num_tar == 1
assert num_jsonl == 2
def lowercase_( SCREAMING_SNAKE_CASE_ ):
'''simple docstring'''
lowerCamelCase : List[Any] = DownloadManager()
for num_file, file in enumerate(dl_manager.iter_files(SCREAMING_SNAKE_CASE_ ) , start=1 ):
assert os.path.basename(SCREAMING_SNAKE_CASE_ ) == ("test.txt" if num_file == 1 else "train.txt")
assert num_file == 2
| 340
| 1
|
'''simple docstring'''
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
convert_to_rgb,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_dimension_format,
)
from ...image_utils import (
OPENAI_CLIP_MEAN,
OPENAI_CLIP_STD,
ChannelDimension,
ImageInput,
PILImageResampling,
make_list_of_images,
to_numpy_array,
valid_images,
)
from ...utils import TensorType, is_vision_available, logging
SCREAMING_SNAKE_CASE__ : str = logging.get_logger(__name__)
if is_vision_available():
import PIL
class __lowerCAmelCase( lowerCAmelCase__ ):
__snake_case : Dict = ['pixel_values']
def __init__( self : Optional[Any] , SCREAMING_SNAKE_CASE : bool = True , SCREAMING_SNAKE_CASE : Dict[str, int] = None , SCREAMING_SNAKE_CASE : PILImageResampling = PILImageResampling.BICUBIC , SCREAMING_SNAKE_CASE : bool = True , SCREAMING_SNAKE_CASE : Dict[str, int] = None , SCREAMING_SNAKE_CASE : bool = True , SCREAMING_SNAKE_CASE : Union[int, float] = 1 / 255 , SCREAMING_SNAKE_CASE : bool = True , SCREAMING_SNAKE_CASE : Optional[Union[float, List[float]]] = None , SCREAMING_SNAKE_CASE : Optional[Union[float, List[float]]] = None , SCREAMING_SNAKE_CASE : bool = True , **SCREAMING_SNAKE_CASE : Union[str, Any] , ):
"""simple docstring"""
super().__init__(**SCREAMING_SNAKE_CASE )
SCREAMING_SNAKE_CASE_ :Tuple = size if size is not None else {'shortest_edge': 224}
SCREAMING_SNAKE_CASE_ :List[Any] = get_size_dict(SCREAMING_SNAKE_CASE , default_to_square=SCREAMING_SNAKE_CASE )
SCREAMING_SNAKE_CASE_ :Optional[Any] = crop_size if crop_size is not None else {'height': 224, 'width': 224}
SCREAMING_SNAKE_CASE_ :List[str] = get_size_dict(SCREAMING_SNAKE_CASE , default_to_square=SCREAMING_SNAKE_CASE , param_name='crop_size' )
SCREAMING_SNAKE_CASE_ :Dict = do_resize
SCREAMING_SNAKE_CASE_ :Any = size
SCREAMING_SNAKE_CASE_ :str = resample
SCREAMING_SNAKE_CASE_ :Any = do_center_crop
SCREAMING_SNAKE_CASE_ :int = crop_size
SCREAMING_SNAKE_CASE_ :Union[str, Any] = do_rescale
SCREAMING_SNAKE_CASE_ :Tuple = rescale_factor
SCREAMING_SNAKE_CASE_ :Tuple = do_normalize
SCREAMING_SNAKE_CASE_ :List[Any] = image_mean if image_mean is not None else OPENAI_CLIP_MEAN
SCREAMING_SNAKE_CASE_ :List[str] = image_std if image_std is not None else OPENAI_CLIP_STD
SCREAMING_SNAKE_CASE_ :List[Any] = do_convert_rgb
def _lowercase ( self : Union[str, Any] , SCREAMING_SNAKE_CASE : np.ndarray , SCREAMING_SNAKE_CASE : Dict[str, int] , SCREAMING_SNAKE_CASE : PILImageResampling = PILImageResampling.BICUBIC , SCREAMING_SNAKE_CASE : Optional[Union[str, ChannelDimension]] = None , **SCREAMING_SNAKE_CASE : Tuple , ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ :Union[str, Any] = get_size_dict(SCREAMING_SNAKE_CASE , default_to_square=SCREAMING_SNAKE_CASE )
if "shortest_edge" not in size:
raise ValueError(f'The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}' )
SCREAMING_SNAKE_CASE_ :Optional[Any] = get_resize_output_image_size(SCREAMING_SNAKE_CASE , size=size['shortest_edge'] , default_to_square=SCREAMING_SNAKE_CASE )
return resize(SCREAMING_SNAKE_CASE , size=SCREAMING_SNAKE_CASE , resample=SCREAMING_SNAKE_CASE , data_format=SCREAMING_SNAKE_CASE , **SCREAMING_SNAKE_CASE )
def _lowercase ( self : Optional[int] , SCREAMING_SNAKE_CASE : np.ndarray , SCREAMING_SNAKE_CASE : Dict[str, int] , SCREAMING_SNAKE_CASE : Optional[Union[str, ChannelDimension]] = None , **SCREAMING_SNAKE_CASE : Union[str, Any] , ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ :int = get_size_dict(SCREAMING_SNAKE_CASE )
if "height" not in size or "width" not in size:
raise ValueError(f'The `size` parameter must contain the keys (height, width). Got {size.keys()}' )
return center_crop(SCREAMING_SNAKE_CASE , size=(size['height'], size['width']) , data_format=SCREAMING_SNAKE_CASE , **SCREAMING_SNAKE_CASE )
def _lowercase ( self : str , SCREAMING_SNAKE_CASE : np.ndarray , SCREAMING_SNAKE_CASE : Union[int, float] , SCREAMING_SNAKE_CASE : Optional[Union[str, ChannelDimension]] = None , **SCREAMING_SNAKE_CASE : Optional[int] , ):
"""simple docstring"""
return rescale(SCREAMING_SNAKE_CASE , scale=SCREAMING_SNAKE_CASE , data_format=SCREAMING_SNAKE_CASE , **SCREAMING_SNAKE_CASE )
def _lowercase ( self : List[Any] , SCREAMING_SNAKE_CASE : np.ndarray , SCREAMING_SNAKE_CASE : Union[float, List[float]] , SCREAMING_SNAKE_CASE : Union[float, List[float]] , SCREAMING_SNAKE_CASE : Optional[Union[str, ChannelDimension]] = None , **SCREAMING_SNAKE_CASE : Union[str, Any] , ):
"""simple docstring"""
return normalize(SCREAMING_SNAKE_CASE , mean=SCREAMING_SNAKE_CASE , std=SCREAMING_SNAKE_CASE , data_format=SCREAMING_SNAKE_CASE , **SCREAMING_SNAKE_CASE )
def _lowercase ( self : Optional[int] , SCREAMING_SNAKE_CASE : ImageInput , SCREAMING_SNAKE_CASE : bool = None , SCREAMING_SNAKE_CASE : Dict[str, int] = None , SCREAMING_SNAKE_CASE : PILImageResampling = None , SCREAMING_SNAKE_CASE : bool = None , SCREAMING_SNAKE_CASE : int = None , SCREAMING_SNAKE_CASE : bool = None , SCREAMING_SNAKE_CASE : float = None , SCREAMING_SNAKE_CASE : bool = None , SCREAMING_SNAKE_CASE : Optional[Union[float, List[float]]] = None , SCREAMING_SNAKE_CASE : Optional[Union[float, List[float]]] = None , SCREAMING_SNAKE_CASE : bool = None , SCREAMING_SNAKE_CASE : Optional[Union[str, TensorType]] = None , SCREAMING_SNAKE_CASE : Optional[ChannelDimension] = ChannelDimension.FIRST , **SCREAMING_SNAKE_CASE : List[Any] , ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ :List[Any] = do_resize if do_resize is not None else self.do_resize
SCREAMING_SNAKE_CASE_ :Optional[int] = size if size is not None else self.size
SCREAMING_SNAKE_CASE_ :List[Any] = get_size_dict(SCREAMING_SNAKE_CASE , param_name='size' , default_to_square=SCREAMING_SNAKE_CASE )
SCREAMING_SNAKE_CASE_ :Tuple = 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_ :Any = crop_size if crop_size is not None else self.crop_size
SCREAMING_SNAKE_CASE_ :Dict = get_size_dict(SCREAMING_SNAKE_CASE , param_name='crop_size' , default_to_square=SCREAMING_SNAKE_CASE )
SCREAMING_SNAKE_CASE_ :Tuple = do_rescale if do_rescale is not None else self.do_rescale
SCREAMING_SNAKE_CASE_ :List[str] = rescale_factor if rescale_factor is not None else self.rescale_factor
SCREAMING_SNAKE_CASE_ :Any = do_normalize if do_normalize is not None else self.do_normalize
SCREAMING_SNAKE_CASE_ :str = image_mean if image_mean is not None else self.image_mean
SCREAMING_SNAKE_CASE_ :Optional[Any] = image_std if image_std is not None else self.image_std
SCREAMING_SNAKE_CASE_ :Optional[Any] = do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb
SCREAMING_SNAKE_CASE_ :Dict = make_list_of_images(SCREAMING_SNAKE_CASE )
if not valid_images(SCREAMING_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.' )
# PIL RGBA images are converted to RGB
if do_convert_rgb:
SCREAMING_SNAKE_CASE_ :str = [convert_to_rgb(SCREAMING_SNAKE_CASE ) for image in images]
# All transformations expect numpy arrays.
SCREAMING_SNAKE_CASE_ :str = [to_numpy_array(SCREAMING_SNAKE_CASE ) for image in images]
if do_resize:
SCREAMING_SNAKE_CASE_ :Optional[Any] = [self.resize(image=SCREAMING_SNAKE_CASE , size=SCREAMING_SNAKE_CASE , resample=SCREAMING_SNAKE_CASE ) for image in images]
if do_center_crop:
SCREAMING_SNAKE_CASE_ :Tuple = [self.center_crop(image=SCREAMING_SNAKE_CASE , size=SCREAMING_SNAKE_CASE ) for image in images]
if do_rescale:
SCREAMING_SNAKE_CASE_ :List[str] = [self.rescale(image=SCREAMING_SNAKE_CASE , scale=SCREAMING_SNAKE_CASE ) for image in images]
if do_normalize:
SCREAMING_SNAKE_CASE_ :List[Any] = [self.normalize(image=SCREAMING_SNAKE_CASE , mean=SCREAMING_SNAKE_CASE , std=SCREAMING_SNAKE_CASE ) for image in images]
SCREAMING_SNAKE_CASE_ :str = [to_channel_dimension_format(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ) for image in images]
SCREAMING_SNAKE_CASE_ :Union[str, Any] = {'pixel_values': images}
return BatchFeature(data=SCREAMING_SNAKE_CASE , tensor_type=SCREAMING_SNAKE_CASE )
| 720
|
'''simple docstring'''
from __future__ import annotations
def SCREAMING_SNAKE_CASE__ ( SCREAMING_SNAKE_CASE ): # This function is recursive
SCREAMING_SNAKE_CASE_ :Union[str, Any] = len(SCREAMING_SNAKE_CASE )
# If the array contains only one element, we return it (it's the stop condition of
# recursion)
if array_length <= 1:
return array
# Else
SCREAMING_SNAKE_CASE_ :Optional[int] = array[0]
SCREAMING_SNAKE_CASE_ :Union[str, Any] = False
SCREAMING_SNAKE_CASE_ :List[Any] = 1
SCREAMING_SNAKE_CASE_ :list[int] = []
while not is_found and i < array_length:
if array[i] < pivot:
SCREAMING_SNAKE_CASE_ :List[Any] = True
SCREAMING_SNAKE_CASE_ :int = [element for element in array[i:] if element >= array[i]]
SCREAMING_SNAKE_CASE_ :Optional[Any] = longest_subsequence(SCREAMING_SNAKE_CASE )
if len(SCREAMING_SNAKE_CASE ) > len(SCREAMING_SNAKE_CASE ):
SCREAMING_SNAKE_CASE_ :int = temp_array
else:
i += 1
SCREAMING_SNAKE_CASE_ :List[Any] = [element for element in array[1:] if element >= pivot]
SCREAMING_SNAKE_CASE_ :Optional[Any] = [pivot, *longest_subsequence(SCREAMING_SNAKE_CASE )]
if len(SCREAMING_SNAKE_CASE ) > len(SCREAMING_SNAKE_CASE ):
return temp_array
else:
return longest_subseq
if __name__ == "__main__":
import doctest
doctest.testmod()
| 233
| 0
|
"""simple docstring"""
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available
if is_vision_available():
from PIL import Image
from transformers import AutoProcessor, BertTokenizer, BlipImageProcessor, BlipProcessor, PreTrainedTokenizerFast
@require_vision
class __UpperCamelCase ( unittest.TestCase ):
def __a ( self ) -> Any:
a : Optional[Any] = tempfile.mkdtemp()
a : str = BlipImageProcessor()
a : List[str] = BertTokenizer.from_pretrained("hf-internal-testing/tiny-random-BertModel" )
a : Dict = BlipProcessor(lowerCAmelCase__ , lowerCAmelCase__ )
processor.save_pretrained(self.tmpdirname )
def __a ( self , **lowerCAmelCase__ ) -> Tuple:
return AutoProcessor.from_pretrained(self.tmpdirname , **lowerCAmelCase__ ).tokenizer
def __a ( self , **lowerCAmelCase__ ) -> Union[str, Any]:
return AutoProcessor.from_pretrained(self.tmpdirname , **lowerCAmelCase__ ).image_processor
def __a ( self ) -> Optional[Any]:
shutil.rmtree(self.tmpdirname )
def __a ( self ) -> Any:
a : Dict = [np.random.randint(255 , size=(3, 30, 400) , dtype=np.uinta )]
a : Tuple = [Image.fromarray(np.moveaxis(lowerCAmelCase__ , 0 , -1 ) ) for x in image_inputs]
return image_inputs
def __a ( self ) -> Dict:
a : str = BlipProcessor(tokenizer=self.get_tokenizer() , image_processor=self.get_image_processor() )
processor.save_pretrained(self.tmpdirname )
a : Tuple = self.get_tokenizer(bos_token="(BOS)" , eos_token="(EOS)" )
a : List[Any] = self.get_image_processor(do_normalize=lowerCAmelCase__ , padding_value=1.0 )
a : List[str] = BlipProcessor.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 __a ( self ) -> Dict:
a : List[Any] = self.get_image_processor()
a : List[Any] = self.get_tokenizer()
a : Tuple = BlipProcessor(tokenizer=lowerCAmelCase__ , image_processor=lowerCAmelCase__ )
a : Union[str, Any] = self.prepare_image_inputs()
a : int = image_processor(lowerCAmelCase__ , return_tensors="np" )
a : List[str] = processor(images=lowerCAmelCase__ , return_tensors="np" )
for key in input_feat_extract.keys():
self.assertAlmostEqual(input_feat_extract[key].sum() , input_processor[key].sum() , delta=1E-2 )
def __a ( self ) -> Tuple:
a : Any = self.get_image_processor()
a : List[Any] = self.get_tokenizer()
a : Union[str, Any] = BlipProcessor(tokenizer=lowerCAmelCase__ , image_processor=lowerCAmelCase__ )
a : Optional[Any] = "lower newer"
a : int = processor(text=lowerCAmelCase__ )
a : Dict = tokenizer(lowerCAmelCase__ , return_token_type_ids=lowerCAmelCase__ )
for key in encoded_tok.keys():
self.assertListEqual(encoded_tok[key] , encoded_processor[key] )
def __a ( self ) -> List[str]:
a : Optional[int] = self.get_image_processor()
a : Union[str, Any] = self.get_tokenizer()
a : Optional[Any] = BlipProcessor(tokenizer=lowerCAmelCase__ , image_processor=lowerCAmelCase__ )
a : Optional[Any] = "lower newer"
a : Any = self.prepare_image_inputs()
a : Dict = processor(text=lowerCAmelCase__ , images=lowerCAmelCase__ )
self.assertListEqual(list(inputs.keys() ) , ["pixel_values", "input_ids", "attention_mask"] )
# test if it raises when no input is passed
with pytest.raises(lowerCAmelCase__ ):
processor()
def __a ( self ) -> List[str]:
a : List[Any] = self.get_image_processor()
a : Any = self.get_tokenizer()
a : List[str] = BlipProcessor(tokenizer=lowerCAmelCase__ , image_processor=lowerCAmelCase__ )
a : Tuple = [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]
a : List[Any] = processor.batch_decode(lowerCAmelCase__ )
a : List[Any] = tokenizer.batch_decode(lowerCAmelCase__ )
self.assertListEqual(lowerCAmelCase__ , lowerCAmelCase__ )
def __a ( self ) -> Tuple:
a : int = self.get_image_processor()
a : Tuple = self.get_tokenizer()
a : List[str] = BlipProcessor(tokenizer=lowerCAmelCase__ , image_processor=lowerCAmelCase__ )
a : List[Any] = "lower newer"
a : int = self.prepare_image_inputs()
a : int = processor(text=lowerCAmelCase__ , images=lowerCAmelCase__ )
# For now the processor supports only ['pixel_values', 'input_ids', 'attention_mask']
self.assertListEqual(list(inputs.keys() ) , ["pixel_values", "input_ids", "attention_mask"] )
| 633
|
"""simple docstring"""
from __future__ import annotations
import matplotlib.pyplot as plt # type: ignore
import numpy
# initial triangle of Koch snowflake
a : int = numpy.array([0, 0])
a : Optional[Any] = numpy.array([0.5, 0.866_0254])
a : Tuple = numpy.array([1, 0])
a : List[str] = [VECTOR_1, VECTOR_2, VECTOR_3, VECTOR_1]
def _SCREAMING_SNAKE_CASE ( _lowercase : list[numpy.ndarray] , _lowercase : int ) ->list[numpy.ndarray]:
'''simple docstring'''
a : List[str] = initial_vectors
for _ in range(_lowercase ):
a : Optional[Any] = iteration_step(_lowercase )
return vectors
def _SCREAMING_SNAKE_CASE ( _lowercase : list[numpy.ndarray] ) ->list[numpy.ndarray]:
'''simple docstring'''
a : Union[str, Any] = []
for i, start_vector in enumerate(vectors[:-1] ):
a : str = vectors[i + 1]
new_vectors.append(_lowercase )
a : Optional[int] = end_vector - start_vector
new_vectors.append(start_vector + difference_vector / 3 )
new_vectors.append(
start_vector + difference_vector / 3 + rotate(difference_vector / 3 , 60 ) )
new_vectors.append(start_vector + difference_vector * 2 / 3 )
new_vectors.append(vectors[-1] )
return new_vectors
def _SCREAMING_SNAKE_CASE ( _lowercase : numpy.ndarray , _lowercase : float ) ->numpy.ndarray:
'''simple docstring'''
a : int = numpy.radians(_lowercase )
a, a : Optional[int] = numpy.cos(_lowercase ), numpy.sin(_lowercase )
a : Dict = numpy.array(((c, -s), (s, c)) )
return numpy.dot(_lowercase , _lowercase )
def _SCREAMING_SNAKE_CASE ( _lowercase : list[numpy.ndarray] ) ->None:
'''simple docstring'''
a : Dict = plt.gca()
axes.set_aspect("equal" )
# matplotlib.pyplot.plot takes a list of all x-coordinates and a list of all
# y-coordinates as inputs, which are constructed from the vector-list using
# zip()
a, a : Any = zip(*_lowercase )
plt.plot(_lowercase , _lowercase )
plt.show()
if __name__ == "__main__":
import doctest
doctest.testmod()
a : Optional[int] = iterate(INITIAL_VECTORS, 5)
plot(processed_vectors)
| 633
| 1
|
'''simple docstring'''
def _SCREAMING_SNAKE_CASE( snake_case_ : int ) ->list:
'''simple docstring'''
_lowercase : str = int(lowercase_ )
if n_element < 1:
_lowercase : Optional[int] = ValueError('''a should be a positive number''' )
raise my_error
_lowercase : int = [1]
_lowercase : str = (0, 0, 0)
_lowercase : Union[str, Any] = 1
while index < n_element:
while hamming_list[i] * 2 <= hamming_list[-1]:
i += 1
while hamming_list[j] * 3 <= hamming_list[-1]:
j += 1
while hamming_list[k] * 5 <= hamming_list[-1]:
k += 1
hamming_list.append(
min(hamming_list[i] * 2 , hamming_list[j] * 3 , hamming_list[k] * 5 ) )
index += 1
return hamming_list
if __name__ == "__main__":
lowerCamelCase__ = input('Enter the last number (nth term) of the Hamming Number Series: ')
print('Formula of Hamming Number Series => 2^i * 3^j * 5^k')
lowerCamelCase__ = hamming(int(n))
print('-----------------------------------------------------')
print(f'''The list with nth numbers is: {hamming_numbers}''')
print('-----------------------------------------------------')
| 720
|
'''simple docstring'''
import unittest
from transformers import DonutProcessor
lowerCamelCase__ = 'naver-clova-ix/donut-base'
class _lowerCAmelCase ( unittest.TestCase ):
'''simple docstring'''
def __lowercase ( self : Tuple ) -> Optional[Any]:
'''simple docstring'''
_lowercase : Optional[Any] = DonutProcessor.from_pretrained(UpperCamelCase_ )
def __lowercase ( self : Tuple ) -> Tuple:
'''simple docstring'''
_lowercase : str = {
'''name''': '''John Doe''',
'''age''': '''99''',
'''city''': '''Atlanta''',
'''state''': '''GA''',
'''zip''': '''30301''',
'''phone''': '''123-4567''',
'''nicknames''': [{'''nickname''': '''Johnny'''}, {'''nickname''': '''JD'''}],
}
_lowercase : List[str] = (
'''<s_name>John Doe</s_name><s_age>99</s_age><s_city>Atlanta</s_city>'''
'''<s_state>GA</s_state><s_zip>30301</s_zip><s_phone>123-4567</s_phone>'''
'''<s_nicknames><s_nickname>Johnny</s_nickname>'''
'''<sep/><s_nickname>JD</s_nickname></s_nicknames>'''
)
_lowercase : str = self.processor.tokenajson(UpperCamelCase_ )
self.assertDictEqual(UpperCamelCase_ , UpperCamelCase_ )
| 411
| 0
|
def lowercase__ ( _UpperCamelCase = 10_00) -> int:
"""simple docstring"""
return sum(2 * a * ((a - 1) // 2) for a in range(3 , n + 1))
if __name__ == "__main__":
print(solution())
| 280
|
def lowercase__ ( _UpperCamelCase) -> list:
"""simple docstring"""
if bit_count < 0:
raise ValueError('The given input must be positive')
# get the generated string sequence
UpperCamelCase = gray_code_sequence_string(_UpperCamelCase)
#
# convert them to integers
for i in range(len(_UpperCamelCase)):
UpperCamelCase = int(sequence[i] , 2)
return sequence
def lowercase__ ( _UpperCamelCase) -> list:
"""simple docstring"""
if bit_count == 0:
return ["0"]
if bit_count == 1:
return ["0", "1"]
UpperCamelCase = 1 << bit_count # defines the length of the sequence
# 1<< n is equivalent to 2^n
# recursive answer will generate answer for n-1 bits
UpperCamelCase = gray_code_sequence_string(bit_count - 1)
UpperCamelCase = []
# append 0 to first half of the smaller sequence generated
for i in range(seq_len // 2):
UpperCamelCase = '0' + smaller_sequence[i]
sequence.append(_UpperCamelCase)
# append 1 to second half ... start from the end of the list
for i in reversed(range(seq_len // 2)):
UpperCamelCase = '1' + smaller_sequence[i]
sequence.append(_UpperCamelCase)
return sequence
if __name__ == "__main__":
import doctest
doctest.testmod()
| 280
| 1
|
'''simple docstring'''
from jiwer import compute_measures
import datasets
A_ : Optional[Any] ='''\
@inproceedings{inproceedings,
author = {Morris, Andrew and Maier, Viktoria and Green, Phil},
year = {2004},
month = {01},
pages = {},
title = {From WER and RIL to MER and WIL: improved evaluation measures for connected speech recognition.}
}
'''
A_ : Optional[Any] ='''\
Word error rate (WER) is a common metric of the performance of an automatic speech recognition system.
The general difficulty of measuring performance lies in the fact that the recognized word sequence can have a different length from the reference word sequence (supposedly the correct one). The WER is derived from the Levenshtein distance, working at the word level instead of the phoneme level. The WER is a valuable tool for comparing different systems as well as for evaluating improvements within one system. This kind of measurement, however, provides no details on the nature of translation errors and further work is therefore required to identify the main source(s) of error and to focus any research effort.
This problem is solved by first aligning the recognized word sequence with the reference (spoken) word sequence using dynamic string alignment. Examination of this issue is seen through a theory called the power law that states the correlation between perplexity and word error rate.
Word error rate can then be computed as:
WER = (S + D + I) / N = (S + D + I) / (S + D + C)
where
S is the number of substitutions,
D is the number of deletions,
I is the number of insertions,
C is the number of correct words,
N is the number of words in the reference (N=S+D+C).
This value indicates the average number of errors per reference word. The lower the value, the better the
performance of the ASR system with a WER of 0 being a perfect score.
'''
A_ : int ='''
Compute WER score of transcribed segments against references.
Args:
references: List of references for each speech input.
predictions: List of transcriptions to score.
concatenate_texts (bool, default=False): Whether to concatenate all input texts or compute WER iteratively.
Returns:
(float): the word error rate
Examples:
>>> predictions = ["this is the prediction", "there is an other sample"]
>>> references = ["this is the reference", "there is another one"]
>>> wer = datasets.load_metric("wer")
>>> wer_score = wer.compute(predictions=predictions, references=references)
>>> print(wer_score)
0.5
'''
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class __UpperCAmelCase ( datasets.Metric ):
def UpperCAmelCase_ ( self ):
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
'''predictions''': datasets.Value('''string''' , id='''sequence''' ),
'''references''': datasets.Value('''string''' , id='''sequence''' ),
} ) , codebase_urls=['''https://github.com/jitsi/jiwer/'''] , reference_urls=[
'''https://en.wikipedia.org/wiki/Word_error_rate''',
] , )
def UpperCAmelCase_ ( self , _lowerCamelCase=None , _lowerCamelCase=None , _lowerCamelCase=False ):
if concatenate_texts:
return compute_measures(_lowerCamelCase , _lowerCamelCase )["wer"]
else:
lowerCAmelCase_ = 0
lowerCAmelCase_ = 0
for prediction, reference in zip(_lowerCamelCase , _lowerCamelCase ):
lowerCAmelCase_ = compute_measures(_lowerCamelCase , _lowerCamelCase )
incorrect += measures["substitutions"] + measures["deletions"] + measures["insertions"]
total += measures["substitutions"] + measures["deletions"] + measures["hits"]
return incorrect / total
| 606
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'''simple docstring'''
def snake_case_ ( __snake_case : int = 1000) -> int:
lowerCAmelCase_ = 3
lowerCAmelCase_ = 0
while a < n:
if a % 3 == 0 or a % 5 == 0:
result += a
elif a % 15 == 0:
result -= a
a += 1
return result
if __name__ == "__main__":
print(f'''{solution() = }''')
| 606
| 1
|
'''simple docstring'''
def A_ ( snake_case , snake_case ):
# "extended trapezoidal rule"
# int(f) = dx/2 * (f1 + 2f2 + ... + fn)
SCREAMING_SNAKE_CASE:List[Any] = (boundary[1] - boundary[0]) / steps
SCREAMING_SNAKE_CASE:Dict = boundary[0]
SCREAMING_SNAKE_CASE:Any = boundary[1]
SCREAMING_SNAKE_CASE:int = make_points(__UpperCamelCase , __UpperCamelCase , __UpperCamelCase )
SCREAMING_SNAKE_CASE:int = 0.0
y += (h / 2.0) * f(__UpperCamelCase )
for i in x_i:
# print(i)
y += h * f(__UpperCamelCase )
y += (h / 2.0) * f(__UpperCamelCase )
return y
def A_ ( snake_case , snake_case , snake_case ):
SCREAMING_SNAKE_CASE:Optional[int] = a + h
while x < (b - h):
yield x
SCREAMING_SNAKE_CASE:Optional[Any] = x + h
def A_ ( snake_case ): # enter your function here
SCREAMING_SNAKE_CASE:str = (x - 0) * (x - 0)
return y
def A_ ( ):
SCREAMING_SNAKE_CASE:List[str] = 0.0 # Lower bound of integration
SCREAMING_SNAKE_CASE:Union[str, Any] = 1.0 # Upper bound of integration
SCREAMING_SNAKE_CASE:str = 10.0 # define number of steps or resolution
SCREAMING_SNAKE_CASE:Tuple = [a, b] # define boundary of integration
SCREAMING_SNAKE_CASE:List[str] = method_a(__UpperCamelCase , __UpperCamelCase )
print(F'''y = {y}''' )
if __name__ == "__main__":
main()
| 143
|
from collections import OrderedDict
from typing import List, Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
SCREAMING_SNAKE_CASE : List[str] = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE : List[Any] = {
"google/efficientnet-b7": "https://huggingface.co/google/efficientnet-b7/resolve/main/config.json",
}
class __lowercase ( A ):
__magic_name__ : List[Any] = '''efficientnet'''
def __init__( self , a__ = 3 , a__ = 6_0_0 , a__ = 2.0 , a__ = 3.1 , a__ = 8 , a__ = [3, 3, 5, 3, 5, 5, 3] , a__ = [3_2, 1_6, 2_4, 4_0, 8_0, 1_1_2, 1_9_2] , a__ = [1_6, 2_4, 4_0, 8_0, 1_1_2, 1_9_2, 3_2_0] , a__ = [] , a__ = [1, 2, 2, 2, 1, 2, 1] , a__ = [1, 2, 2, 3, 3, 4, 1] , a__ = [1, 6, 6, 6, 6, 6, 6] , a__ = 0.25 , a__ = "swish" , a__ = 2_5_6_0 , a__ = "mean" , a__ = 0.02 , a__ = 0.0_01 , a__ = 0.99 , a__ = 0.5 , a__ = 0.2 , **a__ , ) -> Optional[Any]:
'''simple docstring'''
super().__init__(**a__ )
A_ = num_channels
A_ = image_size
A_ = width_coefficient
A_ = depth_coefficient
A_ = depth_divisor
A_ = kernel_sizes
A_ = in_channels
A_ = out_channels
A_ = depthwise_padding
A_ = strides
A_ = num_block_repeats
A_ = expand_ratios
A_ = squeeze_expansion_ratio
A_ = hidden_act
A_ = hidden_dim
A_ = pooling_type
A_ = initializer_range
A_ = batch_norm_eps
A_ = batch_norm_momentum
A_ = dropout_rate
A_ = drop_connect_rate
A_ = sum(a__ ) * 4
class __lowercase ( A ):
__magic_name__ : Any = version.parse('''1.11''' )
@property
def lowerCAmelCase_ ( self ) -> Mapping[str, Mapping[int, str]]:
'''simple docstring'''
return OrderedDict(
[
('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
] )
@property
def lowerCAmelCase_ ( self ) -> float:
'''simple docstring'''
return 1E-5
| 141
| 0
|
import tempfile
import torch
from diffusers import PNDMScheduler
from .test_schedulers import SchedulerCommonTest
class SCREAMING_SNAKE_CASE__ ( UpperCamelCase_ ):
"""simple docstring"""
a_ = (PNDMScheduler,)
a_ = (("num_inference_steps", 5_0),)
def _lowercase ( self : Dict , **__A : Tuple ):
snake_case__ : List[str] = {
"num_train_timesteps": 1_0_0_0,
"beta_start": 0.0_0_0_1,
"beta_end": 0.0_2,
"beta_schedule": "linear",
}
config.update(**__A )
return config
def _lowercase ( self : Optional[Any] , __A : Tuple=0 , **__A : Dict ):
snake_case__ : Optional[int] = dict(self.forward_default_kwargs )
snake_case__ : List[Any] = kwargs.pop("num_inference_steps" , __A )
snake_case__ : Optional[int] = self.dummy_sample
snake_case__ : List[Any] = 0.1 * sample
snake_case__ : Dict = [residual + 0.2, residual + 0.1_5, residual + 0.1, residual + 0.0_5]
for scheduler_class in self.scheduler_classes:
snake_case__ : str = self.get_scheduler_config(**__A )
snake_case__ : int = scheduler_class(**__A )
scheduler.set_timesteps(__A )
# copy over dummy past residuals
snake_case__ : Dict = dummy_past_residuals[:]
with tempfile.TemporaryDirectory() as tmpdirname:
scheduler.save_config(__A )
snake_case__ : Tuple = scheduler_class.from_pretrained(__A )
new_scheduler.set_timesteps(__A )
# copy over dummy past residuals
snake_case__ : List[str] = dummy_past_residuals[:]
snake_case__ : int = scheduler.step_prk(__A , __A , __A , **__A ).prev_sample
snake_case__ : List[Any] = new_scheduler.step_prk(__A , __A , __A , **__A ).prev_sample
assert torch.sum(torch.abs(output - new_output ) ) < 1e-5, "Scheduler outputs are not identical"
snake_case__ : Tuple = scheduler.step_plms(__A , __A , __A , **__A ).prev_sample
snake_case__ : int = new_scheduler.step_plms(__A , __A , __A , **__A ).prev_sample
assert torch.sum(torch.abs(output - new_output ) ) < 1e-5, "Scheduler outputs are not identical"
def _lowercase ( self : Union[str, Any] ):
pass
def _lowercase ( self : Any , __A : Dict=0 , **__A : Union[str, Any] ):
snake_case__ : Optional[Any] = dict(self.forward_default_kwargs )
snake_case__ : Optional[int] = kwargs.pop("num_inference_steps" , __A )
snake_case__ : List[Any] = self.dummy_sample
snake_case__ : List[Any] = 0.1 * sample
snake_case__ : List[str] = [residual + 0.2, residual + 0.1_5, residual + 0.1, residual + 0.0_5]
for scheduler_class in self.scheduler_classes:
snake_case__ : List[str] = self.get_scheduler_config()
snake_case__ : Union[str, Any] = scheduler_class(**__A )
scheduler.set_timesteps(__A )
# copy over dummy past residuals (must be after setting timesteps)
snake_case__ : Tuple = dummy_past_residuals[:]
with tempfile.TemporaryDirectory() as tmpdirname:
scheduler.save_config(__A )
snake_case__ : Tuple = scheduler_class.from_pretrained(__A )
# copy over dummy past residuals
new_scheduler.set_timesteps(__A )
# copy over dummy past residual (must be after setting timesteps)
snake_case__ : Optional[Any] = dummy_past_residuals[:]
snake_case__ : List[str] = scheduler.step_prk(__A , __A , __A , **__A ).prev_sample
snake_case__ : List[str] = new_scheduler.step_prk(__A , __A , __A , **__A ).prev_sample
assert torch.sum(torch.abs(output - new_output ) ) < 1e-5, "Scheduler outputs are not identical"
snake_case__ : Tuple = scheduler.step_plms(__A , __A , __A , **__A ).prev_sample
snake_case__ : Union[str, Any] = new_scheduler.step_plms(__A , __A , __A , **__A ).prev_sample
assert torch.sum(torch.abs(output - new_output ) ) < 1e-5, "Scheduler outputs are not identical"
def _lowercase ( self : List[str] , **__A : int ):
snake_case__ : Any = self.scheduler_classes[0]
snake_case__ : List[str] = self.get_scheduler_config(**__A )
snake_case__ : List[Any] = scheduler_class(**__A )
snake_case__ : Optional[int] = 1_0
snake_case__ : Dict = self.dummy_model()
snake_case__ : Any = self.dummy_sample_deter
scheduler.set_timesteps(__A )
for i, t in enumerate(scheduler.prk_timesteps ):
snake_case__ : List[str] = model(__A , __A )
snake_case__ : Union[str, Any] = scheduler.step_prk(__A , __A , __A ).prev_sample
for i, t in enumerate(scheduler.plms_timesteps ):
snake_case__ : List[Any] = model(__A , __A )
snake_case__ : List[Any] = scheduler.step_plms(__A , __A , __A ).prev_sample
return sample
def _lowercase ( self : Any ):
snake_case__ : Tuple = dict(self.forward_default_kwargs )
snake_case__ : List[Any] = kwargs.pop("num_inference_steps" , __A )
for scheduler_class in self.scheduler_classes:
snake_case__ : int = self.get_scheduler_config()
snake_case__ : Any = scheduler_class(**__A )
snake_case__ : Optional[Any] = self.dummy_sample
snake_case__ : Optional[int] = 0.1 * sample
if num_inference_steps is not None and hasattr(__A , "set_timesteps" ):
scheduler.set_timesteps(__A )
elif num_inference_steps is not None and not hasattr(__A , "set_timesteps" ):
snake_case__ : Tuple = num_inference_steps
# copy over dummy past residuals (must be done after set_timesteps)
snake_case__ : Optional[int] = [residual + 0.2, residual + 0.1_5, residual + 0.1, residual + 0.0_5]
snake_case__ : Optional[Any] = dummy_past_residuals[:]
snake_case__ : Any = scheduler.step_prk(__A , 0 , __A , **__A ).prev_sample
snake_case__ : Dict = scheduler.step_prk(__A , 1 , __A , **__A ).prev_sample
self.assertEqual(output_a.shape , sample.shape )
self.assertEqual(output_a.shape , output_a.shape )
snake_case__ : Dict = scheduler.step_plms(__A , 0 , __A , **__A ).prev_sample
snake_case__ : Optional[Any] = scheduler.step_plms(__A , 1 , __A , **__A ).prev_sample
self.assertEqual(output_a.shape , sample.shape )
self.assertEqual(output_a.shape , output_a.shape )
def _lowercase ( self : Optional[Any] ):
for timesteps in [1_0_0, 1_0_0_0]:
self.check_over_configs(num_train_timesteps=__A )
def _lowercase ( self : Any ):
for steps_offset in [0, 1]:
self.check_over_configs(steps_offset=__A )
snake_case__ : Optional[Any] = self.scheduler_classes[0]
snake_case__ : Optional[int] = self.get_scheduler_config(steps_offset=1 )
snake_case__ : str = scheduler_class(**__A )
scheduler.set_timesteps(1_0 )
assert torch.equal(
scheduler.timesteps , torch.LongTensor(
[9_0_1, 8_5_1, 8_5_1, 8_0_1, 8_0_1, 7_5_1, 7_5_1, 7_0_1, 7_0_1, 6_5_1, 6_5_1, 6_0_1, 6_0_1, 5_0_1, 4_0_1, 3_0_1, 2_0_1, 1_0_1, 1] ) , )
def _lowercase ( self : Tuple ):
for beta_start, beta_end in zip([0.0_0_0_1, 0.0_0_1] , [0.0_0_2, 0.0_2] ):
self.check_over_configs(beta_start=__A , beta_end=__A )
def _lowercase ( self : Union[str, Any] ):
for schedule in ["linear", "squaredcos_cap_v2"]:
self.check_over_configs(beta_schedule=__A )
def _lowercase ( self : Any ):
for prediction_type in ["epsilon", "v_prediction"]:
self.check_over_configs(prediction_type=__A )
def _lowercase ( self : int ):
for t in [1, 5, 1_0]:
self.check_over_forward(time_step=__A )
def _lowercase ( self : Dict ):
for t, num_inference_steps in zip([1, 5, 1_0] , [1_0, 5_0, 1_0_0] ):
self.check_over_forward(num_inference_steps=__A )
def _lowercase ( self : List[str] ):
# earlier version of set_timesteps() caused an error indexing alpha's with inference steps as power of 3
snake_case__ : List[Any] = 2_7
for scheduler_class in self.scheduler_classes:
snake_case__ : List[Any] = self.dummy_sample
snake_case__ : Union[str, Any] = 0.1 * sample
snake_case__ : Dict = self.get_scheduler_config()
snake_case__ : Dict = scheduler_class(**__A )
scheduler.set_timesteps(__A )
# before power of 3 fix, would error on first step, so we only need to do two
for i, t in enumerate(scheduler.prk_timesteps[:2] ):
snake_case__ : Optional[int] = scheduler.step_prk(__A , __A , __A ).prev_sample
def _lowercase ( self : Union[str, Any] ):
with self.assertRaises(__A ):
snake_case__ : Any = self.scheduler_classes[0]
snake_case__ : Optional[int] = self.get_scheduler_config()
snake_case__ : List[str] = scheduler_class(**__A )
scheduler.step_plms(self.dummy_sample , 1 , self.dummy_sample ).prev_sample
def _lowercase ( self : Optional[Any] ):
snake_case__ : int = self.full_loop()
snake_case__ : Optional[Any] = torch.sum(torch.abs(__A ) )
snake_case__ : Any = torch.mean(torch.abs(__A ) )
assert abs(result_sum.item() - 1_9_8.1_3_1_8 ) < 1e-2
assert abs(result_mean.item() - 0.2_5_8_0 ) < 1e-3
def _lowercase ( self : Any ):
snake_case__ : Dict = self.full_loop(prediction_type="v_prediction" )
snake_case__ : List[Any] = torch.sum(torch.abs(__A ) )
snake_case__ : Optional[Any] = torch.mean(torch.abs(__A ) )
assert abs(result_sum.item() - 6_7.3_9_8_6 ) < 1e-2
assert abs(result_mean.item() - 0.0_8_7_8 ) < 1e-3
def _lowercase ( self : Dict ):
# We specify different beta, so that the first alpha is 0.99
snake_case__ : List[str] = self.full_loop(set_alpha_to_one=__A , beta_start=0.0_1 )
snake_case__ : Dict = torch.sum(torch.abs(__A ) )
snake_case__ : str = torch.mean(torch.abs(__A ) )
assert abs(result_sum.item() - 2_3_0.0_3_9_9 ) < 1e-2
assert abs(result_mean.item() - 0.2_9_9_5 ) < 1e-3
def _lowercase ( self : Tuple ):
# We specify different beta, so that the first alpha is 0.99
snake_case__ : Union[str, Any] = self.full_loop(set_alpha_to_one=__A , beta_start=0.0_1 )
snake_case__ : int = torch.sum(torch.abs(__A ) )
snake_case__ : Dict = torch.mean(torch.abs(__A ) )
assert abs(result_sum.item() - 1_8_6.9_4_8_2 ) < 1e-2
assert abs(result_mean.item() - 0.2_4_3_4 ) < 1e-3
| 25
|
from __future__ import annotations
def SCREAMING_SNAKE_CASE ( snake_case_ : int ):
snake_case__ : str = [True] * limit
snake_case__ : str = False
snake_case__ : str = False
snake_case__ : str = True
for i in range(3 , int(limit**0.5 + 1 ) , 2 ):
snake_case__ : Optional[Any] = i * 2
while index < limit:
snake_case__ : Union[str, Any] = False
snake_case__ : Any = index + i
snake_case__ : Optional[Any] = [2]
for i in range(3 , snake_case_ , 2 ):
if is_prime[i]:
primes.append(snake_case_ )
return primes
def SCREAMING_SNAKE_CASE ( snake_case_ : int = 1000000 ):
snake_case__ : Optional[int] = prime_sieve(snake_case_ )
snake_case__ : List[Any] = 0
snake_case__ : List[str] = 0
for i in range(len(snake_case_ ) ):
for j in range(i + length , len(snake_case_ ) ):
snake_case__ : Dict = sum(primes[i:j] )
if sol >= ceiling:
break
if sol in primes:
snake_case__ : Tuple = j - i
snake_case__ : str = sol
return largest
if __name__ == "__main__":
print(f"{solution() = }")
| 25
| 1
|
import os
from collections import namedtuple
import pytest
from datasets import ClassLabel, Features, Sequence, Value
from datasets.commands.test import TestCommand
from datasets.info import DatasetInfo, DatasetInfosDict
A : Dict = namedtuple(
'''_TestCommandArgs''',
[
'''dataset''',
'''name''',
'''cache_dir''',
'''data_dir''',
'''all_configs''',
'''save_infos''',
'''ignore_verifications''',
'''force_redownload''',
'''clear_cache''',
],
defaults=[None, None, None, False, False, False, False, False],
)
def UpperCamelCase__ ( SCREAMING_SNAKE_CASE_ : Optional[int] , SCREAMING_SNAKE_CASE_ : List[Any] ) -> Any:
return (abs(source - target ) / target) < 0.01
@pytest.mark.integration
def UpperCamelCase__ ( SCREAMING_SNAKE_CASE_ : Optional[Any] ) -> List[str]:
_lowercase = _TestCommandArgs(dataset=SCREAMING_SNAKE_CASE_ , all_configs=SCREAMING_SNAKE_CASE_ , save_infos=SCREAMING_SNAKE_CASE_ )
_lowercase = TestCommand(*SCREAMING_SNAKE_CASE_ )
test_command.run()
_lowercase = os.path.join(SCREAMING_SNAKE_CASE_ , """README.md""" )
assert os.path.exists(SCREAMING_SNAKE_CASE_ )
_lowercase = DatasetInfosDict.from_directory(SCREAMING_SNAKE_CASE_ )
_lowercase = DatasetInfosDict(
{
"""default""": DatasetInfo(
features=Features(
{
"""tokens""": Sequence(Value("""string""" ) ),
"""ner_tags""": Sequence(
ClassLabel(names=["""O""", """B-PER""", """I-PER""", """B-ORG""", """I-ORG""", """B-LOC""", """I-LOC"""] ) ),
"""langs""": Sequence(Value("""string""" ) ),
"""spans""": Sequence(Value("""string""" ) ),
} ) , splits=[
{
"""name""": """train""",
"""num_bytes""": 2_35_15_63,
"""num_examples""": 1_00_00,
},
{
"""name""": """validation""",
"""num_bytes""": 23_84_18,
"""num_examples""": 10_00,
},
] , download_size=3_94_06_80 , dataset_size=2_58_99_81 , )
} )
assert dataset_infos.keys() == expected_dataset_infos.keys()
for key in DatasetInfo._INCLUDED_INFO_IN_YAML:
_lowercase , _lowercase = getattr(dataset_infos["""default"""] , SCREAMING_SNAKE_CASE_ ), getattr(expected_dataset_infos["""default"""] , SCREAMING_SNAKE_CASE_ )
if key == "num_bytes":
assert is_apercent_close(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
elif key == "splits":
assert list(SCREAMING_SNAKE_CASE_ ) == list(SCREAMING_SNAKE_CASE_ )
for split in result:
assert result[split].name == expected[split].name
assert result[split].num_examples == expected[split].num_examples
assert is_apercent_close(result[split].num_bytes , expected[split].num_bytes )
else:
result == expected
| 287
|
import warnings
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class a_ ( _a ):
a : Optional[Any] = ['''image_processor''', '''tokenizer''']
a : Optional[Any] = '''ChineseCLIPImageProcessor'''
a : List[str] = ('''BertTokenizer''', '''BertTokenizerFast''')
def __init__( self , __UpperCamelCase=None , __UpperCamelCase=None , **__UpperCamelCase ):
_lowercase = None
if "feature_extractor" in kwargs:
warnings.warn(
"""The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"""
""" instead.""" , __UpperCamelCase , )
_lowercase = kwargs.pop("""feature_extractor""" )
_lowercase = image_processor if image_processor is not None else feature_extractor
if image_processor is None:
raise ValueError("""You need to specify an `image_processor`.""" )
if tokenizer is None:
raise ValueError("""You need to specify a `tokenizer`.""" )
super().__init__(__UpperCamelCase , __UpperCamelCase )
_lowercase = self.image_processor
def __call__( self , __UpperCamelCase=None , __UpperCamelCase=None , __UpperCamelCase=None , **__UpperCamelCase ):
if text is None and images is None:
raise ValueError("""You have to specify either text or images. Both cannot be none.""" )
if text is not None:
_lowercase = self.tokenizer(__UpperCamelCase , return_tensors=__UpperCamelCase , **__UpperCamelCase )
if images is not None:
_lowercase = self.image_processor(__UpperCamelCase , return_tensors=__UpperCamelCase , **__UpperCamelCase )
if text is not None and images is not None:
_lowercase = image_features.pixel_values
return encoding
elif text is not None:
return encoding
else:
return BatchEncoding(data=dict(**__UpperCamelCase ) , tensor_type=__UpperCamelCase )
def UpperCamelCase_ ( self , *__UpperCamelCase , **__UpperCamelCase ):
return self.tokenizer.batch_decode(*__UpperCamelCase , **__UpperCamelCase )
def UpperCamelCase_ ( self , *__UpperCamelCase , **__UpperCamelCase ):
return self.tokenizer.decode(*__UpperCamelCase , **__UpperCamelCase )
@property
def UpperCamelCase_ ( self ):
_lowercase = self.tokenizer.model_input_names
_lowercase = self.image_processor.model_input_names
return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names ) )
@property
def UpperCamelCase_ ( self ):
warnings.warn(
"""`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.""" , __UpperCamelCase , )
return self.image_processor_class
| 287
| 1
|
import json
import os
import tempfile
import unittest
import numpy as np
from datasets import load_dataset
from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin
if is_torch_available():
import torch
if is_vision_available():
from PIL import Image
from transformers import ImageGPTImageProcessor
class A ( unittest.TestCase ):
def __init__( self, UpperCamelCase__, UpperCamelCase__=7, UpperCamelCase__=3, UpperCamelCase__=18, UpperCamelCase__=30, UpperCamelCase__=400, UpperCamelCase__=True, UpperCamelCase__=None, UpperCamelCase__=True, ):
"""simple docstring"""
lowerCAmelCase_ = size if size is not None else {'''height''': 18, '''width''': 18}
lowerCAmelCase_ = parent
lowerCAmelCase_ = batch_size
lowerCAmelCase_ = num_channels
lowerCAmelCase_ = image_size
lowerCAmelCase_ = min_resolution
lowerCAmelCase_ = max_resolution
lowerCAmelCase_ = do_resize
lowerCAmelCase_ = size
lowerCAmelCase_ = do_normalize
def SCREAMING_SNAKE_CASE__ ( self ):
"""simple docstring"""
return {
# here we create 2 clusters for the sake of simplicity
"clusters": np.asarray(
[
[0.8_866_443_634_033_203, 0.6_618_829_369_544_983, 0.3_891_746_401_786_804],
[-0.6_042_559_146_881_104, -0.02_295_008_860_528_469, 0.5_423_797_369_003_296],
] ),
"do_resize": self.do_resize,
"size": self.size,
"do_normalize": self.do_normalize,
}
@require_torch
@require_vision
class A ( __UpperCAmelCase , unittest.TestCase ):
__snake_case = ImageGPTImageProcessor if is_vision_available() else None
def SCREAMING_SNAKE_CASE__ ( self ):
"""simple docstring"""
lowerCAmelCase_ = ImageGPTImageProcessingTester(self )
@property
def SCREAMING_SNAKE_CASE__ ( self ):
"""simple docstring"""
return self.image_processor_tester.prepare_image_processor_dict()
def SCREAMING_SNAKE_CASE__ ( self ):
"""simple docstring"""
lowerCAmelCase_ = self.image_processing_class(**self.image_processor_dict )
self.assertTrue(hasattr(UpperCamelCase__, '''clusters''' ) )
self.assertTrue(hasattr(UpperCamelCase__, '''do_resize''' ) )
self.assertTrue(hasattr(UpperCamelCase__, '''size''' ) )
self.assertTrue(hasattr(UpperCamelCase__, '''do_normalize''' ) )
def SCREAMING_SNAKE_CASE__ ( self ):
"""simple docstring"""
lowerCAmelCase_ = self.image_processing_class.from_dict(self.image_processor_dict )
self.assertEqual(image_processor.size, {'''height''': 18, '''width''': 18} )
lowerCAmelCase_ = self.image_processing_class.from_dict(self.image_processor_dict, size=42 )
self.assertEqual(image_processor.size, {'''height''': 42, '''width''': 42} )
def SCREAMING_SNAKE_CASE__ ( self ):
"""simple docstring"""
lowerCAmelCase_ = self.image_processing_class(**self.image_processor_dict )
lowerCAmelCase_ = json.loads(image_processor.to_json_string() )
for key, value in self.image_processor_dict.items():
if key == "clusters":
self.assertTrue(np.array_equal(UpperCamelCase__, obj[key] ) )
else:
self.assertEqual(obj[key], UpperCamelCase__ )
def SCREAMING_SNAKE_CASE__ ( self ):
"""simple docstring"""
lowerCAmelCase_ = self.image_processing_class(**self.image_processor_dict )
with tempfile.TemporaryDirectory() as tmpdirname:
lowerCAmelCase_ = os.path.join(UpperCamelCase__, '''image_processor.json''' )
image_processor_first.to_json_file(UpperCamelCase__ )
lowerCAmelCase_ = self.image_processing_class.from_json_file(UpperCamelCase__ ).to_dict()
lowerCAmelCase_ = image_processor_first.to_dict()
for key, value in image_processor_first.items():
if key == "clusters":
self.assertTrue(np.array_equal(UpperCamelCase__, image_processor_second[key] ) )
else:
self.assertEqual(image_processor_first[key], UpperCamelCase__ )
def SCREAMING_SNAKE_CASE__ ( self ):
"""simple docstring"""
lowerCAmelCase_ = self.image_processing_class(**self.image_processor_dict )
with tempfile.TemporaryDirectory() as tmpdirname:
image_processor_first.save_pretrained(UpperCamelCase__ )
lowerCAmelCase_ = self.image_processing_class.from_pretrained(UpperCamelCase__ ).to_dict()
lowerCAmelCase_ = image_processor_first.to_dict()
for key, value in image_processor_first.items():
if key == "clusters":
self.assertTrue(np.array_equal(UpperCamelCase__, image_processor_second[key] ) )
else:
self.assertEqual(image_processor_first[key], UpperCamelCase__ )
@unittest.skip('''ImageGPT requires clusters at initialization''' )
def SCREAMING_SNAKE_CASE__ ( self ):
"""simple docstring"""
pass
def __UpperCamelCase ( ):
lowerCAmelCase_ = load_dataset('''hf-internal-testing/fixtures_image_utils''' , split='''test''' )
lowerCAmelCase_ = Image.open(dataset[4]['''file'''] )
lowerCAmelCase_ = Image.open(dataset[5]['''file'''] )
lowerCAmelCase_ = [imagea, imagea]
return images
@require_vision
@require_torch
class A ( unittest.TestCase ):
@slow
def SCREAMING_SNAKE_CASE__ ( self ):
"""simple docstring"""
lowerCAmelCase_ = ImageGPTImageProcessor.from_pretrained('''openai/imagegpt-small''' )
lowerCAmelCase_ = prepare_images()
# test non-batched
lowerCAmelCase_ = image_processing(images[0], return_tensors='''pt''' )
self.assertIsInstance(encoding.input_ids, torch.LongTensor )
self.assertEqual(encoding.input_ids.shape, (1, 1024) )
lowerCAmelCase_ = [306, 191, 191]
self.assertEqual(encoding.input_ids[0, :3].tolist(), UpperCamelCase__ )
# test batched
lowerCAmelCase_ = image_processing(UpperCamelCase__, return_tensors='''pt''' )
self.assertIsInstance(encoding.input_ids, torch.LongTensor )
self.assertEqual(encoding.input_ids.shape, (2, 1024) )
lowerCAmelCase_ = [303, 13, 13]
self.assertEqual(encoding.input_ids[1, -3:].tolist(), UpperCamelCase__ )
| 325
|
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
_A = {
'''configuration_gpt_bigcode''': ['''GPT_BIGCODE_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''GPTBigCodeConfig'''],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_A = [
'''GPT_BIGCODE_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''GPTBigCodeForSequenceClassification''',
'''GPTBigCodeForTokenClassification''',
'''GPTBigCodeForCausalLM''',
'''GPTBigCodeModel''',
'''GPTBigCodePreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_gpt_bigcode import GPT_BIGCODE_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTBigCodeConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_gpt_bigcode import (
GPT_BIGCODE_PRETRAINED_MODEL_ARCHIVE_LIST,
GPTBigCodeForCausalLM,
GPTBigCodeForSequenceClassification,
GPTBigCodeForTokenClassification,
GPTBigCodeModel,
GPTBigCodePreTrainedModel,
)
else:
import sys
_A = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 325
| 1
|
import unittest
from diffusers import FlaxAutoencoderKL
from diffusers.utils import is_flax_available
from diffusers.utils.testing_utils import require_flax
from .test_modeling_common_flax import FlaxModelTesterMixin
if is_flax_available():
import jax
@require_flax
class __lowercase (__SCREAMING_SNAKE_CASE , unittest.TestCase ):
"""simple docstring"""
_UpperCAmelCase = FlaxAutoencoderKL
@property
def UpperCamelCase__ ( self ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Any = 4
SCREAMING_SNAKE_CASE_ : Optional[Any] = 3
SCREAMING_SNAKE_CASE_ : Any = (3_2, 3_2)
SCREAMING_SNAKE_CASE_ : int = jax.random.PRNGKey(0 )
SCREAMING_SNAKE_CASE_ : List[Any] = jax.random.uniform(lowerCAmelCase__ , ((batch_size, num_channels) + sizes) )
return {"sample": image, "prng_key": prng_key}
def UpperCamelCase__ ( self ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Union[str, Any] = {
'block_out_channels': [3_2, 6_4],
'in_channels': 3,
'out_channels': 3,
'down_block_types': ['DownEncoderBlock2D', 'DownEncoderBlock2D'],
'up_block_types': ['UpDecoderBlock2D', 'UpDecoderBlock2D'],
'latent_channels': 4,
}
SCREAMING_SNAKE_CASE_ : Dict = self.dummy_input
return init_dict, inputs_dict
| 101
|
class snake_case__ :
def __init__( self : Any ):
snake_case__ : Optional[Any] = 0
snake_case__ : Tuple = 0
snake_case__ : Any = {}
def UpperCAmelCase__ ( self : Union[str, Any] , _lowerCamelCase : Optional[int] ):
if vertex not in self.adjacency:
snake_case__ : str = {}
self.num_vertices += 1
def UpperCAmelCase__ ( self : Dict , _lowerCamelCase : Any , _lowerCamelCase : Optional[Any] , _lowerCamelCase : Any ):
self.add_vertex(_lowerCamelCase )
self.add_vertex(_lowerCamelCase )
if head == tail:
return
snake_case__ : Optional[Any] = weight
snake_case__ : Union[str, Any] = weight
def UpperCAmelCase__ ( self : Optional[Any] ):
snake_case__ : Optional[int] = self.get_edges()
for edge in edges:
snake_case__ , snake_case__ , snake_case__ : str = edge
edges.remove((tail, head, weight) )
for i in range(len(_lowerCamelCase ) ):
snake_case__ : int = list(edges[i] )
edges.sort(key=lambda _lowerCamelCase : e[2] )
for i in range(len(_lowerCamelCase ) - 1 ):
if edges[i][2] >= edges[i + 1][2]:
snake_case__ : Optional[Any] = edges[i][2] + 1
for edge in edges:
snake_case__ , snake_case__ , snake_case__ : Union[str, Any] = edge
snake_case__ : Tuple = weight
snake_case__ : str = weight
def __str__( self : Optional[int] ):
snake_case__ : Tuple = ''
for tail in self.adjacency:
for head in self.adjacency[tail]:
snake_case__ : str = self.adjacency[head][tail]
string += F'''{head} -> {tail} == {weight}\n'''
return string.rstrip('\n' )
def UpperCAmelCase__ ( self : Optional[Any] ):
snake_case__ : Union[str, Any] = []
for tail in self.adjacency:
for head in self.adjacency[tail]:
output.append((tail, head, self.adjacency[head][tail]) )
return output
def UpperCAmelCase__ ( self : Union[str, Any] ):
return self.adjacency.keys()
@staticmethod
def UpperCAmelCase__ ( _lowerCamelCase : Union[str, Any]=None , _lowerCamelCase : int=None ):
snake_case__ : Optional[Any] = Graph()
if vertices is None:
snake_case__ : Union[str, Any] = []
if edges is None:
snake_case__ : int = []
for vertex in vertices:
g.add_vertex(_lowerCamelCase )
for edge in edges:
g.add_edge(*_lowerCamelCase )
return g
class snake_case__ :
def __init__( self : Tuple ):
snake_case__ : Optional[int] = {}
snake_case__ : Union[str, Any] = {}
def __len__( self : Optional[int] ):
return len(self.parent )
def UpperCAmelCase__ ( self : Optional[Any] , _lowerCamelCase : Any ):
if item in self.parent:
return self.find(_lowerCamelCase )
snake_case__ : Tuple = item
snake_case__ : Union[str, Any] = 0
return item
def UpperCAmelCase__ ( self : Optional[int] , _lowerCamelCase : str ):
if item not in self.parent:
return self.make_set(_lowerCamelCase )
if item != self.parent[item]:
snake_case__ : Optional[int] = self.find(self.parent[item] )
return self.parent[item]
def UpperCAmelCase__ ( self : Optional[int] , _lowerCamelCase : str , _lowerCamelCase : List[Any] ):
snake_case__ : int = self.find(_lowerCamelCase )
snake_case__ : str = self.find(_lowerCamelCase )
if roota == roota:
return roota
if self.rank[roota] > self.rank[roota]:
snake_case__ : Any = roota
return roota
if self.rank[roota] < self.rank[roota]:
snake_case__ : str = roota
return roota
if self.rank[roota] == self.rank[roota]:
self.rank[roota] += 1
snake_case__ : List[Any] = roota
return roota
return None
@staticmethod
def UpperCAmelCase__ ( _lowerCamelCase : Optional[Any] ):
snake_case__ : Any = graph.num_vertices
snake_case__ : Optional[Any] = Graph.UnionFind()
snake_case__ : Optional[int] = []
while num_components > 1:
snake_case__ : Any = {}
for vertex in graph.get_vertices():
snake_case__ : Dict = -1
snake_case__ : Tuple = graph.get_edges()
for edge in edges:
snake_case__ , snake_case__ , snake_case__ : Optional[int] = edge
edges.remove((tail, head, weight) )
for edge in edges:
snake_case__ , snake_case__ , snake_case__ : Dict = edge
snake_case__ : int = union_find.find(_lowerCamelCase )
snake_case__ : int = union_find.find(_lowerCamelCase )
if seta != seta:
if cheap_edge[seta] == -1 or cheap_edge[seta][2] > weight:
snake_case__ : Union[str, Any] = [head, tail, weight]
if cheap_edge[seta] == -1 or cheap_edge[seta][2] > weight:
snake_case__ : Optional[Any] = [head, tail, weight]
for vertex in cheap_edge:
if cheap_edge[vertex] != -1:
snake_case__ , snake_case__ , snake_case__ : Optional[Any] = cheap_edge[vertex]
if union_find.find(_lowerCamelCase ) != union_find.find(_lowerCamelCase ):
union_find.union(_lowerCamelCase , _lowerCamelCase )
mst_edges.append(cheap_edge[vertex] )
snake_case__ : List[Any] = num_components - 1
snake_case__ : Tuple = Graph.build(edges=_lowerCamelCase )
return mst
| 170
| 0
|
'''simple docstring'''
from __future__ import annotations
class lowercase__ :
'''simple docstring'''
def __init__( self , lowerCamelCase__ ):
'''simple docstring'''
UpperCamelCase = TypeError(
'''Matrices must be formed from a list of zero or more lists containing at '''
'''least one and the same number of values, each of which must be of type '''
'''int or float.''' )
if len(lowerCamelCase__ ) != 0:
UpperCamelCase = len(rows[0] )
if cols == 0:
raise error
for row in rows:
if len(lowerCamelCase__ ) != cols:
raise error
for value in row:
if not isinstance(lowerCamelCase__ , (int, float) ):
raise error
UpperCamelCase = rows
else:
UpperCamelCase = []
def UpperCAmelCase ( self ):
'''simple docstring'''
return [[row[i] for row in self.rows] for i in range(len(self.rows[0] ) )]
@property
def UpperCAmelCase ( self ):
'''simple docstring'''
return len(self.rows )
@property
def UpperCAmelCase ( self ):
'''simple docstring'''
return len(self.rows[0] )
@property
def UpperCAmelCase ( self ):
'''simple docstring'''
return (self.num_rows, self.num_columns)
@property
def UpperCAmelCase ( self ):
'''simple docstring'''
return self.order[0] == self.order[1]
def UpperCAmelCase ( self ):
'''simple docstring'''
UpperCamelCase = [
[0 if column_num != row_num else 1 for column_num in range(self.num_rows )]
for row_num in range(self.num_rows )
]
return Matrix(lowerCamelCase__ )
def UpperCAmelCase ( self ):
'''simple docstring'''
if not self.is_square:
return 0
if self.order == (0, 0):
return 1
if self.order == (1, 1):
return int(self.rows[0][0] )
if self.order == (2, 2):
return int(
(self.rows[0][0] * self.rows[1][1])
- (self.rows[0][1] * self.rows[1][0]) )
else:
return sum(
self.rows[0][column] * self.cofactors().rows[0][column]
for column in range(self.num_columns ) )
def UpperCAmelCase ( self ):
'''simple docstring'''
return bool(self.determinant() )
def UpperCAmelCase ( self , lowerCamelCase__ , lowerCamelCase__ ):
'''simple docstring'''
UpperCamelCase = [
[
self.rows[other_row][other_column]
for other_column in range(self.num_columns )
if other_column != column
]
for other_row in range(self.num_rows )
if other_row != row
]
return Matrix(lowerCamelCase__ ).determinant()
def UpperCAmelCase ( self , lowerCamelCase__ , lowerCamelCase__ ):
'''simple docstring'''
if (row + column) % 2 == 0:
return self.get_minor(lowerCamelCase__ , lowerCamelCase__ )
return -1 * self.get_minor(lowerCamelCase__ , lowerCamelCase__ )
def UpperCAmelCase ( self ):
'''simple docstring'''
return Matrix(
[
[self.get_minor(lowerCamelCase__ , lowerCamelCase__ ) for column in range(self.num_columns )]
for row in range(self.num_rows )
] )
def UpperCAmelCase ( self ):
'''simple docstring'''
return Matrix(
[
[
self.minors().rows[row][column]
if (row + column) % 2 == 0
else self.minors().rows[row][column] * -1
for column in range(self.minors().num_columns )
]
for row in range(self.minors().num_rows )
] )
def UpperCAmelCase ( self ):
'''simple docstring'''
UpperCamelCase = [
[self.cofactors().rows[column][row] for column in range(self.num_columns )]
for row in range(self.num_rows )
]
return Matrix(lowerCamelCase__ )
def UpperCAmelCase ( self ):
'''simple docstring'''
UpperCamelCase = self.determinant()
if not determinant:
raise TypeError('''Only matrices with a non-zero determinant have an inverse''' )
return self.adjugate() * (1 / determinant)
def __repr__( self ):
'''simple docstring'''
return str(self.rows )
def __str__( self ):
'''simple docstring'''
if self.num_rows == 0:
return "[]"
if self.num_rows == 1:
return "[[" + ". ".join(str(self.rows[0] ) ) + "]]"
return (
"["
+ "\n ".join(
[
'''[''' + '''. '''.join([str(lowerCamelCase__ ) for value in row] ) + '''.]'''
for row in self.rows
] )
+ "]"
)
def UpperCAmelCase ( self , lowerCamelCase__ , lowerCamelCase__ = None ):
'''simple docstring'''
UpperCamelCase = TypeError('''Row must be a list containing all ints and/or floats''' )
if not isinstance(lowerCamelCase__ , lowerCamelCase__ ):
raise type_error
for value in row:
if not isinstance(lowerCamelCase__ , (int, float) ):
raise type_error
if len(lowerCamelCase__ ) != self.num_columns:
raise ValueError(
'''Row must be equal in length to the other rows in the matrix''' )
if position is None:
self.rows.append(lowerCamelCase__ )
else:
UpperCamelCase = self.rows[0:position] + [row] + self.rows[position:]
def UpperCAmelCase ( self , lowerCamelCase__ , lowerCamelCase__ = None ):
'''simple docstring'''
UpperCamelCase = TypeError(
'''Column must be a list containing all ints and/or floats''' )
if not isinstance(lowerCamelCase__ , lowerCamelCase__ ):
raise type_error
for value in column:
if not isinstance(lowerCamelCase__ , (int, float) ):
raise type_error
if len(lowerCamelCase__ ) != self.num_rows:
raise ValueError(
'''Column must be equal in length to the other columns in the matrix''' )
if position is None:
UpperCamelCase = [self.rows[i] + [column[i]] for i in range(self.num_rows )]
else:
UpperCamelCase = [
self.rows[i][0:position] + [column[i]] + self.rows[i][position:]
for i in range(self.num_rows )
]
def __eq__( self , lowerCamelCase__ ):
'''simple docstring'''
if not isinstance(lowerCamelCase__ , lowerCamelCase__ ):
return NotImplemented
return self.rows == other.rows
def __ne__( self , lowerCamelCase__ ):
'''simple docstring'''
return not self == other
def __neg__( self ):
'''simple docstring'''
return self * -1
def __add__( self , lowerCamelCase__ ):
'''simple docstring'''
if self.order != other.order:
raise ValueError('''Addition requires matrices of the same order''' )
return Matrix(
[
[self.rows[i][j] + other.rows[i][j] for j in range(self.num_columns )]
for i in range(self.num_rows )
] )
def __sub__( self , lowerCamelCase__ ):
'''simple docstring'''
if self.order != other.order:
raise ValueError('''Subtraction requires matrices of the same order''' )
return Matrix(
[
[self.rows[i][j] - other.rows[i][j] for j in range(self.num_columns )]
for i in range(self.num_rows )
] )
def __mul__( self , lowerCamelCase__ ):
'''simple docstring'''
if isinstance(lowerCamelCase__ , (int, float) ):
return Matrix(
[[int(element * other ) for element in row] for row in self.rows] )
elif isinstance(lowerCamelCase__ , lowerCamelCase__ ):
if self.num_columns != other.num_rows:
raise ValueError(
'''The number of columns in the first matrix must '''
'''be equal to the number of rows in the second''' )
return Matrix(
[
[Matrix.dot_product(lowerCamelCase__ , lowerCamelCase__ ) for column in other.columns()]
for row in self.rows
] )
else:
raise TypeError(
'''A Matrix can only be multiplied by an int, float, or another matrix''' )
def __pow__( self , lowerCamelCase__ ):
'''simple docstring'''
if not isinstance(lowerCamelCase__ , lowerCamelCase__ ):
raise TypeError('''A Matrix can only be raised to the power of an int''' )
if not self.is_square:
raise ValueError('''Only square matrices can be raised to a power''' )
if other == 0:
return self.identity()
if other < 0:
if self.is_invertable():
return self.inverse() ** (-other)
raise ValueError(
'''Only invertable matrices can be raised to a negative power''' )
UpperCamelCase = self
for _ in range(other - 1 ):
result *= self
return result
@classmethod
def UpperCAmelCase ( cls , lowerCamelCase__ , lowerCamelCase__ ):
'''simple docstring'''
return sum(row[i] * column[i] for i in range(len(lowerCamelCase__ ) ) )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 350
|
'''simple docstring'''
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_pegasus import PegasusTokenizer
else:
snake_case_ : Dict = None
snake_case_ : int = logging.get_logger(__name__)
snake_case_ : int = '▁'
snake_case_ : Optional[int] = {'vocab_file': 'spiece.model', 'tokenizer_file': 'tokenizer.json'}
snake_case_ : List[Any] = {
'vocab_file': {'google/pegasus-xsum': 'https://huggingface.co/google/pegasus-xsum/resolve/main/spiece.model'},
'tokenizer_file': {
'google/pegasus-xsum': 'https://huggingface.co/google/pegasus-xsum/resolve/main/tokenizer.json'
},
}
snake_case_ : Tuple = {
'google/pegasus-xsum': 512,
}
class lowercase__ ( snake_case_ ):
'''simple docstring'''
_snake_case = VOCAB_FILES_NAMES
_snake_case = PRETRAINED_VOCAB_FILES_MAP
_snake_case = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
_snake_case = PegasusTokenizer
_snake_case = ['''input_ids''', '''attention_mask''']
def __init__( self , lowerCamelCase__=None , lowerCamelCase__=None , lowerCamelCase__="<pad>" , lowerCamelCase__="</s>" , lowerCamelCase__="<unk>" , lowerCamelCase__="<mask_2>" , lowerCamelCase__="<mask_1>" , lowerCamelCase__=None , lowerCamelCase__=1_0_3 , **lowerCamelCase__ , ):
'''simple docstring'''
UpperCamelCase = offset
if additional_special_tokens is not None:
if not isinstance(lowerCamelCase__ , lowerCamelCase__ ):
raise TypeError(
f'additional_special_tokens should be of type {type(lowerCamelCase__ )}, but is'
f' {type(lowerCamelCase__ )}' )
UpperCamelCase = (
([mask_token_sent] + additional_special_tokens)
if mask_token_sent not in additional_special_tokens and mask_token_sent is not None
else additional_special_tokens
)
# fill additional tokens with ..., <unk_token_102> in case not all additional tokens are already taken
additional_special_tokens_extended += [
f'<unk_{i}>' for i in range(len(lowerCamelCase__ ) , self.offset - 1 )
]
if len(set(lowerCamelCase__ ) ) != len(lowerCamelCase__ ):
raise ValueError(
'''Please make sure that the provided additional_special_tokens do not contain an incorrectly'''
f' shifted list of <unk_x> tokens. Found {additional_special_tokens_extended}.' )
UpperCamelCase = additional_special_tokens_extended
else:
UpperCamelCase = [mask_token_sent] if mask_token_sent is not None else []
additional_special_tokens += [f'<unk_{i}>' for i in range(2 , self.offset )]
super().__init__(
lowerCamelCase__ , tokenizer_file=lowerCamelCase__ , pad_token=lowerCamelCase__ , eos_token=lowerCamelCase__ , unk_token=lowerCamelCase__ , mask_token=lowerCamelCase__ , mask_token_sent=lowerCamelCase__ , offset=lowerCamelCase__ , additional_special_tokens=lowerCamelCase__ , **lowerCamelCase__ , )
UpperCamelCase = vocab_file
UpperCamelCase = False if not self.vocab_file else True
def UpperCAmelCase ( self , lowerCamelCase__ ):
'''simple docstring'''
UpperCamelCase = set(self.all_special_ids ) # call it once instead of inside list comp
all_special_ids.remove(self.unk_token_id ) # <unk> is only sometimes special
if all_special_ids != set(range(len(self.additional_special_tokens ) + 3 ) ):
raise ValueError(
'''There should be 3 special tokens: mask_token, pad_token, and eos_token +'''
f' {len(self.additional_special_tokens )} additional_special_tokens, but got {all_special_ids}' )
return [1 if x in all_special_ids else 0 for x in seq]
def UpperCAmelCase ( self , lowerCamelCase__ , lowerCamelCase__ = None , lowerCamelCase__ = False ):
'''simple docstring'''
if already_has_special_tokens:
return self._special_token_mask(lowerCamelCase__ )
elif token_ids_a is None:
return self._special_token_mask(lowerCamelCase__ ) + [1]
else:
return self._special_token_mask(token_ids_a + token_ids_a ) + [1]
def UpperCAmelCase ( self , lowerCamelCase__ , lowerCamelCase__=None ):
'''simple docstring'''
if token_ids_a is None:
return token_ids_a + [self.eos_token_id]
# We don't expect to process pairs, but leave the pair logic for API consistency
return token_ids_a + token_ids_a + [self.eos_token_id]
def UpperCAmelCase ( self , lowerCamelCase__ , lowerCamelCase__ = 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
UpperCamelCase = 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,)
| 350
| 1
|
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