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from math import factorial
lowerCamelCase__ = {str(digit): factorial(digit) for digit in range(10)}
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_ ) -> int:
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 lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_ = 60 , SCREAMING_SNAKE_CASE_ = 1_000_000 ) -> int:
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
lowerCAmelCase__ : Tuple = 0
# the cached sizes of the previous chains
lowerCAmelCase__ : dict[int, int] = {}
for start_chain_element in range(1 , SCREAMING_SNAKE_CASE_ ):
# The temporary set will contain the elements of the chain
lowerCAmelCase__ : Dict = set()
lowerCAmelCase__ : str = 0
# Stop computing the chain when you find a cached size, a repeating item or the
# length is greater then the desired one.
lowerCAmelCase__ : 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
lowerCAmelCase__ : Union[str, Any] = digit_factorial_sum(SCREAMING_SNAKE_CASE_ )
if chain_element in chain_sets_lengths:
chain_set_length += chain_sets_lengths[chain_element]
lowerCAmelCase__ : Tuple = 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()}""")
| 361
|
from __future__ import annotations
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) -> list[list[int]]:
lowerCAmelCase__ : list[list[int]] = []
create_all_state(1 , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , [] , SCREAMING_SNAKE_CASE_ )
return result
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , ) -> None:
if level == 0:
total_list.append(current_list[:] )
return
for i in range(SCREAMING_SNAKE_CASE_ , total_number - level + 2 ):
current_list.append(SCREAMING_SNAKE_CASE_ )
create_all_state(i + 1 , SCREAMING_SNAKE_CASE_ , level - 1 , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
current_list.pop()
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_ ) -> None:
for i in total_list:
print(*SCREAMING_SNAKE_CASE_ )
if __name__ == "__main__":
lowerCamelCase__ = 4
lowerCamelCase__ = 2
lowerCamelCase__ = generate_all_combinations(n, k)
print_all_state(total_list)
| 307
| 0
|
import argparse
import json
import torch
from diffusers import DDPMScheduler, LDMPipeline, UNetaDModel, VQModel
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_=1 ) -> List[Any]:
if n_shave_prefix_segments >= 0:
return ".".join(path.split('.' )[n_shave_prefix_segments:] )
else:
return ".".join(path.split('.' )[:n_shave_prefix_segments] )
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_=0 ) -> Tuple:
lowerCAmelCase__ : Optional[int] = []
for old_item in old_list:
lowerCAmelCase__ : List[Any] = old_item.replace('in_layers.0' , 'norm1' )
lowerCAmelCase__ : str = new_item.replace('in_layers.2' , 'conv1' )
lowerCAmelCase__ : Tuple = new_item.replace('out_layers.0' , 'norm2' )
lowerCAmelCase__ : str = new_item.replace('out_layers.3' , 'conv2' )
lowerCAmelCase__ : str = new_item.replace('emb_layers.1' , 'time_emb_proj' )
lowerCAmelCase__ : Tuple = new_item.replace('skip_connection' , 'conv_shortcut' )
lowerCAmelCase__ : str = shave_segments(SCREAMING_SNAKE_CASE_ , n_shave_prefix_segments=SCREAMING_SNAKE_CASE_ )
mapping.append({'old': old_item, 'new': new_item} )
return mapping
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_=0 ) -> Dict:
lowerCAmelCase__ : List[str] = []
for old_item in old_list:
lowerCAmelCase__ : str = old_item
lowerCAmelCase__ : Tuple = new_item.replace('norm.weight' , 'group_norm.weight' )
lowerCAmelCase__ : Union[str, Any] = new_item.replace('norm.bias' , 'group_norm.bias' )
lowerCAmelCase__ : List[str] = new_item.replace('proj_out.weight' , 'proj_attn.weight' )
lowerCAmelCase__ : Optional[int] = new_item.replace('proj_out.bias' , 'proj_attn.bias' )
lowerCAmelCase__ : Dict = shave_segments(SCREAMING_SNAKE_CASE_ , n_shave_prefix_segments=SCREAMING_SNAKE_CASE_ )
mapping.append({'old': old_item, 'new': new_item} )
return mapping
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_=None , SCREAMING_SNAKE_CASE_=None , SCREAMING_SNAKE_CASE_=None ) -> str:
assert isinstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ), "Paths should be a list of dicts containing 'old' and 'new' keys."
# Splits the attention layers into three variables.
if attention_paths_to_split is not None:
for path, path_map in attention_paths_to_split.items():
lowerCAmelCase__ : List[Any] = old_checkpoint[path]
lowerCAmelCase__ : Dict = old_tensor.shape[0] // 3
lowerCAmelCase__ : Any = (-1, channels) if len(old_tensor.shape ) == 3 else (-1)
lowerCAmelCase__ : str = old_tensor.shape[0] // config['num_head_channels'] // 3
lowerCAmelCase__ : Optional[Any] = old_tensor.reshape((num_heads, 3 * channels // num_heads) + old_tensor.shape[1:] )
lowerCAmelCase__ : List[str] = old_tensor.split(channels // num_heads , dim=1 )
lowerCAmelCase__ : str = query.reshape(SCREAMING_SNAKE_CASE_ )
lowerCAmelCase__ : Optional[int] = key.reshape(SCREAMING_SNAKE_CASE_ )
lowerCAmelCase__ : List[Any] = value.reshape(SCREAMING_SNAKE_CASE_ )
for path in paths:
lowerCAmelCase__ : Optional[Any] = path['new']
# These have already been assigned
if attention_paths_to_split is not None and new_path in attention_paths_to_split:
continue
# Global renaming happens here
lowerCAmelCase__ : List[str] = new_path.replace('middle_block.0' , 'mid_block.resnets.0' )
lowerCAmelCase__ : int = new_path.replace('middle_block.1' , 'mid_block.attentions.0' )
lowerCAmelCase__ : Optional[int] = new_path.replace('middle_block.2' , 'mid_block.resnets.1' )
if additional_replacements is not None:
for replacement in additional_replacements:
lowerCAmelCase__ : str = new_path.replace(replacement['old'] , replacement['new'] )
# proj_attn.weight has to be converted from conv 1D to linear
if "proj_attn.weight" in new_path:
lowerCAmelCase__ : List[str] = old_checkpoint[path['old']][:, :, 0]
else:
lowerCAmelCase__ : List[Any] = old_checkpoint[path['old']]
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) -> Union[str, Any]:
lowerCAmelCase__ : Optional[Any] = {}
lowerCAmelCase__ : Union[str, Any] = checkpoint['time_embed.0.weight']
lowerCAmelCase__ : Union[str, Any] = checkpoint['time_embed.0.bias']
lowerCAmelCase__ : Dict = checkpoint['time_embed.2.weight']
lowerCAmelCase__ : Tuple = checkpoint['time_embed.2.bias']
lowerCAmelCase__ : Optional[Any] = checkpoint['input_blocks.0.0.weight']
lowerCAmelCase__ : List[str] = checkpoint['input_blocks.0.0.bias']
lowerCAmelCase__ : Tuple = checkpoint['out.0.weight']
lowerCAmelCase__ : List[str] = checkpoint['out.0.bias']
lowerCAmelCase__ : Dict = checkpoint['out.2.weight']
lowerCAmelCase__ : List[str] = checkpoint['out.2.bias']
# Retrieves the keys for the input blocks only
lowerCAmelCase__ : Dict = len({'.'.join(layer.split('.' )[:2] ) for layer in checkpoint if 'input_blocks' in layer} )
lowerCAmelCase__ : List[str] = {
layer_id: [key for key in checkpoint if F'''input_blocks.{layer_id}''' in key]
for layer_id in range(SCREAMING_SNAKE_CASE_ )
}
# Retrieves the keys for the middle blocks only
lowerCAmelCase__ : List[str] = len({'.'.join(layer.split('.' )[:2] ) for layer in checkpoint if 'middle_block' in layer} )
lowerCAmelCase__ : Union[str, Any] = {
layer_id: [key for key in checkpoint if F'''middle_block.{layer_id}''' in key]
for layer_id in range(SCREAMING_SNAKE_CASE_ )
}
# Retrieves the keys for the output blocks only
lowerCAmelCase__ : int = len({'.'.join(layer.split('.' )[:2] ) for layer in checkpoint if 'output_blocks' in layer} )
lowerCAmelCase__ : Any = {
layer_id: [key for key in checkpoint if F'''output_blocks.{layer_id}''' in key]
for layer_id in range(SCREAMING_SNAKE_CASE_ )
}
for i in range(1 , SCREAMING_SNAKE_CASE_ ):
lowerCAmelCase__ : Optional[int] = (i - 1) // (config['num_res_blocks'] + 1)
lowerCAmelCase__ : Tuple = (i - 1) % (config['num_res_blocks'] + 1)
lowerCAmelCase__ : List[Any] = [key for key in input_blocks[i] if F'''input_blocks.{i}.0''' in key]
lowerCAmelCase__ : str = [key for key in input_blocks[i] if F'''input_blocks.{i}.1''' in key]
if F'''input_blocks.{i}.0.op.weight''' in checkpoint:
lowerCAmelCase__ : Tuple = checkpoint[
F'''input_blocks.{i}.0.op.weight'''
]
lowerCAmelCase__ : Any = checkpoint[
F'''input_blocks.{i}.0.op.bias'''
]
continue
lowerCAmelCase__ : Dict = renew_resnet_paths(SCREAMING_SNAKE_CASE_ )
lowerCAmelCase__ : str = {'old': F'''input_blocks.{i}.0''', 'new': F'''down_blocks.{block_id}.resnets.{layer_in_block_id}'''}
lowerCAmelCase__ : str = {'old': 'resnets.2.op', 'new': 'downsamplers.0.op'}
assign_to_checkpoint(
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , additional_replacements=[meta_path, resnet_op] , config=SCREAMING_SNAKE_CASE_ )
if len(SCREAMING_SNAKE_CASE_ ):
lowerCAmelCase__ : Dict = renew_attention_paths(SCREAMING_SNAKE_CASE_ )
lowerCAmelCase__ : Optional[int] = {
'old': F'''input_blocks.{i}.1''',
'new': F'''down_blocks.{block_id}.attentions.{layer_in_block_id}''',
}
lowerCAmelCase__ : List[str] = {
F'''input_blocks.{i}.1.qkv.bias''': {
'key': F'''down_blocks.{block_id}.attentions.{layer_in_block_id}.key.bias''',
'query': F'''down_blocks.{block_id}.attentions.{layer_in_block_id}.query.bias''',
'value': F'''down_blocks.{block_id}.attentions.{layer_in_block_id}.value.bias''',
},
F'''input_blocks.{i}.1.qkv.weight''': {
'key': F'''down_blocks.{block_id}.attentions.{layer_in_block_id}.key.weight''',
'query': F'''down_blocks.{block_id}.attentions.{layer_in_block_id}.query.weight''',
'value': F'''down_blocks.{block_id}.attentions.{layer_in_block_id}.value.weight''',
},
}
assign_to_checkpoint(
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , additional_replacements=[meta_path] , attention_paths_to_split=SCREAMING_SNAKE_CASE_ , config=SCREAMING_SNAKE_CASE_ , )
lowerCAmelCase__ : Optional[Any] = middle_blocks[0]
lowerCAmelCase__ : Optional[Any] = middle_blocks[1]
lowerCAmelCase__ : Union[str, Any] = middle_blocks[2]
lowerCAmelCase__ : List[str] = renew_resnet_paths(SCREAMING_SNAKE_CASE_ )
assign_to_checkpoint(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , config=SCREAMING_SNAKE_CASE_ )
lowerCAmelCase__ : Union[str, Any] = renew_resnet_paths(SCREAMING_SNAKE_CASE_ )
assign_to_checkpoint(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , config=SCREAMING_SNAKE_CASE_ )
lowerCAmelCase__ : Dict = renew_attention_paths(SCREAMING_SNAKE_CASE_ )
lowerCAmelCase__ : int = {
'middle_block.1.qkv.bias': {
'key': 'mid_block.attentions.0.key.bias',
'query': 'mid_block.attentions.0.query.bias',
'value': 'mid_block.attentions.0.value.bias',
},
'middle_block.1.qkv.weight': {
'key': 'mid_block.attentions.0.key.weight',
'query': 'mid_block.attentions.0.query.weight',
'value': 'mid_block.attentions.0.value.weight',
},
}
assign_to_checkpoint(
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , attention_paths_to_split=SCREAMING_SNAKE_CASE_ , config=SCREAMING_SNAKE_CASE_ )
for i in range(SCREAMING_SNAKE_CASE_ ):
lowerCAmelCase__ : Dict = i // (config['num_res_blocks'] + 1)
lowerCAmelCase__ : Any = i % (config['num_res_blocks'] + 1)
lowerCAmelCase__ : Optional[int] = [shave_segments(SCREAMING_SNAKE_CASE_ , 2 ) for name in output_blocks[i]]
lowerCAmelCase__ : Union[str, Any] = {}
for layer in output_block_layers:
lowerCAmelCase__ : List[str] = layer.split('.' )[0], shave_segments(SCREAMING_SNAKE_CASE_ , 1 )
if layer_id in output_block_list:
output_block_list[layer_id].append(SCREAMING_SNAKE_CASE_ )
else:
lowerCAmelCase__ : Any = [layer_name]
if len(SCREAMING_SNAKE_CASE_ ) > 1:
lowerCAmelCase__ : str = [key for key in output_blocks[i] if F'''output_blocks.{i}.0''' in key]
lowerCAmelCase__ : Any = [key for key in output_blocks[i] if F'''output_blocks.{i}.1''' in key]
lowerCAmelCase__ : Any = renew_resnet_paths(SCREAMING_SNAKE_CASE_ )
lowerCAmelCase__ : List[str] = renew_resnet_paths(SCREAMING_SNAKE_CASE_ )
lowerCAmelCase__ : List[str] = {'old': F'''output_blocks.{i}.0''', 'new': F'''up_blocks.{block_id}.resnets.{layer_in_block_id}'''}
assign_to_checkpoint(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , additional_replacements=[meta_path] , config=SCREAMING_SNAKE_CASE_ )
if ["conv.weight", "conv.bias"] in output_block_list.values():
lowerCAmelCase__ : Union[str, Any] = list(output_block_list.values() ).index(['conv.weight', 'conv.bias'] )
lowerCAmelCase__ : Optional[int] = checkpoint[
F'''output_blocks.{i}.{index}.conv.weight'''
]
lowerCAmelCase__ : Dict = checkpoint[
F'''output_blocks.{i}.{index}.conv.bias'''
]
# Clear attentions as they have been attributed above.
if len(SCREAMING_SNAKE_CASE_ ) == 2:
lowerCAmelCase__ : str = []
if len(SCREAMING_SNAKE_CASE_ ):
lowerCAmelCase__ : str = renew_attention_paths(SCREAMING_SNAKE_CASE_ )
lowerCAmelCase__ : List[Any] = {
'old': F'''output_blocks.{i}.1''',
'new': F'''up_blocks.{block_id}.attentions.{layer_in_block_id}''',
}
lowerCAmelCase__ : str = {
F'''output_blocks.{i}.1.qkv.bias''': {
'key': F'''up_blocks.{block_id}.attentions.{layer_in_block_id}.key.bias''',
'query': F'''up_blocks.{block_id}.attentions.{layer_in_block_id}.query.bias''',
'value': F'''up_blocks.{block_id}.attentions.{layer_in_block_id}.value.bias''',
},
F'''output_blocks.{i}.1.qkv.weight''': {
'key': F'''up_blocks.{block_id}.attentions.{layer_in_block_id}.key.weight''',
'query': F'''up_blocks.{block_id}.attentions.{layer_in_block_id}.query.weight''',
'value': F'''up_blocks.{block_id}.attentions.{layer_in_block_id}.value.weight''',
},
}
assign_to_checkpoint(
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , additional_replacements=[meta_path] , attention_paths_to_split=to_split if any('qkv' in key for key in attentions ) else None , config=SCREAMING_SNAKE_CASE_ , )
else:
lowerCAmelCase__ : List[str] = renew_resnet_paths(SCREAMING_SNAKE_CASE_ , n_shave_prefix_segments=1 )
for path in resnet_0_paths:
lowerCAmelCase__ : Any = '.'.join(['output_blocks', str(SCREAMING_SNAKE_CASE_ ), path['old']] )
lowerCAmelCase__ : Optional[int] = '.'.join(['up_blocks', str(SCREAMING_SNAKE_CASE_ ), 'resnets', str(SCREAMING_SNAKE_CASE_ ), path['new']] )
lowerCAmelCase__ : str = checkpoint[old_path]
return new_checkpoint
if __name__ == "__main__":
lowerCamelCase__ = argparse.ArgumentParser()
parser.add_argument(
"""--checkpoint_path""", default=None, type=str, required=True, help="""Path to the checkpoint to convert."""
)
parser.add_argument(
"""--config_file""",
default=None,
type=str,
required=True,
help="""The config json file corresponding to the architecture.""",
)
parser.add_argument("""--dump_path""", default=None, type=str, required=True, help="""Path to the output model.""")
lowerCamelCase__ = parser.parse_args()
lowerCamelCase__ = torch.load(args.checkpoint_path)
with open(args.config_file) as f:
lowerCamelCase__ = json.loads(f.read())
lowerCamelCase__ = convert_ldm_checkpoint(checkpoint, config)
if "ldm" in config:
del config["ldm"]
lowerCamelCase__ = UNetaDModel(**config)
model.load_state_dict(converted_checkpoint)
try:
lowerCamelCase__ = DDPMScheduler.from_config("""/""".join(args.checkpoint_path.split("""/""")[:-1]))
lowerCamelCase__ = VQModel.from_pretrained("""/""".join(args.checkpoint_path.split("""/""")[:-1]))
lowerCamelCase__ = LDMPipeline(unet=model, scheduler=scheduler, vae=vqvae)
pipe.save_pretrained(args.dump_path)
except: # noqa: E722
model.save_pretrained(args.dump_path)
| 362
|
import copy
import tempfile
import unittest
from huggingface_hub import HfFolder, delete_repo
from parameterized import parameterized
from requests.exceptions import HTTPError
from transformers import AutoConfig, GenerationConfig
from transformers.testing_utils import TOKEN, USER, is_staging_test
class A__ ( unittest.TestCase ):
@parameterized.expand([(None,), ('foo.json',)] )
def _lowerCamelCase ( self : Dict , a : str ):
'''simple docstring'''
lowerCAmelCase__ : List[str] = GenerationConfig(
do_sample=a , temperature=0.7 , length_penalty=1.0 , bad_words_ids=[[1, 2, 3], [4, 5]] , )
with tempfile.TemporaryDirectory() as tmp_dir:
config.save_pretrained(a , config_name=a )
lowerCAmelCase__ : Tuple = GenerationConfig.from_pretrained(a , config_name=a )
# Checks parameters that were specified
self.assertEqual(loaded_config.do_sample , a )
self.assertEqual(loaded_config.temperature , 0.7 )
self.assertEqual(loaded_config.length_penalty , 1.0 )
self.assertEqual(loaded_config.bad_words_ids , [[1, 2, 3], [4, 5]] )
# Checks parameters that were not specified (defaults)
self.assertEqual(loaded_config.top_k , 50 )
self.assertEqual(loaded_config.max_length , 20 )
self.assertEqual(loaded_config.max_time , a )
def _lowerCamelCase ( self : int ):
'''simple docstring'''
lowerCAmelCase__ : Dict = AutoConfig.from_pretrained('gpt2' )
lowerCAmelCase__ : Any = GenerationConfig.from_model_config(a )
lowerCAmelCase__ : Any = GenerationConfig()
# The generation config has loaded a few non-default parameters from the model config
self.assertNotEqual(a , a )
# One of those parameters is eos_token_id -- check if it matches
self.assertNotEqual(generation_config_from_model.eos_token_id , default_generation_config.eos_token_id )
self.assertEqual(generation_config_from_model.eos_token_id , model_config.eos_token_id )
def _lowerCamelCase ( self : List[str] ):
'''simple docstring'''
lowerCAmelCase__ : Dict = GenerationConfig()
lowerCAmelCase__ : Dict = {
'max_new_tokens': 1_024,
'foo': 'bar',
}
lowerCAmelCase__ : List[Any] = copy.deepcopy(a )
lowerCAmelCase__ : Dict = generation_config.update(**a )
# update_kwargs was not modified (no side effects)
self.assertEqual(a , a )
# update_kwargs was used to update the config on valid attributes
self.assertEqual(generation_config.max_new_tokens , 1_024 )
# `.update()` returns a dictionary of unused kwargs
self.assertEqual(a , {'foo': 'bar'} )
def _lowerCamelCase ( self : Any ):
'''simple docstring'''
lowerCAmelCase__ : Dict = GenerationConfig()
lowerCAmelCase__ : List[Any] = 'bar'
with tempfile.TemporaryDirectory('test-generation-config' ) as tmp_dir:
generation_config.save_pretrained(a )
lowerCAmelCase__ : List[Any] = GenerationConfig.from_pretrained(a )
# update_kwargs was used to update the config on valid attributes
self.assertEqual(new_config.foo , 'bar' )
lowerCAmelCase__ : int = GenerationConfig.from_model_config(a )
assert not hasattr(a , 'foo' ) # no new kwargs should be initialized if from config
def _lowerCamelCase ( self : str ):
'''simple docstring'''
lowerCAmelCase__ : Union[str, Any] = GenerationConfig()
self.assertEqual(default_config.temperature , 1.0 )
self.assertEqual(default_config.do_sample , a )
self.assertEqual(default_config.num_beams , 1 )
lowerCAmelCase__ : List[Any] = GenerationConfig(
do_sample=a , temperature=0.7 , length_penalty=1.0 , bad_words_ids=[[1, 2, 3], [4, 5]] , )
self.assertEqual(config.temperature , 0.7 )
self.assertEqual(config.do_sample , a )
self.assertEqual(config.num_beams , 1 )
with tempfile.TemporaryDirectory() as tmp_dir:
config.save_pretrained(a )
lowerCAmelCase__ : Any = GenerationConfig.from_pretrained(a , temperature=1.0 )
self.assertEqual(loaded_config.temperature , 1.0 )
self.assertEqual(loaded_config.do_sample , a )
self.assertEqual(loaded_config.num_beams , 1 ) # default value
@is_staging_test
class A__ ( unittest.TestCase ):
@classmethod
def _lowerCamelCase ( cls : int ):
'''simple docstring'''
lowerCAmelCase__ : List[str] = TOKEN
HfFolder.save_token(a )
@classmethod
def _lowerCamelCase ( cls : Optional[int] ):
'''simple docstring'''
try:
delete_repo(token=cls._token , repo_id='test-generation-config' )
except HTTPError:
pass
try:
delete_repo(token=cls._token , repo_id='valid_org/test-generation-config-org' )
except HTTPError:
pass
def _lowerCamelCase ( self : Optional[int] ):
'''simple docstring'''
lowerCAmelCase__ : Optional[int] = GenerationConfig(
do_sample=a , temperature=0.7 , length_penalty=1.0 , )
config.push_to_hub('test-generation-config' , use_auth_token=self._token )
lowerCAmelCase__ : Any = GenerationConfig.from_pretrained(f'''{USER}/test-generation-config''' )
for k, v in config.to_dict().items():
if k != "transformers_version":
self.assertEqual(a , getattr(a , a ) )
# Reset repo
delete_repo(token=self._token , repo_id='test-generation-config' )
# Push to hub via save_pretrained
with tempfile.TemporaryDirectory() as tmp_dir:
config.save_pretrained(
a , repo_id='test-generation-config' , push_to_hub=a , use_auth_token=self._token )
lowerCAmelCase__ : Tuple = GenerationConfig.from_pretrained(f'''{USER}/test-generation-config''' )
for k, v in config.to_dict().items():
if k != "transformers_version":
self.assertEqual(a , getattr(a , a ) )
def _lowerCamelCase ( self : Optional[Any] ):
'''simple docstring'''
lowerCAmelCase__ : int = GenerationConfig(
do_sample=a , temperature=0.7 , length_penalty=1.0 , )
config.push_to_hub('valid_org/test-generation-config-org' , use_auth_token=self._token )
lowerCAmelCase__ : Dict = GenerationConfig.from_pretrained('valid_org/test-generation-config-org' )
for k, v in config.to_dict().items():
if k != "transformers_version":
self.assertEqual(a , getattr(a , a ) )
# Reset repo
delete_repo(token=self._token , repo_id='valid_org/test-generation-config-org' )
# Push to hub via save_pretrained
with tempfile.TemporaryDirectory() as tmp_dir:
config.save_pretrained(
a , repo_id='valid_org/test-generation-config-org' , push_to_hub=a , use_auth_token=self._token )
lowerCAmelCase__ : List[str] = GenerationConfig.from_pretrained('valid_org/test-generation-config-org' )
for k, v in config.to_dict().items():
if k != "transformers_version":
self.assertEqual(a , getattr(a , a ) )
| 307
| 0
|
from __future__ import annotations
from math import gcd
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = 2 , SCREAMING_SNAKE_CASE_ = 1 , SCREAMING_SNAKE_CASE_ = 3 , ) -> int | None:
# A value less than 2 can cause an infinite loop in the algorithm.
if num < 2:
raise ValueError('The input value cannot be less than 2' )
# Because of the relationship between ``f(f(x))`` and ``f(x)``, this
# algorithm struggles to find factors that are divisible by two.
# As a workaround, we specifically check for two and even inputs.
# See: https://math.stackexchange.com/a/2856214/165820
if num > 2 and num % 2 == 0:
return 2
# Pollard's Rho algorithm requires a function that returns pseudorandom
# values between 0 <= X < ``num``. It doesn't need to be random in the
# sense that the output value is cryptographically secure or difficult
# to calculate, it only needs to be random in the sense that all output
# values should be equally likely to appear.
# For this reason, Pollard suggested using ``f(x) = (x**2 - 1) % num``
# However, the success of Pollard's algorithm isn't guaranteed and is
# determined in part by the initial seed and the chosen random function.
# To make retries easier, we will instead use ``f(x) = (x**2 + C) % num``
# where ``C`` is a value that we can modify between each attempt.
def rand_fn(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) -> int:
return (pow(SCREAMING_SNAKE_CASE_ , 2 ) + step) % modulus
for _ in range(SCREAMING_SNAKE_CASE_ ):
# These track the position within the cycle detection logic.
lowerCAmelCase__ : int = seed
lowerCAmelCase__ : Dict = seed
while True:
# At each iteration, the tortoise moves one step and the hare moves two.
lowerCAmelCase__ : Dict = rand_fn(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
lowerCAmelCase__ : Any = rand_fn(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
lowerCAmelCase__ : Union[str, Any] = rand_fn(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
# At some point both the tortoise and the hare will enter a cycle whose
# length ``p`` is a divisor of ``num``. Once in that cycle, at some point
# the tortoise and hare will end up on the same value modulo ``p``.
# We can detect when this happens because the position difference between
# the tortoise and the hare will share a common divisor with ``num``.
lowerCAmelCase__ : List[str] = gcd(hare - tortoise , SCREAMING_SNAKE_CASE_ )
if divisor == 1:
# No common divisor yet, just keep searching.
continue
else:
# We found a common divisor!
if divisor == num:
# Unfortunately, the divisor is ``num`` itself and is useless.
break
else:
# The divisor is a nontrivial factor of ``num``!
return divisor
# If we made it here, then this attempt failed.
# We need to pick a new starting seed for the tortoise and hare
# in addition to a new step value for the random function.
# To keep this example implementation deterministic, the
# new values will be generated based on currently available
# values instead of using something like ``random.randint``.
# We can use the hare's position as the new seed.
# This is actually what Richard Brent's the "optimized" variant does.
lowerCAmelCase__ : List[Any] = hare
# The new step value for the random function can just be incremented.
# At first the results will be similar to what the old function would
# have produced, but the value will quickly diverge after a bit.
step += 1
# We haven't found a divisor within the requested number of attempts.
# We were unlucky or ``num`` itself is actually prime.
return None
if __name__ == "__main__":
import argparse
lowerCamelCase__ = argparse.ArgumentParser()
parser.add_argument(
"""num""",
type=int,
help="""The value to find a divisor of""",
)
parser.add_argument(
"""--attempts""",
type=int,
default=3,
help="""The number of attempts before giving up""",
)
lowerCamelCase__ = parser.parse_args()
lowerCamelCase__ = pollard_rho(args.num, attempts=args.attempts)
if divisor is None:
print(F"""{args.num} is probably prime""")
else:
lowerCamelCase__ = args.num // divisor
print(F"""{args.num} = {divisor} * {quotient}""")
| 363
|
import gc
import random
import unittest
import numpy as np
import torch
from transformers import (
CLIPImageProcessor,
CLIPTextConfig,
CLIPTextModelWithProjection,
CLIPTokenizer,
CLIPVisionConfig,
CLIPVisionModelWithProjection,
)
from diffusers import (
DiffusionPipeline,
UnCLIPImageVariationPipeline,
UnCLIPScheduler,
UNetaDConditionModel,
UNetaDModel,
)
from diffusers.pipelines.unclip.text_proj import UnCLIPTextProjModel
from diffusers.utils import floats_tensor, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, load_image, require_torch_gpu, skip_mps
from ..pipeline_params import IMAGE_VARIATION_BATCH_PARAMS, IMAGE_VARIATION_PARAMS
from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference
enable_full_determinism()
class A__ ( __magic_name__ , unittest.TestCase ):
lowercase = UnCLIPImageVariationPipeline
lowercase = IMAGE_VARIATION_PARAMS - {'height', 'width', 'guidance_scale'}
lowercase = IMAGE_VARIATION_BATCH_PARAMS
lowercase = [
'generator',
'return_dict',
'decoder_num_inference_steps',
'super_res_num_inference_steps',
]
lowercase = False
@property
def _lowerCamelCase ( self : List[Any] ):
'''simple docstring'''
return 32
@property
def _lowerCamelCase ( self : Union[str, Any] ):
'''simple docstring'''
return 32
@property
def _lowerCamelCase ( self : int ):
'''simple docstring'''
return self.time_input_dim
@property
def _lowerCamelCase ( self : Optional[int] ):
'''simple docstring'''
return self.time_input_dim * 4
@property
def _lowerCamelCase ( self : Any ):
'''simple docstring'''
return 100
@property
def _lowerCamelCase ( self : Optional[Any] ):
'''simple docstring'''
lowerCAmelCase__ : List[str] = CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip' )
return tokenizer
@property
def _lowerCamelCase ( self : Any ):
'''simple docstring'''
torch.manual_seed(0 )
lowerCAmelCase__ : Optional[int] = CLIPTextConfig(
bos_token_id=0 , eos_token_id=2 , hidden_size=self.text_embedder_hidden_size , projection_dim=self.text_embedder_hidden_size , intermediate_size=37 , layer_norm_eps=1E-05 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=1_000 , )
return CLIPTextModelWithProjection(a )
@property
def _lowerCamelCase ( self : List[str] ):
'''simple docstring'''
torch.manual_seed(0 )
lowerCAmelCase__ : List[Any] = CLIPVisionConfig(
hidden_size=self.text_embedder_hidden_size , projection_dim=self.text_embedder_hidden_size , num_hidden_layers=5 , num_attention_heads=4 , image_size=32 , intermediate_size=37 , patch_size=1 , )
return CLIPVisionModelWithProjection(a )
@property
def _lowerCamelCase ( self : Optional[Any] ):
'''simple docstring'''
torch.manual_seed(0 )
lowerCAmelCase__ : Union[str, Any] = {
'clip_embeddings_dim': self.text_embedder_hidden_size,
'time_embed_dim': self.time_embed_dim,
'cross_attention_dim': self.cross_attention_dim,
}
lowerCAmelCase__ : Optional[Any] = UnCLIPTextProjModel(**a )
return model
@property
def _lowerCamelCase ( self : List[str] ):
'''simple docstring'''
torch.manual_seed(0 )
lowerCAmelCase__ : str = {
'sample_size': 32,
# RGB in channels
'in_channels': 3,
# Out channels is double in channels because predicts mean and variance
'out_channels': 6,
'down_block_types': ('ResnetDownsampleBlock2D', 'SimpleCrossAttnDownBlock2D'),
'up_block_types': ('SimpleCrossAttnUpBlock2D', 'ResnetUpsampleBlock2D'),
'mid_block_type': 'UNetMidBlock2DSimpleCrossAttn',
'block_out_channels': (self.block_out_channels_a, self.block_out_channels_a * 2),
'layers_per_block': 1,
'cross_attention_dim': self.cross_attention_dim,
'attention_head_dim': 4,
'resnet_time_scale_shift': 'scale_shift',
'class_embed_type': 'identity',
}
lowerCAmelCase__ : str = UNetaDConditionModel(**a )
return model
@property
def _lowerCamelCase ( self : str ):
'''simple docstring'''
return {
"sample_size": 64,
"layers_per_block": 1,
"down_block_types": ("ResnetDownsampleBlock2D", "ResnetDownsampleBlock2D"),
"up_block_types": ("ResnetUpsampleBlock2D", "ResnetUpsampleBlock2D"),
"block_out_channels": (self.block_out_channels_a, self.block_out_channels_a * 2),
"in_channels": 6,
"out_channels": 3,
}
@property
def _lowerCamelCase ( self : str ):
'''simple docstring'''
torch.manual_seed(0 )
lowerCAmelCase__ : Any = UNetaDModel(**self.dummy_super_res_kwargs )
return model
@property
def _lowerCamelCase ( self : int ):
'''simple docstring'''
torch.manual_seed(1 )
lowerCAmelCase__ : List[str] = UNetaDModel(**self.dummy_super_res_kwargs )
return model
def _lowerCamelCase ( self : Optional[int] ):
'''simple docstring'''
lowerCAmelCase__ : Optional[Any] = self.dummy_decoder
lowerCAmelCase__ : Optional[int] = self.dummy_text_proj
lowerCAmelCase__ : Any = self.dummy_text_encoder
lowerCAmelCase__ : Any = self.dummy_tokenizer
lowerCAmelCase__ : Any = self.dummy_super_res_first
lowerCAmelCase__ : Optional[int] = self.dummy_super_res_last
lowerCAmelCase__ : Dict = UnCLIPScheduler(
variance_type='learned_range' , prediction_type='epsilon' , num_train_timesteps=1_000 , )
lowerCAmelCase__ : Any = UnCLIPScheduler(
variance_type='fixed_small_log' , prediction_type='epsilon' , num_train_timesteps=1_000 , )
lowerCAmelCase__ : Any = CLIPImageProcessor(crop_size=32 , size=32 )
lowerCAmelCase__ : Optional[int] = self.dummy_image_encoder
return {
"decoder": decoder,
"text_encoder": text_encoder,
"tokenizer": tokenizer,
"text_proj": text_proj,
"feature_extractor": feature_extractor,
"image_encoder": image_encoder,
"super_res_first": super_res_first,
"super_res_last": super_res_last,
"decoder_scheduler": decoder_scheduler,
"super_res_scheduler": super_res_scheduler,
}
def _lowerCamelCase ( self : Any , a : Dict , a : List[str]=0 , a : List[str]=True ):
'''simple docstring'''
lowerCAmelCase__ : Dict = floats_tensor((1, 3, 32, 32) , rng=random.Random(a ) ).to(a )
if str(a ).startswith('mps' ):
lowerCAmelCase__ : Optional[int] = torch.manual_seed(a )
else:
lowerCAmelCase__ : str = torch.Generator(device=a ).manual_seed(a )
if pil_image:
lowerCAmelCase__ : Optional[int] = input_image * 0.5 + 0.5
lowerCAmelCase__ : Dict = input_image.clamp(0 , 1 )
lowerCAmelCase__ : List[Any] = input_image.cpu().permute(0 , 2 , 3 , 1 ).float().numpy()
lowerCAmelCase__ : Union[str, Any] = DiffusionPipeline.numpy_to_pil(a )[0]
return {
"image": input_image,
"generator": generator,
"decoder_num_inference_steps": 2,
"super_res_num_inference_steps": 2,
"output_type": "np",
}
def _lowerCamelCase ( self : Optional[int] ):
'''simple docstring'''
lowerCAmelCase__ : Optional[int] = 'cpu'
lowerCAmelCase__ : Any = self.get_dummy_components()
lowerCAmelCase__ : List[str] = self.pipeline_class(**a )
lowerCAmelCase__ : Dict = pipe.to(a )
pipe.set_progress_bar_config(disable=a )
lowerCAmelCase__ : Dict = self.get_dummy_inputs(a , pil_image=a )
lowerCAmelCase__ : str = pipe(**a )
lowerCAmelCase__ : Optional[Any] = output.images
lowerCAmelCase__ : str = self.get_dummy_inputs(a , pil_image=a )
lowerCAmelCase__ : Optional[int] = pipe(
**a , return_dict=a , )[0]
lowerCAmelCase__ : Optional[Any] = image[0, -3:, -3:, -1]
lowerCAmelCase__ : Tuple = image_from_tuple[0, -3:, -3:, -1]
assert image.shape == (1, 64, 64, 3)
lowerCAmelCase__ : List[str] = np.array(
[
0.9_9_9_7,
0.0_0_0_2,
0.9_9_9_7,
0.9_9_9_7,
0.9_9_6_9,
0.0_0_2_3,
0.9_9_9_7,
0.9_9_6_9,
0.9_9_7_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
def _lowerCamelCase ( self : List[str] ):
'''simple docstring'''
lowerCAmelCase__ : Union[str, Any] = 'cpu'
lowerCAmelCase__ : Dict = self.get_dummy_components()
lowerCAmelCase__ : Optional[int] = self.pipeline_class(**a )
lowerCAmelCase__ : int = pipe.to(a )
pipe.set_progress_bar_config(disable=a )
lowerCAmelCase__ : List[Any] = self.get_dummy_inputs(a , pil_image=a )
lowerCAmelCase__ : List[str] = pipe(**a )
lowerCAmelCase__ : Union[str, Any] = output.images
lowerCAmelCase__ : int = self.get_dummy_inputs(a , pil_image=a )
lowerCAmelCase__ : int = pipe(
**a , return_dict=a , )[0]
lowerCAmelCase__ : Tuple = image[0, -3:, -3:, -1]
lowerCAmelCase__ : Tuple = image_from_tuple[0, -3:, -3:, -1]
assert image.shape == (1, 64, 64, 3)
lowerCAmelCase__ : str = np.array([0.9_9_9_7, 0.0_0_0_3, 0.9_9_9_7, 0.9_9_9_7, 0.9_9_7_0, 0.0_0_2_4, 0.9_9_9_7, 0.9_9_7_1, 0.9_9_7_1] )
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 _lowerCamelCase ( self : List[str] ):
'''simple docstring'''
lowerCAmelCase__ : Tuple = 'cpu'
lowerCAmelCase__ : int = self.get_dummy_components()
lowerCAmelCase__ : Tuple = self.pipeline_class(**a )
lowerCAmelCase__ : Union[str, Any] = pipe.to(a )
pipe.set_progress_bar_config(disable=a )
lowerCAmelCase__ : Tuple = self.get_dummy_inputs(a , pil_image=a )
lowerCAmelCase__ : List[str] = [
pipeline_inputs['image'],
pipeline_inputs['image'],
]
lowerCAmelCase__ : Optional[int] = pipe(**a )
lowerCAmelCase__ : Tuple = output.images
lowerCAmelCase__ : List[str] = self.get_dummy_inputs(a , pil_image=a )
lowerCAmelCase__ : Union[str, Any] = [
tuple_pipeline_inputs['image'],
tuple_pipeline_inputs['image'],
]
lowerCAmelCase__ : str = pipe(
**a , return_dict=a , )[0]
lowerCAmelCase__ : Optional[Any] = image[0, -3:, -3:, -1]
lowerCAmelCase__ : Any = image_from_tuple[0, -3:, -3:, -1]
assert image.shape == (2, 64, 64, 3)
lowerCAmelCase__ : Union[str, Any] = np.array(
[
0.9_9_9_7,
0.9_9_8_9,
0.0_0_0_8,
0.0_0_2_1,
0.9_9_6_0,
0.0_0_1_8,
0.0_0_1_4,
0.0_0_0_2,
0.9_9_3_3,
] )
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 _lowerCamelCase ( self : Tuple ):
'''simple docstring'''
lowerCAmelCase__ : Tuple = torch.device('cpu' )
class A__ :
lowercase = 1
lowerCAmelCase__ : Optional[Any] = self.get_dummy_components()
lowerCAmelCase__ : Dict = self.pipeline_class(**a )
lowerCAmelCase__ : Optional[Any] = pipe.to(a )
pipe.set_progress_bar_config(disable=a )
lowerCAmelCase__ : Optional[int] = torch.Generator(device=a ).manual_seed(0 )
lowerCAmelCase__ : Optional[int] = pipe.decoder.dtype
lowerCAmelCase__ : Union[str, Any] = 1
lowerCAmelCase__ : str = (
batch_size,
pipe.decoder.config.in_channels,
pipe.decoder.config.sample_size,
pipe.decoder.config.sample_size,
)
lowerCAmelCase__ : List[Any] = pipe.prepare_latents(
a , dtype=a , device=a , generator=a , latents=a , scheduler=DummyScheduler() )
lowerCAmelCase__ : List[str] = (
batch_size,
pipe.super_res_first.config.in_channels // 2,
pipe.super_res_first.config.sample_size,
pipe.super_res_first.config.sample_size,
)
lowerCAmelCase__ : Any = pipe.prepare_latents(
a , dtype=a , device=a , generator=a , latents=a , scheduler=DummyScheduler() )
lowerCAmelCase__ : List[Any] = self.get_dummy_inputs(a , pil_image=a )
lowerCAmelCase__ : Optional[int] = pipe(
**a , decoder_latents=a , super_res_latents=a ).images
lowerCAmelCase__ : Optional[Any] = self.get_dummy_inputs(a , pil_image=a )
# Don't pass image, instead pass embedding
lowerCAmelCase__ : Union[str, Any] = pipeline_inputs.pop('image' )
lowerCAmelCase__ : Union[str, Any] = pipe.image_encoder(a ).image_embeds
lowerCAmelCase__ : List[Any] = pipe(
**a , decoder_latents=a , super_res_latents=a , image_embeddings=a , ).images
# make sure passing text embeddings manually is identical
assert np.abs(img_out_a - img_out_a ).max() < 1E-4
@skip_mps
def _lowerCamelCase ( self : List[Any] ):
'''simple docstring'''
lowerCAmelCase__ : Tuple = torch_device == 'cpu'
# Check is relaxed because there is not a torch 2.0 sliced attention added kv processor
lowerCAmelCase__ : int = 1E-2
self._test_attention_slicing_forward_pass(
test_max_difference=a , expected_max_diff=a )
@skip_mps
def _lowerCamelCase ( self : Tuple ):
'''simple docstring'''
lowerCAmelCase__ : Union[str, Any] = torch_device == 'cpu'
lowerCAmelCase__ : Any = True
lowerCAmelCase__ : Optional[Any] = [
'decoder_num_inference_steps',
'super_res_num_inference_steps',
]
self._test_inference_batch_single_identical(
test_max_difference=a , relax_max_difference=a , additional_params_copy_to_batched_inputs=a , )
def _lowerCamelCase ( self : Dict ):
'''simple docstring'''
lowerCAmelCase__ : Tuple = [
'decoder_num_inference_steps',
'super_res_num_inference_steps',
]
if torch_device == "mps":
# TODO: MPS errors with larger batch sizes
lowerCAmelCase__ : List[str] = [2, 3]
self._test_inference_batch_consistent(
batch_sizes=a , additional_params_copy_to_batched_inputs=a , )
else:
self._test_inference_batch_consistent(
additional_params_copy_to_batched_inputs=a )
@skip_mps
def _lowerCamelCase ( self : Optional[Any] ):
'''simple docstring'''
return super().test_dict_tuple_outputs_equivalent()
@skip_mps
def _lowerCamelCase ( self : Optional[int] ):
'''simple docstring'''
return super().test_save_load_local()
@skip_mps
def _lowerCamelCase ( self : str ):
'''simple docstring'''
return super().test_save_load_optional_components()
@slow
@require_torch_gpu
class A__ ( unittest.TestCase ):
def _lowerCamelCase ( self : Tuple ):
'''simple docstring'''
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def _lowerCamelCase ( self : Optional[int] ):
'''simple docstring'''
lowerCAmelCase__ : Dict = load_image(
'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/unclip/cat.png' )
lowerCAmelCase__ : List[Any] = load_numpy(
'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
'/unclip/karlo_v1_alpha_cat_variation_fp16.npy' )
lowerCAmelCase__ : Tuple = UnCLIPImageVariationPipeline.from_pretrained(
'kakaobrain/karlo-v1-alpha-image-variations' , torch_dtype=torch.floataa )
lowerCAmelCase__ : Union[str, Any] = pipeline.to(a )
pipeline.set_progress_bar_config(disable=a )
lowerCAmelCase__ : Dict = torch.Generator(device='cpu' ).manual_seed(0 )
lowerCAmelCase__ : List[str] = pipeline(
a , generator=a , output_type='np' , )
lowerCAmelCase__ : Union[str, Any] = output.images[0]
assert image.shape == (256, 256, 3)
assert_mean_pixel_difference(a , a , 15 )
| 307
| 0
|
def lowerCAmelCase__ ( ) -> int:
return [
a * b * (1_000 - a - b)
for a in range(1 , 999 )
for b in range(SCREAMING_SNAKE_CASE_ , 999 )
if (a * a + b * b == (1_000 - a - b) ** 2)
][0]
if __name__ == "__main__":
print(F"""{solution() = }""")
| 364
|
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_ ) -> str:
stooge(SCREAMING_SNAKE_CASE_ , 0 , len(SCREAMING_SNAKE_CASE_ ) - 1 )
return arr
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) -> Dict:
if i >= h:
return
# If first element is smaller than the last then swap them
if arr[i] > arr[h]:
lowerCAmelCase__ , lowerCAmelCase__ : Tuple = arr[h], arr[i]
# If there are more than 2 elements in the array
if h - i + 1 > 2:
lowerCAmelCase__ : Union[str, Any] = (int)((h - i + 1) / 3 )
# Recursively sort first 2/3 elements
stooge(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , (h - t) )
# Recursively sort last 2/3 elements
stooge(SCREAMING_SNAKE_CASE_ , i + t , (SCREAMING_SNAKE_CASE_) )
# Recursively sort first 2/3 elements
stooge(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , (h - t) )
if __name__ == "__main__":
lowerCamelCase__ = input("""Enter numbers separated by a comma:\n""").strip()
lowerCamelCase__ = [int(item) for item in user_input.split(""",""")]
print(stooge_sort(unsorted))
| 307
| 0
|
import warnings
from typing import List
import numpy as np
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
from ...utils import is_flax_available, is_tf_available, is_torch_available
class A__ ( __magic_name__ ):
lowercase = ['image_processor', 'tokenizer']
lowercase = 'OwlViTImageProcessor'
lowercase = ('CLIPTokenizer', 'CLIPTokenizerFast')
def __init__( self : List[str] , a : Tuple=None , a : int=None , **a : Optional[int] ):
lowerCAmelCase__ : str = None
if "feature_extractor" in kwargs:
warnings.warn(
'The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'
' instead.' , a , )
lowerCAmelCase__ : Tuple = kwargs.pop('feature_extractor' )
lowerCAmelCase__ : int = 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__(a , a )
def __call__( self : Tuple , a : str=None , a : Dict=None , a : List[Any]=None , a : Optional[Any]="max_length" , a : str="np" , **a : Union[str, Any] ):
if text is None and query_images is None and images is None:
raise ValueError(
'You have to specify at least one text or query image or image. All three cannot be none.' )
if text is not None:
if isinstance(a , a ) or (isinstance(a , a ) and not isinstance(text[0] , a )):
lowerCAmelCase__ : List[str] = [self.tokenizer(a , padding=a , return_tensors=a , **a )]
elif isinstance(a , a ) and isinstance(text[0] , a ):
lowerCAmelCase__ : Optional[int] = []
# Maximum number of queries across batch
lowerCAmelCase__ : List[Any] = max([len(a ) for t in text] )
# Pad all batch samples to max number of text queries
for t in text:
if len(a ) != max_num_queries:
lowerCAmelCase__ : Optional[int] = t + [' '] * (max_num_queries - len(a ))
lowerCAmelCase__ : str = self.tokenizer(a , padding=a , return_tensors=a , **a )
encodings.append(a )
else:
raise TypeError('Input text should be a string, a list of strings or a nested list of strings' )
if return_tensors == "np":
lowerCAmelCase__ : List[str] = np.concatenate([encoding['input_ids'] for encoding in encodings] , axis=0 )
lowerCAmelCase__ : Union[str, Any] = np.concatenate([encoding['attention_mask'] for encoding in encodings] , axis=0 )
elif return_tensors == "jax" and is_flax_available():
import jax.numpy as jnp
lowerCAmelCase__ : Tuple = jnp.concatenate([encoding['input_ids'] for encoding in encodings] , axis=0 )
lowerCAmelCase__ : List[Any] = jnp.concatenate([encoding['attention_mask'] for encoding in encodings] , axis=0 )
elif return_tensors == "pt" and is_torch_available():
import torch
lowerCAmelCase__ : Union[str, Any] = torch.cat([encoding['input_ids'] for encoding in encodings] , dim=0 )
lowerCAmelCase__ : Optional[Any] = torch.cat([encoding['attention_mask'] for encoding in encodings] , dim=0 )
elif return_tensors == "tf" and is_tf_available():
import tensorflow as tf
lowerCAmelCase__ : Union[str, Any] = tf.stack([encoding['input_ids'] for encoding in encodings] , axis=0 )
lowerCAmelCase__ : Optional[int] = tf.stack([encoding['attention_mask'] for encoding in encodings] , axis=0 )
else:
raise ValueError('Target return tensor type could not be returned' )
lowerCAmelCase__ : Optional[int] = BatchEncoding()
lowerCAmelCase__ : Dict = input_ids
lowerCAmelCase__ : Dict = attention_mask
if query_images is not None:
lowerCAmelCase__ : List[str] = BatchEncoding()
lowerCAmelCase__ : Union[str, Any] = self.image_processor(
a , return_tensors=a , **a ).pixel_values
lowerCAmelCase__ : Union[str, Any] = query_pixel_values
if images is not None:
lowerCAmelCase__ : Dict = self.image_processor(a , return_tensors=a , **a )
if text is not None and images is not None:
lowerCAmelCase__ : List[Any] = image_features.pixel_values
return encoding
elif query_images is not None and images is not None:
lowerCAmelCase__ : Optional[Any] = image_features.pixel_values
return encoding
elif text is not None or query_images is not None:
return encoding
else:
return BatchEncoding(data=dict(**a ) , tensor_type=a )
def _lowerCamelCase ( self : List[Any] , *a : int , **a : Tuple ):
return self.image_processor.post_process(*a , **a )
def _lowerCamelCase ( self : int , *a : Tuple , **a : List[Any] ):
return self.image_processor.post_process_object_detection(*a , **a )
def _lowerCamelCase ( self : Tuple , *a : Tuple , **a : Optional[int] ):
return self.image_processor.post_process_image_guided_detection(*a , **a )
def _lowerCamelCase ( self : str , *a : Any , **a : Optional[Any] ):
return self.tokenizer.batch_decode(*a , **a )
def _lowerCamelCase ( self : Optional[Any] , *a : List[str] , **a : int ):
return self.tokenizer.decode(*a , **a )
@property
def _lowerCamelCase ( self : Optional[Any] ):
warnings.warn(
'`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.' , a , )
return self.image_processor_class
@property
def _lowerCamelCase ( self : Optional[int] ):
warnings.warn(
'`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.' , a , )
return self.image_processor
| 365
|
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_speech_available,
is_tf_available,
is_torch_available,
)
lowerCamelCase__ = {
"""configuration_speech_to_text""": ["""SPEECH_TO_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """Speech2TextConfig"""],
"""processing_speech_to_text""": ["""Speech2TextProcessor"""],
}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCamelCase__ = ["""Speech2TextTokenizer"""]
try:
if not is_speech_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCamelCase__ = ["""Speech2TextFeatureExtractor"""]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCamelCase__ = [
"""TF_SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""TFSpeech2TextForConditionalGeneration""",
"""TFSpeech2TextModel""",
"""TFSpeech2TextPreTrainedModel""",
]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCamelCase__ = [
"""SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""Speech2TextForConditionalGeneration""",
"""Speech2TextModel""",
"""Speech2TextPreTrainedModel""",
]
if TYPE_CHECKING:
from .configuration_speech_to_text import SPEECH_TO_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP, SpeechaTextConfig
from .processing_speech_to_text import SpeechaTextProcessor
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_speech_to_text import SpeechaTextTokenizer
try:
if not is_speech_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .feature_extraction_speech_to_text import SpeechaTextFeatureExtractor
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_speech_to_text import (
TF_SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST,
TFSpeechaTextForConditionalGeneration,
TFSpeechaTextModel,
TFSpeechaTextPreTrainedModel,
)
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_speech_to_text import (
SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST,
SpeechaTextForConditionalGeneration,
SpeechaTextModel,
SpeechaTextPreTrainedModel,
)
else:
import sys
lowerCamelCase__ = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 307
| 0
|
from __future__ import annotations
import math
import random
from typing import Any
class A__ :
def __init__( self : Tuple ):
'''simple docstring'''
lowerCAmelCase__ : list[Any] = []
lowerCAmelCase__ : int = 0
lowerCAmelCase__ : int = 0
def _lowerCamelCase ( self : int ):
'''simple docstring'''
return self.head == self.tail
def _lowerCamelCase ( self : Tuple , a : Any ):
'''simple docstring'''
self.data.append(a )
lowerCAmelCase__ : int = self.tail + 1
def _lowerCamelCase ( self : List[str] ):
'''simple docstring'''
lowerCAmelCase__ : Optional[int] = self.data[self.head]
lowerCAmelCase__ : Optional[int] = self.head + 1
return ret
def _lowerCamelCase ( self : List[str] ):
'''simple docstring'''
return self.tail - self.head
def _lowerCamelCase ( self : Optional[int] ):
'''simple docstring'''
print(self.data )
print('**************' )
print(self.data[self.head : self.tail] )
class A__ :
def __init__( self : List[Any] , a : Any ):
'''simple docstring'''
lowerCAmelCase__ : Optional[Any] = data
lowerCAmelCase__ : MyNode | None = None
lowerCAmelCase__ : MyNode | None = None
lowerCAmelCase__ : int = 1
def _lowerCamelCase ( self : List[Any] ):
'''simple docstring'''
return self.data
def _lowerCamelCase ( self : List[str] ):
'''simple docstring'''
return self.left
def _lowerCamelCase ( self : Any ):
'''simple docstring'''
return self.right
def _lowerCamelCase ( self : Optional[int] ):
'''simple docstring'''
return self.height
def _lowerCamelCase ( self : List[Any] , a : Any ):
'''simple docstring'''
lowerCAmelCase__ : Optional[Any] = data
def _lowerCamelCase ( self : List[Any] , a : MyNode | None ):
'''simple docstring'''
lowerCAmelCase__ : Optional[Any] = node
def _lowerCamelCase ( self : List[str] , a : MyNode | None ):
'''simple docstring'''
lowerCAmelCase__ : List[str] = node
def _lowerCamelCase ( self : List[Any] , a : int ):
'''simple docstring'''
lowerCAmelCase__ : Dict = height
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_ ) -> int:
if node is None:
return 0
return node.get_height()
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) -> int:
if a > b:
return a
return b
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_ ) -> MyNode:
print('left rotation node:' , node.get_data() )
lowerCAmelCase__ : Optional[Any] = node.get_left()
assert ret is not None
node.set_left(ret.get_right() )
ret.set_right(SCREAMING_SNAKE_CASE_ )
lowerCAmelCase__ : Union[str, Any] = my_max(get_height(node.get_right() ) , get_height(node.get_left() ) ) + 1
node.set_height(SCREAMING_SNAKE_CASE_ )
lowerCAmelCase__ : Tuple = my_max(get_height(ret.get_right() ) , get_height(ret.get_left() ) ) + 1
ret.set_height(SCREAMING_SNAKE_CASE_ )
return ret
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_ ) -> MyNode:
print('right rotation node:' , node.get_data() )
lowerCAmelCase__ : str = node.get_right()
assert ret is not None
node.set_right(ret.get_left() )
ret.set_left(SCREAMING_SNAKE_CASE_ )
lowerCAmelCase__ : Optional[int] = my_max(get_height(node.get_right() ) , get_height(node.get_left() ) ) + 1
node.set_height(SCREAMING_SNAKE_CASE_ )
lowerCAmelCase__ : Dict = my_max(get_height(ret.get_right() ) , get_height(ret.get_left() ) ) + 1
ret.set_height(SCREAMING_SNAKE_CASE_ )
return ret
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_ ) -> MyNode:
lowerCAmelCase__ : Any = node.get_left()
assert left_child is not None
node.set_left(left_rotation(SCREAMING_SNAKE_CASE_ ) )
return right_rotation(SCREAMING_SNAKE_CASE_ )
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_ ) -> MyNode:
lowerCAmelCase__ : Dict = node.get_right()
assert right_child is not None
node.set_right(right_rotation(SCREAMING_SNAKE_CASE_ ) )
return left_rotation(SCREAMING_SNAKE_CASE_ )
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) -> MyNode | None:
if node is None:
return MyNode(SCREAMING_SNAKE_CASE_ )
if data < node.get_data():
node.set_left(insert_node(node.get_left() , SCREAMING_SNAKE_CASE_ ) )
if (
get_height(node.get_left() ) - get_height(node.get_right() ) == 2
): # an unbalance detected
lowerCAmelCase__ : 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
lowerCAmelCase__ : Tuple = right_rotation(SCREAMING_SNAKE_CASE_ )
else:
lowerCAmelCase__ : str = lr_rotation(SCREAMING_SNAKE_CASE_ )
else:
node.set_right(insert_node(node.get_right() , SCREAMING_SNAKE_CASE_ ) )
if get_height(node.get_right() ) - get_height(node.get_left() ) == 2:
lowerCAmelCase__ : Tuple = node.get_right()
assert right_child is not None
if data < right_child.get_data():
lowerCAmelCase__ : Tuple = rl_rotation(SCREAMING_SNAKE_CASE_ )
else:
lowerCAmelCase__ : Union[str, Any] = left_rotation(SCREAMING_SNAKE_CASE_ )
lowerCAmelCase__ : int = my_max(get_height(node.get_right() ) , get_height(node.get_left() ) ) + 1
node.set_height(SCREAMING_SNAKE_CASE_ )
return node
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_ ) -> Any:
while True:
lowerCAmelCase__ : Optional[int] = root.get_right()
if right_child is None:
break
lowerCAmelCase__ : Optional[Any] = right_child
return root.get_data()
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_ ) -> Any:
while True:
lowerCAmelCase__ : Tuple = root.get_left()
if left_child is None:
break
lowerCAmelCase__ : List[Any] = left_child
return root.get_data()
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) -> MyNode | None:
lowerCAmelCase__ : str = root.get_left()
lowerCAmelCase__ : Dict = root.get_right()
if root.get_data() == data:
if left_child is not None and right_child is not None:
lowerCAmelCase__ : Union[str, Any] = get_left_most(SCREAMING_SNAKE_CASE_ )
root.set_data(SCREAMING_SNAKE_CASE_ )
root.set_right(del_node(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) )
elif left_child is not None:
lowerCAmelCase__ : Optional[Any] = left_child
elif right_child is not None:
lowerCAmelCase__ : Optional[int] = 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(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) )
else: # root.get_data() < data
if right_child is None:
return root
else:
root.set_right(del_node(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) )
if get_height(SCREAMING_SNAKE_CASE_ ) - get_height(SCREAMING_SNAKE_CASE_ ) == 2:
assert right_child is not None
if get_height(right_child.get_right() ) > get_height(right_child.get_left() ):
lowerCAmelCase__ : Dict = left_rotation(SCREAMING_SNAKE_CASE_ )
else:
lowerCAmelCase__ : List[str] = rl_rotation(SCREAMING_SNAKE_CASE_ )
elif get_height(SCREAMING_SNAKE_CASE_ ) - get_height(SCREAMING_SNAKE_CASE_ ) == -2:
assert left_child is not None
if get_height(left_child.get_left() ) > get_height(left_child.get_right() ):
lowerCAmelCase__ : Optional[int] = right_rotation(SCREAMING_SNAKE_CASE_ )
else:
lowerCAmelCase__ : Dict = lr_rotation(SCREAMING_SNAKE_CASE_ )
lowerCAmelCase__ : str = my_max(get_height(root.get_right() ) , get_height(root.get_left() ) ) + 1
root.set_height(SCREAMING_SNAKE_CASE_ )
return root
class A__ :
def __init__( self : Any ):
'''simple docstring'''
lowerCAmelCase__ : MyNode | None = None
def _lowerCamelCase ( self : Union[str, Any] ):
'''simple docstring'''
return get_height(self.root )
def _lowerCamelCase ( self : List[Any] , a : Any ):
'''simple docstring'''
print('insert:' + str(a ) )
lowerCAmelCase__ : Optional[Any] = insert_node(self.root , a )
def _lowerCamelCase ( self : Tuple , a : Any ):
'''simple docstring'''
print('delete:' + str(a ) )
if self.root is None:
print('Tree is empty!' )
return
lowerCAmelCase__ : List[str] = del_node(self.root , a )
def __str__( self : Dict , ): # a level traversale, gives a more intuitive look on the tree
'''simple docstring'''
lowerCAmelCase__ : int = ''
lowerCAmelCase__ : int = MyQueue()
q.push(self.root )
lowerCAmelCase__ : Any = self.get_height()
if layer == 0:
return output
lowerCAmelCase__ : Optional[Any] = 0
while not q.is_empty():
lowerCAmelCase__ : Any = q.pop()
lowerCAmelCase__ : str = ' ' * 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
lowerCAmelCase__ : str = cnt + 1
for i in range(100 ):
if cnt == math.pow(2 , a ) - 1:
lowerCAmelCase__ : Dict = layer - 1
if layer == 0:
output += "\n*************************************"
return output
output += "\n"
break
output += "\n*************************************"
return output
def lowerCAmelCase__ ( ) -> None:
import doctest
doctest.testmod()
if __name__ == "__main__":
_test()
lowerCamelCase__ = AVLtree()
lowerCamelCase__ = 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))
| 366
|
import unittest
import numpy as np
from transformers.testing_utils import require_flax, require_tf, require_torch
from transformers.utils import (
expand_dims,
flatten_dict,
is_flax_available,
is_tf_available,
is_torch_available,
reshape,
squeeze,
transpose,
)
if is_flax_available():
import jax.numpy as jnp
if is_tf_available():
import tensorflow as tf
if is_torch_available():
import torch
class A__ ( unittest.TestCase ):
def _lowerCamelCase ( self : Any ):
'''simple docstring'''
lowerCAmelCase__ : Optional[Any] = {
'task_specific_params': {
'summarization': {'length_penalty': 1.0, 'max_length': 128, 'min_length': 12, 'num_beams': 4},
'summarization_cnn': {'length_penalty': 2.0, 'max_length': 142, 'min_length': 56, 'num_beams': 4},
'summarization_xsum': {'length_penalty': 1.0, 'max_length': 62, 'min_length': 11, 'num_beams': 6},
}
}
lowerCAmelCase__ : int = {
'task_specific_params.summarization.length_penalty': 1.0,
'task_specific_params.summarization.max_length': 128,
'task_specific_params.summarization.min_length': 12,
'task_specific_params.summarization.num_beams': 4,
'task_specific_params.summarization_cnn.length_penalty': 2.0,
'task_specific_params.summarization_cnn.max_length': 142,
'task_specific_params.summarization_cnn.min_length': 56,
'task_specific_params.summarization_cnn.num_beams': 4,
'task_specific_params.summarization_xsum.length_penalty': 1.0,
'task_specific_params.summarization_xsum.max_length': 62,
'task_specific_params.summarization_xsum.min_length': 11,
'task_specific_params.summarization_xsum.num_beams': 6,
}
self.assertEqual(flatten_dict(a ) , a )
def _lowerCamelCase ( self : Dict ):
'''simple docstring'''
lowerCAmelCase__ : Union[str, Any] = np.random.randn(3 , 4 )
self.assertTrue(np.allclose(transpose(a ) , x.transpose() ) )
lowerCAmelCase__ : List[str] = np.random.randn(3 , 4 , 5 )
self.assertTrue(np.allclose(transpose(a , axes=(1, 2, 0) ) , x.transpose((1, 2, 0) ) ) )
@require_torch
def _lowerCamelCase ( self : List[Any] ):
'''simple docstring'''
lowerCAmelCase__ : Union[str, Any] = np.random.randn(3 , 4 )
lowerCAmelCase__ : List[Any] = torch.tensor(a )
self.assertTrue(np.allclose(transpose(a ) , transpose(a ).numpy() ) )
lowerCAmelCase__ : str = np.random.randn(3 , 4 , 5 )
lowerCAmelCase__ : int = torch.tensor(a )
self.assertTrue(np.allclose(transpose(a , axes=(1, 2, 0) ) , transpose(a , axes=(1, 2, 0) ).numpy() ) )
@require_tf
def _lowerCamelCase ( self : Optional[Any] ):
'''simple docstring'''
lowerCAmelCase__ : Dict = np.random.randn(3 , 4 )
lowerCAmelCase__ : Any = tf.constant(a )
self.assertTrue(np.allclose(transpose(a ) , transpose(a ).numpy() ) )
lowerCAmelCase__ : str = np.random.randn(3 , 4 , 5 )
lowerCAmelCase__ : Dict = tf.constant(a )
self.assertTrue(np.allclose(transpose(a , axes=(1, 2, 0) ) , transpose(a , axes=(1, 2, 0) ).numpy() ) )
@require_flax
def _lowerCamelCase ( self : Union[str, Any] ):
'''simple docstring'''
lowerCAmelCase__ : Optional[Any] = np.random.randn(3 , 4 )
lowerCAmelCase__ : int = jnp.array(a )
self.assertTrue(np.allclose(transpose(a ) , np.asarray(transpose(a ) ) ) )
lowerCAmelCase__ : Any = np.random.randn(3 , 4 , 5 )
lowerCAmelCase__ : str = jnp.array(a )
self.assertTrue(np.allclose(transpose(a , axes=(1, 2, 0) ) , np.asarray(transpose(a , axes=(1, 2, 0) ) ) ) )
def _lowerCamelCase ( self : Union[str, Any] ):
'''simple docstring'''
lowerCAmelCase__ : Any = np.random.randn(3 , 4 )
self.assertTrue(np.allclose(reshape(a , (4, 3) ) , np.reshape(a , (4, 3) ) ) )
lowerCAmelCase__ : Tuple = np.random.randn(3 , 4 , 5 )
self.assertTrue(np.allclose(reshape(a , (12, 5) ) , np.reshape(a , (12, 5) ) ) )
@require_torch
def _lowerCamelCase ( self : Optional[Any] ):
'''simple docstring'''
lowerCAmelCase__ : Optional[Any] = np.random.randn(3 , 4 )
lowerCAmelCase__ : Dict = torch.tensor(a )
self.assertTrue(np.allclose(reshape(a , (4, 3) ) , reshape(a , (4, 3) ).numpy() ) )
lowerCAmelCase__ : str = np.random.randn(3 , 4 , 5 )
lowerCAmelCase__ : str = torch.tensor(a )
self.assertTrue(np.allclose(reshape(a , (12, 5) ) , reshape(a , (12, 5) ).numpy() ) )
@require_tf
def _lowerCamelCase ( self : List[str] ):
'''simple docstring'''
lowerCAmelCase__ : Dict = np.random.randn(3 , 4 )
lowerCAmelCase__ : List[Any] = tf.constant(a )
self.assertTrue(np.allclose(reshape(a , (4, 3) ) , reshape(a , (4, 3) ).numpy() ) )
lowerCAmelCase__ : Dict = np.random.randn(3 , 4 , 5 )
lowerCAmelCase__ : Any = tf.constant(a )
self.assertTrue(np.allclose(reshape(a , (12, 5) ) , reshape(a , (12, 5) ).numpy() ) )
@require_flax
def _lowerCamelCase ( self : Any ):
'''simple docstring'''
lowerCAmelCase__ : Dict = np.random.randn(3 , 4 )
lowerCAmelCase__ : List[str] = jnp.array(a )
self.assertTrue(np.allclose(reshape(a , (4, 3) ) , np.asarray(reshape(a , (4, 3) ) ) ) )
lowerCAmelCase__ : str = np.random.randn(3 , 4 , 5 )
lowerCAmelCase__ : Union[str, Any] = jnp.array(a )
self.assertTrue(np.allclose(reshape(a , (12, 5) ) , np.asarray(reshape(a , (12, 5) ) ) ) )
def _lowerCamelCase ( self : Union[str, Any] ):
'''simple docstring'''
lowerCAmelCase__ : List[str] = np.random.randn(1 , 3 , 4 )
self.assertTrue(np.allclose(squeeze(a ) , np.squeeze(a ) ) )
lowerCAmelCase__ : int = np.random.randn(1 , 4 , 1 , 5 )
self.assertTrue(np.allclose(squeeze(a , axis=2 ) , np.squeeze(a , axis=2 ) ) )
@require_torch
def _lowerCamelCase ( self : Any ):
'''simple docstring'''
lowerCAmelCase__ : Optional[int] = np.random.randn(1 , 3 , 4 )
lowerCAmelCase__ : str = torch.tensor(a )
self.assertTrue(np.allclose(squeeze(a ) , squeeze(a ).numpy() ) )
lowerCAmelCase__ : Optional[Any] = np.random.randn(1 , 4 , 1 , 5 )
lowerCAmelCase__ : Dict = torch.tensor(a )
self.assertTrue(np.allclose(squeeze(a , axis=2 ) , squeeze(a , axis=2 ).numpy() ) )
@require_tf
def _lowerCamelCase ( self : str ):
'''simple docstring'''
lowerCAmelCase__ : List[str] = np.random.randn(1 , 3 , 4 )
lowerCAmelCase__ : Any = tf.constant(a )
self.assertTrue(np.allclose(squeeze(a ) , squeeze(a ).numpy() ) )
lowerCAmelCase__ : int = np.random.randn(1 , 4 , 1 , 5 )
lowerCAmelCase__ : str = tf.constant(a )
self.assertTrue(np.allclose(squeeze(a , axis=2 ) , squeeze(a , axis=2 ).numpy() ) )
@require_flax
def _lowerCamelCase ( self : Optional[int] ):
'''simple docstring'''
lowerCAmelCase__ : List[str] = np.random.randn(1 , 3 , 4 )
lowerCAmelCase__ : Union[str, Any] = jnp.array(a )
self.assertTrue(np.allclose(squeeze(a ) , np.asarray(squeeze(a ) ) ) )
lowerCAmelCase__ : str = np.random.randn(1 , 4 , 1 , 5 )
lowerCAmelCase__ : Optional[Any] = jnp.array(a )
self.assertTrue(np.allclose(squeeze(a , axis=2 ) , np.asarray(squeeze(a , axis=2 ) ) ) )
def _lowerCamelCase ( self : Any ):
'''simple docstring'''
lowerCAmelCase__ : Union[str, Any] = np.random.randn(3 , 4 )
self.assertTrue(np.allclose(expand_dims(a , axis=1 ) , np.expand_dims(a , axis=1 ) ) )
@require_torch
def _lowerCamelCase ( self : Dict ):
'''simple docstring'''
lowerCAmelCase__ : str = np.random.randn(3 , 4 )
lowerCAmelCase__ : str = torch.tensor(a )
self.assertTrue(np.allclose(expand_dims(a , axis=1 ) , expand_dims(a , axis=1 ).numpy() ) )
@require_tf
def _lowerCamelCase ( self : Union[str, Any] ):
'''simple docstring'''
lowerCAmelCase__ : Tuple = np.random.randn(3 , 4 )
lowerCAmelCase__ : Any = tf.constant(a )
self.assertTrue(np.allclose(expand_dims(a , axis=1 ) , expand_dims(a , axis=1 ).numpy() ) )
@require_flax
def _lowerCamelCase ( self : Optional[Any] ):
'''simple docstring'''
lowerCAmelCase__ : int = np.random.randn(3 , 4 )
lowerCAmelCase__ : Tuple = jnp.array(a )
self.assertTrue(np.allclose(expand_dims(a , axis=1 ) , np.asarray(expand_dims(a , axis=1 ) ) ) )
| 307
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|
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
lowerCamelCase__ = logging.get_logger(__name__)
class A__ ( __magic_name__ , __magic_name__ ):
lowercase = 'maskformer-swin'
lowercase = {
'num_attention_heads': 'num_heads',
'num_hidden_layers': 'num_layers',
}
def __init__( self : Optional[int] , a : List[str]=224 , a : Union[str, Any]=4 , a : Optional[int]=3 , a : List[str]=96 , a : int=[2, 2, 6, 2] , a : Optional[Any]=[3, 6, 12, 24] , a : Optional[int]=7 , a : Union[str, Any]=4.0 , a : Dict=True , a : List[Any]=0.0 , a : List[Any]=0.0 , a : Optional[int]=0.1 , a : int="gelu" , a : Tuple=False , a : Union[str, Any]=0.0_2 , a : Optional[int]=1E-5 , a : Optional[int]=None , a : Optional[Any]=None , **a : Optional[Any] , ):
'''simple docstring'''
super().__init__(**a )
lowerCAmelCase__ : Optional[int] = image_size
lowerCAmelCase__ : Optional[Any] = patch_size
lowerCAmelCase__ : Optional[int] = num_channels
lowerCAmelCase__ : int = embed_dim
lowerCAmelCase__ : Union[str, Any] = depths
lowerCAmelCase__ : List[str] = len(a )
lowerCAmelCase__ : str = num_heads
lowerCAmelCase__ : Any = window_size
lowerCAmelCase__ : List[Any] = mlp_ratio
lowerCAmelCase__ : Dict = qkv_bias
lowerCAmelCase__ : List[Any] = hidden_dropout_prob
lowerCAmelCase__ : List[Any] = attention_probs_dropout_prob
lowerCAmelCase__ : int = drop_path_rate
lowerCAmelCase__ : Optional[int] = hidden_act
lowerCAmelCase__ : Union[str, Any] = use_absolute_embeddings
lowerCAmelCase__ : Tuple = layer_norm_eps
lowerCAmelCase__ : Optional[Any] = initializer_range
# we set the hidden_size attribute in order to make Swin work with VisionEncoderDecoderModel
# this indicates the channel dimension after the last stage of the model
lowerCAmelCase__ : List[Any] = int(embed_dim * 2 ** (len(a ) - 1) )
lowerCAmelCase__ : List[Any] = ['stem'] + [f'''stage{idx}''' for idx in range(1 , len(a ) + 1 )]
lowerCAmelCase__ : int = get_aligned_output_features_output_indices(
out_features=a , out_indices=a , stage_names=self.stage_names )
| 367
|
# Lint as: python3
# pylint: enable=line-too-long
# pylint: disable=g-import-not-at-top,g-bad-import-order,wrong-import-position
lowerCamelCase__ = """2.13.1"""
import platform
import pyarrow
from packaging import version
if version.parse(platform.python_version()) < version.parse("""3.7"""):
raise ImportWarning(
"""To use `datasets`, Python>=3.7 is required, and the current version of Python doesn't match this condition."""
)
if version.parse(pyarrow.__version__).major < 8:
raise ImportWarning(
"""To use `datasets`, the module `pyarrow>=8.0.0` is required, and the current version of `pyarrow` doesn't match this condition.\n"""
"""If you are running this in a Google Colab, you should probably just restart the runtime to use the right version of `pyarrow`."""
)
del platform
del pyarrow
del version
from .arrow_dataset import Dataset
from .arrow_reader import ReadInstruction
from .builder import ArrowBasedBuilder, BeamBasedBuilder, BuilderConfig, DatasetBuilder, GeneratorBasedBuilder
from .combine import concatenate_datasets, interleave_datasets
from .dataset_dict import DatasetDict, IterableDatasetDict
from .download import *
from .features import *
from .fingerprint import disable_caching, enable_caching, is_caching_enabled, set_caching_enabled
from .info import DatasetInfo, MetricInfo
from .inspect import (
get_dataset_config_info,
get_dataset_config_names,
get_dataset_infos,
get_dataset_split_names,
inspect_dataset,
inspect_metric,
list_datasets,
list_metrics,
)
from .iterable_dataset import IterableDataset
from .load import load_dataset, load_dataset_builder, load_from_disk, load_metric
from .metric import Metric
from .splits import (
NamedSplit,
NamedSplitAll,
Split,
SplitBase,
SplitDict,
SplitGenerator,
SplitInfo,
SubSplitInfo,
percent,
)
from .tasks import *
from .utils import *
from .utils import logging
# deprecated modules
from datasets import arrow_dataset as _arrow_dataset # isort:skip
from datasets import utils as _utils # isort:skip
from datasets.utils import download_manager as _deprecated_download_manager # isort:skip
lowerCamelCase__ = concatenate_datasets
lowerCamelCase__ = DownloadConfig
lowerCamelCase__ = DownloadManager
lowerCamelCase__ = DownloadMode
lowerCamelCase__ = DownloadConfig
lowerCamelCase__ = DownloadMode
lowerCamelCase__ = DownloadManager
del _arrow_dataset, _utils, _deprecated_download_manager
| 307
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|
import os
import sys
from contextlib import contextmanager
# Windows only
if os.name == "nt":
import ctypes
import msvcrt # noqa
class A__ ( ctypes.Structure ):
# _fields is a specific attr expected by ctypes
lowercase = [('size', ctypes.c_int), ('visible', ctypes.c_byte)]
def lowerCAmelCase__ ( ) -> Optional[int]:
if os.name == "nt":
lowerCAmelCase__ : List[str] = CursorInfo()
lowerCAmelCase__ : int = ctypes.windll.kernelaa.GetStdHandle(-11 )
ctypes.windll.kernelaa.GetConsoleCursorInfo(SCREAMING_SNAKE_CASE_ , ctypes.byref(SCREAMING_SNAKE_CASE_ ) )
lowerCAmelCase__ : Union[str, Any] = False
ctypes.windll.kernelaa.SetConsoleCursorInfo(SCREAMING_SNAKE_CASE_ , ctypes.byref(SCREAMING_SNAKE_CASE_ ) )
elif os.name == "posix":
sys.stdout.write('\033[?25l' )
sys.stdout.flush()
def lowerCAmelCase__ ( ) -> List[Any]:
if os.name == "nt":
lowerCAmelCase__ : Any = CursorInfo()
lowerCAmelCase__ : str = ctypes.windll.kernelaa.GetStdHandle(-11 )
ctypes.windll.kernelaa.GetConsoleCursorInfo(SCREAMING_SNAKE_CASE_ , ctypes.byref(SCREAMING_SNAKE_CASE_ ) )
lowerCAmelCase__ : List[str] = True
ctypes.windll.kernelaa.SetConsoleCursorInfo(SCREAMING_SNAKE_CASE_ , ctypes.byref(SCREAMING_SNAKE_CASE_ ) )
elif os.name == "posix":
sys.stdout.write('\033[?25h' )
sys.stdout.flush()
@contextmanager
def lowerCAmelCase__ ( ) -> List[Any]:
try:
hide_cursor()
yield
finally:
show_cursor()
| 368
|
import gc
import unittest
import numpy as np
import torch
from diffusers import DanceDiffusionPipeline, IPNDMScheduler, UNetaDModel
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, skip_mps
from ..pipeline_params import UNCONDITIONAL_AUDIO_GENERATION_BATCH_PARAMS, UNCONDITIONAL_AUDIO_GENERATION_PARAMS
from ..test_pipelines_common import PipelineTesterMixin
enable_full_determinism()
class A__ ( __magic_name__ , unittest.TestCase ):
lowercase = DanceDiffusionPipeline
lowercase = UNCONDITIONAL_AUDIO_GENERATION_PARAMS
lowercase = PipelineTesterMixin.required_optional_params - {
'callback',
'latents',
'callback_steps',
'output_type',
'num_images_per_prompt',
}
lowercase = UNCONDITIONAL_AUDIO_GENERATION_BATCH_PARAMS
lowercase = False
lowercase = False
def _lowerCamelCase ( self : List[str] ):
'''simple docstring'''
torch.manual_seed(0 )
lowerCAmelCase__ : Optional[int] = UNetaDModel(
block_out_channels=(32, 32, 64) , extra_in_channels=16 , sample_size=512 , sample_rate=16_000 , in_channels=2 , out_channels=2 , flip_sin_to_cos=a , use_timestep_embedding=a , time_embedding_type='fourier' , mid_block_type='UNetMidBlock1D' , down_block_types=('DownBlock1DNoSkip', 'DownBlock1D', 'AttnDownBlock1D') , up_block_types=('AttnUpBlock1D', 'UpBlock1D', 'UpBlock1DNoSkip') , )
lowerCAmelCase__ : Tuple = IPNDMScheduler()
lowerCAmelCase__ : str = {
'unet': unet,
'scheduler': scheduler,
}
return components
def _lowerCamelCase ( self : int , a : Dict , a : List[str]=0 ):
'''simple docstring'''
if str(a ).startswith('mps' ):
lowerCAmelCase__ : Union[str, Any] = torch.manual_seed(a )
else:
lowerCAmelCase__ : Optional[Any] = torch.Generator(device=a ).manual_seed(a )
lowerCAmelCase__ : Optional[Any] = {
'batch_size': 1,
'generator': generator,
'num_inference_steps': 4,
}
return inputs
def _lowerCamelCase ( self : Optional[Any] ):
'''simple docstring'''
lowerCAmelCase__ : Optional[Any] = 'cpu' # ensure determinism for the device-dependent torch.Generator
lowerCAmelCase__ : int = self.get_dummy_components()
lowerCAmelCase__ : List[str] = DanceDiffusionPipeline(**a )
lowerCAmelCase__ : Any = pipe.to(a )
pipe.set_progress_bar_config(disable=a )
lowerCAmelCase__ : List[str] = self.get_dummy_inputs(a )
lowerCAmelCase__ : List[Any] = pipe(**a )
lowerCAmelCase__ : List[str] = output.audios
lowerCAmelCase__ : Optional[Any] = audio[0, -3:, -3:]
assert audio.shape == (1, 2, components["unet"].sample_size)
lowerCAmelCase__ : List[Any] = np.array([-0.7_2_6_5, 1.0_0_0_0, -0.8_3_8_8, 0.1_1_7_5, 0.9_4_9_8, -1.0_0_0_0] )
assert np.abs(audio_slice.flatten() - expected_slice ).max() < 1E-2
@skip_mps
def _lowerCamelCase ( self : str ):
'''simple docstring'''
return super().test_save_load_local()
@skip_mps
def _lowerCamelCase ( self : Tuple ):
'''simple docstring'''
return super().test_dict_tuple_outputs_equivalent(expected_max_difference=3E-3 )
@skip_mps
def _lowerCamelCase ( self : List[str] ):
'''simple docstring'''
return super().test_save_load_optional_components()
@skip_mps
def _lowerCamelCase ( self : Tuple ):
'''simple docstring'''
return super().test_attention_slicing_forward_pass()
def _lowerCamelCase ( self : List[str] ):
'''simple docstring'''
super().test_inference_batch_single_identical(expected_max_diff=3E-3 )
@slow
@require_torch_gpu
class A__ ( unittest.TestCase ):
def _lowerCamelCase ( self : Dict ):
'''simple docstring'''
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def _lowerCamelCase ( self : Union[str, Any] ):
'''simple docstring'''
lowerCAmelCase__ : Tuple = torch_device
lowerCAmelCase__ : List[str] = DanceDiffusionPipeline.from_pretrained('harmonai/maestro-150k' )
lowerCAmelCase__ : List[str] = pipe.to(a )
pipe.set_progress_bar_config(disable=a )
lowerCAmelCase__ : Optional[int] = torch.manual_seed(0 )
lowerCAmelCase__ : Optional[int] = pipe(generator=a , num_inference_steps=100 , audio_length_in_s=4.0_9_6 )
lowerCAmelCase__ : int = output.audios
lowerCAmelCase__ : List[Any] = audio[0, -3:, -3:]
assert audio.shape == (1, 2, pipe.unet.sample_size)
lowerCAmelCase__ : Dict = np.array([-0.0_1_9_2, -0.0_2_3_1, -0.0_3_1_8, -0.0_0_5_9, 0.0_0_0_2, -0.0_0_2_0] )
assert np.abs(audio_slice.flatten() - expected_slice ).max() < 1E-2
def _lowerCamelCase ( self : Tuple ):
'''simple docstring'''
lowerCAmelCase__ : str = torch_device
lowerCAmelCase__ : List[Any] = DanceDiffusionPipeline.from_pretrained('harmonai/maestro-150k' , torch_dtype=torch.floataa )
lowerCAmelCase__ : Optional[int] = pipe.to(a )
pipe.set_progress_bar_config(disable=a )
lowerCAmelCase__ : str = torch.manual_seed(0 )
lowerCAmelCase__ : Optional[int] = pipe(generator=a , num_inference_steps=100 , audio_length_in_s=4.0_9_6 )
lowerCAmelCase__ : str = output.audios
lowerCAmelCase__ : Tuple = audio[0, -3:, -3:]
assert audio.shape == (1, 2, pipe.unet.sample_size)
lowerCAmelCase__ : int = np.array([-0.0_3_6_7, -0.0_4_8_8, -0.0_7_7_1, -0.0_5_2_5, -0.0_4_4_4, -0.0_3_4_1] )
assert np.abs(audio_slice.flatten() - expected_slice ).max() < 1E-2
| 307
| 0
|
import argparse
from torch import nn
# transformers_old should correspond to branch `save_old_prophetnet_model_structure` here
# original prophetnet_checkpoints are saved under `patrickvonplaten/..._old` respectively
from transformers_old.modeling_prophetnet import (
ProphetNetForConditionalGeneration as ProphetNetForConditionalGenerationOld,
)
from transformers_old.modeling_xlm_prophetnet import (
XLMProphetNetForConditionalGeneration as XLMProphetNetForConditionalGenerationOld,
)
from transformers import ProphetNetForConditionalGeneration, XLMProphetNetForConditionalGeneration, logging
lowerCamelCase__ = logging.get_logger(__name__)
logging.set_verbosity_info()
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) -> int:
if "xprophetnet" in prophetnet_checkpoint_path:
lowerCAmelCase__ : Dict = XLMProphetNetForConditionalGenerationOld.from_pretrained(SCREAMING_SNAKE_CASE_ )
lowerCAmelCase__ : List[Any] = XLMProphetNetForConditionalGeneration.from_pretrained(
SCREAMING_SNAKE_CASE_ , output_loading_info=SCREAMING_SNAKE_CASE_ )
else:
lowerCAmelCase__ : Optional[Any] = ProphetNetForConditionalGenerationOld.from_pretrained(SCREAMING_SNAKE_CASE_ )
lowerCAmelCase__ : List[str] = ProphetNetForConditionalGeneration.from_pretrained(
SCREAMING_SNAKE_CASE_ , output_loading_info=SCREAMING_SNAKE_CASE_ )
lowerCAmelCase__ : Dict = ['key_proj', 'value_proj', 'query_proj']
lowerCAmelCase__ : Union[str, Any] = {
'self_attn': 'ngram_self_attn',
'cross_attn': 'encoder_attn',
'cross_attn_layer_norm': 'encoder_attn_layer_norm',
'feed_forward_layer_norm': 'final_layer_norm',
'feed_forward': '',
'intermediate': 'fc1',
'output': 'fc2',
'key_proj': 'k_proj',
'query_proj': 'q_proj',
'value_proj': 'v_proj',
'word_embeddings': 'embed_tokens',
'embeddings_layer_norm': 'emb_layer_norm',
'relative_pos_embeddings': 'relative_linear',
'ngram_embeddings': 'ngram_input_embed',
'position_embeddings': 'embed_positions',
}
for key in loading_info["missing_keys"]:
lowerCAmelCase__ : Optional[Any] = key.split('.' )
if attributes[0] == "lm_head":
lowerCAmelCase__ : Dict = prophet
lowerCAmelCase__ : Tuple = prophet_old
else:
lowerCAmelCase__ : Optional[Any] = prophet.prophetnet
lowerCAmelCase__ : Optional[int] = prophet_old.model
lowerCAmelCase__ : Optional[int] = False
for attribute in attributes:
if attribute in mapping:
lowerCAmelCase__ : List[Any] = mapping[attribute]
if not hasattr(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) and len(SCREAMING_SNAKE_CASE_ ) > 0:
lowerCAmelCase__ : Union[str, Any] = attribute
elif hasattr(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
lowerCAmelCase__ : List[str] = attribute
if attribute == "weight":
assert old_model.weight.shape == model.weight.shape, "Shapes have to match!"
lowerCAmelCase__ : Optional[Any] = old_model.weight
logger.info(F'''{attribute} is initialized.''' )
lowerCAmelCase__ : Tuple = True
break
elif attribute == "bias":
assert old_model.bias.shape == model.bias.shape, "Shapes have to match!"
lowerCAmelCase__ : Optional[Any] = old_model.bias
logger.info(F'''{attribute} is initialized''' )
lowerCAmelCase__ : str = True
break
elif attribute in special_keys and hasattr(SCREAMING_SNAKE_CASE_ , 'in_proj_weight' ):
lowerCAmelCase__ : Dict = old_model.in_proj_weight.shape[0] // 3
lowerCAmelCase__ : Dict = getattr(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
param.weight.shape == old_model.in_proj_weight[:embed_dim, :].shape, "Shapes have to match"
param.bias.shape == old_model.in_proj_bias[:embed_dim].shape, "Shapes have to match"
if attribute == "query_proj":
lowerCAmelCase__ : str = nn.Parameter(old_model.in_proj_weight[:embed_dim, :] )
lowerCAmelCase__ : List[str] = nn.Parameter(old_model.in_proj_bias[:embed_dim] )
elif attribute == "key_proj":
lowerCAmelCase__ : int = nn.Parameter(old_model.in_proj_weight[embed_dim : 2 * embed_dim, :] )
lowerCAmelCase__ : Union[str, Any] = nn.Parameter(old_model.in_proj_bias[embed_dim : 2 * embed_dim] )
elif attribute == "value_proj":
lowerCAmelCase__ : List[str] = nn.Parameter(old_model.in_proj_weight[2 * embed_dim :, :] )
lowerCAmelCase__ : str = nn.Parameter(old_model.in_proj_bias[2 * embed_dim :] )
lowerCAmelCase__ : Union[str, Any] = True
break
elif attribute == "position_embeddings":
assert (
model.position_embeddings.weight.shape[-1] == old_model.embed_positions.weight.shape[-1]
), "Hidden size has to match"
assert model.position_embeddings.weight.shape[0] == 512, "We want 512 position_embeddings."
lowerCAmelCase__ : Optional[Any] = nn.Parameter(old_model.embed_positions.weight[:512, :] )
lowerCAmelCase__ : str = True
break
if attribute.isdigit():
lowerCAmelCase__ : Any = model[int(SCREAMING_SNAKE_CASE_ )]
lowerCAmelCase__ : Optional[int] = old_model[int(SCREAMING_SNAKE_CASE_ )]
else:
lowerCAmelCase__ : Union[str, Any] = getattr(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
if old_attribute == "":
lowerCAmelCase__ : str = old_model
else:
if not hasattr(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
raise ValueError(F'''{old_model} does not have {old_attribute}''' )
lowerCAmelCase__ : Tuple = getattr(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
if not is_key_init:
raise ValueError(F'''{key} was not correctly initialized!''' )
print(F'''Saving model to {pytorch_dump_folder_path}''' )
prophet.save_pretrained(SCREAMING_SNAKE_CASE_ )
if __name__ == "__main__":
lowerCamelCase__ = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"""--prophetnet_checkpoint_path""", default=None, type=str, required=True, help="""Path the official PyTorch dump."""
)
parser.add_argument(
"""--pytorch_dump_folder_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
)
lowerCamelCase__ = parser.parse_args()
convert_prophetnet_checkpoint_to_pytorch(args.prophetnet_checkpoint_path, args.pytorch_dump_folder_path)
| 369
|
from ..utils import DummyObject, requires_backends
class A__ ( metaclass=__magic_name__ ):
lowercase = ['torch', 'transformers', 'onnx']
def __init__( self : Any , *a : Any , **a : Any ):
'''simple docstring'''
requires_backends(self , ['torch', 'transformers', 'onnx'] )
@classmethod
def _lowerCamelCase ( cls : Union[str, Any] , *a : Optional[int] , **a : Optional[Any] ):
'''simple docstring'''
requires_backends(cls , ['torch', 'transformers', 'onnx'] )
@classmethod
def _lowerCamelCase ( cls : int , *a : List[Any] , **a : int ):
'''simple docstring'''
requires_backends(cls , ['torch', 'transformers', 'onnx'] )
class A__ ( metaclass=__magic_name__ ):
lowercase = ['torch', 'transformers', 'onnx']
def __init__( self : str , *a : Any , **a : Optional[Any] ):
'''simple docstring'''
requires_backends(self , ['torch', 'transformers', 'onnx'] )
@classmethod
def _lowerCamelCase ( cls : Optional[int] , *a : List[str] , **a : Dict ):
'''simple docstring'''
requires_backends(cls , ['torch', 'transformers', 'onnx'] )
@classmethod
def _lowerCamelCase ( cls : Optional[Any] , *a : Optional[Any] , **a : Any ):
'''simple docstring'''
requires_backends(cls , ['torch', 'transformers', 'onnx'] )
class A__ ( metaclass=__magic_name__ ):
lowercase = ['torch', 'transformers', 'onnx']
def __init__( self : Optional[int] , *a : List[Any] , **a : str ):
'''simple docstring'''
requires_backends(self , ['torch', 'transformers', 'onnx'] )
@classmethod
def _lowerCamelCase ( cls : List[Any] , *a : List[str] , **a : List[str] ):
'''simple docstring'''
requires_backends(cls , ['torch', 'transformers', 'onnx'] )
@classmethod
def _lowerCamelCase ( cls : Optional[Any] , *a : Union[str, Any] , **a : Optional[int] ):
'''simple docstring'''
requires_backends(cls , ['torch', 'transformers', 'onnx'] )
class A__ ( metaclass=__magic_name__ ):
lowercase = ['torch', 'transformers', 'onnx']
def __init__( self : List[Any] , *a : Dict , **a : List[str] ):
'''simple docstring'''
requires_backends(self , ['torch', 'transformers', 'onnx'] )
@classmethod
def _lowerCamelCase ( cls : Optional[int] , *a : Dict , **a : List[Any] ):
'''simple docstring'''
requires_backends(cls , ['torch', 'transformers', 'onnx'] )
@classmethod
def _lowerCamelCase ( cls : Optional[int] , *a : List[str] , **a : Dict ):
'''simple docstring'''
requires_backends(cls , ['torch', 'transformers', 'onnx'] )
class A__ ( metaclass=__magic_name__ ):
lowercase = ['torch', 'transformers', 'onnx']
def __init__( self : Dict , *a : str , **a : Union[str, Any] ):
'''simple docstring'''
requires_backends(self , ['torch', 'transformers', 'onnx'] )
@classmethod
def _lowerCamelCase ( cls : Any , *a : Any , **a : Any ):
'''simple docstring'''
requires_backends(cls , ['torch', 'transformers', 'onnx'] )
@classmethod
def _lowerCamelCase ( cls : Any , *a : List[Any] , **a : str ):
'''simple docstring'''
requires_backends(cls , ['torch', 'transformers', 'onnx'] )
class A__ ( metaclass=__magic_name__ ):
lowercase = ['torch', 'transformers', 'onnx']
def __init__( self : str , *a : Union[str, Any] , **a : Optional[Any] ):
'''simple docstring'''
requires_backends(self , ['torch', 'transformers', 'onnx'] )
@classmethod
def _lowerCamelCase ( cls : int , *a : Union[str, Any] , **a : Dict ):
'''simple docstring'''
requires_backends(cls , ['torch', 'transformers', 'onnx'] )
@classmethod
def _lowerCamelCase ( cls : Optional[int] , *a : Tuple , **a : List[str] ):
'''simple docstring'''
requires_backends(cls , ['torch', 'transformers', 'onnx'] )
| 307
| 0
|
import gc
import unittest
from diffusers import FlaxStableDiffusionInpaintPipeline
from diffusers.utils import is_flax_available, load_image, 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 A__ ( unittest.TestCase ):
def _lowerCamelCase ( self : List[str] ):
'''simple docstring'''
super().tearDown()
gc.collect()
def _lowerCamelCase ( self : Optional[Any] ):
'''simple docstring'''
lowerCAmelCase__ : Dict = load_image(
'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
'/sd2-inpaint/init_image.png' )
lowerCAmelCase__ : Dict = load_image(
'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png' )
lowerCAmelCase__ : Any = 'xvjiarui/stable-diffusion-2-inpainting'
lowerCAmelCase__ : str = FlaxStableDiffusionInpaintPipeline.from_pretrained(a , safety_checker=a )
lowerCAmelCase__ : int = 'Face of a yellow cat, high resolution, sitting on a park bench'
lowerCAmelCase__ : str = jax.random.PRNGKey(0 )
lowerCAmelCase__ : int = 50
lowerCAmelCase__ : int = jax.device_count()
lowerCAmelCase__ : Optional[Any] = num_samples * [prompt]
lowerCAmelCase__ : List[str] = num_samples * [init_image]
lowerCAmelCase__ : int = num_samples * [mask_image]
lowerCAmelCase__ : Optional[Any] = pipeline.prepare_inputs(a , a , a )
# shard inputs and rng
lowerCAmelCase__ : int = replicate(a )
lowerCAmelCase__ : str = jax.random.split(a , jax.device_count() )
lowerCAmelCase__ : Any = shard(a )
lowerCAmelCase__ : List[Any] = shard(a )
lowerCAmelCase__ : int = shard(a )
lowerCAmelCase__ : List[Any] = pipeline(
a , a , a , a , a , a , jit=a )
lowerCAmelCase__ : Any = output.images.reshape(a , 512 , 512 , 3 )
lowerCAmelCase__ : Union[str, Any] = images[0, 253:256, 253:256, -1]
lowerCAmelCase__ : Optional[Any] = jnp.asarray(jax.device_get(image_slice.flatten() ) )
lowerCAmelCase__ : str = jnp.array(
[0.3_6_1_1_3_0_7, 0.3_7_6_4_9_7_3_6, 0.3_7_5_7_4_0_8, 0.3_8_2_1_3_9_5_3, 0.3_9_2_9_5_1_6_7, 0.3_8_4_1_6_3_1, 0.4_1_5_5_4_9_7_8, 0.4_1_3_7_4_7_5, 0.4_2_1_7_0_8_4] )
print(f'''output_slice: {output_slice}''' )
assert jnp.abs(output_slice - expected_slice ).max() < 1E-2
| 370
|
import unittest
from parameterized import parameterized
from transformers import LlamaConfig, is_torch_available, set_seed
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 LlamaForCausalLM, LlamaForSequenceClassification, LlamaModel, LlamaTokenizer
class A__ :
def __init__( self : List[str] , a : Any , a : Dict=13 , a : Optional[Any]=7 , a : Tuple=True , a : Tuple=True , a : Dict=False , a : Optional[Any]=True , a : Dict=99 , a : Tuple=32 , a : Optional[Any]=5 , a : str=4 , a : Union[str, Any]=37 , a : Any="gelu" , a : Dict=0.1 , a : Any=0.1 , a : Optional[int]=512 , a : Union[str, Any]=16 , a : Optional[int]=2 , a : Optional[Any]=0.0_2 , a : List[Any]=3 , a : Any=4 , a : Optional[int]=None , ):
'''simple docstring'''
lowerCAmelCase__ : List[str] = parent
lowerCAmelCase__ : str = batch_size
lowerCAmelCase__ : Optional[int] = seq_length
lowerCAmelCase__ : Optional[Any] = is_training
lowerCAmelCase__ : Tuple = use_input_mask
lowerCAmelCase__ : List[Any] = use_token_type_ids
lowerCAmelCase__ : str = use_labels
lowerCAmelCase__ : Dict = vocab_size
lowerCAmelCase__ : Union[str, Any] = hidden_size
lowerCAmelCase__ : Optional[int] = num_hidden_layers
lowerCAmelCase__ : List[Any] = num_attention_heads
lowerCAmelCase__ : int = intermediate_size
lowerCAmelCase__ : Union[str, Any] = hidden_act
lowerCAmelCase__ : Union[str, Any] = hidden_dropout_prob
lowerCAmelCase__ : Any = attention_probs_dropout_prob
lowerCAmelCase__ : Dict = max_position_embeddings
lowerCAmelCase__ : int = type_vocab_size
lowerCAmelCase__ : int = type_sequence_label_size
lowerCAmelCase__ : Dict = initializer_range
lowerCAmelCase__ : List[str] = num_labels
lowerCAmelCase__ : Any = num_choices
lowerCAmelCase__ : List[Any] = scope
def _lowerCamelCase ( self : Tuple ):
'''simple docstring'''
lowerCAmelCase__ : List[Any] = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
lowerCAmelCase__ : Tuple = None
if self.use_input_mask:
lowerCAmelCase__ : str = random_attention_mask([self.batch_size, self.seq_length] )
lowerCAmelCase__ : List[str] = None
if self.use_token_type_ids:
lowerCAmelCase__ : Tuple = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
lowerCAmelCase__ : Optional[Any] = None
lowerCAmelCase__ : Dict = None
lowerCAmelCase__ : str = None
if self.use_labels:
lowerCAmelCase__ : Any = ids_tensor([self.batch_size] , self.type_sequence_label_size )
lowerCAmelCase__ : Union[str, Any] = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
lowerCAmelCase__ : List[Any] = ids_tensor([self.batch_size] , self.num_choices )
lowerCAmelCase__ : List[str] = self.get_config()
return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def _lowerCamelCase ( self : Optional[Any] ):
'''simple docstring'''
return LlamaConfig(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , is_decoder=a , initializer_range=self.initializer_range , )
def _lowerCamelCase ( self : Tuple , a : Dict , a : List[str] , a : str , a : Union[str, Any] , a : Optional[Any] , a : Dict , a : str ):
'''simple docstring'''
lowerCAmelCase__ : str = LlamaModel(config=a )
model.to(a )
model.eval()
lowerCAmelCase__ : Dict = model(a , attention_mask=a )
lowerCAmelCase__ : Union[str, Any] = model(a )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def _lowerCamelCase ( self : int , a : Any , a : Union[str, Any] , a : Dict , a : Dict , a : List[Any] , a : Optional[Any] , a : int , a : Dict , a : Tuple , ):
'''simple docstring'''
lowerCAmelCase__ : int = True
lowerCAmelCase__ : Dict = LlamaModel(a )
model.to(a )
model.eval()
lowerCAmelCase__ : List[Any] = model(
a , attention_mask=a , encoder_hidden_states=a , encoder_attention_mask=a , )
lowerCAmelCase__ : Optional[int] = model(
a , attention_mask=a , encoder_hidden_states=a , )
lowerCAmelCase__ : Union[str, Any] = model(a , attention_mask=a )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def _lowerCamelCase ( self : Union[str, Any] , a : int , a : List[Any] , a : int , a : Tuple , a : List[Any] , a : Union[str, Any] , a : Any , a : List[str] , a : List[str] , ):
'''simple docstring'''
lowerCAmelCase__ : List[Any] = LlamaForCausalLM(config=a )
model.to(a )
model.eval()
lowerCAmelCase__ : Tuple = model(a , attention_mask=a , labels=a )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def _lowerCamelCase ( self : str , a : Any , a : Tuple , a : str , a : Union[str, Any] , a : Optional[Any] , a : List[Any] , a : Optional[Any] , a : Optional[Any] , a : List[str] , ):
'''simple docstring'''
lowerCAmelCase__ : str = True
lowerCAmelCase__ : Optional[int] = True
lowerCAmelCase__ : List[Any] = LlamaForCausalLM(config=a )
model.to(a )
model.eval()
# first forward pass
lowerCAmelCase__ : List[str] = model(
a , attention_mask=a , encoder_hidden_states=a , encoder_attention_mask=a , use_cache=a , )
lowerCAmelCase__ : List[Any] = outputs.past_key_values
# create hypothetical multiple next token and extent to next_input_ids
lowerCAmelCase__ : Union[str, Any] = ids_tensor((self.batch_size, 3) , config.vocab_size )
lowerCAmelCase__ : Tuple = ids_tensor((self.batch_size, 3) , vocab_size=2 )
# append to next input_ids and
lowerCAmelCase__ : int = torch.cat([input_ids, next_tokens] , dim=-1 )
lowerCAmelCase__ : Any = torch.cat([input_mask, next_mask] , dim=-1 )
lowerCAmelCase__ : Union[str, Any] = model(
a , attention_mask=a , encoder_hidden_states=a , encoder_attention_mask=a , output_hidden_states=a , )['hidden_states'][0]
lowerCAmelCase__ : Union[str, Any] = model(
a , attention_mask=a , encoder_hidden_states=a , encoder_attention_mask=a , past_key_values=a , output_hidden_states=a , )['hidden_states'][0]
# select random slice
lowerCAmelCase__ : Any = ids_tensor((1,) , output_from_past.shape[-1] ).item()
lowerCAmelCase__ : Optional[int] = output_from_no_past[:, -3:, random_slice_idx].detach()
lowerCAmelCase__ : Any = output_from_past[:, :, random_slice_idx].detach()
self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1] )
# test that outputs are equal for slice
self.parent.assertTrue(torch.allclose(a , a , atol=1E-3 ) )
def _lowerCamelCase ( self : Any ):
'''simple docstring'''
lowerCAmelCase__ : Optional[int] = self.prepare_config_and_inputs()
(
(
lowerCAmelCase__
) , (
lowerCAmelCase__
) , (
lowerCAmelCase__
) , (
lowerCAmelCase__
) , (
lowerCAmelCase__
) , (
lowerCAmelCase__
) , (
lowerCAmelCase__
) ,
) : Any = config_and_inputs
lowerCAmelCase__ : List[str] = {'input_ids': input_ids, 'attention_mask': input_mask}
return config, inputs_dict
@require_torch
class A__ ( __magic_name__ , __magic_name__ , __magic_name__ , unittest.TestCase ):
lowercase = (LlamaModel, LlamaForCausalLM, LlamaForSequenceClassification) if is_torch_available() else ()
lowercase = (LlamaForCausalLM,) if is_torch_available() else ()
lowercase = (
{
'feature-extraction': LlamaModel,
'text-classification': LlamaForSequenceClassification,
'text-generation': LlamaForCausalLM,
'zero-shot': LlamaForSequenceClassification,
}
if is_torch_available()
else {}
)
lowercase = False
lowercase = False
def _lowerCamelCase ( self : int ):
'''simple docstring'''
lowerCAmelCase__ : Tuple = LlamaModelTester(self )
lowerCAmelCase__ : str = ConfigTester(self , config_class=a , hidden_size=37 )
def _lowerCamelCase ( self : str ):
'''simple docstring'''
self.config_tester.run_common_tests()
def _lowerCamelCase ( self : str ):
'''simple docstring'''
lowerCAmelCase__ : Optional[int] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*a )
def _lowerCamelCase ( self : Union[str, Any] ):
'''simple docstring'''
lowerCAmelCase__ : Any = self.model_tester.prepare_config_and_inputs()
for type in ["absolute", "relative_key", "relative_key_query"]:
lowerCAmelCase__ : int = type
self.model_tester.create_and_check_model(*a )
def _lowerCamelCase ( self : str ):
'''simple docstring'''
lowerCAmelCase__ , lowerCAmelCase__ : List[str] = self.model_tester.prepare_config_and_inputs_for_common()
lowerCAmelCase__ : int = 3
lowerCAmelCase__ : Dict = input_dict['input_ids']
lowerCAmelCase__ : Optional[Any] = input_ids.ne(1 ).to(a )
lowerCAmelCase__ : Tuple = ids_tensor([self.model_tester.batch_size] , self.model_tester.type_sequence_label_size )
lowerCAmelCase__ : Tuple = LlamaForSequenceClassification(a )
model.to(a )
model.eval()
lowerCAmelCase__ : str = model(a , attention_mask=a , labels=a )
self.assertEqual(result.logits.shape , (self.model_tester.batch_size, self.model_tester.num_labels) )
def _lowerCamelCase ( self : Tuple ):
'''simple docstring'''
lowerCAmelCase__ , lowerCAmelCase__ : Optional[Any] = self.model_tester.prepare_config_and_inputs_for_common()
lowerCAmelCase__ : List[Any] = 3
lowerCAmelCase__ : List[str] = 'single_label_classification'
lowerCAmelCase__ : List[Any] = input_dict['input_ids']
lowerCAmelCase__ : List[Any] = input_ids.ne(1 ).to(a )
lowerCAmelCase__ : Optional[int] = ids_tensor([self.model_tester.batch_size] , self.model_tester.type_sequence_label_size )
lowerCAmelCase__ : int = LlamaForSequenceClassification(a )
model.to(a )
model.eval()
lowerCAmelCase__ : Optional[int] = model(a , attention_mask=a , labels=a )
self.assertEqual(result.logits.shape , (self.model_tester.batch_size, self.model_tester.num_labels) )
def _lowerCamelCase ( self : Tuple ):
'''simple docstring'''
lowerCAmelCase__ , lowerCAmelCase__ : Optional[int] = self.model_tester.prepare_config_and_inputs_for_common()
lowerCAmelCase__ : Optional[Any] = 3
lowerCAmelCase__ : Optional[Any] = 'multi_label_classification'
lowerCAmelCase__ : List[str] = input_dict['input_ids']
lowerCAmelCase__ : Tuple = input_ids.ne(1 ).to(a )
lowerCAmelCase__ : Any = ids_tensor(
[self.model_tester.batch_size, config.num_labels] , self.model_tester.type_sequence_label_size ).to(torch.float )
lowerCAmelCase__ : Dict = LlamaForSequenceClassification(a )
model.to(a )
model.eval()
lowerCAmelCase__ : Dict = model(a , attention_mask=a , labels=a )
self.assertEqual(result.logits.shape , (self.model_tester.batch_size, self.model_tester.num_labels) )
@unittest.skip('LLaMA buffers include complex numbers, which breaks this test' )
def _lowerCamelCase ( self : List[Any] ):
'''simple docstring'''
pass
@parameterized.expand([('linear',), ('dynamic',)] )
def _lowerCamelCase ( self : Optional[int] , a : Dict ):
'''simple docstring'''
lowerCAmelCase__ , lowerCAmelCase__ : Any = self.model_tester.prepare_config_and_inputs_for_common()
lowerCAmelCase__ : Tuple = ids_tensor([1, 10] , config.vocab_size )
lowerCAmelCase__ : List[str] = ids_tensor([1, int(config.max_position_embeddings * 1.5 )] , config.vocab_size )
set_seed(42 ) # Fixed seed at init time so the two models get the same random weights
lowerCAmelCase__ : List[Any] = LlamaModel(a )
original_model.to(a )
original_model.eval()
lowerCAmelCase__ : List[Any] = original_model(a ).last_hidden_state
lowerCAmelCase__ : str = original_model(a ).last_hidden_state
set_seed(42 ) # Fixed seed at init time so the two models get the same random weights
lowerCAmelCase__ : Any = {'type': scaling_type, 'factor': 1_0.0}
lowerCAmelCase__ : Union[str, Any] = LlamaModel(a )
scaled_model.to(a )
scaled_model.eval()
lowerCAmelCase__ : Union[str, Any] = scaled_model(a ).last_hidden_state
lowerCAmelCase__ : Optional[int] = scaled_model(a ).last_hidden_state
# Dynamic scaling does not change the RoPE embeddings until it receives an input longer than the original
# maximum sequence length, so the outputs for the short input should match.
if scaling_type == "dynamic":
self.assertTrue(torch.allclose(a , a , atol=1E-5 ) )
else:
self.assertFalse(torch.allclose(a , a , atol=1E-5 ) )
# The output should be different for long inputs
self.assertFalse(torch.allclose(a , a , atol=1E-5 ) )
@require_torch
class A__ ( unittest.TestCase ):
@unittest.skip('Logits are not exactly the same, once we fix the instabalities somehow, will update!' )
@slow
def _lowerCamelCase ( self : Union[str, Any] ):
'''simple docstring'''
lowerCAmelCase__ : int = [1, 306, 4_658, 278, 6_593, 310, 2_834, 338]
lowerCAmelCase__ : List[Any] = LlamaForCausalLM.from_pretrained('meta-llama/Llama-2-7b-hf' , device_map='auto' )
lowerCAmelCase__ : Any = model(torch.tensor([input_ids] ) )
# Expected mean on dim = -1
lowerCAmelCase__ : Dict = torch.tensor([[-6.6_5_5_0, -4.1_2_2_7, -4.9_8_5_9, -3.2_4_0_6, 0.8_2_6_2, -3.0_0_3_3, 1.2_9_6_4, -3.3_6_9_9]] )
torch.testing.assert_close(out.mean(-1 ) , a , atol=1E-2 , rtol=1E-2 )
# slicing logits[0, 0, 0:30]
# fmt: off
lowerCAmelCase__ : List[Any] = torch.tensor([-1_2.8_2_8_1, -7.4_4_5_3, -0.4_6_3_9, -8.0_6_2_5, -7.2_5_0_0, -8.0_0_0_0, -6.4_8_8_3, -7.7_6_9_5, -7.8_4_3_8, -7.0_3_1_2, -6.2_1_8_8, -7.1_3_2_8, -1.8_4_9_6, 1.9_9_6_1, -8.6_2_5_0, -6.7_2_2_7, -1_2.8_2_8_1, -6.9_4_9_2, -7.0_7_4_2, -7.7_8_5_2, -7.5_8_2_0, -7.9_0_6_2, -6.9_3_7_5, -7.9_8_0_5, -8.3_4_3_8, -8.1_5_6_2, -8.0_4_6_9, -7.6_2_5_0, -7.7_4_2_2, -7.3_3_9_8,] )
# fmt: on
torch.testing.assert_close(out[0, 0, :30] , a , atol=1E-5 , rtol=1E-5 )
@unittest.skip('Logits are not exactly the same, once we fix the instabalities somehow, will update!' )
@slow
def _lowerCamelCase ( self : Tuple ):
'''simple docstring'''
lowerCAmelCase__ : str = [1, 306, 4_658, 278, 6_593, 310, 2_834, 338]
lowerCAmelCase__ : Optional[Any] = LlamaForCausalLM.from_pretrained('meta-llama/Llama-2-13b-hf' , device_map='auto' )
lowerCAmelCase__ : List[str] = model(torch.tensor(a ) )
# Expected mean on dim = -1
lowerCAmelCase__ : Union[str, Any] = torch.tensor([[-2.0_6_2_2, -1.2_7_9_4, -1.1_6_3_8, -0.9_7_8_8, -1.4_6_0_3, -1.0_2_3_8, -1.7_8_9_3, -1.4_4_1_1]] )
torch.testing.assert_close(out.mean(-1 ) , a , atol=1E-2 , rtol=1E-2 )
# slicing logits[0, 0, 0:30]
# fmt: off
lowerCAmelCase__ : Any = torch.tensor([-8.1_4_0_6, -8.0_5_4_7, 2.7_4_6_1, -1.2_3_4_4, -0.1_4_4_8, -1.8_2_6_2, -1.0_0_2_0, -1.8_1_5_4, -1.6_8_9_5, -1.8_5_1_6, -2.3_5_7_4, -0.9_2_7_7, 3.7_5_9_8, 6.5_7_4_2, -1.2_9_9_8, -0.1_1_7_7, -8.1_4_0_6, -2.9_6_8_8, -2.9_1_9_9, -3.1_6_9_9, -3.5_2_5_4, -2.3_5_5_5, -2.7_9_8_8, -3.4_1_4_1, -2.8_2_6_2, -4.5_1_9_5, -3.3_3_7_9, -3.3_1_6_4, -2.7_8_3_2, -3.0_2_7_3] )
# fmt: on
torch.testing.assert_close(out[0, 0, :30] , a , atol=1E-5 , rtol=1E-5 )
@unittest.skip('Logits are not exactly the same, once we fix the instabalities somehow, will update!' )
@slow
def _lowerCamelCase ( self : str ):
'''simple docstring'''
lowerCAmelCase__ : int = [1, 306, 4_658, 278, 6_593, 310, 2_834, 338]
lowerCAmelCase__ : Optional[int] = LlamaForCausalLM.from_pretrained('meta-llama/Llama-2-13b-chat-hf' , device_map='auto' )
lowerCAmelCase__ : str = model(torch.tensor(a ) )
# Expected mean on dim = -1
lowerCAmelCase__ : str = torch.tensor([[-0.8_5_6_2, -1.8_5_2_0, -0.7_5_5_1, -0.4_1_6_2, -1.5_1_6_1, -1.2_0_3_8, -2.4_8_2_3, -2.3_2_5_4]] )
torch.testing.assert_close(out.mean(-1 ) , a , atol=1E-2 , rtol=1E-2 )
# slicing logits[0, 0, 0:30]
# fmt: off
lowerCAmelCase__ : List[str] = torch.tensor([-2.2_2_2_7, 4.8_8_2_8, 0.9_0_2_3, -0.4_5_7_8, -0.7_8_7_1, -0.1_0_3_3, -0.6_2_2_1, -0.5_7_8_6, -0.7_8_0_3, -1.0_6_7_4, -1.2_9_2_0, -0.1_5_7_0, 0.8_0_0_8, 2.0_7_2_3, -0.9_4_9_7, 0.2_7_7_1, -2.2_2_2_7, -0.7_6_1_2, -1.4_3_4_6, -1.2_0_6_1, -1.6_4_2_6, -0.3_0_0_0, -0.7_1_3_9, -1.1_9_3_4, -1.8_6_9_1, -1.6_9_7_3, -1.5_9_4_7, -1.2_7_0_5, -0.3_5_2_3, -0.5_5_1_3] )
# fmt: on
torch.testing.assert_close(out.mean(-1 ) , a , atol=1E-2 , rtol=1E-2 )
@unittest.skip(
'Logits are not exactly the same, once we fix the instabalities somehow, will update! Also it is gonna be a `too_slow` test' )
@slow
def _lowerCamelCase ( self : str ):
'''simple docstring'''
lowerCAmelCase__ : Optional[Any] = [1, 306, 4_658, 278, 6_593, 310, 2_834, 338]
lowerCAmelCase__ : List[Any] = LlamaForCausalLM.from_pretrained('meta-llama/Llama-2-70b-hf' , device_map='auto' )
lowerCAmelCase__ : List[str] = model(torch.tensor(a ) )
lowerCAmelCase__ : int = torch.tensor(
[[-4.2_3_2_7, -3.3_3_6_0, -4.6_6_6_5, -4.7_6_3_1, -1.8_1_8_0, -3.4_1_7_0, -1.4_2_1_1, -3.1_8_1_0]] , dtype=torch.floataa )
torch.testing.assert_close(out.mean(-1 ) , a , atol=1E-2 , rtol=1E-2 )
# fmt: off
lowerCAmelCase__ : Optional[int] = torch.tensor([-9.4_9_2_2, -3.9_5_5_1, 1.7_9_9_8, -5.6_7_5_8, -5.1_0_5_5, -5.8_9_8_4, -4.8_3_2_0, -6.8_0_8_6, -6.5_3_9_1, -5.6_1_7_2, -5.5_8_2_0, -5.5_3_5_2, 1.7_8_8_1, 3.6_2_8_9, -6.5_1_1_7, -3.4_7_8_5, -9.5_0_0_0, -6.0_3_5_2, -6.8_1_2_5, -6.0_1_9_5, -6.6_8_3_6, -5.4_7_2_7, -6.2_8_1_2, -6.0_3_9_1, -7.3_3_9_8, -7.4_2_9_7, -7.4_8_4_4, -6.5_8_2_0, -5.8_7_8_9, -5.5_3_1_2] )
# fmt: on
torch.testing.assert_close(out[0, 0, :30] , a , atol=1E-5 , rtol=1E-5 )
@unittest.skip('Model is curently gated' )
@slow
def _lowerCamelCase ( self : List[str] ):
'''simple docstring'''
lowerCAmelCase__ : Optional[Any] = 'Simply put, the theory of relativity states that 1) the laws of physics are the same everywhere in the universe and 2) the passage of time and the length of objects can vary depending on the observer\'s frame of reference.\n\nThe first part of the theory, that the laws of physics are the same everywhere, is known as the "princi'
lowerCAmelCase__ : Tuple = 'Simply put, the theory of relativity states that '
lowerCAmelCase__ : Dict = LlamaTokenizer.from_pretrained('meta-llama/Llama-2-13b-chat-hf' )
lowerCAmelCase__ : Dict = tokenizer.encode(a , return_tensors='pt' )
lowerCAmelCase__ : str = LlamaForCausalLM.from_pretrained(
'meta-llama/Llama-2-13b-chat-hf' , device_map='sequential' , use_safetensors=a )
# greedy generation outputs
lowerCAmelCase__ : Optional[Any] = model.generate(a , max_new_tokens=64 , top_p=a , temperature=1 , do_sample=a )
lowerCAmelCase__ : Tuple = tokenizer.decode(generated_ids[0] , skip_special_tokens=a )
self.assertEqual(a , a )
| 307
| 0
|
import copy
from typing import Any, Dict, List, Optional, Union
import numpy as np
from ...audio_utils import mel_filter_bank, spectrogram, window_function
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import TensorType, logging
lowerCamelCase__ = logging.get_logger(__name__)
class A__ ( __magic_name__ ):
lowercase = ['input_features']
def __init__( self : int , a : Dict=80 , a : Dict=16_000 , a : Any=160 , a : str=30 , a : List[str]=400 , a : Any=0.0 , a : List[str]=False , **a : Optional[int] , ):
'''simple docstring'''
super().__init__(
feature_size=a , sampling_rate=a , padding_value=a , return_attention_mask=a , **a , )
lowerCAmelCase__ : List[str] = n_fft
lowerCAmelCase__ : Union[str, Any] = hop_length
lowerCAmelCase__ : Any = chunk_length
lowerCAmelCase__ : Optional[Any] = chunk_length * sampling_rate
lowerCAmelCase__ : List[Any] = self.n_samples // hop_length
lowerCAmelCase__ : str = sampling_rate
lowerCAmelCase__ : Dict = mel_filter_bank(
num_frequency_bins=1 + n_fft // 2 , num_mel_filters=a , min_frequency=0.0 , max_frequency=8_000.0 , sampling_rate=a , norm='slaney' , mel_scale='slaney' , )
def _lowerCamelCase ( self : Optional[Any] , a : np.array ):
'''simple docstring'''
lowerCAmelCase__ : List[Any] = spectrogram(
a , window_function(self.n_fft , 'hann' ) , frame_length=self.n_fft , hop_length=self.hop_length , power=2.0 , mel_filters=self.mel_filters , log_mel='log10' , )
lowerCAmelCase__ : List[str] = log_spec[:, :-1]
lowerCAmelCase__ : str = np.maximum(a , log_spec.max() - 8.0 )
lowerCAmelCase__ : List[str] = (log_spec + 4.0) / 4.0
return log_spec
@staticmethod
# Copied from transformers.models.wav2vec2.feature_extraction_wav2vec2.Wav2Vec2FeatureExtractor.zero_mean_unit_var_norm
def _lowerCamelCase ( a : List[np.ndarray] , a : List[np.ndarray] , a : float = 0.0 ):
'''simple docstring'''
if attention_mask is not None:
lowerCAmelCase__ : Any = np.array(a , np.intaa )
lowerCAmelCase__ : Union[str, Any] = []
for vector, length in zip(a , attention_mask.sum(-1 ) ):
lowerCAmelCase__ : Optional[Any] = (vector - vector[:length].mean()) / np.sqrt(vector[:length].var() + 1E-7 )
if length < normed_slice.shape[0]:
lowerCAmelCase__ : Dict = padding_value
normed_input_values.append(a )
else:
lowerCAmelCase__ : str = [(x - x.mean()) / np.sqrt(x.var() + 1E-7 ) for x in input_values]
return normed_input_values
def __call__( self : Tuple , a : Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]] , a : bool = True , a : Optional[int] = None , a : Optional[Union[str, TensorType]] = None , a : Optional[bool] = None , a : Optional[str] = "max_length" , a : Optional[int] = None , a : Optional[int] = None , a : Optional[bool] = None , **a : 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.__class__.__name__} was trained using a'''
f''' sampling rate of {self.sampling_rate}. Please make sure that the provided `raw_speech` input'''
f''' was sampled with {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.' )
lowerCAmelCase__ : List[Any] = isinstance(a , 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}''' )
lowerCAmelCase__ : Dict = is_batched_numpy or (
isinstance(a , (list, tuple) ) and (isinstance(raw_speech[0] , (np.ndarray, tuple, list) ))
)
if is_batched:
lowerCAmelCase__ : List[Any] = [np.asarray([speech] , dtype=np.floataa ).T for speech in raw_speech]
elif not is_batched and not isinstance(a , np.ndarray ):
lowerCAmelCase__ : Union[str, Any] = np.asarray(a , dtype=np.floataa )
elif isinstance(a , np.ndarray ) and raw_speech.dtype is np.dtype(np.floataa ):
lowerCAmelCase__ : Union[str, Any] = raw_speech.astype(np.floataa )
# always return batch
if not is_batched:
lowerCAmelCase__ : List[Any] = [np.asarray([raw_speech] ).T]
lowerCAmelCase__ : int = BatchFeature({'input_features': raw_speech} )
# convert into correct format for padding
lowerCAmelCase__ : List[str] = self.pad(
a , padding=a , max_length=max_length if max_length else self.n_samples , truncation=a , pad_to_multiple_of=a , return_attention_mask=return_attention_mask or do_normalize , )
# zero-mean and unit-variance normalization
if do_normalize:
lowerCAmelCase__ : Optional[Any] = self.zero_mean_unit_var_norm(
padded_inputs['input_features'] , attention_mask=padded_inputs['attention_mask'] , padding_value=self.padding_value , )
lowerCAmelCase__ : Union[str, Any] = np.stack(padded_inputs['input_features'] , axis=0 )
# make sure list is in array format
lowerCAmelCase__ : Union[str, Any] = padded_inputs.get('input_features' ).transpose(2 , 0 , 1 )
lowerCAmelCase__ : str = [self._np_extract_fbank_features(a ) for waveform in input_features[0]]
if isinstance(input_features[0] , a ):
lowerCAmelCase__ : str = [np.asarray(a , dtype=np.floataa ) for feature in input_features]
else:
lowerCAmelCase__ : Optional[Any] = input_features
if return_attention_mask:
# rescale from sample (48000) to feature (3000)
lowerCAmelCase__ : int = padded_inputs['attention_mask'][:, :: self.hop_length]
if return_tensors is not None:
lowerCAmelCase__ : Optional[int] = padded_inputs.convert_to_tensors(a )
return padded_inputs
def _lowerCamelCase ( self : int ):
'''simple docstring'''
lowerCAmelCase__ : str = copy.deepcopy(self.__dict__ )
lowerCAmelCase__ : Optional[Any] = self.__class__.__name__
if "mel_filters" in output:
del output["mel_filters"]
return output
| 371
|
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCamelCase__ = logging.get_logger(__name__)
lowerCamelCase__ = {
"""microsoft/unispeech-large-1500h-cv""": (
"""https://huggingface.co/microsoft/unispeech-large-1500h-cv/resolve/main/config.json"""
),
# See all UniSpeech models at https://huggingface.co/models?filter=unispeech
}
class A__ ( __magic_name__ ):
lowercase = 'unispeech'
def __init__( self : Any , a : List[Any]=32 , a : List[Any]=768 , a : Any=12 , a : List[str]=12 , a : List[Any]=3_072 , a : Any="gelu" , a : Dict=0.1 , a : List[str]=0.1 , a : List[str]=0.1 , a : Union[str, Any]=0.0 , a : str=0.0 , a : int=0.1 , a : List[str]=0.1 , a : List[Any]=0.0_2 , a : Optional[int]=1E-5 , a : Optional[int]="group" , a : Optional[Any]="gelu" , a : List[Any]=(512, 512, 512, 512, 512, 512, 512) , a : Optional[Any]=(5, 2, 2, 2, 2, 2, 2) , a : List[str]=(10, 3, 3, 3, 3, 2, 2) , a : Union[str, Any]=False , a : Union[str, Any]=128 , a : Tuple=16 , a : Dict=False , a : str=True , a : str=0.0_5 , a : Union[str, Any]=10 , a : Tuple=2 , a : int=0.0 , a : Optional[Any]=10 , a : List[str]=0 , a : str=320 , a : List[str]=2 , a : Optional[Any]=0.1 , a : Any=100 , a : Dict=256 , a : Any=256 , a : Dict=0.1 , a : List[Any]="mean" , a : Dict=False , a : str=False , a : Optional[int]=256 , a : Any=80 , a : List[Any]=0 , a : Optional[int]=1 , a : int=2 , a : List[Any]=0.5 , **a : int , ):
'''simple docstring'''
super().__init__(**a , pad_token_id=a , bos_token_id=a , eos_token_id=a )
lowerCAmelCase__ : List[str] = hidden_size
lowerCAmelCase__ : List[str] = feat_extract_norm
lowerCAmelCase__ : Optional[Any] = feat_extract_activation
lowerCAmelCase__ : str = list(a )
lowerCAmelCase__ : List[str] = list(a )
lowerCAmelCase__ : Tuple = list(a )
lowerCAmelCase__ : Dict = conv_bias
lowerCAmelCase__ : Optional[int] = num_conv_pos_embeddings
lowerCAmelCase__ : Any = num_conv_pos_embedding_groups
lowerCAmelCase__ : str = len(self.conv_dim )
lowerCAmelCase__ : Any = num_hidden_layers
lowerCAmelCase__ : Dict = intermediate_size
lowerCAmelCase__ : Dict = hidden_act
lowerCAmelCase__ : Union[str, Any] = num_attention_heads
lowerCAmelCase__ : Union[str, Any] = hidden_dropout
lowerCAmelCase__ : Tuple = attention_dropout
lowerCAmelCase__ : str = activation_dropout
lowerCAmelCase__ : Any = feat_proj_dropout
lowerCAmelCase__ : List[Any] = final_dropout
lowerCAmelCase__ : Tuple = layerdrop
lowerCAmelCase__ : Any = layer_norm_eps
lowerCAmelCase__ : Dict = initializer_range
lowerCAmelCase__ : Optional[Any] = num_ctc_classes
lowerCAmelCase__ : Tuple = vocab_size
lowerCAmelCase__ : Dict = do_stable_layer_norm
lowerCAmelCase__ : List[Any] = use_weighted_layer_sum
lowerCAmelCase__ : Any = classifier_proj_size
if (
(len(self.conv_stride ) != self.num_feat_extract_layers)
or (len(self.conv_kernel ) != self.num_feat_extract_layers)
or (len(self.conv_dim ) != self.num_feat_extract_layers)
):
raise ValueError(
'Configuration for convolutional layers is incorrect. It is required that `len(config.conv_dim)` =='
' `len(config.conv_stride)` == `len(config.conv_kernel)`, but is `len(config.conv_dim) ='
f''' {len(self.conv_dim )}`, `len(config.conv_stride) = {len(self.conv_stride )}`,'''
f''' `len(config.conv_kernel) = {len(self.conv_kernel )}`.''' )
# fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
lowerCAmelCase__ : Union[str, Any] = apply_spec_augment
lowerCAmelCase__ : Any = mask_time_prob
lowerCAmelCase__ : Dict = mask_time_length
lowerCAmelCase__ : Tuple = mask_time_min_masks
lowerCAmelCase__ : Optional[int] = mask_feature_prob
lowerCAmelCase__ : Optional[Any] = mask_feature_length
lowerCAmelCase__ : int = mask_feature_min_masks
# parameters for pretraining with codevector quantized representations
lowerCAmelCase__ : int = num_codevectors_per_group
lowerCAmelCase__ : Any = num_codevector_groups
lowerCAmelCase__ : Any = contrastive_logits_temperature
lowerCAmelCase__ : int = feat_quantizer_dropout
lowerCAmelCase__ : List[Any] = num_negatives
lowerCAmelCase__ : List[str] = codevector_dim
lowerCAmelCase__ : Optional[int] = proj_codevector_dim
lowerCAmelCase__ : Dict = diversity_loss_weight
# ctc loss
lowerCAmelCase__ : Any = ctc_loss_reduction
lowerCAmelCase__ : Any = ctc_zero_infinity
# pretraining loss
lowerCAmelCase__ : Union[str, Any] = replace_prob
@property
def _lowerCamelCase ( self : List[str] ):
'''simple docstring'''
return functools.reduce(operator.mul , self.conv_stride , 1 )
| 307
| 0
|
import os
def UpperCamelCase_( _snake_case : str = "input.txt" ):
"""simple docstring"""
with open(os.path.join(os.path.dirname(_snake_case ) , _snake_case ) ) as input_file:
__a =[
[int(_snake_case ) for element in line.split(',' )]
for line in input_file.readlines()
]
__a =len(_snake_case )
__a =len(matrix[0] )
__a =[[-1 for _ in range(_snake_case )] for _ in range(_snake_case )]
for i in range(_snake_case ):
__a =matrix[i][0]
for j in range(1 , _snake_case ):
for i in range(_snake_case ):
__a =minimal_path_sums[i][j - 1] + matrix[i][j]
for i in range(1 , _snake_case ):
__a =min(
minimal_path_sums[i][j] , minimal_path_sums[i - 1][j] + matrix[i][j] )
for i in range(rows - 2 , -1 , -1 ):
__a =min(
minimal_path_sums[i][j] , minimal_path_sums[i + 1][j] + matrix[i][j] )
return min(minimal_path_sums_row[-1] for minimal_path_sums_row in minimal_path_sums )
if __name__ == "__main__":
print(f'''{solution() = }''')
| 308
|
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_lowerCAmelCase : Tuple = {
"configuration_megatron_bert": ["MEGATRON_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "MegatronBertConfig"],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_lowerCAmelCase : List[str] = [
"MEGATRON_BERT_PRETRAINED_MODEL_ARCHIVE_LIST",
"MegatronBertForCausalLM",
"MegatronBertForMaskedLM",
"MegatronBertForMultipleChoice",
"MegatronBertForNextSentencePrediction",
"MegatronBertForPreTraining",
"MegatronBertForQuestionAnswering",
"MegatronBertForSequenceClassification",
"MegatronBertForTokenClassification",
"MegatronBertModel",
"MegatronBertPreTrainedModel",
]
if TYPE_CHECKING:
from .configuration_megatron_bert import MEGATRON_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP, MegatronBertConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_megatron_bert import (
MEGATRON_BERT_PRETRAINED_MODEL_ARCHIVE_LIST,
MegatronBertForCausalLM,
MegatronBertForMaskedLM,
MegatronBertForMultipleChoice,
MegatronBertForNextSentencePrediction,
MegatronBertForPreTraining,
MegatronBertForQuestionAnswering,
MegatronBertForSequenceClassification,
MegatronBertForTokenClassification,
MegatronBertModel,
MegatronBertPreTrainedModel,
)
else:
import sys
_lowerCAmelCase : Optional[int] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 308
| 1
|
# 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 UpperCamelCase_( ):
"""simple docstring"""
__a =ArgumentParser('Accelerate CLI tool' , usage='accelerate <command> [<args>]' , allow_abbrev=_snake_case )
__a =parser.add_subparsers(help='accelerate command helpers' )
# Register commands
get_config_parser(subparsers=_snake_case )
env_command_parser(subparsers=_snake_case )
launch_command_parser(subparsers=_snake_case )
tpu_command_parser(subparsers=_snake_case )
test_command_parser(subparsers=_snake_case )
# Let's go
__a =parser.parse_args()
if not hasattr(_snake_case , 'func' ):
parser.print_help()
exit(1 )
# Run
args.func(_snake_case )
if __name__ == "__main__":
main()
| 308
|
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 __magic_name__ :
@staticmethod
def __magic_name__ ( *__snake_case , **__snake_case ) -> List[str]:
'''simple docstring'''
pass
def UpperCamelCase_( _snake_case : Image ):
"""simple docstring"""
__a =hashlib.mda(image.tobytes() )
return m.hexdigest()
@is_pipeline_test
@require_vision
@require_timm
@require_torch
class __magic_name__ ( unittest.TestCase ):
SCREAMING_SNAKE_CASE = MODEL_FOR_DEPTH_ESTIMATION_MAPPING
def __magic_name__ ( self , __snake_case , __snake_case , __snake_case ) -> Dict:
'''simple docstring'''
__a =DepthEstimationPipeline(model=__snake_case , image_processor=__snake_case )
return depth_estimator, [
"./tests/fixtures/tests_samples/COCO/000000039769.png",
"./tests/fixtures/tests_samples/COCO/000000039769.png",
]
def __magic_name__ ( self , __snake_case , __snake_case ) -> Tuple:
'''simple docstring'''
__a =depth_estimator('./tests/fixtures/tests_samples/COCO/000000039769.png' )
self.assertEqual({'predicted_depth': ANY(torch.Tensor ), 'depth': ANY(Image.Image )} , __snake_case )
import datasets
__a =datasets.load_dataset('hf-internal-testing/fixtures_image_utils' , 'image' , split='test' )
__a =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 )},
] , __snake_case , )
@require_tf
@unittest.skip('Depth estimation is not implemented in TF' )
def __magic_name__ ( self ) -> Optional[Any]:
'''simple docstring'''
pass
@slow
@require_torch
def __magic_name__ ( self ) -> int:
'''simple docstring'''
__a ='Intel/dpt-large'
__a =pipeline('depth-estimation' , model=__snake_case )
__a =depth_estimator('http://images.cocodataset.org/val2017/000000039769.jpg' )
__a =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 __magic_name__ ( self ) -> Any:
'''simple docstring'''
# This is highly irregular to have no small tests.
self.skipTest('There is not hf-internal-testing tiny model for either GLPN nor DPT' )
| 308
| 1
|
def UpperCamelCase_( _snake_case : Union[str, Any] ):
"""simple docstring"""
if collection == []:
return []
# get some information about the collection
__a =len(_snake_case )
__a =max(_snake_case )
__a =min(_snake_case )
# create the counting array
__a =coll_max + 1 - coll_min
__a =[0] * counting_arr_length
# count how much a number appears in the collection
for number in collection:
counting_arr[number - coll_min] += 1
# sum each position with it's predecessors. now, counting_arr[i] tells
# us how many elements <= i has in the collection
for i in range(1 , _snake_case ):
__a =counting_arr[i] + counting_arr[i - 1]
# create the output collection
__a =[0] * coll_len
# place the elements in the output, respecting the original order (stable
# sort) from end to begin, updating counting_arr
for i in reversed(range(0 , _snake_case ) ):
__a =collection[i]
counting_arr[collection[i] - coll_min] -= 1
return ordered
def UpperCamelCase_( _snake_case : Optional[Any] ):
"""simple docstring"""
return "".join([chr(_snake_case ) for i in counting_sort([ord(_snake_case ) for c in string] )] )
if __name__ == "__main__":
# Test string sort
assert counting_sort_string("thisisthestring") == "eghhiiinrsssttt"
_lowerCAmelCase : Union[str, Any] = input("Enter numbers separated by a comma:\n").strip()
_lowerCAmelCase : List[Any] = [int(item) for item in user_input.split(",")]
print(counting_sort(unsorted))
| 308
|
import argparse
import dataclasses
import json
import logging
import os
import shutil
from typing import List, Optional
import datasets
from accelerate import Accelerator
from datasets import load_dataset
from finetuning import finetune
from tqdm.auto import tqdm
import transformers
from transformers import AutoConfig, set_seed
from transformers.trainer_utils import IntervalStrategy
_lowerCAmelCase : Optional[int] = logging.getLogger(__name__)
_lowerCAmelCase : Any = "pytorch_model.bin"
@dataclasses.dataclass
class __magic_name__ :
SCREAMING_SNAKE_CASE = dataclasses.field(
metadata={'help': 'Path to pretrained model or model identifier from huggingface.co/models.'} )
SCREAMING_SNAKE_CASE = dataclasses.field(
default=lowerCAmelCase_ , metadata={'help': 'Where do you want to store the pretrained models downloaded from huggingface.co.'} , )
@dataclasses.dataclass
class __magic_name__ :
SCREAMING_SNAKE_CASE = dataclasses.field(metadata={'help': 'A csv or a json file containing the training data.'} )
SCREAMING_SNAKE_CASE = dataclasses.field(metadata={'help': 'A csv or a json file containing the data to predict on.'} )
SCREAMING_SNAKE_CASE = dataclasses.field(
default=lowerCAmelCase_ , metadata={'help': 'A csv or a json file containing the validation data.'} )
SCREAMING_SNAKE_CASE = dataclasses.field(
default=lowerCAmelCase_ , metadata={'help': 'The name of the task to train on.'} , )
SCREAMING_SNAKE_CASE = dataclasses.field(
default=lowerCAmelCase_ , metadata={'help': 'The list of labels for the task.'} )
@dataclasses.dataclass
class __magic_name__ :
SCREAMING_SNAKE_CASE = dataclasses.field(
metadata={'help': 'The output directory where the model predictions and checkpoints will be written.'} )
SCREAMING_SNAKE_CASE = dataclasses.field(
default='accuracy' , metadata={'help': 'The evaluation metric used for the task.'} )
SCREAMING_SNAKE_CASE = dataclasses.field(
default='no' , metadata={
'help': 'The evaluation strategy to adopt during training. Possible values are: ["no", "step", "epoch]'
} , )
SCREAMING_SNAKE_CASE = dataclasses.field(
default=1_0 , metadata={'help': 'Number of evaluation calls with no improvement after which training will be stopped.'} , )
SCREAMING_SNAKE_CASE = dataclasses.field(
default=0.0 , metadata={
'help': 'How much the specified evaluation metric must improve to satisfy early stopping conditions.'
} , )
SCREAMING_SNAKE_CASE = dataclasses.field(
default=lowerCAmelCase_ , metadata={'help': 'Whether to filter the pseudo-labeled data based on the confidence score.'} , )
SCREAMING_SNAKE_CASE = dataclasses.field(
default=lowerCAmelCase_ , metadata={'help': 'Whether to filter the pseudo-labeled data based on the validation performance.'} , )
SCREAMING_SNAKE_CASE = dataclasses.field(
default=lowerCAmelCase_ , metadata={'help': 'Whether to fine-tune on labeled data after pseudo training.'} , )
SCREAMING_SNAKE_CASE = dataclasses.field(
default=0.0 , metadata={'help': 'Confidence threshold for pseudo-labeled data filtering.'} , )
SCREAMING_SNAKE_CASE = dataclasses.field(
default=1_0_0 , metadata={'help': 'Number of evaluation calls with no improvement after which training will be stopped.'} , )
SCREAMING_SNAKE_CASE = dataclasses.field(
default=lowerCAmelCase_ , metadata={'help': 'Random seed for initialization.'} , )
def UpperCamelCase_( _snake_case : int , _snake_case : str , _snake_case : Optional[int] , _snake_case : str , _snake_case : Union[str, Any] , _snake_case : List[Any] ):
"""simple docstring"""
__a =datasets.concatenate_datasets([infer_input, infer_output] , axis=1 )
if args.do_filter_by_confidence:
__a =dataset.filter(lambda _snake_case : example["probability"] > args.confidence_threshold )
if args.do_filter_by_val_performance:
assert eval_result >= 0.0 and eval_result <= 1.0
__a =int(eval_result * len(_snake_case ) )
print(_snake_case )
__a =dataset.sort('probability' , reverse=_snake_case )
__a =dataset.select(range(_snake_case ) )
__a =dataset.remove_columns(['label', 'probability'] )
__a =dataset.rename_column('prediction' , 'label' )
__a =dataset.map(lambda _snake_case : {"label": idalabel[example["label"]]} )
__a =dataset.shuffle(seed=args.seed )
__a =os.path.join(_snake_case , F'train_pseudo.{args.data_file_extension}' )
if args.data_file_extension == "csv":
dataset.to_csv(_snake_case , index=_snake_case )
else:
dataset.to_json(_snake_case )
def UpperCamelCase_( _snake_case : List[Any] , _snake_case : str , _snake_case : int , _snake_case : Optional[int] , **_snake_case : List[str] ):
"""simple docstring"""
__a =Accelerator()
# Make one log on every process with the configuration for debugging.
logging.basicConfig(
format='%(asctime)s - %(levelname)s - %(name)s - %(message)s' , datefmt='%m/%d/%Y %H:%M:%S' , level=logging.INFO , )
logger.info(accelerator.state )
# Setup logging, we only want one process per machine to log things on the
# screen. accelerator.is_local_main_process is only True for one process per
# machine.
logger.setLevel(logging.INFO if accelerator.is_local_main_process else logging.ERROR )
if accelerator.is_local_main_process:
datasets.utils.logging.set_verbosity_warning()
transformers.utils.logging.set_verbosity_info()
else:
datasets.utils.logging.set_verbosity_error()
transformers.utils.logging.set_verbosity_error()
__a =STModelArguments(model_name_or_path=_snake_case )
__a =STDataArguments(train_file=_snake_case , infer_file=_snake_case )
__a =STTrainingArguments(output_dir=_snake_case )
__a =argparse.Namespace()
for arg_class in (model_args, data_args, training_args):
for key, value in vars(_snake_case ).items():
setattr(_snake_case , _snake_case , _snake_case )
for key, value in kwargs.items():
if hasattr(_snake_case , _snake_case ):
setattr(_snake_case , _snake_case , _snake_case )
# Sanity checks
__a ={}
__a =None
# You need to provide the training data and the data to predict on
assert args.train_file is not None
assert args.infer_file is not None
__a =args.train_file
__a =args.infer_file
if args.evaluation_strategy != IntervalStrategy.NO.value:
assert args.eval_file is not None
__a =args.eval_file
for key in data_files:
__a =data_files[key].split('.' )[-1]
assert extension in ["csv", "json"], F'`{key}_file` should be a csv or a json file.'
if args.data_file_extension is None:
__a =extension
else:
assert extension == args.data_file_extension, F'`{key}_file` should be a {args.data_file_extension} file`.'
assert (
args.eval_metric in datasets.list_metrics()
), F'{args.eval_metric} not in the list of supported metrics {datasets.list_metrics()}.'
# If passed along, set the training seed now.
if args.seed is not None:
set_seed(args.seed )
logger.info('Creating the initial data directory for self-training...' )
__a =F'{args.output_dir}/self-train_iter-{{}}'.format
__a =data_dir_format(0 )
if accelerator.is_main_process:
if args.output_dir is not None:
os.makedirs(args.output_dir , exist_ok=_snake_case )
os.makedirs(_snake_case , exist_ok=_snake_case )
accelerator.wait_for_everyone()
__a =None
__a =None
__a =0
__a =False
# Show the progress bar
__a =tqdm(range(args.max_selftrain_iterations ) , disable=not accelerator.is_local_main_process )
# Self-train
for iteration in range(0 , int(args.max_selftrain_iterations ) ):
__a =data_dir_format(_snake_case )
assert os.path.exists(_snake_case )
# Stage 1: initial fine-tuning for iteration = 0 or pseudo-training for
# iteration > 0
__a =os.path.join(_snake_case , 'stage-1' )
__a ={
'accelerator': accelerator,
'model_name_or_path': args.model_name_or_path,
'cache_dir': args.cache_dir,
'do_train': True,
'train_file': data_files['train'] if iteration == 0 else data_files['train_pseudo'],
'do_eval': True if args.eval_file is not None else False,
'eval_file': data_files['eval'],
'do_predict': True,
'infer_file': data_files['infer'],
'task_name': args.task_name,
'label_list': args.label_list,
'output_dir': current_output_dir,
'eval_metric': args.eval_metric,
'evaluation_strategy': args.evaluation_strategy,
'early_stopping_patience': args.early_stopping_patience,
'early_stopping_threshold': args.early_stopping_threshold,
'seed': args.seed,
}
# Add additional training arguments
for key, value in kwargs.items():
if key not in arguments_dict and not hasattr(_snake_case , _snake_case ):
arguments_dict.update({key: value} )
__a =os.path.join(_snake_case , 'best-checkpoint' , _snake_case )
if os.path.exists(_snake_case ):
logger.info(
'Found existing model checkpoint at %s. Skipping self-training: iteration: %d, stage: 1.' , _snake_case , _snake_case , )
else:
logger.info('***** Running self-training: iteration: %d, stage: 1 *****' , _snake_case )
finetune(**_snake_case )
accelerator.wait_for_everyone()
assert os.path.exists(_snake_case )
logger.info('Self-training job completed: iteration: %d, stage: 1.' , _snake_case )
if iteration > 0 and args.finetune_on_labeled_data:
# Stage 2 (optional): fine-tuning on the original labeled data
__a =os.path.join(_snake_case , 'best-checkpoint' )
__a =os.path.join(_snake_case , 'stage-2' )
# Update arguments_dict
__a =model_path
__a =data_files['train']
__a =current_output_dir
__a =os.path.join(_snake_case , 'best-checkpoint' , _snake_case )
if os.path.exists(_snake_case ):
logger.info(
'Found existing model checkpoint at %s. Skipping self-training: iteration: %d, stage: 2.' , _snake_case , _snake_case , )
else:
logger.info('***** Running self-training: iteration: %d, stage: 2 *****' , _snake_case )
finetune(**_snake_case )
accelerator.wait_for_everyone()
assert os.path.exists(_snake_case )
logger.info('Self-training job completed: iteration: %d, stage: 2.' , _snake_case )
__a =iteration
__a =data_dir_format(iteration + 1 )
__a =AutoConfig.from_pretrained(os.path.join(_snake_case , 'best-checkpoint' ) )
__a =config.idalabel
__a =os.path.join(_snake_case , 'eval_results_best-checkpoint.json' )
__a =os.path.join(_snake_case , 'test_results_best-checkpoint.json' )
assert os.path.exists(_snake_case )
with open(_snake_case , 'r' ) as f:
__a =float(json.load(_snake_case )[args.eval_metric] )
__a =os.path.join(_snake_case , 'infer_output_best-checkpoint.csv' )
assert os.path.exists(_snake_case )
# Loading the dataset from local csv or json files.
__a =load_dataset(args.data_file_extension , data_files={'data': data_files['infer']} )['data']
__a =load_dataset('csv' , data_files={'data': infer_output_file} )['data']
if accelerator.is_main_process:
os.makedirs(_snake_case , exist_ok=_snake_case )
shutil.copy(_snake_case , os.path.join(_snake_case , F'eval_results_iter-{iteration}.json' ) )
if os.path.exists(_snake_case ):
shutil.copy(_snake_case , os.path.join(_snake_case , F'test_results_iter-{iteration}.json' ) )
create_pseudo_labeled_data(_snake_case , _snake_case , _snake_case , _snake_case , _snake_case , _snake_case )
accelerator.wait_for_everyone()
__a =os.path.join(_snake_case , F'train_pseudo.{args.data_file_extension}' )
if args.evaluation_strategy != IntervalStrategy.NO.value:
__a =eval_result
if best_iteration is None:
__a =new_iteration
__a =new_eval_result
else:
if new_eval_result - best_eval_result > args.early_stopping_threshold:
__a =new_iteration
__a =new_eval_result
__a =0
else:
if new_eval_result == best_eval_result:
__a =new_iteration
__a =new_eval_result
early_stopping_patience_counter += 1
if early_stopping_patience_counter >= args.early_stopping_patience:
__a =True
progress_bar.update(1 )
if should_training_stop:
break
if best_iteration is not None:
# Save the best iteration
logger.info('Best iteration: %d' , _snake_case )
logger.info('Best evaluation result: %s = %f' , args.eval_metric , _snake_case )
accelerator.wait_for_everyone()
if accelerator.is_main_process:
shutil.copy(
os.path.join(_snake_case , F'eval_results_iter-{iteration}.json' ) , os.path.join(_snake_case , 'eval_results_best-iteration.json' ) , )
else:
# Assume that the last iteration is the best
logger.info('Best iteration: %d' , args.max_selftrain_iterations - 1 )
logger.info('Best evaluation result: %s = %f' , args.eval_metric , _snake_case )
accelerator.wait_for_everyone()
if accelerator.is_main_process:
shutil.copy(
os.path.join(_snake_case , F'eval_results_iter-{args.max_selftrain_iterations - 1}.json' ) , os.path.join(_snake_case , 'eval_results_best-iteration.json' ) , )
| 308
| 1
|
import inspect
import tempfile
import unittest
from huggingface_hub import hf_hub_download
from transformers import is_torch_available
from transformers.testing_utils import is_flaky, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
_lowerCAmelCase : Union[str, Any] = 1E-4
if is_torch_available():
import torch
from transformers import AutoformerConfig, AutoformerForPrediction, AutoformerModel
from transformers.models.autoformer.modeling_autoformer import AutoformerDecoder, AutoformerEncoder
@require_torch
class __magic_name__ :
def __init__( self , __snake_case , __snake_case=16 , __snake_case=13 , __snake_case=7 , __snake_case=14 , __snake_case=10 , __snake_case=19 , __snake_case=5 , __snake_case=4 , __snake_case=True , __snake_case=16 , __snake_case=2 , __snake_case=4 , __snake_case=4 , __snake_case="gelu" , __snake_case=0.1 , __snake_case=0.1 , __snake_case=[1, 2, 3, 4, 5] , __snake_case=25 , __snake_case=5 , ) -> Any:
'''simple docstring'''
__a =d_model
__a =parent
__a =batch_size
__a =prediction_length
__a =context_length
__a =cardinality
__a =num_time_features
__a =lags_sequence
__a =embedding_dimension
__a =is_training
__a =hidden_size
__a =num_hidden_layers
__a =num_attention_heads
__a =intermediate_size
__a =hidden_act
__a =hidden_dropout_prob
__a =attention_probs_dropout_prob
__a =context_length
__a =prediction_length + label_length
__a =label_length
__a =moving_average
__a =autocorrelation_factor
def __magic_name__ ( self ) -> Tuple:
'''simple docstring'''
return AutoformerConfig(
d_model=self.d_model , encoder_layers=self.num_hidden_layers , decoder_layers=self.num_hidden_layers , encoder_attention_heads=self.num_attention_heads , decoder_attention_heads=self.num_attention_heads , encoder_ffn_dim=self.intermediate_size , decoder_ffn_dim=self.intermediate_size , dropout=self.hidden_dropout_prob , attention_dropout=self.attention_probs_dropout_prob , prediction_length=self.prediction_length , context_length=self.context_length , label_length=self.label_length , lags_sequence=self.lags_sequence , num_time_features=self.num_time_features , num_static_categorical_features=1 , cardinality=[self.cardinality] , embedding_dimension=[self.embedding_dimension] , moving_average=self.moving_average , )
def __magic_name__ ( self , __snake_case ) -> int:
'''simple docstring'''
__a =config.context_length + max(config.lags_sequence )
__a =ids_tensor([self.batch_size, 1] , config.cardinality[0] )
__a =floats_tensor([self.batch_size, _past_length, config.num_time_features] )
__a =floats_tensor([self.batch_size, _past_length] )
__a =floats_tensor([self.batch_size, _past_length] ) > 0.5
# decoder inputs
__a =floats_tensor([self.batch_size, config.prediction_length, config.num_time_features] )
__a =floats_tensor([self.batch_size, config.prediction_length] )
__a ={
'past_values': past_values,
'static_categorical_features': static_categorical_features,
'past_time_features': past_time_features,
'past_observed_mask': past_observed_mask,
'future_time_features': future_time_features,
'future_values': future_values,
}
return inputs_dict
def __magic_name__ ( self ) -> Optional[Any]:
'''simple docstring'''
__a =self.get_config()
__a =self.prepare_autoformer_inputs_dict(__snake_case )
return config, inputs_dict
def __magic_name__ ( self ) -> List[str]:
'''simple docstring'''
__a , __a =self.prepare_config_and_inputs()
return config, inputs_dict
def __magic_name__ ( self , __snake_case , __snake_case ) -> int:
'''simple docstring'''
__a =AutoformerModel(config=__snake_case ).to(__snake_case ).eval()
__a =model(**__snake_case )
__a =outputs.encoder_last_hidden_state
__a =outputs.last_hidden_state
with tempfile.TemporaryDirectory() as tmpdirname:
__a =model.get_encoder()
encoder.save_pretrained(__snake_case )
__a =AutoformerEncoder.from_pretrained(__snake_case ).to(__snake_case )
__a , __a , __a , __a , __a =model.create_network_inputs(**__snake_case )
__a , __a =model.decomposition_layer(transformer_inputs[:, : config.context_length, ...] )
__a =torch.cat(
(transformer_inputs[:, : config.context_length, ...], feature[:, : config.context_length, ...]) , dim=-1 , )
__a =encoder(inputs_embeds=__snake_case )[0]
self.parent.assertTrue((encoder_last_hidden_state_a - encoder_last_hidden_state).abs().max().item() < 1e-3 )
__a =(
torch.mean(transformer_inputs[:, : config.context_length, ...] , dim=1 )
.unsqueeze(1 )
.repeat(1 , config.prediction_length , 1 )
)
__a =torch.zeros(
[transformer_inputs.shape[0], config.prediction_length, transformer_inputs.shape[2]] , device=enc_input.device , )
__a =torch.cat(
(
torch.cat((seasonal_input[:, -config.label_length :, ...], zeros) , dim=1 ),
feature[:, config.context_length - config.label_length :, ...],
) , dim=-1 , )
__a =torch.cat(
(
torch.cat((trend_input[:, -config.label_length :, ...], mean) , dim=1 ),
feature[:, config.context_length - config.label_length :, ...],
) , dim=-1 , )
with tempfile.TemporaryDirectory() as tmpdirname:
__a =model.get_decoder()
decoder.save_pretrained(__snake_case )
__a =AutoformerDecoder.from_pretrained(__snake_case ).to(__snake_case )
__a =decoder(
trend=__snake_case , inputs_embeds=__snake_case , encoder_hidden_states=__snake_case , )[0]
self.parent.assertTrue((last_hidden_state_a - last_hidden_state).abs().max().item() < 1e-3 )
@require_torch
class __magic_name__ ( lowerCAmelCase_ , lowerCAmelCase_ , unittest.TestCase ):
SCREAMING_SNAKE_CASE = (AutoformerModel, AutoformerForPrediction) if is_torch_available() else ()
SCREAMING_SNAKE_CASE = (AutoformerForPrediction,) if is_torch_available() else ()
SCREAMING_SNAKE_CASE = {'feature-extraction': AutoformerModel} if is_torch_available() else {}
SCREAMING_SNAKE_CASE = False
SCREAMING_SNAKE_CASE = False
SCREAMING_SNAKE_CASE = False
SCREAMING_SNAKE_CASE = False
SCREAMING_SNAKE_CASE = False
SCREAMING_SNAKE_CASE = False
def __magic_name__ ( self ) -> List[str]:
'''simple docstring'''
__a =AutoformerModelTester(self )
__a =ConfigTester(self , config_class=__snake_case , has_text_modality=__snake_case )
def __magic_name__ ( self ) -> Union[str, Any]:
'''simple docstring'''
self.config_tester.run_common_tests()
def __magic_name__ ( self ) -> List[str]:
'''simple docstring'''
__a , __a =self.model_tester.prepare_config_and_inputs()
for model_class in self.all_model_classes:
__a =model_class(__snake_case )
with tempfile.TemporaryDirectory() as tmpdirname:
model.save_pretrained(__snake_case )
__a , __a =model_class.from_pretrained(__snake_case , output_loading_info=__snake_case )
self.assertEqual(info['missing_keys'] , [] )
def __magic_name__ ( self ) -> Optional[Any]:
'''simple docstring'''
__a =self.model_tester.prepare_config_and_inputs_for_common()
self.model_tester.check_encoder_decoder_model_standalone(*__snake_case )
@unittest.skip(reason='Model has no tokens embeddings' )
def __magic_name__ ( self ) -> Tuple:
'''simple docstring'''
pass
def __magic_name__ ( self ) -> Union[str, Any]:
'''simple docstring'''
__a =inspect.signature(getattr(__snake_case , 'forward' ) )
# The main input is the name of the argument after `self`
__a =list(model_signature.parameters.keys() )[1]
self.assertEqual(AutoformerModel.main_input_name , __snake_case )
def __magic_name__ ( self ) -> int:
'''simple docstring'''
__a , __a =self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
__a =model_class(__snake_case )
__a =inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
__a =[*signature.parameters.keys()]
__a =[
'past_values',
'past_time_features',
'past_observed_mask',
'static_categorical_features',
'static_real_features',
'future_values',
'future_time_features',
]
if model.__class__.__name__ in ["AutoformerForPrediction"]:
expected_arg_names.append('future_observed_mask' )
expected_arg_names.extend(
[
'decoder_attention_mask',
'head_mask',
'decoder_head_mask',
'cross_attn_head_mask',
'encoder_outputs',
'past_key_values',
'output_hidden_states',
'output_attentions',
'use_cache',
'return_dict',
] )
self.assertListEqual(arg_names[: len(__snake_case )] , __snake_case )
def __magic_name__ ( self ) -> Dict:
'''simple docstring'''
__a , __a =self.model_tester.prepare_config_and_inputs_for_common()
__a =True
__a =getattr(self.model_tester , 'seq_length' , __snake_case )
__a =getattr(self.model_tester , 'decoder_seq_length' , __snake_case )
__a =getattr(self.model_tester , 'encoder_seq_length' , __snake_case )
__a =getattr(self.model_tester , 'd_model' , __snake_case )
__a =getattr(self.model_tester , 'num_attention_heads' , __snake_case )
__a =d_model // num_attention_heads
for model_class in self.all_model_classes:
__a =True
__a =False
__a =True
__a =model_class(__snake_case )
model.to(__snake_case )
model.eval()
with torch.no_grad():
__a =model(**self._prepare_for_class(__snake_case , __snake_case ) )
__a =outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
self.assertEqual(len(__snake_case ) , self.model_tester.num_hidden_layers )
# check that output_attentions also work using config
del inputs_dict["output_attentions"]
__a =True
__a =model_class(__snake_case )
model.to(__snake_case )
model.eval()
with torch.no_grad():
__a =model(**self._prepare_for_class(__snake_case , __snake_case ) )
__a =outputs.encoder_attentions
self.assertEqual(len(__snake_case ) , self.model_tester.num_hidden_layers )
self.assertListEqual(
list(attentions[0].shape[-3:] ) , [self.model_tester.num_attention_heads, encoder_seq_length, dim] , )
__a =len(__snake_case )
__a =7
if "last_hidden_state" in outputs:
correct_outlen += 1
if "trend" in outputs:
correct_outlen += 1
if "past_key_values" in outputs:
correct_outlen += 1 # past_key_values have been returned
if "loss" in outputs:
correct_outlen += 1
if "params" in outputs:
correct_outlen += 1
self.assertEqual(__snake_case , __snake_case )
# decoder attentions
__a =outputs.decoder_attentions
self.assertIsInstance(__snake_case , (list, tuple) )
self.assertEqual(len(__snake_case ) , self.model_tester.num_hidden_layers )
self.assertListEqual(
list(decoder_attentions[0].shape[-3:] ) , [self.model_tester.num_attention_heads, decoder_seq_length, dim] , )
# cross attentions
__a =outputs.cross_attentions
self.assertIsInstance(__snake_case , (list, tuple) )
self.assertEqual(len(__snake_case ) , self.model_tester.num_hidden_layers )
self.assertListEqual(
list(cross_attentions[0].shape[-3:] ) , [self.model_tester.num_attention_heads, decoder_seq_length, dim] , )
# Check attention is always last and order is fine
__a =True
__a =True
__a =model_class(__snake_case )
model.to(__snake_case )
model.eval()
with torch.no_grad():
__a =model(**self._prepare_for_class(__snake_case , __snake_case ) )
self.assertEqual(out_len + 2 , len(__snake_case ) )
__a =outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
self.assertEqual(len(__snake_case ) , self.model_tester.num_hidden_layers )
self.assertListEqual(
list(self_attentions[0].shape[-3:] ) , [self.model_tester.num_attention_heads, encoder_seq_length, dim] , )
@is_flaky()
def __magic_name__ ( self ) -> Optional[Any]:
'''simple docstring'''
super().test_retain_grad_hidden_states_attentions()
def UpperCamelCase_( _snake_case : List[Any]="train-batch.pt" ):
"""simple docstring"""
__a =hf_hub_download(repo_id='hf-internal-testing/tourism-monthly-batch' , filename=_snake_case , repo_type='dataset' )
__a =torch.load(_snake_case , map_location=_snake_case )
return batch
@require_torch
@slow
class __magic_name__ ( unittest.TestCase ):
def __magic_name__ ( self ) -> Tuple:
'''simple docstring'''
__a =AutoformerModel.from_pretrained('huggingface/autoformer-tourism-monthly' ).to(__snake_case )
__a =prepare_batch()
with torch.no_grad():
__a =model(
past_values=batch['past_values'] , past_time_features=batch['past_time_features'] , past_observed_mask=batch['past_observed_mask'] , static_categorical_features=batch['static_categorical_features'] , future_values=batch['future_values'] , future_time_features=batch['future_time_features'] , )[0]
__a =torch.Size(
(64, model.config.prediction_length + model.config.label_length, model.config.feature_size) )
self.assertEqual(output.shape , __snake_case )
__a =torch.tensor(
[[0.3593, -1.3398, 0.6330], [0.2279, 1.5396, -0.1792], [0.0450, 1.3225, -0.2335]] , device=__snake_case )
self.assertTrue(torch.allclose(output[0, :3, :3] , __snake_case , atol=__snake_case ) )
def __magic_name__ ( self ) -> List[str]:
'''simple docstring'''
__a =AutoformerForPrediction.from_pretrained('huggingface/autoformer-tourism-monthly' ).to(__snake_case )
__a =prepare_batch('val-batch.pt' )
with torch.no_grad():
__a =model(
past_values=batch['past_values'] , past_time_features=batch['past_time_features'] , past_observed_mask=batch['past_observed_mask'] , static_categorical_features=batch['static_categorical_features'] , ).encoder_last_hidden_state
__a =torch.Size((64, model.config.context_length, model.config.d_model) )
self.assertEqual(output.shape , __snake_case )
__a =torch.tensor(
[[-0.0734, -0.9036, 0.8358], [4.7186, 2.4113, 1.9581], [1.7953, 2.3558, 1.2970]] , device=__snake_case )
self.assertTrue(torch.allclose(output[0, :3, :3] , __snake_case , atol=__snake_case ) )
def __magic_name__ ( self ) -> List[Any]:
'''simple docstring'''
__a =AutoformerForPrediction.from_pretrained('huggingface/autoformer-tourism-monthly' ).to(__snake_case )
__a =prepare_batch('val-batch.pt' )
with torch.no_grad():
__a =model.generate(
static_categorical_features=batch['static_categorical_features'] , past_time_features=batch['past_time_features'] , past_values=batch['past_values'] , future_time_features=batch['future_time_features'] , past_observed_mask=batch['past_observed_mask'] , )
__a =torch.Size((64, model.config.num_parallel_samples, model.config.prediction_length) )
self.assertEqual(outputs.sequences.shape , __snake_case )
__a =torch.tensor([3130.6763, 4056.5293, 7053.0786] , device=__snake_case )
__a =outputs.sequences.mean(dim=1 )
self.assertTrue(torch.allclose(mean_prediction[0, -3:] , __snake_case , rtol=1e-1 ) )
| 308
|
import shutil
import tempfile
import unittest
from transformers import (
SPIECE_UNDERLINE,
AddedToken,
BatchEncoding,
NllbTokenizer,
NllbTokenizerFast,
is_torch_available,
)
from transformers.testing_utils import (
get_tests_dir,
nested_simplify,
require_sentencepiece,
require_tokenizers,
require_torch,
)
from ...test_tokenization_common import TokenizerTesterMixin
_lowerCAmelCase : Any = get_tests_dir("fixtures/test_sentencepiece.model")
if is_torch_available():
from transformers.models.mam_aaa.modeling_mam_aaa import shift_tokens_right
_lowerCAmelCase : List[Any] = 256_047
_lowerCAmelCase : Dict = 256_145
@require_sentencepiece
@require_tokenizers
class __magic_name__ ( lowerCAmelCase_ , unittest.TestCase ):
SCREAMING_SNAKE_CASE = NllbTokenizer
SCREAMING_SNAKE_CASE = NllbTokenizerFast
SCREAMING_SNAKE_CASE = True
SCREAMING_SNAKE_CASE = True
SCREAMING_SNAKE_CASE = {}
def __magic_name__ ( self ) -> int:
'''simple docstring'''
super().setUp()
# We have a SentencePiece fixture for testing
__a =NllbTokenizer(__snake_case , keep_accents=__snake_case )
tokenizer.save_pretrained(self.tmpdirname )
def __magic_name__ ( self ) -> int:
'''simple docstring'''
__a =NllbTokenizer(__snake_case , keep_accents=__snake_case )
__a =tokenizer.tokenize('This is a test' )
self.assertListEqual(__snake_case , ['▁This', '▁is', '▁a', '▁t', 'est'] )
self.assertListEqual(
tokenizer.convert_tokens_to_ids(__snake_case ) , [value + tokenizer.fairseq_offset for value in [285, 46, 10, 170, 382]] , )
__a =tokenizer.tokenize('I was born in 92000, and this is falsé.' )
self.assertListEqual(
__snake_case , [
SPIECE_UNDERLINE + 'I',
SPIECE_UNDERLINE + 'was',
SPIECE_UNDERLINE + 'b',
'or',
'n',
SPIECE_UNDERLINE + 'in',
SPIECE_UNDERLINE + '',
'9',
'2',
'0',
'0',
'0',
',',
SPIECE_UNDERLINE + 'and',
SPIECE_UNDERLINE + 'this',
SPIECE_UNDERLINE + 'is',
SPIECE_UNDERLINE + 'f',
'al',
's',
'é',
'.',
] , )
__a =tokenizer.convert_tokens_to_ids(__snake_case )
self.assertListEqual(
__snake_case , [
value + tokenizer.fairseq_offset
for value in [8, 21, 84, 55, 24, 19, 7, 2, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 2, 4]
] , )
__a =tokenizer.convert_ids_to_tokens(__snake_case )
self.assertListEqual(
__snake_case , [
SPIECE_UNDERLINE + 'I',
SPIECE_UNDERLINE + 'was',
SPIECE_UNDERLINE + 'b',
'or',
'n',
SPIECE_UNDERLINE + 'in',
SPIECE_UNDERLINE + '',
'<unk>',
'2',
'0',
'0',
'0',
',',
SPIECE_UNDERLINE + 'and',
SPIECE_UNDERLINE + 'this',
SPIECE_UNDERLINE + 'is',
SPIECE_UNDERLINE + 'f',
'al',
's',
'<unk>',
'.',
] , )
def __magic_name__ ( self ) -> Union[str, Any]:
'''simple docstring'''
__a =(self.rust_tokenizer_class, 'hf-internal-testing/tiny-random-nllb', {})
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(f'{tokenizer.__class__.__name__} ({pretrained_name})' ):
__a =self.rust_tokenizer_class.from_pretrained(__snake_case , **__snake_case )
__a =self.tokenizer_class.from_pretrained(__snake_case , **__snake_case )
__a =tempfile.mkdtemp()
__a =tokenizer_r.save_pretrained(__snake_case )
__a =tokenizer_p.save_pretrained(__snake_case )
# Checks it save with the same files + the tokenizer.json file for the fast one
self.assertTrue(any('tokenizer.json' in f for f in tokenizer_r_files ) )
__a =tuple(f for f in tokenizer_r_files if 'tokenizer.json' not in f )
self.assertSequenceEqual(__snake_case , __snake_case )
# Checks everything loads correctly in the same way
__a =tokenizer_r.from_pretrained(__snake_case )
__a =tokenizer_p.from_pretrained(__snake_case )
# Check special tokens are set accordingly on Rust and Python
for key in tokenizer_pp.special_tokens_map:
self.assertTrue(hasattr(__snake_case , __snake_case ) )
shutil.rmtree(__snake_case )
# Save tokenizer rust, legacy_format=True
__a =tempfile.mkdtemp()
__a =tokenizer_r.save_pretrained(__snake_case , legacy_format=__snake_case )
__a =tokenizer_p.save_pretrained(__snake_case )
# Checks it save with the same files
self.assertSequenceEqual(__snake_case , __snake_case )
# Checks everything loads correctly in the same way
__a =tokenizer_r.from_pretrained(__snake_case )
__a =tokenizer_p.from_pretrained(__snake_case )
# Check special tokens are set accordingly on Rust and Python
for key in tokenizer_pp.special_tokens_map:
self.assertTrue(hasattr(__snake_case , __snake_case ) )
shutil.rmtree(__snake_case )
# Save tokenizer rust, legacy_format=False
__a =tempfile.mkdtemp()
__a =tokenizer_r.save_pretrained(__snake_case , legacy_format=__snake_case )
__a =tokenizer_p.save_pretrained(__snake_case )
# Checks it saved the tokenizer.json file
self.assertTrue(any('tokenizer.json' in f for f in tokenizer_r_files ) )
# Checks everything loads correctly in the same way
__a =tokenizer_r.from_pretrained(__snake_case )
__a =tokenizer_p.from_pretrained(__snake_case )
# Check special tokens are set accordingly on Rust and Python
for key in tokenizer_pp.special_tokens_map:
self.assertTrue(hasattr(__snake_case , __snake_case ) )
shutil.rmtree(__snake_case )
@require_torch
def __magic_name__ ( self ) -> Optional[Any]:
'''simple docstring'''
if not self.test_seqaseq:
return
__a =self.get_tokenizers()
for tokenizer in tokenizers:
with self.subTest(f'{tokenizer.__class__.__name__}' ):
# Longer text that will definitely require truncation.
__a =[
' UN Chief Says There Is No Military Solution in Syria',
' Secretary-General Ban Ki-moon says his response to Russia\'s stepped up military support for'
' Syria is that \'there is no military solution\' to the nearly five-year conflict and more weapons'
' will only worsen the violence and misery for millions of people.',
]
__a =[
'Şeful ONU declară că nu există o soluţie militară în Siria',
'Secretarul General Ban Ki-moon declară că răspunsul său la intensificarea sprijinului militar al'
' Rusiei pentru Siria este că "nu există o soluţie militară" la conflictul de aproape cinci ani şi'
' că noi arme nu vor face decât să înrăutăţească violenţele şi mizeria pentru milioane de oameni.',
]
try:
__a =tokenizer.prepare_seqaseq_batch(
src_texts=__snake_case , tgt_texts=__snake_case , max_length=3 , max_target_length=10 , return_tensors='pt' , src_lang='eng_Latn' , tgt_lang='ron_Latn' , )
except NotImplementedError:
return
self.assertEqual(batch.input_ids.shape[1] , 3 )
self.assertEqual(batch.labels.shape[1] , 10 )
# max_target_length will default to max_length if not specified
__a =tokenizer.prepare_seqaseq_batch(
__snake_case , tgt_texts=__snake_case , max_length=3 , return_tensors='pt' )
self.assertEqual(batch.input_ids.shape[1] , 3 )
self.assertEqual(batch.labels.shape[1] , 3 )
__a =tokenizer.prepare_seqaseq_batch(
src_texts=__snake_case , max_length=3 , max_target_length=10 , return_tensors='pt' )
self.assertEqual(batch_encoder_only.input_ids.shape[1] , 3 )
self.assertEqual(batch_encoder_only.attention_mask.shape[1] , 3 )
self.assertNotIn('decoder_input_ids' , __snake_case )
@unittest.skip('Unfortunately way too slow to build a BPE with SentencePiece.' )
def __magic_name__ ( self ) -> Optional[int]:
'''simple docstring'''
pass
def __magic_name__ ( self ) -> Tuple:
'''simple docstring'''
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(f'{tokenizer.__class__.__name__} ({pretrained_name})' ):
__a =[AddedToken('<special>' , lstrip=__snake_case )]
__a =self.rust_tokenizer_class.from_pretrained(
__snake_case , additional_special_tokens=__snake_case , **__snake_case )
__a =tokenizer_r.encode('Hey this is a <special> token' )
__a =tokenizer_r.encode('<special>' , add_special_tokens=__snake_case )[0]
self.assertTrue(special_token_id in r_output )
if self.test_slow_tokenizer:
__a =self.rust_tokenizer_class.from_pretrained(
__snake_case , additional_special_tokens=__snake_case , **__snake_case , )
__a =self.tokenizer_class.from_pretrained(
__snake_case , additional_special_tokens=__snake_case , **__snake_case )
__a =tokenizer_p.encode('Hey this is a <special> token' )
__a =tokenizer_cr.encode('Hey this is a <special> token' )
self.assertEqual(__snake_case , __snake_case )
self.assertEqual(__snake_case , __snake_case )
self.assertTrue(special_token_id in p_output )
self.assertTrue(special_token_id in cr_output )
@require_torch
@require_sentencepiece
@require_tokenizers
class __magic_name__ ( unittest.TestCase ):
SCREAMING_SNAKE_CASE = 'facebook/nllb-200-distilled-600M'
SCREAMING_SNAKE_CASE = [
' UN Chief Says There Is No Military Solution in Syria',
' Secretary-General Ban Ki-moon says his response to Russia\'s stepped up military support for Syria is that "there is no military solution" to the nearly five-year conflict and more weapons will only worsen the violence and misery for millions of people.',
]
SCREAMING_SNAKE_CASE = [
'Şeful ONU declară că nu există o soluţie militară în Siria',
'Secretarul General Ban Ki-moon declară că răspunsul său la intensificarea sprijinului militar al Rusiei'
' pentru Siria este că "nu există o soluţie militară" la conflictul de aproape cinci ani şi că noi arme nu vor'
' face decât să înrăutăţească violenţele şi mizeria pentru milioane de oameni.',
]
SCREAMING_SNAKE_CASE = [
2_5_6_0_4_7,
1_6_2_9_7,
1_3_4_4_0_8,
8_1_6_5,
2_4_8_0_6_6,
1_4_7_3_4,
9_5_0,
1_1_3_5,
1_0_5_7_2_1,
3_5_7_3,
8_3,
2_7_3_5_2,
1_0_8,
4_9_4_8_6,
2,
]
@classmethod
def __magic_name__ ( cls ) -> Tuple:
'''simple docstring'''
__a =NllbTokenizer.from_pretrained(
cls.checkpoint_name , src_lang='eng_Latn' , tgt_lang='ron_Latn' )
__a =1
return cls
def __magic_name__ ( self ) -> List[Any]:
'''simple docstring'''
self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['ace_Arab'] , 25_6001 )
self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['ace_Latn'] , 25_6002 )
self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['fra_Latn'] , 25_6057 )
def __magic_name__ ( self ) -> Dict:
'''simple docstring'''
__a =self.tokenizer.batch_encode_plus(self.src_text ).input_ids[0]
self.assertListEqual(self.expected_src_tokens , __snake_case )
def __magic_name__ ( self ) -> Dict:
'''simple docstring'''
self.assertIn(__snake_case , self.tokenizer.all_special_ids )
# fmt: off
__a =[RO_CODE, 4254, 9_8068, 11_2923, 3_9072, 3909, 713, 10_2767, 26, 1_7314, 3_5642, 1_4683, 3_3118, 2022, 6_6987, 2, 25_6047]
# fmt: on
__a =self.tokenizer.decode(__snake_case , skip_special_tokens=__snake_case )
__a =self.tokenizer.decode(generated_ids[1:] , skip_special_tokens=__snake_case )
self.assertEqual(__snake_case , __snake_case )
self.assertNotIn(self.tokenizer.eos_token , __snake_case )
def __magic_name__ ( self ) -> List[str]:
'''simple docstring'''
__a =['this is gunna be a long sentence ' * 20]
assert isinstance(src_text[0] , __snake_case )
__a =10
__a =self.tokenizer(__snake_case , max_length=__snake_case , truncation=__snake_case ).input_ids[0]
self.assertEqual(ids[-1] , 2 )
self.assertEqual(ids[0] , __snake_case )
self.assertEqual(len(__snake_case ) , __snake_case )
def __magic_name__ ( self ) -> Optional[int]:
'''simple docstring'''
self.assertListEqual(self.tokenizer.convert_tokens_to_ids(['<mask>', 'ar_AR'] ) , [25_6203, 3] )
def __magic_name__ ( self ) -> Optional[int]:
'''simple docstring'''
__a =tempfile.mkdtemp()
__a =self.tokenizer.fairseq_tokens_to_ids
self.tokenizer.save_pretrained(__snake_case )
__a =NllbTokenizer.from_pretrained(__snake_case )
self.assertDictEqual(new_tok.fairseq_tokens_to_ids , __snake_case )
@require_torch
def __magic_name__ ( self ) -> Tuple:
'''simple docstring'''
__a =self.tokenizer(
self.src_text , text_target=self.tgt_text , padding=__snake_case , truncation=__snake_case , max_length=len(self.expected_src_tokens ) , return_tensors='pt' , )
__a =shift_tokens_right(
batch['labels'] , self.tokenizer.pad_token_id , self.tokenizer.lang_code_to_id['ron_Latn'] )
self.assertIsInstance(__snake_case , __snake_case )
self.assertEqual((2, 15) , batch.input_ids.shape )
self.assertEqual((2, 15) , batch.attention_mask.shape )
__a =batch.input_ids.tolist()[0]
self.assertListEqual(self.expected_src_tokens , __snake_case )
self.assertEqual(__snake_case , batch.decoder_input_ids[0, 0] ) # EOS
# Test that special tokens are reset
self.assertEqual(self.tokenizer.prefix_tokens , [EN_CODE] )
self.assertEqual(self.tokenizer.suffix_tokens , [self.tokenizer.eos_token_id] )
def __magic_name__ ( self ) -> List[Any]:
'''simple docstring'''
__a =self.tokenizer(self.src_text , padding=__snake_case , truncation=__snake_case , max_length=3 , return_tensors='pt' )
__a =self.tokenizer(
text_target=self.tgt_text , padding=__snake_case , truncation=__snake_case , max_length=10 , return_tensors='pt' )
__a =targets['input_ids']
__a =shift_tokens_right(
__snake_case , self.tokenizer.pad_token_id , decoder_start_token_id=self.tokenizer.lang_code_to_id[self.tokenizer.tgt_lang] , )
self.assertEqual(batch.input_ids.shape[1] , 3 )
self.assertEqual(batch.decoder_input_ids.shape[1] , 10 )
@require_torch
def __magic_name__ ( self ) -> Optional[Any]:
'''simple docstring'''
__a =self.tokenizer._build_translation_inputs(
'A test' , return_tensors='pt' , src_lang='eng_Latn' , tgt_lang='fra_Latn' )
self.assertEqual(
nested_simplify(__snake_case ) , {
# A, test, EOS, en_XX
'input_ids': [[25_6047, 70, 7356, 2]],
'attention_mask': [[1, 1, 1, 1]],
# ar_AR
'forced_bos_token_id': 25_6057,
} , )
@require_torch
def __magic_name__ ( self ) -> List[Any]:
'''simple docstring'''
__a =True
__a =self.tokenizer(
'UN Chief says there is no military solution in Syria' , src_lang='eng_Latn' , tgt_lang='fra_Latn' )
self.assertEqual(
inputs.input_ids , [1_6297, 13_4408, 2_5653, 6370, 248, 254, 10_3929, 9_4995, 108, 4_9486, 2, 25_6047] )
__a =False
__a =self.tokenizer(
'UN Chief says there is no military solution in Syria' , src_lang='eng_Latn' , tgt_lang='fra_Latn' )
self.assertEqual(
inputs.input_ids , [25_6047, 1_6297, 13_4408, 2_5653, 6370, 248, 254, 10_3929, 9_4995, 108, 4_9486, 2] )
| 308
| 1
|
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
_lowerCAmelCase : Dict = {
"configuration_mask2former": [
"MASK2FORMER_PRETRAINED_CONFIG_ARCHIVE_MAP",
"Mask2FormerConfig",
],
}
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_lowerCAmelCase : Tuple = ["Mask2FormerImageProcessor"]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_lowerCAmelCase : Union[str, Any] = [
"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
_lowerCAmelCase : Tuple = _LazyModule(__name__, globals()["__file__"], _import_structure)
| 308
|
def UpperCamelCase_( _snake_case : str , _snake_case : int ):
"""simple docstring"""
return [sentence[i : i + ngram_size] for i in range(len(_snake_case ) - ngram_size + 1 )]
if __name__ == "__main__":
from doctest import testmod
testmod()
| 308
| 1
|
import torch
from transformers import AutoModel
class __magic_name__ ( torch.nn.Module ):
def __init__( self , __snake_case="sayef/fsner-bert-base-uncased" ) -> Optional[int]:
'''simple docstring'''
super(__snake_case , self ).__init__()
__a =AutoModel.from_pretrained(__snake_case , return_dict=__snake_case )
__a =torch.nn.CosineSimilarity(3 , 1e-08 )
__a =torch.nn.Softmax(dim=1 )
def __magic_name__ ( self , **__snake_case ) -> Any:
'''simple docstring'''
return self.bert(**__snake_case ).last_hidden_state
def __magic_name__ ( self , __snake_case ) -> Any:
'''simple docstring'''
return token_embeddings.sum(2 , keepdim=__snake_case )
def __magic_name__ ( self , __snake_case , __snake_case , __snake_case=1 ) -> Optional[Any]:
'''simple docstring'''
return self.softmax(T * self.cos(__snake_case , __snake_case ) )
def __magic_name__ ( self , __snake_case , __snake_case ) -> Dict:
'''simple docstring'''
__a =W_supports['sizes'].tolist()
__a =W_supports['start_token_id'].item()
__a =W_supports['end_token_id'].item()
del W_supports["sizes"]
del W_supports["start_token_id"]
del W_supports["end_token_id"]
__a =self.BERT(**__snake_case )
__a =self.BERT(**__snake_case )
__a =None
__a =None
__a =W_supports['input_ids'] == start_token_id
__a =W_supports['input_ids'] == end_token_id
for i, size in enumerate(__snake_case ):
if i == 0:
__a =0
else:
__a =support_sizes[i - 1]
__a =S[s : s + size][start_token_masks[s : s + size]]
__a =S[s : s + size][end_token_masks[s : s + size]]
__a =torch.matmul(q[i] , s_start.T ).sum(1 ).softmax(0 )
__a =torch.matmul(q[i] , s_end.T ).sum(1 ).softmax(0 )
if p_starts is not None:
__a =torch.vstack((p_starts, p_start) )
__a =torch.vstack((p_ends, p_end) )
else:
__a =p_start
__a =p_end
return p_starts, p_ends
| 308
|
import argparse
import pytorch_lightning as pl
import torch
from torch import nn
from transformers import LongformerForQuestionAnswering, LongformerModel
class __magic_name__ ( pl.LightningModule ):
def __init__( self , __snake_case ) -> List[Any]:
'''simple docstring'''
super().__init__()
__a =model
__a =2
__a =nn.Linear(self.model.config.hidden_size , self.num_labels )
def __magic_name__ ( self ) -> List[Any]:
'''simple docstring'''
pass
def UpperCamelCase_( _snake_case : str , _snake_case : str , _snake_case : str ):
"""simple docstring"""
__a =LongformerModel.from_pretrained(_snake_case )
__a =LightningModel(_snake_case )
__a =torch.load(_snake_case , map_location=torch.device('cpu' ) )
lightning_model.load_state_dict(ckpt['state_dict'] )
# init longformer question answering model
__a =LongformerForQuestionAnswering.from_pretrained(_snake_case )
# transfer weights
longformer_for_qa.longformer.load_state_dict(lightning_model.model.state_dict() )
longformer_for_qa.qa_outputs.load_state_dict(lightning_model.qa_outputs.state_dict() )
longformer_for_qa.eval()
# save model
longformer_for_qa.save_pretrained(_snake_case )
print(F'Conversion successful. Model saved under {pytorch_dump_folder_path}' )
if __name__ == "__main__":
_lowerCAmelCase : Optional[int] = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--longformer_model",
default=None,
type=str,
required=True,
help="model identifier of longformer. Should be either `longformer-base-4096` or `longformer-large-4096`.",
)
parser.add_argument(
"--longformer_question_answering_ckpt_path",
default=None,
type=str,
required=True,
help="Path the official PyTorch Lightning Checkpoint.",
)
parser.add_argument(
"--pytorch_dump_folder_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
)
_lowerCAmelCase : Optional[int] = parser.parse_args()
convert_longformer_qa_checkpoint_to_pytorch(
args.longformer_model, args.longformer_question_answering_ckpt_path, args.pytorch_dump_folder_path
)
| 308
| 1
|
from typing import Optional, Tuple, Union
import flax
import flax.linen as nn
import jax
import jax.numpy as jnp
from flax.core.frozen_dict import FrozenDict
from ..configuration_utils import ConfigMixin, flax_register_to_config
from ..utils import BaseOutput
from .embeddings_flax import FlaxTimestepEmbedding, FlaxTimesteps
from .modeling_flax_utils import FlaxModelMixin
from .unet_ad_blocks_flax import (
FlaxCrossAttnDownBlockaD,
FlaxCrossAttnUpBlockaD,
FlaxDownBlockaD,
FlaxUNetMidBlockaDCrossAttn,
FlaxUpBlockaD,
)
@flax.struct.dataclass
class __magic_name__ ( lowerCAmelCase_ ):
SCREAMING_SNAKE_CASE = 42
@flax_register_to_config
class __magic_name__ ( nn.Module , lowerCAmelCase_ , lowerCAmelCase_ ):
SCREAMING_SNAKE_CASE = 3_2
SCREAMING_SNAKE_CASE = 4
SCREAMING_SNAKE_CASE = 4
SCREAMING_SNAKE_CASE = (
"CrossAttnDownBlock2D",
"CrossAttnDownBlock2D",
"CrossAttnDownBlock2D",
"DownBlock2D",
)
SCREAMING_SNAKE_CASE = ("UpBlock2D", "CrossAttnUpBlock2D", "CrossAttnUpBlock2D", "CrossAttnUpBlock2D")
SCREAMING_SNAKE_CASE = False
SCREAMING_SNAKE_CASE = (3_2_0, 6_4_0, 1_2_8_0, 1_2_8_0)
SCREAMING_SNAKE_CASE = 2
SCREAMING_SNAKE_CASE = 8
SCREAMING_SNAKE_CASE = None
SCREAMING_SNAKE_CASE = 1_2_8_0
SCREAMING_SNAKE_CASE = 0.0
SCREAMING_SNAKE_CASE = False
SCREAMING_SNAKE_CASE = jnp.floataa
SCREAMING_SNAKE_CASE = True
SCREAMING_SNAKE_CASE = 0
SCREAMING_SNAKE_CASE = False
def __magic_name__ ( self , __snake_case ) -> FrozenDict:
'''simple docstring'''
# init input tensors
__a =(1, self.in_channels, self.sample_size, self.sample_size)
__a =jnp.zeros(__snake_case , dtype=jnp.floataa )
__a =jnp.ones((1,) , dtype=jnp.intaa )
__a =jnp.zeros((1, 1, self.cross_attention_dim) , dtype=jnp.floataa )
__a , __a =jax.random.split(__snake_case )
__a ={'params': params_rng, 'dropout': dropout_rng}
return self.init(__snake_case , __snake_case , __snake_case , __snake_case )["params"]
def __magic_name__ ( self ) -> Tuple:
'''simple docstring'''
__a =self.block_out_channels
__a =block_out_channels[0] * 4
if self.num_attention_heads is not None:
raise ValueError(
'At the moment it is not possible to define the number of attention heads via `num_attention_heads` because of a naming issue as described in https://github.com/huggingface/diffusers/issues/2011#issuecomment-1547958131. Passing `num_attention_heads` will only be supported in diffusers v0.19.' )
# If `num_attention_heads` is not defined (which is the case for most models)
# it will default to `attention_head_dim`. This looks weird upon first reading it and it is.
# The reason for this behavior is to correct for incorrectly named variables that were introduced
# when this library was created. The incorrect naming was only discovered much later in https://github.com/huggingface/diffusers/issues/2011#issuecomment-1547958131
# Changing `attention_head_dim` to `num_attention_heads` for 40,000+ configurations is too backwards breaking
# which is why we correct for the naming here.
__a =self.num_attention_heads or self.attention_head_dim
# input
__a =nn.Conv(
block_out_channels[0] , kernel_size=(3, 3) , strides=(1, 1) , padding=((1, 1), (1, 1)) , dtype=self.dtype , )
# time
__a =FlaxTimesteps(
block_out_channels[0] , flip_sin_to_cos=self.flip_sin_to_cos , freq_shift=self.config.freq_shift )
__a =FlaxTimestepEmbedding(__snake_case , dtype=self.dtype )
__a =self.only_cross_attention
if isinstance(__snake_case , __snake_case ):
__a =(only_cross_attention,) * len(self.down_block_types )
if isinstance(__snake_case , __snake_case ):
__a =(num_attention_heads,) * len(self.down_block_types )
# down
__a =[]
__a =block_out_channels[0]
for i, down_block_type in enumerate(self.down_block_types ):
__a =output_channel
__a =block_out_channels[i]
__a =i == len(__snake_case ) - 1
if down_block_type == "CrossAttnDownBlock2D":
__a =FlaxCrossAttnDownBlockaD(
in_channels=__snake_case , out_channels=__snake_case , dropout=self.dropout , num_layers=self.layers_per_block , num_attention_heads=num_attention_heads[i] , add_downsample=not is_final_block , use_linear_projection=self.use_linear_projection , only_cross_attention=only_cross_attention[i] , use_memory_efficient_attention=self.use_memory_efficient_attention , dtype=self.dtype , )
else:
__a =FlaxDownBlockaD(
in_channels=__snake_case , out_channels=__snake_case , dropout=self.dropout , num_layers=self.layers_per_block , add_downsample=not is_final_block , dtype=self.dtype , )
down_blocks.append(__snake_case )
__a =down_blocks
# mid
__a =FlaxUNetMidBlockaDCrossAttn(
in_channels=block_out_channels[-1] , dropout=self.dropout , num_attention_heads=num_attention_heads[-1] , use_linear_projection=self.use_linear_projection , use_memory_efficient_attention=self.use_memory_efficient_attention , dtype=self.dtype , )
# up
__a =[]
__a =list(reversed(__snake_case ) )
__a =list(reversed(__snake_case ) )
__a =list(reversed(__snake_case ) )
__a =reversed_block_out_channels[0]
for i, up_block_type in enumerate(self.up_block_types ):
__a =output_channel
__a =reversed_block_out_channels[i]
__a =reversed_block_out_channels[min(i + 1 , len(__snake_case ) - 1 )]
__a =i == len(__snake_case ) - 1
if up_block_type == "CrossAttnUpBlock2D":
__a =FlaxCrossAttnUpBlockaD(
in_channels=__snake_case , out_channels=__snake_case , prev_output_channel=__snake_case , num_layers=self.layers_per_block + 1 , num_attention_heads=reversed_num_attention_heads[i] , add_upsample=not is_final_block , dropout=self.dropout , use_linear_projection=self.use_linear_projection , only_cross_attention=only_cross_attention[i] , use_memory_efficient_attention=self.use_memory_efficient_attention , dtype=self.dtype , )
else:
__a =FlaxUpBlockaD(
in_channels=__snake_case , out_channels=__snake_case , prev_output_channel=__snake_case , num_layers=self.layers_per_block + 1 , add_upsample=not is_final_block , dropout=self.dropout , dtype=self.dtype , )
up_blocks.append(__snake_case )
__a =output_channel
__a =up_blocks
# out
__a =nn.GroupNorm(num_groups=32 , epsilon=1e-5 )
__a =nn.Conv(
self.out_channels , kernel_size=(3, 3) , strides=(1, 1) , padding=((1, 1), (1, 1)) , dtype=self.dtype , )
def __call__( self , __snake_case , __snake_case , __snake_case , __snake_case=None , __snake_case=None , __snake_case = True , __snake_case = False , ) -> Union[FlaxUNetaDConditionOutput, Tuple]:
'''simple docstring'''
# 1. time
if not isinstance(__snake_case , jnp.ndarray ):
__a =jnp.array([timesteps] , dtype=jnp.intaa )
elif isinstance(__snake_case , jnp.ndarray ) and len(timesteps.shape ) == 0:
__a =timesteps.astype(dtype=jnp.floataa )
__a =jnp.expand_dims(__snake_case , 0 )
__a =self.time_proj(__snake_case )
__a =self.time_embedding(__snake_case )
# 2. pre-process
__a =jnp.transpose(__snake_case , (0, 2, 3, 1) )
__a =self.conv_in(__snake_case )
# 3. down
__a =(sample,)
for down_block in self.down_blocks:
if isinstance(__snake_case , __snake_case ):
__a , __a =down_block(__snake_case , __snake_case , __snake_case , deterministic=not train )
else:
__a , __a =down_block(__snake_case , __snake_case , deterministic=not train )
down_block_res_samples += res_samples
if down_block_additional_residuals is not None:
__a =()
for down_block_res_sample, down_block_additional_residual in zip(
__snake_case , __snake_case ):
down_block_res_sample += down_block_additional_residual
new_down_block_res_samples += (down_block_res_sample,)
__a =new_down_block_res_samples
# 4. mid
__a =self.mid_block(__snake_case , __snake_case , __snake_case , deterministic=not train )
if mid_block_additional_residual is not None:
sample += mid_block_additional_residual
# 5. up
for up_block in self.up_blocks:
__a =down_block_res_samples[-(self.layers_per_block + 1) :]
__a =down_block_res_samples[: -(self.layers_per_block + 1)]
if isinstance(__snake_case , __snake_case ):
__a =up_block(
__snake_case , temb=__snake_case , encoder_hidden_states=__snake_case , res_hidden_states_tuple=__snake_case , deterministic=not train , )
else:
__a =up_block(__snake_case , temb=__snake_case , res_hidden_states_tuple=__snake_case , deterministic=not train )
# 6. post-process
__a =self.conv_norm_out(__snake_case )
__a =nn.silu(__snake_case )
__a =self.conv_out(__snake_case )
__a =jnp.transpose(__snake_case , (0, 3, 1, 2) )
if not return_dict:
return (sample,)
return FlaxUNetaDConditionOutput(sample=__snake_case )
| 308
|
import logging
import os
import random
import sys
from dataclasses import dataclass, field
from typing import Optional
import datasets
import numpy as np
import pandas as pd
from datasets import load_dataset
import transformers
from transformers import (
AutoConfig,
BartForSequenceClassification,
DataCollatorWithPadding,
EvalPrediction,
HfArgumentParser,
TapexTokenizer,
Trainer,
TrainingArguments,
default_data_collator,
set_seed,
)
from transformers.trainer_utils import get_last_checkpoint
from transformers.utils import check_min_version
from transformers.utils.versions import require_version
# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
check_min_version("4.17.0.dev0")
require_version("datasets>=1.8.0", "To fix: pip install -r examples/pytorch/text-classification/requirements.txt")
_lowerCAmelCase : Optional[Any] = logging.getLogger(__name__)
@dataclass
class __magic_name__ :
SCREAMING_SNAKE_CASE = field(
default='tab_fact' , metadata={'help': 'The name of the dataset to use (via the datasets library).'} )
SCREAMING_SNAKE_CASE = field(
default='tab_fact' , metadata={'help': 'The configuration name of the dataset to use (via the datasets library).'} , )
SCREAMING_SNAKE_CASE = field(
default=1_0_2_4 , metadata={
'help': (
'The maximum total input sequence length after tokenization. Sequences longer '
'than this will be truncated, sequences shorter will be padded.'
)
} , )
SCREAMING_SNAKE_CASE = field(
default=lowerCAmelCase_ , metadata={'help': 'Overwrite the cached preprocessed datasets or not.'} )
SCREAMING_SNAKE_CASE = field(
default=lowerCAmelCase_ , metadata={
'help': (
'Whether to pad all samples to `max_seq_length`. '
'If False, will pad the samples dynamically when batching to the maximum length in the batch.'
)
} , )
SCREAMING_SNAKE_CASE = field(
default=lowerCAmelCase_ , metadata={
'help': (
'For debugging purposes or quicker training, truncate the number of training examples to this '
'value if set.'
)
} , )
SCREAMING_SNAKE_CASE = field(
default=lowerCAmelCase_ , metadata={
'help': (
'For debugging purposes or quicker training, truncate the number of evaluation examples to this '
'value if set.'
)
} , )
SCREAMING_SNAKE_CASE = field(
default=lowerCAmelCase_ , metadata={
'help': (
'For debugging purposes or quicker training, truncate the number of prediction examples to this '
'value if set.'
)
} , )
SCREAMING_SNAKE_CASE = field(
default=lowerCAmelCase_ , metadata={'help': 'A csv or a json file containing the training data.'} )
SCREAMING_SNAKE_CASE = field(
default=lowerCAmelCase_ , metadata={'help': 'A csv or a json file containing the validation data.'} )
SCREAMING_SNAKE_CASE = field(default=lowerCAmelCase_ , metadata={'help': 'A csv or a json file containing the test data.'} )
def __magic_name__ ( self ) -> str:
'''simple docstring'''
if self.dataset_name is not None:
pass
elif self.train_file is None or self.validation_file is None:
raise ValueError('Need either a GLUE task, a training/validation file or a dataset name.' )
else:
__a =self.train_file.split('.' )[-1]
assert train_extension in ["csv", "json"], "`train_file` should be a csv or a json file."
__a =self.validation_file.split('.' )[-1]
assert (
validation_extension == train_extension
), "`validation_file` should have the same extension (csv or json) as `train_file`."
@dataclass
class __magic_name__ :
SCREAMING_SNAKE_CASE = field(
default=lowerCAmelCase_ , metadata={'help': 'Path to pretrained model or model identifier from huggingface.co/models'} )
SCREAMING_SNAKE_CASE = field(
default=lowerCAmelCase_ , metadata={'help': 'Pretrained config name or path if not the same as model_name'} )
SCREAMING_SNAKE_CASE = field(
default=lowerCAmelCase_ , metadata={'help': 'Pretrained tokenizer name or path if not the same as model_name'} )
SCREAMING_SNAKE_CASE = field(
default=lowerCAmelCase_ , metadata={'help': 'Where do you want to store the pretrained models downloaded from huggingface.co'} , )
SCREAMING_SNAKE_CASE = field(
default=lowerCAmelCase_ , metadata={'help': 'Whether to use one of the fast tokenizer (backed by the tokenizers library) or not.'} , )
SCREAMING_SNAKE_CASE = field(
default='main' , metadata={'help': 'The specific model version to use (can be a branch name, tag name or commit id).'} , )
SCREAMING_SNAKE_CASE = field(
default=lowerCAmelCase_ , metadata={
'help': (
'Will use the token generated when running `huggingface-cli login` (necessary to use this script '
'with private models).'
)
} , )
def UpperCamelCase_( ):
"""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()
# 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 )] , )
__a =training_args.get_process_log_level()
logger.setLevel(_snake_case )
datasets.utils.logging.set_verbosity(_snake_case )
transformers.utils.logging.set_verbosity(_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 training and evaluation files (see below)
# or specify a GLUE benchmark task (the dataset will be downloaded automatically from the datasets Hub).
#
# For JSON files, this script will use the `question` column for the input question and `table` column for the corresponding table.
#
# If the CSVs/JSONs contain only one non-label column, the script does single sentence classification on this
# single column. 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.dataset_name is not None:
# Downloading and loading a dataset from the hub.
__a =load_dataset(
data_args.dataset_name , data_args.dataset_config_name , cache_dir=model_args.cache_dir )
else:
# Loading a dataset from your local files.
# CSV/JSON training and evaluation files are needed.
__a ={'train': data_args.train_file, 'validation': data_args.validation_file}
# Get the test dataset: you can provide your own CSV/JSON test file (see below)
# when you use `do_predict` without specifying a GLUE benchmark task.
if training_args.do_predict:
if data_args.test_file is not None:
__a =data_args.train_file.split('.' )[-1]
__a =data_args.test_file.split('.' )[-1]
assert (
test_extension == train_extension
), "`test_file` should have the same extension (csv or json) as `train_file`."
__a =data_args.test_file
else:
raise ValueError('Need either a GLUE task or a test file for `do_predict`.' )
for key in data_files.keys():
logger.info(F'load a local file for {key}: {data_files[key]}' )
if data_args.train_file.endswith('.csv' ):
# Loading a dataset from local csv files
__a =load_dataset('csv' , data_files=_snake_case , cache_dir=model_args.cache_dir )
else:
# Loading a dataset from local json files
__a =load_dataset('json' , data_files=_snake_case , cache_dir=model_args.cache_dir )
# See more about loading any type of standard or custom dataset at
# https://huggingface.co/docs/datasets/loading_datasets.html.
# Labels
__a =raw_datasets['train'].features['label'].names
__a =len(_snake_case )
# Load pretrained model and tokenizer
#
# In 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 , num_labels=_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 , )
# load tapex tokenizer
__a =TapexTokenizer.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 , add_prefix_space=_snake_case , )
__a =BartForSequenceClassification.from_pretrained(
model_args.model_name_or_path , from_tf=bool('.ckpt' in model_args.model_name_or_path ) , config=_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 , )
# Padding strategy
if data_args.pad_to_max_length:
__a ='max_length'
else:
# We will pad later, dynamically at batch creation, to the max sequence length in each batch
__a =False
# Some models have set the order of the labels to use, so let's make sure we do use it.
__a ={'Refused': 0, 'Entailed': 1}
__a ={0: 'Refused', 1: 'Entailed'}
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 )
def preprocess_tabfact_function(_snake_case : Tuple ):
# Tokenize the texts
def _convert_table_text_to_pandas(_snake_case : Optional[Any] ):
__a =[_table_row.split('#' ) for _table_row in _table_text.strip('\n' ).split('\n' )]
__a =pd.DataFrame.from_records(_table_content[1:] , columns=_table_content[0] )
return _table_pd
__a =examples['statement']
__a =list(map(_convert_table_text_to_pandas , examples['table_text'] ) )
__a =tokenizer(_snake_case , _snake_case , padding=_snake_case , max_length=_snake_case , truncation=_snake_case )
__a =examples['label']
return result
with training_args.main_process_first(desc='dataset map pre-processing' ):
__a =raw_datasets.map(
_snake_case , batched=_snake_case , load_from_cache_file=not data_args.overwrite_cache , desc='Running tokenizer on dataset' , )
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 =train_dataset.select(range(data_args.max_train_samples ) )
if training_args.do_eval:
if "validation" not in raw_datasets and "validation_matched" 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 =eval_dataset.select(range(data_args.max_eval_samples ) )
if training_args.do_predict or data_args.test_file is not None:
if "test" not in raw_datasets and "test_matched" not in raw_datasets:
raise ValueError('--do_predict requires a test dataset' )
__a =raw_datasets['test']
if data_args.max_predict_samples is not None:
__a =predict_dataset.select(range(data_args.max_predict_samples ) )
# Log a few random samples from the training set:
if training_args.do_train:
for index in random.sample(range(len(_snake_case ) ) , 3 ):
logger.info(F'Sample {index} of the training set: {train_dataset[index]}.' )
# You can define your custom compute_metrics function. It takes an `EvalPrediction` object (a namedtuple with a
# predictions and label_ids field) and has to return a dictionary string to float.
def compute_metrics(_snake_case : EvalPrediction ):
__a =p.predictions[0] if isinstance(p.predictions , _snake_case ) else p.predictions
__a =np.argmax(_snake_case , axis=1 )
return {"accuracy": (preds == p.label_ids).astype(np.floataa ).mean().item()}
# Data collator will default to DataCollatorWithPadding, so we change it if we already did the padding.
if data_args.pad_to_max_length:
__a =default_data_collator
elif training_args.fpaa:
__a =DataCollatorWithPadding(_snake_case , pad_to_multiple_of=8 )
else:
__a =None
# Initialize our Trainer
__a =Trainer(
model=_snake_case , args=_snake_case , train_dataset=train_dataset if training_args.do_train else None , eval_dataset=eval_dataset if training_args.do_eval else None , compute_metrics=_snake_case , tokenizer=_snake_case , data_collator=_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=_snake_case )
__a =train_result.metrics
__a =(
data_args.max_train_samples if data_args.max_train_samples is not None else len(_snake_case )
)
__a =min(_snake_case , len(_snake_case ) )
trainer.save_model() # Saves the tokenizer too for easy upload
trainer.log_metrics('train' , _snake_case )
trainer.save_metrics('train' , _snake_case )
trainer.save_state()
# Evaluation
if training_args.do_eval:
logger.info('*** Evaluate ***' )
__a =trainer.evaluate(eval_dataset=_snake_case )
__a =data_args.max_eval_samples if data_args.max_eval_samples is not None else len(_snake_case )
__a =min(_snake_case , len(_snake_case ) )
trainer.log_metrics('eval' , _snake_case )
trainer.save_metrics('eval' , _snake_case )
if training_args.do_predict:
logger.info('*** Predict ***' )
# Removing the `label` columns because it contains -1 and Trainer won't like that.
__a =predict_dataset.remove_columns('label' )
__a =trainer.predict(_snake_case , metric_key_prefix='predict' ).predictions
__a =np.argmax(_snake_case , axis=1 )
__a =os.path.join(training_args.output_dir , 'predict_results_tabfact.txt' )
if trainer.is_world_process_zero():
with open(_snake_case , 'w' ) as writer:
logger.info('***** Predict Results *****' )
writer.write('index\tprediction\n' )
for index, item in enumerate(_snake_case ):
__a =label_list[item]
writer.write(F'{index}\t{item}\n' )
__a ={'finetuned_from': model_args.model_name_or_path, 'tasks': 'text-classification'}
if training_args.push_to_hub:
trainer.push_to_hub(**_snake_case )
else:
trainer.create_model_card(**_snake_case )
def UpperCamelCase_( _snake_case : Union[str, Any] ):
"""simple docstring"""
main()
if __name__ == "__main__":
main()
| 308
| 1
|
from math import pi, sqrt, tan
def UpperCamelCase_( _snake_case : float ):
"""simple docstring"""
if side_length < 0:
raise ValueError('surface_area_cube() only accepts non-negative values' )
return 6 * side_length**2
def UpperCamelCase_( _snake_case : float , _snake_case : float , _snake_case : float ):
"""simple docstring"""
if length < 0 or breadth < 0 or height < 0:
raise ValueError('surface_area_cuboid() only accepts non-negative values' )
return 2 * ((length * breadth) + (breadth * height) + (length * height))
def UpperCamelCase_( _snake_case : float ):
"""simple docstring"""
if radius < 0:
raise ValueError('surface_area_sphere() only accepts non-negative values' )
return 4 * pi * radius**2
def UpperCamelCase_( _snake_case : float ):
"""simple docstring"""
if radius < 0:
raise ValueError('surface_area_hemisphere() only accepts non-negative values' )
return 3 * pi * radius**2
def UpperCamelCase_( _snake_case : float , _snake_case : float ):
"""simple docstring"""
if radius < 0 or height < 0:
raise ValueError('surface_area_cone() only accepts non-negative values' )
return pi * radius * (radius + (height**2 + radius**2) ** 0.5)
def UpperCamelCase_( _snake_case : float , _snake_case : float , _snake_case : float ):
"""simple docstring"""
if radius_a < 0 or radius_a < 0 or height < 0:
raise ValueError(
'surface_area_conical_frustum() only accepts non-negative values' )
__a =(height**2 + (radius_a - radius_a) ** 2) ** 0.5
return pi * ((slant_height * (radius_a + radius_a)) + radius_a**2 + radius_a**2)
def UpperCamelCase_( _snake_case : float , _snake_case : float ):
"""simple docstring"""
if radius < 0 or height < 0:
raise ValueError('surface_area_cylinder() only accepts non-negative values' )
return 2 * pi * radius * (height + radius)
def UpperCamelCase_( _snake_case : float , _snake_case : float ):
"""simple docstring"""
if torus_radius < 0 or tube_radius < 0:
raise ValueError('surface_area_torus() only accepts non-negative values' )
if torus_radius < tube_radius:
raise ValueError(
'surface_area_torus() does not support spindle or self intersecting tori' )
return 4 * pow(_snake_case , 2 ) * torus_radius * tube_radius
def UpperCamelCase_( _snake_case : float , _snake_case : float ):
"""simple docstring"""
if length < 0 or width < 0:
raise ValueError('area_rectangle() only accepts non-negative values' )
return length * width
def UpperCamelCase_( _snake_case : float ):
"""simple docstring"""
if side_length < 0:
raise ValueError('area_square() only accepts non-negative values' )
return side_length**2
def UpperCamelCase_( _snake_case : float , _snake_case : float ):
"""simple docstring"""
if base < 0 or height < 0:
raise ValueError('area_triangle() only accepts non-negative values' )
return (base * height) / 2
def UpperCamelCase_( _snake_case : float , _snake_case : float , _snake_case : float ):
"""simple docstring"""
if sidea < 0 or sidea < 0 or sidea < 0:
raise ValueError('area_triangle_three_sides() only accepts non-negative values' )
elif sidea + sidea < sidea or sidea + sidea < sidea or sidea + sidea < sidea:
raise ValueError('Given three sides do not form a triangle' )
__a =(sidea + sidea + sidea) / 2
__a =sqrt(
semi_perimeter
* (semi_perimeter - sidea)
* (semi_perimeter - sidea)
* (semi_perimeter - sidea) )
return area
def UpperCamelCase_( _snake_case : float , _snake_case : float ):
"""simple docstring"""
if base < 0 or height < 0:
raise ValueError('area_parallelogram() only accepts non-negative values' )
return base * height
def UpperCamelCase_( _snake_case : float , _snake_case : float , _snake_case : float ):
"""simple docstring"""
if basea < 0 or basea < 0 or height < 0:
raise ValueError('area_trapezium() only accepts non-negative values' )
return 1 / 2 * (basea + basea) * height
def UpperCamelCase_( _snake_case : float ):
"""simple docstring"""
if radius < 0:
raise ValueError('area_circle() only accepts non-negative values' )
return pi * radius**2
def UpperCamelCase_( _snake_case : float , _snake_case : float ):
"""simple docstring"""
if radius_x < 0 or radius_y < 0:
raise ValueError('area_ellipse() only accepts non-negative values' )
return pi * radius_x * radius_y
def UpperCamelCase_( _snake_case : float , _snake_case : float ):
"""simple docstring"""
if diagonal_a < 0 or diagonal_a < 0:
raise ValueError('area_rhombus() only accepts non-negative values' )
return 1 / 2 * diagonal_a * diagonal_a
def UpperCamelCase_( _snake_case : int , _snake_case : float ):
"""simple docstring"""
if not isinstance(_snake_case , _snake_case ) or sides < 3:
raise ValueError(
'area_reg_polygon() only accepts integers greater than or \
equal to three as number of sides' )
elif length < 0:
raise ValueError(
'area_reg_polygon() only accepts non-negative values as \
length of a side' )
return (sides * length**2) / (4 * tan(pi / sides ))
return (sides * length**2) / (4 * tan(pi / sides ))
if __name__ == "__main__":
import doctest
doctest.testmod(verbose=True) # verbose so we can see methods missing tests
print("[DEMO] Areas of various geometric shapes: \n")
print(f'''Rectangle: {area_rectangle(10, 20) = }''')
print(f'''Square: {area_square(10) = }''')
print(f'''Triangle: {area_triangle(10, 10) = }''')
print(f'''Triangle: {area_triangle_three_sides(5, 12, 13) = }''')
print(f'''Parallelogram: {area_parallelogram(10, 20) = }''')
print(f'''Rhombus: {area_rhombus(10, 20) = }''')
print(f'''Trapezium: {area_trapezium(10, 20, 30) = }''')
print(f'''Circle: {area_circle(20) = }''')
print(f'''Ellipse: {area_ellipse(10, 20) = }''')
print("\nSurface Areas of various geometric shapes: \n")
print(f'''Cube: {surface_area_cube(20) = }''')
print(f'''Cuboid: {surface_area_cuboid(10, 20, 30) = }''')
print(f'''Sphere: {surface_area_sphere(20) = }''')
print(f'''Hemisphere: {surface_area_hemisphere(20) = }''')
print(f'''Cone: {surface_area_cone(10, 20) = }''')
print(f'''Conical Frustum: {surface_area_conical_frustum(10, 20, 30) = }''')
print(f'''Cylinder: {surface_area_cylinder(10, 20) = }''')
print(f'''Torus: {surface_area_torus(20, 10) = }''')
print(f'''Equilateral Triangle: {area_reg_polygon(3, 10) = }''')
print(f'''Square: {area_reg_polygon(4, 10) = }''')
print(f'''Reqular Pentagon: {area_reg_polygon(5, 10) = }''')
| 308
|
from __future__ import annotations
import time
import numpy as np
_lowerCAmelCase : List[str] = [8, 5, 9, 7]
_lowerCAmelCase : List[str] = [
[2, 0, 1, 1],
[0, 1, 2, 1],
[4, 0, 0, 3],
[0, 2, 1, 0],
[1, 0, 3, 0],
]
_lowerCAmelCase : List[Any] = [
[3, 2, 1, 4],
[0, 2, 5, 2],
[5, 1, 0, 5],
[1, 5, 3, 0],
[3, 0, 3, 3],
]
class __magic_name__ :
def __init__( self , __snake_case , __snake_case , __snake_case , ) -> None:
'''simple docstring'''
__a =claim_vector
__a =allocated_resources_table
__a =maximum_claim_table
def __magic_name__ ( self ) -> list[int]:
'''simple docstring'''
return [
sum(p_item[i] for p_item in self.__allocated_resources_table )
for i in range(len(self.__allocated_resources_table[0] ) )
]
def __magic_name__ ( self ) -> list[int]:
'''simple docstring'''
return np.array(self.__claim_vector ) - np.array(
self.__processes_resource_summation() )
def __magic_name__ ( self ) -> list[list[int]]:
'''simple docstring'''
return [
list(np.array(self.__maximum_claim_table[i] ) - np.array(__snake_case ) )
for i, allocated_resource in enumerate(self.__allocated_resources_table )
]
def __magic_name__ ( self ) -> dict[int, list[int]]:
'''simple docstring'''
return {self.__need().index(__snake_case ): i for i in self.__need()}
def __magic_name__ ( self , **__snake_case ) -> None:
'''simple docstring'''
__a =self.__need()
__a =self.__allocated_resources_table
__a =self.__available_resources()
__a =self.__need_index_manager()
for kw, val in kwargs.items():
if kw and val is True:
self.__pretty_data()
print('_' * 50 + '\n' )
while need_list:
__a =False
for each_need in need_list:
__a =True
for index, need in enumerate(__snake_case ):
if need > available_resources[index]:
__a =False
break
if execution:
__a =True
# get the original index of the process from ind_ctrl db
for original_need_index, need_clone in need_index_manager.items():
if each_need == need_clone:
__a =original_need_index
print(f'Process {process_number + 1} is executing.' )
# remove the process run from stack
need_list.remove(__snake_case )
# update available/freed resources stack
__a =np.array(__snake_case ) + np.array(
alloc_resources_table[process_number] )
print(
'Updated available resource stack for processes: '
+ ' '.join([str(__snake_case ) for x in available_resources] ) )
break
if safe:
print('The process is in a safe state.\n' )
else:
print('System in unsafe state. Aborting...\n' )
break
def __magic_name__ ( self ) -> Union[str, Any]:
'''simple docstring'''
print(' ' * 9 + 'Allocated Resource Table' )
for item in self.__allocated_resources_table:
print(
f'P{self.__allocated_resources_table.index(__snake_case ) + 1}'
+ ' '.join(f'{it:>8}' for it in item )
+ '\n' )
print(' ' * 9 + 'System Resource Table' )
for item in self.__maximum_claim_table:
print(
f'P{self.__maximum_claim_table.index(__snake_case ) + 1}'
+ ' '.join(f'{it:>8}' for it in item )
+ '\n' )
print(
'Current Usage by Active Processes: '
+ ' '.join(str(__snake_case ) for x in self.__claim_vector ) )
print(
'Initial Available Resources: '
+ ' '.join(str(__snake_case ) for x in self.__available_resources() ) )
time.sleep(1 )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 308
| 1
|
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
_lowerCAmelCase : List[Any] = logging.get_logger(__name__)
_lowerCAmelCase : List[str] = {
"ut/deta": "https://huggingface.co/ut/deta/resolve/main/config.json",
}
class __magic_name__ ( lowerCAmelCase_ ):
SCREAMING_SNAKE_CASE = 'deta'
SCREAMING_SNAKE_CASE = {
'hidden_size': 'd_model',
'num_attention_heads': 'encoder_attention_heads',
}
def __init__( self , __snake_case=None , __snake_case=900 , __snake_case=2048 , __snake_case=6 , __snake_case=2048 , __snake_case=8 , __snake_case=6 , __snake_case=1024 , __snake_case=8 , __snake_case=0.0 , __snake_case=True , __snake_case="relu" , __snake_case=256 , __snake_case=0.1 , __snake_case=0.0 , __snake_case=0.0 , __snake_case=0.02 , __snake_case=1.0 , __snake_case=True , __snake_case=False , __snake_case="sine" , __snake_case=5 , __snake_case=4 , __snake_case=4 , __snake_case=True , __snake_case=300 , __snake_case=True , __snake_case=True , __snake_case=1 , __snake_case=5 , __snake_case=2 , __snake_case=1 , __snake_case=1 , __snake_case=5 , __snake_case=2 , __snake_case=0.1 , __snake_case=0.25 , **__snake_case , ) -> Union[str, Any]:
'''simple docstring'''
if backbone_config is None:
logger.info('`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone.' )
__a =CONFIG_MAPPING['resnet'](out_features=['stage2', 'stage3', 'stage4'] )
else:
if isinstance(__snake_case , __snake_case ):
__a =backbone_config.pop('model_type' )
__a =CONFIG_MAPPING[backbone_model_type]
__a =config_class.from_dict(__snake_case )
__a =backbone_config
__a =num_queries
__a =max_position_embeddings
__a =d_model
__a =encoder_ffn_dim
__a =encoder_layers
__a =encoder_attention_heads
__a =decoder_ffn_dim
__a =decoder_layers
__a =decoder_attention_heads
__a =dropout
__a =attention_dropout
__a =activation_dropout
__a =activation_function
__a =init_std
__a =init_xavier_std
__a =encoder_layerdrop
__a =auxiliary_loss
__a =position_embedding_type
# deformable attributes
__a =num_feature_levels
__a =encoder_n_points
__a =decoder_n_points
__a =two_stage
__a =two_stage_num_proposals
__a =with_box_refine
__a =assign_first_stage
if two_stage is True and with_box_refine is False:
raise ValueError('If two_stage is True, with_box_refine must be True.' )
# Hungarian matcher
__a =class_cost
__a =bbox_cost
__a =giou_cost
# Loss coefficients
__a =mask_loss_coefficient
__a =dice_loss_coefficient
__a =bbox_loss_coefficient
__a =giou_loss_coefficient
__a =eos_coefficient
__a =focal_alpha
super().__init__(is_encoder_decoder=__snake_case , **__snake_case )
@property
def __magic_name__ ( self ) -> int:
'''simple docstring'''
return self.encoder_attention_heads
@property
def __magic_name__ ( self ) -> int:
'''simple docstring'''
return self.d_model
def __magic_name__ ( self ) -> List[Any]:
'''simple docstring'''
__a =copy.deepcopy(self.__dict__ )
__a =self.backbone_config.to_dict()
__a =self.__class__.model_type
return output
| 308
|
import string
# frequency taken from https://en.wikipedia.org/wiki/Letter_frequency
_lowerCAmelCase : Tuple = {
"E": 12.70,
"T": 9.06,
"A": 8.17,
"O": 7.51,
"I": 6.97,
"N": 6.75,
"S": 6.33,
"H": 6.09,
"R": 5.99,
"D": 4.25,
"L": 4.03,
"C": 2.78,
"U": 2.76,
"M": 2.41,
"W": 2.36,
"F": 2.23,
"G": 2.02,
"Y": 1.97,
"P": 1.93,
"B": 1.29,
"V": 0.98,
"K": 0.77,
"J": 0.15,
"X": 0.15,
"Q": 0.10,
"Z": 0.07,
}
_lowerCAmelCase : Optional[int] = "ETAOINSHRDLCUMWFGYPBVKJXQZ"
_lowerCAmelCase : Any = "ABCDEFGHIJKLMNOPQRSTUVWXYZ"
def UpperCamelCase_( _snake_case : str ):
"""simple docstring"""
__a ={letter: 0 for letter in string.ascii_uppercase}
for letter in message.upper():
if letter in LETTERS:
letter_count[letter] += 1
return letter_count
def UpperCamelCase_( _snake_case : tuple ):
"""simple docstring"""
return x[0]
def UpperCamelCase_( _snake_case : str ):
"""simple docstring"""
__a =get_letter_count(_snake_case )
__a ={
freq: [] for letter, freq in letter_to_freq.items()
}
for letter in LETTERS:
freq_to_letter[letter_to_freq[letter]].append(_snake_case )
__a ={}
for freq in freq_to_letter:
freq_to_letter[freq].sort(key=ETAOIN.find , reverse=_snake_case )
__a =''.join(freq_to_letter[freq] )
__a =list(freq_to_letter_str.items() )
freq_pairs.sort(key=_snake_case , reverse=_snake_case )
__a =[freq_pair[1] for freq_pair in freq_pairs]
return "".join(_snake_case )
def UpperCamelCase_( _snake_case : str ):
"""simple docstring"""
__a =get_frequency_order(_snake_case )
__a =0
for common_letter in ETAOIN[:6]:
if common_letter in freq_order[:6]:
match_score += 1
for uncommon_letter in ETAOIN[-6:]:
if uncommon_letter in freq_order[-6:]:
match_score += 1
return match_score
if __name__ == "__main__":
import doctest
doctest.testmod()
| 308
| 1
|
def UpperCamelCase_( _snake_case : Dict , _snake_case : Dict , _snake_case : int , _snake_case : int ):
"""simple docstring"""
__a =[False] * len(_snake_case )
__a =[]
queue.append(_snake_case )
__a =True
while queue:
__a =queue.pop(0 )
for ind in range(len(graph[u] ) ):
if visited[ind] is False and graph[u][ind] > 0:
queue.append(_snake_case )
__a =True
__a =u
return visited[t]
def UpperCamelCase_( _snake_case : int , _snake_case : str , _snake_case : Union[str, Any] ):
"""simple docstring"""
__a =[-1] * (len(_snake_case ))
__a =0
while bfs(_snake_case , _snake_case , _snake_case , _snake_case ):
__a =float('Inf' )
__a =sink
while s != source:
# Find the minimum value in select path
__a =min(_snake_case , graph[parent[s]][s] )
__a =parent[s]
max_flow += path_flow
__a =sink
while v != source:
__a =parent[v]
graph[u][v] -= path_flow
graph[v][u] += path_flow
__a =parent[v]
return max_flow
_lowerCAmelCase : str = [
[0, 16, 13, 0, 0, 0],
[0, 0, 10, 12, 0, 0],
[0, 4, 0, 0, 14, 0],
[0, 0, 9, 0, 0, 20],
[0, 0, 0, 7, 0, 4],
[0, 0, 0, 0, 0, 0],
]
_lowerCAmelCase , _lowerCAmelCase : Union[str, Any] = 0, 5
print(ford_fulkerson(graph, source, sink))
| 308
|
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowerCAmelCase : Any = logging.get_logger(__name__)
_lowerCAmelCase : int = {
"caidas/swin2sr-classicalsr-x2-64": (
"https://huggingface.co/caidas/swin2sr-classicalsr-x2-64/resolve/main/config.json"
),
}
class __magic_name__ ( lowerCAmelCase_ ):
SCREAMING_SNAKE_CASE = 'swin2sr'
SCREAMING_SNAKE_CASE = {
'hidden_size': 'embed_dim',
'num_attention_heads': 'num_heads',
'num_hidden_layers': 'num_layers',
}
def __init__( self , __snake_case=64 , __snake_case=1 , __snake_case=3 , __snake_case=180 , __snake_case=[6, 6, 6, 6, 6, 6] , __snake_case=[6, 6, 6, 6, 6, 6] , __snake_case=8 , __snake_case=2.0 , __snake_case=True , __snake_case=0.0 , __snake_case=0.0 , __snake_case=0.1 , __snake_case="gelu" , __snake_case=False , __snake_case=0.02 , __snake_case=1e-5 , __snake_case=2 , __snake_case=1.0 , __snake_case="1conv" , __snake_case="pixelshuffle" , **__snake_case , ) -> Dict:
'''simple docstring'''
super().__init__(**__snake_case )
__a =image_size
__a =patch_size
__a =num_channels
__a =embed_dim
__a =depths
__a =len(__snake_case )
__a =num_heads
__a =window_size
__a =mlp_ratio
__a =qkv_bias
__a =hidden_dropout_prob
__a =attention_probs_dropout_prob
__a =drop_path_rate
__a =hidden_act
__a =use_absolute_embeddings
__a =layer_norm_eps
__a =initializer_range
__a =upscale
__a =img_range
__a =resi_connection
__a =upsampler
| 308
| 1
|
def UpperCamelCase_( _snake_case : float , _snake_case : list[float] ):
"""simple docstring"""
if discount_rate < 0:
raise ValueError('Discount rate cannot be negative' )
if not cash_flows:
raise ValueError('Cash flows list cannot be empty' )
__a =sum(
cash_flow / ((1 + discount_rate) ** i) for i, cash_flow in enumerate(_snake_case ) )
return round(_snake_case , ndigits=2 )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 308
|
import os
def UpperCamelCase_( _snake_case : str = "input.txt" ):
"""simple docstring"""
with open(os.path.join(os.path.dirname(_snake_case ) , _snake_case ) ) as input_file:
__a =[
[int(_snake_case ) for element in line.split(',' )]
for line in input_file.readlines()
]
__a =len(_snake_case )
__a =len(matrix[0] )
__a =[[-1 for _ in range(_snake_case )] for _ in range(_snake_case )]
for i in range(_snake_case ):
__a =matrix[i][0]
for j in range(1 , _snake_case ):
for i in range(_snake_case ):
__a =minimal_path_sums[i][j - 1] + matrix[i][j]
for i in range(1 , _snake_case ):
__a =min(
minimal_path_sums[i][j] , minimal_path_sums[i - 1][j] + matrix[i][j] )
for i in range(rows - 2 , -1 , -1 ):
__a =min(
minimal_path_sums[i][j] , minimal_path_sums[i + 1][j] + matrix[i][j] )
return min(minimal_path_sums_row[-1] for minimal_path_sums_row in minimal_path_sums )
if __name__ == "__main__":
print(f'''{solution() = }''')
| 308
| 1
|
import argparse
import logging
import pickle
import random
import time
import numpy as np
from transformers import BertTokenizer, GPTaTokenizer, RobertaTokenizer
logging.basicConfig(
format="%(asctime)s - %(levelname)s - %(name)s - %(message)s", datefmt="%m/%d/%Y %H:%M:%S", level=logging.INFO
)
_lowerCAmelCase : Optional[int] = logging.getLogger(__name__)
def UpperCamelCase_( ):
"""simple docstring"""
__a =argparse.ArgumentParser(
description='Preprocess the data to avoid re-doing it several times by (tokenization + token_to_ids).' )
parser.add_argument('--file_path' , type=_snake_case , default='data/dump.txt' , help='The path to the data.' )
parser.add_argument('--tokenizer_type' , type=_snake_case , default='bert' , choices=['bert', 'roberta', 'gpt2'] )
parser.add_argument('--tokenizer_name' , type=_snake_case , default='bert-base-uncased' , help='The tokenizer to use.' )
parser.add_argument('--dump_file' , type=_snake_case , default='data/dump' , help='The dump file prefix.' )
__a =parser.parse_args()
logger.info(F'Loading Tokenizer ({args.tokenizer_name})' )
if args.tokenizer_type == "bert":
__a =BertTokenizer.from_pretrained(args.tokenizer_name )
__a =tokenizer.special_tokens_map['cls_token'] # `[CLS]`
__a =tokenizer.special_tokens_map['sep_token'] # `[SEP]`
elif args.tokenizer_type == "roberta":
__a =RobertaTokenizer.from_pretrained(args.tokenizer_name )
__a =tokenizer.special_tokens_map['cls_token'] # `<s>`
__a =tokenizer.special_tokens_map['sep_token'] # `</s>`
elif args.tokenizer_type == "gpt2":
__a =GPTaTokenizer.from_pretrained(args.tokenizer_name )
__a =tokenizer.special_tokens_map['bos_token'] # `<|endoftext|>`
__a =tokenizer.special_tokens_map['eos_token'] # `<|endoftext|>`
logger.info(F'Loading text from {args.file_path}' )
with open(args.file_path , 'r' , encoding='utf8' ) as fp:
__a =fp.readlines()
logger.info('Start encoding' )
logger.info(F'{len(_snake_case )} examples to process.' )
__a =[]
__a =0
__a =10000
__a =time.time()
for text in data:
__a =F'{bos} {text.strip()} {sep}'
__a =tokenizer.encode(_snake_case , add_special_tokens=_snake_case )
rslt.append(_snake_case )
iter += 1
if iter % interval == 0:
__a =time.time()
logger.info(F'{iter} examples processed. - {(end-start):.2f}s/{interval}expl' )
__a =time.time()
logger.info('Finished binarization' )
logger.info(F'{len(_snake_case )} examples processed.' )
__a =F'{args.dump_file}.{args.tokenizer_name}.pickle'
__a =tokenizer.vocab_size
if vocab_size < (1 << 16):
__a =[np.uintaa(_snake_case ) for d in rslt]
else:
__a =[np.intaa(_snake_case ) for d in rslt]
random.shuffle(rslt_ )
logger.info(F'Dump to {dp_file}' )
with open(_snake_case , 'wb' ) as handle:
pickle.dump(rslt_ , _snake_case , protocol=pickle.HIGHEST_PROTOCOL )
if __name__ == "__main__":
main()
| 308
|
import warnings
from ...utils import logging
from .image_processing_mobilevit import MobileViTImageProcessor
_lowerCAmelCase : Any = logging.get_logger(__name__)
class __magic_name__ ( lowerCAmelCase_ ):
def __init__( self , *__snake_case , **__snake_case ) -> None:
'''simple docstring'''
warnings.warn(
'The class MobileViTFeatureExtractor is deprecated and will be removed in version 5 of Transformers.'
' Please use MobileViTImageProcessor instead.' , __snake_case , )
super().__init__(*__snake_case , **__snake_case )
| 308
| 1
|
_lowerCAmelCase : str = {0: [2, 3], 1: [0], 2: [1], 3: [4], 4: []}
_lowerCAmelCase : Any = {0: [1, 2, 3], 1: [2], 2: [0], 3: [4], 4: [5], 5: [3]}
def UpperCamelCase_( _snake_case : dict[int, list[int]] , _snake_case : int , _snake_case : list[bool] ):
"""simple docstring"""
__a =True
__a =[]
for neighbour in graph[vert]:
if not visited[neighbour]:
order += topology_sort(_snake_case , _snake_case , _snake_case )
order.append(_snake_case )
return order
def UpperCamelCase_( _snake_case : dict[int, list[int]] , _snake_case : int , _snake_case : list[bool] ):
"""simple docstring"""
__a =True
__a =[vert]
for neighbour in reversed_graph[vert]:
if not visited[neighbour]:
component += find_components(_snake_case , _snake_case , _snake_case )
return component
def UpperCamelCase_( _snake_case : dict[int, list[int]] ):
"""simple docstring"""
__a =len(_snake_case ) * [False]
__a ={vert: [] for vert in range(len(_snake_case ) )}
for vert, neighbours in graph.items():
for neighbour in neighbours:
reversed_graph[neighbour].append(_snake_case )
__a =[]
for i, was_visited in enumerate(_snake_case ):
if not was_visited:
order += topology_sort(_snake_case , _snake_case , _snake_case )
__a =[]
__a =len(_snake_case ) * [False]
for i in range(len(_snake_case ) ):
__a =order[len(_snake_case ) - i - 1]
if not visited[vert]:
__a =find_components(_snake_case , _snake_case , _snake_case )
components_list.append(_snake_case )
return components_list
| 308
|
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available
_lowerCAmelCase : int = {}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_lowerCAmelCase : Dict = ["BartphoTokenizer"]
if TYPE_CHECKING:
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_bartpho import BartphoTokenizer
else:
import sys
_lowerCAmelCase : Optional[Any] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 308
| 1
|
# 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.speechta import SpeechTaForTextToSpeech, SpeechTaHifiGan, SpeechTaProcessor
from ..utils import is_datasets_available
from .base import PipelineTool
if is_datasets_available():
from datasets import load_dataset
class __magic_name__ ( lowerCAmelCase_ ):
SCREAMING_SNAKE_CASE = 'microsoft/speecht5_tts'
SCREAMING_SNAKE_CASE = (
'This is a tool that reads an English text out loud. It takes an input named `text` which should contain the '
'text to read (in English) and returns a waveform object containing the sound.'
)
SCREAMING_SNAKE_CASE = 'text_reader'
SCREAMING_SNAKE_CASE = SpeechTaProcessor
SCREAMING_SNAKE_CASE = SpeechTaForTextToSpeech
SCREAMING_SNAKE_CASE = SpeechTaHifiGan
SCREAMING_SNAKE_CASE = ['text']
SCREAMING_SNAKE_CASE = ['audio']
def __magic_name__ ( self ) -> Union[str, Any]:
'''simple docstring'''
if self.post_processor is None:
__a ='microsoft/speecht5_hifigan'
super().setup()
def __magic_name__ ( self , __snake_case , __snake_case=None ) -> Any:
'''simple docstring'''
__a =self.pre_processor(text=__snake_case , return_tensors='pt' , truncation=__snake_case )
if speaker_embeddings is None:
if not is_datasets_available():
raise ImportError('Datasets needs to be installed if not passing speaker embeddings.' )
__a =load_dataset('Matthijs/cmu-arctic-xvectors' , split='validation' )
__a =torch.tensor(embeddings_dataset[7305]['xvector'] ).unsqueeze(0 )
return {"input_ids": inputs["input_ids"], "speaker_embeddings": speaker_embeddings}
def __magic_name__ ( self , __snake_case ) -> Optional[Any]:
'''simple docstring'''
with torch.no_grad():
return self.model.generate_speech(**__snake_case )
def __magic_name__ ( self , __snake_case ) -> List[str]:
'''simple docstring'''
with torch.no_grad():
return self.post_processor(__snake_case ).cpu().detach()
| 308
|
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
_lowerCAmelCase : List[str] = logging.get_logger(__name__)
_lowerCAmelCase : List[Any] = {
"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 __magic_name__ ( lowerCAmelCase_ ):
SCREAMING_SNAKE_CASE = 'yolos'
def __init__( self , __snake_case=768 , __snake_case=12 , __snake_case=12 , __snake_case=3072 , __snake_case="gelu" , __snake_case=0.0 , __snake_case=0.0 , __snake_case=0.02 , __snake_case=1e-12 , __snake_case=[512, 864] , __snake_case=16 , __snake_case=3 , __snake_case=True , __snake_case=100 , __snake_case=True , __snake_case=False , __snake_case=1 , __snake_case=5 , __snake_case=2 , __snake_case=5 , __snake_case=2 , __snake_case=0.1 , **__snake_case , ) -> str:
'''simple docstring'''
super().__init__(**__snake_case )
__a =hidden_size
__a =num_hidden_layers
__a =num_attention_heads
__a =intermediate_size
__a =hidden_act
__a =hidden_dropout_prob
__a =attention_probs_dropout_prob
__a =initializer_range
__a =layer_norm_eps
__a =image_size
__a =patch_size
__a =num_channels
__a =qkv_bias
__a =num_detection_tokens
__a =use_mid_position_embeddings
__a =auxiliary_loss
# Hungarian matcher
__a =class_cost
__a =bbox_cost
__a =giou_cost
# Loss coefficients
__a =bbox_loss_coefficient
__a =giou_loss_coefficient
__a =eos_coefficient
class __magic_name__ ( lowerCAmelCase_ ):
SCREAMING_SNAKE_CASE = version.parse('1.11' )
@property
def __magic_name__ ( self ) -> Mapping[str, Mapping[int, str]]:
'''simple docstring'''
return OrderedDict(
[
('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'}),
] )
@property
def __magic_name__ ( self ) -> float:
'''simple docstring'''
return 1e-4
@property
def __magic_name__ ( self ) -> int:
'''simple docstring'''
return 12
| 308
| 1
|
import importlib
import os
import fsspec
import pytest
from fsspec import register_implementation
from fsspec.registry import _registry as _fsspec_registry
from datasets.filesystems import COMPRESSION_FILESYSTEMS, HfFileSystem, extract_path_from_uri, is_remote_filesystem
from .utils import require_lza, require_zstandard
def UpperCamelCase_( _snake_case : str ):
"""simple docstring"""
assert "mock" in _fsspec_registry
assert "bz2" in _fsspec_registry
def UpperCamelCase_( ):
"""simple docstring"""
assert "mock" not in _fsspec_registry
assert "bz2" in _fsspec_registry
def UpperCamelCase_( ):
"""simple docstring"""
__a ='mock-s3-bucket'
__a =F's3://{mock_bucket}'
__a =extract_path_from_uri(_snake_case )
assert dataset_path.startswith('s3://' ) is False
__a ='./local/path'
__a =extract_path_from_uri(_snake_case )
assert dataset_path == new_dataset_path
def UpperCamelCase_( _snake_case : Optional[Any] ):
"""simple docstring"""
__a =is_remote_filesystem(_snake_case )
assert is_remote is True
__a =fsspec.filesystem('file' )
__a =is_remote_filesystem(_snake_case )
assert is_remote is False
@pytest.mark.parametrize('compression_fs_class' , _snake_case )
def UpperCamelCase_( _snake_case : List[str] , _snake_case : Any , _snake_case : List[Any] , _snake_case : Tuple , _snake_case : Optional[int] , _snake_case : Optional[int] , _snake_case : Optional[Any] ):
"""simple docstring"""
__a ={'gzip': gz_file, 'xz': xz_file, 'zstd': zstd_file, 'bz2': bza_file, 'lz4': lza_file}
__a =input_paths[compression_fs_class.protocol]
if input_path is None:
__a =F'for \'{compression_fs_class.protocol}\' compression protocol, '
if compression_fs_class.protocol == "lz4":
reason += require_lza.kwargs["reason"]
elif compression_fs_class.protocol == "zstd":
reason += require_zstandard.kwargs["reason"]
pytest.skip(_snake_case )
__a =fsspec.filesystem(compression_fs_class.protocol , fo=_snake_case )
assert isinstance(_snake_case , _snake_case )
__a =os.path.basename(_snake_case )
__a =expected_filename[: expected_filename.rindex('.' )]
assert fs.glob('*' ) == [expected_filename]
with fs.open(_snake_case , 'r' , encoding='utf-8' ) as f, open(_snake_case , encoding='utf-8' ) as expected_file:
assert f.read() == expected_file.read()
@pytest.mark.parametrize('protocol' , ['zip', 'gzip'] )
def UpperCamelCase_( _snake_case : Dict , _snake_case : Dict , _snake_case : Union[str, Any] ):
"""simple docstring"""
__a ={'zip': zip_jsonl_path, 'gzip': jsonl_gz_path}
__a =compressed_file_paths[protocol]
__a ='dataset.jsonl'
__a =F'{protocol}://{member_file_path}::{compressed_file_path}'
__a , *__a =fsspec.get_fs_token_paths(_snake_case )
assert fs.isfile(_snake_case )
assert not fs.isfile('non_existing_' + member_file_path )
@pytest.mark.integration
def UpperCamelCase_( _snake_case : Union[str, Any] , _snake_case : Union[str, Any] , _snake_case : Dict , _snake_case : int ):
"""simple docstring"""
__a =hf_api.dataset_info(_snake_case , token=_snake_case )
__a =HfFileSystem(repo_info=_snake_case , token=_snake_case )
assert sorted(hffs.glob('*' ) ) == [".gitattributes", "data"]
assert hffs.isdir('data' )
assert hffs.isfile('.gitattributes' ) and hffs.isfile('data/text_data.txt' )
with open(_snake_case ) as f:
assert hffs.open('data/text_data.txt' , 'r' ).read() == f.read()
def UpperCamelCase_( ):
"""simple docstring"""
__a ='bz2'
# Import module
import datasets.filesystems
# Overwrite protocol and reload
register_implementation(_snake_case , _snake_case , clobber=_snake_case )
with pytest.warns(_snake_case ) as warning_info:
importlib.reload(datasets.filesystems )
assert len(_snake_case ) == 1
assert (
str(warning_info[0].message )
== F'A filesystem protocol was already set for {protocol} and will be overwritten.'
)
| 308
|
import argparse
import torch
from datasets import load_dataset
from donut import DonutModel
from transformers import (
DonutImageProcessor,
DonutProcessor,
DonutSwinConfig,
DonutSwinModel,
MBartConfig,
MBartForCausalLM,
VisionEncoderDecoderModel,
XLMRobertaTokenizerFast,
)
def UpperCamelCase_( _snake_case : Optional[Any] ):
"""simple docstring"""
__a =model.config
__a =DonutSwinConfig(
image_size=original_config.input_size , patch_size=4 , depths=original_config.encoder_layer , num_heads=[4, 8, 16, 32] , window_size=original_config.window_size , embed_dim=128 , )
__a =MBartConfig(
is_decoder=_snake_case , is_encoder_decoder=_snake_case , add_cross_attention=_snake_case , decoder_layers=original_config.decoder_layer , max_position_embeddings=original_config.max_position_embeddings , vocab_size=len(
model.decoder.tokenizer ) , scale_embedding=_snake_case , add_final_layer_norm=_snake_case , )
return encoder_config, decoder_config
def UpperCamelCase_( _snake_case : Tuple ):
"""simple docstring"""
if "encoder.model" in name:
__a =name.replace('encoder.model' , 'encoder' )
if "decoder.model" in name:
__a =name.replace('decoder.model' , 'decoder' )
if "patch_embed.proj" in name:
__a =name.replace('patch_embed.proj' , 'embeddings.patch_embeddings.projection' )
if "patch_embed.norm" in name:
__a =name.replace('patch_embed.norm' , 'embeddings.norm' )
if name.startswith('encoder' ):
if "layers" in name:
__a ='encoder.' + name
if "attn.proj" in name:
__a =name.replace('attn.proj' , 'attention.output.dense' )
if "attn" in name and "mask" not in name:
__a =name.replace('attn' , 'attention.self' )
if "norm1" in name:
__a =name.replace('norm1' , 'layernorm_before' )
if "norm2" in name:
__a =name.replace('norm2' , 'layernorm_after' )
if "mlp.fc1" in name:
__a =name.replace('mlp.fc1' , 'intermediate.dense' )
if "mlp.fc2" in name:
__a =name.replace('mlp.fc2' , 'output.dense' )
if name == "encoder.norm.weight":
__a ='encoder.layernorm.weight'
if name == "encoder.norm.bias":
__a ='encoder.layernorm.bias'
return name
def UpperCamelCase_( _snake_case : Tuple , _snake_case : str ):
"""simple docstring"""
for key in orig_state_dict.copy().keys():
__a =orig_state_dict.pop(_snake_case )
if "qkv" in key:
__a =key.split('.' )
__a =int(key_split[3] )
__a =int(key_split[5] )
__a =model.encoder.encoder.layers[layer_num].blocks[block_num].attention.self.all_head_size
if "weight" in key:
__a =val[:dim, :]
__a =val[dim : dim * 2, :]
__a =val[-dim:, :]
else:
__a =val[:dim]
__a =val[dim : dim * 2]
__a =val[-dim:]
elif "attn_mask" in key or key in ["encoder.model.norm.weight", "encoder.model.norm.bias"]:
# HuggingFace implementation doesn't use attn_mask buffer
# and model doesn't use final LayerNorms for the encoder
pass
else:
__a =val
return orig_state_dict
def UpperCamelCase_( _snake_case : Tuple , _snake_case : Union[str, Any]=None , _snake_case : List[Any]=False ):
"""simple docstring"""
__a =DonutModel.from_pretrained(_snake_case ).eval()
# load HuggingFace model
__a , __a =get_configs(_snake_case )
__a =DonutSwinModel(_snake_case )
__a =MBartForCausalLM(_snake_case )
__a =VisionEncoderDecoderModel(encoder=_snake_case , decoder=_snake_case )
model.eval()
__a =original_model.state_dict()
__a =convert_state_dict(_snake_case , _snake_case )
model.load_state_dict(_snake_case )
# verify results on scanned document
__a =load_dataset('hf-internal-testing/example-documents' )
__a =dataset['test'][0]['image'].convert('RGB' )
__a =XLMRobertaTokenizerFast.from_pretrained(_snake_case , from_slow=_snake_case )
__a =DonutImageProcessor(
do_align_long_axis=original_model.config.align_long_axis , size=original_model.config.input_size[::-1] )
__a =DonutProcessor(_snake_case , _snake_case )
__a =processor(_snake_case , return_tensors='pt' ).pixel_values
if model_name == "naver-clova-ix/donut-base-finetuned-docvqa":
__a ='<s_docvqa><s_question>{user_input}</s_question><s_answer>'
__a ='When is the coffee break?'
__a =task_prompt.replace('{user_input}' , _snake_case )
elif model_name == "naver-clova-ix/donut-base-finetuned-rvlcdip":
__a ='<s_rvlcdip>'
elif model_name in [
"naver-clova-ix/donut-base-finetuned-cord-v1",
"naver-clova-ix/donut-base-finetuned-cord-v1-2560",
]:
__a ='<s_cord>'
elif model_name == "naver-clova-ix/donut-base-finetuned-cord-v2":
__a ='s_cord-v2>'
elif model_name == "naver-clova-ix/donut-base-finetuned-zhtrainticket":
__a ='<s_zhtrainticket>'
elif model_name in ["naver-clova-ix/donut-proto", "naver-clova-ix/donut-base"]:
# use a random prompt
__a ='hello world'
else:
raise ValueError('Model name not supported' )
__a =original_model.decoder.tokenizer(_snake_case , add_special_tokens=_snake_case , return_tensors='pt' )[
'input_ids'
]
__a =original_model.encoder.model.patch_embed(_snake_case )
__a , __a =model.encoder.embeddings(_snake_case )
assert torch.allclose(_snake_case , _snake_case , atol=1e-3 )
# verify encoder hidden states
__a =original_model.encoder(_snake_case )
__a =model.encoder(_snake_case ).last_hidden_state
assert torch.allclose(_snake_case , _snake_case , atol=1e-2 )
# verify decoder hidden states
__a =original_model(_snake_case , _snake_case , _snake_case ).logits
__a =model(_snake_case , decoder_input_ids=_snake_case ).logits
assert torch.allclose(_snake_case , _snake_case , atol=1e-3 )
print('Looks ok!' )
if pytorch_dump_folder_path is not None:
print(F'Saving model and processor to {pytorch_dump_folder_path}' )
model.save_pretrained(_snake_case )
processor.save_pretrained(_snake_case )
if push_to_hub:
model.push_to_hub('nielsr/' + model_name.split('/' )[-1] , commit_message='Update model' )
processor.push_to_hub('nielsr/' + model_name.split('/' )[-1] , commit_message='Update model' )
if __name__ == "__main__":
_lowerCAmelCase : List[str] = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--model_name",
default="naver-clova-ix/donut-base-finetuned-docvqa",
required=False,
type=str,
help="Name of the original model you'd like to convert.",
)
parser.add_argument(
"--pytorch_dump_folder_path",
default=None,
required=False,
type=str,
help="Path to the output PyTorch model directory.",
)
parser.add_argument(
"--push_to_hub",
action="store_true",
help="Whether or not to push the converted model and processor to the 🤗 hub.",
)
_lowerCAmelCase : List[Any] = parser.parse_args()
convert_donut_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)
| 308
| 1
|
from __future__ import annotations
import unittest
from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
if is_tf_available():
import tensorflow as tf
from transformers import AutoTokenizer, TFAutoModelForSeqaSeqLM
@require_tf
@require_sentencepiece
@require_tokenizers
class __magic_name__ ( unittest.TestCase ):
@slow
def __magic_name__ ( self ) -> List[str]:
'''simple docstring'''
__a =TFAutoModelForSeqaSeqLM.from_pretrained('google/mt5-small' )
__a =AutoTokenizer.from_pretrained('google/mt5-small' )
__a =tokenizer('Hello there' , return_tensors='tf' ).input_ids
__a =tokenizer('Hi I am' , return_tensors='tf' ).input_ids
__a =model(__snake_case , labels=__snake_case ).loss
__a =-tf.math.reduce_mean(__snake_case ).numpy()
__a =-21.22_8168
self.assertTrue(abs(mtf_score - EXPECTED_SCORE ) < 2e-4 )
| 308
|
import json
import os
import unittest
from transformers.models.blenderbot_small.tokenization_blenderbot_small import (
VOCAB_FILES_NAMES,
BlenderbotSmallTokenizer,
)
from ...test_tokenization_common import TokenizerTesterMixin
class __magic_name__ ( lowerCAmelCase_ , unittest.TestCase ):
SCREAMING_SNAKE_CASE = BlenderbotSmallTokenizer
SCREAMING_SNAKE_CASE = False
def __magic_name__ ( self ) -> Union[str, Any]:
'''simple docstring'''
super().setUp()
__a =['__start__', 'adapt', 'act', 'ap@@', 'te', '__end__', '__unk__']
__a =dict(zip(__snake_case , range(len(__snake_case ) ) ) )
__a =['#version: 0.2', 'a p', 't e</w>', 'ap t</w>', 'a d', 'ad apt</w>', 'a c', 'ac t</w>', '']
__a ={'unk_token': '__unk__', 'bos_token': '__start__', 'eos_token': '__end__'}
__a =os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['vocab_file'] )
__a =os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['merges_file'] )
with open(self.vocab_file , 'w' , encoding='utf-8' ) as fp:
fp.write(json.dumps(__snake_case ) + '\n' )
with open(self.merges_file , 'w' , encoding='utf-8' ) as fp:
fp.write('\n'.join(__snake_case ) )
def __magic_name__ ( self , **__snake_case ) -> Any:
'''simple docstring'''
kwargs.update(self.special_tokens_map )
return BlenderbotSmallTokenizer.from_pretrained(self.tmpdirname , **__snake_case )
def __magic_name__ ( self , __snake_case ) -> List[Any]:
'''simple docstring'''
__a ='adapt act apte'
__a ='adapt act apte'
return input_text, output_text
def __magic_name__ ( self ) -> str:
'''simple docstring'''
__a =BlenderbotSmallTokenizer(self.vocab_file , self.merges_file , **self.special_tokens_map )
__a ='adapt act apte'
__a =['adapt', 'act', 'ap@@', 'te']
__a =tokenizer.tokenize(__snake_case )
self.assertListEqual(__snake_case , __snake_case )
__a =[tokenizer.bos_token] + tokens + [tokenizer.eos_token]
__a =[0, 1, 2, 3, 4, 5]
self.assertListEqual(tokenizer.convert_tokens_to_ids(__snake_case ) , __snake_case )
def __magic_name__ ( self ) -> str:
'''simple docstring'''
__a =BlenderbotSmallTokenizer.from_pretrained('facebook/blenderbot-90M' )
assert tok('sam' ).input_ids == [1384]
__a ='I am a small frog.'
__a =tok([src_text] , padding=__snake_case , truncation=__snake_case )['input_ids']
__a =tok.batch_decode(__snake_case , skip_special_tokens=__snake_case , clean_up_tokenization_spaces=__snake_case )[0]
assert src_text != decoded # I wish it did!
assert decoded == "i am a small frog ."
def __magic_name__ ( self ) -> str:
'''simple docstring'''
__a =BlenderbotSmallTokenizer.from_pretrained('facebook/blenderbot-90M' )
__a ='I am a small frog .'
__a ='.'
__a =tok(__snake_case )['input_ids']
__a =tok(__snake_case )['input_ids']
assert encoded[-1] == encoded_dot[0]
| 308
| 1
|
import os
import numpy
import onnx
def UpperCamelCase_( _snake_case : Any , _snake_case : Optional[Any] ):
"""simple docstring"""
__a =a.name
__a =b.name
__a =''
__a =''
__a =a == b
__a =name_a
__a =name_b
return res
def UpperCamelCase_( _snake_case : Any , _snake_case : List[Any] , _snake_case : List[Any] ):
"""simple docstring"""
for i, input_name in enumerate(node_proto.input ):
if input_name == name:
node_proto.input.insert(_snake_case , _snake_case )
node_proto.input.pop(i + 1 )
if node_proto.op_type == "If":
_graph_replace_input_with(node_proto.attribute[0].g , _snake_case , _snake_case )
_graph_replace_input_with(node_proto.attribute[1].g , _snake_case , _snake_case )
if node_proto.op_type == "Loop":
_graph_replace_input_with(node_proto.attribute[0].g , _snake_case , _snake_case )
def UpperCamelCase_( _snake_case : str , _snake_case : Any , _snake_case : str ):
"""simple docstring"""
for n in graph_proto.node:
_node_replace_input_with(_snake_case , _snake_case , _snake_case )
def UpperCamelCase_( _snake_case : List[str] , _snake_case : Tuple , _snake_case : Tuple ):
"""simple docstring"""
__a =list(model.graph.initializer )
__a =list(model_without_ext.graph.initializer )
for i, ref_i in ind_to_replace:
assert inits_with_data[i].name == inits[i].name
assert inits_with_data[ref_i].name == inits[ref_i].name
assert i > ref_i
__a =inits[i].name
__a =inits[ref_i].name
model_without_ext.graph.initializer.remove(inits[i] )
# for n in model.graph.node:
_graph_replace_input_with(model_without_ext.graph , _snake_case , _snake_case )
def UpperCamelCase_( _snake_case : Any ):
"""simple docstring"""
__a =os.path.dirname(_snake_case )
__a =os.path.basename(_snake_case )
__a =onnx.load(os.path.join(_snake_case , _snake_case ) )
__a =list(model.graph.initializer )
__a =set()
__a ={}
__a =[]
__a =0
for i in range(len(_snake_case ) ):
if i in dup_set:
continue
for j in range(i + 1 , len(_snake_case ) ):
if j in dup_set:
continue
if _is_equal_tensor_proto(inits[i] , inits[j] ):
dup_set.add(_snake_case )
dup_set.add(_snake_case )
__a =inits[j].data_type
__a =numpy.prod(inits[j].dims )
if dtype == 1:
mem_size *= 4
elif dtype == 6:
mem_size *= 4
elif dtype == 7 or dtype == 11:
mem_size *= 8
else:
print('unexpected data type: ' , _snake_case )
total_reduced_size += mem_size
__a =inits[i].name
__a =inits[j].name
if name_i in dup_map:
dup_map[name_i].append(_snake_case )
else:
__a =[name_j]
ind_to_replace.append((j, i) )
print('total reduced size: ' , total_reduced_size / 1024 / 1024 / 1024 , 'GB' )
__a =sorted(_snake_case )
_remove_dup_initializers_from_model(_snake_case , _snake_case , _snake_case )
__a ='optimized_' + model_file_name
__a =os.path.join(_snake_case , _snake_case )
onnx.save(_snake_case , _snake_case )
return new_model
| 308
|
import unittest
from transformers import load_tool
from transformers.utils import is_torch_available
if is_torch_available():
import torch
from transformers.testing_utils import require_torch
from .test_tools_common import ToolTesterMixin
@require_torch
class __magic_name__ ( unittest.TestCase , lowerCAmelCase_ ):
def __magic_name__ ( self ) -> Union[str, Any]:
'''simple docstring'''
__a =load_tool('text-to-speech' )
self.tool.setup()
def __magic_name__ ( self ) -> Dict:
'''simple docstring'''
# SpeechT5 isn't deterministic
torch.manual_seed(0 )
__a =self.tool('hey' )
__a =result.to_raw()
self.assertTrue(
torch.allclose(
resulting_tensor[:3] , torch.tensor([-0.000_5966_6688_3211_5829, -0.000_3657_6401_9079_5064, -0.0001_3439_5027_9988_3485] ) , ) )
def __magic_name__ ( self ) -> List[Any]:
'''simple docstring'''
# SpeechT5 isn't deterministic
torch.manual_seed(0 )
__a =self.tool('hey' )
__a =result.to_raw()
self.assertTrue(
torch.allclose(
resulting_tensor[:3] , torch.tensor([-0.000_5966_6688_3211_5829, -0.000_3657_6401_9079_5064, -0.0001_3439_5027_9988_3485] ) , ) )
| 308
| 1
|
import json
import os
import unittest
from transformers.models.blenderbot_small.tokenization_blenderbot_small import (
VOCAB_FILES_NAMES,
BlenderbotSmallTokenizer,
)
from ...test_tokenization_common import TokenizerTesterMixin
class __magic_name__ ( lowerCAmelCase_ , unittest.TestCase ):
SCREAMING_SNAKE_CASE = BlenderbotSmallTokenizer
SCREAMING_SNAKE_CASE = False
def __magic_name__ ( self ) -> Union[str, Any]:
'''simple docstring'''
super().setUp()
__a =['__start__', 'adapt', 'act', 'ap@@', 'te', '__end__', '__unk__']
__a =dict(zip(__snake_case , range(len(__snake_case ) ) ) )
__a =['#version: 0.2', 'a p', 't e</w>', 'ap t</w>', 'a d', 'ad apt</w>', 'a c', 'ac t</w>', '']
__a ={'unk_token': '__unk__', 'bos_token': '__start__', 'eos_token': '__end__'}
__a =os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['vocab_file'] )
__a =os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['merges_file'] )
with open(self.vocab_file , 'w' , encoding='utf-8' ) as fp:
fp.write(json.dumps(__snake_case ) + '\n' )
with open(self.merges_file , 'w' , encoding='utf-8' ) as fp:
fp.write('\n'.join(__snake_case ) )
def __magic_name__ ( self , **__snake_case ) -> Any:
'''simple docstring'''
kwargs.update(self.special_tokens_map )
return BlenderbotSmallTokenizer.from_pretrained(self.tmpdirname , **__snake_case )
def __magic_name__ ( self , __snake_case ) -> List[Any]:
'''simple docstring'''
__a ='adapt act apte'
__a ='adapt act apte'
return input_text, output_text
def __magic_name__ ( self ) -> str:
'''simple docstring'''
__a =BlenderbotSmallTokenizer(self.vocab_file , self.merges_file , **self.special_tokens_map )
__a ='adapt act apte'
__a =['adapt', 'act', 'ap@@', 'te']
__a =tokenizer.tokenize(__snake_case )
self.assertListEqual(__snake_case , __snake_case )
__a =[tokenizer.bos_token] + tokens + [tokenizer.eos_token]
__a =[0, 1, 2, 3, 4, 5]
self.assertListEqual(tokenizer.convert_tokens_to_ids(__snake_case ) , __snake_case )
def __magic_name__ ( self ) -> str:
'''simple docstring'''
__a =BlenderbotSmallTokenizer.from_pretrained('facebook/blenderbot-90M' )
assert tok('sam' ).input_ids == [1384]
__a ='I am a small frog.'
__a =tok([src_text] , padding=__snake_case , truncation=__snake_case )['input_ids']
__a =tok.batch_decode(__snake_case , skip_special_tokens=__snake_case , clean_up_tokenization_spaces=__snake_case )[0]
assert src_text != decoded # I wish it did!
assert decoded == "i am a small frog ."
def __magic_name__ ( self ) -> str:
'''simple docstring'''
__a =BlenderbotSmallTokenizer.from_pretrained('facebook/blenderbot-90M' )
__a ='I am a small frog .'
__a ='.'
__a =tok(__snake_case )['input_ids']
__a =tok(__snake_case )['input_ids']
assert encoded[-1] == encoded_dot[0]
| 308
|
import flax.linen as nn
import jax.numpy as jnp
from .attention_flax import FlaxTransformeraDModel
from .resnet_flax import FlaxDownsampleaD, FlaxResnetBlockaD, FlaxUpsampleaD
class __magic_name__ ( nn.Module ):
SCREAMING_SNAKE_CASE = 42
SCREAMING_SNAKE_CASE = 42
SCREAMING_SNAKE_CASE = 0.0
SCREAMING_SNAKE_CASE = 1
SCREAMING_SNAKE_CASE = 1
SCREAMING_SNAKE_CASE = True
SCREAMING_SNAKE_CASE = False
SCREAMING_SNAKE_CASE = False
SCREAMING_SNAKE_CASE = False
SCREAMING_SNAKE_CASE = jnp.floataa
def __magic_name__ ( self ) -> int:
'''simple docstring'''
__a =[]
__a =[]
for i in range(self.num_layers ):
__a =self.in_channels if i == 0 else self.out_channels
__a =FlaxResnetBlockaD(
in_channels=__snake_case , out_channels=self.out_channels , dropout_prob=self.dropout , dtype=self.dtype , )
resnets.append(__snake_case )
__a =FlaxTransformeraDModel(
in_channels=self.out_channels , n_heads=self.num_attention_heads , d_head=self.out_channels // self.num_attention_heads , depth=1 , use_linear_projection=self.use_linear_projection , only_cross_attention=self.only_cross_attention , use_memory_efficient_attention=self.use_memory_efficient_attention , dtype=self.dtype , )
attentions.append(__snake_case )
__a =resnets
__a =attentions
if self.add_downsample:
__a =FlaxDownsampleaD(self.out_channels , dtype=self.dtype )
def __call__( self , __snake_case , __snake_case , __snake_case , __snake_case=True ) -> Optional[Any]:
'''simple docstring'''
__a =()
for resnet, attn in zip(self.resnets , self.attentions ):
__a =resnet(__snake_case , __snake_case , deterministic=__snake_case )
__a =attn(__snake_case , __snake_case , deterministic=__snake_case )
output_states += (hidden_states,)
if self.add_downsample:
__a =self.downsamplers_a(__snake_case )
output_states += (hidden_states,)
return hidden_states, output_states
class __magic_name__ ( nn.Module ):
SCREAMING_SNAKE_CASE = 42
SCREAMING_SNAKE_CASE = 42
SCREAMING_SNAKE_CASE = 0.0
SCREAMING_SNAKE_CASE = 1
SCREAMING_SNAKE_CASE = True
SCREAMING_SNAKE_CASE = jnp.floataa
def __magic_name__ ( self ) -> int:
'''simple docstring'''
__a =[]
for i in range(self.num_layers ):
__a =self.in_channels if i == 0 else self.out_channels
__a =FlaxResnetBlockaD(
in_channels=__snake_case , out_channels=self.out_channels , dropout_prob=self.dropout , dtype=self.dtype , )
resnets.append(__snake_case )
__a =resnets
if self.add_downsample:
__a =FlaxDownsampleaD(self.out_channels , dtype=self.dtype )
def __call__( self , __snake_case , __snake_case , __snake_case=True ) -> Optional[int]:
'''simple docstring'''
__a =()
for resnet in self.resnets:
__a =resnet(__snake_case , __snake_case , deterministic=__snake_case )
output_states += (hidden_states,)
if self.add_downsample:
__a =self.downsamplers_a(__snake_case )
output_states += (hidden_states,)
return hidden_states, output_states
class __magic_name__ ( nn.Module ):
SCREAMING_SNAKE_CASE = 42
SCREAMING_SNAKE_CASE = 42
SCREAMING_SNAKE_CASE = 42
SCREAMING_SNAKE_CASE = 0.0
SCREAMING_SNAKE_CASE = 1
SCREAMING_SNAKE_CASE = 1
SCREAMING_SNAKE_CASE = True
SCREAMING_SNAKE_CASE = False
SCREAMING_SNAKE_CASE = False
SCREAMING_SNAKE_CASE = False
SCREAMING_SNAKE_CASE = jnp.floataa
def __magic_name__ ( self ) -> List[Any]:
'''simple docstring'''
__a =[]
__a =[]
for i in range(self.num_layers ):
__a =self.in_channels if (i == self.num_layers - 1) else self.out_channels
__a =self.prev_output_channel if i == 0 else self.out_channels
__a =FlaxResnetBlockaD(
in_channels=resnet_in_channels + res_skip_channels , out_channels=self.out_channels , dropout_prob=self.dropout , dtype=self.dtype , )
resnets.append(__snake_case )
__a =FlaxTransformeraDModel(
in_channels=self.out_channels , n_heads=self.num_attention_heads , d_head=self.out_channels // self.num_attention_heads , depth=1 , use_linear_projection=self.use_linear_projection , only_cross_attention=self.only_cross_attention , use_memory_efficient_attention=self.use_memory_efficient_attention , dtype=self.dtype , )
attentions.append(__snake_case )
__a =resnets
__a =attentions
if self.add_upsample:
__a =FlaxUpsampleaD(self.out_channels , dtype=self.dtype )
def __call__( self , __snake_case , __snake_case , __snake_case , __snake_case , __snake_case=True ) -> List[Any]:
'''simple docstring'''
for resnet, attn in zip(self.resnets , self.attentions ):
# pop res hidden states
__a =res_hidden_states_tuple[-1]
__a =res_hidden_states_tuple[:-1]
__a =jnp.concatenate((hidden_states, res_hidden_states) , axis=-1 )
__a =resnet(__snake_case , __snake_case , deterministic=__snake_case )
__a =attn(__snake_case , __snake_case , deterministic=__snake_case )
if self.add_upsample:
__a =self.upsamplers_a(__snake_case )
return hidden_states
class __magic_name__ ( nn.Module ):
SCREAMING_SNAKE_CASE = 42
SCREAMING_SNAKE_CASE = 42
SCREAMING_SNAKE_CASE = 42
SCREAMING_SNAKE_CASE = 0.0
SCREAMING_SNAKE_CASE = 1
SCREAMING_SNAKE_CASE = True
SCREAMING_SNAKE_CASE = jnp.floataa
def __magic_name__ ( self ) -> Union[str, Any]:
'''simple docstring'''
__a =[]
for i in range(self.num_layers ):
__a =self.in_channels if (i == self.num_layers - 1) else self.out_channels
__a =self.prev_output_channel if i == 0 else self.out_channels
__a =FlaxResnetBlockaD(
in_channels=resnet_in_channels + res_skip_channels , out_channels=self.out_channels , dropout_prob=self.dropout , dtype=self.dtype , )
resnets.append(__snake_case )
__a =resnets
if self.add_upsample:
__a =FlaxUpsampleaD(self.out_channels , dtype=self.dtype )
def __call__( self , __snake_case , __snake_case , __snake_case , __snake_case=True ) -> List[Any]:
'''simple docstring'''
for resnet in self.resnets:
# pop res hidden states
__a =res_hidden_states_tuple[-1]
__a =res_hidden_states_tuple[:-1]
__a =jnp.concatenate((hidden_states, res_hidden_states) , axis=-1 )
__a =resnet(__snake_case , __snake_case , deterministic=__snake_case )
if self.add_upsample:
__a =self.upsamplers_a(__snake_case )
return hidden_states
class __magic_name__ ( nn.Module ):
SCREAMING_SNAKE_CASE = 42
SCREAMING_SNAKE_CASE = 0.0
SCREAMING_SNAKE_CASE = 1
SCREAMING_SNAKE_CASE = 1
SCREAMING_SNAKE_CASE = False
SCREAMING_SNAKE_CASE = False
SCREAMING_SNAKE_CASE = jnp.floataa
def __magic_name__ ( self ) -> List[Any]:
'''simple docstring'''
# there is always at least one resnet
__a =[
FlaxResnetBlockaD(
in_channels=self.in_channels , out_channels=self.in_channels , dropout_prob=self.dropout , dtype=self.dtype , )
]
__a =[]
for _ in range(self.num_layers ):
__a =FlaxTransformeraDModel(
in_channels=self.in_channels , n_heads=self.num_attention_heads , d_head=self.in_channels // self.num_attention_heads , depth=1 , use_linear_projection=self.use_linear_projection , use_memory_efficient_attention=self.use_memory_efficient_attention , dtype=self.dtype , )
attentions.append(__snake_case )
__a =FlaxResnetBlockaD(
in_channels=self.in_channels , out_channels=self.in_channels , dropout_prob=self.dropout , dtype=self.dtype , )
resnets.append(__snake_case )
__a =resnets
__a =attentions
def __call__( self , __snake_case , __snake_case , __snake_case , __snake_case=True ) -> List[str]:
'''simple docstring'''
__a =self.resnets[0](__snake_case , __snake_case )
for attn, resnet in zip(self.attentions , self.resnets[1:] ):
__a =attn(__snake_case , __snake_case , deterministic=__snake_case )
__a =resnet(__snake_case , __snake_case , deterministic=__snake_case )
return hidden_states
| 308
| 1
|
from __future__ import annotations
_lowerCAmelCase : Dict = 8.988E9 # units = N * m^s * C^-2
def UpperCamelCase_( _snake_case : float , _snake_case : float , _snake_case : float , _snake_case : float ):
"""simple docstring"""
__a =abs(chargea * chargea )
if (force, chargea, chargea, distance).count(0 ) != 1:
raise ValueError('One and only one argument must be 0' )
if distance < 0:
raise ValueError('Distance cannot be negative' )
if force == 0:
__a =COULOMBS_CONSTANT * charge_product / (distance**2)
return {"force": force}
elif chargea == 0:
__a =abs(_snake_case ) * (distance**2) / (COULOMBS_CONSTANT * chargea)
return {"charge1": chargea}
elif chargea == 0:
__a =abs(_snake_case ) * (distance**2) / (COULOMBS_CONSTANT * chargea)
return {"charge2": chargea}
elif distance == 0:
__a =(COULOMBS_CONSTANT * charge_product / abs(_snake_case )) ** 0.5
return {"distance": distance}
raise ValueError('Exactly one argument must be 0' )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 308
|
import json
import os
import subprocess
import unittest
from ast import literal_eval
import pytest
from parameterized import parameterized, parameterized_class
from . import is_sagemaker_available
if is_sagemaker_available():
from sagemaker import Session, TrainingJobAnalytics
from sagemaker.huggingface import HuggingFace
@pytest.mark.skipif(
literal_eval(os.getenv('TEST_SAGEMAKER' , 'False' ) ) is not True , reason='Skipping test because should only be run when releasing minor transformers version' , )
@pytest.mark.usefixtures('sm_env' )
@parameterized_class(
[
{
'framework': 'pytorch',
'script': 'run_glue.py',
'model_name_or_path': 'distilbert-base-cased',
'instance_type': 'ml.p3.16xlarge',
'results': {'train_runtime': 6_5_0, 'eval_accuracy': 0.7, 'eval_loss': 0.6},
},
{
'framework': 'pytorch',
'script': 'run_ddp.py',
'model_name_or_path': 'distilbert-base-cased',
'instance_type': 'ml.p3.16xlarge',
'results': {'train_runtime': 6_0_0, 'eval_accuracy': 0.7, 'eval_loss': 0.6},
},
{
'framework': 'tensorflow',
'script': 'run_tf_dist.py',
'model_name_or_path': 'distilbert-base-cased',
'instance_type': 'ml.p3.16xlarge',
'results': {'train_runtime': 6_0_0, 'eval_accuracy': 0.6, 'eval_loss': 0.7},
},
] )
class __magic_name__ ( unittest.TestCase ):
def __magic_name__ ( self ) -> int:
'''simple docstring'''
if self.framework == "pytorch":
subprocess.run(
f'cp ./examples/pytorch/text-classification/run_glue.py {self.env.test_path}/run_glue.py'.split() , encoding='utf-8' , check=__snake_case , )
assert hasattr(self , 'env' )
def __magic_name__ ( self , __snake_case ) -> int:
'''simple docstring'''
__a =f'{self.env.base_job_name}-{instance_count}-{"ddp" if "ddp" in self.script else "smd"}'
# distributed data settings
__a ={'smdistributed': {'dataparallel': {'enabled': True}}} if self.script != 'run_ddp.py' else None
# creates estimator
return HuggingFace(
entry_point=self.script , source_dir=self.env.test_path , role=self.env.role , image_uri=self.env.image_uri , base_job_name=__snake_case , instance_count=__snake_case , instance_type=self.instance_type , debugger_hook_config=__snake_case , hyperparameters={**self.env.distributed_hyperparameters, 'model_name_or_path': self.model_name_or_path} , metric_definitions=self.env.metric_definitions , distribution=__snake_case , py_version='py36' , )
def __magic_name__ ( self , __snake_case ) -> Optional[Any]:
'''simple docstring'''
TrainingJobAnalytics(__snake_case ).export_csv(f'{self.env.test_path}/{job_name}_metrics.csv' )
@parameterized.expand([(2,)] )
def __magic_name__ ( self , __snake_case ) -> Optional[int]:
'''simple docstring'''
# create estimator
__a =self.create_estimator(__snake_case )
# run training
estimator.fit()
# result dataframe
__a =TrainingJobAnalytics(estimator.latest_training_job.name ).dataframe()
# extract kpis
__a =list(result_metrics_df[result_metrics_df.metric_name == 'eval_accuracy']['value'] )
__a =list(result_metrics_df[result_metrics_df.metric_name == 'eval_loss']['value'] )
# get train time from SageMaker job, this includes starting, preprocessing, stopping
__a =(
Session().describe_training_job(estimator.latest_training_job.name ).get('TrainingTimeInSeconds' , 99_9999 )
)
# assert kpis
assert train_runtime <= self.results["train_runtime"]
assert all(t >= self.results['eval_accuracy'] for t in eval_accuracy )
assert all(t <= self.results['eval_loss'] for t in eval_loss )
# dump tests result into json file to share in PR
with open(f'{estimator.latest_training_job.name}.json' , 'w' ) as outfile:
json.dump({'train_time': train_runtime, 'eval_accuracy': eval_accuracy, 'eval_loss': eval_loss} , __snake_case )
| 308
| 1
|
from PIL import Image
def UpperCamelCase_( _snake_case : Image , _snake_case : float ):
"""simple docstring"""
def brightness(_snake_case : int ) -> float:
return 128 + level + (c - 128)
if not -255.0 <= level <= 255.0:
raise ValueError('level must be between -255.0 (black) and 255.0 (white)' )
return img.point(_snake_case )
if __name__ == "__main__":
# Load image
with Image.open("image_data/lena.jpg") as img:
# Change brightness to 100
_lowerCAmelCase : str = change_brightness(img, 100)
brigt_img.save("image_data/lena_brightness.png", format="png")
| 308
|
import argparse
import logging
from collections import namedtuple
import torch
from model_bertabs import BertAbsSummarizer
from models.model_builder import AbsSummarizer # The authors' implementation
from transformers import BertTokenizer
logging.basicConfig(level=logging.INFO)
_lowerCAmelCase : List[Any] = logging.getLogger(__name__)
_lowerCAmelCase : Optional[Any] = "Hello world! cécé herlolip"
_lowerCAmelCase : str = namedtuple(
"BertAbsConfig",
[
"temp_dir",
"large",
"use_bert_emb",
"finetune_bert",
"encoder",
"share_emb",
"max_pos",
"enc_layers",
"enc_hidden_size",
"enc_heads",
"enc_ff_size",
"enc_dropout",
"dec_layers",
"dec_hidden_size",
"dec_heads",
"dec_ff_size",
"dec_dropout",
],
)
def UpperCamelCase_( _snake_case : str , _snake_case : List[Any] ):
"""simple docstring"""
__a =BertAbsConfig(
temp_dir='.' , finetune_bert=_snake_case , large=_snake_case , share_emb=_snake_case , use_bert_emb=_snake_case , encoder='bert' , max_pos=512 , enc_layers=6 , enc_hidden_size=512 , enc_heads=8 , enc_ff_size=512 , enc_dropout=0.2 , dec_layers=6 , dec_hidden_size=768 , dec_heads=8 , dec_ff_size=2048 , dec_dropout=0.2 , )
__a =torch.load(_snake_case , lambda _snake_case , _snake_case : storage )
__a =AbsSummarizer(_snake_case , torch.device('cpu' ) , _snake_case )
original.eval()
__a =BertAbsSummarizer(_snake_case , torch.device('cpu' ) )
new_model.eval()
# -------------------
# Convert the weights
# -------------------
logging.info('convert the model' )
new_model.bert.load_state_dict(original.bert.state_dict() )
new_model.decoder.load_state_dict(original.decoder.state_dict() )
new_model.generator.load_state_dict(original.generator.state_dict() )
# ----------------------------------
# Make sure the outpus are identical
# ----------------------------------
logging.info('Make sure that the models\' outputs are identical' )
__a =BertTokenizer.from_pretrained('bert-base-uncased' )
# prepare the model inputs
__a =tokenizer.encode('This is sample éàalj\'-.' )
encoder_input_ids.extend([tokenizer.pad_token_id] * (512 - len(_snake_case )) )
__a =torch.tensor(_snake_case ).unsqueeze(0 )
__a =tokenizer.encode('This is sample 3 éàalj\'-.' )
decoder_input_ids.extend([tokenizer.pad_token_id] * (512 - len(_snake_case )) )
__a =torch.tensor(_snake_case ).unsqueeze(0 )
# failsafe to make sure the weights reset does not affect the
# loaded weights.
assert torch.max(torch.abs(original.generator[0].weight - new_model.generator[0].weight ) ) == 0
# forward pass
__a =encoder_input_ids
__a =decoder_input_ids
__a =__a =None
__a =None
__a =__a =None
__a =__a =None
__a =None
# The original model does not apply the geneator layer immediatly but rather in
# the beam search (where it combines softmax + linear layer). Since we already
# apply the softmax in our generation process we only apply the linear layer here.
# We make sure that the outputs of the full stack are identical
__a =original(_snake_case , _snake_case , _snake_case , _snake_case , _snake_case , _snake_case , _snake_case )[0]
__a =original.generator(_snake_case )
__a =new_model(
_snake_case , _snake_case , _snake_case , _snake_case , _snake_case )[0]
__a =new_model.generator(_snake_case )
__a =torch.max(torch.abs(output_converted_model - output_original_model ) ).item()
print('Maximum absolute difference beween weights: {:.2f}'.format(_snake_case ) )
__a =torch.max(torch.abs(output_converted_generator - output_original_generator ) ).item()
print('Maximum absolute difference beween weights: {:.2f}'.format(_snake_case ) )
__a =torch.allclose(_snake_case , _snake_case , atol=1e-3 )
if are_identical:
logging.info('all weights are equal up to 1e-3' )
else:
raise ValueError('the weights are different. The new model is likely different from the original one.' )
# The model has been saved with torch.save(model) and this is bound to the exact
# directory structure. We save the state_dict instead.
logging.info('saving the model\'s state dictionary' )
torch.save(
new_model.state_dict() , './bertabs-finetuned-cnndm-extractive-abstractive-summarization/pytorch_model.bin' )
if __name__ == "__main__":
_lowerCAmelCase : Optional[int] = argparse.ArgumentParser()
parser.add_argument(
"--bertabs_checkpoint_path",
default=None,
type=str,
required=True,
help="Path the official PyTorch dump.",
)
parser.add_argument(
"--pytorch_dump_folder_path",
default=None,
type=str,
required=True,
help="Path to the output PyTorch model.",
)
_lowerCAmelCase : Optional[Any] = parser.parse_args()
convert_bertabs_checkpoints(
args.bertabs_checkpoint_path,
args.pytorch_dump_folder_path,
)
| 308
| 1
|
import unittest
from transformers import load_tool
from transformers.utils import is_torch_available
if is_torch_available():
import torch
from transformers.testing_utils import require_torch
from .test_tools_common import ToolTesterMixin
@require_torch
class __magic_name__ ( unittest.TestCase , lowerCAmelCase_ ):
def __magic_name__ ( self ) -> Union[str, Any]:
'''simple docstring'''
__a =load_tool('text-to-speech' )
self.tool.setup()
def __magic_name__ ( self ) -> Dict:
'''simple docstring'''
# SpeechT5 isn't deterministic
torch.manual_seed(0 )
__a =self.tool('hey' )
__a =result.to_raw()
self.assertTrue(
torch.allclose(
resulting_tensor[:3] , torch.tensor([-0.000_5966_6688_3211_5829, -0.000_3657_6401_9079_5064, -0.0001_3439_5027_9988_3485] ) , ) )
def __magic_name__ ( self ) -> List[Any]:
'''simple docstring'''
# SpeechT5 isn't deterministic
torch.manual_seed(0 )
__a =self.tool('hey' )
__a =result.to_raw()
self.assertTrue(
torch.allclose(
resulting_tensor[:3] , torch.tensor([-0.000_5966_6688_3211_5829, -0.000_3657_6401_9079_5064, -0.0001_3439_5027_9988_3485] ) , ) )
| 308
|
import unittest
from transformers import XLMConfig, 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 (
XLMForMultipleChoice,
XLMForQuestionAnswering,
XLMForQuestionAnsweringSimple,
XLMForSequenceClassification,
XLMForTokenClassification,
XLMModel,
XLMWithLMHeadModel,
)
from transformers.models.xlm.modeling_xlm import XLM_PRETRAINED_MODEL_ARCHIVE_LIST
class __magic_name__ :
def __init__( self , __snake_case , __snake_case=13 , __snake_case=7 , __snake_case=True , __snake_case=True , __snake_case=True , __snake_case=True , __snake_case=True , __snake_case=False , __snake_case=False , __snake_case=False , __snake_case=2 , __snake_case=99 , __snake_case=0 , __snake_case=32 , __snake_case=5 , __snake_case=4 , __snake_case=0.1 , __snake_case=0.1 , __snake_case=512 , __snake_case=2 , __snake_case=0.02 , __snake_case=2 , __snake_case=4 , __snake_case="last" , __snake_case=True , __snake_case=None , __snake_case=0 , ) -> Optional[Any]:
'''simple docstring'''
__a =parent
__a =batch_size
__a =seq_length
__a =is_training
__a =use_input_lengths
__a =use_token_type_ids
__a =use_labels
__a =gelu_activation
__a =sinusoidal_embeddings
__a =causal
__a =asm
__a =n_langs
__a =vocab_size
__a =n_special
__a =hidden_size
__a =num_hidden_layers
__a =num_attention_heads
__a =hidden_dropout_prob
__a =attention_probs_dropout_prob
__a =max_position_embeddings
__a =type_sequence_label_size
__a =initializer_range
__a =num_labels
__a =num_choices
__a =summary_type
__a =use_proj
__a =scope
__a =bos_token_id
def __magic_name__ ( self ) -> Any:
'''simple docstring'''
__a =ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
__a =random_attention_mask([self.batch_size, self.seq_length] )
__a =None
if self.use_input_lengths:
__a =(
ids_tensor([self.batch_size] , vocab_size=2 ) + self.seq_length - 2
) # small variation of seq_length
__a =None
if self.use_token_type_ids:
__a =ids_tensor([self.batch_size, self.seq_length] , self.n_langs )
__a =None
__a =None
__a =None
if self.use_labels:
__a =ids_tensor([self.batch_size] , self.type_sequence_label_size )
__a =ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
__a =ids_tensor([self.batch_size] , 2 ).float()
__a =ids_tensor([self.batch_size] , self.num_choices )
__a =self.get_config()
return (
config,
input_ids,
token_type_ids,
input_lengths,
sequence_labels,
token_labels,
is_impossible_labels,
choice_labels,
input_mask,
)
def __magic_name__ ( self ) -> Any:
'''simple docstring'''
return XLMConfig(
vocab_size=self.vocab_size , n_special=self.n_special , emb_dim=self.hidden_size , n_layers=self.num_hidden_layers , n_heads=self.num_attention_heads , dropout=self.hidden_dropout_prob , attention_dropout=self.attention_probs_dropout_prob , gelu_activation=self.gelu_activation , sinusoidal_embeddings=self.sinusoidal_embeddings , asm=self.asm , causal=self.causal , n_langs=self.n_langs , max_position_embeddings=self.max_position_embeddings , initializer_range=self.initializer_range , summary_type=self.summary_type , use_proj=self.use_proj , num_labels=self.num_labels , bos_token_id=self.bos_token_id , )
def __magic_name__ ( self , __snake_case , __snake_case , __snake_case , __snake_case , __snake_case , __snake_case , __snake_case , __snake_case , __snake_case , ) -> List[Any]:
'''simple docstring'''
__a =XLMModel(config=__snake_case )
model.to(__snake_case )
model.eval()
__a =model(__snake_case , lengths=__snake_case , langs=__snake_case )
__a =model(__snake_case , langs=__snake_case )
__a =model(__snake_case )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def __magic_name__ ( self , __snake_case , __snake_case , __snake_case , __snake_case , __snake_case , __snake_case , __snake_case , __snake_case , __snake_case , ) -> Optional[int]:
'''simple docstring'''
__a =XLMWithLMHeadModel(__snake_case )
model.to(__snake_case )
model.eval()
__a =model(__snake_case , token_type_ids=__snake_case , labels=__snake_case )
self.parent.assertEqual(result.loss.shape , () )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def __magic_name__ ( self , __snake_case , __snake_case , __snake_case , __snake_case , __snake_case , __snake_case , __snake_case , __snake_case , __snake_case , ) -> Dict:
'''simple docstring'''
__a =XLMForQuestionAnsweringSimple(__snake_case )
model.to(__snake_case )
model.eval()
__a =model(__snake_case )
__a =model(__snake_case , start_positions=__snake_case , end_positions=__snake_case )
__a =outputs
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 __magic_name__ ( self , __snake_case , __snake_case , __snake_case , __snake_case , __snake_case , __snake_case , __snake_case , __snake_case , __snake_case , ) -> List[Any]:
'''simple docstring'''
__a =XLMForQuestionAnswering(__snake_case )
model.to(__snake_case )
model.eval()
__a =model(__snake_case )
__a =model(
__snake_case , start_positions=__snake_case , end_positions=__snake_case , cls_index=__snake_case , is_impossible=__snake_case , p_mask=__snake_case , )
__a =model(
__snake_case , start_positions=__snake_case , end_positions=__snake_case , cls_index=__snake_case , is_impossible=__snake_case , )
((__a) , ) =result_with_labels.to_tuple()
__a =model(__snake_case , start_positions=__snake_case , end_positions=__snake_case )
((__a) , ) =result_with_labels.to_tuple()
self.parent.assertEqual(result_with_labels.loss.shape , () )
self.parent.assertEqual(result.start_top_log_probs.shape , (self.batch_size, model.config.start_n_top) )
self.parent.assertEqual(result.start_top_index.shape , (self.batch_size, model.config.start_n_top) )
self.parent.assertEqual(
result.end_top_log_probs.shape , (self.batch_size, model.config.start_n_top * model.config.end_n_top) )
self.parent.assertEqual(
result.end_top_index.shape , (self.batch_size, model.config.start_n_top * model.config.end_n_top) )
self.parent.assertEqual(result.cls_logits.shape , (self.batch_size,) )
def __magic_name__ ( self , __snake_case , __snake_case , __snake_case , __snake_case , __snake_case , __snake_case , __snake_case , __snake_case , __snake_case , ) -> Optional[Any]:
'''simple docstring'''
__a =XLMForSequenceClassification(__snake_case )
model.to(__snake_case )
model.eval()
__a =model(__snake_case )
__a =model(__snake_case , labels=__snake_case )
self.parent.assertEqual(result.loss.shape , () )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size) )
def __magic_name__ ( self , __snake_case , __snake_case , __snake_case , __snake_case , __snake_case , __snake_case , __snake_case , __snake_case , __snake_case , ) -> Any:
'''simple docstring'''
__a =self.num_labels
__a =XLMForTokenClassification(__snake_case )
model.to(__snake_case )
model.eval()
__a =model(__snake_case , attention_mask=__snake_case , labels=__snake_case )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def __magic_name__ ( self , __snake_case , __snake_case , __snake_case , __snake_case , __snake_case , __snake_case , __snake_case , __snake_case , __snake_case , ) -> Tuple:
'''simple docstring'''
__a =self.num_choices
__a =XLMForMultipleChoice(config=__snake_case )
model.to(__snake_case )
model.eval()
__a =input_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
__a =token_type_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
__a =input_mask.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
__a =model(
__snake_case , attention_mask=__snake_case , token_type_ids=__snake_case , labels=__snake_case , )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_choices) )
def __magic_name__ ( self ) -> Union[str, Any]:
'''simple docstring'''
__a =self.prepare_config_and_inputs()
(
(
__a
) , (
__a
) , (
__a
) , (
__a
) , (
__a
) , (
__a
) , (
__a
) , (
__a
) , (
__a
) ,
) =config_and_inputs
__a ={'input_ids': input_ids, 'token_type_ids': token_type_ids, 'lengths': input_lengths}
return config, inputs_dict
@require_torch
class __magic_name__ ( lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , unittest.TestCase ):
SCREAMING_SNAKE_CASE = (
(
XLMModel,
XLMWithLMHeadModel,
XLMForQuestionAnswering,
XLMForSequenceClassification,
XLMForQuestionAnsweringSimple,
XLMForTokenClassification,
XLMForMultipleChoice,
)
if is_torch_available()
else ()
)
SCREAMING_SNAKE_CASE = (
(XLMWithLMHeadModel,) if is_torch_available() else ()
) # TODO (PVP): Check other models whether language generation is also applicable
SCREAMING_SNAKE_CASE = (
{
'feature-extraction': XLMModel,
'fill-mask': XLMWithLMHeadModel,
'question-answering': XLMForQuestionAnsweringSimple,
'text-classification': XLMForSequenceClassification,
'text-generation': XLMWithLMHeadModel,
'token-classification': XLMForTokenClassification,
'zero-shot': XLMForSequenceClassification,
}
if is_torch_available()
else {}
)
def __magic_name__ ( self , __snake_case , __snake_case , __snake_case , __snake_case , __snake_case ) -> int:
'''simple docstring'''
if (
pipeline_test_casse_name == "QAPipelineTests"
and tokenizer_name is not None
and not tokenizer_name.endswith('Fast' )
):
# `QAPipelineTests` fails for a few models when the slower tokenizer are used.
# (The slower tokenizers were never used for pipeline tests before the pipeline testing rework)
# TODO: check (and possibly fix) the `QAPipelineTests` with slower tokenizer
return True
return False
def __magic_name__ ( self , __snake_case , __snake_case , __snake_case=False ) -> str:
'''simple docstring'''
__a =super()._prepare_for_class(__snake_case , __snake_case , return_labels=__snake_case )
if return_labels:
if model_class.__name__ == "XLMForQuestionAnswering":
__a =torch.zeros(
self.model_tester.batch_size , dtype=torch.long , device=__snake_case )
__a =torch.zeros(
self.model_tester.batch_size , dtype=torch.long , device=__snake_case )
return inputs_dict
def __magic_name__ ( self ) -> Tuple:
'''simple docstring'''
__a =XLMModelTester(self )
__a =ConfigTester(self , config_class=__snake_case , emb_dim=37 )
def __magic_name__ ( self ) -> str:
'''simple docstring'''
self.config_tester.run_common_tests()
def __magic_name__ ( self ) -> List[str]:
'''simple docstring'''
__a =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_xlm_model(*__snake_case )
def __magic_name__ ( self ) -> List[str]:
'''simple docstring'''
__a =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_xlm_lm_head(*__snake_case )
def __magic_name__ ( self ) -> List[str]:
'''simple docstring'''
__a =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_xlm_simple_qa(*__snake_case )
def __magic_name__ ( self ) -> Optional[int]:
'''simple docstring'''
__a =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_xlm_qa(*__snake_case )
def __magic_name__ ( self ) -> Any:
'''simple docstring'''
__a =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_xlm_sequence_classif(*__snake_case )
def __magic_name__ ( self ) -> Optional[int]:
'''simple docstring'''
__a =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_xlm_token_classif(*__snake_case )
def __magic_name__ ( self ) -> Any:
'''simple docstring'''
__a =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_xlm_for_multiple_choice(*__snake_case )
def __magic_name__ ( self , __snake_case , __snake_case , __snake_case , __snake_case , __snake_case , __snake_case=False , __snake_case=1 ) -> Optional[Any]:
'''simple docstring'''
self.assertIsInstance(__snake_case , __snake_case )
self.assertListEqual(
[isinstance(__snake_case , __snake_case ) for iter_attentions in attentions] , [True] * len(__snake_case ) )
self.assertEqual(len(__snake_case ) , (max_length - min_length) * num_beam_groups )
for idx, iter_attentions in enumerate(__snake_case ):
# adds PAD dummy token
__a =min_length + idx + 1
__a =min_length + idx + 1
__a =(
batch_size * num_beam_groups,
config.num_attention_heads,
tgt_len,
src_len,
)
# check attn size
self.assertListEqual(
[layer_attention.shape for layer_attention in iter_attentions] , [expected_shape] * len(__snake_case ) )
def __magic_name__ ( self , __snake_case , __snake_case , __snake_case , __snake_case , __snake_case , __snake_case=False , __snake_case=1 ) -> Dict:
'''simple docstring'''
self.assertIsInstance(__snake_case , __snake_case )
self.assertListEqual(
[isinstance(__snake_case , __snake_case ) for iter_hidden_states in hidden_states] , [True] * len(__snake_case ) , )
self.assertEqual(len(__snake_case ) , (max_length - min_length) * num_beam_groups )
for idx, iter_hidden_states in enumerate(__snake_case ):
# adds PAD dummy token
__a =min_length + idx + 1
__a =(batch_size * num_beam_groups, seq_len, config.hidden_size)
# check hidden size
self.assertListEqual(
[layer_hidden_states.shape for layer_hidden_states in iter_hidden_states] , [expected_shape] * len(__snake_case ) , )
pass
@slow
def __magic_name__ ( self ) -> Union[str, Any]:
'''simple docstring'''
for model_name in XLM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
__a =XLMModel.from_pretrained(__snake_case )
self.assertIsNotNone(__snake_case )
@require_torch
class __magic_name__ ( unittest.TestCase ):
@slow
def __magic_name__ ( self ) -> Tuple:
'''simple docstring'''
__a =XLMWithLMHeadModel.from_pretrained('xlm-mlm-en-2048' )
model.to(__snake_case )
__a =torch.tensor([[14, 447]] , dtype=torch.long , device=__snake_case ) # the president
__a =[
14,
447,
14,
447,
14,
447,
14,
447,
14,
447,
14,
447,
14,
447,
14,
447,
14,
447,
14,
447,
] # the president the president the president the president the president the president the president the president the president the president
# TODO(PVP): this and other input_ids I tried for generation give pretty bad results. Not sure why. Model might just not be made for auto-regressive inference
__a =model.generate(__snake_case , do_sample=__snake_case )
self.assertListEqual(output_ids[0].cpu().numpy().tolist() , __snake_case )
| 308
| 1
|
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
StableDiffusionAttendAndExcitePipeline,
UNetaDConditionModel,
)
from diffusers.utils import load_numpy, skip_mps, slow
from diffusers.utils.testing_utils import require_torch_gpu
from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin
_lowerCAmelCase : List[str] = False
@skip_mps
class __magic_name__ ( lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , unittest.TestCase ):
SCREAMING_SNAKE_CASE = StableDiffusionAttendAndExcitePipeline
SCREAMING_SNAKE_CASE = False
SCREAMING_SNAKE_CASE = TEXT_TO_IMAGE_PARAMS
SCREAMING_SNAKE_CASE = TEXT_TO_IMAGE_BATCH_PARAMS.union({'token_indices'} )
SCREAMING_SNAKE_CASE = TEXT_TO_IMAGE_IMAGE_PARAMS
SCREAMING_SNAKE_CASE = TEXT_TO_IMAGE_IMAGE_PARAMS
@classmethod
def __magic_name__ ( cls ) -> int:
'''simple docstring'''
super().setUpClass()
torch.use_deterministic_algorithms(__snake_case )
@classmethod
def __magic_name__ ( cls ) -> List[str]:
'''simple docstring'''
super().tearDownClass()
torch.use_deterministic_algorithms(__snake_case )
def __magic_name__ ( self ) -> Optional[Any]:
'''simple docstring'''
torch.manual_seed(0 )
__a =UNetaDConditionModel(
block_out_channels=(32, 64) , layers_per_block=1 , sample_size=32 , in_channels=4 , out_channels=4 , down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D') , up_block_types=('CrossAttnUpBlock2D', 'UpBlock2D') , cross_attention_dim=32 , attention_head_dim=(2, 4) , use_linear_projection=__snake_case , )
__a =DDIMScheduler(
beta_start=0.0_0085 , beta_end=0.012 , beta_schedule='scaled_linear' , clip_sample=__snake_case , set_alpha_to_one=__snake_case , )
torch.manual_seed(0 )
__a =AutoencoderKL(
block_out_channels=[32, 64] , in_channels=3 , out_channels=3 , down_block_types=['DownEncoderBlock2D', 'DownEncoderBlock2D'] , up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D'] , latent_channels=4 , sample_size=128 , )
torch.manual_seed(0 )
__a =CLIPTextConfig(
bos_token_id=0 , eos_token_id=2 , hidden_size=32 , intermediate_size=37 , layer_norm_eps=1e-05 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=1000 , hidden_act='gelu' , projection_dim=512 , )
__a =CLIPTextModel(__snake_case )
__a =CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip' )
__a ={
'unet': unet,
'scheduler': scheduler,
'vae': vae,
'text_encoder': text_encoder,
'tokenizer': tokenizer,
'safety_checker': None,
'feature_extractor': None,
}
return components
def __magic_name__ ( self , __snake_case , __snake_case=0 ) -> str:
'''simple docstring'''
if str(__snake_case ).startswith('mps' ):
__a =torch.manual_seed(__snake_case )
else:
__a =torch.Generator(device=__snake_case ).manual_seed(__snake_case )
__a =__a ={
'prompt': 'a cat and a frog',
'token_indices': [2, 5],
'generator': generator,
'num_inference_steps': 1,
'guidance_scale': 6.0,
'output_type': 'numpy',
'max_iter_to_alter': 2,
'thresholds': {0: 0.7},
}
return inputs
def __magic_name__ ( self ) -> List[str]:
'''simple docstring'''
__a ='cpu'
__a =self.get_dummy_components()
__a =self.pipeline_class(**__snake_case )
pipe.to(__snake_case )
pipe.set_progress_bar_config(disable=__snake_case )
__a =self.get_dummy_inputs(__snake_case )
__a =pipe(**__snake_case ).images
__a =image[0, -3:, -3:, -1]
self.assertEqual(image.shape , (1, 64, 64, 3) )
__a =np.array(
[0.6390_5364, 0.6289_7307, 0.4859_9017, 0.513_3624, 0.555_0048, 0.4576_9516, 0.5032_6973, 0.502_3139, 0.4538_4496] )
__a =np.abs(image_slice.flatten() - expected_slice ).max()
self.assertLessEqual(__snake_case , 1e-3 )
def __magic_name__ ( self ) -> Optional[Any]:
'''simple docstring'''
super().test_cpu_offload_forward_pass(expected_max_diff=5e-4 )
def __magic_name__ ( self ) -> str:
'''simple docstring'''
# NOTE: Larger batch sizes cause this test to timeout, only test on smaller batches
self._test_inference_batch_consistent(batch_sizes=[1, 2] )
def __magic_name__ ( self ) -> Any:
'''simple docstring'''
self._test_inference_batch_single_identical(batch_size=2 , expected_max_diff=7e-4 )
def __magic_name__ ( self ) -> Any:
'''simple docstring'''
super().test_dict_tuple_outputs_equivalent(expected_max_difference=3e-3 )
def __magic_name__ ( self ) -> Any:
'''simple docstring'''
super().test_pt_np_pil_outputs_equivalent(expected_max_diff=5e-4 )
def __magic_name__ ( self ) -> str:
'''simple docstring'''
super().test_save_load_local(expected_max_difference=5e-4 )
def __magic_name__ ( self ) -> Any:
'''simple docstring'''
super().test_save_load_optional_components(expected_max_difference=4e-4 )
@require_torch_gpu
@slow
class __magic_name__ ( unittest.TestCase ):
@classmethod
def __magic_name__ ( cls ) -> Tuple:
'''simple docstring'''
super().setUpClass()
torch.use_deterministic_algorithms(__snake_case )
@classmethod
def __magic_name__ ( cls ) -> int:
'''simple docstring'''
super().tearDownClass()
torch.use_deterministic_algorithms(__snake_case )
def __magic_name__ ( self ) -> List[Any]:
'''simple docstring'''
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def __magic_name__ ( self ) -> Union[str, Any]:
'''simple docstring'''
__a =torch.manual_seed(51 )
__a =StableDiffusionAttendAndExcitePipeline.from_pretrained(
'CompVis/stable-diffusion-v1-4' , safety_checker=__snake_case , torch_dtype=torch.floataa )
pipe.to('cuda' )
__a ='a painting of an elephant with glasses'
__a =[5, 7]
__a =pipe(
prompt=__snake_case , token_indices=__snake_case , guidance_scale=7.5 , generator=__snake_case , num_inference_steps=5 , max_iter_to_alter=5 , output_type='numpy' , ).images[0]
__a =load_numpy(
'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/attend-and-excite/elephant_glasses.npy' )
assert np.abs((expected_image - image).max() ) < 5e-1
| 308
|
from __future__ import annotations
import matplotlib.pyplot as plt # type: ignore
import numpy
# initial triangle of Koch snowflake
_lowerCAmelCase : Optional[Any] = numpy.array([0, 0])
_lowerCAmelCase : Dict = numpy.array([0.5, 0.8660254])
_lowerCAmelCase : Any = numpy.array([1, 0])
_lowerCAmelCase : int = [VECTOR_1, VECTOR_2, VECTOR_3, VECTOR_1]
def UpperCamelCase_( _snake_case : list[numpy.ndarray] , _snake_case : int ):
"""simple docstring"""
__a =initial_vectors
for _ in range(_snake_case ):
__a =iteration_step(_snake_case )
return vectors
def UpperCamelCase_( _snake_case : list[numpy.ndarray] ):
"""simple docstring"""
__a =[]
for i, start_vector in enumerate(vectors[:-1] ):
__a =vectors[i + 1]
new_vectors.append(_snake_case )
__a =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 UpperCamelCase_( _snake_case : numpy.ndarray , _snake_case : float ):
"""simple docstring"""
__a =numpy.radians(_snake_case )
__a , __a =numpy.cos(_snake_case ), numpy.sin(_snake_case )
__a =numpy.array(((c, -s), (s, c)) )
return numpy.dot(_snake_case , _snake_case )
def UpperCamelCase_( _snake_case : list[numpy.ndarray] ):
"""simple docstring"""
__a =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 =zip(*_snake_case )
plt.plot(_snake_case , _snake_case )
plt.show()
if __name__ == "__main__":
import doctest
doctest.testmod()
_lowerCAmelCase : List[Any] = iterate(INITIAL_VECTORS, 5)
plot(processed_vectors)
| 308
| 1
|
def UpperCamelCase_( _snake_case : str ):
"""simple docstring"""
assert column_title.isupper()
__a =0
__a =len(_snake_case ) - 1
__a =0
while index >= 0:
__a =(ord(column_title[index] ) - 64) * pow(26 , _snake_case )
answer += value
power += 1
index -= 1
return answer
if __name__ == "__main__":
from doctest import testmod
testmod()
| 308
|
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_lowerCAmelCase : Tuple = {
"configuration_megatron_bert": ["MEGATRON_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "MegatronBertConfig"],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_lowerCAmelCase : List[str] = [
"MEGATRON_BERT_PRETRAINED_MODEL_ARCHIVE_LIST",
"MegatronBertForCausalLM",
"MegatronBertForMaskedLM",
"MegatronBertForMultipleChoice",
"MegatronBertForNextSentencePrediction",
"MegatronBertForPreTraining",
"MegatronBertForQuestionAnswering",
"MegatronBertForSequenceClassification",
"MegatronBertForTokenClassification",
"MegatronBertModel",
"MegatronBertPreTrainedModel",
]
if TYPE_CHECKING:
from .configuration_megatron_bert import MEGATRON_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP, MegatronBertConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_megatron_bert import (
MEGATRON_BERT_PRETRAINED_MODEL_ARCHIVE_LIST,
MegatronBertForCausalLM,
MegatronBertForMaskedLM,
MegatronBertForMultipleChoice,
MegatronBertForNextSentencePrediction,
MegatronBertForPreTraining,
MegatronBertForQuestionAnswering,
MegatronBertForSequenceClassification,
MegatronBertForTokenClassification,
MegatronBertModel,
MegatronBertPreTrainedModel,
)
else:
import sys
_lowerCAmelCase : Optional[int] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 308
| 1
|
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowerCAmelCase : Union[str, Any] = logging.get_logger(__name__)
_lowerCAmelCase : Any = {
"facebook/xglm-564M": "https://huggingface.co/facebook/xglm-564M/resolve/main/config.json",
# See all XGLM models at https://huggingface.co/models?filter=xglm
}
class __magic_name__ ( lowerCAmelCase_ ):
SCREAMING_SNAKE_CASE = 'xglm'
SCREAMING_SNAKE_CASE = ['past_key_values']
SCREAMING_SNAKE_CASE = {
'num_attention_heads': 'attention_heads',
'hidden_size': 'd_model',
'num_hidden_layers': 'num_layers',
}
def __init__( self , __snake_case=25_6008 , __snake_case=2048 , __snake_case=1024 , __snake_case=4096 , __snake_case=24 , __snake_case=16 , __snake_case="gelu" , __snake_case=0.1 , __snake_case=0.1 , __snake_case=0.0 , __snake_case=0.0 , __snake_case=0.02 , __snake_case=True , __snake_case=True , __snake_case=2 , __snake_case=1 , __snake_case=0 , __snake_case=2 , **__snake_case , ) -> List[Any]:
'''simple docstring'''
__a =vocab_size
__a =max_position_embeddings
__a =d_model
__a =ffn_dim
__a =num_layers
__a =attention_heads
__a =activation_function
__a =dropout
__a =attention_dropout
__a =activation_dropout
__a =layerdrop
__a =init_std
__a =scale_embedding # scale factor will be sqrt(d_model) if True
__a =use_cache
super().__init__(
pad_token_id=__snake_case , bos_token_id=__snake_case , eos_token_id=__snake_case , decoder_start_token_id=__snake_case , **__snake_case , )
| 308
|
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 __magic_name__ :
@staticmethod
def __magic_name__ ( *__snake_case , **__snake_case ) -> List[str]:
'''simple docstring'''
pass
def UpperCamelCase_( _snake_case : Image ):
"""simple docstring"""
__a =hashlib.mda(image.tobytes() )
return m.hexdigest()
@is_pipeline_test
@require_vision
@require_timm
@require_torch
class __magic_name__ ( unittest.TestCase ):
SCREAMING_SNAKE_CASE = MODEL_FOR_DEPTH_ESTIMATION_MAPPING
def __magic_name__ ( self , __snake_case , __snake_case , __snake_case ) -> Dict:
'''simple docstring'''
__a =DepthEstimationPipeline(model=__snake_case , image_processor=__snake_case )
return depth_estimator, [
"./tests/fixtures/tests_samples/COCO/000000039769.png",
"./tests/fixtures/tests_samples/COCO/000000039769.png",
]
def __magic_name__ ( self , __snake_case , __snake_case ) -> Tuple:
'''simple docstring'''
__a =depth_estimator('./tests/fixtures/tests_samples/COCO/000000039769.png' )
self.assertEqual({'predicted_depth': ANY(torch.Tensor ), 'depth': ANY(Image.Image )} , __snake_case )
import datasets
__a =datasets.load_dataset('hf-internal-testing/fixtures_image_utils' , 'image' , split='test' )
__a =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 )},
] , __snake_case , )
@require_tf
@unittest.skip('Depth estimation is not implemented in TF' )
def __magic_name__ ( self ) -> Optional[Any]:
'''simple docstring'''
pass
@slow
@require_torch
def __magic_name__ ( self ) -> int:
'''simple docstring'''
__a ='Intel/dpt-large'
__a =pipeline('depth-estimation' , model=__snake_case )
__a =depth_estimator('http://images.cocodataset.org/val2017/000000039769.jpg' )
__a =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 __magic_name__ ( self ) -> Any:
'''simple docstring'''
# This is highly irregular to have no small tests.
self.skipTest('There is not hf-internal-testing tiny model for either GLPN nor DPT' )
| 308
| 1
|
import argparse
from collections import OrderedDict
from pathlib import Path
import torch
from transformers import (
VisualBertConfig,
VisualBertForMultipleChoice,
VisualBertForPreTraining,
VisualBertForQuestionAnswering,
VisualBertForVisualReasoning,
)
from transformers.utils import logging
logging.set_verbosity_info()
_lowerCAmelCase : str = logging.get_logger(__name__)
_lowerCAmelCase : Union[str, Any] = [
("bert.bert", "visual_bert"),
("bert.cls", "cls"),
("bert.classifier", "cls"),
("token_type_embeddings_visual", "visual_token_type_embeddings"),
("position_embeddings_visual", "visual_position_embeddings"),
("projection", "visual_projection"),
]
_lowerCAmelCase : int = [
"nlvr2_coco_pre_trained.th",
"nlvr2_fine_tuned.th",
"nlvr2_pre_trained.th",
"vcr_coco_pre_train.th",
"vcr_fine_tune.th",
"vcr_pre_train.th",
"vqa_coco_pre_trained.th",
"vqa_fine_tuned.th",
"vqa_pre_trained.th",
]
def UpperCamelCase_( _snake_case : int ):
"""simple docstring"""
__a =torch.load(_snake_case , map_location='cpu' )
return sd
def UpperCamelCase_( _snake_case : Optional[int] , _snake_case : List[str] , _snake_case : int=rename_keys_prefix ):
"""simple docstring"""
__a =OrderedDict()
__a =torch.arange(config.max_position_embeddings ).expand((1, -1) )
# detector_d = OrderedDict()
for key in d:
if "detector" in key:
# detector_d[key.replace('detector.','')] = d[key]
continue
__a =key
for name_pair in rename_keys_prefix:
__a =new_key.replace(name_pair[0] , name_pair[1] )
__a =d[key]
if key == "bert.cls.predictions.decoder.weight":
# Old bert code didn't have `decoder.bias`, but was added separately
__a =new_d['cls.predictions.bias']
return new_d
@torch.no_grad()
def UpperCamelCase_( _snake_case : List[Any] , _snake_case : int ):
"""simple docstring"""
assert (
checkpoint_path.split('/' )[-1] in ACCEPTABLE_CHECKPOINTS
), F'The checkpoint provided must be in {ACCEPTABLE_CHECKPOINTS}.'
# Get Config
if "pre" in checkpoint_path:
__a ='pretraining'
if "vcr" in checkpoint_path:
__a ={'visual_embedding_dim': 512}
elif "vqa_advanced" in checkpoint_path:
__a ={'visual_embedding_dim': 2048}
elif "vqa" in checkpoint_path:
__a ={'visual_embedding_dim': 2048}
elif "nlvr" in checkpoint_path:
__a ={'visual_embedding_dim': 1024}
else:
raise NotImplementedError(F'No implementation found for `{checkpoint_path}`.' )
else:
if "vcr" in checkpoint_path:
__a ={'visual_embedding_dim': 512}
__a ='multichoice'
elif "vqa_advanced" in checkpoint_path:
__a ={'visual_embedding_dim': 2048}
__a ='vqa_advanced'
elif "vqa" in checkpoint_path:
__a ={'visual_embedding_dim': 2048, 'num_labels': 3129}
__a ='vqa'
elif "nlvr" in checkpoint_path:
__a ={
'visual_embedding_dim': 1024,
'num_labels': 2,
}
__a ='nlvr'
__a =VisualBertConfig(**_snake_case )
# Load State Dict
__a =load_state_dict(_snake_case )
__a =get_new_dict(_snake_case , _snake_case )
if model_type == "pretraining":
__a =VisualBertForPreTraining(_snake_case )
elif model_type == "vqa":
__a =VisualBertForQuestionAnswering(_snake_case )
elif model_type == "nlvr":
__a =VisualBertForVisualReasoning(_snake_case )
elif model_type == "multichoice":
__a =VisualBertForMultipleChoice(_snake_case )
model.load_state_dict(_snake_case )
# Save Checkpoints
Path(_snake_case ).mkdir(exist_ok=_snake_case )
model.save_pretrained(_snake_case )
if __name__ == "__main__":
_lowerCAmelCase : Tuple = argparse.ArgumentParser()
# Required parameters
parser.add_argument("orig_checkpoint_path", type=str, help="A path to .th on local filesystem.")
parser.add_argument("pytorch_dump_folder_path", type=str, help="Path to the output PyTorch model.")
_lowerCAmelCase : Any = parser.parse_args()
convert_visual_bert_checkpoint(args.orig_checkpoint_path, args.pytorch_dump_folder_path)
| 308
|
import argparse
import dataclasses
import json
import logging
import os
import shutil
from typing import List, Optional
import datasets
from accelerate import Accelerator
from datasets import load_dataset
from finetuning import finetune
from tqdm.auto import tqdm
import transformers
from transformers import AutoConfig, set_seed
from transformers.trainer_utils import IntervalStrategy
_lowerCAmelCase : Optional[int] = logging.getLogger(__name__)
_lowerCAmelCase : Any = "pytorch_model.bin"
@dataclasses.dataclass
class __magic_name__ :
SCREAMING_SNAKE_CASE = dataclasses.field(
metadata={'help': 'Path to pretrained model or model identifier from huggingface.co/models.'} )
SCREAMING_SNAKE_CASE = dataclasses.field(
default=lowerCAmelCase_ , metadata={'help': 'Where do you want to store the pretrained models downloaded from huggingface.co.'} , )
@dataclasses.dataclass
class __magic_name__ :
SCREAMING_SNAKE_CASE = dataclasses.field(metadata={'help': 'A csv or a json file containing the training data.'} )
SCREAMING_SNAKE_CASE = dataclasses.field(metadata={'help': 'A csv or a json file containing the data to predict on.'} )
SCREAMING_SNAKE_CASE = dataclasses.field(
default=lowerCAmelCase_ , metadata={'help': 'A csv or a json file containing the validation data.'} )
SCREAMING_SNAKE_CASE = dataclasses.field(
default=lowerCAmelCase_ , metadata={'help': 'The name of the task to train on.'} , )
SCREAMING_SNAKE_CASE = dataclasses.field(
default=lowerCAmelCase_ , metadata={'help': 'The list of labels for the task.'} )
@dataclasses.dataclass
class __magic_name__ :
SCREAMING_SNAKE_CASE = dataclasses.field(
metadata={'help': 'The output directory where the model predictions and checkpoints will be written.'} )
SCREAMING_SNAKE_CASE = dataclasses.field(
default='accuracy' , metadata={'help': 'The evaluation metric used for the task.'} )
SCREAMING_SNAKE_CASE = dataclasses.field(
default='no' , metadata={
'help': 'The evaluation strategy to adopt during training. Possible values are: ["no", "step", "epoch]'
} , )
SCREAMING_SNAKE_CASE = dataclasses.field(
default=1_0 , metadata={'help': 'Number of evaluation calls with no improvement after which training will be stopped.'} , )
SCREAMING_SNAKE_CASE = dataclasses.field(
default=0.0 , metadata={
'help': 'How much the specified evaluation metric must improve to satisfy early stopping conditions.'
} , )
SCREAMING_SNAKE_CASE = dataclasses.field(
default=lowerCAmelCase_ , metadata={'help': 'Whether to filter the pseudo-labeled data based on the confidence score.'} , )
SCREAMING_SNAKE_CASE = dataclasses.field(
default=lowerCAmelCase_ , metadata={'help': 'Whether to filter the pseudo-labeled data based on the validation performance.'} , )
SCREAMING_SNAKE_CASE = dataclasses.field(
default=lowerCAmelCase_ , metadata={'help': 'Whether to fine-tune on labeled data after pseudo training.'} , )
SCREAMING_SNAKE_CASE = dataclasses.field(
default=0.0 , metadata={'help': 'Confidence threshold for pseudo-labeled data filtering.'} , )
SCREAMING_SNAKE_CASE = dataclasses.field(
default=1_0_0 , metadata={'help': 'Number of evaluation calls with no improvement after which training will be stopped.'} , )
SCREAMING_SNAKE_CASE = dataclasses.field(
default=lowerCAmelCase_ , metadata={'help': 'Random seed for initialization.'} , )
def UpperCamelCase_( _snake_case : int , _snake_case : str , _snake_case : Optional[int] , _snake_case : str , _snake_case : Union[str, Any] , _snake_case : List[Any] ):
"""simple docstring"""
__a =datasets.concatenate_datasets([infer_input, infer_output] , axis=1 )
if args.do_filter_by_confidence:
__a =dataset.filter(lambda _snake_case : example["probability"] > args.confidence_threshold )
if args.do_filter_by_val_performance:
assert eval_result >= 0.0 and eval_result <= 1.0
__a =int(eval_result * len(_snake_case ) )
print(_snake_case )
__a =dataset.sort('probability' , reverse=_snake_case )
__a =dataset.select(range(_snake_case ) )
__a =dataset.remove_columns(['label', 'probability'] )
__a =dataset.rename_column('prediction' , 'label' )
__a =dataset.map(lambda _snake_case : {"label": idalabel[example["label"]]} )
__a =dataset.shuffle(seed=args.seed )
__a =os.path.join(_snake_case , F'train_pseudo.{args.data_file_extension}' )
if args.data_file_extension == "csv":
dataset.to_csv(_snake_case , index=_snake_case )
else:
dataset.to_json(_snake_case )
def UpperCamelCase_( _snake_case : List[Any] , _snake_case : str , _snake_case : int , _snake_case : Optional[int] , **_snake_case : List[str] ):
"""simple docstring"""
__a =Accelerator()
# Make one log on every process with the configuration for debugging.
logging.basicConfig(
format='%(asctime)s - %(levelname)s - %(name)s - %(message)s' , datefmt='%m/%d/%Y %H:%M:%S' , level=logging.INFO , )
logger.info(accelerator.state )
# Setup logging, we only want one process per machine to log things on the
# screen. accelerator.is_local_main_process is only True for one process per
# machine.
logger.setLevel(logging.INFO if accelerator.is_local_main_process else logging.ERROR )
if accelerator.is_local_main_process:
datasets.utils.logging.set_verbosity_warning()
transformers.utils.logging.set_verbosity_info()
else:
datasets.utils.logging.set_verbosity_error()
transformers.utils.logging.set_verbosity_error()
__a =STModelArguments(model_name_or_path=_snake_case )
__a =STDataArguments(train_file=_snake_case , infer_file=_snake_case )
__a =STTrainingArguments(output_dir=_snake_case )
__a =argparse.Namespace()
for arg_class in (model_args, data_args, training_args):
for key, value in vars(_snake_case ).items():
setattr(_snake_case , _snake_case , _snake_case )
for key, value in kwargs.items():
if hasattr(_snake_case , _snake_case ):
setattr(_snake_case , _snake_case , _snake_case )
# Sanity checks
__a ={}
__a =None
# You need to provide the training data and the data to predict on
assert args.train_file is not None
assert args.infer_file is not None
__a =args.train_file
__a =args.infer_file
if args.evaluation_strategy != IntervalStrategy.NO.value:
assert args.eval_file is not None
__a =args.eval_file
for key in data_files:
__a =data_files[key].split('.' )[-1]
assert extension in ["csv", "json"], F'`{key}_file` should be a csv or a json file.'
if args.data_file_extension is None:
__a =extension
else:
assert extension == args.data_file_extension, F'`{key}_file` should be a {args.data_file_extension} file`.'
assert (
args.eval_metric in datasets.list_metrics()
), F'{args.eval_metric} not in the list of supported metrics {datasets.list_metrics()}.'
# If passed along, set the training seed now.
if args.seed is not None:
set_seed(args.seed )
logger.info('Creating the initial data directory for self-training...' )
__a =F'{args.output_dir}/self-train_iter-{{}}'.format
__a =data_dir_format(0 )
if accelerator.is_main_process:
if args.output_dir is not None:
os.makedirs(args.output_dir , exist_ok=_snake_case )
os.makedirs(_snake_case , exist_ok=_snake_case )
accelerator.wait_for_everyone()
__a =None
__a =None
__a =0
__a =False
# Show the progress bar
__a =tqdm(range(args.max_selftrain_iterations ) , disable=not accelerator.is_local_main_process )
# Self-train
for iteration in range(0 , int(args.max_selftrain_iterations ) ):
__a =data_dir_format(_snake_case )
assert os.path.exists(_snake_case )
# Stage 1: initial fine-tuning for iteration = 0 or pseudo-training for
# iteration > 0
__a =os.path.join(_snake_case , 'stage-1' )
__a ={
'accelerator': accelerator,
'model_name_or_path': args.model_name_or_path,
'cache_dir': args.cache_dir,
'do_train': True,
'train_file': data_files['train'] if iteration == 0 else data_files['train_pseudo'],
'do_eval': True if args.eval_file is not None else False,
'eval_file': data_files['eval'],
'do_predict': True,
'infer_file': data_files['infer'],
'task_name': args.task_name,
'label_list': args.label_list,
'output_dir': current_output_dir,
'eval_metric': args.eval_metric,
'evaluation_strategy': args.evaluation_strategy,
'early_stopping_patience': args.early_stopping_patience,
'early_stopping_threshold': args.early_stopping_threshold,
'seed': args.seed,
}
# Add additional training arguments
for key, value in kwargs.items():
if key not in arguments_dict and not hasattr(_snake_case , _snake_case ):
arguments_dict.update({key: value} )
__a =os.path.join(_snake_case , 'best-checkpoint' , _snake_case )
if os.path.exists(_snake_case ):
logger.info(
'Found existing model checkpoint at %s. Skipping self-training: iteration: %d, stage: 1.' , _snake_case , _snake_case , )
else:
logger.info('***** Running self-training: iteration: %d, stage: 1 *****' , _snake_case )
finetune(**_snake_case )
accelerator.wait_for_everyone()
assert os.path.exists(_snake_case )
logger.info('Self-training job completed: iteration: %d, stage: 1.' , _snake_case )
if iteration > 0 and args.finetune_on_labeled_data:
# Stage 2 (optional): fine-tuning on the original labeled data
__a =os.path.join(_snake_case , 'best-checkpoint' )
__a =os.path.join(_snake_case , 'stage-2' )
# Update arguments_dict
__a =model_path
__a =data_files['train']
__a =current_output_dir
__a =os.path.join(_snake_case , 'best-checkpoint' , _snake_case )
if os.path.exists(_snake_case ):
logger.info(
'Found existing model checkpoint at %s. Skipping self-training: iteration: %d, stage: 2.' , _snake_case , _snake_case , )
else:
logger.info('***** Running self-training: iteration: %d, stage: 2 *****' , _snake_case )
finetune(**_snake_case )
accelerator.wait_for_everyone()
assert os.path.exists(_snake_case )
logger.info('Self-training job completed: iteration: %d, stage: 2.' , _snake_case )
__a =iteration
__a =data_dir_format(iteration + 1 )
__a =AutoConfig.from_pretrained(os.path.join(_snake_case , 'best-checkpoint' ) )
__a =config.idalabel
__a =os.path.join(_snake_case , 'eval_results_best-checkpoint.json' )
__a =os.path.join(_snake_case , 'test_results_best-checkpoint.json' )
assert os.path.exists(_snake_case )
with open(_snake_case , 'r' ) as f:
__a =float(json.load(_snake_case )[args.eval_metric] )
__a =os.path.join(_snake_case , 'infer_output_best-checkpoint.csv' )
assert os.path.exists(_snake_case )
# Loading the dataset from local csv or json files.
__a =load_dataset(args.data_file_extension , data_files={'data': data_files['infer']} )['data']
__a =load_dataset('csv' , data_files={'data': infer_output_file} )['data']
if accelerator.is_main_process:
os.makedirs(_snake_case , exist_ok=_snake_case )
shutil.copy(_snake_case , os.path.join(_snake_case , F'eval_results_iter-{iteration}.json' ) )
if os.path.exists(_snake_case ):
shutil.copy(_snake_case , os.path.join(_snake_case , F'test_results_iter-{iteration}.json' ) )
create_pseudo_labeled_data(_snake_case , _snake_case , _snake_case , _snake_case , _snake_case , _snake_case )
accelerator.wait_for_everyone()
__a =os.path.join(_snake_case , F'train_pseudo.{args.data_file_extension}' )
if args.evaluation_strategy != IntervalStrategy.NO.value:
__a =eval_result
if best_iteration is None:
__a =new_iteration
__a =new_eval_result
else:
if new_eval_result - best_eval_result > args.early_stopping_threshold:
__a =new_iteration
__a =new_eval_result
__a =0
else:
if new_eval_result == best_eval_result:
__a =new_iteration
__a =new_eval_result
early_stopping_patience_counter += 1
if early_stopping_patience_counter >= args.early_stopping_patience:
__a =True
progress_bar.update(1 )
if should_training_stop:
break
if best_iteration is not None:
# Save the best iteration
logger.info('Best iteration: %d' , _snake_case )
logger.info('Best evaluation result: %s = %f' , args.eval_metric , _snake_case )
accelerator.wait_for_everyone()
if accelerator.is_main_process:
shutil.copy(
os.path.join(_snake_case , F'eval_results_iter-{iteration}.json' ) , os.path.join(_snake_case , 'eval_results_best-iteration.json' ) , )
else:
# Assume that the last iteration is the best
logger.info('Best iteration: %d' , args.max_selftrain_iterations - 1 )
logger.info('Best evaluation result: %s = %f' , args.eval_metric , _snake_case )
accelerator.wait_for_everyone()
if accelerator.is_main_process:
shutil.copy(
os.path.join(_snake_case , F'eval_results_iter-{args.max_selftrain_iterations - 1}.json' ) , os.path.join(_snake_case , 'eval_results_best-iteration.json' ) , )
| 308
| 1
|
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
_lowerCAmelCase : Union[str, Any] = logging.get_logger(__name__)
class __magic_name__ ( lowerCAmelCase_ ):
SCREAMING_SNAKE_CASE = ['input_values', 'padding_mask']
def __init__( self , __snake_case = 1 , __snake_case = 2_4000 , __snake_case = 0.0 , __snake_case = None , __snake_case = None , **__snake_case , ) -> Optional[int]:
'''simple docstring'''
super().__init__(feature_size=__snake_case , sampling_rate=__snake_case , padding_value=__snake_case , **__snake_case )
__a =chunk_length_s
__a =overlap
@property
def __magic_name__ ( self ) -> Optional[int]:
'''simple docstring'''
if self.chunk_length_s is None:
return None
else:
return int(self.chunk_length_s * self.sampling_rate )
@property
def __magic_name__ ( self ) -> Optional[int]:
'''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 , __snake_case , __snake_case = None , __snake_case = False , __snake_case = None , __snake_case = None , __snake_case = None , ) -> BatchFeature:
'''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
__a =True
__a =bool(
isinstance(__snake_case , (list, tuple) ) and (isinstance(raw_audio[0] , (np.ndarray, tuple, list) )) )
if is_batched:
__a =[np.asarray(__snake_case , dtype=np.floataa ).T for audio in raw_audio]
elif not is_batched and not isinstance(__snake_case , np.ndarray ):
__a =np.asarray(__snake_case , dtype=np.floataa )
elif isinstance(__snake_case , np.ndarray ) and raw_audio.dtype is np.dtype(np.floataa ):
__a =raw_audio.astype(np.floataa )
# always return batch
if not is_batched:
__a =[np.asarray(__snake_case ).T]
# verify inputs are valid
for idx, example in enumerate(__snake_case ):
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' )
__a =None
__a =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:
__a =min(array.shape[0] for array in raw_audio )
__a =int(np.floor(max_length / self.chunk_stride ) )
__a =(nb_step - 1) * self.chunk_stride + self.chunk_length
elif padding:
__a =max(array.shape[0] for array in raw_audio )
__a =int(np.ceil(max_length / self.chunk_stride ) )
__a =(nb_step - 1) * self.chunk_stride + self.chunk_length
__a ='max_length'
else:
__a =input_values
# normal padding on batch
if padded_inputs is None:
__a =self.pad(
__snake_case , max_length=__snake_case , truncation=__snake_case , padding=__snake_case , return_attention_mask=__snake_case , )
if padding:
__a =padded_inputs.pop('attention_mask' )
__a =[]
for example in padded_inputs.pop('input_values' ):
if self.feature_size == 1:
__a =example[..., None]
input_values.append(example.T )
__a =input_values
if return_tensors is not None:
__a =padded_inputs.convert_to_tensors(__snake_case )
return padded_inputs
| 308
|
import shutil
import tempfile
import unittest
from transformers import (
SPIECE_UNDERLINE,
AddedToken,
BatchEncoding,
NllbTokenizer,
NllbTokenizerFast,
is_torch_available,
)
from transformers.testing_utils import (
get_tests_dir,
nested_simplify,
require_sentencepiece,
require_tokenizers,
require_torch,
)
from ...test_tokenization_common import TokenizerTesterMixin
_lowerCAmelCase : Any = get_tests_dir("fixtures/test_sentencepiece.model")
if is_torch_available():
from transformers.models.mam_aaa.modeling_mam_aaa import shift_tokens_right
_lowerCAmelCase : List[Any] = 256_047
_lowerCAmelCase : Dict = 256_145
@require_sentencepiece
@require_tokenizers
class __magic_name__ ( lowerCAmelCase_ , unittest.TestCase ):
SCREAMING_SNAKE_CASE = NllbTokenizer
SCREAMING_SNAKE_CASE = NllbTokenizerFast
SCREAMING_SNAKE_CASE = True
SCREAMING_SNAKE_CASE = True
SCREAMING_SNAKE_CASE = {}
def __magic_name__ ( self ) -> int:
'''simple docstring'''
super().setUp()
# We have a SentencePiece fixture for testing
__a =NllbTokenizer(__snake_case , keep_accents=__snake_case )
tokenizer.save_pretrained(self.tmpdirname )
def __magic_name__ ( self ) -> int:
'''simple docstring'''
__a =NllbTokenizer(__snake_case , keep_accents=__snake_case )
__a =tokenizer.tokenize('This is a test' )
self.assertListEqual(__snake_case , ['▁This', '▁is', '▁a', '▁t', 'est'] )
self.assertListEqual(
tokenizer.convert_tokens_to_ids(__snake_case ) , [value + tokenizer.fairseq_offset for value in [285, 46, 10, 170, 382]] , )
__a =tokenizer.tokenize('I was born in 92000, and this is falsé.' )
self.assertListEqual(
__snake_case , [
SPIECE_UNDERLINE + 'I',
SPIECE_UNDERLINE + 'was',
SPIECE_UNDERLINE + 'b',
'or',
'n',
SPIECE_UNDERLINE + 'in',
SPIECE_UNDERLINE + '',
'9',
'2',
'0',
'0',
'0',
',',
SPIECE_UNDERLINE + 'and',
SPIECE_UNDERLINE + 'this',
SPIECE_UNDERLINE + 'is',
SPIECE_UNDERLINE + 'f',
'al',
's',
'é',
'.',
] , )
__a =tokenizer.convert_tokens_to_ids(__snake_case )
self.assertListEqual(
__snake_case , [
value + tokenizer.fairseq_offset
for value in [8, 21, 84, 55, 24, 19, 7, 2, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 2, 4]
] , )
__a =tokenizer.convert_ids_to_tokens(__snake_case )
self.assertListEqual(
__snake_case , [
SPIECE_UNDERLINE + 'I',
SPIECE_UNDERLINE + 'was',
SPIECE_UNDERLINE + 'b',
'or',
'n',
SPIECE_UNDERLINE + 'in',
SPIECE_UNDERLINE + '',
'<unk>',
'2',
'0',
'0',
'0',
',',
SPIECE_UNDERLINE + 'and',
SPIECE_UNDERLINE + 'this',
SPIECE_UNDERLINE + 'is',
SPIECE_UNDERLINE + 'f',
'al',
's',
'<unk>',
'.',
] , )
def __magic_name__ ( self ) -> Union[str, Any]:
'''simple docstring'''
__a =(self.rust_tokenizer_class, 'hf-internal-testing/tiny-random-nllb', {})
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(f'{tokenizer.__class__.__name__} ({pretrained_name})' ):
__a =self.rust_tokenizer_class.from_pretrained(__snake_case , **__snake_case )
__a =self.tokenizer_class.from_pretrained(__snake_case , **__snake_case )
__a =tempfile.mkdtemp()
__a =tokenizer_r.save_pretrained(__snake_case )
__a =tokenizer_p.save_pretrained(__snake_case )
# Checks it save with the same files + the tokenizer.json file for the fast one
self.assertTrue(any('tokenizer.json' in f for f in tokenizer_r_files ) )
__a =tuple(f for f in tokenizer_r_files if 'tokenizer.json' not in f )
self.assertSequenceEqual(__snake_case , __snake_case )
# Checks everything loads correctly in the same way
__a =tokenizer_r.from_pretrained(__snake_case )
__a =tokenizer_p.from_pretrained(__snake_case )
# Check special tokens are set accordingly on Rust and Python
for key in tokenizer_pp.special_tokens_map:
self.assertTrue(hasattr(__snake_case , __snake_case ) )
shutil.rmtree(__snake_case )
# Save tokenizer rust, legacy_format=True
__a =tempfile.mkdtemp()
__a =tokenizer_r.save_pretrained(__snake_case , legacy_format=__snake_case )
__a =tokenizer_p.save_pretrained(__snake_case )
# Checks it save with the same files
self.assertSequenceEqual(__snake_case , __snake_case )
# Checks everything loads correctly in the same way
__a =tokenizer_r.from_pretrained(__snake_case )
__a =tokenizer_p.from_pretrained(__snake_case )
# Check special tokens are set accordingly on Rust and Python
for key in tokenizer_pp.special_tokens_map:
self.assertTrue(hasattr(__snake_case , __snake_case ) )
shutil.rmtree(__snake_case )
# Save tokenizer rust, legacy_format=False
__a =tempfile.mkdtemp()
__a =tokenizer_r.save_pretrained(__snake_case , legacy_format=__snake_case )
__a =tokenizer_p.save_pretrained(__snake_case )
# Checks it saved the tokenizer.json file
self.assertTrue(any('tokenizer.json' in f for f in tokenizer_r_files ) )
# Checks everything loads correctly in the same way
__a =tokenizer_r.from_pretrained(__snake_case )
__a =tokenizer_p.from_pretrained(__snake_case )
# Check special tokens are set accordingly on Rust and Python
for key in tokenizer_pp.special_tokens_map:
self.assertTrue(hasattr(__snake_case , __snake_case ) )
shutil.rmtree(__snake_case )
@require_torch
def __magic_name__ ( self ) -> Optional[Any]:
'''simple docstring'''
if not self.test_seqaseq:
return
__a =self.get_tokenizers()
for tokenizer in tokenizers:
with self.subTest(f'{tokenizer.__class__.__name__}' ):
# Longer text that will definitely require truncation.
__a =[
' UN Chief Says There Is No Military Solution in Syria',
' Secretary-General Ban Ki-moon says his response to Russia\'s stepped up military support for'
' Syria is that \'there is no military solution\' to the nearly five-year conflict and more weapons'
' will only worsen the violence and misery for millions of people.',
]
__a =[
'Şeful ONU declară că nu există o soluţie militară în Siria',
'Secretarul General Ban Ki-moon declară că răspunsul său la intensificarea sprijinului militar al'
' Rusiei pentru Siria este că "nu există o soluţie militară" la conflictul de aproape cinci ani şi'
' că noi arme nu vor face decât să înrăutăţească violenţele şi mizeria pentru milioane de oameni.',
]
try:
__a =tokenizer.prepare_seqaseq_batch(
src_texts=__snake_case , tgt_texts=__snake_case , max_length=3 , max_target_length=10 , return_tensors='pt' , src_lang='eng_Latn' , tgt_lang='ron_Latn' , )
except NotImplementedError:
return
self.assertEqual(batch.input_ids.shape[1] , 3 )
self.assertEqual(batch.labels.shape[1] , 10 )
# max_target_length will default to max_length if not specified
__a =tokenizer.prepare_seqaseq_batch(
__snake_case , tgt_texts=__snake_case , max_length=3 , return_tensors='pt' )
self.assertEqual(batch.input_ids.shape[1] , 3 )
self.assertEqual(batch.labels.shape[1] , 3 )
__a =tokenizer.prepare_seqaseq_batch(
src_texts=__snake_case , max_length=3 , max_target_length=10 , return_tensors='pt' )
self.assertEqual(batch_encoder_only.input_ids.shape[1] , 3 )
self.assertEqual(batch_encoder_only.attention_mask.shape[1] , 3 )
self.assertNotIn('decoder_input_ids' , __snake_case )
@unittest.skip('Unfortunately way too slow to build a BPE with SentencePiece.' )
def __magic_name__ ( self ) -> Optional[int]:
'''simple docstring'''
pass
def __magic_name__ ( self ) -> Tuple:
'''simple docstring'''
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(f'{tokenizer.__class__.__name__} ({pretrained_name})' ):
__a =[AddedToken('<special>' , lstrip=__snake_case )]
__a =self.rust_tokenizer_class.from_pretrained(
__snake_case , additional_special_tokens=__snake_case , **__snake_case )
__a =tokenizer_r.encode('Hey this is a <special> token' )
__a =tokenizer_r.encode('<special>' , add_special_tokens=__snake_case )[0]
self.assertTrue(special_token_id in r_output )
if self.test_slow_tokenizer:
__a =self.rust_tokenizer_class.from_pretrained(
__snake_case , additional_special_tokens=__snake_case , **__snake_case , )
__a =self.tokenizer_class.from_pretrained(
__snake_case , additional_special_tokens=__snake_case , **__snake_case )
__a =tokenizer_p.encode('Hey this is a <special> token' )
__a =tokenizer_cr.encode('Hey this is a <special> token' )
self.assertEqual(__snake_case , __snake_case )
self.assertEqual(__snake_case , __snake_case )
self.assertTrue(special_token_id in p_output )
self.assertTrue(special_token_id in cr_output )
@require_torch
@require_sentencepiece
@require_tokenizers
class __magic_name__ ( unittest.TestCase ):
SCREAMING_SNAKE_CASE = 'facebook/nllb-200-distilled-600M'
SCREAMING_SNAKE_CASE = [
' UN Chief Says There Is No Military Solution in Syria',
' Secretary-General Ban Ki-moon says his response to Russia\'s stepped up military support for Syria is that "there is no military solution" to the nearly five-year conflict and more weapons will only worsen the violence and misery for millions of people.',
]
SCREAMING_SNAKE_CASE = [
'Şeful ONU declară că nu există o soluţie militară în Siria',
'Secretarul General Ban Ki-moon declară că răspunsul său la intensificarea sprijinului militar al Rusiei'
' pentru Siria este că "nu există o soluţie militară" la conflictul de aproape cinci ani şi că noi arme nu vor'
' face decât să înrăutăţească violenţele şi mizeria pentru milioane de oameni.',
]
SCREAMING_SNAKE_CASE = [
2_5_6_0_4_7,
1_6_2_9_7,
1_3_4_4_0_8,
8_1_6_5,
2_4_8_0_6_6,
1_4_7_3_4,
9_5_0,
1_1_3_5,
1_0_5_7_2_1,
3_5_7_3,
8_3,
2_7_3_5_2,
1_0_8,
4_9_4_8_6,
2,
]
@classmethod
def __magic_name__ ( cls ) -> Tuple:
'''simple docstring'''
__a =NllbTokenizer.from_pretrained(
cls.checkpoint_name , src_lang='eng_Latn' , tgt_lang='ron_Latn' )
__a =1
return cls
def __magic_name__ ( self ) -> List[Any]:
'''simple docstring'''
self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['ace_Arab'] , 25_6001 )
self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['ace_Latn'] , 25_6002 )
self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['fra_Latn'] , 25_6057 )
def __magic_name__ ( self ) -> Dict:
'''simple docstring'''
__a =self.tokenizer.batch_encode_plus(self.src_text ).input_ids[0]
self.assertListEqual(self.expected_src_tokens , __snake_case )
def __magic_name__ ( self ) -> Dict:
'''simple docstring'''
self.assertIn(__snake_case , self.tokenizer.all_special_ids )
# fmt: off
__a =[RO_CODE, 4254, 9_8068, 11_2923, 3_9072, 3909, 713, 10_2767, 26, 1_7314, 3_5642, 1_4683, 3_3118, 2022, 6_6987, 2, 25_6047]
# fmt: on
__a =self.tokenizer.decode(__snake_case , skip_special_tokens=__snake_case )
__a =self.tokenizer.decode(generated_ids[1:] , skip_special_tokens=__snake_case )
self.assertEqual(__snake_case , __snake_case )
self.assertNotIn(self.tokenizer.eos_token , __snake_case )
def __magic_name__ ( self ) -> List[str]:
'''simple docstring'''
__a =['this is gunna be a long sentence ' * 20]
assert isinstance(src_text[0] , __snake_case )
__a =10
__a =self.tokenizer(__snake_case , max_length=__snake_case , truncation=__snake_case ).input_ids[0]
self.assertEqual(ids[-1] , 2 )
self.assertEqual(ids[0] , __snake_case )
self.assertEqual(len(__snake_case ) , __snake_case )
def __magic_name__ ( self ) -> Optional[int]:
'''simple docstring'''
self.assertListEqual(self.tokenizer.convert_tokens_to_ids(['<mask>', 'ar_AR'] ) , [25_6203, 3] )
def __magic_name__ ( self ) -> Optional[int]:
'''simple docstring'''
__a =tempfile.mkdtemp()
__a =self.tokenizer.fairseq_tokens_to_ids
self.tokenizer.save_pretrained(__snake_case )
__a =NllbTokenizer.from_pretrained(__snake_case )
self.assertDictEqual(new_tok.fairseq_tokens_to_ids , __snake_case )
@require_torch
def __magic_name__ ( self ) -> Tuple:
'''simple docstring'''
__a =self.tokenizer(
self.src_text , text_target=self.tgt_text , padding=__snake_case , truncation=__snake_case , max_length=len(self.expected_src_tokens ) , return_tensors='pt' , )
__a =shift_tokens_right(
batch['labels'] , self.tokenizer.pad_token_id , self.tokenizer.lang_code_to_id['ron_Latn'] )
self.assertIsInstance(__snake_case , __snake_case )
self.assertEqual((2, 15) , batch.input_ids.shape )
self.assertEqual((2, 15) , batch.attention_mask.shape )
__a =batch.input_ids.tolist()[0]
self.assertListEqual(self.expected_src_tokens , __snake_case )
self.assertEqual(__snake_case , batch.decoder_input_ids[0, 0] ) # EOS
# Test that special tokens are reset
self.assertEqual(self.tokenizer.prefix_tokens , [EN_CODE] )
self.assertEqual(self.tokenizer.suffix_tokens , [self.tokenizer.eos_token_id] )
def __magic_name__ ( self ) -> List[Any]:
'''simple docstring'''
__a =self.tokenizer(self.src_text , padding=__snake_case , truncation=__snake_case , max_length=3 , return_tensors='pt' )
__a =self.tokenizer(
text_target=self.tgt_text , padding=__snake_case , truncation=__snake_case , max_length=10 , return_tensors='pt' )
__a =targets['input_ids']
__a =shift_tokens_right(
__snake_case , self.tokenizer.pad_token_id , decoder_start_token_id=self.tokenizer.lang_code_to_id[self.tokenizer.tgt_lang] , )
self.assertEqual(batch.input_ids.shape[1] , 3 )
self.assertEqual(batch.decoder_input_ids.shape[1] , 10 )
@require_torch
def __magic_name__ ( self ) -> Optional[Any]:
'''simple docstring'''
__a =self.tokenizer._build_translation_inputs(
'A test' , return_tensors='pt' , src_lang='eng_Latn' , tgt_lang='fra_Latn' )
self.assertEqual(
nested_simplify(__snake_case ) , {
# A, test, EOS, en_XX
'input_ids': [[25_6047, 70, 7356, 2]],
'attention_mask': [[1, 1, 1, 1]],
# ar_AR
'forced_bos_token_id': 25_6057,
} , )
@require_torch
def __magic_name__ ( self ) -> List[Any]:
'''simple docstring'''
__a =True
__a =self.tokenizer(
'UN Chief says there is no military solution in Syria' , src_lang='eng_Latn' , tgt_lang='fra_Latn' )
self.assertEqual(
inputs.input_ids , [1_6297, 13_4408, 2_5653, 6370, 248, 254, 10_3929, 9_4995, 108, 4_9486, 2, 25_6047] )
__a =False
__a =self.tokenizer(
'UN Chief says there is no military solution in Syria' , src_lang='eng_Latn' , tgt_lang='fra_Latn' )
self.assertEqual(
inputs.input_ids , [25_6047, 1_6297, 13_4408, 2_5653, 6370, 248, 254, 10_3929, 9_4995, 108, 4_9486, 2] )
| 308
| 1
|
import os
import re
import sys
import traceback
import warnings
from pathlib import Path
from typing import Dict, Optional, Union
from uuid import uuida
from huggingface_hub import HfFolder, ModelCard, ModelCardData, hf_hub_download, whoami
from huggingface_hub.file_download import REGEX_COMMIT_HASH
from huggingface_hub.utils import (
EntryNotFoundError,
RepositoryNotFoundError,
RevisionNotFoundError,
is_jinja_available,
)
from packaging import version
from requests import HTTPError
from .. import __version__
from .constants import (
DEPRECATED_REVISION_ARGS,
DIFFUSERS_CACHE,
HUGGINGFACE_CO_RESOLVE_ENDPOINT,
SAFETENSORS_WEIGHTS_NAME,
WEIGHTS_NAME,
)
from .import_utils import (
ENV_VARS_TRUE_VALUES,
_flax_version,
_jax_version,
_onnxruntime_version,
_torch_version,
is_flax_available,
is_onnx_available,
is_torch_available,
)
from .logging import get_logger
_lowerCAmelCase : Union[str, Any] = get_logger(__name__)
_lowerCAmelCase : Dict = Path(__file__).parent / "model_card_template.md"
_lowerCAmelCase : Optional[Any] = uuida().hex
_lowerCAmelCase : Any = os.getenv("HF_HUB_OFFLINE", "").upper() in ENV_VARS_TRUE_VALUES
_lowerCAmelCase : Dict = os.getenv("DISABLE_TELEMETRY", "").upper() in ENV_VARS_TRUE_VALUES
_lowerCAmelCase : List[Any] = HUGGINGFACE_CO_RESOLVE_ENDPOINT + "/api/telemetry/"
def UpperCamelCase_( _snake_case : Union[Dict, str, None] = None ):
"""simple docstring"""
__a =F'diffusers/{__version__}; python/{sys.version.split()[0]}; session_id/{SESSION_ID}'
if DISABLE_TELEMETRY or HF_HUB_OFFLINE:
return ua + "; telemetry/off"
if is_torch_available():
ua += F'; torch/{_torch_version}'
if is_flax_available():
ua += F'; jax/{_jax_version}'
ua += F'; flax/{_flax_version}'
if is_onnx_available():
ua += F'; onnxruntime/{_onnxruntime_version}'
# CI will set this value to True
if os.environ.get('DIFFUSERS_IS_CI' , '' ).upper() in ENV_VARS_TRUE_VALUES:
ua += "; is_ci/true"
if isinstance(_snake_case , _snake_case ):
ua += "; " + "; ".join(F'{k}/{v}' for k, v in user_agent.items() )
elif isinstance(_snake_case , _snake_case ):
ua += "; " + user_agent
return ua
def UpperCamelCase_( _snake_case : str , _snake_case : Optional[str] = None , _snake_case : Optional[str] = None ):
"""simple docstring"""
if token is None:
__a =HfFolder.get_token()
if organization is None:
__a =whoami(_snake_case )['name']
return F'{username}/{model_id}'
else:
return F'{organization}/{model_id}'
def UpperCamelCase_( _snake_case : Optional[int] , _snake_case : Union[str, Any] ):
"""simple docstring"""
if not is_jinja_available():
raise ValueError(
'Modelcard rendering is based on Jinja templates.'
' Please make sure to have `jinja` installed before using `create_model_card`.'
' To install it, please run `pip install Jinja2`.' )
if hasattr(_snake_case , 'local_rank' ) and args.local_rank not in [-1, 0]:
return
__a =args.hub_token if hasattr(_snake_case , 'hub_token' ) else None
__a =get_full_repo_name(_snake_case , token=_snake_case )
__a =ModelCard.from_template(
card_data=ModelCardData( # Card metadata object that will be converted to YAML block
language='en' , license='apache-2.0' , library_name='diffusers' , tags=[] , datasets=args.dataset_name , metrics=[] , ) , template_path=_snake_case , model_name=_snake_case , repo_name=_snake_case , dataset_name=args.dataset_name if hasattr(_snake_case , 'dataset_name' ) else None , learning_rate=args.learning_rate , train_batch_size=args.train_batch_size , eval_batch_size=args.eval_batch_size , gradient_accumulation_steps=(
args.gradient_accumulation_steps if hasattr(_snake_case , 'gradient_accumulation_steps' ) else None
) , adam_betaa=args.adam_betaa if hasattr(_snake_case , 'adam_beta1' ) else None , adam_betaa=args.adam_betaa if hasattr(_snake_case , 'adam_beta2' ) else None , adam_weight_decay=args.adam_weight_decay if hasattr(_snake_case , 'adam_weight_decay' ) else None , adam_epsilon=args.adam_epsilon if hasattr(_snake_case , 'adam_epsilon' ) else None , lr_scheduler=args.lr_scheduler if hasattr(_snake_case , 'lr_scheduler' ) else None , lr_warmup_steps=args.lr_warmup_steps if hasattr(_snake_case , 'lr_warmup_steps' ) else None , ema_inv_gamma=args.ema_inv_gamma if hasattr(_snake_case , 'ema_inv_gamma' ) else None , ema_power=args.ema_power if hasattr(_snake_case , 'ema_power' ) else None , ema_max_decay=args.ema_max_decay if hasattr(_snake_case , 'ema_max_decay' ) else None , mixed_precision=args.mixed_precision , )
__a =os.path.join(args.output_dir , 'README.md' )
model_card.save(_snake_case )
def UpperCamelCase_( _snake_case : Optional[str] , _snake_case : Optional[str] = None ):
"""simple docstring"""
if resolved_file is None or commit_hash is not None:
return commit_hash
__a =str(Path(_snake_case ).as_posix() )
__a =re.search(r'snapshots/([^/]+)/' , _snake_case )
if search is None:
return None
__a =search.groups()[0]
return commit_hash if REGEX_COMMIT_HASH.match(_snake_case ) else None
# Old default cache path, potentially to be migrated.
# This logic was more or less taken from `transformers`, with the following differences:
# - Diffusers doesn't use custom environment variables to specify the cache path.
# - There is no need to migrate the cache format, just move the files to the new location.
_lowerCAmelCase : List[str] = os.path.expanduser(
os.getenv("HF_HOME", os.path.join(os.getenv("XDG_CACHE_HOME", "~/.cache"), "huggingface"))
)
_lowerCAmelCase : Any = os.path.join(hf_cache_home, "diffusers")
def UpperCamelCase_( _snake_case : Optional[str] = None , _snake_case : Optional[str] = None ):
"""simple docstring"""
if new_cache_dir is None:
__a =DIFFUSERS_CACHE
if old_cache_dir is None:
__a =old_diffusers_cache
__a =Path(_snake_case ).expanduser()
__a =Path(_snake_case ).expanduser()
for old_blob_path in old_cache_dir.glob('**/blobs/*' ):
if old_blob_path.is_file() and not old_blob_path.is_symlink():
__a =new_cache_dir / old_blob_path.relative_to(_snake_case )
new_blob_path.parent.mkdir(parents=_snake_case , exist_ok=_snake_case )
os.replace(_snake_case , _snake_case )
try:
os.symlink(_snake_case , _snake_case )
except OSError:
logger.warning(
'Could not create symlink between old cache and new cache. If you use an older version of diffusers again, files will be re-downloaded.' )
# At this point, old_cache_dir contains symlinks to the new cache (it can still be used).
_lowerCAmelCase : List[str] = os.path.join(DIFFUSERS_CACHE, "version_diffusers_cache.txt")
if not os.path.isfile(cache_version_file):
_lowerCAmelCase : Optional[Any] = 0
else:
with open(cache_version_file) as f:
try:
_lowerCAmelCase : str = int(f.read())
except ValueError:
_lowerCAmelCase : Optional[int] = 0
if cache_version < 1:
_lowerCAmelCase : Any = os.path.isdir(old_diffusers_cache) and len(os.listdir(old_diffusers_cache)) > 0
if old_cache_is_not_empty:
logger.warning(
"The cache for model files in Diffusers v0.14.0 has moved to a new location. Moving your "
"existing cached models. This is a one-time operation, you can interrupt it or run it "
"later by calling `diffusers.utils.hub_utils.move_cache()`."
)
try:
move_cache()
except Exception as e:
_lowerCAmelCase : Optional[int] = "\n".join(traceback.format_tb(e.__traceback__))
logger.error(
f'''There was a problem when trying to move your cache:\n\n{trace}\n{e.__class__.__name__}: {e}\n\nPlease '''
"file an issue at https://github.com/huggingface/diffusers/issues/new/choose, copy paste this whole "
"message and we will do our best to help."
)
if cache_version < 1:
try:
os.makedirs(DIFFUSERS_CACHE, exist_ok=True)
with open(cache_version_file, "w") as f:
f.write("1")
except Exception:
logger.warning(
f'''There was a problem when trying to write in your cache folder ({DIFFUSERS_CACHE}). Please, ensure '''
"the directory exists and can be written to."
)
def UpperCamelCase_( _snake_case : str , _snake_case : Optional[str] = None ):
"""simple docstring"""
if variant is not None:
__a =weights_name.split('.' )
__a =splits[:-1] + [variant] + splits[-1:]
__a ='.'.join(_snake_case )
return weights_name
def UpperCamelCase_( _snake_case : Dict , *,
_snake_case : Any , _snake_case : Any , _snake_case : Optional[int] , _snake_case : Dict , _snake_case : int , _snake_case : str , _snake_case : Tuple , _snake_case : List[Any] , _snake_case : Optional[int] , _snake_case : Any , _snake_case : Optional[int]=None , ):
"""simple docstring"""
__a =str(_snake_case )
if os.path.isfile(_snake_case ):
return pretrained_model_name_or_path
elif os.path.isdir(_snake_case ):
if os.path.isfile(os.path.join(_snake_case , _snake_case ) ):
# Load from a PyTorch checkpoint
__a =os.path.join(_snake_case , _snake_case )
return model_file
elif subfolder is not None and os.path.isfile(
os.path.join(_snake_case , _snake_case , _snake_case ) ):
__a =os.path.join(_snake_case , _snake_case , _snake_case )
return model_file
else:
raise EnvironmentError(
F'Error no file named {weights_name} found in directory {pretrained_model_name_or_path}.' )
else:
# 1. First check if deprecated way of loading from branches is used
if (
revision in DEPRECATED_REVISION_ARGS
and (weights_name == WEIGHTS_NAME or weights_name == SAFETENSORS_WEIGHTS_NAME)
and version.parse(version.parse(_snake_case ).base_version ) >= version.parse('0.20.0' )
):
try:
__a =hf_hub_download(
_snake_case , filename=_add_variant(_snake_case , _snake_case ) , cache_dir=_snake_case , force_download=_snake_case , proxies=_snake_case , resume_download=_snake_case , local_files_only=_snake_case , use_auth_token=_snake_case , user_agent=_snake_case , subfolder=_snake_case , revision=revision or commit_hash , )
warnings.warn(
F'Loading the variant {revision} from {pretrained_model_name_or_path} via `revision=\'{revision}\'` is deprecated. Loading instead from `revision=\'main\'` with `variant={revision}`. Loading model variants via `revision=\'{revision}\'` will be removed in diffusers v1. Please use `variant=\'{revision}\'` instead.' , _snake_case , )
return model_file
except: # noqa: E722
warnings.warn(
F'You are loading the variant {revision} from {pretrained_model_name_or_path} via `revision=\'{revision}\'`. This behavior is deprecated and will be removed in diffusers v1. One should use `variant=\'{revision}\'` instead. However, it appears that {pretrained_model_name_or_path} currently does not have a {_add_variant(_snake_case , _snake_case )} file in the \'main\' branch of {pretrained_model_name_or_path}. \n The Diffusers team and community would be very grateful if you could open an issue: https://github.com/huggingface/diffusers/issues/new with the title \'{pretrained_model_name_or_path} is missing {_add_variant(_snake_case , _snake_case )}\' so that the correct variant file can be added.' , _snake_case , )
try:
# 2. Load model file as usual
__a =hf_hub_download(
_snake_case , filename=_snake_case , cache_dir=_snake_case , force_download=_snake_case , proxies=_snake_case , resume_download=_snake_case , local_files_only=_snake_case , use_auth_token=_snake_case , user_agent=_snake_case , subfolder=_snake_case , revision=revision or commit_hash , )
return model_file
except RepositoryNotFoundError:
raise EnvironmentError(
F'{pretrained_model_name_or_path} is not a local folder and is not a valid model identifier '
'listed on \'https://huggingface.co/models\'\nIf this is a private repository, make sure to pass a '
'token having permission to this repo with `use_auth_token` or log in with `huggingface-cli '
'login`.' )
except RevisionNotFoundError:
raise EnvironmentError(
F'{revision} is not a valid git identifier (branch name, tag name or commit id) that exists for '
'this model name. Check the model page at '
F'\'https://huggingface.co/{pretrained_model_name_or_path}\' for available revisions.' )
except EntryNotFoundError:
raise EnvironmentError(
F'{pretrained_model_name_or_path} does not appear to have a file named {weights_name}.' )
except HTTPError as err:
raise EnvironmentError(
F'There was a specific connection error when trying to load {pretrained_model_name_or_path}:\n{err}' )
except ValueError:
raise EnvironmentError(
F'We couldn\'t connect to \'{HUGGINGFACE_CO_RESOLVE_ENDPOINT}\' to load this model, couldn\'t find it'
F' in the cached files and it looks like {pretrained_model_name_or_path} is not the path to a'
F' directory containing a file named {weights_name} or'
' \nCheckout your internet connection or see how to run the library in'
' offline mode at \'https://huggingface.co/docs/diffusers/installation#offline-mode\'.' )
except EnvironmentError:
raise EnvironmentError(
F'Can\'t load the model for \'{pretrained_model_name_or_path}\'. If you were trying to load it from '
'\'https://huggingface.co/models\', make sure you don\'t have a local directory with the same name. '
F'Otherwise, make sure \'{pretrained_model_name_or_path}\' is the correct path to a directory '
F'containing a file named {weights_name}' )
| 308
|
def UpperCamelCase_( _snake_case : str , _snake_case : int ):
"""simple docstring"""
return [sentence[i : i + ngram_size] for i in range(len(_snake_case ) - ngram_size + 1 )]
if __name__ == "__main__":
from doctest import testmod
testmod()
| 308
| 1
|
import warnings
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class __magic_name__ ( lowerCAmelCase_ ):
SCREAMING_SNAKE_CASE = ['image_processor', 'tokenizer']
SCREAMING_SNAKE_CASE = 'CLIPImageProcessor'
SCREAMING_SNAKE_CASE = ('CLIPTokenizer', 'CLIPTokenizerFast')
def __init__( self , __snake_case=None , __snake_case=None , **__snake_case ) -> Optional[int]:
'''simple docstring'''
__a =None
if "feature_extractor" in kwargs:
warnings.warn(
'The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'
' instead.' , __snake_case , )
__a =kwargs.pop('feature_extractor' )
__a =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__(__snake_case , __snake_case )
def __call__( self , __snake_case=None , __snake_case=None , __snake_case=None , **__snake_case ) -> Tuple:
'''simple docstring'''
if text is None and images is None:
raise ValueError('You have to specify either text or images. Both cannot be none.' )
if text is not None:
__a =self.tokenizer(__snake_case , return_tensors=__snake_case , **__snake_case )
if images is not None:
__a =self.image_processor(__snake_case , return_tensors=__snake_case , **__snake_case )
if text is not None and images is not None:
__a =image_features.pixel_values
return encoding
elif text is not None:
return encoding
else:
return BatchEncoding(data=dict(**__snake_case ) , tensor_type=__snake_case )
def __magic_name__ ( self , *__snake_case , **__snake_case ) -> List[str]:
'''simple docstring'''
return self.tokenizer.batch_decode(*__snake_case , **__snake_case )
def __magic_name__ ( self , *__snake_case , **__snake_case ) -> Any:
'''simple docstring'''
return self.tokenizer.decode(*__snake_case , **__snake_case )
@property
def __magic_name__ ( self ) -> Optional[Any]:
'''simple docstring'''
__a =self.tokenizer.model_input_names
__a =self.image_processor.model_input_names
return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names ) )
@property
def __magic_name__ ( self ) -> Optional[int]:
'''simple docstring'''
warnings.warn(
'`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.' , __snake_case , )
return self.image_processor_class
@property
def __magic_name__ ( self ) -> Optional[int]:
'''simple docstring'''
warnings.warn(
'`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.' , __snake_case , )
return self.image_processor
| 308
|
import argparse
import pytorch_lightning as pl
import torch
from torch import nn
from transformers import LongformerForQuestionAnswering, LongformerModel
class __magic_name__ ( pl.LightningModule ):
def __init__( self , __snake_case ) -> List[Any]:
'''simple docstring'''
super().__init__()
__a =model
__a =2
__a =nn.Linear(self.model.config.hidden_size , self.num_labels )
def __magic_name__ ( self ) -> List[Any]:
'''simple docstring'''
pass
def UpperCamelCase_( _snake_case : str , _snake_case : str , _snake_case : str ):
"""simple docstring"""
__a =LongformerModel.from_pretrained(_snake_case )
__a =LightningModel(_snake_case )
__a =torch.load(_snake_case , map_location=torch.device('cpu' ) )
lightning_model.load_state_dict(ckpt['state_dict'] )
# init longformer question answering model
__a =LongformerForQuestionAnswering.from_pretrained(_snake_case )
# transfer weights
longformer_for_qa.longformer.load_state_dict(lightning_model.model.state_dict() )
longformer_for_qa.qa_outputs.load_state_dict(lightning_model.qa_outputs.state_dict() )
longformer_for_qa.eval()
# save model
longformer_for_qa.save_pretrained(_snake_case )
print(F'Conversion successful. Model saved under {pytorch_dump_folder_path}' )
if __name__ == "__main__":
_lowerCAmelCase : Optional[int] = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--longformer_model",
default=None,
type=str,
required=True,
help="model identifier of longformer. Should be either `longformer-base-4096` or `longformer-large-4096`.",
)
parser.add_argument(
"--longformer_question_answering_ckpt_path",
default=None,
type=str,
required=True,
help="Path the official PyTorch Lightning Checkpoint.",
)
parser.add_argument(
"--pytorch_dump_folder_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
)
_lowerCAmelCase : Optional[int] = parser.parse_args()
convert_longformer_qa_checkpoint_to_pytorch(
args.longformer_model, args.longformer_question_answering_ckpt_path, args.pytorch_dump_folder_path
)
| 308
| 1
|
from diffusers.utils.testing_utils import require_onnxruntime
@require_onnxruntime
class __magic_name__ :
pass
| 308
|
import logging
import os
import random
import sys
from dataclasses import dataclass, field
from typing import Optional
import datasets
import numpy as np
import pandas as pd
from datasets import load_dataset
import transformers
from transformers import (
AutoConfig,
BartForSequenceClassification,
DataCollatorWithPadding,
EvalPrediction,
HfArgumentParser,
TapexTokenizer,
Trainer,
TrainingArguments,
default_data_collator,
set_seed,
)
from transformers.trainer_utils import get_last_checkpoint
from transformers.utils import check_min_version
from transformers.utils.versions import require_version
# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
check_min_version("4.17.0.dev0")
require_version("datasets>=1.8.0", "To fix: pip install -r examples/pytorch/text-classification/requirements.txt")
_lowerCAmelCase : Optional[Any] = logging.getLogger(__name__)
@dataclass
class __magic_name__ :
SCREAMING_SNAKE_CASE = field(
default='tab_fact' , metadata={'help': 'The name of the dataset to use (via the datasets library).'} )
SCREAMING_SNAKE_CASE = field(
default='tab_fact' , metadata={'help': 'The configuration name of the dataset to use (via the datasets library).'} , )
SCREAMING_SNAKE_CASE = field(
default=1_0_2_4 , metadata={
'help': (
'The maximum total input sequence length after tokenization. Sequences longer '
'than this will be truncated, sequences shorter will be padded.'
)
} , )
SCREAMING_SNAKE_CASE = field(
default=lowerCAmelCase_ , metadata={'help': 'Overwrite the cached preprocessed datasets or not.'} )
SCREAMING_SNAKE_CASE = field(
default=lowerCAmelCase_ , metadata={
'help': (
'Whether to pad all samples to `max_seq_length`. '
'If False, will pad the samples dynamically when batching to the maximum length in the batch.'
)
} , )
SCREAMING_SNAKE_CASE = field(
default=lowerCAmelCase_ , metadata={
'help': (
'For debugging purposes or quicker training, truncate the number of training examples to this '
'value if set.'
)
} , )
SCREAMING_SNAKE_CASE = field(
default=lowerCAmelCase_ , metadata={
'help': (
'For debugging purposes or quicker training, truncate the number of evaluation examples to this '
'value if set.'
)
} , )
SCREAMING_SNAKE_CASE = field(
default=lowerCAmelCase_ , metadata={
'help': (
'For debugging purposes or quicker training, truncate the number of prediction examples to this '
'value if set.'
)
} , )
SCREAMING_SNAKE_CASE = field(
default=lowerCAmelCase_ , metadata={'help': 'A csv or a json file containing the training data.'} )
SCREAMING_SNAKE_CASE = field(
default=lowerCAmelCase_ , metadata={'help': 'A csv or a json file containing the validation data.'} )
SCREAMING_SNAKE_CASE = field(default=lowerCAmelCase_ , metadata={'help': 'A csv or a json file containing the test data.'} )
def __magic_name__ ( self ) -> str:
'''simple docstring'''
if self.dataset_name is not None:
pass
elif self.train_file is None or self.validation_file is None:
raise ValueError('Need either a GLUE task, a training/validation file or a dataset name.' )
else:
__a =self.train_file.split('.' )[-1]
assert train_extension in ["csv", "json"], "`train_file` should be a csv or a json file."
__a =self.validation_file.split('.' )[-1]
assert (
validation_extension == train_extension
), "`validation_file` should have the same extension (csv or json) as `train_file`."
@dataclass
class __magic_name__ :
SCREAMING_SNAKE_CASE = field(
default=lowerCAmelCase_ , metadata={'help': 'Path to pretrained model or model identifier from huggingface.co/models'} )
SCREAMING_SNAKE_CASE = field(
default=lowerCAmelCase_ , metadata={'help': 'Pretrained config name or path if not the same as model_name'} )
SCREAMING_SNAKE_CASE = field(
default=lowerCAmelCase_ , metadata={'help': 'Pretrained tokenizer name or path if not the same as model_name'} )
SCREAMING_SNAKE_CASE = field(
default=lowerCAmelCase_ , metadata={'help': 'Where do you want to store the pretrained models downloaded from huggingface.co'} , )
SCREAMING_SNAKE_CASE = field(
default=lowerCAmelCase_ , metadata={'help': 'Whether to use one of the fast tokenizer (backed by the tokenizers library) or not.'} , )
SCREAMING_SNAKE_CASE = field(
default='main' , metadata={'help': 'The specific model version to use (can be a branch name, tag name or commit id).'} , )
SCREAMING_SNAKE_CASE = field(
default=lowerCAmelCase_ , metadata={
'help': (
'Will use the token generated when running `huggingface-cli login` (necessary to use this script '
'with private models).'
)
} , )
def UpperCamelCase_( ):
"""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()
# 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 )] , )
__a =training_args.get_process_log_level()
logger.setLevel(_snake_case )
datasets.utils.logging.set_verbosity(_snake_case )
transformers.utils.logging.set_verbosity(_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 training and evaluation files (see below)
# or specify a GLUE benchmark task (the dataset will be downloaded automatically from the datasets Hub).
#
# For JSON files, this script will use the `question` column for the input question and `table` column for the corresponding table.
#
# If the CSVs/JSONs contain only one non-label column, the script does single sentence classification on this
# single column. 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.dataset_name is not None:
# Downloading and loading a dataset from the hub.
__a =load_dataset(
data_args.dataset_name , data_args.dataset_config_name , cache_dir=model_args.cache_dir )
else:
# Loading a dataset from your local files.
# CSV/JSON training and evaluation files are needed.
__a ={'train': data_args.train_file, 'validation': data_args.validation_file}
# Get the test dataset: you can provide your own CSV/JSON test file (see below)
# when you use `do_predict` without specifying a GLUE benchmark task.
if training_args.do_predict:
if data_args.test_file is not None:
__a =data_args.train_file.split('.' )[-1]
__a =data_args.test_file.split('.' )[-1]
assert (
test_extension == train_extension
), "`test_file` should have the same extension (csv or json) as `train_file`."
__a =data_args.test_file
else:
raise ValueError('Need either a GLUE task or a test file for `do_predict`.' )
for key in data_files.keys():
logger.info(F'load a local file for {key}: {data_files[key]}' )
if data_args.train_file.endswith('.csv' ):
# Loading a dataset from local csv files
__a =load_dataset('csv' , data_files=_snake_case , cache_dir=model_args.cache_dir )
else:
# Loading a dataset from local json files
__a =load_dataset('json' , data_files=_snake_case , cache_dir=model_args.cache_dir )
# See more about loading any type of standard or custom dataset at
# https://huggingface.co/docs/datasets/loading_datasets.html.
# Labels
__a =raw_datasets['train'].features['label'].names
__a =len(_snake_case )
# Load pretrained model and tokenizer
#
# In 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 , num_labels=_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 , )
# load tapex tokenizer
__a =TapexTokenizer.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 , add_prefix_space=_snake_case , )
__a =BartForSequenceClassification.from_pretrained(
model_args.model_name_or_path , from_tf=bool('.ckpt' in model_args.model_name_or_path ) , config=_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 , )
# Padding strategy
if data_args.pad_to_max_length:
__a ='max_length'
else:
# We will pad later, dynamically at batch creation, to the max sequence length in each batch
__a =False
# Some models have set the order of the labels to use, so let's make sure we do use it.
__a ={'Refused': 0, 'Entailed': 1}
__a ={0: 'Refused', 1: 'Entailed'}
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 )
def preprocess_tabfact_function(_snake_case : Tuple ):
# Tokenize the texts
def _convert_table_text_to_pandas(_snake_case : Optional[Any] ):
__a =[_table_row.split('#' ) for _table_row in _table_text.strip('\n' ).split('\n' )]
__a =pd.DataFrame.from_records(_table_content[1:] , columns=_table_content[0] )
return _table_pd
__a =examples['statement']
__a =list(map(_convert_table_text_to_pandas , examples['table_text'] ) )
__a =tokenizer(_snake_case , _snake_case , padding=_snake_case , max_length=_snake_case , truncation=_snake_case )
__a =examples['label']
return result
with training_args.main_process_first(desc='dataset map pre-processing' ):
__a =raw_datasets.map(
_snake_case , batched=_snake_case , load_from_cache_file=not data_args.overwrite_cache , desc='Running tokenizer on dataset' , )
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 =train_dataset.select(range(data_args.max_train_samples ) )
if training_args.do_eval:
if "validation" not in raw_datasets and "validation_matched" 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 =eval_dataset.select(range(data_args.max_eval_samples ) )
if training_args.do_predict or data_args.test_file is not None:
if "test" not in raw_datasets and "test_matched" not in raw_datasets:
raise ValueError('--do_predict requires a test dataset' )
__a =raw_datasets['test']
if data_args.max_predict_samples is not None:
__a =predict_dataset.select(range(data_args.max_predict_samples ) )
# Log a few random samples from the training set:
if training_args.do_train:
for index in random.sample(range(len(_snake_case ) ) , 3 ):
logger.info(F'Sample {index} of the training set: {train_dataset[index]}.' )
# You can define your custom compute_metrics function. It takes an `EvalPrediction` object (a namedtuple with a
# predictions and label_ids field) and has to return a dictionary string to float.
def compute_metrics(_snake_case : EvalPrediction ):
__a =p.predictions[0] if isinstance(p.predictions , _snake_case ) else p.predictions
__a =np.argmax(_snake_case , axis=1 )
return {"accuracy": (preds == p.label_ids).astype(np.floataa ).mean().item()}
# Data collator will default to DataCollatorWithPadding, so we change it if we already did the padding.
if data_args.pad_to_max_length:
__a =default_data_collator
elif training_args.fpaa:
__a =DataCollatorWithPadding(_snake_case , pad_to_multiple_of=8 )
else:
__a =None
# Initialize our Trainer
__a =Trainer(
model=_snake_case , args=_snake_case , train_dataset=train_dataset if training_args.do_train else None , eval_dataset=eval_dataset if training_args.do_eval else None , compute_metrics=_snake_case , tokenizer=_snake_case , data_collator=_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=_snake_case )
__a =train_result.metrics
__a =(
data_args.max_train_samples if data_args.max_train_samples is not None else len(_snake_case )
)
__a =min(_snake_case , len(_snake_case ) )
trainer.save_model() # Saves the tokenizer too for easy upload
trainer.log_metrics('train' , _snake_case )
trainer.save_metrics('train' , _snake_case )
trainer.save_state()
# Evaluation
if training_args.do_eval:
logger.info('*** Evaluate ***' )
__a =trainer.evaluate(eval_dataset=_snake_case )
__a =data_args.max_eval_samples if data_args.max_eval_samples is not None else len(_snake_case )
__a =min(_snake_case , len(_snake_case ) )
trainer.log_metrics('eval' , _snake_case )
trainer.save_metrics('eval' , _snake_case )
if training_args.do_predict:
logger.info('*** Predict ***' )
# Removing the `label` columns because it contains -1 and Trainer won't like that.
__a =predict_dataset.remove_columns('label' )
__a =trainer.predict(_snake_case , metric_key_prefix='predict' ).predictions
__a =np.argmax(_snake_case , axis=1 )
__a =os.path.join(training_args.output_dir , 'predict_results_tabfact.txt' )
if trainer.is_world_process_zero():
with open(_snake_case , 'w' ) as writer:
logger.info('***** Predict Results *****' )
writer.write('index\tprediction\n' )
for index, item in enumerate(_snake_case ):
__a =label_list[item]
writer.write(F'{index}\t{item}\n' )
__a ={'finetuned_from': model_args.model_name_or_path, 'tasks': 'text-classification'}
if training_args.push_to_hub:
trainer.push_to_hub(**_snake_case )
else:
trainer.create_model_card(**_snake_case )
def UpperCamelCase_( _snake_case : Union[str, Any] ):
"""simple docstring"""
main()
if __name__ == "__main__":
main()
| 308
| 1
|
def UpperCamelCase_( _snake_case : int , _snake_case : int ):
"""simple docstring"""
if a < 0 or b < 0:
raise ValueError('the value of both inputs must be positive' )
__a =str(bin(_snake_case ) )[2:] # remove the leading "0b"
__a =str(bin(_snake_case ) )[2:]
__a =max(len(_snake_case ) , len(_snake_case ) )
return "0b" + "".join(
str(int('1' in (char_a, char_b) ) )
for char_a, char_b in zip(a_binary.zfill(_snake_case ) , b_binary.zfill(_snake_case ) ) )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 308
|
from __future__ import annotations
import time
import numpy as np
_lowerCAmelCase : List[str] = [8, 5, 9, 7]
_lowerCAmelCase : List[str] = [
[2, 0, 1, 1],
[0, 1, 2, 1],
[4, 0, 0, 3],
[0, 2, 1, 0],
[1, 0, 3, 0],
]
_lowerCAmelCase : List[Any] = [
[3, 2, 1, 4],
[0, 2, 5, 2],
[5, 1, 0, 5],
[1, 5, 3, 0],
[3, 0, 3, 3],
]
class __magic_name__ :
def __init__( self , __snake_case , __snake_case , __snake_case , ) -> None:
'''simple docstring'''
__a =claim_vector
__a =allocated_resources_table
__a =maximum_claim_table
def __magic_name__ ( self ) -> list[int]:
'''simple docstring'''
return [
sum(p_item[i] for p_item in self.__allocated_resources_table )
for i in range(len(self.__allocated_resources_table[0] ) )
]
def __magic_name__ ( self ) -> list[int]:
'''simple docstring'''
return np.array(self.__claim_vector ) - np.array(
self.__processes_resource_summation() )
def __magic_name__ ( self ) -> list[list[int]]:
'''simple docstring'''
return [
list(np.array(self.__maximum_claim_table[i] ) - np.array(__snake_case ) )
for i, allocated_resource in enumerate(self.__allocated_resources_table )
]
def __magic_name__ ( self ) -> dict[int, list[int]]:
'''simple docstring'''
return {self.__need().index(__snake_case ): i for i in self.__need()}
def __magic_name__ ( self , **__snake_case ) -> None:
'''simple docstring'''
__a =self.__need()
__a =self.__allocated_resources_table
__a =self.__available_resources()
__a =self.__need_index_manager()
for kw, val in kwargs.items():
if kw and val is True:
self.__pretty_data()
print('_' * 50 + '\n' )
while need_list:
__a =False
for each_need in need_list:
__a =True
for index, need in enumerate(__snake_case ):
if need > available_resources[index]:
__a =False
break
if execution:
__a =True
# get the original index of the process from ind_ctrl db
for original_need_index, need_clone in need_index_manager.items():
if each_need == need_clone:
__a =original_need_index
print(f'Process {process_number + 1} is executing.' )
# remove the process run from stack
need_list.remove(__snake_case )
# update available/freed resources stack
__a =np.array(__snake_case ) + np.array(
alloc_resources_table[process_number] )
print(
'Updated available resource stack for processes: '
+ ' '.join([str(__snake_case ) for x in available_resources] ) )
break
if safe:
print('The process is in a safe state.\n' )
else:
print('System in unsafe state. Aborting...\n' )
break
def __magic_name__ ( self ) -> Union[str, Any]:
'''simple docstring'''
print(' ' * 9 + 'Allocated Resource Table' )
for item in self.__allocated_resources_table:
print(
f'P{self.__allocated_resources_table.index(__snake_case ) + 1}'
+ ' '.join(f'{it:>8}' for it in item )
+ '\n' )
print(' ' * 9 + 'System Resource Table' )
for item in self.__maximum_claim_table:
print(
f'P{self.__maximum_claim_table.index(__snake_case ) + 1}'
+ ' '.join(f'{it:>8}' for it in item )
+ '\n' )
print(
'Current Usage by Active Processes: '
+ ' '.join(str(__snake_case ) for x in self.__claim_vector ) )
print(
'Initial Available Resources: '
+ ' '.join(str(__snake_case ) for x in self.__available_resources() ) )
time.sleep(1 )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 308
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|
import string
def UpperCamelCase_( _snake_case : str ):
"""simple docstring"""
for key in range(len(string.ascii_uppercase ) ):
__a =''
for symbol in message:
if symbol in string.ascii_uppercase:
__a =string.ascii_uppercase.find(_snake_case )
__a =num - key
if num < 0:
__a =num + len(string.ascii_uppercase )
__a =translated + string.ascii_uppercase[num]
else:
__a =translated + symbol
print(F'Decryption using Key #{key}: {translated}' )
def UpperCamelCase_( ):
"""simple docstring"""
__a =input('Encrypted message: ' )
__a =message.upper()
decrypt(_snake_case )
if __name__ == "__main__":
import doctest
doctest.testmod()
main()
| 308
|
import string
# frequency taken from https://en.wikipedia.org/wiki/Letter_frequency
_lowerCAmelCase : Tuple = {
"E": 12.70,
"T": 9.06,
"A": 8.17,
"O": 7.51,
"I": 6.97,
"N": 6.75,
"S": 6.33,
"H": 6.09,
"R": 5.99,
"D": 4.25,
"L": 4.03,
"C": 2.78,
"U": 2.76,
"M": 2.41,
"W": 2.36,
"F": 2.23,
"G": 2.02,
"Y": 1.97,
"P": 1.93,
"B": 1.29,
"V": 0.98,
"K": 0.77,
"J": 0.15,
"X": 0.15,
"Q": 0.10,
"Z": 0.07,
}
_lowerCAmelCase : Optional[int] = "ETAOINSHRDLCUMWFGYPBVKJXQZ"
_lowerCAmelCase : Any = "ABCDEFGHIJKLMNOPQRSTUVWXYZ"
def UpperCamelCase_( _snake_case : str ):
"""simple docstring"""
__a ={letter: 0 for letter in string.ascii_uppercase}
for letter in message.upper():
if letter in LETTERS:
letter_count[letter] += 1
return letter_count
def UpperCamelCase_( _snake_case : tuple ):
"""simple docstring"""
return x[0]
def UpperCamelCase_( _snake_case : str ):
"""simple docstring"""
__a =get_letter_count(_snake_case )
__a ={
freq: [] for letter, freq in letter_to_freq.items()
}
for letter in LETTERS:
freq_to_letter[letter_to_freq[letter]].append(_snake_case )
__a ={}
for freq in freq_to_letter:
freq_to_letter[freq].sort(key=ETAOIN.find , reverse=_snake_case )
__a =''.join(freq_to_letter[freq] )
__a =list(freq_to_letter_str.items() )
freq_pairs.sort(key=_snake_case , reverse=_snake_case )
__a =[freq_pair[1] for freq_pair in freq_pairs]
return "".join(_snake_case )
def UpperCamelCase_( _snake_case : str ):
"""simple docstring"""
__a =get_frequency_order(_snake_case )
__a =0
for common_letter in ETAOIN[:6]:
if common_letter in freq_order[:6]:
match_score += 1
for uncommon_letter in ETAOIN[-6:]:
if uncommon_letter in freq_order[-6:]:
match_score += 1
return match_score
if __name__ == "__main__":
import doctest
doctest.testmod()
| 308
| 1
|
import json
from typing import Dict, List, Optional, Tuple, Union
from tokenizers import pre_tokenizers, processors
from ...tokenization_utils_base import AddedToken, BatchEncoding, EncodedInput
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import PaddingStrategy, logging
from .tokenization_led import LEDTokenizer
_lowerCAmelCase : Optional[Any] = logging.get_logger(__name__)
_lowerCAmelCase : Tuple = {"vocab_file": "vocab.json", "merges_file": "merges.txt", "tokenizer_file": "tokenizer.json"}
_lowerCAmelCase : Optional[Any] = {
"vocab_file": {
"allenai/led-base-16384": "https://huggingface.co/allenai/led-base-16384/resolve/main/vocab.json",
},
"merges_file": {
"allenai/led-base-16384": "https://huggingface.co/allenai/led-base-16384/resolve/main/merges.txt",
},
"tokenizer_file": {
"allenai/led-base-16384": "https://huggingface.co/allenai/led-base-16384/resolve/main/tokenizer.json",
},
}
_lowerCAmelCase : Union[str, Any] = {
"allenai/led-base-16384": 16_384,
}
class __magic_name__ ( lowerCAmelCase_ ):
SCREAMING_SNAKE_CASE = VOCAB_FILES_NAMES
SCREAMING_SNAKE_CASE = PRETRAINED_VOCAB_FILES_MAP
SCREAMING_SNAKE_CASE = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
SCREAMING_SNAKE_CASE = LEDTokenizer
SCREAMING_SNAKE_CASE = ['input_ids', 'attention_mask']
def __init__( self , __snake_case=None , __snake_case=None , __snake_case=None , __snake_case="replace" , __snake_case="<s>" , __snake_case="</s>" , __snake_case="</s>" , __snake_case="<s>" , __snake_case="<unk>" , __snake_case="<pad>" , __snake_case="<mask>" , __snake_case=False , __snake_case=True , **__snake_case , ) -> Optional[int]:
'''simple docstring'''
super().__init__(
__snake_case , __snake_case , tokenizer_file=__snake_case , errors=__snake_case , bos_token=__snake_case , eos_token=__snake_case , sep_token=__snake_case , cls_token=__snake_case , unk_token=__snake_case , pad_token=__snake_case , mask_token=__snake_case , add_prefix_space=__snake_case , trim_offsets=__snake_case , **__snake_case , )
__a =json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__() )
if pre_tok_state.get('add_prefix_space' , __snake_case ) != add_prefix_space:
__a =getattr(__snake_case , pre_tok_state.pop('type' ) )
__a =add_prefix_space
__a =pre_tok_class(**__snake_case )
__a =add_prefix_space
# the pre_tokenizer is already updated in the GPT2TokenizerFast `__init__`
__a ='post_processor'
__a =getattr(self.backend_tokenizer , __snake_case , __snake_case )
if tokenizer_component_instance:
__a =json.loads(tokenizer_component_instance.__getstate__() )
# The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
if "sep" in state:
__a =tuple(state['sep'] )
if "cls" in state:
__a =tuple(state['cls'] )
__a =False
if state.get('add_prefix_space' , __snake_case ) != add_prefix_space:
__a =add_prefix_space
__a =True
if state.get('trim_offsets' , __snake_case ) != trim_offsets:
__a =trim_offsets
__a =True
if changes_to_apply:
__a =getattr(__snake_case , state.pop('type' ) )
__a =component_class(**__snake_case )
setattr(self.backend_tokenizer , __snake_case , __snake_case )
@property
# Copied from transformers.models.bart.tokenization_bart_fast.BartTokenizerFast.mask_token with BART->LED
def __magic_name__ ( self ) -> str:
'''simple docstring'''
if self._mask_token is None:
if self.verbose:
logger.error('Using mask_token, but it is not set yet.' )
return None
return str(self._mask_token )
@mask_token.setter
def __magic_name__ ( self , __snake_case ) -> Any:
'''simple docstring'''
__a =AddedToken(__snake_case , lstrip=__snake_case , rstrip=__snake_case ) if isinstance(__snake_case , __snake_case ) else value
__a =value
def __magic_name__ ( self , *__snake_case , **__snake_case ) -> BatchEncoding:
'''simple docstring'''
__a =kwargs.get('is_split_into_words' , __snake_case )
if is_split_into_words and not self.add_prefix_space:
raise ValueError(
f'You need to instantiate {self.__class__.__name__} with add_prefix_space=True '
'to use it with pretokenized inputs.' )
return super()._batch_encode_plus(*__snake_case , **__snake_case )
def __magic_name__ ( self , *__snake_case , **__snake_case ) -> BatchEncoding:
'''simple docstring'''
__a =kwargs.get('is_split_into_words' , __snake_case )
if is_split_into_words and not self.add_prefix_space:
raise ValueError(
f'You need to instantiate {self.__class__.__name__} with add_prefix_space=True '
'to use it with pretokenized inputs.' )
return super()._encode_plus(*__snake_case , **__snake_case )
def __magic_name__ ( self , __snake_case , __snake_case = None ) -> Tuple[str]:
'''simple docstring'''
__a =self._tokenizer.model.save(__snake_case , name=__snake_case )
return tuple(__snake_case )
def __magic_name__ ( self , __snake_case , __snake_case=None ) -> int:
'''simple docstring'''
__a =[self.bos_token_id] + token_ids_a + [self.eos_token_id]
if token_ids_a is None:
return output
return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]
def __magic_name__ ( self , __snake_case , __snake_case = None ) -> List[int]:
'''simple docstring'''
__a =[self.sep_token_id]
__a =[self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep + sep + token_ids_a + sep ) * [0]
def __magic_name__ ( self , __snake_case , __snake_case = None , __snake_case = PaddingStrategy.DO_NOT_PAD , __snake_case = None , __snake_case = None , ) -> dict:
'''simple docstring'''
__a =super()._pad(
encoded_inputs=__snake_case , max_length=__snake_case , padding_strategy=__snake_case , pad_to_multiple_of=__snake_case , return_attention_mask=__snake_case , )
# Load from model defaults
if return_attention_mask is None:
__a ='attention_mask' in self.model_input_names
if return_attention_mask and "global_attention_mask" in encoded_inputs:
__a =encoded_inputs[self.model_input_names[0]]
# `global_attention_mask` need to have the same length as other (sequential) inputs.
__a =len(encoded_inputs['global_attention_mask'] ) != len(__snake_case )
if needs_to_be_padded:
__a =len(__snake_case ) - len(encoded_inputs['global_attention_mask'] )
if self.padding_side == "right":
# Use `-1` since `0` in `global_attention_mask` means `local attention` instead of `not to attend`
__a =(
encoded_inputs['global_attention_mask'] + [-1] * difference
)
elif self.padding_side == "left":
__a =[-1] * difference + encoded_inputs[
'global_attention_mask'
]
else:
raise ValueError('Invalid padding strategy:' + str(self.padding_side ) )
return encoded_inputs
| 308
|
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowerCAmelCase : Any = logging.get_logger(__name__)
_lowerCAmelCase : int = {
"caidas/swin2sr-classicalsr-x2-64": (
"https://huggingface.co/caidas/swin2sr-classicalsr-x2-64/resolve/main/config.json"
),
}
class __magic_name__ ( lowerCAmelCase_ ):
SCREAMING_SNAKE_CASE = 'swin2sr'
SCREAMING_SNAKE_CASE = {
'hidden_size': 'embed_dim',
'num_attention_heads': 'num_heads',
'num_hidden_layers': 'num_layers',
}
def __init__( self , __snake_case=64 , __snake_case=1 , __snake_case=3 , __snake_case=180 , __snake_case=[6, 6, 6, 6, 6, 6] , __snake_case=[6, 6, 6, 6, 6, 6] , __snake_case=8 , __snake_case=2.0 , __snake_case=True , __snake_case=0.0 , __snake_case=0.0 , __snake_case=0.1 , __snake_case="gelu" , __snake_case=False , __snake_case=0.02 , __snake_case=1e-5 , __snake_case=2 , __snake_case=1.0 , __snake_case="1conv" , __snake_case="pixelshuffle" , **__snake_case , ) -> Dict:
'''simple docstring'''
super().__init__(**__snake_case )
__a =image_size
__a =patch_size
__a =num_channels
__a =embed_dim
__a =depths
__a =len(__snake_case )
__a =num_heads
__a =window_size
__a =mlp_ratio
__a =qkv_bias
__a =hidden_dropout_prob
__a =attention_probs_dropout_prob
__a =drop_path_rate
__a =hidden_act
__a =use_absolute_embeddings
__a =layer_norm_eps
__a =initializer_range
__a =upscale
__a =img_range
__a =resi_connection
__a =upsampler
| 308
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|
from __future__ import annotations
def UpperCamelCase_( _snake_case : list[int] , _snake_case : int , _snake_case : int , _snake_case : int ):
"""simple docstring"""
if (direction == 1 and array[indexa] > array[indexa]) or (
direction == 0 and array[indexa] < array[indexa]
):
__a , __a =array[indexa], array[indexa]
def UpperCamelCase_( _snake_case : list[int] , _snake_case : int , _snake_case : int , _snake_case : int ):
"""simple docstring"""
if length > 1:
__a =int(length / 2 )
for i in range(_snake_case , low + middle ):
comp_and_swap(_snake_case , _snake_case , i + middle , _snake_case )
bitonic_merge(_snake_case , _snake_case , _snake_case , _snake_case )
bitonic_merge(_snake_case , low + middle , _snake_case , _snake_case )
def UpperCamelCase_( _snake_case : list[int] , _snake_case : int , _snake_case : int , _snake_case : int ):
"""simple docstring"""
if length > 1:
__a =int(length / 2 )
bitonic_sort(_snake_case , _snake_case , _snake_case , 1 )
bitonic_sort(_snake_case , low + middle , _snake_case , 0 )
bitonic_merge(_snake_case , _snake_case , _snake_case , _snake_case )
if __name__ == "__main__":
_lowerCAmelCase : Tuple = input("Enter numbers separated by a comma:\n").strip()
_lowerCAmelCase : Optional[int] = [int(item.strip()) for item in user_input.split(",")]
bitonic_sort(unsorted, 0, len(unsorted), 1)
print("\nSorted array in ascending order is: ", end="")
print(*unsorted, sep=", ")
bitonic_merge(unsorted, 0, len(unsorted), 0)
print("Sorted array in descending order is: ", end="")
print(*unsorted, sep=", ")
| 308
|
import os
def UpperCamelCase_( _snake_case : str = "input.txt" ):
"""simple docstring"""
with open(os.path.join(os.path.dirname(_snake_case ) , _snake_case ) ) as input_file:
__a =[
[int(_snake_case ) for element in line.split(',' )]
for line in input_file.readlines()
]
__a =len(_snake_case )
__a =len(matrix[0] )
__a =[[-1 for _ in range(_snake_case )] for _ in range(_snake_case )]
for i in range(_snake_case ):
__a =matrix[i][0]
for j in range(1 , _snake_case ):
for i in range(_snake_case ):
__a =minimal_path_sums[i][j - 1] + matrix[i][j]
for i in range(1 , _snake_case ):
__a =min(
minimal_path_sums[i][j] , minimal_path_sums[i - 1][j] + matrix[i][j] )
for i in range(rows - 2 , -1 , -1 ):
__a =min(
minimal_path_sums[i][j] , minimal_path_sums[i + 1][j] + matrix[i][j] )
return min(minimal_path_sums_row[-1] for minimal_path_sums_row in minimal_path_sums )
if __name__ == "__main__":
print(f'''{solution() = }''')
| 308
| 1
|
import gc
import unittest
import numpy as np
import torch
import torch.nn.functional as F
from transformers import (
ClapTextConfig,
ClapTextModelWithProjection,
RobertaTokenizer,
SpeechTaHifiGan,
SpeechTaHifiGanConfig,
)
from diffusers import (
AudioLDMPipeline,
AutoencoderKL,
DDIMScheduler,
LMSDiscreteScheduler,
PNDMScheduler,
UNetaDConditionModel,
)
from diffusers.utils import is_xformers_available, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism
from ..pipeline_params import TEXT_TO_AUDIO_BATCH_PARAMS, TEXT_TO_AUDIO_PARAMS
from ..test_pipelines_common import PipelineTesterMixin
enable_full_determinism()
class __magic_name__ ( lowerCAmelCase_ , unittest.TestCase ):
SCREAMING_SNAKE_CASE = AudioLDMPipeline
SCREAMING_SNAKE_CASE = TEXT_TO_AUDIO_PARAMS
SCREAMING_SNAKE_CASE = TEXT_TO_AUDIO_BATCH_PARAMS
SCREAMING_SNAKE_CASE = frozenset(
[
'num_inference_steps',
'num_waveforms_per_prompt',
'generator',
'latents',
'output_type',
'return_dict',
'callback',
'callback_steps',
] )
def __magic_name__ ( self ) -> List[Any]:
'''simple docstring'''
torch.manual_seed(0 )
__a =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, 64) , class_embed_type='simple_projection' , projection_class_embeddings_input_dim=32 , class_embeddings_concat=__snake_case , )
__a =DDIMScheduler(
beta_start=0.0_0085 , beta_end=0.012 , beta_schedule='scaled_linear' , clip_sample=__snake_case , set_alpha_to_one=__snake_case , )
torch.manual_seed(0 )
__a =AutoencoderKL(
block_out_channels=[32, 64] , in_channels=1 , out_channels=1 , down_block_types=['DownEncoderBlock2D', 'DownEncoderBlock2D'] , up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D'] , latent_channels=4 , )
torch.manual_seed(0 )
__a =ClapTextConfig(
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 , projection_dim=32 , )
__a =ClapTextModelWithProjection(__snake_case )
__a =RobertaTokenizer.from_pretrained('hf-internal-testing/tiny-random-roberta' , model_max_length=77 )
__a =SpeechTaHifiGanConfig(
model_in_dim=8 , sampling_rate=1_6000 , upsample_initial_channel=16 , upsample_rates=[2, 2] , upsample_kernel_sizes=[4, 4] , resblock_kernel_sizes=[3, 7] , resblock_dilation_sizes=[[1, 3, 5], [1, 3, 5]] , normalize_before=__snake_case , )
__a =SpeechTaHifiGan(__snake_case )
__a ={
'unet': unet,
'scheduler': scheduler,
'vae': vae,
'text_encoder': text_encoder,
'tokenizer': tokenizer,
'vocoder': vocoder,
}
return components
def __magic_name__ ( self , __snake_case , __snake_case=0 ) -> Dict:
'''simple docstring'''
if str(__snake_case ).startswith('mps' ):
__a =torch.manual_seed(__snake_case )
else:
__a =torch.Generator(device=__snake_case ).manual_seed(__snake_case )
__a ={
'prompt': 'A hammer hitting a wooden surface',
'generator': generator,
'num_inference_steps': 2,
'guidance_scale': 6.0,
}
return inputs
def __magic_name__ ( self ) -> Dict:
'''simple docstring'''
__a ='cpu' # ensure determinism for the device-dependent torch.Generator
__a =self.get_dummy_components()
__a =AudioLDMPipeline(**__snake_case )
__a =audioldm_pipe.to(__snake_case )
audioldm_pipe.set_progress_bar_config(disable=__snake_case )
__a =self.get_dummy_inputs(__snake_case )
__a =audioldm_pipe(**__snake_case )
__a =output.audios[0]
assert audio.ndim == 1
assert len(__snake_case ) == 256
__a =audio[:10]
__a =np.array(
[-0.0050, 0.0050, -0.0060, 0.0033, -0.0026, 0.0033, -0.0027, 0.0033, -0.0028, 0.0033] )
assert np.abs(audio_slice - expected_slice ).max() < 1e-2
def __magic_name__ ( self ) -> List[str]:
'''simple docstring'''
__a =self.get_dummy_components()
__a =AudioLDMPipeline(**__snake_case )
__a =audioldm_pipe.to(__snake_case )
__a =audioldm_pipe.to(__snake_case )
audioldm_pipe.set_progress_bar_config(disable=__snake_case )
__a =self.get_dummy_inputs(__snake_case )
__a =3 * [inputs['prompt']]
# forward
__a =audioldm_pipe(**__snake_case )
__a =output.audios[0]
__a =self.get_dummy_inputs(__snake_case )
__a =3 * [inputs.pop('prompt' )]
__a =audioldm_pipe.tokenizer(
__snake_case , padding='max_length' , max_length=audioldm_pipe.tokenizer.model_max_length , truncation=__snake_case , return_tensors='pt' , )
__a =text_inputs['input_ids'].to(__snake_case )
__a =audioldm_pipe.text_encoder(
__snake_case , )
__a =prompt_embeds.text_embeds
# additional L_2 normalization over each hidden-state
__a =F.normalize(__snake_case , dim=-1 )
__a =prompt_embeds
# forward
__a =audioldm_pipe(**__snake_case )
__a =output.audios[0]
assert np.abs(audio_a - audio_a ).max() < 1e-2
def __magic_name__ ( self ) -> Any:
'''simple docstring'''
__a =self.get_dummy_components()
__a =AudioLDMPipeline(**__snake_case )
__a =audioldm_pipe.to(__snake_case )
__a =audioldm_pipe.to(__snake_case )
audioldm_pipe.set_progress_bar_config(disable=__snake_case )
__a =self.get_dummy_inputs(__snake_case )
__a =3 * ['this is a negative prompt']
__a =negative_prompt
__a =3 * [inputs['prompt']]
# forward
__a =audioldm_pipe(**__snake_case )
__a =output.audios[0]
__a =self.get_dummy_inputs(__snake_case )
__a =3 * [inputs.pop('prompt' )]
__a =[]
for p in [prompt, negative_prompt]:
__a =audioldm_pipe.tokenizer(
__snake_case , padding='max_length' , max_length=audioldm_pipe.tokenizer.model_max_length , truncation=__snake_case , return_tensors='pt' , )
__a =text_inputs['input_ids'].to(__snake_case )
__a =audioldm_pipe.text_encoder(
__snake_case , )
__a =text_embeds.text_embeds
# additional L_2 normalization over each hidden-state
__a =F.normalize(__snake_case , dim=-1 )
embeds.append(__snake_case )
__a , __a =embeds
# forward
__a =audioldm_pipe(**__snake_case )
__a =output.audios[0]
assert np.abs(audio_a - audio_a ).max() < 1e-2
def __magic_name__ ( self ) -> Dict:
'''simple docstring'''
__a ='cpu' # ensure determinism for the device-dependent torch.Generator
__a =self.get_dummy_components()
__a =PNDMScheduler(skip_prk_steps=__snake_case )
__a =AudioLDMPipeline(**__snake_case )
__a =audioldm_pipe.to(__snake_case )
audioldm_pipe.set_progress_bar_config(disable=__snake_case )
__a =self.get_dummy_inputs(__snake_case )
__a ='egg cracking'
__a =audioldm_pipe(**__snake_case , negative_prompt=__snake_case )
__a =output.audios[0]
assert audio.ndim == 1
assert len(__snake_case ) == 256
__a =audio[:10]
__a =np.array(
[-0.0051, 0.0050, -0.0060, 0.0034, -0.0026, 0.0033, -0.0027, 0.0033, -0.0028, 0.0032] )
assert np.abs(audio_slice - expected_slice ).max() < 1e-2
def __magic_name__ ( self ) -> Tuple:
'''simple docstring'''
__a ='cpu' # ensure determinism for the device-dependent torch.Generator
__a =self.get_dummy_components()
__a =PNDMScheduler(skip_prk_steps=__snake_case )
__a =AudioLDMPipeline(**__snake_case )
__a =audioldm_pipe.to(__snake_case )
audioldm_pipe.set_progress_bar_config(disable=__snake_case )
__a ='A hammer hitting a wooden surface'
# test num_waveforms_per_prompt=1 (default)
__a =audioldm_pipe(__snake_case , num_inference_steps=2 ).audios
assert audios.shape == (1, 256)
# test num_waveforms_per_prompt=1 (default) for batch of prompts
__a =2
__a =audioldm_pipe([prompt] * batch_size , num_inference_steps=2 ).audios
assert audios.shape == (batch_size, 256)
# test num_waveforms_per_prompt for single prompt
__a =2
__a =audioldm_pipe(__snake_case , num_inference_steps=2 , num_waveforms_per_prompt=__snake_case ).audios
assert audios.shape == (num_waveforms_per_prompt, 256)
# test num_waveforms_per_prompt for batch of prompts
__a =2
__a =audioldm_pipe(
[prompt] * batch_size , num_inference_steps=2 , num_waveforms_per_prompt=__snake_case ).audios
assert audios.shape == (batch_size * num_waveforms_per_prompt, 256)
def __magic_name__ ( self ) -> Optional[int]:
'''simple docstring'''
__a ='cpu' # ensure determinism for the device-dependent torch.Generator
__a =self.get_dummy_components()
__a =AudioLDMPipeline(**__snake_case )
__a =audioldm_pipe.to(__snake_case )
audioldm_pipe.set_progress_bar_config(disable=__snake_case )
__a =audioldm_pipe.vocoder.config.sampling_rate
__a =self.get_dummy_inputs(__snake_case )
__a =audioldm_pipe(audio_length_in_s=0.016 , **__snake_case )
__a =output.audios[0]
assert audio.ndim == 1
assert len(__snake_case ) / vocoder_sampling_rate == 0.016
__a =audioldm_pipe(audio_length_in_s=0.032 , **__snake_case )
__a =output.audios[0]
assert audio.ndim == 1
assert len(__snake_case ) / vocoder_sampling_rate == 0.032
def __magic_name__ ( self ) -> str:
'''simple docstring'''
__a =self.get_dummy_components()
__a =AudioLDMPipeline(**__snake_case )
__a =audioldm_pipe.to(__snake_case )
audioldm_pipe.set_progress_bar_config(disable=__snake_case )
__a =['hey']
__a =audioldm_pipe(__snake_case , num_inference_steps=1 )
__a =output.audios.shape
assert audio_shape == (1, 256)
__a =audioldm_pipe.vocoder.config
config.model_in_dim *= 2
__a =SpeechTaHifiGan(__snake_case ).to(__snake_case )
__a =audioldm_pipe(__snake_case , num_inference_steps=1 )
__a =output.audios.shape
# waveform shape is unchanged, we just have 2x the number of mel channels in the spectrogram
assert audio_shape == (1, 256)
def __magic_name__ ( self ) -> List[str]:
'''simple docstring'''
self._test_attention_slicing_forward_pass(test_mean_pixel_difference=__snake_case )
def __magic_name__ ( self ) -> str:
'''simple docstring'''
self._test_inference_batch_single_identical(test_mean_pixel_difference=__snake_case )
@unittest.skipIf(
torch_device != 'cuda' or not is_xformers_available() , reason='XFormers attention is only available with CUDA and `xformers` installed' , )
def __magic_name__ ( self ) -> Union[str, Any]:
'''simple docstring'''
self._test_xformers_attention_forwardGenerator_pass(test_mean_pixel_difference=__snake_case )
@slow
class __magic_name__ ( unittest.TestCase ):
def __magic_name__ ( self ) -> List[Any]:
'''simple docstring'''
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def __magic_name__ ( self , __snake_case , __snake_case="cpu" , __snake_case=torch.floataa , __snake_case=0 ) -> Union[str, Any]:
'''simple docstring'''
__a =torch.Generator(device=__snake_case ).manual_seed(__snake_case )
__a =np.random.RandomState(__snake_case ).standard_normal((1, 8, 128, 16) )
__a =torch.from_numpy(__snake_case ).to(device=__snake_case , dtype=__snake_case )
__a ={
'prompt': 'A hammer hitting a wooden surface',
'latents': latents,
'generator': generator,
'num_inference_steps': 3,
'guidance_scale': 2.5,
}
return inputs
def __magic_name__ ( self ) -> List[str]:
'''simple docstring'''
__a =AudioLDMPipeline.from_pretrained('cvssp/audioldm' )
__a =audioldm_pipe.to(__snake_case )
audioldm_pipe.set_progress_bar_config(disable=__snake_case )
__a =self.get_inputs(__snake_case )
__a =25
__a =audioldm_pipe(**__snake_case ).audios[0]
assert audio.ndim == 1
assert len(__snake_case ) == 8_1920
__a =audio[7_7230:7_7240]
__a =np.array(
[-0.4884, -0.4607, 0.0023, 0.5007, 0.5896, 0.5151, 0.3813, -0.0208, -0.3687, -0.4315] )
__a =np.abs(expected_slice - audio_slice ).max()
assert max_diff < 1e-2
def __magic_name__ ( self ) -> Union[str, Any]:
'''simple docstring'''
__a =AudioLDMPipeline.from_pretrained('cvssp/audioldm' )
__a =LMSDiscreteScheduler.from_config(audioldm_pipe.scheduler.config )
__a =audioldm_pipe.to(__snake_case )
audioldm_pipe.set_progress_bar_config(disable=__snake_case )
__a =self.get_inputs(__snake_case )
__a =audioldm_pipe(**__snake_case ).audios[0]
assert audio.ndim == 1
assert len(__snake_case ) == 8_1920
__a =audio[2_7780:2_7790]
__a =np.array([-0.2131, -0.0873, -0.0124, -0.0189, 0.0569, 0.1373, 0.1883, 0.2886, 0.3297, 0.2212] )
__a =np.abs(expected_slice - audio_slice ).max()
assert max_diff < 3e-2
| 308
|
import warnings
from ...utils import logging
from .image_processing_mobilevit import MobileViTImageProcessor
_lowerCAmelCase : Any = logging.get_logger(__name__)
class __magic_name__ ( lowerCAmelCase_ ):
def __init__( self , *__snake_case , **__snake_case ) -> None:
'''simple docstring'''
warnings.warn(
'The class MobileViTFeatureExtractor is deprecated and will be removed in version 5 of Transformers.'
' Please use MobileViTImageProcessor instead.' , __snake_case , )
super().__init__(*__snake_case , **__snake_case )
| 308
| 1
|
import unittest
from transformers import XLMConfig, 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 (
XLMForMultipleChoice,
XLMForQuestionAnswering,
XLMForQuestionAnsweringSimple,
XLMForSequenceClassification,
XLMForTokenClassification,
XLMModel,
XLMWithLMHeadModel,
)
from transformers.models.xlm.modeling_xlm import XLM_PRETRAINED_MODEL_ARCHIVE_LIST
class __magic_name__ :
def __init__( self , __snake_case , __snake_case=13 , __snake_case=7 , __snake_case=True , __snake_case=True , __snake_case=True , __snake_case=True , __snake_case=True , __snake_case=False , __snake_case=False , __snake_case=False , __snake_case=2 , __snake_case=99 , __snake_case=0 , __snake_case=32 , __snake_case=5 , __snake_case=4 , __snake_case=0.1 , __snake_case=0.1 , __snake_case=512 , __snake_case=2 , __snake_case=0.02 , __snake_case=2 , __snake_case=4 , __snake_case="last" , __snake_case=True , __snake_case=None , __snake_case=0 , ) -> Optional[Any]:
'''simple docstring'''
__a =parent
__a =batch_size
__a =seq_length
__a =is_training
__a =use_input_lengths
__a =use_token_type_ids
__a =use_labels
__a =gelu_activation
__a =sinusoidal_embeddings
__a =causal
__a =asm
__a =n_langs
__a =vocab_size
__a =n_special
__a =hidden_size
__a =num_hidden_layers
__a =num_attention_heads
__a =hidden_dropout_prob
__a =attention_probs_dropout_prob
__a =max_position_embeddings
__a =type_sequence_label_size
__a =initializer_range
__a =num_labels
__a =num_choices
__a =summary_type
__a =use_proj
__a =scope
__a =bos_token_id
def __magic_name__ ( self ) -> Any:
'''simple docstring'''
__a =ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
__a =random_attention_mask([self.batch_size, self.seq_length] )
__a =None
if self.use_input_lengths:
__a =(
ids_tensor([self.batch_size] , vocab_size=2 ) + self.seq_length - 2
) # small variation of seq_length
__a =None
if self.use_token_type_ids:
__a =ids_tensor([self.batch_size, self.seq_length] , self.n_langs )
__a =None
__a =None
__a =None
if self.use_labels:
__a =ids_tensor([self.batch_size] , self.type_sequence_label_size )
__a =ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
__a =ids_tensor([self.batch_size] , 2 ).float()
__a =ids_tensor([self.batch_size] , self.num_choices )
__a =self.get_config()
return (
config,
input_ids,
token_type_ids,
input_lengths,
sequence_labels,
token_labels,
is_impossible_labels,
choice_labels,
input_mask,
)
def __magic_name__ ( self ) -> Any:
'''simple docstring'''
return XLMConfig(
vocab_size=self.vocab_size , n_special=self.n_special , emb_dim=self.hidden_size , n_layers=self.num_hidden_layers , n_heads=self.num_attention_heads , dropout=self.hidden_dropout_prob , attention_dropout=self.attention_probs_dropout_prob , gelu_activation=self.gelu_activation , sinusoidal_embeddings=self.sinusoidal_embeddings , asm=self.asm , causal=self.causal , n_langs=self.n_langs , max_position_embeddings=self.max_position_embeddings , initializer_range=self.initializer_range , summary_type=self.summary_type , use_proj=self.use_proj , num_labels=self.num_labels , bos_token_id=self.bos_token_id , )
def __magic_name__ ( self , __snake_case , __snake_case , __snake_case , __snake_case , __snake_case , __snake_case , __snake_case , __snake_case , __snake_case , ) -> List[Any]:
'''simple docstring'''
__a =XLMModel(config=__snake_case )
model.to(__snake_case )
model.eval()
__a =model(__snake_case , lengths=__snake_case , langs=__snake_case )
__a =model(__snake_case , langs=__snake_case )
__a =model(__snake_case )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def __magic_name__ ( self , __snake_case , __snake_case , __snake_case , __snake_case , __snake_case , __snake_case , __snake_case , __snake_case , __snake_case , ) -> Optional[int]:
'''simple docstring'''
__a =XLMWithLMHeadModel(__snake_case )
model.to(__snake_case )
model.eval()
__a =model(__snake_case , token_type_ids=__snake_case , labels=__snake_case )
self.parent.assertEqual(result.loss.shape , () )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def __magic_name__ ( self , __snake_case , __snake_case , __snake_case , __snake_case , __snake_case , __snake_case , __snake_case , __snake_case , __snake_case , ) -> Dict:
'''simple docstring'''
__a =XLMForQuestionAnsweringSimple(__snake_case )
model.to(__snake_case )
model.eval()
__a =model(__snake_case )
__a =model(__snake_case , start_positions=__snake_case , end_positions=__snake_case )
__a =outputs
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 __magic_name__ ( self , __snake_case , __snake_case , __snake_case , __snake_case , __snake_case , __snake_case , __snake_case , __snake_case , __snake_case , ) -> List[Any]:
'''simple docstring'''
__a =XLMForQuestionAnswering(__snake_case )
model.to(__snake_case )
model.eval()
__a =model(__snake_case )
__a =model(
__snake_case , start_positions=__snake_case , end_positions=__snake_case , cls_index=__snake_case , is_impossible=__snake_case , p_mask=__snake_case , )
__a =model(
__snake_case , start_positions=__snake_case , end_positions=__snake_case , cls_index=__snake_case , is_impossible=__snake_case , )
((__a) , ) =result_with_labels.to_tuple()
__a =model(__snake_case , start_positions=__snake_case , end_positions=__snake_case )
((__a) , ) =result_with_labels.to_tuple()
self.parent.assertEqual(result_with_labels.loss.shape , () )
self.parent.assertEqual(result.start_top_log_probs.shape , (self.batch_size, model.config.start_n_top) )
self.parent.assertEqual(result.start_top_index.shape , (self.batch_size, model.config.start_n_top) )
self.parent.assertEqual(
result.end_top_log_probs.shape , (self.batch_size, model.config.start_n_top * model.config.end_n_top) )
self.parent.assertEqual(
result.end_top_index.shape , (self.batch_size, model.config.start_n_top * model.config.end_n_top) )
self.parent.assertEqual(result.cls_logits.shape , (self.batch_size,) )
def __magic_name__ ( self , __snake_case , __snake_case , __snake_case , __snake_case , __snake_case , __snake_case , __snake_case , __snake_case , __snake_case , ) -> Optional[Any]:
'''simple docstring'''
__a =XLMForSequenceClassification(__snake_case )
model.to(__snake_case )
model.eval()
__a =model(__snake_case )
__a =model(__snake_case , labels=__snake_case )
self.parent.assertEqual(result.loss.shape , () )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size) )
def __magic_name__ ( self , __snake_case , __snake_case , __snake_case , __snake_case , __snake_case , __snake_case , __snake_case , __snake_case , __snake_case , ) -> Any:
'''simple docstring'''
__a =self.num_labels
__a =XLMForTokenClassification(__snake_case )
model.to(__snake_case )
model.eval()
__a =model(__snake_case , attention_mask=__snake_case , labels=__snake_case )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def __magic_name__ ( self , __snake_case , __snake_case , __snake_case , __snake_case , __snake_case , __snake_case , __snake_case , __snake_case , __snake_case , ) -> Tuple:
'''simple docstring'''
__a =self.num_choices
__a =XLMForMultipleChoice(config=__snake_case )
model.to(__snake_case )
model.eval()
__a =input_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
__a =token_type_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
__a =input_mask.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
__a =model(
__snake_case , attention_mask=__snake_case , token_type_ids=__snake_case , labels=__snake_case , )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_choices) )
def __magic_name__ ( self ) -> Union[str, Any]:
'''simple docstring'''
__a =self.prepare_config_and_inputs()
(
(
__a
) , (
__a
) , (
__a
) , (
__a
) , (
__a
) , (
__a
) , (
__a
) , (
__a
) , (
__a
) ,
) =config_and_inputs
__a ={'input_ids': input_ids, 'token_type_ids': token_type_ids, 'lengths': input_lengths}
return config, inputs_dict
@require_torch
class __magic_name__ ( lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , unittest.TestCase ):
SCREAMING_SNAKE_CASE = (
(
XLMModel,
XLMWithLMHeadModel,
XLMForQuestionAnswering,
XLMForSequenceClassification,
XLMForQuestionAnsweringSimple,
XLMForTokenClassification,
XLMForMultipleChoice,
)
if is_torch_available()
else ()
)
SCREAMING_SNAKE_CASE = (
(XLMWithLMHeadModel,) if is_torch_available() else ()
) # TODO (PVP): Check other models whether language generation is also applicable
SCREAMING_SNAKE_CASE = (
{
'feature-extraction': XLMModel,
'fill-mask': XLMWithLMHeadModel,
'question-answering': XLMForQuestionAnsweringSimple,
'text-classification': XLMForSequenceClassification,
'text-generation': XLMWithLMHeadModel,
'token-classification': XLMForTokenClassification,
'zero-shot': XLMForSequenceClassification,
}
if is_torch_available()
else {}
)
def __magic_name__ ( self , __snake_case , __snake_case , __snake_case , __snake_case , __snake_case ) -> int:
'''simple docstring'''
if (
pipeline_test_casse_name == "QAPipelineTests"
and tokenizer_name is not None
and not tokenizer_name.endswith('Fast' )
):
# `QAPipelineTests` fails for a few models when the slower tokenizer are used.
# (The slower tokenizers were never used for pipeline tests before the pipeline testing rework)
# TODO: check (and possibly fix) the `QAPipelineTests` with slower tokenizer
return True
return False
def __magic_name__ ( self , __snake_case , __snake_case , __snake_case=False ) -> str:
'''simple docstring'''
__a =super()._prepare_for_class(__snake_case , __snake_case , return_labels=__snake_case )
if return_labels:
if model_class.__name__ == "XLMForQuestionAnswering":
__a =torch.zeros(
self.model_tester.batch_size , dtype=torch.long , device=__snake_case )
__a =torch.zeros(
self.model_tester.batch_size , dtype=torch.long , device=__snake_case )
return inputs_dict
def __magic_name__ ( self ) -> Tuple:
'''simple docstring'''
__a =XLMModelTester(self )
__a =ConfigTester(self , config_class=__snake_case , emb_dim=37 )
def __magic_name__ ( self ) -> str:
'''simple docstring'''
self.config_tester.run_common_tests()
def __magic_name__ ( self ) -> List[str]:
'''simple docstring'''
__a =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_xlm_model(*__snake_case )
def __magic_name__ ( self ) -> List[str]:
'''simple docstring'''
__a =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_xlm_lm_head(*__snake_case )
def __magic_name__ ( self ) -> List[str]:
'''simple docstring'''
__a =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_xlm_simple_qa(*__snake_case )
def __magic_name__ ( self ) -> Optional[int]:
'''simple docstring'''
__a =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_xlm_qa(*__snake_case )
def __magic_name__ ( self ) -> Any:
'''simple docstring'''
__a =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_xlm_sequence_classif(*__snake_case )
def __magic_name__ ( self ) -> Optional[int]:
'''simple docstring'''
__a =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_xlm_token_classif(*__snake_case )
def __magic_name__ ( self ) -> Any:
'''simple docstring'''
__a =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_xlm_for_multiple_choice(*__snake_case )
def __magic_name__ ( self , __snake_case , __snake_case , __snake_case , __snake_case , __snake_case , __snake_case=False , __snake_case=1 ) -> Optional[Any]:
'''simple docstring'''
self.assertIsInstance(__snake_case , __snake_case )
self.assertListEqual(
[isinstance(__snake_case , __snake_case ) for iter_attentions in attentions] , [True] * len(__snake_case ) )
self.assertEqual(len(__snake_case ) , (max_length - min_length) * num_beam_groups )
for idx, iter_attentions in enumerate(__snake_case ):
# adds PAD dummy token
__a =min_length + idx + 1
__a =min_length + idx + 1
__a =(
batch_size * num_beam_groups,
config.num_attention_heads,
tgt_len,
src_len,
)
# check attn size
self.assertListEqual(
[layer_attention.shape for layer_attention in iter_attentions] , [expected_shape] * len(__snake_case ) )
def __magic_name__ ( self , __snake_case , __snake_case , __snake_case , __snake_case , __snake_case , __snake_case=False , __snake_case=1 ) -> Dict:
'''simple docstring'''
self.assertIsInstance(__snake_case , __snake_case )
self.assertListEqual(
[isinstance(__snake_case , __snake_case ) for iter_hidden_states in hidden_states] , [True] * len(__snake_case ) , )
self.assertEqual(len(__snake_case ) , (max_length - min_length) * num_beam_groups )
for idx, iter_hidden_states in enumerate(__snake_case ):
# adds PAD dummy token
__a =min_length + idx + 1
__a =(batch_size * num_beam_groups, seq_len, config.hidden_size)
# check hidden size
self.assertListEqual(
[layer_hidden_states.shape for layer_hidden_states in iter_hidden_states] , [expected_shape] * len(__snake_case ) , )
pass
@slow
def __magic_name__ ( self ) -> Union[str, Any]:
'''simple docstring'''
for model_name in XLM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
__a =XLMModel.from_pretrained(__snake_case )
self.assertIsNotNone(__snake_case )
@require_torch
class __magic_name__ ( unittest.TestCase ):
@slow
def __magic_name__ ( self ) -> Tuple:
'''simple docstring'''
__a =XLMWithLMHeadModel.from_pretrained('xlm-mlm-en-2048' )
model.to(__snake_case )
__a =torch.tensor([[14, 447]] , dtype=torch.long , device=__snake_case ) # the president
__a =[
14,
447,
14,
447,
14,
447,
14,
447,
14,
447,
14,
447,
14,
447,
14,
447,
14,
447,
14,
447,
] # the president the president the president the president the president the president the president the president the president the president
# TODO(PVP): this and other input_ids I tried for generation give pretty bad results. Not sure why. Model might just not be made for auto-regressive inference
__a =model.generate(__snake_case , do_sample=__snake_case )
self.assertListEqual(output_ids[0].cpu().numpy().tolist() , __snake_case )
| 308
|
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available
_lowerCAmelCase : int = {}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_lowerCAmelCase : Dict = ["BartphoTokenizer"]
if TYPE_CHECKING:
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_bartpho import BartphoTokenizer
else:
import sys
_lowerCAmelCase : Optional[Any] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 308
| 1
|
import argparse
import json
import os
import torch
from transformers import LukeConfig, LukeModel, LukeTokenizer, RobertaTokenizer
from transformers.tokenization_utils_base import AddedToken
@torch.no_grad()
def UpperCamelCase_( _snake_case : Dict , _snake_case : Tuple , _snake_case : int , _snake_case : Dict , _snake_case : Tuple ):
"""simple docstring"""
with open(_snake_case ) as metadata_file:
__a =json.load(_snake_case )
__a =LukeConfig(use_entity_aware_attention=_snake_case , **metadata['model_config'] )
# Load in the weights from the checkpoint_path
__a =torch.load(_snake_case , map_location='cpu' )
# Load the entity vocab file
__a =load_entity_vocab(_snake_case )
__a =RobertaTokenizer.from_pretrained(metadata['model_config']['bert_model_name'] )
# Add special tokens to the token vocabulary for downstream tasks
__a =AddedToken('<ent>' , lstrip=_snake_case , rstrip=_snake_case )
__a =AddedToken('<ent2>' , lstrip=_snake_case , rstrip=_snake_case )
tokenizer.add_special_tokens({'additional_special_tokens': [entity_token_a, entity_token_a]} )
config.vocab_size += 2
print(F'Saving tokenizer to {pytorch_dump_folder_path}' )
tokenizer.save_pretrained(_snake_case )
with open(os.path.join(_snake_case , LukeTokenizer.vocab_files_names['entity_vocab_file'] ) , 'w' ) as f:
json.dump(_snake_case , _snake_case )
__a =LukeTokenizer.from_pretrained(_snake_case )
# Initialize the embeddings of the special tokens
__a =state_dict['embeddings.word_embeddings.weight']
__a =word_emb[tokenizer.convert_tokens_to_ids(['@'] )[0]].unsqueeze(0 )
__a =word_emb[tokenizer.convert_tokens_to_ids(['#'] )[0]].unsqueeze(0 )
__a =torch.cat([word_emb, ent_emb, enta_emb] )
# Initialize the query layers of the entity-aware self-attention mechanism
for layer_index in range(config.num_hidden_layers ):
for matrix_name in ["query.weight", "query.bias"]:
__a =F'encoder.layer.{layer_index}.attention.self.'
__a =state_dict[prefix + matrix_name]
__a =state_dict[prefix + matrix_name]
__a =state_dict[prefix + matrix_name]
# Initialize the embedding of the [MASK2] entity using that of the [MASK] entity for downstream tasks
__a =state_dict['entity_embeddings.entity_embeddings.weight']
__a =entity_emb[entity_vocab['[MASK]']]
__a =LukeModel(config=_snake_case ).eval()
__a , __a =model.load_state_dict(_snake_case , strict=_snake_case )
if not (len(_snake_case ) == 1 and missing_keys[0] == "embeddings.position_ids"):
raise ValueError(F'Missing keys {", ".join(_snake_case )}. Expected only missing embeddings.position_ids' )
if not (all(key.startswith('entity_predictions' ) or key.startswith('lm_head' ) for key in unexpected_keys )):
raise ValueError(
'Unexpected keys'
F' {", ".join([key for key in unexpected_keys if not (key.startswith("entity_predictions" ) or key.startswith("lm_head" ))] )}' )
# Check outputs
__a =LukeTokenizer.from_pretrained(_snake_case , task='entity_classification' )
__a =(
'Top seed Ana Ivanovic said on Thursday she could hardly believe her luck as a fortuitous netcord helped the'
' new world number one avoid a humiliating second- round exit at Wimbledon .'
)
__a =(39, 42)
__a =tokenizer(_snake_case , entity_spans=[span] , add_prefix_space=_snake_case , return_tensors='pt' )
__a =model(**_snake_case )
# Verify word hidden states
if model_size == "large":
__a =torch.Size((1, 42, 1024) )
__a =torch.tensor(
[[0.0_133, 0.0_865, 0.0_095], [0.3_093, -0.2_576, -0.7_418], [-0.1_720, -0.2_117, -0.2_869]] )
else: # base
__a =torch.Size((1, 42, 768) )
__a =torch.tensor([[0.0_037, 0.1_368, -0.0_091], [0.1_099, 0.3_329, -0.1_095], [0.0_765, 0.5_335, 0.1_179]] )
if not (outputs.last_hidden_state.shape == expected_shape):
raise ValueError(
F'Outputs.last_hidden_state.shape is {outputs.last_hidden_state.shape}, Expected shape is {expected_shape}' )
if not torch.allclose(outputs.last_hidden_state[0, :3, :3] , _snake_case , atol=1e-4 ):
raise ValueError
# Verify entity hidden states
if model_size == "large":
__a =torch.Size((1, 1, 1024) )
__a =torch.tensor([[0.0_466, -0.0_106, -0.0_179]] )
else: # base
__a =torch.Size((1, 1, 768) )
__a =torch.tensor([[0.1_457, 0.1_044, 0.0_174]] )
if not (outputs.entity_last_hidden_state.shape != expected_shape):
raise ValueError(
F'Outputs.entity_last_hidden_state.shape is {outputs.entity_last_hidden_state.shape}, Expected shape is'
F' {expected_shape}' )
if not torch.allclose(outputs.entity_last_hidden_state[0, :3, :3] , _snake_case , atol=1e-4 ):
raise ValueError
# Finally, save our PyTorch model and tokenizer
print('Saving PyTorch model to {}'.format(_snake_case ) )
model.save_pretrained(_snake_case )
def UpperCamelCase_( _snake_case : Optional[int] ):
"""simple docstring"""
__a ={}
with open(_snake_case , 'r' , encoding='utf-8' ) as f:
for index, line in enumerate(_snake_case ):
__a , __a =line.rstrip().split('\t' )
__a =index
return entity_vocab
if __name__ == "__main__":
_lowerCAmelCase : int = argparse.ArgumentParser()
# Required parameters
parser.add_argument("--checkpoint_path", type=str, help="Path to a pytorch_model.bin file.")
parser.add_argument(
"--metadata_path", default=None, type=str, help="Path to a metadata.json file, defining the configuration."
)
parser.add_argument(
"--entity_vocab_path",
default=None,
type=str,
help="Path to an entity_vocab.tsv file, containing the entity vocabulary.",
)
parser.add_argument(
"--pytorch_dump_folder_path", default=None, type=str, help="Path to where to dump the output PyTorch model."
)
parser.add_argument(
"--model_size", default="base", type=str, choices=["base", "large"], help="Size of the model to be converted."
)
_lowerCAmelCase : int = parser.parse_args()
convert_luke_checkpoint(
args.checkpoint_path,
args.metadata_path,
args.entity_vocab_path,
args.pytorch_dump_folder_path,
args.model_size,
)
| 308
|
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
_lowerCAmelCase : List[str] = logging.get_logger(__name__)
_lowerCAmelCase : List[Any] = {
"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 __magic_name__ ( lowerCAmelCase_ ):
SCREAMING_SNAKE_CASE = 'yolos'
def __init__( self , __snake_case=768 , __snake_case=12 , __snake_case=12 , __snake_case=3072 , __snake_case="gelu" , __snake_case=0.0 , __snake_case=0.0 , __snake_case=0.02 , __snake_case=1e-12 , __snake_case=[512, 864] , __snake_case=16 , __snake_case=3 , __snake_case=True , __snake_case=100 , __snake_case=True , __snake_case=False , __snake_case=1 , __snake_case=5 , __snake_case=2 , __snake_case=5 , __snake_case=2 , __snake_case=0.1 , **__snake_case , ) -> str:
'''simple docstring'''
super().__init__(**__snake_case )
__a =hidden_size
__a =num_hidden_layers
__a =num_attention_heads
__a =intermediate_size
__a =hidden_act
__a =hidden_dropout_prob
__a =attention_probs_dropout_prob
__a =initializer_range
__a =layer_norm_eps
__a =image_size
__a =patch_size
__a =num_channels
__a =qkv_bias
__a =num_detection_tokens
__a =use_mid_position_embeddings
__a =auxiliary_loss
# Hungarian matcher
__a =class_cost
__a =bbox_cost
__a =giou_cost
# Loss coefficients
__a =bbox_loss_coefficient
__a =giou_loss_coefficient
__a =eos_coefficient
class __magic_name__ ( lowerCAmelCase_ ):
SCREAMING_SNAKE_CASE = version.parse('1.11' )
@property
def __magic_name__ ( self ) -> Mapping[str, Mapping[int, str]]:
'''simple docstring'''
return OrderedDict(
[
('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'}),
] )
@property
def __magic_name__ ( self ) -> float:
'''simple docstring'''
return 1e-4
@property
def __magic_name__ ( self ) -> int:
'''simple docstring'''
return 12
| 308
| 1
|
import os
from typing import Dict, List, Union
import tensorflow as tf
from keras_nlp.tokenizers import BytePairTokenizer
from tensorflow_text import pad_model_inputs
from .tokenization_gpta import GPTaTokenizer
class __magic_name__ ( tf.keras.layers.Layer ):
def __init__( self , __snake_case , __snake_case , __snake_case = None , __snake_case = None ) -> Optional[int]:
'''simple docstring'''
super().__init__()
__a =pad_token_id
__a =max_length
__a =vocab
__a =merges
__a =BytePairTokenizer(__snake_case , __snake_case , sequence_length=__snake_case )
@classmethod
def __magic_name__ ( cls , __snake_case , *__snake_case , **__snake_case ) -> str:
'''simple docstring'''
__a =[' '.join(__snake_case ) for m in tokenizer.bpe_ranks.keys()]
__a =tokenizer.get_vocab()
return cls(__snake_case , __snake_case , *__snake_case , **__snake_case )
@classmethod
def __magic_name__ ( cls , __snake_case , *__snake_case , **__snake_case ) -> str:
'''simple docstring'''
__a =GPTaTokenizer.from_pretrained(__snake_case , *__snake_case , **__snake_case )
return cls.from_tokenizer(__snake_case , *__snake_case , **__snake_case )
@classmethod
def __magic_name__ ( cls , __snake_case ) -> List[str]:
'''simple docstring'''
return cls(**__snake_case )
def __magic_name__ ( self ) -> Dict:
'''simple docstring'''
return {
"vocab": self.vocab,
"merges": self.merges,
"max_length": self.max_length,
"pad_token_id": self.pad_token_id,
}
def __magic_name__ ( self , __snake_case , __snake_case = None ) -> int:
'''simple docstring'''
__a =self.tf_tokenizer(__snake_case )
__a =tf.ones_like(__snake_case )
if self.pad_token_id is not None:
# pad the tokens up to max length
__a =max_length if max_length is not None else self.max_length
if max_length is not None:
__a , __a =pad_model_inputs(
__snake_case , max_seq_length=__snake_case , pad_value=self.pad_token_id )
return {"attention_mask": attention_mask, "input_ids": input_ids}
| 308
|
import argparse
import torch
from datasets import load_dataset
from donut import DonutModel
from transformers import (
DonutImageProcessor,
DonutProcessor,
DonutSwinConfig,
DonutSwinModel,
MBartConfig,
MBartForCausalLM,
VisionEncoderDecoderModel,
XLMRobertaTokenizerFast,
)
def UpperCamelCase_( _snake_case : Optional[Any] ):
"""simple docstring"""
__a =model.config
__a =DonutSwinConfig(
image_size=original_config.input_size , patch_size=4 , depths=original_config.encoder_layer , num_heads=[4, 8, 16, 32] , window_size=original_config.window_size , embed_dim=128 , )
__a =MBartConfig(
is_decoder=_snake_case , is_encoder_decoder=_snake_case , add_cross_attention=_snake_case , decoder_layers=original_config.decoder_layer , max_position_embeddings=original_config.max_position_embeddings , vocab_size=len(
model.decoder.tokenizer ) , scale_embedding=_snake_case , add_final_layer_norm=_snake_case , )
return encoder_config, decoder_config
def UpperCamelCase_( _snake_case : Tuple ):
"""simple docstring"""
if "encoder.model" in name:
__a =name.replace('encoder.model' , 'encoder' )
if "decoder.model" in name:
__a =name.replace('decoder.model' , 'decoder' )
if "patch_embed.proj" in name:
__a =name.replace('patch_embed.proj' , 'embeddings.patch_embeddings.projection' )
if "patch_embed.norm" in name:
__a =name.replace('patch_embed.norm' , 'embeddings.norm' )
if name.startswith('encoder' ):
if "layers" in name:
__a ='encoder.' + name
if "attn.proj" in name:
__a =name.replace('attn.proj' , 'attention.output.dense' )
if "attn" in name and "mask" not in name:
__a =name.replace('attn' , 'attention.self' )
if "norm1" in name:
__a =name.replace('norm1' , 'layernorm_before' )
if "norm2" in name:
__a =name.replace('norm2' , 'layernorm_after' )
if "mlp.fc1" in name:
__a =name.replace('mlp.fc1' , 'intermediate.dense' )
if "mlp.fc2" in name:
__a =name.replace('mlp.fc2' , 'output.dense' )
if name == "encoder.norm.weight":
__a ='encoder.layernorm.weight'
if name == "encoder.norm.bias":
__a ='encoder.layernorm.bias'
return name
def UpperCamelCase_( _snake_case : Tuple , _snake_case : str ):
"""simple docstring"""
for key in orig_state_dict.copy().keys():
__a =orig_state_dict.pop(_snake_case )
if "qkv" in key:
__a =key.split('.' )
__a =int(key_split[3] )
__a =int(key_split[5] )
__a =model.encoder.encoder.layers[layer_num].blocks[block_num].attention.self.all_head_size
if "weight" in key:
__a =val[:dim, :]
__a =val[dim : dim * 2, :]
__a =val[-dim:, :]
else:
__a =val[:dim]
__a =val[dim : dim * 2]
__a =val[-dim:]
elif "attn_mask" in key or key in ["encoder.model.norm.weight", "encoder.model.norm.bias"]:
# HuggingFace implementation doesn't use attn_mask buffer
# and model doesn't use final LayerNorms for the encoder
pass
else:
__a =val
return orig_state_dict
def UpperCamelCase_( _snake_case : Tuple , _snake_case : Union[str, Any]=None , _snake_case : List[Any]=False ):
"""simple docstring"""
__a =DonutModel.from_pretrained(_snake_case ).eval()
# load HuggingFace model
__a , __a =get_configs(_snake_case )
__a =DonutSwinModel(_snake_case )
__a =MBartForCausalLM(_snake_case )
__a =VisionEncoderDecoderModel(encoder=_snake_case , decoder=_snake_case )
model.eval()
__a =original_model.state_dict()
__a =convert_state_dict(_snake_case , _snake_case )
model.load_state_dict(_snake_case )
# verify results on scanned document
__a =load_dataset('hf-internal-testing/example-documents' )
__a =dataset['test'][0]['image'].convert('RGB' )
__a =XLMRobertaTokenizerFast.from_pretrained(_snake_case , from_slow=_snake_case )
__a =DonutImageProcessor(
do_align_long_axis=original_model.config.align_long_axis , size=original_model.config.input_size[::-1] )
__a =DonutProcessor(_snake_case , _snake_case )
__a =processor(_snake_case , return_tensors='pt' ).pixel_values
if model_name == "naver-clova-ix/donut-base-finetuned-docvqa":
__a ='<s_docvqa><s_question>{user_input}</s_question><s_answer>'
__a ='When is the coffee break?'
__a =task_prompt.replace('{user_input}' , _snake_case )
elif model_name == "naver-clova-ix/donut-base-finetuned-rvlcdip":
__a ='<s_rvlcdip>'
elif model_name in [
"naver-clova-ix/donut-base-finetuned-cord-v1",
"naver-clova-ix/donut-base-finetuned-cord-v1-2560",
]:
__a ='<s_cord>'
elif model_name == "naver-clova-ix/donut-base-finetuned-cord-v2":
__a ='s_cord-v2>'
elif model_name == "naver-clova-ix/donut-base-finetuned-zhtrainticket":
__a ='<s_zhtrainticket>'
elif model_name in ["naver-clova-ix/donut-proto", "naver-clova-ix/donut-base"]:
# use a random prompt
__a ='hello world'
else:
raise ValueError('Model name not supported' )
__a =original_model.decoder.tokenizer(_snake_case , add_special_tokens=_snake_case , return_tensors='pt' )[
'input_ids'
]
__a =original_model.encoder.model.patch_embed(_snake_case )
__a , __a =model.encoder.embeddings(_snake_case )
assert torch.allclose(_snake_case , _snake_case , atol=1e-3 )
# verify encoder hidden states
__a =original_model.encoder(_snake_case )
__a =model.encoder(_snake_case ).last_hidden_state
assert torch.allclose(_snake_case , _snake_case , atol=1e-2 )
# verify decoder hidden states
__a =original_model(_snake_case , _snake_case , _snake_case ).logits
__a =model(_snake_case , decoder_input_ids=_snake_case ).logits
assert torch.allclose(_snake_case , _snake_case , atol=1e-3 )
print('Looks ok!' )
if pytorch_dump_folder_path is not None:
print(F'Saving model and processor to {pytorch_dump_folder_path}' )
model.save_pretrained(_snake_case )
processor.save_pretrained(_snake_case )
if push_to_hub:
model.push_to_hub('nielsr/' + model_name.split('/' )[-1] , commit_message='Update model' )
processor.push_to_hub('nielsr/' + model_name.split('/' )[-1] , commit_message='Update model' )
if __name__ == "__main__":
_lowerCAmelCase : List[str] = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--model_name",
default="naver-clova-ix/donut-base-finetuned-docvqa",
required=False,
type=str,
help="Name of the original model you'd like to convert.",
)
parser.add_argument(
"--pytorch_dump_folder_path",
default=None,
required=False,
type=str,
help="Path to the output PyTorch model directory.",
)
parser.add_argument(
"--push_to_hub",
action="store_true",
help="Whether or not to push the converted model and processor to the 🤗 hub.",
)
_lowerCAmelCase : List[Any] = parser.parse_args()
convert_donut_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)
| 308
| 1
|
from collections import namedtuple
import requests
from lxml import html # type: ignore
_lowerCAmelCase : Tuple = namedtuple("covid_data", "cases deaths recovered")
def UpperCamelCase_( _snake_case : str = "https://www.worldometers.info/coronavirus/" ):
"""simple docstring"""
__a ='//div[@class = "maincounter-number"]/span/text()'
return covid_data(*html.fromstring(requests.get(_snake_case ).content ).xpath(_snake_case ) )
_lowerCAmelCase : str = "Total COVID-19 cases in the world: {}\nTotal deaths due to COVID-19 in the world: {}\nTotal COVID-19 patients recovered in the world: {}"
print(fmt.format(*covid_stats()))
| 308
|
import json
import os
import unittest
from transformers.models.blenderbot_small.tokenization_blenderbot_small import (
VOCAB_FILES_NAMES,
BlenderbotSmallTokenizer,
)
from ...test_tokenization_common import TokenizerTesterMixin
class __magic_name__ ( lowerCAmelCase_ , unittest.TestCase ):
SCREAMING_SNAKE_CASE = BlenderbotSmallTokenizer
SCREAMING_SNAKE_CASE = False
def __magic_name__ ( self ) -> Union[str, Any]:
'''simple docstring'''
super().setUp()
__a =['__start__', 'adapt', 'act', 'ap@@', 'te', '__end__', '__unk__']
__a =dict(zip(__snake_case , range(len(__snake_case ) ) ) )
__a =['#version: 0.2', 'a p', 't e</w>', 'ap t</w>', 'a d', 'ad apt</w>', 'a c', 'ac t</w>', '']
__a ={'unk_token': '__unk__', 'bos_token': '__start__', 'eos_token': '__end__'}
__a =os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['vocab_file'] )
__a =os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['merges_file'] )
with open(self.vocab_file , 'w' , encoding='utf-8' ) as fp:
fp.write(json.dumps(__snake_case ) + '\n' )
with open(self.merges_file , 'w' , encoding='utf-8' ) as fp:
fp.write('\n'.join(__snake_case ) )
def __magic_name__ ( self , **__snake_case ) -> Any:
'''simple docstring'''
kwargs.update(self.special_tokens_map )
return BlenderbotSmallTokenizer.from_pretrained(self.tmpdirname , **__snake_case )
def __magic_name__ ( self , __snake_case ) -> List[Any]:
'''simple docstring'''
__a ='adapt act apte'
__a ='adapt act apte'
return input_text, output_text
def __magic_name__ ( self ) -> str:
'''simple docstring'''
__a =BlenderbotSmallTokenizer(self.vocab_file , self.merges_file , **self.special_tokens_map )
__a ='adapt act apte'
__a =['adapt', 'act', 'ap@@', 'te']
__a =tokenizer.tokenize(__snake_case )
self.assertListEqual(__snake_case , __snake_case )
__a =[tokenizer.bos_token] + tokens + [tokenizer.eos_token]
__a =[0, 1, 2, 3, 4, 5]
self.assertListEqual(tokenizer.convert_tokens_to_ids(__snake_case ) , __snake_case )
def __magic_name__ ( self ) -> str:
'''simple docstring'''
__a =BlenderbotSmallTokenizer.from_pretrained('facebook/blenderbot-90M' )
assert tok('sam' ).input_ids == [1384]
__a ='I am a small frog.'
__a =tok([src_text] , padding=__snake_case , truncation=__snake_case )['input_ids']
__a =tok.batch_decode(__snake_case , skip_special_tokens=__snake_case , clean_up_tokenization_spaces=__snake_case )[0]
assert src_text != decoded # I wish it did!
assert decoded == "i am a small frog ."
def __magic_name__ ( self ) -> str:
'''simple docstring'''
__a =BlenderbotSmallTokenizer.from_pretrained('facebook/blenderbot-90M' )
__a ='I am a small frog .'
__a ='.'
__a =tok(__snake_case )['input_ids']
__a =tok(__snake_case )['input_ids']
assert encoded[-1] == encoded_dot[0]
| 308
| 1
|
import argparse
import os
from io import BytesIO
from pathlib import Path
import requests
from clip_retrieval.clip_client import ClipClient
from PIL import Image
from tqdm import tqdm
def UpperCamelCase_( _snake_case : Optional[int] , _snake_case : Dict , _snake_case : Union[str, Any] ):
"""simple docstring"""
__a =1.5
__a =int(factor * num_class_images )
__a =ClipClient(
url='https://knn.laion.ai/knn-service' , indice_name='laion_400m' , num_images=_snake_case , aesthetic_weight=0.1 )
os.makedirs(F'{class_data_dir}/images' , exist_ok=_snake_case )
if len(list(Path(F'{class_data_dir}/images' ).iterdir() ) ) >= num_class_images:
return
while True:
__a =client.query(text=_snake_case )
if len(_snake_case ) >= factor * num_class_images or num_images > 1e4:
break
else:
__a =int(factor * num_images )
__a =ClipClient(
url='https://knn.laion.ai/knn-service' , indice_name='laion_400m' , num_images=_snake_case , aesthetic_weight=0.1 , )
__a =0
__a =0
__a =tqdm(desc='downloading real regularization images' , total=_snake_case )
with open(F'{class_data_dir}/caption.txt' , 'w' ) as fa, open(F'{class_data_dir}/urls.txt' , 'w' ) as fa, open(
F'{class_data_dir}/images.txt' , 'w' ) as fa:
while total < num_class_images:
__a =class_images[count]
count += 1
try:
__a =requests.get(images['url'] )
if img.status_code == 200:
__a =Image.open(BytesIO(img.content ) )
with open(F'{class_data_dir}/images/{total}.jpg' , 'wb' ) as f:
f.write(img.content )
fa.write(images['caption'] + '\n' )
fa.write(images['url'] + '\n' )
fa.write(F'{class_data_dir}/images/{total}.jpg' + '\n' )
total += 1
pbar.update(1 )
else:
continue
except Exception:
continue
return
def UpperCamelCase_( ):
"""simple docstring"""
__a =argparse.ArgumentParser('' , add_help=_snake_case )
parser.add_argument('--class_prompt' , help='text prompt to retrieve images' , required=_snake_case , type=_snake_case )
parser.add_argument('--class_data_dir' , help='path to save images' , required=_snake_case , type=_snake_case )
parser.add_argument('--num_class_images' , help='number of images to download' , default=200 , type=_snake_case )
return parser.parse_args()
if __name__ == "__main__":
_lowerCAmelCase : List[Any] = parse_args()
retrieve(args.class_prompt, args.class_data_dir, args.num_class_images)
| 308
|
import unittest
from transformers import load_tool
from transformers.utils import is_torch_available
if is_torch_available():
import torch
from transformers.testing_utils import require_torch
from .test_tools_common import ToolTesterMixin
@require_torch
class __magic_name__ ( unittest.TestCase , lowerCAmelCase_ ):
def __magic_name__ ( self ) -> Union[str, Any]:
'''simple docstring'''
__a =load_tool('text-to-speech' )
self.tool.setup()
def __magic_name__ ( self ) -> Dict:
'''simple docstring'''
# SpeechT5 isn't deterministic
torch.manual_seed(0 )
__a =self.tool('hey' )
__a =result.to_raw()
self.assertTrue(
torch.allclose(
resulting_tensor[:3] , torch.tensor([-0.000_5966_6688_3211_5829, -0.000_3657_6401_9079_5064, -0.0001_3439_5027_9988_3485] ) , ) )
def __magic_name__ ( self ) -> List[Any]:
'''simple docstring'''
# SpeechT5 isn't deterministic
torch.manual_seed(0 )
__a =self.tool('hey' )
__a =result.to_raw()
self.assertTrue(
torch.allclose(
resulting_tensor[:3] , torch.tensor([-0.000_5966_6688_3211_5829, -0.000_3657_6401_9079_5064, -0.0001_3439_5027_9988_3485] ) , ) )
| 308
| 1
|
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
_lowerCAmelCase : int = logging.get_logger(__name__)
_lowerCAmelCase : List[Any] = {
"google/bigbird-roberta-base": "https://huggingface.co/google/bigbird-roberta-base/resolve/main/config.json",
"google/bigbird-roberta-large": "https://huggingface.co/google/bigbird-roberta-large/resolve/main/config.json",
"google/bigbird-base-trivia-itc": "https://huggingface.co/google/bigbird-base-trivia-itc/resolve/main/config.json",
# See all BigBird models at https://huggingface.co/models?filter=big_bird
}
class __magic_name__ ( lowerCAmelCase_ ):
SCREAMING_SNAKE_CASE = 'big_bird'
def __init__( self , __snake_case=5_0358 , __snake_case=768 , __snake_case=12 , __snake_case=12 , __snake_case=3072 , __snake_case="gelu_new" , __snake_case=0.1 , __snake_case=0.1 , __snake_case=4096 , __snake_case=2 , __snake_case=0.02 , __snake_case=1e-12 , __snake_case=True , __snake_case=0 , __snake_case=1 , __snake_case=2 , __snake_case=66 , __snake_case="block_sparse" , __snake_case=True , __snake_case=False , __snake_case=64 , __snake_case=3 , __snake_case=None , **__snake_case , ) -> Union[str, Any]:
'''simple docstring'''
super().__init__(
pad_token_id=__snake_case , bos_token_id=__snake_case , eos_token_id=__snake_case , sep_token_id=__snake_case , **__snake_case , )
__a =vocab_size
__a =max_position_embeddings
__a =hidden_size
__a =num_hidden_layers
__a =num_attention_heads
__a =intermediate_size
__a =hidden_act
__a =hidden_dropout_prob
__a =attention_probs_dropout_prob
__a =initializer_range
__a =type_vocab_size
__a =layer_norm_eps
__a =use_cache
__a =rescale_embeddings
__a =attention_type
__a =use_bias
__a =block_size
__a =num_random_blocks
__a =classifier_dropout
class __magic_name__ ( lowerCAmelCase_ ):
@property
def __magic_name__ ( self ) -> Mapping[str, Mapping[int, str]]:
'''simple docstring'''
if self.task == "multiple-choice":
__a ={0: 'batch', 1: 'choice', 2: 'sequence'}
else:
__a ={0: 'batch', 1: 'sequence'}
return OrderedDict(
[
('input_ids', dynamic_axis),
('attention_mask', dynamic_axis),
] )
| 308
|
import flax.linen as nn
import jax.numpy as jnp
from .attention_flax import FlaxTransformeraDModel
from .resnet_flax import FlaxDownsampleaD, FlaxResnetBlockaD, FlaxUpsampleaD
class __magic_name__ ( nn.Module ):
SCREAMING_SNAKE_CASE = 42
SCREAMING_SNAKE_CASE = 42
SCREAMING_SNAKE_CASE = 0.0
SCREAMING_SNAKE_CASE = 1
SCREAMING_SNAKE_CASE = 1
SCREAMING_SNAKE_CASE = True
SCREAMING_SNAKE_CASE = False
SCREAMING_SNAKE_CASE = False
SCREAMING_SNAKE_CASE = False
SCREAMING_SNAKE_CASE = jnp.floataa
def __magic_name__ ( self ) -> int:
'''simple docstring'''
__a =[]
__a =[]
for i in range(self.num_layers ):
__a =self.in_channels if i == 0 else self.out_channels
__a =FlaxResnetBlockaD(
in_channels=__snake_case , out_channels=self.out_channels , dropout_prob=self.dropout , dtype=self.dtype , )
resnets.append(__snake_case )
__a =FlaxTransformeraDModel(
in_channels=self.out_channels , n_heads=self.num_attention_heads , d_head=self.out_channels // self.num_attention_heads , depth=1 , use_linear_projection=self.use_linear_projection , only_cross_attention=self.only_cross_attention , use_memory_efficient_attention=self.use_memory_efficient_attention , dtype=self.dtype , )
attentions.append(__snake_case )
__a =resnets
__a =attentions
if self.add_downsample:
__a =FlaxDownsampleaD(self.out_channels , dtype=self.dtype )
def __call__( self , __snake_case , __snake_case , __snake_case , __snake_case=True ) -> Optional[Any]:
'''simple docstring'''
__a =()
for resnet, attn in zip(self.resnets , self.attentions ):
__a =resnet(__snake_case , __snake_case , deterministic=__snake_case )
__a =attn(__snake_case , __snake_case , deterministic=__snake_case )
output_states += (hidden_states,)
if self.add_downsample:
__a =self.downsamplers_a(__snake_case )
output_states += (hidden_states,)
return hidden_states, output_states
class __magic_name__ ( nn.Module ):
SCREAMING_SNAKE_CASE = 42
SCREAMING_SNAKE_CASE = 42
SCREAMING_SNAKE_CASE = 0.0
SCREAMING_SNAKE_CASE = 1
SCREAMING_SNAKE_CASE = True
SCREAMING_SNAKE_CASE = jnp.floataa
def __magic_name__ ( self ) -> int:
'''simple docstring'''
__a =[]
for i in range(self.num_layers ):
__a =self.in_channels if i == 0 else self.out_channels
__a =FlaxResnetBlockaD(
in_channels=__snake_case , out_channels=self.out_channels , dropout_prob=self.dropout , dtype=self.dtype , )
resnets.append(__snake_case )
__a =resnets
if self.add_downsample:
__a =FlaxDownsampleaD(self.out_channels , dtype=self.dtype )
def __call__( self , __snake_case , __snake_case , __snake_case=True ) -> Optional[int]:
'''simple docstring'''
__a =()
for resnet in self.resnets:
__a =resnet(__snake_case , __snake_case , deterministic=__snake_case )
output_states += (hidden_states,)
if self.add_downsample:
__a =self.downsamplers_a(__snake_case )
output_states += (hidden_states,)
return hidden_states, output_states
class __magic_name__ ( nn.Module ):
SCREAMING_SNAKE_CASE = 42
SCREAMING_SNAKE_CASE = 42
SCREAMING_SNAKE_CASE = 42
SCREAMING_SNAKE_CASE = 0.0
SCREAMING_SNAKE_CASE = 1
SCREAMING_SNAKE_CASE = 1
SCREAMING_SNAKE_CASE = True
SCREAMING_SNAKE_CASE = False
SCREAMING_SNAKE_CASE = False
SCREAMING_SNAKE_CASE = False
SCREAMING_SNAKE_CASE = jnp.floataa
def __magic_name__ ( self ) -> List[Any]:
'''simple docstring'''
__a =[]
__a =[]
for i in range(self.num_layers ):
__a =self.in_channels if (i == self.num_layers - 1) else self.out_channels
__a =self.prev_output_channel if i == 0 else self.out_channels
__a =FlaxResnetBlockaD(
in_channels=resnet_in_channels + res_skip_channels , out_channels=self.out_channels , dropout_prob=self.dropout , dtype=self.dtype , )
resnets.append(__snake_case )
__a =FlaxTransformeraDModel(
in_channels=self.out_channels , n_heads=self.num_attention_heads , d_head=self.out_channels // self.num_attention_heads , depth=1 , use_linear_projection=self.use_linear_projection , only_cross_attention=self.only_cross_attention , use_memory_efficient_attention=self.use_memory_efficient_attention , dtype=self.dtype , )
attentions.append(__snake_case )
__a =resnets
__a =attentions
if self.add_upsample:
__a =FlaxUpsampleaD(self.out_channels , dtype=self.dtype )
def __call__( self , __snake_case , __snake_case , __snake_case , __snake_case , __snake_case=True ) -> List[Any]:
'''simple docstring'''
for resnet, attn in zip(self.resnets , self.attentions ):
# pop res hidden states
__a =res_hidden_states_tuple[-1]
__a =res_hidden_states_tuple[:-1]
__a =jnp.concatenate((hidden_states, res_hidden_states) , axis=-1 )
__a =resnet(__snake_case , __snake_case , deterministic=__snake_case )
__a =attn(__snake_case , __snake_case , deterministic=__snake_case )
if self.add_upsample:
__a =self.upsamplers_a(__snake_case )
return hidden_states
class __magic_name__ ( nn.Module ):
SCREAMING_SNAKE_CASE = 42
SCREAMING_SNAKE_CASE = 42
SCREAMING_SNAKE_CASE = 42
SCREAMING_SNAKE_CASE = 0.0
SCREAMING_SNAKE_CASE = 1
SCREAMING_SNAKE_CASE = True
SCREAMING_SNAKE_CASE = jnp.floataa
def __magic_name__ ( self ) -> Union[str, Any]:
'''simple docstring'''
__a =[]
for i in range(self.num_layers ):
__a =self.in_channels if (i == self.num_layers - 1) else self.out_channels
__a =self.prev_output_channel if i == 0 else self.out_channels
__a =FlaxResnetBlockaD(
in_channels=resnet_in_channels + res_skip_channels , out_channels=self.out_channels , dropout_prob=self.dropout , dtype=self.dtype , )
resnets.append(__snake_case )
__a =resnets
if self.add_upsample:
__a =FlaxUpsampleaD(self.out_channels , dtype=self.dtype )
def __call__( self , __snake_case , __snake_case , __snake_case , __snake_case=True ) -> List[Any]:
'''simple docstring'''
for resnet in self.resnets:
# pop res hidden states
__a =res_hidden_states_tuple[-1]
__a =res_hidden_states_tuple[:-1]
__a =jnp.concatenate((hidden_states, res_hidden_states) , axis=-1 )
__a =resnet(__snake_case , __snake_case , deterministic=__snake_case )
if self.add_upsample:
__a =self.upsamplers_a(__snake_case )
return hidden_states
class __magic_name__ ( nn.Module ):
SCREAMING_SNAKE_CASE = 42
SCREAMING_SNAKE_CASE = 0.0
SCREAMING_SNAKE_CASE = 1
SCREAMING_SNAKE_CASE = 1
SCREAMING_SNAKE_CASE = False
SCREAMING_SNAKE_CASE = False
SCREAMING_SNAKE_CASE = jnp.floataa
def __magic_name__ ( self ) -> List[Any]:
'''simple docstring'''
# there is always at least one resnet
__a =[
FlaxResnetBlockaD(
in_channels=self.in_channels , out_channels=self.in_channels , dropout_prob=self.dropout , dtype=self.dtype , )
]
__a =[]
for _ in range(self.num_layers ):
__a =FlaxTransformeraDModel(
in_channels=self.in_channels , n_heads=self.num_attention_heads , d_head=self.in_channels // self.num_attention_heads , depth=1 , use_linear_projection=self.use_linear_projection , use_memory_efficient_attention=self.use_memory_efficient_attention , dtype=self.dtype , )
attentions.append(__snake_case )
__a =FlaxResnetBlockaD(
in_channels=self.in_channels , out_channels=self.in_channels , dropout_prob=self.dropout , dtype=self.dtype , )
resnets.append(__snake_case )
__a =resnets
__a =attentions
def __call__( self , __snake_case , __snake_case , __snake_case , __snake_case=True ) -> List[str]:
'''simple docstring'''
__a =self.resnets[0](__snake_case , __snake_case )
for attn, resnet in zip(self.attentions , self.resnets[1:] ):
__a =attn(__snake_case , __snake_case , deterministic=__snake_case )
__a =resnet(__snake_case , __snake_case , deterministic=__snake_case )
return hidden_states
| 308
| 1
|
from __future__ import annotations
_lowerCAmelCase : str = {
"A": ["B", "C", "E"],
"B": ["A", "D", "E"],
"C": ["A", "F", "G"],
"D": ["B"],
"E": ["A", "B", "D"],
"F": ["C"],
"G": ["C"],
}
class __magic_name__ :
def __init__( self , __snake_case , __snake_case ) -> None:
'''simple docstring'''
__a =graph
# mapping node to its parent in resulting breadth first tree
__a ={}
__a =source_vertex
def __magic_name__ ( self ) -> None:
'''simple docstring'''
__a ={self.source_vertex}
__a =None
__a =[self.source_vertex] # first in first out queue
while queue:
__a =queue.pop(0 )
for adjacent_vertex in self.graph[vertex]:
if adjacent_vertex not in visited:
visited.add(__snake_case )
__a =vertex
queue.append(__snake_case )
def __magic_name__ ( self , __snake_case ) -> str:
'''simple docstring'''
if target_vertex == self.source_vertex:
return self.source_vertex
__a =self.parent.get(__snake_case )
if target_vertex_parent is None:
__a =(
f'No path from vertex: {self.source_vertex} to vertex: {target_vertex}'
)
raise ValueError(__snake_case )
return self.shortest_path(__snake_case ) + f'->{target_vertex}'
if __name__ == "__main__":
_lowerCAmelCase : List[Any] = Graph(graph, "G")
g.breath_first_search()
print(g.shortest_path("D"))
print(g.shortest_path("G"))
print(g.shortest_path("Foo"))
| 308
|
import json
import os
import subprocess
import unittest
from ast import literal_eval
import pytest
from parameterized import parameterized, parameterized_class
from . import is_sagemaker_available
if is_sagemaker_available():
from sagemaker import Session, TrainingJobAnalytics
from sagemaker.huggingface import HuggingFace
@pytest.mark.skipif(
literal_eval(os.getenv('TEST_SAGEMAKER' , 'False' ) ) is not True , reason='Skipping test because should only be run when releasing minor transformers version' , )
@pytest.mark.usefixtures('sm_env' )
@parameterized_class(
[
{
'framework': 'pytorch',
'script': 'run_glue.py',
'model_name_or_path': 'distilbert-base-cased',
'instance_type': 'ml.p3.16xlarge',
'results': {'train_runtime': 6_5_0, 'eval_accuracy': 0.7, 'eval_loss': 0.6},
},
{
'framework': 'pytorch',
'script': 'run_ddp.py',
'model_name_or_path': 'distilbert-base-cased',
'instance_type': 'ml.p3.16xlarge',
'results': {'train_runtime': 6_0_0, 'eval_accuracy': 0.7, 'eval_loss': 0.6},
},
{
'framework': 'tensorflow',
'script': 'run_tf_dist.py',
'model_name_or_path': 'distilbert-base-cased',
'instance_type': 'ml.p3.16xlarge',
'results': {'train_runtime': 6_0_0, 'eval_accuracy': 0.6, 'eval_loss': 0.7},
},
] )
class __magic_name__ ( unittest.TestCase ):
def __magic_name__ ( self ) -> int:
'''simple docstring'''
if self.framework == "pytorch":
subprocess.run(
f'cp ./examples/pytorch/text-classification/run_glue.py {self.env.test_path}/run_glue.py'.split() , encoding='utf-8' , check=__snake_case , )
assert hasattr(self , 'env' )
def __magic_name__ ( self , __snake_case ) -> int:
'''simple docstring'''
__a =f'{self.env.base_job_name}-{instance_count}-{"ddp" if "ddp" in self.script else "smd"}'
# distributed data settings
__a ={'smdistributed': {'dataparallel': {'enabled': True}}} if self.script != 'run_ddp.py' else None
# creates estimator
return HuggingFace(
entry_point=self.script , source_dir=self.env.test_path , role=self.env.role , image_uri=self.env.image_uri , base_job_name=__snake_case , instance_count=__snake_case , instance_type=self.instance_type , debugger_hook_config=__snake_case , hyperparameters={**self.env.distributed_hyperparameters, 'model_name_or_path': self.model_name_or_path} , metric_definitions=self.env.metric_definitions , distribution=__snake_case , py_version='py36' , )
def __magic_name__ ( self , __snake_case ) -> Optional[Any]:
'''simple docstring'''
TrainingJobAnalytics(__snake_case ).export_csv(f'{self.env.test_path}/{job_name}_metrics.csv' )
@parameterized.expand([(2,)] )
def __magic_name__ ( self , __snake_case ) -> Optional[int]:
'''simple docstring'''
# create estimator
__a =self.create_estimator(__snake_case )
# run training
estimator.fit()
# result dataframe
__a =TrainingJobAnalytics(estimator.latest_training_job.name ).dataframe()
# extract kpis
__a =list(result_metrics_df[result_metrics_df.metric_name == 'eval_accuracy']['value'] )
__a =list(result_metrics_df[result_metrics_df.metric_name == 'eval_loss']['value'] )
# get train time from SageMaker job, this includes starting, preprocessing, stopping
__a =(
Session().describe_training_job(estimator.latest_training_job.name ).get('TrainingTimeInSeconds' , 99_9999 )
)
# assert kpis
assert train_runtime <= self.results["train_runtime"]
assert all(t >= self.results['eval_accuracy'] for t in eval_accuracy )
assert all(t <= self.results['eval_loss'] for t in eval_loss )
# dump tests result into json file to share in PR
with open(f'{estimator.latest_training_job.name}.json' , 'w' ) as outfile:
json.dump({'train_time': train_runtime, 'eval_accuracy': eval_accuracy, 'eval_loss': eval_loss} , __snake_case )
| 308
| 1
|
def UpperCamelCase_( _snake_case : int = 50 ):
"""simple docstring"""
__a =[1] * (length + 1)
for row_length in range(length + 1 ):
for tile_length in range(2 , 5 ):
for tile_start in range(row_length - tile_length + 1 ):
ways_number[row_length] += ways_number[
row_length - tile_start - tile_length
]
return ways_number[length]
if __name__ == "__main__":
print(f'''{solution() = }''')
| 308
|
import argparse
import logging
from collections import namedtuple
import torch
from model_bertabs import BertAbsSummarizer
from models.model_builder import AbsSummarizer # The authors' implementation
from transformers import BertTokenizer
logging.basicConfig(level=logging.INFO)
_lowerCAmelCase : List[Any] = logging.getLogger(__name__)
_lowerCAmelCase : Optional[Any] = "Hello world! cécé herlolip"
_lowerCAmelCase : str = namedtuple(
"BertAbsConfig",
[
"temp_dir",
"large",
"use_bert_emb",
"finetune_bert",
"encoder",
"share_emb",
"max_pos",
"enc_layers",
"enc_hidden_size",
"enc_heads",
"enc_ff_size",
"enc_dropout",
"dec_layers",
"dec_hidden_size",
"dec_heads",
"dec_ff_size",
"dec_dropout",
],
)
def UpperCamelCase_( _snake_case : str , _snake_case : List[Any] ):
"""simple docstring"""
__a =BertAbsConfig(
temp_dir='.' , finetune_bert=_snake_case , large=_snake_case , share_emb=_snake_case , use_bert_emb=_snake_case , encoder='bert' , max_pos=512 , enc_layers=6 , enc_hidden_size=512 , enc_heads=8 , enc_ff_size=512 , enc_dropout=0.2 , dec_layers=6 , dec_hidden_size=768 , dec_heads=8 , dec_ff_size=2048 , dec_dropout=0.2 , )
__a =torch.load(_snake_case , lambda _snake_case , _snake_case : storage )
__a =AbsSummarizer(_snake_case , torch.device('cpu' ) , _snake_case )
original.eval()
__a =BertAbsSummarizer(_snake_case , torch.device('cpu' ) )
new_model.eval()
# -------------------
# Convert the weights
# -------------------
logging.info('convert the model' )
new_model.bert.load_state_dict(original.bert.state_dict() )
new_model.decoder.load_state_dict(original.decoder.state_dict() )
new_model.generator.load_state_dict(original.generator.state_dict() )
# ----------------------------------
# Make sure the outpus are identical
# ----------------------------------
logging.info('Make sure that the models\' outputs are identical' )
__a =BertTokenizer.from_pretrained('bert-base-uncased' )
# prepare the model inputs
__a =tokenizer.encode('This is sample éàalj\'-.' )
encoder_input_ids.extend([tokenizer.pad_token_id] * (512 - len(_snake_case )) )
__a =torch.tensor(_snake_case ).unsqueeze(0 )
__a =tokenizer.encode('This is sample 3 éàalj\'-.' )
decoder_input_ids.extend([tokenizer.pad_token_id] * (512 - len(_snake_case )) )
__a =torch.tensor(_snake_case ).unsqueeze(0 )
# failsafe to make sure the weights reset does not affect the
# loaded weights.
assert torch.max(torch.abs(original.generator[0].weight - new_model.generator[0].weight ) ) == 0
# forward pass
__a =encoder_input_ids
__a =decoder_input_ids
__a =__a =None
__a =None
__a =__a =None
__a =__a =None
__a =None
# The original model does not apply the geneator layer immediatly but rather in
# the beam search (where it combines softmax + linear layer). Since we already
# apply the softmax in our generation process we only apply the linear layer here.
# We make sure that the outputs of the full stack are identical
__a =original(_snake_case , _snake_case , _snake_case , _snake_case , _snake_case , _snake_case , _snake_case )[0]
__a =original.generator(_snake_case )
__a =new_model(
_snake_case , _snake_case , _snake_case , _snake_case , _snake_case )[0]
__a =new_model.generator(_snake_case )
__a =torch.max(torch.abs(output_converted_model - output_original_model ) ).item()
print('Maximum absolute difference beween weights: {:.2f}'.format(_snake_case ) )
__a =torch.max(torch.abs(output_converted_generator - output_original_generator ) ).item()
print('Maximum absolute difference beween weights: {:.2f}'.format(_snake_case ) )
__a =torch.allclose(_snake_case , _snake_case , atol=1e-3 )
if are_identical:
logging.info('all weights are equal up to 1e-3' )
else:
raise ValueError('the weights are different. The new model is likely different from the original one.' )
# The model has been saved with torch.save(model) and this is bound to the exact
# directory structure. We save the state_dict instead.
logging.info('saving the model\'s state dictionary' )
torch.save(
new_model.state_dict() , './bertabs-finetuned-cnndm-extractive-abstractive-summarization/pytorch_model.bin' )
if __name__ == "__main__":
_lowerCAmelCase : Optional[int] = argparse.ArgumentParser()
parser.add_argument(
"--bertabs_checkpoint_path",
default=None,
type=str,
required=True,
help="Path the official PyTorch dump.",
)
parser.add_argument(
"--pytorch_dump_folder_path",
default=None,
type=str,
required=True,
help="Path to the output PyTorch model.",
)
_lowerCAmelCase : Optional[Any] = parser.parse_args()
convert_bertabs_checkpoints(
args.bertabs_checkpoint_path,
args.pytorch_dump_folder_path,
)
| 308
| 1
|
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from timm import create_model
from timm.data import resolve_data_config
from timm.data.transforms_factory import create_transform
from transformers import BitConfig, BitForImageClassification, BitImageProcessor
from transformers.image_utils import PILImageResampling
from transformers.utils import logging
logging.set_verbosity_info()
_lowerCAmelCase : Dict = logging.get_logger(__name__)
def UpperCamelCase_( _snake_case : Tuple ):
"""simple docstring"""
__a ='huggingface/label-files'
__a ='imagenet-1k-id2label.json'
__a =json.load(open(hf_hub_download(_snake_case , _snake_case , repo_type='dataset' ) , 'r' ) )
__a ={int(_snake_case ): v for k, v in idalabel.items()}
__a ={v: k for k, v in idalabel.items()}
__a ='std_conv' if 'bit' in model_name else False
# note that when using BiT as backbone for ViT-hybrid checkpoints,
# one needs to additionally set config.layer_type = "bottleneck", config.stem_type = "same",
# config.conv_layer = "std_conv_same"
__a =BitConfig(
conv_layer=_snake_case , num_labels=1000 , idalabel=_snake_case , labelaid=_snake_case , )
return config
def UpperCamelCase_( _snake_case : str ):
"""simple docstring"""
if "stem.conv" in name:
__a =name.replace('stem.conv' , 'bit.embedder.convolution' )
if "blocks" in name:
__a =name.replace('blocks' , 'layers' )
if "head.fc" in name:
__a =name.replace('head.fc' , 'classifier.1' )
if name.startswith('norm' ):
__a ='bit.' + name
if "bit" not in name and "classifier" not in name:
__a ='bit.encoder.' + name
return name
def UpperCamelCase_( ):
"""simple docstring"""
__a ='http://images.cocodataset.org/val2017/000000039769.jpg'
__a =Image.open(requests.get(_snake_case , stream=_snake_case ).raw )
return im
@torch.no_grad()
def UpperCamelCase_( _snake_case : Optional[Any] , _snake_case : Optional[Any] , _snake_case : Any=False ):
"""simple docstring"""
__a =get_config(_snake_case )
# load original model from timm
__a =create_model(_snake_case , pretrained=_snake_case )
timm_model.eval()
# load state_dict of original model
__a =timm_model.state_dict()
for key in state_dict.copy().keys():
__a =state_dict.pop(_snake_case )
__a =val.squeeze() if 'head' in key else val
# load HuggingFace model
__a =BitForImageClassification(_snake_case )
model.eval()
model.load_state_dict(_snake_case )
# create image processor
__a =create_transform(**resolve_data_config({} , model=_snake_case ) )
__a =transform.transforms
__a ={
'bilinear': PILImageResampling.BILINEAR,
'bicubic': PILImageResampling.BICUBIC,
'nearest': PILImageResampling.NEAREST,
}
__a =BitImageProcessor(
do_resize=_snake_case , size={'shortest_edge': timm_transforms[0].size} , resample=pillow_resamplings[timm_transforms[0].interpolation.value] , do_center_crop=_snake_case , crop_size={'height': timm_transforms[1].size[0], 'width': timm_transforms[1].size[1]} , do_normalize=_snake_case , image_mean=timm_transforms[-1].mean.tolist() , image_std=timm_transforms[-1].std.tolist() , )
__a =prepare_img()
__a =transform(_snake_case ).unsqueeze(0 )
__a =processor(_snake_case , return_tensors='pt' ).pixel_values
# verify pixel values
assert torch.allclose(_snake_case , _snake_case )
# verify logits
with torch.no_grad():
__a =model(_snake_case )
__a =outputs.logits
print('Logits:' , logits[0, :3] )
print('Predicted class:' , model.config.idalabel[logits.argmax(-1 ).item()] )
__a =timm_model(_snake_case )
assert timm_logits.shape == outputs.logits.shape
assert torch.allclose(_snake_case , outputs.logits , atol=1e-3 )
print('Looks ok!' )
if pytorch_dump_folder_path is not None:
Path(_snake_case ).mkdir(exist_ok=_snake_case )
print(F'Saving model {model_name} and processor to {pytorch_dump_folder_path}' )
model.save_pretrained(_snake_case )
processor.save_pretrained(_snake_case )
if push_to_hub:
print(F'Pushing model {model_name} and processor to the hub' )
model.push_to_hub(F'ybelkada/{model_name}' )
processor.push_to_hub(F'ybelkada/{model_name}' )
if __name__ == "__main__":
_lowerCAmelCase : Optional[int] = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--model_name",
default="resnetv2_50x1_bitm",
type=str,
help="Name of the BiT timm model you'd like to convert.",
)
parser.add_argument(
"--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model directory."
)
parser.add_argument(
"--push_to_hub",
action="store_true",
help="Whether to push the model to the hub.",
)
_lowerCAmelCase : Dict = parser.parse_args()
convert_bit_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)
| 308
|
import unittest
from transformers import XLMConfig, 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 (
XLMForMultipleChoice,
XLMForQuestionAnswering,
XLMForQuestionAnsweringSimple,
XLMForSequenceClassification,
XLMForTokenClassification,
XLMModel,
XLMWithLMHeadModel,
)
from transformers.models.xlm.modeling_xlm import XLM_PRETRAINED_MODEL_ARCHIVE_LIST
class __magic_name__ :
def __init__( self , __snake_case , __snake_case=13 , __snake_case=7 , __snake_case=True , __snake_case=True , __snake_case=True , __snake_case=True , __snake_case=True , __snake_case=False , __snake_case=False , __snake_case=False , __snake_case=2 , __snake_case=99 , __snake_case=0 , __snake_case=32 , __snake_case=5 , __snake_case=4 , __snake_case=0.1 , __snake_case=0.1 , __snake_case=512 , __snake_case=2 , __snake_case=0.02 , __snake_case=2 , __snake_case=4 , __snake_case="last" , __snake_case=True , __snake_case=None , __snake_case=0 , ) -> Optional[Any]:
'''simple docstring'''
__a =parent
__a =batch_size
__a =seq_length
__a =is_training
__a =use_input_lengths
__a =use_token_type_ids
__a =use_labels
__a =gelu_activation
__a =sinusoidal_embeddings
__a =causal
__a =asm
__a =n_langs
__a =vocab_size
__a =n_special
__a =hidden_size
__a =num_hidden_layers
__a =num_attention_heads
__a =hidden_dropout_prob
__a =attention_probs_dropout_prob
__a =max_position_embeddings
__a =type_sequence_label_size
__a =initializer_range
__a =num_labels
__a =num_choices
__a =summary_type
__a =use_proj
__a =scope
__a =bos_token_id
def __magic_name__ ( self ) -> Any:
'''simple docstring'''
__a =ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
__a =random_attention_mask([self.batch_size, self.seq_length] )
__a =None
if self.use_input_lengths:
__a =(
ids_tensor([self.batch_size] , vocab_size=2 ) + self.seq_length - 2
) # small variation of seq_length
__a =None
if self.use_token_type_ids:
__a =ids_tensor([self.batch_size, self.seq_length] , self.n_langs )
__a =None
__a =None
__a =None
if self.use_labels:
__a =ids_tensor([self.batch_size] , self.type_sequence_label_size )
__a =ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
__a =ids_tensor([self.batch_size] , 2 ).float()
__a =ids_tensor([self.batch_size] , self.num_choices )
__a =self.get_config()
return (
config,
input_ids,
token_type_ids,
input_lengths,
sequence_labels,
token_labels,
is_impossible_labels,
choice_labels,
input_mask,
)
def __magic_name__ ( self ) -> Any:
'''simple docstring'''
return XLMConfig(
vocab_size=self.vocab_size , n_special=self.n_special , emb_dim=self.hidden_size , n_layers=self.num_hidden_layers , n_heads=self.num_attention_heads , dropout=self.hidden_dropout_prob , attention_dropout=self.attention_probs_dropout_prob , gelu_activation=self.gelu_activation , sinusoidal_embeddings=self.sinusoidal_embeddings , asm=self.asm , causal=self.causal , n_langs=self.n_langs , max_position_embeddings=self.max_position_embeddings , initializer_range=self.initializer_range , summary_type=self.summary_type , use_proj=self.use_proj , num_labels=self.num_labels , bos_token_id=self.bos_token_id , )
def __magic_name__ ( self , __snake_case , __snake_case , __snake_case , __snake_case , __snake_case , __snake_case , __snake_case , __snake_case , __snake_case , ) -> List[Any]:
'''simple docstring'''
__a =XLMModel(config=__snake_case )
model.to(__snake_case )
model.eval()
__a =model(__snake_case , lengths=__snake_case , langs=__snake_case )
__a =model(__snake_case , langs=__snake_case )
__a =model(__snake_case )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def __magic_name__ ( self , __snake_case , __snake_case , __snake_case , __snake_case , __snake_case , __snake_case , __snake_case , __snake_case , __snake_case , ) -> Optional[int]:
'''simple docstring'''
__a =XLMWithLMHeadModel(__snake_case )
model.to(__snake_case )
model.eval()
__a =model(__snake_case , token_type_ids=__snake_case , labels=__snake_case )
self.parent.assertEqual(result.loss.shape , () )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def __magic_name__ ( self , __snake_case , __snake_case , __snake_case , __snake_case , __snake_case , __snake_case , __snake_case , __snake_case , __snake_case , ) -> Dict:
'''simple docstring'''
__a =XLMForQuestionAnsweringSimple(__snake_case )
model.to(__snake_case )
model.eval()
__a =model(__snake_case )
__a =model(__snake_case , start_positions=__snake_case , end_positions=__snake_case )
__a =outputs
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 __magic_name__ ( self , __snake_case , __snake_case , __snake_case , __snake_case , __snake_case , __snake_case , __snake_case , __snake_case , __snake_case , ) -> List[Any]:
'''simple docstring'''
__a =XLMForQuestionAnswering(__snake_case )
model.to(__snake_case )
model.eval()
__a =model(__snake_case )
__a =model(
__snake_case , start_positions=__snake_case , end_positions=__snake_case , cls_index=__snake_case , is_impossible=__snake_case , p_mask=__snake_case , )
__a =model(
__snake_case , start_positions=__snake_case , end_positions=__snake_case , cls_index=__snake_case , is_impossible=__snake_case , )
((__a) , ) =result_with_labels.to_tuple()
__a =model(__snake_case , start_positions=__snake_case , end_positions=__snake_case )
((__a) , ) =result_with_labels.to_tuple()
self.parent.assertEqual(result_with_labels.loss.shape , () )
self.parent.assertEqual(result.start_top_log_probs.shape , (self.batch_size, model.config.start_n_top) )
self.parent.assertEqual(result.start_top_index.shape , (self.batch_size, model.config.start_n_top) )
self.parent.assertEqual(
result.end_top_log_probs.shape , (self.batch_size, model.config.start_n_top * model.config.end_n_top) )
self.parent.assertEqual(
result.end_top_index.shape , (self.batch_size, model.config.start_n_top * model.config.end_n_top) )
self.parent.assertEqual(result.cls_logits.shape , (self.batch_size,) )
def __magic_name__ ( self , __snake_case , __snake_case , __snake_case , __snake_case , __snake_case , __snake_case , __snake_case , __snake_case , __snake_case , ) -> Optional[Any]:
'''simple docstring'''
__a =XLMForSequenceClassification(__snake_case )
model.to(__snake_case )
model.eval()
__a =model(__snake_case )
__a =model(__snake_case , labels=__snake_case )
self.parent.assertEqual(result.loss.shape , () )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size) )
def __magic_name__ ( self , __snake_case , __snake_case , __snake_case , __snake_case , __snake_case , __snake_case , __snake_case , __snake_case , __snake_case , ) -> Any:
'''simple docstring'''
__a =self.num_labels
__a =XLMForTokenClassification(__snake_case )
model.to(__snake_case )
model.eval()
__a =model(__snake_case , attention_mask=__snake_case , labels=__snake_case )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def __magic_name__ ( self , __snake_case , __snake_case , __snake_case , __snake_case , __snake_case , __snake_case , __snake_case , __snake_case , __snake_case , ) -> Tuple:
'''simple docstring'''
__a =self.num_choices
__a =XLMForMultipleChoice(config=__snake_case )
model.to(__snake_case )
model.eval()
__a =input_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
__a =token_type_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
__a =input_mask.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
__a =model(
__snake_case , attention_mask=__snake_case , token_type_ids=__snake_case , labels=__snake_case , )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_choices) )
def __magic_name__ ( self ) -> Union[str, Any]:
'''simple docstring'''
__a =self.prepare_config_and_inputs()
(
(
__a
) , (
__a
) , (
__a
) , (
__a
) , (
__a
) , (
__a
) , (
__a
) , (
__a
) , (
__a
) ,
) =config_and_inputs
__a ={'input_ids': input_ids, 'token_type_ids': token_type_ids, 'lengths': input_lengths}
return config, inputs_dict
@require_torch
class __magic_name__ ( lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , unittest.TestCase ):
SCREAMING_SNAKE_CASE = (
(
XLMModel,
XLMWithLMHeadModel,
XLMForQuestionAnswering,
XLMForSequenceClassification,
XLMForQuestionAnsweringSimple,
XLMForTokenClassification,
XLMForMultipleChoice,
)
if is_torch_available()
else ()
)
SCREAMING_SNAKE_CASE = (
(XLMWithLMHeadModel,) if is_torch_available() else ()
) # TODO (PVP): Check other models whether language generation is also applicable
SCREAMING_SNAKE_CASE = (
{
'feature-extraction': XLMModel,
'fill-mask': XLMWithLMHeadModel,
'question-answering': XLMForQuestionAnsweringSimple,
'text-classification': XLMForSequenceClassification,
'text-generation': XLMWithLMHeadModel,
'token-classification': XLMForTokenClassification,
'zero-shot': XLMForSequenceClassification,
}
if is_torch_available()
else {}
)
def __magic_name__ ( self , __snake_case , __snake_case , __snake_case , __snake_case , __snake_case ) -> int:
'''simple docstring'''
if (
pipeline_test_casse_name == "QAPipelineTests"
and tokenizer_name is not None
and not tokenizer_name.endswith('Fast' )
):
# `QAPipelineTests` fails for a few models when the slower tokenizer are used.
# (The slower tokenizers were never used for pipeline tests before the pipeline testing rework)
# TODO: check (and possibly fix) the `QAPipelineTests` with slower tokenizer
return True
return False
def __magic_name__ ( self , __snake_case , __snake_case , __snake_case=False ) -> str:
'''simple docstring'''
__a =super()._prepare_for_class(__snake_case , __snake_case , return_labels=__snake_case )
if return_labels:
if model_class.__name__ == "XLMForQuestionAnswering":
__a =torch.zeros(
self.model_tester.batch_size , dtype=torch.long , device=__snake_case )
__a =torch.zeros(
self.model_tester.batch_size , dtype=torch.long , device=__snake_case )
return inputs_dict
def __magic_name__ ( self ) -> Tuple:
'''simple docstring'''
__a =XLMModelTester(self )
__a =ConfigTester(self , config_class=__snake_case , emb_dim=37 )
def __magic_name__ ( self ) -> str:
'''simple docstring'''
self.config_tester.run_common_tests()
def __magic_name__ ( self ) -> List[str]:
'''simple docstring'''
__a =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_xlm_model(*__snake_case )
def __magic_name__ ( self ) -> List[str]:
'''simple docstring'''
__a =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_xlm_lm_head(*__snake_case )
def __magic_name__ ( self ) -> List[str]:
'''simple docstring'''
__a =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_xlm_simple_qa(*__snake_case )
def __magic_name__ ( self ) -> Optional[int]:
'''simple docstring'''
__a =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_xlm_qa(*__snake_case )
def __magic_name__ ( self ) -> Any:
'''simple docstring'''
__a =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_xlm_sequence_classif(*__snake_case )
def __magic_name__ ( self ) -> Optional[int]:
'''simple docstring'''
__a =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_xlm_token_classif(*__snake_case )
def __magic_name__ ( self ) -> Any:
'''simple docstring'''
__a =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_xlm_for_multiple_choice(*__snake_case )
def __magic_name__ ( self , __snake_case , __snake_case , __snake_case , __snake_case , __snake_case , __snake_case=False , __snake_case=1 ) -> Optional[Any]:
'''simple docstring'''
self.assertIsInstance(__snake_case , __snake_case )
self.assertListEqual(
[isinstance(__snake_case , __snake_case ) for iter_attentions in attentions] , [True] * len(__snake_case ) )
self.assertEqual(len(__snake_case ) , (max_length - min_length) * num_beam_groups )
for idx, iter_attentions in enumerate(__snake_case ):
# adds PAD dummy token
__a =min_length + idx + 1
__a =min_length + idx + 1
__a =(
batch_size * num_beam_groups,
config.num_attention_heads,
tgt_len,
src_len,
)
# check attn size
self.assertListEqual(
[layer_attention.shape for layer_attention in iter_attentions] , [expected_shape] * len(__snake_case ) )
def __magic_name__ ( self , __snake_case , __snake_case , __snake_case , __snake_case , __snake_case , __snake_case=False , __snake_case=1 ) -> Dict:
'''simple docstring'''
self.assertIsInstance(__snake_case , __snake_case )
self.assertListEqual(
[isinstance(__snake_case , __snake_case ) for iter_hidden_states in hidden_states] , [True] * len(__snake_case ) , )
self.assertEqual(len(__snake_case ) , (max_length - min_length) * num_beam_groups )
for idx, iter_hidden_states in enumerate(__snake_case ):
# adds PAD dummy token
__a =min_length + idx + 1
__a =(batch_size * num_beam_groups, seq_len, config.hidden_size)
# check hidden size
self.assertListEqual(
[layer_hidden_states.shape for layer_hidden_states in iter_hidden_states] , [expected_shape] * len(__snake_case ) , )
pass
@slow
def __magic_name__ ( self ) -> Union[str, Any]:
'''simple docstring'''
for model_name in XLM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
__a =XLMModel.from_pretrained(__snake_case )
self.assertIsNotNone(__snake_case )
@require_torch
class __magic_name__ ( unittest.TestCase ):
@slow
def __magic_name__ ( self ) -> Tuple:
'''simple docstring'''
__a =XLMWithLMHeadModel.from_pretrained('xlm-mlm-en-2048' )
model.to(__snake_case )
__a =torch.tensor([[14, 447]] , dtype=torch.long , device=__snake_case ) # the president
__a =[
14,
447,
14,
447,
14,
447,
14,
447,
14,
447,
14,
447,
14,
447,
14,
447,
14,
447,
14,
447,
] # the president the president the president the president the president the president the president the president the president the president
# TODO(PVP): this and other input_ids I tried for generation give pretty bad results. Not sure why. Model might just not be made for auto-regressive inference
__a =model.generate(__snake_case , do_sample=__snake_case )
self.assertListEqual(output_ids[0].cpu().numpy().tolist() , __snake_case )
| 308
| 1
|
def UpperCamelCase_( _snake_case : int = 10**9 ):
"""simple docstring"""
__a =1
__a =2
__a =0
__a =0
__a =0
while perimeter <= max_perimeter:
perimeters_sum += perimeter
prev_value += 2 * value
value += prev_value
__a =2 * value + 2 if i % 2 == 0 else 2 * value - 2
i += 1
return perimeters_sum
if __name__ == "__main__":
print(f'''{solution() = }''')
| 308
|
from __future__ import annotations
import matplotlib.pyplot as plt # type: ignore
import numpy
# initial triangle of Koch snowflake
_lowerCAmelCase : Optional[Any] = numpy.array([0, 0])
_lowerCAmelCase : Dict = numpy.array([0.5, 0.8660254])
_lowerCAmelCase : Any = numpy.array([1, 0])
_lowerCAmelCase : int = [VECTOR_1, VECTOR_2, VECTOR_3, VECTOR_1]
def UpperCamelCase_( _snake_case : list[numpy.ndarray] , _snake_case : int ):
"""simple docstring"""
__a =initial_vectors
for _ in range(_snake_case ):
__a =iteration_step(_snake_case )
return vectors
def UpperCamelCase_( _snake_case : list[numpy.ndarray] ):
"""simple docstring"""
__a =[]
for i, start_vector in enumerate(vectors[:-1] ):
__a =vectors[i + 1]
new_vectors.append(_snake_case )
__a =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 UpperCamelCase_( _snake_case : numpy.ndarray , _snake_case : float ):
"""simple docstring"""
__a =numpy.radians(_snake_case )
__a , __a =numpy.cos(_snake_case ), numpy.sin(_snake_case )
__a =numpy.array(((c, -s), (s, c)) )
return numpy.dot(_snake_case , _snake_case )
def UpperCamelCase_( _snake_case : list[numpy.ndarray] ):
"""simple docstring"""
__a =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 =zip(*_snake_case )
plt.plot(_snake_case , _snake_case )
plt.show()
if __name__ == "__main__":
import doctest
doctest.testmod()
_lowerCAmelCase : List[Any] = iterate(INITIAL_VECTORS, 5)
plot(processed_vectors)
| 308
| 1
|
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_lowerCAmelCase : str = {
"configuration_swinv2": ["SWINV2_PRETRAINED_CONFIG_ARCHIVE_MAP", "Swinv2Config"],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_lowerCAmelCase : Optional[int] = [
"SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST",
"Swinv2ForImageClassification",
"Swinv2ForMaskedImageModeling",
"Swinv2Model",
"Swinv2PreTrainedModel",
]
if TYPE_CHECKING:
from .configuration_swinva import SWINV2_PRETRAINED_CONFIG_ARCHIVE_MAP, SwinvaConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_swinva import (
SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST,
SwinvaForImageClassification,
SwinvaForMaskedImageModeling,
SwinvaModel,
SwinvaPreTrainedModel,
)
else:
import sys
_lowerCAmelCase : Tuple = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 308
|
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_lowerCAmelCase : Tuple = {
"configuration_megatron_bert": ["MEGATRON_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "MegatronBertConfig"],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_lowerCAmelCase : List[str] = [
"MEGATRON_BERT_PRETRAINED_MODEL_ARCHIVE_LIST",
"MegatronBertForCausalLM",
"MegatronBertForMaskedLM",
"MegatronBertForMultipleChoice",
"MegatronBertForNextSentencePrediction",
"MegatronBertForPreTraining",
"MegatronBertForQuestionAnswering",
"MegatronBertForSequenceClassification",
"MegatronBertForTokenClassification",
"MegatronBertModel",
"MegatronBertPreTrainedModel",
]
if TYPE_CHECKING:
from .configuration_megatron_bert import MEGATRON_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP, MegatronBertConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_megatron_bert import (
MEGATRON_BERT_PRETRAINED_MODEL_ARCHIVE_LIST,
MegatronBertForCausalLM,
MegatronBertForMaskedLM,
MegatronBertForMultipleChoice,
MegatronBertForNextSentencePrediction,
MegatronBertForPreTraining,
MegatronBertForQuestionAnswering,
MegatronBertForSequenceClassification,
MegatronBertForTokenClassification,
MegatronBertModel,
MegatronBertPreTrainedModel,
)
else:
import sys
_lowerCAmelCase : Optional[int] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 308
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|
import json
import pathlib
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_available():
import torch
if is_vision_available():
from PIL import Image
from transformers import DetaImageProcessor
class __magic_name__ ( unittest.TestCase ):
def __init__( self , __snake_case , __snake_case=7 , __snake_case=3 , __snake_case=30 , __snake_case=400 , __snake_case=True , __snake_case=None , __snake_case=True , __snake_case=[0.5, 0.5, 0.5] , __snake_case=[0.5, 0.5, 0.5] , __snake_case=True , __snake_case=1 / 255 , __snake_case=True , ) -> List[Any]:
'''simple docstring'''
# by setting size["longest_edge"] > max_resolution we're effectively not testing this :p
__a =size if size is not None else {'shortest_edge': 18, 'longest_edge': 1333}
__a =parent
__a =batch_size
__a =num_channels
__a =min_resolution
__a =max_resolution
__a =do_resize
__a =size
__a =do_normalize
__a =image_mean
__a =image_std
__a =do_rescale
__a =rescale_factor
__a =do_pad
def __magic_name__ ( self ) -> List[str]:
'''simple docstring'''
return {
"do_resize": self.do_resize,
"size": self.size,
"do_normalize": self.do_normalize,
"image_mean": self.image_mean,
"image_std": self.image_std,
"do_rescale": self.do_rescale,
"rescale_factor": self.rescale_factor,
"do_pad": self.do_pad,
}
def __magic_name__ ( self , __snake_case , __snake_case=False ) -> List[str]:
'''simple docstring'''
if not batched:
__a =image_inputs[0]
if isinstance(__snake_case , Image.Image ):
__a , __a =image.size
else:
__a , __a =image.shape[1], image.shape[2]
if w < h:
__a =int(self.size['shortest_edge'] * h / w )
__a =self.size['shortest_edge']
elif w > h:
__a =self.size['shortest_edge']
__a =int(self.size['shortest_edge'] * w / h )
else:
__a =self.size['shortest_edge']
__a =self.size['shortest_edge']
else:
__a =[]
for image in image_inputs:
__a , __a =self.get_expected_values([image] )
expected_values.append((expected_height, expected_width) )
__a =max(__snake_case , key=lambda __snake_case : item[0] )[0]
__a =max(__snake_case , key=lambda __snake_case : item[1] )[1]
return expected_height, expected_width
@require_torch
@require_vision
class __magic_name__ ( lowerCAmelCase_ , unittest.TestCase ):
SCREAMING_SNAKE_CASE = DetaImageProcessor if is_vision_available() else None
def __magic_name__ ( self ) -> Any:
'''simple docstring'''
__a =DetaImageProcessingTester(self )
@property
def __magic_name__ ( self ) -> Dict:
'''simple docstring'''
return self.image_processor_tester.prepare_image_processor_dict()
def __magic_name__ ( self ) -> List[str]:
'''simple docstring'''
__a =self.image_processing_class(**self.image_processor_dict )
self.assertTrue(hasattr(__snake_case , 'image_mean' ) )
self.assertTrue(hasattr(__snake_case , 'image_std' ) )
self.assertTrue(hasattr(__snake_case , 'do_normalize' ) )
self.assertTrue(hasattr(__snake_case , 'do_resize' ) )
self.assertTrue(hasattr(__snake_case , 'do_rescale' ) )
self.assertTrue(hasattr(__snake_case , 'do_pad' ) )
self.assertTrue(hasattr(__snake_case , 'size' ) )
def __magic_name__ ( self ) -> Union[str, Any]:
'''simple docstring'''
__a =self.image_processing_class.from_dict(self.image_processor_dict )
self.assertEqual(image_processor.size , {'shortest_edge': 18, 'longest_edge': 1333} )
self.assertEqual(image_processor.do_pad , __snake_case )
def __magic_name__ ( self ) -> int:
'''simple docstring'''
pass
def __magic_name__ ( self ) -> str:
'''simple docstring'''
# Initialize image_processing
__a =self.image_processing_class(**self.image_processor_dict )
# create random PIL images
__a =prepare_image_inputs(self.image_processor_tester , equal_resolution=__snake_case )
for image in image_inputs:
self.assertIsInstance(__snake_case , Image.Image )
# Test not batched input
__a =image_processing(image_inputs[0] , return_tensors='pt' ).pixel_values
__a , __a =self.image_processor_tester.get_expected_values(__snake_case )
self.assertEqual(
encoded_images.shape , (1, self.image_processor_tester.num_channels, expected_height, expected_width) , )
# Test batched
__a , __a =self.image_processor_tester.get_expected_values(__snake_case , batched=__snake_case )
__a =image_processing(__snake_case , return_tensors='pt' ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
expected_height,
expected_width,
) , )
def __magic_name__ ( self ) -> Dict:
'''simple docstring'''
# Initialize image_processing
__a =self.image_processing_class(**self.image_processor_dict )
# create random numpy tensors
__a =prepare_image_inputs(self.image_processor_tester , equal_resolution=__snake_case , numpify=__snake_case )
for image in image_inputs:
self.assertIsInstance(__snake_case , np.ndarray )
# Test not batched input
__a =image_processing(image_inputs[0] , return_tensors='pt' ).pixel_values
__a , __a =self.image_processor_tester.get_expected_values(__snake_case )
self.assertEqual(
encoded_images.shape , (1, self.image_processor_tester.num_channels, expected_height, expected_width) , )
# Test batched
__a =image_processing(__snake_case , return_tensors='pt' ).pixel_values
__a , __a =self.image_processor_tester.get_expected_values(__snake_case , batched=__snake_case )
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
expected_height,
expected_width,
) , )
def __magic_name__ ( self ) -> List[str]:
'''simple docstring'''
# Initialize image_processing
__a =self.image_processing_class(**self.image_processor_dict )
# create random PyTorch tensors
__a =prepare_image_inputs(self.image_processor_tester , equal_resolution=__snake_case , torchify=__snake_case )
for image in image_inputs:
self.assertIsInstance(__snake_case , torch.Tensor )
# Test not batched input
__a =image_processing(image_inputs[0] , return_tensors='pt' ).pixel_values
__a , __a =self.image_processor_tester.get_expected_values(__snake_case )
self.assertEqual(
encoded_images.shape , (1, self.image_processor_tester.num_channels, expected_height, expected_width) , )
# Test batched
__a =image_processing(__snake_case , return_tensors='pt' ).pixel_values
__a , __a =self.image_processor_tester.get_expected_values(__snake_case , batched=__snake_case )
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
expected_height,
expected_width,
) , )
@slow
def __magic_name__ ( self ) -> int:
'''simple docstring'''
# prepare image and target
__a =Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png' )
with open('./tests/fixtures/tests_samples/COCO/coco_annotations.txt' , 'r' ) as f:
__a =json.loads(f.read() )
__a ={'image_id': 3_9769, 'annotations': target}
# encode them
__a =DetaImageProcessor()
__a =image_processing(images=__snake_case , annotations=__snake_case , return_tensors='pt' )
# verify pixel values
__a =torch.Size([1, 3, 800, 1066] )
self.assertEqual(encoding['pixel_values'].shape , __snake_case )
__a =torch.tensor([0.2796, 0.3138, 0.3481] )
self.assertTrue(torch.allclose(encoding['pixel_values'][0, 0, 0, :3] , __snake_case , atol=1e-4 ) )
# verify area
__a =torch.tensor([5887.9600, 1_1250.2061, 48_9353.8438, 83_7122.7500, 14_7967.5156, 16_5732.3438] )
self.assertTrue(torch.allclose(encoding['labels'][0]['area'] , __snake_case ) )
# verify boxes
__a =torch.Size([6, 4] )
self.assertEqual(encoding['labels'][0]['boxes'].shape , __snake_case )
__a =torch.tensor([0.5503, 0.2765, 0.0604, 0.2215] )
self.assertTrue(torch.allclose(encoding['labels'][0]['boxes'][0] , __snake_case , atol=1e-3 ) )
# verify image_id
__a =torch.tensor([3_9769] )
self.assertTrue(torch.allclose(encoding['labels'][0]['image_id'] , __snake_case ) )
# verify is_crowd
__a =torch.tensor([0, 0, 0, 0, 0, 0] )
self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd'] , __snake_case ) )
# verify class_labels
__a =torch.tensor([75, 75, 63, 65, 17, 17] )
self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels'] , __snake_case ) )
# verify orig_size
__a =torch.tensor([480, 640] )
self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size'] , __snake_case ) )
# verify size
__a =torch.tensor([800, 1066] )
self.assertTrue(torch.allclose(encoding['labels'][0]['size'] , __snake_case ) )
@slow
def __magic_name__ ( self ) -> List[Any]:
'''simple docstring'''
# prepare image, target and masks_path
__a =Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png' )
with open('./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt' , 'r' ) as f:
__a =json.loads(f.read() )
__a ={'file_name': '000000039769.png', 'image_id': 3_9769, 'segments_info': target}
__a =pathlib.Path('./tests/fixtures/tests_samples/COCO/coco_panoptic' )
# encode them
__a =DetaImageProcessor(format='coco_panoptic' )
__a =image_processing(images=__snake_case , annotations=__snake_case , masks_path=__snake_case , return_tensors='pt' )
# verify pixel values
__a =torch.Size([1, 3, 800, 1066] )
self.assertEqual(encoding['pixel_values'].shape , __snake_case )
__a =torch.tensor([0.2796, 0.3138, 0.3481] )
self.assertTrue(torch.allclose(encoding['pixel_values'][0, 0, 0, :3] , __snake_case , atol=1e-4 ) )
# verify area
__a =torch.tensor([14_7979.6875, 16_5527.0469, 48_4638.5938, 1_1292.9375, 5879.6562, 7634.1147] )
self.assertTrue(torch.allclose(encoding['labels'][0]['area'] , __snake_case ) )
# verify boxes
__a =torch.Size([6, 4] )
self.assertEqual(encoding['labels'][0]['boxes'].shape , __snake_case )
__a =torch.tensor([0.2625, 0.5437, 0.4688, 0.8625] )
self.assertTrue(torch.allclose(encoding['labels'][0]['boxes'][0] , __snake_case , atol=1e-3 ) )
# verify image_id
__a =torch.tensor([3_9769] )
self.assertTrue(torch.allclose(encoding['labels'][0]['image_id'] , __snake_case ) )
# verify is_crowd
__a =torch.tensor([0, 0, 0, 0, 0, 0] )
self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd'] , __snake_case ) )
# verify class_labels
__a =torch.tensor([17, 17, 63, 75, 75, 93] )
self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels'] , __snake_case ) )
# verify masks
__a =82_2873
self.assertEqual(encoding['labels'][0]['masks'].sum().item() , __snake_case )
# verify orig_size
__a =torch.tensor([480, 640] )
self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size'] , __snake_case ) )
# verify size
__a =torch.tensor([800, 1066] )
self.assertTrue(torch.allclose(encoding['labels'][0]['size'] , __snake_case ) )
| 308
|
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 __magic_name__ :
@staticmethod
def __magic_name__ ( *__snake_case , **__snake_case ) -> List[str]:
'''simple docstring'''
pass
def UpperCamelCase_( _snake_case : Image ):
"""simple docstring"""
__a =hashlib.mda(image.tobytes() )
return m.hexdigest()
@is_pipeline_test
@require_vision
@require_timm
@require_torch
class __magic_name__ ( unittest.TestCase ):
SCREAMING_SNAKE_CASE = MODEL_FOR_DEPTH_ESTIMATION_MAPPING
def __magic_name__ ( self , __snake_case , __snake_case , __snake_case ) -> Dict:
'''simple docstring'''
__a =DepthEstimationPipeline(model=__snake_case , image_processor=__snake_case )
return depth_estimator, [
"./tests/fixtures/tests_samples/COCO/000000039769.png",
"./tests/fixtures/tests_samples/COCO/000000039769.png",
]
def __magic_name__ ( self , __snake_case , __snake_case ) -> Tuple:
'''simple docstring'''
__a =depth_estimator('./tests/fixtures/tests_samples/COCO/000000039769.png' )
self.assertEqual({'predicted_depth': ANY(torch.Tensor ), 'depth': ANY(Image.Image )} , __snake_case )
import datasets
__a =datasets.load_dataset('hf-internal-testing/fixtures_image_utils' , 'image' , split='test' )
__a =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 )},
] , __snake_case , )
@require_tf
@unittest.skip('Depth estimation is not implemented in TF' )
def __magic_name__ ( self ) -> Optional[Any]:
'''simple docstring'''
pass
@slow
@require_torch
def __magic_name__ ( self ) -> int:
'''simple docstring'''
__a ='Intel/dpt-large'
__a =pipeline('depth-estimation' , model=__snake_case )
__a =depth_estimator('http://images.cocodataset.org/val2017/000000039769.jpg' )
__a =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 __magic_name__ ( self ) -> Any:
'''simple docstring'''
# This is highly irregular to have no small tests.
self.skipTest('There is not hf-internal-testing tiny model for either GLPN nor DPT' )
| 308
| 1
|
from __future__ import annotations
from statistics import mean
def UpperCamelCase_( _snake_case : list[int] , _snake_case : list[int] , _snake_case : int ):
"""simple docstring"""
__a =[0] * no_of_processes
__a =[0] * no_of_processes
# Initialize remaining_time to waiting_time.
for i in range(_snake_case ):
__a =burst_time[i]
__a =[]
__a =0
__a =0
# When processes are not completed,
# A process whose arrival time has passed \
# and has remaining execution time is put into the ready_process.
# The shortest process in the ready_process, target_process is executed.
while completed != no_of_processes:
__a =[]
__a =-1
for i in range(_snake_case ):
if (arrival_time[i] <= total_time) and (remaining_time[i] > 0):
ready_process.append(_snake_case )
if len(_snake_case ) > 0:
__a =ready_process[0]
for i in ready_process:
if remaining_time[i] < remaining_time[target_process]:
__a =i
total_time += burst_time[target_process]
completed += 1
__a =0
__a =(
total_time - arrival_time[target_process] - burst_time[target_process]
)
else:
total_time += 1
return waiting_time
def UpperCamelCase_( _snake_case : list[int] , _snake_case : int , _snake_case : list[int] ):
"""simple docstring"""
__a =[0] * no_of_processes
for i in range(_snake_case ):
__a =burst_time[i] + waiting_time[i]
return turn_around_time
if __name__ == "__main__":
print("[TEST CASE 01]")
_lowerCAmelCase : Union[str, Any] = 4
_lowerCAmelCase : Tuple = [2, 5, 3, 7]
_lowerCAmelCase : Union[str, Any] = [0, 0, 0, 0]
_lowerCAmelCase : Optional[Any] = calculate_waitingtime(arrival_time, burst_time, no_of_processes)
_lowerCAmelCase : Optional[Any] = calculate_turnaroundtime(
burst_time, no_of_processes, waiting_time
)
# Printing the Result
print("PID\tBurst Time\tArrival Time\tWaiting Time\tTurnaround Time")
for i, process_id in enumerate(list(range(1, 5))):
print(
f'''{process_id}\t{burst_time[i]}\t\t\t{arrival_time[i]}\t\t\t\t'''
f'''{waiting_time[i]}\t\t\t\t{turn_around_time[i]}'''
)
print(f'''\nAverage waiting time = {mean(waiting_time):.5f}''')
print(f'''Average turnaround time = {mean(turn_around_time):.5f}''')
| 308
|
import argparse
import dataclasses
import json
import logging
import os
import shutil
from typing import List, Optional
import datasets
from accelerate import Accelerator
from datasets import load_dataset
from finetuning import finetune
from tqdm.auto import tqdm
import transformers
from transformers import AutoConfig, set_seed
from transformers.trainer_utils import IntervalStrategy
_lowerCAmelCase : Optional[int] = logging.getLogger(__name__)
_lowerCAmelCase : Any = "pytorch_model.bin"
@dataclasses.dataclass
class __magic_name__ :
SCREAMING_SNAKE_CASE = dataclasses.field(
metadata={'help': 'Path to pretrained model or model identifier from huggingface.co/models.'} )
SCREAMING_SNAKE_CASE = dataclasses.field(
default=lowerCAmelCase_ , metadata={'help': 'Where do you want to store the pretrained models downloaded from huggingface.co.'} , )
@dataclasses.dataclass
class __magic_name__ :
SCREAMING_SNAKE_CASE = dataclasses.field(metadata={'help': 'A csv or a json file containing the training data.'} )
SCREAMING_SNAKE_CASE = dataclasses.field(metadata={'help': 'A csv or a json file containing the data to predict on.'} )
SCREAMING_SNAKE_CASE = dataclasses.field(
default=lowerCAmelCase_ , metadata={'help': 'A csv or a json file containing the validation data.'} )
SCREAMING_SNAKE_CASE = dataclasses.field(
default=lowerCAmelCase_ , metadata={'help': 'The name of the task to train on.'} , )
SCREAMING_SNAKE_CASE = dataclasses.field(
default=lowerCAmelCase_ , metadata={'help': 'The list of labels for the task.'} )
@dataclasses.dataclass
class __magic_name__ :
SCREAMING_SNAKE_CASE = dataclasses.field(
metadata={'help': 'The output directory where the model predictions and checkpoints will be written.'} )
SCREAMING_SNAKE_CASE = dataclasses.field(
default='accuracy' , metadata={'help': 'The evaluation metric used for the task.'} )
SCREAMING_SNAKE_CASE = dataclasses.field(
default='no' , metadata={
'help': 'The evaluation strategy to adopt during training. Possible values are: ["no", "step", "epoch]'
} , )
SCREAMING_SNAKE_CASE = dataclasses.field(
default=1_0 , metadata={'help': 'Number of evaluation calls with no improvement after which training will be stopped.'} , )
SCREAMING_SNAKE_CASE = dataclasses.field(
default=0.0 , metadata={
'help': 'How much the specified evaluation metric must improve to satisfy early stopping conditions.'
} , )
SCREAMING_SNAKE_CASE = dataclasses.field(
default=lowerCAmelCase_ , metadata={'help': 'Whether to filter the pseudo-labeled data based on the confidence score.'} , )
SCREAMING_SNAKE_CASE = dataclasses.field(
default=lowerCAmelCase_ , metadata={'help': 'Whether to filter the pseudo-labeled data based on the validation performance.'} , )
SCREAMING_SNAKE_CASE = dataclasses.field(
default=lowerCAmelCase_ , metadata={'help': 'Whether to fine-tune on labeled data after pseudo training.'} , )
SCREAMING_SNAKE_CASE = dataclasses.field(
default=0.0 , metadata={'help': 'Confidence threshold for pseudo-labeled data filtering.'} , )
SCREAMING_SNAKE_CASE = dataclasses.field(
default=1_0_0 , metadata={'help': 'Number of evaluation calls with no improvement after which training will be stopped.'} , )
SCREAMING_SNAKE_CASE = dataclasses.field(
default=lowerCAmelCase_ , metadata={'help': 'Random seed for initialization.'} , )
def UpperCamelCase_( _snake_case : int , _snake_case : str , _snake_case : Optional[int] , _snake_case : str , _snake_case : Union[str, Any] , _snake_case : List[Any] ):
"""simple docstring"""
__a =datasets.concatenate_datasets([infer_input, infer_output] , axis=1 )
if args.do_filter_by_confidence:
__a =dataset.filter(lambda _snake_case : example["probability"] > args.confidence_threshold )
if args.do_filter_by_val_performance:
assert eval_result >= 0.0 and eval_result <= 1.0
__a =int(eval_result * len(_snake_case ) )
print(_snake_case )
__a =dataset.sort('probability' , reverse=_snake_case )
__a =dataset.select(range(_snake_case ) )
__a =dataset.remove_columns(['label', 'probability'] )
__a =dataset.rename_column('prediction' , 'label' )
__a =dataset.map(lambda _snake_case : {"label": idalabel[example["label"]]} )
__a =dataset.shuffle(seed=args.seed )
__a =os.path.join(_snake_case , F'train_pseudo.{args.data_file_extension}' )
if args.data_file_extension == "csv":
dataset.to_csv(_snake_case , index=_snake_case )
else:
dataset.to_json(_snake_case )
def UpperCamelCase_( _snake_case : List[Any] , _snake_case : str , _snake_case : int , _snake_case : Optional[int] , **_snake_case : List[str] ):
"""simple docstring"""
__a =Accelerator()
# Make one log on every process with the configuration for debugging.
logging.basicConfig(
format='%(asctime)s - %(levelname)s - %(name)s - %(message)s' , datefmt='%m/%d/%Y %H:%M:%S' , level=logging.INFO , )
logger.info(accelerator.state )
# Setup logging, we only want one process per machine to log things on the
# screen. accelerator.is_local_main_process is only True for one process per
# machine.
logger.setLevel(logging.INFO if accelerator.is_local_main_process else logging.ERROR )
if accelerator.is_local_main_process:
datasets.utils.logging.set_verbosity_warning()
transformers.utils.logging.set_verbosity_info()
else:
datasets.utils.logging.set_verbosity_error()
transformers.utils.logging.set_verbosity_error()
__a =STModelArguments(model_name_or_path=_snake_case )
__a =STDataArguments(train_file=_snake_case , infer_file=_snake_case )
__a =STTrainingArguments(output_dir=_snake_case )
__a =argparse.Namespace()
for arg_class in (model_args, data_args, training_args):
for key, value in vars(_snake_case ).items():
setattr(_snake_case , _snake_case , _snake_case )
for key, value in kwargs.items():
if hasattr(_snake_case , _snake_case ):
setattr(_snake_case , _snake_case , _snake_case )
# Sanity checks
__a ={}
__a =None
# You need to provide the training data and the data to predict on
assert args.train_file is not None
assert args.infer_file is not None
__a =args.train_file
__a =args.infer_file
if args.evaluation_strategy != IntervalStrategy.NO.value:
assert args.eval_file is not None
__a =args.eval_file
for key in data_files:
__a =data_files[key].split('.' )[-1]
assert extension in ["csv", "json"], F'`{key}_file` should be a csv or a json file.'
if args.data_file_extension is None:
__a =extension
else:
assert extension == args.data_file_extension, F'`{key}_file` should be a {args.data_file_extension} file`.'
assert (
args.eval_metric in datasets.list_metrics()
), F'{args.eval_metric} not in the list of supported metrics {datasets.list_metrics()}.'
# If passed along, set the training seed now.
if args.seed is not None:
set_seed(args.seed )
logger.info('Creating the initial data directory for self-training...' )
__a =F'{args.output_dir}/self-train_iter-{{}}'.format
__a =data_dir_format(0 )
if accelerator.is_main_process:
if args.output_dir is not None:
os.makedirs(args.output_dir , exist_ok=_snake_case )
os.makedirs(_snake_case , exist_ok=_snake_case )
accelerator.wait_for_everyone()
__a =None
__a =None
__a =0
__a =False
# Show the progress bar
__a =tqdm(range(args.max_selftrain_iterations ) , disable=not accelerator.is_local_main_process )
# Self-train
for iteration in range(0 , int(args.max_selftrain_iterations ) ):
__a =data_dir_format(_snake_case )
assert os.path.exists(_snake_case )
# Stage 1: initial fine-tuning for iteration = 0 or pseudo-training for
# iteration > 0
__a =os.path.join(_snake_case , 'stage-1' )
__a ={
'accelerator': accelerator,
'model_name_or_path': args.model_name_or_path,
'cache_dir': args.cache_dir,
'do_train': True,
'train_file': data_files['train'] if iteration == 0 else data_files['train_pseudo'],
'do_eval': True if args.eval_file is not None else False,
'eval_file': data_files['eval'],
'do_predict': True,
'infer_file': data_files['infer'],
'task_name': args.task_name,
'label_list': args.label_list,
'output_dir': current_output_dir,
'eval_metric': args.eval_metric,
'evaluation_strategy': args.evaluation_strategy,
'early_stopping_patience': args.early_stopping_patience,
'early_stopping_threshold': args.early_stopping_threshold,
'seed': args.seed,
}
# Add additional training arguments
for key, value in kwargs.items():
if key not in arguments_dict and not hasattr(_snake_case , _snake_case ):
arguments_dict.update({key: value} )
__a =os.path.join(_snake_case , 'best-checkpoint' , _snake_case )
if os.path.exists(_snake_case ):
logger.info(
'Found existing model checkpoint at %s. Skipping self-training: iteration: %d, stage: 1.' , _snake_case , _snake_case , )
else:
logger.info('***** Running self-training: iteration: %d, stage: 1 *****' , _snake_case )
finetune(**_snake_case )
accelerator.wait_for_everyone()
assert os.path.exists(_snake_case )
logger.info('Self-training job completed: iteration: %d, stage: 1.' , _snake_case )
if iteration > 0 and args.finetune_on_labeled_data:
# Stage 2 (optional): fine-tuning on the original labeled data
__a =os.path.join(_snake_case , 'best-checkpoint' )
__a =os.path.join(_snake_case , 'stage-2' )
# Update arguments_dict
__a =model_path
__a =data_files['train']
__a =current_output_dir
__a =os.path.join(_snake_case , 'best-checkpoint' , _snake_case )
if os.path.exists(_snake_case ):
logger.info(
'Found existing model checkpoint at %s. Skipping self-training: iteration: %d, stage: 2.' , _snake_case , _snake_case , )
else:
logger.info('***** Running self-training: iteration: %d, stage: 2 *****' , _snake_case )
finetune(**_snake_case )
accelerator.wait_for_everyone()
assert os.path.exists(_snake_case )
logger.info('Self-training job completed: iteration: %d, stage: 2.' , _snake_case )
__a =iteration
__a =data_dir_format(iteration + 1 )
__a =AutoConfig.from_pretrained(os.path.join(_snake_case , 'best-checkpoint' ) )
__a =config.idalabel
__a =os.path.join(_snake_case , 'eval_results_best-checkpoint.json' )
__a =os.path.join(_snake_case , 'test_results_best-checkpoint.json' )
assert os.path.exists(_snake_case )
with open(_snake_case , 'r' ) as f:
__a =float(json.load(_snake_case )[args.eval_metric] )
__a =os.path.join(_snake_case , 'infer_output_best-checkpoint.csv' )
assert os.path.exists(_snake_case )
# Loading the dataset from local csv or json files.
__a =load_dataset(args.data_file_extension , data_files={'data': data_files['infer']} )['data']
__a =load_dataset('csv' , data_files={'data': infer_output_file} )['data']
if accelerator.is_main_process:
os.makedirs(_snake_case , exist_ok=_snake_case )
shutil.copy(_snake_case , os.path.join(_snake_case , F'eval_results_iter-{iteration}.json' ) )
if os.path.exists(_snake_case ):
shutil.copy(_snake_case , os.path.join(_snake_case , F'test_results_iter-{iteration}.json' ) )
create_pseudo_labeled_data(_snake_case , _snake_case , _snake_case , _snake_case , _snake_case , _snake_case )
accelerator.wait_for_everyone()
__a =os.path.join(_snake_case , F'train_pseudo.{args.data_file_extension}' )
if args.evaluation_strategy != IntervalStrategy.NO.value:
__a =eval_result
if best_iteration is None:
__a =new_iteration
__a =new_eval_result
else:
if new_eval_result - best_eval_result > args.early_stopping_threshold:
__a =new_iteration
__a =new_eval_result
__a =0
else:
if new_eval_result == best_eval_result:
__a =new_iteration
__a =new_eval_result
early_stopping_patience_counter += 1
if early_stopping_patience_counter >= args.early_stopping_patience:
__a =True
progress_bar.update(1 )
if should_training_stop:
break
if best_iteration is not None:
# Save the best iteration
logger.info('Best iteration: %d' , _snake_case )
logger.info('Best evaluation result: %s = %f' , args.eval_metric , _snake_case )
accelerator.wait_for_everyone()
if accelerator.is_main_process:
shutil.copy(
os.path.join(_snake_case , F'eval_results_iter-{iteration}.json' ) , os.path.join(_snake_case , 'eval_results_best-iteration.json' ) , )
else:
# Assume that the last iteration is the best
logger.info('Best iteration: %d' , args.max_selftrain_iterations - 1 )
logger.info('Best evaluation result: %s = %f' , args.eval_metric , _snake_case )
accelerator.wait_for_everyone()
if accelerator.is_main_process:
shutil.copy(
os.path.join(_snake_case , F'eval_results_iter-{args.max_selftrain_iterations - 1}.json' ) , os.path.join(_snake_case , 'eval_results_best-iteration.json' ) , )
| 308
| 1
|
import argparse
import logging
from collections import namedtuple
import torch
from model_bertabs import BertAbsSummarizer
from models.model_builder import AbsSummarizer # The authors' implementation
from transformers import BertTokenizer
logging.basicConfig(level=logging.INFO)
_lowerCAmelCase : List[Any] = logging.getLogger(__name__)
_lowerCAmelCase : Optional[Any] = "Hello world! cécé herlolip"
_lowerCAmelCase : str = namedtuple(
"BertAbsConfig",
[
"temp_dir",
"large",
"use_bert_emb",
"finetune_bert",
"encoder",
"share_emb",
"max_pos",
"enc_layers",
"enc_hidden_size",
"enc_heads",
"enc_ff_size",
"enc_dropout",
"dec_layers",
"dec_hidden_size",
"dec_heads",
"dec_ff_size",
"dec_dropout",
],
)
def UpperCamelCase_( _snake_case : str , _snake_case : List[Any] ):
"""simple docstring"""
__a =BertAbsConfig(
temp_dir='.' , finetune_bert=_snake_case , large=_snake_case , share_emb=_snake_case , use_bert_emb=_snake_case , encoder='bert' , max_pos=512 , enc_layers=6 , enc_hidden_size=512 , enc_heads=8 , enc_ff_size=512 , enc_dropout=0.2 , dec_layers=6 , dec_hidden_size=768 , dec_heads=8 , dec_ff_size=2048 , dec_dropout=0.2 , )
__a =torch.load(_snake_case , lambda _snake_case , _snake_case : storage )
__a =AbsSummarizer(_snake_case , torch.device('cpu' ) , _snake_case )
original.eval()
__a =BertAbsSummarizer(_snake_case , torch.device('cpu' ) )
new_model.eval()
# -------------------
# Convert the weights
# -------------------
logging.info('convert the model' )
new_model.bert.load_state_dict(original.bert.state_dict() )
new_model.decoder.load_state_dict(original.decoder.state_dict() )
new_model.generator.load_state_dict(original.generator.state_dict() )
# ----------------------------------
# Make sure the outpus are identical
# ----------------------------------
logging.info('Make sure that the models\' outputs are identical' )
__a =BertTokenizer.from_pretrained('bert-base-uncased' )
# prepare the model inputs
__a =tokenizer.encode('This is sample éàalj\'-.' )
encoder_input_ids.extend([tokenizer.pad_token_id] * (512 - len(_snake_case )) )
__a =torch.tensor(_snake_case ).unsqueeze(0 )
__a =tokenizer.encode('This is sample 3 éàalj\'-.' )
decoder_input_ids.extend([tokenizer.pad_token_id] * (512 - len(_snake_case )) )
__a =torch.tensor(_snake_case ).unsqueeze(0 )
# failsafe to make sure the weights reset does not affect the
# loaded weights.
assert torch.max(torch.abs(original.generator[0].weight - new_model.generator[0].weight ) ) == 0
# forward pass
__a =encoder_input_ids
__a =decoder_input_ids
__a =__a =None
__a =None
__a =__a =None
__a =__a =None
__a =None
# The original model does not apply the geneator layer immediatly but rather in
# the beam search (where it combines softmax + linear layer). Since we already
# apply the softmax in our generation process we only apply the linear layer here.
# We make sure that the outputs of the full stack are identical
__a =original(_snake_case , _snake_case , _snake_case , _snake_case , _snake_case , _snake_case , _snake_case )[0]
__a =original.generator(_snake_case )
__a =new_model(
_snake_case , _snake_case , _snake_case , _snake_case , _snake_case )[0]
__a =new_model.generator(_snake_case )
__a =torch.max(torch.abs(output_converted_model - output_original_model ) ).item()
print('Maximum absolute difference beween weights: {:.2f}'.format(_snake_case ) )
__a =torch.max(torch.abs(output_converted_generator - output_original_generator ) ).item()
print('Maximum absolute difference beween weights: {:.2f}'.format(_snake_case ) )
__a =torch.allclose(_snake_case , _snake_case , atol=1e-3 )
if are_identical:
logging.info('all weights are equal up to 1e-3' )
else:
raise ValueError('the weights are different. The new model is likely different from the original one.' )
# The model has been saved with torch.save(model) and this is bound to the exact
# directory structure. We save the state_dict instead.
logging.info('saving the model\'s state dictionary' )
torch.save(
new_model.state_dict() , './bertabs-finetuned-cnndm-extractive-abstractive-summarization/pytorch_model.bin' )
if __name__ == "__main__":
_lowerCAmelCase : Optional[int] = argparse.ArgumentParser()
parser.add_argument(
"--bertabs_checkpoint_path",
default=None,
type=str,
required=True,
help="Path the official PyTorch dump.",
)
parser.add_argument(
"--pytorch_dump_folder_path",
default=None,
type=str,
required=True,
help="Path to the output PyTorch model.",
)
_lowerCAmelCase : Optional[Any] = parser.parse_args()
convert_bertabs_checkpoints(
args.bertabs_checkpoint_path,
args.pytorch_dump_folder_path,
)
| 308
|
import shutil
import tempfile
import unittest
from transformers import (
SPIECE_UNDERLINE,
AddedToken,
BatchEncoding,
NllbTokenizer,
NllbTokenizerFast,
is_torch_available,
)
from transformers.testing_utils import (
get_tests_dir,
nested_simplify,
require_sentencepiece,
require_tokenizers,
require_torch,
)
from ...test_tokenization_common import TokenizerTesterMixin
_lowerCAmelCase : Any = get_tests_dir("fixtures/test_sentencepiece.model")
if is_torch_available():
from transformers.models.mam_aaa.modeling_mam_aaa import shift_tokens_right
_lowerCAmelCase : List[Any] = 256_047
_lowerCAmelCase : Dict = 256_145
@require_sentencepiece
@require_tokenizers
class __magic_name__ ( lowerCAmelCase_ , unittest.TestCase ):
SCREAMING_SNAKE_CASE = NllbTokenizer
SCREAMING_SNAKE_CASE = NllbTokenizerFast
SCREAMING_SNAKE_CASE = True
SCREAMING_SNAKE_CASE = True
SCREAMING_SNAKE_CASE = {}
def __magic_name__ ( self ) -> int:
'''simple docstring'''
super().setUp()
# We have a SentencePiece fixture for testing
__a =NllbTokenizer(__snake_case , keep_accents=__snake_case )
tokenizer.save_pretrained(self.tmpdirname )
def __magic_name__ ( self ) -> int:
'''simple docstring'''
__a =NllbTokenizer(__snake_case , keep_accents=__snake_case )
__a =tokenizer.tokenize('This is a test' )
self.assertListEqual(__snake_case , ['▁This', '▁is', '▁a', '▁t', 'est'] )
self.assertListEqual(
tokenizer.convert_tokens_to_ids(__snake_case ) , [value + tokenizer.fairseq_offset for value in [285, 46, 10, 170, 382]] , )
__a =tokenizer.tokenize('I was born in 92000, and this is falsé.' )
self.assertListEqual(
__snake_case , [
SPIECE_UNDERLINE + 'I',
SPIECE_UNDERLINE + 'was',
SPIECE_UNDERLINE + 'b',
'or',
'n',
SPIECE_UNDERLINE + 'in',
SPIECE_UNDERLINE + '',
'9',
'2',
'0',
'0',
'0',
',',
SPIECE_UNDERLINE + 'and',
SPIECE_UNDERLINE + 'this',
SPIECE_UNDERLINE + 'is',
SPIECE_UNDERLINE + 'f',
'al',
's',
'é',
'.',
] , )
__a =tokenizer.convert_tokens_to_ids(__snake_case )
self.assertListEqual(
__snake_case , [
value + tokenizer.fairseq_offset
for value in [8, 21, 84, 55, 24, 19, 7, 2, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 2, 4]
] , )
__a =tokenizer.convert_ids_to_tokens(__snake_case )
self.assertListEqual(
__snake_case , [
SPIECE_UNDERLINE + 'I',
SPIECE_UNDERLINE + 'was',
SPIECE_UNDERLINE + 'b',
'or',
'n',
SPIECE_UNDERLINE + 'in',
SPIECE_UNDERLINE + '',
'<unk>',
'2',
'0',
'0',
'0',
',',
SPIECE_UNDERLINE + 'and',
SPIECE_UNDERLINE + 'this',
SPIECE_UNDERLINE + 'is',
SPIECE_UNDERLINE + 'f',
'al',
's',
'<unk>',
'.',
] , )
def __magic_name__ ( self ) -> Union[str, Any]:
'''simple docstring'''
__a =(self.rust_tokenizer_class, 'hf-internal-testing/tiny-random-nllb', {})
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(f'{tokenizer.__class__.__name__} ({pretrained_name})' ):
__a =self.rust_tokenizer_class.from_pretrained(__snake_case , **__snake_case )
__a =self.tokenizer_class.from_pretrained(__snake_case , **__snake_case )
__a =tempfile.mkdtemp()
__a =tokenizer_r.save_pretrained(__snake_case )
__a =tokenizer_p.save_pretrained(__snake_case )
# Checks it save with the same files + the tokenizer.json file for the fast one
self.assertTrue(any('tokenizer.json' in f for f in tokenizer_r_files ) )
__a =tuple(f for f in tokenizer_r_files if 'tokenizer.json' not in f )
self.assertSequenceEqual(__snake_case , __snake_case )
# Checks everything loads correctly in the same way
__a =tokenizer_r.from_pretrained(__snake_case )
__a =tokenizer_p.from_pretrained(__snake_case )
# Check special tokens are set accordingly on Rust and Python
for key in tokenizer_pp.special_tokens_map:
self.assertTrue(hasattr(__snake_case , __snake_case ) )
shutil.rmtree(__snake_case )
# Save tokenizer rust, legacy_format=True
__a =tempfile.mkdtemp()
__a =tokenizer_r.save_pretrained(__snake_case , legacy_format=__snake_case )
__a =tokenizer_p.save_pretrained(__snake_case )
# Checks it save with the same files
self.assertSequenceEqual(__snake_case , __snake_case )
# Checks everything loads correctly in the same way
__a =tokenizer_r.from_pretrained(__snake_case )
__a =tokenizer_p.from_pretrained(__snake_case )
# Check special tokens are set accordingly on Rust and Python
for key in tokenizer_pp.special_tokens_map:
self.assertTrue(hasattr(__snake_case , __snake_case ) )
shutil.rmtree(__snake_case )
# Save tokenizer rust, legacy_format=False
__a =tempfile.mkdtemp()
__a =tokenizer_r.save_pretrained(__snake_case , legacy_format=__snake_case )
__a =tokenizer_p.save_pretrained(__snake_case )
# Checks it saved the tokenizer.json file
self.assertTrue(any('tokenizer.json' in f for f in tokenizer_r_files ) )
# Checks everything loads correctly in the same way
__a =tokenizer_r.from_pretrained(__snake_case )
__a =tokenizer_p.from_pretrained(__snake_case )
# Check special tokens are set accordingly on Rust and Python
for key in tokenizer_pp.special_tokens_map:
self.assertTrue(hasattr(__snake_case , __snake_case ) )
shutil.rmtree(__snake_case )
@require_torch
def __magic_name__ ( self ) -> Optional[Any]:
'''simple docstring'''
if not self.test_seqaseq:
return
__a =self.get_tokenizers()
for tokenizer in tokenizers:
with self.subTest(f'{tokenizer.__class__.__name__}' ):
# Longer text that will definitely require truncation.
__a =[
' UN Chief Says There Is No Military Solution in Syria',
' Secretary-General Ban Ki-moon says his response to Russia\'s stepped up military support for'
' Syria is that \'there is no military solution\' to the nearly five-year conflict and more weapons'
' will only worsen the violence and misery for millions of people.',
]
__a =[
'Şeful ONU declară că nu există o soluţie militară în Siria',
'Secretarul General Ban Ki-moon declară că răspunsul său la intensificarea sprijinului militar al'
' Rusiei pentru Siria este că "nu există o soluţie militară" la conflictul de aproape cinci ani şi'
' că noi arme nu vor face decât să înrăutăţească violenţele şi mizeria pentru milioane de oameni.',
]
try:
__a =tokenizer.prepare_seqaseq_batch(
src_texts=__snake_case , tgt_texts=__snake_case , max_length=3 , max_target_length=10 , return_tensors='pt' , src_lang='eng_Latn' , tgt_lang='ron_Latn' , )
except NotImplementedError:
return
self.assertEqual(batch.input_ids.shape[1] , 3 )
self.assertEqual(batch.labels.shape[1] , 10 )
# max_target_length will default to max_length if not specified
__a =tokenizer.prepare_seqaseq_batch(
__snake_case , tgt_texts=__snake_case , max_length=3 , return_tensors='pt' )
self.assertEqual(batch.input_ids.shape[1] , 3 )
self.assertEqual(batch.labels.shape[1] , 3 )
__a =tokenizer.prepare_seqaseq_batch(
src_texts=__snake_case , max_length=3 , max_target_length=10 , return_tensors='pt' )
self.assertEqual(batch_encoder_only.input_ids.shape[1] , 3 )
self.assertEqual(batch_encoder_only.attention_mask.shape[1] , 3 )
self.assertNotIn('decoder_input_ids' , __snake_case )
@unittest.skip('Unfortunately way too slow to build a BPE with SentencePiece.' )
def __magic_name__ ( self ) -> Optional[int]:
'''simple docstring'''
pass
def __magic_name__ ( self ) -> Tuple:
'''simple docstring'''
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(f'{tokenizer.__class__.__name__} ({pretrained_name})' ):
__a =[AddedToken('<special>' , lstrip=__snake_case )]
__a =self.rust_tokenizer_class.from_pretrained(
__snake_case , additional_special_tokens=__snake_case , **__snake_case )
__a =tokenizer_r.encode('Hey this is a <special> token' )
__a =tokenizer_r.encode('<special>' , add_special_tokens=__snake_case )[0]
self.assertTrue(special_token_id in r_output )
if self.test_slow_tokenizer:
__a =self.rust_tokenizer_class.from_pretrained(
__snake_case , additional_special_tokens=__snake_case , **__snake_case , )
__a =self.tokenizer_class.from_pretrained(
__snake_case , additional_special_tokens=__snake_case , **__snake_case )
__a =tokenizer_p.encode('Hey this is a <special> token' )
__a =tokenizer_cr.encode('Hey this is a <special> token' )
self.assertEqual(__snake_case , __snake_case )
self.assertEqual(__snake_case , __snake_case )
self.assertTrue(special_token_id in p_output )
self.assertTrue(special_token_id in cr_output )
@require_torch
@require_sentencepiece
@require_tokenizers
class __magic_name__ ( unittest.TestCase ):
SCREAMING_SNAKE_CASE = 'facebook/nllb-200-distilled-600M'
SCREAMING_SNAKE_CASE = [
' UN Chief Says There Is No Military Solution in Syria',
' Secretary-General Ban Ki-moon says his response to Russia\'s stepped up military support for Syria is that "there is no military solution" to the nearly five-year conflict and more weapons will only worsen the violence and misery for millions of people.',
]
SCREAMING_SNAKE_CASE = [
'Şeful ONU declară că nu există o soluţie militară în Siria',
'Secretarul General Ban Ki-moon declară că răspunsul său la intensificarea sprijinului militar al Rusiei'
' pentru Siria este că "nu există o soluţie militară" la conflictul de aproape cinci ani şi că noi arme nu vor'
' face decât să înrăutăţească violenţele şi mizeria pentru milioane de oameni.',
]
SCREAMING_SNAKE_CASE = [
2_5_6_0_4_7,
1_6_2_9_7,
1_3_4_4_0_8,
8_1_6_5,
2_4_8_0_6_6,
1_4_7_3_4,
9_5_0,
1_1_3_5,
1_0_5_7_2_1,
3_5_7_3,
8_3,
2_7_3_5_2,
1_0_8,
4_9_4_8_6,
2,
]
@classmethod
def __magic_name__ ( cls ) -> Tuple:
'''simple docstring'''
__a =NllbTokenizer.from_pretrained(
cls.checkpoint_name , src_lang='eng_Latn' , tgt_lang='ron_Latn' )
__a =1
return cls
def __magic_name__ ( self ) -> List[Any]:
'''simple docstring'''
self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['ace_Arab'] , 25_6001 )
self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['ace_Latn'] , 25_6002 )
self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['fra_Latn'] , 25_6057 )
def __magic_name__ ( self ) -> Dict:
'''simple docstring'''
__a =self.tokenizer.batch_encode_plus(self.src_text ).input_ids[0]
self.assertListEqual(self.expected_src_tokens , __snake_case )
def __magic_name__ ( self ) -> Dict:
'''simple docstring'''
self.assertIn(__snake_case , self.tokenizer.all_special_ids )
# fmt: off
__a =[RO_CODE, 4254, 9_8068, 11_2923, 3_9072, 3909, 713, 10_2767, 26, 1_7314, 3_5642, 1_4683, 3_3118, 2022, 6_6987, 2, 25_6047]
# fmt: on
__a =self.tokenizer.decode(__snake_case , skip_special_tokens=__snake_case )
__a =self.tokenizer.decode(generated_ids[1:] , skip_special_tokens=__snake_case )
self.assertEqual(__snake_case , __snake_case )
self.assertNotIn(self.tokenizer.eos_token , __snake_case )
def __magic_name__ ( self ) -> List[str]:
'''simple docstring'''
__a =['this is gunna be a long sentence ' * 20]
assert isinstance(src_text[0] , __snake_case )
__a =10
__a =self.tokenizer(__snake_case , max_length=__snake_case , truncation=__snake_case ).input_ids[0]
self.assertEqual(ids[-1] , 2 )
self.assertEqual(ids[0] , __snake_case )
self.assertEqual(len(__snake_case ) , __snake_case )
def __magic_name__ ( self ) -> Optional[int]:
'''simple docstring'''
self.assertListEqual(self.tokenizer.convert_tokens_to_ids(['<mask>', 'ar_AR'] ) , [25_6203, 3] )
def __magic_name__ ( self ) -> Optional[int]:
'''simple docstring'''
__a =tempfile.mkdtemp()
__a =self.tokenizer.fairseq_tokens_to_ids
self.tokenizer.save_pretrained(__snake_case )
__a =NllbTokenizer.from_pretrained(__snake_case )
self.assertDictEqual(new_tok.fairseq_tokens_to_ids , __snake_case )
@require_torch
def __magic_name__ ( self ) -> Tuple:
'''simple docstring'''
__a =self.tokenizer(
self.src_text , text_target=self.tgt_text , padding=__snake_case , truncation=__snake_case , max_length=len(self.expected_src_tokens ) , return_tensors='pt' , )
__a =shift_tokens_right(
batch['labels'] , self.tokenizer.pad_token_id , self.tokenizer.lang_code_to_id['ron_Latn'] )
self.assertIsInstance(__snake_case , __snake_case )
self.assertEqual((2, 15) , batch.input_ids.shape )
self.assertEqual((2, 15) , batch.attention_mask.shape )
__a =batch.input_ids.tolist()[0]
self.assertListEqual(self.expected_src_tokens , __snake_case )
self.assertEqual(__snake_case , batch.decoder_input_ids[0, 0] ) # EOS
# Test that special tokens are reset
self.assertEqual(self.tokenizer.prefix_tokens , [EN_CODE] )
self.assertEqual(self.tokenizer.suffix_tokens , [self.tokenizer.eos_token_id] )
def __magic_name__ ( self ) -> List[Any]:
'''simple docstring'''
__a =self.tokenizer(self.src_text , padding=__snake_case , truncation=__snake_case , max_length=3 , return_tensors='pt' )
__a =self.tokenizer(
text_target=self.tgt_text , padding=__snake_case , truncation=__snake_case , max_length=10 , return_tensors='pt' )
__a =targets['input_ids']
__a =shift_tokens_right(
__snake_case , self.tokenizer.pad_token_id , decoder_start_token_id=self.tokenizer.lang_code_to_id[self.tokenizer.tgt_lang] , )
self.assertEqual(batch.input_ids.shape[1] , 3 )
self.assertEqual(batch.decoder_input_ids.shape[1] , 10 )
@require_torch
def __magic_name__ ( self ) -> Optional[Any]:
'''simple docstring'''
__a =self.tokenizer._build_translation_inputs(
'A test' , return_tensors='pt' , src_lang='eng_Latn' , tgt_lang='fra_Latn' )
self.assertEqual(
nested_simplify(__snake_case ) , {
# A, test, EOS, en_XX
'input_ids': [[25_6047, 70, 7356, 2]],
'attention_mask': [[1, 1, 1, 1]],
# ar_AR
'forced_bos_token_id': 25_6057,
} , )
@require_torch
def __magic_name__ ( self ) -> List[Any]:
'''simple docstring'''
__a =True
__a =self.tokenizer(
'UN Chief says there is no military solution in Syria' , src_lang='eng_Latn' , tgt_lang='fra_Latn' )
self.assertEqual(
inputs.input_ids , [1_6297, 13_4408, 2_5653, 6370, 248, 254, 10_3929, 9_4995, 108, 4_9486, 2, 25_6047] )
__a =False
__a =self.tokenizer(
'UN Chief says there is no military solution in Syria' , src_lang='eng_Latn' , tgt_lang='fra_Latn' )
self.assertEqual(
inputs.input_ids , [25_6047, 1_6297, 13_4408, 2_5653, 6370, 248, 254, 10_3929, 9_4995, 108, 4_9486, 2] )
| 308
| 1
|
from abc import ABC, abstractmethod
from argparse import ArgumentParser
class __magic_name__ ( lowerCAmelCase_ ):
@staticmethod
@abstractmethod
def __magic_name__ ( __snake_case ) -> List[Any]:
'''simple docstring'''
raise NotImplementedError()
@abstractmethod
def __magic_name__ ( self ) -> Optional[int]:
'''simple docstring'''
raise NotImplementedError()
| 308
|
def UpperCamelCase_( _snake_case : str , _snake_case : int ):
"""simple docstring"""
return [sentence[i : i + ngram_size] for i in range(len(_snake_case ) - ngram_size + 1 )]
if __name__ == "__main__":
from doctest import testmod
testmod()
| 308
| 1
|
import argparse
import random
import joblib
import numpy as np
import torch
from igf.igf import (
SecondaryLearner,
collect_objective_set,
compute_perplexity,
generate_datasets,
load_gpta,
recopy_gpta,
set_seed,
train_secondary_learner,
)
from torch.utils.data import DataLoader, RandomSampler
from transformers import GPTaLMHeadModel
def UpperCamelCase_( _snake_case : Union[str, Any]=32 , _snake_case : Any=10 , _snake_case : Optional[Any]=100 , _snake_case : List[Any]=1026 , _snake_case : List[str]=True , _snake_case : str="data/tokenized_stories_train_wikitext103.jbl" , _snake_case : List[str]="igf_context_pairs.jbl" , ):
"""simple docstring"""
set_seed(3 )
# generate train_data and objective_set
__a , __a =generate_datasets(
_snake_case , _snake_case , number=_snake_case , min_len=1026 , trim=_snake_case )
# keeps model same across runs
set_seed(4 )
# model, lm_optimizer, lm_scheduler = recopy_gpt2(model, device, max_steps) # store original model weights
# can we train on GPU?
__a =torch.device('cuda:0' if torch.cuda.is_available() else 'cpu' )
# load pretrained model
__a =load_gpta('gpt2' ).to(_snake_case )
print('computing perplexity on objective set' )
__a =compute_perplexity(_snake_case , _snake_case , _snake_case ).item()
print('perplexity on objective set:' , _snake_case )
# collect igf pairs and save to file demo.jbl
collect_objective_set(_snake_case , _snake_case , _snake_case , _snake_case , _snake_case , _snake_case , _snake_case , _snake_case )
# clean up, delete model and data we don't need anymore
del model, train_data, objective_set
torch.cuda.empty_cache()
def UpperCamelCase_( _snake_case : Optional[Any] , _snake_case : str=15 , _snake_case : Dict=128 , _snake_case : Union[str, Any]=100 , _snake_case : Dict="igf_model.pt" , ):
"""simple docstring"""
set_seed(42 )
# Load pre-trained model
__a =GPTaLMHeadModel.from_pretrained('gpt2' )
# Initialize secondary learner to use embedding weights of model
__a =SecondaryLearner(_snake_case )
# Train secondary learner
__a =train_secondary_learner(
_snake_case , _snake_case , max_epochs=_snake_case , batch_size=_snake_case , eval_freq=100 , igf_model_path=_snake_case , )
del model, secondary_learner_train_data
torch.cuda.empty_cache()
return secondary_learner
def UpperCamelCase_( _snake_case : Optional[Any] , _snake_case : Optional[int] , _snake_case : List[Any] , _snake_case : List[str]=32 , _snake_case : int=1000 , _snake_case : Union[str, Any]=16 , _snake_case : str=1.0 , _snake_case : Union[str, Any]=recopy_gpta , _snake_case : Union[str, Any]=None , _snake_case : int=10 , _snake_case : Any="gpt2_finetuned.pt" , ):
"""simple docstring"""
__a =torch.device('cuda:0' if torch.cuda.is_available() else 'cpu' )
__a =RandomSampler(_snake_case )
__a =DataLoader(_snake_case , sampler=_snake_case )
__a =max_steps // (len(_snake_case )) + 1
__a =0
__a =torch.zeros((1, context_len) , dtype=torch.long , device=_snake_case )
__a , __a , __a =recopy_model(_snake_case , _snake_case , _snake_case )
model.train()
if secondary_learner is not None:
secondary_learner.to(_snake_case )
secondary_learner.eval()
__a =[]
__a =0
__a =[]
__a =[]
# Compute the performance of the transformer model at the beginning
__a =compute_perplexity(_snake_case , _snake_case , _snake_case )
test_perps.append(_snake_case )
print('Test perplexity, step' , _snake_case , ':' , _snake_case )
for epoch in range(int(_snake_case ) ):
for step, example in enumerate(_snake_case ):
torch.cuda.empty_cache()
__a =random.randint(0 , example.size(2 ) - context_len - 1 )
__a =example[0, 0, start : start + context_len]
lm_optimizer.zero_grad()
__a =model(_snake_case , labels=_snake_case )
__a =True
if secondary_learner is not None:
__a =secondary_learner.forward(
torch.tensor(_snake_case , dtype=torch.long , device=_snake_case ).unsqueeze(0 ) )[0].item()
observed_qs.append(float(_snake_case ) )
# Here we implement the simple non-constant threshold for the predicted IG(X) value
# We will decay the selectivity of our secondary learner filter from
# 1 standard deviation above average to 1 below average after 10 batches.
if global_step == 10:
__a =-1
if predicted_q < threshold:
__a =False
# If we passed the filter, add the context to the batch!
if do_backprop:
contexts.append(np.array(context.cpu() ) )
__a =outputs[0]
lm_loss.backward()
examples += 1
del outputs
# Once the batch is filled with enough contexts, backprop on the batch.
if examples == batch_size:
torch.cuda.empty_cache()
__a =0
# Do LM backprop
torch.nn.utils.clip_grad_norm_(model.parameters() , 3.0 )
lm_optimizer.step()
lm_scheduler.step() # Update learning rate schedule
global_step += 1
# Compute the performance of the transformer model at this batch
if global_step % eval_interval == 0:
__a =compute_perplexity(_snake_case , _snake_case , _snake_case )
test_perps.append(_snake_case )
print('Test perplexity, step' , _snake_case , ':' , _snake_case )
# Break out of the loop after 60 batches
if max_steps > 0 and global_step > 60:
break
if max_steps > 0 and global_step > 60:
break
# save finetuned transformer model
torch.save(model.state_dict() , _snake_case )
torch.cuda.empty_cache()
# Do some cleaning up so we can reinitialize for the next run of this function
del lm_optimizer
del lm_scheduler
return model
def UpperCamelCase_( ):
"""simple docstring"""
__a =argparse.ArgumentParser(description='Fine-tune a transformer model with IGF on a language modeling task' )
# Required parameters
parser.add_argument(
'--data_dir' , default=_snake_case , type=_snake_case , required=_snake_case , help='The input data dir. Should contain data files for WikiText.' , )
parser.add_argument(
'--model_name_or_path' , default=_snake_case , type=_snake_case , required=_snake_case , help='Path to pretrained model or model identifier from huggingface.co/models' , )
parser.add_argument(
'--data_file' , type=_snake_case , default=_snake_case , help=(
'A jbl file containing tokenized data which can be split as objective dataset, '
'train_dataset and test_dataset.'
) , )
parser.add_argument(
'--igf_data_file' , type=_snake_case , default=_snake_case , help='A jbl file containing the context and information gain pairs to train secondary learner.' , )
parser.add_argument(
'--output_dir' , default=_snake_case , type=_snake_case , required=_snake_case , help='The output directory where the final fine-tuned model is stored.' , )
parser.add_argument(
'--tokenizer_name' , default=_snake_case , type=_snake_case , help='Pretrained tokenizer name or path if not the same as model_name' , )
parser.add_argument('--seed' , type=_snake_case , default=_snake_case , help='A seed for reproducible training.' )
parser.add_argument(
'--context_len' , default=32 , type=_snake_case , help=(
'The maximum total input sequence length after tokenization. Sequences longer '
'than this will be truncated, sequences shorter will be padded.'
) , )
parser.add_argument(
'--size_objective_set' , default=100 , type=_snake_case , help='number of articles that are long enough to be used as our objective set' , )
parser.add_argument(
'--eval_freq' , default=100 , type=_snake_case , help='secondary model evaluation is triggered at eval_freq' )
parser.add_argument('--max_steps' , default=1000 , type=_snake_case , help='To calculate training epochs' )
parser.add_argument(
'--secondary_learner_batch_size' , default=128 , type=_snake_case , help='batch size of training data for secondary learner' , )
parser.add_argument(
'--batch_size' , default=16 , type=_snake_case , help='batch size of training data of language model(gpt2) ' )
parser.add_argument(
'--eval_interval' , default=10 , type=_snake_case , help=(
'decay the selectivity of our secondary learner filter from'
'1 standard deviation above average to 1 below average after 10 batches'
) , )
parser.add_argument(
'--number' , default=100 , type=_snake_case , help='The number of examples split to be used as objective_set/test_data' )
parser.add_argument(
'--min_len' , default=1026 , type=_snake_case , help='The minimum length of the article to be used as objective set' )
parser.add_argument(
'--secondary_learner_max_epochs' , default=15 , type=_snake_case , help='number of epochs to train secondary learner' )
parser.add_argument('--trim' , default=_snake_case , type=_snake_case , help='truncate the example if it exceeds context length' )
parser.add_argument(
'--threshold' , default=1.0 , type=_snake_case , help=(
'The threshold value used by secondary learner to filter the train_data and allow only'
' informative data as input to the model'
) , )
parser.add_argument('--finetuned_model_name' , default='gpt2_finetuned.pt' , type=_snake_case , help='finetuned_model_name' )
parser.add_argument(
'--recopy_model' , default=_snake_case , type=_snake_case , help='Reset the model to the original pretrained GPT-2 weights after each iteration' , )
# function calls
# Collecting *n* pairs of context and information gain(X, IG(X)) for training the secondary learner
generate_n_pairs(
context_len=32 , max_steps=10 , size_objective_set=100 , min_len=1026 , trim=_snake_case , data_file='data/tokenized_stories_train_wikitext103.jbl' , igf_data_file='igf_context_pairs.jbl' , )
# Load train data for secondary learner
__a =joblib.load('data/IGF_values.jbl' )
# Train secondary learner
__a =training_secondary_learner(
_snake_case , secondary_learner_max_epochs=15 , secondary_learner_batch_size=128 , eval_freq=100 , igf_model_path='igf_model.pt' , )
# load pretrained gpt2 model
__a =GPTaLMHeadModel.from_pretrained('gpt2' )
set_seed(42 )
# Generate train and test data to train and evaluate gpt2 model
__a , __a =generate_datasets(
context_len=32 , file='data/tokenized_stories_train_wikitext103.jbl' , number=100 , min_len=1026 , trim=_snake_case )
# fine-tuning of the gpt2 model using igf (Information Gain Filtration)
finetune(
_snake_case , _snake_case , _snake_case , context_len=32 , max_steps=1000 , batch_size=16 , threshold=1.0 , recopy_model=_snake_case , secondary_learner=_snake_case , eval_interval=10 , finetuned_model_name='gpt2_finetuned.pt' , )
if __name__ == "__main__":
main()
| 308
|
import argparse
import pytorch_lightning as pl
import torch
from torch import nn
from transformers import LongformerForQuestionAnswering, LongformerModel
class __magic_name__ ( pl.LightningModule ):
def __init__( self , __snake_case ) -> List[Any]:
'''simple docstring'''
super().__init__()
__a =model
__a =2
__a =nn.Linear(self.model.config.hidden_size , self.num_labels )
def __magic_name__ ( self ) -> List[Any]:
'''simple docstring'''
pass
def UpperCamelCase_( _snake_case : str , _snake_case : str , _snake_case : str ):
"""simple docstring"""
__a =LongformerModel.from_pretrained(_snake_case )
__a =LightningModel(_snake_case )
__a =torch.load(_snake_case , map_location=torch.device('cpu' ) )
lightning_model.load_state_dict(ckpt['state_dict'] )
# init longformer question answering model
__a =LongformerForQuestionAnswering.from_pretrained(_snake_case )
# transfer weights
longformer_for_qa.longformer.load_state_dict(lightning_model.model.state_dict() )
longformer_for_qa.qa_outputs.load_state_dict(lightning_model.qa_outputs.state_dict() )
longformer_for_qa.eval()
# save model
longformer_for_qa.save_pretrained(_snake_case )
print(F'Conversion successful. Model saved under {pytorch_dump_folder_path}' )
if __name__ == "__main__":
_lowerCAmelCase : Optional[int] = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--longformer_model",
default=None,
type=str,
required=True,
help="model identifier of longformer. Should be either `longformer-base-4096` or `longformer-large-4096`.",
)
parser.add_argument(
"--longformer_question_answering_ckpt_path",
default=None,
type=str,
required=True,
help="Path the official PyTorch Lightning Checkpoint.",
)
parser.add_argument(
"--pytorch_dump_folder_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
)
_lowerCAmelCase : Optional[int] = parser.parse_args()
convert_longformer_qa_checkpoint_to_pytorch(
args.longformer_model, args.longformer_question_answering_ckpt_path, args.pytorch_dump_folder_path
)
| 308
| 1
|
from functools import lru_cache
@lru_cache
def UpperCamelCase_( _snake_case : int ):
"""simple docstring"""
if num < 0:
raise ValueError('Number should not be negative.' )
return 1 if num in (0, 1) else num * factorial(num - 1 )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 308
|
import logging
import os
import random
import sys
from dataclasses import dataclass, field
from typing import Optional
import datasets
import numpy as np
import pandas as pd
from datasets import load_dataset
import transformers
from transformers import (
AutoConfig,
BartForSequenceClassification,
DataCollatorWithPadding,
EvalPrediction,
HfArgumentParser,
TapexTokenizer,
Trainer,
TrainingArguments,
default_data_collator,
set_seed,
)
from transformers.trainer_utils import get_last_checkpoint
from transformers.utils import check_min_version
from transformers.utils.versions import require_version
# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
check_min_version("4.17.0.dev0")
require_version("datasets>=1.8.0", "To fix: pip install -r examples/pytorch/text-classification/requirements.txt")
_lowerCAmelCase : Optional[Any] = logging.getLogger(__name__)
@dataclass
class __magic_name__ :
SCREAMING_SNAKE_CASE = field(
default='tab_fact' , metadata={'help': 'The name of the dataset to use (via the datasets library).'} )
SCREAMING_SNAKE_CASE = field(
default='tab_fact' , metadata={'help': 'The configuration name of the dataset to use (via the datasets library).'} , )
SCREAMING_SNAKE_CASE = field(
default=1_0_2_4 , metadata={
'help': (
'The maximum total input sequence length after tokenization. Sequences longer '
'than this will be truncated, sequences shorter will be padded.'
)
} , )
SCREAMING_SNAKE_CASE = field(
default=lowerCAmelCase_ , metadata={'help': 'Overwrite the cached preprocessed datasets or not.'} )
SCREAMING_SNAKE_CASE = field(
default=lowerCAmelCase_ , metadata={
'help': (
'Whether to pad all samples to `max_seq_length`. '
'If False, will pad the samples dynamically when batching to the maximum length in the batch.'
)
} , )
SCREAMING_SNAKE_CASE = field(
default=lowerCAmelCase_ , metadata={
'help': (
'For debugging purposes or quicker training, truncate the number of training examples to this '
'value if set.'
)
} , )
SCREAMING_SNAKE_CASE = field(
default=lowerCAmelCase_ , metadata={
'help': (
'For debugging purposes or quicker training, truncate the number of evaluation examples to this '
'value if set.'
)
} , )
SCREAMING_SNAKE_CASE = field(
default=lowerCAmelCase_ , metadata={
'help': (
'For debugging purposes or quicker training, truncate the number of prediction examples to this '
'value if set.'
)
} , )
SCREAMING_SNAKE_CASE = field(
default=lowerCAmelCase_ , metadata={'help': 'A csv or a json file containing the training data.'} )
SCREAMING_SNAKE_CASE = field(
default=lowerCAmelCase_ , metadata={'help': 'A csv or a json file containing the validation data.'} )
SCREAMING_SNAKE_CASE = field(default=lowerCAmelCase_ , metadata={'help': 'A csv or a json file containing the test data.'} )
def __magic_name__ ( self ) -> str:
'''simple docstring'''
if self.dataset_name is not None:
pass
elif self.train_file is None or self.validation_file is None:
raise ValueError('Need either a GLUE task, a training/validation file or a dataset name.' )
else:
__a =self.train_file.split('.' )[-1]
assert train_extension in ["csv", "json"], "`train_file` should be a csv or a json file."
__a =self.validation_file.split('.' )[-1]
assert (
validation_extension == train_extension
), "`validation_file` should have the same extension (csv or json) as `train_file`."
@dataclass
class __magic_name__ :
SCREAMING_SNAKE_CASE = field(
default=lowerCAmelCase_ , metadata={'help': 'Path to pretrained model or model identifier from huggingface.co/models'} )
SCREAMING_SNAKE_CASE = field(
default=lowerCAmelCase_ , metadata={'help': 'Pretrained config name or path if not the same as model_name'} )
SCREAMING_SNAKE_CASE = field(
default=lowerCAmelCase_ , metadata={'help': 'Pretrained tokenizer name or path if not the same as model_name'} )
SCREAMING_SNAKE_CASE = field(
default=lowerCAmelCase_ , metadata={'help': 'Where do you want to store the pretrained models downloaded from huggingface.co'} , )
SCREAMING_SNAKE_CASE = field(
default=lowerCAmelCase_ , metadata={'help': 'Whether to use one of the fast tokenizer (backed by the tokenizers library) or not.'} , )
SCREAMING_SNAKE_CASE = field(
default='main' , metadata={'help': 'The specific model version to use (can be a branch name, tag name or commit id).'} , )
SCREAMING_SNAKE_CASE = field(
default=lowerCAmelCase_ , metadata={
'help': (
'Will use the token generated when running `huggingface-cli login` (necessary to use this script '
'with private models).'
)
} , )
def UpperCamelCase_( ):
"""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()
# 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 )] , )
__a =training_args.get_process_log_level()
logger.setLevel(_snake_case )
datasets.utils.logging.set_verbosity(_snake_case )
transformers.utils.logging.set_verbosity(_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 training and evaluation files (see below)
# or specify a GLUE benchmark task (the dataset will be downloaded automatically from the datasets Hub).
#
# For JSON files, this script will use the `question` column for the input question and `table` column for the corresponding table.
#
# If the CSVs/JSONs contain only one non-label column, the script does single sentence classification on this
# single column. 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.dataset_name is not None:
# Downloading and loading a dataset from the hub.
__a =load_dataset(
data_args.dataset_name , data_args.dataset_config_name , cache_dir=model_args.cache_dir )
else:
# Loading a dataset from your local files.
# CSV/JSON training and evaluation files are needed.
__a ={'train': data_args.train_file, 'validation': data_args.validation_file}
# Get the test dataset: you can provide your own CSV/JSON test file (see below)
# when you use `do_predict` without specifying a GLUE benchmark task.
if training_args.do_predict:
if data_args.test_file is not None:
__a =data_args.train_file.split('.' )[-1]
__a =data_args.test_file.split('.' )[-1]
assert (
test_extension == train_extension
), "`test_file` should have the same extension (csv or json) as `train_file`."
__a =data_args.test_file
else:
raise ValueError('Need either a GLUE task or a test file for `do_predict`.' )
for key in data_files.keys():
logger.info(F'load a local file for {key}: {data_files[key]}' )
if data_args.train_file.endswith('.csv' ):
# Loading a dataset from local csv files
__a =load_dataset('csv' , data_files=_snake_case , cache_dir=model_args.cache_dir )
else:
# Loading a dataset from local json files
__a =load_dataset('json' , data_files=_snake_case , cache_dir=model_args.cache_dir )
# See more about loading any type of standard or custom dataset at
# https://huggingface.co/docs/datasets/loading_datasets.html.
# Labels
__a =raw_datasets['train'].features['label'].names
__a =len(_snake_case )
# Load pretrained model and tokenizer
#
# In 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 , num_labels=_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 , )
# load tapex tokenizer
__a =TapexTokenizer.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 , add_prefix_space=_snake_case , )
__a =BartForSequenceClassification.from_pretrained(
model_args.model_name_or_path , from_tf=bool('.ckpt' in model_args.model_name_or_path ) , config=_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 , )
# Padding strategy
if data_args.pad_to_max_length:
__a ='max_length'
else:
# We will pad later, dynamically at batch creation, to the max sequence length in each batch
__a =False
# Some models have set the order of the labels to use, so let's make sure we do use it.
__a ={'Refused': 0, 'Entailed': 1}
__a ={0: 'Refused', 1: 'Entailed'}
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 )
def preprocess_tabfact_function(_snake_case : Tuple ):
# Tokenize the texts
def _convert_table_text_to_pandas(_snake_case : Optional[Any] ):
__a =[_table_row.split('#' ) for _table_row in _table_text.strip('\n' ).split('\n' )]
__a =pd.DataFrame.from_records(_table_content[1:] , columns=_table_content[0] )
return _table_pd
__a =examples['statement']
__a =list(map(_convert_table_text_to_pandas , examples['table_text'] ) )
__a =tokenizer(_snake_case , _snake_case , padding=_snake_case , max_length=_snake_case , truncation=_snake_case )
__a =examples['label']
return result
with training_args.main_process_first(desc='dataset map pre-processing' ):
__a =raw_datasets.map(
_snake_case , batched=_snake_case , load_from_cache_file=not data_args.overwrite_cache , desc='Running tokenizer on dataset' , )
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 =train_dataset.select(range(data_args.max_train_samples ) )
if training_args.do_eval:
if "validation" not in raw_datasets and "validation_matched" 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 =eval_dataset.select(range(data_args.max_eval_samples ) )
if training_args.do_predict or data_args.test_file is not None:
if "test" not in raw_datasets and "test_matched" not in raw_datasets:
raise ValueError('--do_predict requires a test dataset' )
__a =raw_datasets['test']
if data_args.max_predict_samples is not None:
__a =predict_dataset.select(range(data_args.max_predict_samples ) )
# Log a few random samples from the training set:
if training_args.do_train:
for index in random.sample(range(len(_snake_case ) ) , 3 ):
logger.info(F'Sample {index} of the training set: {train_dataset[index]}.' )
# You can define your custom compute_metrics function. It takes an `EvalPrediction` object (a namedtuple with a
# predictions and label_ids field) and has to return a dictionary string to float.
def compute_metrics(_snake_case : EvalPrediction ):
__a =p.predictions[0] if isinstance(p.predictions , _snake_case ) else p.predictions
__a =np.argmax(_snake_case , axis=1 )
return {"accuracy": (preds == p.label_ids).astype(np.floataa ).mean().item()}
# Data collator will default to DataCollatorWithPadding, so we change it if we already did the padding.
if data_args.pad_to_max_length:
__a =default_data_collator
elif training_args.fpaa:
__a =DataCollatorWithPadding(_snake_case , pad_to_multiple_of=8 )
else:
__a =None
# Initialize our Trainer
__a =Trainer(
model=_snake_case , args=_snake_case , train_dataset=train_dataset if training_args.do_train else None , eval_dataset=eval_dataset if training_args.do_eval else None , compute_metrics=_snake_case , tokenizer=_snake_case , data_collator=_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=_snake_case )
__a =train_result.metrics
__a =(
data_args.max_train_samples if data_args.max_train_samples is not None else len(_snake_case )
)
__a =min(_snake_case , len(_snake_case ) )
trainer.save_model() # Saves the tokenizer too for easy upload
trainer.log_metrics('train' , _snake_case )
trainer.save_metrics('train' , _snake_case )
trainer.save_state()
# Evaluation
if training_args.do_eval:
logger.info('*** Evaluate ***' )
__a =trainer.evaluate(eval_dataset=_snake_case )
__a =data_args.max_eval_samples if data_args.max_eval_samples is not None else len(_snake_case )
__a =min(_snake_case , len(_snake_case ) )
trainer.log_metrics('eval' , _snake_case )
trainer.save_metrics('eval' , _snake_case )
if training_args.do_predict:
logger.info('*** Predict ***' )
# Removing the `label` columns because it contains -1 and Trainer won't like that.
__a =predict_dataset.remove_columns('label' )
__a =trainer.predict(_snake_case , metric_key_prefix='predict' ).predictions
__a =np.argmax(_snake_case , axis=1 )
__a =os.path.join(training_args.output_dir , 'predict_results_tabfact.txt' )
if trainer.is_world_process_zero():
with open(_snake_case , 'w' ) as writer:
logger.info('***** Predict Results *****' )
writer.write('index\tprediction\n' )
for index, item in enumerate(_snake_case ):
__a =label_list[item]
writer.write(F'{index}\t{item}\n' )
__a ={'finetuned_from': model_args.model_name_or_path, 'tasks': 'text-classification'}
if training_args.push_to_hub:
trainer.push_to_hub(**_snake_case )
else:
trainer.create_model_card(**_snake_case )
def UpperCamelCase_( _snake_case : Union[str, Any] ):
"""simple docstring"""
main()
if __name__ == "__main__":
main()
| 308
| 1
|
from ..utils import DummyObject, requires_backends
class __magic_name__ ( metaclass=lowerCAmelCase_ ):
SCREAMING_SNAKE_CASE = ['flax']
def __init__( self , *__snake_case , **__snake_case ) -> Any:
'''simple docstring'''
requires_backends(self , ['flax'] )
@classmethod
def __magic_name__ ( cls , *__snake_case , **__snake_case ) -> Optional[int]:
'''simple docstring'''
requires_backends(cls , ['flax'] )
@classmethod
def __magic_name__ ( cls , *__snake_case , **__snake_case ) -> int:
'''simple docstring'''
requires_backends(cls , ['flax'] )
class __magic_name__ ( metaclass=lowerCAmelCase_ ):
SCREAMING_SNAKE_CASE = ['flax']
def __init__( self , *__snake_case , **__snake_case ) -> Optional[int]:
'''simple docstring'''
requires_backends(self , ['flax'] )
@classmethod
def __magic_name__ ( cls , *__snake_case , **__snake_case ) -> List[str]:
'''simple docstring'''
requires_backends(cls , ['flax'] )
@classmethod
def __magic_name__ ( cls , *__snake_case , **__snake_case ) -> List[Any]:
'''simple docstring'''
requires_backends(cls , ['flax'] )
class __magic_name__ ( metaclass=lowerCAmelCase_ ):
SCREAMING_SNAKE_CASE = ['flax']
def __init__( self , *__snake_case , **__snake_case ) -> List[Any]:
'''simple docstring'''
requires_backends(self , ['flax'] )
@classmethod
def __magic_name__ ( cls , *__snake_case , **__snake_case ) -> List[str]:
'''simple docstring'''
requires_backends(cls , ['flax'] )
@classmethod
def __magic_name__ ( cls , *__snake_case , **__snake_case ) -> str:
'''simple docstring'''
requires_backends(cls , ['flax'] )
class __magic_name__ ( metaclass=lowerCAmelCase_ ):
SCREAMING_SNAKE_CASE = ['flax']
def __init__( self , *__snake_case , **__snake_case ) -> Dict:
'''simple docstring'''
requires_backends(self , ['flax'] )
@classmethod
def __magic_name__ ( cls , *__snake_case , **__snake_case ) -> int:
'''simple docstring'''
requires_backends(cls , ['flax'] )
@classmethod
def __magic_name__ ( cls , *__snake_case , **__snake_case ) -> Optional[int]:
'''simple docstring'''
requires_backends(cls , ['flax'] )
class __magic_name__ ( metaclass=lowerCAmelCase_ ):
SCREAMING_SNAKE_CASE = ['flax']
def __init__( self , *__snake_case , **__snake_case ) -> List[Any]:
'''simple docstring'''
requires_backends(self , ['flax'] )
@classmethod
def __magic_name__ ( cls , *__snake_case , **__snake_case ) -> Any:
'''simple docstring'''
requires_backends(cls , ['flax'] )
@classmethod
def __magic_name__ ( cls , *__snake_case , **__snake_case ) -> List[str]:
'''simple docstring'''
requires_backends(cls , ['flax'] )
class __magic_name__ ( metaclass=lowerCAmelCase_ ):
SCREAMING_SNAKE_CASE = ['flax']
def __init__( self , *__snake_case , **__snake_case ) -> Optional[int]:
'''simple docstring'''
requires_backends(self , ['flax'] )
@classmethod
def __magic_name__ ( cls , *__snake_case , **__snake_case ) -> List[Any]:
'''simple docstring'''
requires_backends(cls , ['flax'] )
@classmethod
def __magic_name__ ( cls , *__snake_case , **__snake_case ) -> List[Any]:
'''simple docstring'''
requires_backends(cls , ['flax'] )
class __magic_name__ ( metaclass=lowerCAmelCase_ ):
SCREAMING_SNAKE_CASE = ['flax']
def __init__( self , *__snake_case , **__snake_case ) -> List[str]:
'''simple docstring'''
requires_backends(self , ['flax'] )
@classmethod
def __magic_name__ ( cls , *__snake_case , **__snake_case ) -> Dict:
'''simple docstring'''
requires_backends(cls , ['flax'] )
@classmethod
def __magic_name__ ( cls , *__snake_case , **__snake_case ) -> str:
'''simple docstring'''
requires_backends(cls , ['flax'] )
class __magic_name__ ( metaclass=lowerCAmelCase_ ):
SCREAMING_SNAKE_CASE = ['flax']
def __init__( self , *__snake_case , **__snake_case ) -> Dict:
'''simple docstring'''
requires_backends(self , ['flax'] )
@classmethod
def __magic_name__ ( cls , *__snake_case , **__snake_case ) -> Optional[int]:
'''simple docstring'''
requires_backends(cls , ['flax'] )
@classmethod
def __magic_name__ ( cls , *__snake_case , **__snake_case ) -> Dict:
'''simple docstring'''
requires_backends(cls , ['flax'] )
class __magic_name__ ( metaclass=lowerCAmelCase_ ):
SCREAMING_SNAKE_CASE = ['flax']
def __init__( self , *__snake_case , **__snake_case ) -> Optional[int]:
'''simple docstring'''
requires_backends(self , ['flax'] )
@classmethod
def __magic_name__ ( cls , *__snake_case , **__snake_case ) -> Tuple:
'''simple docstring'''
requires_backends(cls , ['flax'] )
@classmethod
def __magic_name__ ( cls , *__snake_case , **__snake_case ) -> Tuple:
'''simple docstring'''
requires_backends(cls , ['flax'] )
class __magic_name__ ( metaclass=lowerCAmelCase_ ):
SCREAMING_SNAKE_CASE = ['flax']
def __init__( self , *__snake_case , **__snake_case ) -> Union[str, Any]:
'''simple docstring'''
requires_backends(self , ['flax'] )
@classmethod
def __magic_name__ ( cls , *__snake_case , **__snake_case ) -> int:
'''simple docstring'''
requires_backends(cls , ['flax'] )
@classmethod
def __magic_name__ ( cls , *__snake_case , **__snake_case ) -> Optional[int]:
'''simple docstring'''
requires_backends(cls , ['flax'] )
class __magic_name__ ( metaclass=lowerCAmelCase_ ):
SCREAMING_SNAKE_CASE = ['flax']
def __init__( self , *__snake_case , **__snake_case ) -> str:
'''simple docstring'''
requires_backends(self , ['flax'] )
@classmethod
def __magic_name__ ( cls , *__snake_case , **__snake_case ) -> Union[str, Any]:
'''simple docstring'''
requires_backends(cls , ['flax'] )
@classmethod
def __magic_name__ ( cls , *__snake_case , **__snake_case ) -> Optional[Any]:
'''simple docstring'''
requires_backends(cls , ['flax'] )
class __magic_name__ ( metaclass=lowerCAmelCase_ ):
SCREAMING_SNAKE_CASE = ['flax']
def __init__( self , *__snake_case , **__snake_case ) -> int:
'''simple docstring'''
requires_backends(self , ['flax'] )
@classmethod
def __magic_name__ ( cls , *__snake_case , **__snake_case ) -> int:
'''simple docstring'''
requires_backends(cls , ['flax'] )
@classmethod
def __magic_name__ ( cls , *__snake_case , **__snake_case ) -> Dict:
'''simple docstring'''
requires_backends(cls , ['flax'] )
class __magic_name__ ( metaclass=lowerCAmelCase_ ):
SCREAMING_SNAKE_CASE = ['flax']
def __init__( self , *__snake_case , **__snake_case ) -> Dict:
'''simple docstring'''
requires_backends(self , ['flax'] )
@classmethod
def __magic_name__ ( cls , *__snake_case , **__snake_case ) -> Union[str, Any]:
'''simple docstring'''
requires_backends(cls , ['flax'] )
@classmethod
def __magic_name__ ( cls , *__snake_case , **__snake_case ) -> int:
'''simple docstring'''
requires_backends(cls , ['flax'] )
| 308
|
from __future__ import annotations
import time
import numpy as np
_lowerCAmelCase : List[str] = [8, 5, 9, 7]
_lowerCAmelCase : List[str] = [
[2, 0, 1, 1],
[0, 1, 2, 1],
[4, 0, 0, 3],
[0, 2, 1, 0],
[1, 0, 3, 0],
]
_lowerCAmelCase : List[Any] = [
[3, 2, 1, 4],
[0, 2, 5, 2],
[5, 1, 0, 5],
[1, 5, 3, 0],
[3, 0, 3, 3],
]
class __magic_name__ :
def __init__( self , __snake_case , __snake_case , __snake_case , ) -> None:
'''simple docstring'''
__a =claim_vector
__a =allocated_resources_table
__a =maximum_claim_table
def __magic_name__ ( self ) -> list[int]:
'''simple docstring'''
return [
sum(p_item[i] for p_item in self.__allocated_resources_table )
for i in range(len(self.__allocated_resources_table[0] ) )
]
def __magic_name__ ( self ) -> list[int]:
'''simple docstring'''
return np.array(self.__claim_vector ) - np.array(
self.__processes_resource_summation() )
def __magic_name__ ( self ) -> list[list[int]]:
'''simple docstring'''
return [
list(np.array(self.__maximum_claim_table[i] ) - np.array(__snake_case ) )
for i, allocated_resource in enumerate(self.__allocated_resources_table )
]
def __magic_name__ ( self ) -> dict[int, list[int]]:
'''simple docstring'''
return {self.__need().index(__snake_case ): i for i in self.__need()}
def __magic_name__ ( self , **__snake_case ) -> None:
'''simple docstring'''
__a =self.__need()
__a =self.__allocated_resources_table
__a =self.__available_resources()
__a =self.__need_index_manager()
for kw, val in kwargs.items():
if kw and val is True:
self.__pretty_data()
print('_' * 50 + '\n' )
while need_list:
__a =False
for each_need in need_list:
__a =True
for index, need in enumerate(__snake_case ):
if need > available_resources[index]:
__a =False
break
if execution:
__a =True
# get the original index of the process from ind_ctrl db
for original_need_index, need_clone in need_index_manager.items():
if each_need == need_clone:
__a =original_need_index
print(f'Process {process_number + 1} is executing.' )
# remove the process run from stack
need_list.remove(__snake_case )
# update available/freed resources stack
__a =np.array(__snake_case ) + np.array(
alloc_resources_table[process_number] )
print(
'Updated available resource stack for processes: '
+ ' '.join([str(__snake_case ) for x in available_resources] ) )
break
if safe:
print('The process is in a safe state.\n' )
else:
print('System in unsafe state. Aborting...\n' )
break
def __magic_name__ ( self ) -> Union[str, Any]:
'''simple docstring'''
print(' ' * 9 + 'Allocated Resource Table' )
for item in self.__allocated_resources_table:
print(
f'P{self.__allocated_resources_table.index(__snake_case ) + 1}'
+ ' '.join(f'{it:>8}' for it in item )
+ '\n' )
print(' ' * 9 + 'System Resource Table' )
for item in self.__maximum_claim_table:
print(
f'P{self.__maximum_claim_table.index(__snake_case ) + 1}'
+ ' '.join(f'{it:>8}' for it in item )
+ '\n' )
print(
'Current Usage by Active Processes: '
+ ' '.join(str(__snake_case ) for x in self.__claim_vector ) )
print(
'Initial Available Resources: '
+ ' '.join(str(__snake_case ) for x in self.__available_resources() ) )
time.sleep(1 )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 308
| 1
|
from __future__ import annotations
def UpperCamelCase_( _snake_case : int = 4 ):
"""simple docstring"""
__a =abs(_snake_case ) or 4
return [[1 + x + y * row_size for x in range(_snake_case )] for y in range(_snake_case )]
def UpperCamelCase_( _snake_case : list[list[int]] ):
"""simple docstring"""
return reverse_row(transpose(_snake_case ) )
# OR.. transpose(reverse_column(matrix))
def UpperCamelCase_( _snake_case : list[list[int]] ):
"""simple docstring"""
return reverse_row(reverse_column(_snake_case ) )
# OR.. reverse_column(reverse_row(matrix))
def UpperCamelCase_( _snake_case : list[list[int]] ):
"""simple docstring"""
return reverse_column(transpose(_snake_case ) )
# OR.. transpose(reverse_row(matrix))
def UpperCamelCase_( _snake_case : list[list[int]] ):
"""simple docstring"""
__a =[list(_snake_case ) for x in zip(*_snake_case )]
return matrix
def UpperCamelCase_( _snake_case : list[list[int]] ):
"""simple docstring"""
__a =matrix[::-1]
return matrix
def UpperCamelCase_( _snake_case : list[list[int]] ):
"""simple docstring"""
__a =[x[::-1] for x in matrix]
return matrix
def UpperCamelCase_( _snake_case : list[list[int]] ):
"""simple docstring"""
for i in matrix:
print(*_snake_case )
if __name__ == "__main__":
_lowerCAmelCase : Any = make_matrix()
print("\norigin:\n")
print_matrix(matrix)
print("\nrotate 90 counterclockwise:\n")
print_matrix(rotate_aa(matrix))
_lowerCAmelCase : Union[str, Any] = make_matrix()
print("\norigin:\n")
print_matrix(matrix)
print("\nrotate 180:\n")
print_matrix(rotate_aaa(matrix))
_lowerCAmelCase : Any = make_matrix()
print("\norigin:\n")
print_matrix(matrix)
print("\nrotate 270 counterclockwise:\n")
print_matrix(rotate_aaa(matrix))
| 308
|
import string
# frequency taken from https://en.wikipedia.org/wiki/Letter_frequency
_lowerCAmelCase : Tuple = {
"E": 12.70,
"T": 9.06,
"A": 8.17,
"O": 7.51,
"I": 6.97,
"N": 6.75,
"S": 6.33,
"H": 6.09,
"R": 5.99,
"D": 4.25,
"L": 4.03,
"C": 2.78,
"U": 2.76,
"M": 2.41,
"W": 2.36,
"F": 2.23,
"G": 2.02,
"Y": 1.97,
"P": 1.93,
"B": 1.29,
"V": 0.98,
"K": 0.77,
"J": 0.15,
"X": 0.15,
"Q": 0.10,
"Z": 0.07,
}
_lowerCAmelCase : Optional[int] = "ETAOINSHRDLCUMWFGYPBVKJXQZ"
_lowerCAmelCase : Any = "ABCDEFGHIJKLMNOPQRSTUVWXYZ"
def UpperCamelCase_( _snake_case : str ):
"""simple docstring"""
__a ={letter: 0 for letter in string.ascii_uppercase}
for letter in message.upper():
if letter in LETTERS:
letter_count[letter] += 1
return letter_count
def UpperCamelCase_( _snake_case : tuple ):
"""simple docstring"""
return x[0]
def UpperCamelCase_( _snake_case : str ):
"""simple docstring"""
__a =get_letter_count(_snake_case )
__a ={
freq: [] for letter, freq in letter_to_freq.items()
}
for letter in LETTERS:
freq_to_letter[letter_to_freq[letter]].append(_snake_case )
__a ={}
for freq in freq_to_letter:
freq_to_letter[freq].sort(key=ETAOIN.find , reverse=_snake_case )
__a =''.join(freq_to_letter[freq] )
__a =list(freq_to_letter_str.items() )
freq_pairs.sort(key=_snake_case , reverse=_snake_case )
__a =[freq_pair[1] for freq_pair in freq_pairs]
return "".join(_snake_case )
def UpperCamelCase_( _snake_case : str ):
"""simple docstring"""
__a =get_frequency_order(_snake_case )
__a =0
for common_letter in ETAOIN[:6]:
if common_letter in freq_order[:6]:
match_score += 1
for uncommon_letter in ETAOIN[-6:]:
if uncommon_letter in freq_order[-6:]:
match_score += 1
return match_score
if __name__ == "__main__":
import doctest
doctest.testmod()
| 308
| 1
|
def UpperCamelCase_( _snake_case : int = 1000000 ):
"""simple docstring"""
__a =limit + 1
__a =[0] * limit
for first_term in range(1 , _snake_case ):
for n in range(_snake_case , _snake_case , _snake_case ):
__a =first_term + n / first_term
if common_difference % 4: # d must be divisble by 4
continue
else:
common_difference /= 4
if (
first_term > common_difference
and first_term < 4 * common_difference
): # since x,y,z are positive integers
frequency[n] += 1 # so z>0 and a>d ,also 4d<a
__a =sum(1 for x in frequency[1:limit] if x == 10 )
return count
if __name__ == "__main__":
print(f'''{solution() = }''')
| 308
|
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowerCAmelCase : Any = logging.get_logger(__name__)
_lowerCAmelCase : int = {
"caidas/swin2sr-classicalsr-x2-64": (
"https://huggingface.co/caidas/swin2sr-classicalsr-x2-64/resolve/main/config.json"
),
}
class __magic_name__ ( lowerCAmelCase_ ):
SCREAMING_SNAKE_CASE = 'swin2sr'
SCREAMING_SNAKE_CASE = {
'hidden_size': 'embed_dim',
'num_attention_heads': 'num_heads',
'num_hidden_layers': 'num_layers',
}
def __init__( self , __snake_case=64 , __snake_case=1 , __snake_case=3 , __snake_case=180 , __snake_case=[6, 6, 6, 6, 6, 6] , __snake_case=[6, 6, 6, 6, 6, 6] , __snake_case=8 , __snake_case=2.0 , __snake_case=True , __snake_case=0.0 , __snake_case=0.0 , __snake_case=0.1 , __snake_case="gelu" , __snake_case=False , __snake_case=0.02 , __snake_case=1e-5 , __snake_case=2 , __snake_case=1.0 , __snake_case="1conv" , __snake_case="pixelshuffle" , **__snake_case , ) -> Dict:
'''simple docstring'''
super().__init__(**__snake_case )
__a =image_size
__a =patch_size
__a =num_channels
__a =embed_dim
__a =depths
__a =len(__snake_case )
__a =num_heads
__a =window_size
__a =mlp_ratio
__a =qkv_bias
__a =hidden_dropout_prob
__a =attention_probs_dropout_prob
__a =drop_path_rate
__a =hidden_act
__a =use_absolute_embeddings
__a =layer_norm_eps
__a =initializer_range
__a =upscale
__a =img_range
__a =resi_connection
__a =upsampler
| 308
| 1
|
import string
# frequency taken from https://en.wikipedia.org/wiki/Letter_frequency
_lowerCAmelCase : Tuple = {
"E": 12.70,
"T": 9.06,
"A": 8.17,
"O": 7.51,
"I": 6.97,
"N": 6.75,
"S": 6.33,
"H": 6.09,
"R": 5.99,
"D": 4.25,
"L": 4.03,
"C": 2.78,
"U": 2.76,
"M": 2.41,
"W": 2.36,
"F": 2.23,
"G": 2.02,
"Y": 1.97,
"P": 1.93,
"B": 1.29,
"V": 0.98,
"K": 0.77,
"J": 0.15,
"X": 0.15,
"Q": 0.10,
"Z": 0.07,
}
_lowerCAmelCase : Optional[int] = "ETAOINSHRDLCUMWFGYPBVKJXQZ"
_lowerCAmelCase : Any = "ABCDEFGHIJKLMNOPQRSTUVWXYZ"
def UpperCamelCase_( _snake_case : str ):
"""simple docstring"""
__a ={letter: 0 for letter in string.ascii_uppercase}
for letter in message.upper():
if letter in LETTERS:
letter_count[letter] += 1
return letter_count
def UpperCamelCase_( _snake_case : tuple ):
"""simple docstring"""
return x[0]
def UpperCamelCase_( _snake_case : str ):
"""simple docstring"""
__a =get_letter_count(_snake_case )
__a ={
freq: [] for letter, freq in letter_to_freq.items()
}
for letter in LETTERS:
freq_to_letter[letter_to_freq[letter]].append(_snake_case )
__a ={}
for freq in freq_to_letter:
freq_to_letter[freq].sort(key=ETAOIN.find , reverse=_snake_case )
__a =''.join(freq_to_letter[freq] )
__a =list(freq_to_letter_str.items() )
freq_pairs.sort(key=_snake_case , reverse=_snake_case )
__a =[freq_pair[1] for freq_pair in freq_pairs]
return "".join(_snake_case )
def UpperCamelCase_( _snake_case : str ):
"""simple docstring"""
__a =get_frequency_order(_snake_case )
__a =0
for common_letter in ETAOIN[:6]:
if common_letter in freq_order[:6]:
match_score += 1
for uncommon_letter in ETAOIN[-6:]:
if uncommon_letter in freq_order[-6:]:
match_score += 1
return match_score
if __name__ == "__main__":
import doctest
doctest.testmod()
| 308
|
import os
def UpperCamelCase_( _snake_case : str = "input.txt" ):
"""simple docstring"""
with open(os.path.join(os.path.dirname(_snake_case ) , _snake_case ) ) as input_file:
__a =[
[int(_snake_case ) for element in line.split(',' )]
for line in input_file.readlines()
]
__a =len(_snake_case )
__a =len(matrix[0] )
__a =[[-1 for _ in range(_snake_case )] for _ in range(_snake_case )]
for i in range(_snake_case ):
__a =matrix[i][0]
for j in range(1 , _snake_case ):
for i in range(_snake_case ):
__a =minimal_path_sums[i][j - 1] + matrix[i][j]
for i in range(1 , _snake_case ):
__a =min(
minimal_path_sums[i][j] , minimal_path_sums[i - 1][j] + matrix[i][j] )
for i in range(rows - 2 , -1 , -1 ):
__a =min(
minimal_path_sums[i][j] , minimal_path_sums[i + 1][j] + matrix[i][j] )
return min(minimal_path_sums_row[-1] for minimal_path_sums_row in minimal_path_sums )
if __name__ == "__main__":
print(f'''{solution() = }''')
| 308
| 1
|
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 __magic_name__ ( tf.keras.optimizers.schedules.LearningRateSchedule ):
def __init__( self , __snake_case , __snake_case , __snake_case , __snake_case = 1.0 , __snake_case = None , ) -> Optional[Any]:
'''simple docstring'''
super().__init__()
__a =initial_learning_rate
__a =warmup_steps
__a =power
__a =decay_schedule_fn
__a =name
def __call__( self , __snake_case ) -> Optional[Any]:
'''simple docstring'''
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`.
__a =tf.cast(__snake_case , tf.floataa )
__a =tf.cast(self.warmup_steps , tf.floataa )
__a =global_step_float / warmup_steps_float
__a =self.initial_learning_rate * tf.math.pow(__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=__snake_case , )
def __magic_name__ ( self ) -> Optional[Any]:
'''simple docstring'''
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_( _snake_case : float , _snake_case : int , _snake_case : int , _snake_case : float = 0.0 , _snake_case : float = 0.9 , _snake_case : float = 0.999 , _snake_case : float = 1e-8 , _snake_case : Optional[float] = None , _snake_case : Optional[float] = None , _snake_case : float = 0.0 , _snake_case : float = 1.0 , _snake_case : Optional[List[str]] = None , ):
"""simple docstring"""
__a =tf.keras.optimizers.schedules.PolynomialDecay(
initial_learning_rate=_snake_case , decay_steps=num_train_steps - num_warmup_steps , end_learning_rate=init_lr * min_lr_ratio , power=_snake_case , )
if num_warmup_steps:
__a =WarmUp(
initial_learning_rate=_snake_case , decay_schedule_fn=_snake_case , warmup_steps=_snake_case , )
if weight_decay_rate > 0.0:
__a =AdamWeightDecay(
learning_rate=_snake_case , weight_decay_rate=_snake_case , beta_a=_snake_case , beta_a=_snake_case , epsilon=_snake_case , clipnorm=_snake_case , global_clipnorm=_snake_case , exclude_from_weight_decay=['LayerNorm', 'layer_norm', 'bias'] , include_in_weight_decay=_snake_case , )
else:
__a =tf.keras.optimizers.Adam(
learning_rate=_snake_case , beta_a=_snake_case , beta_a=_snake_case , epsilon=_snake_case , clipnorm=_snake_case , global_clipnorm=_snake_case , )
# 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 __magic_name__ ( lowerCAmelCase_ ):
def __init__( self , __snake_case = 0.001 , __snake_case = 0.9 , __snake_case = 0.999 , __snake_case = 1e-7 , __snake_case = False , __snake_case = 0.0 , __snake_case = None , __snake_case = None , __snake_case = "AdamWeightDecay" , **__snake_case , ) -> List[str]:
'''simple docstring'''
super().__init__(__snake_case , __snake_case , __snake_case , __snake_case , __snake_case , __snake_case , **__snake_case )
__a =weight_decay_rate
__a =include_in_weight_decay
__a =exclude_from_weight_decay
@classmethod
def __magic_name__ ( cls , __snake_case ) -> Optional[int]:
'''simple docstring'''
__a ={'WarmUp': WarmUp}
return super(__snake_case , cls ).from_config(__snake_case , custom_objects=__snake_case )
def __magic_name__ ( self , __snake_case , __snake_case , __snake_case ) -> Optional[int]:
'''simple docstring'''
super(__snake_case , self )._prepare_local(__snake_case , __snake_case , __snake_case )
__a =tf.constant(
self.weight_decay_rate , name='adam_weight_decay_rate' )
def __magic_name__ ( self , __snake_case , __snake_case , __snake_case ) -> Optional[Any]:
'''simple docstring'''
__a =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 __magic_name__ ( self , __snake_case , __snake_case=None , **__snake_case ) -> Union[str, Any]:
'''simple docstring'''
__a , __a =list(zip(*__snake_case ) )
return super(__snake_case , self ).apply_gradients(zip(__snake_case , __snake_case ) , name=__snake_case , **__snake_case )
def __magic_name__ ( self , __snake_case , __snake_case , __snake_case ) -> Any:
'''simple docstring'''
if apply_state is None:
return self._decayed_lr_t[var_dtype], {}
__a =apply_state or {}
__a =apply_state.get((var_device, var_dtype) )
if coefficients is None:
__a =self._fallback_apply_state(__snake_case , __snake_case )
__a =coefficients
return coefficients["lr_t"], {"apply_state": apply_state}
def __magic_name__ ( self , __snake_case , __snake_case , __snake_case=None ) -> List[str]:
'''simple docstring'''
__a , __a =self._get_lr(var.device , var.dtype.base_dtype , __snake_case )
__a =self._decay_weights_op(__snake_case , __snake_case , __snake_case )
with tf.control_dependencies([decay] ):
return super(__snake_case , self )._resource_apply_dense(__snake_case , __snake_case , **__snake_case )
def __magic_name__ ( self , __snake_case , __snake_case , __snake_case , __snake_case=None ) -> Optional[Any]:
'''simple docstring'''
__a , __a =self._get_lr(var.device , var.dtype.base_dtype , __snake_case )
__a =self._decay_weights_op(__snake_case , __snake_case , __snake_case )
with tf.control_dependencies([decay] ):
return super(__snake_case , self )._resource_apply_sparse(__snake_case , __snake_case , __snake_case , **__snake_case )
def __magic_name__ ( self ) -> Dict:
'''simple docstring'''
__a =super().get_config()
config.update({'weight_decay_rate': self.weight_decay_rate} )
return config
def __magic_name__ ( self , __snake_case ) -> str:
'''simple docstring'''
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(__snake_case , __snake_case ) is not None:
return True
if self._exclude_from_weight_decay:
for r in self._exclude_from_weight_decay:
if re.search(__snake_case , __snake_case ) is not None:
return False
return True
class __magic_name__ ( lowerCAmelCase_ ):
def __init__( self ) -> Any:
'''simple docstring'''
__a =[]
__a =None
@property
def __magic_name__ ( self ) -> Any:
'''simple docstring'''
if self._accum_steps is None:
__a =tf.Variable(
tf.constant(0 , dtype=tf.intaa ) , trainable=__snake_case , synchronization=tf.VariableSynchronization.ON_READ , aggregation=tf.VariableAggregation.ONLY_FIRST_REPLICA , )
return self._accum_steps.value()
@property
def __magic_name__ ( self ) -> Optional[Any]:
'''simple docstring'''
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 , __snake_case ) -> Union[str, Any]:
'''simple docstring'''
if not self._gradients:
__a =self.step # Create the step variable.
self._gradients.extend(
[
tf.Variable(
tf.zeros_like(__snake_case ) , trainable=__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(__snake_case ) != len(self._gradients ):
raise ValueError(f'Expected {len(self._gradients )} gradients, but got {len(__snake_case )}' )
for accum_gradient, gradient in zip(self._gradients , __snake_case ):
if accum_gradient is not None and gradient is not None:
accum_gradient.assign_add(__snake_case )
self._accum_steps.assign_add(1 )
def __magic_name__ ( self ) -> Dict:
'''simple docstring'''
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(__snake_case ) )
| 308
|
import warnings
from ...utils import logging
from .image_processing_mobilevit import MobileViTImageProcessor
_lowerCAmelCase : Any = logging.get_logger(__name__)
class __magic_name__ ( lowerCAmelCase_ ):
def __init__( self , *__snake_case , **__snake_case ) -> None:
'''simple docstring'''
warnings.warn(
'The class MobileViTFeatureExtractor is deprecated and will be removed in version 5 of Transformers.'
' Please use MobileViTImageProcessor instead.' , __snake_case , )
super().__init__(*__snake_case , **__snake_case )
| 308
| 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 : Dict = logging.get_logger(__name__)
if is_vision_available():
import PIL
class __magic_name__ ( lowerCAmelCase_ ):
SCREAMING_SNAKE_CASE = ['pixel_values']
def __init__( self , __snake_case = True , __snake_case = None , __snake_case = PILImageResampling.BICUBIC , __snake_case = True , __snake_case = None , __snake_case = True , __snake_case = 1 / 255 , __snake_case = True , __snake_case = None , __snake_case = None , __snake_case = True , **__snake_case , ) -> None:
'''simple docstring'''
super().__init__(**__snake_case )
__a =size if size is not None else {'shortest_edge': 224}
__a =get_size_dict(__snake_case , default_to_square=__snake_case )
__a =crop_size if crop_size is not None else {'height': 224, 'width': 224}
__a =get_size_dict(__snake_case , default_to_square=__snake_case , 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 __magic_name__ ( self , __snake_case , __snake_case , __snake_case = PILImageResampling.BICUBIC , __snake_case = None , **__snake_case , ) -> np.ndarray:
'''simple docstring'''
__a =get_size_dict(__snake_case , default_to_square=__snake_case )
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(__snake_case , size=size['shortest_edge'] , default_to_square=__snake_case )
return resize(__snake_case , size=__snake_case , resample=__snake_case , data_format=__snake_case , **__snake_case )
def __magic_name__ ( self , __snake_case , __snake_case , __snake_case = None , **__snake_case , ) -> np.ndarray:
'''simple docstring'''
__a =get_size_dict(__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(__snake_case , size=(size['height'], size['width']) , data_format=__snake_case , **__snake_case )
def __magic_name__ ( self , __snake_case , __snake_case , __snake_case = None , **__snake_case , ) -> int:
'''simple docstring'''
return rescale(__snake_case , scale=__snake_case , data_format=__snake_case , **__snake_case )
def __magic_name__ ( self , __snake_case , __snake_case , __snake_case , __snake_case = None , **__snake_case , ) -> np.ndarray:
'''simple docstring'''
return normalize(__snake_case , mean=__snake_case , std=__snake_case , data_format=__snake_case , **__snake_case )
def __magic_name__ ( self , __snake_case , __snake_case = None , __snake_case = None , __snake_case = None , __snake_case = None , __snake_case = None , __snake_case = None , __snake_case = None , __snake_case = None , __snake_case = None , __snake_case = None , __snake_case = None , __snake_case = None , __snake_case = ChannelDimension.FIRST , **__snake_case , ) -> 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(__snake_case , param_name='size' , default_to_square=__snake_case )
__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(__snake_case , param_name='crop_size' , default_to_square=__snake_case )
__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(__snake_case )
if not valid_images(__snake_case ):
raise ValueError(
'Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '
'torch.Tensor, tf.Tensor or jax.ndarray.' )
if do_resize and size is None:
raise ValueError('Size must be specified if do_resize is True.' )
if do_center_crop and crop_size is None:
raise ValueError('Crop size must be specified if do_center_crop is True.' )
if do_rescale and rescale_factor is None:
raise ValueError('Rescale factor must be specified if do_rescale is True.' )
if do_normalize and (image_mean is None or image_std is None):
raise ValueError('Image mean and std must be specified if do_normalize is True.' )
# PIL RGBA images are converted to RGB
if do_convert_rgb:
__a =[convert_to_rgb(__snake_case ) for image in images]
# All transformations expect numpy arrays.
__a =[to_numpy_array(__snake_case ) for image in images]
if do_resize:
__a =[self.resize(image=__snake_case , size=__snake_case , resample=__snake_case ) for image in images]
if do_center_crop:
__a =[self.center_crop(image=__snake_case , size=__snake_case ) for image in images]
if do_rescale:
__a =[self.rescale(image=__snake_case , scale=__snake_case ) for image in images]
if do_normalize:
__a =[self.normalize(image=__snake_case , mean=__snake_case , std=__snake_case ) for image in images]
__a =[to_channel_dimension_format(__snake_case , __snake_case ) for image in images]
__a ={'pixel_values': images}
return BatchFeature(data=__snake_case , tensor_type=__snake_case )
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from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available
_lowerCAmelCase : int = {}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_lowerCAmelCase : Dict = ["BartphoTokenizer"]
if TYPE_CHECKING:
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_bartpho import BartphoTokenizer
else:
import sys
_lowerCAmelCase : Optional[Any] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
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def UpperCamelCase_( _snake_case : float , _snake_case : float ):
"""simple docstring"""
return price * (1 + tax_rate)
if __name__ == "__main__":
print(f'''{price_plus_tax(100, 0.25) = }''')
print(f'''{price_plus_tax(125.50, 0.05) = }''')
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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
_lowerCAmelCase : List[str] = logging.get_logger(__name__)
_lowerCAmelCase : List[Any] = {
"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 __magic_name__ ( lowerCAmelCase_ ):
SCREAMING_SNAKE_CASE = 'yolos'
def __init__( self , __snake_case=768 , __snake_case=12 , __snake_case=12 , __snake_case=3072 , __snake_case="gelu" , __snake_case=0.0 , __snake_case=0.0 , __snake_case=0.02 , __snake_case=1e-12 , __snake_case=[512, 864] , __snake_case=16 , __snake_case=3 , __snake_case=True , __snake_case=100 , __snake_case=True , __snake_case=False , __snake_case=1 , __snake_case=5 , __snake_case=2 , __snake_case=5 , __snake_case=2 , __snake_case=0.1 , **__snake_case , ) -> str:
'''simple docstring'''
super().__init__(**__snake_case )
__a =hidden_size
__a =num_hidden_layers
__a =num_attention_heads
__a =intermediate_size
__a =hidden_act
__a =hidden_dropout_prob
__a =attention_probs_dropout_prob
__a =initializer_range
__a =layer_norm_eps
__a =image_size
__a =patch_size
__a =num_channels
__a =qkv_bias
__a =num_detection_tokens
__a =use_mid_position_embeddings
__a =auxiliary_loss
# Hungarian matcher
__a =class_cost
__a =bbox_cost
__a =giou_cost
# Loss coefficients
__a =bbox_loss_coefficient
__a =giou_loss_coefficient
__a =eos_coefficient
class __magic_name__ ( lowerCAmelCase_ ):
SCREAMING_SNAKE_CASE = version.parse('1.11' )
@property
def __magic_name__ ( self ) -> Mapping[str, Mapping[int, str]]:
'''simple docstring'''
return OrderedDict(
[
('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'}),
] )
@property
def __magic_name__ ( self ) -> float:
'''simple docstring'''
return 1e-4
@property
def __magic_name__ ( self ) -> int:
'''simple docstring'''
return 12
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def UpperCamelCase_( _snake_case : list ):
"""simple docstring"""
if not grid or not grid[0]:
raise TypeError('The grid does not contain the appropriate information' )
for cell_n in range(1 , len(grid[0] ) ):
grid[0][cell_n] += grid[0][cell_n - 1]
__a =grid[0]
for row_n in range(1 , len(_snake_case ) ):
__a =grid[row_n]
__a =fill_row(_snake_case , _snake_case )
__a =grid[row_n]
return grid[-1][-1]
def UpperCamelCase_( _snake_case : list , _snake_case : list ):
"""simple docstring"""
current_row[0] += row_above[0]
for cell_n in range(1 , len(_snake_case ) ):
current_row[cell_n] += min(current_row[cell_n - 1] , row_above[cell_n] )
return current_row
if __name__ == "__main__":
import doctest
doctest.testmod()
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import argparse
import torch
from datasets import load_dataset
from donut import DonutModel
from transformers import (
DonutImageProcessor,
DonutProcessor,
DonutSwinConfig,
DonutSwinModel,
MBartConfig,
MBartForCausalLM,
VisionEncoderDecoderModel,
XLMRobertaTokenizerFast,
)
def UpperCamelCase_( _snake_case : Optional[Any] ):
"""simple docstring"""
__a =model.config
__a =DonutSwinConfig(
image_size=original_config.input_size , patch_size=4 , depths=original_config.encoder_layer , num_heads=[4, 8, 16, 32] , window_size=original_config.window_size , embed_dim=128 , )
__a =MBartConfig(
is_decoder=_snake_case , is_encoder_decoder=_snake_case , add_cross_attention=_snake_case , decoder_layers=original_config.decoder_layer , max_position_embeddings=original_config.max_position_embeddings , vocab_size=len(
model.decoder.tokenizer ) , scale_embedding=_snake_case , add_final_layer_norm=_snake_case , )
return encoder_config, decoder_config
def UpperCamelCase_( _snake_case : Tuple ):
"""simple docstring"""
if "encoder.model" in name:
__a =name.replace('encoder.model' , 'encoder' )
if "decoder.model" in name:
__a =name.replace('decoder.model' , 'decoder' )
if "patch_embed.proj" in name:
__a =name.replace('patch_embed.proj' , 'embeddings.patch_embeddings.projection' )
if "patch_embed.norm" in name:
__a =name.replace('patch_embed.norm' , 'embeddings.norm' )
if name.startswith('encoder' ):
if "layers" in name:
__a ='encoder.' + name
if "attn.proj" in name:
__a =name.replace('attn.proj' , 'attention.output.dense' )
if "attn" in name and "mask" not in name:
__a =name.replace('attn' , 'attention.self' )
if "norm1" in name:
__a =name.replace('norm1' , 'layernorm_before' )
if "norm2" in name:
__a =name.replace('norm2' , 'layernorm_after' )
if "mlp.fc1" in name:
__a =name.replace('mlp.fc1' , 'intermediate.dense' )
if "mlp.fc2" in name:
__a =name.replace('mlp.fc2' , 'output.dense' )
if name == "encoder.norm.weight":
__a ='encoder.layernorm.weight'
if name == "encoder.norm.bias":
__a ='encoder.layernorm.bias'
return name
def UpperCamelCase_( _snake_case : Tuple , _snake_case : str ):
"""simple docstring"""
for key in orig_state_dict.copy().keys():
__a =orig_state_dict.pop(_snake_case )
if "qkv" in key:
__a =key.split('.' )
__a =int(key_split[3] )
__a =int(key_split[5] )
__a =model.encoder.encoder.layers[layer_num].blocks[block_num].attention.self.all_head_size
if "weight" in key:
__a =val[:dim, :]
__a =val[dim : dim * 2, :]
__a =val[-dim:, :]
else:
__a =val[:dim]
__a =val[dim : dim * 2]
__a =val[-dim:]
elif "attn_mask" in key or key in ["encoder.model.norm.weight", "encoder.model.norm.bias"]:
# HuggingFace implementation doesn't use attn_mask buffer
# and model doesn't use final LayerNorms for the encoder
pass
else:
__a =val
return orig_state_dict
def UpperCamelCase_( _snake_case : Tuple , _snake_case : Union[str, Any]=None , _snake_case : List[Any]=False ):
"""simple docstring"""
__a =DonutModel.from_pretrained(_snake_case ).eval()
# load HuggingFace model
__a , __a =get_configs(_snake_case )
__a =DonutSwinModel(_snake_case )
__a =MBartForCausalLM(_snake_case )
__a =VisionEncoderDecoderModel(encoder=_snake_case , decoder=_snake_case )
model.eval()
__a =original_model.state_dict()
__a =convert_state_dict(_snake_case , _snake_case )
model.load_state_dict(_snake_case )
# verify results on scanned document
__a =load_dataset('hf-internal-testing/example-documents' )
__a =dataset['test'][0]['image'].convert('RGB' )
__a =XLMRobertaTokenizerFast.from_pretrained(_snake_case , from_slow=_snake_case )
__a =DonutImageProcessor(
do_align_long_axis=original_model.config.align_long_axis , size=original_model.config.input_size[::-1] )
__a =DonutProcessor(_snake_case , _snake_case )
__a =processor(_snake_case , return_tensors='pt' ).pixel_values
if model_name == "naver-clova-ix/donut-base-finetuned-docvqa":
__a ='<s_docvqa><s_question>{user_input}</s_question><s_answer>'
__a ='When is the coffee break?'
__a =task_prompt.replace('{user_input}' , _snake_case )
elif model_name == "naver-clova-ix/donut-base-finetuned-rvlcdip":
__a ='<s_rvlcdip>'
elif model_name in [
"naver-clova-ix/donut-base-finetuned-cord-v1",
"naver-clova-ix/donut-base-finetuned-cord-v1-2560",
]:
__a ='<s_cord>'
elif model_name == "naver-clova-ix/donut-base-finetuned-cord-v2":
__a ='s_cord-v2>'
elif model_name == "naver-clova-ix/donut-base-finetuned-zhtrainticket":
__a ='<s_zhtrainticket>'
elif model_name in ["naver-clova-ix/donut-proto", "naver-clova-ix/donut-base"]:
# use a random prompt
__a ='hello world'
else:
raise ValueError('Model name not supported' )
__a =original_model.decoder.tokenizer(_snake_case , add_special_tokens=_snake_case , return_tensors='pt' )[
'input_ids'
]
__a =original_model.encoder.model.patch_embed(_snake_case )
__a , __a =model.encoder.embeddings(_snake_case )
assert torch.allclose(_snake_case , _snake_case , atol=1e-3 )
# verify encoder hidden states
__a =original_model.encoder(_snake_case )
__a =model.encoder(_snake_case ).last_hidden_state
assert torch.allclose(_snake_case , _snake_case , atol=1e-2 )
# verify decoder hidden states
__a =original_model(_snake_case , _snake_case , _snake_case ).logits
__a =model(_snake_case , decoder_input_ids=_snake_case ).logits
assert torch.allclose(_snake_case , _snake_case , atol=1e-3 )
print('Looks ok!' )
if pytorch_dump_folder_path is not None:
print(F'Saving model and processor to {pytorch_dump_folder_path}' )
model.save_pretrained(_snake_case )
processor.save_pretrained(_snake_case )
if push_to_hub:
model.push_to_hub('nielsr/' + model_name.split('/' )[-1] , commit_message='Update model' )
processor.push_to_hub('nielsr/' + model_name.split('/' )[-1] , commit_message='Update model' )
if __name__ == "__main__":
_lowerCAmelCase : List[str] = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--model_name",
default="naver-clova-ix/donut-base-finetuned-docvqa",
required=False,
type=str,
help="Name of the original model you'd like to convert.",
)
parser.add_argument(
"--pytorch_dump_folder_path",
default=None,
required=False,
type=str,
help="Path to the output PyTorch model directory.",
)
parser.add_argument(
"--push_to_hub",
action="store_true",
help="Whether or not to push the converted model and processor to the 🤗 hub.",
)
_lowerCAmelCase : List[Any] = parser.parse_args()
convert_donut_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)
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import math
def UpperCamelCase_( _snake_case : float , _snake_case : float ):
"""simple docstring"""
if initial_intensity < 0:
raise ValueError('The value of intensity cannot be negative' )
# handling of negative values of initial intensity
if angle < 0 or angle > 360:
raise ValueError('In Malus Law, the angle is in the range 0-360 degrees' )
# handling of values out of allowed range
return initial_intensity * (math.cos(math.radians(_snake_case ) ) ** 2)
if __name__ == "__main__":
import doctest
doctest.testmod(name="malus_law")
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import json
import os
import unittest
from transformers.models.blenderbot_small.tokenization_blenderbot_small import (
VOCAB_FILES_NAMES,
BlenderbotSmallTokenizer,
)
from ...test_tokenization_common import TokenizerTesterMixin
class __magic_name__ ( lowerCAmelCase_ , unittest.TestCase ):
SCREAMING_SNAKE_CASE = BlenderbotSmallTokenizer
SCREAMING_SNAKE_CASE = False
def __magic_name__ ( self ) -> Union[str, Any]:
'''simple docstring'''
super().setUp()
__a =['__start__', 'adapt', 'act', 'ap@@', 'te', '__end__', '__unk__']
__a =dict(zip(__snake_case , range(len(__snake_case ) ) ) )
__a =['#version: 0.2', 'a p', 't e</w>', 'ap t</w>', 'a d', 'ad apt</w>', 'a c', 'ac t</w>', '']
__a ={'unk_token': '__unk__', 'bos_token': '__start__', 'eos_token': '__end__'}
__a =os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['vocab_file'] )
__a =os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['merges_file'] )
with open(self.vocab_file , 'w' , encoding='utf-8' ) as fp:
fp.write(json.dumps(__snake_case ) + '\n' )
with open(self.merges_file , 'w' , encoding='utf-8' ) as fp:
fp.write('\n'.join(__snake_case ) )
def __magic_name__ ( self , **__snake_case ) -> Any:
'''simple docstring'''
kwargs.update(self.special_tokens_map )
return BlenderbotSmallTokenizer.from_pretrained(self.tmpdirname , **__snake_case )
def __magic_name__ ( self , __snake_case ) -> List[Any]:
'''simple docstring'''
__a ='adapt act apte'
__a ='adapt act apte'
return input_text, output_text
def __magic_name__ ( self ) -> str:
'''simple docstring'''
__a =BlenderbotSmallTokenizer(self.vocab_file , self.merges_file , **self.special_tokens_map )
__a ='adapt act apte'
__a =['adapt', 'act', 'ap@@', 'te']
__a =tokenizer.tokenize(__snake_case )
self.assertListEqual(__snake_case , __snake_case )
__a =[tokenizer.bos_token] + tokens + [tokenizer.eos_token]
__a =[0, 1, 2, 3, 4, 5]
self.assertListEqual(tokenizer.convert_tokens_to_ids(__snake_case ) , __snake_case )
def __magic_name__ ( self ) -> str:
'''simple docstring'''
__a =BlenderbotSmallTokenizer.from_pretrained('facebook/blenderbot-90M' )
assert tok('sam' ).input_ids == [1384]
__a ='I am a small frog.'
__a =tok([src_text] , padding=__snake_case , truncation=__snake_case )['input_ids']
__a =tok.batch_decode(__snake_case , skip_special_tokens=__snake_case , clean_up_tokenization_spaces=__snake_case )[0]
assert src_text != decoded # I wish it did!
assert decoded == "i am a small frog ."
def __magic_name__ ( self ) -> str:
'''simple docstring'''
__a =BlenderbotSmallTokenizer.from_pretrained('facebook/blenderbot-90M' )
__a ='I am a small frog .'
__a ='.'
__a =tok(__snake_case )['input_ids']
__a =tok(__snake_case )['input_ids']
assert encoded[-1] == encoded_dot[0]
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import itertools
import string
from collections.abc import Generator, Iterable
def UpperCamelCase_( _snake_case : Iterable[str] , _snake_case : int ):
"""simple docstring"""
__a =iter(_snake_case )
while True:
__a =tuple(itertools.islice(_snake_case , _snake_case ) )
if not chunk:
return
yield chunk
def UpperCamelCase_( _snake_case : str ):
"""simple docstring"""
__a =''.join([c.upper() for c in dirty if c in string.ascii_letters] )
__a =''
if len(_snake_case ) < 2:
return dirty
for i in range(len(_snake_case ) - 1 ):
clean += dirty[i]
if dirty[i] == dirty[i + 1]:
clean += "X"
clean += dirty[-1]
if len(_snake_case ) & 1:
clean += "X"
return clean
def UpperCamelCase_( _snake_case : str ):
"""simple docstring"""
__a ='ABCDEFGHIKLMNOPQRSTUVWXYZ'
# we're using a list instead of a '2d' array because it makes the math
# for setting up the table and doing the actual encoding/decoding simpler
__a =[]
# copy key chars into the table if they are in `alphabet` ignoring duplicates
for char in key.upper():
if char not in table and char in alphabet:
table.append(_snake_case )
# fill the rest of the table in with the remaining alphabet chars
for char in alphabet:
if char not in table:
table.append(_snake_case )
return table
def UpperCamelCase_( _snake_case : str , _snake_case : str ):
"""simple docstring"""
__a =generate_table(_snake_case )
__a =prepare_input(_snake_case )
__a =''
# https://en.wikipedia.org/wiki/Playfair_cipher#Description
for chara, chara in chunker(_snake_case , 2 ):
__a , __a =divmod(table.index(_snake_case ) , 5 )
__a , __a =divmod(table.index(_snake_case ) , 5 )
if rowa == rowa:
ciphertext += table[rowa * 5 + (cola + 1) % 5]
ciphertext += table[rowa * 5 + (cola + 1) % 5]
elif cola == cola:
ciphertext += table[((rowa + 1) % 5) * 5 + cola]
ciphertext += table[((rowa + 1) % 5) * 5 + cola]
else: # rectangle
ciphertext += table[rowa * 5 + cola]
ciphertext += table[rowa * 5 + cola]
return ciphertext
def UpperCamelCase_( _snake_case : str , _snake_case : str ):
"""simple docstring"""
__a =generate_table(_snake_case )
__a =''
# https://en.wikipedia.org/wiki/Playfair_cipher#Description
for chara, chara in chunker(_snake_case , 2 ):
__a , __a =divmod(table.index(_snake_case ) , 5 )
__a , __a =divmod(table.index(_snake_case ) , 5 )
if rowa == rowa:
plaintext += table[rowa * 5 + (cola - 1) % 5]
plaintext += table[rowa * 5 + (cola - 1) % 5]
elif cola == cola:
plaintext += table[((rowa - 1) % 5) * 5 + cola]
plaintext += table[((rowa - 1) % 5) * 5 + cola]
else: # rectangle
plaintext += table[rowa * 5 + cola]
plaintext += table[rowa * 5 + cola]
return plaintext
| 308
|
import unittest
from transformers import load_tool
from transformers.utils import is_torch_available
if is_torch_available():
import torch
from transformers.testing_utils import require_torch
from .test_tools_common import ToolTesterMixin
@require_torch
class __magic_name__ ( unittest.TestCase , lowerCAmelCase_ ):
def __magic_name__ ( self ) -> Union[str, Any]:
'''simple docstring'''
__a =load_tool('text-to-speech' )
self.tool.setup()
def __magic_name__ ( self ) -> Dict:
'''simple docstring'''
# SpeechT5 isn't deterministic
torch.manual_seed(0 )
__a =self.tool('hey' )
__a =result.to_raw()
self.assertTrue(
torch.allclose(
resulting_tensor[:3] , torch.tensor([-0.000_5966_6688_3211_5829, -0.000_3657_6401_9079_5064, -0.0001_3439_5027_9988_3485] ) , ) )
def __magic_name__ ( self ) -> List[Any]:
'''simple docstring'''
# SpeechT5 isn't deterministic
torch.manual_seed(0 )
__a =self.tool('hey' )
__a =result.to_raw()
self.assertTrue(
torch.allclose(
resulting_tensor[:3] , torch.tensor([-0.000_5966_6688_3211_5829, -0.000_3657_6401_9079_5064, -0.0001_3439_5027_9988_3485] ) , ) )
| 308
| 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,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_dimension_format,
)
from ...image_utils import (
IMAGENET_STANDARD_MEAN,
IMAGENET_STANDARD_STD,
ChannelDimension,
ImageInput,
PILImageResampling,
is_valid_image,
to_numpy_array,
valid_images,
)
from ...utils import TensorType, is_vision_available, logging
if is_vision_available():
import PIL
_lowerCAmelCase : Optional[Any] = logging.get_logger(__name__)
def UpperCamelCase_( _snake_case : List[str] ):
"""simple docstring"""
if isinstance(_snake_case , (list, tuple) ) and isinstance(videos[0] , (list, tuple) ) and is_valid_image(videos[0][0] ):
return videos
elif isinstance(_snake_case , (list, tuple) ) and is_valid_image(videos[0] ):
return [videos]
elif is_valid_image(_snake_case ):
return [[videos]]
raise ValueError(F'Could not make batched video from {videos}' )
class __magic_name__ ( lowerCAmelCase_ ):
SCREAMING_SNAKE_CASE = ['pixel_values']
def __init__( self , __snake_case = True , __snake_case = None , __snake_case = PILImageResampling.BILINEAR , __snake_case = True , __snake_case = None , __snake_case = True , __snake_case = 1 / 255 , __snake_case = True , __snake_case = None , __snake_case = None , **__snake_case , ) -> None:
'''simple docstring'''
super().__init__(**__snake_case )
__a =size if size is not None else {'shortest_edge': 224}
__a =get_size_dict(__snake_case , default_to_square=__snake_case )
__a =crop_size if crop_size is not None else {'height': 224, 'width': 224}
__a =get_size_dict(__snake_case , param_name='crop_size' )
__a =do_resize
__a =size
__a =do_center_crop
__a =crop_size
__a =resample
__a =do_rescale
__a =rescale_factor
__a =do_normalize
__a =image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
__a =image_std if image_std is not None else IMAGENET_STANDARD_STD
def __magic_name__ ( self , __snake_case , __snake_case , __snake_case = PILImageResampling.BILINEAR , __snake_case = None , **__snake_case , ) -> np.ndarray:
'''simple docstring'''
__a =get_size_dict(__snake_case , default_to_square=__snake_case )
if "shortest_edge" in size:
__a =get_resize_output_image_size(__snake_case , size['shortest_edge'] , default_to_square=__snake_case )
elif "height" in size and "width" in size:
__a =(size['height'], size['width'])
else:
raise ValueError(f'Size must have \'height\' and \'width\' or \'shortest_edge\' as keys. Got {size.keys()}' )
return resize(__snake_case , size=__snake_case , resample=__snake_case , data_format=__snake_case , **__snake_case )
def __magic_name__ ( self , __snake_case , __snake_case , __snake_case = None , **__snake_case , ) -> np.ndarray:
'''simple docstring'''
__a =get_size_dict(__snake_case )
if "height" not in size or "width" not in size:
raise ValueError(f'Size must have \'height\' and \'width\' as keys. Got {size.keys()}' )
return center_crop(__snake_case , size=(size['height'], size['width']) , data_format=__snake_case , **__snake_case )
def __magic_name__ ( self , __snake_case , __snake_case , __snake_case = None , **__snake_case , ) -> str:
'''simple docstring'''
return rescale(__snake_case , scale=__snake_case , data_format=__snake_case , **__snake_case )
def __magic_name__ ( self , __snake_case , __snake_case , __snake_case , __snake_case = None , **__snake_case , ) -> np.ndarray:
'''simple docstring'''
return normalize(__snake_case , mean=__snake_case , std=__snake_case , data_format=__snake_case , **__snake_case )
def __magic_name__ ( self , __snake_case , __snake_case = None , __snake_case = None , __snake_case = None , __snake_case = None , __snake_case = None , __snake_case = None , __snake_case = None , __snake_case = None , __snake_case = None , __snake_case = None , __snake_case = ChannelDimension.FIRST , ) -> np.ndarray:
'''simple docstring'''
if do_resize and size is None or resample is None:
raise ValueError('Size and resample must be specified if do_resize is True.' )
if do_center_crop and crop_size is None:
raise ValueError('Crop size must be specified if do_center_crop is True.' )
if do_rescale and rescale_factor is None:
raise ValueError('Rescale factor must be specified if do_rescale is True.' )
if do_normalize and (image_mean is None or image_std is None):
raise ValueError('Image mean and std must be specified if do_normalize is True.' )
# All transformations expect numpy arrays.
__a =to_numpy_array(__snake_case )
if do_resize:
__a =self.resize(image=__snake_case , size=__snake_case , resample=__snake_case )
if do_center_crop:
__a =self.center_crop(__snake_case , size=__snake_case )
if do_rescale:
__a =self.rescale(image=__snake_case , scale=__snake_case )
if do_normalize:
__a =self.normalize(image=__snake_case , mean=__snake_case , std=__snake_case )
__a =to_channel_dimension_format(__snake_case , __snake_case )
return image
def __magic_name__ ( self , __snake_case , __snake_case = None , __snake_case = None , __snake_case = None , __snake_case = None , __snake_case = None , __snake_case = None , __snake_case = None , __snake_case = None , __snake_case = None , __snake_case = None , __snake_case = None , __snake_case = ChannelDimension.FIRST , **__snake_case , ) -> PIL.Image.Image:
'''simple docstring'''
__a =do_resize if do_resize is not None else self.do_resize
__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 =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 =size if size is not None else self.size
__a =get_size_dict(__snake_case , default_to_square=__snake_case )
__a =crop_size if crop_size is not None else self.crop_size
__a =get_size_dict(__snake_case , param_name='crop_size' )
if not valid_images(__snake_case ):
raise ValueError(
'Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '
'torch.Tensor, tf.Tensor or jax.ndarray.' )
__a =make_batched(__snake_case )
__a =[
[
self._preprocess_image(
image=__snake_case , do_resize=__snake_case , size=__snake_case , resample=__snake_case , do_center_crop=__snake_case , crop_size=__snake_case , do_rescale=__snake_case , rescale_factor=__snake_case , do_normalize=__snake_case , image_mean=__snake_case , image_std=__snake_case , data_format=__snake_case , )
for img in video
]
for video in videos
]
__a ={'pixel_values': videos}
return BatchFeature(data=__snake_case , tensor_type=__snake_case )
| 308
|
import flax.linen as nn
import jax.numpy as jnp
from .attention_flax import FlaxTransformeraDModel
from .resnet_flax import FlaxDownsampleaD, FlaxResnetBlockaD, FlaxUpsampleaD
class __magic_name__ ( nn.Module ):
SCREAMING_SNAKE_CASE = 42
SCREAMING_SNAKE_CASE = 42
SCREAMING_SNAKE_CASE = 0.0
SCREAMING_SNAKE_CASE = 1
SCREAMING_SNAKE_CASE = 1
SCREAMING_SNAKE_CASE = True
SCREAMING_SNAKE_CASE = False
SCREAMING_SNAKE_CASE = False
SCREAMING_SNAKE_CASE = False
SCREAMING_SNAKE_CASE = jnp.floataa
def __magic_name__ ( self ) -> int:
'''simple docstring'''
__a =[]
__a =[]
for i in range(self.num_layers ):
__a =self.in_channels if i == 0 else self.out_channels
__a =FlaxResnetBlockaD(
in_channels=__snake_case , out_channels=self.out_channels , dropout_prob=self.dropout , dtype=self.dtype , )
resnets.append(__snake_case )
__a =FlaxTransformeraDModel(
in_channels=self.out_channels , n_heads=self.num_attention_heads , d_head=self.out_channels // self.num_attention_heads , depth=1 , use_linear_projection=self.use_linear_projection , only_cross_attention=self.only_cross_attention , use_memory_efficient_attention=self.use_memory_efficient_attention , dtype=self.dtype , )
attentions.append(__snake_case )
__a =resnets
__a =attentions
if self.add_downsample:
__a =FlaxDownsampleaD(self.out_channels , dtype=self.dtype )
def __call__( self , __snake_case , __snake_case , __snake_case , __snake_case=True ) -> Optional[Any]:
'''simple docstring'''
__a =()
for resnet, attn in zip(self.resnets , self.attentions ):
__a =resnet(__snake_case , __snake_case , deterministic=__snake_case )
__a =attn(__snake_case , __snake_case , deterministic=__snake_case )
output_states += (hidden_states,)
if self.add_downsample:
__a =self.downsamplers_a(__snake_case )
output_states += (hidden_states,)
return hidden_states, output_states
class __magic_name__ ( nn.Module ):
SCREAMING_SNAKE_CASE = 42
SCREAMING_SNAKE_CASE = 42
SCREAMING_SNAKE_CASE = 0.0
SCREAMING_SNAKE_CASE = 1
SCREAMING_SNAKE_CASE = True
SCREAMING_SNAKE_CASE = jnp.floataa
def __magic_name__ ( self ) -> int:
'''simple docstring'''
__a =[]
for i in range(self.num_layers ):
__a =self.in_channels if i == 0 else self.out_channels
__a =FlaxResnetBlockaD(
in_channels=__snake_case , out_channels=self.out_channels , dropout_prob=self.dropout , dtype=self.dtype , )
resnets.append(__snake_case )
__a =resnets
if self.add_downsample:
__a =FlaxDownsampleaD(self.out_channels , dtype=self.dtype )
def __call__( self , __snake_case , __snake_case , __snake_case=True ) -> Optional[int]:
'''simple docstring'''
__a =()
for resnet in self.resnets:
__a =resnet(__snake_case , __snake_case , deterministic=__snake_case )
output_states += (hidden_states,)
if self.add_downsample:
__a =self.downsamplers_a(__snake_case )
output_states += (hidden_states,)
return hidden_states, output_states
class __magic_name__ ( nn.Module ):
SCREAMING_SNAKE_CASE = 42
SCREAMING_SNAKE_CASE = 42
SCREAMING_SNAKE_CASE = 42
SCREAMING_SNAKE_CASE = 0.0
SCREAMING_SNAKE_CASE = 1
SCREAMING_SNAKE_CASE = 1
SCREAMING_SNAKE_CASE = True
SCREAMING_SNAKE_CASE = False
SCREAMING_SNAKE_CASE = False
SCREAMING_SNAKE_CASE = False
SCREAMING_SNAKE_CASE = jnp.floataa
def __magic_name__ ( self ) -> List[Any]:
'''simple docstring'''
__a =[]
__a =[]
for i in range(self.num_layers ):
__a =self.in_channels if (i == self.num_layers - 1) else self.out_channels
__a =self.prev_output_channel if i == 0 else self.out_channels
__a =FlaxResnetBlockaD(
in_channels=resnet_in_channels + res_skip_channels , out_channels=self.out_channels , dropout_prob=self.dropout , dtype=self.dtype , )
resnets.append(__snake_case )
__a =FlaxTransformeraDModel(
in_channels=self.out_channels , n_heads=self.num_attention_heads , d_head=self.out_channels // self.num_attention_heads , depth=1 , use_linear_projection=self.use_linear_projection , only_cross_attention=self.only_cross_attention , use_memory_efficient_attention=self.use_memory_efficient_attention , dtype=self.dtype , )
attentions.append(__snake_case )
__a =resnets
__a =attentions
if self.add_upsample:
__a =FlaxUpsampleaD(self.out_channels , dtype=self.dtype )
def __call__( self , __snake_case , __snake_case , __snake_case , __snake_case , __snake_case=True ) -> List[Any]:
'''simple docstring'''
for resnet, attn in zip(self.resnets , self.attentions ):
# pop res hidden states
__a =res_hidden_states_tuple[-1]
__a =res_hidden_states_tuple[:-1]
__a =jnp.concatenate((hidden_states, res_hidden_states) , axis=-1 )
__a =resnet(__snake_case , __snake_case , deterministic=__snake_case )
__a =attn(__snake_case , __snake_case , deterministic=__snake_case )
if self.add_upsample:
__a =self.upsamplers_a(__snake_case )
return hidden_states
class __magic_name__ ( nn.Module ):
SCREAMING_SNAKE_CASE = 42
SCREAMING_SNAKE_CASE = 42
SCREAMING_SNAKE_CASE = 42
SCREAMING_SNAKE_CASE = 0.0
SCREAMING_SNAKE_CASE = 1
SCREAMING_SNAKE_CASE = True
SCREAMING_SNAKE_CASE = jnp.floataa
def __magic_name__ ( self ) -> Union[str, Any]:
'''simple docstring'''
__a =[]
for i in range(self.num_layers ):
__a =self.in_channels if (i == self.num_layers - 1) else self.out_channels
__a =self.prev_output_channel if i == 0 else self.out_channels
__a =FlaxResnetBlockaD(
in_channels=resnet_in_channels + res_skip_channels , out_channels=self.out_channels , dropout_prob=self.dropout , dtype=self.dtype , )
resnets.append(__snake_case )
__a =resnets
if self.add_upsample:
__a =FlaxUpsampleaD(self.out_channels , dtype=self.dtype )
def __call__( self , __snake_case , __snake_case , __snake_case , __snake_case=True ) -> List[Any]:
'''simple docstring'''
for resnet in self.resnets:
# pop res hidden states
__a =res_hidden_states_tuple[-1]
__a =res_hidden_states_tuple[:-1]
__a =jnp.concatenate((hidden_states, res_hidden_states) , axis=-1 )
__a =resnet(__snake_case , __snake_case , deterministic=__snake_case )
if self.add_upsample:
__a =self.upsamplers_a(__snake_case )
return hidden_states
class __magic_name__ ( nn.Module ):
SCREAMING_SNAKE_CASE = 42
SCREAMING_SNAKE_CASE = 0.0
SCREAMING_SNAKE_CASE = 1
SCREAMING_SNAKE_CASE = 1
SCREAMING_SNAKE_CASE = False
SCREAMING_SNAKE_CASE = False
SCREAMING_SNAKE_CASE = jnp.floataa
def __magic_name__ ( self ) -> List[Any]:
'''simple docstring'''
# there is always at least one resnet
__a =[
FlaxResnetBlockaD(
in_channels=self.in_channels , out_channels=self.in_channels , dropout_prob=self.dropout , dtype=self.dtype , )
]
__a =[]
for _ in range(self.num_layers ):
__a =FlaxTransformeraDModel(
in_channels=self.in_channels , n_heads=self.num_attention_heads , d_head=self.in_channels // self.num_attention_heads , depth=1 , use_linear_projection=self.use_linear_projection , use_memory_efficient_attention=self.use_memory_efficient_attention , dtype=self.dtype , )
attentions.append(__snake_case )
__a =FlaxResnetBlockaD(
in_channels=self.in_channels , out_channels=self.in_channels , dropout_prob=self.dropout , dtype=self.dtype , )
resnets.append(__snake_case )
__a =resnets
__a =attentions
def __call__( self , __snake_case , __snake_case , __snake_case , __snake_case=True ) -> List[str]:
'''simple docstring'''
__a =self.resnets[0](__snake_case , __snake_case )
for attn, resnet in zip(self.attentions , self.resnets[1:] ):
__a =attn(__snake_case , __snake_case , deterministic=__snake_case )
__a =resnet(__snake_case , __snake_case , deterministic=__snake_case )
return hidden_states
| 308
| 1
|
def UpperCamelCase_( _snake_case : list[list[int]] , _snake_case : int , _snake_case : int , _snake_case : list[int] ):
"""simple docstring"""
if graph[path[curr_ind - 1]][next_ver] == 0:
return False
# 2. Validate that next vertex is not already in path
return not any(vertex == next_ver for vertex in path )
def UpperCamelCase_( _snake_case : list[list[int]] , _snake_case : list[int] , _snake_case : int ):
"""simple docstring"""
if curr_ind == len(_snake_case ):
# return whether path exists between current and starting vertices
return graph[path[curr_ind - 1]][path[0]] == 1
# Recursive Step
for next_ver in range(0 , len(_snake_case ) ):
if valid_connection(_snake_case , _snake_case , _snake_case , _snake_case ):
# Insert current vertex into path as next transition
__a =next_ver
# Validate created path
if util_hamilton_cycle(_snake_case , _snake_case , curr_ind + 1 ):
return True
# Backtrack
__a =-1
return False
def UpperCamelCase_( _snake_case : list[list[int]] , _snake_case : int = 0 ):
"""simple docstring"""
__a =[-1] * (len(_snake_case ) + 1)
# initialize start and end of path with starting index
__a =__a =start_index
# evaluate and if we find answer return path either return empty array
return path if util_hamilton_cycle(_snake_case , _snake_case , 1 ) else []
| 308
|
import json
import os
import subprocess
import unittest
from ast import literal_eval
import pytest
from parameterized import parameterized, parameterized_class
from . import is_sagemaker_available
if is_sagemaker_available():
from sagemaker import Session, TrainingJobAnalytics
from sagemaker.huggingface import HuggingFace
@pytest.mark.skipif(
literal_eval(os.getenv('TEST_SAGEMAKER' , 'False' ) ) is not True , reason='Skipping test because should only be run when releasing minor transformers version' , )
@pytest.mark.usefixtures('sm_env' )
@parameterized_class(
[
{
'framework': 'pytorch',
'script': 'run_glue.py',
'model_name_or_path': 'distilbert-base-cased',
'instance_type': 'ml.p3.16xlarge',
'results': {'train_runtime': 6_5_0, 'eval_accuracy': 0.7, 'eval_loss': 0.6},
},
{
'framework': 'pytorch',
'script': 'run_ddp.py',
'model_name_or_path': 'distilbert-base-cased',
'instance_type': 'ml.p3.16xlarge',
'results': {'train_runtime': 6_0_0, 'eval_accuracy': 0.7, 'eval_loss': 0.6},
},
{
'framework': 'tensorflow',
'script': 'run_tf_dist.py',
'model_name_or_path': 'distilbert-base-cased',
'instance_type': 'ml.p3.16xlarge',
'results': {'train_runtime': 6_0_0, 'eval_accuracy': 0.6, 'eval_loss': 0.7},
},
] )
class __magic_name__ ( unittest.TestCase ):
def __magic_name__ ( self ) -> int:
'''simple docstring'''
if self.framework == "pytorch":
subprocess.run(
f'cp ./examples/pytorch/text-classification/run_glue.py {self.env.test_path}/run_glue.py'.split() , encoding='utf-8' , check=__snake_case , )
assert hasattr(self , 'env' )
def __magic_name__ ( self , __snake_case ) -> int:
'''simple docstring'''
__a =f'{self.env.base_job_name}-{instance_count}-{"ddp" if "ddp" in self.script else "smd"}'
# distributed data settings
__a ={'smdistributed': {'dataparallel': {'enabled': True}}} if self.script != 'run_ddp.py' else None
# creates estimator
return HuggingFace(
entry_point=self.script , source_dir=self.env.test_path , role=self.env.role , image_uri=self.env.image_uri , base_job_name=__snake_case , instance_count=__snake_case , instance_type=self.instance_type , debugger_hook_config=__snake_case , hyperparameters={**self.env.distributed_hyperparameters, 'model_name_or_path': self.model_name_or_path} , metric_definitions=self.env.metric_definitions , distribution=__snake_case , py_version='py36' , )
def __magic_name__ ( self , __snake_case ) -> Optional[Any]:
'''simple docstring'''
TrainingJobAnalytics(__snake_case ).export_csv(f'{self.env.test_path}/{job_name}_metrics.csv' )
@parameterized.expand([(2,)] )
def __magic_name__ ( self , __snake_case ) -> Optional[int]:
'''simple docstring'''
# create estimator
__a =self.create_estimator(__snake_case )
# run training
estimator.fit()
# result dataframe
__a =TrainingJobAnalytics(estimator.latest_training_job.name ).dataframe()
# extract kpis
__a =list(result_metrics_df[result_metrics_df.metric_name == 'eval_accuracy']['value'] )
__a =list(result_metrics_df[result_metrics_df.metric_name == 'eval_loss']['value'] )
# get train time from SageMaker job, this includes starting, preprocessing, stopping
__a =(
Session().describe_training_job(estimator.latest_training_job.name ).get('TrainingTimeInSeconds' , 99_9999 )
)
# assert kpis
assert train_runtime <= self.results["train_runtime"]
assert all(t >= self.results['eval_accuracy'] for t in eval_accuracy )
assert all(t <= self.results['eval_loss'] for t in eval_loss )
# dump tests result into json file to share in PR
with open(f'{estimator.latest_training_job.name}.json' , 'w' ) as outfile:
json.dump({'train_time': train_runtime, 'eval_accuracy': eval_accuracy, 'eval_loss': eval_loss} , __snake_case )
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import argparse
import json
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import ViTImageProcessor, ViTMSNConfig, ViTMSNModel
from transformers.image_utils import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
torch.set_grad_enabled(False)
def UpperCamelCase_( _snake_case : Optional[int] , _snake_case : int=False ):
"""simple docstring"""
__a =[]
for i in range(config.num_hidden_layers ):
# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
rename_keys.append((F'module.blocks.{i}.norm1.weight', F'vit.encoder.layer.{i}.layernorm_before.weight') )
rename_keys.append((F'module.blocks.{i}.norm1.bias', F'vit.encoder.layer.{i}.layernorm_before.bias') )
rename_keys.append(
(F'module.blocks.{i}.attn.proj.weight', F'vit.encoder.layer.{i}.attention.output.dense.weight') )
rename_keys.append((F'module.blocks.{i}.attn.proj.bias', F'vit.encoder.layer.{i}.attention.output.dense.bias') )
rename_keys.append((F'module.blocks.{i}.norm2.weight', F'vit.encoder.layer.{i}.layernorm_after.weight') )
rename_keys.append((F'module.blocks.{i}.norm2.bias', F'vit.encoder.layer.{i}.layernorm_after.bias') )
rename_keys.append((F'module.blocks.{i}.mlp.fc1.weight', F'vit.encoder.layer.{i}.intermediate.dense.weight') )
rename_keys.append((F'module.blocks.{i}.mlp.fc1.bias', F'vit.encoder.layer.{i}.intermediate.dense.bias') )
rename_keys.append((F'module.blocks.{i}.mlp.fc2.weight', F'vit.encoder.layer.{i}.output.dense.weight') )
rename_keys.append((F'module.blocks.{i}.mlp.fc2.bias', F'vit.encoder.layer.{i}.output.dense.bias') )
# projection layer + position embeddings
rename_keys.extend(
[
('module.cls_token', 'vit.embeddings.cls_token'),
('module.patch_embed.proj.weight', 'vit.embeddings.patch_embeddings.projection.weight'),
('module.patch_embed.proj.bias', 'vit.embeddings.patch_embeddings.projection.bias'),
('module.pos_embed', 'vit.embeddings.position_embeddings'),
] )
if base_model:
# layernorm + pooler
rename_keys.extend(
[
('module.norm.weight', 'layernorm.weight'),
('module.norm.bias', 'layernorm.bias'),
] )
# if just the base model, we should remove "vit" from all keys that start with "vit"
__a =[(pair[0], pair[1][4:]) if pair[1].startswith('vit' ) else pair for pair in rename_keys]
else:
# layernorm + classification head
rename_keys.extend(
[
('norm.weight', 'vit.layernorm.weight'),
('norm.bias', 'vit.layernorm.bias'),
('head.weight', 'classifier.weight'),
('head.bias', 'classifier.bias'),
] )
return rename_keys
def UpperCamelCase_( _snake_case : Any , _snake_case : List[Any] , _snake_case : List[str]=False ):
"""simple docstring"""
for i in range(config.num_hidden_layers ):
if base_model:
__a =''
else:
__a ='vit.'
# read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
__a =state_dict.pop(F'module.blocks.{i}.attn.qkv.weight' )
__a =state_dict.pop(F'module.blocks.{i}.attn.qkv.bias' )
# next, add query, keys and values (in that order) to the state dict
__a =in_proj_weight[
: config.hidden_size, :
]
__a =in_proj_bias[: config.hidden_size]
__a =in_proj_weight[
config.hidden_size : config.hidden_size * 2, :
]
__a =in_proj_bias[
config.hidden_size : config.hidden_size * 2
]
__a =in_proj_weight[
-config.hidden_size :, :
]
__a =in_proj_bias[-config.hidden_size :]
def UpperCamelCase_( _snake_case : Optional[Any] ):
"""simple docstring"""
__a =['head.weight', 'head.bias']
for k in ignore_keys:
state_dict.pop(_snake_case , _snake_case )
def UpperCamelCase_( _snake_case : List[Any] ):
"""simple docstring"""
__a =[
'module.fc.fc1.weight',
'module.fc.fc1.bias',
'module.fc.bn1.weight',
'module.fc.bn1.bias',
'module.fc.bn1.running_mean',
'module.fc.bn1.running_var',
'module.fc.bn1.num_batches_tracked',
'module.fc.fc2.weight',
'module.fc.fc2.bias',
'module.fc.bn2.weight',
'module.fc.bn2.bias',
'module.fc.bn2.running_mean',
'module.fc.bn2.running_var',
'module.fc.bn2.num_batches_tracked',
'module.fc.fc3.weight',
'module.fc.fc3.bias',
]
for k in ignore_keys:
state_dict.pop(_snake_case , _snake_case )
def UpperCamelCase_( _snake_case : Optional[Any] , _snake_case : Union[str, Any] , _snake_case : Optional[int] ):
"""simple docstring"""
__a =dct.pop(_snake_case )
__a =val
def UpperCamelCase_( _snake_case : Optional[Any] , _snake_case : Tuple ):
"""simple docstring"""
__a =ViTMSNConfig()
__a =1000
__a ='datasets/huggingface/label-files'
__a ='imagenet-1k-id2label.json'
__a =json.load(open(hf_hub_download(_snake_case , _snake_case ) , 'r' ) )
__a ={int(_snake_case ): v for k, v in idalabel.items()}
__a =idalabel
__a ={v: k for k, v in idalabel.items()}
if "s16" in checkpoint_url:
__a =384
__a =1536
__a =6
elif "l16" in checkpoint_url:
__a =1024
__a =4096
__a =24
__a =16
__a =0.1
elif "b4" in checkpoint_url:
__a =4
elif "l7" in checkpoint_url:
__a =7
__a =1024
__a =4096
__a =24
__a =16
__a =0.1
__a =ViTMSNModel(_snake_case )
__a =torch.hub.load_state_dict_from_url(_snake_case , map_location='cpu' )['target_encoder']
__a =ViTImageProcessor(size=config.image_size )
remove_projection_head(_snake_case )
__a =create_rename_keys(_snake_case , base_model=_snake_case )
for src, dest in rename_keys:
rename_key(_snake_case , _snake_case , _snake_case )
read_in_q_k_v(_snake_case , _snake_case , base_model=_snake_case )
model.load_state_dict(_snake_case )
model.eval()
__a ='http://images.cocodataset.org/val2017/000000039769.jpg'
__a =Image.open(requests.get(_snake_case , stream=_snake_case ).raw )
__a =ViTImageProcessor(
size=config.image_size , image_mean=_snake_case , image_std=_snake_case )
__a =image_processor(images=_snake_case , return_tensors='pt' )
# forward pass
torch.manual_seed(2 )
__a =model(**_snake_case )
__a =outputs.last_hidden_state
# The following Colab Notebook was used to generate these outputs:
# https://colab.research.google.com/gist/sayakpaul/3672419a04f5997827503fd84079bdd1/scratchpad.ipynb
if "s16" in checkpoint_url:
__a =torch.tensor([[-1.0_915, -1.4_876, -1.1_809]] )
elif "b16" in checkpoint_url:
__a =torch.tensor([[14.2_889, -18.9_045, 11.7_281]] )
elif "l16" in checkpoint_url:
__a =torch.tensor([[41.5_028, -22.8_681, 45.6_475]] )
elif "b4" in checkpoint_url:
__a =torch.tensor([[-4.3_868, 5.2_932, -0.4_137]] )
else:
__a =torch.tensor([[-0.1_792, -0.6_465, 2.4_263]] )
# verify logits
assert torch.allclose(last_hidden_state[:, 0, :3] , _snake_case , atol=1e-4 )
print(F'Saving model to {pytorch_dump_folder_path}' )
model.save_pretrained(_snake_case )
print(F'Saving image processor to {pytorch_dump_folder_path}' )
image_processor.save_pretrained(_snake_case )
if __name__ == "__main__":
_lowerCAmelCase : Optional[int] = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--checkpoint_url",
default="https://dl.fbaipublicfiles.com/msn/vits16_800ep.pth.tar",
type=str,
help="URL of the checkpoint you'd like to convert.",
)
parser.add_argument(
"--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model directory."
)
_lowerCAmelCase : Tuple = parser.parse_args()
convert_vit_msn_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)
| 308
|
import argparse
import logging
from collections import namedtuple
import torch
from model_bertabs import BertAbsSummarizer
from models.model_builder import AbsSummarizer # The authors' implementation
from transformers import BertTokenizer
logging.basicConfig(level=logging.INFO)
_lowerCAmelCase : List[Any] = logging.getLogger(__name__)
_lowerCAmelCase : Optional[Any] = "Hello world! cécé herlolip"
_lowerCAmelCase : str = namedtuple(
"BertAbsConfig",
[
"temp_dir",
"large",
"use_bert_emb",
"finetune_bert",
"encoder",
"share_emb",
"max_pos",
"enc_layers",
"enc_hidden_size",
"enc_heads",
"enc_ff_size",
"enc_dropout",
"dec_layers",
"dec_hidden_size",
"dec_heads",
"dec_ff_size",
"dec_dropout",
],
)
def UpperCamelCase_( _snake_case : str , _snake_case : List[Any] ):
"""simple docstring"""
__a =BertAbsConfig(
temp_dir='.' , finetune_bert=_snake_case , large=_snake_case , share_emb=_snake_case , use_bert_emb=_snake_case , encoder='bert' , max_pos=512 , enc_layers=6 , enc_hidden_size=512 , enc_heads=8 , enc_ff_size=512 , enc_dropout=0.2 , dec_layers=6 , dec_hidden_size=768 , dec_heads=8 , dec_ff_size=2048 , dec_dropout=0.2 , )
__a =torch.load(_snake_case , lambda _snake_case , _snake_case : storage )
__a =AbsSummarizer(_snake_case , torch.device('cpu' ) , _snake_case )
original.eval()
__a =BertAbsSummarizer(_snake_case , torch.device('cpu' ) )
new_model.eval()
# -------------------
# Convert the weights
# -------------------
logging.info('convert the model' )
new_model.bert.load_state_dict(original.bert.state_dict() )
new_model.decoder.load_state_dict(original.decoder.state_dict() )
new_model.generator.load_state_dict(original.generator.state_dict() )
# ----------------------------------
# Make sure the outpus are identical
# ----------------------------------
logging.info('Make sure that the models\' outputs are identical' )
__a =BertTokenizer.from_pretrained('bert-base-uncased' )
# prepare the model inputs
__a =tokenizer.encode('This is sample éàalj\'-.' )
encoder_input_ids.extend([tokenizer.pad_token_id] * (512 - len(_snake_case )) )
__a =torch.tensor(_snake_case ).unsqueeze(0 )
__a =tokenizer.encode('This is sample 3 éàalj\'-.' )
decoder_input_ids.extend([tokenizer.pad_token_id] * (512 - len(_snake_case )) )
__a =torch.tensor(_snake_case ).unsqueeze(0 )
# failsafe to make sure the weights reset does not affect the
# loaded weights.
assert torch.max(torch.abs(original.generator[0].weight - new_model.generator[0].weight ) ) == 0
# forward pass
__a =encoder_input_ids
__a =decoder_input_ids
__a =__a =None
__a =None
__a =__a =None
__a =__a =None
__a =None
# The original model does not apply the geneator layer immediatly but rather in
# the beam search (where it combines softmax + linear layer). Since we already
# apply the softmax in our generation process we only apply the linear layer here.
# We make sure that the outputs of the full stack are identical
__a =original(_snake_case , _snake_case , _snake_case , _snake_case , _snake_case , _snake_case , _snake_case )[0]
__a =original.generator(_snake_case )
__a =new_model(
_snake_case , _snake_case , _snake_case , _snake_case , _snake_case )[0]
__a =new_model.generator(_snake_case )
__a =torch.max(torch.abs(output_converted_model - output_original_model ) ).item()
print('Maximum absolute difference beween weights: {:.2f}'.format(_snake_case ) )
__a =torch.max(torch.abs(output_converted_generator - output_original_generator ) ).item()
print('Maximum absolute difference beween weights: {:.2f}'.format(_snake_case ) )
__a =torch.allclose(_snake_case , _snake_case , atol=1e-3 )
if are_identical:
logging.info('all weights are equal up to 1e-3' )
else:
raise ValueError('the weights are different. The new model is likely different from the original one.' )
# The model has been saved with torch.save(model) and this is bound to the exact
# directory structure. We save the state_dict instead.
logging.info('saving the model\'s state dictionary' )
torch.save(
new_model.state_dict() , './bertabs-finetuned-cnndm-extractive-abstractive-summarization/pytorch_model.bin' )
if __name__ == "__main__":
_lowerCAmelCase : Optional[int] = argparse.ArgumentParser()
parser.add_argument(
"--bertabs_checkpoint_path",
default=None,
type=str,
required=True,
help="Path the official PyTorch dump.",
)
parser.add_argument(
"--pytorch_dump_folder_path",
default=None,
type=str,
required=True,
help="Path to the output PyTorch model.",
)
_lowerCAmelCase : Optional[Any] = parser.parse_args()
convert_bertabs_checkpoints(
args.bertabs_checkpoint_path,
args.pytorch_dump_folder_path,
)
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import argparse
import os
import shutil
from pathlib import Path
import onnx
import torch
from packaging import version
from torch.onnx import export
from diffusers import OnnxRuntimeModel, OnnxStableDiffusionPipeline, StableDiffusionPipeline
_lowerCAmelCase : List[str] = version.parse(version.parse(torch.__version__).base_version) < version.parse("1.11")
def UpperCamelCase_( _snake_case : Optional[int] , _snake_case : tuple , _snake_case : Path , _snake_case : Any , _snake_case : Any , _snake_case : Union[str, Any] , _snake_case : Union[str, Any] , _snake_case : int=False , ):
"""simple docstring"""
output_path.parent.mkdir(parents=_snake_case , exist_ok=_snake_case )
# PyTorch deprecated the `enable_onnx_checker` and `use_external_data_format` arguments in v1.11,
# so we check the torch version for backwards compatibility
if is_torch_less_than_1_11:
export(
_snake_case , _snake_case , f=output_path.as_posix() , input_names=_snake_case , output_names=_snake_case , dynamic_axes=_snake_case , do_constant_folding=_snake_case , use_external_data_format=_snake_case , enable_onnx_checker=_snake_case , opset_version=_snake_case , )
else:
export(
_snake_case , _snake_case , f=output_path.as_posix() , input_names=_snake_case , output_names=_snake_case , dynamic_axes=_snake_case , do_constant_folding=_snake_case , opset_version=_snake_case , )
@torch.no_grad()
def UpperCamelCase_( _snake_case : str , _snake_case : str , _snake_case : int , _snake_case : bool = False ):
"""simple docstring"""
__a =torch.floataa if fpaa else torch.floataa
if fpaa and torch.cuda.is_available():
__a ='cuda'
elif fpaa and not torch.cuda.is_available():
raise ValueError('`float16` model export is only supported on GPUs with CUDA' )
else:
__a ='cpu'
__a =StableDiffusionPipeline.from_pretrained(_snake_case , torch_dtype=_snake_case ).to(_snake_case )
__a =Path(_snake_case )
# TEXT ENCODER
__a =pipeline.text_encoder.config.max_position_embeddings
__a =pipeline.text_encoder.config.hidden_size
__a =pipeline.tokenizer(
'A sample prompt' , padding='max_length' , max_length=pipeline.tokenizer.model_max_length , truncation=_snake_case , return_tensors='pt' , )
onnx_export(
pipeline.text_encoder , model_args=(text_input.input_ids.to(device=_snake_case , dtype=torch.intaa )) , output_path=output_path / 'text_encoder' / 'model.onnx' , ordered_input_names=['input_ids'] , output_names=['last_hidden_state', 'pooler_output'] , dynamic_axes={
'input_ids': {0: 'batch', 1: 'sequence'},
} , opset=_snake_case , )
del pipeline.text_encoder
# UNET
__a =pipeline.unet.config.in_channels
__a =pipeline.unet.config.sample_size
__a =output_path / 'unet' / 'model.onnx'
onnx_export(
pipeline.unet , model_args=(
torch.randn(2 , _snake_case , _snake_case , _snake_case ).to(device=_snake_case , dtype=_snake_case ),
torch.randn(2 ).to(device=_snake_case , dtype=_snake_case ),
torch.randn(2 , _snake_case , _snake_case ).to(device=_snake_case , dtype=_snake_case ),
False,
) , output_path=_snake_case , ordered_input_names=['sample', 'timestep', 'encoder_hidden_states', 'return_dict'] , output_names=['out_sample'] , dynamic_axes={
'sample': {0: 'batch', 1: 'channels', 2: 'height', 3: 'width'},
'timestep': {0: 'batch'},
'encoder_hidden_states': {0: 'batch', 1: 'sequence'},
} , opset=_snake_case , use_external_data_format=_snake_case , )
__a =str(unet_path.absolute().as_posix() )
__a =os.path.dirname(_snake_case )
__a =onnx.load(_snake_case )
# clean up existing tensor files
shutil.rmtree(_snake_case )
os.mkdir(_snake_case )
# collate external tensor files into one
onnx.save_model(
_snake_case , _snake_case , save_as_external_data=_snake_case , all_tensors_to_one_file=_snake_case , location='weights.pb' , convert_attribute=_snake_case , )
del pipeline.unet
# VAE ENCODER
__a =pipeline.vae
__a =vae_encoder.config.in_channels
__a =vae_encoder.config.sample_size
# need to get the raw tensor output (sample) from the encoder
__a =lambda _snake_case , _snake_case : vae_encoder.encode(_snake_case , _snake_case )[0].sample()
onnx_export(
_snake_case , model_args=(
torch.randn(1 , _snake_case , _snake_case , _snake_case ).to(device=_snake_case , dtype=_snake_case ),
False,
) , output_path=output_path / 'vae_encoder' / 'model.onnx' , ordered_input_names=['sample', 'return_dict'] , output_names=['latent_sample'] , dynamic_axes={
'sample': {0: 'batch', 1: 'channels', 2: 'height', 3: 'width'},
} , opset=_snake_case , )
# VAE DECODER
__a =pipeline.vae
__a =vae_decoder.config.latent_channels
__a =vae_decoder.config.out_channels
# forward only through the decoder part
__a =vae_encoder.decode
onnx_export(
_snake_case , model_args=(
torch.randn(1 , _snake_case , _snake_case , _snake_case ).to(device=_snake_case , dtype=_snake_case ),
False,
) , output_path=output_path / 'vae_decoder' / 'model.onnx' , ordered_input_names=['latent_sample', 'return_dict'] , output_names=['sample'] , dynamic_axes={
'latent_sample': {0: 'batch', 1: 'channels', 2: 'height', 3: 'width'},
} , opset=_snake_case , )
del pipeline.vae
# SAFETY CHECKER
if pipeline.safety_checker is not None:
__a =pipeline.safety_checker
__a =safety_checker.config.vision_config.num_channels
__a =safety_checker.config.vision_config.image_size
__a =safety_checker.forward_onnx
onnx_export(
pipeline.safety_checker , model_args=(
torch.randn(
1 , _snake_case , _snake_case , _snake_case , ).to(device=_snake_case , dtype=_snake_case ),
torch.randn(1 , _snake_case , _snake_case , _snake_case ).to(device=_snake_case , dtype=_snake_case ),
) , output_path=output_path / 'safety_checker' / 'model.onnx' , ordered_input_names=['clip_input', 'images'] , output_names=['out_images', 'has_nsfw_concepts'] , dynamic_axes={
'clip_input': {0: 'batch', 1: 'channels', 2: 'height', 3: 'width'},
'images': {0: 'batch', 1: 'height', 2: 'width', 3: 'channels'},
} , opset=_snake_case , )
del pipeline.safety_checker
__a =OnnxRuntimeModel.from_pretrained(output_path / 'safety_checker' )
__a =pipeline.feature_extractor
else:
__a =None
__a =None
__a =OnnxStableDiffusionPipeline(
vae_encoder=OnnxRuntimeModel.from_pretrained(output_path / 'vae_encoder' ) , vae_decoder=OnnxRuntimeModel.from_pretrained(output_path / 'vae_decoder' ) , text_encoder=OnnxRuntimeModel.from_pretrained(output_path / 'text_encoder' ) , tokenizer=pipeline.tokenizer , unet=OnnxRuntimeModel.from_pretrained(output_path / 'unet' ) , scheduler=pipeline.scheduler , safety_checker=_snake_case , feature_extractor=_snake_case , requires_safety_checker=safety_checker is not None , )
onnx_pipeline.save_pretrained(_snake_case )
print('ONNX pipeline saved to' , _snake_case )
del pipeline
del onnx_pipeline
__a =OnnxStableDiffusionPipeline.from_pretrained(_snake_case , provider='CPUExecutionProvider' )
print('ONNX pipeline is loadable' )
if __name__ == "__main__":
_lowerCAmelCase : Any = argparse.ArgumentParser()
parser.add_argument(
"--model_path",
type=str,
required=True,
help="Path to the `diffusers` checkpoint to convert (either a local directory or on the Hub).",
)
parser.add_argument("--output_path", type=str, required=True, help="Path to the output model.")
parser.add_argument(
"--opset",
default=14,
type=int,
help="The version of the ONNX operator set to use.",
)
parser.add_argument("--fp16", action="store_true", default=False, help="Export the models in `float16` mode")
_lowerCAmelCase : List[str] = parser.parse_args()
convert_models(args.model_path, args.output_path, args.opset, args.fpaa)
| 308
|
import unittest
from transformers import XLMConfig, 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 (
XLMForMultipleChoice,
XLMForQuestionAnswering,
XLMForQuestionAnsweringSimple,
XLMForSequenceClassification,
XLMForTokenClassification,
XLMModel,
XLMWithLMHeadModel,
)
from transformers.models.xlm.modeling_xlm import XLM_PRETRAINED_MODEL_ARCHIVE_LIST
class __magic_name__ :
def __init__( self , __snake_case , __snake_case=13 , __snake_case=7 , __snake_case=True , __snake_case=True , __snake_case=True , __snake_case=True , __snake_case=True , __snake_case=False , __snake_case=False , __snake_case=False , __snake_case=2 , __snake_case=99 , __snake_case=0 , __snake_case=32 , __snake_case=5 , __snake_case=4 , __snake_case=0.1 , __snake_case=0.1 , __snake_case=512 , __snake_case=2 , __snake_case=0.02 , __snake_case=2 , __snake_case=4 , __snake_case="last" , __snake_case=True , __snake_case=None , __snake_case=0 , ) -> Optional[Any]:
'''simple docstring'''
__a =parent
__a =batch_size
__a =seq_length
__a =is_training
__a =use_input_lengths
__a =use_token_type_ids
__a =use_labels
__a =gelu_activation
__a =sinusoidal_embeddings
__a =causal
__a =asm
__a =n_langs
__a =vocab_size
__a =n_special
__a =hidden_size
__a =num_hidden_layers
__a =num_attention_heads
__a =hidden_dropout_prob
__a =attention_probs_dropout_prob
__a =max_position_embeddings
__a =type_sequence_label_size
__a =initializer_range
__a =num_labels
__a =num_choices
__a =summary_type
__a =use_proj
__a =scope
__a =bos_token_id
def __magic_name__ ( self ) -> Any:
'''simple docstring'''
__a =ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
__a =random_attention_mask([self.batch_size, self.seq_length] )
__a =None
if self.use_input_lengths:
__a =(
ids_tensor([self.batch_size] , vocab_size=2 ) + self.seq_length - 2
) # small variation of seq_length
__a =None
if self.use_token_type_ids:
__a =ids_tensor([self.batch_size, self.seq_length] , self.n_langs )
__a =None
__a =None
__a =None
if self.use_labels:
__a =ids_tensor([self.batch_size] , self.type_sequence_label_size )
__a =ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
__a =ids_tensor([self.batch_size] , 2 ).float()
__a =ids_tensor([self.batch_size] , self.num_choices )
__a =self.get_config()
return (
config,
input_ids,
token_type_ids,
input_lengths,
sequence_labels,
token_labels,
is_impossible_labels,
choice_labels,
input_mask,
)
def __magic_name__ ( self ) -> Any:
'''simple docstring'''
return XLMConfig(
vocab_size=self.vocab_size , n_special=self.n_special , emb_dim=self.hidden_size , n_layers=self.num_hidden_layers , n_heads=self.num_attention_heads , dropout=self.hidden_dropout_prob , attention_dropout=self.attention_probs_dropout_prob , gelu_activation=self.gelu_activation , sinusoidal_embeddings=self.sinusoidal_embeddings , asm=self.asm , causal=self.causal , n_langs=self.n_langs , max_position_embeddings=self.max_position_embeddings , initializer_range=self.initializer_range , summary_type=self.summary_type , use_proj=self.use_proj , num_labels=self.num_labels , bos_token_id=self.bos_token_id , )
def __magic_name__ ( self , __snake_case , __snake_case , __snake_case , __snake_case , __snake_case , __snake_case , __snake_case , __snake_case , __snake_case , ) -> List[Any]:
'''simple docstring'''
__a =XLMModel(config=__snake_case )
model.to(__snake_case )
model.eval()
__a =model(__snake_case , lengths=__snake_case , langs=__snake_case )
__a =model(__snake_case , langs=__snake_case )
__a =model(__snake_case )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def __magic_name__ ( self , __snake_case , __snake_case , __snake_case , __snake_case , __snake_case , __snake_case , __snake_case , __snake_case , __snake_case , ) -> Optional[int]:
'''simple docstring'''
__a =XLMWithLMHeadModel(__snake_case )
model.to(__snake_case )
model.eval()
__a =model(__snake_case , token_type_ids=__snake_case , labels=__snake_case )
self.parent.assertEqual(result.loss.shape , () )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def __magic_name__ ( self , __snake_case , __snake_case , __snake_case , __snake_case , __snake_case , __snake_case , __snake_case , __snake_case , __snake_case , ) -> Dict:
'''simple docstring'''
__a =XLMForQuestionAnsweringSimple(__snake_case )
model.to(__snake_case )
model.eval()
__a =model(__snake_case )
__a =model(__snake_case , start_positions=__snake_case , end_positions=__snake_case )
__a =outputs
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 __magic_name__ ( self , __snake_case , __snake_case , __snake_case , __snake_case , __snake_case , __snake_case , __snake_case , __snake_case , __snake_case , ) -> List[Any]:
'''simple docstring'''
__a =XLMForQuestionAnswering(__snake_case )
model.to(__snake_case )
model.eval()
__a =model(__snake_case )
__a =model(
__snake_case , start_positions=__snake_case , end_positions=__snake_case , cls_index=__snake_case , is_impossible=__snake_case , p_mask=__snake_case , )
__a =model(
__snake_case , start_positions=__snake_case , end_positions=__snake_case , cls_index=__snake_case , is_impossible=__snake_case , )
((__a) , ) =result_with_labels.to_tuple()
__a =model(__snake_case , start_positions=__snake_case , end_positions=__snake_case )
((__a) , ) =result_with_labels.to_tuple()
self.parent.assertEqual(result_with_labels.loss.shape , () )
self.parent.assertEqual(result.start_top_log_probs.shape , (self.batch_size, model.config.start_n_top) )
self.parent.assertEqual(result.start_top_index.shape , (self.batch_size, model.config.start_n_top) )
self.parent.assertEqual(
result.end_top_log_probs.shape , (self.batch_size, model.config.start_n_top * model.config.end_n_top) )
self.parent.assertEqual(
result.end_top_index.shape , (self.batch_size, model.config.start_n_top * model.config.end_n_top) )
self.parent.assertEqual(result.cls_logits.shape , (self.batch_size,) )
def __magic_name__ ( self , __snake_case , __snake_case , __snake_case , __snake_case , __snake_case , __snake_case , __snake_case , __snake_case , __snake_case , ) -> Optional[Any]:
'''simple docstring'''
__a =XLMForSequenceClassification(__snake_case )
model.to(__snake_case )
model.eval()
__a =model(__snake_case )
__a =model(__snake_case , labels=__snake_case )
self.parent.assertEqual(result.loss.shape , () )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size) )
def __magic_name__ ( self , __snake_case , __snake_case , __snake_case , __snake_case , __snake_case , __snake_case , __snake_case , __snake_case , __snake_case , ) -> Any:
'''simple docstring'''
__a =self.num_labels
__a =XLMForTokenClassification(__snake_case )
model.to(__snake_case )
model.eval()
__a =model(__snake_case , attention_mask=__snake_case , labels=__snake_case )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def __magic_name__ ( self , __snake_case , __snake_case , __snake_case , __snake_case , __snake_case , __snake_case , __snake_case , __snake_case , __snake_case , ) -> Tuple:
'''simple docstring'''
__a =self.num_choices
__a =XLMForMultipleChoice(config=__snake_case )
model.to(__snake_case )
model.eval()
__a =input_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
__a =token_type_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
__a =input_mask.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
__a =model(
__snake_case , attention_mask=__snake_case , token_type_ids=__snake_case , labels=__snake_case , )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_choices) )
def __magic_name__ ( self ) -> Union[str, Any]:
'''simple docstring'''
__a =self.prepare_config_and_inputs()
(
(
__a
) , (
__a
) , (
__a
) , (
__a
) , (
__a
) , (
__a
) , (
__a
) , (
__a
) , (
__a
) ,
) =config_and_inputs
__a ={'input_ids': input_ids, 'token_type_ids': token_type_ids, 'lengths': input_lengths}
return config, inputs_dict
@require_torch
class __magic_name__ ( lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , unittest.TestCase ):
SCREAMING_SNAKE_CASE = (
(
XLMModel,
XLMWithLMHeadModel,
XLMForQuestionAnswering,
XLMForSequenceClassification,
XLMForQuestionAnsweringSimple,
XLMForTokenClassification,
XLMForMultipleChoice,
)
if is_torch_available()
else ()
)
SCREAMING_SNAKE_CASE = (
(XLMWithLMHeadModel,) if is_torch_available() else ()
) # TODO (PVP): Check other models whether language generation is also applicable
SCREAMING_SNAKE_CASE = (
{
'feature-extraction': XLMModel,
'fill-mask': XLMWithLMHeadModel,
'question-answering': XLMForQuestionAnsweringSimple,
'text-classification': XLMForSequenceClassification,
'text-generation': XLMWithLMHeadModel,
'token-classification': XLMForTokenClassification,
'zero-shot': XLMForSequenceClassification,
}
if is_torch_available()
else {}
)
def __magic_name__ ( self , __snake_case , __snake_case , __snake_case , __snake_case , __snake_case ) -> int:
'''simple docstring'''
if (
pipeline_test_casse_name == "QAPipelineTests"
and tokenizer_name is not None
and not tokenizer_name.endswith('Fast' )
):
# `QAPipelineTests` fails for a few models when the slower tokenizer are used.
# (The slower tokenizers were never used for pipeline tests before the pipeline testing rework)
# TODO: check (and possibly fix) the `QAPipelineTests` with slower tokenizer
return True
return False
def __magic_name__ ( self , __snake_case , __snake_case , __snake_case=False ) -> str:
'''simple docstring'''
__a =super()._prepare_for_class(__snake_case , __snake_case , return_labels=__snake_case )
if return_labels:
if model_class.__name__ == "XLMForQuestionAnswering":
__a =torch.zeros(
self.model_tester.batch_size , dtype=torch.long , device=__snake_case )
__a =torch.zeros(
self.model_tester.batch_size , dtype=torch.long , device=__snake_case )
return inputs_dict
def __magic_name__ ( self ) -> Tuple:
'''simple docstring'''
__a =XLMModelTester(self )
__a =ConfigTester(self , config_class=__snake_case , emb_dim=37 )
def __magic_name__ ( self ) -> str:
'''simple docstring'''
self.config_tester.run_common_tests()
def __magic_name__ ( self ) -> List[str]:
'''simple docstring'''
__a =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_xlm_model(*__snake_case )
def __magic_name__ ( self ) -> List[str]:
'''simple docstring'''
__a =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_xlm_lm_head(*__snake_case )
def __magic_name__ ( self ) -> List[str]:
'''simple docstring'''
__a =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_xlm_simple_qa(*__snake_case )
def __magic_name__ ( self ) -> Optional[int]:
'''simple docstring'''
__a =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_xlm_qa(*__snake_case )
def __magic_name__ ( self ) -> Any:
'''simple docstring'''
__a =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_xlm_sequence_classif(*__snake_case )
def __magic_name__ ( self ) -> Optional[int]:
'''simple docstring'''
__a =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_xlm_token_classif(*__snake_case )
def __magic_name__ ( self ) -> Any:
'''simple docstring'''
__a =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_xlm_for_multiple_choice(*__snake_case )
def __magic_name__ ( self , __snake_case , __snake_case , __snake_case , __snake_case , __snake_case , __snake_case=False , __snake_case=1 ) -> Optional[Any]:
'''simple docstring'''
self.assertIsInstance(__snake_case , __snake_case )
self.assertListEqual(
[isinstance(__snake_case , __snake_case ) for iter_attentions in attentions] , [True] * len(__snake_case ) )
self.assertEqual(len(__snake_case ) , (max_length - min_length) * num_beam_groups )
for idx, iter_attentions in enumerate(__snake_case ):
# adds PAD dummy token
__a =min_length + idx + 1
__a =min_length + idx + 1
__a =(
batch_size * num_beam_groups,
config.num_attention_heads,
tgt_len,
src_len,
)
# check attn size
self.assertListEqual(
[layer_attention.shape for layer_attention in iter_attentions] , [expected_shape] * len(__snake_case ) )
def __magic_name__ ( self , __snake_case , __snake_case , __snake_case , __snake_case , __snake_case , __snake_case=False , __snake_case=1 ) -> Dict:
'''simple docstring'''
self.assertIsInstance(__snake_case , __snake_case )
self.assertListEqual(
[isinstance(__snake_case , __snake_case ) for iter_hidden_states in hidden_states] , [True] * len(__snake_case ) , )
self.assertEqual(len(__snake_case ) , (max_length - min_length) * num_beam_groups )
for idx, iter_hidden_states in enumerate(__snake_case ):
# adds PAD dummy token
__a =min_length + idx + 1
__a =(batch_size * num_beam_groups, seq_len, config.hidden_size)
# check hidden size
self.assertListEqual(
[layer_hidden_states.shape for layer_hidden_states in iter_hidden_states] , [expected_shape] * len(__snake_case ) , )
pass
@slow
def __magic_name__ ( self ) -> Union[str, Any]:
'''simple docstring'''
for model_name in XLM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
__a =XLMModel.from_pretrained(__snake_case )
self.assertIsNotNone(__snake_case )
@require_torch
class __magic_name__ ( unittest.TestCase ):
@slow
def __magic_name__ ( self ) -> Tuple:
'''simple docstring'''
__a =XLMWithLMHeadModel.from_pretrained('xlm-mlm-en-2048' )
model.to(__snake_case )
__a =torch.tensor([[14, 447]] , dtype=torch.long , device=__snake_case ) # the president
__a =[
14,
447,
14,
447,
14,
447,
14,
447,
14,
447,
14,
447,
14,
447,
14,
447,
14,
447,
14,
447,
] # the president the president the president the president the president the president the president the president the president the president
# TODO(PVP): this and other input_ids I tried for generation give pretty bad results. Not sure why. Model might just not be made for auto-regressive inference
__a =model.generate(__snake_case , do_sample=__snake_case )
self.assertListEqual(output_ids[0].cpu().numpy().tolist() , __snake_case )
| 308
| 1
|
import os
from pathlib import Path
from unittest.mock import patch
import pytest
import zstandard as zstd
from datasets.download.download_config import DownloadConfig
from datasets.utils.file_utils import (
OfflineModeIsEnabled,
cached_path,
fsspec_get,
fsspec_head,
ftp_get,
ftp_head,
get_from_cache,
http_get,
http_head,
)
_lowerCAmelCase : Dict = "\\n Text data.\n Second line of data."
_lowerCAmelCase : Any = "file"
@pytest.fixture(scope='session' )
def UpperCamelCase_( _snake_case : List[Any] ):
"""simple docstring"""
__a =tmp_path_factory.mktemp('data' ) / (FILE_PATH + '.zstd')
__a =bytes(_snake_case , 'utf-8' )
with zstd.open(_snake_case , 'wb' ) as f:
f.write(_snake_case )
return path
@pytest.fixture
def UpperCamelCase_( _snake_case : Union[str, Any] ):
"""simple docstring"""
with open(os.path.join(tmpfs.local_root_dir , _snake_case ) , 'w' ) as f:
f.write(_snake_case )
return FILE_PATH
@pytest.mark.parametrize('compression_format' , ['gzip', 'xz', 'zstd'] )
def UpperCamelCase_( _snake_case : List[Any] , _snake_case : List[str] , _snake_case : List[str] , _snake_case : Union[str, Any] , _snake_case : List[str] , _snake_case : str ):
"""simple docstring"""
__a ={'gzip': gz_file, 'xz': xz_file, 'zstd': zstd_path}
__a =input_paths[compression_format]
__a =tmp_path / 'cache'
__a =DownloadConfig(cache_dir=_snake_case , extract_compressed_file=_snake_case )
__a =cached_path(_snake_case , download_config=_snake_case )
with open(_snake_case ) as f:
__a =f.read()
with open(_snake_case ) as f:
__a =f.read()
assert extracted_file_content == expected_file_content
@pytest.mark.parametrize('default_extracted' , [True, False] )
@pytest.mark.parametrize('default_cache_dir' , [True, False] )
def UpperCamelCase_( _snake_case : List[str] , _snake_case : str , _snake_case : Dict , _snake_case : Any , _snake_case : Tuple ):
"""simple docstring"""
__a ='custom_cache'
__a ='custom_extracted_dir'
__a =tmp_path / 'custom_extracted_path'
if default_extracted:
__a =('downloads' if default_cache_dir else custom_cache_dir, 'extracted')
else:
monkeypatch.setattr('datasets.config.EXTRACTED_DATASETS_DIR' , _snake_case )
monkeypatch.setattr('datasets.config.EXTRACTED_DATASETS_PATH' , str(_snake_case ) )
__a =custom_extracted_path.parts[-2:] if default_cache_dir else (custom_cache_dir, custom_extracted_dir)
__a =xz_file
__a =(
DownloadConfig(extract_compressed_file=_snake_case )
if default_cache_dir
else DownloadConfig(cache_dir=tmp_path / custom_cache_dir , extract_compressed_file=_snake_case )
)
__a =cached_path(_snake_case , download_config=_snake_case )
assert Path(_snake_case ).parent.parts[-2:] == expected
def UpperCamelCase_( _snake_case : int ):
"""simple docstring"""
__a =str(Path(_snake_case ).resolve() )
assert cached_path(_snake_case ) == text_file
# relative path
__a =str(Path(_snake_case ).resolve().relative_to(Path(os.getcwd() ) ) )
assert cached_path(_snake_case ) == text_file
def UpperCamelCase_( _snake_case : Tuple ):
"""simple docstring"""
__a =str(tmp_path.resolve() / '__missing_file__.txt' )
with pytest.raises(_snake_case ):
cached_path(_snake_case )
# relative path
__a ='./__missing_file__.txt'
with pytest.raises(_snake_case ):
cached_path(_snake_case )
def UpperCamelCase_( _snake_case : str ):
"""simple docstring"""
__a =get_from_cache(F'tmp://{tmpfs_file}' )
with open(_snake_case ) as f:
__a =f.read()
assert output_file_content == FILE_CONTENT
@patch('datasets.config.HF_DATASETS_OFFLINE' , _snake_case )
def UpperCamelCase_( ):
"""simple docstring"""
with pytest.raises(_snake_case ):
cached_path('https://huggingface.co' )
@patch('datasets.config.HF_DATASETS_OFFLINE' , _snake_case )
def UpperCamelCase_( _snake_case : Dict ):
"""simple docstring"""
__a =tmp_path_factory.mktemp('data' ) / 'file.html'
with pytest.raises(_snake_case ):
http_get('https://huggingface.co' , temp_file=_snake_case )
with pytest.raises(_snake_case ):
http_head('https://huggingface.co' )
@patch('datasets.config.HF_DATASETS_OFFLINE' , _snake_case )
def UpperCamelCase_( _snake_case : Tuple ):
"""simple docstring"""
__a =tmp_path_factory.mktemp('data' ) / 'file.html'
with pytest.raises(_snake_case ):
ftp_get('ftp://huggingface.co' , temp_file=_snake_case )
with pytest.raises(_snake_case ):
ftp_head('ftp://huggingface.co' )
@patch('datasets.config.HF_DATASETS_OFFLINE' , _snake_case )
def UpperCamelCase_( _snake_case : List[str] ):
"""simple docstring"""
__a =tmp_path_factory.mktemp('data' ) / 'file.html'
with pytest.raises(_snake_case ):
fsspec_get('s3://huggingface.co' , temp_file=_snake_case )
with pytest.raises(_snake_case ):
fsspec_head('s3://huggingface.co' )
| 308
|
from __future__ import annotations
import matplotlib.pyplot as plt # type: ignore
import numpy
# initial triangle of Koch snowflake
_lowerCAmelCase : Optional[Any] = numpy.array([0, 0])
_lowerCAmelCase : Dict = numpy.array([0.5, 0.8660254])
_lowerCAmelCase : Any = numpy.array([1, 0])
_lowerCAmelCase : int = [VECTOR_1, VECTOR_2, VECTOR_3, VECTOR_1]
def UpperCamelCase_( _snake_case : list[numpy.ndarray] , _snake_case : int ):
"""simple docstring"""
__a =initial_vectors
for _ in range(_snake_case ):
__a =iteration_step(_snake_case )
return vectors
def UpperCamelCase_( _snake_case : list[numpy.ndarray] ):
"""simple docstring"""
__a =[]
for i, start_vector in enumerate(vectors[:-1] ):
__a =vectors[i + 1]
new_vectors.append(_snake_case )
__a =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 UpperCamelCase_( _snake_case : numpy.ndarray , _snake_case : float ):
"""simple docstring"""
__a =numpy.radians(_snake_case )
__a , __a =numpy.cos(_snake_case ), numpy.sin(_snake_case )
__a =numpy.array(((c, -s), (s, c)) )
return numpy.dot(_snake_case , _snake_case )
def UpperCamelCase_( _snake_case : list[numpy.ndarray] ):
"""simple docstring"""
__a =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 =zip(*_snake_case )
plt.plot(_snake_case , _snake_case )
plt.show()
if __name__ == "__main__":
import doctest
doctest.testmod()
_lowerCAmelCase : List[Any] = iterate(INITIAL_VECTORS, 5)
plot(processed_vectors)
| 308
| 1
|
class __magic_name__ :
def __init__( self , __snake_case ) -> Optional[Any]:
'''simple docstring'''
__a =n
__a =[None] * self.n
__a =0 # index of the first element
__a =0
__a =0
def __len__( self ) -> int:
'''simple docstring'''
return self.size
def __magic_name__ ( self ) -> bool:
'''simple docstring'''
return self.size == 0
def __magic_name__ ( self ) -> Any:
'''simple docstring'''
return False if self.is_empty() else self.array[self.front]
def __magic_name__ ( self , __snake_case ) -> int:
'''simple docstring'''
if self.size >= self.n:
raise Exception('QUEUE IS FULL' )
__a =data
__a =(self.rear + 1) % self.n
self.size += 1
return self
def __magic_name__ ( self ) -> Union[str, Any]:
'''simple docstring'''
if self.size == 0:
raise Exception('UNDERFLOW' )
__a =self.array[self.front]
__a =None
__a =(self.front + 1) % self.n
self.size -= 1
return temp
| 308
|
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_lowerCAmelCase : Tuple = {
"configuration_megatron_bert": ["MEGATRON_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "MegatronBertConfig"],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_lowerCAmelCase : List[str] = [
"MEGATRON_BERT_PRETRAINED_MODEL_ARCHIVE_LIST",
"MegatronBertForCausalLM",
"MegatronBertForMaskedLM",
"MegatronBertForMultipleChoice",
"MegatronBertForNextSentencePrediction",
"MegatronBertForPreTraining",
"MegatronBertForQuestionAnswering",
"MegatronBertForSequenceClassification",
"MegatronBertForTokenClassification",
"MegatronBertModel",
"MegatronBertPreTrainedModel",
]
if TYPE_CHECKING:
from .configuration_megatron_bert import MEGATRON_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP, MegatronBertConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_megatron_bert import (
MEGATRON_BERT_PRETRAINED_MODEL_ARCHIVE_LIST,
MegatronBertForCausalLM,
MegatronBertForMaskedLM,
MegatronBertForMultipleChoice,
MegatronBertForNextSentencePrediction,
MegatronBertForPreTraining,
MegatronBertForQuestionAnswering,
MegatronBertForSequenceClassification,
MegatronBertForTokenClassification,
MegatronBertModel,
MegatronBertPreTrainedModel,
)
else:
import sys
_lowerCAmelCase : Optional[int] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 308
| 1
|
from __future__ import annotations
import requests
def UpperCamelCase_( _snake_case : str ):
"""simple docstring"""
__a =F'https://hacker-news.firebaseio.com/v0/item/{story_id}.json?print=pretty'
return requests.get(_snake_case ).json()
def UpperCamelCase_( _snake_case : int = 10 ):
"""simple docstring"""
__a ='https://hacker-news.firebaseio.com/v0/topstories.json?print=pretty'
__a =requests.get(_snake_case ).json()[:max_stories]
return [get_hackernews_story(_snake_case ) for story_id in story_ids]
def UpperCamelCase_( _snake_case : int = 10 ):
"""simple docstring"""
__a =hackernews_top_stories(_snake_case )
return "\n".join('* [{title}]({url})'.format(**_snake_case ) for story in stories )
if __name__ == "__main__":
print(hackernews_top_stories_as_markdown())
| 308
|
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 __magic_name__ :
@staticmethod
def __magic_name__ ( *__snake_case , **__snake_case ) -> List[str]:
'''simple docstring'''
pass
def UpperCamelCase_( _snake_case : Image ):
"""simple docstring"""
__a =hashlib.mda(image.tobytes() )
return m.hexdigest()
@is_pipeline_test
@require_vision
@require_timm
@require_torch
class __magic_name__ ( unittest.TestCase ):
SCREAMING_SNAKE_CASE = MODEL_FOR_DEPTH_ESTIMATION_MAPPING
def __magic_name__ ( self , __snake_case , __snake_case , __snake_case ) -> Dict:
'''simple docstring'''
__a =DepthEstimationPipeline(model=__snake_case , image_processor=__snake_case )
return depth_estimator, [
"./tests/fixtures/tests_samples/COCO/000000039769.png",
"./tests/fixtures/tests_samples/COCO/000000039769.png",
]
def __magic_name__ ( self , __snake_case , __snake_case ) -> Tuple:
'''simple docstring'''
__a =depth_estimator('./tests/fixtures/tests_samples/COCO/000000039769.png' )
self.assertEqual({'predicted_depth': ANY(torch.Tensor ), 'depth': ANY(Image.Image )} , __snake_case )
import datasets
__a =datasets.load_dataset('hf-internal-testing/fixtures_image_utils' , 'image' , split='test' )
__a =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 )},
] , __snake_case , )
@require_tf
@unittest.skip('Depth estimation is not implemented in TF' )
def __magic_name__ ( self ) -> Optional[Any]:
'''simple docstring'''
pass
@slow
@require_torch
def __magic_name__ ( self ) -> int:
'''simple docstring'''
__a ='Intel/dpt-large'
__a =pipeline('depth-estimation' , model=__snake_case )
__a =depth_estimator('http://images.cocodataset.org/val2017/000000039769.jpg' )
__a =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 __magic_name__ ( self ) -> Any:
'''simple docstring'''
# This is highly irregular to have no small tests.
self.skipTest('There is not hf-internal-testing tiny model for either GLPN nor DPT' )
| 308
| 1
|
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
_lowerCAmelCase : List[str] = logging.get_logger(__name__)
_lowerCAmelCase : List[Any] = {
"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 __magic_name__ ( lowerCAmelCase_ ):
SCREAMING_SNAKE_CASE = 'yolos'
def __init__( self , __snake_case=768 , __snake_case=12 , __snake_case=12 , __snake_case=3072 , __snake_case="gelu" , __snake_case=0.0 , __snake_case=0.0 , __snake_case=0.02 , __snake_case=1e-12 , __snake_case=[512, 864] , __snake_case=16 , __snake_case=3 , __snake_case=True , __snake_case=100 , __snake_case=True , __snake_case=False , __snake_case=1 , __snake_case=5 , __snake_case=2 , __snake_case=5 , __snake_case=2 , __snake_case=0.1 , **__snake_case , ) -> str:
'''simple docstring'''
super().__init__(**__snake_case )
__a =hidden_size
__a =num_hidden_layers
__a =num_attention_heads
__a =intermediate_size
__a =hidden_act
__a =hidden_dropout_prob
__a =attention_probs_dropout_prob
__a =initializer_range
__a =layer_norm_eps
__a =image_size
__a =patch_size
__a =num_channels
__a =qkv_bias
__a =num_detection_tokens
__a =use_mid_position_embeddings
__a =auxiliary_loss
# Hungarian matcher
__a =class_cost
__a =bbox_cost
__a =giou_cost
# Loss coefficients
__a =bbox_loss_coefficient
__a =giou_loss_coefficient
__a =eos_coefficient
class __magic_name__ ( lowerCAmelCase_ ):
SCREAMING_SNAKE_CASE = version.parse('1.11' )
@property
def __magic_name__ ( self ) -> Mapping[str, Mapping[int, str]]:
'''simple docstring'''
return OrderedDict(
[
('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'}),
] )
@property
def __magic_name__ ( self ) -> float:
'''simple docstring'''
return 1e-4
@property
def __magic_name__ ( self ) -> int:
'''simple docstring'''
return 12
| 308
|
import argparse
import dataclasses
import json
import logging
import os
import shutil
from typing import List, Optional
import datasets
from accelerate import Accelerator
from datasets import load_dataset
from finetuning import finetune
from tqdm.auto import tqdm
import transformers
from transformers import AutoConfig, set_seed
from transformers.trainer_utils import IntervalStrategy
_lowerCAmelCase : Optional[int] = logging.getLogger(__name__)
_lowerCAmelCase : Any = "pytorch_model.bin"
@dataclasses.dataclass
class __magic_name__ :
SCREAMING_SNAKE_CASE = dataclasses.field(
metadata={'help': 'Path to pretrained model or model identifier from huggingface.co/models.'} )
SCREAMING_SNAKE_CASE = dataclasses.field(
default=lowerCAmelCase_ , metadata={'help': 'Where do you want to store the pretrained models downloaded from huggingface.co.'} , )
@dataclasses.dataclass
class __magic_name__ :
SCREAMING_SNAKE_CASE = dataclasses.field(metadata={'help': 'A csv or a json file containing the training data.'} )
SCREAMING_SNAKE_CASE = dataclasses.field(metadata={'help': 'A csv or a json file containing the data to predict on.'} )
SCREAMING_SNAKE_CASE = dataclasses.field(
default=lowerCAmelCase_ , metadata={'help': 'A csv or a json file containing the validation data.'} )
SCREAMING_SNAKE_CASE = dataclasses.field(
default=lowerCAmelCase_ , metadata={'help': 'The name of the task to train on.'} , )
SCREAMING_SNAKE_CASE = dataclasses.field(
default=lowerCAmelCase_ , metadata={'help': 'The list of labels for the task.'} )
@dataclasses.dataclass
class __magic_name__ :
SCREAMING_SNAKE_CASE = dataclasses.field(
metadata={'help': 'The output directory where the model predictions and checkpoints will be written.'} )
SCREAMING_SNAKE_CASE = dataclasses.field(
default='accuracy' , metadata={'help': 'The evaluation metric used for the task.'} )
SCREAMING_SNAKE_CASE = dataclasses.field(
default='no' , metadata={
'help': 'The evaluation strategy to adopt during training. Possible values are: ["no", "step", "epoch]'
} , )
SCREAMING_SNAKE_CASE = dataclasses.field(
default=1_0 , metadata={'help': 'Number of evaluation calls with no improvement after which training will be stopped.'} , )
SCREAMING_SNAKE_CASE = dataclasses.field(
default=0.0 , metadata={
'help': 'How much the specified evaluation metric must improve to satisfy early stopping conditions.'
} , )
SCREAMING_SNAKE_CASE = dataclasses.field(
default=lowerCAmelCase_ , metadata={'help': 'Whether to filter the pseudo-labeled data based on the confidence score.'} , )
SCREAMING_SNAKE_CASE = dataclasses.field(
default=lowerCAmelCase_ , metadata={'help': 'Whether to filter the pseudo-labeled data based on the validation performance.'} , )
SCREAMING_SNAKE_CASE = dataclasses.field(
default=lowerCAmelCase_ , metadata={'help': 'Whether to fine-tune on labeled data after pseudo training.'} , )
SCREAMING_SNAKE_CASE = dataclasses.field(
default=0.0 , metadata={'help': 'Confidence threshold for pseudo-labeled data filtering.'} , )
SCREAMING_SNAKE_CASE = dataclasses.field(
default=1_0_0 , metadata={'help': 'Number of evaluation calls with no improvement after which training will be stopped.'} , )
SCREAMING_SNAKE_CASE = dataclasses.field(
default=lowerCAmelCase_ , metadata={'help': 'Random seed for initialization.'} , )
def UpperCamelCase_( _snake_case : int , _snake_case : str , _snake_case : Optional[int] , _snake_case : str , _snake_case : Union[str, Any] , _snake_case : List[Any] ):
"""simple docstring"""
__a =datasets.concatenate_datasets([infer_input, infer_output] , axis=1 )
if args.do_filter_by_confidence:
__a =dataset.filter(lambda _snake_case : example["probability"] > args.confidence_threshold )
if args.do_filter_by_val_performance:
assert eval_result >= 0.0 and eval_result <= 1.0
__a =int(eval_result * len(_snake_case ) )
print(_snake_case )
__a =dataset.sort('probability' , reverse=_snake_case )
__a =dataset.select(range(_snake_case ) )
__a =dataset.remove_columns(['label', 'probability'] )
__a =dataset.rename_column('prediction' , 'label' )
__a =dataset.map(lambda _snake_case : {"label": idalabel[example["label"]]} )
__a =dataset.shuffle(seed=args.seed )
__a =os.path.join(_snake_case , F'train_pseudo.{args.data_file_extension}' )
if args.data_file_extension == "csv":
dataset.to_csv(_snake_case , index=_snake_case )
else:
dataset.to_json(_snake_case )
def UpperCamelCase_( _snake_case : List[Any] , _snake_case : str , _snake_case : int , _snake_case : Optional[int] , **_snake_case : List[str] ):
"""simple docstring"""
__a =Accelerator()
# Make one log on every process with the configuration for debugging.
logging.basicConfig(
format='%(asctime)s - %(levelname)s - %(name)s - %(message)s' , datefmt='%m/%d/%Y %H:%M:%S' , level=logging.INFO , )
logger.info(accelerator.state )
# Setup logging, we only want one process per machine to log things on the
# screen. accelerator.is_local_main_process is only True for one process per
# machine.
logger.setLevel(logging.INFO if accelerator.is_local_main_process else logging.ERROR )
if accelerator.is_local_main_process:
datasets.utils.logging.set_verbosity_warning()
transformers.utils.logging.set_verbosity_info()
else:
datasets.utils.logging.set_verbosity_error()
transformers.utils.logging.set_verbosity_error()
__a =STModelArguments(model_name_or_path=_snake_case )
__a =STDataArguments(train_file=_snake_case , infer_file=_snake_case )
__a =STTrainingArguments(output_dir=_snake_case )
__a =argparse.Namespace()
for arg_class in (model_args, data_args, training_args):
for key, value in vars(_snake_case ).items():
setattr(_snake_case , _snake_case , _snake_case )
for key, value in kwargs.items():
if hasattr(_snake_case , _snake_case ):
setattr(_snake_case , _snake_case , _snake_case )
# Sanity checks
__a ={}
__a =None
# You need to provide the training data and the data to predict on
assert args.train_file is not None
assert args.infer_file is not None
__a =args.train_file
__a =args.infer_file
if args.evaluation_strategy != IntervalStrategy.NO.value:
assert args.eval_file is not None
__a =args.eval_file
for key in data_files:
__a =data_files[key].split('.' )[-1]
assert extension in ["csv", "json"], F'`{key}_file` should be a csv or a json file.'
if args.data_file_extension is None:
__a =extension
else:
assert extension == args.data_file_extension, F'`{key}_file` should be a {args.data_file_extension} file`.'
assert (
args.eval_metric in datasets.list_metrics()
), F'{args.eval_metric} not in the list of supported metrics {datasets.list_metrics()}.'
# If passed along, set the training seed now.
if args.seed is not None:
set_seed(args.seed )
logger.info('Creating the initial data directory for self-training...' )
__a =F'{args.output_dir}/self-train_iter-{{}}'.format
__a =data_dir_format(0 )
if accelerator.is_main_process:
if args.output_dir is not None:
os.makedirs(args.output_dir , exist_ok=_snake_case )
os.makedirs(_snake_case , exist_ok=_snake_case )
accelerator.wait_for_everyone()
__a =None
__a =None
__a =0
__a =False
# Show the progress bar
__a =tqdm(range(args.max_selftrain_iterations ) , disable=not accelerator.is_local_main_process )
# Self-train
for iteration in range(0 , int(args.max_selftrain_iterations ) ):
__a =data_dir_format(_snake_case )
assert os.path.exists(_snake_case )
# Stage 1: initial fine-tuning for iteration = 0 or pseudo-training for
# iteration > 0
__a =os.path.join(_snake_case , 'stage-1' )
__a ={
'accelerator': accelerator,
'model_name_or_path': args.model_name_or_path,
'cache_dir': args.cache_dir,
'do_train': True,
'train_file': data_files['train'] if iteration == 0 else data_files['train_pseudo'],
'do_eval': True if args.eval_file is not None else False,
'eval_file': data_files['eval'],
'do_predict': True,
'infer_file': data_files['infer'],
'task_name': args.task_name,
'label_list': args.label_list,
'output_dir': current_output_dir,
'eval_metric': args.eval_metric,
'evaluation_strategy': args.evaluation_strategy,
'early_stopping_patience': args.early_stopping_patience,
'early_stopping_threshold': args.early_stopping_threshold,
'seed': args.seed,
}
# Add additional training arguments
for key, value in kwargs.items():
if key not in arguments_dict and not hasattr(_snake_case , _snake_case ):
arguments_dict.update({key: value} )
__a =os.path.join(_snake_case , 'best-checkpoint' , _snake_case )
if os.path.exists(_snake_case ):
logger.info(
'Found existing model checkpoint at %s. Skipping self-training: iteration: %d, stage: 1.' , _snake_case , _snake_case , )
else:
logger.info('***** Running self-training: iteration: %d, stage: 1 *****' , _snake_case )
finetune(**_snake_case )
accelerator.wait_for_everyone()
assert os.path.exists(_snake_case )
logger.info('Self-training job completed: iteration: %d, stage: 1.' , _snake_case )
if iteration > 0 and args.finetune_on_labeled_data:
# Stage 2 (optional): fine-tuning on the original labeled data
__a =os.path.join(_snake_case , 'best-checkpoint' )
__a =os.path.join(_snake_case , 'stage-2' )
# Update arguments_dict
__a =model_path
__a =data_files['train']
__a =current_output_dir
__a =os.path.join(_snake_case , 'best-checkpoint' , _snake_case )
if os.path.exists(_snake_case ):
logger.info(
'Found existing model checkpoint at %s. Skipping self-training: iteration: %d, stage: 2.' , _snake_case , _snake_case , )
else:
logger.info('***** Running self-training: iteration: %d, stage: 2 *****' , _snake_case )
finetune(**_snake_case )
accelerator.wait_for_everyone()
assert os.path.exists(_snake_case )
logger.info('Self-training job completed: iteration: %d, stage: 2.' , _snake_case )
__a =iteration
__a =data_dir_format(iteration + 1 )
__a =AutoConfig.from_pretrained(os.path.join(_snake_case , 'best-checkpoint' ) )
__a =config.idalabel
__a =os.path.join(_snake_case , 'eval_results_best-checkpoint.json' )
__a =os.path.join(_snake_case , 'test_results_best-checkpoint.json' )
assert os.path.exists(_snake_case )
with open(_snake_case , 'r' ) as f:
__a =float(json.load(_snake_case )[args.eval_metric] )
__a =os.path.join(_snake_case , 'infer_output_best-checkpoint.csv' )
assert os.path.exists(_snake_case )
# Loading the dataset from local csv or json files.
__a =load_dataset(args.data_file_extension , data_files={'data': data_files['infer']} )['data']
__a =load_dataset('csv' , data_files={'data': infer_output_file} )['data']
if accelerator.is_main_process:
os.makedirs(_snake_case , exist_ok=_snake_case )
shutil.copy(_snake_case , os.path.join(_snake_case , F'eval_results_iter-{iteration}.json' ) )
if os.path.exists(_snake_case ):
shutil.copy(_snake_case , os.path.join(_snake_case , F'test_results_iter-{iteration}.json' ) )
create_pseudo_labeled_data(_snake_case , _snake_case , _snake_case , _snake_case , _snake_case , _snake_case )
accelerator.wait_for_everyone()
__a =os.path.join(_snake_case , F'train_pseudo.{args.data_file_extension}' )
if args.evaluation_strategy != IntervalStrategy.NO.value:
__a =eval_result
if best_iteration is None:
__a =new_iteration
__a =new_eval_result
else:
if new_eval_result - best_eval_result > args.early_stopping_threshold:
__a =new_iteration
__a =new_eval_result
__a =0
else:
if new_eval_result == best_eval_result:
__a =new_iteration
__a =new_eval_result
early_stopping_patience_counter += 1
if early_stopping_patience_counter >= args.early_stopping_patience:
__a =True
progress_bar.update(1 )
if should_training_stop:
break
if best_iteration is not None:
# Save the best iteration
logger.info('Best iteration: %d' , _snake_case )
logger.info('Best evaluation result: %s = %f' , args.eval_metric , _snake_case )
accelerator.wait_for_everyone()
if accelerator.is_main_process:
shutil.copy(
os.path.join(_snake_case , F'eval_results_iter-{iteration}.json' ) , os.path.join(_snake_case , 'eval_results_best-iteration.json' ) , )
else:
# Assume that the last iteration is the best
logger.info('Best iteration: %d' , args.max_selftrain_iterations - 1 )
logger.info('Best evaluation result: %s = %f' , args.eval_metric , _snake_case )
accelerator.wait_for_everyone()
if accelerator.is_main_process:
shutil.copy(
os.path.join(_snake_case , F'eval_results_iter-{args.max_selftrain_iterations - 1}.json' ) , os.path.join(_snake_case , 'eval_results_best-iteration.json' ) , )
| 308
| 1
|
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
_lowerCAmelCase : int = {
"configuration_altclip": [
"ALTCLIP_PRETRAINED_CONFIG_ARCHIVE_MAP",
"AltCLIPConfig",
"AltCLIPTextConfig",
"AltCLIPVisionConfig",
],
"processing_altclip": ["AltCLIPProcessor"],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_lowerCAmelCase : str = [
"ALTCLIP_PRETRAINED_MODEL_ARCHIVE_LIST",
"AltCLIPPreTrainedModel",
"AltCLIPModel",
"AltCLIPTextModel",
"AltCLIPVisionModel",
]
if TYPE_CHECKING:
from .configuration_altclip import (
ALTCLIP_PRETRAINED_CONFIG_ARCHIVE_MAP,
AltCLIPConfig,
AltCLIPTextConfig,
AltCLIPVisionConfig,
)
from .processing_altclip import AltCLIPProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_altclip import (
ALTCLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
AltCLIPModel,
AltCLIPPreTrainedModel,
AltCLIPTextModel,
AltCLIPVisionModel,
)
else:
import sys
_lowerCAmelCase : Union[str, Any] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 308
|
import shutil
import tempfile
import unittest
from transformers import (
SPIECE_UNDERLINE,
AddedToken,
BatchEncoding,
NllbTokenizer,
NllbTokenizerFast,
is_torch_available,
)
from transformers.testing_utils import (
get_tests_dir,
nested_simplify,
require_sentencepiece,
require_tokenizers,
require_torch,
)
from ...test_tokenization_common import TokenizerTesterMixin
_lowerCAmelCase : Any = get_tests_dir("fixtures/test_sentencepiece.model")
if is_torch_available():
from transformers.models.mam_aaa.modeling_mam_aaa import shift_tokens_right
_lowerCAmelCase : List[Any] = 256_047
_lowerCAmelCase : Dict = 256_145
@require_sentencepiece
@require_tokenizers
class __magic_name__ ( lowerCAmelCase_ , unittest.TestCase ):
SCREAMING_SNAKE_CASE = NllbTokenizer
SCREAMING_SNAKE_CASE = NllbTokenizerFast
SCREAMING_SNAKE_CASE = True
SCREAMING_SNAKE_CASE = True
SCREAMING_SNAKE_CASE = {}
def __magic_name__ ( self ) -> int:
'''simple docstring'''
super().setUp()
# We have a SentencePiece fixture for testing
__a =NllbTokenizer(__snake_case , keep_accents=__snake_case )
tokenizer.save_pretrained(self.tmpdirname )
def __magic_name__ ( self ) -> int:
'''simple docstring'''
__a =NllbTokenizer(__snake_case , keep_accents=__snake_case )
__a =tokenizer.tokenize('This is a test' )
self.assertListEqual(__snake_case , ['▁This', '▁is', '▁a', '▁t', 'est'] )
self.assertListEqual(
tokenizer.convert_tokens_to_ids(__snake_case ) , [value + tokenizer.fairseq_offset for value in [285, 46, 10, 170, 382]] , )
__a =tokenizer.tokenize('I was born in 92000, and this is falsé.' )
self.assertListEqual(
__snake_case , [
SPIECE_UNDERLINE + 'I',
SPIECE_UNDERLINE + 'was',
SPIECE_UNDERLINE + 'b',
'or',
'n',
SPIECE_UNDERLINE + 'in',
SPIECE_UNDERLINE + '',
'9',
'2',
'0',
'0',
'0',
',',
SPIECE_UNDERLINE + 'and',
SPIECE_UNDERLINE + 'this',
SPIECE_UNDERLINE + 'is',
SPIECE_UNDERLINE + 'f',
'al',
's',
'é',
'.',
] , )
__a =tokenizer.convert_tokens_to_ids(__snake_case )
self.assertListEqual(
__snake_case , [
value + tokenizer.fairseq_offset
for value in [8, 21, 84, 55, 24, 19, 7, 2, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 2, 4]
] , )
__a =tokenizer.convert_ids_to_tokens(__snake_case )
self.assertListEqual(
__snake_case , [
SPIECE_UNDERLINE + 'I',
SPIECE_UNDERLINE + 'was',
SPIECE_UNDERLINE + 'b',
'or',
'n',
SPIECE_UNDERLINE + 'in',
SPIECE_UNDERLINE + '',
'<unk>',
'2',
'0',
'0',
'0',
',',
SPIECE_UNDERLINE + 'and',
SPIECE_UNDERLINE + 'this',
SPIECE_UNDERLINE + 'is',
SPIECE_UNDERLINE + 'f',
'al',
's',
'<unk>',
'.',
] , )
def __magic_name__ ( self ) -> Union[str, Any]:
'''simple docstring'''
__a =(self.rust_tokenizer_class, 'hf-internal-testing/tiny-random-nllb', {})
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(f'{tokenizer.__class__.__name__} ({pretrained_name})' ):
__a =self.rust_tokenizer_class.from_pretrained(__snake_case , **__snake_case )
__a =self.tokenizer_class.from_pretrained(__snake_case , **__snake_case )
__a =tempfile.mkdtemp()
__a =tokenizer_r.save_pretrained(__snake_case )
__a =tokenizer_p.save_pretrained(__snake_case )
# Checks it save with the same files + the tokenizer.json file for the fast one
self.assertTrue(any('tokenizer.json' in f for f in tokenizer_r_files ) )
__a =tuple(f for f in tokenizer_r_files if 'tokenizer.json' not in f )
self.assertSequenceEqual(__snake_case , __snake_case )
# Checks everything loads correctly in the same way
__a =tokenizer_r.from_pretrained(__snake_case )
__a =tokenizer_p.from_pretrained(__snake_case )
# Check special tokens are set accordingly on Rust and Python
for key in tokenizer_pp.special_tokens_map:
self.assertTrue(hasattr(__snake_case , __snake_case ) )
shutil.rmtree(__snake_case )
# Save tokenizer rust, legacy_format=True
__a =tempfile.mkdtemp()
__a =tokenizer_r.save_pretrained(__snake_case , legacy_format=__snake_case )
__a =tokenizer_p.save_pretrained(__snake_case )
# Checks it save with the same files
self.assertSequenceEqual(__snake_case , __snake_case )
# Checks everything loads correctly in the same way
__a =tokenizer_r.from_pretrained(__snake_case )
__a =tokenizer_p.from_pretrained(__snake_case )
# Check special tokens are set accordingly on Rust and Python
for key in tokenizer_pp.special_tokens_map:
self.assertTrue(hasattr(__snake_case , __snake_case ) )
shutil.rmtree(__snake_case )
# Save tokenizer rust, legacy_format=False
__a =tempfile.mkdtemp()
__a =tokenizer_r.save_pretrained(__snake_case , legacy_format=__snake_case )
__a =tokenizer_p.save_pretrained(__snake_case )
# Checks it saved the tokenizer.json file
self.assertTrue(any('tokenizer.json' in f for f in tokenizer_r_files ) )
# Checks everything loads correctly in the same way
__a =tokenizer_r.from_pretrained(__snake_case )
__a =tokenizer_p.from_pretrained(__snake_case )
# Check special tokens are set accordingly on Rust and Python
for key in tokenizer_pp.special_tokens_map:
self.assertTrue(hasattr(__snake_case , __snake_case ) )
shutil.rmtree(__snake_case )
@require_torch
def __magic_name__ ( self ) -> Optional[Any]:
'''simple docstring'''
if not self.test_seqaseq:
return
__a =self.get_tokenizers()
for tokenizer in tokenizers:
with self.subTest(f'{tokenizer.__class__.__name__}' ):
# Longer text that will definitely require truncation.
__a =[
' UN Chief Says There Is No Military Solution in Syria',
' Secretary-General Ban Ki-moon says his response to Russia\'s stepped up military support for'
' Syria is that \'there is no military solution\' to the nearly five-year conflict and more weapons'
' will only worsen the violence and misery for millions of people.',
]
__a =[
'Şeful ONU declară că nu există o soluţie militară în Siria',
'Secretarul General Ban Ki-moon declară că răspunsul său la intensificarea sprijinului militar al'
' Rusiei pentru Siria este că "nu există o soluţie militară" la conflictul de aproape cinci ani şi'
' că noi arme nu vor face decât să înrăutăţească violenţele şi mizeria pentru milioane de oameni.',
]
try:
__a =tokenizer.prepare_seqaseq_batch(
src_texts=__snake_case , tgt_texts=__snake_case , max_length=3 , max_target_length=10 , return_tensors='pt' , src_lang='eng_Latn' , tgt_lang='ron_Latn' , )
except NotImplementedError:
return
self.assertEqual(batch.input_ids.shape[1] , 3 )
self.assertEqual(batch.labels.shape[1] , 10 )
# max_target_length will default to max_length if not specified
__a =tokenizer.prepare_seqaseq_batch(
__snake_case , tgt_texts=__snake_case , max_length=3 , return_tensors='pt' )
self.assertEqual(batch.input_ids.shape[1] , 3 )
self.assertEqual(batch.labels.shape[1] , 3 )
__a =tokenizer.prepare_seqaseq_batch(
src_texts=__snake_case , max_length=3 , max_target_length=10 , return_tensors='pt' )
self.assertEqual(batch_encoder_only.input_ids.shape[1] , 3 )
self.assertEqual(batch_encoder_only.attention_mask.shape[1] , 3 )
self.assertNotIn('decoder_input_ids' , __snake_case )
@unittest.skip('Unfortunately way too slow to build a BPE with SentencePiece.' )
def __magic_name__ ( self ) -> Optional[int]:
'''simple docstring'''
pass
def __magic_name__ ( self ) -> Tuple:
'''simple docstring'''
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(f'{tokenizer.__class__.__name__} ({pretrained_name})' ):
__a =[AddedToken('<special>' , lstrip=__snake_case )]
__a =self.rust_tokenizer_class.from_pretrained(
__snake_case , additional_special_tokens=__snake_case , **__snake_case )
__a =tokenizer_r.encode('Hey this is a <special> token' )
__a =tokenizer_r.encode('<special>' , add_special_tokens=__snake_case )[0]
self.assertTrue(special_token_id in r_output )
if self.test_slow_tokenizer:
__a =self.rust_tokenizer_class.from_pretrained(
__snake_case , additional_special_tokens=__snake_case , **__snake_case , )
__a =self.tokenizer_class.from_pretrained(
__snake_case , additional_special_tokens=__snake_case , **__snake_case )
__a =tokenizer_p.encode('Hey this is a <special> token' )
__a =tokenizer_cr.encode('Hey this is a <special> token' )
self.assertEqual(__snake_case , __snake_case )
self.assertEqual(__snake_case , __snake_case )
self.assertTrue(special_token_id in p_output )
self.assertTrue(special_token_id in cr_output )
@require_torch
@require_sentencepiece
@require_tokenizers
class __magic_name__ ( unittest.TestCase ):
SCREAMING_SNAKE_CASE = 'facebook/nllb-200-distilled-600M'
SCREAMING_SNAKE_CASE = [
' UN Chief Says There Is No Military Solution in Syria',
' Secretary-General Ban Ki-moon says his response to Russia\'s stepped up military support for Syria is that "there is no military solution" to the nearly five-year conflict and more weapons will only worsen the violence and misery for millions of people.',
]
SCREAMING_SNAKE_CASE = [
'Şeful ONU declară că nu există o soluţie militară în Siria',
'Secretarul General Ban Ki-moon declară că răspunsul său la intensificarea sprijinului militar al Rusiei'
' pentru Siria este că "nu există o soluţie militară" la conflictul de aproape cinci ani şi că noi arme nu vor'
' face decât să înrăutăţească violenţele şi mizeria pentru milioane de oameni.',
]
SCREAMING_SNAKE_CASE = [
2_5_6_0_4_7,
1_6_2_9_7,
1_3_4_4_0_8,
8_1_6_5,
2_4_8_0_6_6,
1_4_7_3_4,
9_5_0,
1_1_3_5,
1_0_5_7_2_1,
3_5_7_3,
8_3,
2_7_3_5_2,
1_0_8,
4_9_4_8_6,
2,
]
@classmethod
def __magic_name__ ( cls ) -> Tuple:
'''simple docstring'''
__a =NllbTokenizer.from_pretrained(
cls.checkpoint_name , src_lang='eng_Latn' , tgt_lang='ron_Latn' )
__a =1
return cls
def __magic_name__ ( self ) -> List[Any]:
'''simple docstring'''
self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['ace_Arab'] , 25_6001 )
self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['ace_Latn'] , 25_6002 )
self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['fra_Latn'] , 25_6057 )
def __magic_name__ ( self ) -> Dict:
'''simple docstring'''
__a =self.tokenizer.batch_encode_plus(self.src_text ).input_ids[0]
self.assertListEqual(self.expected_src_tokens , __snake_case )
def __magic_name__ ( self ) -> Dict:
'''simple docstring'''
self.assertIn(__snake_case , self.tokenizer.all_special_ids )
# fmt: off
__a =[RO_CODE, 4254, 9_8068, 11_2923, 3_9072, 3909, 713, 10_2767, 26, 1_7314, 3_5642, 1_4683, 3_3118, 2022, 6_6987, 2, 25_6047]
# fmt: on
__a =self.tokenizer.decode(__snake_case , skip_special_tokens=__snake_case )
__a =self.tokenizer.decode(generated_ids[1:] , skip_special_tokens=__snake_case )
self.assertEqual(__snake_case , __snake_case )
self.assertNotIn(self.tokenizer.eos_token , __snake_case )
def __magic_name__ ( self ) -> List[str]:
'''simple docstring'''
__a =['this is gunna be a long sentence ' * 20]
assert isinstance(src_text[0] , __snake_case )
__a =10
__a =self.tokenizer(__snake_case , max_length=__snake_case , truncation=__snake_case ).input_ids[0]
self.assertEqual(ids[-1] , 2 )
self.assertEqual(ids[0] , __snake_case )
self.assertEqual(len(__snake_case ) , __snake_case )
def __magic_name__ ( self ) -> Optional[int]:
'''simple docstring'''
self.assertListEqual(self.tokenizer.convert_tokens_to_ids(['<mask>', 'ar_AR'] ) , [25_6203, 3] )
def __magic_name__ ( self ) -> Optional[int]:
'''simple docstring'''
__a =tempfile.mkdtemp()
__a =self.tokenizer.fairseq_tokens_to_ids
self.tokenizer.save_pretrained(__snake_case )
__a =NllbTokenizer.from_pretrained(__snake_case )
self.assertDictEqual(new_tok.fairseq_tokens_to_ids , __snake_case )
@require_torch
def __magic_name__ ( self ) -> Tuple:
'''simple docstring'''
__a =self.tokenizer(
self.src_text , text_target=self.tgt_text , padding=__snake_case , truncation=__snake_case , max_length=len(self.expected_src_tokens ) , return_tensors='pt' , )
__a =shift_tokens_right(
batch['labels'] , self.tokenizer.pad_token_id , self.tokenizer.lang_code_to_id['ron_Latn'] )
self.assertIsInstance(__snake_case , __snake_case )
self.assertEqual((2, 15) , batch.input_ids.shape )
self.assertEqual((2, 15) , batch.attention_mask.shape )
__a =batch.input_ids.tolist()[0]
self.assertListEqual(self.expected_src_tokens , __snake_case )
self.assertEqual(__snake_case , batch.decoder_input_ids[0, 0] ) # EOS
# Test that special tokens are reset
self.assertEqual(self.tokenizer.prefix_tokens , [EN_CODE] )
self.assertEqual(self.tokenizer.suffix_tokens , [self.tokenizer.eos_token_id] )
def __magic_name__ ( self ) -> List[Any]:
'''simple docstring'''
__a =self.tokenizer(self.src_text , padding=__snake_case , truncation=__snake_case , max_length=3 , return_tensors='pt' )
__a =self.tokenizer(
text_target=self.tgt_text , padding=__snake_case , truncation=__snake_case , max_length=10 , return_tensors='pt' )
__a =targets['input_ids']
__a =shift_tokens_right(
__snake_case , self.tokenizer.pad_token_id , decoder_start_token_id=self.tokenizer.lang_code_to_id[self.tokenizer.tgt_lang] , )
self.assertEqual(batch.input_ids.shape[1] , 3 )
self.assertEqual(batch.decoder_input_ids.shape[1] , 10 )
@require_torch
def __magic_name__ ( self ) -> Optional[Any]:
'''simple docstring'''
__a =self.tokenizer._build_translation_inputs(
'A test' , return_tensors='pt' , src_lang='eng_Latn' , tgt_lang='fra_Latn' )
self.assertEqual(
nested_simplify(__snake_case ) , {
# A, test, EOS, en_XX
'input_ids': [[25_6047, 70, 7356, 2]],
'attention_mask': [[1, 1, 1, 1]],
# ar_AR
'forced_bos_token_id': 25_6057,
} , )
@require_torch
def __magic_name__ ( self ) -> List[Any]:
'''simple docstring'''
__a =True
__a =self.tokenizer(
'UN Chief says there is no military solution in Syria' , src_lang='eng_Latn' , tgt_lang='fra_Latn' )
self.assertEqual(
inputs.input_ids , [1_6297, 13_4408, 2_5653, 6370, 248, 254, 10_3929, 9_4995, 108, 4_9486, 2, 25_6047] )
__a =False
__a =self.tokenizer(
'UN Chief says there is no military solution in Syria' , src_lang='eng_Latn' , tgt_lang='fra_Latn' )
self.assertEqual(
inputs.input_ids , [25_6047, 1_6297, 13_4408, 2_5653, 6370, 248, 254, 10_3929, 9_4995, 108, 4_9486, 2] )
| 308
| 1
|
import shutil
import tempfile
import unittest
from transformers import (
SPIECE_UNDERLINE,
AddedToken,
BatchEncoding,
NllbTokenizer,
NllbTokenizerFast,
is_torch_available,
)
from transformers.testing_utils import (
get_tests_dir,
nested_simplify,
require_sentencepiece,
require_tokenizers,
require_torch,
)
from ...test_tokenization_common import TokenizerTesterMixin
_lowerCAmelCase : Any = get_tests_dir("fixtures/test_sentencepiece.model")
if is_torch_available():
from transformers.models.mam_aaa.modeling_mam_aaa import shift_tokens_right
_lowerCAmelCase : List[Any] = 256_047
_lowerCAmelCase : Dict = 256_145
@require_sentencepiece
@require_tokenizers
class __magic_name__ ( lowerCAmelCase_ , unittest.TestCase ):
SCREAMING_SNAKE_CASE = NllbTokenizer
SCREAMING_SNAKE_CASE = NllbTokenizerFast
SCREAMING_SNAKE_CASE = True
SCREAMING_SNAKE_CASE = True
SCREAMING_SNAKE_CASE = {}
def __magic_name__ ( self ) -> int:
'''simple docstring'''
super().setUp()
# We have a SentencePiece fixture for testing
__a =NllbTokenizer(__snake_case , keep_accents=__snake_case )
tokenizer.save_pretrained(self.tmpdirname )
def __magic_name__ ( self ) -> int:
'''simple docstring'''
__a =NllbTokenizer(__snake_case , keep_accents=__snake_case )
__a =tokenizer.tokenize('This is a test' )
self.assertListEqual(__snake_case , ['▁This', '▁is', '▁a', '▁t', 'est'] )
self.assertListEqual(
tokenizer.convert_tokens_to_ids(__snake_case ) , [value + tokenizer.fairseq_offset for value in [285, 46, 10, 170, 382]] , )
__a =tokenizer.tokenize('I was born in 92000, and this is falsé.' )
self.assertListEqual(
__snake_case , [
SPIECE_UNDERLINE + 'I',
SPIECE_UNDERLINE + 'was',
SPIECE_UNDERLINE + 'b',
'or',
'n',
SPIECE_UNDERLINE + 'in',
SPIECE_UNDERLINE + '',
'9',
'2',
'0',
'0',
'0',
',',
SPIECE_UNDERLINE + 'and',
SPIECE_UNDERLINE + 'this',
SPIECE_UNDERLINE + 'is',
SPIECE_UNDERLINE + 'f',
'al',
's',
'é',
'.',
] , )
__a =tokenizer.convert_tokens_to_ids(__snake_case )
self.assertListEqual(
__snake_case , [
value + tokenizer.fairseq_offset
for value in [8, 21, 84, 55, 24, 19, 7, 2, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 2, 4]
] , )
__a =tokenizer.convert_ids_to_tokens(__snake_case )
self.assertListEqual(
__snake_case , [
SPIECE_UNDERLINE + 'I',
SPIECE_UNDERLINE + 'was',
SPIECE_UNDERLINE + 'b',
'or',
'n',
SPIECE_UNDERLINE + 'in',
SPIECE_UNDERLINE + '',
'<unk>',
'2',
'0',
'0',
'0',
',',
SPIECE_UNDERLINE + 'and',
SPIECE_UNDERLINE + 'this',
SPIECE_UNDERLINE + 'is',
SPIECE_UNDERLINE + 'f',
'al',
's',
'<unk>',
'.',
] , )
def __magic_name__ ( self ) -> Union[str, Any]:
'''simple docstring'''
__a =(self.rust_tokenizer_class, 'hf-internal-testing/tiny-random-nllb', {})
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(f'{tokenizer.__class__.__name__} ({pretrained_name})' ):
__a =self.rust_tokenizer_class.from_pretrained(__snake_case , **__snake_case )
__a =self.tokenizer_class.from_pretrained(__snake_case , **__snake_case )
__a =tempfile.mkdtemp()
__a =tokenizer_r.save_pretrained(__snake_case )
__a =tokenizer_p.save_pretrained(__snake_case )
# Checks it save with the same files + the tokenizer.json file for the fast one
self.assertTrue(any('tokenizer.json' in f for f in tokenizer_r_files ) )
__a =tuple(f for f in tokenizer_r_files if 'tokenizer.json' not in f )
self.assertSequenceEqual(__snake_case , __snake_case )
# Checks everything loads correctly in the same way
__a =tokenizer_r.from_pretrained(__snake_case )
__a =tokenizer_p.from_pretrained(__snake_case )
# Check special tokens are set accordingly on Rust and Python
for key in tokenizer_pp.special_tokens_map:
self.assertTrue(hasattr(__snake_case , __snake_case ) )
shutil.rmtree(__snake_case )
# Save tokenizer rust, legacy_format=True
__a =tempfile.mkdtemp()
__a =tokenizer_r.save_pretrained(__snake_case , legacy_format=__snake_case )
__a =tokenizer_p.save_pretrained(__snake_case )
# Checks it save with the same files
self.assertSequenceEqual(__snake_case , __snake_case )
# Checks everything loads correctly in the same way
__a =tokenizer_r.from_pretrained(__snake_case )
__a =tokenizer_p.from_pretrained(__snake_case )
# Check special tokens are set accordingly on Rust and Python
for key in tokenizer_pp.special_tokens_map:
self.assertTrue(hasattr(__snake_case , __snake_case ) )
shutil.rmtree(__snake_case )
# Save tokenizer rust, legacy_format=False
__a =tempfile.mkdtemp()
__a =tokenizer_r.save_pretrained(__snake_case , legacy_format=__snake_case )
__a =tokenizer_p.save_pretrained(__snake_case )
# Checks it saved the tokenizer.json file
self.assertTrue(any('tokenizer.json' in f for f in tokenizer_r_files ) )
# Checks everything loads correctly in the same way
__a =tokenizer_r.from_pretrained(__snake_case )
__a =tokenizer_p.from_pretrained(__snake_case )
# Check special tokens are set accordingly on Rust and Python
for key in tokenizer_pp.special_tokens_map:
self.assertTrue(hasattr(__snake_case , __snake_case ) )
shutil.rmtree(__snake_case )
@require_torch
def __magic_name__ ( self ) -> Optional[Any]:
'''simple docstring'''
if not self.test_seqaseq:
return
__a =self.get_tokenizers()
for tokenizer in tokenizers:
with self.subTest(f'{tokenizer.__class__.__name__}' ):
# Longer text that will definitely require truncation.
__a =[
' UN Chief Says There Is No Military Solution in Syria',
' Secretary-General Ban Ki-moon says his response to Russia\'s stepped up military support for'
' Syria is that \'there is no military solution\' to the nearly five-year conflict and more weapons'
' will only worsen the violence and misery for millions of people.',
]
__a =[
'Şeful ONU declară că nu există o soluţie militară în Siria',
'Secretarul General Ban Ki-moon declară că răspunsul său la intensificarea sprijinului militar al'
' Rusiei pentru Siria este că "nu există o soluţie militară" la conflictul de aproape cinci ani şi'
' că noi arme nu vor face decât să înrăutăţească violenţele şi mizeria pentru milioane de oameni.',
]
try:
__a =tokenizer.prepare_seqaseq_batch(
src_texts=__snake_case , tgt_texts=__snake_case , max_length=3 , max_target_length=10 , return_tensors='pt' , src_lang='eng_Latn' , tgt_lang='ron_Latn' , )
except NotImplementedError:
return
self.assertEqual(batch.input_ids.shape[1] , 3 )
self.assertEqual(batch.labels.shape[1] , 10 )
# max_target_length will default to max_length if not specified
__a =tokenizer.prepare_seqaseq_batch(
__snake_case , tgt_texts=__snake_case , max_length=3 , return_tensors='pt' )
self.assertEqual(batch.input_ids.shape[1] , 3 )
self.assertEqual(batch.labels.shape[1] , 3 )
__a =tokenizer.prepare_seqaseq_batch(
src_texts=__snake_case , max_length=3 , max_target_length=10 , return_tensors='pt' )
self.assertEqual(batch_encoder_only.input_ids.shape[1] , 3 )
self.assertEqual(batch_encoder_only.attention_mask.shape[1] , 3 )
self.assertNotIn('decoder_input_ids' , __snake_case )
@unittest.skip('Unfortunately way too slow to build a BPE with SentencePiece.' )
def __magic_name__ ( self ) -> Optional[int]:
'''simple docstring'''
pass
def __magic_name__ ( self ) -> Tuple:
'''simple docstring'''
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(f'{tokenizer.__class__.__name__} ({pretrained_name})' ):
__a =[AddedToken('<special>' , lstrip=__snake_case )]
__a =self.rust_tokenizer_class.from_pretrained(
__snake_case , additional_special_tokens=__snake_case , **__snake_case )
__a =tokenizer_r.encode('Hey this is a <special> token' )
__a =tokenizer_r.encode('<special>' , add_special_tokens=__snake_case )[0]
self.assertTrue(special_token_id in r_output )
if self.test_slow_tokenizer:
__a =self.rust_tokenizer_class.from_pretrained(
__snake_case , additional_special_tokens=__snake_case , **__snake_case , )
__a =self.tokenizer_class.from_pretrained(
__snake_case , additional_special_tokens=__snake_case , **__snake_case )
__a =tokenizer_p.encode('Hey this is a <special> token' )
__a =tokenizer_cr.encode('Hey this is a <special> token' )
self.assertEqual(__snake_case , __snake_case )
self.assertEqual(__snake_case , __snake_case )
self.assertTrue(special_token_id in p_output )
self.assertTrue(special_token_id in cr_output )
@require_torch
@require_sentencepiece
@require_tokenizers
class __magic_name__ ( unittest.TestCase ):
SCREAMING_SNAKE_CASE = 'facebook/nllb-200-distilled-600M'
SCREAMING_SNAKE_CASE = [
' UN Chief Says There Is No Military Solution in Syria',
' Secretary-General Ban Ki-moon says his response to Russia\'s stepped up military support for Syria is that "there is no military solution" to the nearly five-year conflict and more weapons will only worsen the violence and misery for millions of people.',
]
SCREAMING_SNAKE_CASE = [
'Şeful ONU declară că nu există o soluţie militară în Siria',
'Secretarul General Ban Ki-moon declară că răspunsul său la intensificarea sprijinului militar al Rusiei'
' pentru Siria este că "nu există o soluţie militară" la conflictul de aproape cinci ani şi că noi arme nu vor'
' face decât să înrăutăţească violenţele şi mizeria pentru milioane de oameni.',
]
SCREAMING_SNAKE_CASE = [
2_5_6_0_4_7,
1_6_2_9_7,
1_3_4_4_0_8,
8_1_6_5,
2_4_8_0_6_6,
1_4_7_3_4,
9_5_0,
1_1_3_5,
1_0_5_7_2_1,
3_5_7_3,
8_3,
2_7_3_5_2,
1_0_8,
4_9_4_8_6,
2,
]
@classmethod
def __magic_name__ ( cls ) -> Tuple:
'''simple docstring'''
__a =NllbTokenizer.from_pretrained(
cls.checkpoint_name , src_lang='eng_Latn' , tgt_lang='ron_Latn' )
__a =1
return cls
def __magic_name__ ( self ) -> List[Any]:
'''simple docstring'''
self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['ace_Arab'] , 25_6001 )
self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['ace_Latn'] , 25_6002 )
self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['fra_Latn'] , 25_6057 )
def __magic_name__ ( self ) -> Dict:
'''simple docstring'''
__a =self.tokenizer.batch_encode_plus(self.src_text ).input_ids[0]
self.assertListEqual(self.expected_src_tokens , __snake_case )
def __magic_name__ ( self ) -> Dict:
'''simple docstring'''
self.assertIn(__snake_case , self.tokenizer.all_special_ids )
# fmt: off
__a =[RO_CODE, 4254, 9_8068, 11_2923, 3_9072, 3909, 713, 10_2767, 26, 1_7314, 3_5642, 1_4683, 3_3118, 2022, 6_6987, 2, 25_6047]
# fmt: on
__a =self.tokenizer.decode(__snake_case , skip_special_tokens=__snake_case )
__a =self.tokenizer.decode(generated_ids[1:] , skip_special_tokens=__snake_case )
self.assertEqual(__snake_case , __snake_case )
self.assertNotIn(self.tokenizer.eos_token , __snake_case )
def __magic_name__ ( self ) -> List[str]:
'''simple docstring'''
__a =['this is gunna be a long sentence ' * 20]
assert isinstance(src_text[0] , __snake_case )
__a =10
__a =self.tokenizer(__snake_case , max_length=__snake_case , truncation=__snake_case ).input_ids[0]
self.assertEqual(ids[-1] , 2 )
self.assertEqual(ids[0] , __snake_case )
self.assertEqual(len(__snake_case ) , __snake_case )
def __magic_name__ ( self ) -> Optional[int]:
'''simple docstring'''
self.assertListEqual(self.tokenizer.convert_tokens_to_ids(['<mask>', 'ar_AR'] ) , [25_6203, 3] )
def __magic_name__ ( self ) -> Optional[int]:
'''simple docstring'''
__a =tempfile.mkdtemp()
__a =self.tokenizer.fairseq_tokens_to_ids
self.tokenizer.save_pretrained(__snake_case )
__a =NllbTokenizer.from_pretrained(__snake_case )
self.assertDictEqual(new_tok.fairseq_tokens_to_ids , __snake_case )
@require_torch
def __magic_name__ ( self ) -> Tuple:
'''simple docstring'''
__a =self.tokenizer(
self.src_text , text_target=self.tgt_text , padding=__snake_case , truncation=__snake_case , max_length=len(self.expected_src_tokens ) , return_tensors='pt' , )
__a =shift_tokens_right(
batch['labels'] , self.tokenizer.pad_token_id , self.tokenizer.lang_code_to_id['ron_Latn'] )
self.assertIsInstance(__snake_case , __snake_case )
self.assertEqual((2, 15) , batch.input_ids.shape )
self.assertEqual((2, 15) , batch.attention_mask.shape )
__a =batch.input_ids.tolist()[0]
self.assertListEqual(self.expected_src_tokens , __snake_case )
self.assertEqual(__snake_case , batch.decoder_input_ids[0, 0] ) # EOS
# Test that special tokens are reset
self.assertEqual(self.tokenizer.prefix_tokens , [EN_CODE] )
self.assertEqual(self.tokenizer.suffix_tokens , [self.tokenizer.eos_token_id] )
def __magic_name__ ( self ) -> List[Any]:
'''simple docstring'''
__a =self.tokenizer(self.src_text , padding=__snake_case , truncation=__snake_case , max_length=3 , return_tensors='pt' )
__a =self.tokenizer(
text_target=self.tgt_text , padding=__snake_case , truncation=__snake_case , max_length=10 , return_tensors='pt' )
__a =targets['input_ids']
__a =shift_tokens_right(
__snake_case , self.tokenizer.pad_token_id , decoder_start_token_id=self.tokenizer.lang_code_to_id[self.tokenizer.tgt_lang] , )
self.assertEqual(batch.input_ids.shape[1] , 3 )
self.assertEqual(batch.decoder_input_ids.shape[1] , 10 )
@require_torch
def __magic_name__ ( self ) -> Optional[Any]:
'''simple docstring'''
__a =self.tokenizer._build_translation_inputs(
'A test' , return_tensors='pt' , src_lang='eng_Latn' , tgt_lang='fra_Latn' )
self.assertEqual(
nested_simplify(__snake_case ) , {
# A, test, EOS, en_XX
'input_ids': [[25_6047, 70, 7356, 2]],
'attention_mask': [[1, 1, 1, 1]],
# ar_AR
'forced_bos_token_id': 25_6057,
} , )
@require_torch
def __magic_name__ ( self ) -> List[Any]:
'''simple docstring'''
__a =True
__a =self.tokenizer(
'UN Chief says there is no military solution in Syria' , src_lang='eng_Latn' , tgt_lang='fra_Latn' )
self.assertEqual(
inputs.input_ids , [1_6297, 13_4408, 2_5653, 6370, 248, 254, 10_3929, 9_4995, 108, 4_9486, 2, 25_6047] )
__a =False
__a =self.tokenizer(
'UN Chief says there is no military solution in Syria' , src_lang='eng_Latn' , tgt_lang='fra_Latn' )
self.assertEqual(
inputs.input_ids , [25_6047, 1_6297, 13_4408, 2_5653, 6370, 248, 254, 10_3929, 9_4995, 108, 4_9486, 2] )
| 308
|
def UpperCamelCase_( _snake_case : str , _snake_case : int ):
"""simple docstring"""
return [sentence[i : i + ngram_size] for i in range(len(_snake_case ) - ngram_size + 1 )]
if __name__ == "__main__":
from doctest import testmod
testmod()
| 308
| 1
|
def UpperCamelCase_( _snake_case : int = 10 , _snake_case : int = 1000 , _snake_case : bool = True ):
"""simple docstring"""
assert (
isinstance(_snake_case , _snake_case )
and isinstance(_snake_case , _snake_case )
and isinstance(_snake_case , _snake_case )
), "Invalid type of value(s) specified to function!"
if min_val > max_val:
raise ValueError('Invalid value for min_val or max_val (min_value < max_value)' )
return min_val if option else max_val
def UpperCamelCase_( _snake_case : int , _snake_case : int ):
"""simple docstring"""
return int((number_a + number_a) / 2 )
def UpperCamelCase_( _snake_case : int , _snake_case : int , _snake_case : int ):
"""simple docstring"""
assert (
isinstance(_snake_case , _snake_case ) and isinstance(_snake_case , _snake_case ) and isinstance(_snake_case , _snake_case )
), 'argument values must be type of "int"'
if lower > higher:
raise ValueError('argument value for lower and higher must be(lower > higher)' )
if not lower < to_guess < higher:
raise ValueError(
'guess value must be within the range of lower and higher value' )
def answer(_snake_case : int ) -> str:
if number > to_guess:
return "high"
elif number < to_guess:
return "low"
else:
return "same"
print('started...' )
__a =lower
__a =higher
__a =[]
while True:
__a =get_avg(_snake_case , _snake_case )
last_numbers.append(_snake_case )
if answer(_snake_case ) == "low":
__a =number
elif answer(_snake_case ) == "high":
__a =number
else:
break
print(F'guess the number : {last_numbers[-1]}' )
print(F'details : {last_numbers!s}' )
def UpperCamelCase_( ):
"""simple docstring"""
__a =int(input('Enter lower value : ' ).strip() )
__a =int(input('Enter high value : ' ).strip() )
__a =int(input('Enter value to guess : ' ).strip() )
guess_the_number(_snake_case , _snake_case , _snake_case )
if __name__ == "__main__":
main()
| 308
|
import argparse
import pytorch_lightning as pl
import torch
from torch import nn
from transformers import LongformerForQuestionAnswering, LongformerModel
class __magic_name__ ( pl.LightningModule ):
def __init__( self , __snake_case ) -> List[Any]:
'''simple docstring'''
super().__init__()
__a =model
__a =2
__a =nn.Linear(self.model.config.hidden_size , self.num_labels )
def __magic_name__ ( self ) -> List[Any]:
'''simple docstring'''
pass
def UpperCamelCase_( _snake_case : str , _snake_case : str , _snake_case : str ):
"""simple docstring"""
__a =LongformerModel.from_pretrained(_snake_case )
__a =LightningModel(_snake_case )
__a =torch.load(_snake_case , map_location=torch.device('cpu' ) )
lightning_model.load_state_dict(ckpt['state_dict'] )
# init longformer question answering model
__a =LongformerForQuestionAnswering.from_pretrained(_snake_case )
# transfer weights
longformer_for_qa.longformer.load_state_dict(lightning_model.model.state_dict() )
longformer_for_qa.qa_outputs.load_state_dict(lightning_model.qa_outputs.state_dict() )
longformer_for_qa.eval()
# save model
longformer_for_qa.save_pretrained(_snake_case )
print(F'Conversion successful. Model saved under {pytorch_dump_folder_path}' )
if __name__ == "__main__":
_lowerCAmelCase : Optional[int] = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--longformer_model",
default=None,
type=str,
required=True,
help="model identifier of longformer. Should be either `longformer-base-4096` or `longformer-large-4096`.",
)
parser.add_argument(
"--longformer_question_answering_ckpt_path",
default=None,
type=str,
required=True,
help="Path the official PyTorch Lightning Checkpoint.",
)
parser.add_argument(
"--pytorch_dump_folder_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
)
_lowerCAmelCase : Optional[int] = parser.parse_args()
convert_longformer_qa_checkpoint_to_pytorch(
args.longformer_model, args.longformer_question_answering_ckpt_path, args.pytorch_dump_folder_path
)
| 308
| 1
|
import argparse
import dataclasses
import json
import logging
import os
import shutil
from typing import List, Optional
import datasets
from accelerate import Accelerator
from datasets import load_dataset
from finetuning import finetune
from tqdm.auto import tqdm
import transformers
from transformers import AutoConfig, set_seed
from transformers.trainer_utils import IntervalStrategy
_lowerCAmelCase : Optional[int] = logging.getLogger(__name__)
_lowerCAmelCase : Any = "pytorch_model.bin"
@dataclasses.dataclass
class __magic_name__ :
SCREAMING_SNAKE_CASE = dataclasses.field(
metadata={'help': 'Path to pretrained model or model identifier from huggingface.co/models.'} )
SCREAMING_SNAKE_CASE = dataclasses.field(
default=lowerCAmelCase_ , metadata={'help': 'Where do you want to store the pretrained models downloaded from huggingface.co.'} , )
@dataclasses.dataclass
class __magic_name__ :
SCREAMING_SNAKE_CASE = dataclasses.field(metadata={'help': 'A csv or a json file containing the training data.'} )
SCREAMING_SNAKE_CASE = dataclasses.field(metadata={'help': 'A csv or a json file containing the data to predict on.'} )
SCREAMING_SNAKE_CASE = dataclasses.field(
default=lowerCAmelCase_ , metadata={'help': 'A csv or a json file containing the validation data.'} )
SCREAMING_SNAKE_CASE = dataclasses.field(
default=lowerCAmelCase_ , metadata={'help': 'The name of the task to train on.'} , )
SCREAMING_SNAKE_CASE = dataclasses.field(
default=lowerCAmelCase_ , metadata={'help': 'The list of labels for the task.'} )
@dataclasses.dataclass
class __magic_name__ :
SCREAMING_SNAKE_CASE = dataclasses.field(
metadata={'help': 'The output directory where the model predictions and checkpoints will be written.'} )
SCREAMING_SNAKE_CASE = dataclasses.field(
default='accuracy' , metadata={'help': 'The evaluation metric used for the task.'} )
SCREAMING_SNAKE_CASE = dataclasses.field(
default='no' , metadata={
'help': 'The evaluation strategy to adopt during training. Possible values are: ["no", "step", "epoch]'
} , )
SCREAMING_SNAKE_CASE = dataclasses.field(
default=1_0 , metadata={'help': 'Number of evaluation calls with no improvement after which training will be stopped.'} , )
SCREAMING_SNAKE_CASE = dataclasses.field(
default=0.0 , metadata={
'help': 'How much the specified evaluation metric must improve to satisfy early stopping conditions.'
} , )
SCREAMING_SNAKE_CASE = dataclasses.field(
default=lowerCAmelCase_ , metadata={'help': 'Whether to filter the pseudo-labeled data based on the confidence score.'} , )
SCREAMING_SNAKE_CASE = dataclasses.field(
default=lowerCAmelCase_ , metadata={'help': 'Whether to filter the pseudo-labeled data based on the validation performance.'} , )
SCREAMING_SNAKE_CASE = dataclasses.field(
default=lowerCAmelCase_ , metadata={'help': 'Whether to fine-tune on labeled data after pseudo training.'} , )
SCREAMING_SNAKE_CASE = dataclasses.field(
default=0.0 , metadata={'help': 'Confidence threshold for pseudo-labeled data filtering.'} , )
SCREAMING_SNAKE_CASE = dataclasses.field(
default=1_0_0 , metadata={'help': 'Number of evaluation calls with no improvement after which training will be stopped.'} , )
SCREAMING_SNAKE_CASE = dataclasses.field(
default=lowerCAmelCase_ , metadata={'help': 'Random seed for initialization.'} , )
def UpperCamelCase_( _snake_case : int , _snake_case : str , _snake_case : Optional[int] , _snake_case : str , _snake_case : Union[str, Any] , _snake_case : List[Any] ):
"""simple docstring"""
__a =datasets.concatenate_datasets([infer_input, infer_output] , axis=1 )
if args.do_filter_by_confidence:
__a =dataset.filter(lambda _snake_case : example["probability"] > args.confidence_threshold )
if args.do_filter_by_val_performance:
assert eval_result >= 0.0 and eval_result <= 1.0
__a =int(eval_result * len(_snake_case ) )
print(_snake_case )
__a =dataset.sort('probability' , reverse=_snake_case )
__a =dataset.select(range(_snake_case ) )
__a =dataset.remove_columns(['label', 'probability'] )
__a =dataset.rename_column('prediction' , 'label' )
__a =dataset.map(lambda _snake_case : {"label": idalabel[example["label"]]} )
__a =dataset.shuffle(seed=args.seed )
__a =os.path.join(_snake_case , F'train_pseudo.{args.data_file_extension}' )
if args.data_file_extension == "csv":
dataset.to_csv(_snake_case , index=_snake_case )
else:
dataset.to_json(_snake_case )
def UpperCamelCase_( _snake_case : List[Any] , _snake_case : str , _snake_case : int , _snake_case : Optional[int] , **_snake_case : List[str] ):
"""simple docstring"""
__a =Accelerator()
# Make one log on every process with the configuration for debugging.
logging.basicConfig(
format='%(asctime)s - %(levelname)s - %(name)s - %(message)s' , datefmt='%m/%d/%Y %H:%M:%S' , level=logging.INFO , )
logger.info(accelerator.state )
# Setup logging, we only want one process per machine to log things on the
# screen. accelerator.is_local_main_process is only True for one process per
# machine.
logger.setLevel(logging.INFO if accelerator.is_local_main_process else logging.ERROR )
if accelerator.is_local_main_process:
datasets.utils.logging.set_verbosity_warning()
transformers.utils.logging.set_verbosity_info()
else:
datasets.utils.logging.set_verbosity_error()
transformers.utils.logging.set_verbosity_error()
__a =STModelArguments(model_name_or_path=_snake_case )
__a =STDataArguments(train_file=_snake_case , infer_file=_snake_case )
__a =STTrainingArguments(output_dir=_snake_case )
__a =argparse.Namespace()
for arg_class in (model_args, data_args, training_args):
for key, value in vars(_snake_case ).items():
setattr(_snake_case , _snake_case , _snake_case )
for key, value in kwargs.items():
if hasattr(_snake_case , _snake_case ):
setattr(_snake_case , _snake_case , _snake_case )
# Sanity checks
__a ={}
__a =None
# You need to provide the training data and the data to predict on
assert args.train_file is not None
assert args.infer_file is not None
__a =args.train_file
__a =args.infer_file
if args.evaluation_strategy != IntervalStrategy.NO.value:
assert args.eval_file is not None
__a =args.eval_file
for key in data_files:
__a =data_files[key].split('.' )[-1]
assert extension in ["csv", "json"], F'`{key}_file` should be a csv or a json file.'
if args.data_file_extension is None:
__a =extension
else:
assert extension == args.data_file_extension, F'`{key}_file` should be a {args.data_file_extension} file`.'
assert (
args.eval_metric in datasets.list_metrics()
), F'{args.eval_metric} not in the list of supported metrics {datasets.list_metrics()}.'
# If passed along, set the training seed now.
if args.seed is not None:
set_seed(args.seed )
logger.info('Creating the initial data directory for self-training...' )
__a =F'{args.output_dir}/self-train_iter-{{}}'.format
__a =data_dir_format(0 )
if accelerator.is_main_process:
if args.output_dir is not None:
os.makedirs(args.output_dir , exist_ok=_snake_case )
os.makedirs(_snake_case , exist_ok=_snake_case )
accelerator.wait_for_everyone()
__a =None
__a =None
__a =0
__a =False
# Show the progress bar
__a =tqdm(range(args.max_selftrain_iterations ) , disable=not accelerator.is_local_main_process )
# Self-train
for iteration in range(0 , int(args.max_selftrain_iterations ) ):
__a =data_dir_format(_snake_case )
assert os.path.exists(_snake_case )
# Stage 1: initial fine-tuning for iteration = 0 or pseudo-training for
# iteration > 0
__a =os.path.join(_snake_case , 'stage-1' )
__a ={
'accelerator': accelerator,
'model_name_or_path': args.model_name_or_path,
'cache_dir': args.cache_dir,
'do_train': True,
'train_file': data_files['train'] if iteration == 0 else data_files['train_pseudo'],
'do_eval': True if args.eval_file is not None else False,
'eval_file': data_files['eval'],
'do_predict': True,
'infer_file': data_files['infer'],
'task_name': args.task_name,
'label_list': args.label_list,
'output_dir': current_output_dir,
'eval_metric': args.eval_metric,
'evaluation_strategy': args.evaluation_strategy,
'early_stopping_patience': args.early_stopping_patience,
'early_stopping_threshold': args.early_stopping_threshold,
'seed': args.seed,
}
# Add additional training arguments
for key, value in kwargs.items():
if key not in arguments_dict and not hasattr(_snake_case , _snake_case ):
arguments_dict.update({key: value} )
__a =os.path.join(_snake_case , 'best-checkpoint' , _snake_case )
if os.path.exists(_snake_case ):
logger.info(
'Found existing model checkpoint at %s. Skipping self-training: iteration: %d, stage: 1.' , _snake_case , _snake_case , )
else:
logger.info('***** Running self-training: iteration: %d, stage: 1 *****' , _snake_case )
finetune(**_snake_case )
accelerator.wait_for_everyone()
assert os.path.exists(_snake_case )
logger.info('Self-training job completed: iteration: %d, stage: 1.' , _snake_case )
if iteration > 0 and args.finetune_on_labeled_data:
# Stage 2 (optional): fine-tuning on the original labeled data
__a =os.path.join(_snake_case , 'best-checkpoint' )
__a =os.path.join(_snake_case , 'stage-2' )
# Update arguments_dict
__a =model_path
__a =data_files['train']
__a =current_output_dir
__a =os.path.join(_snake_case , 'best-checkpoint' , _snake_case )
if os.path.exists(_snake_case ):
logger.info(
'Found existing model checkpoint at %s. Skipping self-training: iteration: %d, stage: 2.' , _snake_case , _snake_case , )
else:
logger.info('***** Running self-training: iteration: %d, stage: 2 *****' , _snake_case )
finetune(**_snake_case )
accelerator.wait_for_everyone()
assert os.path.exists(_snake_case )
logger.info('Self-training job completed: iteration: %d, stage: 2.' , _snake_case )
__a =iteration
__a =data_dir_format(iteration + 1 )
__a =AutoConfig.from_pretrained(os.path.join(_snake_case , 'best-checkpoint' ) )
__a =config.idalabel
__a =os.path.join(_snake_case , 'eval_results_best-checkpoint.json' )
__a =os.path.join(_snake_case , 'test_results_best-checkpoint.json' )
assert os.path.exists(_snake_case )
with open(_snake_case , 'r' ) as f:
__a =float(json.load(_snake_case )[args.eval_metric] )
__a =os.path.join(_snake_case , 'infer_output_best-checkpoint.csv' )
assert os.path.exists(_snake_case )
# Loading the dataset from local csv or json files.
__a =load_dataset(args.data_file_extension , data_files={'data': data_files['infer']} )['data']
__a =load_dataset('csv' , data_files={'data': infer_output_file} )['data']
if accelerator.is_main_process:
os.makedirs(_snake_case , exist_ok=_snake_case )
shutil.copy(_snake_case , os.path.join(_snake_case , F'eval_results_iter-{iteration}.json' ) )
if os.path.exists(_snake_case ):
shutil.copy(_snake_case , os.path.join(_snake_case , F'test_results_iter-{iteration}.json' ) )
create_pseudo_labeled_data(_snake_case , _snake_case , _snake_case , _snake_case , _snake_case , _snake_case )
accelerator.wait_for_everyone()
__a =os.path.join(_snake_case , F'train_pseudo.{args.data_file_extension}' )
if args.evaluation_strategy != IntervalStrategy.NO.value:
__a =eval_result
if best_iteration is None:
__a =new_iteration
__a =new_eval_result
else:
if new_eval_result - best_eval_result > args.early_stopping_threshold:
__a =new_iteration
__a =new_eval_result
__a =0
else:
if new_eval_result == best_eval_result:
__a =new_iteration
__a =new_eval_result
early_stopping_patience_counter += 1
if early_stopping_patience_counter >= args.early_stopping_patience:
__a =True
progress_bar.update(1 )
if should_training_stop:
break
if best_iteration is not None:
# Save the best iteration
logger.info('Best iteration: %d' , _snake_case )
logger.info('Best evaluation result: %s = %f' , args.eval_metric , _snake_case )
accelerator.wait_for_everyone()
if accelerator.is_main_process:
shutil.copy(
os.path.join(_snake_case , F'eval_results_iter-{iteration}.json' ) , os.path.join(_snake_case , 'eval_results_best-iteration.json' ) , )
else:
# Assume that the last iteration is the best
logger.info('Best iteration: %d' , args.max_selftrain_iterations - 1 )
logger.info('Best evaluation result: %s = %f' , args.eval_metric , _snake_case )
accelerator.wait_for_everyone()
if accelerator.is_main_process:
shutil.copy(
os.path.join(_snake_case , F'eval_results_iter-{args.max_selftrain_iterations - 1}.json' ) , os.path.join(_snake_case , 'eval_results_best-iteration.json' ) , )
| 308
|
import logging
import os
import random
import sys
from dataclasses import dataclass, field
from typing import Optional
import datasets
import numpy as np
import pandas as pd
from datasets import load_dataset
import transformers
from transformers import (
AutoConfig,
BartForSequenceClassification,
DataCollatorWithPadding,
EvalPrediction,
HfArgumentParser,
TapexTokenizer,
Trainer,
TrainingArguments,
default_data_collator,
set_seed,
)
from transformers.trainer_utils import get_last_checkpoint
from transformers.utils import check_min_version
from transformers.utils.versions import require_version
# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
check_min_version("4.17.0.dev0")
require_version("datasets>=1.8.0", "To fix: pip install -r examples/pytorch/text-classification/requirements.txt")
_lowerCAmelCase : Optional[Any] = logging.getLogger(__name__)
@dataclass
class __magic_name__ :
SCREAMING_SNAKE_CASE = field(
default='tab_fact' , metadata={'help': 'The name of the dataset to use (via the datasets library).'} )
SCREAMING_SNAKE_CASE = field(
default='tab_fact' , metadata={'help': 'The configuration name of the dataset to use (via the datasets library).'} , )
SCREAMING_SNAKE_CASE = field(
default=1_0_2_4 , metadata={
'help': (
'The maximum total input sequence length after tokenization. Sequences longer '
'than this will be truncated, sequences shorter will be padded.'
)
} , )
SCREAMING_SNAKE_CASE = field(
default=lowerCAmelCase_ , metadata={'help': 'Overwrite the cached preprocessed datasets or not.'} )
SCREAMING_SNAKE_CASE = field(
default=lowerCAmelCase_ , metadata={
'help': (
'Whether to pad all samples to `max_seq_length`. '
'If False, will pad the samples dynamically when batching to the maximum length in the batch.'
)
} , )
SCREAMING_SNAKE_CASE = field(
default=lowerCAmelCase_ , metadata={
'help': (
'For debugging purposes or quicker training, truncate the number of training examples to this '
'value if set.'
)
} , )
SCREAMING_SNAKE_CASE = field(
default=lowerCAmelCase_ , metadata={
'help': (
'For debugging purposes or quicker training, truncate the number of evaluation examples to this '
'value if set.'
)
} , )
SCREAMING_SNAKE_CASE = field(
default=lowerCAmelCase_ , metadata={
'help': (
'For debugging purposes or quicker training, truncate the number of prediction examples to this '
'value if set.'
)
} , )
SCREAMING_SNAKE_CASE = field(
default=lowerCAmelCase_ , metadata={'help': 'A csv or a json file containing the training data.'} )
SCREAMING_SNAKE_CASE = field(
default=lowerCAmelCase_ , metadata={'help': 'A csv or a json file containing the validation data.'} )
SCREAMING_SNAKE_CASE = field(default=lowerCAmelCase_ , metadata={'help': 'A csv or a json file containing the test data.'} )
def __magic_name__ ( self ) -> str:
'''simple docstring'''
if self.dataset_name is not None:
pass
elif self.train_file is None or self.validation_file is None:
raise ValueError('Need either a GLUE task, a training/validation file or a dataset name.' )
else:
__a =self.train_file.split('.' )[-1]
assert train_extension in ["csv", "json"], "`train_file` should be a csv or a json file."
__a =self.validation_file.split('.' )[-1]
assert (
validation_extension == train_extension
), "`validation_file` should have the same extension (csv or json) as `train_file`."
@dataclass
class __magic_name__ :
SCREAMING_SNAKE_CASE = field(
default=lowerCAmelCase_ , metadata={'help': 'Path to pretrained model or model identifier from huggingface.co/models'} )
SCREAMING_SNAKE_CASE = field(
default=lowerCAmelCase_ , metadata={'help': 'Pretrained config name or path if not the same as model_name'} )
SCREAMING_SNAKE_CASE = field(
default=lowerCAmelCase_ , metadata={'help': 'Pretrained tokenizer name or path if not the same as model_name'} )
SCREAMING_SNAKE_CASE = field(
default=lowerCAmelCase_ , metadata={'help': 'Where do you want to store the pretrained models downloaded from huggingface.co'} , )
SCREAMING_SNAKE_CASE = field(
default=lowerCAmelCase_ , metadata={'help': 'Whether to use one of the fast tokenizer (backed by the tokenizers library) or not.'} , )
SCREAMING_SNAKE_CASE = field(
default='main' , metadata={'help': 'The specific model version to use (can be a branch name, tag name or commit id).'} , )
SCREAMING_SNAKE_CASE = field(
default=lowerCAmelCase_ , metadata={
'help': (
'Will use the token generated when running `huggingface-cli login` (necessary to use this script '
'with private models).'
)
} , )
def UpperCamelCase_( ):
"""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()
# 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 )] , )
__a =training_args.get_process_log_level()
logger.setLevel(_snake_case )
datasets.utils.logging.set_verbosity(_snake_case )
transformers.utils.logging.set_verbosity(_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 training and evaluation files (see below)
# or specify a GLUE benchmark task (the dataset will be downloaded automatically from the datasets Hub).
#
# For JSON files, this script will use the `question` column for the input question and `table` column for the corresponding table.
#
# If the CSVs/JSONs contain only one non-label column, the script does single sentence classification on this
# single column. 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.dataset_name is not None:
# Downloading and loading a dataset from the hub.
__a =load_dataset(
data_args.dataset_name , data_args.dataset_config_name , cache_dir=model_args.cache_dir )
else:
# Loading a dataset from your local files.
# CSV/JSON training and evaluation files are needed.
__a ={'train': data_args.train_file, 'validation': data_args.validation_file}
# Get the test dataset: you can provide your own CSV/JSON test file (see below)
# when you use `do_predict` without specifying a GLUE benchmark task.
if training_args.do_predict:
if data_args.test_file is not None:
__a =data_args.train_file.split('.' )[-1]
__a =data_args.test_file.split('.' )[-1]
assert (
test_extension == train_extension
), "`test_file` should have the same extension (csv or json) as `train_file`."
__a =data_args.test_file
else:
raise ValueError('Need either a GLUE task or a test file for `do_predict`.' )
for key in data_files.keys():
logger.info(F'load a local file for {key}: {data_files[key]}' )
if data_args.train_file.endswith('.csv' ):
# Loading a dataset from local csv files
__a =load_dataset('csv' , data_files=_snake_case , cache_dir=model_args.cache_dir )
else:
# Loading a dataset from local json files
__a =load_dataset('json' , data_files=_snake_case , cache_dir=model_args.cache_dir )
# See more about loading any type of standard or custom dataset at
# https://huggingface.co/docs/datasets/loading_datasets.html.
# Labels
__a =raw_datasets['train'].features['label'].names
__a =len(_snake_case )
# Load pretrained model and tokenizer
#
# In 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 , num_labels=_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 , )
# load tapex tokenizer
__a =TapexTokenizer.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 , add_prefix_space=_snake_case , )
__a =BartForSequenceClassification.from_pretrained(
model_args.model_name_or_path , from_tf=bool('.ckpt' in model_args.model_name_or_path ) , config=_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 , )
# Padding strategy
if data_args.pad_to_max_length:
__a ='max_length'
else:
# We will pad later, dynamically at batch creation, to the max sequence length in each batch
__a =False
# Some models have set the order of the labels to use, so let's make sure we do use it.
__a ={'Refused': 0, 'Entailed': 1}
__a ={0: 'Refused', 1: 'Entailed'}
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 )
def preprocess_tabfact_function(_snake_case : Tuple ):
# Tokenize the texts
def _convert_table_text_to_pandas(_snake_case : Optional[Any] ):
__a =[_table_row.split('#' ) for _table_row in _table_text.strip('\n' ).split('\n' )]
__a =pd.DataFrame.from_records(_table_content[1:] , columns=_table_content[0] )
return _table_pd
__a =examples['statement']
__a =list(map(_convert_table_text_to_pandas , examples['table_text'] ) )
__a =tokenizer(_snake_case , _snake_case , padding=_snake_case , max_length=_snake_case , truncation=_snake_case )
__a =examples['label']
return result
with training_args.main_process_first(desc='dataset map pre-processing' ):
__a =raw_datasets.map(
_snake_case , batched=_snake_case , load_from_cache_file=not data_args.overwrite_cache , desc='Running tokenizer on dataset' , )
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 =train_dataset.select(range(data_args.max_train_samples ) )
if training_args.do_eval:
if "validation" not in raw_datasets and "validation_matched" 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 =eval_dataset.select(range(data_args.max_eval_samples ) )
if training_args.do_predict or data_args.test_file is not None:
if "test" not in raw_datasets and "test_matched" not in raw_datasets:
raise ValueError('--do_predict requires a test dataset' )
__a =raw_datasets['test']
if data_args.max_predict_samples is not None:
__a =predict_dataset.select(range(data_args.max_predict_samples ) )
# Log a few random samples from the training set:
if training_args.do_train:
for index in random.sample(range(len(_snake_case ) ) , 3 ):
logger.info(F'Sample {index} of the training set: {train_dataset[index]}.' )
# You can define your custom compute_metrics function. It takes an `EvalPrediction` object (a namedtuple with a
# predictions and label_ids field) and has to return a dictionary string to float.
def compute_metrics(_snake_case : EvalPrediction ):
__a =p.predictions[0] if isinstance(p.predictions , _snake_case ) else p.predictions
__a =np.argmax(_snake_case , axis=1 )
return {"accuracy": (preds == p.label_ids).astype(np.floataa ).mean().item()}
# Data collator will default to DataCollatorWithPadding, so we change it if we already did the padding.
if data_args.pad_to_max_length:
__a =default_data_collator
elif training_args.fpaa:
__a =DataCollatorWithPadding(_snake_case , pad_to_multiple_of=8 )
else:
__a =None
# Initialize our Trainer
__a =Trainer(
model=_snake_case , args=_snake_case , train_dataset=train_dataset if training_args.do_train else None , eval_dataset=eval_dataset if training_args.do_eval else None , compute_metrics=_snake_case , tokenizer=_snake_case , data_collator=_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=_snake_case )
__a =train_result.metrics
__a =(
data_args.max_train_samples if data_args.max_train_samples is not None else len(_snake_case )
)
__a =min(_snake_case , len(_snake_case ) )
trainer.save_model() # Saves the tokenizer too for easy upload
trainer.log_metrics('train' , _snake_case )
trainer.save_metrics('train' , _snake_case )
trainer.save_state()
# Evaluation
if training_args.do_eval:
logger.info('*** Evaluate ***' )
__a =trainer.evaluate(eval_dataset=_snake_case )
__a =data_args.max_eval_samples if data_args.max_eval_samples is not None else len(_snake_case )
__a =min(_snake_case , len(_snake_case ) )
trainer.log_metrics('eval' , _snake_case )
trainer.save_metrics('eval' , _snake_case )
if training_args.do_predict:
logger.info('*** Predict ***' )
# Removing the `label` columns because it contains -1 and Trainer won't like that.
__a =predict_dataset.remove_columns('label' )
__a =trainer.predict(_snake_case , metric_key_prefix='predict' ).predictions
__a =np.argmax(_snake_case , axis=1 )
__a =os.path.join(training_args.output_dir , 'predict_results_tabfact.txt' )
if trainer.is_world_process_zero():
with open(_snake_case , 'w' ) as writer:
logger.info('***** Predict Results *****' )
writer.write('index\tprediction\n' )
for index, item in enumerate(_snake_case ):
__a =label_list[item]
writer.write(F'{index}\t{item}\n' )
__a ={'finetuned_from': model_args.model_name_or_path, 'tasks': 'text-classification'}
if training_args.push_to_hub:
trainer.push_to_hub(**_snake_case )
else:
trainer.create_model_card(**_snake_case )
def UpperCamelCase_( _snake_case : Union[str, Any] ):
"""simple docstring"""
main()
if __name__ == "__main__":
main()
| 308
| 1
|
import pytest
import requests
from datasets.utils.file_utils import http_head
from .utils import OfflineSimulationMode, RequestWouldHangIndefinitelyError, offline
@pytest.mark.integration
def UpperCamelCase_( ):
"""simple docstring"""
with offline(OfflineSimulationMode.CONNECTION_TIMES_OUT ):
with pytest.raises(_snake_case ):
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 UpperCamelCase_( ):
"""simple docstring"""
with offline(OfflineSimulationMode.CONNECTION_FAILS ):
with pytest.raises(requests.exceptions.ConnectionError ):
requests.request('GET' , 'https://huggingface.co' )
def UpperCamelCase_( ):
"""simple docstring"""
with offline(OfflineSimulationMode.HF_DATASETS_OFFLINE_SET_TO_1 ):
with pytest.raises(_snake_case ):
http_head('https://huggingface.co' )
| 308
|
from __future__ import annotations
import time
import numpy as np
_lowerCAmelCase : List[str] = [8, 5, 9, 7]
_lowerCAmelCase : List[str] = [
[2, 0, 1, 1],
[0, 1, 2, 1],
[4, 0, 0, 3],
[0, 2, 1, 0],
[1, 0, 3, 0],
]
_lowerCAmelCase : List[Any] = [
[3, 2, 1, 4],
[0, 2, 5, 2],
[5, 1, 0, 5],
[1, 5, 3, 0],
[3, 0, 3, 3],
]
class __magic_name__ :
def __init__( self , __snake_case , __snake_case , __snake_case , ) -> None:
'''simple docstring'''
__a =claim_vector
__a =allocated_resources_table
__a =maximum_claim_table
def __magic_name__ ( self ) -> list[int]:
'''simple docstring'''
return [
sum(p_item[i] for p_item in self.__allocated_resources_table )
for i in range(len(self.__allocated_resources_table[0] ) )
]
def __magic_name__ ( self ) -> list[int]:
'''simple docstring'''
return np.array(self.__claim_vector ) - np.array(
self.__processes_resource_summation() )
def __magic_name__ ( self ) -> list[list[int]]:
'''simple docstring'''
return [
list(np.array(self.__maximum_claim_table[i] ) - np.array(__snake_case ) )
for i, allocated_resource in enumerate(self.__allocated_resources_table )
]
def __magic_name__ ( self ) -> dict[int, list[int]]:
'''simple docstring'''
return {self.__need().index(__snake_case ): i for i in self.__need()}
def __magic_name__ ( self , **__snake_case ) -> None:
'''simple docstring'''
__a =self.__need()
__a =self.__allocated_resources_table
__a =self.__available_resources()
__a =self.__need_index_manager()
for kw, val in kwargs.items():
if kw and val is True:
self.__pretty_data()
print('_' * 50 + '\n' )
while need_list:
__a =False
for each_need in need_list:
__a =True
for index, need in enumerate(__snake_case ):
if need > available_resources[index]:
__a =False
break
if execution:
__a =True
# get the original index of the process from ind_ctrl db
for original_need_index, need_clone in need_index_manager.items():
if each_need == need_clone:
__a =original_need_index
print(f'Process {process_number + 1} is executing.' )
# remove the process run from stack
need_list.remove(__snake_case )
# update available/freed resources stack
__a =np.array(__snake_case ) + np.array(
alloc_resources_table[process_number] )
print(
'Updated available resource stack for processes: '
+ ' '.join([str(__snake_case ) for x in available_resources] ) )
break
if safe:
print('The process is in a safe state.\n' )
else:
print('System in unsafe state. Aborting...\n' )
break
def __magic_name__ ( self ) -> Union[str, Any]:
'''simple docstring'''
print(' ' * 9 + 'Allocated Resource Table' )
for item in self.__allocated_resources_table:
print(
f'P{self.__allocated_resources_table.index(__snake_case ) + 1}'
+ ' '.join(f'{it:>8}' for it in item )
+ '\n' )
print(' ' * 9 + 'System Resource Table' )
for item in self.__maximum_claim_table:
print(
f'P{self.__maximum_claim_table.index(__snake_case ) + 1}'
+ ' '.join(f'{it:>8}' for it in item )
+ '\n' )
print(
'Current Usage by Active Processes: '
+ ' '.join(str(__snake_case ) for x in self.__claim_vector ) )
print(
'Initial Available Resources: '
+ ' '.join(str(__snake_case ) for x in self.__available_resources() ) )
time.sleep(1 )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 308
| 1
|
_lowerCAmelCase : int = {
0: "0",
1: "1",
2: "2",
3: "3",
4: "4",
5: "5",
6: "6",
7: "7",
8: "8",
9: "9",
10: "a",
11: "b",
12: "c",
13: "d",
14: "e",
15: "f",
}
def UpperCamelCase_( _snake_case : float ):
"""simple docstring"""
assert type(_snake_case ) in (int, float) and decimal == int(_snake_case )
__a =int(_snake_case )
__a =''
__a =False
if decimal < 0:
__a =True
decimal *= -1
while decimal > 0:
__a , __a =divmod(_snake_case , 16 )
__a =values[remainder] + hexadecimal
__a ='0x' + hexadecimal
if negative:
__a ='-' + hexadecimal
return hexadecimal
if __name__ == "__main__":
import doctest
doctest.testmod()
| 308
|
import string
# frequency taken from https://en.wikipedia.org/wiki/Letter_frequency
_lowerCAmelCase : Tuple = {
"E": 12.70,
"T": 9.06,
"A": 8.17,
"O": 7.51,
"I": 6.97,
"N": 6.75,
"S": 6.33,
"H": 6.09,
"R": 5.99,
"D": 4.25,
"L": 4.03,
"C": 2.78,
"U": 2.76,
"M": 2.41,
"W": 2.36,
"F": 2.23,
"G": 2.02,
"Y": 1.97,
"P": 1.93,
"B": 1.29,
"V": 0.98,
"K": 0.77,
"J": 0.15,
"X": 0.15,
"Q": 0.10,
"Z": 0.07,
}
_lowerCAmelCase : Optional[int] = "ETAOINSHRDLCUMWFGYPBVKJXQZ"
_lowerCAmelCase : Any = "ABCDEFGHIJKLMNOPQRSTUVWXYZ"
def UpperCamelCase_( _snake_case : str ):
"""simple docstring"""
__a ={letter: 0 for letter in string.ascii_uppercase}
for letter in message.upper():
if letter in LETTERS:
letter_count[letter] += 1
return letter_count
def UpperCamelCase_( _snake_case : tuple ):
"""simple docstring"""
return x[0]
def UpperCamelCase_( _snake_case : str ):
"""simple docstring"""
__a =get_letter_count(_snake_case )
__a ={
freq: [] for letter, freq in letter_to_freq.items()
}
for letter in LETTERS:
freq_to_letter[letter_to_freq[letter]].append(_snake_case )
__a ={}
for freq in freq_to_letter:
freq_to_letter[freq].sort(key=ETAOIN.find , reverse=_snake_case )
__a =''.join(freq_to_letter[freq] )
__a =list(freq_to_letter_str.items() )
freq_pairs.sort(key=_snake_case , reverse=_snake_case )
__a =[freq_pair[1] for freq_pair in freq_pairs]
return "".join(_snake_case )
def UpperCamelCase_( _snake_case : str ):
"""simple docstring"""
__a =get_frequency_order(_snake_case )
__a =0
for common_letter in ETAOIN[:6]:
if common_letter in freq_order[:6]:
match_score += 1
for uncommon_letter in ETAOIN[-6:]:
if uncommon_letter in freq_order[-6:]:
match_score += 1
return match_score
if __name__ == "__main__":
import doctest
doctest.testmod()
| 308
| 1
|
import os
import tempfile
import unittest
from transformers.models.marian.convert_marian_tatoeba_to_pytorch import DEFAULT_REPO, TatoebaConverter
from transformers.testing_utils import slow
from transformers.utils import cached_property
@unittest.skipUnless(os.path.exists(lowerCAmelCase_ ) , 'Tatoeba directory does not exist.' )
class __magic_name__ ( unittest.TestCase ):
@cached_property
def __magic_name__ ( self ) -> Dict:
'''simple docstring'''
__a =tempfile.mkdtemp()
return TatoebaConverter(save_dir=__snake_case )
@slow
def __magic_name__ ( self ) -> List[str]:
'''simple docstring'''
self.resolver.convert_models(['heb-eng'] )
@slow
def __magic_name__ ( self ) -> Dict:
'''simple docstring'''
__a , __a =self.resolver.write_model_card('opus-mt-he-en' , dry_run=__snake_case )
assert mmeta["long_pair"] == "heb-eng"
| 308
|
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowerCAmelCase : Any = logging.get_logger(__name__)
_lowerCAmelCase : int = {
"caidas/swin2sr-classicalsr-x2-64": (
"https://huggingface.co/caidas/swin2sr-classicalsr-x2-64/resolve/main/config.json"
),
}
class __magic_name__ ( lowerCAmelCase_ ):
SCREAMING_SNAKE_CASE = 'swin2sr'
SCREAMING_SNAKE_CASE = {
'hidden_size': 'embed_dim',
'num_attention_heads': 'num_heads',
'num_hidden_layers': 'num_layers',
}
def __init__( self , __snake_case=64 , __snake_case=1 , __snake_case=3 , __snake_case=180 , __snake_case=[6, 6, 6, 6, 6, 6] , __snake_case=[6, 6, 6, 6, 6, 6] , __snake_case=8 , __snake_case=2.0 , __snake_case=True , __snake_case=0.0 , __snake_case=0.0 , __snake_case=0.1 , __snake_case="gelu" , __snake_case=False , __snake_case=0.02 , __snake_case=1e-5 , __snake_case=2 , __snake_case=1.0 , __snake_case="1conv" , __snake_case="pixelshuffle" , **__snake_case , ) -> Dict:
'''simple docstring'''
super().__init__(**__snake_case )
__a =image_size
__a =patch_size
__a =num_channels
__a =embed_dim
__a =depths
__a =len(__snake_case )
__a =num_heads
__a =window_size
__a =mlp_ratio
__a =qkv_bias
__a =hidden_dropout_prob
__a =attention_probs_dropout_prob
__a =drop_path_rate
__a =hidden_act
__a =use_absolute_embeddings
__a =layer_norm_eps
__a =initializer_range
__a =upscale
__a =img_range
__a =resi_connection
__a =upsampler
| 308
| 1
|
from __future__ import annotations
import bisect
def UpperCamelCase_( _snake_case : list[int] , _snake_case : int , _snake_case : int = 0 , _snake_case : int = -1 ):
"""simple docstring"""
if hi < 0:
__a =len(_snake_case )
while lo < hi:
__a =lo + (hi - lo) // 2
if sorted_collection[mid] < item:
__a =mid + 1
else:
__a =mid
return lo
def UpperCamelCase_( _snake_case : list[int] , _snake_case : int , _snake_case : int = 0 , _snake_case : int = -1 ):
"""simple docstring"""
if hi < 0:
__a =len(_snake_case )
while lo < hi:
__a =lo + (hi - lo) // 2
if sorted_collection[mid] <= item:
__a =mid + 1
else:
__a =mid
return lo
def UpperCamelCase_( _snake_case : list[int] , _snake_case : int , _snake_case : int = 0 , _snake_case : int = -1 ):
"""simple docstring"""
sorted_collection.insert(bisect_left(_snake_case , _snake_case , _snake_case , _snake_case ) , _snake_case )
def UpperCamelCase_( _snake_case : list[int] , _snake_case : int , _snake_case : int = 0 , _snake_case : int = -1 ):
"""simple docstring"""
sorted_collection.insert(bisect_right(_snake_case , _snake_case , _snake_case , _snake_case ) , _snake_case )
def UpperCamelCase_( _snake_case : list[int] , _snake_case : int ):
"""simple docstring"""
__a =0
__a =len(_snake_case ) - 1
while left <= right:
__a =left + (right - left) // 2
__a =sorted_collection[midpoint]
if current_item == item:
return midpoint
elif item < current_item:
__a =midpoint - 1
else:
__a =midpoint + 1
return None
def UpperCamelCase_( _snake_case : list[int] , _snake_case : int ):
"""simple docstring"""
__a =bisect.bisect_left(_snake_case , _snake_case )
if index != len(_snake_case ) and sorted_collection[index] == item:
return index
return None
def UpperCamelCase_( _snake_case : list[int] , _snake_case : int , _snake_case : int , _snake_case : int ):
"""simple docstring"""
if right < left:
return None
__a =left + (right - left) // 2
if sorted_collection[midpoint] == item:
return midpoint
elif sorted_collection[midpoint] > item:
return binary_search_by_recursion(_snake_case , _snake_case , _snake_case , midpoint - 1 )
else:
return binary_search_by_recursion(_snake_case , _snake_case , midpoint + 1 , _snake_case )
if __name__ == "__main__":
_lowerCAmelCase : List[str] = input("Enter numbers separated by comma:\n").strip()
_lowerCAmelCase : Union[str, Any] = sorted(int(item) for item in user_input.split(","))
_lowerCAmelCase : str = int(input("Enter a single number to be found in the list:\n"))
_lowerCAmelCase : List[Any] = binary_search(collection, target)
if result is None:
print(f'''{target} was not found in {collection}.''')
else:
print(f'''{target} was found at position {result} in {collection}.''')
| 308
|
import os
def UpperCamelCase_( _snake_case : str = "input.txt" ):
"""simple docstring"""
with open(os.path.join(os.path.dirname(_snake_case ) , _snake_case ) ) as input_file:
__a =[
[int(_snake_case ) for element in line.split(',' )]
for line in input_file.readlines()
]
__a =len(_snake_case )
__a =len(matrix[0] )
__a =[[-1 for _ in range(_snake_case )] for _ in range(_snake_case )]
for i in range(_snake_case ):
__a =matrix[i][0]
for j in range(1 , _snake_case ):
for i in range(_snake_case ):
__a =minimal_path_sums[i][j - 1] + matrix[i][j]
for i in range(1 , _snake_case ):
__a =min(
minimal_path_sums[i][j] , minimal_path_sums[i - 1][j] + matrix[i][j] )
for i in range(rows - 2 , -1 , -1 ):
__a =min(
minimal_path_sums[i][j] , minimal_path_sums[i + 1][j] + matrix[i][j] )
return min(minimal_path_sums_row[-1] for minimal_path_sums_row in minimal_path_sums )
if __name__ == "__main__":
print(f'''{solution() = }''')
| 308
| 1
|
import torch
from diffusers import DPMSolverSDEScheduler
from diffusers.utils import torch_device
from diffusers.utils.testing_utils import require_torchsde
from .test_schedulers import SchedulerCommonTest
@require_torchsde
class __magic_name__ ( lowerCAmelCase_ ):
SCREAMING_SNAKE_CASE = (DPMSolverSDEScheduler,)
SCREAMING_SNAKE_CASE = 1_0
def __magic_name__ ( self , **__snake_case ) -> Union[str, Any]:
'''simple docstring'''
__a ={
'num_train_timesteps': 1100,
'beta_start': 0.0001,
'beta_end': 0.02,
'beta_schedule': 'linear',
'noise_sampler_seed': 0,
}
config.update(**__snake_case )
return config
def __magic_name__ ( self ) -> int:
'''simple docstring'''
for timesteps in [10, 50, 100, 1000]:
self.check_over_configs(num_train_timesteps=__snake_case )
def __magic_name__ ( self ) -> List[Any]:
'''simple docstring'''
for beta_start, beta_end in zip([0.0_0001, 0.0001, 0.001] , [0.0002, 0.002, 0.02] ):
self.check_over_configs(beta_start=__snake_case , beta_end=__snake_case )
def __magic_name__ ( self ) -> Optional[int]:
'''simple docstring'''
for schedule in ["linear", "scaled_linear"]:
self.check_over_configs(beta_schedule=__snake_case )
def __magic_name__ ( self ) -> Dict:
'''simple docstring'''
for prediction_type in ["epsilon", "v_prediction"]:
self.check_over_configs(prediction_type=__snake_case )
def __magic_name__ ( self ) -> Tuple:
'''simple docstring'''
__a =self.scheduler_classes[0]
__a =self.get_scheduler_config()
__a =scheduler_class(**__snake_case )
scheduler.set_timesteps(self.num_inference_steps )
__a =self.dummy_model()
__a =self.dummy_sample_deter * scheduler.init_noise_sigma
__a =sample.to(__snake_case )
for i, t in enumerate(scheduler.timesteps ):
__a =scheduler.scale_model_input(__snake_case , __snake_case )
__a =model(__snake_case , __snake_case )
__a =scheduler.step(__snake_case , __snake_case , __snake_case )
__a =output.prev_sample
__a =torch.sum(torch.abs(__snake_case ) )
__a =torch.mean(torch.abs(__snake_case ) )
if torch_device in ["mps"]:
assert abs(result_sum.item() - 167.47_8210_4492_1875 ) < 1e-2
assert abs(result_mean.item() - 0.2178_7059_6456_5277 ) < 1e-3
elif torch_device in ["cuda"]:
assert abs(result_sum.item() - 171.59_3521_1181_6406 ) < 1e-2
assert abs(result_mean.item() - 0.2_2342_9068_9229_9652 ) < 1e-3
else:
assert abs(result_sum.item() - 162.52_3834_2285_1562 ) < 1e-2
assert abs(result_mean.item() - 0.211_6195_7085_1326 ) < 1e-3
def __magic_name__ ( self ) -> Optional[int]:
'''simple docstring'''
__a =self.scheduler_classes[0]
__a =self.get_scheduler_config(prediction_type='v_prediction' )
__a =scheduler_class(**__snake_case )
scheduler.set_timesteps(self.num_inference_steps )
__a =self.dummy_model()
__a =self.dummy_sample_deter * scheduler.init_noise_sigma
__a =sample.to(__snake_case )
for i, t in enumerate(scheduler.timesteps ):
__a =scheduler.scale_model_input(__snake_case , __snake_case )
__a =model(__snake_case , __snake_case )
__a =scheduler.step(__snake_case , __snake_case , __snake_case )
__a =output.prev_sample
__a =torch.sum(torch.abs(__snake_case ) )
__a =torch.mean(torch.abs(__snake_case ) )
if torch_device in ["mps"]:
assert abs(result_sum.item() - 124.77_1492_0043_9453 ) < 1e-2
assert abs(result_mean.item() - 0.1_6226_2890_1481_6284 ) < 1e-3
elif torch_device in ["cuda"]:
assert abs(result_sum.item() - 128.1_6633_6059_5703 ) < 1e-2
assert abs(result_mean.item() - 0.1_6688_3260_0116_7297 ) < 1e-3
else:
assert abs(result_sum.item() - 119.8_4875_4882_8125 ) < 1e-2
assert abs(result_mean.item() - 0.1560_5306_6253_6621 ) < 1e-3
def __magic_name__ ( self ) -> str:
'''simple docstring'''
__a =self.scheduler_classes[0]
__a =self.get_scheduler_config()
__a =scheduler_class(**__snake_case )
scheduler.set_timesteps(self.num_inference_steps , device=__snake_case )
__a =self.dummy_model()
__a =self.dummy_sample_deter.to(__snake_case ) * scheduler.init_noise_sigma
for t in scheduler.timesteps:
__a =scheduler.scale_model_input(__snake_case , __snake_case )
__a =model(__snake_case , __snake_case )
__a =scheduler.step(__snake_case , __snake_case , __snake_case )
__a =output.prev_sample
__a =torch.sum(torch.abs(__snake_case ) )
__a =torch.mean(torch.abs(__snake_case ) )
if torch_device in ["mps"]:
assert abs(result_sum.item() - 167.46_9573_9746_0938 ) < 1e-2
assert abs(result_mean.item() - 0.2_1805_9346_0798_2635 ) < 1e-3
elif torch_device in ["cuda"]:
assert abs(result_sum.item() - 171.59_3536_3769_5312 ) < 1e-2
assert abs(result_mean.item() - 0.2_2342_9083_8241_5771 ) < 1e-3
else:
assert abs(result_sum.item() - 162.52_3834_2285_1562 ) < 1e-2
assert abs(result_mean.item() - 0.211_6195_7085_1326 ) < 1e-3
def __magic_name__ ( self ) -> Any:
'''simple docstring'''
__a =self.scheduler_classes[0]
__a =self.get_scheduler_config()
__a =scheduler_class(**__snake_case , use_karras_sigmas=__snake_case )
scheduler.set_timesteps(self.num_inference_steps , device=__snake_case )
__a =self.dummy_model()
__a =self.dummy_sample_deter.to(__snake_case ) * scheduler.init_noise_sigma
__a =sample.to(__snake_case )
for t in scheduler.timesteps:
__a =scheduler.scale_model_input(__snake_case , __snake_case )
__a =model(__snake_case , __snake_case )
__a =scheduler.step(__snake_case , __snake_case , __snake_case )
__a =output.prev_sample
__a =torch.sum(torch.abs(__snake_case ) )
__a =torch.mean(torch.abs(__snake_case ) )
if torch_device in ["mps"]:
assert abs(result_sum.item() - 176.66_9741_3574_2188 ) < 1e-2
assert abs(result_mean.item() - 0.2_3003_8727_3098_1811 ) < 1e-2
elif torch_device in ["cuda"]:
assert abs(result_sum.item() - 177.63_6535_6445_3125 ) < 1e-2
assert abs(result_mean.item() - 0.2_3003_8727_3098_1811 ) < 1e-2
else:
assert abs(result_sum.item() - 170.3_1352_2338_8672 ) < 1e-2
assert abs(result_mean.item() - 0.2_3003_8727_3098_1811 ) < 1e-2
| 308
|
import warnings
from ...utils import logging
from .image_processing_mobilevit import MobileViTImageProcessor
_lowerCAmelCase : Any = logging.get_logger(__name__)
class __magic_name__ ( lowerCAmelCase_ ):
def __init__( self , *__snake_case , **__snake_case ) -> None:
'''simple docstring'''
warnings.warn(
'The class MobileViTFeatureExtractor is deprecated and will be removed in version 5 of Transformers.'
' Please use MobileViTImageProcessor instead.' , __snake_case , )
super().__init__(*__snake_case , **__snake_case )
| 308
| 1
|
import inspect
import os
import torch
from transformers import AutoModel
from transformers.testing_utils import mockenv_context
from transformers.trainer_utils import set_seed
import accelerate
from accelerate.accelerator import Accelerator
from accelerate.state import AcceleratorState
from accelerate.test_utils.testing import (
AccelerateTestCase,
TempDirTestCase,
execute_subprocess_async,
require_cuda,
require_fsdp,
require_multi_gpu,
slow,
)
from accelerate.utils.constants import (
FSDP_AUTO_WRAP_POLICY,
FSDP_BACKWARD_PREFETCH,
FSDP_SHARDING_STRATEGY,
FSDP_STATE_DICT_TYPE,
)
from accelerate.utils.dataclasses import FullyShardedDataParallelPlugin
from accelerate.utils.other import patch_environment
set_seed(42)
_lowerCAmelCase : Tuple = "bert-base-cased"
_lowerCAmelCase : str = "fp16"
_lowerCAmelCase : Optional[int] = "bf16"
_lowerCAmelCase : Optional[Any] = [FPaa, BFaa]
@require_fsdp
@require_cuda
class __magic_name__ ( lowerCAmelCase_ ):
def __magic_name__ ( self ) -> Tuple:
'''simple docstring'''
super().setUp()
__a =dict(
ACCELERATE_USE_FSDP='true' , MASTER_ADDR='localhost' , MASTER_PORT='10999' , RANK='0' , LOCAL_RANK='0' , WORLD_SIZE='1' , )
def __magic_name__ ( self ) -> Union[str, Any]:
'''simple docstring'''
from torch.distributed.fsdp.fully_sharded_data_parallel import ShardingStrategy
for i, strategy in enumerate(__snake_case ):
__a =self.dist_env.copy()
__a =f'{i + 1}'
__a =strategy
with mockenv_context(**__snake_case ):
__a =FullyShardedDataParallelPlugin()
self.assertEqual(fsdp_plugin.sharding_strategy , ShardingStrategy(i + 1 ) )
def __magic_name__ ( self ) -> Union[str, Any]:
'''simple docstring'''
from torch.distributed.fsdp.fully_sharded_data_parallel import BackwardPrefetch
for i, prefetch_policy in enumerate(__snake_case ):
__a =self.dist_env.copy()
__a =prefetch_policy
with mockenv_context(**__snake_case ):
__a =FullyShardedDataParallelPlugin()
if prefetch_policy == "NO_PREFETCH":
self.assertIsNone(fsdp_plugin.backward_prefetch )
else:
self.assertEqual(fsdp_plugin.backward_prefetch , BackwardPrefetch(i + 1 ) )
def __magic_name__ ( self ) -> Optional[Any]:
'''simple docstring'''
from torch.distributed.fsdp.fully_sharded_data_parallel import StateDictType
for i, state_dict_type in enumerate(__snake_case ):
__a =self.dist_env.copy()
__a =state_dict_type
with mockenv_context(**__snake_case ):
__a =FullyShardedDataParallelPlugin()
self.assertEqual(fsdp_plugin.state_dict_type , StateDictType(i + 1 ) )
if state_dict_type == "FULL_STATE_DICT":
self.assertTrue(fsdp_plugin.state_dict_config.offload_to_cpu )
self.assertTrue(fsdp_plugin.state_dict_config.ranka_only )
def __magic_name__ ( self ) -> List[Any]:
'''simple docstring'''
__a =AutoModel.from_pretrained(__snake_case )
for policy in FSDP_AUTO_WRAP_POLICY:
__a =self.dist_env.copy()
__a =policy
if policy == "TRANSFORMER_BASED_WRAP":
__a ='BertLayer'
elif policy == "SIZE_BASED_WRAP":
__a ='2000'
with mockenv_context(**__snake_case ):
__a =FullyShardedDataParallelPlugin()
fsdp_plugin.set_auto_wrap_policy(__snake_case )
if policy == "NO_WRAP":
self.assertIsNone(fsdp_plugin.auto_wrap_policy )
else:
self.assertIsNotNone(fsdp_plugin.auto_wrap_policy )
__a =self.dist_env.copy()
__a ='TRANSFORMER_BASED_WRAP'
__a ='T5Layer'
with mockenv_context(**__snake_case ):
__a =FullyShardedDataParallelPlugin()
with self.assertRaises(__snake_case ) as cm:
fsdp_plugin.set_auto_wrap_policy(__snake_case )
self.assertTrue('Could not find the transformer layer class to wrap in the model.' in str(cm.exception ) )
__a =self.dist_env.copy()
__a ='SIZE_BASED_WRAP'
__a ='0'
with mockenv_context(**__snake_case ):
__a =FullyShardedDataParallelPlugin()
fsdp_plugin.set_auto_wrap_policy(__snake_case )
self.assertIsNone(fsdp_plugin.auto_wrap_policy )
def __magic_name__ ( self ) -> Any:
'''simple docstring'''
from torch.distributed.fsdp.fully_sharded_data_parallel import MixedPrecision
from torch.distributed.fsdp.sharded_grad_scaler import ShardedGradScaler
for mp_dtype in dtypes:
__a =self.dist_env.copy()
__a =mp_dtype
with mockenv_context(**__snake_case ):
__a =Accelerator()
if mp_dtype == "fp16":
__a =torch.floataa
elif mp_dtype == "bf16":
__a =torch.bfloataa
__a =MixedPrecision(param_dtype=__snake_case , reduce_dtype=__snake_case , buffer_dtype=__snake_case )
self.assertEqual(accelerator.state.fsdp_plugin.mixed_precision_policy , __snake_case )
if mp_dtype == FPaa:
self.assertTrue(isinstance(accelerator.scaler , __snake_case ) )
elif mp_dtype == BFaa:
self.assertIsNone(accelerator.scaler )
AcceleratorState._reset_state(__snake_case )
def __magic_name__ ( self ) -> List[str]:
'''simple docstring'''
from torch.distributed.fsdp.fully_sharded_data_parallel import CPUOffload
for flag in [True, False]:
__a =self.dist_env.copy()
__a =str(__snake_case ).lower()
with mockenv_context(**__snake_case ):
__a =FullyShardedDataParallelPlugin()
self.assertEqual(fsdp_plugin.cpu_offload , CPUOffload(offload_params=__snake_case ) )
@require_fsdp
@require_multi_gpu
@slow
class __magic_name__ ( lowerCAmelCase_ ):
def __magic_name__ ( self ) -> Dict:
'''simple docstring'''
super().setUp()
__a =0.82
__a =[
'fsdp_shard_grad_op_transformer_based_wrap',
'fsdp_full_shard_transformer_based_wrap',
]
__a ={
'multi_gpu_fp16': 3200,
'fsdp_shard_grad_op_transformer_based_wrap_fp16': 2000,
'fsdp_full_shard_transformer_based_wrap_fp16': 1900,
# Disabling below test as it overwhelms the RAM memory usage
# on CI self-hosted runner leading to tests getting killed.
# "fsdp_full_shard_cpu_offload_transformer_based_wrap_fp32": 1500, # fp16 was leading to indefinite hang
}
__a =160
__a =160
__a =inspect.getfile(accelerate.test_utils )
__a =os.path.sep.join(mod_file.split(os.path.sep )[:-1] + ['scripts', 'external_deps'] )
def __magic_name__ ( self ) -> Union[str, Any]:
'''simple docstring'''
__a =os.path.join(self.test_scripts_folder , 'test_performance.py' )
__a =['accelerate', 'launch', '--num_processes=2', '--num_machines=1', '--machine_rank=0', '--use_fsdp']
for config in self.performance_configs:
__a =cmd.copy()
for i, strategy in enumerate(__snake_case ):
if strategy.lower() in config:
cmd_config.append(f'--fsdp_sharding_strategy={i+1}' )
break
if "fp32" in config:
cmd_config.append('--mixed_precision=no' )
else:
cmd_config.append('--mixed_precision=fp16' )
if "cpu_offload" in config:
cmd_config.append('--fsdp_offload_params=True' )
for policy in FSDP_AUTO_WRAP_POLICY:
if policy.lower() in config:
cmd_config.append(f'--fsdp_auto_wrap_policy={policy}' )
break
if policy == "TRANSFORMER_BASED_WRAP":
cmd_config.append('--fsdp_transformer_layer_cls_to_wrap=BertLayer' )
elif policy == "SIZE_BASED_WRAP":
cmd_config.append('--fsdp_min_num_params=2000' )
cmd_config.extend(
[
self.test_file_path,
f'--output_dir={self.tmpdir}',
f'--performance_lower_bound={self.performance_lower_bound}',
] )
with patch_environment(omp_num_threads=1 ):
execute_subprocess_async(__snake_case , env=os.environ.copy() )
def __magic_name__ ( self ) -> Union[str, Any]:
'''simple docstring'''
__a =os.path.join(self.test_scripts_folder , 'test_checkpointing.py' )
__a =[
'accelerate',
'launch',
'--num_processes=2',
'--num_machines=1',
'--machine_rank=0',
'--use_fsdp',
'--mixed_precision=fp16',
'--fsdp_transformer_layer_cls_to_wrap=BertLayer',
]
for i, strategy in enumerate(__snake_case ):
__a =cmd.copy()
cmd_config.append(f'--fsdp_sharding_strategy={i+1}' )
if strategy != "FULL_SHARD":
continue
__a =len(__snake_case )
for state_dict_type in FSDP_STATE_DICT_TYPE:
__a =cmd_config[:state_dict_config_index]
cmd_config.append(f'--fsdp_state_dict_type={state_dict_type}' )
cmd_config.extend(
[
self.test_file_path,
f'--output_dir={self.tmpdir}',
'--partial_train_epoch=1',
] )
with patch_environment(omp_num_threads=1 ):
execute_subprocess_async(__snake_case , env=os.environ.copy() )
__a =cmd_config[:-1]
__a =os.path.join(self.tmpdir , 'epoch_0' )
cmd_config.extend(
[
f'--resume_from_checkpoint={resume_from_checkpoint}',
] )
with patch_environment(omp_num_threads=1 ):
execute_subprocess_async(__snake_case , env=os.environ.copy() )
def __magic_name__ ( self ) -> List[Any]:
'''simple docstring'''
__a =os.path.join(self.test_scripts_folder , 'test_peak_memory_usage.py' )
__a =[
'accelerate',
'launch',
'--num_processes=2',
'--num_machines=1',
'--machine_rank=0',
]
for spec, peak_mem_upper_bound in self.peak_memory_usage_upper_bound.items():
__a =cmd.copy()
if "fp16" in spec:
cmd_config.extend(['--mixed_precision=fp16'] )
else:
cmd_config.extend(['--mixed_precision=no'] )
if "multi_gpu" in spec:
continue
else:
cmd_config.extend(['--use_fsdp'] )
for i, strategy in enumerate(__snake_case ):
if strategy.lower() in spec:
cmd_config.append(f'--fsdp_sharding_strategy={i+1}' )
break
if "cpu_offload" in spec:
cmd_config.append('--fsdp_offload_params=True' )
for policy in FSDP_AUTO_WRAP_POLICY:
if policy.lower() in spec:
cmd_config.append(f'--fsdp_auto_wrap_policy={policy}' )
break
if policy == "TRANSFORMER_BASED_WRAP":
cmd_config.append('--fsdp_transformer_layer_cls_to_wrap=BertLayer' )
elif policy == "SIZE_BASED_WRAP":
cmd_config.append('--fsdp_min_num_params=2000' )
cmd_config.extend(
[
self.test_file_path,
f'--output_dir={self.tmpdir}',
f'--peak_memory_upper_bound={peak_mem_upper_bound}',
f'--n_train={self.n_train}',
f'--n_val={self.n_val}',
] )
with patch_environment(omp_num_threads=1 ):
execute_subprocess_async(__snake_case , env=os.environ.copy() )
| 308
|
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available
_lowerCAmelCase : int = {}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_lowerCAmelCase : Dict = ["BartphoTokenizer"]
if TYPE_CHECKING:
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_bartpho import BartphoTokenizer
else:
import sys
_lowerCAmelCase : Optional[Any] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 308
| 1
|
from __future__ import annotations
from random import choice
def UpperCamelCase_( _snake_case : Any ):
"""simple docstring"""
return choice(_snake_case )
def UpperCamelCase_( _snake_case : list[int] , _snake_case : int ):
"""simple docstring"""
__a =random_pivot(_snake_case )
# partition based on pivot
# linear time
__a =[e for e in lst if e < pivot]
__a =[e for e in lst if e > pivot]
# if we get lucky, pivot might be the element we want.
# we can easily see this:
# small (elements smaller than k)
# + pivot (kth element)
# + big (elements larger than k)
if len(_snake_case ) == k - 1:
return pivot
# pivot is in elements bigger than k
elif len(_snake_case ) < k - 1:
return kth_number(_snake_case , k - len(_snake_case ) - 1 )
# pivot is in elements smaller than k
else:
return kth_number(_snake_case , _snake_case )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 308
|
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
_lowerCAmelCase : List[str] = logging.get_logger(__name__)
_lowerCAmelCase : List[Any] = {
"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 __magic_name__ ( lowerCAmelCase_ ):
SCREAMING_SNAKE_CASE = 'yolos'
def __init__( self , __snake_case=768 , __snake_case=12 , __snake_case=12 , __snake_case=3072 , __snake_case="gelu" , __snake_case=0.0 , __snake_case=0.0 , __snake_case=0.02 , __snake_case=1e-12 , __snake_case=[512, 864] , __snake_case=16 , __snake_case=3 , __snake_case=True , __snake_case=100 , __snake_case=True , __snake_case=False , __snake_case=1 , __snake_case=5 , __snake_case=2 , __snake_case=5 , __snake_case=2 , __snake_case=0.1 , **__snake_case , ) -> str:
'''simple docstring'''
super().__init__(**__snake_case )
__a =hidden_size
__a =num_hidden_layers
__a =num_attention_heads
__a =intermediate_size
__a =hidden_act
__a =hidden_dropout_prob
__a =attention_probs_dropout_prob
__a =initializer_range
__a =layer_norm_eps
__a =image_size
__a =patch_size
__a =num_channels
__a =qkv_bias
__a =num_detection_tokens
__a =use_mid_position_embeddings
__a =auxiliary_loss
# Hungarian matcher
__a =class_cost
__a =bbox_cost
__a =giou_cost
# Loss coefficients
__a =bbox_loss_coefficient
__a =giou_loss_coefficient
__a =eos_coefficient
class __magic_name__ ( lowerCAmelCase_ ):
SCREAMING_SNAKE_CASE = version.parse('1.11' )
@property
def __magic_name__ ( self ) -> Mapping[str, Mapping[int, str]]:
'''simple docstring'''
return OrderedDict(
[
('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'}),
] )
@property
def __magic_name__ ( self ) -> float:
'''simple docstring'''
return 1e-4
@property
def __magic_name__ ( self ) -> int:
'''simple docstring'''
return 12
| 308
| 1
|
def UpperCamelCase_( _snake_case : int = 1000 ):
"""simple docstring"""
__a =2**power
__a =0
while n:
__a , __a =r + n % 10, n // 10
return r
if __name__ == "__main__":
print(solution(int(str(input()).strip())))
| 308
|
import argparse
import torch
from datasets import load_dataset
from donut import DonutModel
from transformers import (
DonutImageProcessor,
DonutProcessor,
DonutSwinConfig,
DonutSwinModel,
MBartConfig,
MBartForCausalLM,
VisionEncoderDecoderModel,
XLMRobertaTokenizerFast,
)
def UpperCamelCase_( _snake_case : Optional[Any] ):
"""simple docstring"""
__a =model.config
__a =DonutSwinConfig(
image_size=original_config.input_size , patch_size=4 , depths=original_config.encoder_layer , num_heads=[4, 8, 16, 32] , window_size=original_config.window_size , embed_dim=128 , )
__a =MBartConfig(
is_decoder=_snake_case , is_encoder_decoder=_snake_case , add_cross_attention=_snake_case , decoder_layers=original_config.decoder_layer , max_position_embeddings=original_config.max_position_embeddings , vocab_size=len(
model.decoder.tokenizer ) , scale_embedding=_snake_case , add_final_layer_norm=_snake_case , )
return encoder_config, decoder_config
def UpperCamelCase_( _snake_case : Tuple ):
"""simple docstring"""
if "encoder.model" in name:
__a =name.replace('encoder.model' , 'encoder' )
if "decoder.model" in name:
__a =name.replace('decoder.model' , 'decoder' )
if "patch_embed.proj" in name:
__a =name.replace('patch_embed.proj' , 'embeddings.patch_embeddings.projection' )
if "patch_embed.norm" in name:
__a =name.replace('patch_embed.norm' , 'embeddings.norm' )
if name.startswith('encoder' ):
if "layers" in name:
__a ='encoder.' + name
if "attn.proj" in name:
__a =name.replace('attn.proj' , 'attention.output.dense' )
if "attn" in name and "mask" not in name:
__a =name.replace('attn' , 'attention.self' )
if "norm1" in name:
__a =name.replace('norm1' , 'layernorm_before' )
if "norm2" in name:
__a =name.replace('norm2' , 'layernorm_after' )
if "mlp.fc1" in name:
__a =name.replace('mlp.fc1' , 'intermediate.dense' )
if "mlp.fc2" in name:
__a =name.replace('mlp.fc2' , 'output.dense' )
if name == "encoder.norm.weight":
__a ='encoder.layernorm.weight'
if name == "encoder.norm.bias":
__a ='encoder.layernorm.bias'
return name
def UpperCamelCase_( _snake_case : Tuple , _snake_case : str ):
"""simple docstring"""
for key in orig_state_dict.copy().keys():
__a =orig_state_dict.pop(_snake_case )
if "qkv" in key:
__a =key.split('.' )
__a =int(key_split[3] )
__a =int(key_split[5] )
__a =model.encoder.encoder.layers[layer_num].blocks[block_num].attention.self.all_head_size
if "weight" in key:
__a =val[:dim, :]
__a =val[dim : dim * 2, :]
__a =val[-dim:, :]
else:
__a =val[:dim]
__a =val[dim : dim * 2]
__a =val[-dim:]
elif "attn_mask" in key or key in ["encoder.model.norm.weight", "encoder.model.norm.bias"]:
# HuggingFace implementation doesn't use attn_mask buffer
# and model doesn't use final LayerNorms for the encoder
pass
else:
__a =val
return orig_state_dict
def UpperCamelCase_( _snake_case : Tuple , _snake_case : Union[str, Any]=None , _snake_case : List[Any]=False ):
"""simple docstring"""
__a =DonutModel.from_pretrained(_snake_case ).eval()
# load HuggingFace model
__a , __a =get_configs(_snake_case )
__a =DonutSwinModel(_snake_case )
__a =MBartForCausalLM(_snake_case )
__a =VisionEncoderDecoderModel(encoder=_snake_case , decoder=_snake_case )
model.eval()
__a =original_model.state_dict()
__a =convert_state_dict(_snake_case , _snake_case )
model.load_state_dict(_snake_case )
# verify results on scanned document
__a =load_dataset('hf-internal-testing/example-documents' )
__a =dataset['test'][0]['image'].convert('RGB' )
__a =XLMRobertaTokenizerFast.from_pretrained(_snake_case , from_slow=_snake_case )
__a =DonutImageProcessor(
do_align_long_axis=original_model.config.align_long_axis , size=original_model.config.input_size[::-1] )
__a =DonutProcessor(_snake_case , _snake_case )
__a =processor(_snake_case , return_tensors='pt' ).pixel_values
if model_name == "naver-clova-ix/donut-base-finetuned-docvqa":
__a ='<s_docvqa><s_question>{user_input}</s_question><s_answer>'
__a ='When is the coffee break?'
__a =task_prompt.replace('{user_input}' , _snake_case )
elif model_name == "naver-clova-ix/donut-base-finetuned-rvlcdip":
__a ='<s_rvlcdip>'
elif model_name in [
"naver-clova-ix/donut-base-finetuned-cord-v1",
"naver-clova-ix/donut-base-finetuned-cord-v1-2560",
]:
__a ='<s_cord>'
elif model_name == "naver-clova-ix/donut-base-finetuned-cord-v2":
__a ='s_cord-v2>'
elif model_name == "naver-clova-ix/donut-base-finetuned-zhtrainticket":
__a ='<s_zhtrainticket>'
elif model_name in ["naver-clova-ix/donut-proto", "naver-clova-ix/donut-base"]:
# use a random prompt
__a ='hello world'
else:
raise ValueError('Model name not supported' )
__a =original_model.decoder.tokenizer(_snake_case , add_special_tokens=_snake_case , return_tensors='pt' )[
'input_ids'
]
__a =original_model.encoder.model.patch_embed(_snake_case )
__a , __a =model.encoder.embeddings(_snake_case )
assert torch.allclose(_snake_case , _snake_case , atol=1e-3 )
# verify encoder hidden states
__a =original_model.encoder(_snake_case )
__a =model.encoder(_snake_case ).last_hidden_state
assert torch.allclose(_snake_case , _snake_case , atol=1e-2 )
# verify decoder hidden states
__a =original_model(_snake_case , _snake_case , _snake_case ).logits
__a =model(_snake_case , decoder_input_ids=_snake_case ).logits
assert torch.allclose(_snake_case , _snake_case , atol=1e-3 )
print('Looks ok!' )
if pytorch_dump_folder_path is not None:
print(F'Saving model and processor to {pytorch_dump_folder_path}' )
model.save_pretrained(_snake_case )
processor.save_pretrained(_snake_case )
if push_to_hub:
model.push_to_hub('nielsr/' + model_name.split('/' )[-1] , commit_message='Update model' )
processor.push_to_hub('nielsr/' + model_name.split('/' )[-1] , commit_message='Update model' )
if __name__ == "__main__":
_lowerCAmelCase : List[str] = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--model_name",
default="naver-clova-ix/donut-base-finetuned-docvqa",
required=False,
type=str,
help="Name of the original model you'd like to convert.",
)
parser.add_argument(
"--pytorch_dump_folder_path",
default=None,
required=False,
type=str,
help="Path to the output PyTorch model directory.",
)
parser.add_argument(
"--push_to_hub",
action="store_true",
help="Whether or not to push the converted model and processor to the 🤗 hub.",
)
_lowerCAmelCase : List[Any] = parser.parse_args()
convert_donut_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)
| 308
| 1
|
import logging
import os
import random
import sys
from dataclasses import dataclass, field
from typing import Optional
import datasets
import numpy as np
import pandas as pd
from datasets import load_dataset
import transformers
from transformers import (
AutoConfig,
BartForSequenceClassification,
DataCollatorWithPadding,
EvalPrediction,
HfArgumentParser,
TapexTokenizer,
Trainer,
TrainingArguments,
default_data_collator,
set_seed,
)
from transformers.trainer_utils import get_last_checkpoint
from transformers.utils import check_min_version
from transformers.utils.versions import require_version
# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
check_min_version("4.17.0.dev0")
require_version("datasets>=1.8.0", "To fix: pip install -r examples/pytorch/text-classification/requirements.txt")
_lowerCAmelCase : Optional[Any] = logging.getLogger(__name__)
@dataclass
class __magic_name__ :
SCREAMING_SNAKE_CASE = field(
default='tab_fact' , metadata={'help': 'The name of the dataset to use (via the datasets library).'} )
SCREAMING_SNAKE_CASE = field(
default='tab_fact' , metadata={'help': 'The configuration name of the dataset to use (via the datasets library).'} , )
SCREAMING_SNAKE_CASE = field(
default=1_0_2_4 , metadata={
'help': (
'The maximum total input sequence length after tokenization. Sequences longer '
'than this will be truncated, sequences shorter will be padded.'
)
} , )
SCREAMING_SNAKE_CASE = field(
default=lowerCAmelCase_ , metadata={'help': 'Overwrite the cached preprocessed datasets or not.'} )
SCREAMING_SNAKE_CASE = field(
default=lowerCAmelCase_ , metadata={
'help': (
'Whether to pad all samples to `max_seq_length`. '
'If False, will pad the samples dynamically when batching to the maximum length in the batch.'
)
} , )
SCREAMING_SNAKE_CASE = field(
default=lowerCAmelCase_ , metadata={
'help': (
'For debugging purposes or quicker training, truncate the number of training examples to this '
'value if set.'
)
} , )
SCREAMING_SNAKE_CASE = field(
default=lowerCAmelCase_ , metadata={
'help': (
'For debugging purposes or quicker training, truncate the number of evaluation examples to this '
'value if set.'
)
} , )
SCREAMING_SNAKE_CASE = field(
default=lowerCAmelCase_ , metadata={
'help': (
'For debugging purposes or quicker training, truncate the number of prediction examples to this '
'value if set.'
)
} , )
SCREAMING_SNAKE_CASE = field(
default=lowerCAmelCase_ , metadata={'help': 'A csv or a json file containing the training data.'} )
SCREAMING_SNAKE_CASE = field(
default=lowerCAmelCase_ , metadata={'help': 'A csv or a json file containing the validation data.'} )
SCREAMING_SNAKE_CASE = field(default=lowerCAmelCase_ , metadata={'help': 'A csv or a json file containing the test data.'} )
def __magic_name__ ( self ) -> str:
'''simple docstring'''
if self.dataset_name is not None:
pass
elif self.train_file is None or self.validation_file is None:
raise ValueError('Need either a GLUE task, a training/validation file or a dataset name.' )
else:
__a =self.train_file.split('.' )[-1]
assert train_extension in ["csv", "json"], "`train_file` should be a csv or a json file."
__a =self.validation_file.split('.' )[-1]
assert (
validation_extension == train_extension
), "`validation_file` should have the same extension (csv or json) as `train_file`."
@dataclass
class __magic_name__ :
SCREAMING_SNAKE_CASE = field(
default=lowerCAmelCase_ , metadata={'help': 'Path to pretrained model or model identifier from huggingface.co/models'} )
SCREAMING_SNAKE_CASE = field(
default=lowerCAmelCase_ , metadata={'help': 'Pretrained config name or path if not the same as model_name'} )
SCREAMING_SNAKE_CASE = field(
default=lowerCAmelCase_ , metadata={'help': 'Pretrained tokenizer name or path if not the same as model_name'} )
SCREAMING_SNAKE_CASE = field(
default=lowerCAmelCase_ , metadata={'help': 'Where do you want to store the pretrained models downloaded from huggingface.co'} , )
SCREAMING_SNAKE_CASE = field(
default=lowerCAmelCase_ , metadata={'help': 'Whether to use one of the fast tokenizer (backed by the tokenizers library) or not.'} , )
SCREAMING_SNAKE_CASE = field(
default='main' , metadata={'help': 'The specific model version to use (can be a branch name, tag name or commit id).'} , )
SCREAMING_SNAKE_CASE = field(
default=lowerCAmelCase_ , metadata={
'help': (
'Will use the token generated when running `huggingface-cli login` (necessary to use this script '
'with private models).'
)
} , )
def UpperCamelCase_( ):
"""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()
# 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 )] , )
__a =training_args.get_process_log_level()
logger.setLevel(_snake_case )
datasets.utils.logging.set_verbosity(_snake_case )
transformers.utils.logging.set_verbosity(_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 training and evaluation files (see below)
# or specify a GLUE benchmark task (the dataset will be downloaded automatically from the datasets Hub).
#
# For JSON files, this script will use the `question` column for the input question and `table` column for the corresponding table.
#
# If the CSVs/JSONs contain only one non-label column, the script does single sentence classification on this
# single column. 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.dataset_name is not None:
# Downloading and loading a dataset from the hub.
__a =load_dataset(
data_args.dataset_name , data_args.dataset_config_name , cache_dir=model_args.cache_dir )
else:
# Loading a dataset from your local files.
# CSV/JSON training and evaluation files are needed.
__a ={'train': data_args.train_file, 'validation': data_args.validation_file}
# Get the test dataset: you can provide your own CSV/JSON test file (see below)
# when you use `do_predict` without specifying a GLUE benchmark task.
if training_args.do_predict:
if data_args.test_file is not None:
__a =data_args.train_file.split('.' )[-1]
__a =data_args.test_file.split('.' )[-1]
assert (
test_extension == train_extension
), "`test_file` should have the same extension (csv or json) as `train_file`."
__a =data_args.test_file
else:
raise ValueError('Need either a GLUE task or a test file for `do_predict`.' )
for key in data_files.keys():
logger.info(F'load a local file for {key}: {data_files[key]}' )
if data_args.train_file.endswith('.csv' ):
# Loading a dataset from local csv files
__a =load_dataset('csv' , data_files=_snake_case , cache_dir=model_args.cache_dir )
else:
# Loading a dataset from local json files
__a =load_dataset('json' , data_files=_snake_case , cache_dir=model_args.cache_dir )
# See more about loading any type of standard or custom dataset at
# https://huggingface.co/docs/datasets/loading_datasets.html.
# Labels
__a =raw_datasets['train'].features['label'].names
__a =len(_snake_case )
# Load pretrained model and tokenizer
#
# In 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 , num_labels=_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 , )
# load tapex tokenizer
__a =TapexTokenizer.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 , add_prefix_space=_snake_case , )
__a =BartForSequenceClassification.from_pretrained(
model_args.model_name_or_path , from_tf=bool('.ckpt' in model_args.model_name_or_path ) , config=_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 , )
# Padding strategy
if data_args.pad_to_max_length:
__a ='max_length'
else:
# We will pad later, dynamically at batch creation, to the max sequence length in each batch
__a =False
# Some models have set the order of the labels to use, so let's make sure we do use it.
__a ={'Refused': 0, 'Entailed': 1}
__a ={0: 'Refused', 1: 'Entailed'}
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 )
def preprocess_tabfact_function(_snake_case : Tuple ):
# Tokenize the texts
def _convert_table_text_to_pandas(_snake_case : Optional[Any] ):
__a =[_table_row.split('#' ) for _table_row in _table_text.strip('\n' ).split('\n' )]
__a =pd.DataFrame.from_records(_table_content[1:] , columns=_table_content[0] )
return _table_pd
__a =examples['statement']
__a =list(map(_convert_table_text_to_pandas , examples['table_text'] ) )
__a =tokenizer(_snake_case , _snake_case , padding=_snake_case , max_length=_snake_case , truncation=_snake_case )
__a =examples['label']
return result
with training_args.main_process_first(desc='dataset map pre-processing' ):
__a =raw_datasets.map(
_snake_case , batched=_snake_case , load_from_cache_file=not data_args.overwrite_cache , desc='Running tokenizer on dataset' , )
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 =train_dataset.select(range(data_args.max_train_samples ) )
if training_args.do_eval:
if "validation" not in raw_datasets and "validation_matched" 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 =eval_dataset.select(range(data_args.max_eval_samples ) )
if training_args.do_predict or data_args.test_file is not None:
if "test" not in raw_datasets and "test_matched" not in raw_datasets:
raise ValueError('--do_predict requires a test dataset' )
__a =raw_datasets['test']
if data_args.max_predict_samples is not None:
__a =predict_dataset.select(range(data_args.max_predict_samples ) )
# Log a few random samples from the training set:
if training_args.do_train:
for index in random.sample(range(len(_snake_case ) ) , 3 ):
logger.info(F'Sample {index} of the training set: {train_dataset[index]}.' )
# You can define your custom compute_metrics function. It takes an `EvalPrediction` object (a namedtuple with a
# predictions and label_ids field) and has to return a dictionary string to float.
def compute_metrics(_snake_case : EvalPrediction ):
__a =p.predictions[0] if isinstance(p.predictions , _snake_case ) else p.predictions
__a =np.argmax(_snake_case , axis=1 )
return {"accuracy": (preds == p.label_ids).astype(np.floataa ).mean().item()}
# Data collator will default to DataCollatorWithPadding, so we change it if we already did the padding.
if data_args.pad_to_max_length:
__a =default_data_collator
elif training_args.fpaa:
__a =DataCollatorWithPadding(_snake_case , pad_to_multiple_of=8 )
else:
__a =None
# Initialize our Trainer
__a =Trainer(
model=_snake_case , args=_snake_case , train_dataset=train_dataset if training_args.do_train else None , eval_dataset=eval_dataset if training_args.do_eval else None , compute_metrics=_snake_case , tokenizer=_snake_case , data_collator=_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=_snake_case )
__a =train_result.metrics
__a =(
data_args.max_train_samples if data_args.max_train_samples is not None else len(_snake_case )
)
__a =min(_snake_case , len(_snake_case ) )
trainer.save_model() # Saves the tokenizer too for easy upload
trainer.log_metrics('train' , _snake_case )
trainer.save_metrics('train' , _snake_case )
trainer.save_state()
# Evaluation
if training_args.do_eval:
logger.info('*** Evaluate ***' )
__a =trainer.evaluate(eval_dataset=_snake_case )
__a =data_args.max_eval_samples if data_args.max_eval_samples is not None else len(_snake_case )
__a =min(_snake_case , len(_snake_case ) )
trainer.log_metrics('eval' , _snake_case )
trainer.save_metrics('eval' , _snake_case )
if training_args.do_predict:
logger.info('*** Predict ***' )
# Removing the `label` columns because it contains -1 and Trainer won't like that.
__a =predict_dataset.remove_columns('label' )
__a =trainer.predict(_snake_case , metric_key_prefix='predict' ).predictions
__a =np.argmax(_snake_case , axis=1 )
__a =os.path.join(training_args.output_dir , 'predict_results_tabfact.txt' )
if trainer.is_world_process_zero():
with open(_snake_case , 'w' ) as writer:
logger.info('***** Predict Results *****' )
writer.write('index\tprediction\n' )
for index, item in enumerate(_snake_case ):
__a =label_list[item]
writer.write(F'{index}\t{item}\n' )
__a ={'finetuned_from': model_args.model_name_or_path, 'tasks': 'text-classification'}
if training_args.push_to_hub:
trainer.push_to_hub(**_snake_case )
else:
trainer.create_model_card(**_snake_case )
def UpperCamelCase_( _snake_case : Union[str, Any] ):
"""simple docstring"""
main()
if __name__ == "__main__":
main()
| 308
|
import json
import os
import unittest
from transformers.models.blenderbot_small.tokenization_blenderbot_small import (
VOCAB_FILES_NAMES,
BlenderbotSmallTokenizer,
)
from ...test_tokenization_common import TokenizerTesterMixin
class __magic_name__ ( lowerCAmelCase_ , unittest.TestCase ):
SCREAMING_SNAKE_CASE = BlenderbotSmallTokenizer
SCREAMING_SNAKE_CASE = False
def __magic_name__ ( self ) -> Union[str, Any]:
'''simple docstring'''
super().setUp()
__a =['__start__', 'adapt', 'act', 'ap@@', 'te', '__end__', '__unk__']
__a =dict(zip(__snake_case , range(len(__snake_case ) ) ) )
__a =['#version: 0.2', 'a p', 't e</w>', 'ap t</w>', 'a d', 'ad apt</w>', 'a c', 'ac t</w>', '']
__a ={'unk_token': '__unk__', 'bos_token': '__start__', 'eos_token': '__end__'}
__a =os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['vocab_file'] )
__a =os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['merges_file'] )
with open(self.vocab_file , 'w' , encoding='utf-8' ) as fp:
fp.write(json.dumps(__snake_case ) + '\n' )
with open(self.merges_file , 'w' , encoding='utf-8' ) as fp:
fp.write('\n'.join(__snake_case ) )
def __magic_name__ ( self , **__snake_case ) -> Any:
'''simple docstring'''
kwargs.update(self.special_tokens_map )
return BlenderbotSmallTokenizer.from_pretrained(self.tmpdirname , **__snake_case )
def __magic_name__ ( self , __snake_case ) -> List[Any]:
'''simple docstring'''
__a ='adapt act apte'
__a ='adapt act apte'
return input_text, output_text
def __magic_name__ ( self ) -> str:
'''simple docstring'''
__a =BlenderbotSmallTokenizer(self.vocab_file , self.merges_file , **self.special_tokens_map )
__a ='adapt act apte'
__a =['adapt', 'act', 'ap@@', 'te']
__a =tokenizer.tokenize(__snake_case )
self.assertListEqual(__snake_case , __snake_case )
__a =[tokenizer.bos_token] + tokens + [tokenizer.eos_token]
__a =[0, 1, 2, 3, 4, 5]
self.assertListEqual(tokenizer.convert_tokens_to_ids(__snake_case ) , __snake_case )
def __magic_name__ ( self ) -> str:
'''simple docstring'''
__a =BlenderbotSmallTokenizer.from_pretrained('facebook/blenderbot-90M' )
assert tok('sam' ).input_ids == [1384]
__a ='I am a small frog.'
__a =tok([src_text] , padding=__snake_case , truncation=__snake_case )['input_ids']
__a =tok.batch_decode(__snake_case , skip_special_tokens=__snake_case , clean_up_tokenization_spaces=__snake_case )[0]
assert src_text != decoded # I wish it did!
assert decoded == "i am a small frog ."
def __magic_name__ ( self ) -> str:
'''simple docstring'''
__a =BlenderbotSmallTokenizer.from_pretrained('facebook/blenderbot-90M' )
__a ='I am a small frog .'
__a ='.'
__a =tok(__snake_case )['input_ids']
__a =tok(__snake_case )['input_ids']
assert encoded[-1] == encoded_dot[0]
| 308
| 1
|
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available
_lowerCAmelCase : int = {}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_lowerCAmelCase : Dict = ["BartphoTokenizer"]
if TYPE_CHECKING:
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_bartpho import BartphoTokenizer
else:
import sys
_lowerCAmelCase : Optional[Any] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 308
|
import unittest
from transformers import load_tool
from transformers.utils import is_torch_available
if is_torch_available():
import torch
from transformers.testing_utils import require_torch
from .test_tools_common import ToolTesterMixin
@require_torch
class __magic_name__ ( unittest.TestCase , lowerCAmelCase_ ):
def __magic_name__ ( self ) -> Union[str, Any]:
'''simple docstring'''
__a =load_tool('text-to-speech' )
self.tool.setup()
def __magic_name__ ( self ) -> Dict:
'''simple docstring'''
# SpeechT5 isn't deterministic
torch.manual_seed(0 )
__a =self.tool('hey' )
__a =result.to_raw()
self.assertTrue(
torch.allclose(
resulting_tensor[:3] , torch.tensor([-0.000_5966_6688_3211_5829, -0.000_3657_6401_9079_5064, -0.0001_3439_5027_9988_3485] ) , ) )
def __magic_name__ ( self ) -> List[Any]:
'''simple docstring'''
# SpeechT5 isn't deterministic
torch.manual_seed(0 )
__a =self.tool('hey' )
__a =result.to_raw()
self.assertTrue(
torch.allclose(
resulting_tensor[:3] , torch.tensor([-0.000_5966_6688_3211_5829, -0.000_3657_6401_9079_5064, -0.0001_3439_5027_9988_3485] ) , ) )
| 308
| 1
|
from __future__ import annotations
import unittest
from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
if is_tf_available():
import numpy as np
import tensorflow as tf
from transformers import TFCamembertModel
@require_tf
@require_sentencepiece
@require_tokenizers
class __magic_name__ ( unittest.TestCase ):
@slow
def __magic_name__ ( self ) -> Dict:
'''simple docstring'''
__a =TFCamembertModel.from_pretrained('jplu/tf-camembert-base' )
__a =tf.convert_to_tensor(
[[5, 121, 11, 660, 16, 730, 2_5543, 110, 83, 6]] , dtype=tf.intaa , ) # J'aime le camembert !"
__a =model(__snake_case )['last_hidden_state']
__a =tf.TensorShape((1, 10, 768) )
self.assertEqual(output.shape , __snake_case )
# compare the actual values for a slice.
__a =tf.convert_to_tensor(
[[[-0.0254, 0.0235, 0.1027], [0.0606, -0.1811, -0.0418], [-0.1561, -0.1127, 0.2687]]] , dtype=tf.floataa , )
# camembert = torch.hub.load('pytorch/fairseq', 'camembert.v0')
# camembert.eval()
# expected_slice = roberta.model.forward(input_ids)[0][:, :3, :3].detach()
self.assertTrue(np.allclose(output[:, :3, :3].numpy() , expected_slice.numpy() , atol=1e-4 ) )
| 308
|
import flax.linen as nn
import jax.numpy as jnp
from .attention_flax import FlaxTransformeraDModel
from .resnet_flax import FlaxDownsampleaD, FlaxResnetBlockaD, FlaxUpsampleaD
class __magic_name__ ( nn.Module ):
SCREAMING_SNAKE_CASE = 42
SCREAMING_SNAKE_CASE = 42
SCREAMING_SNAKE_CASE = 0.0
SCREAMING_SNAKE_CASE = 1
SCREAMING_SNAKE_CASE = 1
SCREAMING_SNAKE_CASE = True
SCREAMING_SNAKE_CASE = False
SCREAMING_SNAKE_CASE = False
SCREAMING_SNAKE_CASE = False
SCREAMING_SNAKE_CASE = jnp.floataa
def __magic_name__ ( self ) -> int:
'''simple docstring'''
__a =[]
__a =[]
for i in range(self.num_layers ):
__a =self.in_channels if i == 0 else self.out_channels
__a =FlaxResnetBlockaD(
in_channels=__snake_case , out_channels=self.out_channels , dropout_prob=self.dropout , dtype=self.dtype , )
resnets.append(__snake_case )
__a =FlaxTransformeraDModel(
in_channels=self.out_channels , n_heads=self.num_attention_heads , d_head=self.out_channels // self.num_attention_heads , depth=1 , use_linear_projection=self.use_linear_projection , only_cross_attention=self.only_cross_attention , use_memory_efficient_attention=self.use_memory_efficient_attention , dtype=self.dtype , )
attentions.append(__snake_case )
__a =resnets
__a =attentions
if self.add_downsample:
__a =FlaxDownsampleaD(self.out_channels , dtype=self.dtype )
def __call__( self , __snake_case , __snake_case , __snake_case , __snake_case=True ) -> Optional[Any]:
'''simple docstring'''
__a =()
for resnet, attn in zip(self.resnets , self.attentions ):
__a =resnet(__snake_case , __snake_case , deterministic=__snake_case )
__a =attn(__snake_case , __snake_case , deterministic=__snake_case )
output_states += (hidden_states,)
if self.add_downsample:
__a =self.downsamplers_a(__snake_case )
output_states += (hidden_states,)
return hidden_states, output_states
class __magic_name__ ( nn.Module ):
SCREAMING_SNAKE_CASE = 42
SCREAMING_SNAKE_CASE = 42
SCREAMING_SNAKE_CASE = 0.0
SCREAMING_SNAKE_CASE = 1
SCREAMING_SNAKE_CASE = True
SCREAMING_SNAKE_CASE = jnp.floataa
def __magic_name__ ( self ) -> int:
'''simple docstring'''
__a =[]
for i in range(self.num_layers ):
__a =self.in_channels if i == 0 else self.out_channels
__a =FlaxResnetBlockaD(
in_channels=__snake_case , out_channels=self.out_channels , dropout_prob=self.dropout , dtype=self.dtype , )
resnets.append(__snake_case )
__a =resnets
if self.add_downsample:
__a =FlaxDownsampleaD(self.out_channels , dtype=self.dtype )
def __call__( self , __snake_case , __snake_case , __snake_case=True ) -> Optional[int]:
'''simple docstring'''
__a =()
for resnet in self.resnets:
__a =resnet(__snake_case , __snake_case , deterministic=__snake_case )
output_states += (hidden_states,)
if self.add_downsample:
__a =self.downsamplers_a(__snake_case )
output_states += (hidden_states,)
return hidden_states, output_states
class __magic_name__ ( nn.Module ):
SCREAMING_SNAKE_CASE = 42
SCREAMING_SNAKE_CASE = 42
SCREAMING_SNAKE_CASE = 42
SCREAMING_SNAKE_CASE = 0.0
SCREAMING_SNAKE_CASE = 1
SCREAMING_SNAKE_CASE = 1
SCREAMING_SNAKE_CASE = True
SCREAMING_SNAKE_CASE = False
SCREAMING_SNAKE_CASE = False
SCREAMING_SNAKE_CASE = False
SCREAMING_SNAKE_CASE = jnp.floataa
def __magic_name__ ( self ) -> List[Any]:
'''simple docstring'''
__a =[]
__a =[]
for i in range(self.num_layers ):
__a =self.in_channels if (i == self.num_layers - 1) else self.out_channels
__a =self.prev_output_channel if i == 0 else self.out_channels
__a =FlaxResnetBlockaD(
in_channels=resnet_in_channels + res_skip_channels , out_channels=self.out_channels , dropout_prob=self.dropout , dtype=self.dtype , )
resnets.append(__snake_case )
__a =FlaxTransformeraDModel(
in_channels=self.out_channels , n_heads=self.num_attention_heads , d_head=self.out_channels // self.num_attention_heads , depth=1 , use_linear_projection=self.use_linear_projection , only_cross_attention=self.only_cross_attention , use_memory_efficient_attention=self.use_memory_efficient_attention , dtype=self.dtype , )
attentions.append(__snake_case )
__a =resnets
__a =attentions
if self.add_upsample:
__a =FlaxUpsampleaD(self.out_channels , dtype=self.dtype )
def __call__( self , __snake_case , __snake_case , __snake_case , __snake_case , __snake_case=True ) -> List[Any]:
'''simple docstring'''
for resnet, attn in zip(self.resnets , self.attentions ):
# pop res hidden states
__a =res_hidden_states_tuple[-1]
__a =res_hidden_states_tuple[:-1]
__a =jnp.concatenate((hidden_states, res_hidden_states) , axis=-1 )
__a =resnet(__snake_case , __snake_case , deterministic=__snake_case )
__a =attn(__snake_case , __snake_case , deterministic=__snake_case )
if self.add_upsample:
__a =self.upsamplers_a(__snake_case )
return hidden_states
class __magic_name__ ( nn.Module ):
SCREAMING_SNAKE_CASE = 42
SCREAMING_SNAKE_CASE = 42
SCREAMING_SNAKE_CASE = 42
SCREAMING_SNAKE_CASE = 0.0
SCREAMING_SNAKE_CASE = 1
SCREAMING_SNAKE_CASE = True
SCREAMING_SNAKE_CASE = jnp.floataa
def __magic_name__ ( self ) -> Union[str, Any]:
'''simple docstring'''
__a =[]
for i in range(self.num_layers ):
__a =self.in_channels if (i == self.num_layers - 1) else self.out_channels
__a =self.prev_output_channel if i == 0 else self.out_channels
__a =FlaxResnetBlockaD(
in_channels=resnet_in_channels + res_skip_channels , out_channels=self.out_channels , dropout_prob=self.dropout , dtype=self.dtype , )
resnets.append(__snake_case )
__a =resnets
if self.add_upsample:
__a =FlaxUpsampleaD(self.out_channels , dtype=self.dtype )
def __call__( self , __snake_case , __snake_case , __snake_case , __snake_case=True ) -> List[Any]:
'''simple docstring'''
for resnet in self.resnets:
# pop res hidden states
__a =res_hidden_states_tuple[-1]
__a =res_hidden_states_tuple[:-1]
__a =jnp.concatenate((hidden_states, res_hidden_states) , axis=-1 )
__a =resnet(__snake_case , __snake_case , deterministic=__snake_case )
if self.add_upsample:
__a =self.upsamplers_a(__snake_case )
return hidden_states
class __magic_name__ ( nn.Module ):
SCREAMING_SNAKE_CASE = 42
SCREAMING_SNAKE_CASE = 0.0
SCREAMING_SNAKE_CASE = 1
SCREAMING_SNAKE_CASE = 1
SCREAMING_SNAKE_CASE = False
SCREAMING_SNAKE_CASE = False
SCREAMING_SNAKE_CASE = jnp.floataa
def __magic_name__ ( self ) -> List[Any]:
'''simple docstring'''
# there is always at least one resnet
__a =[
FlaxResnetBlockaD(
in_channels=self.in_channels , out_channels=self.in_channels , dropout_prob=self.dropout , dtype=self.dtype , )
]
__a =[]
for _ in range(self.num_layers ):
__a =FlaxTransformeraDModel(
in_channels=self.in_channels , n_heads=self.num_attention_heads , d_head=self.in_channels // self.num_attention_heads , depth=1 , use_linear_projection=self.use_linear_projection , use_memory_efficient_attention=self.use_memory_efficient_attention , dtype=self.dtype , )
attentions.append(__snake_case )
__a =FlaxResnetBlockaD(
in_channels=self.in_channels , out_channels=self.in_channels , dropout_prob=self.dropout , dtype=self.dtype , )
resnets.append(__snake_case )
__a =resnets
__a =attentions
def __call__( self , __snake_case , __snake_case , __snake_case , __snake_case=True ) -> List[str]:
'''simple docstring'''
__a =self.resnets[0](__snake_case , __snake_case )
for attn, resnet in zip(self.attentions , self.resnets[1:] ):
__a =attn(__snake_case , __snake_case , deterministic=__snake_case )
__a =resnet(__snake_case , __snake_case , deterministic=__snake_case )
return hidden_states
| 308
| 1
|
import os
from itertools import chain
from random import randrange, shuffle
import pytest
from .sola import PokerHand
_lowerCAmelCase : Optional[Any] = (
"4S 3H 2C 7S 5H",
"9D 8H 2C 6S 7H",
"2D 6D 9D TH 7D",
"TC 8C 2S JH 6C",
"JH 8S TH AH QH",
"TS KS 5S 9S AC",
"KD 6S 9D TH AD",
"KS 8D 4D 9S 4S", # pair
"8C 4S KH JS 4D", # pair
"QH 8H KD JH 8S", # pair
"KC 4H KS 2H 8D", # pair
"KD 4S KC 3H 8S", # pair
"AH 8S AS KC JH", # pair
"3H 4C 4H 3S 2H", # 2 pairs
"5S 5D 2C KH KH", # 2 pairs
"3C KH 5D 5S KH", # 2 pairs
"AS 3C KH AD KH", # 2 pairs
"7C 7S 3S 7H 5S", # 3 of a kind
"7C 7S KH 2H 7H", # 3 of a kind
"AC KH QH AH AS", # 3 of a kind
"2H 4D 3C AS 5S", # straight (low ace)
"3C 5C 4C 2C 6H", # straight
"6S 8S 7S 5H 9H", # straight
"JS QS 9H TS KH", # straight
"QC KH TS JS AH", # straight (high ace)
"8C 9C 5C 3C TC", # flush
"3S 8S 9S 5S KS", # flush
"4C 5C 9C 8C KC", # flush
"JH 8H AH KH QH", # flush
"3D 2H 3H 2C 2D", # full house
"2H 2C 3S 3H 3D", # full house
"KH KC 3S 3H 3D", # full house
"JC 6H JS JD JH", # 4 of a kind
"JC 7H JS JD JH", # 4 of a kind
"JC KH JS JD JH", # 4 of a kind
"2S AS 4S 5S 3S", # straight flush (low ace)
"2D 6D 3D 4D 5D", # straight flush
"5C 6C 3C 7C 4C", # straight flush
"JH 9H TH KH QH", # straight flush
"JH AH TH KH QH", # royal flush (high ace straight flush)
)
_lowerCAmelCase : Optional[Any] = (
("2H 3H 4H 5H 6H", "KS AS TS QS JS", "Loss"),
("2H 3H 4H 5H 6H", "AS AD AC AH JD", "Win"),
("AS AH 2H AD AC", "JS JD JC JH 3D", "Win"),
("2S AH 2H AS AC", "JS JD JC JH AD", "Loss"),
("2S AH 2H AS AC", "2H 3H 5H 6H 7H", "Win"),
("AS 3S 4S 8S 2S", "2H 3H 5H 6H 7H", "Win"),
("2H 3H 5H 6H 7H", "2S 3H 4H 5S 6C", "Win"),
("2S 3H 4H 5S 6C", "3D 4C 5H 6H 2S", "Tie"),
("2S 3H 4H 5S 6C", "AH AC 5H 6H AS", "Win"),
("2S 2H 4H 5S 4C", "AH AC 5H 6H AS", "Loss"),
("2S 2H 4H 5S 4C", "AH AC 5H 6H 7S", "Win"),
("6S AD 7H 4S AS", "AH AC 5H 6H 7S", "Loss"),
("2S AH 4H 5S KC", "AH AC 5H 6H 7S", "Loss"),
("2S 3H 6H 7S 9C", "7H 3C TH 6H 9S", "Loss"),
("4S 5H 6H TS AC", "3S 5H 6H TS AC", "Win"),
("2S AH 4H 5S 6C", "AD 4C 5H 6H 2C", "Tie"),
("AS AH 3H AD AC", "AS AH 2H AD AC", "Win"),
("AH AC 5H 5C QS", "AH AC 5H 5C KS", "Loss"),
("AH AC 5H 5C QS", "KH KC 5H 5C QS", "Win"),
("7C 7S KH 2H 7H", "3C 3S AH 2H 3H", "Win"),
("3C 3S AH 2H 3H", "7C 7S KH 2H 7H", "Loss"),
("6H 5H 4H 3H 2H", "5H 4H 3H 2H AH", "Win"),
("5H 4H 3H 2H AH", "5H 4H 3H 2H AH", "Tie"),
("5H 4H 3H 2H AH", "6H 5H 4H 3H 2H", "Loss"),
("AH AD KS KC AC", "AH KD KH AC KC", "Win"),
("2H 4D 3C AS 5S", "2H 4D 3C 6S 5S", "Loss"),
("2H 3S 3C 3H 2S", "3S 3C 2S 2H 2D", "Win"),
("4D 6D 5D 2D JH", "3S 8S 3H TC KH", "Loss"),
("4S 6C 8S 3S 7S", "AD KS 2D 7D 7C", "Loss"),
("6S 4C 7H 8C 3H", "5H JC AH 9D 9C", "Loss"),
("9D 9H JH TC QH", "3C 2S JS 5C 7H", "Win"),
("2H TC 8S AD 9S", "4H TS 7H 2C 5C", "Win"),
("9D 3S 2C 7S 7C", "JC TD 3C TC 9H", "Loss"),
)
_lowerCAmelCase : List[Any] = (
("2H 3H 4H 5H 6H", True),
("AS AH 2H AD AC", False),
("2H 3H 5H 6H 7H", True),
("KS AS TS QS JS", True),
("8H 9H QS JS TH", False),
("AS 3S 4S 8S 2S", True),
)
_lowerCAmelCase : Tuple = (
("2H 3H 4H 5H 6H", True),
("AS AH 2H AD AC", False),
("2H 3H 5H 6H 7H", False),
("KS AS TS QS JS", True),
("8H 9H QS JS TH", True),
)
_lowerCAmelCase : int = (
("2H 4D 3C AS 5S", True, [5, 4, 3, 2, 14]),
("2H 5D 3C AS 5S", False, [14, 5, 5, 3, 2]),
("JH QD KC AS TS", False, [14, 13, 12, 11, 10]),
("9D 3S 2C 7S 7C", False, [9, 7, 7, 3, 2]),
)
_lowerCAmelCase : Optional[int] = (
("JH AH TH KH QH", 0),
("JH 9H TH KH QH", 0),
("JC KH JS JD JH", 7),
("KH KC 3S 3H 3D", 6),
("8C 9C 5C 3C TC", 0),
("JS QS 9H TS KH", 0),
("7C 7S KH 2H 7H", 3),
("3C KH 5D 5S KH", 2),
("QH 8H KD JH 8S", 1),
("2D 6D 9D TH 7D", 0),
)
_lowerCAmelCase : Optional[int] = (
("JH AH TH KH QH", 23),
("JH 9H TH KH QH", 22),
("JC KH JS JD JH", 21),
("KH KC 3S 3H 3D", 20),
("8C 9C 5C 3C TC", 19),
("JS QS 9H TS KH", 18),
("7C 7S KH 2H 7H", 17),
("3C KH 5D 5S KH", 16),
("QH 8H KD JH 8S", 15),
("2D 6D 9D TH 7D", 14),
)
def UpperCamelCase_( ):
"""simple docstring"""
__a , __a =randrange(len(_snake_case ) ), randrange(len(_snake_case ) )
__a =['Loss', 'Tie', 'Win'][(play >= oppo) + (play > oppo)]
__a , __a =SORTED_HANDS[play], SORTED_HANDS[oppo]
return hand, other, expected
def UpperCamelCase_( _snake_case : int = 100 ):
"""simple docstring"""
return (generate_random_hand() for _ in range(_snake_case ))
@pytest.mark.parametrize('hand, expected' , _snake_case )
def UpperCamelCase_( _snake_case : List[Any] , _snake_case : Union[str, Any] ):
"""simple docstring"""
assert PokerHand(_snake_case )._is_flush() == expected
@pytest.mark.parametrize('hand, expected' , _snake_case )
def UpperCamelCase_( _snake_case : Any , _snake_case : int ):
"""simple docstring"""
assert PokerHand(_snake_case )._is_straight() == expected
@pytest.mark.parametrize('hand, expected, card_values' , _snake_case )
def UpperCamelCase_( _snake_case : int , _snake_case : List[str] , _snake_case : Any ):
"""simple docstring"""
__a =PokerHand(_snake_case )
assert player._is_five_high_straight() == expected
assert player._card_values == card_values
@pytest.mark.parametrize('hand, expected' , _snake_case )
def UpperCamelCase_( _snake_case : Optional[int] , _snake_case : str ):
"""simple docstring"""
assert PokerHand(_snake_case )._is_same_kind() == expected
@pytest.mark.parametrize('hand, expected' , _snake_case )
def UpperCamelCase_( _snake_case : List[str] , _snake_case : Tuple ):
"""simple docstring"""
assert PokerHand(_snake_case )._hand_type == expected
@pytest.mark.parametrize('hand, other, expected' , _snake_case )
def UpperCamelCase_( _snake_case : List[Any] , _snake_case : List[str] , _snake_case : str ):
"""simple docstring"""
assert PokerHand(_snake_case ).compare_with(PokerHand(_snake_case ) ) == expected
@pytest.mark.parametrize('hand, other, expected' , generate_random_hands() )
def UpperCamelCase_( _snake_case : Optional[int] , _snake_case : List[str] , _snake_case : Optional[Any] ):
"""simple docstring"""
assert PokerHand(_snake_case ).compare_with(PokerHand(_snake_case ) ) == expected
def UpperCamelCase_( ):
"""simple docstring"""
__a =[PokerHand(_snake_case ) for hand in SORTED_HANDS]
__a =poker_hands.copy()
shuffle(_snake_case )
__a =chain(sorted(_snake_case ) )
for index, hand in enumerate(_snake_case ):
assert hand == poker_hands[index]
def UpperCamelCase_( ):
"""simple docstring"""
__a =[PokerHand('2D AC 3H 4H 5S' ), PokerHand('2S 3H 4H 5S 6C' )]
pokerhands.sort(reverse=_snake_case )
assert pokerhands[0].__str__() == "2S 3H 4H 5S 6C"
def UpperCamelCase_( ):
"""simple docstring"""
__a =PokerHand('2C 4S AS 3D 5C' )
__a =True
__a =[5, 4, 3, 2, 14]
for _ in range(10 ):
assert pokerhand._is_five_high_straight() == expected
assert pokerhand._card_values == expected_card_values
def UpperCamelCase_( ):
"""simple docstring"""
__a =0
__a =os.path.abspath(os.path.dirname(_snake_case ) )
__a =os.path.join(_snake_case , 'poker_hands.txt' )
with open(_snake_case ) as file_hand:
for line in file_hand:
__a =line[:14].strip()
__a =line[15:].strip()
__a , __a =PokerHand(_snake_case ), PokerHand(_snake_case )
__a =player.compare_with(_snake_case )
if output == "Win":
answer += 1
assert answer == 376
| 308
|
import json
import os
import subprocess
import unittest
from ast import literal_eval
import pytest
from parameterized import parameterized, parameterized_class
from . import is_sagemaker_available
if is_sagemaker_available():
from sagemaker import Session, TrainingJobAnalytics
from sagemaker.huggingface import HuggingFace
@pytest.mark.skipif(
literal_eval(os.getenv('TEST_SAGEMAKER' , 'False' ) ) is not True , reason='Skipping test because should only be run when releasing minor transformers version' , )
@pytest.mark.usefixtures('sm_env' )
@parameterized_class(
[
{
'framework': 'pytorch',
'script': 'run_glue.py',
'model_name_or_path': 'distilbert-base-cased',
'instance_type': 'ml.p3.16xlarge',
'results': {'train_runtime': 6_5_0, 'eval_accuracy': 0.7, 'eval_loss': 0.6},
},
{
'framework': 'pytorch',
'script': 'run_ddp.py',
'model_name_or_path': 'distilbert-base-cased',
'instance_type': 'ml.p3.16xlarge',
'results': {'train_runtime': 6_0_0, 'eval_accuracy': 0.7, 'eval_loss': 0.6},
},
{
'framework': 'tensorflow',
'script': 'run_tf_dist.py',
'model_name_or_path': 'distilbert-base-cased',
'instance_type': 'ml.p3.16xlarge',
'results': {'train_runtime': 6_0_0, 'eval_accuracy': 0.6, 'eval_loss': 0.7},
},
] )
class __magic_name__ ( unittest.TestCase ):
def __magic_name__ ( self ) -> int:
'''simple docstring'''
if self.framework == "pytorch":
subprocess.run(
f'cp ./examples/pytorch/text-classification/run_glue.py {self.env.test_path}/run_glue.py'.split() , encoding='utf-8' , check=__snake_case , )
assert hasattr(self , 'env' )
def __magic_name__ ( self , __snake_case ) -> int:
'''simple docstring'''
__a =f'{self.env.base_job_name}-{instance_count}-{"ddp" if "ddp" in self.script else "smd"}'
# distributed data settings
__a ={'smdistributed': {'dataparallel': {'enabled': True}}} if self.script != 'run_ddp.py' else None
# creates estimator
return HuggingFace(
entry_point=self.script , source_dir=self.env.test_path , role=self.env.role , image_uri=self.env.image_uri , base_job_name=__snake_case , instance_count=__snake_case , instance_type=self.instance_type , debugger_hook_config=__snake_case , hyperparameters={**self.env.distributed_hyperparameters, 'model_name_or_path': self.model_name_or_path} , metric_definitions=self.env.metric_definitions , distribution=__snake_case , py_version='py36' , )
def __magic_name__ ( self , __snake_case ) -> Optional[Any]:
'''simple docstring'''
TrainingJobAnalytics(__snake_case ).export_csv(f'{self.env.test_path}/{job_name}_metrics.csv' )
@parameterized.expand([(2,)] )
def __magic_name__ ( self , __snake_case ) -> Optional[int]:
'''simple docstring'''
# create estimator
__a =self.create_estimator(__snake_case )
# run training
estimator.fit()
# result dataframe
__a =TrainingJobAnalytics(estimator.latest_training_job.name ).dataframe()
# extract kpis
__a =list(result_metrics_df[result_metrics_df.metric_name == 'eval_accuracy']['value'] )
__a =list(result_metrics_df[result_metrics_df.metric_name == 'eval_loss']['value'] )
# get train time from SageMaker job, this includes starting, preprocessing, stopping
__a =(
Session().describe_training_job(estimator.latest_training_job.name ).get('TrainingTimeInSeconds' , 99_9999 )
)
# assert kpis
assert train_runtime <= self.results["train_runtime"]
assert all(t >= self.results['eval_accuracy'] for t in eval_accuracy )
assert all(t <= self.results['eval_loss'] for t in eval_loss )
# dump tests result into json file to share in PR
with open(f'{estimator.latest_training_job.name}.json' , 'w' ) as outfile:
json.dump({'train_time': train_runtime, 'eval_accuracy': eval_accuracy, 'eval_loss': eval_loss} , __snake_case )
| 308
| 1
|
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
_lowerCAmelCase : Any = logging.get_logger(__name__)
class __magic_name__ ( lowerCAmelCase_ ):
SCREAMING_SNAKE_CASE = ['input_features', 'attention_mask']
def __init__( self , __snake_case=80 , __snake_case=1_6000 , __snake_case=0.0 , __snake_case=10 , __snake_case=25 , __snake_case="hamming_window" , __snake_case=3_2768.0 , __snake_case=0.97 , __snake_case=1.0 , __snake_case=True , __snake_case=True , __snake_case=False , **__snake_case , ) -> int:
'''simple docstring'''
super().__init__(feature_size=__snake_case , sampling_rate=__snake_case , padding_value=__snake_case , **__snake_case )
__a =feature_size
__a =sampling_rate
__a =padding_value
__a =hop_length
__a =win_length
__a =frame_signal_scale
__a =preemphasis_coeff
__a =mel_floor
__a =normalize_means
__a =normalize_vars
__a =win_function
__a =return_attention_mask
__a =win_length * sampling_rate // 1000
__a =hop_length * sampling_rate // 1000
__a =optimal_fft_length(self.sample_size )
__a =(self.n_fft // 2) + 1
def __magic_name__ ( self , __snake_case ) -> np.ndarray:
'''simple docstring'''
if self.win_function == "hamming_window":
__a =window_function(window_length=self.sample_size , name=self.win_function , periodic=__snake_case )
else:
__a =window_function(window_length=self.sample_size , name=self.win_function )
__a =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 , )
__a =spectrogram(
one_waveform * self.frame_signal_scale , window=__snake_case , frame_length=self.sample_size , hop_length=self.sample_stride , fft_length=self.n_fft , center=__snake_case , preemphasis=self.preemphasis_coeff , mel_filters=__snake_case , mel_floor=self.mel_floor , log_mel='log' , )
return msfc_features.T
def __magic_name__ ( self , __snake_case , __snake_case , __snake_case ) -> int:
'''simple docstring'''
# make sure we normalize float32 arrays
if self.normalize_means:
__a =x[:input_length].mean(axis=0 )
__a =np.subtract(__snake_case , __snake_case )
if self.normalize_vars:
__a =x[:input_length].std(axis=0 )
__a =np.divide(__snake_case , __snake_case )
if input_length < x.shape[0]:
__a =padding_value
# make sure array is in float32
__a =x.astype(np.floataa )
return x
def __magic_name__ ( self , __snake_case , __snake_case = None ) -> List[np.ndarray]:
'''simple docstring'''
__a =attention_mask.sum(-1 ) if attention_mask is not None else [x.shape[0] for x in input_features]
return [self._normalize_one(__snake_case , __snake_case , self.padding_value ) for x, n in zip(__snake_case , __snake_case )]
def __call__( self , __snake_case , __snake_case = False , __snake_case = None , __snake_case = False , __snake_case = None , __snake_case = None , __snake_case = None , __snake_case = None , **__snake_case , ) -> BatchFeature:
'''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.' )
__a =isinstance(__snake_case , 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}' )
__a =is_batched_numpy or (
isinstance(__snake_case , (list, tuple) ) and (isinstance(raw_speech[0] , (np.ndarray, tuple, list) ))
)
if is_batched:
__a =[np.asarray(__snake_case , dtype=np.floataa ) for speech in raw_speech]
elif not is_batched and not isinstance(__snake_case , np.ndarray ):
__a =np.asarray(__snake_case , dtype=np.floataa )
elif isinstance(__snake_case , np.ndarray ) and raw_speech.dtype is np.dtype(np.floataa ):
__a =raw_speech.astype(np.floataa )
# always return batch
if not is_batched:
__a =[raw_speech]
# extract fbank features
__a =[self._extract_mfsc_features(__snake_case ) for one_waveform in raw_speech]
# convert into correct format for padding
__a =BatchFeature({'input_features': features} )
__a =self.pad(
__snake_case , padding=__snake_case , max_length=__snake_case , truncation=__snake_case , pad_to_multiple_of=__snake_case , return_attention_mask=__snake_case , **__snake_case , )
# make sure list is in array format
__a =padded_inputs.get('input_features' )
if isinstance(input_features[0] , __snake_case ):
__a =[np.asarray(__snake_case , dtype=np.floataa ) for feature in input_features]
__a =padded_inputs.get('attention_mask' )
if attention_mask is not None:
__a =[np.asarray(__snake_case , dtype=np.intaa ) for array in attention_mask]
if self.normalize_means or self.normalize_vars:
__a =(
np.array(__snake_case , dtype=np.intaa )
if self._get_padding_strategies(__snake_case , max_length=__snake_case ) is not PaddingStrategy.DO_NOT_PAD
and padding
else None
)
__a =self.normalize(
padded_inputs['input_features'] , attention_mask=__snake_case )
if return_tensors is not None:
__a =padded_inputs.convert_to_tensors(__snake_case )
return padded_inputs
| 308
|
import argparse
import logging
from collections import namedtuple
import torch
from model_bertabs import BertAbsSummarizer
from models.model_builder import AbsSummarizer # The authors' implementation
from transformers import BertTokenizer
logging.basicConfig(level=logging.INFO)
_lowerCAmelCase : List[Any] = logging.getLogger(__name__)
_lowerCAmelCase : Optional[Any] = "Hello world! cécé herlolip"
_lowerCAmelCase : str = namedtuple(
"BertAbsConfig",
[
"temp_dir",
"large",
"use_bert_emb",
"finetune_bert",
"encoder",
"share_emb",
"max_pos",
"enc_layers",
"enc_hidden_size",
"enc_heads",
"enc_ff_size",
"enc_dropout",
"dec_layers",
"dec_hidden_size",
"dec_heads",
"dec_ff_size",
"dec_dropout",
],
)
def UpperCamelCase_( _snake_case : str , _snake_case : List[Any] ):
"""simple docstring"""
__a =BertAbsConfig(
temp_dir='.' , finetune_bert=_snake_case , large=_snake_case , share_emb=_snake_case , use_bert_emb=_snake_case , encoder='bert' , max_pos=512 , enc_layers=6 , enc_hidden_size=512 , enc_heads=8 , enc_ff_size=512 , enc_dropout=0.2 , dec_layers=6 , dec_hidden_size=768 , dec_heads=8 , dec_ff_size=2048 , dec_dropout=0.2 , )
__a =torch.load(_snake_case , lambda _snake_case , _snake_case : storage )
__a =AbsSummarizer(_snake_case , torch.device('cpu' ) , _snake_case )
original.eval()
__a =BertAbsSummarizer(_snake_case , torch.device('cpu' ) )
new_model.eval()
# -------------------
# Convert the weights
# -------------------
logging.info('convert the model' )
new_model.bert.load_state_dict(original.bert.state_dict() )
new_model.decoder.load_state_dict(original.decoder.state_dict() )
new_model.generator.load_state_dict(original.generator.state_dict() )
# ----------------------------------
# Make sure the outpus are identical
# ----------------------------------
logging.info('Make sure that the models\' outputs are identical' )
__a =BertTokenizer.from_pretrained('bert-base-uncased' )
# prepare the model inputs
__a =tokenizer.encode('This is sample éàalj\'-.' )
encoder_input_ids.extend([tokenizer.pad_token_id] * (512 - len(_snake_case )) )
__a =torch.tensor(_snake_case ).unsqueeze(0 )
__a =tokenizer.encode('This is sample 3 éàalj\'-.' )
decoder_input_ids.extend([tokenizer.pad_token_id] * (512 - len(_snake_case )) )
__a =torch.tensor(_snake_case ).unsqueeze(0 )
# failsafe to make sure the weights reset does not affect the
# loaded weights.
assert torch.max(torch.abs(original.generator[0].weight - new_model.generator[0].weight ) ) == 0
# forward pass
__a =encoder_input_ids
__a =decoder_input_ids
__a =__a =None
__a =None
__a =__a =None
__a =__a =None
__a =None
# The original model does not apply the geneator layer immediatly but rather in
# the beam search (where it combines softmax + linear layer). Since we already
# apply the softmax in our generation process we only apply the linear layer here.
# We make sure that the outputs of the full stack are identical
__a =original(_snake_case , _snake_case , _snake_case , _snake_case , _snake_case , _snake_case , _snake_case )[0]
__a =original.generator(_snake_case )
__a =new_model(
_snake_case , _snake_case , _snake_case , _snake_case , _snake_case )[0]
__a =new_model.generator(_snake_case )
__a =torch.max(torch.abs(output_converted_model - output_original_model ) ).item()
print('Maximum absolute difference beween weights: {:.2f}'.format(_snake_case ) )
__a =torch.max(torch.abs(output_converted_generator - output_original_generator ) ).item()
print('Maximum absolute difference beween weights: {:.2f}'.format(_snake_case ) )
__a =torch.allclose(_snake_case , _snake_case , atol=1e-3 )
if are_identical:
logging.info('all weights are equal up to 1e-3' )
else:
raise ValueError('the weights are different. The new model is likely different from the original one.' )
# The model has been saved with torch.save(model) and this is bound to the exact
# directory structure. We save the state_dict instead.
logging.info('saving the model\'s state dictionary' )
torch.save(
new_model.state_dict() , './bertabs-finetuned-cnndm-extractive-abstractive-summarization/pytorch_model.bin' )
if __name__ == "__main__":
_lowerCAmelCase : Optional[int] = argparse.ArgumentParser()
parser.add_argument(
"--bertabs_checkpoint_path",
default=None,
type=str,
required=True,
help="Path the official PyTorch dump.",
)
parser.add_argument(
"--pytorch_dump_folder_path",
default=None,
type=str,
required=True,
help="Path to the output PyTorch model.",
)
_lowerCAmelCase : Optional[Any] = parser.parse_args()
convert_bertabs_checkpoints(
args.bertabs_checkpoint_path,
args.pytorch_dump_folder_path,
)
| 308
| 1
|
import argparse
import pytorch_lightning as pl
import torch
from torch import nn
from transformers import LongformerForQuestionAnswering, LongformerModel
class __magic_name__ ( pl.LightningModule ):
def __init__( self , __snake_case ) -> List[Any]:
'''simple docstring'''
super().__init__()
__a =model
__a =2
__a =nn.Linear(self.model.config.hidden_size , self.num_labels )
def __magic_name__ ( self ) -> List[Any]:
'''simple docstring'''
pass
def UpperCamelCase_( _snake_case : str , _snake_case : str , _snake_case : str ):
"""simple docstring"""
__a =LongformerModel.from_pretrained(_snake_case )
__a =LightningModel(_snake_case )
__a =torch.load(_snake_case , map_location=torch.device('cpu' ) )
lightning_model.load_state_dict(ckpt['state_dict'] )
# init longformer question answering model
__a =LongformerForQuestionAnswering.from_pretrained(_snake_case )
# transfer weights
longformer_for_qa.longformer.load_state_dict(lightning_model.model.state_dict() )
longformer_for_qa.qa_outputs.load_state_dict(lightning_model.qa_outputs.state_dict() )
longformer_for_qa.eval()
# save model
longformer_for_qa.save_pretrained(_snake_case )
print(F'Conversion successful. Model saved under {pytorch_dump_folder_path}' )
if __name__ == "__main__":
_lowerCAmelCase : Optional[int] = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--longformer_model",
default=None,
type=str,
required=True,
help="model identifier of longformer. Should be either `longformer-base-4096` or `longformer-large-4096`.",
)
parser.add_argument(
"--longformer_question_answering_ckpt_path",
default=None,
type=str,
required=True,
help="Path the official PyTorch Lightning Checkpoint.",
)
parser.add_argument(
"--pytorch_dump_folder_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
)
_lowerCAmelCase : Optional[int] = parser.parse_args()
convert_longformer_qa_checkpoint_to_pytorch(
args.longformer_model, args.longformer_question_answering_ckpt_path, args.pytorch_dump_folder_path
)
| 308
|
import unittest
from transformers import XLMConfig, 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 (
XLMForMultipleChoice,
XLMForQuestionAnswering,
XLMForQuestionAnsweringSimple,
XLMForSequenceClassification,
XLMForTokenClassification,
XLMModel,
XLMWithLMHeadModel,
)
from transformers.models.xlm.modeling_xlm import XLM_PRETRAINED_MODEL_ARCHIVE_LIST
class __magic_name__ :
def __init__( self , __snake_case , __snake_case=13 , __snake_case=7 , __snake_case=True , __snake_case=True , __snake_case=True , __snake_case=True , __snake_case=True , __snake_case=False , __snake_case=False , __snake_case=False , __snake_case=2 , __snake_case=99 , __snake_case=0 , __snake_case=32 , __snake_case=5 , __snake_case=4 , __snake_case=0.1 , __snake_case=0.1 , __snake_case=512 , __snake_case=2 , __snake_case=0.02 , __snake_case=2 , __snake_case=4 , __snake_case="last" , __snake_case=True , __snake_case=None , __snake_case=0 , ) -> Optional[Any]:
'''simple docstring'''
__a =parent
__a =batch_size
__a =seq_length
__a =is_training
__a =use_input_lengths
__a =use_token_type_ids
__a =use_labels
__a =gelu_activation
__a =sinusoidal_embeddings
__a =causal
__a =asm
__a =n_langs
__a =vocab_size
__a =n_special
__a =hidden_size
__a =num_hidden_layers
__a =num_attention_heads
__a =hidden_dropout_prob
__a =attention_probs_dropout_prob
__a =max_position_embeddings
__a =type_sequence_label_size
__a =initializer_range
__a =num_labels
__a =num_choices
__a =summary_type
__a =use_proj
__a =scope
__a =bos_token_id
def __magic_name__ ( self ) -> Any:
'''simple docstring'''
__a =ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
__a =random_attention_mask([self.batch_size, self.seq_length] )
__a =None
if self.use_input_lengths:
__a =(
ids_tensor([self.batch_size] , vocab_size=2 ) + self.seq_length - 2
) # small variation of seq_length
__a =None
if self.use_token_type_ids:
__a =ids_tensor([self.batch_size, self.seq_length] , self.n_langs )
__a =None
__a =None
__a =None
if self.use_labels:
__a =ids_tensor([self.batch_size] , self.type_sequence_label_size )
__a =ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
__a =ids_tensor([self.batch_size] , 2 ).float()
__a =ids_tensor([self.batch_size] , self.num_choices )
__a =self.get_config()
return (
config,
input_ids,
token_type_ids,
input_lengths,
sequence_labels,
token_labels,
is_impossible_labels,
choice_labels,
input_mask,
)
def __magic_name__ ( self ) -> Any:
'''simple docstring'''
return XLMConfig(
vocab_size=self.vocab_size , n_special=self.n_special , emb_dim=self.hidden_size , n_layers=self.num_hidden_layers , n_heads=self.num_attention_heads , dropout=self.hidden_dropout_prob , attention_dropout=self.attention_probs_dropout_prob , gelu_activation=self.gelu_activation , sinusoidal_embeddings=self.sinusoidal_embeddings , asm=self.asm , causal=self.causal , n_langs=self.n_langs , max_position_embeddings=self.max_position_embeddings , initializer_range=self.initializer_range , summary_type=self.summary_type , use_proj=self.use_proj , num_labels=self.num_labels , bos_token_id=self.bos_token_id , )
def __magic_name__ ( self , __snake_case , __snake_case , __snake_case , __snake_case , __snake_case , __snake_case , __snake_case , __snake_case , __snake_case , ) -> List[Any]:
'''simple docstring'''
__a =XLMModel(config=__snake_case )
model.to(__snake_case )
model.eval()
__a =model(__snake_case , lengths=__snake_case , langs=__snake_case )
__a =model(__snake_case , langs=__snake_case )
__a =model(__snake_case )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def __magic_name__ ( self , __snake_case , __snake_case , __snake_case , __snake_case , __snake_case , __snake_case , __snake_case , __snake_case , __snake_case , ) -> Optional[int]:
'''simple docstring'''
__a =XLMWithLMHeadModel(__snake_case )
model.to(__snake_case )
model.eval()
__a =model(__snake_case , token_type_ids=__snake_case , labels=__snake_case )
self.parent.assertEqual(result.loss.shape , () )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def __magic_name__ ( self , __snake_case , __snake_case , __snake_case , __snake_case , __snake_case , __snake_case , __snake_case , __snake_case , __snake_case , ) -> Dict:
'''simple docstring'''
__a =XLMForQuestionAnsweringSimple(__snake_case )
model.to(__snake_case )
model.eval()
__a =model(__snake_case )
__a =model(__snake_case , start_positions=__snake_case , end_positions=__snake_case )
__a =outputs
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 __magic_name__ ( self , __snake_case , __snake_case , __snake_case , __snake_case , __snake_case , __snake_case , __snake_case , __snake_case , __snake_case , ) -> List[Any]:
'''simple docstring'''
__a =XLMForQuestionAnswering(__snake_case )
model.to(__snake_case )
model.eval()
__a =model(__snake_case )
__a =model(
__snake_case , start_positions=__snake_case , end_positions=__snake_case , cls_index=__snake_case , is_impossible=__snake_case , p_mask=__snake_case , )
__a =model(
__snake_case , start_positions=__snake_case , end_positions=__snake_case , cls_index=__snake_case , is_impossible=__snake_case , )
((__a) , ) =result_with_labels.to_tuple()
__a =model(__snake_case , start_positions=__snake_case , end_positions=__snake_case )
((__a) , ) =result_with_labels.to_tuple()
self.parent.assertEqual(result_with_labels.loss.shape , () )
self.parent.assertEqual(result.start_top_log_probs.shape , (self.batch_size, model.config.start_n_top) )
self.parent.assertEqual(result.start_top_index.shape , (self.batch_size, model.config.start_n_top) )
self.parent.assertEqual(
result.end_top_log_probs.shape , (self.batch_size, model.config.start_n_top * model.config.end_n_top) )
self.parent.assertEqual(
result.end_top_index.shape , (self.batch_size, model.config.start_n_top * model.config.end_n_top) )
self.parent.assertEqual(result.cls_logits.shape , (self.batch_size,) )
def __magic_name__ ( self , __snake_case , __snake_case , __snake_case , __snake_case , __snake_case , __snake_case , __snake_case , __snake_case , __snake_case , ) -> Optional[Any]:
'''simple docstring'''
__a =XLMForSequenceClassification(__snake_case )
model.to(__snake_case )
model.eval()
__a =model(__snake_case )
__a =model(__snake_case , labels=__snake_case )
self.parent.assertEqual(result.loss.shape , () )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size) )
def __magic_name__ ( self , __snake_case , __snake_case , __snake_case , __snake_case , __snake_case , __snake_case , __snake_case , __snake_case , __snake_case , ) -> Any:
'''simple docstring'''
__a =self.num_labels
__a =XLMForTokenClassification(__snake_case )
model.to(__snake_case )
model.eval()
__a =model(__snake_case , attention_mask=__snake_case , labels=__snake_case )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def __magic_name__ ( self , __snake_case , __snake_case , __snake_case , __snake_case , __snake_case , __snake_case , __snake_case , __snake_case , __snake_case , ) -> Tuple:
'''simple docstring'''
__a =self.num_choices
__a =XLMForMultipleChoice(config=__snake_case )
model.to(__snake_case )
model.eval()
__a =input_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
__a =token_type_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
__a =input_mask.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
__a =model(
__snake_case , attention_mask=__snake_case , token_type_ids=__snake_case , labels=__snake_case , )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_choices) )
def __magic_name__ ( self ) -> Union[str, Any]:
'''simple docstring'''
__a =self.prepare_config_and_inputs()
(
(
__a
) , (
__a
) , (
__a
) , (
__a
) , (
__a
) , (
__a
) , (
__a
) , (
__a
) , (
__a
) ,
) =config_and_inputs
__a ={'input_ids': input_ids, 'token_type_ids': token_type_ids, 'lengths': input_lengths}
return config, inputs_dict
@require_torch
class __magic_name__ ( lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , unittest.TestCase ):
SCREAMING_SNAKE_CASE = (
(
XLMModel,
XLMWithLMHeadModel,
XLMForQuestionAnswering,
XLMForSequenceClassification,
XLMForQuestionAnsweringSimple,
XLMForTokenClassification,
XLMForMultipleChoice,
)
if is_torch_available()
else ()
)
SCREAMING_SNAKE_CASE = (
(XLMWithLMHeadModel,) if is_torch_available() else ()
) # TODO (PVP): Check other models whether language generation is also applicable
SCREAMING_SNAKE_CASE = (
{
'feature-extraction': XLMModel,
'fill-mask': XLMWithLMHeadModel,
'question-answering': XLMForQuestionAnsweringSimple,
'text-classification': XLMForSequenceClassification,
'text-generation': XLMWithLMHeadModel,
'token-classification': XLMForTokenClassification,
'zero-shot': XLMForSequenceClassification,
}
if is_torch_available()
else {}
)
def __magic_name__ ( self , __snake_case , __snake_case , __snake_case , __snake_case , __snake_case ) -> int:
'''simple docstring'''
if (
pipeline_test_casse_name == "QAPipelineTests"
and tokenizer_name is not None
and not tokenizer_name.endswith('Fast' )
):
# `QAPipelineTests` fails for a few models when the slower tokenizer are used.
# (The slower tokenizers were never used for pipeline tests before the pipeline testing rework)
# TODO: check (and possibly fix) the `QAPipelineTests` with slower tokenizer
return True
return False
def __magic_name__ ( self , __snake_case , __snake_case , __snake_case=False ) -> str:
'''simple docstring'''
__a =super()._prepare_for_class(__snake_case , __snake_case , return_labels=__snake_case )
if return_labels:
if model_class.__name__ == "XLMForQuestionAnswering":
__a =torch.zeros(
self.model_tester.batch_size , dtype=torch.long , device=__snake_case )
__a =torch.zeros(
self.model_tester.batch_size , dtype=torch.long , device=__snake_case )
return inputs_dict
def __magic_name__ ( self ) -> Tuple:
'''simple docstring'''
__a =XLMModelTester(self )
__a =ConfigTester(self , config_class=__snake_case , emb_dim=37 )
def __magic_name__ ( self ) -> str:
'''simple docstring'''
self.config_tester.run_common_tests()
def __magic_name__ ( self ) -> List[str]:
'''simple docstring'''
__a =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_xlm_model(*__snake_case )
def __magic_name__ ( self ) -> List[str]:
'''simple docstring'''
__a =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_xlm_lm_head(*__snake_case )
def __magic_name__ ( self ) -> List[str]:
'''simple docstring'''
__a =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_xlm_simple_qa(*__snake_case )
def __magic_name__ ( self ) -> Optional[int]:
'''simple docstring'''
__a =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_xlm_qa(*__snake_case )
def __magic_name__ ( self ) -> Any:
'''simple docstring'''
__a =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_xlm_sequence_classif(*__snake_case )
def __magic_name__ ( self ) -> Optional[int]:
'''simple docstring'''
__a =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_xlm_token_classif(*__snake_case )
def __magic_name__ ( self ) -> Any:
'''simple docstring'''
__a =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_xlm_for_multiple_choice(*__snake_case )
def __magic_name__ ( self , __snake_case , __snake_case , __snake_case , __snake_case , __snake_case , __snake_case=False , __snake_case=1 ) -> Optional[Any]:
'''simple docstring'''
self.assertIsInstance(__snake_case , __snake_case )
self.assertListEqual(
[isinstance(__snake_case , __snake_case ) for iter_attentions in attentions] , [True] * len(__snake_case ) )
self.assertEqual(len(__snake_case ) , (max_length - min_length) * num_beam_groups )
for idx, iter_attentions in enumerate(__snake_case ):
# adds PAD dummy token
__a =min_length + idx + 1
__a =min_length + idx + 1
__a =(
batch_size * num_beam_groups,
config.num_attention_heads,
tgt_len,
src_len,
)
# check attn size
self.assertListEqual(
[layer_attention.shape for layer_attention in iter_attentions] , [expected_shape] * len(__snake_case ) )
def __magic_name__ ( self , __snake_case , __snake_case , __snake_case , __snake_case , __snake_case , __snake_case=False , __snake_case=1 ) -> Dict:
'''simple docstring'''
self.assertIsInstance(__snake_case , __snake_case )
self.assertListEqual(
[isinstance(__snake_case , __snake_case ) for iter_hidden_states in hidden_states] , [True] * len(__snake_case ) , )
self.assertEqual(len(__snake_case ) , (max_length - min_length) * num_beam_groups )
for idx, iter_hidden_states in enumerate(__snake_case ):
# adds PAD dummy token
__a =min_length + idx + 1
__a =(batch_size * num_beam_groups, seq_len, config.hidden_size)
# check hidden size
self.assertListEqual(
[layer_hidden_states.shape for layer_hidden_states in iter_hidden_states] , [expected_shape] * len(__snake_case ) , )
pass
@slow
def __magic_name__ ( self ) -> Union[str, Any]:
'''simple docstring'''
for model_name in XLM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
__a =XLMModel.from_pretrained(__snake_case )
self.assertIsNotNone(__snake_case )
@require_torch
class __magic_name__ ( unittest.TestCase ):
@slow
def __magic_name__ ( self ) -> Tuple:
'''simple docstring'''
__a =XLMWithLMHeadModel.from_pretrained('xlm-mlm-en-2048' )
model.to(__snake_case )
__a =torch.tensor([[14, 447]] , dtype=torch.long , device=__snake_case ) # the president
__a =[
14,
447,
14,
447,
14,
447,
14,
447,
14,
447,
14,
447,
14,
447,
14,
447,
14,
447,
14,
447,
] # the president the president the president the president the president the president the president the president the president the president
# TODO(PVP): this and other input_ids I tried for generation give pretty bad results. Not sure why. Model might just not be made for auto-regressive inference
__a =model.generate(__snake_case , do_sample=__snake_case )
self.assertListEqual(output_ids[0].cpu().numpy().tolist() , __snake_case )
| 308
| 1
|
import logging
import numpy as np
import pytest
from scipy.linalg import eigh
logging.basicConfig(level=logging.INFO, format="%(message)s")
def UpperCamelCase_( _snake_case : np.ndarray ):
"""simple docstring"""
return input_array.reshape((input_array.size, 1) )
def UpperCamelCase_( _snake_case : np.ndarray , _snake_case : np.ndarray , _snake_case : int ):
"""simple docstring"""
__a =np.nan
for i in range(_snake_case ):
__a =features[:, labels == i]
__a =data.mean(1 )
# Centralize the data of class i
__a =data - column_reshape(_snake_case )
if i > 0:
# If covariance_sum is not None
covariance_sum += np.dot(_snake_case , centered_data.T )
else:
# If covariance_sum is np.nan (i.e. first loop)
__a =np.dot(_snake_case , centered_data.T )
return covariance_sum / features.shape[1]
def UpperCamelCase_( _snake_case : np.ndarray , _snake_case : np.ndarray , _snake_case : int ):
"""simple docstring"""
__a =features.mean(1 )
__a =np.nan
for i in range(_snake_case ):
__a =features[:, labels == i]
__a =data.shape[1]
__a =data.mean(1 )
if i > 0:
# If covariance_sum is not None
covariance_sum += device_data * np.dot(
column_reshape(_snake_case ) - column_reshape(_snake_case ) , (column_reshape(_snake_case ) - column_reshape(_snake_case )).T , )
else:
# If covariance_sum is np.nan (i.e. first loop)
__a =device_data * np.dot(
column_reshape(_snake_case ) - column_reshape(_snake_case ) , (column_reshape(_snake_case ) - column_reshape(_snake_case )).T , )
return covariance_sum / features.shape[1]
def UpperCamelCase_( _snake_case : np.ndarray , _snake_case : int ):
"""simple docstring"""
if features.any():
__a =features.mean(1 )
# Center the dataset
__a =features - np.reshape(_snake_case , (data_mean.size, 1) )
__a =np.dot(_snake_case , centered_data.T ) / features.shape[1]
__a , __a =np.linalg.eigh(_snake_case )
# Take all the columns in the reverse order (-1), and then takes only the first
__a =eigenvectors[:, ::-1][:, 0:dimensions]
# Project the database on the new space
__a =np.dot(filtered_eigenvectors.T , _snake_case )
logging.info('Principal Component Analysis computed' )
return projected_data
else:
logging.basicConfig(level=logging.ERROR , format='%(message)s' , force=_snake_case )
logging.error('Dataset empty' )
raise AssertionError
def UpperCamelCase_( _snake_case : np.ndarray , _snake_case : np.ndarray , _snake_case : int , _snake_case : int ):
"""simple docstring"""
assert classes > dimensions
# Check if features have been already loaded
if features.any:
__a , __a =eigh(
covariance_between_classes(_snake_case , _snake_case , _snake_case ) , covariance_within_classes(_snake_case , _snake_case , _snake_case ) , )
__a =eigenvectors[:, ::-1][:, :dimensions]
__a , __a , __a =np.linalg.svd(_snake_case )
__a =svd_matrix[:, 0:dimensions]
__a =np.dot(filtered_svd_matrix.T , _snake_case )
logging.info('Linear Discriminant Analysis computed' )
return projected_data
else:
logging.basicConfig(level=logging.ERROR , format='%(message)s' , force=_snake_case )
logging.error('Dataset empty' )
raise AssertionError
def UpperCamelCase_( ):
"""simple docstring"""
__a =np.array([[1, 2, 3, 4, 5], [2, 3, 4, 5, 6], [3, 4, 5, 6, 7]] )
__a =np.array([0, 0, 0, 1, 1] )
__a =2
__a =2
# Assert that the function raises an AssertionError if dimensions > classes
with pytest.raises(_snake_case ) as error_info:
__a =linear_discriminant_analysis(
_snake_case , _snake_case , _snake_case , _snake_case )
if isinstance(_snake_case , np.ndarray ):
raise AssertionError(
'Did not raise AssertionError for dimensions > classes' )
assert error_info.type is AssertionError
def UpperCamelCase_( ):
"""simple docstring"""
__a =np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]] )
__a =2
__a =np.array([[6.92_820_323, 8.66_025_404, 10.39_230_485], [3.0, 3.0, 3.0]] )
with pytest.raises(_snake_case ) as error_info:
__a =principal_component_analysis(_snake_case , _snake_case )
if not np.allclose(_snake_case , _snake_case ):
raise AssertionError
assert error_info.type is AssertionError
if __name__ == "__main__":
import doctest
doctest.testmod()
| 308
|
from __future__ import annotations
import matplotlib.pyplot as plt # type: ignore
import numpy
# initial triangle of Koch snowflake
_lowerCAmelCase : Optional[Any] = numpy.array([0, 0])
_lowerCAmelCase : Dict = numpy.array([0.5, 0.8660254])
_lowerCAmelCase : Any = numpy.array([1, 0])
_lowerCAmelCase : int = [VECTOR_1, VECTOR_2, VECTOR_3, VECTOR_1]
def UpperCamelCase_( _snake_case : list[numpy.ndarray] , _snake_case : int ):
"""simple docstring"""
__a =initial_vectors
for _ in range(_snake_case ):
__a =iteration_step(_snake_case )
return vectors
def UpperCamelCase_( _snake_case : list[numpy.ndarray] ):
"""simple docstring"""
__a =[]
for i, start_vector in enumerate(vectors[:-1] ):
__a =vectors[i + 1]
new_vectors.append(_snake_case )
__a =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 UpperCamelCase_( _snake_case : numpy.ndarray , _snake_case : float ):
"""simple docstring"""
__a =numpy.radians(_snake_case )
__a , __a =numpy.cos(_snake_case ), numpy.sin(_snake_case )
__a =numpy.array(((c, -s), (s, c)) )
return numpy.dot(_snake_case , _snake_case )
def UpperCamelCase_( _snake_case : list[numpy.ndarray] ):
"""simple docstring"""
__a =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 =zip(*_snake_case )
plt.plot(_snake_case , _snake_case )
plt.show()
if __name__ == "__main__":
import doctest
doctest.testmod()
_lowerCAmelCase : List[Any] = iterate(INITIAL_VECTORS, 5)
plot(processed_vectors)
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from __future__ import annotations
import matplotlib.pyplot as plt # type: ignore
import numpy
# initial triangle of Koch snowflake
_lowerCAmelCase : Optional[Any] = numpy.array([0, 0])
_lowerCAmelCase : Dict = numpy.array([0.5, 0.8660254])
_lowerCAmelCase : Any = numpy.array([1, 0])
_lowerCAmelCase : int = [VECTOR_1, VECTOR_2, VECTOR_3, VECTOR_1]
def UpperCamelCase_( _snake_case : list[numpy.ndarray] , _snake_case : int ):
"""simple docstring"""
__a =initial_vectors
for _ in range(_snake_case ):
__a =iteration_step(_snake_case )
return vectors
def UpperCamelCase_( _snake_case : list[numpy.ndarray] ):
"""simple docstring"""
__a =[]
for i, start_vector in enumerate(vectors[:-1] ):
__a =vectors[i + 1]
new_vectors.append(_snake_case )
__a =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 UpperCamelCase_( _snake_case : numpy.ndarray , _snake_case : float ):
"""simple docstring"""
__a =numpy.radians(_snake_case )
__a , __a =numpy.cos(_snake_case ), numpy.sin(_snake_case )
__a =numpy.array(((c, -s), (s, c)) )
return numpy.dot(_snake_case , _snake_case )
def UpperCamelCase_( _snake_case : list[numpy.ndarray] ):
"""simple docstring"""
__a =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 =zip(*_snake_case )
plt.plot(_snake_case , _snake_case )
plt.show()
if __name__ == "__main__":
import doctest
doctest.testmod()
_lowerCAmelCase : List[Any] = iterate(INITIAL_VECTORS, 5)
plot(processed_vectors)
| 308
|
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_lowerCAmelCase : Tuple = {
"configuration_megatron_bert": ["MEGATRON_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "MegatronBertConfig"],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_lowerCAmelCase : List[str] = [
"MEGATRON_BERT_PRETRAINED_MODEL_ARCHIVE_LIST",
"MegatronBertForCausalLM",
"MegatronBertForMaskedLM",
"MegatronBertForMultipleChoice",
"MegatronBertForNextSentencePrediction",
"MegatronBertForPreTraining",
"MegatronBertForQuestionAnswering",
"MegatronBertForSequenceClassification",
"MegatronBertForTokenClassification",
"MegatronBertModel",
"MegatronBertPreTrainedModel",
]
if TYPE_CHECKING:
from .configuration_megatron_bert import MEGATRON_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP, MegatronBertConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_megatron_bert import (
MEGATRON_BERT_PRETRAINED_MODEL_ARCHIVE_LIST,
MegatronBertForCausalLM,
MegatronBertForMaskedLM,
MegatronBertForMultipleChoice,
MegatronBertForNextSentencePrediction,
MegatronBertForPreTraining,
MegatronBertForQuestionAnswering,
MegatronBertForSequenceClassification,
MegatronBertForTokenClassification,
MegatronBertModel,
MegatronBertPreTrainedModel,
)
else:
import sys
_lowerCAmelCase : Optional[int] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
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|
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