code stringlengths 87 55.2k | code_codestyle int64 0 349 | style_context stringlengths 135 49.1k | style_context_codestyle int64 0 349 | label int64 0 1 |
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import argparse
import pathlib
import fairseq
import torch
from fairseq.models.roberta import RobertaModel as FairseqRobertaModel
from fairseq.modules import TransformerSentenceEncoderLayer
from packaging import version
from transformers import XLMRobertaConfig, XLMRobertaXLForMaskedLM, XLMRobertaXLForSequenceClassification
from transformers.models.bert.modeling_bert import (
BertIntermediate,
BertLayer,
BertOutput,
BertSelfAttention,
BertSelfOutput,
)
from transformers.models.roberta.modeling_roberta import RobertaAttention
from transformers.utils import logging
if version.parse(fairseq.__version__) < version.parse("1.0.0a"):
raise Exception("requires fairseq >= 1.0.0a")
logging.set_verbosity_info()
lowercase__ :int = logging.get_logger(__name__)
lowercase__ :Union[str, Any] = "Hello world! cécé herlolip"
def UpperCamelCase ( lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ ):
'''simple docstring'''
lowercase = FairseqRobertaModel.from_pretrained(lowerCAmelCase__ )
roberta.eval() # disable dropout
lowercase = roberta.model.encoder.sentence_encoder
lowercase = XLMRobertaConfig(
vocab_size=roberta_sent_encoder.embed_tokens.num_embeddings , hidden_size=roberta.cfg.model.encoder_embed_dim , num_hidden_layers=roberta.cfg.model.encoder_layers , num_attention_heads=roberta.cfg.model.encoder_attention_heads , intermediate_size=roberta.cfg.model.encoder_ffn_embed_dim , max_position_embeddings=514 , type_vocab_size=1 , layer_norm_eps=1E-5 , )
if classification_head:
lowercase = roberta.model.classification_heads['''mnli'''].out_proj.weight.shape[0]
print('''Our RoBERTa config:''' , lowerCAmelCase__ )
lowercase = XLMRobertaXLForSequenceClassification(lowerCAmelCase__ ) if classification_head else XLMRobertaXLForMaskedLM(lowerCAmelCase__ )
model.eval()
# Now let's copy all the weights.
# Embeddings
lowercase = roberta_sent_encoder.embed_tokens.weight
lowercase = roberta_sent_encoder.embed_positions.weight
lowercase = torch.zeros_like(
model.roberta.embeddings.token_type_embeddings.weight ) # just zero them out b/c RoBERTa doesn't use them.
lowercase = roberta_sent_encoder.layer_norm.weight
lowercase = roberta_sent_encoder.layer_norm.bias
for i in range(config.num_hidden_layers ):
# Encoder: start of layer
lowercase = model.roberta.encoder.layer[i]
lowercase = roberta_sent_encoder.layers[i]
lowercase = layer.attention
lowercase = roberta_layer.self_attn_layer_norm.weight
lowercase = roberta_layer.self_attn_layer_norm.bias
# self attention
lowercase = layer.attention.self
assert (
roberta_layer.self_attn.k_proj.weight.data.shape
== roberta_layer.self_attn.q_proj.weight.data.shape
== roberta_layer.self_attn.v_proj.weight.data.shape
== torch.Size((config.hidden_size, config.hidden_size) )
)
lowercase = roberta_layer.self_attn.q_proj.weight
lowercase = roberta_layer.self_attn.q_proj.bias
lowercase = roberta_layer.self_attn.k_proj.weight
lowercase = roberta_layer.self_attn.k_proj.bias
lowercase = roberta_layer.self_attn.v_proj.weight
lowercase = roberta_layer.self_attn.v_proj.bias
# self-attention output
lowercase = layer.attention.output
assert self_output.dense.weight.shape == roberta_layer.self_attn.out_proj.weight.shape
lowercase = roberta_layer.self_attn.out_proj.weight
lowercase = roberta_layer.self_attn.out_proj.bias
# this one is final layer norm
lowercase = roberta_layer.final_layer_norm.weight
lowercase = roberta_layer.final_layer_norm.bias
# intermediate
lowercase = layer.intermediate
assert intermediate.dense.weight.shape == roberta_layer.fca.weight.shape
lowercase = roberta_layer.fca.weight
lowercase = roberta_layer.fca.bias
# output
lowercase = layer.output
assert bert_output.dense.weight.shape == roberta_layer.fca.weight.shape
lowercase = roberta_layer.fca.weight
lowercase = roberta_layer.fca.bias
# end of layer
if classification_head:
lowercase = roberta.model.classification_heads['''mnli'''].dense.weight
lowercase = roberta.model.classification_heads['''mnli'''].dense.bias
lowercase = roberta.model.classification_heads['''mnli'''].out_proj.weight
lowercase = roberta.model.classification_heads['''mnli'''].out_proj.bias
else:
# LM Head
lowercase = roberta.model.encoder.lm_head.dense.weight
lowercase = roberta.model.encoder.lm_head.dense.bias
lowercase = roberta.model.encoder.lm_head.layer_norm.weight
lowercase = roberta.model.encoder.lm_head.layer_norm.bias
lowercase = roberta.model.encoder.lm_head.weight
lowercase = roberta.model.encoder.lm_head.bias
# Let's check that we get the same results.
lowercase = roberta.encode(lowerCAmelCase__ ).unsqueeze(0 ) # batch of size 1
lowercase = model(lowerCAmelCase__ )[0]
if classification_head:
lowercase = roberta.model.classification_heads['''mnli'''](roberta.extract_features(lowerCAmelCase__ ) )
else:
lowercase = roberta.model(lowerCAmelCase__ )[0]
print(our_output.shape , their_output.shape )
lowercase = torch.max(torch.abs(our_output - their_output ) ).item()
print(f'max_absolute_diff = {max_absolute_diff}' ) # ~ 1e-7
lowercase = torch.allclose(lowerCAmelCase__ , lowerCAmelCase__ , atol=1E-3 )
print('''Do both models output the same tensors?''' , '''🔥''' if success else '''💩''' )
if not success:
raise Exception('''Something went wRoNg''' )
pathlib.Path(lowerCAmelCase__ ).mkdir(parents=lowerCAmelCase__ , exist_ok=lowerCAmelCase__ )
print(f'Saving model to {pytorch_dump_folder_path}' )
model.save_pretrained(lowerCAmelCase__ )
if __name__ == "__main__":
lowercase__ :Dict = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--roberta_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."
)
parser.add_argument(
"--classification_head", action="store_true", help="Whether to convert a final classification head."
)
lowercase__ :Optional[int] = parser.parse_args()
convert_xlm_roberta_xl_checkpoint_to_pytorch(
args.roberta_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
)
| 101 |
from .glue import glue_convert_examples_to_features, glue_output_modes, glue_processors, glue_tasks_num_labels
from .squad import SquadExample, SquadFeatures, SquadVaProcessor, SquadVaProcessor, squad_convert_examples_to_features
from .utils import DataProcessor, InputExample, InputFeatures, SingleSentenceClassificationProcessor
from .xnli import xnli_output_modes, xnli_processors, xnli_tasks_num_labels
| 92 | 0 |
"""simple docstring"""
from typing import Any, Dict, Optional
import torch
import torch.nn.functional as F
from torch import nn
from ..utils import maybe_allow_in_graph
from .activations import get_activation
from .attention_processor import Attention
from .embeddings import CombinedTimestepLabelEmbeddings
@maybe_allow_in_graph
class _UpperCAmelCase ( nn.Module ):
'''simple docstring'''
def __init__(self , a_ , a_ , a_ , a_=0.0 , a_ = None , a_ = "geglu" , a_ = None , a_ = False , a_ = False , a_ = False , a_ = False , a_ = True , a_ = "layer_norm" , a_ = False , ):
'''simple docstring'''
super().__init__()
__snake_case : List[Any] = only_cross_attention
__snake_case : Union[str, Any] = (num_embeds_ada_norm is not None) and norm_type == '''ada_norm_zero'''
__snake_case : Any = (num_embeds_ada_norm is not None) and norm_type == '''ada_norm'''
if norm_type in ("ada_norm", "ada_norm_zero") and num_embeds_ada_norm is None:
raise ValueError(
f"""`norm_type` is set to {norm_type}, but `num_embeds_ada_norm` is not defined. Please make sure to"""
f""" define `num_embeds_ada_norm` if setting `norm_type` to {norm_type}.""" )
# Define 3 blocks. Each block has its own normalization layer.
# 1. Self-Attn
if self.use_ada_layer_norm:
__snake_case : Dict = AdaLayerNorm(a_ , a_ )
elif self.use_ada_layer_norm_zero:
__snake_case : int = AdaLayerNormZero(a_ , a_ )
else:
__snake_case : Optional[Any] = nn.LayerNorm(a_ , elementwise_affine=a_ )
__snake_case : Any = Attention(
query_dim=a_ , heads=a_ , dim_head=a_ , dropout=a_ , bias=a_ , cross_attention_dim=cross_attention_dim if only_cross_attention else None , upcast_attention=a_ , )
# 2. Cross-Attn
if cross_attention_dim is not None or double_self_attention:
# We currently only use AdaLayerNormZero for self attention where there will only be one attention block.
# I.e. the number of returned modulation chunks from AdaLayerZero would not make sense if returned during
# the second cross attention block.
__snake_case : Dict = (
AdaLayerNorm(a_ , a_ )
if self.use_ada_layer_norm
else nn.LayerNorm(a_ , elementwise_affine=a_ )
)
__snake_case : List[Any] = Attention(
query_dim=a_ , cross_attention_dim=cross_attention_dim if not double_self_attention else None , heads=a_ , dim_head=a_ , dropout=a_ , bias=a_ , upcast_attention=a_ , ) # is self-attn if encoder_hidden_states is none
else:
__snake_case : int = None
__snake_case : Union[str, Any] = None
# 3. Feed-forward
__snake_case : Optional[int] = nn.LayerNorm(a_ , elementwise_affine=a_ )
__snake_case : Optional[Any] = FeedForward(a_ , dropout=a_ , activation_fn=a_ , final_dropout=a_ )
# let chunk size default to None
__snake_case : Tuple = None
__snake_case : int = 0
def SCREAMING_SNAKE_CASE (self , a_ , a_ ):
'''simple docstring'''
__snake_case : int = chunk_size
__snake_case : Optional[Any] = dim
def SCREAMING_SNAKE_CASE (self , a_ , a_ = None , a_ = None , a_ = None , a_ = None , a_ = None , a_ = None , ):
'''simple docstring'''
if self.use_ada_layer_norm:
__snake_case : Tuple = self.norma(a_ , a_ )
elif self.use_ada_layer_norm_zero:
__snake_case , __snake_case , __snake_case , __snake_case , __snake_case : Union[str, Any] = self.norma(
a_ , a_ , a_ , hidden_dtype=hidden_states.dtype )
else:
__snake_case : Optional[Any] = self.norma(a_ )
__snake_case : Optional[Any] = cross_attention_kwargs if cross_attention_kwargs is not None else {}
__snake_case : List[Any] = self.attna(
a_ , encoder_hidden_states=encoder_hidden_states if self.only_cross_attention else None , attention_mask=a_ , **a_ , )
if self.use_ada_layer_norm_zero:
__snake_case : Optional[Any] = gate_msa.unsqueeze(1 ) * attn_output
__snake_case : List[str] = attn_output + hidden_states
# 2. Cross-Attention
if self.attna is not None:
__snake_case : List[Any] = (
self.norma(a_ , a_ ) if self.use_ada_layer_norm else self.norma(a_ )
)
__snake_case : str = self.attna(
a_ , encoder_hidden_states=a_ , attention_mask=a_ , **a_ , )
__snake_case : Union[str, Any] = attn_output + hidden_states
# 3. Feed-forward
__snake_case : Any = self.norma(a_ )
if self.use_ada_layer_norm_zero:
__snake_case : Optional[Any] = norm_hidden_states * (1 + scale_mlp[:, None]) + shift_mlp[:, None]
if self._chunk_size is not None:
# "feed_forward_chunk_size" can be used to save memory
if norm_hidden_states.shape[self._chunk_dim] % self._chunk_size != 0:
raise ValueError(
f"""`hidden_states` dimension to be chunked: {norm_hidden_states.shape[self._chunk_dim]} has to be divisible by chunk size: {self._chunk_size}. Make sure to set an appropriate `chunk_size` when calling `unet.enable_forward_chunking`.""" )
__snake_case : List[str] = norm_hidden_states.shape[self._chunk_dim] // self._chunk_size
__snake_case : Dict = torch.cat(
[self.ff(a_ ) for hid_slice in norm_hidden_states.chunk(a_ , dim=self._chunk_dim )] , dim=self._chunk_dim , )
else:
__snake_case : Tuple = self.ff(a_ )
if self.use_ada_layer_norm_zero:
__snake_case : Any = gate_mlp.unsqueeze(1 ) * ff_output
__snake_case : int = ff_output + hidden_states
return hidden_states
class _UpperCAmelCase ( nn.Module ):
'''simple docstring'''
def __init__(self , a_ , a_ = None , a_ = 4 , a_ = 0.0 , a_ = "geglu" , a_ = False , ):
'''simple docstring'''
super().__init__()
__snake_case : Union[str, Any] = int(dim * mult )
__snake_case : List[Any] = dim_out if dim_out is not None else dim
if activation_fn == "gelu":
__snake_case : Optional[int] = GELU(a_ , a_ )
if activation_fn == "gelu-approximate":
__snake_case : Union[str, Any] = GELU(a_ , a_ , approximate='''tanh''' )
elif activation_fn == "geglu":
__snake_case : Optional[int] = GEGLU(a_ , a_ )
elif activation_fn == "geglu-approximate":
__snake_case : List[Any] = ApproximateGELU(a_ , a_ )
__snake_case : List[str] = nn.ModuleList([] )
# project in
self.net.append(a_ )
# project dropout
self.net.append(nn.Dropout(a_ ) )
# project out
self.net.append(nn.Linear(a_ , a_ ) )
# FF as used in Vision Transformer, MLP-Mixer, etc. have a final dropout
if final_dropout:
self.net.append(nn.Dropout(a_ ) )
def SCREAMING_SNAKE_CASE (self , a_ ):
'''simple docstring'''
for module in self.net:
__snake_case : List[Any] = module(a_ )
return hidden_states
class _UpperCAmelCase ( nn.Module ):
'''simple docstring'''
def __init__(self , a_ , a_ , a_ = "none" ):
'''simple docstring'''
super().__init__()
__snake_case : Optional[int] = nn.Linear(a_ , a_ )
__snake_case : Optional[Any] = approximate
def SCREAMING_SNAKE_CASE (self , a_ ):
'''simple docstring'''
if gate.device.type != "mps":
return F.gelu(a_ , approximate=self.approximate )
# mps: gelu is not implemented for float16
return F.gelu(gate.to(dtype=torch.floataa ) , approximate=self.approximate ).to(dtype=gate.dtype )
def SCREAMING_SNAKE_CASE (self , a_ ):
'''simple docstring'''
__snake_case : Any = self.proj(a_ )
__snake_case : List[str] = self.gelu(a_ )
return hidden_states
class _UpperCAmelCase ( nn.Module ):
'''simple docstring'''
def __init__(self , a_ , a_ ):
'''simple docstring'''
super().__init__()
__snake_case : Union[str, Any] = nn.Linear(a_ , dim_out * 2 )
def SCREAMING_SNAKE_CASE (self , a_ ):
'''simple docstring'''
if gate.device.type != "mps":
return F.gelu(a_ )
# mps: gelu is not implemented for float16
return F.gelu(gate.to(dtype=torch.floataa ) ).to(dtype=gate.dtype )
def SCREAMING_SNAKE_CASE (self , a_ ):
'''simple docstring'''
__snake_case , __snake_case : Tuple = self.proj(a_ ).chunk(2 , dim=-1 )
return hidden_states * self.gelu(a_ )
class _UpperCAmelCase ( nn.Module ):
'''simple docstring'''
def __init__(self , a_ , a_ ):
'''simple docstring'''
super().__init__()
__snake_case : Dict = nn.Linear(a_ , a_ )
def SCREAMING_SNAKE_CASE (self , a_ ):
'''simple docstring'''
__snake_case : Tuple = self.proj(a_ )
return x * torch.sigmoid(1.702 * x )
class _UpperCAmelCase ( nn.Module ):
'''simple docstring'''
def __init__(self , a_ , a_ ):
'''simple docstring'''
super().__init__()
__snake_case : Union[str, Any] = nn.Embedding(a_ , a_ )
__snake_case : Any = nn.SiLU()
__snake_case : str = nn.Linear(a_ , embedding_dim * 2 )
__snake_case : Dict = nn.LayerNorm(a_ , elementwise_affine=a_ )
def SCREAMING_SNAKE_CASE (self , a_ , a_ ):
'''simple docstring'''
__snake_case : Union[str, Any] = self.linear(self.silu(self.emb(a_ ) ) )
__snake_case , __snake_case : int = torch.chunk(a_ , 2 )
__snake_case : Optional[int] = self.norm(a_ ) * (1 + scale) + shift
return x
class _UpperCAmelCase ( nn.Module ):
'''simple docstring'''
def __init__(self , a_ , a_ ):
'''simple docstring'''
super().__init__()
__snake_case : Optional[int] = CombinedTimestepLabelEmbeddings(a_ , a_ )
__snake_case : Optional[int] = nn.SiLU()
__snake_case : List[str] = nn.Linear(a_ , 6 * embedding_dim , bias=a_ )
__snake_case : str = nn.LayerNorm(a_ , elementwise_affine=a_ , eps=1E-6 )
def SCREAMING_SNAKE_CASE (self , a_ , a_ , a_ , a_=None ):
'''simple docstring'''
__snake_case : Any = self.linear(self.silu(self.emb(a_ , a_ , hidden_dtype=a_ ) ) )
__snake_case , __snake_case , __snake_case , __snake_case , __snake_case , __snake_case : str = emb.chunk(6 , dim=1 )
__snake_case : Any = self.norm(a_ ) * (1 + scale_msa[:, None]) + shift_msa[:, None]
return x, gate_msa, shift_mlp, scale_mlp, gate_mlp
class _UpperCAmelCase ( nn.Module ):
'''simple docstring'''
def __init__(self , a_ , a_ , a_ , a_ = None , a_ = 1E-5 ):
'''simple docstring'''
super().__init__()
__snake_case : Optional[Any] = num_groups
__snake_case : Any = eps
if act_fn is None:
__snake_case : Optional[Any] = None
else:
__snake_case : Tuple = get_activation(a_ )
__snake_case : List[str] = nn.Linear(a_ , out_dim * 2 )
def SCREAMING_SNAKE_CASE (self , a_ , a_ ):
'''simple docstring'''
if self.act:
__snake_case : Dict = self.act(a_ )
__snake_case : List[str] = self.linear(a_ )
__snake_case : Tuple = emb[:, :, None, None]
__snake_case , __snake_case : Optional[int] = emb.chunk(2 , dim=1 )
__snake_case : Union[str, Any] = F.group_norm(a_ , self.num_groups , eps=self.eps )
__snake_case : int = x * (1 + scale) + shift
return x
| 102 |
from queue import PriorityQueue
from typing import Any
import numpy as np
def _a ( SCREAMING_SNAKE_CASE_ : dict , SCREAMING_SNAKE_CASE_ : str , SCREAMING_SNAKE_CASE_ : set , SCREAMING_SNAKE_CASE_ : set , SCREAMING_SNAKE_CASE_ : dict , SCREAMING_SNAKE_CASE_ : dict , SCREAMING_SNAKE_CASE_ : PriorityQueue , SCREAMING_SNAKE_CASE_ : dict , SCREAMING_SNAKE_CASE_ : float | int , ):
for nxt, d in graph[v]:
if nxt in visited_forward:
continue
__lowerCAmelCase = cst_fwd.get(SCREAMING_SNAKE_CASE_ , np.inf )
__lowerCAmelCase = cst_fwd[v] + d
if new_cost_f < old_cost_f:
queue.put((new_cost_f, nxt) )
__lowerCAmelCase = new_cost_f
__lowerCAmelCase = v
if nxt in visited_backward:
if cst_fwd[v] + d + cst_bwd[nxt] < shortest_distance:
__lowerCAmelCase = cst_fwd[v] + d + cst_bwd[nxt]
return shortest_distance
def _a ( SCREAMING_SNAKE_CASE_ : str , SCREAMING_SNAKE_CASE_ : str , SCREAMING_SNAKE_CASE_ : dict , SCREAMING_SNAKE_CASE_ : dict ):
__lowerCAmelCase = -1
__lowerCAmelCase = set()
__lowerCAmelCase = set()
__lowerCAmelCase = {source: 0}
__lowerCAmelCase = {destination: 0}
__lowerCAmelCase = {source: None}
__lowerCAmelCase = {destination: None}
__lowerCAmelCase = PriorityQueue()
__lowerCAmelCase = PriorityQueue()
__lowerCAmelCase = np.inf
queue_forward.put((0, source) )
queue_backward.put((0, destination) )
if source == destination:
return 0
while not queue_forward.empty() and not queue_backward.empty():
__lowerCAmelCase , __lowerCAmelCase = queue_forward.get()
visited_forward.add(SCREAMING_SNAKE_CASE_ )
__lowerCAmelCase , __lowerCAmelCase = queue_backward.get()
visited_backward.add(SCREAMING_SNAKE_CASE_ )
__lowerCAmelCase = pass_and_relaxation(
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , )
__lowerCAmelCase = pass_and_relaxation(
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , )
if cst_fwd[v_fwd] + cst_bwd[v_bwd] >= shortest_distance:
break
if shortest_distance != np.inf:
__lowerCAmelCase = shortest_distance
return shortest_path_distance
UpperCamelCase__ = {
"""B""": [["""C""", 1]],
"""C""": [["""D""", 1]],
"""D""": [["""F""", 1]],
"""E""": [["""B""", 1], ["""G""", 2]],
"""F""": [],
"""G""": [["""F""", 1]],
}
UpperCamelCase__ = {
"""B""": [["""E""", 1]],
"""C""": [["""B""", 1]],
"""D""": [["""C""", 1]],
"""F""": [["""D""", 1], ["""G""", 1]],
"""E""": [[None, np.inf]],
"""G""": [["""E""", 2]],
}
if __name__ == "__main__":
import doctest
doctest.testmod()
| 92 | 0 |
from bisect import bisect
from itertools import accumulate
def UpperCamelCase( __UpperCamelCase : Optional[int] ,__UpperCamelCase : Optional[Any] ,__UpperCamelCase : Dict ,__UpperCamelCase : str ):
lowerCAmelCase_ : Union[str, Any] = sorted(zip(__UpperCamelCase ,__UpperCamelCase ) ,key=lambda __UpperCamelCase : x[0] / x[1] ,reverse=__UpperCamelCase )
lowerCAmelCase_ , lowerCAmelCase_ : Any = [i[0] for i in r], [i[1] for i in r]
lowerCAmelCase_ : Union[str, Any] = list(accumulate(__UpperCamelCase ) )
lowerCAmelCase_ : List[Any] = bisect(__UpperCamelCase ,__UpperCamelCase )
return (
0
if k == 0
else sum(vl[:k] ) + (w - acc[k - 1]) * (vl[k]) / (wt[k])
if k != n
else sum(vl[:k] )
)
if __name__ == "__main__":
import doctest
doctest.testmod()
| 103 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCamelCase__ = logging.get_logger(__name__)
UpperCamelCase__ = {
"""edbeeching/decision-transformer-gym-hopper-medium""": (
"""https://huggingface.co/edbeeching/decision-transformer-gym-hopper-medium/resolve/main/config.json"""
),
# See all DecisionTransformer models at https://huggingface.co/models?filter=decision_transformer
}
class a__ ( snake_case__ ):
_a : Optional[int] = """decision_transformer"""
_a : Optional[int] = ["""past_key_values"""]
_a : Dict = {
"""max_position_embeddings""": """n_positions""",
"""num_attention_heads""": """n_head""",
"""num_hidden_layers""": """n_layer""",
}
def __init__( self , _A=1_7 , _A=4 , _A=1_2_8 , _A=4_0_9_6 , _A=True , _A=1 , _A=1_0_2_4 , _A=3 , _A=1 , _A=None , _A="relu" , _A=0.1 , _A=0.1 , _A=0.1 , _A=1E-5 , _A=0.02 , _A=True , _A=True , _A=5_0_2_5_6 , _A=5_0_2_5_6 , _A=False , _A=False , **_A , ):
"""simple docstring"""
__lowerCAmelCase = state_dim
__lowerCAmelCase = act_dim
__lowerCAmelCase = hidden_size
__lowerCAmelCase = max_ep_len
__lowerCAmelCase = action_tanh
__lowerCAmelCase = vocab_size
__lowerCAmelCase = n_positions
__lowerCAmelCase = n_layer
__lowerCAmelCase = n_head
__lowerCAmelCase = n_inner
__lowerCAmelCase = activation_function
__lowerCAmelCase = resid_pdrop
__lowerCAmelCase = embd_pdrop
__lowerCAmelCase = attn_pdrop
__lowerCAmelCase = layer_norm_epsilon
__lowerCAmelCase = initializer_range
__lowerCAmelCase = scale_attn_weights
__lowerCAmelCase = use_cache
__lowerCAmelCase = scale_attn_by_inverse_layer_idx
__lowerCAmelCase = reorder_and_upcast_attn
__lowerCAmelCase = bos_token_id
__lowerCAmelCase = eos_token_id
super().__init__(bos_token_id=_A , eos_token_id=_A , **_A )
| 92 | 0 |
'''simple docstring'''
import json
import os
from functools import lru_cache
from typing import Dict, List, Optional, Tuple, Union
import regex as re
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...tokenization_utils_base import BatchEncoding, EncodedInput
from ...utils import PaddingStrategy, logging
lowerCAmelCase__ = logging.get_logger(__name__)
lowerCAmelCase__ = {'''vocab_file''': '''vocab.json''', '''merges_file''': '''merges.txt'''}
# See all LED models at https://huggingface.co/models?filter=LED
lowerCAmelCase__ = {
'''vocab_file''': {
'''allenai/led-base-16384''': '''https://huggingface.co/allenai/led-base-16384/resolve/main/vocab.json''',
},
'''merges_file''': {
'''allenai/led-base-16384''': '''https://huggingface.co/allenai/led-base-16384/resolve/main/merges.txt''',
},
'''tokenizer_file''': {
'''allenai/led-base-16384''': '''https://huggingface.co/allenai/led-base-16384/resolve/main/tokenizer.json''',
},
}
lowerCAmelCase__ = {
'''allenai/led-base-16384''': 1_6384,
}
@lru_cache()
# Copied from transformers.models.bart.tokenization_bart.bytes_to_unicode
def _A ( ):
"""simple docstring"""
__lowercase = (
list(range(ord('''!''' ) , ord('''~''' ) + 1 ) ) + list(range(ord('''¡''' ) , ord('''¬''' ) + 1 ) ) + list(range(ord('''®''' ) , ord('''ÿ''' ) + 1 ) )
)
__lowercase = bs[:]
__lowercase = 0
for b in range(2**8 ):
if b not in bs:
bs.append(A__ )
cs.append(2**8 + n )
n += 1
__lowercase = [chr(A__ ) for n in cs]
return dict(zip(A__ , A__ ) )
def _A ( A__ ):
"""simple docstring"""
__lowercase = set()
__lowercase = word[0]
for char in word[1:]:
pairs.add((prev_char, char) )
__lowercase = char
return pairs
class lowercase_ (lowerCamelCase__ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : int = VOCAB_FILES_NAMES
SCREAMING_SNAKE_CASE : Optional[int] = PRETRAINED_VOCAB_FILES_MAP
SCREAMING_SNAKE_CASE : Union[str, Any] = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
SCREAMING_SNAKE_CASE : Union[str, Any] = ['input_ids', 'attention_mask']
def __init__( self : Dict ,lowercase__ : Tuple ,lowercase__ : Dict ,lowercase__ : Union[str, Any]="replace" ,lowercase__ : Any="<s>" ,lowercase__ : Dict="</s>" ,lowercase__ : Union[str, Any]="</s>" ,lowercase__ : Dict="<s>" ,lowercase__ : Dict="<unk>" ,lowercase__ : Union[str, Any]="<pad>" ,lowercase__ : Tuple="<mask>" ,lowercase__ : List[str]=False ,**lowercase__ : List[Any] ,):
__lowercase = AddedToken(lowercase__ ,lstrip=lowercase__ ,rstrip=lowercase__ ) if isinstance(lowercase__ ,lowercase__ ) else bos_token
__lowercase = AddedToken(lowercase__ ,lstrip=lowercase__ ,rstrip=lowercase__ ) if isinstance(lowercase__ ,lowercase__ ) else eos_token
__lowercase = AddedToken(lowercase__ ,lstrip=lowercase__ ,rstrip=lowercase__ ) if isinstance(lowercase__ ,lowercase__ ) else sep_token
__lowercase = AddedToken(lowercase__ ,lstrip=lowercase__ ,rstrip=lowercase__ ) if isinstance(lowercase__ ,lowercase__ ) else cls_token
__lowercase = AddedToken(lowercase__ ,lstrip=lowercase__ ,rstrip=lowercase__ ) if isinstance(lowercase__ ,lowercase__ ) else unk_token
__lowercase = AddedToken(lowercase__ ,lstrip=lowercase__ ,rstrip=lowercase__ ) if isinstance(lowercase__ ,lowercase__ ) else pad_token
# Mask token behave like a normal word, i.e. include the space before it
__lowercase = AddedToken(lowercase__ ,lstrip=lowercase__ ,rstrip=lowercase__ ) if isinstance(lowercase__ ,lowercase__ ) else mask_token
super().__init__(
errors=lowercase__ ,bos_token=lowercase__ ,eos_token=lowercase__ ,unk_token=lowercase__ ,sep_token=lowercase__ ,cls_token=lowercase__ ,pad_token=lowercase__ ,mask_token=lowercase__ ,add_prefix_space=lowercase__ ,**lowercase__ ,)
with open(lowercase__ ,encoding='''utf-8''' ) as vocab_handle:
__lowercase = json.load(lowercase__ )
__lowercase = {v: k for k, v in self.encoder.items()}
__lowercase = errors # how to handle errors in decoding
__lowercase = bytes_to_unicode()
__lowercase = {v: k for k, v in self.byte_encoder.items()}
with open(lowercase__ ,encoding='''utf-8''' ) as merges_handle:
__lowercase = merges_handle.read().split('''\n''' )[1:-1]
__lowercase = [tuple(merge.split() ) for merge in bpe_merges]
__lowercase = dict(zip(lowercase__ ,range(len(lowercase__ ) ) ) )
__lowercase = {}
__lowercase = add_prefix_space
# Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
__lowercase = re.compile(r'''\'s|\'t|\'re|\'ve|\'m|\'ll|\'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+''' )
@property
# Copied from transformers.models.bart.tokenization_bart.BartTokenizer.vocab_size
def SCREAMING_SNAKE_CASE ( self : Union[str, Any] ):
return len(self.encoder )
def SCREAMING_SNAKE_CASE ( self : List[str] ):
return dict(self.encoder ,**self.added_tokens_encoder )
def SCREAMING_SNAKE_CASE ( self : Optional[Any] ,lowercase__ : Optional[Any] ):
if token in self.cache:
return self.cache[token]
__lowercase = tuple(lowercase__ )
__lowercase = get_pairs(lowercase__ )
if not pairs:
return token
while True:
__lowercase = min(lowercase__ ,key=lambda lowercase__ : self.bpe_ranks.get(lowercase__ ,float('''inf''' ) ) )
if bigram not in self.bpe_ranks:
break
__lowercase , __lowercase = bigram
__lowercase = []
__lowercase = 0
while i < len(lowercase__ ):
try:
__lowercase = word.index(lowercase__ ,lowercase__ )
except ValueError:
new_word.extend(word[i:] )
break
else:
new_word.extend(word[i:j] )
__lowercase = j
if word[i] == first and i < len(lowercase__ ) - 1 and word[i + 1] == second:
new_word.append(first + second )
i += 2
else:
new_word.append(word[i] )
i += 1
__lowercase = tuple(lowercase__ )
__lowercase = new_word
if len(lowercase__ ) == 1:
break
else:
__lowercase = get_pairs(lowercase__ )
__lowercase = ''' '''.join(lowercase__ )
__lowercase = word
return word
def SCREAMING_SNAKE_CASE ( self : List[Any] ,lowercase__ : List[Any] ):
__lowercase = []
for token in re.findall(self.pat ,lowercase__ ):
__lowercase = ''''''.join(
self.byte_encoder[b] for b in token.encode('''utf-8''' ) ) # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case)
bpe_tokens.extend(bpe_token for bpe_token in self.bpe(lowercase__ ).split(''' ''' ) )
return bpe_tokens
def SCREAMING_SNAKE_CASE ( self : str ,lowercase__ : Tuple ):
return self.encoder.get(lowercase__ ,self.encoder.get(self.unk_token ) )
def SCREAMING_SNAKE_CASE ( self : Union[str, Any] ,lowercase__ : Any ):
return self.decoder.get(lowercase__ )
def SCREAMING_SNAKE_CASE ( self : Optional[int] ,lowercase__ : Optional[Any] ):
__lowercase = ''''''.join(lowercase__ )
__lowercase = bytearray([self.byte_decoder[c] for c in text] ).decode('''utf-8''' ,errors=self.errors )
return text
def SCREAMING_SNAKE_CASE ( self : List[Any] ,lowercase__ : str ,lowercase__ : Optional[str] = None ):
if not os.path.isdir(lowercase__ ):
logger.error(F"Vocabulary path ({save_directory}) should be a directory" )
return
__lowercase = os.path.join(
lowercase__ ,(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file'''] )
__lowercase = os.path.join(
lowercase__ ,(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''merges_file'''] )
with open(lowercase__ ,'''w''' ,encoding='''utf-8''' ) as f:
f.write(json.dumps(self.encoder ,indent=2 ,sort_keys=lowercase__ ,ensure_ascii=lowercase__ ) + '''\n''' )
__lowercase = 0
with open(lowercase__ ,'''w''' ,encoding='''utf-8''' ) as writer:
writer.write('''#version: 0.2\n''' )
for bpe_tokens, token_index in sorted(self.bpe_ranks.items() ,key=lambda lowercase__ : kv[1] ):
if index != token_index:
logger.warning(
F"Saving vocabulary to {merge_file}: BPE merge indices are not consecutive."
''' Please check that the tokenizer is not corrupted!''' )
__lowercase = token_index
writer.write(''' '''.join(lowercase__ ) + '''\n''' )
index += 1
return vocab_file, merge_file
def SCREAMING_SNAKE_CASE ( self : Any ,lowercase__ : List[int] ,lowercase__ : Optional[List[int]] = None ):
if token_ids_a is None:
return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
__lowercase = [self.cls_token_id]
__lowercase = [self.sep_token_id]
return cls + token_ids_a + sep + sep + token_ids_a + sep
def SCREAMING_SNAKE_CASE ( self : Union[str, Any] ,lowercase__ : List[int] ,lowercase__ : Optional[List[int]] = None ,lowercase__ : bool = False ):
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=lowercase__ ,token_ids_a=lowercase__ ,already_has_special_tokens=lowercase__ )
if token_ids_a is None:
return [1] + ([0] * len(lowercase__ )) + [1]
return [1] + ([0] * len(lowercase__ )) + [1, 1] + ([0] * len(lowercase__ )) + [1]
def SCREAMING_SNAKE_CASE ( self : Union[str, Any] ,lowercase__ : List[int] ,lowercase__ : Optional[List[int]] = None ):
__lowercase = [self.sep_token_id]
__lowercase = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep + sep + token_ids_a + sep ) * [0]
def SCREAMING_SNAKE_CASE ( self : int ,lowercase__ : Optional[int] ,lowercase__ : Optional[Any]=False ,**lowercase__ : Optional[int] ):
__lowercase = kwargs.pop('''add_prefix_space''' ,self.add_prefix_space )
if (is_split_into_words or add_prefix_space) and (len(lowercase__ ) > 0 and not text[0].isspace()):
__lowercase = ''' ''' + text
return (text, kwargs)
def SCREAMING_SNAKE_CASE ( self : Any ,lowercase__ : Union[Dict[str, EncodedInput], BatchEncoding] ,lowercase__ : Optional[int] = None ,lowercase__ : PaddingStrategy = PaddingStrategy.DO_NOT_PAD ,lowercase__ : Optional[int] = None ,lowercase__ : Optional[bool] = None ,):
__lowercase = super()._pad(
encoded_inputs=lowercase__ ,max_length=lowercase__ ,padding_strategy=lowercase__ ,pad_to_multiple_of=lowercase__ ,return_attention_mask=lowercase__ ,)
# Load from model defaults
if return_attention_mask is None:
__lowercase = '''attention_mask''' in self.model_input_names
if return_attention_mask and "global_attention_mask" in encoded_inputs:
__lowercase = encoded_inputs[self.model_input_names[0]]
# `global_attention_mask` need to have the same length as other (sequential) inputs.
__lowercase = len(encoded_inputs['''global_attention_mask'''] ) != len(lowercase__ )
if needs_to_be_padded:
__lowercase = len(lowercase__ ) - len(encoded_inputs['''global_attention_mask'''] )
if self.padding_side == "right":
# Use `-1` since `0` in `global_attention_mask` means `local attention` instead of `not to attend`
__lowercase = (
encoded_inputs['''global_attention_mask'''] + [-1] * difference
)
elif self.padding_side == "left":
__lowercase = [-1] * difference + encoded_inputs[
'''global_attention_mask'''
]
else:
raise ValueError('''Invalid padding strategy:''' + str(self.padding_side ) )
return encoded_inputs
| 104 |
import gc
import unittest
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
DDPMScheduler,
PriorTransformer,
StableUnCLIPPipeline,
UNetaDConditionModel,
)
from diffusers.pipelines.stable_diffusion.stable_unclip_image_normalizer import StableUnCLIPImageNormalizer
from diffusers.utils.testing_utils import enable_full_determinism, load_numpy, require_torch_gpu, slow, torch_device
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,
assert_mean_pixel_difference,
)
enable_full_determinism()
class a__ ( snake_case__ , snake_case__ , snake_case__ , unittest.TestCase ):
_a : str = StableUnCLIPPipeline
_a : Union[str, Any] = TEXT_TO_IMAGE_PARAMS
_a : Dict = TEXT_TO_IMAGE_BATCH_PARAMS
_a : Optional[int] = TEXT_TO_IMAGE_IMAGE_PARAMS
_a : Dict = TEXT_TO_IMAGE_IMAGE_PARAMS
# TODO(will) Expected attn_bias.stride(1) == 0 to be true, but got false
_a : Optional[Any] = False
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
__lowerCAmelCase = 3_2
__lowerCAmelCase = embedder_hidden_size
# prior components
torch.manual_seed(0 )
__lowerCAmelCase = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip" )
torch.manual_seed(0 )
__lowerCAmelCase = CLIPTextModelWithProjection(
CLIPTextConfig(
bos_token_id=0 , eos_token_id=2 , hidden_size=_A , projection_dim=_A , intermediate_size=3_7 , layer_norm_eps=1E-0_5 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=1_0_0_0 , ) )
torch.manual_seed(0 )
__lowerCAmelCase = PriorTransformer(
num_attention_heads=2 , attention_head_dim=1_2 , embedding_dim=_A , num_layers=1 , )
torch.manual_seed(0 )
__lowerCAmelCase = DDPMScheduler(
variance_type="fixed_small_log" , prediction_type="sample" , num_train_timesteps=1_0_0_0 , clip_sample=_A , clip_sample_range=5.0 , beta_schedule="squaredcos_cap_v2" , )
# regular denoising components
torch.manual_seed(0 )
__lowerCAmelCase = StableUnCLIPImageNormalizer(embedding_dim=_A )
__lowerCAmelCase = DDPMScheduler(beta_schedule="squaredcos_cap_v2" )
torch.manual_seed(0 )
__lowerCAmelCase = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip" )
torch.manual_seed(0 )
__lowerCAmelCase = CLIPTextModel(
CLIPTextConfig(
bos_token_id=0 , eos_token_id=2 , hidden_size=_A , projection_dim=3_2 , intermediate_size=3_7 , layer_norm_eps=1E-0_5 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=1_0_0_0 , ) )
torch.manual_seed(0 )
__lowerCAmelCase = UNetaDConditionModel(
sample_size=3_2 , in_channels=4 , out_channels=4 , down_block_types=("CrossAttnDownBlock2D", "DownBlock2D") , up_block_types=("UpBlock2D", "CrossAttnUpBlock2D") , block_out_channels=(3_2, 6_4) , attention_head_dim=(2, 4) , class_embed_type="projection" , projection_class_embeddings_input_dim=embedder_projection_dim * 2 , cross_attention_dim=_A , layers_per_block=1 , upcast_attention=_A , use_linear_projection=_A , )
torch.manual_seed(0 )
__lowerCAmelCase = DDIMScheduler(
beta_schedule="scaled_linear" , beta_start=0.0_00_85 , beta_end=0.0_12 , prediction_type="v_prediction" , set_alpha_to_one=_A , steps_offset=1 , )
torch.manual_seed(0 )
__lowerCAmelCase = AutoencoderKL()
__lowerCAmelCase = {
# prior components
"prior_tokenizer": prior_tokenizer,
"prior_text_encoder": prior_text_encoder,
"prior": prior,
"prior_scheduler": prior_scheduler,
# image noising components
"image_normalizer": image_normalizer,
"image_noising_scheduler": image_noising_scheduler,
# regular denoising components
"tokenizer": tokenizer,
"text_encoder": text_encoder,
"unet": unet,
"scheduler": scheduler,
"vae": vae,
}
return components
def __SCREAMING_SNAKE_CASE( self , _A , _A=0 ):
"""simple docstring"""
if str(_A ).startswith("mps" ):
__lowerCAmelCase = torch.manual_seed(_A )
else:
__lowerCAmelCase = torch.Generator(device=_A ).manual_seed(_A )
__lowerCAmelCase = {
"prompt": "A painting of a squirrel eating a burger",
"generator": generator,
"num_inference_steps": 2,
"prior_num_inference_steps": 2,
"output_type": "numpy",
}
return inputs
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
__lowerCAmelCase = torch_device == "cpu"
self._test_attention_slicing_forward_pass(test_max_difference=_A )
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
__lowerCAmelCase = torch_device in ["cpu", "mps"]
self._test_inference_batch_single_identical(test_max_difference=_A )
@slow
@require_torch_gpu
class a__ ( unittest.TestCase ):
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
__lowerCAmelCase = load_numpy(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/stable_unclip_2_1_l_anime_turtle_fp16.npy" )
__lowerCAmelCase = StableUnCLIPPipeline.from_pretrained("fusing/stable-unclip-2-1-l" , torch_dtype=torch.floataa )
pipe.to(_A )
pipe.set_progress_bar_config(disable=_A )
# stable unclip will oom when integration tests are run on a V100,
# so turn on memory savings
pipe.enable_attention_slicing()
pipe.enable_sequential_cpu_offload()
__lowerCAmelCase = torch.Generator(device="cpu" ).manual_seed(0 )
__lowerCAmelCase = pipe("anime turle" , generator=_A , output_type="np" )
__lowerCAmelCase = output.images[0]
assert image.shape == (7_6_8, 7_6_8, 3)
assert_mean_pixel_difference(_A , _A )
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
torch.cuda.empty_cache()
torch.cuda.reset_max_memory_allocated()
torch.cuda.reset_peak_memory_stats()
__lowerCAmelCase = StableUnCLIPPipeline.from_pretrained("fusing/stable-unclip-2-1-l" , torch_dtype=torch.floataa )
__lowerCAmelCase = pipe.to(_A )
pipe.set_progress_bar_config(disable=_A )
pipe.enable_attention_slicing()
pipe.enable_sequential_cpu_offload()
__lowerCAmelCase = pipe(
"anime turtle" , prior_num_inference_steps=2 , num_inference_steps=2 , output_type="np" , )
__lowerCAmelCase = torch.cuda.max_memory_allocated()
# make sure that less than 7 GB is allocated
assert mem_bytes < 7 * 1_0**9
| 92 | 0 |
"""simple docstring"""
import os
from itertools import chain
from random import randrange, shuffle
import pytest
from .sola import PokerHand
a : Optional[int] = (
'''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)
)
a : Tuple = (
('''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'''),
)
a : Union[str, 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),
)
a : str = (
('''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),
)
a : str = (
('''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]),
)
a : 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),
)
a : str = (
('''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 _SCREAMING_SNAKE_CASE ( ) ->Tuple:
'''simple docstring'''
a, a : List[Any] = randrange(len(_lowercase ) ), randrange(len(_lowercase ) )
a : Dict = ["Loss", "Tie", "Win"][(play >= oppo) + (play > oppo)]
a, a : Optional[int] = SORTED_HANDS[play], SORTED_HANDS[oppo]
return hand, other, expected
def _SCREAMING_SNAKE_CASE ( _lowercase : int = 100 ) ->Tuple:
'''simple docstring'''
return (generate_random_hand() for _ in range(_lowercase ))
@pytest.mark.parametrize("hand, expected" , _lowercase )
def _SCREAMING_SNAKE_CASE ( _lowercase : Tuple , _lowercase : Tuple ) ->List[Any]:
'''simple docstring'''
assert PokerHand(_lowercase )._is_flush() == expected
@pytest.mark.parametrize("hand, expected" , _lowercase )
def _SCREAMING_SNAKE_CASE ( _lowercase : List[str] , _lowercase : Any ) ->Union[str, Any]:
'''simple docstring'''
assert PokerHand(_lowercase )._is_straight() == expected
@pytest.mark.parametrize("hand, expected, card_values" , _lowercase )
def _SCREAMING_SNAKE_CASE ( _lowercase : str , _lowercase : Optional[int] , _lowercase : Tuple ) ->int:
'''simple docstring'''
a : str = PokerHand(_lowercase )
assert player._is_five_high_straight() == expected
assert player._card_values == card_values
@pytest.mark.parametrize("hand, expected" , _lowercase )
def _SCREAMING_SNAKE_CASE ( _lowercase : Optional[Any] , _lowercase : int ) ->str:
'''simple docstring'''
assert PokerHand(_lowercase )._is_same_kind() == expected
@pytest.mark.parametrize("hand, expected" , _lowercase )
def _SCREAMING_SNAKE_CASE ( _lowercase : List[Any] , _lowercase : Optional[int] ) ->Optional[Any]:
'''simple docstring'''
assert PokerHand(_lowercase )._hand_type == expected
@pytest.mark.parametrize("hand, other, expected" , _lowercase )
def _SCREAMING_SNAKE_CASE ( _lowercase : int , _lowercase : str , _lowercase : Optional[int] ) ->Union[str, Any]:
'''simple docstring'''
assert PokerHand(_lowercase ).compare_with(PokerHand(_lowercase ) ) == expected
@pytest.mark.parametrize("hand, other, expected" , generate_random_hands() )
def _SCREAMING_SNAKE_CASE ( _lowercase : str , _lowercase : List[Any] , _lowercase : Optional[Any] ) ->Union[str, Any]:
'''simple docstring'''
assert PokerHand(_lowercase ).compare_with(PokerHand(_lowercase ) ) == expected
def _SCREAMING_SNAKE_CASE ( ) ->Optional[int]:
'''simple docstring'''
a : Union[str, Any] = [PokerHand(_lowercase ) for hand in SORTED_HANDS]
a : Union[str, Any] = poker_hands.copy()
shuffle(_lowercase )
a : Tuple = chain(sorted(_lowercase ) )
for index, hand in enumerate(_lowercase ):
assert hand == poker_hands[index]
def _SCREAMING_SNAKE_CASE ( ) ->Optional[int]:
'''simple docstring'''
a : int = [PokerHand("2D AC 3H 4H 5S" ), PokerHand("2S 3H 4H 5S 6C" )]
pokerhands.sort(reverse=_lowercase )
assert pokerhands[0].__str__() == "2S 3H 4H 5S 6C"
def _SCREAMING_SNAKE_CASE ( ) ->List[str]:
'''simple docstring'''
a : List[Any] = PokerHand("2C 4S AS 3D 5C" )
a : Optional[Any] = True
a : Optional[Any] = [5, 4, 3, 2, 14]
for _ in range(10 ):
assert pokerhand._is_five_high_straight() == expected
assert pokerhand._card_values == expected_card_values
def _SCREAMING_SNAKE_CASE ( ) ->Union[str, Any]:
'''simple docstring'''
a : Tuple = 0
a : Union[str, Any] = os.path.abspath(os.path.dirname(_lowercase ) )
a : Optional[Any] = os.path.join(_lowercase , "poker_hands.txt" )
with open(_lowercase ) as file_hand:
for line in file_hand:
a : Tuple = line[:14].strip()
a : List[Any] = line[15:].strip()
a, a : Optional[int] = PokerHand(_lowercase ), PokerHand(_lowercase )
a : str = player.compare_with(_lowercase )
if output == "Win":
answer += 1
assert answer == 376
| 105 |
from typing import TYPE_CHECKING
from ...utils import _LazyModule
UpperCamelCase__ = {"""tokenization_wav2vec2_phoneme""": ["""Wav2Vec2PhonemeCTCTokenizer"""]}
if TYPE_CHECKING:
from .tokenization_wavaveca_phoneme import WavaVecaPhonemeCTCTokenizer
else:
import sys
UpperCamelCase__ = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 92 | 0 |
"""simple docstring"""
from __future__ import annotations
import unittest
from transformers import RoFormerConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import tensorflow as tf
from transformers import (
TFRoFormerForCausalLM,
TFRoFormerForMaskedLM,
TFRoFormerForMultipleChoice,
TFRoFormerForQuestionAnswering,
TFRoFormerForSequenceClassification,
TFRoFormerForTokenClassification,
TFRoFormerModel,
)
from transformers.models.roformer.modeling_tf_roformer import (
TFRoFormerSelfAttention,
TFRoFormerSinusoidalPositionalEmbedding,
)
class SCREAMING_SNAKE_CASE :
"""simple docstring"""
def __init__( self : Dict ,lowercase_ : Tuple ,lowercase_ : List[str]=1_3 ,lowercase_ : int=7 ,lowercase_ : Optional[Any]=True ,lowercase_ : Any=True ,lowercase_ : int=True ,lowercase_ : Any=True ,lowercase_ : str=9_9 ,lowercase_ : Union[str, Any]=3_2 ,lowercase_ : str=2 ,lowercase_ : Union[str, Any]=4 ,lowercase_ : Optional[int]=3_7 ,lowercase_ : Any="gelu" ,lowercase_ : str=0.1 ,lowercase_ : Dict=0.1 ,lowercase_ : Optional[int]=5_1_2 ,lowercase_ : Optional[Any]=1_6 ,lowercase_ : Optional[Any]=2 ,lowercase_ : int=0.02 ,lowercase_ : Union[str, Any]=3 ,lowercase_ : Optional[Any]=4 ,lowercase_ : Dict=None ,):
lowerCAmelCase__ : Tuple = parent
lowerCAmelCase__ : Dict = 1_3
lowerCAmelCase__ : List[str] = 7
lowerCAmelCase__ : Union[str, Any] = True
lowerCAmelCase__ : Any = True
lowerCAmelCase__ : Optional[Any] = True
lowerCAmelCase__ : Optional[int] = True
lowerCAmelCase__ : List[Any] = 9_9
lowerCAmelCase__ : Any = 3_2
lowerCAmelCase__ : Optional[int] = 2
lowerCAmelCase__ : Optional[Any] = 4
lowerCAmelCase__ : Any = 3_7
lowerCAmelCase__ : Tuple = '''gelu'''
lowerCAmelCase__ : Any = 0.1
lowerCAmelCase__ : Union[str, Any] = 0.1
lowerCAmelCase__ : List[Any] = 5_1_2
lowerCAmelCase__ : str = 1_6
lowerCAmelCase__ : Union[str, Any] = 2
lowerCAmelCase__ : List[Any] = 0.02
lowerCAmelCase__ : Optional[Any] = 3
lowerCAmelCase__ : Union[str, Any] = 4
lowerCAmelCase__ : Optional[int] = None
def __lowerCAmelCase ( self : Optional[int] ):
lowerCAmelCase__ : Tuple = ids_tensor([self.batch_size, self.seq_length] ,self.vocab_size )
lowerCAmelCase__ : List[Any] = None
if self.use_input_mask:
lowerCAmelCase__ : Any = random_attention_mask([self.batch_size, self.seq_length] )
lowerCAmelCase__ : List[Any] = None
if self.use_token_type_ids:
lowerCAmelCase__ : List[str] = ids_tensor([self.batch_size, self.seq_length] ,self.type_vocab_size )
lowerCAmelCase__ : Tuple = None
lowerCAmelCase__ : Optional[int] = None
lowerCAmelCase__ : int = None
if self.use_labels:
lowerCAmelCase__ : Union[str, 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__ : Tuple = ids_tensor([self.batch_size] ,self.num_choices )
lowerCAmelCase__ : Tuple = RoFormerConfig(
vocab_size=self.vocab_size ,hidden_size=self.hidden_size ,num_hidden_layers=self.num_hidden_layers ,num_attention_heads=self.num_attention_heads ,intermediate_size=self.intermediate_size ,hidden_act=self.hidden_act ,hidden_dropout_prob=self.hidden_dropout_prob ,attention_probs_dropout_prob=self.attention_probs_dropout_prob ,max_position_embeddings=self.max_position_embeddings ,type_vocab_size=self.type_vocab_size ,initializer_range=self.initializer_range ,return_dict=lowercase_ ,)
return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def __lowerCAmelCase ( self : List[str] ,lowercase_ : List[str] ,lowercase_ : Tuple ,lowercase_ : Tuple ,lowercase_ : Optional[int] ,lowercase_ : str ,lowercase_ : str ,lowercase_ : Dict ):
lowerCAmelCase__ : Optional[int] = TFRoFormerModel(config=lowercase_ )
lowerCAmelCase__ : Optional[Any] = {'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}
lowerCAmelCase__ : str = [input_ids, input_mask]
lowerCAmelCase__ : str = model(lowercase_ )
lowerCAmelCase__ : Optional[int] = model(lowercase_ )
self.parent.assertEqual(result.last_hidden_state.shape ,(self.batch_size, self.seq_length, self.hidden_size) )
def __lowerCAmelCase ( self : List[str] ,lowercase_ : Union[str, Any] ,lowercase_ : Any ,lowercase_ : Union[str, Any] ,lowercase_ : int ,lowercase_ : Optional[int] ,lowercase_ : Optional[Any] ,lowercase_ : Optional[int] ):
lowerCAmelCase__ : Dict = True
lowerCAmelCase__ : Tuple = TFRoFormerForCausalLM(config=lowercase_ )
lowerCAmelCase__ : List[str] = {
'''input_ids''': input_ids,
'''attention_mask''': input_mask,
'''token_type_ids''': token_type_ids,
}
lowerCAmelCase__ : Dict = model(lowercase_ )['''logits''']
self.parent.assertListEqual(
list(prediction_scores.numpy().shape ) ,[self.batch_size, self.seq_length, self.vocab_size] )
def __lowerCAmelCase ( self : int ,lowercase_ : Any ,lowercase_ : int ,lowercase_ : int ,lowercase_ : str ,lowercase_ : str ,lowercase_ : Optional[int] ,lowercase_ : Any ):
lowerCAmelCase__ : Optional[Any] = TFRoFormerForMaskedLM(config=lowercase_ )
lowerCAmelCase__ : Dict = {
'''input_ids''': input_ids,
'''attention_mask''': input_mask,
'''token_type_ids''': token_type_ids,
}
lowerCAmelCase__ : Union[str, Any] = model(lowercase_ )
self.parent.assertEqual(result.logits.shape ,(self.batch_size, self.seq_length, self.vocab_size) )
def __lowerCAmelCase ( self : int ,lowercase_ : List[Any] ,lowercase_ : List[Any] ,lowercase_ : Any ,lowercase_ : str ,lowercase_ : int ,lowercase_ : List[Any] ,lowercase_ : List[str] ):
lowerCAmelCase__ : List[str] = self.num_labels
lowerCAmelCase__ : List[str] = TFRoFormerForSequenceClassification(config=lowercase_ )
lowerCAmelCase__ : int = {
'''input_ids''': input_ids,
'''attention_mask''': input_mask,
'''token_type_ids''': token_type_ids,
}
lowerCAmelCase__ : str = model(lowercase_ )
self.parent.assertEqual(result.logits.shape ,(self.batch_size, self.num_labels) )
def __lowerCAmelCase ( self : int ,lowercase_ : List[str] ,lowercase_ : Dict ,lowercase_ : Any ,lowercase_ : str ,lowercase_ : str ,lowercase_ : List[Any] ,lowercase_ : Optional[int] ):
lowerCAmelCase__ : List[Any] = self.num_choices
lowerCAmelCase__ : Tuple = TFRoFormerForMultipleChoice(config=lowercase_ )
lowerCAmelCase__ : List[str] = tf.tile(tf.expand_dims(lowercase_ ,1 ) ,(1, self.num_choices, 1) )
lowerCAmelCase__ : Tuple = tf.tile(tf.expand_dims(lowercase_ ,1 ) ,(1, self.num_choices, 1) )
lowerCAmelCase__ : Tuple = tf.tile(tf.expand_dims(lowercase_ ,1 ) ,(1, self.num_choices, 1) )
lowerCAmelCase__ : Optional[Any] = {
'''input_ids''': multiple_choice_inputs_ids,
'''attention_mask''': multiple_choice_input_mask,
'''token_type_ids''': multiple_choice_token_type_ids,
}
lowerCAmelCase__ : Union[str, Any] = model(lowercase_ )
self.parent.assertEqual(result.logits.shape ,(self.batch_size, self.num_choices) )
def __lowerCAmelCase ( self : int ,lowercase_ : Optional[Any] ,lowercase_ : List[str] ,lowercase_ : List[str] ,lowercase_ : List[str] ,lowercase_ : Optional[int] ,lowercase_ : Any ,lowercase_ : Dict ):
lowerCAmelCase__ : Any = self.num_labels
lowerCAmelCase__ : List[Any] = TFRoFormerForTokenClassification(config=lowercase_ )
lowerCAmelCase__ : Optional[int] = {
'''input_ids''': input_ids,
'''attention_mask''': input_mask,
'''token_type_ids''': token_type_ids,
}
lowerCAmelCase__ : Optional[int] = model(lowercase_ )
self.parent.assertEqual(result.logits.shape ,(self.batch_size, self.seq_length, self.num_labels) )
def __lowerCAmelCase ( self : str ,lowercase_ : List[str] ,lowercase_ : Any ,lowercase_ : Any ,lowercase_ : Dict ,lowercase_ : str ,lowercase_ : Any ,lowercase_ : Any ):
lowerCAmelCase__ : List[Any] = TFRoFormerForQuestionAnswering(config=lowercase_ )
lowerCAmelCase__ : Optional[int] = {
'''input_ids''': input_ids,
'''attention_mask''': input_mask,
'''token_type_ids''': token_type_ids,
}
lowerCAmelCase__ : str = model(lowercase_ )
self.parent.assertEqual(result.start_logits.shape ,(self.batch_size, self.seq_length) )
self.parent.assertEqual(result.end_logits.shape ,(self.batch_size, self.seq_length) )
def __lowerCAmelCase ( self : Dict ):
lowerCAmelCase__ : Optional[Any] = self.prepare_config_and_inputs()
(
(
lowerCAmelCase__
) ,(
lowerCAmelCase__
) ,(
lowerCAmelCase__
) ,(
lowerCAmelCase__
) ,(
lowerCAmelCase__
) ,(
lowerCAmelCase__
) ,(
lowerCAmelCase__
) ,
) : Optional[int] = config_and_inputs
lowerCAmelCase__ : List[Any] = {'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': input_mask}
return config, inputs_dict
@require_tf
class SCREAMING_SNAKE_CASE ( a_ , a_ , unittest.TestCase ):
"""simple docstring"""
lowercase__ = (
(
TFRoFormerModel,
TFRoFormerForCausalLM,
TFRoFormerForMaskedLM,
TFRoFormerForQuestionAnswering,
TFRoFormerForSequenceClassification,
TFRoFormerForTokenClassification,
TFRoFormerForMultipleChoice,
)
if is_tf_available()
else ()
)
lowercase__ = (
{
"feature-extraction": TFRoFormerModel,
"fill-mask": TFRoFormerForMaskedLM,
"question-answering": TFRoFormerForQuestionAnswering,
"text-classification": TFRoFormerForSequenceClassification,
"text-generation": TFRoFormerForCausalLM,
"token-classification": TFRoFormerForTokenClassification,
"zero-shot": TFRoFormerForSequenceClassification,
}
if is_tf_available()
else {}
)
lowercase__ = False
lowercase__ = False
def __lowerCAmelCase ( self : Union[str, Any] ,lowercase_ : Dict ,lowercase_ : int ,lowercase_ : Optional[int] ,lowercase_ : Optional[Any] ,lowercase_ : str ):
if pipeline_test_casse_name == "TextGenerationPipelineTests":
return True
return False
def __lowerCAmelCase ( self : str ):
lowerCAmelCase__ : Any = TFRoFormerModelTester(self )
lowerCAmelCase__ : Any = ConfigTester(self ,config_class=lowercase_ ,hidden_size=3_7 )
def __lowerCAmelCase ( self : Optional[Any] ):
self.config_tester.run_common_tests()
def __lowerCAmelCase ( self : List[Any] ):
lowerCAmelCase__ : Tuple = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*lowercase_ )
def __lowerCAmelCase ( self : str ):
lowerCAmelCase__ : str = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_lm(*lowercase_ )
def __lowerCAmelCase ( self : Any ):
lowerCAmelCase__ : Optional[int] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_lm_head(*lowercase_ )
def __lowerCAmelCase ( self : List[str] ):
lowerCAmelCase__ : Dict = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_multiple_choice(*lowercase_ )
def __lowerCAmelCase ( self : Tuple ):
lowerCAmelCase__ : Union[str, Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_question_answering(*lowercase_ )
def __lowerCAmelCase ( self : int ):
lowerCAmelCase__ : List[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_sequence_classification(*lowercase_ )
def __lowerCAmelCase ( self : List[str] ):
lowerCAmelCase__ : List[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_token_classification(*lowercase_ )
@slow
def __lowerCAmelCase ( self : Tuple ):
lowerCAmelCase__ : Dict = TFRoFormerModel.from_pretrained('''junnyu/roformer_chinese_base''' )
self.assertIsNotNone(lowercase_ )
@require_tf
class SCREAMING_SNAKE_CASE ( unittest.TestCase ):
"""simple docstring"""
@slow
def __lowerCAmelCase ( self : Tuple ):
lowerCAmelCase__ : Dict = TFRoFormerForMaskedLM.from_pretrained('''junnyu/roformer_chinese_base''' )
lowerCAmelCase__ : Dict = tf.constant([[0, 1, 2, 3, 4, 5]] )
lowerCAmelCase__ : Optional[int] = model(lowercase_ )[0]
# TODO Replace vocab size
lowerCAmelCase__ : int = 5_0_0_0_0
lowerCAmelCase__ : int = [1, 6, vocab_size]
self.assertEqual(output.shape ,lowercase_ )
print(output[:, :3, :3] )
# TODO Replace values below with what was printed above.
lowerCAmelCase__ : Optional[int] = tf.constant(
[
[
[-0.1205_3341, -1.026_4901, 0.2922_1946],
[-1.513_3783, 0.19_7433, 0.1519_0607],
[-5.013_5403, -3.90_0256, -0.8403_8764],
]
] )
tf.debugging.assert_near(output[:, :3, :3] ,lowercase_ ,atol=1E-4 )
@require_tf
class SCREAMING_SNAKE_CASE ( unittest.TestCase ):
"""simple docstring"""
lowercase__ = 1e-4
def __lowerCAmelCase ( self : List[Any] ):
lowerCAmelCase__ : List[Any] = tf.constant([[4, 1_0]] )
lowerCAmelCase__ : Optional[int] = TFRoFormerSinusoidalPositionalEmbedding(num_positions=6 ,embedding_dim=6 )
lowerCAmelCase__ : List[Any] = emba(input_ids.shape )
lowerCAmelCase__ : int = tf.constant(
[[0.0000, 0.0000, 0.0000, 1.0000, 1.0000, 1.0000], [0.8415, 0.0464, 0.0022, 0.5403, 0.9989, 1.0000]] )
tf.debugging.assert_near(lowercase_ ,lowercase_ ,atol=self.tolerance )
def __lowerCAmelCase ( self : List[str] ):
lowerCAmelCase__ : Tuple = tf.constant(
[
[0.0000, 0.0000, 0.0000, 0.0000, 0.0000],
[0.8415, 0.8219, 0.8020, 0.7819, 0.7617],
[0.9093, 0.9364, 0.9581, 0.9749, 0.9870],
] )
lowerCAmelCase__ : Tuple = TFRoFormerSinusoidalPositionalEmbedding(num_positions=5_1_2 ,embedding_dim=5_1_2 )
emba([2, 1_6, 5_1_2] )
lowerCAmelCase__ : int = emba.weight[:3, :5]
tf.debugging.assert_near(lowercase_ ,lowercase_ ,atol=self.tolerance )
@require_tf
class SCREAMING_SNAKE_CASE ( unittest.TestCase ):
"""simple docstring"""
lowercase__ = 1e-4
def __lowerCAmelCase ( self : str ):
# 2,12,16,64
lowerCAmelCase__ : Optional[Any] = tf.reshape(tf.range(2 * 1_2 * 1_6 * 6_4 ,dtype=tf.floataa ) ,shape=(2, 1_2, 1_6, 6_4) ) / 1_0_0
lowerCAmelCase__ : Optional[Any] = -tf.reshape(tf.range(2 * 1_2 * 1_6 * 6_4 ,dtype=tf.floataa ) ,shape=(2, 1_2, 1_6, 6_4) ) / 1_0_0
lowerCAmelCase__ : Optional[int] = TFRoFormerSinusoidalPositionalEmbedding(num_positions=3_2 ,embedding_dim=6_4 )
lowerCAmelCase__ : str = embed_positions([2, 1_6, 7_6_8] )[None, None, :, :]
lowerCAmelCase__ ,lowerCAmelCase__ : Any = TFRoFormerSelfAttention.apply_rotary_position_embeddings(
lowercase_ ,lowercase_ ,lowercase_ )
lowerCAmelCase__ : str = tf.constant(
[
[0.0000, 0.0100, 0.0200, 0.0300, 0.0400, 0.0500, 0.0600, 0.0700],
[-0.2012, 0.8897, 0.0263, 0.9401, 0.2074, 0.9463, 0.3481, 0.9343],
[-1.7057, 0.6271, -1.2145, 1.3897, -0.6303, 1.7647, -0.1173, 1.8985],
[-2.1731, -1.6397, -2.7358, 0.2854, -2.1840, 1.7183, -1.3018, 2.4871],
[0.2717, -3.6173, -2.9206, -2.1988, -3.6638, 0.3858, -2.9155, 2.2980],
[3.9859, -2.1580, -0.7984, -4.4904, -4.1181, -2.0252, -4.4782, 1.1253],
] )
lowerCAmelCase__ : Union[str, Any] = tf.constant(
[
[0.0000, -0.0100, -0.0200, -0.0300, -0.0400, -0.0500, -0.0600, -0.0700],
[0.2012, -0.8897, -0.0263, -0.9401, -0.2074, -0.9463, -0.3481, -0.9343],
[1.7057, -0.6271, 1.2145, -1.3897, 0.6303, -1.7647, 0.1173, -1.8985],
[2.1731, 1.6397, 2.7358, -0.2854, 2.1840, -1.7183, 1.3018, -2.4871],
[-0.2717, 3.6173, 2.9206, 2.1988, 3.6638, -0.3858, 2.9155, -2.2980],
[-3.9859, 2.1580, 0.7984, 4.4904, 4.1181, 2.0252, 4.4782, -1.1253],
] )
tf.debugging.assert_near(query_layer[0, 0, :6, :8] ,lowercase_ ,atol=self.tolerance )
tf.debugging.assert_near(key_layer[0, 0, :6, :8] ,lowercase_ ,atol=self.tolerance )
| 106 |
import unittest
from transformers import DebertaVaTokenizer, DebertaVaTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin
UpperCamelCase__ = get_tests_dir("""fixtures/spiece.model""")
@require_sentencepiece
@require_tokenizers
class a__ ( snake_case__ , unittest.TestCase ):
_a : Optional[Any] = DebertaVaTokenizer
_a : Optional[Any] = DebertaVaTokenizerFast
_a : List[str] = True
_a : Optional[Any] = True
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
super().setUp()
# We have a SentencePiece fixture for testing
__lowerCAmelCase = DebertaVaTokenizer(_A , unk_token="<unk>" )
tokenizer.save_pretrained(self.tmpdirname )
def __SCREAMING_SNAKE_CASE( self , _A ):
"""simple docstring"""
__lowerCAmelCase = "this is a test"
__lowerCAmelCase = "this is a test"
return input_text, output_text
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
__lowerCAmelCase = "<pad>"
__lowerCAmelCase = 0
self.assertEqual(self.get_tokenizer()._convert_token_to_id(_A ) , _A )
self.assertEqual(self.get_tokenizer()._convert_id_to_token(_A ) , _A )
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
__lowerCAmelCase = list(self.get_tokenizer().get_vocab().keys() )
self.assertEqual(vocab_keys[0] , "<pad>" )
self.assertEqual(vocab_keys[1] , "<unk>" )
self.assertEqual(vocab_keys[-1] , "[PAD]" )
self.assertEqual(len(_A ) , 3_0_0_0_1 )
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
self.assertEqual(self.get_tokenizer().vocab_size , 3_0_0_0_0 )
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
__lowerCAmelCase = " \tHeLLo!how \n Are yoU? "
__lowerCAmelCase = ["▁hello", "!", "how", "▁are", "▁you", "?"]
# fmt: on
__lowerCAmelCase = DebertaVaTokenizer(_A , do_lower_case=_A )
__lowerCAmelCase = tokenizer.convert_ids_to_tokens(tokenizer.encode(_A , add_special_tokens=_A ) )
self.assertListEqual(_A , _A )
__lowerCAmelCase = DebertaVaTokenizerFast(_A , do_lower_case=_A )
__lowerCAmelCase = rust_tokenizer.convert_ids_to_tokens(rust_tokenizer.encode(_A , add_special_tokens=_A ) )
self.assertListEqual(_A , _A )
@unittest.skip("There is an inconsistency between slow and fast tokenizer due to a bug in the fast one." )
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
pass
@unittest.skip("There is an inconsistency between slow and fast tokenizer due to a bug in the fast one." )
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
pass
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
__lowerCAmelCase = "I was born in 92000, and this is falsé."
__lowerCAmelCase = ["▁", "<unk>", "▁was", "▁born", "▁in", "▁9", "2000", "▁", ",", "▁and", "▁this", "▁is", "▁fal", "s", "<unk>", "▁", ".", ]
# fmt: on
__lowerCAmelCase = DebertaVaTokenizer(_A , split_by_punct=_A )
__lowerCAmelCase = tokenizer.convert_ids_to_tokens(tokenizer.encode(_A , add_special_tokens=_A ) )
self.assertListEqual(_A , _A )
__lowerCAmelCase = DebertaVaTokenizerFast(_A , split_by_punct=_A )
__lowerCAmelCase = rust_tokenizer.convert_ids_to_tokens(rust_tokenizer.encode(_A , add_special_tokens=_A ) )
self.assertListEqual(_A , _A )
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
__lowerCAmelCase = "I was born in 92000, and this is falsé."
__lowerCAmelCase = ["▁i", "▁was", "▁born", "▁in", "▁9", "2000", "▁", ",", "▁and", "▁this", "▁is", "▁fal", "s", "<unk>", "▁", ".", ]
# fmt: on
__lowerCAmelCase = DebertaVaTokenizer(_A , do_lower_case=_A , split_by_punct=_A )
__lowerCAmelCase = tokenizer.convert_ids_to_tokens(tokenizer.encode(_A , add_special_tokens=_A ) )
self.assertListEqual(_A , _A )
__lowerCAmelCase = DebertaVaTokenizerFast(_A , do_lower_case=_A , split_by_punct=_A )
__lowerCAmelCase = rust_tokenizer.convert_ids_to_tokens(rust_tokenizer.encode(_A , add_special_tokens=_A ) )
self.assertListEqual(_A , _A )
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
__lowerCAmelCase = "I was born in 92000, and this is falsé."
__lowerCAmelCase = ["▁i", "▁was", "▁born", "▁in", "▁9", "2000", ",", "▁and", "▁this", "▁is", "▁fal", "s", "<unk>", ".", ]
# fmt: on
__lowerCAmelCase = DebertaVaTokenizer(_A , do_lower_case=_A , split_by_punct=_A )
__lowerCAmelCase = tokenizer.convert_ids_to_tokens(tokenizer.encode(_A , add_special_tokens=_A ) )
self.assertListEqual(_A , _A )
__lowerCAmelCase = DebertaVaTokenizerFast(_A , do_lower_case=_A , split_by_punct=_A )
__lowerCAmelCase = rust_tokenizer.convert_ids_to_tokens(rust_tokenizer.encode(_A , add_special_tokens=_A ) )
self.assertListEqual(_A , _A )
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
__lowerCAmelCase = "I was born in 92000, and this is falsé."
__lowerCAmelCase = ["▁", "<unk>", "▁was", "▁born", "▁in", "▁9", "2000", "▁", ",", "▁and", "▁this", "▁is", "▁fal", "s", "<unk>", "▁", ".", ]
# fmt: on
__lowerCAmelCase = DebertaVaTokenizer(_A , do_lower_case=_A , split_by_punct=_A )
__lowerCAmelCase = tokenizer.convert_ids_to_tokens(tokenizer.encode(_A , add_special_tokens=_A ) )
self.assertListEqual(_A , _A )
__lowerCAmelCase = DebertaVaTokenizerFast(_A , do_lower_case=_A , split_by_punct=_A )
__lowerCAmelCase = rust_tokenizer.convert_ids_to_tokens(rust_tokenizer.encode(_A , add_special_tokens=_A ) )
self.assertListEqual(_A , _A )
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
__lowerCAmelCase = " \tHeLLo!how \n Are yoU? "
__lowerCAmelCase = ["▁", "<unk>", "e", "<unk>", "o", "!", "how", "▁", "<unk>", "re", "▁yo", "<unk>", "?"]
# fmt: on
__lowerCAmelCase = DebertaVaTokenizer(_A , do_lower_case=_A , split_by_punct=_A )
__lowerCAmelCase = tokenizer.convert_ids_to_tokens(tokenizer.encode(_A , add_special_tokens=_A ) )
self.assertListEqual(_A , _A )
__lowerCAmelCase = DebertaVaTokenizerFast(_A , do_lower_case=_A , split_by_punct=_A )
__lowerCAmelCase = rust_tokenizer.convert_ids_to_tokens(rust_tokenizer.encode(_A , add_special_tokens=_A ) )
self.assertListEqual(_A , _A )
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
__lowerCAmelCase = self.get_tokenizer()
__lowerCAmelCase = self.get_rust_tokenizer()
__lowerCAmelCase = "I was born in 92000, and this is falsé."
__lowerCAmelCase = tokenizer.convert_ids_to_tokens(tokenizer.encode(_A , add_special_tokens=_A ) )
__lowerCAmelCase = rust_tokenizer.convert_ids_to_tokens(rust_tokenizer.encode(_A , add_special_tokens=_A ) )
self.assertListEqual(_A , _A )
__lowerCAmelCase = tokenizer.encode(_A , add_special_tokens=_A )
__lowerCAmelCase = rust_tokenizer.encode(_A , add_special_tokens=_A )
self.assertListEqual(_A , _A )
__lowerCAmelCase = self.get_rust_tokenizer()
__lowerCAmelCase = tokenizer.encode(_A )
__lowerCAmelCase = rust_tokenizer.encode(_A )
self.assertListEqual(_A , _A )
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
__lowerCAmelCase = "This is a test"
__lowerCAmelCase = [1_3, 1, 4_3_9_8, 2_5, 2_1, 1_2_8_9]
__lowerCAmelCase = ["▁", "T", "his", "▁is", "▁a", "▁test"]
__lowerCAmelCase = ["▁", "<unk>", "his", "▁is", "▁a", "▁test"]
__lowerCAmelCase = DebertaVaTokenizer(_A , keep_accents=_A )
__lowerCAmelCase = DebertaVaTokenizerFast(_A , keep_accents=_A )
__lowerCAmelCase = tokenizer.encode(_A , add_special_tokens=_A )
self.assertListEqual(_A , _A )
__lowerCAmelCase = tokenizer.tokenize(_A )
self.assertListEqual(_A , _A )
__lowerCAmelCase = tokenizer.convert_ids_to_tokens(_A )
self.assertListEqual(_A , _A )
__lowerCAmelCase = rust_tokenizer.encode(_A , add_special_tokens=_A )
self.assertListEqual(_A , _A )
__lowerCAmelCase = rust_tokenizer.tokenize(_A )
self.assertListEqual(_A , _A )
__lowerCAmelCase = rust_tokenizer.convert_ids_to_tokens(_A )
self.assertListEqual(_A , _A )
# fmt: off
__lowerCAmelCase = "I was born in 92000, and this is falsé."
__lowerCAmelCase = [1_3, 1, 2_3, 3_8_6, 1_9, 5_6_1, 3_0_5_0, 1_5, 1_7, 4_8, 2_5, 8_2_5_6, 1_8, 1, 9]
__lowerCAmelCase = ["▁", "I", "▁was", "▁born", "▁in", "▁9", "2000", ",", "▁and", "▁this", "▁is", "▁fal", "s", "é", ".", ]
__lowerCAmelCase = ["▁", "<unk>", "▁was", "▁born", "▁in", "▁9", "2000", ",", "▁and", "▁this", "▁is", "▁fal", "s", "<unk>", ".", ]
# fmt: on
__lowerCAmelCase = tokenizer.encode(_A , add_special_tokens=_A )
self.assertListEqual(_A , _A )
__lowerCAmelCase = tokenizer.tokenize(_A )
self.assertListEqual(_A , _A )
__lowerCAmelCase = tokenizer.convert_ids_to_tokens(_A )
self.assertListEqual(_A , _A )
__lowerCAmelCase = rust_tokenizer.encode(_A , add_special_tokens=_A )
self.assertListEqual(_A , _A )
__lowerCAmelCase = rust_tokenizer.tokenize(_A )
self.assertListEqual(_A , _A )
__lowerCAmelCase = rust_tokenizer.convert_ids_to_tokens(_A )
self.assertListEqual(_A , _A )
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
__lowerCAmelCase = DebertaVaTokenizer(_A )
__lowerCAmelCase = tokenizer.encode("sequence builders" )
__lowerCAmelCase = tokenizer.encode("multi-sequence build" )
__lowerCAmelCase = tokenizer.build_inputs_with_special_tokens(_A )
__lowerCAmelCase = tokenizer.build_inputs_with_special_tokens(_A , _A )
self.assertEqual([tokenizer.cls_token_id] + text + [tokenizer.sep_token_id] , _A )
self.assertEqual(
[tokenizer.cls_token_id] + text + [tokenizer.sep_token_id] + text_a + [tokenizer.sep_token_id] , _A , )
@slow
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
__lowerCAmelCase = {"input_ids": [[1, 3_9_8_6_7, 3_6, 1_9_3_9_0, 4_8_6, 2_7, 3_5_0_5_2, 8_1_4_3_6, 1_8, 6_0_6_8_5, 1_2_2_5, 7, 3_5_0_5_2, 8_1_4_3_6, 1_8, 9_3_6_7, 1_6_8_9_9, 1_8, 1_5_9_3_7, 5_3, 5_9_4, 7_7_3, 1_8, 1_6_2_8_7, 3_0_4_6_5, 3_6, 1_5_9_3_7, 6, 4_1_1_3_9, 3_8, 3_6_9_7_9, 6_0_7_6_3, 1_9_1, 6, 3_4_1_3_2, 9_9, 6, 5_0_5_3_8, 3_9_0, 4_3_2_3_0, 6, 3_4_1_3_2, 2_7_7_9, 2_0_8_5_0, 1_4, 6_9_9, 1_0_7_2, 1_1_9_4, 3_6, 3_8_2, 1_0_9_0_1, 5_3, 7, 6_9_9, 1_0_7_2, 2_0_8_4, 3_6, 2_0_4_2_2, 6_3_0, 5_3, 1_9, 1_0_5, 3_0_4_9, 1_8_9_6, 1_0_5_3, 1_6_8_9_9, 1_5_0_6, 1_1, 3_7_9_7_8, 4_2_4_3, 7, 1_2_3_7, 3_1_8_6_9, 2_0_0, 1_6_5_6_6, 6_5_4, 6, 3_5_0_5_2, 8_1_4_3_6, 7, 5_5_6_3_0, 1_3_5_9_3, 4, 2], [1, 2_6, 1_5_0_1_1, 1_3, 6_6_7, 8, 1_0_5_3, 1_8, 2_3_6_1_1, 1_2_3_7, 7_2_3_5_6, 1_2_8_2_0, 3_4, 1_0_4_1_3_4, 1_2_0_9, 3_5, 1_3_3_1_3, 6_6_2_7, 2_1, 2_0_2, 3_4_7, 7, 1_6_4, 2_3_9_9, 1_1, 4_6, 4_4_8_5, 4, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 5, 1_2_3_2, 2_8_6_4, 1_5_7_8_5, 1_4_9_5_1, 1_0_5, 5, 8_5_8_1, 1_2_5_0, 4, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], "token_type_ids": [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], "attention_mask": [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]} # noqa: E501
# fmt: on
self.tokenizer_integration_test_util(
expected_encoding=_A , model_name="microsoft/deberta-v2-xlarge" , revision="ad6e42c1532ddf3a15c39246b63f5559d558b670" , )
| 92 | 0 |
from collections import Counter
import numpy as np
from sklearn import datasets
from sklearn.model_selection import train_test_split
__lowerCAmelCase : Any = datasets.load_iris()
__lowerCAmelCase : List[Any] = np.array(data['data'])
__lowerCAmelCase : List[str] = np.array(data['target'])
__lowerCAmelCase : Dict = data['target_names']
__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase : List[str] = train_test_split(X, y)
def __magic_name__ ( A : Optional[Any], A : Optional[Any] ):
'''simple docstring'''
return np.linalg.norm(np.array(A ) - np.array(A ) )
def __magic_name__ ( A : Tuple, A : List[Any], A : int, A : str, A : int=5 ):
'''simple docstring'''
a = zip(A, A )
# List of distances of all points from the point to be classified
a = []
for data_point in data:
a = euclidean_distance(data_point[0], A )
distances.append((distance, data_point[1]) )
# Choosing 'k' points with the least distances.
a = [i[1] for i in sorted(A )[:k]]
# Most commonly occurring class among them
# is the class into which the point is classified
a = Counter(A ).most_common(1 )[0][0]
return classes[result]
if __name__ == "__main__":
print(classifier(X_train, y_train, classes, [4.4, 3.1, 1.3, 1.4]))
| 107 |
from dataclasses import dataclass, field
from typing import Tuple
from ..utils import cached_property, is_tf_available, logging, requires_backends
from .benchmark_args_utils import BenchmarkArguments
if is_tf_available():
import tensorflow as tf
UpperCamelCase__ = logging.get_logger(__name__)
@dataclass
class a__ ( snake_case__ ):
_a : List[str] = [
"""no_inference""",
"""no_cuda""",
"""no_tpu""",
"""no_speed""",
"""no_memory""",
"""no_env_print""",
"""no_multi_process""",
]
def __init__( self , **_A ):
"""simple docstring"""
for deprecated_arg in self.deprecated_args:
if deprecated_arg in kwargs:
__lowerCAmelCase = deprecated_arg[3:]
__lowerCAmelCase = not kwargs.pop(_A )
logger.warning(
f"""{deprecated_arg} is depreciated. Please use --no-{positive_arg} or"""
f""" {positive_arg}={kwargs[positive_arg]}""" )
__lowerCAmelCase = kwargs.pop("tpu_name" , self.tpu_name )
__lowerCAmelCase = kwargs.pop("device_idx" , self.device_idx )
__lowerCAmelCase = kwargs.pop("eager_mode" , self.eager_mode )
__lowerCAmelCase = kwargs.pop("use_xla" , self.use_xla )
super().__init__(**_A )
_a : str = field(
default=snake_case__ , metadata={"""help""": """Name of TPU"""} , )
_a : int = field(
default=0 , metadata={"""help""": """CPU / GPU device index. Defaults to 0."""} , )
_a : bool = field(default=snake_case__ , metadata={"""help""": """Benchmark models in eager model."""} )
_a : bool = field(
default=snake_case__ , metadata={
"""help""": """Benchmark models using XLA JIT compilation. Note that `eager_model` has to be set to `False`."""
} , )
@cached_property
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
requires_backends(self , ["tf"] )
__lowerCAmelCase = None
if self.tpu:
try:
if self.tpu_name:
__lowerCAmelCase = tf.distribute.cluster_resolver.TPUClusterResolver(self.tpu_name )
else:
__lowerCAmelCase = tf.distribute.cluster_resolver.TPUClusterResolver()
except ValueError:
__lowerCAmelCase = None
return tpu
@cached_property
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
requires_backends(self , ["tf"] )
if self.is_tpu:
tf.config.experimental_connect_to_cluster(self._setup_tpu )
tf.tpu.experimental.initialize_tpu_system(self._setup_tpu )
__lowerCAmelCase = tf.distribute.TPUStrategy(self._setup_tpu )
else:
# currently no multi gpu is allowed
if self.is_gpu:
# TODO: Currently only single GPU is supported
tf.config.set_visible_devices(self.gpu_list[self.device_idx] , "GPU" )
__lowerCAmelCase = tf.distribute.OneDeviceStrategy(device=f"""/gpu:{self.device_idx}""" )
else:
tf.config.set_visible_devices([] , "GPU" ) # disable GPU
__lowerCAmelCase = tf.distribute.OneDeviceStrategy(device=f"""/cpu:{self.device_idx}""" )
return strategy
@property
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
requires_backends(self , ["tf"] )
return self._setup_tpu is not None
@property
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
requires_backends(self , ["tf"] )
return self._setup_strategy
@property
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
requires_backends(self , ["tf"] )
return tf.config.list_physical_devices("GPU" )
@property
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
requires_backends(self , ["tf"] )
if self.cuda:
return len(self.gpu_list )
return 0
@property
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
return self.n_gpu > 0
| 92 | 0 |
"""simple docstring"""
from math import ceil
def a__ ( SCREAMING_SNAKE_CASE : List[str] , SCREAMING_SNAKE_CASE : Tuple ):
'''simple docstring'''
lowerCAmelCase : Union[str, Any] = list(range(0 , SCREAMING_SNAKE_CASE ) )
lowerCAmelCase : Union[str, Any] = [item for sublist in list(device_map.values() ) for item in sublist]
# Duplicate check
lowerCAmelCase : Optional[Any] = []
for i in device_map_blocks:
if device_map_blocks.count(SCREAMING_SNAKE_CASE ) > 1 and i not in duplicate_blocks:
duplicate_blocks.append(SCREAMING_SNAKE_CASE )
# Missing blocks
lowerCAmelCase : Any = [i for i in blocks if i not in device_map_blocks]
lowerCAmelCase : Any = [i for i in device_map_blocks if i not in blocks]
if len(SCREAMING_SNAKE_CASE ) != 0:
raise ValueError(
"Duplicate attention blocks specified in device_map. Attention blocks must be specified to one device."
" These attention blocks were specified more than once: " + str(SCREAMING_SNAKE_CASE ) )
if len(SCREAMING_SNAKE_CASE ) != 0:
raise ValueError(
"There are attention blocks for this model that are not specified in the device_map. Add these attention "
"blocks to a device on the device_map: " + str(SCREAMING_SNAKE_CASE ) )
if len(SCREAMING_SNAKE_CASE ) != 0:
raise ValueError(
"The device_map contains more attention blocks than this model has. Remove these from the device_map:"
+ str(SCREAMING_SNAKE_CASE ) )
def a__ ( SCREAMING_SNAKE_CASE : Dict , SCREAMING_SNAKE_CASE : Any ):
'''simple docstring'''
lowerCAmelCase : List[Any] = list(range(SCREAMING_SNAKE_CASE ) )
lowerCAmelCase : Union[str, Any] = int(ceil(n_layers / len(SCREAMING_SNAKE_CASE ) ) )
lowerCAmelCase : int = [layers[i : i + n_blocks] for i in range(0 , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )]
return dict(zip(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ) )
| 108 |
import unittest
from transformers import CamembertTokenizer, CamembertTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import is_torch_available
from ...test_tokenization_common import TokenizerTesterMixin
UpperCamelCase__ = get_tests_dir("""fixtures/test_sentencepiece.model""")
UpperCamelCase__ = get_tests_dir("""fixtures/test_sentencepiece_bpe.model""")
UpperCamelCase__ = """pt""" if is_torch_available() else """tf"""
@require_sentencepiece
@require_tokenizers
class a__ ( snake_case__ , unittest.TestCase ):
_a : int = CamembertTokenizer
_a : Dict = CamembertTokenizerFast
_a : Tuple = True
_a : List[Any] = True
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
super().setUp()
# We have a SentencePiece fixture for testing
__lowerCAmelCase = CamembertTokenizer(_A )
tokenizer.save_pretrained(self.tmpdirname )
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
__lowerCAmelCase = "<pad>"
__lowerCAmelCase = 1
self.assertEqual(self.get_tokenizer()._convert_token_to_id(_A ) , _A )
self.assertEqual(self.get_tokenizer()._convert_id_to_token(_A ) , _A )
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
__lowerCAmelCase = list(self.get_tokenizer().get_vocab().keys() )
self.assertEqual(vocab_keys[0] , "<s>NOTUSED" )
self.assertEqual(vocab_keys[1] , "<pad>" )
self.assertEqual(vocab_keys[-1] , "<mask>" )
self.assertEqual(len(_A ) , 1_0_0_4 )
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
self.assertEqual(self.get_tokenizer().vocab_size , 1_0_0_5 )
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
__lowerCAmelCase = CamembertTokenizer(_A )
tokenizer.save_pretrained(self.tmpdirname )
__lowerCAmelCase = CamembertTokenizerFast.from_pretrained(self.tmpdirname )
__lowerCAmelCase = "I was born in 92000, and this is falsé."
__lowerCAmelCase = tokenizer.encode(_A )
__lowerCAmelCase = rust_tokenizer.encode(_A )
self.assertListEqual(_A , _A )
__lowerCAmelCase = tokenizer.encode(_A , add_special_tokens=_A )
__lowerCAmelCase = rust_tokenizer.encode(_A , add_special_tokens=_A )
self.assertListEqual(_A , _A )
# <unk> tokens are not the same for `rust` than for `slow`.
# Because spm gives back raw token instead of `unk` in EncodeAsPieces
# tokens = tokenizer.tokenize(sequence)
__lowerCAmelCase = tokenizer.convert_ids_to_tokens(_A )
__lowerCAmelCase = rust_tokenizer.tokenize(_A )
self.assertListEqual(_A , _A )
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
if not self.test_rust_tokenizer:
return
__lowerCAmelCase = self.get_tokenizer()
__lowerCAmelCase = self.get_rust_tokenizer()
__lowerCAmelCase = "I was born in 92000, and this is falsé."
__lowerCAmelCase = tokenizer.tokenize(_A )
__lowerCAmelCase = rust_tokenizer.tokenize(_A )
self.assertListEqual(_A , _A )
__lowerCAmelCase = tokenizer.encode(_A , add_special_tokens=_A )
__lowerCAmelCase = rust_tokenizer.encode(_A , add_special_tokens=_A )
self.assertListEqual(_A , _A )
__lowerCAmelCase = self.get_rust_tokenizer()
__lowerCAmelCase = tokenizer.encode(_A )
__lowerCAmelCase = rust_tokenizer.encode(_A )
self.assertListEqual(_A , _A )
@slow
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
__lowerCAmelCase = {"input_ids": [[5, 5_4, 7_1_9_6, 2_9_7, 3_0, 2_3, 7_7_6, 1_8, 1_1, 3_2_1_5, 3_7_0_5, 8_2_5_2, 2_2, 3_1_6_4, 1_1_8_1, 2_1_1_6, 2_9, 1_6, 8_1_3, 2_5, 7_9_1, 3_3_1_4, 2_0, 3_4_4_6, 3_8, 2_7_5_7_5, 1_2_0, 6, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [5, 4_6_8, 1_7, 1_1, 9_0_8_8, 2_0, 1_5_1_7, 8, 2_2_8_0_4, 1_8_8_1_8, 1_0, 3_8, 6_2_9, 6_0_7, 6_0_7, 1_4_2, 1_9, 7_1_9_6, 8_6_7, 5_6, 1_0_3_2_6, 2_4, 2_2_6_7, 2_0, 4_1_6, 5_0_7_2, 1_5_6_1_2, 2_3_3, 7_3_4, 7, 2_3_9_9, 2_7, 1_6, 3_0_1_5, 1_6_4_9, 7, 2_4, 2_0, 4_3_3_8, 2_3_9_9, 2_7, 1_3, 3_4_0_0, 1_4, 1_3, 6_1_8_9, 8, 9_3_0, 9, 6]], "attention_mask": [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]} # noqa: E501
# fmt: on
# camembert is a french model. So we also use french texts.
__lowerCAmelCase = [
"Le transformeur est un modèle d'apprentissage profond introduit en 2017, "
"utilisé principalement dans le domaine du traitement automatique des langues (TAL).",
"À l'instar des réseaux de neurones récurrents (RNN), les transformeurs sont conçus "
"pour gérer des données séquentielles, telles que le langage naturel, pour des tâches "
"telles que la traduction et la synthèse de texte.",
]
self.tokenizer_integration_test_util(
expected_encoding=_A , model_name="camembert-base" , revision="3a0641d9a1aeb7e848a74299e7e4c4bca216b4cf" , sequences=_A , )
| 92 | 0 |
"""simple docstring"""
import argparse
from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_controlnet_from_original_ckpt
if __name__ == "__main__":
A: Tuple = argparse.ArgumentParser()
parser.add_argument(
"--checkpoint_path", default=None, type=str, required=True, help="Path to the checkpoint to convert."
)
parser.add_argument(
"--original_config_file",
type=str,
required=True,
help="The YAML config file corresponding to the original architecture.",
)
parser.add_argument(
"--num_in_channels",
default=None,
type=int,
help="The number of input channels. If `None` number of input channels will be automatically inferred.",
)
parser.add_argument(
"--image_size",
default=5_1_2,
type=int,
help=(
"The image size that the model was trained on. Use 512 for Stable Diffusion v1.X and Stable Siffusion v2"
" Base. Use 768 for Stable Diffusion v2."
),
)
parser.add_argument(
"--extract_ema",
action="store_true",
help=(
"Only relevant for checkpoints that have both EMA and non-EMA weights. Whether to extract the EMA weights"
" or not. Defaults to `False`. Add `--extract_ema` to extract the EMA weights. EMA weights usually yield"
" higher quality images for inference. Non-EMA weights are usually better to continue fine-tuning."
),
)
parser.add_argument(
"--upcast_attention",
action="store_true",
help=(
"Whether the attention computation should always be upcasted. This is necessary when running stable"
" diffusion 2.1."
),
)
parser.add_argument(
"--from_safetensors",
action="store_true",
help="If `--checkpoint_path` is in `safetensors` format, load checkpoint with safetensors instead of PyTorch.",
)
parser.add_argument(
"--to_safetensors",
action="store_true",
help="Whether to store pipeline in safetensors format or not.",
)
parser.add_argument("--dump_path", default=None, type=str, required=True, help="Path to the output model.")
parser.add_argument("--device", type=str, help="Device to use (e.g. cpu, cuda:0, cuda:1, etc.)")
def _snake_case ( UpperCamelCase : int ):
if string == "True":
return True
elif string == "False":
return False
else:
raise ValueError(F"could not parse string as bool {string}" )
parser.add_argument(
"--use_linear_projection", help="Override for use linear projection", required=False, type=parse_bool
)
parser.add_argument("--cross_attention_dim", help="Override for cross attention_dim", required=False, type=int)
A: Union[str, Any] = parser.parse_args()
A: Tuple = download_controlnet_from_original_ckpt(
checkpoint_path=args.checkpoint_path,
original_config_file=args.original_config_file,
image_size=args.image_size,
extract_ema=args.extract_ema,
num_in_channels=args.num_in_channels,
upcast_attention=args.upcast_attention,
from_safetensors=args.from_safetensors,
device=args.device,
use_linear_projection=args.use_linear_projection,
cross_attention_dim=args.cross_attention_dim,
)
controlnet.save_pretrained(args.dump_path, safe_serialization=args.to_safetensors)
| 109 |
from __future__ import annotations
import collections
import tempfile
import unittest
import numpy as np
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import is_tf_available, is_vision_available
from ...test_modeling_tf_common import floats_tensor, ids_tensor, random_attention_mask
from ..bert.test_modeling_tf_bert import TFBertModelTester
from ..clip.test_modeling_tf_clip import TFCLIPVisionModelTester
from ..deit.test_modeling_tf_deit import TFDeiTModelTester
from ..roberta.test_modeling_tf_roberta import TFRobertaModelTester
from ..vit.test_modeling_tf_vit import TFViTModelTester
if is_tf_available():
from transformers import (
TFBertModel,
TFCLIPVisionModel,
TFDeiTModel,
TFRobertaModel,
TFVisionTextDualEncoderModel,
TFViTModel,
VisionTextDualEncoderConfig,
)
if is_vision_available():
from PIL import Image
from transformers import VisionTextDualEncoderProcessor
def _a ( SCREAMING_SNAKE_CASE_ : Union[str, Any] ):
if isinstance(SCREAMING_SNAKE_CASE_ , collections.abc.Iterable ):
return x
return (x, x)
@require_tf
class a__ :
def __SCREAMING_SNAKE_CASE( self , _A , _A ):
"""simple docstring"""
pass
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
pass
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
pass
def __SCREAMING_SNAKE_CASE( self , _A , _A , _A , _A , _A=None , **_A ):
"""simple docstring"""
__lowerCAmelCase = VisionTextDualEncoderConfig.from_vision_text_configs(_A , _A )
__lowerCAmelCase = TFVisionTextDualEncoderModel(_A )
__lowerCAmelCase = model(input_ids=_A , pixel_values=_A , attention_mask=_A )
self.assertEqual(output["text_embeds"].shape , (input_ids.shape[0], config.projection_dim) )
self.assertEqual(output["image_embeds"].shape , (pixel_values.shape[0], config.projection_dim) )
def __SCREAMING_SNAKE_CASE( self , _A , _A , _A , _A , _A=None , **_A ):
"""simple docstring"""
__lowerCAmelCase , __lowerCAmelCase = self.get_vision_text_model(_A , _A )
__lowerCAmelCase = TFVisionTextDualEncoderModel(vision_model=_A , text_model=_A )
__lowerCAmelCase = model(input_ids=_A , pixel_values=_A , attention_mask=_A )
self.assertEqual(output["text_embeds"].shape , (input_ids.shape[0], model.config.projection_dim) )
self.assertEqual(output["image_embeds"].shape , (pixel_values.shape[0], model.config.projection_dim) )
def __SCREAMING_SNAKE_CASE( self , _A , _A , _A , _A , _A=None , **_A ):
"""simple docstring"""
__lowerCAmelCase , __lowerCAmelCase = self.get_vision_text_model(_A , _A )
__lowerCAmelCase = {"vision_model": vision_model, "text_model": text_model}
__lowerCAmelCase = TFVisionTextDualEncoderModel.from_vision_text_pretrained(**_A )
__lowerCAmelCase = model(input_ids=_A , pixel_values=_A , attention_mask=_A )
self.assertEqual(output["text_embeds"].shape , (input_ids.shape[0], model.config.projection_dim) )
self.assertEqual(output["image_embeds"].shape , (pixel_values.shape[0], model.config.projection_dim) )
def __SCREAMING_SNAKE_CASE( self , _A , _A , _A , _A , _A=None , **_A ):
"""simple docstring"""
__lowerCAmelCase , __lowerCAmelCase = self.get_vision_text_model(_A , _A )
__lowerCAmelCase = TFVisionTextDualEncoderModel(vision_model=_A , text_model=_A )
__lowerCAmelCase = model(input_ids=_A , pixel_values=_A , attention_mask=_A )
__lowerCAmelCase = output[0].numpy()
with tempfile.TemporaryDirectory() as tmpdirname:
model.save_pretrained(_A )
__lowerCAmelCase = TFVisionTextDualEncoderModel.from_pretrained(_A )
__lowerCAmelCase = model(input_ids=_A , pixel_values=_A , attention_mask=_A )
__lowerCAmelCase = after_output[0].numpy()
__lowerCAmelCase = np.amax(np.abs(out_a - out_a ) )
self.assertLessEqual(_A , 1E-5 )
def __SCREAMING_SNAKE_CASE( self , _A , _A , _A , _A , _A=None , **_A ):
"""simple docstring"""
__lowerCAmelCase , __lowerCAmelCase = self.get_vision_text_model(_A , _A )
__lowerCAmelCase = TFVisionTextDualEncoderModel(vision_model=_A , text_model=_A )
__lowerCAmelCase = model(
input_ids=_A , pixel_values=_A , attention_mask=_A , output_attentions=_A )
__lowerCAmelCase = output.vision_model_output.attentions
self.assertEqual(len(_A ) , vision_config.num_hidden_layers )
# in ViT, the seq_len equals the number of patches + 1 (we add 1 for the [CLS] token)
__lowerCAmelCase = to_atuple(vision_model.config.image_size )
__lowerCAmelCase = to_atuple(vision_model.config.patch_size )
__lowerCAmelCase = (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
__lowerCAmelCase = num_patches + 1
self.assertEqual(vision_attentions[0].shape[-3:] , (vision_config.num_attention_heads, seq_len, seq_len) )
__lowerCAmelCase = output.text_model_output.attentions
self.assertEqual(len(_A ) , text_config.num_hidden_layers )
self.assertEqual(
text_attentions[0].shape[-3:] , (text_config.num_attention_heads, input_ids.shape[-1], input_ids.shape[-1]) , )
def __SCREAMING_SNAKE_CASE( self , _A , _A , _A ):
"""simple docstring"""
__lowerCAmelCase = np.abs((a - b) ).max()
self.assertLessEqual(_A , _A , f"""Difference between torch and flax is {diff} (>= {tol}).""" )
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
__lowerCAmelCase = self.prepare_config_and_inputs()
self.check_vision_text_dual_encoder_model(**_A )
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
__lowerCAmelCase = self.prepare_config_and_inputs()
self.check_model_from_pretrained_configs(**_A )
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
__lowerCAmelCase = self.prepare_config_and_inputs()
self.check_vision_text_dual_encoder_from_pretrained(**_A )
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
__lowerCAmelCase = self.prepare_config_and_inputs()
self.check_save_load(**_A )
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
__lowerCAmelCase = self.prepare_config_and_inputs()
self.check_vision_text_output_attention(**_A )
@slow
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
__lowerCAmelCase , __lowerCAmelCase = self.get_pretrained_model_and_inputs()
__lowerCAmelCase = model_a(**_A )
__lowerCAmelCase = outputs[0].numpy()
with tempfile.TemporaryDirectory() as tmp_dirname:
model_a.save_pretrained(_A )
__lowerCAmelCase = TFVisionTextDualEncoderModel.from_pretrained(_A )
__lowerCAmelCase = model_a(**_A )
__lowerCAmelCase = after_outputs[0].numpy()
__lowerCAmelCase = np.amax(np.abs(out_a - out_a ) )
self.assertLessEqual(_A , 1E-5 )
@require_tf
class a__ ( snake_case__ , unittest.TestCase ):
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
__lowerCAmelCase = TFVisionTextDualEncoderModel.from_vision_text_pretrained(
"hf-internal-testing/tiny-random-vit" , "hf-internal-testing/tiny-random-bert" )
__lowerCAmelCase = 1_3
__lowerCAmelCase = floats_tensor(
[
batch_size,
model.vision_model.config.num_channels,
model.vision_model.config.image_size,
model.vision_model.config.image_size,
] )
__lowerCAmelCase = ids_tensor([batch_size, 4] , model.text_model.config.vocab_size )
__lowerCAmelCase = random_attention_mask([batch_size, 4] )
__lowerCAmelCase = {"pixel_values": pixel_values, "input_ids": input_ids, "attention_mask": attention_mask}
return model, inputs
def __SCREAMING_SNAKE_CASE( self , _A , _A ):
"""simple docstring"""
__lowerCAmelCase = TFViTModel(_A , name="vision_model" )
__lowerCAmelCase = TFBertModel(_A , name="text_model" )
return vision_model, text_model
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
__lowerCAmelCase = TFViTModelTester(self )
__lowerCAmelCase = TFBertModelTester(self )
__lowerCAmelCase = vit_model_tester.prepare_config_and_inputs()
__lowerCAmelCase = bert_model_tester.prepare_config_and_inputs()
__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase = vision_config_and_inputs
(
(
__lowerCAmelCase
) , (
__lowerCAmelCase
) , (
__lowerCAmelCase
) , (
__lowerCAmelCase
) , (
__lowerCAmelCase
) , (
__lowerCAmelCase
) , (
__lowerCAmelCase
) ,
) = text_config_and_inputs
return {
"text_config": text_config,
"vision_config": vision_config,
"pixel_values": pixel_values,
"attention_mask": input_mask,
"input_ids": input_ids,
"text_token_type_ids": token_type_ids,
"text_sequence_labels": sequence_labels,
"text_token_labels": token_labels,
"text_choice_labels": choice_labels,
}
@require_tf
class a__ ( snake_case__ , unittest.TestCase ):
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
__lowerCAmelCase = TFVisionTextDualEncoderModel.from_vision_text_pretrained(
"Rocketknight1/tiny-random-deit-tf" , "hf-internal-testing/tiny-random-roberta" )
__lowerCAmelCase = 1_3
__lowerCAmelCase = floats_tensor(
[
batch_size,
model.vision_model.config.num_channels,
model.vision_model.config.image_size,
model.vision_model.config.image_size,
] )
__lowerCAmelCase = ids_tensor([batch_size, 4] , model.text_model.config.vocab_size )
__lowerCAmelCase = random_attention_mask([batch_size, 4] )
__lowerCAmelCase = {"pixel_values": pixel_values, "input_ids": input_ids, "attention_mask": attention_mask}
return model, inputs
def __SCREAMING_SNAKE_CASE( self , _A , _A , _A , _A , _A=None , **_A ):
"""simple docstring"""
__lowerCAmelCase , __lowerCAmelCase = self.get_vision_text_model(_A , _A )
__lowerCAmelCase = TFVisionTextDualEncoderModel(vision_model=_A , text_model=_A )
__lowerCAmelCase = model(
input_ids=_A , pixel_values=_A , attention_mask=_A , output_attentions=_A )
__lowerCAmelCase = output.vision_model_output.attentions
self.assertEqual(len(_A ) , vision_config.num_hidden_layers )
# in DEiT, the seq_len equals the number of patches + 2 (we add 2 for the [CLS] and distillation tokens)
__lowerCAmelCase = to_atuple(vision_model.config.image_size )
__lowerCAmelCase = to_atuple(vision_model.config.patch_size )
__lowerCAmelCase = (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
__lowerCAmelCase = num_patches + 2
self.assertEqual(vision_attentions[0].shape[-3:] , (vision_config.num_attention_heads, seq_len, seq_len) )
__lowerCAmelCase = output.text_model_output.attentions
self.assertEqual(len(_A ) , text_config.num_hidden_layers )
self.assertEqual(
text_attentions[0].shape[-3:] , (text_config.num_attention_heads, input_ids.shape[-1], input_ids.shape[-1]) , )
def __SCREAMING_SNAKE_CASE( self , _A , _A ):
"""simple docstring"""
__lowerCAmelCase = TFDeiTModel(_A , name="vision_model" )
__lowerCAmelCase = TFRobertaModel(_A , name="text_model" )
return vision_model, text_model
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
__lowerCAmelCase = TFDeiTModelTester(self )
__lowerCAmelCase = TFRobertaModelTester(self )
__lowerCAmelCase = vit_model_tester.prepare_config_and_inputs()
__lowerCAmelCase = bert_model_tester.prepare_config_and_inputs()
__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase = vision_config_and_inputs
(
(
__lowerCAmelCase
) , (
__lowerCAmelCase
) , (
__lowerCAmelCase
) , (
__lowerCAmelCase
) , (
__lowerCAmelCase
) , (
__lowerCAmelCase
) , (
__lowerCAmelCase
) ,
) = text_config_and_inputs
return {
"text_config": text_config,
"vision_config": vision_config,
"pixel_values": pixel_values,
"attention_mask": input_mask,
"input_ids": input_ids,
"text_token_type_ids": token_type_ids,
"text_sequence_labels": sequence_labels,
"text_token_labels": token_labels,
"text_choice_labels": choice_labels,
}
@require_tf
class a__ ( snake_case__ , unittest.TestCase ):
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
__lowerCAmelCase = TFVisionTextDualEncoderModel.from_vision_text_pretrained(
"Rocketknight1/tiny-random-clip-tf" , "hf-internal-testing/tiny-random-bert" )
__lowerCAmelCase = 1_3
__lowerCAmelCase = floats_tensor(
[
batch_size,
model.vision_model.config.num_channels,
model.vision_model.config.image_size,
model.vision_model.config.image_size,
] )
__lowerCAmelCase = ids_tensor([batch_size, 4] , model.text_model.config.vocab_size )
__lowerCAmelCase = random_attention_mask([batch_size, 4] )
__lowerCAmelCase = {"pixel_values": pixel_values, "input_ids": input_ids, "attention_mask": attention_mask}
return model, inputs
def __SCREAMING_SNAKE_CASE( self , _A , _A ):
"""simple docstring"""
__lowerCAmelCase = TFCLIPVisionModel(_A , name="vision_model" )
__lowerCAmelCase = TFBertModel(_A , name="text_model" )
return vision_model, text_model
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
__lowerCAmelCase = TFCLIPVisionModelTester(self )
__lowerCAmelCase = TFBertModelTester(self )
__lowerCAmelCase = clip_model_tester.prepare_config_and_inputs()
__lowerCAmelCase = bert_model_tester.prepare_config_and_inputs()
__lowerCAmelCase , __lowerCAmelCase = vision_config_and_inputs
(
(
__lowerCAmelCase
) , (
__lowerCAmelCase
) , (
__lowerCAmelCase
) , (
__lowerCAmelCase
) , (
__lowerCAmelCase
) , (
__lowerCAmelCase
) , (
__lowerCAmelCase
) ,
) = text_config_and_inputs
return {
"text_config": text_config,
"vision_config": vision_config,
"pixel_values": pixel_values,
"attention_mask": input_mask,
"input_ids": input_ids,
"text_token_type_ids": token_type_ids,
"text_sequence_labels": sequence_labels,
"text_token_labels": token_labels,
"text_choice_labels": choice_labels,
}
@require_vision
@require_tf
class a__ ( unittest.TestCase ):
@slow
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
__lowerCAmelCase = TFVisionTextDualEncoderModel.from_pretrained(
"clip-italian/clip-italian" , logit_scale_init_value=1.0 , from_pt=_A )
__lowerCAmelCase = VisionTextDualEncoderProcessor.from_pretrained("clip-italian/clip-italian" )
__lowerCAmelCase = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png" )
__lowerCAmelCase = processor(
text=["una foto di un gatto", "una foto di un cane"] , images=_A , padding=_A , return_tensors="np" )
__lowerCAmelCase = model(**_A )
# verify the logits
self.assertEqual(outputs.logits_per_image.shape , (inputs.pixel_values.shape[0], inputs.input_ids.shape[0]) )
self.assertEqual(
outputs.logits_per_text.shape , (inputs.input_ids.shape[0], inputs.pixel_values.shape[0]) , )
__lowerCAmelCase = np.array([[1.2_28_47_27, 0.3_10_41_22]] )
self.assertTrue(np.allclose(outputs.logits_per_image.numpy() , _A , atol=1E-3 ) )
| 92 | 0 |
from typing import Optional
import pyspark
from .. import Features, NamedSplit
from ..download import DownloadMode
from ..packaged_modules.spark.spark import Spark
from .abc import AbstractDatasetReader
class _a ( UpperCamelCase__ ):
def __init__( self: Optional[Any] , UpperCamelCase_: pyspark.sql.DataFrame , UpperCamelCase_: Optional[NamedSplit] = None , UpperCamelCase_: Optional[Features] = None , UpperCamelCase_: bool = True , UpperCamelCase_: str = None , UpperCamelCase_: bool = False , UpperCamelCase_: str = None , UpperCamelCase_: bool = True , UpperCamelCase_: str = "arrow" , **UpperCamelCase_: Tuple , ) -> Optional[int]:
"""simple docstring"""
super().__init__(
split=UpperCamelCase_ , features=UpperCamelCase_ , cache_dir=UpperCamelCase_ , keep_in_memory=UpperCamelCase_ , streaming=UpperCamelCase_ , **UpperCamelCase_ , )
lowercase__ = load_from_cache_file
lowercase__ = file_format
lowercase__ = Spark(
df=UpperCamelCase_ , features=UpperCamelCase_ , cache_dir=UpperCamelCase_ , working_dir=UpperCamelCase_ , **UpperCamelCase_ , )
def lowerCamelCase_ ( self: str ) -> Optional[int]:
"""simple docstring"""
if self.streaming:
return self.builder.as_streaming_dataset(split=self.split )
lowercase__ = None if self._load_from_cache_file else DownloadMode.FORCE_REDOWNLOAD
self.builder.download_and_prepare(
download_mode=UpperCamelCase_ , file_format=self._file_format , )
return self.builder.as_dataset(split=self.split )
| 110 |
import json
import os
import torch
from diffusers import UNetaDModel
os.makedirs("""hub/hopper-medium-v2/unet/hor32""", exist_ok=True)
os.makedirs("""hub/hopper-medium-v2/unet/hor128""", exist_ok=True)
os.makedirs("""hub/hopper-medium-v2/value_function""", exist_ok=True)
def _a ( SCREAMING_SNAKE_CASE_ : List[Any] ):
if hor == 1_28:
__lowerCAmelCase = ("DownResnetBlock1D", "DownResnetBlock1D", "DownResnetBlock1D")
__lowerCAmelCase = (32, 1_28, 2_56)
__lowerCAmelCase = ("UpResnetBlock1D", "UpResnetBlock1D")
elif hor == 32:
__lowerCAmelCase = ("DownResnetBlock1D", "DownResnetBlock1D", "DownResnetBlock1D", "DownResnetBlock1D")
__lowerCAmelCase = (32, 64, 1_28, 2_56)
__lowerCAmelCase = ("UpResnetBlock1D", "UpResnetBlock1D", "UpResnetBlock1D")
__lowerCAmelCase = torch.load(F"""/Users/bglickenhaus/Documents/diffuser/temporal_unet-hopper-mediumv2-hor{hor}.torch""" )
__lowerCAmelCase = model.state_dict()
__lowerCAmelCase = {
"down_block_types": down_block_types,
"block_out_channels": block_out_channels,
"up_block_types": up_block_types,
"layers_per_block": 1,
"use_timestep_embedding": True,
"out_block_type": "OutConv1DBlock",
"norm_num_groups": 8,
"downsample_each_block": False,
"in_channels": 14,
"out_channels": 14,
"extra_in_channels": 0,
"time_embedding_type": "positional",
"flip_sin_to_cos": False,
"freq_shift": 1,
"sample_size": 6_55_36,
"mid_block_type": "MidResTemporalBlock1D",
"act_fn": "mish",
}
__lowerCAmelCase = UNetaDModel(**SCREAMING_SNAKE_CASE_ )
print(F"""length of state dict: {len(state_dict.keys() )}""" )
print(F"""length of value function dict: {len(hf_value_function.state_dict().keys() )}""" )
__lowerCAmelCase = dict(zip(model.state_dict().keys() , hf_value_function.state_dict().keys() ) )
for k, v in mapping.items():
__lowerCAmelCase = state_dict.pop(SCREAMING_SNAKE_CASE_ )
hf_value_function.load_state_dict(SCREAMING_SNAKE_CASE_ )
torch.save(hf_value_function.state_dict() , F"""hub/hopper-medium-v2/unet/hor{hor}/diffusion_pytorch_model.bin""" )
with open(F"""hub/hopper-medium-v2/unet/hor{hor}/config.json""" , "w" ) as f:
json.dump(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
def _a ( ):
__lowerCAmelCase = {
"in_channels": 14,
"down_block_types": ("DownResnetBlock1D", "DownResnetBlock1D", "DownResnetBlock1D", "DownResnetBlock1D"),
"up_block_types": (),
"out_block_type": "ValueFunction",
"mid_block_type": "ValueFunctionMidBlock1D",
"block_out_channels": (32, 64, 1_28, 2_56),
"layers_per_block": 1,
"downsample_each_block": True,
"sample_size": 6_55_36,
"out_channels": 14,
"extra_in_channels": 0,
"time_embedding_type": "positional",
"use_timestep_embedding": True,
"flip_sin_to_cos": False,
"freq_shift": 1,
"norm_num_groups": 8,
"act_fn": "mish",
}
__lowerCAmelCase = torch.load("/Users/bglickenhaus/Documents/diffuser/value_function-hopper-mediumv2-hor32.torch" )
__lowerCAmelCase = model
__lowerCAmelCase = UNetaDModel(**SCREAMING_SNAKE_CASE_ )
print(F"""length of state dict: {len(state_dict.keys() )}""" )
print(F"""length of value function dict: {len(hf_value_function.state_dict().keys() )}""" )
__lowerCAmelCase = dict(zip(state_dict.keys() , hf_value_function.state_dict().keys() ) )
for k, v in mapping.items():
__lowerCAmelCase = state_dict.pop(SCREAMING_SNAKE_CASE_ )
hf_value_function.load_state_dict(SCREAMING_SNAKE_CASE_ )
torch.save(hf_value_function.state_dict() , "hub/hopper-medium-v2/value_function/diffusion_pytorch_model.bin" )
with open("hub/hopper-medium-v2/value_function/config.json" , "w" ) as f:
json.dump(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
if __name__ == "__main__":
unet(32)
# unet(128)
value_function()
| 92 | 0 |
"""simple docstring"""
import functools
import logging
import os
import sys
import threading
from logging import (
CRITICAL, # NOQA
DEBUG, # NOQA
ERROR, # NOQA
FATAL, # NOQA
INFO, # NOQA
NOTSET, # NOQA
WARN, # NOQA
WARNING, # NOQA
)
from typing import Optional
import huggingface_hub.utils as hf_hub_utils
from tqdm import auto as tqdm_lib
_UpperCamelCase : Optional[Any] = threading.Lock()
_UpperCamelCase : Optional[int] = None
_UpperCamelCase : Optional[Any] = {
'debug': logging.DEBUG,
'info': logging.INFO,
'warning': logging.WARNING,
'error': logging.ERROR,
'critical': logging.CRITICAL,
}
_UpperCamelCase : Optional[int] = logging.WARNING
_UpperCamelCase : str = True
def _SCREAMING_SNAKE_CASE ( ):
'''simple docstring'''
lowercase = os.getenv('TRANSFORMERS_VERBOSITY' , SCREAMING_SNAKE_CASE_ )
if env_level_str:
if env_level_str in log_levels:
return log_levels[env_level_str]
else:
logging.getLogger().warning(
f'Unknown option TRANSFORMERS_VERBOSITY={env_level_str}, '
f'has to be one of: { ", ".join(log_levels.keys() ) }' )
return _default_log_level
def _SCREAMING_SNAKE_CASE ( ):
'''simple docstring'''
return __name__.split('.' )[0]
def _SCREAMING_SNAKE_CASE ( ):
'''simple docstring'''
return logging.getLogger(_get_library_name() )
def _SCREAMING_SNAKE_CASE ( ):
'''simple docstring'''
global _default_handler
with _lock:
if _default_handler:
# This library has already configured the library root logger.
return
lowercase = logging.StreamHandler() # Set sys.stderr as stream.
lowercase = sys.stderr.flush
# Apply our default configuration to the library root logger.
lowercase = _get_library_root_logger()
library_root_logger.addHandler(_default_handler )
library_root_logger.setLevel(_get_default_logging_level() )
lowercase = False
def _SCREAMING_SNAKE_CASE ( ):
'''simple docstring'''
global _default_handler
with _lock:
if not _default_handler:
return
lowercase = _get_library_root_logger()
library_root_logger.removeHandler(_default_handler )
library_root_logger.setLevel(logging.NOTSET )
lowercase = None
def _SCREAMING_SNAKE_CASE ( ):
'''simple docstring'''
return log_levels
def _SCREAMING_SNAKE_CASE ( __snake_case : Optional[str] = None ):
'''simple docstring'''
if name is None:
lowercase = _get_library_name()
_configure_library_root_logger()
return logging.getLogger(SCREAMING_SNAKE_CASE_ )
def _SCREAMING_SNAKE_CASE ( ):
'''simple docstring'''
_configure_library_root_logger()
return _get_library_root_logger().getEffectiveLevel()
def _SCREAMING_SNAKE_CASE ( __snake_case : int ):
'''simple docstring'''
_configure_library_root_logger()
_get_library_root_logger().setLevel(SCREAMING_SNAKE_CASE_ )
def _SCREAMING_SNAKE_CASE ( ):
'''simple docstring'''
return set_verbosity(SCREAMING_SNAKE_CASE_ )
def _SCREAMING_SNAKE_CASE ( ):
'''simple docstring'''
return set_verbosity(SCREAMING_SNAKE_CASE_ )
def _SCREAMING_SNAKE_CASE ( ):
'''simple docstring'''
return set_verbosity(SCREAMING_SNAKE_CASE_ )
def _SCREAMING_SNAKE_CASE ( ):
'''simple docstring'''
return set_verbosity(SCREAMING_SNAKE_CASE_ )
def _SCREAMING_SNAKE_CASE ( ):
'''simple docstring'''
_configure_library_root_logger()
assert _default_handler is not None
_get_library_root_logger().removeHandler(_default_handler )
def _SCREAMING_SNAKE_CASE ( ):
'''simple docstring'''
_configure_library_root_logger()
assert _default_handler is not None
_get_library_root_logger().addHandler(_default_handler )
def _SCREAMING_SNAKE_CASE ( __snake_case : logging.Handler ):
'''simple docstring'''
_configure_library_root_logger()
assert handler is not None
_get_library_root_logger().addHandler(SCREAMING_SNAKE_CASE_ )
def _SCREAMING_SNAKE_CASE ( __snake_case : logging.Handler ):
'''simple docstring'''
_configure_library_root_logger()
assert handler is not None and handler not in _get_library_root_logger().handlers
_get_library_root_logger().removeHandler(SCREAMING_SNAKE_CASE_ )
def _SCREAMING_SNAKE_CASE ( ):
'''simple docstring'''
_configure_library_root_logger()
lowercase = False
def _SCREAMING_SNAKE_CASE ( ):
'''simple docstring'''
_configure_library_root_logger()
lowercase = True
def _SCREAMING_SNAKE_CASE ( ):
'''simple docstring'''
lowercase = _get_library_root_logger().handlers
for handler in handlers:
lowercase = logging.Formatter('[%(levelname)s|%(filename)s:%(lineno)s] %(asctime)s >> %(message)s' )
handler.setFormatter(SCREAMING_SNAKE_CASE_ )
def _SCREAMING_SNAKE_CASE ( ):
'''simple docstring'''
lowercase = _get_library_root_logger().handlers
for handler in handlers:
handler.setFormatter(SCREAMING_SNAKE_CASE_ )
def _SCREAMING_SNAKE_CASE ( self : List[Any] , *__snake_case : Optional[int] , **__snake_case : int ):
'''simple docstring'''
lowercase = os.getenv('TRANSFORMERS_NO_ADVISORY_WARNINGS' , SCREAMING_SNAKE_CASE_ )
if no_advisory_warnings:
return
self.warning(*SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ )
_UpperCamelCase : int = warning_advice
@functools.lru_cache(SCREAMING_SNAKE_CASE_ )
def _SCREAMING_SNAKE_CASE ( self : Tuple , *__snake_case : int , **__snake_case : Optional[int] ):
'''simple docstring'''
self.warning(*SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ )
_UpperCamelCase : List[Any] = warning_once
class a :
def __init__( self , *_lowerCamelCase , **_lowerCamelCase ): # pylint: disable=unused-argument
lowercase = args[0] if args else None
def __iter__( self ):
return iter(self._iterator )
def __getattr__( self , _lowerCamelCase ):
def empty_fn(*_lowerCamelCase , **_lowerCamelCase ): # pylint: disable=unused-argument
return
return empty_fn
def __enter__( self ):
return self
def __exit__( self , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase ):
return
class a :
def __call__( self , *_lowerCamelCase , **_lowerCamelCase ):
if _tqdm_active:
return tqdm_lib.tqdm(*_A , **_A )
else:
return EmptyTqdm(*_A , **_A )
def UpperCamelCase_ ( self , *_lowerCamelCase , **_lowerCamelCase ):
lowercase = None
if _tqdm_active:
return tqdm_lib.tqdm.set_lock(*_A , **_A )
def UpperCamelCase_ ( self ):
if _tqdm_active:
return tqdm_lib.tqdm.get_lock()
_UpperCamelCase : str = _tqdm_cls()
def _SCREAMING_SNAKE_CASE ( ):
'''simple docstring'''
global _tqdm_active
return bool(_tqdm_active )
def _SCREAMING_SNAKE_CASE ( ):
'''simple docstring'''
global _tqdm_active
lowercase = True
hf_hub_utils.enable_progress_bars()
def _SCREAMING_SNAKE_CASE ( ):
'''simple docstring'''
global _tqdm_active
lowercase = False
hf_hub_utils.disable_progress_bars()
| 220 |
import pytest
from datasets import inspect_metric, list_metrics, load_metric
@pytest.fixture
def _a ( SCREAMING_SNAKE_CASE_ : Optional[Any] ):
monkeypatch.setattr("datasets.utils.deprecation_utils._emitted_deprecation_warnings" , set() )
@pytest.fixture
def _a ( SCREAMING_SNAKE_CASE_ : List[Any] ):
class a__ :
def __init__( self , _A ):
"""simple docstring"""
__lowerCAmelCase = metric_id
class a__ :
_a : Optional[int] = [MetricMock(snake_case__ ) for metric_id in ["""accuracy""", """mse""", """precision""", """codeparrot/apps_metric"""]]
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
return self._metrics
monkeypatch.setattr("datasets.inspect.huggingface_hub" , HfhMock() )
@pytest.mark.parametrize(
"func, args" , [(load_metric, ("metrics/mse",)), (list_metrics, ()), (inspect_metric, ("metrics/mse", "tmp_path"))] )
def _a ( SCREAMING_SNAKE_CASE_ : Tuple , SCREAMING_SNAKE_CASE_ : List[str] , SCREAMING_SNAKE_CASE_ : int , SCREAMING_SNAKE_CASE_ : Optional[int] , SCREAMING_SNAKE_CASE_ : Optional[int] ):
if "tmp_path" in args:
__lowerCAmelCase = tuple(arg if arg != "tmp_path" else tmp_path for arg in args )
with pytest.warns(SCREAMING_SNAKE_CASE_ , match="https://huggingface.co/docs/evaluate" ):
func(*SCREAMING_SNAKE_CASE_ )
| 92 | 0 |
from ..utils import DummyObject, requires_backends
class __A( metaclass=snake_case__ ):
snake_case_ = ["""flax"""]
def __init__( self , *_snake_case , **_snake_case ) -> Union[str, Any]:
'''simple docstring'''
requires_backends(self , ['''flax'''] )
@classmethod
def SCREAMING_SNAKE_CASE_ ( cls , *_snake_case , **_snake_case ) -> Union[str, Any]:
'''simple docstring'''
requires_backends(cls , ['''flax'''] )
@classmethod
def SCREAMING_SNAKE_CASE_ ( cls , *_snake_case , **_snake_case ) -> Union[str, Any]:
'''simple docstring'''
requires_backends(cls , ['''flax'''] )
class __A( metaclass=snake_case__ ):
snake_case_ = ["""flax"""]
def __init__( self , *_snake_case , **_snake_case ) -> int:
'''simple docstring'''
requires_backends(self , ['''flax'''] )
@classmethod
def SCREAMING_SNAKE_CASE_ ( cls , *_snake_case , **_snake_case ) -> Optional[Any]:
'''simple docstring'''
requires_backends(cls , ['''flax'''] )
@classmethod
def SCREAMING_SNAKE_CASE_ ( cls , *_snake_case , **_snake_case ) -> Dict:
'''simple docstring'''
requires_backends(cls , ['''flax'''] )
class __A( metaclass=snake_case__ ):
snake_case_ = ["""flax"""]
def __init__( self , *_snake_case , **_snake_case ) -> int:
'''simple docstring'''
requires_backends(self , ['''flax'''] )
@classmethod
def SCREAMING_SNAKE_CASE_ ( cls , *_snake_case , **_snake_case ) -> str:
'''simple docstring'''
requires_backends(cls , ['''flax'''] )
@classmethod
def SCREAMING_SNAKE_CASE_ ( cls , *_snake_case , **_snake_case ) -> Tuple:
'''simple docstring'''
requires_backends(cls , ['''flax'''] )
class __A( metaclass=snake_case__ ):
snake_case_ = ["""flax"""]
def __init__( self , *_snake_case , **_snake_case ) -> List[Any]:
'''simple docstring'''
requires_backends(self , ['''flax'''] )
@classmethod
def SCREAMING_SNAKE_CASE_ ( cls , *_snake_case , **_snake_case ) -> Dict:
'''simple docstring'''
requires_backends(cls , ['''flax'''] )
@classmethod
def SCREAMING_SNAKE_CASE_ ( cls , *_snake_case , **_snake_case ) -> Dict:
'''simple docstring'''
requires_backends(cls , ['''flax'''] )
class __A( metaclass=snake_case__ ):
snake_case_ = ["""flax"""]
def __init__( self , *_snake_case , **_snake_case ) -> Any:
'''simple docstring'''
requires_backends(self , ['''flax'''] )
@classmethod
def SCREAMING_SNAKE_CASE_ ( cls , *_snake_case , **_snake_case ) -> Optional[Any]:
'''simple docstring'''
requires_backends(cls , ['''flax'''] )
@classmethod
def SCREAMING_SNAKE_CASE_ ( cls , *_snake_case , **_snake_case ) -> Optional[Any]:
'''simple docstring'''
requires_backends(cls , ['''flax'''] )
class __A( metaclass=snake_case__ ):
snake_case_ = ["""flax"""]
def __init__( self , *_snake_case , **_snake_case ) -> Dict:
'''simple docstring'''
requires_backends(self , ['''flax'''] )
@classmethod
def SCREAMING_SNAKE_CASE_ ( cls , *_snake_case , **_snake_case ) -> str:
'''simple docstring'''
requires_backends(cls , ['''flax'''] )
@classmethod
def SCREAMING_SNAKE_CASE_ ( cls , *_snake_case , **_snake_case ) -> Any:
'''simple docstring'''
requires_backends(cls , ['''flax'''] )
class __A( metaclass=snake_case__ ):
snake_case_ = ["""flax"""]
def __init__( self , *_snake_case , **_snake_case ) -> str:
'''simple docstring'''
requires_backends(self , ['''flax'''] )
@classmethod
def SCREAMING_SNAKE_CASE_ ( cls , *_snake_case , **_snake_case ) -> List[Any]:
'''simple docstring'''
requires_backends(cls , ['''flax'''] )
@classmethod
def SCREAMING_SNAKE_CASE_ ( cls , *_snake_case , **_snake_case ) -> Any:
'''simple docstring'''
requires_backends(cls , ['''flax'''] )
class __A( metaclass=snake_case__ ):
snake_case_ = ["""flax"""]
def __init__( self , *_snake_case , **_snake_case ) -> Optional[int]:
'''simple docstring'''
requires_backends(self , ['''flax'''] )
@classmethod
def SCREAMING_SNAKE_CASE_ ( cls , *_snake_case , **_snake_case ) -> List[str]:
'''simple docstring'''
requires_backends(cls , ['''flax'''] )
@classmethod
def SCREAMING_SNAKE_CASE_ ( cls , *_snake_case , **_snake_case ) -> Tuple:
'''simple docstring'''
requires_backends(cls , ['''flax'''] )
class __A( metaclass=snake_case__ ):
snake_case_ = ["""flax"""]
def __init__( self , *_snake_case , **_snake_case ) -> Tuple:
'''simple docstring'''
requires_backends(self , ['''flax'''] )
@classmethod
def SCREAMING_SNAKE_CASE_ ( cls , *_snake_case , **_snake_case ) -> Any:
'''simple docstring'''
requires_backends(cls , ['''flax'''] )
@classmethod
def SCREAMING_SNAKE_CASE_ ( cls , *_snake_case , **_snake_case ) -> List[Any]:
'''simple docstring'''
requires_backends(cls , ['''flax'''] )
class __A( metaclass=snake_case__ ):
snake_case_ = ["""flax"""]
def __init__( self , *_snake_case , **_snake_case ) -> List[Any]:
'''simple docstring'''
requires_backends(self , ['''flax'''] )
@classmethod
def SCREAMING_SNAKE_CASE_ ( cls , *_snake_case , **_snake_case ) -> Optional[Any]:
'''simple docstring'''
requires_backends(cls , ['''flax'''] )
@classmethod
def SCREAMING_SNAKE_CASE_ ( cls , *_snake_case , **_snake_case ) -> Any:
'''simple docstring'''
requires_backends(cls , ['''flax'''] )
class __A( metaclass=snake_case__ ):
snake_case_ = ["""flax"""]
def __init__( self , *_snake_case , **_snake_case ) -> Union[str, Any]:
'''simple docstring'''
requires_backends(self , ['''flax'''] )
@classmethod
def SCREAMING_SNAKE_CASE_ ( cls , *_snake_case , **_snake_case ) -> List[Any]:
'''simple docstring'''
requires_backends(cls , ['''flax'''] )
@classmethod
def SCREAMING_SNAKE_CASE_ ( cls , *_snake_case , **_snake_case ) -> Optional[Any]:
'''simple docstring'''
requires_backends(cls , ['''flax'''] )
class __A( metaclass=snake_case__ ):
snake_case_ = ["""flax"""]
def __init__( self , *_snake_case , **_snake_case ) -> List[str]:
'''simple docstring'''
requires_backends(self , ['''flax'''] )
@classmethod
def SCREAMING_SNAKE_CASE_ ( cls , *_snake_case , **_snake_case ) -> Union[str, Any]:
'''simple docstring'''
requires_backends(cls , ['''flax'''] )
@classmethod
def SCREAMING_SNAKE_CASE_ ( cls , *_snake_case , **_snake_case ) -> Optional[int]:
'''simple docstring'''
requires_backends(cls , ['''flax'''] )
class __A( metaclass=snake_case__ ):
snake_case_ = ["""flax"""]
def __init__( self , *_snake_case , **_snake_case ) -> Optional[Any]:
'''simple docstring'''
requires_backends(self , ['''flax'''] )
@classmethod
def SCREAMING_SNAKE_CASE_ ( cls , *_snake_case , **_snake_case ) -> Tuple:
'''simple docstring'''
requires_backends(cls , ['''flax'''] )
@classmethod
def SCREAMING_SNAKE_CASE_ ( cls , *_snake_case , **_snake_case ) -> Any:
'''simple docstring'''
requires_backends(cls , ['''flax'''] ) | 6 |
from random import randint
from tempfile import TemporaryFile
import numpy as np
def _a ( SCREAMING_SNAKE_CASE_ : int , SCREAMING_SNAKE_CASE_ : str , SCREAMING_SNAKE_CASE_ : List[str] ):
__lowerCAmelCase = 0
if start < end:
__lowerCAmelCase = randint(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
__lowerCAmelCase = a[end]
__lowerCAmelCase = a[pivot]
__lowerCAmelCase = temp
__lowerCAmelCase , __lowerCAmelCase = _in_place_partition(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
count += _in_place_quick_sort(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , p - 1 )
count += _in_place_quick_sort(SCREAMING_SNAKE_CASE_ , p + 1 , SCREAMING_SNAKE_CASE_ )
return count
def _a ( SCREAMING_SNAKE_CASE_ : str , SCREAMING_SNAKE_CASE_ : List[Any] , SCREAMING_SNAKE_CASE_ : Optional[int] ):
__lowerCAmelCase = 0
__lowerCAmelCase = randint(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
__lowerCAmelCase = a[end]
__lowerCAmelCase = a[pivot]
__lowerCAmelCase = temp
__lowerCAmelCase = start - 1
for index in range(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
count += 1
if a[index] < a[end]: # check if current val is less than pivot value
__lowerCAmelCase = new_pivot_index + 1
__lowerCAmelCase = a[new_pivot_index]
__lowerCAmelCase = a[index]
__lowerCAmelCase = temp
__lowerCAmelCase = a[new_pivot_index + 1]
__lowerCAmelCase = a[end]
__lowerCAmelCase = temp
return new_pivot_index + 1, count
UpperCamelCase__ = TemporaryFile()
UpperCamelCase__ = 100 # 1000 elements are to be sorted
UpperCamelCase__ , UpperCamelCase__ = 0, 1 # mean and standard deviation
UpperCamelCase__ = np.random.normal(mu, sigma, p)
np.save(outfile, X)
print("""The array is""")
print(X)
outfile.seek(0) # using the same array
UpperCamelCase__ = np.load(outfile)
UpperCamelCase__ = len(M) - 1
UpperCamelCase__ = _in_place_quick_sort(M, 0, r)
print(
"""No of Comparisons for 100 elements selected from a standard normal distribution"""
"""is :"""
)
print(z)
| 92 | 0 |
class _UpperCAmelCase :
"""simple docstring"""
def __init__( self : Tuple, lowerCamelCase : Optional[int], lowerCamelCase : Any, lowerCamelCase : str ):
'''simple docstring'''
lowercase__ = name
lowercase__ = value
lowercase__ = weight
def __repr__( self : Union[str, Any] ):
'''simple docstring'''
return F"""{self.__class__.__name__}({self.name}, {self.value}, {self.weight})"""
def lowercase__ ( self : Tuple ):
'''simple docstring'''
return self.value
def lowercase__ ( self : str ):
'''simple docstring'''
return self.name
def lowercase__ ( self : Optional[Any] ):
'''simple docstring'''
return self.weight
def lowercase__ ( self : Optional[int] ):
'''simple docstring'''
return self.value / self.weight
def a ( lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ ):
'''simple docstring'''
lowercase__ = []
for i in range(len(SCREAMING_SNAKE_CASE_ ) ):
menu.append(Things(name[i] , value[i] , weight[i] ) )
return menu
def a ( lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ ):
'''simple docstring'''
lowercase__ = sorted(SCREAMING_SNAKE_CASE_ , key=SCREAMING_SNAKE_CASE_ , reverse=SCREAMING_SNAKE_CASE_ )
lowercase__ = []
lowercase__ , lowercase__ = 0.0, 0.0
for i in range(len(SCREAMING_SNAKE_CASE_ ) ):
if (total_cost + items_copy[i].get_weight()) <= max_cost:
result.append(items_copy[i] )
total_cost += items_copy[i].get_weight()
total_value += items_copy[i].get_value()
return (result, total_value)
def a ( ):
'''simple docstring'''
pass
if __name__ == "__main__":
import doctest
doctest.testmod()
| 207 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_speech_available, is_torch_available
UpperCamelCase__ = {
"""configuration_audio_spectrogram_transformer""": [
"""AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""ASTConfig""",
]
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCamelCase__ = [
"""AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""ASTForAudioClassification""",
"""ASTModel""",
"""ASTPreTrainedModel""",
]
try:
if not is_speech_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCamelCase__ = ["""ASTFeatureExtractor"""]
if TYPE_CHECKING:
from .configuration_audio_spectrogram_transformer import (
AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
ASTConfig,
)
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_audio_spectrogram_transformer import (
AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
ASTForAudioClassification,
ASTModel,
ASTPreTrainedModel,
)
try:
if not is_speech_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .feature_extraction_audio_spectrogram_transformer import ASTFeatureExtractor
else:
import sys
UpperCamelCase__ = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 92 | 0 |
'''simple docstring'''
from __future__ import annotations
from collections.abc import Iterator
from typing import Generic, TypeVar
lowerCAmelCase: Any = TypeVar('T')
class a__( Generic[T] ):
def __init__( self : Dict , __snake_case : int ):
a : List[str] = data
a : int = None
def __str__( self : Dict ):
return F"""{self.data}"""
class a__( Generic[T] ):
def __init__( self : Tuple ):
a : List[str] = None
def __iter__( self : Union[str, Any] ):
a : str = self.top
while node:
yield node.data
a : List[str] = node.next
def __str__( self : str ):
return "->".join([str(_A ) for item in self] )
def __len__( self : Any ):
return len(tuple(iter(self ) ) )
def lowercase_ ( self : Optional[int] ):
return self.top is None
def lowercase_ ( self : Optional[Any] , __snake_case : List[str] ):
a : List[str] = Node(_A )
if not self.is_empty():
a : str = self.top
a : str = node
def lowercase_ ( self : Union[str, Any] ):
if self.is_empty():
raise IndexError('pop from empty stack' )
assert isinstance(self.top , _A )
a : List[str] = self.top
a : str = self.top.next
return pop_node.data
def lowercase_ ( self : Union[str, Any] ):
if self.is_empty():
raise IndexError('peek from empty stack' )
assert self.top is not None
return self.top.data
def lowercase_ ( self : Optional[int] ):
a : Tuple = None
if __name__ == "__main__":
from doctest import testmod
testmod() | 297 |
import argparse
import os
import re
import packaging.version
UpperCamelCase__ = """examples/"""
UpperCamelCase__ = {
"""examples""": (re.compile(R"""^check_min_version\(\"[^\"]+\"\)\s*$""", re.MULTILINE), """check_min_version(\"VERSION\")\n"""),
"""init""": (re.compile(R"""^__version__\s+=\s+\"([^\"]+)\"\s*$""", re.MULTILINE), """__version__ = \"VERSION\"\n"""),
"""setup""": (re.compile(R"""^(\s*)version\s*=\s*\"[^\"]+\",""", re.MULTILINE), R"""\1version=\"VERSION\","""),
"""doc""": (re.compile(R"""^(\s*)release\s*=\s*\"[^\"]+\"$""", re.MULTILINE), """release = \"VERSION\"\n"""),
}
UpperCamelCase__ = {
"""init""": """src/transformers/__init__.py""",
"""setup""": """setup.py""",
}
UpperCamelCase__ = """README.md"""
def _a ( SCREAMING_SNAKE_CASE_ : Optional[int] , SCREAMING_SNAKE_CASE_ : Any , SCREAMING_SNAKE_CASE_ : List[str] ):
with open(SCREAMING_SNAKE_CASE_ , "r" , encoding="utf-8" , newline="\n" ) as f:
__lowerCAmelCase = f.read()
__lowerCAmelCase , __lowerCAmelCase = REPLACE_PATTERNS[pattern]
__lowerCAmelCase = replace.replace("VERSION" , SCREAMING_SNAKE_CASE_ )
__lowerCAmelCase = re_pattern.sub(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
with open(SCREAMING_SNAKE_CASE_ , "w" , encoding="utf-8" , newline="\n" ) as f:
f.write(SCREAMING_SNAKE_CASE_ )
def _a ( SCREAMING_SNAKE_CASE_ : List[Any] ):
for folder, directories, fnames in os.walk(SCREAMING_SNAKE_CASE_ ):
# Removing some of the folders with non-actively maintained examples from the walk
if "research_projects" in directories:
directories.remove("research_projects" )
if "legacy" in directories:
directories.remove("legacy" )
for fname in fnames:
if fname.endswith(".py" ):
update_version_in_file(os.path.join(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) , SCREAMING_SNAKE_CASE_ , pattern="examples" )
def _a ( SCREAMING_SNAKE_CASE_ : Tuple , SCREAMING_SNAKE_CASE_ : Optional[int]=False ):
for pattern, fname in REPLACE_FILES.items():
update_version_in_file(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
if not patch:
update_version_in_examples(SCREAMING_SNAKE_CASE_ )
def _a ( ):
__lowerCAmelCase = "🤗 Transformers currently provides the following architectures"
__lowerCAmelCase = "1. Want to contribute a new model?"
with open(SCREAMING_SNAKE_CASE_ , "r" , encoding="utf-8" , newline="\n" ) as f:
__lowerCAmelCase = f.readlines()
# Find the start of the list.
__lowerCAmelCase = 0
while not lines[start_index].startswith(_start_prompt ):
start_index += 1
start_index += 1
__lowerCAmelCase = start_index
# Update the lines in the model list.
while not lines[index].startswith(_end_prompt ):
if lines[index].startswith("1." ):
__lowerCAmelCase = lines[index].replace(
"https://huggingface.co/docs/transformers/main/model_doc" , "https://huggingface.co/docs/transformers/model_doc" , )
index += 1
with open(SCREAMING_SNAKE_CASE_ , "w" , encoding="utf-8" , newline="\n" ) as f:
f.writelines(SCREAMING_SNAKE_CASE_ )
def _a ( ):
with open(REPLACE_FILES["init"] , "r" ) as f:
__lowerCAmelCase = f.read()
__lowerCAmelCase = REPLACE_PATTERNS["init"][0].search(SCREAMING_SNAKE_CASE_ ).groups()[0]
return packaging.version.parse(SCREAMING_SNAKE_CASE_ )
def _a ( SCREAMING_SNAKE_CASE_ : List[Any]=False ):
__lowerCAmelCase = get_version()
if patch and default_version.is_devrelease:
raise ValueError("Can't create a patch version from the dev branch, checkout a released version!" )
if default_version.is_devrelease:
__lowerCAmelCase = default_version.base_version
elif patch:
__lowerCAmelCase = F"""{default_version.major}.{default_version.minor}.{default_version.micro + 1}"""
else:
__lowerCAmelCase = F"""{default_version.major}.{default_version.minor + 1}.0"""
# Now let's ask nicely if that's the right one.
__lowerCAmelCase = input(F"""Which version are you releasing? [{default_version}]""" )
if len(SCREAMING_SNAKE_CASE_ ) == 0:
__lowerCAmelCase = default_version
print(F"""Updating version to {version}.""" )
global_version_update(SCREAMING_SNAKE_CASE_ , patch=SCREAMING_SNAKE_CASE_ )
if not patch:
print("Cleaning main README, don't forget to run `make fix-copies`." )
clean_main_ref_in_model_list()
def _a ( ):
__lowerCAmelCase = get_version()
__lowerCAmelCase = F"""{current_version.major}.{current_version.minor + 1}.0.dev0"""
__lowerCAmelCase = current_version.base_version
# Check with the user we got that right.
__lowerCAmelCase = input(F"""Which version are we developing now? [{dev_version}]""" )
if len(SCREAMING_SNAKE_CASE_ ) == 0:
__lowerCAmelCase = dev_version
print(F"""Updating version to {version}.""" )
global_version_update(SCREAMING_SNAKE_CASE_ )
print("Cleaning main README, don't forget to run `make fix-copies`." )
clean_main_ref_in_model_list()
if __name__ == "__main__":
UpperCamelCase__ = argparse.ArgumentParser()
parser.add_argument("""--post_release""", action="""store_true""", help="""Whether this is pre or post release.""")
parser.add_argument("""--patch""", action="""store_true""", help="""Whether or not this is a patch release.""")
UpperCamelCase__ = parser.parse_args()
if not args.post_release:
pre_release_work(patch=args.patch)
elif args.patch:
print("""Nothing to do after a patch :-)""")
else:
post_release_work()
| 92 | 0 |
from __future__ import annotations
UpperCAmelCase : Optional[int] = []
def __lowerCamelCase ( lowerCamelCase__ : list[list[int]] , lowerCamelCase__ : int , lowerCamelCase__ : int ):
'''simple docstring'''
for i in range(len(SCREAMING_SNAKE_CASE_ ) ):
if board[row][i] == 1:
return False
for i in range(len(SCREAMING_SNAKE_CASE_ ) ):
if board[i][column] == 1:
return False
for i, j in zip(range(SCREAMING_SNAKE_CASE_ , -1 , -1 ) , range(SCREAMING_SNAKE_CASE_ , -1 , -1 ) ):
if board[i][j] == 1:
return False
for i, j in zip(range(SCREAMING_SNAKE_CASE_ , -1 , -1 ) , range(SCREAMING_SNAKE_CASE_ , len(SCREAMING_SNAKE_CASE_ ) ) ):
if board[i][j] == 1:
return False
return True
def __lowerCamelCase ( lowerCamelCase__ : list[list[int]] , lowerCamelCase__ : int ):
'''simple docstring'''
if row >= len(SCREAMING_SNAKE_CASE_ ):
solution.append(SCREAMING_SNAKE_CASE_ )
printboard(SCREAMING_SNAKE_CASE_ )
print()
return True
for i in range(len(SCREAMING_SNAKE_CASE_ ) ):
if is_safe(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
lowerCamelCase = 1
solve(SCREAMING_SNAKE_CASE_ , row + 1 )
lowerCamelCase = 0
return False
def __lowerCamelCase ( lowerCamelCase__ : list[list[int]] ):
'''simple docstring'''
for i in range(len(SCREAMING_SNAKE_CASE_ ) ):
for j in range(len(SCREAMING_SNAKE_CASE_ ) ):
if board[i][j] == 1:
print("""Q""" , end=""" """ )
else:
print(""".""" , end=""" """ )
print()
# n=int(input("The no. of queens"))
UpperCAmelCase : Optional[int] = 8
UpperCAmelCase : Optional[Any] = [[0 for i in range(n)] for j in range(n)]
solve(board, 0)
print("The total no. of solutions are :", len(solution))
| 252 |
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import XLMRobertaTokenizerFast
from diffusers import DDIMScheduler, KandinskyImgaImgPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandinsky.text_encoder import MCLIPConfig, MultilingualCLIP
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu
from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference
enable_full_determinism()
class a__ ( snake_case__ , unittest.TestCase ):
_a : Dict = KandinskyImgaImgPipeline
_a : List[Any] = ["""prompt""", """image_embeds""", """negative_image_embeds""", """image"""]
_a : str = [
"""prompt""",
"""negative_prompt""",
"""image_embeds""",
"""negative_image_embeds""",
"""image""",
]
_a : List[Any] = [
"""generator""",
"""height""",
"""width""",
"""strength""",
"""guidance_scale""",
"""negative_prompt""",
"""num_inference_steps""",
"""return_dict""",
"""guidance_scale""",
"""num_images_per_prompt""",
"""output_type""",
"""return_dict""",
]
_a : int = False
@property
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
return 3_2
@property
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
return 3_2
@property
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
return self.time_input_dim
@property
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
return self.time_input_dim * 4
@property
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
return 1_0_0
@property
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
__lowerCAmelCase = XLMRobertaTokenizerFast.from_pretrained("YiYiXu/tiny-random-mclip-base" )
return tokenizer
@property
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
torch.manual_seed(0 )
__lowerCAmelCase = MCLIPConfig(
numDims=self.cross_attention_dim , transformerDimensions=self.text_embedder_hidden_size , hidden_size=self.text_embedder_hidden_size , intermediate_size=3_7 , num_attention_heads=4 , num_hidden_layers=5 , vocab_size=1_0_0_5 , )
__lowerCAmelCase = MultilingualCLIP(_A )
__lowerCAmelCase = text_encoder.eval()
return text_encoder
@property
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
torch.manual_seed(0 )
__lowerCAmelCase = {
"in_channels": 4,
# Out channels is double in channels because predicts mean and variance
"out_channels": 8,
"addition_embed_type": "text_image",
"down_block_types": ("ResnetDownsampleBlock2D", "SimpleCrossAttnDownBlock2D"),
"up_block_types": ("SimpleCrossAttnUpBlock2D", "ResnetUpsampleBlock2D"),
"mid_block_type": "UNetMidBlock2DSimpleCrossAttn",
"block_out_channels": (self.block_out_channels_a, self.block_out_channels_a * 2),
"layers_per_block": 1,
"encoder_hid_dim": self.text_embedder_hidden_size,
"encoder_hid_dim_type": "text_image_proj",
"cross_attention_dim": self.cross_attention_dim,
"attention_head_dim": 4,
"resnet_time_scale_shift": "scale_shift",
"class_embed_type": None,
}
__lowerCAmelCase = UNetaDConditionModel(**_A )
return model
@property
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
return {
"block_out_channels": [3_2, 6_4],
"down_block_types": ["DownEncoderBlock2D", "AttnDownEncoderBlock2D"],
"in_channels": 3,
"latent_channels": 4,
"layers_per_block": 1,
"norm_num_groups": 8,
"norm_type": "spatial",
"num_vq_embeddings": 1_2,
"out_channels": 3,
"up_block_types": [
"AttnUpDecoderBlock2D",
"UpDecoderBlock2D",
],
"vq_embed_dim": 4,
}
@property
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
torch.manual_seed(0 )
__lowerCAmelCase = VQModel(**self.dummy_movq_kwargs )
return model
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
__lowerCAmelCase = self.dummy_text_encoder
__lowerCAmelCase = self.dummy_tokenizer
__lowerCAmelCase = self.dummy_unet
__lowerCAmelCase = self.dummy_movq
__lowerCAmelCase = {
"num_train_timesteps": 1_0_0_0,
"beta_schedule": "linear",
"beta_start": 0.0_00_85,
"beta_end": 0.0_12,
"clip_sample": False,
"set_alpha_to_one": False,
"steps_offset": 0,
"prediction_type": "epsilon",
"thresholding": False,
}
__lowerCAmelCase = DDIMScheduler(**_A )
__lowerCAmelCase = {
"text_encoder": text_encoder,
"tokenizer": tokenizer,
"unet": unet,
"scheduler": scheduler,
"movq": movq,
}
return components
def __SCREAMING_SNAKE_CASE( self , _A , _A=0 ):
"""simple docstring"""
__lowerCAmelCase = floats_tensor((1, self.cross_attention_dim) , rng=random.Random(_A ) ).to(_A )
__lowerCAmelCase = floats_tensor((1, self.cross_attention_dim) , rng=random.Random(seed + 1 ) ).to(_A )
# create init_image
__lowerCAmelCase = floats_tensor((1, 3, 6_4, 6_4) , rng=random.Random(_A ) ).to(_A )
__lowerCAmelCase = image.cpu().permute(0 , 2 , 3 , 1 )[0]
__lowerCAmelCase = Image.fromarray(np.uinta(_A ) ).convert("RGB" ).resize((2_5_6, 2_5_6) )
if str(_A ).startswith("mps" ):
__lowerCAmelCase = torch.manual_seed(_A )
else:
__lowerCAmelCase = torch.Generator(device=_A ).manual_seed(_A )
__lowerCAmelCase = {
"prompt": "horse",
"image": init_image,
"image_embeds": image_embeds,
"negative_image_embeds": negative_image_embeds,
"generator": generator,
"height": 6_4,
"width": 6_4,
"num_inference_steps": 1_0,
"guidance_scale": 7.0,
"strength": 0.2,
"output_type": "np",
}
return inputs
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
__lowerCAmelCase = "cpu"
__lowerCAmelCase = self.get_dummy_components()
__lowerCAmelCase = self.pipeline_class(**_A )
__lowerCAmelCase = pipe.to(_A )
pipe.set_progress_bar_config(disable=_A )
__lowerCAmelCase = pipe(**self.get_dummy_inputs(_A ) )
__lowerCAmelCase = output.images
__lowerCAmelCase = pipe(
**self.get_dummy_inputs(_A ) , return_dict=_A , )[0]
__lowerCAmelCase = image[0, -3:, -3:, -1]
__lowerCAmelCase = image_from_tuple[0, -3:, -3:, -1]
assert image.shape == (1, 6_4, 6_4, 3)
__lowerCAmelCase = np.array(
[0.61_47_49_43, 0.6_07_35_39, 0.43_30_85_44, 0.5_92_82_69, 0.47_49_35_95, 0.46_75_59_73, 0.4_61_38_38, 0.45_36_87_97, 0.50_11_92_33] )
assert (
np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
), f""" expected_slice {expected_slice}, but got {image_slice.flatten()}"""
assert (
np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 1E-2
), f""" expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}"""
@slow
@require_torch_gpu
class a__ ( unittest.TestCase ):
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
__lowerCAmelCase = load_numpy(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
"/kandinsky/kandinsky_img2img_frog.npy" )
__lowerCAmelCase = load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main" "/kandinsky/cat.png" )
__lowerCAmelCase = "A red cartoon frog, 4k"
__lowerCAmelCase = KandinskyPriorPipeline.from_pretrained(
"kandinsky-community/kandinsky-2-1-prior" , torch_dtype=torch.floataa )
pipe_prior.to(_A )
__lowerCAmelCase = KandinskyImgaImgPipeline.from_pretrained(
"kandinsky-community/kandinsky-2-1" , torch_dtype=torch.floataa )
__lowerCAmelCase = pipeline.to(_A )
pipeline.set_progress_bar_config(disable=_A )
__lowerCAmelCase = torch.Generator(device="cpu" ).manual_seed(0 )
__lowerCAmelCase , __lowerCAmelCase = pipe_prior(
_A , generator=_A , num_inference_steps=5 , negative_prompt="" , ).to_tuple()
__lowerCAmelCase = pipeline(
_A , image=_A , image_embeds=_A , negative_image_embeds=_A , generator=_A , num_inference_steps=1_0_0 , height=7_6_8 , width=7_6_8 , strength=0.2 , output_type="np" , )
__lowerCAmelCase = output.images[0]
assert image.shape == (7_6_8, 7_6_8, 3)
assert_mean_pixel_difference(_A , _A )
| 92 | 0 |
'''simple docstring'''
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__a = logging.get_logger(__name__)
__a = {
"BAAI/AltCLIP": "https://huggingface.co/BAAI/AltCLIP/resolve/main/config.json",
# See all AltCLIP models at https://huggingface.co/models?filter=altclip
}
class UpperCAmelCase_ ( snake_case__ ):
"""simple docstring"""
lowercase = """altclip_text_model"""
def __init__( self : List[str] , snake_case_ : int=250_002 , snake_case_ : Tuple=1_024 , snake_case_ : str=24 , snake_case_ : Optional[int]=16 , snake_case_ : str=4_096 , snake_case_ : Optional[int]="gelu" , snake_case_ : Dict=0.1 , snake_case_ : str=0.1 , snake_case_ : List[Any]=514 , snake_case_ : int=1 , snake_case_ : str=0.02 , snake_case_ : List[str]=0.02 , snake_case_ : List[str]=1E-0_5 , snake_case_ : Any=1 , snake_case_ : Optional[int]=0 , snake_case_ : str=2 , snake_case_ : Tuple="absolute" , snake_case_ : List[Any]=True , snake_case_ : List[str]=768 , **snake_case_ : Union[str, Any] , ):
super().__init__(pad_token_id=_A , bos_token_id=_A , eos_token_id=_A , **_A )
snake_case__ : Optional[int] = vocab_size
snake_case__ : Optional[int] = hidden_size
snake_case__ : str = num_hidden_layers
snake_case__ : List[str] = num_attention_heads
snake_case__ : List[str] = hidden_act
snake_case__ : Union[str, Any] = intermediate_size
snake_case__ : Optional[Any] = hidden_dropout_prob
snake_case__ : List[str] = attention_probs_dropout_prob
snake_case__ : str = max_position_embeddings
snake_case__ : Tuple = type_vocab_size
snake_case__ : Union[str, Any] = initializer_range
snake_case__ : Dict = initializer_factor
snake_case__ : Any = layer_norm_eps
snake_case__ : int = position_embedding_type
snake_case__ : List[Any] = use_cache
snake_case__ : List[Any] = project_dim
class UpperCAmelCase_ ( snake_case__ ):
"""simple docstring"""
lowercase = """altclip_vision_model"""
def __init__( self : Any , snake_case_ : Optional[int]=768 , snake_case_ : List[Any]=3_072 , snake_case_ : Tuple=512 , snake_case_ : Optional[Any]=12 , snake_case_ : List[Any]=12 , snake_case_ : Union[str, Any]=3 , snake_case_ : Any=224 , snake_case_ : Union[str, Any]=32 , snake_case_ : Union[str, Any]="quick_gelu" , snake_case_ : List[Any]=1E-5 , snake_case_ : Tuple=0.0 , snake_case_ : Optional[Any]=0.02 , snake_case_ : Any=1.0 , **snake_case_ : List[str] , ):
super().__init__(**_A )
snake_case__ : Optional[int] = hidden_size
snake_case__ : Tuple = intermediate_size
snake_case__ : Optional[int] = projection_dim
snake_case__ : Dict = num_hidden_layers
snake_case__ : Any = num_attention_heads
snake_case__ : Tuple = num_channels
snake_case__ : Optional[int] = patch_size
snake_case__ : int = image_size
snake_case__ : List[Any] = initializer_range
snake_case__ : Any = initializer_factor
snake_case__ : Optional[Any] = attention_dropout
snake_case__ : List[Any] = layer_norm_eps
snake_case__ : Tuple = hidden_act
@classmethod
def lowerCamelCase ( cls : Any , snake_case_ : Union[str, Any] , **snake_case_ : Any ):
cls._set_token_in_kwargs(_A )
snake_case__ , snake_case__ : int = cls.get_config_dict(_A , **_A )
# get the vision config dict if we are loading from AltCLIPConfig
if config_dict.get("""model_type""" ) == "altclip":
snake_case__ : Tuple = config_dict["""vision_config"""]
if "model_type" in config_dict and hasattr(cls , """model_type""" ) and config_dict["model_type"] != cls.model_type:
logger.warning(
f"You are using a model of type {config_dict['model_type']} to instantiate a model of type "
f"{cls.model_type}. This is not supported for all configurations of models and can yield errors." )
return cls.from_dict(_A , **_A )
class UpperCAmelCase_ ( snake_case__ ):
"""simple docstring"""
lowercase = """altclip"""
lowercase = True
def __init__( self : List[Any] , snake_case_ : Union[str, Any]=None , snake_case_ : str=None , snake_case_ : Union[str, Any]=768 , snake_case_ : List[str]=2.6592 , **snake_case_ : List[Any] ):
snake_case__ : Optional[int] = kwargs.pop("""text_config_dict""" , _A )
snake_case__ : Optional[Any] = kwargs.pop("""vision_config_dict""" , _A )
super().__init__(**_A )
# Instead of simply assigning `[text|vision]_config_dict` to `[text|vision]_config`, we use the values in
# `[text|vision]_config_dict` to update the values in `[text|vision]_config`. The values should be same in most
# cases, but we don't want to break anything regarding `_config_dict` that existed before commit `8827e1b2`.
if text_config_dict is not None:
if text_config is None:
snake_case__ : Any = {}
# This is the complete result when using `text_config_dict`.
snake_case__ : Optional[Any] = AltCLIPTextConfig(**_A ).to_dict()
# Give a warning if the values exist in both `_text_config_dict` and `text_config` but being different.
for key, value in _text_config_dict.items():
if key in text_config and value != text_config[key] and key not in ["transformers_version"]:
# If specified in `text_config_dict`
if key in text_config_dict:
snake_case__ : Dict = (
f"`{key}` is found in both `text_config_dict` and `text_config` but with different values. "
f"The value `text_config_dict[\"{key}\"]` will be used instead."
)
# If inferred from default argument values (just to be super careful)
else:
snake_case__ : Optional[Any] = (
f"`text_config_dict` is provided which will be used to initialize `AltCLIPTextConfig`. The "
f"value `text_config[\"{key}\"]` will be overriden."
)
logger.warning(_A )
# Update all values in `text_config` with the ones in `_text_config_dict`.
text_config.update(_text_config_dict )
if vision_config_dict is not None:
if vision_config is None:
snake_case__ : Optional[Any] = {}
# This is the complete result when using `vision_config_dict`.
snake_case__ : str = AltCLIPVisionConfig(**_A ).to_dict()
# convert keys to string instead of integer
if "id2label" in _vision_config_dict:
snake_case__ : Union[str, Any] = {
str(_A ): value for key, value in _vision_config_dict["""id2label"""].items()
}
# Give a warning if the values exist in both `_vision_config_dict` and `vision_config` but being different.
for key, value in _vision_config_dict.items():
if key in vision_config and value != vision_config[key] and key not in ["transformers_version"]:
# If specified in `vision_config_dict`
if key in vision_config_dict:
snake_case__ : List[Any] = (
f"`{key}` is found in both `vision_config_dict` and `vision_config` but with different "
f"values. The value `vision_config_dict[\"{key}\"]` will be used instead."
)
# If inferred from default argument values (just to be super careful)
else:
snake_case__ : List[str] = (
f"`vision_config_dict` is provided which will be used to initialize `AltCLIPVisionConfig`. "
f"The value `vision_config[\"{key}\"]` will be overriden."
)
logger.warning(_A )
# Update all values in `vision_config` with the ones in `_vision_config_dict`.
vision_config.update(_vision_config_dict )
if text_config is None:
snake_case__ : int = {}
logger.info("""`text_config` is `None`. Initializing the `AltCLIPTextConfig` with default values.""" )
if vision_config is None:
snake_case__ : Optional[Any] = {}
logger.info("""`vision_config` is `None`. initializing the `AltCLIPVisionConfig` with default values.""" )
snake_case__ : Optional[Any] = AltCLIPTextConfig(**_A )
snake_case__ : Dict = AltCLIPVisionConfig(**_A )
snake_case__ : List[str] = projection_dim
snake_case__ : Optional[Any] = logit_scale_init_value
snake_case__ : int = 1.0
@classmethod
def lowerCamelCase ( cls : Optional[Any] , snake_case_ : List[Any] , snake_case_ : Tuple , **snake_case_ : Dict ):
return cls(text_config=text_config.to_dict() , vision_config=vision_config.to_dict() , **_A )
def lowerCamelCase ( self : Dict ):
snake_case__ : int = copy.deepcopy(self.__dict__ )
snake_case__ : List[str] = self.text_config.to_dict()
snake_case__ : int = self.vision_config.to_dict()
snake_case__ : List[Any] = self.__class__.model_type
return output
| 35 |
class a__ ( snake_case__ ):
pass
class a__ ( snake_case__ ):
pass
class a__ :
def __init__( self ):
"""simple docstring"""
__lowerCAmelCase = [
[],
[],
[],
]
def __SCREAMING_SNAKE_CASE( self , _A , _A ):
"""simple docstring"""
try:
if len(self.queues[priority] ) >= 1_0_0:
raise OverflowError("Maximum queue size is 100" )
self.queues[priority].append(_A )
except IndexError:
raise ValueError("Valid priorities are 0, 1, and 2" )
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
for queue in self.queues:
if queue:
return queue.pop(0 )
raise UnderFlowError("All queues are empty" )
def __str__( self ):
"""simple docstring"""
return "\n".join(f"""Priority {i}: {q}""" for i, q in enumerate(self.queues ) )
class a__ :
def __init__( self ):
"""simple docstring"""
__lowerCAmelCase = []
def __SCREAMING_SNAKE_CASE( self , _A ):
"""simple docstring"""
if len(self.queue ) == 1_0_0:
raise OverFlowError("Maximum queue size is 100" )
self.queue.append(_A )
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
if not self.queue:
raise UnderFlowError("The queue is empty" )
else:
__lowerCAmelCase = min(self.queue )
self.queue.remove(_A )
return data
def __str__( self ):
"""simple docstring"""
return str(self.queue )
def _a ( ):
__lowerCAmelCase = FixedPriorityQueue()
fpq.enqueue(0 , 10 )
fpq.enqueue(1 , 70 )
fpq.enqueue(0 , 1_00 )
fpq.enqueue(2 , 1 )
fpq.enqueue(2 , 5 )
fpq.enqueue(1 , 7 )
fpq.enqueue(2 , 4 )
fpq.enqueue(1 , 64 )
fpq.enqueue(0 , 1_28 )
print(SCREAMING_SNAKE_CASE_ )
print(fpq.dequeue() )
print(fpq.dequeue() )
print(fpq.dequeue() )
print(fpq.dequeue() )
print(fpq.dequeue() )
print(SCREAMING_SNAKE_CASE_ )
print(fpq.dequeue() )
print(fpq.dequeue() )
print(fpq.dequeue() )
print(fpq.dequeue() )
print(fpq.dequeue() )
def _a ( ):
__lowerCAmelCase = ElementPriorityQueue()
epq.enqueue(10 )
epq.enqueue(70 )
epq.enqueue(1_00 )
epq.enqueue(1 )
epq.enqueue(5 )
epq.enqueue(7 )
epq.enqueue(4 )
epq.enqueue(64 )
epq.enqueue(1_28 )
print(SCREAMING_SNAKE_CASE_ )
print(epq.dequeue() )
print(epq.dequeue() )
print(epq.dequeue() )
print(epq.dequeue() )
print(epq.dequeue() )
print(SCREAMING_SNAKE_CASE_ )
print(epq.dequeue() )
print(epq.dequeue() )
print(epq.dequeue() )
print(epq.dequeue() )
print(epq.dequeue() )
if __name__ == "__main__":
fixed_priority_queue()
element_priority_queue()
| 92 | 0 |
'''simple docstring'''
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import XLMRobertaTokenizerFast
from diffusers import DDIMScheduler, KandinskyImgaImgPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandinsky.text_encoder import MCLIPConfig, MultilingualCLIP
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu
from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference
enable_full_determinism()
class UpperCamelCase__ ( snake_case__ , unittest.TestCase):
UpperCAmelCase__ : Dict = KandinskyImgaImgPipeline
UpperCAmelCase__ : List[Any] = ["""prompt""", """image_embeds""", """negative_image_embeds""", """image"""]
UpperCAmelCase__ : str = [
"""prompt""",
"""negative_prompt""",
"""image_embeds""",
"""negative_image_embeds""",
"""image""",
]
UpperCAmelCase__ : List[Any] = [
"""generator""",
"""height""",
"""width""",
"""strength""",
"""guidance_scale""",
"""negative_prompt""",
"""num_inference_steps""",
"""return_dict""",
"""guidance_scale""",
"""num_images_per_prompt""",
"""output_type""",
"""return_dict""",
]
UpperCAmelCase__ : int = False
@property
def lowercase_ ( self :Dict ) -> Dict:
'''simple docstring'''
return 32
@property
def lowercase_ ( self :Any ) -> Optional[Any]:
'''simple docstring'''
return 32
@property
def lowercase_ ( self :Optional[Any] ) -> List[str]:
'''simple docstring'''
return self.time_input_dim
@property
def lowercase_ ( self :str ) -> int:
'''simple docstring'''
return self.time_input_dim * 4
@property
def lowercase_ ( self :Optional[int] ) -> Any:
'''simple docstring'''
return 100
@property
def lowercase_ ( self :Dict ) -> Optional[Any]:
'''simple docstring'''
__A = XLMRobertaTokenizerFast.from_pretrained('YiYiXu/tiny-random-mclip-base' )
return tokenizer
@property
def lowercase_ ( self :Dict ) -> Optional[Any]:
'''simple docstring'''
torch.manual_seed(0 )
__A = MCLIPConfig(
numDims=self.cross_attention_dim , transformerDimensions=self.text_embedder_hidden_size , hidden_size=self.text_embedder_hidden_size , intermediate_size=37 , num_attention_heads=4 , num_hidden_layers=5 , vocab_size=1_005 , )
__A = MultilingualCLIP(_A )
__A = text_encoder.eval()
return text_encoder
@property
def lowercase_ ( self :str ) -> Optional[Any]:
'''simple docstring'''
torch.manual_seed(0 )
__A = {
'in_channels': 4,
# Out channels is double in channels because predicts mean and variance
'out_channels': 8,
'addition_embed_type': 'text_image',
'down_block_types': ('ResnetDownsampleBlock2D', 'SimpleCrossAttnDownBlock2D'),
'up_block_types': ('SimpleCrossAttnUpBlock2D', 'ResnetUpsampleBlock2D'),
'mid_block_type': 'UNetMidBlock2DSimpleCrossAttn',
'block_out_channels': (self.block_out_channels_a, self.block_out_channels_a * 2),
'layers_per_block': 1,
'encoder_hid_dim': self.text_embedder_hidden_size,
'encoder_hid_dim_type': 'text_image_proj',
'cross_attention_dim': self.cross_attention_dim,
'attention_head_dim': 4,
'resnet_time_scale_shift': 'scale_shift',
'class_embed_type': None,
}
__A = UNetaDConditionModel(**_A )
return model
@property
def lowercase_ ( self :str ) -> Dict:
'''simple docstring'''
return {
"block_out_channels": [32, 64],
"down_block_types": ["DownEncoderBlock2D", "AttnDownEncoderBlock2D"],
"in_channels": 3,
"latent_channels": 4,
"layers_per_block": 1,
"norm_num_groups": 8,
"norm_type": "spatial",
"num_vq_embeddings": 12,
"out_channels": 3,
"up_block_types": [
"AttnUpDecoderBlock2D",
"UpDecoderBlock2D",
],
"vq_embed_dim": 4,
}
@property
def lowercase_ ( self :Tuple ) -> List[Any]:
'''simple docstring'''
torch.manual_seed(0 )
__A = VQModel(**self.dummy_movq_kwargs )
return model
def lowercase_ ( self :Union[str, Any] ) -> Optional[int]:
'''simple docstring'''
__A = self.dummy_text_encoder
__A = self.dummy_tokenizer
__A = self.dummy_unet
__A = self.dummy_movq
__A = {
'num_train_timesteps': 1_000,
'beta_schedule': 'linear',
'beta_start': 0.00_085,
'beta_end': 0.012,
'clip_sample': False,
'set_alpha_to_one': False,
'steps_offset': 0,
'prediction_type': 'epsilon',
'thresholding': False,
}
__A = DDIMScheduler(**_A )
__A = {
'text_encoder': text_encoder,
'tokenizer': tokenizer,
'unet': unet,
'scheduler': scheduler,
'movq': movq,
}
return components
def lowercase_ ( self :List[Any] , _A :Any , _A :Tuple=0 ) -> Dict:
'''simple docstring'''
__A = floats_tensor((1, self.cross_attention_dim) , rng=random.Random(_A ) ).to(_A )
__A = floats_tensor((1, self.cross_attention_dim) , rng=random.Random(seed + 1 ) ).to(_A )
# create init_image
__A = floats_tensor((1, 3, 64, 64) , rng=random.Random(_A ) ).to(_A )
__A = image.cpu().permute(0 , 2 , 3 , 1 )[0]
__A = Image.fromarray(np.uinta(_A ) ).convert('RGB' ).resize((256, 256) )
if str(_A ).startswith('mps' ):
__A = torch.manual_seed(_A )
else:
__A = torch.Generator(device=_A ).manual_seed(_A )
__A = {
'prompt': 'horse',
'image': init_image,
'image_embeds': image_embeds,
'negative_image_embeds': negative_image_embeds,
'generator': generator,
'height': 64,
'width': 64,
'num_inference_steps': 10,
'guidance_scale': 7.0,
'strength': 0.2,
'output_type': 'np',
}
return inputs
def lowercase_ ( self :int ) -> Dict:
'''simple docstring'''
__A = 'cpu'
__A = self.get_dummy_components()
__A = self.pipeline_class(**_A )
__A = pipe.to(_A )
pipe.set_progress_bar_config(disable=_A )
__A = pipe(**self.get_dummy_inputs(_A ) )
__A = output.images
__A = pipe(
**self.get_dummy_inputs(_A ) , return_dict=_A , )[0]
__A = image[0, -3:, -3:, -1]
__A = image_from_tuple[0, -3:, -3:, -1]
assert image.shape == (1, 64, 64, 3)
__A = np.array(
[0.61_474_943, 0.6_073_539, 0.43_308_544, 0.5_928_269, 0.47_493_595, 0.46_755_973, 0.4_613_838, 0.45_368_797, 0.50_119_233] )
assert (
np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
), F' expected_slice {expected_slice}, but got {image_slice.flatten()}'
assert (
np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 1E-2
), F' expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}'
@slow
@require_torch_gpu
class UpperCamelCase__ ( unittest.TestCase):
def lowercase_ ( self :Any ) -> Union[str, Any]:
'''simple docstring'''
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def lowercase_ ( self :Optional[Any] ) -> Tuple:
'''simple docstring'''
__A = load_numpy(
'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
'/kandinsky/kandinsky_img2img_frog.npy' )
__A = load_image(
'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main' '/kandinsky/cat.png' )
__A = 'A red cartoon frog, 4k'
__A = KandinskyPriorPipeline.from_pretrained(
'kandinsky-community/kandinsky-2-1-prior' , torch_dtype=torch.floataa )
pipe_prior.to(_A )
__A = KandinskyImgaImgPipeline.from_pretrained(
'kandinsky-community/kandinsky-2-1' , torch_dtype=torch.floataa )
__A = pipeline.to(_A )
pipeline.set_progress_bar_config(disable=_A )
__A = torch.Generator(device='cpu' ).manual_seed(0 )
__A , __A = pipe_prior(
_A , generator=_A , num_inference_steps=5 , negative_prompt='' , ).to_tuple()
__A = pipeline(
_A , image=_A , image_embeds=_A , negative_image_embeds=_A , generator=_A , num_inference_steps=100 , height=768 , width=768 , strength=0.2 , output_type='np' , )
__A = output.images[0]
assert image.shape == (768, 768, 3)
assert_mean_pixel_difference(_A , _A )
| 161 |
import inspect
import unittest
import warnings
from transformers import DeiTConfig
from transformers.models.auto import get_values
from transformers.testing_utils import (
require_accelerate,
require_torch,
require_torch_gpu,
require_vision,
slow,
torch_device,
)
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from torch import nn
from transformers import (
MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
MODEL_MAPPING,
DeiTForImageClassification,
DeiTForImageClassificationWithTeacher,
DeiTForMaskedImageModeling,
DeiTModel,
)
from transformers.models.deit.modeling_deit import DEIT_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import DeiTImageProcessor
class a__ :
def __init__( self , _A , _A=1_3 , _A=3_0 , _A=2 , _A=3 , _A=True , _A=True , _A=3_2 , _A=5 , _A=4 , _A=3_7 , _A="gelu" , _A=0.1 , _A=0.1 , _A=1_0 , _A=0.02 , _A=3 , _A=None , _A=2 , ):
"""simple docstring"""
__lowerCAmelCase = parent
__lowerCAmelCase = batch_size
__lowerCAmelCase = image_size
__lowerCAmelCase = patch_size
__lowerCAmelCase = num_channels
__lowerCAmelCase = is_training
__lowerCAmelCase = use_labels
__lowerCAmelCase = hidden_size
__lowerCAmelCase = num_hidden_layers
__lowerCAmelCase = num_attention_heads
__lowerCAmelCase = intermediate_size
__lowerCAmelCase = hidden_act
__lowerCAmelCase = hidden_dropout_prob
__lowerCAmelCase = attention_probs_dropout_prob
__lowerCAmelCase = type_sequence_label_size
__lowerCAmelCase = initializer_range
__lowerCAmelCase = scope
__lowerCAmelCase = encoder_stride
# in DeiT, the seq length equals the number of patches + 2 (we add 2 for the [CLS] and distilation tokens)
__lowerCAmelCase = (image_size // patch_size) ** 2
__lowerCAmelCase = num_patches + 2
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
__lowerCAmelCase = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
__lowerCAmelCase = None
if self.use_labels:
__lowerCAmelCase = ids_tensor([self.batch_size] , self.type_sequence_label_size )
__lowerCAmelCase = self.get_config()
return config, pixel_values, labels
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
return DeiTConfig(
image_size=self.image_size , patch_size=self.patch_size , num_channels=self.num_channels , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , is_decoder=_A , initializer_range=self.initializer_range , encoder_stride=self.encoder_stride , )
def __SCREAMING_SNAKE_CASE( self , _A , _A , _A ):
"""simple docstring"""
__lowerCAmelCase = DeiTModel(config=_A )
model.to(_A )
model.eval()
__lowerCAmelCase = model(_A )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def __SCREAMING_SNAKE_CASE( self , _A , _A , _A ):
"""simple docstring"""
__lowerCAmelCase = DeiTForMaskedImageModeling(config=_A )
model.to(_A )
model.eval()
__lowerCAmelCase = model(_A )
self.parent.assertEqual(
result.reconstruction.shape , (self.batch_size, self.num_channels, self.image_size, self.image_size) )
# test greyscale images
__lowerCAmelCase = 1
__lowerCAmelCase = DeiTForMaskedImageModeling(_A )
model.to(_A )
model.eval()
__lowerCAmelCase = floats_tensor([self.batch_size, 1, self.image_size, self.image_size] )
__lowerCAmelCase = model(_A )
self.parent.assertEqual(result.reconstruction.shape , (self.batch_size, 1, self.image_size, self.image_size) )
def __SCREAMING_SNAKE_CASE( self , _A , _A , _A ):
"""simple docstring"""
__lowerCAmelCase = self.type_sequence_label_size
__lowerCAmelCase = DeiTForImageClassification(_A )
model.to(_A )
model.eval()
__lowerCAmelCase = model(_A , labels=_A )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size) )
# test greyscale images
__lowerCAmelCase = 1
__lowerCAmelCase = DeiTForImageClassification(_A )
model.to(_A )
model.eval()
__lowerCAmelCase = floats_tensor([self.batch_size, 1, self.image_size, self.image_size] )
__lowerCAmelCase = model(_A , labels=_A )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size) )
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
__lowerCAmelCase = self.prepare_config_and_inputs()
(
(
__lowerCAmelCase
) , (
__lowerCAmelCase
) , (
__lowerCAmelCase
) ,
) = config_and_inputs
__lowerCAmelCase = {"pixel_values": pixel_values}
return config, inputs_dict
@require_torch
class a__ ( snake_case__ , snake_case__ , unittest.TestCase ):
_a : Optional[Any] = (
(
DeiTModel,
DeiTForImageClassification,
DeiTForImageClassificationWithTeacher,
DeiTForMaskedImageModeling,
)
if is_torch_available()
else ()
)
_a : int = (
{
"""feature-extraction""": DeiTModel,
"""image-classification""": (DeiTForImageClassification, DeiTForImageClassificationWithTeacher),
}
if is_torch_available()
else {}
)
_a : Optional[Any] = False
_a : Tuple = False
_a : Tuple = False
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
__lowerCAmelCase = DeiTModelTester(self )
__lowerCAmelCase = ConfigTester(self , config_class=_A , has_text_modality=_A , hidden_size=3_7 )
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
self.config_tester.run_common_tests()
@unittest.skip(reason="DeiT does not use inputs_embeds" )
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
pass
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
__lowerCAmelCase , __lowerCAmelCase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
__lowerCAmelCase = model_class(_A )
self.assertIsInstance(model.get_input_embeddings() , (nn.Module) )
__lowerCAmelCase = model.get_output_embeddings()
self.assertTrue(x is None or isinstance(_A , nn.Linear ) )
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
__lowerCAmelCase , __lowerCAmelCase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
__lowerCAmelCase = model_class(_A )
__lowerCAmelCase = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
__lowerCAmelCase = [*signature.parameters.keys()]
__lowerCAmelCase = ["pixel_values"]
self.assertListEqual(arg_names[:1] , _A )
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
__lowerCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*_A )
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
__lowerCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_image_modeling(*_A )
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
__lowerCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*_A )
def __SCREAMING_SNAKE_CASE( self , _A , _A , _A=False ):
"""simple docstring"""
__lowerCAmelCase = super()._prepare_for_class(_A , _A , return_labels=_A )
if return_labels:
if model_class.__name__ == "DeiTForImageClassificationWithTeacher":
del inputs_dict["labels"]
return inputs_dict
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
if not self.model_tester.is_training:
return
__lowerCAmelCase , __lowerCAmelCase = self.model_tester.prepare_config_and_inputs_for_common()
__lowerCAmelCase = True
for model_class in self.all_model_classes:
# DeiTForImageClassificationWithTeacher supports inference-only
if (
model_class in get_values(_A )
or model_class.__name__ == "DeiTForImageClassificationWithTeacher"
):
continue
__lowerCAmelCase = model_class(_A )
model.to(_A )
model.train()
__lowerCAmelCase = self._prepare_for_class(_A , _A , return_labels=_A )
__lowerCAmelCase = model(**_A ).loss
loss.backward()
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
__lowerCAmelCase , __lowerCAmelCase = self.model_tester.prepare_config_and_inputs_for_common()
if not self.model_tester.is_training:
return
__lowerCAmelCase = False
__lowerCAmelCase = True
for model_class in self.all_model_classes:
if model_class in get_values(_A ) or not model_class.supports_gradient_checkpointing:
continue
# DeiTForImageClassificationWithTeacher supports inference-only
if model_class.__name__ == "DeiTForImageClassificationWithTeacher":
continue
__lowerCAmelCase = model_class(_A )
model.gradient_checkpointing_enable()
model.to(_A )
model.train()
__lowerCAmelCase = self._prepare_for_class(_A , _A , return_labels=_A )
__lowerCAmelCase = model(**_A ).loss
loss.backward()
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
__lowerCAmelCase , __lowerCAmelCase = self.model_tester.prepare_config_and_inputs_for_common()
__lowerCAmelCase = [
{"title": "multi_label_classification", "num_labels": 2, "dtype": torch.float},
{"title": "single_label_classification", "num_labels": 1, "dtype": torch.long},
{"title": "regression", "num_labels": 1, "dtype": torch.float},
]
for model_class in self.all_model_classes:
if (
model_class
not in [
*get_values(_A ),
*get_values(_A ),
]
or model_class.__name__ == "DeiTForImageClassificationWithTeacher"
):
continue
for problem_type in problem_types:
with self.subTest(msg=f"""Testing {model_class} with {problem_type['title']}""" ):
__lowerCAmelCase = problem_type["title"]
__lowerCAmelCase = problem_type["num_labels"]
__lowerCAmelCase = model_class(_A )
model.to(_A )
model.train()
__lowerCAmelCase = self._prepare_for_class(_A , _A , return_labels=_A )
if problem_type["num_labels"] > 1:
__lowerCAmelCase = inputs["labels"].unsqueeze(1 ).repeat(1 , problem_type["num_labels"] )
__lowerCAmelCase = inputs["labels"].to(problem_type["dtype"] )
# This tests that we do not trigger the warning form PyTorch "Using a target size that is different
# to the input size. This will likely lead to incorrect results due to broadcasting. Please ensure
# they have the same size." which is a symptom something in wrong for the regression problem.
# See https://github.com/huggingface/transformers/issues/11780
with warnings.catch_warnings(record=_A ) as warning_list:
__lowerCAmelCase = model(**_A ).loss
for w in warning_list:
if "Using a target size that is different to the input size" in str(w.message ):
raise ValueError(
f"""Something is going wrong in the regression problem: intercepted {w.message}""" )
loss.backward()
@slow
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
for model_name in DEIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
__lowerCAmelCase = DeiTModel.from_pretrained(_A )
self.assertIsNotNone(_A )
def _a ( ):
__lowerCAmelCase = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png" )
return image
@require_torch
@require_vision
class a__ ( unittest.TestCase ):
@cached_property
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
return (
DeiTImageProcessor.from_pretrained("facebook/deit-base-distilled-patch16-224" )
if is_vision_available()
else None
)
@slow
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
__lowerCAmelCase = DeiTForImageClassificationWithTeacher.from_pretrained("facebook/deit-base-distilled-patch16-224" ).to(
_A )
__lowerCAmelCase = self.default_image_processor
__lowerCAmelCase = prepare_img()
__lowerCAmelCase = image_processor(images=_A , return_tensors="pt" ).to(_A )
# forward pass
with torch.no_grad():
__lowerCAmelCase = model(**_A )
# verify the logits
__lowerCAmelCase = torch.Size((1, 1_0_0_0) )
self.assertEqual(outputs.logits.shape , _A )
__lowerCAmelCase = torch.tensor([-1.02_66, 0.19_12, -1.28_61] ).to(_A )
self.assertTrue(torch.allclose(outputs.logits[0, :3] , _A , atol=1E-4 ) )
@slow
@require_accelerate
@require_torch_gpu
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
__lowerCAmelCase = DeiTModel.from_pretrained(
"facebook/deit-base-distilled-patch16-224" , torch_dtype=torch.floataa , device_map="auto" )
__lowerCAmelCase = self.default_image_processor
__lowerCAmelCase = prepare_img()
__lowerCAmelCase = image_processor(images=_A , return_tensors="pt" )
__lowerCAmelCase = inputs.pixel_values.to(_A )
# forward pass to make sure inference works in fp16
with torch.no_grad():
__lowerCAmelCase = model(_A )
| 92 | 0 |
"""simple docstring"""
import logging
from pathlib import Path
import numpy as np
import pytorch_lightning as pl
import torch
from pytorch_lightning.callbacks import EarlyStopping, ModelCheckpoint
from pytorch_lightning.utilities import rank_zero_only
from utils_rag import save_json
def a__ ( _SCREAMING_SNAKE_CASE ):
"""simple docstring"""
UpperCamelCase = filter(lambda _SCREAMING_SNAKE_CASE : p.requires_grad , model.parameters() )
UpperCamelCase = sum([np.prod(p.size() ) for p in model_parameters] )
return params
lowerCAmelCase__ = logging.getLogger(__name__)
def a__ ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
"""simple docstring"""
if metric == "rouge2":
UpperCamelCase = "{val_avg_rouge2:.4f}-{step_count}"
elif metric == "bleu":
UpperCamelCase = "{val_avg_bleu:.4f}-{step_count}"
elif metric == "em":
UpperCamelCase = "{val_avg_em:.4f}-{step_count}"
elif metric == "loss":
UpperCamelCase = "{val_avg_loss:.4f}-{step_count}"
else:
raise NotImplementedError(
F"seq2seq callbacks only support rouge2 and bleu, got {metric}, You can make your own by adding to this"
" function." )
UpperCamelCase = ModelCheckpoint(
dirpath=SCREAMING_SNAKE_CASE_ , filename=SCREAMING_SNAKE_CASE_ , monitor=F"val_{metric}" , mode="max" , save_top_k=1 , every_n_epochs=1 , )
return checkpoint_callback
def a__ ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
"""simple docstring"""
return EarlyStopping(
monitor=F"val_{metric}" , mode="min" if "loss" in metric else "max" , patience=SCREAMING_SNAKE_CASE_ , verbose=SCREAMING_SNAKE_CASE_ , )
class _lowerCamelCase ( pl.Callback ):
def snake_case_ (self , __a , __a ) -> int:
UpperCamelCase = {F"lr_group_{i}": param["lr"] for i, param in enumerate(pl_module.trainer.optimizers[0].param_groups )}
pl_module.logger.log_metrics(_A )
@rank_zero_only
def snake_case_ (self , __a , __a , __a , __a=True ) -> List[str]:
logger.info(F"***** {type_path} results at step {trainer.global_step:05d} *****" )
UpperCamelCase = trainer.callback_metrics
trainer.logger.log_metrics({k: v for k, v in metrics.items() if k not in ["log", "progress_bar", "preds"]} )
# Log results
UpperCamelCase = Path(pl_module.hparams.output_dir )
if type_path == "test":
UpperCamelCase = od / "test_results.txt"
UpperCamelCase = od / "test_generations.txt"
else:
# this never gets hit. I prefer not to save intermediate generations, and results are in metrics.json
# If people want this it will be easy enough to add back.
UpperCamelCase = od / F"{type_path}_results/{trainer.global_step:05d}.txt"
UpperCamelCase = od / F"{type_path}_generations/{trainer.global_step:05d}.txt"
results_file.parent.mkdir(exist_ok=_A )
generations_file.parent.mkdir(exist_ok=_A )
with open(_A , "a+" ) as writer:
for key in sorted(_A ):
if key in ["log", "progress_bar", "preds"]:
continue
UpperCamelCase = metrics[key]
if isinstance(_A , torch.Tensor ):
UpperCamelCase = val.item()
UpperCamelCase = F"{key}: {val:.6f}\n"
writer.write(_A )
if not save_generations:
return
if "preds" in metrics:
UpperCamelCase = "\n".join(metrics["preds"] )
generations_file.open("w+" ).write(_A )
@rank_zero_only
def snake_case_ (self , __a , __a ) -> List[Any]:
try:
UpperCamelCase = pl_module.model.model.num_parameters()
except AttributeError:
UpperCamelCase = pl_module.model.num_parameters()
UpperCamelCase = count_trainable_parameters(_A )
# mp stands for million parameters
trainer.logger.log_metrics({"n_params": npars, "mp": npars / 1e6, "grad_mp": n_trainable_pars / 1e6} )
@rank_zero_only
def snake_case_ (self , __a , __a ) -> int:
save_json(pl_module.metrics , pl_module.metrics_save_path )
return self._write_logs(_A , _A , "test" )
@rank_zero_only
def snake_case_ (self , __a , __a ) -> Optional[Any]:
save_json(pl_module.metrics , pl_module.metrics_save_path )
# Uncommenting this will save val generations
# return self._write_logs(trainer, pl_module, "valid")
| 153 |
def _a ( SCREAMING_SNAKE_CASE_ : int = 1_00_00_00 ):
__lowerCAmelCase = [i - 1 for i in range(limit + 1 )]
for i in range(2 , limit + 1 ):
if phi[i] == i - 1:
for j in range(2 * i , limit + 1 , SCREAMING_SNAKE_CASE_ ):
phi[j] -= phi[j] // i
return sum(phi[2 : limit + 1] )
if __name__ == "__main__":
print(solution())
| 92 | 0 |
"""simple docstring"""
import argparse
import torch
from transformers import MobileBertConfig, MobileBertForPreTraining, load_tf_weights_in_mobilebert
from transformers.utils import logging
logging.set_verbosity_info()
def __lowerCAmelCase ( lowercase : Optional[int] , lowercase : Union[str, Any] , lowercase : Dict ) -> List[Any]:
"""simple docstring"""
snake_case : Optional[Any] = MobileBertConfig.from_json_file(SCREAMING_SNAKE_CASE_ )
print(F'Building PyTorch model from configuration: {config}' )
snake_case : str = MobileBertForPreTraining(SCREAMING_SNAKE_CASE_ )
# Load weights from tf checkpoint
snake_case : Any = load_tf_weights_in_mobilebert(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
# Save pytorch-model
print(F'Save PyTorch model to {pytorch_dump_path}' )
torch.save(model.state_dict() , SCREAMING_SNAKE_CASE_ )
if __name__ == "__main__":
__snake_case = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"""--tf_checkpoint_path""", default=None, type=str, required=True, help="""Path to the TensorFlow checkpoint path."""
)
parser.add_argument(
"""--mobilebert_config_file""",
default=None,
type=str,
required=True,
help=(
"""The config json file corresponding to the pre-trained MobileBERT model. \n"""
"""This specifies the model architecture."""
),
)
parser.add_argument(
"""--pytorch_dump_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
)
__snake_case = parser.parse_args()
convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.mobilebert_config_file, args.pytorch_dump_path)
| 203 |
import warnings
from diffusers import StableDiffusionImgaImgPipeline # noqa F401
warnings.warn(
"""The `image_to_image.py` script is outdated. Please use directly `from diffusers import"""
""" StableDiffusionImg2ImgPipeline` instead."""
)
| 92 | 0 |
'''simple docstring'''
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
a : Tuple = logging.get_logger(__name__)
a : List[str] = {'vocab_file': 'sentencepiece.bpe.model'}
a : Union[str, Any] = {
'vocab_file': {
'moussaKam/mbarthez': 'https://huggingface.co/moussaKam/mbarthez/resolve/main/sentencepiece.bpe.model',
'moussaKam/barthez': 'https://huggingface.co/moussaKam/barthez/resolve/main/sentencepiece.bpe.model',
'moussaKam/barthez-orangesum-title': (
'https://huggingface.co/moussaKam/barthez-orangesum-title/resolve/main/sentencepiece.bpe.model'
),
},
}
a : List[Any] = {
'moussaKam/mbarthez': 1024,
'moussaKam/barthez': 1024,
'moussaKam/barthez-orangesum-title': 1024,
}
a : Dict = '▁'
class a ( snake_case__ ):
snake_case_ = VOCAB_FILES_NAMES
snake_case_ = PRETRAINED_VOCAB_FILES_MAP
snake_case_ = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
snake_case_ = ["""input_ids""", """attention_mask"""]
def __init__( self : Optional[int] , lowercase_ : List[str] , lowercase_ : List[Any]="<s>" , lowercase_ : Optional[Any]="</s>" , lowercase_ : List[Any]="</s>" , lowercase_ : List[Any]="<s>" , lowercase_ : int="<unk>" , lowercase_ : str="<pad>" , lowercase_ : List[Any]="<mask>" , lowercase_ : List[str] = None , **lowercase_ : List[Any] , ):
snake_case_ = AddedToken(_A , lstrip=_A , rstrip=_A ) if isinstance(_A , _A ) else mask_token
snake_case_ = {} if sp_model_kwargs is None else sp_model_kwargs
super().__init__(
bos_token=_A , eos_token=_A , unk_token=_A , sep_token=_A , cls_token=_A , pad_token=_A , mask_token=_A , sp_model_kwargs=self.sp_model_kwargs , **_A , )
snake_case_ = vocab_file
snake_case_ = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(str(_A ) )
snake_case_ = {'''<s>''': 0, '''<pad>''': 1, '''</s>''': 2, '''<unk>''': 3}
snake_case_ = len(self.sp_model ) - 1
snake_case_ = {v: k for k, v in self.fairseq_tokens_to_ids.items()}
def A_ ( self : str , lowercase_ : List[Any] , lowercase_ : int = None ):
if token_ids_a is None:
return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
snake_case_ = [self.cls_token_id]
snake_case_ = [self.sep_token_id]
return cls + token_ids_a + sep + sep + token_ids_a + sep
def A_ ( self : Optional[int] , lowercase_ : List[str] , lowercase_ : int = None , lowercase_ : int = False ):
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=_A , token_ids_a=_A , already_has_special_tokens=_A )
if token_ids_a is None:
return [1] + ([0] * len(_A )) + [1]
return [1] + ([0] * len(_A )) + [1, 1] + ([0] * len(_A )) + [1]
def A_ ( self : int , lowercase_ : Tuple , lowercase_ : str = None ):
snake_case_ = [self.sep_token_id]
snake_case_ = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep + sep + token_ids_a + sep ) * [0]
@property
def A_ ( self : List[Any] ):
return len(self.sp_model )
def A_ ( self : Optional[int] ):
snake_case_ = {self.convert_ids_to_tokens(_A ): i for i in range(self.vocab_size )}
vocab.update(self.added_tokens_encoder )
return vocab
def A_ ( self : Tuple , lowercase_ : Optional[Any] ):
return self.sp_model.encode(_A , out_type=_A )
def A_ ( self : int , lowercase_ : Tuple ):
if token in self.fairseq_tokens_to_ids:
return self.fairseq_tokens_to_ids[token]
snake_case_ = self.sp_model.PieceToId(_A )
return spm_id if spm_id else self.unk_token_id
def A_ ( self : Dict , lowercase_ : Tuple ):
if index in self.fairseq_ids_to_tokens:
return self.fairseq_ids_to_tokens[index]
return self.sp_model.IdToPiece(_A )
def A_ ( self : int , lowercase_ : Dict ):
snake_case_ = []
snake_case_ = ''''''
snake_case_ = False
for token in tokens:
# make sure that special tokens are not decoded using sentencepiece model
if token in self.all_special_tokens:
if not prev_is_special:
out_string += " "
out_string += self.sp_model.decode(_A ) + token
snake_case_ = True
snake_case_ = []
else:
current_sub_tokens.append(_A )
snake_case_ = False
out_string += self.sp_model.decode(_A )
return out_string.strip()
def __getstate__( self : Union[str, Any] ):
snake_case_ = self.__dict__.copy()
snake_case_ = None
return state
def __setstate__( self : Optional[int] , lowercase_ : Union[str, Any] ):
snake_case_ = d
# for backward compatibility
if not hasattr(self , '''sp_model_kwargs''' ):
snake_case_ = {}
snake_case_ = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(self.vocab_file )
def A_ ( self : Optional[int] , lowercase_ : Union[str, Any] , lowercase_ : Optional[int] = None ):
if not os.path.isdir(_A ):
logger.error(F"Vocabulary path ({save_directory}) should be a directory" )
return
snake_case_ = os.path.join(
_A , (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file'''] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(_A ) and os.path.isfile(self.vocab_file ):
copyfile(self.vocab_file , _A )
elif not os.path.isfile(self.vocab_file ):
with open(_A , '''wb''' ) as fi:
snake_case_ = self.sp_model.serialized_model_proto()
fi.write(_A )
return (out_vocab_file,)
| 56 |
import os
import torch
from ..logging import get_logger
from .constants import FSDP_PYTORCH_VERSION, MODEL_NAME, OPTIMIZER_NAME
from .versions import is_torch_version
if is_torch_version(""">=""", FSDP_PYTORCH_VERSION):
import torch.distributed.checkpoint as dist_cp
from torch.distributed.checkpoint.default_planner import DefaultLoadPlanner, DefaultSavePlanner
from torch.distributed.checkpoint.optimizer import load_sharded_optimizer_state_dict
from torch.distributed.fsdp.fully_sharded_data_parallel import FullyShardedDataParallel as FSDP
from torch.distributed.fsdp.fully_sharded_data_parallel import StateDictType
UpperCamelCase__ = get_logger(__name__)
def _a ( SCREAMING_SNAKE_CASE_ : List[str] , SCREAMING_SNAKE_CASE_ : Tuple , SCREAMING_SNAKE_CASE_ : Tuple , SCREAMING_SNAKE_CASE_ : Optional[Any] , SCREAMING_SNAKE_CASE_ : str=0 ):
os.makedirs(SCREAMING_SNAKE_CASE_ , exist_ok=SCREAMING_SNAKE_CASE_ )
with FSDP.state_dict_type(
SCREAMING_SNAKE_CASE_ , fsdp_plugin.state_dict_type , fsdp_plugin.state_dict_config , fsdp_plugin.optim_state_dict_config ):
__lowerCAmelCase = model.state_dict()
if fsdp_plugin.state_dict_type == StateDictType.FULL_STATE_DICT:
__lowerCAmelCase = F"""{MODEL_NAME}.bin""" if model_index == 0 else F"""{MODEL_NAME}_{model_index}.bin"""
__lowerCAmelCase = os.path.join(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
if accelerator.process_index == 0:
logger.info(F"""Saving model to {output_model_file}""" )
torch.save(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
logger.info(F"""Model saved to {output_model_file}""" )
elif fsdp_plugin.state_dict_type == StateDictType.LOCAL_STATE_DICT:
__lowerCAmelCase = (
F"""{MODEL_NAME}_rank{accelerator.process_index}.bin"""
if model_index == 0
else F"""{MODEL_NAME}_{model_index}_rank{accelerator.process_index}.bin"""
)
__lowerCAmelCase = os.path.join(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
logger.info(F"""Saving model to {output_model_file}""" )
torch.save(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
logger.info(F"""Model saved to {output_model_file}""" )
elif fsdp_plugin.state_dict_type == StateDictType.SHARDED_STATE_DICT:
__lowerCAmelCase = os.path.join(SCREAMING_SNAKE_CASE_ , F"""{MODEL_NAME}_{model_index}""" )
os.makedirs(SCREAMING_SNAKE_CASE_ , exist_ok=SCREAMING_SNAKE_CASE_ )
logger.info(F"""Saving model to {ckpt_dir}""" )
__lowerCAmelCase = {"model": state_dict}
dist_cp.save_state_dict(
state_dict=SCREAMING_SNAKE_CASE_ , storage_writer=dist_cp.FileSystemWriter(SCREAMING_SNAKE_CASE_ ) , planner=DefaultSavePlanner() , )
logger.info(F"""Model saved to {ckpt_dir}""" )
def _a ( SCREAMING_SNAKE_CASE_ : Dict , SCREAMING_SNAKE_CASE_ : int , SCREAMING_SNAKE_CASE_ : Optional[int] , SCREAMING_SNAKE_CASE_ : List[str] , SCREAMING_SNAKE_CASE_ : Any=0 ):
accelerator.wait_for_everyone()
with FSDP.state_dict_type(
SCREAMING_SNAKE_CASE_ , fsdp_plugin.state_dict_type , fsdp_plugin.state_dict_config , fsdp_plugin.optim_state_dict_config ):
if fsdp_plugin.state_dict_type == StateDictType.FULL_STATE_DICT:
if type(SCREAMING_SNAKE_CASE_ ) != FSDP and accelerator.process_index != 0:
if not fsdp_plugin.sync_module_states:
raise ValueError(
"Set the `sync_module_states` flag to `True` so that model states are synced across processes when "
"initializing FSDP object" )
return
__lowerCAmelCase = F"""{MODEL_NAME}.bin""" if model_index == 0 else F"""{MODEL_NAME}_{model_index}.bin"""
__lowerCAmelCase = os.path.join(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
logger.info(F"""Loading model from {input_model_file}""" )
__lowerCAmelCase = torch.load(SCREAMING_SNAKE_CASE_ )
logger.info(F"""Model loaded from {input_model_file}""" )
elif fsdp_plugin.state_dict_type == StateDictType.LOCAL_STATE_DICT:
__lowerCAmelCase = (
F"""{MODEL_NAME}_rank{accelerator.process_index}.bin"""
if model_index == 0
else F"""{MODEL_NAME}_{model_index}_rank{accelerator.process_index}.bin"""
)
__lowerCAmelCase = os.path.join(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
logger.info(F"""Loading model from {input_model_file}""" )
__lowerCAmelCase = torch.load(SCREAMING_SNAKE_CASE_ )
logger.info(F"""Model loaded from {input_model_file}""" )
elif fsdp_plugin.state_dict_type == StateDictType.SHARDED_STATE_DICT:
__lowerCAmelCase = (
os.path.join(SCREAMING_SNAKE_CASE_ , F"""{MODEL_NAME}_{model_index}""" )
if F"""{MODEL_NAME}""" not in input_dir
else input_dir
)
logger.info(F"""Loading model from {ckpt_dir}""" )
__lowerCAmelCase = {"model": model.state_dict()}
dist_cp.load_state_dict(
state_dict=SCREAMING_SNAKE_CASE_ , storage_reader=dist_cp.FileSystemReader(SCREAMING_SNAKE_CASE_ ) , planner=DefaultLoadPlanner() , )
__lowerCAmelCase = state_dict["model"]
logger.info(F"""Model loaded from {ckpt_dir}""" )
model.load_state_dict(SCREAMING_SNAKE_CASE_ )
def _a ( SCREAMING_SNAKE_CASE_ : Dict , SCREAMING_SNAKE_CASE_ : int , SCREAMING_SNAKE_CASE_ : List[str] , SCREAMING_SNAKE_CASE_ : Optional[Any] , SCREAMING_SNAKE_CASE_ : Optional[int] , SCREAMING_SNAKE_CASE_ : str=0 ):
os.makedirs(SCREAMING_SNAKE_CASE_ , exist_ok=SCREAMING_SNAKE_CASE_ )
with FSDP.state_dict_type(
SCREAMING_SNAKE_CASE_ , fsdp_plugin.state_dict_type , fsdp_plugin.state_dict_config , fsdp_plugin.optim_state_dict_config ):
__lowerCAmelCase = FSDP.optim_state_dict(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
if fsdp_plugin.state_dict_type == StateDictType.FULL_STATE_DICT:
if accelerator.process_index == 0:
__lowerCAmelCase = (
F"""{OPTIMIZER_NAME}.bin""" if optimizer_index == 0 else F"""{OPTIMIZER_NAME}_{optimizer_index}.bin"""
)
__lowerCAmelCase = os.path.join(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
logger.info(F"""Saving Optimizer state to {output_optimizer_file}""" )
torch.save(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
logger.info(F"""Optimizer state saved in {output_optimizer_file}""" )
else:
__lowerCAmelCase = os.path.join(SCREAMING_SNAKE_CASE_ , F"""{OPTIMIZER_NAME}_{optimizer_index}""" )
os.makedirs(SCREAMING_SNAKE_CASE_ , exist_ok=SCREAMING_SNAKE_CASE_ )
logger.info(F"""Saving Optimizer state to {ckpt_dir}""" )
dist_cp.save_state_dict(
state_dict={"optimizer": optim_state} , storage_writer=dist_cp.FileSystemWriter(SCREAMING_SNAKE_CASE_ ) , planner=DefaultSavePlanner() , )
logger.info(F"""Optimizer state saved in {ckpt_dir}""" )
def _a ( SCREAMING_SNAKE_CASE_ : Dict , SCREAMING_SNAKE_CASE_ : Dict , SCREAMING_SNAKE_CASE_ : Tuple , SCREAMING_SNAKE_CASE_ : List[str] , SCREAMING_SNAKE_CASE_ : str , SCREAMING_SNAKE_CASE_ : Dict=0 ):
accelerator.wait_for_everyone()
with FSDP.state_dict_type(
SCREAMING_SNAKE_CASE_ , fsdp_plugin.state_dict_type , fsdp_plugin.state_dict_config , fsdp_plugin.optim_state_dict_config ):
if fsdp_plugin.state_dict_type == StateDictType.FULL_STATE_DICT:
__lowerCAmelCase = None
# below check should work but currently it isn't working (mostly opytorch issue),
# in the meantime disabling it at the cost of excess memory usage
# if accelerator.process_index == 0 or not fsdp_plugin.optim_state_dict_config.rank0_only:
__lowerCAmelCase = (
F"""{OPTIMIZER_NAME}.bin""" if optimizer_index == 0 else F"""{OPTIMIZER_NAME}_{optimizer_index}.bin"""
)
__lowerCAmelCase = os.path.join(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
logger.info(F"""Loading Optimizer state from {input_optimizer_file}""" )
__lowerCAmelCase = torch.load(SCREAMING_SNAKE_CASE_ )
logger.info(F"""Optimizer state loaded from {input_optimizer_file}""" )
else:
__lowerCAmelCase = (
os.path.join(SCREAMING_SNAKE_CASE_ , F"""{OPTIMIZER_NAME}_{optimizer_index}""" )
if F"""{OPTIMIZER_NAME}""" not in input_dir
else input_dir
)
logger.info(F"""Loading Optimizer from {ckpt_dir}""" )
__lowerCAmelCase = load_sharded_optimizer_state_dict(
model_state_dict=model.state_dict() , optimizer_key="optimizer" , storage_reader=dist_cp.FileSystemReader(SCREAMING_SNAKE_CASE_ ) , )
__lowerCAmelCase = optim_state["optimizer"]
logger.info(F"""Optimizer loaded from {ckpt_dir}""" )
__lowerCAmelCase = FSDP.optim_state_dict_to_load(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
optimizer.load_state_dict(SCREAMING_SNAKE_CASE_ )
| 92 | 0 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a_ : Dict = logging.get_logger(__name__)
a_ : Optional[Any] = {
"""alibaba-damo/mgp-str-base""": """https://huggingface.co/alibaba-damo/mgp-str-base/resolve/main/config.json""",
}
class __UpperCamelCase ( snake_case__ ):
lowercase : Union[str, Any] ="""mgp-str"""
def __init__( self, lowerCAmelCase=[32, 128], lowerCAmelCase=4, lowerCAmelCase=3, lowerCAmelCase=27, lowerCAmelCase=38, lowerCAmelCase=50_257, lowerCAmelCase=30_522, lowerCAmelCase=768, lowerCAmelCase=12, lowerCAmelCase=12, lowerCAmelCase=4.0, lowerCAmelCase=True, lowerCAmelCase=False, lowerCAmelCase=1e-5, lowerCAmelCase=0.0, lowerCAmelCase=0.0, lowerCAmelCase=0.0, lowerCAmelCase=False, lowerCAmelCase=0.0_2, **lowerCAmelCase, ):
"""simple docstring"""
super().__init__(**_A )
lowerCamelCase_ =image_size
lowerCamelCase_ =patch_size
lowerCamelCase_ =num_channels
lowerCamelCase_ =max_token_length
lowerCamelCase_ =num_character_labels
lowerCamelCase_ =num_bpe_labels
lowerCamelCase_ =num_wordpiece_labels
lowerCamelCase_ =hidden_size
lowerCamelCase_ =num_hidden_layers
lowerCamelCase_ =num_attention_heads
lowerCamelCase_ =mlp_ratio
lowerCamelCase_ =distilled
lowerCamelCase_ =layer_norm_eps
lowerCamelCase_ =drop_rate
lowerCamelCase_ =qkv_bias
lowerCamelCase_ =attn_drop_rate
lowerCamelCase_ =drop_path_rate
lowerCamelCase_ =output_aa_attentions
lowerCamelCase_ =initializer_range
| 75 |
import math
import time
from typing import Dict, List, Optional
from torch.utils.data import Dataset
from transformers import SeqaSeqTrainer, is_torch_tpu_available
from transformers.trainer_utils import PredictionOutput, speed_metrics
if is_torch_tpu_available(check_device=False):
import torch_xla.core.xla_model as xm
import torch_xla.debug.metrics as met
class a__ ( snake_case__ ):
def __init__( self , *_A , _A=None , _A=None , **_A ):
"""simple docstring"""
super().__init__(*_A , **_A )
__lowerCAmelCase = eval_examples
__lowerCAmelCase = post_process_function
def __SCREAMING_SNAKE_CASE( self , _A = None , _A=None , _A = None , _A = "eval" , **_A , ):
"""simple docstring"""
__lowerCAmelCase = gen_kwargs.copy()
__lowerCAmelCase = (
gen_kwargs["max_length"] if gen_kwargs.get("max_length" ) is not None else self.args.generation_max_length
)
__lowerCAmelCase = (
gen_kwargs["num_beams"] if gen_kwargs.get("num_beams" ) is not None else self.args.generation_num_beams
)
__lowerCAmelCase = gen_kwargs
__lowerCAmelCase = self.eval_dataset if eval_dataset is None else eval_dataset
__lowerCAmelCase = self.get_eval_dataloader(_A )
__lowerCAmelCase = self.eval_examples if eval_examples is None else eval_examples
# Temporarily disable metric computation, we will do it in the loop here.
__lowerCAmelCase = self.compute_metrics
__lowerCAmelCase = None
__lowerCAmelCase = time.time()
__lowerCAmelCase = self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
try:
__lowerCAmelCase = eval_loop(
_A , description="Evaluation" , prediction_loss_only=True if compute_metrics is None else None , ignore_keys=_A , metric_key_prefix=_A , )
finally:
__lowerCAmelCase = compute_metrics
__lowerCAmelCase = self.args.eval_batch_size * self.args.world_size
if f"""{metric_key_prefix}_jit_compilation_time""" in output.metrics:
start_time += output.metrics[f"""{metric_key_prefix}_jit_compilation_time"""]
output.metrics.update(
speed_metrics(
_A , _A , num_samples=output.num_samples , num_steps=math.ceil(output.num_samples / total_batch_size ) , ) )
if self.post_process_function is not None and self.compute_metrics is not None and self.args.should_save:
# Only the main node write the results by default
__lowerCAmelCase = self.post_process_function(_A , _A , _A )
__lowerCAmelCase = self.compute_metrics(_A )
# Prefix all keys with metric_key_prefix + '_'
for key in list(metrics.keys() ):
if not key.startswith(f"""{metric_key_prefix}_""" ):
__lowerCAmelCase = metrics.pop(_A )
metrics.update(output.metrics )
else:
__lowerCAmelCase = output.metrics
if self.args.should_log:
# Only the main node log the results by default
self.log(_A )
if self.args.tpu_metrics_debug or self.args.debug:
# tpu-comment: Logging debug metrics for PyTorch/XLA (compile, execute times, ops, etc.)
xm.master_print(met.metrics_report() )
__lowerCAmelCase = self.callback_handler.on_evaluate(self.args , self.state , self.control , _A )
return metrics
def __SCREAMING_SNAKE_CASE( self , _A , _A , _A=None , _A = "test" , **_A ):
"""simple docstring"""
__lowerCAmelCase = gen_kwargs.copy()
__lowerCAmelCase = self.get_test_dataloader(_A )
# Temporarily disable metric computation, we will do it in the loop here.
__lowerCAmelCase = self.compute_metrics
__lowerCAmelCase = None
__lowerCAmelCase = time.time()
__lowerCAmelCase = self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
try:
__lowerCAmelCase = eval_loop(
_A , description="Prediction" , prediction_loss_only=True if compute_metrics is None else None , ignore_keys=_A , metric_key_prefix=_A , )
finally:
__lowerCAmelCase = compute_metrics
__lowerCAmelCase = self.args.eval_batch_size * self.args.world_size
if f"""{metric_key_prefix}_jit_compilation_time""" in output.metrics:
start_time += output.metrics[f"""{metric_key_prefix}_jit_compilation_time"""]
output.metrics.update(
speed_metrics(
_A , _A , num_samples=output.num_samples , num_steps=math.ceil(output.num_samples / total_batch_size ) , ) )
if self.post_process_function is None or self.compute_metrics is None:
return output
__lowerCAmelCase = self.post_process_function(_A , _A , _A , "predict" )
__lowerCAmelCase = self.compute_metrics(_A )
# Prefix all keys with metric_key_prefix + '_'
for key in list(metrics.keys() ):
if not key.startswith(f"""{metric_key_prefix}_""" ):
__lowerCAmelCase = metrics.pop(_A )
metrics.update(output.metrics )
return PredictionOutput(predictions=predictions.predictions , label_ids=predictions.label_ids , metrics=_A )
| 92 | 0 |
"""simple docstring"""
from dataclasses import dataclass, field
from typing import Tuple
from ..utils import cached_property, is_torch_available, is_torch_tpu_available, logging, requires_backends
from .benchmark_args_utils import BenchmarkArguments
if is_torch_available():
import torch
if is_torch_tpu_available(check_device=False):
import torch_xla.core.xla_model as xm
_UpperCamelCase : Dict = logging.get_logger(__name__)
@dataclass
class a ( snake_case__ ):
UpperCAmelCase_ : str =[
"""no_inference""",
"""no_cuda""",
"""no_tpu""",
"""no_speed""",
"""no_memory""",
"""no_env_print""",
"""no_multi_process""",
]
def __init__( self , **_lowerCamelCase ):
for deprecated_arg in self.deprecated_args:
if deprecated_arg in kwargs:
lowercase = deprecated_arg[3:]
setattr(self , _A , not kwargs.pop(_A ) )
logger.warning(
F'{deprecated_arg} is depreciated. Please use --no_{positive_arg} or'
F' {positive_arg}={kwargs[positive_arg]}' )
lowercase = kwargs.pop('torchscript' , self.torchscript )
lowercase = kwargs.pop('torch_xla_tpu_print_metrics' , self.torch_xla_tpu_print_metrics )
lowercase = kwargs.pop('fp16_opt_level' , self.fpaa_opt_level )
super().__init__(**_A )
UpperCAmelCase_ : bool =field(default=snake_case__, metadata={"help": "Trace the models using torchscript"} )
UpperCAmelCase_ : bool =field(default=snake_case__, metadata={"help": "Print Xla/PyTorch tpu metrics"} )
UpperCAmelCase_ : str =field(
default="O1", metadata={
"help": (
"For fp16: Apex AMP optimization level selected in ['O0', 'O1', 'O2', and 'O3']. "
"See details at https://nvidia.github.io/apex/amp.html"
)
}, )
@cached_property
def UpperCamelCase_ ( self ):
requires_backends(self , ['torch'] )
logger.info('PyTorch: setting up devices' )
if not self.cuda:
lowercase = torch.device('cpu' )
lowercase = 0
elif is_torch_tpu_available():
lowercase = xm.xla_device()
lowercase = 0
else:
lowercase = torch.device('cuda' if torch.cuda.is_available() else 'cpu' )
lowercase = torch.cuda.device_count()
return device, n_gpu
@property
def UpperCamelCase_ ( self ):
return is_torch_tpu_available() and self.tpu
@property
def UpperCamelCase_ ( self ):
requires_backends(self , ['torch'] )
# TODO(PVP): currently only single GPU is supported
return torch.cuda.current_device()
@property
def UpperCamelCase_ ( self ):
requires_backends(self , ['torch'] )
return self._setup_devices[0]
@property
def UpperCamelCase_ ( self ):
requires_backends(self , ['torch'] )
return self._setup_devices[1]
@property
def UpperCamelCase_ ( self ):
return self.n_gpu > 0
| 220 |
import logging
from pathlib import Path
import numpy as np
import pytorch_lightning as pl
import torch
from pytorch_lightning.callbacks import EarlyStopping, ModelCheckpoint
from pytorch_lightning.utilities import rank_zero_only
from utils_rag import save_json
def _a ( SCREAMING_SNAKE_CASE_ : Optional[int] ):
__lowerCAmelCase = filter(lambda SCREAMING_SNAKE_CASE_ : p.requires_grad , model.parameters() )
__lowerCAmelCase = sum([np.prod(p.size() ) for p in model_parameters] )
return params
UpperCamelCase__ = logging.getLogger(__name__)
def _a ( SCREAMING_SNAKE_CASE_ : Union[str, Any] , SCREAMING_SNAKE_CASE_ : Any ):
if metric == "rouge2":
__lowerCAmelCase = "{val_avg_rouge2:.4f}-{step_count}"
elif metric == "bleu":
__lowerCAmelCase = "{val_avg_bleu:.4f}-{step_count}"
elif metric == "em":
__lowerCAmelCase = "{val_avg_em:.4f}-{step_count}"
else:
raise NotImplementedError(
F"""seq2seq callbacks only support rouge2 and bleu, got {metric}, You can make your own by adding to this"""
" function." )
__lowerCAmelCase = ModelCheckpoint(
dirpath=SCREAMING_SNAKE_CASE_ , filename=SCREAMING_SNAKE_CASE_ , monitor=F"""val_{metric}""" , mode="max" , save_top_k=3 , every_n_epochs=1 , )
return checkpoint_callback
def _a ( SCREAMING_SNAKE_CASE_ : int , SCREAMING_SNAKE_CASE_ : Union[str, Any] ):
return EarlyStopping(
monitor=F"""val_{metric}""" , mode="min" if "loss" in metric else "max" , patience=SCREAMING_SNAKE_CASE_ , verbose=SCREAMING_SNAKE_CASE_ , )
class a__ ( pl.Callback ):
def __SCREAMING_SNAKE_CASE( self , _A , _A ):
"""simple docstring"""
__lowerCAmelCase = {f"""lr_group_{i}""": param["lr"] for i, param in enumerate(pl_module.trainer.optimizers[0].param_groups )}
pl_module.logger.log_metrics(_A )
@rank_zero_only
def __SCREAMING_SNAKE_CASE( self , _A , _A , _A , _A=True ):
"""simple docstring"""
logger.info(f"""***** {type_path} results at step {trainer.global_step:05d} *****""" )
__lowerCAmelCase = trainer.callback_metrics
trainer.logger.log_metrics({k: v for k, v in metrics.items() if k not in ["log", "progress_bar", "preds"]} )
# Log results
__lowerCAmelCase = Path(pl_module.hparams.output_dir )
if type_path == "test":
__lowerCAmelCase = od / "test_results.txt"
__lowerCAmelCase = od / "test_generations.txt"
else:
# this never gets hit. I prefer not to save intermediate generations, and results are in metrics.json
# If people want this it will be easy enough to add back.
__lowerCAmelCase = od / f"""{type_path}_results/{trainer.global_step:05d}.txt"""
__lowerCAmelCase = od / f"""{type_path}_generations/{trainer.global_step:05d}.txt"""
results_file.parent.mkdir(exist_ok=_A )
generations_file.parent.mkdir(exist_ok=_A )
with open(_A , "a+" ) as writer:
for key in sorted(_A ):
if key in ["log", "progress_bar", "preds"]:
continue
__lowerCAmelCase = metrics[key]
if isinstance(_A , torch.Tensor ):
__lowerCAmelCase = val.item()
__lowerCAmelCase = f"""{key}: {val:.6f}\n"""
writer.write(_A )
if not save_generations:
return
if "preds" in metrics:
__lowerCAmelCase = "\n".join(metrics["preds"] )
generations_file.open("w+" ).write(_A )
@rank_zero_only
def __SCREAMING_SNAKE_CASE( self , _A , _A ):
"""simple docstring"""
try:
__lowerCAmelCase = pl_module.model.model.num_parameters()
except AttributeError:
__lowerCAmelCase = pl_module.model.num_parameters()
__lowerCAmelCase = count_trainable_parameters(_A )
# mp stands for million parameters
trainer.logger.log_metrics({"n_params": npars, "mp": npars / 1E6, "grad_mp": n_trainable_pars / 1E6} )
@rank_zero_only
def __SCREAMING_SNAKE_CASE( self , _A , _A ):
"""simple docstring"""
save_json(pl_module.metrics , pl_module.metrics_save_path )
return self._write_logs(_A , _A , "test" )
@rank_zero_only
def __SCREAMING_SNAKE_CASE( self , _A , _A ):
"""simple docstring"""
save_json(pl_module.metrics , pl_module.metrics_save_path )
# Uncommenting this will save val generations
# return self._write_logs(trainer, pl_module, "valid")
| 92 | 0 |
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
from transformers import BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES, BertTokenizer
from transformers.testing_utils import require_tokenizers, require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available
if is_vision_available():
from PIL import Image
from transformers import VisionTextDualEncoderProcessor, ViTImageProcessor
@require_tokenizers
@require_vision
class __A( unittest.TestCase ):
def SCREAMING_SNAKE_CASE_ ( self ) -> List[Any]:
'''simple docstring'''
__a = tempfile.mkdtemp()
# fmt: off
__a = ['''[UNK]''', '''[CLS]''', '''[SEP]''', '''[PAD]''', '''[MASK]''', '''want''', '''##want''', '''##ed''', '''wa''', '''un''', '''runn''', '''##ing''', ''',''', '''low''', '''lowest''']
# fmt: on
__a = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['''vocab_file'''] )
with open(self.vocab_file , '''w''' , encoding='''utf-8''' ) as vocab_writer:
vocab_writer.write(''''''.join([x + '''\n''' for x in vocab_tokens] ) )
__a = {
'''do_resize''': True,
'''size''': {'''height''': 18, '''width''': 18},
'''do_normalize''': True,
'''image_mean''': [0.5, 0.5, 0.5],
'''image_std''': [0.5, 0.5, 0.5],
}
__a = os.path.join(self.tmpdirname , _A )
with open(self.image_processor_file , '''w''' , encoding='''utf-8''' ) as fp:
json.dump(_A , _A )
def SCREAMING_SNAKE_CASE_ ( self , **_snake_case ) -> Dict:
'''simple docstring'''
return BertTokenizer.from_pretrained(self.tmpdirname , **_A )
def SCREAMING_SNAKE_CASE_ ( self , **_snake_case ) -> Optional[int]:
'''simple docstring'''
return ViTImageProcessor.from_pretrained(self.tmpdirname , **_A )
def SCREAMING_SNAKE_CASE_ ( self ) -> Tuple:
'''simple docstring'''
shutil.rmtree(self.tmpdirname )
def SCREAMING_SNAKE_CASE_ ( self ) -> Any:
'''simple docstring'''
__a = [np.random.randint(255 , size=(3, 30, 400) , dtype=np.uinta )]
__a = [Image.fromarray(np.moveaxis(_A , 0 , -1 ) ) for x in image_inputs]
return image_inputs
def SCREAMING_SNAKE_CASE_ ( self ) -> Union[str, Any]:
'''simple docstring'''
__a = self.get_tokenizer()
__a = self.get_image_processor()
__a = VisionTextDualEncoderProcessor(tokenizer=_A , image_processor=_A )
processor.save_pretrained(self.tmpdirname )
__a = VisionTextDualEncoderProcessor.from_pretrained(self.tmpdirname )
self.assertEqual(processor.tokenizer.get_vocab() , tokenizer.get_vocab() )
self.assertIsInstance(processor.tokenizer , (BertTokenizer, BertTokenizerFast) )
self.assertEqual(processor.image_processor.to_json_string() , image_processor.to_json_string() )
self.assertIsInstance(processor.image_processor , _A )
def SCREAMING_SNAKE_CASE_ ( self ) -> List[str]:
'''simple docstring'''
__a = VisionTextDualEncoderProcessor(
tokenizer=self.get_tokenizer() , image_processor=self.get_image_processor() )
processor.save_pretrained(self.tmpdirname )
__a = self.get_tokenizer(bos_token='''(BOS)''' , eos_token='''(EOS)''' )
__a = self.get_image_processor(do_normalize=_A , padding_value=1.0 )
__a = VisionTextDualEncoderProcessor.from_pretrained(
self.tmpdirname , bos_token='''(BOS)''' , eos_token='''(EOS)''' , do_normalize=_A , padding_value=1.0 )
self.assertEqual(processor.tokenizer.get_vocab() , tokenizer_add_kwargs.get_vocab() )
self.assertIsInstance(processor.tokenizer , (BertTokenizer, BertTokenizerFast) )
self.assertEqual(processor.image_processor.to_json_string() , image_processor_add_kwargs.to_json_string() )
self.assertIsInstance(processor.image_processor , _A )
def SCREAMING_SNAKE_CASE_ ( self ) -> List[str]:
'''simple docstring'''
__a = self.get_image_processor()
__a = self.get_tokenizer()
__a = VisionTextDualEncoderProcessor(tokenizer=_A , image_processor=_A )
__a = self.prepare_image_inputs()
__a = image_processor(_A , return_tensors='''np''' )
__a = processor(images=_A , return_tensors='''np''' )
for key in input_feat_extract.keys():
self.assertAlmostEqual(input_feat_extract[key].sum() , input_processor[key].sum() , delta=1E-2 )
def SCREAMING_SNAKE_CASE_ ( self ) -> Optional[int]:
'''simple docstring'''
__a = self.get_image_processor()
__a = self.get_tokenizer()
__a = VisionTextDualEncoderProcessor(tokenizer=_A , image_processor=_A )
__a = '''lower newer'''
__a = processor(text=_A )
__a = tokenizer(_A )
for key in encoded_tok.keys():
self.assertListEqual(encoded_tok[key] , encoded_processor[key] )
def SCREAMING_SNAKE_CASE_ ( self ) -> List[Any]:
'''simple docstring'''
__a = self.get_image_processor()
__a = self.get_tokenizer()
__a = VisionTextDualEncoderProcessor(tokenizer=_A , image_processor=_A )
__a = '''lower newer'''
__a = self.prepare_image_inputs()
__a = processor(text=_A , images=_A )
self.assertListEqual(list(inputs.keys() ) , ['''input_ids''', '''token_type_ids''', '''attention_mask''', '''pixel_values'''] )
# test if it raises when no input is passed
with self.assertRaises(_A ):
processor()
def SCREAMING_SNAKE_CASE_ ( self ) -> str:
'''simple docstring'''
__a = self.get_image_processor()
__a = self.get_tokenizer()
__a = VisionTextDualEncoderProcessor(tokenizer=_A , image_processor=_A )
__a = [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]
__a = processor.batch_decode(_A )
__a = tokenizer.batch_decode(_A )
self.assertListEqual(_A , _A )
def SCREAMING_SNAKE_CASE_ ( self ) -> Any:
'''simple docstring'''
__a = self.get_image_processor()
__a = self.get_tokenizer()
__a = VisionTextDualEncoderProcessor(tokenizer=_A , image_processor=_A )
__a = '''lower newer'''
__a = self.prepare_image_inputs()
__a = processor(text=_A , images=_A )
self.assertListEqual(list(inputs.keys() ) , processor.model_input_names ) | 6 |
from .glue import glue_convert_examples_to_features, glue_output_modes, glue_processors, glue_tasks_num_labels
from .squad import SquadExample, SquadFeatures, SquadVaProcessor, SquadVaProcessor, squad_convert_examples_to_features
from .utils import DataProcessor, InputExample, InputFeatures, SingleSentenceClassificationProcessor
from .xnli import xnli_output_modes, xnli_processors, xnli_tasks_num_labels
| 92 | 0 |
from . import __version__
# Backward compatibility imports, to make sure all those objects can be found in file_utils
from .utils import (
CLOUDFRONT_DISTRIB_PREFIX,
CONFIG_NAME,
DISABLE_TELEMETRY,
DUMMY_INPUTS,
DUMMY_MASK,
ENV_VARS_TRUE_AND_AUTO_VALUES,
ENV_VARS_TRUE_VALUES,
FEATURE_EXTRACTOR_NAME,
FLAX_WEIGHTS_NAME,
HF_MODULES_CACHE,
HUGGINGFACE_CO_PREFIX,
HUGGINGFACE_CO_RESOLVE_ENDPOINT,
MODEL_CARD_NAME,
MULTIPLE_CHOICE_DUMMY_INPUTS,
PYTORCH_PRETRAINED_BERT_CACHE,
PYTORCH_TRANSFORMERS_CACHE,
S3_BUCKET_PREFIX,
SENTENCEPIECE_UNDERLINE,
SPIECE_UNDERLINE,
TF2_WEIGHTS_NAME,
TF_WEIGHTS_NAME,
TORCH_FX_REQUIRED_VERSION,
TRANSFORMERS_CACHE,
TRANSFORMERS_DYNAMIC_MODULE_NAME,
USE_JAX,
USE_TF,
USE_TORCH,
WEIGHTS_INDEX_NAME,
WEIGHTS_NAME,
ContextManagers,
DummyObject,
EntryNotFoundError,
ExplicitEnum,
ModelOutput,
PaddingStrategy,
PushToHubMixin,
RepositoryNotFoundError,
RevisionNotFoundError,
TensorType,
_LazyModule,
add_code_sample_docstrings,
add_end_docstrings,
add_start_docstrings,
add_start_docstrings_to_model_forward,
cached_property,
copy_func,
default_cache_path,
define_sagemaker_information,
get_cached_models,
get_file_from_repo,
get_full_repo_name,
get_torch_version,
has_file,
http_user_agent,
is_apex_available,
is_bsa_available,
is_coloredlogs_available,
is_datasets_available,
is_detectrona_available,
is_faiss_available,
is_flax_available,
is_ftfy_available,
is_in_notebook,
is_ipex_available,
is_librosa_available,
is_offline_mode,
is_onnx_available,
is_pandas_available,
is_phonemizer_available,
is_protobuf_available,
is_psutil_available,
is_pyanvml_available,
is_pyctcdecode_available,
is_pytesseract_available,
is_pytorch_quantization_available,
is_rjieba_available,
is_sagemaker_dp_enabled,
is_sagemaker_mp_enabled,
is_scipy_available,
is_sentencepiece_available,
is_seqio_available,
is_sklearn_available,
is_soundfile_availble,
is_spacy_available,
is_speech_available,
is_tensor,
is_tensorflow_probability_available,
is_tfaonnx_available,
is_tf_available,
is_timm_available,
is_tokenizers_available,
is_torch_available,
is_torch_bfaa_available,
is_torch_cuda_available,
is_torch_fx_available,
is_torch_fx_proxy,
is_torch_mps_available,
is_torch_tfaa_available,
is_torch_tpu_available,
is_torchaudio_available,
is_training_run_on_sagemaker,
is_vision_available,
replace_return_docstrings,
requires_backends,
to_numpy,
to_py_obj,
torch_only_method,
)
| 207 |
from queue import PriorityQueue
from typing import Any
import numpy as np
def _a ( SCREAMING_SNAKE_CASE_ : dict , SCREAMING_SNAKE_CASE_ : str , SCREAMING_SNAKE_CASE_ : set , SCREAMING_SNAKE_CASE_ : set , SCREAMING_SNAKE_CASE_ : dict , SCREAMING_SNAKE_CASE_ : dict , SCREAMING_SNAKE_CASE_ : PriorityQueue , SCREAMING_SNAKE_CASE_ : dict , SCREAMING_SNAKE_CASE_ : float | int , ):
for nxt, d in graph[v]:
if nxt in visited_forward:
continue
__lowerCAmelCase = cst_fwd.get(SCREAMING_SNAKE_CASE_ , np.inf )
__lowerCAmelCase = cst_fwd[v] + d
if new_cost_f < old_cost_f:
queue.put((new_cost_f, nxt) )
__lowerCAmelCase = new_cost_f
__lowerCAmelCase = v
if nxt in visited_backward:
if cst_fwd[v] + d + cst_bwd[nxt] < shortest_distance:
__lowerCAmelCase = cst_fwd[v] + d + cst_bwd[nxt]
return shortest_distance
def _a ( SCREAMING_SNAKE_CASE_ : str , SCREAMING_SNAKE_CASE_ : str , SCREAMING_SNAKE_CASE_ : dict , SCREAMING_SNAKE_CASE_ : dict ):
__lowerCAmelCase = -1
__lowerCAmelCase = set()
__lowerCAmelCase = set()
__lowerCAmelCase = {source: 0}
__lowerCAmelCase = {destination: 0}
__lowerCAmelCase = {source: None}
__lowerCAmelCase = {destination: None}
__lowerCAmelCase = PriorityQueue()
__lowerCAmelCase = PriorityQueue()
__lowerCAmelCase = np.inf
queue_forward.put((0, source) )
queue_backward.put((0, destination) )
if source == destination:
return 0
while not queue_forward.empty() and not queue_backward.empty():
__lowerCAmelCase , __lowerCAmelCase = queue_forward.get()
visited_forward.add(SCREAMING_SNAKE_CASE_ )
__lowerCAmelCase , __lowerCAmelCase = queue_backward.get()
visited_backward.add(SCREAMING_SNAKE_CASE_ )
__lowerCAmelCase = pass_and_relaxation(
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , )
__lowerCAmelCase = pass_and_relaxation(
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , )
if cst_fwd[v_fwd] + cst_bwd[v_bwd] >= shortest_distance:
break
if shortest_distance != np.inf:
__lowerCAmelCase = shortest_distance
return shortest_path_distance
UpperCamelCase__ = {
"""B""": [["""C""", 1]],
"""C""": [["""D""", 1]],
"""D""": [["""F""", 1]],
"""E""": [["""B""", 1], ["""G""", 2]],
"""F""": [],
"""G""": [["""F""", 1]],
}
UpperCamelCase__ = {
"""B""": [["""E""", 1]],
"""C""": [["""B""", 1]],
"""D""": [["""C""", 1]],
"""F""": [["""D""", 1], ["""G""", 1]],
"""E""": [[None, np.inf]],
"""G""": [["""E""", 2]],
}
if __name__ == "__main__":
import doctest
doctest.testmod()
| 92 | 0 |
'''simple docstring'''
import argparse
from collections import defaultdict
import yaml
lowerCAmelCase: List[str] = 'docs/source/en/_toctree.yml'
def lowerCamelCase__ ( _A ):
a : int = defaultdict(SCREAMING_SNAKE_CASE_ )
for doc in model_doc:
counts[doc["local"]] += 1
a : Any = [key for key, value in counts.items() if value > 1]
a : Any = []
for duplicate_key in duplicates:
a : List[Any] = list({doc['title'] for doc in model_doc if doc['local'] == duplicate_key} )
if len(SCREAMING_SNAKE_CASE_ ) > 1:
raise ValueError(
f"""{duplicate_key} is present several times in the documentation table of content at """
'`docs/source/en/_toctree.yml` with different *Title* values. Choose one of those and remove the '
'others.' )
# Only add this once
new_doc.append({'local': duplicate_key, 'title': titles[0]} )
# Add none duplicate-keys
new_doc.extend([doc for doc in model_doc if counts[doc['local']] == 1] )
# Sort
return sorted(SCREAMING_SNAKE_CASE_ , key=lambda _A : s["title"].lower() )
def lowerCamelCase__ ( _A=False ):
with open(SCREAMING_SNAKE_CASE_ , encoding='utf-8' ) as f:
a : List[str] = yaml.safe_load(f.read() )
# Get to the API doc
a : Dict = 0
while content[api_idx]["title"] != "API":
api_idx += 1
a : List[str] = content[api_idx]['sections']
# Then to the model doc
a : str = 0
while api_doc[model_idx]["title"] != "Models":
model_idx += 1
a : List[str] = api_doc[model_idx]['sections']
a : List[str] = [(idx, section) for idx, section in enumerate(SCREAMING_SNAKE_CASE_ ) if 'sections' in section]
a : Union[str, Any] = False
for idx, modality_doc in modalities_docs:
a : List[str] = modality_doc['sections']
a : Any = clean_model_doc_toc(SCREAMING_SNAKE_CASE_ )
if old_modality_doc != new_modality_doc:
a : str = True
if overwrite:
a : str = new_modality_doc
if diff:
if overwrite:
a : Any = model_doc
a : Tuple = api_doc
with open(SCREAMING_SNAKE_CASE_ , 'w' , encoding='utf-8' ) as f:
f.write(yaml.dump(SCREAMING_SNAKE_CASE_ , allow_unicode=SCREAMING_SNAKE_CASE_ ) )
else:
raise ValueError(
'The model doc part of the table of content is not properly sorted, run `make style` to fix this.' )
if __name__ == "__main__":
lowerCAmelCase: int = argparse.ArgumentParser()
parser.add_argument('--fix_and_overwrite', action='store_true', help='Whether to fix inconsistencies.')
lowerCAmelCase: List[Any] = parser.parse_args()
check_model_doc(args.fix_and_overwrite) | 297 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCamelCase__ = logging.get_logger(__name__)
UpperCamelCase__ = {
"""edbeeching/decision-transformer-gym-hopper-medium""": (
"""https://huggingface.co/edbeeching/decision-transformer-gym-hopper-medium/resolve/main/config.json"""
),
# See all DecisionTransformer models at https://huggingface.co/models?filter=decision_transformer
}
class a__ ( snake_case__ ):
_a : Optional[int] = """decision_transformer"""
_a : Optional[int] = ["""past_key_values"""]
_a : Dict = {
"""max_position_embeddings""": """n_positions""",
"""num_attention_heads""": """n_head""",
"""num_hidden_layers""": """n_layer""",
}
def __init__( self , _A=1_7 , _A=4 , _A=1_2_8 , _A=4_0_9_6 , _A=True , _A=1 , _A=1_0_2_4 , _A=3 , _A=1 , _A=None , _A="relu" , _A=0.1 , _A=0.1 , _A=0.1 , _A=1E-5 , _A=0.02 , _A=True , _A=True , _A=5_0_2_5_6 , _A=5_0_2_5_6 , _A=False , _A=False , **_A , ):
"""simple docstring"""
__lowerCAmelCase = state_dim
__lowerCAmelCase = act_dim
__lowerCAmelCase = hidden_size
__lowerCAmelCase = max_ep_len
__lowerCAmelCase = action_tanh
__lowerCAmelCase = vocab_size
__lowerCAmelCase = n_positions
__lowerCAmelCase = n_layer
__lowerCAmelCase = n_head
__lowerCAmelCase = n_inner
__lowerCAmelCase = activation_function
__lowerCAmelCase = resid_pdrop
__lowerCAmelCase = embd_pdrop
__lowerCAmelCase = attn_pdrop
__lowerCAmelCase = layer_norm_epsilon
__lowerCAmelCase = initializer_range
__lowerCAmelCase = scale_attn_weights
__lowerCAmelCase = use_cache
__lowerCAmelCase = scale_attn_by_inverse_layer_idx
__lowerCAmelCase = reorder_and_upcast_attn
__lowerCAmelCase = bos_token_id
__lowerCAmelCase = eos_token_id
super().__init__(bos_token_id=_A , eos_token_id=_A , **_A )
| 92 | 0 |
import os
import tempfile
import unittest
from pathlib import Path
from transformers import AutoConfig, is_torch_available
from transformers.testing_utils import require_torch, torch_device
if is_torch_available():
from transformers import PyTorchBenchmark, PyTorchBenchmarkArguments
@require_torch
class __lowercase ( unittest.TestCase ):
"""simple docstring"""
def __A ( self , A ) -> Dict:
'''simple docstring'''
for model_result in results.values():
for batch_size, sequence_length in zip(model_result["""bs"""] , model_result["""ss"""] ):
lowerCamelCase = model_result["""result"""][batch_size][sequence_length]
self.assertIsNotNone(_A )
def __A ( self ) -> Any:
'''simple docstring'''
lowerCamelCase = """sshleifer/tiny-gpt2"""
lowerCamelCase = PyTorchBenchmarkArguments(
models=[MODEL_ID] , training=_A , inference=_A , sequence_lengths=[8] , batch_sizes=[1] , multi_process=_A , )
lowerCamelCase = PyTorchBenchmark(_A )
lowerCamelCase = benchmark.run()
self.check_results_dict_not_empty(results.time_inference_result )
self.check_results_dict_not_empty(results.memory_inference_result )
def __A ( self ) -> Dict:
'''simple docstring'''
lowerCamelCase = """sgugger/tiny-distilbert-classification"""
lowerCamelCase = PyTorchBenchmarkArguments(
models=[MODEL_ID] , training=_A , inference=_A , sequence_lengths=[8] , batch_sizes=[1] , multi_process=_A , only_pretrain_model=_A , )
lowerCamelCase = PyTorchBenchmark(_A )
lowerCamelCase = benchmark.run()
self.check_results_dict_not_empty(results.time_inference_result )
self.check_results_dict_not_empty(results.memory_inference_result )
def __A ( self ) -> Any:
'''simple docstring'''
lowerCamelCase = """sshleifer/tiny-gpt2"""
lowerCamelCase = PyTorchBenchmarkArguments(
models=[MODEL_ID] , training=_A , inference=_A , torchscript=_A , sequence_lengths=[8] , batch_sizes=[1] , multi_process=_A , )
lowerCamelCase = PyTorchBenchmark(_A )
lowerCamelCase = benchmark.run()
self.check_results_dict_not_empty(results.time_inference_result )
self.check_results_dict_not_empty(results.memory_inference_result )
@unittest.skipIf(torch_device == """cpu""" , """Cant do half precision""" )
def __A ( self ) -> Dict:
'''simple docstring'''
lowerCamelCase = """sshleifer/tiny-gpt2"""
lowerCamelCase = PyTorchBenchmarkArguments(
models=[MODEL_ID] , training=_A , inference=_A , fpaa=_A , sequence_lengths=[8] , batch_sizes=[1] , multi_process=_A , )
lowerCamelCase = PyTorchBenchmark(_A )
lowerCamelCase = benchmark.run()
self.check_results_dict_not_empty(results.time_inference_result )
self.check_results_dict_not_empty(results.memory_inference_result )
def __A ( self ) -> Any:
'''simple docstring'''
lowerCamelCase = """sshleifer/tiny-gpt2"""
lowerCamelCase = AutoConfig.from_pretrained(_A )
# set architectures equal to `None`
lowerCamelCase = None
lowerCamelCase = PyTorchBenchmarkArguments(
models=[MODEL_ID] , training=_A , inference=_A , sequence_lengths=[8] , batch_sizes=[1] , multi_process=_A , )
lowerCamelCase = PyTorchBenchmark(_A , configs=[config] )
lowerCamelCase = benchmark.run()
self.check_results_dict_not_empty(results.time_inference_result )
self.check_results_dict_not_empty(results.memory_inference_result )
def __A ( self ) -> Tuple:
'''simple docstring'''
lowerCamelCase = """sshleifer/tiny-gpt2"""
lowerCamelCase = PyTorchBenchmarkArguments(
models=[MODEL_ID] , training=_A , inference=_A , sequence_lengths=[8] , batch_sizes=[1] , multi_process=_A , )
lowerCamelCase = PyTorchBenchmark(_A )
lowerCamelCase = benchmark.run()
self.check_results_dict_not_empty(results.time_train_result )
self.check_results_dict_not_empty(results.memory_train_result )
@unittest.skipIf(torch_device == """cpu""" , """Can't do half precision""" )
def __A ( self ) -> int:
'''simple docstring'''
lowerCamelCase = """sshleifer/tiny-gpt2"""
lowerCamelCase = PyTorchBenchmarkArguments(
models=[MODEL_ID] , training=_A , inference=_A , sequence_lengths=[8] , batch_sizes=[1] , fpaa=_A , multi_process=_A , )
lowerCamelCase = PyTorchBenchmark(_A )
lowerCamelCase = benchmark.run()
self.check_results_dict_not_empty(results.time_train_result )
self.check_results_dict_not_empty(results.memory_train_result )
def __A ( self ) -> Tuple:
'''simple docstring'''
lowerCamelCase = """sshleifer/tiny-gpt2"""
lowerCamelCase = AutoConfig.from_pretrained(_A )
lowerCamelCase = PyTorchBenchmarkArguments(
models=[MODEL_ID] , training=_A , inference=_A , sequence_lengths=[8] , batch_sizes=[1] , multi_process=_A , )
lowerCamelCase = PyTorchBenchmark(_A , configs=[config] )
lowerCamelCase = benchmark.run()
self.check_results_dict_not_empty(results.time_inference_result )
self.check_results_dict_not_empty(results.memory_inference_result )
def __A ( self ) -> Optional[Any]:
'''simple docstring'''
lowerCamelCase = """sshleifer/tinier_bart"""
lowerCamelCase = AutoConfig.from_pretrained(_A )
lowerCamelCase = PyTorchBenchmarkArguments(
models=[MODEL_ID] , training=_A , inference=_A , sequence_lengths=[8] , batch_sizes=[1] , multi_process=_A , )
lowerCamelCase = PyTorchBenchmark(_A , configs=[config] )
lowerCamelCase = benchmark.run()
self.check_results_dict_not_empty(results.time_inference_result )
self.check_results_dict_not_empty(results.memory_inference_result )
def __A ( self ) -> List[Any]:
'''simple docstring'''
lowerCamelCase = """sshleifer/tiny-gpt2"""
lowerCamelCase = AutoConfig.from_pretrained(_A )
lowerCamelCase = PyTorchBenchmarkArguments(
models=[MODEL_ID] , training=_A , inference=_A , sequence_lengths=[8] , batch_sizes=[1] , multi_process=_A , )
lowerCamelCase = PyTorchBenchmark(_A , configs=[config] )
lowerCamelCase = benchmark.run()
self.check_results_dict_not_empty(results.time_train_result )
self.check_results_dict_not_empty(results.memory_train_result )
def __A ( self ) -> Union[str, Any]:
'''simple docstring'''
lowerCamelCase = """sshleifer/tinier_bart"""
lowerCamelCase = AutoConfig.from_pretrained(_A )
lowerCamelCase = PyTorchBenchmarkArguments(
models=[MODEL_ID] , training=_A , inference=_A , sequence_lengths=[8] , batch_sizes=[1] , multi_process=_A , )
lowerCamelCase = PyTorchBenchmark(_A , configs=[config] )
lowerCamelCase = benchmark.run()
self.check_results_dict_not_empty(results.time_train_result )
self.check_results_dict_not_empty(results.memory_train_result )
def __A ( self ) -> str:
'''simple docstring'''
lowerCamelCase = """sshleifer/tiny-gpt2"""
with tempfile.TemporaryDirectory() as tmp_dir:
lowerCamelCase = PyTorchBenchmarkArguments(
models=[MODEL_ID] , training=_A , inference=_A , save_to_csv=_A , sequence_lengths=[8] , batch_sizes=[1] , inference_time_csv_file=os.path.join(_A , """inf_time.csv""" ) , train_memory_csv_file=os.path.join(_A , """train_mem.csv""" ) , inference_memory_csv_file=os.path.join(_A , """inf_mem.csv""" ) , train_time_csv_file=os.path.join(_A , """train_time.csv""" ) , env_info_csv_file=os.path.join(_A , """env.csv""" ) , multi_process=_A , )
lowerCamelCase = PyTorchBenchmark(_A )
benchmark.run()
self.assertTrue(Path(os.path.join(_A , """inf_time.csv""" ) ).exists() )
self.assertTrue(Path(os.path.join(_A , """train_time.csv""" ) ).exists() )
self.assertTrue(Path(os.path.join(_A , """inf_mem.csv""" ) ).exists() )
self.assertTrue(Path(os.path.join(_A , """train_mem.csv""" ) ).exists() )
self.assertTrue(Path(os.path.join(_A , """env.csv""" ) ).exists() )
def __A ( self ) -> str:
'''simple docstring'''
lowerCamelCase = """sshleifer/tiny-gpt2"""
def _check_summary_is_not_empty(A ):
self.assertTrue(hasattr(_A , """sequential""" ) )
self.assertTrue(hasattr(_A , """cumulative""" ) )
self.assertTrue(hasattr(_A , """current""" ) )
self.assertTrue(hasattr(_A , """total""" ) )
with tempfile.TemporaryDirectory() as tmp_dir:
lowerCamelCase = PyTorchBenchmarkArguments(
models=[MODEL_ID] , training=_A , inference=_A , sequence_lengths=[8] , batch_sizes=[1] , log_filename=os.path.join(_A , """log.txt""" ) , log_print=_A , trace_memory_line_by_line=_A , multi_process=_A , )
lowerCamelCase = PyTorchBenchmark(_A )
lowerCamelCase = benchmark.run()
_check_summary_is_not_empty(result.inference_summary )
_check_summary_is_not_empty(result.train_summary )
self.assertTrue(Path(os.path.join(_A , """log.txt""" ) ).exists() )
| 252 |
import gc
import unittest
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
DDPMScheduler,
PriorTransformer,
StableUnCLIPPipeline,
UNetaDConditionModel,
)
from diffusers.pipelines.stable_diffusion.stable_unclip_image_normalizer import StableUnCLIPImageNormalizer
from diffusers.utils.testing_utils import enable_full_determinism, load_numpy, require_torch_gpu, slow, torch_device
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,
assert_mean_pixel_difference,
)
enable_full_determinism()
class a__ ( snake_case__ , snake_case__ , snake_case__ , unittest.TestCase ):
_a : str = StableUnCLIPPipeline
_a : Union[str, Any] = TEXT_TO_IMAGE_PARAMS
_a : Dict = TEXT_TO_IMAGE_BATCH_PARAMS
_a : Optional[int] = TEXT_TO_IMAGE_IMAGE_PARAMS
_a : Dict = TEXT_TO_IMAGE_IMAGE_PARAMS
# TODO(will) Expected attn_bias.stride(1) == 0 to be true, but got false
_a : Optional[Any] = False
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
__lowerCAmelCase = 3_2
__lowerCAmelCase = embedder_hidden_size
# prior components
torch.manual_seed(0 )
__lowerCAmelCase = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip" )
torch.manual_seed(0 )
__lowerCAmelCase = CLIPTextModelWithProjection(
CLIPTextConfig(
bos_token_id=0 , eos_token_id=2 , hidden_size=_A , projection_dim=_A , intermediate_size=3_7 , layer_norm_eps=1E-0_5 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=1_0_0_0 , ) )
torch.manual_seed(0 )
__lowerCAmelCase = PriorTransformer(
num_attention_heads=2 , attention_head_dim=1_2 , embedding_dim=_A , num_layers=1 , )
torch.manual_seed(0 )
__lowerCAmelCase = DDPMScheduler(
variance_type="fixed_small_log" , prediction_type="sample" , num_train_timesteps=1_0_0_0 , clip_sample=_A , clip_sample_range=5.0 , beta_schedule="squaredcos_cap_v2" , )
# regular denoising components
torch.manual_seed(0 )
__lowerCAmelCase = StableUnCLIPImageNormalizer(embedding_dim=_A )
__lowerCAmelCase = DDPMScheduler(beta_schedule="squaredcos_cap_v2" )
torch.manual_seed(0 )
__lowerCAmelCase = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip" )
torch.manual_seed(0 )
__lowerCAmelCase = CLIPTextModel(
CLIPTextConfig(
bos_token_id=0 , eos_token_id=2 , hidden_size=_A , projection_dim=3_2 , intermediate_size=3_7 , layer_norm_eps=1E-0_5 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=1_0_0_0 , ) )
torch.manual_seed(0 )
__lowerCAmelCase = UNetaDConditionModel(
sample_size=3_2 , in_channels=4 , out_channels=4 , down_block_types=("CrossAttnDownBlock2D", "DownBlock2D") , up_block_types=("UpBlock2D", "CrossAttnUpBlock2D") , block_out_channels=(3_2, 6_4) , attention_head_dim=(2, 4) , class_embed_type="projection" , projection_class_embeddings_input_dim=embedder_projection_dim * 2 , cross_attention_dim=_A , layers_per_block=1 , upcast_attention=_A , use_linear_projection=_A , )
torch.manual_seed(0 )
__lowerCAmelCase = DDIMScheduler(
beta_schedule="scaled_linear" , beta_start=0.0_00_85 , beta_end=0.0_12 , prediction_type="v_prediction" , set_alpha_to_one=_A , steps_offset=1 , )
torch.manual_seed(0 )
__lowerCAmelCase = AutoencoderKL()
__lowerCAmelCase = {
# prior components
"prior_tokenizer": prior_tokenizer,
"prior_text_encoder": prior_text_encoder,
"prior": prior,
"prior_scheduler": prior_scheduler,
# image noising components
"image_normalizer": image_normalizer,
"image_noising_scheduler": image_noising_scheduler,
# regular denoising components
"tokenizer": tokenizer,
"text_encoder": text_encoder,
"unet": unet,
"scheduler": scheduler,
"vae": vae,
}
return components
def __SCREAMING_SNAKE_CASE( self , _A , _A=0 ):
"""simple docstring"""
if str(_A ).startswith("mps" ):
__lowerCAmelCase = torch.manual_seed(_A )
else:
__lowerCAmelCase = torch.Generator(device=_A ).manual_seed(_A )
__lowerCAmelCase = {
"prompt": "A painting of a squirrel eating a burger",
"generator": generator,
"num_inference_steps": 2,
"prior_num_inference_steps": 2,
"output_type": "numpy",
}
return inputs
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
__lowerCAmelCase = torch_device == "cpu"
self._test_attention_slicing_forward_pass(test_max_difference=_A )
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
__lowerCAmelCase = torch_device in ["cpu", "mps"]
self._test_inference_batch_single_identical(test_max_difference=_A )
@slow
@require_torch_gpu
class a__ ( unittest.TestCase ):
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
__lowerCAmelCase = load_numpy(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/stable_unclip_2_1_l_anime_turtle_fp16.npy" )
__lowerCAmelCase = StableUnCLIPPipeline.from_pretrained("fusing/stable-unclip-2-1-l" , torch_dtype=torch.floataa )
pipe.to(_A )
pipe.set_progress_bar_config(disable=_A )
# stable unclip will oom when integration tests are run on a V100,
# so turn on memory savings
pipe.enable_attention_slicing()
pipe.enable_sequential_cpu_offload()
__lowerCAmelCase = torch.Generator(device="cpu" ).manual_seed(0 )
__lowerCAmelCase = pipe("anime turle" , generator=_A , output_type="np" )
__lowerCAmelCase = output.images[0]
assert image.shape == (7_6_8, 7_6_8, 3)
assert_mean_pixel_difference(_A , _A )
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
torch.cuda.empty_cache()
torch.cuda.reset_max_memory_allocated()
torch.cuda.reset_peak_memory_stats()
__lowerCAmelCase = StableUnCLIPPipeline.from_pretrained("fusing/stable-unclip-2-1-l" , torch_dtype=torch.floataa )
__lowerCAmelCase = pipe.to(_A )
pipe.set_progress_bar_config(disable=_A )
pipe.enable_attention_slicing()
pipe.enable_sequential_cpu_offload()
__lowerCAmelCase = pipe(
"anime turtle" , prior_num_inference_steps=2 , num_inference_steps=2 , output_type="np" , )
__lowerCAmelCase = torch.cuda.max_memory_allocated()
# make sure that less than 7 GB is allocated
assert mem_bytes < 7 * 1_0**9
| 92 | 0 |
'''simple docstring'''
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import ViTConfig, ViTForImageClassification, ViTImageProcessor, ViTModel
from transformers.utils import logging
logging.set_verbosity_info()
__a = logging.get_logger(__name__)
def __snake_case( _lowerCAmelCase , _lowerCAmelCase=False ) -> List[str]:
snake_case__ : Optional[int] = []
for i in range(config.num_hidden_layers ):
# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
rename_keys.append((f"blocks.{i}.norm1.weight", f"vit.encoder.layer.{i}.layernorm_before.weight") )
rename_keys.append((f"blocks.{i}.norm1.bias", f"vit.encoder.layer.{i}.layernorm_before.bias") )
rename_keys.append((f"blocks.{i}.attn.proj.weight", f"vit.encoder.layer.{i}.attention.output.dense.weight") )
rename_keys.append((f"blocks.{i}.attn.proj.bias", f"vit.encoder.layer.{i}.attention.output.dense.bias") )
rename_keys.append((f"blocks.{i}.norm2.weight", f"vit.encoder.layer.{i}.layernorm_after.weight") )
rename_keys.append((f"blocks.{i}.norm2.bias", f"vit.encoder.layer.{i}.layernorm_after.bias") )
rename_keys.append((f"blocks.{i}.mlp.fc1.weight", f"vit.encoder.layer.{i}.intermediate.dense.weight") )
rename_keys.append((f"blocks.{i}.mlp.fc1.bias", f"vit.encoder.layer.{i}.intermediate.dense.bias") )
rename_keys.append((f"blocks.{i}.mlp.fc2.weight", f"vit.encoder.layer.{i}.output.dense.weight") )
rename_keys.append((f"blocks.{i}.mlp.fc2.bias", f"vit.encoder.layer.{i}.output.dense.bias") )
# projection layer + position embeddings
rename_keys.extend(
[
("""cls_token""", """vit.embeddings.cls_token"""),
("""patch_embed.proj.weight""", """vit.embeddings.patch_embeddings.projection.weight"""),
("""patch_embed.proj.bias""", """vit.embeddings.patch_embeddings.projection.bias"""),
("""pos_embed""", """vit.embeddings.position_embeddings"""),
] )
if base_model:
# layernorm + pooler
rename_keys.extend(
[
("""norm.weight""", """layernorm.weight"""),
("""norm.bias""", """layernorm.bias"""),
] )
# if just the base model, we should remove "vit" from all keys that start with "vit"
snake_case__ : List[Any] = [(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 __snake_case( _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase=False ) -> List[str]:
for i in range(config.num_hidden_layers ):
if base_model:
snake_case__ : List[str] = """"""
else:
snake_case__ : Tuple = """vit."""
# read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
snake_case__ : List[Any] = state_dict.pop(f"blocks.{i}.attn.qkv.weight" )
snake_case__ : Optional[Any] = state_dict.pop(f"blocks.{i}.attn.qkv.bias" )
# next, add query, keys and values (in that order) to the state dict
snake_case__ : Optional[int] = in_proj_weight[
: config.hidden_size, :
]
snake_case__ : Optional[Any] = in_proj_bias[: config.hidden_size]
snake_case__ : Union[str, Any] = in_proj_weight[
config.hidden_size : config.hidden_size * 2, :
]
snake_case__ : Optional[int] = in_proj_bias[
config.hidden_size : config.hidden_size * 2
]
snake_case__ : Any = in_proj_weight[
-config.hidden_size :, :
]
snake_case__ : Any = in_proj_bias[-config.hidden_size :]
def __snake_case( _lowerCAmelCase ) -> List[str]:
snake_case__ : Optional[Any] = ["""head.weight""", """head.bias"""]
for k in ignore_keys:
state_dict.pop(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
def __snake_case( _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase ) -> int:
snake_case__ : List[str] = dct.pop(SCREAMING_SNAKE_CASE_ )
snake_case__ : List[Any] = val
def __snake_case( ) -> Union[str, Any]:
snake_case__ : str = """http://images.cocodataset.org/val2017/000000039769.jpg"""
snake_case__ : Optional[int] = Image.open(requests.get(SCREAMING_SNAKE_CASE_ , stream=SCREAMING_SNAKE_CASE_ ).raw )
return im
@torch.no_grad()
def __snake_case( _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase=True ) -> List[Any]:
snake_case__ : str = ViTConfig()
# patch_size
if model_name[-1] == "8":
snake_case__ : Dict = 8
# set labels if required
if not base_model:
snake_case__ : Dict = 1_000
snake_case__ : Any = """huggingface/label-files"""
snake_case__ : Dict = """imagenet-1k-id2label.json"""
snake_case__ : Optional[Any] = json.load(open(hf_hub_download(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , repo_type="""dataset""" ) , """r""" ) )
snake_case__ : int = {int(SCREAMING_SNAKE_CASE_ ): v for k, v in idalabel.items()}
snake_case__ : List[str] = idalabel
snake_case__ : int = {v: k for k, v in idalabel.items()}
# size of the architecture
if model_name in ["dino_vits8", "dino_vits16"]:
snake_case__ : List[Any] = 384
snake_case__ : Any = 1_536
snake_case__ : Tuple = 12
snake_case__ : Optional[int] = 6
# load original model from torch hub
snake_case__ : Optional[int] = torch.hub.load("""facebookresearch/dino:main""" , SCREAMING_SNAKE_CASE_ )
original_model.eval()
# load state_dict of original model, remove and rename some keys
snake_case__ : List[str] = original_model.state_dict()
if base_model:
remove_classification_head_(SCREAMING_SNAKE_CASE_ )
snake_case__ : Any = create_rename_keys(SCREAMING_SNAKE_CASE_ , base_model=SCREAMING_SNAKE_CASE_ )
for src, dest in rename_keys:
rename_key(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
read_in_q_k_v(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
# load HuggingFace model
if base_model:
snake_case__ : Optional[int] = ViTModel(SCREAMING_SNAKE_CASE_ , add_pooling_layer=SCREAMING_SNAKE_CASE_ ).eval()
else:
snake_case__ : int = ViTForImageClassification(SCREAMING_SNAKE_CASE_ ).eval()
model.load_state_dict(SCREAMING_SNAKE_CASE_ )
# Check outputs on an image, prepared by ViTImageProcessor
snake_case__ : List[str] = ViTImageProcessor()
snake_case__ : List[str] = image_processor(images=prepare_img() , return_tensors="""pt""" )
snake_case__ : Any = encoding["""pixel_values"""]
snake_case__ : int = model(SCREAMING_SNAKE_CASE_ )
if base_model:
snake_case__ : Any = original_model(SCREAMING_SNAKE_CASE_ )
assert torch.allclose(SCREAMING_SNAKE_CASE_ , outputs.last_hidden_state[:, 0, :] , atol=1e-1 )
else:
snake_case__ : int = original_model(SCREAMING_SNAKE_CASE_ )
assert logits.shape == outputs.logits.shape
assert torch.allclose(SCREAMING_SNAKE_CASE_ , outputs.logits , atol=1e-3 )
Path(SCREAMING_SNAKE_CASE_ ).mkdir(exist_ok=SCREAMING_SNAKE_CASE_ )
print(f"Saving model {model_name} to {pytorch_dump_folder_path}" )
model.save_pretrained(SCREAMING_SNAKE_CASE_ )
print(f"Saving image processor to {pytorch_dump_folder_path}" )
image_processor.save_pretrained(SCREAMING_SNAKE_CASE_ )
if __name__ == "__main__":
__a = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--model_name",
default="dino_vitb16",
type=str,
help="Name of the model trained with DINO 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(
"--base_model",
action="store_true",
help="Whether to only convert the base model (no projection head weights).",
)
parser.set_defaults(base_model=True)
__a = parser.parse_args()
convert_vit_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.base_model)
| 35 |
from typing import TYPE_CHECKING
from ...utils import _LazyModule
UpperCamelCase__ = {"""tokenization_wav2vec2_phoneme""": ["""Wav2Vec2PhonemeCTCTokenizer"""]}
if TYPE_CHECKING:
from .tokenization_wavaveca_phoneme import WavaVecaPhonemeCTCTokenizer
else:
import sys
UpperCamelCase__ = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 92 | 0 |
'''simple docstring'''
import gc
import random
import unittest
import torch
from diffusers import (
IFImgaImgPipeline,
IFImgaImgSuperResolutionPipeline,
IFInpaintingPipeline,
IFInpaintingSuperResolutionPipeline,
IFPipeline,
IFSuperResolutionPipeline,
)
from diffusers.models.attention_processor import AttnAddedKVProcessor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import floats_tensor, load_numpy, require_torch_gpu, skip_mps, slow, torch_device
from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference
from . import IFPipelineTesterMixin
@skip_mps
class UpperCamelCase__ ( snake_case__ , snake_case__ , unittest.TestCase):
UpperCAmelCase__ : str = IFPipeline
UpperCAmelCase__ : List[Any] = TEXT_TO_IMAGE_PARAMS - {"""width""", """height""", """latents"""}
UpperCAmelCase__ : Any = TEXT_TO_IMAGE_BATCH_PARAMS
UpperCAmelCase__ : Dict = PipelineTesterMixin.required_optional_params - {"""latents"""}
def lowercase_ ( self :Tuple ) -> List[Any]:
'''simple docstring'''
return self._get_dummy_components()
def lowercase_ ( self :str , _A :int , _A :Optional[int]=0 ) -> Optional[Any]:
'''simple docstring'''
if str(_A ).startswith('mps' ):
__A = torch.manual_seed(_A )
else:
__A = torch.Generator(device=_A ).manual_seed(_A )
__A = {
'prompt': 'A painting of a squirrel eating a burger',
'generator': generator,
'num_inference_steps': 2,
'output_type': 'numpy',
}
return inputs
def lowercase_ ( self :Union[str, Any] ) -> Optional[Any]:
'''simple docstring'''
self._test_save_load_optional_components()
@unittest.skipIf(torch_device != 'cuda' , reason='float16 requires CUDA' )
def lowercase_ ( self :int ) -> Optional[int]:
'''simple docstring'''
super().test_save_load_floataa(expected_max_diff=1E-1 )
def lowercase_ ( self :Optional[Any] ) -> str:
'''simple docstring'''
self._test_attention_slicing_forward_pass(expected_max_diff=1E-2 )
def lowercase_ ( self :Any ) -> Union[str, Any]:
'''simple docstring'''
self._test_save_load_local()
def lowercase_ ( self :Dict ) -> List[str]:
'''simple docstring'''
self._test_inference_batch_single_identical(
expected_max_diff=1E-2 , )
@unittest.skipIf(
torch_device != 'cuda' or not is_xformers_available() , reason='XFormers attention is only available with CUDA and `xformers` installed' , )
def lowercase_ ( self :Tuple ) -> Dict:
'''simple docstring'''
self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3 )
@slow
@require_torch_gpu
class UpperCamelCase__ ( unittest.TestCase):
def lowercase_ ( self :Tuple ) -> str:
'''simple docstring'''
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def lowercase_ ( self :Dict ) -> Optional[int]:
'''simple docstring'''
__A = IFPipeline.from_pretrained('DeepFloyd/IF-I-XL-v1.0' , variant='fp16' , torch_dtype=torch.floataa )
__A = IFSuperResolutionPipeline.from_pretrained(
'DeepFloyd/IF-II-L-v1.0' , variant='fp16' , torch_dtype=torch.floataa , text_encoder=_A , tokenizer=_A )
# pre compute text embeddings and remove T5 to save memory
pipe_a.text_encoder.to('cuda' )
__A , __A = pipe_a.encode_prompt('anime turtle' , device='cuda' )
del pipe_a.tokenizer
del pipe_a.text_encoder
gc.collect()
__A = None
__A = None
pipe_a.enable_model_cpu_offload()
pipe_a.enable_model_cpu_offload()
pipe_a.unet.set_attn_processor(AttnAddedKVProcessor() )
pipe_a.unet.set_attn_processor(AttnAddedKVProcessor() )
self._test_if(_A , _A , _A , _A )
pipe_a.remove_all_hooks()
pipe_a.remove_all_hooks()
# img2img
__A = IFImgaImgPipeline(**pipe_a.components )
__A = IFImgaImgSuperResolutionPipeline(**pipe_a.components )
pipe_a.enable_model_cpu_offload()
pipe_a.enable_model_cpu_offload()
pipe_a.unet.set_attn_processor(AttnAddedKVProcessor() )
pipe_a.unet.set_attn_processor(AttnAddedKVProcessor() )
self._test_if_imgaimg(_A , _A , _A , _A )
pipe_a.remove_all_hooks()
pipe_a.remove_all_hooks()
# inpainting
__A = IFInpaintingPipeline(**pipe_a.components )
__A = IFInpaintingSuperResolutionPipeline(**pipe_a.components )
pipe_a.enable_model_cpu_offload()
pipe_a.enable_model_cpu_offload()
pipe_a.unet.set_attn_processor(AttnAddedKVProcessor() )
pipe_a.unet.set_attn_processor(AttnAddedKVProcessor() )
self._test_if_inpainting(_A , _A , _A , _A )
def lowercase_ ( self :Optional[Any] , _A :Dict , _A :int , _A :Optional[Any] , _A :Dict ) -> Dict:
'''simple docstring'''
_start_torch_memory_measurement()
__A = torch.Generator(device='cpu' ).manual_seed(0 )
__A = pipe_a(
prompt_embeds=_A , negative_prompt_embeds=_A , num_inference_steps=2 , generator=_A , output_type='np' , )
__A = output.images[0]
assert image.shape == (64, 64, 3)
__A = torch.cuda.max_memory_allocated()
assert mem_bytes < 13 * 10**9
__A = load_numpy(
'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if.npy' )
assert_mean_pixel_difference(_A , _A )
# pipeline 2
_start_torch_memory_measurement()
__A = torch.Generator(device='cpu' ).manual_seed(0 )
__A = floats_tensor((1, 3, 64, 64) , rng=random.Random(0 ) ).to(_A )
__A = pipe_a(
prompt_embeds=_A , negative_prompt_embeds=_A , image=_A , generator=_A , num_inference_steps=2 , output_type='np' , )
__A = output.images[0]
assert image.shape == (256, 256, 3)
__A = torch.cuda.max_memory_allocated()
assert mem_bytes < 4 * 10**9
__A = load_numpy(
'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_superresolution_stage_II.npy' )
assert_mean_pixel_difference(_A , _A )
def lowercase_ ( self :Union[str, Any] , _A :Tuple , _A :Tuple , _A :str , _A :Optional[int] ) -> List[str]:
'''simple docstring'''
_start_torch_memory_measurement()
__A = floats_tensor((1, 3, 64, 64) , rng=random.Random(0 ) ).to(_A )
__A = torch.Generator(device='cpu' ).manual_seed(0 )
__A = pipe_a(
prompt_embeds=_A , negative_prompt_embeds=_A , image=_A , num_inference_steps=2 , generator=_A , output_type='np' , )
__A = output.images[0]
assert image.shape == (64, 64, 3)
__A = torch.cuda.max_memory_allocated()
assert mem_bytes < 10 * 10**9
__A = load_numpy(
'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_img2img.npy' )
assert_mean_pixel_difference(_A , _A )
# pipeline 2
_start_torch_memory_measurement()
__A = torch.Generator(device='cpu' ).manual_seed(0 )
__A = floats_tensor((1, 3, 256, 256) , rng=random.Random(0 ) ).to(_A )
__A = floats_tensor((1, 3, 64, 64) , rng=random.Random(0 ) ).to(_A )
__A = pipe_a(
prompt_embeds=_A , negative_prompt_embeds=_A , image=_A , original_image=_A , generator=_A , num_inference_steps=2 , output_type='np' , )
__A = output.images[0]
assert image.shape == (256, 256, 3)
__A = torch.cuda.max_memory_allocated()
assert mem_bytes < 4 * 10**9
__A = load_numpy(
'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_img2img_superresolution_stage_II.npy' )
assert_mean_pixel_difference(_A , _A )
def lowercase_ ( self :Tuple , _A :Dict , _A :Optional[Any] , _A :Optional[int] , _A :Dict ) -> str:
'''simple docstring'''
_start_torch_memory_measurement()
__A = floats_tensor((1, 3, 64, 64) , rng=random.Random(0 ) ).to(_A )
__A = floats_tensor((1, 3, 64, 64) , rng=random.Random(1 ) ).to(_A )
__A = torch.Generator(device='cpu' ).manual_seed(0 )
__A = pipe_a(
prompt_embeds=_A , negative_prompt_embeds=_A , image=_A , mask_image=_A , num_inference_steps=2 , generator=_A , output_type='np' , )
__A = output.images[0]
assert image.shape == (64, 64, 3)
__A = torch.cuda.max_memory_allocated()
assert mem_bytes < 10 * 10**9
__A = load_numpy(
'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_inpainting.npy' )
assert_mean_pixel_difference(_A , _A )
# pipeline 2
_start_torch_memory_measurement()
__A = torch.Generator(device='cpu' ).manual_seed(0 )
__A = floats_tensor((1, 3, 64, 64) , rng=random.Random(0 ) ).to(_A )
__A = floats_tensor((1, 3, 256, 256) , rng=random.Random(0 ) ).to(_A )
__A = floats_tensor((1, 3, 256, 256) , rng=random.Random(1 ) ).to(_A )
__A = pipe_a(
prompt_embeds=_A , negative_prompt_embeds=_A , image=_A , mask_image=_A , original_image=_A , generator=_A , num_inference_steps=2 , output_type='np' , )
__A = output.images[0]
assert image.shape == (256, 256, 3)
__A = torch.cuda.max_memory_allocated()
assert mem_bytes < 4 * 10**9
__A = load_numpy(
'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_inpainting_superresolution_stage_II.npy' )
assert_mean_pixel_difference(_A , _A )
def snake_case ( )-> Union[str, Any]:
"""simple docstring"""
torch.cuda.empty_cache()
torch.cuda.reset_max_memory_allocated()
torch.cuda.reset_peak_memory_stats()
| 161 |
import unittest
from transformers import DebertaVaTokenizer, DebertaVaTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin
UpperCamelCase__ = get_tests_dir("""fixtures/spiece.model""")
@require_sentencepiece
@require_tokenizers
class a__ ( snake_case__ , unittest.TestCase ):
_a : Optional[Any] = DebertaVaTokenizer
_a : Optional[Any] = DebertaVaTokenizerFast
_a : List[str] = True
_a : Optional[Any] = True
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
super().setUp()
# We have a SentencePiece fixture for testing
__lowerCAmelCase = DebertaVaTokenizer(_A , unk_token="<unk>" )
tokenizer.save_pretrained(self.tmpdirname )
def __SCREAMING_SNAKE_CASE( self , _A ):
"""simple docstring"""
__lowerCAmelCase = "this is a test"
__lowerCAmelCase = "this is a test"
return input_text, output_text
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
__lowerCAmelCase = "<pad>"
__lowerCAmelCase = 0
self.assertEqual(self.get_tokenizer()._convert_token_to_id(_A ) , _A )
self.assertEqual(self.get_tokenizer()._convert_id_to_token(_A ) , _A )
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
__lowerCAmelCase = list(self.get_tokenizer().get_vocab().keys() )
self.assertEqual(vocab_keys[0] , "<pad>" )
self.assertEqual(vocab_keys[1] , "<unk>" )
self.assertEqual(vocab_keys[-1] , "[PAD]" )
self.assertEqual(len(_A ) , 3_0_0_0_1 )
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
self.assertEqual(self.get_tokenizer().vocab_size , 3_0_0_0_0 )
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
__lowerCAmelCase = " \tHeLLo!how \n Are yoU? "
__lowerCAmelCase = ["▁hello", "!", "how", "▁are", "▁you", "?"]
# fmt: on
__lowerCAmelCase = DebertaVaTokenizer(_A , do_lower_case=_A )
__lowerCAmelCase = tokenizer.convert_ids_to_tokens(tokenizer.encode(_A , add_special_tokens=_A ) )
self.assertListEqual(_A , _A )
__lowerCAmelCase = DebertaVaTokenizerFast(_A , do_lower_case=_A )
__lowerCAmelCase = rust_tokenizer.convert_ids_to_tokens(rust_tokenizer.encode(_A , add_special_tokens=_A ) )
self.assertListEqual(_A , _A )
@unittest.skip("There is an inconsistency between slow and fast tokenizer due to a bug in the fast one." )
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
pass
@unittest.skip("There is an inconsistency between slow and fast tokenizer due to a bug in the fast one." )
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
pass
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
__lowerCAmelCase = "I was born in 92000, and this is falsé."
__lowerCAmelCase = ["▁", "<unk>", "▁was", "▁born", "▁in", "▁9", "2000", "▁", ",", "▁and", "▁this", "▁is", "▁fal", "s", "<unk>", "▁", ".", ]
# fmt: on
__lowerCAmelCase = DebertaVaTokenizer(_A , split_by_punct=_A )
__lowerCAmelCase = tokenizer.convert_ids_to_tokens(tokenizer.encode(_A , add_special_tokens=_A ) )
self.assertListEqual(_A , _A )
__lowerCAmelCase = DebertaVaTokenizerFast(_A , split_by_punct=_A )
__lowerCAmelCase = rust_tokenizer.convert_ids_to_tokens(rust_tokenizer.encode(_A , add_special_tokens=_A ) )
self.assertListEqual(_A , _A )
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
__lowerCAmelCase = "I was born in 92000, and this is falsé."
__lowerCAmelCase = ["▁i", "▁was", "▁born", "▁in", "▁9", "2000", "▁", ",", "▁and", "▁this", "▁is", "▁fal", "s", "<unk>", "▁", ".", ]
# fmt: on
__lowerCAmelCase = DebertaVaTokenizer(_A , do_lower_case=_A , split_by_punct=_A )
__lowerCAmelCase = tokenizer.convert_ids_to_tokens(tokenizer.encode(_A , add_special_tokens=_A ) )
self.assertListEqual(_A , _A )
__lowerCAmelCase = DebertaVaTokenizerFast(_A , do_lower_case=_A , split_by_punct=_A )
__lowerCAmelCase = rust_tokenizer.convert_ids_to_tokens(rust_tokenizer.encode(_A , add_special_tokens=_A ) )
self.assertListEqual(_A , _A )
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
__lowerCAmelCase = "I was born in 92000, and this is falsé."
__lowerCAmelCase = ["▁i", "▁was", "▁born", "▁in", "▁9", "2000", ",", "▁and", "▁this", "▁is", "▁fal", "s", "<unk>", ".", ]
# fmt: on
__lowerCAmelCase = DebertaVaTokenizer(_A , do_lower_case=_A , split_by_punct=_A )
__lowerCAmelCase = tokenizer.convert_ids_to_tokens(tokenizer.encode(_A , add_special_tokens=_A ) )
self.assertListEqual(_A , _A )
__lowerCAmelCase = DebertaVaTokenizerFast(_A , do_lower_case=_A , split_by_punct=_A )
__lowerCAmelCase = rust_tokenizer.convert_ids_to_tokens(rust_tokenizer.encode(_A , add_special_tokens=_A ) )
self.assertListEqual(_A , _A )
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
__lowerCAmelCase = "I was born in 92000, and this is falsé."
__lowerCAmelCase = ["▁", "<unk>", "▁was", "▁born", "▁in", "▁9", "2000", "▁", ",", "▁and", "▁this", "▁is", "▁fal", "s", "<unk>", "▁", ".", ]
# fmt: on
__lowerCAmelCase = DebertaVaTokenizer(_A , do_lower_case=_A , split_by_punct=_A )
__lowerCAmelCase = tokenizer.convert_ids_to_tokens(tokenizer.encode(_A , add_special_tokens=_A ) )
self.assertListEqual(_A , _A )
__lowerCAmelCase = DebertaVaTokenizerFast(_A , do_lower_case=_A , split_by_punct=_A )
__lowerCAmelCase = rust_tokenizer.convert_ids_to_tokens(rust_tokenizer.encode(_A , add_special_tokens=_A ) )
self.assertListEqual(_A , _A )
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
__lowerCAmelCase = " \tHeLLo!how \n Are yoU? "
__lowerCAmelCase = ["▁", "<unk>", "e", "<unk>", "o", "!", "how", "▁", "<unk>", "re", "▁yo", "<unk>", "?"]
# fmt: on
__lowerCAmelCase = DebertaVaTokenizer(_A , do_lower_case=_A , split_by_punct=_A )
__lowerCAmelCase = tokenizer.convert_ids_to_tokens(tokenizer.encode(_A , add_special_tokens=_A ) )
self.assertListEqual(_A , _A )
__lowerCAmelCase = DebertaVaTokenizerFast(_A , do_lower_case=_A , split_by_punct=_A )
__lowerCAmelCase = rust_tokenizer.convert_ids_to_tokens(rust_tokenizer.encode(_A , add_special_tokens=_A ) )
self.assertListEqual(_A , _A )
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
__lowerCAmelCase = self.get_tokenizer()
__lowerCAmelCase = self.get_rust_tokenizer()
__lowerCAmelCase = "I was born in 92000, and this is falsé."
__lowerCAmelCase = tokenizer.convert_ids_to_tokens(tokenizer.encode(_A , add_special_tokens=_A ) )
__lowerCAmelCase = rust_tokenizer.convert_ids_to_tokens(rust_tokenizer.encode(_A , add_special_tokens=_A ) )
self.assertListEqual(_A , _A )
__lowerCAmelCase = tokenizer.encode(_A , add_special_tokens=_A )
__lowerCAmelCase = rust_tokenizer.encode(_A , add_special_tokens=_A )
self.assertListEqual(_A , _A )
__lowerCAmelCase = self.get_rust_tokenizer()
__lowerCAmelCase = tokenizer.encode(_A )
__lowerCAmelCase = rust_tokenizer.encode(_A )
self.assertListEqual(_A , _A )
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
__lowerCAmelCase = "This is a test"
__lowerCAmelCase = [1_3, 1, 4_3_9_8, 2_5, 2_1, 1_2_8_9]
__lowerCAmelCase = ["▁", "T", "his", "▁is", "▁a", "▁test"]
__lowerCAmelCase = ["▁", "<unk>", "his", "▁is", "▁a", "▁test"]
__lowerCAmelCase = DebertaVaTokenizer(_A , keep_accents=_A )
__lowerCAmelCase = DebertaVaTokenizerFast(_A , keep_accents=_A )
__lowerCAmelCase = tokenizer.encode(_A , add_special_tokens=_A )
self.assertListEqual(_A , _A )
__lowerCAmelCase = tokenizer.tokenize(_A )
self.assertListEqual(_A , _A )
__lowerCAmelCase = tokenizer.convert_ids_to_tokens(_A )
self.assertListEqual(_A , _A )
__lowerCAmelCase = rust_tokenizer.encode(_A , add_special_tokens=_A )
self.assertListEqual(_A , _A )
__lowerCAmelCase = rust_tokenizer.tokenize(_A )
self.assertListEqual(_A , _A )
__lowerCAmelCase = rust_tokenizer.convert_ids_to_tokens(_A )
self.assertListEqual(_A , _A )
# fmt: off
__lowerCAmelCase = "I was born in 92000, and this is falsé."
__lowerCAmelCase = [1_3, 1, 2_3, 3_8_6, 1_9, 5_6_1, 3_0_5_0, 1_5, 1_7, 4_8, 2_5, 8_2_5_6, 1_8, 1, 9]
__lowerCAmelCase = ["▁", "I", "▁was", "▁born", "▁in", "▁9", "2000", ",", "▁and", "▁this", "▁is", "▁fal", "s", "é", ".", ]
__lowerCAmelCase = ["▁", "<unk>", "▁was", "▁born", "▁in", "▁9", "2000", ",", "▁and", "▁this", "▁is", "▁fal", "s", "<unk>", ".", ]
# fmt: on
__lowerCAmelCase = tokenizer.encode(_A , add_special_tokens=_A )
self.assertListEqual(_A , _A )
__lowerCAmelCase = tokenizer.tokenize(_A )
self.assertListEqual(_A , _A )
__lowerCAmelCase = tokenizer.convert_ids_to_tokens(_A )
self.assertListEqual(_A , _A )
__lowerCAmelCase = rust_tokenizer.encode(_A , add_special_tokens=_A )
self.assertListEqual(_A , _A )
__lowerCAmelCase = rust_tokenizer.tokenize(_A )
self.assertListEqual(_A , _A )
__lowerCAmelCase = rust_tokenizer.convert_ids_to_tokens(_A )
self.assertListEqual(_A , _A )
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
__lowerCAmelCase = DebertaVaTokenizer(_A )
__lowerCAmelCase = tokenizer.encode("sequence builders" )
__lowerCAmelCase = tokenizer.encode("multi-sequence build" )
__lowerCAmelCase = tokenizer.build_inputs_with_special_tokens(_A )
__lowerCAmelCase = tokenizer.build_inputs_with_special_tokens(_A , _A )
self.assertEqual([tokenizer.cls_token_id] + text + [tokenizer.sep_token_id] , _A )
self.assertEqual(
[tokenizer.cls_token_id] + text + [tokenizer.sep_token_id] + text_a + [tokenizer.sep_token_id] , _A , )
@slow
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
__lowerCAmelCase = {"input_ids": [[1, 3_9_8_6_7, 3_6, 1_9_3_9_0, 4_8_6, 2_7, 3_5_0_5_2, 8_1_4_3_6, 1_8, 6_0_6_8_5, 1_2_2_5, 7, 3_5_0_5_2, 8_1_4_3_6, 1_8, 9_3_6_7, 1_6_8_9_9, 1_8, 1_5_9_3_7, 5_3, 5_9_4, 7_7_3, 1_8, 1_6_2_8_7, 3_0_4_6_5, 3_6, 1_5_9_3_7, 6, 4_1_1_3_9, 3_8, 3_6_9_7_9, 6_0_7_6_3, 1_9_1, 6, 3_4_1_3_2, 9_9, 6, 5_0_5_3_8, 3_9_0, 4_3_2_3_0, 6, 3_4_1_3_2, 2_7_7_9, 2_0_8_5_0, 1_4, 6_9_9, 1_0_7_2, 1_1_9_4, 3_6, 3_8_2, 1_0_9_0_1, 5_3, 7, 6_9_9, 1_0_7_2, 2_0_8_4, 3_6, 2_0_4_2_2, 6_3_0, 5_3, 1_9, 1_0_5, 3_0_4_9, 1_8_9_6, 1_0_5_3, 1_6_8_9_9, 1_5_0_6, 1_1, 3_7_9_7_8, 4_2_4_3, 7, 1_2_3_7, 3_1_8_6_9, 2_0_0, 1_6_5_6_6, 6_5_4, 6, 3_5_0_5_2, 8_1_4_3_6, 7, 5_5_6_3_0, 1_3_5_9_3, 4, 2], [1, 2_6, 1_5_0_1_1, 1_3, 6_6_7, 8, 1_0_5_3, 1_8, 2_3_6_1_1, 1_2_3_7, 7_2_3_5_6, 1_2_8_2_0, 3_4, 1_0_4_1_3_4, 1_2_0_9, 3_5, 1_3_3_1_3, 6_6_2_7, 2_1, 2_0_2, 3_4_7, 7, 1_6_4, 2_3_9_9, 1_1, 4_6, 4_4_8_5, 4, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 5, 1_2_3_2, 2_8_6_4, 1_5_7_8_5, 1_4_9_5_1, 1_0_5, 5, 8_5_8_1, 1_2_5_0, 4, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], "token_type_ids": [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], "attention_mask": [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]} # noqa: E501
# fmt: on
self.tokenizer_integration_test_util(
expected_encoding=_A , model_name="microsoft/deberta-v2-xlarge" , revision="ad6e42c1532ddf3a15c39246b63f5559d558b670" , )
| 92 | 0 |
"""simple docstring"""
def a__ ( _SCREAMING_SNAKE_CASE ): # noqa: E741
"""simple docstring"""
UpperCamelCase = len(SCREAMING_SNAKE_CASE_ )
UpperCamelCase = 0
UpperCamelCase = [0] * n
UpperCamelCase = [False] * n
UpperCamelCase = [False] * n
def dfs(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
if parent == root:
out_edge_count += 1
UpperCamelCase = True
UpperCamelCase = at
for to in l[at]:
if to == parent:
pass
elif not visited[to]:
UpperCamelCase = dfs(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
UpperCamelCase = min(low[at] , low[to] )
# AP found via bridge
if at < low[to]:
UpperCamelCase = True
# AP found via cycle
if at == low[to]:
UpperCamelCase = True
else:
UpperCamelCase = min(low[at] , SCREAMING_SNAKE_CASE_ )
return out_edge_count
for i in range(SCREAMING_SNAKE_CASE_ ):
if not visited[i]:
UpperCamelCase = 0
UpperCamelCase = dfs(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , -1 , SCREAMING_SNAKE_CASE_ )
UpperCamelCase = out_edge_count > 1
for x in range(len(SCREAMING_SNAKE_CASE_ ) ):
if is_art[x] is True:
print(SCREAMING_SNAKE_CASE_ )
# Adjacency list of graph
lowerCAmelCase__ = {
0: [1, 2],
1: [0, 2],
2: [0, 1, 3, 5],
3: [2, 4],
4: [3],
5: [2, 6, 8],
6: [5, 7],
7: [6, 8],
8: [5, 7],
}
compute_ap(data)
| 153 |
from dataclasses import dataclass, field
from typing import Tuple
from ..utils import cached_property, is_tf_available, logging, requires_backends
from .benchmark_args_utils import BenchmarkArguments
if is_tf_available():
import tensorflow as tf
UpperCamelCase__ = logging.get_logger(__name__)
@dataclass
class a__ ( snake_case__ ):
_a : List[str] = [
"""no_inference""",
"""no_cuda""",
"""no_tpu""",
"""no_speed""",
"""no_memory""",
"""no_env_print""",
"""no_multi_process""",
]
def __init__( self , **_A ):
"""simple docstring"""
for deprecated_arg in self.deprecated_args:
if deprecated_arg in kwargs:
__lowerCAmelCase = deprecated_arg[3:]
__lowerCAmelCase = not kwargs.pop(_A )
logger.warning(
f"""{deprecated_arg} is depreciated. Please use --no-{positive_arg} or"""
f""" {positive_arg}={kwargs[positive_arg]}""" )
__lowerCAmelCase = kwargs.pop("tpu_name" , self.tpu_name )
__lowerCAmelCase = kwargs.pop("device_idx" , self.device_idx )
__lowerCAmelCase = kwargs.pop("eager_mode" , self.eager_mode )
__lowerCAmelCase = kwargs.pop("use_xla" , self.use_xla )
super().__init__(**_A )
_a : str = field(
default=snake_case__ , metadata={"""help""": """Name of TPU"""} , )
_a : int = field(
default=0 , metadata={"""help""": """CPU / GPU device index. Defaults to 0."""} , )
_a : bool = field(default=snake_case__ , metadata={"""help""": """Benchmark models in eager model."""} )
_a : bool = field(
default=snake_case__ , metadata={
"""help""": """Benchmark models using XLA JIT compilation. Note that `eager_model` has to be set to `False`."""
} , )
@cached_property
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
requires_backends(self , ["tf"] )
__lowerCAmelCase = None
if self.tpu:
try:
if self.tpu_name:
__lowerCAmelCase = tf.distribute.cluster_resolver.TPUClusterResolver(self.tpu_name )
else:
__lowerCAmelCase = tf.distribute.cluster_resolver.TPUClusterResolver()
except ValueError:
__lowerCAmelCase = None
return tpu
@cached_property
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
requires_backends(self , ["tf"] )
if self.is_tpu:
tf.config.experimental_connect_to_cluster(self._setup_tpu )
tf.tpu.experimental.initialize_tpu_system(self._setup_tpu )
__lowerCAmelCase = tf.distribute.TPUStrategy(self._setup_tpu )
else:
# currently no multi gpu is allowed
if self.is_gpu:
# TODO: Currently only single GPU is supported
tf.config.set_visible_devices(self.gpu_list[self.device_idx] , "GPU" )
__lowerCAmelCase = tf.distribute.OneDeviceStrategy(device=f"""/gpu:{self.device_idx}""" )
else:
tf.config.set_visible_devices([] , "GPU" ) # disable GPU
__lowerCAmelCase = tf.distribute.OneDeviceStrategy(device=f"""/cpu:{self.device_idx}""" )
return strategy
@property
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
requires_backends(self , ["tf"] )
return self._setup_tpu is not None
@property
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
requires_backends(self , ["tf"] )
return self._setup_strategy
@property
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
requires_backends(self , ["tf"] )
return tf.config.list_physical_devices("GPU" )
@property
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
requires_backends(self , ["tf"] )
if self.cuda:
return len(self.gpu_list )
return 0
@property
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
return self.n_gpu > 0
| 92 | 0 |
"""simple docstring"""
def __lowerCAmelCase ( lowercase : list ) -> List[str]:
"""simple docstring"""
if len(SCREAMING_SNAKE_CASE_ ) <= 1:
return [tuple(SCREAMING_SNAKE_CASE_ )]
snake_case : str = []
def generate(lowercase : int , lowercase : list ):
if k == 1:
res.append(tuple(arr[:] ) )
return
generate(k - 1 , SCREAMING_SNAKE_CASE_ )
for i in range(k - 1 ):
if k % 2 == 0: # k is even
snake_case ,snake_case : List[Any] = arr[k - 1], arr[i]
else: # k is odd
snake_case ,snake_case : Dict = arr[k - 1], arr[0]
generate(k - 1 , SCREAMING_SNAKE_CASE_ )
generate(len(SCREAMING_SNAKE_CASE_ ) , SCREAMING_SNAKE_CASE_ )
return res
if __name__ == "__main__":
__snake_case = input("""Enter numbers separated by a comma:\n""").strip()
__snake_case = [int(item) for item in user_input.split(""",""")]
print(heaps(arr))
| 203 |
import unittest
from transformers import CamembertTokenizer, CamembertTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import is_torch_available
from ...test_tokenization_common import TokenizerTesterMixin
UpperCamelCase__ = get_tests_dir("""fixtures/test_sentencepiece.model""")
UpperCamelCase__ = get_tests_dir("""fixtures/test_sentencepiece_bpe.model""")
UpperCamelCase__ = """pt""" if is_torch_available() else """tf"""
@require_sentencepiece
@require_tokenizers
class a__ ( snake_case__ , unittest.TestCase ):
_a : int = CamembertTokenizer
_a : Dict = CamembertTokenizerFast
_a : Tuple = True
_a : List[Any] = True
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
super().setUp()
# We have a SentencePiece fixture for testing
__lowerCAmelCase = CamembertTokenizer(_A )
tokenizer.save_pretrained(self.tmpdirname )
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
__lowerCAmelCase = "<pad>"
__lowerCAmelCase = 1
self.assertEqual(self.get_tokenizer()._convert_token_to_id(_A ) , _A )
self.assertEqual(self.get_tokenizer()._convert_id_to_token(_A ) , _A )
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
__lowerCAmelCase = list(self.get_tokenizer().get_vocab().keys() )
self.assertEqual(vocab_keys[0] , "<s>NOTUSED" )
self.assertEqual(vocab_keys[1] , "<pad>" )
self.assertEqual(vocab_keys[-1] , "<mask>" )
self.assertEqual(len(_A ) , 1_0_0_4 )
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
self.assertEqual(self.get_tokenizer().vocab_size , 1_0_0_5 )
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
__lowerCAmelCase = CamembertTokenizer(_A )
tokenizer.save_pretrained(self.tmpdirname )
__lowerCAmelCase = CamembertTokenizerFast.from_pretrained(self.tmpdirname )
__lowerCAmelCase = "I was born in 92000, and this is falsé."
__lowerCAmelCase = tokenizer.encode(_A )
__lowerCAmelCase = rust_tokenizer.encode(_A )
self.assertListEqual(_A , _A )
__lowerCAmelCase = tokenizer.encode(_A , add_special_tokens=_A )
__lowerCAmelCase = rust_tokenizer.encode(_A , add_special_tokens=_A )
self.assertListEqual(_A , _A )
# <unk> tokens are not the same for `rust` than for `slow`.
# Because spm gives back raw token instead of `unk` in EncodeAsPieces
# tokens = tokenizer.tokenize(sequence)
__lowerCAmelCase = tokenizer.convert_ids_to_tokens(_A )
__lowerCAmelCase = rust_tokenizer.tokenize(_A )
self.assertListEqual(_A , _A )
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
if not self.test_rust_tokenizer:
return
__lowerCAmelCase = self.get_tokenizer()
__lowerCAmelCase = self.get_rust_tokenizer()
__lowerCAmelCase = "I was born in 92000, and this is falsé."
__lowerCAmelCase = tokenizer.tokenize(_A )
__lowerCAmelCase = rust_tokenizer.tokenize(_A )
self.assertListEqual(_A , _A )
__lowerCAmelCase = tokenizer.encode(_A , add_special_tokens=_A )
__lowerCAmelCase = rust_tokenizer.encode(_A , add_special_tokens=_A )
self.assertListEqual(_A , _A )
__lowerCAmelCase = self.get_rust_tokenizer()
__lowerCAmelCase = tokenizer.encode(_A )
__lowerCAmelCase = rust_tokenizer.encode(_A )
self.assertListEqual(_A , _A )
@slow
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
__lowerCAmelCase = {"input_ids": [[5, 5_4, 7_1_9_6, 2_9_7, 3_0, 2_3, 7_7_6, 1_8, 1_1, 3_2_1_5, 3_7_0_5, 8_2_5_2, 2_2, 3_1_6_4, 1_1_8_1, 2_1_1_6, 2_9, 1_6, 8_1_3, 2_5, 7_9_1, 3_3_1_4, 2_0, 3_4_4_6, 3_8, 2_7_5_7_5, 1_2_0, 6, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [5, 4_6_8, 1_7, 1_1, 9_0_8_8, 2_0, 1_5_1_7, 8, 2_2_8_0_4, 1_8_8_1_8, 1_0, 3_8, 6_2_9, 6_0_7, 6_0_7, 1_4_2, 1_9, 7_1_9_6, 8_6_7, 5_6, 1_0_3_2_6, 2_4, 2_2_6_7, 2_0, 4_1_6, 5_0_7_2, 1_5_6_1_2, 2_3_3, 7_3_4, 7, 2_3_9_9, 2_7, 1_6, 3_0_1_5, 1_6_4_9, 7, 2_4, 2_0, 4_3_3_8, 2_3_9_9, 2_7, 1_3, 3_4_0_0, 1_4, 1_3, 6_1_8_9, 8, 9_3_0, 9, 6]], "attention_mask": [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]} # noqa: E501
# fmt: on
# camembert is a french model. So we also use french texts.
__lowerCAmelCase = [
"Le transformeur est un modèle d'apprentissage profond introduit en 2017, "
"utilisé principalement dans le domaine du traitement automatique des langues (TAL).",
"À l'instar des réseaux de neurones récurrents (RNN), les transformeurs sont conçus "
"pour gérer des données séquentielles, telles que le langage naturel, pour des tâches "
"telles que la traduction et la synthèse de texte.",
]
self.tokenizer_integration_test_util(
expected_encoding=_A , model_name="camembert-base" , revision="3a0641d9a1aeb7e848a74299e7e4c4bca216b4cf" , sequences=_A , )
| 92 | 0 |
'''simple docstring'''
import argparse
import json
import logging
import os
import sys
from unittest.mock import patch
from transformers.testing_utils import TestCasePlus, get_gpu_count, slow
a : Tuple = [
os.path.join(os.path.dirname(__file__), dirname)
for dirname in [
'text-classification',
'language-modeling',
'summarization',
'token-classification',
'question-answering',
]
]
sys.path.extend(SRC_DIRS)
if SRC_DIRS is not None:
import run_clm_flax
import run_flax_glue
import run_flax_ner
import run_mlm_flax
import run_qa
import run_summarization_flax
import run_ta_mlm_flax
logging.basicConfig(level=logging.DEBUG)
a : str = logging.getLogger()
def __magic_name__ ( ) -> Any:
'''simple docstring'''
snake_case_ = argparse.ArgumentParser()
parser.add_argument('''-f''' )
snake_case_ = parser.parse_args()
return args.f
def __magic_name__ ( __UpperCAmelCase, __UpperCAmelCase="eval" ) -> Tuple:
'''simple docstring'''
snake_case_ = os.path.join(SCREAMING_SNAKE_CASE_, F"{split}_results.json" )
if os.path.exists(SCREAMING_SNAKE_CASE_ ):
with open(SCREAMING_SNAKE_CASE_, '''r''' ) as f:
return json.load(SCREAMING_SNAKE_CASE_ )
raise ValueError(F"can't find {path}" )
a : str = logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)
class a ( snake_case__ ):
def A_ ( self : str ):
snake_case_ = self.get_auto_remove_tmp_dir()
snake_case_ = F"\n run_glue.py\n --model_name_or_path distilbert-base-uncased\n --output_dir {tmp_dir}\n --train_file ./tests/fixtures/tests_samples/MRPC/train.csv\n --validation_file ./tests/fixtures/tests_samples/MRPC/dev.csv\n --per_device_train_batch_size=2\n --per_device_eval_batch_size=1\n --learning_rate=1e-4\n --eval_steps=2\n --warmup_steps=2\n --seed=42\n --max_seq_length=128\n ".split()
with patch.object(_A , '''argv''' , _A ):
run_flax_glue.main()
snake_case_ = get_results(_A )
self.assertGreaterEqual(result['''eval_accuracy'''] , 0.75 )
@slow
def A_ ( self : Optional[Any] ):
snake_case_ = self.get_auto_remove_tmp_dir()
snake_case_ = F"\n run_clm_flax.py\n --model_name_or_path distilgpt2\n --train_file ./tests/fixtures/sample_text.txt\n --validation_file ./tests/fixtures/sample_text.txt\n --do_train\n --do_eval\n --block_size 128\n --per_device_train_batch_size 4\n --per_device_eval_batch_size 4\n --num_train_epochs 2\n --logging_steps 2 --eval_steps 2\n --output_dir {tmp_dir}\n --overwrite_output_dir\n ".split()
with patch.object(_A , '''argv''' , _A ):
run_clm_flax.main()
snake_case_ = get_results(_A )
self.assertLess(result['''eval_perplexity'''] , 100 )
@slow
def A_ ( self : Union[str, Any] ):
snake_case_ = self.get_auto_remove_tmp_dir()
snake_case_ = F"\n run_summarization.py\n --model_name_or_path t5-small\n --train_file tests/fixtures/tests_samples/xsum/sample.json\n --validation_file tests/fixtures/tests_samples/xsum/sample.json\n --test_file tests/fixtures/tests_samples/xsum/sample.json\n --output_dir {tmp_dir}\n --overwrite_output_dir\n --num_train_epochs=3\n --warmup_steps=8\n --do_train\n --do_eval\n --do_predict\n --learning_rate=2e-4\n --per_device_train_batch_size=2\n --per_device_eval_batch_size=1\n --predict_with_generate\n ".split()
with patch.object(_A , '''argv''' , _A ):
run_summarization_flax.main()
snake_case_ = get_results(_A , split='''test''' )
self.assertGreaterEqual(result['''test_rouge1'''] , 10 )
self.assertGreaterEqual(result['''test_rouge2'''] , 2 )
self.assertGreaterEqual(result['''test_rougeL'''] , 7 )
self.assertGreaterEqual(result['''test_rougeLsum'''] , 7 )
@slow
def A_ ( self : Union[str, Any] ):
snake_case_ = self.get_auto_remove_tmp_dir()
snake_case_ = F"\n run_mlm.py\n --model_name_or_path distilroberta-base\n --train_file ./tests/fixtures/sample_text.txt\n --validation_file ./tests/fixtures/sample_text.txt\n --output_dir {tmp_dir}\n --overwrite_output_dir\n --max_seq_length 128\n --per_device_train_batch_size 4\n --per_device_eval_batch_size 4\n --logging_steps 2 --eval_steps 2\n --do_train\n --do_eval\n --num_train_epochs=1\n ".split()
with patch.object(_A , '''argv''' , _A ):
run_mlm_flax.main()
snake_case_ = get_results(_A )
self.assertLess(result['''eval_perplexity'''] , 42 )
@slow
def A_ ( self : Optional[Any] ):
snake_case_ = self.get_auto_remove_tmp_dir()
snake_case_ = F"\n run_t5_mlm_flax.py\n --model_name_or_path t5-small\n --train_file ./tests/fixtures/sample_text.txt\n --validation_file ./tests/fixtures/sample_text.txt\n --do_train\n --do_eval\n --max_seq_length 128\n --per_device_train_batch_size 4\n --per_device_eval_batch_size 4\n --num_train_epochs 2\n --logging_steps 2 --eval_steps 2\n --output_dir {tmp_dir}\n --overwrite_output_dir\n ".split()
with patch.object(_A , '''argv''' , _A ):
run_ta_mlm_flax.main()
snake_case_ = get_results(_A )
self.assertGreaterEqual(result['''eval_accuracy'''] , 0.42 )
@slow
def A_ ( self : Optional[Any] ):
snake_case_ = 7 if get_gpu_count() > 1 else 2
snake_case_ = self.get_auto_remove_tmp_dir()
snake_case_ = F"\n run_flax_ner.py\n --model_name_or_path bert-base-uncased\n --train_file tests/fixtures/tests_samples/conll/sample.json\n --validation_file tests/fixtures/tests_samples/conll/sample.json\n --output_dir {tmp_dir}\n --overwrite_output_dir\n --do_train\n --do_eval\n --warmup_steps=2\n --learning_rate=2e-4\n --logging_steps 2 --eval_steps 2\n --per_device_train_batch_size=2\n --per_device_eval_batch_size=2\n --num_train_epochs={epochs}\n --seed 7\n ".split()
with patch.object(_A , '''argv''' , _A ):
run_flax_ner.main()
snake_case_ = get_results(_A )
self.assertGreaterEqual(result['''eval_accuracy'''] , 0.75 )
self.assertGreaterEqual(result['''eval_f1'''] , 0.3 )
@slow
def A_ ( self : Optional[Any] ):
snake_case_ = self.get_auto_remove_tmp_dir()
snake_case_ = F"\n run_qa.py\n --model_name_or_path bert-base-uncased\n --version_2_with_negative\n --train_file tests/fixtures/tests_samples/SQUAD/sample.json\n --validation_file tests/fixtures/tests_samples/SQUAD/sample.json\n --output_dir {tmp_dir}\n --overwrite_output_dir\n --num_train_epochs=3\n --warmup_steps=2\n --do_train\n --do_eval\n --logging_steps 2 --eval_steps 2\n --learning_rate=2e-4\n --per_device_train_batch_size=2\n --per_device_eval_batch_size=1\n ".split()
with patch.object(_A , '''argv''' , _A ):
run_qa.main()
snake_case_ = get_results(_A )
self.assertGreaterEqual(result['''eval_f1'''] , 30 )
self.assertGreaterEqual(result['''eval_exact'''] , 30 )
| 56 |
from __future__ import annotations
import collections
import tempfile
import unittest
import numpy as np
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import is_tf_available, is_vision_available
from ...test_modeling_tf_common import floats_tensor, ids_tensor, random_attention_mask
from ..bert.test_modeling_tf_bert import TFBertModelTester
from ..clip.test_modeling_tf_clip import TFCLIPVisionModelTester
from ..deit.test_modeling_tf_deit import TFDeiTModelTester
from ..roberta.test_modeling_tf_roberta import TFRobertaModelTester
from ..vit.test_modeling_tf_vit import TFViTModelTester
if is_tf_available():
from transformers import (
TFBertModel,
TFCLIPVisionModel,
TFDeiTModel,
TFRobertaModel,
TFVisionTextDualEncoderModel,
TFViTModel,
VisionTextDualEncoderConfig,
)
if is_vision_available():
from PIL import Image
from transformers import VisionTextDualEncoderProcessor
def _a ( SCREAMING_SNAKE_CASE_ : Union[str, Any] ):
if isinstance(SCREAMING_SNAKE_CASE_ , collections.abc.Iterable ):
return x
return (x, x)
@require_tf
class a__ :
def __SCREAMING_SNAKE_CASE( self , _A , _A ):
"""simple docstring"""
pass
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
pass
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
pass
def __SCREAMING_SNAKE_CASE( self , _A , _A , _A , _A , _A=None , **_A ):
"""simple docstring"""
__lowerCAmelCase = VisionTextDualEncoderConfig.from_vision_text_configs(_A , _A )
__lowerCAmelCase = TFVisionTextDualEncoderModel(_A )
__lowerCAmelCase = model(input_ids=_A , pixel_values=_A , attention_mask=_A )
self.assertEqual(output["text_embeds"].shape , (input_ids.shape[0], config.projection_dim) )
self.assertEqual(output["image_embeds"].shape , (pixel_values.shape[0], config.projection_dim) )
def __SCREAMING_SNAKE_CASE( self , _A , _A , _A , _A , _A=None , **_A ):
"""simple docstring"""
__lowerCAmelCase , __lowerCAmelCase = self.get_vision_text_model(_A , _A )
__lowerCAmelCase = TFVisionTextDualEncoderModel(vision_model=_A , text_model=_A )
__lowerCAmelCase = model(input_ids=_A , pixel_values=_A , attention_mask=_A )
self.assertEqual(output["text_embeds"].shape , (input_ids.shape[0], model.config.projection_dim) )
self.assertEqual(output["image_embeds"].shape , (pixel_values.shape[0], model.config.projection_dim) )
def __SCREAMING_SNAKE_CASE( self , _A , _A , _A , _A , _A=None , **_A ):
"""simple docstring"""
__lowerCAmelCase , __lowerCAmelCase = self.get_vision_text_model(_A , _A )
__lowerCAmelCase = {"vision_model": vision_model, "text_model": text_model}
__lowerCAmelCase = TFVisionTextDualEncoderModel.from_vision_text_pretrained(**_A )
__lowerCAmelCase = model(input_ids=_A , pixel_values=_A , attention_mask=_A )
self.assertEqual(output["text_embeds"].shape , (input_ids.shape[0], model.config.projection_dim) )
self.assertEqual(output["image_embeds"].shape , (pixel_values.shape[0], model.config.projection_dim) )
def __SCREAMING_SNAKE_CASE( self , _A , _A , _A , _A , _A=None , **_A ):
"""simple docstring"""
__lowerCAmelCase , __lowerCAmelCase = self.get_vision_text_model(_A , _A )
__lowerCAmelCase = TFVisionTextDualEncoderModel(vision_model=_A , text_model=_A )
__lowerCAmelCase = model(input_ids=_A , pixel_values=_A , attention_mask=_A )
__lowerCAmelCase = output[0].numpy()
with tempfile.TemporaryDirectory() as tmpdirname:
model.save_pretrained(_A )
__lowerCAmelCase = TFVisionTextDualEncoderModel.from_pretrained(_A )
__lowerCAmelCase = model(input_ids=_A , pixel_values=_A , attention_mask=_A )
__lowerCAmelCase = after_output[0].numpy()
__lowerCAmelCase = np.amax(np.abs(out_a - out_a ) )
self.assertLessEqual(_A , 1E-5 )
def __SCREAMING_SNAKE_CASE( self , _A , _A , _A , _A , _A=None , **_A ):
"""simple docstring"""
__lowerCAmelCase , __lowerCAmelCase = self.get_vision_text_model(_A , _A )
__lowerCAmelCase = TFVisionTextDualEncoderModel(vision_model=_A , text_model=_A )
__lowerCAmelCase = model(
input_ids=_A , pixel_values=_A , attention_mask=_A , output_attentions=_A )
__lowerCAmelCase = output.vision_model_output.attentions
self.assertEqual(len(_A ) , vision_config.num_hidden_layers )
# in ViT, the seq_len equals the number of patches + 1 (we add 1 for the [CLS] token)
__lowerCAmelCase = to_atuple(vision_model.config.image_size )
__lowerCAmelCase = to_atuple(vision_model.config.patch_size )
__lowerCAmelCase = (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
__lowerCAmelCase = num_patches + 1
self.assertEqual(vision_attentions[0].shape[-3:] , (vision_config.num_attention_heads, seq_len, seq_len) )
__lowerCAmelCase = output.text_model_output.attentions
self.assertEqual(len(_A ) , text_config.num_hidden_layers )
self.assertEqual(
text_attentions[0].shape[-3:] , (text_config.num_attention_heads, input_ids.shape[-1], input_ids.shape[-1]) , )
def __SCREAMING_SNAKE_CASE( self , _A , _A , _A ):
"""simple docstring"""
__lowerCAmelCase = np.abs((a - b) ).max()
self.assertLessEqual(_A , _A , f"""Difference between torch and flax is {diff} (>= {tol}).""" )
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
__lowerCAmelCase = self.prepare_config_and_inputs()
self.check_vision_text_dual_encoder_model(**_A )
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
__lowerCAmelCase = self.prepare_config_and_inputs()
self.check_model_from_pretrained_configs(**_A )
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
__lowerCAmelCase = self.prepare_config_and_inputs()
self.check_vision_text_dual_encoder_from_pretrained(**_A )
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
__lowerCAmelCase = self.prepare_config_and_inputs()
self.check_save_load(**_A )
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
__lowerCAmelCase = self.prepare_config_and_inputs()
self.check_vision_text_output_attention(**_A )
@slow
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
__lowerCAmelCase , __lowerCAmelCase = self.get_pretrained_model_and_inputs()
__lowerCAmelCase = model_a(**_A )
__lowerCAmelCase = outputs[0].numpy()
with tempfile.TemporaryDirectory() as tmp_dirname:
model_a.save_pretrained(_A )
__lowerCAmelCase = TFVisionTextDualEncoderModel.from_pretrained(_A )
__lowerCAmelCase = model_a(**_A )
__lowerCAmelCase = after_outputs[0].numpy()
__lowerCAmelCase = np.amax(np.abs(out_a - out_a ) )
self.assertLessEqual(_A , 1E-5 )
@require_tf
class a__ ( snake_case__ , unittest.TestCase ):
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
__lowerCAmelCase = TFVisionTextDualEncoderModel.from_vision_text_pretrained(
"hf-internal-testing/tiny-random-vit" , "hf-internal-testing/tiny-random-bert" )
__lowerCAmelCase = 1_3
__lowerCAmelCase = floats_tensor(
[
batch_size,
model.vision_model.config.num_channels,
model.vision_model.config.image_size,
model.vision_model.config.image_size,
] )
__lowerCAmelCase = ids_tensor([batch_size, 4] , model.text_model.config.vocab_size )
__lowerCAmelCase = random_attention_mask([batch_size, 4] )
__lowerCAmelCase = {"pixel_values": pixel_values, "input_ids": input_ids, "attention_mask": attention_mask}
return model, inputs
def __SCREAMING_SNAKE_CASE( self , _A , _A ):
"""simple docstring"""
__lowerCAmelCase = TFViTModel(_A , name="vision_model" )
__lowerCAmelCase = TFBertModel(_A , name="text_model" )
return vision_model, text_model
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
__lowerCAmelCase = TFViTModelTester(self )
__lowerCAmelCase = TFBertModelTester(self )
__lowerCAmelCase = vit_model_tester.prepare_config_and_inputs()
__lowerCAmelCase = bert_model_tester.prepare_config_and_inputs()
__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase = vision_config_and_inputs
(
(
__lowerCAmelCase
) , (
__lowerCAmelCase
) , (
__lowerCAmelCase
) , (
__lowerCAmelCase
) , (
__lowerCAmelCase
) , (
__lowerCAmelCase
) , (
__lowerCAmelCase
) ,
) = text_config_and_inputs
return {
"text_config": text_config,
"vision_config": vision_config,
"pixel_values": pixel_values,
"attention_mask": input_mask,
"input_ids": input_ids,
"text_token_type_ids": token_type_ids,
"text_sequence_labels": sequence_labels,
"text_token_labels": token_labels,
"text_choice_labels": choice_labels,
}
@require_tf
class a__ ( snake_case__ , unittest.TestCase ):
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
__lowerCAmelCase = TFVisionTextDualEncoderModel.from_vision_text_pretrained(
"Rocketknight1/tiny-random-deit-tf" , "hf-internal-testing/tiny-random-roberta" )
__lowerCAmelCase = 1_3
__lowerCAmelCase = floats_tensor(
[
batch_size,
model.vision_model.config.num_channels,
model.vision_model.config.image_size,
model.vision_model.config.image_size,
] )
__lowerCAmelCase = ids_tensor([batch_size, 4] , model.text_model.config.vocab_size )
__lowerCAmelCase = random_attention_mask([batch_size, 4] )
__lowerCAmelCase = {"pixel_values": pixel_values, "input_ids": input_ids, "attention_mask": attention_mask}
return model, inputs
def __SCREAMING_SNAKE_CASE( self , _A , _A , _A , _A , _A=None , **_A ):
"""simple docstring"""
__lowerCAmelCase , __lowerCAmelCase = self.get_vision_text_model(_A , _A )
__lowerCAmelCase = TFVisionTextDualEncoderModel(vision_model=_A , text_model=_A )
__lowerCAmelCase = model(
input_ids=_A , pixel_values=_A , attention_mask=_A , output_attentions=_A )
__lowerCAmelCase = output.vision_model_output.attentions
self.assertEqual(len(_A ) , vision_config.num_hidden_layers )
# in DEiT, the seq_len equals the number of patches + 2 (we add 2 for the [CLS] and distillation tokens)
__lowerCAmelCase = to_atuple(vision_model.config.image_size )
__lowerCAmelCase = to_atuple(vision_model.config.patch_size )
__lowerCAmelCase = (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
__lowerCAmelCase = num_patches + 2
self.assertEqual(vision_attentions[0].shape[-3:] , (vision_config.num_attention_heads, seq_len, seq_len) )
__lowerCAmelCase = output.text_model_output.attentions
self.assertEqual(len(_A ) , text_config.num_hidden_layers )
self.assertEqual(
text_attentions[0].shape[-3:] , (text_config.num_attention_heads, input_ids.shape[-1], input_ids.shape[-1]) , )
def __SCREAMING_SNAKE_CASE( self , _A , _A ):
"""simple docstring"""
__lowerCAmelCase = TFDeiTModel(_A , name="vision_model" )
__lowerCAmelCase = TFRobertaModel(_A , name="text_model" )
return vision_model, text_model
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
__lowerCAmelCase = TFDeiTModelTester(self )
__lowerCAmelCase = TFRobertaModelTester(self )
__lowerCAmelCase = vit_model_tester.prepare_config_and_inputs()
__lowerCAmelCase = bert_model_tester.prepare_config_and_inputs()
__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase = vision_config_and_inputs
(
(
__lowerCAmelCase
) , (
__lowerCAmelCase
) , (
__lowerCAmelCase
) , (
__lowerCAmelCase
) , (
__lowerCAmelCase
) , (
__lowerCAmelCase
) , (
__lowerCAmelCase
) ,
) = text_config_and_inputs
return {
"text_config": text_config,
"vision_config": vision_config,
"pixel_values": pixel_values,
"attention_mask": input_mask,
"input_ids": input_ids,
"text_token_type_ids": token_type_ids,
"text_sequence_labels": sequence_labels,
"text_token_labels": token_labels,
"text_choice_labels": choice_labels,
}
@require_tf
class a__ ( snake_case__ , unittest.TestCase ):
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
__lowerCAmelCase = TFVisionTextDualEncoderModel.from_vision_text_pretrained(
"Rocketknight1/tiny-random-clip-tf" , "hf-internal-testing/tiny-random-bert" )
__lowerCAmelCase = 1_3
__lowerCAmelCase = floats_tensor(
[
batch_size,
model.vision_model.config.num_channels,
model.vision_model.config.image_size,
model.vision_model.config.image_size,
] )
__lowerCAmelCase = ids_tensor([batch_size, 4] , model.text_model.config.vocab_size )
__lowerCAmelCase = random_attention_mask([batch_size, 4] )
__lowerCAmelCase = {"pixel_values": pixel_values, "input_ids": input_ids, "attention_mask": attention_mask}
return model, inputs
def __SCREAMING_SNAKE_CASE( self , _A , _A ):
"""simple docstring"""
__lowerCAmelCase = TFCLIPVisionModel(_A , name="vision_model" )
__lowerCAmelCase = TFBertModel(_A , name="text_model" )
return vision_model, text_model
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
__lowerCAmelCase = TFCLIPVisionModelTester(self )
__lowerCAmelCase = TFBertModelTester(self )
__lowerCAmelCase = clip_model_tester.prepare_config_and_inputs()
__lowerCAmelCase = bert_model_tester.prepare_config_and_inputs()
__lowerCAmelCase , __lowerCAmelCase = vision_config_and_inputs
(
(
__lowerCAmelCase
) , (
__lowerCAmelCase
) , (
__lowerCAmelCase
) , (
__lowerCAmelCase
) , (
__lowerCAmelCase
) , (
__lowerCAmelCase
) , (
__lowerCAmelCase
) ,
) = text_config_and_inputs
return {
"text_config": text_config,
"vision_config": vision_config,
"pixel_values": pixel_values,
"attention_mask": input_mask,
"input_ids": input_ids,
"text_token_type_ids": token_type_ids,
"text_sequence_labels": sequence_labels,
"text_token_labels": token_labels,
"text_choice_labels": choice_labels,
}
@require_vision
@require_tf
class a__ ( unittest.TestCase ):
@slow
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
__lowerCAmelCase = TFVisionTextDualEncoderModel.from_pretrained(
"clip-italian/clip-italian" , logit_scale_init_value=1.0 , from_pt=_A )
__lowerCAmelCase = VisionTextDualEncoderProcessor.from_pretrained("clip-italian/clip-italian" )
__lowerCAmelCase = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png" )
__lowerCAmelCase = processor(
text=["una foto di un gatto", "una foto di un cane"] , images=_A , padding=_A , return_tensors="np" )
__lowerCAmelCase = model(**_A )
# verify the logits
self.assertEqual(outputs.logits_per_image.shape , (inputs.pixel_values.shape[0], inputs.input_ids.shape[0]) )
self.assertEqual(
outputs.logits_per_text.shape , (inputs.input_ids.shape[0], inputs.pixel_values.shape[0]) , )
__lowerCAmelCase = np.array([[1.2_28_47_27, 0.3_10_41_22]] )
self.assertTrue(np.allclose(outputs.logits_per_image.numpy() , _A , atol=1E-3 ) )
| 92 | 0 |
'''simple docstring'''
import os
import posixpath
import uuid
from dataclasses import dataclass
from typing import TYPE_CHECKING, Iterable, List, Optional, Tuple, Union
import numpy as np
import pyarrow as pa
import datasets
from datasets.arrow_writer import ArrowWriter, ParquetWriter
from datasets.config import MAX_SHARD_SIZE
from datasets.filesystems import (
is_remote_filesystem,
rename,
)
from datasets.iterable_dataset import _BaseExamplesIterable
from datasets.utils.py_utils import convert_file_size_to_int
a_ : Any = datasets.utils.logging.get_logger(__name__)
if TYPE_CHECKING:
import pyspark
@dataclass
class __UpperCamelCase ( datasets.BuilderConfig ):
lowercase : Optional[datasets.Features] =None
def a_ ( __snake_case : "pyspark.sql.DataFrame" , __snake_case : List[int] , ) -> Dict:
"""simple docstring"""
import pyspark
def generate_fn():
lowerCamelCase_ =df.select('''*''' , pyspark.sql.functions.spark_partition_id().alias('''part_id''' ) )
for partition_id in partition_order:
lowerCamelCase_ =df_with_partition_id.select('''*''' ).where(F'''part_id = {partition_id}''' ).drop('''part_id''' )
lowerCamelCase_ =partition_df.collect()
lowerCamelCase_ =0
for row in rows:
yield F'''{partition_id}_{row_id}''', row.asDict()
row_id += 1
return generate_fn
class __UpperCamelCase ( _BaseExamplesIterable ):
def __init__( self, lowerCAmelCase, lowerCAmelCase=None, ):
"""simple docstring"""
lowerCamelCase_ =df
lowerCamelCase_ =partition_order or range(self.df.rdd.getNumPartitions() )
lowerCamelCase_ =_generate_iterable_examples(self.df, self.partition_order )
def __iter__( self ):
"""simple docstring"""
yield from self.generate_examples_fn()
def lowercase__ ( self, lowerCAmelCase ):
"""simple docstring"""
lowerCamelCase_ =list(range(self.df.rdd.getNumPartitions() ) )
generator.shuffle(_A )
return SparkExamplesIterable(self.df, partition_order=_A )
def lowercase__ ( self, lowerCAmelCase, lowerCAmelCase ):
"""simple docstring"""
lowerCamelCase_ =self.split_shard_indices_by_worker(_A, _A )
return SparkExamplesIterable(self.df, partition_order=_A )
@property
def lowercase__ ( self ):
"""simple docstring"""
return len(self.partition_order )
class __UpperCamelCase ( datasets.DatasetBuilder ):
lowercase : Optional[Any] =SparkConfig
def __init__( self, lowerCAmelCase, lowerCAmelCase = None, lowerCAmelCase = None, **lowerCAmelCase, ):
"""simple docstring"""
import pyspark
lowerCamelCase_ =pyspark.sql.SparkSession.builder.getOrCreate()
lowerCamelCase_ =df
lowerCamelCase_ =working_dir
super().__init__(
cache_dir=_A, config_name=str(self.df.semanticHash() ), **_A, )
def lowercase__ ( self ):
"""simple docstring"""
def create_cache_and_write_probe(lowerCAmelCase ):
# makedirs with exist_ok will recursively create the directory. It will not throw an error if directories
# already exist.
os.makedirs(self._cache_dir, exist_ok=_A )
lowerCamelCase_ =os.path.join(self._cache_dir, '''fs_test''' + uuid.uuida().hex )
# Opening the file in append mode will create a new file unless it already exists, in which case it will not
# change the file contents.
open(_A, '''a''' )
return [probe_file]
if self._spark.conf.get('''spark.master''', '''''' ).startswith('''local''' ):
return
# If the cluster is multi-node, make sure that the user provided a cache_dir and that it is on an NFS
# accessible to the driver.
# TODO: Stream batches to the driver using ArrowCollectSerializer instead of throwing an error.
if self._cache_dir:
lowerCamelCase_ =(
self._spark.sparkContext.parallelize(range(1 ), 1 ).mapPartitions(_A ).collect()
)
if os.path.isfile(probe[0] ):
return
raise ValueError(
'''When using Dataset.from_spark on a multi-node cluster, the driver and all workers should be able to access cache_dir''' )
def lowercase__ ( self ):
"""simple docstring"""
return datasets.DatasetInfo(features=self.config.features )
def lowercase__ ( self, lowerCAmelCase ):
"""simple docstring"""
return [datasets.SplitGenerator(name=datasets.Split.TRAIN )]
def lowercase__ ( self, lowerCAmelCase ):
"""simple docstring"""
import pyspark
def get_arrow_batch_size(lowerCAmelCase ):
for batch in it:
yield pa.RecordBatch.from_pydict({'''batch_bytes''': [batch.nbytes]} )
lowerCamelCase_ =self.df.count()
lowerCamelCase_ =df_num_rows if df_num_rows <= 100 else 100
# Approximate the size of each row (in Arrow format) by averaging over a max-100-row sample.
lowerCamelCase_ =(
self.df.limit(_A )
.repartition(1 )
.mapInArrow(_A, '''batch_bytes: long''' )
.agg(pyspark.sql.functions.sum('''batch_bytes''' ).alias('''sample_bytes''' ) )
.collect()[0]
.sample_bytes
/ sample_num_rows
)
lowerCamelCase_ =approx_bytes_per_row * df_num_rows
if approx_total_size > max_shard_size:
# Make sure there is at least one row per partition.
lowerCamelCase_ =min(_A, int(approx_total_size / max_shard_size ) )
lowerCamelCase_ =self.df.repartition(_A )
def lowercase__ ( self, lowerCAmelCase, lowerCAmelCase, lowerCAmelCase, ):
"""simple docstring"""
import pyspark
lowerCamelCase_ =ParquetWriter if file_format == '''parquet''' else ArrowWriter
lowerCamelCase_ =os.path.join(self._working_dir, os.path.basename(_A ) ) if self._working_dir else fpath
lowerCamelCase_ =file_format == '''parquet'''
# Define these so that we don't reference self in write_arrow, which will result in a pickling error due to
# pickling the SparkContext.
lowerCamelCase_ =self.config.features
lowerCamelCase_ =self._writer_batch_size
lowerCamelCase_ =self._fs.storage_options
def write_arrow(lowerCAmelCase ):
# Within the same SparkContext, no two task attempts will share the same attempt ID.
lowerCamelCase_ =pyspark.TaskContext().taskAttemptId()
lowerCamelCase_ =next(_A, _A )
if first_batch is None:
# Some partitions might not receive any data.
return pa.RecordBatch.from_arrays(
[[task_id], [0], [0]], names=['''task_id''', '''num_examples''', '''num_bytes'''], )
lowerCamelCase_ =0
lowerCamelCase_ =writer_class(
features=_A, path=working_fpath.replace('''SSSSS''', f'''{shard_id:05d}''' ).replace('''TTTTT''', f'''{task_id:05d}''' ), writer_batch_size=_A, storage_options=_A, embed_local_files=_A, )
lowerCamelCase_ =pa.Table.from_batches([first_batch] )
writer.write_table(_A )
for batch in it:
if max_shard_size is not None and writer._num_bytes >= max_shard_size:
lowerCamelCase_, lowerCamelCase_ =writer.finalize()
writer.close()
yield pa.RecordBatch.from_arrays(
[[task_id], [num_examples], [num_bytes]], names=['''task_id''', '''num_examples''', '''num_bytes'''], )
shard_id += 1
lowerCamelCase_ =writer_class(
features=writer._features, path=working_fpath.replace('''SSSSS''', f'''{shard_id:05d}''' ).replace('''TTTTT''', f'''{task_id:05d}''' ), writer_batch_size=_A, storage_options=_A, embed_local_files=_A, )
lowerCamelCase_ =pa.Table.from_batches([batch] )
writer.write_table(_A )
if writer._num_bytes > 0:
lowerCamelCase_, lowerCamelCase_ =writer.finalize()
writer.close()
yield pa.RecordBatch.from_arrays(
[[task_id], [num_examples], [num_bytes]], names=['''task_id''', '''num_examples''', '''num_bytes'''], )
if working_fpath != fpath:
for file in os.listdir(os.path.dirname(_A ) ):
lowerCamelCase_ =os.path.join(os.path.dirname(_A ), os.path.basename(_A ) )
shutil.move(_A, _A )
lowerCamelCase_ =(
self.df.mapInArrow(_A, '''task_id: long, num_examples: long, num_bytes: long''' )
.groupBy('''task_id''' )
.agg(
pyspark.sql.functions.sum('''num_examples''' ).alias('''total_num_examples''' ), pyspark.sql.functions.sum('''num_bytes''' ).alias('''total_num_bytes''' ), pyspark.sql.functions.count('''num_bytes''' ).alias('''num_shards''' ), pyspark.sql.functions.collect_list('''num_examples''' ).alias('''shard_lengths''' ), )
.collect()
)
for row in stats:
yield row.task_id, (row.total_num_examples, row.total_num_bytes, row.num_shards, row.shard_lengths)
def lowercase__ ( self, lowerCAmelCase, lowerCAmelCase = "arrow", lowerCAmelCase = None, lowerCAmelCase = None, **lowerCAmelCase, ):
"""simple docstring"""
self._validate_cache_dir()
lowerCamelCase_ =convert_file_size_to_int(max_shard_size or MAX_SHARD_SIZE )
self._repartition_df_if_needed(_A )
lowerCamelCase_ =not is_remote_filesystem(self._fs )
lowerCamelCase_ =os.path.join if is_local else posixpath.join
lowerCamelCase_ ='''-TTTTT-SSSSS-of-NNNNN'''
lowerCamelCase_ =f'''{self.name}-{split_generator.name}{SUFFIX}.{file_format}'''
lowerCamelCase_ =path_join(self._output_dir, _A )
lowerCamelCase_ =0
lowerCamelCase_ =0
lowerCamelCase_ =0
lowerCamelCase_ =[]
lowerCamelCase_ =[]
for task_id, content in self._prepare_split_single(_A, _A, _A ):
(
(
lowerCamelCase_
), (
lowerCamelCase_
), (
lowerCamelCase_
), (
lowerCamelCase_
),
) =content
if num_bytes > 0:
total_num_examples += num_examples
total_num_bytes += num_bytes
total_shards += num_shards
task_id_and_num_shards.append((task_id, num_shards) )
all_shard_lengths.extend(_A )
lowerCamelCase_ =total_num_examples
lowerCamelCase_ =total_num_bytes
# should rename everything at the end
logger.debug(f'''Renaming {total_shards} shards.''' )
if total_shards > 1:
lowerCamelCase_ =all_shard_lengths
# Define fs outside of _rename_shard so that we don't reference self in the function, which will result in a
# pickling error due to pickling the SparkContext.
lowerCamelCase_ =self._fs
# use the -SSSSS-of-NNNNN pattern
def _rename_shard(
lowerCAmelCase, lowerCAmelCase, lowerCAmelCase, ):
rename(
_A, fpath.replace('''SSSSS''', f'''{shard_id:05d}''' ).replace('''TTTTT''', f'''{task_id:05d}''' ), fpath.replace('''TTTTT-SSSSS''', f'''{global_shard_id:05d}''' ).replace('''NNNNN''', f'''{total_shards:05d}''' ), )
lowerCamelCase_ =[]
lowerCamelCase_ =0
for i in range(len(_A ) ):
lowerCamelCase_, lowerCamelCase_ =task_id_and_num_shards[i]
for shard_id in range(_A ):
args.append([task_id, shard_id, global_shard_id] )
global_shard_id += 1
self._spark.sparkContext.parallelize(_A, len(_A ) ).map(lambda lowerCAmelCase : _rename_shard(*_A ) ).collect()
else:
# don't use any pattern
lowerCamelCase_ =0
lowerCamelCase_ =task_id_and_num_shards[0][0]
self._rename(
fpath.replace('''SSSSS''', f'''{shard_id:05d}''' ).replace('''TTTTT''', f'''{task_id:05d}''' ), fpath.replace(_A, '''''' ), )
def lowercase__ ( self, lowerCAmelCase, ):
"""simple docstring"""
return SparkExamplesIterable(self.df )
| 75 |
import json
import os
import torch
from diffusers import UNetaDModel
os.makedirs("""hub/hopper-medium-v2/unet/hor32""", exist_ok=True)
os.makedirs("""hub/hopper-medium-v2/unet/hor128""", exist_ok=True)
os.makedirs("""hub/hopper-medium-v2/value_function""", exist_ok=True)
def _a ( SCREAMING_SNAKE_CASE_ : List[Any] ):
if hor == 1_28:
__lowerCAmelCase = ("DownResnetBlock1D", "DownResnetBlock1D", "DownResnetBlock1D")
__lowerCAmelCase = (32, 1_28, 2_56)
__lowerCAmelCase = ("UpResnetBlock1D", "UpResnetBlock1D")
elif hor == 32:
__lowerCAmelCase = ("DownResnetBlock1D", "DownResnetBlock1D", "DownResnetBlock1D", "DownResnetBlock1D")
__lowerCAmelCase = (32, 64, 1_28, 2_56)
__lowerCAmelCase = ("UpResnetBlock1D", "UpResnetBlock1D", "UpResnetBlock1D")
__lowerCAmelCase = torch.load(F"""/Users/bglickenhaus/Documents/diffuser/temporal_unet-hopper-mediumv2-hor{hor}.torch""" )
__lowerCAmelCase = model.state_dict()
__lowerCAmelCase = {
"down_block_types": down_block_types,
"block_out_channels": block_out_channels,
"up_block_types": up_block_types,
"layers_per_block": 1,
"use_timestep_embedding": True,
"out_block_type": "OutConv1DBlock",
"norm_num_groups": 8,
"downsample_each_block": False,
"in_channels": 14,
"out_channels": 14,
"extra_in_channels": 0,
"time_embedding_type": "positional",
"flip_sin_to_cos": False,
"freq_shift": 1,
"sample_size": 6_55_36,
"mid_block_type": "MidResTemporalBlock1D",
"act_fn": "mish",
}
__lowerCAmelCase = UNetaDModel(**SCREAMING_SNAKE_CASE_ )
print(F"""length of state dict: {len(state_dict.keys() )}""" )
print(F"""length of value function dict: {len(hf_value_function.state_dict().keys() )}""" )
__lowerCAmelCase = dict(zip(model.state_dict().keys() , hf_value_function.state_dict().keys() ) )
for k, v in mapping.items():
__lowerCAmelCase = state_dict.pop(SCREAMING_SNAKE_CASE_ )
hf_value_function.load_state_dict(SCREAMING_SNAKE_CASE_ )
torch.save(hf_value_function.state_dict() , F"""hub/hopper-medium-v2/unet/hor{hor}/diffusion_pytorch_model.bin""" )
with open(F"""hub/hopper-medium-v2/unet/hor{hor}/config.json""" , "w" ) as f:
json.dump(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
def _a ( ):
__lowerCAmelCase = {
"in_channels": 14,
"down_block_types": ("DownResnetBlock1D", "DownResnetBlock1D", "DownResnetBlock1D", "DownResnetBlock1D"),
"up_block_types": (),
"out_block_type": "ValueFunction",
"mid_block_type": "ValueFunctionMidBlock1D",
"block_out_channels": (32, 64, 1_28, 2_56),
"layers_per_block": 1,
"downsample_each_block": True,
"sample_size": 6_55_36,
"out_channels": 14,
"extra_in_channels": 0,
"time_embedding_type": "positional",
"use_timestep_embedding": True,
"flip_sin_to_cos": False,
"freq_shift": 1,
"norm_num_groups": 8,
"act_fn": "mish",
}
__lowerCAmelCase = torch.load("/Users/bglickenhaus/Documents/diffuser/value_function-hopper-mediumv2-hor32.torch" )
__lowerCAmelCase = model
__lowerCAmelCase = UNetaDModel(**SCREAMING_SNAKE_CASE_ )
print(F"""length of state dict: {len(state_dict.keys() )}""" )
print(F"""length of value function dict: {len(hf_value_function.state_dict().keys() )}""" )
__lowerCAmelCase = dict(zip(state_dict.keys() , hf_value_function.state_dict().keys() ) )
for k, v in mapping.items():
__lowerCAmelCase = state_dict.pop(SCREAMING_SNAKE_CASE_ )
hf_value_function.load_state_dict(SCREAMING_SNAKE_CASE_ )
torch.save(hf_value_function.state_dict() , "hub/hopper-medium-v2/value_function/diffusion_pytorch_model.bin" )
with open("hub/hopper-medium-v2/value_function/config.json" , "w" ) as f:
json.dump(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
if __name__ == "__main__":
unet(32)
# unet(128)
value_function()
| 92 | 0 |
"""simple docstring"""
from typing import List
import datasets
from datasets.tasks import AudioClassification
from ..folder_based_builder import folder_based_builder
_UpperCamelCase : List[str] = datasets.utils.logging.get_logger(__name__)
class a ( folder_based_builder.FolderBasedBuilderConfig ):
UpperCAmelCase_ : bool =None
UpperCAmelCase_ : bool =None
class a ( folder_based_builder.FolderBasedBuilder ):
UpperCAmelCase_ : int =datasets.Audio()
UpperCAmelCase_ : Optional[Any] ="""audio"""
UpperCAmelCase_ : str =AudioFolderConfig
UpperCAmelCase_ : List[str] # definition at the bottom of the script
UpperCAmelCase_ : Union[str, Any] =AudioClassification(audio_column="audio", label_column="label" )
_UpperCamelCase : str = [
'.aiff',
'.au',
'.avr',
'.caf',
'.flac',
'.htk',
'.svx',
'.mat4',
'.mat5',
'.mpc2k',
'.ogg',
'.paf',
'.pvf',
'.raw',
'.rf64',
'.sd2',
'.sds',
'.ircam',
'.voc',
'.w64',
'.wav',
'.nist',
'.wavex',
'.wve',
'.xi',
'.mp3',
'.opus',
]
_UpperCamelCase : List[Any] = AUDIO_EXTENSIONS
| 220 |
import pytest
from datasets import inspect_metric, list_metrics, load_metric
@pytest.fixture
def _a ( SCREAMING_SNAKE_CASE_ : Optional[Any] ):
monkeypatch.setattr("datasets.utils.deprecation_utils._emitted_deprecation_warnings" , set() )
@pytest.fixture
def _a ( SCREAMING_SNAKE_CASE_ : List[Any] ):
class a__ :
def __init__( self , _A ):
"""simple docstring"""
__lowerCAmelCase = metric_id
class a__ :
_a : Optional[int] = [MetricMock(snake_case__ ) for metric_id in ["""accuracy""", """mse""", """precision""", """codeparrot/apps_metric"""]]
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
return self._metrics
monkeypatch.setattr("datasets.inspect.huggingface_hub" , HfhMock() )
@pytest.mark.parametrize(
"func, args" , [(load_metric, ("metrics/mse",)), (list_metrics, ()), (inspect_metric, ("metrics/mse", "tmp_path"))] )
def _a ( SCREAMING_SNAKE_CASE_ : Tuple , SCREAMING_SNAKE_CASE_ : List[str] , SCREAMING_SNAKE_CASE_ : int , SCREAMING_SNAKE_CASE_ : Optional[int] , SCREAMING_SNAKE_CASE_ : Optional[int] ):
if "tmp_path" in args:
__lowerCAmelCase = tuple(arg if arg != "tmp_path" else tmp_path for arg in args )
with pytest.warns(SCREAMING_SNAKE_CASE_ , match="https://huggingface.co/docs/evaluate" ):
func(*SCREAMING_SNAKE_CASE_ )
| 92 | 0 |
def __lowerCAmelCase ( a__ , a__ ) -> List[Any]:
__a = len(SCREAMING_SNAKE_CASE_ )
print('''The following activities are selected:''' )
# The first activity is always selected
__a = 0
print(SCREAMING_SNAKE_CASE_ , end=''',''' )
# Consider rest of the activities
for j in range(SCREAMING_SNAKE_CASE_ ):
# If this activity has start time greater than
# or equal to the finish time of previously
# selected activity, then select it
if start[j] >= finish[i]:
print(SCREAMING_SNAKE_CASE_ , end=''',''' )
__a = j
if __name__ == "__main__":
import doctest
doctest.testmod()
A : List[str] = [1, 3, 0, 5, 8, 5]
A : Optional[Any] = [2, 4, 6, 7, 9, 9]
print_max_activities(start, finish) | 6 |
from random import randint
from tempfile import TemporaryFile
import numpy as np
def _a ( SCREAMING_SNAKE_CASE_ : int , SCREAMING_SNAKE_CASE_ : str , SCREAMING_SNAKE_CASE_ : List[str] ):
__lowerCAmelCase = 0
if start < end:
__lowerCAmelCase = randint(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
__lowerCAmelCase = a[end]
__lowerCAmelCase = a[pivot]
__lowerCAmelCase = temp
__lowerCAmelCase , __lowerCAmelCase = _in_place_partition(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
count += _in_place_quick_sort(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , p - 1 )
count += _in_place_quick_sort(SCREAMING_SNAKE_CASE_ , p + 1 , SCREAMING_SNAKE_CASE_ )
return count
def _a ( SCREAMING_SNAKE_CASE_ : str , SCREAMING_SNAKE_CASE_ : List[Any] , SCREAMING_SNAKE_CASE_ : Optional[int] ):
__lowerCAmelCase = 0
__lowerCAmelCase = randint(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
__lowerCAmelCase = a[end]
__lowerCAmelCase = a[pivot]
__lowerCAmelCase = temp
__lowerCAmelCase = start - 1
for index in range(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
count += 1
if a[index] < a[end]: # check if current val is less than pivot value
__lowerCAmelCase = new_pivot_index + 1
__lowerCAmelCase = a[new_pivot_index]
__lowerCAmelCase = a[index]
__lowerCAmelCase = temp
__lowerCAmelCase = a[new_pivot_index + 1]
__lowerCAmelCase = a[end]
__lowerCAmelCase = temp
return new_pivot_index + 1, count
UpperCamelCase__ = TemporaryFile()
UpperCamelCase__ = 100 # 1000 elements are to be sorted
UpperCamelCase__ , UpperCamelCase__ = 0, 1 # mean and standard deviation
UpperCamelCase__ = np.random.normal(mu, sigma, p)
np.save(outfile, X)
print("""The array is""")
print(X)
outfile.seek(0) # using the same array
UpperCamelCase__ = np.load(outfile)
UpperCamelCase__ = len(M) - 1
UpperCamelCase__ = _in_place_quick_sort(M, 0, r)
print(
"""No of Comparisons for 100 elements selected from a standard normal distribution"""
"""is :"""
)
print(z)
| 92 | 0 |
from __future__ import annotations
from math import pi
# Define the Reduced Planck Constant ℏ (H bar), speed of light C, value of
# Pi and the function
A__ : Tuple = 1.054_571_817e-34 # unit of ℏ : J * s
A__ : int = 3e8 # unit of c : m * s^-1
def a ( lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ ):
'''simple docstring'''
if (force, area, distance).count(0 ) != 1:
raise ValueError('''One and only one argument must be 0''' )
if force < 0:
raise ValueError('''Magnitude of force can not be negative''' )
if distance < 0:
raise ValueError('''Distance can not be negative''' )
if area < 0:
raise ValueError('''Area can not be negative''' )
if force == 0:
lowercase__ = (REDUCED_PLANCK_CONSTANT * SPEED_OF_LIGHT * pi**2 * area) / (
240 * (distance) ** 4
)
return {"force": force}
elif area == 0:
lowercase__ = (240 * force * (distance) ** 4) / (
REDUCED_PLANCK_CONSTANT * SPEED_OF_LIGHT * pi**2
)
return {"area": area}
elif distance == 0:
lowercase__ = (
(REDUCED_PLANCK_CONSTANT * SPEED_OF_LIGHT * pi**2 * area) / (240 * force)
) ** (1 / 4)
return {"distance": distance}
raise ValueError('''One and only one argument must be 0''' )
# Run doctest
if __name__ == "__main__":
import doctest
doctest.testmod()
| 207 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_speech_available, is_torch_available
UpperCamelCase__ = {
"""configuration_audio_spectrogram_transformer""": [
"""AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""ASTConfig""",
]
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCamelCase__ = [
"""AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""ASTForAudioClassification""",
"""ASTModel""",
"""ASTPreTrainedModel""",
]
try:
if not is_speech_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCamelCase__ = ["""ASTFeatureExtractor"""]
if TYPE_CHECKING:
from .configuration_audio_spectrogram_transformer import (
AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
ASTConfig,
)
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_audio_spectrogram_transformer import (
AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
ASTForAudioClassification,
ASTModel,
ASTPreTrainedModel,
)
try:
if not is_speech_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .feature_extraction_audio_spectrogram_transformer import ASTFeatureExtractor
else:
import sys
UpperCamelCase__ = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 92 | 0 |
'''simple docstring'''
from __future__ import annotations
def lowerCamelCase__ ( _A ):
a : List[str] = 0.00
a : List[Any] = 0
for resistor in resistors:
if resistor <= 0:
a : int = f"""Resistor at index {index} has a negative or zero value!"""
raise ValueError(SCREAMING_SNAKE_CASE_ )
first_sum += 1 / float(SCREAMING_SNAKE_CASE_ )
index += 1
return 1 / first_sum
def lowerCamelCase__ ( _A ):
a : Optional[Any] = 0.00
a : Any = 0
for resistor in resistors:
sum_r += resistor
if resistor < 0:
a : Dict = f"""Resistor at index {index} has a negative value!"""
raise ValueError(SCREAMING_SNAKE_CASE_ )
index += 1
return sum_r
if __name__ == "__main__":
import doctest
doctest.testmod() | 297 |
import argparse
import os
import re
import packaging.version
UpperCamelCase__ = """examples/"""
UpperCamelCase__ = {
"""examples""": (re.compile(R"""^check_min_version\(\"[^\"]+\"\)\s*$""", re.MULTILINE), """check_min_version(\"VERSION\")\n"""),
"""init""": (re.compile(R"""^__version__\s+=\s+\"([^\"]+)\"\s*$""", re.MULTILINE), """__version__ = \"VERSION\"\n"""),
"""setup""": (re.compile(R"""^(\s*)version\s*=\s*\"[^\"]+\",""", re.MULTILINE), R"""\1version=\"VERSION\","""),
"""doc""": (re.compile(R"""^(\s*)release\s*=\s*\"[^\"]+\"$""", re.MULTILINE), """release = \"VERSION\"\n"""),
}
UpperCamelCase__ = {
"""init""": """src/transformers/__init__.py""",
"""setup""": """setup.py""",
}
UpperCamelCase__ = """README.md"""
def _a ( SCREAMING_SNAKE_CASE_ : Optional[int] , SCREAMING_SNAKE_CASE_ : Any , SCREAMING_SNAKE_CASE_ : List[str] ):
with open(SCREAMING_SNAKE_CASE_ , "r" , encoding="utf-8" , newline="\n" ) as f:
__lowerCAmelCase = f.read()
__lowerCAmelCase , __lowerCAmelCase = REPLACE_PATTERNS[pattern]
__lowerCAmelCase = replace.replace("VERSION" , SCREAMING_SNAKE_CASE_ )
__lowerCAmelCase = re_pattern.sub(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
with open(SCREAMING_SNAKE_CASE_ , "w" , encoding="utf-8" , newline="\n" ) as f:
f.write(SCREAMING_SNAKE_CASE_ )
def _a ( SCREAMING_SNAKE_CASE_ : List[Any] ):
for folder, directories, fnames in os.walk(SCREAMING_SNAKE_CASE_ ):
# Removing some of the folders with non-actively maintained examples from the walk
if "research_projects" in directories:
directories.remove("research_projects" )
if "legacy" in directories:
directories.remove("legacy" )
for fname in fnames:
if fname.endswith(".py" ):
update_version_in_file(os.path.join(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) , SCREAMING_SNAKE_CASE_ , pattern="examples" )
def _a ( SCREAMING_SNAKE_CASE_ : Tuple , SCREAMING_SNAKE_CASE_ : Optional[int]=False ):
for pattern, fname in REPLACE_FILES.items():
update_version_in_file(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
if not patch:
update_version_in_examples(SCREAMING_SNAKE_CASE_ )
def _a ( ):
__lowerCAmelCase = "🤗 Transformers currently provides the following architectures"
__lowerCAmelCase = "1. Want to contribute a new model?"
with open(SCREAMING_SNAKE_CASE_ , "r" , encoding="utf-8" , newline="\n" ) as f:
__lowerCAmelCase = f.readlines()
# Find the start of the list.
__lowerCAmelCase = 0
while not lines[start_index].startswith(_start_prompt ):
start_index += 1
start_index += 1
__lowerCAmelCase = start_index
# Update the lines in the model list.
while not lines[index].startswith(_end_prompt ):
if lines[index].startswith("1." ):
__lowerCAmelCase = lines[index].replace(
"https://huggingface.co/docs/transformers/main/model_doc" , "https://huggingface.co/docs/transformers/model_doc" , )
index += 1
with open(SCREAMING_SNAKE_CASE_ , "w" , encoding="utf-8" , newline="\n" ) as f:
f.writelines(SCREAMING_SNAKE_CASE_ )
def _a ( ):
with open(REPLACE_FILES["init"] , "r" ) as f:
__lowerCAmelCase = f.read()
__lowerCAmelCase = REPLACE_PATTERNS["init"][0].search(SCREAMING_SNAKE_CASE_ ).groups()[0]
return packaging.version.parse(SCREAMING_SNAKE_CASE_ )
def _a ( SCREAMING_SNAKE_CASE_ : List[Any]=False ):
__lowerCAmelCase = get_version()
if patch and default_version.is_devrelease:
raise ValueError("Can't create a patch version from the dev branch, checkout a released version!" )
if default_version.is_devrelease:
__lowerCAmelCase = default_version.base_version
elif patch:
__lowerCAmelCase = F"""{default_version.major}.{default_version.minor}.{default_version.micro + 1}"""
else:
__lowerCAmelCase = F"""{default_version.major}.{default_version.minor + 1}.0"""
# Now let's ask nicely if that's the right one.
__lowerCAmelCase = input(F"""Which version are you releasing? [{default_version}]""" )
if len(SCREAMING_SNAKE_CASE_ ) == 0:
__lowerCAmelCase = default_version
print(F"""Updating version to {version}.""" )
global_version_update(SCREAMING_SNAKE_CASE_ , patch=SCREAMING_SNAKE_CASE_ )
if not patch:
print("Cleaning main README, don't forget to run `make fix-copies`." )
clean_main_ref_in_model_list()
def _a ( ):
__lowerCAmelCase = get_version()
__lowerCAmelCase = F"""{current_version.major}.{current_version.minor + 1}.0.dev0"""
__lowerCAmelCase = current_version.base_version
# Check with the user we got that right.
__lowerCAmelCase = input(F"""Which version are we developing now? [{dev_version}]""" )
if len(SCREAMING_SNAKE_CASE_ ) == 0:
__lowerCAmelCase = dev_version
print(F"""Updating version to {version}.""" )
global_version_update(SCREAMING_SNAKE_CASE_ )
print("Cleaning main README, don't forget to run `make fix-copies`." )
clean_main_ref_in_model_list()
if __name__ == "__main__":
UpperCamelCase__ = argparse.ArgumentParser()
parser.add_argument("""--post_release""", action="""store_true""", help="""Whether this is pre or post release.""")
parser.add_argument("""--patch""", action="""store_true""", help="""Whether or not this is a patch release.""")
UpperCamelCase__ = parser.parse_args()
if not args.post_release:
pre_release_work(patch=args.patch)
elif args.patch:
print("""Nothing to do after a patch :-)""")
else:
post_release_work()
| 92 | 0 |
from ..utils import DummyObject, requires_backends
class __lowercase ( metaclass=snake_case__ ):
"""simple docstring"""
UpperCamelCase : List[str] = ["""keras_nlp"""]
def __init__( self , *A , **A ) -> Tuple:
'''simple docstring'''
requires_backends(self , ["""keras_nlp"""] )
| 252 |
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import XLMRobertaTokenizerFast
from diffusers import DDIMScheduler, KandinskyImgaImgPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandinsky.text_encoder import MCLIPConfig, MultilingualCLIP
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu
from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference
enable_full_determinism()
class a__ ( snake_case__ , unittest.TestCase ):
_a : Dict = KandinskyImgaImgPipeline
_a : List[Any] = ["""prompt""", """image_embeds""", """negative_image_embeds""", """image"""]
_a : str = [
"""prompt""",
"""negative_prompt""",
"""image_embeds""",
"""negative_image_embeds""",
"""image""",
]
_a : List[Any] = [
"""generator""",
"""height""",
"""width""",
"""strength""",
"""guidance_scale""",
"""negative_prompt""",
"""num_inference_steps""",
"""return_dict""",
"""guidance_scale""",
"""num_images_per_prompt""",
"""output_type""",
"""return_dict""",
]
_a : int = False
@property
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
return 3_2
@property
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
return 3_2
@property
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
return self.time_input_dim
@property
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
return self.time_input_dim * 4
@property
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
return 1_0_0
@property
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
__lowerCAmelCase = XLMRobertaTokenizerFast.from_pretrained("YiYiXu/tiny-random-mclip-base" )
return tokenizer
@property
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
torch.manual_seed(0 )
__lowerCAmelCase = MCLIPConfig(
numDims=self.cross_attention_dim , transformerDimensions=self.text_embedder_hidden_size , hidden_size=self.text_embedder_hidden_size , intermediate_size=3_7 , num_attention_heads=4 , num_hidden_layers=5 , vocab_size=1_0_0_5 , )
__lowerCAmelCase = MultilingualCLIP(_A )
__lowerCAmelCase = text_encoder.eval()
return text_encoder
@property
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
torch.manual_seed(0 )
__lowerCAmelCase = {
"in_channels": 4,
# Out channels is double in channels because predicts mean and variance
"out_channels": 8,
"addition_embed_type": "text_image",
"down_block_types": ("ResnetDownsampleBlock2D", "SimpleCrossAttnDownBlock2D"),
"up_block_types": ("SimpleCrossAttnUpBlock2D", "ResnetUpsampleBlock2D"),
"mid_block_type": "UNetMidBlock2DSimpleCrossAttn",
"block_out_channels": (self.block_out_channels_a, self.block_out_channels_a * 2),
"layers_per_block": 1,
"encoder_hid_dim": self.text_embedder_hidden_size,
"encoder_hid_dim_type": "text_image_proj",
"cross_attention_dim": self.cross_attention_dim,
"attention_head_dim": 4,
"resnet_time_scale_shift": "scale_shift",
"class_embed_type": None,
}
__lowerCAmelCase = UNetaDConditionModel(**_A )
return model
@property
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
return {
"block_out_channels": [3_2, 6_4],
"down_block_types": ["DownEncoderBlock2D", "AttnDownEncoderBlock2D"],
"in_channels": 3,
"latent_channels": 4,
"layers_per_block": 1,
"norm_num_groups": 8,
"norm_type": "spatial",
"num_vq_embeddings": 1_2,
"out_channels": 3,
"up_block_types": [
"AttnUpDecoderBlock2D",
"UpDecoderBlock2D",
],
"vq_embed_dim": 4,
}
@property
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
torch.manual_seed(0 )
__lowerCAmelCase = VQModel(**self.dummy_movq_kwargs )
return model
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
__lowerCAmelCase = self.dummy_text_encoder
__lowerCAmelCase = self.dummy_tokenizer
__lowerCAmelCase = self.dummy_unet
__lowerCAmelCase = self.dummy_movq
__lowerCAmelCase = {
"num_train_timesteps": 1_0_0_0,
"beta_schedule": "linear",
"beta_start": 0.0_00_85,
"beta_end": 0.0_12,
"clip_sample": False,
"set_alpha_to_one": False,
"steps_offset": 0,
"prediction_type": "epsilon",
"thresholding": False,
}
__lowerCAmelCase = DDIMScheduler(**_A )
__lowerCAmelCase = {
"text_encoder": text_encoder,
"tokenizer": tokenizer,
"unet": unet,
"scheduler": scheduler,
"movq": movq,
}
return components
def __SCREAMING_SNAKE_CASE( self , _A , _A=0 ):
"""simple docstring"""
__lowerCAmelCase = floats_tensor((1, self.cross_attention_dim) , rng=random.Random(_A ) ).to(_A )
__lowerCAmelCase = floats_tensor((1, self.cross_attention_dim) , rng=random.Random(seed + 1 ) ).to(_A )
# create init_image
__lowerCAmelCase = floats_tensor((1, 3, 6_4, 6_4) , rng=random.Random(_A ) ).to(_A )
__lowerCAmelCase = image.cpu().permute(0 , 2 , 3 , 1 )[0]
__lowerCAmelCase = Image.fromarray(np.uinta(_A ) ).convert("RGB" ).resize((2_5_6, 2_5_6) )
if str(_A ).startswith("mps" ):
__lowerCAmelCase = torch.manual_seed(_A )
else:
__lowerCAmelCase = torch.Generator(device=_A ).manual_seed(_A )
__lowerCAmelCase = {
"prompt": "horse",
"image": init_image,
"image_embeds": image_embeds,
"negative_image_embeds": negative_image_embeds,
"generator": generator,
"height": 6_4,
"width": 6_4,
"num_inference_steps": 1_0,
"guidance_scale": 7.0,
"strength": 0.2,
"output_type": "np",
}
return inputs
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
__lowerCAmelCase = "cpu"
__lowerCAmelCase = self.get_dummy_components()
__lowerCAmelCase = self.pipeline_class(**_A )
__lowerCAmelCase = pipe.to(_A )
pipe.set_progress_bar_config(disable=_A )
__lowerCAmelCase = pipe(**self.get_dummy_inputs(_A ) )
__lowerCAmelCase = output.images
__lowerCAmelCase = pipe(
**self.get_dummy_inputs(_A ) , return_dict=_A , )[0]
__lowerCAmelCase = image[0, -3:, -3:, -1]
__lowerCAmelCase = image_from_tuple[0, -3:, -3:, -1]
assert image.shape == (1, 6_4, 6_4, 3)
__lowerCAmelCase = np.array(
[0.61_47_49_43, 0.6_07_35_39, 0.43_30_85_44, 0.5_92_82_69, 0.47_49_35_95, 0.46_75_59_73, 0.4_61_38_38, 0.45_36_87_97, 0.50_11_92_33] )
assert (
np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
), f""" expected_slice {expected_slice}, but got {image_slice.flatten()}"""
assert (
np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 1E-2
), f""" expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}"""
@slow
@require_torch_gpu
class a__ ( unittest.TestCase ):
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
__lowerCAmelCase = load_numpy(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
"/kandinsky/kandinsky_img2img_frog.npy" )
__lowerCAmelCase = load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main" "/kandinsky/cat.png" )
__lowerCAmelCase = "A red cartoon frog, 4k"
__lowerCAmelCase = KandinskyPriorPipeline.from_pretrained(
"kandinsky-community/kandinsky-2-1-prior" , torch_dtype=torch.floataa )
pipe_prior.to(_A )
__lowerCAmelCase = KandinskyImgaImgPipeline.from_pretrained(
"kandinsky-community/kandinsky-2-1" , torch_dtype=torch.floataa )
__lowerCAmelCase = pipeline.to(_A )
pipeline.set_progress_bar_config(disable=_A )
__lowerCAmelCase = torch.Generator(device="cpu" ).manual_seed(0 )
__lowerCAmelCase , __lowerCAmelCase = pipe_prior(
_A , generator=_A , num_inference_steps=5 , negative_prompt="" , ).to_tuple()
__lowerCAmelCase = pipeline(
_A , image=_A , image_embeds=_A , negative_image_embeds=_A , generator=_A , num_inference_steps=1_0_0 , height=7_6_8 , width=7_6_8 , strength=0.2 , output_type="np" , )
__lowerCAmelCase = output.images[0]
assert image.shape == (7_6_8, 7_6_8, 3)
assert_mean_pixel_difference(_A , _A )
| 92 | 0 |
'''simple docstring'''
class UpperCAmelCase_ :
"""simple docstring"""
def __init__( self : List[str] , snake_case_ : Dict ):
snake_case__ : Tuple = len(_A )
snake_case__ : List[str] = [0] * len_array
if len_array > 0:
snake_case__ : int = array[0]
for i in range(1 , _A ):
snake_case__ : Union[str, Any] = self.prefix_sum[i - 1] + array[i]
def lowerCamelCase ( self : Any , snake_case_ : List[str] , snake_case_ : Optional[int] ):
if start == 0:
return self.prefix_sum[end]
return self.prefix_sum[end] - self.prefix_sum[start - 1]
def lowerCamelCase ( self : Dict , snake_case_ : Optional[Any] ):
snake_case__ : Any = {0}
for sum_item in self.prefix_sum:
if sum_item - target_sum in sums:
return True
sums.add(_A )
return False
if __name__ == "__main__":
import doctest
doctest.testmod()
| 35 |
class a__ ( snake_case__ ):
pass
class a__ ( snake_case__ ):
pass
class a__ :
def __init__( self ):
"""simple docstring"""
__lowerCAmelCase = [
[],
[],
[],
]
def __SCREAMING_SNAKE_CASE( self , _A , _A ):
"""simple docstring"""
try:
if len(self.queues[priority] ) >= 1_0_0:
raise OverflowError("Maximum queue size is 100" )
self.queues[priority].append(_A )
except IndexError:
raise ValueError("Valid priorities are 0, 1, and 2" )
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
for queue in self.queues:
if queue:
return queue.pop(0 )
raise UnderFlowError("All queues are empty" )
def __str__( self ):
"""simple docstring"""
return "\n".join(f"""Priority {i}: {q}""" for i, q in enumerate(self.queues ) )
class a__ :
def __init__( self ):
"""simple docstring"""
__lowerCAmelCase = []
def __SCREAMING_SNAKE_CASE( self , _A ):
"""simple docstring"""
if len(self.queue ) == 1_0_0:
raise OverFlowError("Maximum queue size is 100" )
self.queue.append(_A )
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
if not self.queue:
raise UnderFlowError("The queue is empty" )
else:
__lowerCAmelCase = min(self.queue )
self.queue.remove(_A )
return data
def __str__( self ):
"""simple docstring"""
return str(self.queue )
def _a ( ):
__lowerCAmelCase = FixedPriorityQueue()
fpq.enqueue(0 , 10 )
fpq.enqueue(1 , 70 )
fpq.enqueue(0 , 1_00 )
fpq.enqueue(2 , 1 )
fpq.enqueue(2 , 5 )
fpq.enqueue(1 , 7 )
fpq.enqueue(2 , 4 )
fpq.enqueue(1 , 64 )
fpq.enqueue(0 , 1_28 )
print(SCREAMING_SNAKE_CASE_ )
print(fpq.dequeue() )
print(fpq.dequeue() )
print(fpq.dequeue() )
print(fpq.dequeue() )
print(fpq.dequeue() )
print(SCREAMING_SNAKE_CASE_ )
print(fpq.dequeue() )
print(fpq.dequeue() )
print(fpq.dequeue() )
print(fpq.dequeue() )
print(fpq.dequeue() )
def _a ( ):
__lowerCAmelCase = ElementPriorityQueue()
epq.enqueue(10 )
epq.enqueue(70 )
epq.enqueue(1_00 )
epq.enqueue(1 )
epq.enqueue(5 )
epq.enqueue(7 )
epq.enqueue(4 )
epq.enqueue(64 )
epq.enqueue(1_28 )
print(SCREAMING_SNAKE_CASE_ )
print(epq.dequeue() )
print(epq.dequeue() )
print(epq.dequeue() )
print(epq.dequeue() )
print(epq.dequeue() )
print(SCREAMING_SNAKE_CASE_ )
print(epq.dequeue() )
print(epq.dequeue() )
print(epq.dequeue() )
print(epq.dequeue() )
print(epq.dequeue() )
if __name__ == "__main__":
fixed_priority_queue()
element_priority_queue()
| 92 | 0 |
'''simple docstring'''
import numpy as np
import torch
import tqdm
from ...models.unet_ad import UNetaDModel
from ...pipelines import DiffusionPipeline
from ...utils import randn_tensor
from ...utils.dummy_pt_objects import DDPMScheduler
class UpperCamelCase__ ( snake_case__):
def __init__( self :str , _A :Optional[int] , _A :List[str] , _A :Optional[Any] , _A :int , ) -> Tuple:
'''simple docstring'''
super().__init__()
__A = value_function
__A = unet
__A = scheduler
__A = env
__A = env.get_dataset()
__A = {}
for key in self.data.keys():
try:
__A = self.data[key].mean()
except: # noqa: E722
pass
__A = {}
for key in self.data.keys():
try:
__A = self.data[key].std()
except: # noqa: E722
pass
__A = env.observation_space.shape[0]
__A = env.action_space.shape[0]
def lowercase_ ( self :List[Any] , _A :int , _A :Optional[int] ) -> Dict:
'''simple docstring'''
return (x_in - self.means[key]) / self.stds[key]
def lowercase_ ( self :Union[str, Any] , _A :Dict , _A :Union[str, Any] ) -> Any:
'''simple docstring'''
return x_in * self.stds[key] + self.means[key]
def lowercase_ ( self :str , _A :Dict ) -> Tuple:
'''simple docstring'''
if type(_A ) is dict:
return {k: self.to_torch(_A ) for k, v in x_in.items()}
elif torch.is_tensor(_A ):
return x_in.to(self.unet.device )
return torch.tensor(_A , device=self.unet.device )
def lowercase_ ( self :Any , _A :int , _A :List[Any] , _A :List[Any] ) -> Dict:
'''simple docstring'''
for key, val in cond.items():
__A = val.clone()
return x_in
def lowercase_ ( self :str , _A :Dict , _A :Tuple , _A :List[Any] , _A :Union[str, Any] ) -> List[str]:
'''simple docstring'''
__A = x.shape[0]
__A = None
for i in tqdm.tqdm(self.scheduler.timesteps ):
# create batch of timesteps to pass into model
__A = torch.full((batch_size,) , _A , device=self.unet.device , dtype=torch.long )
for _ in range(_A ):
with torch.enable_grad():
x.requires_grad_()
# permute to match dimension for pre-trained models
__A = self.value_function(x.permute(0 , 2 , 1 ) , _A ).sample
__A = torch.autograd.grad([y.sum()] , [x] )[0]
__A = self.scheduler._get_variance(_A )
__A = torch.exp(0.5 * posterior_variance )
__A = model_std * grad
__A = 0
__A = x.detach()
__A = x + scale * grad
__A = self.reset_xa(_A , _A , self.action_dim )
__A = self.unet(x.permute(0 , 2 , 1 ) , _A ).sample.permute(0 , 2 , 1 )
# TODO: verify deprecation of this kwarg
__A = self.scheduler.step(_A , _A , _A , predict_epsilon=_A )['prev_sample']
# apply conditions to the trajectory (set the initial state)
__A = self.reset_xa(_A , _A , self.action_dim )
__A = self.to_torch(_A )
return x, y
def __call__( self :List[str] , _A :Union[str, Any] , _A :str=64 , _A :Optional[int]=32 , _A :Union[str, Any]=2 , _A :List[str]=0.1 ) -> List[str]:
'''simple docstring'''
__A = self.normalize(_A , 'observations' )
__A = obs[None].repeat(_A , axis=0 )
__A = {0: self.to_torch(_A )}
__A = (batch_size, planning_horizon, self.state_dim + self.action_dim)
# generate initial noise and apply our conditions (to make the trajectories start at current state)
__A = randn_tensor(_A , device=self.unet.device )
__A = self.reset_xa(_A , _A , self.action_dim )
__A = self.to_torch(_A )
# run the diffusion process
__A , __A = self.run_diffusion(_A , _A , _A , _A )
# sort output trajectories by value
__A = y.argsort(0 , descending=_A ).squeeze()
__A = x[sorted_idx]
__A = sorted_values[:, :, : self.action_dim]
__A = actions.detach().cpu().numpy()
__A = self.de_normalize(_A , key='actions' )
# select the action with the highest value
if y is not None:
__A = 0
else:
# if we didn't run value guiding, select a random action
__A = np.random.randint(0 , _A )
__A = denorm_actions[selected_index, 0]
return denorm_actions
| 161 |
import inspect
import unittest
import warnings
from transformers import DeiTConfig
from transformers.models.auto import get_values
from transformers.testing_utils import (
require_accelerate,
require_torch,
require_torch_gpu,
require_vision,
slow,
torch_device,
)
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from torch import nn
from transformers import (
MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
MODEL_MAPPING,
DeiTForImageClassification,
DeiTForImageClassificationWithTeacher,
DeiTForMaskedImageModeling,
DeiTModel,
)
from transformers.models.deit.modeling_deit import DEIT_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import DeiTImageProcessor
class a__ :
def __init__( self , _A , _A=1_3 , _A=3_0 , _A=2 , _A=3 , _A=True , _A=True , _A=3_2 , _A=5 , _A=4 , _A=3_7 , _A="gelu" , _A=0.1 , _A=0.1 , _A=1_0 , _A=0.02 , _A=3 , _A=None , _A=2 , ):
"""simple docstring"""
__lowerCAmelCase = parent
__lowerCAmelCase = batch_size
__lowerCAmelCase = image_size
__lowerCAmelCase = patch_size
__lowerCAmelCase = num_channels
__lowerCAmelCase = is_training
__lowerCAmelCase = use_labels
__lowerCAmelCase = hidden_size
__lowerCAmelCase = num_hidden_layers
__lowerCAmelCase = num_attention_heads
__lowerCAmelCase = intermediate_size
__lowerCAmelCase = hidden_act
__lowerCAmelCase = hidden_dropout_prob
__lowerCAmelCase = attention_probs_dropout_prob
__lowerCAmelCase = type_sequence_label_size
__lowerCAmelCase = initializer_range
__lowerCAmelCase = scope
__lowerCAmelCase = encoder_stride
# in DeiT, the seq length equals the number of patches + 2 (we add 2 for the [CLS] and distilation tokens)
__lowerCAmelCase = (image_size // patch_size) ** 2
__lowerCAmelCase = num_patches + 2
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
__lowerCAmelCase = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
__lowerCAmelCase = None
if self.use_labels:
__lowerCAmelCase = ids_tensor([self.batch_size] , self.type_sequence_label_size )
__lowerCAmelCase = self.get_config()
return config, pixel_values, labels
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
return DeiTConfig(
image_size=self.image_size , patch_size=self.patch_size , num_channels=self.num_channels , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , is_decoder=_A , initializer_range=self.initializer_range , encoder_stride=self.encoder_stride , )
def __SCREAMING_SNAKE_CASE( self , _A , _A , _A ):
"""simple docstring"""
__lowerCAmelCase = DeiTModel(config=_A )
model.to(_A )
model.eval()
__lowerCAmelCase = model(_A )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def __SCREAMING_SNAKE_CASE( self , _A , _A , _A ):
"""simple docstring"""
__lowerCAmelCase = DeiTForMaskedImageModeling(config=_A )
model.to(_A )
model.eval()
__lowerCAmelCase = model(_A )
self.parent.assertEqual(
result.reconstruction.shape , (self.batch_size, self.num_channels, self.image_size, self.image_size) )
# test greyscale images
__lowerCAmelCase = 1
__lowerCAmelCase = DeiTForMaskedImageModeling(_A )
model.to(_A )
model.eval()
__lowerCAmelCase = floats_tensor([self.batch_size, 1, self.image_size, self.image_size] )
__lowerCAmelCase = model(_A )
self.parent.assertEqual(result.reconstruction.shape , (self.batch_size, 1, self.image_size, self.image_size) )
def __SCREAMING_SNAKE_CASE( self , _A , _A , _A ):
"""simple docstring"""
__lowerCAmelCase = self.type_sequence_label_size
__lowerCAmelCase = DeiTForImageClassification(_A )
model.to(_A )
model.eval()
__lowerCAmelCase = model(_A , labels=_A )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size) )
# test greyscale images
__lowerCAmelCase = 1
__lowerCAmelCase = DeiTForImageClassification(_A )
model.to(_A )
model.eval()
__lowerCAmelCase = floats_tensor([self.batch_size, 1, self.image_size, self.image_size] )
__lowerCAmelCase = model(_A , labels=_A )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size) )
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
__lowerCAmelCase = self.prepare_config_and_inputs()
(
(
__lowerCAmelCase
) , (
__lowerCAmelCase
) , (
__lowerCAmelCase
) ,
) = config_and_inputs
__lowerCAmelCase = {"pixel_values": pixel_values}
return config, inputs_dict
@require_torch
class a__ ( snake_case__ , snake_case__ , unittest.TestCase ):
_a : Optional[Any] = (
(
DeiTModel,
DeiTForImageClassification,
DeiTForImageClassificationWithTeacher,
DeiTForMaskedImageModeling,
)
if is_torch_available()
else ()
)
_a : int = (
{
"""feature-extraction""": DeiTModel,
"""image-classification""": (DeiTForImageClassification, DeiTForImageClassificationWithTeacher),
}
if is_torch_available()
else {}
)
_a : Optional[Any] = False
_a : Tuple = False
_a : Tuple = False
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
__lowerCAmelCase = DeiTModelTester(self )
__lowerCAmelCase = ConfigTester(self , config_class=_A , has_text_modality=_A , hidden_size=3_7 )
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
self.config_tester.run_common_tests()
@unittest.skip(reason="DeiT does not use inputs_embeds" )
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
pass
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
__lowerCAmelCase , __lowerCAmelCase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
__lowerCAmelCase = model_class(_A )
self.assertIsInstance(model.get_input_embeddings() , (nn.Module) )
__lowerCAmelCase = model.get_output_embeddings()
self.assertTrue(x is None or isinstance(_A , nn.Linear ) )
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
__lowerCAmelCase , __lowerCAmelCase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
__lowerCAmelCase = model_class(_A )
__lowerCAmelCase = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
__lowerCAmelCase = [*signature.parameters.keys()]
__lowerCAmelCase = ["pixel_values"]
self.assertListEqual(arg_names[:1] , _A )
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
__lowerCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*_A )
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
__lowerCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_image_modeling(*_A )
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
__lowerCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*_A )
def __SCREAMING_SNAKE_CASE( self , _A , _A , _A=False ):
"""simple docstring"""
__lowerCAmelCase = super()._prepare_for_class(_A , _A , return_labels=_A )
if return_labels:
if model_class.__name__ == "DeiTForImageClassificationWithTeacher":
del inputs_dict["labels"]
return inputs_dict
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
if not self.model_tester.is_training:
return
__lowerCAmelCase , __lowerCAmelCase = self.model_tester.prepare_config_and_inputs_for_common()
__lowerCAmelCase = True
for model_class in self.all_model_classes:
# DeiTForImageClassificationWithTeacher supports inference-only
if (
model_class in get_values(_A )
or model_class.__name__ == "DeiTForImageClassificationWithTeacher"
):
continue
__lowerCAmelCase = model_class(_A )
model.to(_A )
model.train()
__lowerCAmelCase = self._prepare_for_class(_A , _A , return_labels=_A )
__lowerCAmelCase = model(**_A ).loss
loss.backward()
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
__lowerCAmelCase , __lowerCAmelCase = self.model_tester.prepare_config_and_inputs_for_common()
if not self.model_tester.is_training:
return
__lowerCAmelCase = False
__lowerCAmelCase = True
for model_class in self.all_model_classes:
if model_class in get_values(_A ) or not model_class.supports_gradient_checkpointing:
continue
# DeiTForImageClassificationWithTeacher supports inference-only
if model_class.__name__ == "DeiTForImageClassificationWithTeacher":
continue
__lowerCAmelCase = model_class(_A )
model.gradient_checkpointing_enable()
model.to(_A )
model.train()
__lowerCAmelCase = self._prepare_for_class(_A , _A , return_labels=_A )
__lowerCAmelCase = model(**_A ).loss
loss.backward()
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
__lowerCAmelCase , __lowerCAmelCase = self.model_tester.prepare_config_and_inputs_for_common()
__lowerCAmelCase = [
{"title": "multi_label_classification", "num_labels": 2, "dtype": torch.float},
{"title": "single_label_classification", "num_labels": 1, "dtype": torch.long},
{"title": "regression", "num_labels": 1, "dtype": torch.float},
]
for model_class in self.all_model_classes:
if (
model_class
not in [
*get_values(_A ),
*get_values(_A ),
]
or model_class.__name__ == "DeiTForImageClassificationWithTeacher"
):
continue
for problem_type in problem_types:
with self.subTest(msg=f"""Testing {model_class} with {problem_type['title']}""" ):
__lowerCAmelCase = problem_type["title"]
__lowerCAmelCase = problem_type["num_labels"]
__lowerCAmelCase = model_class(_A )
model.to(_A )
model.train()
__lowerCAmelCase = self._prepare_for_class(_A , _A , return_labels=_A )
if problem_type["num_labels"] > 1:
__lowerCAmelCase = inputs["labels"].unsqueeze(1 ).repeat(1 , problem_type["num_labels"] )
__lowerCAmelCase = inputs["labels"].to(problem_type["dtype"] )
# This tests that we do not trigger the warning form PyTorch "Using a target size that is different
# to the input size. This will likely lead to incorrect results due to broadcasting. Please ensure
# they have the same size." which is a symptom something in wrong for the regression problem.
# See https://github.com/huggingface/transformers/issues/11780
with warnings.catch_warnings(record=_A ) as warning_list:
__lowerCAmelCase = model(**_A ).loss
for w in warning_list:
if "Using a target size that is different to the input size" in str(w.message ):
raise ValueError(
f"""Something is going wrong in the regression problem: intercepted {w.message}""" )
loss.backward()
@slow
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
for model_name in DEIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
__lowerCAmelCase = DeiTModel.from_pretrained(_A )
self.assertIsNotNone(_A )
def _a ( ):
__lowerCAmelCase = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png" )
return image
@require_torch
@require_vision
class a__ ( unittest.TestCase ):
@cached_property
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
return (
DeiTImageProcessor.from_pretrained("facebook/deit-base-distilled-patch16-224" )
if is_vision_available()
else None
)
@slow
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
__lowerCAmelCase = DeiTForImageClassificationWithTeacher.from_pretrained("facebook/deit-base-distilled-patch16-224" ).to(
_A )
__lowerCAmelCase = self.default_image_processor
__lowerCAmelCase = prepare_img()
__lowerCAmelCase = image_processor(images=_A , return_tensors="pt" ).to(_A )
# forward pass
with torch.no_grad():
__lowerCAmelCase = model(**_A )
# verify the logits
__lowerCAmelCase = torch.Size((1, 1_0_0_0) )
self.assertEqual(outputs.logits.shape , _A )
__lowerCAmelCase = torch.tensor([-1.02_66, 0.19_12, -1.28_61] ).to(_A )
self.assertTrue(torch.allclose(outputs.logits[0, :3] , _A , atol=1E-4 ) )
@slow
@require_accelerate
@require_torch_gpu
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
__lowerCAmelCase = DeiTModel.from_pretrained(
"facebook/deit-base-distilled-patch16-224" , torch_dtype=torch.floataa , device_map="auto" )
__lowerCAmelCase = self.default_image_processor
__lowerCAmelCase = prepare_img()
__lowerCAmelCase = image_processor(images=_A , return_tensors="pt" )
__lowerCAmelCase = inputs.pixel_values.to(_A )
# forward pass to make sure inference works in fp16
with torch.no_grad():
__lowerCAmelCase = model(_A )
| 92 | 0 |
"""simple docstring"""
import argparse
import shlex
import runhouse as rh
if __name__ == "__main__":
# Refer to https://runhouse-docs.readthedocs-hosted.com/en/latest/api/python/cluster.html#hardware-setup for cloud access
# setup instructions, if using on-demand hardware
# If user passes --user <user> --host <host> --key_path <key_path> <example> <args>, fill them in as BYO cluster
# If user passes --instance <instance> --provider <provider> <example> <args>, fill them in as on-demand cluster
# Throw an error if user passes both BYO and on-demand cluster args
# Otherwise, use default values
lowerCAmelCase__ = argparse.ArgumentParser()
parser.add_argument('''--user''', type=str, default='''ubuntu''')
parser.add_argument('''--host''', type=str, default='''localhost''')
parser.add_argument('''--key_path''', type=str, default=None)
parser.add_argument('''--instance''', type=str, default='''V100:1''')
parser.add_argument('''--provider''', type=str, default='''cheapest''')
parser.add_argument('''--use_spot''', type=bool, default=False)
parser.add_argument('''--example''', type=str, default='''pytorch/text-generation/run_generation.py''')
lowerCAmelCase__ , lowerCAmelCase__ = parser.parse_known_args()
if args.host != "localhost":
if args.instance != "V100:1" or args.provider != "cheapest":
raise ValueError('''Cannot specify both BYO and on-demand cluster args''')
lowerCAmelCase__ = rh.cluster(
name='''rh-cluster''', ips=[args.host], ssh_creds={'''ssh_user''': args.user, '''ssh_private_key''': args.key_path}
)
else:
lowerCAmelCase__ = rh.cluster(
name='''rh-cluster''', instance_type=args.instance, provider=args.provider, use_spot=args.use_spot
)
lowerCAmelCase__ = args.example.rsplit('''/''', 1)[0]
# Set up remote environment
cluster.install_packages(['''pip:./''']) # Installs transformers from local source
# Note transformers is copied into the home directory on the remote machine, so we can install from there
cluster.run([f'''pip install -r transformers/examples/{example_dir}/requirements.txt'''])
cluster.run(['''pip install torch --upgrade --extra-index-url https://download.pytorch.org/whl/cu117'''])
# Run example. You can bypass the CLI wrapper and paste your own code here.
cluster.run([f'''python transformers/examples/{args.example} {' '.join(shlex.quote(arg) for arg in unknown)}'''])
# Alternatively, we can just import and run a training function (especially if there's no wrapper CLI):
# from my_script... import train
# reqs = ['pip:./', 'torch', 'datasets', 'accelerate', 'evaluate', 'tqdm', 'scipy', 'scikit-learn', 'tensorboard']
# launch_train_gpu = rh.function(fn=train,
# system=gpu,
# reqs=reqs,
# name='train_bert_glue')
#
# We can pass in arguments just like we would to a function:
# launch_train_gpu(num_epochs = 3, lr = 2e-5, seed = 42, batch_size = 16
# stream_logs=True)
| 153 |
def _a ( SCREAMING_SNAKE_CASE_ : int = 1_00_00_00 ):
__lowerCAmelCase = [i - 1 for i in range(limit + 1 )]
for i in range(2 , limit + 1 ):
if phi[i] == i - 1:
for j in range(2 * i , limit + 1 , SCREAMING_SNAKE_CASE_ ):
phi[j] -= phi[j] // i
return sum(phi[2 : limit + 1] )
if __name__ == "__main__":
print(solution())
| 92 | 0 |
"""simple docstring"""
from __future__ import annotations
from collections.abc import Callable
def __lowerCAmelCase ( lowercase : Callable[[int | float], int | float] , lowercase : int | float , lowercase : int | float , lowercase : int = 100 , ) -> int:
"""simple docstring"""
snake_case : Optional[int] = x_start
snake_case : Optional[int] = fnc(SCREAMING_SNAKE_CASE_ )
snake_case : Dict = 0.0
for _ in range(SCREAMING_SNAKE_CASE_ ):
# Approximates small segments of curve as linear and solve
# for trapezoidal area
snake_case : List[str] = (x_end - x_start) / steps + xa
snake_case : Union[str, Any] = fnc(SCREAMING_SNAKE_CASE_ )
area += abs(fxa + fxa ) * (xa - xa) / 2
# Increment step
snake_case : int = xa
snake_case : str = fxa
return area
if __name__ == "__main__":
def __lowerCAmelCase ( lowercase : Tuple ) -> Optional[int]:
"""simple docstring"""
return x**3 + x**2
print("""f(x) = x^3 + x^2""")
print("""The area between the curve, x = -5, x = 5 and the x axis is:""")
__snake_case = 10
while i <= 100000:
print(F'''with {i} steps: {trapezoidal_area(f, -5, 5, i)}''')
i *= 10
| 203 |
import warnings
from diffusers import StableDiffusionImgaImgPipeline # noqa F401
warnings.warn(
"""The `image_to_image.py` script is outdated. Please use directly `from diffusers import"""
""" StableDiffusionImg2ImgPipeline` instead."""
)
| 92 | 0 |
'''simple docstring'''
from typing import Dict, Iterable, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import normalize, rescale, resize, to_channel_dimension_format, to_pil_image
from ...image_utils import (
IMAGENET_STANDARD_MEAN,
IMAGENET_STANDARD_STD,
ChannelDimension,
ImageInput,
PILImageResampling,
make_list_of_images,
to_numpy_array,
valid_images,
)
from ...utils import TensorType, is_pytesseract_available, is_vision_available, logging, requires_backends
if is_vision_available():
import PIL
# soft dependency
if is_pytesseract_available():
import pytesseract
a : Dict = logging.get_logger(__name__)
def __magic_name__ ( __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase ) -> List[Any]:
'''simple docstring'''
return [
int(1000 * (box[0] / width) ),
int(1000 * (box[1] / height) ),
int(1000 * (box[2] / width) ),
int(1000 * (box[3] / height) ),
]
def __magic_name__ ( __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase ) -> List[str]:
'''simple docstring'''
snake_case_ = to_pil_image(SCREAMING_SNAKE_CASE_ )
snake_case_ ,snake_case_ = pil_image.size
snake_case_ = pytesseract.image_to_data(SCREAMING_SNAKE_CASE_, lang=SCREAMING_SNAKE_CASE_, output_type='''dict''', config=SCREAMING_SNAKE_CASE_ )
snake_case_ ,snake_case_ ,snake_case_ ,snake_case_ ,snake_case_ = data['''text'''], data['''left'''], data['''top'''], data['''width'''], data['''height''']
# filter empty words and corresponding coordinates
snake_case_ = [idx for idx, word in enumerate(SCREAMING_SNAKE_CASE_ ) if not word.strip()]
snake_case_ = [word for idx, word in enumerate(SCREAMING_SNAKE_CASE_ ) if idx not in irrelevant_indices]
snake_case_ = [coord for idx, coord in enumerate(SCREAMING_SNAKE_CASE_ ) if idx not in irrelevant_indices]
snake_case_ = [coord for idx, coord in enumerate(SCREAMING_SNAKE_CASE_ ) if idx not in irrelevant_indices]
snake_case_ = [coord for idx, coord in enumerate(SCREAMING_SNAKE_CASE_ ) if idx not in irrelevant_indices]
snake_case_ = [coord for idx, coord in enumerate(SCREAMING_SNAKE_CASE_ ) if idx not in irrelevant_indices]
# turn coordinates into (left, top, left+width, top+height) format
snake_case_ = []
for x, y, w, h in zip(SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_ ):
snake_case_ = [x, y, x + w, y + h]
actual_boxes.append(SCREAMING_SNAKE_CASE_ )
# finally, normalize the bounding boxes
snake_case_ = []
for box in actual_boxes:
normalized_boxes.append(normalize_box(SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_ ) )
assert len(SCREAMING_SNAKE_CASE_ ) == len(SCREAMING_SNAKE_CASE_ ), "Not as many words as there are bounding boxes"
return words, normalized_boxes
class a ( snake_case__ ):
snake_case_ = ["""pixel_values"""]
def __init__( self : List[str] , lowercase_ : Dict = True , lowercase_ : Tuple = None , lowercase_ : Union[str, Any] = PILImageResampling.BILINEAR , lowercase_ : List[str] = True , lowercase_ : Dict = 1 / 255 , lowercase_ : str = True , lowercase_ : List[Any] = None , lowercase_ : List[str] = None , lowercase_ : List[Any] = True , lowercase_ : Optional[Any] = None , lowercase_ : Optional[Any] = "" , **lowercase_ : Optional[int] , ):
super().__init__(**_A )
snake_case_ = size if size is not None else {'''height''': 224, '''width''': 224}
snake_case_ = get_size_dict(_A )
snake_case_ = do_resize
snake_case_ = size
snake_case_ = resample
snake_case_ = do_rescale
snake_case_ = rescale_value
snake_case_ = do_normalize
snake_case_ = image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
snake_case_ = image_std if image_std is not None else IMAGENET_STANDARD_STD
snake_case_ = apply_ocr
snake_case_ = ocr_lang
snake_case_ = tesseract_config
def A_ ( self : Tuple , lowercase_ : Optional[Any] , lowercase_ : Union[str, Any] , lowercase_ : Dict = PILImageResampling.BILINEAR , lowercase_ : List[str] = None , **lowercase_ : str , ):
snake_case_ = get_size_dict(_A )
if "height" not in size or "width" not in size:
raise ValueError(F"The size dictionary must contain the keys 'height' and 'width'. Got {size.keys()}" )
snake_case_ = (size['''height'''], size['''width'''])
return resize(_A , size=_A , resample=_A , data_format=_A , **_A )
def A_ ( self : str , lowercase_ : List[Any] , lowercase_ : Union[str, Any] , lowercase_ : int = None , **lowercase_ : Tuple , ):
return rescale(_A , scale=_A , data_format=_A , **_A )
def A_ ( self : Union[str, Any] , lowercase_ : Dict , lowercase_ : Optional[Any] , lowercase_ : Any , lowercase_ : Optional[Any] = None , **lowercase_ : Any , ):
return normalize(_A , mean=_A , std=_A , data_format=_A , **_A )
def A_ ( self : List[str] , lowercase_ : int , lowercase_ : Tuple = None , lowercase_ : List[Any] = None , lowercase_ : str=None , lowercase_ : Tuple = None , lowercase_ : Tuple = None , lowercase_ : Optional[int] = None , lowercase_ : List[str] = None , lowercase_ : int = None , lowercase_ : List[str] = None , lowercase_ : List[Any] = None , lowercase_ : List[Any] = None , lowercase_ : Union[str, Any] = None , lowercase_ : List[str] = ChannelDimension.FIRST , **lowercase_ : List[str] , ):
snake_case_ = do_resize if do_resize is not None else self.do_resize
snake_case_ = size if size is not None else self.size
snake_case_ = get_size_dict(_A )
snake_case_ = resample if resample is not None else self.resample
snake_case_ = do_rescale if do_rescale is not None else self.do_rescale
snake_case_ = rescale_factor if rescale_factor is not None else self.rescale_factor
snake_case_ = do_normalize if do_normalize is not None else self.do_normalize
snake_case_ = image_mean if image_mean is not None else self.image_mean
snake_case_ = image_std if image_std is not None else self.image_std
snake_case_ = apply_ocr if apply_ocr is not None else self.apply_ocr
snake_case_ = ocr_lang if ocr_lang is not None else self.ocr_lang
snake_case_ = tesseract_config if tesseract_config is not None else self.tesseract_config
snake_case_ = make_list_of_images(_A )
if not valid_images(_A ):
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_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('''If do_normalize is True, image_mean and image_std must be specified.''' )
# All transformations expect numpy arrays.
snake_case_ = [to_numpy_array(_A ) for image in images]
# Tesseract OCR to get words + normalized bounding boxes
if apply_ocr:
requires_backends(self , '''pytesseract''' )
snake_case_ = []
snake_case_ = []
for image in images:
snake_case_ ,snake_case_ = apply_tesseract(_A , _A , _A )
words_batch.append(_A )
boxes_batch.append(_A )
if do_resize:
snake_case_ = [self.resize(image=_A , size=_A , resample=_A ) for image in images]
if do_rescale:
snake_case_ = [self.rescale(image=_A , scale=_A ) for image in images]
if do_normalize:
snake_case_ = [self.normalize(image=_A , mean=_A , std=_A ) for image in images]
snake_case_ = [to_channel_dimension_format(_A , _A ) for image in images]
snake_case_ = BatchFeature(data={'''pixel_values''': images} , tensor_type=_A )
if apply_ocr:
snake_case_ = words_batch
snake_case_ = boxes_batch
return data
| 56 |
import os
import torch
from ..logging import get_logger
from .constants import FSDP_PYTORCH_VERSION, MODEL_NAME, OPTIMIZER_NAME
from .versions import is_torch_version
if is_torch_version(""">=""", FSDP_PYTORCH_VERSION):
import torch.distributed.checkpoint as dist_cp
from torch.distributed.checkpoint.default_planner import DefaultLoadPlanner, DefaultSavePlanner
from torch.distributed.checkpoint.optimizer import load_sharded_optimizer_state_dict
from torch.distributed.fsdp.fully_sharded_data_parallel import FullyShardedDataParallel as FSDP
from torch.distributed.fsdp.fully_sharded_data_parallel import StateDictType
UpperCamelCase__ = get_logger(__name__)
def _a ( SCREAMING_SNAKE_CASE_ : List[str] , SCREAMING_SNAKE_CASE_ : Tuple , SCREAMING_SNAKE_CASE_ : Tuple , SCREAMING_SNAKE_CASE_ : Optional[Any] , SCREAMING_SNAKE_CASE_ : str=0 ):
os.makedirs(SCREAMING_SNAKE_CASE_ , exist_ok=SCREAMING_SNAKE_CASE_ )
with FSDP.state_dict_type(
SCREAMING_SNAKE_CASE_ , fsdp_plugin.state_dict_type , fsdp_plugin.state_dict_config , fsdp_plugin.optim_state_dict_config ):
__lowerCAmelCase = model.state_dict()
if fsdp_plugin.state_dict_type == StateDictType.FULL_STATE_DICT:
__lowerCAmelCase = F"""{MODEL_NAME}.bin""" if model_index == 0 else F"""{MODEL_NAME}_{model_index}.bin"""
__lowerCAmelCase = os.path.join(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
if accelerator.process_index == 0:
logger.info(F"""Saving model to {output_model_file}""" )
torch.save(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
logger.info(F"""Model saved to {output_model_file}""" )
elif fsdp_plugin.state_dict_type == StateDictType.LOCAL_STATE_DICT:
__lowerCAmelCase = (
F"""{MODEL_NAME}_rank{accelerator.process_index}.bin"""
if model_index == 0
else F"""{MODEL_NAME}_{model_index}_rank{accelerator.process_index}.bin"""
)
__lowerCAmelCase = os.path.join(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
logger.info(F"""Saving model to {output_model_file}""" )
torch.save(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
logger.info(F"""Model saved to {output_model_file}""" )
elif fsdp_plugin.state_dict_type == StateDictType.SHARDED_STATE_DICT:
__lowerCAmelCase = os.path.join(SCREAMING_SNAKE_CASE_ , F"""{MODEL_NAME}_{model_index}""" )
os.makedirs(SCREAMING_SNAKE_CASE_ , exist_ok=SCREAMING_SNAKE_CASE_ )
logger.info(F"""Saving model to {ckpt_dir}""" )
__lowerCAmelCase = {"model": state_dict}
dist_cp.save_state_dict(
state_dict=SCREAMING_SNAKE_CASE_ , storage_writer=dist_cp.FileSystemWriter(SCREAMING_SNAKE_CASE_ ) , planner=DefaultSavePlanner() , )
logger.info(F"""Model saved to {ckpt_dir}""" )
def _a ( SCREAMING_SNAKE_CASE_ : Dict , SCREAMING_SNAKE_CASE_ : int , SCREAMING_SNAKE_CASE_ : Optional[int] , SCREAMING_SNAKE_CASE_ : List[str] , SCREAMING_SNAKE_CASE_ : Any=0 ):
accelerator.wait_for_everyone()
with FSDP.state_dict_type(
SCREAMING_SNAKE_CASE_ , fsdp_plugin.state_dict_type , fsdp_plugin.state_dict_config , fsdp_plugin.optim_state_dict_config ):
if fsdp_plugin.state_dict_type == StateDictType.FULL_STATE_DICT:
if type(SCREAMING_SNAKE_CASE_ ) != FSDP and accelerator.process_index != 0:
if not fsdp_plugin.sync_module_states:
raise ValueError(
"Set the `sync_module_states` flag to `True` so that model states are synced across processes when "
"initializing FSDP object" )
return
__lowerCAmelCase = F"""{MODEL_NAME}.bin""" if model_index == 0 else F"""{MODEL_NAME}_{model_index}.bin"""
__lowerCAmelCase = os.path.join(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
logger.info(F"""Loading model from {input_model_file}""" )
__lowerCAmelCase = torch.load(SCREAMING_SNAKE_CASE_ )
logger.info(F"""Model loaded from {input_model_file}""" )
elif fsdp_plugin.state_dict_type == StateDictType.LOCAL_STATE_DICT:
__lowerCAmelCase = (
F"""{MODEL_NAME}_rank{accelerator.process_index}.bin"""
if model_index == 0
else F"""{MODEL_NAME}_{model_index}_rank{accelerator.process_index}.bin"""
)
__lowerCAmelCase = os.path.join(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
logger.info(F"""Loading model from {input_model_file}""" )
__lowerCAmelCase = torch.load(SCREAMING_SNAKE_CASE_ )
logger.info(F"""Model loaded from {input_model_file}""" )
elif fsdp_plugin.state_dict_type == StateDictType.SHARDED_STATE_DICT:
__lowerCAmelCase = (
os.path.join(SCREAMING_SNAKE_CASE_ , F"""{MODEL_NAME}_{model_index}""" )
if F"""{MODEL_NAME}""" not in input_dir
else input_dir
)
logger.info(F"""Loading model from {ckpt_dir}""" )
__lowerCAmelCase = {"model": model.state_dict()}
dist_cp.load_state_dict(
state_dict=SCREAMING_SNAKE_CASE_ , storage_reader=dist_cp.FileSystemReader(SCREAMING_SNAKE_CASE_ ) , planner=DefaultLoadPlanner() , )
__lowerCAmelCase = state_dict["model"]
logger.info(F"""Model loaded from {ckpt_dir}""" )
model.load_state_dict(SCREAMING_SNAKE_CASE_ )
def _a ( SCREAMING_SNAKE_CASE_ : Dict , SCREAMING_SNAKE_CASE_ : int , SCREAMING_SNAKE_CASE_ : List[str] , SCREAMING_SNAKE_CASE_ : Optional[Any] , SCREAMING_SNAKE_CASE_ : Optional[int] , SCREAMING_SNAKE_CASE_ : str=0 ):
os.makedirs(SCREAMING_SNAKE_CASE_ , exist_ok=SCREAMING_SNAKE_CASE_ )
with FSDP.state_dict_type(
SCREAMING_SNAKE_CASE_ , fsdp_plugin.state_dict_type , fsdp_plugin.state_dict_config , fsdp_plugin.optim_state_dict_config ):
__lowerCAmelCase = FSDP.optim_state_dict(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
if fsdp_plugin.state_dict_type == StateDictType.FULL_STATE_DICT:
if accelerator.process_index == 0:
__lowerCAmelCase = (
F"""{OPTIMIZER_NAME}.bin""" if optimizer_index == 0 else F"""{OPTIMIZER_NAME}_{optimizer_index}.bin"""
)
__lowerCAmelCase = os.path.join(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
logger.info(F"""Saving Optimizer state to {output_optimizer_file}""" )
torch.save(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
logger.info(F"""Optimizer state saved in {output_optimizer_file}""" )
else:
__lowerCAmelCase = os.path.join(SCREAMING_SNAKE_CASE_ , F"""{OPTIMIZER_NAME}_{optimizer_index}""" )
os.makedirs(SCREAMING_SNAKE_CASE_ , exist_ok=SCREAMING_SNAKE_CASE_ )
logger.info(F"""Saving Optimizer state to {ckpt_dir}""" )
dist_cp.save_state_dict(
state_dict={"optimizer": optim_state} , storage_writer=dist_cp.FileSystemWriter(SCREAMING_SNAKE_CASE_ ) , planner=DefaultSavePlanner() , )
logger.info(F"""Optimizer state saved in {ckpt_dir}""" )
def _a ( SCREAMING_SNAKE_CASE_ : Dict , SCREAMING_SNAKE_CASE_ : Dict , SCREAMING_SNAKE_CASE_ : Tuple , SCREAMING_SNAKE_CASE_ : List[str] , SCREAMING_SNAKE_CASE_ : str , SCREAMING_SNAKE_CASE_ : Dict=0 ):
accelerator.wait_for_everyone()
with FSDP.state_dict_type(
SCREAMING_SNAKE_CASE_ , fsdp_plugin.state_dict_type , fsdp_plugin.state_dict_config , fsdp_plugin.optim_state_dict_config ):
if fsdp_plugin.state_dict_type == StateDictType.FULL_STATE_DICT:
__lowerCAmelCase = None
# below check should work but currently it isn't working (mostly opytorch issue),
# in the meantime disabling it at the cost of excess memory usage
# if accelerator.process_index == 0 or not fsdp_plugin.optim_state_dict_config.rank0_only:
__lowerCAmelCase = (
F"""{OPTIMIZER_NAME}.bin""" if optimizer_index == 0 else F"""{OPTIMIZER_NAME}_{optimizer_index}.bin"""
)
__lowerCAmelCase = os.path.join(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
logger.info(F"""Loading Optimizer state from {input_optimizer_file}""" )
__lowerCAmelCase = torch.load(SCREAMING_SNAKE_CASE_ )
logger.info(F"""Optimizer state loaded from {input_optimizer_file}""" )
else:
__lowerCAmelCase = (
os.path.join(SCREAMING_SNAKE_CASE_ , F"""{OPTIMIZER_NAME}_{optimizer_index}""" )
if F"""{OPTIMIZER_NAME}""" not in input_dir
else input_dir
)
logger.info(F"""Loading Optimizer from {ckpt_dir}""" )
__lowerCAmelCase = load_sharded_optimizer_state_dict(
model_state_dict=model.state_dict() , optimizer_key="optimizer" , storage_reader=dist_cp.FileSystemReader(SCREAMING_SNAKE_CASE_ ) , )
__lowerCAmelCase = optim_state["optimizer"]
logger.info(F"""Optimizer loaded from {ckpt_dir}""" )
__lowerCAmelCase = FSDP.optim_state_dict_to_load(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
optimizer.load_state_dict(SCREAMING_SNAKE_CASE_ )
| 92 | 0 |
'''simple docstring'''
from __future__ import annotations
a_ : List[Any] = 10
def a_ ( __snake_case : list[int] ) -> List[str]:
"""simple docstring"""
lowerCamelCase_ =1
lowerCamelCase_ =max(SCREAMING_SNAKE_CASE_ )
while placement <= max_digit:
# declare and initialize empty buckets
lowerCamelCase_ =[[] for _ in range(SCREAMING_SNAKE_CASE_ )]
# split list_of_ints between the buckets
for i in list_of_ints:
lowerCamelCase_ =int((i / placement) % RADIX )
buckets[tmp].append(SCREAMING_SNAKE_CASE_ )
# put each buckets' contents into list_of_ints
lowerCamelCase_ =0
for b in range(SCREAMING_SNAKE_CASE_ ):
for i in buckets[b]:
lowerCamelCase_ =i
a += 1
# move to next
placement *= RADIX
return list_of_ints
if __name__ == "__main__":
import doctest
doctest.testmod()
| 75 |
import math
import time
from typing import Dict, List, Optional
from torch.utils.data import Dataset
from transformers import SeqaSeqTrainer, is_torch_tpu_available
from transformers.trainer_utils import PredictionOutput, speed_metrics
if is_torch_tpu_available(check_device=False):
import torch_xla.core.xla_model as xm
import torch_xla.debug.metrics as met
class a__ ( snake_case__ ):
def __init__( self , *_A , _A=None , _A=None , **_A ):
"""simple docstring"""
super().__init__(*_A , **_A )
__lowerCAmelCase = eval_examples
__lowerCAmelCase = post_process_function
def __SCREAMING_SNAKE_CASE( self , _A = None , _A=None , _A = None , _A = "eval" , **_A , ):
"""simple docstring"""
__lowerCAmelCase = gen_kwargs.copy()
__lowerCAmelCase = (
gen_kwargs["max_length"] if gen_kwargs.get("max_length" ) is not None else self.args.generation_max_length
)
__lowerCAmelCase = (
gen_kwargs["num_beams"] if gen_kwargs.get("num_beams" ) is not None else self.args.generation_num_beams
)
__lowerCAmelCase = gen_kwargs
__lowerCAmelCase = self.eval_dataset if eval_dataset is None else eval_dataset
__lowerCAmelCase = self.get_eval_dataloader(_A )
__lowerCAmelCase = self.eval_examples if eval_examples is None else eval_examples
# Temporarily disable metric computation, we will do it in the loop here.
__lowerCAmelCase = self.compute_metrics
__lowerCAmelCase = None
__lowerCAmelCase = time.time()
__lowerCAmelCase = self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
try:
__lowerCAmelCase = eval_loop(
_A , description="Evaluation" , prediction_loss_only=True if compute_metrics is None else None , ignore_keys=_A , metric_key_prefix=_A , )
finally:
__lowerCAmelCase = compute_metrics
__lowerCAmelCase = self.args.eval_batch_size * self.args.world_size
if f"""{metric_key_prefix}_jit_compilation_time""" in output.metrics:
start_time += output.metrics[f"""{metric_key_prefix}_jit_compilation_time"""]
output.metrics.update(
speed_metrics(
_A , _A , num_samples=output.num_samples , num_steps=math.ceil(output.num_samples / total_batch_size ) , ) )
if self.post_process_function is not None and self.compute_metrics is not None and self.args.should_save:
# Only the main node write the results by default
__lowerCAmelCase = self.post_process_function(_A , _A , _A )
__lowerCAmelCase = self.compute_metrics(_A )
# Prefix all keys with metric_key_prefix + '_'
for key in list(metrics.keys() ):
if not key.startswith(f"""{metric_key_prefix}_""" ):
__lowerCAmelCase = metrics.pop(_A )
metrics.update(output.metrics )
else:
__lowerCAmelCase = output.metrics
if self.args.should_log:
# Only the main node log the results by default
self.log(_A )
if self.args.tpu_metrics_debug or self.args.debug:
# tpu-comment: Logging debug metrics for PyTorch/XLA (compile, execute times, ops, etc.)
xm.master_print(met.metrics_report() )
__lowerCAmelCase = self.callback_handler.on_evaluate(self.args , self.state , self.control , _A )
return metrics
def __SCREAMING_SNAKE_CASE( self , _A , _A , _A=None , _A = "test" , **_A ):
"""simple docstring"""
__lowerCAmelCase = gen_kwargs.copy()
__lowerCAmelCase = self.get_test_dataloader(_A )
# Temporarily disable metric computation, we will do it in the loop here.
__lowerCAmelCase = self.compute_metrics
__lowerCAmelCase = None
__lowerCAmelCase = time.time()
__lowerCAmelCase = self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
try:
__lowerCAmelCase = eval_loop(
_A , description="Prediction" , prediction_loss_only=True if compute_metrics is None else None , ignore_keys=_A , metric_key_prefix=_A , )
finally:
__lowerCAmelCase = compute_metrics
__lowerCAmelCase = self.args.eval_batch_size * self.args.world_size
if f"""{metric_key_prefix}_jit_compilation_time""" in output.metrics:
start_time += output.metrics[f"""{metric_key_prefix}_jit_compilation_time"""]
output.metrics.update(
speed_metrics(
_A , _A , num_samples=output.num_samples , num_steps=math.ceil(output.num_samples / total_batch_size ) , ) )
if self.post_process_function is None or self.compute_metrics is None:
return output
__lowerCAmelCase = self.post_process_function(_A , _A , _A , "predict" )
__lowerCAmelCase = self.compute_metrics(_A )
# Prefix all keys with metric_key_prefix + '_'
for key in list(metrics.keys() ):
if not key.startswith(f"""{metric_key_prefix}_""" ):
__lowerCAmelCase = metrics.pop(_A )
metrics.update(output.metrics )
return PredictionOutput(predictions=predictions.predictions , label_ids=predictions.label_ids , metrics=_A )
| 92 | 0 |
"""simple docstring"""
class a ( snake_case__ ):
pass
class a ( snake_case__ ):
pass
class a :
def __init__( self ):
lowercase = [
[],
[],
[],
]
def UpperCamelCase_ ( self , _lowerCamelCase , _lowerCamelCase ):
try:
if len(self.queues[priority] ) >= 1_0_0:
raise OverflowError('Maximum queue size is 100' )
self.queues[priority].append(_A )
except IndexError:
raise ValueError('Valid priorities are 0, 1, and 2' )
def UpperCamelCase_ ( self ):
for queue in self.queues:
if queue:
return queue.pop(0 )
raise UnderFlowError('All queues are empty' )
def __str__( self ):
return "\n".join(F'Priority {i}: {q}' for i, q in enumerate(self.queues ) )
class a :
def __init__( self ):
lowercase = []
def UpperCamelCase_ ( self , _lowerCamelCase ):
if len(self.queue ) == 1_0_0:
raise OverFlowError('Maximum queue size is 100' )
self.queue.append(_A )
def UpperCamelCase_ ( self ):
if not self.queue:
raise UnderFlowError('The queue is empty' )
else:
lowercase = min(self.queue )
self.queue.remove(_A )
return data
def __str__( self ):
return str(self.queue )
def _SCREAMING_SNAKE_CASE ( ):
'''simple docstring'''
lowercase = FixedPriorityQueue()
fpq.enqueue(0 , 10 )
fpq.enqueue(1 , 70 )
fpq.enqueue(0 , 1_00 )
fpq.enqueue(2 , 1 )
fpq.enqueue(2 , 5 )
fpq.enqueue(1 , 7 )
fpq.enqueue(2 , 4 )
fpq.enqueue(1 , 64 )
fpq.enqueue(0 , 1_28 )
print(SCREAMING_SNAKE_CASE_ )
print(fpq.dequeue() )
print(fpq.dequeue() )
print(fpq.dequeue() )
print(fpq.dequeue() )
print(fpq.dequeue() )
print(SCREAMING_SNAKE_CASE_ )
print(fpq.dequeue() )
print(fpq.dequeue() )
print(fpq.dequeue() )
print(fpq.dequeue() )
print(fpq.dequeue() )
def _SCREAMING_SNAKE_CASE ( ):
'''simple docstring'''
lowercase = ElementPriorityQueue()
epq.enqueue(10 )
epq.enqueue(70 )
epq.enqueue(1_00 )
epq.enqueue(1 )
epq.enqueue(5 )
epq.enqueue(7 )
epq.enqueue(4 )
epq.enqueue(64 )
epq.enqueue(1_28 )
print(SCREAMING_SNAKE_CASE_ )
print(epq.dequeue() )
print(epq.dequeue() )
print(epq.dequeue() )
print(epq.dequeue() )
print(epq.dequeue() )
print(SCREAMING_SNAKE_CASE_ )
print(epq.dequeue() )
print(epq.dequeue() )
print(epq.dequeue() )
print(epq.dequeue() )
print(epq.dequeue() )
if __name__ == "__main__":
fixed_priority_queue()
element_priority_queue()
| 220 |
import logging
from pathlib import Path
import numpy as np
import pytorch_lightning as pl
import torch
from pytorch_lightning.callbacks import EarlyStopping, ModelCheckpoint
from pytorch_lightning.utilities import rank_zero_only
from utils_rag import save_json
def _a ( SCREAMING_SNAKE_CASE_ : Optional[int] ):
__lowerCAmelCase = filter(lambda SCREAMING_SNAKE_CASE_ : p.requires_grad , model.parameters() )
__lowerCAmelCase = sum([np.prod(p.size() ) for p in model_parameters] )
return params
UpperCamelCase__ = logging.getLogger(__name__)
def _a ( SCREAMING_SNAKE_CASE_ : Union[str, Any] , SCREAMING_SNAKE_CASE_ : Any ):
if metric == "rouge2":
__lowerCAmelCase = "{val_avg_rouge2:.4f}-{step_count}"
elif metric == "bleu":
__lowerCAmelCase = "{val_avg_bleu:.4f}-{step_count}"
elif metric == "em":
__lowerCAmelCase = "{val_avg_em:.4f}-{step_count}"
else:
raise NotImplementedError(
F"""seq2seq callbacks only support rouge2 and bleu, got {metric}, You can make your own by adding to this"""
" function." )
__lowerCAmelCase = ModelCheckpoint(
dirpath=SCREAMING_SNAKE_CASE_ , filename=SCREAMING_SNAKE_CASE_ , monitor=F"""val_{metric}""" , mode="max" , save_top_k=3 , every_n_epochs=1 , )
return checkpoint_callback
def _a ( SCREAMING_SNAKE_CASE_ : int , SCREAMING_SNAKE_CASE_ : Union[str, Any] ):
return EarlyStopping(
monitor=F"""val_{metric}""" , mode="min" if "loss" in metric else "max" , patience=SCREAMING_SNAKE_CASE_ , verbose=SCREAMING_SNAKE_CASE_ , )
class a__ ( pl.Callback ):
def __SCREAMING_SNAKE_CASE( self , _A , _A ):
"""simple docstring"""
__lowerCAmelCase = {f"""lr_group_{i}""": param["lr"] for i, param in enumerate(pl_module.trainer.optimizers[0].param_groups )}
pl_module.logger.log_metrics(_A )
@rank_zero_only
def __SCREAMING_SNAKE_CASE( self , _A , _A , _A , _A=True ):
"""simple docstring"""
logger.info(f"""***** {type_path} results at step {trainer.global_step:05d} *****""" )
__lowerCAmelCase = trainer.callback_metrics
trainer.logger.log_metrics({k: v for k, v in metrics.items() if k not in ["log", "progress_bar", "preds"]} )
# Log results
__lowerCAmelCase = Path(pl_module.hparams.output_dir )
if type_path == "test":
__lowerCAmelCase = od / "test_results.txt"
__lowerCAmelCase = od / "test_generations.txt"
else:
# this never gets hit. I prefer not to save intermediate generations, and results are in metrics.json
# If people want this it will be easy enough to add back.
__lowerCAmelCase = od / f"""{type_path}_results/{trainer.global_step:05d}.txt"""
__lowerCAmelCase = od / f"""{type_path}_generations/{trainer.global_step:05d}.txt"""
results_file.parent.mkdir(exist_ok=_A )
generations_file.parent.mkdir(exist_ok=_A )
with open(_A , "a+" ) as writer:
for key in sorted(_A ):
if key in ["log", "progress_bar", "preds"]:
continue
__lowerCAmelCase = metrics[key]
if isinstance(_A , torch.Tensor ):
__lowerCAmelCase = val.item()
__lowerCAmelCase = f"""{key}: {val:.6f}\n"""
writer.write(_A )
if not save_generations:
return
if "preds" in metrics:
__lowerCAmelCase = "\n".join(metrics["preds"] )
generations_file.open("w+" ).write(_A )
@rank_zero_only
def __SCREAMING_SNAKE_CASE( self , _A , _A ):
"""simple docstring"""
try:
__lowerCAmelCase = pl_module.model.model.num_parameters()
except AttributeError:
__lowerCAmelCase = pl_module.model.num_parameters()
__lowerCAmelCase = count_trainable_parameters(_A )
# mp stands for million parameters
trainer.logger.log_metrics({"n_params": npars, "mp": npars / 1E6, "grad_mp": n_trainable_pars / 1E6} )
@rank_zero_only
def __SCREAMING_SNAKE_CASE( self , _A , _A ):
"""simple docstring"""
save_json(pl_module.metrics , pl_module.metrics_save_path )
return self._write_logs(_A , _A , "test" )
@rank_zero_only
def __SCREAMING_SNAKE_CASE( self , _A , _A ):
"""simple docstring"""
save_json(pl_module.metrics , pl_module.metrics_save_path )
# Uncommenting this will save val generations
# return self._write_logs(trainer, pl_module, "valid")
| 92 | 0 |
from __future__ import annotations
from scipy.special import comb # type: ignore
class __A:
def __init__( self , _snake_case ) -> Dict:
'''simple docstring'''
__a = list_of_points
# Degree determines the flexibility of the curve.
# Degree = 1 will produce a straight line.
__a = len(_A ) - 1
def SCREAMING_SNAKE_CASE_ ( self , _snake_case ) -> List[Any]:
'''simple docstring'''
assert 0 <= t <= 1, "Time t must be between 0 and 1."
__a = []
for i in range(len(self.list_of_points ) ):
# basis function for each i
output_values.append(
comb(self.degree , _A ) * ((1 - t) ** (self.degree - i)) * (t**i) )
# the basis must sum up to 1 for it to produce a valid Bezier curve.
assert round(sum(_A ) , 5 ) == 1
return output_values
def SCREAMING_SNAKE_CASE_ ( self , _snake_case ) -> str:
'''simple docstring'''
assert 0 <= t <= 1, "Time t must be between 0 and 1."
__a = self.basis_function(_A )
__a = 0.0
__a = 0.0
for i in range(len(self.list_of_points ) ):
# For all points, sum up the product of i-th basis function and i-th point.
x += basis_function[i] * self.list_of_points[i][0]
y += basis_function[i] * self.list_of_points[i][1]
return (x, y)
def SCREAMING_SNAKE_CASE_ ( self , _snake_case = 0.01 ) -> int:
'''simple docstring'''
from matplotlib import pyplot as plt # type: ignore
__a = [] # x coordinates of points to plot
__a = [] # y coordinates of points to plot
__a = 0.0
while t <= 1:
__a = self.bezier_curve_function(_A )
to_plot_x.append(value[0] )
to_plot_y.append(value[1] )
t += step_size
__a = [i[0] for i in self.list_of_points]
__a = [i[1] for i in self.list_of_points]
plt.plot(
_A , _A , color='''blue''' , label='''Curve of Degree ''' + str(self.degree ) , )
plt.scatter(_A , _A , color='''red''' , label='''Control Points''' )
plt.legend()
plt.show()
if __name__ == "__main__":
import doctest
doctest.testmod()
BezierCurve([(1, 2), (3, 5)]).plot_curve() # degree 1
BezierCurve([(0, 0), (5, 5), (5, 0)]).plot_curve() # degree 2
BezierCurve([(0, 0), (5, 5), (5, 0), (2.5, -2.5)]).plot_curve() # degree 3 | 6 |
from .glue import glue_convert_examples_to_features, glue_output_modes, glue_processors, glue_tasks_num_labels
from .squad import SquadExample, SquadFeatures, SquadVaProcessor, SquadVaProcessor, squad_convert_examples_to_features
from .utils import DataProcessor, InputExample, InputFeatures, SingleSentenceClassificationProcessor
from .xnli import xnli_output_modes, xnli_processors, xnli_tasks_num_labels
| 92 | 0 |
import os
from typing import Optional
import fsspec
from fsspec.archive import AbstractArchiveFileSystem
from fsspec.utils import DEFAULT_BLOCK_SIZE
class _UpperCAmelCase ( snake_case__ ):
"""simple docstring"""
lowercase__ = """"""
lowercase__ = (
None # protocol passed in prefix to the url. ex: "gzip", for gzip://file.txt::http://foo.bar/file.txt.gz
)
lowercase__ = None # compression type in fsspec. ex: "gzip"
lowercase__ = None # extension of the filename to strip. ex: "".gz" to get file.txt from file.txt.gz
def __init__( self : Tuple, lowerCamelCase : Optional[Any] = "", lowerCamelCase : List[str] = None, lowerCamelCase : int = None, **lowerCamelCase : str ):
'''simple docstring'''
super().__init__(self, **_A )
# always open as "rb" since fsspec can then use the TextIOWrapper to make it work for "r" mode
lowercase__ = fsspec.open(
_A, mode='''rb''', protocol=_A, compression=self.compression, client_kwargs={
'''requote_redirect_url''': False, # see https://github.com/huggingface/datasets/pull/5459
'''trust_env''': True, # Enable reading proxy env variables.
**(target_options or {}).pop('''client_kwargs''', {} ), # To avoid issues if it was already passed.
}, **(target_options or {}), )
lowercase__ = os.path.basename(self.file.path.split('''::''' )[0] )
lowercase__ = (
self.compressed_name[: self.compressed_name.rindex('''.''' )]
if '''.''' in self.compressed_name
else self.compressed_name
)
lowercase__ = None
@classmethod
def lowercase__ ( cls : List[str], lowerCamelCase : int ):
'''simple docstring'''
return super()._strip_protocol(_A ).lstrip('''/''' )
def lowercase__ ( self : Any ):
'''simple docstring'''
if self.dir_cache is None:
lowercase__ = {**self.file.fs.info(self.file.path ), '''name''': self.uncompressed_name}
lowercase__ = {f['''name''']: f}
def lowercase__ ( self : Union[str, Any], lowerCamelCase : Union[str, Any] ):
'''simple docstring'''
return self.file.open().read()
def lowercase__ ( self : Any, lowerCamelCase : Optional[int], lowerCamelCase : List[str] = "rb", lowerCamelCase : List[str]=None, lowerCamelCase : List[Any]=True, lowerCamelCase : str=None, **lowerCamelCase : List[Any], ):
'''simple docstring'''
lowercase__ = self._strip_protocol(_A )
if mode != "rb":
raise ValueError(F"""Tried to read with mode {mode} on file {self.file.path} opened with mode 'rb'""" )
return self.file.open()
class _UpperCAmelCase ( snake_case__ ):
"""simple docstring"""
lowercase__ = """bz2"""
lowercase__ = """bz2"""
lowercase__ = """.bz2"""
class _UpperCAmelCase ( snake_case__ ):
"""simple docstring"""
lowercase__ = """gzip"""
lowercase__ = """gzip"""
lowercase__ = """.gz"""
class _UpperCAmelCase ( snake_case__ ):
"""simple docstring"""
lowercase__ = """lz4"""
lowercase__ = """lz4"""
lowercase__ = """.lz4"""
class _UpperCAmelCase ( snake_case__ ):
"""simple docstring"""
lowercase__ = """xz"""
lowercase__ = """xz"""
lowercase__ = """.xz"""
class _UpperCAmelCase ( snake_case__ ):
"""simple docstring"""
lowercase__ = """zstd"""
lowercase__ = """zstd"""
lowercase__ = """.zst"""
def __init__( self : Tuple, lowerCamelCase : Tuple, lowerCamelCase : Optional[int] = "rb", lowerCamelCase : List[Any] = None, lowerCamelCase : Optional[int] = None, lowerCamelCase : Tuple = DEFAULT_BLOCK_SIZE, **lowerCamelCase : int, ):
'''simple docstring'''
super().__init__(
fo=_A, mode=_A, target_protocol=_A, target_options=_A, block_size=_A, **_A, )
# We need to wrap the zstd decompressor to avoid this error in fsspec==2021.7.0 and zstandard==0.15.2:
#
# File "/Users/user/.virtualenvs/hf-datasets/lib/python3.7/site-packages/fsspec/core.py", line 145, in open
# out.close = close
# AttributeError: 'zstd.ZstdDecompressionReader' object attribute 'close' is read-only
#
# see https://github.com/intake/filesystem_spec/issues/725
lowercase__ = self.file.__enter__
class _UpperCAmelCase :
"""simple docstring"""
def __init__( self : Any, lowerCamelCase : List[Any] ):
'''simple docstring'''
lowercase__ = file_
def __enter__( self : Union[str, Any] ):
'''simple docstring'''
self._file.__enter__()
return self
def __exit__( self : Optional[int], *lowerCamelCase : Optional[Any], **lowerCamelCase : int ):
'''simple docstring'''
self._file.__exit__(*_A, **_A )
def __iter__( self : List[str] ):
'''simple docstring'''
return iter(self._file )
def lowercase__ ( self : Tuple ):
'''simple docstring'''
return next(self._file )
def __getattr__( self : Any, lowerCamelCase : int ):
'''simple docstring'''
return getattr(self._file, _A )
def fixed_enter(*lowerCamelCase : Union[str, Any], **lowerCamelCase : Optional[Any] ):
return WrappedFile(_enter(*_A, **_A ) )
lowercase__ = fixed_enter
| 207 |
from queue import PriorityQueue
from typing import Any
import numpy as np
def _a ( SCREAMING_SNAKE_CASE_ : dict , SCREAMING_SNAKE_CASE_ : str , SCREAMING_SNAKE_CASE_ : set , SCREAMING_SNAKE_CASE_ : set , SCREAMING_SNAKE_CASE_ : dict , SCREAMING_SNAKE_CASE_ : dict , SCREAMING_SNAKE_CASE_ : PriorityQueue , SCREAMING_SNAKE_CASE_ : dict , SCREAMING_SNAKE_CASE_ : float | int , ):
for nxt, d in graph[v]:
if nxt in visited_forward:
continue
__lowerCAmelCase = cst_fwd.get(SCREAMING_SNAKE_CASE_ , np.inf )
__lowerCAmelCase = cst_fwd[v] + d
if new_cost_f < old_cost_f:
queue.put((new_cost_f, nxt) )
__lowerCAmelCase = new_cost_f
__lowerCAmelCase = v
if nxt in visited_backward:
if cst_fwd[v] + d + cst_bwd[nxt] < shortest_distance:
__lowerCAmelCase = cst_fwd[v] + d + cst_bwd[nxt]
return shortest_distance
def _a ( SCREAMING_SNAKE_CASE_ : str , SCREAMING_SNAKE_CASE_ : str , SCREAMING_SNAKE_CASE_ : dict , SCREAMING_SNAKE_CASE_ : dict ):
__lowerCAmelCase = -1
__lowerCAmelCase = set()
__lowerCAmelCase = set()
__lowerCAmelCase = {source: 0}
__lowerCAmelCase = {destination: 0}
__lowerCAmelCase = {source: None}
__lowerCAmelCase = {destination: None}
__lowerCAmelCase = PriorityQueue()
__lowerCAmelCase = PriorityQueue()
__lowerCAmelCase = np.inf
queue_forward.put((0, source) )
queue_backward.put((0, destination) )
if source == destination:
return 0
while not queue_forward.empty() and not queue_backward.empty():
__lowerCAmelCase , __lowerCAmelCase = queue_forward.get()
visited_forward.add(SCREAMING_SNAKE_CASE_ )
__lowerCAmelCase , __lowerCAmelCase = queue_backward.get()
visited_backward.add(SCREAMING_SNAKE_CASE_ )
__lowerCAmelCase = pass_and_relaxation(
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , )
__lowerCAmelCase = pass_and_relaxation(
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , )
if cst_fwd[v_fwd] + cst_bwd[v_bwd] >= shortest_distance:
break
if shortest_distance != np.inf:
__lowerCAmelCase = shortest_distance
return shortest_path_distance
UpperCamelCase__ = {
"""B""": [["""C""", 1]],
"""C""": [["""D""", 1]],
"""D""": [["""F""", 1]],
"""E""": [["""B""", 1], ["""G""", 2]],
"""F""": [],
"""G""": [["""F""", 1]],
}
UpperCamelCase__ = {
"""B""": [["""E""", 1]],
"""C""": [["""B""", 1]],
"""D""": [["""C""", 1]],
"""F""": [["""D""", 1], ["""G""", 1]],
"""E""": [[None, np.inf]],
"""G""": [["""E""", 2]],
}
if __name__ == "__main__":
import doctest
doctest.testmod()
| 92 | 0 |
'''simple docstring'''
# Lint as: python3
import sys
from collections.abc import Mapping
from typing import TYPE_CHECKING, Dict, Optional
import numpy as np
import pyarrow as pa
from .. import config
from ..utils.logging import get_logger
from ..utils.py_utils import map_nested
from .formatting import TensorFormatter
if TYPE_CHECKING:
import jax
import jaxlib
lowerCAmelCase: Tuple = get_logger()
lowerCAmelCase: List[Any] = None
class a__( TensorFormatter[Mapping, """jax.Array""", Mapping] ):
def __init__( self : Union[str, Any] , __snake_case : Any=None , __snake_case : Dict=None , **__snake_case : int ):
super().__init__(features=_A )
import jax
from jaxlib.xla_client import Device
if isinstance(_A , _A ):
raise ValueError(
F"""Expected {device} to be a `str` not {type(_A )}, as `jaxlib.xla_extension.Device` """
'is not serializable neither with `pickle` nor with `dill`. Instead you can surround '
'the device with `str()` to get its string identifier that will be internally mapped '
'to the actual `jaxlib.xla_extension.Device`.' )
a : Optional[int] = device if isinstance(_A , _A ) else str(jax.devices()[0] )
# using global variable since `jaxlib.xla_extension.Device` is not serializable neither
# with `pickle` nor with `dill`, so we need to use a global variable instead
global DEVICE_MAPPING
if DEVICE_MAPPING is None:
a : Optional[Any] = self._map_devices_to_str()
if self.device not in list(DEVICE_MAPPING.keys() ):
logger.warning(
F"""Device with string identifier {self.device} not listed among the available """
F"""devices: {list(DEVICE_MAPPING.keys() )}, so falling back to the default """
F"""device: {str(jax.devices()[0] )}.""" )
a : Tuple = str(jax.devices()[0] )
a : Optional[int] = jnp_array_kwargs
@staticmethod
def lowercase_ ( ):
import jax
return {str(_A ): device for device in jax.devices()}
def lowercase_ ( self : Dict , __snake_case : Tuple ):
import jax
import jax.numpy as jnp
if isinstance(_A , _A ) and column:
if all(
isinstance(_A , jax.Array ) and x.shape == column[0].shape and x.dtype == column[0].dtype for x in column ):
return jnp.stack(_A , axis=0 )
return column
def lowercase_ ( self : Union[str, Any] , __snake_case : Union[str, Any] ):
import jax
import jax.numpy as jnp
if isinstance(_A , (str, bytes, type(_A )) ):
return value
elif isinstance(_A , (np.character, np.ndarray) ) and np.issubdtype(value.dtype , np.character ):
return value.tolist()
a : List[str] = {}
if isinstance(_A , (np.number, np.ndarray) ) and np.issubdtype(value.dtype , np.integer ):
# the default int precision depends on the jax config
# see https://jax.readthedocs.io/en/latest/notebooks/Common_Gotchas_in_JAX.html#double-64bit-precision
if jax.config.jax_enable_xaa:
a : Union[str, Any] = {'dtype': jnp.intaa}
else:
a : Dict = {'dtype': jnp.intaa}
elif isinstance(_A , (np.number, np.ndarray) ) and np.issubdtype(value.dtype , np.floating ):
a : Any = {'dtype': jnp.floataa}
elif config.PIL_AVAILABLE and "PIL" in sys.modules:
import PIL.Image
if isinstance(_A , PIL.Image.Image ):
a : str = np.asarray(_A )
# using global variable since `jaxlib.xla_extension.Device` is not serializable neither
# with `pickle` nor with `dill`, so we need to use a global variable instead
global DEVICE_MAPPING
if DEVICE_MAPPING is None:
a : Tuple = self._map_devices_to_str()
with jax.default_device(DEVICE_MAPPING[self.device] ):
# calling jnp.array on a np.ndarray does copy the data
# see https://github.com/google/jax/issues/4486
return jnp.array(_A , **{**default_dtype, **self.jnp_array_kwargs} )
def lowercase_ ( self : int , __snake_case : Optional[Any] ):
import jax
# support for torch, tf, jax etc.
if config.TORCH_AVAILABLE and "torch" in sys.modules:
import torch
if isinstance(_A , torch.Tensor ):
return self._tensorize(data_struct.detach().cpu().numpy()[()] )
if hasattr(_A , '__array__' ) and not isinstance(_A , jax.Array ):
a : Optional[Any] = data_struct.__array__()
# support for nested types like struct of list of struct
if isinstance(_A , np.ndarray ):
if data_struct.dtype == object: # jax arrays cannot be instantied from an array of objects
return self._consolidate([self.recursive_tensorize(_A ) for substruct in data_struct] )
elif isinstance(_A , (list, tuple) ):
return self._consolidate([self.recursive_tensorize(_A ) for substruct in data_struct] )
return self._tensorize(_A )
def lowercase_ ( self : Dict , __snake_case : List[Any] ):
return map_nested(self._recursive_tensorize , _A , map_list=_A )
def lowercase_ ( self : Optional[Any] , __snake_case : List[Any] ):
a : Tuple = self.numpy_arrow_extractor().extract_row(_A )
a : int = self.python_features_decoder.decode_row(_A )
return self.recursive_tensorize(_A )
def lowercase_ ( self : Union[str, Any] , __snake_case : Optional[int] ):
a : Optional[int] = self.numpy_arrow_extractor().extract_column(_A )
a : Dict = self.python_features_decoder.decode_column(_A , pa_table.column_names[0] )
a : Optional[Any] = self.recursive_tensorize(_A )
a : Optional[int] = self._consolidate(_A )
return column
def lowercase_ ( self : List[str] , __snake_case : Union[str, Any] ):
a : str = self.numpy_arrow_extractor().extract_batch(_A )
a : Optional[Any] = self.python_features_decoder.decode_batch(_A )
a : Optional[Any] = self.recursive_tensorize(_A )
for column_name in batch:
a : int = self._consolidate(batch[column_name] )
return batch | 297 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCamelCase__ = logging.get_logger(__name__)
UpperCamelCase__ = {
"""edbeeching/decision-transformer-gym-hopper-medium""": (
"""https://huggingface.co/edbeeching/decision-transformer-gym-hopper-medium/resolve/main/config.json"""
),
# See all DecisionTransformer models at https://huggingface.co/models?filter=decision_transformer
}
class a__ ( snake_case__ ):
_a : Optional[int] = """decision_transformer"""
_a : Optional[int] = ["""past_key_values"""]
_a : Dict = {
"""max_position_embeddings""": """n_positions""",
"""num_attention_heads""": """n_head""",
"""num_hidden_layers""": """n_layer""",
}
def __init__( self , _A=1_7 , _A=4 , _A=1_2_8 , _A=4_0_9_6 , _A=True , _A=1 , _A=1_0_2_4 , _A=3 , _A=1 , _A=None , _A="relu" , _A=0.1 , _A=0.1 , _A=0.1 , _A=1E-5 , _A=0.02 , _A=True , _A=True , _A=5_0_2_5_6 , _A=5_0_2_5_6 , _A=False , _A=False , **_A , ):
"""simple docstring"""
__lowerCAmelCase = state_dim
__lowerCAmelCase = act_dim
__lowerCAmelCase = hidden_size
__lowerCAmelCase = max_ep_len
__lowerCAmelCase = action_tanh
__lowerCAmelCase = vocab_size
__lowerCAmelCase = n_positions
__lowerCAmelCase = n_layer
__lowerCAmelCase = n_head
__lowerCAmelCase = n_inner
__lowerCAmelCase = activation_function
__lowerCAmelCase = resid_pdrop
__lowerCAmelCase = embd_pdrop
__lowerCAmelCase = attn_pdrop
__lowerCAmelCase = layer_norm_epsilon
__lowerCAmelCase = initializer_range
__lowerCAmelCase = scale_attn_weights
__lowerCAmelCase = use_cache
__lowerCAmelCase = scale_attn_by_inverse_layer_idx
__lowerCAmelCase = reorder_and_upcast_attn
__lowerCAmelCase = bos_token_id
__lowerCAmelCase = eos_token_id
super().__init__(bos_token_id=_A , eos_token_id=_A , **_A )
| 92 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
is_vision_available,
)
UpperCAmelCase : Dict = {"configuration_vit": ["VIT_PRETRAINED_CONFIG_ARCHIVE_MAP", "ViTConfig", "ViTOnnxConfig"]}
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCAmelCase : Union[str, Any] = ["ViTFeatureExtractor"]
UpperCAmelCase : int = ["ViTImageProcessor"]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCAmelCase : Optional[int] = [
"VIT_PRETRAINED_MODEL_ARCHIVE_LIST",
"ViTForImageClassification",
"ViTForMaskedImageModeling",
"ViTModel",
"ViTPreTrainedModel",
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCAmelCase : List[str] = [
"TFViTForImageClassification",
"TFViTModel",
"TFViTPreTrainedModel",
]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCAmelCase : List[str] = [
"FlaxViTForImageClassification",
"FlaxViTModel",
"FlaxViTPreTrainedModel",
]
if TYPE_CHECKING:
from .configuration_vit import VIT_PRETRAINED_CONFIG_ARCHIVE_MAP, ViTConfig, ViTOnnxConfig
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .feature_extraction_vit import ViTFeatureExtractor
from .image_processing_vit import ViTImageProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_vit import (
VIT_PRETRAINED_MODEL_ARCHIVE_LIST,
ViTForImageClassification,
ViTForMaskedImageModeling,
ViTModel,
ViTPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_vit import TFViTForImageClassification, TFViTModel, TFViTPreTrainedModel
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_vit import FlaxViTForImageClassification, FlaxViTModel, FlaxViTPreTrainedModel
else:
import sys
UpperCAmelCase : Dict = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 252 |
import gc
import unittest
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
DDPMScheduler,
PriorTransformer,
StableUnCLIPPipeline,
UNetaDConditionModel,
)
from diffusers.pipelines.stable_diffusion.stable_unclip_image_normalizer import StableUnCLIPImageNormalizer
from diffusers.utils.testing_utils import enable_full_determinism, load_numpy, require_torch_gpu, slow, torch_device
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,
assert_mean_pixel_difference,
)
enable_full_determinism()
class a__ ( snake_case__ , snake_case__ , snake_case__ , unittest.TestCase ):
_a : str = StableUnCLIPPipeline
_a : Union[str, Any] = TEXT_TO_IMAGE_PARAMS
_a : Dict = TEXT_TO_IMAGE_BATCH_PARAMS
_a : Optional[int] = TEXT_TO_IMAGE_IMAGE_PARAMS
_a : Dict = TEXT_TO_IMAGE_IMAGE_PARAMS
# TODO(will) Expected attn_bias.stride(1) == 0 to be true, but got false
_a : Optional[Any] = False
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
__lowerCAmelCase = 3_2
__lowerCAmelCase = embedder_hidden_size
# prior components
torch.manual_seed(0 )
__lowerCAmelCase = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip" )
torch.manual_seed(0 )
__lowerCAmelCase = CLIPTextModelWithProjection(
CLIPTextConfig(
bos_token_id=0 , eos_token_id=2 , hidden_size=_A , projection_dim=_A , intermediate_size=3_7 , layer_norm_eps=1E-0_5 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=1_0_0_0 , ) )
torch.manual_seed(0 )
__lowerCAmelCase = PriorTransformer(
num_attention_heads=2 , attention_head_dim=1_2 , embedding_dim=_A , num_layers=1 , )
torch.manual_seed(0 )
__lowerCAmelCase = DDPMScheduler(
variance_type="fixed_small_log" , prediction_type="sample" , num_train_timesteps=1_0_0_0 , clip_sample=_A , clip_sample_range=5.0 , beta_schedule="squaredcos_cap_v2" , )
# regular denoising components
torch.manual_seed(0 )
__lowerCAmelCase = StableUnCLIPImageNormalizer(embedding_dim=_A )
__lowerCAmelCase = DDPMScheduler(beta_schedule="squaredcos_cap_v2" )
torch.manual_seed(0 )
__lowerCAmelCase = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip" )
torch.manual_seed(0 )
__lowerCAmelCase = CLIPTextModel(
CLIPTextConfig(
bos_token_id=0 , eos_token_id=2 , hidden_size=_A , projection_dim=3_2 , intermediate_size=3_7 , layer_norm_eps=1E-0_5 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=1_0_0_0 , ) )
torch.manual_seed(0 )
__lowerCAmelCase = UNetaDConditionModel(
sample_size=3_2 , in_channels=4 , out_channels=4 , down_block_types=("CrossAttnDownBlock2D", "DownBlock2D") , up_block_types=("UpBlock2D", "CrossAttnUpBlock2D") , block_out_channels=(3_2, 6_4) , attention_head_dim=(2, 4) , class_embed_type="projection" , projection_class_embeddings_input_dim=embedder_projection_dim * 2 , cross_attention_dim=_A , layers_per_block=1 , upcast_attention=_A , use_linear_projection=_A , )
torch.manual_seed(0 )
__lowerCAmelCase = DDIMScheduler(
beta_schedule="scaled_linear" , beta_start=0.0_00_85 , beta_end=0.0_12 , prediction_type="v_prediction" , set_alpha_to_one=_A , steps_offset=1 , )
torch.manual_seed(0 )
__lowerCAmelCase = AutoencoderKL()
__lowerCAmelCase = {
# prior components
"prior_tokenizer": prior_tokenizer,
"prior_text_encoder": prior_text_encoder,
"prior": prior,
"prior_scheduler": prior_scheduler,
# image noising components
"image_normalizer": image_normalizer,
"image_noising_scheduler": image_noising_scheduler,
# regular denoising components
"tokenizer": tokenizer,
"text_encoder": text_encoder,
"unet": unet,
"scheduler": scheduler,
"vae": vae,
}
return components
def __SCREAMING_SNAKE_CASE( self , _A , _A=0 ):
"""simple docstring"""
if str(_A ).startswith("mps" ):
__lowerCAmelCase = torch.manual_seed(_A )
else:
__lowerCAmelCase = torch.Generator(device=_A ).manual_seed(_A )
__lowerCAmelCase = {
"prompt": "A painting of a squirrel eating a burger",
"generator": generator,
"num_inference_steps": 2,
"prior_num_inference_steps": 2,
"output_type": "numpy",
}
return inputs
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
__lowerCAmelCase = torch_device == "cpu"
self._test_attention_slicing_forward_pass(test_max_difference=_A )
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
__lowerCAmelCase = torch_device in ["cpu", "mps"]
self._test_inference_batch_single_identical(test_max_difference=_A )
@slow
@require_torch_gpu
class a__ ( unittest.TestCase ):
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
__lowerCAmelCase = load_numpy(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/stable_unclip_2_1_l_anime_turtle_fp16.npy" )
__lowerCAmelCase = StableUnCLIPPipeline.from_pretrained("fusing/stable-unclip-2-1-l" , torch_dtype=torch.floataa )
pipe.to(_A )
pipe.set_progress_bar_config(disable=_A )
# stable unclip will oom when integration tests are run on a V100,
# so turn on memory savings
pipe.enable_attention_slicing()
pipe.enable_sequential_cpu_offload()
__lowerCAmelCase = torch.Generator(device="cpu" ).manual_seed(0 )
__lowerCAmelCase = pipe("anime turle" , generator=_A , output_type="np" )
__lowerCAmelCase = output.images[0]
assert image.shape == (7_6_8, 7_6_8, 3)
assert_mean_pixel_difference(_A , _A )
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
torch.cuda.empty_cache()
torch.cuda.reset_max_memory_allocated()
torch.cuda.reset_peak_memory_stats()
__lowerCAmelCase = StableUnCLIPPipeline.from_pretrained("fusing/stable-unclip-2-1-l" , torch_dtype=torch.floataa )
__lowerCAmelCase = pipe.to(_A )
pipe.set_progress_bar_config(disable=_A )
pipe.enable_attention_slicing()
pipe.enable_sequential_cpu_offload()
__lowerCAmelCase = pipe(
"anime turtle" , prior_num_inference_steps=2 , num_inference_steps=2 , output_type="np" , )
__lowerCAmelCase = torch.cuda.max_memory_allocated()
# make sure that less than 7 GB is allocated
assert mem_bytes < 7 * 1_0**9
| 92 | 0 |
'''simple docstring'''
from __future__ import annotations
def __snake_case( _lowerCAmelCase ) -> Any:
if not nums:
return 0
snake_case__ : List[Any] = nums[0]
snake_case__ : Optional[Any] = 0
for num in nums[1:]:
snake_case__ , snake_case__ : Optional[Any] = (
max_excluding + num,
max(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ),
)
return max(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 35 |
from typing import TYPE_CHECKING
from ...utils import _LazyModule
UpperCamelCase__ = {"""tokenization_wav2vec2_phoneme""": ["""Wav2Vec2PhonemeCTCTokenizer"""]}
if TYPE_CHECKING:
from .tokenization_wavaveca_phoneme import WavaVecaPhonemeCTCTokenizer
else:
import sys
UpperCamelCase__ = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 92 | 0 |
'''simple docstring'''
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 UpperCamelCase__ ( snake_case__):
UpperCAmelCase__ : List[Any] = ["""image_processor""", """tokenizer"""]
UpperCAmelCase__ : int = """OwlViTImageProcessor"""
UpperCAmelCase__ : Optional[Any] = ("""CLIPTokenizer""", """CLIPTokenizerFast""")
def __init__( self :Tuple , _A :List[str]=None , _A :Optional[Any]=None , **_A :List[Any] ) -> 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.' , _A , )
__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__(_A , _A )
def __call__( self :Any , _A :int=None , _A :Any=None , _A :Dict=None , _A :int="max_length" , _A :List[str]="np" , **_A :Any ) -> Optional[Any]:
'''simple docstring'''
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 )):
__A = [self.tokenizer(_A , padding=_A , return_tensors=_A , **_A )]
elif isinstance(_A , _A ) and isinstance(text[0] , _A ):
__A = []
# Maximum number of queries across batch
__A = 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:
__A = t + [' '] * (max_num_queries - len(_A ))
__A = 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":
__A = np.concatenate([encoding['input_ids'] for encoding in encodings] , axis=0 )
__A = np.concatenate([encoding['attention_mask'] for encoding in encodings] , axis=0 )
elif return_tensors == "jax" and is_flax_available():
import jax.numpy as jnp
__A = jnp.concatenate([encoding['input_ids'] for encoding in encodings] , axis=0 )
__A = jnp.concatenate([encoding['attention_mask'] for encoding in encodings] , axis=0 )
elif return_tensors == "pt" and is_torch_available():
import torch
__A = torch.cat([encoding['input_ids'] for encoding in encodings] , dim=0 )
__A = torch.cat([encoding['attention_mask'] for encoding in encodings] , dim=0 )
elif return_tensors == "tf" and is_tf_available():
import tensorflow as tf
__A = tf.stack([encoding['input_ids'] for encoding in encodings] , axis=0 )
__A = tf.stack([encoding['attention_mask'] for encoding in encodings] , axis=0 )
else:
raise ValueError('Target return tensor type could not be returned' )
__A = BatchEncoding()
__A = input_ids
__A = attention_mask
if query_images is not None:
__A = BatchEncoding()
__A = self.image_processor(
_A , return_tensors=_A , **_A ).pixel_values
__A = query_pixel_values
if images is not None:
__A = self.image_processor(_A , return_tensors=_A , **_A )
if text is not None and images is not None:
__A = image_features.pixel_values
return encoding
elif query_images is not None and images is not None:
__A = 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 lowercase_ ( self :str , *_A :str , **_A :Tuple ) -> Tuple:
'''simple docstring'''
return self.image_processor.post_process(*_A , **_A )
def lowercase_ ( self :List[str] , *_A :int , **_A :Dict ) -> Any:
'''simple docstring'''
return self.image_processor.post_process_object_detection(*_A , **_A )
def lowercase_ ( self :int , *_A :Tuple , **_A :int ) -> Any:
'''simple docstring'''
return self.image_processor.post_process_image_guided_detection(*_A , **_A )
def lowercase_ ( self :Any , *_A :Dict , **_A :List[str] ) -> Union[str, Any]:
'''simple docstring'''
return self.tokenizer.batch_decode(*_A , **_A )
def lowercase_ ( self :int , *_A :Dict , **_A :Union[str, Any] ) -> Optional[int]:
'''simple docstring'''
return self.tokenizer.decode(*_A , **_A )
@property
def lowercase_ ( self :List[str] ) -> Optional[Any]:
'''simple docstring'''
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 lowercase_ ( self :Union[str, Any] ) -> Optional[Any]:
'''simple docstring'''
warnings.warn(
'`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.' , _A , )
return self.image_processor
| 161 |
import unittest
from transformers import DebertaVaTokenizer, DebertaVaTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin
UpperCamelCase__ = get_tests_dir("""fixtures/spiece.model""")
@require_sentencepiece
@require_tokenizers
class a__ ( snake_case__ , unittest.TestCase ):
_a : Optional[Any] = DebertaVaTokenizer
_a : Optional[Any] = DebertaVaTokenizerFast
_a : List[str] = True
_a : Optional[Any] = True
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
super().setUp()
# We have a SentencePiece fixture for testing
__lowerCAmelCase = DebertaVaTokenizer(_A , unk_token="<unk>" )
tokenizer.save_pretrained(self.tmpdirname )
def __SCREAMING_SNAKE_CASE( self , _A ):
"""simple docstring"""
__lowerCAmelCase = "this is a test"
__lowerCAmelCase = "this is a test"
return input_text, output_text
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
__lowerCAmelCase = "<pad>"
__lowerCAmelCase = 0
self.assertEqual(self.get_tokenizer()._convert_token_to_id(_A ) , _A )
self.assertEqual(self.get_tokenizer()._convert_id_to_token(_A ) , _A )
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
__lowerCAmelCase = list(self.get_tokenizer().get_vocab().keys() )
self.assertEqual(vocab_keys[0] , "<pad>" )
self.assertEqual(vocab_keys[1] , "<unk>" )
self.assertEqual(vocab_keys[-1] , "[PAD]" )
self.assertEqual(len(_A ) , 3_0_0_0_1 )
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
self.assertEqual(self.get_tokenizer().vocab_size , 3_0_0_0_0 )
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
__lowerCAmelCase = " \tHeLLo!how \n Are yoU? "
__lowerCAmelCase = ["▁hello", "!", "how", "▁are", "▁you", "?"]
# fmt: on
__lowerCAmelCase = DebertaVaTokenizer(_A , do_lower_case=_A )
__lowerCAmelCase = tokenizer.convert_ids_to_tokens(tokenizer.encode(_A , add_special_tokens=_A ) )
self.assertListEqual(_A , _A )
__lowerCAmelCase = DebertaVaTokenizerFast(_A , do_lower_case=_A )
__lowerCAmelCase = rust_tokenizer.convert_ids_to_tokens(rust_tokenizer.encode(_A , add_special_tokens=_A ) )
self.assertListEqual(_A , _A )
@unittest.skip("There is an inconsistency between slow and fast tokenizer due to a bug in the fast one." )
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
pass
@unittest.skip("There is an inconsistency between slow and fast tokenizer due to a bug in the fast one." )
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
pass
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
__lowerCAmelCase = "I was born in 92000, and this is falsé."
__lowerCAmelCase = ["▁", "<unk>", "▁was", "▁born", "▁in", "▁9", "2000", "▁", ",", "▁and", "▁this", "▁is", "▁fal", "s", "<unk>", "▁", ".", ]
# fmt: on
__lowerCAmelCase = DebertaVaTokenizer(_A , split_by_punct=_A )
__lowerCAmelCase = tokenizer.convert_ids_to_tokens(tokenizer.encode(_A , add_special_tokens=_A ) )
self.assertListEqual(_A , _A )
__lowerCAmelCase = DebertaVaTokenizerFast(_A , split_by_punct=_A )
__lowerCAmelCase = rust_tokenizer.convert_ids_to_tokens(rust_tokenizer.encode(_A , add_special_tokens=_A ) )
self.assertListEqual(_A , _A )
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
__lowerCAmelCase = "I was born in 92000, and this is falsé."
__lowerCAmelCase = ["▁i", "▁was", "▁born", "▁in", "▁9", "2000", "▁", ",", "▁and", "▁this", "▁is", "▁fal", "s", "<unk>", "▁", ".", ]
# fmt: on
__lowerCAmelCase = DebertaVaTokenizer(_A , do_lower_case=_A , split_by_punct=_A )
__lowerCAmelCase = tokenizer.convert_ids_to_tokens(tokenizer.encode(_A , add_special_tokens=_A ) )
self.assertListEqual(_A , _A )
__lowerCAmelCase = DebertaVaTokenizerFast(_A , do_lower_case=_A , split_by_punct=_A )
__lowerCAmelCase = rust_tokenizer.convert_ids_to_tokens(rust_tokenizer.encode(_A , add_special_tokens=_A ) )
self.assertListEqual(_A , _A )
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
__lowerCAmelCase = "I was born in 92000, and this is falsé."
__lowerCAmelCase = ["▁i", "▁was", "▁born", "▁in", "▁9", "2000", ",", "▁and", "▁this", "▁is", "▁fal", "s", "<unk>", ".", ]
# fmt: on
__lowerCAmelCase = DebertaVaTokenizer(_A , do_lower_case=_A , split_by_punct=_A )
__lowerCAmelCase = tokenizer.convert_ids_to_tokens(tokenizer.encode(_A , add_special_tokens=_A ) )
self.assertListEqual(_A , _A )
__lowerCAmelCase = DebertaVaTokenizerFast(_A , do_lower_case=_A , split_by_punct=_A )
__lowerCAmelCase = rust_tokenizer.convert_ids_to_tokens(rust_tokenizer.encode(_A , add_special_tokens=_A ) )
self.assertListEqual(_A , _A )
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
__lowerCAmelCase = "I was born in 92000, and this is falsé."
__lowerCAmelCase = ["▁", "<unk>", "▁was", "▁born", "▁in", "▁9", "2000", "▁", ",", "▁and", "▁this", "▁is", "▁fal", "s", "<unk>", "▁", ".", ]
# fmt: on
__lowerCAmelCase = DebertaVaTokenizer(_A , do_lower_case=_A , split_by_punct=_A )
__lowerCAmelCase = tokenizer.convert_ids_to_tokens(tokenizer.encode(_A , add_special_tokens=_A ) )
self.assertListEqual(_A , _A )
__lowerCAmelCase = DebertaVaTokenizerFast(_A , do_lower_case=_A , split_by_punct=_A )
__lowerCAmelCase = rust_tokenizer.convert_ids_to_tokens(rust_tokenizer.encode(_A , add_special_tokens=_A ) )
self.assertListEqual(_A , _A )
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
__lowerCAmelCase = " \tHeLLo!how \n Are yoU? "
__lowerCAmelCase = ["▁", "<unk>", "e", "<unk>", "o", "!", "how", "▁", "<unk>", "re", "▁yo", "<unk>", "?"]
# fmt: on
__lowerCAmelCase = DebertaVaTokenizer(_A , do_lower_case=_A , split_by_punct=_A )
__lowerCAmelCase = tokenizer.convert_ids_to_tokens(tokenizer.encode(_A , add_special_tokens=_A ) )
self.assertListEqual(_A , _A )
__lowerCAmelCase = DebertaVaTokenizerFast(_A , do_lower_case=_A , split_by_punct=_A )
__lowerCAmelCase = rust_tokenizer.convert_ids_to_tokens(rust_tokenizer.encode(_A , add_special_tokens=_A ) )
self.assertListEqual(_A , _A )
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
__lowerCAmelCase = self.get_tokenizer()
__lowerCAmelCase = self.get_rust_tokenizer()
__lowerCAmelCase = "I was born in 92000, and this is falsé."
__lowerCAmelCase = tokenizer.convert_ids_to_tokens(tokenizer.encode(_A , add_special_tokens=_A ) )
__lowerCAmelCase = rust_tokenizer.convert_ids_to_tokens(rust_tokenizer.encode(_A , add_special_tokens=_A ) )
self.assertListEqual(_A , _A )
__lowerCAmelCase = tokenizer.encode(_A , add_special_tokens=_A )
__lowerCAmelCase = rust_tokenizer.encode(_A , add_special_tokens=_A )
self.assertListEqual(_A , _A )
__lowerCAmelCase = self.get_rust_tokenizer()
__lowerCAmelCase = tokenizer.encode(_A )
__lowerCAmelCase = rust_tokenizer.encode(_A )
self.assertListEqual(_A , _A )
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
__lowerCAmelCase = "This is a test"
__lowerCAmelCase = [1_3, 1, 4_3_9_8, 2_5, 2_1, 1_2_8_9]
__lowerCAmelCase = ["▁", "T", "his", "▁is", "▁a", "▁test"]
__lowerCAmelCase = ["▁", "<unk>", "his", "▁is", "▁a", "▁test"]
__lowerCAmelCase = DebertaVaTokenizer(_A , keep_accents=_A )
__lowerCAmelCase = DebertaVaTokenizerFast(_A , keep_accents=_A )
__lowerCAmelCase = tokenizer.encode(_A , add_special_tokens=_A )
self.assertListEqual(_A , _A )
__lowerCAmelCase = tokenizer.tokenize(_A )
self.assertListEqual(_A , _A )
__lowerCAmelCase = tokenizer.convert_ids_to_tokens(_A )
self.assertListEqual(_A , _A )
__lowerCAmelCase = rust_tokenizer.encode(_A , add_special_tokens=_A )
self.assertListEqual(_A , _A )
__lowerCAmelCase = rust_tokenizer.tokenize(_A )
self.assertListEqual(_A , _A )
__lowerCAmelCase = rust_tokenizer.convert_ids_to_tokens(_A )
self.assertListEqual(_A , _A )
# fmt: off
__lowerCAmelCase = "I was born in 92000, and this is falsé."
__lowerCAmelCase = [1_3, 1, 2_3, 3_8_6, 1_9, 5_6_1, 3_0_5_0, 1_5, 1_7, 4_8, 2_5, 8_2_5_6, 1_8, 1, 9]
__lowerCAmelCase = ["▁", "I", "▁was", "▁born", "▁in", "▁9", "2000", ",", "▁and", "▁this", "▁is", "▁fal", "s", "é", ".", ]
__lowerCAmelCase = ["▁", "<unk>", "▁was", "▁born", "▁in", "▁9", "2000", ",", "▁and", "▁this", "▁is", "▁fal", "s", "<unk>", ".", ]
# fmt: on
__lowerCAmelCase = tokenizer.encode(_A , add_special_tokens=_A )
self.assertListEqual(_A , _A )
__lowerCAmelCase = tokenizer.tokenize(_A )
self.assertListEqual(_A , _A )
__lowerCAmelCase = tokenizer.convert_ids_to_tokens(_A )
self.assertListEqual(_A , _A )
__lowerCAmelCase = rust_tokenizer.encode(_A , add_special_tokens=_A )
self.assertListEqual(_A , _A )
__lowerCAmelCase = rust_tokenizer.tokenize(_A )
self.assertListEqual(_A , _A )
__lowerCAmelCase = rust_tokenizer.convert_ids_to_tokens(_A )
self.assertListEqual(_A , _A )
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
__lowerCAmelCase = DebertaVaTokenizer(_A )
__lowerCAmelCase = tokenizer.encode("sequence builders" )
__lowerCAmelCase = tokenizer.encode("multi-sequence build" )
__lowerCAmelCase = tokenizer.build_inputs_with_special_tokens(_A )
__lowerCAmelCase = tokenizer.build_inputs_with_special_tokens(_A , _A )
self.assertEqual([tokenizer.cls_token_id] + text + [tokenizer.sep_token_id] , _A )
self.assertEqual(
[tokenizer.cls_token_id] + text + [tokenizer.sep_token_id] + text_a + [tokenizer.sep_token_id] , _A , )
@slow
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
__lowerCAmelCase = {"input_ids": [[1, 3_9_8_6_7, 3_6, 1_9_3_9_0, 4_8_6, 2_7, 3_5_0_5_2, 8_1_4_3_6, 1_8, 6_0_6_8_5, 1_2_2_5, 7, 3_5_0_5_2, 8_1_4_3_6, 1_8, 9_3_6_7, 1_6_8_9_9, 1_8, 1_5_9_3_7, 5_3, 5_9_4, 7_7_3, 1_8, 1_6_2_8_7, 3_0_4_6_5, 3_6, 1_5_9_3_7, 6, 4_1_1_3_9, 3_8, 3_6_9_7_9, 6_0_7_6_3, 1_9_1, 6, 3_4_1_3_2, 9_9, 6, 5_0_5_3_8, 3_9_0, 4_3_2_3_0, 6, 3_4_1_3_2, 2_7_7_9, 2_0_8_5_0, 1_4, 6_9_9, 1_0_7_2, 1_1_9_4, 3_6, 3_8_2, 1_0_9_0_1, 5_3, 7, 6_9_9, 1_0_7_2, 2_0_8_4, 3_6, 2_0_4_2_2, 6_3_0, 5_3, 1_9, 1_0_5, 3_0_4_9, 1_8_9_6, 1_0_5_3, 1_6_8_9_9, 1_5_0_6, 1_1, 3_7_9_7_8, 4_2_4_3, 7, 1_2_3_7, 3_1_8_6_9, 2_0_0, 1_6_5_6_6, 6_5_4, 6, 3_5_0_5_2, 8_1_4_3_6, 7, 5_5_6_3_0, 1_3_5_9_3, 4, 2], [1, 2_6, 1_5_0_1_1, 1_3, 6_6_7, 8, 1_0_5_3, 1_8, 2_3_6_1_1, 1_2_3_7, 7_2_3_5_6, 1_2_8_2_0, 3_4, 1_0_4_1_3_4, 1_2_0_9, 3_5, 1_3_3_1_3, 6_6_2_7, 2_1, 2_0_2, 3_4_7, 7, 1_6_4, 2_3_9_9, 1_1, 4_6, 4_4_8_5, 4, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 5, 1_2_3_2, 2_8_6_4, 1_5_7_8_5, 1_4_9_5_1, 1_0_5, 5, 8_5_8_1, 1_2_5_0, 4, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], "token_type_ids": [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], "attention_mask": [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]} # noqa: E501
# fmt: on
self.tokenizer_integration_test_util(
expected_encoding=_A , model_name="microsoft/deberta-v2-xlarge" , revision="ad6e42c1532ddf3a15c39246b63f5559d558b670" , )
| 92 | 0 |
"""simple docstring"""
import contextlib
import csv
import json
import os
import sqlitea
import tarfile
import textwrap
import zipfile
import pyarrow as pa
import pyarrow.parquet as pq
import pytest
import datasets
import datasets.config
@pytest.fixture(scope="session" )
def a__ ( ):
"""simple docstring"""
UpperCamelCase = 10
UpperCamelCase = datasets.Features(
{
"tokens": datasets.Sequence(datasets.Value("string" ) ),
"labels": datasets.Sequence(datasets.ClassLabel(names=["negative", "positive"] ) ),
"answers": datasets.Sequence(
{
"text": datasets.Value("string" ),
"answer_start": datasets.Value("int32" ),
} ),
"id": datasets.Value("int64" ),
} )
UpperCamelCase = datasets.Dataset.from_dict(
{
"tokens": [["foo"] * 5] * n,
"labels": [[1] * 5] * n,
"answers": [{"answer_start": [97], "text": ["1976"]}] * 10,
"id": list(range(SCREAMING_SNAKE_CASE_ ) ),
} , features=SCREAMING_SNAKE_CASE_ , )
return dataset
@pytest.fixture(scope="session" )
def a__ ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
"""simple docstring"""
UpperCamelCase = str(tmp_path_factory.mktemp("data" ) / "file.arrow" )
dataset.map(cache_file_name=SCREAMING_SNAKE_CASE_ )
return filename
# FILE_CONTENT + files
lowerCAmelCase__ = '''\
Text data.
Second line of data.'''
@pytest.fixture(scope="session" )
def a__ ( _SCREAMING_SNAKE_CASE ):
"""simple docstring"""
UpperCamelCase = tmp_path_factory.mktemp("data" ) / "file.txt"
UpperCamelCase = FILE_CONTENT
with open(SCREAMING_SNAKE_CASE_ , "w" ) as f:
f.write(SCREAMING_SNAKE_CASE_ )
return filename
@pytest.fixture(scope="session" )
def a__ ( _SCREAMING_SNAKE_CASE ):
"""simple docstring"""
import bza
UpperCamelCase = tmp_path_factory.mktemp("data" ) / "file.txt.bz2"
UpperCamelCase = bytes(SCREAMING_SNAKE_CASE_ , "utf-8" )
with bza.open(SCREAMING_SNAKE_CASE_ , "wb" ) as f:
f.write(SCREAMING_SNAKE_CASE_ )
return path
@pytest.fixture(scope="session" )
def a__ ( _SCREAMING_SNAKE_CASE ):
"""simple docstring"""
import gzip
UpperCamelCase = str(tmp_path_factory.mktemp("data" ) / "file.txt.gz" )
UpperCamelCase = bytes(SCREAMING_SNAKE_CASE_ , "utf-8" )
with gzip.open(SCREAMING_SNAKE_CASE_ , "wb" ) as f:
f.write(SCREAMING_SNAKE_CASE_ )
return path
@pytest.fixture(scope="session" )
def a__ ( _SCREAMING_SNAKE_CASE ):
"""simple docstring"""
if datasets.config.LZ4_AVAILABLE:
import lza.frame
UpperCamelCase = tmp_path_factory.mktemp("data" ) / "file.txt.lz4"
UpperCamelCase = bytes(SCREAMING_SNAKE_CASE_ , "utf-8" )
with lza.frame.open(SCREAMING_SNAKE_CASE_ , "wb" ) as f:
f.write(SCREAMING_SNAKE_CASE_ )
return path
@pytest.fixture(scope="session" )
def a__ ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
"""simple docstring"""
if datasets.config.PY7ZR_AVAILABLE:
import pyazr
UpperCamelCase = tmp_path_factory.mktemp("data" ) / "file.txt.7z"
with pyazr.SevenZipFile(SCREAMING_SNAKE_CASE_ , "w" ) as archive:
archive.write(SCREAMING_SNAKE_CASE_ , arcname=os.path.basename(SCREAMING_SNAKE_CASE_ ) )
return path
@pytest.fixture(scope="session" )
def a__ ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
"""simple docstring"""
import tarfile
UpperCamelCase = tmp_path_factory.mktemp("data" ) / "file.txt.tar"
with tarfile.TarFile(SCREAMING_SNAKE_CASE_ , "w" ) as f:
f.add(SCREAMING_SNAKE_CASE_ , arcname=os.path.basename(SCREAMING_SNAKE_CASE_ ) )
return path
@pytest.fixture(scope="session" )
def a__ ( _SCREAMING_SNAKE_CASE ):
"""simple docstring"""
import lzma
UpperCamelCase = tmp_path_factory.mktemp("data" ) / "file.txt.xz"
UpperCamelCase = bytes(SCREAMING_SNAKE_CASE_ , "utf-8" )
with lzma.open(SCREAMING_SNAKE_CASE_ , "wb" ) as f:
f.write(SCREAMING_SNAKE_CASE_ )
return path
@pytest.fixture(scope="session" )
def a__ ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
"""simple docstring"""
import zipfile
UpperCamelCase = tmp_path_factory.mktemp("data" ) / "file.txt.zip"
with zipfile.ZipFile(SCREAMING_SNAKE_CASE_ , "w" ) as f:
f.write(SCREAMING_SNAKE_CASE_ , arcname=os.path.basename(SCREAMING_SNAKE_CASE_ ) )
return path
@pytest.fixture(scope="session" )
def a__ ( _SCREAMING_SNAKE_CASE ):
"""simple docstring"""
if datasets.config.ZSTANDARD_AVAILABLE:
import zstandard as zstd
UpperCamelCase = tmp_path_factory.mktemp("data" ) / "file.txt.zst"
UpperCamelCase = bytes(SCREAMING_SNAKE_CASE_ , "utf-8" )
with zstd.open(SCREAMING_SNAKE_CASE_ , "wb" ) as f:
f.write(SCREAMING_SNAKE_CASE_ )
return path
@pytest.fixture(scope="session" )
def a__ ( _SCREAMING_SNAKE_CASE ):
"""simple docstring"""
UpperCamelCase = tmp_path_factory.mktemp("data" ) / "file.xml"
UpperCamelCase = textwrap.dedent(
"\\n <?xml version=\"1.0\" encoding=\"UTF-8\" ?>\n <tmx version=\"1.4\">\n <header segtype=\"sentence\" srclang=\"ca\" />\n <body>\n <tu>\n <tuv xml:lang=\"ca\"><seg>Contingut 1</seg></tuv>\n <tuv xml:lang=\"en\"><seg>Content 1</seg></tuv>\n </tu>\n <tu>\n <tuv xml:lang=\"ca\"><seg>Contingut 2</seg></tuv>\n <tuv xml:lang=\"en\"><seg>Content 2</seg></tuv>\n </tu>\n <tu>\n <tuv xml:lang=\"ca\"><seg>Contingut 3</seg></tuv>\n <tuv xml:lang=\"en\"><seg>Content 3</seg></tuv>\n </tu>\n <tu>\n <tuv xml:lang=\"ca\"><seg>Contingut 4</seg></tuv>\n <tuv xml:lang=\"en\"><seg>Content 4</seg></tuv>\n </tu>\n <tu>\n <tuv xml:lang=\"ca\"><seg>Contingut 5</seg></tuv>\n <tuv xml:lang=\"en\"><seg>Content 5</seg></tuv>\n </tu>\n </body>\n </tmx>" )
with open(SCREAMING_SNAKE_CASE_ , "w" ) as f:
f.write(SCREAMING_SNAKE_CASE_ )
return filename
lowerCAmelCase__ = [
{'''col_1''': '''0''', '''col_2''': 0, '''col_3''': 0.0},
{'''col_1''': '''1''', '''col_2''': 1, '''col_3''': 1.0},
{'''col_1''': '''2''', '''col_2''': 2, '''col_3''': 2.0},
{'''col_1''': '''3''', '''col_2''': 3, '''col_3''': 3.0},
]
lowerCAmelCase__ = [
{'''col_1''': '''4''', '''col_2''': 4, '''col_3''': 4.0},
{'''col_1''': '''5''', '''col_2''': 5, '''col_3''': 5.0},
]
lowerCAmelCase__ = {
'''col_1''': ['''0''', '''1''', '''2''', '''3'''],
'''col_2''': [0, 1, 2, 3],
'''col_3''': [0.0, 1.0, 2.0, 3.0],
}
lowerCAmelCase__ = [
{'''col_3''': 0.0, '''col_1''': '''0''', '''col_2''': 0},
{'''col_3''': 1.0, '''col_1''': '''1''', '''col_2''': 1},
]
lowerCAmelCase__ = [
{'''col_1''': '''s0''', '''col_2''': 0, '''col_3''': 0.0},
{'''col_1''': '''s1''', '''col_2''': 1, '''col_3''': 1.0},
{'''col_1''': '''s2''', '''col_2''': 2, '''col_3''': 2.0},
{'''col_1''': '''s3''', '''col_2''': 3, '''col_3''': 3.0},
]
@pytest.fixture(scope="session" )
def a__ ( ):
"""simple docstring"""
return DATA_DICT_OF_LISTS
@pytest.fixture(scope="session" )
def a__ ( _SCREAMING_SNAKE_CASE ):
"""simple docstring"""
UpperCamelCase = datasets.Dataset.from_dict(SCREAMING_SNAKE_CASE_ )
UpperCamelCase = str(tmp_path_factory.mktemp("data" ) / "dataset.arrow" )
dataset.map(cache_file_name=SCREAMING_SNAKE_CASE_ )
return path
@pytest.fixture(scope="session" )
def a__ ( _SCREAMING_SNAKE_CASE ):
"""simple docstring"""
UpperCamelCase = str(tmp_path_factory.mktemp("data" ) / "dataset.sqlite" )
with contextlib.closing(sqlitea.connect(SCREAMING_SNAKE_CASE_ ) ) as con:
UpperCamelCase = con.cursor()
cur.execute("CREATE TABLE dataset(col_1 text, col_2 int, col_3 real)" )
for item in DATA:
cur.execute("INSERT INTO dataset(col_1, col_2, col_3) VALUES (?, ?, ?)" , tuple(item.values() ) )
con.commit()
return path
@pytest.fixture(scope="session" )
def a__ ( _SCREAMING_SNAKE_CASE ):
"""simple docstring"""
UpperCamelCase = str(tmp_path_factory.mktemp("data" ) / "dataset.csv" )
with open(SCREAMING_SNAKE_CASE_ , "w" , newline="" ) as f:
UpperCamelCase = csv.DictWriter(SCREAMING_SNAKE_CASE_ , fieldnames=["col_1", "col_2", "col_3"] )
writer.writeheader()
for item in DATA:
writer.writerow(SCREAMING_SNAKE_CASE_ )
return path
@pytest.fixture(scope="session" )
def a__ ( _SCREAMING_SNAKE_CASE ):
"""simple docstring"""
UpperCamelCase = str(tmp_path_factory.mktemp("data" ) / "dataset2.csv" )
with open(SCREAMING_SNAKE_CASE_ , "w" , newline="" ) as f:
UpperCamelCase = csv.DictWriter(SCREAMING_SNAKE_CASE_ , fieldnames=["col_1", "col_2", "col_3"] )
writer.writeheader()
for item in DATA:
writer.writerow(SCREAMING_SNAKE_CASE_ )
return path
@pytest.fixture(scope="session" )
def a__ ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
"""simple docstring"""
import bza
UpperCamelCase = tmp_path_factory.mktemp("data" ) / "dataset.csv.bz2"
with open(SCREAMING_SNAKE_CASE_ , "rb" ) as f:
UpperCamelCase = f.read()
# data = bytes(FILE_CONTENT, "utf-8")
with bza.open(SCREAMING_SNAKE_CASE_ , "wb" ) as f:
f.write(SCREAMING_SNAKE_CASE_ )
return path
@pytest.fixture(scope="session" )
def a__ ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
"""simple docstring"""
UpperCamelCase = tmp_path_factory.mktemp("data" ) / "dataset.csv.zip"
with zipfile.ZipFile(SCREAMING_SNAKE_CASE_ , "w" ) as f:
f.write(SCREAMING_SNAKE_CASE_ , arcname=os.path.basename(SCREAMING_SNAKE_CASE_ ) )
f.write(SCREAMING_SNAKE_CASE_ , arcname=os.path.basename(SCREAMING_SNAKE_CASE_ ) )
return path
@pytest.fixture(scope="session" )
def a__ ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
"""simple docstring"""
UpperCamelCase = tmp_path_factory.mktemp("data" ) / "dataset.csv.zip"
with zipfile.ZipFile(SCREAMING_SNAKE_CASE_ , "w" ) as f:
f.write(SCREAMING_SNAKE_CASE_ , arcname=os.path.basename(csv_path.replace(".csv" , ".CSV" ) ) )
f.write(SCREAMING_SNAKE_CASE_ , arcname=os.path.basename(csva_path.replace(".csv" , ".CSV" ) ) )
return path
@pytest.fixture(scope="session" )
def a__ ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
"""simple docstring"""
UpperCamelCase = tmp_path_factory.mktemp("data" ) / "dataset_with_dir.csv.zip"
with zipfile.ZipFile(SCREAMING_SNAKE_CASE_ , "w" ) as f:
f.write(SCREAMING_SNAKE_CASE_ , arcname=os.path.join("main_dir" , os.path.basename(SCREAMING_SNAKE_CASE_ ) ) )
f.write(SCREAMING_SNAKE_CASE_ , arcname=os.path.join("main_dir" , os.path.basename(SCREAMING_SNAKE_CASE_ ) ) )
return path
@pytest.fixture(scope="session" )
def a__ ( _SCREAMING_SNAKE_CASE ):
"""simple docstring"""
UpperCamelCase = str(tmp_path_factory.mktemp("data" ) / "dataset.parquet" )
UpperCamelCase = pa.schema(
{
"col_1": pa.string(),
"col_2": pa.intaa(),
"col_3": pa.floataa(),
} )
with open(SCREAMING_SNAKE_CASE_ , "wb" ) as f:
UpperCamelCase = pq.ParquetWriter(SCREAMING_SNAKE_CASE_ , schema=SCREAMING_SNAKE_CASE_ )
UpperCamelCase = pa.Table.from_pydict({k: [DATA[i][k] for i in range(len(SCREAMING_SNAKE_CASE_ ) )] for k in DATA[0]} , schema=SCREAMING_SNAKE_CASE_ )
writer.write_table(SCREAMING_SNAKE_CASE_ )
writer.close()
return path
@pytest.fixture(scope="session" )
def a__ ( _SCREAMING_SNAKE_CASE ):
"""simple docstring"""
UpperCamelCase = str(tmp_path_factory.mktemp("data" ) / "dataset.json" )
UpperCamelCase = {"data": DATA}
with open(SCREAMING_SNAKE_CASE_ , "w" ) as f:
json.dump(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
return path
@pytest.fixture(scope="session" )
def a__ ( _SCREAMING_SNAKE_CASE ):
"""simple docstring"""
UpperCamelCase = str(tmp_path_factory.mktemp("data" ) / "dataset.json" )
UpperCamelCase = {"data": DATA_DICT_OF_LISTS}
with open(SCREAMING_SNAKE_CASE_ , "w" ) as f:
json.dump(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
return path
@pytest.fixture(scope="session" )
def a__ ( _SCREAMING_SNAKE_CASE ):
"""simple docstring"""
UpperCamelCase = str(tmp_path_factory.mktemp("data" ) / "dataset.jsonl" )
with open(SCREAMING_SNAKE_CASE_ , "w" ) as f:
for item in DATA:
f.write(json.dumps(SCREAMING_SNAKE_CASE_ ) + "\n" )
return path
@pytest.fixture(scope="session" )
def a__ ( _SCREAMING_SNAKE_CASE ):
"""simple docstring"""
UpperCamelCase = str(tmp_path_factory.mktemp("data" ) / "dataset2.jsonl" )
with open(SCREAMING_SNAKE_CASE_ , "w" ) as f:
for item in DATA:
f.write(json.dumps(SCREAMING_SNAKE_CASE_ ) + "\n" )
return path
@pytest.fixture(scope="session" )
def a__ ( _SCREAMING_SNAKE_CASE ):
"""simple docstring"""
UpperCamelCase = str(tmp_path_factory.mktemp("data" ) / "dataset_312.jsonl" )
with open(SCREAMING_SNAKE_CASE_ , "w" ) as f:
for item in DATA_312:
f.write(json.dumps(SCREAMING_SNAKE_CASE_ ) + "\n" )
return path
@pytest.fixture(scope="session" )
def a__ ( _SCREAMING_SNAKE_CASE ):
"""simple docstring"""
UpperCamelCase = str(tmp_path_factory.mktemp("data" ) / "dataset-str.jsonl" )
with open(SCREAMING_SNAKE_CASE_ , "w" ) as f:
for item in DATA_STR:
f.write(json.dumps(SCREAMING_SNAKE_CASE_ ) + "\n" )
return path
@pytest.fixture(scope="session" )
def a__ ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
"""simple docstring"""
import gzip
UpperCamelCase = str(tmp_path_factory.mktemp("data" ) / "dataset.txt.gz" )
with open(SCREAMING_SNAKE_CASE_ , "rb" ) as orig_file:
with gzip.open(SCREAMING_SNAKE_CASE_ , "wb" ) as zipped_file:
zipped_file.writelines(SCREAMING_SNAKE_CASE_ )
return path
@pytest.fixture(scope="session" )
def a__ ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
"""simple docstring"""
import gzip
UpperCamelCase = str(tmp_path_factory.mktemp("data" ) / "dataset.jsonl.gz" )
with open(SCREAMING_SNAKE_CASE_ , "rb" ) as orig_file:
with gzip.open(SCREAMING_SNAKE_CASE_ , "wb" ) as zipped_file:
zipped_file.writelines(SCREAMING_SNAKE_CASE_ )
return path
@pytest.fixture(scope="session" )
def a__ ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
"""simple docstring"""
UpperCamelCase = tmp_path_factory.mktemp("data" ) / "dataset.jsonl.zip"
with zipfile.ZipFile(SCREAMING_SNAKE_CASE_ , "w" ) as f:
f.write(SCREAMING_SNAKE_CASE_ , arcname=os.path.basename(SCREAMING_SNAKE_CASE_ ) )
f.write(SCREAMING_SNAKE_CASE_ , arcname=os.path.basename(SCREAMING_SNAKE_CASE_ ) )
return path
@pytest.fixture(scope="session" )
def a__ ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
"""simple docstring"""
UpperCamelCase = tmp_path_factory.mktemp("data" ) / "dataset_nested.jsonl.zip"
with zipfile.ZipFile(SCREAMING_SNAKE_CASE_ , "w" ) as f:
f.write(SCREAMING_SNAKE_CASE_ , arcname=os.path.join("nested" , os.path.basename(SCREAMING_SNAKE_CASE_ ) ) )
return path
@pytest.fixture(scope="session" )
def a__ ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
"""simple docstring"""
UpperCamelCase = tmp_path_factory.mktemp("data" ) / "dataset_with_dir.jsonl.zip"
with zipfile.ZipFile(SCREAMING_SNAKE_CASE_ , "w" ) as f:
f.write(SCREAMING_SNAKE_CASE_ , arcname=os.path.join("main_dir" , os.path.basename(SCREAMING_SNAKE_CASE_ ) ) )
f.write(SCREAMING_SNAKE_CASE_ , arcname=os.path.join("main_dir" , os.path.basename(SCREAMING_SNAKE_CASE_ ) ) )
return path
@pytest.fixture(scope="session" )
def a__ ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
"""simple docstring"""
UpperCamelCase = tmp_path_factory.mktemp("data" ) / "dataset.jsonl.tar"
with tarfile.TarFile(SCREAMING_SNAKE_CASE_ , "w" ) as f:
f.add(SCREAMING_SNAKE_CASE_ , arcname=os.path.basename(SCREAMING_SNAKE_CASE_ ) )
f.add(SCREAMING_SNAKE_CASE_ , arcname=os.path.basename(SCREAMING_SNAKE_CASE_ ) )
return path
@pytest.fixture(scope="session" )
def a__ ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
"""simple docstring"""
UpperCamelCase = tmp_path_factory.mktemp("data" ) / "dataset_nested.jsonl.tar"
with tarfile.TarFile(SCREAMING_SNAKE_CASE_ , "w" ) as f:
f.add(SCREAMING_SNAKE_CASE_ , arcname=os.path.join("nested" , os.path.basename(SCREAMING_SNAKE_CASE_ ) ) )
return path
@pytest.fixture(scope="session" )
def a__ ( _SCREAMING_SNAKE_CASE ):
"""simple docstring"""
UpperCamelCase = ["0", "1", "2", "3"]
UpperCamelCase = str(tmp_path_factory.mktemp("data" ) / "dataset.txt" )
with open(SCREAMING_SNAKE_CASE_ , "w" ) as f:
for item in data:
f.write(item + "\n" )
return path
@pytest.fixture(scope="session" )
def a__ ( _SCREAMING_SNAKE_CASE ):
"""simple docstring"""
UpperCamelCase = ["0", "1", "2", "3"]
UpperCamelCase = str(tmp_path_factory.mktemp("data" ) / "dataset2.txt" )
with open(SCREAMING_SNAKE_CASE_ , "w" ) as f:
for item in data:
f.write(item + "\n" )
return path
@pytest.fixture(scope="session" )
def a__ ( _SCREAMING_SNAKE_CASE ):
"""simple docstring"""
UpperCamelCase = ["0", "1", "2", "3"]
UpperCamelCase = tmp_path_factory.mktemp("data" ) / "dataset.abc"
with open(SCREAMING_SNAKE_CASE_ , "w" ) as f:
for item in data:
f.write(item + "\n" )
return path
@pytest.fixture(scope="session" )
def a__ ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
"""simple docstring"""
UpperCamelCase = tmp_path_factory.mktemp("data" ) / "dataset.text.zip"
with zipfile.ZipFile(SCREAMING_SNAKE_CASE_ , "w" ) as f:
f.write(SCREAMING_SNAKE_CASE_ , arcname=os.path.basename(SCREAMING_SNAKE_CASE_ ) )
f.write(SCREAMING_SNAKE_CASE_ , arcname=os.path.basename(SCREAMING_SNAKE_CASE_ ) )
return path
@pytest.fixture(scope="session" )
def a__ ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
"""simple docstring"""
UpperCamelCase = tmp_path_factory.mktemp("data" ) / "dataset_with_dir.text.zip"
with zipfile.ZipFile(SCREAMING_SNAKE_CASE_ , "w" ) as f:
f.write(SCREAMING_SNAKE_CASE_ , arcname=os.path.join("main_dir" , os.path.basename(SCREAMING_SNAKE_CASE_ ) ) )
f.write(SCREAMING_SNAKE_CASE_ , arcname=os.path.join("main_dir" , os.path.basename(SCREAMING_SNAKE_CASE_ ) ) )
return path
@pytest.fixture(scope="session" )
def a__ ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
"""simple docstring"""
UpperCamelCase = tmp_path_factory.mktemp("data" ) / "dataset.ext.zip"
with zipfile.ZipFile(SCREAMING_SNAKE_CASE_ , "w" ) as f:
f.write(SCREAMING_SNAKE_CASE_ , arcname=os.path.basename("unsupported.ext" ) )
f.write(SCREAMING_SNAKE_CASE_ , arcname=os.path.basename("unsupported_2.ext" ) )
return path
@pytest.fixture(scope="session" )
def a__ ( _SCREAMING_SNAKE_CASE ):
"""simple docstring"""
UpperCamelCase = "\n".join(["First", "Second\u2029with Unicode new line", "Third"] )
UpperCamelCase = str(tmp_path_factory.mktemp("data" ) / "dataset_with_unicode_new_lines.txt" )
with open(SCREAMING_SNAKE_CASE_ , "w" , encoding="utf-8" ) as f:
f.write(SCREAMING_SNAKE_CASE_ )
return path
@pytest.fixture(scope="session" )
def a__ ( ):
"""simple docstring"""
return os.path.join("tests" , "features" , "data" , "test_image_rgb.jpg" )
@pytest.fixture(scope="session" )
def a__ ( ):
"""simple docstring"""
return os.path.join("tests" , "features" , "data" , "test_audio_44100.wav" )
@pytest.fixture(scope="session" )
def a__ ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
"""simple docstring"""
UpperCamelCase = tmp_path_factory.mktemp("data" ) / "dataset.img.zip"
with zipfile.ZipFile(SCREAMING_SNAKE_CASE_ , "w" ) as f:
f.write(SCREAMING_SNAKE_CASE_ , arcname=os.path.basename(SCREAMING_SNAKE_CASE_ ) )
f.write(SCREAMING_SNAKE_CASE_ , arcname=os.path.basename(SCREAMING_SNAKE_CASE_ ).replace(".jpg" , "2.jpg" ) )
return path
@pytest.fixture(scope="session" )
def a__ ( _SCREAMING_SNAKE_CASE ):
"""simple docstring"""
UpperCamelCase = tmp_path_factory.mktemp("data_dir" )
(data_dir / "subdir").mkdir()
with open(data_dir / "subdir" / "train.txt" , "w" ) as f:
f.write("foo\n" * 10 )
with open(data_dir / "subdir" / "test.txt" , "w" ) as f:
f.write("bar\n" * 10 )
# hidden file
with open(data_dir / "subdir" / ".test.txt" , "w" ) as f:
f.write("bar\n" * 10 )
# hidden directory
(data_dir / ".subdir").mkdir()
with open(data_dir / ".subdir" / "train.txt" , "w" ) as f:
f.write("foo\n" * 10 )
with open(data_dir / ".subdir" / "test.txt" , "w" ) as f:
f.write("bar\n" * 10 )
return data_dir
| 153 |
from dataclasses import dataclass, field
from typing import Tuple
from ..utils import cached_property, is_tf_available, logging, requires_backends
from .benchmark_args_utils import BenchmarkArguments
if is_tf_available():
import tensorflow as tf
UpperCamelCase__ = logging.get_logger(__name__)
@dataclass
class a__ ( snake_case__ ):
_a : List[str] = [
"""no_inference""",
"""no_cuda""",
"""no_tpu""",
"""no_speed""",
"""no_memory""",
"""no_env_print""",
"""no_multi_process""",
]
def __init__( self , **_A ):
"""simple docstring"""
for deprecated_arg in self.deprecated_args:
if deprecated_arg in kwargs:
__lowerCAmelCase = deprecated_arg[3:]
__lowerCAmelCase = not kwargs.pop(_A )
logger.warning(
f"""{deprecated_arg} is depreciated. Please use --no-{positive_arg} or"""
f""" {positive_arg}={kwargs[positive_arg]}""" )
__lowerCAmelCase = kwargs.pop("tpu_name" , self.tpu_name )
__lowerCAmelCase = kwargs.pop("device_idx" , self.device_idx )
__lowerCAmelCase = kwargs.pop("eager_mode" , self.eager_mode )
__lowerCAmelCase = kwargs.pop("use_xla" , self.use_xla )
super().__init__(**_A )
_a : str = field(
default=snake_case__ , metadata={"""help""": """Name of TPU"""} , )
_a : int = field(
default=0 , metadata={"""help""": """CPU / GPU device index. Defaults to 0."""} , )
_a : bool = field(default=snake_case__ , metadata={"""help""": """Benchmark models in eager model."""} )
_a : bool = field(
default=snake_case__ , metadata={
"""help""": """Benchmark models using XLA JIT compilation. Note that `eager_model` has to be set to `False`."""
} , )
@cached_property
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
requires_backends(self , ["tf"] )
__lowerCAmelCase = None
if self.tpu:
try:
if self.tpu_name:
__lowerCAmelCase = tf.distribute.cluster_resolver.TPUClusterResolver(self.tpu_name )
else:
__lowerCAmelCase = tf.distribute.cluster_resolver.TPUClusterResolver()
except ValueError:
__lowerCAmelCase = None
return tpu
@cached_property
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
requires_backends(self , ["tf"] )
if self.is_tpu:
tf.config.experimental_connect_to_cluster(self._setup_tpu )
tf.tpu.experimental.initialize_tpu_system(self._setup_tpu )
__lowerCAmelCase = tf.distribute.TPUStrategy(self._setup_tpu )
else:
# currently no multi gpu is allowed
if self.is_gpu:
# TODO: Currently only single GPU is supported
tf.config.set_visible_devices(self.gpu_list[self.device_idx] , "GPU" )
__lowerCAmelCase = tf.distribute.OneDeviceStrategy(device=f"""/gpu:{self.device_idx}""" )
else:
tf.config.set_visible_devices([] , "GPU" ) # disable GPU
__lowerCAmelCase = tf.distribute.OneDeviceStrategy(device=f"""/cpu:{self.device_idx}""" )
return strategy
@property
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
requires_backends(self , ["tf"] )
return self._setup_tpu is not None
@property
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
requires_backends(self , ["tf"] )
return self._setup_strategy
@property
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
requires_backends(self , ["tf"] )
return tf.config.list_physical_devices("GPU" )
@property
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
requires_backends(self , ["tf"] )
if self.cuda:
return len(self.gpu_list )
return 0
@property
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
return self.n_gpu > 0
| 92 | 0 |
"""simple docstring"""
def __lowerCAmelCase ( lowercase : int , lowercase : int ) -> str:
"""simple docstring"""
while b:
snake_case ,snake_case : int = b, a % b
return a
def __lowerCAmelCase ( lowercase : int , lowercase : int ) -> str:
"""simple docstring"""
return a if b == 0 else euclidean_gcd_recursive(SCREAMING_SNAKE_CASE_ , a % b )
def __lowerCAmelCase ( ) -> List[Any]:
"""simple docstring"""
print(F'euclidean_gcd(3, 5) = {euclidean_gcd(3 , 5 )}' )
print(F'euclidean_gcd(5, 3) = {euclidean_gcd(5 , 3 )}' )
print(F'euclidean_gcd(1, 3) = {euclidean_gcd(1 , 3 )}' )
print(F'euclidean_gcd(3, 6) = {euclidean_gcd(3 , 6 )}' )
print(F'euclidean_gcd(6, 3) = {euclidean_gcd(6 , 3 )}' )
print(F'euclidean_gcd_recursive(3, 5) = {euclidean_gcd_recursive(3 , 5 )}' )
print(F'euclidean_gcd_recursive(5, 3) = {euclidean_gcd_recursive(5 , 3 )}' )
print(F'euclidean_gcd_recursive(1, 3) = {euclidean_gcd_recursive(1 , 3 )}' )
print(F'euclidean_gcd_recursive(3, 6) = {euclidean_gcd_recursive(3 , 6 )}' )
print(F'euclidean_gcd_recursive(6, 3) = {euclidean_gcd_recursive(6 , 3 )}' )
if __name__ == "__main__":
main()
| 203 |
import unittest
from transformers import CamembertTokenizer, CamembertTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import is_torch_available
from ...test_tokenization_common import TokenizerTesterMixin
UpperCamelCase__ = get_tests_dir("""fixtures/test_sentencepiece.model""")
UpperCamelCase__ = get_tests_dir("""fixtures/test_sentencepiece_bpe.model""")
UpperCamelCase__ = """pt""" if is_torch_available() else """tf"""
@require_sentencepiece
@require_tokenizers
class a__ ( snake_case__ , unittest.TestCase ):
_a : int = CamembertTokenizer
_a : Dict = CamembertTokenizerFast
_a : Tuple = True
_a : List[Any] = True
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
super().setUp()
# We have a SentencePiece fixture for testing
__lowerCAmelCase = CamembertTokenizer(_A )
tokenizer.save_pretrained(self.tmpdirname )
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
__lowerCAmelCase = "<pad>"
__lowerCAmelCase = 1
self.assertEqual(self.get_tokenizer()._convert_token_to_id(_A ) , _A )
self.assertEqual(self.get_tokenizer()._convert_id_to_token(_A ) , _A )
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
__lowerCAmelCase = list(self.get_tokenizer().get_vocab().keys() )
self.assertEqual(vocab_keys[0] , "<s>NOTUSED" )
self.assertEqual(vocab_keys[1] , "<pad>" )
self.assertEqual(vocab_keys[-1] , "<mask>" )
self.assertEqual(len(_A ) , 1_0_0_4 )
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
self.assertEqual(self.get_tokenizer().vocab_size , 1_0_0_5 )
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
__lowerCAmelCase = CamembertTokenizer(_A )
tokenizer.save_pretrained(self.tmpdirname )
__lowerCAmelCase = CamembertTokenizerFast.from_pretrained(self.tmpdirname )
__lowerCAmelCase = "I was born in 92000, and this is falsé."
__lowerCAmelCase = tokenizer.encode(_A )
__lowerCAmelCase = rust_tokenizer.encode(_A )
self.assertListEqual(_A , _A )
__lowerCAmelCase = tokenizer.encode(_A , add_special_tokens=_A )
__lowerCAmelCase = rust_tokenizer.encode(_A , add_special_tokens=_A )
self.assertListEqual(_A , _A )
# <unk> tokens are not the same for `rust` than for `slow`.
# Because spm gives back raw token instead of `unk` in EncodeAsPieces
# tokens = tokenizer.tokenize(sequence)
__lowerCAmelCase = tokenizer.convert_ids_to_tokens(_A )
__lowerCAmelCase = rust_tokenizer.tokenize(_A )
self.assertListEqual(_A , _A )
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
if not self.test_rust_tokenizer:
return
__lowerCAmelCase = self.get_tokenizer()
__lowerCAmelCase = self.get_rust_tokenizer()
__lowerCAmelCase = "I was born in 92000, and this is falsé."
__lowerCAmelCase = tokenizer.tokenize(_A )
__lowerCAmelCase = rust_tokenizer.tokenize(_A )
self.assertListEqual(_A , _A )
__lowerCAmelCase = tokenizer.encode(_A , add_special_tokens=_A )
__lowerCAmelCase = rust_tokenizer.encode(_A , add_special_tokens=_A )
self.assertListEqual(_A , _A )
__lowerCAmelCase = self.get_rust_tokenizer()
__lowerCAmelCase = tokenizer.encode(_A )
__lowerCAmelCase = rust_tokenizer.encode(_A )
self.assertListEqual(_A , _A )
@slow
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
__lowerCAmelCase = {"input_ids": [[5, 5_4, 7_1_9_6, 2_9_7, 3_0, 2_3, 7_7_6, 1_8, 1_1, 3_2_1_5, 3_7_0_5, 8_2_5_2, 2_2, 3_1_6_4, 1_1_8_1, 2_1_1_6, 2_9, 1_6, 8_1_3, 2_5, 7_9_1, 3_3_1_4, 2_0, 3_4_4_6, 3_8, 2_7_5_7_5, 1_2_0, 6, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [5, 4_6_8, 1_7, 1_1, 9_0_8_8, 2_0, 1_5_1_7, 8, 2_2_8_0_4, 1_8_8_1_8, 1_0, 3_8, 6_2_9, 6_0_7, 6_0_7, 1_4_2, 1_9, 7_1_9_6, 8_6_7, 5_6, 1_0_3_2_6, 2_4, 2_2_6_7, 2_0, 4_1_6, 5_0_7_2, 1_5_6_1_2, 2_3_3, 7_3_4, 7, 2_3_9_9, 2_7, 1_6, 3_0_1_5, 1_6_4_9, 7, 2_4, 2_0, 4_3_3_8, 2_3_9_9, 2_7, 1_3, 3_4_0_0, 1_4, 1_3, 6_1_8_9, 8, 9_3_0, 9, 6]], "attention_mask": [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]} # noqa: E501
# fmt: on
# camembert is a french model. So we also use french texts.
__lowerCAmelCase = [
"Le transformeur est un modèle d'apprentissage profond introduit en 2017, "
"utilisé principalement dans le domaine du traitement automatique des langues (TAL).",
"À l'instar des réseaux de neurones récurrents (RNN), les transformeurs sont conçus "
"pour gérer des données séquentielles, telles que le langage naturel, pour des tâches "
"telles que la traduction et la synthèse de texte.",
]
self.tokenizer_integration_test_util(
expected_encoding=_A , model_name="camembert-base" , revision="3a0641d9a1aeb7e848a74299e7e4c4bca216b4cf" , sequences=_A , )
| 92 | 0 |
'''simple docstring'''
def __magic_name__ ( __UpperCAmelCase, __UpperCAmelCase ) -> Union[str, Any]:
'''simple docstring'''
return int((input_a, input_a).count(1 ) != 0 )
def __magic_name__ ( ) -> int:
'''simple docstring'''
assert or_gate(0, 0 ) == 0
assert or_gate(0, 1 ) == 1
assert or_gate(1, 0 ) == 1
assert or_gate(1, 1 ) == 1
if __name__ == "__main__":
print(or_gate(0, 1))
print(or_gate(1, 0))
print(or_gate(0, 0))
print(or_gate(1, 1))
| 56 |
from __future__ import annotations
import collections
import tempfile
import unittest
import numpy as np
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import is_tf_available, is_vision_available
from ...test_modeling_tf_common import floats_tensor, ids_tensor, random_attention_mask
from ..bert.test_modeling_tf_bert import TFBertModelTester
from ..clip.test_modeling_tf_clip import TFCLIPVisionModelTester
from ..deit.test_modeling_tf_deit import TFDeiTModelTester
from ..roberta.test_modeling_tf_roberta import TFRobertaModelTester
from ..vit.test_modeling_tf_vit import TFViTModelTester
if is_tf_available():
from transformers import (
TFBertModel,
TFCLIPVisionModel,
TFDeiTModel,
TFRobertaModel,
TFVisionTextDualEncoderModel,
TFViTModel,
VisionTextDualEncoderConfig,
)
if is_vision_available():
from PIL import Image
from transformers import VisionTextDualEncoderProcessor
def _a ( SCREAMING_SNAKE_CASE_ : Union[str, Any] ):
if isinstance(SCREAMING_SNAKE_CASE_ , collections.abc.Iterable ):
return x
return (x, x)
@require_tf
class a__ :
def __SCREAMING_SNAKE_CASE( self , _A , _A ):
"""simple docstring"""
pass
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
pass
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
pass
def __SCREAMING_SNAKE_CASE( self , _A , _A , _A , _A , _A=None , **_A ):
"""simple docstring"""
__lowerCAmelCase = VisionTextDualEncoderConfig.from_vision_text_configs(_A , _A )
__lowerCAmelCase = TFVisionTextDualEncoderModel(_A )
__lowerCAmelCase = model(input_ids=_A , pixel_values=_A , attention_mask=_A )
self.assertEqual(output["text_embeds"].shape , (input_ids.shape[0], config.projection_dim) )
self.assertEqual(output["image_embeds"].shape , (pixel_values.shape[0], config.projection_dim) )
def __SCREAMING_SNAKE_CASE( self , _A , _A , _A , _A , _A=None , **_A ):
"""simple docstring"""
__lowerCAmelCase , __lowerCAmelCase = self.get_vision_text_model(_A , _A )
__lowerCAmelCase = TFVisionTextDualEncoderModel(vision_model=_A , text_model=_A )
__lowerCAmelCase = model(input_ids=_A , pixel_values=_A , attention_mask=_A )
self.assertEqual(output["text_embeds"].shape , (input_ids.shape[0], model.config.projection_dim) )
self.assertEqual(output["image_embeds"].shape , (pixel_values.shape[0], model.config.projection_dim) )
def __SCREAMING_SNAKE_CASE( self , _A , _A , _A , _A , _A=None , **_A ):
"""simple docstring"""
__lowerCAmelCase , __lowerCAmelCase = self.get_vision_text_model(_A , _A )
__lowerCAmelCase = {"vision_model": vision_model, "text_model": text_model}
__lowerCAmelCase = TFVisionTextDualEncoderModel.from_vision_text_pretrained(**_A )
__lowerCAmelCase = model(input_ids=_A , pixel_values=_A , attention_mask=_A )
self.assertEqual(output["text_embeds"].shape , (input_ids.shape[0], model.config.projection_dim) )
self.assertEqual(output["image_embeds"].shape , (pixel_values.shape[0], model.config.projection_dim) )
def __SCREAMING_SNAKE_CASE( self , _A , _A , _A , _A , _A=None , **_A ):
"""simple docstring"""
__lowerCAmelCase , __lowerCAmelCase = self.get_vision_text_model(_A , _A )
__lowerCAmelCase = TFVisionTextDualEncoderModel(vision_model=_A , text_model=_A )
__lowerCAmelCase = model(input_ids=_A , pixel_values=_A , attention_mask=_A )
__lowerCAmelCase = output[0].numpy()
with tempfile.TemporaryDirectory() as tmpdirname:
model.save_pretrained(_A )
__lowerCAmelCase = TFVisionTextDualEncoderModel.from_pretrained(_A )
__lowerCAmelCase = model(input_ids=_A , pixel_values=_A , attention_mask=_A )
__lowerCAmelCase = after_output[0].numpy()
__lowerCAmelCase = np.amax(np.abs(out_a - out_a ) )
self.assertLessEqual(_A , 1E-5 )
def __SCREAMING_SNAKE_CASE( self , _A , _A , _A , _A , _A=None , **_A ):
"""simple docstring"""
__lowerCAmelCase , __lowerCAmelCase = self.get_vision_text_model(_A , _A )
__lowerCAmelCase = TFVisionTextDualEncoderModel(vision_model=_A , text_model=_A )
__lowerCAmelCase = model(
input_ids=_A , pixel_values=_A , attention_mask=_A , output_attentions=_A )
__lowerCAmelCase = output.vision_model_output.attentions
self.assertEqual(len(_A ) , vision_config.num_hidden_layers )
# in ViT, the seq_len equals the number of patches + 1 (we add 1 for the [CLS] token)
__lowerCAmelCase = to_atuple(vision_model.config.image_size )
__lowerCAmelCase = to_atuple(vision_model.config.patch_size )
__lowerCAmelCase = (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
__lowerCAmelCase = num_patches + 1
self.assertEqual(vision_attentions[0].shape[-3:] , (vision_config.num_attention_heads, seq_len, seq_len) )
__lowerCAmelCase = output.text_model_output.attentions
self.assertEqual(len(_A ) , text_config.num_hidden_layers )
self.assertEqual(
text_attentions[0].shape[-3:] , (text_config.num_attention_heads, input_ids.shape[-1], input_ids.shape[-1]) , )
def __SCREAMING_SNAKE_CASE( self , _A , _A , _A ):
"""simple docstring"""
__lowerCAmelCase = np.abs((a - b) ).max()
self.assertLessEqual(_A , _A , f"""Difference between torch and flax is {diff} (>= {tol}).""" )
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
__lowerCAmelCase = self.prepare_config_and_inputs()
self.check_vision_text_dual_encoder_model(**_A )
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
__lowerCAmelCase = self.prepare_config_and_inputs()
self.check_model_from_pretrained_configs(**_A )
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
__lowerCAmelCase = self.prepare_config_and_inputs()
self.check_vision_text_dual_encoder_from_pretrained(**_A )
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
__lowerCAmelCase = self.prepare_config_and_inputs()
self.check_save_load(**_A )
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
__lowerCAmelCase = self.prepare_config_and_inputs()
self.check_vision_text_output_attention(**_A )
@slow
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
__lowerCAmelCase , __lowerCAmelCase = self.get_pretrained_model_and_inputs()
__lowerCAmelCase = model_a(**_A )
__lowerCAmelCase = outputs[0].numpy()
with tempfile.TemporaryDirectory() as tmp_dirname:
model_a.save_pretrained(_A )
__lowerCAmelCase = TFVisionTextDualEncoderModel.from_pretrained(_A )
__lowerCAmelCase = model_a(**_A )
__lowerCAmelCase = after_outputs[0].numpy()
__lowerCAmelCase = np.amax(np.abs(out_a - out_a ) )
self.assertLessEqual(_A , 1E-5 )
@require_tf
class a__ ( snake_case__ , unittest.TestCase ):
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
__lowerCAmelCase = TFVisionTextDualEncoderModel.from_vision_text_pretrained(
"hf-internal-testing/tiny-random-vit" , "hf-internal-testing/tiny-random-bert" )
__lowerCAmelCase = 1_3
__lowerCAmelCase = floats_tensor(
[
batch_size,
model.vision_model.config.num_channels,
model.vision_model.config.image_size,
model.vision_model.config.image_size,
] )
__lowerCAmelCase = ids_tensor([batch_size, 4] , model.text_model.config.vocab_size )
__lowerCAmelCase = random_attention_mask([batch_size, 4] )
__lowerCAmelCase = {"pixel_values": pixel_values, "input_ids": input_ids, "attention_mask": attention_mask}
return model, inputs
def __SCREAMING_SNAKE_CASE( self , _A , _A ):
"""simple docstring"""
__lowerCAmelCase = TFViTModel(_A , name="vision_model" )
__lowerCAmelCase = TFBertModel(_A , name="text_model" )
return vision_model, text_model
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
__lowerCAmelCase = TFViTModelTester(self )
__lowerCAmelCase = TFBertModelTester(self )
__lowerCAmelCase = vit_model_tester.prepare_config_and_inputs()
__lowerCAmelCase = bert_model_tester.prepare_config_and_inputs()
__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase = vision_config_and_inputs
(
(
__lowerCAmelCase
) , (
__lowerCAmelCase
) , (
__lowerCAmelCase
) , (
__lowerCAmelCase
) , (
__lowerCAmelCase
) , (
__lowerCAmelCase
) , (
__lowerCAmelCase
) ,
) = text_config_and_inputs
return {
"text_config": text_config,
"vision_config": vision_config,
"pixel_values": pixel_values,
"attention_mask": input_mask,
"input_ids": input_ids,
"text_token_type_ids": token_type_ids,
"text_sequence_labels": sequence_labels,
"text_token_labels": token_labels,
"text_choice_labels": choice_labels,
}
@require_tf
class a__ ( snake_case__ , unittest.TestCase ):
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
__lowerCAmelCase = TFVisionTextDualEncoderModel.from_vision_text_pretrained(
"Rocketknight1/tiny-random-deit-tf" , "hf-internal-testing/tiny-random-roberta" )
__lowerCAmelCase = 1_3
__lowerCAmelCase = floats_tensor(
[
batch_size,
model.vision_model.config.num_channels,
model.vision_model.config.image_size,
model.vision_model.config.image_size,
] )
__lowerCAmelCase = ids_tensor([batch_size, 4] , model.text_model.config.vocab_size )
__lowerCAmelCase = random_attention_mask([batch_size, 4] )
__lowerCAmelCase = {"pixel_values": pixel_values, "input_ids": input_ids, "attention_mask": attention_mask}
return model, inputs
def __SCREAMING_SNAKE_CASE( self , _A , _A , _A , _A , _A=None , **_A ):
"""simple docstring"""
__lowerCAmelCase , __lowerCAmelCase = self.get_vision_text_model(_A , _A )
__lowerCAmelCase = TFVisionTextDualEncoderModel(vision_model=_A , text_model=_A )
__lowerCAmelCase = model(
input_ids=_A , pixel_values=_A , attention_mask=_A , output_attentions=_A )
__lowerCAmelCase = output.vision_model_output.attentions
self.assertEqual(len(_A ) , vision_config.num_hidden_layers )
# in DEiT, the seq_len equals the number of patches + 2 (we add 2 for the [CLS] and distillation tokens)
__lowerCAmelCase = to_atuple(vision_model.config.image_size )
__lowerCAmelCase = to_atuple(vision_model.config.patch_size )
__lowerCAmelCase = (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
__lowerCAmelCase = num_patches + 2
self.assertEqual(vision_attentions[0].shape[-3:] , (vision_config.num_attention_heads, seq_len, seq_len) )
__lowerCAmelCase = output.text_model_output.attentions
self.assertEqual(len(_A ) , text_config.num_hidden_layers )
self.assertEqual(
text_attentions[0].shape[-3:] , (text_config.num_attention_heads, input_ids.shape[-1], input_ids.shape[-1]) , )
def __SCREAMING_SNAKE_CASE( self , _A , _A ):
"""simple docstring"""
__lowerCAmelCase = TFDeiTModel(_A , name="vision_model" )
__lowerCAmelCase = TFRobertaModel(_A , name="text_model" )
return vision_model, text_model
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
__lowerCAmelCase = TFDeiTModelTester(self )
__lowerCAmelCase = TFRobertaModelTester(self )
__lowerCAmelCase = vit_model_tester.prepare_config_and_inputs()
__lowerCAmelCase = bert_model_tester.prepare_config_and_inputs()
__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase = vision_config_and_inputs
(
(
__lowerCAmelCase
) , (
__lowerCAmelCase
) , (
__lowerCAmelCase
) , (
__lowerCAmelCase
) , (
__lowerCAmelCase
) , (
__lowerCAmelCase
) , (
__lowerCAmelCase
) ,
) = text_config_and_inputs
return {
"text_config": text_config,
"vision_config": vision_config,
"pixel_values": pixel_values,
"attention_mask": input_mask,
"input_ids": input_ids,
"text_token_type_ids": token_type_ids,
"text_sequence_labels": sequence_labels,
"text_token_labels": token_labels,
"text_choice_labels": choice_labels,
}
@require_tf
class a__ ( snake_case__ , unittest.TestCase ):
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
__lowerCAmelCase = TFVisionTextDualEncoderModel.from_vision_text_pretrained(
"Rocketknight1/tiny-random-clip-tf" , "hf-internal-testing/tiny-random-bert" )
__lowerCAmelCase = 1_3
__lowerCAmelCase = floats_tensor(
[
batch_size,
model.vision_model.config.num_channels,
model.vision_model.config.image_size,
model.vision_model.config.image_size,
] )
__lowerCAmelCase = ids_tensor([batch_size, 4] , model.text_model.config.vocab_size )
__lowerCAmelCase = random_attention_mask([batch_size, 4] )
__lowerCAmelCase = {"pixel_values": pixel_values, "input_ids": input_ids, "attention_mask": attention_mask}
return model, inputs
def __SCREAMING_SNAKE_CASE( self , _A , _A ):
"""simple docstring"""
__lowerCAmelCase = TFCLIPVisionModel(_A , name="vision_model" )
__lowerCAmelCase = TFBertModel(_A , name="text_model" )
return vision_model, text_model
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
__lowerCAmelCase = TFCLIPVisionModelTester(self )
__lowerCAmelCase = TFBertModelTester(self )
__lowerCAmelCase = clip_model_tester.prepare_config_and_inputs()
__lowerCAmelCase = bert_model_tester.prepare_config_and_inputs()
__lowerCAmelCase , __lowerCAmelCase = vision_config_and_inputs
(
(
__lowerCAmelCase
) , (
__lowerCAmelCase
) , (
__lowerCAmelCase
) , (
__lowerCAmelCase
) , (
__lowerCAmelCase
) , (
__lowerCAmelCase
) , (
__lowerCAmelCase
) ,
) = text_config_and_inputs
return {
"text_config": text_config,
"vision_config": vision_config,
"pixel_values": pixel_values,
"attention_mask": input_mask,
"input_ids": input_ids,
"text_token_type_ids": token_type_ids,
"text_sequence_labels": sequence_labels,
"text_token_labels": token_labels,
"text_choice_labels": choice_labels,
}
@require_vision
@require_tf
class a__ ( unittest.TestCase ):
@slow
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
__lowerCAmelCase = TFVisionTextDualEncoderModel.from_pretrained(
"clip-italian/clip-italian" , logit_scale_init_value=1.0 , from_pt=_A )
__lowerCAmelCase = VisionTextDualEncoderProcessor.from_pretrained("clip-italian/clip-italian" )
__lowerCAmelCase = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png" )
__lowerCAmelCase = processor(
text=["una foto di un gatto", "una foto di un cane"] , images=_A , padding=_A , return_tensors="np" )
__lowerCAmelCase = model(**_A )
# verify the logits
self.assertEqual(outputs.logits_per_image.shape , (inputs.pixel_values.shape[0], inputs.input_ids.shape[0]) )
self.assertEqual(
outputs.logits_per_text.shape , (inputs.input_ids.shape[0], inputs.pixel_values.shape[0]) , )
__lowerCAmelCase = np.array([[1.2_28_47_27, 0.3_10_41_22]] )
self.assertTrue(np.allclose(outputs.logits_per_image.numpy() , _A , atol=1E-3 ) )
| 92 | 0 |
'''simple docstring'''
from collections import OrderedDict
from ...utils import logging
from .auto_factory import _BaseAutoModelClass, _LazyAutoMapping, auto_class_update
from .configuration_auto import CONFIG_MAPPING_NAMES
a_ : Union[str, Any] = logging.get_logger(__name__)
a_ : Union[str, Any] = OrderedDict(
[
# Base model mapping
("""albert""", """FlaxAlbertModel"""),
("""bart""", """FlaxBartModel"""),
("""beit""", """FlaxBeitModel"""),
("""bert""", """FlaxBertModel"""),
("""big_bird""", """FlaxBigBirdModel"""),
("""blenderbot""", """FlaxBlenderbotModel"""),
("""blenderbot-small""", """FlaxBlenderbotSmallModel"""),
("""clip""", """FlaxCLIPModel"""),
("""distilbert""", """FlaxDistilBertModel"""),
("""electra""", """FlaxElectraModel"""),
("""gpt-sw3""", """FlaxGPT2Model"""),
("""gpt2""", """FlaxGPT2Model"""),
("""gpt_neo""", """FlaxGPTNeoModel"""),
("""gptj""", """FlaxGPTJModel"""),
("""longt5""", """FlaxLongT5Model"""),
("""marian""", """FlaxMarianModel"""),
("""mbart""", """FlaxMBartModel"""),
("""mt5""", """FlaxMT5Model"""),
("""opt""", """FlaxOPTModel"""),
("""pegasus""", """FlaxPegasusModel"""),
("""regnet""", """FlaxRegNetModel"""),
("""resnet""", """FlaxResNetModel"""),
("""roberta""", """FlaxRobertaModel"""),
("""roberta-prelayernorm""", """FlaxRobertaPreLayerNormModel"""),
("""roformer""", """FlaxRoFormerModel"""),
("""t5""", """FlaxT5Model"""),
("""vision-text-dual-encoder""", """FlaxVisionTextDualEncoderModel"""),
("""vit""", """FlaxViTModel"""),
("""wav2vec2""", """FlaxWav2Vec2Model"""),
("""whisper""", """FlaxWhisperModel"""),
("""xglm""", """FlaxXGLMModel"""),
("""xlm-roberta""", """FlaxXLMRobertaModel"""),
]
)
a_ : Dict = OrderedDict(
[
# Model for pre-training mapping
("""albert""", """FlaxAlbertForPreTraining"""),
("""bart""", """FlaxBartForConditionalGeneration"""),
("""bert""", """FlaxBertForPreTraining"""),
("""big_bird""", """FlaxBigBirdForPreTraining"""),
("""electra""", """FlaxElectraForPreTraining"""),
("""longt5""", """FlaxLongT5ForConditionalGeneration"""),
("""mbart""", """FlaxMBartForConditionalGeneration"""),
("""mt5""", """FlaxMT5ForConditionalGeneration"""),
("""roberta""", """FlaxRobertaForMaskedLM"""),
("""roberta-prelayernorm""", """FlaxRobertaPreLayerNormForMaskedLM"""),
("""roformer""", """FlaxRoFormerForMaskedLM"""),
("""t5""", """FlaxT5ForConditionalGeneration"""),
("""wav2vec2""", """FlaxWav2Vec2ForPreTraining"""),
("""whisper""", """FlaxWhisperForConditionalGeneration"""),
("""xlm-roberta""", """FlaxXLMRobertaForMaskedLM"""),
]
)
a_ : Optional[Any] = OrderedDict(
[
# Model for Masked LM mapping
("""albert""", """FlaxAlbertForMaskedLM"""),
("""bart""", """FlaxBartForConditionalGeneration"""),
("""bert""", """FlaxBertForMaskedLM"""),
("""big_bird""", """FlaxBigBirdForMaskedLM"""),
("""distilbert""", """FlaxDistilBertForMaskedLM"""),
("""electra""", """FlaxElectraForMaskedLM"""),
("""mbart""", """FlaxMBartForConditionalGeneration"""),
("""roberta""", """FlaxRobertaForMaskedLM"""),
("""roberta-prelayernorm""", """FlaxRobertaPreLayerNormForMaskedLM"""),
("""roformer""", """FlaxRoFormerForMaskedLM"""),
("""xlm-roberta""", """FlaxXLMRobertaForMaskedLM"""),
]
)
a_ : Optional[int] = OrderedDict(
[
# Model for Seq2Seq Causal LM mapping
("""bart""", """FlaxBartForConditionalGeneration"""),
("""blenderbot""", """FlaxBlenderbotForConditionalGeneration"""),
("""blenderbot-small""", """FlaxBlenderbotSmallForConditionalGeneration"""),
("""encoder-decoder""", """FlaxEncoderDecoderModel"""),
("""longt5""", """FlaxLongT5ForConditionalGeneration"""),
("""marian""", """FlaxMarianMTModel"""),
("""mbart""", """FlaxMBartForConditionalGeneration"""),
("""mt5""", """FlaxMT5ForConditionalGeneration"""),
("""pegasus""", """FlaxPegasusForConditionalGeneration"""),
("""t5""", """FlaxT5ForConditionalGeneration"""),
]
)
a_ : Any = OrderedDict(
[
# Model for Image-classsification
("""beit""", """FlaxBeitForImageClassification"""),
("""regnet""", """FlaxRegNetForImageClassification"""),
("""resnet""", """FlaxResNetForImageClassification"""),
("""vit""", """FlaxViTForImageClassification"""),
]
)
a_ : str = OrderedDict(
[
("""vision-encoder-decoder""", """FlaxVisionEncoderDecoderModel"""),
]
)
a_ : List[str] = OrderedDict(
[
# Model for Causal LM mapping
("""bart""", """FlaxBartForCausalLM"""),
("""bert""", """FlaxBertForCausalLM"""),
("""big_bird""", """FlaxBigBirdForCausalLM"""),
("""electra""", """FlaxElectraForCausalLM"""),
("""gpt-sw3""", """FlaxGPT2LMHeadModel"""),
("""gpt2""", """FlaxGPT2LMHeadModel"""),
("""gpt_neo""", """FlaxGPTNeoForCausalLM"""),
("""gptj""", """FlaxGPTJForCausalLM"""),
("""opt""", """FlaxOPTForCausalLM"""),
("""roberta""", """FlaxRobertaForCausalLM"""),
("""roberta-prelayernorm""", """FlaxRobertaPreLayerNormForCausalLM"""),
("""xglm""", """FlaxXGLMForCausalLM"""),
("""xlm-roberta""", """FlaxXLMRobertaForCausalLM"""),
]
)
a_ : Tuple = OrderedDict(
[
# Model for Sequence Classification mapping
("""albert""", """FlaxAlbertForSequenceClassification"""),
("""bart""", """FlaxBartForSequenceClassification"""),
("""bert""", """FlaxBertForSequenceClassification"""),
("""big_bird""", """FlaxBigBirdForSequenceClassification"""),
("""distilbert""", """FlaxDistilBertForSequenceClassification"""),
("""electra""", """FlaxElectraForSequenceClassification"""),
("""mbart""", """FlaxMBartForSequenceClassification"""),
("""roberta""", """FlaxRobertaForSequenceClassification"""),
("""roberta-prelayernorm""", """FlaxRobertaPreLayerNormForSequenceClassification"""),
("""roformer""", """FlaxRoFormerForSequenceClassification"""),
("""xlm-roberta""", """FlaxXLMRobertaForSequenceClassification"""),
]
)
a_ : List[Any] = OrderedDict(
[
# Model for Question Answering mapping
("""albert""", """FlaxAlbertForQuestionAnswering"""),
("""bart""", """FlaxBartForQuestionAnswering"""),
("""bert""", """FlaxBertForQuestionAnswering"""),
("""big_bird""", """FlaxBigBirdForQuestionAnswering"""),
("""distilbert""", """FlaxDistilBertForQuestionAnswering"""),
("""electra""", """FlaxElectraForQuestionAnswering"""),
("""mbart""", """FlaxMBartForQuestionAnswering"""),
("""roberta""", """FlaxRobertaForQuestionAnswering"""),
("""roberta-prelayernorm""", """FlaxRobertaPreLayerNormForQuestionAnswering"""),
("""roformer""", """FlaxRoFormerForQuestionAnswering"""),
("""xlm-roberta""", """FlaxXLMRobertaForQuestionAnswering"""),
]
)
a_ : List[Any] = OrderedDict(
[
# Model for Token Classification mapping
("""albert""", """FlaxAlbertForTokenClassification"""),
("""bert""", """FlaxBertForTokenClassification"""),
("""big_bird""", """FlaxBigBirdForTokenClassification"""),
("""distilbert""", """FlaxDistilBertForTokenClassification"""),
("""electra""", """FlaxElectraForTokenClassification"""),
("""roberta""", """FlaxRobertaForTokenClassification"""),
("""roberta-prelayernorm""", """FlaxRobertaPreLayerNormForTokenClassification"""),
("""roformer""", """FlaxRoFormerForTokenClassification"""),
("""xlm-roberta""", """FlaxXLMRobertaForTokenClassification"""),
]
)
a_ : List[str] = OrderedDict(
[
# Model for Multiple Choice mapping
("""albert""", """FlaxAlbertForMultipleChoice"""),
("""bert""", """FlaxBertForMultipleChoice"""),
("""big_bird""", """FlaxBigBirdForMultipleChoice"""),
("""distilbert""", """FlaxDistilBertForMultipleChoice"""),
("""electra""", """FlaxElectraForMultipleChoice"""),
("""roberta""", """FlaxRobertaForMultipleChoice"""),
("""roberta-prelayernorm""", """FlaxRobertaPreLayerNormForMultipleChoice"""),
("""roformer""", """FlaxRoFormerForMultipleChoice"""),
("""xlm-roberta""", """FlaxXLMRobertaForMultipleChoice"""),
]
)
a_ : Optional[int] = OrderedDict(
[
("""bert""", """FlaxBertForNextSentencePrediction"""),
]
)
a_ : List[str] = OrderedDict(
[
("""speech-encoder-decoder""", """FlaxSpeechEncoderDecoderModel"""),
("""whisper""", """FlaxWhisperForConditionalGeneration"""),
]
)
a_ : Any = OrderedDict(
[
("""whisper""", """FlaxWhisperForAudioClassification"""),
]
)
a_ : str = _LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_MAPPING_NAMES)
a_ : Tuple = _LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_PRETRAINING_MAPPING_NAMES)
a_ : List[str] = _LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_MASKED_LM_MAPPING_NAMES)
a_ : Any = _LazyAutoMapping(
CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES
)
a_ : List[Any] = _LazyAutoMapping(
CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING_NAMES
)
a_ : Dict = _LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_VISION_2_SEQ_MAPPING_NAMES)
a_ : List[Any] = _LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_CAUSAL_LM_MAPPING_NAMES)
a_ : int = _LazyAutoMapping(
CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING_NAMES
)
a_ : Union[str, Any] = _LazyAutoMapping(
CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_QUESTION_ANSWERING_MAPPING_NAMES
)
a_ : str = _LazyAutoMapping(
CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING_NAMES
)
a_ : Optional[Any] = _LazyAutoMapping(
CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_MULTIPLE_CHOICE_MAPPING_NAMES
)
a_ : Any = _LazyAutoMapping(
CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING_NAMES
)
a_ : int = _LazyAutoMapping(
CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING_NAMES
)
a_ : List[str] = _LazyAutoMapping(
CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING_NAMES
)
class __UpperCamelCase ( _BaseAutoModelClass ):
lowercase : Tuple =FLAX_MODEL_MAPPING
a_ : Union[str, Any] = auto_class_update(FlaxAutoModel)
class __UpperCamelCase ( _BaseAutoModelClass ):
lowercase : Any =FLAX_MODEL_FOR_PRETRAINING_MAPPING
a_ : List[str] = auto_class_update(FlaxAutoModelForPreTraining, head_doc="""pretraining""")
class __UpperCamelCase ( _BaseAutoModelClass ):
lowercase : List[Any] =FLAX_MODEL_FOR_CAUSAL_LM_MAPPING
a_ : Optional[Any] = auto_class_update(FlaxAutoModelForCausalLM, head_doc="""causal language modeling""")
class __UpperCamelCase ( _BaseAutoModelClass ):
lowercase : Optional[int] =FLAX_MODEL_FOR_MASKED_LM_MAPPING
a_ : Optional[int] = auto_class_update(FlaxAutoModelForMaskedLM, head_doc="""masked language modeling""")
class __UpperCamelCase ( _BaseAutoModelClass ):
lowercase : Dict =FLAX_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
a_ : int = auto_class_update(
FlaxAutoModelForSeqaSeqLM, head_doc="""sequence-to-sequence language modeling""", checkpoint_for_example="""t5-base"""
)
class __UpperCamelCase ( _BaseAutoModelClass ):
lowercase : Any =FLAX_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
a_ : List[Any] = auto_class_update(
FlaxAutoModelForSequenceClassification, head_doc="""sequence classification"""
)
class __UpperCamelCase ( _BaseAutoModelClass ):
lowercase : int =FLAX_MODEL_FOR_QUESTION_ANSWERING_MAPPING
a_ : int = auto_class_update(FlaxAutoModelForQuestionAnswering, head_doc="""question answering""")
class __UpperCamelCase ( _BaseAutoModelClass ):
lowercase : Tuple =FLAX_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING
a_ : Dict = auto_class_update(
FlaxAutoModelForTokenClassification, head_doc="""token classification"""
)
class __UpperCamelCase ( _BaseAutoModelClass ):
lowercase : int =FLAX_MODEL_FOR_MULTIPLE_CHOICE_MAPPING
a_ : Optional[Any] = auto_class_update(FlaxAutoModelForMultipleChoice, head_doc="""multiple choice""")
class __UpperCamelCase ( _BaseAutoModelClass ):
lowercase : List[Any] =FLAX_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING
a_ : Tuple = auto_class_update(
FlaxAutoModelForNextSentencePrediction, head_doc="""next sentence prediction"""
)
class __UpperCamelCase ( _BaseAutoModelClass ):
lowercase : Tuple =FLAX_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING
a_ : Union[str, Any] = auto_class_update(
FlaxAutoModelForImageClassification, head_doc="""image classification"""
)
class __UpperCamelCase ( _BaseAutoModelClass ):
lowercase : int =FLAX_MODEL_FOR_VISION_2_SEQ_MAPPING
a_ : str = auto_class_update(FlaxAutoModelForVisionaSeq, head_doc="""vision-to-text modeling""")
class __UpperCamelCase ( _BaseAutoModelClass ):
lowercase : int =FLAX_MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING
a_ : Dict = auto_class_update(
FlaxAutoModelForSpeechSeqaSeq, head_doc="""sequence-to-sequence speech-to-text modeling"""
)
| 75 |
import json
import os
import torch
from diffusers import UNetaDModel
os.makedirs("""hub/hopper-medium-v2/unet/hor32""", exist_ok=True)
os.makedirs("""hub/hopper-medium-v2/unet/hor128""", exist_ok=True)
os.makedirs("""hub/hopper-medium-v2/value_function""", exist_ok=True)
def _a ( SCREAMING_SNAKE_CASE_ : List[Any] ):
if hor == 1_28:
__lowerCAmelCase = ("DownResnetBlock1D", "DownResnetBlock1D", "DownResnetBlock1D")
__lowerCAmelCase = (32, 1_28, 2_56)
__lowerCAmelCase = ("UpResnetBlock1D", "UpResnetBlock1D")
elif hor == 32:
__lowerCAmelCase = ("DownResnetBlock1D", "DownResnetBlock1D", "DownResnetBlock1D", "DownResnetBlock1D")
__lowerCAmelCase = (32, 64, 1_28, 2_56)
__lowerCAmelCase = ("UpResnetBlock1D", "UpResnetBlock1D", "UpResnetBlock1D")
__lowerCAmelCase = torch.load(F"""/Users/bglickenhaus/Documents/diffuser/temporal_unet-hopper-mediumv2-hor{hor}.torch""" )
__lowerCAmelCase = model.state_dict()
__lowerCAmelCase = {
"down_block_types": down_block_types,
"block_out_channels": block_out_channels,
"up_block_types": up_block_types,
"layers_per_block": 1,
"use_timestep_embedding": True,
"out_block_type": "OutConv1DBlock",
"norm_num_groups": 8,
"downsample_each_block": False,
"in_channels": 14,
"out_channels": 14,
"extra_in_channels": 0,
"time_embedding_type": "positional",
"flip_sin_to_cos": False,
"freq_shift": 1,
"sample_size": 6_55_36,
"mid_block_type": "MidResTemporalBlock1D",
"act_fn": "mish",
}
__lowerCAmelCase = UNetaDModel(**SCREAMING_SNAKE_CASE_ )
print(F"""length of state dict: {len(state_dict.keys() )}""" )
print(F"""length of value function dict: {len(hf_value_function.state_dict().keys() )}""" )
__lowerCAmelCase = dict(zip(model.state_dict().keys() , hf_value_function.state_dict().keys() ) )
for k, v in mapping.items():
__lowerCAmelCase = state_dict.pop(SCREAMING_SNAKE_CASE_ )
hf_value_function.load_state_dict(SCREAMING_SNAKE_CASE_ )
torch.save(hf_value_function.state_dict() , F"""hub/hopper-medium-v2/unet/hor{hor}/diffusion_pytorch_model.bin""" )
with open(F"""hub/hopper-medium-v2/unet/hor{hor}/config.json""" , "w" ) as f:
json.dump(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
def _a ( ):
__lowerCAmelCase = {
"in_channels": 14,
"down_block_types": ("DownResnetBlock1D", "DownResnetBlock1D", "DownResnetBlock1D", "DownResnetBlock1D"),
"up_block_types": (),
"out_block_type": "ValueFunction",
"mid_block_type": "ValueFunctionMidBlock1D",
"block_out_channels": (32, 64, 1_28, 2_56),
"layers_per_block": 1,
"downsample_each_block": True,
"sample_size": 6_55_36,
"out_channels": 14,
"extra_in_channels": 0,
"time_embedding_type": "positional",
"use_timestep_embedding": True,
"flip_sin_to_cos": False,
"freq_shift": 1,
"norm_num_groups": 8,
"act_fn": "mish",
}
__lowerCAmelCase = torch.load("/Users/bglickenhaus/Documents/diffuser/value_function-hopper-mediumv2-hor32.torch" )
__lowerCAmelCase = model
__lowerCAmelCase = UNetaDModel(**SCREAMING_SNAKE_CASE_ )
print(F"""length of state dict: {len(state_dict.keys() )}""" )
print(F"""length of value function dict: {len(hf_value_function.state_dict().keys() )}""" )
__lowerCAmelCase = dict(zip(state_dict.keys() , hf_value_function.state_dict().keys() ) )
for k, v in mapping.items():
__lowerCAmelCase = state_dict.pop(SCREAMING_SNAKE_CASE_ )
hf_value_function.load_state_dict(SCREAMING_SNAKE_CASE_ )
torch.save(hf_value_function.state_dict() , "hub/hopper-medium-v2/value_function/diffusion_pytorch_model.bin" )
with open("hub/hopper-medium-v2/value_function/config.json" , "w" ) as f:
json.dump(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
if __name__ == "__main__":
unet(32)
# unet(128)
value_function()
| 92 | 0 |
"""simple docstring"""
from __future__ import annotations
_UpperCamelCase : str = 'Muhammad Umer Farooq'
_UpperCamelCase : Optional[Any] = 'MIT'
_UpperCamelCase : str = '1.0.0'
_UpperCamelCase : Union[str, Any] = 'Muhammad Umer Farooq'
_UpperCamelCase : Dict = 'contact@muhammadumerfarooq.me'
_UpperCamelCase : List[Any] = 'Alpha'
import re
from html.parser import HTMLParser
from urllib import parse
import requests
class a ( snake_case__ ):
def __init__( self , _lowerCamelCase ):
super().__init__()
lowercase = []
lowercase = domain
def UpperCamelCase_ ( self , _lowerCamelCase , _lowerCamelCase ):
if tag == "a":
# Check the list of defined attributes.
for name, value in attrs:
# If href is defined, and not empty nor # print it.
if name == "href" and value != "#" and value != "":
# If not already in urls.
if value not in self.urls:
lowercase = parse.urljoin(self.domain , _A )
self.urls.append(_A )
def _SCREAMING_SNAKE_CASE ( __snake_case : str ):
'''simple docstring'''
return ".".join(get_sub_domain_name(SCREAMING_SNAKE_CASE_ ).split('.' )[-2:] )
def _SCREAMING_SNAKE_CASE ( __snake_case : str ):
'''simple docstring'''
return parse.urlparse(SCREAMING_SNAKE_CASE_ ).netloc
def _SCREAMING_SNAKE_CASE ( __snake_case : str = "https://github.com" ):
'''simple docstring'''
lowercase = get_domain_name(SCREAMING_SNAKE_CASE_ )
# Initialize the parser
lowercase = Parser(SCREAMING_SNAKE_CASE_ )
try:
# Open URL
lowercase = requests.get(SCREAMING_SNAKE_CASE_ )
# pass the raw HTML to the parser to get links
parser.feed(r.text )
# Get links and loop through
lowercase = set()
for link in parser.urls:
# open URL.
# read = requests.get(link)
try:
lowercase = requests.get(SCREAMING_SNAKE_CASE_ )
# Get the valid email.
lowercase = re.findall('[a-zA-Z0-9]+@' + domain , read.text )
# If not in list then append it.
for email in emails:
valid_emails.add(SCREAMING_SNAKE_CASE_ )
except ValueError:
pass
except ValueError:
raise SystemExit(1 )
# Finally return a sorted list of email addresses with no duplicates.
return sorted(SCREAMING_SNAKE_CASE_ )
if __name__ == "__main__":
_UpperCamelCase : Dict = emails_from_url('https://github.com')
print(F'''{len(emails)} emails found:''')
print('\n'.join(sorted(emails)))
| 220 |
import pytest
from datasets import inspect_metric, list_metrics, load_metric
@pytest.fixture
def _a ( SCREAMING_SNAKE_CASE_ : Optional[Any] ):
monkeypatch.setattr("datasets.utils.deprecation_utils._emitted_deprecation_warnings" , set() )
@pytest.fixture
def _a ( SCREAMING_SNAKE_CASE_ : List[Any] ):
class a__ :
def __init__( self , _A ):
"""simple docstring"""
__lowerCAmelCase = metric_id
class a__ :
_a : Optional[int] = [MetricMock(snake_case__ ) for metric_id in ["""accuracy""", """mse""", """precision""", """codeparrot/apps_metric"""]]
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
return self._metrics
monkeypatch.setattr("datasets.inspect.huggingface_hub" , HfhMock() )
@pytest.mark.parametrize(
"func, args" , [(load_metric, ("metrics/mse",)), (list_metrics, ()), (inspect_metric, ("metrics/mse", "tmp_path"))] )
def _a ( SCREAMING_SNAKE_CASE_ : Tuple , SCREAMING_SNAKE_CASE_ : List[str] , SCREAMING_SNAKE_CASE_ : int , SCREAMING_SNAKE_CASE_ : Optional[int] , SCREAMING_SNAKE_CASE_ : Optional[int] ):
if "tmp_path" in args:
__lowerCAmelCase = tuple(arg if arg != "tmp_path" else tmp_path for arg in args )
with pytest.warns(SCREAMING_SNAKE_CASE_ , match="https://huggingface.co/docs/evaluate" ):
func(*SCREAMING_SNAKE_CASE_ )
| 92 | 0 |
from __future__ import annotations
import numpy as np
def __lowerCAmelCase ( a__ ) -> List[str]:
return np.maximum(0 , SCREAMING_SNAKE_CASE_ )
if __name__ == "__main__":
print(np.array(relu([-1, 0, 5]))) # --> [0, 0, 5] | 6 |
from random import randint
from tempfile import TemporaryFile
import numpy as np
def _a ( SCREAMING_SNAKE_CASE_ : int , SCREAMING_SNAKE_CASE_ : str , SCREAMING_SNAKE_CASE_ : List[str] ):
__lowerCAmelCase = 0
if start < end:
__lowerCAmelCase = randint(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
__lowerCAmelCase = a[end]
__lowerCAmelCase = a[pivot]
__lowerCAmelCase = temp
__lowerCAmelCase , __lowerCAmelCase = _in_place_partition(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
count += _in_place_quick_sort(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , p - 1 )
count += _in_place_quick_sort(SCREAMING_SNAKE_CASE_ , p + 1 , SCREAMING_SNAKE_CASE_ )
return count
def _a ( SCREAMING_SNAKE_CASE_ : str , SCREAMING_SNAKE_CASE_ : List[Any] , SCREAMING_SNAKE_CASE_ : Optional[int] ):
__lowerCAmelCase = 0
__lowerCAmelCase = randint(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
__lowerCAmelCase = a[end]
__lowerCAmelCase = a[pivot]
__lowerCAmelCase = temp
__lowerCAmelCase = start - 1
for index in range(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
count += 1
if a[index] < a[end]: # check if current val is less than pivot value
__lowerCAmelCase = new_pivot_index + 1
__lowerCAmelCase = a[new_pivot_index]
__lowerCAmelCase = a[index]
__lowerCAmelCase = temp
__lowerCAmelCase = a[new_pivot_index + 1]
__lowerCAmelCase = a[end]
__lowerCAmelCase = temp
return new_pivot_index + 1, count
UpperCamelCase__ = TemporaryFile()
UpperCamelCase__ = 100 # 1000 elements are to be sorted
UpperCamelCase__ , UpperCamelCase__ = 0, 1 # mean and standard deviation
UpperCamelCase__ = np.random.normal(mu, sigma, p)
np.save(outfile, X)
print("""The array is""")
print(X)
outfile.seek(0) # using the same array
UpperCamelCase__ = np.load(outfile)
UpperCamelCase__ = len(M) - 1
UpperCamelCase__ = _in_place_quick_sort(M, 0, r)
print(
"""No of Comparisons for 100 elements selected from a standard normal distribution"""
"""is :"""
)
print(z)
| 92 | 0 |
from .glue import glue_convert_examples_to_features, glue_output_modes, glue_processors, glue_tasks_num_labels
from .squad import SquadExample, SquadFeatures, SquadVaProcessor, SquadVaProcessor, squad_convert_examples_to_features
from .utils import DataProcessor, InputExample, InputFeatures, SingleSentenceClassificationProcessor
from .xnli import xnli_output_modes, xnli_processors, xnli_tasks_num_labels
| 207 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_speech_available, is_torch_available
UpperCamelCase__ = {
"""configuration_audio_spectrogram_transformer""": [
"""AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""ASTConfig""",
]
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCamelCase__ = [
"""AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""ASTForAudioClassification""",
"""ASTModel""",
"""ASTPreTrainedModel""",
]
try:
if not is_speech_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCamelCase__ = ["""ASTFeatureExtractor"""]
if TYPE_CHECKING:
from .configuration_audio_spectrogram_transformer import (
AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
ASTConfig,
)
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_audio_spectrogram_transformer import (
AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
ASTForAudioClassification,
ASTModel,
ASTPreTrainedModel,
)
try:
if not is_speech_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .feature_extraction_audio_spectrogram_transformer import ASTFeatureExtractor
else:
import sys
UpperCamelCase__ = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 92 | 0 |
'''simple docstring'''
import argparse
import os
import re
import packaging.version
lowerCAmelCase: Optional[int] = 'examples/'
lowerCAmelCase: Optional[Any] = {
'examples': (re.compile(r'^check_min_version\(\"[^\"]+\"\)\s*$', re.MULTILINE), 'check_min_version(\"VERSION\")\n'),
'init': (re.compile(r'^__version__\s+=\s+\"([^\"]+)\"\s*$', re.MULTILINE), '__version__ = \"VERSION\"\n'),
'setup': (re.compile(r'^(\s*)version\s*=\s*\"[^\"]+\",', re.MULTILINE), r'\1version=\"VERSION\",'),
'doc': (re.compile(r'^(\s*)release\s*=\s*\"[^\"]+\"$', re.MULTILINE), 'release = \"VERSION\"\n'),
}
lowerCAmelCase: Union[str, Any] = {
'init': 'src/transformers/__init__.py',
'setup': 'setup.py',
}
lowerCAmelCase: Any = 'README.md'
def lowerCamelCase__ ( _A , _A , _A ):
with open(SCREAMING_SNAKE_CASE_ , 'r' , encoding='utf-8' , newline='\n' ) as f:
a : List[str] = f.read()
a , a : Optional[Any] = REPLACE_PATTERNS[pattern]
a : List[Any] = replace.replace('VERSION' , SCREAMING_SNAKE_CASE_ )
a : Union[str, Any] = re_pattern.sub(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
with open(SCREAMING_SNAKE_CASE_ , 'w' , encoding='utf-8' , newline='\n' ) as f:
f.write(SCREAMING_SNAKE_CASE_ )
def lowerCamelCase__ ( _A ):
for folder, directories, fnames in os.walk(SCREAMING_SNAKE_CASE_ ):
# Removing some of the folders with non-actively maintained examples from the walk
if "research_projects" in directories:
directories.remove('research_projects' )
if "legacy" in directories:
directories.remove('legacy' )
for fname in fnames:
if fname.endswith('.py' ):
update_version_in_file(os.path.join(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) , SCREAMING_SNAKE_CASE_ , pattern='examples' )
def lowerCamelCase__ ( _A , _A=False ):
for pattern, fname in REPLACE_FILES.items():
update_version_in_file(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
if not patch:
update_version_in_examples(SCREAMING_SNAKE_CASE_ )
def lowerCamelCase__ ( ):
a : Any = '🤗 Transformers currently provides the following architectures'
a : Dict = '1. Want to contribute a new model?'
with open(SCREAMING_SNAKE_CASE_ , 'r' , encoding='utf-8' , newline='\n' ) as f:
a : List[str] = f.readlines()
# Find the start of the list.
a : Optional[int] = 0
while not lines[start_index].startswith(_start_prompt ):
start_index += 1
start_index += 1
a : Optional[int] = start_index
# Update the lines in the model list.
while not lines[index].startswith(_end_prompt ):
if lines[index].startswith('1.' ):
a : Dict = lines[index].replace(
'https://huggingface.co/docs/transformers/main/model_doc' , 'https://huggingface.co/docs/transformers/model_doc' , )
index += 1
with open(SCREAMING_SNAKE_CASE_ , 'w' , encoding='utf-8' , newline='\n' ) as f:
f.writelines(SCREAMING_SNAKE_CASE_ )
def lowerCamelCase__ ( ):
with open(REPLACE_FILES['init'] , 'r' ) as f:
a : Union[str, Any] = f.read()
a : Dict = REPLACE_PATTERNS['init'][0].search(SCREAMING_SNAKE_CASE_ ).groups()[0]
return packaging.version.parse(SCREAMING_SNAKE_CASE_ )
def lowerCamelCase__ ( _A=False ):
a : Union[str, Any] = get_version()
if patch and default_version.is_devrelease:
raise ValueError('Can\'t create a patch version from the dev branch, checkout a released version!' )
if default_version.is_devrelease:
a : int = default_version.base_version
elif patch:
a : str = f"""{default_version.major}.{default_version.minor}.{default_version.micro + 1}"""
else:
a : Optional[int] = f"""{default_version.major}.{default_version.minor + 1}.0"""
# Now let's ask nicely if that's the right one.
a : Any = input(f"""Which version are you releasing? [{default_version}]""" )
if len(SCREAMING_SNAKE_CASE_ ) == 0:
a : Optional[Any] = default_version
print(f"""Updating version to {version}.""" )
global_version_update(SCREAMING_SNAKE_CASE_ , patch=SCREAMING_SNAKE_CASE_ )
if not patch:
print('Cleaning main README, don\'t forget to run `make fix-copies`.' )
clean_main_ref_in_model_list()
def lowerCamelCase__ ( ):
a : Dict = get_version()
a : Any = f"""{current_version.major}.{current_version.minor + 1}.0.dev0"""
a : Optional[int] = current_version.base_version
# Check with the user we got that right.
a : int = input(f"""Which version are we developing now? [{dev_version}]""" )
if len(SCREAMING_SNAKE_CASE_ ) == 0:
a : Union[str, Any] = dev_version
print(f"""Updating version to {version}.""" )
global_version_update(SCREAMING_SNAKE_CASE_ )
print('Cleaning main README, don\'t forget to run `make fix-copies`.' )
clean_main_ref_in_model_list()
if __name__ == "__main__":
lowerCAmelCase: Optional[int] = argparse.ArgumentParser()
parser.add_argument('--post_release', action='store_true', help='Whether this is pre or post release.')
parser.add_argument('--patch', action='store_true', help='Whether or not this is a patch release.')
lowerCAmelCase: List[str] = parser.parse_args()
if not args.post_release:
pre_release_work(patch=args.patch)
elif args.patch:
print('Nothing to do after a patch :-)')
else:
post_release_work() | 297 |
import argparse
import os
import re
import packaging.version
UpperCamelCase__ = """examples/"""
UpperCamelCase__ = {
"""examples""": (re.compile(R"""^check_min_version\(\"[^\"]+\"\)\s*$""", re.MULTILINE), """check_min_version(\"VERSION\")\n"""),
"""init""": (re.compile(R"""^__version__\s+=\s+\"([^\"]+)\"\s*$""", re.MULTILINE), """__version__ = \"VERSION\"\n"""),
"""setup""": (re.compile(R"""^(\s*)version\s*=\s*\"[^\"]+\",""", re.MULTILINE), R"""\1version=\"VERSION\","""),
"""doc""": (re.compile(R"""^(\s*)release\s*=\s*\"[^\"]+\"$""", re.MULTILINE), """release = \"VERSION\"\n"""),
}
UpperCamelCase__ = {
"""init""": """src/transformers/__init__.py""",
"""setup""": """setup.py""",
}
UpperCamelCase__ = """README.md"""
def _a ( SCREAMING_SNAKE_CASE_ : Optional[int] , SCREAMING_SNAKE_CASE_ : Any , SCREAMING_SNAKE_CASE_ : List[str] ):
with open(SCREAMING_SNAKE_CASE_ , "r" , encoding="utf-8" , newline="\n" ) as f:
__lowerCAmelCase = f.read()
__lowerCAmelCase , __lowerCAmelCase = REPLACE_PATTERNS[pattern]
__lowerCAmelCase = replace.replace("VERSION" , SCREAMING_SNAKE_CASE_ )
__lowerCAmelCase = re_pattern.sub(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
with open(SCREAMING_SNAKE_CASE_ , "w" , encoding="utf-8" , newline="\n" ) as f:
f.write(SCREAMING_SNAKE_CASE_ )
def _a ( SCREAMING_SNAKE_CASE_ : List[Any] ):
for folder, directories, fnames in os.walk(SCREAMING_SNAKE_CASE_ ):
# Removing some of the folders with non-actively maintained examples from the walk
if "research_projects" in directories:
directories.remove("research_projects" )
if "legacy" in directories:
directories.remove("legacy" )
for fname in fnames:
if fname.endswith(".py" ):
update_version_in_file(os.path.join(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) , SCREAMING_SNAKE_CASE_ , pattern="examples" )
def _a ( SCREAMING_SNAKE_CASE_ : Tuple , SCREAMING_SNAKE_CASE_ : Optional[int]=False ):
for pattern, fname in REPLACE_FILES.items():
update_version_in_file(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
if not patch:
update_version_in_examples(SCREAMING_SNAKE_CASE_ )
def _a ( ):
__lowerCAmelCase = "🤗 Transformers currently provides the following architectures"
__lowerCAmelCase = "1. Want to contribute a new model?"
with open(SCREAMING_SNAKE_CASE_ , "r" , encoding="utf-8" , newline="\n" ) as f:
__lowerCAmelCase = f.readlines()
# Find the start of the list.
__lowerCAmelCase = 0
while not lines[start_index].startswith(_start_prompt ):
start_index += 1
start_index += 1
__lowerCAmelCase = start_index
# Update the lines in the model list.
while not lines[index].startswith(_end_prompt ):
if lines[index].startswith("1." ):
__lowerCAmelCase = lines[index].replace(
"https://huggingface.co/docs/transformers/main/model_doc" , "https://huggingface.co/docs/transformers/model_doc" , )
index += 1
with open(SCREAMING_SNAKE_CASE_ , "w" , encoding="utf-8" , newline="\n" ) as f:
f.writelines(SCREAMING_SNAKE_CASE_ )
def _a ( ):
with open(REPLACE_FILES["init"] , "r" ) as f:
__lowerCAmelCase = f.read()
__lowerCAmelCase = REPLACE_PATTERNS["init"][0].search(SCREAMING_SNAKE_CASE_ ).groups()[0]
return packaging.version.parse(SCREAMING_SNAKE_CASE_ )
def _a ( SCREAMING_SNAKE_CASE_ : List[Any]=False ):
__lowerCAmelCase = get_version()
if patch and default_version.is_devrelease:
raise ValueError("Can't create a patch version from the dev branch, checkout a released version!" )
if default_version.is_devrelease:
__lowerCAmelCase = default_version.base_version
elif patch:
__lowerCAmelCase = F"""{default_version.major}.{default_version.minor}.{default_version.micro + 1}"""
else:
__lowerCAmelCase = F"""{default_version.major}.{default_version.minor + 1}.0"""
# Now let's ask nicely if that's the right one.
__lowerCAmelCase = input(F"""Which version are you releasing? [{default_version}]""" )
if len(SCREAMING_SNAKE_CASE_ ) == 0:
__lowerCAmelCase = default_version
print(F"""Updating version to {version}.""" )
global_version_update(SCREAMING_SNAKE_CASE_ , patch=SCREAMING_SNAKE_CASE_ )
if not patch:
print("Cleaning main README, don't forget to run `make fix-copies`." )
clean_main_ref_in_model_list()
def _a ( ):
__lowerCAmelCase = get_version()
__lowerCAmelCase = F"""{current_version.major}.{current_version.minor + 1}.0.dev0"""
__lowerCAmelCase = current_version.base_version
# Check with the user we got that right.
__lowerCAmelCase = input(F"""Which version are we developing now? [{dev_version}]""" )
if len(SCREAMING_SNAKE_CASE_ ) == 0:
__lowerCAmelCase = dev_version
print(F"""Updating version to {version}.""" )
global_version_update(SCREAMING_SNAKE_CASE_ )
print("Cleaning main README, don't forget to run `make fix-copies`." )
clean_main_ref_in_model_list()
if __name__ == "__main__":
UpperCamelCase__ = argparse.ArgumentParser()
parser.add_argument("""--post_release""", action="""store_true""", help="""Whether this is pre or post release.""")
parser.add_argument("""--patch""", action="""store_true""", help="""Whether or not this is a patch release.""")
UpperCamelCase__ = parser.parse_args()
if not args.post_release:
pre_release_work(patch=args.patch)
elif args.patch:
print("""Nothing to do after a patch :-)""")
else:
post_release_work()
| 92 | 0 |
UpperCAmelCase : Optional[int] = [4, 1, 7, 4, 2, 6, 4, 1, 5, 3, 7, 5]
UpperCAmelCase : int = [3, 7, 7, 4, 2, 6, 4, 1, 5, 3, 7, 5]
UpperCAmelCase : int = {
0: "Sunday",
1: "Monday",
2: "Tuesday",
3: "Wednesday",
4: "Thursday",
5: "Friday",
6: "Saturday",
}
def __lowerCamelCase ( lowerCamelCase__ : int , lowerCamelCase__ : int , lowerCamelCase__ : int ):
'''simple docstring'''
assert len(str(SCREAMING_SNAKE_CASE_ ) ) > 2, "year should be in YYYY format"
assert 1 <= month <= 12, "month should be between 1 to 12"
assert 1 <= day <= 31, "day should be between 1 to 31"
# Doomsday algorithm:
lowerCamelCase = year // 100
lowerCamelCase = (5 * (century % 4) + 2) % 7
lowerCamelCase = year % 100
lowerCamelCase = centurian % 12
lowerCamelCase = (
(centurian // 12) + centurian_m + (centurian_m // 4) + century_anchor
) % 7
lowerCamelCase = (
DOOMSDAY_NOT_LEAP[month - 1]
if (year % 4 != 0) or (centurian == 0 and (year % 400) == 0)
else DOOMSDAY_LEAP[month - 1]
)
lowerCamelCase = (dooms_day + day - day_anchor) % 7
return WEEK_DAY_NAMES[week_day]
if __name__ == "__main__":
import doctest
doctest.testmod()
| 252 |
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import XLMRobertaTokenizerFast
from diffusers import DDIMScheduler, KandinskyImgaImgPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandinsky.text_encoder import MCLIPConfig, MultilingualCLIP
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu
from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference
enable_full_determinism()
class a__ ( snake_case__ , unittest.TestCase ):
_a : Dict = KandinskyImgaImgPipeline
_a : List[Any] = ["""prompt""", """image_embeds""", """negative_image_embeds""", """image"""]
_a : str = [
"""prompt""",
"""negative_prompt""",
"""image_embeds""",
"""negative_image_embeds""",
"""image""",
]
_a : List[Any] = [
"""generator""",
"""height""",
"""width""",
"""strength""",
"""guidance_scale""",
"""negative_prompt""",
"""num_inference_steps""",
"""return_dict""",
"""guidance_scale""",
"""num_images_per_prompt""",
"""output_type""",
"""return_dict""",
]
_a : int = False
@property
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
return 3_2
@property
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
return 3_2
@property
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
return self.time_input_dim
@property
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
return self.time_input_dim * 4
@property
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
return 1_0_0
@property
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
__lowerCAmelCase = XLMRobertaTokenizerFast.from_pretrained("YiYiXu/tiny-random-mclip-base" )
return tokenizer
@property
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
torch.manual_seed(0 )
__lowerCAmelCase = MCLIPConfig(
numDims=self.cross_attention_dim , transformerDimensions=self.text_embedder_hidden_size , hidden_size=self.text_embedder_hidden_size , intermediate_size=3_7 , num_attention_heads=4 , num_hidden_layers=5 , vocab_size=1_0_0_5 , )
__lowerCAmelCase = MultilingualCLIP(_A )
__lowerCAmelCase = text_encoder.eval()
return text_encoder
@property
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
torch.manual_seed(0 )
__lowerCAmelCase = {
"in_channels": 4,
# Out channels is double in channels because predicts mean and variance
"out_channels": 8,
"addition_embed_type": "text_image",
"down_block_types": ("ResnetDownsampleBlock2D", "SimpleCrossAttnDownBlock2D"),
"up_block_types": ("SimpleCrossAttnUpBlock2D", "ResnetUpsampleBlock2D"),
"mid_block_type": "UNetMidBlock2DSimpleCrossAttn",
"block_out_channels": (self.block_out_channels_a, self.block_out_channels_a * 2),
"layers_per_block": 1,
"encoder_hid_dim": self.text_embedder_hidden_size,
"encoder_hid_dim_type": "text_image_proj",
"cross_attention_dim": self.cross_attention_dim,
"attention_head_dim": 4,
"resnet_time_scale_shift": "scale_shift",
"class_embed_type": None,
}
__lowerCAmelCase = UNetaDConditionModel(**_A )
return model
@property
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
return {
"block_out_channels": [3_2, 6_4],
"down_block_types": ["DownEncoderBlock2D", "AttnDownEncoderBlock2D"],
"in_channels": 3,
"latent_channels": 4,
"layers_per_block": 1,
"norm_num_groups": 8,
"norm_type": "spatial",
"num_vq_embeddings": 1_2,
"out_channels": 3,
"up_block_types": [
"AttnUpDecoderBlock2D",
"UpDecoderBlock2D",
],
"vq_embed_dim": 4,
}
@property
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
torch.manual_seed(0 )
__lowerCAmelCase = VQModel(**self.dummy_movq_kwargs )
return model
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
__lowerCAmelCase = self.dummy_text_encoder
__lowerCAmelCase = self.dummy_tokenizer
__lowerCAmelCase = self.dummy_unet
__lowerCAmelCase = self.dummy_movq
__lowerCAmelCase = {
"num_train_timesteps": 1_0_0_0,
"beta_schedule": "linear",
"beta_start": 0.0_00_85,
"beta_end": 0.0_12,
"clip_sample": False,
"set_alpha_to_one": False,
"steps_offset": 0,
"prediction_type": "epsilon",
"thresholding": False,
}
__lowerCAmelCase = DDIMScheduler(**_A )
__lowerCAmelCase = {
"text_encoder": text_encoder,
"tokenizer": tokenizer,
"unet": unet,
"scheduler": scheduler,
"movq": movq,
}
return components
def __SCREAMING_SNAKE_CASE( self , _A , _A=0 ):
"""simple docstring"""
__lowerCAmelCase = floats_tensor((1, self.cross_attention_dim) , rng=random.Random(_A ) ).to(_A )
__lowerCAmelCase = floats_tensor((1, self.cross_attention_dim) , rng=random.Random(seed + 1 ) ).to(_A )
# create init_image
__lowerCAmelCase = floats_tensor((1, 3, 6_4, 6_4) , rng=random.Random(_A ) ).to(_A )
__lowerCAmelCase = image.cpu().permute(0 , 2 , 3 , 1 )[0]
__lowerCAmelCase = Image.fromarray(np.uinta(_A ) ).convert("RGB" ).resize((2_5_6, 2_5_6) )
if str(_A ).startswith("mps" ):
__lowerCAmelCase = torch.manual_seed(_A )
else:
__lowerCAmelCase = torch.Generator(device=_A ).manual_seed(_A )
__lowerCAmelCase = {
"prompt": "horse",
"image": init_image,
"image_embeds": image_embeds,
"negative_image_embeds": negative_image_embeds,
"generator": generator,
"height": 6_4,
"width": 6_4,
"num_inference_steps": 1_0,
"guidance_scale": 7.0,
"strength": 0.2,
"output_type": "np",
}
return inputs
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
__lowerCAmelCase = "cpu"
__lowerCAmelCase = self.get_dummy_components()
__lowerCAmelCase = self.pipeline_class(**_A )
__lowerCAmelCase = pipe.to(_A )
pipe.set_progress_bar_config(disable=_A )
__lowerCAmelCase = pipe(**self.get_dummy_inputs(_A ) )
__lowerCAmelCase = output.images
__lowerCAmelCase = pipe(
**self.get_dummy_inputs(_A ) , return_dict=_A , )[0]
__lowerCAmelCase = image[0, -3:, -3:, -1]
__lowerCAmelCase = image_from_tuple[0, -3:, -3:, -1]
assert image.shape == (1, 6_4, 6_4, 3)
__lowerCAmelCase = np.array(
[0.61_47_49_43, 0.6_07_35_39, 0.43_30_85_44, 0.5_92_82_69, 0.47_49_35_95, 0.46_75_59_73, 0.4_61_38_38, 0.45_36_87_97, 0.50_11_92_33] )
assert (
np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
), f""" expected_slice {expected_slice}, but got {image_slice.flatten()}"""
assert (
np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 1E-2
), f""" expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}"""
@slow
@require_torch_gpu
class a__ ( unittest.TestCase ):
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
__lowerCAmelCase = load_numpy(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
"/kandinsky/kandinsky_img2img_frog.npy" )
__lowerCAmelCase = load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main" "/kandinsky/cat.png" )
__lowerCAmelCase = "A red cartoon frog, 4k"
__lowerCAmelCase = KandinskyPriorPipeline.from_pretrained(
"kandinsky-community/kandinsky-2-1-prior" , torch_dtype=torch.floataa )
pipe_prior.to(_A )
__lowerCAmelCase = KandinskyImgaImgPipeline.from_pretrained(
"kandinsky-community/kandinsky-2-1" , torch_dtype=torch.floataa )
__lowerCAmelCase = pipeline.to(_A )
pipeline.set_progress_bar_config(disable=_A )
__lowerCAmelCase = torch.Generator(device="cpu" ).manual_seed(0 )
__lowerCAmelCase , __lowerCAmelCase = pipe_prior(
_A , generator=_A , num_inference_steps=5 , negative_prompt="" , ).to_tuple()
__lowerCAmelCase = pipeline(
_A , image=_A , image_embeds=_A , negative_image_embeds=_A , generator=_A , num_inference_steps=1_0_0 , height=7_6_8 , width=7_6_8 , strength=0.2 , output_type="np" , )
__lowerCAmelCase = output.images[0]
assert image.shape == (7_6_8, 7_6_8, 3)
assert_mean_pixel_difference(_A , _A )
| 92 | 0 |
'''simple docstring'''
import argparse
import csv
import logging
import os
import random
import numpy as np
import torch
from torch.utils.data import DataLoader, RandomSampler, SequentialSampler, TensorDataset
from tqdm import tqdm, trange
from transformers import (
CONFIG_NAME,
WEIGHTS_NAME,
AdamW,
OpenAIGPTDoubleHeadsModel,
OpenAIGPTTokenizer,
get_linear_schedule_with_warmup,
)
logging.basicConfig(
format="%(asctime)s - %(levelname)s - %(name)s - %(message)s", datefmt="%m/%d/%Y %H:%M:%S", level=logging.INFO
)
__a = logging.getLogger(__name__)
def __snake_case( _lowerCAmelCase , _lowerCAmelCase ) -> Any:
snake_case__ : Dict = np.argmax(SCREAMING_SNAKE_CASE_ , axis=1 )
return np.sum(outputs == labels )
def __snake_case( _lowerCAmelCase ) -> Optional[Any]:
with open(SCREAMING_SNAKE_CASE_ , encoding="""utf_8""" ) as f:
snake_case__ : List[str] = csv.reader(SCREAMING_SNAKE_CASE_ )
snake_case__ : int = []
next(SCREAMING_SNAKE_CASE_ ) # skip the first line
for line in tqdm(SCREAMING_SNAKE_CASE_ ):
output.append((""" """.join(line[1:5] ), line[5], line[6], int(line[-1] ) - 1) )
return output
def __snake_case( _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase ) -> int:
snake_case__ : str = []
for dataset in encoded_datasets:
snake_case__ : int = len(SCREAMING_SNAKE_CASE_ )
snake_case__ : Any = np.zeros((n_batch, 2, input_len) , dtype=np.intaa )
snake_case__ : Union[str, Any] = np.zeros((n_batch, 2) , dtype=np.intaa )
snake_case__ : str = np.full((n_batch, 2, input_len) , fill_value=-100 , dtype=np.intaa )
snake_case__ : List[str] = np.zeros((n_batch,) , dtype=np.intaa )
for (
i,
(story, conta, conta, mc_label),
) in enumerate(SCREAMING_SNAKE_CASE_ ):
snake_case__ : Union[str, Any] = [start_token] + story[:cap_length] + [delimiter_token] + conta[:cap_length] + [clf_token]
snake_case__ : Dict = [start_token] + story[:cap_length] + [delimiter_token] + conta[:cap_length] + [clf_token]
snake_case__ : List[Any] = with_conta
snake_case__ : List[Any] = with_conta
snake_case__ : List[Any] = len(SCREAMING_SNAKE_CASE_ ) - 1
snake_case__ : Dict = len(SCREAMING_SNAKE_CASE_ ) - 1
snake_case__ : Optional[Any] = with_conta
snake_case__ : Union[str, Any] = with_conta
snake_case__ : str = mc_label
snake_case__ : List[str] = (input_ids, mc_token_ids, lm_labels, mc_labels)
tensor_datasets.append(tuple(torch.tensor(SCREAMING_SNAKE_CASE_ ) for t in all_inputs ) )
return tensor_datasets
def __snake_case( ) -> int:
snake_case__ : Tuple = argparse.ArgumentParser()
parser.add_argument("""--model_name""" , type=SCREAMING_SNAKE_CASE_ , default="""openai-gpt""" , help="""pretrained model name""" )
parser.add_argument("""--do_train""" , action="""store_true""" , help="""Whether to run training.""" )
parser.add_argument("""--do_eval""" , action="""store_true""" , help="""Whether to run eval on the dev set.""" )
parser.add_argument(
"""--output_dir""" , default=SCREAMING_SNAKE_CASE_ , type=SCREAMING_SNAKE_CASE_ , required=SCREAMING_SNAKE_CASE_ , help="""The output directory where the model predictions and checkpoints will be written.""" , )
parser.add_argument("""--train_dataset""" , type=SCREAMING_SNAKE_CASE_ , default="""""" )
parser.add_argument("""--eval_dataset""" , type=SCREAMING_SNAKE_CASE_ , default="""""" )
parser.add_argument("""--seed""" , type=SCREAMING_SNAKE_CASE_ , default=42 )
parser.add_argument("""--num_train_epochs""" , type=SCREAMING_SNAKE_CASE_ , default=3 )
parser.add_argument("""--train_batch_size""" , type=SCREAMING_SNAKE_CASE_ , default=8 )
parser.add_argument("""--eval_batch_size""" , type=SCREAMING_SNAKE_CASE_ , default=16 )
parser.add_argument("""--adam_epsilon""" , default=1e-8 , type=SCREAMING_SNAKE_CASE_ , help="""Epsilon for Adam optimizer.""" )
parser.add_argument("""--max_grad_norm""" , type=SCREAMING_SNAKE_CASE_ , default=1 )
parser.add_argument(
"""--max_steps""" , default=-1 , type=SCREAMING_SNAKE_CASE_ , help=(
"""If > 0: set total number of training steps to perform. Override num_train_epochs."""
) , )
parser.add_argument(
"""--gradient_accumulation_steps""" , type=SCREAMING_SNAKE_CASE_ , default=1 , help="""Number of updates steps to accumulate before performing a backward/update pass.""" , )
parser.add_argument("""--learning_rate""" , type=SCREAMING_SNAKE_CASE_ , default=6.25e-5 )
parser.add_argument("""--warmup_steps""" , default=0 , type=SCREAMING_SNAKE_CASE_ , help="""Linear warmup over warmup_steps.""" )
parser.add_argument("""--lr_schedule""" , type=SCREAMING_SNAKE_CASE_ , default="""warmup_linear""" )
parser.add_argument("""--weight_decay""" , type=SCREAMING_SNAKE_CASE_ , default=0.01 )
parser.add_argument("""--lm_coef""" , type=SCREAMING_SNAKE_CASE_ , default=0.9 )
parser.add_argument("""--n_valid""" , type=SCREAMING_SNAKE_CASE_ , default=374 )
parser.add_argument("""--server_ip""" , type=SCREAMING_SNAKE_CASE_ , default="""""" , help="""Can be used for distant debugging.""" )
parser.add_argument("""--server_port""" , type=SCREAMING_SNAKE_CASE_ , default="""""" , help="""Can be used for distant debugging.""" )
snake_case__ : List[str] = parser.parse_args()
print(SCREAMING_SNAKE_CASE_ )
if args.server_ip and args.server_port:
# Distant debugging - see https://code.visualstudio.com/docs/python/debugging#_attach-to-a-local-script
import ptvsd
print("""Waiting for debugger attach""" )
ptvsd.enable_attach(address=(args.server_ip, args.server_port) , redirect_output=SCREAMING_SNAKE_CASE_ )
ptvsd.wait_for_attach()
random.seed(args.seed )
np.random.seed(args.seed )
torch.manual_seed(args.seed )
torch.cuda.manual_seed_all(args.seed )
snake_case__ : int = torch.device("""cuda""" if torch.cuda.is_available() else """cpu""" )
snake_case__ : Tuple = torch.cuda.device_count()
logger.info("""device: {}, n_gpu {}""".format(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) )
if not args.do_train and not args.do_eval:
raise ValueError("""At least one of `do_train` or `do_eval` must be True.""" )
if not os.path.exists(args.output_dir ):
os.makedirs(args.output_dir )
# Load tokenizer and model
# This loading functions also add new tokens and embeddings called `special tokens`
# These new embeddings will be fine-tuned on the RocStories dataset
snake_case__ : Tuple = ["""_start_""", """_delimiter_""", """_classify_"""]
snake_case__ : Tuple = OpenAIGPTTokenizer.from_pretrained(args.model_name )
tokenizer.add_tokens(SCREAMING_SNAKE_CASE_ )
snake_case__ : Union[str, Any] = tokenizer.convert_tokens_to_ids(SCREAMING_SNAKE_CASE_ )
snake_case__ : Optional[Any] = OpenAIGPTDoubleHeadsModel.from_pretrained(args.model_name )
model.resize_token_embeddings(len(SCREAMING_SNAKE_CASE_ ) )
model.to(SCREAMING_SNAKE_CASE_ )
# Load and encode the datasets
def tokenize_and_encode(_lowerCAmelCase ):
if isinstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
return tokenizer.convert_tokens_to_ids(tokenizer.tokenize(SCREAMING_SNAKE_CASE_ ) )
elif isinstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
return obj
return [tokenize_and_encode(SCREAMING_SNAKE_CASE_ ) for o in obj]
logger.info("""Encoding dataset...""" )
snake_case__ : Optional[int] = load_rocstories_dataset(args.train_dataset )
snake_case__ : List[str] = load_rocstories_dataset(args.eval_dataset )
snake_case__ : Union[str, Any] = (train_dataset, eval_dataset)
snake_case__ : Dict = tokenize_and_encode(SCREAMING_SNAKE_CASE_ )
# Compute the max input length for the Transformer
snake_case__ : List[Any] = model.config.n_positions // 2 - 2
snake_case__ : Any = max(
len(story[:max_length] ) + max(len(conta[:max_length] ) , len(conta[:max_length] ) ) + 3
for dataset in encoded_datasets
for story, conta, conta, _ in dataset )
snake_case__ : Optional[Any] = min(SCREAMING_SNAKE_CASE_ , model.config.n_positions ) # Max size of input for the pre-trained model
# Prepare inputs tensors and dataloaders
snake_case__ : Optional[int] = pre_process_datasets(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , *SCREAMING_SNAKE_CASE_ )
snake_case__ , snake_case__ : Optional[Any] = tensor_datasets[0], tensor_datasets[1]
snake_case__ : Optional[Any] = TensorDataset(*SCREAMING_SNAKE_CASE_ )
snake_case__ : str = RandomSampler(SCREAMING_SNAKE_CASE_ )
snake_case__ : Union[str, Any] = DataLoader(SCREAMING_SNAKE_CASE_ , sampler=SCREAMING_SNAKE_CASE_ , batch_size=args.train_batch_size )
snake_case__ : Optional[int] = TensorDataset(*SCREAMING_SNAKE_CASE_ )
snake_case__ : List[str] = SequentialSampler(SCREAMING_SNAKE_CASE_ )
snake_case__ : Optional[Any] = DataLoader(SCREAMING_SNAKE_CASE_ , sampler=SCREAMING_SNAKE_CASE_ , batch_size=args.eval_batch_size )
# Prepare optimizer
if args.do_train:
if args.max_steps > 0:
snake_case__ : Tuple = args.max_steps
snake_case__ : Any = args.max_steps // (len(SCREAMING_SNAKE_CASE_ ) // args.gradient_accumulation_steps) + 1
else:
snake_case__ : Dict = len(SCREAMING_SNAKE_CASE_ ) // args.gradient_accumulation_steps * args.num_train_epochs
snake_case__ : str = list(model.named_parameters() )
snake_case__ : str = ["""bias""", """LayerNorm.bias""", """LayerNorm.weight"""]
snake_case__ : List[str] = [
{
"""params""": [p for n, p in param_optimizer if not any(nd in n for nd in no_decay )],
"""weight_decay""": args.weight_decay,
},
{"""params""": [p for n, p in param_optimizer if any(nd in n for nd in no_decay )], """weight_decay""": 0.0},
]
snake_case__ : int = AdamW(SCREAMING_SNAKE_CASE_ , lr=args.learning_rate , eps=args.adam_epsilon )
snake_case__ : List[Any] = get_linear_schedule_with_warmup(
SCREAMING_SNAKE_CASE_ , num_warmup_steps=args.warmup_steps , num_training_steps=SCREAMING_SNAKE_CASE_ )
if args.do_train:
snake_case__ , snake_case__ , snake_case__ : Optional[int] = 0, 0, None
model.train()
for _ in trange(int(args.num_train_epochs ) , desc="""Epoch""" ):
snake_case__ : str = 0
snake_case__ : Optional[int] = 0
snake_case__ : Dict = tqdm(SCREAMING_SNAKE_CASE_ , desc="""Training""" )
for step, batch in enumerate(SCREAMING_SNAKE_CASE_ ):
snake_case__ : Union[str, Any] = tuple(t.to(SCREAMING_SNAKE_CASE_ ) for t in batch )
snake_case__ , snake_case__ , snake_case__ , snake_case__ : Any = batch
snake_case__ : Tuple = model(SCREAMING_SNAKE_CASE_ , mc_token_ids=SCREAMING_SNAKE_CASE_ , lm_labels=SCREAMING_SNAKE_CASE_ , mc_labels=SCREAMING_SNAKE_CASE_ )
snake_case__ : Any = args.lm_coef * losses[0] + losses[1]
loss.backward()
optimizer.step()
scheduler.step()
optimizer.zero_grad()
tr_loss += loss.item()
snake_case__ : List[str] = (
loss.item() if exp_average_loss is None else 0.7 * exp_average_loss + 0.3 * loss.item()
)
nb_tr_steps += 1
snake_case__ : Tuple = """Training loss: {:.2e} lr: {:.2e}""".format(SCREAMING_SNAKE_CASE_ , scheduler.get_lr()[0] )
# Save a trained model
if args.do_train:
# Save a trained model, configuration and tokenizer
snake_case__ : str = model.module if hasattr(SCREAMING_SNAKE_CASE_ , """module""" ) else model # Only save the model itself
# If we save using the predefined names, we can load using `from_pretrained`
snake_case__ : Optional[Any] = os.path.join(args.output_dir , SCREAMING_SNAKE_CASE_ )
snake_case__ : int = os.path.join(args.output_dir , SCREAMING_SNAKE_CASE_ )
torch.save(model_to_save.state_dict() , SCREAMING_SNAKE_CASE_ )
model_to_save.config.to_json_file(SCREAMING_SNAKE_CASE_ )
tokenizer.save_vocabulary(args.output_dir )
# Load a trained model and vocabulary that you have fine-tuned
snake_case__ : int = OpenAIGPTDoubleHeadsModel.from_pretrained(args.output_dir )
snake_case__ : Union[str, Any] = OpenAIGPTTokenizer.from_pretrained(args.output_dir )
model.to(SCREAMING_SNAKE_CASE_ )
if args.do_eval:
model.eval()
snake_case__ , snake_case__ : List[str] = 0, 0
snake_case__ , snake_case__ : Union[str, Any] = 0, 0
for batch in tqdm(SCREAMING_SNAKE_CASE_ , desc="""Evaluating""" ):
snake_case__ : Union[str, Any] = tuple(t.to(SCREAMING_SNAKE_CASE_ ) for t in batch )
snake_case__ , snake_case__ , snake_case__ , snake_case__ : Any = batch
with torch.no_grad():
snake_case__ , snake_case__ , snake_case__ , snake_case__ : List[str] = model(
SCREAMING_SNAKE_CASE_ , mc_token_ids=SCREAMING_SNAKE_CASE_ , lm_labels=SCREAMING_SNAKE_CASE_ , mc_labels=SCREAMING_SNAKE_CASE_ )
snake_case__ : Optional[int] = mc_logits.detach().cpu().numpy()
snake_case__ : Union[str, Any] = mc_labels.to("""cpu""" ).numpy()
snake_case__ : Union[str, Any] = accuracy(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
eval_loss += mc_loss.mean().item()
eval_accuracy += tmp_eval_accuracy
nb_eval_examples += input_ids.size(0 )
nb_eval_steps += 1
snake_case__ : Optional[int] = eval_loss / nb_eval_steps
snake_case__ : Any = eval_accuracy / nb_eval_examples
snake_case__ : Optional[int] = tr_loss / nb_tr_steps if args.do_train else None
snake_case__ : Optional[int] = {"""eval_loss""": eval_loss, """eval_accuracy""": eval_accuracy, """train_loss""": train_loss}
snake_case__ : Optional[int] = os.path.join(args.output_dir , """eval_results.txt""" )
with open(SCREAMING_SNAKE_CASE_ , """w""" ) as writer:
logger.info("""***** Eval results *****""" )
for key in sorted(result.keys() ):
logger.info(""" %s = %s""" , SCREAMING_SNAKE_CASE_ , str(result[key] ) )
writer.write("""%s = %s\n""" % (key, str(result[key] )) )
if __name__ == "__main__":
main()
| 35 |
class a__ ( snake_case__ ):
pass
class a__ ( snake_case__ ):
pass
class a__ :
def __init__( self ):
"""simple docstring"""
__lowerCAmelCase = [
[],
[],
[],
]
def __SCREAMING_SNAKE_CASE( self , _A , _A ):
"""simple docstring"""
try:
if len(self.queues[priority] ) >= 1_0_0:
raise OverflowError("Maximum queue size is 100" )
self.queues[priority].append(_A )
except IndexError:
raise ValueError("Valid priorities are 0, 1, and 2" )
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
for queue in self.queues:
if queue:
return queue.pop(0 )
raise UnderFlowError("All queues are empty" )
def __str__( self ):
"""simple docstring"""
return "\n".join(f"""Priority {i}: {q}""" for i, q in enumerate(self.queues ) )
class a__ :
def __init__( self ):
"""simple docstring"""
__lowerCAmelCase = []
def __SCREAMING_SNAKE_CASE( self , _A ):
"""simple docstring"""
if len(self.queue ) == 1_0_0:
raise OverFlowError("Maximum queue size is 100" )
self.queue.append(_A )
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
if not self.queue:
raise UnderFlowError("The queue is empty" )
else:
__lowerCAmelCase = min(self.queue )
self.queue.remove(_A )
return data
def __str__( self ):
"""simple docstring"""
return str(self.queue )
def _a ( ):
__lowerCAmelCase = FixedPriorityQueue()
fpq.enqueue(0 , 10 )
fpq.enqueue(1 , 70 )
fpq.enqueue(0 , 1_00 )
fpq.enqueue(2 , 1 )
fpq.enqueue(2 , 5 )
fpq.enqueue(1 , 7 )
fpq.enqueue(2 , 4 )
fpq.enqueue(1 , 64 )
fpq.enqueue(0 , 1_28 )
print(SCREAMING_SNAKE_CASE_ )
print(fpq.dequeue() )
print(fpq.dequeue() )
print(fpq.dequeue() )
print(fpq.dequeue() )
print(fpq.dequeue() )
print(SCREAMING_SNAKE_CASE_ )
print(fpq.dequeue() )
print(fpq.dequeue() )
print(fpq.dequeue() )
print(fpq.dequeue() )
print(fpq.dequeue() )
def _a ( ):
__lowerCAmelCase = ElementPriorityQueue()
epq.enqueue(10 )
epq.enqueue(70 )
epq.enqueue(1_00 )
epq.enqueue(1 )
epq.enqueue(5 )
epq.enqueue(7 )
epq.enqueue(4 )
epq.enqueue(64 )
epq.enqueue(1_28 )
print(SCREAMING_SNAKE_CASE_ )
print(epq.dequeue() )
print(epq.dequeue() )
print(epq.dequeue() )
print(epq.dequeue() )
print(epq.dequeue() )
print(SCREAMING_SNAKE_CASE_ )
print(epq.dequeue() )
print(epq.dequeue() )
print(epq.dequeue() )
print(epq.dequeue() )
print(epq.dequeue() )
if __name__ == "__main__":
fixed_priority_queue()
element_priority_queue()
| 92 | 0 |
'''simple docstring'''
import os
from math import logaa
def snake_case ( UpperCAmelCase = "base_exp.txt" )-> Optional[Any]:
"""simple docstring"""
__A = 0
__A = 0
for i, line in enumerate(open(os.path.join(os.path.dirname(SCREAMING_SNAKE_CASE_ ) , SCREAMING_SNAKE_CASE_ ) ) ):
__A , __A = list(map(SCREAMING_SNAKE_CASE_ , line.split(',' ) ) )
if x * logaa(SCREAMING_SNAKE_CASE_ ) > largest:
__A = x * logaa(SCREAMING_SNAKE_CASE_ )
__A = i + 1
return result
if __name__ == "__main__":
print(solution())
| 161 |
import inspect
import unittest
import warnings
from transformers import DeiTConfig
from transformers.models.auto import get_values
from transformers.testing_utils import (
require_accelerate,
require_torch,
require_torch_gpu,
require_vision,
slow,
torch_device,
)
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from torch import nn
from transformers import (
MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
MODEL_MAPPING,
DeiTForImageClassification,
DeiTForImageClassificationWithTeacher,
DeiTForMaskedImageModeling,
DeiTModel,
)
from transformers.models.deit.modeling_deit import DEIT_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import DeiTImageProcessor
class a__ :
def __init__( self , _A , _A=1_3 , _A=3_0 , _A=2 , _A=3 , _A=True , _A=True , _A=3_2 , _A=5 , _A=4 , _A=3_7 , _A="gelu" , _A=0.1 , _A=0.1 , _A=1_0 , _A=0.02 , _A=3 , _A=None , _A=2 , ):
"""simple docstring"""
__lowerCAmelCase = parent
__lowerCAmelCase = batch_size
__lowerCAmelCase = image_size
__lowerCAmelCase = patch_size
__lowerCAmelCase = num_channels
__lowerCAmelCase = is_training
__lowerCAmelCase = use_labels
__lowerCAmelCase = hidden_size
__lowerCAmelCase = num_hidden_layers
__lowerCAmelCase = num_attention_heads
__lowerCAmelCase = intermediate_size
__lowerCAmelCase = hidden_act
__lowerCAmelCase = hidden_dropout_prob
__lowerCAmelCase = attention_probs_dropout_prob
__lowerCAmelCase = type_sequence_label_size
__lowerCAmelCase = initializer_range
__lowerCAmelCase = scope
__lowerCAmelCase = encoder_stride
# in DeiT, the seq length equals the number of patches + 2 (we add 2 for the [CLS] and distilation tokens)
__lowerCAmelCase = (image_size // patch_size) ** 2
__lowerCAmelCase = num_patches + 2
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
__lowerCAmelCase = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
__lowerCAmelCase = None
if self.use_labels:
__lowerCAmelCase = ids_tensor([self.batch_size] , self.type_sequence_label_size )
__lowerCAmelCase = self.get_config()
return config, pixel_values, labels
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
return DeiTConfig(
image_size=self.image_size , patch_size=self.patch_size , num_channels=self.num_channels , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , is_decoder=_A , initializer_range=self.initializer_range , encoder_stride=self.encoder_stride , )
def __SCREAMING_SNAKE_CASE( self , _A , _A , _A ):
"""simple docstring"""
__lowerCAmelCase = DeiTModel(config=_A )
model.to(_A )
model.eval()
__lowerCAmelCase = model(_A )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def __SCREAMING_SNAKE_CASE( self , _A , _A , _A ):
"""simple docstring"""
__lowerCAmelCase = DeiTForMaskedImageModeling(config=_A )
model.to(_A )
model.eval()
__lowerCAmelCase = model(_A )
self.parent.assertEqual(
result.reconstruction.shape , (self.batch_size, self.num_channels, self.image_size, self.image_size) )
# test greyscale images
__lowerCAmelCase = 1
__lowerCAmelCase = DeiTForMaskedImageModeling(_A )
model.to(_A )
model.eval()
__lowerCAmelCase = floats_tensor([self.batch_size, 1, self.image_size, self.image_size] )
__lowerCAmelCase = model(_A )
self.parent.assertEqual(result.reconstruction.shape , (self.batch_size, 1, self.image_size, self.image_size) )
def __SCREAMING_SNAKE_CASE( self , _A , _A , _A ):
"""simple docstring"""
__lowerCAmelCase = self.type_sequence_label_size
__lowerCAmelCase = DeiTForImageClassification(_A )
model.to(_A )
model.eval()
__lowerCAmelCase = model(_A , labels=_A )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size) )
# test greyscale images
__lowerCAmelCase = 1
__lowerCAmelCase = DeiTForImageClassification(_A )
model.to(_A )
model.eval()
__lowerCAmelCase = floats_tensor([self.batch_size, 1, self.image_size, self.image_size] )
__lowerCAmelCase = model(_A , labels=_A )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size) )
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
__lowerCAmelCase = self.prepare_config_and_inputs()
(
(
__lowerCAmelCase
) , (
__lowerCAmelCase
) , (
__lowerCAmelCase
) ,
) = config_and_inputs
__lowerCAmelCase = {"pixel_values": pixel_values}
return config, inputs_dict
@require_torch
class a__ ( snake_case__ , snake_case__ , unittest.TestCase ):
_a : Optional[Any] = (
(
DeiTModel,
DeiTForImageClassification,
DeiTForImageClassificationWithTeacher,
DeiTForMaskedImageModeling,
)
if is_torch_available()
else ()
)
_a : int = (
{
"""feature-extraction""": DeiTModel,
"""image-classification""": (DeiTForImageClassification, DeiTForImageClassificationWithTeacher),
}
if is_torch_available()
else {}
)
_a : Optional[Any] = False
_a : Tuple = False
_a : Tuple = False
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
__lowerCAmelCase = DeiTModelTester(self )
__lowerCAmelCase = ConfigTester(self , config_class=_A , has_text_modality=_A , hidden_size=3_7 )
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
self.config_tester.run_common_tests()
@unittest.skip(reason="DeiT does not use inputs_embeds" )
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
pass
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
__lowerCAmelCase , __lowerCAmelCase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
__lowerCAmelCase = model_class(_A )
self.assertIsInstance(model.get_input_embeddings() , (nn.Module) )
__lowerCAmelCase = model.get_output_embeddings()
self.assertTrue(x is None or isinstance(_A , nn.Linear ) )
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
__lowerCAmelCase , __lowerCAmelCase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
__lowerCAmelCase = model_class(_A )
__lowerCAmelCase = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
__lowerCAmelCase = [*signature.parameters.keys()]
__lowerCAmelCase = ["pixel_values"]
self.assertListEqual(arg_names[:1] , _A )
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
__lowerCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*_A )
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
__lowerCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_image_modeling(*_A )
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
__lowerCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*_A )
def __SCREAMING_SNAKE_CASE( self , _A , _A , _A=False ):
"""simple docstring"""
__lowerCAmelCase = super()._prepare_for_class(_A , _A , return_labels=_A )
if return_labels:
if model_class.__name__ == "DeiTForImageClassificationWithTeacher":
del inputs_dict["labels"]
return inputs_dict
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
if not self.model_tester.is_training:
return
__lowerCAmelCase , __lowerCAmelCase = self.model_tester.prepare_config_and_inputs_for_common()
__lowerCAmelCase = True
for model_class in self.all_model_classes:
# DeiTForImageClassificationWithTeacher supports inference-only
if (
model_class in get_values(_A )
or model_class.__name__ == "DeiTForImageClassificationWithTeacher"
):
continue
__lowerCAmelCase = model_class(_A )
model.to(_A )
model.train()
__lowerCAmelCase = self._prepare_for_class(_A , _A , return_labels=_A )
__lowerCAmelCase = model(**_A ).loss
loss.backward()
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
__lowerCAmelCase , __lowerCAmelCase = self.model_tester.prepare_config_and_inputs_for_common()
if not self.model_tester.is_training:
return
__lowerCAmelCase = False
__lowerCAmelCase = True
for model_class in self.all_model_classes:
if model_class in get_values(_A ) or not model_class.supports_gradient_checkpointing:
continue
# DeiTForImageClassificationWithTeacher supports inference-only
if model_class.__name__ == "DeiTForImageClassificationWithTeacher":
continue
__lowerCAmelCase = model_class(_A )
model.gradient_checkpointing_enable()
model.to(_A )
model.train()
__lowerCAmelCase = self._prepare_for_class(_A , _A , return_labels=_A )
__lowerCAmelCase = model(**_A ).loss
loss.backward()
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
__lowerCAmelCase , __lowerCAmelCase = self.model_tester.prepare_config_and_inputs_for_common()
__lowerCAmelCase = [
{"title": "multi_label_classification", "num_labels": 2, "dtype": torch.float},
{"title": "single_label_classification", "num_labels": 1, "dtype": torch.long},
{"title": "regression", "num_labels": 1, "dtype": torch.float},
]
for model_class in self.all_model_classes:
if (
model_class
not in [
*get_values(_A ),
*get_values(_A ),
]
or model_class.__name__ == "DeiTForImageClassificationWithTeacher"
):
continue
for problem_type in problem_types:
with self.subTest(msg=f"""Testing {model_class} with {problem_type['title']}""" ):
__lowerCAmelCase = problem_type["title"]
__lowerCAmelCase = problem_type["num_labels"]
__lowerCAmelCase = model_class(_A )
model.to(_A )
model.train()
__lowerCAmelCase = self._prepare_for_class(_A , _A , return_labels=_A )
if problem_type["num_labels"] > 1:
__lowerCAmelCase = inputs["labels"].unsqueeze(1 ).repeat(1 , problem_type["num_labels"] )
__lowerCAmelCase = inputs["labels"].to(problem_type["dtype"] )
# This tests that we do not trigger the warning form PyTorch "Using a target size that is different
# to the input size. This will likely lead to incorrect results due to broadcasting. Please ensure
# they have the same size." which is a symptom something in wrong for the regression problem.
# See https://github.com/huggingface/transformers/issues/11780
with warnings.catch_warnings(record=_A ) as warning_list:
__lowerCAmelCase = model(**_A ).loss
for w in warning_list:
if "Using a target size that is different to the input size" in str(w.message ):
raise ValueError(
f"""Something is going wrong in the regression problem: intercepted {w.message}""" )
loss.backward()
@slow
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
for model_name in DEIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
__lowerCAmelCase = DeiTModel.from_pretrained(_A )
self.assertIsNotNone(_A )
def _a ( ):
__lowerCAmelCase = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png" )
return image
@require_torch
@require_vision
class a__ ( unittest.TestCase ):
@cached_property
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
return (
DeiTImageProcessor.from_pretrained("facebook/deit-base-distilled-patch16-224" )
if is_vision_available()
else None
)
@slow
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
__lowerCAmelCase = DeiTForImageClassificationWithTeacher.from_pretrained("facebook/deit-base-distilled-patch16-224" ).to(
_A )
__lowerCAmelCase = self.default_image_processor
__lowerCAmelCase = prepare_img()
__lowerCAmelCase = image_processor(images=_A , return_tensors="pt" ).to(_A )
# forward pass
with torch.no_grad():
__lowerCAmelCase = model(**_A )
# verify the logits
__lowerCAmelCase = torch.Size((1, 1_0_0_0) )
self.assertEqual(outputs.logits.shape , _A )
__lowerCAmelCase = torch.tensor([-1.02_66, 0.19_12, -1.28_61] ).to(_A )
self.assertTrue(torch.allclose(outputs.logits[0, :3] , _A , atol=1E-4 ) )
@slow
@require_accelerate
@require_torch_gpu
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
__lowerCAmelCase = DeiTModel.from_pretrained(
"facebook/deit-base-distilled-patch16-224" , torch_dtype=torch.floataa , device_map="auto" )
__lowerCAmelCase = self.default_image_processor
__lowerCAmelCase = prepare_img()
__lowerCAmelCase = image_processor(images=_A , return_tensors="pt" )
__lowerCAmelCase = inputs.pixel_values.to(_A )
# forward pass to make sure inference works in fp16
with torch.no_grad():
__lowerCAmelCase = model(_A )
| 92 | 0 |
"""simple docstring"""
import os
import time
import warnings
from dataclasses import dataclass, field
from enum import Enum
from typing import List, Optional, Union
import torch
from filelock import FileLock
from torch.utils.data import Dataset
from ...tokenization_utils_base import PreTrainedTokenizerBase
from ...utils import logging
from ..processors.glue import glue_convert_examples_to_features, glue_output_modes, glue_processors
from ..processors.utils import InputFeatures
lowerCAmelCase__ = logging.get_logger(__name__)
@dataclass
class _lowerCamelCase :
UpperCAmelCase_ = field(metadata={"help": "The name of the task to train on: " + ", ".join(glue_processors.keys() )} )
UpperCAmelCase_ = field(
metadata={"help": "The input data dir. Should contain the .tsv files (or other data files) for the task."} )
UpperCAmelCase_ = field(
default=128 , metadata={
"help": (
"The maximum total input sequence length after tokenization. Sequences longer "
"than this will be truncated, sequences shorter will be padded."
)
} , )
UpperCAmelCase_ = field(
default=snake_case__ , metadata={"help": "Overwrite the cached training and evaluation sets"} )
def snake_case_ (self ) -> Optional[Any]:
UpperCamelCase = self.task_name.lower()
class _lowerCamelCase ( snake_case__ ):
UpperCAmelCase_ = """train"""
UpperCAmelCase_ = """dev"""
UpperCAmelCase_ = """test"""
class _lowerCamelCase ( snake_case__ ):
UpperCAmelCase_ = 42
UpperCAmelCase_ = 42
UpperCAmelCase_ = 42
def __init__(self , __a , __a , __a = None , __a = Split.train , __a = None , ) -> Optional[Any]:
warnings.warn(
"This dataset will be removed from the library soon, preprocessing should be handled with the 🤗 Datasets "
"library. You can have a look at this example script for pointers: "
"https://github.com/huggingface/transformers/blob/main/examples/pytorch/text-classification/run_glue.py" , _A , )
UpperCamelCase = args
UpperCamelCase = glue_processors[args.task_name]()
UpperCamelCase = glue_output_modes[args.task_name]
if isinstance(_A , _A ):
try:
UpperCamelCase = Split[mode]
except KeyError:
raise KeyError("mode is not a valid split name" )
# Load data features from cache or dataset file
UpperCamelCase = os.path.join(
cache_dir if cache_dir is not None else args.data_dir , F"cached_{mode.value}_{tokenizer.__class__.__name__}_{args.max_seq_length}_{args.task_name}" , )
UpperCamelCase = self.processor.get_labels()
if args.task_name in ["mnli", "mnli-mm"] and tokenizer.__class__.__name__ in (
"RobertaTokenizer",
"RobertaTokenizerFast",
"XLMRobertaTokenizer",
"BartTokenizer",
"BartTokenizerFast",
):
# HACK(label indices are swapped in RoBERTa pretrained model)
UpperCamelCase , UpperCamelCase = label_list[2], label_list[1]
UpperCamelCase = label_list
# Make sure only the first process in distributed training processes the dataset,
# and the others will use the cache.
UpperCamelCase = cached_features_file + ".lock"
with FileLock(_A ):
if os.path.exists(_A ) and not args.overwrite_cache:
UpperCamelCase = time.time()
UpperCamelCase = torch.load(_A )
logger.info(
F"Loading features from cached file {cached_features_file} [took %.3f s]" , time.time() - start )
else:
logger.info(F"Creating features from dataset file at {args.data_dir}" )
if mode == Split.dev:
UpperCamelCase = self.processor.get_dev_examples(args.data_dir )
elif mode == Split.test:
UpperCamelCase = self.processor.get_test_examples(args.data_dir )
else:
UpperCamelCase = self.processor.get_train_examples(args.data_dir )
if limit_length is not None:
UpperCamelCase = examples[:limit_length]
UpperCamelCase = glue_convert_examples_to_features(
_A , _A , max_length=args.max_seq_length , label_list=_A , output_mode=self.output_mode , )
UpperCamelCase = time.time()
torch.save(self.features , _A )
# ^ This seems to take a lot of time so I want to investigate why and how we can improve.
logger.info(
F"Saving features into cached file {cached_features_file} [took {time.time() - start:.3f} s]" )
def __len__(self ) -> Union[str, Any]:
return len(self.features )
def __getitem__(self , __a ) -> Any:
return self.features[i]
def snake_case_ (self ) -> int:
return self.label_list
| 153 |
def _a ( SCREAMING_SNAKE_CASE_ : int = 1_00_00_00 ):
__lowerCAmelCase = [i - 1 for i in range(limit + 1 )]
for i in range(2 , limit + 1 ):
if phi[i] == i - 1:
for j in range(2 * i , limit + 1 , SCREAMING_SNAKE_CASE_ ):
phi[j] -= phi[j] // i
return sum(phi[2 : limit + 1] )
if __name__ == "__main__":
print(solution())
| 92 | 0 |
"""simple docstring"""
import json
import logging
import math
import os
import sys
from dataclasses import dataclass, field
from typing import Optional
from datasets import Dataset, load_dataset
import transformers
from transformers import (
CONFIG_MAPPING,
MODEL_FOR_MASKED_LM_MAPPING,
AutoConfig,
AutoModelForMaskedLM,
AutoTokenizer,
DataCollatorForWholeWordMask,
HfArgumentParser,
Trainer,
TrainingArguments,
set_seed,
)
from transformers.trainer_utils import get_last_checkpoint, is_main_process
__snake_case = logging.getLogger(__name__)
__snake_case = list(MODEL_FOR_MASKED_LM_MAPPING.keys())
__snake_case = tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)
@dataclass
class _lowerCAmelCase :
__UpperCAmelCase : Optional[str] = field(
default=snake_case__ , metadata={
'''help''': (
'''The model checkpoint for weights initialization.Don\'t set if you want to train a model from scratch.'''
)
} , )
__UpperCAmelCase : Optional[str] = field(
default=snake_case__ , metadata={'''help''': '''If training from scratch, pass a model type from the list: ''' + ''', '''.join(snake_case__ )} , )
__UpperCAmelCase : Optional[str] = field(
default=snake_case__ , metadata={
'''help''': (
'''Override some existing default config settings when a model is trained from scratch. Example: '''
'''n_embd=10,resid_pdrop=0.2,scale_attn_weights=false,summary_type=cls_index'''
)
} , )
__UpperCAmelCase : Optional[str] = field(
default=snake_case__ , metadata={'''help''': '''Pretrained config name or path if not the same as model_name'''} )
__UpperCAmelCase : Optional[str] = field(
default=snake_case__ , metadata={'''help''': '''Pretrained tokenizer name or path if not the same as model_name'''} )
__UpperCAmelCase : Optional[str] = field(
default=snake_case__ , metadata={'''help''': '''Where do you want to store the pretrained models downloaded from huggingface.co'''} , )
__UpperCAmelCase : bool = field(
default=snake_case__ , metadata={'''help''': '''Whether to use one of the fast tokenizer (backed by the tokenizers library) or not.'''} , )
__UpperCAmelCase : str = field(
default='''main''' , metadata={'''help''': '''The specific model version to use (can be a branch name, tag name or commit id).'''} , )
__UpperCAmelCase : bool = field(
default=snake_case__ , metadata={
'''help''': (
'''Will use the token generated when running `huggingface-cli login` (necessary to use this script '''
'''with private models).'''
)
} , )
def lowerCamelCase ( self ) -> List[str]:
'''simple docstring'''
if self.config_overrides is not None and (self.config_name is not None or self.model_name_or_path is not None):
raise ValueError(
"--config_overrides can't be used in combination with --config_name or --model_name_or_path" )
@dataclass
class _lowerCAmelCase :
__UpperCAmelCase : Optional[str] = field(
default=snake_case__ , metadata={'''help''': '''The name of the dataset to use (via the datasets library).'''} )
__UpperCAmelCase : Optional[str] = field(
default=snake_case__ , metadata={'''help''': '''The configuration name of the dataset to use (via the datasets library).'''} )
__UpperCAmelCase : Optional[str] = field(default=snake_case__ , metadata={'''help''': '''The input training data file (a text file).'''} )
__UpperCAmelCase : Optional[str] = field(
default=snake_case__ , metadata={'''help''': '''An optional input evaluation data file to evaluate the perplexity on (a text file).'''} , )
__UpperCAmelCase : Optional[str] = field(
default=snake_case__ , metadata={'''help''': '''An optional input train ref data file for whole word masking in Chinese.'''} , )
__UpperCAmelCase : Optional[str] = field(
default=snake_case__ , metadata={'''help''': '''An optional input validation ref data file for whole word masking in Chinese.'''} , )
__UpperCAmelCase : bool = field(
default=snake_case__ , metadata={'''help''': '''Overwrite the cached training and evaluation sets'''} )
__UpperCAmelCase : Optional[int] = field(
default=5 , metadata={
'''help''': '''The percentage of the train set used as validation set in case there\'s no validation split'''
} , )
__UpperCAmelCase : Optional[int] = field(
default=snake_case__ , metadata={
'''help''': (
'''The maximum total input sequence length after tokenization. Sequences longer '''
'''than this will be truncated. Default to the max input length of the model.'''
)
} , )
__UpperCAmelCase : Optional[int] = field(
default=snake_case__ , metadata={'''help''': '''The number of processes to use for the preprocessing.'''} , )
__UpperCAmelCase : float = field(
default=0.15 , metadata={'''help''': '''Ratio of tokens to mask for masked language modeling loss'''} )
__UpperCAmelCase : bool = field(
default=snake_case__ , 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.'''
)
} , )
def lowerCamelCase ( self ) -> List[str]:
'''simple docstring'''
if self.train_file is not None:
snake_case : Dict = self.train_file.split("." )[-1]
assert extension in ["csv", "json", "txt"], "`train_file` should be a csv, a json or a txt file."
if self.validation_file is not None:
snake_case : Tuple = self.validation_file.split("." )[-1]
assert extension in ["csv", "json", "txt"], "`validation_file` should be a csv, a json or a txt file."
def __lowerCAmelCase ( lowercase : Union[str, Any] , lowercase : Optional[int] ) -> str:
"""simple docstring"""
with open(SCREAMING_SNAKE_CASE_ , "r" , encoding="utf-8" ) as f:
snake_case : List[str] = [json.loads(SCREAMING_SNAKE_CASE_ ) for line in f.read().splitlines() if (len(SCREAMING_SNAKE_CASE_ ) > 0 and not line.isspace())]
assert len(SCREAMING_SNAKE_CASE_ ) == len(SCREAMING_SNAKE_CASE_ )
snake_case : List[Any] = {c: dataset[c] for c in dataset.column_names}
snake_case : List[Any] = refs
return Dataset.from_dict(SCREAMING_SNAKE_CASE_ )
def __lowerCAmelCase ( ) -> Optional[Any]:
"""simple docstring"""
snake_case : Tuple = 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.
snake_case ,snake_case ,snake_case : Optional[Any] = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1] ) )
else:
snake_case ,snake_case ,snake_case : List[str] = parser.parse_args_into_dataclasses()
# Detecting last checkpoint.
snake_case : str = None
if os.path.isdir(training_args.output_dir ) and training_args.do_train and not training_args.overwrite_output_dir:
snake_case : List[Any] = get_last_checkpoint(training_args.output_dir )
if last_checkpoint is None and len(os.listdir(training_args.output_dir ) ) > 0:
raise ValueError(
F'Output directory ({training_args.output_dir}) already exists and is not empty. '
"Use --overwrite_output_dir to overcome." )
elif last_checkpoint is not None:
logger.info(
F'Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change '
"the `--output_dir` or add `--overwrite_output_dir` to train from scratch." )
# Setup logging
logging.basicConfig(
format="%(asctime)s - %(levelname)s - %(name)s - %(message)s" , datefmt="%m/%d/%Y %H:%M:%S" , handlers=[logging.StreamHandler(sys.stdout )] , )
logger.setLevel(logging.INFO if is_main_process(training_args.local_rank ) else logging.WARN )
# Log on each process the small summary:
logger.warning(
F'Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}'
+ F'distributed training: {bool(training_args.local_rank != -1 )}, 16-bits training: {training_args.fpaa}' )
# Set the verbosity to info of the Transformers logger (on main process only):
if is_main_process(training_args.local_rank ):
transformers.utils.logging.set_verbosity_info()
transformers.utils.logging.enable_default_handler()
transformers.utils.logging.enable_explicit_format()
logger.info("Training/evaluation parameters %s" , SCREAMING_SNAKE_CASE_ )
# Set seed before initializing model.
set_seed(training_args.seed )
# Get the datasets: you can either provide your own CSV/JSON/TXT training and evaluation files (see below)
# or just provide the name of one of the public datasets available on the hub at https://huggingface.co/datasets/
# (the dataset will be downloaded automatically from the datasets Hub).
#
# For CSV/JSON files, this script will use the column called 'text' or the first column if no column called
# 'text' is found. You can easily tweak this behavior (see below).
#
# In distributed training, the load_dataset function guarantee that only one local process can concurrently
# download the dataset.
if data_args.dataset_name is not None:
# Downloading and loading a dataset from the hub.
snake_case : Any = load_dataset(data_args.dataset_name , data_args.dataset_config_name )
if "validation" not in datasets.keys():
snake_case : List[str] = load_dataset(
data_args.dataset_name , data_args.dataset_config_name , split=F'train[:{data_args.validation_split_percentage}%]' , )
snake_case : Any = load_dataset(
data_args.dataset_name , data_args.dataset_config_name , split=F'train[{data_args.validation_split_percentage}%:]' , )
else:
snake_case : Dict = {}
if data_args.train_file is not None:
snake_case : Optional[Any] = data_args.train_file
if data_args.validation_file is not None:
snake_case : Union[str, Any] = data_args.validation_file
snake_case : List[Any] = data_args.train_file.split("." )[-1]
if extension == "txt":
snake_case : Dict = "text"
snake_case : Any = load_dataset(SCREAMING_SNAKE_CASE_ , data_files=SCREAMING_SNAKE_CASE_ )
# See more about loading any type of standard or custom dataset (from files, python dict, pandas DataFrame, etc) at
# https://huggingface.co/docs/datasets/loading_datasets.html.
# Load pretrained model and tokenizer
#
# Distributed training:
# The .from_pretrained methods guarantee that only one local process can concurrently
# download model & vocab.
snake_case : List[str] = {
"cache_dir": model_args.cache_dir,
"revision": model_args.model_revision,
"use_auth_token": True if model_args.use_auth_token else None,
}
if model_args.config_name:
snake_case : Tuple = AutoConfig.from_pretrained(model_args.config_name , **SCREAMING_SNAKE_CASE_ )
elif model_args.model_name_or_path:
snake_case : int = AutoConfig.from_pretrained(model_args.model_name_or_path , **SCREAMING_SNAKE_CASE_ )
else:
snake_case : str = CONFIG_MAPPING[model_args.model_type]()
logger.warning("You are instantiating a new config instance from scratch." )
if model_args.config_overrides is not None:
logger.info(F'Overriding config: {model_args.config_overrides}' )
config.update_from_string(model_args.config_overrides )
logger.info(F'New config: {config}' )
snake_case : Tuple = {
"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,
}
if model_args.tokenizer_name:
snake_case : List[str] = AutoTokenizer.from_pretrained(model_args.tokenizer_name , **SCREAMING_SNAKE_CASE_ )
elif model_args.model_name_or_path:
snake_case : List[Any] = AutoTokenizer.from_pretrained(model_args.model_name_or_path , **SCREAMING_SNAKE_CASE_ )
else:
raise ValueError(
"You are instantiating a new tokenizer from scratch. This is not supported by this script."
"You can do it from another script, save it, and load it from here, using --tokenizer_name." )
if model_args.model_name_or_path:
snake_case : str = AutoModelForMaskedLM.from_pretrained(
model_args.model_name_or_path , from_tf=bool(".ckpt" in model_args.model_name_or_path ) , config=SCREAMING_SNAKE_CASE_ , cache_dir=model_args.cache_dir , revision=model_args.model_revision , use_auth_token=True if model_args.use_auth_token else None , )
else:
logger.info("Training new model from scratch" )
snake_case : Dict = AutoModelForMaskedLM.from_config(SCREAMING_SNAKE_CASE_ )
model.resize_token_embeddings(len(SCREAMING_SNAKE_CASE_ ) )
# Preprocessing the datasets.
# First we tokenize all the texts.
if training_args.do_train:
snake_case : Union[str, Any] = datasets["train"].column_names
else:
snake_case : Union[str, Any] = datasets["validation"].column_names
snake_case : str = "text" if "text" in column_names else column_names[0]
snake_case : Union[str, Any] = "max_length" if data_args.pad_to_max_length else False
def tokenize_function(lowercase : Union[str, Any] ):
# Remove empty lines
snake_case : Any = [line for line in examples["text"] if len(SCREAMING_SNAKE_CASE_ ) > 0 and not line.isspace()]
return tokenizer(examples["text"] , padding=SCREAMING_SNAKE_CASE_ , truncation=SCREAMING_SNAKE_CASE_ , max_length=data_args.max_seq_length )
snake_case : Union[str, Any] = datasets.map(
SCREAMING_SNAKE_CASE_ , batched=SCREAMING_SNAKE_CASE_ , num_proc=data_args.preprocessing_num_workers , remove_columns=[text_column_name] , load_from_cache_file=not data_args.overwrite_cache , )
# Add the chinese references if provided
if data_args.train_ref_file is not None:
snake_case : int = add_chinese_references(tokenized_datasets["train"] , data_args.train_ref_file )
if data_args.validation_ref_file is not None:
snake_case : str = add_chinese_references(
tokenized_datasets["validation"] , data_args.validation_ref_file )
# If we have ref files, need to avoid it removed by trainer
snake_case : Tuple = data_args.train_ref_file or data_args.validation_ref_file
if has_ref:
snake_case : Any = False
# Data collator
# This one will take care of randomly masking the tokens.
snake_case : Dict = DataCollatorForWholeWordMask(tokenizer=SCREAMING_SNAKE_CASE_ , mlm_probability=data_args.mlm_probability )
# Initialize our Trainer
snake_case : Optional[int] = Trainer(
model=SCREAMING_SNAKE_CASE_ , args=SCREAMING_SNAKE_CASE_ , train_dataset=tokenized_datasets["train"] if training_args.do_train else None , eval_dataset=tokenized_datasets["validation"] if training_args.do_eval else None , tokenizer=SCREAMING_SNAKE_CASE_ , data_collator=SCREAMING_SNAKE_CASE_ , )
# Training
if training_args.do_train:
if last_checkpoint is not None:
snake_case : str = last_checkpoint
elif model_args.model_name_or_path is not None and os.path.isdir(model_args.model_name_or_path ):
snake_case : Any = model_args.model_name_or_path
else:
snake_case : Union[str, Any] = None
snake_case : Tuple = trainer.train(resume_from_checkpoint=SCREAMING_SNAKE_CASE_ )
trainer.save_model() # Saves the tokenizer too for easy upload
snake_case : Tuple = os.path.join(training_args.output_dir , "train_results.txt" )
if trainer.is_world_process_zero():
with open(SCREAMING_SNAKE_CASE_ , "w" ) as writer:
logger.info("***** Train results *****" )
for key, value in sorted(train_result.metrics.items() ):
logger.info(F' {key} = {value}' )
writer.write(F'{key} = {value}\n' )
# Need to save the state, since Trainer.save_model saves only the tokenizer with the model
trainer.state.save_to_json(os.path.join(training_args.output_dir , "trainer_state.json" ) )
# Evaluation
snake_case : int = {}
if training_args.do_eval:
logger.info("*** Evaluate ***" )
snake_case : List[Any] = trainer.evaluate()
snake_case : List[str] = math.exp(eval_output["eval_loss"] )
snake_case : int = perplexity
snake_case : str = os.path.join(training_args.output_dir , "eval_results_mlm_wwm.txt" )
if trainer.is_world_process_zero():
with open(SCREAMING_SNAKE_CASE_ , "w" ) as writer:
logger.info("***** Eval results *****" )
for key, value in sorted(results.items() ):
logger.info(F' {key} = {value}' )
writer.write(F'{key} = {value}\n' )
return results
def __lowerCAmelCase ( lowercase : Any ) -> Tuple:
"""simple docstring"""
main()
if __name__ == "__main__":
main()
| 203 |
import warnings
from diffusers import StableDiffusionImgaImgPipeline # noqa F401
warnings.warn(
"""The `image_to_image.py` script is outdated. Please use directly `from diffusers import"""
""" StableDiffusionImg2ImgPipeline` instead."""
)
| 92 | 0 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a : Optional[int] = logging.get_logger(__name__)
a : Union[str, Any] = {
'edbeeching/decision-transformer-gym-hopper-medium': (
'https://huggingface.co/edbeeching/decision-transformer-gym-hopper-medium/resolve/main/config.json'
),
# See all DecisionTransformer models at https://huggingface.co/models?filter=decision_transformer
}
class a ( snake_case__ ):
snake_case_ = """decision_transformer"""
snake_case_ = ["""past_key_values"""]
snake_case_ = {
"""max_position_embeddings""": """n_positions""",
"""num_attention_heads""": """n_head""",
"""num_hidden_layers""": """n_layer""",
}
def __init__( self : Optional[int] , lowercase_ : Optional[int]=17 , lowercase_ : List[Any]=4 , lowercase_ : List[str]=128 , lowercase_ : Tuple=4096 , lowercase_ : Tuple=True , lowercase_ : Tuple=1 , lowercase_ : List[Any]=1024 , lowercase_ : Optional[Any]=3 , lowercase_ : Union[str, Any]=1 , lowercase_ : Optional[Any]=None , lowercase_ : Tuple="relu" , lowercase_ : str=0.1 , lowercase_ : Optional[int]=0.1 , lowercase_ : Optional[Any]=0.1 , lowercase_ : Dict=1e-5 , lowercase_ : Tuple=0.02 , lowercase_ : Any=True , lowercase_ : str=True , lowercase_ : List[Any]=5_0256 , lowercase_ : int=5_0256 , lowercase_ : str=False , lowercase_ : Union[str, Any]=False , **lowercase_ : List[str] , ):
snake_case_ = state_dim
snake_case_ = act_dim
snake_case_ = hidden_size
snake_case_ = max_ep_len
snake_case_ = action_tanh
snake_case_ = vocab_size
snake_case_ = n_positions
snake_case_ = n_layer
snake_case_ = n_head
snake_case_ = n_inner
snake_case_ = activation_function
snake_case_ = resid_pdrop
snake_case_ = embd_pdrop
snake_case_ = attn_pdrop
snake_case_ = layer_norm_epsilon
snake_case_ = initializer_range
snake_case_ = scale_attn_weights
snake_case_ = use_cache
snake_case_ = scale_attn_by_inverse_layer_idx
snake_case_ = reorder_and_upcast_attn
snake_case_ = bos_token_id
snake_case_ = eos_token_id
super().__init__(bos_token_id=_A , eos_token_id=_A , **_A )
| 56 |
import os
import torch
from ..logging import get_logger
from .constants import FSDP_PYTORCH_VERSION, MODEL_NAME, OPTIMIZER_NAME
from .versions import is_torch_version
if is_torch_version(""">=""", FSDP_PYTORCH_VERSION):
import torch.distributed.checkpoint as dist_cp
from torch.distributed.checkpoint.default_planner import DefaultLoadPlanner, DefaultSavePlanner
from torch.distributed.checkpoint.optimizer import load_sharded_optimizer_state_dict
from torch.distributed.fsdp.fully_sharded_data_parallel import FullyShardedDataParallel as FSDP
from torch.distributed.fsdp.fully_sharded_data_parallel import StateDictType
UpperCamelCase__ = get_logger(__name__)
def _a ( SCREAMING_SNAKE_CASE_ : List[str] , SCREAMING_SNAKE_CASE_ : Tuple , SCREAMING_SNAKE_CASE_ : Tuple , SCREAMING_SNAKE_CASE_ : Optional[Any] , SCREAMING_SNAKE_CASE_ : str=0 ):
os.makedirs(SCREAMING_SNAKE_CASE_ , exist_ok=SCREAMING_SNAKE_CASE_ )
with FSDP.state_dict_type(
SCREAMING_SNAKE_CASE_ , fsdp_plugin.state_dict_type , fsdp_plugin.state_dict_config , fsdp_plugin.optim_state_dict_config ):
__lowerCAmelCase = model.state_dict()
if fsdp_plugin.state_dict_type == StateDictType.FULL_STATE_DICT:
__lowerCAmelCase = F"""{MODEL_NAME}.bin""" if model_index == 0 else F"""{MODEL_NAME}_{model_index}.bin"""
__lowerCAmelCase = os.path.join(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
if accelerator.process_index == 0:
logger.info(F"""Saving model to {output_model_file}""" )
torch.save(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
logger.info(F"""Model saved to {output_model_file}""" )
elif fsdp_plugin.state_dict_type == StateDictType.LOCAL_STATE_DICT:
__lowerCAmelCase = (
F"""{MODEL_NAME}_rank{accelerator.process_index}.bin"""
if model_index == 0
else F"""{MODEL_NAME}_{model_index}_rank{accelerator.process_index}.bin"""
)
__lowerCAmelCase = os.path.join(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
logger.info(F"""Saving model to {output_model_file}""" )
torch.save(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
logger.info(F"""Model saved to {output_model_file}""" )
elif fsdp_plugin.state_dict_type == StateDictType.SHARDED_STATE_DICT:
__lowerCAmelCase = os.path.join(SCREAMING_SNAKE_CASE_ , F"""{MODEL_NAME}_{model_index}""" )
os.makedirs(SCREAMING_SNAKE_CASE_ , exist_ok=SCREAMING_SNAKE_CASE_ )
logger.info(F"""Saving model to {ckpt_dir}""" )
__lowerCAmelCase = {"model": state_dict}
dist_cp.save_state_dict(
state_dict=SCREAMING_SNAKE_CASE_ , storage_writer=dist_cp.FileSystemWriter(SCREAMING_SNAKE_CASE_ ) , planner=DefaultSavePlanner() , )
logger.info(F"""Model saved to {ckpt_dir}""" )
def _a ( SCREAMING_SNAKE_CASE_ : Dict , SCREAMING_SNAKE_CASE_ : int , SCREAMING_SNAKE_CASE_ : Optional[int] , SCREAMING_SNAKE_CASE_ : List[str] , SCREAMING_SNAKE_CASE_ : Any=0 ):
accelerator.wait_for_everyone()
with FSDP.state_dict_type(
SCREAMING_SNAKE_CASE_ , fsdp_plugin.state_dict_type , fsdp_plugin.state_dict_config , fsdp_plugin.optim_state_dict_config ):
if fsdp_plugin.state_dict_type == StateDictType.FULL_STATE_DICT:
if type(SCREAMING_SNAKE_CASE_ ) != FSDP and accelerator.process_index != 0:
if not fsdp_plugin.sync_module_states:
raise ValueError(
"Set the `sync_module_states` flag to `True` so that model states are synced across processes when "
"initializing FSDP object" )
return
__lowerCAmelCase = F"""{MODEL_NAME}.bin""" if model_index == 0 else F"""{MODEL_NAME}_{model_index}.bin"""
__lowerCAmelCase = os.path.join(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
logger.info(F"""Loading model from {input_model_file}""" )
__lowerCAmelCase = torch.load(SCREAMING_SNAKE_CASE_ )
logger.info(F"""Model loaded from {input_model_file}""" )
elif fsdp_plugin.state_dict_type == StateDictType.LOCAL_STATE_DICT:
__lowerCAmelCase = (
F"""{MODEL_NAME}_rank{accelerator.process_index}.bin"""
if model_index == 0
else F"""{MODEL_NAME}_{model_index}_rank{accelerator.process_index}.bin"""
)
__lowerCAmelCase = os.path.join(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
logger.info(F"""Loading model from {input_model_file}""" )
__lowerCAmelCase = torch.load(SCREAMING_SNAKE_CASE_ )
logger.info(F"""Model loaded from {input_model_file}""" )
elif fsdp_plugin.state_dict_type == StateDictType.SHARDED_STATE_DICT:
__lowerCAmelCase = (
os.path.join(SCREAMING_SNAKE_CASE_ , F"""{MODEL_NAME}_{model_index}""" )
if F"""{MODEL_NAME}""" not in input_dir
else input_dir
)
logger.info(F"""Loading model from {ckpt_dir}""" )
__lowerCAmelCase = {"model": model.state_dict()}
dist_cp.load_state_dict(
state_dict=SCREAMING_SNAKE_CASE_ , storage_reader=dist_cp.FileSystemReader(SCREAMING_SNAKE_CASE_ ) , planner=DefaultLoadPlanner() , )
__lowerCAmelCase = state_dict["model"]
logger.info(F"""Model loaded from {ckpt_dir}""" )
model.load_state_dict(SCREAMING_SNAKE_CASE_ )
def _a ( SCREAMING_SNAKE_CASE_ : Dict , SCREAMING_SNAKE_CASE_ : int , SCREAMING_SNAKE_CASE_ : List[str] , SCREAMING_SNAKE_CASE_ : Optional[Any] , SCREAMING_SNAKE_CASE_ : Optional[int] , SCREAMING_SNAKE_CASE_ : str=0 ):
os.makedirs(SCREAMING_SNAKE_CASE_ , exist_ok=SCREAMING_SNAKE_CASE_ )
with FSDP.state_dict_type(
SCREAMING_SNAKE_CASE_ , fsdp_plugin.state_dict_type , fsdp_plugin.state_dict_config , fsdp_plugin.optim_state_dict_config ):
__lowerCAmelCase = FSDP.optim_state_dict(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
if fsdp_plugin.state_dict_type == StateDictType.FULL_STATE_DICT:
if accelerator.process_index == 0:
__lowerCAmelCase = (
F"""{OPTIMIZER_NAME}.bin""" if optimizer_index == 0 else F"""{OPTIMIZER_NAME}_{optimizer_index}.bin"""
)
__lowerCAmelCase = os.path.join(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
logger.info(F"""Saving Optimizer state to {output_optimizer_file}""" )
torch.save(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
logger.info(F"""Optimizer state saved in {output_optimizer_file}""" )
else:
__lowerCAmelCase = os.path.join(SCREAMING_SNAKE_CASE_ , F"""{OPTIMIZER_NAME}_{optimizer_index}""" )
os.makedirs(SCREAMING_SNAKE_CASE_ , exist_ok=SCREAMING_SNAKE_CASE_ )
logger.info(F"""Saving Optimizer state to {ckpt_dir}""" )
dist_cp.save_state_dict(
state_dict={"optimizer": optim_state} , storage_writer=dist_cp.FileSystemWriter(SCREAMING_SNAKE_CASE_ ) , planner=DefaultSavePlanner() , )
logger.info(F"""Optimizer state saved in {ckpt_dir}""" )
def _a ( SCREAMING_SNAKE_CASE_ : Dict , SCREAMING_SNAKE_CASE_ : Dict , SCREAMING_SNAKE_CASE_ : Tuple , SCREAMING_SNAKE_CASE_ : List[str] , SCREAMING_SNAKE_CASE_ : str , SCREAMING_SNAKE_CASE_ : Dict=0 ):
accelerator.wait_for_everyone()
with FSDP.state_dict_type(
SCREAMING_SNAKE_CASE_ , fsdp_plugin.state_dict_type , fsdp_plugin.state_dict_config , fsdp_plugin.optim_state_dict_config ):
if fsdp_plugin.state_dict_type == StateDictType.FULL_STATE_DICT:
__lowerCAmelCase = None
# below check should work but currently it isn't working (mostly opytorch issue),
# in the meantime disabling it at the cost of excess memory usage
# if accelerator.process_index == 0 or not fsdp_plugin.optim_state_dict_config.rank0_only:
__lowerCAmelCase = (
F"""{OPTIMIZER_NAME}.bin""" if optimizer_index == 0 else F"""{OPTIMIZER_NAME}_{optimizer_index}.bin"""
)
__lowerCAmelCase = os.path.join(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
logger.info(F"""Loading Optimizer state from {input_optimizer_file}""" )
__lowerCAmelCase = torch.load(SCREAMING_SNAKE_CASE_ )
logger.info(F"""Optimizer state loaded from {input_optimizer_file}""" )
else:
__lowerCAmelCase = (
os.path.join(SCREAMING_SNAKE_CASE_ , F"""{OPTIMIZER_NAME}_{optimizer_index}""" )
if F"""{OPTIMIZER_NAME}""" not in input_dir
else input_dir
)
logger.info(F"""Loading Optimizer from {ckpt_dir}""" )
__lowerCAmelCase = load_sharded_optimizer_state_dict(
model_state_dict=model.state_dict() , optimizer_key="optimizer" , storage_reader=dist_cp.FileSystemReader(SCREAMING_SNAKE_CASE_ ) , )
__lowerCAmelCase = optim_state["optimizer"]
logger.info(F"""Optimizer loaded from {ckpt_dir}""" )
__lowerCAmelCase = FSDP.optim_state_dict_to_load(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
optimizer.load_state_dict(SCREAMING_SNAKE_CASE_ )
| 92 | 0 |
'''simple docstring'''
import math
import time
from typing import Dict, List, Optional
from torch.utils.data import Dataset
from transformers import SeqaSeqTrainer, is_torch_tpu_available
from transformers.trainer_utils import PredictionOutput, speed_metrics
if is_torch_tpu_available(check_device=False):
import torch_xla.core.xla_model as xm
import torch_xla.debug.metrics as met
class __UpperCamelCase ( snake_case__ ):
def __init__( self, *lowerCAmelCase, lowerCAmelCase=None, lowerCAmelCase=None, **lowerCAmelCase ):
"""simple docstring"""
super().__init__(*_A, **_A )
lowerCamelCase_ =eval_examples
lowerCamelCase_ =post_process_function
def lowercase__ ( self, lowerCAmelCase = None, lowerCAmelCase=None, lowerCAmelCase = None, lowerCAmelCase = "eval", **lowerCAmelCase, ):
"""simple docstring"""
lowerCamelCase_ =gen_kwargs.copy()
lowerCamelCase_ =(
gen_kwargs['''max_length'''] if gen_kwargs.get('''max_length''' ) is not None else self.args.generation_max_length
)
lowerCamelCase_ =(
gen_kwargs['''num_beams'''] if gen_kwargs.get('''num_beams''' ) is not None else self.args.generation_num_beams
)
lowerCamelCase_ =gen_kwargs
lowerCamelCase_ =self.eval_dataset if eval_dataset is None else eval_dataset
lowerCamelCase_ =self.get_eval_dataloader(_A )
lowerCamelCase_ =self.eval_examples if eval_examples is None else eval_examples
# Temporarily disable metric computation, we will do it in the loop here.
lowerCamelCase_ =self.compute_metrics
lowerCamelCase_ =None
lowerCamelCase_ =time.time()
lowerCamelCase_ =self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
try:
lowerCamelCase_ =eval_loop(
_A, description='''Evaluation''', prediction_loss_only=True if compute_metrics is None else None, ignore_keys=_A, metric_key_prefix=_A, )
finally:
lowerCamelCase_ =compute_metrics
lowerCamelCase_ =self.args.eval_batch_size * self.args.world_size
if f'''{metric_key_prefix}_jit_compilation_time''' in output.metrics:
start_time += output.metrics[f'''{metric_key_prefix}_jit_compilation_time''']
output.metrics.update(
speed_metrics(
_A, _A, num_samples=output.num_samples, num_steps=math.ceil(output.num_samples / total_batch_size ), ) )
if self.post_process_function is not None and self.compute_metrics is not None and self.args.should_save:
# Only the main node write the results by default
lowerCamelCase_ =self.post_process_function(_A, _A, _A )
lowerCamelCase_ =self.compute_metrics(_A )
# Prefix all keys with metric_key_prefix + '_'
for key in list(metrics.keys() ):
if not key.startswith(f'''{metric_key_prefix}_''' ):
lowerCamelCase_ =metrics.pop(_A )
metrics.update(output.metrics )
else:
lowerCamelCase_ =output.metrics
if self.args.should_log:
# Only the main node log the results by default
self.log(_A )
if self.args.tpu_metrics_debug or self.args.debug:
# tpu-comment: Logging debug metrics for PyTorch/XLA (compile, execute times, ops, etc.)
xm.master_print(met.metrics_report() )
lowerCamelCase_ =self.callback_handler.on_evaluate(self.args, self.state, self.control, _A )
return metrics
def lowercase__ ( self, lowerCAmelCase, lowerCAmelCase, lowerCAmelCase=None, lowerCAmelCase = "test", **lowerCAmelCase ):
"""simple docstring"""
lowerCamelCase_ =gen_kwargs.copy()
lowerCamelCase_ =self.get_test_dataloader(_A )
# Temporarily disable metric computation, we will do it in the loop here.
lowerCamelCase_ =self.compute_metrics
lowerCamelCase_ =None
lowerCamelCase_ =time.time()
lowerCamelCase_ =self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
try:
lowerCamelCase_ =eval_loop(
_A, description='''Prediction''', prediction_loss_only=True if compute_metrics is None else None, ignore_keys=_A, metric_key_prefix=_A, )
finally:
lowerCamelCase_ =compute_metrics
lowerCamelCase_ =self.args.eval_batch_size * self.args.world_size
if f'''{metric_key_prefix}_jit_compilation_time''' in output.metrics:
start_time += output.metrics[f'''{metric_key_prefix}_jit_compilation_time''']
output.metrics.update(
speed_metrics(
_A, _A, num_samples=output.num_samples, num_steps=math.ceil(output.num_samples / total_batch_size ), ) )
if self.post_process_function is None or self.compute_metrics is None:
return output
lowerCamelCase_ =self.post_process_function(_A, _A, _A, '''predict''' )
lowerCamelCase_ =self.compute_metrics(_A )
# Prefix all keys with metric_key_prefix + '_'
for key in list(metrics.keys() ):
if not key.startswith(f'''{metric_key_prefix}_''' ):
lowerCamelCase_ =metrics.pop(_A )
metrics.update(output.metrics )
return PredictionOutput(predictions=predictions.predictions, label_ids=predictions.label_ids, metrics=_A )
| 75 |
import math
import time
from typing import Dict, List, Optional
from torch.utils.data import Dataset
from transformers import SeqaSeqTrainer, is_torch_tpu_available
from transformers.trainer_utils import PredictionOutput, speed_metrics
if is_torch_tpu_available(check_device=False):
import torch_xla.core.xla_model as xm
import torch_xla.debug.metrics as met
class a__ ( snake_case__ ):
def __init__( self , *_A , _A=None , _A=None , **_A ):
"""simple docstring"""
super().__init__(*_A , **_A )
__lowerCAmelCase = eval_examples
__lowerCAmelCase = post_process_function
def __SCREAMING_SNAKE_CASE( self , _A = None , _A=None , _A = None , _A = "eval" , **_A , ):
"""simple docstring"""
__lowerCAmelCase = gen_kwargs.copy()
__lowerCAmelCase = (
gen_kwargs["max_length"] if gen_kwargs.get("max_length" ) is not None else self.args.generation_max_length
)
__lowerCAmelCase = (
gen_kwargs["num_beams"] if gen_kwargs.get("num_beams" ) is not None else self.args.generation_num_beams
)
__lowerCAmelCase = gen_kwargs
__lowerCAmelCase = self.eval_dataset if eval_dataset is None else eval_dataset
__lowerCAmelCase = self.get_eval_dataloader(_A )
__lowerCAmelCase = self.eval_examples if eval_examples is None else eval_examples
# Temporarily disable metric computation, we will do it in the loop here.
__lowerCAmelCase = self.compute_metrics
__lowerCAmelCase = None
__lowerCAmelCase = time.time()
__lowerCAmelCase = self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
try:
__lowerCAmelCase = eval_loop(
_A , description="Evaluation" , prediction_loss_only=True if compute_metrics is None else None , ignore_keys=_A , metric_key_prefix=_A , )
finally:
__lowerCAmelCase = compute_metrics
__lowerCAmelCase = self.args.eval_batch_size * self.args.world_size
if f"""{metric_key_prefix}_jit_compilation_time""" in output.metrics:
start_time += output.metrics[f"""{metric_key_prefix}_jit_compilation_time"""]
output.metrics.update(
speed_metrics(
_A , _A , num_samples=output.num_samples , num_steps=math.ceil(output.num_samples / total_batch_size ) , ) )
if self.post_process_function is not None and self.compute_metrics is not None and self.args.should_save:
# Only the main node write the results by default
__lowerCAmelCase = self.post_process_function(_A , _A , _A )
__lowerCAmelCase = self.compute_metrics(_A )
# Prefix all keys with metric_key_prefix + '_'
for key in list(metrics.keys() ):
if not key.startswith(f"""{metric_key_prefix}_""" ):
__lowerCAmelCase = metrics.pop(_A )
metrics.update(output.metrics )
else:
__lowerCAmelCase = output.metrics
if self.args.should_log:
# Only the main node log the results by default
self.log(_A )
if self.args.tpu_metrics_debug or self.args.debug:
# tpu-comment: Logging debug metrics for PyTorch/XLA (compile, execute times, ops, etc.)
xm.master_print(met.metrics_report() )
__lowerCAmelCase = self.callback_handler.on_evaluate(self.args , self.state , self.control , _A )
return metrics
def __SCREAMING_SNAKE_CASE( self , _A , _A , _A=None , _A = "test" , **_A ):
"""simple docstring"""
__lowerCAmelCase = gen_kwargs.copy()
__lowerCAmelCase = self.get_test_dataloader(_A )
# Temporarily disable metric computation, we will do it in the loop here.
__lowerCAmelCase = self.compute_metrics
__lowerCAmelCase = None
__lowerCAmelCase = time.time()
__lowerCAmelCase = self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
try:
__lowerCAmelCase = eval_loop(
_A , description="Prediction" , prediction_loss_only=True if compute_metrics is None else None , ignore_keys=_A , metric_key_prefix=_A , )
finally:
__lowerCAmelCase = compute_metrics
__lowerCAmelCase = self.args.eval_batch_size * self.args.world_size
if f"""{metric_key_prefix}_jit_compilation_time""" in output.metrics:
start_time += output.metrics[f"""{metric_key_prefix}_jit_compilation_time"""]
output.metrics.update(
speed_metrics(
_A , _A , num_samples=output.num_samples , num_steps=math.ceil(output.num_samples / total_batch_size ) , ) )
if self.post_process_function is None or self.compute_metrics is None:
return output
__lowerCAmelCase = self.post_process_function(_A , _A , _A , "predict" )
__lowerCAmelCase = self.compute_metrics(_A )
# Prefix all keys with metric_key_prefix + '_'
for key in list(metrics.keys() ):
if not key.startswith(f"""{metric_key_prefix}_""" ):
__lowerCAmelCase = metrics.pop(_A )
metrics.update(output.metrics )
return PredictionOutput(predictions=predictions.predictions , label_ids=predictions.label_ids , metrics=_A )
| 92 | 0 |
"""simple docstring"""
import os
def _SCREAMING_SNAKE_CASE ( __snake_case : str = "matrix.txt" ):
'''simple docstring'''
with open(os.path.join(os.path.dirname(SCREAMING_SNAKE_CASE_ ) , SCREAMING_SNAKE_CASE_ ) ) as in_file:
lowercase = in_file.read()
lowercase = [[int(SCREAMING_SNAKE_CASE_ ) for cell in row.split(',' )] for row in data.strip().splitlines()]
lowercase = [[0 for cell in row] for row in grid]
lowercase = len(grid[0] )
lowercase = [[0 for i in range(SCREAMING_SNAKE_CASE_ )] for j in range(SCREAMING_SNAKE_CASE_ )]
lowercase = grid[0][0]
for i in range(1 , SCREAMING_SNAKE_CASE_ ):
lowercase = grid[0][i] + dp[0][i - 1]
for i in range(1 , SCREAMING_SNAKE_CASE_ ):
lowercase = grid[i][0] + dp[i - 1][0]
for i in range(1 , SCREAMING_SNAKE_CASE_ ):
for j in range(1 , SCREAMING_SNAKE_CASE_ ):
lowercase = grid[i][j] + min(dp[i - 1][j] , dp[i][j - 1] )
return dp[-1][-1]
if __name__ == "__main__":
print(F'''{solution() = }''')
| 220 |
import logging
from pathlib import Path
import numpy as np
import pytorch_lightning as pl
import torch
from pytorch_lightning.callbacks import EarlyStopping, ModelCheckpoint
from pytorch_lightning.utilities import rank_zero_only
from utils_rag import save_json
def _a ( SCREAMING_SNAKE_CASE_ : Optional[int] ):
__lowerCAmelCase = filter(lambda SCREAMING_SNAKE_CASE_ : p.requires_grad , model.parameters() )
__lowerCAmelCase = sum([np.prod(p.size() ) for p in model_parameters] )
return params
UpperCamelCase__ = logging.getLogger(__name__)
def _a ( SCREAMING_SNAKE_CASE_ : Union[str, Any] , SCREAMING_SNAKE_CASE_ : Any ):
if metric == "rouge2":
__lowerCAmelCase = "{val_avg_rouge2:.4f}-{step_count}"
elif metric == "bleu":
__lowerCAmelCase = "{val_avg_bleu:.4f}-{step_count}"
elif metric == "em":
__lowerCAmelCase = "{val_avg_em:.4f}-{step_count}"
else:
raise NotImplementedError(
F"""seq2seq callbacks only support rouge2 and bleu, got {metric}, You can make your own by adding to this"""
" function." )
__lowerCAmelCase = ModelCheckpoint(
dirpath=SCREAMING_SNAKE_CASE_ , filename=SCREAMING_SNAKE_CASE_ , monitor=F"""val_{metric}""" , mode="max" , save_top_k=3 , every_n_epochs=1 , )
return checkpoint_callback
def _a ( SCREAMING_SNAKE_CASE_ : int , SCREAMING_SNAKE_CASE_ : Union[str, Any] ):
return EarlyStopping(
monitor=F"""val_{metric}""" , mode="min" if "loss" in metric else "max" , patience=SCREAMING_SNAKE_CASE_ , verbose=SCREAMING_SNAKE_CASE_ , )
class a__ ( pl.Callback ):
def __SCREAMING_SNAKE_CASE( self , _A , _A ):
"""simple docstring"""
__lowerCAmelCase = {f"""lr_group_{i}""": param["lr"] for i, param in enumerate(pl_module.trainer.optimizers[0].param_groups )}
pl_module.logger.log_metrics(_A )
@rank_zero_only
def __SCREAMING_SNAKE_CASE( self , _A , _A , _A , _A=True ):
"""simple docstring"""
logger.info(f"""***** {type_path} results at step {trainer.global_step:05d} *****""" )
__lowerCAmelCase = trainer.callback_metrics
trainer.logger.log_metrics({k: v for k, v in metrics.items() if k not in ["log", "progress_bar", "preds"]} )
# Log results
__lowerCAmelCase = Path(pl_module.hparams.output_dir )
if type_path == "test":
__lowerCAmelCase = od / "test_results.txt"
__lowerCAmelCase = od / "test_generations.txt"
else:
# this never gets hit. I prefer not to save intermediate generations, and results are in metrics.json
# If people want this it will be easy enough to add back.
__lowerCAmelCase = od / f"""{type_path}_results/{trainer.global_step:05d}.txt"""
__lowerCAmelCase = od / f"""{type_path}_generations/{trainer.global_step:05d}.txt"""
results_file.parent.mkdir(exist_ok=_A )
generations_file.parent.mkdir(exist_ok=_A )
with open(_A , "a+" ) as writer:
for key in sorted(_A ):
if key in ["log", "progress_bar", "preds"]:
continue
__lowerCAmelCase = metrics[key]
if isinstance(_A , torch.Tensor ):
__lowerCAmelCase = val.item()
__lowerCAmelCase = f"""{key}: {val:.6f}\n"""
writer.write(_A )
if not save_generations:
return
if "preds" in metrics:
__lowerCAmelCase = "\n".join(metrics["preds"] )
generations_file.open("w+" ).write(_A )
@rank_zero_only
def __SCREAMING_SNAKE_CASE( self , _A , _A ):
"""simple docstring"""
try:
__lowerCAmelCase = pl_module.model.model.num_parameters()
except AttributeError:
__lowerCAmelCase = pl_module.model.num_parameters()
__lowerCAmelCase = count_trainable_parameters(_A )
# mp stands for million parameters
trainer.logger.log_metrics({"n_params": npars, "mp": npars / 1E6, "grad_mp": n_trainable_pars / 1E6} )
@rank_zero_only
def __SCREAMING_SNAKE_CASE( self , _A , _A ):
"""simple docstring"""
save_json(pl_module.metrics , pl_module.metrics_save_path )
return self._write_logs(_A , _A , "test" )
@rank_zero_only
def __SCREAMING_SNAKE_CASE( self , _A , _A ):
"""simple docstring"""
save_json(pl_module.metrics , pl_module.metrics_save_path )
# Uncommenting this will save val generations
# return self._write_logs(trainer, pl_module, "valid")
| 92 | 0 |
# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
A : str = {
'configuration_xmod': [
'XMOD_PRETRAINED_CONFIG_ARCHIVE_MAP',
'XmodConfig',
'XmodOnnxConfig',
],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A : Any = [
'XMOD_PRETRAINED_MODEL_ARCHIVE_LIST',
'XmodForCausalLM',
'XmodForMaskedLM',
'XmodForMultipleChoice',
'XmodForQuestionAnswering',
'XmodForSequenceClassification',
'XmodForTokenClassification',
'XmodModel',
'XmodPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_xmod import XMOD_PRETRAINED_CONFIG_ARCHIVE_MAP, XmodConfig, XmodOnnxConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_xmod import (
XMOD_PRETRAINED_MODEL_ARCHIVE_LIST,
XmodForCausalLM,
XmodForMaskedLM,
XmodForMultipleChoice,
XmodForQuestionAnswering,
XmodForSequenceClassification,
XmodForTokenClassification,
XmodModel,
XmodPreTrainedModel,
)
else:
import sys
A : str = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__) | 6 |
from .glue import glue_convert_examples_to_features, glue_output_modes, glue_processors, glue_tasks_num_labels
from .squad import SquadExample, SquadFeatures, SquadVaProcessor, SquadVaProcessor, squad_convert_examples_to_features
from .utils import DataProcessor, InputExample, InputFeatures, SingleSentenceClassificationProcessor
from .xnli import xnli_output_modes, xnli_processors, xnli_tasks_num_labels
| 92 | 0 |
#
# This a `torch.distributed` diagnostics script that checks that all GPUs in the cluster (one or
# many nodes) can talk to each other via nccl and allocate gpu memory.
#
# To run first adjust the number of processes and nodes:
#
# python -m torch.distributed.run --nproc_per_node 2 --nnodes 1 torch-distributed-gpu-test.py
#
# You may need to add --master_addr $MASTER_ADDR --master_port $MASTER_PORT if using a custom addr:port
#
# You can also use the rdzv API: --rdzv_endpoint $MASTER_ADDR:$MASTER_PORT --rdzv_backend c10d
#
# use torch.distributed.launch instead of torch.distributed.run for torch < 1.9
#
# If you get a hanging in `barrier` calls you have some network issues, you may try to debug this with:
#
# NCCL_DEBUG=INFO python -m torch.distributed.run --nproc_per_node 2 --nnodes 1 torch-distributed-gpu-test.py
#
# which should tell you what's going on behind the scenes.
#
#
# This script can be run via `srun` in the SLURM environment as well. Here is a SLURM script that
# runs on 2 nodes of 4 gpus per node:
#
# #SBATCH --job-name=test-nodes # name
# #SBATCH --nodes=2 # nodes
# #SBATCH --ntasks-per-node=1 # crucial - only 1 task per dist per node!
# #SBATCH --cpus-per-task=10 # number of cores per tasks
# #SBATCH --gres=gpu:4 # number of gpus
# #SBATCH --time 0:05:00 # maximum execution time (HH:MM:SS)
# #SBATCH --output=%x-%j.out # output file name
#
# GPUS_PER_NODE=4
# MASTER_ADDR=$(scontrol show hostnames $SLURM_JOB_NODELIST | head -n 1)
# MASTER_PORT=6000
#
# srun --jobid $SLURM_JOBID bash -c 'python -m torch.distributed.run \
# --nproc_per_node $GPUS_PER_NODE --nnodes $SLURM_NNODES --node_rank $SLURM_PROCID \
# --master_addr $MASTER_ADDR --master_port $MASTER_PORT \
# torch-distributed-gpu-test.py'
#
import fcntl
import os
import socket
import torch
import torch.distributed as dist
def a ( *lowerCamelCase_ ):
'''simple docstring'''
with open(SCREAMING_SNAKE_CASE_ , '''r''' ) as fh:
fcntl.flock(SCREAMING_SNAKE_CASE_ , fcntl.LOCK_EX )
try:
print(*SCREAMING_SNAKE_CASE_ )
finally:
fcntl.flock(SCREAMING_SNAKE_CASE_ , fcntl.LOCK_UN )
A__ : int = int(os.environ['LOCAL_RANK'])
torch.cuda.set_device(local_rank)
A__ : List[str] = torch.device('cuda', local_rank)
A__ : Union[str, Any] = socket.gethostname()
A__ : str = F"[{hostname}-{local_rank}]"
try:
# test distributed
dist.init_process_group('nccl')
dist.all_reduce(torch.ones(1).to(device), op=dist.ReduceOp.SUM)
dist.barrier()
# test cuda is available and can allocate memory
torch.cuda.is_available()
torch.ones(1).cuda(local_rank)
# global rank
A__ : Tuple = dist.get_rank()
A__ : Optional[int] = dist.get_world_size()
printflock(F"{gpu} is OK (global rank: {rank}/{world_size})")
dist.barrier()
if rank == 0:
printflock(F"pt={torch.__version__}, cuda={torch.version.cuda}, nccl={torch.cuda.nccl.version()}")
except Exception:
printflock(F"{gpu} is broken")
raise
| 207 |
from queue import PriorityQueue
from typing import Any
import numpy as np
def _a ( SCREAMING_SNAKE_CASE_ : dict , SCREAMING_SNAKE_CASE_ : str , SCREAMING_SNAKE_CASE_ : set , SCREAMING_SNAKE_CASE_ : set , SCREAMING_SNAKE_CASE_ : dict , SCREAMING_SNAKE_CASE_ : dict , SCREAMING_SNAKE_CASE_ : PriorityQueue , SCREAMING_SNAKE_CASE_ : dict , SCREAMING_SNAKE_CASE_ : float | int , ):
for nxt, d in graph[v]:
if nxt in visited_forward:
continue
__lowerCAmelCase = cst_fwd.get(SCREAMING_SNAKE_CASE_ , np.inf )
__lowerCAmelCase = cst_fwd[v] + d
if new_cost_f < old_cost_f:
queue.put((new_cost_f, nxt) )
__lowerCAmelCase = new_cost_f
__lowerCAmelCase = v
if nxt in visited_backward:
if cst_fwd[v] + d + cst_bwd[nxt] < shortest_distance:
__lowerCAmelCase = cst_fwd[v] + d + cst_bwd[nxt]
return shortest_distance
def _a ( SCREAMING_SNAKE_CASE_ : str , SCREAMING_SNAKE_CASE_ : str , SCREAMING_SNAKE_CASE_ : dict , SCREAMING_SNAKE_CASE_ : dict ):
__lowerCAmelCase = -1
__lowerCAmelCase = set()
__lowerCAmelCase = set()
__lowerCAmelCase = {source: 0}
__lowerCAmelCase = {destination: 0}
__lowerCAmelCase = {source: None}
__lowerCAmelCase = {destination: None}
__lowerCAmelCase = PriorityQueue()
__lowerCAmelCase = PriorityQueue()
__lowerCAmelCase = np.inf
queue_forward.put((0, source) )
queue_backward.put((0, destination) )
if source == destination:
return 0
while not queue_forward.empty() and not queue_backward.empty():
__lowerCAmelCase , __lowerCAmelCase = queue_forward.get()
visited_forward.add(SCREAMING_SNAKE_CASE_ )
__lowerCAmelCase , __lowerCAmelCase = queue_backward.get()
visited_backward.add(SCREAMING_SNAKE_CASE_ )
__lowerCAmelCase = pass_and_relaxation(
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , )
__lowerCAmelCase = pass_and_relaxation(
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , )
if cst_fwd[v_fwd] + cst_bwd[v_bwd] >= shortest_distance:
break
if shortest_distance != np.inf:
__lowerCAmelCase = shortest_distance
return shortest_path_distance
UpperCamelCase__ = {
"""B""": [["""C""", 1]],
"""C""": [["""D""", 1]],
"""D""": [["""F""", 1]],
"""E""": [["""B""", 1], ["""G""", 2]],
"""F""": [],
"""G""": [["""F""", 1]],
}
UpperCamelCase__ = {
"""B""": [["""E""", 1]],
"""C""": [["""B""", 1]],
"""D""": [["""C""", 1]],
"""F""": [["""D""", 1], ["""G""", 1]],
"""E""": [[None, np.inf]],
"""G""": [["""E""", 2]],
}
if __name__ == "__main__":
import doctest
doctest.testmod()
| 92 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
lowerCAmelCase: Optional[Any] = {'configuration_deit': ['DEIT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'DeiTConfig', 'DeiTOnnxConfig']}
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase: List[str] = ['DeiTFeatureExtractor']
lowerCAmelCase: Optional[Any] = ['DeiTImageProcessor']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase: Tuple = [
'DEIT_PRETRAINED_MODEL_ARCHIVE_LIST',
'DeiTForImageClassification',
'DeiTForImageClassificationWithTeacher',
'DeiTForMaskedImageModeling',
'DeiTModel',
'DeiTPreTrainedModel',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase: int = [
'TF_DEIT_PRETRAINED_MODEL_ARCHIVE_LIST',
'TFDeiTForImageClassification',
'TFDeiTForImageClassificationWithTeacher',
'TFDeiTForMaskedImageModeling',
'TFDeiTModel',
'TFDeiTPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_deit import DEIT_PRETRAINED_CONFIG_ARCHIVE_MAP, DeiTConfig, DeiTOnnxConfig
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .feature_extraction_deit import DeiTFeatureExtractor
from .image_processing_deit import DeiTImageProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_deit import (
DEIT_PRETRAINED_MODEL_ARCHIVE_LIST,
DeiTForImageClassification,
DeiTForImageClassificationWithTeacher,
DeiTForMaskedImageModeling,
DeiTModel,
DeiTPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_deit import (
TF_DEIT_PRETRAINED_MODEL_ARCHIVE_LIST,
TFDeiTForImageClassification,
TFDeiTForImageClassificationWithTeacher,
TFDeiTForMaskedImageModeling,
TFDeiTModel,
TFDeiTPreTrainedModel,
)
else:
import sys
lowerCAmelCase: Any = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__) | 297 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCamelCase__ = logging.get_logger(__name__)
UpperCamelCase__ = {
"""edbeeching/decision-transformer-gym-hopper-medium""": (
"""https://huggingface.co/edbeeching/decision-transformer-gym-hopper-medium/resolve/main/config.json"""
),
# See all DecisionTransformer models at https://huggingface.co/models?filter=decision_transformer
}
class a__ ( snake_case__ ):
_a : Optional[int] = """decision_transformer"""
_a : Optional[int] = ["""past_key_values"""]
_a : Dict = {
"""max_position_embeddings""": """n_positions""",
"""num_attention_heads""": """n_head""",
"""num_hidden_layers""": """n_layer""",
}
def __init__( self , _A=1_7 , _A=4 , _A=1_2_8 , _A=4_0_9_6 , _A=True , _A=1 , _A=1_0_2_4 , _A=3 , _A=1 , _A=None , _A="relu" , _A=0.1 , _A=0.1 , _A=0.1 , _A=1E-5 , _A=0.02 , _A=True , _A=True , _A=5_0_2_5_6 , _A=5_0_2_5_6 , _A=False , _A=False , **_A , ):
"""simple docstring"""
__lowerCAmelCase = state_dim
__lowerCAmelCase = act_dim
__lowerCAmelCase = hidden_size
__lowerCAmelCase = max_ep_len
__lowerCAmelCase = action_tanh
__lowerCAmelCase = vocab_size
__lowerCAmelCase = n_positions
__lowerCAmelCase = n_layer
__lowerCAmelCase = n_head
__lowerCAmelCase = n_inner
__lowerCAmelCase = activation_function
__lowerCAmelCase = resid_pdrop
__lowerCAmelCase = embd_pdrop
__lowerCAmelCase = attn_pdrop
__lowerCAmelCase = layer_norm_epsilon
__lowerCAmelCase = initializer_range
__lowerCAmelCase = scale_attn_weights
__lowerCAmelCase = use_cache
__lowerCAmelCase = scale_attn_by_inverse_layer_idx
__lowerCAmelCase = reorder_and_upcast_attn
__lowerCAmelCase = bos_token_id
__lowerCAmelCase = eos_token_id
super().__init__(bos_token_id=_A , eos_token_id=_A , **_A )
| 92 | 0 |
from __future__ import annotations
from math import pow, sqrt
def __lowerCamelCase ( lowerCamelCase__ : float , lowerCamelCase__ : float , lowerCamelCase__ : float ):
'''simple docstring'''
if (resistance, reactance, impedance).count(0 ) != 1:
raise ValueError("""One and only one argument must be 0""" )
if resistance == 0:
return {"resistance": sqrt(pow(SCREAMING_SNAKE_CASE_ , 2 ) - pow(SCREAMING_SNAKE_CASE_ , 2 ) )}
elif reactance == 0:
return {"reactance": sqrt(pow(SCREAMING_SNAKE_CASE_ , 2 ) - pow(SCREAMING_SNAKE_CASE_ , 2 ) )}
elif impedance == 0:
return {"impedance": sqrt(pow(SCREAMING_SNAKE_CASE_ , 2 ) + pow(SCREAMING_SNAKE_CASE_ , 2 ) )}
else:
raise ValueError("""Exactly one argument must be 0""" )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 252 |
import gc
import unittest
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
DDPMScheduler,
PriorTransformer,
StableUnCLIPPipeline,
UNetaDConditionModel,
)
from diffusers.pipelines.stable_diffusion.stable_unclip_image_normalizer import StableUnCLIPImageNormalizer
from diffusers.utils.testing_utils import enable_full_determinism, load_numpy, require_torch_gpu, slow, torch_device
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,
assert_mean_pixel_difference,
)
enable_full_determinism()
class a__ ( snake_case__ , snake_case__ , snake_case__ , unittest.TestCase ):
_a : str = StableUnCLIPPipeline
_a : Union[str, Any] = TEXT_TO_IMAGE_PARAMS
_a : Dict = TEXT_TO_IMAGE_BATCH_PARAMS
_a : Optional[int] = TEXT_TO_IMAGE_IMAGE_PARAMS
_a : Dict = TEXT_TO_IMAGE_IMAGE_PARAMS
# TODO(will) Expected attn_bias.stride(1) == 0 to be true, but got false
_a : Optional[Any] = False
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
__lowerCAmelCase = 3_2
__lowerCAmelCase = embedder_hidden_size
# prior components
torch.manual_seed(0 )
__lowerCAmelCase = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip" )
torch.manual_seed(0 )
__lowerCAmelCase = CLIPTextModelWithProjection(
CLIPTextConfig(
bos_token_id=0 , eos_token_id=2 , hidden_size=_A , projection_dim=_A , intermediate_size=3_7 , layer_norm_eps=1E-0_5 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=1_0_0_0 , ) )
torch.manual_seed(0 )
__lowerCAmelCase = PriorTransformer(
num_attention_heads=2 , attention_head_dim=1_2 , embedding_dim=_A , num_layers=1 , )
torch.manual_seed(0 )
__lowerCAmelCase = DDPMScheduler(
variance_type="fixed_small_log" , prediction_type="sample" , num_train_timesteps=1_0_0_0 , clip_sample=_A , clip_sample_range=5.0 , beta_schedule="squaredcos_cap_v2" , )
# regular denoising components
torch.manual_seed(0 )
__lowerCAmelCase = StableUnCLIPImageNormalizer(embedding_dim=_A )
__lowerCAmelCase = DDPMScheduler(beta_schedule="squaredcos_cap_v2" )
torch.manual_seed(0 )
__lowerCAmelCase = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip" )
torch.manual_seed(0 )
__lowerCAmelCase = CLIPTextModel(
CLIPTextConfig(
bos_token_id=0 , eos_token_id=2 , hidden_size=_A , projection_dim=3_2 , intermediate_size=3_7 , layer_norm_eps=1E-0_5 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=1_0_0_0 , ) )
torch.manual_seed(0 )
__lowerCAmelCase = UNetaDConditionModel(
sample_size=3_2 , in_channels=4 , out_channels=4 , down_block_types=("CrossAttnDownBlock2D", "DownBlock2D") , up_block_types=("UpBlock2D", "CrossAttnUpBlock2D") , block_out_channels=(3_2, 6_4) , attention_head_dim=(2, 4) , class_embed_type="projection" , projection_class_embeddings_input_dim=embedder_projection_dim * 2 , cross_attention_dim=_A , layers_per_block=1 , upcast_attention=_A , use_linear_projection=_A , )
torch.manual_seed(0 )
__lowerCAmelCase = DDIMScheduler(
beta_schedule="scaled_linear" , beta_start=0.0_00_85 , beta_end=0.0_12 , prediction_type="v_prediction" , set_alpha_to_one=_A , steps_offset=1 , )
torch.manual_seed(0 )
__lowerCAmelCase = AutoencoderKL()
__lowerCAmelCase = {
# prior components
"prior_tokenizer": prior_tokenizer,
"prior_text_encoder": prior_text_encoder,
"prior": prior,
"prior_scheduler": prior_scheduler,
# image noising components
"image_normalizer": image_normalizer,
"image_noising_scheduler": image_noising_scheduler,
# regular denoising components
"tokenizer": tokenizer,
"text_encoder": text_encoder,
"unet": unet,
"scheduler": scheduler,
"vae": vae,
}
return components
def __SCREAMING_SNAKE_CASE( self , _A , _A=0 ):
"""simple docstring"""
if str(_A ).startswith("mps" ):
__lowerCAmelCase = torch.manual_seed(_A )
else:
__lowerCAmelCase = torch.Generator(device=_A ).manual_seed(_A )
__lowerCAmelCase = {
"prompt": "A painting of a squirrel eating a burger",
"generator": generator,
"num_inference_steps": 2,
"prior_num_inference_steps": 2,
"output_type": "numpy",
}
return inputs
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
__lowerCAmelCase = torch_device == "cpu"
self._test_attention_slicing_forward_pass(test_max_difference=_A )
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
__lowerCAmelCase = torch_device in ["cpu", "mps"]
self._test_inference_batch_single_identical(test_max_difference=_A )
@slow
@require_torch_gpu
class a__ ( unittest.TestCase ):
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
__lowerCAmelCase = load_numpy(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/stable_unclip_2_1_l_anime_turtle_fp16.npy" )
__lowerCAmelCase = StableUnCLIPPipeline.from_pretrained("fusing/stable-unclip-2-1-l" , torch_dtype=torch.floataa )
pipe.to(_A )
pipe.set_progress_bar_config(disable=_A )
# stable unclip will oom when integration tests are run on a V100,
# so turn on memory savings
pipe.enable_attention_slicing()
pipe.enable_sequential_cpu_offload()
__lowerCAmelCase = torch.Generator(device="cpu" ).manual_seed(0 )
__lowerCAmelCase = pipe("anime turle" , generator=_A , output_type="np" )
__lowerCAmelCase = output.images[0]
assert image.shape == (7_6_8, 7_6_8, 3)
assert_mean_pixel_difference(_A , _A )
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
torch.cuda.empty_cache()
torch.cuda.reset_max_memory_allocated()
torch.cuda.reset_peak_memory_stats()
__lowerCAmelCase = StableUnCLIPPipeline.from_pretrained("fusing/stable-unclip-2-1-l" , torch_dtype=torch.floataa )
__lowerCAmelCase = pipe.to(_A )
pipe.set_progress_bar_config(disable=_A )
pipe.enable_attention_slicing()
pipe.enable_sequential_cpu_offload()
__lowerCAmelCase = pipe(
"anime turtle" , prior_num_inference_steps=2 , num_inference_steps=2 , output_type="np" , )
__lowerCAmelCase = torch.cuda.max_memory_allocated()
# make sure that less than 7 GB is allocated
assert mem_bytes < 7 * 1_0**9
| 92 | 0 |
'''simple docstring'''
from __future__ import annotations
from statistics import mean
def __snake_case( _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase ) -> Optional[int]:
snake_case__ : Optional[Any] = [0] * no_of_processes
snake_case__ : Union[str, Any] = [0] * no_of_processes
# Initialize remaining_time to waiting_time.
for i in range(SCREAMING_SNAKE_CASE_ ):
snake_case__ : List[str] = burst_time[i]
snake_case__ : List[Any] = []
snake_case__ : Tuple = 0
snake_case__ : List[str] = 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:
snake_case__ : Dict = []
snake_case__ : Optional[Any] = -1
for i in range(SCREAMING_SNAKE_CASE_ ):
if (arrival_time[i] <= total_time) and (remaining_time[i] > 0):
ready_process.append(SCREAMING_SNAKE_CASE_ )
if len(SCREAMING_SNAKE_CASE_ ) > 0:
snake_case__ : Optional[int] = ready_process[0]
for i in ready_process:
if remaining_time[i] < remaining_time[target_process]:
snake_case__ : Union[str, Any] = i
total_time += burst_time[target_process]
completed += 1
snake_case__ : List[Any] = 0
snake_case__ : Tuple = (
total_time - arrival_time[target_process] - burst_time[target_process]
)
else:
total_time += 1
return waiting_time
def __snake_case( _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase ) -> Any:
snake_case__ : Any = [0] * no_of_processes
for i in range(SCREAMING_SNAKE_CASE_ ):
snake_case__ : Optional[int] = burst_time[i] + waiting_time[i]
return turn_around_time
if __name__ == "__main__":
print("[TEST CASE 01]")
__a = 4
__a = [2, 5, 3, 7]
__a = [0, 0, 0, 0]
__a = calculate_waitingtime(arrival_time, burst_time, no_of_processes)
__a = 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}")
| 35 |
from typing import TYPE_CHECKING
from ...utils import _LazyModule
UpperCamelCase__ = {"""tokenization_wav2vec2_phoneme""": ["""Wav2Vec2PhonemeCTCTokenizer"""]}
if TYPE_CHECKING:
from .tokenization_wavaveca_phoneme import WavaVecaPhonemeCTCTokenizer
else:
import sys
UpperCamelCase__ = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 92 | 0 |
'''simple docstring'''
from queue import PriorityQueue
from typing import Any
import numpy as np
def snake_case ( UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , )-> int:
"""simple docstring"""
for nxt, d in graph[v]:
if nxt in visited_forward:
continue
__A = cst_fwd.get(SCREAMING_SNAKE_CASE_ , np.inf )
__A = cst_fwd[v] + d
if new_cost_f < old_cost_f:
queue.put((new_cost_f, nxt) )
__A = new_cost_f
__A = v
if nxt in visited_backward:
if cst_fwd[v] + d + cst_bwd[nxt] < shortest_distance:
__A = cst_fwd[v] + d + cst_bwd[nxt]
return shortest_distance
def snake_case ( UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase )-> str:
"""simple docstring"""
__A = -1
__A = set()
__A = set()
__A = {source: 0}
__A = {destination: 0}
__A = {source: None}
__A = {destination: None}
__A = PriorityQueue()
__A = PriorityQueue()
__A = np.inf
queue_forward.put((0, source) )
queue_backward.put((0, destination) )
if source == destination:
return 0
while not queue_forward.empty() and not queue_backward.empty():
__A , __A = queue_forward.get()
visited_forward.add(SCREAMING_SNAKE_CASE_ )
__A , __A = queue_backward.get()
visited_backward.add(SCREAMING_SNAKE_CASE_ )
__A = pass_and_relaxation(
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , )
__A = pass_and_relaxation(
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , )
if cst_fwd[v_fwd] + cst_bwd[v_bwd] >= shortest_distance:
break
if shortest_distance != np.inf:
__A = shortest_distance
return shortest_path_distance
a__ : Optional[Any] = {
"B": [["C", 1]],
"C": [["D", 1]],
"D": [["F", 1]],
"E": [["B", 1], ["G", 2]],
"F": [],
"G": [["F", 1]],
}
a__ : Union[str, Any] = {
"B": [["E", 1]],
"C": [["B", 1]],
"D": [["C", 1]],
"F": [["D", 1], ["G", 1]],
"E": [[None, np.inf]],
"G": [["E", 2]],
}
if __name__ == "__main__":
import doctest
doctest.testmod()
| 161 |
import unittest
from transformers import DebertaVaTokenizer, DebertaVaTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin
UpperCamelCase__ = get_tests_dir("""fixtures/spiece.model""")
@require_sentencepiece
@require_tokenizers
class a__ ( snake_case__ , unittest.TestCase ):
_a : Optional[Any] = DebertaVaTokenizer
_a : Optional[Any] = DebertaVaTokenizerFast
_a : List[str] = True
_a : Optional[Any] = True
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
super().setUp()
# We have a SentencePiece fixture for testing
__lowerCAmelCase = DebertaVaTokenizer(_A , unk_token="<unk>" )
tokenizer.save_pretrained(self.tmpdirname )
def __SCREAMING_SNAKE_CASE( self , _A ):
"""simple docstring"""
__lowerCAmelCase = "this is a test"
__lowerCAmelCase = "this is a test"
return input_text, output_text
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
__lowerCAmelCase = "<pad>"
__lowerCAmelCase = 0
self.assertEqual(self.get_tokenizer()._convert_token_to_id(_A ) , _A )
self.assertEqual(self.get_tokenizer()._convert_id_to_token(_A ) , _A )
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
__lowerCAmelCase = list(self.get_tokenizer().get_vocab().keys() )
self.assertEqual(vocab_keys[0] , "<pad>" )
self.assertEqual(vocab_keys[1] , "<unk>" )
self.assertEqual(vocab_keys[-1] , "[PAD]" )
self.assertEqual(len(_A ) , 3_0_0_0_1 )
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
self.assertEqual(self.get_tokenizer().vocab_size , 3_0_0_0_0 )
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
__lowerCAmelCase = " \tHeLLo!how \n Are yoU? "
__lowerCAmelCase = ["▁hello", "!", "how", "▁are", "▁you", "?"]
# fmt: on
__lowerCAmelCase = DebertaVaTokenizer(_A , do_lower_case=_A )
__lowerCAmelCase = tokenizer.convert_ids_to_tokens(tokenizer.encode(_A , add_special_tokens=_A ) )
self.assertListEqual(_A , _A )
__lowerCAmelCase = DebertaVaTokenizerFast(_A , do_lower_case=_A )
__lowerCAmelCase = rust_tokenizer.convert_ids_to_tokens(rust_tokenizer.encode(_A , add_special_tokens=_A ) )
self.assertListEqual(_A , _A )
@unittest.skip("There is an inconsistency between slow and fast tokenizer due to a bug in the fast one." )
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
pass
@unittest.skip("There is an inconsistency between slow and fast tokenizer due to a bug in the fast one." )
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
pass
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
__lowerCAmelCase = "I was born in 92000, and this is falsé."
__lowerCAmelCase = ["▁", "<unk>", "▁was", "▁born", "▁in", "▁9", "2000", "▁", ",", "▁and", "▁this", "▁is", "▁fal", "s", "<unk>", "▁", ".", ]
# fmt: on
__lowerCAmelCase = DebertaVaTokenizer(_A , split_by_punct=_A )
__lowerCAmelCase = tokenizer.convert_ids_to_tokens(tokenizer.encode(_A , add_special_tokens=_A ) )
self.assertListEqual(_A , _A )
__lowerCAmelCase = DebertaVaTokenizerFast(_A , split_by_punct=_A )
__lowerCAmelCase = rust_tokenizer.convert_ids_to_tokens(rust_tokenizer.encode(_A , add_special_tokens=_A ) )
self.assertListEqual(_A , _A )
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
__lowerCAmelCase = "I was born in 92000, and this is falsé."
__lowerCAmelCase = ["▁i", "▁was", "▁born", "▁in", "▁9", "2000", "▁", ",", "▁and", "▁this", "▁is", "▁fal", "s", "<unk>", "▁", ".", ]
# fmt: on
__lowerCAmelCase = DebertaVaTokenizer(_A , do_lower_case=_A , split_by_punct=_A )
__lowerCAmelCase = tokenizer.convert_ids_to_tokens(tokenizer.encode(_A , add_special_tokens=_A ) )
self.assertListEqual(_A , _A )
__lowerCAmelCase = DebertaVaTokenizerFast(_A , do_lower_case=_A , split_by_punct=_A )
__lowerCAmelCase = rust_tokenizer.convert_ids_to_tokens(rust_tokenizer.encode(_A , add_special_tokens=_A ) )
self.assertListEqual(_A , _A )
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
__lowerCAmelCase = "I was born in 92000, and this is falsé."
__lowerCAmelCase = ["▁i", "▁was", "▁born", "▁in", "▁9", "2000", ",", "▁and", "▁this", "▁is", "▁fal", "s", "<unk>", ".", ]
# fmt: on
__lowerCAmelCase = DebertaVaTokenizer(_A , do_lower_case=_A , split_by_punct=_A )
__lowerCAmelCase = tokenizer.convert_ids_to_tokens(tokenizer.encode(_A , add_special_tokens=_A ) )
self.assertListEqual(_A , _A )
__lowerCAmelCase = DebertaVaTokenizerFast(_A , do_lower_case=_A , split_by_punct=_A )
__lowerCAmelCase = rust_tokenizer.convert_ids_to_tokens(rust_tokenizer.encode(_A , add_special_tokens=_A ) )
self.assertListEqual(_A , _A )
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
__lowerCAmelCase = "I was born in 92000, and this is falsé."
__lowerCAmelCase = ["▁", "<unk>", "▁was", "▁born", "▁in", "▁9", "2000", "▁", ",", "▁and", "▁this", "▁is", "▁fal", "s", "<unk>", "▁", ".", ]
# fmt: on
__lowerCAmelCase = DebertaVaTokenizer(_A , do_lower_case=_A , split_by_punct=_A )
__lowerCAmelCase = tokenizer.convert_ids_to_tokens(tokenizer.encode(_A , add_special_tokens=_A ) )
self.assertListEqual(_A , _A )
__lowerCAmelCase = DebertaVaTokenizerFast(_A , do_lower_case=_A , split_by_punct=_A )
__lowerCAmelCase = rust_tokenizer.convert_ids_to_tokens(rust_tokenizer.encode(_A , add_special_tokens=_A ) )
self.assertListEqual(_A , _A )
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
__lowerCAmelCase = " \tHeLLo!how \n Are yoU? "
__lowerCAmelCase = ["▁", "<unk>", "e", "<unk>", "o", "!", "how", "▁", "<unk>", "re", "▁yo", "<unk>", "?"]
# fmt: on
__lowerCAmelCase = DebertaVaTokenizer(_A , do_lower_case=_A , split_by_punct=_A )
__lowerCAmelCase = tokenizer.convert_ids_to_tokens(tokenizer.encode(_A , add_special_tokens=_A ) )
self.assertListEqual(_A , _A )
__lowerCAmelCase = DebertaVaTokenizerFast(_A , do_lower_case=_A , split_by_punct=_A )
__lowerCAmelCase = rust_tokenizer.convert_ids_to_tokens(rust_tokenizer.encode(_A , add_special_tokens=_A ) )
self.assertListEqual(_A , _A )
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
__lowerCAmelCase = self.get_tokenizer()
__lowerCAmelCase = self.get_rust_tokenizer()
__lowerCAmelCase = "I was born in 92000, and this is falsé."
__lowerCAmelCase = tokenizer.convert_ids_to_tokens(tokenizer.encode(_A , add_special_tokens=_A ) )
__lowerCAmelCase = rust_tokenizer.convert_ids_to_tokens(rust_tokenizer.encode(_A , add_special_tokens=_A ) )
self.assertListEqual(_A , _A )
__lowerCAmelCase = tokenizer.encode(_A , add_special_tokens=_A )
__lowerCAmelCase = rust_tokenizer.encode(_A , add_special_tokens=_A )
self.assertListEqual(_A , _A )
__lowerCAmelCase = self.get_rust_tokenizer()
__lowerCAmelCase = tokenizer.encode(_A )
__lowerCAmelCase = rust_tokenizer.encode(_A )
self.assertListEqual(_A , _A )
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
__lowerCAmelCase = "This is a test"
__lowerCAmelCase = [1_3, 1, 4_3_9_8, 2_5, 2_1, 1_2_8_9]
__lowerCAmelCase = ["▁", "T", "his", "▁is", "▁a", "▁test"]
__lowerCAmelCase = ["▁", "<unk>", "his", "▁is", "▁a", "▁test"]
__lowerCAmelCase = DebertaVaTokenizer(_A , keep_accents=_A )
__lowerCAmelCase = DebertaVaTokenizerFast(_A , keep_accents=_A )
__lowerCAmelCase = tokenizer.encode(_A , add_special_tokens=_A )
self.assertListEqual(_A , _A )
__lowerCAmelCase = tokenizer.tokenize(_A )
self.assertListEqual(_A , _A )
__lowerCAmelCase = tokenizer.convert_ids_to_tokens(_A )
self.assertListEqual(_A , _A )
__lowerCAmelCase = rust_tokenizer.encode(_A , add_special_tokens=_A )
self.assertListEqual(_A , _A )
__lowerCAmelCase = rust_tokenizer.tokenize(_A )
self.assertListEqual(_A , _A )
__lowerCAmelCase = rust_tokenizer.convert_ids_to_tokens(_A )
self.assertListEqual(_A , _A )
# fmt: off
__lowerCAmelCase = "I was born in 92000, and this is falsé."
__lowerCAmelCase = [1_3, 1, 2_3, 3_8_6, 1_9, 5_6_1, 3_0_5_0, 1_5, 1_7, 4_8, 2_5, 8_2_5_6, 1_8, 1, 9]
__lowerCAmelCase = ["▁", "I", "▁was", "▁born", "▁in", "▁9", "2000", ",", "▁and", "▁this", "▁is", "▁fal", "s", "é", ".", ]
__lowerCAmelCase = ["▁", "<unk>", "▁was", "▁born", "▁in", "▁9", "2000", ",", "▁and", "▁this", "▁is", "▁fal", "s", "<unk>", ".", ]
# fmt: on
__lowerCAmelCase = tokenizer.encode(_A , add_special_tokens=_A )
self.assertListEqual(_A , _A )
__lowerCAmelCase = tokenizer.tokenize(_A )
self.assertListEqual(_A , _A )
__lowerCAmelCase = tokenizer.convert_ids_to_tokens(_A )
self.assertListEqual(_A , _A )
__lowerCAmelCase = rust_tokenizer.encode(_A , add_special_tokens=_A )
self.assertListEqual(_A , _A )
__lowerCAmelCase = rust_tokenizer.tokenize(_A )
self.assertListEqual(_A , _A )
__lowerCAmelCase = rust_tokenizer.convert_ids_to_tokens(_A )
self.assertListEqual(_A , _A )
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
__lowerCAmelCase = DebertaVaTokenizer(_A )
__lowerCAmelCase = tokenizer.encode("sequence builders" )
__lowerCAmelCase = tokenizer.encode("multi-sequence build" )
__lowerCAmelCase = tokenizer.build_inputs_with_special_tokens(_A )
__lowerCAmelCase = tokenizer.build_inputs_with_special_tokens(_A , _A )
self.assertEqual([tokenizer.cls_token_id] + text + [tokenizer.sep_token_id] , _A )
self.assertEqual(
[tokenizer.cls_token_id] + text + [tokenizer.sep_token_id] + text_a + [tokenizer.sep_token_id] , _A , )
@slow
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
__lowerCAmelCase = {"input_ids": [[1, 3_9_8_6_7, 3_6, 1_9_3_9_0, 4_8_6, 2_7, 3_5_0_5_2, 8_1_4_3_6, 1_8, 6_0_6_8_5, 1_2_2_5, 7, 3_5_0_5_2, 8_1_4_3_6, 1_8, 9_3_6_7, 1_6_8_9_9, 1_8, 1_5_9_3_7, 5_3, 5_9_4, 7_7_3, 1_8, 1_6_2_8_7, 3_0_4_6_5, 3_6, 1_5_9_3_7, 6, 4_1_1_3_9, 3_8, 3_6_9_7_9, 6_0_7_6_3, 1_9_1, 6, 3_4_1_3_2, 9_9, 6, 5_0_5_3_8, 3_9_0, 4_3_2_3_0, 6, 3_4_1_3_2, 2_7_7_9, 2_0_8_5_0, 1_4, 6_9_9, 1_0_7_2, 1_1_9_4, 3_6, 3_8_2, 1_0_9_0_1, 5_3, 7, 6_9_9, 1_0_7_2, 2_0_8_4, 3_6, 2_0_4_2_2, 6_3_0, 5_3, 1_9, 1_0_5, 3_0_4_9, 1_8_9_6, 1_0_5_3, 1_6_8_9_9, 1_5_0_6, 1_1, 3_7_9_7_8, 4_2_4_3, 7, 1_2_3_7, 3_1_8_6_9, 2_0_0, 1_6_5_6_6, 6_5_4, 6, 3_5_0_5_2, 8_1_4_3_6, 7, 5_5_6_3_0, 1_3_5_9_3, 4, 2], [1, 2_6, 1_5_0_1_1, 1_3, 6_6_7, 8, 1_0_5_3, 1_8, 2_3_6_1_1, 1_2_3_7, 7_2_3_5_6, 1_2_8_2_0, 3_4, 1_0_4_1_3_4, 1_2_0_9, 3_5, 1_3_3_1_3, 6_6_2_7, 2_1, 2_0_2, 3_4_7, 7, 1_6_4, 2_3_9_9, 1_1, 4_6, 4_4_8_5, 4, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 5, 1_2_3_2, 2_8_6_4, 1_5_7_8_5, 1_4_9_5_1, 1_0_5, 5, 8_5_8_1, 1_2_5_0, 4, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], "token_type_ids": [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], "attention_mask": [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]} # noqa: E501
# fmt: on
self.tokenizer_integration_test_util(
expected_encoding=_A , model_name="microsoft/deberta-v2-xlarge" , revision="ad6e42c1532ddf3a15c39246b63f5559d558b670" , )
| 92 | 0 |
"""simple docstring"""
import time
import warnings
from abc import ABC
from copy import deepcopy
from typing import Optional
import torch
from ..utils import add_start_docstrings, logging
lowerCAmelCase__ = logging.get_logger(__name__)
lowerCAmelCase__ = r'''
Args:
input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
Indices of input sequence tokens in the vocabulary.
Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
[`PreTrainedTokenizer.__call__`] for details.
[What are input IDs?](../glossary#input-ids)
scores (`torch.FloatTensor` of shape `(batch_size, config.vocab_size)`):
Prediction scores of a language modeling head. These can be scores for each vocabulary token before SoftMax
or scores for each vocabulary token after SoftMax.
kwargs (`Dict[str, Any]`, *optional*):
Additional stopping criteria specific kwargs.
Return:
`bool`. `False` indicates we should continue, `True` indicates we should stop.
'''
class _lowerCamelCase ( snake_case__ ):
@add_start_docstrings(_A )
def __call__(self , __a , __a , **__a ) -> List[Any]:
raise NotImplementedError("StoppingCriteria needs to be subclassed" )
class _lowerCamelCase ( snake_case__ ):
def __init__(self , __a , __a = None ) -> int:
UpperCamelCase = max_length
UpperCamelCase = max_position_embeddings
@add_start_docstrings(_A )
def __call__(self , __a , __a , **__a ) -> Dict:
UpperCamelCase = input_ids.shape[-1]
UpperCamelCase = cur_len >= self.max_length
if self.max_position_embeddings is not None and not is_done and cur_len >= self.max_position_embeddings:
logger.warning_once(
"This is a friendly reminder - the current text generation call will exceed the model's predefined "
F"maximum length ({self.max_position_embeddings}). Depending on the model, you may observe "
"exceptions, performance degradation, or nothing at all." )
return is_done
class _lowerCamelCase ( snake_case__ ):
def __init__(self , __a , __a ) -> Tuple:
warnings.warn(
"The class `MaxNewTokensCriteria` is deprecated. "
F"Please use `MaxLengthCriteria(max_length={start_length + max_new_tokens})` "
"with `max_length = start_length + max_new_tokens` instead." , _A , )
UpperCamelCase = start_length
UpperCamelCase = max_new_tokens
UpperCamelCase = start_length + max_new_tokens
@add_start_docstrings(_A )
def __call__(self , __a , __a , **__a ) -> List[str]:
return input_ids.shape[-1] >= self.max_length
class _lowerCamelCase ( snake_case__ ):
def __init__(self , __a , __a = None ) -> List[str]:
UpperCamelCase = max_time
UpperCamelCase = time.time() if initial_timestamp is None else initial_timestamp
@add_start_docstrings(_A )
def __call__(self , __a , __a , **__a ) -> List[str]:
return time.time() - self.initial_timestamp > self.max_time
class _lowerCamelCase ( snake_case__ ):
@add_start_docstrings(_A )
def __call__(self , __a , __a , **__a ) -> Tuple:
return any(criteria(_A , _A ) for criteria in self )
@property
def snake_case_ (self ) -> Any:
for stopping_criterium in self:
if isinstance(_A , _A ):
return stopping_criterium.max_length
elif isinstance(_A , _A ):
return stopping_criterium.max_length
return None
def a__ ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
"""simple docstring"""
UpperCamelCase = stopping_criteria.max_length
UpperCamelCase = deepcopy(SCREAMING_SNAKE_CASE_ )
if stopping_max_length is not None and stopping_max_length != max_length:
warnings.warn("You set different `max_length` for stopping criteria and `max_length` parameter" , SCREAMING_SNAKE_CASE_ )
elif stopping_max_length is None:
new_stopping_criteria.append(MaxLengthCriteria(max_length=SCREAMING_SNAKE_CASE_ ) )
return new_stopping_criteria
| 153 |
from dataclasses import dataclass, field
from typing import Tuple
from ..utils import cached_property, is_tf_available, logging, requires_backends
from .benchmark_args_utils import BenchmarkArguments
if is_tf_available():
import tensorflow as tf
UpperCamelCase__ = logging.get_logger(__name__)
@dataclass
class a__ ( snake_case__ ):
_a : List[str] = [
"""no_inference""",
"""no_cuda""",
"""no_tpu""",
"""no_speed""",
"""no_memory""",
"""no_env_print""",
"""no_multi_process""",
]
def __init__( self , **_A ):
"""simple docstring"""
for deprecated_arg in self.deprecated_args:
if deprecated_arg in kwargs:
__lowerCAmelCase = deprecated_arg[3:]
__lowerCAmelCase = not kwargs.pop(_A )
logger.warning(
f"""{deprecated_arg} is depreciated. Please use --no-{positive_arg} or"""
f""" {positive_arg}={kwargs[positive_arg]}""" )
__lowerCAmelCase = kwargs.pop("tpu_name" , self.tpu_name )
__lowerCAmelCase = kwargs.pop("device_idx" , self.device_idx )
__lowerCAmelCase = kwargs.pop("eager_mode" , self.eager_mode )
__lowerCAmelCase = kwargs.pop("use_xla" , self.use_xla )
super().__init__(**_A )
_a : str = field(
default=snake_case__ , metadata={"""help""": """Name of TPU"""} , )
_a : int = field(
default=0 , metadata={"""help""": """CPU / GPU device index. Defaults to 0."""} , )
_a : bool = field(default=snake_case__ , metadata={"""help""": """Benchmark models in eager model."""} )
_a : bool = field(
default=snake_case__ , metadata={
"""help""": """Benchmark models using XLA JIT compilation. Note that `eager_model` has to be set to `False`."""
} , )
@cached_property
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
requires_backends(self , ["tf"] )
__lowerCAmelCase = None
if self.tpu:
try:
if self.tpu_name:
__lowerCAmelCase = tf.distribute.cluster_resolver.TPUClusterResolver(self.tpu_name )
else:
__lowerCAmelCase = tf.distribute.cluster_resolver.TPUClusterResolver()
except ValueError:
__lowerCAmelCase = None
return tpu
@cached_property
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
requires_backends(self , ["tf"] )
if self.is_tpu:
tf.config.experimental_connect_to_cluster(self._setup_tpu )
tf.tpu.experimental.initialize_tpu_system(self._setup_tpu )
__lowerCAmelCase = tf.distribute.TPUStrategy(self._setup_tpu )
else:
# currently no multi gpu is allowed
if self.is_gpu:
# TODO: Currently only single GPU is supported
tf.config.set_visible_devices(self.gpu_list[self.device_idx] , "GPU" )
__lowerCAmelCase = tf.distribute.OneDeviceStrategy(device=f"""/gpu:{self.device_idx}""" )
else:
tf.config.set_visible_devices([] , "GPU" ) # disable GPU
__lowerCAmelCase = tf.distribute.OneDeviceStrategy(device=f"""/cpu:{self.device_idx}""" )
return strategy
@property
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
requires_backends(self , ["tf"] )
return self._setup_tpu is not None
@property
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
requires_backends(self , ["tf"] )
return self._setup_strategy
@property
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
requires_backends(self , ["tf"] )
return tf.config.list_physical_devices("GPU" )
@property
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
requires_backends(self , ["tf"] )
if self.cuda:
return len(self.gpu_list )
return 0
@property
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
return self.n_gpu > 0
| 92 | 0 |
"""simple docstring"""
def __lowerCAmelCase ( lowercase : int = 100 ) -> Optional[Any]:
"""simple docstring"""
snake_case : Tuple = (n * (n + 1) // 2) ** 2
snake_case : Tuple = n * (n + 1) * (2 * n + 1) // 6
return sum_cubes - sum_squares
if __name__ == "__main__":
print(F'''{solution() = }''')
| 203 |
import unittest
from transformers import CamembertTokenizer, CamembertTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import is_torch_available
from ...test_tokenization_common import TokenizerTesterMixin
UpperCamelCase__ = get_tests_dir("""fixtures/test_sentencepiece.model""")
UpperCamelCase__ = get_tests_dir("""fixtures/test_sentencepiece_bpe.model""")
UpperCamelCase__ = """pt""" if is_torch_available() else """tf"""
@require_sentencepiece
@require_tokenizers
class a__ ( snake_case__ , unittest.TestCase ):
_a : int = CamembertTokenizer
_a : Dict = CamembertTokenizerFast
_a : Tuple = True
_a : List[Any] = True
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
super().setUp()
# We have a SentencePiece fixture for testing
__lowerCAmelCase = CamembertTokenizer(_A )
tokenizer.save_pretrained(self.tmpdirname )
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
__lowerCAmelCase = "<pad>"
__lowerCAmelCase = 1
self.assertEqual(self.get_tokenizer()._convert_token_to_id(_A ) , _A )
self.assertEqual(self.get_tokenizer()._convert_id_to_token(_A ) , _A )
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
__lowerCAmelCase = list(self.get_tokenizer().get_vocab().keys() )
self.assertEqual(vocab_keys[0] , "<s>NOTUSED" )
self.assertEqual(vocab_keys[1] , "<pad>" )
self.assertEqual(vocab_keys[-1] , "<mask>" )
self.assertEqual(len(_A ) , 1_0_0_4 )
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
self.assertEqual(self.get_tokenizer().vocab_size , 1_0_0_5 )
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
__lowerCAmelCase = CamembertTokenizer(_A )
tokenizer.save_pretrained(self.tmpdirname )
__lowerCAmelCase = CamembertTokenizerFast.from_pretrained(self.tmpdirname )
__lowerCAmelCase = "I was born in 92000, and this is falsé."
__lowerCAmelCase = tokenizer.encode(_A )
__lowerCAmelCase = rust_tokenizer.encode(_A )
self.assertListEqual(_A , _A )
__lowerCAmelCase = tokenizer.encode(_A , add_special_tokens=_A )
__lowerCAmelCase = rust_tokenizer.encode(_A , add_special_tokens=_A )
self.assertListEqual(_A , _A )
# <unk> tokens are not the same for `rust` than for `slow`.
# Because spm gives back raw token instead of `unk` in EncodeAsPieces
# tokens = tokenizer.tokenize(sequence)
__lowerCAmelCase = tokenizer.convert_ids_to_tokens(_A )
__lowerCAmelCase = rust_tokenizer.tokenize(_A )
self.assertListEqual(_A , _A )
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
if not self.test_rust_tokenizer:
return
__lowerCAmelCase = self.get_tokenizer()
__lowerCAmelCase = self.get_rust_tokenizer()
__lowerCAmelCase = "I was born in 92000, and this is falsé."
__lowerCAmelCase = tokenizer.tokenize(_A )
__lowerCAmelCase = rust_tokenizer.tokenize(_A )
self.assertListEqual(_A , _A )
__lowerCAmelCase = tokenizer.encode(_A , add_special_tokens=_A )
__lowerCAmelCase = rust_tokenizer.encode(_A , add_special_tokens=_A )
self.assertListEqual(_A , _A )
__lowerCAmelCase = self.get_rust_tokenizer()
__lowerCAmelCase = tokenizer.encode(_A )
__lowerCAmelCase = rust_tokenizer.encode(_A )
self.assertListEqual(_A , _A )
@slow
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
__lowerCAmelCase = {"input_ids": [[5, 5_4, 7_1_9_6, 2_9_7, 3_0, 2_3, 7_7_6, 1_8, 1_1, 3_2_1_5, 3_7_0_5, 8_2_5_2, 2_2, 3_1_6_4, 1_1_8_1, 2_1_1_6, 2_9, 1_6, 8_1_3, 2_5, 7_9_1, 3_3_1_4, 2_0, 3_4_4_6, 3_8, 2_7_5_7_5, 1_2_0, 6, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [5, 4_6_8, 1_7, 1_1, 9_0_8_8, 2_0, 1_5_1_7, 8, 2_2_8_0_4, 1_8_8_1_8, 1_0, 3_8, 6_2_9, 6_0_7, 6_0_7, 1_4_2, 1_9, 7_1_9_6, 8_6_7, 5_6, 1_0_3_2_6, 2_4, 2_2_6_7, 2_0, 4_1_6, 5_0_7_2, 1_5_6_1_2, 2_3_3, 7_3_4, 7, 2_3_9_9, 2_7, 1_6, 3_0_1_5, 1_6_4_9, 7, 2_4, 2_0, 4_3_3_8, 2_3_9_9, 2_7, 1_3, 3_4_0_0, 1_4, 1_3, 6_1_8_9, 8, 9_3_0, 9, 6]], "attention_mask": [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]} # noqa: E501
# fmt: on
# camembert is a french model. So we also use french texts.
__lowerCAmelCase = [
"Le transformeur est un modèle d'apprentissage profond introduit en 2017, "
"utilisé principalement dans le domaine du traitement automatique des langues (TAL).",
"À l'instar des réseaux de neurones récurrents (RNN), les transformeurs sont conçus "
"pour gérer des données séquentielles, telles que le langage naturel, pour des tâches "
"telles que la traduction et la synthèse de texte.",
]
self.tokenizer_integration_test_util(
expected_encoding=_A , model_name="camembert-base" , revision="3a0641d9a1aeb7e848a74299e7e4c4bca216b4cf" , sequences=_A , )
| 92 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
a : Optional[Any] = {
'configuration_canine': ['CANINE_PRETRAINED_CONFIG_ARCHIVE_MAP', 'CanineConfig'],
'tokenization_canine': ['CanineTokenizer'],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a : List[Any] = [
'CANINE_PRETRAINED_MODEL_ARCHIVE_LIST',
'CanineForMultipleChoice',
'CanineForQuestionAnswering',
'CanineForSequenceClassification',
'CanineForTokenClassification',
'CanineLayer',
'CanineModel',
'CaninePreTrainedModel',
'load_tf_weights_in_canine',
]
if TYPE_CHECKING:
from .configuration_canine import CANINE_PRETRAINED_CONFIG_ARCHIVE_MAP, CanineConfig
from .tokenization_canine import CanineTokenizer
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_canine import (
CANINE_PRETRAINED_MODEL_ARCHIVE_LIST,
CanineForMultipleChoice,
CanineForQuestionAnswering,
CanineForSequenceClassification,
CanineForTokenClassification,
CanineLayer,
CanineModel,
CaninePreTrainedModel,
load_tf_weights_in_canine,
)
else:
import sys
a : Optional[int] = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 56 |
from __future__ import annotations
import collections
import tempfile
import unittest
import numpy as np
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import is_tf_available, is_vision_available
from ...test_modeling_tf_common import floats_tensor, ids_tensor, random_attention_mask
from ..bert.test_modeling_tf_bert import TFBertModelTester
from ..clip.test_modeling_tf_clip import TFCLIPVisionModelTester
from ..deit.test_modeling_tf_deit import TFDeiTModelTester
from ..roberta.test_modeling_tf_roberta import TFRobertaModelTester
from ..vit.test_modeling_tf_vit import TFViTModelTester
if is_tf_available():
from transformers import (
TFBertModel,
TFCLIPVisionModel,
TFDeiTModel,
TFRobertaModel,
TFVisionTextDualEncoderModel,
TFViTModel,
VisionTextDualEncoderConfig,
)
if is_vision_available():
from PIL import Image
from transformers import VisionTextDualEncoderProcessor
def _a ( SCREAMING_SNAKE_CASE_ : Union[str, Any] ):
if isinstance(SCREAMING_SNAKE_CASE_ , collections.abc.Iterable ):
return x
return (x, x)
@require_tf
class a__ :
def __SCREAMING_SNAKE_CASE( self , _A , _A ):
"""simple docstring"""
pass
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
pass
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
pass
def __SCREAMING_SNAKE_CASE( self , _A , _A , _A , _A , _A=None , **_A ):
"""simple docstring"""
__lowerCAmelCase = VisionTextDualEncoderConfig.from_vision_text_configs(_A , _A )
__lowerCAmelCase = TFVisionTextDualEncoderModel(_A )
__lowerCAmelCase = model(input_ids=_A , pixel_values=_A , attention_mask=_A )
self.assertEqual(output["text_embeds"].shape , (input_ids.shape[0], config.projection_dim) )
self.assertEqual(output["image_embeds"].shape , (pixel_values.shape[0], config.projection_dim) )
def __SCREAMING_SNAKE_CASE( self , _A , _A , _A , _A , _A=None , **_A ):
"""simple docstring"""
__lowerCAmelCase , __lowerCAmelCase = self.get_vision_text_model(_A , _A )
__lowerCAmelCase = TFVisionTextDualEncoderModel(vision_model=_A , text_model=_A )
__lowerCAmelCase = model(input_ids=_A , pixel_values=_A , attention_mask=_A )
self.assertEqual(output["text_embeds"].shape , (input_ids.shape[0], model.config.projection_dim) )
self.assertEqual(output["image_embeds"].shape , (pixel_values.shape[0], model.config.projection_dim) )
def __SCREAMING_SNAKE_CASE( self , _A , _A , _A , _A , _A=None , **_A ):
"""simple docstring"""
__lowerCAmelCase , __lowerCAmelCase = self.get_vision_text_model(_A , _A )
__lowerCAmelCase = {"vision_model": vision_model, "text_model": text_model}
__lowerCAmelCase = TFVisionTextDualEncoderModel.from_vision_text_pretrained(**_A )
__lowerCAmelCase = model(input_ids=_A , pixel_values=_A , attention_mask=_A )
self.assertEqual(output["text_embeds"].shape , (input_ids.shape[0], model.config.projection_dim) )
self.assertEqual(output["image_embeds"].shape , (pixel_values.shape[0], model.config.projection_dim) )
def __SCREAMING_SNAKE_CASE( self , _A , _A , _A , _A , _A=None , **_A ):
"""simple docstring"""
__lowerCAmelCase , __lowerCAmelCase = self.get_vision_text_model(_A , _A )
__lowerCAmelCase = TFVisionTextDualEncoderModel(vision_model=_A , text_model=_A )
__lowerCAmelCase = model(input_ids=_A , pixel_values=_A , attention_mask=_A )
__lowerCAmelCase = output[0].numpy()
with tempfile.TemporaryDirectory() as tmpdirname:
model.save_pretrained(_A )
__lowerCAmelCase = TFVisionTextDualEncoderModel.from_pretrained(_A )
__lowerCAmelCase = model(input_ids=_A , pixel_values=_A , attention_mask=_A )
__lowerCAmelCase = after_output[0].numpy()
__lowerCAmelCase = np.amax(np.abs(out_a - out_a ) )
self.assertLessEqual(_A , 1E-5 )
def __SCREAMING_SNAKE_CASE( self , _A , _A , _A , _A , _A=None , **_A ):
"""simple docstring"""
__lowerCAmelCase , __lowerCAmelCase = self.get_vision_text_model(_A , _A )
__lowerCAmelCase = TFVisionTextDualEncoderModel(vision_model=_A , text_model=_A )
__lowerCAmelCase = model(
input_ids=_A , pixel_values=_A , attention_mask=_A , output_attentions=_A )
__lowerCAmelCase = output.vision_model_output.attentions
self.assertEqual(len(_A ) , vision_config.num_hidden_layers )
# in ViT, the seq_len equals the number of patches + 1 (we add 1 for the [CLS] token)
__lowerCAmelCase = to_atuple(vision_model.config.image_size )
__lowerCAmelCase = to_atuple(vision_model.config.patch_size )
__lowerCAmelCase = (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
__lowerCAmelCase = num_patches + 1
self.assertEqual(vision_attentions[0].shape[-3:] , (vision_config.num_attention_heads, seq_len, seq_len) )
__lowerCAmelCase = output.text_model_output.attentions
self.assertEqual(len(_A ) , text_config.num_hidden_layers )
self.assertEqual(
text_attentions[0].shape[-3:] , (text_config.num_attention_heads, input_ids.shape[-1], input_ids.shape[-1]) , )
def __SCREAMING_SNAKE_CASE( self , _A , _A , _A ):
"""simple docstring"""
__lowerCAmelCase = np.abs((a - b) ).max()
self.assertLessEqual(_A , _A , f"""Difference between torch and flax is {diff} (>= {tol}).""" )
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
__lowerCAmelCase = self.prepare_config_and_inputs()
self.check_vision_text_dual_encoder_model(**_A )
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
__lowerCAmelCase = self.prepare_config_and_inputs()
self.check_model_from_pretrained_configs(**_A )
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
__lowerCAmelCase = self.prepare_config_and_inputs()
self.check_vision_text_dual_encoder_from_pretrained(**_A )
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
__lowerCAmelCase = self.prepare_config_and_inputs()
self.check_save_load(**_A )
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
__lowerCAmelCase = self.prepare_config_and_inputs()
self.check_vision_text_output_attention(**_A )
@slow
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
__lowerCAmelCase , __lowerCAmelCase = self.get_pretrained_model_and_inputs()
__lowerCAmelCase = model_a(**_A )
__lowerCAmelCase = outputs[0].numpy()
with tempfile.TemporaryDirectory() as tmp_dirname:
model_a.save_pretrained(_A )
__lowerCAmelCase = TFVisionTextDualEncoderModel.from_pretrained(_A )
__lowerCAmelCase = model_a(**_A )
__lowerCAmelCase = after_outputs[0].numpy()
__lowerCAmelCase = np.amax(np.abs(out_a - out_a ) )
self.assertLessEqual(_A , 1E-5 )
@require_tf
class a__ ( snake_case__ , unittest.TestCase ):
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
__lowerCAmelCase = TFVisionTextDualEncoderModel.from_vision_text_pretrained(
"hf-internal-testing/tiny-random-vit" , "hf-internal-testing/tiny-random-bert" )
__lowerCAmelCase = 1_3
__lowerCAmelCase = floats_tensor(
[
batch_size,
model.vision_model.config.num_channels,
model.vision_model.config.image_size,
model.vision_model.config.image_size,
] )
__lowerCAmelCase = ids_tensor([batch_size, 4] , model.text_model.config.vocab_size )
__lowerCAmelCase = random_attention_mask([batch_size, 4] )
__lowerCAmelCase = {"pixel_values": pixel_values, "input_ids": input_ids, "attention_mask": attention_mask}
return model, inputs
def __SCREAMING_SNAKE_CASE( self , _A , _A ):
"""simple docstring"""
__lowerCAmelCase = TFViTModel(_A , name="vision_model" )
__lowerCAmelCase = TFBertModel(_A , name="text_model" )
return vision_model, text_model
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
__lowerCAmelCase = TFViTModelTester(self )
__lowerCAmelCase = TFBertModelTester(self )
__lowerCAmelCase = vit_model_tester.prepare_config_and_inputs()
__lowerCAmelCase = bert_model_tester.prepare_config_and_inputs()
__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase = vision_config_and_inputs
(
(
__lowerCAmelCase
) , (
__lowerCAmelCase
) , (
__lowerCAmelCase
) , (
__lowerCAmelCase
) , (
__lowerCAmelCase
) , (
__lowerCAmelCase
) , (
__lowerCAmelCase
) ,
) = text_config_and_inputs
return {
"text_config": text_config,
"vision_config": vision_config,
"pixel_values": pixel_values,
"attention_mask": input_mask,
"input_ids": input_ids,
"text_token_type_ids": token_type_ids,
"text_sequence_labels": sequence_labels,
"text_token_labels": token_labels,
"text_choice_labels": choice_labels,
}
@require_tf
class a__ ( snake_case__ , unittest.TestCase ):
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
__lowerCAmelCase = TFVisionTextDualEncoderModel.from_vision_text_pretrained(
"Rocketknight1/tiny-random-deit-tf" , "hf-internal-testing/tiny-random-roberta" )
__lowerCAmelCase = 1_3
__lowerCAmelCase = floats_tensor(
[
batch_size,
model.vision_model.config.num_channels,
model.vision_model.config.image_size,
model.vision_model.config.image_size,
] )
__lowerCAmelCase = ids_tensor([batch_size, 4] , model.text_model.config.vocab_size )
__lowerCAmelCase = random_attention_mask([batch_size, 4] )
__lowerCAmelCase = {"pixel_values": pixel_values, "input_ids": input_ids, "attention_mask": attention_mask}
return model, inputs
def __SCREAMING_SNAKE_CASE( self , _A , _A , _A , _A , _A=None , **_A ):
"""simple docstring"""
__lowerCAmelCase , __lowerCAmelCase = self.get_vision_text_model(_A , _A )
__lowerCAmelCase = TFVisionTextDualEncoderModel(vision_model=_A , text_model=_A )
__lowerCAmelCase = model(
input_ids=_A , pixel_values=_A , attention_mask=_A , output_attentions=_A )
__lowerCAmelCase = output.vision_model_output.attentions
self.assertEqual(len(_A ) , vision_config.num_hidden_layers )
# in DEiT, the seq_len equals the number of patches + 2 (we add 2 for the [CLS] and distillation tokens)
__lowerCAmelCase = to_atuple(vision_model.config.image_size )
__lowerCAmelCase = to_atuple(vision_model.config.patch_size )
__lowerCAmelCase = (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
__lowerCAmelCase = num_patches + 2
self.assertEqual(vision_attentions[0].shape[-3:] , (vision_config.num_attention_heads, seq_len, seq_len) )
__lowerCAmelCase = output.text_model_output.attentions
self.assertEqual(len(_A ) , text_config.num_hidden_layers )
self.assertEqual(
text_attentions[0].shape[-3:] , (text_config.num_attention_heads, input_ids.shape[-1], input_ids.shape[-1]) , )
def __SCREAMING_SNAKE_CASE( self , _A , _A ):
"""simple docstring"""
__lowerCAmelCase = TFDeiTModel(_A , name="vision_model" )
__lowerCAmelCase = TFRobertaModel(_A , name="text_model" )
return vision_model, text_model
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
__lowerCAmelCase = TFDeiTModelTester(self )
__lowerCAmelCase = TFRobertaModelTester(self )
__lowerCAmelCase = vit_model_tester.prepare_config_and_inputs()
__lowerCAmelCase = bert_model_tester.prepare_config_and_inputs()
__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase = vision_config_and_inputs
(
(
__lowerCAmelCase
) , (
__lowerCAmelCase
) , (
__lowerCAmelCase
) , (
__lowerCAmelCase
) , (
__lowerCAmelCase
) , (
__lowerCAmelCase
) , (
__lowerCAmelCase
) ,
) = text_config_and_inputs
return {
"text_config": text_config,
"vision_config": vision_config,
"pixel_values": pixel_values,
"attention_mask": input_mask,
"input_ids": input_ids,
"text_token_type_ids": token_type_ids,
"text_sequence_labels": sequence_labels,
"text_token_labels": token_labels,
"text_choice_labels": choice_labels,
}
@require_tf
class a__ ( snake_case__ , unittest.TestCase ):
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
__lowerCAmelCase = TFVisionTextDualEncoderModel.from_vision_text_pretrained(
"Rocketknight1/tiny-random-clip-tf" , "hf-internal-testing/tiny-random-bert" )
__lowerCAmelCase = 1_3
__lowerCAmelCase = floats_tensor(
[
batch_size,
model.vision_model.config.num_channels,
model.vision_model.config.image_size,
model.vision_model.config.image_size,
] )
__lowerCAmelCase = ids_tensor([batch_size, 4] , model.text_model.config.vocab_size )
__lowerCAmelCase = random_attention_mask([batch_size, 4] )
__lowerCAmelCase = {"pixel_values": pixel_values, "input_ids": input_ids, "attention_mask": attention_mask}
return model, inputs
def __SCREAMING_SNAKE_CASE( self , _A , _A ):
"""simple docstring"""
__lowerCAmelCase = TFCLIPVisionModel(_A , name="vision_model" )
__lowerCAmelCase = TFBertModel(_A , name="text_model" )
return vision_model, text_model
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
__lowerCAmelCase = TFCLIPVisionModelTester(self )
__lowerCAmelCase = TFBertModelTester(self )
__lowerCAmelCase = clip_model_tester.prepare_config_and_inputs()
__lowerCAmelCase = bert_model_tester.prepare_config_and_inputs()
__lowerCAmelCase , __lowerCAmelCase = vision_config_and_inputs
(
(
__lowerCAmelCase
) , (
__lowerCAmelCase
) , (
__lowerCAmelCase
) , (
__lowerCAmelCase
) , (
__lowerCAmelCase
) , (
__lowerCAmelCase
) , (
__lowerCAmelCase
) ,
) = text_config_and_inputs
return {
"text_config": text_config,
"vision_config": vision_config,
"pixel_values": pixel_values,
"attention_mask": input_mask,
"input_ids": input_ids,
"text_token_type_ids": token_type_ids,
"text_sequence_labels": sequence_labels,
"text_token_labels": token_labels,
"text_choice_labels": choice_labels,
}
@require_vision
@require_tf
class a__ ( unittest.TestCase ):
@slow
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
__lowerCAmelCase = TFVisionTextDualEncoderModel.from_pretrained(
"clip-italian/clip-italian" , logit_scale_init_value=1.0 , from_pt=_A )
__lowerCAmelCase = VisionTextDualEncoderProcessor.from_pretrained("clip-italian/clip-italian" )
__lowerCAmelCase = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png" )
__lowerCAmelCase = processor(
text=["una foto di un gatto", "una foto di un cane"] , images=_A , padding=_A , return_tensors="np" )
__lowerCAmelCase = model(**_A )
# verify the logits
self.assertEqual(outputs.logits_per_image.shape , (inputs.pixel_values.shape[0], inputs.input_ids.shape[0]) )
self.assertEqual(
outputs.logits_per_text.shape , (inputs.input_ids.shape[0], inputs.pixel_values.shape[0]) , )
__lowerCAmelCase = np.array([[1.2_28_47_27, 0.3_10_41_22]] )
self.assertTrue(np.allclose(outputs.logits_per_image.numpy() , _A , atol=1E-3 ) )
| 92 | 0 |
'''simple docstring'''
def a_ ( __snake_case : int = 1000 ) -> str:
"""simple docstring"""
lowerCamelCase_ =3
lowerCamelCase_ =0
while a < n:
if a % 3 == 0 or a % 5 == 0:
result += a
elif a % 15 == 0:
result -= a
a += 1
return result
if __name__ == "__main__":
print(F"""{solution() = }""")
| 75 |
import json
import os
import torch
from diffusers import UNetaDModel
os.makedirs("""hub/hopper-medium-v2/unet/hor32""", exist_ok=True)
os.makedirs("""hub/hopper-medium-v2/unet/hor128""", exist_ok=True)
os.makedirs("""hub/hopper-medium-v2/value_function""", exist_ok=True)
def _a ( SCREAMING_SNAKE_CASE_ : List[Any] ):
if hor == 1_28:
__lowerCAmelCase = ("DownResnetBlock1D", "DownResnetBlock1D", "DownResnetBlock1D")
__lowerCAmelCase = (32, 1_28, 2_56)
__lowerCAmelCase = ("UpResnetBlock1D", "UpResnetBlock1D")
elif hor == 32:
__lowerCAmelCase = ("DownResnetBlock1D", "DownResnetBlock1D", "DownResnetBlock1D", "DownResnetBlock1D")
__lowerCAmelCase = (32, 64, 1_28, 2_56)
__lowerCAmelCase = ("UpResnetBlock1D", "UpResnetBlock1D", "UpResnetBlock1D")
__lowerCAmelCase = torch.load(F"""/Users/bglickenhaus/Documents/diffuser/temporal_unet-hopper-mediumv2-hor{hor}.torch""" )
__lowerCAmelCase = model.state_dict()
__lowerCAmelCase = {
"down_block_types": down_block_types,
"block_out_channels": block_out_channels,
"up_block_types": up_block_types,
"layers_per_block": 1,
"use_timestep_embedding": True,
"out_block_type": "OutConv1DBlock",
"norm_num_groups": 8,
"downsample_each_block": False,
"in_channels": 14,
"out_channels": 14,
"extra_in_channels": 0,
"time_embedding_type": "positional",
"flip_sin_to_cos": False,
"freq_shift": 1,
"sample_size": 6_55_36,
"mid_block_type": "MidResTemporalBlock1D",
"act_fn": "mish",
}
__lowerCAmelCase = UNetaDModel(**SCREAMING_SNAKE_CASE_ )
print(F"""length of state dict: {len(state_dict.keys() )}""" )
print(F"""length of value function dict: {len(hf_value_function.state_dict().keys() )}""" )
__lowerCAmelCase = dict(zip(model.state_dict().keys() , hf_value_function.state_dict().keys() ) )
for k, v in mapping.items():
__lowerCAmelCase = state_dict.pop(SCREAMING_SNAKE_CASE_ )
hf_value_function.load_state_dict(SCREAMING_SNAKE_CASE_ )
torch.save(hf_value_function.state_dict() , F"""hub/hopper-medium-v2/unet/hor{hor}/diffusion_pytorch_model.bin""" )
with open(F"""hub/hopper-medium-v2/unet/hor{hor}/config.json""" , "w" ) as f:
json.dump(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
def _a ( ):
__lowerCAmelCase = {
"in_channels": 14,
"down_block_types": ("DownResnetBlock1D", "DownResnetBlock1D", "DownResnetBlock1D", "DownResnetBlock1D"),
"up_block_types": (),
"out_block_type": "ValueFunction",
"mid_block_type": "ValueFunctionMidBlock1D",
"block_out_channels": (32, 64, 1_28, 2_56),
"layers_per_block": 1,
"downsample_each_block": True,
"sample_size": 6_55_36,
"out_channels": 14,
"extra_in_channels": 0,
"time_embedding_type": "positional",
"use_timestep_embedding": True,
"flip_sin_to_cos": False,
"freq_shift": 1,
"norm_num_groups": 8,
"act_fn": "mish",
}
__lowerCAmelCase = torch.load("/Users/bglickenhaus/Documents/diffuser/value_function-hopper-mediumv2-hor32.torch" )
__lowerCAmelCase = model
__lowerCAmelCase = UNetaDModel(**SCREAMING_SNAKE_CASE_ )
print(F"""length of state dict: {len(state_dict.keys() )}""" )
print(F"""length of value function dict: {len(hf_value_function.state_dict().keys() )}""" )
__lowerCAmelCase = dict(zip(state_dict.keys() , hf_value_function.state_dict().keys() ) )
for k, v in mapping.items():
__lowerCAmelCase = state_dict.pop(SCREAMING_SNAKE_CASE_ )
hf_value_function.load_state_dict(SCREAMING_SNAKE_CASE_ )
torch.save(hf_value_function.state_dict() , "hub/hopper-medium-v2/value_function/diffusion_pytorch_model.bin" )
with open("hub/hopper-medium-v2/value_function/config.json" , "w" ) as f:
json.dump(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
if __name__ == "__main__":
unet(32)
# unet(128)
value_function()
| 92 | 0 |
"""simple docstring"""
import collections
import os
from typing import List, Optional, Tuple
from transformers.utils import is_jieba_available, requires_backends
if is_jieba_available():
import jieba
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
_UpperCamelCase : Any = logging.get_logger(__name__)
_UpperCamelCase : Any = {'vocab_file': 'vocab.txt'}
_UpperCamelCase : List[Any] = {
'vocab_file': {
'openbmb/cpm-ant-10b': 'https://huggingface.co/openbmb/cpm-ant-10b/blob/main/vocab.txt',
},
}
_UpperCamelCase : Union[str, Any] = {
'openbmb/cpm-ant-10b': 1_0_2_4,
}
def _SCREAMING_SNAKE_CASE ( __snake_case : Optional[Any] ):
'''simple docstring'''
lowercase = collections.OrderedDict()
with open(SCREAMING_SNAKE_CASE_ , 'r' , encoding='utf-8' ) as reader:
lowercase = reader.readlines()
for index, token in enumerate(SCREAMING_SNAKE_CASE_ ):
lowercase = token.rstrip('\n' )
lowercase = index
return vocab
class a ( snake_case__ ):
def __init__( self , _lowerCamelCase , _lowerCamelCase="<unk>" , _lowerCamelCase=2_0_0 ):
lowercase = vocab
lowercase = unk_token
lowercase = max_input_chars_per_word
def UpperCamelCase_ ( self , _lowerCamelCase ):
lowercase = list(_A )
if len(_A ) > self.max_input_chars_per_word:
return [self.unk_token]
lowercase = 0
lowercase = []
while start < len(_A ):
lowercase = len(_A )
lowercase = None
while start < end:
lowercase = ''.join(chars[start:end] )
if substr in self.vocab:
lowercase = substr
break
end -= 1
if cur_substr is None:
sub_tokens.append(self.unk_token )
start += 1
else:
sub_tokens.append(_A )
lowercase = end
return sub_tokens
class a ( snake_case__ ):
UpperCAmelCase_ : Any =VOCAB_FILES_NAMES
UpperCAmelCase_ : str =PRETRAINED_VOCAB_FILES_MAP
UpperCAmelCase_ : Optional[Any] =PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
UpperCAmelCase_ : Any =["""input_ids""", """attention_mask"""]
UpperCAmelCase_ : int =False
def __init__( self , _lowerCamelCase , _lowerCamelCase="<d>" , _lowerCamelCase="</d>" , _lowerCamelCase="<s>" , _lowerCamelCase="</s>" , _lowerCamelCase="<pad>" , _lowerCamelCase="<unk>" , _lowerCamelCase="</n>" , _lowerCamelCase="</_>" , _lowerCamelCase="left" , **_lowerCamelCase , ):
requires_backends(self , ['jieba'] )
super().__init__(
bod_token=_A , eod_token=_A , bos_token=_A , eos_token=_A , pad_token=_A , unk_token=_A , line_token=_A , space_token=_A , padding_side=_A , **_A , )
lowercase = bod_token
lowercase = eod_token
lowercase = load_vocab(_A )
lowercase = self.encoder[space_token]
lowercase = self.encoder[line_token]
del self.encoder[space_token]
del self.encoder[line_token]
lowercase = collections.OrderedDict(sorted(self.encoder.items() , key=lambda _lowerCamelCase : x[1] ) )
lowercase = {v: k for k, v in self.encoder.items()}
lowercase = WordpieceTokenizer(vocab=self.encoder , unk_token=self.unk_token )
@property
def UpperCamelCase_ ( self ):
return self.encoder[self.bod_token]
@property
def UpperCamelCase_ ( self ):
return self.encoder[self.eod_token]
@property
def UpperCamelCase_ ( self ):
return self.encoder["\n"]
@property
def UpperCamelCase_ ( self ):
return len(self.encoder )
def UpperCamelCase_ ( self ):
return dict(self.encoder , **self.added_tokens_encoder )
def UpperCamelCase_ ( self , _lowerCamelCase ):
lowercase = []
for x in jieba.cut(_A , cut_all=_A ):
output_tokens.extend(self.wordpiece_tokenizer.tokenize(_A ) )
return output_tokens
def UpperCamelCase_ ( self , _lowerCamelCase , **_lowerCamelCase ):
lowercase = [i for i in token_ids if i >= 0]
lowercase = [
x for x in token_ids if x != self.pad_token_id and x != self.eos_token_id and x != self.bos_token_id
]
return super()._decode(_A , **_A )
def UpperCamelCase_ ( self , _lowerCamelCase ):
return token in self.encoder
def UpperCamelCase_ ( self , _lowerCamelCase ):
return "".join(_A )
def UpperCamelCase_ ( self , _lowerCamelCase ):
return self.encoder.get(_A , self.encoder.get(self.unk_token ) )
def UpperCamelCase_ ( self , _lowerCamelCase ):
return self.decoder.get(_A , self.unk_token )
def UpperCamelCase_ ( self , _lowerCamelCase , _lowerCamelCase = None ):
if os.path.isdir(_A ):
lowercase = os.path.join(
_A , (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file'] )
else:
lowercase = (filename_prefix + '-' if filename_prefix else '') + save_directory
lowercase = 0
if " " in self.encoder:
lowercase = self.encoder[' ']
del self.encoder[" "]
if "\n" in self.encoder:
lowercase = self.encoder['\n']
del self.encoder["\n"]
lowercase = collections.OrderedDict(sorted(self.encoder.items() , key=lambda _lowerCamelCase : x[1] ) )
with open(_A , 'w' , encoding='utf-8' ) as writer:
for token, token_index in self.encoder.items():
if index != token_index:
logger.warning(
F'Saving vocabulary to {vocab_file}: vocabulary indices are not consecutive.'
' Please check that the vocabulary is not corrupted!' )
lowercase = token_index
writer.write(token + '\n' )
index += 1
return (vocab_file,)
def UpperCamelCase_ ( self , _lowerCamelCase , _lowerCamelCase = None ):
if token_ids_a is None:
return [self.bos_token_id] + token_ids_a
return [self.bos_token_id] + token_ids_a + [self.bos_token_id] + token_ids_a
def UpperCamelCase_ ( self , _lowerCamelCase , _lowerCamelCase = None , _lowerCamelCase = False ):
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=_A , token_ids_a=_A , already_has_special_tokens=_A )
if token_ids_a is not None:
return [1] + ([0] * len(_A )) + [1] + ([0] * len(_A ))
return [1] + ([0] * len(_A ))
| 220 |
import pytest
from datasets import inspect_metric, list_metrics, load_metric
@pytest.fixture
def _a ( SCREAMING_SNAKE_CASE_ : Optional[Any] ):
monkeypatch.setattr("datasets.utils.deprecation_utils._emitted_deprecation_warnings" , set() )
@pytest.fixture
def _a ( SCREAMING_SNAKE_CASE_ : List[Any] ):
class a__ :
def __init__( self , _A ):
"""simple docstring"""
__lowerCAmelCase = metric_id
class a__ :
_a : Optional[int] = [MetricMock(snake_case__ ) for metric_id in ["""accuracy""", """mse""", """precision""", """codeparrot/apps_metric"""]]
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
return self._metrics
monkeypatch.setattr("datasets.inspect.huggingface_hub" , HfhMock() )
@pytest.mark.parametrize(
"func, args" , [(load_metric, ("metrics/mse",)), (list_metrics, ()), (inspect_metric, ("metrics/mse", "tmp_path"))] )
def _a ( SCREAMING_SNAKE_CASE_ : Tuple , SCREAMING_SNAKE_CASE_ : List[str] , SCREAMING_SNAKE_CASE_ : int , SCREAMING_SNAKE_CASE_ : Optional[int] , SCREAMING_SNAKE_CASE_ : Optional[int] ):
if "tmp_path" in args:
__lowerCAmelCase = tuple(arg if arg != "tmp_path" else tmp_path for arg in args )
with pytest.warns(SCREAMING_SNAKE_CASE_ , match="https://huggingface.co/docs/evaluate" ):
func(*SCREAMING_SNAKE_CASE_ )
| 92 | 0 |
import json
import os
import unittest
from transformers import AutoTokenizer, GPTaTokenizer, GPTaTokenizerFast
from transformers.models.gpta.tokenization_gpta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class __A( snake_case__ , unittest.TestCase ):
snake_case_ = GPTaTokenizer
snake_case_ = GPTaTokenizerFast
snake_case_ = True
snake_case_ = {"""add_prefix_space""": True}
snake_case_ = False
def SCREAMING_SNAKE_CASE_ ( self ) -> Any:
'''simple docstring'''
super().setUp()
# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
__a = [
'''l''',
'''o''',
'''w''',
'''e''',
'''r''',
'''s''',
'''t''',
'''i''',
'''d''',
'''n''',
'''\u0120''',
'''\u0120l''',
'''\u0120n''',
'''\u0120lo''',
'''\u0120low''',
'''er''',
'''\u0120lowest''',
'''\u0120newer''',
'''\u0120wider''',
'''<unk>''',
'''<|endoftext|>''',
]
__a = dict(zip(_A , range(len(_A ) ) ) )
__a = ['''#version: 0.2''', '''\u0120 l''', '''\u0120l o''', '''\u0120lo w''', '''e r''', '''''']
__a = {'''unk_token''': '''<unk>'''}
__a = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['''vocab_file'''] )
__a = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['''merges_file'''] )
with open(self.vocab_file , '''w''' , encoding='''utf-8''' ) as fp:
fp.write(json.dumps(_A ) + '''\n''' )
with open(self.merges_file , '''w''' , encoding='''utf-8''' ) as fp:
fp.write('''\n'''.join(_A ) )
def SCREAMING_SNAKE_CASE_ ( self , **_snake_case ) -> str:
'''simple docstring'''
kwargs.update(self.special_tokens_map )
return GPTaTokenizer.from_pretrained(self.tmpdirname , **_A )
def SCREAMING_SNAKE_CASE_ ( self , **_snake_case ) -> Any:
'''simple docstring'''
kwargs.update(self.special_tokens_map )
return GPTaTokenizerFast.from_pretrained(self.tmpdirname , **_A )
def SCREAMING_SNAKE_CASE_ ( self , _snake_case ) -> Optional[Any]:
'''simple docstring'''
__a = '''lower newer'''
__a = '''lower newer'''
return input_text, output_text
def SCREAMING_SNAKE_CASE_ ( self ) -> int:
'''simple docstring'''
__a = GPTaTokenizer(self.vocab_file , self.merges_file , **self.special_tokens_map )
__a = '''lower newer'''
__a = ['''\u0120low''', '''er''', '''\u0120''', '''n''', '''e''', '''w''', '''er''']
__a = tokenizer.tokenize(_A , add_prefix_space=_A )
self.assertListEqual(_A , _A )
__a = tokens + [tokenizer.unk_token]
__a = [14, 15, 10, 9, 3, 2, 15, 19]
self.assertListEqual(tokenizer.convert_tokens_to_ids(_A ) , _A )
def SCREAMING_SNAKE_CASE_ ( self ) -> Tuple:
'''simple docstring'''
if not self.test_rust_tokenizer:
return
__a = self.get_tokenizer()
__a = self.get_rust_tokenizer(add_prefix_space=_A )
__a = '''lower newer'''
# Testing tokenization
__a = tokenizer.tokenize(_A , add_prefix_space=_A )
__a = rust_tokenizer.tokenize(_A )
self.assertListEqual(_A , _A )
# Testing conversion to ids without special tokens
__a = tokenizer.encode(_A , add_special_tokens=_A , add_prefix_space=_A )
__a = rust_tokenizer.encode(_A , add_special_tokens=_A )
self.assertListEqual(_A , _A )
# Testing conversion to ids with special tokens
__a = self.get_rust_tokenizer(add_prefix_space=_A )
__a = tokenizer.encode(_A , add_prefix_space=_A )
__a = rust_tokenizer.encode(_A )
self.assertListEqual(_A , _A )
# Testing the unknown token
__a = tokens + [rust_tokenizer.unk_token]
__a = [14, 15, 10, 9, 3, 2, 15, 19]
self.assertListEqual(rust_tokenizer.convert_tokens_to_ids(_A ) , _A )
def SCREAMING_SNAKE_CASE_ ( self , *_snake_case , **_snake_case ) -> Union[str, Any]:
'''simple docstring'''
pass
def SCREAMING_SNAKE_CASE_ ( self , _snake_case=15 ) -> List[str]:
'''simple docstring'''
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(F"""{tokenizer.__class__.__name__} ({pretrained_name})""" ):
__a = self.rust_tokenizer_class.from_pretrained(_A , **_A )
# Simple input
__a = '''This is a simple input'''
__a = ['''This is a simple input 1''', '''This is a simple input 2''']
__a = ('''This is a simple input''', '''This is a pair''')
__a = [
('''This is a simple input 1''', '''This is a simple input 2'''),
('''This is a simple pair 1''', '''This is a simple pair 2'''),
]
# Simple input tests
self.assertRaises(_A , tokenizer_r.encode , _A , max_length=_A , padding='''max_length''' )
# Simple input
self.assertRaises(_A , tokenizer_r.encode_plus , _A , max_length=_A , padding='''max_length''' )
# Simple input
self.assertRaises(
_A , tokenizer_r.batch_encode_plus , _A , max_length=_A , padding='''max_length''' , )
# Pair input
self.assertRaises(_A , tokenizer_r.encode , _A , max_length=_A , padding='''max_length''' )
# Pair input
self.assertRaises(_A , tokenizer_r.encode_plus , _A , max_length=_A , padding='''max_length''' )
# Pair input
self.assertRaises(
_A , tokenizer_r.batch_encode_plus , _A , max_length=_A , padding='''max_length''' , )
def SCREAMING_SNAKE_CASE_ ( self ) -> List[str]:
'''simple docstring'''
__a = GPTaTokenizer.from_pretrained(self.tmpdirname , pad_token='''<pad>''' )
# Simple input
__a = '''This is a simple input'''
__a = ['''This is a simple input looooooooong''', '''This is a simple input''']
__a = ('''This is a simple input''', '''This is a pair''')
__a = [
('''This is a simple input loooooong''', '''This is a simple input'''),
('''This is a simple pair loooooong''', '''This is a simple pair'''),
]
__a = tokenizer.pad_token_id
__a = tokenizer(_A , padding='''max_length''' , max_length=30 , return_tensors='''np''' )
__a = tokenizer(_A , padding=_A , truncate=_A , return_tensors='''np''' )
__a = tokenizer(*_A , padding='''max_length''' , max_length=60 , return_tensors='''np''' )
__a = tokenizer(_A , padding=_A , truncate=_A , return_tensors='''np''' )
# s
# test single string max_length padding
self.assertEqual(out_s['''input_ids'''].shape[-1] , 30 )
self.assertTrue(pad_token_id in out_s['''input_ids'''] )
self.assertTrue(0 in out_s['''attention_mask'''] )
# s2
# test automatic padding
self.assertEqual(out_sa['''input_ids'''].shape[-1] , 33 )
# long slice doesn't have padding
self.assertFalse(pad_token_id in out_sa['''input_ids'''][0] )
self.assertFalse(0 in out_sa['''attention_mask'''][0] )
# short slice does have padding
self.assertTrue(pad_token_id in out_sa['''input_ids'''][1] )
self.assertTrue(0 in out_sa['''attention_mask'''][1] )
# p
# test single pair max_length padding
self.assertEqual(out_p['''input_ids'''].shape[-1] , 60 )
self.assertTrue(pad_token_id in out_p['''input_ids'''] )
self.assertTrue(0 in out_p['''attention_mask'''] )
# p2
# test automatic padding pair
self.assertEqual(out_pa['''input_ids'''].shape[-1] , 52 )
# long slice pair doesn't have padding
self.assertFalse(pad_token_id in out_pa['''input_ids'''][0] )
self.assertFalse(0 in out_pa['''attention_mask'''][0] )
# short slice pair does have padding
self.assertTrue(pad_token_id in out_pa['''input_ids'''][1] )
self.assertTrue(0 in out_pa['''attention_mask'''][1] )
def SCREAMING_SNAKE_CASE_ ( self ) -> Optional[int]:
'''simple docstring'''
__a = '''$$$'''
__a = GPTaTokenizer.from_pretrained(self.tmpdirname , bos_token=_A , add_bos_token=_A )
__a = '''This is a simple input'''
__a = ['''This is a simple input 1''', '''This is a simple input 2''']
__a = tokenizer.bos_token_id
__a = tokenizer(_A )
__a = tokenizer(_A )
self.assertEqual(out_s.input_ids[0] , _A )
self.assertTrue(all(o[0] == bos_token_id for o in out_sa.input_ids ) )
__a = tokenizer.decode(out_s.input_ids )
__a = tokenizer.batch_decode(out_sa.input_ids )
self.assertEqual(decode_s.split()[0] , _A )
self.assertTrue(all(d.split()[0] == bos_token for d in decode_sa ) )
def SCREAMING_SNAKE_CASE_ ( self ) -> Any:
'''simple docstring'''
pass
def SCREAMING_SNAKE_CASE_ ( self ) -> Union[str, Any]:
'''simple docstring'''
__a = [self.get_tokenizer(do_lower_case=_A , add_bos_token=_A )]
for tokenizer in tokenizers:
with self.subTest(F"""{tokenizer.__class__.__name__}""" ):
__a = '''Encode this.'''
__a = '''This one too please.'''
__a = tokenizer.encode(_A , add_special_tokens=_A )
encoded_sequence += tokenizer.encode(_A , add_special_tokens=_A )
__a = tokenizer.encode_plus(
_A , _A , add_special_tokens=_A , return_special_tokens_mask=_A , )
__a = encoded_sequence_dict['''input_ids''']
__a = encoded_sequence_dict['''special_tokens_mask''']
self.assertEqual(len(_A ) , len(_A ) )
__a = [
(x if not special_tokens_mask[i] else None) for i, x in enumerate(_A )
]
__a = [x for x in filtered_sequence if x is not None]
self.assertEqual(_A , _A )
@require_tokenizers
class __A( unittest.TestCase ):
def SCREAMING_SNAKE_CASE_ ( self ) -> Union[str, Any]:
'''simple docstring'''
__a = AutoTokenizer.from_pretrained('''facebook/opt-350m''' , from_slow=_A )
__a = '''A photo of a cat'''
__a = tokenizer.encode(
_A , )
self.assertEqual(_A , [2, 250, 1_345, 9, 10, 4_758] )
tokenizer.save_pretrained('''test_opt''' )
__a = AutoTokenizer.from_pretrained('''./test_opt''' )
__a = tokenizer.encode(
_A , )
self.assertEqual(_A , [2, 250, 1_345, 9, 10, 4_758] )
def SCREAMING_SNAKE_CASE_ ( self ) -> int:
'''simple docstring'''
__a = AutoTokenizer.from_pretrained('''facebook/opt-350m''' , use_slow=_A )
__a = '''A photo of a cat'''
__a = tokenizer.encode(
_A , )
# Same as above
self.assertEqual(_A , [2, 250, 1_345, 9, 10, 4_758] )
@unittest.skip('''This test is failing because of a bug in the fast tokenizer''' )
def SCREAMING_SNAKE_CASE_ ( self ) -> List[str]:
'''simple docstring'''
__a = AutoTokenizer.from_pretrained('''facebook/opt-350m''' , from_slow=_A )
__a = '''bos'''
__a = tokenizer.get_vocab()['''bos''']
__a = '''A photo of a cat'''
__a = tokenizer.encode(
_A , )
# We changed the bos token
self.assertEqual(_A , [31_957, 250, 1_345, 9, 10, 4_758] )
tokenizer.save_pretrained('''./tok''' )
__a = AutoTokenizer.from_pretrained('''./tok''' )
self.assertTrue(tokenizer.is_fast )
__a = tokenizer.encode(
_A , )
self.assertEqual(_A , [31_957, 250, 1_345, 9, 10, 4_758] ) | 6 |
from random import randint
from tempfile import TemporaryFile
import numpy as np
def _a ( SCREAMING_SNAKE_CASE_ : int , SCREAMING_SNAKE_CASE_ : str , SCREAMING_SNAKE_CASE_ : List[str] ):
__lowerCAmelCase = 0
if start < end:
__lowerCAmelCase = randint(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
__lowerCAmelCase = a[end]
__lowerCAmelCase = a[pivot]
__lowerCAmelCase = temp
__lowerCAmelCase , __lowerCAmelCase = _in_place_partition(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
count += _in_place_quick_sort(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , p - 1 )
count += _in_place_quick_sort(SCREAMING_SNAKE_CASE_ , p + 1 , SCREAMING_SNAKE_CASE_ )
return count
def _a ( SCREAMING_SNAKE_CASE_ : str , SCREAMING_SNAKE_CASE_ : List[Any] , SCREAMING_SNAKE_CASE_ : Optional[int] ):
__lowerCAmelCase = 0
__lowerCAmelCase = randint(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
__lowerCAmelCase = a[end]
__lowerCAmelCase = a[pivot]
__lowerCAmelCase = temp
__lowerCAmelCase = start - 1
for index in range(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
count += 1
if a[index] < a[end]: # check if current val is less than pivot value
__lowerCAmelCase = new_pivot_index + 1
__lowerCAmelCase = a[new_pivot_index]
__lowerCAmelCase = a[index]
__lowerCAmelCase = temp
__lowerCAmelCase = a[new_pivot_index + 1]
__lowerCAmelCase = a[end]
__lowerCAmelCase = temp
return new_pivot_index + 1, count
UpperCamelCase__ = TemporaryFile()
UpperCamelCase__ = 100 # 1000 elements are to be sorted
UpperCamelCase__ , UpperCamelCase__ = 0, 1 # mean and standard deviation
UpperCamelCase__ = np.random.normal(mu, sigma, p)
np.save(outfile, X)
print("""The array is""")
print(X)
outfile.seek(0) # using the same array
UpperCamelCase__ = np.load(outfile)
UpperCamelCase__ = len(M) - 1
UpperCamelCase__ = _in_place_quick_sort(M, 0, r)
print(
"""No of Comparisons for 100 elements selected from a standard normal distribution"""
"""is :"""
)
print(z)
| 92 | 0 |
from __future__ import annotations
def a ( lowerCamelCase_ ):
'''simple docstring'''
if len(SCREAMING_SNAKE_CASE_ ) < 2:
raise ValueError('''Monogons and Digons are not polygons in the Euclidean space''' )
if any(i <= 0 for i in nums ):
raise ValueError('''All values must be greater than 0''' )
lowercase__ = nums.copy()
copy_nums.sort()
return copy_nums[-1] < sum(copy_nums[:-1] )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 207 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_speech_available, is_torch_available
UpperCamelCase__ = {
"""configuration_audio_spectrogram_transformer""": [
"""AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""ASTConfig""",
]
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCamelCase__ = [
"""AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""ASTForAudioClassification""",
"""ASTModel""",
"""ASTPreTrainedModel""",
]
try:
if not is_speech_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCamelCase__ = ["""ASTFeatureExtractor"""]
if TYPE_CHECKING:
from .configuration_audio_spectrogram_transformer import (
AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
ASTConfig,
)
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_audio_spectrogram_transformer import (
AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
ASTForAudioClassification,
ASTModel,
ASTPreTrainedModel,
)
try:
if not is_speech_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .feature_extraction_audio_spectrogram_transformer import ASTFeatureExtractor
else:
import sys
UpperCamelCase__ = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 92 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
lowerCAmelCase: Tuple = {'configuration_plbart': ['PLBART_PRETRAINED_CONFIG_ARCHIVE_MAP', 'PLBartConfig']}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase: Any = ['PLBartTokenizer']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase: int = [
'PLBART_PRETRAINED_MODEL_ARCHIVE_LIST',
'PLBartForCausalLM',
'PLBartForConditionalGeneration',
'PLBartForSequenceClassification',
'PLBartModel',
'PLBartPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_plbart import PLBART_PRETRAINED_CONFIG_ARCHIVE_MAP, PLBartConfig
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_plbart import PLBartTokenizer
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_plbart import (
PLBART_PRETRAINED_MODEL_ARCHIVE_LIST,
PLBartForCausalLM,
PLBartForConditionalGeneration,
PLBartForSequenceClassification,
PLBartModel,
PLBartPreTrainedModel,
)
else:
import sys
lowerCAmelCase: Any = _LazyModule(__name__, globals()['__file__'], _import_structure) | 297 |
import argparse
import os
import re
import packaging.version
UpperCamelCase__ = """examples/"""
UpperCamelCase__ = {
"""examples""": (re.compile(R"""^check_min_version\(\"[^\"]+\"\)\s*$""", re.MULTILINE), """check_min_version(\"VERSION\")\n"""),
"""init""": (re.compile(R"""^__version__\s+=\s+\"([^\"]+)\"\s*$""", re.MULTILINE), """__version__ = \"VERSION\"\n"""),
"""setup""": (re.compile(R"""^(\s*)version\s*=\s*\"[^\"]+\",""", re.MULTILINE), R"""\1version=\"VERSION\","""),
"""doc""": (re.compile(R"""^(\s*)release\s*=\s*\"[^\"]+\"$""", re.MULTILINE), """release = \"VERSION\"\n"""),
}
UpperCamelCase__ = {
"""init""": """src/transformers/__init__.py""",
"""setup""": """setup.py""",
}
UpperCamelCase__ = """README.md"""
def _a ( SCREAMING_SNAKE_CASE_ : Optional[int] , SCREAMING_SNAKE_CASE_ : Any , SCREAMING_SNAKE_CASE_ : List[str] ):
with open(SCREAMING_SNAKE_CASE_ , "r" , encoding="utf-8" , newline="\n" ) as f:
__lowerCAmelCase = f.read()
__lowerCAmelCase , __lowerCAmelCase = REPLACE_PATTERNS[pattern]
__lowerCAmelCase = replace.replace("VERSION" , SCREAMING_SNAKE_CASE_ )
__lowerCAmelCase = re_pattern.sub(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
with open(SCREAMING_SNAKE_CASE_ , "w" , encoding="utf-8" , newline="\n" ) as f:
f.write(SCREAMING_SNAKE_CASE_ )
def _a ( SCREAMING_SNAKE_CASE_ : List[Any] ):
for folder, directories, fnames in os.walk(SCREAMING_SNAKE_CASE_ ):
# Removing some of the folders with non-actively maintained examples from the walk
if "research_projects" in directories:
directories.remove("research_projects" )
if "legacy" in directories:
directories.remove("legacy" )
for fname in fnames:
if fname.endswith(".py" ):
update_version_in_file(os.path.join(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) , SCREAMING_SNAKE_CASE_ , pattern="examples" )
def _a ( SCREAMING_SNAKE_CASE_ : Tuple , SCREAMING_SNAKE_CASE_ : Optional[int]=False ):
for pattern, fname in REPLACE_FILES.items():
update_version_in_file(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
if not patch:
update_version_in_examples(SCREAMING_SNAKE_CASE_ )
def _a ( ):
__lowerCAmelCase = "🤗 Transformers currently provides the following architectures"
__lowerCAmelCase = "1. Want to contribute a new model?"
with open(SCREAMING_SNAKE_CASE_ , "r" , encoding="utf-8" , newline="\n" ) as f:
__lowerCAmelCase = f.readlines()
# Find the start of the list.
__lowerCAmelCase = 0
while not lines[start_index].startswith(_start_prompt ):
start_index += 1
start_index += 1
__lowerCAmelCase = start_index
# Update the lines in the model list.
while not lines[index].startswith(_end_prompt ):
if lines[index].startswith("1." ):
__lowerCAmelCase = lines[index].replace(
"https://huggingface.co/docs/transformers/main/model_doc" , "https://huggingface.co/docs/transformers/model_doc" , )
index += 1
with open(SCREAMING_SNAKE_CASE_ , "w" , encoding="utf-8" , newline="\n" ) as f:
f.writelines(SCREAMING_SNAKE_CASE_ )
def _a ( ):
with open(REPLACE_FILES["init"] , "r" ) as f:
__lowerCAmelCase = f.read()
__lowerCAmelCase = REPLACE_PATTERNS["init"][0].search(SCREAMING_SNAKE_CASE_ ).groups()[0]
return packaging.version.parse(SCREAMING_SNAKE_CASE_ )
def _a ( SCREAMING_SNAKE_CASE_ : List[Any]=False ):
__lowerCAmelCase = get_version()
if patch and default_version.is_devrelease:
raise ValueError("Can't create a patch version from the dev branch, checkout a released version!" )
if default_version.is_devrelease:
__lowerCAmelCase = default_version.base_version
elif patch:
__lowerCAmelCase = F"""{default_version.major}.{default_version.minor}.{default_version.micro + 1}"""
else:
__lowerCAmelCase = F"""{default_version.major}.{default_version.minor + 1}.0"""
# Now let's ask nicely if that's the right one.
__lowerCAmelCase = input(F"""Which version are you releasing? [{default_version}]""" )
if len(SCREAMING_SNAKE_CASE_ ) == 0:
__lowerCAmelCase = default_version
print(F"""Updating version to {version}.""" )
global_version_update(SCREAMING_SNAKE_CASE_ , patch=SCREAMING_SNAKE_CASE_ )
if not patch:
print("Cleaning main README, don't forget to run `make fix-copies`." )
clean_main_ref_in_model_list()
def _a ( ):
__lowerCAmelCase = get_version()
__lowerCAmelCase = F"""{current_version.major}.{current_version.minor + 1}.0.dev0"""
__lowerCAmelCase = current_version.base_version
# Check with the user we got that right.
__lowerCAmelCase = input(F"""Which version are we developing now? [{dev_version}]""" )
if len(SCREAMING_SNAKE_CASE_ ) == 0:
__lowerCAmelCase = dev_version
print(F"""Updating version to {version}.""" )
global_version_update(SCREAMING_SNAKE_CASE_ )
print("Cleaning main README, don't forget to run `make fix-copies`." )
clean_main_ref_in_model_list()
if __name__ == "__main__":
UpperCamelCase__ = argparse.ArgumentParser()
parser.add_argument("""--post_release""", action="""store_true""", help="""Whether this is pre or post release.""")
parser.add_argument("""--patch""", action="""store_true""", help="""Whether or not this is a patch release.""")
UpperCamelCase__ = parser.parse_args()
if not args.post_release:
pre_release_work(patch=args.patch)
elif args.patch:
print("""Nothing to do after a patch :-)""")
else:
post_release_work()
| 92 | 0 |
import argparse
import logging
import sys
from unittest.mock import patch
import run_glue_deebert
from transformers.testing_utils import TestCasePlus, get_gpu_count, require_torch_non_multi_gpu, slow
logging.basicConfig(level=logging.DEBUG)
UpperCAmelCase : Optional[Any] = logging.getLogger()
def __lowerCamelCase ( ):
'''simple docstring'''
lowerCamelCase = argparse.ArgumentParser()
parser.add_argument("""-f""" )
lowerCamelCase = parser.parse_args()
return args.f
class __lowercase ( snake_case__ ):
"""simple docstring"""
def __A ( self ) -> Dict:
'''simple docstring'''
lowerCamelCase = logging.StreamHandler(sys.stdout )
logger.addHandler(_A )
def __A ( self , A ) -> List[Any]:
'''simple docstring'''
lowerCamelCase = get_gpu_count()
if n_gpu > 1:
pass
# XXX: doesn't quite work with n_gpu > 1 https://github.com/huggingface/transformers/issues/10560
# script = f"{self.examples_dir_str}/research_projects/deebert/run_glue_deebert.py"
# distributed_args = f"-m torch.distributed.launch --nproc_per_node={n_gpu} {script}".split()
# cmd = [sys.executable] + distributed_args + args
# execute_subprocess_async(cmd, env=self.get_env())
# XXX: test the results - need to save them first into .json file
else:
args.insert(0 , """run_glue_deebert.py""" )
with patch.object(_A , """argv""" , _A ):
lowerCamelCase = run_glue_deebert.main()
for value in result.values():
self.assertGreaterEqual(_A , 0.666 )
@slow
@require_torch_non_multi_gpu
def __A ( self ) -> Tuple:
'''simple docstring'''
lowerCamelCase = """\n --model_type roberta\n --model_name_or_path roberta-base\n --task_name MRPC\n --do_train\n --do_eval\n --do_lower_case\n --data_dir ./tests/fixtures/tests_samples/MRPC/\n --max_seq_length 128\n --per_gpu_eval_batch_size=1\n --per_gpu_train_batch_size=8\n --learning_rate 2e-4\n --num_train_epochs 3\n --overwrite_output_dir\n --seed 42\n --output_dir ./examples/deebert/saved_models/roberta-base/MRPC/two_stage\n --plot_data_dir ./examples/deebert/results/\n --save_steps 0\n --overwrite_cache\n --eval_after_first_stage\n """.split()
self.run_and_check(_A )
lowerCamelCase = """\n --model_type roberta\n --model_name_or_path ./examples/deebert/saved_models/roberta-base/MRPC/two_stage\n --task_name MRPC\n --do_eval\n --do_lower_case\n --data_dir ./tests/fixtures/tests_samples/MRPC/\n --output_dir ./examples/deebert/saved_models/roberta-base/MRPC/two_stage\n --plot_data_dir ./examples/deebert/results/\n --max_seq_length 128\n --eval_each_highway\n --eval_highway\n --overwrite_cache\n --per_gpu_eval_batch_size=1\n """.split()
self.run_and_check(_A )
lowerCamelCase = """\n --model_type roberta\n --model_name_or_path ./examples/deebert/saved_models/roberta-base/MRPC/two_stage\n --task_name MRPC\n --do_eval\n --do_lower_case\n --data_dir ./tests/fixtures/tests_samples/MRPC/\n --output_dir ./examples/deebert/saved_models/roberta-base/MRPC/two_stage\n --plot_data_dir ./examples/deebert/results/\n --max_seq_length 128\n --early_exit_entropy 0.1\n --eval_highway\n --overwrite_cache\n --per_gpu_eval_batch_size=1\n """.split()
self.run_and_check(_A )
| 252 |
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import XLMRobertaTokenizerFast
from diffusers import DDIMScheduler, KandinskyImgaImgPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandinsky.text_encoder import MCLIPConfig, MultilingualCLIP
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu
from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference
enable_full_determinism()
class a__ ( snake_case__ , unittest.TestCase ):
_a : Dict = KandinskyImgaImgPipeline
_a : List[Any] = ["""prompt""", """image_embeds""", """negative_image_embeds""", """image"""]
_a : str = [
"""prompt""",
"""negative_prompt""",
"""image_embeds""",
"""negative_image_embeds""",
"""image""",
]
_a : List[Any] = [
"""generator""",
"""height""",
"""width""",
"""strength""",
"""guidance_scale""",
"""negative_prompt""",
"""num_inference_steps""",
"""return_dict""",
"""guidance_scale""",
"""num_images_per_prompt""",
"""output_type""",
"""return_dict""",
]
_a : int = False
@property
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
return 3_2
@property
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
return 3_2
@property
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
return self.time_input_dim
@property
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
return self.time_input_dim * 4
@property
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
return 1_0_0
@property
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
__lowerCAmelCase = XLMRobertaTokenizerFast.from_pretrained("YiYiXu/tiny-random-mclip-base" )
return tokenizer
@property
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
torch.manual_seed(0 )
__lowerCAmelCase = MCLIPConfig(
numDims=self.cross_attention_dim , transformerDimensions=self.text_embedder_hidden_size , hidden_size=self.text_embedder_hidden_size , intermediate_size=3_7 , num_attention_heads=4 , num_hidden_layers=5 , vocab_size=1_0_0_5 , )
__lowerCAmelCase = MultilingualCLIP(_A )
__lowerCAmelCase = text_encoder.eval()
return text_encoder
@property
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
torch.manual_seed(0 )
__lowerCAmelCase = {
"in_channels": 4,
# Out channels is double in channels because predicts mean and variance
"out_channels": 8,
"addition_embed_type": "text_image",
"down_block_types": ("ResnetDownsampleBlock2D", "SimpleCrossAttnDownBlock2D"),
"up_block_types": ("SimpleCrossAttnUpBlock2D", "ResnetUpsampleBlock2D"),
"mid_block_type": "UNetMidBlock2DSimpleCrossAttn",
"block_out_channels": (self.block_out_channels_a, self.block_out_channels_a * 2),
"layers_per_block": 1,
"encoder_hid_dim": self.text_embedder_hidden_size,
"encoder_hid_dim_type": "text_image_proj",
"cross_attention_dim": self.cross_attention_dim,
"attention_head_dim": 4,
"resnet_time_scale_shift": "scale_shift",
"class_embed_type": None,
}
__lowerCAmelCase = UNetaDConditionModel(**_A )
return model
@property
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
return {
"block_out_channels": [3_2, 6_4],
"down_block_types": ["DownEncoderBlock2D", "AttnDownEncoderBlock2D"],
"in_channels": 3,
"latent_channels": 4,
"layers_per_block": 1,
"norm_num_groups": 8,
"norm_type": "spatial",
"num_vq_embeddings": 1_2,
"out_channels": 3,
"up_block_types": [
"AttnUpDecoderBlock2D",
"UpDecoderBlock2D",
],
"vq_embed_dim": 4,
}
@property
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
torch.manual_seed(0 )
__lowerCAmelCase = VQModel(**self.dummy_movq_kwargs )
return model
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
__lowerCAmelCase = self.dummy_text_encoder
__lowerCAmelCase = self.dummy_tokenizer
__lowerCAmelCase = self.dummy_unet
__lowerCAmelCase = self.dummy_movq
__lowerCAmelCase = {
"num_train_timesteps": 1_0_0_0,
"beta_schedule": "linear",
"beta_start": 0.0_00_85,
"beta_end": 0.0_12,
"clip_sample": False,
"set_alpha_to_one": False,
"steps_offset": 0,
"prediction_type": "epsilon",
"thresholding": False,
}
__lowerCAmelCase = DDIMScheduler(**_A )
__lowerCAmelCase = {
"text_encoder": text_encoder,
"tokenizer": tokenizer,
"unet": unet,
"scheduler": scheduler,
"movq": movq,
}
return components
def __SCREAMING_SNAKE_CASE( self , _A , _A=0 ):
"""simple docstring"""
__lowerCAmelCase = floats_tensor((1, self.cross_attention_dim) , rng=random.Random(_A ) ).to(_A )
__lowerCAmelCase = floats_tensor((1, self.cross_attention_dim) , rng=random.Random(seed + 1 ) ).to(_A )
# create init_image
__lowerCAmelCase = floats_tensor((1, 3, 6_4, 6_4) , rng=random.Random(_A ) ).to(_A )
__lowerCAmelCase = image.cpu().permute(0 , 2 , 3 , 1 )[0]
__lowerCAmelCase = Image.fromarray(np.uinta(_A ) ).convert("RGB" ).resize((2_5_6, 2_5_6) )
if str(_A ).startswith("mps" ):
__lowerCAmelCase = torch.manual_seed(_A )
else:
__lowerCAmelCase = torch.Generator(device=_A ).manual_seed(_A )
__lowerCAmelCase = {
"prompt": "horse",
"image": init_image,
"image_embeds": image_embeds,
"negative_image_embeds": negative_image_embeds,
"generator": generator,
"height": 6_4,
"width": 6_4,
"num_inference_steps": 1_0,
"guidance_scale": 7.0,
"strength": 0.2,
"output_type": "np",
}
return inputs
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
__lowerCAmelCase = "cpu"
__lowerCAmelCase = self.get_dummy_components()
__lowerCAmelCase = self.pipeline_class(**_A )
__lowerCAmelCase = pipe.to(_A )
pipe.set_progress_bar_config(disable=_A )
__lowerCAmelCase = pipe(**self.get_dummy_inputs(_A ) )
__lowerCAmelCase = output.images
__lowerCAmelCase = pipe(
**self.get_dummy_inputs(_A ) , return_dict=_A , )[0]
__lowerCAmelCase = image[0, -3:, -3:, -1]
__lowerCAmelCase = image_from_tuple[0, -3:, -3:, -1]
assert image.shape == (1, 6_4, 6_4, 3)
__lowerCAmelCase = np.array(
[0.61_47_49_43, 0.6_07_35_39, 0.43_30_85_44, 0.5_92_82_69, 0.47_49_35_95, 0.46_75_59_73, 0.4_61_38_38, 0.45_36_87_97, 0.50_11_92_33] )
assert (
np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
), f""" expected_slice {expected_slice}, but got {image_slice.flatten()}"""
assert (
np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 1E-2
), f""" expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}"""
@slow
@require_torch_gpu
class a__ ( unittest.TestCase ):
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
__lowerCAmelCase = load_numpy(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
"/kandinsky/kandinsky_img2img_frog.npy" )
__lowerCAmelCase = load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main" "/kandinsky/cat.png" )
__lowerCAmelCase = "A red cartoon frog, 4k"
__lowerCAmelCase = KandinskyPriorPipeline.from_pretrained(
"kandinsky-community/kandinsky-2-1-prior" , torch_dtype=torch.floataa )
pipe_prior.to(_A )
__lowerCAmelCase = KandinskyImgaImgPipeline.from_pretrained(
"kandinsky-community/kandinsky-2-1" , torch_dtype=torch.floataa )
__lowerCAmelCase = pipeline.to(_A )
pipeline.set_progress_bar_config(disable=_A )
__lowerCAmelCase = torch.Generator(device="cpu" ).manual_seed(0 )
__lowerCAmelCase , __lowerCAmelCase = pipe_prior(
_A , generator=_A , num_inference_steps=5 , negative_prompt="" , ).to_tuple()
__lowerCAmelCase = pipeline(
_A , image=_A , image_embeds=_A , negative_image_embeds=_A , generator=_A , num_inference_steps=1_0_0 , height=7_6_8 , width=7_6_8 , strength=0.2 , output_type="np" , )
__lowerCAmelCase = output.images[0]
assert image.shape == (7_6_8, 7_6_8, 3)
assert_mean_pixel_difference(_A , _A )
| 92 | 0 |
'''simple docstring'''
import numpy as np
def __snake_case( _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase = 1e-12 , _lowerCAmelCase = 100 , ) -> Optional[int]:
assert np.shape(SCREAMING_SNAKE_CASE_ )[0] == np.shape(SCREAMING_SNAKE_CASE_ )[1]
# Ensure proper dimensionality.
assert np.shape(SCREAMING_SNAKE_CASE_ )[0] == np.shape(SCREAMING_SNAKE_CASE_ )[0]
# Ensure inputs are either both complex or both real
assert np.iscomplexobj(SCREAMING_SNAKE_CASE_ ) == np.iscomplexobj(SCREAMING_SNAKE_CASE_ )
snake_case__ : Union[str, Any] = np.iscomplexobj(SCREAMING_SNAKE_CASE_ )
if is_complex:
# Ensure complex input_matrix is Hermitian
assert np.array_equal(SCREAMING_SNAKE_CASE_ , input_matrix.conj().T )
# Set convergence to False. Will define convergence when we exceed max_iterations
# or when we have small changes from one iteration to next.
snake_case__ : int = False
snake_case__ : Optional[int] = 0
snake_case__ : Optional[int] = 0
snake_case__ : Optional[Any] = 1e12
while not convergence:
# Multiple matrix by the vector.
snake_case__ : Union[str, Any] = np.dot(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
# Normalize the resulting output vector.
snake_case__ : int = w / np.linalg.norm(SCREAMING_SNAKE_CASE_ )
# Find rayleigh quotient
# (faster than usual b/c we know vector is normalized already)
snake_case__ : str = vector.conj().T if is_complex else vector.T
snake_case__ : Union[str, Any] = np.dot(SCREAMING_SNAKE_CASE_ , np.dot(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) )
# Check convergence.
snake_case__ : Tuple = np.abs(lambda_ - lambda_previous ) / lambda_
iterations += 1
if error <= error_tol or iterations >= max_iterations:
snake_case__ : Union[str, Any] = True
snake_case__ : Union[str, Any] = lambda_
if is_complex:
snake_case__ : Any = np.real(lambda_ )
return lambda_, vector
def __snake_case( ) -> Tuple:
snake_case__ : List[Any] = np.array([[41, 4, 20], [4, 26, 30], [20, 30, 50]] )
snake_case__ : Optional[int] = np.array([41, 4, 20] )
snake_case__ : List[Any] = real_input_matrix.astype(np.complexaaa )
snake_case__ : Tuple = np.triu(1J * complex_input_matrix , 1 )
complex_input_matrix += imag_matrix
complex_input_matrix += -1 * imag_matrix.T
snake_case__ : Tuple = np.array([41, 4, 20] ).astype(np.complexaaa )
for problem_type in ["real", "complex"]:
if problem_type == "real":
snake_case__ : Any = real_input_matrix
snake_case__ : List[str] = real_vector
elif problem_type == "complex":
snake_case__ : Tuple = complex_input_matrix
snake_case__ : int = complex_vector
# Our implementation.
snake_case__ , snake_case__ : Optional[int] = power_iteration(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
# Numpy implementation.
# Get eigenvalues and eigenvectors using built-in numpy
# eigh (eigh used for symmetric or hermetian matrices).
snake_case__ , snake_case__ : int = np.linalg.eigh(SCREAMING_SNAKE_CASE_ )
# Last eigenvalue is the maximum one.
snake_case__ : Union[str, Any] = eigen_values[-1]
# Last column in this matrix is eigenvector corresponding to largest eigenvalue.
snake_case__ : Union[str, Any] = eigen_vectors[:, -1]
# Check our implementation and numpy gives close answers.
assert np.abs(eigen_value - eigen_value_max ) <= 1e-6
# Take absolute values element wise of each eigenvector.
# as they are only unique to a minus sign.
assert np.linalg.norm(np.abs(SCREAMING_SNAKE_CASE_ ) - np.abs(SCREAMING_SNAKE_CASE_ ) ) <= 1e-6
if __name__ == "__main__":
import doctest
doctest.testmod()
test_power_iteration()
| 35 |
class a__ ( snake_case__ ):
pass
class a__ ( snake_case__ ):
pass
class a__ :
def __init__( self ):
"""simple docstring"""
__lowerCAmelCase = [
[],
[],
[],
]
def __SCREAMING_SNAKE_CASE( self , _A , _A ):
"""simple docstring"""
try:
if len(self.queues[priority] ) >= 1_0_0:
raise OverflowError("Maximum queue size is 100" )
self.queues[priority].append(_A )
except IndexError:
raise ValueError("Valid priorities are 0, 1, and 2" )
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
for queue in self.queues:
if queue:
return queue.pop(0 )
raise UnderFlowError("All queues are empty" )
def __str__( self ):
"""simple docstring"""
return "\n".join(f"""Priority {i}: {q}""" for i, q in enumerate(self.queues ) )
class a__ :
def __init__( self ):
"""simple docstring"""
__lowerCAmelCase = []
def __SCREAMING_SNAKE_CASE( self , _A ):
"""simple docstring"""
if len(self.queue ) == 1_0_0:
raise OverFlowError("Maximum queue size is 100" )
self.queue.append(_A )
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
if not self.queue:
raise UnderFlowError("The queue is empty" )
else:
__lowerCAmelCase = min(self.queue )
self.queue.remove(_A )
return data
def __str__( self ):
"""simple docstring"""
return str(self.queue )
def _a ( ):
__lowerCAmelCase = FixedPriorityQueue()
fpq.enqueue(0 , 10 )
fpq.enqueue(1 , 70 )
fpq.enqueue(0 , 1_00 )
fpq.enqueue(2 , 1 )
fpq.enqueue(2 , 5 )
fpq.enqueue(1 , 7 )
fpq.enqueue(2 , 4 )
fpq.enqueue(1 , 64 )
fpq.enqueue(0 , 1_28 )
print(SCREAMING_SNAKE_CASE_ )
print(fpq.dequeue() )
print(fpq.dequeue() )
print(fpq.dequeue() )
print(fpq.dequeue() )
print(fpq.dequeue() )
print(SCREAMING_SNAKE_CASE_ )
print(fpq.dequeue() )
print(fpq.dequeue() )
print(fpq.dequeue() )
print(fpq.dequeue() )
print(fpq.dequeue() )
def _a ( ):
__lowerCAmelCase = ElementPriorityQueue()
epq.enqueue(10 )
epq.enqueue(70 )
epq.enqueue(1_00 )
epq.enqueue(1 )
epq.enqueue(5 )
epq.enqueue(7 )
epq.enqueue(4 )
epq.enqueue(64 )
epq.enqueue(1_28 )
print(SCREAMING_SNAKE_CASE_ )
print(epq.dequeue() )
print(epq.dequeue() )
print(epq.dequeue() )
print(epq.dequeue() )
print(epq.dequeue() )
print(SCREAMING_SNAKE_CASE_ )
print(epq.dequeue() )
print(epq.dequeue() )
print(epq.dequeue() )
print(epq.dequeue() )
print(epq.dequeue() )
if __name__ == "__main__":
fixed_priority_queue()
element_priority_queue()
| 92 | 0 |
'''simple docstring'''
def snake_case ( UpperCAmelCase )-> List[Any]:
"""simple docstring"""
__A , __A = [], []
while len(SCREAMING_SNAKE_CASE_ ) > 1:
__A , __A = min(SCREAMING_SNAKE_CASE_ ), max(SCREAMING_SNAKE_CASE_ )
start.append(SCREAMING_SNAKE_CASE_ )
end.append(SCREAMING_SNAKE_CASE_ )
collection.remove(SCREAMING_SNAKE_CASE_ )
collection.remove(SCREAMING_SNAKE_CASE_ )
end.reverse()
return start + collection + end
if __name__ == "__main__":
a__ : str = input("Enter numbers separated by a comma:\n").strip()
a__ : List[str] = [int(item) for item in user_input.split(",")]
print(*merge_sort(unsorted), sep=",")
| 161 |
import inspect
import unittest
import warnings
from transformers import DeiTConfig
from transformers.models.auto import get_values
from transformers.testing_utils import (
require_accelerate,
require_torch,
require_torch_gpu,
require_vision,
slow,
torch_device,
)
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from torch import nn
from transformers import (
MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
MODEL_MAPPING,
DeiTForImageClassification,
DeiTForImageClassificationWithTeacher,
DeiTForMaskedImageModeling,
DeiTModel,
)
from transformers.models.deit.modeling_deit import DEIT_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import DeiTImageProcessor
class a__ :
def __init__( self , _A , _A=1_3 , _A=3_0 , _A=2 , _A=3 , _A=True , _A=True , _A=3_2 , _A=5 , _A=4 , _A=3_7 , _A="gelu" , _A=0.1 , _A=0.1 , _A=1_0 , _A=0.02 , _A=3 , _A=None , _A=2 , ):
"""simple docstring"""
__lowerCAmelCase = parent
__lowerCAmelCase = batch_size
__lowerCAmelCase = image_size
__lowerCAmelCase = patch_size
__lowerCAmelCase = num_channels
__lowerCAmelCase = is_training
__lowerCAmelCase = use_labels
__lowerCAmelCase = hidden_size
__lowerCAmelCase = num_hidden_layers
__lowerCAmelCase = num_attention_heads
__lowerCAmelCase = intermediate_size
__lowerCAmelCase = hidden_act
__lowerCAmelCase = hidden_dropout_prob
__lowerCAmelCase = attention_probs_dropout_prob
__lowerCAmelCase = type_sequence_label_size
__lowerCAmelCase = initializer_range
__lowerCAmelCase = scope
__lowerCAmelCase = encoder_stride
# in DeiT, the seq length equals the number of patches + 2 (we add 2 for the [CLS] and distilation tokens)
__lowerCAmelCase = (image_size // patch_size) ** 2
__lowerCAmelCase = num_patches + 2
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
__lowerCAmelCase = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
__lowerCAmelCase = None
if self.use_labels:
__lowerCAmelCase = ids_tensor([self.batch_size] , self.type_sequence_label_size )
__lowerCAmelCase = self.get_config()
return config, pixel_values, labels
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
return DeiTConfig(
image_size=self.image_size , patch_size=self.patch_size , num_channels=self.num_channels , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , is_decoder=_A , initializer_range=self.initializer_range , encoder_stride=self.encoder_stride , )
def __SCREAMING_SNAKE_CASE( self , _A , _A , _A ):
"""simple docstring"""
__lowerCAmelCase = DeiTModel(config=_A )
model.to(_A )
model.eval()
__lowerCAmelCase = model(_A )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def __SCREAMING_SNAKE_CASE( self , _A , _A , _A ):
"""simple docstring"""
__lowerCAmelCase = DeiTForMaskedImageModeling(config=_A )
model.to(_A )
model.eval()
__lowerCAmelCase = model(_A )
self.parent.assertEqual(
result.reconstruction.shape , (self.batch_size, self.num_channels, self.image_size, self.image_size) )
# test greyscale images
__lowerCAmelCase = 1
__lowerCAmelCase = DeiTForMaskedImageModeling(_A )
model.to(_A )
model.eval()
__lowerCAmelCase = floats_tensor([self.batch_size, 1, self.image_size, self.image_size] )
__lowerCAmelCase = model(_A )
self.parent.assertEqual(result.reconstruction.shape , (self.batch_size, 1, self.image_size, self.image_size) )
def __SCREAMING_SNAKE_CASE( self , _A , _A , _A ):
"""simple docstring"""
__lowerCAmelCase = self.type_sequence_label_size
__lowerCAmelCase = DeiTForImageClassification(_A )
model.to(_A )
model.eval()
__lowerCAmelCase = model(_A , labels=_A )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size) )
# test greyscale images
__lowerCAmelCase = 1
__lowerCAmelCase = DeiTForImageClassification(_A )
model.to(_A )
model.eval()
__lowerCAmelCase = floats_tensor([self.batch_size, 1, self.image_size, self.image_size] )
__lowerCAmelCase = model(_A , labels=_A )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size) )
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
__lowerCAmelCase = self.prepare_config_and_inputs()
(
(
__lowerCAmelCase
) , (
__lowerCAmelCase
) , (
__lowerCAmelCase
) ,
) = config_and_inputs
__lowerCAmelCase = {"pixel_values": pixel_values}
return config, inputs_dict
@require_torch
class a__ ( snake_case__ , snake_case__ , unittest.TestCase ):
_a : Optional[Any] = (
(
DeiTModel,
DeiTForImageClassification,
DeiTForImageClassificationWithTeacher,
DeiTForMaskedImageModeling,
)
if is_torch_available()
else ()
)
_a : int = (
{
"""feature-extraction""": DeiTModel,
"""image-classification""": (DeiTForImageClassification, DeiTForImageClassificationWithTeacher),
}
if is_torch_available()
else {}
)
_a : Optional[Any] = False
_a : Tuple = False
_a : Tuple = False
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
__lowerCAmelCase = DeiTModelTester(self )
__lowerCAmelCase = ConfigTester(self , config_class=_A , has_text_modality=_A , hidden_size=3_7 )
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
self.config_tester.run_common_tests()
@unittest.skip(reason="DeiT does not use inputs_embeds" )
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
pass
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
__lowerCAmelCase , __lowerCAmelCase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
__lowerCAmelCase = model_class(_A )
self.assertIsInstance(model.get_input_embeddings() , (nn.Module) )
__lowerCAmelCase = model.get_output_embeddings()
self.assertTrue(x is None or isinstance(_A , nn.Linear ) )
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
__lowerCAmelCase , __lowerCAmelCase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
__lowerCAmelCase = model_class(_A )
__lowerCAmelCase = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
__lowerCAmelCase = [*signature.parameters.keys()]
__lowerCAmelCase = ["pixel_values"]
self.assertListEqual(arg_names[:1] , _A )
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
__lowerCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*_A )
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
__lowerCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_image_modeling(*_A )
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
__lowerCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*_A )
def __SCREAMING_SNAKE_CASE( self , _A , _A , _A=False ):
"""simple docstring"""
__lowerCAmelCase = super()._prepare_for_class(_A , _A , return_labels=_A )
if return_labels:
if model_class.__name__ == "DeiTForImageClassificationWithTeacher":
del inputs_dict["labels"]
return inputs_dict
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
if not self.model_tester.is_training:
return
__lowerCAmelCase , __lowerCAmelCase = self.model_tester.prepare_config_and_inputs_for_common()
__lowerCAmelCase = True
for model_class in self.all_model_classes:
# DeiTForImageClassificationWithTeacher supports inference-only
if (
model_class in get_values(_A )
or model_class.__name__ == "DeiTForImageClassificationWithTeacher"
):
continue
__lowerCAmelCase = model_class(_A )
model.to(_A )
model.train()
__lowerCAmelCase = self._prepare_for_class(_A , _A , return_labels=_A )
__lowerCAmelCase = model(**_A ).loss
loss.backward()
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
__lowerCAmelCase , __lowerCAmelCase = self.model_tester.prepare_config_and_inputs_for_common()
if not self.model_tester.is_training:
return
__lowerCAmelCase = False
__lowerCAmelCase = True
for model_class in self.all_model_classes:
if model_class in get_values(_A ) or not model_class.supports_gradient_checkpointing:
continue
# DeiTForImageClassificationWithTeacher supports inference-only
if model_class.__name__ == "DeiTForImageClassificationWithTeacher":
continue
__lowerCAmelCase = model_class(_A )
model.gradient_checkpointing_enable()
model.to(_A )
model.train()
__lowerCAmelCase = self._prepare_for_class(_A , _A , return_labels=_A )
__lowerCAmelCase = model(**_A ).loss
loss.backward()
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
__lowerCAmelCase , __lowerCAmelCase = self.model_tester.prepare_config_and_inputs_for_common()
__lowerCAmelCase = [
{"title": "multi_label_classification", "num_labels": 2, "dtype": torch.float},
{"title": "single_label_classification", "num_labels": 1, "dtype": torch.long},
{"title": "regression", "num_labels": 1, "dtype": torch.float},
]
for model_class in self.all_model_classes:
if (
model_class
not in [
*get_values(_A ),
*get_values(_A ),
]
or model_class.__name__ == "DeiTForImageClassificationWithTeacher"
):
continue
for problem_type in problem_types:
with self.subTest(msg=f"""Testing {model_class} with {problem_type['title']}""" ):
__lowerCAmelCase = problem_type["title"]
__lowerCAmelCase = problem_type["num_labels"]
__lowerCAmelCase = model_class(_A )
model.to(_A )
model.train()
__lowerCAmelCase = self._prepare_for_class(_A , _A , return_labels=_A )
if problem_type["num_labels"] > 1:
__lowerCAmelCase = inputs["labels"].unsqueeze(1 ).repeat(1 , problem_type["num_labels"] )
__lowerCAmelCase = inputs["labels"].to(problem_type["dtype"] )
# This tests that we do not trigger the warning form PyTorch "Using a target size that is different
# to the input size. This will likely lead to incorrect results due to broadcasting. Please ensure
# they have the same size." which is a symptom something in wrong for the regression problem.
# See https://github.com/huggingface/transformers/issues/11780
with warnings.catch_warnings(record=_A ) as warning_list:
__lowerCAmelCase = model(**_A ).loss
for w in warning_list:
if "Using a target size that is different to the input size" in str(w.message ):
raise ValueError(
f"""Something is going wrong in the regression problem: intercepted {w.message}""" )
loss.backward()
@slow
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
for model_name in DEIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
__lowerCAmelCase = DeiTModel.from_pretrained(_A )
self.assertIsNotNone(_A )
def _a ( ):
__lowerCAmelCase = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png" )
return image
@require_torch
@require_vision
class a__ ( unittest.TestCase ):
@cached_property
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
return (
DeiTImageProcessor.from_pretrained("facebook/deit-base-distilled-patch16-224" )
if is_vision_available()
else None
)
@slow
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
__lowerCAmelCase = DeiTForImageClassificationWithTeacher.from_pretrained("facebook/deit-base-distilled-patch16-224" ).to(
_A )
__lowerCAmelCase = self.default_image_processor
__lowerCAmelCase = prepare_img()
__lowerCAmelCase = image_processor(images=_A , return_tensors="pt" ).to(_A )
# forward pass
with torch.no_grad():
__lowerCAmelCase = model(**_A )
# verify the logits
__lowerCAmelCase = torch.Size((1, 1_0_0_0) )
self.assertEqual(outputs.logits.shape , _A )
__lowerCAmelCase = torch.tensor([-1.02_66, 0.19_12, -1.28_61] ).to(_A )
self.assertTrue(torch.allclose(outputs.logits[0, :3] , _A , atol=1E-4 ) )
@slow
@require_accelerate
@require_torch_gpu
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
__lowerCAmelCase = DeiTModel.from_pretrained(
"facebook/deit-base-distilled-patch16-224" , torch_dtype=torch.floataa , device_map="auto" )
__lowerCAmelCase = self.default_image_processor
__lowerCAmelCase = prepare_img()
__lowerCAmelCase = image_processor(images=_A , return_tensors="pt" )
__lowerCAmelCase = inputs.pixel_values.to(_A )
# forward pass to make sure inference works in fp16
with torch.no_grad():
__lowerCAmelCase = model(_A )
| 92 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
lowerCAmelCase__ = {
'''configuration_rembert''': ['''REMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''RemBertConfig''', '''RemBertOnnxConfig''']
}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase__ = ['''RemBertTokenizer''']
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase__ = ['''RemBertTokenizerFast''']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase__ = [
'''REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''RemBertForCausalLM''',
'''RemBertForMaskedLM''',
'''RemBertForMultipleChoice''',
'''RemBertForQuestionAnswering''',
'''RemBertForSequenceClassification''',
'''RemBertForTokenClassification''',
'''RemBertLayer''',
'''RemBertModel''',
'''RemBertPreTrainedModel''',
'''load_tf_weights_in_rembert''',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase__ = [
'''TF_REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''TFRemBertForCausalLM''',
'''TFRemBertForMaskedLM''',
'''TFRemBertForMultipleChoice''',
'''TFRemBertForQuestionAnswering''',
'''TFRemBertForSequenceClassification''',
'''TFRemBertForTokenClassification''',
'''TFRemBertLayer''',
'''TFRemBertModel''',
'''TFRemBertPreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_rembert import REMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, RemBertConfig, RemBertOnnxConfig
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_rembert import RemBertTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_rembert_fast import RemBertTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_rembert import (
REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
RemBertForCausalLM,
RemBertForMaskedLM,
RemBertForMultipleChoice,
RemBertForQuestionAnswering,
RemBertForSequenceClassification,
RemBertForTokenClassification,
RemBertLayer,
RemBertModel,
RemBertPreTrainedModel,
load_tf_weights_in_rembert,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_rembert import (
TF_REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
TFRemBertForCausalLM,
TFRemBertForMaskedLM,
TFRemBertForMultipleChoice,
TFRemBertForQuestionAnswering,
TFRemBertForSequenceClassification,
TFRemBertForTokenClassification,
TFRemBertLayer,
TFRemBertModel,
TFRemBertPreTrainedModel,
)
else:
import sys
lowerCAmelCase__ = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 153 |
def _a ( SCREAMING_SNAKE_CASE_ : int = 1_00_00_00 ):
__lowerCAmelCase = [i - 1 for i in range(limit + 1 )]
for i in range(2 , limit + 1 ):
if phi[i] == i - 1:
for j in range(2 * i , limit + 1 , SCREAMING_SNAKE_CASE_ ):
phi[j] -= phi[j] // i
return sum(phi[2 : limit + 1] )
if __name__ == "__main__":
print(solution())
| 92 | 0 |
"""simple docstring"""
from typing import Any, Dict, List, Union
from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, ChunkPipeline
if is_vision_available():
from PIL import Image
from ..image_utils import load_image
if is_torch_available():
import torch
from transformers.modeling_outputs import BaseModelOutput
from ..models.auto.modeling_auto import MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING
__snake_case = logging.get_logger(__name__)
@add_end_docstrings(snake_case__ )
class _lowerCAmelCase ( snake_case__ ):
def __init__( self , **UpperCamelCase__ ) -> Optional[int]:
'''simple docstring'''
super().__init__(**_A )
if self.framework == "tf":
raise ValueError(F'The {self.__class__} is only available in PyTorch.' )
requires_backends(self , "vision" )
self.check_model_type(_A )
def __call__( self , UpperCamelCase__ , UpperCamelCase__ = None , **UpperCamelCase__ , ) -> Union[str, Any]:
'''simple docstring'''
if "text_queries" in kwargs:
snake_case : Dict = kwargs.pop("text_queries" )
if isinstance(_A , (str, Image.Image) ):
snake_case : Optional[int] = {"image": image, "candidate_labels": candidate_labels}
else:
snake_case : str = image
snake_case : Any = super().__call__(_A , **_A )
return results
def lowerCamelCase ( self , **UpperCamelCase__ ) -> Optional[int]:
'''simple docstring'''
snake_case : Optional[int] = {}
if "threshold" in kwargs:
snake_case : Any = kwargs["threshold"]
if "top_k" in kwargs:
snake_case : Dict = kwargs["top_k"]
return {}, {}, postprocess_params
def lowerCamelCase ( self , UpperCamelCase__ ) -> List[str]:
'''simple docstring'''
snake_case : int = load_image(inputs["image"] )
snake_case : Union[str, Any] = inputs["candidate_labels"]
if isinstance(_A , _A ):
snake_case : List[str] = candidate_labels.split("," )
snake_case : List[str] = torch.tensor([[image.height, image.width]] , dtype=torch.intaa )
for i, candidate_label in enumerate(_A ):
snake_case : Union[str, Any] = self.tokenizer(_A , return_tensors=self.framework )
snake_case : Optional[int] = self.image_processor(_A , return_tensors=self.framework )
yield {
"is_last": i == len(_A ) - 1,
"target_size": target_size,
"candidate_label": candidate_label,
**text_inputs,
**image_features,
}
def lowerCamelCase ( self , UpperCamelCase__ ) -> Any:
'''simple docstring'''
snake_case : Optional[int] = model_inputs.pop("target_size" )
snake_case : Union[str, Any] = model_inputs.pop("candidate_label" )
snake_case : Tuple = model_inputs.pop("is_last" )
snake_case : str = self.model(**_A )
snake_case : Dict = {"target_size": target_size, "candidate_label": candidate_label, "is_last": is_last, **outputs}
return model_outputs
def lowerCamelCase ( self , UpperCamelCase__ , UpperCamelCase__=0.1 , UpperCamelCase__=None ) -> List[Any]:
'''simple docstring'''
snake_case : Union[str, Any] = []
for model_output in model_outputs:
snake_case : Optional[Any] = model_output["candidate_label"]
snake_case : Dict = BaseModelOutput(_A )
snake_case : int = self.image_processor.post_process_object_detection(
outputs=_A , threshold=_A , target_sizes=model_output["target_size"] )[0]
for index in outputs["scores"].nonzero():
snake_case : Any = outputs["scores"][index].item()
snake_case : List[Any] = self._get_bounding_box(outputs["boxes"][index][0] )
snake_case : Optional[int] = {"score": score, "label": label, "box": box}
results.append(_A )
snake_case : Optional[Any] = sorted(_A , key=lambda UpperCamelCase__ : x["score"] , reverse=_A )
if top_k:
snake_case : int = results[:top_k]
return results
def lowerCamelCase ( self , UpperCamelCase__ ) -> Optional[int]:
'''simple docstring'''
if self.framework != "pt":
raise ValueError("The ZeroShotObjectDetectionPipeline is only available in PyTorch." )
snake_case ,snake_case ,snake_case ,snake_case : Tuple = box.int().tolist()
snake_case : Optional[int] = {
"xmin": xmin,
"ymin": ymin,
"xmax": xmax,
"ymax": ymax,
}
return bbox
| 203 |
import warnings
from diffusers import StableDiffusionImgaImgPipeline # noqa F401
warnings.warn(
"""The `image_to_image.py` script is outdated. Please use directly `from diffusers import"""
""" StableDiffusionImg2ImgPipeline` instead."""
)
| 92 | 0 |
'''simple docstring'''
import math
class a :
def A_ ( self : Tuple , lowercase_ : Optional[int] , lowercase_ : Optional[Any] ):
snake_case_ = 0.0
snake_case_ = 0.0
for i in range(len(_A ) ):
da += math.pow((sample[i] - weights[0][i]) , 2 )
da += math.pow((sample[i] - weights[1][i]) , 2 )
return 0 if da > da else 1
return 0
def A_ ( self : Tuple , lowercase_ : int , lowercase_ : Optional[Any] , lowercase_ : Optional[int] , lowercase_ : List[str] ):
for i in range(len(_A ) ):
weights[j][i] += alpha * (sample[i] - weights[j][i])
return weights
def __magic_name__ ( ) -> Optional[Any]:
'''simple docstring'''
snake_case_ = [[1, 1, 0, 0], [0, 0, 0, 1], [1, 0, 0, 0], [0, 0, 1, 1]]
# weight initialization ( n, C )
snake_case_ = [[0.2, 0.6, 0.5, 0.9], [0.8, 0.4, 0.7, 0.3]]
# training
snake_case_ = SelfOrganizingMap()
snake_case_ = 3
snake_case_ = 0.5
for _ in range(SCREAMING_SNAKE_CASE_ ):
for j in range(len(SCREAMING_SNAKE_CASE_ ) ):
# training sample
snake_case_ = training_samples[j]
# Compute the winning vector
snake_case_ = self_organizing_map.get_winner(SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_ )
# Update the winning vector
snake_case_ = self_organizing_map.update(SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_ )
# classify test sample
snake_case_ = [0, 0, 0, 1]
snake_case_ = self_organizing_map.get_winner(SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_ )
# results
print(F"Clusters that the test sample belongs to : {winner}" )
print(F"Weights that have been trained : {weights}" )
# running the main() function
if __name__ == "__main__":
main()
| 56 |
import os
import torch
from ..logging import get_logger
from .constants import FSDP_PYTORCH_VERSION, MODEL_NAME, OPTIMIZER_NAME
from .versions import is_torch_version
if is_torch_version(""">=""", FSDP_PYTORCH_VERSION):
import torch.distributed.checkpoint as dist_cp
from torch.distributed.checkpoint.default_planner import DefaultLoadPlanner, DefaultSavePlanner
from torch.distributed.checkpoint.optimizer import load_sharded_optimizer_state_dict
from torch.distributed.fsdp.fully_sharded_data_parallel import FullyShardedDataParallel as FSDP
from torch.distributed.fsdp.fully_sharded_data_parallel import StateDictType
UpperCamelCase__ = get_logger(__name__)
def _a ( SCREAMING_SNAKE_CASE_ : List[str] , SCREAMING_SNAKE_CASE_ : Tuple , SCREAMING_SNAKE_CASE_ : Tuple , SCREAMING_SNAKE_CASE_ : Optional[Any] , SCREAMING_SNAKE_CASE_ : str=0 ):
os.makedirs(SCREAMING_SNAKE_CASE_ , exist_ok=SCREAMING_SNAKE_CASE_ )
with FSDP.state_dict_type(
SCREAMING_SNAKE_CASE_ , fsdp_plugin.state_dict_type , fsdp_plugin.state_dict_config , fsdp_plugin.optim_state_dict_config ):
__lowerCAmelCase = model.state_dict()
if fsdp_plugin.state_dict_type == StateDictType.FULL_STATE_DICT:
__lowerCAmelCase = F"""{MODEL_NAME}.bin""" if model_index == 0 else F"""{MODEL_NAME}_{model_index}.bin"""
__lowerCAmelCase = os.path.join(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
if accelerator.process_index == 0:
logger.info(F"""Saving model to {output_model_file}""" )
torch.save(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
logger.info(F"""Model saved to {output_model_file}""" )
elif fsdp_plugin.state_dict_type == StateDictType.LOCAL_STATE_DICT:
__lowerCAmelCase = (
F"""{MODEL_NAME}_rank{accelerator.process_index}.bin"""
if model_index == 0
else F"""{MODEL_NAME}_{model_index}_rank{accelerator.process_index}.bin"""
)
__lowerCAmelCase = os.path.join(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
logger.info(F"""Saving model to {output_model_file}""" )
torch.save(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
logger.info(F"""Model saved to {output_model_file}""" )
elif fsdp_plugin.state_dict_type == StateDictType.SHARDED_STATE_DICT:
__lowerCAmelCase = os.path.join(SCREAMING_SNAKE_CASE_ , F"""{MODEL_NAME}_{model_index}""" )
os.makedirs(SCREAMING_SNAKE_CASE_ , exist_ok=SCREAMING_SNAKE_CASE_ )
logger.info(F"""Saving model to {ckpt_dir}""" )
__lowerCAmelCase = {"model": state_dict}
dist_cp.save_state_dict(
state_dict=SCREAMING_SNAKE_CASE_ , storage_writer=dist_cp.FileSystemWriter(SCREAMING_SNAKE_CASE_ ) , planner=DefaultSavePlanner() , )
logger.info(F"""Model saved to {ckpt_dir}""" )
def _a ( SCREAMING_SNAKE_CASE_ : Dict , SCREAMING_SNAKE_CASE_ : int , SCREAMING_SNAKE_CASE_ : Optional[int] , SCREAMING_SNAKE_CASE_ : List[str] , SCREAMING_SNAKE_CASE_ : Any=0 ):
accelerator.wait_for_everyone()
with FSDP.state_dict_type(
SCREAMING_SNAKE_CASE_ , fsdp_plugin.state_dict_type , fsdp_plugin.state_dict_config , fsdp_plugin.optim_state_dict_config ):
if fsdp_plugin.state_dict_type == StateDictType.FULL_STATE_DICT:
if type(SCREAMING_SNAKE_CASE_ ) != FSDP and accelerator.process_index != 0:
if not fsdp_plugin.sync_module_states:
raise ValueError(
"Set the `sync_module_states` flag to `True` so that model states are synced across processes when "
"initializing FSDP object" )
return
__lowerCAmelCase = F"""{MODEL_NAME}.bin""" if model_index == 0 else F"""{MODEL_NAME}_{model_index}.bin"""
__lowerCAmelCase = os.path.join(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
logger.info(F"""Loading model from {input_model_file}""" )
__lowerCAmelCase = torch.load(SCREAMING_SNAKE_CASE_ )
logger.info(F"""Model loaded from {input_model_file}""" )
elif fsdp_plugin.state_dict_type == StateDictType.LOCAL_STATE_DICT:
__lowerCAmelCase = (
F"""{MODEL_NAME}_rank{accelerator.process_index}.bin"""
if model_index == 0
else F"""{MODEL_NAME}_{model_index}_rank{accelerator.process_index}.bin"""
)
__lowerCAmelCase = os.path.join(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
logger.info(F"""Loading model from {input_model_file}""" )
__lowerCAmelCase = torch.load(SCREAMING_SNAKE_CASE_ )
logger.info(F"""Model loaded from {input_model_file}""" )
elif fsdp_plugin.state_dict_type == StateDictType.SHARDED_STATE_DICT:
__lowerCAmelCase = (
os.path.join(SCREAMING_SNAKE_CASE_ , F"""{MODEL_NAME}_{model_index}""" )
if F"""{MODEL_NAME}""" not in input_dir
else input_dir
)
logger.info(F"""Loading model from {ckpt_dir}""" )
__lowerCAmelCase = {"model": model.state_dict()}
dist_cp.load_state_dict(
state_dict=SCREAMING_SNAKE_CASE_ , storage_reader=dist_cp.FileSystemReader(SCREAMING_SNAKE_CASE_ ) , planner=DefaultLoadPlanner() , )
__lowerCAmelCase = state_dict["model"]
logger.info(F"""Model loaded from {ckpt_dir}""" )
model.load_state_dict(SCREAMING_SNAKE_CASE_ )
def _a ( SCREAMING_SNAKE_CASE_ : Dict , SCREAMING_SNAKE_CASE_ : int , SCREAMING_SNAKE_CASE_ : List[str] , SCREAMING_SNAKE_CASE_ : Optional[Any] , SCREAMING_SNAKE_CASE_ : Optional[int] , SCREAMING_SNAKE_CASE_ : str=0 ):
os.makedirs(SCREAMING_SNAKE_CASE_ , exist_ok=SCREAMING_SNAKE_CASE_ )
with FSDP.state_dict_type(
SCREAMING_SNAKE_CASE_ , fsdp_plugin.state_dict_type , fsdp_plugin.state_dict_config , fsdp_plugin.optim_state_dict_config ):
__lowerCAmelCase = FSDP.optim_state_dict(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
if fsdp_plugin.state_dict_type == StateDictType.FULL_STATE_DICT:
if accelerator.process_index == 0:
__lowerCAmelCase = (
F"""{OPTIMIZER_NAME}.bin""" if optimizer_index == 0 else F"""{OPTIMIZER_NAME}_{optimizer_index}.bin"""
)
__lowerCAmelCase = os.path.join(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
logger.info(F"""Saving Optimizer state to {output_optimizer_file}""" )
torch.save(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
logger.info(F"""Optimizer state saved in {output_optimizer_file}""" )
else:
__lowerCAmelCase = os.path.join(SCREAMING_SNAKE_CASE_ , F"""{OPTIMIZER_NAME}_{optimizer_index}""" )
os.makedirs(SCREAMING_SNAKE_CASE_ , exist_ok=SCREAMING_SNAKE_CASE_ )
logger.info(F"""Saving Optimizer state to {ckpt_dir}""" )
dist_cp.save_state_dict(
state_dict={"optimizer": optim_state} , storage_writer=dist_cp.FileSystemWriter(SCREAMING_SNAKE_CASE_ ) , planner=DefaultSavePlanner() , )
logger.info(F"""Optimizer state saved in {ckpt_dir}""" )
def _a ( SCREAMING_SNAKE_CASE_ : Dict , SCREAMING_SNAKE_CASE_ : Dict , SCREAMING_SNAKE_CASE_ : Tuple , SCREAMING_SNAKE_CASE_ : List[str] , SCREAMING_SNAKE_CASE_ : str , SCREAMING_SNAKE_CASE_ : Dict=0 ):
accelerator.wait_for_everyone()
with FSDP.state_dict_type(
SCREAMING_SNAKE_CASE_ , fsdp_plugin.state_dict_type , fsdp_plugin.state_dict_config , fsdp_plugin.optim_state_dict_config ):
if fsdp_plugin.state_dict_type == StateDictType.FULL_STATE_DICT:
__lowerCAmelCase = None
# below check should work but currently it isn't working (mostly opytorch issue),
# in the meantime disabling it at the cost of excess memory usage
# if accelerator.process_index == 0 or not fsdp_plugin.optim_state_dict_config.rank0_only:
__lowerCAmelCase = (
F"""{OPTIMIZER_NAME}.bin""" if optimizer_index == 0 else F"""{OPTIMIZER_NAME}_{optimizer_index}.bin"""
)
__lowerCAmelCase = os.path.join(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
logger.info(F"""Loading Optimizer state from {input_optimizer_file}""" )
__lowerCAmelCase = torch.load(SCREAMING_SNAKE_CASE_ )
logger.info(F"""Optimizer state loaded from {input_optimizer_file}""" )
else:
__lowerCAmelCase = (
os.path.join(SCREAMING_SNAKE_CASE_ , F"""{OPTIMIZER_NAME}_{optimizer_index}""" )
if F"""{OPTIMIZER_NAME}""" not in input_dir
else input_dir
)
logger.info(F"""Loading Optimizer from {ckpt_dir}""" )
__lowerCAmelCase = load_sharded_optimizer_state_dict(
model_state_dict=model.state_dict() , optimizer_key="optimizer" , storage_reader=dist_cp.FileSystemReader(SCREAMING_SNAKE_CASE_ ) , )
__lowerCAmelCase = optim_state["optimizer"]
logger.info(F"""Optimizer loaded from {ckpt_dir}""" )
__lowerCAmelCase = FSDP.optim_state_dict_to_load(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
optimizer.load_state_dict(SCREAMING_SNAKE_CASE_ )
| 92 | 0 |
'''simple docstring'''
# limitations under the License.
from typing import Optional, Tuple, Union
import torch
from diffusers import DiffusionPipeline, ImagePipelineOutput
class __UpperCamelCase ( snake_case__ ):
def __init__( self, lowerCAmelCase, lowerCAmelCase ):
"""simple docstring"""
super().__init__()
self.register_modules(unet=_A, scheduler=_A )
@torch.no_grad()
def __call__( self, lowerCAmelCase = 1, lowerCAmelCase = None, lowerCAmelCase = 50, lowerCAmelCase = "pil", lowerCAmelCase = True, **lowerCAmelCase, ):
"""simple docstring"""
lowerCamelCase_ =torch.randn(
(batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size), generator=_A, )
lowerCamelCase_ =image.to(self.device )
# set step values
self.scheduler.set_timesteps(_A )
for t in self.progress_bar(self.scheduler.timesteps ):
# 1. predict noise model_output
lowerCamelCase_ =self.unet(_A, _A ).sample
# 2. predict previous mean of image x_t-1 and add variance depending on eta
# eta corresponds to η in paper and should be between [0, 1]
# do x_t -> x_t-1
lowerCamelCase_ =self.scheduler.step(_A, _A, _A ).prev_sample
lowerCamelCase_ =(image / 2 + 0.5).clamp(0, 1 )
lowerCamelCase_ =image.cpu().permute(0, 2, 3, 1 ).numpy()
if output_type == "pil":
lowerCamelCase_ =self.numpy_to_pil(_A )
if not return_dict:
return (image,), "This is a local test"
return ImagePipelineOutput(images=_A ), "This is a local test"
| 75 |
import math
import time
from typing import Dict, List, Optional
from torch.utils.data import Dataset
from transformers import SeqaSeqTrainer, is_torch_tpu_available
from transformers.trainer_utils import PredictionOutput, speed_metrics
if is_torch_tpu_available(check_device=False):
import torch_xla.core.xla_model as xm
import torch_xla.debug.metrics as met
class a__ ( snake_case__ ):
def __init__( self , *_A , _A=None , _A=None , **_A ):
"""simple docstring"""
super().__init__(*_A , **_A )
__lowerCAmelCase = eval_examples
__lowerCAmelCase = post_process_function
def __SCREAMING_SNAKE_CASE( self , _A = None , _A=None , _A = None , _A = "eval" , **_A , ):
"""simple docstring"""
__lowerCAmelCase = gen_kwargs.copy()
__lowerCAmelCase = (
gen_kwargs["max_length"] if gen_kwargs.get("max_length" ) is not None else self.args.generation_max_length
)
__lowerCAmelCase = (
gen_kwargs["num_beams"] if gen_kwargs.get("num_beams" ) is not None else self.args.generation_num_beams
)
__lowerCAmelCase = gen_kwargs
__lowerCAmelCase = self.eval_dataset if eval_dataset is None else eval_dataset
__lowerCAmelCase = self.get_eval_dataloader(_A )
__lowerCAmelCase = self.eval_examples if eval_examples is None else eval_examples
# Temporarily disable metric computation, we will do it in the loop here.
__lowerCAmelCase = self.compute_metrics
__lowerCAmelCase = None
__lowerCAmelCase = time.time()
__lowerCAmelCase = self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
try:
__lowerCAmelCase = eval_loop(
_A , description="Evaluation" , prediction_loss_only=True if compute_metrics is None else None , ignore_keys=_A , metric_key_prefix=_A , )
finally:
__lowerCAmelCase = compute_metrics
__lowerCAmelCase = self.args.eval_batch_size * self.args.world_size
if f"""{metric_key_prefix}_jit_compilation_time""" in output.metrics:
start_time += output.metrics[f"""{metric_key_prefix}_jit_compilation_time"""]
output.metrics.update(
speed_metrics(
_A , _A , num_samples=output.num_samples , num_steps=math.ceil(output.num_samples / total_batch_size ) , ) )
if self.post_process_function is not None and self.compute_metrics is not None and self.args.should_save:
# Only the main node write the results by default
__lowerCAmelCase = self.post_process_function(_A , _A , _A )
__lowerCAmelCase = self.compute_metrics(_A )
# Prefix all keys with metric_key_prefix + '_'
for key in list(metrics.keys() ):
if not key.startswith(f"""{metric_key_prefix}_""" ):
__lowerCAmelCase = metrics.pop(_A )
metrics.update(output.metrics )
else:
__lowerCAmelCase = output.metrics
if self.args.should_log:
# Only the main node log the results by default
self.log(_A )
if self.args.tpu_metrics_debug or self.args.debug:
# tpu-comment: Logging debug metrics for PyTorch/XLA (compile, execute times, ops, etc.)
xm.master_print(met.metrics_report() )
__lowerCAmelCase = self.callback_handler.on_evaluate(self.args , self.state , self.control , _A )
return metrics
def __SCREAMING_SNAKE_CASE( self , _A , _A , _A=None , _A = "test" , **_A ):
"""simple docstring"""
__lowerCAmelCase = gen_kwargs.copy()
__lowerCAmelCase = self.get_test_dataloader(_A )
# Temporarily disable metric computation, we will do it in the loop here.
__lowerCAmelCase = self.compute_metrics
__lowerCAmelCase = None
__lowerCAmelCase = time.time()
__lowerCAmelCase = self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
try:
__lowerCAmelCase = eval_loop(
_A , description="Prediction" , prediction_loss_only=True if compute_metrics is None else None , ignore_keys=_A , metric_key_prefix=_A , )
finally:
__lowerCAmelCase = compute_metrics
__lowerCAmelCase = self.args.eval_batch_size * self.args.world_size
if f"""{metric_key_prefix}_jit_compilation_time""" in output.metrics:
start_time += output.metrics[f"""{metric_key_prefix}_jit_compilation_time"""]
output.metrics.update(
speed_metrics(
_A , _A , num_samples=output.num_samples , num_steps=math.ceil(output.num_samples / total_batch_size ) , ) )
if self.post_process_function is None or self.compute_metrics is None:
return output
__lowerCAmelCase = self.post_process_function(_A , _A , _A , "predict" )
__lowerCAmelCase = self.compute_metrics(_A )
# Prefix all keys with metric_key_prefix + '_'
for key in list(metrics.keys() ):
if not key.startswith(f"""{metric_key_prefix}_""" ):
__lowerCAmelCase = metrics.pop(_A )
metrics.update(output.metrics )
return PredictionOutput(predictions=predictions.predictions , label_ids=predictions.label_ids , metrics=_A )
| 92 | 0 |
"""simple docstring"""
import pickle
import numpy as np
from matplotlib import pyplot as plt
class a :
def __init__( self , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase=0.2 , _lowerCamelCase=0.2 ):
lowercase = bp_numa
lowercase = bp_numa
lowercase = bp_numa
lowercase = conva_get[:2]
lowercase = conva_get[2]
lowercase = size_pa
lowercase = rate_w
lowercase = rate_t
lowercase = [
np.mat(-1 * np.random.rand(self.conva[0] , self.conva[0] ) + 0.5 )
for i in range(self.conva[1] )
]
lowercase = np.mat(-1 * np.random.rand(self.num_bpa , self.num_bpa ) + 0.5 )
lowercase = np.mat(-1 * np.random.rand(self.num_bpa , self.num_bpa ) + 0.5 )
lowercase = -2 * np.random.rand(self.conva[1] ) + 1
lowercase = -2 * np.random.rand(self.num_bpa ) + 1
lowercase = -2 * np.random.rand(self.num_bpa ) + 1
def UpperCamelCase_ ( self , _lowerCamelCase ):
lowercase = {
'num_bp1': self.num_bpa,
'num_bp2': self.num_bpa,
'num_bp3': self.num_bpa,
'conv1': self.conva,
'step_conv1': self.step_conva,
'size_pooling1': self.size_poolinga,
'rate_weight': self.rate_weight,
'rate_thre': self.rate_thre,
'w_conv1': self.w_conva,
'wkj': self.wkj,
'vji': self.vji,
'thre_conv1': self.thre_conva,
'thre_bp2': self.thre_bpa,
'thre_bp3': self.thre_bpa,
}
with open(_A , 'wb' ) as f:
pickle.dump(_A , _A )
print(F'Model saved： {save_path}' )
@classmethod
def UpperCamelCase_ ( cls , _lowerCamelCase ):
with open(_A , 'rb' ) as f:
lowercase = pickle.load(_A ) # noqa: S301
lowercase = model_dic.get('conv1' )
conv_get.append(model_dic.get('step_conv1' ) )
lowercase = model_dic.get('size_pooling1' )
lowercase = model_dic.get('num_bp1' )
lowercase = model_dic.get('num_bp2' )
lowercase = model_dic.get('num_bp3' )
lowercase = model_dic.get('rate_weight' )
lowercase = model_dic.get('rate_thre' )
# create model instance
lowercase = CNN(_A , _A , _A , _A , _A , _A , _A )
# modify model parameter
lowercase = model_dic.get('w_conv1' )
lowercase = model_dic.get('wkj' )
lowercase = model_dic.get('vji' )
lowercase = model_dic.get('thre_conv1' )
lowercase = model_dic.get('thre_bp2' )
lowercase = model_dic.get('thre_bp3' )
return conv_ins
def UpperCamelCase_ ( self , _lowerCamelCase ):
return 1 / (1 + np.exp(-1 * x ))
def UpperCamelCase_ ( self , _lowerCamelCase ):
return round(_A , 3 )
def UpperCamelCase_ ( self , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase ):
lowercase = convs[0]
lowercase = convs[1]
lowercase = np.shape(_A )[0]
# get the data slice of original image data, data_focus
lowercase = []
for i_focus in range(0 , size_data - size_conv + 1 , _A ):
for j_focus in range(0 , size_data - size_conv + 1 , _A ):
lowercase = data[
i_focus : i_focus + size_conv, j_focus : j_focus + size_conv
]
data_focus.append(_A )
# calculate the feature map of every single kernel, and saved as list of matrix
lowercase = []
lowercase = int((size_data - size_conv) / conv_step + 1 )
for i_map in range(_A ):
lowercase = []
for i_focus in range(len(_A ) ):
lowercase = (
np.sum(np.multiply(data_focus[i_focus] , w_convs[i_map] ) )
- thre_convs[i_map]
)
featuremap.append(self.sig(_A ) )
lowercase = np.asmatrix(_A ).reshape(
_A , _A )
data_featuremap.append(_A )
# expanding the data slice to One dimenssion
lowercase = []
for each_focus in data_focus:
focusa_list.extend(self.Expand_Mat(_A ) )
lowercase = np.asarray(_A )
return focus_list, data_featuremap
def UpperCamelCase_ ( self , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase="average_pool" ):
lowercase = len(featuremaps[0] )
lowercase = int(size_map / size_pooling )
lowercase = []
for i_map in range(len(_A ) ):
lowercase = featuremaps[i_map]
lowercase = []
for i_focus in range(0 , _A , _A ):
for j_focus in range(0 , _A , _A ):
lowercase = feature_map[
i_focus : i_focus + size_pooling,
j_focus : j_focus + size_pooling,
]
if pooling_type == "average_pool":
# average pooling
map_pooled.append(np.average(_A ) )
elif pooling_type == "max_pooling":
# max pooling
map_pooled.append(np.max(_A ) )
lowercase = np.asmatrix(_A ).reshape(_A , _A )
featuremap_pooled.append(_A )
return featuremap_pooled
def UpperCamelCase_ ( self , _lowerCamelCase ):
lowercase = []
for i in range(len(_A ) ):
lowercase = np.shape(data[i] )
lowercase = data[i].reshape(1 , shapes[0] * shapes[1] )
lowercase = data_listed.getA().tolist()[0]
data_expanded.extend(_A )
lowercase = np.asarray(_A )
return data_expanded
def UpperCamelCase_ ( self , _lowerCamelCase ):
lowercase = np.asarray(_A )
lowercase = np.shape(_A )
lowercase = data_mat.reshape(1 , shapes[0] * shapes[1] )
return data_expanded
def UpperCamelCase_ ( self , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase ):
lowercase = []
lowercase = 0
for i_map in range(_A ):
lowercase = np.ones((size_map, size_map) )
for i in range(0 , _A , _A ):
for j in range(0 , _A , _A ):
lowercase = pd_pool[
i_pool
]
lowercase = i_pool + 1
lowercase = np.multiply(
_A , np.multiply(out_map[i_map] , (1 - out_map[i_map]) ) )
pd_all.append(_A )
return pd_all
def UpperCamelCase_ ( self , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase=bool ):
print('----------------------Start Training-------------------------' )
print((' - - Shape: Train_Data ', np.shape(_A )) )
print((' - - Shape: Teach_Data ', np.shape(_A )) )
lowercase = 0
lowercase = []
lowercase = 1_0_0_0_0
while rp < n_repeat and mse >= error_accuracy:
lowercase = 0
print(F'-------------Learning Time {rp}--------------' )
for p in range(len(_A ) ):
# print('------------Learning Image: %d--------------'%p)
lowercase = np.asmatrix(datas_train[p] )
lowercase = np.asarray(datas_teach[p] )
lowercase , lowercase = self.convolute(
_A , self.conva , self.w_conva , self.thre_conva , conv_step=self.step_conva , )
lowercase = self.pooling(_A , self.size_poolinga )
lowercase = np.shape(_A )
lowercase = self._expand(_A )
lowercase = data_bp_input
lowercase = np.dot(_A , self.vji.T ) - self.thre_bpa
lowercase = self.sig(_A )
lowercase = np.dot(_A , self.wkj.T ) - self.thre_bpa
lowercase = self.sig(_A )
# --------------Model Leaning ------------------------
# calculate error and gradient---------------
lowercase = np.multiply(
(data_teach - bp_outa) , np.multiply(_A , (1 - bp_outa) ) )
lowercase = np.multiply(
np.dot(_A , self.wkj ) , np.multiply(_A , (1 - bp_outa) ) )
lowercase = np.dot(_A , self.vji )
lowercase = pd_i_all / (self.size_poolinga * self.size_poolinga)
lowercase = pd_conva_pooled.T.getA().tolist()
lowercase = self._calculate_gradient_from_pool(
_A , _A , shape_featuremapa[0] , shape_featuremapa[1] , self.size_poolinga , )
# weight and threshold learning process---------
# convolution layer
for k_conv in range(self.conva[1] ):
lowercase = self._expand_mat(pd_conva_all[k_conv] )
lowercase = self.rate_weight * np.dot(_A , _A )
lowercase = self.w_conva[k_conv] + delta_w.reshape(
(self.conva[0], self.conva[0]) )
lowercase = (
self.thre_conva[k_conv]
- np.sum(pd_conva_all[k_conv] ) * self.rate_thre
)
# all connected layer
lowercase = self.wkj + pd_k_all.T * bp_outa * self.rate_weight
lowercase = self.vji + pd_j_all.T * bp_outa * self.rate_weight
lowercase = self.thre_bpa - pd_k_all * self.rate_thre
lowercase = self.thre_bpa - pd_j_all * self.rate_thre
# calculate the sum error of all single image
lowercase = np.sum(abs(data_teach - bp_outa ) )
error_count += errors
# print(' ----Teach ',data_teach)
# print(' ----BP_output ',bp_out3)
lowercase = rp + 1
lowercase = error_count / patterns
all_mse.append(_A )
def draw_error():
lowercase = [error_accuracy for i in range(int(n_repeat * 1.2 ) )]
plt.plot(_A , '+-' )
plt.plot(_A , 'r--' )
plt.xlabel('Learning Times' )
plt.ylabel('All_mse' )
plt.grid(_A , alpha=0.5 )
plt.show()
print('------------------Training Complished---------------------' )
print((' - - Training epoch: ', rp, F' - - Mse: {mse:.6f}') )
if draw_e:
draw_error()
return mse
def UpperCamelCase_ ( self , _lowerCamelCase ):
lowercase = []
print('-------------------Start Testing-------------------------' )
print((' - - Shape: Test_Data ', np.shape(_A )) )
for p in range(len(_A ) ):
lowercase = np.asmatrix(datas_test[p] )
lowercase , lowercase = self.convolute(
_A , self.conva , self.w_conva , self.thre_conva , conv_step=self.step_conva , )
lowercase = self.pooling(_A , self.size_poolinga )
lowercase = self._expand(_A )
lowercase = data_bp_input
lowercase = bp_outa * self.vji.T - self.thre_bpa
lowercase = self.sig(_A )
lowercase = bp_outa * self.wkj.T - self.thre_bpa
lowercase = self.sig(_A )
produce_out.extend(bp_outa.getA().tolist() )
lowercase = [list(map(self.do_round , _A ) ) for each in produce_out]
return np.asarray(_A )
def UpperCamelCase_ ( self , _lowerCamelCase ):
lowercase = np.asmatrix(_A )
lowercase , lowercase = self.convolute(
_A , self.conva , self.w_conva , self.thre_conva , conv_step=self.step_conva , )
lowercase = self.pooling(_A , self.size_poolinga )
return data_conveda, data_pooleda
if __name__ == "__main__":
pass
| 220 |
import logging
from pathlib import Path
import numpy as np
import pytorch_lightning as pl
import torch
from pytorch_lightning.callbacks import EarlyStopping, ModelCheckpoint
from pytorch_lightning.utilities import rank_zero_only
from utils_rag import save_json
def _a ( SCREAMING_SNAKE_CASE_ : Optional[int] ):
__lowerCAmelCase = filter(lambda SCREAMING_SNAKE_CASE_ : p.requires_grad , model.parameters() )
__lowerCAmelCase = sum([np.prod(p.size() ) for p in model_parameters] )
return params
UpperCamelCase__ = logging.getLogger(__name__)
def _a ( SCREAMING_SNAKE_CASE_ : Union[str, Any] , SCREAMING_SNAKE_CASE_ : Any ):
if metric == "rouge2":
__lowerCAmelCase = "{val_avg_rouge2:.4f}-{step_count}"
elif metric == "bleu":
__lowerCAmelCase = "{val_avg_bleu:.4f}-{step_count}"
elif metric == "em":
__lowerCAmelCase = "{val_avg_em:.4f}-{step_count}"
else:
raise NotImplementedError(
F"""seq2seq callbacks only support rouge2 and bleu, got {metric}, You can make your own by adding to this"""
" function." )
__lowerCAmelCase = ModelCheckpoint(
dirpath=SCREAMING_SNAKE_CASE_ , filename=SCREAMING_SNAKE_CASE_ , monitor=F"""val_{metric}""" , mode="max" , save_top_k=3 , every_n_epochs=1 , )
return checkpoint_callback
def _a ( SCREAMING_SNAKE_CASE_ : int , SCREAMING_SNAKE_CASE_ : Union[str, Any] ):
return EarlyStopping(
monitor=F"""val_{metric}""" , mode="min" if "loss" in metric else "max" , patience=SCREAMING_SNAKE_CASE_ , verbose=SCREAMING_SNAKE_CASE_ , )
class a__ ( pl.Callback ):
def __SCREAMING_SNAKE_CASE( self , _A , _A ):
"""simple docstring"""
__lowerCAmelCase = {f"""lr_group_{i}""": param["lr"] for i, param in enumerate(pl_module.trainer.optimizers[0].param_groups )}
pl_module.logger.log_metrics(_A )
@rank_zero_only
def __SCREAMING_SNAKE_CASE( self , _A , _A , _A , _A=True ):
"""simple docstring"""
logger.info(f"""***** {type_path} results at step {trainer.global_step:05d} *****""" )
__lowerCAmelCase = trainer.callback_metrics
trainer.logger.log_metrics({k: v for k, v in metrics.items() if k not in ["log", "progress_bar", "preds"]} )
# Log results
__lowerCAmelCase = Path(pl_module.hparams.output_dir )
if type_path == "test":
__lowerCAmelCase = od / "test_results.txt"
__lowerCAmelCase = od / "test_generations.txt"
else:
# this never gets hit. I prefer not to save intermediate generations, and results are in metrics.json
# If people want this it will be easy enough to add back.
__lowerCAmelCase = od / f"""{type_path}_results/{trainer.global_step:05d}.txt"""
__lowerCAmelCase = od / f"""{type_path}_generations/{trainer.global_step:05d}.txt"""
results_file.parent.mkdir(exist_ok=_A )
generations_file.parent.mkdir(exist_ok=_A )
with open(_A , "a+" ) as writer:
for key in sorted(_A ):
if key in ["log", "progress_bar", "preds"]:
continue
__lowerCAmelCase = metrics[key]
if isinstance(_A , torch.Tensor ):
__lowerCAmelCase = val.item()
__lowerCAmelCase = f"""{key}: {val:.6f}\n"""
writer.write(_A )
if not save_generations:
return
if "preds" in metrics:
__lowerCAmelCase = "\n".join(metrics["preds"] )
generations_file.open("w+" ).write(_A )
@rank_zero_only
def __SCREAMING_SNAKE_CASE( self , _A , _A ):
"""simple docstring"""
try:
__lowerCAmelCase = pl_module.model.model.num_parameters()
except AttributeError:
__lowerCAmelCase = pl_module.model.num_parameters()
__lowerCAmelCase = count_trainable_parameters(_A )
# mp stands for million parameters
trainer.logger.log_metrics({"n_params": npars, "mp": npars / 1E6, "grad_mp": n_trainable_pars / 1E6} )
@rank_zero_only
def __SCREAMING_SNAKE_CASE( self , _A , _A ):
"""simple docstring"""
save_json(pl_module.metrics , pl_module.metrics_save_path )
return self._write_logs(_A , _A , "test" )
@rank_zero_only
def __SCREAMING_SNAKE_CASE( self , _A , _A ):
"""simple docstring"""
save_json(pl_module.metrics , pl_module.metrics_save_path )
# Uncommenting this will save val generations
# return self._write_logs(trainer, pl_module, "valid")
| 92 | 0 |
import json
import os
from functools import lru_cache
from typing import List, Optional, Tuple
import regex as re
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
A : Any = logging.get_logger(__name__)
A : List[Any] = {'vocab_file': 'vocab.json', 'merges_file': 'merges.txt'}
A : Tuple = {
'vocab_file': {
'allenai/longformer-base-4096': 'https://huggingface.co/allenai/longformer-base-4096/resolve/main/vocab.json',
'allenai/longformer-large-4096': (
'https://huggingface.co/allenai/longformer-large-4096/resolve/main/vocab.json'
),
'allenai/longformer-large-4096-finetuned-triviaqa': (
'https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/vocab.json'
),
'allenai/longformer-base-4096-extra.pos.embd.only': (
'https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/vocab.json'
),
'allenai/longformer-large-4096-extra.pos.embd.only': (
'https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/vocab.json'
),
},
'merges_file': {
'allenai/longformer-base-4096': 'https://huggingface.co/allenai/longformer-base-4096/resolve/main/merges.txt',
'allenai/longformer-large-4096': (
'https://huggingface.co/allenai/longformer-large-4096/resolve/main/merges.txt'
),
'allenai/longformer-large-4096-finetuned-triviaqa': (
'https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/merges.txt'
),
'allenai/longformer-base-4096-extra.pos.embd.only': (
'https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/merges.txt'
),
'allenai/longformer-large-4096-extra.pos.embd.only': (
'https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/merges.txt'
),
},
}
A : str = {
'allenai/longformer-base-4096': 4_0_9_6,
'allenai/longformer-large-4096': 4_0_9_6,
'allenai/longformer-large-4096-finetuned-triviaqa': 4_0_9_6,
'allenai/longformer-base-4096-extra.pos.embd.only': 4_0_9_6,
'allenai/longformer-large-4096-extra.pos.embd.only': 4_0_9_6,
}
@lru_cache()
# Copied from transformers.models.roberta.tokenization_roberta.bytes_to_unicode
def __lowerCAmelCase ( ) -> str:
__a = (
list(range(ord('''!''' ) , ord('''~''' ) + 1 ) ) + list(range(ord('''¡''' ) , ord('''¬''' ) + 1 ) ) + list(range(ord('''®''' ) , ord('''ÿ''' ) + 1 ) )
)
__a = bs[:]
__a = 0
for b in range(2**8 ):
if b not in bs:
bs.append(SCREAMING_SNAKE_CASE_ )
cs.append(2**8 + n )
n += 1
__a = [chr(SCREAMING_SNAKE_CASE_ ) for n in cs]
return dict(zip(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) )
def __lowerCAmelCase ( a__ ) -> Optional[Any]:
__a = set()
__a = word[0]
for char in word[1:]:
pairs.add((prev_char, char) )
__a = char
return pairs
class __A( snake_case__ ):
snake_case_ = VOCAB_FILES_NAMES
snake_case_ = PRETRAINED_VOCAB_FILES_MAP
snake_case_ = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
snake_case_ = ["""input_ids""", """attention_mask"""]
def __init__( self , _snake_case , _snake_case , _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 , ) -> List[Any]:
'''simple docstring'''
__a = AddedToken(_A , lstrip=_A , rstrip=_A ) if isinstance(_A , _A ) else bos_token
__a = AddedToken(_A , lstrip=_A , rstrip=_A ) if isinstance(_A , _A ) else eos_token
__a = AddedToken(_A , lstrip=_A , rstrip=_A ) if isinstance(_A , _A ) else sep_token
__a = AddedToken(_A , lstrip=_A , rstrip=_A ) if isinstance(_A , _A ) else cls_token
__a = AddedToken(_A , lstrip=_A , rstrip=_A ) if isinstance(_A , _A ) else unk_token
__a = AddedToken(_A , lstrip=_A , rstrip=_A ) if isinstance(_A , _A ) else pad_token
# Mask token behave like a normal word, i.e. include the space before it
__a = AddedToken(_A , lstrip=_A , rstrip=_A ) if isinstance(_A , _A ) else mask_token
super().__init__(
errors=_A , bos_token=_A , eos_token=_A , unk_token=_A , sep_token=_A , cls_token=_A , pad_token=_A , mask_token=_A , add_prefix_space=_A , **_A , )
with open(_A , encoding='''utf-8''' ) as vocab_handle:
__a = json.load(_A )
__a = {v: k for k, v in self.encoder.items()}
__a = errors # how to handle errors in decoding
__a = bytes_to_unicode()
__a = {v: k for k, v in self.byte_encoder.items()}
with open(_A , encoding='''utf-8''' ) as merges_handle:
__a = merges_handle.read().split('''\n''' )[1:-1]
__a = [tuple(merge.split() ) for merge in bpe_merges]
__a = dict(zip(_A , range(len(_A ) ) ) )
__a = {}
__a = add_prefix_space
# Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
__a = re.compile(r'''\'s|\'t|\'re|\'ve|\'m|\'ll|\'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+''' )
@property
def SCREAMING_SNAKE_CASE_ ( self ) -> Optional[int]:
'''simple docstring'''
return len(self.encoder )
def SCREAMING_SNAKE_CASE_ ( self ) -> List[str]:
'''simple docstring'''
return dict(self.encoder , **self.added_tokens_encoder )
def SCREAMING_SNAKE_CASE_ ( self , _snake_case ) -> Union[str, Any]:
'''simple docstring'''
if token in self.cache:
return self.cache[token]
__a = tuple(_A )
__a = get_pairs(_A )
if not pairs:
return token
while True:
__a = min(_A , key=lambda _snake_case : self.bpe_ranks.get(_A , float('''inf''' ) ) )
if bigram not in self.bpe_ranks:
break
__a , __a = bigram
__a = []
__a = 0
while i < len(_A ):
try:
__a = word.index(_A , _A )
except ValueError:
new_word.extend(word[i:] )
break
else:
new_word.extend(word[i:j] )
__a = j
if word[i] == first and i < len(_A ) - 1 and word[i + 1] == second:
new_word.append(first + second )
i += 2
else:
new_word.append(word[i] )
i += 1
__a = tuple(_A )
__a = new_word
if len(_A ) == 1:
break
else:
__a = get_pairs(_A )
__a = ''' '''.join(_A )
__a = word
return word
def SCREAMING_SNAKE_CASE_ ( self , _snake_case ) -> Union[str, Any]:
'''simple docstring'''
__a = []
for token in re.findall(self.pat , _A ):
__a = ''''''.join(
self.byte_encoder[b] for b in token.encode('''utf-8''' ) ) # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case)
bpe_tokens.extend(bpe_token for bpe_token in self.bpe(_A ).split(''' ''' ) )
return bpe_tokens
def SCREAMING_SNAKE_CASE_ ( self , _snake_case ) -> Dict:
'''simple docstring'''
return self.encoder.get(_A , self.encoder.get(self.unk_token ) )
def SCREAMING_SNAKE_CASE_ ( self , _snake_case ) -> int:
'''simple docstring'''
return self.decoder.get(_A )
def SCREAMING_SNAKE_CASE_ ( self , _snake_case ) -> List[str]:
'''simple docstring'''
__a = ''''''.join(_A )
__a = bytearray([self.byte_decoder[c] for c in text] ).decode('''utf-8''' , errors=self.errors )
return text
def SCREAMING_SNAKE_CASE_ ( self , _snake_case , _snake_case = None ) -> Optional[int]:
'''simple docstring'''
if not os.path.isdir(_A ):
logger.error(F"""Vocabulary path ({save_directory}) should be a directory""" )
return
__a = os.path.join(
_A , (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file'''] )
__a = os.path.join(
_A , (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''merges_file'''] )
with open(_A , '''w''' , encoding='''utf-8''' ) as f:
f.write(json.dumps(self.encoder , indent=2 , sort_keys=_A , ensure_ascii=_A ) + '''\n''' )
__a = 0
with open(_A , '''w''' , encoding='''utf-8''' ) as writer:
writer.write('''#version: 0.2\n''' )
for bpe_tokens, token_index in sorted(self.bpe_ranks.items() , key=lambda _snake_case : kv[1] ):
if index != token_index:
logger.warning(
F"""Saving vocabulary to {merge_file}: BPE merge indices are not consecutive."""
''' Please check that the tokenizer is not corrupted!''' )
__a = token_index
writer.write(''' '''.join(_A ) + '''\n''' )
index += 1
return vocab_file, merge_file
def SCREAMING_SNAKE_CASE_ ( self , _snake_case , _snake_case = None ) -> str:
'''simple docstring'''
if token_ids_a is None:
return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
__a = [self.cls_token_id]
__a = [self.sep_token_id]
return cls + token_ids_a + sep + sep + token_ids_a + sep
def SCREAMING_SNAKE_CASE_ ( self , _snake_case , _snake_case = None , _snake_case = False ) -> List[str]:
'''simple docstring'''
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=_A , token_ids_a=_A , already_has_special_tokens=_A )
if token_ids_a is None:
return [1] + ([0] * len(_A )) + [1]
return [1] + ([0] * len(_A )) + [1, 1] + ([0] * len(_A )) + [1]
def SCREAMING_SNAKE_CASE_ ( self , _snake_case , _snake_case = None ) -> Union[str, Any]:
'''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 SCREAMING_SNAKE_CASE_ ( self , _snake_case , _snake_case=False , **_snake_case ) -> Dict:
'''simple docstring'''
__a = kwargs.pop('''add_prefix_space''' , self.add_prefix_space )
if (is_split_into_words or add_prefix_space) and (len(_A ) > 0 and not text[0].isspace()):
__a = ''' ''' + text
return (text, kwargs) | 6 |
from .glue import glue_convert_examples_to_features, glue_output_modes, glue_processors, glue_tasks_num_labels
from .squad import SquadExample, SquadFeatures, SquadVaProcessor, SquadVaProcessor, squad_convert_examples_to_features
from .utils import DataProcessor, InputExample, InputFeatures, SingleSentenceClassificationProcessor
from .xnli import xnli_output_modes, xnli_processors, xnli_tasks_num_labels
| 92 | 0 |
import logging
import os
from dataclasses import dataclass, field
from typing import Dict, Optional
import numpy as np
from utils_multiple_choice import MultipleChoiceDataset, Split, processors
import transformers
from transformers import (
AutoConfig,
AutoModelForMultipleChoice,
AutoTokenizer,
DataCollatorWithPadding,
EvalPrediction,
HfArgumentParser,
Trainer,
TrainingArguments,
set_seed,
)
from transformers.trainer_utils import is_main_process
A__ : Any = logging.getLogger(__name__)
def a ( lowerCamelCase_ , lowerCamelCase_ ):
'''simple docstring'''
return (preds == labels).mean()
@dataclass
class _UpperCAmelCase :
"""simple docstring"""
lowercase__ = field(
metadata={"""help""": """Path to pretrained model or model identifier from huggingface.co/models"""} )
lowercase__ = field(
default=snake_case__ ,metadata={"""help""": """Pretrained config name or path if not the same as model_name"""} )
lowercase__ = field(
default=snake_case__ ,metadata={"""help""": """Pretrained tokenizer name or path if not the same as model_name"""} )
lowercase__ = field(
default=snake_case__ ,metadata={"""help""": """Where do you want to store the pretrained models downloaded from huggingface.co"""} ,)
@dataclass
class _UpperCAmelCase :
"""simple docstring"""
lowercase__ = field(metadata={"""help""": """The name of the task to train on: """ + """, """.join(processors.keys() )} )
lowercase__ = field(metadata={"""help""": """Should contain the data files for the task."""} )
lowercase__ = field(
default=128 ,metadata={
"""help""": (
"""The maximum total input sequence length after tokenization. Sequences longer """
"""than this will be truncated, sequences shorter will be padded."""
)
} ,)
lowercase__ = field(
default=snake_case__ ,metadata={"""help""": """Overwrite the cached training and evaluation sets"""} )
def a ( ):
'''simple docstring'''
# See all possible arguments in src/transformers/training_args.py
# or by passing the --help flag to this script.
# We now keep distinct sets of args, for a cleaner separation of concerns.
lowercase__ = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments) )
lowercase__ , lowercase__ , lowercase__ = parser.parse_args_into_dataclasses()
if (
os.path.exists(training_args.output_dir )
and os.listdir(training_args.output_dir )
and training_args.do_train
and not training_args.overwrite_output_dir
):
raise ValueError(
F"""Output directory ({training_args.output_dir}) already exists and is not empty. Use"""
''' --overwrite_output_dir to overcome.''' )
# Setup logging
logging.basicConfig(
format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s''' , datefmt='''%m/%d/%Y %H:%M:%S''' , level=logging.INFO if training_args.local_rank in [-1, 0] else logging.WARN , )
logger.warning(
'''Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s''' , training_args.local_rank , training_args.device , training_args.n_gpu , bool(training_args.local_rank != -1 ) , training_args.fpaa , )
# Set the verbosity to info of the Transformers logger (on main process only):
if is_main_process(training_args.local_rank ):
transformers.utils.logging.set_verbosity_info()
transformers.utils.logging.enable_default_handler()
transformers.utils.logging.enable_explicit_format()
logger.info('''Training/evaluation parameters %s''' , SCREAMING_SNAKE_CASE_ )
# Set seed
set_seed(training_args.seed )
try:
lowercase__ = processors[data_args.task_name]()
lowercase__ = processor.get_labels()
lowercase__ = len(SCREAMING_SNAKE_CASE_ )
except KeyError:
raise ValueError('''Task not found: %s''' % (data_args.task_name) )
# Load pretrained model and tokenizer
#
# Distributed training:
# The .from_pretrained methods guarantee that only one local process can concurrently
# download model & vocab.
lowercase__ = AutoConfig.from_pretrained(
model_args.config_name if model_args.config_name else model_args.model_name_or_path , num_labels=SCREAMING_SNAKE_CASE_ , finetuning_task=data_args.task_name , cache_dir=model_args.cache_dir , )
lowercase__ = AutoTokenizer.from_pretrained(
model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path , cache_dir=model_args.cache_dir , )
lowercase__ = AutoModelForMultipleChoice.from_pretrained(
model_args.model_name_or_path , from_tf=bool('''.ckpt''' in model_args.model_name_or_path ) , config=SCREAMING_SNAKE_CASE_ , cache_dir=model_args.cache_dir , )
# Get datasets
lowercase__ = (
MultipleChoiceDataset(
data_dir=data_args.data_dir , tokenizer=SCREAMING_SNAKE_CASE_ , task=data_args.task_name , max_seq_length=data_args.max_seq_length , overwrite_cache=data_args.overwrite_cache , mode=Split.train , )
if training_args.do_train
else None
)
lowercase__ = (
MultipleChoiceDataset(
data_dir=data_args.data_dir , tokenizer=SCREAMING_SNAKE_CASE_ , task=data_args.task_name , max_seq_length=data_args.max_seq_length , overwrite_cache=data_args.overwrite_cache , mode=Split.dev , )
if training_args.do_eval
else None
)
def compute_metrics(lowerCamelCase_ ) -> Dict:
lowercase__ = np.argmax(p.predictions , axis=1 )
return {"acc": simple_accuracy(SCREAMING_SNAKE_CASE_ , p.label_ids )}
# Data collator
lowercase__ = DataCollatorWithPadding(SCREAMING_SNAKE_CASE_ , pad_to_multiple_of=8 ) if training_args.fpaa else None
# Initialize our Trainer
lowercase__ = Trainer(
model=SCREAMING_SNAKE_CASE_ , args=SCREAMING_SNAKE_CASE_ , train_dataset=SCREAMING_SNAKE_CASE_ , eval_dataset=SCREAMING_SNAKE_CASE_ , compute_metrics=SCREAMING_SNAKE_CASE_ , data_collator=SCREAMING_SNAKE_CASE_ , )
# Training
if training_args.do_train:
trainer.train(
model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path ) else None )
trainer.save_model()
# For convenience, we also re-save the tokenizer to the same directory,
# so that you can share your model easily on huggingface.co/models =)
if trainer.is_world_master():
tokenizer.save_pretrained(training_args.output_dir )
# Evaluation
lowercase__ = {}
if training_args.do_eval:
logger.info('''*** Evaluate ***''' )
lowercase__ = trainer.evaluate()
lowercase__ = os.path.join(training_args.output_dir , '''eval_results.txt''' )
if trainer.is_world_master():
with open(SCREAMING_SNAKE_CASE_ , '''w''' ) as writer:
logger.info('''***** Eval results *****''' )
for key, value in result.items():
logger.info(''' %s = %s''' , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
writer.write('''%s = %s\n''' % (key, value) )
results.update(SCREAMING_SNAKE_CASE_ )
return results
def a ( lowerCamelCase_ ):
'''simple docstring'''
# For xla_spawn (TPUs)
main()
if __name__ == "__main__":
main()
| 207 |
from queue import PriorityQueue
from typing import Any
import numpy as np
def _a ( SCREAMING_SNAKE_CASE_ : dict , SCREAMING_SNAKE_CASE_ : str , SCREAMING_SNAKE_CASE_ : set , SCREAMING_SNAKE_CASE_ : set , SCREAMING_SNAKE_CASE_ : dict , SCREAMING_SNAKE_CASE_ : dict , SCREAMING_SNAKE_CASE_ : PriorityQueue , SCREAMING_SNAKE_CASE_ : dict , SCREAMING_SNAKE_CASE_ : float | int , ):
for nxt, d in graph[v]:
if nxt in visited_forward:
continue
__lowerCAmelCase = cst_fwd.get(SCREAMING_SNAKE_CASE_ , np.inf )
__lowerCAmelCase = cst_fwd[v] + d
if new_cost_f < old_cost_f:
queue.put((new_cost_f, nxt) )
__lowerCAmelCase = new_cost_f
__lowerCAmelCase = v
if nxt in visited_backward:
if cst_fwd[v] + d + cst_bwd[nxt] < shortest_distance:
__lowerCAmelCase = cst_fwd[v] + d + cst_bwd[nxt]
return shortest_distance
def _a ( SCREAMING_SNAKE_CASE_ : str , SCREAMING_SNAKE_CASE_ : str , SCREAMING_SNAKE_CASE_ : dict , SCREAMING_SNAKE_CASE_ : dict ):
__lowerCAmelCase = -1
__lowerCAmelCase = set()
__lowerCAmelCase = set()
__lowerCAmelCase = {source: 0}
__lowerCAmelCase = {destination: 0}
__lowerCAmelCase = {source: None}
__lowerCAmelCase = {destination: None}
__lowerCAmelCase = PriorityQueue()
__lowerCAmelCase = PriorityQueue()
__lowerCAmelCase = np.inf
queue_forward.put((0, source) )
queue_backward.put((0, destination) )
if source == destination:
return 0
while not queue_forward.empty() and not queue_backward.empty():
__lowerCAmelCase , __lowerCAmelCase = queue_forward.get()
visited_forward.add(SCREAMING_SNAKE_CASE_ )
__lowerCAmelCase , __lowerCAmelCase = queue_backward.get()
visited_backward.add(SCREAMING_SNAKE_CASE_ )
__lowerCAmelCase = pass_and_relaxation(
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , )
__lowerCAmelCase = pass_and_relaxation(
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , )
if cst_fwd[v_fwd] + cst_bwd[v_bwd] >= shortest_distance:
break
if shortest_distance != np.inf:
__lowerCAmelCase = shortest_distance
return shortest_path_distance
UpperCamelCase__ = {
"""B""": [["""C""", 1]],
"""C""": [["""D""", 1]],
"""D""": [["""F""", 1]],
"""E""": [["""B""", 1], ["""G""", 2]],
"""F""": [],
"""G""": [["""F""", 1]],
}
UpperCamelCase__ = {
"""B""": [["""E""", 1]],
"""C""": [["""B""", 1]],
"""D""": [["""C""", 1]],
"""F""": [["""D""", 1], ["""G""", 1]],
"""E""": [[None, np.inf]],
"""G""": [["""E""", 2]],
}
if __name__ == "__main__":
import doctest
doctest.testmod()
| 92 | 0 |
'''simple docstring'''
import unittest
import numpy as np
from transformers import is_flax_available
from transformers.testing_utils import require_flax
from ..test_modeling_flax_common import ids_tensor
if is_flax_available():
import jax
import jax.numpy as jnp
from transformers.generation import (
FlaxForcedBOSTokenLogitsProcessor,
FlaxForcedEOSTokenLogitsProcessor,
FlaxLogitsProcessorList,
FlaxMinLengthLogitsProcessor,
FlaxTemperatureLogitsWarper,
FlaxTopKLogitsWarper,
FlaxTopPLogitsWarper,
)
@require_flax
class a__( unittest.TestCase ):
def lowercase_ ( self : Optional[Any] , __snake_case : str , __snake_case : Dict ):
a : Optional[Any] = jnp.ones((batch_size, length) ) / length
return scores
def lowercase_ ( self : str ):
a : Tuple = None
a : Optional[int] = 20
a : List[str] = self._get_uniform_logits(batch_size=2 , length=_A )
# tweak scores to not be uniform anymore
a : int = scores.at[1, 5].set((1 / length) + 0.1 ) # peak, 1st batch
a : int = scores.at[1, 10].set((1 / length) - 0.4 ) # valley, 1st batch
# compute softmax
a : str = jax.nn.softmax(_A , axis=-1 )
a : List[str] = FlaxTemperatureLogitsWarper(temperature=0.5 )
a : List[Any] = FlaxTemperatureLogitsWarper(temperature=1.3 )
a : Tuple = jax.nn.softmax(temp_dist_warper_sharper(_A , scores.copy() , cur_len=_A ) , axis=-1 )
a : str = jax.nn.softmax(temp_dist_warper_smoother(_A , scores.copy() , cur_len=_A ) , axis=-1 )
# uniform distribution stays uniform
self.assertTrue(jnp.allclose(probs[0, :] , warped_prob_sharp[0, :] , atol=1e-3 ) )
self.assertTrue(jnp.allclose(probs[0, :] , warped_prob_smooth[0, :] , atol=1e-3 ) )
# sharp peaks get higher, valleys get lower
self.assertLess(probs[1, :].max() , warped_prob_sharp[1, :].max() )
self.assertGreater(probs[1, :].min() , warped_prob_sharp[1, :].min() )
# smooth peaks get lower, valleys get higher
self.assertGreater(probs[1, :].max() , warped_prob_smooth[1, :].max() )
self.assertLess(probs[1, :].min() , warped_prob_smooth[1, :].min() )
def lowercase_ ( self : Any ):
a : int = None
a : Optional[int] = 10
a : List[str] = 2
# create ramp distribution
a : int = np.broadcast_to(np.arange(_A )[None, :] , (batch_size, vocab_size) ).copy()
a : List[Any] = ramp_logits[1:, : vocab_size // 2] + vocab_size
a : List[Any] = FlaxTopKLogitsWarper(3 )
a : str = top_k_warp(_A , _A , cur_len=_A )
# check that correct tokens are filtered
self.assertListEqual(jnp.isinf(scores[0] ).tolist() , 7 * [True] + 3 * [False] )
self.assertListEqual(jnp.isinf(scores[1] ).tolist() , 2 * [True] + 3 * [False] + 5 * [True] )
# check special case
a : Any = 5
a : Any = FlaxTopKLogitsWarper(top_k=1 , filter_value=0.0 , min_tokens_to_keep=3 )
a : str = np.broadcast_to(np.arange(_A )[None, :] , (batch_size, length) ).copy()
a : Dict = top_k_warp_safety_check(_A , _A , cur_len=_A )
# min_tokens overwrites k: 3 tokens are kept => 2 tokens are nullified
self.assertListEqual((scores == 0.0).sum(axis=-1 ).tolist() , [2, 2] )
def lowercase_ ( self : List[Any] ):
a : Optional[Any] = None
a : Optional[Any] = 10
a : str = 2
# create distribution and take log (inverse to Softmax as taken in TopPLogitsWarper)
a : Optional[Any] = np.log(np.array([[0.3, 0.1, 0.1, 0.5], [0.15, 0.3, 0.3, 0.25]] ) )
a : Optional[int] = FlaxTopPLogitsWarper(0.8 )
a : Dict = np.exp(top_p_warp(_A , _A , cur_len=_A ) )
# dist should be filtered to keep min num values so that sum is >= top_p
# exp (-inf) => 0
a : Union[str, Any] = np.array([[0.3, 0.0, 0.0, 0.5], [0.0, 0.3, 0.3, 0.25]] )
self.assertTrue(np.allclose(_A , _A , atol=1e-3 ) )
# check edge cases with negative and extreme logits
a : int = np.broadcast_to(np.arange(_A )[None, :] , (batch_size, vocab_size) ).copy() - (
vocab_size // 2
)
# make ramp_logits more extreme
a : str = ramp_logits[1] * 1_00.0
# make sure at least 2 tokens are kept
a : List[str] = FlaxTopPLogitsWarper(0.9 , min_tokens_to_keep=2 , filter_value=0.0 )
a : Union[str, Any] = top_p_warp(_A , _A , cur_len=_A )
# first batch should keep three tokens, second batch would keep only 1, but due to `min_tokens_to_keep=2` keeps 2.
self.assertListEqual((filtered_dist != 0.0).sum(axis=-1 ).tolist() , [3, 2] )
def lowercase_ ( self : int ):
a : Dict = 20
a : Union[str, Any] = 4
a : Optional[int] = 0
a : List[Any] = FlaxMinLengthLogitsProcessor(min_length=10 , eos_token_id=_A )
# check that min length is applied at length 5
a : Dict = ids_tensor((batch_size, 20) , vocab_size=20 )
a : str = 5
a : List[Any] = self._get_uniform_logits(_A , _A )
a : List[Any] = min_dist_processor(_A , _A , cur_len=_A )
self.assertListEqual(scores_before_min_length[:, eos_token_id].tolist() , 4 * [-float('inf' )] )
# check that min length is not applied anymore at length 15
a : int = self._get_uniform_logits(_A , _A )
a : Union[str, Any] = 15
a : Union[str, Any] = min_dist_processor(_A , _A , cur_len=_A )
self.assertFalse(jnp.isinf(_A ).any() )
def lowercase_ ( self : Any ):
a : List[str] = 20
a : Dict = 4
a : str = 0
a : List[str] = FlaxForcedBOSTokenLogitsProcessor(bos_token_id=_A )
# check that all scores are -inf except the bos_token_id score
a : Tuple = ids_tensor((batch_size, 1) , vocab_size=20 )
a : List[str] = 1
a : List[str] = self._get_uniform_logits(_A , _A )
a : Dict = logits_processor(_A , _A , cur_len=_A )
self.assertTrue(jnp.isneginf(scores[:, bos_token_id + 1 :] ).all() )
self.assertListEqual(scores[:, bos_token_id].tolist() , 4 * [0] ) # score for bos_token_id shold be zero
# check that bos_token_id is not forced if current length is greater than 1
a : int = 3
a : Any = self._get_uniform_logits(_A , _A )
a : List[str] = logits_processor(_A , _A , cur_len=_A )
self.assertFalse(jnp.isinf(_A ).any() )
def lowercase_ ( self : Dict ):
a : List[Any] = 20
a : str = 4
a : Optional[int] = 0
a : Optional[Any] = 5
a : List[Any] = FlaxForcedEOSTokenLogitsProcessor(max_length=_A , eos_token_id=_A )
# check that all scores are -inf except the eos_token_id when max_length is reached
a : str = ids_tensor((batch_size, 4) , vocab_size=20 )
a : int = 4
a : Dict = self._get_uniform_logits(_A , _A )
a : List[str] = logits_processor(_A , _A , cur_len=_A )
self.assertTrue(jnp.isneginf(scores[:, eos_token_id + 1 :] ).all() )
self.assertListEqual(scores[:, eos_token_id].tolist() , 4 * [0] ) # score for eos_token_id should be zero
# check that eos_token_id is not forced if max_length is not reached
a : str = 3
a : Dict = self._get_uniform_logits(_A , _A )
a : Tuple = logits_processor(_A , _A , cur_len=_A )
self.assertFalse(jnp.isinf(_A ).any() )
def lowercase_ ( self : Any ):
a : str = 4
a : int = 10
a : str = 15
a : Optional[int] = 2
a : Optional[Any] = 1
a : List[Any] = 15
# dummy input_ids and scores
a : List[str] = ids_tensor((batch_size, sequence_length) , _A )
a : Any = input_ids.copy()
a : Union[str, Any] = self._get_uniform_logits(_A , _A )
a : List[Any] = scores.copy()
# instantiate all dist processors
a : str = FlaxTemperatureLogitsWarper(temperature=0.5 )
a : Any = FlaxTopKLogitsWarper(3 )
a : str = FlaxTopPLogitsWarper(0.8 )
# instantiate all logits processors
a : Union[str, Any] = FlaxMinLengthLogitsProcessor(min_length=10 , eos_token_id=_A )
a : int = FlaxForcedBOSTokenLogitsProcessor(bos_token_id=_A )
a : Any = FlaxForcedEOSTokenLogitsProcessor(max_length=_A , eos_token_id=_A )
a : str = 10
# no processor list
a : Tuple = temp_dist_warp(_A , _A , cur_len=_A )
a : Any = top_k_warp(_A , _A , cur_len=_A )
a : List[Any] = top_p_warp(_A , _A , cur_len=_A )
a : Dict = min_dist_proc(_A , _A , cur_len=_A )
a : Optional[Any] = bos_dist_proc(_A , _A , cur_len=_A )
a : Any = eos_dist_proc(_A , _A , cur_len=_A )
# with processor list
a : str = FlaxLogitsProcessorList(
[temp_dist_warp, top_k_warp, top_p_warp, min_dist_proc, bos_dist_proc, eos_dist_proc] )
a : Any = processor(_A , _A , cur_len=_A )
# scores should be equal
self.assertTrue(jnp.allclose(_A , _A , atol=1e-3 ) )
# input_ids should never be changed
self.assertListEqual(input_ids.tolist() , input_ids_comp.tolist() )
def lowercase_ ( self : Union[str, Any] ):
a : Tuple = 4
a : Optional[Any] = 10
a : Tuple = 15
a : str = 2
a : Tuple = 1
a : str = 15
# dummy input_ids and scores
a : int = ids_tensor((batch_size, sequence_length) , _A )
a : List[str] = input_ids.copy()
a : Union[str, Any] = self._get_uniform_logits(_A , _A )
a : Optional[Any] = scores.copy()
# instantiate all dist processors
a : List[Any] = FlaxTemperatureLogitsWarper(temperature=0.5 )
a : Optional[int] = FlaxTopKLogitsWarper(3 )
a : int = FlaxTopPLogitsWarper(0.8 )
# instantiate all logits processors
a : List[Any] = FlaxMinLengthLogitsProcessor(min_length=10 , eos_token_id=_A )
a : Any = FlaxForcedBOSTokenLogitsProcessor(bos_token_id=_A )
a : int = FlaxForcedEOSTokenLogitsProcessor(max_length=_A , eos_token_id=_A )
a : List[Any] = 10
# no processor list
def run_no_processor_list(__snake_case : int , __snake_case : Optional[int] , __snake_case : List[Any] ):
a : Optional[Any] = temp_dist_warp(_A , _A , cur_len=_A )
a : Optional[Any] = top_k_warp(_A , _A , cur_len=_A )
a : int = top_p_warp(_A , _A , cur_len=_A )
a : Tuple = min_dist_proc(_A , _A , cur_len=_A )
a : List[Any] = bos_dist_proc(_A , _A , cur_len=_A )
a : Optional[int] = eos_dist_proc(_A , _A , cur_len=_A )
return scores
# with processor list
def run_processor_list(__snake_case : Union[str, Any] , __snake_case : Union[str, Any] , __snake_case : Any ):
a : Union[str, Any] = FlaxLogitsProcessorList(
[temp_dist_warp, top_k_warp, top_p_warp, min_dist_proc, bos_dist_proc, eos_dist_proc] )
a : Optional[Any] = processor(_A , _A , cur_len=_A )
return scores
a : Optional[Any] = jax.jit(_A )
a : Dict = jax.jit(_A )
a : List[Any] = jitted_run_no_processor_list(_A , _A , _A )
a : int = jitted_run_processor_list(_A , _A , _A )
# scores should be equal
self.assertTrue(jnp.allclose(_A , _A , atol=1e-3 ) )
# input_ids should never be changed
self.assertListEqual(input_ids.tolist() , input_ids_comp.tolist() ) | 297 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCamelCase__ = logging.get_logger(__name__)
UpperCamelCase__ = {
"""edbeeching/decision-transformer-gym-hopper-medium""": (
"""https://huggingface.co/edbeeching/decision-transformer-gym-hopper-medium/resolve/main/config.json"""
),
# See all DecisionTransformer models at https://huggingface.co/models?filter=decision_transformer
}
class a__ ( snake_case__ ):
_a : Optional[int] = """decision_transformer"""
_a : Optional[int] = ["""past_key_values"""]
_a : Dict = {
"""max_position_embeddings""": """n_positions""",
"""num_attention_heads""": """n_head""",
"""num_hidden_layers""": """n_layer""",
}
def __init__( self , _A=1_7 , _A=4 , _A=1_2_8 , _A=4_0_9_6 , _A=True , _A=1 , _A=1_0_2_4 , _A=3 , _A=1 , _A=None , _A="relu" , _A=0.1 , _A=0.1 , _A=0.1 , _A=1E-5 , _A=0.02 , _A=True , _A=True , _A=5_0_2_5_6 , _A=5_0_2_5_6 , _A=False , _A=False , **_A , ):
"""simple docstring"""
__lowerCAmelCase = state_dim
__lowerCAmelCase = act_dim
__lowerCAmelCase = hidden_size
__lowerCAmelCase = max_ep_len
__lowerCAmelCase = action_tanh
__lowerCAmelCase = vocab_size
__lowerCAmelCase = n_positions
__lowerCAmelCase = n_layer
__lowerCAmelCase = n_head
__lowerCAmelCase = n_inner
__lowerCAmelCase = activation_function
__lowerCAmelCase = resid_pdrop
__lowerCAmelCase = embd_pdrop
__lowerCAmelCase = attn_pdrop
__lowerCAmelCase = layer_norm_epsilon
__lowerCAmelCase = initializer_range
__lowerCAmelCase = scale_attn_weights
__lowerCAmelCase = use_cache
__lowerCAmelCase = scale_attn_by_inverse_layer_idx
__lowerCAmelCase = reorder_and_upcast_attn
__lowerCAmelCase = bos_token_id
__lowerCAmelCase = eos_token_id
super().__init__(bos_token_id=_A , eos_token_id=_A , **_A )
| 92 | 0 |
from ...utils import is_note_seq_available, is_transformers_available, is_torch_available
from ...utils import OptionalDependencyNotAvailable
try:
if not (is_transformers_available() and is_torch_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ...utils.dummy_torch_and_transformers_objects import * # noqa F403
else:
from .notes_encoder import SpectrogramNotesEncoder
from .continous_encoder import SpectrogramContEncoder
from .pipeline_spectrogram_diffusion import (
SpectrogramContEncoder,
SpectrogramDiffusionPipeline,
TaFilmDecoder,
)
try:
if not (is_transformers_available() and is_torch_available() and is_note_seq_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ...utils.dummy_transformers_and_torch_and_note_seq_objects import * # noqa F403
else:
from .midi_utils import MidiProcessor
| 252 |
import gc
import unittest
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
DDPMScheduler,
PriorTransformer,
StableUnCLIPPipeline,
UNetaDConditionModel,
)
from diffusers.pipelines.stable_diffusion.stable_unclip_image_normalizer import StableUnCLIPImageNormalizer
from diffusers.utils.testing_utils import enable_full_determinism, load_numpy, require_torch_gpu, slow, torch_device
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,
assert_mean_pixel_difference,
)
enable_full_determinism()
class a__ ( snake_case__ , snake_case__ , snake_case__ , unittest.TestCase ):
_a : str = StableUnCLIPPipeline
_a : Union[str, Any] = TEXT_TO_IMAGE_PARAMS
_a : Dict = TEXT_TO_IMAGE_BATCH_PARAMS
_a : Optional[int] = TEXT_TO_IMAGE_IMAGE_PARAMS
_a : Dict = TEXT_TO_IMAGE_IMAGE_PARAMS
# TODO(will) Expected attn_bias.stride(1) == 0 to be true, but got false
_a : Optional[Any] = False
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
__lowerCAmelCase = 3_2
__lowerCAmelCase = embedder_hidden_size
# prior components
torch.manual_seed(0 )
__lowerCAmelCase = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip" )
torch.manual_seed(0 )
__lowerCAmelCase = CLIPTextModelWithProjection(
CLIPTextConfig(
bos_token_id=0 , eos_token_id=2 , hidden_size=_A , projection_dim=_A , intermediate_size=3_7 , layer_norm_eps=1E-0_5 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=1_0_0_0 , ) )
torch.manual_seed(0 )
__lowerCAmelCase = PriorTransformer(
num_attention_heads=2 , attention_head_dim=1_2 , embedding_dim=_A , num_layers=1 , )
torch.manual_seed(0 )
__lowerCAmelCase = DDPMScheduler(
variance_type="fixed_small_log" , prediction_type="sample" , num_train_timesteps=1_0_0_0 , clip_sample=_A , clip_sample_range=5.0 , beta_schedule="squaredcos_cap_v2" , )
# regular denoising components
torch.manual_seed(0 )
__lowerCAmelCase = StableUnCLIPImageNormalizer(embedding_dim=_A )
__lowerCAmelCase = DDPMScheduler(beta_schedule="squaredcos_cap_v2" )
torch.manual_seed(0 )
__lowerCAmelCase = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip" )
torch.manual_seed(0 )
__lowerCAmelCase = CLIPTextModel(
CLIPTextConfig(
bos_token_id=0 , eos_token_id=2 , hidden_size=_A , projection_dim=3_2 , intermediate_size=3_7 , layer_norm_eps=1E-0_5 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=1_0_0_0 , ) )
torch.manual_seed(0 )
__lowerCAmelCase = UNetaDConditionModel(
sample_size=3_2 , in_channels=4 , out_channels=4 , down_block_types=("CrossAttnDownBlock2D", "DownBlock2D") , up_block_types=("UpBlock2D", "CrossAttnUpBlock2D") , block_out_channels=(3_2, 6_4) , attention_head_dim=(2, 4) , class_embed_type="projection" , projection_class_embeddings_input_dim=embedder_projection_dim * 2 , cross_attention_dim=_A , layers_per_block=1 , upcast_attention=_A , use_linear_projection=_A , )
torch.manual_seed(0 )
__lowerCAmelCase = DDIMScheduler(
beta_schedule="scaled_linear" , beta_start=0.0_00_85 , beta_end=0.0_12 , prediction_type="v_prediction" , set_alpha_to_one=_A , steps_offset=1 , )
torch.manual_seed(0 )
__lowerCAmelCase = AutoencoderKL()
__lowerCAmelCase = {
# prior components
"prior_tokenizer": prior_tokenizer,
"prior_text_encoder": prior_text_encoder,
"prior": prior,
"prior_scheduler": prior_scheduler,
# image noising components
"image_normalizer": image_normalizer,
"image_noising_scheduler": image_noising_scheduler,
# regular denoising components
"tokenizer": tokenizer,
"text_encoder": text_encoder,
"unet": unet,
"scheduler": scheduler,
"vae": vae,
}
return components
def __SCREAMING_SNAKE_CASE( self , _A , _A=0 ):
"""simple docstring"""
if str(_A ).startswith("mps" ):
__lowerCAmelCase = torch.manual_seed(_A )
else:
__lowerCAmelCase = torch.Generator(device=_A ).manual_seed(_A )
__lowerCAmelCase = {
"prompt": "A painting of a squirrel eating a burger",
"generator": generator,
"num_inference_steps": 2,
"prior_num_inference_steps": 2,
"output_type": "numpy",
}
return inputs
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
__lowerCAmelCase = torch_device == "cpu"
self._test_attention_slicing_forward_pass(test_max_difference=_A )
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
__lowerCAmelCase = torch_device in ["cpu", "mps"]
self._test_inference_batch_single_identical(test_max_difference=_A )
@slow
@require_torch_gpu
class a__ ( unittest.TestCase ):
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
__lowerCAmelCase = load_numpy(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/stable_unclip_2_1_l_anime_turtle_fp16.npy" )
__lowerCAmelCase = StableUnCLIPPipeline.from_pretrained("fusing/stable-unclip-2-1-l" , torch_dtype=torch.floataa )
pipe.to(_A )
pipe.set_progress_bar_config(disable=_A )
# stable unclip will oom when integration tests are run on a V100,
# so turn on memory savings
pipe.enable_attention_slicing()
pipe.enable_sequential_cpu_offload()
__lowerCAmelCase = torch.Generator(device="cpu" ).manual_seed(0 )
__lowerCAmelCase = pipe("anime turle" , generator=_A , output_type="np" )
__lowerCAmelCase = output.images[0]
assert image.shape == (7_6_8, 7_6_8, 3)
assert_mean_pixel_difference(_A , _A )
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
torch.cuda.empty_cache()
torch.cuda.reset_max_memory_allocated()
torch.cuda.reset_peak_memory_stats()
__lowerCAmelCase = StableUnCLIPPipeline.from_pretrained("fusing/stable-unclip-2-1-l" , torch_dtype=torch.floataa )
__lowerCAmelCase = pipe.to(_A )
pipe.set_progress_bar_config(disable=_A )
pipe.enable_attention_slicing()
pipe.enable_sequential_cpu_offload()
__lowerCAmelCase = pipe(
"anime turtle" , prior_num_inference_steps=2 , num_inference_steps=2 , output_type="np" , )
__lowerCAmelCase = torch.cuda.max_memory_allocated()
# make sure that less than 7 GB is allocated
assert mem_bytes < 7 * 1_0**9
| 92 | 0 |
'''simple docstring'''
import pytest
from datasets import inspect_metric, list_metrics, load_metric
@pytest.fixture
def __snake_case( _lowerCAmelCase ) -> Dict:
monkeypatch.setattr("""datasets.utils.deprecation_utils._emitted_deprecation_warnings""" , set() )
@pytest.fixture
def __snake_case( _lowerCAmelCase ) -> Optional[int]:
class UpperCAmelCase_ :
"""simple docstring"""
def __init__( self : List[Any] , snake_case_ : str ):
snake_case__ : Union[str, Any] = metric_id
class UpperCAmelCase_ :
"""simple docstring"""
lowercase = [MetricMock(snake_case__ ) for metric_id in ["""accuracy""", """mse""", """precision""", """codeparrot/apps_metric"""]]
def lowerCamelCase ( self : Optional[int] ):
return self._metrics
monkeypatch.setattr("""datasets.inspect.huggingface_hub""" , HfhMock() )
@pytest.mark.parametrize(
"""func, args""" , [(load_metric, ("""metrics/mse""",)), (list_metrics, ()), (inspect_metric, ("""metrics/mse""", """tmp_path"""))] )
def __snake_case( _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase ) -> str:
if "tmp_path" in args:
snake_case__ : Optional[int] = tuple(arg if arg != """tmp_path""" else tmp_path for arg in args )
with pytest.warns(SCREAMING_SNAKE_CASE_ , match="""https://huggingface.co/docs/evaluate""" ):
func(*SCREAMING_SNAKE_CASE_ )
| 35 |
from typing import TYPE_CHECKING
from ...utils import _LazyModule
UpperCamelCase__ = {"""tokenization_wav2vec2_phoneme""": ["""Wav2Vec2PhonemeCTCTokenizer"""]}
if TYPE_CHECKING:
from .tokenization_wavaveca_phoneme import WavaVecaPhonemeCTCTokenizer
else:
import sys
UpperCamelCase__ = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 92 | 0 |
'''simple docstring'''
import unittest
from transformers import TrOCRConfig
from transformers.testing_utils import is_torch_available, require_torch, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers.models.trocr.modeling_trocr import TrOCRDecoder, TrOCRForCausalLM
@require_torch
class UpperCamelCase__ :
def __init__( self :Optional[Any] , _A :Optional[int] , _A :Optional[Any]=99 , _A :Optional[int]=13 , _A :int=16 , _A :str=7 , _A :List[Any]=True , _A :List[Any]=True , _A :Any=True , _A :List[str]=False , _A :int=True , _A :Union[str, Any]=2 , _A :List[str]=32 , _A :Optional[Any]=4 , _A :int=4 , _A :int=30 , _A :Dict=0 , _A :List[str]=1 , _A :Union[str, Any]=2 , _A :int=None , ) -> Optional[int]:
'''simple docstring'''
__A = parent
__A = batch_size
__A = decoder_seq_length
# For common tests
__A = self.decoder_seq_length
__A = is_training
__A = use_attention_mask
__A = use_labels
__A = vocab_size
__A = d_model
__A = d_model
__A = decoder_layers
__A = decoder_layers
__A = decoder_ffn_dim
__A = decoder_attention_heads
__A = decoder_attention_heads
__A = eos_token_id
__A = bos_token_id
__A = pad_token_id
__A = decoder_start_token_id
__A = use_cache
__A = max_position_embeddings
__A = None
__A = decoder_seq_length
__A = 2
__A = 1
def lowercase_ ( self :List[str] ) -> Any:
'''simple docstring'''
__A = ids_tensor([self.batch_size, self.decoder_seq_length] , self.vocab_size )
__A = None
if self.use_attention_mask:
__A = ids_tensor([self.batch_size, self.decoder_seq_length] , vocab_size=2 )
__A = None
if self.use_labels:
__A = ids_tensor([self.batch_size, self.decoder_seq_length] , self.vocab_size )
__A = TrOCRConfig(
vocab_size=self.vocab_size , d_model=self.d_model , decoder_layers=self.decoder_layers , decoder_ffn_dim=self.decoder_ffn_dim , decoder_attention_heads=self.decoder_attention_heads , eos_token_id=self.eos_token_id , bos_token_id=self.bos_token_id , use_cache=self.use_cache , pad_token_id=self.pad_token_id , decoder_start_token_id=self.decoder_start_token_id , max_position_embeddings=self.max_position_embeddings , )
return (config, input_ids, attention_mask, lm_labels)
def lowercase_ ( self :int , _A :Tuple , _A :Union[str, Any] , _A :Dict , _A :Any , ) -> int:
'''simple docstring'''
__A = True
__A = TrOCRDecoder(config=_A ).to(_A ).eval()
__A = input_ids[:2]
input_ids[input_ids == 0] += 1
# first forward pass
__A = model(_A , use_cache=_A )
__A = model(_A )
__A = model(_A , use_cache=_A )
self.parent.assertTrue(len(_A ) == len(_A ) )
self.parent.assertTrue(len(_A ) == len(_A ) + 1 )
__A = outputs['past_key_values']
# create hypothetical next token and extent to next_input_ids
__A = ids_tensor((2, 1) , config.vocab_size - 1 ) + 1
# append to next input_ids and
__A = torch.cat([input_ids, next_tokens] , dim=-1 )
__A = model(_A )['last_hidden_state']
__A = model(_A , past_key_values=_A )['last_hidden_state']
# select random slice
__A = ids_tensor((1,) , output_from_past.shape[-1] ).item()
__A = output_from_no_past[:, next_input_ids.shape[-1] - 1, random_slice_idx].detach()
__A = output_from_past[:, 0, random_slice_idx].detach()
# test that outputs are equal for slice
assert torch.allclose(_A , _A , atol=1E-3 )
def lowercase_ ( self :Optional[int] ) -> Optional[Any]:
'''simple docstring'''
__A = self.prepare_config_and_inputs()
__A , __A , __A , __A = config_and_inputs
__A = {'input_ids': input_ids, 'attention_mask': attention_mask}
return config, inputs_dict
@require_torch
class UpperCamelCase__ ( snake_case__ , snake_case__ , snake_case__ , unittest.TestCase):
UpperCAmelCase__ : List[Any] = (TrOCRDecoder, TrOCRForCausalLM) if is_torch_available() else ()
UpperCAmelCase__ : Dict = (TrOCRForCausalLM,) if is_torch_available() else ()
UpperCAmelCase__ : Union[str, Any] = {"""text-generation""": TrOCRForCausalLM} if is_torch_available() else {}
UpperCAmelCase__ : List[Any] = True
UpperCAmelCase__ : Dict = False
def lowercase_ ( self :Any ) -> List[str]:
'''simple docstring'''
__A = TrOCRStandaloneDecoderModelTester(self , is_training=_A )
__A = ConfigTester(self , config_class=_A )
def lowercase_ ( self :Optional[int] ) -> Optional[int]:
'''simple docstring'''
pass
def lowercase_ ( self :List[Any] ) -> Dict:
'''simple docstring'''
pass
def lowercase_ ( self :Any ) -> Tuple:
'''simple docstring'''
pass
def lowercase_ ( self :Optional[int] ) -> Optional[int]:
'''simple docstring'''
self.config_tester.run_common_tests()
def lowercase_ ( self :Optional[int] ) -> Optional[Any]:
'''simple docstring'''
__A = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_decoder_model_past(*_A )
def lowercase_ ( self :Union[str, Any] ) -> Any:
'''simple docstring'''
return
@unittest.skip('The model doesn\'t support left padding' ) # and it's not used enough to be worth fixing :)
def lowercase_ ( self :Any ) -> Union[str, Any]:
'''simple docstring'''
pass
| 161 |
import unittest
from transformers import DebertaVaTokenizer, DebertaVaTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin
UpperCamelCase__ = get_tests_dir("""fixtures/spiece.model""")
@require_sentencepiece
@require_tokenizers
class a__ ( snake_case__ , unittest.TestCase ):
_a : Optional[Any] = DebertaVaTokenizer
_a : Optional[Any] = DebertaVaTokenizerFast
_a : List[str] = True
_a : Optional[Any] = True
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
super().setUp()
# We have a SentencePiece fixture for testing
__lowerCAmelCase = DebertaVaTokenizer(_A , unk_token="<unk>" )
tokenizer.save_pretrained(self.tmpdirname )
def __SCREAMING_SNAKE_CASE( self , _A ):
"""simple docstring"""
__lowerCAmelCase = "this is a test"
__lowerCAmelCase = "this is a test"
return input_text, output_text
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
__lowerCAmelCase = "<pad>"
__lowerCAmelCase = 0
self.assertEqual(self.get_tokenizer()._convert_token_to_id(_A ) , _A )
self.assertEqual(self.get_tokenizer()._convert_id_to_token(_A ) , _A )
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
__lowerCAmelCase = list(self.get_tokenizer().get_vocab().keys() )
self.assertEqual(vocab_keys[0] , "<pad>" )
self.assertEqual(vocab_keys[1] , "<unk>" )
self.assertEqual(vocab_keys[-1] , "[PAD]" )
self.assertEqual(len(_A ) , 3_0_0_0_1 )
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
self.assertEqual(self.get_tokenizer().vocab_size , 3_0_0_0_0 )
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
__lowerCAmelCase = " \tHeLLo!how \n Are yoU? "
__lowerCAmelCase = ["▁hello", "!", "how", "▁are", "▁you", "?"]
# fmt: on
__lowerCAmelCase = DebertaVaTokenizer(_A , do_lower_case=_A )
__lowerCAmelCase = tokenizer.convert_ids_to_tokens(tokenizer.encode(_A , add_special_tokens=_A ) )
self.assertListEqual(_A , _A )
__lowerCAmelCase = DebertaVaTokenizerFast(_A , do_lower_case=_A )
__lowerCAmelCase = rust_tokenizer.convert_ids_to_tokens(rust_tokenizer.encode(_A , add_special_tokens=_A ) )
self.assertListEqual(_A , _A )
@unittest.skip("There is an inconsistency between slow and fast tokenizer due to a bug in the fast one." )
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
pass
@unittest.skip("There is an inconsistency between slow and fast tokenizer due to a bug in the fast one." )
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
pass
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
__lowerCAmelCase = "I was born in 92000, and this is falsé."
__lowerCAmelCase = ["▁", "<unk>", "▁was", "▁born", "▁in", "▁9", "2000", "▁", ",", "▁and", "▁this", "▁is", "▁fal", "s", "<unk>", "▁", ".", ]
# fmt: on
__lowerCAmelCase = DebertaVaTokenizer(_A , split_by_punct=_A )
__lowerCAmelCase = tokenizer.convert_ids_to_tokens(tokenizer.encode(_A , add_special_tokens=_A ) )
self.assertListEqual(_A , _A )
__lowerCAmelCase = DebertaVaTokenizerFast(_A , split_by_punct=_A )
__lowerCAmelCase = rust_tokenizer.convert_ids_to_tokens(rust_tokenizer.encode(_A , add_special_tokens=_A ) )
self.assertListEqual(_A , _A )
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
__lowerCAmelCase = "I was born in 92000, and this is falsé."
__lowerCAmelCase = ["▁i", "▁was", "▁born", "▁in", "▁9", "2000", "▁", ",", "▁and", "▁this", "▁is", "▁fal", "s", "<unk>", "▁", ".", ]
# fmt: on
__lowerCAmelCase = DebertaVaTokenizer(_A , do_lower_case=_A , split_by_punct=_A )
__lowerCAmelCase = tokenizer.convert_ids_to_tokens(tokenizer.encode(_A , add_special_tokens=_A ) )
self.assertListEqual(_A , _A )
__lowerCAmelCase = DebertaVaTokenizerFast(_A , do_lower_case=_A , split_by_punct=_A )
__lowerCAmelCase = rust_tokenizer.convert_ids_to_tokens(rust_tokenizer.encode(_A , add_special_tokens=_A ) )
self.assertListEqual(_A , _A )
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
__lowerCAmelCase = "I was born in 92000, and this is falsé."
__lowerCAmelCase = ["▁i", "▁was", "▁born", "▁in", "▁9", "2000", ",", "▁and", "▁this", "▁is", "▁fal", "s", "<unk>", ".", ]
# fmt: on
__lowerCAmelCase = DebertaVaTokenizer(_A , do_lower_case=_A , split_by_punct=_A )
__lowerCAmelCase = tokenizer.convert_ids_to_tokens(tokenizer.encode(_A , add_special_tokens=_A ) )
self.assertListEqual(_A , _A )
__lowerCAmelCase = DebertaVaTokenizerFast(_A , do_lower_case=_A , split_by_punct=_A )
__lowerCAmelCase = rust_tokenizer.convert_ids_to_tokens(rust_tokenizer.encode(_A , add_special_tokens=_A ) )
self.assertListEqual(_A , _A )
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
__lowerCAmelCase = "I was born in 92000, and this is falsé."
__lowerCAmelCase = ["▁", "<unk>", "▁was", "▁born", "▁in", "▁9", "2000", "▁", ",", "▁and", "▁this", "▁is", "▁fal", "s", "<unk>", "▁", ".", ]
# fmt: on
__lowerCAmelCase = DebertaVaTokenizer(_A , do_lower_case=_A , split_by_punct=_A )
__lowerCAmelCase = tokenizer.convert_ids_to_tokens(tokenizer.encode(_A , add_special_tokens=_A ) )
self.assertListEqual(_A , _A )
__lowerCAmelCase = DebertaVaTokenizerFast(_A , do_lower_case=_A , split_by_punct=_A )
__lowerCAmelCase = rust_tokenizer.convert_ids_to_tokens(rust_tokenizer.encode(_A , add_special_tokens=_A ) )
self.assertListEqual(_A , _A )
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
__lowerCAmelCase = " \tHeLLo!how \n Are yoU? "
__lowerCAmelCase = ["▁", "<unk>", "e", "<unk>", "o", "!", "how", "▁", "<unk>", "re", "▁yo", "<unk>", "?"]
# fmt: on
__lowerCAmelCase = DebertaVaTokenizer(_A , do_lower_case=_A , split_by_punct=_A )
__lowerCAmelCase = tokenizer.convert_ids_to_tokens(tokenizer.encode(_A , add_special_tokens=_A ) )
self.assertListEqual(_A , _A )
__lowerCAmelCase = DebertaVaTokenizerFast(_A , do_lower_case=_A , split_by_punct=_A )
__lowerCAmelCase = rust_tokenizer.convert_ids_to_tokens(rust_tokenizer.encode(_A , add_special_tokens=_A ) )
self.assertListEqual(_A , _A )
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
__lowerCAmelCase = self.get_tokenizer()
__lowerCAmelCase = self.get_rust_tokenizer()
__lowerCAmelCase = "I was born in 92000, and this is falsé."
__lowerCAmelCase = tokenizer.convert_ids_to_tokens(tokenizer.encode(_A , add_special_tokens=_A ) )
__lowerCAmelCase = rust_tokenizer.convert_ids_to_tokens(rust_tokenizer.encode(_A , add_special_tokens=_A ) )
self.assertListEqual(_A , _A )
__lowerCAmelCase = tokenizer.encode(_A , add_special_tokens=_A )
__lowerCAmelCase = rust_tokenizer.encode(_A , add_special_tokens=_A )
self.assertListEqual(_A , _A )
__lowerCAmelCase = self.get_rust_tokenizer()
__lowerCAmelCase = tokenizer.encode(_A )
__lowerCAmelCase = rust_tokenizer.encode(_A )
self.assertListEqual(_A , _A )
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
__lowerCAmelCase = "This is a test"
__lowerCAmelCase = [1_3, 1, 4_3_9_8, 2_5, 2_1, 1_2_8_9]
__lowerCAmelCase = ["▁", "T", "his", "▁is", "▁a", "▁test"]
__lowerCAmelCase = ["▁", "<unk>", "his", "▁is", "▁a", "▁test"]
__lowerCAmelCase = DebertaVaTokenizer(_A , keep_accents=_A )
__lowerCAmelCase = DebertaVaTokenizerFast(_A , keep_accents=_A )
__lowerCAmelCase = tokenizer.encode(_A , add_special_tokens=_A )
self.assertListEqual(_A , _A )
__lowerCAmelCase = tokenizer.tokenize(_A )
self.assertListEqual(_A , _A )
__lowerCAmelCase = tokenizer.convert_ids_to_tokens(_A )
self.assertListEqual(_A , _A )
__lowerCAmelCase = rust_tokenizer.encode(_A , add_special_tokens=_A )
self.assertListEqual(_A , _A )
__lowerCAmelCase = rust_tokenizer.tokenize(_A )
self.assertListEqual(_A , _A )
__lowerCAmelCase = rust_tokenizer.convert_ids_to_tokens(_A )
self.assertListEqual(_A , _A )
# fmt: off
__lowerCAmelCase = "I was born in 92000, and this is falsé."
__lowerCAmelCase = [1_3, 1, 2_3, 3_8_6, 1_9, 5_6_1, 3_0_5_0, 1_5, 1_7, 4_8, 2_5, 8_2_5_6, 1_8, 1, 9]
__lowerCAmelCase = ["▁", "I", "▁was", "▁born", "▁in", "▁9", "2000", ",", "▁and", "▁this", "▁is", "▁fal", "s", "é", ".", ]
__lowerCAmelCase = ["▁", "<unk>", "▁was", "▁born", "▁in", "▁9", "2000", ",", "▁and", "▁this", "▁is", "▁fal", "s", "<unk>", ".", ]
# fmt: on
__lowerCAmelCase = tokenizer.encode(_A , add_special_tokens=_A )
self.assertListEqual(_A , _A )
__lowerCAmelCase = tokenizer.tokenize(_A )
self.assertListEqual(_A , _A )
__lowerCAmelCase = tokenizer.convert_ids_to_tokens(_A )
self.assertListEqual(_A , _A )
__lowerCAmelCase = rust_tokenizer.encode(_A , add_special_tokens=_A )
self.assertListEqual(_A , _A )
__lowerCAmelCase = rust_tokenizer.tokenize(_A )
self.assertListEqual(_A , _A )
__lowerCAmelCase = rust_tokenizer.convert_ids_to_tokens(_A )
self.assertListEqual(_A , _A )
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
__lowerCAmelCase = DebertaVaTokenizer(_A )
__lowerCAmelCase = tokenizer.encode("sequence builders" )
__lowerCAmelCase = tokenizer.encode("multi-sequence build" )
__lowerCAmelCase = tokenizer.build_inputs_with_special_tokens(_A )
__lowerCAmelCase = tokenizer.build_inputs_with_special_tokens(_A , _A )
self.assertEqual([tokenizer.cls_token_id] + text + [tokenizer.sep_token_id] , _A )
self.assertEqual(
[tokenizer.cls_token_id] + text + [tokenizer.sep_token_id] + text_a + [tokenizer.sep_token_id] , _A , )
@slow
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
__lowerCAmelCase = {"input_ids": [[1, 3_9_8_6_7, 3_6, 1_9_3_9_0, 4_8_6, 2_7, 3_5_0_5_2, 8_1_4_3_6, 1_8, 6_0_6_8_5, 1_2_2_5, 7, 3_5_0_5_2, 8_1_4_3_6, 1_8, 9_3_6_7, 1_6_8_9_9, 1_8, 1_5_9_3_7, 5_3, 5_9_4, 7_7_3, 1_8, 1_6_2_8_7, 3_0_4_6_5, 3_6, 1_5_9_3_7, 6, 4_1_1_3_9, 3_8, 3_6_9_7_9, 6_0_7_6_3, 1_9_1, 6, 3_4_1_3_2, 9_9, 6, 5_0_5_3_8, 3_9_0, 4_3_2_3_0, 6, 3_4_1_3_2, 2_7_7_9, 2_0_8_5_0, 1_4, 6_9_9, 1_0_7_2, 1_1_9_4, 3_6, 3_8_2, 1_0_9_0_1, 5_3, 7, 6_9_9, 1_0_7_2, 2_0_8_4, 3_6, 2_0_4_2_2, 6_3_0, 5_3, 1_9, 1_0_5, 3_0_4_9, 1_8_9_6, 1_0_5_3, 1_6_8_9_9, 1_5_0_6, 1_1, 3_7_9_7_8, 4_2_4_3, 7, 1_2_3_7, 3_1_8_6_9, 2_0_0, 1_6_5_6_6, 6_5_4, 6, 3_5_0_5_2, 8_1_4_3_6, 7, 5_5_6_3_0, 1_3_5_9_3, 4, 2], [1, 2_6, 1_5_0_1_1, 1_3, 6_6_7, 8, 1_0_5_3, 1_8, 2_3_6_1_1, 1_2_3_7, 7_2_3_5_6, 1_2_8_2_0, 3_4, 1_0_4_1_3_4, 1_2_0_9, 3_5, 1_3_3_1_3, 6_6_2_7, 2_1, 2_0_2, 3_4_7, 7, 1_6_4, 2_3_9_9, 1_1, 4_6, 4_4_8_5, 4, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 5, 1_2_3_2, 2_8_6_4, 1_5_7_8_5, 1_4_9_5_1, 1_0_5, 5, 8_5_8_1, 1_2_5_0, 4, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], "token_type_ids": [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], "attention_mask": [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]} # noqa: E501
# fmt: on
self.tokenizer_integration_test_util(
expected_encoding=_A , model_name="microsoft/deberta-v2-xlarge" , revision="ad6e42c1532ddf3a15c39246b63f5559d558b670" , )
| 92 | 0 |
"""simple docstring"""
def a__ ( _SCREAMING_SNAKE_CASE ):
"""simple docstring"""
if not all(x.isalpha() for x in string ):
raise ValueError("String must only contain alphabetic characters." )
UpperCamelCase = sorted(string.lower() )
return len(SCREAMING_SNAKE_CASE_ ) == len(set(SCREAMING_SNAKE_CASE_ ) )
if __name__ == "__main__":
lowerCAmelCase__ = input('''Enter a string ''').strip()
lowerCAmelCase__ = is_isogram(input_str)
print(f'''{input_str} is {'an' if isogram else 'not an'} isogram.''')
| 153 |
from dataclasses import dataclass, field
from typing import Tuple
from ..utils import cached_property, is_tf_available, logging, requires_backends
from .benchmark_args_utils import BenchmarkArguments
if is_tf_available():
import tensorflow as tf
UpperCamelCase__ = logging.get_logger(__name__)
@dataclass
class a__ ( snake_case__ ):
_a : List[str] = [
"""no_inference""",
"""no_cuda""",
"""no_tpu""",
"""no_speed""",
"""no_memory""",
"""no_env_print""",
"""no_multi_process""",
]
def __init__( self , **_A ):
"""simple docstring"""
for deprecated_arg in self.deprecated_args:
if deprecated_arg in kwargs:
__lowerCAmelCase = deprecated_arg[3:]
__lowerCAmelCase = not kwargs.pop(_A )
logger.warning(
f"""{deprecated_arg} is depreciated. Please use --no-{positive_arg} or"""
f""" {positive_arg}={kwargs[positive_arg]}""" )
__lowerCAmelCase = kwargs.pop("tpu_name" , self.tpu_name )
__lowerCAmelCase = kwargs.pop("device_idx" , self.device_idx )
__lowerCAmelCase = kwargs.pop("eager_mode" , self.eager_mode )
__lowerCAmelCase = kwargs.pop("use_xla" , self.use_xla )
super().__init__(**_A )
_a : str = field(
default=snake_case__ , metadata={"""help""": """Name of TPU"""} , )
_a : int = field(
default=0 , metadata={"""help""": """CPU / GPU device index. Defaults to 0."""} , )
_a : bool = field(default=snake_case__ , metadata={"""help""": """Benchmark models in eager model."""} )
_a : bool = field(
default=snake_case__ , metadata={
"""help""": """Benchmark models using XLA JIT compilation. Note that `eager_model` has to be set to `False`."""
} , )
@cached_property
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
requires_backends(self , ["tf"] )
__lowerCAmelCase = None
if self.tpu:
try:
if self.tpu_name:
__lowerCAmelCase = tf.distribute.cluster_resolver.TPUClusterResolver(self.tpu_name )
else:
__lowerCAmelCase = tf.distribute.cluster_resolver.TPUClusterResolver()
except ValueError:
__lowerCAmelCase = None
return tpu
@cached_property
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
requires_backends(self , ["tf"] )
if self.is_tpu:
tf.config.experimental_connect_to_cluster(self._setup_tpu )
tf.tpu.experimental.initialize_tpu_system(self._setup_tpu )
__lowerCAmelCase = tf.distribute.TPUStrategy(self._setup_tpu )
else:
# currently no multi gpu is allowed
if self.is_gpu:
# TODO: Currently only single GPU is supported
tf.config.set_visible_devices(self.gpu_list[self.device_idx] , "GPU" )
__lowerCAmelCase = tf.distribute.OneDeviceStrategy(device=f"""/gpu:{self.device_idx}""" )
else:
tf.config.set_visible_devices([] , "GPU" ) # disable GPU
__lowerCAmelCase = tf.distribute.OneDeviceStrategy(device=f"""/cpu:{self.device_idx}""" )
return strategy
@property
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
requires_backends(self , ["tf"] )
return self._setup_tpu is not None
@property
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
requires_backends(self , ["tf"] )
return self._setup_strategy
@property
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
requires_backends(self , ["tf"] )
return tf.config.list_physical_devices("GPU" )
@property
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
requires_backends(self , ["tf"] )
if self.cuda:
return len(self.gpu_list )
return 0
@property
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
return self.n_gpu > 0
| 92 | 0 |
"""simple docstring"""
__snake_case = """Alexander Joslin"""
import operator as op
from .stack import Stack
def __lowerCAmelCase ( lowercase : str ) -> int:
"""simple docstring"""
snake_case : List[Any] = {"*": op.mul, "/": op.truediv, "+": op.add, "-": op.sub}
snake_case : List[Any] = Stack()
snake_case : Tuple = Stack()
for i in equation:
if i.isdigit():
# RULE 1
operand_stack.push(int(SCREAMING_SNAKE_CASE_ ) )
elif i in operators:
# RULE 2
operator_stack.push(SCREAMING_SNAKE_CASE_ )
elif i == ")":
# RULE 4
snake_case : int = operator_stack.peek()
operator_stack.pop()
snake_case : Dict = operand_stack.peek()
operand_stack.pop()
snake_case : List[Any] = operand_stack.peek()
operand_stack.pop()
snake_case : Optional[Any] = operators[opr](SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
operand_stack.push(SCREAMING_SNAKE_CASE_ )
# RULE 5
return operand_stack.peek()
if __name__ == "__main__":
__snake_case = """(5 + ((4 * 2) * (2 + 3)))"""
# answer = 45
print(F'''{equation} = {dijkstras_two_stack_algorithm(equation)}''')
| 203 |
import unittest
from transformers import CamembertTokenizer, CamembertTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import is_torch_available
from ...test_tokenization_common import TokenizerTesterMixin
UpperCamelCase__ = get_tests_dir("""fixtures/test_sentencepiece.model""")
UpperCamelCase__ = get_tests_dir("""fixtures/test_sentencepiece_bpe.model""")
UpperCamelCase__ = """pt""" if is_torch_available() else """tf"""
@require_sentencepiece
@require_tokenizers
class a__ ( snake_case__ , unittest.TestCase ):
_a : int = CamembertTokenizer
_a : Dict = CamembertTokenizerFast
_a : Tuple = True
_a : List[Any] = True
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
super().setUp()
# We have a SentencePiece fixture for testing
__lowerCAmelCase = CamembertTokenizer(_A )
tokenizer.save_pretrained(self.tmpdirname )
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
__lowerCAmelCase = "<pad>"
__lowerCAmelCase = 1
self.assertEqual(self.get_tokenizer()._convert_token_to_id(_A ) , _A )
self.assertEqual(self.get_tokenizer()._convert_id_to_token(_A ) , _A )
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
__lowerCAmelCase = list(self.get_tokenizer().get_vocab().keys() )
self.assertEqual(vocab_keys[0] , "<s>NOTUSED" )
self.assertEqual(vocab_keys[1] , "<pad>" )
self.assertEqual(vocab_keys[-1] , "<mask>" )
self.assertEqual(len(_A ) , 1_0_0_4 )
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
self.assertEqual(self.get_tokenizer().vocab_size , 1_0_0_5 )
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
__lowerCAmelCase = CamembertTokenizer(_A )
tokenizer.save_pretrained(self.tmpdirname )
__lowerCAmelCase = CamembertTokenizerFast.from_pretrained(self.tmpdirname )
__lowerCAmelCase = "I was born in 92000, and this is falsé."
__lowerCAmelCase = tokenizer.encode(_A )
__lowerCAmelCase = rust_tokenizer.encode(_A )
self.assertListEqual(_A , _A )
__lowerCAmelCase = tokenizer.encode(_A , add_special_tokens=_A )
__lowerCAmelCase = rust_tokenizer.encode(_A , add_special_tokens=_A )
self.assertListEqual(_A , _A )
# <unk> tokens are not the same for `rust` than for `slow`.
# Because spm gives back raw token instead of `unk` in EncodeAsPieces
# tokens = tokenizer.tokenize(sequence)
__lowerCAmelCase = tokenizer.convert_ids_to_tokens(_A )
__lowerCAmelCase = rust_tokenizer.tokenize(_A )
self.assertListEqual(_A , _A )
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
if not self.test_rust_tokenizer:
return
__lowerCAmelCase = self.get_tokenizer()
__lowerCAmelCase = self.get_rust_tokenizer()
__lowerCAmelCase = "I was born in 92000, and this is falsé."
__lowerCAmelCase = tokenizer.tokenize(_A )
__lowerCAmelCase = rust_tokenizer.tokenize(_A )
self.assertListEqual(_A , _A )
__lowerCAmelCase = tokenizer.encode(_A , add_special_tokens=_A )
__lowerCAmelCase = rust_tokenizer.encode(_A , add_special_tokens=_A )
self.assertListEqual(_A , _A )
__lowerCAmelCase = self.get_rust_tokenizer()
__lowerCAmelCase = tokenizer.encode(_A )
__lowerCAmelCase = rust_tokenizer.encode(_A )
self.assertListEqual(_A , _A )
@slow
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
__lowerCAmelCase = {"input_ids": [[5, 5_4, 7_1_9_6, 2_9_7, 3_0, 2_3, 7_7_6, 1_8, 1_1, 3_2_1_5, 3_7_0_5, 8_2_5_2, 2_2, 3_1_6_4, 1_1_8_1, 2_1_1_6, 2_9, 1_6, 8_1_3, 2_5, 7_9_1, 3_3_1_4, 2_0, 3_4_4_6, 3_8, 2_7_5_7_5, 1_2_0, 6, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [5, 4_6_8, 1_7, 1_1, 9_0_8_8, 2_0, 1_5_1_7, 8, 2_2_8_0_4, 1_8_8_1_8, 1_0, 3_8, 6_2_9, 6_0_7, 6_0_7, 1_4_2, 1_9, 7_1_9_6, 8_6_7, 5_6, 1_0_3_2_6, 2_4, 2_2_6_7, 2_0, 4_1_6, 5_0_7_2, 1_5_6_1_2, 2_3_3, 7_3_4, 7, 2_3_9_9, 2_7, 1_6, 3_0_1_5, 1_6_4_9, 7, 2_4, 2_0, 4_3_3_8, 2_3_9_9, 2_7, 1_3, 3_4_0_0, 1_4, 1_3, 6_1_8_9, 8, 9_3_0, 9, 6]], "attention_mask": [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]} # noqa: E501
# fmt: on
# camembert is a french model. So we also use french texts.
__lowerCAmelCase = [
"Le transformeur est un modèle d'apprentissage profond introduit en 2017, "
"utilisé principalement dans le domaine du traitement automatique des langues (TAL).",
"À l'instar des réseaux de neurones récurrents (RNN), les transformeurs sont conçus "
"pour gérer des données séquentielles, telles que le langage naturel, pour des tâches "
"telles que la traduction et la synthèse de texte.",
]
self.tokenizer_integration_test_util(
expected_encoding=_A , model_name="camembert-base" , revision="3a0641d9a1aeb7e848a74299e7e4c4bca216b4cf" , sequences=_A , )
| 92 | 0 |
'''simple docstring'''
from __future__ import annotations
a : Any = 1.60_21E-19 # units = C
def __magic_name__ ( __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase, ) -> Optional[int]:
'''simple docstring'''
if (conductivity, electron_conc, mobility).count(0 ) != 1:
raise ValueError('''You cannot supply more or less than 2 values''' )
elif conductivity < 0:
raise ValueError('''Conductivity cannot be negative''' )
elif electron_conc < 0:
raise ValueError('''Electron concentration cannot be negative''' )
elif mobility < 0:
raise ValueError('''mobility cannot be negative''' )
elif conductivity == 0:
return (
"conductivity",
mobility * electron_conc * ELECTRON_CHARGE,
)
elif electron_conc == 0:
return (
"electron_conc",
conductivity / (mobility * ELECTRON_CHARGE),
)
else:
return (
"mobility",
conductivity / (electron_conc * ELECTRON_CHARGE),
)
if __name__ == "__main__":
import doctest
doctest.testmod()
| 56 |
from __future__ import annotations
import collections
import tempfile
import unittest
import numpy as np
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import is_tf_available, is_vision_available
from ...test_modeling_tf_common import floats_tensor, ids_tensor, random_attention_mask
from ..bert.test_modeling_tf_bert import TFBertModelTester
from ..clip.test_modeling_tf_clip import TFCLIPVisionModelTester
from ..deit.test_modeling_tf_deit import TFDeiTModelTester
from ..roberta.test_modeling_tf_roberta import TFRobertaModelTester
from ..vit.test_modeling_tf_vit import TFViTModelTester
if is_tf_available():
from transformers import (
TFBertModel,
TFCLIPVisionModel,
TFDeiTModel,
TFRobertaModel,
TFVisionTextDualEncoderModel,
TFViTModel,
VisionTextDualEncoderConfig,
)
if is_vision_available():
from PIL import Image
from transformers import VisionTextDualEncoderProcessor
def _a ( SCREAMING_SNAKE_CASE_ : Union[str, Any] ):
if isinstance(SCREAMING_SNAKE_CASE_ , collections.abc.Iterable ):
return x
return (x, x)
@require_tf
class a__ :
def __SCREAMING_SNAKE_CASE( self , _A , _A ):
"""simple docstring"""
pass
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
pass
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
pass
def __SCREAMING_SNAKE_CASE( self , _A , _A , _A , _A , _A=None , **_A ):
"""simple docstring"""
__lowerCAmelCase = VisionTextDualEncoderConfig.from_vision_text_configs(_A , _A )
__lowerCAmelCase = TFVisionTextDualEncoderModel(_A )
__lowerCAmelCase = model(input_ids=_A , pixel_values=_A , attention_mask=_A )
self.assertEqual(output["text_embeds"].shape , (input_ids.shape[0], config.projection_dim) )
self.assertEqual(output["image_embeds"].shape , (pixel_values.shape[0], config.projection_dim) )
def __SCREAMING_SNAKE_CASE( self , _A , _A , _A , _A , _A=None , **_A ):
"""simple docstring"""
__lowerCAmelCase , __lowerCAmelCase = self.get_vision_text_model(_A , _A )
__lowerCAmelCase = TFVisionTextDualEncoderModel(vision_model=_A , text_model=_A )
__lowerCAmelCase = model(input_ids=_A , pixel_values=_A , attention_mask=_A )
self.assertEqual(output["text_embeds"].shape , (input_ids.shape[0], model.config.projection_dim) )
self.assertEqual(output["image_embeds"].shape , (pixel_values.shape[0], model.config.projection_dim) )
def __SCREAMING_SNAKE_CASE( self , _A , _A , _A , _A , _A=None , **_A ):
"""simple docstring"""
__lowerCAmelCase , __lowerCAmelCase = self.get_vision_text_model(_A , _A )
__lowerCAmelCase = {"vision_model": vision_model, "text_model": text_model}
__lowerCAmelCase = TFVisionTextDualEncoderModel.from_vision_text_pretrained(**_A )
__lowerCAmelCase = model(input_ids=_A , pixel_values=_A , attention_mask=_A )
self.assertEqual(output["text_embeds"].shape , (input_ids.shape[0], model.config.projection_dim) )
self.assertEqual(output["image_embeds"].shape , (pixel_values.shape[0], model.config.projection_dim) )
def __SCREAMING_SNAKE_CASE( self , _A , _A , _A , _A , _A=None , **_A ):
"""simple docstring"""
__lowerCAmelCase , __lowerCAmelCase = self.get_vision_text_model(_A , _A )
__lowerCAmelCase = TFVisionTextDualEncoderModel(vision_model=_A , text_model=_A )
__lowerCAmelCase = model(input_ids=_A , pixel_values=_A , attention_mask=_A )
__lowerCAmelCase = output[0].numpy()
with tempfile.TemporaryDirectory() as tmpdirname:
model.save_pretrained(_A )
__lowerCAmelCase = TFVisionTextDualEncoderModel.from_pretrained(_A )
__lowerCAmelCase = model(input_ids=_A , pixel_values=_A , attention_mask=_A )
__lowerCAmelCase = after_output[0].numpy()
__lowerCAmelCase = np.amax(np.abs(out_a - out_a ) )
self.assertLessEqual(_A , 1E-5 )
def __SCREAMING_SNAKE_CASE( self , _A , _A , _A , _A , _A=None , **_A ):
"""simple docstring"""
__lowerCAmelCase , __lowerCAmelCase = self.get_vision_text_model(_A , _A )
__lowerCAmelCase = TFVisionTextDualEncoderModel(vision_model=_A , text_model=_A )
__lowerCAmelCase = model(
input_ids=_A , pixel_values=_A , attention_mask=_A , output_attentions=_A )
__lowerCAmelCase = output.vision_model_output.attentions
self.assertEqual(len(_A ) , vision_config.num_hidden_layers )
# in ViT, the seq_len equals the number of patches + 1 (we add 1 for the [CLS] token)
__lowerCAmelCase = to_atuple(vision_model.config.image_size )
__lowerCAmelCase = to_atuple(vision_model.config.patch_size )
__lowerCAmelCase = (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
__lowerCAmelCase = num_patches + 1
self.assertEqual(vision_attentions[0].shape[-3:] , (vision_config.num_attention_heads, seq_len, seq_len) )
__lowerCAmelCase = output.text_model_output.attentions
self.assertEqual(len(_A ) , text_config.num_hidden_layers )
self.assertEqual(
text_attentions[0].shape[-3:] , (text_config.num_attention_heads, input_ids.shape[-1], input_ids.shape[-1]) , )
def __SCREAMING_SNAKE_CASE( self , _A , _A , _A ):
"""simple docstring"""
__lowerCAmelCase = np.abs((a - b) ).max()
self.assertLessEqual(_A , _A , f"""Difference between torch and flax is {diff} (>= {tol}).""" )
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
__lowerCAmelCase = self.prepare_config_and_inputs()
self.check_vision_text_dual_encoder_model(**_A )
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
__lowerCAmelCase = self.prepare_config_and_inputs()
self.check_model_from_pretrained_configs(**_A )
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
__lowerCAmelCase = self.prepare_config_and_inputs()
self.check_vision_text_dual_encoder_from_pretrained(**_A )
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
__lowerCAmelCase = self.prepare_config_and_inputs()
self.check_save_load(**_A )
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
__lowerCAmelCase = self.prepare_config_and_inputs()
self.check_vision_text_output_attention(**_A )
@slow
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
__lowerCAmelCase , __lowerCAmelCase = self.get_pretrained_model_and_inputs()
__lowerCAmelCase = model_a(**_A )
__lowerCAmelCase = outputs[0].numpy()
with tempfile.TemporaryDirectory() as tmp_dirname:
model_a.save_pretrained(_A )
__lowerCAmelCase = TFVisionTextDualEncoderModel.from_pretrained(_A )
__lowerCAmelCase = model_a(**_A )
__lowerCAmelCase = after_outputs[0].numpy()
__lowerCAmelCase = np.amax(np.abs(out_a - out_a ) )
self.assertLessEqual(_A , 1E-5 )
@require_tf
class a__ ( snake_case__ , unittest.TestCase ):
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
__lowerCAmelCase = TFVisionTextDualEncoderModel.from_vision_text_pretrained(
"hf-internal-testing/tiny-random-vit" , "hf-internal-testing/tiny-random-bert" )
__lowerCAmelCase = 1_3
__lowerCAmelCase = floats_tensor(
[
batch_size,
model.vision_model.config.num_channels,
model.vision_model.config.image_size,
model.vision_model.config.image_size,
] )
__lowerCAmelCase = ids_tensor([batch_size, 4] , model.text_model.config.vocab_size )
__lowerCAmelCase = random_attention_mask([batch_size, 4] )
__lowerCAmelCase = {"pixel_values": pixel_values, "input_ids": input_ids, "attention_mask": attention_mask}
return model, inputs
def __SCREAMING_SNAKE_CASE( self , _A , _A ):
"""simple docstring"""
__lowerCAmelCase = TFViTModel(_A , name="vision_model" )
__lowerCAmelCase = TFBertModel(_A , name="text_model" )
return vision_model, text_model
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
__lowerCAmelCase = TFViTModelTester(self )
__lowerCAmelCase = TFBertModelTester(self )
__lowerCAmelCase = vit_model_tester.prepare_config_and_inputs()
__lowerCAmelCase = bert_model_tester.prepare_config_and_inputs()
__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase = vision_config_and_inputs
(
(
__lowerCAmelCase
) , (
__lowerCAmelCase
) , (
__lowerCAmelCase
) , (
__lowerCAmelCase
) , (
__lowerCAmelCase
) , (
__lowerCAmelCase
) , (
__lowerCAmelCase
) ,
) = text_config_and_inputs
return {
"text_config": text_config,
"vision_config": vision_config,
"pixel_values": pixel_values,
"attention_mask": input_mask,
"input_ids": input_ids,
"text_token_type_ids": token_type_ids,
"text_sequence_labels": sequence_labels,
"text_token_labels": token_labels,
"text_choice_labels": choice_labels,
}
@require_tf
class a__ ( snake_case__ , unittest.TestCase ):
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
__lowerCAmelCase = TFVisionTextDualEncoderModel.from_vision_text_pretrained(
"Rocketknight1/tiny-random-deit-tf" , "hf-internal-testing/tiny-random-roberta" )
__lowerCAmelCase = 1_3
__lowerCAmelCase = floats_tensor(
[
batch_size,
model.vision_model.config.num_channels,
model.vision_model.config.image_size,
model.vision_model.config.image_size,
] )
__lowerCAmelCase = ids_tensor([batch_size, 4] , model.text_model.config.vocab_size )
__lowerCAmelCase = random_attention_mask([batch_size, 4] )
__lowerCAmelCase = {"pixel_values": pixel_values, "input_ids": input_ids, "attention_mask": attention_mask}
return model, inputs
def __SCREAMING_SNAKE_CASE( self , _A , _A , _A , _A , _A=None , **_A ):
"""simple docstring"""
__lowerCAmelCase , __lowerCAmelCase = self.get_vision_text_model(_A , _A )
__lowerCAmelCase = TFVisionTextDualEncoderModel(vision_model=_A , text_model=_A )
__lowerCAmelCase = model(
input_ids=_A , pixel_values=_A , attention_mask=_A , output_attentions=_A )
__lowerCAmelCase = output.vision_model_output.attentions
self.assertEqual(len(_A ) , vision_config.num_hidden_layers )
# in DEiT, the seq_len equals the number of patches + 2 (we add 2 for the [CLS] and distillation tokens)
__lowerCAmelCase = to_atuple(vision_model.config.image_size )
__lowerCAmelCase = to_atuple(vision_model.config.patch_size )
__lowerCAmelCase = (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
__lowerCAmelCase = num_patches + 2
self.assertEqual(vision_attentions[0].shape[-3:] , (vision_config.num_attention_heads, seq_len, seq_len) )
__lowerCAmelCase = output.text_model_output.attentions
self.assertEqual(len(_A ) , text_config.num_hidden_layers )
self.assertEqual(
text_attentions[0].shape[-3:] , (text_config.num_attention_heads, input_ids.shape[-1], input_ids.shape[-1]) , )
def __SCREAMING_SNAKE_CASE( self , _A , _A ):
"""simple docstring"""
__lowerCAmelCase = TFDeiTModel(_A , name="vision_model" )
__lowerCAmelCase = TFRobertaModel(_A , name="text_model" )
return vision_model, text_model
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
__lowerCAmelCase = TFDeiTModelTester(self )
__lowerCAmelCase = TFRobertaModelTester(self )
__lowerCAmelCase = vit_model_tester.prepare_config_and_inputs()
__lowerCAmelCase = bert_model_tester.prepare_config_and_inputs()
__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase = vision_config_and_inputs
(
(
__lowerCAmelCase
) , (
__lowerCAmelCase
) , (
__lowerCAmelCase
) , (
__lowerCAmelCase
) , (
__lowerCAmelCase
) , (
__lowerCAmelCase
) , (
__lowerCAmelCase
) ,
) = text_config_and_inputs
return {
"text_config": text_config,
"vision_config": vision_config,
"pixel_values": pixel_values,
"attention_mask": input_mask,
"input_ids": input_ids,
"text_token_type_ids": token_type_ids,
"text_sequence_labels": sequence_labels,
"text_token_labels": token_labels,
"text_choice_labels": choice_labels,
}
@require_tf
class a__ ( snake_case__ , unittest.TestCase ):
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
__lowerCAmelCase = TFVisionTextDualEncoderModel.from_vision_text_pretrained(
"Rocketknight1/tiny-random-clip-tf" , "hf-internal-testing/tiny-random-bert" )
__lowerCAmelCase = 1_3
__lowerCAmelCase = floats_tensor(
[
batch_size,
model.vision_model.config.num_channels,
model.vision_model.config.image_size,
model.vision_model.config.image_size,
] )
__lowerCAmelCase = ids_tensor([batch_size, 4] , model.text_model.config.vocab_size )
__lowerCAmelCase = random_attention_mask([batch_size, 4] )
__lowerCAmelCase = {"pixel_values": pixel_values, "input_ids": input_ids, "attention_mask": attention_mask}
return model, inputs
def __SCREAMING_SNAKE_CASE( self , _A , _A ):
"""simple docstring"""
__lowerCAmelCase = TFCLIPVisionModel(_A , name="vision_model" )
__lowerCAmelCase = TFBertModel(_A , name="text_model" )
return vision_model, text_model
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
__lowerCAmelCase = TFCLIPVisionModelTester(self )
__lowerCAmelCase = TFBertModelTester(self )
__lowerCAmelCase = clip_model_tester.prepare_config_and_inputs()
__lowerCAmelCase = bert_model_tester.prepare_config_and_inputs()
__lowerCAmelCase , __lowerCAmelCase = vision_config_and_inputs
(
(
__lowerCAmelCase
) , (
__lowerCAmelCase
) , (
__lowerCAmelCase
) , (
__lowerCAmelCase
) , (
__lowerCAmelCase
) , (
__lowerCAmelCase
) , (
__lowerCAmelCase
) ,
) = text_config_and_inputs
return {
"text_config": text_config,
"vision_config": vision_config,
"pixel_values": pixel_values,
"attention_mask": input_mask,
"input_ids": input_ids,
"text_token_type_ids": token_type_ids,
"text_sequence_labels": sequence_labels,
"text_token_labels": token_labels,
"text_choice_labels": choice_labels,
}
@require_vision
@require_tf
class a__ ( unittest.TestCase ):
@slow
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
__lowerCAmelCase = TFVisionTextDualEncoderModel.from_pretrained(
"clip-italian/clip-italian" , logit_scale_init_value=1.0 , from_pt=_A )
__lowerCAmelCase = VisionTextDualEncoderProcessor.from_pretrained("clip-italian/clip-italian" )
__lowerCAmelCase = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png" )
__lowerCAmelCase = processor(
text=["una foto di un gatto", "una foto di un cane"] , images=_A , padding=_A , return_tensors="np" )
__lowerCAmelCase = model(**_A )
# verify the logits
self.assertEqual(outputs.logits_per_image.shape , (inputs.pixel_values.shape[0], inputs.input_ids.shape[0]) )
self.assertEqual(
outputs.logits_per_text.shape , (inputs.input_ids.shape[0], inputs.pixel_values.shape[0]) , )
__lowerCAmelCase = np.array([[1.2_28_47_27, 0.3_10_41_22]] )
self.assertTrue(np.allclose(outputs.logits_per_image.numpy() , _A , atol=1E-3 ) )
| 92 | 0 |
'''simple docstring'''
import datasets
import faiss
import numpy as np
import streamlit as st
import torch
from elasticsearch import Elasticsearch
from elia_utils import (
embed_questions_for_retrieval,
make_qa_sas_model,
qa_sas_generate,
query_es_index,
query_qa_dense_index,
)
import transformers
from transformers import AutoModel, AutoModelForSeqaSeqLM, AutoTokenizer
a_ : str = """bart"""
a_ : List[Any] = True
@st.cache(allow_output_mutation=SCREAMING_SNAKE_CASE_ )
def a_ ( ) -> List[Any]:
"""simple docstring"""
if LOAD_DENSE_INDEX:
lowerCamelCase_ =AutoTokenizer.from_pretrained('''yjernite/retribert-base-uncased''' )
lowerCamelCase_ =AutoModel.from_pretrained('''yjernite/retribert-base-uncased''' ).to('''cuda:0''' )
lowerCamelCase_ =qar_model.eval()
else:
lowerCamelCase_, lowerCamelCase_ =(None, None)
if MODEL_TYPE == "bart":
lowerCamelCase_ =AutoTokenizer.from_pretrained('''yjernite/bart_eli5''' )
lowerCamelCase_ =AutoModelForSeqaSeqLM.from_pretrained('''yjernite/bart_eli5''' ).to('''cuda:0''' )
lowerCamelCase_ =torch.load('''seq2seq_models/eli5_bart_model_blm_2.pth''' )
sas_model.load_state_dict(save_dict['''model'''] )
lowerCamelCase_ =sas_model.eval()
else:
lowerCamelCase_, lowerCamelCase_ =make_qa_sas_model(
model_name='''t5-small''' , from_file='''seq2seq_models/eli5_t5_model_1024_4.pth''' , device='''cuda:0''' )
return (qar_tokenizer, qar_model, sas_tokenizer, sas_model)
@st.cache(allow_output_mutation=SCREAMING_SNAKE_CASE_ )
def a_ ( ) -> Optional[Any]:
"""simple docstring"""
if LOAD_DENSE_INDEX:
lowerCamelCase_ =faiss.StandardGpuResources()
lowerCamelCase_ =datasets.load_dataset(path='''wiki_snippets''' , name='''wiki40b_en_100_0''' )['''train''']
lowerCamelCase_ =np.memmap(
'''wiki40b_passages_reps_32_l-8_h-768_b-512-512.dat''' , dtype='''float32''' , mode='''r''' , shape=(wikiaab_passages.num_rows, 128) , )
lowerCamelCase_ =faiss.IndexFlatIP(128 )
lowerCamelCase_ =faiss.index_cpu_to_gpu(SCREAMING_SNAKE_CASE_ , 1 , SCREAMING_SNAKE_CASE_ )
wikiaab_gpu_index_flat.add(SCREAMING_SNAKE_CASE_ ) # TODO fix for larger GPU
else:
lowerCamelCase_, lowerCamelCase_ =(None, None)
lowerCamelCase_ =Elasticsearch([{'''host''': '''localhost''', '''port''': '''9200'''}] )
return (wikiaab_passages, wikiaab_gpu_index_flat, es_client)
@st.cache(allow_output_mutation=SCREAMING_SNAKE_CASE_ )
def a_ ( ) -> Union[str, Any]:
"""simple docstring"""
lowerCamelCase_ =datasets.load_dataset('''eli5''' , name='''LFQA_reddit''' )
lowerCamelCase_ =elia['''train_eli5''']
lowerCamelCase_ =np.memmap(
'''eli5_questions_reps.dat''' , dtype='''float32''' , mode='''r''' , shape=(elia_train.num_rows, 128) )
lowerCamelCase_ =faiss.IndexFlatIP(128 )
eli5_train_q_index.add(SCREAMING_SNAKE_CASE_ )
return (elia_train, eli5_train_q_index)
a_ , a_ , a_ : List[str] = load_indexes()
a_ , a_ , a_ , a_ : int = load_models()
a_ , a_ : Union[str, Any] = load_train_data()
def a_ ( __snake_case : int , __snake_case : Optional[int]=10 ) -> Union[str, Any]:
"""simple docstring"""
lowerCamelCase_ =embed_questions_for_retrieval([question] , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
lowerCamelCase_, lowerCamelCase_ =eli5_train_q_index.search(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ =[elia_train[int(SCREAMING_SNAKE_CASE_ )] for i in I[0]]
return nn_examples
def a_ ( __snake_case : List[str] , __snake_case : int="wiki40b" , __snake_case : List[Any]="dense" , __snake_case : int=10 ) -> str:
"""simple docstring"""
if source == "none":
lowerCamelCase_, lowerCamelCase_ =(''' <P> '''.join(['''''' for _ in range(11 )] ).strip(), [])
else:
if method == "dense":
lowerCamelCase_, lowerCamelCase_ =query_qa_dense_index(
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
else:
lowerCamelCase_, lowerCamelCase_ =query_es_index(
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , index_name='''english_wiki40b_snippets_100w''' , n_results=SCREAMING_SNAKE_CASE_ , )
lowerCamelCase_ =[
(res['''article_title'''], res['''section_title'''].strip(), res['''score'''], res['''passage_text''']) for res in hit_lst
]
lowerCamelCase_ ='''question: {} context: {}'''.format(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
return question_doc, support_list
@st.cache(
hash_funcs={
torch.Tensor: (lambda __snake_case : None),
transformers.models.bart.tokenization_bart.BartTokenizer: (lambda __snake_case : None),
} )
def a_ ( __snake_case : Any , __snake_case : Optional[Any] , __snake_case : Optional[int] , __snake_case : Any=64 , __snake_case : Dict=256 , __snake_case : List[str]=False , __snake_case : List[str]=2 , __snake_case : List[str]=0.9_5 , __snake_case : Union[str, Any]=0.8 ) -> Optional[int]:
"""simple docstring"""
with torch.no_grad():
lowerCamelCase_ =qa_sas_generate(
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , num_answers=1 , num_beams=SCREAMING_SNAKE_CASE_ , min_len=SCREAMING_SNAKE_CASE_ , max_len=SCREAMING_SNAKE_CASE_ , do_sample=SCREAMING_SNAKE_CASE_ , temp=SCREAMING_SNAKE_CASE_ , top_p=SCREAMING_SNAKE_CASE_ , top_k=SCREAMING_SNAKE_CASE_ , max_input_length=1024 , device='''cuda:0''' , )[0]
return (answer, support_list)
st.title("""Long Form Question Answering with ELI5""")
# Start sidebar
a_ : List[Any] = """<img src='https://huggingface.co/front/assets/huggingface_logo.svg'>"""
a_ : Union[str, Any] = """
<html>
<head>
<style>
.img-container {
padding-left: 90px;
padding-right: 90px;
padding-top: 50px;
padding-bottom: 50px;
background-color: #f0f3f9;
}
</style>
</head>
<body>
<span class=\"img-container\"> <!-- Inline parent element -->
%s
</span>
</body>
</html>
""" % (
header_html,
)
st.sidebar.markdown(
header_full,
unsafe_allow_html=True,
)
# Long Form QA with ELI5 and Wikipedia
a_ : str = """
This demo presents a model trained to [provide long-form answers to open-domain questions](https://yjernite.github.io/lfqa.html).
First, a document retriever fetches a set of relevant Wikipedia passages given the question from the [Wiki40b](https://research.google/pubs/pub49029/) dataset,
a pre-processed fixed snapshot of Wikipedia.
"""
st.sidebar.markdown(description, unsafe_allow_html=True)
a_ : str = [
"""Answer the question""",
"""View the retrieved document only""",
"""View the most similar ELI5 question and answer""",
"""Show me everything, please!""",
]
a_ : List[Any] = st.sidebar.checkbox("""Demo options""")
if demo_options:
a_ : List[Any] = st.sidebar.selectbox(
"""""",
action_list,
index=3,
)
a_ : Dict = action_list.index(action_st)
a_ : Dict = st.sidebar.selectbox(
"""""",
["""Show full text of passages""", """Show passage section titles"""],
index=0,
)
a_ : Optional[int] = show_type == """Show full text of passages"""
else:
a_ : List[Any] = 3
a_ : List[str] = True
a_ : List[Any] = st.sidebar.checkbox("""Retrieval options""")
if retrieval_options:
a_ : List[str] = """
### Information retriever options
The **sparse** retriever uses ElasticSearch, while the **dense** retriever uses max-inner-product search between a question and passage embedding
trained using the [ELI5](https://arxiv.org/abs/1907.09190) questions-answer pairs.
The answer is then generated by sequence to sequence model which takes the question and retrieved document as input.
"""
st.sidebar.markdown(retriever_info)
a_ : Any = st.sidebar.selectbox("""Which Wikipedia format should the model use?""", ["""wiki40b""", """none"""])
a_ : Dict = st.sidebar.selectbox("""Which Wikipedia indexer should the model use?""", ["""dense""", """sparse""", """mixed"""])
else:
a_ : Optional[Any] = """wiki40b"""
a_ : Optional[Any] = """dense"""
a_ : str = """beam"""
a_ : Optional[Any] = 2
a_ : str = 64
a_ : Union[str, Any] = 2_56
a_ : str = None
a_ : Any = None
a_ : Optional[Any] = st.sidebar.checkbox("""Generation options""")
if generate_options:
a_ : Tuple = """
### Answer generation options
The sequence-to-sequence model was initialized with [BART](https://huggingface.co/facebook/bart-large)
weights and fine-tuned on the ELI5 QA pairs and retrieved documents. You can use the model for greedy decoding with
**beam** search, or **sample** from the decoder's output probabilities.
"""
st.sidebar.markdown(generate_info)
a_ : Union[str, Any] = st.sidebar.selectbox("""Would you like to use beam search or sample an answer?""", ["""beam""", """sampled"""])
a_ : Optional[int] = st.sidebar.slider(
"""Minimum generation length""", min_value=8, max_value=2_56, value=64, step=8, format=None, key=None
)
a_ : Optional[int] = st.sidebar.slider(
"""Maximum generation length""", min_value=64, max_value=5_12, value=2_56, step=16, format=None, key=None
)
if sampled == "beam":
a_ : int = st.sidebar.slider("""Beam size""", min_value=1, max_value=8, value=2, step=None, format=None, key=None)
else:
a_ : Tuple = st.sidebar.slider(
"""Nucleus sampling p""", min_value=0.1, max_value=1.0, value=0.95, step=0.01, format=None, key=None
)
a_ : str = st.sidebar.slider(
"""Temperature""", min_value=0.1, max_value=1.0, value=0.7, step=0.01, format=None, key=None
)
a_ : Optional[int] = None
# start main text
a_ : List[Any] = [
"""<MY QUESTION>""",
"""How do people make chocolate?""",
"""Why do we get a fever when we are sick?""",
"""How can different animals perceive different colors?""",
"""What is natural language processing?""",
"""What's the best way to treat a sunburn?""",
"""What exactly are vitamins ?""",
"""How does nuclear energy provide electricity?""",
"""What's the difference between viruses and bacteria?""",
"""Why are flutes classified as woodwinds when most of them are made out of metal ?""",
"""Why do people like drinking coffee even though it tastes so bad?""",
"""What happens when wine ages? How does it make the wine taste better?""",
"""If an animal is an herbivore, where does it get the protein that it needs to survive if it only eats grass?""",
"""How can we set a date to the beginning or end of an artistic period? Doesn't the change happen gradually?""",
"""How does New Zealand have so many large bird predators?""",
]
a_ : str = st.selectbox(
"""What would you like to ask? ---- select <MY QUESTION> to enter a new query""",
questions_list,
index=1,
)
if question_s == "<MY QUESTION>":
a_ : Tuple = st.text_input("""Enter your question here:""", """""")
else:
a_ : Optional[Any] = question_s
if st.button("""Show me!"""):
if action in [0, 1, 3]:
if index_type == "mixed":
a_ , a_ : Any = make_support(question, source=wiki_source, method="""dense""", n_results=10)
a_ , a_ : Dict = make_support(question, source=wiki_source, method="""sparse""", n_results=10)
a_ : Dict = []
for res_d, res_s in zip(support_list_dense, support_list_sparse):
if tuple(res_d) not in support_list:
support_list += [tuple(res_d)]
if tuple(res_s) not in support_list:
support_list += [tuple(res_s)]
a_ : int = support_list[:10]
a_ : Optional[int] = """<P> """ + """ <P> """.join([res[-1] for res in support_list])
else:
a_ , a_ : Dict = make_support(question, source=wiki_source, method=index_type, n_results=10)
if action in [0, 3]:
a_ , a_ : int = answer_question(
question_doc,
sas_model,
sas_tokenizer,
min_len=min_len,
max_len=int(max_len),
sampling=(sampled == """sampled"""),
n_beams=n_beams,
top_p=top_p,
temp=temp,
)
st.markdown("""### The model generated answer is:""")
st.write(answer)
if action in [0, 1, 3] and wiki_source != "none":
st.markdown("""--- \n ### The model is drawing information from the following Wikipedia passages:""")
for i, res in enumerate(support_list):
a_ : Dict = """https://en.wikipedia.org/wiki/{}""".format(res[0].replace(""" """, """_"""))
a_ : Tuple = res[1].strip()
if sec_titles == "":
a_ : Any = """[{}]({})""".format(res[0], wiki_url)
else:
a_ : int = sec_titles.split(""" & """)
a_ : List[str] = """ & """.join(
["""[{}]({}#{})""".format(sec.strip(), wiki_url, sec.strip().replace(""" """, """_""")) for sec in sec_list]
)
st.markdown(
"""{0:02d} - **Article**: {1:<18} <br> _Section_: {2}""".format(i + 1, res[0], sections),
unsafe_allow_html=True,
)
if show_passages:
st.write(
"""> <span style=\"font-family:arial; font-size:10pt;\">""" + res[-1] + """</span>""", unsafe_allow_html=True
)
if action in [2, 3]:
a_ : List[Any] = find_nearest_training(question)
a_ : Union[str, Any] = nn_train_list[0]
st.markdown(
"""--- \n ### The most similar question in the ELI5 training set was: \n\n {}""".format(train_exple["""title"""])
)
a_ : Union[str, Any] = [
"""{}. {}""".format(i + 1, """ \n""".join([line.strip() for line in ans.split("""\n""") if line.strip() != """"""]))
for i, (ans, sc) in enumerate(zip(train_exple["""answers"""]["""text"""], train_exple["""answers"""]["""score"""]))
if i == 0 or sc > 2
]
st.markdown("""##### Its answers were: \n\n {}""".format("""\n""".join(answers_st)))
a_ : str = """
---
**Disclaimer**
*The intent of this app is to provide some (hopefully entertaining) insights into the behavior of a current LFQA system.
Evaluating biases of such a model and ensuring factual generations are still very much open research problems.
Therefore, until some significant progress is achieved, we caution against using the generated answers for practical purposes.*
"""
st.sidebar.markdown(disclaimer, unsafe_allow_html=True)
| 75 |
import json
import os
import torch
from diffusers import UNetaDModel
os.makedirs("""hub/hopper-medium-v2/unet/hor32""", exist_ok=True)
os.makedirs("""hub/hopper-medium-v2/unet/hor128""", exist_ok=True)
os.makedirs("""hub/hopper-medium-v2/value_function""", exist_ok=True)
def _a ( SCREAMING_SNAKE_CASE_ : List[Any] ):
if hor == 1_28:
__lowerCAmelCase = ("DownResnetBlock1D", "DownResnetBlock1D", "DownResnetBlock1D")
__lowerCAmelCase = (32, 1_28, 2_56)
__lowerCAmelCase = ("UpResnetBlock1D", "UpResnetBlock1D")
elif hor == 32:
__lowerCAmelCase = ("DownResnetBlock1D", "DownResnetBlock1D", "DownResnetBlock1D", "DownResnetBlock1D")
__lowerCAmelCase = (32, 64, 1_28, 2_56)
__lowerCAmelCase = ("UpResnetBlock1D", "UpResnetBlock1D", "UpResnetBlock1D")
__lowerCAmelCase = torch.load(F"""/Users/bglickenhaus/Documents/diffuser/temporal_unet-hopper-mediumv2-hor{hor}.torch""" )
__lowerCAmelCase = model.state_dict()
__lowerCAmelCase = {
"down_block_types": down_block_types,
"block_out_channels": block_out_channels,
"up_block_types": up_block_types,
"layers_per_block": 1,
"use_timestep_embedding": True,
"out_block_type": "OutConv1DBlock",
"norm_num_groups": 8,
"downsample_each_block": False,
"in_channels": 14,
"out_channels": 14,
"extra_in_channels": 0,
"time_embedding_type": "positional",
"flip_sin_to_cos": False,
"freq_shift": 1,
"sample_size": 6_55_36,
"mid_block_type": "MidResTemporalBlock1D",
"act_fn": "mish",
}
__lowerCAmelCase = UNetaDModel(**SCREAMING_SNAKE_CASE_ )
print(F"""length of state dict: {len(state_dict.keys() )}""" )
print(F"""length of value function dict: {len(hf_value_function.state_dict().keys() )}""" )
__lowerCAmelCase = dict(zip(model.state_dict().keys() , hf_value_function.state_dict().keys() ) )
for k, v in mapping.items():
__lowerCAmelCase = state_dict.pop(SCREAMING_SNAKE_CASE_ )
hf_value_function.load_state_dict(SCREAMING_SNAKE_CASE_ )
torch.save(hf_value_function.state_dict() , F"""hub/hopper-medium-v2/unet/hor{hor}/diffusion_pytorch_model.bin""" )
with open(F"""hub/hopper-medium-v2/unet/hor{hor}/config.json""" , "w" ) as f:
json.dump(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
def _a ( ):
__lowerCAmelCase = {
"in_channels": 14,
"down_block_types": ("DownResnetBlock1D", "DownResnetBlock1D", "DownResnetBlock1D", "DownResnetBlock1D"),
"up_block_types": (),
"out_block_type": "ValueFunction",
"mid_block_type": "ValueFunctionMidBlock1D",
"block_out_channels": (32, 64, 1_28, 2_56),
"layers_per_block": 1,
"downsample_each_block": True,
"sample_size": 6_55_36,
"out_channels": 14,
"extra_in_channels": 0,
"time_embedding_type": "positional",
"use_timestep_embedding": True,
"flip_sin_to_cos": False,
"freq_shift": 1,
"norm_num_groups": 8,
"act_fn": "mish",
}
__lowerCAmelCase = torch.load("/Users/bglickenhaus/Documents/diffuser/value_function-hopper-mediumv2-hor32.torch" )
__lowerCAmelCase = model
__lowerCAmelCase = UNetaDModel(**SCREAMING_SNAKE_CASE_ )
print(F"""length of state dict: {len(state_dict.keys() )}""" )
print(F"""length of value function dict: {len(hf_value_function.state_dict().keys() )}""" )
__lowerCAmelCase = dict(zip(state_dict.keys() , hf_value_function.state_dict().keys() ) )
for k, v in mapping.items():
__lowerCAmelCase = state_dict.pop(SCREAMING_SNAKE_CASE_ )
hf_value_function.load_state_dict(SCREAMING_SNAKE_CASE_ )
torch.save(hf_value_function.state_dict() , "hub/hopper-medium-v2/value_function/diffusion_pytorch_model.bin" )
with open("hub/hopper-medium-v2/value_function/config.json" , "w" ) as f:
json.dump(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
if __name__ == "__main__":
unet(32)
# unet(128)
value_function()
| 92 | 0 |
"""simple docstring"""
from sympy import diff, lambdify, symbols
from sympy.functions import * # noqa: F403
def _SCREAMING_SNAKE_CASE ( __snake_case : str , __snake_case : complex , __snake_case : str = "x" , __snake_case : float = 10**-10 , __snake_case : int = 1 , ):
'''simple docstring'''
lowercase = symbols(SCREAMING_SNAKE_CASE_ )
lowercase = lambdify(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
lowercase = lambdify(SCREAMING_SNAKE_CASE_ , diff(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) )
lowercase = starting_point
while True:
if diff_function(SCREAMING_SNAKE_CASE_ ) != 0:
lowercase = prev_guess - multiplicity * func(SCREAMING_SNAKE_CASE_ ) / diff_function(
SCREAMING_SNAKE_CASE_ )
else:
raise ZeroDivisionError('Could not find root' ) from None
# Precision is checked by comparing the difference of consecutive guesses
if abs(next_guess - prev_guess ) < precision:
return next_guess
lowercase = next_guess
# Let's Execute
if __name__ == "__main__":
# Find root of trigonometric function
# Find value of pi
print(F'''The root of sin(x) = 0 is {newton_raphson('sin(x)', 2)}''')
# Find root of polynomial
# Find fourth Root of 5
print(F'''The root of x**4 - 5 = 0 is {newton_raphson('x**4 -5', 0.4 +5J)}''')
# Find value of e
print(
'The root of log(y) - 1 = 0 is ',
F'''{newton_raphson('log(y) - 1', 2, variable='y')}''',
)
# Exponential Roots
print(
'The root of exp(x) - 1 = 0 is',
F'''{newton_raphson('exp(x) - 1', 1_0, precision=0.0_0_5)}''',
)
# Find root of cos(x)
print(F'''The root of cos(x) = 0 is {newton_raphson('cos(x)', 0)}''')
| 220 |
import pytest
from datasets import inspect_metric, list_metrics, load_metric
@pytest.fixture
def _a ( SCREAMING_SNAKE_CASE_ : Optional[Any] ):
monkeypatch.setattr("datasets.utils.deprecation_utils._emitted_deprecation_warnings" , set() )
@pytest.fixture
def _a ( SCREAMING_SNAKE_CASE_ : List[Any] ):
class a__ :
def __init__( self , _A ):
"""simple docstring"""
__lowerCAmelCase = metric_id
class a__ :
_a : Optional[int] = [MetricMock(snake_case__ ) for metric_id in ["""accuracy""", """mse""", """precision""", """codeparrot/apps_metric"""]]
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
return self._metrics
monkeypatch.setattr("datasets.inspect.huggingface_hub" , HfhMock() )
@pytest.mark.parametrize(
"func, args" , [(load_metric, ("metrics/mse",)), (list_metrics, ()), (inspect_metric, ("metrics/mse", "tmp_path"))] )
def _a ( SCREAMING_SNAKE_CASE_ : Tuple , SCREAMING_SNAKE_CASE_ : List[str] , SCREAMING_SNAKE_CASE_ : int , SCREAMING_SNAKE_CASE_ : Optional[int] , SCREAMING_SNAKE_CASE_ : Optional[int] ):
if "tmp_path" in args:
__lowerCAmelCase = tuple(arg if arg != "tmp_path" else tmp_path for arg in args )
with pytest.warns(SCREAMING_SNAKE_CASE_ , match="https://huggingface.co/docs/evaluate" ):
func(*SCREAMING_SNAKE_CASE_ )
| 92 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
A : List[Any] = {
'configuration_poolformer': [
'POOLFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
'PoolFormerConfig',
'PoolFormerOnnxConfig',
]
}
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A : Tuple = ['PoolFormerFeatureExtractor']
A : List[str] = ['PoolFormerImageProcessor']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A : int = [
'POOLFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
'PoolFormerForImageClassification',
'PoolFormerModel',
'PoolFormerPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_poolformer import (
POOLFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
PoolFormerConfig,
PoolFormerOnnxConfig,
)
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .feature_extraction_poolformer import PoolFormerFeatureExtractor
from .image_processing_poolformer import PoolFormerImageProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_poolformer import (
POOLFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
PoolFormerForImageClassification,
PoolFormerModel,
PoolFormerPreTrainedModel,
)
else:
import sys
A : List[str] = _LazyModule(__name__, globals()['__file__'], _import_structure) | 6 |
from random import randint
from tempfile import TemporaryFile
import numpy as np
def _a ( SCREAMING_SNAKE_CASE_ : int , SCREAMING_SNAKE_CASE_ : str , SCREAMING_SNAKE_CASE_ : List[str] ):
__lowerCAmelCase = 0
if start < end:
__lowerCAmelCase = randint(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
__lowerCAmelCase = a[end]
__lowerCAmelCase = a[pivot]
__lowerCAmelCase = temp
__lowerCAmelCase , __lowerCAmelCase = _in_place_partition(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
count += _in_place_quick_sort(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , p - 1 )
count += _in_place_quick_sort(SCREAMING_SNAKE_CASE_ , p + 1 , SCREAMING_SNAKE_CASE_ )
return count
def _a ( SCREAMING_SNAKE_CASE_ : str , SCREAMING_SNAKE_CASE_ : List[Any] , SCREAMING_SNAKE_CASE_ : Optional[int] ):
__lowerCAmelCase = 0
__lowerCAmelCase = randint(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
__lowerCAmelCase = a[end]
__lowerCAmelCase = a[pivot]
__lowerCAmelCase = temp
__lowerCAmelCase = start - 1
for index in range(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
count += 1
if a[index] < a[end]: # check if current val is less than pivot value
__lowerCAmelCase = new_pivot_index + 1
__lowerCAmelCase = a[new_pivot_index]
__lowerCAmelCase = a[index]
__lowerCAmelCase = temp
__lowerCAmelCase = a[new_pivot_index + 1]
__lowerCAmelCase = a[end]
__lowerCAmelCase = temp
return new_pivot_index + 1, count
UpperCamelCase__ = TemporaryFile()
UpperCamelCase__ = 100 # 1000 elements are to be sorted
UpperCamelCase__ , UpperCamelCase__ = 0, 1 # mean and standard deviation
UpperCamelCase__ = np.random.normal(mu, sigma, p)
np.save(outfile, X)
print("""The array is""")
print(X)
outfile.seek(0) # using the same array
UpperCamelCase__ = np.load(outfile)
UpperCamelCase__ = len(M) - 1
UpperCamelCase__ = _in_place_quick_sort(M, 0, r)
print(
"""No of Comparisons for 100 elements selected from a standard normal distribution"""
"""is :"""
)
print(z)
| 92 | 0 |
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