
'''simple docstring'''





import torch

from transformers import AutoModel







class        lowerCAmelCase__      (   torch.nn.Module	):

						def __init__(			self       ,							a="sayef/fsner-bert-base-uncased"				)					->      List[str]:


								'''simple docstring'''

								super(_snake_case       ,							self				).__init__()

								_UpperCamelCase						    =	AutoModel.from_pretrained(_snake_case       ,							return_dict=_snake_case				)
								_UpperCamelCase						    =	torch.nn.CosineSimilarity(3       ,							1e-08				)
								_UpperCamelCase						    =	torch.nn.Softmax(dim=1				)

						def  A_				(			self       ,							**a				)					->      Optional[int]:


								'''simple docstring'''

								return self.bert(**_snake_case				).last_hidden_state

						def  A_				(			self       ,							a				)					->      str:


								'''simple docstring'''

								return token_embeddings.sum(2       ,							keepdim=_snake_case				)

						def  A_				(			self       ,							a       ,							a       ,							a=1				)					->      Dict:


								'''simple docstring'''

								return self.softmax(T * self.cos(_snake_case       ,							_snake_case				)				)







						def  A_				(			self       ,							a       ,							a				)					->      str:


								'''simple docstring'''

								_UpperCamelCase						    =	W_supports["""sizes"""].tolist()
								_UpperCamelCase						    =	W_supports["""start_token_id"""].item()
								_UpperCamelCase						    =	W_supports["""end_token_id"""].item()

								del W_supports["sizes"]
								del W_supports["start_token_id"]
								del W_supports["end_token_id"]

								_UpperCamelCase						    =	self.BERT(**_snake_case				)
								_UpperCamelCase						    =	self.BERT(**_snake_case				)

								_UpperCamelCase						    =	None
								_UpperCamelCase						    =	None

								_UpperCamelCase						    =	W_supports["""input_ids"""] == start_token_id
								_UpperCamelCase						    =	W_supports["""input_ids"""] == end_token_id

								for i, size in enumerate(_snake_case				):
										if i == 0:
												_UpperCamelCase						    =	0
										else:
												_UpperCamelCase						    =	support_sizes[i - 1]

										_UpperCamelCase						    =	S[s : s + size][start_token_masks[s : s + size]]
										_UpperCamelCase						    =	S[s : s + size][end_token_masks[s : s + size]]

										_UpperCamelCase						    =	torch.matmul(q[i]       ,							s_start.T				).sum(1				).softmax(0				)
										_UpperCamelCase						    =	torch.matmul(q[i]       ,							s_end.T				).sum(1				).softmax(0				)

										if p_starts is not None:
												_UpperCamelCase						    =	torch.vstack((p_starts, p_start)				)
												_UpperCamelCase						    =	torch.vstack((p_ends, p_end)				)
										else:
												_UpperCamelCase						    =	p_start
												_UpperCamelCase						    =	p_end

								return p_starts, p_ends





import platform
from argparse import ArgumentParser

import huggingface_hub

from .. import __version__ as version
from ..utils import is_accelerate_available, is_torch_available, is_transformers_available, is_xformers_available
from . import BaseDiffusersCLICommand


def 							__A(lowerCAmelCase )		->				Optional[int]:

		"""simple docstring"""






		return EnvironmentCommand()







class        lowerCAmelCase__      (   __lowercase	):

						@staticmethod
						def  A_				(			a				)					->      List[str]:


								'''simple docstring'''

								_UpperCamelCase						    =	parser.add_parser("""env"""				)
								download_parser.set_defaults(func=a				)

						def  A_				(			self				)					->      List[str]:


								'''simple docstring'''

								_UpperCamelCase						    =	huggingface_hub.__version__

								_UpperCamelCase						    =	"""not installed"""
								_UpperCamelCase						    =	"""NA"""
								if is_torch_available():
										import torch

										_UpperCamelCase						    =	torch.__version__
										_UpperCamelCase						    =	torch.cuda.is_available()

								_UpperCamelCase						    =	"""not installed"""
								if is_transformers_available():
										import transformers

										_UpperCamelCase						    =	transformers.__version__

								_UpperCamelCase						    =	"""not installed"""
								if is_accelerate_available():
										import accelerate

										_UpperCamelCase						    =	accelerate.__version__

								_UpperCamelCase						    =	"""not installed"""
								if is_xformers_available():
										import xformers

										_UpperCamelCase						    =	xformers.__version__

								_UpperCamelCase						    =	{
								    """`diffusers` version""": version,
								    """Platform""": platform.platform(),
								    """Python version""": platform.python_version(),
								    """PyTorch version (GPU?)""": F'{pt_version} ({pt_cuda_available})',
								    """Huggingface_hub version""": hub_version,
								    """Transformers version""": transformers_version,
								    """Accelerate version""": accelerate_version,
								    """xFormers version""": xformers_version,
								    """Using GPU in script?""": """<fill in>""",
								    """Using distributed or parallel set-up in script?""": """<fill in>""",
								}

								print("""\nCopy-and-paste the text below in your GitHub issue and FILL OUT the two last points.\n"""				)
								print(self.format_dict(a				)				)

								return info







						@staticmethod
						def  A_				(			a				)					->      Dict:


								'''simple docstring'''

								return "\n".join([F'- {prop}: {val}' for prop, val in d.items()]				) + "\n"



