






import json
import os
import tempfile
import unittest

from transformers.modelcard import ModelCard


class 					__SCREAMING_SNAKE_CASE(		unittest.TestCase		):
						def lowerCAmelCase_	(       self: int  )						->						Optional[Any]:
										snake_case__													=       {
										    'model_details': {
										        'Organization': 'testing',
										        'Model date': 'today',
										        'Model version': 'v2.1, Developed by Test Corp in 2019.',
										        'Architecture': 'Convolutional Neural Network.',
										    },
										    'metrics': 'BLEU and ROUGE-1',
										    'evaluation_data': {
										        'Datasets': {'BLEU': 'My-great-dataset-v1', 'ROUGE-1': 'My-short-dataset-v2.1'},
										        'Preprocessing': 'See details on https://arxiv.org/pdf/1810.03993.pdf',
										    },
										    'training_data': {
										        'Dataset': 'English Wikipedia dump dated 2018-12-01',
										        'Preprocessing': (
										            'Using SentencePiece vocabulary of size 52k tokens. See details on'
										            ' https://arxiv.org/pdf/1810.03993.pdf'
										        ),
										    },
										    'quantitative_analyses': {'BLEU': 55.1, 'ROUGE-1': 76},
										}
						def lowerCAmelCase_	(       self: Optional[int]  )						->						List[str]:
										snake_case__													=       ModelCard.from_dict(self.inputs_dict  )
										self.assertTrue(hasattr(UpperCamelCase    ,		'model_details'  )  )
										self.assertTrue(hasattr(UpperCamelCase    ,		'intended_use'  )  )
										self.assertTrue(hasattr(UpperCamelCase    ,		'factors'  )  )
										self.assertTrue(hasattr(UpperCamelCase    ,		'metrics'  )  )
										self.assertTrue(hasattr(UpperCamelCase    ,		'evaluation_data'  )  )
										self.assertTrue(hasattr(UpperCamelCase    ,		'training_data'  )  )
										self.assertTrue(hasattr(UpperCamelCase    ,		'quantitative_analyses'  )  )
										self.assertTrue(hasattr(UpperCamelCase    ,		'ethical_considerations'  )  )
										self.assertTrue(hasattr(UpperCamelCase    ,		'caveats_and_recommendations'  )  )
						def lowerCAmelCase_	(       self: Optional[int]  )						->						Optional[Any]:
										snake_case__													=       ModelCard.from_dict(self.inputs_dict  )
										snake_case__													=       json.loads(modelcard.to_json_string()  )
										for key, value in self.inputs_dict.items():
														self.assertEqual(obj[key]    ,		UpperCamelCase  )
						def lowerCAmelCase_	(       self: Union[str, Any]  )						->						Any:
										snake_case__													=       ModelCard.from_dict(self.inputs_dict  )

										with tempfile.TemporaryDirectory() as tmpdirname:
														snake_case__													=       os.path.join(UpperCamelCase    ,		'modelcard.json'  )
														model_card_first.to_json_file(UpperCamelCase  )
														snake_case__													=       ModelCard.from_json_file(UpperCamelCase  )

										self.assertEqual(model_card_second.to_dict()    ,		model_card_first.to_dict()  )


						def lowerCAmelCase_	(       self: Any  )						->						Optional[int]:
										snake_case__													=       ModelCard.from_dict(self.inputs_dict  )

										with tempfile.TemporaryDirectory() as tmpdirname:
														model_card_first.save_pretrained(UpperCamelCase  )
														snake_case__													=       ModelCard.from_pretrained(UpperCamelCase  )

										self.assertEqual(model_card_second.to_dict()    ,		model_card_first.to_dict()  )












import inspect
from typing import Callable, List, Optional, Set, Tuple, Union

import torch
from packaging import version
from safetensors.torch import storage_ptr, storage_size
from torch import nn

from .utils import logging


__UpperCamelCase   :						int							=  [nn.LayerNorm]

__UpperCamelCase   :						List[str]							=  logging.get_logger(__name__)

__UpperCamelCase   :						str							=  version.parse(version.parse(torch.__version__).base_version)

__UpperCamelCase   :						List[str]							=  parsed_torch_version_base >= version.parse("""2.0""")
__UpperCamelCase   :						Dict							=  parsed_torch_version_base >= version.parse("""1.12""")
__UpperCamelCase   :						Tuple							=  parsed_torch_version_base >= version.parse("""1.11""")
__UpperCamelCase   :						Union[str, Any]							=  parsed_torch_version_base < version.parse("""1.11""")
def        a_	( _A ,				_A ,				_A ,				_A ,				self					)     ->     Any:







				"""simple docstring"""
				from torch import _softmax_backward_data

				if is_torch_less_than_1_11:
								return _softmax_backward_data(_A ,				_A ,				parent.dim ,				self					)
				else:
								return _softmax_backward_data(_A ,				_A ,				parent.dim ,				self.dtype					)
def        a_	( _A ,				_A ,				_A = 0					)     ->     nn.Linear:







				"""simple docstring"""
				snake_case__													=       index.to(layer.weight.device					)
				snake_case__													=       layer.weight.index_select(_A ,				_A					).clone().detach()
				if layer.bias is not None:
								if dim == 1:
												snake_case__													=       layer.bias.clone().detach()
								else:
												snake_case__													=       layer.bias[index].clone().detach()
				snake_case__													=       list(layer.weight.size()					)
				snake_case__													=       len(_A					)
				snake_case__													=       nn.Linear(new_size[1] ,				new_size[0] ,				bias=layer.bias is not None					).to(layer.weight.device					)
				snake_case__													=       False
				new_layer.weight.copy_(W.contiguous()					)
				snake_case__													=       True
				if layer.bias is not None:
								snake_case__													=       False
								new_layer.bias.copy_(b.contiguous()					)
								snake_case__													=       True
				return new_layer


class 					__SCREAMING_SNAKE_CASE(		nn.Module		):
							def __init__(       self: Union[str, Any]    ,		UpperCamelCase: Dict    ,		UpperCamelCase: List[Any]  )						->						Any:
											super().__init__()
											snake_case__													=       nf
											snake_case__													=       nn.Parameter(torch.empty(UpperCamelCase    ,		UpperCamelCase  )  )
											snake_case__													=       nn.Parameter(torch.zeros(UpperCamelCase  )  )
											nn.init.normal_(self.weight    ,		std=0.02  )


							def lowerCAmelCase_	(       self: Optional[Any]    ,		UpperCamelCase: int  )						->						List[Any]:
											snake_case__													=       x.size()[:-1] + (self.nf,)
											snake_case__													=       torch.addmm(self.bias    ,		x.view(-1    ,		x.size(-1  )  )    ,		self.weight  )
											snake_case__													=       x.view(UpperCamelCase  )
											return x


def        a_	( _A ,				_A ,				_A = 1					)     ->     ConvaD:







				"""simple docstring"""
				snake_case__													=       index.to(layer.weight.device					)
				snake_case__													=       layer.weight.index_select(_A ,				_A					).clone().detach()
				if dim == 0:
								snake_case__													=       layer.bias.clone().detach()
				else:
								snake_case__													=       layer.bias[index].clone().detach()
				snake_case__													=       list(layer.weight.size()					)
				snake_case__													=       len(_A					)
				snake_case__													=       ConvaD(new_size[1] ,				new_size[0]					).to(layer.weight.device					)
				snake_case__													=       False
				new_layer.weight.copy_(W.contiguous()					)
				snake_case__													=       True
				snake_case__													=       False
				new_layer.bias.copy_(b.contiguous()					)
				snake_case__													=       True
				return new_layer
def        a_	( _A ,				_A ,				_A = None					)     ->     Union[nn.Linear, ConvaD]:







				"""simple docstring"""
				if isinstance(_A ,				nn.Linear					):
								return prune_linear_layer(_A ,				_A ,				dim=0 if dim is None else dim					)
				elif isinstance(_A ,				_A					):
								return prune_convad_layer(_A ,				_A ,				dim=1 if dim is None else dim					)
				else:
								raise ValueError(f'''Can\'t prune layer of class {layer.__class__}'''					)
def        a_	( _A ,				_A ,				_A ,				*_A					)     ->     torch.Tensor:







				"""simple docstring"""
				assert len(_A					) > 0, f'''{input_tensors} has to be a tuple/list of tensors'''

				# inspect.signature exist since python 3.5 and is a python method -> no problem with backward compatibility
				snake_case__													=       len(inspect.signature(_A					).parameters					)
				if num_args_in_forward_chunk_fn != len(_A					):
								raise ValueError(
								    f'''forward_chunk_fn expects {num_args_in_forward_chunk_fn} arguments, but only {len(_A					)} input '''
								    'tensors are given'					)

				if chunk_size > 0:
								snake_case__													=       input_tensors[0].shape[chunk_dim]
								for input_tensor in input_tensors:
												if input_tensor.shape[chunk_dim] != tensor_shape:
																raise ValueError(
																    f'''All input tenors have to be of the same shape: {tensor_shape}, '''
																    f'''found shape {input_tensor.shape[chunk_dim]}'''					)

								if input_tensors[0].shape[chunk_dim] % chunk_size != 0:
												raise ValueError(
												    f'''The dimension to be chunked {input_tensors[0].shape[chunk_dim]} has to be a multiple of the chunk '''
												    f'''size {chunk_size}'''					)

								snake_case__													=       input_tensors[0].shape[chunk_dim] // chunk_size

								# chunk input tensor into tuples
								snake_case__													=       tuple(input_tensor.chunk(_A ,				dim=_A					) for input_tensor in input_tensors					)
								# apply forward fn to every tuple
								snake_case__													=       tuple(forward_fn(*_A					) for input_tensors_chunk in zip(*_A					)					)
								# concatenate output at same dimension
								return torch.cat(_A ,				dim=_A					)

				return forward_fn(*_A					)
def        a_	( _A ,				_A ,				_A ,				_A					)     ->     Tuple[Set[int], torch.LongTensor]:







				"""simple docstring"""
				snake_case__													=       torch.ones(_A ,				_A					)
				snake_case__													=       set(_A					) - already_pruned_heads  # Convert to set and remove already pruned heads
				for head in heads:
								# Compute how many pruned heads are before the head and move the index accordingly
								snake_case__													=       head - sum(1 if h < head else 0 for h in already_pruned_heads					)
								snake_case__													=       0
				snake_case__													=       mask.view(-1					).contiguous().eq(1					)
				snake_case__													=       torch.arange(len(_A					)					)[mask].long()
				return heads, index
def        a_	( *_A ,				_A = None					)     ->     Tuple[torch.Tensor, ...]:







				"""simple docstring"""
				return torch.meshgrid(*_A ,				indexing=_A					)


def        a_	( _A					)     ->     Tuple[torch.device, int, int]:







				"""simple docstring"""
				return tensor.device, storage_ptr(_A					), storage_size(_A					)




