






from typing import List, Optional, Tuple, Union

from . import logging
from .import_utils import is_torch_available, is_torch_version


if is_torch_available():
      import torch

__UpperCAmelCase      =				logging.get_logger(__name__)  # pylint: disable=invalid-name

try:
      from torch._dynamo import allow_in_graph as maybe_allow_in_graph
except (ImportError, ModuleNotFoundError):





      def 						A_					(						cls			)				->List[Any]:







       """simple docstring"""

       return cls





def 						A_					(						lowercase_	,  lowercase_ = None	,  lowercase_ = None	,  lowercase_ = None	,  lowercase_ = None	,  )				->Optional[Any]:







 """simple docstring"""

 SCREAMING_SNAKE_CASE				    =		device
 SCREAMING_SNAKE_CASE				    =		shape[0]

 SCREAMING_SNAKE_CASE				    =		layout or torch.strided
 SCREAMING_SNAKE_CASE				    =		device or torch.device('cpu'			)

 if generator is not None:
  SCREAMING_SNAKE_CASE				    =		generator.device.type if not isinstance(lowercase_	,  lowercase_			) else generator[0].device.type
  if gen_device_type != device.type and gen_device_type == "cpu":
   SCREAMING_SNAKE_CASE				    =		'cpu'
   if device != "mps":
    logger.info(
        f'''The passed generator was created on \'cpu\' even though a tensor on {device} was expected.'''
        f''' Tensors will be created on \'cpu\' and then moved to {device}. Note that one can probably'''
        f''' slighly speed up this function by passing a generator that was created on the {device} device.'''			)
  elif gen_device_type != device.type and gen_device_type == "cuda":
   raise ValueError(f'''Cannot generate a {device} tensor from a generator of type {gen_device_type}.'''			)

 if isinstance(lowercase_	,  lowercase_			):
  SCREAMING_SNAKE_CASE				    =		(1,) + shape[1:]
  SCREAMING_SNAKE_CASE				    =		[
      torch.randn(lowercase_	,  generator=generator[i]	,  device=lowercase_	,  dtype=lowercase_	,  layout=lowercase_			)
      for i in range(lowercase_			)
  ]
  SCREAMING_SNAKE_CASE				    =		torch.cat(lowercase_	,  dim=0			).to(lowercase_			)
 else:
  SCREAMING_SNAKE_CASE				    =		torch.randn(lowercase_	,  generator=lowercase_	,  device=lowercase_	,  dtype=lowercase_	,  layout=lowercase_			).to(lowercase_			)

 return latents





def 						A_					(						lowercase_			)				->Optional[Any]:







 """simple docstring"""

 if is_torch_version('<'	,  '2.0.0'			) or not hasattr(lowercase_	,  '_dynamo'			):
  return False
 return isinstance(lowercase_	,  torch._dynamo.eval_frame.OptimizedModule			)












import json
import os
import tempfile
import unittest

from transformers.modelcard import ModelCard
class 	a_(		unittest.TestCase	):



     """simple docstring"""



     def     __UpperCamelCase						(		self					:	Optional[int])							->	List[str]:

      """simple docstring"""
      SCREAMING_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': 7_6},
      }





     def     __UpperCamelCase						(		self					:	Optional[Any])							->	Any:

      """simple docstring"""
      SCREAMING_SNAKE_CASE				    =		ModelCard.from_dict(self.inputs_dict)
      self.assertTrue(hasattr(lowerCAmelCase__  ,  'model_details'))
      self.assertTrue(hasattr(lowerCAmelCase__  ,  'intended_use'))
      self.assertTrue(hasattr(lowerCAmelCase__  ,  'factors'))
      self.assertTrue(hasattr(lowerCAmelCase__  ,  'metrics'))
      self.assertTrue(hasattr(lowerCAmelCase__  ,  'evaluation_data'))
      self.assertTrue(hasattr(lowerCAmelCase__  ,  'training_data'))
      self.assertTrue(hasattr(lowerCAmelCase__  ,  'quantitative_analyses'))
      self.assertTrue(hasattr(lowerCAmelCase__  ,  'ethical_considerations'))
      self.assertTrue(hasattr(lowerCAmelCase__  ,  'caveats_and_recommendations'))





     def     __UpperCamelCase						(		self					:	Any)							->	Any:

      """simple docstring"""
      SCREAMING_SNAKE_CASE				    =		ModelCard.from_dict(self.inputs_dict)
      SCREAMING_SNAKE_CASE				    =		json.loads(modelcard.to_json_string())
      for key, value in self.inputs_dict.items():
       self.assertEqual(obj[key]  ,  lowerCAmelCase__)





     def     __UpperCamelCase						(		self					:	int)							->	Optional[Any]:

      """simple docstring"""
      SCREAMING_SNAKE_CASE				    =		ModelCard.from_dict(self.inputs_dict)

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

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





     def     __UpperCamelCase						(		self					:	Optional[int])							->	Any:

      """simple docstring"""
      SCREAMING_SNAKE_CASE				    =		ModelCard.from_dict(self.inputs_dict)

      with tempfile.TemporaryDirectory() as tmpdirname:
       model_card_first.save_pretrained(lowerCAmelCase__)
       SCREAMING_SNAKE_CASE				    =		ModelCard.from_pretrained(lowerCAmelCase__)

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




