





"""simple docstring"""


import json
import os
import tempfile
import unittest

from transformers.modelcard import ModelCard







class 	__a   (  unittest.TestCase		):



     def 						snake_case_			(       self   ):
            _lowerCamelCase    	=					{
                '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 						snake_case_			(       self   ):
            _lowerCamelCase    	=					ModelCard.from_dict(self.inputs_dict   )
            self.assertTrue(hasattr(a__					,     'model_details'   )   )
            self.assertTrue(hasattr(a__					,     'intended_use'   )   )
            self.assertTrue(hasattr(a__					,     'factors'   )   )
            self.assertTrue(hasattr(a__					,     'metrics'   )   )
            self.assertTrue(hasattr(a__					,     'evaluation_data'   )   )
            self.assertTrue(hasattr(a__					,     'training_data'   )   )
            self.assertTrue(hasattr(a__					,     'quantitative_analyses'   )   )
            self.assertTrue(hasattr(a__					,     'ethical_considerations'   )   )
            self.assertTrue(hasattr(a__					,     'caveats_and_recommendations'   )   )



     def 						snake_case_			(       self   ):
            _lowerCamelCase    	=					ModelCard.from_dict(self.inputs_dict   )
            _lowerCamelCase    	=					json.loads(modelcard.to_json_string()   )
            for key, value in self.inputs_dict.items():
                   self.assertEqual(obj[key]					,     a__   )



     def 						snake_case_			(       self   ):
            _lowerCamelCase    	=					ModelCard.from_dict(self.inputs_dict   )

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

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



     def 						snake_case_			(       self   ):
            _lowerCamelCase    	=					ModelCard.from_dict(self.inputs_dict   )

            with tempfile.TemporaryDirectory() as tmpdirname:
                   model_card_first.save_pretrained(a__   )
                   _lowerCamelCase    	=					ModelCard.from_pretrained(a__   )

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













"""simple docstring"""


import random
from typing import List, Optional, Union

import numpy as np
import torch

from ..state import AcceleratorState
from .constants import CUDA_DISTRIBUTED_TYPES
from .dataclasses import DistributedType, RNGType
from .imports import is_npu_available, is_tpu_available, is_xpu_available


if is_tpu_available(check_device=False):
      import torch_xla.core.xla_model as xm





def SCREAMING_SNAKE_CASE_   (       snake_case   :					int						,  snake_case   :					bool = False		)->		Any:
       if device_specific:
              seed += AcceleratorState().process_index
       random.seed(snake_case		)
       np.random.seed(snake_case		)
       torch.manual_seed(snake_case		)
       if is_xpu_available():
              torch.xpu.manual_seed_all(snake_case		)
       elif is_npu_available():
              torch.npu.manual_seed_all(snake_case		)
       else:
              torch.cuda.manual_seed_all(snake_case		)
       # ^^ safe to call this function even if cuda is not available
       if is_tpu_available():
              xm.set_rng_state(snake_case		)





def SCREAMING_SNAKE_CASE_   (       snake_case   :					Optional[RNGType] = None						,  snake_case   :					Optional[torch.Generator] = None		)->		str:
       # Get the proper rng state
       if rng_type == RNGType.TORCH:
              _lowerCamelCase    	=					torch.get_rng_state()
       elif rng_type == RNGType.CUDA:
              _lowerCamelCase    	=					torch.cuda.get_rng_state()
       elif rng_type == RNGType.XLA:
              assert is_tpu_available(), "Can't synchronize XLA seeds on an environment without TPUs."
              _lowerCamelCase    	=					torch.tensor(xm.get_rng_state()		)
       elif rng_type == RNGType.NPU:
              assert is_npu_available(), "Can't synchronize NPU seeds on an environment without NPUs."
              _lowerCamelCase    	=					torch.npu.get_rng_state()
       elif rng_type == RNGType.XPU:
              assert is_xpu_available(), "Can't synchronize XPU seeds on an environment without XPUs."
              _lowerCamelCase    	=					torch.xpu.get_rng_state()
       elif rng_type == RNGType.GENERATOR:
              assert generator is not None, "Need a generator to synchronize its seed."
              _lowerCamelCase    	=					generator.get_state()

       # Broadcast the rng state from device 0 to other devices
       _lowerCamelCase    	=					AcceleratorState()
       if state.distributed_type == DistributedType.TPU:
              _lowerCamelCase    	=					rng_state.to(xm.xla_device()		)
              xm.collective_broadcast([rng_state]		)
              xm.mark_step()
              _lowerCamelCase    	=					rng_state.cpu()
       elif (
           state.distributed_type in CUDA_DISTRIBUTED_TYPES
           or state.distributed_type == DistributedType.MULTI_NPU
           or state.distributed_type == DistributedType.MULTI_XPU
       ):
              _lowerCamelCase    	=					rng_state.to(state.device		)
              torch.distributed.broadcast(snake_case						,  0		)
              _lowerCamelCase    	=					rng_state.cpu()
       elif state.distributed_type == DistributedType.MULTI_CPU:
              torch.distributed.broadcast(snake_case						,  0		)

       # Set the broadcast rng state
       if rng_type == RNGType.TORCH:
              torch.set_rng_state(snake_case		)
       elif rng_type == RNGType.CUDA:
              torch.cuda.set_rng_state(snake_case		)
       elif rng_type == RNGType.NPU:
              torch.npu.set_rng_state(snake_case		)
       elif rng_type == RNGType.XPU:
              torch.xpu.set_rng_state(snake_case		)
       elif rng_type == RNGType.XLA:
              xm.set_rng_state(rng_state.item()		)
       elif rng_type == RNGType.GENERATOR:
              generator.set_state(snake_case		)





def SCREAMING_SNAKE_CASE_   (       snake_case   :					List[Union[str, RNGType]]						,  snake_case   :					Optional[torch.Generator] = None		)->		int:
       for rng_type in rng_types:
              synchronize_rng_state(RNGType(snake_case		)						,  generator=snake_case		)






