
'''simple docstring'''
import json
import os
import tempfile
import unittest

from transformers.modelcard import ModelCard


class        SCREAMING_SNAKE_CASE__	(			unittest.TestCase):







				def 		UpperCAmelCase_  (    self			)->						List[Any]:







											'''simple docstring'''






											UpperCamelCase					       =			{
											    '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 		UpperCAmelCase_  (    self			)->						Optional[int]:







											'''simple docstring'''






											UpperCamelCase					       =			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 		UpperCAmelCase_  (    self			)->						Optional[int]:







											'''simple docstring'''






											UpperCamelCase					       =			ModelCard.from_dict(self.inputs_dict			)
											UpperCamelCase					       =			json.loads(modelcard.to_json_string()			)
											for key, value in self.inputs_dict.items():
																		self.assertEqual(obj[key]		,							A_			)







				def 		UpperCAmelCase_  (    self			)->						Union[str, Any]:







											'''simple docstring'''






											UpperCamelCase					       =			ModelCard.from_dict(self.inputs_dict			)

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

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







				def 		UpperCAmelCase_  (    self			)->						Any:







											'''simple docstring'''






											UpperCamelCase					       =			ModelCard.from_dict(self.inputs_dict			)

											with tempfile.TemporaryDirectory() as tmpdirname:
																		model_card_first.save_pretrained(A_			)
																		UpperCamelCase					       =			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 						A_(			A		:						int						,			A		:						bool = False):
							if device_specific:
														seed += AcceleratorState().process_index
							random.seed(A)
							np.random.seed(A)
							torch.manual_seed(A)
							if is_xpu_available():
														torch.xpu.manual_seed_all(A)
							elif is_npu_available():
														torch.npu.manual_seed_all(A)
							else:
														torch.cuda.manual_seed_all(A)
							# ^^ safe to call this function even if cuda is not available
							if is_tpu_available():
														xm.set_rng_state(A)







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

							# Broadcast the rng state from device 0 to other devices
							UpperCamelCase					       =			AcceleratorState()
							if state.distributed_type == DistributedType.TPU:
														UpperCamelCase					       =			rng_state.to(xm.xla_device())
														xm.collective_broadcast([rng_state])
														xm.mark_step()
														UpperCamelCase					       =			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
							):
														UpperCamelCase					       =			rng_state.to(state.device)
														torch.distributed.broadcast(A						,			0)
														UpperCamelCase					       =			rng_state.cpu()
							elif state.distributed_type == DistributedType.MULTI_CPU:
														torch.distributed.broadcast(A						,			0)

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







def 						A_(			A		:						List[Union[str, RNGType]]						,			A		:						Optional[torch.Generator] = None):
							for rng_type in rng_types:
														synchronize_rng_state(RNGType(A)						,			generator=A)




