




import unittest

from parameterized import parameterized

from transformers import AutoTokenizer, GPTNeoXConfig, is_torch_available, set_seed
from transformers.testing_utils import require_torch, slow, torch_device

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
				import torch

				from transformers import (
				    GPTNeoXForCausalLM,
				    GPTNeoXForQuestionAnswering,
				    GPTNeoXForSequenceClassification,
				    GPTNeoXForTokenClassification,
				    GPTNeoXModel,
				)



class       a_       :





	def __init__(							self      ,     SCREAMING_SNAKE_CASE      ,     SCREAMING_SNAKE_CASE=13      ,     SCREAMING_SNAKE_CASE=7      ,     SCREAMING_SNAKE_CASE=True      ,     SCREAMING_SNAKE_CASE=True      ,     SCREAMING_SNAKE_CASE=True      ,     SCREAMING_SNAKE_CASE=True      ,     SCREAMING_SNAKE_CASE=99      ,     SCREAMING_SNAKE_CASE=64      ,     SCREAMING_SNAKE_CASE=5      ,     SCREAMING_SNAKE_CASE=4      ,     SCREAMING_SNAKE_CASE=37      ,     SCREAMING_SNAKE_CASE="gelu"      ,     SCREAMING_SNAKE_CASE=0.1      ,     SCREAMING_SNAKE_CASE=0.1      ,     SCREAMING_SNAKE_CASE=512      ,     SCREAMING_SNAKE_CASE=16      ,     SCREAMING_SNAKE_CASE=2      ,     SCREAMING_SNAKE_CASE=0.0_2      ,     SCREAMING_SNAKE_CASE=3      ,     SCREAMING_SNAKE_CASE=4      ,     SCREAMING_SNAKE_CASE=None      ,     )  ->					Union[str, Any]:




				"""simple docstring"""



				SCREAMING_SNAKE_CASE_								=						parent
				SCREAMING_SNAKE_CASE_								=						batch_size
				SCREAMING_SNAKE_CASE_								=						seq_length
				SCREAMING_SNAKE_CASE_								=						is_training
				SCREAMING_SNAKE_CASE_								=						use_input_mask
				SCREAMING_SNAKE_CASE_								=						use_token_type_ids
				SCREAMING_SNAKE_CASE_								=						use_labels
				SCREAMING_SNAKE_CASE_								=						vocab_size
				SCREAMING_SNAKE_CASE_								=						hidden_size
				SCREAMING_SNAKE_CASE_								=						num_hidden_layers
				SCREAMING_SNAKE_CASE_								=						num_attention_heads
				SCREAMING_SNAKE_CASE_								=						intermediate_size
				SCREAMING_SNAKE_CASE_								=						hidden_act
				SCREAMING_SNAKE_CASE_								=						hidden_dropout_prob
				SCREAMING_SNAKE_CASE_								=						attention_probs_dropout_prob
				SCREAMING_SNAKE_CASE_								=						max_position_embeddings
				SCREAMING_SNAKE_CASE_								=						type_vocab_size
				SCREAMING_SNAKE_CASE_								=						type_sequence_label_size
				SCREAMING_SNAKE_CASE_								=						initializer_range
				SCREAMING_SNAKE_CASE_								=						num_labels
				SCREAMING_SNAKE_CASE_								=						num_choices
				SCREAMING_SNAKE_CASE_								=						scope
				SCREAMING_SNAKE_CASE_								=						vocab_size - 1





	def 						A_(							self      )  ->					int:




				"""simple docstring"""



				SCREAMING_SNAKE_CASE_								=						ids_tensor([self.batch_size, self.seq_length]      ,     self.vocab_size      )

				SCREAMING_SNAKE_CASE_								=						None
				if self.use_input_mask:
							SCREAMING_SNAKE_CASE_								=						random_attention_mask([self.batch_size, self.seq_length]      )

				SCREAMING_SNAKE_CASE_								=						None
				if self.use_labels:
							SCREAMING_SNAKE_CASE_								=						ids_tensor([self.batch_size, self.seq_length]      ,     self.num_labels      )

				SCREAMING_SNAKE_CASE_								=						self.get_config()

				return config, input_ids, input_mask, token_labels





	def 						A_(							self      )  ->					Tuple:




				"""simple docstring"""



				return GPTNeoXConfig(
				    vocab_size=self.vocab_size      ,     hidden_size=self.hidden_size      ,     num_hidden_layers=self.num_hidden_layers      ,     num_attention_heads=self.num_attention_heads      ,     intermediate_size=self.intermediate_size      ,     hidden_act=self.hidden_act      ,     hidden_dropout_prob=self.hidden_dropout_prob      ,     attention_probs_dropout_prob=self.attention_probs_dropout_prob      ,     max_position_embeddings=self.max_position_embeddings      ,     type_vocab_size=self.type_vocab_size      ,     is_decoder=SCREAMING_SNAKE_CASE      ,     initializer_range=self.initializer_range      ,     pad_token_id=self.pad_token_id      ,     )





	def 						A_(							self      )  ->					Dict:




				"""simple docstring"""



				SCREAMING_SNAKE_CASE_						,		SCREAMING_SNAKE_CASE_						,		SCREAMING_SNAKE_CASE_						,		SCREAMING_SNAKE_CASE_								=						self.prepare_config_and_inputs()

				SCREAMING_SNAKE_CASE_								=						True

				return config, input_ids, input_mask, token_labels





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




				"""simple docstring"""



				SCREAMING_SNAKE_CASE_								=						GPTNeoXModel(config=SCREAMING_SNAKE_CASE      )
				model.to(SCREAMING_SNAKE_CASE      )
				model.eval()
				SCREAMING_SNAKE_CASE_								=						model(SCREAMING_SNAKE_CASE      ,     attention_mask=SCREAMING_SNAKE_CASE      )
				SCREAMING_SNAKE_CASE_								=						model(SCREAMING_SNAKE_CASE      )
				self.parent.assertEqual(result.last_hidden_state.shape      ,     (self.batch_size, self.seq_length, self.hidden_size)      )





	def 						A_(							self      ,     SCREAMING_SNAKE_CASE      ,     SCREAMING_SNAKE_CASE      ,     SCREAMING_SNAKE_CASE      )  ->					int:




				"""simple docstring"""



				SCREAMING_SNAKE_CASE_								=						True
				SCREAMING_SNAKE_CASE_								=						GPTNeoXModel(SCREAMING_SNAKE_CASE      )
				model.to(SCREAMING_SNAKE_CASE      )
				model.eval()
				SCREAMING_SNAKE_CASE_								=						model(SCREAMING_SNAKE_CASE      ,     attention_mask=SCREAMING_SNAKE_CASE      )
				self.parent.assertEqual(result.last_hidden_state.shape      ,     (self.batch_size, self.seq_length, self.hidden_size)      )





	def 						A_(							self      ,     SCREAMING_SNAKE_CASE      ,     SCREAMING_SNAKE_CASE      ,     SCREAMING_SNAKE_CASE      ,     SCREAMING_SNAKE_CASE      )  ->					Dict:




				"""simple docstring"""



				SCREAMING_SNAKE_CASE_								=						GPTNeoXForCausalLM(config=SCREAMING_SNAKE_CASE      )
				model.to(SCREAMING_SNAKE_CASE      )
				model.eval()
				SCREAMING_SNAKE_CASE_								=						model(SCREAMING_SNAKE_CASE      ,     attention_mask=SCREAMING_SNAKE_CASE      ,     labels=SCREAMING_SNAKE_CASE      )
				self.parent.assertEqual(result.logits.shape      ,     (self.batch_size, self.seq_length, self.vocab_size)      )





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




				"""simple docstring"""



				SCREAMING_SNAKE_CASE_								=						self.num_labels
				SCREAMING_SNAKE_CASE_								=						GPTNeoXForQuestionAnswering(SCREAMING_SNAKE_CASE      )
				model.to(SCREAMING_SNAKE_CASE      )
				model.eval()
				SCREAMING_SNAKE_CASE_								=						model(SCREAMING_SNAKE_CASE      ,     attention_mask=SCREAMING_SNAKE_CASE      )
				self.parent.assertEqual(result.start_logits.shape      ,     (self.batch_size, self.seq_length)      )
				self.parent.assertEqual(result.end_logits.shape      ,     (self.batch_size, self.seq_length)      )





	def 						A_(							self      ,     SCREAMING_SNAKE_CASE      ,     SCREAMING_SNAKE_CASE      ,     SCREAMING_SNAKE_CASE      ,     SCREAMING_SNAKE_CASE      )  ->					Dict:




				"""simple docstring"""



				SCREAMING_SNAKE_CASE_								=						self.num_labels
				SCREAMING_SNAKE_CASE_								=						GPTNeoXForSequenceClassification(SCREAMING_SNAKE_CASE      )
				model.to(SCREAMING_SNAKE_CASE      )
				model.eval()
				SCREAMING_SNAKE_CASE_								=						ids_tensor([self.batch_size]      ,     self.type_sequence_label_size      )
				SCREAMING_SNAKE_CASE_								=						model(SCREAMING_SNAKE_CASE      ,     attention_mask=SCREAMING_SNAKE_CASE      ,     labels=SCREAMING_SNAKE_CASE      )
				self.parent.assertEqual(result.logits.shape      ,     (self.batch_size, self.num_labels)      )





	def 						A_(							self      ,     SCREAMING_SNAKE_CASE      ,     SCREAMING_SNAKE_CASE      ,     SCREAMING_SNAKE_CASE      ,     SCREAMING_SNAKE_CASE      )  ->					Any:




				"""simple docstring"""



				SCREAMING_SNAKE_CASE_								=						self.num_labels
				SCREAMING_SNAKE_CASE_								=						GPTNeoXForTokenClassification(SCREAMING_SNAKE_CASE      )
				model.to(SCREAMING_SNAKE_CASE      )
				model.eval()
				SCREAMING_SNAKE_CASE_								=						model(SCREAMING_SNAKE_CASE      ,     attention_mask=SCREAMING_SNAKE_CASE      ,     labels=SCREAMING_SNAKE_CASE      )
				self.parent.assertEqual(result.logits.shape      ,     (self.batch_size, self.seq_length, self.num_labels)      )





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




				"""simple docstring"""



				SCREAMING_SNAKE_CASE_								=						True
				SCREAMING_SNAKE_CASE_								=						GPTNeoXForCausalLM(config=SCREAMING_SNAKE_CASE      )
				model.to(SCREAMING_SNAKE_CASE      )
				model.eval()

				# first forward pass
				SCREAMING_SNAKE_CASE_								=						model(SCREAMING_SNAKE_CASE      ,     attention_mask=SCREAMING_SNAKE_CASE      ,     use_cache=SCREAMING_SNAKE_CASE      )
				SCREAMING_SNAKE_CASE_								=						outputs.past_key_values

				# create hypothetical multiple next token and extent to next_input_ids
				SCREAMING_SNAKE_CASE_								=						ids_tensor((self.batch_size, 3)      ,     config.vocab_size      )
				SCREAMING_SNAKE_CASE_								=						ids_tensor((self.batch_size, 3)      ,     vocab_size=2      )

				# append to next input_ids and
				SCREAMING_SNAKE_CASE_								=						torch.cat([input_ids, next_tokens]      ,     dim=-1      )
				SCREAMING_SNAKE_CASE_								=						torch.cat([input_mask, next_mask]      ,     dim=-1      )

				SCREAMING_SNAKE_CASE_								=						model(SCREAMING_SNAKE_CASE      ,     attention_mask=SCREAMING_SNAKE_CASE      ,     output_hidden_states=SCREAMING_SNAKE_CASE      )
				SCREAMING_SNAKE_CASE_								=						output_from_no_past['hidden_states'][0]
				SCREAMING_SNAKE_CASE_								=						model(
				    SCREAMING_SNAKE_CASE      ,     attention_mask=SCREAMING_SNAKE_CASE      ,     past_key_values=SCREAMING_SNAKE_CASE      ,     output_hidden_states=SCREAMING_SNAKE_CASE      ,     )['hidden_states'][0]

				# select random slice
				SCREAMING_SNAKE_CASE_								=						ids_tensor((1,)      ,     output_from_past.shape[-1]      ).item()
				SCREAMING_SNAKE_CASE_								=						output_from_no_past[:, -3:, random_slice_idx].detach()
				SCREAMING_SNAKE_CASE_								=						output_from_past[:, :, random_slice_idx].detach()

				self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1]      )

				# test that outputs are equal for slice
				self.parent.assertTrue(torch.allclose(SCREAMING_SNAKE_CASE      ,     SCREAMING_SNAKE_CASE      ,     atol=1e-3      )      )





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




				"""simple docstring"""



				SCREAMING_SNAKE_CASE_								=						self.prepare_config_and_inputs()
				SCREAMING_SNAKE_CASE_						,		SCREAMING_SNAKE_CASE_						,		SCREAMING_SNAKE_CASE_						,		SCREAMING_SNAKE_CASE_								=						config_and_inputs
				SCREAMING_SNAKE_CASE_								=						{'input_ids': input_ids, 'attention_mask': input_mask}
				return config, inputs_dict




@require_torch
class       a_       (     SCREAMING_SNAKE_CASE__  ,    SCREAMING_SNAKE_CASE__  ,    SCREAMING_SNAKE_CASE__  ,    unittest.TestCase     ):
	A    							= (
	    (
	        GPTNeoXModel,
	        GPTNeoXForCausalLM,
	        GPTNeoXForQuestionAnswering,
	        GPTNeoXForSequenceClassification,
	        GPTNeoXForTokenClassification,
	    )
	    if is_torch_available()
	    else ()
	)
	A    							= (GPTNeoXForCausalLM,) if is_torch_available() else ()
	A    							= (
	    {
	        '''feature-extraction''': GPTNeoXModel,
	        '''question-answering''': GPTNeoXForQuestionAnswering,
	        '''text-classification''': GPTNeoXForSequenceClassification,
	        '''text-generation''': GPTNeoXForCausalLM,
	        '''token-classification''': GPTNeoXForTokenClassification,
	        '''zero-shot''': GPTNeoXForSequenceClassification,
	    }
	    if is_torch_available()
	    else {}
	)
	A    							= False
	A    							= False
	A    							= False
	A    							= False





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




				"""simple docstring"""



				SCREAMING_SNAKE_CASE_								=						GPTNeoXModelTester(self      )
				SCREAMING_SNAKE_CASE_								=						ConfigTester(self      ,     config_class=SCREAMING_SNAKE_CASE      ,     hidden_size=64      ,     num_attention_heads=8      )





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




				"""simple docstring"""



				self.config_tester.run_common_tests()





	def 						A_(							self      )  ->					Dict:




				"""simple docstring"""



				SCREAMING_SNAKE_CASE_						,		SCREAMING_SNAKE_CASE_						,		SCREAMING_SNAKE_CASE_						,		SCREAMING_SNAKE_CASE_								=						self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_model(SCREAMING_SNAKE_CASE      ,     SCREAMING_SNAKE_CASE      ,     SCREAMING_SNAKE_CASE      )





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




				"""simple docstring"""



				SCREAMING_SNAKE_CASE_						,		SCREAMING_SNAKE_CASE_						,		SCREAMING_SNAKE_CASE_						,		SCREAMING_SNAKE_CASE_								=						self.model_tester.prepare_config_and_inputs_for_decoder()
				self.model_tester.create_and_check_model_as_decoder(SCREAMING_SNAKE_CASE      ,     SCREAMING_SNAKE_CASE      ,     SCREAMING_SNAKE_CASE      )





	def 						A_(							self      )  ->					Any:




				"""simple docstring"""



				SCREAMING_SNAKE_CASE_						,		SCREAMING_SNAKE_CASE_						,		SCREAMING_SNAKE_CASE_						,		SCREAMING_SNAKE_CASE_								=						self.model_tester.prepare_config_and_inputs_for_decoder()

				SCREAMING_SNAKE_CASE_								=						None

				self.model_tester.create_and_check_model_as_decoder(SCREAMING_SNAKE_CASE      ,     SCREAMING_SNAKE_CASE      ,     SCREAMING_SNAKE_CASE      )





	def 						A_(							self      )  ->					str:




				"""simple docstring"""



				SCREAMING_SNAKE_CASE_						,		SCREAMING_SNAKE_CASE_						,		SCREAMING_SNAKE_CASE_						,		SCREAMING_SNAKE_CASE_								=						self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_decoder_model_past_large_inputs(SCREAMING_SNAKE_CASE      ,     SCREAMING_SNAKE_CASE      ,     SCREAMING_SNAKE_CASE      )





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




				"""simple docstring"""



				SCREAMING_SNAKE_CASE_								=						self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_for_causal_lm(*SCREAMING_SNAKE_CASE      )





	def 						A_(							self      )  ->					Dict:




				"""simple docstring"""



				SCREAMING_SNAKE_CASE_								=						self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_for_question_answering(*SCREAMING_SNAKE_CASE      )





	def 						A_(							self      )  ->					Any:




				"""simple docstring"""



				SCREAMING_SNAKE_CASE_								=						self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_for_sequence_classification(*SCREAMING_SNAKE_CASE      )





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




				"""simple docstring"""



				SCREAMING_SNAKE_CASE_								=						self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_for_token_classification(*SCREAMING_SNAKE_CASE      )





	@unittest.skip(reason='Feed forward chunking is not implemented'      )
	def 						A_(							self      )  ->					Tuple:




				"""simple docstring"""



				pass





	@parameterized.expand([('linear',), ('dynamic',)]      )
	def 						A_(							self      ,     SCREAMING_SNAKE_CASE      )  ->					Dict:




				"""simple docstring"""



				SCREAMING_SNAKE_CASE_						,		SCREAMING_SNAKE_CASE_								=						self.model_tester.prepare_config_and_inputs_for_common()
				SCREAMING_SNAKE_CASE_								=						ids_tensor([1, 10]      ,     config.vocab_size      )
				SCREAMING_SNAKE_CASE_								=						ids_tensor([1, int(config.max_position_embeddings * 1.5      )]      ,     config.vocab_size      )

				set_seed(42      )  # Fixed seed at init time so the two models get the same random weights
				SCREAMING_SNAKE_CASE_								=						GPTNeoXModel(SCREAMING_SNAKE_CASE      )
				original_model.to(SCREAMING_SNAKE_CASE      )
				original_model.eval()
				SCREAMING_SNAKE_CASE_								=						original_model(SCREAMING_SNAKE_CASE      ).last_hidden_state
				SCREAMING_SNAKE_CASE_								=						original_model(SCREAMING_SNAKE_CASE      ).last_hidden_state

				set_seed(42      )  # Fixed seed at init time so the two models get the same random weights
				SCREAMING_SNAKE_CASE_								=						{'type': scaling_type, 'factor': 1_0.0}
				SCREAMING_SNAKE_CASE_								=						GPTNeoXModel(SCREAMING_SNAKE_CASE      )
				scaled_model.to(SCREAMING_SNAKE_CASE      )
				scaled_model.eval()
				SCREAMING_SNAKE_CASE_								=						scaled_model(SCREAMING_SNAKE_CASE      ).last_hidden_state
				SCREAMING_SNAKE_CASE_								=						scaled_model(SCREAMING_SNAKE_CASE      ).last_hidden_state

				# Dynamic scaling does not change the RoPE embeddings until it receives an input longer than the original
				# maximum sequence length, so the outputs for the short input should match.
				if scaling_type == "dynamic":
							self.assertTrue(torch.allclose(SCREAMING_SNAKE_CASE      ,     SCREAMING_SNAKE_CASE      ,     atol=1e-5      )      )
				else:
							self.assertFalse(torch.allclose(SCREAMING_SNAKE_CASE      ,     SCREAMING_SNAKE_CASE      ,     atol=1e-5      )      )

				# The output should be different for long inputs
				self.assertFalse(torch.allclose(SCREAMING_SNAKE_CASE      ,     SCREAMING_SNAKE_CASE      ,     atol=1e-5      )      )




@require_torch
class       a_       (     unittest.TestCase     ):





	@slow
	def 						A_(							self      )  ->					Any:




				"""simple docstring"""



				SCREAMING_SNAKE_CASE_								=						AutoTokenizer.from_pretrained('EleutherAI/pythia-410m-deduped'      )
				for checkpointing in [True, False]:
							SCREAMING_SNAKE_CASE_								=						GPTNeoXForCausalLM.from_pretrained('EleutherAI/pythia-410m-deduped'      )

							if checkpointing:
										model.gradient_checkpointing_enable()
							else:
										model.gradient_checkpointing_disable()
							model.to(SCREAMING_SNAKE_CASE      )

							SCREAMING_SNAKE_CASE_								=						tokenizer('My favorite food is'      ,     return_tensors='pt'      ).to(SCREAMING_SNAKE_CASE      )
							# The hub repo. is updated on 2023-04-04, resulting in poor outputs.
							# See: https://github.com/huggingface/transformers/pull/24193
							SCREAMING_SNAKE_CASE_								=						'My favorite food is a good old-fashioned, old-fashioned, old-fashioned.\n\nI\'m not sure'

							SCREAMING_SNAKE_CASE_								=						model.generate(**SCREAMING_SNAKE_CASE      ,     do_sample=SCREAMING_SNAKE_CASE      ,     max_new_tokens=20      )
							SCREAMING_SNAKE_CASE_								=						tokenizer.batch_decode(SCREAMING_SNAKE_CASE      )[0]

							self.assertEqual(SCREAMING_SNAKE_CASE      ,     SCREAMING_SNAKE_CASE      )






# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import torch

from accelerate import PartialState
from accelerate.utils.operations import broadcast, gather, gather_object, pad_across_processes, reduce

def lowercase    (  SCREAMING_SNAKE_CASE )			->							str:


			'''simple docstring'''







			return (torch.arange(state.num_processes ) + 1.0 + (state.num_processes * state.process_index)).to(state.device )

def lowercase    (  SCREAMING_SNAKE_CASE )			->							Dict:


			'''simple docstring'''







			SCREAMING_SNAKE_CASE_								=						create_tensor(SCREAMING_SNAKE_CASE )
			SCREAMING_SNAKE_CASE_								=						gather(SCREAMING_SNAKE_CASE )
			assert gathered_tensor.tolist() == list(range(1       ,    state.num_processes**2 + 1 ) )

def lowercase    (  SCREAMING_SNAKE_CASE )			->							List[str]:


			'''simple docstring'''







			SCREAMING_SNAKE_CASE_								=						[state.process_index]
			SCREAMING_SNAKE_CASE_								=						gather_object(SCREAMING_SNAKE_CASE )
			assert len(SCREAMING_SNAKE_CASE ) == state.num_processes, F'{gathered_obj}, {len(SCREAMING_SNAKE_CASE )} != {state.num_processes}'
			assert gathered_obj == list(range(state.num_processes ) ), F'{gathered_obj} != {list(range(state.num_processes ) )}'

def lowercase    (  SCREAMING_SNAKE_CASE )			->							int:


			'''simple docstring'''







			SCREAMING_SNAKE_CASE_								=						create_tensor(SCREAMING_SNAKE_CASE )
			SCREAMING_SNAKE_CASE_								=						broadcast(SCREAMING_SNAKE_CASE )
			assert broadcasted_tensor.shape == torch.Size([state.num_processes] )
			assert broadcasted_tensor.tolist() == list(range(1       ,    state.num_processes + 1 ) )

def lowercase    (  SCREAMING_SNAKE_CASE )			->							Union[str, Any]:


			'''simple docstring'''







			if state.is_main_process:
						SCREAMING_SNAKE_CASE_								=						torch.arange(state.num_processes + 1 ).to(state.device )
			else:
						SCREAMING_SNAKE_CASE_								=						torch.arange(state.num_processes ).to(state.device )
			SCREAMING_SNAKE_CASE_								=						pad_across_processes(SCREAMING_SNAKE_CASE )
			assert padded_tensor.shape == torch.Size([state.num_processes + 1] )
			if not state.is_main_process:
						assert padded_tensor.tolist() == list(range(0       ,    state.num_processes ) ) + [0]

def lowercase    (  SCREAMING_SNAKE_CASE )			->							str:


			'''simple docstring'''







			if state.num_processes != 2:
						return
			SCREAMING_SNAKE_CASE_								=						create_tensor(SCREAMING_SNAKE_CASE )
			SCREAMING_SNAKE_CASE_								=						reduce(SCREAMING_SNAKE_CASE       ,    'sum' )
			SCREAMING_SNAKE_CASE_								=						torch.tensor([4.0, 6] ).to(state.device )
			assert torch.allclose(SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE ), F'{reduced_tensor} != {truth_tensor}'

def lowercase    (  SCREAMING_SNAKE_CASE )			->							Dict:


			'''simple docstring'''







			if state.num_processes != 2:
						return
			SCREAMING_SNAKE_CASE_								=						create_tensor(SCREAMING_SNAKE_CASE )
			SCREAMING_SNAKE_CASE_								=						reduce(SCREAMING_SNAKE_CASE       ,    'mean' )
			SCREAMING_SNAKE_CASE_								=						torch.tensor([2.0, 3] ).to(state.device )
			assert torch.allclose(SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE ), F'{reduced_tensor} != {truth_tensor}'

def lowercase    (  SCREAMING_SNAKE_CASE )			->							List[str]:


			'''simple docstring'''







			main()

def lowercase    (  )			->							str:


			'''simple docstring'''







			SCREAMING_SNAKE_CASE_								=						PartialState()
			state.print(F'State: {state}' )
			state.print('testing gather' )
			test_gather(SCREAMING_SNAKE_CASE )
			state.print('testing gather_object' )
			test_gather_object(SCREAMING_SNAKE_CASE )
			state.print('testing broadcast' )
			test_broadcast(SCREAMING_SNAKE_CASE )
			state.print('testing pad_across_processes' )
			test_pad_across_processes(SCREAMING_SNAKE_CASE )
			state.print('testing reduce_sum' )
			test_reduce_sum(SCREAMING_SNAKE_CASE )
			state.print('testing reduce_mean' )
			test_reduce_mean(SCREAMING_SNAKE_CASE )


if __name__ == "__main__":
				main()
