


import unittest

from transformers import SqueezeBertConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device

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 (
        SQUEEZEBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
        SqueezeBertForMaskedLM,
        SqueezeBertForMultipleChoice,
        SqueezeBertForQuestionAnswering,
        SqueezeBertForSequenceClassification,
        SqueezeBertForTokenClassification,
        SqueezeBertModel,
    )



class 	__lowerCamelCase			(_a						):







       def __init__(			self: Optional[int],A_: Dict,A_: List[str]=13,A_: List[str]=7,A_: Dict=True,A_: Any=True,A_: Optional[Any]=False,A_: Any=True,A_: List[Any]=99,A_: Optional[Any]=32,A_: Union[str, Any]=5,A_: str=4,A_: Optional[int]=64,A_: Tuple="gelu",A_: Optional[int]=0.1,A_: str=0.1,A_: Union[str, Any]=512,A_: Optional[Any]=16,A_: Any=2,A_: Dict=0.0_2,A_: Dict=3,A_: List[Any]=4,A_: Optional[Any]=None,A_: Tuple=2,A_: Optional[int]=2,A_: int=2,A_: List[Any]=2,A_: Optional[Any]=4,A_: Any=1,):






            '''simple docstring'''




            __UpperCamelCase =			parent
            __UpperCamelCase =			batch_size
            __UpperCamelCase =			seq_length
            __UpperCamelCase =			is_training
            __UpperCamelCase =			use_input_mask
            __UpperCamelCase =			use_token_type_ids
            __UpperCamelCase =			use_labels
            __UpperCamelCase =			vocab_size
            __UpperCamelCase =			hidden_size
            __UpperCamelCase =			num_hidden_layers
            __UpperCamelCase =			num_attention_heads
            __UpperCamelCase =			intermediate_size
            __UpperCamelCase =			hidden_act
            __UpperCamelCase =			hidden_dropout_prob
            __UpperCamelCase =			attention_probs_dropout_prob
            __UpperCamelCase =			max_position_embeddings
            __UpperCamelCase =			type_vocab_size
            __UpperCamelCase =			type_sequence_label_size
            __UpperCamelCase =			initializer_range
            __UpperCamelCase =			num_labels
            __UpperCamelCase =			num_choices
            __UpperCamelCase =			scope
            __UpperCamelCase =			q_groups
            __UpperCamelCase =			k_groups
            __UpperCamelCase =			v_groups
            __UpperCamelCase =			post_attention_groups
            __UpperCamelCase =			intermediate_groups
            __UpperCamelCase =			output_groups







       def 		snake_case_	(			self: Optional[int]					):






            '''simple docstring'''




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

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

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

            __UpperCamelCase =			self.get_config()

            return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels







       def 		snake_case_	(			self: List[Any]					):






            '''simple docstring'''




            return SqueezeBertConfig(
                embedding_size=self.hidden_size,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,attention_probs_dropout_prob=self.hidden_dropout_prob,attention_dropout=self.attention_probs_dropout_prob,max_position_embeddings=self.max_position_embeddings,initializer_range=self.initializer_range,q_groups=self.q_groups,k_groups=self.k_groups,v_groups=self.v_groups,post_attention_groups=self.post_attention_groups,intermediate_groups=self.intermediate_groups,output_groups=self.output_groups,)







       def 		snake_case_	(			self: List[str],A_: str,A_: str,A_: str,A_: List[Any],A_: Any,A_: Optional[Any]					):






            '''simple docstring'''




            __UpperCamelCase =			SqueezeBertModel(config=A_					)
            model.to(A_					)
            model.eval()
            __UpperCamelCase =			model(A_,A_					)
            __UpperCamelCase =			model(A_					)
            self.parent.assertEqual(result.last_hidden_state.shape,(self.batch_size, self.seq_length, self.hidden_size)					)







       def 		snake_case_	(			self: int,A_: Optional[Any],A_: List[Any],A_: List[str],A_: List[str],A_: Optional[Any],A_: Optional[int]					):






            '''simple docstring'''




            __UpperCamelCase =			SqueezeBertForMaskedLM(config=A_					)
            model.to(A_					)
            model.eval()
            __UpperCamelCase =			model(A_,attention_mask=A_,labels=A_					)
            self.parent.assertEqual(result.logits.shape,(self.batch_size, self.seq_length, self.vocab_size)					)







       def 		snake_case_	(			self: str,A_: str,A_: Union[str, Any],A_: Tuple,A_: Any,A_: List[str],A_: Dict					):






            '''simple docstring'''




            __UpperCamelCase =			SqueezeBertForQuestionAnswering(config=A_					)
            model.to(A_					)
            model.eval()
            __UpperCamelCase =			model(
                A_,attention_mask=A_,start_positions=A_,end_positions=A_					)
            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 		snake_case_	(			self: Optional[int],A_: Optional[int],A_: List[str],A_: int,A_: Optional[int],A_: Dict,A_: int					):






            '''simple docstring'''




            __UpperCamelCase =			self.num_labels
            __UpperCamelCase =			SqueezeBertForSequenceClassification(A_					)
            model.to(A_					)
            model.eval()
            __UpperCamelCase =			model(A_,attention_mask=A_,labels=A_					)
            self.parent.assertEqual(result.logits.shape,(self.batch_size, self.num_labels)					)







       def 		snake_case_	(			self: str,A_: Any,A_: str,A_: Optional[Any],A_: Optional[Any],A_: Optional[Any],A_: Dict					):






            '''simple docstring'''




            __UpperCamelCase =			self.num_labels
            __UpperCamelCase =			SqueezeBertForTokenClassification(config=A_					)
            model.to(A_					)
            model.eval()

            __UpperCamelCase =			model(A_,attention_mask=A_,labels=A_					)
            self.parent.assertEqual(result.logits.shape,(self.batch_size, self.seq_length, self.num_labels)					)







       def 		snake_case_	(			self: Union[str, Any],A_: Tuple,A_: str,A_: Any,A_: Union[str, Any],A_: Dict,A_: str					):






            '''simple docstring'''




            __UpperCamelCase =			self.num_choices
            __UpperCamelCase =			SqueezeBertForMultipleChoice(config=A_					)
            model.to(A_					)
            model.eval()
            __UpperCamelCase =			input_ids.unsqueeze(1					).expand(-1,self.num_choices,-1					).contiguous()
            __UpperCamelCase =			input_mask.unsqueeze(1					).expand(-1,self.num_choices,-1					).contiguous()
            __UpperCamelCase =			model(
                A_,attention_mask=A_,labels=A_,)
            self.parent.assertEqual(result.logits.shape,(self.batch_size, self.num_choices)					)







       def 		snake_case_	(			self: Dict					):






            '''simple docstring'''




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



@require_torch
class 	__lowerCamelCase			(_a				,      _a				,      unittest.TestCase						):
       _lowercase											=     (
           (
               SqueezeBertModel,
               SqueezeBertForMaskedLM,
               SqueezeBertForMultipleChoice,
               SqueezeBertForQuestionAnswering,
               SqueezeBertForSequenceClassification,
               SqueezeBertForTokenClassification,
           )
           if is_torch_available()
           else None
       )
       _lowercase											=     (
           {
               """feature-extraction""": SqueezeBertModel,
               """fill-mask""": SqueezeBertForMaskedLM,
               """question-answering""": SqueezeBertForQuestionAnswering,
               """text-classification""": SqueezeBertForSequenceClassification,
               """token-classification""": SqueezeBertForTokenClassification,
               """zero-shot""": SqueezeBertForSequenceClassification,
           }
           if is_torch_available()
           else {}
       )
       _lowercase											=     False
       _lowercase											=     True
       _lowercase											=     False







       def 		snake_case_	(			self: Union[str, Any]					):






            '''simple docstring'''




            __UpperCamelCase =			SqueezeBertModelTester(self					)
            __UpperCamelCase =			ConfigTester(self,config_class=A_,dim=37					)







       def 		snake_case_	(			self: int					):






            '''simple docstring'''




            self.config_tester.run_common_tests()







       def 		snake_case_	(			self: str					):






            '''simple docstring'''




            __UpperCamelCase =			self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_squeezebert_model(*A_					)







       def 		snake_case_	(			self: Any					):






            '''simple docstring'''




            __UpperCamelCase =			self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_squeezebert_for_masked_lm(*A_					)







       def 		snake_case_	(			self: Dict					):






            '''simple docstring'''




            __UpperCamelCase =			self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_squeezebert_for_question_answering(*A_					)







       def 		snake_case_	(			self: Union[str, Any]					):






            '''simple docstring'''




            __UpperCamelCase =			self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_squeezebert_for_sequence_classification(*A_					)







       def 		snake_case_	(			self: Optional[Any]					):






            '''simple docstring'''




            __UpperCamelCase =			self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_squeezebert_for_token_classification(*A_					)







       def 		snake_case_	(			self: Any					):






            '''simple docstring'''




            __UpperCamelCase =			self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_squeezebert_for_multiple_choice(*A_					)







       @slow
       def 		snake_case_	(			self: int					):






            '''simple docstring'''




            for model_name in SQUEEZEBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                 __UpperCamelCase =			SqueezeBertModel.from_pretrained(A_					)
                 self.assertIsNotNone(A_					)



@require_sentencepiece
@require_tokenizers
@require_torch
class 	__lowerCamelCase			(unittest.TestCase						):







       @slow
       def 		snake_case_	(			self: List[Any]					):






            '''simple docstring'''




            __UpperCamelCase =			SqueezeBertForSequenceClassification.from_pretrained('squeezebert/squeezebert-mnli'					)

            __UpperCamelCase =			torch.tensor([[1, 2_9414, 232, 328, 740, 1140, 1_2695, 69, 13, 1588, 2]]					)
            __UpperCamelCase =			model(A_					)[0]
            __UpperCamelCase =			torch.Size((1, 3)					)
            self.assertEqual(output.shape,A_					)
            __UpperCamelCase =			torch.tensor([[0.6_4_0_1, -0.0_3_4_9, -0.6_0_4_1]]					)
            self.assertTrue(torch.allclose(A_,A_,atol=1E-4					)					)






#
# This a `torch.distributed` diagnostics script that checks that all GPUs in the cluster (one or
# many nodes) can talk to each other via nccl and allocate gpu memory.
#
# To run first adjust the number of processes and nodes:
#
# python -m torch.distributed.run --nproc_per_node 2 --nnodes 1 torch-distributed-gpu-test.py
#
# You may need to add --master_addr $MASTER_ADDR --master_port $MASTER_PORT if using a custom addr:port
#
# You can also use the rdzv API: --rdzv_endpoint $MASTER_ADDR:$MASTER_PORT --rdzv_backend c10d
#
# use torch.distributed.launch instead of torch.distributed.run for torch < 1.9
#
# If you get a hanging in `barrier` calls you have some network issues, you may try to debug this with:
#
# NCCL_DEBUG=INFO python -m torch.distributed.run --nproc_per_node 2 --nnodes 1 torch-distributed-gpu-test.py
#
# which should tell you what's going on behind the scenes.
#
#
# This script can be run via `srun` in the SLURM environment as well. Here is a SLURM script that
# runs on 2 nodes of 4 gpus per node:
#
# #SBATCH --job-name=test-nodes        # name
# #SBATCH --nodes=2                    # nodes
# #SBATCH --ntasks-per-node=1          # crucial - only 1 task per dist per node!
# #SBATCH --cpus-per-task=10           # number of cores per tasks
# #SBATCH --gres=gpu:4                 # number of gpus
# #SBATCH --time 0:05:00               # maximum execution time (HH:MM:SS)
# #SBATCH --output=%x-%j.out           # output file name
#
# GPUS_PER_NODE=4
# MASTER_ADDR=$(scontrol show hostnames $SLURM_JOB_NODELIST | head -n 1)
# MASTER_PORT=6000
#
# srun --jobid $SLURM_JOBID bash -c 'python -m torch.distributed.run \
# --nproc_per_node $GPUS_PER_NODE --nnodes $SLURM_NNODES --node_rank $SLURM_PROCID \
# --master_addr $MASTER_ADDR --master_port $MASTER_PORT \
# torch-distributed-gpu-test.py'
#

import fcntl
import os
import socket

import torch
import torch.distributed as dist






def 		_A		(		*_lowercase )					-> Tuple:


     """simple docstring"""


     with open(_lowercase					,  'r' ) as fh:
          fcntl.flock(_lowercase					,  fcntl.LOCK_EX )
          try:
               print(*_lowercase )
          finally:
               fcntl.flock(_lowercase					,  fcntl.LOCK_UN )


__snake_case   							=						int(os.environ['''LOCAL_RANK'''])
torch.cuda.set_device(local_rank)
__snake_case   							=						torch.device('''cuda''', local_rank)
__snake_case   							=						socket.gethostname()

__snake_case   							=						f"""[{hostname}-{local_rank}]"""

try:
    # test distributed
    dist.init_process_group('''nccl''')
    dist.all_reduce(torch.ones(1).to(device), op=dist.ReduceOp.SUM)
    dist.barrier()

    # test cuda is available and can allocate memory
    torch.cuda.is_available()
    torch.ones(1).cuda(local_rank)

    # global rank
    __snake_case   							=						dist.get_rank()
    __snake_case   							=						dist.get_world_size()

    printflock(f"""{gpu} is OK (global rank: {rank}/{world_size})""")

    dist.barrier()
    if rank == 0:
        printflock(f"""pt={torch.__version__}, cuda={torch.version.cuda}, nccl={torch.cuda.nccl.version()}""")

except Exception:
    printflock(f"""{gpu} is broken""")
    raise


