



'''simple docstring'''





import inspect
import unittest

from transformers import ViTMSNConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
 import torch
 from torch import nn

 from transformers import ViTMSNForImageClassification, ViTMSNModel
 from transformers.models.vit_msn.modeling_vit_msn import VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
 from PIL import Image

 from transformers import ViTImageProcessor

class 					_A :



       def __init__(	self							:  Dict  ,	__SCREAMING_SNAKE_CASE							:  Dict  ,	__SCREAMING_SNAKE_CASE							:  Tuple=13  ,	__SCREAMING_SNAKE_CASE							:  Union[str, Any]=30  ,	__SCREAMING_SNAKE_CASE							:  int=2  ,	__SCREAMING_SNAKE_CASE							:  str=3  ,	__SCREAMING_SNAKE_CASE							:  Optional[int]=True  ,	__SCREAMING_SNAKE_CASE							:  str=True  ,	__SCREAMING_SNAKE_CASE							:  List[Any]=32  ,	__SCREAMING_SNAKE_CASE							:  Optional[Any]=5  ,	__SCREAMING_SNAKE_CASE							:  Any=4  ,	__SCREAMING_SNAKE_CASE							:  int=37  ,	__SCREAMING_SNAKE_CASE							:  Union[str, Any]="gelu"  ,	__SCREAMING_SNAKE_CASE							:  str=0.1  ,	__SCREAMING_SNAKE_CASE							:  Union[str, Any]=0.1  ,	__SCREAMING_SNAKE_CASE							:  Any=10  ,	__SCREAMING_SNAKE_CASE							:  Optional[int]=0.02  ,	__SCREAMING_SNAKE_CASE							:  Any=None  ,	):


           '''simple docstring'''

           __a		 =       parent
           __a		 =       batch_size
           __a		 =       image_size
           __a		 =       patch_size
           __a		 =       num_channels
           __a		 =       is_training
           __a		 =       use_labels
           __a		 =       hidden_size
           __a		 =       num_hidden_layers
           __a		 =       num_attention_heads
           __a		 =       intermediate_size
           __a		 =       hidden_act
           __a		 =       hidden_dropout_prob
           __a		 =       attention_probs_dropout_prob
           __a		 =       type_sequence_label_size
           __a		 =       initializer_range
           __a		 =       scope

           # in ViT MSN, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
           __a		 =       (image_size // patch_size) ** 2
           __a		 =       num_patches + 1



       def 	_lowerCamelCase    (	self							:  Tuple):


           '''simple docstring'''

           __a		 =       floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])

           __a		 =       None
           if self.use_labels:
               __a		 =       ids_tensor([self.batch_size]  ,	self.type_sequence_label_size)

           __a		 =       self.get_config()

           return config, pixel_values, labels



       def 	_lowerCamelCase    (	self							:  Any):


           '''simple docstring'''

           return ViTMSNConfig(
               image_size=self.image_size  ,	patch_size=self.patch_size  ,	num_channels=self.num_channels  ,	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  ,	initializer_range=self.initializer_range  ,	)



       def 	_lowerCamelCase    (	self							:  str  ,	__SCREAMING_SNAKE_CASE							:  Optional[Any]  ,	__SCREAMING_SNAKE_CASE							:  Dict  ,	__SCREAMING_SNAKE_CASE							:  int):


           '''simple docstring'''

           __a		 =       ViTMSNModel(config=__SCREAMING_SNAKE_CASE)
           model.to(__SCREAMING_SNAKE_CASE)
           model.eval()
           __a		 =       model(__SCREAMING_SNAKE_CASE)
           self.parent.assertEqual(result.last_hidden_state.shape  ,	(self.batch_size, self.seq_length, self.hidden_size))



       def 	_lowerCamelCase    (	self							:  Union[str, Any]  ,	__SCREAMING_SNAKE_CASE							:  Dict  ,	__SCREAMING_SNAKE_CASE							:  List[Any]  ,	__SCREAMING_SNAKE_CASE							:  List[Any]):


           '''simple docstring'''

           __a		 =       self.type_sequence_label_size
           __a		 =       ViTMSNForImageClassification(__SCREAMING_SNAKE_CASE)
           model.to(__SCREAMING_SNAKE_CASE)
           model.eval()
           __a		 =       model(__SCREAMING_SNAKE_CASE  ,	labels=__SCREAMING_SNAKE_CASE)
           print('''Pixel and labels shape: {pixel_values.shape}, {labels.shape}''')
           print('''Labels: {labels}''')
           self.parent.assertEqual(result.logits.shape  ,	(self.batch_size, self.type_sequence_label_size))

           # test greyscale images
           __a		 =       1
           __a		 =       ViTMSNForImageClassification(__SCREAMING_SNAKE_CASE)
           model.to(__SCREAMING_SNAKE_CASE)
           model.eval()

           __a		 =       floats_tensor([self.batch_size, 1, self.image_size, self.image_size])
           __a		 =       model(__SCREAMING_SNAKE_CASE)
           self.parent.assertEqual(result.logits.shape  ,	(self.batch_size, self.type_sequence_label_size))





       def 	_lowerCamelCase    (	self							:  int):


           '''simple docstring'''

           __a		 =       self.prepare_config_and_inputs()
           __a ,     __a ,     __a		 =       config_and_inputs
           __a		 =       {'''pixel_values''': pixel_values}
           return config, inputs_dict


@require_torch
class 					_A (			__UpperCAmelCase   ,__UpperCAmelCase   ,unittest.TestCase					):
       UpperCamelCase__						:					List[Any]		      =		(ViTMSNModel, ViTMSNForImageClassification) if is_torch_available() else ()
       UpperCamelCase__						:					Optional[int]		      =		(
           {'''feature-extraction''': ViTMSNModel, '''image-classification''': ViTMSNForImageClassification}
           if is_torch_available()
           else {}
       )

       UpperCamelCase__						:					int		      =		False
       UpperCamelCase__						:					Optional[Any]		      =		False
       UpperCamelCase__						:					Any		      =		False
       UpperCamelCase__						:					Any		      =		False



       def 	_lowerCamelCase    (	self							:  Tuple):


           '''simple docstring'''

           __a		 =       ViTMSNModelTester(self)
           __a		 =       ConfigTester(self  ,	config_class=__SCREAMING_SNAKE_CASE  ,	has_text_modality=__SCREAMING_SNAKE_CASE  ,	hidden_size=37)



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


           '''simple docstring'''

           self.config_tester.run_common_tests()



       @unittest.skip(reason='''ViTMSN does not use inputs_embeds''')
       def 	_lowerCamelCase    (	self							:  Dict):


           '''simple docstring'''

           pass



       def 	_lowerCamelCase    (	self							:  Optional[int]):


           '''simple docstring'''

           __a ,     __a		 =       self.model_tester.prepare_config_and_inputs_for_common()

           for model_class in self.all_model_classes:
               __a		 =       model_class(__SCREAMING_SNAKE_CASE)
               self.assertIsInstance(model.get_input_embeddings()  ,	(nn.Module))
               __a		 =       model.get_output_embeddings()
               self.assertTrue(x is None or isinstance(__SCREAMING_SNAKE_CASE  ,	nn.Linear))



       def 	_lowerCamelCase    (	self							:  List[Any]):


           '''simple docstring'''

           __a ,     __a		 =       self.model_tester.prepare_config_and_inputs_for_common()

           for model_class in self.all_model_classes:
               __a		 =       model_class(__SCREAMING_SNAKE_CASE)
               __a		 =       inspect.signature(model.forward)
               # signature.parameters is an OrderedDict => so arg_names order is deterministic
               __a		 =       [*signature.parameters.keys()]

               __a		 =       ['''pixel_values''']
               self.assertListEqual(arg_names[:1]  ,	__SCREAMING_SNAKE_CASE)



       def 	_lowerCamelCase    (	self							:  Optional[Any]):


           '''simple docstring'''

           __a		 =       self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_model(*__SCREAMING_SNAKE_CASE)



       def 	_lowerCamelCase    (	self							:  Optional[Any]):


           '''simple docstring'''

           __a		 =       self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_image_classification(*__SCREAMING_SNAKE_CASE)





       @slow
       def 	_lowerCamelCase    (	self							:  Dict):


           '''simple docstring'''

           for model_name in VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
               __a		 =       ViTMSNModel.from_pretrained(__SCREAMING_SNAKE_CASE)
               self.assertIsNotNone(__SCREAMING_SNAKE_CASE)





def   __snake_case			(    ):
    __a		 =       Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''       )
    return image

@require_torch
@require_vision
class 					_A (			unittest.TestCase					):



       @cached_property
       def 	_lowerCamelCase    (	self							:  Optional[Any]):


           '''simple docstring'''

           return ViTImageProcessor.from_pretrained('''facebook/vit-msn-small''') if is_vision_available() else None





       @slow
       def 	_lowerCamelCase    (	self							:  Union[str, Any]):


           '''simple docstring'''

           torch.manual_seed(2)
           __a		 =       ViTMSNForImageClassification.from_pretrained('''facebook/vit-msn-small''').to(__SCREAMING_SNAKE_CASE)

           __a		 =       self.default_image_processor
           __a		 =       prepare_img()
           __a		 =       image_processor(images=__SCREAMING_SNAKE_CASE  ,	return_tensors='''pt''').to(__SCREAMING_SNAKE_CASE)

           # forward pass
           with torch.no_grad():
               __a		 =       model(**__SCREAMING_SNAKE_CASE)

           # verify the logits
           __a		 =       torch.Size((1, 1_000))
           self.assertEqual(outputs.logits.shape  ,	__SCREAMING_SNAKE_CASE)

           __a		 =       torch.tensor([-0.08_03, -0.44_54, -0.23_75]).to(__SCREAMING_SNAKE_CASE)

           self.assertTrue(torch.allclose(outputs.logits[0, :3]  ,	__SCREAMING_SNAKE_CASE  ,	atol=1E-4))










# XXX: we want transformers master here - in the absense of conftest manipulating sys.path:
# hack it in for now:
import sys
from pathlib import Path


__snake_case		:Union[str, Any]   	=  Path(__file__).resolve().parents[3] / '''src'''
sys.path.insert(1, str(git_repo_path))

import dataclasses  # noqa
import io  # noqa
import itertools  # noqa
import json  # noqa
import os  # noqa
import unittest  # noqa
from copy import deepcopy  # noqa

from parameterized import parameterized  # noqa
from transformers import TrainingArguments, is_torch_available  # noqa
from transformers.deepspeed import is_deepspeed_available  # noqa
from transformers.file_utils import WEIGHTS_NAME  # noqa
from transformers.testing_utils import (  # noqa
    CaptureLogger,
    ExtendSysPath,
    TestCasePlus,
    execute_subprocess_async,
    get_gpu_count,
    mockenv_context,
    require_deepspeed,
    require_torch_gpu,
    require_torch_multi_gpu,
    slow,
)
from transformers.trainer_utils import set_seed  # noqa


set_seed(42)

__snake_case		:str   	=  {'''base''': '''patrickvonplaten/wav2vec2_tiny_random''', '''robust''': '''patrickvonplaten/wav2vec2_tiny_random_robust'''}

__snake_case		:List[Any]   	=  '''zero2'''
__snake_case		:Optional[Any]   	=  '''zero3'''
__snake_case		:str   	=  [ZEROa, ZEROa]





def   __snake_case			(    _UpperCAmelCase     ,				_UpperCAmelCase     ,				_UpperCAmelCase       ):
    # customize the test name generator function as we want both params to appear in the sub-test
    # name, as by default it shows only the first param
    __a		 =       parameterized.to_safe_name('''_'''.join(str(_UpperCAmelCase       ) for x in param.args       )       )
    return f'{func.__name__}_{param_based_name}'


# Cartesian-product of zero stages with models to test
__snake_case		:List[Any]   	=  list(itertools.product(stages, models.keys()))

@slow
@require_deepspeed
@require_torch_gpu
class 					_A (			__UpperCAmelCase					):



       @parameterized.expand(__SCREAMING_SNAKE_CASE  ,	name_func=__SCREAMING_SNAKE_CASE)
       def 	_lowerCamelCase    (	self							:  Dict  ,	__SCREAMING_SNAKE_CASE							:  Optional[Any]  ,	__SCREAMING_SNAKE_CASE							:  Any):


           '''simple docstring'''

           self.run_and_check(
               stage=__SCREAMING_SNAKE_CASE  ,	model=__SCREAMING_SNAKE_CASE  ,	distributed=__SCREAMING_SNAKE_CASE  ,	fpaa=__SCREAMING_SNAKE_CASE  ,	)



       @require_torch_multi_gpu
       @parameterized.expand(__SCREAMING_SNAKE_CASE  ,	name_func=__SCREAMING_SNAKE_CASE)
       def 	_lowerCamelCase    (	self							:  str  ,	__SCREAMING_SNAKE_CASE							:  Optional[int]  ,	__SCREAMING_SNAKE_CASE							:  List[Any]):


           '''simple docstring'''

           self.run_and_check(
               stage=__SCREAMING_SNAKE_CASE  ,	model=__SCREAMING_SNAKE_CASE  ,	distributed=__SCREAMING_SNAKE_CASE  ,	fpaa=__SCREAMING_SNAKE_CASE  ,	)



       @parameterized.expand(__SCREAMING_SNAKE_CASE  ,	name_func=__SCREAMING_SNAKE_CASE)
       def 	_lowerCamelCase    (	self							:  Tuple  ,	__SCREAMING_SNAKE_CASE							:  Optional[int]  ,	__SCREAMING_SNAKE_CASE							:  Any):


           '''simple docstring'''

           self.run_and_check(
               stage=__SCREAMING_SNAKE_CASE  ,	model=__SCREAMING_SNAKE_CASE  ,	distributed=__SCREAMING_SNAKE_CASE  ,	fpaa=__SCREAMING_SNAKE_CASE  ,	)



       @require_torch_multi_gpu
       @parameterized.expand(__SCREAMING_SNAKE_CASE  ,	name_func=__SCREAMING_SNAKE_CASE)
       def 	_lowerCamelCase    (	self							:  List[Any]  ,	__SCREAMING_SNAKE_CASE							:  Optional[int]  ,	__SCREAMING_SNAKE_CASE							:  List[Any]):


           '''simple docstring'''

           self.run_and_check(
               stage=__SCREAMING_SNAKE_CASE  ,	model=__SCREAMING_SNAKE_CASE  ,	distributed=__SCREAMING_SNAKE_CASE  ,	fpaa=__SCREAMING_SNAKE_CASE  ,	)



       def 	_lowerCamelCase    (	self							:  List[Any]  ,	__SCREAMING_SNAKE_CASE							:  List[str]):


           '''simple docstring'''

           pass



       def 	_lowerCamelCase    (	self							:  Any  ,	__SCREAMING_SNAKE_CASE							:  str  ,	__SCREAMING_SNAKE_CASE							:  str  ,	__SCREAMING_SNAKE_CASE							:  int = 10  ,	__SCREAMING_SNAKE_CASE							:  bool = True  ,	__SCREAMING_SNAKE_CASE							:  bool = True  ,	__SCREAMING_SNAKE_CASE							:  bool = True  ,	):


           '''simple docstring'''

           __a		 =       models[model]

           __a		 =       self.run_trainer(
               stage=__SCREAMING_SNAKE_CASE  ,	model_name=__SCREAMING_SNAKE_CASE  ,	eval_steps=__SCREAMING_SNAKE_CASE  ,	num_train_epochs=1  ,	distributed=__SCREAMING_SNAKE_CASE  ,	fpaa=__SCREAMING_SNAKE_CASE  ,	)

           self.do_checks(__SCREAMING_SNAKE_CASE)

           return output_dir



       def 	_lowerCamelCase    (	self							:  List[str]  ,	__SCREAMING_SNAKE_CASE							:  str  ,	__SCREAMING_SNAKE_CASE							:  str  ,	__SCREAMING_SNAKE_CASE							:  int = 10  ,	__SCREAMING_SNAKE_CASE							:  int = 1  ,	__SCREAMING_SNAKE_CASE							:  bool = True  ,	__SCREAMING_SNAKE_CASE							:  bool = True  ,	):


           '''simple docstring'''

           __a		 =       self.get_auto_remove_tmp_dir('''./xxx'''  ,	after=__SCREAMING_SNAKE_CASE)
           __a		 =       F'\n            --model_name_or_path {model_name}\n            --dataset_name hf-internal-testing/librispeech_asr_dummy\n            --dataset_config_name clean\n            --train_split_name validation\n            --validation_split_name validation\n            --output_dir {output_dir}\n            --num_train_epochs {str(__SCREAMING_SNAKE_CASE)}\n            --per_device_train_batch_size 2\n            --per_device_eval_batch_size 2\n            --evaluation_strategy steps\n            --learning_rate 5e-4\n            --warmup_steps 8\n            --orthography timit\n            --preprocessing_num_workers 1\n            --group_by_length\n            --freeze_feature_extractor\n            --report_to none\n            --save_steps 0\n            --eval_steps {eval_steps}\n            --report_to none\n        '.split()

           if fpaa:
               args.extend(['''--fp16'''])

           # currently ds_config_wav2vec2_zero.json requires "zero_optimization.find_unused_parameters": true,
           # hence the separate config files
           __a		 =       F'--deepspeed {self.test_file_dir_str}/ds_config_wav2vec2_{stage}.json'.split()
           __a		 =       [F'{self.examples_dir_str}/research_projects/wav2vec2/run_asr.py']
           __a		 =       self.get_launcher(__SCREAMING_SNAKE_CASE)

           __a		 =       launcher + script + args + ds_args
           # keep for quick debug
           # print(" ".join([f"\nPYTHONPATH={self.src_dir_str}"] +cmd)); die
           execute_subprocess_async(__SCREAMING_SNAKE_CASE  ,	env=self.get_env())

           return output_dir





       def 	_lowerCamelCase    (	self							:  int  ,	__SCREAMING_SNAKE_CASE							:  List[Any]=False):


           '''simple docstring'''

           __a		 =       min(2  ,	get_gpu_count()) if distributed else 1
           return F'deepspeed --num_nodes 1 --num_gpus {num_gpus}'.split()





