


import argparse
import logging
import os
import sys
import tempfile
from pathlib import Path

import lightning_base
import pytest
import pytorch_lightning as pl
import torch
from convert_pl_checkpoint_to_hf import convert_pl_to_hf
from distillation import distill_main
from finetune import SummarizationModule, main
from huggingface_hub import list_models
from parameterized import parameterized
from run_eval import generate_summaries_or_translations
from torch import nn

from transformers import AutoConfig, AutoModelForSeqaSeqLM
from transformers.testing_utils import CaptureStderr, CaptureStdout, TestCasePlus, require_torch_gpu, slow
from utils import label_smoothed_nll_loss, lmap, load_json


logging.basicConfig(level=logging.DEBUG)

lowerCAmelCase__						:Any         =							logging.getLogger()
lowerCAmelCase__						:Optional[int]         =							torch.cuda.is_available()
lowerCAmelCase__						:Union[str, Any]         =							{
    '''max_tokens_per_batch''': None,
    '''supervise_forward''': True,
    '''normalize_hidden''': True,
    '''label_smoothing''': 0.2,
    '''eval_max_gen_length''': None,
    '''eval_beams''': 1,
    '''val_metric''': '''loss''',
    '''save_top_k''': 1,
    '''adafactor''': True,
    '''early_stopping_patience''': 2,
    '''logger_name''': '''default''',
    '''length_penalty''': 0.5,
    '''cache_dir''': '''''',
    '''task''': '''summarization''',
    '''num_workers''': 2,
    '''alpha_hid''': 0,
    '''freeze_embeds''': True,
    '''enc_only''': False,
    '''tgt_suffix''': '''''',
    '''resume_from_checkpoint''': None,
    '''sortish_sampler''': True,
    '''student_decoder_layers''': 1,
    '''val_check_interval''': 1.0,
    '''output_dir''': '''''',
    '''fp16''': False,  # TODO(SS): set this to CUDA_AVAILABLE if ci installs apex or start using native amp
    '''no_teacher''': False,
    '''fp16_opt_level''': '''O1''',
    '''gpus''': 1 if CUDA_AVAILABLE else 0,
    '''n_tpu_cores''': 0,
    '''max_grad_norm''': 1.0,
    '''do_train''': True,
    '''do_predict''': True,
    '''accumulate_grad_batches''': 1,
    '''server_ip''': '''''',
    '''server_port''': '''''',
    '''seed''': 4_2,
    '''model_name_or_path''': '''sshleifer/bart-tiny-random''',
    '''config_name''': '''''',
    '''tokenizer_name''': '''facebook/bart-large''',
    '''do_lower_case''': False,
    '''learning_rate''': 0.3,
    '''lr_scheduler''': '''linear''',
    '''weight_decay''': 0.0,
    '''adam_epsilon''': 1e-08,
    '''warmup_steps''': 0,
    '''max_epochs''': 1,
    '''train_batch_size''': 2,
    '''eval_batch_size''': 2,
    '''max_source_length''': 1_2,
    '''max_target_length''': 1_2,
    '''val_max_target_length''': 1_2,
    '''test_max_target_length''': 1_2,
    '''fast_dev_run''': False,
    '''no_cache''': False,
    '''n_train''': -1,
    '''n_val''': -1,
    '''n_test''': -1,
    '''student_encoder_layers''': 1,
    '''freeze_encoder''': False,
    '''auto_scale_batch_size''': False,
    '''overwrite_output_dir''': False,
    '''student''': None,
}

def  lowerCAmelCase__				(    a__:	Path    ,						a__:	list )						->	int:


							'''simple docstring'''





							_UpperCAmelCase					=    '\n'.join(a__ )
							Path(a__ ).open('w' ).writelines(a__ )


lowerCAmelCase__						:Union[str, Any]         =							[''' Sam ate lunch today.''', '''Sams lunch ingredients.''']
lowerCAmelCase__						:Any         =							['''A very interesting story about what I ate for lunch.''', '''Avocado, celery, turkey, coffee''']
lowerCAmelCase__						:List[Any]         =							'''patrickvonplaten/t5-tiny-random'''
lowerCAmelCase__						:Union[str, Any]         =							'''sshleifer/t5-tinier-random'''
lowerCAmelCase__						:Optional[Any]         =							'''sshleifer/bart-tiny-random'''
lowerCAmelCase__						:Dict         =							'''sshleifer/tiny-mbart'''
lowerCAmelCase__						:Any         =							'''sshleifer/tiny-marian-en-de'''
lowerCAmelCase__						:Optional[int]         =							'''stas/tiny-wmt19-en-de'''


lowerCAmelCase__						:str         =							logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)
logging.disable(logging.CRITICAL)  # remove noisy download output from tracebacks

def  lowerCAmelCase__				(    a__:	int )						->	Optional[int]:


							'''simple docstring'''





							for split in ["train", "val", "test"]:
														_dump_articles(os.path.join(a__    ,						F'''{split}.source''' )    ,						a__ )
														_dump_articles(os.path.join(a__    ,						F'''{split}.target''' )    ,						a__ )
							return tmp_dir

class 	__a   (       UpperCAmelCase    ):
		@classmethod
		def 			UpperCAmelCase__       (    cls		)				-> List[str]:




									"""simple docstring"""





									logging.disable(logging.CRITICAL		)  # remove noisy download output from tracebacks
									return cls
		@slow
		@require_torch_gpu
		def 			UpperCAmelCase__       (    self		)				-> Any:




									"""simple docstring"""





									_UpperCAmelCase					=    list_models()
									_UpperCAmelCase					=    'sshleifer'
									_UpperCAmelCase					=    [x.modelId for x in model_list if x.modelId.startswith(_SCREAMING_SNAKE_CASE		)]
									_UpperCAmelCase					=    ['sshleifer/blenderbot-3B', 'sshleifer/blenderbot-90M']
									_UpperCAmelCase					=    []
									for m in model_ids:
																if m in allowed_to_be_broken:
																							continue
																try:
																							AutoConfig.from_pretrained(_SCREAMING_SNAKE_CASE		)
																except Exception:
																							failures.append(_SCREAMING_SNAKE_CASE		)
									assert not failures, f'''The following models could not be loaded through AutoConfig: {failures}'''
		def 			UpperCAmelCase__       (    self		)				-> Union[str, Any]:




									"""simple docstring"""





									_UpperCAmelCase					=    {'student_encoder_layers': 2, 'student_decoder_layers': 1, 'no_teacher': True}
									self._test_distiller_cli(_SCREAMING_SNAKE_CASE		)
		def 			UpperCAmelCase__       (    self		)				-> List[Any]:




									"""simple docstring"""





									_UpperCAmelCase					=    {
									    'student_encoder_layers': 2,
									    'student_decoder_layers': 1,
									    'max_epochs': 4,
									    'val_check_interval': 0.25,
									    'alpha_hid': 2.0,
									    'model_name_or_path': 'IGNORE_THIS_IT_DOESNT_GET_USED',
									}
									_UpperCAmelCase					=    self._test_distiller_cli(_SCREAMING_SNAKE_CASE    ,					check_contents=_SCREAMING_SNAKE_CASE		)

									_UpperCAmelCase					=    list(Path(model.output_dir		).glob('*.ckpt'		)		)
									self.assertEqual(1    ,					len(_SCREAMING_SNAKE_CASE		)		)
									_UpperCAmelCase					=    list(Path(model.output_dir		).glob('**/*.bin'		)		)
									self.assertEqual(len(_SCREAMING_SNAKE_CASE		)    ,					2		)
									_UpperCAmelCase					=    lmap(str.strip    ,					Path(model.hparams.data_dir		).joinpath('test.source'		).open().readlines()		)
									_UpperCAmelCase					=    tempfile.mktemp()  # XXX: not being cleaned up
									generate_summaries_or_translations(_SCREAMING_SNAKE_CASE    ,					_SCREAMING_SNAKE_CASE    ,					str(model.output_dir / 'best_tfmr'		)		)
									self.assertTrue(Path(_SCREAMING_SNAKE_CASE		).exists()		)

									_UpperCAmelCase					=    self.get_auto_remove_tmp_dir()
									convert_pl_to_hf(ckpts[0]    ,					transformer_ckpts[0].parent    ,					_SCREAMING_SNAKE_CASE		)
									assert os.path.exists(os.path.join(_SCREAMING_SNAKE_CASE    ,					'pytorch_model.bin'		)		)
		def 			UpperCAmelCase__       (    self		)				-> int:




									"""simple docstring"""





									_UpperCAmelCase					=    AutoModelForSeqaSeqLM.from_pretrained(_SCREAMING_SNAKE_CASE		)
									_UpperCAmelCase	, _UpperCAmelCase					=    model.dummy_inputs['input_ids'], model.dummy_inputs['attention_mask']
									_UpperCAmelCase					=    torch.tensor([[0, 4, 8, 2], [0, 8, 2, 1]]    ,					dtype=torch.long    ,					device=model.device		)
									_UpperCAmelCase					=    target_ids[:, :-1].contiguous()  # Why this line?
									_UpperCAmelCase					=    target_ids[:, 1:].clone()  # why clone?
									_UpperCAmelCase					=    model(
									    _SCREAMING_SNAKE_CASE    ,					attention_mask=_SCREAMING_SNAKE_CASE    ,					decoder_input_ids=_SCREAMING_SNAKE_CASE    ,					labels=_SCREAMING_SNAKE_CASE    ,					use_cache=_SCREAMING_SNAKE_CASE		).loss

									_UpperCAmelCase					=    model(_SCREAMING_SNAKE_CASE    ,					attention_mask=_SCREAMING_SNAKE_CASE    ,					decoder_input_ids=_SCREAMING_SNAKE_CASE    ,					use_cache=_SCREAMING_SNAKE_CASE		).logits

									_UpperCAmelCase					=    nn.functional.log_softmax(_SCREAMING_SNAKE_CASE    ,					dim=-1		)
									_UpperCAmelCase	, _UpperCAmelCase					=    label_smoothed_nll_loss(
									    _SCREAMING_SNAKE_CASE    ,					_SCREAMING_SNAKE_CASE    ,					0.1    ,					ignore_index=model.config.pad_token_id		)
									with self.assertRaises(_SCREAMING_SNAKE_CASE		):
																# TODO: understand why this breaks
																self.assertEqual(_SCREAMING_SNAKE_CASE    ,					_SCREAMING_SNAKE_CASE		)
		def 			UpperCAmelCase__       (    self		)				-> Optional[int]:




									"""simple docstring"""





									_UpperCAmelCase					=    {
									    'student_encoder_layers': 2,
									    'student_decoder_layers': 1,
									    'num_train_epochs': 4,
									    'val_check_interval': 0.25,
									    'alpha_hid': 2.0,
									    'task': 'translation',
									    'model_name_or_path': 'IGNORE_THIS_IT_DOESNT_GET_USED',
									    'tokenizer_name': MBART_TINY,
									    'teacher': MBART_TINY,
									    'src_lang': 'en_XX',
									    'tgt_lang': 'ro_RO',
									}
									_UpperCAmelCase					=    self._test_distiller_cli(_SCREAMING_SNAKE_CASE    ,					check_contents=_SCREAMING_SNAKE_CASE		)
									assert model.model.config.model_type == "mbart"

									_UpperCAmelCase					=    list(Path(model.output_dir		).glob('*.ckpt'		)		)
									self.assertEqual(1    ,					len(_SCREAMING_SNAKE_CASE		)		)
									_UpperCAmelCase					=    list(Path(model.output_dir		).glob('**/*.bin'		)		)
									_UpperCAmelCase					=    list(Path(model.output_dir		).glob('best_tfmr/*'		)		)
									assert len(_SCREAMING_SNAKE_CASE		) > 2
									self.assertEqual(len(_SCREAMING_SNAKE_CASE		)    ,					2		)
		def 			UpperCAmelCase__       (    self		)				-> List[Any]:




									"""simple docstring"""





									_UpperCAmelCase					=    {
									    'student_encoder_layers': 1,
									    'student_decoder_layers': 1,
									    'alpha_hid': 2.0,
									    'teacher': T5_TINY,
									    'model_name_or_path': T5_TINY,
									    'tokenizer_name': T5_TINY,
									}
									self._test_distiller_cli(_SCREAMING_SNAKE_CASE		)
		def 			UpperCAmelCase__       (    self		)				-> List[str]:




									"""simple docstring"""





									_UpperCAmelCase					=    {
									    'teacher': T5_TINY,
									    'student': T5_TINIER,
									    'model_name_or_path': T5_TINIER,
									    'tokenizer_name': T5_TINIER,
									}
									self._test_distiller_cli(_SCREAMING_SNAKE_CASE		)






		def 			UpperCAmelCase__       (    self    ,					_SCREAMING_SNAKE_CASE    ,					_SCREAMING_SNAKE_CASE=True		)				-> Dict:




									"""simple docstring"""





									_UpperCAmelCase					=    {
									    'label_smoothing': 0.0,
									    'early_stopping_patience': -1,
									    'train_batch_size': 1,
									    'eval_batch_size': 2,
									    'max_epochs': 2,
									    'alpha_mlm': 0.2,
									    'alpha_ce': 0.8,
									    'do_predict': True,
									    'model_name_or_path': 'sshleifer/tinier_bart',
									    'teacher': CHEAP_ARGS['model_name_or_path'],
									    'val_check_interval': 0.5,
									}
									default_updates.update(_SCREAMING_SNAKE_CASE		)
									_UpperCAmelCase					=    CHEAP_ARGS.copy()
									_UpperCAmelCase					=    make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()		)
									_UpperCAmelCase					=    self.get_auto_remove_tmp_dir()

									args_d.update(data_dir=_SCREAMING_SNAKE_CASE    ,					output_dir=_SCREAMING_SNAKE_CASE    ,					**_SCREAMING_SNAKE_CASE		)
									_UpperCAmelCase					=    distill_main(argparse.Namespace(**_SCREAMING_SNAKE_CASE		)		)
									if not check_contents:
																return model
									_UpperCAmelCase					=    os.listdir(_SCREAMING_SNAKE_CASE		)
									_UpperCAmelCase					=    {os.path.basename(_SCREAMING_SNAKE_CASE		) for p in contents}
									_UpperCAmelCase					=    [p for p in contents if p.endswith('ckpt'		)]
									assert len(_SCREAMING_SNAKE_CASE		) > 0

									self.assertIn('test_generations.txt'    ,					_SCREAMING_SNAKE_CASE		)
									self.assertIn('test_results.txt'    ,					_SCREAMING_SNAKE_CASE		)

									_UpperCAmelCase					=    load_json(model.metrics_save_path		)
									_UpperCAmelCase					=    metrics['val'][-1]
									self.assertGreaterEqual(last_step_stats['val_avg_gen_time']    ,					0.01		)
									self.assertGreaterEqual(1.0    ,					last_step_stats['val_avg_gen_time']		)
									self.assertIsInstance(last_step_stats[f'''val_avg_{model.val_metric}''']    ,					_SCREAMING_SNAKE_CASE		)
									_UpperCAmelCase					=    int(args_d['max_epochs'] * (1 / args_d['val_check_interval']) + 1		)
									self.assertEqual(len(metrics['val']		)    ,					_SCREAMING_SNAKE_CASE		)
									self.assertEqual(len(metrics['test']		)    ,					1		)
									return model



class 	__a   (       UpperCAmelCase    ):
		@parameterized.expand(
		    [T5_TINY, BART_TINY, MBART_TINY, MARIAN_TINY, FSMT_TINY]    ,					)
		def 			UpperCAmelCase__       (    self    ,					_SCREAMING_SNAKE_CASE		)				-> List[str]:




									"""simple docstring"""





									_UpperCAmelCase					=    CHEAP_ARGS.copy()
									_UpperCAmelCase					=    'translation' if model in [MBART_TINY, MARIAN_TINY, FSMT_TINY] else 'summarization'
									_UpperCAmelCase					=    0.1 if task == 'translation' else 0

									_UpperCAmelCase					=    make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()		)
									_UpperCAmelCase					=    self.get_auto_remove_tmp_dir()
									args_d.update(
									    data_dir=_SCREAMING_SNAKE_CASE    ,					model_name_or_path=_SCREAMING_SNAKE_CASE    ,					tokenizer_name=_SCREAMING_SNAKE_CASE    ,					train_batch_size=2    ,					eval_batch_size=2    ,					output_dir=_SCREAMING_SNAKE_CASE    ,					do_predict=_SCREAMING_SNAKE_CASE    ,					task=_SCREAMING_SNAKE_CASE    ,					src_lang='en_XX'    ,					tgt_lang='ro_RO'    ,					freeze_encoder=_SCREAMING_SNAKE_CASE    ,					freeze_embeds=_SCREAMING_SNAKE_CASE    ,					)
									assert "n_train" in args_d
									_UpperCAmelCase					=    argparse.Namespace(**_SCREAMING_SNAKE_CASE		)
									_UpperCAmelCase					=    main(_SCREAMING_SNAKE_CASE		)

									_UpperCAmelCase					=    module.model.get_input_embeddings()
									assert not input_embeds.weight.requires_grad
									if model == T5_TINY:
																_UpperCAmelCase					=    module.model.lm_head
																assert not lm_head.weight.requires_grad
																assert (lm_head.weight == input_embeds.weight).all().item()
									elif model == FSMT_TINY:
																_UpperCAmelCase					=    module.model.model
																_UpperCAmelCase					=    fsmt.decoder.embed_positions
																assert not embed_pos.weight.requires_grad
																assert not fsmt.decoder.embed_tokens.weight.requires_grad
																# check that embeds are not the same
																assert fsmt.decoder.embed_tokens != fsmt.encoder.embed_tokens
									else:
																_UpperCAmelCase					=    module.model.model
																_UpperCAmelCase					=    bart.decoder.embed_positions
																assert not embed_pos.weight.requires_grad
																assert not bart.shared.weight.requires_grad
																# check that embeds are the same
																assert bart.decoder.embed_tokens == bart.encoder.embed_tokens
																assert bart.decoder.embed_tokens == bart.shared

									_UpperCAmelCase					=    load_json(module.output_dir / 'text_batch.json'		)
									assert isinstance(_SCREAMING_SNAKE_CASE    ,					_SCREAMING_SNAKE_CASE		)
									assert len(_SCREAMING_SNAKE_CASE		) >= 4
		def 			UpperCAmelCase__       (    self		)				-> List[Any]:




									"""simple docstring"""





									_UpperCAmelCase					=    CHEAP_ARGS.copy()

									_UpperCAmelCase					=    'summarization'
									_UpperCAmelCase					=    make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()		)

									args_d.update(
									    data_dir=_SCREAMING_SNAKE_CASE    ,					tokenizer_name=_SCREAMING_SNAKE_CASE    ,					train_batch_size=2    ,					eval_batch_size=2    ,					do_predict=_SCREAMING_SNAKE_CASE    ,					task=_SCREAMING_SNAKE_CASE    ,					src_lang='en_XX'    ,					tgt_lang='ro_RO'    ,					freeze_encoder=_SCREAMING_SNAKE_CASE    ,					freeze_embeds=_SCREAMING_SNAKE_CASE    ,					)

									# test models whose config includes the extra_model_args
									_UpperCAmelCase					=    BART_TINY
									_UpperCAmelCase					=    self.get_auto_remove_tmp_dir()
									_UpperCAmelCase					=    args_d.copy()
									args_da.update(
									    model_name_or_path=_SCREAMING_SNAKE_CASE    ,					output_dir=_SCREAMING_SNAKE_CASE    ,					)
									_UpperCAmelCase					=    ('encoder_layerdrop', 'decoder_layerdrop', 'dropout', 'attention_dropout')
									for p in extra_model_params:
																_UpperCAmelCase					=    0.5
									_UpperCAmelCase					=    argparse.Namespace(**_SCREAMING_SNAKE_CASE		)
									_UpperCAmelCase					=    main(_SCREAMING_SNAKE_CASE		)
									for p in extra_model_params:
																assert getattr(model.config    ,					_SCREAMING_SNAKE_CASE		) == 0.5, f'''failed to override the model config for param {p}'''

									# test models whose config doesn't include the extra_model_args
									_UpperCAmelCase					=    T5_TINY
									_UpperCAmelCase					=    self.get_auto_remove_tmp_dir()
									_UpperCAmelCase					=    args_d.copy()
									args_da.update(
									    model_name_or_path=_SCREAMING_SNAKE_CASE    ,					output_dir=_SCREAMING_SNAKE_CASE    ,					)
									_UpperCAmelCase					=    'encoder_layerdrop'
									_UpperCAmelCase					=    0.5
									_UpperCAmelCase					=    argparse.Namespace(**_SCREAMING_SNAKE_CASE		)
									with pytest.raises(_SCREAMING_SNAKE_CASE		) as excinfo:
																_UpperCAmelCase					=    main(_SCREAMING_SNAKE_CASE		)
									assert str(excinfo.value		) == f'''model config doesn\'t have a `{unsupported_param}` attribute'''






		def 			UpperCAmelCase__       (    self		)				-> str:




									"""simple docstring"""





									_UpperCAmelCase					=    CHEAP_ARGS.copy()

									_UpperCAmelCase					=    'summarization'
									_UpperCAmelCase					=    make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()		)

									_UpperCAmelCase					=    BART_TINY
									_UpperCAmelCase					=    self.get_auto_remove_tmp_dir()

									args_d.update(
									    data_dir=_SCREAMING_SNAKE_CASE    ,					model_name_or_path=_SCREAMING_SNAKE_CASE    ,					output_dir=_SCREAMING_SNAKE_CASE    ,					tokenizer_name=_SCREAMING_SNAKE_CASE    ,					train_batch_size=2    ,					eval_batch_size=2    ,					do_predict=_SCREAMING_SNAKE_CASE    ,					task=_SCREAMING_SNAKE_CASE    ,					src_lang='en_XX'    ,					tgt_lang='ro_RO'    ,					freeze_encoder=_SCREAMING_SNAKE_CASE    ,					freeze_embeds=_SCREAMING_SNAKE_CASE    ,					)

									# emulate finetune.py
									_UpperCAmelCase					=    argparse.ArgumentParser()
									_UpperCAmelCase					=    pl.Trainer.add_argparse_args(_SCREAMING_SNAKE_CASE		)
									_UpperCAmelCase					=    SummarizationModule.add_model_specific_args(_SCREAMING_SNAKE_CASE    ,					os.getcwd()		)
									_UpperCAmelCase					=    {'--help': True}

									# --help test
									with pytest.raises(_SCREAMING_SNAKE_CASE		) as excinfo:
																with CaptureStdout() as cs:
																							_UpperCAmelCase					=    parser.parse_args(_SCREAMING_SNAKE_CASE		)
																assert False, "--help is expected to sys.exit"
									assert excinfo.type == SystemExit
									_UpperCAmelCase					=    lightning_base.arg_to_scheduler_metavar
									assert expected in cs.out, "--help is expected to list the supported schedulers"

									# --lr_scheduler=non_existing_scheduler test
									_UpperCAmelCase					=    'non_existing_scheduler'
									_UpperCAmelCase					=    {f'''--lr_scheduler={unsupported_param}'''}
									with pytest.raises(_SCREAMING_SNAKE_CASE		) as excinfo:
																with CaptureStderr() as cs:
																							_UpperCAmelCase					=    parser.parse_args(_SCREAMING_SNAKE_CASE		)
																assert False, "invalid argument is expected to sys.exit"
									assert excinfo.type == SystemExit
									_UpperCAmelCase					=    f'''invalid choice: \'{unsupported_param}\''''
									assert expected in cs.err, f'''should have bailed on invalid choice of scheduler {unsupported_param}'''

									# --lr_scheduler=existing_scheduler test
									_UpperCAmelCase					=    'cosine'
									_UpperCAmelCase					=    args_d.copy()
									_UpperCAmelCase					=    supported_param
									_UpperCAmelCase					=    argparse.Namespace(**_SCREAMING_SNAKE_CASE		)
									_UpperCAmelCase					=    main(_SCREAMING_SNAKE_CASE		)
									assert (
									    getattr(model.hparams    ,					'lr_scheduler'		) == supported_param
									), f'''lr_scheduler={supported_param} shouldn\'t fail'''




import inspect
import unittest

from transformers import ResNetConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from transformers.utils import cached_property, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor


if is_flax_available():
		import jax
		import jax.numpy as jnp

		from transformers.models.resnet.modeling_flax_resnet import FlaxResNetForImageClassification, FlaxResNetModel

if is_vision_available():
		from PIL import Image

		from transformers import AutoImageProcessor

class 	__a   (       unittest.TestCase    ):
		def __init__(    self    ,					_SCREAMING_SNAKE_CASE    ,					_SCREAMING_SNAKE_CASE=3    ,					_SCREAMING_SNAKE_CASE=32    ,					_SCREAMING_SNAKE_CASE=3    ,					_SCREAMING_SNAKE_CASE=10    ,					_SCREAMING_SNAKE_CASE=[10, 20, 30, 40]    ,					_SCREAMING_SNAKE_CASE=[1, 1, 2, 1]    ,					_SCREAMING_SNAKE_CASE=True    ,					_SCREAMING_SNAKE_CASE=True    ,					_SCREAMING_SNAKE_CASE="relu"    ,					_SCREAMING_SNAKE_CASE=3    ,					_SCREAMING_SNAKE_CASE=None    ,					)				-> Tuple:




									"""simple docstring"""





									_UpperCAmelCase					=    parent
									_UpperCAmelCase					=    batch_size
									_UpperCAmelCase					=    image_size
									_UpperCAmelCase					=    num_channels
									_UpperCAmelCase					=    embeddings_size
									_UpperCAmelCase					=    hidden_sizes
									_UpperCAmelCase					=    depths
									_UpperCAmelCase					=    is_training
									_UpperCAmelCase					=    use_labels
									_UpperCAmelCase					=    hidden_act
									_UpperCAmelCase					=    num_labels
									_UpperCAmelCase					=    scope
									_UpperCAmelCase					=    len(_SCREAMING_SNAKE_CASE		)
		def 			UpperCAmelCase__       (    self		)				-> List[Any]:




									"""simple docstring"""





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

									_UpperCAmelCase					=    self.get_config()

									return config, pixel_values
		def 			UpperCAmelCase__       (    self		)				-> Union[str, Any]:




									"""simple docstring"""





									return ResNetConfig(
									    num_channels=self.num_channels    ,					embeddings_size=self.embeddings_size    ,					hidden_sizes=self.hidden_sizes    ,					depths=self.depths    ,					hidden_act=self.hidden_act    ,					num_labels=self.num_labels    ,					image_size=self.image_size    ,					)
		def 			UpperCAmelCase__       (    self    ,					_SCREAMING_SNAKE_CASE    ,					_SCREAMING_SNAKE_CASE		)				-> Optional[Any]:




									"""simple docstring"""





									_UpperCAmelCase					=    FlaxResNetModel(config=_SCREAMING_SNAKE_CASE		)
									_UpperCAmelCase					=    model(_SCREAMING_SNAKE_CASE		)

									# Output shape (b, c, h, w)
									self.parent.assertEqual(
									    result.last_hidden_state.shape    ,					(self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32)    ,					)
		def 			UpperCAmelCase__       (    self    ,					_SCREAMING_SNAKE_CASE    ,					_SCREAMING_SNAKE_CASE		)				-> Any:




									"""simple docstring"""





									_UpperCAmelCase					=    self.num_labels
									_UpperCAmelCase					=    FlaxResNetForImageClassification(config=_SCREAMING_SNAKE_CASE		)
									_UpperCAmelCase					=    model(_SCREAMING_SNAKE_CASE		)
									self.parent.assertEqual(result.logits.shape    ,					(self.batch_size, self.num_labels)		)






		def 			UpperCAmelCase__       (    self		)				-> Any:




									"""simple docstring"""





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



@require_flax
class 	__a   (       UpperCAmelCase					,   unittest.TestCase    ):
		_a			:						Any    			=			(FlaxResNetModel, FlaxResNetForImageClassification) if is_flax_available() else ()

		_a			:						Dict    			=			False
		_a			:						Optional[Any]    			=			False
		_a			:						Optional[Any]    			=			False
		def 			UpperCAmelCase__       (    self		)				-> None:




									"""simple docstring"""





									_UpperCAmelCase					=    FlaxResNetModelTester(self		)
									_UpperCAmelCase					=    ConfigTester(self    ,					config_class=_SCREAMING_SNAKE_CASE    ,					has_text_modality=_SCREAMING_SNAKE_CASE		)
		def 			UpperCAmelCase__       (    self		)				-> str:




									"""simple docstring"""





									self.create_and_test_config_common_properties()
									self.config_tester.create_and_test_config_to_json_string()
									self.config_tester.create_and_test_config_to_json_file()
									self.config_tester.create_and_test_config_from_and_save_pretrained()
									self.config_tester.create_and_test_config_with_num_labels()
									self.config_tester.check_config_can_be_init_without_params()
									self.config_tester.check_config_arguments_init()
		def 			UpperCAmelCase__       (    self		)				-> int:




									"""simple docstring"""





									return
		def 			UpperCAmelCase__       (    self		)				-> Tuple:




									"""simple docstring"""





									_UpperCAmelCase					=    self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_model(*_SCREAMING_SNAKE_CASE		)
		def 			UpperCAmelCase__       (    self		)				-> List[Any]:




									"""simple docstring"""





									_UpperCAmelCase					=    self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_image_classification(*_SCREAMING_SNAKE_CASE		)
		@unittest.skip(reason='ResNet does not use inputs_embeds'		)
		def 			UpperCAmelCase__       (    self		)				-> Optional[Any]:




									"""simple docstring"""





									pass
		@unittest.skip(reason='ResNet does not support input and output embeddings'		)
		def 			UpperCAmelCase__       (    self		)				-> Optional[int]:




									"""simple docstring"""





									pass
		def 			UpperCAmelCase__       (    self		)				-> Dict:




									"""simple docstring"""





									_UpperCAmelCase	, _UpperCAmelCase					=    self.model_tester.prepare_config_and_inputs_for_common()

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

																_UpperCAmelCase					=    ['pixel_values']
																self.assertListEqual(arg_names[:1]    ,					_SCREAMING_SNAKE_CASE		)
		def 			UpperCAmelCase__       (    self		)				-> int:




									"""simple docstring"""





									def check_hidden_states_output(_SCREAMING_SNAKE_CASE    ,					_SCREAMING_SNAKE_CASE    ,					_SCREAMING_SNAKE_CASE		):
																_UpperCAmelCase					=    model_class(_SCREAMING_SNAKE_CASE		)

																_UpperCAmelCase					=    model(**self._prepare_for_class(_SCREAMING_SNAKE_CASE    ,					_SCREAMING_SNAKE_CASE		)		)

																_UpperCAmelCase					=    outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

																_UpperCAmelCase					=    self.model_tester.num_stages
																self.assertEqual(len(_SCREAMING_SNAKE_CASE		)    ,					expected_num_stages + 1		)
		@unittest.skip(reason='ResNet does not use feedforward chunking'		)
		def 			UpperCAmelCase__       (    self		)				-> List[Any]:




									"""simple docstring"""





									pass






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




									"""simple docstring"""





									_UpperCAmelCase	, _UpperCAmelCase					=    self.model_tester.prepare_config_and_inputs_for_common()

									for model_class in self.all_model_classes:
																with self.subTest(model_class.__name__		):
																							_UpperCAmelCase					=    self._prepare_for_class(_SCREAMING_SNAKE_CASE    ,					_SCREAMING_SNAKE_CASE		)
																							_UpperCAmelCase					=    model_class(_SCREAMING_SNAKE_CASE		)

																							@jax.jit
																							def model_jitted(_SCREAMING_SNAKE_CASE    ,					**_SCREAMING_SNAKE_CASE		):
																														return model(pixel_values=_SCREAMING_SNAKE_CASE    ,					**_SCREAMING_SNAKE_CASE		)

																							with self.subTest('JIT Enabled'		):
																														_UpperCAmelCase					=    model_jitted(**_SCREAMING_SNAKE_CASE		).to_tuple()

																							with self.subTest('JIT Disabled'		):
																														with jax.disable_jit():
																																					_UpperCAmelCase					=    model_jitted(**_SCREAMING_SNAKE_CASE		).to_tuple()

																							self.assertEqual(len(_SCREAMING_SNAKE_CASE		)    ,					len(_SCREAMING_SNAKE_CASE		)		)
																							for jitted_output, output in zip(_SCREAMING_SNAKE_CASE    ,					_SCREAMING_SNAKE_CASE		):
																														self.assertEqual(jitted_output.shape    ,					output.shape		)



def  lowerCAmelCase__				(    )						->	Optional[int]:


							'''simple docstring'''





							_UpperCAmelCase					=    Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png' )
							return image







@require_flax
class 	__a   (       unittest.TestCase    ):
		@cached_property
		def 			UpperCAmelCase__       (    self		)				-> List[str]:




									"""simple docstring"""





									return AutoImageProcessor.from_pretrained('microsoft/resnet-50'		) if is_vision_available() else None






		@slow
		def 			UpperCAmelCase__       (    self		)				-> List[str]:




									"""simple docstring"""





									_UpperCAmelCase					=    FlaxResNetForImageClassification.from_pretrained('microsoft/resnet-50'		)

									_UpperCAmelCase					=    self.default_image_processor
									_UpperCAmelCase					=    prepare_img()
									_UpperCAmelCase					=    image_processor(images=_SCREAMING_SNAKE_CASE    ,					return_tensors='np'		)

									_UpperCAmelCase					=    model(**_SCREAMING_SNAKE_CASE		)

									# verify the logits
									_UpperCAmelCase					=    (1, 1000)
									self.assertEqual(outputs.logits.shape    ,					_SCREAMING_SNAKE_CASE		)

									_UpperCAmelCase					=    jnp.array([-11.1069, -9.7877, -8.3777]		)

									self.assertTrue(jnp.allclose(outputs.logits[0, :3]    ,					_SCREAMING_SNAKE_CASE    ,					atol=1e-4		)		)
