"""simple docstring"""







import argparse
import os
import sys
from unittest.mock import patch

import pytorch_lightning as pl
import timeout_decorator
import torch
from distillation import SummarizationDistiller, distill_main
from finetune import SummarizationModule, main

from transformers import MarianMTModel
from transformers.file_utils import cached_path
from transformers.testing_utils import TestCasePlus, require_torch_gpu, slow
from utils import load_json


__SCREAMING_SNAKE_CASE    	="sshleifer/mar_enro_6_3_student"







class        UpperCamelCase		(			lowercase_  ):
   def 			_UpperCAmelCase   (			self			)	->      Any:


        '''simple docstring'''


        super().setUp()

        lowercase_	:				List[str]													=	cached_path(
            'https://cdn-datasets.huggingface.co/translation/wmt_en_ro-tr40k-va0.5k-te0.5k.tar.gz'     ,extract_compressed_file=__UpperCamelCase     ,)
        lowercase_	:				Tuple													=	f'''{data_cached}/wmt_en_ro-tr40k-va0.5k-te0.5k'''
   @slow
   @require_torch_gpu
   def 			_UpperCAmelCase   (			self			)	->      Optional[Any]:


        '''simple docstring'''


        MarianMTModel.from_pretrained(__UpperCamelCase			)







   @slow
   @require_torch_gpu
   def 			_UpperCAmelCase   (			self			)	->      Optional[int]:


        '''simple docstring'''


        lowercase_	:				Dict													=	{
            '$MAX_LEN': 64,
            '$BS': 64,
            '$GAS': 1,
            '$ENRO_DIR': self.data_dir,
            'facebook/mbart-large-cc25': MARIAN_MODEL,
            # "val_check_interval=0.25": "val_check_interval=1.0",
            '--learning_rate=3e-5': '--learning_rate 3e-4',
            '--num_train_epochs 6': '--num_train_epochs 1',
        }

        # Clean up bash script
        lowercase_	:				Optional[int]													=	(self.test_file_dir / 'train_mbart_cc25_enro.sh').open().read().split('finetune.py'			)[1].strip()
        lowercase_	:				Optional[Any]													=	bash_script.replace('\\\n'     ,''			).strip().replace('"$@"'     ,''			)
        for k, v in env_vars_to_replace.items():
             lowercase_	:				int													=	bash_script.replace(__UpperCamelCase     ,str(__UpperCamelCase			)			)
        lowercase_	:				Optional[Any]													=	self.get_auto_remove_tmp_dir()

        # bash_script = bash_script.replace("--fp16 ", "")
        lowercase_	:				List[str]													=	f'''
            --output_dir {output_dir}
            --tokenizer_name Helsinki-NLP/opus-mt-en-ro
            --sortish_sampler
            --do_predict
            --gpus 1
            --freeze_encoder
            --n_train 40000
            --n_val 500
            --n_test 500
            --fp16_opt_level O1
            --num_sanity_val_steps 0
            --eval_beams 2
        '''.split()
        # XXX: args.gpus > 1 : handle multi_gpu in the future

        lowercase_	:				int													=	['finetune.py'] + bash_script.split() + args
        with patch.object(__UpperCamelCase     ,'argv'     ,__UpperCamelCase			):
             lowercase_	:				Optional[int]													=	argparse.ArgumentParser()
             lowercase_	:				Any													=	pl.Trainer.add_argparse_args(__UpperCamelCase			)
             lowercase_	:				Tuple													=	SummarizationModule.add_model_specific_args(__UpperCamelCase     ,os.getcwd()			)
             lowercase_	:				Tuple													=	parser.parse_args()
             lowercase_	:				Optional[Any]													=	main(__UpperCamelCase			)

        # Check metrics
        lowercase_	:				int													=	load_json(model.metrics_save_path			)
        lowercase_	:				Tuple													=	metrics['val'][0]
        lowercase_	:				str													=	metrics['val'][-1]
        self.assertEqual(len(metrics['val']			)     ,(args.max_epochs / args.val_check_interval)			)
        assert isinstance(last_step_stats[f'''val_avg_{model.val_metric}''']     ,__UpperCamelCase			)

        self.assertGreater(last_step_stats['val_avg_gen_time']     ,0.01			)
        # model hanging on generate. Maybe bad config was saved. (XXX: old comment/assert?)
        self.assertLessEqual(last_step_stats['val_avg_gen_time']     ,1.0			)

        # test learning requirements:

        # 1. BLEU improves over the course of training by more than 2 pts
        self.assertGreater(last_step_stats['val_avg_bleu'] - first_step_stats['val_avg_bleu']     ,2			)

        # 2. BLEU finishes above 17
        self.assertGreater(last_step_stats['val_avg_bleu']     ,17			)

        # 3. test BLEU and val BLEU within ~1.1 pt.
        self.assertLess(abs(metrics['val'][-1]['val_avg_bleu'] - metrics['test'][-1]['test_avg_bleu']			)     ,1.1			)

        # check lightning ckpt can be loaded and has a reasonable statedict
        lowercase_	:				Dict													=	os.listdir(__UpperCamelCase			)
        lowercase_	:				int													=	[x for x in contents if x.endswith('.ckpt'			)][0]
        lowercase_	:				Tuple													=	os.path.join(args.output_dir     ,__UpperCamelCase			)
        lowercase_	:				List[str]													=	torch.load(__UpperCamelCase     ,map_location='cpu'			)
        lowercase_	:				List[Any]													=	'model.model.decoder.layers.0.encoder_attn_layer_norm.weight'
        assert expected_key in ckpt["state_dict"]
        assert ckpt["state_dict"]["model.model.decoder.layers.0.encoder_attn_layer_norm.weight"].dtype == torch.floataa

        # TODO: turn on args.do_predict when PL bug fixed.
        if args.do_predict:
             lowercase_	:				Any													=	{os.path.basename(__UpperCamelCase			) for p in contents}
             assert "test_generations.txt" in contents
             assert "test_results.txt" in contents
             # assert len(metrics["val"]) ==  desired_n_evals
             assert len(metrics['test']			) == 1







class        UpperCamelCase		(			lowercase_  ):
   @timeout_decorator.timeout(600			)
   @slow
   @require_torch_gpu
   def 			_UpperCAmelCase   (			self			)	->      Tuple:


        '''simple docstring'''


        lowercase_	:				Optional[int]													=	f'''{self.test_file_dir_str}/test_data/wmt_en_ro'''
        lowercase_	:				Union[str, Any]													=	{
            '--fp16_opt_level=O1': '',
            '$MAX_LEN': 128,
            '$BS': 16,
            '$GAS': 1,
            '$ENRO_DIR': data_dir,
            '$m': 'sshleifer/student_marian_en_ro_6_1',
            'val_check_interval=0.25': 'val_check_interval=1.0',
        }

        # Clean up bash script
        lowercase_	:				Any													=	(
            (self.test_file_dir / 'distil_marian_no_teacher.sh').open().read().split('distillation.py'			)[1].strip()
        )
        lowercase_	:				Tuple													=	bash_script.replace('\\\n'     ,''			).strip().replace('"$@"'     ,''			)
        lowercase_	:				Union[str, Any]													=	bash_script.replace('--fp16 '     ,' '			)

        for k, v in env_vars_to_replace.items():
             lowercase_	:				Dict													=	bash_script.replace(__UpperCamelCase     ,str(__UpperCamelCase			)			)
        lowercase_	:				List[Any]													=	self.get_auto_remove_tmp_dir()
        lowercase_	:				int													=	bash_script.replace('--fp16'     ,''			)
        lowercase_	:				str													=	6
        lowercase_	:				List[Any]													=	(
            ['distillation.py']
            + bash_script.split()
            + [
                f'''--output_dir={output_dir}''',
                '--gpus=1',
                '--learning_rate=1e-3',
                f'''--num_train_epochs={epochs}''',
                '--warmup_steps=10',
                '--val_check_interval=1.0',
                '--do_predict',
            ]
        )
        with patch.object(__UpperCamelCase     ,'argv'     ,__UpperCamelCase			):
             lowercase_	:				Any													=	argparse.ArgumentParser()
             lowercase_	:				int													=	pl.Trainer.add_argparse_args(__UpperCamelCase			)
             lowercase_	:				int													=	SummarizationDistiller.add_model_specific_args(__UpperCamelCase     ,os.getcwd()			)
             lowercase_	:				str													=	parser.parse_args()
             # assert args.gpus == gpus THIS BREAKS for multi_gpu

             lowercase_	:				Optional[int]													=	distill_main(__UpperCamelCase			)

        # Check metrics
        lowercase_	:				Optional[int]													=	load_json(model.metrics_save_path			)
        lowercase_	:				Optional[Any]													=	metrics['val'][0]
        lowercase_	:				Union[str, Any]													=	metrics['val'][-1]
        assert len(metrics['val']			) >= (args.max_epochs / args.val_check_interval)  # +1 accounts for val_sanity_check

        assert last_step_stats["val_avg_gen_time"] >= 0.01

        assert first_step_stats["val_avg_bleu"] < last_step_stats["val_avg_bleu"]  # model learned nothing
        assert 1.0 >= last_step_stats["val_avg_gen_time"]  # model hanging on generate. Maybe bad config was saved.
        assert isinstance(last_step_stats[f'''val_avg_{model.val_metric}''']     ,__UpperCamelCase			)

        # check lightning ckpt can be loaded and has a reasonable statedict
        lowercase_	:				Union[str, Any]													=	os.listdir(__UpperCamelCase			)
        lowercase_	:				int													=	[x for x in contents if x.endswith('.ckpt'			)][0]
        lowercase_	:				Optional[Any]													=	os.path.join(args.output_dir     ,__UpperCamelCase			)
        lowercase_	:				Union[str, Any]													=	torch.load(__UpperCamelCase     ,map_location='cpu'			)
        lowercase_	:				Any													=	'model.model.decoder.layers.0.encoder_attn_layer_norm.weight'
        assert expected_key in ckpt["state_dict"]
        assert ckpt["state_dict"]["model.model.decoder.layers.0.encoder_attn_layer_norm.weight"].dtype == torch.floataa

        # TODO: turn on args.do_predict when PL bug fixed.
        if args.do_predict:
             lowercase_	:				str													=	{os.path.basename(__UpperCamelCase			) for p in contents}
             assert "test_generations.txt" in contents
             assert "test_results.txt" in contents
             # assert len(metrics["val"]) ==  desired_n_evals
             assert len(metrics['test']			) == 1


"""simple docstring"""







import inspect
import unittest

import numpy as np

from transformers import ViTConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

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


if is_flax_available():
       import jax

       from transformers.models.vit.modeling_flax_vit import FlaxViTForImageClassification, FlaxViTModel







class        UpperCamelCase		(			unittest.TestCase  ):
   def __init__(			self     ,__UpperCamelCase     ,__UpperCamelCase=13     ,__UpperCamelCase=30     ,__UpperCamelCase=2     ,__UpperCamelCase=3     ,__UpperCamelCase=True     ,__UpperCamelCase=True     ,__UpperCamelCase=32     ,__UpperCamelCase=5     ,__UpperCamelCase=4     ,__UpperCamelCase=37     ,__UpperCamelCase="gelu"     ,__UpperCamelCase=0.1     ,__UpperCamelCase=0.1     ,__UpperCamelCase=10     ,__UpperCamelCase=0.02     ,)	->      Tuple:


        '''simple docstring'''


        lowercase_	:				Tuple													=	parent
        lowercase_	:				Union[str, Any]													=	batch_size
        lowercase_	:				int													=	image_size
        lowercase_	:				Tuple													=	patch_size
        lowercase_	:				Optional[int]													=	num_channels
        lowercase_	:				Union[str, Any]													=	is_training
        lowercase_	:				Dict													=	use_labels
        lowercase_	:				Optional[int]													=	hidden_size
        lowercase_	:				List[str]													=	num_hidden_layers
        lowercase_	:				Optional[Any]													=	num_attention_heads
        lowercase_	:				Optional[int]													=	intermediate_size
        lowercase_	:				Tuple													=	hidden_act
        lowercase_	:				int													=	hidden_dropout_prob
        lowercase_	:				str													=	attention_probs_dropout_prob
        lowercase_	:				str													=	type_sequence_label_size
        lowercase_	:				Optional[int]													=	initializer_range

        # in ViT, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
        lowercase_	:				str													=	(image_size // patch_size) ** 2
        lowercase_	:				Optional[int]													=	num_patches + 1
   def 			_UpperCAmelCase   (			self			)	->      Tuple:


        '''simple docstring'''


        lowercase_	:				Tuple													=	floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]			)

        lowercase_	:				List[Any]													=	ViTConfig(
            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     ,is_decoder=__UpperCamelCase     ,initializer_range=self.initializer_range     ,)

        return config, pixel_values
   def 			_UpperCAmelCase   (			self     ,__UpperCamelCase     ,__UpperCamelCase			)	->      Dict:


        '''simple docstring'''


        lowercase_	:				List[Any]													=	FlaxViTModel(config=__UpperCamelCase			)
        lowercase_	:				Dict													=	model(__UpperCamelCase			)
        # expected sequence length = num_patches + 1 (we add 1 for the [CLS] token)
        lowercase_	:				Union[str, Any]													=	(self.image_size, self.image_size)
        lowercase_	:				List[Any]													=	(self.patch_size, self.patch_size)
        lowercase_	:				List[str]													=	(image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
        self.parent.assertEqual(result.last_hidden_state.shape     ,(self.batch_size, num_patches + 1, self.hidden_size)			)
   def 			_UpperCAmelCase   (			self     ,__UpperCamelCase     ,__UpperCamelCase			)	->      Union[str, Any]:


        '''simple docstring'''


        lowercase_	:				List[Any]													=	self.type_sequence_label_size
        lowercase_	:				str													=	FlaxViTForImageClassification(config=__UpperCamelCase			)
        lowercase_	:				Optional[Any]													=	model(__UpperCamelCase			)
        self.parent.assertEqual(result.logits.shape     ,(self.batch_size, self.type_sequence_label_size)			)

        # test greyscale images
        lowercase_	:				Union[str, Any]													=	1
        lowercase_	:				Optional[int]													=	FlaxViTForImageClassification(__UpperCamelCase			)

        lowercase_	:				List[Any]													=	floats_tensor([self.batch_size, 1, self.image_size, self.image_size]			)
        lowercase_	:				str													=	model(__UpperCamelCase			)







   def 			_UpperCAmelCase   (			self			)	->      Any:


        '''simple docstring'''


        lowercase_	:				Optional[int]													=	self.prepare_config_and_inputs()
        (
            (
            lowercase_
        )							,  (
            lowercase_
        )							,  
        )	:				List[Any]													=	config_and_inputs
        lowercase_	:				Tuple													=	{'pixel_values': pixel_values}
        return config, inputs_dict







@require_flax
class        UpperCamelCase		(			lowercase_						,      unittest.TestCase  ):
   lowercase							=  (FlaxViTModel, FlaxViTForImageClassification) if is_flax_available() else ()
   def 			_UpperCAmelCase   (			self			)	->      None:


        '''simple docstring'''


        lowercase_	:				Optional[Any]													=	FlaxViTModelTester(self			)
        lowercase_	:				Union[str, Any]													=	ConfigTester(self     ,config_class=__UpperCamelCase     ,has_text_modality=__UpperCamelCase     ,hidden_size=37			)
   def 			_UpperCAmelCase   (			self			)	->      List[str]:


        '''simple docstring'''


        self.config_tester.run_common_tests()
   def 			_UpperCAmelCase   (			self			)	->      Optional[Any]:


        '''simple docstring'''


        lowercase_	:				Optional[Any]													=	self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*__UpperCamelCase			)
   def 			_UpperCAmelCase   (			self			)	->      Optional[Any]:


        '''simple docstring'''


        lowercase_	:				List[Any]													=	self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_image_classification(*__UpperCamelCase			)
   def 			_UpperCAmelCase   (			self			)	->      List[Any]:


        '''simple docstring'''


        lowercase_							,  lowercase_	:				Any													=	self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
             lowercase_	:				Optional[Any]													=	model_class(__UpperCamelCase			)
             lowercase_	:				Tuple													=	inspect.signature(model.__call__			)
             # signature.parameters is an OrderedDict => so arg_names order is deterministic
             lowercase_	:				Union[str, Any]													=	[*signature.parameters.keys()]

             lowercase_	:				str													=	['pixel_values']
             self.assertListEqual(arg_names[:1]     ,__UpperCamelCase			)
   def 			_UpperCAmelCase   (			self			)	->      Optional[Any]:


        '''simple docstring'''


        lowercase_							,  lowercase_	:				str													=	self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
             with self.subTest(model_class.__name__			):
                  lowercase_	:				Optional[Any]													=	self._prepare_for_class(__UpperCamelCase     ,__UpperCamelCase			)
                  lowercase_	:				Dict													=	model_class(__UpperCamelCase			)

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

                  with self.subTest('JIT Enabled'			):
                       lowercase_	:				Optional[int]													=	model_jitted(**__UpperCamelCase			).to_tuple()

                  with self.subTest('JIT Disabled'			):
                       with jax.disable_jit():
                            lowercase_	:				List[str]													=	model_jitted(**__UpperCamelCase			).to_tuple()

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







   @slow
   def 			_UpperCAmelCase   (			self			)	->      Optional[int]:


        '''simple docstring'''


        for model_class_name in self.all_model_classes:
             lowercase_	:				Optional[int]													=	model_class_name.from_pretrained('google/vit-base-patch16-224'			)
             lowercase_	:				int													=	model(np.ones((1, 3, 224, 224)			)			)
             self.assertIsNotNone(__UpperCamelCase			)

