




"""simple docstring"""







import inspect
import unittest

from transformers import RegNetConfig
from transformers.file_utils import cached_property, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision, slow, torch_device

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 RegNetForImageClassification, RegNetModel
  from transformers.models.regnet.modeling_regnet import REGNET_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
  from PIL import Image

  from transformers import AutoImageProcessor

class 				snake_case_			:







   """simple docstring"""




   def __init__(		self       ,					lowerCamelCase_       ,					lowerCamelCase_=3       ,					lowerCamelCase_=3_2       ,					lowerCamelCase_=3       ,					lowerCamelCase_=1_0       ,					lowerCamelCase_=[1_0, 2_0, 3_0, 4_0]       ,					lowerCamelCase_=[1, 1, 2, 1]       ,					lowerCamelCase_=True       ,					lowerCamelCase_=True       ,					lowerCamelCase_="relu"       ,					lowerCamelCase_=3       ,					lowerCamelCase_=None       ,					)				->   Union[str, Any]:
       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(lowercase__)







   def   UpperCAmelCase__			(		self)				->   str:
       UpperCamelCase              =	floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])

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

       UpperCamelCase              =	self.get_config()

       return config, pixel_values, labels







   def   UpperCAmelCase__			(		self)				->   List[Any]:
       return RegNetConfig(
           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       ,					)







   def   UpperCAmelCase__			(		self       ,					lowerCamelCase_       ,					lowerCamelCase_       ,					lowerCamelCase_)				->   List[str]:
       UpperCamelCase              =	RegNetModel(config=lowercase__)
       model.to(lowercase__)
       model.eval()
       UpperCamelCase              =	model(lowercase__)
       # expected last hidden states: B, C, H // 32, W // 32
       self.parent.assertEqual(
           result.last_hidden_state.shape       ,					(self.batch_size, self.hidden_sizes[-1], self.image_size // 3_2, self.image_size // 3_2)       ,					)







   def   UpperCAmelCase__			(		self       ,					lowerCamelCase_       ,					lowerCamelCase_       ,					lowerCamelCase_)				->   Dict:
       UpperCamelCase              =	self.num_labels
       UpperCamelCase              =	RegNetForImageClassification(lowercase__)
       model.to(lowercase__)
       model.eval()
       UpperCamelCase              =	model(lowercase__       ,					labels=lowercase__)
       self.parent.assertEqual(result.logits.shape       ,					(self.batch_size, self.num_labels))







   def   UpperCAmelCase__			(		self)				->   str:
       UpperCamelCase              =	self.prepare_config_and_inputs()
       UpperCamelCase              =	config_and_inputs
       UpperCamelCase              =	{"""pixel_values""": pixel_values}
       return config, inputs_dict

@require_torch
class 				snake_case_			(					snake_case_	, snake_case_	, unittest.TestCase			):







   """simple docstring"""




   A_				      =       (RegNetModel, RegNetForImageClassification) if is_torch_available() else ()
   A_				      =       (
       {'''feature-extraction''': RegNetModel, '''image-classification''': RegNetForImageClassification}
       if is_torch_available()
       else {}
   )

   A_				      =       False
   A_				      =       False
   A_				      =       False
   A_				      =       False







   def   UpperCAmelCase__			(		self)				->   str:
       UpperCamelCase              =	RegNetModelTester(self)
       UpperCamelCase              =	ConfigTester(self       ,					config_class=lowercase__       ,					has_text_modality=lowercase__)







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







   @unittest.skip(reason='''RegNet does not use inputs_embeds''')
   def   UpperCAmelCase__			(		self)				->   Dict:
       pass







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







   def   UpperCAmelCase__			(		self)				->   Optional[int]:
       UpperCamelCase              =	self.model_tester.prepare_config_and_inputs_for_common()

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

           UpperCamelCase              =	["""pixel_values"""]
           self.assertListEqual(arg_names[:1]       ,					lowercase__)







   def   UpperCAmelCase__			(		self)				->   List[str]:
       UpperCamelCase              =	self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*lowercase__)







   def   UpperCAmelCase__			(		self)				->   List[Any]:
       UpperCamelCase              =	self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
           UpperCamelCase              =	model_class(config=lowercase__)
           for name, module in model.named_modules():
               if isinstance(lowercase__       ,					(nn.BatchNormad, nn.GroupNorm)):
                   self.assertTrue(
                       torch.all(module.weight == 1)       ,					msg=F'Parameter {name} of model {model_class} seems not properly initialized'       ,					)
                   self.assertTrue(
                       torch.all(module.bias == 0)       ,					msg=F'Parameter {name} of model {model_class} seems not properly initialized'       ,					)







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

       def check_hidden_states_output(lowerCamelCase_       ,					lowerCamelCase_       ,					lowerCamelCase_):
           UpperCamelCase              =	model_class(lowercase__)
           model.to(lowercase__)
           model.eval()

           with torch.no_grad():
               UpperCamelCase              =	model(**self._prepare_for_class(lowercase__       ,					lowercase__))

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

           UpperCamelCase              =	self.model_tester.num_stages
           self.assertEqual(len(lowercase__)       ,					expected_num_stages + 1)

           # RegNet's feature maps are of shape (batch_size, num_channels, height, width)
           self.assertListEqual(
               list(hidden_states[0].shape[-2:])       ,					[self.model_tester.image_size // 2, self.model_tester.image_size // 2]       ,					)

       UpperCamelCase              =	self.model_tester.prepare_config_and_inputs_for_common()
       UpperCamelCase              =	["""basic""", """bottleneck"""]
       for model_class in self.all_model_classes:
           for layer_type in layers_type:
               UpperCamelCase              =	layer_type
               UpperCamelCase              =	True
               check_hidden_states_output(lowercase__       ,					lowercase__       ,					lowercase__)

               # check that output_hidden_states also work using config
               del inputs_dict["output_hidden_states"]
               UpperCamelCase              =	True

               check_hidden_states_output(lowercase__       ,					lowercase__       ,					lowercase__)







   def   UpperCAmelCase__			(		self)				->   Any:
       UpperCamelCase              =	self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_image_classification(*lowercase__)







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

       for model_name in REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
           UpperCamelCase              =	RegNetModel.from_pretrained(lowercase__)
           self.assertIsNotNone(lowercase__)





def      __snake_case (				):


    """simple docstring"""







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


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







   """simple docstring"""




   @cached_property
   def   UpperCAmelCase__			(		self)				->   Union[str, Any]:
       return (
           AutoImageProcessor.from_pretrained(REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[0])
           if is_vision_available()
           else None
       )







   @slow
   def   UpperCAmelCase__			(		self)				->   int:
       UpperCamelCase              =	RegNetForImageClassification.from_pretrained(REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]).to(lowercase__)

       UpperCamelCase              =	self.default_image_processor
       UpperCamelCase              =	prepare_img()
       UpperCamelCase              =	image_processor(images=lowercase__       ,					return_tensors='''pt''').to(lowercase__)

       # forward pass
       with torch.no_grad():
           UpperCamelCase              =	model(**lowercase__)

       # verify the logits
       UpperCamelCase              =	torch.Size((1, 1_0_0_0))
       self.assertEqual(outputs.logits.shape       ,					lowercase__)

       UpperCamelCase              =	torch.tensor([-0.4180, -1.5051, -3.4836]).to(lowercase__)

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





'''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


lowerCAmelCase_      :  List[str]				     =  'sshleifer/mar_enro_6_3_student'







class      SCREAMING_SNAKE_CASE    (						snake_case_				):






      def 							lowercase_						( self   :  int       ):


           '''simple docstring'''

           super().setUp()

           a_     :							Optional[int]     						=  cached_path(
               """https://cdn-datasets.huggingface.co/translation/wmt_en_ro-tr40k-va0.5k-te0.5k.tar.gz"""						,						extract_compressed_file=lowercase__						,						)
           a_     :							List[Any]     						=  F"{data_cached}/wmt_en_ro-tr40k-va0.5k-te0.5k"






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


           '''simple docstring'''

           MarianMTModel.from_pretrained(lowercase__       )






      @slow
      @require_torch_gpu
      def 							lowercase_						( self   :  Tuple       ):


           '''simple docstring'''

           a_     :							Union[str, Any]     						=  {
               """$MAX_LEN""": 64,
               """$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
           a_     :							Optional[int]     						=  (self.test_file_dir / """train_mbart_cc25_enro.sh""").open().read().split("""finetune.py"""       )[1].strip()
           a_     :							List[str]     						=  bash_script.replace("""\\\n"""						,						""""""       ).strip().replace("""\"$@\""""						,						""""""       )
           for k, v in env_vars_to_replace.items():
                a_     :							Optional[Any]     						=  bash_script.replace(lowercase__						,						str(lowercase__       )       )
           a_     :							Union[str, Any]     						=  self.get_auto_remove_tmp_dir()

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

           a_     :							Dict     						=  ["""finetune.py"""] + bash_script.split() + args
           with patch.object(lowercase__						,						"""argv"""						,						lowercase__       ):
                a_     :							List[Any]     						=  argparse.ArgumentParser()
                a_     :							int     						=  pl.Trainer.add_argparse_args(lowercase__       )
                a_     :							Dict     						=  SummarizationModule.add_model_specific_args(lowercase__						,						os.getcwd()       )
                a_     :							List[str]     						=  parser.parse_args()
                a_     :							List[Any]     						=  main(lowercase__       )

           # Check metrics
           a_     :							List[str]     						=  load_json(model.metrics_save_path       )
           a_     :							Any     						=  metrics["""val"""][0]
           a_     :							int     						=  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}"]						,						lowercase__       )

           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
           a_     :							Dict     						=  os.listdir(lowercase__       )
           a_     :							Union[str, Any]     						=  [x for x in contents if x.endswith(""".ckpt"""       )][0]
           a_     :							Optional[Any]     						=  os.path.join(args.output_dir						,						lowercase__       )
           a_     :							Dict     						=  torch.load(lowercase__						,						map_location="""cpu"""       )
           a_     :							Tuple     						=  """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:
                a_     :							int     						=  {os.path.basename(lowercase__       ) 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      SCREAMING_SNAKE_CASE    (						snake_case_				):






      @timeout_decorator.timeout(600       )
      @slow
      @require_torch_gpu
      def 							lowercase_						( self   :  Optional[int]       ):


           '''simple docstring'''

           a_     :							int     						=  F"{self.test_file_dir_str}/test_data/wmt_en_ro"
           a_     :							Dict     						=  {
               """--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
           a_     :							List[str]     						=  (
               (self.test_file_dir / """distil_marian_no_teacher.sh""").open().read().split("""distillation.py"""       )[1].strip()
           )
           a_     :							List[str]     						=  bash_script.replace("""\\\n"""						,						""""""       ).strip().replace("""\"$@\""""						,						""""""       )
           a_     :							Tuple     						=  bash_script.replace("""--fp16 """						,						""" """       )

           for k, v in env_vars_to_replace.items():
                a_     :							Union[str, Any]     						=  bash_script.replace(lowercase__						,						str(lowercase__       )       )
           a_     :							List[str]     						=  self.get_auto_remove_tmp_dir()
           a_     :							List[str]     						=  bash_script.replace("""--fp16"""						,						""""""       )
           a_     :							str     						=  6
           a_     :							Union[str, 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(lowercase__						,						"""argv"""						,						lowercase__       ):
                a_     :							Dict     						=  argparse.ArgumentParser()
                a_     :							Tuple     						=  pl.Trainer.add_argparse_args(lowercase__       )
                a_     :							int     						=  SummarizationDistiller.add_model_specific_args(lowercase__						,						os.getcwd()       )
                a_     :							List[Any]     						=  parser.parse_args()
                # assert args.gpus == gpus THIS BREAKS for multi_gpu

                a_     :							Union[str, Any]     						=  distill_main(lowercase__       )

           # Check metrics
           a_     :							Union[str, Any]     						=  load_json(model.metrics_save_path       )
           a_     :							List[str]     						=  metrics["""val"""][0]
           a_     :							Optional[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}"]						,						lowercase__       )

           # check lightning ckpt can be loaded and has a reasonable statedict
           a_     :							Optional[int]     						=  os.listdir(lowercase__       )
           a_     :							Dict     						=  [x for x in contents if x.endswith(""".ckpt"""       )][0]
           a_     :							Optional[Any]     						=  os.path.join(args.output_dir						,						lowercase__       )
           a_     :							Optional[Any]     						=  torch.load(lowercase__						,						map_location="""cpu"""       )
           a_     :							List[str]     						=  """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:
                a_     :							int     						=  {os.path.basename(lowercase__       ) 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
