
"""simple docstring"""


import unittest

import numpy as np
import PIL
import torch

from diffusers.image_processor import VaeImageProcessor







class 	lowerCamelCase__      (      unittest.TestCase       ):





			'''simple docstring'''



			@property
			def       _lowerCAmelCase	(							self						)				->  Optional[int]:
				_lowerCAmelCase			=1
				_lowerCAmelCase			=3
				_lowerCAmelCase			=8
				_lowerCAmelCase			=8

				_lowerCAmelCase			=torch.rand((batch_size, num_channels, height, width)						)

				return sample



			def       _lowerCAmelCase	(							self  ,  __UpperCAmelCase						)				->  str:
				if isinstance(image[0]  ,  PIL.Image.Image						):
					return np.stack([np.array(__UpperCAmelCase						) for i in image]  ,  axis=0						)
				elif isinstance(__UpperCAmelCase  ,  torch.Tensor						):
					return image.cpu().numpy().transpose(0  ,  2  ,  3  ,  1						)
				return image



			def       _lowerCAmelCase	(							self						)				->  Optional[Any]:
				_lowerCAmelCase			=VaeImageProcessor(do_resize=__UpperCAmelCase  ,  do_normalize=__UpperCAmelCase						)

				_lowerCAmelCase			=self.dummy_sample
				_lowerCAmelCase			=self.to_np(__UpperCAmelCase						)

				for output_type in ["pt", "np", "pil"]:
					_lowerCAmelCase			=image_processor.postprocess(
					    image_processor.preprocess(__UpperCAmelCase						)  ,  output_type=__UpperCAmelCase  ,  )
					_lowerCAmelCase			=self.to_np(__UpperCAmelCase						)
					_lowerCAmelCase			=(input_np * 2_55).round() if output_type == """pil""" else input_np
					assert (
					    np.abs(in_np - out_np						).max() < 1e-6
					), f'''decoded output does not match input for output_type {output_type}'''



			def       _lowerCAmelCase	(							self						)				->  Optional[Any]:
				_lowerCAmelCase			=VaeImageProcessor(do_resize=__UpperCAmelCase  ,  do_normalize=__UpperCAmelCase						)
				_lowerCAmelCase			=self.dummy_sample.cpu().numpy().transpose(0  ,  2  ,  3  ,  1						)

				for output_type in ["pt", "np", "pil"]:
					_lowerCAmelCase			=image_processor.postprocess(image_processor.preprocess(__UpperCAmelCase						)  ,  output_type=__UpperCAmelCase						)

					_lowerCAmelCase			=self.to_np(__UpperCAmelCase						)
					_lowerCAmelCase			=(input_np * 2_55).round() if output_type == """pil""" else input_np
					assert (
					    np.abs(in_np - out_np						).max() < 1e-6
					), f'''decoded output does not match input for output_type {output_type}'''



			def       _lowerCAmelCase	(							self						)				->  Tuple:
				_lowerCAmelCase			=VaeImageProcessor(do_resize=__UpperCAmelCase  ,  do_normalize=__UpperCAmelCase						)

				_lowerCAmelCase			=self.dummy_sample.cpu().numpy().transpose(0  ,  2  ,  3  ,  1						)
				_lowerCAmelCase			=image_processor.numpy_to_pil(__UpperCAmelCase						)

				for output_type in ["pt", "np", "pil"]:
					_lowerCAmelCase			=image_processor.postprocess(image_processor.preprocess(__UpperCAmelCase						)  ,  output_type=__UpperCAmelCase						)
					for i, o in zip(__UpperCAmelCase  ,  __UpperCAmelCase						):
						_lowerCAmelCase			=np.array(__UpperCAmelCase						)
						_lowerCAmelCase			=self.to_np(__UpperCAmelCase						) if output_type == """pil""" else (self.to_np(__UpperCAmelCase						) * 2_55).round()
						assert (
						    np.abs(in_np - out_np						).max() < 1e-6
						), f'''decoded output does not match input for output_type {output_type}'''



			def       _lowerCAmelCase	(							self						)				->  Dict:
				_lowerCAmelCase			=VaeImageProcessor(do_resize=__UpperCAmelCase  ,  do_normalize=__UpperCAmelCase						)

				_lowerCAmelCase			=self.dummy_sample
				_lowerCAmelCase			=input_pt_ad.squeeze(0						)

				_lowerCAmelCase			=image_processor.postprocess(
				    image_processor.preprocess(__UpperCAmelCase						)  ,  output_type="""np"""  ,  )
				_lowerCAmelCase			=image_processor.postprocess(
				    image_processor.preprocess(__UpperCAmelCase						)  ,  output_type="""np"""  ,  )

				_lowerCAmelCase			=self.to_np(self.dummy_sample						)
				_lowerCAmelCase			=input_np_ad.squeeze(0						)

				_lowerCAmelCase			=image_processor.postprocess(
				    image_processor.preprocess(__UpperCAmelCase						)  ,  output_type="""np"""  ,  )
				_lowerCAmelCase			=image_processor.postprocess(
				    image_processor.preprocess(__UpperCAmelCase						)  ,  output_type="""np"""  ,  )

				assert np.abs(out_pt_ad - out_pt_ad						).max() < 1e-6
				assert np.abs(out_np_ad - out_np_ad						).max() < 1e-6



			def       _lowerCAmelCase	(							self						)				->  int:
				_lowerCAmelCase			=VaeImageProcessor(do_resize=__UpperCAmelCase  ,  do_normalize=__UpperCAmelCase						)

				_lowerCAmelCase			=self.dummy_sample
				_lowerCAmelCase			=list(__UpperCAmelCase						)

				_lowerCAmelCase			=image_processor.postprocess(
				    image_processor.preprocess(__UpperCAmelCase						)  ,  output_type="""np"""  ,  )

				_lowerCAmelCase			=image_processor.postprocess(
				    image_processor.preprocess(__UpperCAmelCase						)  ,  output_type="""np"""  ,  )

				_lowerCAmelCase			=self.to_np(self.dummy_sample						)
				list(__UpperCAmelCase						)

				_lowerCAmelCase			=image_processor.postprocess(
				    image_processor.preprocess(__UpperCAmelCase						)  ,  output_type="""np"""  ,  )

				_lowerCAmelCase			=image_processor.postprocess(
				    image_processor.preprocess(__UpperCAmelCase						)  ,  output_type="""np"""  ,  )

				assert np.abs(out_pt_ad - out_pt_list						).max() < 1e-6
				assert np.abs(out_np_ad - out_np_list						).max() < 1e-6





"""simple docstring"""


import argparse
import json
import logging
import os
import sys
from unittest import skip
from unittest.mock import patch

import tensorflow as tf

from transformers.testing_utils import TestCasePlus, get_gpu_count, slow


__A 		= [
    os.path.join(os.path.dirname(__file__), dirname)
    for dirname in [
        'text-generation',
        'text-classification',
        'token-classification',
        'language-modeling',
        'multiple-choice',
        'question-answering',
        'summarization',
        'translation',
        'image-classification',
    ]
]
sys.path.extend(SRC_DIRS)


if SRC_DIRS is not None:
			import run_clm
			import run_image_classification
			import run_mlm
			import run_ner
			import run_qa as run_squad
			import run_summarization
			import run_swag
			import run_text_classification
			import run_translation


logging.basicConfig(level=logging.DEBUG)

__A 		= logging.getLogger()



def 					_lowerCamelCase()       ->				str:
	_lowerCAmelCase			=argparse.ArgumentParser()
	parser.add_argument("""-f""" )
	_lowerCAmelCase			=parser.parse_args()
	return args.f



def 					_lowerCamelCase(__UpperCamelCase )       ->				List[str]:
	_lowerCAmelCase			={}
	_lowerCAmelCase			=os.path.join(__UpperCamelCase					,				"""all_results.json""" )
	if os.path.exists(__UpperCamelCase ):
		with open(__UpperCamelCase					,				"""r""" ) as f:
			_lowerCAmelCase			=json.load(__UpperCamelCase )
	else:
		raise ValueError(F'''can\'t find {path}''' )
	return results



def 					_lowerCamelCase()       ->				List[Any]:
	return bool(tf.config.list_physical_devices("""GPU""" ) )


__A 		= logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)







class 	lowerCamelCase__      (      __magic_name__       ):





			'''simple docstring'''



			@skip("""Skipping until shape inference for to_tf_dataset PR is merged."""						)
			def       _lowerCAmelCase	(							self						)				->  List[str]:
				_lowerCAmelCase			=self.get_auto_remove_tmp_dir()
				_lowerCAmelCase			=f'''
            run_text_classification.py
            --model_name_or_path distilbert-base-uncased
            --output_dir {tmp_dir}
            --overwrite_output_dir
            --train_file ./tests/fixtures/tests_samples/MRPC/train.csv
            --validation_file ./tests/fixtures/tests_samples/MRPC/dev.csv
            --do_train
            --do_eval
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
            --learning_rate=1e-4
            --max_steps=10
            --warmup_steps=2
            --seed=42
            --max_seq_length=128
            '''.split()

				if is_cuda_available():
					testargs.append("""--fp16"""						)

				with patch.object(__UpperCAmelCase  ,  """argv"""  ,  __UpperCAmelCase						):
					run_text_classification.main()
					# Reset the mixed precision policy so we don't break other tests
					tf.keras.mixed_precision.set_global_policy("""float32"""						)
					_lowerCAmelCase			=get_results(__UpperCAmelCase						)
					self.assertGreaterEqual(result["""eval_accuracy"""]  ,  0.7_5						)



			def       _lowerCAmelCase	(							self						)				->  int:
				_lowerCAmelCase			=self.get_auto_remove_tmp_dir()
				_lowerCAmelCase			=f'''
            run_clm.py
            --model_name_or_path distilgpt2
            --train_file ./tests/fixtures/sample_text.txt
            --validation_file ./tests/fixtures/sample_text.txt
            --do_train
            --do_eval
            --block_size 128
            --per_device_train_batch_size 2
            --per_device_eval_batch_size 1
            --num_train_epochs 2
            --output_dir {tmp_dir}
            --overwrite_output_dir
            '''.split()

				if len(tf.config.list_physical_devices("""GPU"""						)						) > 1:
					# Skipping because there are not enough batches to train the model + would need a drop_last to work.
					return

				with patch.object(__UpperCAmelCase  ,  """argv"""  ,  __UpperCAmelCase						):
					run_clm.main()
					_lowerCAmelCase			=get_results(__UpperCAmelCase						)
					self.assertLess(result["""eval_perplexity"""]  ,  1_00						)



			def       _lowerCAmelCase	(							self						)				->  str:
				_lowerCAmelCase			=self.get_auto_remove_tmp_dir()
				_lowerCAmelCase			=f'''
            run_mlm.py
            --model_name_or_path distilroberta-base
            --train_file ./tests/fixtures/sample_text.txt
            --validation_file ./tests/fixtures/sample_text.txt
            --max_seq_length 64
            --output_dir {tmp_dir}
            --overwrite_output_dir
            --do_train
            --do_eval
            --prediction_loss_only
            --num_train_epochs=1
            --learning_rate=1e-4
        '''.split()

				with patch.object(__UpperCAmelCase  ,  """argv"""  ,  __UpperCAmelCase						):
					run_mlm.main()
					_lowerCAmelCase			=get_results(__UpperCAmelCase						)
					self.assertLess(result["""eval_perplexity"""]  ,  42						)



			def       _lowerCAmelCase	(							self						)				->  Union[str, Any]:
				# with so little data distributed training needs more epochs to get the score on par with 0/1 gpu
				_lowerCAmelCase			=7 if get_gpu_count() > 1 else 2

				_lowerCAmelCase			=self.get_auto_remove_tmp_dir()
				_lowerCAmelCase			=f'''
            run_ner.py
            --model_name_or_path bert-base-uncased
            --train_file tests/fixtures/tests_samples/conll/sample.json
            --validation_file tests/fixtures/tests_samples/conll/sample.json
            --output_dir {tmp_dir}
            --overwrite_output_dir
            --do_train
            --do_eval
            --warmup_steps=2
            --learning_rate=2e-4
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=2
            --num_train_epochs={epochs}
            --seed 7
        '''.split()

				with patch.object(__UpperCAmelCase  ,  """argv"""  ,  __UpperCAmelCase						):
					run_ner.main()
					_lowerCAmelCase			=get_results(__UpperCAmelCase						)
					self.assertGreaterEqual(result["""accuracy"""]  ,  0.7_5						)



			def       _lowerCAmelCase	(							self						)				->  str:
				_lowerCAmelCase			=self.get_auto_remove_tmp_dir()
				_lowerCAmelCase			=f'''
            run_qa.py
            --model_name_or_path bert-base-uncased
            --version_2_with_negative
            --train_file tests/fixtures/tests_samples/SQUAD/sample.json
            --validation_file tests/fixtures/tests_samples/SQUAD/sample.json
            --output_dir {tmp_dir}
            --overwrite_output_dir
            --max_steps=10
            --warmup_steps=2
            --do_train
            --do_eval
            --learning_rate=2e-4
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
        '''.split()

				with patch.object(__UpperCAmelCase  ,  """argv"""  ,  __UpperCAmelCase						):
					run_squad.main()
					_lowerCAmelCase			=get_results(__UpperCAmelCase						)
					self.assertGreaterEqual(result["""f1"""]  ,  30						)
					self.assertGreaterEqual(result["""exact"""]  ,  30						)



			def       _lowerCAmelCase	(							self						)				->  int:
				_lowerCAmelCase			=self.get_auto_remove_tmp_dir()
				_lowerCAmelCase			=f'''
            run_swag.py
            --model_name_or_path bert-base-uncased
            --train_file tests/fixtures/tests_samples/swag/sample.json
            --validation_file tests/fixtures/tests_samples/swag/sample.json
            --output_dir {tmp_dir}
            --overwrite_output_dir
            --max_steps=20
            --warmup_steps=2
            --do_train
            --do_eval
            --learning_rate=2e-4
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
        '''.split()

				with patch.object(__UpperCAmelCase  ,  """argv"""  ,  __UpperCAmelCase						):
					run_swag.main()
					_lowerCAmelCase			=get_results(__UpperCAmelCase						)
					self.assertGreaterEqual(result["""val_accuracy"""]  ,  0.8						)



			@slow
			def       _lowerCAmelCase	(							self						)				->  Tuple:
				_lowerCAmelCase			=self.get_auto_remove_tmp_dir()
				_lowerCAmelCase			=f'''
            run_summarization.py
            --model_name_or_path t5-small
            --train_file tests/fixtures/tests_samples/xsum/sample.json
            --validation_file tests/fixtures/tests_samples/xsum/sample.json
            --output_dir {tmp_dir}
            --overwrite_output_dir
            --max_steps=50
            --warmup_steps=8
            --do_train
            --do_eval
            --learning_rate=2e-4
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
        '''.split()

				with patch.object(__UpperCAmelCase  ,  """argv"""  ,  __UpperCAmelCase						):
					run_summarization.main()
					_lowerCAmelCase			=get_results(__UpperCAmelCase						)
					self.assertGreaterEqual(result["""rouge1"""]  ,  10						)
					self.assertGreaterEqual(result["""rouge2"""]  ,  2						)
					self.assertGreaterEqual(result["""rougeL"""]  ,  7						)
					self.assertGreaterEqual(result["""rougeLsum"""]  ,  7						)



			@slow
			def       _lowerCAmelCase	(							self						)				->  Any:
				_lowerCAmelCase			=self.get_auto_remove_tmp_dir()
				_lowerCAmelCase			=f'''
            run_translation.py
            --model_name_or_path Rocketknight1/student_marian_en_ro_6_1
            --source_lang en
            --target_lang ro
            --train_file tests/fixtures/tests_samples/wmt16/sample.json
            --validation_file tests/fixtures/tests_samples/wmt16/sample.json
            --output_dir {tmp_dir}
            --overwrite_output_dir
            --warmup_steps=8
            --do_train
            --do_eval
            --learning_rate=3e-3
            --num_train_epochs 12
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
            --source_lang en_XX
            --target_lang ro_RO
        '''.split()

				with patch.object(__UpperCAmelCase  ,  """argv"""  ,  __UpperCAmelCase						):
					run_translation.main()
					_lowerCAmelCase			=get_results(__UpperCAmelCase						)
					self.assertGreaterEqual(result["""bleu"""]  ,  30						)



			def       _lowerCAmelCase	(							self						)				->  Optional[int]:
				_lowerCAmelCase			=self.get_auto_remove_tmp_dir()
				_lowerCAmelCase			=f'''
            run_image_classification.py
            --dataset_name hf-internal-testing/cats_vs_dogs_sample
            --model_name_or_path microsoft/resnet-18
            --do_train
            --do_eval
            --learning_rate 1e-4
            --per_device_train_batch_size 2
            --per_device_eval_batch_size 1
            --output_dir {tmp_dir}
            --overwrite_output_dir
            --dataloader_num_workers 16
            --num_train_epochs 2
            --train_val_split 0.1
            --seed 42
            --ignore_mismatched_sizes True
            '''.split()

				with patch.object(__UpperCAmelCase  ,  """argv"""  ,  __UpperCAmelCase						):
					run_image_classification.main()
					_lowerCAmelCase			=get_results(__UpperCAmelCase						)
					self.assertGreaterEqual(result["""accuracy"""]  ,  0.7						)



