






import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
					import torch

if is_vision_available():
					from PIL import Image

					from transformers import PoolFormerImageProcessor
class 						lowerCamelCase_				(      unittest.TestCase ):







				def __init__(						self       ,					__lowerCAmelCase       ,					__lowerCAmelCase=7       ,					__lowerCAmelCase=3       ,					__lowerCAmelCase=3_0       ,					__lowerCAmelCase=4_0_0       ,					__lowerCAmelCase=True       ,					__lowerCAmelCase=None       ,					__lowerCAmelCase=0.9       ,					__lowerCAmelCase=None       ,					__lowerCAmelCase=True       ,					__lowerCAmelCase=[0.5, 0.5, 0.5]       ,					__lowerCAmelCase=[0.5, 0.5, 0.5]       ,					):
								"""simple docstring"""
								__magic_name__		:int        =      size if size is not None else {'''shortest_edge''': 3_0}
								__magic_name__		:Optional[Any]        =      crop_size if crop_size is not None else {'''height''': 3_0, '''width''': 3_0}
								__magic_name__		:Union[str, Any]        =      parent
								__magic_name__		:Optional[int]        =      batch_size
								__magic_name__		:Union[str, Any]        =      num_channels
								__magic_name__		:List[str]        =      min_resolution
								__magic_name__		:Optional[Any]        =      max_resolution
								__magic_name__		:List[str]        =      do_resize_and_center_crop
								__magic_name__		:List[str]        =      size
								__magic_name__		:Optional[int]        =      crop_pct
								__magic_name__		:Tuple        =      crop_size
								__magic_name__		:Optional[int]        =      do_normalize
								__magic_name__		:int        =      image_mean
								__magic_name__		:int        =      image_std







				def 			A	(						self     ):
								"""simple docstring"""
								return {
								    "size": self.size,
								    "do_resize_and_center_crop": self.do_resize_and_center_crop,
								    "crop_pct": self.crop_pct,
								    "crop_size": self.crop_size,
								    "do_normalize": self.do_normalize,
								    "image_mean": self.image_mean,
								    "image_std": self.image_std,
								}







@require_torch
@require_vision
class 						lowerCamelCase_				(      lowerCamelCase			,				unittest.TestCase ):
				a__					       =  PoolFormerImageProcessor if is_vision_available() else None







				def 			A	(						self     ):
								"""simple docstring"""
								__magic_name__		:List[str]        =      PoolFormerImageProcessingTester(self     )







				@property
				def 			A	(						self     ):
								"""simple docstring"""
								return self.image_processor_tester.prepare_image_processor_dict()







				def 			A	(						self     ):
								"""simple docstring"""
								__magic_name__		:int        =      self.image_processing_class(**self.image_processor_dict     )
								self.assertTrue(hasattr(__lowerCAmelCase       ,					'''do_resize_and_center_crop'''     )     )
								self.assertTrue(hasattr(__lowerCAmelCase       ,					'''size'''     )     )
								self.assertTrue(hasattr(__lowerCAmelCase       ,					'''crop_pct'''     )     )
								self.assertTrue(hasattr(__lowerCAmelCase       ,					'''do_normalize'''     )     )
								self.assertTrue(hasattr(__lowerCAmelCase       ,					'''image_mean'''     )     )
								self.assertTrue(hasattr(__lowerCAmelCase       ,					'''image_std'''     )     )







				def 			A	(						self     ):
								"""simple docstring"""
								__magic_name__		:Dict        =      self.image_processing_class.from_dict(self.image_processor_dict     )
								self.assertEqual(image_processor.size       ,					{'''shortest_edge''': 3_0}     )
								self.assertEqual(image_processor.crop_size       ,					{'''height''': 3_0, '''width''': 3_0}     )

								__magic_name__		:str        =      self.image_processing_class.from_dict(self.image_processor_dict       ,					size=4_2       ,					crop_size=8_4     )
								self.assertEqual(image_processor.size       ,					{'''shortest_edge''': 4_2}     )
								self.assertEqual(image_processor.crop_size       ,					{'''height''': 8_4, '''width''': 8_4}     )







				def 			A	(						self     ):
								"""simple docstring"""
								pass







				def 			A	(						self     ):
								"""simple docstring"""
								# Initialize image_processing
								__magic_name__		:Union[str, Any]        =      self.image_processing_class(**self.image_processor_dict     )
								# create random PIL images
								__magic_name__		:Optional[int]        =      prepare_image_inputs(self.image_processor_tester       ,					equal_resolution=__lowerCAmelCase     )
								for image in image_inputs:
												self.assertIsInstance(__lowerCAmelCase       ,					Image.Image     )

								# Test not batched input
								__magic_name__		:Dict        =      image_processing(image_inputs[0]       ,					return_tensors='''pt'''     ).pixel_values

								self.assertEqual(
								    encoded_images.shape       ,					(
								        1,
								        self.image_processor_tester.num_channels,
								        self.image_processor_tester.crop_size['''height'''],
								        self.image_processor_tester.crop_size['''width'''],
								    )       ,					)

								# Test batched
								__magic_name__		:List[str]        =      image_processing(__lowerCAmelCase       ,					return_tensors='''pt'''     ).pixel_values
								self.assertEqual(
								    encoded_images.shape       ,					(
								        self.image_processor_tester.batch_size,
								        self.image_processor_tester.num_channels,
								        self.image_processor_tester.crop_size['''height'''],
								        self.image_processor_tester.crop_size['''width'''],
								    )       ,					)







				def 			A	(						self     ):
								"""simple docstring"""
								# Initialize image_processing
								__magic_name__		:Tuple        =      self.image_processing_class(**self.image_processor_dict     )
								# create random numpy tensors
								__magic_name__		:List[Any]        =      prepare_image_inputs(self.image_processor_tester       ,					equal_resolution=__lowerCAmelCase       ,					numpify=__lowerCAmelCase     )
								for image in image_inputs:
												self.assertIsInstance(__lowerCAmelCase       ,					np.ndarray     )

								# Test not batched input
								__magic_name__		:List[Any]        =      image_processing(image_inputs[0]       ,					return_tensors='''pt'''     ).pixel_values
								self.assertEqual(
								    encoded_images.shape       ,					(
								        1,
								        self.image_processor_tester.num_channels,
								        self.image_processor_tester.crop_size['''height'''],
								        self.image_processor_tester.crop_size['''width'''],
								    )       ,					)

								# Test batched
								__magic_name__		:Union[str, Any]        =      image_processing(__lowerCAmelCase       ,					return_tensors='''pt'''     ).pixel_values
								self.assertEqual(
								    encoded_images.shape       ,					(
								        self.image_processor_tester.batch_size,
								        self.image_processor_tester.num_channels,
								        self.image_processor_tester.crop_size['''height'''],
								        self.image_processor_tester.crop_size['''width'''],
								    )       ,					)







				def 			A	(						self     ):
								"""simple docstring"""
								# Initialize image_processing
								__magic_name__		:List[Any]        =      self.image_processing_class(**self.image_processor_dict     )
								# create random PyTorch tensors
								__magic_name__		:int        =      prepare_image_inputs(self.image_processor_tester       ,					equal_resolution=__lowerCAmelCase       ,					torchify=__lowerCAmelCase     )
								for image in image_inputs:
												self.assertIsInstance(__lowerCAmelCase       ,					torch.Tensor     )

								# Test not batched input
								__magic_name__		:Optional[Any]        =      image_processing(image_inputs[0]       ,					return_tensors='''pt'''     ).pixel_values
								self.assertEqual(
								    encoded_images.shape       ,					(
								        1,
								        self.image_processor_tester.num_channels,
								        self.image_processor_tester.crop_size['''height'''],
								        self.image_processor_tester.crop_size['''width'''],
								    )       ,					)

								# Test batched
								__magic_name__		:Any        =      image_processing(__lowerCAmelCase       ,					return_tensors='''pt'''     ).pixel_values
								self.assertEqual(
								    encoded_images.shape       ,					(
								        self.image_processor_tester.batch_size,
								        self.image_processor_tester.num_channels,
								        self.image_processor_tester.crop_size['''height'''],
								        self.image_processor_tester.crop_size['''width'''],
								    )       ,					)














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

from transformers.testing_utils import TestCasePlus, get_gpu_count, slow


SCREAMING_SNAKE_CASE__      :			List[str]							=		[
    os.path.join(os.path.dirname(__file__), dirname)
    for dirname in [
        """text-classification""",
        """language-modeling""",
        """summarization""",
        """token-classification""",
        """question-answering""",
    ]
]
sys.path.extend(SRC_DIRS)


if SRC_DIRS is not None:
					import run_clm_flax
					import run_flax_glue
					import run_flax_ner
					import run_mlm_flax
					import run_qa
					import run_summarization_flax
					import run_ta_mlm_flax


logging.basicConfig(level=logging.DEBUG)

SCREAMING_SNAKE_CASE__      :			int							=		logging.getLogger()

def      __lowercase					( ):







				"""simple docstring"""






				__magic_name__		:int        =      argparse.ArgumentParser()
				parser.add_argument('''-f''' )
				__magic_name__		:Tuple        =      parser.parse_args()
				return args.f

def      __lowercase					( snake_case,  snake_case="eval" ):







				"""simple docstring"""






				__magic_name__		:str        =      os.path.join(snake_case,  f'''{split}_results.json''' )
				if os.path.exists(snake_case ):
								with open(snake_case,  '''r''' ) as f:
												return json.load(snake_case )
				raise ValueError(f'''can\'t find {path}''' )


SCREAMING_SNAKE_CASE__      :			Tuple							=		logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)

class 						lowerCamelCase_				(      lowerCamelCase ):







				def 			A	(						self     ):
								"""simple docstring"""
								__magic_name__		:Optional[Any]        =      self.get_auto_remove_tmp_dir()
								__magic_name__		:Optional[Any]        =      F'''
            run_glue.py
            --model_name_or_path distilbert-base-uncased
            --output_dir {tmp_dir}
            --train_file ./tests/fixtures/tests_samples/MRPC/train.csv
            --validation_file ./tests/fixtures/tests_samples/MRPC/dev.csv
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
            --learning_rate=1e-4
            --eval_steps=2
            --warmup_steps=2
            --seed=42
            --max_seq_length=128
            '''.split()

								with patch.object(__lowerCAmelCase       ,					'''argv'''       ,					__lowerCAmelCase     ):
												run_flax_glue.main()
												__magic_name__		:int        =      get_results(__lowerCAmelCase     )
												self.assertGreaterEqual(result['''eval_accuracy''']       ,					0.75     )







				@slow
				def 			A	(						self     ):
								"""simple docstring"""
								__magic_name__		:List[str]        =      self.get_auto_remove_tmp_dir()
								__magic_name__		:Tuple        =      F'''
            run_clm_flax.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 4
            --per_device_eval_batch_size 4
            --num_train_epochs 2
            --logging_steps 2 --eval_steps 2
            --output_dir {tmp_dir}
            --overwrite_output_dir
            '''.split()

								with patch.object(__lowerCAmelCase       ,					'''argv'''       ,					__lowerCAmelCase     ):
												run_clm_flax.main()
												__magic_name__		:int        =      get_results(__lowerCAmelCase     )
												self.assertLess(result['''eval_perplexity''']       ,					1_0_0     )







				@slow
				def 			A	(						self     ):
								"""simple docstring"""
								__magic_name__		:Union[str, Any]        =      self.get_auto_remove_tmp_dir()
								__magic_name__		:List[str]        =      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
            --test_file tests/fixtures/tests_samples/xsum/sample.json
            --output_dir {tmp_dir}
            --overwrite_output_dir
            --num_train_epochs=3
            --warmup_steps=8
            --do_train
            --do_eval
            --do_predict
            --learning_rate=2e-4
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
            --predict_with_generate
        '''.split()

								with patch.object(__lowerCAmelCase       ,					'''argv'''       ,					__lowerCAmelCase     ):
												run_summarization_flax.main()
												__magic_name__		:int        =      get_results(__lowerCAmelCase       ,					split='''test'''     )
												self.assertGreaterEqual(result['''test_rouge1''']       ,					1_0     )
												self.assertGreaterEqual(result['''test_rouge2''']       ,					2     )
												self.assertGreaterEqual(result['''test_rougeL''']       ,					7     )
												self.assertGreaterEqual(result['''test_rougeLsum''']       ,					7     )







				@slow
				def 			A	(						self     ):
								"""simple docstring"""
								__magic_name__		:Dict        =      self.get_auto_remove_tmp_dir()
								__magic_name__		:Optional[Any]        =      F'''
            run_mlm.py
            --model_name_or_path distilroberta-base
            --train_file ./tests/fixtures/sample_text.txt
            --validation_file ./tests/fixtures/sample_text.txt
            --output_dir {tmp_dir}
            --overwrite_output_dir
            --max_seq_length 128
            --per_device_train_batch_size 4
            --per_device_eval_batch_size 4
            --logging_steps 2 --eval_steps 2
            --do_train
            --do_eval
            --num_train_epochs=1
        '''.split()

								with patch.object(__lowerCAmelCase       ,					'''argv'''       ,					__lowerCAmelCase     ):
												run_mlm_flax.main()
												__magic_name__		:int        =      get_results(__lowerCAmelCase     )
												self.assertLess(result['''eval_perplexity''']       ,					4_2     )







				@slow
				def 			A	(						self     ):
								"""simple docstring"""
								__magic_name__		:Tuple        =      self.get_auto_remove_tmp_dir()
								__magic_name__		:List[Any]        =      F'''
            run_t5_mlm_flax.py
            --model_name_or_path t5-small
            --train_file ./tests/fixtures/sample_text.txt
            --validation_file ./tests/fixtures/sample_text.txt
            --do_train
            --do_eval
            --max_seq_length 128
            --per_device_train_batch_size 4
            --per_device_eval_batch_size 4
            --num_train_epochs 2
            --logging_steps 2 --eval_steps 2
            --output_dir {tmp_dir}
            --overwrite_output_dir
            '''.split()

								with patch.object(__lowerCAmelCase       ,					'''argv'''       ,					__lowerCAmelCase     ):
												run_ta_mlm_flax.main()
												__magic_name__		:Dict        =      get_results(__lowerCAmelCase     )
												self.assertGreaterEqual(result['''eval_accuracy''']       ,					0.42     )







				@slow
				def 			A	(						self     ):
								"""simple docstring"""
								# with so little data distributed training needs more epochs to get the score on par with 0/1 gpu
								__magic_name__		:int        =      7 if get_gpu_count() > 1 else 2

								__magic_name__		:str        =      self.get_auto_remove_tmp_dir()
								__magic_name__		:str        =      F'''
            run_flax_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
            --logging_steps 2 --eval_steps 2
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=2
            --num_train_epochs={epochs}
            --seed 7
        '''.split()

								with patch.object(__lowerCAmelCase       ,					'''argv'''       ,					__lowerCAmelCase     ):
												run_flax_ner.main()
												__magic_name__		:Any        =      get_results(__lowerCAmelCase     )
												self.assertGreaterEqual(result['''eval_accuracy''']       ,					0.75     )
												self.assertGreaterEqual(result['''eval_f1''']       ,					0.3     )







				@slow
				def 			A	(						self     ):
								"""simple docstring"""
								__magic_name__		:Union[str, Any]        =      self.get_auto_remove_tmp_dir()
								__magic_name__		:int        =      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
            --num_train_epochs=3
            --warmup_steps=2
            --do_train
            --do_eval
            --logging_steps 2 --eval_steps 2
            --learning_rate=2e-4
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
        '''.split()

								with patch.object(__lowerCAmelCase       ,					'''argv'''       ,					__lowerCAmelCase     ):
												run_qa.main()
												__magic_name__		:int        =      get_results(__lowerCAmelCase     )
												self.assertGreaterEqual(result['''eval_f1''']       ,					3_0     )
												self.assertGreaterEqual(result['''eval_exact''']       ,					3_0     )






