


'''simple docstring'''


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


UpperCAmelCase_		:   Tuple							   =		[
    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)

UpperCAmelCase_		:   Any							   =		logging.getLogger()







def 			snake_case_   (  ):







				"""simple docstring"""





				_SCREAMING_SNAKE_CASE :       List[Any]     		=      argparse.ArgumentParser()
				parser.add_argument("""-f"""     )
				_SCREAMING_SNAKE_CASE :       int     		=      parser.parse_args()
				return args.f







def 			snake_case_   (  SCREAMING_SNAKE_CASE__							,      SCREAMING_SNAKE_CASE__="eval"     ):







				"""simple docstring"""





				_SCREAMING_SNAKE_CASE :       Dict     		=      os.path.join(SCREAMING_SNAKE_CASE__							,      f"""{split}_results.json"""     )
				if os.path.exists(SCREAMING_SNAKE_CASE__     ):
								with open(SCREAMING_SNAKE_CASE__							,      """r"""     ) as f:
												return json.load(SCREAMING_SNAKE_CASE__     )
				raise ValueError(f"""can't find {path}"""     )


UpperCAmelCase_		:   List[Any]							   =		logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)







class     lowercase__  (						_snake_case					):





							'''simple docstring'''


							def UpperCAmelCase_	(    self  ):
											_SCREAMING_SNAKE_CASE :       Optional[Any]     		=      self.get_auto_remove_tmp_dir()
											_SCREAMING_SNAKE_CASE :       Tuple     		=      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(__snake_case    ,					"""argv"""    ,					__snake_case  ):
															run_flax_glue.main()
															_SCREAMING_SNAKE_CASE :       Optional[Any]     		=      get_results(__snake_case  )
															self.assertGreaterEqual(result["""eval_accuracy"""]    ,					0.75  )






							@slow
							def UpperCAmelCase_	(    self  ):
											_SCREAMING_SNAKE_CASE :       Tuple     		=      self.get_auto_remove_tmp_dir()
											_SCREAMING_SNAKE_CASE :       int     		=      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(__snake_case    ,					"""argv"""    ,					__snake_case  ):
															run_clm_flax.main()
															_SCREAMING_SNAKE_CASE :       Union[str, Any]     		=      get_results(__snake_case  )
															self.assertLess(result["""eval_perplexity"""]    ,					100  )






							@slow
							def UpperCAmelCase_	(    self  ):
											_SCREAMING_SNAKE_CASE :       Optional[Any]     		=      self.get_auto_remove_tmp_dir()
											_SCREAMING_SNAKE_CASE :       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(__snake_case    ,					"""argv"""    ,					__snake_case  ):
															run_summarization_flax.main()
															_SCREAMING_SNAKE_CASE :       Optional[int]     		=      get_results(__snake_case    ,					split="""test"""  )
															self.assertGreaterEqual(result["""test_rouge1"""]    ,					10  )
															self.assertGreaterEqual(result["""test_rouge2"""]    ,					2  )
															self.assertGreaterEqual(result["""test_rougeL"""]    ,					7  )
															self.assertGreaterEqual(result["""test_rougeLsum"""]    ,					7  )






							@slow
							def UpperCAmelCase_	(    self  ):
											_SCREAMING_SNAKE_CASE :       Any     		=      self.get_auto_remove_tmp_dir()
											_SCREAMING_SNAKE_CASE :       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(__snake_case    ,					"""argv"""    ,					__snake_case  ):
															run_mlm_flax.main()
															_SCREAMING_SNAKE_CASE :       Dict     		=      get_results(__snake_case  )
															self.assertLess(result["""eval_perplexity"""]    ,					42  )






							@slow
							def UpperCAmelCase_	(    self  ):
											_SCREAMING_SNAKE_CASE :       Dict     		=      self.get_auto_remove_tmp_dir()
											_SCREAMING_SNAKE_CASE :       List[str]     		=      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(__snake_case    ,					"""argv"""    ,					__snake_case  ):
															run_ta_mlm_flax.main()
															_SCREAMING_SNAKE_CASE :       List[Any]     		=      get_results(__snake_case  )
															self.assertGreaterEqual(result["""eval_accuracy"""]    ,					0.42  )






							@slow
							def UpperCAmelCase_	(    self  ):
											# with so little data distributed training needs more epochs to get the score on par with 0/1 gpu
											_SCREAMING_SNAKE_CASE :       Tuple     		=      7 if get_gpu_count() > 1 else 2

											_SCREAMING_SNAKE_CASE :       str     		=      self.get_auto_remove_tmp_dir()
											_SCREAMING_SNAKE_CASE :       Optional[int]     		=      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(__snake_case    ,					"""argv"""    ,					__snake_case  ):
															run_flax_ner.main()
															_SCREAMING_SNAKE_CASE :       List[Any]     		=      get_results(__snake_case  )
															self.assertGreaterEqual(result["""eval_accuracy"""]    ,					0.75  )
															self.assertGreaterEqual(result["""eval_f1"""]    ,					0.3  )






							@slow
							def UpperCAmelCase_	(    self  ):
											_SCREAMING_SNAKE_CASE :       Optional[Any]     		=      self.get_auto_remove_tmp_dir()
											_SCREAMING_SNAKE_CASE :       Any     		=      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(__snake_case    ,					"""argv"""    ,					__snake_case  ):
															run_qa.main()
															_SCREAMING_SNAKE_CASE :       str     		=      get_results(__snake_case  )
															self.assertGreaterEqual(result["""eval_f1"""]    ,					30  )
															self.assertGreaterEqual(result["""eval_exact"""]    ,					30  )








'''simple docstring'''


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 MobileNetVaImageProcessor





class     lowercase__  (						unittest.TestCase					):





							'''simple docstring'''


							def __init__(    self    ,					__snake_case    ,					__snake_case=7    ,					__snake_case=3    ,					__snake_case=18    ,					__snake_case=30    ,					__snake_case=400    ,					__snake_case=True    ,					__snake_case=None    ,					__snake_case=True    ,					__snake_case=None    ,					):
											_SCREAMING_SNAKE_CASE :       Any     		=      size if size is not None else {"""shortest_edge""": 20}
											_SCREAMING_SNAKE_CASE :       int     		=      crop_size if crop_size is not None else {"""height""": 18, """width""": 18}
											_SCREAMING_SNAKE_CASE :       Dict     		=      parent
											_SCREAMING_SNAKE_CASE :       Any     		=      batch_size
											_SCREAMING_SNAKE_CASE :       Any     		=      num_channels
											_SCREAMING_SNAKE_CASE :       Dict     		=      image_size
											_SCREAMING_SNAKE_CASE :       Any     		=      min_resolution
											_SCREAMING_SNAKE_CASE :       Any     		=      max_resolution
											_SCREAMING_SNAKE_CASE :       Union[str, Any]     		=      do_resize
											_SCREAMING_SNAKE_CASE :       Tuple     		=      size
											_SCREAMING_SNAKE_CASE :       Optional[int]     		=      do_center_crop
											_SCREAMING_SNAKE_CASE :       str     		=      crop_size






							def UpperCAmelCase_	(    self  ):
											return {
											    "do_resize": self.do_resize,
											    "size": self.size,
											    "do_center_crop": self.do_center_crop,
											    "crop_size": self.crop_size,
											}





@require_torch
@require_vision
class     lowercase__  (						_snake_case     ,     unittest.TestCase					):





							'''simple docstring'''


							A_							:      Union[str, Any]      	=  MobileNetVaImageProcessor if is_vision_available() else None






							def UpperCAmelCase_	(    self  ):
											_SCREAMING_SNAKE_CASE :       int     		=      MobileNetVaImageProcessingTester(self  )






							@property
							def UpperCAmelCase_	(    self  ):
											return self.image_processor_tester.prepare_image_processor_dict()






							def UpperCAmelCase_	(    self  ):
											_SCREAMING_SNAKE_CASE :       str     		=      self.image_processing_class(**self.image_processor_dict  )
											self.assertTrue(hasattr(__snake_case    ,					"""do_resize"""  )  )
											self.assertTrue(hasattr(__snake_case    ,					"""size"""  )  )
											self.assertTrue(hasattr(__snake_case    ,					"""do_center_crop"""  )  )
											self.assertTrue(hasattr(__snake_case    ,					"""crop_size"""  )  )






							def UpperCAmelCase_	(    self  ):
											_SCREAMING_SNAKE_CASE :       str     		=      self.image_processing_class.from_dict(self.image_processor_dict  )
											self.assertEqual(image_processor.size    ,					{"""shortest_edge""": 20}  )
											self.assertEqual(image_processor.crop_size    ,					{"""height""": 18, """width""": 18}  )

											_SCREAMING_SNAKE_CASE :       Dict     		=      self.image_processing_class.from_dict(self.image_processor_dict    ,					size=42    ,					crop_size=84  )
											self.assertEqual(image_processor.size    ,					{"""shortest_edge""": 42}  )
											self.assertEqual(image_processor.crop_size    ,					{"""height""": 84, """width""": 84}  )






							def UpperCAmelCase_	(    self  ):
											pass






							def UpperCAmelCase_	(    self  ):
											# Initialize image_processing
											_SCREAMING_SNAKE_CASE :       str     		=      self.image_processing_class(**self.image_processor_dict  )
											# create random PIL images
											_SCREAMING_SNAKE_CASE :       List[str]     		=      prepare_image_inputs(self.image_processor_tester    ,					equal_resolution=__snake_case  )
											for image in image_inputs:
															self.assertIsInstance(__snake_case    ,					Image.Image  )

											# Test not batched input
											_SCREAMING_SNAKE_CASE :       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
											_SCREAMING_SNAKE_CASE :       int     		=      image_processing(__snake_case    ,					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 UpperCAmelCase_	(    self  ):
											# Initialize image_processing
											_SCREAMING_SNAKE_CASE :       Optional[int]     		=      self.image_processing_class(**self.image_processor_dict  )
											# create random numpy tensors
											_SCREAMING_SNAKE_CASE :       Tuple     		=      prepare_image_inputs(self.image_processor_tester    ,					equal_resolution=__snake_case    ,					numpify=__snake_case  )
											for image in image_inputs:
															self.assertIsInstance(__snake_case    ,					np.ndarray  )

											# Test not batched input
											_SCREAMING_SNAKE_CASE :       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
											_SCREAMING_SNAKE_CASE :       Optional[Any]     		=      image_processing(__snake_case    ,					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 UpperCAmelCase_	(    self  ):
											# Initialize image_processing
											_SCREAMING_SNAKE_CASE :       Optional[Any]     		=      self.image_processing_class(**self.image_processor_dict  )
											# create random PyTorch tensors
											_SCREAMING_SNAKE_CASE :       str     		=      prepare_image_inputs(self.image_processor_tester    ,					equal_resolution=__snake_case    ,					torchify=__snake_case  )
											for image in image_inputs:
															self.assertIsInstance(__snake_case    ,					torch.Tensor  )

											# Test not batched input
											_SCREAMING_SNAKE_CASE :       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
											_SCREAMING_SNAKE_CASE :       str     		=      image_processing(__snake_case    ,					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"""],
											    )    ,					)




