

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_            = [
    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_            = logging.getLogger()

def 							__SCREAMING_SNAKE_CASE							(			)				->    Dict:
				_UpperCAmelCase    :						str							=      argparse.ArgumentParser()
				parser.add_argument("-f"	)
				_UpperCAmelCase    :						int							=      parser.parse_args()
				return args.f

def 							__SCREAMING_SNAKE_CASE							(			lowerCAmelCase:					int					, lowerCAmelCase:					Dict="eval"	)				->    Union[str, Any]:
				_UpperCAmelCase    :						Dict							=      os.path.join(lowerCAmelCase					, F'{split}_results.json'	)
				if os.path.exists(lowerCAmelCase	):
								with open(lowerCAmelCase					, "r"	) as f:
												return json.load(lowerCAmelCase	)
				raise ValueError(F'can\'t find {path}'	)


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



class 			a					(		UpperCAmelCase			):


						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										_UpperCAmelCase    :						Optional[int]							=      self.get_auto_remove_tmp_dir()
										_UpperCAmelCase    :						Tuple							=      f'\n            run_glue.py\n            --model_name_or_path distilbert-base-uncased\n            --output_dir {tmp_dir}\n            --train_file ./tests/fixtures/tests_samples/MRPC/train.csv\n            --validation_file ./tests/fixtures/tests_samples/MRPC/dev.csv\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=1\n            --learning_rate=1e-4\n            --eval_steps=2\n            --warmup_steps=2\n            --seed=42\n            --max_seq_length=128\n            '.split()

										with patch.object(A_  ,   "argv"  ,   A_		):
														run_flax_glue.main()
														_UpperCAmelCase    :						Union[str, Any]							=      get_results(A_		)
														self.assertGreaterEqual(result["eval_accuracy"]  ,   0.75		)


						@slow
						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										_UpperCAmelCase    :						Optional[Any]							=      self.get_auto_remove_tmp_dir()
										_UpperCAmelCase    :						Optional[Any]							=      f'\n            run_clm_flax.py\n            --model_name_or_path distilgpt2\n            --train_file ./tests/fixtures/sample_text.txt\n            --validation_file ./tests/fixtures/sample_text.txt\n            --do_train\n            --do_eval\n            --block_size 128\n            --per_device_train_batch_size 4\n            --per_device_eval_batch_size 4\n            --num_train_epochs 2\n            --logging_steps 2 --eval_steps 2\n            --output_dir {tmp_dir}\n            --overwrite_output_dir\n            '.split()

										with patch.object(A_  ,   "argv"  ,   A_		):
														run_clm_flax.main()
														_UpperCAmelCase    :						Any							=      get_results(A_		)
														self.assertLess(result["eval_perplexity"]  ,   100		)


						@slow
						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										_UpperCAmelCase    :						Dict							=      self.get_auto_remove_tmp_dir()
										_UpperCAmelCase    :						List[Any]							=      f'\n            run_summarization.py\n            --model_name_or_path t5-small\n            --train_file tests/fixtures/tests_samples/xsum/sample.json\n            --validation_file tests/fixtures/tests_samples/xsum/sample.json\n            --test_file tests/fixtures/tests_samples/xsum/sample.json\n            --output_dir {tmp_dir}\n            --overwrite_output_dir\n            --num_train_epochs=3\n            --warmup_steps=8\n            --do_train\n            --do_eval\n            --do_predict\n            --learning_rate=2e-4\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=1\n            --predict_with_generate\n        '.split()

										with patch.object(A_  ,   "argv"  ,   A_		):
														run_summarization_flax.main()
														_UpperCAmelCase    :						Optional[Any]							=      get_results(A_  ,   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		):







										'''simple docstring'''


										_UpperCAmelCase    :						List[Any]							=      self.get_auto_remove_tmp_dir()
										_UpperCAmelCase    :						Optional[Any]							=      f'\n            run_mlm.py\n            --model_name_or_path distilroberta-base\n            --train_file ./tests/fixtures/sample_text.txt\n            --validation_file ./tests/fixtures/sample_text.txt\n            --output_dir {tmp_dir}\n            --overwrite_output_dir\n            --max_seq_length 128\n            --per_device_train_batch_size 4\n            --per_device_eval_batch_size 4\n            --logging_steps 2 --eval_steps 2\n            --do_train\n            --do_eval\n            --num_train_epochs=1\n        '.split()

										with patch.object(A_  ,   "argv"  ,   A_		):
														run_mlm_flax.main()
														_UpperCAmelCase    :						Tuple							=      get_results(A_		)
														self.assertLess(result["eval_perplexity"]  ,   42		)


						@slow
						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										_UpperCAmelCase    :						Dict							=      self.get_auto_remove_tmp_dir()
										_UpperCAmelCase    :						Union[str, Any]							=      f'\n            run_t5_mlm_flax.py\n            --model_name_or_path t5-small\n            --train_file ./tests/fixtures/sample_text.txt\n            --validation_file ./tests/fixtures/sample_text.txt\n            --do_train\n            --do_eval\n            --max_seq_length 128\n            --per_device_train_batch_size 4\n            --per_device_eval_batch_size 4\n            --num_train_epochs 2\n            --logging_steps 2 --eval_steps 2\n            --output_dir {tmp_dir}\n            --overwrite_output_dir\n            '.split()

										with patch.object(A_  ,   "argv"  ,   A_		):
														run_ta_mlm_flax.main()
														_UpperCAmelCase    :						List[Any]							=      get_results(A_		)
														self.assertGreaterEqual(result["eval_accuracy"]  ,   0.42		)


						@slow
						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										_UpperCAmelCase    :						Tuple							=      7 if get_gpu_count() > 1 else 2

										_UpperCAmelCase    :						int							=      self.get_auto_remove_tmp_dir()
										_UpperCAmelCase    :						Tuple							=      f'\n            run_flax_ner.py\n            --model_name_or_path bert-base-uncased\n            --train_file tests/fixtures/tests_samples/conll/sample.json\n            --validation_file tests/fixtures/tests_samples/conll/sample.json\n            --output_dir {tmp_dir}\n            --overwrite_output_dir\n            --do_train\n            --do_eval\n            --warmup_steps=2\n            --learning_rate=2e-4\n            --logging_steps 2 --eval_steps 2\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=2\n            --num_train_epochs={epochs}\n            --seed 7\n        '.split()

										with patch.object(A_  ,   "argv"  ,   A_		):
														run_flax_ner.main()
														_UpperCAmelCase    :						Tuple							=      get_results(A_		)
														self.assertGreaterEqual(result["eval_accuracy"]  ,   0.75		)
														self.assertGreaterEqual(result["eval_f1"]  ,   0.3		)





						@slow
						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										_UpperCAmelCase    :						List[str]							=      self.get_auto_remove_tmp_dir()
										_UpperCAmelCase    :						str							=      f'\n            run_qa.py\n            --model_name_or_path bert-base-uncased\n            --version_2_with_negative\n            --train_file tests/fixtures/tests_samples/SQUAD/sample.json\n            --validation_file tests/fixtures/tests_samples/SQUAD/sample.json\n            --output_dir {tmp_dir}\n            --overwrite_output_dir\n            --num_train_epochs=3\n            --warmup_steps=2\n            --do_train\n            --do_eval\n            --logging_steps 2 --eval_steps 2\n            --learning_rate=2e-4\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=1\n        '.split()

										with patch.object(A_  ,   "argv"  ,   A_		):
														run_qa.main()
														_UpperCAmelCase    :						Union[str, Any]							=      get_results(A_		)
														self.assertGreaterEqual(result["eval_f1"]  ,   30		)
														self.assertGreaterEqual(result["eval_exact"]  ,   30		)





import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import is_speech_available, is_vision_available
from transformers.testing_utils import require_torch


if is_vision_available():
					from transformers import TvltImageProcessor

if is_speech_available():
					from transformers import TvltFeatureExtractor

from transformers import TvltProcessor

@require_torch
class 			a					(		unittest.TestCase			):


						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										_UpperCAmelCase    :						int							=      "ZinengTang/tvlt-base"
										_UpperCAmelCase    :						int							=      tempfile.mkdtemp()


						def     _UpperCAmelCase			(    self  ,   **A_		):







										'''simple docstring'''


										return TvltImageProcessor.from_pretrained(self.checkpoint  ,   **A_		)


						def     _UpperCAmelCase			(    self  ,   **A_		):







										'''simple docstring'''


										return TvltFeatureExtractor.from_pretrained(self.checkpoint  ,   **A_		)


						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										shutil.rmtree(self.tmpdirname		)


						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										_UpperCAmelCase    :						Dict							=      self.get_image_processor()
										_UpperCAmelCase    :						Optional[int]							=      self.get_feature_extractor()

										_UpperCAmelCase    :						Tuple							=      TvltProcessor(image_processor=A_  ,   feature_extractor=A_		)
										processor.save_pretrained(self.tmpdirname		)
										_UpperCAmelCase    :						str							=      TvltProcessor.from_pretrained(self.tmpdirname		)

										self.assertIsInstance(processor.feature_extractor  ,   A_		)
										self.assertIsInstance(processor.image_processor  ,   A_		)


						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										_UpperCAmelCase    :						List[Any]							=      self.get_image_processor()
										_UpperCAmelCase    :						int							=      self.get_feature_extractor()

										_UpperCAmelCase    :						Tuple							=      TvltProcessor(image_processor=A_  ,   feature_extractor=A_		)

										_UpperCAmelCase    :						List[str]							=      np.ones([12000]		)

										_UpperCAmelCase    :						int							=      feature_extractor(A_  ,   return_tensors="np"		)
										_UpperCAmelCase    :						int							=      processor(audio=A_  ,   return_tensors="np"		)

										for key in audio_dict.keys():
														self.assertAlmostEqual(audio_dict[key].sum()  ,   input_processor[key].sum()  ,   delta=1e-2		)


						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										_UpperCAmelCase    :						List[str]							=      self.get_image_processor()
										_UpperCAmelCase    :						int							=      self.get_feature_extractor()

										_UpperCAmelCase    :						Optional[Any]							=      TvltProcessor(image_processor=A_  ,   feature_extractor=A_		)

										_UpperCAmelCase    :						Union[str, Any]							=      np.ones([3, 224, 224]		)

										_UpperCAmelCase    :						Tuple							=      image_processor(A_  ,   return_tensors="np"		)
										_UpperCAmelCase    :						List[str]							=      processor(images=A_  ,   return_tensors="np"		)

										for key in image_dict.keys():
														self.assertAlmostEqual(image_dict[key].sum()  ,   input_processor[key].sum()  ,   delta=1e-2		)


						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										_UpperCAmelCase    :						List[str]							=      self.get_image_processor()
										_UpperCAmelCase    :						Any							=      self.get_feature_extractor()

										_UpperCAmelCase    :						Dict							=      TvltProcessor(image_processor=A_  ,   feature_extractor=A_		)

										_UpperCAmelCase    :						str							=      np.ones([12000]		)
										_UpperCAmelCase    :						Optional[Any]							=      np.ones([3, 224, 224]		)

										_UpperCAmelCase    :						List[Any]							=      processor(audio=A_  ,   images=A_		)

										self.assertListEqual(list(inputs.keys()		)  ,   ["audio_values", "audio_mask", "pixel_values", "pixel_mask"]		)

										# test if it raises when no input is passed
										with pytest.raises(A_		):
														processor()





						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										_UpperCAmelCase    :						Optional[Any]							=      self.get_image_processor()
										_UpperCAmelCase    :						int							=      self.get_feature_extractor()

										_UpperCAmelCase    :						str							=      TvltProcessor(image_processor=A_  ,   feature_extractor=A_		)

										self.assertListEqual(
										    processor.model_input_names  ,   image_processor.model_input_names + feature_extractor.model_input_names  ,   msg="`processor` and `image_processor`+`feature_extractor` model input names do not match"  ,   )


