





import argparse
import json
import logging
import os
import shutil
import sys
import tempfile
import unittest
from unittest import mock

import torch
from accelerate.utils import write_basic_config

from transformers.testing_utils import TestCasePlus, get_gpu_count, run_command, slow, torch_device
from transformers.utils import is_apex_available


logging.basicConfig(level=logging.DEBUG)

_a 						=      logging.getLogger()





def lowerCAmelCase__()				->      Optional[Any]:







	'''simple docstring'''







	lowerCamelCase__								=		argparse.ArgumentParser()
	parser.add_argument('''-f'''							)
	lowerCamelCase__								=		parser.parse_args()
	return args.f





def lowerCAmelCase__(__snake_case							)				->      Tuple:







	'''simple docstring'''







	lowerCamelCase__								=		{}
	lowerCamelCase__								=		os.path.join(__snake_case       ,'''all_results.json'''							)
	if os.path.exists(__snake_case							):
		with open(__snake_case       ,'''r'''							) as f:
			lowerCamelCase__								=		json.load(__snake_case							)
	else:
		raise ValueError(F'can\'t find {path}'							)
	return results





def lowerCAmelCase__()				->      Tuple:







	'''simple docstring'''







	lowerCamelCase__								=		torch.cuda.is_available() and torch_device == '''cuda'''
	return is_using_cuda and is_apex_available()


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






class 					__A    (    lowerCAmelCase ):

			'''simple docstring'''







			@classmethod
			def 		__lowerCamelCase				(    cls							):



				'''simple docstring'''







				lowerCamelCase__								=		tempfile.mkdtemp()
				lowerCamelCase__								=		os.path.join(cls.tmpdir       ,  '''default_config.yml'''							)
				write_basic_config(save_location=cls.configPath							)
				lowerCamelCase__								=		['''accelerate''', '''launch''', '''--config_file''', cls.configPath]





			@classmethod
			def 		__lowerCamelCase				(    cls							):



				'''simple docstring'''







				shutil.rmtree(cls.tmpdir							)





			@mock.patch.dict(os.environ       ,  {'''WANDB_MODE''': '''offline'''}							)
			def 		__lowerCamelCase				(    self							):



				'''simple docstring'''







				lowerCamelCase__								=		self.get_auto_remove_tmp_dir()
				lowerCamelCase__								=		F'\n            {self.examples_dir}/pytorch/text-classification/run_glue_no_trainer.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            --seed=42\n            --checkpointing_steps epoch\n            --with_tracking\n        '.split()

				if is_cuda_and_apex_available():
					testargs.append('''--fp16'''							)

				run_command(self._launch_args + testargs							)
				lowerCamelCase__								=		get_results(__lowerCAmelCase							)
				self.assertGreaterEqual(result['''eval_accuracy''']       ,  0.75							)
				self.assertTrue(os.path.exists(os.path.join(__lowerCAmelCase       ,  '''epoch_0'''							)							)							)
				self.assertTrue(os.path.exists(os.path.join(__lowerCAmelCase       ,  '''glue_no_trainer'''							)							)							)





			@mock.patch.dict(os.environ       ,  {'''WANDB_MODE''': '''offline'''}							)
			def 		__lowerCamelCase				(    self							):



				'''simple docstring'''







				lowerCamelCase__								=		self.get_auto_remove_tmp_dir()
				lowerCamelCase__								=		F'\n            {self.examples_dir}/pytorch/language-modeling/run_clm_no_trainer.py\n            --model_name_or_path distilgpt2\n            --train_file ./tests/fixtures/sample_text.txt\n            --validation_file ./tests/fixtures/sample_text.txt\n            --block_size 128\n            --per_device_train_batch_size 5\n            --per_device_eval_batch_size 5\n            --num_train_epochs 2\n            --output_dir {tmp_dir}\n            --checkpointing_steps epoch\n            --with_tracking\n        '.split()

				if torch.cuda.device_count() > 1:
					# Skipping because there are not enough batches to train the model + would need a drop_last to work.
					return

				run_command(self._launch_args + testargs							)
				lowerCamelCase__								=		get_results(__lowerCAmelCase							)
				self.assertLess(result['''perplexity''']       ,  1_0_0							)
				self.assertTrue(os.path.exists(os.path.join(__lowerCAmelCase       ,  '''epoch_0'''							)							)							)
				self.assertTrue(os.path.exists(os.path.join(__lowerCAmelCase       ,  '''clm_no_trainer'''							)							)							)





			@mock.patch.dict(os.environ       ,  {'''WANDB_MODE''': '''offline'''}							)
			def 		__lowerCamelCase				(    self							):



				'''simple docstring'''







				lowerCamelCase__								=		self.get_auto_remove_tmp_dir()
				lowerCamelCase__								=		F'\n            {self.examples_dir}/pytorch/language-modeling/run_mlm_no_trainer.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            --num_train_epochs=1\n            --checkpointing_steps epoch\n            --with_tracking\n        '.split()

				run_command(self._launch_args + testargs							)
				lowerCamelCase__								=		get_results(__lowerCAmelCase							)
				self.assertLess(result['''perplexity''']       ,  4_2							)
				self.assertTrue(os.path.exists(os.path.join(__lowerCAmelCase       ,  '''epoch_0'''							)							)							)
				self.assertTrue(os.path.exists(os.path.join(__lowerCAmelCase       ,  '''mlm_no_trainer'''							)							)							)





			@mock.patch.dict(os.environ       ,  {'''WANDB_MODE''': '''offline'''}							)
			def 		__lowerCamelCase				(    self							):



				'''simple docstring'''







				lowerCamelCase__								=		7 if get_gpu_count() > 1 else 2

				lowerCamelCase__								=		self.get_auto_remove_tmp_dir()
				lowerCamelCase__								=		F'\n            {self.examples_dir}/pytorch/token-classification/run_ner_no_trainer.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            --learning_rate=2e-4\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=2\n            --num_train_epochs={epochs}\n            --seed 7\n            --checkpointing_steps epoch\n            --with_tracking\n        '.split()

				run_command(self._launch_args + testargs							)
				lowerCamelCase__								=		get_results(__lowerCAmelCase							)
				self.assertGreaterEqual(result['''eval_accuracy''']       ,  0.75							)
				self.assertLess(result['''train_loss''']       ,  0.5							)
				self.assertTrue(os.path.exists(os.path.join(__lowerCAmelCase       ,  '''epoch_0'''							)							)							)
				self.assertTrue(os.path.exists(os.path.join(__lowerCAmelCase       ,  '''ner_no_trainer'''							)							)							)





			@unittest.skip(reason='''Fix me @muellerzr'''							)
			@mock.patch.dict(os.environ       ,  {'''WANDB_MODE''': '''offline'''}							)
			def 		__lowerCamelCase				(    self							):



				'''simple docstring'''







				lowerCamelCase__								=		self.get_auto_remove_tmp_dir()
				lowerCamelCase__								=		F'\n            {self.examples_dir}/pytorch/question-answering/run_qa_no_trainer.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            --seed=42\n            --max_train_steps=10\n            --num_warmup_steps=2\n            --learning_rate=2e-4\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=1\n            --checkpointing_steps epoch\n            --with_tracking\n        '.split()

				run_command(self._launch_args + testargs							)
				lowerCamelCase__								=		get_results(__lowerCAmelCase							)
				# Because we use --version_2_with_negative the testing script uses SQuAD v2 metrics.
				self.assertGreaterEqual(result['''eval_f1''']       ,  2_8							)
				self.assertGreaterEqual(result['''eval_exact''']       ,  2_8							)
				self.assertTrue(os.path.exists(os.path.join(__lowerCAmelCase       ,  '''epoch_0'''							)							)							)
				self.assertTrue(os.path.exists(os.path.join(__lowerCAmelCase       ,  '''qa_no_trainer'''							)							)							)





			@mock.patch.dict(os.environ       ,  {'''WANDB_MODE''': '''offline'''}							)
			def 		__lowerCamelCase				(    self							):



				'''simple docstring'''







				lowerCamelCase__								=		self.get_auto_remove_tmp_dir()
				lowerCamelCase__								=		F'\n            {self.examples_dir}/pytorch/multiple-choice/run_swag_no_trainer.py\n            --model_name_or_path bert-base-uncased\n            --train_file tests/fixtures/tests_samples/swag/sample.json\n            --validation_file tests/fixtures/tests_samples/swag/sample.json\n            --output_dir {tmp_dir}\n            --max_train_steps=20\n            --num_warmup_steps=2\n            --learning_rate=2e-4\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=1\n            --with_tracking\n        '.split()

				run_command(self._launch_args + testargs							)
				lowerCamelCase__								=		get_results(__lowerCAmelCase							)
				self.assertGreaterEqual(result['''eval_accuracy''']       ,  0.8							)
				self.assertTrue(os.path.exists(os.path.join(__lowerCAmelCase       ,  '''swag_no_trainer'''							)							)							)





			@slow
			@mock.patch.dict(os.environ       ,  {'''WANDB_MODE''': '''offline'''}							)
			def 		__lowerCamelCase				(    self							):



				'''simple docstring'''







				lowerCamelCase__								=		self.get_auto_remove_tmp_dir()
				lowerCamelCase__								=		F'\n            {self.examples_dir}/pytorch/summarization/run_summarization_no_trainer.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            --output_dir {tmp_dir}\n            --max_train_steps=50\n            --num_warmup_steps=8\n            --learning_rate=2e-4\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=1\n            --checkpointing_steps epoch\n            --with_tracking\n        '.split()

				run_command(self._launch_args + testargs							)
				lowerCamelCase__								=		get_results(__lowerCAmelCase							)
				self.assertGreaterEqual(result['''eval_rouge1''']       ,  1_0							)
				self.assertGreaterEqual(result['''eval_rouge2''']       ,  2							)
				self.assertGreaterEqual(result['''eval_rougeL''']       ,  7							)
				self.assertGreaterEqual(result['''eval_rougeLsum''']       ,  7							)
				self.assertTrue(os.path.exists(os.path.join(__lowerCAmelCase       ,  '''epoch_0'''							)							)							)
				self.assertTrue(os.path.exists(os.path.join(__lowerCAmelCase       ,  '''summarization_no_trainer'''							)							)							)





			@slow
			@mock.patch.dict(os.environ       ,  {'''WANDB_MODE''': '''offline'''}							)
			def 		__lowerCamelCase				(    self							):



				'''simple docstring'''







				lowerCamelCase__								=		self.get_auto_remove_tmp_dir()
				lowerCamelCase__								=		F'\n            {self.examples_dir}/pytorch/translation/run_translation_no_trainer.py\n            --model_name_or_path sshleifer/student_marian_en_ro_6_1\n            --source_lang en\n            --target_lang ro\n            --train_file tests/fixtures/tests_samples/wmt16/sample.json\n            --validation_file tests/fixtures/tests_samples/wmt16/sample.json\n            --output_dir {tmp_dir}\n            --max_train_steps=50\n            --num_warmup_steps=8\n            --num_beams=6\n            --learning_rate=3e-3\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=1\n            --source_lang en_XX\n            --target_lang ro_RO\n            --checkpointing_steps epoch\n            --with_tracking\n        '.split()

				run_command(self._launch_args + testargs							)
				lowerCamelCase__								=		get_results(__lowerCAmelCase							)
				self.assertGreaterEqual(result['''eval_bleu''']       ,  3_0							)
				self.assertTrue(os.path.exists(os.path.join(__lowerCAmelCase       ,  '''epoch_0'''							)							)							)
				self.assertTrue(os.path.exists(os.path.join(__lowerCAmelCase       ,  '''translation_no_trainer'''							)							)							)





			@slow
			def 		__lowerCamelCase				(    self							):



				'''simple docstring'''







				lowerCamelCase__								=		logging.StreamHandler(sys.stdout							)
				logger.addHandler(__lowerCAmelCase							)

				lowerCamelCase__								=		self.get_auto_remove_tmp_dir()
				lowerCamelCase__								=		F'\n            {self.examples_dir}/pytorch/semantic-segmentation/run_semantic_segmentation_no_trainer.py\n            --dataset_name huggingface/semantic-segmentation-test-sample\n            --output_dir {tmp_dir}\n            --max_train_steps=10\n            --num_warmup_steps=2\n            --learning_rate=2e-4\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=1\n            --checkpointing_steps epoch\n        '.split()

				run_command(self._launch_args + testargs							)
				lowerCamelCase__								=		get_results(__lowerCAmelCase							)
				self.assertGreaterEqual(result['''eval_overall_accuracy''']       ,  0.10							)





			@mock.patch.dict(os.environ       ,  {'''WANDB_MODE''': '''offline'''}							)
			def 		__lowerCamelCase				(    self							):



				'''simple docstring'''







				lowerCamelCase__								=		self.get_auto_remove_tmp_dir()
				lowerCamelCase__								=		F'\n            {self.examples_dir}/pytorch/image-classification/run_image_classification_no_trainer.py\n            --model_name_or_path google/vit-base-patch16-224-in21k\n            --dataset_name hf-internal-testing/cats_vs_dogs_sample\n            --learning_rate 1e-4\n            --per_device_train_batch_size 2\n            --per_device_eval_batch_size 1\n            --max_train_steps 2\n            --train_val_split 0.1\n            --seed 42\n            --output_dir {tmp_dir}\n            --with_tracking\n            --checkpointing_steps 1\n        '.split()

				if is_cuda_and_apex_available():
					testargs.append('''--fp16'''							)

				run_command(self._launch_args + testargs							)
				lowerCamelCase__								=		get_results(__lowerCAmelCase							)
				# The base model scores a 25%
				self.assertGreaterEqual(result['''eval_accuracy''']       ,  0.6							)
				self.assertTrue(os.path.exists(os.path.join(__lowerCAmelCase       ,  '''step_1'''							)							)							)
				self.assertTrue(os.path.exists(os.path.join(__lowerCAmelCase       ,  '''image_classification_no_trainer'''							)							)							)










import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin






class 					__A    (    lowerCAmelCase ):

			'''simple docstring'''







			lowerCAmelCase_							     =						"""Speech2TextFeatureExtractor"""
			lowerCAmelCase_							     =						"""Speech2TextTokenizer"""





			def __init__(    self       ,  __lowerCAmelCase       ,  __lowerCAmelCase							):



				'''simple docstring'''







				super().__init__(__lowerCAmelCase       ,  __lowerCAmelCase							)
				lowerCamelCase__								=		self.feature_extractor
				lowerCamelCase__								=		False





			def __call__(    self       ,  *__lowerCAmelCase       ,  **__lowerCAmelCase							):



				'''simple docstring'''







				if self._in_target_context_manager:
					return self.current_processor(*__lowerCAmelCase       ,  **__lowerCAmelCase							)

				if "raw_speech" in kwargs:
					warnings.warn('''Using `raw_speech` as a keyword argument is deprecated. Use `audio` instead.'''							)
					lowerCamelCase__								=		kwargs.pop('''raw_speech'''							)
				else:
					lowerCamelCase__								=		kwargs.pop('''audio'''       ,  __lowerCAmelCase							)
				lowerCamelCase__								=		kwargs.pop('''sampling_rate'''       ,  __lowerCAmelCase							)
				lowerCamelCase__								=		kwargs.pop('''text'''       ,  __lowerCAmelCase							)
				if len(__lowerCAmelCase							) > 0:
					lowerCamelCase__								=		args[0]
					lowerCamelCase__								=		args[1:]

				if audio is None and text is None:
					raise ValueError('''You need to specify either an `audio` or `text` input to process.'''							)

				if audio is not None:
					lowerCamelCase__								=		self.feature_extractor(__lowerCAmelCase       ,  *__lowerCAmelCase       ,  sampling_rate=__lowerCAmelCase       ,  **__lowerCAmelCase							)
				if text is not None:
					lowerCamelCase__								=		self.tokenizer(__lowerCAmelCase       ,  **__lowerCAmelCase							)

				if text is None:
					return inputs
				elif audio is None:
					return encodings
				else:
					lowerCamelCase__								=		encodings['''input_ids''']
					return inputs





			def 		__lowerCamelCase				(    self       ,  *__lowerCAmelCase       ,  **__lowerCAmelCase							):



				'''simple docstring'''







				return self.tokenizer.batch_decode(*__lowerCAmelCase       ,  **__lowerCAmelCase							)





			def 		__lowerCamelCase				(    self       ,  *__lowerCAmelCase       ,  **__lowerCAmelCase							):



				'''simple docstring'''







				return self.tokenizer.decode(*__lowerCAmelCase       ,  **__lowerCAmelCase							)





			@contextmanager
			def 		__lowerCamelCase				(    self							):



				'''simple docstring'''







				warnings.warn(
				    '''`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your '''
				    '''labels by using the argument `text` of the regular `__call__` method (either in the same call as '''
				    '''your audio inputs, or in a separate call.'''							)
				lowerCamelCase__								=		True
				lowerCamelCase__								=		self.tokenizer
				yield
				lowerCamelCase__								=		self.feature_extractor
				lowerCamelCase__								=		False



