
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)

_SCREAMING_SNAKE_CASE    :      str          =			logging.getLogger()

def 				SCREAMING_SNAKE_CASE     (      ) ->       List[Any]:

				"""simple docstring"""







				A__  :	Optional[Any]         =							argparse.ArgumentParser()
				parser.add_argument('''-f'''       )
				A__  :	Dict         =							parser.parse_args()
				return args.f

def 				SCREAMING_SNAKE_CASE     (      __UpperCamelCase :						Any       ) ->       Optional[Any]:

				"""simple docstring"""







				A__  :	Tuple         =							{}
				A__  :	Optional[int]         =							os.path.join(__UpperCamelCase							,						'''all_results.json'''       )
				if os.path.exists(__UpperCamelCase       ):
								with open(__UpperCamelCase							,						'''r'''       ) as f:
												A__  :	Optional[int]         =							json.load(__UpperCamelCase       )
				else:
								raise ValueError(F"can't find {path}"       )
				return results

def 				SCREAMING_SNAKE_CASE     (      ) ->       Dict:

				"""simple docstring"""







				A__  :	str         =							torch.cuda.is_available() and torch_device == '''cuda'''
				return is_using_cuda and is_apex_available()


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







class  UpperCamelCase__		(    SCREAMING_SNAKE_CASE_   ):







	'''simple docstring'''





	@classmethod
	def   __snake_case				(			cls		):
					# Write Accelerate config, will pick up on CPU, GPU, and multi-GPU
					A__  :	Any         =							tempfile.mkdtemp()
					A__  :	List[Any]         =							os.path.join(cls.tmpdir				,       '''default_config.yml'''		)
					write_basic_config(save_location=cls.configPath		)
					A__  :	int         =							['''accelerate''', '''launch''', '''--config_file''', cls.configPath]





	@classmethod
	def   __snake_case				(			cls		):
					shutil.rmtree(cls.tmpdir		)





	@mock.patch.dict(os.environ				,       {'''WANDB_MODE''': '''offline'''}		)
	def   __snake_case				(			self		):
					A__  :	Dict         =							self.get_auto_remove_tmp_dir()
					A__  :	Optional[int]         =							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		)
					A__  :	Tuple         =							get_results(UpperCamelCase__		)
					self.assertGreaterEqual(result['''eval_accuracy''']				,       0.7_5		)
					self.assertTrue(os.path.exists(os.path.join(UpperCamelCase__				,       '''epoch_0'''		)		)		)
					self.assertTrue(os.path.exists(os.path.join(UpperCamelCase__				,       '''glue_no_trainer'''		)		)		)





	@mock.patch.dict(os.environ				,       {'''WANDB_MODE''': '''offline'''}		)
	def   __snake_case				(			self		):
					A__  :	List[Any]         =							self.get_auto_remove_tmp_dir()
					A__  :	int         =							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		)
					A__  :	Optional[Any]         =							get_results(UpperCamelCase__		)
					self.assertLess(result['''perplexity''']				,       100		)
					self.assertTrue(os.path.exists(os.path.join(UpperCamelCase__				,       '''epoch_0'''		)		)		)
					self.assertTrue(os.path.exists(os.path.join(UpperCamelCase__				,       '''clm_no_trainer'''		)		)		)





	@mock.patch.dict(os.environ				,       {'''WANDB_MODE''': '''offline'''}		)
	def   __snake_case				(			self		):
					A__  :	Tuple         =							self.get_auto_remove_tmp_dir()
					A__  :	str         =							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		)
					A__  :	Optional[int]         =							get_results(UpperCamelCase__		)
					self.assertLess(result['''perplexity''']				,       42		)
					self.assertTrue(os.path.exists(os.path.join(UpperCamelCase__				,       '''epoch_0'''		)		)		)
					self.assertTrue(os.path.exists(os.path.join(UpperCamelCase__				,       '''mlm_no_trainer'''		)		)		)





	@mock.patch.dict(os.environ				,       {'''WANDB_MODE''': '''offline'''}		)
	def   __snake_case				(			self		):
					# with so little data distributed training needs more epochs to get the score on par with 0/1 gpu
					A__  :	Optional[int]         =							7 if get_gpu_count() > 1 else 2

					A__  :	Dict         =							self.get_auto_remove_tmp_dir()
					A__  :	Dict         =							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		)
					A__  :	str         =							get_results(UpperCamelCase__		)
					self.assertGreaterEqual(result['''eval_accuracy''']				,       0.7_5		)
					self.assertLess(result['''train_loss''']				,       0.5		)
					self.assertTrue(os.path.exists(os.path.join(UpperCamelCase__				,       '''epoch_0'''		)		)		)
					self.assertTrue(os.path.exists(os.path.join(UpperCamelCase__				,       '''ner_no_trainer'''		)		)		)





	@unittest.skip(reason='''Fix me @muellerzr'''		)
	@mock.patch.dict(os.environ				,       {'''WANDB_MODE''': '''offline'''}		)
	def   __snake_case				(			self		):
					A__  :	List[Any]         =							self.get_auto_remove_tmp_dir()
					A__  :	Any         =							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		)
					A__  :	Tuple         =							get_results(UpperCamelCase__		)
					# Because we use --version_2_with_negative the testing script uses SQuAD v2 metrics.
					self.assertGreaterEqual(result['''eval_f1''']				,       28		)
					self.assertGreaterEqual(result['''eval_exact''']				,       28		)
					self.assertTrue(os.path.exists(os.path.join(UpperCamelCase__				,       '''epoch_0'''		)		)		)
					self.assertTrue(os.path.exists(os.path.join(UpperCamelCase__				,       '''qa_no_trainer'''		)		)		)





	@mock.patch.dict(os.environ				,       {'''WANDB_MODE''': '''offline'''}		)
	def   __snake_case				(			self		):
					A__  :	Optional[int]         =							self.get_auto_remove_tmp_dir()
					A__  :	Dict         =							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		)
					A__  :	List[Any]         =							get_results(UpperCamelCase__		)
					self.assertGreaterEqual(result['''eval_accuracy''']				,       0.8		)
					self.assertTrue(os.path.exists(os.path.join(UpperCamelCase__				,       '''swag_no_trainer'''		)		)		)





	@slow
	@mock.patch.dict(os.environ				,       {'''WANDB_MODE''': '''offline'''}		)
	def   __snake_case				(			self		):
					A__  :	List[Any]         =							self.get_auto_remove_tmp_dir()
					A__  :	Tuple         =							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		)
					A__  :	Tuple         =							get_results(UpperCamelCase__		)
					self.assertGreaterEqual(result['''eval_rouge1''']				,       10		)
					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(UpperCamelCase__				,       '''epoch_0'''		)		)		)
					self.assertTrue(os.path.exists(os.path.join(UpperCamelCase__				,       '''summarization_no_trainer'''		)		)		)





	@slow
	@mock.patch.dict(os.environ				,       {'''WANDB_MODE''': '''offline'''}		)
	def   __snake_case				(			self		):
					A__  :	Any         =							self.get_auto_remove_tmp_dir()
					A__  :	Optional[int]         =							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		)
					A__  :	Optional[int]         =							get_results(UpperCamelCase__		)
					self.assertGreaterEqual(result['''eval_bleu''']				,       30		)
					self.assertTrue(os.path.exists(os.path.join(UpperCamelCase__				,       '''epoch_0'''		)		)		)
					self.assertTrue(os.path.exists(os.path.join(UpperCamelCase__				,       '''translation_no_trainer'''		)		)		)





	@slow
	def   __snake_case				(			self		):
					A__  :	List[Any]         =							logging.StreamHandler(sys.stdout		)
					logger.addHandler(UpperCamelCase__		)

					A__  :	Optional[int]         =							self.get_auto_remove_tmp_dir()
					A__  :	List[Any]         =							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		)
					A__  :	Dict         =							get_results(UpperCamelCase__		)
					self.assertGreaterEqual(result['''eval_overall_accuracy''']				,       0.1_0		)





	@mock.patch.dict(os.environ				,       {'''WANDB_MODE''': '''offline'''}		)
	def   __snake_case				(			self		):
					A__  :	Optional[Any]         =							self.get_auto_remove_tmp_dir()
					A__  :	List[Any]         =							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		)
					A__  :	Dict         =							get_results(UpperCamelCase__		)
					# The base model scores a 25%
					self.assertGreaterEqual(result['''eval_accuracy''']				,       0.6		)
					self.assertTrue(os.path.exists(os.path.join(UpperCamelCase__				,       '''step_1'''		)		)		)
					self.assertTrue(os.path.exists(os.path.join(UpperCamelCase__				,       '''image_classification_no_trainer'''		)		)		)

# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import torch

from ..models.speechta import SpeechTaForTextToSpeech, SpeechTaHifiGan, SpeechTaProcessor
from ..utils import is_datasets_available
from .base import PipelineTool


if is_datasets_available():
					from datasets import load_dataset







class  UpperCamelCase__		(    SCREAMING_SNAKE_CASE_   ):







	'''simple docstring'''
	_lowerCAmelCase							       =				"microsoft/speecht5_tts"
	_lowerCAmelCase							       =				(
	    "This is a tool that reads an English text out loud. It takes an input named `text` which should contain the "
	    "text to read (in English) and returns a waveform object containing the sound."
	)
	_lowerCAmelCase							       =				"text_reader"
	_lowerCAmelCase							       =				SpeechTaProcessor
	_lowerCAmelCase							       =				SpeechTaForTextToSpeech
	_lowerCAmelCase							       =				SpeechTaHifiGan

	_lowerCAmelCase							       =				["text"]
	_lowerCAmelCase							       =				["audio"]





	def   __snake_case				(			self		):
					if self.post_processor is None:
									A__  :	int         =							'''microsoft/speecht5_hifigan'''
					super().setup()





	def   __snake_case				(			self				,       UpperCamelCase__				,       UpperCamelCase__=None		):
					A__  :	List[Any]         =							self.pre_processor(text=UpperCamelCase__				,       return_tensors='''pt'''				,       truncation=UpperCamelCase__		)

					if speaker_embeddings is None:
									if not is_datasets_available():
													raise ImportError('''Datasets needs to be installed if not passing speaker embeddings.'''		)

									A__  :	List[Any]         =							load_dataset('''Matthijs/cmu-arctic-xvectors'''				,       split='''validation'''		)
									A__  :	Dict         =							torch.tensor(embeddings_dataset[7305]['''xvector''']		).unsqueeze(0		)

					return {"input_ids": inputs["input_ids"], "speaker_embeddings": speaker_embeddings}





	def   __snake_case				(			self				,       UpperCamelCase__		):
					with torch.no_grad():
									return self.model.generate_speech(**UpperCamelCase__		)





	def   __snake_case				(			self				,       UpperCamelCase__		):
					with torch.no_grad():
									return self.post_processor(UpperCamelCase__		).cpu().detach()