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)

__UpperCamelCase		:       List[str]									=     logging.getLogger()
def       A	(    ):
						SCREAMING_SNAKE_CASE							:    Tuple							=							argparse.ArgumentParser()
						parser.add_argument('''-f'''						)
						SCREAMING_SNAKE_CASE							:    Optional[int]							=							parser.parse_args()
						return args.f
def       A	(    _lowercase						):
						SCREAMING_SNAKE_CASE							:    Optional[Any]							=							{}
						SCREAMING_SNAKE_CASE							:    Optional[int]							=							os.path.join(_lowercase		,  '''all_results.json'''						)
						if os.path.exists(_lowercase						):
												with open(_lowercase		,  '''r'''						) as f:
																		SCREAMING_SNAKE_CASE							:    Tuple							=							json.load(_lowercase						)
						else:
												raise ValueError(f"""can't find {path}"""						)
						return results
def       A	(    ):
						SCREAMING_SNAKE_CASE							:    Tuple							=							torch.cuda.is_available() and torch_device == '''cuda'''
						return is_using_cuda and is_apex_available()


__UpperCamelCase		:       int									=     logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)





class 					lowercase__						(   UpperCamelCase_):


						@classmethod
						def        __A       (       cls	:				Optional[Any]						):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:    List[Any]							=							tempfile.mkdtemp()
												SCREAMING_SNAKE_CASE							:    Dict							=							os.path.join(cls.tmpdir    ,		'''default_config.yml'''						)
												write_basic_config(save_location=cls.configPath						)
												SCREAMING_SNAKE_CASE							:    List[str]							=							['''accelerate''', '''launch''', '''--config_file''', cls.configPath]


						@classmethod
						def        __A       (       cls	:				Optional[Any]						):





												'''simple docstring'''





												shutil.rmtree(cls.tmpdir						)


						@mock.patch.dict(os.environ    ,		{'''WANDB_MODE''': '''offline'''}						)
						def        __A       (       self	:				List[str]						):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:    Any							=							self.get_auto_remove_tmp_dir()
												SCREAMING_SNAKE_CASE							:    Optional[int]							=							f"""
            {self.examples_dir}/pytorch/text-classification/run_glue_no_trainer.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
            --seed=42
            --checkpointing_steps epoch
            --with_tracking
        """.split()

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

												run_command(self._launch_args + testargs						)
												SCREAMING_SNAKE_CASE							:    List[str]							=							get_results(UpperCamelCase__						)
												self.assertGreaterEqual(result['''eval_accuracy''']    ,		0.75						)
												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        __A       (       self	:				Union[str, Any]						):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							self.get_auto_remove_tmp_dir()
												SCREAMING_SNAKE_CASE							:    Tuple							=							f"""
            {self.examples_dir}/pytorch/language-modeling/run_clm_no_trainer.py
            --model_name_or_path distilgpt2
            --train_file ./tests/fixtures/sample_text.txt
            --validation_file ./tests/fixtures/sample_text.txt
            --block_size 128
            --per_device_train_batch_size 5
            --per_device_eval_batch_size 5
            --num_train_epochs 2
            --output_dir {tmp_dir}
            --checkpointing_steps epoch
            --with_tracking
        """.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						)
												SCREAMING_SNAKE_CASE							:    List[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        __A       (       self	:				Optional[int]						):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:    List[str]							=							self.get_auto_remove_tmp_dir()
												SCREAMING_SNAKE_CASE							:    List[str]							=							f"""
            {self.examples_dir}/pytorch/language-modeling/run_mlm_no_trainer.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}
            --num_train_epochs=1
            --checkpointing_steps epoch
            --with_tracking
        """.split()

												run_command(self._launch_args + testargs						)
												SCREAMING_SNAKE_CASE							:    List[str]							=							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        __A       (       self	:				Any						):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:    Optional[int]							=							7 if get_gpu_count() > 1 else 2

												SCREAMING_SNAKE_CASE							:    Any							=							self.get_auto_remove_tmp_dir()
												SCREAMING_SNAKE_CASE							:    int							=							f"""
            {self.examples_dir}/pytorch/token-classification/run_ner_no_trainer.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}
            --learning_rate=2e-4
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=2
            --num_train_epochs={epochs}
            --seed 7
            --checkpointing_steps epoch
            --with_tracking
        """.split()

												run_command(self._launch_args + testargs						)
												SCREAMING_SNAKE_CASE							:    Optional[Any]							=							get_results(UpperCamelCase__						)
												self.assertGreaterEqual(result['''eval_accuracy''']    ,		0.75						)
												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        __A       (       self	:				Optional[int]						):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:    Tuple							=							self.get_auto_remove_tmp_dir()
												SCREAMING_SNAKE_CASE							:    List[Any]							=							f"""
            {self.examples_dir}/pytorch/question-answering/run_qa_no_trainer.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}
            --seed=42
            --max_train_steps=10
            --num_warmup_steps=2
            --learning_rate=2e-4
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
            --checkpointing_steps epoch
            --with_tracking
        """.split()

												run_command(self._launch_args + testargs						)
												SCREAMING_SNAKE_CASE							:    Optional[int]							=							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        __A       (       self	:				Optional[Any]						):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:    List[Any]							=							self.get_auto_remove_tmp_dir()
												SCREAMING_SNAKE_CASE							:    int							=							f"""
            {self.examples_dir}/pytorch/multiple-choice/run_swag_no_trainer.py
            --model_name_or_path bert-base-uncased
            --train_file tests/fixtures/tests_samples/swag/sample.json
            --validation_file tests/fixtures/tests_samples/swag/sample.json
            --output_dir {tmp_dir}
            --max_train_steps=20
            --num_warmup_steps=2
            --learning_rate=2e-4
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
            --with_tracking
        """.split()

												run_command(self._launch_args + testargs						)
												SCREAMING_SNAKE_CASE							:    List[str]							=							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        __A       (       self	:				Dict						):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:    Dict							=							self.get_auto_remove_tmp_dir()
												SCREAMING_SNAKE_CASE							:    int							=							f"""
            {self.examples_dir}/pytorch/summarization/run_summarization_no_trainer.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
            --output_dir {tmp_dir}
            --max_train_steps=50
            --num_warmup_steps=8
            --learning_rate=2e-4
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
            --checkpointing_steps epoch
            --with_tracking
        """.split()

												run_command(self._launch_args + testargs						)
												SCREAMING_SNAKE_CASE							:    Optional[Any]							=							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        __A       (       self	:				Any						):





												'''simple docstring'''





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

												run_command(self._launch_args + testargs						)
												SCREAMING_SNAKE_CASE							:    Any							=							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        __A       (       self	:				Tuple						):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:    List[str]							=							logging.StreamHandler(sys.stdout						)
												logger.addHandler(UpperCamelCase__						)

												SCREAMING_SNAKE_CASE							:    Tuple							=							self.get_auto_remove_tmp_dir()
												SCREAMING_SNAKE_CASE							:    Tuple							=							f"""
            {self.examples_dir}/pytorch/semantic-segmentation/run_semantic_segmentation_no_trainer.py
            --dataset_name huggingface/semantic-segmentation-test-sample
            --output_dir {tmp_dir}
            --max_train_steps=10
            --num_warmup_steps=2
            --learning_rate=2e-4
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
            --checkpointing_steps epoch
        """.split()

												run_command(self._launch_args + testargs						)
												SCREAMING_SNAKE_CASE							:    Any							=							get_results(UpperCamelCase__						)
												self.assertGreaterEqual(result['''eval_overall_accuracy''']    ,		0.10						)



						@mock.patch.dict(os.environ    ,		{'''WANDB_MODE''': '''offline'''}						)
						def        __A       (       self	:				Optional[Any]						):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:    str							=							self.get_auto_remove_tmp_dir()
												SCREAMING_SNAKE_CASE							:    List[str]							=							f"""
            {self.examples_dir}/pytorch/image-classification/run_image_classification_no_trainer.py
            --model_name_or_path google/vit-base-patch16-224-in21k
            --dataset_name hf-internal-testing/cats_vs_dogs_sample
            --learning_rate 1e-4
            --per_device_train_batch_size 2
            --per_device_eval_batch_size 1
            --max_train_steps 2
            --train_val_split 0.1
            --seed 42
            --output_dir {tmp_dir}
            --with_tracking
            --checkpointing_steps 1
        """.split()

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

												run_command(self._launch_args + testargs						)
												SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							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'''						)						)						)






from __future__ import annotations

import unittest

from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow


if is_tf_available():
					import tensorflow as tf

					from transformers import AutoTokenizer, TFAutoModelForSeqaSeqLM





@require_tf
@require_sentencepiece
@require_tokenizers
class 					lowercase__						(   unittest.TestCase):


						@slow
						def        __A       (       self	:				Union[str, Any]						):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:    Any							=							TFAutoModelForSeqaSeqLM.from_pretrained('''google/mt5-small'''						)
												SCREAMING_SNAKE_CASE							:    List[str]							=							AutoTokenizer.from_pretrained('''google/mt5-small'''						)

												SCREAMING_SNAKE_CASE							:    Optional[int]							=							tokenizer('''Hello there'''    ,		return_tensors='''tf'''						).input_ids
												SCREAMING_SNAKE_CASE							:    Optional[Any]							=							tokenizer('''Hi I am'''    ,		return_tensors='''tf'''						).input_ids

												SCREAMING_SNAKE_CASE							:    List[Any]							=							model(UpperCamelCase__    ,		labels=UpperCamelCase__						).loss
												SCREAMING_SNAKE_CASE							:    int							=							-tf.math.reduce_mean(UpperCamelCase__						).numpy()

												SCREAMING_SNAKE_CASE							:    List[str]							=							-21.22_8168
												self.assertTrue(abs(mtf_score - EXPECTED_SCORE						) < 2E-4						)





