

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)

lowerCAmelCase_       	=	logging.getLogger()


def   __SCREAMING_SNAKE_CASE					():
						snake_case_   =	argparse.ArgumentParser()
						parser.add_argument('''-f'''       )
						snake_case_   =	parser.parse_args()
						return args.f


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


def   __SCREAMING_SNAKE_CASE					():
						snake_case_   =	torch.cuda.is_available() and torch_device == '''cuda'''
						return is_using_cuda and is_apex_available()


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





class 	snake_case_   (		__A					):







			'''simple docstring'''

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







			@classmethod
			def 				snake_case__(     cls :				Dict							)      ->str:
									shutil.rmtree(cls.tmpdir							)







			@mock.patch.dict(os.environ							,   {'''WANDB_MODE''': '''offline'''}							)
			def 				snake_case__(     self :				int							)      ->Optional[int]:
									snake_case_   =	self.get_auto_remove_tmp_dir()
									snake_case_   =	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							)
									snake_case_   =	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 				snake_case__(     self :				Any							)      ->int:
									snake_case_   =	self.get_auto_remove_tmp_dir()
									snake_case_   =	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							)
									snake_case_   =	get_results(_UpperCamelCase							)
									self.assertLess(result['''perplexity''']							,   1_0_0							)
									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 :				str							)      ->Union[str, Any]:
									snake_case_   =	self.get_auto_remove_tmp_dir()
									snake_case_   =	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							)
									snake_case_   =	get_results(_UpperCamelCase							)
									self.assertLess(result['''perplexity''']							,   4_2							)
									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 :				Tuple							)      ->Union[str, Any]:
									# with so little data distributed training needs more epochs to get the score on par with 0/1 gpu
									snake_case_   =	7 if get_gpu_count() > 1 else 2

									snake_case_   =	self.get_auto_remove_tmp_dir()
									snake_case_   =	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							)
									snake_case_   =	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 				snake_case__(     self :				Optional[int]							)      ->str:
									snake_case_   =	self.get_auto_remove_tmp_dir()
									snake_case_   =	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							)
									snake_case_   =	get_results(_UpperCamelCase							)
									# 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(_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 :				List[str]							)      ->List[str]:
									snake_case_   =	self.get_auto_remove_tmp_dir()
									snake_case_   =	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							)
									snake_case_   =	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 :				str							)      ->Any:
									snake_case_   =	self.get_auto_remove_tmp_dir()
									snake_case_   =	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							)
									snake_case_   =	get_results(_UpperCamelCase							)
									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(_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 :				Any							)      ->List[str]:
									snake_case_   =	self.get_auto_remove_tmp_dir()
									snake_case_   =	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							)
									snake_case_   =	get_results(_UpperCamelCase							)
									self.assertGreaterEqual(result['''eval_bleu''']							,   3_0							)
									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 :				Optional[Any]							)      ->Union[str, Any]:
									snake_case_   =	logging.StreamHandler(sys.stdout							)
									logger.addHandler(_UpperCamelCase							)

									snake_case_   =	self.get_auto_remove_tmp_dir()
									snake_case_   =	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							)
									snake_case_   =	get_results(_UpperCamelCase							)
									self.assertGreaterEqual(result['''eval_overall_accuracy''']							,   0.10							)







			@mock.patch.dict(os.environ							,   {'''WANDB_MODE''': '''offline'''}							)
			def 				snake_case__(     self :				Any							)      ->Union[str, Any]:
									snake_case_   =	self.get_auto_remove_tmp_dir()
									snake_case_   =	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							)
									snake_case_   =	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'''							)							)							)


import gc
import unittest

from parameterized import parameterized

from diffusers import FlaxUNetaDConditionModel
from diffusers.utils import is_flax_available
from diffusers.utils.testing_utils import load_hf_numpy, require_flax, slow


if is_flax_available():
					import jax
					import jax.numpy as jnp





@slow
@require_flax
class 	snake_case_   (		unittest.TestCase					):







			'''simple docstring'''

			def 				snake_case__(     self :				Any							,   _UpperCamelCase :				Any							,   _UpperCamelCase :				Tuple							)      ->List[Any]:
									return f'''gaussian_noise_s={seed}_shape={'_'.join([str(_UpperCamelCase							) for s in shape]							)}.npy'''







			def 				snake_case__(     self :				Any							)      ->List[str]:
									# clean up the VRAM after each test
									super().tearDown()
									gc.collect()







			def 				snake_case__(     self :				int							,   _UpperCamelCase :				Union[str, Any]=0							,   _UpperCamelCase :				int=(4, 4, 6_4, 6_4)							,   _UpperCamelCase :				Optional[int]=False							)      ->Tuple:
									snake_case_   =	jnp.bfloataa if fpaa else jnp.floataa
									snake_case_   =	jnp.array(load_hf_numpy(self.get_file_format(_UpperCamelCase							,   _UpperCamelCase							)							)							,   dtype=_UpperCamelCase							)
									return image







			def 				snake_case__(     self :				List[Any]							,   _UpperCamelCase :				Optional[Any]=False							,   _UpperCamelCase :				Optional[int]="CompVis/stable-diffusion-v1-4"							)      ->Optional[Any]:
									snake_case_   =	jnp.bfloataa if fpaa else jnp.floataa
									snake_case_   =	'''bf16''' if fpaa else None

									snake_case_,    snake_case_   =	FlaxUNetaDConditionModel.from_pretrained(
									    _UpperCamelCase							,   subfolder='''unet'''							,   dtype=_UpperCamelCase							,   revision=_UpperCamelCase							)
									return model, params







			def 				snake_case__(     self :				Dict							,   _UpperCamelCase :				List[Any]=0							,   _UpperCamelCase :				Tuple=(4, 7_7, 7_6_8)							,   _UpperCamelCase :				List[Any]=False							)      ->int:
									snake_case_   =	jnp.bfloataa if fpaa else jnp.floataa
									snake_case_   =	jnp.array(load_hf_numpy(self.get_file_format(_UpperCamelCase							,   _UpperCamelCase							)							)							,   dtype=_UpperCamelCase							)
									return hidden_states







			@parameterized.expand(
			    [
			        # fmt: off
			        [8_3, 4, [-0.2323, -0.1304, 0.0813, -0.3093, -0.0919, -0.1571, -0.1125, -0.5806]],
			        [1_7, 0.55, [-0.0831, -0.2443, 0.0901, -0.0919, 0.3396, 0.0103, -0.3743, 0.0701]],
			        [8, 0.89, [-0.4863, 0.0859, 0.0875, -0.1658, 0.9199, -0.0114, 0.4839, 0.4639]],
			        [3, 1_0_0_0, [-0.5649, 0.2402, -0.5518, 0.1248, 1.1328, -0.2443, -0.0325, -1.0078]],
			        # fmt: on
			    ]							)
			def 				snake_case__(     self :				Optional[Any]							,   _UpperCamelCase :				List[Any]							,   _UpperCamelCase :				List[str]							,   _UpperCamelCase :				Optional[int]							)      ->Union[str, Any]:
									snake_case_,    snake_case_   =	self.get_unet_model(model_id='''CompVis/stable-diffusion-v1-4'''							,   fpaa=_UpperCamelCase							)
									snake_case_   =	self.get_latents(_UpperCamelCase							,   fpaa=_UpperCamelCase							)
									snake_case_   =	self.get_encoder_hidden_states(_UpperCamelCase							,   fpaa=_UpperCamelCase							)

									snake_case_   =	model.apply(
									    {'''params''': params}							,   _UpperCamelCase							,   jnp.array(_UpperCamelCase							,   dtype=jnp.intaa							)							,   encoder_hidden_states=_UpperCamelCase							,   ).sample

									assert sample.shape == latents.shape

									snake_case_   =	jnp.asarray(jax.device_get((sample[-1, -2:, -2:, :2].flatten())							)							,   dtype=jnp.floataa							)
									snake_case_   =	jnp.array(_UpperCamelCase							,   dtype=jnp.floataa							)

									# Found torch (float16) and flax (bfloat16) outputs to be within this tolerance, in the same hardware
									assert jnp.allclose(_UpperCamelCase							,   _UpperCamelCase							,   atol=1e-2							)







			@parameterized.expand(
			    [
			        # fmt: off
			        [8_3, 4, [0.1514, 0.0807, 0.1624, 0.1016, -0.1896, 0.0263, 0.0677, 0.2310]],
			        [1_7, 0.55, [0.1164, -0.0216, 0.0170, 0.1589, -0.3120, 0.1005, -0.0581, -0.1458]],
			        [8, 0.89, [-0.1758, -0.0169, 0.1004, -0.1411, 0.1312, 0.1103, -0.1996, 0.2139]],
			        [3, 1_0_0_0, [0.1214, 0.0352, -0.0731, -0.1562, -0.0994, -0.0906, -0.2340, -0.0539]],
			        # fmt: on
			    ]							)
			def 				snake_case__(     self :				Optional[int]							,   _UpperCamelCase :				Optional[Any]							,   _UpperCamelCase :				Union[str, Any]							,   _UpperCamelCase :				str							)      ->Dict:
									snake_case_,    snake_case_   =	self.get_unet_model(model_id='''stabilityai/stable-diffusion-2'''							,   fpaa=_UpperCamelCase							)
									snake_case_   =	self.get_latents(_UpperCamelCase							,   shape=(4, 4, 9_6, 9_6)							,   fpaa=_UpperCamelCase							)
									snake_case_   =	self.get_encoder_hidden_states(_UpperCamelCase							,   shape=(4, 7_7, 1_0_2_4)							,   fpaa=_UpperCamelCase							)

									snake_case_   =	model.apply(
									    {'''params''': params}							,   _UpperCamelCase							,   jnp.array(_UpperCamelCase							,   dtype=jnp.intaa							)							,   encoder_hidden_states=_UpperCamelCase							,   ).sample

									assert sample.shape == latents.shape

									snake_case_   =	jnp.asarray(jax.device_get((sample[-1, -2:, -2:, :2].flatten())							)							,   dtype=jnp.floataa							)
									snake_case_   =	jnp.array(_UpperCamelCase							,   dtype=jnp.floataa							)

									# Found torch (float16) and flax (bfloat16) outputs to be within this tolerance, on the same hardware
									assert jnp.allclose(_UpperCamelCase							,   _UpperCamelCase							,   atol=1e-2							)