
'''simple docstring'''
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)

lowercase__						       =      logging.getLogger()




def    _UpperCamelCase						(       )  ->							Union[str, Any]:


		'''simple docstring'''


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




def    _UpperCamelCase						(       SCREAMING_SNAKE_CASE__			)  ->							Optional[Any]:


		'''simple docstring'''


		snake_case  :       str     			=						{}
		snake_case  :       Union[str, Any]     			=						os.path.join(a_ ,       '''all_results.json'''			)
		if os.path.exists(a_			):
				with open(a_ ,       '''r'''			) as f:
						snake_case  :       Any     			=						json.load(a_			)
		else:
				raise ValueError(F'can\'t find {path}'			)
		return results




def    _UpperCamelCase						(       )  ->							Optional[Any]:


		'''simple docstring'''


		snake_case  :       Optional[int]     			=						torch.cuda.is_available() and torch_device == '''cuda'''
		return is_using_cuda and is_apex_available()


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






class 	snake_case__       (      snake_case_					):







		"""simple docstring"""

		@classmethod
		def       lowerCAmelCase				(      cls				:			Tuple      )						->			str:






				"""simple docstring"""







				snake_case  :       Optional[Any]     			=						tempfile.mkdtemp()
				snake_case  :       Optional[int]     			=						os.path.join(cls.tmpdir  ,	'''default_config.yml'''      )
				write_basic_config(save_location=cls.configPath      )
				snake_case  :       Tuple     			=						['''accelerate''', '''launch''', '''--config_file''', cls.configPath]



		@classmethod
		def       lowerCAmelCase				(      cls				:			Optional[Any]      )						->			Any:






				"""simple docstring"""







				shutil.rmtree(cls.tmpdir      )



		@mock.patch.dict(os.environ  ,	{'''WANDB_MODE''': '''offline'''}      )
		def       lowerCAmelCase				(      self				:			Any      )						->			str:






				"""simple docstring"""







				snake_case  :       Optional[Any]     			=						self.get_auto_remove_tmp_dir()
				snake_case  :       Dict     			=						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      )
				snake_case  :       Dict     			=						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       lowerCAmelCase				(      self				:			Tuple      )						->			Optional[Any]:






				"""simple docstring"""







				snake_case  :       Dict     			=						self.get_auto_remove_tmp_dir()
				snake_case  :       Dict     			=						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      )
				snake_case  :       str     			=						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       lowerCAmelCase				(      self				:			Dict      )						->			Optional[int]:






				"""simple docstring"""







				snake_case  :       Optional[int]     			=						self.get_auto_remove_tmp_dir()
				snake_case  :       Union[str, Any]     			=						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      )
				snake_case  :       Optional[Any]     			=						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       lowerCAmelCase				(      self				:			int      )						->			Any:






				"""simple docstring"""







				snake_case  :       str     			=						7 if get_gpu_count() > 1 else 2

				snake_case  :       Optional[int]     			=						self.get_auto_remove_tmp_dir()
				snake_case  :       Tuple     			=						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      )
				snake_case  :       int     			=						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       lowerCAmelCase				(      self				:			Any      )						->			Union[str, Any]:






				"""simple docstring"""







				snake_case  :       List[Any]     			=						self.get_auto_remove_tmp_dir()
				snake_case  :       Dict     			=						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      )
				snake_case  :       Union[str, Any]     			=						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       lowerCAmelCase				(      self				:			List[Any]      )						->			Dict:






				"""simple docstring"""







				snake_case  :       Optional[Any]     			=						self.get_auto_remove_tmp_dir()
				snake_case  :       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      )
				snake_case  :       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       lowerCAmelCase				(      self				:			Optional[Any]      )						->			int:






				"""simple docstring"""







				snake_case  :       str     			=						self.get_auto_remove_tmp_dir()
				snake_case  :       str     			=						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      )
				snake_case  :       List[str]     			=						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       lowerCAmelCase				(      self				:			Dict      )						->			int:






				"""simple docstring"""







				snake_case  :       str     			=						self.get_auto_remove_tmp_dir()
				snake_case  :       List[Any]     			=						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      )
				snake_case  :       List[str]     			=						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       lowerCAmelCase				(      self				:			int      )						->			Tuple:






				"""simple docstring"""







				snake_case  :       Optional[int]     			=						logging.StreamHandler(sys.stdout      )
				logger.addHandler(UpperCamelCase__      )

				snake_case  :       List[str]     			=						self.get_auto_remove_tmp_dir()
				snake_case  :       str     			=						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      )
				snake_case  :       str     			=						get_results(UpperCamelCase__      )
				self.assertGreaterEqual(result['''eval_overall_accuracy''']  ,	0.10      )



		@mock.patch.dict(os.environ  ,	{'''WANDB_MODE''': '''offline'''}      )
		def       lowerCAmelCase				(      self				:			Optional[int]      )						->			Optional[int]:






				"""simple docstring"""







				snake_case  :       Union[str, Any]     			=						self.get_auto_remove_tmp_dir()
				snake_case  :       Dict     			=						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      )
				snake_case  :       Tuple     			=						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 typing import List, Optional, Tuple, Union

import torch

from ...schedulers import DDIMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput




class 					UpperCamelCase_ (     snake_case_							):



	'''simple docstring'''




	def __init__(			self			,     a			,     a   )			->       Optional[Any]:
								super().__init__()

								# make sure scheduler can always be converted to DDIM
								snake_case_           =				DDIMScheduler.from_config(scheduler.config   )

								self.register_modules(unet=a			,     scheduler=a   )






	@torch.no_grad()
	def __call__(			self			,     a = 1			,     a = None			,     a = 0.0			,     a = 50			,     a = None			,     a = "pil"			,     a = True			,     )			->       Union[ImagePipelineOutput, Tuple]:

								# Sample gaussian noise to begin loop
								if isinstance(self.unet.config.sample_size			,     a   ):
															snake_case_           =				(
															    batch_size,
															    self.unet.config.in_channels,
															    self.unet.config.sample_size,
															    self.unet.config.sample_size,
															)
								else:
															snake_case_           =				(batch_size, self.unet.config.in_channels, *self.unet.config.sample_size)

								if isinstance(a			,     a   ) and len(a   ) != batch_size:
															raise ValueError(
															    F'''You have passed a list of generators of length {len(a   )}, but requested an effective batch'''
															    F''' size of {batch_size}. Make sure the batch size matches the length of the generators.'''   )

								snake_case_           =				randn_tensor(a			,     generator=a			,     device=self.device			,     dtype=self.unet.dtype   )

								# set step values
								self.scheduler.set_timesteps(a   )

								for t in self.progress_bar(self.scheduler.timesteps   ):
															# 1. predict noise model_output
															snake_case_           =				self.unet(a			,     a   ).sample

															# 2. predict previous mean of image x_t-1 and add variance depending on eta
															# eta corresponds to η in paper and should be between [0, 1]
															# do x_t -> x_t-1
															snake_case_           =				self.scheduler.step(
															    a			,     a			,     a			,     eta=a			,     use_clipped_model_output=a			,     generator=a   ).prev_sample

								snake_case_           =				(image / 2 + 0.5).clamp(0			,     1   )
								snake_case_           =				image.cpu().permute(0			,     2			,     3			,     1   ).numpy()
								if output_type == "pil":
															snake_case_           =				self.numpy_to_pil(a   )

								if not return_dict:
															return (image,)

								return ImagePipelineOutput(images=a   )





