


'''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)

__UpperCAmelCase       							=				logging.getLogger()


def    _snake_case  (	)		->       int:
							lowerCAmelCase__			  =      argparse.ArgumentParser()
							parser.add_argument('''-f'''			)
							lowerCAmelCase__			  =      parser.parse_args()
							return args.f


def    _snake_case  (	A			)		->       Tuple:
							lowerCAmelCase__			  =      {}
							lowerCAmelCase__			  =      os.path.join(A					,       '''all_results.json'''			)
							if os.path.exists(A			):
														with open(A					,       '''r'''			) as f:
																					lowerCAmelCase__			  =      json.load(A			)
							else:
														raise ValueError(F"""can't find {path}"""			)
							return results


def    _snake_case  (	)		->       Any:
							lowerCAmelCase__			  =      torch.cuda.is_available() and torch_device == '''cuda'''
							return is_using_cuda and is_apex_available()


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






class 						a__							(				a__    ):





							'''simple docstring'''






							@classmethod
							def     __SCREAMING_SNAKE_CASE				(						cls  )			->				Any:
														# Write Accelerate config, will pick up on CPU, GPU, and multi-GPU
														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     __SCREAMING_SNAKE_CASE				(						cls  )			->				Tuple:
														shutil.rmtree(cls.tmpdir  )







							@mock.patch.dict(os.environ   ,       {'''WANDB_MODE''': '''offline'''}  )
							def     __SCREAMING_SNAKE_CASE				(						self  )			->				Tuple:
														lowerCAmelCase__			  =      self.get_auto_remove_tmp_dir()
														lowerCAmelCase__			  =      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  )
														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     __SCREAMING_SNAKE_CASE				(						self  )			->				Tuple:
														lowerCAmelCase__			  =      self.get_auto_remove_tmp_dir()
														lowerCAmelCase__			  =      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  )
														lowerCAmelCase__			  =      get_results(lowerCamelCase_  )
														self.assertLess(result['''perplexity''']   ,       1_00  )
														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     __SCREAMING_SNAKE_CASE				(						self  )			->				Any:
														lowerCAmelCase__			  =      self.get_auto_remove_tmp_dir()
														lowerCAmelCase__			  =      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  )
														lowerCAmelCase__			  =      get_results(lowerCamelCase_  )
														self.assertLess(result['''perplexity''']   ,       42  )
														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     __SCREAMING_SNAKE_CASE				(						self  )			->				List[str]:
														# with so little data distributed training needs more epochs to get the score on par with 0/1 gpu
														lowerCAmelCase__			  =      7 if get_gpu_count() > 1 else 2

														lowerCAmelCase__			  =      self.get_auto_remove_tmp_dir()
														lowerCAmelCase__			  =      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  )
														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     __SCREAMING_SNAKE_CASE				(						self  )			->				List[Any]:
														lowerCAmelCase__			  =      self.get_auto_remove_tmp_dir()
														lowerCAmelCase__			  =      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  )
														lowerCAmelCase__			  =      get_results(lowerCamelCase_  )
														# 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(lowerCamelCase_   ,       '''epoch_0'''  )  )  )
														self.assertTrue(os.path.exists(os.path.join(lowerCamelCase_   ,       '''qa_no_trainer'''  )  )  )







							@mock.patch.dict(os.environ   ,       {'''WANDB_MODE''': '''offline'''}  )
							def     __SCREAMING_SNAKE_CASE				(						self  )			->				Optional[int]:
														lowerCAmelCase__			  =      self.get_auto_remove_tmp_dir()
														lowerCAmelCase__			  =      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  )
														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     __SCREAMING_SNAKE_CASE				(						self  )			->				Optional[Any]:
														lowerCAmelCase__			  =      self.get_auto_remove_tmp_dir()
														lowerCAmelCase__			  =      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  )
														lowerCAmelCase__			  =      get_results(lowerCamelCase_  )
														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(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     __SCREAMING_SNAKE_CASE				(						self  )			->				int:
														lowerCAmelCase__			  =      self.get_auto_remove_tmp_dir()
														lowerCAmelCase__			  =      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  )
														lowerCAmelCase__			  =      get_results(lowerCamelCase_  )
														self.assertGreaterEqual(result['''eval_bleu''']   ,       30  )
														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     __SCREAMING_SNAKE_CASE				(						self  )			->				Union[str, Any]:
														lowerCAmelCase__			  =      logging.StreamHandler(sys.stdout  )
														logger.addHandler(lowerCamelCase_  )

														lowerCAmelCase__			  =      self.get_auto_remove_tmp_dir()
														lowerCAmelCase__			  =      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  )
														lowerCAmelCase__			  =      get_results(lowerCamelCase_  )
														self.assertGreaterEqual(result['''eval_overall_accuracy''']   ,       0.10  )







							@mock.patch.dict(os.environ   ,       {'''WANDB_MODE''': '''offline'''}  )
							def     __SCREAMING_SNAKE_CASE				(						self  )			->				Union[str, Any]:
														lowerCAmelCase__			  =      self.get_auto_remove_tmp_dir()
														lowerCAmelCase__			  =      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  )
														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 gc
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModelWithProjection, CLIPTokenizer

from diffusers import HeunDiscreteScheduler, PriorTransformer, ShapEPipeline
from diffusers.pipelines.shap_e import ShapERenderer
from diffusers.utils import load_numpy, slow
from diffusers.utils.testing_utils import require_torch_gpu, torch_device

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference
class     lowerCAmelCase_     (       __lowercase,  unittest.TestCase ):
							UpperCAmelCase				   =     ShapEPipeline
							UpperCAmelCase				   =     ["prompt"]
							UpperCAmelCase				   =     ["prompt"]
							UpperCAmelCase				   =     [
							    "num_images_per_prompt",
							    "num_inference_steps",
							    "generator",
							    "latents",
							    "guidance_scale",
							    "frame_size",
							    "output_type",
							    "return_dict",
							]
							UpperCAmelCase				   =     False







							@property
							def  UpperCamelCase_						(   self      :     Union[str, Any]		):
												return 32







							@property
							def  UpperCamelCase_						(   self      :     int		):
												return 32







							@property
							def  UpperCamelCase_						(   self      :     List[str]		):
												return self.time_input_dim * 4







							@property
							def  UpperCamelCase_						(   self      :     Optional[Any]		):
												return 8







							@property
							def  UpperCamelCase_						(   self      :     int		):
												_UpperCamelCase			     =     CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''		)
												return tokenizer







							@property
							def  UpperCamelCase_						(   self      :     List[Any]		):
												torch.manual_seed(0		)
												_UpperCamelCase			     =     CLIPTextConfig(
												    bos_token_id=0 ,			eos_token_id=2 ,			hidden_size=self.text_embedder_hidden_size ,			projection_dim=self.text_embedder_hidden_size ,			intermediate_size=37 ,			layer_norm_eps=1e-05 ,			num_attention_heads=4 ,			num_hidden_layers=5 ,			pad_token_id=1 ,			vocab_size=1000 ,			)
												return CLIPTextModelWithProjection(_A		)







							@property
							def  UpperCamelCase_						(   self      :     int		):
												torch.manual_seed(0		)

												_UpperCamelCase			     =     {
												    '''num_attention_heads''': 2,
												    '''attention_head_dim''': 16,
												    '''embedding_dim''': self.time_input_dim,
												    '''num_embeddings''': 32,
												    '''embedding_proj_dim''': self.text_embedder_hidden_size,
												    '''time_embed_dim''': self.time_embed_dim,
												    '''num_layers''': 1,
												    '''clip_embed_dim''': self.time_input_dim * 2,
												    '''additional_embeddings''': 0,
												    '''time_embed_act_fn''': '''gelu''',
												    '''norm_in_type''': '''layer''',
												    '''encoder_hid_proj_type''': None,
												    '''added_emb_type''': None,
												}

												_UpperCamelCase			     =     PriorTransformer(**_A		)
												return model







							@property
							def  UpperCamelCase_						(   self      :     Union[str, Any]		):
												torch.manual_seed(0		)

												_UpperCamelCase			     =     {
												    '''param_shapes''': (
												        (self.renderer_dim, 93),
												        (self.renderer_dim, 8),
												        (self.renderer_dim, 8),
												        (self.renderer_dim, 8),
												    ),
												    '''d_latent''': self.time_input_dim,
												    '''d_hidden''': self.renderer_dim,
												    '''n_output''': 12,
												    '''background''': (
												        0.1,
												        0.1,
												        0.1,
												    ),
												}
												_UpperCamelCase			     =     ShapERenderer(**_A		)
												return model







							def  UpperCamelCase_						(   self      :     str		):
												_UpperCamelCase			     =     self.dummy_prior
												_UpperCamelCase			     =     self.dummy_text_encoder
												_UpperCamelCase			     =     self.dummy_tokenizer
												_UpperCamelCase			     =     self.dummy_renderer

												_UpperCamelCase			     =     HeunDiscreteScheduler(
												    beta_schedule='''exp''' ,			num_train_timesteps=1024 ,			prediction_type='''sample''' ,			use_karras_sigmas=_A ,			clip_sample=_A ,			clip_sample_range=1.0 ,			)
												_UpperCamelCase			     =     {
												    '''prior''': prior,
												    '''text_encoder''': text_encoder,
												    '''tokenizer''': tokenizer,
												    '''renderer''': renderer,
												    '''scheduler''': scheduler,
												}

												return components







							def  UpperCamelCase_						(   self      :     Tuple ,			_A      :     Tuple ,			_A      :     Optional[int]=0		):
												if str(_A		).startswith('''mps'''		):
																	_UpperCamelCase			     =     torch.manual_seed(_A		)
												else:
																	_UpperCamelCase			     =     torch.Generator(device=_A		).manual_seed(_A		)
												_UpperCamelCase			     =     {
												    '''prompt''': '''horse''',
												    '''generator''': generator,
												    '''num_inference_steps''': 1,
												    '''frame_size''': 32,
												    '''output_type''': '''np''',
												}
												return inputs







							def  UpperCamelCase_						(   self      :     Any		):
												_UpperCamelCase			     =     '''cpu'''

												_UpperCamelCase			     =     self.get_dummy_components()

												_UpperCamelCase			     =     self.pipeline_class(**_A		)
												_UpperCamelCase			     =     pipe.to(_A		)

												pipe.set_progress_bar_config(disable=_A		)

												_UpperCamelCase			     =     pipe(**self.get_dummy_inputs(_A		)		)
												_UpperCamelCase			     =     output.images[0]
												_UpperCamelCase			     =     image[0, -3:, -3:, -1]

												assert image.shape == (20, 32, 32, 3)

												_UpperCamelCase			     =     np.array(
												    [
												        0.0003_9216,
												        0.0003_9216,
												        0.0003_9216,
												        0.0003_9216,
												        0.0003_9216,
												        0.0003_9216,
												        0.0003_9216,
												        0.0003_9216,
												        0.0003_9216,
												    ]		)

												assert np.abs(image_slice.flatten() - expected_slice		).max() < 1e-2







							def  UpperCamelCase_						(   self      :     Any		):
												# NOTE: Larger batch sizes cause this test to timeout, only test on smaller batches
												self._test_inference_batch_consistent(batch_sizes=[1, 2]		)







							def  UpperCamelCase_						(   self      :     Any		):
												_UpperCamelCase			     =     torch_device == '''cpu'''
												_UpperCamelCase			     =     True

												self._test_inference_batch_single_identical(
												    batch_size=2 ,			test_max_difference=_A ,			relax_max_difference=_A ,			)







							def  UpperCamelCase_						(   self      :     Any		):
												_UpperCamelCase			     =     self.get_dummy_components()
												_UpperCamelCase			     =     self.pipeline_class(**_A		)
												_UpperCamelCase			     =     pipe.to(_A		)
												pipe.set_progress_bar_config(disable=_A		)

												_UpperCamelCase			     =     1
												_UpperCamelCase			     =     2

												_UpperCamelCase			     =     self.get_dummy_inputs(_A		)

												for key in inputs.keys():
																	if key in self.batch_params:
																						_UpperCamelCase			     =     batch_size * [inputs[key]]

												_UpperCamelCase			     =     pipe(**_A ,			num_images_per_prompt=_A		)[0]

												assert images.shape[0] == batch_size * num_images_per_prompt




@slow
@require_torch_gpu
class     lowerCAmelCase_     (       unittest.TestCase ):







							def  UpperCamelCase_						(   self      :     str		):
												# clean up the VRAM after each test
												super().tearDown()
												gc.collect()
												torch.cuda.empty_cache()







							def  UpperCamelCase_						(   self      :     List[str]		):
												_UpperCamelCase			     =     load_numpy(
												    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
												    '''/shap_e/test_shap_e_np_out.npy'''		)
												_UpperCamelCase			     =     ShapEPipeline.from_pretrained('''openai/shap-e'''		)
												_UpperCamelCase			     =     pipe.to(_A		)
												pipe.set_progress_bar_config(disable=_A		)

												_UpperCamelCase			     =     torch.Generator(device=_A		).manual_seed(0		)

												_UpperCamelCase			     =     pipe(
												    '''a shark''' ,			generator=_A ,			guidance_scale=15.0 ,			num_inference_steps=64 ,			frame_size=64 ,			output_type='''np''' ,			).images[0]

												assert images.shape == (20, 64, 64, 3)

												assert_mean_pixel_difference(_A ,			_A		)
