





'''simple docstring'''

import gc
import random
import unittest

import numpy as np
import torch

from diffusers import (
    DDIMScheduler,
    KandinskyVaaControlnetPipeline,
    KandinskyVaaPriorPipeline,
    UNetaDConditionModel,
    VQModel,
)
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()






class   UpperCAmelCase      (			UpperCamelCase__     ,			unittest.TestCase							):
 __lowercase  						=							KandinskyVaaControlnetPipeline
 __lowercase  						=							["""image_embeds""", """negative_image_embeds""", """hint"""]
 __lowercase  						=							["""image_embeds""", """negative_image_embeds""", """hint"""]
 __lowercase  						=							[
     """generator""",
     """height""",
     """width""",
     """latents""",
     """guidance_scale""",
     """num_inference_steps""",
     """return_dict""",
     """guidance_scale""",
     """num_images_per_prompt""",
     """output_type""",
     """return_dict""",
 ]
 __lowercase  						=							False
 @property
 def 	UpperCAmelCase_				(							self :List[str]				)->				Tuple:
     return 32
 @property
 def 	UpperCAmelCase_				(							self :Tuple				)->				Union[str, Any]:
     return 32
 @property
 def 	UpperCAmelCase_				(							self :List[str]				)->				str:
     return self.time_input_dim
 @property
 def 	UpperCAmelCase_				(							self :List[Any]				)->				Optional[Any]:
     return self.time_input_dim * 4
 @property
 def 	UpperCAmelCase_				(							self :Optional[int]				)->				str:
     return 1_00
 @property
 def 	UpperCAmelCase_				(							self :Optional[int]				)->				int:
     torch.manual_seed(0				)

     A__							=     {
         "in_channels": 8,
         # Out channels is double in channels because predicts mean and variance
         "out_channels": 8,
         "addition_embed_type": "image_hint",
         "down_block_types": ("ResnetDownsampleBlock2D", "SimpleCrossAttnDownBlock2D"),
         "up_block_types": ("SimpleCrossAttnUpBlock2D", "ResnetUpsampleBlock2D"),
         "mid_block_type": "UNetMidBlock2DSimpleCrossAttn",
         "block_out_channels": (self.block_out_channels_a, self.block_out_channels_a * 2),
         "layers_per_block": 1,
         "encoder_hid_dim": self.text_embedder_hidden_size,
         "encoder_hid_dim_type": "image_proj",
         "cross_attention_dim": self.cross_attention_dim,
         "attention_head_dim": 4,
         "resnet_time_scale_shift": "scale_shift",
         "class_embed_type": None,
     }

     A__							=     UNetaDConditionModel(**lowercase_				)
     return model
 @property
 def 	UpperCAmelCase_				(							self :Dict				)->				str:
     return {
         "block_out_channels": [32, 32, 64, 64],
         "down_block_types": [
             "DownEncoderBlock2D",
             "DownEncoderBlock2D",
             "DownEncoderBlock2D",
             "AttnDownEncoderBlock2D",
         ],
         "in_channels": 3,
         "latent_channels": 4,
         "layers_per_block": 1,
         "norm_num_groups": 8,
         "norm_type": "spatial",
         "num_vq_embeddings": 12,
         "out_channels": 3,
         "up_block_types": ["AttnUpDecoderBlock2D", "UpDecoderBlock2D", "UpDecoderBlock2D", "UpDecoderBlock2D"],
         "vq_embed_dim": 4,
     }
 @property
 def 	UpperCAmelCase_				(							self :List[str]				)->				Tuple:
     torch.manual_seed(0				)
     A__							=     VQModel(**self.dummy_movq_kwargs				)
     return model
 def 	UpperCAmelCase_				(							self :Optional[int]				)->				Dict:
     A__							=     self.dummy_unet
     A__							=     self.dummy_movq

     A__							=     DDIMScheduler(
         num_train_timesteps=10_00		,			beta_schedule="linear"		,			beta_start=0.0_0_0_8_5		,			beta_end=0.0_1_2		,			clip_sample=lowercase_		,			set_alpha_to_one=lowercase_		,			steps_offset=1		,			prediction_type="epsilon"		,			thresholding=lowercase_		,			)

     A__							=     {
         "unet": unet,
         "scheduler": scheduler,
         "movq": movq,
     }
     return components
 def 	UpperCAmelCase_				(							self :Tuple		,			lowercase_ :int		,			lowercase_ :Tuple=0				)->				int:
     A__							=     floats_tensor((1, self.text_embedder_hidden_size)		,			rng=random.Random(lowercase_				)				).to(lowercase_				)
     A__							=     floats_tensor((1, self.text_embedder_hidden_size)		,			rng=random.Random(seed + 1				)				).to(
         lowercase_				)

     # create hint
     A__							=     floats_tensor((1, 3, 64, 64)		,			rng=random.Random(lowercase_				)				).to(lowercase_				)

     if str(lowercase_				).startswith("mps"				):
         A__							=     torch.manual_seed(lowercase_				)
     else:
         A__							=     torch.Generator(device=lowercase_				).manual_seed(lowercase_				)
     A__							=     {
         "image_embeds": image_embeds,
         "negative_image_embeds": negative_image_embeds,
         "hint": hint,
         "generator": generator,
         "height": 64,
         "width": 64,
         "guidance_scale": 4.0,
         "num_inference_steps": 2,
         "output_type": "np",
     }
     return inputs



 def 	UpperCAmelCase_				(							self :Any				)->				Any:
     A__							=     "cpu"

     A__							=     self.get_dummy_components()

     A__							=     self.pipeline_class(**lowercase_				)
     A__							=     pipe.to(lowercase_				)

     pipe.set_progress_bar_config(disable=lowercase_				)

     A__							=     pipe(**self.get_dummy_inputs(lowercase_				)				)
     A__							=     output.images

     A__							=     pipe(
         **self.get_dummy_inputs(lowercase_				)		,			return_dict=lowercase_		,			)[0]

     A__							=     image[0, -3:, -3:, -1]
     A__							=     image_from_tuple[0, -3:, -3:, -1]

     assert image.shape == (1, 64, 64, 3)

     A__							=     np.array(
         [0.6_9_5_9_8_2_6, 0.8_6_8_2_7_9, 0.7_5_5_8_0_9_2, 0.6_8_7_6_9_4_6_7, 0.8_5_8_0_5_8_0_4, 0.6_5_9_7_7_4_9_6, 0.4_4_8_8_5_3_0_2, 0.5_9_5_9_1_1_1, 0.4_2_5_1_5_9_5]				)

     assert (
         np.abs(image_slice.flatten() - expected_slice				).max() < 1E-2
     ), F" expected_slice {expected_slice}, but got {image_slice.flatten()}"

     assert (
         np.abs(image_from_tuple_slice.flatten() - expected_slice				).max() < 1E-2
     ), F" expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}"






@slow
@require_torch_gpu
class   UpperCAmelCase      (			unittest.TestCase							):
 def 	UpperCAmelCase_				(							self :Union[str, Any]				)->				Optional[int]:
     # clean up the VRAM after each test
     super().tearDown()
     gc.collect()
     torch.cuda.empty_cache()



 def 	UpperCAmelCase_				(							self :List[Any]				)->				Optional[int]:
     A__							=     load_numpy(
         "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
         "/kandinskyv22/kandinskyv22_controlnet_robotcat_fp16.npy"				)

     A__							=     load_image(
         "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
         "/kandinskyv22/hint_image_cat.png"				)
     A__							=     torch.from_numpy(np.array(lowercase_				)				).float() / 2_5_5.0
     A__							=     hint.permute(2		,			0		,			1				).unsqueeze(0				)

     A__							=     KandinskyVaaPriorPipeline.from_pretrained(
         "kandinsky-community/kandinsky-2-2-prior"		,			torch_dtype=torch.floataa				)
     pipe_prior.to(lowercase_				)

     A__							=     KandinskyVaaControlnetPipeline.from_pretrained(
         "kandinsky-community/kandinsky-2-2-controlnet-depth"		,			torch_dtype=torch.floataa				)
     A__							=     pipeline.to(lowercase_				)
     pipeline.set_progress_bar_config(disable=lowercase_				)

     A__							=     "A robot, 4k photo"

     A__							=     torch.Generator(device="cuda"				).manual_seed(0				)
     A__,      A__							=     pipe_prior(
         lowercase_		,			generator=lowercase_		,			num_inference_steps=5		,			negative_prompt=""		,			).to_tuple()

     A__							=     torch.Generator(device="cuda"				).manual_seed(0				)
     A__							=     pipeline(
         image_embeds=lowercase_		,			negative_image_embeds=lowercase_		,			hint=lowercase_		,			generator=lowercase_		,			num_inference_steps=1_00		,			output_type="np"		,			)

     A__							=     output.images[0]

     assert image.shape == (5_12, 5_12, 3)

     assert_mean_pixel_difference(lowercase_		,			lowercase_				)










'''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)

__lowerCAmelCase				:      Dict						      =logging.getLogger()





def 		UpperCamelCase							(				):
    A__							=     argparse.ArgumentParser()
    parser.add_argument("-f"			)
    A__							=     parser.parse_args()
    return args.f





def 		UpperCamelCase							(				_lowerCamelCase   :   List[str]			):
    A__							=     {}
    A__							=     os.path.join(_lowerCamelCase		,			"all_results.json"			)
    if os.path.exists(_lowerCamelCase			):
        with open(_lowerCamelCase		,			"r"			) as f:
            A__							=     json.load(_lowerCamelCase			)
    else:
        raise ValueError(F"can't find {path}"			)
    return results





def 		UpperCamelCase							(				):
    A__							=     torch.cuda.is_available() and torch_device == "cuda"
    return is_using_cuda and is_apex_available()


__lowerCAmelCase				:      Union[str, Any]						      =logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)






class   UpperCAmelCase      (			UpperCamelCase__							):
 @classmethod
 def 	UpperCAmelCase_				(							cls :List[Any]				)->				Union[str, Any]:
     # Write Accelerate config, will pick up on CPU, GPU, and multi-GPU
     A__							=     tempfile.mkdtemp()
     A__							=     os.path.join(cls.tmpdir		,			"default_config.yml"				)
     write_basic_config(save_location=cls.configPath				)
     A__							=     ["accelerate", "launch", "--config_file", cls.configPath]
 @classmethod
 def 	UpperCAmelCase_				(							cls :Tuple				)->				List[Any]:
     shutil.rmtree(cls.tmpdir				)
 @mock.patch.dict(os.environ		,			{"WANDB_MODE": "offline"}				)
 def 	UpperCAmelCase_				(							self :Union[str, Any]				)->				Any:
     A__							=     self.get_auto_remove_tmp_dir()
     A__							=     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__							=     get_results(lowercase_				)
     self.assertGreaterEqual(result["eval_accuracy"]		,			0.7_5				)
     self.assertTrue(os.path.exists(os.path.join(lowercase_		,			"epoch_0"				)				)				)
     self.assertTrue(os.path.exists(os.path.join(lowercase_		,			"glue_no_trainer"				)				)				)
 @mock.patch.dict(os.environ		,			{"WANDB_MODE": "offline"}				)
 def 	UpperCAmelCase_				(							self :List[Any]				)->				Tuple:
     A__							=     self.get_auto_remove_tmp_dir()
     A__							=     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__							=     get_results(lowercase_				)
     self.assertLess(result["perplexity"]		,			1_00				)
     self.assertTrue(os.path.exists(os.path.join(lowercase_		,			"epoch_0"				)				)				)
     self.assertTrue(os.path.exists(os.path.join(lowercase_		,			"clm_no_trainer"				)				)				)
 @mock.patch.dict(os.environ		,			{"WANDB_MODE": "offline"}				)
 def 	UpperCAmelCase_				(							self :Any				)->				str:
     A__							=     self.get_auto_remove_tmp_dir()
     A__							=     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__							=     get_results(lowercase_				)
     self.assertLess(result["perplexity"]		,			42				)
     self.assertTrue(os.path.exists(os.path.join(lowercase_		,			"epoch_0"				)				)				)
     self.assertTrue(os.path.exists(os.path.join(lowercase_		,			"mlm_no_trainer"				)				)				)
 @mock.patch.dict(os.environ		,			{"WANDB_MODE": "offline"}				)
 def 	UpperCAmelCase_				(							self :Optional[Any]				)->				List[str]:
     # with so little data distributed training needs more epochs to get the score on par with 0/1 gpu
     A__							=     7 if get_gpu_count() > 1 else 2

     A__							=     self.get_auto_remove_tmp_dir()
     A__							=     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__							=     get_results(lowercase_				)
     self.assertGreaterEqual(result["eval_accuracy"]		,			0.7_5				)
     self.assertLess(result["train_loss"]		,			0.5				)
     self.assertTrue(os.path.exists(os.path.join(lowercase_		,			"epoch_0"				)				)				)
     self.assertTrue(os.path.exists(os.path.join(lowercase_		,			"ner_no_trainer"				)				)				)
 @unittest.skip(reason="Fix me @muellerzr"				)
 @mock.patch.dict(os.environ		,			{"WANDB_MODE": "offline"}				)
 def 	UpperCAmelCase_				(							self :Any				)->				Optional[int]:
     A__							=     self.get_auto_remove_tmp_dir()
     A__							=     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__							=     get_results(lowercase_				)
     # 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(lowercase_		,			"epoch_0"				)				)				)
     self.assertTrue(os.path.exists(os.path.join(lowercase_		,			"qa_no_trainer"				)				)				)
 @mock.patch.dict(os.environ		,			{"WANDB_MODE": "offline"}				)
 def 	UpperCAmelCase_				(							self :Optional[int]				)->				List[str]:
     A__							=     self.get_auto_remove_tmp_dir()
     A__							=     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__							=     get_results(lowercase_				)
     self.assertGreaterEqual(result["eval_accuracy"]		,			0.8				)
     self.assertTrue(os.path.exists(os.path.join(lowercase_		,			"swag_no_trainer"				)				)				)
 @slow
 @mock.patch.dict(os.environ		,			{"WANDB_MODE": "offline"}				)
 def 	UpperCAmelCase_				(							self :Tuple				)->				Any:
     A__							=     self.get_auto_remove_tmp_dir()
     A__							=     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__							=     get_results(lowercase_				)
     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(lowercase_		,			"epoch_0"				)				)				)
     self.assertTrue(os.path.exists(os.path.join(lowercase_		,			"summarization_no_trainer"				)				)				)
 @slow
 @mock.patch.dict(os.environ		,			{"WANDB_MODE": "offline"}				)
 def 	UpperCAmelCase_				(							self :Union[str, Any]				)->				Optional[int]:
     A__							=     self.get_auto_remove_tmp_dir()
     A__							=     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__							=     get_results(lowercase_				)
     self.assertGreaterEqual(result["eval_bleu"]		,			30				)
     self.assertTrue(os.path.exists(os.path.join(lowercase_		,			"epoch_0"				)				)				)
     self.assertTrue(os.path.exists(os.path.join(lowercase_		,			"translation_no_trainer"				)				)				)
 @slow
 def 	UpperCAmelCase_				(							self :Optional[int]				)->				List[Any]:
     A__							=     logging.StreamHandler(sys.stdout				)
     logger.addHandler(lowercase_				)

     A__							=     self.get_auto_remove_tmp_dir()
     A__							=     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__							=     get_results(lowercase_				)
     self.assertGreaterEqual(result["eval_overall_accuracy"]		,			0.1_0				)



 @mock.patch.dict(os.environ		,			{"WANDB_MODE": "offline"}				)
 def 	UpperCAmelCase_				(							self :str				)->				Dict:
     A__							=     self.get_auto_remove_tmp_dir()
     A__							=     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__							=     get_results(lowercase_				)
     # The base model scores a 25%
     self.assertGreaterEqual(result["eval_accuracy"]		,			0.6				)
     self.assertTrue(os.path.exists(os.path.join(lowercase_		,			"step_1"				)				)				)
     self.assertTrue(os.path.exists(os.path.join(lowercase_		,			"image_classification_no_trainer"				)				)				)



