




'''simple docstring'''







from math import acos, sin
from typing import List, Tuple, Union

import numpy as np
import torch
from PIL import Image

from ...models import AutoencoderKL, UNetaDConditionModel
from ...schedulers import DDIMScheduler, DDPMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import AudioPipelineOutput, BaseOutput, DiffusionPipeline, ImagePipelineOutput
from .mel import Mel







class 					_a (UpperCamelCase__	):





     '''simple docstring'''



     lowerCAmelCase_  :     int          =					["""vqvae"""]







     def __init__(				self							,__a							,__a							,__a							,__a							,)      ->				Optional[Any]:
          super().__init__()
          self.register_modules(unet=lowercase_							,scheduler=lowercase_							,mel=lowercase_							,vqvae=lowercase_							)







     def 			snake_case_      (				self							)      ->				int:
          return 50 if isinstance(self.scheduler							,lowercase_							) else 1_000







     @torch.no_grad()
     def __call__(				self							,__a = 1							,__a = None							,__a = None							,__a = 0							,__a = 0							,__a = None							,__a = None							,__a = 0							,__a = 0							,__a = None							,__a = 0							,__a = None							,__a = None							,__a=True							,)      ->				Union[
         Union[AudioPipelineOutput, ImagePipelineOutput],
         Tuple[List[Image.Image], Tuple[int, List[np.ndarray]]],
     ]:
          snake_case			:      Optional[int]        =			steps or self.get_default_steps()
          self.scheduler.set_timesteps(lowercase_							)
          snake_case			:      Dict        =			step_generator or generator
          # For backwards compatibility
          if type(self.unet.config.sample_size							) == int:
               snake_case			:      Tuple        =			(self.unet.config.sample_size, self.unet.config.sample_size)
          if noise is None:
               snake_case			:      str        =			randn_tensor(
                   (
                       batch_size,
                       self.unet.config.in_channels,
                       self.unet.config.sample_size[0],
                       self.unet.config.sample_size[1],
                   )							,generator=lowercase_							,device=self.device							,)
          snake_case			:      Any        =			noise
          snake_case			:      Any        =			None

          if audio_file is not None or raw_audio is not None:
               self.mel.load_audio(lowercase_							,lowercase_							)
               snake_case			:      List[str]        =			self.mel.audio_slice_to_image(lowercase_							)
               snake_case			:      List[str]        =			np.frombuffer(input_image.tobytes()							,dtype="""uint8"""							).reshape(
                   (input_image.height, input_image.width)							)
               snake_case			:      int        =			(input_image / 255) * 2 - 1
               snake_case			:      List[Any]        =			torch.tensor(input_image[np.newaxis, :, :]							,dtype=torch.float							).to(self.device							)

               if self.vqvae is not None:
                    snake_case			:      List[str]        =			self.vqvae.encode(torch.unsqueeze(lowercase_							,0							)							).latent_dist.sample(
                        generator=lowercase_							)[0]
                    snake_case			:      List[str]        =			self.vqvae.config.scaling_factor * input_images

               if start_step > 0:
                    snake_case			:      int        =			self.scheduler.add_noise(lowercase_							,lowercase_							,self.scheduler.timesteps[start_step - 1]							)

               snake_case			:      Union[str, Any]        =			(
                   self.unet.config.sample_size[1] * self.mel.get_sample_rate() / self.mel.x_res / self.mel.hop_length
               )
               snake_case			:      Union[str, Any]        =			int(mask_start_secs * pixels_per_second							)
               snake_case			:      Tuple        =			int(mask_end_secs * pixels_per_second							)
               snake_case			:      Tuple        =			self.scheduler.add_noise(lowercase_							,lowercase_							,torch.tensor(self.scheduler.timesteps[start_step:]							)							)

          for step, t in enumerate(self.progress_bar(self.scheduler.timesteps[start_step:]							)							):
               if isinstance(self.unet							,lowercase_							):
                    snake_case			:      Any        =			self.unet(lowercase_							,lowercase_							,lowercase_							)["""sample"""]
               else:
                    snake_case			:      Optional[Any]        =			self.unet(lowercase_							,lowercase_							)["""sample"""]

               if isinstance(self.scheduler							,lowercase_							):
                    snake_case			:      List[str]        =			self.scheduler.step(
                        model_output=lowercase_							,timestep=lowercase_							,sample=lowercase_							,eta=lowercase_							,generator=lowercase_							,)["""prev_sample"""]
               else:
                    snake_case			:      Tuple        =			self.scheduler.step(
                        model_output=lowercase_							,timestep=lowercase_							,sample=lowercase_							,generator=lowercase_							,)["""prev_sample"""]

               if mask is not None:
                    if mask_start > 0:
                         snake_case			:      Dict        =			mask[:, step, :, :mask_start]
                    if mask_end > 0:
                         snake_case			:      Union[str, Any]        =			mask[:, step, :, -mask_end:]

          if self.vqvae is not None:
               # 0.18215 was scaling factor used in training to ensure unit variance
               snake_case			:      Optional[Any]        =			1 / self.vqvae.config.scaling_factor * images
               snake_case			:      Optional[Any]        =			self.vqvae.decode(lowercase_							)["""sample"""]

          snake_case			:      Tuple        =			(images / 2 + 0.5).clamp(0							,1							)
          snake_case			:      Dict        =			images.cpu().permute(0							,2							,3							,1							).numpy()
          snake_case			:      Optional[Any]        =			(images * 255).round().astype("""uint8"""							)
          snake_case			:      Union[str, Any]        =			list(
              (Image.fromarray(_[:, :, 0]							) for _ in images)
              if images.shape[3] == 1
              else (Image.fromarray(lowercase_							,mode="""RGB"""							).convert("""L"""							) for _ in images)							)

          snake_case			:      Any        =			[self.mel.image_to_audio(lowercase_							) for _ in images]
          if not return_dict:
               return images, (self.mel.get_sample_rate(), audios)

          return BaseOutput(**AudioPipelineOutput(np.array(lowercase_							)[:, np.newaxis, :]							)							,**ImagePipelineOutput(lowercase_							)							)







     @torch.no_grad()
     def 			snake_case_      (				self							,__a							,__a = 50							)      ->				np.ndarray:
          assert isinstance(self.scheduler							,lowercase_							)
          self.scheduler.set_timesteps(lowercase_							)
          snake_case			:      Optional[int]        =			np.array(
              [np.frombuffer(image.tobytes()							,dtype="""uint8"""							).reshape((1, image.height, image.width)							) for image in images]							)
          snake_case			:      Union[str, Any]        =			(sample / 255) * 2 - 1
          snake_case			:      int        =			torch.Tensor(lowercase_							).to(self.device							)

          for t in self.progress_bar(torch.flip(self.scheduler.timesteps							,(0,)							)							):
               snake_case			:      List[str]        =			t - self.scheduler.config.num_train_timesteps // self.scheduler.num_inference_steps
               snake_case			:      Optional[int]        =			self.scheduler.alphas_cumprod[t]
               snake_case			:      List[Any]        =			(
                   self.scheduler.alphas_cumprod[prev_timestep]
                   if prev_timestep >= 0
                   else self.scheduler.final_alpha_cumprod
               )
               snake_case			:      Dict        =			1 - alpha_prod_t
               snake_case			:      Union[str, Any]        =			self.unet(lowercase_							,lowercase_							)["""sample"""]
               snake_case			:      Union[str, Any]        =			(1 - alpha_prod_t_prev) ** 0.5 * model_output
               snake_case			:      List[str]        =			(sample - pred_sample_direction) * alpha_prod_t_prev ** (-0.5)
               snake_case			:      int        =			sample * alpha_prod_t ** 0.5 + beta_prod_t ** 0.5 * model_output

          return sample







     @staticmethod
     def 			snake_case_      (				__a							,__a							,__a							)      ->				torch.Tensor:
          snake_case			:      str        =			acos(torch.dot(torch.flatten(lowercase_							)							,torch.flatten(lowercase_							)							) / torch.norm(lowercase_							) / torch.norm(lowercase_							)							)
          return sin((1 - alpha) * theta							) * xa / sin(lowercase_							) + sin(alpha * theta							) * xa / sin(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"				)				)				)



