






'''simple docstring'''
import unittest

import numpy as np
import PIL
import torch

from diffusers.image_processor import VaeImageProcessor


class    _snake_case   (				unittest.TestCase					):

					"""simple docstring"""





					@property
					def  __SCREAMING_SNAKE_CASE (     self     )      ->	Optional[int]:
										a_									=   1
										a_									=   3
										a_									=   8
										a_									=   8

										a_									=   torch.rand((batch_size, num_channels, height, width)     )

										return sample




					def  __SCREAMING_SNAKE_CASE (     self   , UpperCAmelCase__     )      ->	Union[str, Any]:







										if isinstance(image[0]   , PIL.Image.Image     ):
															return np.stack([np.array(UpperCAmelCase__     ) for i in image]   , axis=0     )
										elif isinstance(UpperCAmelCase__   , torch.Tensor     ):
															return image.cpu().numpy().transpose(0   , 2   , 3   , 1     )
										return image




					def  __SCREAMING_SNAKE_CASE (     self     )      ->	Union[str, Any]:
										a_									=   VaeImageProcessor(do_resize=UpperCAmelCase__   , do_normalize=UpperCAmelCase__     )

										a_									=   self.dummy_sample
										a_									=   self.to_np(UpperCAmelCase__     )

										for output_type in ["pt", "np", "pil"]:
															a_									=   image_processor.postprocess(
															    image_processor.preprocess(UpperCAmelCase__     )   , output_type=UpperCAmelCase__   , )
															a_									=   self.to_np(UpperCAmelCase__     )
															a_									=   (input_np * 255).round() if output_type == "pil" else input_np
															assert (
															    np.abs(in_np - out_np     ).max() < 1e-6
															), F'''decoded output does not match input for output_type {output_type}'''




					def  __SCREAMING_SNAKE_CASE (     self     )      ->	Tuple:
										a_									=   VaeImageProcessor(do_resize=UpperCAmelCase__   , do_normalize=UpperCAmelCase__     )
										a_									=   self.dummy_sample.cpu().numpy().transpose(0   , 2   , 3   , 1     )

										for output_type in ["pt", "np", "pil"]:
															a_									=   image_processor.postprocess(image_processor.preprocess(UpperCAmelCase__     )   , output_type=UpperCAmelCase__     )

															a_									=   self.to_np(UpperCAmelCase__     )
															a_									=   (input_np * 255).round() if output_type == "pil" else input_np
															assert (
															    np.abs(in_np - out_np     ).max() < 1e-6
															), F'''decoded output does not match input for output_type {output_type}'''




					def  __SCREAMING_SNAKE_CASE (     self     )      ->	Any:
										a_									=   VaeImageProcessor(do_resize=UpperCAmelCase__   , do_normalize=UpperCAmelCase__     )

										a_									=   self.dummy_sample.cpu().numpy().transpose(0   , 2   , 3   , 1     )
										a_									=   image_processor.numpy_to_pil(UpperCAmelCase__     )

										for output_type in ["pt", "np", "pil"]:
															a_									=   image_processor.postprocess(image_processor.preprocess(UpperCAmelCase__     )   , output_type=UpperCAmelCase__     )
															for i, o in zip(UpperCAmelCase__   , UpperCAmelCase__     ):
																				a_									=   np.array(UpperCAmelCase__     )
																				a_									=   self.to_np(UpperCAmelCase__     ) if output_type == "pil" else (self.to_np(UpperCAmelCase__     ) * 255).round()
																				assert (
																				    np.abs(in_np - out_np     ).max() < 1e-6
																				), F'''decoded output does not match input for output_type {output_type}'''




					def  __SCREAMING_SNAKE_CASE (     self     )      ->	List[str]:
										a_									=   VaeImageProcessor(do_resize=UpperCAmelCase__   , do_normalize=UpperCAmelCase__     )

										a_									=   self.dummy_sample
										a_									=   input_pt_ad.squeeze(0     )

										a_									=   image_processor.postprocess(
										    image_processor.preprocess(UpperCAmelCase__     )   , output_type='np'   , )
										a_									=   image_processor.postprocess(
										    image_processor.preprocess(UpperCAmelCase__     )   , output_type='np'   , )

										a_									=   self.to_np(self.dummy_sample     )
										a_									=   input_np_ad.squeeze(0     )

										a_									=   image_processor.postprocess(
										    image_processor.preprocess(UpperCAmelCase__     )   , output_type='np'   , )
										a_									=   image_processor.postprocess(
										    image_processor.preprocess(UpperCAmelCase__     )   , output_type='np'   , )

										assert np.abs(out_pt_ad - out_pt_ad     ).max() < 1e-6
										assert np.abs(out_np_ad - out_np_ad     ).max() < 1e-6




					def  __SCREAMING_SNAKE_CASE (     self     )      ->	Optional[Any]:
										a_									=   VaeImageProcessor(do_resize=UpperCAmelCase__   , do_normalize=UpperCAmelCase__     )

										a_									=   self.dummy_sample
										a_									=   list(UpperCAmelCase__     )

										a_									=   image_processor.postprocess(
										    image_processor.preprocess(UpperCAmelCase__     )   , output_type='np'   , )

										a_									=   image_processor.postprocess(
										    image_processor.preprocess(UpperCAmelCase__     )   , output_type='np'   , )

										a_									=   self.to_np(self.dummy_sample     )
										list(UpperCAmelCase__     )

										a_									=   image_processor.postprocess(
										    image_processor.preprocess(UpperCAmelCase__     )   , output_type='np'   , )

										a_									=   image_processor.postprocess(
										    image_processor.preprocess(UpperCAmelCase__     )   , output_type='np'   , )

										assert np.abs(out_pt_ad - out_pt_list     ).max() < 1e-6
										assert np.abs(out_np_ad - out_np_list     ).max() < 1e-6









'''simple docstring'''
import os
import shutil
import unittest
from unittest.mock import patch

from transformers.testing_utils import CaptureStd, is_pt_tf_cross_test, require_torch


class    _snake_case   (				unittest.TestCase					):

					"""simple docstring"""





					@patch('sys.argv'   , ['fakeprogrampath', 'env']     )
					def  __SCREAMING_SNAKE_CASE (     self     )      ->	Tuple:
										# test transformers-cli env
										import transformers.commands.transformers_cli

										with CaptureStd() as cs:
															transformers.commands.transformers_cli.main()
										self.assertIn('Python version'   , cs.out     )
										self.assertIn('Platform'   , cs.out     )
										self.assertIn('Using distributed or parallel set-up in script?'   , cs.out     )




					@is_pt_tf_cross_test
					@patch(
					    'sys.argv'   , ['fakeprogrampath', 'pt-to-tf', '--model-name', 'hf-internal-testing/tiny-random-gptj', '--no-pr']     )
					def  __SCREAMING_SNAKE_CASE (     self     )      ->	List[str]:
										import transformers.commands.transformers_cli

										shutil.rmtree('/tmp/hf-internal-testing/tiny-random-gptj'   , ignore_errors=UpperCAmelCase__     )  # cleans potential past runs
										transformers.commands.transformers_cli.main()

										# The original repo has no TF weights -- if they exist, they were created by the CLI
										self.assertTrue(os.path.exists('/tmp/hf-internal-testing/tiny-random-gptj/tf_model.h5'     )     )




					@require_torch
					@patch('sys.argv'   , ['fakeprogrampath', 'download', 'hf-internal-testing/tiny-random-gptj', '--cache-dir', '/tmp']     )
					def  __SCREAMING_SNAKE_CASE (     self     )      ->	List[Any]:
										import transformers.commands.transformers_cli

										# # remove any previously downloaded model to start clean
										shutil.rmtree('/tmp/models--hf-internal-testing--tiny-random-gptj'   , ignore_errors=UpperCAmelCase__     )

										# run the command
										transformers.commands.transformers_cli.main()

										# check if the model files are downloaded correctly on /tmp/models--hf-internal-testing--tiny-random-gptj
										self.assertTrue(os.path.exists('/tmp/models--hf-internal-testing--tiny-random-gptj/blobs'     )     )
										self.assertTrue(os.path.exists('/tmp/models--hf-internal-testing--tiny-random-gptj/refs'     )     )
										self.assertTrue(os.path.exists('/tmp/models--hf-internal-testing--tiny-random-gptj/snapshots'     )     )




					@require_torch
					@patch(
					    'sys.argv'   , [
					        'fakeprogrampath',
					        'download',
					        'hf-internal-testing/test_dynamic_model_with_tokenizer',
					        '--trust-remote-code',
					        '--cache-dir',
					        '/tmp',
					    ]   , )
					def  __SCREAMING_SNAKE_CASE (     self     )      ->	int:
										import transformers.commands.transformers_cli

										# # remove any previously downloaded model to start clean
										shutil.rmtree('/tmp/models--hf-internal-testing--test_dynamic_model_with_tokenizer'   , ignore_errors=UpperCAmelCase__     )

										# run the command
										transformers.commands.transformers_cli.main()

										# check if the model files are downloaded correctly on /tmp/models--hf-internal-testing--test_dynamic_model_with_tokenizer
										self.assertTrue(os.path.exists('/tmp/models--hf-internal-testing--test_dynamic_model_with_tokenizer/blobs'     )     )
										self.assertTrue(os.path.exists('/tmp/models--hf-internal-testing--test_dynamic_model_with_tokenizer/refs'     )     )
										self.assertTrue(
										    os.path.exists('/tmp/models--hf-internal-testing--test_dynamic_model_with_tokenizer/snapshots'     )     )

