



"""simple docstring"""
import argparse
import logging
import sys
from unittest.mock import patch

import run_glue_deebert

from transformers.testing_utils import TestCasePlus, get_gpu_count, require_torch_non_multi_gpu, slow


logging.basicConfig(level=logging.DEBUG)

__SCREAMING_SNAKE_CASE  :						str    							=      logging.getLogger()



def        lowerCAmelCase_(   )	->       str:

				'''simple docstring'''





				_lowerCamelCase        =				argparse.ArgumentParser()
				parser.add_argument('''-f'''							)
				_lowerCamelCase        =				parser.parse_args()
				return args.f




class 							lowerCamelCase_(   A__    ):





					'''simple docstring'''



					def 			snake_case__      (    self   ):
									_lowerCamelCase        =				logging.StreamHandler(sys.stdout   )
									logger.addHandler(__UpperCamelCase   )

					def 			snake_case__      (    self				,       lowerCamelCase__   ):
									_lowerCamelCase        =				get_gpu_count()

									if n_gpu > 1:
													pass
													# XXX: doesn't quite work with n_gpu > 1 https://github.com/huggingface/transformers/issues/10560
													# script = f"{self.examples_dir_str}/research_projects/deebert/run_glue_deebert.py"
													# distributed_args = f"-m torch.distributed.launch --nproc_per_node={n_gpu} {script}".split()
													# cmd = [sys.executable] + distributed_args + args
													# execute_subprocess_async(cmd, env=self.get_env())
													# XXX: test the results - need to save them first into .json file
									else:
													args.insert(0				,       '''run_glue_deebert.py'''   )
													with patch.object(__UpperCamelCase				,       '''argv'''				,       __UpperCamelCase   ):
																	_lowerCamelCase        =				run_glue_deebert.main()
																	for value in result.values():
																					self.assertGreaterEqual(__UpperCamelCase				,       0.6_6_6   )





					@slow
					@require_torch_non_multi_gpu
					def 			snake_case__      (    self   ):
									_lowerCamelCase        =				'''
            --model_type roberta
            --model_name_or_path roberta-base
            --task_name MRPC
            --do_train
            --do_eval
            --do_lower_case
            --data_dir ./tests/fixtures/tests_samples/MRPC/
            --max_seq_length 128
            --per_gpu_eval_batch_size=1
            --per_gpu_train_batch_size=8
            --learning_rate 2e-4
            --num_train_epochs 3
            --overwrite_output_dir
            --seed 42
            --output_dir ./examples/deebert/saved_models/roberta-base/MRPC/two_stage
            --plot_data_dir ./examples/deebert/results/
            --save_steps 0
            --overwrite_cache
            --eval_after_first_stage
            '''.split()
									self.run_and_check(__UpperCamelCase   )

									_lowerCamelCase        =				'''
            --model_type roberta
            --model_name_or_path ./examples/deebert/saved_models/roberta-base/MRPC/two_stage
            --task_name MRPC
            --do_eval
            --do_lower_case
            --data_dir ./tests/fixtures/tests_samples/MRPC/
            --output_dir ./examples/deebert/saved_models/roberta-base/MRPC/two_stage
            --plot_data_dir ./examples/deebert/results/
            --max_seq_length 128
            --eval_each_highway
            --eval_highway
            --overwrite_cache
            --per_gpu_eval_batch_size=1
            '''.split()
									self.run_and_check(__UpperCamelCase   )

									_lowerCamelCase        =				'''
            --model_type roberta
            --model_name_or_path ./examples/deebert/saved_models/roberta-base/MRPC/two_stage
            --task_name MRPC
            --do_eval
            --do_lower_case
            --data_dir ./tests/fixtures/tests_samples/MRPC/
            --output_dir ./examples/deebert/saved_models/roberta-base/MRPC/two_stage
            --plot_data_dir ./examples/deebert/results/
            --max_seq_length 128
            --early_exit_entropy 0.1
            --eval_highway
            --overwrite_cache
            --per_gpu_eval_batch_size=1
            '''.split()
									self.run_and_check(__UpperCamelCase   )





"""simple docstring"""
import gc
import random
import unittest

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

from diffusers import (
    AutoencoderKL,
    DiffusionPipeline,
    EulerDiscreteScheduler,
    StableDiffusionXLImgaImgPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import floats_tensor, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import (
    IMAGE_TO_IMAGE_IMAGE_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()


class 							lowerCamelCase_(   A__,				A__,				unittest.TestCase    ):





					'''simple docstring'''



					lowercase__   :       Tuple										= StableDiffusionXLImgaImgPipeline
					lowercase__   :       List[str]										= TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {'height', 'width'}
					lowercase__   :       int										= PipelineTesterMixin.required_optional_params - {'latents'}
					lowercase__   :       Union[str, Any]										= TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
					lowercase__   :       Dict										= IMAGE_TO_IMAGE_IMAGE_PARAMS
					lowercase__   :       List[str]										= IMAGE_TO_IMAGE_IMAGE_PARAMS

					def 			snake_case__      (    self   ):
									torch.manual_seed(0   )
									_lowerCamelCase        =				UNetaDConditionModel(
									    block_out_channels=(3_2, 6_4)				,       layers_per_block=2				,       sample_size=3_2				,       in_channels=4				,       out_channels=4				,       down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''')				,       up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D''')				,       attention_head_dim=(2, 4)				,       use_linear_projection=lowerCamelCase__				,       addition_embed_type='''text_time'''				,       addition_time_embed_dim=8				,       transformer_layers_per_block=(1, 2)				,       projection_class_embeddings_input_dim=8_0				,       cross_attention_dim=6_4				,       )
									_lowerCamelCase        =				EulerDiscreteScheduler(
									    beta_start=0.0_0_0_8_5				,       beta_end=0.0_1_2				,       steps_offset=1				,       beta_schedule='''scaled_linear'''				,       timestep_spacing='''leading'''				,       )
									torch.manual_seed(0   )
									_lowerCamelCase        =				AutoencoderKL(
									    block_out_channels=[3_2, 6_4]				,       in_channels=3				,       out_channels=3				,       down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D''']				,       up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D''']				,       latent_channels=4				,       sample_size=1_2_8				,       )
									torch.manual_seed(0   )
									_lowerCamelCase        =				CLIPTextConfig(
									    bos_token_id=0				,       eos_token_id=2				,       hidden_size=3_2				,       intermediate_size=3_7				,       layer_norm_eps=1e-05				,       num_attention_heads=4				,       num_hidden_layers=5				,       pad_token_id=1				,       vocab_size=1_0_0_0				,       hidden_act='''gelu'''				,       projection_dim=3_2				,       )
									_lowerCamelCase        =				CLIPTextModel(lowerCamelCase__   )
									_lowerCamelCase        =				CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''				,       local_files_only=lowerCamelCase__   )

									_lowerCamelCase        =				CLIPTextModelWithProjection(lowerCamelCase__   )
									_lowerCamelCase        =				CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''				,       local_files_only=lowerCamelCase__   )

									_lowerCamelCase        =				{
									    '''unet''': unet,
									    '''scheduler''': scheduler,
									    '''vae''': vae,
									    '''text_encoder''': text_encoder,
									    '''tokenizer''': tokenizer,
									    '''text_encoder_2''': text_encoder_a,
									    '''tokenizer_2''': tokenizer_a,
									    # "safety_checker": None,
									    # "feature_extractor": None,
									}
									return components

					def 			snake_case__      (    self				,       lowerCamelCase__				,       lowerCamelCase__=0   ):
									_lowerCamelCase        =				floats_tensor((1, 3, 3_2, 3_2)				,       rng=random.Random(lowerCamelCase__   )   ).to(lowerCamelCase__   )
									_lowerCamelCase        =				image / 2 + 0.5
									if str(lowerCamelCase__   ).startswith('''mps'''   ):
													_lowerCamelCase        =				torch.manual_seed(lowerCamelCase__   )
									else:
													_lowerCamelCase        =				torch.Generator(device=lowerCamelCase__   ).manual_seed(lowerCamelCase__   )
									_lowerCamelCase        =				{
									    '''prompt''': '''A painting of a squirrel eating a burger''',
									    '''image''': image,
									    '''generator''': generator,
									    '''num_inference_steps''': 2,
									    '''guidance_scale''': 5.0,
									    '''output_type''': '''numpy''',
									    '''strength''': 0.7_5,
									}
									return inputs

					def 			snake_case__      (    self   ):
									_lowerCamelCase        =				'''cpu'''  # ensure determinism for the device-dependent torch.Generator
									_lowerCamelCase        =				self.get_dummy_components()
									_lowerCamelCase        =				StableDiffusionXLImgaImgPipeline(**lowerCamelCase__   )
									_lowerCamelCase        =				sd_pipe.to(lowerCamelCase__   )
									sd_pipe.set_progress_bar_config(disable=lowerCamelCase__   )

									_lowerCamelCase        =				self.get_dummy_inputs(lowerCamelCase__   )
									_lowerCamelCase        =				sd_pipe(**lowerCamelCase__   ).images
									_lowerCamelCase        =				image[0, -3:, -3:, -1]

									assert image.shape == (1, 3_2, 3_2, 3)

									_lowerCamelCase        =				np.array([0.4_6_5_6, 0.4_8_4_0, 0.4_4_3_9, 0.6_6_9_8, 0.5_5_7_4, 0.4_5_2_4, 0.5_7_9_9, 0.5_9_4_3, 0.5_1_6_5]   )

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

					def 			snake_case__      (    self   ):
									super().test_attention_slicing_forward_pass(expected_max_diff=3e-3   )

					def 			snake_case__      (    self   ):
									super().test_inference_batch_single_identical(expected_max_diff=3e-3   )

					def 			snake_case__      (    self   ):
									pass





					def 			snake_case__      (    self   ):
									_lowerCamelCase        =				self.get_dummy_components()
									_lowerCamelCase        =				StableDiffusionXLImgaImgPipeline(**lowerCamelCase__   )
									_lowerCamelCase        =				sd_pipe.to(lowerCamelCase__   )
									_lowerCamelCase        =				sd_pipe.to(lowerCamelCase__   )
									sd_pipe.set_progress_bar_config(disable=lowerCamelCase__   )

									# forward without prompt embeds
									_lowerCamelCase        =				self.get_dummy_inputs(lowerCamelCase__   )
									_lowerCamelCase        =				3 * ['''this is a negative prompt''']
									_lowerCamelCase        =				negative_prompt
									_lowerCamelCase        =				3 * [inputs['''prompt''']]

									_lowerCamelCase        =				sd_pipe(**lowerCamelCase__   )
									_lowerCamelCase        =				output.images[0, -3:, -3:, -1]

									# forward with prompt embeds
									_lowerCamelCase        =				self.get_dummy_inputs(lowerCamelCase__   )
									_lowerCamelCase        =				3 * ['''this is a negative prompt''']
									_lowerCamelCase        =				3 * [inputs.pop('''prompt'''   )]

									(
									    (
									    _lowerCamelCase
									)  ,     (
									    _lowerCamelCase
									)  ,     (
									    _lowerCamelCase
									)  ,     (
									    _lowerCamelCase
									)  ,     
									)        =				sd_pipe.encode_prompt(lowerCamelCase__				,       negative_prompt=lowerCamelCase__   )

									_lowerCamelCase        =				sd_pipe(
									    **lowerCamelCase__				,       prompt_embeds=lowerCamelCase__				,       negative_prompt_embeds=lowerCamelCase__				,       pooled_prompt_embeds=lowerCamelCase__				,       negative_pooled_prompt_embeds=lowerCamelCase__				,       )
									_lowerCamelCase        =				output.images[0, -3:, -3:, -1]

									# make sure that it's equal
									assert np.abs(image_slice_a.flatten() - image_slice_a.flatten()   ).max() < 1e-4


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





					'''simple docstring'''



					def 			snake_case__      (    self   ):
									super().tearDown()
									gc.collect()
									torch.cuda.empty_cache()

					def 			snake_case__      (    self				,       lowerCamelCase__				,       lowerCamelCase__="cpu"				,       lowerCamelCase__=torch.floataa				,       lowerCamelCase__=0   ):
									_lowerCamelCase        =				torch.Generator(device=lowerCamelCase__   ).manual_seed(lowerCamelCase__   )
									_lowerCamelCase        =				np.random.RandomState(lowerCamelCase__   ).standard_normal((1, 4, 6_4, 6_4)   )
									_lowerCamelCase        =				torch.from_numpy(lowerCamelCase__   ).to(device=lowerCamelCase__				,       dtype=lowerCamelCase__   )
									_lowerCamelCase        =				{
									    '''prompt''': '''a photograph of an astronaut riding a horse''',
									    '''latents''': latents,
									    '''generator''': generator,
									    '''num_inference_steps''': 3,
									    '''guidance_scale''': 7.5,
									    '''output_type''': '''numpy''',
									}
									return inputs





					def 			snake_case__      (    self   ):
									_lowerCamelCase        =				DiffusionPipeline.from_pretrained('''stabilityai/stable-diffusion-2-base'''   )
									pipe.to(lowerCamelCase__   )
									pipe.set_progress_bar_config(disable=lowerCamelCase__   )

									_lowerCamelCase        =				self.get_inputs(lowerCamelCase__   )
									_lowerCamelCase        =				pipe(**lowerCamelCase__   ).images
									_lowerCamelCase        =				image[0, -3:, -3:, -1].flatten()

									assert image.shape == (1, 5_1_2, 5_1_2, 3)
									_lowerCamelCase        =				np.array([0.4_9_4_9_3, 0.4_7_8_9_6, 0.4_0_7_9_8, 0.5_4_2_1_4, 0.5_3_2_1_2, 0.4_8_2_0_2, 0.4_7_6_5_6, 0.4_6_3_2_9, 0.4_8_5_0_6]   )
									assert np.abs(image_slice - expected_slice   ).max() < 7e-3
