





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   __SCREAMING_SNAKE_CASE     (					lowerCAmelCase_	,lowerCAmelCase_	,unittest.TestCase   ):
       _UpperCAmelCase :    int					       =       StableDiffusionXLImgaImgPipeline
       _UpperCAmelCase :    Any					       =       TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"height", "width"}
       _UpperCAmelCase :    int					       =       PipelineTesterMixin.required_optional_params - {"latents"}
       _UpperCAmelCase :    List[str]					       =       TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
       _UpperCAmelCase :    List[str]					       =       IMAGE_TO_IMAGE_IMAGE_PARAMS
       _UpperCAmelCase :    Optional[Any]					       =       IMAGE_TO_IMAGE_IMAGE_PARAMS







       def 		__lowerCamelCase       ( self	:      int     )  ->Union[str, Any]:
          torch.manual_seed(0     )
          lowerCamelCase__  :		Optional[Any]     =				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=A						,   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__  :		Tuple     =				EulerDiscreteScheduler(
              beta_start=0.0_00_85						,   beta_end=0.0_12						,   steps_offset=1						,   beta_schedule='''scaled_linear'''						,   timestep_spacing='''leading'''						,   )
          torch.manual_seed(0     )
          lowerCamelCase__  :		Any     =				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__  :		Union[str, Any]     =				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__  :		int     =				CLIPTextModel(A     )
          lowerCamelCase__  :		Tuple     =				CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''						,   local_files_only=A     )

          lowerCamelCase__  :		List[str]     =				CLIPTextModelWithProjection(A     )
          lowerCamelCase__  :		str     =				CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''						,   local_files_only=A     )

          lowerCamelCase__  :		int     =				{
              '''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 		__lowerCamelCase       ( self	:      Optional[Any]						,   A	:      int						,   A	:      int=0     )  ->Dict:
          lowerCamelCase__  :		Tuple     =				floats_tensor((1, 3, 3_2, 3_2)						,   rng=random.Random(A     )     ).to(A     )
          lowerCamelCase__  :		str     =				image / 2 + 0.5
          if str(A     ).startswith('''mps'''     ):
             lowerCamelCase__  :		Any     =				torch.manual_seed(A     )
          else:
             lowerCamelCase__  :		Optional[int]     =				torch.Generator(device=A     ).manual_seed(A     )
          lowerCamelCase__  :		Optional[int]     =				{
              '''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''': 0.75,
          }
          return inputs







       def 		__lowerCamelCase       ( self	:      Optional[Any]     )  ->str:
          lowerCamelCase__  :		Any     =				'''cpu'''  # ensure determinism for the device-dependent torch.Generator
          lowerCamelCase__  :		str     =				self.get_dummy_components()
          lowerCamelCase__  :		List[Any]     =				StableDiffusionXLImgaImgPipeline(**A     )
          lowerCamelCase__  :		int     =				sd_pipe.to(A     )
          sd_pipe.set_progress_bar_config(disable=A     )

          lowerCamelCase__  :		List[str]     =				self.get_dummy_inputs(A     )
          lowerCamelCase__  :		Any     =				sd_pipe(**A     ).images
          lowerCamelCase__  :		List[Any]     =				image[0, -3:, -3:, -1]

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

          lowerCamelCase__  :		Optional[int]     =				np.array([0.46_56, 0.48_40, 0.44_39, 0.66_98, 0.55_74, 0.45_24, 0.57_99, 0.59_43, 0.51_65]     )

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







       def 		__lowerCamelCase       ( self	:      int     )  ->Union[str, Any]:
          super().test_attention_slicing_forward_pass(expected_max_diff=3e-3     )







       def 		__lowerCamelCase       ( self	:      Any     )  ->Union[str, Any]:
          super().test_inference_batch_single_identical(expected_max_diff=3e-3     )







       def 		__lowerCamelCase       ( self	:      str     )  ->Tuple:
          pass







       def 		__lowerCamelCase       ( self	:      int     )  ->Dict:
          lowerCamelCase__  :		List[Any]     =				self.get_dummy_components()
          lowerCamelCase__  :		str     =				StableDiffusionXLImgaImgPipeline(**A     )
          lowerCamelCase__  :		List[Any]     =				sd_pipe.to(A     )
          lowerCamelCase__  :		List[str]     =				sd_pipe.to(A     )
          sd_pipe.set_progress_bar_config(disable=A     )

          # forward without prompt embeds
          lowerCamelCase__  :		Tuple     =				self.get_dummy_inputs(A     )
          lowerCamelCase__  :		Optional[int]     =				3 * ['''this is a negative prompt''']
          lowerCamelCase__  :		Tuple     =				negative_prompt
          lowerCamelCase__  :		int     =				3 * [inputs['''prompt''']]

          lowerCamelCase__  :		Optional[int]     =				sd_pipe(**A     )
          lowerCamelCase__  :		Optional[Any]     =				output.images[0, -3:, -3:, -1]

          # forward with prompt embeds
          lowerCamelCase__  :		Tuple     =				self.get_dummy_inputs(A     )
          lowerCamelCase__  :		str     =				3 * ['''this is a negative prompt''']
          lowerCamelCase__  :		Dict     =				3 * [inputs.pop('''prompt'''     )]

          (
              (
              lowerCamelCase__						
          )  ,				(
              lowerCamelCase__						
          )  ,				(
              lowerCamelCase__						
          )  ,				(
              lowerCamelCase__						
          )  ,										
          )  :		Any     =				sd_pipe.encode_prompt(A						,   negative_prompt=A     )

          lowerCamelCase__  :		List[Any]     =				sd_pipe(
              **A						,   prompt_embeds=A						,   negative_prompt_embeds=A						,   pooled_prompt_embeds=A						,   negative_pooled_prompt_embeds=A						,   )
          lowerCamelCase__  :		Tuple     =				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   __SCREAMING_SNAKE_CASE     (					unittest.TestCase   ):







       def 		__lowerCamelCase       ( self	:      List[str]     )  ->Tuple:
          super().tearDown()
          gc.collect()
          torch.cuda.empty_cache()







       def 		__lowerCamelCase       ( self	:      List[Any]						,   A	:      str						,   A	:      int="cpu"						,   A	:      List[Any]=torch.floataa						,   A	:      str=0     )  ->str:
          lowerCamelCase__  :		Tuple     =				torch.Generator(device=A     ).manual_seed(A     )
          lowerCamelCase__  :		Any     =				np.random.RandomState(A     ).standard_normal((1, 4, 6_4, 6_4)     )
          lowerCamelCase__  :		Optional[int]     =				torch.from_numpy(A     ).to(device=A						,   dtype=A     )
          lowerCamelCase__  :		List[Any]     =				{
              '''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 		__lowerCamelCase       ( self	:      Any     )  ->Dict:
          lowerCamelCase__  :		Optional[int]     =				DiffusionPipeline.from_pretrained('''stabilityai/stable-diffusion-2-base'''     )
          pipe.to(A     )
          pipe.set_progress_bar_config(disable=A     )

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

          assert image.shape == (1, 5_1_2, 5_1_2, 3)
          lowerCamelCase__  :		Optional[int]     =				np.array([0.4_94_93, 0.4_78_96, 0.4_07_98, 0.5_42_14, 0.5_32_12, 0.4_82_02, 0.4_76_56, 0.4_63_29, 0.4_85_06]     )
          assert np.abs(image_slice - expected_slice     ).max() < 7e-3








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)

a__   :					Optional[Any] 						=      logging.getLogger()

def 	_lowerCAmelCase (    ):
      lowercase__  =	argparse.ArgumentParser()
      parser.add_argument('-f'						)
      lowercase__  =	parser.parse_args()
      return args.f



class    UpperCAmelCase__(				lowerCamelCase						):

   '''simple docstring'''




   def       UpperCAmelCase		(    self	:      List[str])						->					None:




         """simple docstring"""

         lowercase__  =	logging.StreamHandler(sys.stdout)
         logger.addHandler(lowerCAmelCase)


   def       UpperCAmelCase		(    self	:      List[str]      ,					lowerCAmelCase	:      Union[str, Any])						->					List[Any]:




         """simple docstring"""

         lowercase__  =	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(lowerCAmelCase      ,					'argv'      ,					lowerCAmelCase):
                     lowercase__  =	run_glue_deebert.main()
                     for value in result.values():
                           self.assertGreaterEqual(lowerCAmelCase      ,					0.6_66)



   @slow
   @require_torch_non_multi_gpu
   def       UpperCAmelCase		(    self	:      Optional[Any])						->					Union[str, Any]:




         """simple docstring"""

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

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

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