




import os
import sys
from pathlib import Path

import torch

from transformers.testing_utils import TestCasePlus, execute_subprocess_async, require_torch_multi_gpu
from utils import load_json


SCREAMING_SNAKE_CASE_    :       Tuple       =							torch.cuda.is_available()
SCREAMING_SNAKE_CASE_    :       List[str]       =							[''' Sam ate lunch today.''', '''Sams lunch ingredients.''']
SCREAMING_SNAKE_CASE_    :       List[Any]       =							['''A very interesting story about what I ate for lunch.''', '''Avocado, celery, turkey, coffee''']
SCREAMING_SNAKE_CASE_    :       List[Any]       =							{
    '''max_tokens_per_batch''': None,
    '''supervise_forward''': True,
    '''normalize_hidden''': True,
    '''label_smoothing''': 0.2,
    '''eval_max_gen_length''': None,
    '''eval_beams''': 1,
    '''val_metric''': '''loss''',
    '''save_top_k''': 1,
    '''adafactor''': True,
    '''early_stopping_patience''': 2,
    '''logger_name''': '''default''',
    '''length_penalty''': 0.5,
    '''cache_dir''': '''''',
    '''task''': '''summarization''',
    '''num_workers''': 2,
    '''alpha_hid''': 0,
    '''freeze_embeds''': True,
    '''enc_only''': False,
    '''tgt_suffix''': '''''',
    '''resume_from_checkpoint''': None,
    '''sortish_sampler''': True,
    '''student_decoder_layers''': 1,
    '''val_check_interval''': 1.0,
    '''output_dir''': '''''',
    '''fp16''': False,  # TODO(SS): set this to CUDA_AVAILABLE if ci installs apex or start using native amp
    '''no_teacher''': False,
    '''fp16_opt_level''': '''O1''',
    '''gpus''': 1 if CUDA_AVAILABLE else 0,
    '''n_tpu_cores''': 0,
    '''max_grad_norm''': 1.0,
    '''do_train''': True,
    '''do_predict''': True,
    '''accumulate_grad_batches''': 1,
    '''server_ip''': '''''',
    '''server_port''': '''''',
    '''seed''': 42,
    '''model_name_or_path''': '''sshleifer/bart-tiny-random''',
    '''config_name''': '''''',
    '''tokenizer_name''': '''facebook/bart-large''',
    '''do_lower_case''': False,
    '''learning_rate''': 0.3,
    '''lr_scheduler''': '''linear''',
    '''weight_decay''': 0.0,
    '''adam_epsilon''': 1e-08,
    '''warmup_steps''': 0,
    '''max_epochs''': 1,
    '''train_batch_size''': 2,
    '''eval_batch_size''': 2,
    '''max_source_length''': 12,
    '''max_target_length''': 12,
    '''val_max_target_length''': 12,
    '''test_max_target_length''': 12,
    '''fast_dev_run''': False,
    '''no_cache''': False,
    '''n_train''': -1,
    '''n_val''': -1,
    '''n_test''': -1,
    '''student_encoder_layers''': 1,
    '''freeze_encoder''': False,
    '''auto_scale_batch_size''': False,
    '''overwrite_output_dir''': False,
    '''student''': None,
}
def 							__lowerCAmelCase			(UpperCAmelCase_ ,     UpperCAmelCase_		):
    a__  			=							"""\n""".join(UpperCAmelCase_		)
    Path(UpperCAmelCase_		).open("""w"""		).writelines(UpperCAmelCase_		)
def 							__lowerCAmelCase			(UpperCAmelCase_		):
    for split in ["train", "val", "test"]:
        _dump_articles(os.path.join(UpperCAmelCase_ ,     F"""{split}.source"""		) ,     UpperCAmelCase_		)
        _dump_articles(os.path.join(UpperCAmelCase_ ,     F"""{split}.target"""		) ,     UpperCAmelCase_		)
    return tmp_dir







class 		__UpperCAmelCase(		lowercase__			):





     """simple docstring"""



     @classmethod
     def 					__SCREAMING_SNAKE_CASE		(   cls			:							str     )      ->      Any:






         """simple docstring"""
         return cls

     @require_torch_multi_gpu
     def 					__SCREAMING_SNAKE_CASE		(   self			:							Any     )      ->      Optional[int]:






         """simple docstring"""
         a__  			=							{
             """no_teacher""": True,
             """freeze_encoder""": True,
             """gpus""": 2,
             """overwrite_output_dir""": True,
             """sortish_sampler""": True,
         }
         self._test_distiller_cli_fork(A    ,					check_contents=A     )






     def 					__SCREAMING_SNAKE_CASE		(   self			:							Any    ,					A			:							Any    ,					A			:							int=True     )      ->      Dict:






         """simple docstring"""
         a__  			=							{
             """label_smoothing""": 0.0,
             """early_stopping_patience""": -1,
             """train_batch_size""": 1,
             """eval_batch_size""": 2,
             """max_epochs""": 2,
             """alpha_mlm""": 0.2,
             """alpha_ce""": 0.8,
             """do_predict""": True,
             """model_name_or_path""": """sshleifer/tinier_bart""",
             """teacher""": CHEAP_ARGS["""model_name_or_path"""],
             """val_check_interval""": 0.5,
         }
         default_updates.update(A     )
         a__  			=							CHEAP_ARGS.copy()
         a__  			=							make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()     )
         a__  			=							self.get_auto_remove_tmp_dir()
         args_d.update(data_dir=A    ,					output_dir=A    ,					**A     )

         def convert(A			:							Dict    ,					A			:							Optional[Any]     ):
             if k in ["tgt_suffix", "server_ip", "server_port", "out", "n_tpu_cores"]:
                 return ""
             if v is False or v is None:
                 return ""
             if v is True:  # or len(str(v))==0:
                 return f"""--{k}"""
             return f"""--{k}={v}"""

         a__  			=							[x for x in (convert(A    ,					A     ) for k, v in args_d.items()) if len(A     )]
         a__  			=							[sys.executable, f"""{self.test_file_dir}/distillation.py"""] + cli_args
         execute_subprocess_async(A    ,					env=self.get_env()     )

         a__  			=							os.listdir(A     )
         a__  			=							{os.path.basename(A     ) for p in contents}
         a__  			=							[p for p in contents if p.endswith("""ckpt"""     )]
         assert len(A     ) > 0

         self.assertIn("""test_generations.txt"""    ,					A     )
         self.assertIn("""test_results.txt"""    ,					A     )

         # get the following from the module, (we don't have access to `model` here)
         a__  			=							os.path.join(A    ,					"""metrics.json"""     )
         a__  			=							"""rouge2"""

         a__  			=							load_json(A     )
         # {'test': [{'test_avg_loss': 10.63731575012207, 'test_avg_rouge1': 0.0, 'test_avg_rouge2': 0.0, 'test_avg_rougeL': 0.0, 'test_avg_gen_time': 0.1822289228439331, 'test_avg_gen_len': 142.0, 'step_count': 1}]}
         print(A     )
         a__  			=							metrics["""val"""][-1]
         self.assertGreaterEqual(last_step_stats["""val_avg_gen_time"""]    ,					0.0_1     )
         self.assertIsInstance(last_step_stats[f"""val_avg_{val_metric}"""]    ,					A     )
         self.assertEqual(len(metrics["""test"""]     )    ,					1     )
         a__  			=							int(args_d["""max_epochs"""] * (1 / args_d["""val_check_interval"""]) / 2 + 1     )
         self.assertEqual(len(metrics["""val"""]     )    ,					A     )












import random
import unittest

import numpy as np
import torch
from PIL import Image
from torch import nn
from transformers import (
    CLIPImageProcessor,
    CLIPTextConfig,
    CLIPTextModelWithProjection,
    CLIPTokenizer,
    CLIPVisionConfig,
    CLIPVisionModelWithProjection,
)

from diffusers import KandinskyVaaPriorEmbaEmbPipeline, PriorTransformer, UnCLIPScheduler
from diffusers.utils import floats_tensor, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, skip_mps

from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()







class 		__UpperCAmelCase(		lowercase__							,						unittest.TestCase			):





     """simple docstring"""



     __UpperCAmelCase						:			Optional[int]							=     KandinskyVaaPriorEmbaEmbPipeline
     __UpperCAmelCase						:			str							=     ["""prompt""", """image"""]
     __UpperCAmelCase						:			Dict							=     ["""prompt""", """image"""]
     __UpperCAmelCase						:			Optional[Any]							=     [
         """num_images_per_prompt""",
         """strength""",
         """generator""",
         """num_inference_steps""",
         """latents""",
         """negative_prompt""",
         """guidance_scale""",
         """output_type""",
         """return_dict""",
     ]
     __UpperCAmelCase						:			Dict							=     False

     @property
     def 					__SCREAMING_SNAKE_CASE		(   self			:							Any     )      ->      int:






         """simple docstring"""
         return 32

     @property
     def 					__SCREAMING_SNAKE_CASE		(   self			:							Optional[Any]     )      ->      Any:






         """simple docstring"""
         return 32

     @property
     def 					__SCREAMING_SNAKE_CASE		(   self			:							Tuple     )      ->      int:






         """simple docstring"""
         return self.time_input_dim

     @property
     def 					__SCREAMING_SNAKE_CASE		(   self			:							int     )      ->      List[Any]:






         """simple docstring"""
         return self.time_input_dim * 4

     @property
     def 					__SCREAMING_SNAKE_CASE		(   self			:							Optional[int]     )      ->      Tuple:






         """simple docstring"""
         return 1_00

     @property
     def 					__SCREAMING_SNAKE_CASE		(   self			:							Union[str, Any]     )      ->      List[str]:






         """simple docstring"""
         a__  			=							CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""     )
         return tokenizer

     @property
     def 					__SCREAMING_SNAKE_CASE		(   self			:							int     )      ->      Optional[int]:






         """simple docstring"""
         torch.manual_seed(0     )
         a__  			=							CLIPTextConfig(
             bos_token_id=0    ,					eos_token_id=2    ,					hidden_size=self.text_embedder_hidden_size    ,					projection_dim=self.text_embedder_hidden_size    ,					intermediate_size=37    ,					layer_norm_eps=1E-05    ,					num_attention_heads=4    ,					num_hidden_layers=5    ,					pad_token_id=1    ,					vocab_size=10_00    ,					)
         return CLIPTextModelWithProjection(A     )

     @property
     def 					__SCREAMING_SNAKE_CASE		(   self			:							Dict     )      ->      List[Any]:






         """simple docstring"""
         torch.manual_seed(0     )

         a__  			=							{
             """num_attention_heads""": 2,
             """attention_head_dim""": 12,
             """embedding_dim""": self.text_embedder_hidden_size,
             """num_layers""": 1,
         }

         a__  			=							PriorTransformer(**A     )
         # clip_std and clip_mean is initialized to be 0 so PriorTransformer.post_process_latents will always return 0 - set clip_std to be 1 so it won't return 0
         a__  			=							nn.Parameter(torch.ones(model.clip_std.shape     )     )
         return model

     @property
     def 					__SCREAMING_SNAKE_CASE		(   self			:							Dict     )      ->      Optional[int]:






         """simple docstring"""
         torch.manual_seed(0     )
         a__  			=							CLIPVisionConfig(
             hidden_size=self.text_embedder_hidden_size    ,					image_size=2_24    ,					projection_dim=self.text_embedder_hidden_size    ,					intermediate_size=37    ,					num_attention_heads=4    ,					num_channels=3    ,					num_hidden_layers=5    ,					patch_size=14    ,					)

         a__  			=							CLIPVisionModelWithProjection(A     )
         return model

     @property
     def 					__SCREAMING_SNAKE_CASE		(   self			:							int     )      ->      List[str]:






         """simple docstring"""
         a__  			=							CLIPImageProcessor(
             crop_size=2_24    ,					do_center_crop=A    ,					do_normalize=A    ,					do_resize=A    ,					image_mean=[0.4_8_1_4_5_4_6_6, 0.4_5_7_8_2_7_5, 0.4_0_8_2_1_0_7_3]    ,					image_std=[0.2_6_8_6_2_9_5_4, 0.2_6_1_3_0_2_5_8, 0.2_7_5_7_7_7_1_1]    ,					resample=3    ,					size=2_24    ,					)

         return image_processor

     def 					__SCREAMING_SNAKE_CASE		(   self			:							List[Any]     )      ->      Tuple:






         """simple docstring"""
         a__  			=							self.dummy_prior
         a__  			=							self.dummy_image_encoder
         a__  			=							self.dummy_text_encoder
         a__  			=							self.dummy_tokenizer
         a__  			=							self.dummy_image_processor

         a__  			=							UnCLIPScheduler(
             variance_type="""fixed_small_log"""    ,					prediction_type="""sample"""    ,					num_train_timesteps=10_00    ,					clip_sample=A    ,					clip_sample_range=1_0.0    ,					)

         a__  			=							{
             """prior""": prior,
             """image_encoder""": image_encoder,
             """text_encoder""": text_encoder,
             """tokenizer""": tokenizer,
             """scheduler""": scheduler,
             """image_processor""": image_processor,
         }

         return components

     def 					__SCREAMING_SNAKE_CASE		(   self			:							Optional[int]    ,					A			:							Tuple    ,					A			:							Optional[Any]=0     )      ->      Any:






         """simple docstring"""
         if str(A     ).startswith("""mps"""     ):
             a__  			=							torch.manual_seed(A     )
         else:
             a__  			=							torch.Generator(device=A     ).manual_seed(A     )

         a__  			=							floats_tensor((1, 3, 64, 64)    ,					rng=random.Random(A     )     ).to(A     )
         a__  			=							image.cpu().permute(0    ,					2    ,					3    ,					1     )[0]
         a__  			=							Image.fromarray(np.uinta(A     )     ).convert("""RGB"""     ).resize((2_56, 2_56)     )

         a__  			=							{
             """prompt""": """horse""",
             """image""": init_image,
             """strength""": 0.5,
             """generator""": generator,
             """guidance_scale""": 4.0,
             """num_inference_steps""": 2,
             """output_type""": """np""",
         }
         return inputs

     def 					__SCREAMING_SNAKE_CASE		(   self			:							Dict     )      ->      Optional[int]:






         """simple docstring"""
         a__  			=							"""cpu"""

         a__  			=							self.get_dummy_components()

         a__  			=							self.pipeline_class(**A     )
         a__  			=							pipe.to(A     )

         pipe.set_progress_bar_config(disable=A     )

         a__  			=							pipe(**self.get_dummy_inputs(A     )     )
         a__  			=							output.image_embeds

         a__  			=							pipe(
             **self.get_dummy_inputs(A     )    ,					return_dict=A    ,					)[0]

         a__  			=							image[0, -10:]
         a__  			=							image_from_tuple[0, -10:]

         assert image.shape == (1, 32)

         a__  			=							np.array(
             [
                 0.1_0_7_1_2_8_4,
                 1.3_3_3_0_2_7_1,
                 0.6_1_2_6_0_2_2_3,
                 -0.6_6_9_1_0_6_5,
                 -0.3_8_4_6_8_5_2,
                 -1.0_3_0_3_6_6_1,
                 0.2_2_7_1_6_1_1_1,
                 0.0_3_3_4_8_9_0_1,
                 0.3_0_0_4_0_6_7_5,
                 -0.2_4_8_0_5_0_2_9,
             ]     )

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

     @skip_mps
     def 					__SCREAMING_SNAKE_CASE		(   self			:							str     )      ->      Optional[Any]:






         """simple docstring"""
         a__  			=							torch_device == """cpu"""
         a__  			=							True
         a__  			=							False

         self._test_inference_batch_single_identical(
             test_max_difference=A    ,					relax_max_difference=A    ,					test_mean_pixel_difference=A    ,					)






     @skip_mps
     def 					__SCREAMING_SNAKE_CASE		(   self			:							Tuple     )      ->      Union[str, Any]:






         """simple docstring"""
         a__  			=							torch_device == """cpu"""
         a__  			=							False

         self._test_attention_slicing_forward_pass(
             test_max_difference=A    ,					test_mean_pixel_difference=A    ,					)






