


'''simple docstring'''






import unittest

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

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

from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()






class      SCREAMING_SNAKE_CASE_	( snake_case					,					unittest.TestCase		):
			__a					:      List[str]					=  KandinskyPriorPipeline
			__a					:      str					=  ['''prompt''']
			__a					:      Tuple					=  ['''prompt''', '''negative_prompt''']
			__a					:      List[Any]					=  [
			    '''num_images_per_prompt''',
			    '''generator''',
			    '''num_inference_steps''',
			    '''latents''',
			    '''negative_prompt''',
			    '''guidance_scale''',
			    '''output_type''',
			    '''return_dict''',
			]
			__a					:      Dict					=  False




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


							'''simple docstring'''






							return 3_2




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


							'''simple docstring'''






							return 3_2




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


							'''simple docstring'''






							return self.time_input_dim




			@property
			def 			_snake_case   (      self       )			->						int:


							'''simple docstring'''






							return self.time_input_dim * 4




			@property
			def 			_snake_case   (      self       )			->						int:


							'''simple docstring'''






							return 1_0_0




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


							'''simple docstring'''






							__SCREAMING_SNAKE_CASE					:							List[str]			   = CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''       )
							return tokenizer




			@property
			def 			_snake_case   (      self       )			->						int:


							'''simple docstring'''






							torch.manual_seed(0       )
							__SCREAMING_SNAKE_CASE					:							Optional[int]			   = 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=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					,      )
							return CLIPTextModelWithProjection(lowercase       )




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


							'''simple docstring'''






							torch.manual_seed(0       )

							__SCREAMING_SNAKE_CASE					:							Dict			   = {
							    '''num_attention_heads''': 2,
							    '''attention_head_dim''': 1_2,
							    '''embedding_dim''': self.text_embedder_hidden_size,
							    '''num_layers''': 1,
							}

							__SCREAMING_SNAKE_CASE					:							List[Any]			   = PriorTransformer(**lowercase       )
							# 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
							__SCREAMING_SNAKE_CASE					:							Tuple			   = nn.Parameter(torch.ones(model.clip_std.shape       )       )
							return model




			@property
			def 			_snake_case   (      self       )			->						Dict:


							'''simple docstring'''






							torch.manual_seed(0       )
							__SCREAMING_SNAKE_CASE					:							Optional[Any]			   = CLIPVisionConfig(
							    hidden_size=self.text_embedder_hidden_size					,      image_size=2_2_4					,      projection_dim=self.text_embedder_hidden_size					,      intermediate_size=3_7					,      num_attention_heads=4					,      num_channels=3					,      num_hidden_layers=5					,      patch_size=1_4					,      )

							__SCREAMING_SNAKE_CASE					:							Tuple			   = CLIPVisionModelWithProjection(lowercase       )
							return model




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


							'''simple docstring'''






							__SCREAMING_SNAKE_CASE					:							Tuple			   = CLIPImageProcessor(
							    crop_size=2_2_4					,      do_center_crop=lowercase					,      do_normalize=lowercase					,      do_resize=lowercase					,      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_2_4					,      )

							return image_processor




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


							'''simple docstring'''






							__SCREAMING_SNAKE_CASE					:							Optional[Any]			   = self.dummy_prior
							__SCREAMING_SNAKE_CASE					:							Dict			   = self.dummy_image_encoder
							__SCREAMING_SNAKE_CASE					:							int			   = self.dummy_text_encoder
							__SCREAMING_SNAKE_CASE					:							Optional[int]			   = self.dummy_tokenizer
							__SCREAMING_SNAKE_CASE					:							List[Any]			   = self.dummy_image_processor

							__SCREAMING_SNAKE_CASE					:							Dict			   = UnCLIPScheduler(
							    variance_type='''fixed_small_log'''					,      prediction_type='''sample'''					,      num_train_timesteps=1_0_0_0					,      clip_sample=lowercase					,      clip_sample_range=1_0.0					,      )

							__SCREAMING_SNAKE_CASE					:							Optional[int]			   = {
							    '''prior''': prior,
							    '''image_encoder''': image_encoder,
							    '''text_encoder''': text_encoder,
							    '''tokenizer''': tokenizer,
							    '''scheduler''': scheduler,
							    '''image_processor''': image_processor,
							}

							return components




			def 			_snake_case   (      self					,      lowercase					,      lowercase=0       )			->						List[str]:


							'''simple docstring'''






							if str(lowercase       ).startswith('''mps'''       ):
											__SCREAMING_SNAKE_CASE					:							Any			   = torch.manual_seed(lowercase       )
							else:
											__SCREAMING_SNAKE_CASE					:							Dict			   = torch.Generator(device=lowercase       ).manual_seed(lowercase       )
							__SCREAMING_SNAKE_CASE					:							Optional[int]			   = {
							    '''prompt''': '''horse''',
							    '''generator''': generator,
							    '''guidance_scale''': 4.0,
							    '''num_inference_steps''': 2,
							    '''output_type''': '''np''',
							}
							return inputs




			def 			_snake_case   (      self       )			->						List[str]:


							'''simple docstring'''






							__SCREAMING_SNAKE_CASE					:							str			   = '''cpu'''

							__SCREAMING_SNAKE_CASE					:							Optional[Any]			   = self.get_dummy_components()

							__SCREAMING_SNAKE_CASE					:							Tuple			   = self.pipeline_class(**lowercase       )
							__SCREAMING_SNAKE_CASE					:							List[str]			   = pipe.to(lowercase       )

							pipe.set_progress_bar_config(disable=lowercase       )

							__SCREAMING_SNAKE_CASE					:							str			   = pipe(**self.get_dummy_inputs(lowercase       )       )
							__SCREAMING_SNAKE_CASE					:							Union[str, Any]			   = output.image_embeds

							__SCREAMING_SNAKE_CASE					:							List[Any]			   = pipe(
							    **self.get_dummy_inputs(lowercase       )					,      return_dict=lowercase					,      )[0]

							__SCREAMING_SNAKE_CASE					:							List[Any]			   = image[0, -1_0:]
							__SCREAMING_SNAKE_CASE					:							Optional[int]			   = image_from_tuple[0, -1_0:]

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

							__SCREAMING_SNAKE_CASE					:							Optional[Any]			   = np.array(
							    [-0.0_5_3_2, 1.7_1_2_0, 0.3_6_5_6, -1.0_8_5_2, -0.8_9_4_6, -1.1_7_5_6, 0.4_3_4_8, 0.2_4_8_2, 0.5_1_4_6, -0.1_1_5_6]       )

							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 			_snake_case   (      self       )			->						List[str]:


							'''simple docstring'''






							__SCREAMING_SNAKE_CASE					:							str			   = torch_device == '''cpu'''
							__SCREAMING_SNAKE_CASE					:							Optional[Any]			   = True
							__SCREAMING_SNAKE_CASE					:							Optional[int]			   = False

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




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


							'''simple docstring'''






							__SCREAMING_SNAKE_CASE					:							Any			   = torch_device == '''cpu'''
							__SCREAMING_SNAKE_CASE					:							Union[str, Any]			   = False

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






'''simple docstring'''






import argparse
import itertools
import operator
import sys
from collections import OrderedDict

from run_eval import datetime_now, run_generate

from utils import ROUGE_KEYS


# A table of supported tasks and the list of scores in the order of importance to be sorted by.
# To add a new task, simply list the score names that `run_eval.run_generate()` returns
_A      =     {
    """translation""": ["""bleu"""],
    """summarization""": ROUGE_KEYS,
}


def A_					(				__SCREAMING_SNAKE_CASE :					str       )     ->  Dict:
					__SCREAMING_SNAKE_CASE					:							Tuple			   = search.split()
					__SCREAMING_SNAKE_CASE					:							List[Any]			   = dict((g.split('''='''       ) for g in groups)       )
					__SCREAMING_SNAKE_CASE					:							List[Any]			   = list(entries.keys()       )
					__SCREAMING_SNAKE_CASE					:							Dict			   = [[f"""--{k} {v}""" for v in vs.split(''':'''       )] for k, vs in entries.items()]
					__SCREAMING_SNAKE_CASE					:							int			   = [list(__SCREAMING_SNAKE_CASE       ) for x in itertools.product(*__SCREAMING_SNAKE_CASE       )]
					return matrix, entry_names


def A_					(				)     ->  int:
					__SCREAMING_SNAKE_CASE					:							Optional[Any]			   = sys.argv[0]

					__SCREAMING_SNAKE_CASE					:							Optional[Any]			   = argparse.ArgumentParser(
					    usage=(
					        '''\n\nImportant: this script accepts all arguments `run_eval.py` accepts and then a few extra, therefore'''
					        ''' refer to `run_eval.py -h` for the complete list.'''
					    )       )
					parser.add_argument(
					    '''--search'''	,   type=__SCREAMING_SNAKE_CASE	,   required=__SCREAMING_SNAKE_CASE	,   help='''param space to search, e.g. "num_beams=5:10 length_penalty=0.8:1.0:1.2"'''	,   )
					parser.add_argument(
					    '''--bs'''	,   type=__SCREAMING_SNAKE_CASE	,   default=8	,   required=__SCREAMING_SNAKE_CASE	,   help='''initial batch size (may get reduced if it\'s too big)'''       )
					parser.add_argument('''--task'''	,   type=__SCREAMING_SNAKE_CASE	,   help='''used for task_specific_params + metrics'''       )
					parser.add_argument(
					    '''--info'''	,   nargs='''?'''	,   type=__SCREAMING_SNAKE_CASE	,   const=datetime_now()	,   help=(
					        '''add custom notes to be printed before the results table. If no value is passed, the current datetime'''
					        ''' string will be used.'''
					    )	,   )
					__SCREAMING_SNAKE_CASE   ,   __SCREAMING_SNAKE_CASE					:							str			   = parser.parse_known_args()
					# we share some of the args
					args_main.extend(['''--task''', args.task]       )
					__SCREAMING_SNAKE_CASE					:							List[Any]			   = [prog] + args_main

					# to support variations like translation_en_to_de"
					__SCREAMING_SNAKE_CASE					:							List[str]			   = '''translation''' if '''translation''' in args.task else '''summarization'''

					__SCREAMING_SNAKE_CASE   ,   __SCREAMING_SNAKE_CASE					:							Tuple			   = parse_search_arg(args.search       )
					__SCREAMING_SNAKE_CASE					:							Union[str, Any]			   = task_score_names[task]  # score cols first
					__SCREAMING_SNAKE_CASE					:							Optional[int]			   = {col: len(str(__SCREAMING_SNAKE_CASE       )       ) for col in col_names}
					__SCREAMING_SNAKE_CASE					:							Dict			   = []
					for r in matrix:
										__SCREAMING_SNAKE_CASE					:							List[str]			   = dict((x.replace('''--'''	,   ''''''       ).split() for x in r)       )
										__SCREAMING_SNAKE_CASE					:							Tuple			   = ''' '''.join(__SCREAMING_SNAKE_CASE       ).split()
										args_exp.extend(['''--bs''', str(args.bs       )]       )  # in case we need to reduce its size due to CUDA OOM
										__SCREAMING_SNAKE_CASE					:							Dict			   = args_normal + args_exp

										# XXX: need to trap CUDA OOM and lower args.bs if that happens and retry

										__SCREAMING_SNAKE_CASE					:							Union[str, Any]			   = run_generate(verbose=__SCREAMING_SNAKE_CASE       )
										# make sure scores are first in the table
										__SCREAMING_SNAKE_CASE					:							Any			   = OrderedDict()
										for score in task_score_names[task]:
															__SCREAMING_SNAKE_CASE					:							int			   = scores[score]
										result.update(__SCREAMING_SNAKE_CASE       )
										results.append(__SCREAMING_SNAKE_CASE       )

										# find widest entries
										for k, v in result.items():
															__SCREAMING_SNAKE_CASE					:							List[Any]			   = len(str(__SCREAMING_SNAKE_CASE       )       )
															if l > col_widths[k]:
																				__SCREAMING_SNAKE_CASE					:							Any			   = l

					__SCREAMING_SNAKE_CASE					:							Optional[int]			   = sorted(__SCREAMING_SNAKE_CASE	,   key=operator.itemgetter(*task_score_names[task]       )	,   reverse=__SCREAMING_SNAKE_CASE       )
					print(''' | '''.join([f"""{col:{col_widths[col]}}""" for col in col_names]       )       )
					print(''' | '''.join([f"""{"-"*col_widths[col]}""" for col in col_names]       )       )
					for row in results_sorted:
										print(''' | '''.join([f"""{row[col]:{col_widths[col]}}""" for col in col_names]       )       )

					__SCREAMING_SNAKE_CASE					:							Any			   = results_sorted[0]
					for score in task_score_names[task]:
										del best[score]
					__SCREAMING_SNAKE_CASE					:							Union[str, Any]			   = [f"""--{k} {v}""" for k, v in best.items()]
					__SCREAMING_SNAKE_CASE					:							Tuple			   = ['''--bs''', str(args.bs       )]
					if args.info:
										print(f"""\nInfo: {args.info}"""       )
					print('''\nBest score args:'''       )
					print(''' '''.join(args_main + best_args + dyn_args       )       )

					return results_sorted


if __name__ == "__main__":
						# Usage:
						# [normal-run_eval_search.py cmd plus] \
						# --search="num_beams=1:5:10 length_penalty=0.8:1:1.2 early_stopping=true:false"
						#
						# Example:
						# PYTHONPATH="src:examples/seq2seq" python examples/seq2seq/run_eval_search.py $MODEL_NAME \
						# $DATA_DIR/val.source $SAVE_DIR/test_translations.txt --reference_path $DATA_DIR/val.target \
						# --score_path $SAVE_DIR/test_bleu.json --bs $BS --task translation \
						# --search="num_beams=1:5:10 length_penalty=0.8:1:1.2 early_stopping=true:false"
						run_search()


