

import gc
import random
import unittest

import numpy as np
import torch
from transformers import XLMRobertaTokenizerFast

from diffusers import DDIMScheduler, KandinskyPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandinsky.text_encoder import MCLIPConfig, MultilingualCLIP
from diffusers.utils import floats_tensor, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()





class A__     (			A	,		unittest.TestCase			):



 """simple docstring"""







 _lowercase  :       Optional[int]  			=    KandinskyPipeline
 _lowercase  :       List[Any]  			=    [
     '''prompt''',
     '''image_embeds''',
     '''negative_image_embeds''',
 ]
 _lowercase  :       List[Any]  			=    ['''prompt''', '''negative_prompt''', '''image_embeds''', '''negative_image_embeds''']
 _lowercase  :       Tuple  			=    [
     '''generator''',
     '''height''',
     '''width''',
     '''latents''',
     '''guidance_scale''',
     '''negative_prompt''',
     '''num_inference_steps''',
     '''return_dict''',
     '''guidance_scale''',
     '''num_images_per_prompt''',
     '''output_type''',
     '''return_dict''',
 ]
 _lowercase  :       Tuple  			=    False

 @property
 def  __magic_name__							(		self			:						List[Any]							):






      '''simple docstring'''




      return 3_2

 @property
 def  __magic_name__							(		self			:						Optional[Any]							):






      '''simple docstring'''




      return 3_2

 @property
 def  __magic_name__							(		self			:						Union[str, Any]							):






      '''simple docstring'''




      return self.time_input_dim

 @property
 def  __magic_name__							(		self			:						Optional[int]							):






      '''simple docstring'''




      return self.time_input_dim * 4

 @property
 def  __magic_name__							(		self			:						Optional[int]							):






      '''simple docstring'''




      return 1_0_0

 @property
 def  __magic_name__							(		self			:						str							):






      '''simple docstring'''




      _lowerCAmelCase :       Union[str, Any]	   =     XLMRobertaTokenizerFast.from_pretrained("YiYiXu/tiny-random-mclip-base"							)
      return tokenizer

 @property
 def  __magic_name__							(		self			:						Optional[int]							):






      '''simple docstring'''




      torch.manual_seed(0							)
      _lowerCAmelCase :       Any	   =     MCLIPConfig(
          numDims=self.cross_attention_dim  ,    transformerDimensions=self.text_embedder_hidden_size  ,    hidden_size=self.text_embedder_hidden_size  ,    intermediate_size=3_7  ,    num_attention_heads=4  ,    num_hidden_layers=5  ,    vocab_size=1_0_0_5  ,    )

      _lowerCAmelCase :       Tuple	   =     MultilingualCLIP(A_							)
      _lowerCAmelCase :       Optional[int]	   =     text_encoder.eval()

      return text_encoder

 @property
 def  __magic_name__							(		self			:						Optional[Any]							):






      '''simple docstring'''




      torch.manual_seed(0							)

      _lowerCAmelCase :       List[Any]	   =     {
          "in_channels": 4,
          # Out channels is double in channels because predicts mean and variance
          "out_channels": 8,
          "addition_embed_type": "text_image",
          "down_block_types": ("ResnetDownsampleBlock2D", "SimpleCrossAttnDownBlock2D"),
          "up_block_types": ("SimpleCrossAttnUpBlock2D", "ResnetUpsampleBlock2D"),
          "mid_block_type": "UNetMidBlock2DSimpleCrossAttn",
          "block_out_channels": (self.block_out_channels_a, self.block_out_channels_a * 2),
          "layers_per_block": 1,
          "encoder_hid_dim": self.text_embedder_hidden_size,
          "encoder_hid_dim_type": "text_image_proj",
          "cross_attention_dim": self.cross_attention_dim,
          "attention_head_dim": 4,
          "resnet_time_scale_shift": "scale_shift",
          "class_embed_type": None,
      }

      _lowerCAmelCase :       Optional[Any]	   =     UNetaDConditionModel(**A_							)
      return model

 @property
 def  __magic_name__							(		self			:						Dict							):






      '''simple docstring'''




      return {
          "block_out_channels": [3_2, 6_4],
          "down_block_types": ["DownEncoderBlock2D", "AttnDownEncoderBlock2D"],
          "in_channels": 3,
          "latent_channels": 4,
          "layers_per_block": 1,
          "norm_num_groups": 8,
          "norm_type": "spatial",
          "num_vq_embeddings": 1_2,
          "out_channels": 3,
          "up_block_types": [
              "AttnUpDecoderBlock2D",
              "UpDecoderBlock2D",
          ],
          "vq_embed_dim": 4,
      }

 @property
 def  __magic_name__							(		self			:						str							):






      '''simple docstring'''




      torch.manual_seed(0							)
      _lowerCAmelCase :       Any	   =     VQModel(**self.dummy_movq_kwargs							)
      return model

 def  __magic_name__							(		self			:						int							):






      '''simple docstring'''




      _lowerCAmelCase :       str	   =     self.dummy_text_encoder
      _lowerCAmelCase :       List[str]	   =     self.dummy_tokenizer
      _lowerCAmelCase :       Tuple	   =     self.dummy_unet
      _lowerCAmelCase :       Dict	   =     self.dummy_movq

      _lowerCAmelCase :       Tuple	   =     DDIMScheduler(
          num_train_timesteps=1_0_0_0  ,    beta_schedule="linear"  ,    beta_start=0.00085  ,    beta_end=0.012  ,    clip_sample=A_  ,    set_alpha_to_one=A_  ,    steps_offset=1  ,    prediction_type="epsilon"  ,    thresholding=A_  ,    )

      _lowerCAmelCase :       Optional[Any]	   =     {
          "text_encoder": text_encoder,
          "tokenizer": tokenizer,
          "unet": unet,
          "scheduler": scheduler,
          "movq": movq,
      }
      return components

 def  __magic_name__							(		self			:						Optional[int]  ,    A_			:						Tuple  ,    A_			:						int=0							):






      '''simple docstring'''




      _lowerCAmelCase :       Tuple	   =     floats_tensor((1, self.cross_attention_dim)  ,    rng=random.Random(A_							)							).to(A_							)
      _lowerCAmelCase :       Union[str, Any]	   =     floats_tensor((1, self.cross_attention_dim)  ,    rng=random.Random(seed + 1							)							).to(A_							)
      if str(A_							).startswith("mps"							):
           _lowerCAmelCase :       Any	   =     torch.manual_seed(A_							)
      else:
           _lowerCAmelCase :       str	   =     torch.Generator(device=A_							).manual_seed(A_							)
      _lowerCAmelCase :       str	   =     {
          "prompt": "horse",
          "image_embeds": image_embeds,
          "negative_image_embeds": negative_image_embeds,
          "generator": generator,
          "height": 6_4,
          "width": 6_4,
          "guidance_scale": 4.0,
          "num_inference_steps": 2,
          "output_type": "np",
      }
      return inputs


 def  __magic_name__							(		self			:						Optional[Any]							):






      '''simple docstring'''




      _lowerCAmelCase :       int	   =     "cpu"

      _lowerCAmelCase :       Any	   =     self.get_dummy_components()

      _lowerCAmelCase :       List[Any]	   =     self.pipeline_class(**A_							)
      _lowerCAmelCase :       Union[str, Any]	   =     pipe.to(A_							)

      pipe.set_progress_bar_config(disable=A_							)

      _lowerCAmelCase :       Any	   =     pipe(**self.get_dummy_inputs(A_							)							)
      _lowerCAmelCase :       Optional[Any]	   =     output.images

      _lowerCAmelCase :       Optional[int]	   =     pipe(
          **self.get_dummy_inputs(A_							)  ,    return_dict=A_  ,    )[0]

      _lowerCAmelCase :       int	   =     image[0, -3:, -3:, -1]
      _lowerCAmelCase :       Union[str, Any]	   =     image_from_tuple[0, -3:, -3:, -1]

      assert image.shape == (1, 6_4, 6_4, 3)

      _lowerCAmelCase :       Tuple	   =     np.array(
          [0.328663, 1.0, 0.23216873, 1.0, 0.92717564, 0.4639046, 0.96894777, 0.31713378, 0.6293953]							)

      assert (
          np.abs(image_slice.flatten() - expected_slice							).max() < 1E-2
      ), F''' expected_slice {expected_slice}, but got {image_slice.flatten()}'''
      assert (
          np.abs(image_from_tuple_slice.flatten() - expected_slice							).max() < 1E-2
      ), F''' expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}'''





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



 """simple docstring"""







 def  __magic_name__							(		self			:						Any							):






      '''simple docstring'''




      super().tearDown()
      gc.collect()
      torch.cuda.empty_cache()


 def  __magic_name__							(		self			:						int							):






      '''simple docstring'''




      _lowerCAmelCase :       Dict	   =     load_numpy(
          "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
          "/kandinsky/kandinsky_text2img_cat_fp16.npy"							)

      _lowerCAmelCase :       Union[str, Any]	   =     KandinskyPriorPipeline.from_pretrained(
          "kandinsky-community/kandinsky-2-1-prior"  ,    torch_dtype=torch.floataa							)
      pipe_prior.to(A_							)

      _lowerCAmelCase :       Optional[int]	   =     KandinskyPipeline.from_pretrained("kandinsky-community/kandinsky-2-1"  ,    torch_dtype=torch.floataa							)
      _lowerCAmelCase :       List[Any]	   =     pipeline.to(A_							)
      pipeline.set_progress_bar_config(disable=A_							)

      _lowerCAmelCase :       List[Any]	   =     "red cat, 4k photo"

      _lowerCAmelCase :       Any	   =     torch.Generator(device="cuda"							).manual_seed(0							)
      _lowerCAmelCase   ,      _lowerCAmelCase :       Any	   =     pipe_prior(
          A_  ,    generator=A_  ,    num_inference_steps=5  ,    negative_prompt=""  ,    ).to_tuple()

      _lowerCAmelCase :       str	   =     torch.Generator(device="cuda"							).manual_seed(0							)
      _lowerCAmelCase :       str	   =     pipeline(
          A_  ,    image_embeds=A_  ,    negative_image_embeds=A_  ,    generator=A_  ,    num_inference_steps=1_0_0  ,    output_type="np"  ,    )

      _lowerCAmelCase :       Union[str, Any]	   =     output.images[0]

      assert image.shape == (5_1_2, 5_1_2, 3)

      assert_mean_pixel_difference(A_  ,    A_							)









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
__UpperCAmelCase     					=  {
    'translation': ['bleu'],
    'summarization': ROUGE_KEYS,
}







def 						_snake_case		(     SCREAMING_SNAKE_CASE					)							->   Optional[Any]:

     """simple docstring"""







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







def 						_snake_case		(     )							->   Tuple:

     """simple docstring"""







     _lowerCAmelCase :       List[str]	   =     sys.argv[0]

     _lowerCAmelCase :       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."
         )		,				)
     _lowerCAmelCase   ,      _lowerCAmelCase :       List[str]	   =     parser.parse_known_args()
     # we share some of the args
     args_main.extend(["--task", args.task]					)
     _lowerCAmelCase :       str	   =     [prog] + args_main

     # to support variations like translation_en_to_de"
     _lowerCAmelCase :       Any	   =     "translation" if "translation" in args.task else "summarization"

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

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

          _lowerCAmelCase :       Any	   =     run_generate(verbose=SCREAMING_SNAKE_CASE					)
          # make sure scores are first in the table
          _lowerCAmelCase :       Any	   =     OrderedDict()
          for score in task_score_names[task]:
               _lowerCAmelCase :       Dict	   =     scores[score]
          result.update(SCREAMING_SNAKE_CASE					)
          results.append(SCREAMING_SNAKE_CASE					)

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

     _lowerCAmelCase :       Optional[Any]	   =     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]					)					)

     _lowerCAmelCase :       Optional[int]	   =     results_sorted[0]
     for score in task_score_names[task]:
          del best[score]
     _lowerCAmelCase :       Tuple	   =     [f'''--{k} {v}''' for k, v in best.items()]
     _lowerCAmelCase :       Union[str, Any]	   =     ["--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()






