


'''simple docstring'''






import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import XLMRobertaTokenizerFast

from diffusers import DDIMScheduler, KandinskyImgaImgPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandinsky.text_encoder import MCLIPConfig, MultilingualCLIP
from diffusers.utils import floats_tensor, load_image, 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        __snake_case	(       _SCREAMING_SNAKE_CASE     ,unittest.TestCase):







    """simple docstring"""



    lowercase       			= KandinskyImgaImgPipeline
    lowercase       			= ['prompt', 'image_embeds', 'negative_image_embeds', 'image']
    lowercase       			= [
        'prompt',
        'negative_prompt',
        'image_embeds',
        'negative_image_embeds',
        'image',
    ]
    lowercase       			= [
        'generator',
        'height',
        'width',
        'strength',
        'guidance_scale',
        'negative_prompt',
        'num_inference_steps',
        'return_dict',
        'guidance_scale',
        'num_images_per_prompt',
        'output_type',
        'return_dict',
    ]
    lowercase       			= False

    @property
    def __lowercase       (       self   :  List[Any]		)       ->  str:
      return 32

    @property
    def __lowercase       (       self   :  Dict		)       ->  Optional[Any]:
      return 32

    @property
    def __lowercase       (       self   :  int		)       ->  Optional[int]:
      return self.time_input_dim

    @property
    def __lowercase       (       self   :  Optional[int]		)       ->  Any:
      return self.time_input_dim * 4

    @property
    def __lowercase       (       self   :  List[str]		)       ->  Dict:
      return 1_00

    @property
    def __lowercase       (       self   :  Dict		)       ->  Optional[Any]:
      lowerCAmelCase_      :				Any      =       XLMRobertaTokenizerFast.from_pretrained("""YiYiXu/tiny-random-mclip-base"""		)
      return tokenizer

    @property
    def __lowercase       (       self   :  Optional[int]		)       ->  Optional[int]:
      torch.manual_seed(0		)
      lowerCAmelCase_      :				Dict      =       MCLIPConfig(
          numDims=self.cross_attention_dim   ,						transformerDimensions=self.text_embedder_hidden_size   ,						hidden_size=self.text_embedder_hidden_size   ,						intermediate_size=37   ,						num_attention_heads=4   ,						num_hidden_layers=5   ,						vocab_size=10_05   ,						)

      lowerCAmelCase_      :				str      =       MultilingualCLIP(lowerCamelCase		)
      lowerCAmelCase_      :				Union[str, Any]      =       text_encoder.eval()

      return text_encoder

    @property
    def __lowercase       (       self   :  List[Any]		)       ->  int:
      torch.manual_seed(0		)

      lowerCAmelCase_      :				Optional[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_      :				List[Any]      =       UNetaDConditionModel(**lowerCamelCase		)
      return model

    @property
    def __lowercase       (       self   :  int		)       ->  Union[str, Any]:
      return {
          "block_out_channels": [32, 64],
          "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": 12,
          "out_channels": 3,
          "up_block_types": [
              "AttnUpDecoderBlock2D",
              "UpDecoderBlock2D",
          ],
          "vq_embed_dim": 4,
      }

    @property
    def __lowercase       (       self   :  Tuple		)       ->  Optional[int]:
      torch.manual_seed(0		)
      lowerCAmelCase_      :				Any      =       VQModel(**self.dummy_movq_kwargs		)
      return model

    def __lowercase       (       self   :  Optional[int]		)       ->  Optional[Any]:
      lowerCAmelCase_      :				List[Any]      =       self.dummy_text_encoder
      lowerCAmelCase_      :				List[str]      =       self.dummy_tokenizer
      lowerCAmelCase_      :				Union[str, Any]      =       self.dummy_unet
      lowerCAmelCase_      :				Union[str, Any]      =       self.dummy_movq

      lowerCAmelCase_      :				Any      =       {
          """num_train_timesteps""": 10_00,
          """beta_schedule""": """linear""",
          """beta_start""": 0.00_085,
          """beta_end""": 0.012,
          """clip_sample""": False,
          """set_alpha_to_one""": False,
          """steps_offset""": 0,
          """prediction_type""": """epsilon""",
          """thresholding""": False,
      }

      lowerCAmelCase_      :				List[Any]      =       DDIMScheduler(**lowerCamelCase		)

      lowerCAmelCase_      :				List[str]      =       {
          """text_encoder""": text_encoder,
          """tokenizer""": tokenizer,
          """unet""": unet,
          """scheduler""": scheduler,
          """movq""": movq,
      }

      return components

    def __lowercase       (       self   :  Dict   ,						lowerCamelCase   :  Dict   ,						lowerCamelCase   :  int=0		)       ->  Any:
      lowerCAmelCase_      :				List[str]      =       floats_tensor((1, self.cross_attention_dim)   ,						rng=random.Random(lowerCamelCase		)		).to(lowerCamelCase		)
      lowerCAmelCase_      :				Union[str, Any]      =       floats_tensor((1, self.cross_attention_dim)   ,						rng=random.Random(seed + 1		)		).to(lowerCamelCase		)
      # create init_image
      lowerCAmelCase_      :				int      =       floats_tensor((1, 3, 64, 64)   ,						rng=random.Random(lowerCamelCase		)		).to(lowerCamelCase		)
      lowerCAmelCase_      :				Union[str, Any]      =       image.cpu().permute(0   ,						2   ,						3   ,						1		)[0]
      lowerCAmelCase_      :				Optional[int]      =       Image.fromarray(np.uinta(lowerCamelCase		)		).convert("""RGB"""		).resize((2_56, 2_56)		)

      if str(lowerCamelCase		).startswith("""mps"""		):
        lowerCAmelCase_      :				Any      =       torch.manual_seed(lowerCamelCase		)
      else:
        lowerCAmelCase_      :				Optional[int]      =       torch.Generator(device=lowerCamelCase		).manual_seed(lowerCamelCase		)
      lowerCAmelCase_      :				str      =       {
          """prompt""": """horse""",
          """image""": init_image,
          """image_embeds""": image_embeds,
          """negative_image_embeds""": negative_image_embeds,
          """generator""": generator,
          """height""": 64,
          """width""": 64,
          """num_inference_steps""": 10,
          """guidance_scale""": 7.0,
          """strength""": 0.2,
          """output_type""": """np""",
      }
      return inputs





    def __lowercase       (       self   :  List[Any]		)       ->  Union[str, Any]:
      lowerCAmelCase_      :				Union[str, Any]      =       """cpu"""

      lowerCAmelCase_      :				Optional[int]      =       self.get_dummy_components()

      lowerCAmelCase_      :				Optional[Any]      =       self.pipeline_class(**lowerCamelCase		)
      lowerCAmelCase_      :				Union[str, Any]      =       pipe.to(lowerCamelCase		)

      pipe.set_progress_bar_config(disable=lowerCamelCase		)

      lowerCAmelCase_      :				int      =       pipe(**self.get_dummy_inputs(lowerCamelCase		)		)
      lowerCAmelCase_      :				Tuple      =       output.images

      lowerCAmelCase_      :				int      =       pipe(
          **self.get_dummy_inputs(lowerCamelCase		)   ,						return_dict=lowerCamelCase   ,						)[0]

      lowerCAmelCase_      :				Union[str, Any]      =       image[0, -3:, -3:, -1]
      lowerCAmelCase_      :				Optional[Any]      =       image_from_tuple[0, -3:, -3:, -1]

      assert image.shape == (1, 64, 64, 3)

      lowerCAmelCase_      :				Tuple      =       np.array(
          [0.61_474_943, 0.6_073_539, 0.43_308_544, 0.5_928_269, 0.47_493_595, 0.46_755_973, 0.4_613_838, 0.45_368_797, 0.50_119_233]		)
      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        __snake_case	(       unittest.TestCase):







    """simple docstring"""



    def __lowercase       (       self   :  Dict		)       ->  Optional[Any]:
      # clean up the VRAM after each test
      super().tearDown()
      gc.collect()
      torch.cuda.empty_cache()





    def __lowercase       (       self   :  Optional[Any]		)       ->  List[str]:
      lowerCAmelCase_      :				List[str]      =       load_numpy(
          """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
          """/kandinsky/kandinsky_img2img_frog.npy"""		)

      lowerCAmelCase_      :				Tuple      =       load_image(
          """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main""" """/kandinsky/cat.png"""		)
      lowerCAmelCase_      :				int      =       """A red cartoon frog, 4k"""

      lowerCAmelCase_      :				Dict      =       KandinskyPriorPipeline.from_pretrained(
          """kandinsky-community/kandinsky-2-1-prior"""   ,						torch_dtype=torch.floataa		)
      pipe_prior.to(lowerCamelCase		)

      lowerCAmelCase_      :				Optional[Any]      =       KandinskyImgaImgPipeline.from_pretrained(
          """kandinsky-community/kandinsky-2-1"""   ,						torch_dtype=torch.floataa		)
      lowerCAmelCase_      :				Any      =       pipeline.to(lowerCamelCase		)

      pipeline.set_progress_bar_config(disable=lowerCamelCase		)

      lowerCAmelCase_      :				int      =       torch.Generator(device="""cpu"""		).manual_seed(0		)
      lowerCAmelCase_,							lowerCAmelCase_      :				Optional[Any]      =       pipe_prior(
          lowerCamelCase   ,						generator=lowerCamelCase   ,						num_inference_steps=5   ,						negative_prompt=""""""   ,						).to_tuple()

      lowerCAmelCase_      :				Tuple      =       pipeline(
          lowerCamelCase   ,						image=lowerCamelCase   ,						image_embeds=lowerCamelCase   ,						negative_image_embeds=lowerCamelCase   ,						generator=lowerCamelCase   ,						num_inference_steps=1_00   ,						height=7_68   ,						width=7_68   ,						strength=0.2   ,						output_type="""np"""   ,						)

      lowerCAmelCase_      :				List[str]      =       output.images[0]

      assert image.shape == (7_68, 7_68, 3)

      assert_mean_pixel_difference(lowerCamelCase   ,						lowerCamelCase		)







'''simple docstring'''






import argparse
import os
import sys
from unittest.mock import patch

import pytorch_lightning as pl
import timeout_decorator
import torch
from distillation import SummarizationDistiller, distill_main
from finetune import SummarizationModule, main

from transformers import MarianMTModel
from transformers.file_utils import cached_path
from transformers.testing_utils import TestCasePlus, require_torch_gpu, slow
from utils import load_json


__A :  Tuple    							=				"sshleifer/mar_enro_6_3_student"






class        __snake_case	(       _SCREAMING_SNAKE_CASE):







    """simple docstring"""



    def __lowercase       (       self   :  List[Any]		)       ->  Optional[Any]:
      super().setUp()

      lowerCAmelCase_      :				Any      =       cached_path(
          """https://cdn-datasets.huggingface.co/translation/wmt_en_ro-tr40k-va0.5k-te0.5k.tar.gz"""   ,						extract_compressed_file=lowerCamelCase   ,						)
      lowerCAmelCase_      :				Optional[Any]      =       F'{data_cached}/wmt_en_ro-tr40k-va0.5k-te0.5k'

    @slow
    @require_torch_gpu
    def __lowercase       (       self   :  str		)       ->  str:
      MarianMTModel.from_pretrained(lowerCamelCase		)





    @slow
    @require_torch_gpu
    def __lowercase       (       self   :  List[Any]		)       ->  Union[str, Any]:
      lowerCAmelCase_      :				str      =       {
          """$MAX_LEN""": 64,
          """$BS""": 64,
          """$GAS""": 1,
          """$ENRO_DIR""": self.data_dir,
          """facebook/mbart-large-cc25""": MARIAN_MODEL,
          # "val_check_interval=0.25": "val_check_interval=1.0",
          """--learning_rate=3e-5""": """--learning_rate 3e-4""",
          """--num_train_epochs 6""": """--num_train_epochs 1""",
      }

      # Clean up bash script
      lowerCAmelCase_      :				Dict      =       (self.test_file_dir / """train_mbart_cc25_enro.sh""").open().read().split("""finetune.py"""		)[1].strip()
      lowerCAmelCase_      :				Optional[int]      =       bash_script.replace("""\\\n"""   ,						""""""		).strip().replace("""\"$@\""""   ,						""""""		)
      for k, v in env_vars_to_replace.items():
        lowerCAmelCase_      :				Optional[int]      =       bash_script.replace(lowerCamelCase   ,						str(lowerCamelCase		)		)
      lowerCAmelCase_      :				Optional[Any]      =       self.get_auto_remove_tmp_dir()

      # bash_script = bash_script.replace("--fp16 ", "")
      lowerCAmelCase_      :				Tuple      =       F'\n            --output_dir {output_dir}\n            --tokenizer_name Helsinki-NLP/opus-mt-en-ro\n            --sortish_sampler\n            --do_predict\n            --gpus 1\n            --freeze_encoder\n            --n_train 40000\n            --n_val 500\n            --n_test 500\n            --fp16_opt_level O1\n            --num_sanity_val_steps 0\n            --eval_beams 2\n        '.split()
      # XXX: args.gpus > 1 : handle multi_gpu in the future

      lowerCAmelCase_      :				Tuple      =       ["""finetune.py"""] + bash_script.split() + args
      with patch.object(lowerCamelCase   ,						"""argv"""   ,						lowerCamelCase		):
        lowerCAmelCase_      :				Optional[Any]      =       argparse.ArgumentParser()
        lowerCAmelCase_      :				Any      =       pl.Trainer.add_argparse_args(lowerCamelCase		)
        lowerCAmelCase_      :				List[str]      =       SummarizationModule.add_model_specific_args(lowerCamelCase   ,						os.getcwd()		)
        lowerCAmelCase_      :				Tuple      =       parser.parse_args()
        lowerCAmelCase_      :				Dict      =       main(lowerCamelCase		)

      # Check metrics
      lowerCAmelCase_      :				int      =       load_json(model.metrics_save_path		)
      lowerCAmelCase_      :				Optional[Any]      =       metrics["""val"""][0]
      lowerCAmelCase_      :				Tuple      =       metrics["""val"""][-1]
      self.assertEqual(len(metrics["""val"""]		)   ,						(args.max_epochs / args.val_check_interval)		)
      assert isinstance(last_step_stats[F'val_avg_{model.val_metric}']   ,						lowerCamelCase		)

      self.assertGreater(last_step_stats["""val_avg_gen_time"""]   ,						0.01		)
      # model hanging on generate. Maybe bad config was saved. (XXX: old comment/assert?)
      self.assertLessEqual(last_step_stats["""val_avg_gen_time"""]   ,						1.0		)

      # test learning requirements:

      # 1. BLEU improves over the course of training by more than 2 pts
      self.assertGreater(last_step_stats["""val_avg_bleu"""] - first_step_stats["""val_avg_bleu"""]   ,						2		)

      # 2. BLEU finishes above 17
      self.assertGreater(last_step_stats["""val_avg_bleu"""]   ,						17		)

      # 3. test BLEU and val BLEU within ~1.1 pt.
      self.assertLess(abs(metrics["""val"""][-1]["""val_avg_bleu"""] - metrics["""test"""][-1]["""test_avg_bleu"""]		)   ,						1.1		)

      # check lightning ckpt can be loaded and has a reasonable statedict
      lowerCAmelCase_      :				Union[str, Any]      =       os.listdir(lowerCamelCase		)
      lowerCAmelCase_      :				Any      =       [x for x in contents if x.endswith(""".ckpt"""		)][0]
      lowerCAmelCase_      :				Union[str, Any]      =       os.path.join(args.output_dir   ,						lowerCamelCase		)
      lowerCAmelCase_      :				int      =       torch.load(lowerCamelCase   ,						map_location="""cpu"""		)
      lowerCAmelCase_      :				List[str]      =       """model.model.decoder.layers.0.encoder_attn_layer_norm.weight"""
      assert expected_key in ckpt["state_dict"]
      assert ckpt["state_dict"]["model.model.decoder.layers.0.encoder_attn_layer_norm.weight"].dtype == torch.floataa

      # TODO: turn on args.do_predict when PL bug fixed.
      if args.do_predict:
        lowerCAmelCase_      :				List[str]      =       {os.path.basename(lowerCamelCase		) for p in contents}
        assert "test_generations.txt" in contents
        assert "test_results.txt" in contents
        # assert len(metrics["val"]) ==  desired_n_evals
        assert len(metrics["""test"""]		) == 1







class        __snake_case	(       _SCREAMING_SNAKE_CASE):







    """simple docstring"""



    @timeout_decorator.timeout(6_00		)
    @slow
    @require_torch_gpu
    def __lowercase       (       self   :  Optional[Any]		)       ->  Dict:
      lowerCAmelCase_      :				List[str]      =       F'{self.test_file_dir_str}/test_data/wmt_en_ro'
      lowerCAmelCase_      :				Dict      =       {
          """--fp16_opt_level=O1""": """""",
          """$MAX_LEN""": 1_28,
          """$BS""": 16,
          """$GAS""": 1,
          """$ENRO_DIR""": data_dir,
          """$m""": """sshleifer/student_marian_en_ro_6_1""",
          """val_check_interval=0.25""": """val_check_interval=1.0""",
      }

      # Clean up bash script
      lowerCAmelCase_      :				int      =       (
          (self.test_file_dir / """distil_marian_no_teacher.sh""").open().read().split("""distillation.py"""		)[1].strip()
      )
      lowerCAmelCase_      :				str      =       bash_script.replace("""\\\n"""   ,						""""""		).strip().replace("""\"$@\""""   ,						""""""		)
      lowerCAmelCase_      :				Tuple      =       bash_script.replace("""--fp16 """   ,						""" """		)

      for k, v in env_vars_to_replace.items():
        lowerCAmelCase_      :				Optional[int]      =       bash_script.replace(lowerCamelCase   ,						str(lowerCamelCase		)		)
      lowerCAmelCase_      :				int      =       self.get_auto_remove_tmp_dir()
      lowerCAmelCase_      :				Optional[Any]      =       bash_script.replace("""--fp16"""   ,						""""""		)
      lowerCAmelCase_      :				Dict      =       6
      lowerCAmelCase_      :				List[Any]      =       (
          ["""distillation.py"""]
          + bash_script.split()
          + [
              F'--output_dir={output_dir}',
              """--gpus=1""",
              """--learning_rate=1e-3""",
              F'--num_train_epochs={epochs}',
              """--warmup_steps=10""",
              """--val_check_interval=1.0""",
              """--do_predict""",
          ]
      )
      with patch.object(lowerCamelCase   ,						"""argv"""   ,						lowerCamelCase		):
        lowerCAmelCase_      :				Dict      =       argparse.ArgumentParser()
        lowerCAmelCase_      :				int      =       pl.Trainer.add_argparse_args(lowerCamelCase		)
        lowerCAmelCase_      :				List[str]      =       SummarizationDistiller.add_model_specific_args(lowerCamelCase   ,						os.getcwd()		)
        lowerCAmelCase_      :				List[Any]      =       parser.parse_args()
        # assert args.gpus == gpus THIS BREAKS for multi_gpu

        lowerCAmelCase_      :				str      =       distill_main(lowerCamelCase		)

      # Check metrics
      lowerCAmelCase_      :				Union[str, Any]      =       load_json(model.metrics_save_path		)
      lowerCAmelCase_      :				Union[str, Any]      =       metrics["""val"""][0]
      lowerCAmelCase_      :				Union[str, Any]      =       metrics["""val"""][-1]
      assert len(metrics["""val"""]		) >= (args.max_epochs / args.val_check_interval)  # +1 accounts for val_sanity_check

      assert last_step_stats["val_avg_gen_time"] >= 0.01

      assert first_step_stats["val_avg_bleu"] < last_step_stats["val_avg_bleu"]  # model learned nothing
      assert 1.0 >= last_step_stats["val_avg_gen_time"]  # model hanging on generate. Maybe bad config was saved.
      assert isinstance(last_step_stats[F'val_avg_{model.val_metric}']   ,						lowerCamelCase		)

      # check lightning ckpt can be loaded and has a reasonable statedict
      lowerCAmelCase_      :				Union[str, Any]      =       os.listdir(lowerCamelCase		)
      lowerCAmelCase_      :				Dict      =       [x for x in contents if x.endswith(""".ckpt"""		)][0]
      lowerCAmelCase_      :				Optional[int]      =       os.path.join(args.output_dir   ,						lowerCamelCase		)
      lowerCAmelCase_      :				int      =       torch.load(lowerCamelCase   ,						map_location="""cpu"""		)
      lowerCAmelCase_      :				Tuple      =       """model.model.decoder.layers.0.encoder_attn_layer_norm.weight"""
      assert expected_key in ckpt["state_dict"]
      assert ckpt["state_dict"]["model.model.decoder.layers.0.encoder_attn_layer_norm.weight"].dtype == torch.floataa

      # TODO: turn on args.do_predict when PL bug fixed.
      if args.do_predict:
        lowerCAmelCase_      :				Union[str, Any]      =       {os.path.basename(lowerCamelCase		) for p in contents}
        assert "test_generations.txt" in contents
        assert "test_results.txt" in contents
        # assert len(metrics["val"]) ==  desired_n_evals
        assert len(metrics["""test"""]		) == 1



