

"""simple docstring"""




import logging
import os
import sys
from pathlib import Path
from unittest.mock import patch

from parameterized import parameterized
from run_eval import run_generate
from run_eval_search import run_search

from transformers.testing_utils import CaptureStdout, TestCasePlus, slow
from utils import ROUGE_KEYS


logging.basicConfig(level=logging.DEBUG)
SCREAMING_SNAKE_CASE_     :			Union[str, Any]	  =			logging.getLogger()



def 	UpperCAmelCase__   (				A__	,    A__   )      ->      int:




   """simple docstring"""
   lowerCamelCase__						=			"\n".join(A__   )
   Path(A__   ).open("w"   ).writelines(A__   )


SCREAMING_SNAKE_CASE_     :			str	  =			'''patrickvonplaten/t5-tiny-random'''
SCREAMING_SNAKE_CASE_     :			str	  =			'''sshleifer/bart-tiny-random'''
SCREAMING_SNAKE_CASE_     :			Tuple	  =			'''sshleifer/tiny-mbart'''

SCREAMING_SNAKE_CASE_     :			int	  =			logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)
logging.disable(logging.CRITICAL)  # remove noisy download output from tracebacks


class 		_A       (			__a					):





      def 					_lowerCamelCase  (  self    ,						SCREAMING_SNAKE_CASE__		)   ->					Optional[Any]:
         lowerCamelCase__						=			Path(self.get_auto_remove_tmp_dir()		) / "utest_input.source"
         lowerCamelCase__						=			input_file_name.parent / "utest_output.txt"
         assert not output_file_name.exists()
         lowerCamelCase__						=			[" New York (CNN)When Liana Barrientos was 23 years old, she got married in Westchester County."]
         _dump_articles(SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__		)

         lowerCamelCase__						=			str(Path(self.get_auto_remove_tmp_dir()		) / "scores.json"		)
         lowerCamelCase__						=			"translation_en_to_de" if model == T5_TINY else "summarization"
         lowerCamelCase__						=			f'\n            run_eval_search.py\n            {model}\n            {input_file_name}\n            {output_file_name}\n            --score_path {score_path}\n            --task {task}\n            --num_beams 2\n            --length_penalty 2.0\n            '.split()

         with patch.object(SCREAMING_SNAKE_CASE__    ,						"argv"    ,						SCREAMING_SNAKE_CASE__		):
            run_generate()
            assert Path(SCREAMING_SNAKE_CASE__		).exists()
            # os.remove(Path(output_file_name))





      def 					_lowerCamelCase  (  self		)   ->					Optional[Any]:
         self.run_eval_tester(SCREAMING_SNAKE_CASE__		)





      @parameterized.expand([BART_TINY, MBART_TINY]		)
      @slow
      def 					_lowerCamelCase  (  self    ,						SCREAMING_SNAKE_CASE__		)   ->					Optional[Any]:
         self.run_eval_tester(SCREAMING_SNAKE_CASE__		)





      @parameterized.expand([T5_TINY, MBART_TINY]		)
      @slow
      def 					_lowerCamelCase  (  self    ,						SCREAMING_SNAKE_CASE__		)   ->					Optional[int]:
         lowerCamelCase__						=			Path(self.get_auto_remove_tmp_dir()		) / "utest_input.source"
         lowerCamelCase__						=			input_file_name.parent / "utest_output.txt"
         assert not output_file_name.exists()

         lowerCamelCase__						=			{
             "en": ["Machine learning is great, isn't it?", "I like to eat bananas", "Tomorrow is another great day!"],
             "de": [
                 "Maschinelles Lernen ist großartig, oder?",
                 "Ich esse gerne Bananen",
                 "Morgen ist wieder ein toller Tag!",
             ],
         }

         lowerCamelCase__						=			Path(self.get_auto_remove_tmp_dir()		)
         lowerCamelCase__						=			str(tmp_dir / "scores.json"		)
         lowerCamelCase__						=			str(tmp_dir / "val.target"		)
         _dump_articles(SCREAMING_SNAKE_CASE__    ,						text["en"]		)
         _dump_articles(SCREAMING_SNAKE_CASE__    ,						text["de"]		)
         lowerCamelCase__						=			"translation_en_to_de" if model == T5_TINY else "summarization"
         lowerCamelCase__						=			f'\n            run_eval_search.py\n            {model}\n            {str(SCREAMING_SNAKE_CASE__		)}\n            {str(SCREAMING_SNAKE_CASE__		)}\n            --score_path {score_path}\n            --reference_path {reference_path}\n            --task {task}\n            '.split()
         testargs.extend(["--search", "num_beams=1:2 length_penalty=0.9:1.0"]		)

         with patch.object(SCREAMING_SNAKE_CASE__    ,						"argv"    ,						SCREAMING_SNAKE_CASE__		):
            with CaptureStdout() as cs:
               run_search()
            lowerCamelCase__						=			[" num_beams | length_penalty", model, "Best score args"]
            lowerCamelCase__						=			["Info"]
            if "translation" in task:
               expected_strings.append("bleu"		)
            else:
               expected_strings.extend(SCREAMING_SNAKE_CASE__		)
            for w in expected_strings:
               assert w in cs.out
            for w in un_expected_strings:
               assert w not in cs.out
            assert Path(SCREAMING_SNAKE_CASE__		).exists()
            os.remove(Path(SCREAMING_SNAKE_CASE__		)		)







"""simple docstring"""




import gc
import unittest

from diffusers import FlaxControlNetModel, FlaxStableDiffusionControlNetPipeline
from diffusers.utils import is_flax_available, load_image, slow
from diffusers.utils.testing_utils import require_flax


if is_flax_available():
     import jax
     import jax.numpy as jnp
     from flax.jax_utils import replicate
     from flax.training.common_utils import shard


@slow
@require_flax
class 		_A       (			unittest.TestCase					):





      def 					_lowerCamelCase  (  self		)   ->					Tuple:
         # clean up the VRAM after each test
         super().tearDown()
         gc.collect()





      def 					_lowerCamelCase  (  self		)   ->					Optional[Any]:
         lowerCamelCase__					,       lowerCamelCase__						=			FlaxControlNetModel.from_pretrained(
             "lllyasviel/sd-controlnet-canny"    ,						from_pt=SCREAMING_SNAKE_CASE__    ,						dtype=jnp.bfloataa		)
         lowerCamelCase__					,       lowerCamelCase__						=			FlaxStableDiffusionControlNetPipeline.from_pretrained(
             "runwayml/stable-diffusion-v1-5"    ,						controlnet=SCREAMING_SNAKE_CASE__    ,						from_pt=SCREAMING_SNAKE_CASE__    ,						dtype=jnp.bfloataa		)
         lowerCamelCase__						=			controlnet_params

         lowerCamelCase__						=			"bird"
         lowerCamelCase__						=			jax.device_count()
         lowerCamelCase__						=			pipe.prepare_text_inputs([prompts] * num_samples		)

         lowerCamelCase__						=			load_image(
             "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/bird_canny.png"		)
         lowerCamelCase__						=			pipe.prepare_image_inputs([canny_image] * num_samples		)

         lowerCamelCase__						=			jax.random.PRNGKey(0		)
         lowerCamelCase__						=			jax.random.split(SCREAMING_SNAKE_CASE__    ,						jax.device_count()		)

         lowerCamelCase__						=			replicate(SCREAMING_SNAKE_CASE__		)
         lowerCamelCase__						=			shard(SCREAMING_SNAKE_CASE__		)
         lowerCamelCase__						=			shard(SCREAMING_SNAKE_CASE__		)

         lowerCamelCase__						=			pipe(
             prompt_ids=SCREAMING_SNAKE_CASE__    ,						image=SCREAMING_SNAKE_CASE__    ,						params=SCREAMING_SNAKE_CASE__    ,						prng_seed=SCREAMING_SNAKE_CASE__    ,						num_inference_steps=50    ,						jit=SCREAMING_SNAKE_CASE__    ,						).images
         assert images.shape == (jax.device_count(), 1, 768, 512, 3)

         lowerCamelCase__						=			images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:]		)
         lowerCamelCase__						=			images[0, 253:256, 253:256, -1]

         lowerCamelCase__						=			jnp.asarray(jax.device_get(image_slice.flatten()		)		)
         lowerCamelCase__						=			jnp.array(
             [0.16_79_69, 0.11_66_99, 0.08_15_43, 0.15_42_97, 0.13_28_12, 0.10_88_87, 0.16_99_22, 0.16_99_22, 0.20_50_78]		)
         print(f'output_slice: {output_slice}'		)
         assert jnp.abs(output_slice - expected_slice		).max() < 1e-2





      def 					_lowerCamelCase  (  self		)   ->					str:
         lowerCamelCase__					,       lowerCamelCase__						=			FlaxControlNetModel.from_pretrained(
             "lllyasviel/sd-controlnet-openpose"    ,						from_pt=SCREAMING_SNAKE_CASE__    ,						dtype=jnp.bfloataa		)
         lowerCamelCase__					,       lowerCamelCase__						=			FlaxStableDiffusionControlNetPipeline.from_pretrained(
             "runwayml/stable-diffusion-v1-5"    ,						controlnet=SCREAMING_SNAKE_CASE__    ,						from_pt=SCREAMING_SNAKE_CASE__    ,						dtype=jnp.bfloataa		)
         lowerCamelCase__						=			controlnet_params

         lowerCamelCase__						=			"Chef in the kitchen"
         lowerCamelCase__						=			jax.device_count()
         lowerCamelCase__						=			pipe.prepare_text_inputs([prompts] * num_samples		)

         lowerCamelCase__						=			load_image(
             "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/pose.png"		)
         lowerCamelCase__						=			pipe.prepare_image_inputs([pose_image] * num_samples		)

         lowerCamelCase__						=			jax.random.PRNGKey(0		)
         lowerCamelCase__						=			jax.random.split(SCREAMING_SNAKE_CASE__    ,						jax.device_count()		)

         lowerCamelCase__						=			replicate(SCREAMING_SNAKE_CASE__		)
         lowerCamelCase__						=			shard(SCREAMING_SNAKE_CASE__		)
         lowerCamelCase__						=			shard(SCREAMING_SNAKE_CASE__		)

         lowerCamelCase__						=			pipe(
             prompt_ids=SCREAMING_SNAKE_CASE__    ,						image=SCREAMING_SNAKE_CASE__    ,						params=SCREAMING_SNAKE_CASE__    ,						prng_seed=SCREAMING_SNAKE_CASE__    ,						num_inference_steps=50    ,						jit=SCREAMING_SNAKE_CASE__    ,						).images
         assert images.shape == (jax.device_count(), 1, 768, 512, 3)

         lowerCamelCase__						=			images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:]		)
         lowerCamelCase__						=			images[0, 253:256, 253:256, -1]

         lowerCamelCase__						=			jnp.asarray(jax.device_get(image_slice.flatten()		)		)
         lowerCamelCase__						=			jnp.array(
             [[0.27_14_84, 0.26_17_19, 0.27_53_91, 0.27_73_44, 0.27_92_97, 0.29_10_16, 0.29_49_22, 0.30_27_34, 0.30_27_34]]		)
         print(f'output_slice: {output_slice}'		)
         assert jnp.abs(output_slice - expected_slice		).max() < 1e-2




