
"""simple docstring"""




import gc
import unittest

from diffusers import FlaxDPMSolverMultistepScheduler, FlaxStableDiffusionPipeline
from diffusers.utils import is_flax_available, 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     lowercase(unittest.TestCase       ):



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







         """simple docstring"""



         super().tearDown()
         gc.collect()



     def 			lowercase__  (							self   )		->     Tuple:







         """simple docstring"""



         a__	,     a__		=			FlaxStableDiffusionPipeline.from_pretrained(
             'stabilityai/stable-diffusion-2'     ,				revision='bf16'     ,				dtype=jnp.bfloataa     ,				)

         a__		=			'A painting of a squirrel eating a burger'
         a__		=			jax.device_count()
         a__		=			num_samples * [prompt]
         a__		=			sd_pipe.prepare_inputs(__SCREAMING_SNAKE_CASE   )

         a__		=			replicate(__SCREAMING_SNAKE_CASE   )
         a__		=			shard(__SCREAMING_SNAKE_CASE   )

         a__		=			jax.random.PRNGKey(0   )
         a__		=			jax.random.split(__SCREAMING_SNAKE_CASE     ,				jax.device_count()   )

         a__		=			sd_pipe(__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE     ,				num_inference_steps=2_5     ,				jit=__SCREAMING_SNAKE_CASE   )[0]
         assert images.shape == (jax.device_count(), 1, 7_6_8, 7_6_8, 3)

         a__		=			images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:]   )
         a__		=			images[0, 2_5_3:2_5_6, 2_5_3:2_5_6, -1]

         a__		=			jnp.asarray(jax.device_get(image_slice.flatten()   )   )
         a__		=			jnp.array([0.42_38, 0.44_14, 0.43_95, 0.44_53, 0.46_29, 0.45_90, 0.45_31, 0.4_55_08, 0.45_12]   )
         print(f'output_slice: {output_slice}'   )
         assert jnp.abs(output_slice - expected_slice   ).max() < 1e-2




     def 			lowercase__  (							self   )		->     List[Any]:







         """simple docstring"""



         a__		=			'stabilityai/stable-diffusion-2'
         a__	,     a__		=			FlaxDPMSolverMultistepScheduler.from_pretrained(__SCREAMING_SNAKE_CASE     ,				subfolder='scheduler'   )
         a__	,     a__		=			FlaxStableDiffusionPipeline.from_pretrained(
             __SCREAMING_SNAKE_CASE     ,				scheduler=__SCREAMING_SNAKE_CASE     ,				revision='bf16'     ,				dtype=jnp.bfloataa     ,				)
         a__		=			scheduler_params

         a__		=			'A painting of a squirrel eating a burger'
         a__		=			jax.device_count()
         a__		=			num_samples * [prompt]
         a__		=			sd_pipe.prepare_inputs(__SCREAMING_SNAKE_CASE   )

         a__		=			replicate(__SCREAMING_SNAKE_CASE   )
         a__		=			shard(__SCREAMING_SNAKE_CASE   )

         a__		=			jax.random.PRNGKey(0   )
         a__		=			jax.random.split(__SCREAMING_SNAKE_CASE     ,				jax.device_count()   )

         a__		=			sd_pipe(__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE     ,				num_inference_steps=2_5     ,				jit=__SCREAMING_SNAKE_CASE   )[0]
         assert images.shape == (jax.device_count(), 1, 7_6_8, 7_6_8, 3)

         a__		=			images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:]   )
         a__		=			images[0, 2_5_3:2_5_6, 2_5_3:2_5_6, -1]

         a__		=			jnp.asarray(jax.device_get(image_slice.flatten()   )   )
         a__		=			jnp.array([0.43_36, 0.4_29_69, 0.44_53, 0.41_99, 0.42_97, 0.45_31, 0.44_34, 0.44_34, 0.42_97]   )
         print(f'output_slice: {output_slice}'   )
         assert jnp.abs(output_slice - expected_slice   ).max() < 1e-2




"""simple docstring"""




import io
import json
import unittest

from parameterized import parameterized

from transformers import FSMTForConditionalGeneration, FSMTTokenizer
from transformers.testing_utils import get_tests_dir, require_torch, slow, torch_device
from utils import calculate_bleu


a		:       Any     						=       get_tests_dir() + '/test_data/fsmt/fsmt_val_data.json'
with io.open(filename, 'r', encoding='utf-8') as f:
    a		:       Tuple     						=       json.load(f)







@require_torch
class     lowercase(unittest.TestCase       ):



     def 			lowercase__  (							self     ,				__SCREAMING_SNAKE_CASE   )		->     Optional[Any]:







         """simple docstring"""



         return FSMTTokenizer.from_pretrained(__SCREAMING_SNAKE_CASE   )



     def 			lowercase__  (							self     ,				__SCREAMING_SNAKE_CASE   )		->     int:







         """simple docstring"""



         a__		=			FSMTForConditionalGeneration.from_pretrained(__SCREAMING_SNAKE_CASE   ).to(__SCREAMING_SNAKE_CASE   )
         if torch_device == "cuda":
             model.half()
         return model




     @parameterized.expand(
         [
             ['en-ru', 26.0],
             ['ru-en', 22.0],
             ['en-de', 22.0],
             ['de-en', 29.0],
         ]   )
     @slow
     def 			lowercase__  (							self     ,				__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE   )		->     Tuple:







         """simple docstring"""



         a__		=			f'facebook/wmt19-{pair}'
         a__		=			self.get_tokenizer(__SCREAMING_SNAKE_CASE   )
         a__		=			self.get_model(__SCREAMING_SNAKE_CASE   )

         a__		=			bleu_data[pair]['src']
         a__		=			bleu_data[pair]['tgt']

         a__		=			tokenizer(__SCREAMING_SNAKE_CASE     ,				return_tensors='pt'     ,				truncation=__SCREAMING_SNAKE_CASE     ,				padding='longest'   ).to(__SCREAMING_SNAKE_CASE   )
         a__		=			model.generate(
             input_ids=batch.input_ids     ,				num_beams=8     ,				)
         a__		=			tokenizer.batch_decode(
             __SCREAMING_SNAKE_CASE     ,				skip_special_tokens=__SCREAMING_SNAKE_CASE     ,				clean_up_tokenization_spaces=__SCREAMING_SNAKE_CASE   )
         a__		=			calculate_bleu(__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE   )
         print(__SCREAMING_SNAKE_CASE   )
         self.assertGreaterEqual(scores['bleu']     ,				__SCREAMING_SNAKE_CASE   )


