





"""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


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




@require_torch
class 							snake_case							(			unittest.TestCase ):

      '''simple docstring'''



      def       A_					(							self       :      int						,				__lowercase       :      int ):


        '''simple docstring'''




        return FSMTTokenizer.from_pretrained(__lowercase )

      def       A_					(							self       :      int						,				__lowercase       :      List[str] ):


        '''simple docstring'''




        __UpperCAmelCase    :						str       =						FSMTForConditionalGeneration.from_pretrained(__lowercase ).to(__lowercase )
        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       A_					(							self       :      Dict						,				__lowercase       :      List[str]						,				__lowercase       :      Any ):


        '''simple docstring'''




        __UpperCAmelCase    :						Optional[Any]       =						f'''facebook/wmt19-{pair}'''
        __UpperCAmelCase    :						List[Any]       =						self.get_tokenizer(__lowercase )
        __UpperCAmelCase    :						str       =						self.get_model(__lowercase )

        __UpperCAmelCase    :						List[str]       =						bleu_data[pair]['''src''']
        __UpperCAmelCase    :						Union[str, Any]       =						bleu_data[pair]['''tgt''']

        __UpperCAmelCase    :						List[Any]       =						tokenizer(__lowercase						,				return_tensors='''pt'''						,				truncation=__lowercase						,				padding='''longest''' ).to(__lowercase )
        __UpperCAmelCase    :						Any       =						model.generate(
            input_ids=batch.input_ids						,				num_beams=8						,				)
        __UpperCAmelCase    :						Optional[int]       =						tokenizer.batch_decode(
            __lowercase						,				skip_special_tokens=__lowercase						,				clean_up_tokenization_spaces=__lowercase )
        __UpperCAmelCase    :						Optional[int]       =						calculate_bleu(__lowercase						,				__lowercase )
        print(__lowercase )
        self.assertGreaterEqual(scores['''bleu''']						,				__lowercase )






"""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 							snake_case							(			unittest.TestCase ):

      '''simple docstring'''



      def       A_					(							self       :      str ):


        '''simple docstring'''




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

      def       A_					(							self       :      List[Any] ):


        '''simple docstring'''




        __UpperCAmelCase       ,     __UpperCAmelCase    :						List[Any]       =						FlaxStableDiffusionPipeline.from_pretrained(
            '''stabilityai/stable-diffusion-2'''						,				revision='''bf16'''						,				dtype=jnp.bfloataa						,				)

        __UpperCAmelCase    :						Dict       =						'''A painting of a squirrel eating a burger'''
        __UpperCAmelCase    :						Any       =						jax.device_count()
        __UpperCAmelCase    :						Optional[int]       =						num_samples * [prompt]
        __UpperCAmelCase    :						Tuple       =						sd_pipe.prepare_inputs(__lowercase )

        __UpperCAmelCase    :						List[Any]       =						replicate(__lowercase )
        __UpperCAmelCase    :						Optional[Any]       =						shard(__lowercase )

        __UpperCAmelCase    :						str       =						jax.random.PRNGKey(0 )
        __UpperCAmelCase    :						Dict       =						jax.random.split(__lowercase						,				jax.device_count() )

        __UpperCAmelCase    :						Tuple       =						sd_pipe(__lowercase						,				__lowercase						,				__lowercase						,				num_inference_steps=25						,				jit=__lowercase )[0]
        assert images.shape == (jax.device_count(), 1, 768, 768, 3)

        __UpperCAmelCase    :						Any       =						images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:] )
        __UpperCAmelCase    :						Union[str, Any]       =						images[0, 253:256, 253:256, -1]

        __UpperCAmelCase    :						str       =						jnp.asarray(jax.device_get(image_slice.flatten() ) )
        __UpperCAmelCase    :						List[Any]       =						jnp.array([0.4_2_3_8, 0.4_4_1_4, 0.4_3_9_5, 0.4_4_5_3, 0.4_6_2_9, 0.4_5_9_0, 0.4_5_3_1, 0.4_5_5_0_8, 0.4_5_1_2] )
        print(f'''output_slice: {output_slice}''' )
        assert jnp.abs(output_slice - expected_slice ).max() < 1e-2







      def       A_					(							self       :      Union[str, Any] ):


        '''simple docstring'''




        __UpperCAmelCase    :						Optional[int]       =						'''stabilityai/stable-diffusion-2'''
        __UpperCAmelCase       ,     __UpperCAmelCase    :						Any       =						FlaxDPMSolverMultistepScheduler.from_pretrained(__lowercase						,				subfolder='''scheduler''' )
        __UpperCAmelCase       ,     __UpperCAmelCase    :						Optional[int]       =						FlaxStableDiffusionPipeline.from_pretrained(
            __lowercase						,				scheduler=__lowercase						,				revision='''bf16'''						,				dtype=jnp.bfloataa						,				)
        __UpperCAmelCase    :						Dict       =						scheduler_params

        __UpperCAmelCase    :						int       =						'''A painting of a squirrel eating a burger'''
        __UpperCAmelCase    :						List[Any]       =						jax.device_count()
        __UpperCAmelCase    :						Optional[int]       =						num_samples * [prompt]
        __UpperCAmelCase    :						Tuple       =						sd_pipe.prepare_inputs(__lowercase )

        __UpperCAmelCase    :						Optional[int]       =						replicate(__lowercase )
        __UpperCAmelCase    :						List[Any]       =						shard(__lowercase )

        __UpperCAmelCase    :						Union[str, Any]       =						jax.random.PRNGKey(0 )
        __UpperCAmelCase    :						List[Any]       =						jax.random.split(__lowercase						,				jax.device_count() )

        __UpperCAmelCase    :						List[Any]       =						sd_pipe(__lowercase						,				__lowercase						,				__lowercase						,				num_inference_steps=25						,				jit=__lowercase )[0]
        assert images.shape == (jax.device_count(), 1, 768, 768, 3)

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

        __UpperCAmelCase    :						List[str]       =						jnp.asarray(jax.device_get(image_slice.flatten() ) )
        __UpperCAmelCase    :						Any       =						jnp.array([0.4_3_3_6, 0.4_2_9_6_9, 0.4_4_5_3, 0.4_1_9_9, 0.4_2_9_7, 0.4_5_3_1, 0.4_4_3_4, 0.4_4_3_4, 0.4_2_9_7] )
        print(f'''output_slice: {output_slice}''' )
        assert jnp.abs(output_slice - expected_slice ).max() < 1e-2