




'''simple docstring'''







import os
import tempfile
import unittest

import numpy as np

from diffusers.utils import is_flax_available
from diffusers.utils.testing_utils import require_flax, slow


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

 from diffusers import FlaxDDIMScheduler, FlaxDiffusionPipeline, FlaxStableDiffusionPipeline


@require_flax
class  lowerCAmelCase_						(       unittest.TestCase      ):





  """simple docstring"""



  def __a						(					self     :							Optional[int]						):





       '''simple docstring'''

       with tempfile.TemporaryDirectory() as tmpdirname:
            # pipeline has Flax weights
            __a       =   FlaxDiffusionPipeline.from_pretrained(
                """hf-internal-testing/tiny-stable-diffusion-pipe"""				,							safety_checker=UpperCamelCase__				,							cache_dir=UpperCamelCase__						)

            __a       =   [t[-1] for t in os.walk(os.path.join(UpperCamelCase__				,							os.listdir(UpperCamelCase__						)[0]				,							"""snapshots"""						)						)]
            __a       =   [item for sublist in all_root_files for item in sublist]

            # None of the downloaded files should be a PyTorch file even if we have some here:
            # https://huggingface.co/hf-internal-testing/tiny-stable-diffusion-pipe/blob/main/unet/diffusion_pytorch_model.bin
            assert not any(f.endswith(""".bin"""						) for f in files						)


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





  """simple docstring"""



  def __a						(					self     :							Any						):





       '''simple docstring'''

       __a       =   FlaxStableDiffusionPipeline.from_pretrained(
           """hf-internal-testing/tiny-stable-diffusion-pipe"""				,							safety_checker=UpperCamelCase__						)

       __a       =   (
           '''A cinematic film still of Morgan Freeman starring as Jimi Hendrix, portrait, 40mm lens, shallow depth of'''
           ''' field, close up, split lighting, cinematic'''
       )

       __a       =   jax.random.PRNGKey(0						)
       __a       =   4

       __a       =   jax.device_count()
       __a       =   num_samples * [prompt]
       __a       =   pipeline.prepare_inputs(UpperCamelCase__						)

       # shard inputs and rng
       __a       =   replicate(UpperCamelCase__						)
       __a       =   jax.random.split(UpperCamelCase__				,							UpperCamelCase__						)
       __a       =   shard(UpperCamelCase__						)

       __a       =   pipeline(UpperCamelCase__				,							UpperCamelCase__				,							UpperCamelCase__				,							UpperCamelCase__				,							jit=UpperCamelCase__						).images

       assert images.shape == (num_samples, 1, 6_4, 6_4, 3)
       if jax.device_count() == 8:
            assert np.abs(np.abs(images[0, 0, :2, :2, -2:]				,							dtype=np.floataa						).sum() - 4.1_5_1_4_7_4_5						) < 1E-3
            assert np.abs(np.abs(UpperCamelCase__				,							dtype=np.floataa						).sum() - 4_9_9_4_7.8_7_5						) < 5E-1

       __a       =   pipeline.numpy_to_pil(np.asarray(images.reshape((num_samples,) + images.shape[-3:]						)						)						)
       assert len(UpperCamelCase__						) == num_samples

  def __a						(					self     :							Optional[Any]						):





       '''simple docstring'''

       __a       =   FlaxStableDiffusionPipeline.from_pretrained(
           """CompVis/stable-diffusion-v1-4"""				,							revision="""flax"""				,							safety_checker=UpperCamelCase__						)

       __a       =   (
           '''A cinematic film still of Morgan Freeman starring as Jimi Hendrix, portrait, 40mm lens, shallow depth of'''
           ''' field, close up, split lighting, cinematic'''
       )

       __a       =   jax.random.PRNGKey(0						)
       __a       =   5_0

       __a       =   jax.device_count()
       __a       =   num_samples * [prompt]
       __a       =   pipeline.prepare_inputs(UpperCamelCase__						)

       # shard inputs and rng
       __a       =   replicate(UpperCamelCase__						)
       __a       =   jax.random.split(UpperCamelCase__				,							UpperCamelCase__						)
       __a       =   shard(UpperCamelCase__						)

       __a       =   pipeline(UpperCamelCase__				,							UpperCamelCase__				,							UpperCamelCase__				,							UpperCamelCase__				,							jit=UpperCamelCase__						).images

       assert images.shape == (num_samples, 1, 5_1_2, 5_1_2, 3)
       if jax.device_count() == 8:
            assert np.abs((np.abs(images[0, 0, :2, :2, -2:]				,							dtype=np.floataa						).sum() - 0.0_5_6_5_2_4_0_1)						) < 1E-3
            assert np.abs((np.abs(UpperCamelCase__				,							dtype=np.floataa						).sum() - 2_3_8_3_8_0_8.2)						) < 5E-1

  def __a						(					self     :							Optional[Any]						):





       '''simple docstring'''

       __a       =   FlaxStableDiffusionPipeline.from_pretrained(
           """CompVis/stable-diffusion-v1-4"""				,							revision="""bf16"""				,							dtype=jnp.bfloataa				,							safety_checker=UpperCamelCase__						)

       __a       =   (
           '''A cinematic film still of Morgan Freeman starring as Jimi Hendrix, portrait, 40mm lens, shallow depth of'''
           ''' field, close up, split lighting, cinematic'''
       )

       __a       =   jax.random.PRNGKey(0						)
       __a       =   5_0

       __a       =   jax.device_count()
       __a       =   num_samples * [prompt]
       __a       =   pipeline.prepare_inputs(UpperCamelCase__						)

       # shard inputs and rng
       __a       =   replicate(UpperCamelCase__						)
       __a       =   jax.random.split(UpperCamelCase__				,							UpperCamelCase__						)
       __a       =   shard(UpperCamelCase__						)

       __a       =   pipeline(UpperCamelCase__				,							UpperCamelCase__				,							UpperCamelCase__				,							UpperCamelCase__				,							jit=UpperCamelCase__						).images

       assert images.shape == (num_samples, 1, 5_1_2, 5_1_2, 3)
       if jax.device_count() == 8:
            assert np.abs((np.abs(images[0, 0, :2, :2, -2:]				,							dtype=np.floataa						).sum() - 0.0_4_0_0_3_9_0_6)						) < 1E-3
            assert np.abs((np.abs(UpperCamelCase__				,							dtype=np.floataa						).sum() - 2_3_7_3_5_1_6.7_5)						) < 5E-1

  def __a						(					self     :							Dict						):





       '''simple docstring'''

       __a       =   FlaxStableDiffusionPipeline.from_pretrained(
           """CompVis/stable-diffusion-v1-4"""				,							revision="""bf16"""				,							dtype=jnp.bfloataa						)

       __a       =   (
           '''A cinematic film still of Morgan Freeman starring as Jimi Hendrix, portrait, 40mm lens, shallow depth of'''
           ''' field, close up, split lighting, cinematic'''
       )

       __a       =   jax.random.PRNGKey(0						)
       __a       =   5_0

       __a       =   jax.device_count()
       __a       =   num_samples * [prompt]
       __a       =   pipeline.prepare_inputs(UpperCamelCase__						)

       # shard inputs and rng
       __a       =   replicate(UpperCamelCase__						)
       __a       =   jax.random.split(UpperCamelCase__				,							UpperCamelCase__						)
       __a       =   shard(UpperCamelCase__						)

       __a       =   pipeline(UpperCamelCase__				,							UpperCamelCase__				,							UpperCamelCase__				,							UpperCamelCase__				,							jit=UpperCamelCase__						).images

       assert images.shape == (num_samples, 1, 5_1_2, 5_1_2, 3)
       if jax.device_count() == 8:
            assert np.abs((np.abs(images[0, 0, :2, :2, -2:]				,							dtype=np.floataa						).sum() - 0.0_4_0_0_3_9_0_6)						) < 1E-3
            assert np.abs((np.abs(UpperCamelCase__				,							dtype=np.floataa						).sum() - 2_3_7_3_5_1_6.7_5)						) < 5E-1

  def __a						(					self     :							int						):





       '''simple docstring'''

       __a       =   FlaxDDIMScheduler(
           beta_start=0.0_0_0_8_5				,							beta_end=0.0_1_2				,							beta_schedule="""scaled_linear"""				,							set_alpha_to_one=UpperCamelCase__				,							steps_offset=1				,							)

       __a       =   FlaxStableDiffusionPipeline.from_pretrained(
           """CompVis/stable-diffusion-v1-4"""				,							revision="""bf16"""				,							dtype=jnp.bfloataa				,							scheduler=UpperCamelCase__				,							safety_checker=UpperCamelCase__				,							)
       __a       =   scheduler.create_state()

       __a       =   scheduler_state

       __a       =   (
           '''A cinematic film still of Morgan Freeman starring as Jimi Hendrix, portrait, 40mm lens, shallow depth of'''
           ''' field, close up, split lighting, cinematic'''
       )

       __a       =   jax.random.PRNGKey(0						)
       __a       =   5_0

       __a       =   jax.device_count()
       __a       =   num_samples * [prompt]
       __a       =   pipeline.prepare_inputs(UpperCamelCase__						)

       # shard inputs and rng
       __a       =   replicate(UpperCamelCase__						)
       __a       =   jax.random.split(UpperCamelCase__				,							UpperCamelCase__						)
       __a       =   shard(UpperCamelCase__						)

       __a       =   pipeline(UpperCamelCase__				,							UpperCamelCase__				,							UpperCamelCase__				,							UpperCamelCase__				,							jit=UpperCamelCase__						).images

       assert images.shape == (num_samples, 1, 5_1_2, 5_1_2, 3)
       if jax.device_count() == 8:
            assert np.abs((np.abs(images[0, 0, :2, :2, -2:]				,							dtype=np.floataa						).sum() - 0.0_4_5_0_4_3_9_4_5)						) < 1E-3
            assert np.abs((np.abs(UpperCamelCase__				,							dtype=np.floataa						).sum() - 2_3_4_7_6_9_3.5)						) < 5E-1


  def __a						(					self     :							Optional[int]						):





       '''simple docstring'''

       __a       =   (
           '''A cinematic film still of Morgan Freeman starring as Jimi Hendrix, portrait, 40mm lens, shallow depth of'''
           ''' field, close up, split lighting, cinematic'''
       )

       __a       =   jax.device_count()
       __a       =   num_samples * [prompt]
       __a       =   jax.random.split(jax.random.PRNGKey(0						)				,							UpperCamelCase__						)

       __a       =   FlaxStableDiffusionPipeline.from_pretrained(
           """CompVis/stable-diffusion-v1-4"""				,							revision="""bf16"""				,							dtype=jnp.bfloataa				,							safety_checker=UpperCamelCase__				,							)

       __a       =   replicate(UpperCamelCase__						)
       __a       =   pipeline.prepare_inputs(UpperCamelCase__						)
       __a       =   shard(UpperCamelCase__						)
       __a       =   pipeline(UpperCamelCase__				,							UpperCamelCase__				,							UpperCamelCase__				,							jit=UpperCamelCase__						).images
       assert images.shape == (num_samples, 1, 5_1_2, 5_1_2, 3)
       __a       =   images[2, 0, 2_5_6, 1_0:1_7, 1]

       # With memory efficient attention
       __a       =   FlaxStableDiffusionPipeline.from_pretrained(
           """CompVis/stable-diffusion-v1-4"""				,							revision="""bf16"""				,							dtype=jnp.bfloataa				,							safety_checker=UpperCamelCase__				,							use_memory_efficient_attention=UpperCamelCase__				,							)

       __a       =   replicate(UpperCamelCase__						)
       __a       =   pipeline.prepare_inputs(UpperCamelCase__						)
       __a       =   shard(UpperCamelCase__						)
       __a       =   pipeline(UpperCamelCase__				,							UpperCamelCase__				,							UpperCamelCase__				,							jit=UpperCamelCase__						).images
       assert images_eff.shape == (num_samples, 1, 5_1_2, 5_1_2, 3)
       __a       =   images[2, 0, 2_5_6, 1_0:1_7, 1]

       # I checked the results visually and they are very similar. However, I saw that the max diff is `1` and the `sum`
       # over the 8 images is exactly `256`, which is very suspicious. Testing a random slice for now.
       assert abs(slice_eff - slice						).max() < 1E-2










'''simple docstring'''







import unittest

from transformers import (
    MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
    TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
    TextClassificationPipeline,
    pipeline,
)
from transformers.testing_utils import is_pipeline_test, nested_simplify, require_tf, require_torch, slow

from .test_pipelines_common import ANY


# These 2 model types require different inputs than those of the usual text models.
SCREAMING_SNAKE_CASE_      =			{'LayoutLMv2Config', 'LayoutLMv3Config'}


@is_pipeline_test
class  lowerCAmelCase_						(       unittest.TestCase      ):





  """simple docstring"""



  a_       :List[Any]												=MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
  a_       :List[Any]												=TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING

  if model_mapping is not None:
    a_       :List[Any]												={config: model for config, model in model_mapping.items() if config.__name__ not in _TO_SKIP}
  if tf_model_mapping is not None:
    a_       :Tuple												={
        config: model for config, model in tf_model_mapping.items() if config.__name__ not in _TO_SKIP
    }

  @require_torch
  def __a						(					self     :							int						):





       '''simple docstring'''

       __a       =   pipeline(
           task="""text-classification"""				,							model="""hf-internal-testing/tiny-random-distilbert"""				,							framework="""pt"""						)

       __a       =   text_classifier("""This is great !"""						)
       self.assertEqual(nested_simplify(SCREAMING_SNAKE_CASE__						)				,							[{"""label""": """LABEL_0""", """score""": 0.5_0_4}]						)

       __a       =   text_classifier("""This is great !"""				,							top_k=2						)
       self.assertEqual(
           nested_simplify(SCREAMING_SNAKE_CASE__						)				,							[{"""label""": """LABEL_0""", """score""": 0.5_0_4}, {"""label""": """LABEL_1""", """score""": 0.4_9_6}]						)

       __a       =   text_classifier(["""This is great !""", """This is bad"""]				,							top_k=2						)
       self.assertEqual(
           nested_simplify(SCREAMING_SNAKE_CASE__						)				,							[
               [{"""label""": """LABEL_0""", """score""": 0.5_0_4}, {"""label""": """LABEL_1""", """score""": 0.4_9_6}],
               [{"""label""": """LABEL_0""", """score""": 0.5_0_4}, {"""label""": """LABEL_1""", """score""": 0.4_9_6}],
           ]				,							)

       __a       =   text_classifier("""This is great !"""				,							top_k=1						)
       self.assertEqual(nested_simplify(SCREAMING_SNAKE_CASE__						)				,							[{"""label""": """LABEL_0""", """score""": 0.5_0_4}]						)

       # Legacy behavior
       __a       =   text_classifier("""This is great !"""				,							return_all_scores=SCREAMING_SNAKE_CASE__						)
       self.assertEqual(nested_simplify(SCREAMING_SNAKE_CASE__						)				,							[{"""label""": """LABEL_0""", """score""": 0.5_0_4}]						)

       __a       =   text_classifier("""This is great !"""				,							return_all_scores=SCREAMING_SNAKE_CASE__						)
       self.assertEqual(
           nested_simplify(SCREAMING_SNAKE_CASE__						)				,							[[{"""label""": """LABEL_0""", """score""": 0.5_0_4}, {"""label""": """LABEL_1""", """score""": 0.4_9_6}]]						)

       __a       =   text_classifier(["""This is great !""", """Something else"""]				,							return_all_scores=SCREAMING_SNAKE_CASE__						)
       self.assertEqual(
           nested_simplify(SCREAMING_SNAKE_CASE__						)				,							[
               [{"""label""": """LABEL_0""", """score""": 0.5_0_4}, {"""label""": """LABEL_1""", """score""": 0.4_9_6}],
               [{"""label""": """LABEL_0""", """score""": 0.5_0_4}, {"""label""": """LABEL_1""", """score""": 0.4_9_6}],
           ]				,							)

       __a       =   text_classifier(["""This is great !""", """Something else"""]				,							return_all_scores=SCREAMING_SNAKE_CASE__						)
       self.assertEqual(
           nested_simplify(SCREAMING_SNAKE_CASE__						)				,							[
               {"""label""": """LABEL_0""", """score""": 0.5_0_4},
               {"""label""": """LABEL_0""", """score""": 0.5_0_4},
           ]				,							)

  @require_torch
  def __a						(					self     :							Optional[int]						):





       '''simple docstring'''

       import torch

       __a       =   pipeline(
           task="""text-classification"""				,							model="""hf-internal-testing/tiny-random-distilbert"""				,							framework="""pt"""				,							device=torch.device("""cpu"""						)				,							)

       __a       =   text_classifier("""This is great !"""						)
       self.assertEqual(nested_simplify(SCREAMING_SNAKE_CASE__						)				,							[{"""label""": """LABEL_0""", """score""": 0.5_0_4}]						)

  @require_tf
  def __a						(					self     :							List[str]						):





       '''simple docstring'''

       __a       =   pipeline(
           task="""text-classification"""				,							model="""hf-internal-testing/tiny-random-distilbert"""				,							framework="""tf"""						)

       __a       =   text_classifier("""This is great !"""						)
       self.assertEqual(nested_simplify(SCREAMING_SNAKE_CASE__						)				,							[{"""label""": """LABEL_0""", """score""": 0.5_0_4}]						)

  @slow
  @require_torch
  def __a						(					self     :							int						):





       '''simple docstring'''

       __a       =   pipeline("""text-classification"""						)

       __a       =   text_classifier("""This is great !"""						)
       self.assertEqual(nested_simplify(SCREAMING_SNAKE_CASE__						)				,							[{"""label""": """POSITIVE""", """score""": 1.0}]						)
       __a       =   text_classifier("""This is bad !"""						)
       self.assertEqual(nested_simplify(SCREAMING_SNAKE_CASE__						)				,							[{"""label""": """NEGATIVE""", """score""": 1.0}]						)
       __a       =   text_classifier("""Birds are a type of animal"""						)
       self.assertEqual(nested_simplify(SCREAMING_SNAKE_CASE__						)				,							[{"""label""": """POSITIVE""", """score""": 0.9_8_8}]						)

  @slow
  @require_tf
  def __a						(					self     :							Union[str, Any]						):





       '''simple docstring'''

       __a       =   pipeline("""text-classification"""				,							framework="""tf"""						)

       __a       =   text_classifier("""This is great !"""						)
       self.assertEqual(nested_simplify(SCREAMING_SNAKE_CASE__						)				,							[{"""label""": """POSITIVE""", """score""": 1.0}]						)
       __a       =   text_classifier("""This is bad !"""						)
       self.assertEqual(nested_simplify(SCREAMING_SNAKE_CASE__						)				,							[{"""label""": """NEGATIVE""", """score""": 1.0}]						)
       __a       =   text_classifier("""Birds are a type of animal"""						)
       self.assertEqual(nested_simplify(SCREAMING_SNAKE_CASE__						)				,							[{"""label""": """POSITIVE""", """score""": 0.9_8_8}]						)

  def __a						(					self     :							Tuple				,							SCREAMING_SNAKE_CASE__     :							int				,							SCREAMING_SNAKE_CASE__     :							str				,							SCREAMING_SNAKE_CASE__     :							Tuple						):





       '''simple docstring'''

       __a       =   TextClassificationPipeline(model=SCREAMING_SNAKE_CASE__				,							tokenizer=SCREAMING_SNAKE_CASE__						)
       return text_classifier, ["HuggingFace is in", "This is another test"]


  def __a						(					self     :							Any				,							SCREAMING_SNAKE_CASE__     :							Any				,							SCREAMING_SNAKE_CASE__     :							Tuple						):





       '''simple docstring'''

       __a       =   text_classifier.model
       # Small inputs because BartTokenizer tiny has maximum position embeddings = 22
       __a       =   """HuggingFace is in"""
       __a       =   text_classifier(SCREAMING_SNAKE_CASE__						)

       self.assertEqual(nested_simplify(SCREAMING_SNAKE_CASE__						)				,							[{"""label""": ANY(SCREAMING_SNAKE_CASE__						), """score""": ANY(SCREAMING_SNAKE_CASE__						)}]						)
       self.assertTrue(outputs[0]["""label"""] in model.config.idalabel.values()						)

       __a       =   ["""HuggingFace is in """, """Paris is in France"""]
       __a       =   text_classifier(SCREAMING_SNAKE_CASE__						)
       self.assertEqual(
           nested_simplify(SCREAMING_SNAKE_CASE__						)				,							[{"""label""": ANY(SCREAMING_SNAKE_CASE__						), """score""": ANY(SCREAMING_SNAKE_CASE__						)}, {"""label""": ANY(SCREAMING_SNAKE_CASE__						), """score""": ANY(SCREAMING_SNAKE_CASE__						)}]				,							)
       self.assertTrue(outputs[0]["""label"""] in model.config.idalabel.values()						)
       self.assertTrue(outputs[1]["""label"""] in model.config.idalabel.values()						)

       # Forcing to get all results with `top_k=None`
       # This is NOT the legacy format
       __a       =   text_classifier(SCREAMING_SNAKE_CASE__				,							top_k=SCREAMING_SNAKE_CASE__						)
       __a       =   len(model.config.idalabel.values()						)
       self.assertEqual(
           nested_simplify(SCREAMING_SNAKE_CASE__						)				,							[[{"""label""": ANY(SCREAMING_SNAKE_CASE__						), """score""": ANY(SCREAMING_SNAKE_CASE__						)}] * N, [{"""label""": ANY(SCREAMING_SNAKE_CASE__						), """score""": ANY(SCREAMING_SNAKE_CASE__						)}] * N]				,							)

       __a       =   {"""text""": """HuggingFace is in """, """text_pair""": """Paris is in France"""}
       __a       =   text_classifier(SCREAMING_SNAKE_CASE__						)
       self.assertEqual(
           nested_simplify(SCREAMING_SNAKE_CASE__						)				,							{"""label""": ANY(SCREAMING_SNAKE_CASE__						), """score""": ANY(SCREAMING_SNAKE_CASE__						)}				,							)
       self.assertTrue(outputs["""label"""] in model.config.idalabel.values()						)

       # This might be used a text pair, but tokenizer + pipe interaction
       # makes it hard to understand that it's not using the pair properly
       # https://github.com/huggingface/transformers/issues/17305
       # We disabled this usage instead as it was outputting wrong outputs.
       __a       =   [["""HuggingFace is in """, """Paris is in France"""]]
       with self.assertRaises(SCREAMING_SNAKE_CASE__						):
            text_classifier(SCREAMING_SNAKE_CASE__						)

       # This used to be valid for doing text pairs
       # We're keeping it working because of backward compatibility
       __a       =   text_classifier([[["""HuggingFace is in """, """Paris is in France"""]]]						)
       self.assertEqual(
           nested_simplify(SCREAMING_SNAKE_CASE__						)				,							[{"""label""": ANY(SCREAMING_SNAKE_CASE__						), """score""": ANY(SCREAMING_SNAKE_CASE__						)}]				,							)
       self.assertTrue(outputs[0]["""label"""] in model.config.idalabel.values()						)






