
'''simple docstring'''
import unittest

from transformers import SPIECE_UNDERLINE, ReformerTokenizer, ReformerTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, require_torch, slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


__lowerCAmelCase												=	get_tests_dir('fixtures/test_sentencepiece.model')


@require_sentencepiece
@require_tokenizers
class       _lowerCAmelCase   (   __snake_case					,							unittest.TestCase				):







							'''simple docstring'''

							lowerCAmelCase_ 						=						ReformerTokenizer
							lowerCAmelCase_ 						=						ReformerTokenizerFast
							lowerCAmelCase_ 						=						True
							lowerCAmelCase_ 						=						False
							lowerCAmelCase_ 						=						True

							def 	lowercase							(self  )					->       Any:
														super().setUp()

														_snake_case       =  ReformerTokenizer(UpperCAmelCase  ,  keep_accents=UpperCAmelCase  )
														tokenizer.save_pretrained(self.tmpdirname  )

							def 	lowercase							(self  )					->       Any:
														_snake_case       =  """<s>"""
														_snake_case       =  1

														self.assertEqual(self.get_tokenizer()._convert_token_to_id(UpperCAmelCase  )  ,  UpperCAmelCase  )
														self.assertEqual(self.get_tokenizer()._convert_id_to_token(UpperCAmelCase  )  ,  UpperCAmelCase  )

							def 	lowercase							(self  )					->       Any:
														_snake_case       =  list(self.get_tokenizer().get_vocab().keys()  )

														self.assertEqual(vocab_keys[0]  ,  """<unk>"""  )
														self.assertEqual(vocab_keys[1]  ,  """<s>"""  )
														self.assertEqual(vocab_keys[-1]  ,  """j"""  )
														self.assertEqual(len(UpperCAmelCase  )  ,  1000  )

							def 	lowercase							(self  )					->       List[str]:
														self.assertEqual(self.get_tokenizer().vocab_size  ,  1000  )

							def 	lowercase							(self  )					->       Union[str, Any]:
														if not self.test_rust_tokenizer:
																					return

														_snake_case       =  self.get_tokenizer()
														_snake_case       =  self.get_rust_tokenizer()

														_snake_case       =  """I was born in 92000, and this is falsé."""

														_snake_case       =  tokenizer.tokenize(UpperCAmelCase  )
														_snake_case       =  rust_tokenizer.tokenize(UpperCAmelCase  )
														self.assertListEqual(UpperCAmelCase  ,  UpperCAmelCase  )

														_snake_case       =  tokenizer.encode(UpperCAmelCase  ,  add_special_tokens=UpperCAmelCase  )
														_snake_case       =  rust_tokenizer.encode(UpperCAmelCase  ,  add_special_tokens=UpperCAmelCase  )
														self.assertListEqual(UpperCAmelCase  ,  UpperCAmelCase  )

														_snake_case       =  self.get_rust_tokenizer()
														_snake_case       =  tokenizer.encode(UpperCAmelCase  )
														_snake_case       =  rust_tokenizer.encode(UpperCAmelCase  )
														self.assertListEqual(UpperCAmelCase  ,  UpperCAmelCase  )

							def 	lowercase							(self  ,  UpperCAmelCase=15  )					->       Union[str, Any]:
														for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
																					with self.subTest(f"""{tokenizer.__class__.__name__} ({pretrained_name})"""  ):
																												_snake_case       =  self.rust_tokenizer_class.from_pretrained(UpperCAmelCase  ,  **UpperCAmelCase  )

																												# Simple input
																												_snake_case       =  """This is a simple input"""
																												_snake_case       =  ["""This is a simple input 1""", """This is a simple input 2"""]
																												_snake_case       =  ("""This is a simple input""", """This is a pair""")
																												_snake_case       =  [
																												    ("""This is a simple input 1""", """This is a simple input 2"""),
																												    ("""This is a simple pair 1""", """This is a simple pair 2"""),
																												]

																												# Simple input tests
																												self.assertRaises(UpperCAmelCase  ,  tokenizer_r.encode  ,  UpperCAmelCase  ,  max_length=UpperCAmelCase  ,  padding="""max_length"""  )

																												# Simple input
																												self.assertRaises(UpperCAmelCase  ,  tokenizer_r.encode_plus  ,  UpperCAmelCase  ,  max_length=UpperCAmelCase  ,  padding="""max_length"""  )

																												# Simple input
																												self.assertRaises(
																												    UpperCAmelCase  ,  tokenizer_r.batch_encode_plus  ,  UpperCAmelCase  ,  max_length=UpperCAmelCase  ,  padding="""max_length"""  ,  )

																												# Pair input
																												self.assertRaises(UpperCAmelCase  ,  tokenizer_r.encode  ,  UpperCAmelCase  ,  max_length=UpperCAmelCase  ,  padding="""max_length"""  )

																												# Pair input
																												self.assertRaises(UpperCAmelCase  ,  tokenizer_r.encode_plus  ,  UpperCAmelCase  ,  max_length=UpperCAmelCase  ,  padding="""max_length"""  )

																												# Pair input
																												self.assertRaises(
																												    UpperCAmelCase  ,  tokenizer_r.batch_encode_plus  ,  UpperCAmelCase  ,  max_length=UpperCAmelCase  ,  padding="""max_length"""  ,  )

							def 	lowercase							(self  )					->       str:
														pass

							def 	lowercase							(self  )					->       str:
														_snake_case       =  ReformerTokenizer(UpperCAmelCase  ,  keep_accents=UpperCAmelCase  )

														_snake_case       =  tokenizer.tokenize("""This is a test"""  )
														self.assertListEqual(UpperCAmelCase  ,  ["""▁This""", """▁is""", """▁a""", """▁t""", """est"""]  )

														self.assertListEqual(
														    tokenizer.convert_tokens_to_ids(UpperCAmelCase  )  ,  [285, 46, 10, 170, 382]  ,  )

														_snake_case       =  tokenizer.tokenize("""I was born in 92000, and this is falsé."""  )
														self.assertListEqual(
														    UpperCAmelCase  ,  [
														        SPIECE_UNDERLINE + """I""",
														        SPIECE_UNDERLINE + """was""",
														        SPIECE_UNDERLINE + """b""",
														        """or""",
														        """n""",
														        SPIECE_UNDERLINE + """in""",
														        SPIECE_UNDERLINE + """""",
														        """9""",
														        """2""",
														        """0""",
														        """0""",
														        """0""",
														        """,""",
														        SPIECE_UNDERLINE + """and""",
														        SPIECE_UNDERLINE + """this""",
														        SPIECE_UNDERLINE + """is""",
														        SPIECE_UNDERLINE + """f""",
														        """al""",
														        """s""",
														        """é""",
														        """.""",
														    ]  ,  )
														_snake_case       =  tokenizer.convert_tokens_to_ids(UpperCAmelCase  )
														self.assertListEqual(
														    UpperCAmelCase  ,  [8, 21, 84, 55, 24, 19, 7, 0, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 0, 4]  ,  )

														_snake_case       =  tokenizer.convert_ids_to_tokens(UpperCAmelCase  )
														self.assertListEqual(
														    UpperCAmelCase  ,  [
														        SPIECE_UNDERLINE + """I""",
														        SPIECE_UNDERLINE + """was""",
														        SPIECE_UNDERLINE + """b""",
														        """or""",
														        """n""",
														        SPIECE_UNDERLINE + """in""",
														        SPIECE_UNDERLINE + """""",
														        """<unk>""",
														        """2""",
														        """0""",
														        """0""",
														        """0""",
														        """,""",
														        SPIECE_UNDERLINE + """and""",
														        SPIECE_UNDERLINE + """this""",
														        SPIECE_UNDERLINE + """is""",
														        SPIECE_UNDERLINE + """f""",
														        """al""",
														        """s""",
														        """<unk>""",
														        """.""",
														    ]  ,  )

							@cached_property
							def 	lowercase							(self  )					->       Any:
														return ReformerTokenizer.from_pretrained("""google/reformer-crime-and-punishment"""  )

							@slow
							def 	lowercase							(self  )					->       Tuple:
														_snake_case       =  """Hello World!"""
														_snake_case       =  [126, 32, 262, 152, 38, 72, 287]

														self.assertListEqual(UpperCAmelCase  ,  self.big_tokenizer.encode(UpperCAmelCase  )  )

							@slow
							def 	lowercase							(self  )					->       Any:
														_snake_case       =  (
														    """This is a very long text with a lot of weird characters, such as: . , ~ ? ( ) \" [ ] ! : - . Also we will"""
														    """ add words that should not exsist and be tokenized to <unk>, such as saoneuhaoesuth"""
														)
														_snake_case       =  [
														    108,
														    265,
														    24,
														    111,
														    4,
														    258,
														    156,
														    35,
														    28,
														    275,
														    3,
														    259,
														    297,
														    260,
														    84,
														    4,
														    35,
														    110,
														    44,
														    8,
														    259,
														    91,
														    268,
														    21,
														    11,
														    209,
														    274,
														    109,
														    266,
														    277,
														    117,
														    86,
														    93,
														    315,
														    258,
														    278,
														    258,
														    277,
														    258,
														    0,
														    258,
														    288,
														    258,
														    319,
														    258,
														    0,
														    258,
														    0,
														    258,
														    0,
														    258,
														    0,
														    258,
														    287,
														    258,
														    315,
														    258,
														    289,
														    258,
														    278,
														    99,
														    269,
														    266,
														    262,
														    8,
														    259,
														    241,
														    4,
														    217,
														    230,
														    268,
														    266,
														    55,
														    168,
														    106,
														    75,
														    193,
														    266,
														    223,
														    27,
														    49,
														    26,
														    282,
														    25,
														    264,
														    299,
														    19,
														    26,
														    0,
														    258,
														    277,
														    117,
														    86,
														    93,
														    176,
														    183,
														    270,
														    11,
														    262,
														    42,
														    61,
														    265,
														]

														self.assertListEqual(UpperCAmelCase  ,  self.big_tokenizer.encode(UpperCAmelCase  )  )

							@require_torch
							@slow
							def 	lowercase							(self  )					->       int:
														import torch

														from transformers import ReformerConfig, ReformerModel

														# Build sequence
														_snake_case       =  list(self.big_tokenizer.get_vocab().keys()  )[:10]
														_snake_case       =  """ """.join(UpperCAmelCase  )
														_snake_case       =  self.big_tokenizer.encode_plus(UpperCAmelCase  ,  return_tensors="""pt"""  )
														_snake_case       =  self.big_tokenizer.batch_encode_plus([sequence, sequence]  ,  return_tensors="""pt"""  )

														_snake_case       =  ReformerConfig()
														# The input gets padded during training so adjust the axial position encodings from the pretrained model value of (512, 1024)
														_snake_case       =  encoded_sequence["""input_ids"""].shape
														_snake_case       =  ReformerModel(UpperCAmelCase  )

														# Reformer has config.vocab_size == tokenizer.vocab_size == len(tokenizer) - 1 = 320; len(tokenizer) is 321 (including a pad token with id 320)
														assert model.get_input_embeddings().weight.shape[0] >= self.big_tokenizer.vocab_size

														with torch.no_grad():
																					model(**UpperCAmelCase  )
																					model(**UpperCAmelCase  )





							@slow
							def 	lowercase							(self  )					->       List[Any]:
														# fmt: off
														_snake_case       =  {"""input_ids""": [[108, 265, 24, 111, 4, 258, 156, 7, 51, 279, 58, 7, 76, 25, 69, 278], [140, 243, 264, 134, 17, 267, 77, 263, 22, 262, 297, 258, 304, 177, 279, 266, 14, 89, 13, 35, 261, 299, 272, 137, 275, 278]], """attention_mask""": [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]}  # noqa: E501
														# fmt: on

														# This tokenizer does not know some characters like ")".
														# That is the reason why we use very simple texts here.
														# Also see https://github.com/huggingface/transformers/pull/11737#issuecomment-850769064
														_snake_case       =  [
														    """This is a very simple sentence.""",
														    """The quick brown fox jumps over the lazy dog.""",
														]

														self.tokenizer_integration_test_util(
														    expected_encoding=UpperCAmelCase  ,  model_name="""google/reformer-crime-and-punishment"""  ,  revision="""0e6c3decb8211d49bf881013425dc8b0448b3f5a"""  ,  padding=UpperCAmelCase  ,  sequences=UpperCAmelCase  ,  )

'''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       _lowerCAmelCase   (   unittest.TestCase				):







							'''simple docstring'''

							def 	lowercase							(self  )					->       Optional[int]:
														# clean up the VRAM after each test
														super().tearDown()
														gc.collect()

							def 	lowercase							(self  )					->       Dict:
														_snake_case,							_snake_case       =  FlaxControlNetModel.from_pretrained(
														    """lllyasviel/sd-controlnet-canny"""  ,  from_pt=UpperCAmelCase  ,  dtype=jnp.bfloataa  )
														_snake_case,							_snake_case       =  FlaxStableDiffusionControlNetPipeline.from_pretrained(
														    """runwayml/stable-diffusion-v1-5"""  ,  controlnet=UpperCAmelCase  ,  from_pt=UpperCAmelCase  ,  dtype=jnp.bfloataa  )
														_snake_case       =  controlnet_params

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

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

														_snake_case       =  jax.random.PRNGKey(0  )
														_snake_case       =  jax.random.split(UpperCAmelCase  ,  jax.device_count()  )

														_snake_case       =  replicate(UpperCAmelCase  )
														_snake_case       =  shard(UpperCAmelCase  )
														_snake_case       =  shard(UpperCAmelCase  )

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

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

														_snake_case       =  jnp.asarray(jax.device_get(image_slice.flatten()  )  )
														_snake_case       =  jnp.array(
														    [0.16_7969, 0.11_6699, 0.08_1543, 0.15_4297, 0.13_2812, 0.10_8887, 0.16_9922, 0.16_9922, 0.20_5078]  )
														print(f"""output_slice: {output_slice}"""  )
														assert jnp.abs(output_slice - expected_slice  ).max() < 1e-2





							def 	lowercase							(self  )					->       Optional[int]:
														_snake_case,							_snake_case       =  FlaxControlNetModel.from_pretrained(
														    """lllyasviel/sd-controlnet-openpose"""  ,  from_pt=UpperCAmelCase  ,  dtype=jnp.bfloataa  )
														_snake_case,							_snake_case       =  FlaxStableDiffusionControlNetPipeline.from_pretrained(
														    """runwayml/stable-diffusion-v1-5"""  ,  controlnet=UpperCAmelCase  ,  from_pt=UpperCAmelCase  ,  dtype=jnp.bfloataa  )
														_snake_case       =  controlnet_params

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

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

														_snake_case       =  jax.random.PRNGKey(0  )
														_snake_case       =  jax.random.split(UpperCAmelCase  ,  jax.device_count()  )

														_snake_case       =  replicate(UpperCAmelCase  )
														_snake_case       =  shard(UpperCAmelCase  )
														_snake_case       =  shard(UpperCAmelCase  )

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

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

														_snake_case       =  jnp.asarray(jax.device_get(image_slice.flatten()  )  )
														_snake_case       =  jnp.array(
														    [[0.27_1484, 0.26_1719, 0.27_5391, 0.27_7344, 0.27_9297, 0.29_1016, 0.29_4922, 0.30_2734, 0.30_2734]]  )
														print(f"""output_slice: {output_slice}"""  )
														assert jnp.abs(output_slice - expected_slice  ).max() < 1e-2