import unittest

from transformers import BertGenerationTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_torch, slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


A     :  Union[str, Any]					     =       "▁"

A     :  Union[str, Any]					     =       get_tests_dir("fixtures/test_sentencepiece.model")


@require_sentencepiece
class   lowerCamelCase					(SCREAMING_SNAKE_CASE__  ,      unittest.TestCase					):
  """simple docstring"""







  lowerCamelCase__     =						BertGenerationTokenizer
  lowerCamelCase__     =						False
  lowerCamelCase__     =						True







  def    __A     (							self :       Optional[Any]   )  ->	Tuple:
        super().setUp()

        SCREAMING_SNAKE_CASE_          =  BertGenerationTokenizer(__magic_name__  ,     keep_accents=__magic_name__   )
        tokenizer.save_pretrained(self.tmpdirname   )







  def    __A     (							self :       str   )  ->	Dict:
        SCREAMING_SNAKE_CASE_          =  "<s>"
        SCREAMING_SNAKE_CASE_          =  1

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







  def    __A     (							self :       int   )  ->	Optional[Any]:
        SCREAMING_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]  ,     "<pad>"   )
        self.assertEqual(len(__magic_name__   )  ,     1_002   )







  def    __A     (							self :       List[str]   )  ->	int:
        self.assertEqual(self.get_tokenizer().vocab_size  ,     1_000   )







  def    __A     (							self :       Any   )  ->	str:
        SCREAMING_SNAKE_CASE_          =  BertGenerationTokenizer(__magic_name__  ,     keep_accents=__magic_name__   )

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

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

        SCREAMING_SNAKE_CASE_          =  tokenizer.tokenize("I was born in 92000, and this is falsé."   )
        self.assertListEqual(
            __magic_name__  ,     [
                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",
                ".",
            ]  ,     )
        SCREAMING_SNAKE_CASE_          =  tokenizer.convert_tokens_to_ids(__magic_name__   )
        self.assertListEqual(
            __magic_name__  ,     [8, 21, 84, 55, 24, 19, 7, 0, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 0, 4]  ,     )

        SCREAMING_SNAKE_CASE_          =  tokenizer.convert_ids_to_tokens(__magic_name__   )
        self.assertListEqual(
            __magic_name__  ,     [
                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    __A     (							self :       int   )  ->	Optional[int]:
        return BertGenerationTokenizer.from_pretrained("google/bert_for_seq_generation_L-24_bbc_encoder"   )







  @slow
  def    __A     (							self :       List[Any]   )  ->	List[str]:
        SCREAMING_SNAKE_CASE_          =  "Hello World!"
        SCREAMING_SNAKE_CASE_          =  [18_536, 2_260, 101]

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







  @slow
  def    __A     (							self :       Optional[int]   )  ->	Dict:
        SCREAMING_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"
        )
        SCREAMING_SNAKE_CASE_          =  [
            871,
            419,
            358,
            946,
            991,
            2_521,
            452,
            358,
            1_357,
            387,
            7_751,
            3_536,
            112,
            985,
            456,
            126,
            865,
            938,
            5_400,
            5_734,
            458,
            1_368,
            467,
            786,
            2_462,
            5_246,
            1_159,
            633,
            865,
            4_519,
            457,
            582,
            852,
            2_557,
            427,
            916,
            508,
            405,
            34_324,
            497,
            391,
            408,
            11_342,
            1_244,
            385,
            100,
            938,
            985,
            456,
            574,
            362,
            12_597,
            3_200,
            3_129,
            1_172,
        ]

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







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

        from transformers import BertGenerationConfig, BertGenerationEncoder

        # Build sequence
        SCREAMING_SNAKE_CASE_          =  list(self.big_tokenizer.get_vocab().keys()   )[:10]
        SCREAMING_SNAKE_CASE_          =  " ".join(__magic_name__   )
        SCREAMING_SNAKE_CASE_          =  self.big_tokenizer.encode_plus(__magic_name__  ,     return_tensors="pt"  ,     return_token_type_ids=__magic_name__   )
        SCREAMING_SNAKE_CASE_          =  self.big_tokenizer.batch_encode_plus(
            [sequence + " " + sequence]  ,     return_tensors="pt"  ,     return_token_type_ids=__magic_name__   )

        SCREAMING_SNAKE_CASE_          =  BertGenerationConfig()
        SCREAMING_SNAKE_CASE_          =  BertGenerationEncoder(__magic_name__   )

        assert model.get_input_embeddings().weight.shape[0] >= self.big_tokenizer.vocab_size

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







  @slow
  def    __A     (							self :       str   )  ->	Optional[int]:
        # fmt: off
        SCREAMING_SNAKE_CASE_          =  {"input_ids": [[39_286, 458, 36_335, 2_001, 456, 13_073, 13_266, 455, 113, 7_746, 1_741, 11_157, 391, 13_073, 13_266, 455, 113, 3_967, 35_412, 113, 4_936, 109, 3_870, 2_377, 113, 30_084, 45_720, 458, 134, 17_496, 112, 503, 11_672, 113, 118, 112, 5_665, 13_347, 38_687, 112, 1_496, 31_389, 112, 3_268, 47_264, 134, 962, 112, 16_377, 8_035, 23_130, 430, 12_169, 15_518, 28_592, 458, 146, 41_697, 109, 391, 12_169, 15_518, 16_689, 458, 146, 41_358, 109, 452, 726, 4_034, 111, 763, 35_412, 5_082, 388, 1_903, 111, 9_051, 391, 2_870, 48_918, 1_900, 1_123, 550, 998, 112, 9_586, 15_985, 455, 391, 410, 22_955, 37_636, 114], [448, 17_496, 419, 3_663, 385, 763, 113, 27_533, 2_870, 3_283, 13_043, 1_639, 24_713, 523, 656, 24_013, 18_550, 2_521, 517, 27_014, 21_244, 420, 1_212, 1_465, 391, 927, 4_833, 388, 578, 11_786, 114, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [484, 2_169, 7_687, 21_932, 18_146, 726, 363, 17_032, 3_391, 114, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], "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, 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, 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}  # noqa: E501
        # fmt: on

        self.tokenizer_integration_test_util(
            expected_encoding=__magic_name__  ,     model_name="google/bert_for_seq_generation_L-24_bbc_encoder"  ,     revision="c817d1fd1be2ffa69431227a1fe320544943d4db"  ,     )



import gc
import tempfile
import unittest

import numpy as np
import torch

from diffusers import VersatileDiffusionTextToImagePipeline
from diffusers.utils.testing_utils import nightly, require_torch_gpu, torch_device


A     :  List[str]					     =       False


class   lowerCamelCase					(unittest.TestCase					):
  """simple docstring"""







  pass



@nightly
@require_torch_gpu
class   lowerCamelCase					(unittest.TestCase					):
  """simple docstring"""







  def    __A     (							self :       Union[str, Any]   )  ->	List[Any]:
        # clean up the VRAM after each test
        super().tearDown()
        gc.collect()
        torch.cuda.empty_cache()







  def    __A     (							self :       str   )  ->	Any:
        SCREAMING_SNAKE_CASE_          =  VersatileDiffusionTextToImagePipeline.from_pretrained("shi-labs/versatile-diffusion"   )
        # remove text_unet
        pipe.remove_unused_weights()
        pipe.to(__magic_name__   )
        pipe.set_progress_bar_config(disable=__magic_name__   )

        SCREAMING_SNAKE_CASE_          =  "A painting of a squirrel eating a burger "
        SCREAMING_SNAKE_CASE_          =  torch.manual_seed(0   )
        SCREAMING_SNAKE_CASE_          =  pipe(
            prompt=__magic_name__  ,     generator=__magic_name__  ,     guidance_scale=7.5  ,     num_inference_steps=2  ,     output_type="numpy"   ).images

        with tempfile.TemporaryDirectory() as tmpdirname:
              pipe.save_pretrained(__magic_name__   )
              SCREAMING_SNAKE_CASE_          =  VersatileDiffusionTextToImagePipeline.from_pretrained(__magic_name__   )
        pipe.to(__magic_name__   )
        pipe.set_progress_bar_config(disable=__magic_name__   )

        SCREAMING_SNAKE_CASE_          =  generator.manual_seed(0   )
        SCREAMING_SNAKE_CASE_          =  pipe(
            prompt=__magic_name__  ,     generator=__magic_name__  ,     guidance_scale=7.5  ,     num_inference_steps=2  ,     output_type="numpy"   ).images

        assert np.abs(image - new_image   ).sum() < 1e-5, "Models don't have the same forward pass"







  def    __A     (							self :       int   )  ->	Optional[int]:
        SCREAMING_SNAKE_CASE_          =  VersatileDiffusionTextToImagePipeline.from_pretrained(
            "shi-labs/versatile-diffusion"  ,     torch_dtype=torch.floataa   )
        pipe.to(__magic_name__   )
        pipe.set_progress_bar_config(disable=__magic_name__   )

        SCREAMING_SNAKE_CASE_          =  "A painting of a squirrel eating a burger "
        SCREAMING_SNAKE_CASE_          =  torch.manual_seed(0   )
        SCREAMING_SNAKE_CASE_          =  pipe(
            prompt=__magic_name__  ,     generator=__magic_name__  ,     guidance_scale=7.5  ,     num_inference_steps=50  ,     output_type="numpy"   ).images

        SCREAMING_SNAKE_CASE_          =  image[0, 253:256, 253:256, -1]

        assert image.shape == (1, 512, 512, 3)
        SCREAMING_SNAKE_CASE_          =  np.array([0.3367, 0.3169, 0.2656, 0.3870, 0.4790, 0.3796, 0.4009, 0.4878, 0.4778]   )

        assert np.abs(image_slice.flatten() - expected_slice   ).max() < 1e-2


