


import gc
import unittest

from parameterized import parameterized

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


if is_flax_available():
    import jax
    import jax.numpy as jnp







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


       """simple docstring"""




       def 		lowerCAmelCase  (     self							:    List[str]      ,   __lowerCamelCase							:    Union[str, Any]      ,   __lowerCamelCase							:    Optional[Any] ):


             '''simple docstring'''



             return f"gaussian_noise_s={seed}_shape={'_'.join([str(__lowerCamelCase ) for s in shape] )}.npy"





       def 		lowerCAmelCase  (     self							:    Tuple ):


             '''simple docstring'''



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





       def 		lowerCAmelCase  (     self							:    List[str]      ,   __lowerCamelCase							:    List[Any]=0      ,   __lowerCamelCase							:    List[Any]=(4, 4, 64, 64)      ,   __lowerCamelCase							:    Dict=False ):


             '''simple docstring'''



             lowerCamelCase__	:       Optional[int]					=						jnp.bfloataa if fpaa else jnp.floataa
             lowerCamelCase__	:       Any					=						jnp.array(load_hf_numpy(self.get_file_format(__lowerCamelCase      ,   __lowerCamelCase ) )      ,   dtype=__lowerCamelCase )
             return image





       def 		lowerCAmelCase  (     self							:    str      ,   __lowerCamelCase							:    Dict=False      ,   __lowerCamelCase							:    Optional[Any]="CompVis/stable-diffusion-v1-4" ):


             '''simple docstring'''



             lowerCamelCase__	:       Optional[int]					=						jnp.bfloataa if fpaa else jnp.floataa
             lowerCamelCase__	:       Optional[int]					=						"bf16" if fpaa else None

             lowerCamelCase__	:       Optional[int]					=						FlaxUNetaDConditionModel.from_pretrained(
                 __lowerCamelCase      ,   subfolder="unet"      ,   dtype=__lowerCamelCase      ,   revision=__lowerCamelCase )
             return model, params





       def 		lowerCAmelCase  (     self							:    Tuple      ,   __lowerCamelCase							:    Tuple=0      ,   __lowerCamelCase							:    List[Any]=(4, 77, 768)      ,   __lowerCamelCase							:    Dict=False ):


             '''simple docstring'''



             lowerCamelCase__	:       str					=						jnp.bfloataa if fpaa else jnp.floataa
             lowerCamelCase__	:       Optional[Any]					=						jnp.array(load_hf_numpy(self.get_file_format(__lowerCamelCase      ,   __lowerCamelCase ) )      ,   dtype=__lowerCamelCase )
             return hidden_states





       @parameterized.expand(
           [
               # fmt: off
               [83, 4, [-0.2_3_2_3, -0.1_3_0_4, 0.0_8_1_3, -0.3_0_9_3, -0.0_9_1_9, -0.1_5_7_1, -0.1_1_2_5, -0.5_8_0_6]],
               [17, 0.5_5, [-0.0_8_3_1, -0.2_4_4_3, 0.0_9_0_1, -0.0_9_1_9, 0.3_3_9_6, 0.0_1_0_3, -0.3_7_4_3, 0.0_7_0_1]],
               [8, 0.8_9, [-0.4_8_6_3, 0.0_8_5_9, 0.0_8_7_5, -0.1_6_5_8, 0.9_1_9_9, -0.0_1_1_4, 0.4_8_3_9, 0.4_6_3_9]],
               [3, 1000, [-0.5_6_4_9, 0.2_4_0_2, -0.5_5_1_8, 0.1_2_4_8, 1.1_3_2_8, -0.2_4_4_3, -0.0_3_2_5, -1.0_0_7_8]],
               # fmt: on
           ] )
       def 		lowerCAmelCase  (     self							:    List[str]      ,   __lowerCamelCase							:    Optional[Any]      ,   __lowerCamelCase							:    List[str]      ,   __lowerCamelCase							:    Optional[Any] ):


             '''simple docstring'''



             lowerCamelCase__	:       Tuple					=						self.get_unet_model(model_id="CompVis/stable-diffusion-v1-4"      ,   fpaa=__lowerCamelCase )
             lowerCamelCase__	:       int					=						self.get_latents(__lowerCamelCase      ,   fpaa=__lowerCamelCase )
             lowerCamelCase__	:       int					=						self.get_encoder_hidden_states(__lowerCamelCase      ,   fpaa=__lowerCamelCase )

             lowerCamelCase__	:       int					=						model.apply(
                 {"params": params}      ,   __lowerCamelCase      ,   jnp.array(__lowerCamelCase      ,   dtype=jnp.intaa )      ,   encoder_hidden_states=__lowerCamelCase      ,   ).sample

             assert sample.shape == latents.shape

             lowerCamelCase__	:       Optional[int]					=						jnp.asarray(jax.device_get((sample[-1, -2:, -2:, :2].flatten()) )      ,   dtype=jnp.floataa )
             lowerCamelCase__	:       List[Any]					=						jnp.array(__lowerCamelCase      ,   dtype=jnp.floataa )

             # Found torch (float16) and flax (bfloat16) outputs to be within this tolerance, in the same hardware
             assert jnp.allclose(__lowerCamelCase      ,   __lowerCamelCase      ,   atol=1E-2 )





       @parameterized.expand(
           [
               # fmt: off
               [83, 4, [0.1_5_1_4, 0.0_8_0_7, 0.1_6_2_4, 0.1_0_1_6, -0.1_8_9_6, 0.0_2_6_3, 0.0_6_7_7, 0.2_3_1_0]],
               [17, 0.5_5, [0.1_1_6_4, -0.0_2_1_6, 0.0_1_7_0, 0.1_5_8_9, -0.3_1_2_0, 0.1_0_0_5, -0.0_5_8_1, -0.1_4_5_8]],
               [8, 0.8_9, [-0.1_7_5_8, -0.0_1_6_9, 0.1_0_0_4, -0.1_4_1_1, 0.1_3_1_2, 0.1_1_0_3, -0.1_9_9_6, 0.2_1_3_9]],
               [3, 1000, [0.1_2_1_4, 0.0_3_5_2, -0.0_7_3_1, -0.1_5_6_2, -0.0_9_9_4, -0.0_9_0_6, -0.2_3_4_0, -0.0_5_3_9]],
               # fmt: on
           ] )
       def 		lowerCAmelCase  (     self							:    Optional[int]      ,   __lowerCamelCase							:    int      ,   __lowerCamelCase							:    Dict      ,   __lowerCamelCase							:    Optional[Any] ):


             '''simple docstring'''



             lowerCamelCase__	:       str					=						self.get_unet_model(model_id="stabilityai/stable-diffusion-2"      ,   fpaa=__lowerCamelCase )
             lowerCamelCase__	:       Dict					=						self.get_latents(__lowerCamelCase      ,   shape=(4, 4, 96, 96)      ,   fpaa=__lowerCamelCase )
             lowerCamelCase__	:       str					=						self.get_encoder_hidden_states(__lowerCamelCase      ,   shape=(4, 77, 1024)      ,   fpaa=__lowerCamelCase )

             lowerCamelCase__	:       Union[str, Any]					=						model.apply(
                 {"params": params}      ,   __lowerCamelCase      ,   jnp.array(__lowerCamelCase      ,   dtype=jnp.intaa )      ,   encoder_hidden_states=__lowerCamelCase      ,   ).sample

             assert sample.shape == latents.shape

             lowerCamelCase__	:       str					=						jnp.asarray(jax.device_get((sample[-1, -2:, -2:, :2].flatten()) )      ,   dtype=jnp.floataa )
             lowerCamelCase__	:       Optional[Any]					=						jnp.array(__lowerCamelCase      ,   dtype=jnp.floataa )

             # Found torch (float16) and flax (bfloat16) outputs to be within this tolerance, on the same hardware
             assert jnp.allclose(__lowerCamelCase      ,   __lowerCamelCase      ,   atol=1E-2 )






import unittest

from transformers import BarthezTokenizer, BarthezTokenizerFast, BatchEncoding
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow

from ...test_tokenization_common import TokenizerTesterMixin







@require_tokenizers
@require_sentencepiece
@slow  # see https://github.com/huggingface/transformers/issues/11457
class     _lowercase				(							lowercase__							,						unittest.TestCase):


       """simple docstring"""




       A__      		=    BarthezTokenizer
       A__      		=    BarthezTokenizerFast
       A__      		=    True
       A__      		=    True





       def 		lowerCAmelCase  (     self							:    int ):


             '''simple docstring'''



             super().setUp()

             lowerCamelCase__	:       List[str]					=						BarthezTokenizerFast.from_pretrained("moussaKam/mbarthez" )
             tokenizer.save_pretrained(self.tmpdirname )
             tokenizer.save_pretrained(self.tmpdirname      ,   legacy_format=__lowerCamelCase )
             lowerCamelCase__	:       Tuple					=						tokenizer





       def 		lowerCAmelCase  (     self							:    Dict ):


             '''simple docstring'''



             lowerCamelCase__	:       Any					=						"<pad>"
             lowerCamelCase__	:       Tuple					=						1

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





       def 		lowerCAmelCase  (     self							:    Dict ):


             '''simple docstring'''



             lowerCamelCase__	:       Dict					=						list(self.get_tokenizer().get_vocab().keys() )

             self.assertEqual(vocab_keys[0]      ,   "<s>" )
             self.assertEqual(vocab_keys[1]      ,   "<pad>" )
             self.assertEqual(vocab_keys[-1]      ,   "<mask>" )
             self.assertEqual(len(__lowerCamelCase )      ,   101122 )





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


             '''simple docstring'''



             self.assertEqual(self.get_tokenizer().vocab_size      ,   101122 )





       @require_torch
       def 		lowerCAmelCase  (     self							:    int ):


             '''simple docstring'''



             lowerCamelCase__	:       int					=						["A long paragraph for summarization.", "Another paragraph for summarization."]
             lowerCamelCase__	:       str					=						[0, 57, 3018, 70307, 91, 2]

             lowerCamelCase__	:       Tuple					=						self.tokenizer(
                 __lowerCamelCase      ,   max_length=len(__lowerCamelCase )      ,   padding=__lowerCamelCase      ,   truncation=__lowerCamelCase      ,   return_tensors="pt" )
             self.assertIsInstance(__lowerCamelCase      ,   __lowerCamelCase )

             self.assertEqual((2, 6)      ,   batch.input_ids.shape )
             self.assertEqual((2, 6)      ,   batch.attention_mask.shape )
             lowerCamelCase__	:       Any					=						batch.input_ids.tolist()[0]
             self.assertListEqual(__lowerCamelCase      ,   __lowerCamelCase )





       def 		lowerCAmelCase  (     self							:    Any ):


             '''simple docstring'''



             if not self.test_rust_tokenizer:
                   return

             lowerCamelCase__	:       Any					=						self.get_tokenizer()
             lowerCamelCase__	:       Tuple					=						self.get_rust_tokenizer()

             lowerCamelCase__	:       Union[str, Any]					=						"I was born in 92000, and this is falsé."

             lowerCamelCase__	:       Dict					=						tokenizer.tokenize(__lowerCamelCase )
             lowerCamelCase__	:       Optional[int]					=						rust_tokenizer.tokenize(__lowerCamelCase )
             self.assertListEqual(__lowerCamelCase      ,   __lowerCamelCase )

             lowerCamelCase__	:       Tuple					=						tokenizer.encode(__lowerCamelCase      ,   add_special_tokens=__lowerCamelCase )
             lowerCamelCase__	:       List[Any]					=						rust_tokenizer.encode(__lowerCamelCase      ,   add_special_tokens=__lowerCamelCase )
             self.assertListEqual(__lowerCamelCase      ,   __lowerCamelCase )

             lowerCamelCase__	:       List[str]					=						self.get_rust_tokenizer()
             lowerCamelCase__	:       Optional[Any]					=						tokenizer.encode(__lowerCamelCase )
             lowerCamelCase__	:       List[Any]					=						rust_tokenizer.encode(__lowerCamelCase )
             self.assertListEqual(__lowerCamelCase      ,   __lowerCamelCase )





       @slow
       def 		lowerCAmelCase  (     self							:    Dict ):


             '''simple docstring'''



             lowerCamelCase__	:       int					=						{"input_ids": [[0, 490, 14328, 4507, 354, 47, 43669, 95, 25, 78117, 20215, 19779, 190, 22, 400, 4, 35343, 80310, 603, 86, 24937, 105, 33438, 94762, 196, 39642, 7, 15, 15933, 173, 2, 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, 10534, 87, 25, 66, 3358, 196, 55289, 8, 82961, 81, 2204, 75203, 7, 15, 763, 12956, 216, 178, 14328, 9595, 1377, 69693, 7, 448, 71021, 196, 18106, 1437, 13974, 108, 9083, 4, 49315, 7, 39, 86, 1326, 2793, 46333, 4, 448, 196, 74588, 7, 49315, 7, 39, 21, 822, 38470, 74, 21, 66723, 62480, 8, 22050, 5, 2]], "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, 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, 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]]}  # noqa: E501
             # fmt: on

             # moussaKam/mbarthez is a french model. So we also use french texts.
             lowerCamelCase__	:       List[str]					=						[
                 "Le transformeur est un modèle d'apprentissage profond introduit en 2017, "
                 "utilisé principalement dans le domaine du traitement automatique des langues (TAL).",
                 "À l'instar des réseaux de neurones récurrents (RNN), les transformeurs sont conçus "
                 "pour gérer des données séquentielles, telles que le langage naturel, pour des tâches "
                 "telles que la traduction et la synthèse de texte.",
             ]

             self.tokenizer_integration_test_util(
                 expected_encoding=__lowerCamelCase      ,   model_name="moussaKam/mbarthez"      ,   revision="c2e4ecbca5e3cd2c37fe1ac285ca4fbdf1366fb6"      ,   sequences=__lowerCamelCase      ,   )


