






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 				lowerCAmelCase_	(		__snake_case   ,		unittest.TestCase			):
				_UpperCamelCase					:       List[str]							       = BarthezTokenizer
				_UpperCamelCase					:       List[str]							       = BarthezTokenizerFast
				_UpperCamelCase					:       List[Any]							       = True
				_UpperCamelCase					:       Tuple							       = True


				def 						__a					(					self      ):
											super().setUp()

											_lowercase							:   int 							=   BarthezTokenizerFast.from_pretrained('moussaKam/mbarthez'      )
											tokenizer.save_pretrained(self.tmpdirname      )
											tokenizer.save_pretrained(self.tmpdirname ,						legacy_format=_lowerCAmelCase      )
											_lowercase							:   Union[str, Any] 							=   tokenizer


				def 						__a					(					self      ):
											_lowercase							:   Dict 							=   '<pad>'
											_lowercase							:   Any 							=   1

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


				def 						__a					(					self      ):
											_lowercase							:   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      ) ,						1_0_1_1_2_2      )


				def 						__a					(					self      ):
											self.assertEqual(self.get_tokenizer().vocab_size ,						1_0_1_1_2_2      )


				@require_torch
				def 						__a					(					self      ):
											_lowercase							:   Union[str, Any] 							=   ['A long paragraph for summarization.', 'Another paragraph for summarization.']
											_lowercase							:   Optional[Any] 							=   [0, 5_7, 3_0_1_8, 7_0_3_0_7, 9_1, 2]

											_lowercase							:   Any 							=   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      )
											_lowercase							:   Optional[int] 							=   batch.input_ids.tolist()[0]
											self.assertListEqual(_lowerCAmelCase ,						_lowerCAmelCase      )


				def 						__a					(					self      ):
											if not self.test_rust_tokenizer:
																		return

											_lowercase							:   int 							=   self.get_tokenizer()
											_lowercase							:   Tuple 							=   self.get_rust_tokenizer()

											_lowercase							:   List[str] 							=   'I was born in 92000, and this is falsé.'

											_lowercase							:   str 							=   tokenizer.tokenize(_lowerCAmelCase      )
											_lowercase							:   Optional[int] 							=   rust_tokenizer.tokenize(_lowerCAmelCase      )
											self.assertListEqual(_lowerCAmelCase ,						_lowerCAmelCase      )

											_lowercase							:   Optional[Any] 							=   tokenizer.encode(_lowerCAmelCase ,						add_special_tokens=_lowerCAmelCase      )
											_lowercase							:   Tuple 							=   rust_tokenizer.encode(_lowerCAmelCase ,						add_special_tokens=_lowerCAmelCase      )
											self.assertListEqual(_lowerCAmelCase ,						_lowerCAmelCase      )

											_lowercase							:   Dict 							=   self.get_rust_tokenizer()
											_lowercase							:   Optional[int] 							=   tokenizer.encode(_lowerCAmelCase      )
											_lowercase							:   int 							=   rust_tokenizer.encode(_lowerCAmelCase      )
											self.assertListEqual(_lowerCAmelCase ,						_lowerCAmelCase      )


				@slow
				def 						__a					(					self      ):
											# fmt: off
											_lowercase							:   List[str] 							=   {'input_ids': [[0, 4_9_0, 1_4_3_2_8, 4_5_0_7, 3_5_4, 4_7, 4_3_6_6_9, 9_5, 2_5, 7_8_1_1_7, 2_0_2_1_5, 1_9_7_7_9, 1_9_0, 2_2, 4_0_0, 4, 3_5_3_4_3, 8_0_3_1_0, 6_0_3, 8_6, 2_4_9_3_7, 1_0_5, 3_3_4_3_8, 9_4_7_6_2, 1_9_6, 3_9_6_4_2, 7, 1_5, 1_5_9_3_3, 1_7_3, 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, 1_0_5_3_4, 8_7, 2_5, 6_6, 3_3_5_8, 1_9_6, 5_5_2_8_9, 8, 8_2_9_6_1, 8_1, 2_2_0_4, 7_5_2_0_3, 7, 1_5, 7_6_3, 1_2_9_5_6, 2_1_6, 1_7_8, 1_4_3_2_8, 9_5_9_5, 1_3_7_7, 6_9_6_9_3, 7, 4_4_8, 7_1_0_2_1, 1_9_6, 1_8_1_0_6, 1_4_3_7, 1_3_9_7_4, 1_0_8, 9_0_8_3, 4, 4_9_3_1_5, 7, 3_9, 8_6, 1_3_2_6, 2_7_9_3, 4_6_3_3_3, 4, 4_4_8, 1_9_6, 7_4_5_8_8, 7, 4_9_3_1_5, 7, 3_9, 2_1, 8_2_2, 3_8_4_7_0, 7_4, 2_1, 6_6_7_2_3, 6_2_4_8_0, 8, 2_2_0_5_0, 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.
											_lowercase							:   Union[str, Any] 							=   [
											    '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 ,						)







'''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 				_snake_case					(   unittest.TestCase			):

  def   _SCREAMING_SNAKE_CASE				(				self	):





      '''simple docstring'''

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

  def   _SCREAMING_SNAKE_CASE				(				self	):





      '''simple docstring'''

      lowerCAmelCase						,   lowerCAmelCase 						=							FlaxControlNetModel.from_pretrained(
          'lllyasviel/sd-controlnet-canny'      , from_pt=_SCREAMING_SNAKE_CASE      , dtype=jnp.bfloataa	)
      lowerCAmelCase						,   lowerCAmelCase 						=							FlaxStableDiffusionControlNetPipeline.from_pretrained(
          'runwayml/stable-diffusion-v1-5'      , controlnet=_SCREAMING_SNAKE_CASE      , from_pt=_SCREAMING_SNAKE_CASE      , dtype=jnp.bfloataa	)
      lowerCAmelCase 						=							controlnet_params

      lowerCAmelCase 						=							'bird'
      lowerCAmelCase 						=							jax.device_count()
      lowerCAmelCase 						=							pipe.prepare_text_inputs([prompts] * num_samples	)

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

      lowerCAmelCase 						=							jax.random.PRNGKey(0	)
      lowerCAmelCase 						=							jax.random.split(_SCREAMING_SNAKE_CASE      , jax.device_count()	)

      lowerCAmelCase 						=							replicate(_SCREAMING_SNAKE_CASE	)
      lowerCAmelCase 						=							shard(_SCREAMING_SNAKE_CASE	)
      lowerCAmelCase 						=							shard(_SCREAMING_SNAKE_CASE	)

      lowerCAmelCase 						=							pipe(
          prompt_ids=_SCREAMING_SNAKE_CASE      , image=_SCREAMING_SNAKE_CASE      , params=_SCREAMING_SNAKE_CASE      , prng_seed=_SCREAMING_SNAKE_CASE      , num_inference_steps=50      , jit=_SCREAMING_SNAKE_CASE      , ).images
      assert images.shape == (jax.device_count(), 1, 7_68, 5_12, 3)

      lowerCAmelCase 						=							images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:]	)
      lowerCAmelCase 						=							images[0, 2_53:2_56, 2_53:2_56, -1]

      lowerCAmelCase 						=							jnp.asarray(jax.device_get(image_slice.flatten()	)	)
      lowerCAmelCase 						=							jnp.array(
          [0.167_969, 0.116_699, 0.081_543, 0.154_297, 0.132_812, 0.108_887, 0.169_922, 0.169_922, 0.205_078]	)
      print(F'output_slice: {output_slice}'	)
      assert jnp.abs(output_slice - expected_slice	).max() < 1e-2


  def   _SCREAMING_SNAKE_CASE				(				self	):





      '''simple docstring'''

      lowerCAmelCase						,   lowerCAmelCase 						=							FlaxControlNetModel.from_pretrained(
          'lllyasviel/sd-controlnet-openpose'      , from_pt=_SCREAMING_SNAKE_CASE      , dtype=jnp.bfloataa	)
      lowerCAmelCase						,   lowerCAmelCase 						=							FlaxStableDiffusionControlNetPipeline.from_pretrained(
          'runwayml/stable-diffusion-v1-5'      , controlnet=_SCREAMING_SNAKE_CASE      , from_pt=_SCREAMING_SNAKE_CASE      , dtype=jnp.bfloataa	)
      lowerCAmelCase 						=							controlnet_params

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

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

      lowerCAmelCase 						=							jax.random.PRNGKey(0	)
      lowerCAmelCase 						=							jax.random.split(_SCREAMING_SNAKE_CASE      , jax.device_count()	)

      lowerCAmelCase 						=							replicate(_SCREAMING_SNAKE_CASE	)
      lowerCAmelCase 						=							shard(_SCREAMING_SNAKE_CASE	)
      lowerCAmelCase 						=							shard(_SCREAMING_SNAKE_CASE	)

      lowerCAmelCase 						=							pipe(
          prompt_ids=_SCREAMING_SNAKE_CASE      , image=_SCREAMING_SNAKE_CASE      , params=_SCREAMING_SNAKE_CASE      , prng_seed=_SCREAMING_SNAKE_CASE      , num_inference_steps=50      , jit=_SCREAMING_SNAKE_CASE      , ).images
      assert images.shape == (jax.device_count(), 1, 7_68, 5_12, 3)

      lowerCAmelCase 						=							images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:]	)
      lowerCAmelCase 						=							images[0, 2_53:2_56, 2_53:2_56, -1]

      lowerCAmelCase 						=							jnp.asarray(jax.device_get(image_slice.flatten()	)	)
      lowerCAmelCase 						=							jnp.array(
          [[0.271_484, 0.261_719, 0.275_391, 0.277_344, 0.279_297, 0.291_016, 0.294_922, 0.302_734, 0.302_734]]	)
      print(F'output_slice: {output_slice}'	)
      assert jnp.abs(output_slice - expected_slice	).max() < 1e-2





