


"""simple docstring"""






import json
import os
import unittest

from transformers import BatchEncoding, MvpTokenizer, MvpTokenizerFast
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin, filter_roberta_detectors





@require_tokenizers
class 	lowerCamelCase__  (	__UpperCAmelCase	,       unittest.TestCase							):







					"""simple docstring"""


					__a      			=				MvpTokenizer
					__a      			=				MvpTokenizerFast
					__a      			=				True
					__a      			=				filter_roberta_detectors






					def lowerCamelCase__				(			self       :					Any    ):
												'''simple docstring'''






												super().setUp()
												__UpperCAmelCase  :		Optional[int] 				=   [
												    'l',
												    'o',
												    'w',
												    'e',
												    'r',
												    's',
												    't',
												    'i',
												    'd',
												    'n',
												    '\u0120',
												    '\u0120l',
												    '\u0120n',
												    '\u0120lo',
												    '\u0120low',
												    'er',
												    '\u0120lowest',
												    '\u0120newer',
												    '\u0120wider',
												    '<unk>',
												]
												__UpperCAmelCase  :		Any 				=   dict(zip(lowerCAmelCase_					,			range(len(lowerCAmelCase_    )    )    )    )
												__UpperCAmelCase  :		Dict 				=   ['#version: 0.2', '\u0120 l', '\u0120l o', '\u0120lo w', 'e r', '']
												__UpperCAmelCase  :		Tuple 				=   {'unk_token': '<unk>'}

												__UpperCAmelCase  :		int 				=   os.path.join(self.tmpdirname					,			VOCAB_FILES_NAMES["""vocab_file"""]    )
												__UpperCAmelCase  :		Dict 				=   os.path.join(self.tmpdirname					,			VOCAB_FILES_NAMES["""merges_file"""]    )
												with open(self.vocab_file					,			"""w"""					,			encoding="""utf-8"""    ) as fp:
																			fp.write(json.dumps(lowerCAmelCase_    ) + """\n"""    )
												with open(self.merges_file					,			"""w"""					,			encoding="""utf-8"""    ) as fp:
																			fp.write("""\n""".join(lowerCAmelCase_    )    )






					def lowerCamelCase__				(			self       :					Any					,			**UpperCamelCase       :					str    ):
												'''simple docstring'''






												kwargs.update(self.special_tokens_map    )
												return self.tokenizer_class.from_pretrained(self.tmpdirname					,			**lowerCAmelCase_    )






					def lowerCamelCase__				(			self       :					Optional[int]					,			**UpperCamelCase       :					Union[str, Any]    ):
												'''simple docstring'''






												kwargs.update(self.special_tokens_map    )
												return self.rust_tokenizer_class.from_pretrained(self.tmpdirname					,			**lowerCAmelCase_    )






					def lowerCamelCase__				(			self       :					List[Any]					,			UpperCamelCase       :					Dict    ):
												'''simple docstring'''






												return "lower newer", "lower newer"






					@cached_property
					def lowerCamelCase__				(			self       :					str    ):
												'''simple docstring'''






												return MvpTokenizer.from_pretrained("""RUCAIBox/mvp"""    )






					@cached_property
					def lowerCamelCase__				(			self       :					List[Any]    ):
												'''simple docstring'''






												return MvpTokenizerFast.from_pretrained("""RUCAIBox/mvp"""    )






					@require_torch
					def lowerCamelCase__				(			self       :					Tuple    ):
												'''simple docstring'''






												__UpperCAmelCase  :		Optional[int] 				=   ['A long paragraph for summarization.', 'Another paragraph for summarization.']
												__UpperCAmelCase  :		List[str] 				=   [0, 250, 251, 17_818, 13, 39_186, 1_938, 4, 2]

												for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
																			__UpperCAmelCase  :		Optional[int] 				=   tokenizer(lowerCAmelCase_					,			max_length=len(lowerCAmelCase_    )					,			padding=lowerCAmelCase_					,			return_tensors="""pt"""    )
																			self.assertIsInstance(lowerCAmelCase_					,			lowerCAmelCase_    )

																			self.assertEqual((2, 9)					,			batch.input_ids.shape    )
																			self.assertEqual((2, 9)					,			batch.attention_mask.shape    )
																			__UpperCAmelCase  :		Optional[int] 				=   batch.input_ids.tolist()[0]
																			self.assertListEqual(lowerCAmelCase_					,			lowerCAmelCase_    )
																			# Test that special tokens are reset






					@require_torch
					def lowerCamelCase__				(			self       :					Any    ):
												'''simple docstring'''






												__UpperCAmelCase  :		Dict 				=   ['A long paragraph for summarization.', 'Another paragraph for summarization.']
												for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
																			__UpperCAmelCase  :		Union[str, Any] 				=   tokenizer(lowerCAmelCase_					,			padding=lowerCAmelCase_					,			return_tensors="""pt"""    )
																			# check if input_ids are returned and no labels
																			self.assertIn("""input_ids"""					,			lowerCAmelCase_    )
																			self.assertIn("""attention_mask"""					,			lowerCAmelCase_    )
																			self.assertNotIn("""labels"""					,			lowerCAmelCase_    )
																			self.assertNotIn("""decoder_attention_mask"""					,			lowerCAmelCase_    )






					@require_torch
					def lowerCamelCase__				(			self       :					Any    ):
												'''simple docstring'''






												__UpperCAmelCase  :		Optional[Any] 				=   [
												    'Summary of the text.',
												    'Another summary.',
												]
												for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
																			__UpperCAmelCase  :		Union[str, Any] 				=   tokenizer(text_target=lowerCAmelCase_					,			max_length=32					,			padding="""max_length"""					,			return_tensors="""pt"""    )
																			self.assertEqual(32					,			targets["""input_ids"""].shape[1]    )






					@require_torch
					def lowerCamelCase__				(			self       :					Tuple    ):
												'''simple docstring'''






												for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
																			__UpperCAmelCase  :		str 				=   tokenizer(
																			    ["""I am a small frog""" * 1_024, """I am a small frog"""]					,			padding=lowerCAmelCase_					,			truncation=lowerCAmelCase_					,			return_tensors="""pt"""    )
																			self.assertIsInstance(lowerCAmelCase_					,			lowerCAmelCase_    )
																			self.assertEqual(batch.input_ids.shape					,			(2, 1_024)    )






					@require_torch
					def lowerCamelCase__				(			self       :					List[Any]    ):
												'''simple docstring'''






												__UpperCAmelCase  :		Dict 				=   ['A long paragraph for summarization.']
												__UpperCAmelCase  :		Any 				=   [
												    'Summary of the text.',
												]
												for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
																			__UpperCAmelCase  :		Tuple 				=   tokenizer(lowerCAmelCase_					,			text_target=lowerCAmelCase_					,			return_tensors="""pt"""    )
																			__UpperCAmelCase  :		int 				=   inputs['input_ids']
																			__UpperCAmelCase  :		int 				=   inputs['labels']
																			self.assertTrue((input_ids[:, 0] == tokenizer.bos_token_id).all().item()    )
																			self.assertTrue((labels[:, 0] == tokenizer.bos_token_id).all().item()    )
																			self.assertTrue((input_ids[:, -1] == tokenizer.eos_token_id).all().item()    )
																			self.assertTrue((labels[:, -1] == tokenizer.eos_token_id).all().item()    )






					def lowerCamelCase__				(			self       :					str    ):
												'''simple docstring'''






												pass






					def lowerCamelCase__				(			self       :					Dict    ):
												'''simple docstring'''






												for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
																			with self.subTest(f'''{tokenizer.__class__.__name__} ({pretrained_name})'''    ):
																										__UpperCAmelCase  :		int 				=   self.rust_tokenizer_class.from_pretrained(lowerCAmelCase_					,			**lowerCAmelCase_    )
																										__UpperCAmelCase  :		Union[str, Any] 				=   self.tokenizer_class.from_pretrained(lowerCAmelCase_					,			**lowerCAmelCase_    )
																										__UpperCAmelCase  :		int 				=   'A, <mask> AllenNLP sentence.'
																										__UpperCAmelCase  :		Any 				=   tokenizer_r.encode_plus(lowerCAmelCase_					,			add_special_tokens=lowerCAmelCase_					,			return_token_type_ids=lowerCAmelCase_    )
																										__UpperCAmelCase  :		Any 				=   tokenizer_p.encode_plus(lowerCAmelCase_					,			add_special_tokens=lowerCAmelCase_					,			return_token_type_ids=lowerCAmelCase_    )

																										# token_type_ids should put 0 everywhere
																										self.assertEqual(sum(tokens_r["""token_type_ids"""]    )					,			sum(tokens_p["""token_type_ids"""]    )    )

																										# attention_mask should put 1 everywhere, so sum over length should be 1
																										self.assertEqual(
																										    sum(tokens_r["""attention_mask"""]    ) / len(tokens_r["""attention_mask"""]    )					,			sum(tokens_p["""attention_mask"""]    ) / len(tokens_p["""attention_mask"""]    )					,			)

																										__UpperCAmelCase  :		Union[str, Any] 				=   tokenizer_r.convert_ids_to_tokens(tokens_r["""input_ids"""]    )
																										__UpperCAmelCase  :		Optional[int] 				=   tokenizer_p.convert_ids_to_tokens(tokens_p["""input_ids"""]    )

																										# Rust correctly handles the space before the mask while python doesnt
																										self.assertSequenceEqual(tokens_p["""input_ids"""]					,			[0, 250, 6, 50_264, 3_823, 487, 21_992, 3_645, 4, 2]    )
																										self.assertSequenceEqual(tokens_r["""input_ids"""]					,			[0, 250, 6, 50_264, 3_823, 487, 21_992, 3_645, 4, 2]    )

																										self.assertSequenceEqual(
																										    lowerCAmelCase_					,			["""<s>""", """A""", """,""", """<mask>""", """ĠAllen""", """N""", """LP""", """Ġsentence""", """.""", """</s>"""]    )
																										self.assertSequenceEqual(
																										    lowerCAmelCase_					,			["""<s>""", """A""", """,""", """<mask>""", """ĠAllen""", """N""", """LP""", """Ġsentence""", """.""", """</s>"""]    )








"""simple docstring"""
import gc
import unittest

from diffusers import FlaxDPMSolverMultistepScheduler, FlaxStableDiffusionPipeline
from diffusers.utils import is_flax_available, 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       ):







						def 			A      (    self							)		->							Any:
										# clean up the VRAM after each test
										super().tearDown()
										gc.collect()







						def 			A      (    self							)		->							int:
										_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE   :		Optional[int]       		=    FlaxStableDiffusionPipeline.from_pretrained(
										    'stabilityai/stable-diffusion-2'   ,				revision='bf16'   ,				dtype=jnp.bfloataa   ,				)

										_SCREAMING_SNAKE_CASE   :		Union[str, Any]       		=    'A painting of a squirrel eating a burger'
										_SCREAMING_SNAKE_CASE   :		Optional[Any]       		=    jax.device_count()
										_SCREAMING_SNAKE_CASE   :		List[Any]       		=    num_samples * [prompt]
										_SCREAMING_SNAKE_CASE   :		Any       		=    sd_pipe.prepare_inputs(lowerCAmelCase_							)

										_SCREAMING_SNAKE_CASE   :		List[Any]       		=    replicate(lowerCAmelCase_							)
										_SCREAMING_SNAKE_CASE   :		Union[str, Any]       		=    shard(lowerCAmelCase_							)

										_SCREAMING_SNAKE_CASE   :		Any       		=    jax.random.PRNGKey(0							)
										_SCREAMING_SNAKE_CASE   :		Optional[int]       		=    jax.random.split(lowerCAmelCase_   ,				jax.device_count()							)

										_SCREAMING_SNAKE_CASE   :		Optional[Any]       		=    sd_pipe(lowerCAmelCase_   ,				lowerCAmelCase_   ,				lowerCAmelCase_   ,				num_inference_steps=2_5   ,				jit=lowerCAmelCase_							)[0]
										assert images.shape == (jax.device_count(), 1, 7_6_8, 7_6_8, 3)

										_SCREAMING_SNAKE_CASE   :		int       		=    images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:]							)
										_SCREAMING_SNAKE_CASE   :		Optional[Any]       		=    images[0, 2_5_3:2_5_6, 2_5_3:2_5_6, -1]

										_SCREAMING_SNAKE_CASE   :		Tuple       		=    jnp.asarray(jax.device_get(image_slice.flatten()							)							)
										_SCREAMING_SNAKE_CASE   :		List[Any]       		=    jnp.array([0.4_238, 0.4_414, 0.4_395, 0.4_453, 0.4_629, 0.4_590, 0.4_531, 0.45_508, 0.4_512]							)
										print(F"""output_slice: {output_slice}"""							)
										assert jnp.abs(output_slice - expected_slice							).max() < 1e-2







						def 			A      (    self							)		->							Optional[int]:
										_SCREAMING_SNAKE_CASE   :		str       		=    'stabilityai/stable-diffusion-2'
										_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE   :		int       		=    FlaxDPMSolverMultistepScheduler.from_pretrained(lowerCAmelCase_   ,				subfolder='scheduler'							)
										_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE   :		List[str]       		=    FlaxStableDiffusionPipeline.from_pretrained(
										    lowerCAmelCase_   ,				scheduler=lowerCAmelCase_   ,				revision='bf16'   ,				dtype=jnp.bfloataa   ,				)
										_SCREAMING_SNAKE_CASE   :		Any       		=    scheduler_params

										_SCREAMING_SNAKE_CASE   :		int       		=    'A painting of a squirrel eating a burger'
										_SCREAMING_SNAKE_CASE   :		Tuple       		=    jax.device_count()
										_SCREAMING_SNAKE_CASE   :		Union[str, Any]       		=    num_samples * [prompt]
										_SCREAMING_SNAKE_CASE   :		Any       		=    sd_pipe.prepare_inputs(lowerCAmelCase_							)

										_SCREAMING_SNAKE_CASE   :		Dict       		=    replicate(lowerCAmelCase_							)
										_SCREAMING_SNAKE_CASE   :		Any       		=    shard(lowerCAmelCase_							)

										_SCREAMING_SNAKE_CASE   :		Any       		=    jax.random.PRNGKey(0							)
										_SCREAMING_SNAKE_CASE   :		Optional[Any]       		=    jax.random.split(lowerCAmelCase_   ,				jax.device_count()							)

										_SCREAMING_SNAKE_CASE   :		int       		=    sd_pipe(lowerCAmelCase_   ,				lowerCAmelCase_   ,				lowerCAmelCase_   ,				num_inference_steps=2_5   ,				jit=lowerCAmelCase_							)[0]
										assert images.shape == (jax.device_count(), 1, 7_6_8, 7_6_8, 3)

										_SCREAMING_SNAKE_CASE   :		str       		=    images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:]							)
										_SCREAMING_SNAKE_CASE   :		str       		=    images[0, 2_5_3:2_5_6, 2_5_3:2_5_6, -1]

										_SCREAMING_SNAKE_CASE   :		int       		=    jnp.asarray(jax.device_get(image_slice.flatten()							)							)
										_SCREAMING_SNAKE_CASE   :		Optional[int]       		=    jnp.array([0.4_336, 0.42_969, 0.4_453, 0.4_199, 0.4_297, 0.4_531, 0.4_434, 0.4_434, 0.4_297]							)
										print(F"""output_slice: {output_slice}"""							)
										assert jnp.abs(output_slice - expected_slice							).max() < 1e-2

