






import unittest

from datasets import load_dataset

from transformers import BloomTokenizerFast
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin


@require_tokenizers
class        __UpperCamelCase    (     _lowerCAmelCase   ,			unittest.TestCase   ):
	__snake_case	:Optional[int]   =       None
	__snake_case	:Union[str, Any]   =       BloomTokenizerFast
	__snake_case	:List[Any]   =       BloomTokenizerFast
	__snake_case	:Union[str, Any]   =       True
	__snake_case	:List[Any]   =       False
	__snake_case	:Dict   =       'tokenizer_file'
	__snake_case	:int   =       {'bos_token': '<s>', 'eos_token': '</s>', 'unk_token': '<unk>', 'pad_token': '<pad>'}







	def  _a   (  self      :							Union[str, Any]			)		-> Dict:
		"""simple docstring"""







		super().setUp()
		__lowercase             =  BloomTokenizerFast.from_pretrained("""bigscience/tokenizer"""			)
		tokenizer.save_pretrained(self.tmpdirname			)







	def  _a   (  self      :							Dict       ,				**_lowerCAmelCase      :							Optional[Any]			)		-> Optional[Any]:
		"""simple docstring"""







		kwargs.update(self.special_tokens_map			)
		return BloomTokenizerFast.from_pretrained(self.tmpdirname       ,				**_lowerCAmelCase			)







	def  _a   (  self      :							Optional[Any]			)		-> Optional[int]:
		"""simple docstring"""







		__lowercase             =  self.get_rust_tokenizer()

		__lowercase             =  ["""The quick brown fox</s>""", """jumps over the lazy dog</s>"""]
		__lowercase             =  [[2175, 2_3714, 7_3173, 14_4252, 2], [77, 13_2619, 3478, 368, 10_9586, 3_5433, 2]]

		__lowercase             =  tokenizer.batch_encode_plus(_lowerCAmelCase			)["""input_ids"""]
		self.assertListEqual(_lowerCAmelCase       ,				_lowerCAmelCase			)

		__lowercase             =  tokenizer.batch_decode(_lowerCAmelCase			)
		self.assertListEqual(_lowerCAmelCase       ,				_lowerCAmelCase			)







	def  _a   (  self      :							Union[str, Any]       ,				_lowerCAmelCase      :							Dict=6			)		-> Dict:
		"""simple docstring"""







		for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
			with self.subTest(F'{tokenizer.__class__.__name__} ({pretrained_name})'			):
				__lowercase             =  self.rust_tokenizer_class.from_pretrained(_lowerCAmelCase       ,				**_lowerCAmelCase			)
				# tokenizer_r.pad_token = None # Hotfixing padding = None
				# Simple input
				__lowercase             =  """This is a simple input"""
				__lowercase             =  ["""This is a simple input 1""", """This is a simple input 2"""]
				__lowercase             =  ("""This is a simple input""", """This is a pair""")
				__lowercase             =  [
				    ("""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
				try:
					tokenizer_r.encode(_lowerCAmelCase       ,				max_length=_lowerCAmelCase			)
					tokenizer_r.encode_plus(_lowerCAmelCase       ,				max_length=_lowerCAmelCase			)

					tokenizer_r.batch_encode_plus(_lowerCAmelCase       ,				max_length=_lowerCAmelCase			)
					tokenizer_r.encode(_lowerCAmelCase       ,				max_length=_lowerCAmelCase			)
					tokenizer_r.batch_encode_plus(_lowerCAmelCase       ,				max_length=_lowerCAmelCase			)
				except ValueError:
					self.fail("""Bloom Tokenizer should be able to deal with padding"""			)

				__lowercase             =  None  # Hotfixing padding = None
				self.assertRaises(_lowerCAmelCase       ,				tokenizer_r.encode       ,				_lowerCAmelCase       ,				max_length=_lowerCAmelCase       ,				padding="""max_length"""			)

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

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

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

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

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







	def  _a   (  self      :							Dict			)		-> Any:
		"""simple docstring"""







		__lowercase             =  self.get_rust_tokenizer()
		__lowercase             =  load_dataset("""xnli"""       ,				"""all_languages"""       ,				split="""test"""       ,				streaming=_lowerCAmelCase			)

		__lowercase             =  next(iter(_lowerCAmelCase			)			)["""premise"""]  # pick up one data
		__lowercase             =  list(sample_data.values()			)

		__lowercase             =  list(map(tokenizer.encode       ,				_lowerCAmelCase			)			)
		__lowercase             =  [tokenizer.decode(_lowerCAmelCase       ,				clean_up_tokenization_spaces=_lowerCAmelCase			) for x in output_tokens]
		self.assertListEqual(_lowerCAmelCase       ,				_lowerCAmelCase			)







	def  _a   (  self      :							Dict			)		-> Optional[int]:
		"""simple docstring"""







		self.assertGreaterEqual(len(self.tokenizer_class.pretrained_vocab_files_map			)       ,				1			)
		self.assertGreaterEqual(len(list(self.tokenizer_class.pretrained_vocab_files_map.values()			)[0]			)       ,				1			)









import random
import unittest

import torch

from diffusers import IFInpaintingPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device

from ..pipeline_params import (
    TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_INPAINTING_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin
from . import IFPipelineTesterMixin


@skip_mps
class        __UpperCamelCase    (     _lowerCAmelCase   ,			_lowerCAmelCase   ,			unittest.TestCase   ):
	__snake_case	:Any   =       IFInpaintingPipeline
	__snake_case	:str   =       TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {'width', 'height'}
	__snake_case	:Optional[int]   =       TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
	__snake_case	:str   =       PipelineTesterMixin.required_optional_params - {'latents'}







	def  _a   (  self      :							Union[str, Any]			)		-> Tuple:
		"""simple docstring"""







		return self._get_dummy_components()







	def  _a   (  self      :							Optional[int]       ,				_lowerCAmelCase      :							List[str]       ,				_lowerCAmelCase      :							Dict=0			)		-> Any:
		"""simple docstring"""







		if str(_lowerCAmelCase			).startswith("""mps"""			):
			__lowercase             =  torch.manual_seed(_lowerCAmelCase			)
		else:
			__lowercase             =  torch.Generator(device=_lowerCAmelCase			).manual_seed(_lowerCAmelCase			)

		__lowercase             =  floats_tensor((1, 3, 32, 32)       ,				rng=random.Random(_lowerCAmelCase			)			).to(_lowerCAmelCase			)
		__lowercase             =  floats_tensor((1, 3, 32, 32)       ,				rng=random.Random(_lowerCAmelCase			)			).to(_lowerCAmelCase			)

		__lowercase             =  {
		    """prompt""": """A painting of a squirrel eating a burger""",
		    """image""": image,
		    """mask_image""": mask_image,
		    """generator""": generator,
		    """num_inference_steps""": 2,
		    """output_type""": """numpy""",
		}

		return inputs







	@unittest.skipIf(
	    torch_device != """cuda""" or not is_xformers_available()       ,				reason="""XFormers attention is only available with CUDA and `xformers` installed"""       ,				)
	def  _a   (  self      :							Tuple			)		-> Dict:
		"""simple docstring"""







		self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3			)







	def  _a   (  self      :							Union[str, Any]			)		-> Dict:
		"""simple docstring"""







		self._test_save_load_optional_components()







	@unittest.skipIf(torch_device != """cuda"""       ,				reason="""float16 requires CUDA"""			)
	def  _a   (  self      :							List[str]			)		-> Tuple:
		"""simple docstring"""







		super().test_save_load_floataa(expected_max_diff=1e-1			)







	def  _a   (  self      :							Tuple			)		-> Optional[Any]:
		"""simple docstring"""







		self._test_attention_slicing_forward_pass(expected_max_diff=1e-2			)







	def  _a   (  self      :							str			)		-> Optional[int]:
		"""simple docstring"""







		self._test_save_load_local()







	def  _a   (  self      :							List[str]			)		-> List[Any]:
		"""simple docstring"""







		self._test_inference_batch_single_identical(
		    expected_max_diff=1e-2       ,				)

