



'''simple docstring'''





import json
import os
import unittest

from transformers import BatchEncoding, LEDTokenizer, LEDTokenizerFast
from transformers.models.led.tokenization_led 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






@require_tokenizers
class 	lowerCAmelCase_(    SCREAMING_SNAKE_CASE_						,			unittest.TestCase					):
  '''simple docstring'''




  __lowercase							:				List[str]           =				LEDTokenizer
  __lowercase							:				Any           =				LEDTokenizerFast
  __lowercase							:				Tuple           =				True




  def 							UpperCAmelCase_     (   self				)   ->		Any:
      super().setUp()
      lowerCAmelCase__			:  Tuple											= [
          """l""",
          """o""",
          """w""",
          """e""",
          """r""",
          """s""",
          """t""",
          """i""",
          """d""",
          """n""",
          """\u0120""",
          """\u0120l""",
          """\u0120n""",
          """\u0120lo""",
          """\u0120low""",
          """er""",
          """\u0120lowest""",
          """\u0120newer""",
          """\u0120wider""",
          """<unk>""",
      ]
      lowerCAmelCase__			:  Dict											= dict(zip(__UpperCAmelCase  ,range(len(__UpperCAmelCase				)				)				)				)
      lowerCAmelCase__			:  Optional[int]											= ["""#version: 0.2""", """\u0120 l""", """\u0120l o""", """\u0120lo w""", """e r""", """"""]
      lowerCAmelCase__			:  List[Any]											= {"""unk_token""": """<unk>"""}

      lowerCAmelCase__			:  Any											= os.path.join(self.tmpdirname  ,VOCAB_FILES_NAMES["""vocab_file"""]				)
      lowerCAmelCase__			:  List[Any]											= 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(__UpperCAmelCase				) + """\n"""				)
      with open(self.merges_file  ,"""w"""  ,encoding="""utf-8"""				) as fp:
          fp.write("""\n""".join(__UpperCAmelCase				)				)




  def 							UpperCAmelCase_     (   self  ,**__UpperCAmelCase				)   ->		Any:
      kwargs.update(self.special_tokens_map				)
      return self.tokenizer_class.from_pretrained(self.tmpdirname  ,**__UpperCAmelCase				)




  def 							UpperCAmelCase_     (   self  ,**__UpperCAmelCase				)   ->		Tuple:
      kwargs.update(self.special_tokens_map				)
      return self.rust_tokenizer_class.from_pretrained(self.tmpdirname  ,**__UpperCAmelCase				)




  def 							UpperCAmelCase_     (   self  ,__UpperCAmelCase				)   ->		int:
      return "lower newer", "lower newer"




  @cached_property
  def 							UpperCAmelCase_     (   self				)   ->		Tuple:
      return LEDTokenizer.from_pretrained("""allenai/led-base-16384"""				)




  @cached_property
  def 							UpperCAmelCase_     (   self				)   ->		Any:
      return LEDTokenizerFast.from_pretrained("""allenai/led-base-16384"""				)




  @require_torch
  def 							UpperCAmelCase_     (   self				)   ->		Any:
      lowerCAmelCase__			:  int											= ["""A long paragraph for summarization.""", """Another paragraph for summarization."""]
      lowerCAmelCase__			:  List[str]											= [0, 250, 251, 1_7818, 13, 3_9186, 1938, 4, 2]

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

          self.assertEqual((2, 9)  ,batch.input_ids.shape				)
          self.assertEqual((2, 9)  ,batch.attention_mask.shape				)
          lowerCAmelCase__			:  Any											= batch.input_ids.tolist()[0]
          self.assertListEqual(__UpperCAmelCase  ,__UpperCAmelCase				)




  @require_torch
  def 							UpperCAmelCase_     (   self				)   ->		Dict:
      lowerCAmelCase__			:  Dict											= ["""A long paragraph for summarization.""", """Another paragraph for summarization."""]
      for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
          lowerCAmelCase__			:  int											= tokenizer(__UpperCAmelCase  ,padding=__UpperCAmelCase  ,return_tensors="""pt"""				)
          self.assertIn("""input_ids"""  ,__UpperCAmelCase				)
          self.assertIn("""attention_mask"""  ,__UpperCAmelCase				)
          self.assertNotIn("""labels"""  ,__UpperCAmelCase				)
          self.assertNotIn("""decoder_attention_mask"""  ,__UpperCAmelCase				)




  @require_torch
  def 							UpperCAmelCase_     (   self				)   ->		Any:
      lowerCAmelCase__			:  List[Any]											= [
          """Summary of the text.""",
          """Another summary.""",
      ]
      for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
          lowerCAmelCase__			:  Tuple											= tokenizer(text_target=__UpperCAmelCase  ,max_length=32  ,padding="""max_length"""  ,return_tensors="""pt"""				)
          self.assertEqual(32  ,targets["""input_ids"""].shape[1]				)




  @require_torch
  def 							UpperCAmelCase_     (   self				)   ->		str:
      for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
          lowerCAmelCase__			:  Tuple											= tokenizer(
              ["""I am a small frog""" * 1024, """I am a small frog"""]  ,padding=__UpperCAmelCase  ,truncation=__UpperCAmelCase  ,return_tensors="""pt"""				)
          self.assertIsInstance(__UpperCAmelCase  ,__UpperCAmelCase				)
          self.assertEqual(batch.input_ids.shape  ,(2, 5122)				)




  @require_torch
  def 							UpperCAmelCase_     (   self				)   ->		Tuple:
      lowerCAmelCase__			:  Dict											= ["""A long paragraph for summarization."""]
      lowerCAmelCase__			:  List[str]											= [
          """Summary of the text.""",
      ]
      for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
          lowerCAmelCase__			:  str											= tokenizer(__UpperCAmelCase  ,return_tensors="""pt"""				)
          lowerCAmelCase__			:  List[str]											= tokenizer(text_target=__UpperCAmelCase  ,return_tensors="""pt"""				)
          lowerCAmelCase__			:  str											= inputs["""input_ids"""]
          lowerCAmelCase__			:  Optional[Any]											= targets["""input_ids"""]
          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()				)




  @require_torch
  def 							UpperCAmelCase_     (   self				)   ->		Any:
      for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
          lowerCAmelCase__			:  Optional[Any]											= ["""Summary of the text.""", """Another summary."""]
          lowerCAmelCase__			:  List[Any]											= [[0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, -1, -1]]

          lowerCAmelCase__			:  Union[str, Any]											= tokenizer(__UpperCAmelCase  ,padding=__UpperCAmelCase				)
          lowerCAmelCase__			:  str											= [[0] * len(__UpperCAmelCase				) for x in encoded_output["""input_ids"""]]
          lowerCAmelCase__			:  Dict											= tokenizer.pad(__UpperCAmelCase				)
          self.assertSequenceEqual(outputs["""global_attention_mask"""]  ,__UpperCAmelCase				)




  def 							UpperCAmelCase_     (   self				)   ->		int:
      pass






  def 							UpperCAmelCase_     (   self				)   ->		Tuple:
      for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
          with self.subTest(F"""{tokenizer.__class__.__name__} ({pretrained_name})"""				):
              lowerCAmelCase__			:  Optional[int]											= self.rust_tokenizer_class.from_pretrained(__UpperCAmelCase  ,**__UpperCAmelCase				)
              lowerCAmelCase__			:  Tuple											= self.tokenizer_class.from_pretrained(__UpperCAmelCase  ,**__UpperCAmelCase				)
              lowerCAmelCase__			:  Optional[Any]											= """A, <mask> AllenNLP sentence."""
              lowerCAmelCase__			:  Tuple											= tokenizer_r.encode_plus(__UpperCAmelCase  ,add_special_tokens=__UpperCAmelCase  ,return_token_type_ids=__UpperCAmelCase				)
              lowerCAmelCase__			:  List[Any]											= tokenizer_p.encode_plus(__UpperCAmelCase  ,add_special_tokens=__UpperCAmelCase  ,return_token_type_ids=__UpperCAmelCase				)
              self.assertEqual(sum(tokens_r["""token_type_ids"""]				)  ,sum(tokens_p["""token_type_ids"""]				)				)
              self.assertEqual(
                  sum(tokens_r["""attention_mask"""]				) / len(tokens_r["""attention_mask"""]				)  ,sum(tokens_p["""attention_mask"""]				) / len(tokens_p["""attention_mask"""]				)  ,)

              lowerCAmelCase__			:  Any											= tokenizer_r.convert_ids_to_tokens(tokens_r["""input_ids"""]				)
              lowerCAmelCase__			:  Optional[int]											= tokenizer_p.convert_ids_to_tokens(tokens_p["""input_ids"""]				)
              self.assertSequenceEqual(tokens_p["""input_ids"""]  ,[0, 250, 6, 5_0264, 3823, 487, 2_1992, 3645, 4, 2]				)
              self.assertSequenceEqual(tokens_r["""input_ids"""]  ,[0, 250, 6, 5_0264, 3823, 487, 2_1992, 3645, 4, 2]				)

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







'''simple docstring'''





import random
import unittest

import torch

from diffusers import IFInpaintingSuperResolutionPipeline
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 	lowerCAmelCase_(    SCREAMING_SNAKE_CASE_						,			SCREAMING_SNAKE_CASE_						,			unittest.TestCase					):
  '''simple docstring'''




  __lowercase							:				List[str]           =				IFInpaintingSuperResolutionPipeline
  __lowercase							:				int           =				TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {'''width''', '''height'''}
  __lowercase							:				List[str]           =				TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS.union({'''original_image'''}					)
  __lowercase							:				Optional[Any]           =				PipelineTesterMixin.required_optional_params - {'''latents'''}




  def 							UpperCAmelCase_     (   self				)   ->		Any:
      return self._get_superresolution_dummy_components()




  def 							UpperCAmelCase_     (   self  ,__UpperCAmelCase  ,__UpperCAmelCase=0				)   ->		List[Any]:
      if str(__UpperCAmelCase				).startswith("""mps"""				):
          lowerCAmelCase__			:  Any											= torch.manual_seed(__UpperCAmelCase				)
      else:
          lowerCAmelCase__			:  Dict											= torch.Generator(device=__UpperCAmelCase				).manual_seed(__UpperCAmelCase				)

      lowerCAmelCase__			:  Tuple											= floats_tensor((1, 3, 16, 16)  ,rng=random.Random(__UpperCAmelCase				)				).to(__UpperCAmelCase				)
      lowerCAmelCase__			:  Union[str, Any]											= floats_tensor((1, 3, 32, 32)  ,rng=random.Random(__UpperCAmelCase				)				).to(__UpperCAmelCase				)
      lowerCAmelCase__			:  Any											= floats_tensor((1, 3, 32, 32)  ,rng=random.Random(__UpperCAmelCase				)				).to(__UpperCAmelCase				)

      lowerCAmelCase__			:  Dict											= {
          """prompt""": """A painting of a squirrel eating a burger""",
          """image""": image,
          """original_image""": original_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 							UpperCAmelCase_     (   self				)   ->		Union[str, Any]:
      self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3				)




  def 							UpperCAmelCase_     (   self				)   ->		Optional[int]:
      self._test_save_load_optional_components()




  @unittest.skipIf(torch_device != """cuda"""  ,reason="""float16 requires CUDA"""				)
  def 							UpperCAmelCase_     (   self				)   ->		List[Any]:
      # Due to non-determinism in save load of the hf-internal-testing/tiny-random-t5 text encoder
      super().test_save_load_floataa(expected_max_diff=1E-1				)




  def 							UpperCAmelCase_     (   self				)   ->		str:
      self._test_attention_slicing_forward_pass(expected_max_diff=1E-2				)




  def 							UpperCAmelCase_     (   self				)   ->		List[Any]:
      self._test_save_load_local()






  def 							UpperCAmelCase_     (   self				)   ->		int:
      self._test_inference_batch_single_identical(
          expected_max_diff=1E-2  ,)


