




'''simple docstring'''





import random
import unittest

import numpy as np
import torch

from diffusers import (
    DPMSolverMultistepScheduler,
    EulerAncestralDiscreteScheduler,
    EulerDiscreteScheduler,
    LMSDiscreteScheduler,
    OnnxStableDiffusionUpscalePipeline,
    PNDMScheduler,
)
from diffusers.utils import floats_tensor
from diffusers.utils.testing_utils import (
    is_onnx_available,
    load_image,
    nightly,
    require_onnxruntime,
    require_torch_gpu,
)

from ..test_pipelines_onnx_common import OnnxPipelineTesterMixin


if is_onnx_available():
   import onnxruntime as ort

class        _a    (						__lowerCAmelCase , unittest.TestCase							):
     # TODO: is there an appropriate internal test set?
     SCREAMING_SNAKE_CASE_							:   Any       =						"""ssube/stable-diffusion-x4-upscaler-onnx"""






     def     _lowercase     (			self ,_SCREAMING_SNAKE_CASE=0   )							->      List[str]:
          _snake_case							=				floats_tensor((1, 3, 128, 128) ,rng=random.Random(_SCREAMING_SNAKE_CASE   )   )
          _snake_case							=				torch.manual_seed(_SCREAMING_SNAKE_CASE   )
          _snake_case							=				{
              "prompt": "A painting of a squirrel eating a burger",
              "image": image,
              "generator": generator,
              "num_inference_steps": 3,
              "guidance_scale": 7.5,
              "output_type": "numpy",
          }
          return inputs






     def     _lowercase     (			self   )							->      Any:
          _snake_case							=				OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint ,provider="CPUExecutionProvider"   )
          pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE   )

          _snake_case							=				self.get_dummy_inputs()
          _snake_case							=				pipe(**_SCREAMING_SNAKE_CASE   ).images
          _snake_case							=				image[0, -3:, -3:, -1].flatten()

          # started as 128, should now be 512
          assert image.shape == (1, 512, 512, 3)
          _snake_case							=				np.array(
              [0.6_9_7_4_7_8_2, 0.6_8_9_0_2_0_9_3, 0.7_0_1_3_5_8_8_5, 0.7_5_8_3_6_1_8, 0.7_8_0_4_5_4_5, 0.7_8_5_4_9_1_2, 0.7_8_6_6_7_4_2_6, 0.7_8_7_4_3_8_6_3, 0.7_8_0_7_0_2_2_3]   )
          assert np.abs(image_slice - expected_slice   ).max() < 1e-1






     def     _lowercase     (			self   )							->      Any:
          _snake_case							=				OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint ,provider="CPUExecutionProvider"   )
          _snake_case							=				PNDMScheduler.from_config(pipe.scheduler.config ,skip_prk_steps=_SCREAMING_SNAKE_CASE   )
          pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE   )

          _snake_case							=				self.get_dummy_inputs()
          _snake_case							=				pipe(**_SCREAMING_SNAKE_CASE   ).images
          _snake_case							=				image[0, -3:, -3:, -1]

          assert image.shape == (1, 512, 512, 3)
          _snake_case							=				np.array(
              [0.6_8_9_8_8_9_2, 0.5_9_2_4_0_5_5_6, 0.5_2_4_9_9_5_2_7, 0.5_8_8_6_6_2_1_5, 0.5_2_2_5_8_2_3_5, 0.5_2_5_7_2_7_1_5, 0.6_2_4_1_4_4_7_3, 0.6_1_7_4_3_8_7, 0.6_2_1_4_9_6_4]   )
          assert np.abs(image_slice.flatten() - expected_slice   ).max() < 1e-1






     def     _lowercase     (			self   )							->      Any:
          _snake_case							=				OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint ,provider="CPUExecutionProvider"   )
          _snake_case							=				DPMSolverMultistepScheduler.from_config(pipe.scheduler.config   )
          pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE   )

          _snake_case							=				self.get_dummy_inputs()
          _snake_case							=				pipe(**_SCREAMING_SNAKE_CASE   ).images
          _snake_case							=				image[0, -3:, -3:, -1]

          assert image.shape == (1, 512, 512, 3)
          _snake_case							=				np.array(
              [0.7_6_5_9_2_7_8, 0.7_6_4_3_7_6_6_4, 0.7_5_5_7_9_1_0_7, 0.7_6_9_1_1_1_6, 0.7_7_6_6_6_9_8_6, 0.7_7_2_7_6_7_2, 0.7_7_5_8_6_6_4, 0.7_8_1_2_2_2_6, 0.7_6_9_4_2_5_1_5]   )

          assert np.abs(image_slice.flatten() - expected_slice   ).max() < 1e-1






     def     _lowercase     (			self   )							->      Tuple:
          _snake_case							=				OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint ,provider="CPUExecutionProvider"   )
          _snake_case							=				EulerDiscreteScheduler.from_config(pipe.scheduler.config   )
          pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE   )

          _snake_case							=				self.get_dummy_inputs()
          _snake_case							=				pipe(**_SCREAMING_SNAKE_CASE   ).images
          _snake_case							=				image[0, -3:, -3:, -1]

          assert image.shape == (1, 512, 512, 3)
          _snake_case							=				np.array(
              [0.6_9_7_4_7_8_2, 0.6_8_9_0_2_0_9_3, 0.7_0_1_3_5_8_8_5, 0.7_5_8_3_6_1_8, 0.7_8_0_4_5_4_5, 0.7_8_5_4_9_1_2, 0.7_8_6_6_7_4_2_6, 0.7_8_7_4_3_8_6_3, 0.7_8_0_7_0_2_2_3]   )
          assert np.abs(image_slice.flatten() - expected_slice   ).max() < 1e-1






     def     _lowercase     (			self   )							->      str:
          _snake_case							=				OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint ,provider="CPUExecutionProvider"   )
          _snake_case							=				EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config   )
          pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE   )

          _snake_case							=				self.get_dummy_inputs()
          _snake_case							=				pipe(**_SCREAMING_SNAKE_CASE   ).images
          _snake_case							=				image[0, -3:, -3:, -1]

          assert image.shape == (1, 512, 512, 3)
          _snake_case							=				np.array(
              [0.7_7_4_2_4_4_9_6, 0.7_7_3_6_0_1, 0.7_6_4_5_2_8_8, 0.7_7_6_9_5_9_8, 0.7_7_7_2_7_3_9, 0.7_7_3_8_6_8_8, 0.7_8_1_8_7_2_3_3, 0.7_7_8_7_9_5_8_4, 0.7_6_7_0_4_3]   )

          assert np.abs(image_slice.flatten() - expected_slice   ).max() < 1e-1







@nightly
@require_onnxruntime
@require_torch_gpu
class        _a    (						unittest.TestCase							):






     @property
     def     _lowercase     (			self   )							->      Optional[int]:
          return (
              "CUDAExecutionProvider",
              {
                  "gpu_mem_limit": "15000000000",  # 15GB
                  "arena_extend_strategy": "kSameAsRequested",
              },
          )






     @property
     def     _lowercase     (			self   )							->      Any:
          _snake_case							=				ort.SessionOptions()
          _snake_case							=				False
          return options






     def     _lowercase     (			self   )							->      int:
          _snake_case							=				load_image(
              "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
              "/img2img/sketch-mountains-input.jpg"   )
          _snake_case							=				init_image.resize((128, 128)   )
          # using the PNDM scheduler by default
          _snake_case							=				OnnxStableDiffusionUpscalePipeline.from_pretrained(
              "ssube/stable-diffusion-x4-upscaler-onnx" ,provider=self.gpu_provider ,sess_options=self.gpu_options ,)
          pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE   )

          _snake_case							=				"A fantasy landscape, trending on artstation"

          _snake_case							=				torch.manual_seed(0   )
          _snake_case							=				pipe(
              prompt=_SCREAMING_SNAKE_CASE ,image=_SCREAMING_SNAKE_CASE ,guidance_scale=7.5 ,num_inference_steps=10 ,generator=_SCREAMING_SNAKE_CASE ,output_type="np" ,)
          _snake_case							=				output.images
          _snake_case							=				images[0, 255:258, 383:386, -1]

          assert images.shape == (1, 512, 512, 3)
          _snake_case							=				np.array([0.4_8_8_3, 0.4_9_4_7, 0.4_9_8_0, 0.4_9_7_5, 0.4_9_8_2, 0.4_9_8_0, 0.5_0_0_0, 0.5_0_0_6, 0.4_9_7_2]   )
          # TODO: lower the tolerance after finding the cause of onnxruntime reproducibility issues

          assert np.abs(image_slice.flatten() - expected_slice   ).max() < 2e-2






     def     _lowercase     (			self   )							->      Optional[int]:
          _snake_case							=				load_image(
              "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
              "/img2img/sketch-mountains-input.jpg"   )
          _snake_case							=				init_image.resize((128, 128)   )
          _snake_case							=				LMSDiscreteScheduler.from_pretrained(
              "ssube/stable-diffusion-x4-upscaler-onnx" ,subfolder="scheduler"   )
          _snake_case							=				OnnxStableDiffusionUpscalePipeline.from_pretrained(
              "ssube/stable-diffusion-x4-upscaler-onnx" ,scheduler=_SCREAMING_SNAKE_CASE ,provider=self.gpu_provider ,sess_options=self.gpu_options ,)
          pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE   )

          _snake_case							=				"A fantasy landscape, trending on artstation"

          _snake_case							=				torch.manual_seed(0   )
          _snake_case							=				pipe(
              prompt=_SCREAMING_SNAKE_CASE ,image=_SCREAMING_SNAKE_CASE ,guidance_scale=7.5 ,num_inference_steps=20 ,generator=_SCREAMING_SNAKE_CASE ,output_type="np" ,)
          _snake_case							=				output.images
          _snake_case							=				images[0, 255:258, 383:386, -1]

          assert images.shape == (1, 512, 512, 3)
          _snake_case							=				np.array(
              [0.5_0_1_7_3_7_5_3, 0.5_0_2_2_3_3_5_6, 0.5_0_2_0_3_9, 0.5_0_2_3_3_0_3_6, 0.5_0_2_3_7_2_5, 0.5_0_2_2_6_0_1, 0.5_0_1_8_7_5_8, 0.5_0_2_3_4_0_8_5, 0.5_0_2_4_1_5_6_6]   )
          # TODO: lower the tolerance after finding the cause of onnxruntime reproducibility issues

          assert np.abs(image_slice.flatten() - expected_slice   ).max() < 2e-2








'''simple docstring'''





import shutil
import tempfile
import unittest

from transformers import SPIECE_UNDERLINE, BatchEncoding, MBartTokenizer, MBartTokenizerFast, is_torch_available
from transformers.testing_utils import (
    get_tests_dir,
    nested_simplify,
    require_sentencepiece,
    require_tokenizers,
    require_torch,
)

from ...test_tokenization_common import TokenizerTesterMixin


UpperCamelCase_						:      List[str]       =						get_tests_dir('''fixtures/test_sentencepiece.model''')


if is_torch_available():
   from transformers.models.mbart.modeling_mbart import shift_tokens_right

UpperCamelCase_						:      Optional[Any]       =						250004
UpperCamelCase_						:      Union[str, Any]       =						250020

@require_sentencepiece
@require_tokenizers
class        _a    (						__lowerCAmelCase , unittest.TestCase							):
     SCREAMING_SNAKE_CASE_							:   Optional[int]       =						MBartTokenizer
     SCREAMING_SNAKE_CASE_							:   List[str]       =						MBartTokenizerFast
     SCREAMING_SNAKE_CASE_							:   Union[str, Any]       =						True
     SCREAMING_SNAKE_CASE_							:   List[Any]       =						True






     def     _lowercase     (			self   )							->      Optional[Any]:
          super().setUp()

          # We have a SentencePiece fixture for testing
          _snake_case							=				MBartTokenizer(_SCREAMING_SNAKE_CASE ,keep_accents=_SCREAMING_SNAKE_CASE   )
          tokenizer.save_pretrained(self.tmpdirname   )






     def     _lowercase     (			self   )							->      Dict:
          _snake_case							=				MBartTokenizer(_SCREAMING_SNAKE_CASE ,keep_accents=_SCREAMING_SNAKE_CASE   )

          _snake_case							=				tokenizer.tokenize("This is a test"   )
          self.assertListEqual(_SCREAMING_SNAKE_CASE ,["▁This", "▁is", "▁a", "▁t", "est"]   )

          self.assertListEqual(
              tokenizer.convert_tokens_to_ids(_SCREAMING_SNAKE_CASE   ) ,[value + tokenizer.fairseq_offset for value in [285, 46, 10, 170, 382]] ,)

          _snake_case							=				tokenizer.tokenize("I was born in 92000, and this is falsé."   )
          self.assertListEqual(
              _SCREAMING_SNAKE_CASE ,[
                  SPIECE_UNDERLINE + "I",
                  SPIECE_UNDERLINE + "was",
                  SPIECE_UNDERLINE + "b",
                  "or",
                  "n",
                  SPIECE_UNDERLINE + "in",
                  SPIECE_UNDERLINE + "",
                  "9",
                  "2",
                  "0",
                  "0",
                  "0",
                  ",",
                  SPIECE_UNDERLINE + "and",
                  SPIECE_UNDERLINE + "this",
                  SPIECE_UNDERLINE + "is",
                  SPIECE_UNDERLINE + "f",
                  "al",
                  "s",
                  "é",
                  ".",
              ] ,)
          _snake_case							=				tokenizer.convert_tokens_to_ids(_SCREAMING_SNAKE_CASE   )
          self.assertListEqual(
              _SCREAMING_SNAKE_CASE ,[
                  value + tokenizer.fairseq_offset
                  for value in [8, 21, 84, 55, 24, 19, 7, 2, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 2, 4]
                  #                                       ^ unk: 2 + 1 = 3                  unk: 2 + 1 = 3 ^
              ] ,)

          _snake_case							=				tokenizer.convert_ids_to_tokens(_SCREAMING_SNAKE_CASE   )
          self.assertListEqual(
              _SCREAMING_SNAKE_CASE ,[
                  SPIECE_UNDERLINE + "I",
                  SPIECE_UNDERLINE + "was",
                  SPIECE_UNDERLINE + "b",
                  "or",
                  "n",
                  SPIECE_UNDERLINE + "in",
                  SPIECE_UNDERLINE + "",
                  "<unk>",
                  "2",
                  "0",
                  "0",
                  "0",
                  ",",
                  SPIECE_UNDERLINE + "and",
                  SPIECE_UNDERLINE + "this",
                  SPIECE_UNDERLINE + "is",
                  SPIECE_UNDERLINE + "f",
                  "al",
                  "s",
                  "<unk>",
                  ".",
              ] ,)






     def     _lowercase     (			self   )							->      List[str]:
          if not self.test_slow_tokenizer:
               # as we don't have a slow version, we can't compare the outputs between slow and fast versions
               return

          _snake_case							=				(self.rust_tokenizer_class, "hf-internal-testing/tiny-random-mbart", {})
          for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
               with self.subTest(f"""{tokenizer.__class__.__name__} ({pretrained_name})"""   ):
                    _snake_case							=				self.rust_tokenizer_class.from_pretrained(_SCREAMING_SNAKE_CASE ,**_SCREAMING_SNAKE_CASE   )
                    _snake_case							=				self.tokenizer_class.from_pretrained(_SCREAMING_SNAKE_CASE ,**_SCREAMING_SNAKE_CASE   )

                    _snake_case							=				tempfile.mkdtemp()

                    _snake_case							=				tokenizer_r.save_pretrained(_SCREAMING_SNAKE_CASE   )
                    _snake_case							=				tokenizer_p.save_pretrained(_SCREAMING_SNAKE_CASE   )

                    # Checks it save with the same files + the tokenizer.json file for the fast one
                    self.assertTrue(any("tokenizer.json" in f for f in tokenizer_r_files   )   )
                    _snake_case							=				tuple(f for f in tokenizer_r_files if "tokenizer.json" not in f   )
                    self.assertSequenceEqual(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE   )

                    # Checks everything loads correctly in the same way
                    _snake_case							=				tokenizer_r.from_pretrained(_SCREAMING_SNAKE_CASE   )
                    _snake_case							=				tokenizer_p.from_pretrained(_SCREAMING_SNAKE_CASE   )

                    # Check special tokens are set accordingly on Rust and Python
                    for key in tokenizer_pp.special_tokens_map:
                         self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE   )   )
                         # self.assertEqual(getattr(tokenizer_rp, key), getattr(tokenizer_pp, key))
                         # self.assertEqual(getattr(tokenizer_rp, key + "_id"), getattr(tokenizer_pp, key + "_id"))

                    shutil.rmtree(_SCREAMING_SNAKE_CASE   )

                    # Save tokenizer rust, legacy_format=True
                    _snake_case							=				tempfile.mkdtemp()

                    _snake_case							=				tokenizer_r.save_pretrained(_SCREAMING_SNAKE_CASE ,legacy_format=_SCREAMING_SNAKE_CASE   )
                    _snake_case							=				tokenizer_p.save_pretrained(_SCREAMING_SNAKE_CASE   )

                    # Checks it save with the same files
                    self.assertSequenceEqual(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE   )

                    # Checks everything loads correctly in the same way
                    _snake_case							=				tokenizer_r.from_pretrained(_SCREAMING_SNAKE_CASE   )
                    _snake_case							=				tokenizer_p.from_pretrained(_SCREAMING_SNAKE_CASE   )

                    # Check special tokens are set accordingly on Rust and Python
                    for key in tokenizer_pp.special_tokens_map:
                         self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE   )   )

                    shutil.rmtree(_SCREAMING_SNAKE_CASE   )

                    # Save tokenizer rust, legacy_format=False
                    _snake_case							=				tempfile.mkdtemp()

                    _snake_case							=				tokenizer_r.save_pretrained(_SCREAMING_SNAKE_CASE ,legacy_format=_SCREAMING_SNAKE_CASE   )
                    _snake_case							=				tokenizer_p.save_pretrained(_SCREAMING_SNAKE_CASE   )

                    # Checks it saved the tokenizer.json file
                    self.assertTrue(any("tokenizer.json" in f for f in tokenizer_r_files   )   )

                    # Checks everything loads correctly in the same way
                    _snake_case							=				tokenizer_r.from_pretrained(_SCREAMING_SNAKE_CASE   )
                    _snake_case							=				tokenizer_p.from_pretrained(_SCREAMING_SNAKE_CASE   )

                    # Check special tokens are set accordingly on Rust and Python
                    for key in tokenizer_pp.special_tokens_map:
                         self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE   )   )

                    shutil.rmtree(_SCREAMING_SNAKE_CASE   )







@require_torch
@require_sentencepiece
@require_tokenizers
class        _a    (						unittest.TestCase							):
     SCREAMING_SNAKE_CASE_							:   Optional[Any]       =						"""facebook/mbart-large-en-ro"""
     SCREAMING_SNAKE_CASE_							:   Optional[Any]       =						[
         """ UN Chief Says There Is No Military Solution in Syria""",
         """ Secretary-General Ban Ki-moon says his response to Russia's stepped up military support for Syria is that \"there is no military solution\" to the nearly five-year conflict and more weapons will only worsen the violence and misery for millions of people.""",
     ]
     SCREAMING_SNAKE_CASE_							:   Dict       =						[
         """Şeful ONU declară că nu există o soluţie militară în Siria""",
         """Secretarul General Ban Ki-moon declară că răspunsul său la intensificarea sprijinului militar al Rusiei"""
         """ pentru Siria este că \"nu există o soluţie militară\" la conflictul de aproape cinci ani şi că noi arme nu vor"""
         """ face decât să înrăutăţească violenţele şi mizeria pentru milioane de oameni.""",
     ]
     SCREAMING_SNAKE_CASE_							:   Optional[int]       =						[82_74, 12_78_73, 2_59_16, 7, 86_22, 20_71, 4_38, 6_74_85, 53, 18_78_95, 23, 5_17_12, 2, EN_CODE]






     @classmethod
     def     _lowercase     (			cls   )							->      List[str]:
          _snake_case							=				MBartTokenizer.from_pretrained(
              cls.checkpoint_name ,src_lang="en_XX" ,tgt_lang="ro_RO"   )
          _snake_case							=				1
          return cls






     def     _lowercase     (			self   )							->      Dict:
          self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["ar_AR"] ,250_001   )
          self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["en_EN"] ,250_004   )
          self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["ro_RO"] ,250_020   )






     def     _lowercase     (			self   )							->      Tuple:
          _snake_case							=				self.tokenizer.batch_encode_plus(self.src_text   ).input_ids[0]
          self.assertListEqual(self.expected_src_tokens ,_SCREAMING_SNAKE_CASE   )






     def     _lowercase     (			self   )							->      Optional[int]:
          self.assertIn(_SCREAMING_SNAKE_CASE ,self.tokenizer.all_special_ids   )
          _snake_case							=				[RO_CODE, 884, 9_019, 96, 9, 916, 86_792, 36, 18_743, 15_596, 5, 2]
          _snake_case							=				self.tokenizer.decode(_SCREAMING_SNAKE_CASE ,skip_special_tokens=_SCREAMING_SNAKE_CASE   )
          _snake_case							=				self.tokenizer.decode(generated_ids[1:] ,skip_special_tokens=_SCREAMING_SNAKE_CASE   )
          self.assertEqual(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE   )
          self.assertNotIn(self.tokenizer.eos_token ,_SCREAMING_SNAKE_CASE   )






     def     _lowercase     (			self   )							->      List[Any]:
          _snake_case							=				["this is gunna be a long sentence " * 20]
          assert isinstance(src_text[0] ,_SCREAMING_SNAKE_CASE   )
          _snake_case							=				10
          _snake_case							=				self.tokenizer(_SCREAMING_SNAKE_CASE ,max_length=_SCREAMING_SNAKE_CASE ,truncation=_SCREAMING_SNAKE_CASE   ).input_ids[0]
          self.assertEqual(ids[-2] ,2   )
          self.assertEqual(ids[-1] ,_SCREAMING_SNAKE_CASE   )
          self.assertEqual(len(_SCREAMING_SNAKE_CASE   ) ,_SCREAMING_SNAKE_CASE   )






     def     _lowercase     (			self   )							->      Optional[int]:
          self.assertListEqual(self.tokenizer.convert_tokens_to_ids(["<mask>", "ar_AR"]   ) ,[250_026, 250_001]   )






     def     _lowercase     (			self   )							->      str:
          _snake_case							=				tempfile.mkdtemp()
          _snake_case							=				self.tokenizer.fairseq_tokens_to_ids
          self.tokenizer.save_pretrained(_SCREAMING_SNAKE_CASE   )
          _snake_case							=				MBartTokenizer.from_pretrained(_SCREAMING_SNAKE_CASE   )
          self.assertDictEqual(new_tok.fairseq_tokens_to_ids ,_SCREAMING_SNAKE_CASE   )






     @require_torch
     def     _lowercase     (			self   )							->      Dict:
          _snake_case							=				self.tokenizer(self.src_text ,text_target=self.tgt_text ,padding=_SCREAMING_SNAKE_CASE ,return_tensors="pt"   )
          _snake_case							=				shift_tokens_right(batch["labels"] ,self.tokenizer.pad_token_id   )

          # fairseq batch: https://gist.github.com/sshleifer/cba08bc2109361a74ac3760a7e30e4f4
          assert batch.input_ids[1][-2:].tolist() == [2, EN_CODE]
          assert batch.decoder_input_ids[1][0].tolist() == RO_CODE
          assert batch.decoder_input_ids[1][-1] == 2
          assert batch.labels[1][-2:].tolist() == [2, RO_CODE]






     @require_torch
     def     _lowercase     (			self   )							->      Optional[int]:
          _snake_case							=				self.tokenizer(
              self.src_text ,text_target=self.tgt_text ,padding=_SCREAMING_SNAKE_CASE ,truncation=_SCREAMING_SNAKE_CASE ,max_length=len(self.expected_src_tokens   ) ,return_tensors="pt" ,)

          _snake_case							=				shift_tokens_right(batch["labels"] ,self.tokenizer.pad_token_id   )

          self.assertIsInstance(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE   )

          self.assertEqual((2, 14) ,batch.input_ids.shape   )
          self.assertEqual((2, 14) ,batch.attention_mask.shape   )
          _snake_case							=				batch.input_ids.tolist()[0]
          self.assertListEqual(self.expected_src_tokens ,_SCREAMING_SNAKE_CASE   )
          self.assertEqual(2 ,batch.decoder_input_ids[0, -1]   )  # EOS
          # Test that special tokens are reset
          self.assertEqual(self.tokenizer.prefix_tokens ,[]   )
          self.assertEqual(self.tokenizer.suffix_tokens ,[self.tokenizer.eos_token_id, EN_CODE]   )






     def     _lowercase     (			self   )							->      str:
          _snake_case							=				self.tokenizer(self.src_text ,padding=_SCREAMING_SNAKE_CASE ,truncation=_SCREAMING_SNAKE_CASE ,max_length=3 ,return_tensors="pt"   )
          _snake_case							=				self.tokenizer(
              text_target=self.tgt_text ,padding=_SCREAMING_SNAKE_CASE ,truncation=_SCREAMING_SNAKE_CASE ,max_length=10 ,return_tensors="pt"   )
          _snake_case							=				targets["input_ids"]
          _snake_case							=				shift_tokens_right(_SCREAMING_SNAKE_CASE ,self.tokenizer.pad_token_id   )

          self.assertEqual(batch.input_ids.shape[1] ,3   )
          self.assertEqual(batch.decoder_input_ids.shape[1] ,10   )






     @require_torch
     def     _lowercase     (			self   )							->      Any:
          _snake_case							=				self.tokenizer._build_translation_inputs(
              "A test" ,return_tensors="pt" ,src_lang="en_XX" ,tgt_lang="ar_AR"   )

          self.assertEqual(
              nested_simplify(_SCREAMING_SNAKE_CASE   ) ,{
                  # A, test, EOS, en_XX
                  "input_ids": [[62, 3_034, 2, 250_004]],
                  "attention_mask": [[1, 1, 1, 1]],
                  # ar_AR
                  "forced_bos_token_id": 250_001,
              } ,)


