



"""simple docstring"""





import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    EulerAncestralDiscreteScheduler,
    LMSDiscreteScheduler,
    PNDMScheduler,
    StableDiffusionInstructPixaPixPipeline,
    UNetaDConditionModel,
)
from diffusers.image_processor import VaeImageProcessor
from diffusers.utils import floats_tensor, load_image, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import (
    IMAGE_TO_IMAGE_IMAGE_PARAMS,
    TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()
class  lowercase__  (       SCREAMING_SNAKE_CASE							,   SCREAMING_SNAKE_CASE							,   SCREAMING_SNAKE_CASE							,   unittest.TestCase ):







       '''simple docstring'''


       UpperCamelCase		=	StableDiffusionInstructPixaPixPipeline
       UpperCamelCase		=	TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {'''height''', '''width''', '''cross_attention_kwargs'''}
       UpperCamelCase		=	TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
       UpperCamelCase		=	IMAGE_TO_IMAGE_IMAGE_PARAMS
       UpperCamelCase		=	IMAGE_TO_IMAGE_IMAGE_PARAMS



       def 	lowercase__							(				self     :    List[str]       )					->		Any:


             '''simple docstring'''




             torch.manual_seed(0       )
             UpperCAmelCase_     					=				UNetaDConditionModel(
                 block_out_channels=(32, 64)						,							layers_per_block=2						,							sample_size=32						,							in_channels=8						,							out_channels=4						,							down_block_types=("DownBlock2D", "CrossAttnDownBlock2D")						,							up_block_types=("CrossAttnUpBlock2D", "UpBlock2D")						,							cross_attention_dim=32						,							)
             UpperCAmelCase_     					=				PNDMScheduler(skip_prk_steps=_UpperCAmelCase       )
             torch.manual_seed(0       )
             UpperCAmelCase_     					=				AutoencoderKL(
                 block_out_channels=[32, 64]						,							in_channels=3						,							out_channels=3						,							down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"]						,							up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"]						,							latent_channels=4						,							)
             torch.manual_seed(0       )
             UpperCAmelCase_     					=				CLIPTextConfig(
                 bos_token_id=0						,							eos_token_id=2						,							hidden_size=32						,							intermediate_size=37						,							layer_norm_eps=1e-05						,							num_attention_heads=4						,							num_hidden_layers=5						,							pad_token_id=1						,							vocab_size=1000						,							)
             UpperCAmelCase_     					=				CLIPTextModel(_UpperCAmelCase       )
             UpperCAmelCase_     					=				CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"       )

             UpperCAmelCase_     					=				{
                 "unet": unet,
                 "scheduler": scheduler,
                 "vae": vae,
                 "text_encoder": text_encoder,
                 "tokenizer": tokenizer,
                 "safety_checker": None,
                 "feature_extractor": None,
             }
             return components



       def 	lowercase__							(				self     :    Optional[int]						,							_UpperCAmelCase     :    List[Any]						,							_UpperCAmelCase     :    Dict=0       )					->		Optional[Any]:


             '''simple docstring'''




             UpperCAmelCase_     					=				floats_tensor((1, 3, 32, 32)						,							rng=random.Random(_UpperCAmelCase       )       ).to(_UpperCAmelCase       )
             UpperCAmelCase_     					=				image.cpu().permute(0						,							2						,							3						,							1       )[0]
             UpperCAmelCase_     					=				Image.fromarray(np.uinta(_UpperCAmelCase       )       ).convert("RGB"       )
             if str(_UpperCAmelCase       ).startswith("mps"       ):
                   UpperCAmelCase_     					=				torch.manual_seed(_UpperCAmelCase       )
             else:
                   UpperCAmelCase_     					=				torch.Generator(device=_UpperCAmelCase       ).manual_seed(_UpperCAmelCase       )
             UpperCAmelCase_     					=				{
                 "prompt": "A painting of a squirrel eating a burger",
                 "image": image,
                 "generator": generator,
                 "num_inference_steps": 2,
                 "guidance_scale": 6.0,
                 "image_guidance_scale": 1,
                 "output_type": "numpy",
             }
             return inputs



       def 	lowercase__							(				self     :    int       )					->		Union[str, Any]:


             '''simple docstring'''




             UpperCAmelCase_     					=				"cpu"  # ensure determinism for the device-dependent torch.Generator
             UpperCAmelCase_     					=				self.get_dummy_components()
             UpperCAmelCase_     					=				StableDiffusionInstructPixaPixPipeline(**_UpperCAmelCase       )
             UpperCAmelCase_     					=				sd_pipe.to(_UpperCAmelCase       )
             sd_pipe.set_progress_bar_config(disable=_UpperCAmelCase       )

             UpperCAmelCase_     					=				self.get_dummy_inputs(_UpperCAmelCase       )
             UpperCAmelCase_     					=				sd_pipe(**_UpperCAmelCase       ).images
             UpperCAmelCase_     					=				image[0, -3:, -3:, -1]
             assert image.shape == (1, 32, 32, 3)
             UpperCAmelCase_     					=				np.array([0.7526, 0.3750, 0.4547, 0.6117, 0.5866, 0.5016, 0.4327, 0.5642, 0.4815]       )

             assert np.abs(image_slice.flatten() - expected_slice       ).max() < 1e-3



       def 	lowercase__							(				self     :    Union[str, Any]       )					->		Any:


             '''simple docstring'''




             UpperCAmelCase_     					=				"cpu"  # ensure determinism for the device-dependent torch.Generator
             UpperCAmelCase_     					=				self.get_dummy_components()
             UpperCAmelCase_     					=				StableDiffusionInstructPixaPixPipeline(**_UpperCAmelCase       )
             UpperCAmelCase_     					=				sd_pipe.to(_UpperCAmelCase       )
             sd_pipe.set_progress_bar_config(disable=_UpperCAmelCase       )

             UpperCAmelCase_     					=				self.get_dummy_inputs(_UpperCAmelCase       )
             UpperCAmelCase_     					=				"french fries"
             UpperCAmelCase_     					=				sd_pipe(**_UpperCAmelCase						,							negative_prompt=_UpperCAmelCase       )
             UpperCAmelCase_     					=				output.images
             UpperCAmelCase_     					=				image[0, -3:, -3:, -1]

             assert image.shape == (1, 32, 32, 3)
             UpperCAmelCase_     					=				np.array([0.7511, 0.3642, 0.4553, 0.6236, 0.5797, 0.5013, 0.4343, 0.5611, 0.4831]       )

             assert np.abs(image_slice.flatten() - expected_slice       ).max() < 1e-3



       def 	lowercase__							(				self     :    Tuple       )					->		Dict:


             '''simple docstring'''




             UpperCAmelCase_     					=				"cpu"  # ensure determinism for the device-dependent torch.Generator
             UpperCAmelCase_     					=				self.get_dummy_components()
             UpperCAmelCase_     					=				StableDiffusionInstructPixaPixPipeline(**_UpperCAmelCase       )
             UpperCAmelCase_     					=				sd_pipe.to(_UpperCAmelCase       )
             sd_pipe.set_progress_bar_config(disable=_UpperCAmelCase       )

             UpperCAmelCase_     					=				self.get_dummy_inputs(_UpperCAmelCase       )
             UpperCAmelCase_     					=				[inputs["prompt"]] * 2

             UpperCAmelCase_     					=				np.array(inputs["image"]       ).astype(np.floataa       ) / 255.0
             UpperCAmelCase_     					=				torch.from_numpy(_UpperCAmelCase       ).unsqueeze(0       ).to(_UpperCAmelCase       )
             UpperCAmelCase_     					=				image / 2 + 0.5
             UpperCAmelCase_     					=				image.permute(0						,							3						,							1						,							2       )
             UpperCAmelCase_     					=				image.repeat(2						,							1						,							1						,							1       )

             UpperCAmelCase_     					=				sd_pipe(**_UpperCAmelCase       ).images
             UpperCAmelCase_     					=				image[-1, -3:, -3:, -1]

             assert image.shape == (2, 32, 32, 3)
             UpperCAmelCase_     					=				np.array([0.5812, 0.5748, 0.5222, 0.5908, 0.5695, 0.7174, 0.6804, 0.5523, 0.5579]       )

             assert np.abs(image_slice.flatten() - expected_slice       ).max() < 1e-3



       def 	lowercase__							(				self     :    str       )					->		Union[str, Any]:


             '''simple docstring'''




             UpperCAmelCase_     					=				"cpu"  # ensure determinism for the device-dependent torch.Generator
             UpperCAmelCase_     					=				self.get_dummy_components()
             UpperCAmelCase_     					=				EulerAncestralDiscreteScheduler(
                 beta_start=0.0_0085						,							beta_end=0.012						,							beta_schedule="scaled_linear"       )
             UpperCAmelCase_     					=				StableDiffusionInstructPixaPixPipeline(**_UpperCAmelCase       )
             UpperCAmelCase_     					=				sd_pipe.to(_UpperCAmelCase       )
             sd_pipe.set_progress_bar_config(disable=_UpperCAmelCase       )

             UpperCAmelCase_     					=				self.get_dummy_inputs(_UpperCAmelCase       )
             UpperCAmelCase_     					=				sd_pipe(**_UpperCAmelCase       ).images
             UpperCAmelCase_     					=				image[0, -3:, -3:, -1]

             UpperCAmelCase_     					=				[round(_UpperCAmelCase						,							4       ) for x in image_slice.flatten().tolist()]
             print(",".join([str(_UpperCAmelCase       ) for x in slice]       )       )

             assert image.shape == (1, 32, 32, 3)
             UpperCAmelCase_     					=				np.array([0.7417, 0.3842, 0.4732, 0.5776, 0.5891, 0.5139, 0.4052, 0.5673, 0.4986]       )

             assert np.abs(image_slice.flatten() - expected_slice       ).max() < 1e-3



       def 	lowercase__							(				self     :    List[str]       )					->		Union[str, Any]:


             '''simple docstring'''




             super().test_inference_batch_single_identical(expected_max_diff=3e-3       )







       def 	lowercase__							(				self     :    int       )					->		Tuple:


             '''simple docstring'''




             UpperCAmelCase_     					=				self.get_dummy_components()
             UpperCAmelCase_     					=				StableDiffusionInstructPixaPixPipeline(**_UpperCAmelCase       )
             UpperCAmelCase_     					=				VaeImageProcessor(do_resize=_UpperCAmelCase						,							do_normalize=_UpperCAmelCase       )
             UpperCAmelCase_     					=				pipe.to(_UpperCAmelCase       )
             pipe.set_progress_bar_config(disable=_UpperCAmelCase       )

             UpperCAmelCase_     					=				pipe(**self.get_dummy_inputs_by_type(_UpperCAmelCase						,							input_image_type="pt"       )       )[0]

             UpperCAmelCase_     					=				components["vae"]
             UpperCAmelCase_     					=				self.get_dummy_inputs_by_type(_UpperCAmelCase						,							input_image_type="pt"       )

             for image_param in self.image_latents_params:
                   if image_param in inputs.keys():
                         UpperCAmelCase_     					=				vae.encode(inputs[image_param]       ).latent_dist.mode()

             UpperCAmelCase_     					=				pipe(**_UpperCAmelCase       )[0]

             UpperCAmelCase_     					=				np.abs(out - out_latents_inputs       ).max()
             self.assertLess(_UpperCAmelCase						,							1e-4						,							"passing latents as image input generate different result from passing image"       )



@slow
@require_torch_gpu
class  lowercase__  (       unittest.TestCase ):







       '''simple docstring'''


       def 	lowercase__							(				self     :    List[str]       )					->		Optional[Any]:


             '''simple docstring'''




             super().tearDown()
             gc.collect()
             torch.cuda.empty_cache()



       def 	lowercase__							(				self     :    Optional[int]						,							_UpperCAmelCase     :    Optional[int]=0       )					->		int:


             '''simple docstring'''




             UpperCAmelCase_     					=				torch.manual_seed(_UpperCAmelCase       )
             UpperCAmelCase_     					=				load_image(
                 "https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/stable_diffusion_pix2pix/example.jpg"       )
             UpperCAmelCase_     					=				{
                 "prompt": "turn him into a cyborg",
                 "image": image,
                 "generator": generator,
                 "num_inference_steps": 3,
                 "guidance_scale": 7.5,
                 "image_guidance_scale": 1.0,
                 "output_type": "numpy",
             }
             return inputs



       def 	lowercase__							(				self     :    str       )					->		str:


             '''simple docstring'''




             UpperCAmelCase_     					=				StableDiffusionInstructPixaPixPipeline.from_pretrained(
                 "timbrooks/instruct-pix2pix"						,							safety_checker=_UpperCAmelCase       )
             pipe.to(_UpperCAmelCase       )
             pipe.set_progress_bar_config(disable=_UpperCAmelCase       )
             pipe.enable_attention_slicing()

             UpperCAmelCase_     					=				self.get_inputs()
             UpperCAmelCase_     					=				pipe(**_UpperCAmelCase       ).images
             UpperCAmelCase_     					=				image[0, -3:, -3:, -1].flatten()

             assert image.shape == (1, 512, 512, 3)
             UpperCAmelCase_     					=				np.array([0.5902, 0.6015, 0.6027, 0.5983, 0.6092, 0.6061, 0.5765, 0.5785, 0.5555]       )

             assert np.abs(expected_slice - image_slice       ).max() < 1e-3



       def 	lowercase__							(				self     :    Optional[int]       )					->		Dict:


             '''simple docstring'''




             UpperCAmelCase_     					=				StableDiffusionInstructPixaPixPipeline.from_pretrained(
                 "timbrooks/instruct-pix2pix"						,							safety_checker=_UpperCAmelCase       )
             UpperCAmelCase_     					=				LMSDiscreteScheduler.from_config(pipe.scheduler.config       )
             pipe.to(_UpperCAmelCase       )
             pipe.set_progress_bar_config(disable=_UpperCAmelCase       )
             pipe.enable_attention_slicing()

             UpperCAmelCase_     					=				self.get_inputs()
             UpperCAmelCase_     					=				pipe(**_UpperCAmelCase       ).images
             UpperCAmelCase_     					=				image[0, -3:, -3:, -1].flatten()

             assert image.shape == (1, 512, 512, 3)
             UpperCAmelCase_     					=				np.array([0.6578, 0.6817, 0.6972, 0.6761, 0.6856, 0.6916, 0.6428, 0.6516, 0.6301]       )

             assert np.abs(expected_slice - image_slice       ).max() < 1e-3



       def 	lowercase__							(				self     :    Optional[Any]       )					->		List[str]:


             '''simple docstring'''




             UpperCAmelCase_     					=				StableDiffusionInstructPixaPixPipeline.from_pretrained(
                 "timbrooks/instruct-pix2pix"						,							safety_checker=_UpperCAmelCase       )
             UpperCAmelCase_     					=				DDIMScheduler.from_config(pipe.scheduler.config       )
             pipe.to(_UpperCAmelCase       )
             pipe.set_progress_bar_config(disable=_UpperCAmelCase       )
             pipe.enable_attention_slicing()

             UpperCAmelCase_     					=				self.get_inputs()
             UpperCAmelCase_     					=				pipe(**_UpperCAmelCase       ).images
             UpperCAmelCase_     					=				image[0, -3:, -3:, -1].flatten()

             assert image.shape == (1, 512, 512, 3)
             UpperCAmelCase_     					=				np.array([0.3828, 0.3834, 0.3818, 0.3792, 0.3865, 0.3752, 0.3792, 0.3847, 0.3753]       )

             assert np.abs(expected_slice - image_slice       ).max() < 1e-3



       def 	lowercase__							(				self     :    Any       )					->		str:


             '''simple docstring'''




             UpperCAmelCase_     					=				0

             def callback_fn(_UpperCAmelCase     :    int						,							_UpperCAmelCase     :    int						,							_UpperCAmelCase     :    torch.FloatTensor       ) -> None:
                   UpperCAmelCase_     					=				True
                   nonlocal number_of_steps
                   number_of_steps += 1
                   if step == 1:
                         UpperCAmelCase_     					=				latents.detach().cpu().numpy()
                         assert latents.shape == (1, 4, 64, 64)
                         UpperCAmelCase_     					=				latents[0, -3:, -3:, -1]
                         UpperCAmelCase_     					=				np.array([-0.2463, -0.4644, -0.9756, 1.5176, 1.4414, 0.7866, 0.9897, 0.8521, 0.7983]       )

                         assert np.abs(latents_slice.flatten() - expected_slice       ).max() < 5e-2
                   elif step == 2:
                         UpperCAmelCase_     					=				latents.detach().cpu().numpy()
                         assert latents.shape == (1, 4, 64, 64)
                         UpperCAmelCase_     					=				latents[0, -3:, -3:, -1]
                         UpperCAmelCase_     					=				np.array([-0.2644, -0.4626, -0.9653, 1.5176, 1.4551, 0.7686, 0.9805, 0.8452, 0.8115]       )

                         assert np.abs(latents_slice.flatten() - expected_slice       ).max() < 5e-2

             UpperCAmelCase_     					=				False

             UpperCAmelCase_     					=				StableDiffusionInstructPixaPixPipeline.from_pretrained(
                 "timbrooks/instruct-pix2pix"						,							safety_checker=_UpperCAmelCase						,							torch_dtype=torch.floataa       )
             UpperCAmelCase_     					=				pipe.to(_UpperCAmelCase       )
             pipe.set_progress_bar_config(disable=_UpperCAmelCase       )
             pipe.enable_attention_slicing()

             UpperCAmelCase_     					=				self.get_inputs()
             pipe(**_UpperCAmelCase						,							callback=_UpperCAmelCase						,							callback_steps=1       )
             assert callback_fn.has_been_called
             assert number_of_steps == 3



       def 	lowercase__							(				self     :    Tuple       )					->		Dict:


             '''simple docstring'''




             torch.cuda.empty_cache()
             torch.cuda.reset_max_memory_allocated()
             torch.cuda.reset_peak_memory_stats()

             UpperCAmelCase_     					=				StableDiffusionInstructPixaPixPipeline.from_pretrained(
                 "timbrooks/instruct-pix2pix"						,							safety_checker=_UpperCAmelCase						,							torch_dtype=torch.floataa       )
             UpperCAmelCase_     					=				pipe.to(_UpperCAmelCase       )
             pipe.set_progress_bar_config(disable=_UpperCAmelCase       )
             pipe.enable_attention_slicing(1       )
             pipe.enable_sequential_cpu_offload()

             UpperCAmelCase_     					=				self.get_inputs()
             UpperCAmelCase_     					=				pipe(**_UpperCAmelCase       )

             UpperCAmelCase_     					=				torch.cuda.max_memory_allocated()
             # make sure that less than 2.2 GB is allocated
             assert mem_bytes < 2.2 * 10**9







       def 	lowercase__							(				self     :    Tuple       )					->		Any:


             '''simple docstring'''




             UpperCAmelCase_     					=				self.get_inputs()
             # resize to resolution that is divisible by 8 but not 16 or 32
             UpperCAmelCase_     					=				inputs["image"].resize((504, 504)       )

             UpperCAmelCase_     					=				"timbrooks/instruct-pix2pix"
             UpperCAmelCase_     					=				StableDiffusionInstructPixaPixPipeline.from_pretrained(
                 _UpperCAmelCase						,							safety_checker=_UpperCAmelCase						,							)
             pipe.to(_UpperCAmelCase       )
             pipe.set_progress_bar_config(disable=_UpperCAmelCase       )
             pipe.enable_attention_slicing()

             UpperCAmelCase_     					=				pipe(**_UpperCAmelCase       )
             UpperCAmelCase_     					=				output.images[0]

             UpperCAmelCase_     					=				image[255:258, 383:386, -1]

             assert image.shape == (504, 504, 3)
             UpperCAmelCase_     					=				np.array([0.2726, 0.2529, 0.2664, 0.2655, 0.2641, 0.2642, 0.2591, 0.2649, 0.2590]       )

             assert np.abs(image_slice.flatten() - expected_slice       ).max() < 5e-3







'''simple docstring'''





import tempfile
import unittest
from pathlib import Path
from shutil import copyfile

from transformers import MaMaaaTokenizer, is_torch_available
from transformers.testing_utils import (
    get_tests_dir,
    nested_simplify,
    require_sentencepiece,
    require_tokenizers,
    require_torch,
    slow,
)
from transformers.utils import is_sentencepiece_available


if is_sentencepiece_available():
							from transformers.models.mam_aaa.tokenization_mam_aaa import VOCAB_FILES_NAMES, save_json

from ...test_tokenization_common import TokenizerTesterMixin


if is_sentencepiece_available():
							UpperCamelCase_    						=       get_tests_dir("""fixtures/test_sentencepiece.model""")


if is_torch_available():
							from transformers.models.mam_aaa.modeling_mam_aaa import shift_tokens_right

UpperCamelCase_    						=       128022
UpperCamelCase_    						=       128028





@require_sentencepiece
class 		__SCREAMING_SNAKE_CASE							(			lowercase__						,							unittest.TestCase      ):
				lowerCamelCase_		       =    MaMaaaTokenizer
				lowerCamelCase_		       =    False
				lowerCamelCase_		       =    False
				lowerCamelCase_		       =    True







				def  lowerCamelCase_	(       self     :		Dict   ):
										'''simple docstring'''
										super().setUp()

										lowercase :							Dict    		=['''</s>''', '''<unk>''', '''▁This''', '''▁is''', '''▁a''', '''▁t''', '''est''', '''\u0120''', '''<pad>''']
										lowercase :							List[Any]    		=dict(zip(UpperCAmelCase__		,					range(len(UpperCAmelCase__   )   )   )   )
										lowercase :							List[Any]    		=Path(self.tmpdirname   )
										save_json(UpperCAmelCase__		,					save_dir / VOCAB_FILES_NAMES['''vocab_file''']   )
										if not (save_dir / VOCAB_FILES_NAMES["spm_file"]).exists():
																copyfile(UpperCAmelCase__		,					save_dir / VOCAB_FILES_NAMES['''spm_file''']   )

										lowercase :							Tuple    		=MaMaaaTokenizer.from_pretrained(self.tmpdirname   )
										tokenizer.save_pretrained(self.tmpdirname   )







				def  lowerCamelCase_	(       self     :		Any		,					**UpperCAmelCase__     :		int   ):
										'''simple docstring'''
										return MaMaaaTokenizer.from_pretrained(self.tmpdirname		,					**UpperCAmelCase__   )







				def  lowerCamelCase_	(       self     :		List[Any]		,					UpperCAmelCase__     :		Dict   ):
										'''simple docstring'''
										return (
										    "This is a test",
										    "This is a test",
										)







				def  lowerCamelCase_	(       self     :		Any   ):
										'''simple docstring'''
										lowercase :							Tuple    		='''</s>'''
										lowercase :							Union[str, Any]    		=0

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







				def  lowerCamelCase_	(       self     :		List[str]   ):
										'''simple docstring'''
										lowercase :							List[Any]    		=self.get_tokenizer()
										lowercase :							Optional[Any]    		=list(tokenizer.get_vocab().keys()   )

										self.assertEqual(vocab_keys[0]		,					'''</s>'''   )
										self.assertEqual(vocab_keys[1]		,					'''<unk>'''   )
										self.assertEqual(vocab_keys[-1]		,					'''<s>'''   )
										self.assertEqual(len(UpperCAmelCase__   )		,					tokenizer.vocab_size + len(tokenizer.get_added_vocab()   )   )







				@unittest.skip('''Skip this test while all models are still to be uploaded.'''   )
				def  lowerCamelCase_	(       self     :		Optional[int]   ):
										'''simple docstring'''
										pass







				def  lowerCamelCase_	(       self     :		List[Any]   ):
										'''simple docstring'''
										lowercase :							Union[str, Any]    		=self.get_tokenizer()

										lowercase :							str    		=tokenizer.tokenize('''This is a test'''   )
										self.assertListEqual(UpperCAmelCase__		,					['''▁This''', '''▁is''', '''▁a''', '''▁t''', '''est''']   )

										self.assertListEqual(
										    tokenizer.convert_tokens_to_ids(UpperCAmelCase__   )		,					[2, 3, 4, 5, 6]		,					)

										lowercase :							Optional[int]    		=tokenizer.convert_ids_to_tokens([2, 3, 4, 5, 6]   )
										self.assertListEqual(UpperCAmelCase__		,					['''▁This''', '''▁is''', '''▁a''', '''▁t''', '''est''']   )

										lowercase :							Tuple    		=tokenizer.convert_tokens_to_string(UpperCAmelCase__   )
										self.assertEqual(UpperCAmelCase__		,					'''This is a test'''   )







				@slow
				def  lowerCamelCase_	(       self     :		List[str]   ):
										'''simple docstring'''
										# fmt: off
										lowercase :							int    		={'''input_ids''': [[128022, 110108, 397, 11, 38272, 2247, 124811, 285, 18105, 1586, 207, 7, 39534, 4428, 397, 1019, 18105, 1586, 207, 7, 41337, 16786, 241, 7, 20214, 17, 125690, 10398, 7, 44378, 58069, 68342, 7798, 7343, 11, 299, 33310, 4, 158, 37350, 94077, 4569, 299, 33310, 90, 4, 52840, 290, 4, 31270, 112, 299, 682, 4, 52840, 39953, 14079, 193, 52519, 90894, 17894, 120697, 11, 40445, 551, 17, 1019, 52519, 90894, 17756, 963, 11, 40445, 480, 17, 9792, 1120, 5173, 1393, 6240, 16786, 241, 120996, 28, 1245, 1393, 118240, 11123, 1019, 93612, 2691, 10618, 98058, 120409, 1928, 279, 4, 40683, 367, 178, 207, 1019, 103, 103121, 506, 65296, 5, 2], [128022, 21217, 367, 117, 125450, 128, 719, 7, 7308, 40, 93612, 12669, 1116, 16704, 71, 17785, 3699, 15592, 35, 144, 9584, 241, 11943, 713, 950, 799, 2247, 88427, 150, 149, 118813, 120706, 1019, 106906, 81518, 28, 1224, 22799, 397, 5, 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, 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], [128022, 1658, 123311, 5155, 5578, 4722, 279, 14947, 2366, 1120, 1197, 14, 1348, 9232, 5, 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, 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, 1, 1, 1, 1, 1]], '''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, 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, 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, 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, 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, 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, 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, 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, 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, 0, 0, 0, 0, 0, 0, 0, 0]]}  # noqa: E501
										# fmt: on

										self.tokenizer_integration_test_util(
										    expected_encoding=UpperCAmelCase__		,					model_name='''facebook/m2m100_418M'''		,					revision='''c168bae485c864188cf9aa0e4108b0b6934dc91e'''		,					)





@require_torch
@require_sentencepiece
@require_tokenizers
class 		__SCREAMING_SNAKE_CASE							(			unittest.TestCase      ):
				lowerCamelCase_		       =    'facebook/m2m100_418M'
				lowerCamelCase_		       =    [
				    'In my opinion, there are two levels of response from the French government.',
				    'NSA Affair Emphasizes Complete Lack of Debate on Intelligence',
				]
				lowerCamelCase_		       =    [
				    'Selon moi, il y a deux niveaux de réponse de la part du gouvernement français.',
				    'L\'affaire NSA souligne l\'absence totale de débat sur le renseignement',
				]

				# fmt: off
				lowerCamelCase_		       =    [EN_CODE, 5_93, 19_49, 11_57_81, 4, 7_15_86, 42_34, 6_06_33, 12_62_33, 4_32, 12_38_08, 1_55_92, 11_97, 11_71_32, 12_06_18, 5, 2]







				@classmethod
				def  lowerCamelCase_	(       cls     :		Optional[Any]   ):
										'''simple docstring'''
										lowercase :							MaMaaaTokenizer    		=MaMaaaTokenizer.from_pretrained(
										    cls.checkpoint_name		,					src_lang='''en'''		,					tgt_lang='''fr'''   )
										lowercase :							Optional[int]    		=1
										return cls







				def  lowerCamelCase_	(       self     :		List[Any]   ):
										'''simple docstring'''
										self.assertEqual(self.tokenizer.get_lang_id('''ar'''   )		,					128006   )
										self.assertEqual(self.tokenizer.get_lang_id('''en'''   )		,					128022   )
										self.assertEqual(self.tokenizer.get_lang_id('''ro'''   )		,					128076   )
										self.assertEqual(self.tokenizer.get_lang_id('''mr'''   )		,					128063   )







				def  lowerCamelCase_	(       self     :		int   ):
										'''simple docstring'''
										lowercase :							List[str]    		=self.tokenizer.get_vocab()
										self.assertEqual(len(UpperCAmelCase__   )		,					self.tokenizer.vocab_size   )
										self.assertEqual(vocab['''<unk>''']		,					3   )
										self.assertIn(self.tokenizer.get_lang_token('''en'''   )		,					UpperCAmelCase__   )







				def  lowerCamelCase_	(       self     :		Union[str, Any]   ):
										'''simple docstring'''
										lowercase :							List[Any]    		='''en'''
										lowercase :							Optional[Any]    		=self.tokenizer.batch_encode_plus(self.src_text   ).input_ids[0]
										self.assertListEqual(self.expected_src_tokens		,					UpperCAmelCase__   )







				def  lowerCamelCase_	(       self     :		int   ):
										'''simple docstring'''
										self.assertIn(UpperCAmelCase__		,					self.tokenizer.all_special_ids   )
										# fmt: off
										lowercase :							str    		=[FR_CODE, 5364, 82, 8642, 4, 294, 47, 8, 14028, 136, 3286, 9706, 6, 90797, 6, 144012, 162, 88128, 30061, 5, 2]
										# fmt: on
										lowercase :							Optional[Any]    		=self.tokenizer.decode(UpperCAmelCase__		,					skip_special_tokens=UpperCAmelCase__   )
										lowercase :							Optional[Any]    		=self.tokenizer.decode(generated_ids[1:]		,					skip_special_tokens=UpperCAmelCase__   )
										self.assertEqual(UpperCAmelCase__		,					UpperCAmelCase__   )
										self.assertNotIn(self.tokenizer.eos_token		,					UpperCAmelCase__   )







				def  lowerCamelCase_	(       self     :		int   ):
										'''simple docstring'''
										lowercase :							Any    		=tempfile.mkdtemp()
										lowercase :							Tuple    		=self.tokenizer.lang_token_to_id
										self.tokenizer.save_pretrained(UpperCAmelCase__   )
										lowercase :							Union[str, Any]    		=MaMaaaTokenizer.from_pretrained(UpperCAmelCase__   )
										self.assertDictEqual(new_tok.lang_token_to_id		,					UpperCAmelCase__   )







				@require_torch
				def  lowerCamelCase_	(       self     :		List[str]   ):
										'''simple docstring'''
										lowercase :							List[str]    		='''en'''
										lowercase :							int    		='''fr'''

										lowercase :							Union[str, Any]    		=self.tokenizer(self.src_text		,					text_target=self.tgt_text		,					padding=UpperCAmelCase__		,					return_tensors='''pt'''   )

										lowercase :							str    		=shift_tokens_right(
										    batch['''labels''']		,					self.tokenizer.pad_token_id		,					self.tokenizer.eos_token_id   )

										for k in batch:
																lowercase :							int    		=batch[k].tolist()
										# batch = {k: v.tolist() for k,v in batch.items()}
										# fairseq batch: https://gist.github.com/sshleifer/cba08bc2109361a74ac3760a7e30e4f4
										# batch.decoder_inputs_ids[0][0] ==
										assert batch.input_ids[1][0] == EN_CODE
										assert batch.input_ids[1][-1] == 2
										assert batch.labels[1][0] == FR_CODE
										assert batch.labels[1][-1] == 2
										assert batch.decoder_input_ids[1][:2] == [2, FR_CODE]







				@require_torch
				def  lowerCamelCase_	(       self     :		int   ):
										'''simple docstring'''
										lowercase :							Optional[int]    		='''mr'''
										self.assertListEqual(self.tokenizer.prefix_tokens		,					[self.tokenizer.get_lang_id('''mr'''   )]   )
										self.assertListEqual(self.tokenizer.suffix_tokens		,					[self.tokenizer.eos_token_id]   )

										lowercase :							Union[str, Any]    		='''zh'''
										self.assertListEqual(self.tokenizer.prefix_tokens		,					[self.tokenizer.get_lang_id('''zh'''   )]   )
										self.assertListEqual(self.tokenizer.suffix_tokens		,					[self.tokenizer.eos_token_id]   )







				@require_torch
				def  lowerCamelCase_	(       self     :		Union[str, Any]   ):
										'''simple docstring'''
										lowercase :							int    		='''mr'''
										self.tokenizer._switch_to_target_mode()
										self.assertListEqual(self.tokenizer.prefix_tokens		,					[self.tokenizer.get_lang_id('''mr'''   )]   )
										self.assertListEqual(self.tokenizer.suffix_tokens		,					[self.tokenizer.eos_token_id]   )
										self.tokenizer._switch_to_input_mode()
										self.assertListEqual(self.tokenizer.prefix_tokens		,					[self.tokenizer.get_lang_id(self.tokenizer.src_lang   )]   )

										lowercase :							Optional[Any]    		='''zh'''
										self.tokenizer._switch_to_target_mode()
										self.assertListEqual(self.tokenizer.prefix_tokens		,					[self.tokenizer.get_lang_id('''zh'''   )]   )
										self.assertListEqual(self.tokenizer.suffix_tokens		,					[self.tokenizer.eos_token_id]   )
										self.tokenizer._switch_to_input_mode()
										self.assertListEqual(self.tokenizer.prefix_tokens		,					[self.tokenizer.get_lang_id(self.tokenizer.src_lang   )]   )







				@require_torch
				def  lowerCamelCase_	(       self     :		Any   ):
										'''simple docstring'''
										lowercase :							Optional[Any]    		=self.tokenizer._build_translation_inputs('''A test'''		,					return_tensors='''pt'''		,					src_lang='''en'''		,					tgt_lang='''ar'''   )

										self.assertEqual(
										    nested_simplify(UpperCAmelCase__   )		,					{
										        # en_XX, A, test, EOS
										        '''input_ids''': [[128022, 58, 4183, 2]],
										        '''attention_mask''': [[1, 1, 1, 1]],
										        # ar_AR
										        '''forced_bos_token_id''': 128006,
										    }		,					)





