import json
import os
import unittest

from transformers.models.fsmt.tokenization_fsmt import VOCAB_FILES_NAMES, FSMTTokenizer
from transformers.testing_utils import slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


# using a different tiny model than the one used for default params defined in init to ensure proper testing
lowerCAmelCase__      		=					'stas/tiny-wmt19-en-ru'
class 							a_					(		SCREAMING_SNAKE_CASE			,	unittest.TestCase		):






 '''simple docstring'''






 UpperCAmelCase_      					=   FSMTTokenizer
 UpperCAmelCase_      					=   False




 def 						__snake_case		(     self   :    Any):







        '''simple docstring'''



        super().setUp()

        # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
        lowerCAmelCase__	      =   [
            "l",
            "o",
            "w",
            "e",
            "r",
            "s",
            "t",
            "i",
            "d",
            "n",
            "w</w>",
            "r</w>",
            "t</w>",
            "lo",
            "low",
            "er</w>",
            "low</w>",
            "lowest</w>",
            "newer</w>",
            "wider</w>",
            "<unk>",
        ]
        lowerCAmelCase__	      =   dict(zip(_lowerCamelCase     ,       range(len(_lowerCamelCase))))
        lowerCAmelCase__	      =   ["l o 123", "lo w 1456", "e r</w> 1789", ""]

        lowerCAmelCase__	      =   ["en", "ru"]
        lowerCAmelCase__	      =   {
            "langs": self.langs,
            "src_vocab_size": 10,
            "tgt_vocab_size": 20,
        }

        lowerCAmelCase__	      =   os.path.join(self.tmpdirname     ,       VOCAB_FILES_NAMES['src_vocab_file'])
        lowerCAmelCase__	      =   os.path.join(self.tmpdirname     ,       VOCAB_FILES_NAMES['tgt_vocab_file'])
        lowerCAmelCase__	      =   os.path.join(self.tmpdirname     ,       'tokenizer_config.json')
        lowerCAmelCase__	      =   os.path.join(self.tmpdirname     ,       VOCAB_FILES_NAMES['merges_file'])
        with open(self.src_vocab_file     ,       'w') as fp:
               fp.write(json.dumps(_lowerCamelCase))
        with open(self.tgt_vocab_file     ,       'w') as fp:
               fp.write(json.dumps(_lowerCamelCase))
        with open(self.merges_file     ,       'w') as fp:
               fp.write('\n'.join(_lowerCamelCase))
        with open(_lowerCamelCase     ,       'w') as fp:
               fp.write(json.dumps(_lowerCamelCase))




 @cached_property
 def 						__snake_case		(     self   :    str):







        '''simple docstring'''



        return FSMTTokenizer.from_pretrained('facebook/wmt19-ru-en')




 @cached_property
 def 						__snake_case		(     self   :    Optional[Any]):







        '''simple docstring'''



        return FSMTTokenizer.from_pretrained('facebook/wmt19-en-ru')




 def 						__snake_case		(     self   :    List[str]):







        '''simple docstring'''



        lowerCAmelCase__	      =   FSMTTokenizer.from_pretrained(_lowerCamelCase)
        self.assertListEqual([tokenizer.src_lang, tokenizer.tgt_lang]     ,       ['en', 'ru'])
        self.assertEqual(tokenizer.src_vocab_size     ,       21)
        self.assertEqual(tokenizer.tgt_vocab_size     ,       21)




 def 						__snake_case		(     self   :    Any):







        '''simple docstring'''



        lowerCAmelCase__	      =   FSMTTokenizer(self.langs     ,       self.src_vocab_file     ,       self.tgt_vocab_file     ,       self.merges_file)

        lowerCAmelCase__	      =   "lower"
        lowerCAmelCase__	      =   ["low", "er</w>"]
        lowerCAmelCase__	      =   tokenizer.tokenize(_lowerCamelCase)
        self.assertListEqual(_lowerCamelCase     ,       _lowerCamelCase)

        lowerCAmelCase__	      =   tokens + ["<unk>"]
        lowerCAmelCase__	      =   [14, 15, 20]
        self.assertListEqual(tokenizer.convert_tokens_to_ids(_lowerCamelCase)     ,       _lowerCamelCase)




 @slow
 def 						__snake_case		(     self   :    int):







        '''simple docstring'''



        lowerCAmelCase__	      =   self.tokenizer_ru_en

        lowerCAmelCase__	      =   tokenizer.encode('sequence builders'     ,       add_special_tokens=_lowerCamelCase)
        lowerCAmelCase__	      =   tokenizer.encode('multi-sequence build'     ,       add_special_tokens=_lowerCamelCase)

        lowerCAmelCase__	      =   tokenizer.build_inputs_with_special_tokens(_lowerCamelCase)
        lowerCAmelCase__	      =   tokenizer.build_inputs_with_special_tokens(_lowerCamelCase     ,       _lowerCamelCase)

        assert encoded_sentence == text + [2]
        assert encoded_pair == text + [2] + text_a + [2]




 @slow
 def 						__snake_case		(     self   :    List[str]):







        '''simple docstring'''



        lowerCAmelCase__	      =   self.tokenizer_en_ru
        lowerCAmelCase__	      =   self.tokenizer_ru_en

        lowerCAmelCase__	      =   [
            [
                "Here's a little song I wrote. Don't worry, be happy.",
                [2_470, 39, 11, 2_349, 7_222, 70, 5_979, 7, 8_450, 1_050, 13_160, 5, 26, 6_445, 7, 2],
            ],
            ["This is it. No more. I'm done!", [132, 21, 37, 7, 1_434, 86, 7, 70, 6_476, 1_305, 427, 2]],
        ]

        # if data needs to be recreated or added, run:
        # import torch
        # model = torch.hub.load("pytorch/fairseq", "transformer.wmt19.en-ru", checkpoint_file="model4.pt", tokenizer="moses", bpe="fastbpe")
        # for src_text, _ in targets: print(f"""[\n"{src_text}",\n {model.encode(src_text).tolist()}\n],""")

        for src_text, tgt_input_ids in targets:
               lowerCAmelCase__	      =   tokenizer_enc.encode(_lowerCamelCase     ,       return_tensors=_lowerCamelCase)
               self.assertListEqual(_lowerCamelCase     ,       _lowerCamelCase)

               # and decode backward, using the reversed languages model
               lowerCAmelCase__	      =   tokenizer_dec.decode(_lowerCamelCase     ,       skip_special_tokens=_lowerCamelCase)
               self.assertEqual(_lowerCamelCase     ,       _lowerCamelCase)




 @slow
 def 						__snake_case		(     self   :    List[str]):







        '''simple docstring'''



        lowerCAmelCase__	      =   FSMTTokenizer.from_pretrained('facebook/wmt19-ru-en'     ,       do_lower_case=_lowerCamelCase)
        lowerCAmelCase__	      =   tokenizer.tokenize('USA is United States of America')
        lowerCAmelCase__	      =   ["us", "a</w>", "is</w>", "un", "i", "ted</w>", "st", "ates</w>", "of</w>", "am", "er", "ica</w>"]
        self.assertListEqual(_lowerCamelCase     ,       _lowerCamelCase)




 @unittest.skip('FSMTConfig.__init__  requires non-optional args')
 def 						__snake_case		(     self   :    str):







        '''simple docstring'''



        pass




 @unittest.skip('FSMTConfig.__init__  requires non-optional args')
 def 						__snake_case		(     self   :    Tuple):







        '''simple docstring'''



        pass


import gc
import unittest

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

from diffusers import (
    AutoencoderKL,
    DDIMParallelScheduler,
    DDPMParallelScheduler,
    StableDiffusionParadigmsPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import (
    enable_full_determinism,
    require_torch_gpu,
)

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()
class    _UpperCamelCase(   SCREAMING_SNAKE_CASE		,						SCREAMING_SNAKE_CASE		,						unittest.TestCase				):
						__A:						Optional[Any]				   =      StableDiffusionParadigmsPipeline
						__A:						Optional[int]				   =      TEXT_TO_IMAGE_PARAMS
						__A:						Dict				   =      TEXT_TO_IMAGE_BATCH_PARAMS
						__A:						Any				   =      TEXT_TO_IMAGE_IMAGE_PARAMS
						__A:						List[Any]				   =      TEXT_TO_IMAGE_IMAGE_PARAMS

						def 						a__ (					self		:			Optional[Any]			):
													torch.manual_seed(0			)
													_UpperCAmelCase  :			str	       =		UNetaDConditionModel(
													    block_out_channels=(32, 64)	,						layers_per_block=2	,						sample_size=32	,						in_channels=4	,						out_channels=4	,						down_block_types=("DownBlock2D", "CrossAttnDownBlock2D")	,						up_block_types=("CrossAttnUpBlock2D", "UpBlock2D")	,						cross_attention_dim=32	,						attention_head_dim=(2, 4)	,						use_linear_projection=_lowerCamelCase	,						)
													_UpperCAmelCase  :			Dict	       =		DDIMParallelScheduler(
													    beta_start=0.0_00_85	,						beta_end=0.0_12	,						beta_schedule="scaled_linear"	,						clip_sample=_lowerCamelCase	,						set_alpha_to_one=_lowerCamelCase	,						)
													torch.manual_seed(0			)
													_UpperCAmelCase  :			Tuple	       =		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	,						sample_size=1_28	,						)
													torch.manual_seed(0			)
													_UpperCAmelCase  :			Optional[Any]	       =		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=10_00	,						hidden_act="gelu"	,						projection_dim=5_12	,						)
													_UpperCAmelCase  :			Tuple	       =		CLIPTextModel(_lowerCamelCase			)
													_UpperCAmelCase  :			Dict	       =		CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"			)

													_UpperCAmelCase  :			Optional[int]	       =		{
													    "unet": unet,
													    "scheduler": scheduler,
													    "vae": vae,
													    "text_encoder": text_encoder,
													    "tokenizer": tokenizer,
													    "safety_checker": None,
													    "feature_extractor": None,
													}
													return components

						def 						a__ (					self		:			Dict	,						_lowerCamelCase		:			str	,						_lowerCamelCase		:			int=0			):
													if str(_lowerCamelCase			).startswith("mps"			):
																				_UpperCAmelCase  :			Tuple	       =		torch.manual_seed(_lowerCamelCase			)
													else:
																				_UpperCAmelCase  :			Optional[int]	       =		torch.Generator(device=_lowerCamelCase			).manual_seed(_lowerCamelCase			)
													_UpperCAmelCase  :			str	       =		{
													    "prompt": "a photograph of an astronaut riding a horse",
													    "generator": generator,
													    "num_inference_steps": 10,
													    "guidance_scale": 6.0,
													    "output_type": "numpy",
													    "parallel": 3,
													    "debug": True,
													}
													return inputs

						def 						a__ (					self		:			Optional[Any]			):
													_UpperCAmelCase  :			Union[str, Any]	       =		"cpu"  # ensure determinism for the device-dependent torch.Generator
													_UpperCAmelCase  :			List[str]	       =		self.get_dummy_components()
													_UpperCAmelCase  :			List[Any]	       =		StableDiffusionParadigmsPipeline(**_lowerCamelCase			)
													_UpperCAmelCase  :			List[str]	       =		sd_pipe.to(_lowerCamelCase			)
													sd_pipe.set_progress_bar_config(disable=_lowerCamelCase			)

													_UpperCAmelCase  :			List[Any]	       =		self.get_dummy_inputs(_lowerCamelCase			)
													_UpperCAmelCase  :			int	       =		sd_pipe(**_lowerCamelCase			).images
													_UpperCAmelCase  :			Tuple	       =		image[0, -3:, -3:, -1]
													assert image.shape == (1, 64, 64, 3)

													_UpperCAmelCase  :			str	       =		np.array([0.47_73, 0.54_17, 0.47_23, 0.49_25, 0.56_31, 0.47_52, 0.52_40, 0.49_35, 0.50_23]			)

													assert np.abs(image_slice.flatten() - expected_slice			).max() < 1E-2

						def 						a__ (					self		:			Optional[Any]			):
													_UpperCAmelCase  :			Dict	       =		"cpu"  # ensure determinism for the device-dependent torch.Generator
													_UpperCAmelCase  :			Dict	       =		self.get_dummy_components()
													torch.manual_seed(0			)
													_UpperCAmelCase  :			str	       =		DDPMParallelScheduler()
													torch.manual_seed(0			)
													_UpperCAmelCase  :			int	       =		StableDiffusionParadigmsPipeline(**_lowerCamelCase			)
													_UpperCAmelCase  :			Any	       =		sd_pipe.to(_lowerCamelCase			)
													sd_pipe.set_progress_bar_config(disable=_lowerCamelCase			)

													_UpperCAmelCase  :			int	       =		self.get_dummy_inputs(_lowerCamelCase			)
													_UpperCAmelCase  :			Dict	       =		sd_pipe(**_lowerCamelCase			).images
													_UpperCAmelCase  :			str	       =		image[0, -3:, -3:, -1]
													assert image.shape == (1, 64, 64, 3)

													_UpperCAmelCase  :			Optional[Any]	       =		np.array([0.35_73, 0.44_20, 0.49_60, 0.47_99, 0.37_96, 0.38_79, 0.48_19, 0.43_65, 0.44_68]			)

													assert np.abs(image_slice.flatten() - expected_slice			).max() < 1E-2

						def 						a__ (					self		:			List[str]			):
													super().test_inference_batch_consistent(batch_sizes=[1, 2]			)

						def 						a__ (					self		:			List[str]			):
													super().test_inference_batch_single_identical(batch_size=2	,						expected_max_diff=3E-3			)






						def 						a__ (					self		:			Optional[Any]			):
													_UpperCAmelCase  :			int	       =		"cpu"  # ensure determinism for the device-dependent torch.Generator
													_UpperCAmelCase  :			Tuple	       =		self.get_dummy_components()
													_UpperCAmelCase  :			Dict	       =		StableDiffusionParadigmsPipeline(**_lowerCamelCase			)
													_UpperCAmelCase  :			str	       =		sd_pipe.to(_lowerCamelCase			)
													sd_pipe.set_progress_bar_config(disable=_lowerCamelCase			)

													_UpperCAmelCase  :			Optional[int]	       =		self.get_dummy_inputs(_lowerCamelCase			)
													_UpperCAmelCase  :			Optional[Any]	       =		"french fries"
													_UpperCAmelCase  :			Tuple	       =		sd_pipe(**_lowerCamelCase	,						negative_prompt=_lowerCamelCase			)
													_UpperCAmelCase  :			Optional[Any]	       =		output.images
													_UpperCAmelCase  :			Union[str, Any]	       =		image[0, -3:, -3:, -1]

													assert image.shape == (1, 64, 64, 3)

													_UpperCAmelCase  :			Optional[Any]	       =		np.array([0.47_71, 0.54_20, 0.46_83, 0.49_18, 0.56_36, 0.47_25, 0.52_30, 0.49_23, 0.50_15]			)

													assert np.abs(image_slice.flatten() - expected_slice			).max() < 1E-2







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

						def 						a__ (					self		:			int			):
													super().tearDown()
													gc.collect()
													torch.cuda.empty_cache()

						def 						a__ (					self		:			Tuple	,						_lowerCamelCase		:			Optional[int]=0			):
													_UpperCAmelCase  :			Union[str, Any]	       =		torch.Generator(device=_lowerCamelCase			).manual_seed(_lowerCamelCase			)
													_UpperCAmelCase  :			List[str]	       =		{
													    "prompt": "a photograph of an astronaut riding a horse",
													    "generator": generator,
													    "num_inference_steps": 10,
													    "guidance_scale": 7.5,
													    "output_type": "numpy",
													    "parallel": 3,
													    "debug": True,
													}
													return inputs






						def 						a__ (					self		:			Any			):
													_UpperCAmelCase  :			str	       =		"stabilityai/stable-diffusion-2-base"
													_UpperCAmelCase  :			List[str]	       =		DDIMParallelScheduler.from_pretrained(_lowerCamelCase	,						subfolder="scheduler"			)
													_UpperCAmelCase  :			Union[str, Any]	       =		StableDiffusionParadigmsPipeline.from_pretrained(_lowerCamelCase	,						scheduler=_lowerCamelCase	,						safety_checker=_lowerCamelCase			)
													pipe.to(_lowerCamelCase			)
													pipe.set_progress_bar_config(disable=_lowerCamelCase			)
													pipe.enable_attention_slicing()

													_UpperCAmelCase  :			Optional[Any]	       =		self.get_inputs()
													_UpperCAmelCase  :			List[str]	       =		pipe(**_lowerCamelCase			).images
													_UpperCAmelCase  :			Tuple	       =		image[0, -3:, -3:, -1].flatten()

													assert image.shape == (1, 5_12, 5_12, 3)

													_UpperCAmelCase  :			Union[str, Any]	       =		np.array([0.96_22, 0.96_02, 0.97_48, 0.95_91, 0.96_30, 0.96_91, 0.96_61, 0.96_31, 0.97_41]			)

													assert np.abs(expected_slice - image_slice			).max() < 1E-2




