"""simple docstring"""

import unittest

import pytest

from transformers import (
    MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
    TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
    MBartaaTokenizerFast,
    MBartConfig,
    MBartForConditionalGeneration,
    TranslationPipeline,
    pipeline,
)
from transformers.testing_utils import is_pipeline_test, require_tf, require_torch, slow

from .test_pipelines_common import ANY






@is_pipeline_test
class 		_lowercase (						unittest.TestCase      ):



						"""simple docstring"""




						lowercase__       =  MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
						lowercase__       =  TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING





						def   UpperCAmelCase_ (				self							:    int     , UpperCamelCase__							:    List[Any]     , UpperCamelCase__							:    int     , UpperCamelCase__							:    str				)				->				Any:
													'''simple docstring'''



													if isinstance(model.config     , UpperCamelCase__				):
																				__UpperCamelCase	,      __UpperCamelCase							=list(tokenizer.lang_code_to_id.keys()				)[:2]
																				__UpperCamelCase							=TranslationPipeline(model=UpperCamelCase__     , tokenizer=UpperCamelCase__     , src_lang=UpperCamelCase__     , tgt_lang=UpperCamelCase__				)
													else:
																				__UpperCamelCase							=TranslationPipeline(model=UpperCamelCase__     , tokenizer=UpperCamelCase__				)
													return translator, ["Some string", "Some other text"]





						def   UpperCAmelCase_ (				self							:    List[Any]     , UpperCamelCase__							:    Union[str, Any]     , UpperCamelCase__							:    Any				)				->				Tuple:
													'''simple docstring'''



													__UpperCamelCase							=translator('''Some string'''				)
													self.assertEqual(UpperCamelCase__     , [{'''translation_text''': ANY(UpperCamelCase__				)}]				)

													__UpperCamelCase							=translator(['''Some string''']				)
													self.assertEqual(UpperCamelCase__     , [{'''translation_text''': ANY(UpperCamelCase__				)}]				)

													__UpperCamelCase							=translator(['''Some string''', '''other string''']				)
													self.assertEqual(UpperCamelCase__     , [{'''translation_text''': ANY(UpperCamelCase__				)}, {'''translation_text''': ANY(UpperCamelCase__				)}]				)





						@require_torch
						def   UpperCAmelCase_ (				self							:    Optional[Any]				)				->				str:
													'''simple docstring'''



													__UpperCamelCase							=pipeline('''translation_en_to_ro'''     , model='''patrickvonplaten/t5-tiny-random'''     , framework='''pt'''				)
													__UpperCamelCase							=translator('''This is a test string'''     , max_length=20				)
													self.assertEqual(
													    UpperCamelCase__     , [
													        {
													            '''translation_text''': (
													                '''Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide'''
													                ''' Beide Beide'''
													            )
													        }
													    ]     , )





						@require_tf
						def   UpperCAmelCase_ (				self							:    List[Any]				)				->				Union[str, Any]:
													'''simple docstring'''



													__UpperCamelCase							=pipeline('''translation_en_to_ro'''     , model='''patrickvonplaten/t5-tiny-random'''     , framework='''tf'''				)
													__UpperCamelCase							=translator('''This is a test string'''     , max_length=20				)
													self.assertEqual(
													    UpperCamelCase__     , [
													        {
													            '''translation_text''': (
													                '''Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide'''
													                ''' Beide Beide'''
													            )
													        }
													    ]     , )





						@require_torch
						def   UpperCAmelCase_ (				self							:    List[str]				)				->				Optional[int]:
													'''simple docstring'''



													__UpperCamelCase							=pipeline('''translation_en_to_de'''     , model='''patrickvonplaten/t5-tiny-random'''     , framework='''pt'''				)
													__UpperCamelCase							=translator('''This is a test string'''     , max_length=20				)
													self.assertEqual(
													    UpperCamelCase__     , [
													        {
													            '''translation_text''': (
													                '''monoton monoton monoton monoton monoton monoton monoton monoton monoton monoton urine urine'''
													                ''' urine urine urine urine urine urine urine'''
													            )
													        }
													    ]     , )





						@require_tf
						def   UpperCAmelCase_ (				self							:    Any				)				->				Tuple:
													'''simple docstring'''



													__UpperCamelCase							=pipeline('''translation_en_to_de'''     , model='''patrickvonplaten/t5-tiny-random'''     , framework='''tf'''				)
													__UpperCamelCase							=translator('''This is a test string'''     , max_length=20				)
													self.assertEqual(
													    UpperCamelCase__     , [
													        {
													            '''translation_text''': (
													                '''monoton monoton monoton monoton monoton monoton monoton monoton monoton monoton urine urine'''
													                ''' urine urine urine urine urine urine urine'''
													            )
													        }
													    ]     , )






class 		_lowercase (						unittest.TestCase      ):



						"""simple docstring"""




						@require_torch
						@slow
						def   UpperCAmelCase_ (				self							:    Tuple				)				->				str:
													'''simple docstring'''



													with self.assertRaises(UpperCamelCase__				):
																				pipeline(task='''translation_cn_to_ar'''				)

													# but we do for this one
													__UpperCamelCase							=pipeline(task='''translation_en_to_de'''				)
													self.assertEqual(translator._preprocess_params['''src_lang''']     , '''en'''				)
													self.assertEqual(translator._preprocess_params['''tgt_lang''']     , '''de'''				)





						@require_torch
						@slow
						def   UpperCAmelCase_ (				self							:    str				)				->				Dict:
													'''simple docstring'''



													__UpperCamelCase							=MBartForConditionalGeneration.from_pretrained('''facebook/mbart-large-50-many-to-many-mmt'''				)
													__UpperCamelCase							=MBartaaTokenizerFast.from_pretrained('''facebook/mbart-large-50-many-to-many-mmt'''				)

													__UpperCamelCase							=pipeline(task='''translation'''     , model=UpperCamelCase__     , tokenizer=UpperCamelCase__				)
													# Missing src_lang, tgt_lang
													with self.assertRaises(UpperCamelCase__				):
																				translator('''This is a test'''				)

													__UpperCamelCase							=translator('''This is a test'''     , src_lang='''en_XX'''     , tgt_lang='''ar_AR'''				)
													self.assertEqual(UpperCamelCase__     , [{'''translation_text''': '''هذا إختبار'''}]				)

													__UpperCamelCase							=translator('''This is a test'''     , src_lang='''en_XX'''     , tgt_lang='''hi_IN'''				)
													self.assertEqual(UpperCamelCase__     , [{'''translation_text''': '''यह एक परीक्षण है'''}]				)

													# src_lang, tgt_lang can be defined at pipeline call time
													__UpperCamelCase							=pipeline(task='''translation'''     , model=UpperCamelCase__     , tokenizer=UpperCamelCase__     , src_lang='''en_XX'''     , tgt_lang='''ar_AR'''				)
													__UpperCamelCase							=translator('''This is a test'''				)
													self.assertEqual(UpperCamelCase__     , [{'''translation_text''': '''هذا إختبار'''}]				)





						@require_torch
						def   UpperCAmelCase_ (				self							:    List[Any]				)				->				Any:
													'''simple docstring'''



													__UpperCamelCase							='''patrickvonplaten/t5-tiny-random'''
													__UpperCamelCase							=pipeline(task='''translation_cn_to_ar'''     , model=UpperCamelCase__				)
													self.assertEqual(translator._preprocess_params['''src_lang''']     , '''cn'''				)
													self.assertEqual(translator._preprocess_params['''tgt_lang''']     , '''ar'''				)





						@require_torch
						def   UpperCAmelCase_ (				self							:    Union[str, Any]				)				->				Any:
													'''simple docstring'''



													__UpperCamelCase							='''patrickvonplaten/t5-tiny-random'''
													with pytest.warns(UpperCamelCase__     , match=R'''.*translation_en_to_de.*'''				):
																				__UpperCamelCase							=pipeline(task='''translation'''     , model=UpperCamelCase__				)
													self.assertEqual(translator.task     , '''translation_en_to_de'''				)
													self.assertEqual(translator._preprocess_params['''src_lang''']     , '''en'''				)
													self.assertEqual(translator._preprocess_params['''tgt_lang''']     , '''de'''				)





						@require_torch
						def   UpperCAmelCase_ (				self							:    List[str]				)				->				Optional[Any]:
													'''simple docstring'''



													with self.assertRaises(UpperCamelCase__				):
																				pipeline(task='''translation'''				)




"""simple docstring"""

import gc
import tempfile
import unittest

import numpy as np
import torch

from diffusers import VersatileDiffusionDualGuidedPipeline
from diffusers.utils.testing_utils import load_image, nightly, require_torch_gpu, torch_device


__lowercase    =				False






@nightly
@require_torch_gpu
class 		_lowercase (						unittest.TestCase      ):



						"""simple docstring"""




						def   UpperCAmelCase_ (				self							:    List[str]				)				->				List[Any]:
													'''simple docstring'''



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





						def   UpperCAmelCase_ (				self							:    List[Any]				)				->				List[Any]:
													'''simple docstring'''



													__UpperCamelCase							=VersatileDiffusionDualGuidedPipeline.from_pretrained('''shi-labs/versatile-diffusion'''				)
													# remove text_unet
													pipe.remove_unused_weights()
													pipe.to(UpperCamelCase__				)
													pipe.set_progress_bar_config(disable=UpperCamelCase__				)

													__UpperCamelCase							=load_image(
													    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg'''				)

													__UpperCamelCase							=torch.manual_seed(0				)
													__UpperCamelCase							=pipe(
													    prompt='''first prompt'''     , image=UpperCamelCase__     , text_to_image_strength=0.75     , generator=UpperCamelCase__     , guidance_scale=7.5     , num_inference_steps=2     , output_type='''numpy'''     , ).images

													with tempfile.TemporaryDirectory() as tmpdirname:
																				pipe.save_pretrained(UpperCamelCase__				)
																				__UpperCamelCase							=VersatileDiffusionDualGuidedPipeline.from_pretrained(UpperCamelCase__				)

													pipe.to(UpperCamelCase__				)
													pipe.set_progress_bar_config(disable=UpperCamelCase__				)

													__UpperCamelCase							=generator.manual_seed(0				)
													__UpperCamelCase							=pipe(
													    prompt='''first prompt'''     , image=UpperCamelCase__     , text_to_image_strength=0.75     , generator=UpperCamelCase__     , guidance_scale=7.5     , num_inference_steps=2     , output_type='''numpy'''     , ).images

													assert np.abs(image - new_image				).sum() < 1E-5, "Models don't have the same forward pass"





						def   UpperCAmelCase_ (				self							:    Optional[int]				)				->				Optional[int]:
													'''simple docstring'''



													__UpperCamelCase							=VersatileDiffusionDualGuidedPipeline.from_pretrained('''shi-labs/versatile-diffusion'''				)
													pipe.remove_unused_weights()
													pipe.to(UpperCamelCase__				)
													pipe.set_progress_bar_config(disable=UpperCamelCase__				)

													__UpperCamelCase							='''cyberpunk 2077'''
													__UpperCamelCase							=load_image(
													    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg'''				)
													__UpperCamelCase							=torch.manual_seed(0				)
													__UpperCamelCase							=pipe(
													    prompt=UpperCamelCase__     , image=UpperCamelCase__     , text_to_image_strength=0.75     , generator=UpperCamelCase__     , guidance_scale=7.5     , num_inference_steps=50     , output_type='''numpy'''     , ).images

													__UpperCamelCase							=image[0, 253:256, 253:256, -1]

													assert image.shape == (1, 512, 512, 3)
													__UpperCamelCase							=np.array([0.07_87, 0.08_49, 0.08_26, 0.08_12, 0.08_07, 0.07_95, 0.08_18, 0.07_98, 0.07_79]				)

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



