"""simple docstring"""


import unittest

import numpy as np
import torch
from torch import nn
from transformers import (
    CLIPImageProcessor,
    CLIPTextConfig,
    CLIPTextModelWithProjection,
    CLIPTokenizer,
    CLIPVisionConfig,
    CLIPVisionModelWithProjection,
)

from diffusers import KandinskyPriorPipeline, PriorTransformer, UnCLIPScheduler
from diffusers.utils import torch_device
from diffusers.utils.testing_utils import enable_full_determinism, skip_mps

from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()






class    __UpperCAmelCase						(	__lowerCAmelCase      ,      unittest.TestCase	):
				A__      :   Any  							=       KandinskyPriorPipeline
				A__      :   int  							=       ['''prompt''']
				A__      :   List[Any]  							=       ['''prompt''', '''negative_prompt''']
				A__      :   Union[str, Any]  							=       [
				    '''num_images_per_prompt''',
				    '''generator''',
				    '''num_inference_steps''',
				    '''latents''',
				    '''negative_prompt''',
				    '''guidance_scale''',
				    '''output_type''',
				    '''return_dict''',
				]
				A__      :   Optional[int]  							=       False

				@property
				def 		_a	(      self      ):
										return 32

				@property
				def 		_a	(      self      ):
										return 32

				@property
				def 		_a	(      self      ):
										return self.time_input_dim

				@property
				def 		_a	(      self      ):
										return self.time_input_dim * 4

				@property
				def 		_a	(      self      ):
										return 100

				@property
				def 		_a	(      self      ):
										lowerCamelCase__					=CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"      )
										return tokenizer

				@property
				def 		_a	(      self      ):
										torch.manual_seed(0      )
										lowerCamelCase__					=CLIPTextConfig(
										    bos_token_id=0	, eos_token_id=2	, hidden_size=self.text_embedder_hidden_size	, projection_dim=self.text_embedder_hidden_size	, intermediate_size=37	, layer_norm_eps=1E-05	, num_attention_heads=4	, num_hidden_layers=5	, pad_token_id=1	, vocab_size=1000	, )
										return CLIPTextModelWithProjection(_lowerCamelCase      )

				@property
				def 		_a	(      self      ):
										torch.manual_seed(0      )

										lowerCamelCase__					={
										    "num_attention_heads": 2,
										    "attention_head_dim": 12,
										    "embedding_dim": self.text_embedder_hidden_size,
										    "num_layers": 1,
										}

										lowerCamelCase__					=PriorTransformer(**_lowerCamelCase      )
										# clip_std and clip_mean is initialized to be 0 so PriorTransformer.post_process_latents will always return 0 - set clip_std to be 1 so it won't return 0
										lowerCamelCase__					=nn.Parameter(torch.ones(model.clip_std.shape      )      )
										return model

				@property
				def 		_a	(      self      ):
										torch.manual_seed(0      )
										lowerCamelCase__					=CLIPVisionConfig(
										    hidden_size=self.text_embedder_hidden_size	, image_size=224	, projection_dim=self.text_embedder_hidden_size	, intermediate_size=37	, num_attention_heads=4	, num_channels=3	, num_hidden_layers=5	, patch_size=14	, )

										lowerCamelCase__					=CLIPVisionModelWithProjection(_lowerCamelCase      )
										return model

				@property
				def 		_a	(      self      ):
										lowerCamelCase__					=CLIPImageProcessor(
										    crop_size=224	, do_center_crop=_lowerCamelCase	, do_normalize=_lowerCamelCase	, do_resize=_lowerCamelCase	, image_mean=[0.4_8_1_4_5_4_6_6, 0.4_5_7_8_2_7_5, 0.4_0_8_2_1_0_7_3]	, image_std=[0.2_6_8_6_2_9_5_4, 0.2_6_1_3_0_2_5_8, 0.2_7_5_7_7_7_1_1]	, resample=3	, size=224	, )

										return image_processor

				def 		_a	(      self      ):
										lowerCamelCase__					=self.dummy_prior
										lowerCamelCase__					=self.dummy_image_encoder
										lowerCamelCase__					=self.dummy_text_encoder
										lowerCamelCase__					=self.dummy_tokenizer
										lowerCamelCase__					=self.dummy_image_processor

										lowerCamelCase__					=UnCLIPScheduler(
										    variance_type="fixed_small_log"	, prediction_type="sample"	, num_train_timesteps=1000	, clip_sample=_lowerCamelCase	, clip_sample_range=1_0.0	, )

										lowerCamelCase__					={
										    "prior": prior,
										    "image_encoder": image_encoder,
										    "text_encoder": text_encoder,
										    "tokenizer": tokenizer,
										    "scheduler": scheduler,
										    "image_processor": image_processor,
										}

										return components

				def 		_a	(      self	, _lowerCamelCase	, _lowerCamelCase=0      ):
										if str(_lowerCamelCase      ).startswith("mps"      ):
																lowerCamelCase__					=torch.manual_seed(_lowerCamelCase      )
										else:
																lowerCamelCase__					=torch.Generator(device=_lowerCamelCase      ).manual_seed(_lowerCamelCase      )
										lowerCamelCase__					={
										    "prompt": "horse",
										    "generator": generator,
										    "guidance_scale": 4.0,
										    "num_inference_steps": 2,
										    "output_type": "np",
										}
										return inputs

				def 		_a	(      self      ):
										lowerCamelCase__					="cpu"

										lowerCamelCase__					=self.get_dummy_components()

										lowerCamelCase__					=self.pipeline_class(**_lowerCamelCase      )
										lowerCamelCase__					=pipe.to(_lowerCamelCase      )

										pipe.set_progress_bar_config(disable=_lowerCamelCase      )

										lowerCamelCase__					=pipe(**self.get_dummy_inputs(_lowerCamelCase      )      )
										lowerCamelCase__					=output.image_embeds

										lowerCamelCase__					=pipe(
										    **self.get_dummy_inputs(_lowerCamelCase      )	, return_dict=_lowerCamelCase	, )[0]

										lowerCamelCase__					=image[0, -10:]
										lowerCamelCase__					=image_from_tuple[0, -10:]

										assert image.shape == (1, 32)

										lowerCamelCase__					=np.array(
										    [-0.0_5_3_2, 1.7_1_2_0, 0.3_6_5_6, -1.0_8_5_2, -0.8_9_4_6, -1.1_7_5_6, 0.4_3_4_8, 0.2_4_8_2, 0.5_1_4_6, -0.1_1_5_6]      )

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

				@skip_mps
				def 		_a	(      self      ):
										lowerCamelCase__					=torch_device == "cpu"
										lowerCamelCase__					=True
										lowerCamelCase__					=False

										self._test_inference_batch_single_identical(
										    test_max_difference=_lowerCamelCase	, relax_max_difference=_lowerCamelCase	, test_mean_pixel_difference=_lowerCamelCase	, )






				@skip_mps
				def 		_a	(      self      ):
										lowerCamelCase__					=torch_device == "cpu"
										lowerCamelCase__					=False

										self._test_attention_slicing_forward_pass(
										    test_max_difference=_lowerCamelCase	, test_mean_pixel_difference=_lowerCamelCase	, )



"""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    __UpperCAmelCase						(	unittest.TestCase	):
				A__      :   Optional[Any]  							=       MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
				A__      :   int  							=       TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING

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

				def 		_a	(      self	, _lowerCamelCase	, _lowerCamelCase      ):
										lowerCamelCase__					=translator("Some string"      )
										self.assertEqual(_lowerCamelCase	, [{"translation_text": ANY(_lowerCamelCase      )}]      )

										lowerCamelCase__					=translator(["Some string"]      )
										self.assertEqual(_lowerCamelCase	, [{"translation_text": ANY(_lowerCamelCase      )}]      )

										lowerCamelCase__					=translator(["Some string", "other string"]      )
										self.assertEqual(_lowerCamelCase	, [{"translation_text": ANY(_lowerCamelCase      )}, {"translation_text": ANY(_lowerCamelCase      )}]      )

				@require_torch
				def 		_a	(      self      ):
										lowerCamelCase__					=pipeline("translation_en_to_ro"	, model="patrickvonplaten/t5-tiny-random"	, framework="pt"      )
										lowerCamelCase__					=translator("This is a test string"	, max_length=20      )
										self.assertEqual(
										    _lowerCamelCase	, [
										        {
										            "translation_text": (
										                "Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide"
										                " Beide Beide"
										            )
										        }
										    ]	, )

				@require_tf
				def 		_a	(      self      ):
										lowerCamelCase__					=pipeline("translation_en_to_ro"	, model="patrickvonplaten/t5-tiny-random"	, framework="tf"      )
										lowerCamelCase__					=translator("This is a test string"	, max_length=20      )
										self.assertEqual(
										    _lowerCamelCase	, [
										        {
										            "translation_text": (
										                "Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide"
										                " Beide Beide"
										            )
										        }
										    ]	, )

				@require_torch
				def 		_a	(      self      ):
										lowerCamelCase__					=pipeline("translation_en_to_de"	, model="patrickvonplaten/t5-tiny-random"	, framework="pt"      )
										lowerCamelCase__					=translator("This is a test string"	, max_length=20      )
										self.assertEqual(
										    _lowerCamelCase	, [
										        {
										            "translation_text": (
										                "monoton monoton monoton monoton monoton monoton monoton monoton monoton monoton urine urine"
										                " urine urine urine urine urine urine urine"
										            )
										        }
										    ]	, )






				@require_tf
				def 		_a	(      self      ):
										lowerCamelCase__					=pipeline("translation_en_to_de"	, model="patrickvonplaten/t5-tiny-random"	, framework="tf"      )
										lowerCamelCase__					=translator("This is a test string"	, max_length=20      )
										self.assertEqual(
										    _lowerCamelCase	, [
										        {
										            "translation_text": (
										                "monoton monoton monoton monoton monoton monoton monoton monoton monoton monoton urine urine"
										                " urine urine urine urine urine urine urine"
										            )
										        }
										    ]	, )






class    __UpperCAmelCase						(	unittest.TestCase	):

				@require_torch
				@slow
				def 		_a	(      self      ):
										# We don't provide a default for this pair
										with self.assertRaises(_lowerCamelCase      ):
																pipeline(task="translation_cn_to_ar"      )

										# but we do for this one
										lowerCamelCase__					=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 		_a	(      self      ):
										lowerCamelCase__					=MBartForConditionalGeneration.from_pretrained("facebook/mbart-large-50-many-to-many-mmt"      )
										lowerCamelCase__					=MBartaaTokenizerFast.from_pretrained("facebook/mbart-large-50-many-to-many-mmt"      )

										lowerCamelCase__					=pipeline(task="translation"	, model=_lowerCamelCase	, tokenizer=_lowerCamelCase      )
										# Missing src_lang, tgt_lang
										with self.assertRaises(_lowerCamelCase      ):
																translator("This is a test"      )

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

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

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

				@require_torch
				def 		_a	(      self      ):
										lowerCamelCase__					="patrickvonplaten/t5-tiny-random"
										lowerCamelCase__					=pipeline(task="translation_cn_to_ar"	, model=_lowerCamelCase      )
										self.assertEqual(translator._preprocess_params["src_lang"]	, "cn"      )
										self.assertEqual(translator._preprocess_params["tgt_lang"]	, "ar"      )

				@require_torch
				def 		_a	(      self      ):
										lowerCamelCase__					="patrickvonplaten/t5-tiny-random"
										with pytest.warns(_lowerCamelCase	, match=r".*translation_en_to_de.*"      ):
																lowerCamelCase__					=pipeline(task="translation"	, model=_lowerCamelCase      )
										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 		_a	(      self      ):
										with self.assertRaises(_lowerCamelCase      ):
																pipeline(task="translation"      )


