
import random
import unittest

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

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

from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()





class 							A__		(     __SCREAMING_SNAKE_CASE  ,       unittest.TestCase):
						_UpperCAmelCase    :   Optional[int]   	=      KandinskyVaaPriorEmbaEmbPipeline
						_UpperCAmelCase    :   Tuple   	=      ["""prompt""", """image"""]
						_UpperCAmelCase    :   Dict   	=      ["""prompt""", """image"""]
						_UpperCAmelCase    :   Optional[int]   	=      [
						    """num_images_per_prompt""",
						    """strength""",
						    """generator""",
						    """num_inference_steps""",
						    """latents""",
						    """negative_prompt""",
						    """guidance_scale""",
						    """output_type""",
						    """return_dict""",
						]
						_UpperCAmelCase    :   Optional[Any]   	=      False
						@property
						def 						UpperCamelCase__							(				self				):
							return 3_2
						@property
						def 						UpperCamelCase__							(				self				):
							return 3_2
						@property
						def 						UpperCamelCase__							(				self				):
							return self.time_input_dim
						@property
						def 						UpperCamelCase__							(				self				):
							return self.time_input_dim * 4
						@property
						def 						UpperCamelCase__							(				self				):
							return 1_0_0
						@property
						def 						UpperCamelCase__							(				self				):
							lowerCamelCase		:     Any          =				CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""				)
							return tokenizer
						@property
						def 						UpperCamelCase__							(				self				):
							torch.manual_seed(0				)
							lowerCamelCase		:     Optional[int]          =				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=3_7   ,  layer_norm_eps=1e-05   ,  num_attention_heads=4   ,  num_hidden_layers=5   ,  pad_token_id=1   ,  vocab_size=1_0_0_0   ,  )
							return CLIPTextModelWithProjection(__magic_name__				)
						@property
						def 						UpperCamelCase__							(				self				):
							torch.manual_seed(0				)

							lowerCamelCase		:     int          =				{
							    """num_attention_heads""": 2,
							    """attention_head_dim""": 1_2,
							    """embedding_dim""": self.text_embedder_hidden_size,
							    """num_layers""": 1,
							}

							lowerCamelCase		:     Optional[int]          =				PriorTransformer(**__magic_name__				)
							# 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		:     Optional[Any]          =				nn.Parameter(torch.ones(model.clip_std.shape				)				)
							return model
						@property
						def 						UpperCamelCase__							(				self				):
							torch.manual_seed(0				)
							lowerCamelCase		:     Optional[Any]          =				CLIPVisionConfig(
							    hidden_size=self.text_embedder_hidden_size   ,  image_size=2_2_4   ,  projection_dim=self.text_embedder_hidden_size   ,  intermediate_size=3_7   ,  num_attention_heads=4   ,  num_channels=3   ,  num_hidden_layers=5   ,  patch_size=1_4   ,  )

							lowerCamelCase		:     Any          =				CLIPVisionModelWithProjection(__magic_name__				)
							return model
						@property
						def 						UpperCamelCase__							(				self				):
							lowerCamelCase		:     List[str]          =				CLIPImageProcessor(
							    crop_size=2_2_4   ,  do_center_crop=__magic_name__   ,  do_normalize=__magic_name__   ,  do_resize=__magic_name__   ,  image_mean=[0.48_145_466, 0.4_578_275, 0.40_821_073]   ,  image_std=[0.26_862_954, 0.26_130_258, 0.27_577_711]   ,  resample=3   ,  size=2_2_4   ,  )

							return image_processor
						def 						UpperCamelCase__							(				self				):
							lowerCamelCase		:     List[Any]          =				self.dummy_prior
							lowerCamelCase		:     str          =				self.dummy_image_encoder
							lowerCamelCase		:     str          =				self.dummy_text_encoder
							lowerCamelCase		:     int          =				self.dummy_tokenizer
							lowerCamelCase		:     Tuple          =				self.dummy_image_processor

							lowerCamelCase		:     List[Any]          =				UnCLIPScheduler(
							    variance_type="""fixed_small_log"""   ,  prediction_type="""sample"""   ,  num_train_timesteps=1_0_0_0   ,  clip_sample=__magic_name__   ,  clip_sample_range=10.0   ,  )

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

							return components
						def 						UpperCamelCase__							(				self   ,  __magic_name__   ,  __magic_name__=0				):
							if str(__magic_name__				).startswith("""mps"""				):
								lowerCamelCase		:     int          =				torch.manual_seed(__magic_name__				)
							else:
								lowerCamelCase		:     Optional[Any]          =				torch.Generator(device=__magic_name__				).manual_seed(__magic_name__				)

							lowerCamelCase		:     Dict          =				floats_tensor((1, 3, 6_4, 6_4)   ,  rng=random.Random(__magic_name__				)				).to(__magic_name__				)
							lowerCamelCase		:     Dict          =				image.cpu().permute(0   ,  2   ,  3   ,  1				)[0]
							lowerCamelCase		:     str          =				Image.fromarray(np.uinta(__magic_name__				)				).convert("""RGB"""				).resize((2_5_6, 2_5_6)				)

							lowerCamelCase		:     str          =				{
							    """prompt""": """horse""",
							    """image""": init_image,
							    """strength""": 0.5,
							    """generator""": generator,
							    """guidance_scale""": 4.0,
							    """num_inference_steps""": 2,
							    """output_type""": """np""",
							}
							return inputs
						def 						UpperCamelCase__							(				self				):
							lowerCamelCase		:     Optional[Any]          =				"""cpu"""

							lowerCamelCase		:     int          =				self.get_dummy_components()

							lowerCamelCase		:     Optional[int]          =				self.pipeline_class(**__magic_name__				)
							lowerCamelCase		:     Union[str, Any]          =				pipe.to(__magic_name__				)

							pipe.set_progress_bar_config(disable=__magic_name__				)

							lowerCamelCase		:     Union[str, Any]          =				pipe(**self.get_dummy_inputs(__magic_name__				)				)
							lowerCamelCase		:     Union[str, Any]          =				output.image_embeds

							lowerCamelCase		:     Any          =				pipe(
							    **self.get_dummy_inputs(__magic_name__				)   ,  return_dict=__magic_name__   ,  )[0]

							lowerCamelCase		:     Any          =				image[0, -1_0:]
							lowerCamelCase		:     Optional[Any]          =				image_from_tuple[0, -1_0:]

							assert image.shape == (1, 3_2)

							lowerCamelCase		:     List[Any]          =				np.array(
							    [
							        0.1_071_284,
							        1.3_330_271,
							        0.61_260_223,
							        -0.6_691_065,
							        -0.3_846_852,
							        -1.0_303_661,
							        0.22_716_111,
							        0.03_348_901,
							        0.30_040_675,
							        -0.24_805_029,
							    ]				)

							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 						UpperCamelCase__							(				self				):
							lowerCamelCase		:     List[str]          =				torch_device == """cpu"""
							lowerCamelCase		:     List[str]          =				True
							lowerCamelCase		:     int          =				False

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






						@skip_mps
						def 						UpperCamelCase__							(				self				):
							lowerCamelCase		:     Union[str, Any]          =				torch_device == """cpu"""
							lowerCamelCase		:     int          =				False

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






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 							A__		(     unittest.TestCase):
      _UpperCAmelCase    :   Tuple   	=      MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
      _UpperCAmelCase    :   Optional[Any]   	=      TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
      def 						UpperCamelCase__							(				self   ,  __magic_name__   ,  __magic_name__   ,  __magic_name__				):
       if isinstance(model.config   ,  __magic_name__				):
        lowerCamelCase      ,   lowerCamelCase		:     Union[str, Any]          =				list(tokenizer.lang_code_to_id.keys()				)[:2]
        lowerCamelCase		:     Tuple          =				TranslationPipeline(model=__magic_name__   ,  tokenizer=__magic_name__   ,  src_lang=__magic_name__   ,  tgt_lang=__magic_name__				)
       else:
        lowerCamelCase		:     Tuple          =				TranslationPipeline(model=__magic_name__   ,  tokenizer=__magic_name__				)
       return translator, ["Some string", "Some other text"]
      def 						UpperCamelCase__							(				self   ,  __magic_name__   ,  __magic_name__				):
       lowerCamelCase		:     Optional[Any]          =				translator("""Some string"""				)
       self.assertEqual(__magic_name__   ,  [{"""translation_text""": ANY(__magic_name__				)}]				)

       lowerCamelCase		:     Dict          =				translator(["""Some string"""]				)
       self.assertEqual(__magic_name__   ,  [{"""translation_text""": ANY(__magic_name__				)}]				)

       lowerCamelCase		:     Any          =				translator(["""Some string""", """other string"""]				)
       self.assertEqual(__magic_name__   ,  [{"""translation_text""": ANY(__magic_name__				)}, {"""translation_text""": ANY(__magic_name__				)}]				)
      @require_torch
      def 						UpperCamelCase__							(				self				):
       lowerCamelCase		:     Any          =				pipeline("""translation_en_to_ro"""   ,  model="""patrickvonplaten/t5-tiny-random"""   ,  framework="""pt"""				)
       lowerCamelCase		:     Union[str, Any]          =				translator("""This is a test string"""   ,  max_length=2_0				)
       self.assertEqual(
           __magic_name__   ,  [
               {
                   """translation_text""": (
                       """Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide"""
                       """ Beide Beide"""
                   )
               }
           ]   ,  )
      @require_tf
      def 						UpperCamelCase__							(				self				):
       lowerCamelCase		:     str          =				pipeline("""translation_en_to_ro"""   ,  model="""patrickvonplaten/t5-tiny-random"""   ,  framework="""tf"""				)
       lowerCamelCase		:     Tuple          =				translator("""This is a test string"""   ,  max_length=2_0				)
       self.assertEqual(
           __magic_name__   ,  [
               {
                   """translation_text""": (
                       """Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide"""
                       """ Beide Beide"""
                   )
               }
           ]   ,  )
      @require_torch
      def 						UpperCamelCase__							(				self				):
       lowerCamelCase		:     int          =				pipeline("""translation_en_to_de"""   ,  model="""patrickvonplaten/t5-tiny-random"""   ,  framework="""pt"""				)
       lowerCamelCase		:     List[Any]          =				translator("""This is a test string"""   ,  max_length=2_0				)
       self.assertEqual(
           __magic_name__   ,  [
               {
                   """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				):
       lowerCamelCase		:     Union[str, Any]          =				pipeline("""translation_en_to_de"""   ,  model="""patrickvonplaten/t5-tiny-random"""   ,  framework="""tf"""				)
       lowerCamelCase		:     Union[str, Any]          =				translator("""This is a test string"""   ,  max_length=2_0				)
       self.assertEqual(
           __magic_name__   ,  [
               {
                   """translation_text""": (
                       """monoton monoton monoton monoton monoton monoton monoton monoton monoton monoton urine urine"""
                       """ urine urine urine urine urine urine urine"""
                   )
               }
           ]   ,  )





class 							A__		(     unittest.TestCase):
      @require_torch
      @slow
      def 						UpperCamelCase__							(				self				):
       # We don't provide a default for this pair
       with self.assertRaises(__magic_name__				):
        pipeline(task="""translation_cn_to_ar"""				)

       # but we do for this one
       lowerCamelCase		:     Optional[Any]          =				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				):
       lowerCamelCase		:     Optional[int]          =				MBartForConditionalGeneration.from_pretrained("""facebook/mbart-large-50-many-to-many-mmt"""				)
       lowerCamelCase		:     List[Any]          =				MBartaaTokenizerFast.from_pretrained("""facebook/mbart-large-50-many-to-many-mmt"""				)

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

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

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

       # src_lang, tgt_lang can be defined at pipeline call time
       lowerCamelCase		:     str          =				pipeline(task="""translation"""   ,  model=__magic_name__   ,  tokenizer=__magic_name__   ,  src_lang="""en_XX"""   ,  tgt_lang="""ar_AR"""				)
       lowerCamelCase		:     Union[str, Any]          =				translator("""This is a test"""				)
       self.assertEqual(__magic_name__   ,  [{"""translation_text""": """هذا إختبار"""}]				)
      @require_torch
      def 						UpperCamelCase__							(				self				):
       lowerCamelCase		:     List[str]          =				"""patrickvonplaten/t5-tiny-random"""
       lowerCamelCase		:     Tuple          =				pipeline(task="""translation_cn_to_ar"""   ,  model=__magic_name__				)
       self.assertEqual(translator._preprocess_params["""src_lang"""]   ,  """cn"""				)
       self.assertEqual(translator._preprocess_params["""tgt_lang"""]   ,  """ar"""				)
      @require_torch
      def 						UpperCamelCase__							(				self				):
       lowerCamelCase		:     Dict          =				"""patrickvonplaten/t5-tiny-random"""
       with pytest.warns(__magic_name__   ,  match=r""".*translation_en_to_de.*"""				):
        lowerCamelCase		:     Optional[int]          =				pipeline(task="""translation"""   ,  model=__magic_name__				)
       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				):
       with self.assertRaises(__magic_name__				):
        pipeline(task="""translation"""				)




