
import gc
import random
import unittest

import numpy as np
import torch
from transformers import XLMRobertaTokenizerFast

from diffusers import DDIMScheduler, KandinskyPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandinsky.text_encoder import MCLIPConfig, MultilingualCLIP
from diffusers.utils import floats_tensor, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()





class   __UpperCamelCase				(    __UpperCAmelCase				,		unittest.TestCase   ):






     """simple docstring"""







     lowerCAmelCase_     =	KandinskyPipeline
     lowerCAmelCase_     =	[
         """prompt""",
         """image_embeds""",
         """negative_image_embeds""",
     ]
     lowerCAmelCase_     =	["""prompt""", """negative_prompt""", """image_embeds""", """negative_image_embeds"""]
     lowerCAmelCase_     =	[
         """generator""",
         """height""",
         """width""",
         """latents""",
         """guidance_scale""",
         """negative_prompt""",
         """num_inference_steps""",
         """return_dict""",
         """guidance_scale""",
         """num_images_per_prompt""",
         """output_type""",
         """return_dict""",
     ]
     lowerCAmelCase_     =	False



     @property
     def 				UpperCAmelCase__    (							self    :    Union[str, Any]				):







       """simple docstring"""




       return 32



     @property
     def 				UpperCAmelCase__    (							self    :    List[str]				):







       """simple docstring"""




       return 32



     @property
     def 				UpperCAmelCase__    (							self    :    List[Any]				):







       """simple docstring"""




       return self.time_input_dim



     @property
     def 				UpperCAmelCase__    (							self    :    Any				):







       """simple docstring"""




       return self.time_input_dim * 4



     @property
     def 				UpperCAmelCase__    (							self    :    List[str]				):







       """simple docstring"""




       return 100



     @property
     def 				UpperCAmelCase__    (							self    :    List[Any]				):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE     :					Optional[Any]			=						XLMRobertaTokenizerFast.from_pretrained('''YiYiXu/tiny-random-mclip-base'''				)
       return tokenizer



     @property
     def 				UpperCAmelCase__    (							self    :    Optional[int]				):







       """simple docstring"""




       torch.manual_seed(0				)
       __SCREAMING_SNAKE_CASE     :					List[str]			=						MCLIPConfig(
           numDims=self.cross_attention_dim      ,		transformerDimensions=self.text_embedder_hidden_size      ,		hidden_size=self.text_embedder_hidden_size      ,		intermediate_size=37      ,		num_attention_heads=4      ,		num_hidden_layers=5      ,		vocab_size=1005      ,		)

       __SCREAMING_SNAKE_CASE     :					str			=						MultilingualCLIP(lowerCamelCase__				)
       __SCREAMING_SNAKE_CASE     :					Union[str, Any]			=						text_encoder.eval()

       return text_encoder



     @property
     def 				UpperCAmelCase__    (							self    :    Optional[Any]				):







       """simple docstring"""




       torch.manual_seed(0				)

       __SCREAMING_SNAKE_CASE     :					Union[str, Any]			=						{
           '''in_channels''': 4,
           # Out channels is double in channels because predicts mean and variance
           '''out_channels''': 8,
           '''addition_embed_type''': '''text_image''',
           '''down_block_types''': ('''ResnetDownsampleBlock2D''', '''SimpleCrossAttnDownBlock2D'''),
           '''up_block_types''': ('''SimpleCrossAttnUpBlock2D''', '''ResnetUpsampleBlock2D'''),
           '''mid_block_type''': '''UNetMidBlock2DSimpleCrossAttn''',
           '''block_out_channels''': (self.block_out_channels_a, self.block_out_channels_a * 2),
           '''layers_per_block''': 1,
           '''encoder_hid_dim''': self.text_embedder_hidden_size,
           '''encoder_hid_dim_type''': '''text_image_proj''',
           '''cross_attention_dim''': self.cross_attention_dim,
           '''attention_head_dim''': 4,
           '''resnet_time_scale_shift''': '''scale_shift''',
           '''class_embed_type''': None,
       }

       __SCREAMING_SNAKE_CASE     :					Union[str, Any]			=						UNetaDConditionModel(**lowerCamelCase__				)
       return model



     @property
     def 				UpperCAmelCase__    (							self    :    Optional[int]				):







       """simple docstring"""




       return {
           "block_out_channels": [32, 64],
           "down_block_types": ["DownEncoderBlock2D", "AttnDownEncoderBlock2D"],
           "in_channels": 3,
           "latent_channels": 4,
           "layers_per_block": 1,
           "norm_num_groups": 8,
           "norm_type": "spatial",
           "num_vq_embeddings": 12,
           "out_channels": 3,
           "up_block_types": [
               "AttnUpDecoderBlock2D",
               "UpDecoderBlock2D",
           ],
           "vq_embed_dim": 4,
       }



     @property
     def 				UpperCAmelCase__    (							self    :    Dict				):







       """simple docstring"""




       torch.manual_seed(0				)
       __SCREAMING_SNAKE_CASE     :					Union[str, Any]			=						VQModel(**self.dummy_movq_kwargs				)
       return model



     def 				UpperCAmelCase__    (							self    :    Union[str, Any]				):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE     :					List[Any]			=						self.dummy_text_encoder
       __SCREAMING_SNAKE_CASE     :					int			=						self.dummy_tokenizer
       __SCREAMING_SNAKE_CASE     :					Dict			=						self.dummy_unet
       __SCREAMING_SNAKE_CASE     :					List[Any]			=						self.dummy_movq

       __SCREAMING_SNAKE_CASE     :					Optional[Any]			=						DDIMScheduler(
           num_train_timesteps=1000      ,		beta_schedule='''linear'''      ,		beta_start=0.0_00_85      ,		beta_end=0.0_12      ,		clip_sample=lowerCamelCase__      ,		set_alpha_to_one=lowerCamelCase__      ,		steps_offset=1      ,		prediction_type='''epsilon'''      ,		thresholding=lowerCamelCase__      ,		)

       __SCREAMING_SNAKE_CASE     :					Any			=						{
           '''text_encoder''': text_encoder,
           '''tokenizer''': tokenizer,
           '''unet''': unet,
           '''scheduler''': scheduler,
           '''movq''': movq,
       }
       return components



     def 				UpperCAmelCase__    (							self    :    Optional[int]      ,		_A    :    List[str]      ,		_A    :    Union[str, Any]=0				):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE     :					str			=						floats_tensor((1, self.cross_attention_dim)      ,		rng=random.Random(lowerCamelCase__				)				).to(lowerCamelCase__				)
       __SCREAMING_SNAKE_CASE     :					int			=						floats_tensor((1, self.cross_attention_dim)      ,		rng=random.Random(seed + 1				)				).to(lowerCamelCase__				)
       if str(lowerCamelCase__				).startswith('''mps'''				):
         __SCREAMING_SNAKE_CASE     :					Dict			=						torch.manual_seed(lowerCamelCase__				)
       else:
         __SCREAMING_SNAKE_CASE     :					Optional[int]			=						torch.Generator(device=lowerCamelCase__				).manual_seed(lowerCamelCase__				)
       __SCREAMING_SNAKE_CASE     :					str			=						{
           '''prompt''': '''horse''',
           '''image_embeds''': image_embeds,
           '''negative_image_embeds''': negative_image_embeds,
           '''generator''': generator,
           '''height''': 64,
           '''width''': 64,
           '''guidance_scale''': 4.0,
           '''num_inference_steps''': 2,
           '''output_type''': '''np''',
       }
       return inputs




     def 				UpperCAmelCase__    (							self    :    List[str]				):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE     :					Any			=						'''cpu'''

       __SCREAMING_SNAKE_CASE     :					Union[str, Any]			=						self.get_dummy_components()

       __SCREAMING_SNAKE_CASE     :					str			=						self.pipeline_class(**lowerCamelCase__				)
       __SCREAMING_SNAKE_CASE     :					List[str]			=						pipe.to(lowerCamelCase__				)

       pipe.set_progress_bar_config(disable=lowerCamelCase__				)

       __SCREAMING_SNAKE_CASE     :					Any			=						pipe(**self.get_dummy_inputs(lowerCamelCase__				)				)
       __SCREAMING_SNAKE_CASE     :					Optional[int]			=						output.images

       __SCREAMING_SNAKE_CASE     :					Optional[int]			=						pipe(
           **self.get_dummy_inputs(lowerCamelCase__				)      ,		return_dict=lowerCamelCase__      ,		)[0]

       __SCREAMING_SNAKE_CASE     :					Optional[Any]			=						image[0, -3:, -3:, -1]
       __SCREAMING_SNAKE_CASE     :					Optional[int]			=						image_from_tuple[0, -3:, -3:, -1]

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

       __SCREAMING_SNAKE_CASE     :					Any			=						np.array(
           [0.32_86_63, 1.0, 0.23_21_68_73, 1.0, 0.92_71_75_64, 0.4_63_90_46, 0.96_89_47_77, 0.31_71_33_78, 0.6_29_39_53]				)

       assert (
           np.abs(image_slice.flatten() - expected_slice				).max() < 1e-2
       ), F''' expected_slice {expected_slice}, but got {image_slice.flatten()}'''
       assert (
           np.abs(image_from_tuple_slice.flatten() - expected_slice				).max() < 1e-2
       ), F''' expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}'''






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






     """simple docstring"""







     def 				UpperCAmelCase__    (							self    :    Dict				):







       """simple docstring"""




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




     def 				UpperCAmelCase__    (							self    :    Tuple				):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE     :					Optional[Any]			=						load_numpy(
           '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
           '''/kandinsky/kandinsky_text2img_cat_fp16.npy'''				)

       __SCREAMING_SNAKE_CASE     :					Optional[Any]			=						KandinskyPriorPipeline.from_pretrained(
           '''kandinsky-community/kandinsky-2-1-prior'''      ,		torch_dtype=torch.floataa				)
       pipe_prior.to(lowerCamelCase__				)

       __SCREAMING_SNAKE_CASE     :					Any			=						KandinskyPipeline.from_pretrained('''kandinsky-community/kandinsky-2-1'''      ,		torch_dtype=torch.floataa				)
       __SCREAMING_SNAKE_CASE     :					str			=						pipeline.to(lowerCamelCase__				)
       pipeline.set_progress_bar_config(disable=lowerCamelCase__				)

       __SCREAMING_SNAKE_CASE     :					Tuple			=						'''red cat, 4k photo'''

       __SCREAMING_SNAKE_CASE     :					Optional[int]			=						torch.Generator(device='''cuda'''				).manual_seed(0				)
       __SCREAMING_SNAKE_CASE,						__SCREAMING_SNAKE_CASE     :					str			=						pipe_prior(
           lowerCamelCase__      ,		generator=lowerCamelCase__      ,		num_inference_steps=5      ,		negative_prompt=''''''      ,		).to_tuple()

       __SCREAMING_SNAKE_CASE     :					Optional[Any]			=						torch.Generator(device='''cuda'''				).manual_seed(0				)
       __SCREAMING_SNAKE_CASE     :					Optional[Any]			=						pipeline(
           lowerCamelCase__      ,		image_embeds=lowerCamelCase__      ,		negative_image_embeds=lowerCamelCase__      ,		generator=lowerCamelCase__      ,		num_inference_steps=100      ,		output_type='''np'''      ,		)

       __SCREAMING_SNAKE_CASE     :					Any			=						output.images[0]

       assert image.shape == (512, 512, 3)

       assert_mean_pixel_difference(lowerCamelCase__      ,		lowerCamelCase__				)


import unittest

from transformers import load_tool
from transformers.tools.agent_types import AGENT_TYPE_MAPPING

from .test_tools_common import ToolTesterMixin, output_types





class   __UpperCamelCase				(    unittest.TestCase				,		lowerCAmelCase__   ):






     """simple docstring"""







     def 				UpperCAmelCase__    (							self    :    Optional[Any]				):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE     :					str			=						load_tool('''translation'''				)
       self.tool.setup()
       __SCREAMING_SNAKE_CASE     :					List[Any]			=						load_tool('''translation'''      ,		remote=_A				)



     def 				UpperCAmelCase__    (							self    :    List[Any]				):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE     :					Any			=						self.tool('''Hey, what\'s up?'''      ,		src_lang='''English'''      ,		tgt_lang='''French'''				)
       self.assertEqual(_A      ,		'''- Hé, comment ça va?'''				)



     def 				UpperCAmelCase__    (							self    :    List[str]				):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE     :					List[str]			=						self.remote_tool('''Hey, what\'s up?'''      ,		src_lang='''English'''      ,		tgt_lang='''French'''				)
       self.assertEqual(_A      ,		'''- Hé, comment ça va?'''				)



     def 				UpperCAmelCase__    (							self    :    Optional[Any]				):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE     :					int			=						self.tool(text='''Hey, what\'s up?'''      ,		src_lang='''English'''      ,		tgt_lang='''French'''				)
       self.assertEqual(_A      ,		'''- Hé, comment ça va?'''				)



     def 				UpperCAmelCase__    (							self    :    int				):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE     :					List[Any]			=						self.remote_tool(text='''Hey, what\'s up?'''      ,		src_lang='''English'''      ,		tgt_lang='''French'''				)
       self.assertEqual(_A      ,		'''- Hé, comment ça va?'''				)



     def 				UpperCAmelCase__    (							self    :    Union[str, Any]				):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE     :					Optional[int]			=						['''Hey, what\'s up?''', '''English''', '''Spanish''']
       __SCREAMING_SNAKE_CASE     :					List[Any]			=						self.tool(*_A				)

       # There is a single output
       if len(self.tool.outputs				) == 1:
         __SCREAMING_SNAKE_CASE     :					Any			=						[outputs]

       self.assertListEqual(output_types(_A				)      ,		self.tool.outputs				)



     def 				UpperCAmelCase__    (							self    :    Union[str, Any]				):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE     :					int			=						['''Hey, what\'s up?''', '''English''', '''Spanish''']
       __SCREAMING_SNAKE_CASE     :					List[str]			=						self.tool(*_A				)

       if not isinstance(_A      ,		_A				):
         __SCREAMING_SNAKE_CASE     :					Dict			=						[outputs]

       self.assertEqual(len(_A				)      ,		len(self.tool.outputs				)				)

       for output, output_type in zip(_A      ,		self.tool.outputs				):
         __SCREAMING_SNAKE_CASE     :					List[Any]			=						AGENT_TYPE_MAPPING[output_type]
         self.assertTrue(isinstance(_A      ,		_A				)				)




     def 				UpperCAmelCase__    (							self    :    List[str]				):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE     :					Dict			=						['''Hey, what\'s up?''', '''English''', '''Spanish''']

       __SCREAMING_SNAKE_CASE     :					Optional[Any]			=						[]

       for _input, input_type in zip(_A      ,		self.tool.inputs				):
         if isinstance(_A      ,		_A				):
           _inputs.append([AGENT_TYPE_MAPPING[_input_type](_input				) for _input_type in input_type]				)
         else:
           _inputs.append(AGENT_TYPE_MAPPING[input_type](_input				)				)

        # Should not raise an error
       __SCREAMING_SNAKE_CASE     :					Union[str, Any]			=						self.tool(*_A				)

       if not isinstance(_A      ,		_A				):
         __SCREAMING_SNAKE_CASE     :					Dict			=						[outputs]

       self.assertEqual(len(_A				)      ,		len(self.tool.outputs				)				)
