






'''simple docstring'''
from pathlib import Path
from typing import List

from transformers import is_torch_available, is_vision_available
from transformers.testing_utils import get_tests_dir, is_tool_test
from transformers.tools.agent_types import AGENT_TYPE_MAPPING, AgentAudio, AgentImage, AgentText


if is_torch_available():
 import torch

if is_vision_available():
 from PIL import Image


UpperCamelCase__ =			['text', 'image', 'audio']




def 		__SCREAMING_SNAKE_CASE					(    _UpperCamelCase							):


   """simple docstring"""




   lowercase_	:    List[Any]           =     []

   for input_type in input_types:
      if input_type == "text":
         inputs.append("Text input"							)
      elif input_type == "image":
         inputs.append(
             Image.open(Path(get_tests_dir("fixtures/tests_samples/COCO"							)							) / "000000039769.png"							).resize((512, 512)							)							)
      elif input_type == "audio":
         inputs.append(torch.ones(3000							)							)
      elif isinstance(_UpperCamelCase						,		_UpperCamelCase							):
         inputs.append(create_inputs(_UpperCamelCase							)							)
      else:
         raise ValueError(F"""Invalid type requested: {input_type}"""							)

   return inputs




def 		__SCREAMING_SNAKE_CASE					(    _UpperCamelCase							):


   """simple docstring"""




   lowercase_	:    Optional[int]           =     []

   for output in outputs:
      if isinstance(_UpperCamelCase						,		(str, AgentText)							):
         output_types.append("text"							)
      elif isinstance(_UpperCamelCase						,		(Image.Image, AgentImage)							):
         output_types.append("image"							)
      elif isinstance(_UpperCamelCase						,		(torch.Tensor, AgentAudio)							):
         output_types.append("audio"							)
      else:
         raise ValueError(F"""Invalid output: {output}"""							)

   return output_types





@is_tool_test
class        _UpperCAmelCase						:




    def 				lowerCAmelCase__				(			self  :       List[Any]			):






       '''simple docstring'''






       self.assertTrue(hasattr(self.tool  ,     "inputs"			)			)
       self.assertTrue(hasattr(self.tool  ,     "outputs"			)			)

       lowercase_	:    Optional[Any]           =     self.tool.inputs
       for _input in inputs:
          if isinstance(_input  ,     a			):
             for __input in _input:
                self.assertTrue(__input in authorized_types			)
          else:
             self.assertTrue(_input in authorized_types			)

       lowercase_	:    Any           =     self.tool.outputs
       for _output in outputs:
          self.assertTrue(_output in authorized_types			)




    def 				lowerCAmelCase__				(			self  :       Any			):






       '''simple docstring'''






       lowercase_	:    List[str]           =     create_inputs(self.tool.inputs			)
       lowercase_	:    List[str]           =     self.tool(*a			)

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

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




    def 				lowerCAmelCase__				(			self  :       List[str]			):






       '''simple docstring'''






       self.assertTrue(hasattr(self.tool  ,     "description"			)			)
       self.assertTrue(hasattr(self.tool  ,     "default_checkpoint"			)			)
       self.assertTrue(self.tool.description.startswith("This is a tool that"			)			)




    def 				lowerCAmelCase__				(			self  :       Any			):






       '''simple docstring'''






       lowercase_	:    Any           =     create_inputs(self.tool.inputs			)
       lowercase_	:    str           =     self.tool(*a			)

       if not isinstance(a  ,     a			):
          lowercase_	:    List[Any]           =     [outputs]

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

       for output, output_type in zip(a  ,     self.tool.outputs			):
          lowercase_	:    int           =     AGENT_TYPE_MAPPING[output_type]
          self.assertTrue(isinstance(a  ,     a			)			)





    def 				lowerCAmelCase__				(			self  :       Tuple			):






       '''simple docstring'''






       lowercase_	:    Dict           =     create_inputs(self.tool.inputs			)

       lowercase_	:    Optional[int]           =     []

       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
       lowercase_	:    Any           =     self.tool(*a			)

       if not isinstance(a  ,     a			):
          lowercase_	:    Any           =     [outputs]

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












'''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 KandinskyVaaPriorPipeline, 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						(		snake_case				,					unittest.TestCase			):
    __lowerCamelCase:   Dict		       =    KandinskyVaaPriorPipeline
    __lowerCamelCase:   Optional[int]		       =    ['prompt']
    __lowerCamelCase:   Any		       =    ['prompt', 'negative_prompt']
    __lowerCamelCase:   List[Any]		       =    [
        'num_images_per_prompt',
        'generator',
        'num_inference_steps',
        'latents',
        'negative_prompt',
        'guidance_scale',
        'output_type',
        'return_dict',
    ]
    __lowerCamelCase:   List[Any]		       =    False




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






       '''simple docstring'''






       return 3_2




    @property
    def 				lowerCAmelCase__				(			self  :       Any			):






       '''simple docstring'''






       return 3_2




    @property
    def 				lowerCAmelCase__				(			self  :       Any			):






       '''simple docstring'''






       return self.time_input_dim




    @property
    def 				lowerCAmelCase__				(			self  :       str			):






       '''simple docstring'''






       return self.time_input_dim * 4




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






       '''simple docstring'''






       return 1_0_0




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






       '''simple docstring'''






       lowercase_	:    Any           =     CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"			)
       return tokenizer




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






       '''simple docstring'''






       torch.manual_seed(0			)
       lowercase_	:    Tuple           =     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(a			)




    @property
    def 				lowerCAmelCase__				(			self  :       Tuple			):






       '''simple docstring'''






       torch.manual_seed(0			)

       lowercase_	:    List[str]           =     {
           "num_attention_heads": 2,
           "attention_head_dim": 1_2,
           "embedding_dim": self.text_embedder_hidden_size,
           "num_layers": 1,
       }

       lowercase_	:    Union[str, Any]           =     PriorTransformer(**a			)
       # 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
       lowercase_	:    List[Any]           =     nn.Parameter(torch.ones(model.clip_std.shape			)			)
       return model




    @property
    def 				lowerCAmelCase__				(			self  :       Tuple			):






       '''simple docstring'''






       torch.manual_seed(0			)
       lowercase_	:    Dict           =     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  ,     )

       lowercase_	:    Optional[Any]           =     CLIPVisionModelWithProjection(a			)
       return model




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






       '''simple docstring'''






       lowercase_	:    List[str]           =     CLIPImageProcessor(
           crop_size=2_2_4  ,     do_center_crop=a  ,     do_normalize=a  ,     do_resize=a  ,     image_mean=[0.4814_5466, 0.457_8275, 0.4082_1073]  ,     image_std=[0.2686_2954, 0.2613_0258, 0.2757_7711]  ,     resample=3  ,     size=2_2_4  ,     )

       return image_processor




    def 				lowerCAmelCase__				(			self  :       List[str]			):






       '''simple docstring'''






       lowercase_	:    Any           =     self.dummy_prior
       lowercase_	:    Optional[Any]           =     self.dummy_image_encoder
       lowercase_	:    List[Any]           =     self.dummy_text_encoder
       lowercase_	:    Any           =     self.dummy_tokenizer
       lowercase_	:    Optional[Any]           =     self.dummy_image_processor

       lowercase_	:    List[str]           =     UnCLIPScheduler(
           variance_type="fixed_small_log"  ,     prediction_type="sample"  ,     num_train_timesteps=1_0_0_0  ,     clip_sample=a  ,     clip_sample_range=10.0  ,     )

       lowercase_	:    List[Any]           =     {
           "prior": prior,
           "image_encoder": image_encoder,
           "text_encoder": text_encoder,
           "tokenizer": tokenizer,
           "scheduler": scheduler,
           "image_processor": image_processor,
       }

       return components




    def 				lowerCAmelCase__				(			self  :       Any  ,     a  :       Dict  ,     a  :       Dict=0			):






       '''simple docstring'''






       if str(a			).startswith("mps"			):
          lowercase_	:    int           =     torch.manual_seed(a			)
       else:
          lowercase_	:    Optional[Any]           =     torch.Generator(device=a			).manual_seed(a			)
       lowercase_	:    Any           =     {
           "prompt": "horse",
           "generator": generator,
           "guidance_scale": 4.0,
           "num_inference_steps": 2,
           "output_type": "np",
       }
       return inputs




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






       '''simple docstring'''






       lowercase_	:    str           =     "cpu"

       lowercase_	:    Any           =     self.get_dummy_components()

       lowercase_	:    int           =     self.pipeline_class(**a			)
       lowercase_	:    Any           =     pipe.to(a			)

       pipe.set_progress_bar_config(disable=a			)

       lowercase_	:    Any           =     pipe(**self.get_dummy_inputs(a			)			)
       lowercase_	:    List[Any]           =     output.image_embeds

       lowercase_	:    str           =     pipe(
           **self.get_dummy_inputs(a			)  ,     return_dict=a  ,     )[0]

       lowercase_	:    Any           =     image[0, -1_0:]
       lowercase_	:    Dict           =     image_from_tuple[0, -1_0:]

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

       lowercase_	:    int           =     np.array(
           [-0.0532, 1.7120, 0.3656, -1.0852, -0.8946, -1.1756, 0.4348, 0.2482, 0.5146, -0.1156]			)

       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 				lowerCAmelCase__				(			self  :       Optional[Any]			):






       '''simple docstring'''






       lowercase_	:    int           =     torch_device == "cpu"
       lowercase_	:    Tuple           =     True
       lowercase_	:    str           =     False

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





    @skip_mps
    def 				lowerCAmelCase__				(			self  :       Union[str, Any]			):






       '''simple docstring'''






       lowercase_	:    Any           =     torch_device == "cpu"
       lowercase_	:    int           =     False

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




