



"""simple docstring"""






import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import XLMRobertaTokenizerFast

from diffusers import DDIMScheduler, KandinskyImgaImgPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandinsky.text_encoder import MCLIPConfig, MultilingualCLIP
from diffusers.utils import floats_tensor, load_image, 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        __snake_case       (	_lowercase  ,     unittest.TestCase):
  snake_case__				:   List[str]  			=						KandinskyImgaImgPipeline
  snake_case__				:   Any  			=						["prompt", "image_embeds", "negative_image_embeds", "image"]
  snake_case__				:   str  			=						[
      "prompt",
      "negative_prompt",
      "image_embeds",
      "negative_image_embeds",
      "image",
  ]
  snake_case__				:   Optional[int]  			=						[
      "generator",
      "height",
      "width",
      "strength",
      "guidance_scale",
      "negative_prompt",
      "num_inference_steps",
      "return_dict",
      "guidance_scale",
      "num_images_per_prompt",
      "output_type",
      "return_dict",
  ]
  snake_case__				:   List[Any]  			=						False




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






         """simple docstring"""






         return 3_2




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






         """simple docstring"""






         return 3_2




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






         """simple docstring"""






         return self.time_input_dim




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






         """simple docstring"""






         return self.time_input_dim * 4




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






         """simple docstring"""






         return 1_0_0




  @property
  def 						SCREAMING_SNAKE_CASE				( self     :						str		):






         """simple docstring"""






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




  @property
  def 						SCREAMING_SNAKE_CASE				( self     :						str		):






         """simple docstring"""






         torch.manual_seed(0		)
         _lowerCamelCase						:				Optional[Any]    			= MCLIPConfig(
             numDims=self.cross_attention_dim						,					transformerDimensions=self.text_embedder_hidden_size						,					hidden_size=self.text_embedder_hidden_size						,					intermediate_size=3_7						,					num_attention_heads=4						,					num_hidden_layers=5						,					vocab_size=1_0_0_5						,					)

         _lowerCamelCase						:				Tuple    			= MultilingualCLIP(__lowerCAmelCase		)
         _lowerCamelCase						:				Optional[int]    			= text_encoder.eval()

         return text_encoder




  @property
  def 						SCREAMING_SNAKE_CASE				( self     :						str		):






         """simple docstring"""






         torch.manual_seed(0		)

         _lowerCamelCase						:				List[str]    			= {
             '''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,
         }

         _lowerCamelCase						:				List[Any]    			= UNetaDConditionModel(**__lowerCAmelCase		)
         return model




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






         """simple docstring"""






         return {
             "block_out_channels": [3_2, 6_4],
             "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": 1_2,
             "out_channels": 3,
             "up_block_types": [
                 "AttnUpDecoderBlock2D",
                 "UpDecoderBlock2D",
             ],
             "vq_embed_dim": 4,
         }




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






         """simple docstring"""






         torch.manual_seed(0		)
         _lowerCamelCase						:				Optional[Any]    			= VQModel(**self.dummy_movq_kwargs		)
         return model




  def 						SCREAMING_SNAKE_CASE				( self     :						Optional[int]		):






         """simple docstring"""






         _lowerCamelCase						:				Dict    			= self.dummy_text_encoder
         _lowerCamelCase						:				Tuple    			= self.dummy_tokenizer
         _lowerCamelCase						:				Dict    			= self.dummy_unet
         _lowerCamelCase						:				int    			= self.dummy_movq

         _lowerCamelCase						:				Any    			= {
             '''num_train_timesteps''': 1_0_0_0,
             '''beta_schedule''': '''linear''',
             '''beta_start''': 0.0_00_85,
             '''beta_end''': 0.0_12,
             '''clip_sample''': False,
             '''set_alpha_to_one''': False,
             '''steps_offset''': 0,
             '''prediction_type''': '''epsilon''',
             '''thresholding''': False,
         }

         _lowerCamelCase						:				Optional[Any]    			= DDIMScheduler(**__lowerCAmelCase		)

         _lowerCamelCase						:				List[str]    			= {
             '''text_encoder''': text_encoder,
             '''tokenizer''': tokenizer,
             '''unet''': unet,
             '''scheduler''': scheduler,
             '''movq''': movq,
         }

         return components




  def 						SCREAMING_SNAKE_CASE				( self     :						str						,					__lowerCAmelCase     :						Any						,					__lowerCAmelCase     :						int=0		):






         """simple docstring"""






         _lowerCamelCase						:				int    			= floats_tensor((1, self.cross_attention_dim)						,					rng=random.Random(__lowerCAmelCase		)		).to(__lowerCAmelCase		)
         _lowerCamelCase						:				Optional[Any]    			= floats_tensor((1, self.cross_attention_dim)						,					rng=random.Random(seed + 1		)		).to(__lowerCAmelCase		)
         # create init_image
         _lowerCamelCase						:				Any    			= floats_tensor((1, 3, 6_4, 6_4)						,					rng=random.Random(__lowerCAmelCase		)		).to(__lowerCAmelCase		)
         _lowerCamelCase						:				Optional[Any]    			= image.cpu().permute(0						,					2						,					3						,					1		)[0]
         _lowerCamelCase						:				Dict    			= Image.fromarray(np.uinta(__lowerCAmelCase		)		).convert('''RGB'''		).resize((2_5_6, 2_5_6)		)

         if str(__lowerCAmelCase		).startswith('''mps'''		):
                _lowerCamelCase						:				Dict    			= torch.manual_seed(__lowerCAmelCase		)
         else:
                _lowerCamelCase						:				int    			= torch.Generator(device=__lowerCAmelCase		).manual_seed(__lowerCAmelCase		)
         _lowerCamelCase						:				Tuple    			= {
             '''prompt''': '''horse''',
             '''image''': init_image,
             '''image_embeds''': image_embeds,
             '''negative_image_embeds''': negative_image_embeds,
             '''generator''': generator,
             '''height''': 6_4,
             '''width''': 6_4,
             '''num_inference_steps''': 1_0,
             '''guidance_scale''': 7.0,
             '''strength''': 0.2,
             '''output_type''': '''np''',
         }
         return inputs




  def 						SCREAMING_SNAKE_CASE				( self     :						str		):






         """simple docstring"""






         _lowerCamelCase						:				Optional[int]    			= '''cpu'''

         _lowerCamelCase						:				List[Any]    			= self.get_dummy_components()

         _lowerCamelCase						:				Optional[int]    			= self.pipeline_class(**__lowerCAmelCase		)
         _lowerCamelCase						:				Optional[Any]    			= pipe.to(__lowerCAmelCase		)

         pipe.set_progress_bar_config(disable=__lowerCAmelCase		)

         _lowerCamelCase						:				Dict    			= pipe(**self.get_dummy_inputs(__lowerCAmelCase		)		)
         _lowerCamelCase						:				Optional[int]    			= output.images

         _lowerCamelCase						:				int    			= pipe(
             **self.get_dummy_inputs(__lowerCAmelCase		)						,					return_dict=__lowerCAmelCase						,					)[0]

         _lowerCamelCase						:				List[Any]    			= image[0, -3:, -3:, -1]
         _lowerCamelCase						:				List[str]    			= image_from_tuple[0, -3:, -3:, -1]

         assert image.shape == (1, 6_4, 6_4, 3)

         _lowerCamelCase						:				List[Any]    			= np.array(
             [0.61_47_49_43, 0.6_07_35_39, 0.43_30_85_44, 0.5_92_82_69, 0.47_49_35_95, 0.46_75_59_73, 0.4_61_38_38, 0.45_36_87_97, 0.50_11_92_33]		)
         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        __snake_case       (	unittest.TestCase):




  def 						SCREAMING_SNAKE_CASE				( self     :						List[str]		):






         """simple docstring"""






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




  def 						SCREAMING_SNAKE_CASE				( self     :						Tuple		):






         """simple docstring"""






         _lowerCamelCase						:				Any    			= load_numpy(
             '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
             '''/kandinsky/kandinsky_img2img_frog.npy'''		)

         _lowerCamelCase						:				Dict    			= load_image(
             '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main''' '''/kandinsky/cat.png'''		)
         _lowerCamelCase						:				List[Any]    			= '''A red cartoon frog, 4k'''

         _lowerCamelCase						:				int    			= KandinskyPriorPipeline.from_pretrained(
             '''kandinsky-community/kandinsky-2-1-prior'''						,					torch_dtype=torch.floataa		)
         pipe_prior.to(__lowerCAmelCase		)

         _lowerCamelCase						:				List[Any]    			= KandinskyImgaImgPipeline.from_pretrained(
             '''kandinsky-community/kandinsky-2-1'''						,					torch_dtype=torch.floataa		)
         _lowerCamelCase						:				Optional[Any]    			= pipeline.to(__lowerCAmelCase		)

         pipeline.set_progress_bar_config(disable=__lowerCAmelCase		)

         _lowerCamelCase						:				Dict    			= torch.Generator(device='''cpu'''		).manual_seed(0		)
         _lowerCamelCase			,    _lowerCamelCase						:				List[str]    			= pipe_prior(
             __lowerCAmelCase						,					generator=__lowerCAmelCase						,					num_inference_steps=5						,					negative_prompt=''''''						,					).to_tuple()

         _lowerCamelCase						:				List[Any]    			= pipeline(
             __lowerCAmelCase						,					image=__lowerCAmelCase						,					image_embeds=__lowerCAmelCase						,					negative_image_embeds=__lowerCAmelCase						,					generator=__lowerCAmelCase						,					num_inference_steps=1_0_0						,					height=7_6_8						,					width=7_6_8						,					strength=0.2						,					output_type='''np'''						,					)

         _lowerCamelCase						:				List[str]    			= output.images[0]

         assert image.shape == (7_6_8, 7_6_8, 3)

         assert_mean_pixel_difference(__lowerCAmelCase						,					__lowerCAmelCase		)





from ....configuration_utils import PretrainedConfig
from ....utils import logging


_UpperCamelCase 			=		logging.get_logger(__name__)

_UpperCamelCase 			=		{
    "CarlCochet/trajectory-transformer-halfcheetah-medium-v2": (
        "https://huggingface.co/CarlCochet/trajectory-transformer-halfcheetah-medium-v2/resolve/main/config.json"
    ),
    # See all TrajectoryTransformer models at https://huggingface.co/models?filter=trajectory_transformer
}







class   __lowercase      (_UpperCAmelCase ):
 _UpperCamelCase			  =  """trajectory_transformer"""
 _UpperCamelCase			  =  ["""past_key_values"""]
 _UpperCamelCase			  =  {
     """hidden_size""": """n_embd""",
     """num_attention_heads""": """n_head""",
     """num_hidden_layers""": """n_layer""",
 }
 def __init__(							self      ,							A_=100      ,							A_=5      ,							A_=1      ,							A_=1      ,							A_=249      ,							A_=6      ,							A_=17      ,							A_=25      ,							A_=4      ,							A_=4      ,							A_=128      ,							A_=0.1      ,							A_=0.1      ,							A_=0.1      ,							A_=0.0_006      ,							A_=512      ,							A_=0.02      ,							A_=1e-12      ,							A_=1      ,							A_=True      ,							A_=1      ,							A_=5_0256      ,							A_=5_0256      ,							**A_      ,							)					->int:



       '''simple docstring'''





       __lowerCAmelCase					:						Any					=					vocab_size
       __lowerCAmelCase					:						Tuple					=					action_weight
       __lowerCAmelCase					:						Tuple					=					reward_weight
       __lowerCAmelCase					:						Union[str, Any]					=					value_weight
       __lowerCAmelCase					:						List[str]					=					max_position_embeddings
       __lowerCAmelCase					:						str					=					block_size
       __lowerCAmelCase					:						Optional[Any]					=					action_dim
       __lowerCAmelCase					:						Union[str, Any]					=					observation_dim
       __lowerCAmelCase					:						Union[str, Any]					=					transition_dim
       __lowerCAmelCase					:						Dict					=					learning_rate
       __lowerCAmelCase					:						Any					=					n_layer
       __lowerCAmelCase					:						Any					=					n_head
       __lowerCAmelCase					:						Optional[int]					=					n_embd
       __lowerCAmelCase					:						str					=					embd_pdrop
       __lowerCAmelCase					:						Dict					=					attn_pdrop
       __lowerCAmelCase					:						Optional[int]					=					resid_pdrop
       __lowerCAmelCase					:						Union[str, Any]					=					initializer_range
       __lowerCAmelCase					:						Optional[int]					=					layer_norm_eps
       __lowerCAmelCase					:						Any					=					kaiming_initializer_range
       __lowerCAmelCase					:						List[str]					=					use_cache
       super().__init__(pad_token_id=A_      ,							bos_token_id=A_      ,							eos_token_id=A_      ,							**A_					)


