"""simple docstring"""




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 				__lowerCAmelCase					(    __A,		unittest.TestCase  ):



 '''simple docstring'''
 _lowerCamelCase  :		List[Any]			      =	KandinskyPipeline
 _lowerCamelCase  :		Dict			      =	[
     'prompt',
     'image_embeds',
     'negative_image_embeds',
 ]
 _lowerCamelCase  :		Tuple			      =	['prompt', 'negative_prompt', 'image_embeds', 'negative_image_embeds']
 _lowerCamelCase  :		str			      =	[
     'generator',
     'height',
     'width',
     'latents',
     'guidance_scale',
     'negative_prompt',
     'num_inference_steps',
     'return_dict',
     'guidance_scale',
     'num_images_per_prompt',
     'output_type',
     'return_dict',
 ]
 _lowerCamelCase  :		List[str]			      =	False

 @property
 def    __snake_case   (     self    :						Dict					)	->							List[str]:
        return 3_2

 @property
 def    __snake_case   (     self    :						List[Any]					)	->							Optional[Any]:
        return 3_2

 @property
 def    __snake_case   (     self    :						Union[str, Any]					)	->							Optional[Any]:
        return self.time_input_dim

 @property
 def    __snake_case   (     self    :						Any					)	->							Union[str, Any]:
        return self.time_input_dim * 4

 @property
 def    __snake_case   (     self    :						List[Any]					)	->							Union[str, Any]:
        return 1_0_0

 @property
 def    __snake_case   (     self    :						Any					)	->							Optional[Any]:
        _UpperCamelCase       :    Any					  =					XLMRobertaTokenizerFast.from_pretrained('YiYiXu/tiny-random-mclip-base'					)
        return tokenizer

 @property
 def    __snake_case   (     self    :						Union[str, Any]					)	->							Dict:
        torch.manual_seed(0					)
        _UpperCamelCase       :    Dict					  =					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    ,     )

        _UpperCamelCase       :    List[str]					  =					MultilingualCLIP(lowerCamelCase					)
        _UpperCamelCase       :    int					  =					text_encoder.eval()

        return text_encoder

 @property
 def    __snake_case   (     self    :						Dict					)	->							Tuple:
        torch.manual_seed(0					)

        _UpperCamelCase       :    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,
        }

        _UpperCamelCase       :    Tuple					  =					UNetaDConditionModel(**lowerCamelCase					)
        return model

 @property
 def    __snake_case   (     self    :						Dict					)	->							Any:
        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    __snake_case   (     self    :						Any					)	->							List[str]:
        torch.manual_seed(0					)
        _UpperCamelCase       :    int					  =					VQModel(**self.dummy_movq_kwargs					)
        return model

 def    __snake_case   (     self    :						str					)	->							Any:
        _UpperCamelCase       :    List[Any]					  =					self.dummy_text_encoder
        _UpperCamelCase       :    str					  =					self.dummy_tokenizer
        _UpperCamelCase       :    Union[str, Any]					  =					self.dummy_unet
        _UpperCamelCase       :    Optional[Any]					  =					self.dummy_movq

        _UpperCamelCase       :    Dict					  =					DDIMScheduler(
            num_train_timesteps=1_0_0_0    ,     beta_schedule='linear'    ,     beta_start=0.0_0_0_8_5    ,     beta_end=0.0_1_2    ,     clip_sample=lowerCamelCase    ,     set_alpha_to_one=lowerCamelCase    ,     steps_offset=1    ,     prediction_type='epsilon'    ,     thresholding=lowerCamelCase    ,     )

        _UpperCamelCase       :    Union[str, Any]					  =					{
            "text_encoder": text_encoder,
            "tokenizer": tokenizer,
            "unet": unet,
            "scheduler": scheduler,
            "movq": movq,
        }
        return components

 def    __snake_case   (     self    :						Optional[Any]    ,     lowerCamelCase    :						int    ,     lowerCamelCase    :						str=0					)	->							Optional[Any]:
        _UpperCamelCase       :    Optional[int]					  =					floats_tensor((1, self.cross_attention_dim)    ,     rng=random.Random(lowerCamelCase					)					).to(lowerCamelCase					)
        _UpperCamelCase       :    int					  =					floats_tensor((1, self.cross_attention_dim)    ,     rng=random.Random(seed + 1					)					).to(lowerCamelCase					)
        if str(lowerCamelCase					).startswith('mps'					):
               _UpperCamelCase       :    Any					  =					torch.manual_seed(lowerCamelCase					)
        else:
               _UpperCamelCase       :    Any					  =					torch.Generator(device=lowerCamelCase					).manual_seed(lowerCamelCase					)
        _UpperCamelCase       :    Tuple					  =					{
            "prompt": "horse",
            "image_embeds": image_embeds,
            "negative_image_embeds": negative_image_embeds,
            "generator": generator,
            "height": 6_4,
            "width": 6_4,
            "guidance_scale": 4.0,
            "num_inference_steps": 2,
            "output_type": "np",
        }
        return inputs

 def    __snake_case   (     self    :						List[str]					)	->							Dict:
        _UpperCamelCase       :    Tuple					  =					"cpu"

        _UpperCamelCase       :    str					  =					self.get_dummy_components()

        _UpperCamelCase       :    Tuple					  =					self.pipeline_class(**lowerCamelCase					)
        _UpperCamelCase       :    List[Any]					  =					pipe.to(lowerCamelCase					)

        pipe.set_progress_bar_config(disable=lowerCamelCase					)

        _UpperCamelCase       :    Tuple					  =					pipe(**self.get_dummy_inputs(lowerCamelCase					)					)
        _UpperCamelCase       :    Optional[Any]					  =					output.images

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

        _UpperCamelCase       :    Optional[int]					  =					image[0, -3:, -3:, -1]
        _UpperCamelCase       :    Union[str, Any]					  =					image_from_tuple[0, -3:, -3:, -1]

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

        _UpperCamelCase       :    Dict					  =					np.array(
            [0.3_2_8_6_6_3, 1.0, 0.2_3_2_1_6_8_7_3, 1.0, 0.9_2_7_1_7_5_6_4, 0.4_6_3_9_0_4_6, 0.9_6_8_9_4_7_7_7, 0.3_1_7_1_3_3_7_8, 0.6_2_9_3_9_5_3]					)

        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 				__lowerCAmelCase					(    unittest.TestCase  ):



 '''simple docstring'''

 def    __snake_case   (     self    :						List[str]					)	->							List[str]:
        super().tearDown()
        gc.collect()
        torch.cuda.empty_cache()

 def    __snake_case   (     self    :						Any					)	->							Any:
        _UpperCamelCase       :    Optional[int]					  =					load_numpy(
            'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
            '/kandinsky/kandinsky_text2img_cat_fp16.npy'					)

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

        _UpperCamelCase       :    Optional[int]					  =					KandinskyPipeline.from_pretrained('kandinsky-community/kandinsky-2-1'    ,     torch_dtype=torch.floataa					)
        _UpperCamelCase       :    List[str]					  =					pipeline.to(lowerCamelCase					)
        pipeline.set_progress_bar_config(disable=lowerCamelCase					)

        _UpperCamelCase       :    List[str]					  =					"red cat, 4k photo"

        _UpperCamelCase       :    Any					  =					torch.Generator(device='cuda'					).manual_seed(0					)
        _UpperCamelCase       :    int					  =					pipe_prior(
            lowerCamelCase    ,     generator=lowerCamelCase    ,     num_inference_steps=5    ,     negative_prompt=''    ,     ).to_tuple()

        _UpperCamelCase       :    Optional[int]					  =					torch.Generator(device='cuda'					).manual_seed(0					)
        _UpperCamelCase       :    List[Any]					  =					pipeline(
            lowerCamelCase    ,     image_embeds=lowerCamelCase    ,     negative_image_embeds=lowerCamelCase    ,     generator=lowerCamelCase    ,     num_inference_steps=1_0_0    ,     output_type='np'    ,     )

        _UpperCamelCase       :    Optional[Any]					  =					output.images[0]

        assert image.shape == (5_1_2, 5_1_2, 3)

        assert_mean_pixel_difference(lowerCamelCase    ,     lowerCamelCase					)




import torch
import trajectory.utils as utils

from transformers import TrajectoryTransformerModel




class 				__lowerCAmelCase					(    utils.Parser  ):



 '''simple docstring'''
 _lowerCamelCase  :		str			      =	"halfcheetah-medium-expert-v2"
 _lowerCamelCase  :		str			      =	"config.offline"







def SCREAMING_SNAKE_CASE			(       lowercase_:		Optional[Any]						,  lowercase_:		Tuple						,  lowercase_:		Dict						,  lowercase_:		Union[str, Any]						,  lowercase_:		List[Any]			):
       _UpperCamelCase  , _UpperCamelCase       :    List[Any]					  =					utils.load_model(lowercase_						,  lowercase_						,  lowercase_						,  epoch=lowercase_						,  device=lowercase_			)
       _UpperCamelCase       :    Tuple					  =					TrajectoryTransformerModel(gpt.config			)

       trajectory_transformer.tok_emb.load_state_dict(gpt.tok_emb.state_dict()			)
       _UpperCamelCase       :    Any					  =					gpt.pos_emb
       trajectory_transformer.drop.load_state_dict(gpt.drop.state_dict()			)
       trajectory_transformer.ln_f.load_state_dict(gpt.ln_f.state_dict()			)
       trajectory_transformer.head.load_state_dict(gpt.head.state_dict()			)

       for i, block in enumerate(gpt.blocks			):
              trajectory_transformer.blocks[i].lna.load_state_dict(gpt.blocks[i].lna.state_dict()			)
              trajectory_transformer.blocks[i].lna.load_state_dict(gpt.blocks[i].lna.state_dict()			)
              trajectory_transformer.blocks[i].attn.load_state_dict(gpt.blocks[i].attn.state_dict()			)

              trajectory_transformer.blocks[i].la.load_state_dict(gpt.blocks[i].mlp[0].state_dict()			)
              trajectory_transformer.blocks[i].act.load_state_dict(gpt.blocks[i].mlp[1].state_dict()			)
              trajectory_transformer.blocks[i].la.load_state_dict(gpt.blocks[i].mlp[2].state_dict()			)
              trajectory_transformer.blocks[i].drop.load_state_dict(gpt.blocks[i].mlp[3].state_dict()			)

       torch.save(trajectory_transformer.state_dict()						,  'pytorch_model.bin'			)


if __name__ == "__main__":
 UpperCAmelCase     						=			Parser().parse_args('''plan''')
 convert_trajectory_transformer_original_pytorch_checkpoint_to_pytorch(
     args.logbase, args.dataset, args.gpt_loadpath, args.gpt_epoch, args.device
 )



