
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      SCREAMING_SNAKE_CASE__  (			UpperCAmelCase							,unittest.TestCase      ):





	'''simple docstring'''



	_UpperCAmelCase :	Union[str, Any]					     =      KandinskyPipeline
	_UpperCAmelCase :	List[str]					     =      [
	    "prompt",
	    "image_embeds",
	    "negative_image_embeds",
	]
	_UpperCAmelCase :	List[Any]					     =      ["prompt", "negative_prompt", "image_embeds", "negative_image_embeds"]
	_UpperCAmelCase :	List[str]					     =      [
	    "generator",
	    "height",
	    "width",
	    "latents",
	    "guidance_scale",
	    "negative_prompt",
	    "num_inference_steps",
	    "return_dict",
	    "guidance_scale",
	    "num_images_per_prompt",
	    "output_type",
	    "return_dict",
	]
	_UpperCAmelCase :	List[Any]					     =      False



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


				'''simple docstring'''


				return 32



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


				'''simple docstring'''


				return 32



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


				'''simple docstring'''


				return self.time_input_dim



	@property
	def 				A							(						self			:					Tuple			):


				'''simple docstring'''


				return self.time_input_dim * 4



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


				'''simple docstring'''


				return 100



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


				'''simple docstring'''


				_snake_case						=    XLMRobertaTokenizerFast.from_pretrained('YiYiXu/tiny-random-mclip-base'			)
				return tokenizer



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


				'''simple docstring'''


				torch.manual_seed(0			)
				_snake_case						=    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=1_005    ,						)

				_snake_case						=    MultilingualCLIP(lowercase			)
				_snake_case						=    text_encoder.eval()

				return text_encoder



	@property
	def 				A							(						self			:					int			):


				'''simple docstring'''


				torch.manual_seed(0			)

				_snake_case						=    {
				    '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,
				}

				_snake_case						=    UNetaDConditionModel(**lowercase			)
				return model



	@property
	def 				A							(						self			:					Dict			):


				'''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 				A							(						self			:					Dict			):


				'''simple docstring'''


				torch.manual_seed(0			)
				_snake_case						=    VQModel(**self.dummy_movq_kwargs			)
				return model



	def 				A							(						self			:					str			):


				'''simple docstring'''


				_snake_case						=    self.dummy_text_encoder
				_snake_case						=    self.dummy_tokenizer
				_snake_case						=    self.dummy_unet
				_snake_case						=    self.dummy_movq

				_snake_case						=    DDIMScheduler(
				    num_train_timesteps=1_000    ,						beta_schedule='linear'    ,						beta_start=0.00085    ,						beta_end=0.012    ,						clip_sample=lowercase    ,						set_alpha_to_one=lowercase    ,						steps_offset=1    ,						prediction_type='epsilon'    ,						thresholding=lowercase    ,						)

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



	def 				A							(						self			:					Dict    ,						lowercase			:					List[str]    ,						lowercase			:					Tuple=0			):


				'''simple docstring'''


				_snake_case						=    floats_tensor((1, self.cross_attention_dim)    ,						rng=random.Random(lowercase			)			).to(lowercase			)
				_snake_case						=    floats_tensor((1, self.cross_attention_dim)    ,						rng=random.Random(seed + 1			)			).to(lowercase			)
				if str(lowercase			).startswith('mps'			):
							_snake_case						=    torch.manual_seed(lowercase			)
				else:
							_snake_case						=    torch.Generator(device=lowercase			).manual_seed(lowercase			)
				_snake_case						=    {
				    '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 				A							(						self			:					Tuple			):


				'''simple docstring'''


				_snake_case						=    'cpu'

				_snake_case						=    self.get_dummy_components()

				_snake_case						=    self.pipeline_class(**lowercase			)
				_snake_case						=    pipe.to(lowercase			)

				pipe.set_progress_bar_config(disable=lowercase			)

				_snake_case						=    pipe(**self.get_dummy_inputs(lowercase			)			)
				_snake_case						=    output.images

				_snake_case						=    pipe(
				    **self.get_dummy_inputs(lowercase			)    ,						return_dict=lowercase    ,						)[0]

				_snake_case						=    image[0, -3:, -3:, -1]
				_snake_case						=    image_from_tuple[0, -3:, -3:, -1]

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

				_snake_case						=    np.array(
				    [0.328663, 1.0, 0.23216873, 1.0, 0.92717564, 0.4639046, 0.96894777, 0.31713378, 0.6293953]			)

				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      SCREAMING_SNAKE_CASE__  (			unittest.TestCase      ):





	'''simple docstring'''



	def 				A							(						self			:					Optional[Any]			):


				'''simple docstring'''


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





	def 				A							(						self			:					Any			):


				'''simple docstring'''


				_snake_case						=    load_numpy(
				    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
				    '/kandinsky/kandinsky_text2img_cat_fp16.npy'			)

				_snake_case						=    KandinskyPriorPipeline.from_pretrained(
				    'kandinsky-community/kandinsky-2-1-prior'    ,						torch_dtype=torch.floataa			)
				pipe_prior.to(lowercase			)

				_snake_case						=    KandinskyPipeline.from_pretrained('kandinsky-community/kandinsky-2-1'    ,						torch_dtype=torch.floataa			)
				_snake_case						=    pipeline.to(lowercase			)
				pipeline.set_progress_bar_config(disable=lowercase			)

				_snake_case						=    'red cat, 4k photo'

				_snake_case						=    torch.Generator(device='cuda'			).manual_seed(0			)
				_snake_case			,      _snake_case						=    pipe_prior(
				    lowercase    ,						generator=lowercase    ,						num_inference_steps=5    ,						negative_prompt=''    ,						).to_tuple()

				_snake_case						=    torch.Generator(device='cuda'			).manual_seed(0			)
				_snake_case						=    pipeline(
				    lowercase    ,						image_embeds=lowercase    ,						negative_image_embeds=lowercase    ,						generator=lowercase    ,						num_inference_steps=100    ,						output_type='np'    ,						)

				_snake_case						=    output.images[0]

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

				assert_mean_pixel_difference(lowercase    ,						lowercase			)

import argparse

import torch

from transformers import ImageGPTConfig, ImageGPTForCausalLM, load_tf_weights_in_imagegpt
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging


logging.set_verbosity_info()






def  a_	( __lowercase     :			Optional[Any] ,      __lowercase     :			Union[str, Any] ,      __lowercase     :			str				)				->						Any:
			# Construct configuration depending on size
			_snake_case						=    {'small': (512, 8, 24), 'medium': (1_024, 8, 36), 'large': (1_536, 16, 48)}
			_snake_case			,      _snake_case			,      _snake_case						=    MODELS[model_size]  # set model hyperparameters
			_snake_case						=    ImageGPTConfig(n_embd=__lowercase ,      n_layer=__lowercase ,      n_head=__lowercase				)
			_snake_case						=    ImageGPTForCausalLM(__lowercase				)

			# Load weights from numpy
			load_tf_weights_in_imagegpt(__lowercase ,      __lowercase ,      __lowercase				)

			# Save pytorch-model
			_snake_case						=    pytorch_dump_folder_path + '/' + WEIGHTS_NAME
			_snake_case						=    pytorch_dump_folder_path + '/' + CONFIG_NAME
			print(f'''Save PyTorch model to {pytorch_weights_dump_path}'''				)
			torch.save(model.state_dict() ,      __lowercase				)
			print(f'''Save configuration file to {pytorch_config_dump_path}'''				)
			with open(__lowercase ,      'w' ,      encoding='utf-8'				) as f:
						f.write(config.to_json_string()				)


if __name__ == "__main__":
			_lowerCamelCase :       List[Any]     					=    argparse.ArgumentParser()
			# Required parameters
			parser.add_argument(
			    '''--imagegpt_checkpoint_path''',
			    default=None,
			    type=str,
			    required=True,
			    help='''Path to the TensorFlow checkpoint path.''',
			)
			parser.add_argument(
			    '''--model_size''',
			    default=None,
			    type=str,
			    required=True,
			    help='''Size of the model (can be either \'small\', \'medium\' or \'large\').''',
			)
			parser.add_argument(
			    '''--pytorch_dump_folder_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
			)
			_lowerCamelCase :       Optional[Any]     					=    parser.parse_args()
			convert_imagegpt_checkpoint_to_pytorch(
			    args.imagegpt_checkpoint_path, args.model_size, args.pytorch_dump_folder_path
			)