

import gc
import random
import unittest

import numpy as np
import torch
from transformers import (
    CLIPImageProcessor,
    CLIPTextConfig,
    CLIPTextModelWithProjection,
    CLIPTokenizer,
    CLIPVisionConfig,
    CLIPVisionModelWithProjection,
)

from diffusers import (
    DiffusionPipeline,
    UnCLIPImageVariationPipeline,
    UnCLIPScheduler,
    UNetaDConditionModel,
    UNetaDModel,
)
from diffusers.pipelines.unclip.text_proj import UnCLIPTextProjModel
from diffusers.utils import floats_tensor, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, load_image, require_torch_gpu, skip_mps

from ..pipeline_params import IMAGE_VARIATION_BATCH_PARAMS, IMAGE_VARIATION_PARAMS
from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()







class 					lowercase							(			lowercase_      ,    unittest.TestCase     ):
				__SCREAMING_SNAKE_CASE   :					Optional[Any]			     =     UnCLIPImageVariationPipeline
				__SCREAMING_SNAKE_CASE   :					Tuple			     =     IMAGE_VARIATION_PARAMS - {'''height''', '''width''', '''guidance_scale'''}
				__SCREAMING_SNAKE_CASE   :					List[str]			     =     IMAGE_VARIATION_BATCH_PARAMS

				__SCREAMING_SNAKE_CASE   :					List[str]			     =     [
				    '''generator''',
				    '''return_dict''',
				    '''decoder_num_inference_steps''',
				    '''super_res_num_inference_steps''',
				]
				__SCREAMING_SNAKE_CASE   :					Optional[int]			     =     False




				@property
				def        a				(   self			):
											return 32




				@property
				def        a				(   self			):
											return 32




				@property
				def        a				(   self			):
											return self.time_input_dim




				@property
				def        a				(   self			):
											return self.time_input_dim * 4




				@property
				def        a				(   self			):
											return 100




				@property
				def        a				(   self			):
											snake_case_     							=       CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'			)
											return tokenizer




				@property
				def        a				(   self			):
											torch.manual_seed(0			)
											snake_case_     							=       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=37 ,  layer_norm_eps=1e-0_5 ,  num_attention_heads=4 ,  num_hidden_layers=5 ,  pad_token_id=1 ,  vocab_size=1000 ,  )
											return CLIPTextModelWithProjection(snake_case			)




				@property
				def        a				(   self			):
											torch.manual_seed(0			)
											snake_case_     							=       CLIPVisionConfig(
											    hidden_size=self.text_embedder_hidden_size ,  projection_dim=self.text_embedder_hidden_size ,  num_hidden_layers=5 ,  num_attention_heads=4 ,  image_size=32 ,  intermediate_size=37 ,  patch_size=1 ,  )
											return CLIPVisionModelWithProjection(snake_case			)




				@property
				def        a				(   self			):
											torch.manual_seed(0			)

											snake_case_     							=       {
											    'clip_embeddings_dim': self.text_embedder_hidden_size,
											    'time_embed_dim': self.time_embed_dim,
											    'cross_attention_dim': self.cross_attention_dim,
											}

											snake_case_     							=       UnCLIPTextProjModel(**snake_case			)
											return model




				@property
				def        a				(   self			):
											torch.manual_seed(0			)

											snake_case_     							=       {
											    'sample_size': 32,
											    # RGB in channels
											    'in_channels': 3,
											    # Out channels is double in channels because predicts mean and variance
											    'out_channels': 6,
											    '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,
											    'cross_attention_dim': self.cross_attention_dim,
											    'attention_head_dim': 4,
											    'resnet_time_scale_shift': 'scale_shift',
											    'class_embed_type': 'identity',
											}

											snake_case_     							=       UNetaDConditionModel(**snake_case			)
											return model




				@property
				def        a				(   self			):
											return {
											    "sample_size": 64,
											    "layers_per_block": 1,
											    "down_block_types": ("ResnetDownsampleBlock2D", "ResnetDownsampleBlock2D"),
											    "up_block_types": ("ResnetUpsampleBlock2D", "ResnetUpsampleBlock2D"),
											    "block_out_channels": (self.block_out_channels_a, self.block_out_channels_a * 2),
											    "in_channels": 6,
											    "out_channels": 3,
											}




				@property
				def        a				(   self			):
											torch.manual_seed(0			)

											snake_case_     							=       UNetaDModel(**self.dummy_super_res_kwargs			)
											return model




				@property
				def        a				(   self			):
											# seeded differently to get different unet than `self.dummy_super_res_first`
											torch.manual_seed(1			)

											snake_case_     							=       UNetaDModel(**self.dummy_super_res_kwargs			)
											return model




				def        a				(   self			):
											snake_case_     							=       self.dummy_decoder
											snake_case_     							=       self.dummy_text_proj
											snake_case_     							=       self.dummy_text_encoder
											snake_case_     							=       self.dummy_tokenizer
											snake_case_     							=       self.dummy_super_res_first
											snake_case_     							=       self.dummy_super_res_last

											snake_case_     							=       UnCLIPScheduler(
											    variance_type='learned_range' ,  prediction_type='epsilon' ,  num_train_timesteps=1000 ,  )

											snake_case_     							=       UnCLIPScheduler(
											    variance_type='fixed_small_log' ,  prediction_type='epsilon' ,  num_train_timesteps=1000 ,  )

											snake_case_     							=       CLIPImageProcessor(crop_size=32 ,  size=32			)

											snake_case_     							=       self.dummy_image_encoder

											return {
											    "decoder": decoder,
											    "text_encoder": text_encoder,
											    "tokenizer": tokenizer,
											    "text_proj": text_proj,
											    "feature_extractor": feature_extractor,
											    "image_encoder": image_encoder,
											    "super_res_first": super_res_first,
											    "super_res_last": super_res_last,
											    "decoder_scheduler": decoder_scheduler,
											    "super_res_scheduler": super_res_scheduler,
											}




				def        a				(   self ,  snake_case ,  snake_case=0 ,  snake_case=True			):
											snake_case_     							=       floats_tensor((1, 3, 32, 32) ,  rng=random.Random(snake_case			)			).to(snake_case			)
											if str(snake_case			).startswith('mps'			):
																		snake_case_     							=       torch.manual_seed(snake_case			)
											else:
																		snake_case_     							=       torch.Generator(device=snake_case			).manual_seed(snake_case			)

											if pil_image:
																		snake_case_     							=       input_image * 0.5 + 0.5
																		snake_case_     							=       input_image.clamp(0 ,  1			)
																		snake_case_     							=       input_image.cpu().permute(0 ,  2 ,  3 ,  1			).float().numpy()
																		snake_case_     							=       DiffusionPipeline.numpy_to_pil(snake_case			)[0]

											return {
											    "image": input_image,
											    "generator": generator,
											    "decoder_num_inference_steps": 2,
											    "super_res_num_inference_steps": 2,
											    "output_type": "np",
											}




				def        a				(   self			):
											snake_case_     							=       'cpu'

											snake_case_     							=       self.get_dummy_components()

											snake_case_     							=       self.pipeline_class(**snake_case			)
											snake_case_     							=       pipe.to(snake_case			)

											pipe.set_progress_bar_config(disable=snake_case			)

											snake_case_     							=       self.get_dummy_inputs(snake_case ,  pil_image=snake_case			)

											snake_case_     							=       pipe(**snake_case			)
											snake_case_     							=       output.images

											snake_case_     							=       self.get_dummy_inputs(snake_case ,  pil_image=snake_case			)

											snake_case_     							=       pipe(
											    **snake_case ,  return_dict=snake_case ,  )[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(
											    [
											        0.99_97,
											        0.00_02,
											        0.99_97,
											        0.99_97,
											        0.99_69,
											        0.00_23,
											        0.99_97,
											        0.99_69,
											        0.99_70,
											    ]			)

											assert np.abs(image_slice.flatten() - expected_slice			).max() < 1e-2
											assert np.abs(image_from_tuple_slice.flatten() - expected_slice			).max() < 1e-2




				def        a				(   self			):
											snake_case_     							=       'cpu'

											snake_case_     							=       self.get_dummy_components()

											snake_case_     							=       self.pipeline_class(**snake_case			)
											snake_case_     							=       pipe.to(snake_case			)

											pipe.set_progress_bar_config(disable=snake_case			)

											snake_case_     							=       self.get_dummy_inputs(snake_case ,  pil_image=snake_case			)

											snake_case_     							=       pipe(**snake_case			)
											snake_case_     							=       output.images

											snake_case_     							=       self.get_dummy_inputs(snake_case ,  pil_image=snake_case			)

											snake_case_     							=       pipe(
											    **snake_case ,  return_dict=snake_case ,  )[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([0.99_97, 0.00_03, 0.99_97, 0.99_97, 0.99_70, 0.00_24, 0.99_97, 0.99_71, 0.99_71]			)

											assert np.abs(image_slice.flatten() - expected_slice			).max() < 1e-2
											assert np.abs(image_from_tuple_slice.flatten() - expected_slice			).max() < 1e-2




				def        a				(   self			):
											snake_case_     							=       'cpu'

											snake_case_     							=       self.get_dummy_components()

											snake_case_     							=       self.pipeline_class(**snake_case			)
											snake_case_     							=       pipe.to(snake_case			)

											pipe.set_progress_bar_config(disable=snake_case			)

											snake_case_     							=       self.get_dummy_inputs(snake_case ,  pil_image=snake_case			)
											snake_case_     							=       [
											    pipeline_inputs['image'],
											    pipeline_inputs['image'],
											]

											snake_case_     							=       pipe(**snake_case			)
											snake_case_     							=       output.images

											snake_case_     							=       self.get_dummy_inputs(snake_case ,  pil_image=snake_case			)
											snake_case_     							=       [
											    tuple_pipeline_inputs['image'],
											    tuple_pipeline_inputs['image'],
											]

											snake_case_     							=       pipe(
											    **snake_case ,  return_dict=snake_case ,  )[0]

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

											assert image.shape == (2, 64, 64, 3)

											snake_case_     							=       np.array(
											    [
											        0.99_97,
											        0.99_89,
											        0.00_08,
											        0.00_21,
											        0.99_60,
											        0.00_18,
											        0.00_14,
											        0.00_02,
											        0.99_33,
											    ]			)

											assert np.abs(image_slice.flatten() - expected_slice			).max() < 1e-2
											assert np.abs(image_from_tuple_slice.flatten() - expected_slice			).max() < 1e-2




				def        a				(   self			):
											snake_case_     							=       torch.device('cpu'			)







											class 					lowercase							:
															__SCREAMING_SNAKE_CASE   :					Any			     =     1



											snake_case_     							=       self.get_dummy_components()

											snake_case_     							=       self.pipeline_class(**snake_case			)
											snake_case_     							=       pipe.to(snake_case			)

											pipe.set_progress_bar_config(disable=snake_case			)

											snake_case_     							=       torch.Generator(device=snake_case			).manual_seed(0			)
											snake_case_     							=       pipe.decoder.dtype
											snake_case_     							=       1

											snake_case_     							=       (
											    batch_size,
											    pipe.decoder.config.in_channels,
											    pipe.decoder.config.sample_size,
											    pipe.decoder.config.sample_size,
											)
											snake_case_     							=       pipe.prepare_latents(
											    snake_case ,  dtype=snake_case ,  device=snake_case ,  generator=snake_case ,  latents=snake_case ,  scheduler=DummyScheduler()			)

											snake_case_     							=       (
											    batch_size,
											    pipe.super_res_first.config.in_channels // 2,
											    pipe.super_res_first.config.sample_size,
											    pipe.super_res_first.config.sample_size,
											)
											snake_case_     							=       pipe.prepare_latents(
											    snake_case ,  dtype=snake_case ,  device=snake_case ,  generator=snake_case ,  latents=snake_case ,  scheduler=DummyScheduler()			)

											snake_case_     							=       self.get_dummy_inputs(snake_case ,  pil_image=snake_case			)

											snake_case_     							=       pipe(
											    **snake_case ,  decoder_latents=snake_case ,  super_res_latents=snake_case			).images

											snake_case_     							=       self.get_dummy_inputs(snake_case ,  pil_image=snake_case			)
											# Don't pass image, instead pass embedding
											snake_case_     							=       pipeline_inputs.pop('image'			)
											snake_case_     							=       pipe.image_encoder(snake_case			).image_embeds

											snake_case_     							=       pipe(
											    **snake_case ,  decoder_latents=snake_case ,  super_res_latents=snake_case ,  image_embeddings=snake_case ,  ).images

											# make sure passing text embeddings manually is identical
											assert np.abs(img_out_a - img_out_a			).max() < 1e-4




				@skip_mps
				def        a				(   self			):
											snake_case_     							=       torch_device == 'cpu'

											# Check is relaxed because there is not a torch 2.0 sliced attention added kv processor
											snake_case_     							=       1e-2

											self._test_attention_slicing_forward_pass(
											    test_max_difference=snake_case ,  expected_max_diff=snake_case			)




				@skip_mps
				def        a				(   self			):
											snake_case_     							=       torch_device == 'cpu'
											snake_case_     							=       True
											snake_case_     							=       [
											    'decoder_num_inference_steps',
											    'super_res_num_inference_steps',
											]

											self._test_inference_batch_single_identical(
											    test_max_difference=snake_case ,  relax_max_difference=snake_case ,  additional_params_copy_to_batched_inputs=snake_case ,  )




				def        a				(   self			):
											snake_case_     							=       [
											    'decoder_num_inference_steps',
											    'super_res_num_inference_steps',
											]

											if torch_device == "mps":
																		# TODO: MPS errors with larger batch sizes
																		snake_case_     							=       [2, 3]
																		self._test_inference_batch_consistent(
																		    batch_sizes=snake_case ,  additional_params_copy_to_batched_inputs=snake_case ,  )
											else:
																		self._test_inference_batch_consistent(
																		    additional_params_copy_to_batched_inputs=snake_case			)




				@skip_mps
				def        a				(   self			):
											return super().test_dict_tuple_outputs_equivalent()




				@skip_mps
				def        a				(   self			):
											return super().test_save_load_local()




				@skip_mps
				def        a				(   self			):
											return super().test_save_load_optional_components()







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




				def        a				(   self			):
											# clean up the VRAM after each test
											super().tearDown()
											gc.collect()
											torch.cuda.empty_cache()




				def        a				(   self			):
											snake_case_     							=       load_image(
											    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/unclip/cat.png'			)
											snake_case_     							=       load_numpy(
											    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
											    '/unclip/karlo_v1_alpha_cat_variation_fp16.npy'			)

											snake_case_     							=       UnCLIPImageVariationPipeline.from_pretrained(
											    'kakaobrain/karlo-v1-alpha-image-variations' ,  torch_dtype=torch.floataa			)
											snake_case_     							=       pipeline.to(snake_case			)
											pipeline.set_progress_bar_config(disable=snake_case			)

											snake_case_     							=       torch.Generator(device='cpu'			).manual_seed(0			)
											snake_case_     							=       pipeline(
											    snake_case ,  generator=snake_case ,  output_type='np' ,  )

											snake_case_     							=       output.images[0]

											assert image.shape == (256, 256, 3)

											assert_mean_pixel_difference(snake_case ,  snake_case ,  15			)







import argparse

import torch

from transformers import BertConfig, BertForPreTraining, load_tf_weights_in_bert
from transformers.utils import logging


logging.set_verbosity_info()





def    __lowerCamelCase      (					UpperCamelCase__  ,				UpperCamelCase__  ,				UpperCamelCase__ ):







							'''simple docstring'''






							snake_case_     							=       BertConfig.from_json_file(UpperCamelCase__ )
							print(F'''Building PyTorch model from configuration: {config}''' )
							snake_case_     							=       BertForPreTraining(UpperCamelCase__ )

							# Load weights from tf checkpoint
							load_tf_weights_in_bert(UpperCamelCase__  ,				UpperCamelCase__  ,				UpperCamelCase__ )

							# Save pytorch-model
							print(F'''Save PyTorch model to {pytorch_dump_path}''' )
							torch.save(model.state_dict()  ,				UpperCamelCase__ )


if __name__ == "__main__":
					_UpperCAmelCase						:  Union[str, Any]									=			argparse.ArgumentParser()
					# Required parameters
					parser.add_argument(
					    """--tf_checkpoint_path""", default=None, type=str, required=True, help="""Path to the TensorFlow checkpoint path."""
					)
					parser.add_argument(
					    """--bert_config_file""",
					    default=None,
					    type=str,
					    required=True,
					    help=(
					        """The config json file corresponding to the pre-trained BERT model. \n"""
					        """This specifies the model architecture."""
					    ),
					)
					parser.add_argument(
					    """--pytorch_dump_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
					)
					_UpperCAmelCase						:  Any									=			parser.parse_args()
					convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)




