import random
import unittest

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

from diffusers import KandinskyVaaPriorEmbaEmbPipeline, PriorTransformer, UnCLIPScheduler
from diffusers.utils import floats_tensor, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, skip_mps

from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()

class 						_SCREAMING_SNAKE_CASE				(  _lowercase   ,  unittest.TestCase				):






		"""simple docstring"""







		A_    			=   KandinskyVaaPriorEmbaEmbPipeline
		A_    			=   ['''prompt''', '''image''']
		A_    			=   ['''prompt''', '''image''']
		A_    			=   [
		    '''num_images_per_prompt''',
		    '''strength''',
		    '''generator''',
		    '''num_inference_steps''',
		    '''latents''',
		    '''negative_prompt''',
		    '''guidance_scale''',
		    '''output_type''',
		    '''return_dict''',
		]
		A_    			=   False



		@property
		def 					_a					(      self     :					Dict ):






					"""simple docstring"""
					return 32



		@property
		def 					_a					(      self     :					Dict ):






					"""simple docstring"""
					return 32



		@property
		def 					_a					(      self     :					int ):






					"""simple docstring"""
					return self.time_input_dim



		@property
		def 					_a					(      self     :					Dict ):






					"""simple docstring"""
					return self.time_input_dim * 4



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






					"""simple docstring"""
					return 100



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






					"""simple docstring"""
					_snake_case														=   CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip" )
					return tokenizer



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






					"""simple docstring"""
					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-05	,       num_attention_heads=4	,       num_hidden_layers=5	,       pad_token_id=1	,       vocab_size=1_000	,       )
					return CLIPTextModelWithProjection(__SCREAMING_SNAKE_CASE )



		@property
		def 					_a					(      self     :					Tuple ):






					"""simple docstring"""
					torch.manual_seed(0 )

					_snake_case														=   {
					    "num_attention_heads": 2,
					    "attention_head_dim": 12,
					    "embedding_dim": self.text_embedder_hidden_size,
					    "num_layers": 1,
					}

					_snake_case														=   PriorTransformer(**__SCREAMING_SNAKE_CASE )
					# 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
					_snake_case														=   nn.Parameter(torch.ones(model.clip_std.shape ) )
					return model



		@property
		def 					_a					(      self     :					Dict ):






					"""simple docstring"""
					torch.manual_seed(0 )
					_snake_case														=   CLIPVisionConfig(
					    hidden_size=self.text_embedder_hidden_size	,       image_size=224	,       projection_dim=self.text_embedder_hidden_size	,       intermediate_size=37	,       num_attention_heads=4	,       num_channels=3	,       num_hidden_layers=5	,       patch_size=14	,       )

					_snake_case														=   CLIPVisionModelWithProjection(__SCREAMING_SNAKE_CASE )
					return model



		@property
		def 					_a					(      self     :					Any ):






					"""simple docstring"""
					_snake_case														=   CLIPImageProcessor(
					    crop_size=224	,       do_center_crop=__SCREAMING_SNAKE_CASE	,       do_normalize=__SCREAMING_SNAKE_CASE	,       do_resize=__SCREAMING_SNAKE_CASE	,       image_mean=[0.4_8_1_4_5_4_6_6, 0.4_5_7_8_2_7_5, 0.4_0_8_2_1_0_7_3]	,       image_std=[0.2_6_8_6_2_9_5_4, 0.2_6_1_3_0_2_5_8, 0.2_7_5_7_7_7_1_1]	,       resample=3	,       size=224	,       )

					return image_processor



		def 					_a					(      self     :					List[Any] ):






					"""simple docstring"""
					_snake_case														=   self.dummy_prior
					_snake_case														=   self.dummy_image_encoder
					_snake_case														=   self.dummy_text_encoder
					_snake_case														=   self.dummy_tokenizer
					_snake_case														=   self.dummy_image_processor

					_snake_case														=   UnCLIPScheduler(
					    variance_type="fixed_small_log"	,       prediction_type="sample"	,       num_train_timesteps=1_000	,       clip_sample=__SCREAMING_SNAKE_CASE	,       clip_sample_range=1_0.0	,       )

					_snake_case														=   {
					    "prior": prior,
					    "image_encoder": image_encoder,
					    "text_encoder": text_encoder,
					    "tokenizer": tokenizer,
					    "scheduler": scheduler,
					    "image_processor": image_processor,
					}

					return components



		def 					_a					(      self     :					Tuple	,       __SCREAMING_SNAKE_CASE     :					Optional[int]	,       __SCREAMING_SNAKE_CASE     :					Dict=0 ):






					"""simple docstring"""
					if str(__SCREAMING_SNAKE_CASE ).startswith("mps" ):
								_snake_case														=   torch.manual_seed(__SCREAMING_SNAKE_CASE )
					else:
								_snake_case														=   torch.Generator(device=__SCREAMING_SNAKE_CASE ).manual_seed(__SCREAMING_SNAKE_CASE )

					_snake_case														=   floats_tensor((1, 3, 64, 64)	,       rng=random.Random(__SCREAMING_SNAKE_CASE ) ).to(__SCREAMING_SNAKE_CASE )
					_snake_case														=   image.cpu().permute(0	,       2	,       3	,       1 )[0]
					_snake_case														=   Image.fromarray(np.uinta(__SCREAMING_SNAKE_CASE ) ).convert("RGB" ).resize((256, 256) )

					_snake_case														=   {
					    "prompt": "horse",
					    "image": init_image,
					    "strength": 0.5,
					    "generator": generator,
					    "guidance_scale": 4.0,
					    "num_inference_steps": 2,
					    "output_type": "np",
					}
					return inputs



		def 					_a					(      self     :					Optional[int] ):






					"""simple docstring"""
					_snake_case														=   "cpu"

					_snake_case														=   self.get_dummy_components()

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

					pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE )

					_snake_case														=   pipe(**self.get_dummy_inputs(__SCREAMING_SNAKE_CASE ) )
					_snake_case														=   output.image_embeds

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

					_snake_case														=   image[0, -10:]
					_snake_case														=   image_from_tuple[0, -10:]

					assert image.shape == (1, 32)

					_snake_case														=   np.array(
					    [
					        0.1_0_7_1_2_8_4,
					        1.3_3_3_0_2_7_1,
					        0.6_1_2_6_0_2_2_3,
					        -0.6_6_9_1_0_6_5,
					        -0.3_8_4_6_8_5_2,
					        -1.0_3_0_3_6_6_1,
					        0.2_2_7_1_6_1_1_1,
					        0.0_3_3_4_8_9_0_1,
					        0.3_0_0_4_0_6_7_5,
					        -0.2_4_8_0_5_0_2_9,
					    ] )

					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 					_a					(      self     :					Optional[Any] ):






					"""simple docstring"""
					_snake_case														=   torch_device == "cpu"
					_snake_case														=   True
					_snake_case														=   False

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







		@skip_mps
		def 					_a					(      self     :					str ):






					"""simple docstring"""
					_snake_case														=   torch_device == "cpu"
					_snake_case														=   False

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


import sys

from transformers import AutoTokenizer


_SCREAMING_SNAKE_CASE:    Optional[int]     						=							sys.argv[1]
_SCREAMING_SNAKE_CASE:    int     						=							sys.argv[2]
_SCREAMING_SNAKE_CASE:    str     						=							int(sys.argv[3])

_SCREAMING_SNAKE_CASE:    Union[str, Any]     						=							0

_SCREAMING_SNAKE_CASE:    Any     						=							AutoTokenizer.from_pretrained(model_name_or_path)
max_len -= tokenizer.num_special_tokens_to_add()

with open(dataset, '''rt''') as f_p:
			for line in f_p:
						_SCREAMING_SNAKE_CASE:    Any     						=							line.rstrip()

						if not line:
									print(line)
									_SCREAMING_SNAKE_CASE:    str     						=							0
									continue

						_SCREAMING_SNAKE_CASE:    Optional[int]     						=							line.split()[0]

						_SCREAMING_SNAKE_CASE:    Tuple     						=							len(tokenizer.tokenize(token))

						# Token contains strange control characters like \x96 or \x95
						# Just filter out the complete line
						if current_subwords_len == 0:
									continue

						if (subword_len_counter + current_subwords_len) > max_len:
									print('''''')
									print(line)
									_SCREAMING_SNAKE_CASE:    Any     						=							current_subwords_len
									continue

						subword_len_counter += current_subwords_len

						print(line)

