
import unittest
from pathlib import Path
from tempfile import TemporaryDirectory

from transformers import AutoConfig, TFGPTaLMHeadModel, is_keras_nlp_available, is_tf_available
from transformers.models.gpta.tokenization_gpta import GPTaTokenizer
from transformers.testing_utils import require_keras_nlp, require_tf, slow


if is_tf_available():
						import tensorflow as tf

if is_keras_nlp_available():
						from transformers.models.gpta import TFGPTaTokenizer


_snake_case				:   Tuple     =				['gpt2']
_snake_case				:   Optional[Any]     =				'gpt2'

if is_tf_available():


						class 			lowerCAmelCase (				tf.Module			):




								def __init__(				self					,						UpperCamelCase      ):
														super().__init__()
														_SCREAMING_SNAKE_CASE				= tokenizer
														_SCREAMING_SNAKE_CASE				= AutoConfig.from_pretrained(__A      )
														_SCREAMING_SNAKE_CASE				= TFGPTaLMHeadModel.from_config(__A      )




								@tf.function(input_signature=(tf.TensorSpec((None,)					,						tf.string					,						name="text"      ),)      )
								def     lowercase       (				self					,						UpperCamelCase      ):
														_SCREAMING_SNAKE_CASE				= self.tokenizer(__A      )
														_SCREAMING_SNAKE_CASE				= tokenized["input_ids"].to_tensor()

														_SCREAMING_SNAKE_CASE				= tf.cast(input_ids_dense > 0					,						tf.intaa      )
														# input_mask = tf.reshape(input_mask, [-1, MAX_SEQ_LEN])

														_SCREAMING_SNAKE_CASE				= self.model(input_ids=__A					,						attention_mask=__A      )["logits"]

														return outputs


@require_tf
@require_keras_nlp
class 			lowerCAmelCase (				unittest.TestCase			):




		def     lowercase       (				self      ):
								super().setUp()

								_SCREAMING_SNAKE_CASE				= [GPTaTokenizer.from_pretrained(__A      ) for checkpoint in (TOKENIZER_CHECKPOINTS)]
								_SCREAMING_SNAKE_CASE				= [TFGPTaTokenizer.from_pretrained(__A      ) for checkpoint in TOKENIZER_CHECKPOINTS]
								assert len(self.tokenizers      ) == len(self.tf_tokenizers      )

								_SCREAMING_SNAKE_CASE				= [
								    "This is a straightforward English test sentence.",
								    "This one has some weird characters\rto\nsee\r\nif  those\u00E9break things.",
								    "Now we're going to add some Chinese: 一 二 三 一二三",
								    "And some much more rare Chinese: 齉 堃 齉堃",
								    "Je vais aussi écrire en français pour tester les accents",
								    "Classical Irish also has some unusual characters, so in they go: Gaelaċ, ꝼ",
								]
								_SCREAMING_SNAKE_CASE				= list(zip(self.test_sentences					,						self.test_sentences[::-1]      )      )




		def     lowercase       (				self      ):
								for tokenizer, tf_tokenizer in zip(self.tokenizers					,						self.tf_tokenizers      ):
														for test_inputs in self.test_sentences:
																				_SCREAMING_SNAKE_CASE				= tokenizer([test_inputs]					,						return_tensors="tf"      )
																				_SCREAMING_SNAKE_CASE				= tf_tokenizer([test_inputs]      )

																				for key in python_outputs.keys():
																										# convert them to numpy to avoid messing with ragged tensors
																										_SCREAMING_SNAKE_CASE				= python_outputs[key].numpy()
																										_SCREAMING_SNAKE_CASE				= tf_outputs[key].numpy()

																										self.assertTrue(tf.reduce_all(python_outputs_values.shape == tf_outputs_values.shape      )      )
																										self.assertTrue(tf.reduce_all(tf.cast(__A					,						tf.intaa      ) == tf_outputs_values      )      )




		@slow
		def     lowercase       (				self      ):
								for tf_tokenizer in self.tf_tokenizers:
														_SCREAMING_SNAKE_CASE				= tf.function(__A      )
														for test_inputs in self.test_sentences:
																				_SCREAMING_SNAKE_CASE				= tf.constant(__A      )
																				_SCREAMING_SNAKE_CASE				= compiled_tokenizer(__A      )
																				_SCREAMING_SNAKE_CASE				= tf_tokenizer(__A      )

																				for key in eager_outputs.keys():
																										self.assertTrue(tf.reduce_all(eager_outputs[key] == compiled_outputs[key]      )      )




		@slow
		def     lowercase       (				self      ):
								for tf_tokenizer in self.tf_tokenizers:
														_SCREAMING_SNAKE_CASE				= ModelToSave(tokenizer=__A      )
														_SCREAMING_SNAKE_CASE				= tf.convert_to_tensor([self.test_sentences[0]]      )
														_SCREAMING_SNAKE_CASE				= model.serving(__A      )  # Build model with some sample inputs
														with TemporaryDirectory() as tempdir:
																				_SCREAMING_SNAKE_CASE				= Path(__A      ) / "saved.model"
																				tf.saved_model.save(__A					,						__A					,						signatures={"serving_default": model.serving}      )
																				_SCREAMING_SNAKE_CASE				= tf.saved_model.load(__A      )
														_SCREAMING_SNAKE_CASE				= loaded_model.signatures["serving_default"](__A      )["output_0"]
														# We may see small differences because the loaded model is compiled, so we need an epsilon for the test
														self.assertTrue(tf.reduce_all(out == loaded_output      )      )




		@slow
		def     lowercase       (				self      ):
								for tf_tokenizer in self.tf_tokenizers:
														_SCREAMING_SNAKE_CASE				= tf.convert_to_tensor([self.test_sentences[0]]      )
														_SCREAMING_SNAKE_CASE				= tf_tokenizer(__A      )  # Build model with some sample inputs

														_SCREAMING_SNAKE_CASE				= tf_tokenizer.get_config()
														_SCREAMING_SNAKE_CASE				= TFGPTaTokenizer.from_config(__A      )
														_SCREAMING_SNAKE_CASE				= model_from_config(__A      )

														for key in from_config_output.keys():
																				self.assertTrue(tf.reduce_all(from_config_output[key] == out[key]      )      )




		@slow
		def     lowercase       (				self      ):
								for tf_tokenizer in self.tf_tokenizers:
														# for the test to run
														_SCREAMING_SNAKE_CASE				= 123_123

														for max_length in [3, 5, 1_024]:
																				_SCREAMING_SNAKE_CASE				= tf.convert_to_tensor([self.test_sentences[0]]      )
																				_SCREAMING_SNAKE_CASE				= tf_tokenizer(__A					,						max_length=__A      )

																				_SCREAMING_SNAKE_CASE				= out["input_ids"].numpy().shape[1]

																				assert out_length == max_length

'''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 			lowerCAmelCase (				__UpperCAmelCase		,     unittest.TestCase			):
		a							:					Optional[int]       =					KandinskyImgaImgPipeline
		a							:					Union[str, Any]       =					["""prompt""", """image_embeds""", """negative_image_embeds""", """image"""]
		a							:					Union[str, Any]       =					[
		    """prompt""",
		    """negative_prompt""",
		    """image_embeds""",
		    """negative_image_embeds""",
		    """image""",
		]
		a							:					int       =					[
		    """generator""",
		    """height""",
		    """width""",
		    """strength""",
		    """guidance_scale""",
		    """negative_prompt""",
		    """num_inference_steps""",
		    """return_dict""",
		    """guidance_scale""",
		    """num_images_per_prompt""",
		    """output_type""",
		    """return_dict""",
		]
		a							:					Tuple       =					False




		@property
		def     lowercase       (				self      ):
								return 32




		@property
		def     lowercase       (				self      ):
								return 32




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




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




		@property
		def     lowercase       (				self      ):
								return 100




		@property
		def     lowercase       (				self      ):
								_SCREAMING_SNAKE_CASE				= XLMRobertaTokenizerFast.from_pretrained("YiYiXu/tiny-random-mclip-base"      )
								return tokenizer




		@property
		def     lowercase       (				self      ):
								torch.manual_seed(0      )
								_SCREAMING_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					,						)

								_SCREAMING_SNAKE_CASE				= MultilingualCLIP(UpperCamelCase      )
								_SCREAMING_SNAKE_CASE				= text_encoder.eval()

								return text_encoder




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

								_SCREAMING_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,
								}

								_SCREAMING_SNAKE_CASE				= UNetaDConditionModel(**UpperCamelCase      )
								return model




		@property
		def     lowercase       (				self      ):
								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     lowercase       (				self      ):
								torch.manual_seed(0      )
								_SCREAMING_SNAKE_CASE				= VQModel(**self.dummy_movq_kwargs      )
								return model




		def     lowercase       (				self      ):
								_SCREAMING_SNAKE_CASE				= self.dummy_text_encoder
								_SCREAMING_SNAKE_CASE				= self.dummy_tokenizer
								_SCREAMING_SNAKE_CASE				= self.dummy_unet
								_SCREAMING_SNAKE_CASE				= self.dummy_movq

								_SCREAMING_SNAKE_CASE				= {
								    "num_train_timesteps": 1_000,
								    "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,
								}

								_SCREAMING_SNAKE_CASE				= DDIMScheduler(**UpperCamelCase      )

								_SCREAMING_SNAKE_CASE				= {
								    "text_encoder": text_encoder,
								    "tokenizer": tokenizer,
								    "unet": unet,
								    "scheduler": scheduler,
								    "movq": movq,
								}

								return components




		def     lowercase       (				self					,						UpperCamelCase					,						UpperCamelCase=0      ):
								_SCREAMING_SNAKE_CASE				= floats_tensor((1, self.cross_attention_dim)					,						rng=random.Random(UpperCamelCase      )      ).to(UpperCamelCase      )
								_SCREAMING_SNAKE_CASE				= floats_tensor((1, self.cross_attention_dim)					,						rng=random.Random(seed + 1      )      ).to(UpperCamelCase      )
								# create init_image
								_SCREAMING_SNAKE_CASE				= floats_tensor((1, 3, 64, 64)					,						rng=random.Random(UpperCamelCase      )      ).to(UpperCamelCase      )
								_SCREAMING_SNAKE_CASE				= image.cpu().permute(0					,						2					,						3					,						1      )[0]
								_SCREAMING_SNAKE_CASE				= Image.fromarray(np.uinta(UpperCamelCase      )      ).convert("RGB"      ).resize((256, 256)      )

								if str(UpperCamelCase      ).startswith("mps"      ):
														_SCREAMING_SNAKE_CASE				= torch.manual_seed(UpperCamelCase      )
								else:
														_SCREAMING_SNAKE_CASE				= torch.Generator(device=UpperCamelCase      ).manual_seed(UpperCamelCase      )
								_SCREAMING_SNAKE_CASE				= {
								    "prompt": "horse",
								    "image": init_image,
								    "image_embeds": image_embeds,
								    "negative_image_embeds": negative_image_embeds,
								    "generator": generator,
								    "height": 64,
								    "width": 64,
								    "num_inference_steps": 10,
								    "guidance_scale": 7.0,
								    "strength": 0.2,
								    "output_type": "np",
								}
								return inputs




		def     lowercase       (				self      ):
								_SCREAMING_SNAKE_CASE				= "cpu"

								_SCREAMING_SNAKE_CASE				= self.get_dummy_components()

								_SCREAMING_SNAKE_CASE				= self.pipeline_class(**UpperCamelCase      )
								_SCREAMING_SNAKE_CASE				= pipe.to(UpperCamelCase      )

								pipe.set_progress_bar_config(disable=UpperCamelCase      )

								_SCREAMING_SNAKE_CASE				= pipe(**self.get_dummy_inputs(UpperCamelCase      )      )
								_SCREAMING_SNAKE_CASE				= output.images

								_SCREAMING_SNAKE_CASE				= pipe(
								    **self.get_dummy_inputs(UpperCamelCase      )					,						return_dict=UpperCamelCase					,						)[0]

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

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

								_SCREAMING_SNAKE_CASE				= 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 			lowerCAmelCase (				unittest.TestCase			):




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




		def     lowercase       (				self      ):
								_SCREAMING_SNAKE_CASE				= load_numpy(
								    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
								    "/kandinsky/kandinsky_img2img_frog.npy"      )

								_SCREAMING_SNAKE_CASE				= load_image(
								    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main" "/kandinsky/cat.png"      )
								_SCREAMING_SNAKE_CASE				= "A red cartoon frog, 4k"

								_SCREAMING_SNAKE_CASE				= KandinskyPriorPipeline.from_pretrained(
								    "kandinsky-community/kandinsky-2-1-prior"					,						torch_dtype=torch.floataa      )
								pipe_prior.to(UpperCamelCase      )

								_SCREAMING_SNAKE_CASE				= KandinskyImgaImgPipeline.from_pretrained(
								    "kandinsky-community/kandinsky-2-1"					,						torch_dtype=torch.floataa      )
								_SCREAMING_SNAKE_CASE				= pipeline.to(UpperCamelCase      )

								pipeline.set_progress_bar_config(disable=UpperCamelCase      )

								_SCREAMING_SNAKE_CASE				= torch.Generator(device="cpu"      ).manual_seed(0      )
								_SCREAMING_SNAKE_CASE,							_SCREAMING_SNAKE_CASE				= pipe_prior(
								    UpperCamelCase					,						generator=UpperCamelCase					,						num_inference_steps=5					,						negative_prompt=""					,						).to_tuple()

								_SCREAMING_SNAKE_CASE				= pipeline(
								    UpperCamelCase					,						image=UpperCamelCase					,						image_embeds=UpperCamelCase					,						negative_image_embeds=UpperCamelCase					,						generator=UpperCamelCase					,						num_inference_steps=100					,						height=768					,						width=768					,						strength=0.2					,						output_type="np"					,						)

								_SCREAMING_SNAKE_CASE				= output.images[0]

								assert image.shape == (768, 768, 3)

								assert_mean_pixel_difference(UpperCamelCase					,						UpperCamelCase      )