




"""simple docstring"""
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(      UpperCamelCase_	,	unittest.TestCase				):




					'''simple docstring'''


					__a						:						Optional[int]     		= UnCLIPImageVariationPipeline
					__a						:						List[str]     		= IMAGE_VARIATION_PARAMS - {'height', 'width', 'guidance_scale'}
					__a						:						int     		= IMAGE_VARIATION_BATCH_PARAMS

					__a						:						List[str]     		= [
					    'generator',
					    'return_dict',
					    'decoder_num_inference_steps',
					    'super_res_num_inference_steps',
					]
					__a						:						int     		= False
					@property
					def     snake_case_				(    self     ):
								return 32
					@property
					def     snake_case_				(    self     ):
								return 32
					@property
					def     snake_case_				(    self     ):
								return self.time_input_dim
					@property
					def     snake_case_				(    self     ):
								return self.time_input_dim * 4
					@property
					def     snake_case_				(    self     ):
								return 100
					@property
					def     snake_case_				(    self     ):
								__lowerCamelCase					:	int	       =				CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'     )
								return tokenizer
					@property
					def     snake_case_				(    self     ):
								torch.manual_seed(0     )
								__lowerCamelCase					:	List[Any]	       =				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=1000						,	)
								return CLIPTextModelWithProjection(__a     )
					@property
					def     snake_case_				(    self     ):
								torch.manual_seed(0     )
								__lowerCamelCase					:	str	       =				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(__a     )
					@property
					def     snake_case_				(    self     ):
								torch.manual_seed(0     )

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

								__lowerCamelCase					:	Union[str, Any]	       =				UnCLIPTextProjModel(**__a     )
								return model
					@property
					def     snake_case_				(    self     ):
								torch.manual_seed(0     )

								__lowerCamelCase					:	str	       =				{
								    '''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''',
								}

								__lowerCamelCase					:	Union[str, Any]	       =				UNetaDConditionModel(**__a     )
								return model
					@property
					def     snake_case_				(    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     snake_case_				(    self     ):
								torch.manual_seed(0     )

								__lowerCamelCase					:	Any	       =				UNetaDModel(**self.dummy_super_res_kwargs     )
								return model
					@property
					def     snake_case_				(    self     ):
								# seeded differently to get different unet than `self.dummy_super_res_first`
								torch.manual_seed(1     )

								__lowerCamelCase					:	Union[str, Any]	       =				UNetaDModel(**self.dummy_super_res_kwargs     )
								return model
					def     snake_case_				(    self     ):
								__lowerCamelCase					:	Optional[Any]	       =				self.dummy_decoder
								__lowerCamelCase					:	Dict	       =				self.dummy_text_proj
								__lowerCamelCase					:	Any	       =				self.dummy_text_encoder
								__lowerCamelCase					:	Any	       =				self.dummy_tokenizer
								__lowerCamelCase					:	Union[str, Any]	       =				self.dummy_super_res_first
								__lowerCamelCase					:	int	       =				self.dummy_super_res_last

								__lowerCamelCase					:	Any	       =				UnCLIPScheduler(
								    variance_type='learned_range'						,	prediction_type='epsilon'						,	num_train_timesteps=1000						,	)

								__lowerCamelCase					:	int	       =				UnCLIPScheduler(
								    variance_type='fixed_small_log'						,	prediction_type='epsilon'						,	num_train_timesteps=1000						,	)

								__lowerCamelCase					:	Dict	       =				CLIPImageProcessor(crop_size=32						,	size=32     )

								__lowerCamelCase					:	List[str]	       =				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     snake_case_				(    self						,	__a						,	__a=0						,	__a=True     ):
								__lowerCamelCase					:	Tuple	       =				floats_tensor((1, 3, 32, 32)						,	rng=random.Random(__a     )     ).to(__a     )
								if str(__a     ).startswith('mps'     ):
											__lowerCamelCase					:	Optional[Any]	       =				torch.manual_seed(__a     )
								else:
											__lowerCamelCase					:	Tuple	       =				torch.Generator(device=__a     ).manual_seed(__a     )

								if pil_image:
											__lowerCamelCase					:	Optional[int]	       =				input_image * 0.5 + 0.5
											__lowerCamelCase					:	Optional[int]	       =				input_image.clamp(0						,	1     )
											__lowerCamelCase					:	int	       =				input_image.cpu().permute(0						,	2						,	3						,	1     ).float().numpy()
											__lowerCamelCase					:	Union[str, Any]	       =				DiffusionPipeline.numpy_to_pil(__a     )[0]

								return {
								    "image": input_image,
								    "generator": generator,
								    "decoder_num_inference_steps": 2,
								    "super_res_num_inference_steps": 2,
								    "output_type": "np",
								}
					def     snake_case_				(    self     ):
								__lowerCamelCase					:	Optional[Any]	       =				'''cpu'''

								__lowerCamelCase					:	Any	       =				self.get_dummy_components()

								__lowerCamelCase					:	Union[str, Any]	       =				self.pipeline_class(**__a     )
								__lowerCamelCase					:	str	       =				pipe.to(__a     )

								pipe.set_progress_bar_config(disable=__a     )

								__lowerCamelCase					:	int	       =				self.get_dummy_inputs(__a						,	pil_image=__a     )

								__lowerCamelCase					:	Optional[int]	       =				pipe(**__a     )
								__lowerCamelCase					:	Tuple	       =				output.images

								__lowerCamelCase					:	Optional[int]	       =				self.get_dummy_inputs(__a						,	pil_image=__a     )

								__lowerCamelCase					:	Optional[Any]	       =				pipe(
								    **__a						,	return_dict=__a						,	)[0]

								__lowerCamelCase					:	Any	       =				image[0, -3:, -3:, -1]
								__lowerCamelCase					:	List[str]	       =				image_from_tuple[0, -3:, -3:, -1]

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

								__lowerCamelCase					:	Union[str, Any]	       =				np.array(
								    [
								        0.9_997,
								        0.0_002,
								        0.9_997,
								        0.9_997,
								        0.9_969,
								        0.0_023,
								        0.9_997,
								        0.9_969,
								        0.9_970,
								    ]     )

								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     snake_case_				(    self     ):
								__lowerCamelCase					:	Any	       =				'''cpu'''

								__lowerCamelCase					:	Union[str, Any]	       =				self.get_dummy_components()

								__lowerCamelCase					:	Union[str, Any]	       =				self.pipeline_class(**__a     )
								__lowerCamelCase					:	Dict	       =				pipe.to(__a     )

								pipe.set_progress_bar_config(disable=__a     )

								__lowerCamelCase					:	List[Any]	       =				self.get_dummy_inputs(__a						,	pil_image=__a     )

								__lowerCamelCase					:	int	       =				pipe(**__a     )
								__lowerCamelCase					:	Dict	       =				output.images

								__lowerCamelCase					:	List[str]	       =				self.get_dummy_inputs(__a						,	pil_image=__a     )

								__lowerCamelCase					:	Any	       =				pipe(
								    **__a						,	return_dict=__a						,	)[0]

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

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

								__lowerCamelCase					:	str	       =				np.array([0.9_997, 0.0_003, 0.9_997, 0.9_997, 0.9_970, 0.0_024, 0.9_997, 0.9_971, 0.9_971]     )

								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     snake_case_				(    self     ):
								__lowerCamelCase					:	Any	       =				'''cpu'''

								__lowerCamelCase					:	Optional[int]	       =				self.get_dummy_components()

								__lowerCamelCase					:	Union[str, Any]	       =				self.pipeline_class(**__a     )
								__lowerCamelCase					:	int	       =				pipe.to(__a     )

								pipe.set_progress_bar_config(disable=__a     )

								__lowerCamelCase					:	Optional[Any]	       =				self.get_dummy_inputs(__a						,	pil_image=__a     )
								__lowerCamelCase					:	Optional[int]	       =				[
								    pipeline_inputs['''image'''],
								    pipeline_inputs['''image'''],
								]

								__lowerCamelCase					:	Any	       =				pipe(**__a     )
								__lowerCamelCase					:	Tuple	       =				output.images

								__lowerCamelCase					:	Tuple	       =				self.get_dummy_inputs(__a						,	pil_image=__a     )
								__lowerCamelCase					:	Union[str, Any]	       =				[
								    tuple_pipeline_inputs['''image'''],
								    tuple_pipeline_inputs['''image'''],
								]

								__lowerCamelCase					:	Optional[Any]	       =				pipe(
								    **__a						,	return_dict=__a						,	)[0]

								__lowerCamelCase					:	Any	       =				image[0, -3:, -3:, -1]
								__lowerCamelCase					:	Dict	       =				image_from_tuple[0, -3:, -3:, -1]

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

								__lowerCamelCase					:	str	       =				np.array(
								    [
								        0.9_997,
								        0.9_989,
								        0.0_008,
								        0.0_021,
								        0.9_960,
								        0.0_018,
								        0.0_014,
								        0.0_002,
								        0.9_933,
								    ]     )

								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     snake_case_				(    self     ):
								__lowerCamelCase					:	Optional[int]	       =				torch.device('cpu'     )

								class __lowercase:




													'''simple docstring'''


													__a						:						Union[str, Any]     		= 1



								__lowerCamelCase					:	Optional[int]	       =				self.get_dummy_components()

								__lowerCamelCase					:	int	       =				self.pipeline_class(**__a     )
								__lowerCamelCase					:	List[Any]	       =				pipe.to(__a     )

								pipe.set_progress_bar_config(disable=__a     )

								__lowerCamelCase					:	Any	       =				torch.Generator(device=__a     ).manual_seed(0     )
								__lowerCamelCase					:	Any	       =				pipe.decoder.dtype
								__lowerCamelCase					:	Dict	       =				1

								__lowerCamelCase					:	Optional[int]	       =				(
								    batch_size,
								    pipe.decoder.config.in_channels,
								    pipe.decoder.config.sample_size,
								    pipe.decoder.config.sample_size,
								)
								__lowerCamelCase					:	Any	       =				pipe.prepare_latents(
								    __a						,	dtype=__a						,	device=__a						,	generator=__a						,	latents=__a						,	scheduler=DummyScheduler()     )

								__lowerCamelCase					:	Any	       =				(
								    batch_size,
								    pipe.super_res_first.config.in_channels // 2,
								    pipe.super_res_first.config.sample_size,
								    pipe.super_res_first.config.sample_size,
								)
								__lowerCamelCase					:	Dict	       =				pipe.prepare_latents(
								    __a						,	dtype=__a						,	device=__a						,	generator=__a						,	latents=__a						,	scheduler=DummyScheduler()     )

								__lowerCamelCase					:	List[str]	       =				self.get_dummy_inputs(__a						,	pil_image=__a     )

								__lowerCamelCase					:	int	       =				pipe(
								    **__a						,	decoder_latents=__a						,	super_res_latents=__a     ).images

								__lowerCamelCase					:	List[Any]	       =				self.get_dummy_inputs(__a						,	pil_image=__a     )
								# Don't pass image, instead pass embedding
								__lowerCamelCase					:	Optional[int]	       =				pipeline_inputs.pop('image'     )
								__lowerCamelCase					:	Dict	       =				pipe.image_encoder(__a     ).image_embeds

								__lowerCamelCase					:	Optional[int]	       =				pipe(
								    **__a						,	decoder_latents=__a						,	super_res_latents=__a						,	image_embeddings=__a						,	).images

								# make sure passing text embeddings manually is identical
								assert np.abs(img_out_a - img_out_a     ).max() < 1E-4
					@skip_mps
					def     snake_case_				(    self     ):
								__lowerCamelCase					:	List[Any]	       =				torch_device == '''cpu'''

								# Check is relaxed because there is not a torch 2.0 sliced attention added kv processor
								__lowerCamelCase					:	Optional[int]	       =				1E-2

								self._test_attention_slicing_forward_pass(
								    test_max_difference=__a						,	expected_max_diff=__a     )
					@skip_mps
					def     snake_case_				(    self     ):
								__lowerCamelCase					:	List[Any]	       =				torch_device == '''cpu'''
								__lowerCamelCase					:	Optional[int]	       =				True
								__lowerCamelCase					:	Any	       =				[
								    '''decoder_num_inference_steps''',
								    '''super_res_num_inference_steps''',
								]

								self._test_inference_batch_single_identical(
								    test_max_difference=__a						,	relax_max_difference=__a						,	additional_params_copy_to_batched_inputs=__a						,	)
					def     snake_case_				(    self     ):
								__lowerCamelCase					:	int	       =				[
								    '''decoder_num_inference_steps''',
								    '''super_res_num_inference_steps''',
								]

								if torch_device == "mps":
											# TODO: MPS errors with larger batch sizes
											__lowerCamelCase					:	List[Any]	       =				[2, 3]
											self._test_inference_batch_consistent(
											    batch_sizes=__a						,	additional_params_copy_to_batched_inputs=__a						,	)
								else:
											self._test_inference_batch_consistent(
											    additional_params_copy_to_batched_inputs=__a     )
					@skip_mps
					def     snake_case_				(    self     ):
								return super().test_dict_tuple_outputs_equivalent()
					@skip_mps
					def     snake_case_				(    self     ):
								return super().test_save_load_local()





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



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




					'''simple docstring'''


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





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

								__lowerCamelCase					:	Tuple	       =				UnCLIPImageVariationPipeline.from_pretrained(
								    'kakaobrain/karlo-v1-alpha-image-variations'						,	torch_dtype=torch.floataa     )
								__lowerCamelCase					:	Any	       =				pipeline.to(__a     )
								pipeline.set_progress_bar_config(disable=__a     )

								__lowerCamelCase					:	Any	       =				torch.Generator(device='cpu'     ).manual_seed(0     )
								__lowerCamelCase					:	Any	       =				pipeline(
								    __a						,	generator=__a						,	output_type='np'						,	)

								__lowerCamelCase					:	Tuple	       =				output.images[0]

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

								assert_mean_pixel_difference(__a						,	__a						,	15     )









"""simple docstring"""
from __future__ import annotations

import unittest

from transformers import LEDConfig, is_tf_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
					import tensorflow as tf

					from transformers import TFLEDForConditionalGeneration, TFLEDModel

@require_tf
class __lowercase:




					'''simple docstring'''


					__a						:						Optional[Any]     		= LEDConfig
					__a						:						Dict     		= {}
					__a						:						int     		= 'gelu'
					def __init__(    self						,	__a						,	__a=13						,	__a=7						,	__a=True						,	__a=False						,	__a=99						,	__a=32						,	__a=2						,	__a=4						,	__a=37						,	__a=0.1						,	__a=0.1						,	__a=20						,	__a=2						,	__a=1						,	__a=0						,	__a=4						,	):
								__lowerCamelCase					:	str	       =				parent
								__lowerCamelCase					:	Optional[int]	       =				batch_size
								__lowerCamelCase					:	List[str]	       =				seq_length
								__lowerCamelCase					:	Optional[Any]	       =				is_training
								__lowerCamelCase					:	Dict	       =				use_labels
								__lowerCamelCase					:	Any	       =				vocab_size
								__lowerCamelCase					:	int	       =				hidden_size
								__lowerCamelCase					:	Any	       =				num_hidden_layers
								__lowerCamelCase					:	Union[str, Any]	       =				num_attention_heads
								__lowerCamelCase					:	List[str]	       =				intermediate_size

								__lowerCamelCase					:	str	       =				hidden_dropout_prob
								__lowerCamelCase					:	Union[str, Any]	       =				attention_probs_dropout_prob
								__lowerCamelCase					:	Any	       =				max_position_embeddings
								__lowerCamelCase					:	str	       =				eos_token_id
								__lowerCamelCase					:	str	       =				pad_token_id
								__lowerCamelCase					:	str	       =				bos_token_id
								__lowerCamelCase					:	int	       =				attention_window

								# `ModelTesterMixin.test_attention_outputs` is expecting attention tensors to be of size
								# [num_attention_heads, encoder_seq_length, encoder_key_length], but TFLongformerSelfAttention
								# returns attention of shape [num_attention_heads, encoder_seq_length, self.attention_window + 1]
								# because its local attention only attends to `self.attention_window` and one before and one after
								__lowerCamelCase					:	Dict	       =				self.attention_window + 2

								# because of padding `encoder_seq_length`, is different from `seq_length`. Relevant for
								# the `test_attention_outputs` and `test_hidden_states_output` tests
								__lowerCamelCase					:	int	       =				(
								    self.seq_length + (self.attention_window - self.seq_length % self.attention_window) % self.attention_window
								)
					def     snake_case_				(    self     ):
								__lowerCamelCase					:	Tuple	       =				ids_tensor([self.batch_size, self.seq_length - 1]						,	self.vocab_size     )
								__lowerCamelCase					:	Optional[int]	       =				tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size     )						,	1     )
								__lowerCamelCase					:	Union[str, Any]	       =				tf.concat([input_ids, eos_tensor]						,	axis=1     )

								__lowerCamelCase					:	Any	       =				ids_tensor([self.batch_size, self.seq_length]						,	self.vocab_size     )

								__lowerCamelCase					:	Dict	       =				self.config_cls(
								    vocab_size=self.vocab_size						,	d_model=self.hidden_size						,	encoder_layers=self.num_hidden_layers						,	decoder_layers=self.num_hidden_layers						,	encoder_attention_heads=self.num_attention_heads						,	decoder_attention_heads=self.num_attention_heads						,	encoder_ffn_dim=self.intermediate_size						,	decoder_ffn_dim=self.intermediate_size						,	dropout=self.hidden_dropout_prob						,	attention_dropout=self.attention_probs_dropout_prob						,	max_position_embeddings=self.max_position_embeddings						,	eos_token_ids=[2]						,	bos_token_id=self.bos_token_id						,	pad_token_id=self.pad_token_id						,	decoder_start_token_id=self.pad_token_id						,	attention_window=self.attention_window						,	**self.config_updates						,	)
								__lowerCamelCase					:	int	       =				prepare_led_inputs_dict(__a						,	__a						,	__a     )
								__lowerCamelCase					:	Union[str, Any]	       =				tf.concat(
								    [tf.zeros_like(__a     )[:, :-1], tf.ones_like(__a     )[:, -1:]]						,	axis=-1						,	)
								__lowerCamelCase					:	Union[str, Any]	       =				global_attention_mask
								return config, inputs_dict





					def     snake_case_				(    self						,	__a						,	__a     ):
								__lowerCamelCase					:	Optional[int]	       =				TFLEDModel(config=__a     ).get_decoder()
								__lowerCamelCase					:	List[str]	       =				inputs_dict['input_ids']

								__lowerCamelCase					:	Dict	       =				input_ids[:1, :]
								__lowerCamelCase					:	Any	       =				inputs_dict['attention_mask'][:1, :]
								__lowerCamelCase					:	Tuple	       =				1

								# first forward pass
								__lowerCamelCase					:	List[str]	       =				model(__a						,	attention_mask=__a						,	use_cache=__a     )

								__lowerCamelCase       ,      __lowerCamelCase					:	int	       =				outputs.to_tuple()

								# create hypothetical next token and extent to next_input_ids
								__lowerCamelCase					:	Optional[int]	       =				ids_tensor((self.batch_size, 3)						,	config.vocab_size     )
								__lowerCamelCase					:	List[str]	       =				tf.cast(ids_tensor((self.batch_size, 3)						,	2     )						,	tf.inta     )

								# append to next input_ids and
								__lowerCamelCase					:	Any	       =				tf.concat([input_ids, next_tokens]						,	axis=-1     )
								__lowerCamelCase					:	Any	       =				tf.concat([attention_mask, next_attn_mask]						,	axis=-1     )

								__lowerCamelCase					:	Dict	       =				model(__a						,	attention_mask=__a     )[0]
								__lowerCamelCase					:	Optional[int]	       =				model(__a						,	attention_mask=__a						,	past_key_values=__a     )[0]

								self.parent.assertEqual(next_tokens.shape[1]						,	output_from_past.shape[1]     )

								# select random slice
								__lowerCamelCase					:	Any	       =				int(ids_tensor((1,)						,	output_from_past.shape[-1]     )     )
								__lowerCamelCase					:	int	       =				output_from_no_past[:, -3:, random_slice_idx]
								__lowerCamelCase					:	Union[str, Any]	       =				output_from_past[:, :, random_slice_idx]

								# test that outputs are equal for slice
								tf.debugging.assert_near(__a						,	__a						,	rtol=1E-3     )





def 						UpperCAmelCase				(			A__:    Any							,				A__:    Union[str, Any]							,				A__:    Dict							,				A__:    int=None							,				A__:    str=None							,				A__:    Tuple=None							,				A__:    List[Any]=None							,				)  ->     Optional[Any]:
			if attention_mask is None:
						__lowerCamelCase					:	Optional[int]	       =				tf.cast(tf.math.not_equal(A__							,				config.pad_token_id     )							,				tf.inta     )
			if decoder_attention_mask is None:
						__lowerCamelCase					:	Dict	       =				tf.concat(
						    [
						        tf.ones(decoder_input_ids[:, :1].shape							,				dtype=tf.inta     ),
						        tf.cast(tf.math.not_equal(decoder_input_ids[:, 1:]							,				config.pad_token_id     )							,				tf.inta     ),
						    ]							,				axis=-1							,				)
			if head_mask is None:
						__lowerCamelCase					:	Dict	       =				tf.ones((config.encoder_layers, config.encoder_attention_heads)     )
			if decoder_head_mask is None:
						__lowerCamelCase					:	Tuple	       =				tf.ones((config.decoder_layers, config.decoder_attention_heads)     )
			return {
			    "input_ids": input_ids,
			    "attention_mask": attention_mask,
			    "decoder_input_ids": decoder_input_ids,
			    "decoder_attention_mask": decoder_attention_mask,
			    "head_mask": head_mask,
			    "decoder_head_mask": decoder_head_mask,
			}

@require_tf
class __lowercase(      lowercase__	,	lowercase__	,	unittest.TestCase				):




					'''simple docstring'''


					__a						:						Union[str, Any]     		= (TFLEDForConditionalGeneration, TFLEDModel) if is_tf_available() else ()
					__a						:						Any     		= (TFLEDForConditionalGeneration,) if is_tf_available() else ()
					__a						:						str     		= (
					    {
					        'conversational': TFLEDForConditionalGeneration,
					        'feature-extraction': TFLEDModel,
					        'summarization': TFLEDForConditionalGeneration,
					        'text2text-generation': TFLEDForConditionalGeneration,
					        'translation': TFLEDForConditionalGeneration,
					    }
					    if is_tf_available()
					    else {}
					)
					__a						:						Union[str, Any]     		= True
					__a						:						str     		= False
					__a						:						Dict     		= False
					__a						:						List[Any]     		= False
					def     snake_case_				(    self     ):
								__lowerCamelCase					:	int	       =				TFLEDModelTester(self     )
								__lowerCamelCase					:	List[str]	       =				ConfigTester(self						,	config_class=__a     )
					def     snake_case_				(    self     ):
								self.config_tester.run_common_tests()
					def     snake_case_				(    self     ):
								__lowerCamelCase					:	Optional[int]	       =				self.model_tester.prepare_config_and_inputs_for_common()
								self.model_tester.check_decoder_model_past_large_inputs(*__a     )
					def     snake_case_				(    self     ):
								__lowerCamelCase       ,      __lowerCamelCase					:	Optional[Any]	       =				self.model_tester.prepare_config_and_inputs_for_common()
								__lowerCamelCase					:	Optional[Any]	       =				tf.zeros_like(inputs_dict['attention_mask']     )
								__lowerCamelCase					:	Any	       =				2
								__lowerCamelCase					:	Dict	       =				tf.where(
								    tf.range(self.model_tester.seq_length     )[None, :] < num_global_attn_indices						,	1						,	inputs_dict['global_attention_mask']						,	)

								__lowerCamelCase					:	List[Any]	       =				True
								__lowerCamelCase					:	Tuple	       =				self.model_tester.seq_length
								__lowerCamelCase					:	Dict	       =				self.model_tester.encoder_seq_length

								def check_decoder_attentions_output(__a     ):
											__lowerCamelCase					:	List[Any]	       =				outputs.decoder_attentions
											self.assertEqual(len(__a     )						,	self.model_tester.num_hidden_layers     )
											self.assertListEqual(
											    list(decoder_attentions[0].shape[-3:]     )						,	[self.model_tester.num_attention_heads, seq_length, seq_length]						,	)

								def check_encoder_attentions_output(__a     ):
											__lowerCamelCase					:	List[str]	       =				[t.numpy() for t in outputs.encoder_attentions]
											__lowerCamelCase					:	Optional[Any]	       =				[t.numpy() for t in outputs.encoder_global_attentions]
											self.assertEqual(len(__a     )						,	self.model_tester.num_hidden_layers     )
											self.assertEqual(len(__a     )						,	self.model_tester.num_hidden_layers     )
											self.assertListEqual(
											    list(attentions[0].shape[-3:]     )						,	[self.model_tester.num_attention_heads, seq_length, seq_length]						,	)
											self.assertListEqual(
											    list(global_attentions[0].shape[-3:]     )						,	[self.model_tester.num_attention_heads, encoder_seq_length, num_global_attn_indices]						,	)

								for model_class in self.all_model_classes:
											__lowerCamelCase					:	int	       =				True
											__lowerCamelCase					:	int	       =				False
											__lowerCamelCase					:	Tuple	       =				False
											__lowerCamelCase					:	List[Any]	       =				model_class(__a     )
											__lowerCamelCase					:	Optional[Any]	       =				model(self._prepare_for_class(__a						,	__a     )     )
											__lowerCamelCase					:	Tuple	       =				len(__a     )
											self.assertEqual(config.output_hidden_states						,	__a     )
											check_encoder_attentions_output(__a     )

											if self.is_encoder_decoder:
														__lowerCamelCase					:	Any	       =				model_class(__a     )
														__lowerCamelCase					:	List[str]	       =				model(self._prepare_for_class(__a						,	__a     )     )
														self.assertEqual(config.output_hidden_states						,	__a     )
														check_decoder_attentions_output(__a     )

											# Check that output attentions can also be changed via the config
											del inputs_dict["output_attentions"]
											__lowerCamelCase					:	Tuple	       =				True
											__lowerCamelCase					:	Dict	       =				model_class(__a     )
											__lowerCamelCase					:	List[Any]	       =				model(self._prepare_for_class(__a						,	__a     )     )
											self.assertEqual(config.output_hidden_states						,	__a     )
											check_encoder_attentions_output(__a     )

											# Check attention is always last and order is fine
											__lowerCamelCase					:	List[Any]	       =				True
											__lowerCamelCase					:	int	       =				True
											__lowerCamelCase					:	List[str]	       =				model_class(__a     )
											__lowerCamelCase					:	Dict	       =				model(self._prepare_for_class(__a						,	__a     )     )

											self.assertEqual(out_len + (2 if self.is_encoder_decoder else 1)						,	len(__a     )     )
											self.assertEqual(model.config.output_hidden_states						,	__a     )
											check_encoder_attentions_output(__a     )
					@unittest.skip('LED keeps using potentially symbolic tensors in conditionals and breaks tracing.'     )
					def     snake_case_				(    self     ):
								pass





					def     snake_case_				(    self     ):
								# TODO: Head-masking not yet implement
								pass





def 						UpperCAmelCase				(			A__:    Union[str, Any]     )  ->     List[Any]:
			return tf.constant(A__							,				dtype=tf.intaa     )


a_				:    Tuple										=     1e-4






@slow
@require_tf
class __lowercase(      unittest.TestCase				):




					'''simple docstring'''


					def     snake_case_				(    self     ):
								__lowerCamelCase					:	Tuple	       =				TFLEDForConditionalGeneration.from_pretrained('allenai/led-base-16384'     ).led

								# change to intended input here
								__lowerCamelCase					:	Union[str, Any]	       =				_long_tensor([512 * [0, 31414, 232, 328, 740, 1140, 12695, 69]]     )
								__lowerCamelCase					:	Dict	       =				_long_tensor([128 * [0, 31414, 232, 328, 740, 1140, 12695, 69]]     )
								__lowerCamelCase					:	Optional[int]	       =				prepare_led_inputs_dict(model.config						,	__a						,	__a     )
								__lowerCamelCase					:	str	       =				model(**__a     )[0]
								__lowerCamelCase					:	Optional[int]	       =				(1, 1024, 768)
								self.assertEqual(output.shape						,	__a     )
								# change to expected output here
								__lowerCamelCase					:	int	       =				tf.convert_to_tensor(
								    [[2.3_050, 2.8_279, 0.6_531], [-1.8_457, -0.1_455, -3.5_661], [-1.0_186, 0.4_586, -2.2_043]]						,	)
								tf.debugging.assert_near(output[:, :3, :3]						,	__a						,	atol=1E-3     )





					def     snake_case_				(    self     ):
								__lowerCamelCase					:	List[str]	       =				TFLEDForConditionalGeneration.from_pretrained('allenai/led-base-16384'     )

								# change to intended input here
								__lowerCamelCase					:	Union[str, Any]	       =				_long_tensor([512 * [0, 31414, 232, 328, 740, 1140, 12695, 69]]     )
								__lowerCamelCase					:	Dict	       =				_long_tensor([128 * [0, 31414, 232, 328, 740, 1140, 12695, 69]]     )
								__lowerCamelCase					:	Optional[Any]	       =				prepare_led_inputs_dict(model.config						,	__a						,	__a     )
								__lowerCamelCase					:	Dict	       =				model(**__a     )[0]
								__lowerCamelCase					:	List[Any]	       =				(1, 1024, model.config.vocab_size)
								self.assertEqual(output.shape						,	__a     )
								# change to expected output here
								__lowerCamelCase					:	Optional[Any]	       =				tf.convert_to_tensor(
								    [[33.6_507, 6.4_572, 16.8_089], [5.8_739, -2.4_238, 11.2_902], [-3.2_139, -4.3_149, 4.2_783]]						,	)
								tf.debugging.assert_near(output[:, :3, :3]						,	__a						,	atol=1E-3						,	rtol=1E-3     )



