






"""simple docstring"""
import gc
import unittest

import numpy as np
import torch

from diffusers import AutoencoderKL, DDIMScheduler, DiTPipeline, DPMSolverMultistepScheduler, TransformeraDModel
from diffusers.utils import is_xformers_available, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import (
    CLASS_CONDITIONED_IMAGE_GENERATION_BATCH_PARAMS,
    CLASS_CONDITIONED_IMAGE_GENERATION_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()


class _lowerCAmelCase						(    __UpperCAmelCase   ,  unittest.TestCase       ):
						SCREAMING_SNAKE_CASE_:					Union[str, Any] 					=				DiTPipeline
						SCREAMING_SNAKE_CASE_:					Optional[Any] 					=				CLASS_CONDITIONED_IMAGE_GENERATION_PARAMS
						SCREAMING_SNAKE_CASE_:					Any 					=				PipelineTesterMixin.required_optional_params - {
						    'latents',
						    'num_images_per_prompt',
						    'callback',
						    'callback_steps',
						}
						SCREAMING_SNAKE_CASE_:					Union[str, Any] 					=				CLASS_CONDITIONED_IMAGE_GENERATION_BATCH_PARAMS
						SCREAMING_SNAKE_CASE_:					Optional[int] 					=				False







						def 			A      (    self							)		->							List[str]:
										torch.manual_seed(0							)
										_SCREAMING_SNAKE_CASE   :		Any       		=    TransformeraDModel(
										    sample_size=1_6   ,				num_layers=2   ,				patch_size=4   ,				attention_head_dim=8   ,				num_attention_heads=2   ,				in_channels=4   ,				out_channels=8   ,				attention_bias=lowerCAmelCase_   ,				activation_fn='gelu-approximate'   ,				num_embeds_ada_norm=1_0_0_0   ,				norm_type='ada_norm_zero'   ,				norm_elementwise_affine=lowerCAmelCase_   ,				)
										_SCREAMING_SNAKE_CASE   :		Dict       		=    AutoencoderKL()
										_SCREAMING_SNAKE_CASE   :		Optional[int]       		=    DDIMScheduler()
										_SCREAMING_SNAKE_CASE   :		Union[str, Any]       		=    {'transformer': transformer.eval(), 'vae': vae.eval(), 'scheduler': scheduler}
										return components







						def 			A      (    self   ,				lowerCAmelCase_   ,				lowerCAmelCase_=0							)		->							Union[str, Any]:
										if str(lowerCAmelCase_							).startswith('mps'							):
														_SCREAMING_SNAKE_CASE   :		str       		=    torch.manual_seed(lowerCAmelCase_							)
										else:
														_SCREAMING_SNAKE_CASE   :		Union[str, Any]       		=    torch.Generator(device=lowerCAmelCase_							).manual_seed(lowerCAmelCase_							)
										_SCREAMING_SNAKE_CASE   :		Optional[int]       		=    {
										    'class_labels': [1],
										    'generator': generator,
										    'num_inference_steps': 2,
										    'output_type': 'numpy',
										}
										return inputs







						def 			A      (    self							)		->							Optional[int]:
										_SCREAMING_SNAKE_CASE   :		List[Any]       		=    'cpu'

										_SCREAMING_SNAKE_CASE   :		Union[str, Any]       		=    self.get_dummy_components()
										_SCREAMING_SNAKE_CASE   :		Dict       		=    self.pipeline_class(**lowerCAmelCase_							)
										pipe.to(lowerCAmelCase_							)
										pipe.set_progress_bar_config(disable=lowerCAmelCase_							)

										_SCREAMING_SNAKE_CASE   :		Union[str, Any]       		=    self.get_dummy_inputs(lowerCAmelCase_							)
										_SCREAMING_SNAKE_CASE   :		Optional[Any]       		=    pipe(**lowerCAmelCase_							).images
										_SCREAMING_SNAKE_CASE   :		str       		=    image[0, -3:, -3:, -1]

										self.assertEqual(image.shape   ,				(1, 1_6, 1_6, 3)							)
										_SCREAMING_SNAKE_CASE   :		str       		=    np.array([0.2_946, 0.6_601, 0.4_329, 0.3_296, 0.4_144, 0.5_319, 0.7_273, 0.5_013, 0.4_457]							)
										_SCREAMING_SNAKE_CASE   :		Tuple       		=    np.abs(image_slice.flatten() - expected_slice							).max()
										self.assertLessEqual(lowerCAmelCase_   ,				1e-3							)







						def 			A      (    self							)		->							List[str]:
										self._test_inference_batch_single_identical(relax_max_difference=lowerCAmelCase_   ,				expected_max_diff=1e-3							)







						@unittest.skipIf(
						    torch_device != 'cuda' or not is_xformers_available()   ,				reason='XFormers attention is only available with CUDA and `xformers` installed'   ,				)
						def 			A      (    self							)		->							int:
										self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3							)







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







						def 			A      (    self							)		->							Optional[Any]:
										super().tearDown()
										gc.collect()
										torch.cuda.empty_cache()







						def 			A      (    self							)		->							int:
										_SCREAMING_SNAKE_CASE   :		Any       		=    torch.manual_seed(0							)

										_SCREAMING_SNAKE_CASE   :		Union[str, Any]       		=    DiTPipeline.from_pretrained('facebook/DiT-XL-2-256'							)
										pipe.to('cuda'							)

										_SCREAMING_SNAKE_CASE   :		Tuple       		=    ['vase', 'umbrella', 'white shark', 'white wolf']
										_SCREAMING_SNAKE_CASE   :		Any       		=    pipe.get_label_ids(lowerCAmelCase_							)

										_SCREAMING_SNAKE_CASE   :		Optional[int]       		=    pipe(lowerCAmelCase_   ,				generator=lowerCAmelCase_   ,				num_inference_steps=4_0   ,				output_type='np'							).images

										for word, image in zip(lowerCAmelCase_   ,				lowerCAmelCase_							):
														_SCREAMING_SNAKE_CASE   :		List[str]       		=    load_numpy(
														    F"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/dit/{word}.npy"""							)
														assert np.abs((expected_image - image).max()							) < 1e-2







						def 			A      (    self							)		->							int:
										_SCREAMING_SNAKE_CASE   :		List[Any]       		=    DiTPipeline.from_pretrained('facebook/DiT-XL-2-512'							)
										_SCREAMING_SNAKE_CASE   :		Dict       		=    DPMSolverMultistepScheduler.from_config(pipe.scheduler.config							)
										pipe.to('cuda'							)

										_SCREAMING_SNAKE_CASE   :		Optional[int]       		=    ['vase', 'umbrella']
										_SCREAMING_SNAKE_CASE   :		Union[str, Any]       		=    pipe.get_label_ids(lowerCAmelCase_							)

										_SCREAMING_SNAKE_CASE   :		Any       		=    torch.manual_seed(0							)
										_SCREAMING_SNAKE_CASE   :		str       		=    pipe(lowerCAmelCase_   ,				generator=lowerCAmelCase_   ,				num_inference_steps=2_5   ,				output_type='np'							).images

										for word, image in zip(lowerCAmelCase_   ,				lowerCAmelCase_							):
														_SCREAMING_SNAKE_CASE   :		List[str]       		=    load_numpy(
														    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
														    F"""/dit/{word}_512.npy"""							)

														assert np.abs((expected_image - image).max()							) < 1e-1









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

import unittest

from transformers import AutoTokenizer, MBartConfig, is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
from transformers.utils import cached_property

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 TFAutoModelForSeqaSeqLM, TFMBartForConditionalGeneration, TFMBartModel


@require_tf
class _lowerCAmelCase						:
						SCREAMING_SNAKE_CASE_:					Union[str, Any] 					=				MBartConfig
						SCREAMING_SNAKE_CASE_:					Optional[Any] 					=				{}
						SCREAMING_SNAKE_CASE_:					Tuple 					=				'gelu'







						def __init__(    self   ,				lowerCAmelCase_   ,				lowerCAmelCase_=1_3   ,				lowerCAmelCase_=7   ,				lowerCAmelCase_=True   ,				lowerCAmelCase_=False   ,				lowerCAmelCase_=9_9   ,				lowerCAmelCase_=3_2   ,				lowerCAmelCase_=2   ,				lowerCAmelCase_=4   ,				lowerCAmelCase_=3_7   ,				lowerCAmelCase_=0.1   ,				lowerCAmelCase_=0.1   ,				lowerCAmelCase_=2_0   ,				lowerCAmelCase_=2   ,				lowerCAmelCase_=1   ,				lowerCAmelCase_=0   ,				)		->							int:
										_SCREAMING_SNAKE_CASE   :		Optional[Any]       		=    parent
										_SCREAMING_SNAKE_CASE   :		str       		=    batch_size
										_SCREAMING_SNAKE_CASE   :		str       		=    seq_length
										_SCREAMING_SNAKE_CASE   :		Dict       		=    is_training
										_SCREAMING_SNAKE_CASE   :		int       		=    use_labels
										_SCREAMING_SNAKE_CASE   :		str       		=    vocab_size
										_SCREAMING_SNAKE_CASE   :		List[Any]       		=    hidden_size
										_SCREAMING_SNAKE_CASE   :		Tuple       		=    num_hidden_layers
										_SCREAMING_SNAKE_CASE   :		Tuple       		=    num_attention_heads
										_SCREAMING_SNAKE_CASE   :		Union[str, Any]       		=    intermediate_size
										_SCREAMING_SNAKE_CASE   :		Optional[int]       		=    hidden_dropout_prob
										_SCREAMING_SNAKE_CASE   :		int       		=    attention_probs_dropout_prob
										_SCREAMING_SNAKE_CASE   :		List[str]       		=    max_position_embeddings
										_SCREAMING_SNAKE_CASE   :		Optional[int]       		=    eos_token_id
										_SCREAMING_SNAKE_CASE   :		Optional[int]       		=    pad_token_id
										_SCREAMING_SNAKE_CASE   :		Tuple       		=    bos_token_id







						def 			A      (    self							)		->							Tuple:
										_SCREAMING_SNAKE_CASE   :		List[str]       		=    ids_tensor([self.batch_size, self.seq_length - 1]   ,				self.vocab_size							)
										_SCREAMING_SNAKE_CASE   :		List[Any]       		=    tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size							)   ,				1							)
										_SCREAMING_SNAKE_CASE   :		Union[str, Any]       		=    tf.concat([input_ids, eos_tensor]   ,				axis=1							)

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

										_SCREAMING_SNAKE_CASE   :		Tuple       		=    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   ,				**self.config_updates   ,				)
										_SCREAMING_SNAKE_CASE   :		Any       		=    prepare_mbart_inputs_dict(lowerCAmelCase_   ,				lowerCAmelCase_   ,				lowerCAmelCase_							)
										return config, inputs_dict







						def 			A      (    self   ,				lowerCAmelCase_   ,				lowerCAmelCase_							)		->							int:
										_SCREAMING_SNAKE_CASE   :		int       		=    TFMBartModel(config=lowerCAmelCase_							).get_decoder()
										_SCREAMING_SNAKE_CASE   :		int       		=    inputs_dict['input_ids']

										_SCREAMING_SNAKE_CASE   :		Tuple       		=    input_ids[:1, :]
										_SCREAMING_SNAKE_CASE   :		Any       		=    inputs_dict['attention_mask'][:1, :]
										_SCREAMING_SNAKE_CASE   :		List[str]       		=    inputs_dict['head_mask']
										_SCREAMING_SNAKE_CASE   :		List[Any]       		=    1

										# first forward pass
										_SCREAMING_SNAKE_CASE   :		Tuple       		=    model(lowerCAmelCase_   ,				attention_mask=lowerCAmelCase_   ,				head_mask=lowerCAmelCase_   ,				use_cache=lowerCAmelCase_							)

										_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE   :		str       		=    outputs.to_tuple()
										_SCREAMING_SNAKE_CASE   :		str       		=    past_key_values[1]







def 				lowercase__		(					lowerCamelCase,						lowerCamelCase,						lowerCamelCase,						lowerCamelCase=None,						lowerCamelCase=None,						lowerCamelCase=None,						lowerCamelCase=None,						lowerCamelCase=None,						):
				if attention_mask is None:
								_SCREAMING_SNAKE_CASE   :		Optional[Any]       		=    tf.cast(tf.math.not_equal(lowerCamelCase,						config.pad_token_id       ),						tf.inta       )
				if decoder_attention_mask is None:
								_SCREAMING_SNAKE_CASE   :		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:
								_SCREAMING_SNAKE_CASE   :		Dict       		=    tf.ones((config.encoder_layers, config.encoder_attention_heads)       )
				if decoder_head_mask is None:
								_SCREAMING_SNAKE_CASE   :		int       		=    tf.ones((config.decoder_layers, config.decoder_attention_heads)       )
				if cross_attn_head_mask is None:
								_SCREAMING_SNAKE_CASE   :		int       		=    tf.ones((config.decoder_layers, config.decoder_attention_heads)       )
				return {
				    "input_ids": input_ids,
				    "decoder_input_ids": decoder_input_ids,
				    "attention_mask": attention_mask,
				    "decoder_attention_mask": decoder_attention_mask,
				    "head_mask": head_mask,
				    "decoder_head_mask": decoder_head_mask,
				    "cross_attn_head_mask": cross_attn_head_mask,
				}


@require_tf
class _lowerCAmelCase						(    __UpperCAmelCase   ,  __UpperCAmelCase   ,  unittest.TestCase       ):
						SCREAMING_SNAKE_CASE_:					List[str] 					=				(TFMBartForConditionalGeneration, TFMBartModel) if is_tf_available() else ()
						SCREAMING_SNAKE_CASE_:					Dict 					=				(TFMBartForConditionalGeneration,) if is_tf_available() else ()
						SCREAMING_SNAKE_CASE_:					Tuple 					=				(
						    {
						        'conversational': TFMBartForConditionalGeneration,
						        'feature-extraction': TFMBartModel,
						        'summarization': TFMBartForConditionalGeneration,
						        'text2text-generation': TFMBartForConditionalGeneration,
						        'translation': TFMBartForConditionalGeneration,
						    }
						    if is_tf_available()
						    else {}
						)
						SCREAMING_SNAKE_CASE_:					Any 					=				True
						SCREAMING_SNAKE_CASE_:					int 					=				False
						SCREAMING_SNAKE_CASE_:					Optional[int] 					=				False







						def 			A      (    self   ,				lowerCAmelCase_   ,				lowerCAmelCase_   ,				lowerCAmelCase_   ,				lowerCAmelCase_   ,				lowerCAmelCase_							)		->							List[Any]:
										if pipeline_test_casse_name != "FeatureExtractionPipelineTests":
														# Exception encountered when calling layer '...'
														return True

										return False







						def 			A      (    self							)		->							Optional[int]:
										_SCREAMING_SNAKE_CASE   :		List[Any]       		=    TFMBartModelTester(self							)
										_SCREAMING_SNAKE_CASE   :		Optional[Any]       		=    ConfigTester(self   ,				config_class=lowerCAmelCase_							)







						def 			A      (    self							)		->							List[Any]:
										self.config_tester.run_common_tests()







						def 			A      (    self							)		->							Dict:
										_SCREAMING_SNAKE_CASE   :		Dict       		=    self.model_tester.prepare_config_and_inputs_for_common()
										self.model_tester.check_decoder_model_past_large_inputs(*lowerCAmelCase_							)







@require_sentencepiece
@require_tokenizers
@require_tf
class _lowerCAmelCase						(    unittest.TestCase       ):
						SCREAMING_SNAKE_CASE_:					str 					=				[
						    ' UN Chief Says There Is No Military Solution in Syria',
						]
						SCREAMING_SNAKE_CASE_:					Union[str, Any] 					=				[
						    'Şeful ONU declară că nu există o soluţie militară în Siria',
						]
						SCREAMING_SNAKE_CASE_:					Dict 					=				'facebook/mbart-large-en-ro'







						@cached_property
						def 			A      (    self							)		->							Union[str, Any]:
										return AutoTokenizer.from_pretrained(self.model_name							)







						@cached_property
						def 			A      (    self							)		->							Optional[int]:
										_SCREAMING_SNAKE_CASE   :		List[str]       		=    TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name							)
										return model







						def 			A      (    self   ,				**lowerCAmelCase_							)		->							Optional[int]:
										_SCREAMING_SNAKE_CASE   :		Tuple       		=    self.translate_src_text(**lowerCAmelCase_							)
										self.assertListEqual(self.expected_text   ,				lowerCAmelCase_							)







						def 			A      (    self   ,				**lowerCAmelCase_							)		->							Tuple:
										_SCREAMING_SNAKE_CASE   :		Tuple       		=    self.tokenizer(self.src_text   ,				**lowerCAmelCase_   ,				return_tensors='tf'							)
										_SCREAMING_SNAKE_CASE   :		int       		=    self.model.generate(
										    model_inputs.input_ids   ,				attention_mask=model_inputs.attention_mask   ,				num_beams=2							)
										_SCREAMING_SNAKE_CASE   :		int       		=    self.tokenizer.batch_decode(lowerCAmelCase_   ,				skip_special_tokens=lowerCAmelCase_							)
										return generated_words







						@slow
						def 			A      (    self							)		->							Any:
										self._assert_generated_batch_equal_expected()

