






import gc
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import AutoencoderKL, DDIMScheduler, LDMTextToImagePipeline, UNetaDConditionModel
from diffusers.utils.testing_utils import (
    enable_full_determinism,
    load_numpy,
    nightly,
    require_torch_gpu,
    slow,
    torch_device,
)

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()
class        a       (					lowercase__				,			unittest.TestCase  ):



      """simple docstring"""


      a    :					Any							=      LDMTextToImagePipeline
      a    :					Any							=      TEXT_TO_IMAGE_PARAMS - {
          'negative_prompt',
          'negative_prompt_embeds',
          'cross_attention_kwargs',
          'prompt_embeds',
      }
      a    :					Union[str, Any]							=      PipelineTesterMixin.required_optional_params - {
          'num_images_per_prompt',
          'callback',
          'callback_steps',
      }
      a    :					Tuple							=      TEXT_TO_IMAGE_BATCH_PARAMS
      a    :					List[str]							=      False
      def       UpperCAmelCase ( self   :     Tuple   )				-> Any:
        torch.manual_seed(0   )
        __UpperCAmelCase				:      str	 =    UNetaDConditionModel(
            block_out_channels=(32, 64)						, layers_per_block=2						, sample_size=32						, in_channels=4						, out_channels=4						, down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""")						, up_block_types=("""CrossAttnUpBlock2D""", """UpBlock2D""")						, cross_attention_dim=32						, )
        __UpperCAmelCase				:      List[Any]	 =    DDIMScheduler(
            beta_start=0.00_085						, beta_end=0.012						, beta_schedule="""scaled_linear"""						, clip_sample=__lowercase						, set_alpha_to_one=__lowercase						, )
        torch.manual_seed(0   )
        __UpperCAmelCase				:      Union[str, Any]	 =    AutoencoderKL(
            block_out_channels=(32, 64)						, in_channels=3						, out_channels=3						, down_block_types=("""DownEncoderBlock2D""", """DownEncoderBlock2D""")						, up_block_types=("""UpDecoderBlock2D""", """UpDecoderBlock2D""")						, latent_channels=4						, )
        torch.manual_seed(0   )
        __UpperCAmelCase				:      int	 =    CLIPTextConfig(
            bos_token_id=0						, eos_token_id=2						, hidden_size=32						, intermediate_size=37						, layer_norm_eps=1e-0_5						, num_attention_heads=4						, num_hidden_layers=5						, pad_token_id=1						, vocab_size=1000						, )
        __UpperCAmelCase				:      Optional[int]	 =    CLIPTextModel(__lowercase   )
        __UpperCAmelCase				:      Optional[int]	 =    CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""   )

        __UpperCAmelCase				:      Union[str, Any]	 =    {
            """unet""": unet,
            """scheduler""": scheduler,
            """vqvae""": vae,
            """bert""": text_encoder,
            """tokenizer""": tokenizer,
        }
        return components
      def       UpperCAmelCase ( self   :     Any						, __lowercase   :     Optional[int]						, __lowercase   :     int=0   )				-> Optional[int]:
        if str(__lowercase   ).startswith("""mps"""   ):
          __UpperCAmelCase				:      Tuple	 =    torch.manual_seed(__lowercase   )
        else:
          __UpperCAmelCase				:      Dict	 =    torch.Generator(device=__lowercase   ).manual_seed(__lowercase   )
        __UpperCAmelCase				:      Optional[int]	 =    {
            """prompt""": """A painting of a squirrel eating a burger""",
            """generator""": generator,
            """num_inference_steps""": 2,
            """guidance_scale""": 6.0,
            """output_type""": """numpy""",
        }
        return inputs




      def       UpperCAmelCase ( self   :     int   )				-> Any:
        __UpperCAmelCase				:      int	 =    """cpu"""  # ensure determinism for the device-dependent torch.Generator

        __UpperCAmelCase				:      Tuple	 =    self.get_dummy_components()
        __UpperCAmelCase				:      Any	 =    LDMTextToImagePipeline(**__lowercase   )
        pipe.to(__lowercase   )
        pipe.set_progress_bar_config(disable=__lowercase   )

        __UpperCAmelCase				:      Dict	 =    self.get_dummy_inputs(__lowercase   )
        __UpperCAmelCase				:      Union[str, Any]	 =    pipe(**__lowercase   ).images
        __UpperCAmelCase				:      Any	 =    image[0, -3:, -3:, -1]

        assert image.shape == (1, 16, 16, 3)
        __UpperCAmelCase				:      Optional[Any]	 =    np.array([0.6_101, 0.6_156, 0.5_622, 0.4_895, 0.6_661, 0.3_804, 0.5_748, 0.6_136, 0.5_014]   )

        assert np.abs(image_slice.flatten() - expected_slice   ).max() < 1e-3


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



      """simple docstring"""


      def       UpperCAmelCase ( self   :     str   )				-> str:
        super().tearDown()
        gc.collect()
        torch.cuda.empty_cache()
      def       UpperCAmelCase ( self   :     Optional[Any]						, __lowercase   :     Any						, __lowercase   :     int=torch.floataa						, __lowercase   :     Any=0   )				-> Optional[Any]:
        __UpperCAmelCase				:      List[Any]	 =    torch.manual_seed(__lowercase   )
        __UpperCAmelCase				:      Union[str, Any]	 =    np.random.RandomState(__lowercase   ).standard_normal((1, 4, 32, 32)   )
        __UpperCAmelCase				:      List[str]	 =    torch.from_numpy(__lowercase   ).to(device=__lowercase						, dtype=__lowercase   )
        __UpperCAmelCase				:      Optional[Any]	 =    {
            """prompt""": """A painting of a squirrel eating a burger""",
            """latents""": latents,
            """generator""": generator,
            """num_inference_steps""": 3,
            """guidance_scale""": 6.0,
            """output_type""": """numpy""",
        }
        return inputs




      def       UpperCAmelCase ( self   :     str   )				-> Union[str, Any]:
        __UpperCAmelCase				:      Union[str, Any]	 =    LDMTextToImagePipeline.from_pretrained("""CompVis/ldm-text2im-large-256"""   ).to(__lowercase   )
        pipe.set_progress_bar_config(disable=__lowercase   )

        __UpperCAmelCase				:      str	 =    self.get_inputs(__lowercase   )
        __UpperCAmelCase				:      List[str]	 =    pipe(**__lowercase   ).images
        __UpperCAmelCase				:      str	 =    image[0, -3:, -3:, -1].flatten()

        assert image.shape == (1, 256, 256, 3)
        __UpperCAmelCase				:      int	 =    np.array([0.51_825, 0.52_850, 0.52_543, 0.54_258, 0.52_304, 0.52_569, 0.54_363, 0.55_276, 0.56_878]   )
        __UpperCAmelCase				:      Tuple	 =    np.abs(expected_slice - image_slice   ).max()
        assert max_diff < 1e-3


@nightly
@require_torch_gpu
class        a       (					unittest.TestCase  ):



      """simple docstring"""


      def       UpperCAmelCase ( self   :     Dict   )				-> Dict:
        super().tearDown()
        gc.collect()
        torch.cuda.empty_cache()
      def       UpperCAmelCase ( self   :     Optional[Any]						, __lowercase   :     int						, __lowercase   :     List[str]=torch.floataa						, __lowercase   :     Union[str, Any]=0   )				-> Optional[int]:
        __UpperCAmelCase				:      Tuple	 =    torch.manual_seed(__lowercase   )
        __UpperCAmelCase				:      Dict	 =    np.random.RandomState(__lowercase   ).standard_normal((1, 4, 32, 32)   )
        __UpperCAmelCase				:      str	 =    torch.from_numpy(__lowercase   ).to(device=__lowercase						, dtype=__lowercase   )
        __UpperCAmelCase				:      Dict	 =    {
            """prompt""": """A painting of a squirrel eating a burger""",
            """latents""": latents,
            """generator""": generator,
            """num_inference_steps""": 50,
            """guidance_scale""": 6.0,
            """output_type""": """numpy""",
        }
        return inputs




      def       UpperCAmelCase ( self   :     List[str]   )				-> List[str]:
        __UpperCAmelCase				:      int	 =    LDMTextToImagePipeline.from_pretrained("""CompVis/ldm-text2im-large-256"""   ).to(__lowercase   )
        pipe.set_progress_bar_config(disable=__lowercase   )

        __UpperCAmelCase				:      Union[str, Any]	 =    self.get_inputs(__lowercase   )
        __UpperCAmelCase				:      Dict	 =    pipe(**__lowercase   ).images[0]

        __UpperCAmelCase				:      Optional[int]	 =    load_numpy(
            """https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/ldm_text2img/ldm_large_256_ddim.npy"""   )
        __UpperCAmelCase				:      str	 =    np.abs(expected_image - image   ).max()
        assert max_diff < 1e-3






'''simple docstring'''





from __future__ import annotations

import unittest

from transformers import DistilBertConfig, 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, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
					import tensorflow as tf

					from transformers.models.distilbert.modeling_tf_distilbert import (
					    TF_DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
					    TFDistilBertForMaskedLM,
					    TFDistilBertForMultipleChoice,
					    TFDistilBertForQuestionAnswering,
					    TFDistilBertForSequenceClassification,
					    TFDistilBertForTokenClassification,
					    TFDistilBertModel,
					)
class 						a__					:





			'''simple docstring'''

			def __init__(  self							:			str			,    lowerCAmelCase_							:			str			,    )      ->		Optional[int]:
						__A=	parent
						__A=	13
						__A=	7
						__A=	True
						__A=	True
						__A=	False
						__A=	True
						__A=	99
						__A=	32
						__A=	2
						__A=	4
						__A=	37
						__A=	'gelu'
						__A=	0.1
						__A=	0.1
						__A=	512
						__A=	16
						__A=	2
						__A=	0.02
						__A=	3
						__A=	4
						__A=	None
			def 					lowerCAmelCase     (  self							:			Optional[Any]	)      ->		str:
						__A=	ids_tensor([self.batch_size, self.seq_length]			,    self.vocab_size	)

						__A=	None
						if self.use_input_mask:
									__A=	random_attention_mask([self.batch_size, self.seq_length]	)

						__A=	None
						__A=	None
						__A=	None
						if self.use_labels:
									__A=	ids_tensor([self.batch_size]			,    self.type_sequence_label_size	)
									__A=	ids_tensor([self.batch_size, self.seq_length]			,    self.num_labels	)
									__A=	ids_tensor([self.batch_size]			,    self.num_choices	)

						__A=	DistilBertConfig(
						    vocab_size=self.vocab_size			,    dim=self.hidden_size			,    n_layers=self.num_hidden_layers			,    n_heads=self.num_attention_heads			,    hidden_dim=self.intermediate_size			,    hidden_act=self.hidden_act			,    dropout=self.hidden_dropout_prob			,    attention_dropout=self.attention_probs_dropout_prob			,    max_position_embeddings=self.max_position_embeddings			,    initializer_range=self.initializer_range			,    )

						return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels
			def 					lowerCAmelCase     (  self							:			str			,    lowerCAmelCase_							:			Any			,    lowerCAmelCase_							:			int			,    lowerCAmelCase_							:			int			,    lowerCAmelCase_							:			Dict			,    lowerCAmelCase_							:			Any			,    lowerCAmelCase_							:			Optional[Any]	)      ->		Any:
						__A=	TFDistilBertModel(config=lowerCAmelCase_	)
						__A=	{'input_ids': input_ids, 'attention_mask': input_mask}

						__A=	model(lowerCAmelCase_	)

						__A=	[input_ids, input_mask]

						__A=	model(lowerCAmelCase_	)

						self.parent.assertEqual(result.last_hidden_state.shape			,    (self.batch_size, self.seq_length, self.hidden_size)	)
			def 					lowerCAmelCase     (  self							:			Optional[int]			,    lowerCAmelCase_							:			Optional[Any]			,    lowerCAmelCase_							:			List[Any]			,    lowerCAmelCase_							:			str			,    lowerCAmelCase_							:			Union[str, Any]			,    lowerCAmelCase_							:			Dict			,    lowerCAmelCase_							:			Tuple	)      ->		Optional[int]:
						__A=	TFDistilBertForMaskedLM(config=lowerCAmelCase_	)
						__A=	{'input_ids': input_ids, 'attention_mask': input_mask}
						__A=	model(lowerCAmelCase_	)
						self.parent.assertEqual(result.logits.shape			,    (self.batch_size, self.seq_length, self.vocab_size)	)
			def 					lowerCAmelCase     (  self							:			str			,    lowerCAmelCase_							:			Dict			,    lowerCAmelCase_							:			Optional[Any]			,    lowerCAmelCase_							:			Tuple			,    lowerCAmelCase_							:			str			,    lowerCAmelCase_							:			Dict			,    lowerCAmelCase_							:			List[str]	)      ->		int:
						__A=	TFDistilBertForQuestionAnswering(config=lowerCAmelCase_	)
						__A=	{
						    'input_ids': input_ids,
						    'attention_mask': input_mask,
						}
						__A=	model(lowerCAmelCase_	)
						self.parent.assertEqual(result.start_logits.shape			,    (self.batch_size, self.seq_length)	)
						self.parent.assertEqual(result.end_logits.shape			,    (self.batch_size, self.seq_length)	)
			def 					lowerCAmelCase     (  self							:			List[str]			,    lowerCAmelCase_							:			Any			,    lowerCAmelCase_							:			Optional[int]			,    lowerCAmelCase_							:			Tuple			,    lowerCAmelCase_							:			int			,    lowerCAmelCase_							:			Dict			,    lowerCAmelCase_							:			List[str]	)      ->		Union[str, Any]:
						__A=	self.num_labels
						__A=	TFDistilBertForSequenceClassification(lowerCAmelCase_	)
						__A=	{'input_ids': input_ids, 'attention_mask': input_mask}
						__A=	model(lowerCAmelCase_	)
						self.parent.assertEqual(result.logits.shape			,    (self.batch_size, self.num_labels)	)
			def 					lowerCAmelCase     (  self							:			str			,    lowerCAmelCase_							:			Any			,    lowerCAmelCase_							:			Union[str, Any]			,    lowerCAmelCase_							:			List[Any]			,    lowerCAmelCase_							:			str			,    lowerCAmelCase_							:			Union[str, Any]			,    lowerCAmelCase_							:			int	)      ->		Optional[Any]:
						__A=	self.num_choices
						__A=	TFDistilBertForMultipleChoice(lowerCAmelCase_	)
						__A=	tf.tile(tf.expand_dims(lowerCAmelCase_			,    1	)			,    (1, self.num_choices, 1)	)
						__A=	tf.tile(tf.expand_dims(lowerCAmelCase_			,    1	)			,    (1, self.num_choices, 1)	)
						__A=	{
						    'input_ids': multiple_choice_inputs_ids,
						    'attention_mask': multiple_choice_input_mask,
						}
						__A=	model(lowerCAmelCase_	)
						self.parent.assertEqual(result.logits.shape			,    (self.batch_size, self.num_choices)	)
			def 					lowerCAmelCase     (  self							:			List[Any]			,    lowerCAmelCase_							:			Optional[Any]			,    lowerCAmelCase_							:			Dict			,    lowerCAmelCase_							:			Union[str, Any]			,    lowerCAmelCase_							:			Optional[int]			,    lowerCAmelCase_							:			List[str]			,    lowerCAmelCase_							:			Optional[Any]	)      ->		Dict:
						__A=	self.num_labels
						__A=	TFDistilBertForTokenClassification(lowerCAmelCase_	)
						__A=	{'input_ids': input_ids, 'attention_mask': input_mask}
						__A=	model(lowerCAmelCase_	)
						self.parent.assertEqual(result.logits.shape			,    (self.batch_size, self.seq_length, self.num_labels)	)

			def 					lowerCAmelCase     (  self							:			Union[str, Any]	)      ->		int:
						__A=	self.prepare_config_and_inputs()
						((__A),	(__A),	(__A),	(__A),	(__A),	(__A))=	config_and_inputs
						__A=	{'input_ids': input_ids, 'attention_mask': input_mask}
						return config, inputs_dict


@require_tf
class 						a__					(					a_      ,  a_      ,  unittest.TestCase   ):





			'''simple docstring'''

			A						:				Optional[Any]					      =			(
			    (
			        TFDistilBertModel,
			        TFDistilBertForMaskedLM,
			        TFDistilBertForQuestionAnswering,
			        TFDistilBertForSequenceClassification,
			        TFDistilBertForTokenClassification,
			        TFDistilBertForMultipleChoice,
			    )
			    if is_tf_available()
			    else None
			)
			A						:				Optional[int]					      =			(
			    {
			        '''feature-extraction''': TFDistilBertModel,
			        '''fill-mask''': TFDistilBertForMaskedLM,
			        '''question-answering''': TFDistilBertForQuestionAnswering,
			        '''text-classification''': TFDistilBertForSequenceClassification,
			        '''token-classification''': TFDistilBertForTokenClassification,
			        '''zero-shot''': TFDistilBertForSequenceClassification,
			    }
			    if is_tf_available()
			    else {}
			)
			A						:				str					      =			False
			A						:				List[Any]					      =			False
			def 					lowerCAmelCase     (  self							:			List[str]	)      ->		Optional[Any]:
						__A=	TFDistilBertModelTester(self	)
						__A=	ConfigTester(self			,    config_class=lowerCAmelCase_			,    dim=37	)
			def 					lowerCAmelCase     (  self							:			Dict	)      ->		Tuple:
						self.config_tester.run_common_tests()
			def 					lowerCAmelCase     (  self							:			Union[str, Any]	)      ->		Optional[int]:
						__A=	self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_distilbert_model(*lowerCAmelCase_	)
			def 					lowerCAmelCase     (  self							:			Optional[int]	)      ->		Optional[Any]:
						__A=	self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_distilbert_for_masked_lm(*lowerCAmelCase_	)
			def 					lowerCAmelCase     (  self							:			str	)      ->		Any:
						__A=	self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_distilbert_for_question_answering(*lowerCAmelCase_	)
			def 					lowerCAmelCase     (  self							:			Tuple	)      ->		Optional[Any]:
						__A=	self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_distilbert_for_sequence_classification(*lowerCAmelCase_	)
			def 					lowerCAmelCase     (  self							:			Optional[Any]	)      ->		Optional[Any]:
						__A=	self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_distilbert_for_multiple_choice(*lowerCAmelCase_	)
			def 					lowerCAmelCase     (  self							:			Any	)      ->		Optional[int]:
						__A=	self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_distilbert_for_token_classification(*lowerCAmelCase_	)

			@slow
			def 					lowerCAmelCase     (  self							:			int	)      ->		Tuple:
						for model_name in list(TF_DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]	):
									__A=	TFDistilBertModel.from_pretrained(lowerCAmelCase_	)
									self.assertIsNotNone(lowerCAmelCase_	)


@require_tf
class 						a__					(					unittest.TestCase   ):





			'''simple docstring'''

			@slow
			def 					lowerCAmelCase     (  self							:			Any	)      ->		List[Any]:
						__A=	TFDistilBertModel.from_pretrained('distilbert-base-uncased'	)
						__A=	tf.constant([[0, 1, 2, 3, 4, 5]]	)
						__A=	model(lowerCAmelCase_	)[0]

						__A=	[1, 6, 768]
						self.assertEqual(output.shape			,    lowerCAmelCase_	)

						__A=	tf.constant(
						    [
						        [
						            [0.19_26_18_85, -0.13_73_29_55, 0.4_11_97_99],
						            [0.22_15_01_56, -0.07_42_26_61, 0.39_03_72_04],
						            [0.22_75_60_18, -0.0_89_64_14, 0.3_70_14_67],
						        ]
						    ]	)
						tf.debugging.assert_near(output[:, :3, :3]			,    lowerCAmelCase_			,    atol=1E-4	)



