




from __future__ import annotations

import unittest

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

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


if is_tf_available():
					import numpy
					import tensorflow as tf

					from transformers.models.esm.modeling_tf_esm import (
					    TF_ESM_PRETRAINED_MODEL_ARCHIVE_LIST,
					    TFEsmForMaskedLM,
					    TFEsmForSequenceClassification,
					    TFEsmForTokenClassification,
					    TFEsmModel,
					)
class 			a__    :






						def __init__(   self		,			_A		,			):




											"""simple docstring"""



											__lowerCAmelCase				   =       parent
											__lowerCAmelCase				   =       1_3
											__lowerCAmelCase				   =       7
											__lowerCAmelCase				   =       True
											__lowerCAmelCase				   =       True
											__lowerCAmelCase				   =       True
											__lowerCAmelCase				   =       9_9
											__lowerCAmelCase				   =       3_2
											__lowerCAmelCase				   =       2
											__lowerCAmelCase				   =       4
											__lowerCAmelCase				   =       3_7
											__lowerCAmelCase				   =       "gelu"
											__lowerCAmelCase				   =       0.1
											__lowerCAmelCase				   =       0.1
											__lowerCAmelCase				   =       5_1_2
											__lowerCAmelCase				   =       1_6
											__lowerCAmelCase				   =       2
											__lowerCAmelCase				   =       0.02
											__lowerCAmelCase				   =       3
											__lowerCAmelCase				   =       4
											__lowerCAmelCase				   =       None






						def 				__SCREAMING_SNAKE_CASE(   self			):




											"""simple docstring"""



											__lowerCAmelCase				   =       ids_tensor([self.batch_size, self.seq_length]		,			self.vocab_size			)

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

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

											__lowerCAmelCase				   =       EsmConfig(
											    vocab_size=self.vocab_size		,			hidden_size=self.hidden_size		,			num_hidden_layers=self.num_hidden_layers		,			pad_token_id=1		,			num_attention_heads=self.num_attention_heads		,			intermediate_size=self.intermediate_size		,			hidden_act=self.hidden_act		,			hidden_dropout_prob=self.hidden_dropout_prob		,			attention_probs_dropout_prob=self.attention_probs_dropout_prob		,			max_position_embeddings=self.max_position_embeddings		,			type_vocab_size=self.type_vocab_size		,			initializer_range=self.initializer_range		,			)

											return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels






						def 				__SCREAMING_SNAKE_CASE(   self			):




											"""simple docstring"""



											(
											    (
											    __lowerCAmelCase
											)   ,							(
											    __lowerCAmelCase
											)   ,							(
											    __lowerCAmelCase
											)   ,							(
											    __lowerCAmelCase
											)   ,							(
											    __lowerCAmelCase
											)   ,							(
											    __lowerCAmelCase
											)   ,							
											)				   =       self.prepare_config_and_inputs()

											__lowerCAmelCase				   =       True
											__lowerCAmelCase				   =       floats_tensor([self.batch_size, self.seq_length, self.hidden_size]			)
											__lowerCAmelCase				   =       ids_tensor([self.batch_size, self.seq_length]		,			vocab_size=2			)

											return (
											    config,
											    input_ids,
											    input_mask,
											    sequence_labels,
											    token_labels,
											    choice_labels,
											    encoder_hidden_states,
											    encoder_attention_mask,
											)






						def 				__SCREAMING_SNAKE_CASE(   self		,			_A		,			_A		,			_A		,			_A		,			_A		,			_A			):




											"""simple docstring"""



											__lowerCAmelCase				   =       TFEsmModel(config=_A			)
											__lowerCAmelCase				   =       {"input_ids": input_ids, "attention_mask": input_mask}
											__lowerCAmelCase				   =       model(_A			)

											__lowerCAmelCase				   =       [input_ids, input_mask]
											__lowerCAmelCase				   =       model(_A			)

											__lowerCAmelCase				   =       model(_A			)

											self.parent.assertEqual(result.last_hidden_state.shape		,			(self.batch_size, self.seq_length, self.hidden_size)			)






						def 				__SCREAMING_SNAKE_CASE(   self		,			_A		,			_A		,			_A		,			_A		,			_A		,			_A		,			_A		,			_A		,			):




											"""simple docstring"""



											__lowerCAmelCase				   =       True

											__lowerCAmelCase				   =       TFEsmModel(config=_A			)
											__lowerCAmelCase				   =       {
											    "input_ids": input_ids,
											    "attention_mask": input_mask,
											    "encoder_hidden_states": encoder_hidden_states,
											    "encoder_attention_mask": encoder_attention_mask,
											}
											__lowerCAmelCase				   =       model(_A			)

											__lowerCAmelCase				   =       [input_ids, input_mask]
											__lowerCAmelCase				   =       model(_A		,			encoder_hidden_states=_A			)

											# Also check the case where encoder outputs are not passed
											__lowerCAmelCase				   =       model(_A		,			attention_mask=_A			)

											self.parent.assertEqual(result.last_hidden_state.shape		,			(self.batch_size, self.seq_length, self.hidden_size)			)






						def 				__SCREAMING_SNAKE_CASE(   self		,			_A		,			_A		,			_A		,			_A		,			_A		,			_A			):




											"""simple docstring"""



											__lowerCAmelCase				   =       TFEsmForMaskedLM(config=_A			)
											__lowerCAmelCase				   =       model([input_ids, input_mask]			)
											self.parent.assertEqual(result.logits.shape		,			(self.batch_size, self.seq_length, self.vocab_size)			)






						def 				__SCREAMING_SNAKE_CASE(   self		,			_A		,			_A		,			_A		,			_A		,			_A		,			_A			):




											"""simple docstring"""



											__lowerCAmelCase				   =       self.num_labels
											__lowerCAmelCase				   =       TFEsmForTokenClassification(config=_A			)
											__lowerCAmelCase				   =       {"input_ids": input_ids, "attention_mask": input_mask}
											__lowerCAmelCase				   =       model(_A			)
											self.parent.assertEqual(result.logits.shape		,			(self.batch_size, self.seq_length, self.num_labels)			)






						def 				__SCREAMING_SNAKE_CASE(   self			):




											"""simple docstring"""



											__lowerCAmelCase				   =       self.prepare_config_and_inputs()
											(
											    (
											    __lowerCAmelCase
											)   ,							(
											    __lowerCAmelCase
											)   ,							(
											    __lowerCAmelCase
											)   ,							(
											    __lowerCAmelCase
											)   ,							(
											    __lowerCAmelCase
											)   ,							(
											    __lowerCAmelCase
											)   ,							
											)				   =       config_and_inputs
											__lowerCAmelCase				   =       {"input_ids": input_ids, "attention_mask": input_mask}
											return config, inputs_dict






@require_tf
class 			a__    (					snake_case__ ,				snake_case__ ,				unittest.TestCase      ):
						_a							:    Any					=    (
						    (
						        TFEsmModel,
						        TFEsmForMaskedLM,
						        TFEsmForSequenceClassification,
						        TFEsmForTokenClassification,
						    )
						    if is_tf_available()
						    else ()
						)
						_a							:    List[Any]					=    (
						    {
						        """feature-extraction""": TFEsmModel,
						        """fill-mask""": TFEsmForMaskedLM,
						        """text-classification""": TFEsmForSequenceClassification,
						        """token-classification""": TFEsmForTokenClassification,
						        """zero-shot""": TFEsmForSequenceClassification,
						    }
						    if is_tf_available()
						    else {}
						)
						_a							:    Union[str, Any]					=    False
						_a							:    List[Any]					=    False






						def 				__SCREAMING_SNAKE_CASE(   self			):




											"""simple docstring"""



											__lowerCAmelCase				   =       TFEsmModelTester(self			)
											__lowerCAmelCase				   =       ConfigTester(self		,			config_class=_A		,			hidden_size=3_7			)






						def 				__SCREAMING_SNAKE_CASE(   self			):




											"""simple docstring"""



											self.config_tester.run_common_tests()






						def 				__SCREAMING_SNAKE_CASE(   self			):




											"""simple docstring"""



											__lowerCAmelCase				   =       self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_model(*_A			)






						def 				__SCREAMING_SNAKE_CASE(   self			):




											"""simple docstring"""



											__lowerCAmelCase				   =       self.model_tester.prepare_config_and_inputs_for_decoder()
											self.model_tester.create_and_check_model_as_decoder(*_A			)






						def 				__SCREAMING_SNAKE_CASE(   self			):




											"""simple docstring"""



											__lowerCAmelCase				   =       self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_for_masked_lm(*_A			)






						def 				__SCREAMING_SNAKE_CASE(   self			):




											"""simple docstring"""



											__lowerCAmelCase				   =       self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_for_token_classification(*_A			)






						@slow
						def 				__SCREAMING_SNAKE_CASE(   self			):




											"""simple docstring"""



											for model_name in TF_ESM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																__lowerCAmelCase				   =       TFEsmModel.from_pretrained(_A			)
																self.assertIsNotNone(_A			)






						@unittest.skip("Protein models do not support embedding resizing."			)
						def 				__SCREAMING_SNAKE_CASE(   self			):




											"""simple docstring"""



											pass






						@unittest.skip("Protein models do not support embedding resizing."			)
						def 				__SCREAMING_SNAKE_CASE(   self			):




											"""simple docstring"""



											pass






						def 				__SCREAMING_SNAKE_CASE(   self			):




											"""simple docstring"""



											__lowerCAmelCase   ,							__lowerCAmelCase				   =       self.model_tester.prepare_config_and_inputs_for_common()

											for model_class in self.all_model_classes:
																__lowerCAmelCase				   =       model_class(_A			)
																assert isinstance(model.get_input_embeddings()		,			tf.keras.layers.Layer			)
																if model_class is TFEsmForMaskedLM:
																					# Output embedding test differs from the main test because they're a matrix, not a layer
																					__lowerCAmelCase				   =       model.get_bias()
																					assert isinstance(_A		,			_A			)
																					for k, v in name.items():
																										assert isinstance(_A		,			tf.Variable			)
																else:
																					__lowerCAmelCase				   =       model.get_output_embeddings()
																					assert x is None
																					__lowerCAmelCase				   =       model.get_bias()
																					assert name is None






@require_tf
class 			a__    (					unittest.TestCase      ):






						@slow
						def 				__SCREAMING_SNAKE_CASE(   self			):




											"""simple docstring"""



											__lowerCAmelCase				   =       TFEsmForMaskedLM.from_pretrained("facebook/esm2_t6_8M_UR50D"			)

											__lowerCAmelCase				   =       tf.constant([[0, 1, 2, 3, 4, 5]]			)
											__lowerCAmelCase				   =       model(_A			)[0]
											__lowerCAmelCase				   =       [1, 6, 3_3]
											self.assertEqual(list(output.numpy().shape			)		,			_A			)
											# compare the actual values for a slice.
											__lowerCAmelCase				   =       tf.constant(
											    [
											        [
											            [8.92_15_18, -10.58_98_14, -6.4_67_13_07],
											            [-6.3_96_71_56, -13.91_13_77, -1.1_21_19_15],
											            [-7.78_12_47, -13.95_15_57, -3.74_05_92],
											        ]
											    ]			)
											self.assertTrue(numpy.allclose(output[:, :3, :3].numpy()		,			expected_slice.numpy()		,			atol=1E-2			)			)






						@slow
						def 				__SCREAMING_SNAKE_CASE(   self			):




											"""simple docstring"""



											__lowerCAmelCase				   =       TFEsmModel.from_pretrained("facebook/esm2_t6_8M_UR50D"			)

											__lowerCAmelCase				   =       tf.constant([[0, 6, 4, 1_3, 5, 4, 1_6, 1_2, 1_1, 7, 2]]			)
											__lowerCAmelCase				   =       model(_A			)[0]
											# compare the actual values for a slice.
											__lowerCAmelCase				   =       tf.constant(
											    [
											        [
											            [0.14_44_30_92, 0.54_12_53_27, 0.3_24_77_39],
											            [0.30_34_04_84, 0.00_52_66_76, 0.31_07_77_22],
											            [0.32_27_80_43, -0.24_98_70_96, 0.3_41_46_28],
											        ]
											    ]			)
											self.assertTrue(numpy.allclose(output[:, :3, :3].numpy()		,			expected_slice.numpy()		,			atol=1E-4			)			)










import gc
import unittest

import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DDPMScheduler,
    PriorTransformer,
    StableUnCLIPPipeline,
    UNetaDConditionModel,
)
from diffusers.pipelines.stable_diffusion.stable_unclip_image_normalizer import StableUnCLIPImageNormalizer
from diffusers.utils.testing_utils import enable_full_determinism, load_numpy, require_torch_gpu, slow, torch_device

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import (
    PipelineKarrasSchedulerTesterMixin,
    PipelineLatentTesterMixin,
    PipelineTesterMixin,
    assert_mean_pixel_difference,
)


enable_full_determinism()
class 			a__    (					snake_case__ ,				snake_case__ ,				snake_case__ ,				unittest.TestCase      ):
						_a							:    str					=    StableUnCLIPPipeline
						_a							:    Union[str, Any]					=    TEXT_TO_IMAGE_PARAMS
						_a							:    Dict					=    TEXT_TO_IMAGE_BATCH_PARAMS
						_a							:    Optional[int]					=    TEXT_TO_IMAGE_IMAGE_PARAMS
						_a							:    Dict					=    TEXT_TO_IMAGE_IMAGE_PARAMS

						# TODO(will) Expected attn_bias.stride(1) == 0 to be true, but got false
						_a							:    Optional[Any]					=    False






						def 				__SCREAMING_SNAKE_CASE(   self			):




											"""simple docstring"""



											__lowerCAmelCase				   =       3_2
											__lowerCAmelCase				   =       embedder_hidden_size

											# prior components

											torch.manual_seed(0			)
											__lowerCAmelCase				   =       CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"			)

											torch.manual_seed(0			)
											__lowerCAmelCase				   =       CLIPTextModelWithProjection(
											    CLIPTextConfig(
											        bos_token_id=0		,			eos_token_id=2		,			hidden_size=_A		,			projection_dim=_A		,			intermediate_size=3_7		,			layer_norm_eps=1E-0_5		,			num_attention_heads=4		,			num_hidden_layers=5		,			pad_token_id=1		,			vocab_size=1_0_0_0		,			)			)

											torch.manual_seed(0			)
											__lowerCAmelCase				   =       PriorTransformer(
											    num_attention_heads=2		,			attention_head_dim=1_2		,			embedding_dim=_A		,			num_layers=1		,			)

											torch.manual_seed(0			)
											__lowerCAmelCase				   =       DDPMScheduler(
											    variance_type="fixed_small_log"		,			prediction_type="sample"		,			num_train_timesteps=1_0_0_0		,			clip_sample=_A		,			clip_sample_range=5.0		,			beta_schedule="squaredcos_cap_v2"		,			)

											# regular denoising components

											torch.manual_seed(0			)
											__lowerCAmelCase				   =       StableUnCLIPImageNormalizer(embedding_dim=_A			)
											__lowerCAmelCase				   =       DDPMScheduler(beta_schedule="squaredcos_cap_v2"			)

											torch.manual_seed(0			)
											__lowerCAmelCase				   =       CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"			)

											torch.manual_seed(0			)
											__lowerCAmelCase				   =       CLIPTextModel(
											    CLIPTextConfig(
											        bos_token_id=0		,			eos_token_id=2		,			hidden_size=_A		,			projection_dim=3_2		,			intermediate_size=3_7		,			layer_norm_eps=1E-0_5		,			num_attention_heads=4		,			num_hidden_layers=5		,			pad_token_id=1		,			vocab_size=1_0_0_0		,			)			)

											torch.manual_seed(0			)
											__lowerCAmelCase				   =       UNetaDConditionModel(
											    sample_size=3_2		,			in_channels=4		,			out_channels=4		,			down_block_types=("CrossAttnDownBlock2D", "DownBlock2D")		,			up_block_types=("UpBlock2D", "CrossAttnUpBlock2D")		,			block_out_channels=(3_2, 6_4)		,			attention_head_dim=(2, 4)		,			class_embed_type="projection"		,			projection_class_embeddings_input_dim=embedder_projection_dim * 2		,			cross_attention_dim=_A		,			layers_per_block=1		,			upcast_attention=_A		,			use_linear_projection=_A		,			)

											torch.manual_seed(0			)
											__lowerCAmelCase				   =       DDIMScheduler(
											    beta_schedule="scaled_linear"		,			beta_start=0.0_00_85		,			beta_end=0.0_12		,			prediction_type="v_prediction"		,			set_alpha_to_one=_A		,			steps_offset=1		,			)

											torch.manual_seed(0			)
											__lowerCAmelCase				   =       AutoencoderKL()

											__lowerCAmelCase				   =       {
											    # prior components
											    "prior_tokenizer": prior_tokenizer,
											    "prior_text_encoder": prior_text_encoder,
											    "prior": prior,
											    "prior_scheduler": prior_scheduler,
											    # image noising components
											    "image_normalizer": image_normalizer,
											    "image_noising_scheduler": image_noising_scheduler,
											    # regular denoising components
											    "tokenizer": tokenizer,
											    "text_encoder": text_encoder,
											    "unet": unet,
											    "scheduler": scheduler,
											    "vae": vae,
											}

											return components






						def 				__SCREAMING_SNAKE_CASE(   self		,			_A		,			_A=0			):




											"""simple docstring"""



											if str(_A			).startswith("mps"			):
																__lowerCAmelCase				   =       torch.manual_seed(_A			)
											else:
																__lowerCAmelCase				   =       torch.Generator(device=_A			).manual_seed(_A			)
											__lowerCAmelCase				   =       {
											    "prompt": "A painting of a squirrel eating a burger",
											    "generator": generator,
											    "num_inference_steps": 2,
											    "prior_num_inference_steps": 2,
											    "output_type": "numpy",
											}
											return inputs






						def 				__SCREAMING_SNAKE_CASE(   self			):




											"""simple docstring"""



											__lowerCAmelCase				   =       torch_device == "cpu"

											self._test_attention_slicing_forward_pass(test_max_difference=_A			)






						def 				__SCREAMING_SNAKE_CASE(   self			):




											"""simple docstring"""



											__lowerCAmelCase				   =       torch_device in ["cpu", "mps"]

											self._test_inference_batch_single_identical(test_max_difference=_A			)






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






						def 				__SCREAMING_SNAKE_CASE(   self			):




											"""simple docstring"""



											super().tearDown()
											gc.collect()
											torch.cuda.empty_cache()






						def 				__SCREAMING_SNAKE_CASE(   self			):




											"""simple docstring"""



											__lowerCAmelCase				   =       load_numpy(
											    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/stable_unclip_2_1_l_anime_turtle_fp16.npy"			)

											__lowerCAmelCase				   =       StableUnCLIPPipeline.from_pretrained("fusing/stable-unclip-2-1-l"		,			torch_dtype=torch.floataa			)
											pipe.to(_A			)
											pipe.set_progress_bar_config(disable=_A			)
											# stable unclip will oom when integration tests are run on a V100,
											# so turn on memory savings
											pipe.enable_attention_slicing()
											pipe.enable_sequential_cpu_offload()

											__lowerCAmelCase				   =       torch.Generator(device="cpu"			).manual_seed(0			)
											__lowerCAmelCase				   =       pipe("anime turle"		,			generator=_A		,			output_type="np"			)

											__lowerCAmelCase				   =       output.images[0]

											assert image.shape == (7_6_8, 7_6_8, 3)

											assert_mean_pixel_difference(_A		,			_A			)






						def 				__SCREAMING_SNAKE_CASE(   self			):




											"""simple docstring"""



											torch.cuda.empty_cache()
											torch.cuda.reset_max_memory_allocated()
											torch.cuda.reset_peak_memory_stats()

											__lowerCAmelCase				   =       StableUnCLIPPipeline.from_pretrained("fusing/stable-unclip-2-1-l"		,			torch_dtype=torch.floataa			)
											__lowerCAmelCase				   =       pipe.to(_A			)
											pipe.set_progress_bar_config(disable=_A			)
											pipe.enable_attention_slicing()
											pipe.enable_sequential_cpu_offload()

											__lowerCAmelCase				   =       pipe(
											    "anime turtle"		,			prior_num_inference_steps=2		,			num_inference_steps=2		,			output_type="np"		,			)

											__lowerCAmelCase				   =       torch.cuda.max_memory_allocated()
											# make sure that less than 7 GB is allocated
											assert mem_bytes < 7 * 1_0**9




