"""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  _A						(       lowerCAmelCase  ,    unittest.TestCase	):
      snake_case__			:   Union[str, Any]							  =	DiTPipeline
      snake_case__			:   Any							  =	CLASS_CONDITIONED_IMAGE_GENERATION_PARAMS
      snake_case__			:   int							  =	PipelineTesterMixin.required_optional_params - {
          'latents',
          'num_images_per_prompt',
          'callback',
          'callback_steps',
      }
      snake_case__			:   Optional[Any]							  =	CLASS_CONDITIONED_IMAGE_GENERATION_BATCH_PARAMS
      snake_case__			:   List[Any]							  =	False

      def 				A__       (						self						):







            """simple docstring"""



            torch.manual_seed(0						)
            lowercase														=	TransformeraDModel(
                sample_size=16		,       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=1000		,       norm_type="""ada_norm_zero"""		,       norm_elementwise_affine=__lowerCAmelCase		,       )
            lowercase														=	AutoencoderKL()
            lowercase														=	DDIMScheduler()
            lowercase														=	{"""transformer""": transformer.eval(), """vae""": vae.eval(), """scheduler""": scheduler}
            return components

      def 				A__       (						self		,       __lowerCAmelCase		,       __lowerCAmelCase=0						):







            """simple docstring"""



            if str(__lowerCAmelCase						).startswith("""mps"""						):
                  lowercase														=	torch.manual_seed(__lowerCAmelCase						)
            else:
                  lowercase														=	torch.Generator(device=__lowerCAmelCase						).manual_seed(__lowerCAmelCase						)
            lowercase														=	{
                """class_labels""": [1],
                """generator""": generator,
                """num_inference_steps""": 2,
                """output_type""": """numpy""",
            }
            return inputs

      def 				A__       (						self						):







            """simple docstring"""



            lowercase														=	"""cpu"""

            lowercase														=	self.get_dummy_components()
            lowercase														=	self.pipeline_class(**__lowerCAmelCase						)
            pipe.to(__lowerCAmelCase						)
            pipe.set_progress_bar_config(disable=__lowerCAmelCase						)

            lowercase														=	self.get_dummy_inputs(__lowerCAmelCase						)
            lowercase														=	pipe(**__lowerCAmelCase						).images
            lowercase														=	image[0, -3:, -3:, -1]

            self.assertEqual(image.shape		,       (1, 16, 16, 3)						)
            lowercase														=	np.array([0.2_9_4_6, 0.6_6_0_1, 0.4_3_2_9, 0.3_2_9_6, 0.4_1_4_4, 0.5_3_1_9, 0.7_2_7_3, 0.5_0_1_3, 0.4_4_5_7]						)
            lowercase														=	np.abs(image_slice.flatten() - expected_slice						).max()
            self.assertLessEqual(__lowerCAmelCase		,       1E-3						)

      def 				A__       (						self						):







            """simple docstring"""



            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						):







            """simple docstring"""



            self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3						)






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

      def 				A__       (						self						):







            """simple docstring"""



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

      def 				A__       (						self						):







            """simple docstring"""



            lowercase														=	torch.manual_seed(0						)

            lowercase														=	DiTPipeline.from_pretrained("""facebook/DiT-XL-2-256"""						)
            pipe.to("""cuda"""						)

            lowercase														=	["""vase""", """umbrella""", """white shark""", """white wolf"""]
            lowercase														=	pipe.get_label_ids(__lowerCAmelCase						)

            lowercase														=	pipe(__lowerCAmelCase		,       generator=__lowerCAmelCase		,       num_inference_steps=40		,       output_type="""np"""						).images

            for word, image in zip(__lowerCAmelCase		,       __lowerCAmelCase						):
                  lowercase														=	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						):







            """simple docstring"""



            lowercase														=	DiTPipeline.from_pretrained("""facebook/DiT-XL-2-512"""						)
            lowercase														=	DPMSolverMultistepScheduler.from_config(pipe.scheduler.config						)
            pipe.to("""cuda"""						)

            lowercase														=	["""vase""", """umbrella"""]
            lowercase														=	pipe.get_label_ids(__lowerCAmelCase						)

            lowercase														=	torch.manual_seed(0						)
            lowercase														=	pipe(__lowerCAmelCase		,       generator=__lowerCAmelCase		,       num_inference_steps=25		,       output_type="""np"""						).images

            for word, image in zip(__lowerCAmelCase		,       __lowerCAmelCase						):
                  lowercase														=	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 ...configuration_utils import PretrainedConfig
from ...utils import logging


__lowerCAmelCase    :       List[Any]					     =logging.get_logger(__name__)

__lowerCAmelCase    :       Union[str, Any]					     ={
    """s-JoL/Open-Llama-V1""": """https://huggingface.co/s-JoL/Open-Llama-V1/blob/main/config.json""",
}





class  _A						(       lowerCAmelCase	):
      snake_case__			:   Union[str, Any]							  =	'open-llama'

      def __init__(						self		,       __lowerCAmelCase=10_0000		,       __lowerCAmelCase=4096		,       __lowerCAmelCase=1_1008		,       __lowerCAmelCase=32		,       __lowerCAmelCase=32		,       __lowerCAmelCase="silu"		,       __lowerCAmelCase=2048		,       __lowerCAmelCase=0.0_2		,       __lowerCAmelCase=1E-6		,       __lowerCAmelCase=True		,       __lowerCAmelCase=0		,       __lowerCAmelCase=1		,       __lowerCAmelCase=2		,       __lowerCAmelCase=False		,       __lowerCAmelCase=True		,       __lowerCAmelCase=0.1		,       __lowerCAmelCase=0.1		,       __lowerCAmelCase=True		,       __lowerCAmelCase=True		,       __lowerCAmelCase=None		,       **__lowerCAmelCase		,       ):







            """simple docstring"""



            lowercase														=	vocab_size
            lowercase														=	max_position_embeddings
            lowercase														=	hidden_size
            lowercase														=	intermediate_size
            lowercase														=	num_hidden_layers
            lowercase														=	num_attention_heads
            lowercase														=	hidden_act
            lowercase														=	initializer_range
            lowercase														=	rms_norm_eps
            lowercase														=	use_cache
            lowercase														=	kwargs.pop(
                """use_memorry_efficient_attention"""		,       __lowerCAmelCase						)
            lowercase														=	hidden_dropout_prob
            lowercase														=	attention_dropout_prob
            lowercase														=	use_stable_embedding
            lowercase														=	shared_input_output_embedding
            lowercase														=	rope_scaling
            self._rope_scaling_validation()

            super().__init__(
                pad_token_id=__lowerCAmelCase		,       bos_token_id=__lowerCAmelCase		,       eos_token_id=__lowerCAmelCase		,       tie_word_embeddings=__lowerCAmelCase		,       **__lowerCAmelCase		,       )







      def 				A__       (						self						):







            """simple docstring"""



            if self.rope_scaling is None:
                  return

            if not isinstance(self.rope_scaling		,       __lowerCAmelCase						) or len(self.rope_scaling						) != 2:
                  raise ValueError(
                      """`rope_scaling` must be a dictionary with with two fields, `name` and `factor`, """
                      f'got {self.rope_scaling}'						)
            lowercase														=	self.rope_scaling.get("""type"""		,       __lowerCAmelCase						)
            lowercase														=	self.rope_scaling.get("""factor"""		,       __lowerCAmelCase						)
            if rope_scaling_type is None or rope_scaling_type not in ["linear", "dynamic"]:
                  raise ValueError(
                      f'`rope_scaling`\'s name field must be one of [\'linear\', \'dynamic\'], got {rope_scaling_type}'						)
            if rope_scaling_factor is None or not isinstance(__lowerCAmelCase		,       __lowerCAmelCase						) or rope_scaling_factor <= 1.0:
                  raise ValueError(f'`rope_scaling`\'s factor field must be an float > 1, got {rope_scaling_factor}'						)



