


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  SCREAMING_SNAKE_CASE__  (				lowercase__						,    unittest.TestCase			):
      snake_case__				:     int        =						DiTPipeline
      snake_case__				:     Optional[int]        =						CLASS_CONDITIONED_IMAGE_GENERATION_PARAMS
      snake_case__				:     Optional[Any]        =						PipelineTesterMixin.required_optional_params - {
          '''latents''',
          '''num_images_per_prompt''',
          '''callback''',
          '''callback_steps''',
      }
      snake_case__				:     Optional[Any]        =						CLASS_CONDITIONED_IMAGE_GENERATION_BATCH_PARAMS
      snake_case__				:     Optional[Any]        =						False
      def        SCREAMING_SNAKE_CASE							(       self				:       Union[str, Any]     )		->			Tuple:
             torch.manual_seed(0     )
             a_					:		List[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=__UpperCAmelCase     ,							activation_fn='gelu-approximate'     ,							num_embeds_ada_norm=1_0_0_0     ,							norm_type='ada_norm_zero'     ,							norm_elementwise_affine=__UpperCAmelCase     ,							)
             a_					:		Union[str, Any]							=  AutoencoderKL()
             a_					:		List[str]							=  DDIMScheduler()
             a_					:		Any							=  {'transformer': transformer.eval(), 'vae': vae.eval(), 'scheduler': scheduler}
             return components
      def        SCREAMING_SNAKE_CASE							(       self				:       List[Any]     ,							SCREAMING_SNAKE_CASE__				:       List[Any]     ,							SCREAMING_SNAKE_CASE__				:       Optional[Any]=0     )		->			Any:


             if str(__UpperCAmelCase     ).startswith('mps'     ):
                    a_					:		Any							=  torch.manual_seed(__UpperCAmelCase     )
             else:
                    a_					:		int							=  torch.Generator(device=__UpperCAmelCase     ).manual_seed(__UpperCAmelCase     )
             a_					:		str							=  {
                 'class_labels': [1],
                 'generator': generator,
                 'num_inference_steps': 2,
                 'output_type': 'numpy',
             }
             return inputs
      def        SCREAMING_SNAKE_CASE							(       self				:       List[Any]     )		->			int:
             a_					:		List[Any]							=  'cpu'

             a_					:		List[Any]							=  self.get_dummy_components()
             a_					:		Optional[Any]							=  self.pipeline_class(**__UpperCAmelCase     )
             pipe.to(__UpperCAmelCase     )
             pipe.set_progress_bar_config(disable=__UpperCAmelCase     )

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

             self.assertEqual(image.shape     ,							(1, 1_6, 1_6, 3)     )
             a_					:		str							=  np.array([0.2946, 0.6601, 0.4329, 0.3296, 0.4144, 0.5319, 0.7273, 0.5013, 0.4457]     )
             a_					:		Optional[int]							=  np.abs(image_slice.flatten() - expected_slice     ).max()
             self.assertLessEqual(__UpperCAmelCase     ,							1E-3     )
      def        SCREAMING_SNAKE_CASE							(       self				:       Union[str, Any]     )		->			Union[str, Any]:
             self._test_inference_batch_single_identical(relax_max_difference=__UpperCAmelCase     ,							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        SCREAMING_SNAKE_CASE							(       self				:       Any     )		->			int:
             self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3     )
@require_torch_gpu
@slow
class  SCREAMING_SNAKE_CASE__  (				unittest.TestCase			):
      def        SCREAMING_SNAKE_CASE							(       self				:       List[Any]     )		->			str:
             super().tearDown()
             gc.collect()
             torch.cuda.empty_cache()
      def        SCREAMING_SNAKE_CASE							(       self				:       Tuple     )		->			int:
             a_					:		List[Any]							=  torch.manual_seed(0     )

             a_					:		List[Any]							=  DiTPipeline.from_pretrained('facebook/DiT-XL-2-256'     )
             pipe.to('cuda'     )

             a_					:		Union[str, Any]							=  ['vase', 'umbrella', 'white shark', 'white wolf']
             a_					:		Any							=  pipe.get_label_ids(__UpperCAmelCase     )

             a_					:		List[str]							=  pipe(__UpperCAmelCase     ,							generator=__UpperCAmelCase     ,							num_inference_steps=4_0     ,							output_type='np'     ).images

             for word, image in zip(__UpperCAmelCase     ,							__UpperCAmelCase     ):
                    a_					:		Union[str, Any]							=  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        SCREAMING_SNAKE_CASE							(       self				:       str     )		->			Tuple:
             a_					:		str							=  DiTPipeline.from_pretrained('facebook/DiT-XL-2-512'     )
             a_					:		Dict							=  DPMSolverMultistepScheduler.from_config(pipe.scheduler.config     )
             pipe.to('cuda'     )

             a_					:		Dict							=  ['vase', 'umbrella']
             a_					:		int							=  pipe.get_label_ids(__UpperCAmelCase     )

             a_					:		List[str]							=  torch.manual_seed(0     )
             a_					:		List[str]							=  pipe(__UpperCAmelCase     ,							generator=__UpperCAmelCase     ,							num_inference_steps=2_5     ,							output_type='np'     ).images

             for word, image in zip(__UpperCAmelCase     ,							__UpperCAmelCase     ):
                    a_					:		Dict							=  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










from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxSeqaSeqConfigWithPast
from ...utils import logging


UpperCAmelCase__				   =		logging.get_logger(__name__)

UpperCAmelCase__				   =		{
    "t5-small": "https://huggingface.co/t5-small/resolve/main/config.json",
    "t5-base": "https://huggingface.co/t5-base/resolve/main/config.json",
    "t5-large": "https://huggingface.co/t5-large/resolve/main/config.json",
    "t5-3b": "https://huggingface.co/t5-3b/resolve/main/config.json",
    "t5-11b": "https://huggingface.co/t5-11b/resolve/main/config.json",
}






class 		lowercase_					(	lowercase   ):






      '''simple docstring'''




      __snake_case       =  '''t5'''
      __snake_case       =  ['''past_key_values''']
      __snake_case       =  {'''hidden_size''': '''d_model''', '''num_attention_heads''': '''num_heads''', '''num_hidden_layers''': '''num_layers'''}







      def __init__(      self      :						Optional[Any] ,					__UpperCAmelCase      :						Optional[Any]=32_128 ,					__UpperCAmelCase      :						List[Any]=512 ,					__UpperCAmelCase      :						Dict=64 ,					__UpperCAmelCase      :						Tuple=2_048 ,					__UpperCAmelCase      :						int=6 ,					__UpperCAmelCase      :						Optional[int]=None ,					__UpperCAmelCase      :						Optional[int]=8 ,					__UpperCAmelCase      :						str=32 ,					__UpperCAmelCase      :						Tuple=128 ,					__UpperCAmelCase      :						Optional[Any]=0.1 ,					__UpperCAmelCase      :						int=1e-6 ,					__UpperCAmelCase      :						int=1.0 ,					__UpperCAmelCase      :						List[str]="relu" ,					__UpperCAmelCase      :						int=True ,					__UpperCAmelCase      :						int=True ,					__UpperCAmelCase      :						List[Any]=0 ,					__UpperCAmelCase      :						int=1 ,					**__UpperCAmelCase      :						str ,					)    ->Optional[Any]:




       """simple docstring"""


       a		  =      vocab_size
       a		  =      d_model
       a		  =      d_kv
       a		  =      d_ff
       a		  =      num_layers
       a		  =      (
           num_decoder_layers if num_decoder_layers is not None else self.num_layers
       )  # default = symmetry
       a		  =      num_heads
       a		  =      relative_attention_num_buckets
       a		  =      relative_attention_max_distance
       a		  =      dropout_rate
       a		  =      layer_norm_epsilon
       a		  =      initializer_factor
       a		  =      feed_forward_proj
       a		  =      use_cache

       a		  =      self.feed_forward_proj.split('''-'''  )
       a		  =      act_info[-1]
       a		  =      act_info[0] == '''gated'''

       if len(__UpperCAmelCase  ) > 1 and act_info[0] != "gated" or len(__UpperCAmelCase  ) > 2:
        raise ValueError(
            F"""`feed_forward_proj`: {feed_forward_proj} is not a valid activation function of the dense layer."""
            '''Please make sure `feed_forward_proj` is of the format `gated-{ACT_FN}` or `{ACT_FN}`, e.g. '''
            '''\'gated-gelu\' or \'relu\''''  )

       # for backwards compatibility
       if feed_forward_proj == "gated-gelu":
        a		  =      '''gelu_new'''

       super().__init__(
           pad_token_id=__UpperCAmelCase ,					eos_token_id=__UpperCAmelCase ,					is_encoder_decoder=__UpperCAmelCase ,					**__UpperCAmelCase ,					)






class 		lowercase_					(	lowercase   ):






      '''simple docstring'''




      @property
      def       __lowerCAmelCase					(      self      :						Optional[Any]  )    ->Mapping[str, Mapping[int, str]]:




       """simple docstring"""


       a		  =      {
           '''input_ids''': {0: '''batch''', 1: '''encoder_sequence'''},
           '''attention_mask''': {0: '''batch''', 1: '''encoder_sequence'''},
       }
       if self.use_past:
        a		  =      '''past_encoder_sequence + sequence'''
        a		  =      {0: '''batch'''}
        a		  =      {0: '''batch''', 1: '''past_decoder_sequence + sequence'''}
       else:
        a		  =      {0: '''batch''', 1: '''decoder_sequence'''}
        a		  =      {0: '''batch''', 1: '''decoder_sequence'''}

       if self.use_past:
        self.fill_with_past_key_values_(__UpperCAmelCase ,					direction='''inputs'''  )

       return common_inputs







      @property
      def       __lowerCAmelCase					(      self      :						Union[str, Any]  )    ->int:




       """simple docstring"""


       return 13


