






"""simple docstring"""





import unittest

from transformers import PegasusConfig, PegasusTokenizer, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor


if is_flax_available():
    import os

    # The slow tests are often failing with OOM error on GPU
    # This makes JAX allocate exactly what is needed on demand, and deallocate memory that is no longer needed
    # but will be slower as stated here https://jax.readthedocs.io/en/latest/gpu_memory_allocation.html
    UpperCamelCase_	:				List[str]										= """platform"""
    import jax
    import jax.numpy as jnp
    import numpy as np

    from transformers import FlaxPegasusForConditionalGeneration, FlaxPegasusModel


@require_flax
class      _lowercase						:
      _a   :     Optional[int]								=   PegasusConfig
      _a   :     Dict								=   {}
      _a   :     Union[str, Any]								=   '''gelu'''


      def __init__(    self				:	Tuple							,   a				:	int							,   a				:	str=1_3							,   a				:	Any=7							,   a				:	List[str]=True							,   a				:	List[Any]=False							,   a				:	Tuple=9_9							,   a				:	Union[str, Any]=3_2							,   a				:	str=5							,   a				:	Dict=4							,   a				:	Any=3_7							,   a				:	int=0.1							,   a				:	Optional[Any]=0.1							,   a				:	Union[str, Any]=2_0							,   a				:	List[Any]=2							,   a				:	str=1							,   a				:	Optional[int]=0							,   ):





            """simple docstring"""







            __snake_case      :    Optional[Any]  			=parent
            __snake_case      :    List[str]  			=batch_size
            __snake_case      :    Optional[int]  			=seq_length
            __snake_case      :    Any  			=is_training
            __snake_case      :    Any  			=use_labels
            __snake_case      :    List[str]  			=vocab_size
            __snake_case      :    int  			=hidden_size
            __snake_case      :    Union[str, Any]  			=num_hidden_layers
            __snake_case      :    Optional[Any]  			=num_attention_heads
            __snake_case      :    int  			=intermediate_size

            __snake_case      :    Tuple  			=hidden_dropout_prob
            __snake_case      :    str  			=attention_probs_dropout_prob
            __snake_case      :    Tuple  			=max_position_embeddings
            __snake_case      :    List[Any]  			=eos_token_id
            __snake_case      :    List[Any]  			=pad_token_id
            __snake_case      :    Tuple  			=bos_token_id


      def _UpperCamelCase						(    self				:	List[str]						):





            """simple docstring"""







            __snake_case      :    Dict  			=ids_tensor([self.batch_size, self.seq_length - 1]							,   self.vocab_size						).clip(3							,   self.vocab_size						)
            __snake_case      :    List[Any]  			=np.expand_dims(np.array([self.eos_token_id] * self.batch_size						)							,   1						)
            __snake_case      :    Union[str, Any]  			=np.concatenate([input_ids, eos_tensor]							,   axis=1						)

            __snake_case      :    Dict  			=ids_tensor([self.batch_size, self.seq_length]							,   self.vocab_size						)

            __snake_case      :    List[str]  			=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							,   )
            __snake_case      :    Union[str, Any]  			=prepare_pegasus_inputs_dict(_SCREAMING_SNAKE_CASE							,   _SCREAMING_SNAKE_CASE							,   _SCREAMING_SNAKE_CASE						)
            return config, inputs_dict


      def _UpperCamelCase						(    self				:	Optional[int]							,   a				:	int							,   a				:	Optional[Any]							,   a				:	Optional[Any]						):





            """simple docstring"""







            __snake_case      :    List[Any]  			=2_0
            __snake_case      :    Union[str, Any]  			=model_class_name(_SCREAMING_SNAKE_CASE						)

            __snake_case      :    Optional[int]  			=model.encode(inputs_dict['''input_ids''']						)

            __snake_case			,				__snake_case      :    Union[str, Any]  			=(
                inputs_dict['''decoder_input_ids'''],
                inputs_dict['''decoder_attention_mask'''],
            )

            __snake_case      :    Dict  			=model.init_cache(decoder_input_ids.shape[0]							,   _SCREAMING_SNAKE_CASE							,   _SCREAMING_SNAKE_CASE						)
            __snake_case      :    Optional[int]  			=jnp.ones((decoder_input_ids.shape[0], max_decoder_length)							,   dtype='''i4'''						)

            __snake_case      :    Dict  			=jnp.broadcast_to(
                jnp.arange(decoder_input_ids.shape[-1] - 1						)[None, :]							,   (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)							,   )
            __snake_case      :    str  			=model.decode(
                decoder_input_ids[:, :-1]							,   _SCREAMING_SNAKE_CASE							,   decoder_attention_mask=_SCREAMING_SNAKE_CASE							,   past_key_values=_SCREAMING_SNAKE_CASE							,   decoder_position_ids=_SCREAMING_SNAKE_CASE							,   )

            __snake_case      :    Dict  			=jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]							,   dtype='''i4'''						)
            __snake_case      :    Any  			=model.decode(
                decoder_input_ids[:, -1:]							,   _SCREAMING_SNAKE_CASE							,   decoder_attention_mask=_SCREAMING_SNAKE_CASE							,   past_key_values=outputs_cache.past_key_values							,   decoder_position_ids=_SCREAMING_SNAKE_CASE							,   )

            __snake_case      :    Optional[int]  			=model.decode(_SCREAMING_SNAKE_CASE							,   _SCREAMING_SNAKE_CASE						)

            __snake_case      :    Tuple  			=np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])						)						)
            self.parent.assertTrue(diff < 1e-3							,   msg=f'''Max diff is {diff}'''						)







      def _UpperCamelCase						(    self				:	Optional[int]							,   a				:	Any							,   a				:	Optional[int]							,   a				:	Any						):





            """simple docstring"""







            __snake_case      :    Optional[Any]  			=2_0
            __snake_case      :    Tuple  			=model_class_name(_SCREAMING_SNAKE_CASE						)

            __snake_case      :    Optional[int]  			=model.encode(inputs_dict['''input_ids''']						)

            __snake_case			,				__snake_case      :    List[Any]  			=(
                inputs_dict['''decoder_input_ids'''],
                inputs_dict['''decoder_attention_mask'''],
            )

            __snake_case      :    List[str]  			=jnp.concatenate(
                [
                    decoder_attention_mask,
                    jnp.zeros((decoder_attention_mask.shape[0], max_decoder_length - decoder_attention_mask.shape[1])						),
                ]							,   axis=-1							,   )

            __snake_case      :    Dict  			=model.init_cache(decoder_input_ids.shape[0]							,   _SCREAMING_SNAKE_CASE							,   _SCREAMING_SNAKE_CASE						)
            __snake_case      :    int  			=jnp.broadcast_to(
                jnp.arange(decoder_input_ids.shape[-1] - 1						)[None, :]							,   (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)							,   )

            __snake_case      :    Dict  			=model.decode(
                decoder_input_ids[:, :-1]							,   _SCREAMING_SNAKE_CASE							,   decoder_attention_mask=_SCREAMING_SNAKE_CASE							,   past_key_values=_SCREAMING_SNAKE_CASE							,   decoder_position_ids=_SCREAMING_SNAKE_CASE							,   )
            __snake_case      :    Tuple  			=jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]							,   dtype='''i4'''						)
            __snake_case      :    int  			=model.decode(
                decoder_input_ids[:, -1:]							,   _SCREAMING_SNAKE_CASE							,   past_key_values=outputs_cache.past_key_values							,   decoder_attention_mask=_SCREAMING_SNAKE_CASE							,   decoder_position_ids=_SCREAMING_SNAKE_CASE							,   )

            __snake_case      :    int  			=model.decode(_SCREAMING_SNAKE_CASE							,   _SCREAMING_SNAKE_CASE							,   decoder_attention_mask=_SCREAMING_SNAKE_CASE						)

            __snake_case      :    Dict  			=np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])						)						)
            self.parent.assertTrue(diff < 1e-3							,   msg=f'''Max diff is {diff}'''						)



def     __lowercase					(    a :				List[Any]       ,		a :				Tuple       ,		a :				Union[str, Any]       ,		a :				str=None       ,		a :				Union[str, Any]=None       ,		)					->					Tuple:
      if attention_mask is None:
            __snake_case      :    int  			=np.not_equal(__UpperCamelCase       ,		config.pad_token_id ).astype(np.inta )
      if decoder_attention_mask is None:
            __snake_case      :    List[str]  			=np.concatenate(
                [
                    np.ones(decoder_input_ids[:, :1].shape       ,		dtype=np.inta ),
                    np.not_equal(decoder_input_ids[:, 1:]       ,		config.pad_token_id ).astype(np.inta ),
                ]       ,		axis=-1       ,		)
      return {
          "input_ids": input_ids,
          "decoder_input_ids": decoder_input_ids,
          "attention_mask": attention_mask,
          "decoder_attention_mask": decoder_attention_mask,
      }


@require_flax
class      _lowercase						(     lowerCAmelCase		,  unittest.TestCase						):
      _a   :     Any								=   (
          (
              FlaxPegasusForConditionalGeneration,
              FlaxPegasusModel,
          )
          if is_flax_available()
          else ()
      )
      _a   :     Any								=   (FlaxPegasusForConditionalGeneration,) if is_flax_available() else ()
      _a   :     Optional[int]								=   True
      _a   :     Tuple								=   False
      _a   :     int								=   False
      _a   :     int								=   False


      def _UpperCamelCase						(    self				:	List[str]						):





            """simple docstring"""







            __snake_case      :    Any  			=FlaxPegasusModelTester(self						)
            __snake_case      :    Dict  			=ConfigTester(self							,   config_class=_SCREAMING_SNAKE_CASE						)


      def _UpperCamelCase						(    self				:	Optional[Any]						):





            """simple docstring"""







            self.config_tester.run_common_tests()


      def _UpperCamelCase						(    self				:	List[Any]						):





            """simple docstring"""







            __snake_case			,				__snake_case      :    Tuple  			=self.model_tester.prepare_config_and_inputs_for_common()
            for model_class in self.all_model_classes:
                  self.model_tester.check_use_cache_forward(_SCREAMING_SNAKE_CASE							,   _SCREAMING_SNAKE_CASE							,   _SCREAMING_SNAKE_CASE						)


      def _UpperCamelCase						(    self				:	Optional[int]						):





            """simple docstring"""







            __snake_case			,				__snake_case      :    Tuple  			=self.model_tester.prepare_config_and_inputs_for_common()
            for model_class in self.all_model_classes:
                  self.model_tester.check_use_cache_forward_with_attn_mask(_SCREAMING_SNAKE_CASE							,   _SCREAMING_SNAKE_CASE							,   _SCREAMING_SNAKE_CASE						)


      def _UpperCamelCase						(    self				:	Optional[int]						):





            """simple docstring"""







            __snake_case			,				__snake_case      :    Any  			=self.model_tester.prepare_config_and_inputs_for_common()

            for model_class in self.all_model_classes:
                  with self.subTest(model_class.__name__						):
                        __snake_case      :    Dict  			=self._prepare_for_class(_SCREAMING_SNAKE_CASE							,   _SCREAMING_SNAKE_CASE						)
                        __snake_case      :    Optional[int]  			=model_class(_SCREAMING_SNAKE_CASE						)

                        @jax.jit
                        def encode_jitted(a				:	str							,   a				:	str=None							,   **a				:	List[Any]						):
                              return model.encode(input_ids=_SCREAMING_SNAKE_CASE							,   attention_mask=_SCREAMING_SNAKE_CASE						)

                        with self.subTest('''JIT Enabled'''						):
                              __snake_case      :    Any  			=encode_jitted(**_SCREAMING_SNAKE_CASE						).to_tuple()

                        with self.subTest('''JIT Disabled'''						):
                              with jax.disable_jit():
                                    __snake_case      :    List[Any]  			=encode_jitted(**_SCREAMING_SNAKE_CASE						).to_tuple()

                        self.assertEqual(len(_SCREAMING_SNAKE_CASE						)							,   len(_SCREAMING_SNAKE_CASE						)						)
                        for jitted_output, output in zip(_SCREAMING_SNAKE_CASE							,   _SCREAMING_SNAKE_CASE						):
                              self.assertEqual(jitted_output.shape							,   output.shape						)


      def _UpperCamelCase						(    self				:	List[str]						):





            """simple docstring"""







            __snake_case			,				__snake_case      :    Union[str, Any]  			=self.model_tester.prepare_config_and_inputs_for_common()

            for model_class in self.all_model_classes:
                  with self.subTest(model_class.__name__						):
                        __snake_case      :    List[str]  			=model_class(_SCREAMING_SNAKE_CASE						)
                        __snake_case      :    List[str]  			=model.encode(inputs_dict['''input_ids''']							,   inputs_dict['''attention_mask''']						)

                        __snake_case      :    List[Any]  			={
                            '''decoder_input_ids''': inputs_dict['''decoder_input_ids'''],
                            '''decoder_attention_mask''': inputs_dict['''decoder_attention_mask'''],
                            '''encoder_outputs''': encoder_outputs,
                        }

                        @jax.jit
                        def decode_jitted(a				:	Tuple							,   a				:	List[Any]							,   a				:	int						):
                              return model.decode(
                                  decoder_input_ids=_SCREAMING_SNAKE_CASE							,   decoder_attention_mask=_SCREAMING_SNAKE_CASE							,   encoder_outputs=_SCREAMING_SNAKE_CASE							,   )

                        with self.subTest('''JIT Enabled'''						):
                              __snake_case      :    List[Any]  			=decode_jitted(**_SCREAMING_SNAKE_CASE						).to_tuple()

                        with self.subTest('''JIT Disabled'''						):
                              with jax.disable_jit():
                                    __snake_case      :    Any  			=decode_jitted(**_SCREAMING_SNAKE_CASE						).to_tuple()

                        self.assertEqual(len(_SCREAMING_SNAKE_CASE						)							,   len(_SCREAMING_SNAKE_CASE						)						)
                        for jitted_output, output in zip(_SCREAMING_SNAKE_CASE							,   _SCREAMING_SNAKE_CASE						):
                              self.assertEqual(jitted_output.shape							,   output.shape						)


      @slow
      def _UpperCamelCase						(    self				:	List[Any]						):





            """simple docstring"""







            for model_class_name in self.all_model_classes:
                  __snake_case      :    Optional[Any]  			=model_class_name.from_pretrained('''google/pegasus-large'''							,   from_pt=_SCREAMING_SNAKE_CASE						)
                  __snake_case      :    Dict  			=np.ones((1, 1)						)
                  __snake_case      :    int  			=model(_SCREAMING_SNAKE_CASE						)
                  self.assertIsNotNone(_SCREAMING_SNAKE_CASE						)







      @slow
      def _UpperCamelCase						(    self				:	List[str]						):





            """simple docstring"""







            __snake_case      :    List[str]  			=FlaxPegasusForConditionalGeneration.from_pretrained('''google/pegasus-xsum'''						)
            __snake_case      :    List[str]  			=PegasusTokenizer.from_pretrained('''google/pegasus-xsum'''						)

            __snake_case      :    Optional[Any]  			=[
                ''' PG&E stated it scheduled the blackouts in response to forecasts for high winds amid dry conditions. The aim is to reduce the risk of wildfires. Nearly 800 thousand customers were scheduled to be affected by the shutoffs which were expected to last through at least midday tomorrow.''',
                ''' The London trio are up for best UK act and best album, as well as getting two nominations in the best song category.\"We got told like this morning \'Oh I think you\'re nominated\'\", said Dappy.\"And I was like \'Oh yeah, which one?\' And now we\'ve got nominated for four awards. I mean, wow!\"Bandmate Fazer added: \"We thought it\'s best of us to come down and mingle with everyone and say hello to the cameras. And now we find we\'ve got four nominations.\"The band have two shots at the best song prize, getting the nod for their Tynchy Stryder collaboration Number One, and single Strong Again.Their album Uncle B will also go up against records by the likes of Beyonce and Kanye West.N-Dubz picked up the best newcomer Mobo in 2007, but female member Tulisa said they wouldn\'t be too disappointed if they didn\'t win this time around.\"At the end of the day we\'re grateful to be where we are in our careers.\"If it don\'t happen then it don\'t happen - live to fight another day and keep on making albums and hits for the fans.\"Dappy also revealed they could be performing live several times on the night.The group will be doing Number One and also a possible rendition of the War Child single, I Got Soul.The charity song is a  re-working of The Killers\' All These Things That I\'ve Done and is set to feature artists like Chipmunk, Ironik and Pixie Lott.This year\'s Mobos will be held outside of London for the first time, in Glasgow on 30 September.N-Dubz said they were looking forward to performing for their Scottish fans and boasted about their recent shows north of the border.\"We just done Edinburgh the other day,\" said Dappy.\"We smashed up an N-Dubz show over there. We done Aberdeen about three or four months ago - we smashed up that show over there! Everywhere we go we smash it up!\" ''',
            ]

            __snake_case      :    int  			=[
                '''California\'s largest electricity provider has turned off power to hundreds of thousands of customers.''',
                '''Pop group N-Dubz have revealed they were surprised to get four nominations for this year\'s Mobo Awards.''',
            ]

            __snake_case      :    Tuple  			=tokenizer(_SCREAMING_SNAKE_CASE							,   return_tensors='''np'''							,   truncation=_SCREAMING_SNAKE_CASE							,   max_length=5_1_2							,   padding=_SCREAMING_SNAKE_CASE						)
            __snake_case      :    int  			=model.generate(**_SCREAMING_SNAKE_CASE							,   num_beams=2						).sequences
            __snake_case      :    Dict  			=tokenizer.batch_decode(_SCREAMING_SNAKE_CASE							,   skip_special_tokens=_SCREAMING_SNAKE_CASE						)
            assert tgt_text == decoded










"""simple docstring"""





import tempfile
import unittest

import numpy as np

from diffusers import (
    DDIMScheduler,
    DPMSolverMultistepScheduler,
    EulerAncestralDiscreteScheduler,
    EulerDiscreteScheduler,
    LMSDiscreteScheduler,
    OnnxStableDiffusionPipeline,
    PNDMScheduler,
)
from diffusers.utils.testing_utils import is_onnx_available, nightly, require_onnxruntime, require_torch_gpu

from ..test_pipelines_onnx_common import OnnxPipelineTesterMixin


if is_onnx_available():
    import onnxruntime as ort


class      _lowercase (     lowerCAmelCase		,  unittest.TestCase						):
      _a   :     Tuple								=   '''hf-internal-testing/tiny-random-OnnxStableDiffusionPipeline'''


      def _UpperCamelCase						(    self				:	Dict							,   a				:	Optional[Any]=0						):





            """simple docstring"""







            __snake_case      :    List[str]  			=np.random.RandomState(a						)
            __snake_case      :    Union[str, Any]  			={
                '''prompt''': '''A painting of a squirrel eating a burger''',
                '''generator''': generator,
                '''num_inference_steps''': 2,
                '''guidance_scale''': 7.5,
                '''output_type''': '''numpy''',
            }
            return inputs


      def _UpperCamelCase						(    self				:	str						):





            """simple docstring"""







            __snake_case      :    Dict  			=OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint							,   provider='''CPUExecutionProvider'''						)
            pipe.set_progress_bar_config(disable=a						)

            __snake_case      :    Tuple  			=self.get_dummy_inputs()
            __snake_case      :    List[str]  			=pipe(**a						).images
            __snake_case      :    int  			=image[0, -3:, -3:, -1]

            assert image.shape == (1, 1_2_8, 1_2_8, 3)
            __snake_case      :    str  			=np.array([0.6_5_0_7_2, 0.5_8_4_9_2, 0.4_8_2_1_9, 0.5_5_5_2_1, 0.5_3_1_8_0, 0.5_5_9_3_9, 0.5_0_6_9_7, 0.3_9_8_0_0, 0.4_6_4_5_5]						)

            assert np.abs(image_slice.flatten() - expected_slice						).max() < 1e-2


      def _UpperCamelCase						(    self				:	Union[str, Any]						):





            """simple docstring"""







            __snake_case      :    Dict  			=OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint							,   provider='''CPUExecutionProvider'''						)
            __snake_case      :    Dict  			=PNDMScheduler.from_config(pipe.scheduler.config							,   skip_prk_steps=a						)
            pipe.set_progress_bar_config(disable=a						)

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

            assert image.shape == (1, 1_2_8, 1_2_8, 3)
            __snake_case      :    Optional[Any]  			=np.array([0.6_5_8_6_3, 0.5_9_4_2_5, 0.4_9_3_2_6, 0.5_6_3_1_3, 0.5_3_8_7_5, 0.5_6_6_2_7, 0.5_1_0_6_5, 0.3_9_7_7_7, 0.4_6_3_3_0]						)

            assert np.abs(image_slice.flatten() - expected_slice						).max() < 1e-2


      def _UpperCamelCase						(    self				:	Optional[int]						):





            """simple docstring"""







            __snake_case      :    Any  			=OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint							,   provider='''CPUExecutionProvider'''						)
            __snake_case      :    Any  			=LMSDiscreteScheduler.from_config(pipe.scheduler.config						)
            pipe.set_progress_bar_config(disable=a						)

            __snake_case      :    int  			=self.get_dummy_inputs()
            __snake_case      :    List[str]  			=pipe(**a						).images
            __snake_case      :    str  			=image[0, -3:, -3:, -1]

            assert image.shape == (1, 1_2_8, 1_2_8, 3)
            __snake_case      :    int  			=np.array([0.5_3_7_5_5, 0.6_0_7_8_6, 0.4_7_4_0_2, 0.4_9_4_8_8, 0.5_1_8_6_9, 0.4_9_8_1_9, 0.4_7_9_8_5, 0.3_8_9_5_7, 0.4_4_2_7_9]						)

            assert np.abs(image_slice.flatten() - expected_slice						).max() < 1e-2


      def _UpperCamelCase						(    self				:	List[str]						):





            """simple docstring"""







            __snake_case      :    Optional[int]  			=OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint							,   provider='''CPUExecutionProvider'''						)
            __snake_case      :    Optional[Any]  			=EulerDiscreteScheduler.from_config(pipe.scheduler.config						)
            pipe.set_progress_bar_config(disable=a						)

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

            assert image.shape == (1, 1_2_8, 1_2_8, 3)
            __snake_case      :    Union[str, Any]  			=np.array([0.5_3_7_5_5, 0.6_0_7_8_6, 0.4_7_4_0_2, 0.4_9_4_8_8, 0.5_1_8_6_9, 0.4_9_8_1_9, 0.4_7_9_8_5, 0.3_8_9_5_7, 0.4_4_2_7_9]						)

            assert np.abs(image_slice.flatten() - expected_slice						).max() < 1e-2


      def _UpperCamelCase						(    self				:	List[Any]						):





            """simple docstring"""







            __snake_case      :    Any  			=OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint							,   provider='''CPUExecutionProvider'''						)
            __snake_case      :    List[str]  			=EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config						)
            pipe.set_progress_bar_config(disable=a						)

            __snake_case      :    List[str]  			=self.get_dummy_inputs()
            __snake_case      :    Dict  			=pipe(**a						).images
            __snake_case      :    List[Any]  			=image[0, -3:, -3:, -1]

            assert image.shape == (1, 1_2_8, 1_2_8, 3)
            __snake_case      :    Tuple  			=np.array([0.5_3_8_1_7, 0.6_0_8_1_2, 0.4_7_3_8_4, 0.4_9_5_3_0, 0.5_1_8_9_4, 0.4_9_8_1_4, 0.4_7_9_8_4, 0.3_8_9_5_8, 0.4_4_2_7_1]						)

            assert np.abs(image_slice.flatten() - expected_slice						).max() < 1e-2


      def _UpperCamelCase						(    self				:	Dict						):





            """simple docstring"""







            __snake_case      :    Tuple  			=OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint							,   provider='''CPUExecutionProvider'''						)
            __snake_case      :    Any  			=DPMSolverMultistepScheduler.from_config(pipe.scheduler.config						)
            pipe.set_progress_bar_config(disable=a						)

            __snake_case      :    Tuple  			=self.get_dummy_inputs()
            __snake_case      :    Tuple  			=pipe(**a						).images
            __snake_case      :    Optional[Any]  			=image[0, -3:, -3:, -1]

            assert image.shape == (1, 1_2_8, 1_2_8, 3)
            __snake_case      :    Dict  			=np.array([0.5_3_8_9_5, 0.6_0_8_0_8, 0.4_7_9_3_3, 0.4_9_6_0_8, 0.5_1_8_8_6, 0.4_9_9_5_0, 0.4_8_0_5_3, 0.3_8_9_5_7, 0.4_4_2_0_0]						)

            assert np.abs(image_slice.flatten() - expected_slice						).max() < 1e-2


      def _UpperCamelCase						(    self				:	int						):





            """simple docstring"""







            __snake_case      :    Dict  			=OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint							,   provider='''CPUExecutionProvider'''						)
            pipe.set_progress_bar_config(disable=a						)

            __snake_case      :    Optional[int]  			=self.get_dummy_inputs()
            __snake_case      :    Any  			=3 * [inputs['''prompt''']]

            # forward
            __snake_case      :    Any  			=pipe(**a						)
            __snake_case      :    str  			=output.images[0, -3:, -3:, -1]

            __snake_case      :    Tuple  			=self.get_dummy_inputs()
            __snake_case      :    Any  			=3 * [inputs.pop('''prompt'''						)]

            __snake_case      :    Optional[Any]  			=pipe.tokenizer(
                a							,   padding='''max_length'''							,   max_length=pipe.tokenizer.model_max_length							,   truncation=a							,   return_tensors='''np'''							,   )
            __snake_case      :    List[Any]  			=text_inputs['''input_ids''']

            __snake_case      :    str  			=pipe.text_encoder(input_ids=text_inputs.astype(np.intaa						)						)[0]

            __snake_case      :    Optional[Any]  			=prompt_embeds

            # forward
            __snake_case      :    Dict  			=pipe(**a						)
            __snake_case      :    Any  			=output.images[0, -3:, -3:, -1]

            assert np.abs(image_slice_a.flatten() - image_slice_a.flatten()						).max() < 1e-4







      def _UpperCamelCase						(    self				:	Any						):





            """simple docstring"""







            __snake_case      :    Dict  			=OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint							,   provider='''CPUExecutionProvider'''						)
            pipe.set_progress_bar_config(disable=a						)

            __snake_case      :    List[Any]  			=self.get_dummy_inputs()
            __snake_case      :    Optional[Any]  			=3 * ['''this is a negative prompt''']
            __snake_case      :    List[str]  			=negative_prompt
            __snake_case      :    str  			=3 * [inputs['''prompt''']]

            # forward
            __snake_case      :    int  			=pipe(**a						)
            __snake_case      :    Union[str, Any]  			=output.images[0, -3:, -3:, -1]

            __snake_case      :    Tuple  			=self.get_dummy_inputs()
            __snake_case      :    Union[str, Any]  			=3 * [inputs.pop('''prompt'''						)]

            __snake_case      :    Optional[int]  			=[]
            for p in [prompt, negative_prompt]:
                  __snake_case      :    Optional[int]  			=pipe.tokenizer(
                      a							,   padding='''max_length'''							,   max_length=pipe.tokenizer.model_max_length							,   truncation=a							,   return_tensors='''np'''							,   )
                  __snake_case      :    Optional[Any]  			=text_inputs['''input_ids''']

                  embeds.append(pipe.text_encoder(input_ids=text_inputs.astype(np.intaa						)						)[0]						)

            __snake_case			,				__snake_case      :    Optional[Any]  			=embeds

            # forward
            __snake_case      :    Any  			=pipe(**a						)
            __snake_case      :    Union[str, Any]  			=output.images[0, -3:, -3:, -1]

            assert np.abs(image_slice_a.flatten() - image_slice_a.flatten()						).max() < 1e-4



@nightly
@require_onnxruntime
@require_torch_gpu
class      _lowercase (     unittest.TestCase						):


      @property
      def _UpperCamelCase						(    self				:	List[Any]						):





            """simple docstring"""







            return (
                "CUDAExecutionProvider",
                {
                    "gpu_mem_limit": "15000000000",  # 15GB
                    "arena_extend_strategy": "kSameAsRequested",
                },
            )


      @property
      def _UpperCamelCase						(    self				:	Union[str, Any]						):





            """simple docstring"""







            __snake_case      :    List[str]  			=ort.SessionOptions()
            __snake_case      :    Optional[int]  			=False
            return options


      def _UpperCamelCase						(    self				:	Any						):





            """simple docstring"""







            __snake_case      :    List[Any]  			=OnnxStableDiffusionPipeline.from_pretrained(
                '''CompVis/stable-diffusion-v1-4'''							,   revision='''onnx'''							,   safety_checker=a							,   feature_extractor=a							,   provider=self.gpu_provider							,   sess_options=self.gpu_options							,   )
            sd_pipe.set_progress_bar_config(disable=a						)

            __snake_case      :    List[str]  			='''A painting of a squirrel eating a burger'''
            np.random.seed(0						)
            __snake_case      :    Tuple  			=sd_pipe([prompt]							,   guidance_scale=6.0							,   num_inference_steps=1_0							,   output_type='''np'''						)
            __snake_case      :    Union[str, Any]  			=output.images

            __snake_case      :    Optional[Any]  			=image[0, -3:, -3:, -1]

            assert image.shape == (1, 5_1_2, 5_1_2, 3)
            __snake_case      :    Any  			=np.array([0.0_4_5_2, 0.0_3_9_0, 0.0_0_8_7, 0.0_3_5_0, 0.0_6_1_7, 0.0_3_6_4, 0.0_5_4_4, 0.0_5_2_3, 0.0_7_2_0]						)

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


      def _UpperCamelCase						(    self				:	Optional[Any]						):





            """simple docstring"""







            __snake_case      :    List[str]  			=DDIMScheduler.from_pretrained(
                '''runwayml/stable-diffusion-v1-5'''							,   subfolder='''scheduler'''							,   revision='''onnx'''						)
            __snake_case      :    List[Any]  			=OnnxStableDiffusionPipeline.from_pretrained(
                '''runwayml/stable-diffusion-v1-5'''							,   revision='''onnx'''							,   scheduler=a							,   safety_checker=a							,   feature_extractor=a							,   provider=self.gpu_provider							,   sess_options=self.gpu_options							,   )
            sd_pipe.set_progress_bar_config(disable=a						)

            __snake_case      :    Optional[Any]  			='''open neural network exchange'''
            __snake_case      :    Optional[int]  			=np.random.RandomState(0						)
            __snake_case      :    int  			=sd_pipe([prompt]							,   guidance_scale=7.5							,   num_inference_steps=1_0							,   generator=a							,   output_type='''np'''						)
            __snake_case      :    Union[str, Any]  			=output.images
            __snake_case      :    str  			=image[0, -3:, -3:, -1]

            assert image.shape == (1, 5_1_2, 5_1_2, 3)
            __snake_case      :    Union[str, Any]  			=np.array([0.2_8_6_7, 0.1_9_7_4, 0.1_4_8_1, 0.7_2_9_4, 0.7_2_5_1, 0.6_6_6_7, 0.4_1_9_4, 0.5_6_4_2, 0.6_4_8_6]						)

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


      def _UpperCamelCase						(    self				:	Tuple						):





            """simple docstring"""







            __snake_case      :    List[str]  			=LMSDiscreteScheduler.from_pretrained(
                '''runwayml/stable-diffusion-v1-5'''							,   subfolder='''scheduler'''							,   revision='''onnx'''						)
            __snake_case      :    int  			=OnnxStableDiffusionPipeline.from_pretrained(
                '''runwayml/stable-diffusion-v1-5'''							,   revision='''onnx'''							,   scheduler=a							,   safety_checker=a							,   feature_extractor=a							,   provider=self.gpu_provider							,   sess_options=self.gpu_options							,   )
            sd_pipe.set_progress_bar_config(disable=a						)

            __snake_case      :    Optional[int]  			='''open neural network exchange'''
            __snake_case      :    Optional[Any]  			=np.random.RandomState(0						)
            __snake_case      :    Any  			=sd_pipe([prompt]							,   guidance_scale=7.5							,   num_inference_steps=1_0							,   generator=a							,   output_type='''np'''						)
            __snake_case      :    Optional[int]  			=output.images
            __snake_case      :    Any  			=image[0, -3:, -3:, -1]

            assert image.shape == (1, 5_1_2, 5_1_2, 3)
            __snake_case      :    Optional[int]  			=np.array([0.2_3_0_6, 0.1_9_5_9, 0.1_5_9_3, 0.6_5_4_9, 0.6_3_9_4, 0.5_4_0_8, 0.5_0_6_5, 0.6_0_1_0, 0.6_1_6_1]						)

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


      def _UpperCamelCase						(    self				:	Union[str, Any]						):





            """simple docstring"""







            __snake_case      :    Union[str, Any]  			=0

            def test_callback_fn(a				:	int							,   a				:	int							,   a				:	np.ndarray						) -> None:
                  __snake_case      :    Dict  			=True
                  nonlocal number_of_steps
                  number_of_steps += 1
                  if step == 0:
                        assert latents.shape == (1, 4, 6_4, 6_4)
                        __snake_case      :    Union[str, Any]  			=latents[0, -3:, -3:, -1]
                        __snake_case      :    str  			=np.array(
                            [-0.6_7_7_2, -0.3_8_3_5, -1.2_4_5_6, 0.1_9_0_5, -1.0_9_7_4, 0.6_9_6_7, -1.9_3_5_3, 0.0_1_7_8, 1.0_1_6_7]						)

                        assert np.abs(latents_slice.flatten() - expected_slice						).max() < 1e-3
                  elif step == 5:
                        assert latents.shape == (1, 4, 6_4, 6_4)
                        __snake_case      :    List[Any]  			=latents[0, -3:, -3:, -1]
                        __snake_case      :    List[Any]  			=np.array(
                            [-0.3_3_5_1, 0.2_2_4_1, -0.1_8_3_7, -0.2_3_2_5, -0.6_5_7_7, 0.3_3_9_3, -0.0_2_4_1, 0.5_8_9_9, 1.3_8_7_5]						)

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

            __snake_case      :    str  			=False

            __snake_case      :    int  			=OnnxStableDiffusionPipeline.from_pretrained(
                '''runwayml/stable-diffusion-v1-5'''							,   revision='''onnx'''							,   safety_checker=a							,   feature_extractor=a							,   provider=self.gpu_provider							,   sess_options=self.gpu_options							,   )
            pipe.set_progress_bar_config(disable=a						)

            __snake_case      :    List[Any]  			='''Andromeda galaxy in a bottle'''

            __snake_case      :    Optional[int]  			=np.random.RandomState(0						)
            pipe(
                prompt=a							,   num_inference_steps=5							,   guidance_scale=7.5							,   generator=a							,   callback=a							,   callback_steps=1							,   )
            assert test_callback_fn.has_been_called
            assert number_of_steps == 6







      def _UpperCamelCase						(    self				:	List[Any]						):





            """simple docstring"""







            __snake_case      :    Optional[Any]  			=OnnxStableDiffusionPipeline.from_pretrained(
                '''runwayml/stable-diffusion-v1-5'''							,   revision='''onnx'''							,   safety_checker=a							,   feature_extractor=a							,   provider=self.gpu_provider							,   sess_options=self.gpu_options							,   )
            assert isinstance(a							,   a						)
            assert pipe.safety_checker is None

            __snake_case      :    int  			=pipe('''example prompt'''							,   num_inference_steps=2						).images[0]
            assert image is not None

            # check that there's no error when saving a pipeline with one of the models being None
            with tempfile.TemporaryDirectory() as tmpdirname:
                  pipe.save_pretrained(a						)
                  __snake_case      :    List[Any]  			=OnnxStableDiffusionPipeline.from_pretrained(a						)

            # sanity check that the pipeline still works
            assert pipe.safety_checker is None
            __snake_case      :    Any  			=pipe('''example prompt'''							,   num_inference_steps=2						).images[0]
            assert image is not None


