





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 					__snake_case	(  __SCREAMING_SNAKE_CASE			,     unittest.TestCase    ):


    '''simple docstring'''







    lowerCamelCase__    :		Optional[Any]				=	"""hf-internal-testing/tiny-random-OnnxStableDiffusionPipeline"""



    def  lowercase_					(       self   ,				A_=0     ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__											=			np.random.RandomState(A_     )
        SCREAMING_SNAKE_CASE__											=			{
            '''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  lowercase_					(       self     ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__											=			OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint   ,				provider='''CPUExecutionProvider'''     )
        pipe.set_progress_bar_config(disable=A_     )

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

        assert image.shape == (1, 1_28, 1_28, 3)
        SCREAMING_SNAKE_CASE__											=			np.array([0.65072, 0.58492, 0.48219, 0.55521, 0.53180, 0.55939, 0.50697, 0.39800, 0.46455]     )

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



    def  lowercase_					(       self     ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__											=			OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint   ,				provider='''CPUExecutionProvider'''     )
        SCREAMING_SNAKE_CASE__											=			PNDMScheduler.from_config(pipe.scheduler.config   ,				skip_prk_steps=A_     )
        pipe.set_progress_bar_config(disable=A_     )

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

        assert image.shape == (1, 1_28, 1_28, 3)
        SCREAMING_SNAKE_CASE__											=			np.array([0.65863, 0.59425, 0.49326, 0.56313, 0.53875, 0.56627, 0.51065, 0.39777, 0.46330]     )

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



    def  lowercase_					(       self     ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__											=			OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint   ,				provider='''CPUExecutionProvider'''     )
        SCREAMING_SNAKE_CASE__											=			LMSDiscreteScheduler.from_config(pipe.scheduler.config     )
        pipe.set_progress_bar_config(disable=A_     )

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

        assert image.shape == (1, 1_28, 1_28, 3)
        SCREAMING_SNAKE_CASE__											=			np.array([0.53755, 0.60786, 0.47402, 0.49488, 0.51869, 0.49819, 0.47985, 0.38957, 0.44279]     )

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



    def  lowercase_					(       self     ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__											=			OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint   ,				provider='''CPUExecutionProvider'''     )
        SCREAMING_SNAKE_CASE__											=			EulerDiscreteScheduler.from_config(pipe.scheduler.config     )
        pipe.set_progress_bar_config(disable=A_     )

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

        assert image.shape == (1, 1_28, 1_28, 3)
        SCREAMING_SNAKE_CASE__											=			np.array([0.53755, 0.60786, 0.47402, 0.49488, 0.51869, 0.49819, 0.47985, 0.38957, 0.44279]     )

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



    def  lowercase_					(       self     ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__											=			OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint   ,				provider='''CPUExecutionProvider'''     )
        SCREAMING_SNAKE_CASE__											=			EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config     )
        pipe.set_progress_bar_config(disable=A_     )

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

        assert image.shape == (1, 1_28, 1_28, 3)
        SCREAMING_SNAKE_CASE__											=			np.array([0.53817, 0.60812, 0.47384, 0.49530, 0.51894, 0.49814, 0.47984, 0.38958, 0.44271]     )

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



    def  lowercase_					(       self     ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__											=			OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint   ,				provider='''CPUExecutionProvider'''     )
        SCREAMING_SNAKE_CASE__											=			DPMSolverMultistepScheduler.from_config(pipe.scheduler.config     )
        pipe.set_progress_bar_config(disable=A_     )

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

        assert image.shape == (1, 1_28, 1_28, 3)
        SCREAMING_SNAKE_CASE__											=			np.array([0.53895, 0.60808, 0.47933, 0.49608, 0.51886, 0.49950, 0.48053, 0.38957, 0.44200]     )

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



    def  lowercase_					(       self     ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__											=			OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint   ,				provider='''CPUExecutionProvider'''     )
        pipe.set_progress_bar_config(disable=A_     )

        SCREAMING_SNAKE_CASE__											=			self.get_dummy_inputs()
        SCREAMING_SNAKE_CASE__											=			3 * [inputs['''prompt''']]

        # forward
        SCREAMING_SNAKE_CASE__											=			pipe(**A_     )
        SCREAMING_SNAKE_CASE__											=			output.images[0, -3:, -3:, -1]

        SCREAMING_SNAKE_CASE__											=			self.get_dummy_inputs()
        SCREAMING_SNAKE_CASE__											=			3 * [inputs.pop('''prompt'''     )]

        SCREAMING_SNAKE_CASE__											=			pipe.tokenizer(
            A_   ,				padding='''max_length'''   ,				max_length=pipe.tokenizer.model_max_length   ,				truncation=A_   ,				return_tensors='''np'''   ,				)
        SCREAMING_SNAKE_CASE__											=			text_inputs['''input_ids''']

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

        SCREAMING_SNAKE_CASE__											=			prompt_embeds

        # forward
        SCREAMING_SNAKE_CASE__											=			pipe(**A_     )
        SCREAMING_SNAKE_CASE__											=			output.images[0, -3:, -3:, -1]

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




    def  lowercase_					(       self     ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__											=			OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint   ,				provider='''CPUExecutionProvider'''     )
        pipe.set_progress_bar_config(disable=A_     )

        SCREAMING_SNAKE_CASE__											=			self.get_dummy_inputs()
        SCREAMING_SNAKE_CASE__											=			3 * ['''this is a negative prompt''']
        SCREAMING_SNAKE_CASE__											=			negative_prompt
        SCREAMING_SNAKE_CASE__											=			3 * [inputs['''prompt''']]

        # forward
        SCREAMING_SNAKE_CASE__											=			pipe(**A_     )
        SCREAMING_SNAKE_CASE__											=			output.images[0, -3:, -3:, -1]

        SCREAMING_SNAKE_CASE__											=			self.get_dummy_inputs()
        SCREAMING_SNAKE_CASE__											=			3 * [inputs.pop('''prompt'''     )]

        SCREAMING_SNAKE_CASE__											=			[]
        for p in [prompt, negative_prompt]:
            SCREAMING_SNAKE_CASE__											=			pipe.tokenizer(
                A_   ,				padding='''max_length'''   ,				max_length=pipe.tokenizer.model_max_length   ,				truncation=A_   ,				return_tensors='''np'''   ,				)
            SCREAMING_SNAKE_CASE__											=			text_inputs['''input_ids''']

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

        SCREAMING_SNAKE_CASE__				,		SCREAMING_SNAKE_CASE__											=			embeds

        # forward
        SCREAMING_SNAKE_CASE__											=			pipe(**A_     )
        SCREAMING_SNAKE_CASE__											=			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 					__snake_case	(  unittest.TestCase    ):


    '''simple docstring'''







    @property
    def  lowercase_					(       self     ):





        '''simple docstring'''

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



    @property
    def  lowercase_					(       self     ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__											=			ort.SessionOptions()
        SCREAMING_SNAKE_CASE__											=			False
        return options



    def  lowercase_					(       self     ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__											=			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_     )

        SCREAMING_SNAKE_CASE__											=			'''A painting of a squirrel eating a burger'''
        np.random.seed(0     )
        SCREAMING_SNAKE_CASE__											=			sd_pipe([prompt]   ,				guidance_scale=6.0   ,				num_inference_steps=10   ,				output_type='''np'''     )
        SCREAMING_SNAKE_CASE__											=			output.images

        SCREAMING_SNAKE_CASE__											=			image[0, -3:, -3:, -1]

        assert image.shape == (1, 5_12, 5_12, 3)
        SCREAMING_SNAKE_CASE__											=			np.array([0.0452, 0.0390, 0.0087, 0.0350, 0.0617, 0.0364, 0.0544, 0.0523, 0.0720]     )

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



    def  lowercase_					(       self     ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__											=			DDIMScheduler.from_pretrained(
            '''runwayml/stable-diffusion-v1-5'''   ,				subfolder='''scheduler'''   ,				revision='''onnx'''     )
        SCREAMING_SNAKE_CASE__											=			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_     )

        SCREAMING_SNAKE_CASE__											=			'''open neural network exchange'''
        SCREAMING_SNAKE_CASE__											=			np.random.RandomState(0     )
        SCREAMING_SNAKE_CASE__											=			sd_pipe([prompt]   ,				guidance_scale=7.5   ,				num_inference_steps=10   ,				generator=A_   ,				output_type='''np'''     )
        SCREAMING_SNAKE_CASE__											=			output.images
        SCREAMING_SNAKE_CASE__											=			image[0, -3:, -3:, -1]

        assert image.shape == (1, 5_12, 5_12, 3)
        SCREAMING_SNAKE_CASE__											=			np.array([0.2867, 0.1974, 0.1481, 0.7294, 0.7251, 0.6667, 0.4194, 0.5642, 0.6486]     )

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



    def  lowercase_					(       self     ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__											=			LMSDiscreteScheduler.from_pretrained(
            '''runwayml/stable-diffusion-v1-5'''   ,				subfolder='''scheduler'''   ,				revision='''onnx'''     )
        SCREAMING_SNAKE_CASE__											=			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_     )

        SCREAMING_SNAKE_CASE__											=			'''open neural network exchange'''
        SCREAMING_SNAKE_CASE__											=			np.random.RandomState(0     )
        SCREAMING_SNAKE_CASE__											=			sd_pipe([prompt]   ,				guidance_scale=7.5   ,				num_inference_steps=10   ,				generator=A_   ,				output_type='''np'''     )
        SCREAMING_SNAKE_CASE__											=			output.images
        SCREAMING_SNAKE_CASE__											=			image[0, -3:, -3:, -1]

        assert image.shape == (1, 5_12, 5_12, 3)
        SCREAMING_SNAKE_CASE__											=			np.array([0.2306, 0.1959, 0.1593, 0.6549, 0.6394, 0.5408, 0.5065, 0.6010, 0.6161]     )

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



    def  lowercase_					(       self     ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__											=			0

        def test_callback_fn(A_   ,				A_   ,				A_     ) -> None:
            SCREAMING_SNAKE_CASE__											=			True
            nonlocal number_of_steps
            number_of_steps += 1
            if step == 0:
                assert latents.shape == (1, 4, 64, 64)
                SCREAMING_SNAKE_CASE__											=			latents[0, -3:, -3:, -1]
                SCREAMING_SNAKE_CASE__											=			np.array(
                    [-0.6772, -0.3835, -1.2456, 0.1905, -1.0974, 0.6967, -1.9353, 0.0178, 1.0167]     )

                assert np.abs(latents_slice.flatten() - expected_slice     ).max() < 1E-3
            elif step == 5:
                assert latents.shape == (1, 4, 64, 64)
                SCREAMING_SNAKE_CASE__											=			latents[0, -3:, -3:, -1]
                SCREAMING_SNAKE_CASE__											=			np.array(
                    [-0.3351, 0.2241, -0.1837, -0.2325, -0.6577, 0.3393, -0.0241, 0.5899, 1.3875]     )

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

        SCREAMING_SNAKE_CASE__											=			False

        SCREAMING_SNAKE_CASE__											=			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_     )

        SCREAMING_SNAKE_CASE__											=			'''Andromeda galaxy in a bottle'''

        SCREAMING_SNAKE_CASE__											=			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  lowercase_					(       self     ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__											=			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

        SCREAMING_SNAKE_CASE__											=			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_     )
            SCREAMING_SNAKE_CASE__											=			OnnxStableDiffusionPipeline.from_pretrained(A_     )

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


'''simple docstring'''
from unittest.mock import patch

import pyspark

from datasets.packaged_modules.spark.spark import (
    Spark,
    SparkExamplesIterable,
    _generate_iterable_examples,
)

from ..utils import (
    require_dill_gt_0_3_2,
    require_not_windows,
)






def      UpperCamelCase_				(      A__  ,      A__						):
	a_      				=						[]
	for part_id in partition_order:
		a_      				=						df.where(F'''SPARK_PARTITION_ID() = {part_id}'''						).collect()
		for row_idx, row in enumerate(A__						):
			expected_row_ids_and_row_dicts.append((F'''{part_id}_{row_idx}''', row.asDict())						)
	return expected_row_ids_and_row_dicts






@require_not_windows
@require_dill_gt_0_3_2
def      UpperCamelCase_				(      ):
	a_      				=						pyspark.sql.SparkSession.builder.master("""local[*]"""						).appName("""pyspark"""						).getOrCreate()
	a_      				=						spark.range(1_00						).repartition(1						)
	a_      				=						Spark(A__						)
	# The id ints will be converted to Pyarrow int64s, so each row will be 8 bytes. Setting a max_shard_size of 16 means
	# that each partition can hold 2 rows.
	spark_builder._repartition_df_if_needed(max_shard_size=16						)
	# Given that the dataframe has 100 rows and each partition has 2 rows, we expect 50 partitions.
	assert spark_builder.df.rdd.getNumPartitions() == 50






@require_not_windows
@require_dill_gt_0_3_2
def      UpperCamelCase_				(      ):
	a_      				=						pyspark.sql.SparkSession.builder.master("""local[*]"""						).appName("""pyspark"""						).getOrCreate()
	a_      				=						spark.range(10						).repartition(2						)
	a_      				=						[1, 0]
	a_      				=						_generate_iterable_examples(A__  ,      A__						)  # Reverse the partitions.
	a_      				=						_get_expected_row_ids_and_row_dicts_for_partition_order(A__  ,      A__						)

	for i, (row_id, row_dict) in enumerate(generate_fn()						):
		a_				,  a_      				=						expected_row_ids_and_row_dicts[i]
		assert row_id == expected_row_id
		assert row_dict == expected_row_dict






@require_not_windows
@require_dill_gt_0_3_2
def      UpperCamelCase_				(      ):
	a_      				=						pyspark.sql.SparkSession.builder.master("""local[*]"""						).appName("""pyspark"""						).getOrCreate()
	a_      				=						spark.range(10						).repartition(1						)
	a_      				=						SparkExamplesIterable(A__						)
	assert it.n_shards == 1
	for i, (row_id, row_dict) in enumerate(A__						):
		assert row_id == F'''0_{i}'''
		assert row_dict == {"id": i}






@require_not_windows
@require_dill_gt_0_3_2
def      UpperCamelCase_				(      ):
	a_      				=						pyspark.sql.SparkSession.builder.master("""local[*]"""						).appName("""pyspark"""						).getOrCreate()
	a_      				=						spark.range(30						).repartition(3						)
	# Mock the generator so that shuffle reverses the partition indices.
	with patch("""numpy.random.Generator"""						) as generator_mock:
		a_      				=						lambda A__						: x.reverse()
		a_      				=						_get_expected_row_ids_and_row_dicts_for_partition_order(A__  ,      [2, 1, 0]						)

		a_      				=						SparkExamplesIterable(A__						).shuffle_data_sources(A__						)
		assert shuffled_it.n_shards == 3
		for i, (row_id, row_dict) in enumerate(A__						):
			a_				,  a_      				=						expected_row_ids_and_row_dicts[i]
			assert row_id == expected_row_id
			assert row_dict == expected_row_dict






@require_not_windows
@require_dill_gt_0_3_2
def      UpperCamelCase_				(      ):
	a_      				=						pyspark.sql.SparkSession.builder.master("""local[*]"""						).appName("""pyspark"""						).getOrCreate()
	a_      				=						spark.range(20						).repartition(4						)

	# Partitions 0 and 2
	a_      				=						SparkExamplesIterable(A__						).shard_data_sources(worker_id=0  ,      num_workers=2						)
	assert shard_it_a.n_shards == 2
	a_      				=						_get_expected_row_ids_and_row_dicts_for_partition_order(A__  ,      [0, 2]						)
	for i, (row_id, row_dict) in enumerate(A__						):
		a_				,  a_      				=						expected_row_ids_and_row_dicts_a[i]
		assert row_id == expected_row_id
		assert row_dict == expected_row_dict

	# Partitions 1 and 3
	a_      				=						SparkExamplesIterable(A__						).shard_data_sources(worker_id=1  ,      num_workers=2						)
	assert shard_it_a.n_shards == 2
	a_      				=						_get_expected_row_ids_and_row_dicts_for_partition_order(A__  ,      [1, 3]						)
	for i, (row_id, row_dict) in enumerate(A__						):
		a_				,  a_      				=						expected_row_ids_and_row_dicts_a[i]
		assert row_id == expected_row_id
		assert row_dict == expected_row_dict






@require_not_windows
@require_dill_gt_0_3_2
def      UpperCamelCase_				(      ):
	a_      				=						pyspark.sql.SparkSession.builder.master("""local[*]"""						).appName("""pyspark"""						).getOrCreate()
	a_      				=						spark.range(1_00						).repartition(1						)
	a_      				=						Spark(A__						)
	# Choose a small max_shard_size for maximum partitioning.
	spark_builder._repartition_df_if_needed(max_shard_size=1						)
	# The new number of partitions should not be greater than the number of rows.
	assert spark_builder.df.rdd.getNumPartitions() == 1_00
