





'''simple docstring'''

import os
import sys
import unittest


lowercase__						:      Any	=    os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, 'utils'))

import check_dummies  # noqa: E402
from check_dummies import create_dummy_files, create_dummy_object, find_backend, read_init  # noqa: E402


# Align TRANSFORMERS_PATH in check_dummies with the current path
lowercase__						:      List[Any]	=    os.path.join(git_repo_path, 'src', 'transformers')

lowercase__						:      List[str]	=    '\n{0} = None\n'

lowercase__						:      List[str]	=    '\nclass {0}(metaclass=DummyObject):\n    _backends = {1}\n\n    def __init__(self, *args, **kwargs):\n        requires_backends(self, {1})\n'


lowercase__						:      List[str]	=    '\ndef {0}(*args, **kwargs):\n    requires_backends({0}, {1})\n'
class     __lowerCAmelCase					(   unittest.TestCase			):






       """simple docstring"""





       def snake_case__						(							self       :		str					)   ->					Union[str, Any]:

            '''simple docstring'''




            _UpperCamelCase       =	find_backend('''    _import_structure["models.albert"].append("AlbertTokenizerFast")'''					)
            self.assertIsNone(lowerCAmelCase__					)

            _UpperCamelCase       =	find_backend('''    if not is_tokenizers_available():'''					)
            self.assertEqual(lowerCAmelCase__	,			'''tokenizers'''					)

            _UpperCamelCase       =	find_backend('''    if not is_tensorflow_text_available():'''					)
            self.assertEqual(lowerCAmelCase__	,			'''tensorflow_text'''					)

            _UpperCamelCase       =	find_backend('''    if not (is_sentencepiece_available() and is_tokenizers_available()):'''					)
            self.assertEqual(lowerCAmelCase__	,			'''sentencepiece_and_tokenizers'''					)

            _UpperCamelCase       =	find_backend(
                '''    if not (is_sentencepiece_available() and is_tensorflow_text_available()):'''					)
            self.assertEqual(lowerCAmelCase__	,			'''sentencepiece_and_tensorflow_text'''					)

            _UpperCamelCase       =	find_backend(
                '''    if not (is_sentencepiece_available() and is_tokenizers_available() and is_vision_available()):'''					)
            self.assertEqual(lowerCAmelCase__	,			'''sentencepiece_and_tokenizers_and_vision'''					)





       def snake_case__						(							self       :		Union[str, Any]					)   ->					List[str]:

            '''simple docstring'''




            _UpperCamelCase       =	read_init()
            # We don't assert on the exact list of keys to allow for smooth grow of backend-specific objects
            self.assertIn('''torch'''	,			lowerCAmelCase__					)
            self.assertIn('''tensorflow_text'''	,			lowerCAmelCase__					)
            self.assertIn('''sentencepiece_and_tokenizers'''	,			lowerCAmelCase__					)

            # Likewise, we can't assert on the exact content of a key
            self.assertIn('''BertModel'''	,			objects['''torch''']					)
            self.assertIn('''TFBertModel'''	,			objects['''tf''']					)
            self.assertIn('''FlaxBertModel'''	,			objects['''flax''']					)
            self.assertIn('''BertModel'''	,			objects['''torch''']					)
            self.assertIn('''TFBertTokenizer'''	,			objects['''tensorflow_text''']					)
            self.assertIn('''convert_slow_tokenizer'''	,			objects['''sentencepiece_and_tokenizers''']					)





       def snake_case__						(							self       :		str					)   ->					Optional[Any]:

            '''simple docstring'''




            _UpperCamelCase       =	create_dummy_object('''CONSTANT'''	,			'''\'torch\''''					)
            self.assertEqual(lowerCAmelCase__	,			'''\nCONSTANT = None\n'''					)

            _UpperCamelCase       =	create_dummy_object('''function'''	,			'''\'torch\''''					)
            self.assertEqual(
                lowerCAmelCase__	,			'''\ndef function(*args, **kwargs):\n    requires_backends(function, \'torch\')\n'''					)

            _UpperCamelCase       =	'''
class FakeClass(metaclass=DummyObject):
    _backends = \'torch\'

    def __init__(self, *args, **kwargs):
        requires_backends(self, \'torch\')
'''
            _UpperCamelCase       =	create_dummy_object('''FakeClass'''	,			'''\'torch\''''					)
            self.assertEqual(lowerCAmelCase__	,			lowerCAmelCase__					)







       def snake_case__						(							self       :		List[Any]					)   ->					List[Any]:

            '''simple docstring'''




            _UpperCamelCase       =	'''# This file is autogenerated by the command `make fix-copies`, do not edit.
from ..utils import DummyObject, requires_backends


CONSTANT = None


def function(*args, **kwargs):
    requires_backends(function, ["torch"])


class FakeClass(metaclass=DummyObject):
    _backends = ["torch"]

    def __init__(self, *args, **kwargs):
        requires_backends(self, ["torch"])
'''
            _UpperCamelCase       =	create_dummy_files({'''torch''': ['''CONSTANT''', '''function''', '''FakeClass''']}					)
            self.assertEqual(dummy_files['''torch''']	,			lowerCAmelCase__					)










'''simple docstring'''

import gc
import unittest

from diffusers import FlaxControlNetModel, FlaxStableDiffusionControlNetPipeline
from diffusers.utils import is_flax_available, load_image, slow
from diffusers.utils.testing_utils import require_flax


if is_flax_available():
   import jax
   import jax.numpy as jnp
   from flax.jax_utils import replicate
   from flax.training.common_utils import shard
@slow
@require_flax
class     __lowerCAmelCase					(   unittest.TestCase			):






       """simple docstring"""





       def snake_case__						(							self       :		List[str]					)   ->					Optional[int]:

            '''simple docstring'''




            super().tearDown()
            gc.collect()





       def snake_case__						(							self       :		int					)   ->					Union[str, Any]:

            '''simple docstring'''




            _UpperCamelCase   ,    _UpperCamelCase       =	FlaxControlNetModel.from_pretrained(
                '''lllyasviel/sd-controlnet-canny'''	,			from_pt=lowerCAmelCase__	,			dtype=jnp.bfloataa					)
            _UpperCamelCase   ,    _UpperCamelCase       =	FlaxStableDiffusionControlNetPipeline.from_pretrained(
                '''runwayml/stable-diffusion-v1-5'''	,			controlnet=lowerCAmelCase__	,			from_pt=lowerCAmelCase__	,			dtype=jnp.bfloataa					)
            _UpperCamelCase       =	controlnet_params

            _UpperCamelCase       =	'''bird'''
            _UpperCamelCase       =	jax.device_count()
            _UpperCamelCase       =	pipe.prepare_text_inputs([prompts] * num_samples					)

            _UpperCamelCase       =	load_image(
                '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/bird_canny.png'''					)
            _UpperCamelCase       =	pipe.prepare_image_inputs([canny_image] * num_samples					)

            _UpperCamelCase       =	jax.random.PRNGKey(0					)
            _UpperCamelCase       =	jax.random.split(lowerCAmelCase__	,			jax.device_count()					)

            _UpperCamelCase       =	replicate(lowerCAmelCase__					)
            _UpperCamelCase       =	shard(lowerCAmelCase__					)
            _UpperCamelCase       =	shard(lowerCAmelCase__					)

            _UpperCamelCase       =	pipe(
                prompt_ids=lowerCAmelCase__	,			image=lowerCAmelCase__	,			params=lowerCAmelCase__	,			prng_seed=lowerCAmelCase__	,			num_inference_steps=50	,			jit=lowerCAmelCase__	,			).images
            assert images.shape == (jax.device_count(), 1, 768, 512, 3)

            _UpperCamelCase       =	images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:]					)
            _UpperCamelCase       =	images[0, 253:256, 253:256, -1]

            _UpperCamelCase       =	jnp.asarray(jax.device_get(image_slice.flatten()					)					)
            _UpperCamelCase       =	jnp.array(
                [0.167969, 0.116699, 0.081543, 0.154297, 0.132812, 0.108887, 0.169922, 0.169922, 0.205078]					)
            print(f"""output_slice: {output_slice}"""					)
            assert jnp.abs(output_slice - expected_slice					).max() < 1e-2







       def snake_case__						(							self       :		Union[str, Any]					)   ->					Union[str, Any]:

            '''simple docstring'''




            _UpperCamelCase   ,    _UpperCamelCase       =	FlaxControlNetModel.from_pretrained(
                '''lllyasviel/sd-controlnet-openpose'''	,			from_pt=lowerCAmelCase__	,			dtype=jnp.bfloataa					)
            _UpperCamelCase   ,    _UpperCamelCase       =	FlaxStableDiffusionControlNetPipeline.from_pretrained(
                '''runwayml/stable-diffusion-v1-5'''	,			controlnet=lowerCAmelCase__	,			from_pt=lowerCAmelCase__	,			dtype=jnp.bfloataa					)
            _UpperCamelCase       =	controlnet_params

            _UpperCamelCase       =	'''Chef in the kitchen'''
            _UpperCamelCase       =	jax.device_count()
            _UpperCamelCase       =	pipe.prepare_text_inputs([prompts] * num_samples					)

            _UpperCamelCase       =	load_image(
                '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/pose.png'''					)
            _UpperCamelCase       =	pipe.prepare_image_inputs([pose_image] * num_samples					)

            _UpperCamelCase       =	jax.random.PRNGKey(0					)
            _UpperCamelCase       =	jax.random.split(lowerCAmelCase__	,			jax.device_count()					)

            _UpperCamelCase       =	replicate(lowerCAmelCase__					)
            _UpperCamelCase       =	shard(lowerCAmelCase__					)
            _UpperCamelCase       =	shard(lowerCAmelCase__					)

            _UpperCamelCase       =	pipe(
                prompt_ids=lowerCAmelCase__	,			image=lowerCAmelCase__	,			params=lowerCAmelCase__	,			prng_seed=lowerCAmelCase__	,			num_inference_steps=50	,			jit=lowerCAmelCase__	,			).images
            assert images.shape == (jax.device_count(), 1, 768, 512, 3)

            _UpperCamelCase       =	images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:]					)
            _UpperCamelCase       =	images[0, 253:256, 253:256, -1]

            _UpperCamelCase       =	jnp.asarray(jax.device_get(image_slice.flatten()					)					)
            _UpperCamelCase       =	jnp.array(
                [[0.271484, 0.261719, 0.275391, 0.277344, 0.279297, 0.291016, 0.294922, 0.302734, 0.302734]]					)
            print(f"""output_slice: {output_slice}"""					)
            assert jnp.abs(output_slice - expected_slice					).max() < 1e-2



