"""simple docstring"""






from typing import List, Optional, Union

import numpy as np
import tensorflow as tf

from .utils import logging


_lowercase			:			List[str]										=						logging.get_logger(__name__)






def 					snake_case__       (		__lowerCamelCase    :   Union[tf.Tensor, np.ndarray]     ):
			"""simple docstring"""
			if isinstance(__lowerCamelCase							,		np.ndarray     ):
						return list(tensor.shape     )

			lowerCamelCase__		:		List[Any]		     =tf.shape(__lowerCamelCase     )

			if tensor.shape == tf.TensorShape(__lowerCamelCase     ):
						return dynamic

			lowerCamelCase__		:		List[str]		     =tensor.shape.as_list()

			return [dynamic[i] if s is None else s for i, s in enumerate(__lowerCamelCase     )]






def 					snake_case__       (		__lowerCamelCase    :   tf.Tensor							,		__lowerCamelCase    :   Optional[int] = None							,		__lowerCamelCase    :   Optional[str] = None     ):
			"""simple docstring"""
			return tf.nn.softmax(logits=logits + 1e-9							,		axis=__lowerCamelCase							,		name=__lowerCamelCase     )






def 					snake_case__       (		__lowerCamelCase    :   Optional[Any]							,		__lowerCamelCase    :   str							,		__lowerCamelCase    :   Optional[int]							,		__lowerCamelCase    :   Any=1e-5							,		__lowerCamelCase    :   str=-1     ):
			"""simple docstring"""
			# This is a very simplified functional layernorm, designed to duplicate
			# the functionality of PyTorch nn.functional.layer_norm when this is needed to port
			# models in Transformers.

			if weight.shape.rank != 1 or bias.shape.rank != 1 or not isinstance(__lowerCamelCase							,		__lowerCamelCase     ):
						raise NotImplementedError('''Only 1D weight and bias tensors are supported for now, with only a single axis.'''     )

			# Get mean and variance on the axis to be normalized
			lowerCamelCase__		:		Optional[int]		     =tf.nn.moments(__lowerCamelCase							,		axes=[axis]							,		keepdims=__lowerCamelCase     )

			if axis != -1:
						# Reshape scale and weight to have the same rank as inputs, but with 1 dimensions
						# on every dimension except axis
						lowerCamelCase__		:		Optional[Any]		     =[1] * inputs.shape.rank
						lowerCamelCase__		:		Union[str, Any]		     =shape_list(__lowerCamelCase     )[axis]
						lowerCamelCase__		:		Optional[Any]		     =tf.reshape(__lowerCamelCase							,		__lowerCamelCase     )
						lowerCamelCase__		:		Any		     =tf.reshape(__lowerCamelCase							,		__lowerCamelCase     )

			# Compute layer normalization using the batch_normalization
			# function.
			lowerCamelCase__		:		List[str]		     =tf.nn.batch_normalization(
			    __lowerCamelCase							,		__lowerCamelCase							,		__lowerCamelCase							,		offset=__lowerCamelCase							,		scale=__lowerCamelCase							,		variance_epsilon=__lowerCamelCase							,		)
			return outputs






def 					snake_case__       (		__lowerCamelCase    :   Any							,		__lowerCamelCase    :   List[Any]=0							,		__lowerCamelCase    :   int=-1     ):
			"""simple docstring"""
			# Replicates the behavior of torch.flatten in TF

			# If end_dim or start_dim is negative, count them from the end
			if end_dim < 0:
						end_dim += input.shape.rank
			if start_dim < 0:
						start_dim += input.shape.rank

			if start_dim == end_dim:
						return input

			lowerCamelCase__		:		Optional[int]		     =tf.shape(__lowerCamelCase     )
			lowerCamelCase__		:		Optional[int]		     =tf.math.reduce_prod(in_shape[start_dim : end_dim + 1]     )
			lowerCamelCase__		:		List[Any]		     =tf.concat([in_shape[:start_dim], [flattened_dim], in_shape[end_dim + 1 :]]							,		axis=0     )
			return tf.reshape(__lowerCamelCase							,		__lowerCamelCase     )






def 					snake_case__       (		__lowerCamelCase    :   tf.Tensor     ):
			"""simple docstring"""
			if not isinstance(__lowerCamelCase							,		tf.Tensor     ):
						lowerCamelCase__		:		List[Any]		     =tf.convert_to_tensor(__lowerCamelCase     )  # Catches stray NumPy inputs
			if encoder_attention_mask.shape.rank == 3:
						lowerCamelCase__		:		int		     =encoder_attention_mask[:, None, :, :]
			if encoder_attention_mask.shape.rank == 2:
						lowerCamelCase__		:		int		     =encoder_attention_mask[:, None, None, :]
			# T5 has a mask that can compare sequence ids, we can simulate this here with this transposition
			# Cf. https://github.com/tensorflow/mesh/blob/8d2465e9bc93129b913b5ccc6a59aa97abd96ec6/mesh_tensorflow
			# /transformer/transformer_layers.py#L270
			# encoder_extended_attention_mask = (encoder_extended_attention_mask ==
			# encoder_extended_attention_mask.transpose(-1, -2))
			lowerCamelCase__		:		str		     =(
			    tf.cast(1							,		encoder_attention_mask.dtype     ) - encoder_extended_attention_mask
			) * encoder_extended_attention_mask.dtype.min

			return encoder_extended_attention_mask






def 					snake_case__       (		__lowerCamelCase    :   tf.Tensor							,		__lowerCamelCase    :   int							,		__lowerCamelCase    :   str = "input_ids"     ):
			"""simple docstring"""
			tf.debugging.assert_less(
			    __lowerCamelCase							,		tf.cast(__lowerCamelCase							,		dtype=tensor.dtype     )							,		message=(
			        f'''The maximum value of {tensor_name} ({tf.math.reduce_max(__lowerCamelCase     )}) must be smaller than the embedding '''
			        f'''layer\'s input dimension ({embed_dim}). The likely cause is some problem at tokenization time.'''
			    )							,		)






def 					snake_case__       (		__lowerCamelCase    :   Tuple							,		__lowerCamelCase    :   List[str]							,		__lowerCamelCase    :   Dict     ):
			"""simple docstring"""
			lowerCamelCase__		:		Any		     =64512
			# Check that no item in `data` is larger than `HDF5_OBJECT_HEADER_LIMIT`
			# because in that case even chunking the array would not make the saving
			# possible.
			lowerCamelCase__		:		Tuple		     =[x for x in data if len(__lowerCamelCase     ) > HDF5_OBJECT_HEADER_LIMIT]

			# Expecting this to never be true.
			if bad_attributes:
						raise RuntimeError(
						    '''The following attributes cannot be saved to HDF5 file because '''
						    f'''they are larger than {HDF5_OBJECT_HEADER_LIMIT} '''
						    f'''bytes: {bad_attributes}'''     )

			lowerCamelCase__		:		Optional[Any]		     =np.asarray(__lowerCamelCase     )

			lowerCamelCase__		:		str		     =1
			lowerCamelCase__		:		List[Any]		     =np.array_split(__lowerCamelCase							,		__lowerCamelCase     )

			# This will never loop forever thanks to the test above.
			while any(x.nbytes > HDF5_OBJECT_HEADER_LIMIT for x in chunked_data     ):
						num_chunks += 1
						lowerCamelCase__		:		Union[str, Any]		     =np.array_split(__lowerCamelCase							,		__lowerCamelCase     )

			if num_chunks > 1:
						for chunk_id, chunk_data in enumerate(__lowerCamelCase     ):
									lowerCamelCase__		:		List[str]		     =chunk_data
			else:
						lowerCamelCase__		:		Dict		     =data






def 					snake_case__       (		__lowerCamelCase    :   Dict							,		__lowerCamelCase    :   Optional[Any]     ):
			"""simple docstring"""
			if name in group.attrs:
						lowerCamelCase__		:		Optional[int]		     =[n.decode('''utf8'''     ) if hasattr(__lowerCamelCase							,		'''decode'''     ) else n for n in group.attrs[name]]
			else:
						lowerCamelCase__		:		str		     =[]
						lowerCamelCase__		:		str		     =0
						while "%s%d" % (name, chunk_id) in group.attrs:
									data.extend(
									    [n.decode('''utf8'''     ) if hasattr(__lowerCamelCase							,		'''decode'''     ) else n for n in group.attrs['''%s%d''' % (name, chunk_id)]]     )
									chunk_id += 1
			return data






def 					snake_case__       (		__lowerCamelCase    :   Dict     ):
			"""simple docstring"""

			def _expand_single_ad_tensor(__lowerCamelCase    :   List[Any]     ):
						if isinstance(__lowerCamelCase							,		tf.Tensor     ) and t.shape.rank == 1:
									return tf.expand_dims(__lowerCamelCase							,		axis=-1     )
						return t

			return tf.nest.map_structure(_expand_single_ad_tensor							,		__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 	__SCREAMING_SNAKE_CASE     (				unittest.TestCase       ):


					'''simple docstring'''





					def 		snake_case	(	self							:  Union[str, Any]					)-> Dict:
								# clean up the VRAM after each test
								super().tearDown()
								gc.collect()



					def 		snake_case	(	self							:  str					)-> Any:
								lowerCamelCase__     ,       lowerCamelCase__		:		Optional[Any]		     =FlaxControlNetModel.from_pretrained(
								    '''lllyasviel/sd-controlnet-canny''',  from_pt=lowerCamelCase,  dtype=jnp.bfloataa					)
								lowerCamelCase__     ,       lowerCamelCase__		:		Optional[int]		     =FlaxStableDiffusionControlNetPipeline.from_pretrained(
								    '''runwayml/stable-diffusion-v1-5''',  controlnet=lowerCamelCase,  from_pt=lowerCamelCase,  dtype=jnp.bfloataa					)
								lowerCamelCase__		:		Optional[int]		     =controlnet_params

								lowerCamelCase__		:		Dict		     ='''bird'''
								lowerCamelCase__		:		List[str]		     =jax.device_count()
								lowerCamelCase__		:		Optional[Any]		     =pipe.prepare_text_inputs([prompts] * num_samples					)

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

								lowerCamelCase__		:		Optional[int]		     =jax.random.PRNGKey(0					)
								lowerCamelCase__		:		Dict		     =jax.random.split(lowerCamelCase,  jax.device_count()					)

								lowerCamelCase__		:		Tuple		     =replicate(lowerCamelCase					)
								lowerCamelCase__		:		Tuple		     =shard(lowerCamelCase					)
								lowerCamelCase__		:		Optional[int]		     =shard(lowerCamelCase					)

								lowerCamelCase__		:		Tuple		     =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)

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

								lowerCamelCase__		:		Optional[Any]		     =jnp.asarray(jax.device_get(image_slice.flatten()					)					)
								lowerCamelCase__		:		Dict		     =jnp.array(
								    [0.167_969, 0.116_699, 0.081_543, 0.154_297, 0.132_812, 0.108_887, 0.169_922, 0.169_922, 0.205_078]					)
								print(F'''output_slice: {output_slice}'''					)
								assert jnp.abs(output_slice - expected_slice					).max() < 1E-2




					def 		snake_case	(	self							:  Optional[int]					)-> Optional[int]:
								lowerCamelCase__     ,       lowerCamelCase__		:		Dict		     =FlaxControlNetModel.from_pretrained(
								    '''lllyasviel/sd-controlnet-openpose''',  from_pt=lowerCamelCase,  dtype=jnp.bfloataa					)
								lowerCamelCase__     ,       lowerCamelCase__		:		List[Any]		     =FlaxStableDiffusionControlNetPipeline.from_pretrained(
								    '''runwayml/stable-diffusion-v1-5''',  controlnet=lowerCamelCase,  from_pt=lowerCamelCase,  dtype=jnp.bfloataa					)
								lowerCamelCase__		:		Optional[Any]		     =controlnet_params

								lowerCamelCase__		:		int		     ='''Chef in the kitchen'''
								lowerCamelCase__		:		Optional[Any]		     =jax.device_count()
								lowerCamelCase__		:		Any		     =pipe.prepare_text_inputs([prompts] * num_samples					)

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

								lowerCamelCase__		:		Tuple		     =jax.random.PRNGKey(0					)
								lowerCamelCase__		:		Optional[Any]		     =jax.random.split(lowerCamelCase,  jax.device_count()					)

								lowerCamelCase__		:		int		     =replicate(lowerCamelCase					)
								lowerCamelCase__		:		List[Any]		     =shard(lowerCamelCase					)
								lowerCamelCase__		:		int		     =shard(lowerCamelCase					)

								lowerCamelCase__		:		Tuple		     =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)

								lowerCamelCase__		:		Dict		     =images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:]					)
								lowerCamelCase__		:		List[str]		     =images[0, 253:256, 253:256, -1]

								lowerCamelCase__		:		int		     =jnp.asarray(jax.device_get(image_slice.flatten()					)					)
								lowerCamelCase__		:		Any		     =jnp.array(
								    [[0.271_484, 0.261_719, 0.275_391, 0.277_344, 0.279_297, 0.291_016, 0.294_922, 0.302_734, 0.302_734]]					)
								print(F'''output_slice: {output_slice}'''					)
								assert jnp.abs(output_slice - expected_slice					).max() < 1E-2




