





'''simple docstring'''

import os

import numpy
import onnx







def  a__    (    lowercase :					List[str], lowercase :					Optional[int]				)		->		Union[str, Any]:





     """simple docstring"""

     _UpperCamelCase       =	a.name
     _UpperCamelCase       =	b.name

     _UpperCamelCase       =	''
     _UpperCamelCase       =	''

     _UpperCamelCase       =	a == b

     _UpperCamelCase       =	name_a
     _UpperCamelCase       =	name_b

     return res







def  a__    (    lowercase :					Tuple, lowercase :					Any, lowercase :					Tuple				)		->		Any:





     """simple docstring"""

     for i, input_name in enumerate(node_proto.input				):
          if input_name == name:
               node_proto.input.insert(_UpperCAmelCase, _UpperCAmelCase				)
               node_proto.input.pop(i + 1				)

     if node_proto.op_type == "If":
          _graph_replace_input_with(node_proto.attribute[0].g, _UpperCAmelCase, _UpperCAmelCase				)
          _graph_replace_input_with(node_proto.attribute[1].g, _UpperCAmelCase, _UpperCAmelCase				)
     if node_proto.op_type == "Loop":
          _graph_replace_input_with(node_proto.attribute[0].g, _UpperCAmelCase, _UpperCAmelCase				)







def  a__    (    lowercase :					List[str], lowercase :					Optional[Any], lowercase :					Any				)		->		Optional[int]:





     """simple docstring"""

     for n in graph_proto.node:
          _node_replace_input_with(_UpperCAmelCase, _UpperCAmelCase, _UpperCAmelCase				)







def  a__    (    lowercase :					str, lowercase :					Dict, lowercase :					Tuple				)		->		Tuple:





     """simple docstring"""

     _UpperCamelCase       =	list(model.graph.initializer				)
     _UpperCamelCase       =	list(model_without_ext.graph.initializer				)
     for i, ref_i in ind_to_replace:
          assert inits_with_data[i].name == inits[i].name
          assert inits_with_data[ref_i].name == inits[ref_i].name
          assert i > ref_i

          _UpperCamelCase       =	inits[i].name
          _UpperCamelCase       =	inits[ref_i].name

          model_without_ext.graph.initializer.remove(inits[i]				)

          # for n in model.graph.node:
          _graph_replace_input_with(model_without_ext.graph, _UpperCAmelCase, _UpperCAmelCase				)







def  a__    (    lowercase :					Any				)		->		Any:





     """simple docstring"""

     _UpperCamelCase       =	os.path.dirname(_UpperCAmelCase				)
     _UpperCamelCase       =	os.path.basename(_UpperCAmelCase				)

     _UpperCamelCase       =	onnx.load(os.path.join(_UpperCAmelCase, _UpperCAmelCase				)				)

     _UpperCamelCase       =	list(model.graph.initializer				)

     _UpperCamelCase       =	set()
     _UpperCamelCase       =	{}
     _UpperCamelCase       =	[]

     _UpperCamelCase       =	0

     for i in range(len(_UpperCAmelCase				)				):
          if i in dup_set:
               continue

          for j in range(i + 1, len(_UpperCAmelCase				)				):
               if j in dup_set:
                    continue
               if _is_equal_tensor_proto(inits[i], inits[j]				):
                    dup_set.add(_UpperCAmelCase				)
                    dup_set.add(_UpperCAmelCase				)

                    _UpperCamelCase       =	inits[j].data_type
                    _UpperCamelCase       =	numpy.prod(inits[j].dims				)
                    if dtype == 1:
                         mem_size *= 4
                    elif dtype == 6:
                         mem_size *= 4
                    elif dtype == 7 or dtype == 11:
                         mem_size *= 8
                    else:
                         print('''unexpected data type: ''', _UpperCAmelCase				)
                    total_reduced_size += mem_size

                    _UpperCamelCase       =	inits[i].name
                    _UpperCamelCase       =	inits[j].name

                    if name_i in dup_map:
                         dup_map[name_i].append(_UpperCAmelCase				)
                    else:
                         _UpperCamelCase       =	[name_j]
                    ind_to_replace.append((j, i)				)

     print('''total reduced size: ''', total_reduced_size / 1024 / 1024 / 1024, '''GB'''				)

     _UpperCamelCase       =	sorted(_UpperCAmelCase				)
     _remove_dup_initializers_from_model(_UpperCAmelCase, _UpperCAmelCase, _UpperCAmelCase				)

     _UpperCamelCase       =	'optimized_' + model_file_name
     _UpperCamelCase       =	os.path.join(_UpperCAmelCase, _UpperCAmelCase				)
     onnx.save(_UpperCAmelCase, _UpperCAmelCase				)

     return new_model













'''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



