
import gc
import unittest

from diffusers import FlaxDPMSolverMultistepScheduler, FlaxStableDiffusionPipeline
from diffusers.utils import is_flax_available, 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 __snake_case		(      unittest.TestCase     ):
    def       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           super().tearDown()
           gc.collect()
    def       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           lowercase	,			lowercase							:     Any       	=      FlaxStableDiffusionPipeline.from_pretrained(
               """stabilityai/stable-diffusion-2"""		,revision="""bf16"""		,dtype=jnp.bfloataa		,)

           lowercase							:     Optional[Any]       	=      """A painting of a squirrel eating a burger"""
           lowercase							:     Any       	=      jax.device_count()
           lowercase							:     Tuple       	=      num_samples * [prompt]
           lowercase							:     Any       	=      sd_pipe.prepare_inputs(snake_case      )

           lowercase							:     Tuple       	=      replicate(snake_case      )
           lowercase							:     Optional[int]       	=      shard(snake_case      )

           lowercase							:     Optional[int]       	=      jax.random.PRNGKey(0      )
           lowercase							:     Tuple       	=      jax.random.split(snake_case		,jax.device_count()      )

           lowercase							:     List[Any]       	=      sd_pipe(snake_case		,snake_case		,snake_case		,num_inference_steps=25		,jit=snake_case      )[0]
           assert images.shape == (jax.device_count(), 1, 768, 768, 3)

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

           lowercase							:     Dict       	=      jnp.asarray(jax.device_get(image_slice.flatten()      )      )
           lowercase							:     Union[str, Any]       	=      jnp.array([0.4_238, 0.4_414, 0.4_395, 0.4_453, 0.4_629, 0.4_590, 0.4_531, 0.45_508, 0.4_512]      )
           print(f"output_slice: {output_slice}"      )
           assert jnp.abs(output_slice - expected_slice      ).max() < 1e-2
    def       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           lowercase							:     str       	=      """stabilityai/stable-diffusion-2"""
           lowercase	,			lowercase							:     Dict       	=      FlaxDPMSolverMultistepScheduler.from_pretrained(snake_case		,subfolder="""scheduler"""      )
           lowercase	,			lowercase							:     List[str]       	=      FlaxStableDiffusionPipeline.from_pretrained(
               snake_case		,scheduler=snake_case		,revision="""bf16"""		,dtype=jnp.bfloataa		,)
           lowercase							:     Optional[int]       	=      scheduler_params

           lowercase							:     List[str]       	=      """A painting of a squirrel eating a burger"""
           lowercase							:     Any       	=      jax.device_count()
           lowercase							:     Dict       	=      num_samples * [prompt]
           lowercase							:     Any       	=      sd_pipe.prepare_inputs(snake_case      )

           lowercase							:     Tuple       	=      replicate(snake_case      )
           lowercase							:     Optional[Any]       	=      shard(snake_case      )

           lowercase							:     Tuple       	=      jax.random.PRNGKey(0      )
           lowercase							:     int       	=      jax.random.split(snake_case		,jax.device_count()      )

           lowercase							:     Any       	=      sd_pipe(snake_case		,snake_case		,snake_case		,num_inference_steps=25		,jit=snake_case      )[0]
           assert images.shape == (jax.device_count(), 1, 768, 768, 3)

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

           lowercase							:     int       	=      jnp.asarray(jax.device_get(image_slice.flatten()      )      )
           lowercase							:     List[Any]       	=      jnp.array([0.4_336, 0.42_969, 0.4_453, 0.4_199, 0.4_297, 0.4_531, 0.4_434, 0.4_434, 0.4_297]      )
           print(f"output_slice: {output_slice}"      )
           assert jnp.abs(output_slice - expected_slice      ).max() < 1e-2


import os

import numpy
import onnx







def 						_snake_case(  SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				) ->    Union[str, Any]:
       lowercase							:     int       	=      a.name
       lowercase							:     Any       	=      b.name

       lowercase							:     Optional[Any]       	=      """"""
       lowercase							:     Dict       	=      """"""

       lowercase							:     int       	=      a == b

       lowercase							:     int       	=      name_a
       lowercase							:     List[str]       	=      name_b

       return res







def 						_snake_case(  SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				) ->    str:
       for i, input_name in enumerate(node_proto.input				):
              if input_name == name:
                     node_proto.input.insert(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				)
                     node_proto.input.pop(i + 1				)

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







def 						_snake_case(  SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				) ->    Union[str, Any]:
       for n in graph_proto.node:
              _node_replace_input_with(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				)







def 						_snake_case(  SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				) ->    List[Any]:
       lowercase							:     Any       	=      list(model.graph.initializer				)
       lowercase							:     Dict       	=      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

              lowercase							:     Union[str, Any]       	=      inits[i].name
              lowercase							:     Dict       	=      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					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				)







def 						_snake_case(  SCREAMING_SNAKE_CASE__				) ->    List[str]:
       lowercase							:     Union[str, Any]       	=      os.path.dirname(SCREAMING_SNAKE_CASE__				)
       lowercase							:     Dict       	=      os.path.basename(SCREAMING_SNAKE_CASE__				)

       lowercase							:     str       	=      onnx.load(os.path.join(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				)				)

       lowercase							:     List[str]       	=      list(model.graph.initializer				)

       lowercase							:     Tuple       	=      set()
       lowercase							:     int       	=      {}
       lowercase							:     Optional[Any]       	=      []

       lowercase							:     Dict       	=      0

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

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

                            lowercase							:     int       	=      inits[j].data_type
                            lowercase							:     Optional[int]       	=      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: """					,	SCREAMING_SNAKE_CASE__				)
                            total_reduced_size += mem_size

                            lowercase							:     Tuple       	=      inits[i].name
                            lowercase							:     int       	=      inits[j].name

                            if name_i in dup_map:
                                   dup_map[name_i].append(SCREAMING_SNAKE_CASE__				)
                            else:
                                   lowercase							:     List[str]       	=      [name_j]
                            ind_to_replace.append((j, i)				)

       print("""total reduced size: """					,	total_reduced_size / 1_024 / 1_024 / 1_024					,	"""GB"""				)

       lowercase							:     str       	=      sorted(SCREAMING_SNAKE_CASE__				)
       _remove_dup_initializers_from_model(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				)

       lowercase							:     Optional[Any]       	=      """optimized_""" + model_file_name
       lowercase							:     Dict       	=      os.path.join(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				)
       onnx.save(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				)

       return new_model
