
"""simple docstring"""

import torch

from diffusers import UnCLIPScheduler

from .test_schedulers import SchedulerCommonTest

class __lowerCAmelCase	(   __SCREAMING_SNAKE_CASE    ):
     lowercase		     =		(UnCLIPScheduler,)





     def 		UpperCAmelCase      (			self			,  **__UpperCAmelCase   ):

      '''simple docstring'''




      __UpperCamelCase       						= {
          'num_train_timesteps': 1000,
          'variance_type': 'fixed_small_log',
          'clip_sample': True,
          'clip_sample_range': 1.0,
          'prediction_type': 'epsilon',
      }

      config.update(**__UpperCAmelCase   )
      return config





     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      for timesteps in [1, 5, 100, 1000]:
       self.check_over_configs(num_train_timesteps=__UpperCAmelCase   )





     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      for variance in ["fixed_small_log", "learned_range"]:
       self.check_over_configs(variance_type=__UpperCAmelCase   )





     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      for clip_sample in [True, False]:
       self.check_over_configs(clip_sample=__UpperCAmelCase   )





     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      for clip_sample_range in [1, 5, 10, 20]:
       self.check_over_configs(clip_sample_range=__UpperCAmelCase   )





     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      for prediction_type in ["epsilon", "sample"]:
       self.check_over_configs(prediction_type=__UpperCAmelCase   )





     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      for time_step in [0, 500, 999]:
       for prev_timestep in [None, 5, 100, 250, 500, 750]:
        if prev_timestep is not None and prev_timestep >= time_step:
         continue

        self.check_over_forward(time_step=__UpperCAmelCase			,  prev_timestep=__UpperCAmelCase   )





     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      __UpperCamelCase       						= self.scheduler_classes[0]
      __UpperCamelCase       						= self.get_scheduler_config(variance_type='fixed_small_log'   )
      __UpperCamelCase       						= scheduler_class(**__UpperCAmelCase   )

      assert torch.sum(torch.abs(scheduler._get_variance(0   ) - 1.0000E-10   )   ) < 1E-5
      assert torch.sum(torch.abs(scheduler._get_variance(487   ) - 0.0_5_4_9_6_2_5   )   ) < 1E-5
      assert torch.sum(torch.abs(scheduler._get_variance(999   ) - 0.9_9_9_4_9_8_7   )   ) < 1E-5





     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      __UpperCamelCase       						= self.scheduler_classes[0]
      __UpperCamelCase       						= self.get_scheduler_config(variance_type='learned_range'   )
      __UpperCamelCase       						= scheduler_class(**__UpperCAmelCase   )

      __UpperCamelCase       						= 0.5

      assert scheduler._get_variance(1			,  predicted_variance=__UpperCAmelCase   ) - -1_0.1_7_1_2_7_9_0 < 1E-5
      assert scheduler._get_variance(487			,  predicted_variance=__UpperCAmelCase   ) - -5.7_9_9_8_0_5_2 < 1E-5
      assert scheduler._get_variance(999			,  predicted_variance=__UpperCAmelCase   ) - -0.0_0_1_0_0_1_1 < 1E-5





     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      __UpperCamelCase       						= self.scheduler_classes[0]
      __UpperCamelCase       						= self.get_scheduler_config()
      __UpperCamelCase       						= scheduler_class(**__UpperCAmelCase   )

      __UpperCamelCase       						= scheduler.timesteps

      __UpperCamelCase       						= self.dummy_model()
      __UpperCamelCase       						= self.dummy_sample_deter
      __UpperCamelCase       						= torch.manual_seed(0   )

      for i, t in enumerate(__UpperCAmelCase   ):
       # 1. predict noise residual
       __UpperCamelCase       						= model(__UpperCAmelCase			,  __UpperCAmelCase   )

       # 2. predict previous mean of sample x_t-1
       __UpperCamelCase       						= scheduler.step(__UpperCAmelCase			,  __UpperCAmelCase			,  __UpperCAmelCase			,  generator=__UpperCAmelCase   ).prev_sample

       __UpperCamelCase       						= pred_prev_sample

      __UpperCamelCase       						= torch.sum(torch.abs(__UpperCAmelCase   )   )
      __UpperCamelCase       						= torch.mean(torch.abs(__UpperCAmelCase   )   )

      assert abs(result_sum.item() - 2_5_2.2_6_8_2_4_9_5   ) < 1E-2
      assert abs(result_mean.item() - 0.3_2_8_4_7_4_3   ) < 1E-3





     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      __UpperCamelCase       						= self.scheduler_classes[0]
      __UpperCamelCase       						= self.get_scheduler_config()
      __UpperCamelCase       						= scheduler_class(**__UpperCAmelCase   )

      scheduler.set_timesteps(25   )

      __UpperCamelCase       						= scheduler.timesteps

      __UpperCamelCase       						= self.dummy_model()
      __UpperCamelCase       						= self.dummy_sample_deter
      __UpperCamelCase       						= torch.manual_seed(0   )

      for i, t in enumerate(__UpperCAmelCase   ):
       # 1. predict noise residual
       __UpperCamelCase       						= model(__UpperCAmelCase			,  __UpperCAmelCase   )

       if i + 1 == timesteps.shape[0]:
        __UpperCamelCase       						= None
       else:
        __UpperCamelCase       						= timesteps[i + 1]

       # 2. predict previous mean of sample x_t-1
       __UpperCamelCase       						= scheduler.step(
           __UpperCAmelCase			,  __UpperCAmelCase			,  __UpperCAmelCase			,  prev_timestep=__UpperCAmelCase			,  generator=__UpperCAmelCase   ).prev_sample

       __UpperCamelCase       						= pred_prev_sample

      __UpperCamelCase       						= torch.sum(torch.abs(__UpperCAmelCase   )   )
      __UpperCamelCase       						= torch.mean(torch.abs(__UpperCAmelCase   )   )

      assert abs(result_sum.item() - 2_5_8.2_0_4_4_9_8_3   ) < 1E-2
      assert abs(result_mean.item() - 0.3_3_6_2_0_3_8   ) < 1E-3





     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      pass







     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      pass





"""simple docstring"""

import argparse
import json
import os

from tensorflow.core.protobuf.saved_model_pba import SavedModel


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_copies.py
UpperCamelCase			:       Tuple									=				"."

# Internal TensorFlow ops that can be safely ignored (mostly specific to a saved model)
UpperCamelCase			:       Dict									=				[
    "Assert",
    "AssignVariableOp",
    "EmptyTensorList",
    "MergeV2Checkpoints",
    "ReadVariableOp",
    "ResourceGather",
    "RestoreV2",
    "SaveV2",
    "ShardedFilename",
    "StatefulPartitionedCall",
    "StaticRegexFullMatch",
    "VarHandleOp",
]






def 							A		(       snake_case   :List[str]       ,      snake_case   :Optional[int]       ,      snake_case   :Optional[Any]	)   ->			Any:
 __UpperCamelCase       						= SavedModel()
 __UpperCamelCase       						= []

 with open(os.path.join(snake_case       ,      'utils'       ,      'tf_ops'       ,      'onnx.json'	)	) as f:
  __UpperCamelCase       						= json.load(snake_case	)['opsets']

 for i in range(1       ,      opset + 1	):
  onnx_ops.extend(onnx_opsets[str(snake_case	)]	)

 with open(snake_case       ,      'rb'	) as f:
  saved_model.ParseFromString(f.read()	)

 __UpperCamelCase       						= set()

 # Iterate over every metagraph in case there is more than one (a saved model can contain multiple graphs)
 for meta_graph in saved_model.meta_graphs:
  # Add operations in the graph definition
  model_op_names.update(node.op for node in meta_graph.graph_def.node	)

  # Go through the functions in the graph definition
  for func in meta_graph.graph_def.library.function:
   # Add operations in each function
   model_op_names.update(node.op for node in func.node_def	)

    # Convert to list, sorted if you want
 __UpperCamelCase       						= sorted(snake_case	)
 __UpperCamelCase       						= []

 for op in model_op_names:
  if op not in onnx_ops and op not in INTERNAL_OPS:
   incompatible_ops.append(snake_case	)

 if strict and len(snake_case	) > 0:
  raise Exception(f'Found the following incompatible ops for the opset {opset}:\n' + incompatible_ops	)
 elif len(snake_case	) > 0:
  print(f'Found the following incompatible ops for the opset {opset}:'	)
  print(*snake_case       ,      sep='\n'	)
 else:
  print(f'The saved model {saved_model_path} can properly be converted with ONNX.'	)


if __name__ == "__main__":
      UpperCamelCase			:       List[str]									=				argparse.ArgumentParser()
      parser.add_argument("--saved_model_path", help="Path of the saved model to check (the .pb file).")
      parser.add_argument(
          "--opset", default=1_2, type=int, help="The ONNX opset against which the model has to be tested."
      )
      parser.add_argument(
          "--framework", choices=["onnx"], default="onnx", help="Frameworks against which to test the saved model."
      )
      parser.add_argument(
          "--strict", action="store_true", help="Whether make the checking strict (raise errors) or not (raise warnings)"
      )
      UpperCamelCase			:       str									=				parser.parse_args()

      if args.framework == "onnx":
            onnx_compliancy(args.saved_model_path, args.strict, args.opset)



