
'''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
__lowerCAmelCase					:			int			      =		"."

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


def     UpperCAmelCase_  (						__lowerCAmelCase	,					__lowerCAmelCase	,					__lowerCAmelCase	) ->						int:
    __lowercase		:			Dict	    =       SavedModel()
    __lowercase		:			int	    =       []

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

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

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

    __lowercase		:			str	    =       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
    __lowercase		:			List[Any]	    =       sorted(__lowerCAmelCase	)
    __lowercase		:			Optional[Any]	    =       []

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

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


if __name__ == "__main__":
 __lowerCAmelCase					:			int			      =		argparse.ArgumentParser()
 parser.add_argument("--saved_model_path", help="Path of the saved model to check (the .pb file).")
 parser.add_argument(
     "--opset", default=12, 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)"
 )
 __lowerCAmelCase					:			List[Any]			      =		parser.parse_args()

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




import tempfile

import numpy as np
import torch
from transformers import AutoTokenizer, TaEncoderModel

from diffusers import DDPMScheduler, UNetaDConditionModel
from diffusers.models.attention_processor import AttnAddedKVProcessor
from diffusers.pipelines.deepfloyd_if import IFWatermarker
from diffusers.utils.testing_utils import torch_device

from ..test_pipelines_common import to_np


class 						__lowerCAmelCase	:



      """simple docstring"""







      def 		snake_case_			(      self		:       str				):
          torch.manual_seed(0				)
          __lowercase		:			Optional[int]	    =       TaEncoderModel.from_pretrained('''hf-internal-testing/tiny-random-t5'''				)

          torch.manual_seed(0				)
          __lowercase		:			Optional[Any]	    =       AutoTokenizer.from_pretrained('''hf-internal-testing/tiny-random-t5'''				)

          torch.manual_seed(0				)
          __lowercase		:			Tuple	    =       UNetaDConditionModel(
              sample_size=32				,       layers_per_block=1				,       block_out_channels=[32, 64]				,       down_block_types=[
                  '''ResnetDownsampleBlock2D''',
                  '''SimpleCrossAttnDownBlock2D''',
              ]				,       mid_block_type='''UNetMidBlock2DSimpleCrossAttn'''				,       up_block_types=['''SimpleCrossAttnUpBlock2D''', '''ResnetUpsampleBlock2D''']				,       in_channels=3				,       out_channels=6				,       cross_attention_dim=32				,       encoder_hid_dim=32				,       attention_head_dim=8				,       addition_embed_type='''text'''				,       addition_embed_type_num_heads=2				,       cross_attention_norm='''group_norm'''				,       resnet_time_scale_shift='''scale_shift'''				,       act_fn='''gelu'''				,       )
          unet.set_attn_processor(AttnAddedKVProcessor()				)  # For reproducibility tests

          torch.manual_seed(0				)
          __lowercase		:			List[str]	    =       DDPMScheduler(
              num_train_timesteps=1000				,       beta_schedule='''squaredcos_cap_v2'''				,       beta_start=0.00_01				,       beta_end=0.02				,       thresholding=_snake_case				,       dynamic_thresholding_ratio=0.95				,       sample_max_value=1.0				,       prediction_type='''epsilon'''				,       variance_type='''learned_range'''				,       )

          torch.manual_seed(0				)
          __lowercase		:			str	    =       IFWatermarker()

          return {
              "text_encoder": text_encoder,
              "tokenizer": tokenizer,
              "unet": unet,
              "scheduler": scheduler,
              "watermarker": watermarker,
              "safety_checker": None,
              "feature_extractor": None,
          }




      def 		snake_case_			(      self		:       Optional[int]				):
          torch.manual_seed(0				)
          __lowercase		:			Optional[int]	    =       TaEncoderModel.from_pretrained('''hf-internal-testing/tiny-random-t5'''				)

          torch.manual_seed(0				)
          __lowercase		:			Tuple	    =       AutoTokenizer.from_pretrained('''hf-internal-testing/tiny-random-t5'''				)

          torch.manual_seed(0				)
          __lowercase		:			Dict	    =       UNetaDConditionModel(
              sample_size=32				,       layers_per_block=[1, 2]				,       block_out_channels=[32, 64]				,       down_block_types=[
                  '''ResnetDownsampleBlock2D''',
                  '''SimpleCrossAttnDownBlock2D''',
              ]				,       mid_block_type='''UNetMidBlock2DSimpleCrossAttn'''				,       up_block_types=['''SimpleCrossAttnUpBlock2D''', '''ResnetUpsampleBlock2D''']				,       in_channels=6				,       out_channels=6				,       cross_attention_dim=32				,       encoder_hid_dim=32				,       attention_head_dim=8				,       addition_embed_type='''text'''				,       addition_embed_type_num_heads=2				,       cross_attention_norm='''group_norm'''				,       resnet_time_scale_shift='''scale_shift'''				,       act_fn='''gelu'''				,       class_embed_type='''timestep'''				,       mid_block_scale_factor=1.4_14				,       time_embedding_act_fn='''gelu'''				,       time_embedding_dim=32				,       )
          unet.set_attn_processor(AttnAddedKVProcessor()				)  # For reproducibility tests

          torch.manual_seed(0				)
          __lowercase		:			Dict	    =       DDPMScheduler(
              num_train_timesteps=1000				,       beta_schedule='''squaredcos_cap_v2'''				,       beta_start=0.00_01				,       beta_end=0.02				,       thresholding=_snake_case				,       dynamic_thresholding_ratio=0.95				,       sample_max_value=1.0				,       prediction_type='''epsilon'''				,       variance_type='''learned_range'''				,       )

          torch.manual_seed(0				)
          __lowercase		:			Optional[int]	    =       DDPMScheduler(
              num_train_timesteps=1000				,       beta_schedule='''squaredcos_cap_v2'''				,       beta_start=0.00_01				,       beta_end=0.02				,       )

          torch.manual_seed(0				)
          __lowercase		:			Any	    =       IFWatermarker()

          return {
              "text_encoder": text_encoder,
              "tokenizer": tokenizer,
              "unet": unet,
              "scheduler": scheduler,
              "image_noising_scheduler": image_noising_scheduler,
              "watermarker": watermarker,
              "safety_checker": None,
              "feature_extractor": None,
          }




      def 		snake_case_			(      self		:       Any				):
          __lowercase		:			Tuple	    =       self.get_dummy_components()
          __lowercase		:			Any	    =       self.pipeline_class(**_snake_case				)
          pipe.to(_snake_case				)
          pipe.set_progress_bar_config(disable=_snake_case				)

          __lowercase		:			List[str]	    =       self.get_dummy_inputs(_snake_case				)

          __lowercase		:			List[Any]	    =       inputs['''prompt''']
          __lowercase		:			int	    =       inputs['''generator''']
          __lowercase		:			Dict	    =       inputs['''num_inference_steps''']
          __lowercase		:			Optional[int]	    =       inputs['''output_type''']

          if "image" in inputs:
              __lowercase		:			Tuple	    =       inputs['''image''']
          else:
              __lowercase		:			Dict	    =       None

          if "mask_image" in inputs:
              __lowercase		:			Tuple	    =       inputs['''mask_image''']
          else:
              __lowercase		:			List[Any]	    =       None

          if "original_image" in inputs:
              __lowercase		:			List[Any]	    =       inputs['''original_image''']
          else:
              __lowercase		:			Optional[int]	    =       None

          __lowercase    ,      __lowercase		:			Optional[int]	    =       pipe.encode_prompt(_snake_case				)

          # inputs with prompt converted to embeddings
          __lowercase		:			int	    =       {
              '''prompt_embeds''': prompt_embeds,
              '''negative_prompt_embeds''': negative_prompt_embeds,
              '''generator''': generator,
              '''num_inference_steps''': num_inference_steps,
              '''output_type''': output_type,
          }

          if image is not None:
              __lowercase		:			Tuple	    =       image

          if mask_image is not None:
              __lowercase		:			List[str]	    =       mask_image

          if original_image is not None:
              __lowercase		:			List[Any]	    =       original_image

          # set all optional components to None
          for optional_component in pipe._optional_components:
              setattr(_snake_case				,       _snake_case				,       _snake_case				)

          __lowercase		:			List[str]	    =       pipe(**_snake_case				)[0]

          with tempfile.TemporaryDirectory() as tmpdir:
              pipe.save_pretrained(_snake_case				)
              __lowercase		:			List[Any]	    =       self.pipeline_class.from_pretrained(_snake_case				)
              pipe_loaded.to(_snake_case				)
              pipe_loaded.set_progress_bar_config(disable=_snake_case				)

          pipe_loaded.unet.set_attn_processor(AttnAddedKVProcessor()				)  # For reproducibility tests

          for optional_component in pipe._optional_components:
              self.assertTrue(
                  getattr(_snake_case				,       _snake_case				) is None				,       F'`{optional_component}` did not stay set to None after loading.'				,       )

          __lowercase		:			int	    =       self.get_dummy_inputs(_snake_case				)

          __lowercase		:			Union[str, Any]	    =       inputs['''generator''']
          __lowercase		:			Any	    =       inputs['''num_inference_steps''']
          __lowercase		:			Tuple	    =       inputs['''output_type''']

          # inputs with prompt converted to embeddings
          __lowercase		:			List[str]	    =       {
              '''prompt_embeds''': prompt_embeds,
              '''negative_prompt_embeds''': negative_prompt_embeds,
              '''generator''': generator,
              '''num_inference_steps''': num_inference_steps,
              '''output_type''': output_type,
          }

          if image is not None:
              __lowercase		:			Dict	    =       image

          if mask_image is not None:
              __lowercase		:			Tuple	    =       mask_image

          if original_image is not None:
              __lowercase		:			Any	    =       original_image

          __lowercase		:			List[str]	    =       pipe_loaded(**_snake_case				)[0]

          __lowercase		:			Optional[int]	    =       np.abs(to_np(_snake_case				) - to_np(_snake_case				)				).max()
          self.assertLess(_snake_case				,       1E-4				)




      def 		snake_case_			(      self		:       Optional[int]				):
          __lowercase		:			Union[str, Any]	    =       self.get_dummy_components()
          __lowercase		:			Dict	    =       self.pipeline_class(**_snake_case				)
          pipe.to(_snake_case				)
          pipe.set_progress_bar_config(disable=_snake_case				)

          __lowercase		:			Dict	    =       self.get_dummy_inputs(_snake_case				)
          __lowercase		:			Any	    =       pipe(**_snake_case				)[0]

          with tempfile.TemporaryDirectory() as tmpdir:
              pipe.save_pretrained(_snake_case				)
              __lowercase		:			Dict	    =       self.pipeline_class.from_pretrained(_snake_case				)
              pipe_loaded.to(_snake_case				)
              pipe_loaded.set_progress_bar_config(disable=_snake_case				)

          pipe_loaded.unet.set_attn_processor(AttnAddedKVProcessor()				)  # For reproducibility tests

          __lowercase		:			Optional[Any]	    =       self.get_dummy_inputs(_snake_case				)
          __lowercase		:			str	    =       pipe_loaded(**_snake_case				)[0]

          __lowercase		:			int	    =       np.abs(to_np(_snake_case				) - to_np(_snake_case				)				).max()
          self.assertLess(_snake_case				,       1E-4				)


