





"""simple docstring"""


import unittest
from pathlib import Path
from tempfile import NamedTemporaryFile, TemporaryDirectory

from transformers import BertConfig, BertTokenizerFast, FeatureExtractionPipeline
from transformers.convert_graph_to_onnx import (
    convert,
    ensure_valid_input,
    generate_identified_filename,
    infer_shapes,
    quantize,
)
from transformers.testing_utils import require_tf, require_tokenizers, require_torch, slow



class   __SCREAMING_SNAKE_CASE     :







  '''simple docstring'''


  def  __SCREAMING_SNAKE_CASE					(		self :							Dict       , __a :							Tuple       , __a :							int       , __a :							Dict						)		->					Tuple:
        return None





class   __SCREAMING_SNAKE_CASE     :







  '''simple docstring'''


  def  __SCREAMING_SNAKE_CASE					(		self :							List[Any]       , __a :							Any       , __a :							Optional[int]       , __a :							Optional[Any]       , __a :							Any						)		->					Optional[Any]:
        return None





class   __SCREAMING_SNAKE_CASE     (    unittest.TestCase ):







  '''simple docstring'''


  SCREAMING_SNAKE_CASE__				:List[str]						  =       [
      # (model_name, model_kwargs)
      ("bert-base-cased", {}),
      ("gpt2", {"use_cache": False}),  # We don't support exporting GPT2 past keys anymore
  ]

  @require_tf
  @slow
  def  __SCREAMING_SNAKE_CASE					(		self :							Union[str, Any]						)		->					Union[str, Any]:
        for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
              self._test_export(__a       , "tf"       , 12       , **__a						)

  @require_torch
  @slow
  def  __SCREAMING_SNAKE_CASE					(		self :							str						)		->					Tuple:
        for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
              self._test_export(__a       , "pt"       , 12       , **__a						)

  @require_torch
  @slow
  def  __SCREAMING_SNAKE_CASE					(		self :							List[Any]						)		->					Dict:
        from transformers import BertModel

        _UpperCamelCase   :						str    =							["[UNK]", "[SEP]", "[CLS]", "[PAD]", "[MASK]", "some", "other", "words"]
        with NamedTemporaryFile(mode="w+t"						) as vocab_file:
              vocab_file.write("\n".join(__a						)						)
              vocab_file.flush()
              _UpperCamelCase   :						Dict    =							BertTokenizerFast(vocab_file.name						)

        with TemporaryDirectory() as bert_save_dir:
              _UpperCamelCase   :						Optional[Any]    =							BertModel(BertConfig(vocab_size=len(__a						)						)						)
              model.save_pretrained(__a						)
              self._test_export(__a       , "pt"       , 12       , __a						)

  @require_tf
  @slow
  def  __SCREAMING_SNAKE_CASE					(		self :							str						)		->					Dict:
        for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
              _UpperCamelCase   :						str    =							self._test_export(__a       , "tf"       , 12       , **__a						)
              _UpperCamelCase   :						str    =							quantize(Path(__a						)						)

              # Ensure the actual quantized model is not bigger than the original one
              if quantized_path.stat().st_size >= Path(__a						).stat().st_size:
                    self.fail("Quantized model is bigger than initial ONNX model"						)

  @require_torch
  @slow
  def  __SCREAMING_SNAKE_CASE					(		self :							Union[str, Any]						)		->					List[Any]:
        for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
              _UpperCamelCase   :						Optional[Any]    =							self._test_export(__a       , "pt"       , 12       , **__a						)
              _UpperCamelCase   :						int    =							quantize(__a						)

              # Ensure the actual quantized model is not bigger than the original one
              if quantized_path.stat().st_size >= Path(__a						).stat().st_size:
                    self.fail("Quantized model is bigger than initial ONNX model"						)

  def  __SCREAMING_SNAKE_CASE					(		self :							str       , __a :							Dict       , __a :							Tuple       , __a :							Optional[int]       , __a :							Optional[Any]=None       , **__a :							str						)		->					Any:
        try:
              # Compute path
              with TemporaryDirectory() as tempdir:
                    _UpperCamelCase   :						Optional[Any]    =							Path(__a						).joinpath("model.onnx"						)

              # Remove folder if exists
              if path.parent.exists():
                    path.parent.rmdir()

              # Export
              convert(__a       , __a       , __a       , __a       , __a       , **__a						)

              return path
        except Exception as e:
              self.fail(__a						)

  @require_torch
  @require_tokenizers
  @slow
  def  __SCREAMING_SNAKE_CASE					(		self :							str						)		->					Any:
        from transformers import BertModel

        _UpperCamelCase   :						Dict    =							BertModel(BertConfig.from_pretrained("lysandre/tiny-bert-random"						)						)
        _UpperCamelCase   :						int    =							BertTokenizerFast.from_pretrained("lysandre/tiny-bert-random"						)
        self._test_infer_dynamic_axis(__a       , __a       , "pt"						)

  @require_tf
  @require_tokenizers
  @slow
  def  __SCREAMING_SNAKE_CASE					(		self :							str						)		->					Optional[Any]:
        from transformers import TFBertModel

        _UpperCamelCase   :						str    =							TFBertModel(BertConfig.from_pretrained("lysandre/tiny-bert-random"						)						)
        _UpperCamelCase   :						Optional[int]    =							BertTokenizerFast.from_pretrained("lysandre/tiny-bert-random"						)
        self._test_infer_dynamic_axis(__a       , __a       , "tf"						)

  def  __SCREAMING_SNAKE_CASE					(		self :							Optional[int]       , __a :							Optional[Any]       , __a :							Dict       , __a :							Any						)		->					Union[str, Any]:
        _UpperCamelCase   :						List[Any]    =							FeatureExtractionPipeline(__a       , __a						)

        _UpperCamelCase   :						Tuple    =							["input_ids", "token_type_ids", "attention_mask", "output_0", "output_1"]
        _UpperCamelCase,	_UpperCamelCase,	_UpperCamelCase,	_UpperCamelCase   :						List[Any]    =							infer_shapes(__a       , __a						)

        # Assert all variables are present
        self.assertEqual(len(__a						)       , len(__a						)						)
        self.assertTrue(all(var_name in shapes for var_name in variable_names						)						)
        self.assertSequenceEqual(variable_names[:3]       , __a						)
        self.assertSequenceEqual(variable_names[3:]       , __a						)

        # Assert inputs are {0: batch, 1: sequence}
        for var_name in ["input_ids", "token_type_ids", "attention_mask"]:
              self.assertDictEqual(shapes[var_name]       , {0: "batch", 1: "sequence"}						)

        # Assert outputs are {0: batch, 1: sequence} and {0: batch}
        self.assertDictEqual(shapes["output_0"]       , {0: "batch", 1: "sequence"}						)
        self.assertDictEqual(shapes["output_1"]       , {0: "batch"}						)

  def  __SCREAMING_SNAKE_CASE					(		self :							str						)		->					Dict:
        _UpperCamelCase   :						str    =							["input_ids", "attention_mask", "token_type_ids"]
        _UpperCamelCase   :						Dict    =							{"input_ids": [1, 2, 3, 4], "attention_mask": [0, 0, 0, 0], "token_type_ids": [1, 1, 1, 1]}
        _UpperCamelCase,	_UpperCamelCase   :						Union[str, Any]    =							ensure_valid_input(FuncContiguousArgs()       , __a       , __a						)

        # Should have exactly the same number of args (all are valid)
        self.assertEqual(len(__a						)       , 3						)

        # Should have exactly the same input names
        self.assertEqual(set(__a						)       , set(__a						)						)

        # Parameter should be reordered according to their respective place in the function:
        # (input_ids, token_type_ids, attention_mask)
        self.assertEqual(__a       , (tokens["input_ids"], tokens["token_type_ids"], tokens["attention_mask"])						)

        # Generated args are interleaved with another args (for instance parameter "past" in GPT2)
        _UpperCamelCase,	_UpperCamelCase   :						Tuple    =							ensure_valid_input(FuncNonContiguousArgs()       , __a       , __a						)

        # Should have exactly the one arg (all before the one not provided "some_other_args")
        self.assertEqual(len(__a						)       , 1						)
        self.assertEqual(len(__a						)       , 1						)

        # Should have only "input_ids"
        self.assertEqual(inputs_args[0]       , tokens["input_ids"]						)
        self.assertEqual(ordered_input_names[0]       , "input_ids"						)


  def  __SCREAMING_SNAKE_CASE					(		self :							Optional[Any]						)		->					str:
        _UpperCamelCase   :						Tuple    =							generate_identified_filename(Path("/home/something/my_fake_model.onnx"						)       , "-test"						)
        self.assertEqual("/home/something/my_fake_model-test.onnx"       , generated.as_posix()						)







"""simple docstring"""


import argparse
import json
import os

import torch
from transformers.file_utils import has_file

from diffusers import UNetaDConditionModel, UNetaDModel


lowerCamelCase__						= False
lowerCamelCase__						= True
lowerCamelCase__						= False


if __name__ == "__main__":
  lowerCamelCase__						= argparse.ArgumentParser()

  parser.add_argument(
      "--repo_path",
      default=None,
      type=str,
      required=True,
      help="The config json file corresponding to the architecture.",
  )

  parser.add_argument("--dump_path", default=None, type=str, required=True, help="Path to the output model.")

  lowerCamelCase__						= parser.parse_args()

  lowerCamelCase__						= {
      "image_size": "sample_size",
      "num_res_blocks": "layers_per_block",
      "block_channels": "block_out_channels",
      "down_blocks": "down_block_types",
      "up_blocks": "up_block_types",
      "downscale_freq_shift": "freq_shift",
      "resnet_num_groups": "norm_num_groups",
      "resnet_act_fn": "act_fn",
      "resnet_eps": "norm_eps",
      "num_head_channels": "attention_head_dim",
  }

  lowerCamelCase__						= {
      "time_steps": "time_proj",
      "mid": "mid_block",
      "downsample_blocks": "down_blocks",
      "upsample_blocks": "up_blocks",
  }

  lowerCamelCase__						= "" if has_file(args.repo_path, "config.json") else "unet"

  with open(os.path.join(args.repo_path, subfolder, "config.json"), "r", encoding="utf-8") as reader:
    lowerCamelCase__						= reader.read()
    lowerCamelCase__						= json.loads(text)

  if do_only_config:
    for key in config_parameters_to_change.keys():
      config.pop(key, None)

  if has_file(args.repo_path, "config.json"):
    lowerCamelCase__						= UNetaDModel(**config)
  else:
    lowerCamelCase__						= UNetaDConditionModel if "ldm-text2im-large-256" in args.repo_path else UNetaDModel
    lowerCamelCase__						= class_name(**config)

  if do_only_config:
    model.save_config(os.path.join(args.repo_path, subfolder))

  lowerCamelCase__						= dict(model.config)

  if do_only_renaming:
    for key, value in config_parameters_to_change.items():
      if key in config:
        lowerCamelCase__						= config[key]
        del config[key]

    lowerCamelCase__						= [k.replace("UNetRes", "") for k in config["down_block_types"]]
    lowerCamelCase__						= [k.replace("UNetRes", "") for k in config["up_block_types"]]

  if do_only_weights:
    lowerCamelCase__						= torch.load(os.path.join(args.repo_path, subfolder, "diffusion_pytorch_model.bin"))

    lowerCamelCase__						= {}
    for param_key, param_value in state_dict.items():
      if param_key.endswith(".op.bias") or param_key.endswith(".op.weight"):
        continue
      lowerCamelCase__						= False
      for key, new_key in key_parameters_to_change.items():
        if not has_changed and param_key.split(".")[0] == key:
          lowerCamelCase__						= param_value
          lowerCamelCase__						= True
      if not has_changed:
        lowerCamelCase__						= param_value

    model.load_state_dict(new_state_dict)
    model.save_pretrained(os.path.join(args.repo_path, subfolder))
