
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 _UpperCamelCase							:
			'''simple docstring'''







			def 							__UpperCamelCase (			self							: Any      ,      a							: Dict      ,      a							: int      ,      a							: Union[str, Any]	)							->       Tuple:

				"""simple docstring"""

				return None






class _UpperCamelCase							:
			'''simple docstring'''







			def 							__UpperCamelCase (			self							: Dict      ,      a							: Optional[Any]      ,      a							: Dict      ,      a							: Optional[Any]      ,      a							: Union[str, Any]	)							->       List[str]:

				"""simple docstring"""

				return None






class _UpperCamelCase							(							unittest.TestCase   ):
			'''simple docstring'''







			lowerCamelCase__           =[
			    # (model_name, model_kwargs)
			    ('''bert-base-cased''', {}),
			    ('''gpt2''', {'''use_cache''': False}),  # We don't support exporting GPT2 past keys anymore
			]
			@require_tf
			@slow
			def 							__UpperCamelCase (			self							: Dict	)							->       List[Any]:

				"""simple docstring"""

				for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
					self._test_export(UpperCamelCase__      ,      "tf"      ,      12      ,      **UpperCamelCase__	)
			@require_torch
			@slow
			def 							__UpperCamelCase (			self							: Any	)							->       str:

				"""simple docstring"""

				for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
					self._test_export(UpperCamelCase__      ,      "pt"      ,      12      ,      **UpperCamelCase__	)
			@require_torch
			@slow
			def 							__UpperCamelCase (			self							: Any	)							->       Optional[Any]:

				"""simple docstring"""

				from transformers import BertModel

				SCREAMING_SNAKE_CASE   :      Optional[int]				     =       ["[UNK]", "[SEP]", "[CLS]", "[PAD]", "[MASK]", "some", "other", "words"]
				with NamedTemporaryFile(mode="w+t"	) as vocab_file:
					vocab_file.write("\n".join(UpperCamelCase__	)	)
					vocab_file.flush()
					SCREAMING_SNAKE_CASE   :      Any				     =       BertTokenizerFast(vocab_file.name	)

				with TemporaryDirectory() as bert_save_dir:
					SCREAMING_SNAKE_CASE   :      List[str]				     =       BertModel(BertConfig(vocab_size=len(UpperCamelCase__	)	)	)
					model.save_pretrained(UpperCamelCase__	)
					self._test_export(UpperCamelCase__      ,      "pt"      ,      12      ,      UpperCamelCase__	)
			@require_tf
			@slow
			def 							__UpperCamelCase (			self							: int	)							->       int:

				"""simple docstring"""

				for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
					SCREAMING_SNAKE_CASE   :      Tuple				     =       self._test_export(UpperCamelCase__      ,      "tf"      ,      12      ,      **UpperCamelCase__	)
					SCREAMING_SNAKE_CASE   :      str				     =       quantize(Path(UpperCamelCase__	)	)

					# Ensure the actual quantized model is not bigger than the original one
					if quantized_path.stat().st_size >= Path(UpperCamelCase__	).stat().st_size:
						self.fail("Quantized model is bigger than initial ONNX model"	)
			@require_torch
			@slow
			def 							__UpperCamelCase (			self							: Any	)							->       Union[str, Any]:

				"""simple docstring"""

				for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
					SCREAMING_SNAKE_CASE   :      Any				     =       self._test_export(UpperCamelCase__      ,      "pt"      ,      12      ,      **UpperCamelCase__	)
					SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       quantize(UpperCamelCase__	)

					# Ensure the actual quantized model is not bigger than the original one
					if quantized_path.stat().st_size >= Path(UpperCamelCase__	).stat().st_size:
						self.fail("Quantized model is bigger than initial ONNX model"	)
			def 							__UpperCamelCase (			self							: Optional[int]      ,      a							: Dict      ,      a							: str      ,      a							: Tuple      ,      a							: str=None      ,      **a							: List[str]	)							->       Dict:

				"""simple docstring"""

				try:
					# Compute path
					with TemporaryDirectory() as tempdir:
						SCREAMING_SNAKE_CASE   :      Tuple				     =       Path(UpperCamelCase__	).joinpath("model.onnx"	)

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

					# Export
					convert(UpperCamelCase__      ,      UpperCamelCase__      ,      UpperCamelCase__      ,      UpperCamelCase__      ,      UpperCamelCase__      ,      **UpperCamelCase__	)

					return path
				except Exception as e:
					self.fail(UpperCamelCase__	)
			@require_torch
			@require_tokenizers
			@slow
			def 							__UpperCamelCase (			self							: Optional[Any]	)							->       Tuple:

				"""simple docstring"""

				from transformers import BertModel

				SCREAMING_SNAKE_CASE   :      Optional[int]				     =       BertModel(BertConfig.from_pretrained("lysandre/tiny-bert-random"	)	)
				SCREAMING_SNAKE_CASE   :      List[str]				     =       BertTokenizerFast.from_pretrained("lysandre/tiny-bert-random"	)
				self._test_infer_dynamic_axis(UpperCamelCase__      ,      UpperCamelCase__      ,      "pt"	)
			@require_tf
			@require_tokenizers
			@slow
			def 							__UpperCamelCase (			self							: Tuple	)							->       Dict:

				"""simple docstring"""

				from transformers import TFBertModel

				SCREAMING_SNAKE_CASE   :      int				     =       TFBertModel(BertConfig.from_pretrained("lysandre/tiny-bert-random"	)	)
				SCREAMING_SNAKE_CASE   :      int				     =       BertTokenizerFast.from_pretrained("lysandre/tiny-bert-random"	)
				self._test_infer_dynamic_axis(UpperCamelCase__      ,      UpperCamelCase__      ,      "tf"	)
			def 							__UpperCamelCase (			self							: Optional[Any]      ,      a							: int      ,      a							: str      ,      a							: Dict	)							->       Dict:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      str				     =       FeatureExtractionPipeline(UpperCamelCase__      ,      UpperCamelCase__	)

				SCREAMING_SNAKE_CASE   :      Tuple				     =       ["input_ids", "token_type_ids", "attention_mask", "output_0", "output_1"]
				SCREAMING_SNAKE_CASE     ,SCREAMING_SNAKE_CASE     ,SCREAMING_SNAKE_CASE     ,SCREAMING_SNAKE_CASE   :      str				     =       infer_shapes(UpperCamelCase__      ,      UpperCamelCase__	)

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

				# 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 							__UpperCamelCase (			self							: Dict	)							->       List[Any]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       ["input_ids", "attention_mask", "token_type_ids"]
				SCREAMING_SNAKE_CASE   :      List[str]				     =       {"input_ids": [1, 2, 3, 4], "attention_mask": [0, 0, 0, 0], "token_type_ids": [1, 1, 1, 1]}
				SCREAMING_SNAKE_CASE     ,SCREAMING_SNAKE_CASE   :      List[Any]				     =       ensure_valid_input(FuncContiguousArgs()      ,      UpperCamelCase__      ,      UpperCamelCase__	)

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

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

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

				# Generated args are interleaved with another args (for instance parameter "past" in GPT2)
				SCREAMING_SNAKE_CASE     ,SCREAMING_SNAKE_CASE   :      Any				     =       ensure_valid_input(FuncNonContiguousArgs()      ,      UpperCamelCase__      ,      UpperCamelCase__	)

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

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






			def 							__UpperCamelCase (			self							: List[Any]	)							->       Optional[int]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      Optional[int]				     =       generate_identified_filename(Path("/home/something/my_fake_model.onnx"	)      ,      "-test"	)
				self.assertEqual("/home/something/my_fake_model-test.onnx"      ,      generated.as_posix()	)


import json
import os

import torch

from diffusers import UNetaDModel


os.makedirs('hub/hopper-medium-v2/unet/hor32', exist_ok=True)
os.makedirs('hub/hopper-medium-v2/unet/hor128', exist_ok=True)

os.makedirs('hub/hopper-medium-v2/value_function', exist_ok=True)

def  lowerCamelCase__ (					_a):
	if hor == 128:
		SCREAMING_SNAKE_CASE   :      str				     =       ("DownResnetBlock1D", "DownResnetBlock1D", "DownResnetBlock1D")
		SCREAMING_SNAKE_CASE   :      List[str]				     =       (32, 128, 256)
		SCREAMING_SNAKE_CASE   :      List[str]				     =       ("UpResnetBlock1D", "UpResnetBlock1D")

	elif hor == 32:
		SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       ("DownResnetBlock1D", "DownResnetBlock1D", "DownResnetBlock1D", "DownResnetBlock1D")
		SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       (32, 64, 128, 256)
		SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       ("UpResnetBlock1D", "UpResnetBlock1D", "UpResnetBlock1D")
	SCREAMING_SNAKE_CASE   :      Any				     =       torch.load(f"/Users/bglickenhaus/Documents/diffuser/temporal_unet-hopper-mediumv2-hor{hor}.torch")
	SCREAMING_SNAKE_CASE   :      int				     =       model.state_dict()
	SCREAMING_SNAKE_CASE   :      Dict				     =       {
	    "down_block_types": down_block_types,
	    "block_out_channels": block_out_channels,
	    "up_block_types": up_block_types,
	    "layers_per_block": 1,
	    "use_timestep_embedding": True,
	    "out_block_type": "OutConv1DBlock",
	    "norm_num_groups": 8,
	    "downsample_each_block": False,
	    "in_channels": 14,
	    "out_channels": 14,
	    "extra_in_channels": 0,
	    "time_embedding_type": "positional",
	    "flip_sin_to_cos": False,
	    "freq_shift": 1,
	    "sample_size": 65536,
	    "mid_block_type": "MidResTemporalBlock1D",
	    "act_fn": "mish",
	}
	SCREAMING_SNAKE_CASE   :      str				     =       UNetaDModel(**_a)
	print(f"length of state dict: {len(state_dict.keys())}")
	print(f"length of value function dict: {len(hf_value_function.state_dict().keys())}")
	SCREAMING_SNAKE_CASE   :      List[str]				     =       dict(zip(model.state_dict().keys()							,			hf_value_function.state_dict().keys()))
	for k, v in mapping.items():
		SCREAMING_SNAKE_CASE   :      int				     =       state_dict.pop(_a)
	hf_value_function.load_state_dict(_a)

	torch.save(hf_value_function.state_dict()							,			f"hub/hopper-medium-v2/unet/hor{hor}/diffusion_pytorch_model.bin")
	with open(f"hub/hopper-medium-v2/unet/hor{hor}/config.json"							,			"w") as f:
		json.dump(_a							,			_a)

def  lowerCamelCase__ (					):
	SCREAMING_SNAKE_CASE   :      int				     =       {
	    "in_channels": 14,
	    "down_block_types": ("DownResnetBlock1D", "DownResnetBlock1D", "DownResnetBlock1D", "DownResnetBlock1D"),
	    "up_block_types": (),
	    "out_block_type": "ValueFunction",
	    "mid_block_type": "ValueFunctionMidBlock1D",
	    "block_out_channels": (32, 64, 128, 256),
	    "layers_per_block": 1,
	    "downsample_each_block": True,
	    "sample_size": 65536,
	    "out_channels": 14,
	    "extra_in_channels": 0,
	    "time_embedding_type": "positional",
	    "use_timestep_embedding": True,
	    "flip_sin_to_cos": False,
	    "freq_shift": 1,
	    "norm_num_groups": 8,
	    "act_fn": "mish",
	}

	SCREAMING_SNAKE_CASE   :      Dict				     =       torch.load("/Users/bglickenhaus/Documents/diffuser/value_function-hopper-mediumv2-hor32.torch")
	SCREAMING_SNAKE_CASE   :      Dict				     =       model
	SCREAMING_SNAKE_CASE   :      List[str]				     =       UNetaDModel(**_a)
	print(f"length of state dict: {len(state_dict.keys())}")
	print(f"length of value function dict: {len(hf_value_function.state_dict().keys())}")

	SCREAMING_SNAKE_CASE   :      int				     =       dict(zip(state_dict.keys()							,			hf_value_function.state_dict().keys()))
	for k, v in mapping.items():
		SCREAMING_SNAKE_CASE   :      Optional[int]				     =       state_dict.pop(_a)

	hf_value_function.load_state_dict(_a)

	torch.save(hf_value_function.state_dict()							,			"hub/hopper-medium-v2/value_function/diffusion_pytorch_model.bin")
	with open("hub/hopper-medium-v2/value_function/config.json"							,			"w") as f:
		json.dump(_a							,			_a)


if __name__ == "__main__":
			unet(32)
			# unet(128)
			value_function()