
"""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 			UpperCamelCase      :
     def 				__SCREAMING_SNAKE_CASE							(     self  ,     snake_case__  ,     snake_case__  ,     snake_case__				):




       """simple docstring"""


       return None
class 			UpperCamelCase      :
     def 				__SCREAMING_SNAKE_CASE							(     self  ,     snake_case__  ,     snake_case__  ,     snake_case__  ,     snake_case__				):




       """simple docstring"""


       return None
class 			UpperCamelCase      (							unittest.TestCase  ):
     A__      =			[
         # (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				):




       """simple docstring"""


       for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
         self._test_export(snake_case__  ,     "tf"  ,     12  ,     **snake_case__				)
     @require_torch
     @slow
     def 				__SCREAMING_SNAKE_CASE							(     self				):




       """simple docstring"""


       for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
         self._test_export(snake_case__  ,     "pt"  ,     12  ,     **snake_case__				)
     @require_torch
     @slow
     def 				__SCREAMING_SNAKE_CASE							(     self				):




       """simple docstring"""


       from transformers import BertModel

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

       with TemporaryDirectory() as bert_save_dir:
         _SCREAMING_SNAKE_CASE   :				List[str]							    = BertModel(BertConfig(vocab_size=len(snake_case__				)				)				)
         model.save_pretrained(snake_case__				)
         self._test_export(snake_case__  ,     "pt"  ,     12  ,     snake_case__				)
     @require_tf
     @slow
     def 				__SCREAMING_SNAKE_CASE							(     self				):




       """simple docstring"""


       for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
         _SCREAMING_SNAKE_CASE   :				Any							    = self._test_export(snake_case__  ,     "tf"  ,     12  ,     **snake_case__				)
         _SCREAMING_SNAKE_CASE   :				Dict							    = quantize(Path(snake_case__				)				)

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




       """simple docstring"""


       for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
         _SCREAMING_SNAKE_CASE   :				str							    = self._test_export(snake_case__  ,     "pt"  ,     12  ,     **snake_case__				)
         _SCREAMING_SNAKE_CASE   :				List[Any]							    = quantize(snake_case__				)

         # Ensure the actual quantized model is not bigger than the original one
         if quantized_path.stat().st_size >= Path(snake_case__				).stat().st_size:
           self.fail("Quantized model is bigger than initial ONNX model"				)
     def 				__SCREAMING_SNAKE_CASE							(     self  ,     snake_case__  ,     snake_case__  ,     snake_case__  ,     snake_case__=None  ,     **snake_case__				):




       """simple docstring"""


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

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

         # Export
         convert(snake_case__  ,     snake_case__  ,     snake_case__  ,     snake_case__  ,     snake_case__  ,     **snake_case__				)

         return path
       except Exception as e:
         self.fail(snake_case__				)
     @require_torch
     @require_tokenizers
     @slow
     def 				__SCREAMING_SNAKE_CASE							(     self				):




       """simple docstring"""


       from transformers import BertModel

       _SCREAMING_SNAKE_CASE   :				str							    = BertModel(BertConfig.from_pretrained("lysandre/tiny-bert-random"				)				)
       _SCREAMING_SNAKE_CASE   :				Union[str, Any]							    = BertTokenizerFast.from_pretrained("lysandre/tiny-bert-random"				)
       self._test_infer_dynamic_axis(snake_case__  ,     snake_case__  ,     "pt"				)
     @require_tf
     @require_tokenizers
     @slow
     def 				__SCREAMING_SNAKE_CASE							(     self				):




       """simple docstring"""


       from transformers import TFBertModel

       _SCREAMING_SNAKE_CASE   :				List[Any]							    = TFBertModel(BertConfig.from_pretrained("lysandre/tiny-bert-random"				)				)
       _SCREAMING_SNAKE_CASE   :				str							    = BertTokenizerFast.from_pretrained("lysandre/tiny-bert-random"				)
       self._test_infer_dynamic_axis(snake_case__  ,     snake_case__  ,     "tf"				)
     def 				__SCREAMING_SNAKE_CASE							(     self  ,     snake_case__  ,     snake_case__  ,     snake_case__				):




       """simple docstring"""


       _SCREAMING_SNAKE_CASE   :				Optional[Any]							    = FeatureExtractionPipeline(snake_case__  ,     snake_case__				)

       _SCREAMING_SNAKE_CASE   :				Optional[Any]							    = ["input_ids", "token_type_ids", "attention_mask", "output_0", "output_1"]
       _SCREAMING_SNAKE_CASE    ,		_SCREAMING_SNAKE_CASE    ,		_SCREAMING_SNAKE_CASE    ,		_SCREAMING_SNAKE_CASE   :				Optional[Any]							    = infer_shapes(snake_case__  ,     snake_case__				)

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

       # 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				):




       """simple docstring"""


       _SCREAMING_SNAKE_CASE   :				Dict							    = ["input_ids", "attention_mask", "token_type_ids"]
       _SCREAMING_SNAKE_CASE   :				Optional[Any]							    = {"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   :				Tuple							    = ensure_valid_input(FuncContiguousArgs()  ,     snake_case__  ,     snake_case__				)

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

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

       # Parameter should be reordered according to their respective place in the function:
       # (input_ids, token_type_ids, attention_mask)
       self.assertEqual(snake_case__  ,     (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   :				Dict							    = ensure_valid_input(FuncNonContiguousArgs()  ,     snake_case__  ,     snake_case__				)

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




       """simple docstring"""


       _SCREAMING_SNAKE_CASE   :				List[Any]							    = 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"""
from dataclasses import dataclass
from typing import Optional, Tuple, Union

import torch
import torch.nn as nn

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, apply_forward_hook
from .modeling_utils import ModelMixin
from .vae import Decoder, DecoderOutput, Encoder, VectorQuantizer
@dataclass
class 			UpperCamelCase      (							__SCREAMING_SNAKE_CASE  ):
     A__      =			42
class 			UpperCamelCase      (							__SCREAMING_SNAKE_CASE      ,       __SCREAMING_SNAKE_CASE  ):
     @register_to_config
     def __init__(     self  ,     snake_case__ = 3  ,     snake_case__ = 3  ,     snake_case__ = ("DownEncoderBlock2D",)  ,     snake_case__ = ("UpDecoderBlock2D",)  ,     snake_case__ = (64,)  ,     snake_case__ = 1  ,     snake_case__ = "silu"  ,     snake_case__ = 3  ,     snake_case__ = 32  ,     snake_case__ = 256  ,     snake_case__ = 32  ,     snake_case__ = None  ,     snake_case__ = 0.18_215  ,     snake_case__ = "group"  ,     ):




       """simple docstring"""


       super().__init__()

       # pass init params to Encoder
       _SCREAMING_SNAKE_CASE   :				Optional[Any]							    = Encoder(
           in_channels=snake_case__  ,     out_channels=snake_case__  ,     down_block_types=snake_case__  ,     block_out_channels=snake_case__  ,     layers_per_block=snake_case__  ,     act_fn=snake_case__  ,     norm_num_groups=snake_case__  ,     double_z=snake_case__  ,     )

       _SCREAMING_SNAKE_CASE   :				Dict							    = vq_embed_dim if vq_embed_dim is not None else latent_channels

       _SCREAMING_SNAKE_CASE   :				Tuple							    = nn.Convad(snake_case__  ,     snake_case__  ,     1				)
       _SCREAMING_SNAKE_CASE   :				Optional[int]							    = VectorQuantizer(snake_case__  ,     snake_case__  ,     beta=0.25  ,     remap=snake_case__  ,     sane_index_shape=snake_case__				)
       _SCREAMING_SNAKE_CASE   :				List[str]							    = nn.Convad(snake_case__  ,     snake_case__  ,     1				)

       # pass init params to Decoder
       _SCREAMING_SNAKE_CASE   :				Optional[int]							    = Decoder(
           in_channels=snake_case__  ,     out_channels=snake_case__  ,     up_block_types=snake_case__  ,     block_out_channels=snake_case__  ,     layers_per_block=snake_case__  ,     act_fn=snake_case__  ,     norm_num_groups=snake_case__  ,     norm_type=snake_case__  ,     )
     @apply_forward_hook
     def 				__SCREAMING_SNAKE_CASE							(     self  ,     snake_case__  ,     snake_case__ = True				):




       """simple docstring"""


       _SCREAMING_SNAKE_CASE   :				List[str]							    = self.encoder(snake_case__				)
       _SCREAMING_SNAKE_CASE   :				Dict							    = self.quant_conv(snake_case__				)

       if not return_dict:
         return (h,)

       return VQEncoderOutput(latents=snake_case__				)
     @apply_forward_hook
     def 				__SCREAMING_SNAKE_CASE							(     self  ,     snake_case__  ,     snake_case__ = False  ,     snake_case__ = True				):




       """simple docstring"""


       if not force_not_quantize:
         _SCREAMING_SNAKE_CASE    ,		_SCREAMING_SNAKE_CASE    ,		_SCREAMING_SNAKE_CASE   :				int							    = self.quantize(snake_case__				)
       else:
         _SCREAMING_SNAKE_CASE   :				int							    = h
       _SCREAMING_SNAKE_CASE   :				Dict							    = self.post_quant_conv(snake_case__				)
       _SCREAMING_SNAKE_CASE   :				int							    = self.decoder(snake_case__  ,     quant if self.config.norm_type == "spatial" else None				)

       if not return_dict:
         return (dec,)

       return DecoderOutput(sample=snake_case__				)




     def 				__SCREAMING_SNAKE_CASE							(     self  ,     snake_case__  ,     snake_case__ = True				):




       """simple docstring"""


       _SCREAMING_SNAKE_CASE   :				Tuple							    = sample
       _SCREAMING_SNAKE_CASE   :				Union[str, Any]							    = self.encode(snake_case__				).latents
       _SCREAMING_SNAKE_CASE   :				Any							    = self.decode(snake_case__				).sample

       if not return_dict:
         return (dec,)

       return DecoderOutput(sample=snake_case__				)



