


'''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 						a__							:





							'''simple docstring'''






							def     __SCREAMING_SNAKE_CASE				(						self   ,       lowerCamelCase_   ,       lowerCamelCase_   ,       lowerCamelCase_  )			->				Optional[int]:
														return None




class 						a__							:





							'''simple docstring'''






							def     __SCREAMING_SNAKE_CASE				(						self   ,       lowerCamelCase_   ,       lowerCamelCase_   ,       lowerCamelCase_   ,       lowerCamelCase_  )			->				Optional[Any]:
														return None




class 						a__							(				unittest.TestCase    ):





							'''simple docstring'''






							lowercase__     :  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  )			->				Optional[Any]:
														for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
																					self._test_export(lowercase_   ,       '''tf'''   ,       12   ,       **lowercase_  )







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







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

														lowerCAmelCase__			  =      ['''[UNK]''', '''[SEP]''', '''[CLS]''', '''[PAD]''', '''[MASK]''', '''some''', '''other''', '''words''']
														with NamedTemporaryFile(mode='''w+t'''  ) as vocab_file:
																					vocab_file.write('''\n'''.join(lowercase_  )  )
																					vocab_file.flush()
																					lowerCAmelCase__			  =      BertTokenizerFast(vocab_file.name  )

														with TemporaryDirectory() as bert_save_dir:
																					lowerCAmelCase__			  =      BertModel(BertConfig(vocab_size=len(lowercase_  )  )  )
																					model.save_pretrained(lowercase_  )
																					self._test_export(lowercase_   ,       '''pt'''   ,       12   ,       lowercase_  )







							@require_tf
							@slow
							def     __SCREAMING_SNAKE_CASE				(						self  )			->				Tuple:
														for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
																					lowerCAmelCase__			  =      self._test_export(lowercase_   ,       '''tf'''   ,       12   ,       **lowercase_  )
																					lowerCAmelCase__			  =      quantize(Path(lowercase_  )  )

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







							@require_torch
							@slow
							def     __SCREAMING_SNAKE_CASE				(						self  )			->				Tuple:
														for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
																					lowerCAmelCase__			  =      self._test_export(lowercase_   ,       '''pt'''   ,       12   ,       **lowercase_  )
																					lowerCAmelCase__			  =      quantize(lowercase_  )

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







							def     __SCREAMING_SNAKE_CASE				(						self   ,       lowerCamelCase_   ,       lowerCamelCase_   ,       lowerCamelCase_   ,       lowerCamelCase_=None   ,       **lowerCamelCase_  )			->				int:
														try:
																					# Compute path
																					with TemporaryDirectory() as tempdir:
																												lowerCAmelCase__			  =      Path(lowercase_  ).joinpath('''model.onnx'''  )

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

																					# Export
																					convert(lowercase_   ,       lowercase_   ,       lowercase_   ,       lowercase_   ,       lowercase_   ,       **lowercase_  )

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







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

														lowerCAmelCase__			  =      BertModel(BertConfig.from_pretrained('''lysandre/tiny-bert-random'''  )  )
														lowerCAmelCase__			  =      BertTokenizerFast.from_pretrained('''lysandre/tiny-bert-random'''  )
														self._test_infer_dynamic_axis(lowercase_   ,       lowercase_   ,       '''pt'''  )







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

														lowerCAmelCase__			  =      TFBertModel(BertConfig.from_pretrained('''lysandre/tiny-bert-random'''  )  )
														lowerCAmelCase__			  =      BertTokenizerFast.from_pretrained('''lysandre/tiny-bert-random'''  )
														self._test_infer_dynamic_axis(lowercase_   ,       lowercase_   ,       '''tf'''  )







							def     __SCREAMING_SNAKE_CASE				(						self   ,       lowerCamelCase_   ,       lowerCamelCase_   ,       lowerCamelCase_  )			->				Dict:
														lowerCAmelCase__			  =      FeatureExtractionPipeline(lowercase_   ,       lowercase_  )

														lowerCAmelCase__			  =      ['''input_ids''', '''token_type_ids''', '''attention_mask''', '''output_0''', '''output_1''']
														lowerCAmelCase__  ,   lowerCAmelCase__  ,   lowerCAmelCase__  ,   lowerCAmelCase__			  =      infer_shapes(lowercase_   ,       lowercase_  )

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

														# 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  )			->				List[str]:
														lowerCAmelCase__			  =      ['''input_ids''', '''attention_mask''', '''token_type_ids''']
														lowerCAmelCase__			  =      {'''input_ids''': [1, 2, 3, 4], '''attention_mask''': [0, 0, 0, 0], '''token_type_ids''': [1, 1, 1, 1]}
														lowerCAmelCase__  ,   lowerCAmelCase__			  =      ensure_valid_input(FuncContiguousArgs()   ,       lowercase_   ,       lowercase_  )

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

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

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

														# Generated args are interleaved with another args (for instance parameter "past" in GPT2)
														lowerCAmelCase__  ,   lowerCAmelCase__			  =      ensure_valid_input(FuncNonContiguousArgs()   ,       lowercase_   ,       lowercase_  )

														# Should have exactly the one arg (all before the one not provided "some_other_args")
														self.assertEqual(len(lowercase_  )   ,       1  )
														self.assertEqual(len(lowercase_  )   ,       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  )			->				str:
														lowerCAmelCase__			  =      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      (			UpperCamelCase__							):
 __lowercase  						=							42






class   UpperCAmelCase      (			UpperCamelCase__     ,			UpperCamelCase__							):
 @register_to_config
 def __init__(							self :Tuple		,			lowercase_ :int = 3		,			lowercase_ :int = 3		,			lowercase_ :Tuple[str] = ("DownEncoderBlock2D",)		,			lowercase_ :Tuple[str] = ("UpDecoderBlock2D",)		,			lowercase_ :Tuple[int] = (64,)		,			lowercase_ :int = 1		,			lowercase_ :str = "silu"		,			lowercase_ :int = 3		,			lowercase_ :int = 32		,			lowercase_ :int = 2_56		,			lowercase_ :int = 32		,			lowercase_ :Optional[int] = None		,			lowercase_ :float = 0.1_8_2_1_5		,			lowercase_ :str = "group"		,			)->				Optional[int]:
     super().__init__()

     # pass init params to Encoder
     A__							=     Encoder(
         in_channels=lowercase_		,			out_channels=lowercase_		,			down_block_types=lowercase_		,			block_out_channels=lowercase_		,			layers_per_block=lowercase_		,			act_fn=lowercase_		,			norm_num_groups=lowercase_		,			double_z=lowercase_		,			)

     A__							=     vq_embed_dim if vq_embed_dim is not None else latent_channels

     A__							=     nn.Convad(lowercase_		,			lowercase_		,			1				)
     A__							=     VectorQuantizer(lowercase_		,			lowercase_		,			beta=0.2_5		,			remap=lowercase_		,			sane_index_shape=lowercase_				)
     A__							=     nn.Convad(lowercase_		,			lowercase_		,			1				)

     # pass init params to Decoder
     A__							=     Decoder(
         in_channels=lowercase_		,			out_channels=lowercase_		,			up_block_types=lowercase_		,			block_out_channels=lowercase_		,			layers_per_block=lowercase_		,			act_fn=lowercase_		,			norm_num_groups=lowercase_		,			norm_type=lowercase_		,			)
 @apply_forward_hook
 def 	UpperCAmelCase_				(							self :str		,			lowercase_ :torch.FloatTensor		,			lowercase_ :bool = True				)->				VQEncoderOutput:
     A__							=     self.encoder(lowercase_				)
     A__							=     self.quant_conv(lowercase_				)

     if not return_dict:
         return (h,)

     return VQEncoderOutput(latents=lowercase_				)
 @apply_forward_hook
 def 	UpperCAmelCase_				(							self :Dict		,			lowercase_ :torch.FloatTensor		,			lowercase_ :bool = False		,			lowercase_ :bool = True				)->				Union[DecoderOutput, torch.FloatTensor]:
     # also go through quantization layer
     if not force_not_quantize:
         A__,      A__,      A__							=     self.quantize(lowercase_				)
     else:
         A__							=     h
     A__							=     self.post_quant_conv(lowercase_				)
     A__							=     self.decoder(lowercase_		,			quant if self.config.norm_type == "spatial" else None				)

     if not return_dict:
         return (dec,)

     return DecoderOutput(sample=lowercase_				)



 def 	UpperCAmelCase_				(							self :str		,			lowercase_ :torch.FloatTensor		,			lowercase_ :bool = True				)->				Union[DecoderOutput, torch.FloatTensor]:
     A__							=     sample
     A__							=     self.encode(lowercase_				).latents
     A__							=     self.decode(lowercase_				).sample

     if not return_dict:
         return (dec,)

     return DecoderOutput(sample=lowercase_				)



