


from collections import OrderedDict
from typing import Any, List, Mapping, Optional

from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from ...utils import logging


__magic_name__            =  logging.get_logger(__name__)

__magic_name__            =  {
    '''EleutherAI/gpt-j-6B''': '''https://huggingface.co/EleutherAI/gpt-j-6B/resolve/main/config.json''',
    # See all GPT-J models at https://huggingface.co/models?filter=gpt_j
}




class 					_SCREAMING_SNAKE_CASE  (      _a ):
      _A						:		int    	=     'gptj'
      _A						:		int    	=     {
          'max_position_embeddings': 'n_positions',
          'hidden_size': 'n_embd',
          'num_attention_heads': 'n_head',
          'num_hidden_layers': 'n_layer',
      }
      def __init__(			self		,	lowerCamelCase=5_04_00		,	lowerCamelCase=20_48		,	lowerCamelCase=40_96		,	lowerCamelCase=28		,	lowerCamelCase=16		,	lowerCamelCase=64		,	lowerCamelCase=None		,	lowerCamelCase="gelu_new"		,	lowerCamelCase=0.0		,	lowerCamelCase=0.0		,	lowerCamelCase=0.0		,	lowerCamelCase=1e-5		,	lowerCamelCase=0.0_2		,	lowerCamelCase=True		,	lowerCamelCase=5_02_56		,	lowerCamelCase=5_02_56		,	lowerCamelCase=False		,	**lowerCamelCase		,	):
          snake_case__						=				vocab_size
          snake_case__						=				n_positions
          snake_case__						=				n_embd
          snake_case__						=				n_layer
          snake_case__						=				n_head
          snake_case__						=				n_inner
          snake_case__						=				rotary_dim
          snake_case__						=				activation_function
          snake_case__						=				resid_pdrop
          snake_case__						=				embd_pdrop
          snake_case__						=				attn_pdrop
          snake_case__						=				layer_norm_epsilon
          snake_case__						=				initializer_range
          snake_case__						=				use_cache

          snake_case__						=				bos_token_id
          snake_case__						=				eos_token_id

          super().__init__(
              bos_token_id=_A		,	eos_token_id=_A		,	tie_word_embeddings=_A		,	**_A )





class 					_SCREAMING_SNAKE_CASE  (      _a ):
      def __init__(			self		,	lowerCamelCase		,	lowerCamelCase = "default"		,	lowerCamelCase = None		,	lowerCamelCase = False		,	):
          super().__init__(_A		,	task=_A		,	patching_specs=_A		,	use_past=_A )
          if not getattr(self._config		,	"pad_token_id"		,	_A ):
              # TODO: how to do that better?
              snake_case__						=				0
      @property
      def A_			(			self ):
          snake_case__						=				OrderedDict({"input_ids": {0: "batch", 1: "sequence"}} )
          if self.use_past:
              self.fill_with_past_key_values_(_A		,	direction="inputs" )
              snake_case__						=				{0: "batch", 1: "past_sequence + sequence"}
          else:
              snake_case__						=				{0: "batch", 1: "sequence"}

          return common_inputs
      @property
      def A_			(			self ):
          return self._config.n_layer
      @property
      def A_			(			self ):
          return self._config.n_head
      def A_			(			self		,	lowerCamelCase		,	lowerCamelCase = -1		,	lowerCamelCase = -1		,	lowerCamelCase = False		,	lowerCamelCase = None		,	):
          snake_case__						=				super(_A		,	self ).generate_dummy_inputs(
              _A		,	batch_size=_A		,	seq_length=_A		,	is_pair=_A		,	framework=_A )

          # We need to order the input in the way they appears in the forward()
          snake_case__						=				OrderedDict({"input_ids": common_inputs["input_ids"]} )

          # Need to add the past_keys
          if self.use_past:
              if not is_torch_available():
                  raise ValueError("Cannot generate dummy past_keys inputs without PyTorch installed." )
              else:
                  import torch

                  snake_case__						,					snake_case__						=				common_inputs["input_ids"].shape
                  # Not using the same length for past_key_values
                  snake_case__						=				seqlen + 2
                  snake_case__						=				(
                      batch,
                      self.num_attention_heads,
                      past_key_values_length,
                      self._config.hidden_size // self.num_attention_heads,
                  )
                  snake_case__						=				[
                      (torch.zeros(_A ), torch.zeros(_A )) for _ in range(self.num_layers )
                  ]

          snake_case__						=				common_inputs["attention_mask"]
          if self.use_past:
              snake_case__						=				ordered_inputs["attention_mask"].dtype
              snake_case__						=				torch.cat(
                  [ordered_inputs["attention_mask"], torch.ones(_A		,	_A		,	dtype=_A )]		,	dim=1 )

          return ordered_inputs



      @property
      def A_			(			self ):
          return 13





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  :
      def A_			(			self		,	lowerCamelCase		,	lowerCamelCase		,	lowerCamelCase ):
          return None





class 					_SCREAMING_SNAKE_CASE  :
      def A_			(			self		,	lowerCamelCase		,	lowerCamelCase		,	lowerCamelCase		,	lowerCamelCase ):
          return None





class 					_SCREAMING_SNAKE_CASE  (      unittest.TestCase ):
      _A						:		int    	=     [
          # (model_name, model_kwargs)
          ('bert-base-cased', {}),
          ('gpt2', {'use_cache': False}),  # We don't support exporting GPT2 past keys anymore
      ]
      @require_tf
      @slow
      def A_			(			self ):
          for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
              self._test_export(lowerCamelCase		,	"tf"		,	12		,	**lowerCamelCase )
      @require_torch
      @slow
      def A_			(			self ):
          for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
              self._test_export(lowerCamelCase		,	"pt"		,	12		,	**lowerCamelCase )
      @require_torch
      @slow
      def A_			(			self ):
          from transformers import BertModel

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

          with TemporaryDirectory() as bert_save_dir:
              snake_case__						=				BertModel(BertConfig(vocab_size=len(lowerCamelCase ) ) )
              model.save_pretrained(lowerCamelCase )
              self._test_export(lowerCamelCase		,	"pt"		,	12		,	lowerCamelCase )
      @require_tf
      @slow
      def A_			(			self ):
          for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
              snake_case__						=				self._test_export(lowerCamelCase		,	"tf"		,	12		,	**lowerCamelCase )
              snake_case__						=				quantize(Path(lowerCamelCase ) )

              # Ensure the actual quantized model is not bigger than the original one
              if quantized_path.stat().st_size >= Path(lowerCamelCase ).stat().st_size:
                  self.fail("Quantized model is bigger than initial ONNX model" )
      @require_torch
      @slow
      def A_			(			self ):
          for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
              snake_case__						=				self._test_export(lowerCamelCase		,	"pt"		,	12		,	**lowerCamelCase )
              snake_case__						=				quantize(lowerCamelCase )

              # Ensure the actual quantized model is not bigger than the original one
              if quantized_path.stat().st_size >= Path(lowerCamelCase ).stat().st_size:
                  self.fail("Quantized model is bigger than initial ONNX model" )
      def A_			(			self		,	lowerCamelCase		,	lowerCamelCase		,	lowerCamelCase		,	lowerCamelCase=None		,	**lowerCamelCase ):
          try:
              # Compute path
              with TemporaryDirectory() as tempdir:
                  snake_case__						=				Path(lowerCamelCase ).joinpath("model.onnx" )

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

              # Export
              convert(lowerCamelCase		,	lowerCamelCase		,	lowerCamelCase		,	lowerCamelCase		,	lowerCamelCase		,	**lowerCamelCase )

              return path
          except Exception as e:
              self.fail(lowerCamelCase )
      @require_torch
      @require_tokenizers
      @slow
      def A_			(			self ):
          from transformers import BertModel

          snake_case__						=				BertModel(BertConfig.from_pretrained("lysandre/tiny-bert-random" ) )
          snake_case__						=				BertTokenizerFast.from_pretrained("lysandre/tiny-bert-random" )
          self._test_infer_dynamic_axis(lowerCamelCase		,	lowerCamelCase		,	"pt" )
      @require_tf
      @require_tokenizers
      @slow
      def A_			(			self ):
          from transformers import TFBertModel

          snake_case__						=				TFBertModel(BertConfig.from_pretrained("lysandre/tiny-bert-random" ) )
          snake_case__						=				BertTokenizerFast.from_pretrained("lysandre/tiny-bert-random" )
          self._test_infer_dynamic_axis(lowerCamelCase		,	lowerCamelCase		,	"tf" )
      def A_			(			self		,	lowerCamelCase		,	lowerCamelCase		,	lowerCamelCase ):
          snake_case__						=				FeatureExtractionPipeline(lowerCamelCase		,	lowerCamelCase )

          snake_case__						=				["input_ids", "token_type_ids", "attention_mask", "output_0", "output_1"]
          snake_case__						,					snake_case__						,					snake_case__						,					snake_case__						=				infer_shapes(lowerCamelCase		,	lowerCamelCase )

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

          # 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 A_			(			self ):
          snake_case__						=				["input_ids", "attention_mask", "token_type_ids"]
          snake_case__						=				{"input_ids": [1, 2, 3, 4], "attention_mask": [0, 0, 0, 0], "token_type_ids": [1, 1, 1, 1]}
          snake_case__						,					snake_case__						=				ensure_valid_input(FuncContiguousArgs()		,	lowerCamelCase		,	lowerCamelCase )

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

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

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

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

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

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



      def A_			(			self ):
          snake_case__						=				generate_identified_filename(Path("/home/something/my_fake_model.onnx" )		,	"-test" )
          self.assertEqual("/home/something/my_fake_model-test.onnx"		,	generated.as_posix() )



