
from __future__ import annotations

import unittest

from transformers import DistilBertConfig, is_tf_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
     import tensorflow as tf

     from transformers.models.distilbert.modeling_tf_distilbert import (
         TF_DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
         TFDistilBertForMaskedLM,
         TFDistilBertForMultipleChoice,
         TFDistilBertForQuestionAnswering,
         TFDistilBertForSequenceClassification,
         TFDistilBertForTokenClassification,
         TFDistilBertModel,
     )




class 					_SCREAMING_SNAKE_CASE  :
      def __init__(			self		,	lowerCamelCase		,	):
          snake_case__						=				parent
          snake_case__						=				13
          snake_case__						=				7
          snake_case__						=				True
          snake_case__						=				True
          snake_case__						=				False
          snake_case__						=				True
          snake_case__						=				99
          snake_case__						=				32
          snake_case__						=				2
          snake_case__						=				4
          snake_case__						=				37
          snake_case__						=				"gelu"
          snake_case__						=				0.1
          snake_case__						=				0.1
          snake_case__						=				5_12
          snake_case__						=				16
          snake_case__						=				2
          snake_case__						=				0.0_2
          snake_case__						=				3
          snake_case__						=				4
          snake_case__						=				None
      def A_			(			self ):
          snake_case__						=				ids_tensor([self.batch_size, self.seq_length]		,	self.vocab_size )

          snake_case__						=				None
          if self.use_input_mask:
              snake_case__						=				random_attention_mask([self.batch_size, self.seq_length] )

          snake_case__						=				None
          snake_case__						=				None
          snake_case__						=				None
          if self.use_labels:
              snake_case__						=				ids_tensor([self.batch_size]		,	self.type_sequence_label_size )
              snake_case__						=				ids_tensor([self.batch_size, self.seq_length]		,	self.num_labels )
              snake_case__						=				ids_tensor([self.batch_size]		,	self.num_choices )

          snake_case__						=				DistilBertConfig(
              vocab_size=self.vocab_size		,	dim=self.hidden_size		,	n_layers=self.num_hidden_layers		,	n_heads=self.num_attention_heads		,	hidden_dim=self.intermediate_size		,	hidden_act=self.hidden_act		,	dropout=self.hidden_dropout_prob		,	attention_dropout=self.attention_probs_dropout_prob		,	max_position_embeddings=self.max_position_embeddings		,	initializer_range=self.initializer_range		,	)

          return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels
      def A_			(			self		,	lowerCamelCase		,	lowerCamelCase		,	lowerCamelCase		,	lowerCamelCase		,	lowerCamelCase		,	lowerCamelCase ):
          snake_case__						=				TFDistilBertModel(config=lowerCamelCase )
          snake_case__						=				{"input_ids": input_ids, "attention_mask": input_mask}

          snake_case__						=				model(lowerCamelCase )

          snake_case__						=				[input_ids, input_mask]

          snake_case__						=				model(lowerCamelCase )

          self.parent.assertEqual(result.last_hidden_state.shape		,	(self.batch_size, self.seq_length, self.hidden_size) )
      def A_			(			self		,	lowerCamelCase		,	lowerCamelCase		,	lowerCamelCase		,	lowerCamelCase		,	lowerCamelCase		,	lowerCamelCase ):
          snake_case__						=				TFDistilBertForMaskedLM(config=lowerCamelCase )
          snake_case__						=				{"input_ids": input_ids, "attention_mask": input_mask}
          snake_case__						=				model(lowerCamelCase )
          self.parent.assertEqual(result.logits.shape		,	(self.batch_size, self.seq_length, self.vocab_size) )
      def A_			(			self		,	lowerCamelCase		,	lowerCamelCase		,	lowerCamelCase		,	lowerCamelCase		,	lowerCamelCase		,	lowerCamelCase ):
          snake_case__						=				TFDistilBertForQuestionAnswering(config=lowerCamelCase )
          snake_case__						=				{
              "input_ids": input_ids,
              "attention_mask": input_mask,
          }
          snake_case__						=				model(lowerCamelCase )
          self.parent.assertEqual(result.start_logits.shape		,	(self.batch_size, self.seq_length) )
          self.parent.assertEqual(result.end_logits.shape		,	(self.batch_size, self.seq_length) )
      def A_			(			self		,	lowerCamelCase		,	lowerCamelCase		,	lowerCamelCase		,	lowerCamelCase		,	lowerCamelCase		,	lowerCamelCase ):
          snake_case__						=				self.num_labels
          snake_case__						=				TFDistilBertForSequenceClassification(lowerCamelCase )
          snake_case__						=				{"input_ids": input_ids, "attention_mask": input_mask}
          snake_case__						=				model(lowerCamelCase )
          self.parent.assertEqual(result.logits.shape		,	(self.batch_size, self.num_labels) )
      def A_			(			self		,	lowerCamelCase		,	lowerCamelCase		,	lowerCamelCase		,	lowerCamelCase		,	lowerCamelCase		,	lowerCamelCase ):
          snake_case__						=				self.num_choices
          snake_case__						=				TFDistilBertForMultipleChoice(lowerCamelCase )
          snake_case__						=				tf.tile(tf.expand_dims(lowerCamelCase		,	1 )		,	(1, self.num_choices, 1) )
          snake_case__						=				tf.tile(tf.expand_dims(lowerCamelCase		,	1 )		,	(1, self.num_choices, 1) )
          snake_case__						=				{
              "input_ids": multiple_choice_inputs_ids,
              "attention_mask": multiple_choice_input_mask,
          }
          snake_case__						=				model(lowerCamelCase )
          self.parent.assertEqual(result.logits.shape		,	(self.batch_size, self.num_choices) )
      def A_			(			self		,	lowerCamelCase		,	lowerCamelCase		,	lowerCamelCase		,	lowerCamelCase		,	lowerCamelCase		,	lowerCamelCase ):
          snake_case__						=				self.num_labels
          snake_case__						=				TFDistilBertForTokenClassification(lowerCamelCase )
          snake_case__						=				{"input_ids": input_ids, "attention_mask": input_mask}
          snake_case__						=				model(lowerCamelCase )
          self.parent.assertEqual(result.logits.shape		,	(self.batch_size, self.seq_length, self.num_labels) )



      def A_			(			self ):
          snake_case__						=				self.prepare_config_and_inputs()
          ((snake_case__)						,					(snake_case__)						,					(snake_case__)						,					(snake_case__)						,					(snake_case__)						,					(snake_case__))						=				config_and_inputs
          snake_case__						=				{"input_ids": input_ids, "attention_mask": input_mask}
          return config, inputs_dict





@require_tf
class 					_SCREAMING_SNAKE_CASE  (      __UpperCamelCase       ,					__UpperCamelCase       ,					unittest.TestCase ):
      _A						:		str    	=     (
          (
              TFDistilBertModel,
              TFDistilBertForMaskedLM,
              TFDistilBertForQuestionAnswering,
              TFDistilBertForSequenceClassification,
              TFDistilBertForTokenClassification,
              TFDistilBertForMultipleChoice,
          )
          if is_tf_available()
          else None
      )
      _A						:		str    	=     (
          {
              'feature-extraction': TFDistilBertModel,
              'fill-mask': TFDistilBertForMaskedLM,
              'question-answering': TFDistilBertForQuestionAnswering,
              'text-classification': TFDistilBertForSequenceClassification,
              'token-classification': TFDistilBertForTokenClassification,
              'zero-shot': TFDistilBertForSequenceClassification,
          }
          if is_tf_available()
          else {}
      )
      _A						:		Optional[Any]    	=     False
      _A						:		str    	=     False
      def A_			(			self ):
          snake_case__						=				TFDistilBertModelTester(self )
          snake_case__						=				ConfigTester(self		,	config_class=lowerCamelCase		,	dim=37 )
      def A_			(			self ):
          self.config_tester.run_common_tests()
      def A_			(			self ):
          snake_case__						=				self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_distilbert_model(*lowerCamelCase )
      def A_			(			self ):
          snake_case__						=				self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_distilbert_for_masked_lm(*lowerCamelCase )
      def A_			(			self ):
          snake_case__						=				self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_distilbert_for_question_answering(*lowerCamelCase )
      def A_			(			self ):
          snake_case__						=				self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_distilbert_for_sequence_classification(*lowerCamelCase )
      def A_			(			self ):
          snake_case__						=				self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_distilbert_for_multiple_choice(*lowerCamelCase )
      def A_			(			self ):
          snake_case__						=				self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_distilbert_for_token_classification(*lowerCamelCase )



      @slow
      def A_			(			self ):
          for model_name in list(TF_DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1] ):
              snake_case__						=				TFDistilBertModel.from_pretrained(lowerCamelCase )
              self.assertIsNotNone(lowerCamelCase )





@require_tf
class 					_SCREAMING_SNAKE_CASE  (      unittest.TestCase ):
      @slow
      def A_			(			self ):
          snake_case__						=				TFDistilBertModel.from_pretrained("distilbert-base-uncased" )
          snake_case__						=				tf.constant([[0, 1, 2, 3, 4, 5]] )
          snake_case__						=				model(lowerCamelCase )[0]

          snake_case__						=				[1, 6, 7_68]
          self.assertEqual(output.shape		,	lowerCamelCase )

          snake_case__						=				tf.constant(
              [
                  [
                      [0.1_9_2_6_1_8_8_5, -0.1_3_7_3_2_9_5_5, 0.4_1_1_9_7_9_9],
                      [0.2_2_1_5_0_1_5_6, -0.0_7_4_2_2_6_6_1, 0.3_9_0_3_7_2_0_4],
                      [0.2_2_7_5_6_0_1_8, -0.0_8_9_6_4_1_4, 0.3_7_0_1_4_6_7],
                  ]
              ] )
          tf.debugging.assert_near(output[:, :3, :3]		,	lowerCamelCase		,	atol=1e-4 )





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() )



