


import unittest

from transformers import BigBirdConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask


if is_flax_available():
     import jax

     from transformers.models.big_bird.modeling_flax_big_bird import (
         FlaxBigBirdForCausalLM,
         FlaxBigBirdForMaskedLM,
         FlaxBigBirdForMultipleChoice,
         FlaxBigBirdForPreTraining,
         FlaxBigBirdForQuestionAnswering,
         FlaxBigBirdForSequenceClassification,
         FlaxBigBirdForTokenClassification,
         FlaxBigBirdModel,
     )




class 					_SCREAMING_SNAKE_CASE  (      unittest.TestCase ):
      def __init__(			self		,	lowerCamelCase		,	lowerCamelCase=2		,	lowerCamelCase=56		,	lowerCamelCase=True		,	lowerCamelCase=True		,	lowerCamelCase=True		,	lowerCamelCase=True		,	lowerCamelCase=99		,	lowerCamelCase=32		,	lowerCamelCase=2		,	lowerCamelCase=2		,	lowerCamelCase=7		,	lowerCamelCase="gelu_new"		,	lowerCamelCase=0.1		,	lowerCamelCase=0.1		,	lowerCamelCase=5_12		,	lowerCamelCase=16		,	lowerCamelCase=2		,	lowerCamelCase=0.0_2		,	lowerCamelCase=4		,	lowerCamelCase="block_sparse"		,	lowerCamelCase=True		,	lowerCamelCase=False		,	lowerCamelCase=2		,	lowerCamelCase=3		,	):
          snake_case__						=				parent
          snake_case__						=				batch_size
          snake_case__						=				seq_length
          snake_case__						=				is_training
          snake_case__						=				use_attention_mask
          snake_case__						=				use_token_type_ids
          snake_case__						=				use_labels
          snake_case__						=				vocab_size
          snake_case__						=				hidden_size
          snake_case__						=				num_hidden_layers
          snake_case__						=				num_attention_heads
          snake_case__						=				intermediate_size
          snake_case__						=				hidden_act
          snake_case__						=				hidden_dropout_prob
          snake_case__						=				attention_probs_dropout_prob
          snake_case__						=				max_position_embeddings
          snake_case__						=				type_vocab_size
          snake_case__						=				type_sequence_label_size
          snake_case__						=				initializer_range
          snake_case__						=				num_choices

          snake_case__						=				rescale_embeddings
          snake_case__						=				attention_type
          snake_case__						=				use_bias
          snake_case__						=				block_size
          snake_case__						=				num_random_blocks
      def A_			(			self ):
          snake_case__						=				ids_tensor([self.batch_size, self.seq_length]		,	self.vocab_size )

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

          snake_case__						=				None
          if self.use_token_type_ids:
              snake_case__						=				ids_tensor([self.batch_size, self.seq_length]		,	self.type_vocab_size )

          snake_case__						=				BigBirdConfig(
              vocab_size=self.vocab_size		,	hidden_size=self.hidden_size		,	num_hidden_layers=self.num_hidden_layers		,	num_attention_heads=self.num_attention_heads		,	intermediate_size=self.intermediate_size		,	hidden_act=self.hidden_act		,	hidden_dropout_prob=self.hidden_dropout_prob		,	attention_probs_dropout_prob=self.attention_probs_dropout_prob		,	max_position_embeddings=self.max_position_embeddings		,	type_vocab_size=self.type_vocab_size		,	is_decoder=lowerCamelCase		,	initializer_range=self.initializer_range		,	attention_type=self.attention_type		,	block_size=self.block_size		,	num_random_blocks=self.num_random_blocks		,	use_bias=self.use_bias		,	rescale_embeddings=self.rescale_embeddings		,	)

          return config, input_ids, token_type_ids, attention_mask



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





@require_flax
class 					_SCREAMING_SNAKE_CASE  (      __UpperCamelCase       ,					unittest.TestCase ):
      _A						:		Dict    	=     (
          (
              FlaxBigBirdForCausalLM,
              FlaxBigBirdModel,
              FlaxBigBirdForPreTraining,
              FlaxBigBirdForMaskedLM,
              FlaxBigBirdForMultipleChoice,
              FlaxBigBirdForQuestionAnswering,
              FlaxBigBirdForSequenceClassification,
              FlaxBigBirdForTokenClassification,
          )
          if is_flax_available()
          else ()
      )

      _A						:		int    	=     False
      _A						:		List[Any]    	=     False
      def A_			(			self ):
          snake_case__						=				FlaxBigBirdModelTester(self )
      @slow
      # copied from `test_modeling_flax_common` because it takes much longer than other models
      def A_			(			self ):
          super().test_from_pretrained_save_pretrained()
      @slow
      # copied from `test_modeling_flax_common` because it takes much longer than other models
      def A_			(			self ):
          super().test_from_pretrained_with_no_automatic_init()
      @slow
      # copied from `test_modeling_flax_common` because it takes much longer than other models
      def A_			(			self ):
          super().test_no_automatic_init()
      @slow
      # copied from `test_modeling_flax_common` because it takes much longer than other models
      def A_			(			self ):
          super().test_hidden_states_output()
      @slow
      def A_			(			self ):
          for model_class_name in self.all_model_classes:
              snake_case__						=				model_class_name.from_pretrained("google/bigbird-roberta-base" )
              self.assertIsNotNone(lowerCamelCase )
      def A_			(			self ):
          if self.test_attn_probs:
              super().test_attention_outputs()
      @slow
      # copied from `test_modeling_flax_common` because it takes much longer than other models
      def A_			(			self ):
          snake_case__						,					snake_case__						=				self.model_tester.prepare_config_and_inputs_for_common()

          for model_class in self.all_model_classes:
              with self.subTest(model_class.__name__ ):
                  snake_case__						=				self._prepare_for_class(lowerCamelCase		,	lowerCamelCase )
                  snake_case__						=				model_class(lowerCamelCase )

                  @jax.jit
                  def model_jitted(lowerCamelCase		,	lowerCamelCase=None		,	**lowerCamelCase ):
                      return model(input_ids=lowerCamelCase		,	attention_mask=lowerCamelCase		,	**lowerCamelCase )

                  with self.subTest("JIT Enabled" ):
                      snake_case__						=				model_jitted(**lowerCamelCase ).to_tuple()

                  with self.subTest("JIT Disabled" ):
                      with jax.disable_jit():
                          snake_case__						=				model_jitted(**lowerCamelCase ).to_tuple()

                  self.assertEqual(len(lowerCamelCase )		,	len(lowerCamelCase ) )
                  for jitted_output, output in zip(lowerCamelCase		,	lowerCamelCase ):
                      self.assertEqual(jitted_output.shape		,	output.shape )



      def A_			(			self		,	lowerCamelCase		,	lowerCamelCase		,	lowerCamelCase		,	lowerCamelCase=1e-5		,	lowerCamelCase="outputs"		,	lowerCamelCase=None ):
          # `bigbird_block_sparse_attention` in `FlaxBigBird` returns `attention_probs = None`, while in PyTorch version,
          # an effort was done to return `attention_probs` (yet to be verified).
          if name.startswith("outputs.attentions" ):
              return
          else:
              super().check_pt_flax_outputs(lowerCamelCase		,	lowerCamelCase		,	lowerCamelCase		,	lowerCamelCase		,	lowerCamelCase		,	lowerCamelCase )





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() )



