
import copy
import inspect
import unittest

from transformers import AutoBackbone
from transformers.configuration_utils import PretrainedConfig
from transformers.testing_utils import require_timm, require_torch, torch_device
from transformers.utils.import_utils import is_torch_available

from ...test_backbone_common import BackboneTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor


if is_torch_available():
     import torch

     from transformers import TimmBackbone, TimmBackboneConfig

from ...test_pipeline_mixin import PipelineTesterMixin




class 					_SCREAMING_SNAKE_CASE  :
      def __init__(			self		,	lowerCamelCase		,	lowerCamelCase=None		,	lowerCamelCase=None		,	lowerCamelCase=None		,	lowerCamelCase="resnet50"		,	lowerCamelCase=3		,	lowerCamelCase=32		,	lowerCamelCase=3		,	lowerCamelCase=True		,	lowerCamelCase=True		,	):
          snake_case__						=				parent
          snake_case__						=				out_indices if out_indices is not None else [4]
          snake_case__						=				stage_names
          snake_case__						=				out_features
          snake_case__						=				backbone
          snake_case__						=				batch_size
          snake_case__						=				image_size
          snake_case__						=				num_channels
          snake_case__						=				use_pretrained_backbone
          snake_case__						=				is_training
      def A_			(			self ):
          snake_case__						=				floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
          snake_case__						=				self.get_config()

          return config, pixel_values
      def A_			(			self ):
          return TimmBackboneConfig(
              image_size=self.image_size		,	num_channels=self.num_channels		,	out_features=self.out_features		,	out_indices=self.out_indices		,	stage_names=self.stage_names		,	use_pretrained_backbone=self.use_pretrained_backbone		,	backbone=self.backbone		,	)
      def A_			(			self		,	lowerCamelCase		,	lowerCamelCase ):
          snake_case__						=				TimmBackbone(config=lowerCamelCase )
          model.to(lowerCamelCase )
          model.eval()
          with torch.no_grad():
              snake_case__						=				model(lowerCamelCase )
          self.parent.assertEqual(
              result.feature_map[-1].shape		,	(self.batch_size, model.channels[-1], 14, 14)		,	)



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





@require_torch
@require_timm
class 					_SCREAMING_SNAKE_CASE  (      __UpperCamelCase       ,					__UpperCamelCase       ,					__UpperCamelCase       ,					unittest.TestCase ):
      _A						:		Union[str, Any]    	=     (TimmBackbone,) if is_torch_available() else ()
      _A						:		List[str]    	=     {'feature-extraction': TimmBackbone} if is_torch_available() else {}
      _A						:		Union[str, Any]    	=     False
      _A						:		Optional[Any]    	=     False
      _A						:		Any    	=     False
      _A						:		Optional[int]    	=     False
      def A_			(			self ):
          snake_case__						=				TimmBackboneModelTester(self )
          snake_case__						=				ConfigTester(self		,	config_class=lowerCamelCase		,	has_text_modality=lowerCamelCase )
      def A_			(			self ):
          self.config_tester.create_and_test_config_to_json_string()
          self.config_tester.create_and_test_config_to_json_file()
          self.config_tester.create_and_test_config_from_and_save_pretrained()
          self.config_tester.create_and_test_config_with_num_labels()
          self.config_tester.check_config_can_be_init_without_params()
          self.config_tester.check_config_arguments_init()
      def A_			(			self ):
          snake_case__						=				"resnet18"
          snake_case__						=				"microsoft/resnet-18"

          snake_case__						=				AutoBackbone.from_pretrained(lowerCamelCase		,	use_timm_backbone=lowerCamelCase )
          snake_case__						=				AutoBackbone.from_pretrained(lowerCamelCase )

          self.assertEqual(len(timm_model.out_features )		,	len(transformers_model.out_features ) )
          self.assertEqual(len(timm_model.stage_names )		,	len(transformers_model.stage_names ) )
          self.assertEqual(timm_model.channels		,	transformers_model.channels )
          # Out indices are set to the last layer by default. For timm models, we don't know
          # the number of layers in advance, so we set it to (-1,), whereas for transformers
          # models, we set it to [len(stage_names) - 1] (kept for backward compatibility).
          self.assertEqual(timm_model.out_indices		,	(-1,) )
          self.assertEqual(transformers_model.out_indices		,	[len(timm_model.stage_names ) - 1] )

          snake_case__						=				AutoBackbone.from_pretrained(lowerCamelCase		,	use_timm_backbone=lowerCamelCase		,	out_indices=[1, 2, 3] )
          snake_case__						=				AutoBackbone.from_pretrained(lowerCamelCase		,	out_indices=[1, 2, 3] )

          self.assertEqual(timm_model.out_indices		,	transformers_model.out_indices )
          self.assertEqual(len(timm_model.out_features )		,	len(transformers_model.out_features ) )
          self.assertEqual(timm_model.channels		,	transformers_model.channels )
      @unittest.skip("TimmBackbone doesn't support feed forward chunking" )
      def A_			(			self ):
          pass
      @unittest.skip("TimmBackbone doesn't have num_hidden_layers attribute" )
      def A_			(			self ):
          pass
      @unittest.skip("TimmBackbone initialization is managed on the timm side" )
      def A_			(			self ):
          pass
      @unittest.skip("TimmBackbone models doesn't have inputs_embeds" )
      def A_			(			self ):
          pass
      @unittest.skip("TimmBackbone models doesn't have inputs_embeds" )
      def A_			(			self ):
          pass
      @unittest.skip("TimmBackbone model cannot be created without specifying a backbone checkpoint" )
      def A_			(			self ):
          pass
      @unittest.skip("Only checkpoints on timm can be loaded into TimmBackbone" )
      def A_			(			self ):
          pass
      @unittest.skip("model weights aren't tied in TimmBackbone." )
      def A_			(			self ):
          pass
      @unittest.skip("model weights aren't tied in TimmBackbone." )
      def A_			(			self ):
          pass
      @unittest.skip("Only checkpoints on timm can be loaded into TimmBackbone" )
      def A_			(			self ):
          pass
      @unittest.skip("Only checkpoints on timm can be loaded into TimmBackbone" )
      def A_			(			self ):
          pass
      @unittest.skip("TimmBackbone doesn't have hidden size info in its configuration." )
      def A_			(			self ):
          pass
      @unittest.skip("TimmBackbone doesn't support output_attentions." )
      def A_			(			self ):
          pass
      @unittest.skip("Safetensors is not supported by timm." )
      def A_			(			self ):
          pass
      @unittest.skip("Will be fixed soon by reducing the size of the model used for common tests." )
      def A_			(			self ):
          pass
      def A_			(			self ):
          snake_case__						,					snake_case__						=				self.model_tester.prepare_config_and_inputs_for_common()

          for model_class in self.all_model_classes:
              snake_case__						=				model_class(lowerCamelCase )
              snake_case__						=				inspect.signature(model.forward )
              # signature.parameters is an OrderedDict => so arg_names order is deterministic
              snake_case__						=				[*signature.parameters.keys()]

              snake_case__						=				["pixel_values"]
              self.assertListEqual(arg_names[:1]		,	lowerCamelCase )
      def A_			(			self ):
          snake_case__						,					snake_case__						=				self.model_tester.prepare_config_and_inputs_for_common()
          snake_case__						=				True
          snake_case__						=				self.has_attentions

          # no need to test all models as different heads yield the same functionality
          snake_case__						=				self.all_model_classes[0]
          snake_case__						=				model_class(lowerCamelCase )
          model.to(lowerCamelCase )

          snake_case__						=				self._prepare_for_class(lowerCamelCase		,	lowerCamelCase )
          snake_case__						=				model(**lowerCamelCase )
          snake_case__						=				outputs[0][-1]

          # Encoder-/Decoder-only models
          snake_case__						=				outputs.hidden_states[0]
          hidden_states.retain_grad()

          if self.has_attentions:
              snake_case__						=				outputs.attentions[0]
              attentions.retain_grad()

          output.flatten()[0].backward(retain_graph=lowerCamelCase )

          self.assertIsNotNone(hidden_states.grad )

          if self.has_attentions:
              self.assertIsNotNone(attentions.grad )



      def A_			(			self ):
          snake_case__						,					snake_case__						=				self.model_tester.prepare_config_and_inputs_for_common()

          for model_class in self.all_model_classes:
              snake_case__						=				model_class(lowerCamelCase )
              model.to(lowerCamelCase )
              model.eval()
              snake_case__						=				model(**lowerCamelCase )

              self.assertEqual(len(result.feature_maps )		,	len(config.out_indices ) )
              self.assertEqual(len(model.channels )		,	len(config.out_indices ) )

              # Check output of last stage is taken if out_features=None, out_indices=None
              snake_case__						=				copy.deepcopy(lowerCamelCase )
              snake_case__						=				None
              snake_case__						=				model_class(lowerCamelCase )
              model.to(lowerCamelCase )
              model.eval()
              snake_case__						=				model(**lowerCamelCase )

              self.assertEqual(len(result.feature_maps )		,	1 )
              self.assertEqual(len(model.channels )		,	1 )

              # Check backbone can be initialized with fresh weights
              snake_case__						=				copy.deepcopy(lowerCamelCase )
              snake_case__						=				False
              snake_case__						=				model_class(lowerCamelCase )
              model.to(lowerCamelCase )
              model.eval()
              snake_case__						=				model(**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() )



