


# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__magic_name__            =  {
    '''configuration_mgp_str''': ['''MGP_STR_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''MgpstrConfig'''],
    '''processing_mgp_str''': ['''MgpstrProcessor'''],
    '''tokenization_mgp_str''': ['''MgpstrTokenizer'''],
}

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     __magic_name__            =  [
         '''MGP_STR_PRETRAINED_MODEL_ARCHIVE_LIST''',
         '''MgpstrModel''',
         '''MgpstrPreTrainedModel''',
         '''MgpstrForSceneTextRecognition''',
     ]

if TYPE_CHECKING:
     from .configuration_mgp_str import MGP_STR_PRETRAINED_CONFIG_ARCHIVE_MAP, MgpstrConfig
     from .processing_mgp_str import MgpstrProcessor
     from .tokenization_mgp_str import MgpstrTokenizer

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_mgp_str import (
              MGP_STR_PRETRAINED_MODEL_ARCHIVE_LIST,
              MgpstrForSceneTextRecognition,
              MgpstrModel,
              MgpstrPreTrainedModel,
          )
else:
     import sys

     __magic_name__            =  _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)





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() )



