'''simple docstring'''




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 					__magic_name__		:







   def 			SCREAMING_SNAKE_CASE_  (			self       :						Dict       ,    lowercase_       :						Optional[Any]       ,    lowercase_       :						Any       ,    lowercase_       :						int					):
          return None





class 					__magic_name__		:







   def 			SCREAMING_SNAKE_CASE_  (			self       :						int       ,    lowercase_       :						Dict       ,    lowercase_       :						Union[str, Any]       ,    lowercase_       :						Union[str, Any]       ,    lowercase_       :						List[Any]					):
          return None





class 					__magic_name__		(					unittest.TestCase):
   UpperCamelCase__       = [
       # (model_name, model_kwargs)
       ('''bert-base-cased''', {}),
       ('''gpt2''', {'''use_cache''': False}),  # We don't support exporting GPT2 past keys anymore
   ]







   @require_tf
   @slow
   def 			SCREAMING_SNAKE_CASE_  (			self       :						Optional[int]					):
          for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
                 self._test_export(lowercase_       ,    """tf"""       ,    12       ,    **lowercase_					)







   @require_torch
   @slow
   def 			SCREAMING_SNAKE_CASE_  (			self       :						Tuple					):
          for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
                 self._test_export(lowercase_       ,    """pt"""       ,    12       ,    **lowercase_					)







   @require_torch
   @slow
   def 			SCREAMING_SNAKE_CASE_  (			self       :						Optional[int]					):
          from transformers import BertModel

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

          with TemporaryDirectory() as bert_save_dir:
                 lowercase_      :    Any       		= BertModel(BertConfig(vocab_size=len(lowercase_					)					)					)
                 model.save_pretrained(lowercase_					)
                 self._test_export(lowercase_       ,    """pt"""       ,    12       ,    lowercase_					)







   @require_tf
   @slow
   def 			SCREAMING_SNAKE_CASE_  (			self       :						List[Any]					):
          for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
                 lowercase_      :    Optional[int]       		= self._test_export(lowercase_       ,    """tf"""       ,    12       ,    **lowercase_					)
                 lowercase_      :    Dict       		= quantize(Path(lowercase_					)					)

                 # Ensure the actual quantized model is not bigger than the original one
                 if quantized_path.stat().st_size >= Path(lowercase_					).stat().st_size:
                        self.fail("""Quantized model is bigger than initial ONNX model"""					)







   @require_torch
   @slow
   def 			SCREAMING_SNAKE_CASE_  (			self       :						Any					):
          for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
                 lowercase_      :    Union[str, Any]       		= self._test_export(lowercase_       ,    """pt"""       ,    12       ,    **lowercase_					)
                 lowercase_      :    List[Any]       		= quantize(lowercase_					)

                 # Ensure the actual quantized model is not bigger than the original one
                 if quantized_path.stat().st_size >= Path(lowercase_					).stat().st_size:
                        self.fail("""Quantized model is bigger than initial ONNX model"""					)







   def 			SCREAMING_SNAKE_CASE_  (			self       :						Dict       ,    lowercase_       :						List[str]       ,    lowercase_       :						int       ,    lowercase_       :						List[str]       ,    lowercase_       :						List[str]=None       ,    **lowercase_       :						Tuple					):
          try:
                 # Compute path
                 with TemporaryDirectory() as tempdir:
                        lowercase_      :    List[str]       		= Path(lowercase_					).joinpath("""model.onnx"""					)

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

                 # Export
                 convert(lowercase_       ,    lowercase_       ,    lowercase_       ,    lowercase_       ,    lowercase_       ,    **lowercase_					)

                 return path
          except Exception as e:
                 self.fail(lowercase_					)







   @require_torch
   @require_tokenizers
   @slow
   def 			SCREAMING_SNAKE_CASE_  (			self       :						int					):
          from transformers import BertModel

          lowercase_      :    List[str]       		= BertModel(BertConfig.from_pretrained("""lysandre/tiny-bert-random"""					)					)
          lowercase_      :    Any       		= BertTokenizerFast.from_pretrained("""lysandre/tiny-bert-random"""					)
          self._test_infer_dynamic_axis(lowercase_       ,    lowercase_       ,    """pt"""					)







   @require_tf
   @require_tokenizers
   @slow
   def 			SCREAMING_SNAKE_CASE_  (			self       :						Tuple					):
          from transformers import TFBertModel

          lowercase_      :    Any       		= TFBertModel(BertConfig.from_pretrained("""lysandre/tiny-bert-random"""					)					)
          lowercase_      :    str       		= BertTokenizerFast.from_pretrained("""lysandre/tiny-bert-random"""					)
          self._test_infer_dynamic_axis(lowercase_       ,    lowercase_       ,    """tf"""					)







   def 			SCREAMING_SNAKE_CASE_  (			self       :						Any       ,    lowercase_       :						Tuple       ,    lowercase_       :						Union[str, Any]       ,    lowercase_       :						Optional[int]					):
          lowercase_      :    Optional[int]       		= FeatureExtractionPipeline(lowercase_       ,    lowercase_					)

          lowercase_      :    List[Any]       		= ["""input_ids""", """token_type_ids""", """attention_mask""", """output_0""", """output_1"""]
          lowercase_      :    Any       		= infer_shapes(lowercase_       ,    lowercase_					)

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

          # 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 			SCREAMING_SNAKE_CASE_  (			self       :						Tuple					):
          lowercase_      :    int       		= ["""input_ids""", """attention_mask""", """token_type_ids"""]
          lowercase_      :    List[Any]       		= {"""input_ids""": [1, 2, 3, 4], """attention_mask""": [0, 0, 0, 0], """token_type_ids""": [1, 1, 1, 1]}
          lowercase_      :    Optional[Any]       		= ensure_valid_input(FuncContiguousArgs()       ,    lowercase_       ,    lowercase_					)

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

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

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

          # Generated args are interleaved with another args (for instance parameter "past" in GPT2)
          lowercase_      :    List[Any]       		= ensure_valid_input(FuncNonContiguousArgs()       ,    lowercase_       ,    lowercase_					)

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

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







   def 			SCREAMING_SNAKE_CASE_  (			self       :						Any					):
          lowercase_      :    Tuple       		= generate_identified_filename(Path("""/home/something/my_fake_model.onnx"""					)       ,    """-test"""					)
          self.assertEqual("""/home/something/my_fake_model-test.onnx"""       ,    generated.as_posix()					)






'''simple docstring'''




from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


_lowercase		:				Dict	       =		{
    "configuration_bloom": ["BLOOM_PRETRAINED_CONFIG_ARCHIVE_MAP", "BloomConfig", "BloomOnnxConfig"],
}
try:
						if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						_lowercase		:				Dict	       =		["BloomTokenizerFast"]

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						_lowercase		:				Dict	       =		[
						    "BLOOM_PRETRAINED_MODEL_ARCHIVE_LIST",
						    "BloomForCausalLM",
						    "BloomModel",
						    "BloomPreTrainedModel",
						    "BloomForSequenceClassification",
						    "BloomForTokenClassification",
						    "BloomForQuestionAnswering",
						]

if TYPE_CHECKING:
						from .configuration_bloom import BLOOM_PRETRAINED_CONFIG_ARCHIVE_MAP, BloomConfig, BloomOnnxConfig

						try:
												if not is_tokenizers_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .tokenization_bloom_fast import BloomTokenizerFast

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_bloom import (
												    BLOOM_PRETRAINED_MODEL_ARCHIVE_LIST,
												    BloomForCausalLM,
												    BloomForQuestionAnswering,
												    BloomForSequenceClassification,
												    BloomForTokenClassification,
												    BloomModel,
												    BloomPreTrainedModel,
												)

else:
						import sys

						_lowercase		:				Optional[int]	       =		_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)





