

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 					_snake_case						:






	def    lowerCAmelCase_							(		self					,UpperCamelCase					,UpperCamelCase					,UpperCamelCase       )						->    List[Any]:
					return None




class 					_snake_case						:






	def    lowerCAmelCase_							(		self					,UpperCamelCase					,UpperCamelCase					,UpperCamelCase					,UpperCamelCase       )						->    Tuple:
					return None




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






	@require_tf
	@slow
	def    lowerCAmelCase_							(		self       )						->    str:



					for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
									self._test_export(UpperCAmelCase_					,"tf"					,12					,**UpperCAmelCase_       )






	@require_torch
	@slow
	def    lowerCAmelCase_							(		self       )						->    Optional[int]:



					for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
									self._test_export(UpperCAmelCase_					,"pt"					,12					,**UpperCAmelCase_       )






	@require_torch
	@slow
	def    lowerCAmelCase_							(		self       )						->    str:
					from transformers import BertModel

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

					with TemporaryDirectory() as bert_save_dir:
									snake_case__      :List[str]         =   BertModel(BertConfig(vocab_size=len(UpperCAmelCase_       )       )       )
									model.save_pretrained(UpperCAmelCase_       )
									self._test_export(UpperCAmelCase_					,"pt"					,12					,UpperCAmelCase_       )






	@require_tf
	@slow
	def    lowerCAmelCase_							(		self       )						->    List[Any]:



					for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
									snake_case__      :Union[str, Any]         =   self._test_export(UpperCAmelCase_					,"tf"					,12					,**UpperCAmelCase_       )
									snake_case__      :Any         =   quantize(Path(UpperCAmelCase_       )       )

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






	@require_torch
	@slow
	def    lowerCAmelCase_							(		self       )						->    Optional[Any]:



					for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
									snake_case__      :Dict         =   self._test_export(UpperCAmelCase_					,"pt"					,12					,**UpperCAmelCase_       )
									snake_case__      :str         =   quantize(UpperCAmelCase_       )

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






	def    lowerCAmelCase_							(		self					,UpperCamelCase					,UpperCamelCase					,UpperCamelCase					,UpperCamelCase=None					,**UpperCamelCase       )						->    int:



					try:
									# Compute path
									with TemporaryDirectory() as tempdir:
													snake_case__      :Optional[int]         =   Path(UpperCAmelCase_       ).joinpath("model.onnx"       )

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

									# Export
									convert(UpperCAmelCase_					,UpperCAmelCase_					,UpperCAmelCase_					,UpperCAmelCase_					,UpperCAmelCase_					,**UpperCAmelCase_       )

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






	@require_torch
	@require_tokenizers
	@slow
	def    lowerCAmelCase_							(		self       )						->    Dict:
					from transformers import BertModel

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






	@require_tf
	@require_tokenizers
	@slow
	def    lowerCAmelCase_							(		self       )						->    Optional[Any]:
					from transformers import TFBertModel

					snake_case__      :Union[str, Any]         =   TFBertModel(BertConfig.from_pretrained("lysandre/tiny-bert-random"       )       )
					snake_case__      :List[str]         =   BertTokenizerFast.from_pretrained("lysandre/tiny-bert-random"       )
					self._test_infer_dynamic_axis(UpperCAmelCase_					,UpperCAmelCase_					,"tf"       )






	def    lowerCAmelCase_							(		self					,UpperCamelCase					,UpperCamelCase					,UpperCamelCase       )						->    Union[str, Any]:
					snake_case__      :Tuple         =   FeatureExtractionPipeline(UpperCAmelCase_					,UpperCAmelCase_       )

					snake_case__      :Union[str, Any]         =   ["input_ids", "token_type_ids", "attention_mask", "output_0", "output_1"]
					snake_case__    ,						snake_case__    ,						snake_case__    ,						snake_case__      :Any         =   infer_shapes(UpperCAmelCase_					,UpperCAmelCase_       )

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

					# 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    lowerCAmelCase_							(		self       )						->    Tuple:
					snake_case__      :Optional[Any]         =   ["input_ids", "attention_mask", "token_type_ids"]
					snake_case__      :Union[str, Any]         =   {"input_ids": [1, 2, 3, 4], "attention_mask": [0, 0, 0, 0], "token_type_ids": [1, 1, 1, 1]}
					snake_case__    ,						snake_case__      :int         =   ensure_valid_input(FuncContiguousArgs()					,UpperCAmelCase_					,UpperCAmelCase_       )

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

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

					# Parameter should be reordered according to their respective place in the function:
					# (input_ids, token_type_ids, attention_mask)
					self.assertEqual(UpperCAmelCase_					,(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__      :str         =   ensure_valid_input(FuncNonContiguousArgs()					,UpperCAmelCase_					,UpperCAmelCase_       )

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

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






	def    lowerCAmelCase_							(		self       )						->    Optional[Any]:
					snake_case__      :str         =   generate_identified_filename(Path("/home/something/my_fake_model.onnx"       )					,"-test"       )
					self.assertEqual("/home/something/my_fake_model-test.onnx"					,generated.as_posix()       )







from ...utils import (
    OptionalDependencyNotAvailable,
    is_flax_available,
    is_torch_available,
    is_transformers_available,
)


try:
	if not (is_transformers_available() and is_torch_available()):
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	from ...utils.dummy_torch_and_transformers_objects import *  # noqa F403
else:
	from .multicontrolnet import MultiControlNetModel
	from .pipeline_controlnet import StableDiffusionControlNetPipeline
	from .pipeline_controlnet_imgaimg import StableDiffusionControlNetImgaImgPipeline
	from .pipeline_controlnet_inpaint import StableDiffusionControlNetInpaintPipeline


if is_transformers_available() and is_flax_available():
	from .pipeline_flax_controlnet import FlaxStableDiffusionControlNetPipeline