



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 						UpperCAmelCase_					:







							def      snake_case__   (     self,    __a,    __a,    __a):

														'''simple docstring'''

														return None



class 						UpperCAmelCase_					:







							def      snake_case__   (     self,    __a,    __a,    __a,    __a):

														'''simple docstring'''

														return None



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







							@require_tf
							@slow
							def      snake_case__   (     self):

														'''simple docstring'''

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







							@require_torch
							@slow
							def      snake_case__   (     self):

														'''simple docstring'''

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







							@require_torch
							@slow
							def      snake_case__   (     self):

														'''simple docstring'''

														from transformers import BertModel

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

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







							@require_tf
							@slow
							def      snake_case__   (     self):

														'''simple docstring'''

														for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
																					_lowerCAmelCase  :							Tuple				     =					self._test_export(__a,    "tf",    12,    **__a)
																					_lowerCAmelCase  :							int				     =					quantize(Path(__a))

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







							@require_torch
							@slow
							def      snake_case__   (     self):

														'''simple docstring'''

														for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
																					_lowerCAmelCase  :							str				     =					self._test_export(__a,    "pt",    12,    **__a)
																					_lowerCAmelCase  :							int				     =					quantize(__a)

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







							def      snake_case__   (     self,    __a,    __a,    __a,    __a=None,    **__a):

														'''simple docstring'''

														try:
																					# Compute path
																					with TemporaryDirectory() as tempdir:
																												_lowerCAmelCase  :							Optional[Any]				     =					Path(__a).joinpath("model.onnx")

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

																					# Export
																					convert(__a,    __a,    __a,    __a,    __a,    **__a)

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







							@require_torch
							@require_tokenizers
							@slow
							def      snake_case__   (     self):

														'''simple docstring'''

														from transformers import BertModel

														_lowerCAmelCase  :							Dict				     =					BertModel(BertConfig.from_pretrained("lysandre/tiny-bert-random"))
														_lowerCAmelCase  :							Any				     =					BertTokenizerFast.from_pretrained("lysandre/tiny-bert-random")
														self._test_infer_dynamic_axis(__a,    __a,    "pt")







							@require_tf
							@require_tokenizers
							@slow
							def      snake_case__   (     self):

														'''simple docstring'''

														from transformers import TFBertModel

														_lowerCAmelCase  :							int				     =					TFBertModel(BertConfig.from_pretrained("lysandre/tiny-bert-random"))
														_lowerCAmelCase  :							Optional[Any]				     =					BertTokenizerFast.from_pretrained("lysandre/tiny-bert-random")
														self._test_infer_dynamic_axis(__a,    __a,    "tf")







							def      snake_case__   (     self,    __a,    __a,    __a):

														'''simple docstring'''

														_lowerCAmelCase  :							str				     =					FeatureExtractionPipeline(__a,    __a)

														_lowerCAmelCase  :							Any				     =					["input_ids", "token_type_ids", "attention_mask", "output_0", "output_1"]
														_lowerCAmelCase  :							Optional[int]				     =					infer_shapes(__a,    __a)

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

														# 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      snake_case__   (     self):

														'''simple docstring'''

														_lowerCAmelCase  :							List[Any]				     =					["input_ids", "attention_mask", "token_type_ids"]
														_lowerCAmelCase  :							Optional[Any]				     =					{"input_ids": [1, 2, 3, 4], "attention_mask": [0, 0, 0, 0], "token_type_ids": [1, 1, 1, 1]}
														_lowerCAmelCase  :							Union[str, Any]				     =					ensure_valid_input(FuncContiguousArgs(),    __a,    __a)

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

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

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

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

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

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







							def      snake_case__   (     self):

														'''simple docstring'''

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




from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


_snake_case	=						logging.get_logger(__name__)

_snake_case	=						{
    "facebook/data2vec-vision-base-ft": (
        "https://huggingface.co/facebook/data2vec-vision-base-ft/resolve/main/config.json"
    ),
}



class 						UpperCAmelCase_					( a):
							lowerCamelCase__         =	'data2vec-vision'







							def __init__(     self,    __a=768,    __a=12,    __a=12,    __a=3072,    __a="gelu",    __a=0.0,    __a=0.0,    __a=0.02,    __a=1E-12,    __a=224,    __a=16,    __a=3,    __a=False,    __a=False,    __a=False,    __a=False,    __a=0.1,    __a=0.1,    __a=True,    __a=[3, 5, 7, 11],    __a=[1, 2, 3, 6],    __a=True,    __a=0.4,    __a=256,    __a=1,    __a=False,    __a=255,    **__a,    ):

														'''simple docstring'''

														super().__init__(**__a)

														_lowerCAmelCase  :							Dict				     =					hidden_size
														_lowerCAmelCase  :							List[Any]				     =					num_hidden_layers
														_lowerCAmelCase  :							Any				     =					num_attention_heads
														_lowerCAmelCase  :							str				     =					intermediate_size
														_lowerCAmelCase  :							Optional[Any]				     =					hidden_act
														_lowerCAmelCase  :							int				     =					hidden_dropout_prob
														_lowerCAmelCase  :							Dict				     =					attention_probs_dropout_prob
														_lowerCAmelCase  :							Dict				     =					initializer_range
														_lowerCAmelCase  :							List[str]				     =					layer_norm_eps

														_lowerCAmelCase  :							Optional[int]				     =					image_size
														_lowerCAmelCase  :							List[Any]				     =					patch_size
														_lowerCAmelCase  :							Optional[Any]				     =					num_channels
														_lowerCAmelCase  :							str				     =					use_mask_token
														_lowerCAmelCase  :							List[str]				     =					use_absolute_position_embeddings
														_lowerCAmelCase  :							str				     =					use_relative_position_bias
														_lowerCAmelCase  :							List[str]				     =					use_shared_relative_position_bias
														_lowerCAmelCase  :							List[str]				     =					layer_scale_init_value
														_lowerCAmelCase  :							List[Any]				     =					drop_path_rate
														_lowerCAmelCase  :							Union[str, Any]				     =					use_mean_pooling
														# decode head attributes (semantic segmentation)
														_lowerCAmelCase  :							Tuple				     =					out_indices
														_lowerCAmelCase  :							Tuple				     =					pool_scales
														# auxiliary head attributes (semantic segmentation)
														_lowerCAmelCase  :							Optional[int]				     =					use_auxiliary_head
														_lowerCAmelCase  :							Optional[Any]				     =					auxiliary_loss_weight
														_lowerCAmelCase  :							int				     =					auxiliary_channels
														_lowerCAmelCase  :							Optional[Any]				     =					auxiliary_num_convs
														_lowerCAmelCase  :							int				     =					auxiliary_concat_input
														_lowerCAmelCase  :							Dict				     =					semantic_loss_ignore_index



class 						UpperCAmelCase_					( a):
							lowerCamelCase__         =	version.parse('1.11')







							@property
							def      snake_case__   (     self):

														'''simple docstring'''

														return OrderedDict(
														    [
														        ("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
														    ])







							@property
							def      snake_case__   (     self):

														'''simple docstring'''

														return 1E-4
