






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  lowerCAmelCase_       :




							def 						__snake_case					(   self     :   Any ,							SCREAMING_SNAKE_CASE_     :   Dict ,							SCREAMING_SNAKE_CASE_     :   Tuple ,							SCREAMING_SNAKE_CASE_     :   str	):
											return None



class  lowerCAmelCase_       :




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



class  lowerCAmelCase_       (				unittest.TestCase					):
							UpperCamelCase_		: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 						__snake_case					(   self     :   Any	):






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




							@require_torch
							@slow
							def 						__snake_case					(   self     :   Dict	):






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




							@require_torch
							@slow
							def 						__snake_case					(   self     :   str	):
											from transformers import BertModel

											lowerCAmelCase__      			=						["[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()
															lowerCAmelCase__      			=						BertTokenizerFast(vocab_file.name	)

											with TemporaryDirectory() as bert_save_dir:
															lowerCAmelCase__      			=						BertModel(BertConfig(vocab_size=len(lowercase__	)	)	)
															model.save_pretrained(lowercase__	)
															self._test_export(lowercase__ ,							'''pt''' ,							12 ,							lowercase__	)




							@require_tf
							@slow
							def 						__snake_case					(   self     :   int	):






											for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
															lowerCAmelCase__      			=						self._test_export(lowercase__ ,							'''tf''' ,							12 ,							**lowercase__	)
															lowerCAmelCase__      			=						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 						__snake_case					(   self     :   List[Any]	):






											for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
															lowerCAmelCase__      			=						self._test_export(lowercase__ ,							'''pt''' ,							12 ,							**lowercase__	)
															lowerCAmelCase__      			=						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 						__snake_case					(   self     :   int ,							SCREAMING_SNAKE_CASE_     :   str ,							SCREAMING_SNAKE_CASE_     :   List[str] ,							SCREAMING_SNAKE_CASE_     :   Optional[Any] ,							SCREAMING_SNAKE_CASE_     :   List[str]=None ,							**SCREAMING_SNAKE_CASE_     :   Optional[Any]	):






											try:
															# Compute path
															with TemporaryDirectory() as tempdir:
																			lowerCAmelCase__      			=						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 						__snake_case					(   self     :   List[str]	):
											from transformers import BertModel

											lowerCAmelCase__      			=						BertModel(BertConfig.from_pretrained('''lysandre/tiny-bert-random'''	)	)
											lowerCAmelCase__      			=						BertTokenizerFast.from_pretrained('''lysandre/tiny-bert-random'''	)
											self._test_infer_dynamic_axis(lowercase__ ,							lowercase__ ,							'''pt'''	)




							@require_tf
							@require_tokenizers
							@slow
							def 						__snake_case					(   self     :   Dict	):
											from transformers import TFBertModel

											lowerCAmelCase__      			=						TFBertModel(BertConfig.from_pretrained('''lysandre/tiny-bert-random'''	)	)
											lowerCAmelCase__      			=						BertTokenizerFast.from_pretrained('''lysandre/tiny-bert-random'''	)
											self._test_infer_dynamic_axis(lowercase__ ,							lowercase__ ,							'''tf'''	)




							def 						__snake_case					(   self     :   Optional[int] ,							SCREAMING_SNAKE_CASE_     :   List[str] ,							SCREAMING_SNAKE_CASE_     :   Union[str, Any] ,							SCREAMING_SNAKE_CASE_     :   int	):
											lowerCAmelCase__      			=						FeatureExtractionPipeline(lowercase__ ,							lowercase__	)

											lowerCAmelCase__      			=						["input_ids", "token_type_ids", "attention_mask", "output_0", "output_1"]
											lowerCAmelCase__      			=						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 						__snake_case					(   self     :   Tuple	):
											lowerCAmelCase__      			=						["input_ids", "attention_mask", "token_type_ids"]
											lowerCAmelCase__      			=						{"input_ids": [1, 2, 3, 4], "attention_mask": [0, 0, 0, 0], "token_type_ids": [1, 1, 1, 1]}
											lowerCAmelCase__      			=						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)
											lowerCAmelCase__      			=						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 						__snake_case					(   self     :   List[Any]	):
											lowerCAmelCase__      			=						generate_identified_filename(Path('''/home/something/my_fake_model.onnx'''	) ,							'''-test'''	)
											self.assertEqual('''/home/something/my_fake_model-test.onnx''' ,							generated.as_posix()	)













from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_UpperCAmelCase			:		int   				=    {
    "configuration_git": ["GIT_PRETRAINED_CONFIG_ARCHIVE_MAP", "GitConfig", "GitVisionConfig"],
    "processing_git": ["GitProcessor"],
}

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_UpperCAmelCase			:		Dict   				=    [
			    "GIT_PRETRAINED_MODEL_ARCHIVE_LIST",
			    "GitForCausalLM",
			    "GitModel",
			    "GitPreTrainedModel",
			    "GitVisionModel",
			]

if TYPE_CHECKING:
			from .configuration_git import GIT_PRETRAINED_CONFIG_ARCHIVE_MAP, GitConfig, GitVisionConfig
			from .processing_git import GitProcessor

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_git import (
						    GIT_PRETRAINED_MODEL_ARCHIVE_LIST,
						    GitForCausalLM,
						    GitModel,
						    GitPreTrainedModel,
						    GitVisionModel,
						)

else:
			import sys

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





