



'''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 	lowerCamelCase_							:







						def 					__lowercase    (  self   :      Optional[int]				,    lowerCAmelCase__   :      Tuple				,    lowerCAmelCase__   :      List[str]				,    lowerCAmelCase__   :      Optional[Any]				):

													"""simple docstring"""
													return None


class 	lowerCamelCase_							:







						def 					__lowercase    (  self   :      Union[str, Any]				,    lowerCAmelCase__   :      Optional[int]				,    lowerCAmelCase__   :      int				,    lowerCAmelCase__   :      Tuple				,    lowerCAmelCase__   :      List[str]				):

													"""simple docstring"""
													return None


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







						@require_tf
						@slow
						def 					__lowercase    (  self   :      List[Any]				):

													"""simple docstring"""
													for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
																				self._test_export(lowerCAmelCase__				,    '''tf'''				,    12				,    **lowerCAmelCase__				)







						@require_torch
						@slow
						def 					__lowercase    (  self   :      Any				):

													"""simple docstring"""
													for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
																				self._test_export(lowerCAmelCase__				,    '''pt'''				,    12				,    **lowerCAmelCase__				)







						@require_torch
						@slow
						def 					__lowercase    (  self   :      List[Any]				):

													"""simple docstring"""
													from transformers import BertModel

													SCREAMING_SNAKE_CASE					:      int     					=			['''[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()
																				SCREAMING_SNAKE_CASE					:      int     					=			BertTokenizerFast(vocab_file.name				)

													with TemporaryDirectory() as bert_save_dir:
																				SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			BertModel(BertConfig(vocab_size=len(lowerCAmelCase__				)				)				)
																				model.save_pretrained(lowerCAmelCase__				)
																				self._test_export(lowerCAmelCase__				,    '''pt'''				,    12				,    lowerCAmelCase__				)







						@require_tf
						@slow
						def 					__lowercase    (  self   :      List[str]				):

													"""simple docstring"""
													for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
																				SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			self._test_export(lowerCAmelCase__				,    '''tf'''				,    12				,    **lowerCAmelCase__				)
																				SCREAMING_SNAKE_CASE					:      str     					=			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 					__lowercase    (  self   :      List[Any]				):

													"""simple docstring"""
													for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
																				SCREAMING_SNAKE_CASE					:      str     					=			self._test_export(lowerCAmelCase__				,    '''pt'''				,    12				,    **lowerCAmelCase__				)
																				SCREAMING_SNAKE_CASE					:      List[str]     					=			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 					__lowercase    (  self   :      Tuple				,    lowerCAmelCase__   :      Union[str, Any]				,    lowerCAmelCase__   :      List[str]				,    lowerCAmelCase__   :      List[str]				,    lowerCAmelCase__   :      Any=None				,    **lowerCAmelCase__   :      List[str]				):

													"""simple docstring"""
													try:
																				# Compute path
																				with TemporaryDirectory() as tempdir:
																											SCREAMING_SNAKE_CASE					:      Any     					=			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 					__lowercase    (  self   :      Dict				):

													"""simple docstring"""
													from transformers import BertModel

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







						@require_tf
						@require_tokenizers
						@slow
						def 					__lowercase    (  self   :      List[Any]				):

													"""simple docstring"""
													from transformers import TFBertModel

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







						def 					__lowercase    (  self   :      Tuple				,    lowerCAmelCase__   :      Tuple				,    lowerCAmelCase__   :      List[str]				,    lowerCAmelCase__   :      List[Any]				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      Dict     					=			FeatureExtractionPipeline(lowerCAmelCase__				,    lowerCAmelCase__				)

													SCREAMING_SNAKE_CASE					:      Any     					=			['''input_ids''', '''token_type_ids''', '''attention_mask''', '''output_0''', '''output_1''']
													SCREAMING_SNAKE_CASE					,SCREAMING_SNAKE_CASE					,SCREAMING_SNAKE_CASE					,SCREAMING_SNAKE_CASE					:      Tuple     					=			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 					__lowercase    (  self   :      Optional[int]				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      Dict     					=			['''input_ids''', '''attention_mask''', '''token_type_ids''']
													SCREAMING_SNAKE_CASE					:      List[Any]     					=			{'''input_ids''': [1, 2, 3, 4], '''attention_mask''': [0, 0, 0, 0], '''token_type_ids''': [1, 1, 1, 1]}
													SCREAMING_SNAKE_CASE					,SCREAMING_SNAKE_CASE					:      Any     					=			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)
													SCREAMING_SNAKE_CASE					,SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			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 					__lowercase    (  self   :      Optional[Any]				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      Optional[int]     					=			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 ...utils import (
    OptionalDependencyNotAvailable,
    is_torch_available,
    is_transformers_available,
    is_transformers_version,
)


try:
							if not (is_transformers_available() and is_torch_available()):
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							from ...utils.dummy_torch_and_transformers_objects import (
							    ImageTextPipelineOutput,
							    UniDiffuserPipeline,
							)
else:
							from .modeling_text_decoder import UniDiffuserTextDecoder
							from .modeling_uvit import UniDiffuserModel, UTransformeraDModel
							from .pipeline_unidiffuser import ImageTextPipelineOutput, UniDiffuserPipeline




