





'''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   _lowerCamelCase    (   self		,      a_		,      a_		,      a_	):
			return None






class 		lowerCamelCase  :



	def   _lowerCamelCase    (   self		,      a_		,      a_		,      a_		,      a_	):
			return None






class 		lowerCamelCase  (      unittest.TestCase       ):
	snake_case_      =   [
	    # (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	):
			for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
					self._test_export(a_		,      "tf"		,      12		,      **a_	)



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



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

			lowerCAmelCase     :    int						=   ["[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     :    Union[str, Any]						=   BertTokenizerFast(vocab_file.name	)

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



	@require_tf
	@slow
	def   _lowerCamelCase    (   self	):
			for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
					lowerCAmelCase     :    List[str]						=   self._test_export(a_		,      "tf"		,      12		,      **a_	)
					lowerCAmelCase     :    Any						=   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   _lowerCamelCase    (   self	):
			for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
					lowerCAmelCase     :    Any						=   self._test_export(a_		,      "pt"		,      12		,      **a_	)
					lowerCAmelCase     :    Optional[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   _lowerCamelCase    (   self		,      a_		,      a_		,      a_		,      a_=None		,      **a_	):
			try:
					# Compute path
					with TemporaryDirectory() as tempdir:
							lowerCAmelCase     :    List[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   _lowerCamelCase    (   self	):
			from transformers import BertModel

			lowerCAmelCase     :    Dict						=   BertModel(BertConfig.from_pretrained("lysandre/tiny-bert-random"	)	)
			lowerCAmelCase     :    Optional[int]						=   BertTokenizerFast.from_pretrained("lysandre/tiny-bert-random"	)
			self._test_infer_dynamic_axis(a_		,      a_		,      "pt"	)



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

			lowerCAmelCase     :    Tuple						=   TFBertModel(BertConfig.from_pretrained("lysandre/tiny-bert-random"	)	)
			lowerCAmelCase     :    List[Any]						=   BertTokenizerFast.from_pretrained("lysandre/tiny-bert-random"	)
			self._test_infer_dynamic_axis(a_		,      a_		,      "tf"	)



	def   _lowerCamelCase    (   self		,      a_		,      a_		,      a_	):
			lowerCAmelCase     :    str						=   FeatureExtractionPipeline(a_		,      a_	)

			lowerCAmelCase     :    Tuple						=   ["input_ids", "token_type_ids", "attention_mask", "output_0", "output_1"]
			lowerCAmelCase	,		lowerCAmelCase	,		lowerCAmelCase	,		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   _lowerCamelCase    (   self	):
			lowerCAmelCase     :    Any						=   ["input_ids", "attention_mask", "token_type_ids"]
			lowerCAmelCase     :    str						=   {"input_ids": [1, 2, 3, 4], "attention_mask": [0, 0, 0, 0], "token_type_ids": [1, 1, 1, 1]}
			lowerCAmelCase	,		lowerCAmelCase     :    List[str]						=   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	,		lowerCAmelCase     :    Optional[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   _lowerCamelCase    (   self	):
			lowerCAmelCase     :    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 ...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 KandinskyPipeline, KandinskyPriorPipeline
else:
					from .pipeline_kandinsky import KandinskyPipeline
					from .pipeline_kandinsky_imgaimg import KandinskyImgaImgPipeline
					from .pipeline_kandinsky_inpaint import KandinskyInpaintPipeline
					from .pipeline_kandinsky_prior import KandinskyPriorPipeline, KandinskyPriorPipelineOutput
					from .text_encoder import MultilingualCLIP



