
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 				_SCREAMING_SNAKE_CASE	(  self		,snake_case		,snake_case		,snake_case				):



           '''simple docstring'''







           return None






class __snake_case		:
    def 				_SCREAMING_SNAKE_CASE	(  self		,snake_case		,snake_case		,snake_case		,snake_case				):



           '''simple docstring'''







           return None






class __snake_case		(				unittest.TestCase					):
    _a		:						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 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
                  self._test_export(snake_case		,"""tf"""		,12		,**snake_case				)
    @require_torch
    @slow
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







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



           '''simple docstring'''







           from transformers import BertModel

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

           with TemporaryDirectory() as bert_save_dir:
                  lowercase							:					List[str]	     =				BertModel(BertConfig(vocab_size=len(snake_case				)				)				)
                  model.save_pretrained(snake_case				)
                  self._test_export(snake_case		,"""pt"""		,12		,snake_case				)
    @require_tf
    @slow
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







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

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



           '''simple docstring'''







           for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
                  lowercase							:					Tuple	     =				self._test_export(snake_case		,"""pt"""		,12		,**snake_case				)
                  lowercase							:					str	     =				quantize(snake_case				)

                  # Ensure the actual quantized model is not bigger than the original one
                  if quantized_path.stat().st_size >= Path(snake_case				).stat().st_size:
                         self.fail("""Quantized model is bigger than initial ONNX model"""				)
    def 				_SCREAMING_SNAKE_CASE	(  self		,snake_case		,snake_case		,snake_case		,snake_case=None		,**snake_case				):



           '''simple docstring'''







           try:
                  # Compute path
                  with TemporaryDirectory() as tempdir:
                         lowercase							:					Dict	     =				Path(snake_case				).joinpath("""model.onnx"""				)

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

                  # Export
                  convert(snake_case		,snake_case		,snake_case		,snake_case		,snake_case		,**snake_case				)

                  return path
           except Exception as e:
                  self.fail(snake_case				)
    @require_torch
    @require_tokenizers
    @slow
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           from transformers import BertModel

           lowercase							:					List[Any]	     =				BertModel(BertConfig.from_pretrained("""lysandre/tiny-bert-random"""				)				)
           lowercase							:					Optional[int]	     =				BertTokenizerFast.from_pretrained("""lysandre/tiny-bert-random"""				)
           self._test_infer_dynamic_axis(snake_case		,snake_case		,"""pt"""				)
    @require_tf
    @require_tokenizers
    @slow
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           from transformers import TFBertModel

           lowercase							:					List[str]	     =				TFBertModel(BertConfig.from_pretrained("""lysandre/tiny-bert-random"""				)				)
           lowercase							:					Dict	     =				BertTokenizerFast.from_pretrained("""lysandre/tiny-bert-random"""				)
           self._test_infer_dynamic_axis(snake_case		,snake_case		,"""tf"""				)
    def 				_SCREAMING_SNAKE_CASE	(  self		,snake_case		,snake_case		,snake_case				):



           '''simple docstring'''







           lowercase							:					Any	     =				FeatureExtractionPipeline(snake_case		,snake_case				)

           lowercase							:					Optional[int]	     =				["""input_ids""", """token_type_ids""", """attention_mask""", """output_0""", """output_1"""]
           lowercase     ,      lowercase     ,      lowercase     ,      lowercase							:					Optional[int]	     =				infer_shapes(snake_case		,snake_case				)

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

           # 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 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           lowercase							:					Any	     =				["""input_ids""", """attention_mask""", """token_type_ids"""]
           lowercase							:					Tuple	     =				{"""input_ids""": [1, 2, 3, 4], """attention_mask""": [0, 0, 0, 0], """token_type_ids""": [1, 1, 1, 1]}
           lowercase     ,      lowercase							:					Optional[int]	     =				ensure_valid_input(FuncContiguousArgs()		,snake_case		,snake_case				)

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

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

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

           # Generated args are interleaved with another args (for instance parameter "past" in GPT2)
           lowercase     ,      lowercase							:					int	     =				ensure_valid_input(FuncNonContiguousArgs()		,snake_case		,snake_case				)

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

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



           '''simple docstring'''







           lowercase							:					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()				)


import json
import os
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import BertTokenizer, BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import FEATURE_EXTRACTOR_NAME, is_vision_available


if is_vision_available():
  from PIL import Image

  from transformers import ChineseCLIPImageProcessor, ChineseCLIPProcessor

@require_vision
class __snake_case		(				unittest.TestCase					):
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           lowercase							:					str	     =				tempfile.mkdtemp()

           lowercase							:					List[str]	     =				[
               """[UNK]""",
               """[CLS]""",
               """[SEP]""",
               """[PAD]""",
               """[MASK]""",
               """的""",
               """价""",
               """格""",
               """是""",
               """15""",
               """便""",
               """alex""",
               """##andra""",
               """，""",
               """。""",
               """-""",
               """t""",
               """shirt""",
           ]
           lowercase							:					Dict	     =				os.path.join(self.tmpdirname		,VOCAB_FILES_NAMES["""vocab_file"""]				)
           with open(self.vocab_file		,"""w"""		,encoding="""utf-8"""				) as vocab_writer:
                  vocab_writer.write("""""".join([x + """\n""" for x in vocab_tokens]				)				)

           lowercase							:					Any	     =				{
               """do_resize""": True,
               """size""": {"""height""": 224, """width""": 224},
               """do_center_crop""": True,
               """crop_size""": {"""height""": 18, """width""": 18},
               """do_normalize""": True,
               """image_mean""": [0.48_145_466, 0.4_578_275, 0.40_821_073],
               """image_std""": [0.26_862_954, 0.26_130_258, 0.27_577_711],
               """do_convert_rgb""": True,
           }
           lowercase							:					List[str]	     =				os.path.join(self.tmpdirname		,snake_case				)
           with open(self.image_processor_file		,"""w"""		,encoding="""utf-8"""				) as fp:
                  json.dump(snake_case		,snake_case				)
    def 				_SCREAMING_SNAKE_CASE	(  self		,**snake_case				):



           '''simple docstring'''







           return BertTokenizer.from_pretrained(self.tmpdirname		,**snake_case				)
    def 				_SCREAMING_SNAKE_CASE	(  self		,**snake_case				):



           '''simple docstring'''







           return BertTokenizerFast.from_pretrained(self.tmpdirname		,**snake_case				)
    def 				_SCREAMING_SNAKE_CASE	(  self		,**snake_case				):



           '''simple docstring'''







           return ChineseCLIPImageProcessor.from_pretrained(self.tmpdirname		,**snake_case				)
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           shutil.rmtree(self.tmpdirname				)
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           lowercase							:					Optional[int]	     =				[np.random.randint(255		,size=(3, 30, 400)		,dtype=np.uinta				)]

           lowercase							:					str	     =				[Image.fromarray(np.moveaxis(snake_case		,0		,-1				)				) for x in image_inputs]

           return image_inputs
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           lowercase							:					List[str]	     =				self.get_tokenizer()
           lowercase							:					Dict	     =				self.get_rust_tokenizer()
           lowercase							:					Union[str, Any]	     =				self.get_image_processor()

           lowercase							:					Optional[Any]	     =				ChineseCLIPProcessor(tokenizer=snake_case		,image_processor=snake_case				)
           processor_slow.save_pretrained(self.tmpdirname				)
           lowercase							:					Any	     =				ChineseCLIPProcessor.from_pretrained(self.tmpdirname		,use_fast=snake_case				)

           lowercase							:					List[Any]	     =				ChineseCLIPProcessor(tokenizer=snake_case		,image_processor=snake_case				)
           processor_fast.save_pretrained(self.tmpdirname				)
           lowercase							:					Tuple	     =				ChineseCLIPProcessor.from_pretrained(self.tmpdirname				)

           self.assertEqual(processor_slow.tokenizer.get_vocab()		,tokenizer_slow.get_vocab()				)
           self.assertEqual(processor_fast.tokenizer.get_vocab()		,tokenizer_fast.get_vocab()				)
           self.assertEqual(tokenizer_slow.get_vocab()		,tokenizer_fast.get_vocab()				)
           self.assertIsInstance(processor_slow.tokenizer		,snake_case				)
           self.assertIsInstance(processor_fast.tokenizer		,snake_case				)

           self.assertEqual(processor_slow.image_processor.to_json_string()		,image_processor.to_json_string()				)
           self.assertEqual(processor_fast.image_processor.to_json_string()		,image_processor.to_json_string()				)
           self.assertIsInstance(processor_slow.image_processor		,snake_case				)
           self.assertIsInstance(processor_fast.image_processor		,snake_case				)
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           lowercase							:					List[Any]	     =				ChineseCLIPProcessor(tokenizer=self.get_tokenizer()		,image_processor=self.get_image_processor()				)
           processor.save_pretrained(self.tmpdirname				)

           lowercase							:					int	     =				self.get_tokenizer(cls_token="""(CLS)"""		,sep_token="""(SEP)"""				)
           lowercase							:					Dict	     =				self.get_image_processor(do_normalize=snake_case				)

           lowercase							:					Optional[Any]	     =				ChineseCLIPProcessor.from_pretrained(
               self.tmpdirname		,cls_token="""(CLS)"""		,sep_token="""(SEP)"""		,do_normalize=snake_case				)

           self.assertEqual(processor.tokenizer.get_vocab()		,tokenizer_add_kwargs.get_vocab()				)
           self.assertIsInstance(processor.tokenizer		,snake_case				)

           self.assertEqual(processor.image_processor.to_json_string()		,image_processor_add_kwargs.to_json_string()				)
           self.assertIsInstance(processor.image_processor		,snake_case				)
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           lowercase							:					str	     =				self.get_image_processor()
           lowercase							:					List[str]	     =				self.get_tokenizer()

           lowercase							:					Optional[Any]	     =				ChineseCLIPProcessor(tokenizer=snake_case		,image_processor=snake_case				)

           lowercase							:					Optional[int]	     =				self.prepare_image_inputs()

           lowercase							:					Tuple	     =				image_processor(snake_case		,return_tensors="""np"""				)
           lowercase							:					Optional[int]	     =				processor(images=snake_case		,return_tensors="""np"""				)

           for key in input_feat_extract.keys():
                  self.assertAlmostEqual(input_feat_extract[key].sum()		,input_processor[key].sum()		,delta=1e-2				)
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           lowercase							:					str	     =				self.get_image_processor()
           lowercase							:					Union[str, Any]	     =				self.get_tokenizer()

           lowercase							:					Any	     =				ChineseCLIPProcessor(tokenizer=snake_case		,image_processor=snake_case				)

           lowercase							:					Any	     =				"""Alexandra，T-shirt的价格是15便士。"""

           lowercase							:					int	     =				processor(text=snake_case				)

           lowercase							:					Dict	     =				tokenizer(snake_case				)

           for key in encoded_tok.keys():
                  self.assertListEqual(encoded_tok[key]		,encoded_processor[key]				)
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           lowercase							:					Optional[Any]	     =				self.get_image_processor()
           lowercase							:					Optional[Any]	     =				self.get_tokenizer()

           lowercase							:					Dict	     =				ChineseCLIPProcessor(tokenizer=snake_case		,image_processor=snake_case				)

           lowercase							:					List[str]	     =				"""Alexandra，T-shirt的价格是15便士。"""
           lowercase							:					Any	     =				self.prepare_image_inputs()

           lowercase							:					Optional[Any]	     =				processor(text=snake_case		,images=snake_case				)

           self.assertListEqual(list(inputs.keys()				)		,["""input_ids""", """token_type_ids""", """attention_mask""", """pixel_values"""]				)

           # test if it raises when no input is passed
           with pytest.raises(snake_case				):
                  processor()
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           lowercase							:					str	     =				self.get_image_processor()
           lowercase							:					str	     =				self.get_tokenizer()

           lowercase							:					Dict	     =				ChineseCLIPProcessor(tokenizer=snake_case		,image_processor=snake_case				)

           lowercase							:					str	     =				[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

           lowercase							:					Any	     =				processor.batch_decode(snake_case				)
           lowercase							:					str	     =				tokenizer.batch_decode(snake_case				)

           self.assertListEqual(snake_case		,snake_case				)
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           lowercase							:					Dict	     =				self.get_image_processor()
           lowercase							:					Dict	     =				self.get_tokenizer()

           lowercase							:					List[Any]	     =				ChineseCLIPProcessor(tokenizer=snake_case		,image_processor=snake_case				)

           lowercase							:					str	     =				"""Alexandra，T-shirt的价格是15便士。"""
           lowercase							:					Optional[Any]	     =				self.prepare_image_inputs()

           lowercase							:					Dict	     =				processor(text=snake_case		,images=snake_case				)

           self.assertListEqual(list(inputs.keys()				)		,processor.model_input_names				)
