





'''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    UpperCAmelCase	:
 def      UpperCAmelCase_							(				self      :List[Any]						,       lowercase_      :List[Any]						,       lowercase_      :Tuple						,       lowercase_      :Any							)->							str:
     return None






class    UpperCAmelCase	:
 def      UpperCAmelCase_							(				self      :Union[str, Any]						,       lowercase_      :Tuple						,       lowercase_      :Any						,       lowercase_      :Union[str, Any]						,       lowercase_      :Dict							)->							Union[str, Any]:
     return None






class    UpperCAmelCase	(       unittest.TestCase				):
 __lowercase   		=				[
     # (model_name, model_kwargs)
     ("""bert-base-cased""", {}),
     ("""gpt2""", {"""use_cache""": False}),  # We don't support exporting GPT2 past keys anymore
 ]
 @require_tf
 @slow
 def      UpperCAmelCase_							(				self      :int							)->							Optional[int]:
     for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
         self._test_export(lowercase_						,       "tf"						,       12						,       **lowercase_							)
 @require_torch
 @slow
 def      UpperCAmelCase_							(				self      :Optional[int]							)->							Dict:
     for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
         self._test_export(lowercase_						,       "pt"						,       12						,       **lowercase_							)
 @require_torch
 @slow
 def      UpperCAmelCase_							(				self      :str							)->							List[str]:
     from transformers import BertModel

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

     with TemporaryDirectory() as bert_save_dir:
         A__				=			BertModel(BertConfig(vocab_size=len(lowercase_							)							)							)
         model.save_pretrained(lowercase_							)
         self._test_export(lowercase_						,       "pt"						,       12						,       lowercase_							)
 @require_tf
 @slow
 def      UpperCAmelCase_							(				self      :Dict							)->							Union[str, Any]:
     for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
         A__				=			self._test_export(lowercase_						,       "tf"						,       12						,       **lowercase_							)
         A__				=			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      UpperCAmelCase_							(				self      :Union[str, Any]							)->							Union[str, Any]:
     for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
         A__				=			self._test_export(lowercase_						,       "pt"						,       12						,       **lowercase_							)
         A__				=			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      UpperCAmelCase_							(				self      :int						,       lowercase_      :List[str]						,       lowercase_      :Dict						,       lowercase_      :Optional[int]						,       lowercase_      :int=None						,       **lowercase_      :str							)->							Optional[int]:
     try:
         # Compute path
         with TemporaryDirectory() as tempdir:
             A__				=			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      UpperCAmelCase_							(				self      :Optional[Any]							)->							Any:
     from transformers import BertModel

     A__				=			BertModel(BertConfig.from_pretrained("lysandre/tiny-bert-random"							)							)
     A__				=			BertTokenizerFast.from_pretrained("lysandre/tiny-bert-random"							)
     self._test_infer_dynamic_axis(lowercase_						,       lowercase_						,       "pt"							)
 @require_tf
 @require_tokenizers
 @slow
 def      UpperCAmelCase_							(				self      :List[Any]							)->							Optional[Any]:
     from transformers import TFBertModel

     A__				=			TFBertModel(BertConfig.from_pretrained("lysandre/tiny-bert-random"							)							)
     A__				=			BertTokenizerFast.from_pretrained("lysandre/tiny-bert-random"							)
     self._test_infer_dynamic_axis(lowercase_						,       lowercase_						,       "tf"							)
 def      UpperCAmelCase_							(				self      :Any						,       lowercase_      :List[str]						,       lowercase_      :Optional[int]						,       lowercase_      :str							)->							Tuple:
     A__				=			FeatureExtractionPipeline(lowercase_						,       lowercase_							)

     A__				=			["input_ids", "token_type_ids", "attention_mask", "output_0", "output_1"]
     A__,	A__,	A__,	A__				=			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      UpperCAmelCase_							(				self      :Union[str, Any]							)->							Any:
     A__				=			["input_ids", "attention_mask", "token_type_ids"]
     A__				=			{"input_ids": [1, 2, 3, 4], "attention_mask": [0, 0, 0, 0], "token_type_ids": [1, 1, 1, 1]}
     A__,	A__				=			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)
     A__,	A__				=			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      UpperCAmelCase_							(				self      :List[Any]							)->							List[Any]:
     A__				=			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'''

import warnings

from transformers import AutoTokenizer
from transformers.utils import is_torch_available
from transformers.utils.generic import ExplicitEnum

from ...processing_utils import ProcessorMixin


if is_torch_available():
  import torch






class    UpperCAmelCase	(       UpperCamelCase__				):
 __lowercase   		=				"""char"""
 __lowercase   		=				"""bpe"""
 __lowercase   		=				"""wp"""

__lowerCAmelCase							:	Union[str, Any]			=(DecodeType.CHARACTER, DecodeType.BPE, DecodeType.WORDPIECE)






class    UpperCAmelCase	(       UpperCamelCase__				):
 __lowercase   		=				["""image_processor""", """char_tokenizer"""]
 __lowercase   		=				"""ViTImageProcessor"""
 __lowercase   		=				"""MgpstrTokenizer"""
 def __init__(				self      :int						,       lowercase_      :int=None						,       lowercase_      :List[str]=None						,       **lowercase_      :List[Any]							)->							Optional[Any]:
     A__				=			None
     if "feature_extractor" in kwargs:
         warnings.warn(
             "The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
             " instead."						,       lowercase_						,       )
         A__				=			kwargs.pop("feature_extractor"							)

     A__				=			image_processor if image_processor is not None else feature_extractor
     if image_processor is None:
         raise ValueError("You need to specify an `image_processor`."							)
     if tokenizer is None:
         raise ValueError("You need to specify a `tokenizer`."							)

     A__				=			tokenizer
     A__				=			AutoTokenizer.from_pretrained("gpt2"							)
     A__				=			AutoTokenizer.from_pretrained("bert-base-uncased"							)

     super().__init__(lowercase_						,       lowercase_							)
 def __call__(				self      :Optional[Any]						,       lowercase_      :Any=None						,       lowercase_      :Tuple=None						,       lowercase_      :List[str]=None						,       **lowercase_      :Union[str, Any]							)->							Optional[Any]:
     if images is None and text is None:
         raise ValueError("You need to specify either an `images` or `text` input to process."							)

     if images is not None:
         A__				=			self.image_processor(lowercase_						,       return_tensors=lowercase_						,       **lowercase_							)
     if text is not None:
         A__				=			self.char_tokenizer(lowercase_						,       return_tensors=lowercase_						,       **lowercase_							)

     if text is None:
         return inputs
     elif images is None:
         return encodings
     else:
         A__				=			encodings["input_ids"]
         return inputs
 def      UpperCAmelCase_							(				self      :List[str]						,       lowercase_      :int							)->							int:
     A__,	A__,	A__				=			sequences
     A__				=			char_preds.size(0							)

     A__,	A__				=			self._decode_helper(lowercase_						,       "char"							)
     A__,	A__				=			self._decode_helper(lowercase_						,       "bpe"							)
     A__,	A__				=			self._decode_helper(lowercase_						,       "wp"							)

     A__				=			[]
     A__				=			[]
     for i in range(lowercase_							):
         A__				=			[char_scores[i], bpe_scores[i], wp_scores[i]]
         A__				=			[char_strs[i], bpe_strs[i], wp_strs[i]]
         A__				=			scores.index(max(lowercase_							)							)
         final_strs.append(strs[max_score_index]							)
         final_scores.append(scores[max_score_index]							)

     A__				=			{}
     A__				=			final_strs
     A__				=			final_scores
     A__				=			char_strs
     A__				=			bpe_strs
     A__				=			wp_strs
     return out
 def      UpperCAmelCase_							(				self      :Union[str, Any]						,       lowercase_      :List[str]						,       lowercase_      :str							)->							Optional[Any]:
     if format == DecodeType.CHARACTER:
         A__				=			self.char_decode
         A__				=			1
         A__				=			"[s]"
     elif format == DecodeType.BPE:
         A__				=			self.bpe_decode
         A__				=			2
         A__				=			"#"
     elif format == DecodeType.WORDPIECE:
         A__				=			self.wp_decode
         A__				=			1_02
         A__				=			"[SEP]"
     else:
         raise ValueError(F"Format {format} is not supported."							)

     A__,	A__				=			[], []
     A__				=			pred_logits.size(0							)
     A__				=			pred_logits.size(1							)
     A__,	A__				=			pred_logits.topk(1						,       dim=-1						,       largest=lowercase_						,       sorted=lowercase_							)
     A__				=			preds_index.view(-1						,       lowercase_							)[:, 1:]
     A__				=			decoder(lowercase_							)
     A__,	A__				=			torch.nn.functional.softmax(lowercase_						,       dim=2							).max(dim=2							)
     A__				=			preds_max_prob[:, 1:]

     for index in range(lowercase_							):
         A__				=			preds_str[index].find(lowercase_							)
         A__				=			preds_str[index][:pred_eos]
         A__				=			preds_index[index].cpu().tolist()
         A__				=			pred_index.index(lowercase_							) if eos_token in pred_index else -1
         A__				=			preds_max_prob[index][: pred_eos_index + 1]
         A__				=			pred_max_prob.cumprod(dim=0							)[-1] if pred_max_prob.nelement() != 0 else 0.0
         dec_strs.append(lowercase_							)
         conf_scores.append(lowercase_							)

     return dec_strs, conf_scores
 def      UpperCAmelCase_							(				self      :Dict						,       lowercase_      :Optional[Any]							)->							int:
     A__				=			[seq.replace(" "						,       ""							) for seq in self.char_tokenizer.batch_decode(lowercase_							)]
     return decode_strs
 def      UpperCAmelCase_							(				self      :List[Any]						,       lowercase_      :Optional[Any]							)->							List[str]:
     return self.bpe_tokenizer.batch_decode(lowercase_							)



 def      UpperCAmelCase_							(				self      :Optional[int]						,       lowercase_      :List[str]							)->							Union[str, Any]:
     A__				=			[seq.replace(" "						,       ""							) for seq in self.wp_tokenizer.batch_decode(lowercase_							)]
     return decode_strs



