import numpy as np
import torch
from torch.utils.data import Dataset, IterableDataset

from ..utils.generic import ModelOutput







class __snake_case       (		__lowerCamelCase	):




   def __init__(					self    :       Union[str, Any] ,			_lowercase    :       Optional[Any] ,			_lowercase    :       Union[str, Any] ,			_lowercase    :       Tuple   ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__    							=   dataset
     SCREAMING_SNAKE_CASE__    							=   process
     SCREAMING_SNAKE_CASE__    							=   params




   def __len__(					self    :       Tuple   ):



     """simple docstring"""






     return len(self.dataset   )




   def __getitem__(					self    :       str ,			_lowercase    :       str   ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__    							=   self.dataset[i]
     SCREAMING_SNAKE_CASE__    							=   self.process(a_ ,			**self.params   )
     return processed







class __snake_case       (		__lowerCamelCase	):




   def __init__(					self    :       Tuple ,			_lowercase    :       int ,			_lowercase    :       Optional[Any] ,			_lowercase    :       int ,			_lowercase    :       str=None   ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__    							=   loader
     SCREAMING_SNAKE_CASE__    							=   infer
     SCREAMING_SNAKE_CASE__    							=   params
     if loader_batch_size == 1:
       # Let's spare some time by deactivating altogether
       SCREAMING_SNAKE_CASE__    							=   None
     SCREAMING_SNAKE_CASE__    							=   loader_batch_size

     # Internal bookkeeping
     SCREAMING_SNAKE_CASE__    							=   None
     SCREAMING_SNAKE_CASE__    							=   None




   def __len__(					self    :       Tuple   ):



     """simple docstring"""






     return len(self.loader   )




   def __iter__(					self    :       List[str]   ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__    							=   iter(self.loader   )
     return self




   def 			__a		(					self    :       str   ):



     """simple docstring"""






     if isinstance(self._loader_batch_data ,			torch.Tensor   ):
       # Batch data is simple tensor, just fetch the slice
       SCREAMING_SNAKE_CASE__    							=   self._loader_batch_data[self._loader_batch_index]
     else:
       # Batch data is assumed to be BaseModelOutput (or dict)
       SCREAMING_SNAKE_CASE__    							=   {}
       for k, element in self._loader_batch_data.items():
         if isinstance(a_ ,			a_   ):
           # Convert ModelOutput to tuple first
           SCREAMING_SNAKE_CASE__    							=   element.to_tuple()
           if isinstance(element[0] ,			torch.Tensor   ):
             SCREAMING_SNAKE_CASE__    							=   tuple(el[self._loader_batch_index].unsqueeze(0   ) for el in element   )
           elif isinstance(element[0] ,			np.ndarray   ):
             SCREAMING_SNAKE_CASE__    							=   tuple(np.expand_dims(el[self._loader_batch_index] ,			0   ) for el in element   )
           continue
         if k in {"hidden_states", "past_key_values", "attentions"} and isinstance(a_ ,			a_   ):
           # Those are stored as lists of tensors so need specific unbatching.
           if isinstance(element[0] ,			torch.Tensor   ):
             SCREAMING_SNAKE_CASE__    							=   tuple(el[self._loader_batch_index].unsqueeze(0   ) for el in element   )
           elif isinstance(element[0] ,			np.ndarray   ):
             SCREAMING_SNAKE_CASE__    							=   tuple(np.expand_dims(el[self._loader_batch_index] ,			0   ) for el in element   )
           continue
         if element is None:
           # This can happen for optional data that get passed around
           SCREAMING_SNAKE_CASE__    							=   None
         elif isinstance(element[self._loader_batch_index] ,			torch.Tensor   ):
           # Take correct batch data, but make it looked like batch_size=1
           # For compatibility with other methods within transformers

           SCREAMING_SNAKE_CASE__    							=   element[self._loader_batch_index].unsqueeze(0   )
         elif isinstance(element[self._loader_batch_index] ,			np.ndarray   ):
           # Take correct batch data, but make it looked like batch_size=1
           # For compatibility with other methods within transformers
           SCREAMING_SNAKE_CASE__    							=   np.expand_dims(element[self._loader_batch_index] ,			0   )
         else:
           # This is typically a list, so no need to `unsqueeze`.
           SCREAMING_SNAKE_CASE__    							=   element[self._loader_batch_index]
            # Recreate the element by reusing the original class to make it look
            # batch_size=1
       SCREAMING_SNAKE_CASE__    							=   self._loader_batch_data.__class__(a_   )
     self._loader_batch_index += 1
     return result




   def 			__a		(					self    :       int   ):



     """simple docstring"""






     if self._loader_batch_index is not None and self._loader_batch_index < self.loader_batch_size:
       # We are currently unrolling a batch so we just need to return
       # the current item within a batch
       return self.loader_batch_item()

     # We're out of items within a batch
     SCREAMING_SNAKE_CASE__    							=   next(self.iterator   )
     SCREAMING_SNAKE_CASE__    							=   self.infer(a_ ,			**self.params   )
     # We now have a batch of "inferred things".
     if self.loader_batch_size is not None:
       # Try to infer the size of the batch
       if isinstance(a_ ,			torch.Tensor   ):
         SCREAMING_SNAKE_CASE__    							=   processed
       else:
         SCREAMING_SNAKE_CASE__    							=   list(processed.keys()   )[0]
         SCREAMING_SNAKE_CASE__    							=   processed[key]
       if isinstance(a_ ,			a_   ):
         SCREAMING_SNAKE_CASE__    							=   len(a_   )
       else:
         SCREAMING_SNAKE_CASE__    							=   first_tensor.shape[0]
       if 0 < observed_batch_size < self.loader_batch_size:
         # could be last batch so we can't unroll as many
         # elements.
         SCREAMING_SNAKE_CASE__    							=   observed_batch_size
       # Setting internal index to unwrap the batch
       SCREAMING_SNAKE_CASE__    							=   processed
       SCREAMING_SNAKE_CASE__    							=   0
       return self.loader_batch_item()
     else:
       # We're not unrolling batches
       return processed







class __snake_case       (		__lowerCamelCase	):




   def __init__(					self    :       Any ,			_lowercase    :       Optional[Any] ,			_lowercase    :       Optional[int] ,			_lowercase    :       Optional[Any] ,			_lowercase    :       int=None   ):



     """simple docstring"""






     super().__init__(a_ ,			a_ ,			a_   )




   def __iter__(					self    :       Tuple   ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__    							=   iter(self.loader   )
     SCREAMING_SNAKE_CASE__    							=   None
     return self




   def 			__a		(					self    :       int   ):



     """simple docstring"""






     if self.subiterator is None:
       SCREAMING_SNAKE_CASE__    							=   self.infer(next(self.iterator   ) ,			**self.params   )
     try:
       # Try to return next item
       SCREAMING_SNAKE_CASE__    							=   next(self.subiterator   )
     except StopIteration:
       # When a preprocess iterator ends, we can start lookig at the next item
       # ChunkIterator will keep feeding until ALL elements of iterator
       # all have created their subiterator and have been iterating against.
       #
       # Another way to look at it, is we're basically flattening lists of lists
       # into a single list, but with generators
       SCREAMING_SNAKE_CASE__    							=   self.infer(next(self.iterator   ) ,			**self.params   )
       SCREAMING_SNAKE_CASE__    							=   next(self.subiterator   )
     return processed







class __snake_case       (		__lowerCamelCase	):




   def __iter__(					self    :       int   ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__    							=   iter(self.loader   )
     return self




   def 			__a		(					self    :       Tuple   ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__    							=   False
     SCREAMING_SNAKE_CASE__    							=   []
     if self._loader_batch_index is not None and self._loader_batch_index < self.loader_batch_size:
       while self._loader_batch_index < self.loader_batch_size:
         SCREAMING_SNAKE_CASE__    							=   self.loader_batch_item()
         SCREAMING_SNAKE_CASE__    							=   item.pop("""is_last"""   )
         accumulator.append(a_   )
         if is_last:
           return accumulator

     while not is_last:
       SCREAMING_SNAKE_CASE__    							=   self.infer(next(self.iterator   ) ,			**self.params   )
       if self.loader_batch_size is not None:
         if isinstance(a_ ,			torch.Tensor   ):
           SCREAMING_SNAKE_CASE__    							=   processed
         else:
           SCREAMING_SNAKE_CASE__    							=   list(processed.keys()   )[0]
           SCREAMING_SNAKE_CASE__    							=   processed[key]
         if isinstance(a_ ,			a_   ):
           SCREAMING_SNAKE_CASE__    							=   len(a_   )
         else:
           SCREAMING_SNAKE_CASE__    							=   first_tensor.shape[0]
         if 0 < observed_batch_size < self.loader_batch_size:
           # could be last batch so we can't unroll as many
           # elements.
           SCREAMING_SNAKE_CASE__    							=   observed_batch_size
         SCREAMING_SNAKE_CASE__    							=   processed
         SCREAMING_SNAKE_CASE__    							=   0
         while self._loader_batch_index < self.loader_batch_size:
           SCREAMING_SNAKE_CASE__    							=   self.loader_batch_item()
           SCREAMING_SNAKE_CASE__    							=   item.pop("""is_last"""   )
           accumulator.append(a_   )
           if is_last:
             return accumulator
       else:
         SCREAMING_SNAKE_CASE__    							=   processed
         SCREAMING_SNAKE_CASE__    							=   item.pop("""is_last"""   )
         accumulator.append(a_   )
     return accumulator







class __snake_case       (		__lowerCamelCase	):




   def __init__(					self    :       int ,			_lowercase    :       Any ,			_lowercase    :       Optional[int]   ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__    							=   dataset
     SCREAMING_SNAKE_CASE__    							=   key




   def __len__(					self    :       Optional[Any]   ):



     """simple docstring"""






     return len(self.dataset   )




   def __getitem__(					self    :       int ,			_lowercase    :       Any   ):



     """simple docstring"""






     return self.dataset[i][self.key]







class __snake_case       (		__lowerCamelCase	):




   def __init__(					self    :       int ,			_lowercase    :       List[Any] ,			_lowercase    :       Optional[int] ,			_lowercase    :       Any   ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__    							=   dataset
     SCREAMING_SNAKE_CASE__    							=   keya
     SCREAMING_SNAKE_CASE__    							=   keya




   def __len__(					self    :       Any   ):



     """simple docstring"""






     return len(self.dataset   )




   def __getitem__(					self    :       Any ,			_lowercase    :       Optional[Any]   ):



     """simple docstring"""






     return {"text": self.dataset[i][self.keya], "text_pair": self.dataset[i][self.keya]}

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 			__a		(					self    :       str ,			_lowercase    :       str ,			_lowercase    :       Dict ,			_lowercase    :       List[Any]   ):



     """simple docstring"""






     return None







class __snake_case       :




   def 			__a		(					self    :       List[str] ,			_lowercase    :       List[str] ,			_lowercase    :       str ,			_lowercase    :       List[Any] ,			_lowercase    :       str   ):



     """simple docstring"""






     return None







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




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



     """simple docstring"""






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




   @require_torch
   @slow
   def 			__a		(					self    :       Optional[Any]   ):



     """simple docstring"""






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




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



     """simple docstring"""






     from transformers import BertModel

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

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




   @require_tf
   @slow
   def 			__a		(					self    :       Dict   ):



     """simple docstring"""






     for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
       SCREAMING_SNAKE_CASE__    							=   self._test_export(_lowercase ,			"""tf""" ,			12 ,			**_lowercase   )
       SCREAMING_SNAKE_CASE__    							=   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 			__a		(					self    :       Any   ):



     """simple docstring"""






     for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
       SCREAMING_SNAKE_CASE__    							=   self._test_export(_lowercase ,			"""pt""" ,			12 ,			**_lowercase   )
       SCREAMING_SNAKE_CASE__    							=   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 			__a		(					self    :       Union[str, Any] ,			_lowercase    :       List[str] ,			_lowercase    :       Tuple ,			_lowercase    :       Optional[Any] ,			_lowercase    :       str=None ,			**_lowercase    :       List[str]   ):



     """simple docstring"""






     try:
       # Compute path
       with TemporaryDirectory() as tempdir:
         SCREAMING_SNAKE_CASE__    							=   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 			__a		(					self    :       List[str]   ):



     """simple docstring"""






     from transformers import BertModel

     SCREAMING_SNAKE_CASE__    							=   BertModel(BertConfig.from_pretrained("""lysandre/tiny-bert-random"""   )   )
     SCREAMING_SNAKE_CASE__    							=   BertTokenizerFast.from_pretrained("""lysandre/tiny-bert-random"""   )
     self._test_infer_dynamic_axis(_lowercase ,			_lowercase ,			"""pt"""   )




   @require_tf
   @require_tokenizers
   @slow
   def 			__a		(					self    :       Dict   ):



     """simple docstring"""






     from transformers import TFBertModel

     SCREAMING_SNAKE_CASE__    							=   TFBertModel(BertConfig.from_pretrained("""lysandre/tiny-bert-random"""   )   )
     SCREAMING_SNAKE_CASE__    							=   BertTokenizerFast.from_pretrained("""lysandre/tiny-bert-random"""   )
     self._test_infer_dynamic_axis(_lowercase ,			_lowercase ,			"""tf"""   )




   def 			__a		(					self    :       List[Any] ,			_lowercase    :       List[Any] ,			_lowercase    :       Optional[int] ,			_lowercase    :       Dict   ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__    							=   FeatureExtractionPipeline(_lowercase ,			_lowercase   )

     SCREAMING_SNAKE_CASE__    							=   ["""input_ids""", """token_type_ids""", """attention_mask""", """output_0""", """output_1"""]
     SCREAMING_SNAKE_CASE__							,  SCREAMING_SNAKE_CASE__							,  SCREAMING_SNAKE_CASE__							,  SCREAMING_SNAKE_CASE__    							=   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 			__a		(					self    :       Dict   ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__    							=   ["""input_ids""", """attention_mask""", """token_type_ids"""]
     SCREAMING_SNAKE_CASE__    							=   {"""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__    							=   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)
     SCREAMING_SNAKE_CASE__							,  SCREAMING_SNAKE_CASE__    							=   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 			__a		(					self    :       List[str]   ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__    							=   generate_identified_filename(Path("""/home/something/my_fake_model.onnx"""   ) ,			"""-test"""   )
     self.assertEqual("""/home/something/my_fake_model-test.onnx""" ,			generated.as_posix()   )
