


import tempfile
import unittest

from transformers import TaConfig, is_torch_available
from transformers.testing_utils import (
    require_sentencepiece,
    require_tokenizers,
    require_torch,
    slow,
    torch_device,
)

from ...generation.test_utils import GenerationTesterMixin
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
  import torch

  from transformers import AutoTokenizer, UMTaForConditionalGeneration, UMTaForQuestionAnswering, UMTaModel







class       lowerCamelCase__   :





     def __init__(self		,     UpperCAmelCase		,     UpperCAmelCase=9_9		,     UpperCAmelCase=1_3		,     UpperCAmelCase=7		,     UpperCAmelCase=9		,     UpperCAmelCase=True		,     UpperCAmelCase=True		,     UpperCAmelCase=False		,     UpperCAmelCase=3_2		,     UpperCAmelCase=5		,     UpperCAmelCase=4		,     UpperCAmelCase=3_7		,     UpperCAmelCase=8		,     UpperCAmelCase=0.1		,     UpperCAmelCase=0.002		,     UpperCAmelCase=1		,     UpperCAmelCase=0		,     UpperCAmelCase=0		,     UpperCAmelCase=None		,     UpperCAmelCase=None		,     )     ->   Union[str, Any]:
      _lowercase							      =parent
      _lowercase							      =batch_size
      _lowercase							      =encoder_seq_length
      _lowercase							      =decoder_seq_length
      # For common tests
      _lowercase							      =self.decoder_seq_length
      _lowercase							      =is_training
      _lowercase							      =use_attention_mask
      _lowercase							      =use_labels
      _lowercase							      =vocab_size
      _lowercase							      =hidden_size
      _lowercase							      =num_hidden_layers
      _lowercase							      =num_attention_heads
      _lowercase							      =d_ff
      _lowercase							      =relative_attention_num_buckets
      _lowercase							      =dropout_rate
      _lowercase							      =initializer_factor
      _lowercase							      =eos_token_id
      _lowercase							      =pad_token_id
      _lowercase							      =decoder_start_token_id
      _lowercase							      =None
      _lowercase							      =decoder_layers





     def      __A					(self       )     ->   List[str]:
      return TaConfig.from_pretrained('''google/umt5-base'''       )





     def      __A					(self		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase=None		,     UpperCAmelCase=None		,     UpperCAmelCase=None		,     UpperCAmelCase=None		,     UpperCAmelCase=None		,     )     ->   Optional[int]:
      if attention_mask is None:
       _lowercase							      =input_ids.ne(config.pad_token_id       )
      if decoder_attention_mask is None:
       _lowercase							      =decoder_input_ids.ne(config.pad_token_id       )
      if head_mask is None:
       _lowercase							      =torch.ones(config.num_hidden_layers		,     config.num_attention_heads		,     device=UpperCAmelCase       )
      if decoder_head_mask is None:
       _lowercase							      =torch.ones(config.num_decoder_layers		,     config.num_attention_heads		,     device=UpperCAmelCase       )
      if cross_attn_head_mask is None:
       _lowercase							      =torch.ones(
           config.num_decoder_layers		,     config.num_attention_heads		,     device=UpperCAmelCase       )
      return {
          "input_ids": input_ids,
          "decoder_input_ids": decoder_input_ids,
          "attention_mask": attention_mask,
          "decoder_attention_mask": decoder_attention_mask,
          "head_mask": head_mask,
          "decoder_head_mask": decoder_head_mask,
          "cross_attn_head_mask": cross_attn_head_mask,
      }





     def      __A					(self       )     ->   str:
      _lowercase							      =ids_tensor([self.batch_size, self.encoder_seq_length]		,     self.vocab_size       )
      _lowercase							      =ids_tensor([self.batch_size, self.decoder_seq_length]		,     self.vocab_size       )

      # we need to clamp the input ids here to avoid having pad token in between
      # this is because for NllbMoe the position_ids are prepared such that
      # all pad tokens have pos id = 2 and rest are between 2..seq_length
      # and the seq_length here is seq_length - num_pad_tokens
      # but when using past, there is no way of knowing if the past input ids had
      # pad tokens in them, which results in incorrect seq_lenth and which in turn results in
      # position_ids being off by num_pad_tokens in past input
      _lowercase							      =input_ids.clamp(self.pad_token_id + 1       )
      _lowercase							      =decoder_input_ids.clamp(self.pad_token_id + 1       )

      _lowercase							      =self.get_config()
      _lowercase							      =config.num_attention_heads
      _lowercase							      =self.prepare_inputs_dict(UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase       )
      return config, input_dict





     def      __A					(self       )     ->   Dict:
      _lowercase      ,    _lowercase							      =self.prepare_config_and_inputs()
      return config, inputs_dict





     def      __A					(self       )     ->   Dict:
      return TaConfig(
          vocab_size=1_6_6		,     d_model=self.hidden_size		,     d_ff=self.d_ff		,     d_kv=self.hidden_size // self.num_attention_heads		,     num_layers=self.num_hidden_layers		,     num_decoder_layers=self.decoder_layers		,     num_heads=self.num_attention_heads		,     relative_attention_num_buckets=self.relative_attention_num_buckets		,     dropout_rate=self.dropout_rate		,     initializer_factor=self.initializer_factor		,     eos_token_id=self.eos_token_id		,     bos_token_id=self.pad_token_id		,     pad_token_id=self.pad_token_id		,     decoder_start_token_id=self.decoder_start_token_id		,     )





     def      __A					(self       )     ->   List[str]:
      return TaConfig(
          vocab_size=self.vocab_size		,     d_model=self.hidden_size		,     d_ff=self.d_ff		,     d_kv=self.hidden_size // self.num_attention_heads		,     num_layers=self.num_hidden_layers		,     num_decoder_layers=self.decoder_layers		,     num_heads=self.num_attention_heads		,     relative_attention_num_buckets=self.relative_attention_num_buckets		,     dropout_rate=self.dropout_rate		,     initializer_factor=self.initializer_factor		,     eos_token_id=self.eos_token_id		,     bos_token_id=self.pad_token_id		,     pad_token_id=self.pad_token_id		,     decoder_start_token_id=self.decoder_start_token_id		,     )





     def      __A					(self		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase		,     )     ->   Optional[Any]:
      _lowercase							      =UMTaModel(config=UpperCAmelCase       )
      model.to(UpperCAmelCase       )
      model.eval()
      _lowercase							      =model(
          input_ids=UpperCAmelCase		,     decoder_input_ids=UpperCAmelCase		,     attention_mask=UpperCAmelCase		,     decoder_attention_mask=UpperCAmelCase		,     )
      _lowercase							      =model(input_ids=UpperCAmelCase		,     decoder_input_ids=UpperCAmelCase       )
      _lowercase							      =result.last_hidden_state
      _lowercase							      =result.past_key_values
      _lowercase							      =result.encoder_last_hidden_state

      self.parent.assertEqual(encoder_output.size()		,     (self.batch_size, self.encoder_seq_length, self.hidden_size)       )
      self.parent.assertEqual(decoder_output.size()		,     (self.batch_size, self.decoder_seq_length, self.hidden_size)       )
      # There should be `num_layers` key value embeddings stored in decoder_past
      self.parent.assertEqual(len(UpperCAmelCase       )		,     config.num_layers       )
      # There should be a self attn key, a self attn value, a cross attn key and a cross attn value stored in each decoder_past tuple
      self.parent.assertEqual(len(decoder_past[0]       )		,     4       )





     def      __A					(self		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase		,     )     ->   Optional[int]:
      _lowercase							      =UMTaModel(config=UpperCAmelCase       ).get_decoder().to(UpperCAmelCase       ).eval()
      # first forward pass
      _lowercase							      =model(UpperCAmelCase		,     use_cache=UpperCAmelCase       )
      _lowercase							      =model(UpperCAmelCase       )
      _lowercase							      =model(UpperCAmelCase		,     use_cache=UpperCAmelCase       )

      self.parent.assertTrue(len(UpperCAmelCase       ) == len(UpperCAmelCase       )       )
      self.parent.assertTrue(len(UpperCAmelCase       ) == len(UpperCAmelCase       ) + 1       )

      _lowercase      ,    _lowercase							      =outputs.to_tuple()

      # create hypothetical next token and extent to next_input_ids
      _lowercase							      =ids_tensor((self.batch_size, 1)		,     config.vocab_size       )

      # append to next input_ids and
      _lowercase							      =torch.cat([input_ids, next_tokens]		,     dim=-1       )

      _lowercase							      =model(UpperCAmelCase       )['''last_hidden_state''']
      _lowercase							      =model(UpperCAmelCase		,     past_key_values=UpperCAmelCase       )['''last_hidden_state''']

      # select random slice
      _lowercase							      =ids_tensor((1,)		,     output_from_past.shape[-1]       ).item()
      _lowercase							      =output_from_no_past[:, -1, random_slice_idx].detach()
      _lowercase							      =output_from_past[:, 0, random_slice_idx].detach()

      # test that outputs are equal for slice
      self.parent.assertTrue(torch.allclose(UpperCAmelCase		,     UpperCAmelCase		,     atol=1e-3       )       )





     def      __A					(self		,     UpperCAmelCase		,     UpperCAmelCase		,     )     ->   Optional[int]:
      _lowercase							      =UMTaModel(config=UpperCAmelCase       ).to(UpperCAmelCase       ).half().eval()
      _lowercase							      =model(**UpperCAmelCase       )['''last_hidden_state''']
      self.parent.assertFalse(torch.isnan(UpperCAmelCase       ).any().item()       )







@require_torch
class       lowerCamelCase__   (       lowerCAmelCase   ,							lowerCAmelCase   ,							lowerCAmelCase   ,							unittest.TestCase):
     SCREAMING_SNAKE_CASE__      			=			(
         (UMTaModel, UMTaForConditionalGeneration, UMTaForQuestionAnswering) if is_torch_available() else ()
     )
     SCREAMING_SNAKE_CASE__      			=			(UMTaForConditionalGeneration,) if is_torch_available() else ()
     SCREAMING_SNAKE_CASE__      			=			(
         {
             '''conversational''': UMTaForConditionalGeneration,
             '''feature-extraction''': UMTaModel,
             '''summarization''': UMTaForConditionalGeneration,
             '''text2text-generation''': UMTaForConditionalGeneration,
             '''translation''': UMTaForConditionalGeneration,
             '''question-answering''': UMTaForQuestionAnswering,
         }
         if is_torch_available()
         else {}
     )
     SCREAMING_SNAKE_CASE__      			=			True
     SCREAMING_SNAKE_CASE__      			=			False
     SCREAMING_SNAKE_CASE__      			=			False
     SCREAMING_SNAKE_CASE__      			=			True
     SCREAMING_SNAKE_CASE__      			=			True
     # The small UMT5 model needs higher percentages for CPU/MP tests
     SCREAMING_SNAKE_CASE__      			=			[0.8, 0.9]





     def      __A					(self       )     ->   Dict:
      _lowercase							      =UMTaModelTester(self       )





     @unittest.skip('''Test has a segmentation fault on torch 1.8.0'''       )
     def      __A					(self       )     ->   Optional[int]:
      _lowercase							      =self.model_tester.prepare_config_and_inputs()
      _lowercase							      =UMTaModel(config_and_inputs[0]       ).to(UpperCAmelCase       )
      with tempfile.TemporaryDirectory() as tmpdirname:
       torch.onnx.export(
           UpperCAmelCase		,     (config_and_inputs[1], config_and_inputs[3], config_and_inputs[2])		,     f"{tmpdirname}/t5_test.onnx"		,     export_params=UpperCAmelCase		,     opset_version=9		,     input_names=['''input_ids''', '''decoder_input_ids''']		,     )





     @unittest.skipIf(torch_device == '''cpu'''		,     '''Cant do half precision'''       )
     def      __A					(self       )     ->   List[Any]:
      _lowercase							      =self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_model_fpaa_forward(*UpperCAmelCase       )





     def      __A					(self       )     ->   Dict:
      _lowercase							      =['''encoder_attentions''', '''decoder_attentions''', '''cross_attentions''']
      _lowercase							      =self.model_tester.prepare_config_and_inputs()
      _lowercase							      =config_and_inputs[0]
      _lowercase							      =UMTaForConditionalGeneration(UpperCAmelCase       ).eval()
      model.to(UpperCAmelCase       )

      _lowercase							      ={
          '''head_mask''': torch.zeros(config.num_layers		,     config.num_heads		,     device=UpperCAmelCase       ),
          '''decoder_head_mask''': torch.zeros(config.num_decoder_layers		,     config.num_heads		,     device=UpperCAmelCase       ),
          '''cross_attn_head_mask''': torch.zeros(config.num_decoder_layers		,     config.num_heads		,     device=UpperCAmelCase       ),
      }

      for attn_name, (name, mask) in zip(UpperCAmelCase		,     head_masking.items()       ):
       _lowercase							      ={name: mask}
       # Explicitly pass decoder_head_mask as it is required from T5 model when head_mask specified
       if name == "head_mask":
        _lowercase							      =torch.ones(
            config.num_decoder_layers		,     config.num_heads		,     device=UpperCAmelCase       )

       _lowercase							      =model.generate(
           config_and_inputs[1]['''input_ids''']		,     num_beams=1		,     max_length=3		,     output_attentions=UpperCAmelCase		,     return_dict_in_generate=UpperCAmelCase		,     **UpperCAmelCase		,     )
       # We check the state of decoder_attentions and cross_attentions just from the last step
       _lowercase							      =out[attn_name] if attn_name == attention_names[0] else out[attn_name][-1]
       self.assertEqual(sum([w.sum().item() for w in attn_weights]       )		,     0.0       )





     @unittest.skip('''Does not work on the tiny model as we keep hitting edge cases.'''       )
     def      __A					(self       )     ->   Any:
      pass







@require_torch
@require_sentencepiece
@require_tokenizers
class       lowerCamelCase__   (       unittest.TestCase):





     @slow
     @unittest.skip(
         '''Unless we stop stripping left and right by default for all special tokens, the expected ids obtained here will not match the original ones. Wait for https://github.com/huggingface/transformers/pull/23909 to be merged'''       )
     def      __A					(self       )     ->   Optional[int]:
      _lowercase							      =UMTaForConditionalGeneration.from_pretrained('''google/umt5-small'''		,     return_dict=UpperCAmelCase       ).to(UpperCAmelCase       )
      _lowercase							      =AutoTokenizer.from_pretrained('''google/umt5-small'''		,     use_fast=UpperCAmelCase		,     legacy=UpperCAmelCase       )
      _lowercase							      =[
          '''Bonjour monsieur <extra_id_0> bien <extra_id_1>.''',
          '''No se como puedo <extra_id_0>.''',
          '''This is the reason why we <extra_id_0> them.''',
          '''The <extra_id_0> walks in <extra_id_1>, seats''',
          '''A <extra_id_0> walks into a bar and orders a <extra_id_1> with <extra_id_2> pinch of <extra_id_3>.''',
      ]
      _lowercase							      =tokenizer(UpperCAmelCase		,     return_tensors='''pt'''		,     padding=UpperCAmelCase       ).input_ids
      # fmt: off
      _lowercase							      =torch.tensor(
          [
              [ 3_8_5_3_0, 2_1_0_7_0_3, 2_5_6_2_9_9, 1_4_1_0, 2_5_6_2_9_8, 2_7_4, 1, 0,0, 0, 0, 0, 0, 0, 0, 0,0, 0],
              [   8_2_6, 3_2_1, 6_7_1, 2_5_9_2_2, 2_5_6_2_9_9, 2_7_4, 1, 0,0, 0, 0, 0, 0, 0, 0, 0,0, 0],
              [  1_4_6_0, 3_3_9, 3_1_2, 1_9_0_1_4, 1_0_6_2_0, 7_5_8, 2_5_6_2_9_9, 2_3_5_5,2_7_4, 1, 0, 0, 0, 0, 0, 0,0, 0],
              [   5_1_7, 2_5_6_2_9_9, 1_4_8_6_9, 2_8_1, 3_0_1, 2_5_6_2_9_8, 2_7_5, 1_1_9_9_8_3,1, 0, 0, 0, 0, 0, 0, 0,0, 0],
              [   3_2_0, 2_5_6_2_9_9, 1_4_8_6_9, 2_8_1, 2_2_3_4, 2_8_9, 2_2_7_5, 3_3_3,6_1_3_9_1, 2_8_9, 2_5_6_2_9_8, 5_4_3, 2_5_6_2_9_7, 1_6_8_7_1_4, 3_2_9, 2_5_6_2_9_6,2_7_4, 1],
          ]       )
      # fmt: on
      torch.testing.assert_allclose(UpperCAmelCase		,     UpperCAmelCase       )

      _lowercase							      =model.generate(input_ids.to(UpperCAmelCase       )       )
      _lowercase							      =[
          '''<pad><extra_id_0> et<extra_id_1> [eod] <extra_id_2><extra_id_55>.. [eod] 💐 💐 💐 💐 💐 💐 💐 💐 💐 💐 💐 <extra_id_56>ajšietosto<extra_id_56>lleux<extra_id_19><extra_id_6>ajšie</s>''',
          '''<pad><extra_id_0>.<extra_id_1>.,<0x0A>...spech <0x0A><extra_id_20> <extra_id_21></s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>''',
          '''<pad><extra_id_0> are not going to be a part of the world. We are not going to be a part of<extra_id_1> and<extra_id_2><0x0A><extra_id_48>.<extra_id_48></s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>''',
          '''<pad><extra_id_0> door<extra_id_1>, the door<extra_id_2> 피해[/</s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>''',
          '''<pad><extra_id_0>nyone who<extra_id_1> drink<extra_id_2> a<extra_id_3> alcohol<extra_id_4> A<extra_id_5> A. This<extra_id_6> I<extra_id_7><extra_id_52><extra_id_53></s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>''',
      ]
      _lowercase							      =tokenizer.batch_decode(UpperCAmelCase       )
      self.assertEqual(UpperCAmelCase		,     UpperCAmelCase       )






import argparse
from pathlib import Path

import torch
from packaging import version
from torch.onnx import export

from diffusers import AutoencoderKL


SCREAMING_SNAKE_CASE_:List[Any]             =						version.parse(version.parse(torch.__version__).base_version) < version.parse("""1.11""")




def 	__UpperCamelCase	(  _lowerCAmelCase      ,     _lowerCAmelCase      ,     _lowerCAmelCase      ,     _lowerCAmelCase      ,     _lowerCAmelCase      ,     _lowerCAmelCase      ,     _lowerCAmelCase      ,     _lowerCAmelCase=False      ,     )       ->  int:



    """simple docstring"""






    output_path.parent.mkdir(parents=_lowerCAmelCase      ,     exist_ok=_lowerCAmelCase		)
    # PyTorch deprecated the `enable_onnx_checker` and `use_external_data_format` arguments in v1.11,
    # so we check the torch version for backwards compatibility
    if is_torch_less_than_1_11:
        export(
            _lowerCAmelCase      ,     _lowerCAmelCase      ,     f=output_path.as_posix()      ,     input_names=_lowerCAmelCase      ,     output_names=_lowerCAmelCase      ,     dynamic_axes=_lowerCAmelCase      ,     do_constant_folding=_lowerCAmelCase      ,     use_external_data_format=_lowerCAmelCase      ,     enable_onnx_checker=_lowerCAmelCase      ,     opset_version=_lowerCAmelCase      ,     )
    else:
        export(
            _lowerCAmelCase      ,     _lowerCAmelCase      ,     f=output_path.as_posix()      ,     input_names=_lowerCAmelCase      ,     output_names=_lowerCAmelCase      ,     dynamic_axes=_lowerCAmelCase      ,     do_constant_folding=_lowerCAmelCase      ,     opset_version=_lowerCAmelCase      ,     )




@torch.no_grad()
def 	__UpperCamelCase	(  _lowerCAmelCase      ,     _lowerCAmelCase      ,     _lowerCAmelCase      ,     _lowerCAmelCase = False		)       ->  List[Any]:



    """simple docstring"""






    A :       Tuple          =   torch.floataa if fpaa else torch.floataa
    if fpaa and torch.cuda.is_available():
        A :       Union[str, Any]          =   """cuda"""
    elif fpaa and not torch.cuda.is_available():
        raise ValueError("""`float16` model export is only supported on GPUs with CUDA"""		)
    else:
        A :       Any          =   """cpu"""
    A :       Any          =   Path(_lowerCAmelCase		)

    # VAE DECODER
    A :       Union[str, Any]          =   AutoencoderKL.from_pretrained(model_path + """/vae"""		)
    A :       Any          =   vae_decoder.config.latent_channels
    # forward only through the decoder part
    A :       Optional[int]          =   vae_decoder.decode
    onnx_export(
        _lowerCAmelCase      ,     model_args=(
            torch.randn(1      ,     _lowerCAmelCase      ,     25      ,     25		).to(device=_lowerCAmelCase      ,     dtype=_lowerCAmelCase		),
            False,
        )      ,     output_path=output_path / """vae_decoder""" / """model.onnx"""      ,     ordered_input_names=["""latent_sample""", """return_dict"""]      ,     output_names=["""sample"""]      ,     dynamic_axes={
            """latent_sample""": {0: """batch""", 1: """channels""", 2: """height""", 3: """width"""},
        }      ,     opset=_lowerCAmelCase      ,     )
    del vae_decoder


if __name__ == "__main__":
     SCREAMING_SNAKE_CASE_:Tuple             =						argparse.ArgumentParser()

     parser.add_argument(
         """--model_path""",
         type=str,
         required=True,
         help="""Path to the `diffusers` checkpoint to convert (either a local directory or on the Hub).""",
     )

     parser.add_argument("""--output_path""", type=str, required=True, help="""Path to the output model.""")
     parser.add_argument(
         """--opset""",
         default=14,
         type=int,
         help="""The version of the ONNX operator set to use.""",
     )
     parser.add_argument("""--fp16""", action="""store_true""", default=False, help="""Export the models in `float16` mode""")

     SCREAMING_SNAKE_CASE_:Tuple             =						parser.parse_args()
     print(args.output_path)
     convert_models(args.model_path, args.output_path, args.opset, args.fpaa)
     print("""SD: Done: ONNX""")


