


# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import argparse

from ...utils.dataclasses import (
    ComputeEnvironment,
    DistributedType,
    DynamoBackend,
    PrecisionType,
    SageMakerDistributedType,
)
from ..menu import BulletMenu


UpperCAmelCase      =   [
    """EAGER""",
    """AOT_EAGER""",
    """INDUCTOR""",
    """NVFUSER""",
    """AOT_NVFUSER""",
    """AOT_CUDAGRAPHS""",
    """OFI""",
    """FX2TRT""",
    """ONNXRT""",
    """IPEX""",
]






def 	__lowerCAmelCase   (SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE=None , SCREAMING_SNAKE_CASE=None , SCREAMING_SNAKE_CASE=None )->  int:




      """simple docstring"""

      snake_case_	=				True
      while ask_again:
            snake_case_	=				input(SCREAMING_SNAKE_CASE )
            try:
                  if default is not None and len(SCREAMING_SNAKE_CASE ) == 0:
                        return default
                  return convert_value(SCREAMING_SNAKE_CASE ) if convert_value is not None else result
            except Exception:
                  if error_message is not None:
                        print(SCREAMING_SNAKE_CASE )






def 	__lowerCAmelCase   (SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE=[] , SCREAMING_SNAKE_CASE=None , SCREAMING_SNAKE_CASE=0 )->  List[Any]:




      """simple docstring"""

      snake_case_	=				BulletMenu(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
      snake_case_	=				menu.run(default_choice=SCREAMING_SNAKE_CASE )
      return convert_value(SCREAMING_SNAKE_CASE ) if convert_value is not None else result






def 	__lowerCAmelCase   (SCREAMING_SNAKE_CASE )->  int:




      """simple docstring"""

      snake_case_	=				int(SCREAMING_SNAKE_CASE )
      return ComputeEnvironment(['''LOCAL_MACHINE''', '''AMAZON_SAGEMAKER'''][value] )






def 	__lowerCAmelCase   (SCREAMING_SNAKE_CASE )->  List[Any]:




      """simple docstring"""

      snake_case_	=				int(SCREAMING_SNAKE_CASE )
      return DistributedType(['''NO''', '''MULTI_CPU''', '''MULTI_XPU''', '''MULTI_GPU''', '''MULTI_NPU''', '''TPU'''][value] )






def 	__lowerCAmelCase   (SCREAMING_SNAKE_CASE )->  str:




      """simple docstring"""

      snake_case_	=				int(SCREAMING_SNAKE_CASE )
      return DynamoBackend(DYNAMO_BACKENDS[value] ).value






def 	__lowerCAmelCase   (SCREAMING_SNAKE_CASE )->  Any:




      """simple docstring"""

      snake_case_	=				int(SCREAMING_SNAKE_CASE )
      return PrecisionType(['''no''', '''fp16''', '''bf16''', '''fp8'''][value] )






def 	__lowerCAmelCase   (SCREAMING_SNAKE_CASE )->  Optional[Any]:




      """simple docstring"""

      snake_case_	=				int(SCREAMING_SNAKE_CASE )
      return SageMakerDistributedType(['''NO''', '''DATA_PARALLEL''', '''MODEL_PARALLEL'''][value] )






def 	__lowerCAmelCase   (SCREAMING_SNAKE_CASE )->  Union[str, Any]:




      """simple docstring"""

      return {"yes": True, "no": False}[value.lower()]







class lowerCAmelCase_						(			argparse.RawDescriptionHelpFormatter	):

 '''simple docstring'''

 def 						UpperCamelCase__					(     self		,				_UpperCAmelCase		,				_UpperCAmelCase		,				_UpperCAmelCase		,				_UpperCAmelCase ):
       snake_case_	=				super()._format_usage(_UpperCAmelCase		,				_UpperCAmelCase		,				_UpperCAmelCase		,				_UpperCAmelCase )
       snake_case_	=				usage.replace('''<command> [<args>] '''		,				'''''' )
       return usage



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_						:

 '''simple docstring'''

 def __init__(     self		,				_UpperCAmelCase		,				_UpperCAmelCase=99		,				_UpperCAmelCase=13		,				_UpperCAmelCase=7		,				_UpperCAmelCase=9		,				_UpperCAmelCase=True		,				_UpperCAmelCase=True		,				_UpperCAmelCase=False		,				_UpperCAmelCase=32		,				_UpperCAmelCase=5		,				_UpperCAmelCase=4		,				_UpperCAmelCase=37		,				_UpperCAmelCase=8		,				_UpperCAmelCase=0.1		,				_UpperCAmelCase=0.002		,				_UpperCAmelCase=1		,				_UpperCAmelCase=0		,				_UpperCAmelCase=0		,				_UpperCAmelCase=None		,				_UpperCAmelCase=None		,				):
       snake_case_	=				parent
       snake_case_	=				batch_size
       snake_case_	=				encoder_seq_length
       snake_case_	=				decoder_seq_length
       # For common tests
       snake_case_	=				self.decoder_seq_length
       snake_case_	=				is_training
       snake_case_	=				use_attention_mask
       snake_case_	=				use_labels
       snake_case_	=				vocab_size
       snake_case_	=				hidden_size
       snake_case_	=				num_hidden_layers
       snake_case_	=				num_attention_heads
       snake_case_	=				d_ff
       snake_case_	=				relative_attention_num_buckets
       snake_case_	=				dropout_rate
       snake_case_	=				initializer_factor
       snake_case_	=				eos_token_id
       snake_case_	=				pad_token_id
       snake_case_	=				decoder_start_token_id
       snake_case_	=				None
       snake_case_	=				decoder_layers



 def 						UpperCamelCase__					(     self ):
       return TaConfig.from_pretrained('''google/umt5-base''' )



 def 						UpperCamelCase__					(     self		,				_UpperCAmelCase		,				_UpperCAmelCase		,				_UpperCAmelCase		,				_UpperCAmelCase=None		,				_UpperCAmelCase=None		,				_UpperCAmelCase=None		,				_UpperCAmelCase=None		,				_UpperCAmelCase=None		,				):
       if attention_mask is None:
             snake_case_	=				input_ids.ne(config.pad_token_id )
       if decoder_attention_mask is None:
             snake_case_	=				decoder_input_ids.ne(config.pad_token_id )
       if head_mask is None:
             snake_case_	=				torch.ones(config.num_hidden_layers		,				config.num_attention_heads		,				device=_UpperCAmelCase )
       if decoder_head_mask is None:
             snake_case_	=				torch.ones(config.num_decoder_layers		,				config.num_attention_heads		,				device=_UpperCAmelCase )
       if cross_attn_head_mask is None:
             snake_case_	=				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 						UpperCamelCase__					(     self ):
       snake_case_	=				ids_tensor([self.batch_size, self.encoder_seq_length]		,				self.vocab_size )
       snake_case_	=				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
       snake_case_	=				input_ids.clamp(self.pad_token_id + 1 )
       snake_case_	=				decoder_input_ids.clamp(self.pad_token_id + 1 )

       snake_case_	=				self.get_config()
       snake_case_	=				config.num_attention_heads
       snake_case_	=				self.prepare_inputs_dict(_UpperCAmelCase		,				_UpperCAmelCase		,				_UpperCAmelCase )
       return config, input_dict



 def 						UpperCamelCase__					(     self ):
       snake_case_	,   snake_case_	=				self.prepare_config_and_inputs()
       return config, inputs_dict



 def 						UpperCamelCase__					(     self ):
       return TaConfig(
           vocab_size=1_66		,				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 						UpperCamelCase__					(     self ):
       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 						UpperCamelCase__					(     self		,				_UpperCAmelCase		,				_UpperCAmelCase		,				_UpperCAmelCase		,				_UpperCAmelCase		,				_UpperCAmelCase		,				_UpperCAmelCase		,				):
       snake_case_	=				UMTaModel(config=_UpperCAmelCase )
       model.to(_UpperCAmelCase )
       model.eval()
       snake_case_	=				model(
           input_ids=_UpperCAmelCase		,				decoder_input_ids=_UpperCAmelCase		,				attention_mask=_UpperCAmelCase		,				decoder_attention_mask=_UpperCAmelCase		,				)
       snake_case_	=				model(input_ids=_UpperCAmelCase		,				decoder_input_ids=_UpperCAmelCase )
       snake_case_	=				result.last_hidden_state
       snake_case_	=				result.past_key_values
       snake_case_	=				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 						UpperCamelCase__					(     self		,				_UpperCAmelCase		,				_UpperCAmelCase		,				_UpperCAmelCase		,				_UpperCAmelCase		,				_UpperCAmelCase		,				_UpperCAmelCase		,				):
       snake_case_	=				UMTaModel(config=_UpperCAmelCase ).get_decoder().to(_UpperCAmelCase ).eval()
       # first forward pass
       snake_case_	=				model(_UpperCAmelCase		,				use_cache=_UpperCAmelCase )
       snake_case_	=				model(_UpperCAmelCase )
       snake_case_	=				model(_UpperCAmelCase		,				use_cache=_UpperCAmelCase )

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

       snake_case_	,   snake_case_	=				outputs.to_tuple()

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

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

       snake_case_	=				model(_UpperCAmelCase )['''last_hidden_state''']
       snake_case_	=				model(_UpperCAmelCase		,				past_key_values=_UpperCAmelCase )['''last_hidden_state''']

       # select random slice
       snake_case_	=				ids_tensor((1,)		,				output_from_past.shape[-1] ).item()
       snake_case_	=				output_from_no_past[:, -1, random_slice_idx].detach()
       snake_case_	=				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 						UpperCamelCase__					(     self		,				_UpperCAmelCase		,				_UpperCAmelCase		,				):
       snake_case_	=				UMTaModel(config=_UpperCAmelCase ).to(_UpperCAmelCase ).half().eval()
       snake_case_	=				model(**_UpperCAmelCase )['''last_hidden_state''']
       self.parent.assertFalse(torch.isnan(_UpperCAmelCase ).any().item() )







@require_torch
class lowerCAmelCase_						(			lowerCamelCase__ ,						lowerCamelCase__ ,						lowerCamelCase__ ,						unittest.TestCase	):

 '''simple docstring'''

 __snake_case												=  (
     (UMTaModel, UMTaForConditionalGeneration, UMTaForQuestionAnswering) if is_torch_available() else ()
 )
 __snake_case												=  (UMTaForConditionalGeneration,) if is_torch_available() else ()
 __snake_case												=  (
     {
         "conversational": UMTaForConditionalGeneration,
         "feature-extraction": UMTaModel,
         "summarization": UMTaForConditionalGeneration,
         "text2text-generation": UMTaForConditionalGeneration,
         "translation": UMTaForConditionalGeneration,
         "question-answering": UMTaForQuestionAnswering,
     }
     if is_torch_available()
     else {}
 )
 __snake_case												=  True
 __snake_case												=  False
 __snake_case												=  False
 __snake_case												=  True
 __snake_case												=  True
 # The small UMT5 model needs higher percentages for CPU/MP tests
 __snake_case												=  [0.8, 0.9]



 def 						UpperCamelCase__					(     self ):
       snake_case_	=				UMTaModelTester(self )



 @unittest.skip('''Test has a segmentation fault on torch 1.8.0''' )
 def 						UpperCamelCase__					(     self ):
       snake_case_	=				self.model_tester.prepare_config_and_inputs()
       snake_case_	=				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 						UpperCamelCase__					(     self ):
       snake_case_	=				self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model_fpaa_forward(*_UpperCAmelCase )



 def 						UpperCamelCase__					(     self ):
       snake_case_	=				['''encoder_attentions''', '''decoder_attentions''', '''cross_attentions''']
       snake_case_	=				self.model_tester.prepare_config_and_inputs()
       snake_case_	=				config_and_inputs[0]
       snake_case_	=				UMTaForConditionalGeneration(_UpperCAmelCase ).eval()
       model.to(_UpperCAmelCase )

       snake_case_	=				{
           '''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() ):
             snake_case_	=				{name: mask}
             # Explicitly pass decoder_head_mask as it is required from T5 model when head_mask specified
             if name == "head_mask":
                   snake_case_	=				torch.ones(
                       config.num_decoder_layers		,				config.num_heads		,				device=_UpperCAmelCase )

             snake_case_	=				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
             snake_case_	=				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 						UpperCamelCase__					(     self ):
       pass







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

 '''simple docstring'''

 @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 						UpperCamelCase__					(     self ):
       snake_case_	=				UMTaForConditionalGeneration.from_pretrained('''google/umt5-small'''		,				return_dict=_UpperCAmelCase ).to(_UpperCAmelCase )
       snake_case_	=				AutoTokenizer.from_pretrained('''google/umt5-small'''		,				use_fast=_UpperCAmelCase		,				legacy=_UpperCAmelCase )
       snake_case_	=				[
           '''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>.''',
       ]
       snake_case_	=				tokenizer(_UpperCAmelCase		,				return_tensors='''pt'''		,				padding=_UpperCAmelCase ).input_ids
       # fmt: off
       snake_case_	=				torch.tensor(
           [
               [ 3_85_30, 21_07_03, 25_62_99, 14_10, 25_62_98, 2_74, 1, 0,0, 0, 0, 0, 0, 0, 0, 0,0, 0],
               [   8_26, 3_21, 6_71, 2_59_22, 25_62_99, 2_74, 1, 0,0, 0, 0, 0, 0, 0, 0, 0,0, 0],
               [  14_60, 3_39, 3_12, 1_90_14, 1_06_20, 7_58, 25_62_99, 23_55,2_74, 1, 0, 0, 0, 0, 0, 0,0, 0],
               [   5_17, 25_62_99, 1_48_69, 2_81, 3_01, 25_62_98, 2_75, 11_99_83,1, 0, 0, 0, 0, 0, 0, 0,0, 0],
               [   3_20, 25_62_99, 1_48_69, 2_81, 22_34, 2_89, 22_75, 3_33,6_13_91, 2_89, 25_62_98, 5_43, 25_62_97, 16_87_14, 3_29, 25_62_96,2_74, 1],
           ] )
       # fmt: on
       torch.testing.assert_allclose(_UpperCAmelCase		,				_UpperCAmelCase )

       snake_case_	=				model.generate(input_ids.to(_UpperCAmelCase ) )
       snake_case_	=				[
           '''<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>''',
       ]
       snake_case_	=				tokenizer.batch_decode(_UpperCAmelCase )
       self.assertEqual(_UpperCAmelCase		,				_UpperCAmelCase )