


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        a	:




       """simple docstring"""


       def __init__(							self :						List[str]					,			lowerCamelCase :						Dict					,			lowerCamelCase :						Optional[int]=99					,			lowerCamelCase :						Any=13					,			lowerCamelCase :						Dict=7					,			lowerCamelCase :						Tuple=9					,			lowerCamelCase :						int=True					,			lowerCamelCase :						Dict=True					,			lowerCamelCase :						Tuple=False					,			lowerCamelCase :						Dict=32					,			lowerCamelCase :						List[Any]=5					,			lowerCamelCase :						int=4					,			lowerCamelCase :						Tuple=37					,			lowerCamelCase :						Tuple=8					,			lowerCamelCase :						int=0.1					,			lowerCamelCase :						int=0.0_02					,			lowerCamelCase :						int=1					,			lowerCamelCase :						Tuple=0					,			lowerCamelCase :						Dict=0					,			lowerCamelCase :						List[Any]=None					,			lowerCamelCase :						Optional[int]=None					,			)			->					str:
              __snake_case	:    Any											=     parent
              __snake_case	:    int											=     batch_size
              __snake_case	:    Dict											=     encoder_seq_length
              __snake_case	:    Optional[Any]											=     decoder_seq_length
              # For common tests
              __snake_case	:    str											=     self.decoder_seq_length
              __snake_case	:    Any											=     is_training
              __snake_case	:    int											=     use_attention_mask
              __snake_case	:    Any											=     use_labels
              __snake_case	:    str											=     vocab_size
              __snake_case	:    Union[str, Any]											=     hidden_size
              __snake_case	:    Tuple											=     num_hidden_layers
              __snake_case	:    str											=     num_attention_heads
              __snake_case	:    List[Any]											=     d_ff
              __snake_case	:    List[str]											=     relative_attention_num_buckets
              __snake_case	:    str											=     dropout_rate
              __snake_case	:    int											=     initializer_factor
              __snake_case	:    Optional[Any]											=     eos_token_id
              __snake_case	:    Tuple											=     pad_token_id
              __snake_case	:    List[str]											=     decoder_start_token_id
              __snake_case	:    Optional[Any]											=     None
              __snake_case	:    List[str]											=     decoder_layers

       def 					__snake_case      (							self :						Union[str, Any]       )			->					Dict:
              return TaConfig.from_pretrained("google/umt5-base"       )

       def 					__snake_case      (							self :						Any					,			lowerCamelCase :						Optional[int]					,			lowerCamelCase :						str					,			lowerCamelCase :						Optional[Any]					,			lowerCamelCase :						int=None					,			lowerCamelCase :						Any=None					,			lowerCamelCase :						List[Any]=None					,			lowerCamelCase :						Any=None					,			lowerCamelCase :						Any=None					,			)			->					Dict:
              if attention_mask is None:
                     __snake_case	:    Any											=     input_ids.ne(config.pad_token_id       )
              if decoder_attention_mask is None:
                     __snake_case	:    Union[str, Any]											=     decoder_input_ids.ne(config.pad_token_id       )
              if head_mask is None:
                     __snake_case	:    Dict											=     torch.ones(config.num_hidden_layers					,			config.num_attention_heads					,			device=__A       )
              if decoder_head_mask is None:
                     __snake_case	:    Tuple											=     torch.ones(config.num_decoder_layers					,			config.num_attention_heads					,			device=__A       )
              if cross_attn_head_mask is None:
                     __snake_case	:    Any											=     torch.ones(
                         config.num_decoder_layers					,			config.num_attention_heads					,			device=__A       )
              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 					__snake_case      (							self :						Tuple       )			->					Union[str, Any]:
              __snake_case	:    List[Any]											=     ids_tensor([self.batch_size, self.encoder_seq_length]					,			self.vocab_size       )
              __snake_case	:    Optional[Any]											=     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	:    List[Any]											=     input_ids.clamp(self.pad_token_id + 1       )
              __snake_case	:    List[str]											=     decoder_input_ids.clamp(self.pad_token_id + 1       )

              __snake_case	:    Optional[Any]											=     self.get_config()
              __snake_case	:    List[str]											=     config.num_attention_heads
              __snake_case	:    Union[str, Any]											=     self.prepare_inputs_dict(__A					,			__A					,			__A       )
              return config, input_dict

       def 					__snake_case      (							self :						Optional[int]       )			->					Tuple:
              __snake_case	:    int											=     self.prepare_config_and_inputs()
              return config, inputs_dict

       def 					__snake_case      (							self :						str       )			->					Optional[int]:
              return TaConfig(
                  vocab_size=166					,			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 					__snake_case      (							self :						Tuple       )			->					Any:
              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 					__snake_case      (							self :						List[str]					,			lowerCamelCase :						Optional[int]					,			lowerCamelCase :						Optional[int]					,			lowerCamelCase :						List[Any]					,			lowerCamelCase :						str					,			lowerCamelCase :						Any					,			lowerCamelCase :						List[str]					,			)			->					List[Any]:
              __snake_case	:    List[Any]											=     UMTaModel(config=__A       )
              model.to(__A       )
              model.eval()
              __snake_case	:    List[Any]											=     model(
                  input_ids=__A					,			decoder_input_ids=__A					,			attention_mask=__A					,			decoder_attention_mask=__A					,			)
              __snake_case	:    List[str]											=     model(input_ids=__A					,			decoder_input_ids=__A       )
              __snake_case	:    int											=     result.last_hidden_state
              __snake_case	:    Optional[Any]											=     result.past_key_values
              __snake_case	:    Optional[Any]											=     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(__A       )					,			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 					__snake_case      (							self :						List[str]					,			lowerCamelCase :						Tuple					,			lowerCamelCase :						Dict					,			lowerCamelCase :						int					,			lowerCamelCase :						List[str]					,			lowerCamelCase :						Optional[int]					,			lowerCamelCase :						Any					,			)			->					List[str]:
              __snake_case	:    Optional[Any]											=     UMTaModel(config=__A       ).get_decoder().to(__A       ).eval()
              # first forward pass
              __snake_case	:    Tuple											=     model(__A					,			use_cache=__A       )
              __snake_case	:    List[str]											=     model(__A       )
              __snake_case	:    List[str]											=     model(__A					,			use_cache=__A       )

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

              __snake_case	:    Union[str, Any]											=     outputs.to_tuple()

              # create hypothetical next token and extent to next_input_ids
              __snake_case	:    Optional[Any]											=     ids_tensor((self.batch_size, 1)					,			config.vocab_size       )

              # append to next input_ids and
              __snake_case	:    List[Any]											=     torch.cat([input_ids, next_tokens]					,			dim=-1       )

              __snake_case	:    Tuple											=     model(__A       )["last_hidden_state"]
              __snake_case	:    Tuple											=     model(__A					,			past_key_values=__A       )["last_hidden_state"]

              # select random slice
              __snake_case	:    str											=     ids_tensor((1,)					,			output_from_past.shape[-1]       ).item()
              __snake_case	:    Optional[Any]											=     output_from_no_past[:, -1, random_slice_idx].detach()
              __snake_case	:    List[str]											=     output_from_past[:, 0, random_slice_idx].detach()

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






       def 					__snake_case      (							self :						int					,			lowerCamelCase :						Optional[int]					,			lowerCamelCase :						List[str]					,			)			->					Any:
              __snake_case	:    List[str]											=     UMTaModel(config=__A       ).to(__A       ).half().eval()
              __snake_case	:    Any											=     model(**__A       )["last_hidden_state"]
              self.parent.assertFalse(torch.isnan(__A       ).any().item()       )

@require_torch
class        a	(UpperCamelCase_  ,       UpperCamelCase_  ,       UpperCamelCase_  ,       unittest.TestCase   ):




       """simple docstring"""


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

       def 					__snake_case      (							self :						int       )			->					Optional[Any]:
              __snake_case	:    Tuple											=     UMTaModelTester(self       )

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

       @unittest.skipIf(torch_device == "cpu"					,			"Cant do half precision"       )
       def 					__snake_case      (							self :						Optional[int]       )			->					str:
              __snake_case	:    Dict											=     self.model_tester.prepare_config_and_inputs()
              self.model_tester.create_and_check_model_fpaa_forward(*__A       )

       def 					__snake_case      (							self :						Tuple       )			->					Optional[Any]:
              __snake_case	:    Union[str, Any]											=     ["encoder_attentions", "decoder_attentions", "cross_attentions"]
              __snake_case	:    List[Any]											=     self.model_tester.prepare_config_and_inputs()
              __snake_case	:    List[Any]											=     config_and_inputs[0]
              __snake_case	:    Optional[int]											=     UMTaForConditionalGeneration(__A       ).eval()
              model.to(__A       )

              __snake_case	:    Optional[Any]											=     {
                  "head_mask": torch.zeros(config.num_layers					,			config.num_heads					,			device=__A       ),
                  "decoder_head_mask": torch.zeros(config.num_decoder_layers					,			config.num_heads					,			device=__A       ),
                  "cross_attn_head_mask": torch.zeros(config.num_decoder_layers					,			config.num_heads					,			device=__A       ),
              }

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

                     __snake_case	:    str											=     model.generate(
                         config_and_inputs[1]["input_ids"]					,			num_beams=1					,			max_length=3					,			output_attentions=__A					,			return_dict_in_generate=__A					,			**__A					,			)
                     # We check the state of decoder_attentions and cross_attentions just from the last step
                     __snake_case	:    int											=     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 					__snake_case      (							self :						List[str]       )			->					Union[str, Any]:
              pass

@require_torch
@require_sentencepiece
@require_tokenizers
class        a	(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 					__snake_case      (							self :						Dict       )			->					Dict:
              __snake_case	:    List[Any]											=     UMTaForConditionalGeneration.from_pretrained("google/umt5-small"					,			return_dict=__A       ).to(__A       )
              __snake_case	:    Dict											=     AutoTokenizer.from_pretrained("google/umt5-small"					,			use_fast=__A					,			legacy=__A       )
              __snake_case	:    Any											=     [
                  "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	:    List[str]											=     tokenizer(__A					,			return_tensors="pt"					,			padding=__A       ).input_ids
              # fmt: off
              __snake_case	:    Optional[int]											=     torch.tensor(
                  [
                      [ 38530, 210703, 256299, 1410, 256298, 274, 1, 0,0, 0, 0, 0, 0, 0, 0, 0,0, 0],
                      [   826, 321, 671, 25922, 256299, 274, 1, 0,0, 0, 0, 0, 0, 0, 0, 0,0, 0],
                      [  1460, 339, 312, 19014, 10620, 758, 256299, 2355,274, 1, 0, 0, 0, 0, 0, 0,0, 0],
                      [   517, 256299, 14869, 281, 301, 256298, 275, 119983,1, 0, 0, 0, 0, 0, 0, 0,0, 0],
                      [   320, 256299, 14869, 281, 2234, 289, 2275, 333,61391, 289, 256298, 543, 256297, 168714, 329, 256296,274, 1],
                  ]       )
              # fmt: on
              torch.testing.assert_allclose(__A					,			__A       )

              __snake_case	:    Dict											=     model.generate(input_ids.to(__A       )       )
              __snake_case	:    Optional[int]											=     [
                  "<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	:    str											=     tokenizer.batch_decode(__A       )
              self.assertEqual(__A					,			__A       )





def        lowerCAmelCase_  (      __lowerCamelCase							,			__lowerCamelCase			):
							if number < 0 or shift_amount < 0:
														raise ValueError("both inputs must be positive integers"			)

							__snake_case	:    Dict											=     str(bin(__lowerCamelCase			)			)
							binary_number += "0" * shift_amount
							return binary_number







def        lowerCAmelCase_  (      __lowerCamelCase							,			__lowerCamelCase			):
							if number < 0 or shift_amount < 0:
														raise ValueError("both inputs must be positive integers"			)

							__snake_case	:    List[str]											=     str(bin(__lowerCamelCase			)			)[2:]
							if shift_amount >= len(__lowerCamelCase			):
														return "0b0"
							__snake_case	:    Union[str, Any]											=     binary_number[: len(__lowerCamelCase			) - shift_amount]
							return "0b" + shifted_binary_number







def        lowerCAmelCase_  (      __lowerCamelCase							,			__lowerCamelCase			):
							if number >= 0:  # Get binary representation of positive number
														__snake_case	:    Optional[int]											=     "0" + str(bin(__lowerCamelCase			)			).strip("-"			)[2:]
							else:  # Get binary (2's complement) representation of negative number
														__snake_case	:    Tuple											=     len(bin(__lowerCamelCase			)[3:]			)  # Find 2's complement of number
														__snake_case	:    Any											=     bin(abs(__lowerCamelCase			) - (1 << binary_number_length)			)[3:]
														__snake_case	:    Optional[Any]											=     (
														    "1" + "0" * (binary_number_length - len(__lowerCamelCase			)) + binary_number
														)

							if shift_amount >= len(__lowerCamelCase			):
														return "0b" + binary_number[0] * len(__lowerCamelCase			)
							return (
							    "0b"
							    + binary_number[0] * shift_amount
							    + binary_number[: len(__lowerCamelCase			) - shift_amount]
							)


if __name__ == "__main__":
			import doctest

			doctest.testmod()

