




"""simple docstring"""


from __future__ import annotations

from typing import Any


class _lowerCamelCase    :






   def __init__(		self       :		Tuple   ,     UpperCamelCase       :		int   ,     UpperCamelCase       :		int   ,     UpperCamelCase       :		float = 0   )			->						None:

         """simple docstring"""
         lowerCAmelCase__ ,   lowerCAmelCase__      :			List[str]   =	row, column
         lowerCAmelCase__      :			Any   =	[[default_value for c in range(UpperCamelCase   )] for r in range(UpperCamelCase   )]






   def __str__(		self       :		Optional[int]   )			->						str:

         """simple docstring"""
         lowerCAmelCase__      :			List[Any]   =	f"""Matrix consist of {self.row} rows and {self.column} columns\n"""

         # Make string identifier
         lowerCAmelCase__      :			Optional[int]   =	0
         for row_vector in self.array:
               for obj in row_vector:
                     lowerCAmelCase__      :			str   =	max(UpperCamelCase   ,     len(str(UpperCamelCase   )   )   )
         lowerCAmelCase__      :			List[str]   =	f"""%{max_element_length}s"""

         # Make string and return
         def single_line(UpperCamelCase       :		list[float]   ) -> str:
               nonlocal string_format_identifier
               lowerCAmelCase__      :			List[Any]   =	"""["""
               line += ", ".join(string_format_identifier % (obj,) for obj in row_vector   )
               line += "]"
               return line

         s += "\n".join(single_line(UpperCamelCase   ) for row_vector in self.array   )
         return s






   def __repr__(		self       :		Optional[Any]   )			->						str:

         """simple docstring"""
         return str(self   )






   def 	_lowerCAmelCase   (		self       :		Union[str, Any]   ,     UpperCamelCase       :		tuple[int, int]   )			->						bool:

         """simple docstring"""
         if not (isinstance(UpperCamelCase   ,     (list, tuple)   ) and len(UpperCamelCase   ) == 2):
               return False
         elif not (0 <= loc[0] < self.row and 0 <= loc[1] < self.column):
               return False
         else:
               return True






   def __getitem__(		self       :		Optional[Any]   ,     UpperCamelCase       :		tuple[int, int]   )			->						Any:

         """simple docstring"""
         assert self.validate_indicies(UpperCamelCase   )
         return self.array[loc[0]][loc[1]]






   def __setitem__(		self       :		Union[str, Any]   ,     UpperCamelCase       :		tuple[int, int]   ,     UpperCamelCase       :		float   )			->						None:

         """simple docstring"""
         assert self.validate_indicies(UpperCamelCase   )
         lowerCAmelCase__      :			Union[str, Any]   =	value






   def __add__(		self       :		Optional[int]   ,     UpperCamelCase       :		Matrix   )			->						Matrix:

         """simple docstring"""
         assert isinstance(UpperCamelCase   ,     UpperCamelCase   )
         assert self.row == another.row and self.column == another.column

         # Add
         lowerCAmelCase__      :			Any   =	Matrix(self.row   ,     self.column   )
         for r in range(self.row   ):
               for c in range(self.column   ):
                     lowerCAmelCase__      :			str   =	self[r, c] + another[r, c]
         return result






   def __neg__(		self       :		Tuple   )			->						Matrix:

         """simple docstring"""
         lowerCAmelCase__      :			Optional[int]   =	Matrix(self.row   ,     self.column   )
         for r in range(self.row   ):
               for c in range(self.column   ):
                     lowerCAmelCase__      :			List[Any]   =	-self[r, c]
         return result






   def __sub__(		self       :		Union[str, Any]   ,     UpperCamelCase       :		Matrix   )			->						Matrix:

         """simple docstring"""
         return self + (-another)






   def __mul__(		self       :		Optional[int]   ,     UpperCamelCase       :		int | float | Matrix   )			->						Matrix:

         """simple docstring"""

         if isinstance(UpperCamelCase   ,     (int, float)   ):  # Scalar multiplication
               lowerCAmelCase__      :			str   =	Matrix(self.row   ,     self.column   )
               for r in range(self.row   ):
                     for c in range(self.column   ):
                           lowerCAmelCase__      :			int   =	self[r, c] * another
               return result
         elif isinstance(UpperCamelCase   ,     UpperCamelCase   ):  # Matrix multiplication
               assert self.column == another.row
               lowerCAmelCase__      :			Optional[int]   =	Matrix(self.row   ,     another.column   )
               for r in range(self.row   ):
                     for c in range(another.column   ):
                           for i in range(self.column   ):
                                 result[r, c] += self[r, i] * another[i, c]
               return result
         else:
               lowerCAmelCase__      :			str   =	f"""Unsupported type given for another ({type(UpperCamelCase   )})"""
               raise TypeError(UpperCamelCase   )






   def 	_lowerCAmelCase   (		self       :		Tuple   )			->						Matrix:

         """simple docstring"""
         lowerCAmelCase__      :			Any   =	Matrix(self.column   ,     self.row   )
         for r in range(self.row   ):
               for c in range(self.column   ):
                     lowerCAmelCase__      :			Dict   =	self[r, c]
         return result






   def 	_lowerCAmelCase   (		self       :		Optional[int]   ,     UpperCamelCase       :		Matrix   ,     UpperCamelCase       :		Matrix   )			->						Any:

         """simple docstring"""
         assert isinstance(UpperCamelCase   ,     UpperCamelCase   ) and isinstance(UpperCamelCase   ,     UpperCamelCase   )
         assert self.row == self.column == u.row == v.row  # u, v should be column vector
         assert u.column == v.column == 1  # u, v should be column vector

         # Calculate
         lowerCAmelCase__      :			Dict   =	v.transpose()
         lowerCAmelCase__      :			List[str]   =	(v_t * self * u)[0, 0] + 1
         if numerator_factor == 0:
               return None  # It's not invertable
         return self - ((self * u) * (v_t * self) * (1.0 / numerator_factor))


# Testing
if __name__ == "__main__":



  def 	lowercase_				(			)						->    None:
        # a^(-1)
        lowerCAmelCase__      :			str   =	Matrix(3    ,		3    ,		0				)
        for i in range(3				):
              lowerCAmelCase__      :			Union[str, Any]   =	1
        print(f"""a^(-1) is {ainv}"""				)
        # u, v
        lowerCAmelCase__      :			Optional[int]   =	Matrix(3    ,		1    ,		0				)
        lowerCAmelCase__ ,   lowerCAmelCase__ ,   lowerCAmelCase__      :			str   =	1, 2, -3
        lowerCAmelCase__      :			Tuple   =	Matrix(3    ,		1    ,		0				)
        lowerCAmelCase__ ,   lowerCAmelCase__ ,   lowerCAmelCase__      :			List[str]   =	4, -2, 5
        print(f"""u is {u}"""				)
        print(f"""v is {v}"""				)
        print(f"""uv^T is {u * v.transpose()}"""				)
        # Sherman Morrison
        print(f"""(a + uv^T)^(-1) is {ainv.sherman_morrison(__UpperCAmelCase    ,		__UpperCAmelCase				)}"""				)



  def 	lowercase_				(			)						->    None:
        import doctest

        doctest.testmod()

  testa()











"""simple docstring"""


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       :		str   ,     UpperCamelCase       :		int   ,     UpperCamelCase       :		str=99   ,     UpperCamelCase       :		Optional[int]=13   ,     UpperCamelCase       :		Dict=7   ,     UpperCamelCase       :		List[Any]=9   ,     UpperCamelCase       :		Optional[int]=True   ,     UpperCamelCase       :		Any=True   ,     UpperCamelCase       :		Union[str, Any]=False   ,     UpperCamelCase       :		Optional[int]=32   ,     UpperCamelCase       :		str=5   ,     UpperCamelCase       :		int=4   ,     UpperCamelCase       :		Optional[Any]=37   ,     UpperCamelCase       :		Tuple=8   ,     UpperCamelCase       :		Any=0.1   ,     UpperCamelCase       :		Union[str, Any]=0.002   ,     UpperCamelCase       :		List[Any]=1   ,     UpperCamelCase       :		Any=0   ,     UpperCamelCase       :		Optional[Any]=0   ,     UpperCamelCase       :		Dict=None   ,     UpperCamelCase       :		str=None   ,     )			->						Any:

         """simple docstring"""
         lowerCAmelCase__      :			Optional[Any]   =	parent
         lowerCAmelCase__      :			Union[str, Any]   =	batch_size
         lowerCAmelCase__      :			List[str]   =	encoder_seq_length
         lowerCAmelCase__      :			Any   =	decoder_seq_length
         # For common tests
         lowerCAmelCase__      :			Union[str, Any]   =	self.decoder_seq_length
         lowerCAmelCase__      :			List[Any]   =	is_training
         lowerCAmelCase__      :			Optional[Any]   =	use_attention_mask
         lowerCAmelCase__      :			str   =	use_labels
         lowerCAmelCase__      :			Any   =	vocab_size
         lowerCAmelCase__      :			Any   =	hidden_size
         lowerCAmelCase__      :			Optional[int]   =	num_hidden_layers
         lowerCAmelCase__      :			Any   =	num_attention_heads
         lowerCAmelCase__      :			int   =	d_ff
         lowerCAmelCase__      :			int   =	relative_attention_num_buckets
         lowerCAmelCase__      :			Union[str, Any]   =	dropout_rate
         lowerCAmelCase__      :			str   =	initializer_factor
         lowerCAmelCase__      :			Tuple   =	eos_token_id
         lowerCAmelCase__      :			List[str]   =	pad_token_id
         lowerCAmelCase__      :			str   =	decoder_start_token_id
         lowerCAmelCase__      :			Optional[Any]   =	None
         lowerCAmelCase__      :			Dict   =	decoder_layers






   def 	_lowerCAmelCase   (		self       :		Dict   )			->						Union[str, Any]:

         """simple docstring"""
         return TaConfig.from_pretrained("""google/umt5-base"""   )






   def 	_lowerCAmelCase   (		self       :		str   ,     UpperCamelCase       :		Optional[int]   ,     UpperCamelCase       :		Dict   ,     UpperCamelCase       :		Optional[int]   ,     UpperCamelCase       :		List[Any]=None   ,     UpperCamelCase       :		List[Any]=None   ,     UpperCamelCase       :		List[Any]=None   ,     UpperCamelCase       :		int=None   ,     UpperCamelCase       :		List[Any]=None   ,     )			->						List[Any]:

         """simple docstring"""
         if attention_mask is None:
               lowerCAmelCase__      :			Optional[Any]   =	input_ids.ne(config.pad_token_id   )
         if decoder_attention_mask is None:
               lowerCAmelCase__      :			List[str]   =	decoder_input_ids.ne(config.pad_token_id   )
         if head_mask is None:
               lowerCAmelCase__      :			str   =	torch.ones(config.num_hidden_layers   ,     config.num_attention_heads   ,     device=UpperCamelCase   )
         if decoder_head_mask is None:
               lowerCAmelCase__      :			Optional[int]   =	torch.ones(config.num_decoder_layers   ,     config.num_attention_heads   ,     device=UpperCamelCase   )
         if cross_attn_head_mask is None:
               lowerCAmelCase__      :			Optional[int]   =	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 	_lowerCAmelCase   (		self       :		Any   )			->						Dict:

         """simple docstring"""
         lowerCAmelCase__      :			Tuple   =	ids_tensor([self.batch_size, self.encoder_seq_length]   ,     self.vocab_size   )
         lowerCAmelCase__      :			Tuple   =	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
         lowerCAmelCase__      :			int   =	input_ids.clamp(self.pad_token_id + 1   )
         lowerCAmelCase__      :			Optional[Any]   =	decoder_input_ids.clamp(self.pad_token_id + 1   )

         lowerCAmelCase__      :			Tuple   =	self.get_config()
         lowerCAmelCase__      :			Dict   =	config.num_attention_heads
         lowerCAmelCase__      :			Dict   =	self.prepare_inputs_dict(UpperCamelCase   ,     UpperCamelCase   ,     UpperCamelCase   )
         return config, input_dict






   def 	_lowerCAmelCase   (		self       :		Dict   )			->						Optional[int]:

         """simple docstring"""
         lowerCAmelCase__ ,   lowerCAmelCase__      :			Tuple   =	self.prepare_config_and_inputs()
         return config, inputs_dict






   def 	_lowerCAmelCase   (		self       :		Dict   )			->						List[str]:

         """simple docstring"""
         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 	_lowerCAmelCase   (		self       :		Optional[int]   )			->						Optional[int]:

         """simple docstring"""
         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 	_lowerCAmelCase   (		self       :		List[Any]   ,     UpperCamelCase       :		List[Any]   ,     UpperCamelCase       :		Optional[int]   ,     UpperCamelCase       :		List[str]   ,     UpperCamelCase       :		Optional[int]   ,     UpperCamelCase       :		Any   ,     UpperCamelCase       :		Any   ,     )			->						List[Any]:

         """simple docstring"""
         lowerCAmelCase__      :			List[Any]   =	UMTaModel(config=UpperCamelCase   )
         model.to(UpperCamelCase   )
         model.eval()
         lowerCAmelCase__      :			Optional[int]   =	model(
             input_ids=UpperCamelCase   ,     decoder_input_ids=UpperCamelCase   ,     attention_mask=UpperCamelCase   ,     decoder_attention_mask=UpperCamelCase   ,     )
         lowerCAmelCase__      :			Optional[int]   =	model(input_ids=UpperCamelCase   ,     decoder_input_ids=UpperCamelCase   )
         lowerCAmelCase__      :			List[Any]   =	result.last_hidden_state
         lowerCAmelCase__      :			Any   =	result.past_key_values
         lowerCAmelCase__      :			str   =	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 	_lowerCAmelCase   (		self       :		Optional[int]   ,     UpperCamelCase       :		List[str]   ,     UpperCamelCase       :		int   ,     UpperCamelCase       :		Optional[int]   ,     UpperCamelCase       :		Any   ,     UpperCamelCase       :		Optional[int]   ,     UpperCamelCase       :		Optional[int]   ,     )			->						Tuple:

         """simple docstring"""
         lowerCAmelCase__      :			Dict   =	UMTaModel(config=UpperCamelCase   ).get_decoder().to(UpperCamelCase   ).eval()
         # first forward pass
         lowerCAmelCase__      :			Optional[int]   =	model(UpperCamelCase   ,     use_cache=UpperCamelCase   )
         lowerCAmelCase__      :			Any   =	model(UpperCamelCase   )
         lowerCAmelCase__      :			Union[str, Any]   =	model(UpperCamelCase   ,     use_cache=UpperCamelCase   )

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

         lowerCAmelCase__ ,   lowerCAmelCase__      :			List[str]   =	outputs.to_tuple()

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

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

         lowerCAmelCase__      :			List[str]   =	model(UpperCamelCase   )["""last_hidden_state"""]
         lowerCAmelCase__      :			Any   =	model(UpperCamelCase   ,     past_key_values=UpperCamelCase   )["""last_hidden_state"""]

         # select random slice
         lowerCAmelCase__      :			Optional[int]   =	ids_tensor((1,)   ,     output_from_past.shape[-1]   ).item()
         lowerCAmelCase__      :			List[Any]   =	output_from_no_past[:, -1, random_slice_idx].detach()
         lowerCAmelCase__      :			Optional[int]   =	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 	_lowerCAmelCase   (		self       :		Optional[Any]   ,     UpperCamelCase       :		str   ,     UpperCamelCase       :		int   ,     )			->						Dict:

         """simple docstring"""
         lowerCAmelCase__      :			Union[str, Any]   =	UMTaModel(config=UpperCamelCase   ).to(UpperCamelCase   ).half().eval()
         lowerCAmelCase__      :			Dict   =	model(**UpperCamelCase   )["""last_hidden_state"""]
         self.parent.assertFalse(torch.isnan(UpperCamelCase   ).any().item()   )






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






   def 	_lowerCAmelCase   (		self       :		Dict   )			->						List[str]:

         """simple docstring"""
         lowerCAmelCase__      :			Any   =	UMTaModelTester(self   )






   @unittest.skip("""Test has a segmentation fault on torch 1.8.0"""   )
   def 	_lowerCAmelCase   (		self       :		Union[str, Any]   )			->						str:

         """simple docstring"""
         lowerCAmelCase__      :			str   =	self.model_tester.prepare_config_and_inputs()
         lowerCAmelCase__      :			List[str]   =	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 	_lowerCAmelCase   (		self       :		Union[str, Any]   )			->						Optional[int]:

         """simple docstring"""
         lowerCAmelCase__      :			int   =	self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model_fpaa_forward(*UpperCamelCase   )






   def 	_lowerCAmelCase   (		self       :		int   )			->						Union[str, Any]:

         """simple docstring"""
         lowerCAmelCase__      :			Optional[int]   =	["""encoder_attentions""", """decoder_attentions""", """cross_attentions"""]
         lowerCAmelCase__      :			Any   =	self.model_tester.prepare_config_and_inputs()
         lowerCAmelCase__      :			List[Any]   =	config_and_inputs[0]
         lowerCAmelCase__      :			int   =	UMTaForConditionalGeneration(UpperCamelCase   ).eval()
         model.to(UpperCamelCase   )

         lowerCAmelCase__      :			List[Any]   =	{
             """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()   ):
               lowerCAmelCase__      :			Union[str, Any]   =	{name: mask}
               # Explicitly pass decoder_head_mask as it is required from T5 model when head_mask specified
               if name == "head_mask":
                     lowerCAmelCase__      :			Tuple   =	torch.ones(
                         config.num_decoder_layers   ,     config.num_heads   ,     device=UpperCamelCase   )

               lowerCAmelCase__      :			Union[str, Any]   =	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
               lowerCAmelCase__      :			str   =	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 	_lowerCAmelCase   (		self       :		List[Any]   )			->						Union[str, Any]:

         """simple docstring"""
         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 	_lowerCAmelCase   (		self       :		List[Any]   )			->						Optional[int]:

         """simple docstring"""
         lowerCAmelCase__      :			Dict   =	UMTaForConditionalGeneration.from_pretrained("""google/umt5-small"""   ,     return_dict=UpperCamelCase   ).to(UpperCamelCase   )
         lowerCAmelCase__      :			Dict   =	AutoTokenizer.from_pretrained("""google/umt5-small"""   ,     use_fast=UpperCamelCase   ,     legacy=UpperCamelCase   )
         lowerCAmelCase__      :			int   =	[
             """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>.""",
         ]
         lowerCAmelCase__      :			Union[str, Any]   =	tokenizer(UpperCamelCase   ,     return_tensors="""pt"""   ,     padding=UpperCamelCase   ).input_ids
         # fmt: off
         lowerCAmelCase__      :			List[Any]   =	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   )

         lowerCAmelCase__      :			Optional[Any]   =	model.generate(input_ids.to(UpperCamelCase   )   )
         lowerCAmelCase__      :			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>""",
         ]
         lowerCAmelCase__      :			Any   =	tokenizer.batch_decode(UpperCamelCase   )
         self.assertEqual(UpperCamelCase   ,     UpperCamelCase   )





