


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      , __snake_case      , __snake_case=9_9      , __snake_case=1_3      , __snake_case=7      , __snake_case=9      , __snake_case=True      , __snake_case=True      , __snake_case=False      , __snake_case=3_2      , __snake_case=5      , __snake_case=4      , __snake_case=3_7      , __snake_case=8      , __snake_case=0.1      , __snake_case=0.002      , __snake_case=1      , __snake_case=0      , __snake_case=0      , __snake_case=None      , __snake_case=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 		a_      (	self   ):
         return TaConfig.from_pretrained('''google/umt5-base'''   )
    def 		a_      (	self      , __snake_case      , __snake_case      , __snake_case      , __snake_case=None      , __snake_case=None      , __snake_case=None      , __snake_case=None      , __snake_case=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=__snake_case   )
         if decoder_head_mask is None:
              snake_case					=    torch.ones(config.num_decoder_layers      , config.num_attention_heads      , device=__snake_case   )
         if cross_attn_head_mask is None:
              snake_case					=    torch.ones(
                  config.num_decoder_layers      , config.num_attention_heads      , device=__snake_case   )
         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   ):
         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(__snake_case      , __snake_case      , __snake_case   )
         return config, input_dict
    def 		a_      (	self   ):
         snake_case			,  snake_case					=    self.prepare_config_and_inputs()
         return config, inputs_dict
    def 		a_      (	self   ):
         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   ):
         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      , __snake_case      , __snake_case      , __snake_case      , __snake_case      , __snake_case      , __snake_case      , ):
         snake_case					=    UMTaModel(config=__snake_case   )
         model.to(__snake_case   )
         model.eval()
         snake_case					=    model(
             input_ids=__snake_case      , decoder_input_ids=__snake_case      , attention_mask=__snake_case      , decoder_attention_mask=__snake_case      , )
         snake_case					=    model(input_ids=__snake_case      , decoder_input_ids=__snake_case   )
         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(__snake_case   )      , 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      , __snake_case      , __snake_case      , __snake_case      , __snake_case      , __snake_case      , __snake_case      , ):
         snake_case					=    UMTaModel(config=__snake_case   ).get_decoder().to(__snake_case   ).eval()
         # first forward pass
         snake_case					=    model(__snake_case      , use_cache=__snake_case   )
         snake_case					=    model(__snake_case   )
         snake_case					=    model(__snake_case      , use_cache=__snake_case   )

         self.parent.assertTrue(len(__snake_case   ) == len(__snake_case   )   )
         self.parent.assertTrue(len(__snake_case   ) == len(__snake_case   ) + 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(__snake_case   )['''last_hidden_state''']
         snake_case					=    model(__snake_case      , past_key_values=__snake_case   )['''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(__snake_case      , __snake_case      , atol=1E-3   )   )




    def 		a_      (	self      , __snake_case      , __snake_case      , ):
         snake_case					=    UMTaModel(config=__snake_case   ).to(__snake_case   ).half().eval()
         snake_case					=    model(**__snake_case   )['''last_hidden_state''']
         self.parent.assertFalse(torch.isnan(__snake_case   ).any().item()   )






@require_torch
class   A__ (   snake_case__	, snake_case__	, snake_case__	, unittest.TestCase     ):







    """simple docstring"""

    __magic_name__        =		(
        (UMTaModel, UMTaForConditionalGeneration, UMTaForQuestionAnswering) if is_torch_available() else ()
    )
    __magic_name__        =		(UMTaForConditionalGeneration,) if is_torch_available() else ()
    __magic_name__        =		(
        {
            'conversational': UMTaForConditionalGeneration,
            'feature-extraction': UMTaModel,
            'summarization': UMTaForConditionalGeneration,
            'text2text-generation': UMTaForConditionalGeneration,
            'translation': UMTaForConditionalGeneration,
            'question-answering': UMTaForQuestionAnswering,
        }
        if is_torch_available()
        else {}
    )
    __magic_name__        =		True
    __magic_name__        =		False
    __magic_name__        =		False
    __magic_name__        =		True
    __magic_name__        =		True
    # The small UMT5 model needs higher percentages for CPU/MP tests
    __magic_name__        =		[0.8, 0.9]
    def 		a_      (	self   ):
         snake_case					=    UMTaModelTester(self   )
    @unittest.skip('''Test has a segmentation fault on torch 1.8.0'''   )
    def 		a_      (	self   ):
         snake_case					=    self.model_tester.prepare_config_and_inputs()
         snake_case					=    UMTaModel(config_and_inputs[0]   ).to(__snake_case   )
         with tempfile.TemporaryDirectory() as tmpdirname:
              torch.onnx.export(
                  __snake_case      , (config_and_inputs[1], config_and_inputs[3], config_and_inputs[2])      , F'''{tmpdirname}/t5_test.onnx'''      , export_params=__snake_case      , opset_version=9      , input_names=['''input_ids''', '''decoder_input_ids''']      , )
    @unittest.skipIf(torch_device == '''cpu'''      , '''Cant do half precision'''   )
    def 		a_      (	self   ):
         snake_case					=    self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model_fpaa_forward(*__snake_case   )
    def 		a_      (	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(__snake_case   ).eval()
         model.to(__snake_case   )

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

         for attn_name, (name, mask) in zip(__snake_case      , 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=__snake_case   )

              snake_case					=    model.generate(
                  config_and_inputs[1]['''input_ids''']      , num_beams=1      , max_length=3      , output_attentions=__snake_case      , return_dict_in_generate=__snake_case      , **__snake_case      , )
              # 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 		a_      (	self   ):
         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 		a_      (	self   ):
         snake_case					=    UMTaForConditionalGeneration.from_pretrained('''google/umt5-small'''      , return_dict=__snake_case   ).to(__snake_case   )
         snake_case					=    AutoTokenizer.from_pretrained('''google/umt5-small'''      , use_fast=__snake_case      , legacy=__snake_case   )
         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(__snake_case      , return_tensors='''pt'''      , padding=__snake_case   ).input_ids
         # fmt: off
         snake_case					=    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(__snake_case      , __snake_case   )

         snake_case					=    model.generate(input_ids.to(__snake_case   )   )
         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(__snake_case   )
         self.assertEqual(__snake_case      , __snake_case   )








from __future__ import annotations

import time

_SCREAMING_SNAKE_CASE      :			List[Any]      				=   list[tuple[int, int]]

_SCREAMING_SNAKE_CASE      :			Any      				=   [
    [0, 0, 0, 0, 0, 0, 0],
    [0, 1, 0, 0, 0, 0, 0],  # 0 are free path whereas 1's are obstacles
    [0, 0, 0, 0, 0, 0, 0],
    [0, 0, 1, 0, 0, 0, 0],
    [1, 0, 1, 0, 0, 0, 0],
    [0, 0, 0, 0, 0, 0, 0],
    [0, 0, 0, 0, 1, 0, 0],
]

_SCREAMING_SNAKE_CASE      :			Any      				=   [[-1, 0], [0, -1], [1, 0], [0, 1]]  # up, left, down, right

class   A__ :







    """simple docstring"""

    def __init__(	self      , __snake_case      , __snake_case      , __snake_case      , __snake_case      , __snake_case   ):
         snake_case					=    pos_x
         snake_case					=    pos_y
         snake_case					=    (pos_y, pos_x)
         snake_case					=    goal_x
         snake_case					=    goal_y
         snake_case					=    parent






class   A__ :







    """simple docstring"""

    def __init__(	self      , __snake_case      , __snake_case   ):
         snake_case					=    Node(start[1]      , start[0]      , goal[1]      , goal[0]      , __snake_case   )
         snake_case					=    Node(goal[1]      , goal[0]      , goal[1]      , goal[0]      , __snake_case   )

         snake_case					=    [self.start]
         snake_case					=    False
    def 		a_      (	self   ):
         while self.node_queue:
              snake_case					=    self.node_queue.pop(0   )

              if current_node.pos == self.target.pos:
                   snake_case					=    True
                   return self.retrace_path(__snake_case   )

              snake_case					=    self.get_successors(__snake_case   )

              for node in successors:
                   self.node_queue.append(__snake_case   )

         if not self.reached:
              return [self.start.pos]
         return None
    def 		a_      (	self      , __snake_case   ):
         snake_case					=    []
         for action in delta:
              snake_case					=    parent.pos_x + action[1]
              snake_case					=    parent.pos_y + action[0]
              if not (0 <= pos_x <= len(grid[0]   ) - 1 and 0 <= pos_y <= len(__snake_case   ) - 1):
                   continue

              if grid[pos_y][pos_x] != 0:
                   continue

              successors.append(
                  Node(__snake_case      , __snake_case      , self.target.pos_y      , self.target.pos_x      , __snake_case   )   )
         return successors




    def 		a_      (	self      , __snake_case   ):
         snake_case					=    node
         snake_case					=    []
         while current_node is not None:
              path.append((current_node.pos_y, current_node.pos_x)   )
              snake_case					=    current_node.parent
         path.reverse()
         return path






class   A__ :







    """simple docstring"""

    def __init__(	self      , __snake_case      , __snake_case   ):
         snake_case					=    BreadthFirstSearch(__snake_case      , __snake_case   )
         snake_case					=    BreadthFirstSearch(__snake_case      , __snake_case   )
         snake_case					=    False
    def 		a_      (	self   ):
         while self.fwd_bfs.node_queue or self.bwd_bfs.node_queue:
              snake_case					=    self.fwd_bfs.node_queue.pop(0   )
              snake_case					=    self.bwd_bfs.node_queue.pop(0   )

              if current_bwd_node.pos == current_fwd_node.pos:
                   snake_case					=    True
                   return self.retrace_bidirectional_path(
                       __snake_case      , __snake_case   )

              snake_case					=    current_bwd_node
              snake_case					=    current_fwd_node

              snake_case					=    {
                  self.fwd_bfs: self.fwd_bfs.get_successors(__snake_case   ),
                  self.bwd_bfs: self.bwd_bfs.get_successors(__snake_case   ),
              }

              for bfs in [self.fwd_bfs, self.bwd_bfs]:
                   for node in successors[bfs]:
                        bfs.node_queue.append(__snake_case   )

         if not self.reached:
              return [self.fwd_bfs.start.pos]
         return None




    def 		a_      (	self      , __snake_case      , __snake_case   ):
         snake_case					=    self.fwd_bfs.retrace_path(__snake_case   )
         snake_case					=    self.bwd_bfs.retrace_path(__snake_case   )
         bwd_path.pop()
         bwd_path.reverse()
         snake_case					=    fwd_path + bwd_path
         return path


if __name__ == "__main__":
 # all coordinates are given in format [y,x]
 import doctest

 doctest.testmod()
 _SCREAMING_SNAKE_CASE      :			Optional[Any]      				=   (0, 0)
 _SCREAMING_SNAKE_CASE      :			List[Any]      				=   (len(grid) - 1, len(grid[0]) - 1)
 for elem in grid:
  print(elem)

 _SCREAMING_SNAKE_CASE      :			List[Any]      				=   time.time()
 _SCREAMING_SNAKE_CASE      :			List[Any]      				=   BreadthFirstSearch(init, goal)
 _SCREAMING_SNAKE_CASE      :			List[str]      				=   bfs.search()
 _SCREAMING_SNAKE_CASE      :			int      				=   time.time() - start_bfs_time

 print("Unidirectional BFS computation time : ", bfs_time)

 _SCREAMING_SNAKE_CASE      :			Any      				=   time.time()
 _SCREAMING_SNAKE_CASE      :			Union[str, Any]      				=   BidirectionalBreadthFirstSearch(init, goal)
 _SCREAMING_SNAKE_CASE      :			Union[str, Any]      				=   bd_bfs.search()
 _SCREAMING_SNAKE_CASE      :			Tuple      				=   time.time() - start_bd_bfs_time

 print("Bidirectional BFS computation time : ", bd_bfs_time)




