





import logging
import os
import sys
from pathlib import Path
from unittest.mock import patch

from parameterized import parameterized
from run_eval import run_generate
from run_eval_search import run_search

from transformers.testing_utils import CaptureStdout, TestCasePlus, slow
from utils import ROUGE_KEYS


logging.basicConfig(level=logging.DEBUG)
_snake_case  :   Optional[Any]						   =  logging.getLogger()

def   a_   (			lowerCAmelCase_				:      Path,     lowerCAmelCase_				:      list     ):
   __lowerCAmelCase							     =     '\n'.join(lowerCAmelCase_     )
   Path(lowerCAmelCase_     ).open('w'     ).writelines(lowerCAmelCase_     )


_snake_case  :   Dict						   =  'patrickvonplaten/t5-tiny-random'
_snake_case  :   Dict						   =  'sshleifer/bart-tiny-random'
_snake_case  :   Any						   =  'sshleifer/tiny-mbart'

_snake_case  :   Any						   =  logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)
logging.disable(logging.CRITICAL)  # remove noisy download output from tracebacks




class     _UpperCAmelCase   (   _UpperCamelCase   ):

  """simple docstring"""


  def     lowercase		(    self			:		Dict ,  lowerCAmelCase_			:		Optional[Any]					)							->						Union[str, Any]:
     __lowerCAmelCase							     =     Path(self.get_auto_remove_tmp_dir()					) / 'utest_input.source'
     __lowerCAmelCase							     =     input_file_name.parent / 'utest_output.txt'
     assert not output_file_name.exists()
     __lowerCAmelCase							     =     [' New York (CNN)When Liana Barrientos was 23 years old, she got married in Westchester County.']
     _dump_articles(lowerCAmelCase_ ,  lowerCAmelCase_					)

     __lowerCAmelCase							     =     str(Path(self.get_auto_remove_tmp_dir()					) / 'scores.json'					)
     __lowerCAmelCase							     =     'translation_en_to_de' if model == T5_TINY else 'summarization'
     __lowerCAmelCase							     =     f"""
            run_eval_search.py
            {model}
            {input_file_name}
            {output_file_name}
            --score_path {score_path}
            --task {task}
            --num_beams 2
            --length_penalty 2.0
            """.split()

     with patch.object(lowerCAmelCase_ ,  'argv' ,  lowerCAmelCase_					):
        run_generate()
        assert Path(lowerCAmelCase_					).exists()
        # os.remove(Path(output_file_name))






  def     lowercase		(    self			:		Dict					)							->						Optional[int]:
     self.run_eval_tester(lowerCAmelCase_					)






  @parameterized.expand([BART_TINY, MBART_TINY]					)
  @slow
  def     lowercase		(    self			:		List[str] ,  lowerCAmelCase_			:		Any					)							->						Tuple:
     self.run_eval_tester(lowerCAmelCase_					)






  @parameterized.expand([T5_TINY, MBART_TINY]					)
  @slow
  def     lowercase		(    self			:		Any ,  lowerCAmelCase_			:		Optional[Any]					)							->						Union[str, Any]:
     __lowerCAmelCase							     =     Path(self.get_auto_remove_tmp_dir()					) / 'utest_input.source'
     __lowerCAmelCase							     =     input_file_name.parent / 'utest_output.txt'
     assert not output_file_name.exists()

     __lowerCAmelCase							     =     {
         'en': ['Machine learning is great, isn\'t it?', 'I like to eat bananas', 'Tomorrow is another great day!'],
         'de': [
             'Maschinelles Lernen ist großartig, oder?',
             'Ich esse gerne Bananen',
             'Morgen ist wieder ein toller Tag!',
         ],
     }

     __lowerCAmelCase							     =     Path(self.get_auto_remove_tmp_dir()					)
     __lowerCAmelCase							     =     str(tmp_dir / 'scores.json'					)
     __lowerCAmelCase							     =     str(tmp_dir / 'val.target'					)
     _dump_articles(lowerCAmelCase_ ,  text['en']					)
     _dump_articles(lowerCAmelCase_ ,  text['de']					)
     __lowerCAmelCase							     =     'translation_en_to_de' if model == T5_TINY else 'summarization'
     __lowerCAmelCase							     =     f"""
            run_eval_search.py
            {model}
            {str(lowerCAmelCase_					)}
            {str(lowerCAmelCase_					)}
            --score_path {score_path}
            --reference_path {reference_path}
            --task {task}
            """.split()
     testargs.extend(['--search', 'num_beams=1:2 length_penalty=0.9:1.0']					)

     with patch.object(lowerCAmelCase_ ,  'argv' ,  lowerCAmelCase_					):
        with CaptureStdout() as cs:
           run_search()
        __lowerCAmelCase							     =     [' num_beams | length_penalty', model, 'Best score args']
        __lowerCAmelCase							     =     ['Info']
        if "translation" in task:
           expected_strings.append('bleu'					)
        else:
           expected_strings.extend(lowerCAmelCase_					)
        for w in expected_strings:
           assert w in cs.out
        for w in un_expected_strings:
           assert w not in cs.out
        assert Path(lowerCAmelCase_					).exists()
        os.remove(Path(lowerCAmelCase_					)					)








from dataclasses import dataclass
from typing import Optional

import torch
from torch import nn

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .attention import BasicTransformerBlock
from .modeling_utils import ModelMixin



@dataclass
class     _UpperCAmelCase   (   _UpperCamelCase   ):

  """simple docstring"""


  a_      =			42





class     _UpperCAmelCase   (   _UpperCamelCase		,   _UpperCamelCase   ):

  """simple docstring"""


  @register_to_config
  def __init__(    self			:		Optional[int] ,  lowerCAmelCase_			:		int = 1_6 ,  lowerCAmelCase_			:		int = 8_8 ,  lowerCAmelCase_			:		Optional[int] = None ,  lowerCAmelCase_			:		Optional[int] = None ,  lowerCAmelCase_			:		int = 1 ,  lowerCAmelCase_			:		float = 0.0 ,  lowerCAmelCase_			:		int = 3_2 ,  lowerCAmelCase_			:		Optional[int] = None ,  lowerCAmelCase_			:		bool = False ,  lowerCAmelCase_			:		Optional[int] = None ,  lowerCAmelCase_			:		str = "geglu" ,  lowerCAmelCase_			:		bool = True ,  lowerCAmelCase_			:		bool = True ,  )							->						Dict:
     super().__init__()
     __lowerCAmelCase							     =     num_attention_heads
     __lowerCAmelCase							     =     attention_head_dim
     __lowerCAmelCase							     =     num_attention_heads * attention_head_dim

     __lowerCAmelCase							     =     in_channels

     __lowerCAmelCase							     =     torch.nn.GroupNorm(num_groups=lowerCAmelCase_ ,  num_channels=lowerCAmelCase_ ,  eps=1e-6 ,  affine=lowerCAmelCase_					)
     __lowerCAmelCase							     =     nn.Linear(lowerCAmelCase_ ,  lowerCAmelCase_					)

     # 3. Define transformers blocks
     __lowerCAmelCase							     =     nn.ModuleList(
         [
             BasicTransformerBlock(
                 lowerCAmelCase_ ,  lowerCAmelCase_ ,  lowerCAmelCase_ ,  dropout=lowerCAmelCase_ ,  cross_attention_dim=lowerCAmelCase_ ,  activation_fn=lowerCAmelCase_ ,  attention_bias=lowerCAmelCase_ ,  double_self_attention=lowerCAmelCase_ ,  norm_elementwise_affine=lowerCAmelCase_ ,  )
             for d in range(lowerCAmelCase_					)
         ]					)

     __lowerCAmelCase							     =     nn.Linear(lowerCAmelCase_ ,  lowerCAmelCase_					)






  def     lowercase		(    self			:		Dict ,  lowerCAmelCase_			:		Tuple ,  lowerCAmelCase_			:		List[Any]=None ,  lowerCAmelCase_			:		Optional[int]=None ,  lowerCAmelCase_			:		Union[str, Any]=None ,  lowerCAmelCase_			:		Optional[int]=1 ,  lowerCAmelCase_			:		Optional[int]=None ,  lowerCAmelCase_			:		bool = True ,  )							->						str:
     __lowerCAmelCase     ,     __lowerCAmelCase     ,     __lowerCAmelCase     ,     __lowerCAmelCase							     =     hidden_states.shape
     __lowerCAmelCase							     =     batch_frames // num_frames

     __lowerCAmelCase							     =     hidden_states

     __lowerCAmelCase							     =     hidden_states[None, :].reshape(lowerCAmelCase_ ,  lowerCAmelCase_ ,  lowerCAmelCase_ ,  lowerCAmelCase_ ,  lowerCAmelCase_					)
     __lowerCAmelCase							     =     hidden_states.permute(0 ,  2 ,  1 ,  3 ,  4					)

     __lowerCAmelCase							     =     self.norm(lowerCAmelCase_					)
     __lowerCAmelCase							     =     hidden_states.permute(0 ,  3 ,  4 ,  2 ,  1					).reshape(batch_size * height * width ,  lowerCAmelCase_ ,  lowerCAmelCase_					)

     __lowerCAmelCase							     =     self.proj_in(lowerCAmelCase_					)

     # 2. Blocks
     for block in self.transformer_blocks:
        __lowerCAmelCase							     =     block(
            lowerCAmelCase_ ,  encoder_hidden_states=lowerCAmelCase_ ,  timestep=lowerCAmelCase_ ,  cross_attention_kwargs=lowerCAmelCase_ ,  class_labels=lowerCAmelCase_ ,  )

     # 3. Output
     __lowerCAmelCase							     =     self.proj_out(lowerCAmelCase_					)
     __lowerCAmelCase							     =     (
         hidden_states[None, None, :]
         .reshape(lowerCAmelCase_ ,  lowerCAmelCase_ ,  lowerCAmelCase_ ,  lowerCAmelCase_ ,  lowerCAmelCase_					)
         .permute(0 ,  3 ,  4 ,  1 ,  2					)
         .contiguous()
     )
     __lowerCAmelCase							     =     hidden_states.reshape(lowerCAmelCase_ ,  lowerCAmelCase_ ,  lowerCAmelCase_ ,  lowerCAmelCase_					)

     __lowerCAmelCase							     =     hidden_states + residual

     if not return_dict:
        return (output,)

     return TransformerTemporalModelOutput(sample=lowerCAmelCase_					)

