
import unittest

import numpy as np

from transformers.testing_utils import require_flax, require_tf, require_torch
from transformers.utils import (
    expand_dims,
    flatten_dict,
    is_flax_available,
    is_tf_available,
    is_torch_available,
    reshape,
    squeeze,
    transpose,
)


if is_flax_available():
			import jax.numpy as jnp

if is_tf_available():
			import tensorflow as tf

if is_torch_available():
			import torch




class        SCREAMING_SNAKE_CASE__					(     unittest.TestCase ):





	'''simple docstring'''



	def 			A							(  self     :   Optional[int]     ):


				'''simple docstring'''


				_snake_case          =      {
				    'task_specific_params': {
				        'summarization': {'length_penalty': 1.0, 'max_length': 128, 'min_length': 12, 'num_beams': 4},
				        'summarization_cnn': {'length_penalty': 2.0, 'max_length': 142, 'min_length': 56, 'num_beams': 4},
				        'summarization_xsum': {'length_penalty': 1.0, 'max_length': 62, 'min_length': 11, 'num_beams': 6},
				    }
				}
				_snake_case          =      {
				    'task_specific_params.summarization.length_penalty': 1.0,
				    'task_specific_params.summarization.max_length': 128,
				    'task_specific_params.summarization.min_length': 12,
				    'task_specific_params.summarization.num_beams': 4,
				    'task_specific_params.summarization_cnn.length_penalty': 2.0,
				    'task_specific_params.summarization_cnn.max_length': 142,
				    'task_specific_params.summarization_cnn.min_length': 56,
				    'task_specific_params.summarization_cnn.num_beams': 4,
				    'task_specific_params.summarization_xsum.length_penalty': 1.0,
				    'task_specific_params.summarization_xsum.max_length': 62,
				    'task_specific_params.summarization_xsum.min_length': 11,
				    'task_specific_params.summarization_xsum.num_beams': 6,
				}

				self.assertEqual(flatten_dict(lowercase     )      ,  lowercase     )



	def 			A							(  self     :   Any     ):


				'''simple docstring'''


				_snake_case          =      np.random.randn(3      ,  4     )
				self.assertTrue(np.allclose(transpose(lowercase     )      ,  x.transpose()     )     )

				_snake_case          =      np.random.randn(3      ,  4      ,  5     )
				self.assertTrue(np.allclose(transpose(lowercase      ,  axes=(1, 2, 0)     )      ,  x.transpose((1, 2, 0)     )     )     )



	@require_torch
	def 			A							(  self     :   List[str]     ):


				'''simple docstring'''


				_snake_case          =      np.random.randn(3      ,  4     )
				_snake_case          =      torch.tensor(lowercase     )
				self.assertTrue(np.allclose(transpose(lowercase     )      ,  transpose(lowercase     ).numpy()     )     )

				_snake_case          =      np.random.randn(3      ,  4      ,  5     )
				_snake_case          =      torch.tensor(lowercase     )
				self.assertTrue(np.allclose(transpose(lowercase      ,  axes=(1, 2, 0)     )      ,  transpose(lowercase      ,  axes=(1, 2, 0)     ).numpy()     )     )



	@require_tf
	def 			A							(  self     :   int     ):


				'''simple docstring'''


				_snake_case          =      np.random.randn(3      ,  4     )
				_snake_case          =      tf.constant(lowercase     )
				self.assertTrue(np.allclose(transpose(lowercase     )      ,  transpose(lowercase     ).numpy()     )     )

				_snake_case          =      np.random.randn(3      ,  4      ,  5     )
				_snake_case          =      tf.constant(lowercase     )
				self.assertTrue(np.allclose(transpose(lowercase      ,  axes=(1, 2, 0)     )      ,  transpose(lowercase      ,  axes=(1, 2, 0)     ).numpy()     )     )



	@require_flax
	def 			A							(  self     :   Tuple     ):


				'''simple docstring'''


				_snake_case          =      np.random.randn(3      ,  4     )
				_snake_case          =      jnp.array(lowercase     )
				self.assertTrue(np.allclose(transpose(lowercase     )      ,  np.asarray(transpose(lowercase     )     )     )     )

				_snake_case          =      np.random.randn(3      ,  4      ,  5     )
				_snake_case          =      jnp.array(lowercase     )
				self.assertTrue(np.allclose(transpose(lowercase      ,  axes=(1, 2, 0)     )      ,  np.asarray(transpose(lowercase      ,  axes=(1, 2, 0)     )     )     )     )



	def 			A							(  self     :   Tuple     ):


				'''simple docstring'''


				_snake_case          =      np.random.randn(3      ,  4     )
				self.assertTrue(np.allclose(reshape(lowercase      ,  (4, 3)     )      ,  np.reshape(lowercase      ,  (4, 3)     )     )     )

				_snake_case          =      np.random.randn(3      ,  4      ,  5     )
				self.assertTrue(np.allclose(reshape(lowercase      ,  (12, 5)     )      ,  np.reshape(lowercase      ,  (12, 5)     )     )     )



	@require_torch
	def 			A							(  self     :   int     ):


				'''simple docstring'''


				_snake_case          =      np.random.randn(3      ,  4     )
				_snake_case          =      torch.tensor(lowercase     )
				self.assertTrue(np.allclose(reshape(lowercase      ,  (4, 3)     )      ,  reshape(lowercase      ,  (4, 3)     ).numpy()     )     )

				_snake_case          =      np.random.randn(3      ,  4      ,  5     )
				_snake_case          =      torch.tensor(lowercase     )
				self.assertTrue(np.allclose(reshape(lowercase      ,  (12, 5)     )      ,  reshape(lowercase      ,  (12, 5)     ).numpy()     )     )



	@require_tf
	def 			A							(  self     :   Union[str, Any]     ):


				'''simple docstring'''


				_snake_case          =      np.random.randn(3      ,  4     )
				_snake_case          =      tf.constant(lowercase     )
				self.assertTrue(np.allclose(reshape(lowercase      ,  (4, 3)     )      ,  reshape(lowercase      ,  (4, 3)     ).numpy()     )     )

				_snake_case          =      np.random.randn(3      ,  4      ,  5     )
				_snake_case          =      tf.constant(lowercase     )
				self.assertTrue(np.allclose(reshape(lowercase      ,  (12, 5)     )      ,  reshape(lowercase      ,  (12, 5)     ).numpy()     )     )



	@require_flax
	def 			A							(  self     :   Union[str, Any]     ):


				'''simple docstring'''


				_snake_case          =      np.random.randn(3      ,  4     )
				_snake_case          =      jnp.array(lowercase     )
				self.assertTrue(np.allclose(reshape(lowercase      ,  (4, 3)     )      ,  np.asarray(reshape(lowercase      ,  (4, 3)     )     )     )     )

				_snake_case          =      np.random.randn(3      ,  4      ,  5     )
				_snake_case          =      jnp.array(lowercase     )
				self.assertTrue(np.allclose(reshape(lowercase      ,  (12, 5)     )      ,  np.asarray(reshape(lowercase      ,  (12, 5)     )     )     )     )



	def 			A							(  self     :   int     ):


				'''simple docstring'''


				_snake_case          =      np.random.randn(1      ,  3      ,  4     )
				self.assertTrue(np.allclose(squeeze(lowercase     )      ,  np.squeeze(lowercase     )     )     )

				_snake_case          =      np.random.randn(1      ,  4      ,  1      ,  5     )
				self.assertTrue(np.allclose(squeeze(lowercase      ,  axis=2     )      ,  np.squeeze(lowercase      ,  axis=2     )     )     )



	@require_torch
	def 			A							(  self     :   Optional[Any]     ):


				'''simple docstring'''


				_snake_case          =      np.random.randn(1      ,  3      ,  4     )
				_snake_case          =      torch.tensor(lowercase     )
				self.assertTrue(np.allclose(squeeze(lowercase     )      ,  squeeze(lowercase     ).numpy()     )     )

				_snake_case          =      np.random.randn(1      ,  4      ,  1      ,  5     )
				_snake_case          =      torch.tensor(lowercase     )
				self.assertTrue(np.allclose(squeeze(lowercase      ,  axis=2     )      ,  squeeze(lowercase      ,  axis=2     ).numpy()     )     )



	@require_tf
	def 			A							(  self     :   str     ):


				'''simple docstring'''


				_snake_case          =      np.random.randn(1      ,  3      ,  4     )
				_snake_case          =      tf.constant(lowercase     )
				self.assertTrue(np.allclose(squeeze(lowercase     )      ,  squeeze(lowercase     ).numpy()     )     )

				_snake_case          =      np.random.randn(1      ,  4      ,  1      ,  5     )
				_snake_case          =      tf.constant(lowercase     )
				self.assertTrue(np.allclose(squeeze(lowercase      ,  axis=2     )      ,  squeeze(lowercase      ,  axis=2     ).numpy()     )     )



	@require_flax
	def 			A							(  self     :   Any     ):


				'''simple docstring'''


				_snake_case          =      np.random.randn(1      ,  3      ,  4     )
				_snake_case          =      jnp.array(lowercase     )
				self.assertTrue(np.allclose(squeeze(lowercase     )      ,  np.asarray(squeeze(lowercase     )     )     )     )

				_snake_case          =      np.random.randn(1      ,  4      ,  1      ,  5     )
				_snake_case          =      jnp.array(lowercase     )
				self.assertTrue(np.allclose(squeeze(lowercase      ,  axis=2     )      ,  np.asarray(squeeze(lowercase      ,  axis=2     )     )     )     )



	def 			A							(  self     :   List[Any]     ):


				'''simple docstring'''


				_snake_case          =      np.random.randn(3      ,  4     )
				self.assertTrue(np.allclose(expand_dims(lowercase      ,  axis=1     )      ,  np.expand_dims(lowercase      ,  axis=1     )     )     )



	@require_torch
	def 			A							(  self     :   Optional[int]     ):


				'''simple docstring'''


				_snake_case          =      np.random.randn(3      ,  4     )
				_snake_case          =      torch.tensor(lowercase     )
				self.assertTrue(np.allclose(expand_dims(lowercase      ,  axis=1     )      ,  expand_dims(lowercase      ,  axis=1     ).numpy()     )     )



	@require_tf
	def 			A							(  self     :   str     ):


				'''simple docstring'''


				_snake_case          =      np.random.randn(3      ,  4     )
				_snake_case          =      tf.constant(lowercase     )
				self.assertTrue(np.allclose(expand_dims(lowercase      ,  axis=1     )      ,  expand_dims(lowercase      ,  axis=1     ).numpy()     )     )





	@require_flax
	def 			A							(  self     :   Tuple     ):


				'''simple docstring'''


				_snake_case          =      np.random.randn(3      ,  4     )
				_snake_case          =      jnp.array(lowercase     )
				self.assertTrue(np.allclose(expand_dims(lowercase      ,  axis=1     )      ,  np.asarray(expand_dims(lowercase      ,  axis=1     )     )     )     )

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)
_lowerCamelCase	:    Union[str, Any]          =      logging.getLogger()






def 	a_		(	__lowercase       :     Path	, __lowercase       :     list			)			->  Tuple:
			_snake_case          =      '\n'.join(__lowercase			)
			Path(__lowercase			).open('w'			).writelines(__lowercase			)


_lowerCamelCase	:    Any          =      '''patrickvonplaten/t5-tiny-random'''
_lowerCamelCase	:    List[Any]          =      '''sshleifer/bart-tiny-random'''
_lowerCamelCase	:    List[Any]          =      '''sshleifer/tiny-mbart'''

_lowerCamelCase	:    Union[str, Any]          =      logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)
logging.disable(logging.CRITICAL)  # remove noisy download output from tracebacks




class        SCREAMING_SNAKE_CASE__					(     UpperCAmelCase ):





	'''simple docstring'''



	def 			A							(  self     :   Optional[int]      ,  lowercase     :   int     ):


				'''simple docstring'''


				_snake_case          =      Path(self.get_auto_remove_tmp_dir()     ) / 'utest_input.source'
				_snake_case          =      input_file_name.parent / 'utest_output.txt'
				assert not output_file_name.exists()
				_snake_case          =      [' New York (CNN)When Liana Barrientos was 23 years old, she got married in Westchester County.']
				_dump_articles(lowercase      ,  lowercase     )

				_snake_case          =      str(Path(self.get_auto_remove_tmp_dir()     ) / 'scores.json'     )
				_snake_case          =      'translation_en_to_de' if model == T5_TINY else 'summarization'
				_snake_case          =      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(lowercase      ,  'argv'      ,  lowercase     ):
							run_generate()
							assert Path(lowercase     ).exists()
							# os.remove(Path(output_file_name))



	def 			A							(  self     :   List[Any]     ):


				'''simple docstring'''


				self.run_eval_tester(lowercase     )



	@parameterized.expand([BART_TINY, MBART_TINY]     )
	@slow
	def 			A							(  self     :   Any      ,  lowercase     :   int     ):


				'''simple docstring'''


				self.run_eval_tester(lowercase     )





	@parameterized.expand([T5_TINY, MBART_TINY]     )
	@slow
	def 			A							(  self     :   Any      ,  lowercase     :   str     ):


				'''simple docstring'''


				_snake_case          =      Path(self.get_auto_remove_tmp_dir()     ) / 'utest_input.source'
				_snake_case          =      input_file_name.parent / 'utest_output.txt'
				assert not output_file_name.exists()

				_snake_case          =      {
				    '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!',
				    ],
				}

				_snake_case          =      Path(self.get_auto_remove_tmp_dir()     )
				_snake_case          =      str(tmp_dir / 'scores.json'     )
				_snake_case          =      str(tmp_dir / 'val.target'     )
				_dump_articles(lowercase      ,  text['en']     )
				_dump_articles(lowercase      ,  text['de']     )
				_snake_case          =      'translation_en_to_de' if model == T5_TINY else 'summarization'
				_snake_case          =      f'''
            run_eval_search.py
            {model}
            {str(lowercase     )}
            {str(lowercase     )}
            --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(lowercase      ,  'argv'      ,  lowercase     ):
							with CaptureStdout() as cs:
										run_search()
							_snake_case          =      [' num_beams | length_penalty', model, 'Best score args']
							_snake_case          =      ['Info']
							if "translation" in task:
										expected_strings.append('bleu'     )
							else:
										expected_strings.extend(lowercase     )
							for w in expected_strings:
										assert w in cs.out
							for w in un_expected_strings:
										assert w not in cs.out
							assert Path(lowercase     ).exists()
							os.remove(Path(lowercase     )     )