



import doctest
import glob
import importlib
import inspect
import os
import re
from contextlib import contextmanager
from functools import wraps
from unittest.mock import patch

import numpy as np
import pytest
from absl.testing import parameterized

import datasets
from datasets import load_metric

from .utils import for_all_test_methods, local, slow


# mark all tests as integration
snake_case__					:     List[str]    		=   pytest.mark.integration


snake_case__					:     int    		=   {"""comet"""}
snake_case__					:     Optional[int]    		=   importlib.util.find_spec("""fairseq""") is not None

snake_case__					:     List[Any]    		=   {"""code_eval"""}
snake_case__					:     str    		=   os.name == """nt"""

snake_case__					:     Dict    		=   {"""bertscore""", """frugalscore""", """perplexity"""}
snake_case__					:     int    		=   importlib.util.find_spec("""transformers""") is not None



def    snake_case_				(    _SCREAMING_SNAKE_CASE   ):
	@wraps(_SCREAMING_SNAKE_CASE   )
	def wrapper(self  ,	_SCREAMING_SNAKE_CASE   ):
		if not _has_fairseq and metric_name in REQUIRE_FAIRSEQ:
			self.skipTest("\"test requires Fairseq\""   )
		else:
			test_case(self  ,	_SCREAMING_SNAKE_CASE   )

	return wrapper



def    snake_case_				(    _SCREAMING_SNAKE_CASE   ):
	@wraps(_SCREAMING_SNAKE_CASE   )
	def wrapper(self  ,	_SCREAMING_SNAKE_CASE   ):
		if not _has_transformers and metric_name in REQUIRE_TRANSFORMERS:
			self.skipTest("\"test requires transformers\""   )
		else:
			test_case(self  ,	_SCREAMING_SNAKE_CASE   )

	return wrapper



def    snake_case_				(    _SCREAMING_SNAKE_CASE   ):
	@wraps(_SCREAMING_SNAKE_CASE   )
	def wrapper(self  ,	_SCREAMING_SNAKE_CASE   ):
		if _on_windows and metric_name in UNSUPPORTED_ON_WINDOWS:
			self.skipTest("\"test not supported on Windows\""   )
		else:
			test_case(self  ,	_SCREAMING_SNAKE_CASE   )

	return wrapper



def    snake_case_				(    ):
	__lowercase		       =			[metric_dir.split(os.sep   )[-2] for metric_dir in glob.glob("./metrics/*/"   )]
	return [{"testcase_name": x, "metric_name": x} for x in metrics if x != "gleu"]  # gleu is unfinished

@parameterized.named_parameters(get_local_metric_names()	)
@for_all_test_methods(
    _lowercase  ,						_lowercase  ,						_lowercase	)
@local
class   _A   ( parameterized.TestCase	):


						'''simple docstring'''





						_snake_case			:     Optional[Any]      							=      {}
						_snake_case			:     List[Any]      							=      None



						@pytest.mark.filterwarnings("ignore:metric_module_factory is deprecated:FutureWarning"	)
						@pytest.mark.filterwarnings("ignore:load_metric is deprecated:FutureWarning"	)
						def     _snake_case	(	self	:							str							,      lowerCamelCase	:							Any	):






							'''simple docstring'''



							__lowercase		       =			"[...]"
							__lowercase		       =			importlib.import_module(
							    datasets.load.metric_module_factory(os.path.join("metrics"							,      lowerCamelCase	)	).module_path	)
							__lowercase		       =			datasets.load.import_main_class(metric_module.__name__							,      dataset=lowerCamelCase	)
							# check parameters
							__lowercase		       =			inspect.signature(metric._compute	).parameters
							self.assertTrue(all(p.kind != p.VAR_KEYWORD for p in parameters.values()	)	)  # no **kwargs
							# run doctest
							with self.patch_intensive_calls(lowerCamelCase							,      metric_module.__name__	):
								with self.use_local_metrics():
									try:
										__lowercase		       =			doctest.testmod(lowerCamelCase							,      verbose=lowerCamelCase							,      raise_on_error=lowerCamelCase	)
									except doctest.UnexpectedException as e:
										raise e.exc_info[1]  # raise the exception that doctest caught
							self.assertEqual(results.failed							,      0	)
							self.assertGreater(results.attempted							,      1	)



						@slow
						def     _snake_case	(	self	:							List[str]							,      lowerCamelCase	:							List[str]	):






							'''simple docstring'''



							__lowercase		       =			"[...]"
							__lowercase		       =			importlib.import_module(
							    datasets.load.metric_module_factory(os.path.join("metrics"							,      lowerCamelCase	)	).module_path	)
							# run doctest
							with self.use_local_metrics():
								__lowercase		       =			doctest.testmod(lowerCamelCase							,      verbose=lowerCamelCase							,      raise_on_error=lowerCamelCase	)
							self.assertEqual(results.failed							,      0	)
							self.assertGreater(results.attempted							,      1	)



						@contextmanager
						def     _snake_case	(	self	:							List[Any]							,      lowerCamelCase	:							Optional[int]							,      lowerCamelCase	:							List[str]	):






							'''simple docstring'''



							if metric_name in self.INTENSIVE_CALLS_PATCHER:
								with self.INTENSIVE_CALLS_PATCHER[metric_name](lowerCamelCase	):
									yield
							else:
								yield



						@contextmanager
						def     _snake_case	(	self	:							List[Any]	):






							'''simple docstring'''



							def load_local_metric(lowerCamelCase	:							Optional[int]							,      *lowerCamelCase	:							Any							,      **lowerCamelCase	:							Dict	):
								return load_metric(os.path.join("metrics"							,      lowerCamelCase	)							,      *lowerCamelCase							,      **lowerCamelCase	)

							with patch("datasets.load_metric"	) as mock_load_metric:
								__lowercase		       =			load_local_metric
								yield



						@classmethod
						def     _snake_case	(	cls	:							Union[str, Any]							,      lowerCamelCase	:							Optional[Any]	):






							'''simple docstring'''



							def wrapper(lowerCamelCase	:							Union[str, Any]	):
								__lowercase		       =			contextmanager(lowerCamelCase	)
								__lowercase		       =			patcher
								return patcher

							return wrapper



@LocalMetricTest.register_intensive_calls_patcher("bleurt"   )
def    snake_case_				(    _SCREAMING_SNAKE_CASE   ):
	import tensorflow.compat.va as tf
	from bleurt.score import Predictor

	tf.flags.DEFINE_string("sv"  ,	""  ,	""   )  # handle pytest cli flags

	class   _A   ( _lowercase	):


							'''simple docstring'''





							def     _snake_case	(	self	:							Any							,      lowerCamelCase	:							int	):






								'''simple docstring'''



								assert len(input_dict["input_ids"]	) == 2
								return np.array([1.03, 1.04]	)

    # mock predict_fn which is supposed to do a forward pass with a bleurt model
	with patch("bleurt.score._create_predictor"   ) as mock_create_predictor:
		__lowercase		       =			MockedPredictor()
		yield



@LocalMetricTest.register_intensive_calls_patcher("bertscore"   )
def    snake_case_				(    _SCREAMING_SNAKE_CASE   ):
	import torch

	def bert_cos_score_idf(_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE  ,	*_SCREAMING_SNAKE_CASE  ,	**_SCREAMING_SNAKE_CASE   ):
		return torch.tensor([[1.0, 1.0, 1.0]] * len(_SCREAMING_SNAKE_CASE   )   )

	# mock get_model which is supposed to do download a bert model
	# mock bert_cos_score_idf which is supposed to do a forward pass with a bert model
	with patch("bert_score.scorer.get_model"   ), patch(
	    "bert_score.scorer.bert_cos_score_idf"   ) as mock_bert_cos_score_idf:
		__lowercase		       =			bert_cos_score_idf
		yield



@LocalMetricTest.register_intensive_calls_patcher("comet"   )
def    snake_case_				(    _SCREAMING_SNAKE_CASE   ):
	def load_from_checkpoint(_SCREAMING_SNAKE_CASE   ):

		class   _A   :


								'''simple docstring'''





								def     _snake_case	(	self	:							str							,      lowerCamelCase	:							Any							,      *lowerCamelCase	:							List[str]							,      **lowerCamelCase	:							List[str]	):






									'''simple docstring'''



									assert len(lowerCamelCase	) == 2
									__lowercase		       =			[0.19, 0.92]
									return scores, sum(lowerCamelCase	) / len(lowerCamelCase	)

		return Model()

	# mock load_from_checkpoint which is supposed to do download a bert model
	# mock load_from_checkpoint which is supposed to do download a bert model
	with patch("comet.download_model"   ) as mock_download_model:
		__lowercase		       =			None
		with patch("comet.load_from_checkpoint"   ) as mock_load_from_checkpoint:
			__lowercase		       =			load_from_checkpoint
			yield



def    snake_case_				(    ):
	__lowercase		       =			load_metric(os.path.join("metrics"  ,	"seqeval"   )   )
	__lowercase		       =			"ERROR"
	__lowercase		       =			F"""Scheme should be one of [IOB1, IOB2, IOE1, IOE2, IOBES, BILOU], got {wrong_scheme}"""
	with pytest.raises(_SCREAMING_SNAKE_CASE  ,	match=re.escape(_SCREAMING_SNAKE_CASE   )   ):
		metric.compute(predictions=[]  ,	references=[]  ,	scheme=_SCREAMING_SNAKE_CASE   )








from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


snake_case__					:     List[Any]    		=   logging.get_logger(__name__)

snake_case__					:     List[str]    		=   {
    """hustvl/yolos-small""": """https://huggingface.co/hustvl/yolos-small/resolve/main/config.json""",
    # See all YOLOS models at https://huggingface.co/models?filter=yolos
}

class   _A   ( _lowercase	):


						'''simple docstring'''





						_snake_case			:     List[Any]      							=      """yolos"""



						def __init__(	self	:							Union[str, Any]							,      lowerCamelCase	:							Union[str, Any]=768							,      lowerCamelCase	:							int=12							,      lowerCamelCase	:							Union[str, Any]=12							,      lowerCamelCase	:							Optional[Any]=3_072							,      lowerCamelCase	:							Optional[int]="gelu"							,      lowerCamelCase	:							Dict=0.0							,      lowerCamelCase	:							Optional[Any]=0.0							,      lowerCamelCase	:							Any=0.02							,      lowerCamelCase	:							Optional[Any]=1e-12							,      lowerCamelCase	:							Optional[Any]=[512, 864]							,      lowerCamelCase	:							str=16							,      lowerCamelCase	:							Dict=3							,      lowerCamelCase	:							str=True							,      lowerCamelCase	:							List[Any]=100							,      lowerCamelCase	:							Dict=True							,      lowerCamelCase	:							Dict=False							,      lowerCamelCase	:							List[str]=1							,      lowerCamelCase	:							str=5							,      lowerCamelCase	:							Any=2							,      lowerCamelCase	:							str=5							,      lowerCamelCase	:							Optional[int]=2							,      lowerCamelCase	:							List[Any]=0.1							,      **lowerCamelCase	:							List[Any]							,      ):






							'''simple docstring'''



							super().__init__(**lowerCamelCase	)

							__lowercase		       =			hidden_size
							__lowercase		       =			num_hidden_layers
							__lowercase		       =			num_attention_heads
							__lowercase		       =			intermediate_size
							__lowercase		       =			hidden_act
							__lowercase		       =			hidden_dropout_prob
							__lowercase		       =			attention_probs_dropout_prob
							__lowercase		       =			initializer_range
							__lowercase		       =			layer_norm_eps
							__lowercase		       =			image_size
							__lowercase		       =			patch_size
							__lowercase		       =			num_channels
							__lowercase		       =			qkv_bias
							__lowercase		       =			num_detection_tokens
							__lowercase		       =			use_mid_position_embeddings
							__lowercase		       =			auxiliary_loss
							# Hungarian matcher
							__lowercase		       =			class_cost
							__lowercase		       =			bbox_cost
							__lowercase		       =			giou_cost
							# Loss coefficients
							__lowercase		       =			bbox_loss_coefficient
							__lowercase		       =			giou_loss_coefficient
							__lowercase		       =			eos_coefficient

class   _A   ( _lowercase	):


						'''simple docstring'''





						_snake_case			:     Dict      							=      version.parse("""1.11"""	)



						@property
						def     _snake_case	(	self	:							Union[str, Any]	):






							'''simple docstring'''



							return OrderedDict(
							    [
							        ("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
							    ]	)



						@property
						def     _snake_case	(	self	:							str	):






							'''simple docstring'''



							return 1e-4



						@property
						def     _snake_case	(	self	:							Tuple	):






							'''simple docstring'''



							return 12



