

"""simple docstring"""




import copy
import os
from typing import Union

from ...configuration_utils import PretrainedConfig
from ...utils import logging


A_							:			Union[str, Any]											= logging.get_logger(__name__)

A_							:			Tuple											= {
    '''google/pix2struct-textcaps-base''': (
        '''https://huggingface.co/google/pix2struct-textcaps-base/resolve/main/config.json'''
    ),
}





class    lowerCamelCase						(__a			):
			lowerCamelCase__   :		List[str]							=							'''pix2struct_text_model'''
			lowerCamelCase__   :		int							=							['''past_key_values''']
			lowerCamelCase__   :		Optional[Any]							=							{
			    '''hidden_size''': '''hidden_size''',
			    '''num_attention_heads''': '''num_heads''',
			    '''num_hidden_layers''': '''num_layers''',
			}

			def __init__(					self				:						int    ,				__UpperCAmelCase				:						str=5_0_2_4_4    ,				__UpperCAmelCase				:						Dict=7_6_8    ,				__UpperCAmelCase				:						Optional[Any]=6_4    ,				__UpperCAmelCase				:						Union[str, Any]=2_0_4_8    ,				__UpperCAmelCase				:						Union[str, Any]=1_2    ,				__UpperCAmelCase				:						str=1_2    ,				__UpperCAmelCase				:						int=3_2    ,				__UpperCAmelCase				:						List[Any]=1_2_8    ,				__UpperCAmelCase				:						Optional[int]=0.1    ,				__UpperCAmelCase				:						int=1e-6    ,				__UpperCAmelCase				:						int=1.0    ,				__UpperCAmelCase				:						Dict="gelu_new"    ,				__UpperCAmelCase				:						Union[str, Any]=0    ,				__UpperCAmelCase				:						str=False    ,				__UpperCAmelCase				:						List[str]=0    ,				__UpperCAmelCase				:						str=1    ,				__UpperCAmelCase				:						Optional[Any]=False    ,				__UpperCAmelCase				:						Tuple=True    ,				**__UpperCAmelCase				:						List[str]    ,				)   ->    List[str]:
						SCREAMING_SNAKE_CASE__								=      vocab_size
						SCREAMING_SNAKE_CASE__								=      hidden_size
						SCREAMING_SNAKE_CASE__								=      d_kv
						SCREAMING_SNAKE_CASE__								=      d_ff
						SCREAMING_SNAKE_CASE__								=      num_layers
						SCREAMING_SNAKE_CASE__								=      num_heads
						SCREAMING_SNAKE_CASE__								=      relative_attention_num_buckets
						SCREAMING_SNAKE_CASE__								=      relative_attention_max_distance
						SCREAMING_SNAKE_CASE__								=      dropout_rate
						SCREAMING_SNAKE_CASE__								=      layer_norm_epsilon
						SCREAMING_SNAKE_CASE__								=      initializer_factor
						SCREAMING_SNAKE_CASE__								=      use_cache

						SCREAMING_SNAKE_CASE__								=      eos_token_id
						SCREAMING_SNAKE_CASE__								=      decoder_start_token_id

						# for backwards compatibility
						SCREAMING_SNAKE_CASE__								=      dense_act_fn

						super().__init__(
						    pad_token_id=snake_case__    ,				eos_token_id=snake_case__    ,				decoder_start_token_id=snake_case__    ,				tie_word_embeddings=snake_case__    ,				is_decoder=snake_case__    ,				**snake_case__    ,				)





			@classmethod
			def 					SCREAMING_SNAKE_CASE				(					cls				:						Optional[int]    ,				__UpperCAmelCase				:						Union[str, os.PathLike]    ,				**__UpperCAmelCase				:						int       )   ->    Dict:
						cls._set_token_in_kwargs(snake_case__       )

						SCREAMING_SNAKE_CASE__							,		SCREAMING_SNAKE_CASE__								=      cls.get_config_dict(snake_case__    ,				**snake_case__       )

						# get the text config dict if we are loading from Pix2StructConfig
						if config_dict.get("""model_type"""       ) == "pix2struct":
									SCREAMING_SNAKE_CASE__								=      config_dict["""text_config"""]

						if "model_type" in config_dict and hasattr(cls    ,				"""model_type"""       ) and config_dict["model_type"] != cls.model_type:
									logger.warning(
									    F"""You are using a model of type {config_dict["model_type"]} to instantiate a model of type """
									    F"""{cls.model_type}. This is not supported for all configurations of models and can yield errors."""       )

						return cls.from_dict(snake_case__    ,				**snake_case__       )





class    lowerCamelCase						(__a			):
			lowerCamelCase__   :		Optional[int]							=							'''pix2struct_vision_model'''

			def __init__(					self				:						Tuple    ,				__UpperCAmelCase				:						Union[str, Any]=7_6_8    ,				__UpperCAmelCase				:						Any=7_6_8    ,				__UpperCAmelCase				:						Dict=2_0_4_8    ,				__UpperCAmelCase				:						int=6_4    ,				__UpperCAmelCase				:						str=1_2    ,				__UpperCAmelCase				:						Optional[int]=1_2    ,				__UpperCAmelCase				:						Union[str, Any]="gelu_new"    ,				__UpperCAmelCase				:						Union[str, Any]=1e-6    ,				__UpperCAmelCase				:						int=0.0    ,				__UpperCAmelCase				:						Tuple=0.0    ,				__UpperCAmelCase				:						Optional[int]=1e-10    ,				__UpperCAmelCase				:						Optional[int]=1.0    ,				__UpperCAmelCase				:						Optional[Any]=4_0_9_6    ,				__UpperCAmelCase				:						Optional[int]=3_2    ,				__UpperCAmelCase				:						List[Any]=1_2_8    ,				**__UpperCAmelCase				:						Union[str, Any]    ,				)   ->    Optional[Any]:
						super().__init__(**snake_case__       )

						SCREAMING_SNAKE_CASE__								=      hidden_size
						SCREAMING_SNAKE_CASE__								=      patch_embed_hidden_size
						SCREAMING_SNAKE_CASE__								=      d_ff
						SCREAMING_SNAKE_CASE__								=      dropout_rate
						SCREAMING_SNAKE_CASE__								=      num_hidden_layers
						SCREAMING_SNAKE_CASE__								=      num_attention_heads
						SCREAMING_SNAKE_CASE__								=      initializer_range
						SCREAMING_SNAKE_CASE__								=      initializer_factor
						SCREAMING_SNAKE_CASE__								=      attention_dropout
						SCREAMING_SNAKE_CASE__								=      layer_norm_eps
						SCREAMING_SNAKE_CASE__								=      dense_act_fn
						SCREAMING_SNAKE_CASE__								=      seq_len
						SCREAMING_SNAKE_CASE__								=      relative_attention_num_buckets
						SCREAMING_SNAKE_CASE__								=      relative_attention_max_distance
						SCREAMING_SNAKE_CASE__								=      d_kv





			@classmethod
			def 					SCREAMING_SNAKE_CASE				(					cls				:						List[Any]    ,				__UpperCAmelCase				:						Union[str, os.PathLike]    ,				**__UpperCAmelCase				:						int       )   ->    Optional[int]:
						cls._set_token_in_kwargs(snake_case__       )

						SCREAMING_SNAKE_CASE__							,		SCREAMING_SNAKE_CASE__								=      cls.get_config_dict(snake_case__    ,				**snake_case__       )

						# get the vision config dict if we are loading from Pix2StructConfig
						if config_dict.get("""model_type"""       ) == "pix2struct":
									SCREAMING_SNAKE_CASE__								=      config_dict["""vision_config"""]

						if "model_type" in config_dict and hasattr(cls    ,				"""model_type"""       ) and config_dict["model_type"] != cls.model_type:
									logger.warning(
									    F"""You are using a model of type {config_dict["model_type"]} to instantiate a model of type """
									    F"""{cls.model_type}. This is not supported for all configurations of models and can yield errors."""       )

						return cls.from_dict(snake_case__    ,				**snake_case__       )





class    lowerCamelCase						(__a			):
			lowerCamelCase__   :		int							=							'''pix2struct'''
			lowerCamelCase__   :		str							=							True

			def __init__(					self				:						Optional[int]    ,				__UpperCAmelCase				:						List[str]=None    ,				__UpperCAmelCase				:						Optional[Any]=None    ,				__UpperCAmelCase				:						List[Any]=1.0    ,				__UpperCAmelCase				:						Any=0.02    ,				__UpperCAmelCase				:						Tuple=False    ,				__UpperCAmelCase				:						Union[str, Any]=False    ,				__UpperCAmelCase				:						Tuple=True    ,				**__UpperCAmelCase				:						int    ,				)   ->    Any:
						super().__init__(tie_word_embeddings=snake_case__    ,				is_encoder_decoder=snake_case__    ,				**snake_case__       )

						if text_config is None:
									SCREAMING_SNAKE_CASE__								=      {}
									logger.info("""text_config is None. Initializing the Pix2StructTextConfig with default values."""       )

						if vision_config is None:
									SCREAMING_SNAKE_CASE__								=      {}
									logger.info("""vision_config is None. Initializing the Pix2StructVisionConfig with default values."""       )

						SCREAMING_SNAKE_CASE__								=      PixaStructTextConfig(**snake_case__       )
						SCREAMING_SNAKE_CASE__								=      PixaStructVisionConfig(**snake_case__       )

						SCREAMING_SNAKE_CASE__								=      self.text_config.decoder_start_token_id
						SCREAMING_SNAKE_CASE__								=      self.text_config.pad_token_id
						SCREAMING_SNAKE_CASE__								=      self.text_config.eos_token_id

						SCREAMING_SNAKE_CASE__								=      initializer_factor
						SCREAMING_SNAKE_CASE__								=      initializer_range

						SCREAMING_SNAKE_CASE__								=      self.initializer_range
						SCREAMING_SNAKE_CASE__								=      self.initializer_range

						SCREAMING_SNAKE_CASE__								=      is_vqa

			@classmethod
			def 					SCREAMING_SNAKE_CASE				(					cls				:						Tuple    ,				__UpperCAmelCase				:						PixaStructTextConfig    ,				__UpperCAmelCase				:						PixaStructVisionConfig    ,				**__UpperCAmelCase				:						Any       )   ->    Optional[int]:
						return cls(text_config=text_config.to_dict()    ,				vision_config=vision_config.to_dict()    ,				**snake_case__       )





			def 					SCREAMING_SNAKE_CASE				(					self				:						str       )   ->    Tuple:
						SCREAMING_SNAKE_CASE__								=      copy.deepcopy(self.__dict__       )
						SCREAMING_SNAKE_CASE__								=      self.text_config.to_dict()
						SCREAMING_SNAKE_CASE__								=      self.vision_config.to_dict()
						SCREAMING_SNAKE_CASE__								=      self.__class__.model_type
						return output





"""simple docstring"""




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
A_							:			Optional[Any]											= pytest.mark.integration


A_							:			Union[str, Any]											= {"comet"}
A_							:			str											= importlib.util.find_spec("fairseq") is not None

A_							:			Any											= {"code_eval"}
A_							:			Tuple											= os.name == "nt"

A_							:			List[Any]											= {"bertscore", "frugalscore", "perplexity"}
A_							:			Any											= importlib.util.find_spec("transformers") is not None

def 	A       (     snake_case__    ):




			'''simple docstring'''


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

			return wrapper

def 	A       (     snake_case__    ):




			'''simple docstring'''


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

			return wrapper

def 	A       (     snake_case__    ):




			'''simple docstring'''


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

			return wrapper

def 	A       (     ):




			'''simple docstring'''


			SCREAMING_SNAKE_CASE__								=      [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(
    A__			,A__			,A__			)
@local
class    lowerCamelCase						(parameterized.TestCase			):
			lowerCamelCase__   :		int							=							{}
			lowerCamelCase__   :		Dict							=							None

			@pytest.mark.filterwarnings("""ignore:metric_module_factory is deprecated:FutureWarning"""       )
			@pytest.mark.filterwarnings("""ignore:load_metric is deprecated:FutureWarning"""       )
			def 					SCREAMING_SNAKE_CASE				(					self				:						str    ,				__UpperCAmelCase				:						str       )   ->    Union[str, Any]:
						SCREAMING_SNAKE_CASE__								=      """[...]"""
						SCREAMING_SNAKE_CASE__								=      importlib.import_module(
						    datasets.load.metric_module_factory(os.path.join("""metrics"""    ,				__UpperCAmelCase       )       ).module_path       )
						SCREAMING_SNAKE_CASE__								=      datasets.load.import_main_class(metric_module.__name__    ,				dataset=__UpperCAmelCase       )
						# check parameters
						SCREAMING_SNAKE_CASE__								=      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(__UpperCAmelCase    ,				metric_module.__name__       ):
									with self.use_local_metrics():
												try:
															SCREAMING_SNAKE_CASE__								=      doctest.testmod(__UpperCAmelCase    ,				verbose=__UpperCAmelCase    ,				raise_on_error=__UpperCAmelCase       )
												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 					SCREAMING_SNAKE_CASE				(					self				:						Optional[Any]    ,				__UpperCAmelCase				:						Optional[Any]       )   ->    str:
						SCREAMING_SNAKE_CASE__								=      """[...]"""
						SCREAMING_SNAKE_CASE__								=      importlib.import_module(
						    datasets.load.metric_module_factory(os.path.join("""metrics"""    ,				__UpperCAmelCase       )       ).module_path       )
						# run doctest
						with self.use_local_metrics():
									SCREAMING_SNAKE_CASE__								=      doctest.testmod(__UpperCAmelCase    ,				verbose=__UpperCAmelCase    ,				raise_on_error=__UpperCAmelCase       )
						self.assertEqual(results.failed    ,				0       )
						self.assertGreater(results.attempted    ,				1       )

			@contextmanager
			def 					SCREAMING_SNAKE_CASE				(					self				:						Any    ,				__UpperCAmelCase				:						List[str]    ,				__UpperCAmelCase				:						Optional[Any]       )   ->    str:
						if metric_name in self.INTENSIVE_CALLS_PATCHER:
									with self.INTENSIVE_CALLS_PATCHER[metric_name](__UpperCAmelCase       ):
												yield
						else:
									yield

			@contextmanager
			def 					SCREAMING_SNAKE_CASE				(					self				:						Optional[int]       )   ->    Union[str, Any]:
						def load_local_metric(__UpperCAmelCase				:						int    ,				*__UpperCAmelCase				:						int    ,				**__UpperCAmelCase				:						Optional[int]       ):
									return load_metric(os.path.join("""metrics"""    ,				__UpperCAmelCase       )    ,				*__UpperCAmelCase    ,				**__UpperCAmelCase       )

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





			@classmethod
			def 					SCREAMING_SNAKE_CASE				(					cls				:						Dict    ,				__UpperCAmelCase				:						int       )   ->    Optional[Any]:
						def wrapper(__UpperCAmelCase				:						List[str]       ):
									SCREAMING_SNAKE_CASE__								=      contextmanager(__UpperCAmelCase       )
									SCREAMING_SNAKE_CASE__								=      patcher
									return patcher

						return wrapper

@LocalMetricTest.register_intensive_calls_patcher("""bleurt"""    )
def 	A       (     snake_case__    ):




			'''simple docstring'''


			import tensorflow.compat.va as tf
			from bleurt.score import Predictor

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





			class    lowerCamelCase						(A__			):

						def 					SCREAMING_SNAKE_CASE				(					self				:						Any    ,				__UpperCAmelCase				:						Union[str, Any]       )   ->    List[str]:
									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:
						SCREAMING_SNAKE_CASE__								=      MockedPredictor()
						yield

@LocalMetricTest.register_intensive_calls_patcher("""bertscore"""    )
def 	A       (     snake_case__    ):




			'''simple docstring'''


			import torch

			def bert_cos_score_idf(snake_case__		,				snake_case__		,				*snake_case__		,				**snake_case__    ):
						return torch.tensor([[1.0, 1.0, 1.0]] * len(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:
						SCREAMING_SNAKE_CASE__								=      bert_cos_score_idf
						yield

@LocalMetricTest.register_intensive_calls_patcher("""comet"""    )
def 	A       (     snake_case__    ):




			'''simple docstring'''


			def load_from_checkpoint(snake_case__    ):





						class    lowerCamelCase						:

									def 					SCREAMING_SNAKE_CASE				(					self				:						List[str]    ,				__UpperCAmelCase				:						Any    ,				*__UpperCAmelCase				:						str    ,				**__UpperCAmelCase				:						List[Any]       )   ->    Optional[int]:
												assert len(__UpperCAmelCase       ) == 2
												SCREAMING_SNAKE_CASE__								=      [0.19, 0.92]
												return scores, sum(__UpperCAmelCase       ) / len(__UpperCAmelCase       )
						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:
						SCREAMING_SNAKE_CASE__								=      None
						with patch("""comet.load_from_checkpoint"""    ) as mock_load_from_checkpoint:
									SCREAMING_SNAKE_CASE__								=      load_from_checkpoint
									yield




def 	A       (     ):




			'''simple docstring'''


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


