



import copy
import os
from typing import Union

from ...configuration_utils import PretrainedConfig
from ...models.auto.modeling_auto import MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
from ...utils import logging
from ..auto import CONFIG_MAPPING


lowerCAmelCase__						:	Optional[int]					=logging.get_logger(__name__)

lowerCAmelCase__						:	List[str]					={
    '''Salesforce/instruct-blip-flan-t5''': '''https://huggingface.co/Salesforce/instruct-blip-flan-t5/resolve/main/config.json''',
}

class   UpperCAmelCase_    (  __a			):

						'''simple docstring'''




						UpperCamelCase__     :					Tuple							    =    '''instructblip_vision_model'''






						def __init__(       self ,				_A=1_408 ,				_A=6_144 ,				_A=39 ,				_A=16 ,				_A=224 ,				_A=14 ,				_A="gelu" ,				_A=1e-6 ,				_A=0.0 ,				_A=1e-10 ,				_A=True ,				**_A ,				):







													'''simple docstring'''

													super().__init__(**_UpperCamelCase						)

													__SCREAMING_SNAKE_CASE										=   hidden_size
													__SCREAMING_SNAKE_CASE										=   intermediate_size
													__SCREAMING_SNAKE_CASE										=   num_hidden_layers
													__SCREAMING_SNAKE_CASE										=   num_attention_heads
													__SCREAMING_SNAKE_CASE										=   patch_size
													__SCREAMING_SNAKE_CASE										=   image_size
													__SCREAMING_SNAKE_CASE										=   initializer_range
													__SCREAMING_SNAKE_CASE										=   attention_dropout
													__SCREAMING_SNAKE_CASE										=   layer_norm_eps
													__SCREAMING_SNAKE_CASE										=   hidden_act
													__SCREAMING_SNAKE_CASE										=   qkv_bias






						@classmethod
						def 			_A      (       cls ,				_A ,				**_A						):







													'''simple docstring'''

													cls._set_token_in_kwargs(_UpperCamelCase						)

													__SCREAMING_SNAKE_CASE				,			__SCREAMING_SNAKE_CASE										=   cls.get_config_dict(_UpperCamelCase ,				**_UpperCamelCase						)

													# get the vision config dict if we are loading from InstructBlipConfig
													if config_dict.get('model_type'						) == "instructblip":
																				__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(_UpperCamelCase ,				**_UpperCamelCase						)







class   UpperCAmelCase_    (  __a			):

						'''simple docstring'''




						UpperCamelCase__     :					Any							    =    '''instructblip_qformer'''






						def __init__(       self ,				_A=30_522 ,				_A=768 ,				_A=12 ,				_A=12 ,				_A=3_072 ,				_A="gelu" ,				_A=0.1 ,				_A=0.1 ,				_A=512 ,				_A=0.0_2 ,				_A=1e-12 ,				_A=0 ,				_A="absolute" ,				_A=2 ,				_A=1_408 ,				**_A ,				):







													'''simple docstring'''

													super().__init__(pad_token_id=_UpperCamelCase ,				**_UpperCamelCase						)

													__SCREAMING_SNAKE_CASE										=   vocab_size
													__SCREAMING_SNAKE_CASE										=   hidden_size
													__SCREAMING_SNAKE_CASE										=   num_hidden_layers
													__SCREAMING_SNAKE_CASE										=   num_attention_heads
													__SCREAMING_SNAKE_CASE										=   hidden_act
													__SCREAMING_SNAKE_CASE										=   intermediate_size
													__SCREAMING_SNAKE_CASE										=   hidden_dropout_prob
													__SCREAMING_SNAKE_CASE										=   attention_probs_dropout_prob
													__SCREAMING_SNAKE_CASE										=   max_position_embeddings
													__SCREAMING_SNAKE_CASE										=   initializer_range
													__SCREAMING_SNAKE_CASE										=   layer_norm_eps
													__SCREAMING_SNAKE_CASE										=   position_embedding_type
													__SCREAMING_SNAKE_CASE										=   cross_attention_frequency
													__SCREAMING_SNAKE_CASE										=   encoder_hidden_size






						@classmethod
						def 			_A      (       cls ,				_A ,				**_A						):







													'''simple docstring'''

													cls._set_token_in_kwargs(_UpperCamelCase						)

													__SCREAMING_SNAKE_CASE				,			__SCREAMING_SNAKE_CASE										=   cls.get_config_dict(_UpperCamelCase ,				**_UpperCamelCase						)

													# get the qformer config dict if we are loading from InstructBlipConfig
													if config_dict.get('model_type'						) == "instructblip":
																				__SCREAMING_SNAKE_CASE										=   config_dict['qformer_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(_UpperCamelCase ,				**_UpperCamelCase						)







class   UpperCAmelCase_    (  __a			):

						'''simple docstring'''




						UpperCamelCase__     :					str							    =    '''instructblip'''
						UpperCamelCase__     :					Dict							    =    True






						def __init__(       self ,				_A=None ,				_A=None ,				_A=None ,				_A=32 ,				**_A						):







													'''simple docstring'''

													super().__init__(**_UpperCamelCase						)

													if vision_config is None:
																				__SCREAMING_SNAKE_CASE										=   {}
																				logger.info('vision_config is None. initializing the InstructBlipVisionConfig with default values.'						)

													if qformer_config is None:
																				__SCREAMING_SNAKE_CASE										=   {}
																				logger.info('qformer_config is None. Initializing the InstructBlipQFormerConfig with default values.'						)

													if text_config is None:
																				__SCREAMING_SNAKE_CASE										=   {}
																				logger.info('text_config is None. Initializing the text config with default values (`OPTConfig`).'						)

													__SCREAMING_SNAKE_CASE										=   InstructBlipVisionConfig(**_UpperCamelCase						)
													__SCREAMING_SNAKE_CASE										=   InstructBlipQFormerConfig(**_UpperCamelCase						)
													__SCREAMING_SNAKE_CASE										=   text_config['model_type'] if 'model_type' in text_config else 'opt'
													__SCREAMING_SNAKE_CASE										=   CONFIG_MAPPING[text_model_type](**_UpperCamelCase						)

													__SCREAMING_SNAKE_CASE										=   self.text_config.tie_word_embeddings
													__SCREAMING_SNAKE_CASE										=   self.text_config.is_encoder_decoder

													__SCREAMING_SNAKE_CASE										=   num_query_tokens
													__SCREAMING_SNAKE_CASE										=   self.vision_config.hidden_size
													__SCREAMING_SNAKE_CASE										=   self.text_config.model_type in MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
													__SCREAMING_SNAKE_CASE										=   1.0
													__SCREAMING_SNAKE_CASE										=   0.0_2






						@classmethod
						def 			_A      (       cls ,				_A ,				_A ,				_A ,				**_A ,				):







													'''simple docstring'''

													return cls(
													    vision_config=vision_config.to_dict() ,				qformer_config=qformer_config.to_dict() ,				text_config=text_config.to_dict() ,				**_UpperCamelCase ,				)






						def 			_A      (       self						):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE										=   copy.deepcopy(self.__dict__						)
													__SCREAMING_SNAKE_CASE										=   self.vision_config.to_dict()
													__SCREAMING_SNAKE_CASE										=   self.qformer_config.to_dict()
													__SCREAMING_SNAKE_CASE										=   self.text_config.to_dict()
													__SCREAMING_SNAKE_CASE										=   self.__class__.model_type
													return output








import sacrebleu as scb
from packaging import version
from sacrebleu import TER

import datasets


_A  =	"\\n@inproceedings{snover-etal-2006-study,\n    title = \"A Study of Translation Edit Rate with Targeted Human Annotation\",\n    author = \"Snover, Matthew  and\n      Dorr, Bonnie  and\n      Schwartz, Rich  and\n      Micciulla, Linnea  and\n      Makhoul, John\",\n    booktitle = \"Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers\",\n    month = aug # \" 8-12\",\n    year = \"2006\",\n    address = \"Cambridge, Massachusetts, USA\",\n    publisher = \"Association for Machine Translation in the Americas\",\n    url = \"https://aclanthology.org/2006.amta-papers.25\",\n    pages = \"223--231\",\n}\n@inproceedings{post-2018-call,\n    title = \"A Call for Clarity in Reporting {BLEU} Scores\",\n    author = \"Post, Matt\",\n    booktitle = \"Proceedings of the Third Conference on Machine Translation: Research Papers\",\n    month = oct,\n    year = \"2018\",\n    address = \"Belgium, Brussels\",\n    publisher = \"Association for Computational Linguistics\",\n    url = \"https://www.aclweb.org/anthology/W18-6319\",\n    pages = \"186--191\",\n}\n"

_A  =	"\\nTER (Translation Edit Rate, also called Translation Error Rate) is a metric to quantify the edit operations that a\nhypothesis requires to match a reference translation. We use the implementation that is already present in sacrebleu\n(https://github.com/mjpost/sacreBLEU#ter), which in turn is inspired by the TERCOM implementation, which can be found\nhere: https://github.com/jhclark/tercom.\n\nThe implementation here is slightly different from sacrebleu in terms of the required input format. The length of\nthe references and hypotheses lists need to be the same, so you may need to transpose your references compared to\nsacrebleu's required input format. See https://github.com/huggingface/datasets/issues/3154#issuecomment-950746534\n\nSee the README.md file at https://github.com/mjpost/sacreBLEU#ter for more information.\n"

_A  =	"\nProduces TER scores alongside the number of edits and reference length.\n\nArgs:\n    predictions (list of str): The system stream (a sequence of segments).\n    references (list of list of str): A list of one or more reference streams (each a sequence of segments).\n    normalized (boolean): If `True`, applies basic tokenization and normalization to sentences. Defaults to `False`.\n    ignore_punct (boolean): If `True`, applies basic tokenization and normalization to sentences. Defaults to `False`.\n    support_zh_ja_chars (boolean): If `True`, tokenization/normalization supports processing of Chinese characters,\n                                    as well as Japanese Kanji, Hiragana, Katakana, and Phonetic Extensions of Katakana.\n                                    Only applies if `normalized = True`. Defaults to `False`.\n    case_sensitive (boolean): If `False`, makes all predictions and references lowercase to ignore differences in case. Defaults to `False`.\n\nReturns:\n    'score' (float): TER score (num_edits / sum_ref_lengths * 100)\n    'num_edits' (int): The cumulative number of edits\n    'ref_length' (float): The cumulative average reference length\n\nExamples:\n    Example 1:\n        >>> predictions = [\"does this sentence match??\",\n        ...                     \"what about this sentence?\",\n        ...                     \"What did the TER metric user say to the developer?\"]\n        >>> references = [[\"does this sentence match\", \"does this sentence match!?!\"],\n        ...             [\"wHaT aBoUt ThIs SeNtEnCe?\", \"wHaT aBoUt ThIs SeNtEnCe?\"],\n        ...             [\"Your jokes are...\", \"...TERrible\"]]\n        >>> ter = datasets.load_metric(\"ter\")\n        >>> results = ter.compute(predictions=predictions,\n        ...                         references=references,\n        ...                         case_sensitive=True)\n        >>> print(results)\n        {'score': 150.0, 'num_edits': 15, 'ref_length': 10.0}\n\n    Example 2:\n        >>> predictions = [\"does this sentence match??\",\n        ...                     \"what about this sentence?\"]\n        >>> references = [[\"does this sentence match\", \"does this sentence match!?!\"],\n        ...             [\"wHaT aBoUt ThIs SeNtEnCe?\", \"wHaT aBoUt ThIs SeNtEnCe?\"]]\n        >>> ter = datasets.load_metric(\"ter\")\n        >>> results = ter.compute(predictions=predictions,\n        ...                         references=references,\n        ...                         case_sensitive=True)\n        >>> print(results)\n        {'score': 62.5, 'num_edits': 5, 'ref_length': 8.0}\n\n    Example 3:\n        >>> predictions = [\"does this sentence match??\",\n        ...                     \"what about this sentence?\"]\n        >>> references = [[\"does this sentence match\", \"does this sentence match!?!\"],\n        ...             [\"wHaT aBoUt ThIs SeNtEnCe?\", \"wHaT aBoUt ThIs SeNtEnCe?\"]]\n        >>> ter = datasets.load_metric(\"ter\")\n        >>> results = ter.compute(predictions=predictions,\n        ...                         references=references,\n        ...                         normalized=True,\n        ...                         case_sensitive=True)\n        >>> print(results)\n        {'score': 57.14285714285714, 'num_edits': 6, 'ref_length': 10.5}\n\n    Example 4:\n        >>> predictions = [\"does this sentence match??\",\n        ...                     \"what about this sentence?\"]\n        >>> references = [[\"does this sentence match\", \"does this sentence match!?!\"],\n        ...             [\"wHaT aBoUt ThIs SeNtEnCe?\", \"wHaT aBoUt ThIs SeNtEnCe?\"]]\n        >>> ter = datasets.load_metric(\"ter\")\n        >>> results = ter.compute(predictions=predictions,\n        ...                         references=references,\n        ...                         ignore_punct=True,\n        ...                         case_sensitive=False)\n        >>> print(results)\n        {'score': 0.0, 'num_edits': 0, 'ref_length': 8.0}\n\n    Example 5:\n        >>> predictions = [\"does this sentence match??\",\n        ...                    \"what about this sentence?\",\n        ...                    \"What did the TER metric user say to the developer?\"]\n        >>> references = [[\"does this sentence match\", \"does this sentence match!?!\"],\n        ...             [\"wHaT aBoUt ThIs SeNtEnCe?\", \"wHaT aBoUt ThIs SeNtEnCe?\"],\n        ...             [\"Your jokes are...\", \"...TERrible\"]]\n        >>> ter = datasets.load_metric(\"ter\")\n        >>> results = ter.compute(predictions=predictions,\n        ...                         references=references,\n        ...                         ignore_punct=True,\n        ...                         case_sensitive=False)\n        >>> print(results)\n        {'score': 100.0, 'num_edits': 10, 'ref_length': 10.0}\n"





@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION   , _KWARGS_DESCRIPTION			)
class       _lowerCAmelCase   (      datasets.Metric			):




				def 	__a		(   self	)							-> str:
							if version.parse(scb.__version__	) < version.parse("1.4.12"	):
										raise ImportWarning(
										    "To use `sacrebleu`, the module `sacrebleu>=1.4.12` is required, and the current version of `sacrebleu` doesn't match this condition.\n"
										    "You can install it with `pip install \"sacrebleu>=1.4.12\"`."	)
							return datasets.MetricInfo(
							    description=_DESCRIPTION							,							citation=_CITATION							,							homepage="http://www.cs.umd.edu/~snover/tercom/"							,							inputs_description=_KWARGS_DESCRIPTION							,							features=datasets.Features(
							        {
							            "predictions": datasets.Value("string"							,							id="sequence"	),
							            "references": datasets.Sequence(datasets.Value("string"							,							id="sequence"	)							,							id="references"	),
							        }	)							,							codebase_urls=["https://github.com/mjpost/sacreBLEU#ter"]							,							reference_urls=[
							        "https://github.com/jhclark/tercom",
							    ]							,							)




				def 	__a		(   self							,							_UpperCamelCase							,							_UpperCamelCase							,							_UpperCamelCase = False							,							_UpperCamelCase = False							,							_UpperCamelCase = False							,							_UpperCamelCase = False							,							)							-> Any:
							lowerCAmelCase_         =      len(references[0]	)
							if any(len(_UpperCamelCase	) != references_per_prediction for refs in references	):
										raise ValueError("Sacrebleu requires the same number of references for each prediction"	)
							lowerCAmelCase_         =      [[refs[i] for refs in references] for i in range(_UpperCamelCase	)]

							lowerCAmelCase_         =      TER(
							    normalized=_UpperCamelCase							,							no_punct=_UpperCamelCase							,							asian_support=_UpperCamelCase							,							case_sensitive=_UpperCamelCase							,							)
							lowerCAmelCase_         =      sb_ter.corpus_score(_UpperCamelCase							,							_UpperCamelCase	)

							return {"score": output.score, "num_edits": output.num_edits, "ref_length": output.ref_length}



