
import json
import sys
import tempfile
import unittest
from pathlib import Path

import transformers
from transformers import (
    CONFIG_MAPPING,
    IMAGE_PROCESSOR_MAPPING,
    AutoConfig,
    AutoImageProcessor,
    CLIPConfig,
    CLIPImageProcessor,
)
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER


sys.path.append(str(Path(__file__).parent.parent.parent.parent / "utils"))

from test_module.custom_configuration import CustomConfig  # noqa E402
from test_module.custom_image_processing import CustomImageProcessor  # noqa E402





class       _snake_case					(     unittest.TestCase				):







			def 			SCREAMING_SNAKE_CASE    (				self		):
				__magic_name__		:		Tuple	   =     0







			def 			SCREAMING_SNAKE_CASE    (				self		):
				__magic_name__		:		Dict	   =     AutoImageProcessor.from_pretrained("openai/clip-vit-base-patch32"		)
				self.assertIsInstance(_a						,   _a		)







			def 			SCREAMING_SNAKE_CASE    (				self		):
				with tempfile.TemporaryDirectory() as tmpdirname:
					__magic_name__		:		Tuple	   =     Path(_a		) / "preprocessor_config.json"
					__magic_name__		:		str	   =     Path(_a		) / "config.json"
					json.dump(
					    {"image_processor_type": "CLIPImageProcessor", "processor_class": "CLIPProcessor"}						,   open(_a						,   "w"		)						,   )
					json.dump({"model_type": "clip"}						,   open(_a						,   "w"		)		)

					__magic_name__		:		List[str]	   =     AutoImageProcessor.from_pretrained(_a		)
					self.assertIsInstance(_a						,   _a		)







			def 			SCREAMING_SNAKE_CASE    (				self		):
				# Ensure we can load the image processor from the feature extractor config
				with tempfile.TemporaryDirectory() as tmpdirname:
					__magic_name__		:		Dict	   =     Path(_a		) / "preprocessor_config.json"
					__magic_name__		:		List[str]	   =     Path(_a		) / "config.json"
					json.dump(
					    {"feature_extractor_type": "CLIPFeatureExtractor", "processor_class": "CLIPProcessor"}						,   open(_a						,   "w"		)						,   )
					json.dump({"model_type": "clip"}						,   open(_a						,   "w"		)		)

					__magic_name__		:		List[Any]	   =     AutoImageProcessor.from_pretrained(_a		)
					self.assertIsInstance(_a						,   _a		)







			def 			SCREAMING_SNAKE_CASE    (				self		):
				with tempfile.TemporaryDirectory() as tmpdirname:
					__magic_name__		:		List[str]	   =     CLIPConfig()

					# Create a dummy config file with image_proceesor_type
					__magic_name__		:		Tuple	   =     Path(_a		) / "preprocessor_config.json"
					__magic_name__		:		int	   =     Path(_a		) / "config.json"
					json.dump(
					    {"image_processor_type": "CLIPImageProcessor", "processor_class": "CLIPProcessor"}						,   open(_a						,   "w"		)						,   )
					json.dump({"model_type": "clip"}						,   open(_a						,   "w"		)		)

					# remove image_processor_type to make sure config.json alone is enough to load image processor locally
					__magic_name__		:		Tuple	   =     AutoImageProcessor.from_pretrained(_a		).to_dict()

					config_dict.pop("image_processor_type"		)
					__magic_name__		:		List[Any]	   =     CLIPImageProcessor(**_a		)

					# save in new folder
					model_config.save_pretrained(_a		)
					config.save_pretrained(_a		)

					__magic_name__		:		Tuple	   =     AutoImageProcessor.from_pretrained(_a		)

					# make sure private variable is not incorrectly saved
					__magic_name__		:		int	   =     json.loads(config.to_json_string()		)
					self.assertTrue("_processor_class" not in dict_as_saved		)

				self.assertIsInstance(_a						,   _a		)







			def 			SCREAMING_SNAKE_CASE    (				self		):
				with tempfile.TemporaryDirectory() as tmpdirname:
					__magic_name__		:		Optional[int]	   =     Path(_a		) / "preprocessor_config.json"
					json.dump(
					    {"image_processor_type": "CLIPImageProcessor", "processor_class": "CLIPProcessor"}						,   open(_a						,   "w"		)						,   )

					__magic_name__		:		Optional[int]	   =     AutoImageProcessor.from_pretrained(_a		)
					self.assertIsInstance(_a						,   _a		)







			def 			SCREAMING_SNAKE_CASE    (				self		):
				with self.assertRaisesRegex(
				    _a						,   "clip-base is not a local folder and is not a valid model identifier"		):
					__magic_name__		:		Union[str, Any]	   =     AutoImageProcessor.from_pretrained("clip-base"		)







			def 			SCREAMING_SNAKE_CASE    (				self		):
				with self.assertRaisesRegex(
				    _a						,   r"aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)"		):
					__magic_name__		:		List[Any]	   =     AutoImageProcessor.from_pretrained(_a						,   revision="aaaaaa"		)







			def 			SCREAMING_SNAKE_CASE    (				self		):
				with self.assertRaisesRegex(
				    _a						,   "hf-internal-testing/config-no-model does not appear to have a file named preprocessor_config.json."						,   ):
					__magic_name__		:		str	   =     AutoImageProcessor.from_pretrained("hf-internal-testing/config-no-model"		)







			def 			SCREAMING_SNAKE_CASE    (				self		):
				# If remote code is not set, we will time out when asking whether to load the model.
				with self.assertRaises(_a		):
					__magic_name__		:		List[str]	   =     AutoImageProcessor.from_pretrained("hf-internal-testing/test_dynamic_image_processor"		)
				# If remote code is disabled, we can't load this config.
				with self.assertRaises(_a		):
					__magic_name__		:		Union[str, Any]	   =     AutoImageProcessor.from_pretrained(
					    "hf-internal-testing/test_dynamic_image_processor"						,   trust_remote_code=_a		)

				__magic_name__		:		Any	   =     AutoImageProcessor.from_pretrained(
				    "hf-internal-testing/test_dynamic_image_processor"						,   trust_remote_code=_a		)
				self.assertEqual(image_processor.__class__.__name__						,   "NewImageProcessor"		)

				# Test image processor can be reloaded.
				with tempfile.TemporaryDirectory() as tmp_dir:
					image_processor.save_pretrained(_a		)
					__magic_name__		:		List[str]	   =     AutoImageProcessor.from_pretrained(_a						,   trust_remote_code=_a		)
				self.assertEqual(reloaded_image_processor.__class__.__name__						,   "NewImageProcessor"		)







			def 			SCREAMING_SNAKE_CASE    (				self		):
				try:
					AutoConfig.register("custom"						,   _a		)
					AutoImageProcessor.register(_a						,   _a		)
					# Trying to register something existing in the Transformers library will raise an error
					with self.assertRaises(_a		):
						AutoImageProcessor.register(_a						,   _a		)

					with tempfile.TemporaryDirectory() as tmpdirname:
						__magic_name__		:		Dict	   =     Path(_a		) / "preprocessor_config.json"
						__magic_name__		:		Union[str, Any]	   =     Path(_a		) / "config.json"
						json.dump(
						    {"feature_extractor_type": "CLIPFeatureExtractor", "processor_class": "CLIPProcessor"}						,   open(_a						,   "w"		)						,   )
						json.dump({"model_type": "clip"}						,   open(_a						,   "w"		)		)

						__magic_name__		:		Dict	   =     CustomImageProcessor.from_pretrained(_a		)

					# Now that the config is registered, it can be used as any other config with the auto-API
					with tempfile.TemporaryDirectory() as tmp_dir:
						image_processor.save_pretrained(_a		)
						__magic_name__		:		Any	   =     AutoImageProcessor.from_pretrained(_a		)
						self.assertIsInstance(_a						,   _a		)

				finally:
					if "custom" in CONFIG_MAPPING._extra_content:
						del CONFIG_MAPPING._extra_content["custom"]
					if CustomConfig in IMAGE_PROCESSOR_MAPPING._extra_content:
						del IMAGE_PROCESSOR_MAPPING._extra_content[CustomConfig]







			def 			SCREAMING_SNAKE_CASE    (				self		):





				class       _snake_case					(     snake_case				):
							UpperCamelCase__          =    True

				try:
					AutoConfig.register("custom"						,   _a		)
					AutoImageProcessor.register(_a						,   _a		)
					# If remote code is not set, the default is to use local
					__magic_name__		:		Optional[int]	   =     AutoImageProcessor.from_pretrained("hf-internal-testing/test_dynamic_image_processor"		)
					self.assertEqual(image_processor.__class__.__name__						,   "NewImageProcessor"		)
					self.assertTrue(image_processor.is_local		)

					# If remote code is disabled, we load the local one.
					__magic_name__		:		Optional[int]	   =     AutoImageProcessor.from_pretrained(
					    "hf-internal-testing/test_dynamic_image_processor"						,   trust_remote_code=_a		)
					self.assertEqual(image_processor.__class__.__name__						,   "NewImageProcessor"		)
					self.assertTrue(image_processor.is_local		)

					# If remote is enabled, we load from the Hub
					__magic_name__		:		List[Any]	   =     AutoImageProcessor.from_pretrained(
					    "hf-internal-testing/test_dynamic_image_processor"						,   trust_remote_code=_a		)
					self.assertEqual(image_processor.__class__.__name__						,   "NewImageProcessor"		)
					self.assertTrue(not hasattr(_a						,   "is_local"		)		)

				finally:
					if "custom" in CONFIG_MAPPING._extra_content:
						del CONFIG_MAPPING._extra_content["custom"]
					if CustomConfig in IMAGE_PROCESSOR_MAPPING._extra_content:
						del IMAGE_PROCESSOR_MAPPING._extra_content[CustomConfig]


import absl  # noqa: F401 # Here to have a nice missing dependency error message early on
import nltk  # noqa: F401 # Here to have a nice missing dependency error message early on
import numpy  # noqa: F401 # Here to have a nice missing dependency error message early on
import six  # noqa: F401 # Here to have a nice missing dependency error message early on
from rouge_score import rouge_scorer, scoring

import datasets


snake_case :		List[Any]							      =  "\\n@inproceedings{lin-2004-rouge,\n    title = \"{ROUGE}: A Package for Automatic Evaluation of Summaries\",\n    author = \"Lin, Chin-Yew\",\n    booktitle = \"Text Summarization Branches Out\",\n    month = jul,\n    year = \"2004\",\n    address = \"Barcelona, Spain\",\n    publisher = \"Association for Computational Linguistics\",\n    url = \"https://www.aclweb.org/anthology/W04-1013\",\n    pages = \"74--81\",\n}\n"

snake_case :		Any							      =  "\\nROUGE, or Recall-Oriented Understudy for Gisting Evaluation, is a set of metrics and a software package used for\nevaluating automatic summarization and machine translation software in natural language processing.\nThe metrics compare an automatically produced summary or translation against a reference or a set of references (human-produced) summary or translation.\n\nNote that ROUGE is case insensitive, meaning that upper case letters are treated the same way as lower case letters.\n\nThis metrics is a wrapper around Google Research reimplementation of ROUGE:\nhttps://github.com/google-research/google-research/tree/master/rouge\n"

snake_case :		str							      =  "\nCalculates average rouge scores for a list of hypotheses and references\nArgs:\n    predictions: list of predictions to score. Each prediction\n        should be a string with tokens separated by spaces.\n    references: list of reference for each prediction. Each\n        reference should be a string with tokens separated by spaces.\n    rouge_types: A list of rouge types to calculate.\n        Valid names:\n        `\"rouge{n}\"` (e.g. `\"rouge1\"`, `\"rouge2\"`) where: {n} is the n-gram based scoring,\n        `\"rougeL\"`: Longest common subsequence based scoring.\n        `\"rougeLSum\"`: rougeLsum splits text using `\"\n\"`.\n        See details in https://github.com/huggingface/datasets/issues/617\n    use_stemmer: Bool indicating whether Porter stemmer should be used to strip word suffixes.\n    use_aggregator: Return aggregates if this is set to True\nReturns:\n    rouge1: rouge_1 (precision, recall, f1),\n    rouge2: rouge_2 (precision, recall, f1),\n    rougeL: rouge_l (precision, recall, f1),\n    rougeLsum: rouge_lsum (precision, recall, f1)\nExamples:\n\n    >>> rouge = datasets.load_metric('rouge')\n    >>> predictions = [\"hello there\", \"general kenobi\"]\n    >>> references = [\"hello there\", \"general kenobi\"]\n    >>> results = rouge.compute(predictions=predictions, references=references)\n    >>> print(list(results.keys()))\n    ['rouge1', 'rouge2', 'rougeL', 'rougeLsum']\n    >>> print(results[\"rouge1\"])\n    AggregateScore(low=Score(precision=1.0, recall=1.0, fmeasure=1.0), mid=Score(precision=1.0, recall=1.0, fmeasure=1.0), high=Score(precision=1.0, recall=1.0, fmeasure=1.0))\n    >>> print(results[\"rouge1\"].mid.fmeasure)\n    1.0\n"





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







			def 			SCREAMING_SNAKE_CASE    (				self		):
				return datasets.MetricInfo(
				    description=_DESCRIPTION						,   citation=_CITATION						,   inputs_description=_KWARGS_DESCRIPTION						,   features=datasets.Features(
				        {
				            "predictions": datasets.Value("string"						,   id="sequence"		),
				            "references": datasets.Value("string"						,   id="sequence"		),
				        }		)						,   codebase_urls=["https://github.com/google-research/google-research/tree/master/rouge"]						,   reference_urls=[
				        "https://en.wikipedia.org/wiki/ROUGE_(metric)",
				        "https://github.com/google-research/google-research/tree/master/rouge",
				    ]						,   )







			def 			SCREAMING_SNAKE_CASE    (				self						,   _a						,   _a						,   _a=None						,   _a=True						,   _a=False		):
				if rouge_types is None:
					__magic_name__		:		str	   =     ["rouge1", "rouge2", "rougeL", "rougeLsum"]

				__magic_name__		:		List[str]	   =     rouge_scorer.RougeScorer(rouge_types=_a						,   use_stemmer=_a		)
				if use_aggregator:
					__magic_name__		:		Dict	   =     scoring.BootstrapAggregator()
				else:
					__magic_name__		:		str	   =     []

				for ref, pred in zip(_a						,   _a		):
					__magic_name__		:		Union[str, Any]	   =     scorer.score(_a						,   _a		)
					if use_aggregator:
						aggregator.add_scores(_a		)
					else:
						scores.append(_a		)

				if use_aggregator:
					__magic_name__		:		Any	   =     aggregator.aggregate()
				else:
					__magic_name__		:		List[Any]	   =     {}
					for key in scores[0]:
						__magic_name__		:		str	   =     [score[key] for score in scores]

				return result
