

"""simple docstring"""




import sys
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path

from huggingface_hub import HfFolder, delete_repo
from requests.exceptions import HTTPError

from transformers import AutoImageProcessor, ViTImageProcessor
from transformers.testing_utils import TOKEN, USER, get_tests_dir, is_staging_test


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

from test_module.custom_image_processing import CustomImageProcessor  # noqa E402


A_							:			List[Any]											= get_tests_dir("fixtures")





class    lowerCamelCase						(unittest.TestCase			):

			def 					SCREAMING_SNAKE_CASE				(					self				:						Tuple       )   ->    List[str]:
						# A mock response for an HTTP head request to emulate server down
						SCREAMING_SNAKE_CASE__								=      mock.Mock()
						SCREAMING_SNAKE_CASE__								=      5_0_0
						SCREAMING_SNAKE_CASE__								=      {}
						SCREAMING_SNAKE_CASE__								=      HTTPError
						SCREAMING_SNAKE_CASE__								=      {}

						# Download this model to make sure it's in the cache.
						SCREAMING_SNAKE_CASE__								=      ViTImageProcessor.from_pretrained("""hf-internal-testing/tiny-random-vit"""       )
						# Under the mock environment we get a 500 error when trying to reach the model.
						with mock.patch("""requests.Session.request"""    ,				return_value=__UpperCAmelCase       ) as mock_head:
									SCREAMING_SNAKE_CASE__								=      ViTImageProcessor.from_pretrained("""hf-internal-testing/tiny-random-vit"""       )
									# This check we did call the fake head request
									mock_head.assert_called()

			def 					SCREAMING_SNAKE_CASE				(					self				:						Union[str, Any]       )   ->    Tuple:
						# This test is for deprecated behavior and can be removed in v5
						SCREAMING_SNAKE_CASE__								=      ViTImageProcessor.from_pretrained(
						    """https://huggingface.co/hf-internal-testing/tiny-random-vit/resolve/main/preprocessor_config.json"""       )





			def 					SCREAMING_SNAKE_CASE				(					self				:						Dict       )   ->    Dict:
						with self.assertRaises(__UpperCAmelCase       ):
									# config is in subfolder, the following should not work without specifying the subfolder
									SCREAMING_SNAKE_CASE__								=      AutoImageProcessor.from_pretrained("""hf-internal-testing/stable-diffusion-all-variants"""       )

						SCREAMING_SNAKE_CASE__								=      AutoImageProcessor.from_pretrained(
						    """hf-internal-testing/stable-diffusion-all-variants"""    ,				subfolder="""feature_extractor"""       )

						self.assertIsNotNone(__UpperCAmelCase       )





@is_staging_test
class    lowerCamelCase						(unittest.TestCase			):

			@classmethod
			def 					SCREAMING_SNAKE_CASE				(					cls				:						Any       )   ->    str:
						SCREAMING_SNAKE_CASE__								=      TOKEN
						HfFolder.save_token(__UpperCAmelCase       )

			@classmethod
			def 					SCREAMING_SNAKE_CASE				(					cls				:						List[Any]       )   ->    int:
						try:
									delete_repo(token=cls._token    ,				repo_id="""test-image-processor"""       )
						except HTTPError:
									pass

						try:
									delete_repo(token=cls._token    ,				repo_id="""valid_org/test-image-processor-org"""       )
						except HTTPError:
									pass

						try:
									delete_repo(token=cls._token    ,				repo_id="""test-dynamic-image-processor"""       )
						except HTTPError:
									pass

			def 					SCREAMING_SNAKE_CASE				(					self				:						Optional[int]       )   ->    Dict:
						SCREAMING_SNAKE_CASE__								=      ViTImageProcessor.from_pretrained(__UpperCAmelCase       )
						image_processor.push_to_hub("""test-image-processor"""    ,				use_auth_token=self._token       )

						SCREAMING_SNAKE_CASE__								=      ViTImageProcessor.from_pretrained(F"""{USER}/test-image-processor"""       )
						for k, v in image_processor.__dict__.items():
									self.assertEqual(__UpperCAmelCase    ,				getattr(__UpperCAmelCase    ,				__UpperCAmelCase       )       )

						# Reset repo
						delete_repo(token=self._token    ,				repo_id="""test-image-processor"""       )

						# Push to hub via save_pretrained
						with tempfile.TemporaryDirectory() as tmp_dir:
									image_processor.save_pretrained(
									    __UpperCAmelCase    ,				repo_id="""test-image-processor"""    ,				push_to_hub=__UpperCAmelCase    ,				use_auth_token=self._token       )

						SCREAMING_SNAKE_CASE__								=      ViTImageProcessor.from_pretrained(F"""{USER}/test-image-processor"""       )
						for k, v in image_processor.__dict__.items():
									self.assertEqual(__UpperCAmelCase    ,				getattr(__UpperCAmelCase    ,				__UpperCAmelCase       )       )

			def 					SCREAMING_SNAKE_CASE				(					self				:						List[Any]       )   ->    Any:
						SCREAMING_SNAKE_CASE__								=      ViTImageProcessor.from_pretrained(__UpperCAmelCase       )
						image_processor.push_to_hub("""valid_org/test-image-processor"""    ,				use_auth_token=self._token       )

						SCREAMING_SNAKE_CASE__								=      ViTImageProcessor.from_pretrained("""valid_org/test-image-processor"""       )
						for k, v in image_processor.__dict__.items():
									self.assertEqual(__UpperCAmelCase    ,				getattr(__UpperCAmelCase    ,				__UpperCAmelCase       )       )

						# Reset repo
						delete_repo(token=self._token    ,				repo_id="""valid_org/test-image-processor"""       )

						# Push to hub via save_pretrained
						with tempfile.TemporaryDirectory() as tmp_dir:
									image_processor.save_pretrained(
									    __UpperCAmelCase    ,				repo_id="""valid_org/test-image-processor-org"""    ,				push_to_hub=__UpperCAmelCase    ,				use_auth_token=self._token       )

						SCREAMING_SNAKE_CASE__								=      ViTImageProcessor.from_pretrained("""valid_org/test-image-processor-org"""       )
						for k, v in image_processor.__dict__.items():
									self.assertEqual(__UpperCAmelCase    ,				getattr(__UpperCAmelCase    ,				__UpperCAmelCase       )       )





			def 					SCREAMING_SNAKE_CASE				(					self				:						Optional[Any]       )   ->    Dict:
						CustomImageProcessor.register_for_auto_class()
						SCREAMING_SNAKE_CASE__								=      CustomImageProcessor.from_pretrained(__UpperCAmelCase       )

						image_processor.push_to_hub("""test-dynamic-image-processor"""    ,				use_auth_token=self._token       )

						# This has added the proper auto_map field to the config
						self.assertDictEqual(
						    image_processor.auto_map    ,				{"""AutoImageProcessor""": """custom_image_processing.CustomImageProcessor"""}    ,				)

						SCREAMING_SNAKE_CASE__								=      AutoImageProcessor.from_pretrained(
						    F"""{USER}/test-dynamic-image-processor"""    ,				trust_remote_code=__UpperCAmelCase       )
						# Can't make an isinstance check because the new_image_processor is from the CustomImageProcessor class of a dynamic module
						self.assertEqual(new_image_processor.__class__.__name__    ,				"""CustomImageProcessor"""       )





"""simple docstring"""




import argparse
import logging
from collections import namedtuple

import torch
from model_bertabs import BertAbsSummarizer
from models.model_builder import AbsSummarizer  # The authors' implementation

from transformers import BertTokenizer


logging.basicConfig(level=logging.INFO)
A_							:			Tuple											= logging.getLogger(__name__)


A_							:			Tuple											= "Hello world! cécé herlolip"


A_							:			Dict											= namedtuple(
    "BertAbsConfig",
    [
        "temp_dir",
        "large",
        "use_bert_emb",
        "finetune_bert",
        "encoder",
        "share_emb",
        "max_pos",
        "enc_layers",
        "enc_hidden_size",
        "enc_heads",
        "enc_ff_size",
        "enc_dropout",
        "dec_layers",
        "dec_hidden_size",
        "dec_heads",
        "dec_ff_size",
        "dec_dropout",
    ],
)

def 	A       (     snake_case__		,				snake_case__    ):




			'''simple docstring'''


			SCREAMING_SNAKE_CASE__								=      BertAbsConfig(
			    temp_dir="""."""		,				finetune_bert=snake_case__		,				large=snake_case__		,				share_emb=snake_case__		,				use_bert_emb=snake_case__		,				encoder="""bert"""		,				max_pos=5_12		,				enc_layers=6		,				enc_hidden_size=5_12		,				enc_heads=8		,				enc_ff_size=5_12		,				enc_dropout=0.2		,				dec_layers=6		,				dec_hidden_size=7_68		,				dec_heads=8		,				dec_ff_size=20_48		,				dec_dropout=0.2		,				)
			SCREAMING_SNAKE_CASE__								=      torch.load(snake_case__		,				lambda snake_case__		,				snake_case__    : storage    )
			SCREAMING_SNAKE_CASE__								=      AbsSummarizer(snake_case__		,				torch.device("""cpu"""    )		,				snake_case__    )
			original.eval()

			SCREAMING_SNAKE_CASE__								=      BertAbsSummarizer(snake_case__		,				torch.device("""cpu"""    )    )
			new_model.eval()

			# -------------------
			# Convert the weights
			# -------------------

			logging.info("""convert the model"""    )
			new_model.bert.load_state_dict(original.bert.state_dict()    )
			new_model.decoder.load_state_dict(original.decoder.state_dict()    )
			new_model.generator.load_state_dict(original.generator.state_dict()    )

			# ----------------------------------
			# Make sure the outpus are identical
			# ----------------------------------

			logging.info("""Make sure that the models' outputs are identical"""    )
			SCREAMING_SNAKE_CASE__								=      BertTokenizer.from_pretrained("""bert-base-uncased"""    )

			# prepare the model inputs
			SCREAMING_SNAKE_CASE__								=      tokenizer.encode("""This is sample éàalj'-."""    )
			encoder_input_ids.extend([tokenizer.pad_token_id] * (5_12 - len(snake_case__    ))    )
			SCREAMING_SNAKE_CASE__								=      torch.tensor(snake_case__    ).unsqueeze(0    )
			SCREAMING_SNAKE_CASE__								=      tokenizer.encode("""This is sample 3 éàalj'-."""    )
			decoder_input_ids.extend([tokenizer.pad_token_id] * (5_12 - len(snake_case__    ))    )
			SCREAMING_SNAKE_CASE__								=      torch.tensor(snake_case__    ).unsqueeze(0    )

			# failsafe to make sure the weights reset does not affect the
			# loaded weights.
			assert torch.max(torch.abs(original.generator[0].weight - new_model.generator[0].weight    )    ) == 0

			# forward pass
			SCREAMING_SNAKE_CASE__								=      encoder_input_ids
			SCREAMING_SNAKE_CASE__								=      decoder_input_ids
			SCREAMING_SNAKE_CASE__								=      SCREAMING_SNAKE_CASE__								=      None
			SCREAMING_SNAKE_CASE__								=      None
			SCREAMING_SNAKE_CASE__								=      SCREAMING_SNAKE_CASE__								=      None
			SCREAMING_SNAKE_CASE__								=      SCREAMING_SNAKE_CASE__								=      None
			SCREAMING_SNAKE_CASE__								=      None

			# The original model does not apply the geneator layer immediatly but rather in
			# the beam search (where it combines softmax + linear layer). Since we already
			# apply the softmax in our generation process we only apply the linear layer here.
			# We make sure that the outputs of the full stack are identical
			SCREAMING_SNAKE_CASE__								=      original(snake_case__		,				snake_case__		,				snake_case__		,				snake_case__		,				snake_case__		,				snake_case__		,				snake_case__    )[0]
			SCREAMING_SNAKE_CASE__								=      original.generator(snake_case__    )

			SCREAMING_SNAKE_CASE__								=      new_model(
			    snake_case__		,				snake_case__		,				snake_case__		,				snake_case__		,				snake_case__    )[0]
			SCREAMING_SNAKE_CASE__								=      new_model.generator(snake_case__    )

			SCREAMING_SNAKE_CASE__								=      torch.max(torch.abs(output_converted_model - output_original_model    )    ).item()
			print("""Maximum absolute difference beween weights: {:.2f}""".format(snake_case__    )    )
			SCREAMING_SNAKE_CASE__								=      torch.max(torch.abs(output_converted_generator - output_original_generator    )    ).item()
			print("""Maximum absolute difference beween weights: {:.2f}""".format(snake_case__    )    )

			SCREAMING_SNAKE_CASE__								=      torch.allclose(snake_case__		,				snake_case__		,				atol=1e-3    )
			if are_identical:
						logging.info("""all weights are equal up to 1e-3"""    )
			else:
						raise ValueError("""the weights are different. The new model is likely different from the original one."""    )

			# The model has been saved with torch.save(model) and this is bound to the exact
			# directory structure. We save the state_dict instead.
			logging.info("""saving the model's state dictionary"""    )
			torch.save(
			    new_model.state_dict()		,				"""./bertabs-finetuned-cnndm-extractive-abstractive-summarization/pytorch_model.bin"""    )


if __name__ == "__main__":
						A_							:			Dict											= argparse.ArgumentParser()
						parser.add_argument(
						    "--bertabs_checkpoint_path",
						    default=None,
						    type=str,
						    required=True,
						    help="Path the official PyTorch dump.",
						)
						parser.add_argument(
						    "--pytorch_dump_folder_path",
						    default=None,
						    type=str,
						    required=True,
						    help="Path to the output PyTorch model.",
						)
						A_							:			Tuple											= parser.parse_args()

						convert_bertabs_checkpoints(
						    args.bertabs_checkpoint_path,
						    args.pytorch_dump_folder_path,
						)


