




import argparse
from pathlib import Path

import torch

from transformers import OPTConfig, OPTModel
from transformers.utils import logging


logging.set_verbosity_info()
UpperCamelCase__					    =    logging.get_logger(__name__)







def 			_a   (		SCREAMING_SNAKE_CASE_							:       List[Any]  ):

					'''simple docstring'''





					__lowerCAmelCase            = torch.load(SCREAMING_SNAKE_CASE_   ,   map_location="cpu"  )
					if "model" in sd.keys():
										__lowerCAmelCase            = torch.load(SCREAMING_SNAKE_CASE_   ,   map_location="cpu"  )["model"]

					# pop unnecessary weights
					__lowerCAmelCase            = [
					    "decoder.version",
					    "decoder.output_projection.weight",
					]
					for key in keys_to_delete:
										if key in sd:
															sd.pop(SCREAMING_SNAKE_CASE_  )

					__lowerCAmelCase            = {
					    "decoder.project_in_dim.weight": "decoder.project_in.weight",
					    "decoder.project_out_dim.weight": "decoder.project_out.weight",
					    "decoder.layer_norm.weight": "decoder.final_layer_norm.weight",
					    "decoder.layer_norm.bias": "decoder.final_layer_norm.bias",
					}
					for old_key, new_key in keys_to_rename.items():
										if old_key in sd:
															__lowerCAmelCase            = sd.pop(SCREAMING_SNAKE_CASE_  )

					__lowerCAmelCase            = list(sd.keys()  )
					for key in keys:
										if ".qkv_proj." in key:
															__lowerCAmelCase            = sd[key]
															# We split QKV in separate Q,K,V

															__lowerCAmelCase            = key.replace(".qkv_proj."   ,   ".q_proj."  )
															__lowerCAmelCase            = key.replace(".qkv_proj."   ,   ".k_proj."  )
															__lowerCAmelCase            = key.replace(".qkv_proj."   ,   ".v_proj."  )

															__lowerCAmelCase            = value.shape[0]
															assert depth % 3 == 0
															# `SequeuceParallelTransformerBlock` has QKV weight is separated in K,V,Q despite the naming:
															# https://cs.github.com/facebookresearch/metaseq/blob/51871bd73cd04c038f239ea2a26db1d7f6b37927/metaseq/modules/sequence_parallel_transformer_layer.py#L97
															__lowerCAmelCase     ,							__lowerCAmelCase     ,							__lowerCAmelCase            = torch.split(SCREAMING_SNAKE_CASE_   ,   depth // 3   ,   dim=0  )

															__lowerCAmelCase            = q
															__lowerCAmelCase            = k
															__lowerCAmelCase            = v
															del sd[key]

					return sd







@torch.no_grad()
def 			_a   (		SCREAMING_SNAKE_CASE_							:       Any   ,   SCREAMING_SNAKE_CASE_							:       str   ,   SCREAMING_SNAKE_CASE_							:       Optional[Any]=None  ):

					'''simple docstring'''





					__lowerCAmelCase            = load_checkpoint(SCREAMING_SNAKE_CASE_  )

					if config is not None:
										__lowerCAmelCase            = OPTConfig.from_pretrained(SCREAMING_SNAKE_CASE_  )
					else:
										__lowerCAmelCase            = OPTConfig()

					__lowerCAmelCase            = OPTModel(SCREAMING_SNAKE_CASE_  ).half().eval()
					model.load_state_dict(SCREAMING_SNAKE_CASE_  )

					# Check results
					Path(SCREAMING_SNAKE_CASE_  ).mkdir(exist_ok=SCREAMING_SNAKE_CASE_  )
					model.save_pretrained(SCREAMING_SNAKE_CASE_  )


if __name__ == "__main__":
					UpperCamelCase__					    =    argparse.ArgumentParser()
					# Required parameters
					parser.add_argument(
					    """--fairseq_path""",
					    type=str,
					    help=(
					        """path to fairseq checkpoint in correct format. You can find all checkpoints in the correct format here:"""
					        """ https://huggingface.co/models?other=opt_metasq"""
					    ),
					)
					parser.add_argument("""--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model.""")
					parser.add_argument("""--hf_config""", default=None, type=str, help="""Define HF config.""")
					UpperCamelCase__					    =    parser.parse_args()
					convert_opt_checkpoint(args.fairseq_path, args.pytorch_dump_folder_path, config=args.hf_config)










import unittest

from transformers import MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING, is_vision_available
from transformers.pipelines import pipeline
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_tf,
    require_torch,
    require_vision,
    slow,
)

from .test_pipelines_common import ANY


if is_vision_available():
					from PIL import Image
else:
					class     a__					:






											@staticmethod
											def        __SCREAMING_SNAKE_CASE(     *_A	,    **_A    ):




																"""simple docstring"""



																pass
@is_pipeline_test
@require_torch
@require_vision
class     a__					(      unittest.TestCase   ):
						_a							:					Any      =					MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING






						def        __SCREAMING_SNAKE_CASE(     self	,    _A	,    _A	,    _A    ):




											"""simple docstring"""



											__lowerCAmelCase            = pipeline("visual-question-answering"	,    model="hf-internal-testing/tiny-vilt-random-vqa"    )
											__lowerCAmelCase            = [
											    {
											        "image": Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"    ),
											        "question": "How many cats are there?",
											    },
											    {
											        "image": "./tests/fixtures/tests_samples/COCO/000000039769.png",
											        "question": "How many cats are there?",
											    },
											]
											return vqa_pipeline, examples






						def        __SCREAMING_SNAKE_CASE(     self	,    _A	,    _A    ):




											"""simple docstring"""



											__lowerCAmelCase            = vqa_pipeline(_A	,    top_k=1    )
											self.assertEqual(
											    _A	,    [
											        [{"score": ANY(_A    ), "answer": ANY(_A    )}],
											        [{"score": ANY(_A    ), "answer": ANY(_A    )}],
											    ]	,    )






						@require_torch
						def        __SCREAMING_SNAKE_CASE(     self    ):




											"""simple docstring"""



											__lowerCAmelCase            = pipeline("visual-question-answering"	,    model="hf-internal-testing/tiny-vilt-random-vqa"    )
											__lowerCAmelCase            = "./tests/fixtures/tests_samples/COCO/000000039769.png"
											__lowerCAmelCase            = "How many cats are there?"

											__lowerCAmelCase            = vqa_pipeline(image=_A	,    question="How many cats are there?"	,    top_k=2    )
											self.assertEqual(
											    _A	,    [{"score": ANY(_A    ), "answer": ANY(_A    )}, {"score": ANY(_A    ), "answer": ANY(_A    )}]    )

											__lowerCAmelCase            = vqa_pipeline({"image": image, "question": question}	,    top_k=2    )
											self.assertEqual(
											    _A	,    [{"score": ANY(_A    ), "answer": ANY(_A    )}, {"score": ANY(_A    ), "answer": ANY(_A    )}]    )






						@slow
						@require_torch
						def        __SCREAMING_SNAKE_CASE(     self    ):




											"""simple docstring"""



											__lowerCAmelCase            = pipeline("visual-question-answering"	,    model="dandelin/vilt-b32-finetuned-vqa"    )
											__lowerCAmelCase            = "./tests/fixtures/tests_samples/COCO/000000039769.png"
											__lowerCAmelCase            = "How many cats are there?"

											__lowerCAmelCase            = vqa_pipeline(image=_A	,    question=_A	,    top_k=2    )
											self.assertEqual(
											    nested_simplify(_A	,    decimals=4    )	,    [{"score": 0.87_99, "answer": "2"}, {"score": 0.2_96, "answer": "1"}]    )

											__lowerCAmelCase            = vqa_pipeline({"image": image, "question": question}	,    top_k=2    )
											self.assertEqual(
											    nested_simplify(_A	,    decimals=4    )	,    [{"score": 0.87_99, "answer": "2"}, {"score": 0.2_96, "answer": "1"}]    )

											__lowerCAmelCase            = vqa_pipeline(
											    [{"image": image, "question": question}, {"image": image, "question": question}]	,    top_k=2    )
											self.assertEqual(
											    nested_simplify(_A	,    decimals=4    )	,    [[{"score": 0.87_99, "answer": "2"}, {"score": 0.2_96, "answer": "1"}]] * 2	,    )






						@require_tf
						@unittest.skip("Visual question answering not implemented in TF"    )
						def        __SCREAMING_SNAKE_CASE(     self    ):




											"""simple docstring"""



											pass




