



import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available


if is_vision_available():
 from PIL import Image

 from transformers import AutoProcessor, BlipaProcessor, BlipImageProcessor, GPTaTokenizer, PreTrainedTokenizerFast




@require_vision
class 					snake_case__						(       unittest.TestCase     ):






     """simple docstring"""


     def    lowercase_   (       self  :  Tuple					)   ->str:
            snake_case__							:	int   	=	tempfile.mkdtemp()

            snake_case__							:	Tuple   	=	BlipImageProcessor()
            snake_case__							:	str   	=	GPTaTokenizer.from_pretrained('hf-internal-testing/tiny-random-GPT2Model'					)

            snake_case__							:	List[Any]   	=	BlipaProcessor(_snake_case, _snake_case					)

            processor.save_pretrained(self.tmpdirname					)






     def    lowercase_   (       self  :  List[str], **_snake_case  :  str					)   ->str:
            return AutoProcessor.from_pretrained(self.tmpdirname, **_snake_case					).tokenizer






     def    lowercase_   (       self  :  Optional[int], **_snake_case  :  List[str]					)   ->Tuple:
            return AutoProcessor.from_pretrained(self.tmpdirname, **_snake_case					).image_processor






     def    lowercase_   (       self  :  Optional[Any]					)   ->Optional[Any]:
            shutil.rmtree(self.tmpdirname					)






     def    lowercase_   (       self  :  Tuple					)   ->Any:
            snake_case__							:	Optional[Any]   	=	[np.random.randint(2_5_5, size=(3, 3_0, 4_0_0), dtype=np.uinta					)]

            snake_case__							:	Optional[Any]   	=	[Image.fromarray(np.moveaxis(_snake_case, 0, -1					)					) for x in image_inputs]

            return image_inputs






     def    lowercase_   (       self  :  Dict					)   ->List[str]:
            snake_case__							:	Optional[Any]   	=	BlipaProcessor(tokenizer=self.get_tokenizer(), image_processor=self.get_image_processor()					)
            processor.save_pretrained(self.tmpdirname					)

            snake_case__							:	Optional[Any]   	=	self.get_tokenizer(bos_token='(BOS)', eos_token='(EOS)'					)
            snake_case__							:	Union[str, Any]   	=	self.get_image_processor(do_normalize=_snake_case, padding_value=1.0					)

            snake_case__							:	str   	=	BlipaProcessor.from_pretrained(
                self.tmpdirname, bos_token='(BOS)', eos_token='(EOS)', do_normalize=_snake_case, padding_value=1.0					)

            self.assertEqual(processor.tokenizer.get_vocab(), tokenizer_add_kwargs.get_vocab()					)
            self.assertIsInstance(processor.tokenizer, _snake_case					)

            self.assertEqual(processor.image_processor.to_json_string(), image_processor_add_kwargs.to_json_string()					)
            self.assertIsInstance(processor.image_processor, _snake_case					)






     def    lowercase_   (       self  :  Tuple					)   ->str:
            snake_case__							:	Dict   	=	self.get_image_processor()
            snake_case__							:	List[Any]   	=	self.get_tokenizer()

            snake_case__							:	Any   	=	BlipaProcessor(tokenizer=_snake_case, image_processor=_snake_case					)

            snake_case__							:	Union[str, Any]   	=	self.prepare_image_inputs()

            snake_case__							:	List[Any]   	=	image_processor(_snake_case, return_tensors='np'					)
            snake_case__							:	List[Any]   	=	processor(images=_snake_case, return_tensors='np'					)

            for key in input_feat_extract.keys():
                   self.assertAlmostEqual(input_feat_extract[key].sum(), input_processor[key].sum(), delta=1e-2					)






     def    lowercase_   (       self  :  int					)   ->Any:
            snake_case__							:	Optional[Any]   	=	self.get_image_processor()
            snake_case__							:	List[str]   	=	self.get_tokenizer()

            snake_case__							:	int   	=	BlipaProcessor(tokenizer=_snake_case, image_processor=_snake_case					)

            snake_case__							:	Optional[Any]   	=	'lower newer'

            snake_case__							:	Optional[int]   	=	processor(text=_snake_case					)

            snake_case__							:	Tuple   	=	tokenizer(_snake_case, return_token_type_ids=_snake_case					)

            for key in encoded_tok.keys():
                   self.assertListEqual(encoded_tok[key], encoded_processor[key]					)






     def    lowercase_   (       self  :  Tuple					)   ->List[Any]:
            snake_case__							:	Any   	=	self.get_image_processor()
            snake_case__							:	Dict   	=	self.get_tokenizer()

            snake_case__							:	Optional[Any]   	=	BlipaProcessor(tokenizer=_snake_case, image_processor=_snake_case					)

            snake_case__							:	Union[str, Any]   	=	'lower newer'
            snake_case__							:	Optional[Any]   	=	self.prepare_image_inputs()

            snake_case__							:	Optional[Any]   	=	processor(text=_snake_case, images=_snake_case					)

            self.assertListEqual(list(inputs.keys()					), ['pixel_values', 'input_ids', 'attention_mask']					)

            # test if it raises when no input is passed
            with pytest.raises(_snake_case					):
                   processor()






     def    lowercase_   (       self  :  Dict					)   ->Union[str, Any]:
            snake_case__							:	Optional[int]   	=	self.get_image_processor()
            snake_case__							:	Optional[int]   	=	self.get_tokenizer()

            snake_case__							:	str   	=	BlipaProcessor(tokenizer=_snake_case, image_processor=_snake_case					)

            snake_case__							:	int   	=	[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

            snake_case__							:	str   	=	processor.batch_decode(_snake_case					)
            snake_case__							:	str   	=	tokenizer.batch_decode(_snake_case					)

            self.assertListEqual(_snake_case, _snake_case					)






     def    lowercase_   (       self  :  Tuple					)   ->str:
            snake_case__							:	Optional[int]   	=	self.get_image_processor()
            snake_case__							:	List[Any]   	=	self.get_tokenizer()

            snake_case__							:	List[str]   	=	BlipaProcessor(tokenizer=_snake_case, image_processor=_snake_case					)

            snake_case__							:	Any   	=	'lower newer'
            snake_case__							:	Optional[Any]   	=	self.prepare_image_inputs()

            snake_case__							:	Union[str, Any]   	=	processor(text=_snake_case, images=_snake_case					)

            # For now the processor supports only ['pixel_values', 'input_ids', 'attention_mask']
            self.assertListEqual(list(inputs.keys()					), ['pixel_values', 'input_ids', 'attention_mask']					)













import argparse

import torch
from torch import nn

from transformers import MaMaaaConfig, MaMaaaForConditionalGeneration


def        SCREAMING_SNAKE_CASE      (       _UpperCAmelCase )			->     str:
    lowerCamelCase__    :      Optional[int]					 =		[
        'encoder.version',
        'decoder.version',
        'model.encoder.version',
        'model.decoder.version',
        'decoder.output_projection.weight',
        '_float_tensor',
        'encoder.embed_positions._float_tensor',
        'decoder.embed_positions._float_tensor',
    ]
    for k in ignore_keys:
        state_dict.pop(_UpperCAmelCase						,	_UpperCAmelCase )


def        SCREAMING_SNAKE_CASE      (       _UpperCAmelCase )			->     List[Any]:
    lowerCamelCase__ ,							lowerCamelCase__    :      List[str]					 =		emb.weight.shape
    lowerCamelCase__    :      Tuple					 =		nn.Linear(_UpperCAmelCase						,	_UpperCAmelCase						,	bias=_UpperCAmelCase )
    lowerCamelCase__    :      Dict					 =		emb.weight.data
    return lin_layer


def        SCREAMING_SNAKE_CASE      (       _UpperCAmelCase )			->     str:
    lowerCamelCase__    :      Tuple					 =		torch.load(_UpperCAmelCase						,	map_location='cpu' )
    lowerCamelCase__    :      List[str]					 =		mam_aaa['args'] or mam_aaa['cfg']['model']
    lowerCamelCase__    :      Optional[int]					 =		mam_aaa['model']
    remove_ignore_keys_(_UpperCAmelCase )
    lowerCamelCase__    :      str					 =		state_dict['encoder.embed_tokens.weight'].shape[0]

    lowerCamelCase__    :      Union[str, Any]					 =		MaMaaaConfig(
        vocab_size=_UpperCAmelCase						,	max_position_embeddings=1024						,	encoder_layers=args.encoder_layers						,	decoder_layers=args.decoder_layers						,	encoder_attention_heads=args.encoder_attention_heads						,	decoder_attention_heads=args.decoder_attention_heads						,	encoder_ffn_dim=args.encoder_ffn_embed_dim						,	decoder_ffn_dim=args.decoder_ffn_embed_dim						,	d_model=args.encoder_embed_dim						,	encoder_layerdrop=args.encoder_layerdrop						,	decoder_layerdrop=args.decoder_layerdrop						,	dropout=args.dropout						,	attention_dropout=args.attention_dropout						,	activation_dropout=args.activation_dropout						,	activation_function='relu'						,	)

    lowerCamelCase__    :      Optional[Any]					 =		state_dict['decoder.embed_tokens.weight']
    lowerCamelCase__    :      Union[str, Any]					 =		MaMaaaForConditionalGeneration(_UpperCAmelCase )
    model.model.load_state_dict(_UpperCAmelCase						,	strict=_UpperCAmelCase )
    lowerCamelCase__    :      List[str]					 =		make_linear_from_emb(model.model.shared )

    return model


if __name__ == "__main__":
    _UpperCAmelCase				:       Tuple	   =				argparse.ArgumentParser()
    # Required parameters
    parser.add_argument("""fairseq_path""", type=str, help="""path to a model.pt on local filesystem.""")
    parser.add_argument("""pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model.""")
    _UpperCAmelCase				:       str	   =				parser.parse_args()
    _UpperCAmelCase				:       Optional[Any]	   =				convert_fairseq_mamaaa_checkpoint_from_disk(args.fairseq_pathß)
    model.save_pretrained(args.pytorch_dump_folder_path)





