





import argparse

import torch

from transformers import BlenderbotConfig, BlenderbotForConditionalGeneration
from transformers.utils import logging


logging.set_verbosity_info()
_UpperCAmelCase							:			List[Any]        =							logging.get_logger(__name__)

_UpperCAmelCase							:			Any        =							[
    ["""attention""", """attn"""],
    ["""encoder_attention""", """encoder_attn"""],
    ["""q_lin""", """q_proj"""],
    ["""k_lin""", """k_proj"""],
    ["""v_lin""", """v_proj"""],
    ["""out_lin""", """out_proj"""],
    ["""norm_embeddings""", """layernorm_embedding"""],
    ["""position_embeddings""", """embed_positions"""],
    ["""embeddings""", """embed_tokens"""],
    ["""ffn.lin""", """fc"""],
]


def 			SCREAMING_SNAKE_CASE (			_UpperCAmelCase					)     ->	List[str]:
    if k == "embeddings.weight":
        return "shared.weight"

    for parlai_name, hf_name in PATTERNS:
        lowerCamelCase__       : Optional[Any]   					=						k.replace(_UpperCAmelCase		,      _UpperCAmelCase					)

    if k.startswith('encoder'					):
        lowerCamelCase__       : Union[str, Any]   					=						k.replace('.attn'		,      '.self_attn'					)
        lowerCamelCase__       : Dict   					=						k.replace('norm1'		,      'self_attn_layer_norm'					)
        lowerCamelCase__       : Any   					=						k.replace('norm2'		,      'final_layer_norm'					)
    elif k.startswith('decoder'					):
        lowerCamelCase__       : str   					=						k.replace('norm1'		,      'self_attn_layer_norm'					)
        lowerCamelCase__       : Optional[Any]   					=						k.replace('norm2'		,      'encoder_attn_layer_norm'					)
        lowerCamelCase__       : Optional[int]   					=						k.replace('norm3'		,      'final_layer_norm'					)
    return k


def 			SCREAMING_SNAKE_CASE (			_UpperCAmelCase					)     ->	Union[str, Any]:
    lowerCamelCase__       : Optional[Any]   					=						[
        'model.encoder.layernorm_embedding.weight',
        'model.encoder.layernorm_embedding.bias',
        'model.decoder.layernorm_embedding.weight',
        'model.decoder.layernorm_embedding.bias',
    ]
    for k in keys:
        lowerCamelCase__       : Tuple   					=						sd.pop(_UpperCAmelCase					)
        lowerCamelCase__       : Any   					=						k.replace('layernorm_embedding'		,      'layer_norm'					)
        assert new_k not in sd
        lowerCamelCase__       : Any   					=						v


_UpperCAmelCase							:			str        =							["""START"""]


@torch.no_grad()
def 			SCREAMING_SNAKE_CASE (			_UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase					)     ->	Dict:
    lowerCamelCase__       : Optional[Any]   					=						torch.load(_UpperCAmelCase		,      map_location='cpu'					)
    lowerCamelCase__       : Tuple   					=						model['model']
    lowerCamelCase__       : Union[str, Any]   					=						BlenderbotConfig.from_json_file(_UpperCAmelCase					)
    lowerCamelCase__       : Optional[Any]   					=						BlenderbotForConditionalGeneration(_UpperCAmelCase					)
    lowerCamelCase__       : Optional[Any]   					=						m.model.state_dict().keys()
    lowerCamelCase__       : Tuple   					=						[]
    lowerCamelCase__       : Union[str, Any]   					=						{}
    for k, v in sd.items():
        if k in IGNORE_KEYS:
            continue

        lowerCamelCase__       : Optional[Any]   					=						rename_state_dict_key(_UpperCAmelCase					)
        if new_k not in valid_keys:
            failures.append([k, new_k]					)
        else:
            lowerCamelCase__       : Tuple   					=						v
    if cfg.normalize_before:  # Blenderbot-3B checkpoints. Rename layernorm_embedding -> layer_norm
        rename_layernorm_keys(_UpperCAmelCase					)
    m.model.load_state_dict(_UpperCAmelCase		,      strict=_UpperCAmelCase					)
    m.half()
    m.save_pretrained(_UpperCAmelCase					)


if __name__ == "__main__":
    _UpperCAmelCase							:			List[str]        =							argparse.ArgumentParser()
    # Required parameters
    parser.add_argument("""--src_path""", type=str, help="""like blenderbot-model.bin""")
    parser.add_argument("""--save_dir""", default="""hf_blenderbot""", type=str, help="""Where to save converted model.""")
    parser.add_argument(
        """--hf_config_json""", default="""blenderbot-3b-config.json""", type=str, help="""Path to config to use"""
    )
    _UpperCAmelCase							:			List[str]        =							parser.parse_args()
    convert_parlai_checkpoint(args.src_path, args.save_dir, args.hf_config_json)












import unittest

import numpy as np

from transformers.testing_utils import is_flaky, require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
    import torch

if is_vision_available():
    from PIL import Image

    from transformers import DonutImageProcessor

class   lowerCAmelCase				(   unittest.TestCase     ):
 def __init__( self    : Any     ,	UpperCAmelCase    : Optional[int]     ,	UpperCAmelCase    : Dict=7     ,	UpperCAmelCase    : Union[str, Any]=3     ,	UpperCAmelCase    : List[str]=18     ,	UpperCAmelCase    : Dict=30     ,	UpperCAmelCase    : List[Any]=400     ,	UpperCAmelCase    : Optional[Any]=True     ,	UpperCAmelCase    : Tuple=None     ,	UpperCAmelCase    : str=True     ,	UpperCAmelCase    : Tuple=False     ,	UpperCAmelCase    : List[str]=True     ,	UpperCAmelCase    : Optional[int]=True     ,	UpperCAmelCase    : Optional[Any]=[0.5, 0.5, 0.5]     ,	UpperCAmelCase    : List[str]=[0.5, 0.5, 0.5]     ,	)     ->      List[str]:
     lowerCamelCase__       : List[Any]   					=						parent
     lowerCamelCase__       : str   					=						batch_size
     lowerCamelCase__       : List[str]   					=						num_channels
     lowerCamelCase__       : str   					=						image_size
     lowerCamelCase__       : List[Any]   					=						min_resolution
     lowerCamelCase__       : int   					=						max_resolution
     lowerCamelCase__       : int   					=						do_resize
     lowerCamelCase__       : int   					=						size if size is not None else {'height': 18, 'width': 20}
     lowerCamelCase__       : Tuple   					=						do_thumbnail
     lowerCamelCase__       : str   					=						do_align_axis
     lowerCamelCase__       : str   					=						do_pad
     lowerCamelCase__       : Optional[Any]   					=						do_normalize
     lowerCamelCase__       : List[str]   					=						image_mean
     lowerCamelCase__       : Dict   					=						image_std







 def 	A_      ( self    : str      )     ->      Any:
     return {
         "do_resize": self.do_resize,
         "size": self.size,
         "do_thumbnail": self.do_thumbnail,
         "do_align_long_axis": self.do_align_axis,
         "do_pad": self.do_pad,
         "do_normalize": self.do_normalize,
         "image_mean": self.image_mean,
         "image_std": self.image_std,
     }


@require_torch
@require_vision
class   lowerCAmelCase				(   __UpperCamelCase,    unittest.TestCase     ):
 UpperCAmelCase__						=						DonutImageProcessor if is_vision_available() else None
 def 	A_      ( self    : List[Any]      )     ->      int:
     lowerCamelCase__       : Union[str, Any]   					=						DonutImageProcessingTester(self      )
 @property
 def 	A_      ( self    : Dict      )     ->      Union[str, Any]:
     return self.image_processor_tester.prepare_image_processor_dict()
 def 	A_      ( self    : Dict      )     ->      Any:
     lowerCamelCase__       : Tuple   					=						self.image_processing_class(**self.image_processor_dict      )
     self.assertTrue(hasattr(UpperCAmelCase     ,	'do_resize'      )      )
     self.assertTrue(hasattr(UpperCAmelCase     ,	'size'      )      )
     self.assertTrue(hasattr(UpperCAmelCase     ,	'do_thumbnail'      )      )
     self.assertTrue(hasattr(UpperCAmelCase     ,	'do_align_long_axis'      )      )
     self.assertTrue(hasattr(UpperCAmelCase     ,	'do_pad'      )      )
     self.assertTrue(hasattr(UpperCAmelCase     ,	'do_normalize'      )      )
     self.assertTrue(hasattr(UpperCAmelCase     ,	'image_mean'      )      )
     self.assertTrue(hasattr(UpperCAmelCase     ,	'image_std'      )      )
 def 	A_      ( self    : Tuple      )     ->      Union[str, Any]:
     lowerCamelCase__       : List[Any]   					=						self.image_processing_class.from_dict(self.image_processor_dict      )
     self.assertEqual(image_processor.size     ,	{'height': 18, 'width': 20}      )

     lowerCamelCase__       : int   					=						self.image_processing_class.from_dict(self.image_processor_dict     ,	size=42      )
     self.assertEqual(image_processor.size     ,	{'height': 42, 'width': 42}      )

     # Previous config had dimensions in (width, height) order
     lowerCamelCase__       : Dict   					=						self.image_processing_class.from_dict(self.image_processor_dict     ,	size=(42, 84)      )
     self.assertEqual(image_processor.size     ,	{'height': 84, 'width': 42}      )
 def 	A_      ( self    : Optional[Any]      )     ->      List[str]:
     pass
 @is_flaky()
 def 	A_      ( self    : List[str]      )     ->      Any:
     # Initialize image_processing
     lowerCamelCase__       : str   					=						self.image_processing_class(**self.image_processor_dict      )
     # create random PIL images
     lowerCamelCase__       : List[str]   					=						prepare_image_inputs(self.image_processor_tester     ,	equal_resolution=UpperCAmelCase      )
     for image in image_inputs:
         self.assertIsInstance(UpperCAmelCase     ,	Image.Image      )

     # Test not batched input
     lowerCamelCase__       : List[Any]   					=						image_processing(image_inputs[0]     ,	return_tensors='pt'      ).pixel_values
     self.assertEqual(
         encoded_images.shape     ,	(
             1,
             self.image_processor_tester.num_channels,
             self.image_processor_tester.size['height'],
             self.image_processor_tester.size['width'],
         )     ,	)

     # Test batched
     lowerCamelCase__       : Tuple   					=						image_processing(UpperCAmelCase     ,	return_tensors='pt'      ).pixel_values
     self.assertEqual(
         encoded_images.shape     ,	(
             self.image_processor_tester.batch_size,
             self.image_processor_tester.num_channels,
             self.image_processor_tester.size['height'],
             self.image_processor_tester.size['width'],
         )     ,	)
 @is_flaky()
 def 	A_      ( self    : int      )     ->      Tuple:
     # Initialize image_processing
     lowerCamelCase__       : Tuple   					=						self.image_processing_class(**self.image_processor_dict      )
     # create random numpy tensors
     lowerCamelCase__       : List[str]   					=						prepare_image_inputs(self.image_processor_tester     ,	equal_resolution=UpperCAmelCase     ,	numpify=UpperCAmelCase      )
     for image in image_inputs:
         self.assertIsInstance(UpperCAmelCase     ,	np.ndarray      )

     # Test not batched input
     lowerCamelCase__       : Optional[Any]   					=						image_processing(image_inputs[0]     ,	return_tensors='pt'      ).pixel_values
     self.assertEqual(
         encoded_images.shape     ,	(
             1,
             self.image_processor_tester.num_channels,
             self.image_processor_tester.size['height'],
             self.image_processor_tester.size['width'],
         )     ,	)

     # Test batched
     lowerCamelCase__       : Tuple   					=						image_processing(UpperCAmelCase     ,	return_tensors='pt'      ).pixel_values
     self.assertEqual(
         encoded_images.shape     ,	(
             self.image_processor_tester.batch_size,
             self.image_processor_tester.num_channels,
             self.image_processor_tester.size['height'],
             self.image_processor_tester.size['width'],
         )     ,	)







 @is_flaky()
 def 	A_      ( self    : Any      )     ->      Tuple:
     # Initialize image_processing
     lowerCamelCase__       : int   					=						self.image_processing_class(**self.image_processor_dict      )
     # create random PyTorch tensors
     lowerCamelCase__       : Tuple   					=						prepare_image_inputs(self.image_processor_tester     ,	equal_resolution=UpperCAmelCase     ,	torchify=UpperCAmelCase      )
     for image in image_inputs:
         self.assertIsInstance(UpperCAmelCase     ,	torch.Tensor      )

     # Test not batched input
     lowerCamelCase__       : List[str]   					=						image_processing(image_inputs[0]     ,	return_tensors='pt'      ).pixel_values
     self.assertEqual(
         encoded_images.shape     ,	(
             1,
             self.image_processor_tester.num_channels,
             self.image_processor_tester.size['height'],
             self.image_processor_tester.size['width'],
         )     ,	)

     # Test batched
     lowerCamelCase__       : Dict   					=						image_processing(UpperCAmelCase     ,	return_tensors='pt'      ).pixel_values
     self.assertEqual(
         encoded_images.shape     ,	(
             self.image_processor_tester.batch_size,
             self.image_processor_tester.num_channels,
             self.image_processor_tester.size['height'],
             self.image_processor_tester.size['width'],
         )     ,	)





