




import argparse

import torch

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


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

_snake_case										=       [
    ["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 				lowerCAmelCase_	(     snake_case_     ):
      if k == "embeddings.weight":
            return "shared.weight"

      for parlai_name, hf_name in PATTERNS:
            _A							:     str			=						k.replace(snake_case_,snake_case_     )

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







def 				lowerCAmelCase_	(     snake_case_     ):
      _A							:     List[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:
            _A							:     str			=						sd.pop(snake_case_     )
            _A							:     Optional[int]			=						k.replace("""layernorm_embedding""","""layer_norm"""     )
            assert new_k not in sd
            _A							:     Optional[int]			=						v


_snake_case										=       ["START"]







@torch.no_grad()
def 				lowerCAmelCase_	(     snake_case_,snake_case_,snake_case_     ):
      _A							:     Tuple			=						torch.load(snake_case_,map_location="""cpu"""     )
      _A							:     List[Any]			=						model["""model"""]
      _A							:     Optional[Any]			=						BlenderbotConfig.from_json_file(snake_case_     )
      _A							:     List[str]			=						BlenderbotForConditionalGeneration(snake_case_     )
      _A							:     Tuple			=						m.model.state_dict().keys()
      _A							:     Any			=						[]
      _A							:     Dict			=						{}
      for k, v in sd.items():
            if k in IGNORE_KEYS:
                  continue

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


if __name__ == "__main__":
  _snake_case										=       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"
  )
  _snake_case										=       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 						lowercase							(   unittest.TestCase		):

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



     def 					a__	(  self				)						->							Optional[int]:
           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 						lowercase							(   UpperCamelCase__,unittest.TestCase		):
     _a							 =						DonutImageProcessor if is_vision_available() else None

     def 					a__	(  self				)						->							Optional[int]:
           _A							:     List[str]			=						DonutImageProcessingTester(self				)

     @property
     def 					a__	(  self				)						->							List[Any]:
           return self.image_processor_tester.prepare_image_processor_dict()

     def 					a__	(  self				)						->							Optional[Any]:
           _A							:     Union[str, Any]			=						self.image_processing_class(**self.image_processor_dict				)
           self.assertTrue(hasattr(_a		,     """do_resize"""				)				)
           self.assertTrue(hasattr(_a		,     """size"""				)				)
           self.assertTrue(hasattr(_a		,     """do_thumbnail"""				)				)
           self.assertTrue(hasattr(_a		,     """do_align_long_axis"""				)				)
           self.assertTrue(hasattr(_a		,     """do_pad"""				)				)
           self.assertTrue(hasattr(_a		,     """do_normalize"""				)				)
           self.assertTrue(hasattr(_a		,     """image_mean"""				)				)
           self.assertTrue(hasattr(_a		,     """image_std"""				)				)

     def 					a__	(  self				)						->							List[Any]:
           _A							:     Union[str, Any]			=						self.image_processing_class.from_dict(self.image_processor_dict				)
           self.assertEqual(image_processor.size		,     {"""height""": 18, """width""": 20}				)

           _A							:     Optional[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
           _A							:     List[str]			=						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				)						->							Union[str, Any]:
           pass

     @is_flaky()
     def 					a__	(  self				)						->							Optional[int]:
           # Initialize image_processing
           _A							:     List[Any]			=						self.image_processing_class(**self.image_processor_dict				)
           # create random PIL images
           _A							:     Dict			=						prepare_image_inputs(self.image_processor_tester		,     equal_resolution=_a				)
           for image in image_inputs:
                 self.assertIsInstance(_a		,     Image.Image				)

           # Test not batched input
           _A							:     Union[str, 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
           _A							:     Any			=						image_processing(_a		,     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				)						->							Dict:
           # Initialize image_processing
           _A							:     str			=						self.image_processing_class(**self.image_processor_dict				)
           # create random numpy tensors
           _A							:     Tuple			=						prepare_image_inputs(self.image_processor_tester		,     equal_resolution=_a		,     numpify=_a				)
           for image in image_inputs:
                 self.assertIsInstance(_a		,     np.ndarray				)

           # Test not batched input
           _A							:     int			=						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
           _A							:     List[str]			=						image_processing(_a		,     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				)						->							Optional[int]:
           # Initialize image_processing
           _A							:     str			=						self.image_processing_class(**self.image_processor_dict				)
           # create random PyTorch tensors
           _A							:     Optional[int]			=						prepare_image_inputs(self.image_processor_tester		,     equal_resolution=_a		,     torchify=_a				)
           for image in image_inputs:
                 self.assertIsInstance(_a		,     torch.Tensor				)

           # Test not batched input
           _A							:     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
           _A							:     str			=						image_processing(_a		,     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"""],
               )		,     )

