
'''simple docstring'''





import argparse

# utilities to import the model weights and config file
import os
import pickle as pkl

# PyTorch + new model classes
import torch
from torch import nn

from transformers import AutoTokenizer, MegaConfig, MegaForMaskedLM


# import the EncoderLayer class used to pretrain
# !! NOTE !! this requires the version of fairseq that is built when you install the Mega source
try:
 from fairseq.modules.mega_layer import MegaEncoderLayer
except ImportError:
 raise ImportError("You need to install the version of fairseq from the Mega repo!")


class 			UpperCamelCase__	(     nn.Module):






 def __init__(      self	:Union[str, Any]						,					_A	:List[Any]						,					_A	:Tuple						,					_A	:List[str]      )					->      int:







       '''simple docstring'''



       super().__init__()
       __A													=      mega_args
       __A													=      nn.Embedding(_A						,					self.mega_args.encoder_embed_dim      )
       __A													=      nn.ModuleList([MegaEncoderLayer(self.mega_args      ) for _ in range(_A      )]      )
       __A													=      depth






 def 						lowercase_    (      self	:Optional[Any]						,					_A	:int						,					_A	:Dict						,					_A	:int=True						,					_A	:Tuple=0      )					->      Tuple:







       '''simple docstring'''



       if batch_first:
             __A													=      input_ids.T

       # to make things more confusing, Mega expects the attention mask to
       # be (batch, time), but with values of 0 (normal token) and 1 (ignore token)
       # which is the opposite of what HF returns
       if ignore_mask_value == 0:
             __A													=      1 - attention_mask

       # get token embeddings from IDs
       __A													=      self.embedding_layer(_A      )

       # pass through the Mega layers
       # input is (time, batch, encoder dim) and output is the same
       for encoder in self.encoders:
             __A													=      encoder(_A						,					_A      )

       # return according to the shape specified
       if batch_first:
             # (T, B, H) --> (B, T, H)
             return torch.transpose(_A						,					0						,					1      )
       else:
             return embeds


class 			UpperCamelCase__	(     nn.Module):






 def __init__(      self	:List[str]						,					_A	:Optional[Any]						,					_A	:Tuple						,					_A	:str      )					->      Optional[Any]:







       '''simple docstring'''



       super().__init__()
       __A													=      MegaLM(_A						,					_A						,					_A      )
       __A													=      nn.Linear(mega_args.encoder_embed_dim						,					_A      )
       __A													=      nn.Dropout(p=0.1      )






 def 						lowercase_    (      self	:Optional[int]						,					_A	:Union[str, Any]						,					_A	:Any						,					_A	:Union[str, Any]=True						,					_A	:List[str]=0      )					->      Union[str, Any]:







       '''simple docstring'''



       __A													=      self.mega(_A						,					_A						,					_A						,					_A      )
       return self.mlm_head(self.dropout(_A      )      )





def 							snake_case				(		UpperCAmelCase   ,	UpperCAmelCase   ,	UpperCAmelCase						)->      Dict:







      """simple docstring"""
      with open(os.path.join(UpperCAmelCase   ,	'model_args.pkl'						)   ,	'rb'						) as f:
            __A													=      pkl.load(UpperCAmelCase						)

      # load the original encoder
      __A													=      OriginalMegaForMaskedLM(**UpperCAmelCase						).eval()

      # load its weights
      print(
          'Original Mega encoder:'   ,	original_mlm.mega.load_state_dict(
              torch.load(os.path.join(UpperCAmelCase   ,	'encoder_weights.pt'						)   ,	map_location='cpu'						)						)   ,	)
      print(
          'Original Mega MLM layer:'   ,	original_mlm.mlm_head.load_state_dict(
              torch.load(os.path.join(UpperCAmelCase   ,	'mlm_head_weights.pt'						)   ,	map_location='cpu'						)						)   ,	)

      # create a new config from the old one
      __A													=      MegaConfig(
          num_hidden_layers=mega_original_args['depth']   ,	vocab_size=mega_original_args['vocab_size']   ,	hidden_size=mega_original_args['mega_args'].encoder_embed_dim   ,	shared_representation_size=mega_original_args['mega_args'].encoder_z_dim   ,	intermediate_size=mega_original_args['mega_args'].encoder_hidden_dim   ,	ema_projection_size=mega_original_args['mega_args'].encoder_n_dim   ,	dropout_prob=mega_original_args['mega_args'].dropout   ,	attention_probs_dropout_prob=mega_original_args['mega_args'].attention_dropout   ,	hidden_dropout_prob=mega_original_args['mega_args'].hidden_dropout   ,	activation=mega_original_args['mega_args'].activation_fn   ,	attention_activation=mega_original_args['mega_args'].attention_activation_fn   ,	bidirectional=mega_original_args['mega_args'].bidirectional   ,	use_chunking=mega_original_args['mega_args'].encoder_chunk_size > 0   ,	chunk_size=mega_original_args['mega_args'].encoder_chunk_size   ,	truncation=mega_original_args['mega_args'].truncation_length   ,	normalization_type=mega_original_args['mega_args'].normalization_type   ,	normalize_before_mega=UpperCAmelCase   ,	norm_affine=UpperCAmelCase   ,	use_feature_dropout=mega_original_args['mega_args'].feature_dropout   ,	relative_positional_bias=mega_original_args['mega_args'].rel_pos_bias   ,	max_positions=mega_original_args['mega_args'].max_source_positions   ,	nffn_hidden_size=mega_original_args['mega_args'].encoder_ffn_embed_dim   ,	normalize_before_ffn=mega_original_args['mega_args'].normalize_before   ,	nffn_activation_dropout_prob=0.0   ,	add_token_type_embeddings=UpperCAmelCase   ,	add_lm_hidden_dense_layer=UpperCAmelCase   ,	)

      __A													=      MegaForMaskedLM(UpperCAmelCase						).eval()

      # the originl checkpoint just uses nn.Embedding for the word embeddings
      # we use a wrapper module for embeddings to add support for positional embeddings
      __A													=      original_mlm.mega.embedding_layer.weight

      # modify the state dictionary of the original checkpoint to account for naming issues in the Hugging Face
      # ecosystem -- any names containing "beta" or "gamma" aren't safe to use and are renamed upon _load_pretrained,
      # also renaming previously confusing parameter names
      __A													=      original_mlm.mega.encoders.state_dict()
      __A													=      {}
      for module_name in original_state_dict.keys():
            __A													=      None
            # have to handle gamma, beta, and alpha differently due to their use
            # in multiple modules within the original repository;
            # beta is used in EMA, MovingAverageGatedAttention, and RotaryRelativePositionalBias, and must be renamed due to flax/tf weights
            # the EMA sublayer was renamed from "move" to "ema_gate" for readability, so that is also done here
            if "beta" in module_name:
                  # EMA sub-layers were always called "move" in the original repo
                  if "move.beta" in module_name:
                        __A													=      module_name.replace('move.beta'   ,	'ema_gate.ema_expansion_matrix'						)
                  elif "mega_layer.beta" in module_name:
                        __A													=      module_name.replace('beta'   ,	'qk_bias'						)
                  else:
                        __A													=      module_name.replace('beta'   ,	'b_param'						)
        # beta is used in EMA and MovingAverageGatedAttention, and must be renamed due to flax/tf weights
            elif "gamma" in module_name:
                  if "move.gamma" in module_name:
                        __A													=      module_name.replace('move.gamma'   ,	'ema_gate.kernel_projection_matrix'						)
                  elif "mega_layer.gamma" in module_name:
                        __A													=      module_name.replace('gamma'   ,	'qk_weight'						)
                  else:
                        __A													=      module_name.replace('gamma'   ,	'g_param'						)
        # alpha is used in EMA and positional bias; renaming to improve readability
            elif "move.alpha" in module_name:
                  __A													=      module_name.replace('move.alpha'   ,	'ema_gate.decay_factor'						)
            # delta is only used in EMA; renaming to improve readability
            elif "move.delta" in module_name:
                  __A													=      module_name.replace('move.delta'   ,	'ema_gate.damping_factor'						)
            # omega is only used in EMA; renaming to improve readability
            elif "omega" in module_name:
                  __A													=      module_name.replace('move.omega'   ,	'ema_gate.residual_weight'						)

            if new_module_name:
                  __A													=      new_module_name

      if len(UpperCAmelCase						) != 0:
            print(f'Renaming these keys: {updated_keys.keys()}'						)
      else:
            print('No need to rename state dict entries'						)
      for old, new in updated_keys.items():
            __A													=      original_state_dict.pop(UpperCAmelCase						)

      # now attempt to load the state dictionary with updated names
      # note that we now call it `mega.layers` instead of `mega.encoders` due to hugging face style
      print('HF Mega encoder:'   ,	hf_mlm.mega.layers.load_state_dict(UpperCAmelCase						)						)

      # load the MLM head weights directly
      print(
          'HF Mega MLM layer:'   ,	hf_mlm.mlm_head.load_state_dict(
              torch.load(os.path.join(UpperCAmelCase   ,	'mlm_head_weights.pt'						)   ,	map_location='cpu'						)						)   ,	)

      # test on a randomly generated input sequence
      __A													=      torch.randint(0   ,	hf_config.vocab_size   ,	size=(4, 2_5_6)						)
      __A													=      torch.ones_like(UpperCAmelCase						)
      # mask a few tokens to make sure masking is applied appropriately :)
      __A													=      0

      # run forward passes
      __A													=      original_mlm(UpperCAmelCase   ,	UpperCAmelCase   ,	batch_first=UpperCAmelCase   ,	ignore_mask_value=0						)
      __A													=      hf_mlm(UpperCAmelCase   ,	UpperCAmelCase						)[0]

      # print shapes and diff
      print(f'original output {original_output.shape}'						)
      print(f'hf output {hf_output.shape}'						)
      print(f'max diff: {(original_output - hf_output).max()}'						)  # 0.0
      __A													=      torch.allclose(UpperCAmelCase   ,	UpperCAmelCase   ,	atol=1e-3						)

      if success:
            print('Yay!'						)
            hf_mlm.save_pretrained(UpperCAmelCase						)
      else:
            raise RuntimeError(f'Something\'s broken :(\nOriginal:\n{original_output}\n\nHF\n{hf_output}\n{hf_mlm}'						)

      if includes_tokenizer:
            print('Transferring tokenizer'						)
            __A													=      AutoTokenizer.from_pretrained(UpperCAmelCase						)
            tokenizer.save_pretrained(UpperCAmelCase						)


if __name__ == "__main__":
 a__		: str  							=							argparse.ArgumentParser()

 parser.add_argument(
     "--pretrained_checkpoint_path",
     default=None,
     type=str,
     required=True,
     help="Point to the directory containing your model weights using the official Mega repo",
 )

 parser.add_argument(
     "--output_path", default=None, type=str, required=True, help="Location to save the Hugging Face version"
 )

 parser.add_argument(
     "--includes_tokenizer",
     action="store_true",
     help="Use this flag if there is a Hugging Face tokenizer in the original checkpoint repo",
 )

 a__		: Tuple  							=							parser.parse_args()

 convert_checkpoint_to_huggingface(args.pretrained_checkpoint_path, args.output_path, args.includes_tokenizer)














import unittest

import numpy as np
from datasets import load_dataset

from transformers.testing_utils import 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 SegformerImageProcessor





class        a__      (	unittest.TestCase		):






		def __init__(      self							, lowercase__							, lowercase__=7							, lowercase__=3							, lowercase__=30							, lowercase__=400							, lowercase__=True							, lowercase__=None							, lowercase__=True							, lowercase__=[0.5, 0.5, 0.5]							, lowercase__=[0.5, 0.5, 0.5]							, lowercase__=False							, )				->				Tuple:
				__A    =							size if size is not None else {"height": 30, "width": 30}
				__A    =							parent
				__A    =							batch_size
				__A    =							num_channels
				__A    =							min_resolution
				__A    =							max_resolution
				__A    =							do_resize
				__A    =							size
				__A    =							do_normalize
				__A    =							image_mean
				__A    =							image_std
				__A    =							do_reduce_labels






		def 					_lowerCamelCase					(      self						)				->				Union[str, Any]:
				return {
				    "do_resize": self.do_resize,
				    "size": self.size,
				    "do_normalize": self.do_normalize,
				    "image_mean": self.image_mean,
				    "image_std": self.image_std,
				    "do_reduce_labels": self.do_reduce_labels,
				}



def      UpperCAmelCase						( ):


		'''simple docstring'''

		__A    =							load_dataset("hf-internal-testing/fixtures_ade20k"					,     split="test"	)

		__A    =							Image.open(dataset[0]["file"]	)
		__A    =							Image.open(dataset[1]["file"]	)

		return image, map



def      UpperCAmelCase						( ):


		'''simple docstring'''

		__A    =							load_dataset("hf-internal-testing/fixtures_ade20k"					,     split="test"	)

		__A    =							Image.open(dataset[0]["file"]	)
		__A    =							Image.open(dataset[1]["file"]	)
		__A    =							Image.open(dataset[2]["file"]	)
		__A    =							Image.open(dataset[3]["file"]	)

		return [imagea, imagea], [mapa, mapa]





@require_torch
@require_vision
class        a__      (	lowerCAmelCase__	,       unittest.TestCase		):
		UpperCAmelCase_							:       Dict 					= SegformerImageProcessor if is_vision_available() else None






		def 					_lowerCamelCase					(      self						)				->				List[str]:
				__A    =							SegformerImageProcessingTester(self						)






		@property
		def 					_lowerCamelCase					(      self						)				->				str:
				return self.image_processor_tester.prepare_image_processor_dict()






		def 					_lowerCamelCase					(      self						)				->				Union[str, Any]:
				__A    =							self.image_processing_class(**self.image_processor_dict						)
				self.assertTrue(hasattr(lowercase__							, "do_resize"						)						)
				self.assertTrue(hasattr(lowercase__							, "size"						)						)
				self.assertTrue(hasattr(lowercase__							, "do_normalize"						)						)
				self.assertTrue(hasattr(lowercase__							, "image_mean"						)						)
				self.assertTrue(hasattr(lowercase__							, "image_std"						)						)
				self.assertTrue(hasattr(lowercase__							, "do_reduce_labels"						)						)






		def 					_lowerCamelCase					(      self						)				->				Union[str, Any]:
				__A    =							self.image_processing_class.from_dict(self.image_processor_dict						)
				self.assertEqual(image_processor.size							, {"height": 30, "width": 30}						)
				self.assertEqual(image_processor.do_reduce_labels							, lowercase__						)

				__A    =							self.image_processing_class.from_dict(self.image_processor_dict							, size=42							, reduce_labels=lowercase__						)
				self.assertEqual(image_processor.size							, {"height": 42, "width": 42}						)
				self.assertEqual(image_processor.do_reduce_labels							, lowercase__						)






		def 					_lowerCamelCase					(      self						)				->				Optional[int]:
				pass






		def 					_lowerCamelCase					(      self						)				->				Union[str, Any]:
				# Initialize image_processing
				__A    =							self.image_processing_class(**self.image_processor_dict						)
				# create random PIL images
				__A    =							prepare_image_inputs(self.image_processor_tester							, equal_resolution=lowercase__						)
				for image in image_inputs:
						self.assertIsInstance(lowercase__							, Image.Image						)

				# Test not batched input
				__A    =							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    =							image_processing(lowercase__							, 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"],
				    )							, )






		def 					_lowerCamelCase					(      self						)				->				List[str]:
				# Initialize image_processing
				__A    =							self.image_processing_class(**self.image_processor_dict						)
				# create random numpy tensors
				__A    =							prepare_image_inputs(self.image_processor_tester							, equal_resolution=lowercase__							, numpify=lowercase__						)
				for image in image_inputs:
						self.assertIsInstance(lowercase__							, np.ndarray						)

				# Test not batched input
				__A    =							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    =							image_processing(lowercase__							, 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"],
				    )							, )






		def 					_lowerCamelCase					(      self						)				->				List[str]:
				# Initialize image_processing
				__A    =							self.image_processing_class(**self.image_processor_dict						)
				# create random PyTorch tensors
				__A    =							prepare_image_inputs(self.image_processor_tester							, equal_resolution=lowercase__							, torchify=lowercase__						)
				for image in image_inputs:
						self.assertIsInstance(lowercase__							, torch.Tensor						)

				# Test not batched input
				__A    =							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    =							image_processing(lowercase__							, 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"],
				    )							, )






		def 					_lowerCamelCase					(      self						)				->				Dict:
				# Initialize image_processing
				__A    =							self.image_processing_class(**self.image_processor_dict						)
				# create random PyTorch tensors
				__A    =							prepare_image_inputs(self.image_processor_tester							, equal_resolution=lowercase__							, torchify=lowercase__						)
				__A    =							[]
				for image in image_inputs:
						self.assertIsInstance(lowercase__							, torch.Tensor						)
						maps.append(torch.zeros(image.shape[-2:]						).long()						)

				# Test not batched input
				__A    =							image_processing(image_inputs[0]							, maps[0]							, return_tensors="pt"						)
				self.assertEqual(
				    encoding["pixel_values"].shape							, (
				        1,
				        self.image_processor_tester.num_channels,
				        self.image_processor_tester.size["height"],
				        self.image_processor_tester.size["width"],
				    )							, )
				self.assertEqual(
				    encoding["labels"].shape							, (
				        1,
				        self.image_processor_tester.size["height"],
				        self.image_processor_tester.size["width"],
				    )							, )
				self.assertEqual(encoding["labels"].dtype							, torch.long						)
				self.assertTrue(encoding["labels"].min().item() >= 0						)
				self.assertTrue(encoding["labels"].max().item() <= 255						)

				# Test batched
				__A    =							image_processing(lowercase__							, lowercase__							, return_tensors="pt"						)
				self.assertEqual(
				    encoding["pixel_values"].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"],
				    )							, )
				self.assertEqual(
				    encoding["labels"].shape							, (
				        self.image_processor_tester.batch_size,
				        self.image_processor_tester.size["height"],
				        self.image_processor_tester.size["width"],
				    )							, )
				self.assertEqual(encoding["labels"].dtype							, torch.long						)
				self.assertTrue(encoding["labels"].min().item() >= 0						)
				self.assertTrue(encoding["labels"].max().item() <= 255						)

				# Test not batched input (PIL images)
				__A ,   __A    =							prepare_semantic_single_inputs()

				__A    =							image_processing(lowercase__							, lowercase__							, return_tensors="pt"						)
				self.assertEqual(
				    encoding["pixel_values"].shape							, (
				        1,
				        self.image_processor_tester.num_channels,
				        self.image_processor_tester.size["height"],
				        self.image_processor_tester.size["width"],
				    )							, )
				self.assertEqual(
				    encoding["labels"].shape							, (
				        1,
				        self.image_processor_tester.size["height"],
				        self.image_processor_tester.size["width"],
				    )							, )
				self.assertEqual(encoding["labels"].dtype							, torch.long						)
				self.assertTrue(encoding["labels"].min().item() >= 0						)
				self.assertTrue(encoding["labels"].max().item() <= 255						)

				# Test batched input (PIL images)
				__A ,   __A    =							prepare_semantic_batch_inputs()

				__A    =							image_processing(lowercase__							, lowercase__							, return_tensors="pt"						)
				self.assertEqual(
				    encoding["pixel_values"].shape							, (
				        2,
				        self.image_processor_tester.num_channels,
				        self.image_processor_tester.size["height"],
				        self.image_processor_tester.size["width"],
				    )							, )
				self.assertEqual(
				    encoding["labels"].shape							, (
				        2,
				        self.image_processor_tester.size["height"],
				        self.image_processor_tester.size["width"],
				    )							, )
				self.assertEqual(encoding["labels"].dtype							, torch.long						)
				self.assertTrue(encoding["labels"].min().item() >= 0						)
				self.assertTrue(encoding["labels"].max().item() <= 255						)






		def 					_lowerCamelCase					(      self						)				->				Tuple:
				# Initialize image_processing
				__A    =							self.image_processing_class(**self.image_processor_dict						)

				# ADE20k has 150 classes, and the background is included, so labels should be between 0 and 150
				__A ,   __A    =							prepare_semantic_single_inputs()
				__A    =							image_processing(lowercase__							, lowercase__							, return_tensors="pt"						)
				self.assertTrue(encoding["labels"].min().item() >= 0						)
				self.assertTrue(encoding["labels"].max().item() <= 150						)

				__A    =							True
				__A    =							image_processing(lowercase__							, lowercase__							, return_tensors="pt"						)
				self.assertTrue(encoding["labels"].min().item() >= 0						)
				self.assertTrue(encoding["labels"].max().item() <= 255						)

