






import argparse

import torch

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


logging.set_verbosity_info()
A__			:    Any         =				logging.get_logger(__name__)

A__			:    List[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 						a    (		lowerCamelCase_       ):






				'''simple docstring'''


				if k == "embeddings.weight":
								return "shared.weight"

				for parlai_name, hf_name in PATTERNS:
								lowercase__     				=    k.replace(_lowerCamelCase	,     _lowerCamelCase       )

				if k.startswith('''encoder'''       ):
								lowercase__     				=    k.replace('''.attn'''	,     '''.self_attn'''       )
								lowercase__     				=    k.replace('''norm1'''	,     '''self_attn_layer_norm'''       )
								lowercase__     				=    k.replace('''norm2'''	,     '''final_layer_norm'''       )
				elif k.startswith('''decoder'''       ):
								lowercase__     				=    k.replace('''norm1'''	,     '''self_attn_layer_norm'''       )
								lowercase__     				=    k.replace('''norm2'''	,     '''encoder_attn_layer_norm'''       )
								lowercase__     				=    k.replace('''norm3'''	,     '''final_layer_norm'''       )
				return k






def 						a    (		lowerCamelCase_       ):






				'''simple docstring'''
				lowercase__     				=    [
				    "model.encoder.layernorm_embedding.weight",
				    "model.encoder.layernorm_embedding.bias",
				    "model.decoder.layernorm_embedding.weight",
				    "model.decoder.layernorm_embedding.bias",
				]
				for k in keys:
								lowercase__     				=    sd.pop(_lowerCamelCase       )
								lowercase__     				=    k.replace('''layernorm_embedding'''	,     '''layer_norm'''       )
								assert new_k not in sd
								lowercase__     				=    v


A__			:    List[str]         =				["START"]






@torch.no_grad()
def 						a    (		lowerCamelCase_	,     lowerCamelCase_	,     lowerCamelCase_       ):






				'''simple docstring'''
				lowercase__     				=    torch.load(_lowerCamelCase	,     map_location='''cpu'''       )
				lowercase__     				=    model["model"]
				lowercase__     				=    BlenderbotConfig.from_json_file(_lowerCamelCase       )
				lowercase__     				=    BlenderbotForConditionalGeneration(_lowerCamelCase       )
				lowercase__     				=    m.model.state_dict().keys()
				lowercase__     				=    []
				lowercase__     				=    {}
				for k, v in sd.items():
								if k in IGNORE_KEYS:
												continue

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


if __name__ == "__main__":
				A__			:    Dict         =				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'
				)
				A__			:    List[str]         =				parser.parse_args()
				convert_parlai_checkpoint(args.src_path, args.save_dir, args.hf_config_json)












import json
import os
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import MgpstrTokenizer
from transformers.models.mgp_str.tokenization_mgp_str import VOCAB_FILES_NAMES
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_torch_available, is_vision_available


if is_torch_available():
							import torch


if is_vision_available():
							from PIL import Image

							from transformers import MgpstrProcessor, ViTImageProcessor
@require_torch
@require_vision
class    _UpperCAmelCase      ( unittest.TestCase			):

				"""simple docstring"""




				lowercase__       							= ViTImageProcessor if is_vision_available() else None




				@property
				def        lowercase__     (  self		:					List[str]						):

								'''simple docstring'''
								return self.image_processor_tester.prepare_image_processor_dict()




				def        lowercase__     (  self		:					Any						):

								'''simple docstring'''
								lowercase__     				=    (3, 32, 128)
								lowercase__     				=    tempfile.mkdtemp()

								# fmt: off
								lowercase__     				=    ['''[GO]''', '''[s]''', '''0''', '''1''', '''2''', '''3''', '''4''', '''5''', '''6''', '''7''', '''8''', '''9''', '''a''', '''b''', '''c''', '''d''', '''e''', '''f''', '''g''', '''h''', '''i''', '''j''', '''k''', '''l''', '''m''', '''n''', '''o''', '''p''', '''q''', '''r''', '''s''', '''t''', '''u''', '''v''', '''w''', '''x''', '''y''', '''z''']
								# fmt: on
								lowercase__     				=    dict(zip(lowerCamelCase,						range(len(lowerCamelCase						)						)						)						)

								lowercase__     				=    os.path.join(self.tmpdirname,						VOCAB_FILES_NAMES['''vocab_file''']						)
								with open(self.vocab_file,						'''w''',						encoding='''utf-8'''						) as fp:
												fp.write(json.dumps(lowerCamelCase						) + '''\n'''						)

								lowercase__     				=    {
								    '''do_normalize''': False,
								    '''do_resize''': True,
								    '''image_processor_type''': '''ViTImageProcessor''',
								    '''resample''': 3,
								    '''size''': {'''height''': 32, '''width''': 128},
								}
								lowercase__     				=    os.path.join(self.tmpdirname,						lowerCamelCase						)
								with open(self.image_processor_file,						'''w''',						encoding='''utf-8'''						) as fp:
												json.dump(lowerCamelCase,						lowerCamelCase						)




				def        lowercase__     (  self		:					int,						**lowerCamelCase		:					Optional[Any]						):

								'''simple docstring'''
								return MgpstrTokenizer.from_pretrained(self.tmpdirname,						**lowerCamelCase						)




				def        lowercase__     (  self		:					str,						**lowerCamelCase		:					Union[str, Any]						):

								'''simple docstring'''
								return ViTImageProcessor.from_pretrained(self.tmpdirname,						**lowerCamelCase						)




				def        lowercase__     (  self		:					int						):

								'''simple docstring'''
								shutil.rmtree(self.tmpdirname						)




				def        lowercase__     (  self		:					List[Any]						):

								'''simple docstring'''
								lowercase__     				=    np.random.randint(255,						size=(3, 30, 400),						dtype=np.uinta						)

								lowercase__     				=    Image.fromarray(np.moveaxis(lowerCamelCase,						0,						-1						)						)

								return image_input




				def        lowercase__     (  self		:					int						):

								'''simple docstring'''
								lowercase__     				=    self.get_tokenizer()
								lowercase__     				=    self.get_image_processor()

								lowercase__     				=    MgpstrProcessor(tokenizer=lowerCamelCase,						image_processor=lowerCamelCase						)
								processor.save_pretrained(self.tmpdirname						)
								lowercase__     				=    MgpstrProcessor.from_pretrained(self.tmpdirname,						use_fast=lowerCamelCase						)

								self.assertEqual(processor.char_tokenizer.get_vocab(),						tokenizer.get_vocab()						)
								self.assertIsInstance(processor.char_tokenizer,						lowerCamelCase						)

								self.assertEqual(processor.image_processor.to_json_string(),						image_processor.to_json_string()						)
								self.assertIsInstance(processor.image_processor,						lowerCamelCase						)




				def        lowercase__     (  self		:					Optional[Any]						):

								'''simple docstring'''
								lowercase__     				=    self.get_tokenizer()
								lowercase__     				=    self.get_image_processor()

								lowercase__     				=    MgpstrProcessor(tokenizer=lowerCamelCase,						image_processor=lowerCamelCase						)
								processor.save_pretrained(self.tmpdirname						)

								lowercase__     				=    self.get_tokenizer(bos_token='''(BOS)''',						eos_token='''(EOS)'''						)
								lowercase__     				=    self.get_image_processor(do_normalize=lowerCamelCase,						padding_value=1.0						)

								lowercase__     				=    MgpstrProcessor.from_pretrained(
								    self.tmpdirname,						bos_token='''(BOS)''',						eos_token='''(EOS)''',						do_normalize=lowerCamelCase,						padding_value=1.0						)

								self.assertEqual(processor.char_tokenizer.get_vocab(),						tokenizer_add_kwargs.get_vocab()						)
								self.assertIsInstance(processor.char_tokenizer,						lowerCamelCase						)

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




				def        lowercase__     (  self		:					Tuple						):

								'''simple docstring'''
								lowercase__     				=    self.get_image_processor()
								lowercase__     				=    self.get_tokenizer()

								lowercase__     				=    MgpstrProcessor(tokenizer=lowerCamelCase,						image_processor=lowerCamelCase						)

								lowercase__     				=    self.prepare_image_inputs()

								lowercase__     				=    image_processor(lowerCamelCase,						return_tensors='''np'''						)
								lowercase__     				=    processor(images=lowerCamelCase,						return_tensors='''np'''						)

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




				def        lowercase__     (  self		:					Union[str, Any]						):

								'''simple docstring'''
								lowercase__     				=    self.get_image_processor()
								lowercase__     				=    self.get_tokenizer()

								lowercase__     				=    MgpstrProcessor(tokenizer=lowerCamelCase,						image_processor=lowerCamelCase						)

								lowercase__     				=    '''test'''

								lowercase__     				=    processor(text=lowerCamelCase						)

								lowercase__     				=    tokenizer(lowerCamelCase						)
								for key in encoded_tok.keys():
												self.assertListEqual(encoded_tok[key],						encoded_processor[key]						)




				def        lowercase__     (  self		:					List[str]						):

								'''simple docstring'''
								lowercase__     				=    self.get_image_processor()
								lowercase__     				=    self.get_tokenizer()

								lowercase__     				=    MgpstrProcessor(tokenizer=lowerCamelCase,						image_processor=lowerCamelCase						)

								lowercase__     				=    '''test'''
								lowercase__     				=    self.prepare_image_inputs()

								lowercase__     				=    processor(text=lowerCamelCase,						images=lowerCamelCase						)

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

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




				def        lowercase__     (  self		:					int						):

								'''simple docstring'''
								lowercase__     				=    self.get_image_processor()
								lowercase__     				=    self.get_tokenizer()

								lowercase__     				=    MgpstrProcessor(tokenizer=lowerCamelCase,						image_processor=lowerCamelCase						)

								lowercase__     				=    [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9], [3, 4, 3, 1, 1, 8, 9]]

								lowercase__     				=    processor.char_decode(lowerCamelCase						)
								lowercase__     				=    tokenizer.batch_decode(lowerCamelCase						)
								lowercase__     				=    [seq.replace(''' ''',						''''''						) for seq in decoded_tok]

								self.assertListEqual(lowerCamelCase,						lowerCamelCase						)




				def        lowercase__     (  self		:					Union[str, Any]						):

								'''simple docstring'''
								lowercase__     				=    self.get_image_processor()
								lowercase__     				=    self.get_tokenizer()

								lowercase__     				=    MgpstrProcessor(tokenizer=lowerCamelCase,						image_processor=lowerCamelCase						)

								lowercase__     				=    None
								lowercase__     				=    self.prepare_image_inputs()

								lowercase__     				=    processor(text=lowerCamelCase,						images=lowerCamelCase						)

								self.assertListEqual(list(inputs.keys()						),						processor.model_input_names						)




				def        lowercase__     (  self		:					Optional[Any]						):

								'''simple docstring'''
								lowercase__     				=    self.get_image_processor()
								lowercase__     				=    self.get_tokenizer()

								lowercase__     				=    MgpstrProcessor(tokenizer=lowerCamelCase,						image_processor=lowerCamelCase						)

								lowercase__     				=    torch.randn(1,						27,						38						)
								lowercase__     				=    torch.randn(1,						27,						50_257						)
								lowercase__     				=    torch.randn(1,						27,						30_522						)

								lowercase__     				=    processor.batch_decode([char_input, bpe_input, wp_input]						)

								self.assertListEqual(list(results.keys()						),						['''generated_text''', '''scores''', '''char_preds''', '''bpe_preds''', '''wp_preds''']						)




