






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        lowerCAmelCase__      (   unittest.TestCase	):

						def  A_				(			self				)					->      int:


								'''simple docstring'''

								_UpperCamelCase						    =	tempfile.mkdtemp()

								_UpperCamelCase						    =	BlipImageProcessor()
								_UpperCamelCase						    =	GPTaTokenizer.from_pretrained("""hf-internal-testing/tiny-random-GPT2Model"""				)

								_UpperCamelCase						    =	BlipaProcessor(a       ,							a				)

								processor.save_pretrained(self.tmpdirname				)

						def  A_				(			self       ,							**a				)					->      Tuple:


								'''simple docstring'''

								return AutoProcessor.from_pretrained(self.tmpdirname       ,							**a				).tokenizer

						def  A_				(			self       ,							**a				)					->      List[str]:


								'''simple docstring'''

								return AutoProcessor.from_pretrained(self.tmpdirname       ,							**a				).image_processor

						def  A_				(			self				)					->      Dict:


								'''simple docstring'''

								shutil.rmtree(self.tmpdirname				)

						def  A_				(			self				)					->      Optional[int]:


								'''simple docstring'''

								_UpperCamelCase						    =	[np.random.randint(2_55       ,							size=(3, 30, 4_00)       ,							dtype=np.uinta				)]

								_UpperCamelCase						    =	[Image.fromarray(np.moveaxis(a       ,							0       ,							-1				)				) for x in image_inputs]

								return image_inputs

						def  A_				(			self				)					->      Any:


								'''simple docstring'''

								_UpperCamelCase						    =	BlipaProcessor(tokenizer=self.get_tokenizer()       ,							image_processor=self.get_image_processor()				)
								processor.save_pretrained(self.tmpdirname				)

								_UpperCamelCase						    =	self.get_tokenizer(bos_token="""(BOS)"""       ,							eos_token="""(EOS)"""				)
								_UpperCamelCase						    =	self.get_image_processor(do_normalize=a       ,							padding_value=1.0				)

								_UpperCamelCase						    =	BlipaProcessor.from_pretrained(
								    self.tmpdirname       ,							bos_token="""(BOS)"""       ,							eos_token="""(EOS)"""       ,							do_normalize=a       ,							padding_value=1.0				)

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

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

						def  A_				(			self				)					->      Optional[Any]:


								'''simple docstring'''

								_UpperCamelCase						    =	self.get_image_processor()
								_UpperCamelCase						    =	self.get_tokenizer()

								_UpperCamelCase						    =	BlipaProcessor(tokenizer=a       ,							image_processor=a				)

								_UpperCamelCase						    =	self.prepare_image_inputs()

								_UpperCamelCase						    =	image_processor(a       ,							return_tensors="""np"""				)
								_UpperCamelCase						    =	processor(images=a       ,							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  A_				(			self				)					->      Optional[int]:


								'''simple docstring'''

								_UpperCamelCase						    =	self.get_image_processor()
								_UpperCamelCase						    =	self.get_tokenizer()

								_UpperCamelCase						    =	BlipaProcessor(tokenizer=a       ,							image_processor=a				)

								_UpperCamelCase						    =	"""lower newer"""

								_UpperCamelCase						    =	processor(text=a				)

								_UpperCamelCase						    =	tokenizer(a       ,							return_token_type_ids=a				)

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

						def  A_				(			self				)					->      Tuple:


								'''simple docstring'''

								_UpperCamelCase						    =	self.get_image_processor()
								_UpperCamelCase						    =	self.get_tokenizer()

								_UpperCamelCase						    =	BlipaProcessor(tokenizer=a       ,							image_processor=a				)

								_UpperCamelCase						    =	"""lower newer"""
								_UpperCamelCase						    =	self.prepare_image_inputs()

								_UpperCamelCase						    =	processor(text=a       ,							images=a				)

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

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

						def  A_				(			self				)					->      Optional[int]:


								'''simple docstring'''

								_UpperCamelCase						    =	self.get_image_processor()
								_UpperCamelCase						    =	self.get_tokenizer()

								_UpperCamelCase						    =	BlipaProcessor(tokenizer=a       ,							image_processor=a				)

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

								_UpperCamelCase						    =	processor.batch_decode(a				)
								_UpperCamelCase						    =	tokenizer.batch_decode(a				)

								self.assertListEqual(a       ,							a				)







						def  A_				(			self				)					->      int:


								'''simple docstring'''

								_UpperCamelCase						    =	self.get_image_processor()
								_UpperCamelCase						    =	self.get_tokenizer()

								_UpperCamelCase						    =	BlipaProcessor(tokenizer=a       ,							image_processor=a				)

								_UpperCamelCase						    =	"""lower newer"""
								_UpperCamelCase						    =	self.prepare_image_inputs()

								_UpperCamelCase						    =	processor(text=a       ,							images=a				)

								# 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
from argparse import Namespace

import torch
from torch import nn

from transformers import XGLMConfig, XGLMForCausalLM


def 							__A(lowerCAmelCase )		->				Any:

		"""simple docstring"""






		_UpperCamelCase						    =	[
		    """decoder.version""",
		    """decoder.output_projection.weight""",
		    """_float_tensor""",
		    """decoder.embed_positions._float_tensor""",
		]
		for k in ignore_keys:
				state_dict.pop(lowerCAmelCase						,  lowerCAmelCase )


def 							__A(lowerCAmelCase )		->				Optional[int]:

		"""simple docstring"""






		_UpperCamelCase    ,	_UpperCamelCase						    =	emb.weight.shape
		_UpperCamelCase						    =	nn.Linear(lowerCAmelCase						,  lowerCAmelCase						,  bias=lowerCAmelCase )
		_UpperCamelCase						    =	emb.weight.data
		return lin_layer


def 							__A(lowerCAmelCase )		->				Tuple:

		"""simple docstring"""






		_UpperCamelCase						    =	torch.load(lowerCAmelCase						,  map_location="""cpu""" )
		_UpperCamelCase						    =	Namespace(**checkpoint["""cfg"""]["""model"""] )
		_UpperCamelCase						    =	checkpoint["""model"""]
		remove_ignore_keys_(lowerCAmelCase )
		_UpperCamelCase						    =	state_dict["""decoder.embed_tokens.weight"""].shape[0]

		_UpperCamelCase						    =	{key.replace("""decoder"""						,  """model""" ): val for key, val in state_dict.items()}

		_UpperCamelCase						    =	XGLMConfig(
		    vocab_size=lowerCAmelCase						,  max_position_embeddings=args.max_target_positions						,  num_layers=args.decoder_layers						,  attention_heads=args.decoder_attention_heads						,  ffn_dim=args.decoder_ffn_embed_dim						,  d_model=args.decoder_embed_dim						,  layerdrop=args.decoder_layerdrop						,  dropout=args.dropout						,  attention_dropout=args.attention_dropout						,  activation_dropout=args.activation_dropout						,  activation_function="""gelu"""						,  scale_embedding=not args.no_scale_embedding						,  tie_word_embeddings=args.share_decoder_input_output_embed						,  )

		_UpperCamelCase						    =	XGLMForCausalLM(lowerCAmelCase )
		_UpperCamelCase						    =	model.load_state_dict(lowerCAmelCase						,  strict=lowerCAmelCase )
		print(lowerCAmelCase )
		_UpperCamelCase						    =	make_linear_from_emb(model.model.embed_tokens )

		return model


if __name__ == "__main__":
		lowerCamelCase__	=							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.")
		lowerCamelCase__	=							parser.parse_args()
		lowerCamelCase__	=							convert_fairseq_xglm_checkpoint_from_disk(args.fairseq_path)
		model.save_pretrained(args.pytorch_dump_folder_path)



