
import os
import tempfile
import unittest
import uuid
from pathlib import Path

from transformers.testing_utils import get_tests_dir, require_soundfile, require_torch, require_vision
from transformers.tools.agent_types import AgentAudio, AgentImage, AgentText
from transformers.utils import is_soundfile_availble, is_torch_available, is_vision_available


if is_torch_available():
				import torch

if is_soundfile_availble():
				import soundfile as sf

if is_vision_available():
				from PIL import Image






def 	a_		(	__lowercase       :     Union[str, Any]=""			)			->  str:
			_snake_case          =      tempfile.mkdtemp()
			return os.path.join(__lowercase	, str(uuid.uuida()			) + suffix			)




@require_soundfile
@require_torch
class        SCREAMING_SNAKE_CASE__					(     unittest.TestCase ):





	'''simple docstring'''



	def 			A							(  self     :   List[str]     ):


				'''simple docstring'''


				_snake_case          =      torch.rand(12      ,  dtype=torch.floataa     ) - 0.5
				_snake_case          =      AgentAudio(lowercase     )
				_snake_case          =      str(agent_type.to_string()     )

				# Ensure that the tensor and the agent_type's tensor are the same
				self.assertTrue(torch.allclose(lowercase      ,  agent_type.to_raw()      ,  atol=1E-4     )     )

				del agent_type

				# Ensure the path remains even after the object deletion
				self.assertTrue(os.path.exists(lowercase     )     )

				# Ensure that the file contains the same value as the original tensor
				_snake_case     , _snake_case          =      sf.read(lowercase     )
				self.assertTrue(torch.allclose(lowercase      ,  torch.tensor(lowercase     )      ,  atol=1E-4     )     )





	def 			A							(  self     :   int     ):


				'''simple docstring'''


				_snake_case          =      torch.rand(12      ,  dtype=torch.floataa     ) - 0.5
				_snake_case          =      get_new_path(suffix='.wav'     )
				sf.write(lowercase      ,  lowercase      ,  16_000     )

				_snake_case          =      AgentAudio(lowercase     )

				self.assertTrue(torch.allclose(lowercase      ,  agent_type.to_raw()      ,  atol=1E-4     )     )
				self.assertEqual(agent_type.to_string()      ,  lowercase     )




@require_vision
@require_torch
class        SCREAMING_SNAKE_CASE__					(     unittest.TestCase ):





	'''simple docstring'''



	def 			A							(  self     :   str     ):


				'''simple docstring'''


				_snake_case          =      torch.randint(0      ,  256      ,  (64, 64, 3)     )
				_snake_case          =      AgentImage(lowercase     )
				_snake_case          =      str(agent_type.to_string()     )

				# Ensure that the tensor and the agent_type's tensor are the same
				self.assertTrue(torch.allclose(lowercase      ,  agent_type._tensor      ,  atol=1E-4     )     )

				self.assertIsInstance(agent_type.to_raw()      ,  Image.Image     )

				# Ensure the path remains even after the object deletion
				del agent_type
				self.assertTrue(os.path.exists(lowercase     )     )



	def 			A							(  self     :   Any     ):


				'''simple docstring'''


				_snake_case          =      Path(get_tests_dir('fixtures/tests_samples/COCO'     )     ) / '000000039769.png'
				_snake_case          =      Image.open(lowercase     )
				_snake_case          =      AgentImage(lowercase     )

				self.assertTrue(path.samefile(agent_type.to_string()     )     )
				self.assertTrue(image == agent_type.to_raw()     )

				# Ensure the path remains even after the object deletion
				del agent_type
				self.assertTrue(os.path.exists(lowercase     )     )





	def 			A							(  self     :   List[Any]     ):


				'''simple docstring'''


				_snake_case          =      Path(get_tests_dir('fixtures/tests_samples/COCO'     )     ) / '000000039769.png'
				_snake_case          =      Image.open(lowercase     )
				_snake_case          =      AgentImage(lowercase     )

				self.assertFalse(path.samefile(agent_type.to_string()     )     )
				self.assertTrue(image == agent_type.to_raw()     )

				# Ensure the path remains even after the object deletion
				del agent_type
				self.assertTrue(os.path.exists(lowercase     )     )




class        SCREAMING_SNAKE_CASE__					(     unittest.TestCase ):





	'''simple docstring'''



	def 			A							(  self     :   Optional[int]     ):


				'''simple docstring'''


				_snake_case          =      'Hey!'
				_snake_case          =      AgentText(lowercase     )

				self.assertEqual(lowercase      ,  agent_type.to_string()     )
				self.assertEqual(lowercase      ,  agent_type.to_raw()     )
				self.assertEqual(lowercase      ,  lowercase     )

import argparse

import torch
from torch import nn

from transformers import MBartConfig, MBartForConditionalGeneration






def 	a_		(	__lowercase       :     Dict			)			->  int:
			_snake_case          =      [
			    'encoder.version',
			    'decoder.version',
			    'model.encoder.version',
			    'model.decoder.version',
			    '_float_tensor',
			    'decoder.output_projection.weight',
			]
			for k in ignore_keys:
						state_dict.pop(__lowercase	, __lowercase			)






def 	a_		(	__lowercase       :     List[str]			)			->  str:
			_snake_case     , _snake_case          =      emb.weight.shape
			_snake_case          =      nn.Linear(__lowercase	, __lowercase	, bias=__lowercase			)
			_snake_case          =      emb.weight.data
			return lin_layer






def 	a_		(	__lowercase       :     Optional[int]	, __lowercase       :     Optional[Any]="facebook/mbart-large-en-ro"	, __lowercase       :     Union[str, Any]=False	, __lowercase       :     Optional[Any]=False			)			->  int:
			_snake_case          =      torch.load(__lowercase	, map_location='cpu'			)['model']
			remove_ignore_keys_(__lowercase			)
			_snake_case          =      state_dict['encoder.embed_tokens.weight'].shape[0]

			_snake_case          =      MBartConfig.from_pretrained(__lowercase	, vocab_size=__lowercase			)
			if mbart_aa and finetuned:
						_snake_case          =      'relu'

			_snake_case          =      state_dict['decoder.embed_tokens.weight']
			_snake_case          =      MBartForConditionalGeneration(__lowercase			)
			model.model.load_state_dict(__lowercase			)

			if finetuned:
						_snake_case          =      make_linear_from_emb(model.model.shared			)

			return model


if __name__ == "__main__":
			_lowerCamelCase	:    List[str]          =      argparse.ArgumentParser()
			# Required parameters
			parser.add_argument(
			    '''fairseq_path''', type=str, help='''bart.large, bart.large.cnn or a 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.''')
			parser.add_argument(
			    '''--hf_config''',
			    default='''facebook/mbart-large-cc25''',
			    type=str,
			    help='''Which huggingface architecture to use: mbart-large''',
			)
			parser.add_argument('''--mbart_50''', action='''store_true''', help='''whether the model is mMART-50 checkpoint''')
			parser.add_argument('''--finetuned''', action='''store_true''', help='''whether the model is a fine-tuned checkpoint''')
			_lowerCamelCase	:    List[str]          =      parser.parse_args()
			_lowerCamelCase	:    Tuple          =      convert_fairseq_mbart_checkpoint_from_disk(
			    args.fairseq_path, hf_config_path=args.hf_config, finetuned=args.finetuned, mbart_aa=args.mbart_aa
			)
			model.save_pretrained(args.pytorch_dump_folder_path)