
"""simple docstring"""


import unittest
from queue import Empty
from threading import Thread

from transformers import AutoTokenizer, TextIteratorStreamer, TextStreamer, is_torch_available
from transformers.testing_utils import CaptureStdout, require_torch, torch_device

from ..test_modeling_common import ids_tensor


if is_torch_available():
						import torch

						from transformers import AutoModelForCausalLM




@require_torch
class  __lowerCamelCase  ( unittest.TestCase			):




				def        UpperCAmelCase__						(				self   ):
							lowerCamelCase_					=  AutoTokenizer.from_pretrained('''hf-internal-testing/tiny-random-gpt2'''   )
							lowerCamelCase_					=  AutoModelForCausalLM.from_pretrained('''hf-internal-testing/tiny-random-gpt2'''   ).to(UpperCAmelCase   )
							lowerCamelCase_					=  -1

							lowerCamelCase_					=  ids_tensor((1, 5)							,						vocab_size=model.config.vocab_size   ).to(UpperCAmelCase   )
							lowerCamelCase_					=  model.generate(UpperCAmelCase							,						max_new_tokens=10							,						do_sample=UpperCAmelCase   )
							lowerCamelCase_					=  tokenizer.decode(greedy_ids[0]   )

							with CaptureStdout() as cs:
										lowerCamelCase_					=  TextStreamer(UpperCAmelCase   )
										model.generate(UpperCAmelCase							,						max_new_tokens=10							,						do_sample=UpperCAmelCase							,						streamer=UpperCAmelCase   )
							# The greedy text should be printed to stdout, except for the final "\n" in the streamer
							lowerCamelCase_					=  cs.out[:-1]

							self.assertEqual(UpperCAmelCase							,						UpperCAmelCase   )




				def        UpperCAmelCase__						(				self   ):
							lowerCamelCase_					=  AutoTokenizer.from_pretrained('''hf-internal-testing/tiny-random-gpt2'''   )
							lowerCamelCase_					=  AutoModelForCausalLM.from_pretrained('''hf-internal-testing/tiny-random-gpt2'''   ).to(UpperCAmelCase   )
							lowerCamelCase_					=  -1

							lowerCamelCase_					=  ids_tensor((1, 5)							,						vocab_size=model.config.vocab_size   ).to(UpperCAmelCase   )
							lowerCamelCase_					=  model.generate(UpperCAmelCase							,						max_new_tokens=10							,						do_sample=UpperCAmelCase   )
							lowerCamelCase_					=  tokenizer.decode(greedy_ids[0]   )

							lowerCamelCase_					=  TextIteratorStreamer(UpperCAmelCase   )
							lowerCamelCase_					=  {'''input_ids''': input_ids, '''max_new_tokens''': 10, '''do_sample''': False, '''streamer''': streamer}
							lowerCamelCase_					=  Thread(target=model.generate							,						kwargs=UpperCAmelCase   )
							thread.start()
							lowerCamelCase_					=  ''''''
							for new_text in streamer:
										streamer_text += new_text

							self.assertEqual(UpperCAmelCase							,						UpperCAmelCase   )




				def        UpperCAmelCase__						(				self   ):
							lowerCamelCase_					=  AutoTokenizer.from_pretrained('''hf-internal-testing/tiny-random-gpt2'''   )
							lowerCamelCase_					=  AutoModelForCausalLM.from_pretrained('''hf-internal-testing/tiny-random-gpt2'''   ).to(UpperCAmelCase   )
							lowerCamelCase_					=  -1

							lowerCamelCase_					=  ids_tensor((1, 5)							,						vocab_size=model.config.vocab_size   ).to(UpperCAmelCase   )
							lowerCamelCase_					=  model.generate(UpperCAmelCase							,						max_new_tokens=10							,						do_sample=UpperCAmelCase   )
							lowerCamelCase_					=  greedy_ids[:, input_ids.shape[1] :]
							lowerCamelCase_					=  tokenizer.decode(new_greedy_ids[0]   )

							with CaptureStdout() as cs:
										lowerCamelCase_					=  TextStreamer(UpperCAmelCase							,						skip_prompt=UpperCAmelCase   )
										model.generate(UpperCAmelCase							,						max_new_tokens=10							,						do_sample=UpperCAmelCase							,						streamer=UpperCAmelCase   )
							# The greedy text should be printed to stdout, except for the final "\n" in the streamer
							lowerCamelCase_					=  cs.out[:-1]

							self.assertEqual(UpperCAmelCase							,						UpperCAmelCase   )




				def        UpperCAmelCase__						(				self   ):
							# Tests that we can pass `decode_kwargs` to the streamer to control how the tokens are decoded. Must be tested
							# with actual models -- the dummy models' tokenizers are not aligned with their models, and
							# `skip_special_tokens=True` has no effect on them
							lowerCamelCase_					=  AutoTokenizer.from_pretrained('''distilgpt2'''   )
							lowerCamelCase_					=  AutoModelForCausalLM.from_pretrained('''distilgpt2'''   ).to(UpperCAmelCase   )
							lowerCamelCase_					=  -1

							lowerCamelCase_					=  torch.ones((1, 5)							,						device=UpperCAmelCase   ).long() * model.config.bos_token_id
							with CaptureStdout() as cs:
										lowerCamelCase_					=  TextStreamer(UpperCAmelCase							,						skip_special_tokens=UpperCAmelCase   )
										model.generate(UpperCAmelCase							,						max_new_tokens=1							,						do_sample=UpperCAmelCase							,						streamer=UpperCAmelCase   )

							# The prompt contains a special token, so the streamer should not print it. As such, the output text, when
							# re-tokenized, must only contain one token
							lowerCamelCase_					=  cs.out[:-1]  # Remove the final "\n"
							lowerCamelCase_					=  tokenizer(UpperCAmelCase							,						return_tensors='''pt'''   )
							self.assertEqual(streamer_text_tokenized.input_ids.shape							,						(1, 1)   )





				def        UpperCAmelCase__						(				self   ):
							lowerCamelCase_					=  AutoTokenizer.from_pretrained('''hf-internal-testing/tiny-random-gpt2'''   )
							lowerCamelCase_					=  AutoModelForCausalLM.from_pretrained('''hf-internal-testing/tiny-random-gpt2'''   ).to(UpperCAmelCase   )
							lowerCamelCase_					=  -1

							lowerCamelCase_					=  ids_tensor((1, 5)							,						vocab_size=model.config.vocab_size   ).to(UpperCAmelCase   )
							lowerCamelCase_					=  TextIteratorStreamer(UpperCAmelCase							,						timeout=0.0_0_1   )
							lowerCamelCase_					=  {'''input_ids''': input_ids, '''max_new_tokens''': 10, '''do_sample''': False, '''streamer''': streamer}
							lowerCamelCase_					=  Thread(target=model.generate							,						kwargs=UpperCAmelCase   )
							thread.start()

							# The streamer will timeout after 0.001 seconds, so an exception will be raised
							with self.assertRaises(UpperCAmelCase   ):
										lowerCamelCase_					=  ''''''
										for new_text in streamer:
													streamer_text += new_text





"""simple docstring"""


import argparse

import torch
from torch import nn

from transformers import MBartConfig, MBartForConditionalGeneration






def  lowercase						(	lowerCAmelCase__						):
			lowerCamelCase_					=  [
			    '''encoder.version''',
			    '''decoder.version''',
			    '''model.encoder.version''',
			    '''model.decoder.version''',
			    '''_float_tensor''',
			    '''decoder.output_projection.weight''',
			]
			for k in ignore_keys:
						state_dict.pop(lowerCAmelCase__	,lowerCAmelCase__						)






def  lowercase						(	lowerCAmelCase__						):
			lowerCamelCase_		, lowerCamelCase_					=  emb.weight.shape
			lowerCamelCase_					=  nn.Linear(lowerCAmelCase__	,lowerCAmelCase__	,bias=lowerCAmelCase__						)
			lowerCamelCase_					=  emb.weight.data
			return lin_layer






def  lowercase						(	lowerCAmelCase__	,lowerCAmelCase__="facebook/mbart-large-en-ro"	,lowerCAmelCase__=False	,lowerCAmelCase__=False						):
			lowerCamelCase_					=  torch.load(lowerCAmelCase__	,map_location='''cpu'''						)['''model''']
			remove_ignore_keys_(lowerCAmelCase__						)
			lowerCamelCase_					=  state_dict['''encoder.embed_tokens.weight'''].shape[0]

			lowerCamelCase_					=  MBartConfig.from_pretrained(lowerCAmelCase__	,vocab_size=lowerCAmelCase__						)
			if mbart_aa and finetuned:
						lowerCamelCase_					=  '''relu'''

			lowerCamelCase_					=  state_dict['''decoder.embed_tokens.weight''']
			lowerCamelCase_					=  MBartForConditionalGeneration(lowerCAmelCase__						)
			model.model.load_state_dict(lowerCAmelCase__						)

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

			return model


if __name__ == "__main__":
						A_    							=					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""")
						A_    							=					parser.parse_args()
						A_    							=					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)



