

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   _a					(			unittest.TestCase				):




      """simple docstring"""

      def 		__A	(   self							:				Union[str, Any]	):
            A_						  =				AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-gpt2"	)
            A_						  =				AutoModelForCausalLM.from_pretrained("hf-internal-testing/tiny-random-gpt2"	).to(UpperCAmelCase	)
            A_						  =				-1

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

            with CaptureStdout() as cs:
                  A_						  =				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
            A_						  =				cs.out[:-1]

            self.assertEqual(UpperCAmelCase						,			UpperCAmelCase	)





      def 		__A	(   self							:				Tuple	):
            A_						  =				AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-gpt2"	)
            A_						  =				AutoModelForCausalLM.from_pretrained("hf-internal-testing/tiny-random-gpt2"	).to(UpperCAmelCase	)
            A_						  =				-1

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

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

            self.assertEqual(UpperCAmelCase						,			UpperCAmelCase	)





      def 		__A	(   self							:				List[str]	):
            A_						  =				AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-gpt2"	)
            A_						  =				AutoModelForCausalLM.from_pretrained("hf-internal-testing/tiny-random-gpt2"	).to(UpperCAmelCase	)
            A_						  =				-1

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

            with CaptureStdout() as cs:
                  A_						  =				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
            A_						  =				cs.out[:-1]

            self.assertEqual(UpperCAmelCase						,			UpperCAmelCase	)





      def 		__A	(   self							:				Optional[Any]	):
            # 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
            A_						  =				AutoTokenizer.from_pretrained("distilgpt2"	)
            A_						  =				AutoModelForCausalLM.from_pretrained("distilgpt2"	).to(UpperCAmelCase	)
            A_						  =				-1

            A_						  =				torch.ones((1, 5)						,			device=UpperCAmelCase	).long() * model.config.bos_token_id
            with CaptureStdout() as cs:
                  A_						  =				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
            A_						  =				cs.out[:-1]  # Remove the final "\n"
            A_						  =				tokenizer(UpperCAmelCase						,			return_tensors="pt"	)
            self.assertEqual(streamer_text_tokenized.input_ids.shape						,			(1, 1)	)







      def 		__A	(   self							:				int	):
            A_						  =				AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-gpt2"	)
            A_						  =				AutoModelForCausalLM.from_pretrained("hf-internal-testing/tiny-random-gpt2"	).to(UpperCAmelCase	)
            A_						  =				-1

            A_						  =				ids_tensor((1, 5)						,			vocab_size=model.config.vocab_size	).to(UpperCAmelCase	)
            A_						  =				TextIteratorStreamer(UpperCAmelCase						,			timeout=0.001	)
            A_						  =				{"input_ids": input_ids, "max_new_tokens": 10, "do_sample": False, "streamer": streamer}
            A_						  =				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	):
                  A_						  =				""
                  for new_text in streamer:
                        streamer_text += new_text


import argparse

import torch

from transformers import MobileBertConfig, MobileBertForPreTraining, load_tf_weights_in_mobilebert
from transformers.utils import logging


logging.set_verbosity_info()
def 							__SCREAMING_SNAKE_CASE     (   a__    :   Optional[Any]			,a__    :   Union[str, Any]			,a__    :   Optional[int]       )					->				List[Any]:
	# Initialise PyTorch model
	__A :			Dict		    =			MobileBertConfig.from_json_file(a__       )
	print(f"""Building PyTorch model from configuration: {config}"""       )
	__A :			Tuple		    =			MobileBertForPreTraining(a__       )
	# Load weights from tf checkpoint
	__A :			Dict		    =			load_tf_weights_in_mobilebert(a__			,a__			,a__       )
	# Save pytorch-model
	print(f"""Save PyTorch model to {pytorch_dump_path}"""       )
	torch.save(model.state_dict()			,a__       )


if __name__ == "__main__":
			UpperCAmelCase_							:					Any		 =   argparse.ArgumentParser()
			# Required parameters
			parser.add_argument(
			    '''--tf_checkpoint_path''', default=None, type=str, required=True, help='''Path to the TensorFlow checkpoint path.'''
			)
			parser.add_argument(
			    '''--mobilebert_config_file''',
			    default=None,
			    type=str,
			    required=True,
			    help=(
			        '''The config json file corresponding to the pre-trained MobileBERT model. \n'''
			        '''This specifies the model architecture.'''
			    ),
			)
			parser.add_argument(
			    '''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
			)
			UpperCAmelCase_							:					Tuple		 =   parser.parse_args()
			convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.mobilebert_config_file, args.pytorch_dump_path)





