



import unittest

from transformers import (
    MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
    TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
    TextaTextGenerationPipeline,
    pipeline,
)
from transformers.testing_utils import is_pipeline_test, require_tf, require_torch
from transformers.utils import is_torch_available

from .test_pipelines_common import ANY


if is_torch_available():
			import torch



@is_pipeline_test
class 	lowerCamelCase      (						unittest.TestCase  ):







					'''simple docstring'''







					lowerCAmelCase_				:    str									=  MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
					lowerCAmelCase_				:    int									=  TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING



					def    A__		(			self    ,	lowerCAmelCase    ,	lowerCAmelCase    ,	lowerCAmelCase     ):
							UpperCAmelCase_						=				TextaTextGenerationPipeline(model=A__    ,	tokenizer=A__     )
							return generator, ["Something to write", "Something else"]



					def    A__		(			self    ,	lowerCAmelCase    ,	lowerCAmelCase     ):
							UpperCAmelCase_						=				generator("Something there"     )
							self.assertEqual(A__    ,	[{"generated_text": ANY(A__     )}]     )
							# These are encoder decoder, they don't just append to incoming string
							self.assertFalse(outputs[0]["generated_text"].startswith("Something there"     )     )

							UpperCAmelCase_						=				generator(["This is great !", "Something else"]    ,	num_return_sequences=2    ,	do_sample=A__     )
							self.assertEqual(
							    A__    ,	[
							        [{"generated_text": ANY(A__     )}, {"generated_text": ANY(A__     )}],
							        [{"generated_text": ANY(A__     )}, {"generated_text": ANY(A__     )}],
							    ]    ,	)

							UpperCAmelCase_						=				generator(
							    ["This is great !", "Something else"]    ,	num_return_sequences=2    ,	batch_size=2    ,	do_sample=A__     )
							self.assertEqual(
							    A__    ,	[
							        [{"generated_text": ANY(A__     )}, {"generated_text": ANY(A__     )}],
							        [{"generated_text": ANY(A__     )}, {"generated_text": ANY(A__     )}],
							    ]    ,	)

							with self.assertRaises(A__     ):
									generator(4     )



					@require_torch
					def    A__		(			self     ):
							UpperCAmelCase_						=				pipeline("text2text-generation"    ,	model="patrickvonplaten/t5-tiny-random"    ,	framework="pt"     )
							# do_sample=False necessary for reproducibility
							UpperCAmelCase_						=				generator("Something there"    ,	do_sample=A__     )
							self.assertEqual(A__    ,	[{"generated_text": ""}]     )

							UpperCAmelCase_						=				3
							UpperCAmelCase_						=				generator(
							    "Something there"    ,	num_return_sequences=A__    ,	num_beams=A__    ,	)
							UpperCAmelCase_						=				[
							    {"generated_text": "Beide Beide Beide Beide Beide Beide Beide Beide Beide"},
							    {"generated_text": "Beide Beide Beide Beide Beide Beide Beide Beide"},
							    {"generated_text": ""},
							]
							self.assertEqual(A__    ,	A__     )

							UpperCAmelCase_						=				generator("This is a test"    ,	do_sample=A__    ,	num_return_sequences=2    ,	return_tensors=A__     )
							self.assertEqual(
							    A__    ,	[
							        {"generated_token_ids": ANY(torch.Tensor     )},
							        {"generated_token_ids": ANY(torch.Tensor     )},
							    ]    ,	)
							UpperCAmelCase_						=				generator.model.config.eos_token_id
							UpperCAmelCase_						=				"<pad>"
							UpperCAmelCase_						=				generator(
							    ["This is a test", "This is a second test"]    ,	do_sample=A__    ,	num_return_sequences=2    ,	batch_size=2    ,	return_tensors=A__    ,	)
							self.assertEqual(
							    A__    ,	[
							        [
							            {"generated_token_ids": ANY(torch.Tensor     )},
							            {"generated_token_ids": ANY(torch.Tensor     )},
							        ],
							        [
							            {"generated_token_ids": ANY(torch.Tensor     )},
							            {"generated_token_ids": ANY(torch.Tensor     )},
							        ],
							    ]    ,	)



					@require_tf
					def    A__		(			self     ):
							UpperCAmelCase_						=				pipeline("text2text-generation"    ,	model="patrickvonplaten/t5-tiny-random"    ,	framework="tf"     )
							# do_sample=False necessary for reproducibility
							UpperCAmelCase_						=				generator("Something there"    ,	do_sample=A__     )
							self.assertEqual(A__    ,	[{"generated_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 snake_case__  (			__SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE )		->							List[Any]:
		# Initialise PyTorch model
		UpperCAmelCase_						=				MobileBertConfig.from_json_file(__SCREAMING_SNAKE_CASE )
		print(f'''Building PyTorch model from configuration: {config}''' )
		UpperCAmelCase_						=				MobileBertForPreTraining(__SCREAMING_SNAKE_CASE )
		# Load weights from tf checkpoint
		UpperCAmelCase_						=				load_tf_weights_in_mobilebert(__SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE )
		# Save pytorch-model
		print(f'''Save PyTorch model to {pytorch_dump_path}''' )
		torch.save(model.state_dict()			,     __SCREAMING_SNAKE_CASE )


if __name__ == "__main__":
			SCREAMING_SNAKE_CASE     				=						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."
			)
			SCREAMING_SNAKE_CASE     				=						parser.parse_args()
			convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.mobilebert_config_file, args.pytorch_dump_path)

