"""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      ):






					'''simple docstring'''


					def 			lowerCAmelCase_					(						self			:				str		)							->       List[str]:
									SCREAMING_SNAKE_CASE__		  =				AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-gpt2"		)
									SCREAMING_SNAKE_CASE__		  =				AutoModelForCausalLM.from_pretrained("hf-internal-testing/tiny-random-gpt2"		).to(_snake_case		)
									SCREAMING_SNAKE_CASE__		  =				-1

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

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

									self.assertEqual(_snake_case   , _snake_case		)



					def 			lowerCAmelCase_					(						self			:				List[Any]		)							->       Any:
									SCREAMING_SNAKE_CASE__		  =				AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-gpt2"		)
									SCREAMING_SNAKE_CASE__		  =				AutoModelForCausalLM.from_pretrained("hf-internal-testing/tiny-random-gpt2"		).to(_snake_case		)
									SCREAMING_SNAKE_CASE__		  =				-1

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

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

									self.assertEqual(_snake_case   , _snake_case		)



					def 			lowerCAmelCase_					(						self			:				int		)							->       int:
									SCREAMING_SNAKE_CASE__		  =				AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-gpt2"		)
									SCREAMING_SNAKE_CASE__		  =				AutoModelForCausalLM.from_pretrained("hf-internal-testing/tiny-random-gpt2"		).to(_snake_case		)
									SCREAMING_SNAKE_CASE__		  =				-1

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

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

									self.assertEqual(_snake_case   , _snake_case		)



					def 			lowerCAmelCase_					(						self			:				Optional[Any]		)							->       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
									SCREAMING_SNAKE_CASE__		  =				AutoTokenizer.from_pretrained("distilgpt2"		)
									SCREAMING_SNAKE_CASE__		  =				AutoModelForCausalLM.from_pretrained("distilgpt2"		).to(_snake_case		)
									SCREAMING_SNAKE_CASE__		  =				-1

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

									# 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
									SCREAMING_SNAKE_CASE__		  =				cs.out[:-1]  # Remove the final "\n"
									SCREAMING_SNAKE_CASE__		  =				tokenizer(_snake_case   , return_tensors="pt"		)
									self.assertEqual(streamer_text_tokenized.input_ids.shape   , (1, 1)		)



					def 			lowerCAmelCase_					(						self			:				str		)							->       Optional[Any]:
									SCREAMING_SNAKE_CASE__		  =				AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-gpt2"		)
									SCREAMING_SNAKE_CASE__		  =				AutoModelForCausalLM.from_pretrained("hf-internal-testing/tiny-random-gpt2"		).to(_snake_case		)
									SCREAMING_SNAKE_CASE__		  =				-1

									SCREAMING_SNAKE_CASE__		  =				ids_tensor((1, 5)   , vocab_size=model.config.vocab_size		).to(_snake_case		)
									SCREAMING_SNAKE_CASE__		  =				TextIteratorStreamer(_snake_case   , timeout=0.001		)
									SCREAMING_SNAKE_CASE__		  =				{"input_ids": input_ids, "max_new_tokens": 10, "do_sample": False, "streamer": streamer}
									SCREAMING_SNAKE_CASE__		  =				Thread(target=model.generate   , kwargs=_snake_case		)
									thread.start()

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





"""simple docstring"""






import argparse

from torch import nn

# transformers_old should correspond to branch `save_old_prophetnet_model_structure` here
# original prophetnet_checkpoints are saved under `patrickvonplaten/..._old` respectively
from transformers_old.modeling_prophetnet import (
    ProphetNetForConditionalGeneration as ProphetNetForConditionalGenerationOld,
)
from transformers_old.modeling_xlm_prophetnet import (
    XLMProphetNetForConditionalGeneration as XLMProphetNetForConditionalGenerationOld,
)

from transformers import ProphetNetForConditionalGeneration, XLMProphetNetForConditionalGeneration, logging


_A   					=    logging.get_logger(__name__)
logging.set_verbosity_info()




def 						SCREAMING_SNAKE_CASE					(  __UpperCAmelCase  ,					__UpperCAmelCase       )							->   List[str]:
				if "xprophetnet" in prophetnet_checkpoint_path:
								SCREAMING_SNAKE_CASE__		  =				XLMProphetNetForConditionalGenerationOld.from_pretrained(__UpperCAmelCase       )
								SCREAMING_SNAKE_CASE__  ,       SCREAMING_SNAKE_CASE__		  =				XLMProphetNetForConditionalGeneration.from_pretrained(
								    __UpperCAmelCase  ,					output_loading_info=__UpperCAmelCase       )
				else:
								SCREAMING_SNAKE_CASE__		  =				ProphetNetForConditionalGenerationOld.from_pretrained(__UpperCAmelCase       )
								SCREAMING_SNAKE_CASE__  ,       SCREAMING_SNAKE_CASE__		  =				ProphetNetForConditionalGeneration.from_pretrained(
								    __UpperCAmelCase  ,					output_loading_info=__UpperCAmelCase       )

				SCREAMING_SNAKE_CASE__		  =				["key_proj", "value_proj", "query_proj"]

				SCREAMING_SNAKE_CASE__		  =				{
				    "self_attn": "ngram_self_attn",
				    "cross_attn": "encoder_attn",
				    "cross_attn_layer_norm": "encoder_attn_layer_norm",
				    "feed_forward_layer_norm": "final_layer_norm",
				    "feed_forward": "",
				    "intermediate": "fc1",
				    "output": "fc2",
				    "key_proj": "k_proj",
				    "query_proj": "q_proj",
				    "value_proj": "v_proj",
				    "word_embeddings": "embed_tokens",
				    "embeddings_layer_norm": "emb_layer_norm",
				    "relative_pos_embeddings": "relative_linear",
				    "ngram_embeddings": "ngram_input_embed",
				    "position_embeddings": "embed_positions",
				}

				for key in loading_info["missing_keys"]:
								SCREAMING_SNAKE_CASE__		  =				key.split("."       )

								if attributes[0] == "lm_head":
												SCREAMING_SNAKE_CASE__		  =				prophet
												SCREAMING_SNAKE_CASE__		  =				prophet_old
								else:
												SCREAMING_SNAKE_CASE__		  =				prophet.prophetnet
												SCREAMING_SNAKE_CASE__		  =				prophet_old.model

								SCREAMING_SNAKE_CASE__		  =				False
								for attribute in attributes:
												if attribute in mapping:
																SCREAMING_SNAKE_CASE__		  =				mapping[attribute]
																if not hasattr(__UpperCAmelCase  ,					__UpperCAmelCase       ) and len(__UpperCAmelCase       ) > 0:
																				SCREAMING_SNAKE_CASE__		  =				attribute
												elif hasattr(__UpperCAmelCase  ,					__UpperCAmelCase       ):
																SCREAMING_SNAKE_CASE__		  =				attribute

												if attribute == "weight":
																assert old_model.weight.shape == model.weight.shape, "Shapes have to match!"
																SCREAMING_SNAKE_CASE__		  =				old_model.weight
																logger.info(F"""{attribute} is initialized."""       )
																SCREAMING_SNAKE_CASE__		  =				True
																break
												elif attribute == "bias":
																assert old_model.bias.shape == model.bias.shape, "Shapes have to match!"
																SCREAMING_SNAKE_CASE__		  =				old_model.bias
																logger.info(F"""{attribute} is initialized"""       )
																SCREAMING_SNAKE_CASE__		  =				True
																break
												elif attribute in special_keys and hasattr(__UpperCAmelCase  ,					"in_proj_weight"       ):
																SCREAMING_SNAKE_CASE__		  =				old_model.in_proj_weight.shape[0] // 3
																SCREAMING_SNAKE_CASE__		  =				getattr(__UpperCAmelCase  ,					__UpperCAmelCase       )
																param.weight.shape == old_model.in_proj_weight[:embed_dim, :].shape, "Shapes have to match"
																param.bias.shape == old_model.in_proj_bias[:embed_dim].shape, "Shapes have to match"
																if attribute == "query_proj":
																				SCREAMING_SNAKE_CASE__		  =				nn.Parameter(old_model.in_proj_weight[:embed_dim, :]       )
																				SCREAMING_SNAKE_CASE__		  =				nn.Parameter(old_model.in_proj_bias[:embed_dim]       )

																elif attribute == "key_proj":
																				SCREAMING_SNAKE_CASE__		  =				nn.Parameter(old_model.in_proj_weight[embed_dim : 2 * embed_dim, :]       )
																				SCREAMING_SNAKE_CASE__		  =				nn.Parameter(old_model.in_proj_bias[embed_dim : 2 * embed_dim]       )
																elif attribute == "value_proj":
																				SCREAMING_SNAKE_CASE__		  =				nn.Parameter(old_model.in_proj_weight[2 * embed_dim :, :]       )
																				SCREAMING_SNAKE_CASE__		  =				nn.Parameter(old_model.in_proj_bias[2 * embed_dim :]       )
																SCREAMING_SNAKE_CASE__		  =				True
																break
												elif attribute == "position_embeddings":
																assert (
																    model.position_embeddings.weight.shape[-1] == old_model.embed_positions.weight.shape[-1]
																), "Hidden size has to match"
																assert model.position_embeddings.weight.shape[0] == 512, "We want 512 position_embeddings."
																SCREAMING_SNAKE_CASE__		  =				nn.Parameter(old_model.embed_positions.weight[:512, :]       )
																SCREAMING_SNAKE_CASE__		  =				True
																break

												if attribute.isdigit():
																SCREAMING_SNAKE_CASE__		  =				model[int(__UpperCAmelCase       )]
																SCREAMING_SNAKE_CASE__		  =				old_model[int(__UpperCAmelCase       )]
												else:
																SCREAMING_SNAKE_CASE__		  =				getattr(__UpperCAmelCase  ,					__UpperCAmelCase       )

																if old_attribute == "":
																				SCREAMING_SNAKE_CASE__		  =				old_model
																else:
																				if not hasattr(__UpperCAmelCase  ,					__UpperCAmelCase       ):
																								raise ValueError(F"""{old_model} does not have {old_attribute}"""       )
																				SCREAMING_SNAKE_CASE__		  =				getattr(__UpperCAmelCase  ,					__UpperCAmelCase       )

								if not is_key_init:
												raise ValueError(F"""{key} was not correctly initialized!"""       )

				print(F"""Saving model to {pytorch_dump_folder_path}"""       )
				prophet.save_pretrained(__UpperCAmelCase       )


if __name__ == "__main__":
						_A   					=    argparse.ArgumentParser()
						# Required parameters
						parser.add_argument(
						    '--prophetnet_checkpoint_path', default=None, type=str, required=True, help='Path the official PyTorch dump.'
						)
						parser.add_argument(
						    '--pytorch_dump_folder_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
						)
						_A   					=    parser.parse_args()
						convert_prophetnet_checkpoint_to_pytorch(args.prophetnet_checkpoint_path, args.pytorch_dump_folder_path)




