

"""simple docstring"""


import argparse
import datetime
import json
import time
import warnings
from logging import getLogger
from pathlib import Path
from typing import Dict, List

import torch
from tqdm import tqdm

from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from utils import calculate_bleu, calculate_rouge, chunks, parse_numeric_n_bool_cl_kwargs, use_task_specific_params


__A						       =      getLogger(__name__)


__A						       =      "cuda" if torch.cuda.is_available() else "cpu"






def    a__   (   __SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE = 8 ,			__SCREAMING_SNAKE_CASE = DEFAULT_DEVICE ,			__SCREAMING_SNAKE_CASE=False ,			__SCREAMING_SNAKE_CASE="summarization" ,			__SCREAMING_SNAKE_CASE=None ,			**__SCREAMING_SNAKE_CASE ,			)     ->     Dict:
		__lowerCAmelCase:	Optional[int]   				=      Path(__SCREAMING_SNAKE_CASE			).open("w" ,			encoding="utf-8"			)
		__lowerCAmelCase:	Any   				=      str(__SCREAMING_SNAKE_CASE			)
		__lowerCAmelCase:	Dict   				=      AutoModelForSeqaSeqLM.from_pretrained(__SCREAMING_SNAKE_CASE			).to(__SCREAMING_SNAKE_CASE			)
		if fpaa:
				__lowerCAmelCase:	Union[str, Any]   				=      model.half()

		__lowerCAmelCase:	List[Any]   				=      AutoTokenizer.from_pretrained(__SCREAMING_SNAKE_CASE			)
		logger.info(F"Inferred tokenizer type: {tokenizer.__class__}"			)  # if this is wrong, check config.model_type.

		__lowerCAmelCase:	List[str]   				=      time.time()
		# update config with task specific params
		use_task_specific_params(__SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE			)
		if prefix is None:
				__lowerCAmelCase:	Any   				=      prefix or getattr(model.config ,			"prefix" ,			""			) or ""
		for examples_chunk in tqdm(list(chunks(__SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE			)			)			):
				__lowerCAmelCase:	Any   				=      [prefix + text for text in examples_chunk]
				__lowerCAmelCase:	List[Any]   				=      tokenizer(__SCREAMING_SNAKE_CASE ,			return_tensors="pt" ,			truncation=__SCREAMING_SNAKE_CASE ,			padding="longest"			).to(__SCREAMING_SNAKE_CASE			)
				__lowerCAmelCase:	Tuple   				=      model.generate(
				    input_ids=batch.input_ids ,			attention_mask=batch.attention_mask ,			**__SCREAMING_SNAKE_CASE ,			)
				__lowerCAmelCase:	Optional[Any]   				=      tokenizer.batch_decode(__SCREAMING_SNAKE_CASE ,			skip_special_tokens=__SCREAMING_SNAKE_CASE ,			clean_up_tokenization_spaces=__SCREAMING_SNAKE_CASE			)
				for hypothesis in dec:
						fout.write(hypothesis + "\n"			)
						fout.flush()
		fout.close()
		__lowerCAmelCase:	Optional[int]   				=      int(time.time() - start_time			)  # seconds
		__lowerCAmelCase:	Optional[Any]   				=      len(__SCREAMING_SNAKE_CASE			)
		return {"n_obs": n_obs, "runtime": runtime, "seconds_per_sample": round(runtime / n_obs ,			4			)}






def    a__   (   )     ->     List[Any]:
		return datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S"			)






def    a__   (   __SCREAMING_SNAKE_CASE=True			)     ->     Dict:
		__lowerCAmelCase:	Union[str, Any]   				=      argparse.ArgumentParser()
		parser.add_argument("model_name" ,			type=__SCREAMING_SNAKE_CASE ,			help="like facebook/bart-large-cnn,t5-base, etc."			)
		parser.add_argument("input_path" ,			type=__SCREAMING_SNAKE_CASE ,			help="like cnn_dm/test.source"			)
		parser.add_argument("save_path" ,			type=__SCREAMING_SNAKE_CASE ,			help="where to save summaries"			)
		parser.add_argument("--reference_path" ,			type=__SCREAMING_SNAKE_CASE ,			required=__SCREAMING_SNAKE_CASE ,			help="like cnn_dm/test.target"			)
		parser.add_argument("--score_path" ,			type=__SCREAMING_SNAKE_CASE ,			required=__SCREAMING_SNAKE_CASE ,			default="metrics.json" ,			help="where to save metrics"			)
		parser.add_argument("--device" ,			type=__SCREAMING_SNAKE_CASE ,			required=__SCREAMING_SNAKE_CASE ,			default=__SCREAMING_SNAKE_CASE ,			help="cuda, cuda:1, cpu etc."			)
		parser.add_argument(
		    "--prefix" ,			type=__SCREAMING_SNAKE_CASE ,			required=__SCREAMING_SNAKE_CASE ,			default=__SCREAMING_SNAKE_CASE ,			help="will be added to the begininng of src examples"			)
		parser.add_argument("--task" ,			type=__SCREAMING_SNAKE_CASE ,			default="summarization" ,			help="used for task_specific_params + metrics"			)
		parser.add_argument("--bs" ,			type=__SCREAMING_SNAKE_CASE ,			default=8 ,			required=__SCREAMING_SNAKE_CASE ,			help="batch size"			)
		parser.add_argument(
		    "--n_obs" ,			type=__SCREAMING_SNAKE_CASE ,			default=-1 ,			required=__SCREAMING_SNAKE_CASE ,			help="How many observations. Defaults to all."			)
		parser.add_argument("--fp16" ,			action="store_true"			)
		parser.add_argument("--dump-args" ,			action="store_true" ,			help="print the custom hparams with the results"			)
		parser.add_argument(
		    "--info" ,			nargs="?" ,			type=__SCREAMING_SNAKE_CASE ,			const=datetime_now() ,			help=(
		        "use in conjunction w/ --dump-args to print with the results whatever other info you'd like, e.g."
		        " lang=en-ru. If no value is passed, the current datetime string will be used."
		    ) ,			)
		# Unspecified args like --num_beams=2 --decoder_start_token_id=4 are passed to model.generate
		__lowerCAmelCase:	List[str]   				=      parser.parse_known_args()
		__lowerCAmelCase:	List[str]   				=      parse_numeric_n_bool_cl_kwargs(__SCREAMING_SNAKE_CASE			)
		if parsed_args and verbose:
				print(F"parsed the following generate kwargs: {parsed_args}"			)
		__lowerCAmelCase:	Any   				=      [" " + x.rstrip() if "t5" in args.model_name else x.rstrip() for x in open(args.input_path			).readlines()]
		if args.n_obs > 0:
				__lowerCAmelCase:	Optional[int]   				=      examples[: args.n_obs]
		Path(args.save_path			).parent.mkdir(exist_ok=__SCREAMING_SNAKE_CASE			)

		if args.reference_path is None and Path(args.score_path			).exists():
				warnings.warn(F"score_path {args.score_path} will be overwritten unless you type ctrl-c."			)

		if args.device == "cpu" and args.fpaa:
				# this mix leads to RuntimeError: "threshold_cpu" not implemented for 'Half'
				raise ValueError("Can't mix --fp16 and --device cpu"			)

		__lowerCAmelCase:	Dict   				=      generate_summaries_or_translations(
		    __SCREAMING_SNAKE_CASE ,			args.save_path ,			args.model_name ,			batch_size=args.bs ,			device=args.device ,			fpaa=args.fpaa ,			task=args.task ,			prefix=args.prefix ,			**__SCREAMING_SNAKE_CASE ,			)

		if args.reference_path is None:
				return {}

		# Compute scores
		__lowerCAmelCase:	List[Any]   				=      calculate_bleu if "translation" in args.task else calculate_rouge
		__lowerCAmelCase:	Optional[int]   				=      [x.rstrip() for x in open(args.save_path			).readlines()]
		__lowerCAmelCase:	Optional[Any]   				=      [x.rstrip() for x in open(args.reference_path			).readlines()][: len(__SCREAMING_SNAKE_CASE			)]
		__lowerCAmelCase:	dict   				=      score_fn(__SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE			)
		scores.update(__SCREAMING_SNAKE_CASE			)

		if args.dump_args:
				scores.update(__SCREAMING_SNAKE_CASE			)
		if args.info:
				__lowerCAmelCase:	Optional[int]   				=      args.info

		if verbose:
				print(__SCREAMING_SNAKE_CASE			)

		if args.score_path is not None:
				json.dump(__SCREAMING_SNAKE_CASE ,			open(args.score_path ,			"w"			)			)

		return scores


if __name__ == "__main__":
	# Usage for MT:
	# python run_eval.py MODEL_NAME $DATA_DIR/test.source $save_dir/test_translations.txt --reference_path $DATA_DIR/test.target --score_path $save_dir/test_bleu.json  --task translation $@
	run_generate(verbose=True)





"""simple docstring"""


import gc
import inspect
import unittest

import torch
from parameterized import parameterized

from diffusers import PriorTransformer
from diffusers.utils import floats_tensor, slow, torch_all_close, torch_device
from diffusers.utils.testing_utils import enable_full_determinism

from .test_modeling_common import ModelTesterMixin


enable_full_determinism()





class    snake_case     (    __snake_case,							unittest.TestCase  ):
				SCREAMING_SNAKE_CASE_       :				Tuple       				=  PriorTransformer
				SCREAMING_SNAKE_CASE_       :				List[str]       				=  """hidden_states"""

				@property
				def 							lowercase_					(     self							:    Dict)->       str:



						'''simple docstring'''


						__lowerCAmelCase:	str   				=      4
						__lowerCAmelCase:	int   				=      8
						__lowerCAmelCase:	int   				=      7

						__lowerCAmelCase:	str   				=      floats_tensor((batch_size, embedding_dim)).to(UpperCamelCase__)

						__lowerCAmelCase:	Optional[Any]   				=      floats_tensor((batch_size, embedding_dim)).to(UpperCamelCase__)
						__lowerCAmelCase:	Any   				=      floats_tensor((batch_size, num_embeddings, embedding_dim)).to(UpperCamelCase__)

						return {
						    "hidden_states": hidden_states,
						    "timestep": 2,
						    "proj_embedding": proj_embedding,
						    "encoder_hidden_states": encoder_hidden_states,
						}

				def 							lowercase_					(     self							:    Optional[int]					,  UpperCamelCase__							:    str=0)->       str:



						'''simple docstring'''


						torch.manual_seed(UpperCamelCase__)
						__lowerCAmelCase:	List[Any]   				=      4
						__lowerCAmelCase:	Dict   				=      8
						__lowerCAmelCase:	int   				=      7

						__lowerCAmelCase:	List[str]   				=      torch.randn((batch_size, embedding_dim)).to(UpperCamelCase__)

						__lowerCAmelCase:	Tuple   				=      torch.randn((batch_size, embedding_dim)).to(UpperCamelCase__)
						__lowerCAmelCase:	List[Any]   				=      torch.randn((batch_size, num_embeddings, embedding_dim)).to(UpperCamelCase__)

						return {
						    "hidden_states": hidden_states,
						    "timestep": 2,
						    "proj_embedding": proj_embedding,
						    "encoder_hidden_states": encoder_hidden_states,
						}

				@property
				def 							lowercase_					(     self							:    Dict)->       List[Any]:



						'''simple docstring'''


						return (4, 8)

				@property
				def 							lowercase_					(     self							:    Optional[int])->       int:



						'''simple docstring'''


						return (4, 8)

				def 							lowercase_					(     self							:    Optional[int])->       Tuple:



						'''simple docstring'''


						__lowerCAmelCase:	str   				=      {
						    "num_attention_heads": 2,
						    "attention_head_dim": 4,
						    "num_layers": 2,
						    "embedding_dim": 8,
						    "num_embeddings": 7,
						    "additional_embeddings": 4,
						}
						__lowerCAmelCase:	Any   				=      self.dummy_input
						return init_dict, inputs_dict

				def 							lowercase_					(     self							:    List[Any])->       int:



						'''simple docstring'''


						__lowerCAmelCase				,			__lowerCAmelCase:	Optional[int]   				=      PriorTransformer.from_pretrained(
						    "hf-internal-testing/prior-dummy"					,  output_loading_info=UpperCamelCase__)
						self.assertIsNotNone(UpperCamelCase__)
						self.assertEqual(len(loading_info["missing_keys"])					,  0)

						model.to(UpperCamelCase__)
						__lowerCAmelCase:	Dict   				=      model(**self.dummy_input)[0]

						assert hidden_states is not None, "Make sure output is not None"

				def 							lowercase_					(     self							:    List[str])->       Tuple:



						'''simple docstring'''


						__lowerCAmelCase				,			__lowerCAmelCase:	Optional[Any]   				=      self.prepare_init_args_and_inputs_for_common()

						__lowerCAmelCase:	Tuple   				=      self.model_class(**UpperCamelCase__)
						__lowerCAmelCase:	List[str]   				=      inspect.signature(model.forward)
						# signature.parameters is an OrderedDict => so arg_names order is deterministic
						__lowerCAmelCase:	List[Any]   				=      [*signature.parameters.keys()]

						__lowerCAmelCase:	Any   				=      ["hidden_states", "timestep"]
						self.assertListEqual(arg_names[:2]					,  UpperCamelCase__)




				def 							lowercase_					(     self							:    Optional[int])->       List[str]:



						'''simple docstring'''


						__lowerCAmelCase:	int   				=      PriorTransformer.from_pretrained("hf-internal-testing/prior-dummy")
						__lowerCAmelCase:	Union[str, Any]   				=      model.to(UpperCamelCase__)

						if hasattr(UpperCamelCase__					,  "set_default_attn_processor"):
								model.set_default_attn_processor()

						__lowerCAmelCase:	str   				=      self.get_dummy_seed_input()

						with torch.no_grad():
								__lowerCAmelCase:	Dict   				=      model(**UpperCamelCase__)[0]

						__lowerCAmelCase:	Dict   				=      output[0, :5].flatten().cpu()
						print(UpperCamelCase__)

						# Since the VAE Gaussian prior's generator is seeded on the appropriate device,
						# the expected output slices are not the same for CPU and GPU.
						__lowerCAmelCase:	List[str]   				=      torch.tensor([-1.3436, -0.2870, 0.7538, 0.4368, -0.0239])
						self.assertTrue(torch_all_close(UpperCamelCase__					,  UpperCamelCase__					,  rtol=1e-2))





@slow
class    snake_case     (    unittest.TestCase  ):

				def 							lowercase_					(     self							:    int					,  UpperCamelCase__							:    Dict=1					,  UpperCamelCase__							:    str=7_6_8					,  UpperCamelCase__							:    int=7_7					,  UpperCamelCase__							:    Any=0)->       Union[str, Any]:



						'''simple docstring'''


						torch.manual_seed(UpperCamelCase__)
						__lowerCAmelCase:	List[Any]   				=      batch_size
						__lowerCAmelCase:	Any   				=      embedding_dim
						__lowerCAmelCase:	Dict   				=      num_embeddings

						__lowerCAmelCase:	Dict   				=      torch.randn((batch_size, embedding_dim)).to(UpperCamelCase__)

						__lowerCAmelCase:	str   				=      torch.randn((batch_size, embedding_dim)).to(UpperCamelCase__)
						__lowerCAmelCase:	int   				=      torch.randn((batch_size, num_embeddings, embedding_dim)).to(UpperCamelCase__)

						return {
						    "hidden_states": hidden_states,
						    "timestep": 2,
						    "proj_embedding": proj_embedding,
						    "encoder_hidden_states": encoder_hidden_states,
						}

				def 							lowercase_					(     self							:    List[Any])->       Union[str, Any]:



						'''simple docstring'''


						super().tearDown()
						gc.collect()
						torch.cuda.empty_cache()




				@parameterized.expand(
				    [
				        # fmt: off
				        [1_3, [-0.5861,  0.1283, -0.0931,  0.0882,  0.4476,  0.1329, -0.0498,  0.0640]],
				        [3_7, [-0.4913,  0.0110, -0.0483,  0.0541,  0.4954, -0.0170,  0.0354,  0.1651]],
				        # fmt: on
				    ])
				def 							lowercase_					(     self							:    Optional[int]					,  UpperCamelCase__							:    List[str]					,  UpperCamelCase__							:    int)->       List[Any]:



						'''simple docstring'''


						__lowerCAmelCase:	List[str]   				=      PriorTransformer.from_pretrained("kandinsky-community/kandinsky-2-1-prior"					,  subfolder="prior")
						model.to(UpperCamelCase__)
						__lowerCAmelCase:	Dict   				=      self.get_dummy_seed_input(seed=UpperCamelCase__)

						with torch.no_grad():
								__lowerCAmelCase:	Optional[Any]   				=      model(**UpperCamelCase__)[0]

						assert list(sample.shape) == [1, 7_6_8]

						__lowerCAmelCase:	Dict   				=      sample[0, :8].flatten().cpu()
						print(UpperCamelCase__)
						__lowerCAmelCase:	Union[str, Any]   				=      torch.tensor(UpperCamelCase__)

						assert torch_all_close(UpperCamelCase__					,  UpperCamelCase__					,  atol=1e-3)


