
from __future__ import annotations

import unittest

from transformers import OpenAIGPTConfig, is_tf_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
 import tensorflow as tf

 from transformers.models.openai.modeling_tf_openai import (
     TF_OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST,
     TFOpenAIGPTDoubleHeadsModel,
     TFOpenAIGPTForSequenceClassification,
     TFOpenAIGPTLMHeadModel,
     TFOpenAIGPTModel,
 )







class  _lowerCamelCase			:

     def __init__(  self							,       SCREAMING_SNAKE_CASE_							,       ):
            __snake_case						       =   parent
            __snake_case						       =   13
            __snake_case						       =   7
            __snake_case						       =   True
            __snake_case						       =   True
            __snake_case						       =   True
            __snake_case						       =   True
            __snake_case						       =   True
            __snake_case						       =   99
            __snake_case						       =   32
            __snake_case						       =   2
            __snake_case						       =   4
            __snake_case						       =   37
            __snake_case						       =   'gelu'
            __snake_case						       =   0.1
            __snake_case						       =   0.1
            __snake_case						       =   512
            __snake_case						       =   16
            __snake_case						       =   2
            __snake_case						       =   0.0_2
            __snake_case						       =   3
            __snake_case						       =   4
            __snake_case						       =   None
            __snake_case						       =   self.vocab_size - 1

     def 						__lowerCamelCase				(  self    ):
            __snake_case						       =   ids_tensor([self.batch_size, self.seq_length]							,       self.vocab_size    )

            __snake_case						       =   None
            if self.use_input_mask:
                   __snake_case						       =   random_attention_mask([self.batch_size, self.seq_length]    )

            __snake_case						       =   None
            if self.use_token_type_ids:
                   __snake_case						       =   ids_tensor([self.batch_size, self.seq_length]							,       self.type_vocab_size    )

            __snake_case						       =   None
            if self.use_mc_token_ids:
                   __snake_case						       =   ids_tensor([self.batch_size, self.num_choices]							,       self.seq_length    )

            __snake_case						       =   None
            __snake_case						       =   None
            __snake_case						       =   None
            if self.use_labels:
                   __snake_case						       =   ids_tensor([self.batch_size]							,       self.type_sequence_label_size    )
                   __snake_case						       =   ids_tensor([self.batch_size, self.seq_length]							,       self.num_labels    )
                   __snake_case						       =   ids_tensor([self.batch_size]							,       self.num_choices    )

            __snake_case						       =   OpenAIGPTConfig(
                vocab_size=self.vocab_size							,       n_embd=self.hidden_size							,       n_layer=self.num_hidden_layers							,       n_head=self.num_attention_heads							,       n_positions=self.max_position_embeddings							,       pad_token_id=self.pad_token_id							,       )

            __snake_case						       =   ids_tensor([self.num_hidden_layers, self.num_attention_heads]							,       2    )

            return (
                config,
                input_ids,
                input_mask,
                head_mask,
                token_type_ids,
                mc_token_ids,
                sequence_labels,
                token_labels,
                choice_labels,
            )

     def 						__lowerCamelCase				(  self							,       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_							,       *SCREAMING_SNAKE_CASE_    ):
            __snake_case						       =   TFOpenAIGPTModel(config=SCREAMING_SNAKE_CASE_    )
            __snake_case						       =   {'input_ids': input_ids, 'attention_mask': input_mask, 'token_type_ids': token_type_ids}
            __snake_case						       =   model(SCREAMING_SNAKE_CASE_    )

            __snake_case						       =   [input_ids, input_mask]
            __snake_case						       =   model(SCREAMING_SNAKE_CASE_    )

            __snake_case						       =   model(SCREAMING_SNAKE_CASE_    )

            self.parent.assertEqual(result.last_hidden_state.shape							,       (self.batch_size, self.seq_length, self.hidden_size)    )

     def 						__lowerCamelCase				(  self							,       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_							,       *SCREAMING_SNAKE_CASE_    ):
            __snake_case						       =   TFOpenAIGPTLMHeadModel(config=SCREAMING_SNAKE_CASE_    )
            __snake_case						       =   {'input_ids': input_ids, 'attention_mask': input_mask, 'token_type_ids': token_type_ids}
            __snake_case						       =   model(SCREAMING_SNAKE_CASE_    )
            self.parent.assertEqual(result.logits.shape							,       (self.batch_size, self.seq_length, self.vocab_size)    )

     def 						__lowerCamelCase				(  self							,       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_							,       *SCREAMING_SNAKE_CASE_    ):
            __snake_case						       =   TFOpenAIGPTDoubleHeadsModel(config=SCREAMING_SNAKE_CASE_    )

            __snake_case						       =   tf.tile(tf.expand_dims(SCREAMING_SNAKE_CASE_							,       1    )							,       (1, self.num_choices, 1)    )
            __snake_case						       =   tf.tile(tf.expand_dims(SCREAMING_SNAKE_CASE_							,       1    )							,       (1, self.num_choices, 1)    )
            __snake_case						       =   tf.tile(tf.expand_dims(SCREAMING_SNAKE_CASE_							,       1    )							,       (1, self.num_choices, 1)    )

            __snake_case						       =   {
                'input_ids': multiple_choice_inputs_ids,
                'mc_token_ids': mc_token_ids,
                'attention_mask': multiple_choice_input_mask,
                'token_type_ids': multiple_choice_token_type_ids,
            }
            __snake_case						       =   model(SCREAMING_SNAKE_CASE_    )
            self.parent.assertEqual(
                result.logits.shape							,       (self.batch_size, self.num_choices, self.seq_length, self.vocab_size)    )
            self.parent.assertEqual(result.mc_logits.shape							,       (self.batch_size, self.num_choices)    )

     def 						__lowerCamelCase				(  self							,       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_							,       *SCREAMING_SNAKE_CASE_    ):
            __snake_case						       =   self.num_labels
            __snake_case						       =   ids_tensor([self.batch_size]							,       self.type_sequence_label_size    )
            __snake_case						       =   {
                'input_ids': input_ids,
                'attention_mask': input_mask,
                'token_type_ids': token_type_ids,
                'labels': sequence_labels,
            }
            __snake_case						       =   TFOpenAIGPTForSequenceClassification(SCREAMING_SNAKE_CASE_    )
            __snake_case						       =   model(SCREAMING_SNAKE_CASE_    )
            self.parent.assertEqual(result.logits.shape							,       (self.batch_size, self.num_labels)    )

     def 						__lowerCamelCase				(  self    ):
            __snake_case						       =   self.prepare_config_and_inputs()

            (
                (
                __snake_case
            )       ,       (
                __snake_case
            )       ,       (
                __snake_case
            )       ,       (
                __snake_case
            )       ,       (
                __snake_case
            )       ,       (
                __snake_case
            )       ,       (
                __snake_case
            )       ,       (
                __snake_case
            )       ,       (
                __snake_case
            )       ,       
            )						       =   config_and_inputs

            __snake_case						       =   {'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': input_mask}
            return config, inputs_dict







@require_tf
class  _lowerCamelCase			(lowerCamelCase							,					lowerCamelCase							,					unittest.TestCase					):
     lowercase__				  =			(
         (TFOpenAIGPTModel, TFOpenAIGPTLMHeadModel, TFOpenAIGPTDoubleHeadsModel, TFOpenAIGPTForSequenceClassification)
         if is_tf_available()
         else ()
     )
     lowercase__				  =			(
         (TFOpenAIGPTLMHeadModel,) if is_tf_available() else ()
     )  # TODO (PVP): Add Double HeadsModel when generate() function is changed accordingly
     lowercase__				  =			(
         {
             """feature-extraction""": TFOpenAIGPTModel,
             """text-classification""": TFOpenAIGPTForSequenceClassification,
             """text-generation""": TFOpenAIGPTLMHeadModel,
             """zero-shot""": TFOpenAIGPTForSequenceClassification,
         }
         if is_tf_available()
         else {}
     )
     lowercase__				  =			False
     lowercase__				  =			False

     def 						__lowerCamelCase				(  self							,       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_    ):
            if pipeline_test_casse_name == "ZeroShotClassificationPipelineTests":
                   # Get `tokenizer does not have a padding token` error for both fast/slow tokenizers.
                   # `OpenAIGPTConfig` was never used in pipeline tests, either because of a missing checkpoint or because a
                   # tiny config could not be created.
                   return True

            return False

     def 						__lowerCamelCase				(  self    ):
            __snake_case						       =   TFOpenAIGPTModelTester(self    )
            __snake_case						       =   ConfigTester(self							,       config_class=SCREAMING_SNAKE_CASE_							,       n_embd=37    )

     def 						__lowerCamelCase				(  self    ):
            self.config_tester.run_common_tests()

     def 						__lowerCamelCase				(  self    ):
            __snake_case						       =   self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_openai_gpt_model(*SCREAMING_SNAKE_CASE_    )

     def 						__lowerCamelCase				(  self    ):
            __snake_case						       =   self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_openai_gpt_lm_head(*SCREAMING_SNAKE_CASE_    )

     def 						__lowerCamelCase				(  self    ):
            __snake_case						       =   self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_openai_gpt_double_head(*SCREAMING_SNAKE_CASE_    )

     def 						__lowerCamelCase				(  self    ):
            __snake_case						       =   self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_openai_gpt_for_sequence_classification(*SCREAMING_SNAKE_CASE_    )

     @slow
     def 						__lowerCamelCase				(  self    ):
            for model_name in TF_OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                   __snake_case						       =   TFOpenAIGPTModel.from_pretrained(SCREAMING_SNAKE_CASE_    )
                   self.assertIsNotNone(SCREAMING_SNAKE_CASE_    )







@require_tf
class  _lowerCamelCase			(unittest.TestCase					):

     @slow
     def 						__lowerCamelCase				(  self    ):
            __snake_case						       =   TFOpenAIGPTLMHeadModel.from_pretrained('openai-gpt'    )
            __snake_case						       =   tf.convert_to_tensor([[481, 4_735, 544]]							,       dtype=tf.intaa    )  # the president is
            __snake_case						       =   [
                481,
                4_735,
                544,
                246,
                963,
                870,
                762,
                239,
                244,
                40_477,
                244,
                249,
                719,
                881,
                487,
                544,
                240,
                244,
                603,
                481,
            ]  # the president is a very good man. " \n " i\'m sure he is, " said the

            __snake_case						       =   model.generate(SCREAMING_SNAKE_CASE_							,       do_sample=SCREAMING_SNAKE_CASE_    )
            self.assertListEqual(output_ids[0].numpy().tolist()							,       SCREAMING_SNAKE_CASE_    )


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


lowerCamelCase_				:							List[str]       =    getLogger(__name__)


lowerCamelCase_				:							Dict       =    "cuda" if torch.cuda.is_available() else "cpu"
def        __lowercase(							__snake_case				:		List[str]    ,__snake_case				:		str    ,__snake_case				:		str    ,__snake_case				:		int = 8    ,__snake_case				:		str = DEFAULT_DEVICE    ,__snake_case				:		str=False    ,__snake_case				:		Tuple="summarization"    ,__snake_case				:		List[str]=None    ,**__snake_case				:		Dict    ,)       ->   Dict:
       __snake_case						       =   Path(__snake_case					).open('w'    ,encoding='utf-8'					)
       __snake_case						       =   str(__snake_case					)
       __snake_case						       =   AutoModelForSeqaSeqLM.from_pretrained(__snake_case					).to(__snake_case					)
       if fpaa:
              __snake_case						       =   model.half()

       __snake_case						       =   AutoTokenizer.from_pretrained(__snake_case					)
       logger.info(f'''Inferred tokenizer type: {tokenizer.__class__}'''					)  # if this is wrong, check config.model_type.

       __snake_case						       =   time.time()
       # update config with task specific params
       use_task_specific_params(__snake_case    ,__snake_case					)
       if prefix is None:
              __snake_case						       =   prefix or getattr(model.config    ,'prefix'    ,''					) or ''
       for examples_chunk in tqdm(list(chunks(__snake_case    ,__snake_case					)					)					):
              __snake_case						       =   [prefix + text for text in examples_chunk]
              __snake_case						       =   tokenizer(__snake_case    ,return_tensors='pt'    ,truncation=__snake_case    ,padding='longest'					).to(__snake_case					)
              __snake_case						       =   model.generate(
                  input_ids=batch.input_ids    ,attention_mask=batch.attention_mask    ,**__snake_case    ,)
              __snake_case						       =   tokenizer.batch_decode(__snake_case    ,skip_special_tokens=__snake_case    ,clean_up_tokenization_spaces=__snake_case					)
              for hypothesis in dec:
                     fout.write(hypothesis + '\n'					)
                     fout.flush()
       fout.close()
       __snake_case						       =   int(time.time() - start_time					)  # seconds
       __snake_case						       =   len(__snake_case					)
       return {"n_obs": n_obs, "runtime": runtime, "seconds_per_sample": round(runtime / n_obs    ,4					)}
def        __lowercase(							)       ->   Optional[int]:
       return datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S'					)
def        __lowercase(							__snake_case				:		Optional[int]=True					)       ->   Dict:
       __snake_case						       =   argparse.ArgumentParser()
       parser.add_argument('model_name'    ,type=__snake_case    ,help='like facebook/bart-large-cnn,t5-base, etc.'					)
       parser.add_argument('input_path'    ,type=__snake_case    ,help='like cnn_dm/test.source'					)
       parser.add_argument('save_path'    ,type=__snake_case    ,help='where to save summaries'					)
       parser.add_argument('--reference_path'    ,type=__snake_case    ,required=__snake_case    ,help='like cnn_dm/test.target'					)
       parser.add_argument('--score_path'    ,type=__snake_case    ,required=__snake_case    ,default='metrics.json'    ,help='where to save metrics'					)
       parser.add_argument('--device'    ,type=__snake_case    ,required=__snake_case    ,default=__snake_case    ,help='cuda, cuda:1, cpu etc.'					)
       parser.add_argument(
           '--prefix'    ,type=__snake_case    ,required=__snake_case    ,default=__snake_case    ,help='will be added to the begininng of src examples'					)
       parser.add_argument('--task'    ,type=__snake_case    ,default='summarization'    ,help='used for task_specific_params + metrics'					)
       parser.add_argument('--bs'    ,type=__snake_case    ,default=8    ,required=__snake_case    ,help='batch size'					)
       parser.add_argument(
           '--n_obs'    ,type=__snake_case    ,default=-1    ,required=__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=__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
       __snake_case       ,       __snake_case						       =   parser.parse_known_args()
       __snake_case						       =   parse_numeric_n_bool_cl_kwargs(__snake_case					)
       if parsed_args and verbose:
              print(f'''parsed the following generate kwargs: {parsed_args}'''					)
       with open(args.input_path					) as f:
              __snake_case						       =   [' ' + x.rstrip() if 't5' in args.model_name else x.rstrip() for x in f.readlines()]
       if args.n_obs > 0:
              __snake_case						       =   examples[: args.n_obs]
       Path(args.save_path					).parent.mkdir(exist_ok=__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.'''					)
       __snake_case						       =   generate_summaries_or_translations(
           __snake_case    ,args.save_path    ,args.model_name    ,batch_size=args.bs    ,device=args.device    ,fpaa=args.fpaa    ,task=args.task    ,prefix=args.prefix    ,**__snake_case    ,)

       if args.reference_path is None:
              return {}

       # Compute scores
       __snake_case						       =   calculate_bleu if 'translation' in args.task else calculate_rouge
       __snake_case						       =   [x.rstrip() for x in open(args.save_path					).readlines()]
       __snake_case						       =   [x.rstrip() for x in open(args.reference_path					).readlines()][: len(__snake_case					)]
       __snake_case						       =   score_fn(__snake_case    ,__snake_case					)
       scores.update(__snake_case					)

       if args.dump_args:
              scores.update(__snake_case					)
       if args.info:
              __snake_case						       =   args.info

       if verbose:
              print(__snake_case					)

       if args.score_path is not None:
              json.dump(__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)
