

from __future__ import annotations

import inspect
import unittest
from math import floor

import numpy as np

from transformers import CvtConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available

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


if is_tf_available():
     import tensorflow as tf

     from transformers import TFCvtForImageClassification, TFCvtModel
     from transformers.models.cvt.modeling_tf_cvt import TF_CVT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
     from PIL import Image

     from transformers import AutoImageProcessor


class 			A						(							_UpperCAmelCase			):







    """simple docstring"""





    def 						snake_case__					(				self			:	Dict		)->   List[Any]:





           '''simple docstring'''







           A__   				=       self.config_class(**self.inputs_dict		)
           self.parent.assertTrue(hasattr(__a,'embed_dim'		)		)
           self.parent.assertTrue(hasattr(__a,'num_heads'		)		)




class 			A						:







    """simple docstring"""





    def __init__(				self			:	Union[str, Any],lowercase_			:	Optional[int],lowercase_			:	Any=1_3,lowercase_			:	Optional[Any]=6_4,lowercase_			:	Union[str, Any]=3,lowercase_			:	List[str]=[1_6, 4_8, 9_6],lowercase_			:	Optional[int]=[1, 3, 6],lowercase_			:	str=[1, 2, 1_0],lowercase_			:	Tuple=[7, 3, 3],lowercase_			:	Optional[int]=[4, 2, 2],lowercase_			:	Dict=[2, 1, 1],lowercase_			:	Any=[2, 2, 2],lowercase_			:	Dict=[False, False, True],lowercase_			:	List[Any]=[0.0, 0.0, 0.0],lowercase_			:	Any=0.02,lowercase_			:	str=1E-12,lowercase_			:	Any=True,lowercase_			:	Union[str, Any]=True,lowercase_			:	Union[str, Any]=2,)->   Tuple:





           '''simple docstring'''







           A__   				=       parent
           A__   				=       batch_size
           A__   				=       image_size
           A__   				=       patch_sizes
           A__   				=       patch_stride
           A__   				=       patch_padding
           A__   				=       is_training
           A__   				=       use_labels
           A__   				=       num_labels
           A__   				=       num_channels
           A__   				=       embed_dim
           A__   				=       num_heads
           A__   				=       stride_kv
           A__   				=       depth
           A__   				=       cls_token
           A__   				=       attention_drop_rate
           A__   				=       initializer_range
           A__   				=       layer_norm_eps


    def 						snake_case__					(				self			:	Optional[int]		)->   Union[str, Any]:





           '''simple docstring'''







           A__   				=       floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]		)

           A__   				=       None
           if self.use_labels:
                  # create a random int32 tensor of given shape
                  A__   				=       ids_tensor([self.batch_size],self.num_labels		)

           A__   				=       self.get_config()
           return config, pixel_values, labels


    def 						snake_case__					(				self			:	str		)->   int:





           '''simple docstring'''







           return CvtConfig(
               image_size=self.image_size,num_labels=self.num_labels,num_channels=self.num_channels,embed_dim=self.embed_dim,num_heads=self.num_heads,patch_sizes=self.patch_sizes,patch_padding=self.patch_padding,patch_stride=self.patch_stride,stride_kv=self.stride_kv,depth=self.depth,cls_token=self.cls_token,attention_drop_rate=self.attention_drop_rate,initializer_range=self.initializer_range,)


    def 						snake_case__					(				self			:	List[Any],lowercase_			:	List[Any],lowercase_			:	List[Any],lowercase_			:	Tuple		)->   List[Any]:





           '''simple docstring'''







           A__   				=       TFCvtModel(config=__a		)
           A__   				=       model(__a,training=__a		)
           A__   				=       (self.image_size, self.image_size)
           A__				,						A__   				=       image_size[0], image_size[1]
           for i in range(len(self.depth		)		):
                  A__   				=       floor(((height + 2 * self.patch_padding[i] - self.patch_sizes[i]) / self.patch_stride[i]) + 1		)
                  A__   				=       floor(((width + 2 * self.patch_padding[i] - self.patch_sizes[i]) / self.patch_stride[i]) + 1		)
           self.parent.assertEqual(result.last_hidden_state.shape,(self.batch_size, self.embed_dim[-1], height, width)		)


    def 						snake_case__					(				self			:	List[Any],lowercase_			:	int,lowercase_			:	Dict,lowercase_			:	int		)->   List[str]:





           '''simple docstring'''







           A__   				=       self.num_labels
           A__   				=       TFCvtForImageClassification(__a		)
           A__   				=       model(__a,labels=__a,training=__a		)
           self.parent.assertEqual(result.logits.shape,(self.batch_size, self.num_labels)		)


    def 						snake_case__					(				self			:	Optional[int]		)->   Any:





           '''simple docstring'''







           A__   				=       self.prepare_config_and_inputs()
           A__				,						A__				,						A__   				=       config_and_inputs
           A__   				=       {'pixel_values': pixel_values}
           return config, inputs_dict




@require_tf
class 			A						(							_UpperCAmelCase   ,							_UpperCAmelCase   ,							unittest.TestCase			):







    """simple docstring"""





    lowerCamelCase		     =							(TFCvtModel, TFCvtForImageClassification) if is_tf_available() else ()
    lowerCamelCase		     =							(
        {'feature-extraction': TFCvtModel, 'image-classification': TFCvtForImageClassification}
        if is_tf_available()
        else {}
    )
    lowerCamelCase		     =							False
    lowerCamelCase		     =							False
    lowerCamelCase		     =							False
    lowerCamelCase		     =							False
    lowerCamelCase		     =							False


    def 						snake_case__					(				self			:	Any		)->   Any:





           '''simple docstring'''







           A__   				=       TFCvtModelTester(self		)
           A__   				=       TFCvtConfigTester(self,config_class=__a,has_text_modality=__a,hidden_size=3_7		)


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





           '''simple docstring'''







           self.config_tester.create_and_test_config_common_properties()
           self.config_tester.create_and_test_config_to_json_string()
           self.config_tester.create_and_test_config_to_json_file()
           self.config_tester.create_and_test_config_from_and_save_pretrained()
           self.config_tester.create_and_test_config_with_num_labels()
           self.config_tester.check_config_can_be_init_without_params()
           self.config_tester.check_config_arguments_init()


    @unittest.skip(reason='Cvt does not output attentions'		)
    def 						snake_case__					(				self			:	int		)->   Union[str, Any]:





           '''simple docstring'''







           pass


    @unittest.skip(reason='Cvt does not use inputs_embeds'		)
    def 						snake_case__					(				self			:	Optional[int]		)->   List[str]:





           '''simple docstring'''







           pass


    @unittest.skip(reason='Cvt does not support input and output embeddings'		)
    def 						snake_case__					(				self			:	List[str]		)->   int:





           '''simple docstring'''







           pass


    @unittest.skipIf(
        not is_tf_available() or len(tf.config.list_physical_devices('GPU'		)		) == 0,reason='TF does not support backprop for grouped convolutions on CPU.',)
    def 						snake_case__					(				self			:	Tuple		)->   Tuple:





           '''simple docstring'''







           super().test_dataset_conversion()


    @unittest.skipIf(
        not is_tf_available() or len(tf.config.list_physical_devices('GPU'		)		) == 0,reason='TF does not support backprop for grouped convolutions on CPU.',)
    @slow
    def 						snake_case__					(				self			:	List[Any]		)->   List[str]:





           '''simple docstring'''







           super().test_keras_fit()


    @unittest.skip(reason='Get `Failed to determine best cudnn convolution algo.` error after using TF 2.12+cuda 11.8'		)
    def 						snake_case__					(				self			:	Optional[int]		)->   Dict:





           '''simple docstring'''







           A__   				=       tf.keras.mixed_precision.Policy('mixed_float16'		)
           tf.keras.mixed_precision.set_global_policy(__a		)
           super().test_keras_fit()
           tf.keras.mixed_precision.set_global_policy('float32'		)


    def 						snake_case__					(				self			:	int		)->   str:





           '''simple docstring'''







           A__				,						A__   				=       self.model_tester.prepare_config_and_inputs_for_common()

           for model_class in self.all_model_classes:
                  A__   				=       model_class(__a		)
                  A__   				=       inspect.signature(model.call		)
                  # signature.parameters is an OrderedDict => so arg_names order is deterministic
                  A__   				=       [*signature.parameters.keys()]

                  A__   				=       ['pixel_values']
                  self.assertListEqual(arg_names[:1],__a		)


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





           '''simple docstring'''







           def check_hidden_states_output(lowercase_			:	Union[str, Any],lowercase_			:	Union[str, Any],lowercase_			:	int		):
                  A__   				=       model_class(__a		)

                  A__   				=       model(**self._prepare_for_class(__a,__a		)		)
                  A__   				=       outputs.hidden_states

                  A__   				=       len(self.model_tester.depth		)
                  self.assertEqual(len(__a		),__a		)

                  # verify the first hidden states (first block)
                  self.assertListEqual(
                      list(hidden_states[0].shape[-3:]		),[
                          self.model_tester.embed_dim[0],
                          self.model_tester.image_size // 4,
                          self.model_tester.image_size // 4,
                      ],)

           A__				,						A__   				=       self.model_tester.prepare_config_and_inputs_for_common()

           for model_class in self.all_model_classes:
                  A__   				=       True
                  check_hidden_states_output(__a,__a,__a		)

                  # check that output_hidden_states also work using config
                  del inputs_dict["output_hidden_states"]
                  A__   				=       True

                  check_hidden_states_output(__a,__a,__a		)


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





           '''simple docstring'''







           A__   				=       self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_model(*__a		)


    def 						snake_case__					(				self			:	int		)->   Tuple:





           '''simple docstring'''







           A__   				=       self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_image_classification(*__a		)


    @slow
    def 						snake_case__					(				self			:	Dict		)->   Tuple:





           '''simple docstring'''







           for model_name in TF_CVT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                  A__   				=       TFCvtModel.from_pretrained(__a		)
                  self.assertIsNotNone(__a		)




def 						_snake_case(   )							->				Tuple:





       '''simple docstring'''






       A__   				=       Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'				)
       return image


@require_tf
@require_vision
class 			A						(							unittest.TestCase			):







    """simple docstring"""





    @cached_property
    def 						snake_case__					(				self			:	Any		)->   Tuple:





           '''simple docstring'''







           return AutoImageProcessor.from_pretrained(TF_CVT_PRETRAINED_MODEL_ARCHIVE_LIST[0]		)


    @slow
    def 						snake_case__					(				self			:	Dict		)->   Dict:





           '''simple docstring'''







           A__   				=       TFCvtForImageClassification.from_pretrained(TF_CVT_PRETRAINED_MODEL_ARCHIVE_LIST[0]		)

           A__   				=       self.default_image_processor
           A__   				=       prepare_img()
           A__   				=       image_processor(images=__a,return_tensors='tf'		)

           # forward pass
           A__   				=       model(**__a		)

           # verify the logits
           A__   				=       tf.TensorShape((1, 1_0_0_0)		)
           self.assertEqual(outputs.logits.shape,__a		)

           A__   				=       tf.constant([0.9_285, 0.9_015, -0.3_150]		)
           self.assertTrue(np.allclose(outputs.logits[0, :3].numpy(),__a,atol=1E-4		)		)




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


lowercase_			=  getLogger(__name__)


lowercase_			=  "cuda" if torch.cuda.is_available() else "cpu"
def 						_snake_case(   SCREAMING_SNAKE_CASE__      :     List[str]   ,      SCREAMING_SNAKE_CASE__      :     str   ,      SCREAMING_SNAKE_CASE__      :     str   ,      SCREAMING_SNAKE_CASE__      :     int = 8   ,      SCREAMING_SNAKE_CASE__      :     str = DEFAULT_DEVICE   ,      SCREAMING_SNAKE_CASE__      :     Optional[Any]=False   ,      SCREAMING_SNAKE_CASE__      :     Dict="summarization"   ,      SCREAMING_SNAKE_CASE__      :     Any=None   ,      **SCREAMING_SNAKE_CASE__      :     str   ,      )							->				Dict:





    '''simple docstring'''






    A__   				=       Path(SCREAMING_SNAKE_CASE__				).open('w'   ,      encoding='utf-8'				)
    A__   				=       str(SCREAMING_SNAKE_CASE__				)
    A__   				=       AutoModelForSeqaSeqLM.from_pretrained(SCREAMING_SNAKE_CASE__				).to(SCREAMING_SNAKE_CASE__				)
    if fpaa:
        A__   				=       model.half()

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

    A__   				=       time.time()
    # update config with task specific params
    use_task_specific_params(SCREAMING_SNAKE_CASE__   ,      SCREAMING_SNAKE_CASE__				)
    if prefix is None:
        A__   				=       prefix or getattr(model.config   ,      'prefix'   ,      ''				) or ''
    for examples_chunk in tqdm(list(chunks(SCREAMING_SNAKE_CASE__   ,      SCREAMING_SNAKE_CASE__				)				)				):
        A__   				=       [prefix + text for text in examples_chunk]
        A__   				=       tokenizer(SCREAMING_SNAKE_CASE__   ,      return_tensors='pt'   ,      truncation=SCREAMING_SNAKE_CASE__   ,      padding='longest'				).to(SCREAMING_SNAKE_CASE__				)
        A__   				=       model.generate(
            input_ids=batch.input_ids   ,      attention_mask=batch.attention_mask   ,      **SCREAMING_SNAKE_CASE__   ,      )
        A__   				=       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()
    A__   				=       int(time.time() - start_time				)  # seconds
    A__   				=       len(SCREAMING_SNAKE_CASE__				)
    return {"n_obs": n_obs, "runtime": runtime, "seconds_per_sample": round(runtime / n_obs   ,      4				)}
def 						_snake_case(   )							->				Tuple:





    '''simple docstring'''






    return datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S'				)
def 						_snake_case(   SCREAMING_SNAKE_CASE__      :     Optional[int]=True				)							->				Dict:





    '''simple docstring'''






    A__   				=       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
    A__				,						A__   				=       parser.parse_known_args()
    A__   				=       parse_numeric_n_bool_cl_kwargs(SCREAMING_SNAKE_CASE__				)
    if parsed_args and verbose:
        print(f'parsed the following generate kwargs: {parsed_args}'				)
    A__   				=       [' ' + x.rstrip() if 't5' in args.model_name else x.rstrip() for x in open(args.input_path				).readlines()]
    if args.n_obs > 0:
        A__   				=       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'				)

    A__   				=       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
    A__   				=       calculate_bleu if 'translation' in args.task else calculate_rouge
    A__   				=       [x.rstrip() for x in open(args.save_path				).readlines()]
    A__   				=       [x.rstrip() for x in open(args.reference_path				).readlines()][: len(SCREAMING_SNAKE_CASE__				)]
    A__   				=       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:
        A__   				=       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)

