

from io import BytesIO
from typing import List, Union

import requests

from ..utils import add_end_docstrings, is_decord_available, is_torch_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_decord_available():
     import numpy as np
     from decord import VideoReader


if is_torch_available():
     from ..models.auto.modeling_auto import MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING

lowercase_       		=  logging.get_logger(__name__)


@add_end_docstrings(_UpperCAmelCase )
class  A						(							_UpperCAmelCase ):







    """simple docstring"""





    def __init__(      self :       List[str],*lowercase_ :       Dict,**lowercase_ :       List[Any]		)->    Dict:





        '''simple docstring'''







        super().__init__(*lowercase_,**lowercase_		)
        requires_backends(self,'decord'		)
        self.check_model_type(lowercase_		)


    def 						snake_case__     (      self :       Optional[Any],lowercase_ :       List[str]=None,lowercase_ :       int=None,lowercase_ :       List[Any]=None		)->    List[Any]:





        '''simple docstring'''







        A__          =   {}
        if frame_sampling_rate is not None:
            A__          =   frame_sampling_rate
        if num_frames is not None:
            A__          =   num_frames

        A__          =   {}
        if top_k is not None:
            A__          =   top_k
        return preprocess_params, {}, postprocess_params


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





        '''simple docstring'''







        return super().__call__(lowercase_,**lowercase_		)


    def 						snake_case__     (      self :       Optional[Any],lowercase_ :       Union[str, Any],lowercase_ :       List[str]=None,lowercase_ :       Tuple=1		)->    int:





        '''simple docstring'''







        if num_frames is None:
            A__          =   self.model.config.num_frames

        if video.startswith('http://'		) or video.startswith('https://'		):
            A__          =   BytesIO(requests.get(lowercase_		).content		)

        A__          =   VideoReader(lowercase_		)
        videoreader.seek(0		)

        A__          =   0
        A__          =   num_frames * frame_sampling_rate - 1
        A__          =   np.linspace(lowercase_,lowercase_,num=lowercase_,dtype=np.intaa		)

        A__          =   videoreader.get_batch(lowercase_		).asnumpy()
        A__          =   list(lowercase_		)

        A__          =   self.image_processor(lowercase_,return_tensors=self.framework		)
        return model_inputs


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





        '''simple docstring'''







        A__          =   self.model(**lowercase_		)
        return model_outputs


    def 						snake_case__     (      self :       List[Any],lowercase_ :       List[str],lowercase_ :       Union[str, Any]=5		)->    str:





        '''simple docstring'''







        if top_k > self.model.config.num_labels:
            A__          =   self.model.config.num_labels

        if self.framework == "pt":
            A__          =   model_outputs.logits.softmax(-1		)[0]
            A__      ,						A__          =   probs.topk(lowercase_		)
        else:
            raise ValueError(F'Unsupported framework: {self.framework}'		)

        A__          =   scores.tolist()
        A__          =   ids.tolist()
        return [{"score": score, "label": self.model.config.idalabel[_id]} for score, _id in zip(lowercase_,lowercase_		)]









'''simple docstring'''







# This script creates a super tiny model that is useful inside tests, when we just want to test that
# the machinery works, without needing to the check the quality of the outcomes.
#
# This version creates a tiny vocab first, and then a tiny model - so the outcome is truly tiny -
# all files ~60KB. As compared to taking a full-size model, reducing to the minimum its layers and
# emb dimensions, but keeping the full vocab + merges files, leading to ~3MB in total for all files.
# The latter is done by `fsmt-make-super-tiny-model.py`.
#
# It will be used then as "stas/tiny-wmt19-en-ru"

from pathlib import Path
import json
import tempfile

from transformers import FSMTTokenizer, FSMTConfig, FSMTForConditionalGeneration
from transformers.models.fsmt.tokenization_fsmt import VOCAB_FILES_NAMES

_A	:			Dict         ='''tiny-wmt19-en-ru'''

# Build

# borrowed from a test
_A	:			List[str]         =[
    '''l''',
    '''o''',
    '''w''',
    '''e''',
    '''r''',
    '''s''',
    '''t''',
    '''i''',
    '''d''',
    '''n''',
    '''w</w>''',
    '''r</w>''',
    '''t</w>''',
    '''lo''',
    '''low''',
    '''er</w>''',
    '''low</w>''',
    '''lowest</w>''',
    '''newer</w>''',
    '''wider</w>''',
    '''<unk>''',
]
_A	:			str         =dict(zip(vocab, range(len(vocab))))
_A	:			List[str]         =['''l o 123''', '''lo w 1456''', '''e r</w> 1789''', '''''']

with tempfile.TemporaryDirectory() as tmpdirname:
			_A	:			Union[str, Any]         =Path(tmpdirname)
			_A	:			str         =build_dir / VOCAB_FILES_NAMES['''src_vocab_file''']
			_A	:			int         =build_dir / VOCAB_FILES_NAMES['''tgt_vocab_file''']
			_A	:			List[Any]         =build_dir / VOCAB_FILES_NAMES['''merges_file''']
			with open(src_vocab_file, '''w''') as fp:
						fp.write(json.dumps(vocab_tokens))
			with open(tgt_vocab_file, '''w''') as fp:
						fp.write(json.dumps(vocab_tokens))
			with open(merges_file, '''w''') as fp:
						fp.write('''\n'''.join(merges))

			_A	:			int         =FSMTTokenizer(
			    langs=['''en''', '''ru'''],
			    src_vocab_size=len(vocab),
			    tgt_vocab_size=len(vocab),
			    src_vocab_file=src_vocab_file,
			    tgt_vocab_file=tgt_vocab_file,
			    merges_file=merges_file,
			)

_A	:			List[str]         =FSMTConfig(
    langs=['''ru''', '''en'''],
    src_vocab_size=1_000,
    tgt_vocab_size=1_000,
    d_model=4,
    encoder_layers=1,
    decoder_layers=1,
    encoder_ffn_dim=4,
    decoder_ffn_dim=4,
    encoder_attention_heads=1,
    decoder_attention_heads=1,
)

_A	:			Union[str, Any]         =FSMTForConditionalGeneration(config)
print(F'num of params {tiny_model.num_parameters()}')

# Test
_A	:			List[str]         =tokenizer(['''Making tiny model'''], return_tensors='''pt''')
_A	:			Tuple         =tiny_model(**batch)

print('''test output:''', len(outputs.logits[0]))

# Save
tiny_model.half()  # makes it smaller
tiny_model.save_pretrained(mname_tiny)
tokenizer.save_pretrained(mname_tiny)

print(F'Generated {mname_tiny}')

# Upload
# transformers-cli upload tiny-wmt19-en-ru

