




from typing import List

import numpy as np







def 			_a   (		SCREAMING_SNAKE_CASE_							:       dict  ):
					__lowerCAmelCase            = {key: len(SCREAMING_SNAKE_CASE_  ) for key, value in gen_kwargs.items() if isinstance(SCREAMING_SNAKE_CASE_   ,   SCREAMING_SNAKE_CASE_  )}
					if len(set(lists_lengths.values()  )  ) > 1:
										raise RuntimeError(
										    (
										        "Sharding is ambiguous for this dataset: "
										        + "we found several data sources lists of different lengths, and we don't know over which list we should parallelize:\n"
										        + "\n".join(F"""\t- key {key} has length {length}""" for key, length in lists_lengths.items()  )
										        + "\nTo fix this, check the 'gen_kwargs' and make sure to use lists only for data sources, "
										        + "and use tuples otherwise. In the end there should only be one single list, or several lists with the same length."
										    )  )
					__lowerCAmelCase            = max(lists_lengths.values()   ,   default=0  )
					return max(1   ,   SCREAMING_SNAKE_CASE_  )







def 			_a   (		SCREAMING_SNAKE_CASE_							:       int   ,   SCREAMING_SNAKE_CASE_							:       int  ):
					__lowerCAmelCase            = []
					for group_idx in range(SCREAMING_SNAKE_CASE_  ):
										__lowerCAmelCase            = num_shards // max_num_jobs + (group_idx < (num_shards % max_num_jobs))
										if num_shards_to_add == 0:
															break
										__lowerCAmelCase            = shards_indices_per_group[-1].stop if shards_indices_per_group else 0
										__lowerCAmelCase            = range(SCREAMING_SNAKE_CASE_   ,   start + num_shards_to_add  )
										shards_indices_per_group.append(SCREAMING_SNAKE_CASE_  )
					return shards_indices_per_group







def 			_a   (		SCREAMING_SNAKE_CASE_							:       dict   ,   SCREAMING_SNAKE_CASE_							:       int  ):
					__lowerCAmelCase            = _number_of_shards_in_gen_kwargs(SCREAMING_SNAKE_CASE_  )
					if num_shards == 1:
										return [dict(SCREAMING_SNAKE_CASE_  )]
					else:
										__lowerCAmelCase            = _distribute_shards(num_shards=SCREAMING_SNAKE_CASE_   ,   max_num_jobs=SCREAMING_SNAKE_CASE_  )
										return [
										    {
										        key: [value[shard_idx] for shard_idx in shard_indices_per_group[group_idx]]
										        if isinstance(SCREAMING_SNAKE_CASE_   ,   SCREAMING_SNAKE_CASE_  )
										        else value
										        for key, value in gen_kwargs.items()
										    }
										    for group_idx in range(len(SCREAMING_SNAKE_CASE_  )  )
										]







def 			_a   (		SCREAMING_SNAKE_CASE_							:       List[dict]  ):
					return {
					    key: [value for gen_kwargs in gen_kwargs_list for value in gen_kwargs[key]]
					    if isinstance(gen_kwargs_list[0][key]   ,   SCREAMING_SNAKE_CASE_  )
					    else gen_kwargs_list[0][key]
					    for key in gen_kwargs_list[0]
					}







def 			_a   (		SCREAMING_SNAKE_CASE_							:       np.random.Generator   ,   SCREAMING_SNAKE_CASE_							:       dict  ):
					__lowerCAmelCase            = {len(SCREAMING_SNAKE_CASE_  ) for value in gen_kwargs.values() if isinstance(SCREAMING_SNAKE_CASE_   ,   SCREAMING_SNAKE_CASE_  )}
					__lowerCAmelCase            = {}
					for size in list_sizes:
										__lowerCAmelCase            = list(range(SCREAMING_SNAKE_CASE_  )  )
										rng.shuffle(indices_per_size[size]  )
					# Now let's copy the gen_kwargs and shuffle the lists based on their sizes
					__lowerCAmelCase            = dict(SCREAMING_SNAKE_CASE_  )
					for key, value in shuffled_kwargs.items():
										if isinstance(SCREAMING_SNAKE_CASE_   ,   SCREAMING_SNAKE_CASE_  ):
															__lowerCAmelCase            = [value[i] for i in indices_per_size[len(SCREAMING_SNAKE_CASE_  )]]
					return shuffled_kwargs










from typing import List, Union

from ..utils import (
    add_end_docstrings,
    is_tf_available,
    is_torch_available,
    is_vision_available,
    logging,
    requires_backends,
)
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_vision_available():
					from PIL import Image

					from ..image_utils import load_image

if is_tf_available():
					import tensorflow as tf

					from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING
					from ..tf_utils import stable_softmax

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

UpperCamelCase__					    =    logging.get_logger(__name__)
@add_end_docstrings(snake_case__   )
class     a__					(      snake_case__   ):






						def __init__(     self	,    *_A	,    **_A    ):




											"""simple docstring"""



											super().__init__(*_A	,    **_A    )
											requires_backends(self	,    "vision"    )
											self.check_model_type(
											    TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING
											    if self.framework == "tf"
											    else MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING    )






						def        __SCREAMING_SNAKE_CASE(     self	,    _A=None    ):




											"""simple docstring"""



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






						def __call__(     self	,    _A	,    **_A    ):




											"""simple docstring"""



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






						def        __SCREAMING_SNAKE_CASE(     self	,    _A    ):




											"""simple docstring"""



											__lowerCAmelCase            = load_image(_A    )
											__lowerCAmelCase            = self.image_processor(images=_A	,    return_tensors=self.framework    )
											return model_inputs






						def        __SCREAMING_SNAKE_CASE(     self	,    _A    ):




											"""simple docstring"""



											__lowerCAmelCase            = self.model(**_A    )
											return model_outputs






						def        __SCREAMING_SNAKE_CASE(     self	,    _A	,    _A=5    ):




											"""simple docstring"""



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

											if self.framework == "pt":
																__lowerCAmelCase            = model_outputs.logits.softmax(-1    )[0]
																__lowerCAmelCase     ,							__lowerCAmelCase            = probs.topk(_A    )
											elif self.framework == "tf":
																__lowerCAmelCase            = stable_softmax(model_outputs.logits	,    axis=-1    )[0]
																__lowerCAmelCase            = tf.math.top_k(_A	,    k=_A    )
																__lowerCAmelCase     ,							__lowerCAmelCase            = topk.values.numpy(), topk.indices.numpy()
											else:
																raise ValueError(f"""Unsupported framework: {self.framework}"""    )

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




