


"""simple docstring"""


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

A     :   List[Any]			    =			logging.get_logger(__name__)
@add_end_docstrings(lowerCAmelCase__		)
class   _UpperCamelCase  (   lowerCAmelCase__		):
							'''simple docstring'''







							def __init__(     self    ,   *__a    ,   **__a	):
										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 			snake_case				(     self    ,   __a=None	):
										__lowerCAmelCase				    =			{}
										if top_k is not None:
													__lowerCAmelCase				    =			top_k
										return {}, {}, postprocess_params

							def __call__(     self    ,   __a    ,   **__a	):
										return super().__call__(__a    ,   **__a	)

							def 			snake_case				(     self    ,   __a	):
										__lowerCAmelCase				    =			load_image(__a	)
										__lowerCAmelCase				    =			self.image_processor(images=__a    ,   return_tensors=self.framework	)
										return model_inputs

							def 			snake_case				(     self    ,   __a	):
										__lowerCAmelCase				    =			self.model(**__a	)
										return model_outputs



							def 			snake_case				(     self    ,   __a    ,   __a=5	):
										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	)]








"""simple docstring"""


import unittest

from transformers import (
    MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
    TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
    TextaTextGenerationPipeline,
    pipeline,
)
from transformers.testing_utils import is_pipeline_test, require_tf, require_torch
from transformers.utils import is_torch_available

from .test_pipelines_common import ANY


if is_torch_available():
	import torch
@is_pipeline_test
class   _UpperCamelCase  (   unittest.TestCase		):
							'''simple docstring'''







							__UpperCAmelCase						:    Union[str, Any]				      =MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
							__UpperCAmelCase						:    Union[str, Any]				      =TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING

							def 			snake_case				(     self    ,   __a    ,   __a    ,   __a	):
										__lowerCAmelCase				    =			TextaTextGenerationPipeline(model=__a    ,   tokenizer=__a	)
										return generator, ["Something to write", "Something else"]

							def 			snake_case				(     self    ,   __a    ,   __a	):
										__lowerCAmelCase				    =			generator("Something there"	)
										self.assertEqual(__a    ,   [{"generated_text": ANY(__a	)}]	)
										# These are encoder decoder, they don't just append to incoming string
										self.assertFalse(outputs[0]["generated_text"].startswith("Something there"	)	)

										__lowerCAmelCase				    =			generator(["This is great !", "Something else"]    ,   num_return_sequences=2    ,   do_sample=__a	)
										self.assertEqual(
										    __a    ,   [
										        [{"generated_text": ANY(__a	)}, {"generated_text": ANY(__a	)}],
										        [{"generated_text": ANY(__a	)}, {"generated_text": ANY(__a	)}],
										    ]    ,   )

										__lowerCAmelCase				    =			generator(
										    ["This is great !", "Something else"]    ,   num_return_sequences=2    ,   batch_size=2    ,   do_sample=__a	)
										self.assertEqual(
										    __a    ,   [
										        [{"generated_text": ANY(__a	)}, {"generated_text": ANY(__a	)}],
										        [{"generated_text": ANY(__a	)}, {"generated_text": ANY(__a	)}],
										    ]    ,   )

										with self.assertRaises(__a	):
													generator(4	)

							@require_torch
							def 			snake_case				(     self	):
										__lowerCAmelCase				    =			pipeline("text2text-generation"    ,   model="patrickvonplaten/t5-tiny-random"    ,   framework="pt"	)
										# do_sample=False necessary for reproducibility
										__lowerCAmelCase				    =			generator("Something there"    ,   do_sample=__a	)
										self.assertEqual(__a    ,   [{"generated_text": ""}]	)

										__lowerCAmelCase				    =			3
										__lowerCAmelCase				    =			generator(
										    "Something there"    ,   num_return_sequences=__a    ,   num_beams=__a    ,   )
										__lowerCAmelCase				    =			[
										    {"generated_text": "Beide Beide Beide Beide Beide Beide Beide Beide Beide"},
										    {"generated_text": "Beide Beide Beide Beide Beide Beide Beide Beide"},
										    {"generated_text": ""},
										]
										self.assertEqual(__a    ,   __a	)

										__lowerCAmelCase				    =			generator("This is a test"    ,   do_sample=__a    ,   num_return_sequences=2    ,   return_tensors=__a	)
										self.assertEqual(
										    __a    ,   [
										        {"generated_token_ids": ANY(torch.Tensor	)},
										        {"generated_token_ids": ANY(torch.Tensor	)},
										    ]    ,   )
										__lowerCAmelCase				    =			generator.model.config.eos_token_id
										__lowerCAmelCase				    =			"<pad>"
										__lowerCAmelCase				    =			generator(
										    ["This is a test", "This is a second test"]    ,   do_sample=__a    ,   num_return_sequences=2    ,   batch_size=2    ,   return_tensors=__a    ,   )
										self.assertEqual(
										    __a    ,   [
										        [
										            {"generated_token_ids": ANY(torch.Tensor	)},
										            {"generated_token_ids": ANY(torch.Tensor	)},
										        ],
										        [
										            {"generated_token_ids": ANY(torch.Tensor	)},
										            {"generated_token_ids": ANY(torch.Tensor	)},
										        ],
										    ]    ,   )



							@require_tf
							def 			snake_case				(     self	):
										__lowerCAmelCase				    =			pipeline("text2text-generation"    ,   model="patrickvonplaten/t5-tiny-random"    ,   framework="tf"	)
										# do_sample=False necessary for reproducibility
										__lowerCAmelCase				    =			generator("Something there"    ,   do_sample=__a	)
										self.assertEqual(__a    ,   [{"generated_text": ""}]	)




