





from collections import UserDict
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_torch_available():
					from ..models.auto.modeling_auto import MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING

if is_tf_available():
					from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING
					from ..tf_utils import stable_softmax

UpperCamelCase__											= logging.get_logger(__name__)

@add_end_docstrings(_a       )
class        __SCREAMING_SNAKE_CASE					(     _a       ):






							def __init__(						self  , **__lowerCAmelCase  ):
														super().__init__(**__lowerCAmelCase  )

														requires_backends(self  , """vision"""  )
														self.check_model_type(
														    TF_MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING
														    if self.framework == """tf"""
														    else MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING  )






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






							def 				_lowerCamelCase		(						self  , **__lowerCAmelCase  ):
														UpperCamelCase__				     =			{}
														if "candidate_labels" in kwargs:
																					UpperCamelCase__				     =			kwargs["""candidate_labels"""]
														if "hypothesis_template" in kwargs:
																					UpperCamelCase__				     =			kwargs["""hypothesis_template"""]

														return preprocess_params, {}, {}






							def 				_lowerCamelCase		(						self  , __lowerCAmelCase  , __lowerCAmelCase=None  , __lowerCAmelCase="This is a photo of {}."  ):
														UpperCamelCase__				     =			load_image(__lowerCAmelCase  )
														UpperCamelCase__				     =			self.image_processor(images=[image]  , return_tensors=self.framework  )
														UpperCamelCase__				     =			candidate_labels
														UpperCamelCase__				     =			[hypothesis_template.format(__lowerCAmelCase  ) for x in candidate_labels]
														UpperCamelCase__				     =			self.tokenizer(__lowerCAmelCase  , return_tensors=self.framework  , padding=__lowerCAmelCase  )
														UpperCamelCase__				     =			[text_inputs]
														return inputs






							def 				_lowerCamelCase		(						self  , __lowerCAmelCase  ):
														UpperCamelCase__				     =			model_inputs.pop("""candidate_labels"""  )
														UpperCamelCase__				     =			model_inputs.pop("""text_inputs"""  )
														if isinstance(text_inputs[0]  , __lowerCAmelCase  ):
																					UpperCamelCase__				     =			text_inputs[0]
														else:
																					# Batching case.
																					UpperCamelCase__				     =			text_inputs[0][0]

														UpperCamelCase__				     =			self.model(**__lowerCAmelCase  , **__lowerCAmelCase  )

														UpperCamelCase__				     =			{
														    """candidate_labels""": candidate_labels,
														    """logits""": outputs.logits_per_image,
														}
														return model_outputs







							def 				_lowerCamelCase		(						self  , __lowerCAmelCase  ):
														UpperCamelCase__				     =			model_outputs.pop("""candidate_labels"""  )
														UpperCamelCase__				     =			model_outputs["""logits"""][0]
														if self.framework == "pt":
																					UpperCamelCase__				     =			logits.softmax(dim=-1  ).squeeze(-1  )
																					UpperCamelCase__				     =			probs.tolist()
																					if not isinstance(__lowerCAmelCase  , __lowerCAmelCase  ):
																												UpperCamelCase__				     =			[scores]
														elif self.framework == "tf":
																					UpperCamelCase__				     =			stable_softmax(__lowerCAmelCase  , axis=-1  )
																					UpperCamelCase__				     =			probs.numpy().tolist()
														else:
																					raise ValueError(f"""Unsupported framework: {self.framework}"""  )

														UpperCamelCase__				     =			[
														    {"""score""": score, """label""": candidate_label}
														    for score, candidate_label in sorted(zip(__lowerCAmelCase  , __lowerCAmelCase  )  , key=lambda __lowerCAmelCase  : -x[0]  )
														]
														return result











from __future__ import annotations

import unittest

from transformers import AutoTokenizer, PegasusConfig, is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
from transformers.utils import cached_property

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


if is_tf_available():
					import tensorflow as tf

					from transformers import TFAutoModelForSeqaSeqLM, TFPegasusForConditionalGeneration, TFPegasusModel

@require_tf
class        __SCREAMING_SNAKE_CASE					:
							snake_case     :      Dict						     =       PegasusConfig
							snake_case     :      Any						     =       {}
							snake_case     :      int						     =       """gelu"""






							def __init__(						self  , __lowerCAmelCase  , __lowerCAmelCase=13  , __lowerCAmelCase=7  , __lowerCAmelCase=True  , __lowerCAmelCase=False  , __lowerCAmelCase=99  , __lowerCAmelCase=32  , __lowerCAmelCase=2  , __lowerCAmelCase=4  , __lowerCAmelCase=37  , __lowerCAmelCase=0.1  , __lowerCAmelCase=0.1  , __lowerCAmelCase=40  , __lowerCAmelCase=2  , __lowerCAmelCase=1  , __lowerCAmelCase=0  , ):
														UpperCamelCase__				     =			parent
														UpperCamelCase__				     =			batch_size
														UpperCamelCase__				     =			seq_length
														UpperCamelCase__				     =			is_training
														UpperCamelCase__				     =			use_labels
														UpperCamelCase__				     =			vocab_size
														UpperCamelCase__				     =			hidden_size
														UpperCamelCase__				     =			num_hidden_layers
														UpperCamelCase__				     =			num_attention_heads
														UpperCamelCase__				     =			intermediate_size

														UpperCamelCase__				     =			hidden_dropout_prob
														UpperCamelCase__				     =			attention_probs_dropout_prob
														UpperCamelCase__				     =			max_position_embeddings
														UpperCamelCase__				     =			eos_token_id
														UpperCamelCase__				     =			pad_token_id
														UpperCamelCase__				     =			bos_token_id






							def 				_lowerCamelCase		(						self  ):
														UpperCamelCase__				     =			ids_tensor([self.batch_size, self.seq_length - 1]  , self.vocab_size  )
														UpperCamelCase__				     =			tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size  )  , 1  )
														UpperCamelCase__				     =			tf.concat([input_ids, eos_tensor]  , axis=1  )

														UpperCamelCase__				     =			ids_tensor([self.batch_size, self.seq_length]  , self.vocab_size  )

														UpperCamelCase__				     =			self.config_cls(
														    vocab_size=self.vocab_size  , d_model=self.hidden_size  , encoder_layers=self.num_hidden_layers  , decoder_layers=self.num_hidden_layers  , encoder_attention_heads=self.num_attention_heads  , decoder_attention_heads=self.num_attention_heads  , encoder_ffn_dim=self.intermediate_size  , decoder_ffn_dim=self.intermediate_size  , dropout=self.hidden_dropout_prob  , attention_dropout=self.attention_probs_dropout_prob  , max_position_embeddings=self.max_position_embeddings  , eos_token_ids=[2]  , bos_token_id=self.bos_token_id  , pad_token_id=self.pad_token_id  , decoder_start_token_id=self.pad_token_id  , **self.config_updates  , )
														UpperCamelCase__				     =			prepare_pegasus_inputs_dict(__lowerCAmelCase  , __lowerCAmelCase  , __lowerCAmelCase  )
														return config, inputs_dict







							def 				_lowerCamelCase		(						self  , __lowerCAmelCase  , __lowerCAmelCase  ):
														UpperCamelCase__				     =			TFPegasusModel(config=__lowerCAmelCase  ).get_decoder()
														UpperCamelCase__				     =			inputs_dict["""input_ids"""]

														UpperCamelCase__				     =			input_ids[:1, :]
														UpperCamelCase__				     =			inputs_dict["""attention_mask"""][:1, :]
														UpperCamelCase__				     =			inputs_dict["""head_mask"""]
														UpperCamelCase__				     =			1

														# first forward pass
														UpperCamelCase__				     =			model(__lowerCAmelCase  , attention_mask=__lowerCAmelCase  , head_mask=__lowerCAmelCase  , use_cache=__lowerCAmelCase  )

														UpperCamelCase__     ,     UpperCamelCase__				     =			outputs.to_tuple()

														# create hypothetical next token and extent to next_input_ids
														UpperCamelCase__				     =			ids_tensor((self.batch_size, 3)  , config.vocab_size  )
														UpperCamelCase__				     =			tf.cast(ids_tensor((self.batch_size, 3)  , 2  )  , tf.inta  )

														# append to next input_ids and
														UpperCamelCase__				     =			tf.concat([input_ids, next_tokens]  , axis=-1  )
														UpperCamelCase__				     =			tf.concat([attention_mask, next_attn_mask]  , axis=-1  )

														UpperCamelCase__				     =			model(__lowerCAmelCase  , attention_mask=__lowerCAmelCase  )[0]
														UpperCamelCase__				     =			model(__lowerCAmelCase  , attention_mask=__lowerCAmelCase  , past_key_values=__lowerCAmelCase  )[0]

														self.parent.assertEqual(next_tokens.shape[1]  , output_from_past.shape[1]  )

														# select random slice
														UpperCamelCase__				     =			int(ids_tensor((1,)  , output_from_past.shape[-1]  )  )
														UpperCamelCase__				     =			output_from_no_past[:, -3:, random_slice_idx]
														UpperCamelCase__				     =			output_from_past[:, :, random_slice_idx]

														# test that outputs are equal for slice
														tf.debugging.assert_near(__lowerCAmelCase  , __lowerCAmelCase  , rtol=1E-3  )






def     _UpperCamelCase     (a__   :List[str]   ,			a__   :Any   ,			a__   :str   ,			a__   :Optional[int]=None   ,			a__   :Union[str, Any]=None   ,			a__   :Optional[int]=None   ,			a__   :Optional[int]=None   ,			a__   :List[str]=None   ,			):






							"""simple docstring"""





							if attention_mask is None:
														UpperCamelCase__				     =			tf.cast(tf.math.not_equal(a__   ,			config.pad_token_id       )   ,			tf.inta       )
							if decoder_attention_mask is None:
														UpperCamelCase__				     =			tf.concat(
														    [
														        tf.ones(decoder_input_ids[:, :1].shape   ,			dtype=tf.inta       ),
														        tf.cast(tf.math.not_equal(decoder_input_ids[:, 1:]   ,			config.pad_token_id       )   ,			tf.inta       ),
														    ]   ,			axis=-1   ,			)
							if head_mask is None:
														UpperCamelCase__				     =			tf.ones((config.encoder_layers, config.encoder_attention_heads)       )
							if decoder_head_mask is None:
														UpperCamelCase__				     =			tf.ones((config.decoder_layers, config.decoder_attention_heads)       )
							if cross_attn_head_mask is None:
														UpperCamelCase__				     =			tf.ones((config.decoder_layers, config.decoder_attention_heads)       )
							return {
							    "input_ids": input_ids,
							    "decoder_input_ids": decoder_input_ids,
							    "attention_mask": attention_mask,
							    "decoder_attention_mask": decoder_attention_mask,
							    "head_mask": head_mask,
							    "decoder_head_mask": decoder_head_mask,
							    "cross_attn_head_mask": cross_attn_head_mask,
							}

@require_tf
class        __SCREAMING_SNAKE_CASE					(     _a   ,		_a   ,		unittest.TestCase       ):
							snake_case     :      Optional[int]						     =       (TFPegasusForConditionalGeneration, TFPegasusModel) if is_tf_available() else ()
							snake_case     :      Dict						     =       (TFPegasusForConditionalGeneration,) if is_tf_available() else ()
							snake_case     :      Optional[int]						     =       (
							    {
							        """conversational""": TFPegasusForConditionalGeneration,
							        """feature-extraction""": TFPegasusModel,
							        """summarization""": TFPegasusForConditionalGeneration,
							        """text2text-generation""": TFPegasusForConditionalGeneration,
							        """translation""": TFPegasusForConditionalGeneration,
							    }
							    if is_tf_available()
							    else {}
							)
							snake_case     :      Optional[Any]						     =       True
							snake_case     :      int						     =       False
							snake_case     :      int						     =       False






							def 				_lowerCamelCase		(						self  ):
														UpperCamelCase__				     =			TFPegasusModelTester(self  )
														UpperCamelCase__				     =			ConfigTester(self  , config_class=__lowerCAmelCase  )






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







							def 				_lowerCamelCase		(						self  ):
														UpperCamelCase__				     =			self.model_tester.prepare_config_and_inputs_for_common()
														self.model_tester.check_decoder_model_past_large_inputs(*__lowerCAmelCase  )






@require_sentencepiece
@require_tokenizers
@require_tf
class        __SCREAMING_SNAKE_CASE					(     unittest.TestCase       ):
							snake_case     :      str						     =       [
							    """ PG&E stated it scheduled the blackouts in response to forecasts for high winds amid dry conditions. The aim is to reduce the risk of wildfires. Nearly 800 thousand customers were scheduled to be affected by the shutoffs which were expected to last through at least midday tomorrow.""",
							    """ The London trio are up for best UK act and best album, as well as getting two nominations in the best song category.\"We got told like this morning 'Oh I think you're nominated'\", said Dappy.\"And I was like 'Oh yeah, which one?' And now we've got nominated for four awards. I mean, wow!\"Bandmate Fazer added: \"We thought it's best of us to come down and mingle with everyone and say hello to the cameras. And now we find we've got four nominations.\"The band have two shots at the best song prize, getting the nod for their Tynchy Stryder collaboration Number One, and single Strong Again.Their album Uncle B will also go up against records by the likes of Beyonce and Kanye West.N-Dubz picked up the best newcomer Mobo in 2007, but female member Tulisa said they wouldn't be too disappointed if they didn't win this time around.\"At the end of the day we're grateful to be where we are in our careers.\"If it don't happen then it don't happen - live to fight another day and keep on making albums and hits for the fans.\"Dappy also revealed they could be performing live several times on the night.The group will be doing Number One and also a possible rendition of the War Child single, I Got Soul.The charity song is a  re-working of The Killers' All These Things That I've Done and is set to feature artists like Chipmunk, Ironik and Pixie Lott.This year's Mobos will be held outside of London for the first time, in Glasgow on 30 September.N-Dubz said they were looking forward to performing for their Scottish fans and boasted about their recent shows north of the border.\"We just done Edinburgh the other day,\" said Dappy.\"We smashed up an N-Dubz show over there. We done Aberdeen about three or four months ago - we smashed up that show over there! Everywhere we go we smash it up!\" """,
							]
							snake_case     :      List[Any]						     =       [
							    """California's largest electricity provider has cut power to hundreds of thousands of customers in an effort to"""
							    """ reduce the risk of wildfires.""",
							    """N-Dubz have revealed they\'re \"grateful\" to have been nominated for four Mobo Awards.""",
							]  # differs slightly from pytorch, likely due to numerical differences in linear layers
							snake_case     :      Dict						     =       """google/pegasus-xsum"""






							@cached_property
							def 				_lowerCamelCase		(						self  ):
														return AutoTokenizer.from_pretrained(self.model_name  )






							@cached_property
							def 				_lowerCamelCase		(						self  ):
														UpperCamelCase__				     =			TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name  )
														return model






							def 				_lowerCamelCase		(						self  , **__lowerCAmelCase  ):
														UpperCamelCase__				     =			self.translate_src_text(**__lowerCAmelCase  )
														assert self.expected_text == generated_words






							def 				_lowerCamelCase		(						self  , **__lowerCAmelCase  ):
														UpperCamelCase__				     =			self.tokenizer(self.src_text  , **__lowerCAmelCase  , padding=__lowerCAmelCase  , return_tensors="""tf"""  )
														UpperCamelCase__				     =			self.model.generate(
														    model_inputs.input_ids  , attention_mask=model_inputs.attention_mask  , num_beams=2  , use_cache=__lowerCAmelCase  , )
														UpperCamelCase__				     =			self.tokenizer.batch_decode(generated_ids.numpy()  , skip_special_tokens=__lowerCAmelCase  )
														return generated_words







							@slow
							def 				_lowerCamelCase		(						self  ):
														self._assert_generated_batch_equal_expected()




