






'''simple docstring'''




import unittest

from transformers import (
    MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
    TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
    SummarizationPipeline,
    TFPreTrainedModel,
    pipeline,
)
from transformers.testing_utils import get_gpu_count, is_pipeline_test, require_tf, require_torch, slow, torch_device
from transformers.tokenization_utils import TruncationStrategy

from .test_pipelines_common import ANY


lowerCamelCase_							      =      -1 if torch_device == """cpu""" else 0

@is_pipeline_test
class      _UpperCAmelCase    (  unittest.TestCase		):
   snake_case					       =		MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
   snake_case					       =		TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING

   def 		lowerCAmelCase				(	self				:			List[str]   ,   __UpperCAmelCase				:			str   ,   __UpperCAmelCase				:			Dict   ,   __UpperCAmelCase				:			Tuple	):





         '''simple docstring'''





         _A      =			SummarizationPipeline(model=__SCREAMING_SNAKE_CASE   ,   tokenizer=__SCREAMING_SNAKE_CASE	)
         return summarizer, ["(CNN)The Palestinian Authority officially became", "Some other text"]

   def 		lowerCAmelCase				(	self				:			Dict   ,   __UpperCAmelCase				:			int   ,   __UpperCAmelCase				:			Dict	):





         '''simple docstring'''





         _A      =			summarizer.model

         _A      =			summarizer("(CNN)The Palestinian Authority officially became"	)
         self.assertEqual(__SCREAMING_SNAKE_CASE   ,   [{"summary_text": ANY(__SCREAMING_SNAKE_CASE	)}]	)

         _A      =			summarizer(
             "(CNN)The Palestinian Authority officially became "   ,   num_beams=2   ,   min_length=2   ,   max_length=5   ,   )
         self.assertEqual(__SCREAMING_SNAKE_CASE   ,   [{"summary_text": ANY(__SCREAMING_SNAKE_CASE	)}]	)

         # Some models (Switch Transformers, LED, T5, LongT5, etc) can handle long sequences.
         _A      =			[
             "SwitchTransformersConfig",
             "T5Config",
             "LongT5Config",
             "LEDConfig",
             "PegasusXConfig",
             "FSMTConfig",
             "M2M100Config",
             "ProphetNetConfig",  # positional embeddings up to a fixed maximum size (otherwise clamping the values)
         ]
         if model.config.__class__.__name__ not in model_can_handle_longer_seq:
               # Too long and exception is expected.
               # For TF models, if the weights are initialized in GPU context, we won't get expected index error from
               # the embedding layer.
               if not (
                   isinstance(__SCREAMING_SNAKE_CASE   ,   __SCREAMING_SNAKE_CASE	)
                   and get_gpu_count() > 0
                   and len(summarizer.model.trainable_weights	) > 0
               ):
                     with self.assertRaises(__SCREAMING_SNAKE_CASE	):
                           _A      =			summarizer("This " * 1000	)
         _A      =			summarizer("This " * 1000   ,   truncation=TruncationStrategy.ONLY_FIRST	)

   @require_torch
   def 		lowerCAmelCase				(	self				:			List[Any]	):





         '''simple docstring'''





         _A      =			pipeline(task="summarization"   ,   model="sshleifer/tiny-mbart"   ,   framework="pt"	)
         _A      =			summarizer("This is a small test"	)
         self.assertEqual(
             __SCREAMING_SNAKE_CASE   ,   [
                 {
                     "summary_text": "เข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไป"
                 }
             ]   ,   )

   @require_tf
   def 		lowerCAmelCase				(	self				:			Optional[Any]	):





         '''simple docstring'''





         _A      =			pipeline(task="summarization"   ,   model="sshleifer/tiny-mbart"   ,   framework="tf"	)
         _A      =			summarizer("This is a small test"	)
         self.assertEqual(
             __SCREAMING_SNAKE_CASE   ,   [
                 {
                     "summary_text": "เข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไป"
                 }
             ]   ,   )

   @require_torch
   @slow
   def 		lowerCAmelCase				(	self				:			Union[str, Any]	):





         '''simple docstring'''





         _A      =			pipeline(task="summarization"   ,   device=__SCREAMING_SNAKE_CASE	)
         _A      =			(
             " (CNN)The Palestinian Authority officially became the 123rd member of the International Criminal Court on"
             " Wednesday, a step that gives the court jurisdiction over alleged crimes in Palestinian territories. The"
             " formal accession was marked with a ceremony at The Hague, in the Netherlands, where the court is based."
             " The Palestinians signed the ICC's founding Rome Statute in January, when they also accepted its"
             " jurisdiction over alleged crimes committed \"in the occupied Palestinian territory, including East"
             " Jerusalem, since June 13, 2014.\" Later that month, the ICC opened a preliminary examination into the"
             " situation in Palestinian territories, paving the way for possible war crimes investigations against"
             " Israelis. As members of the court, Palestinians may be subject to counter-charges as well. Israel and"
             " the United States, neither of which is an ICC member, opposed the Palestinians' efforts to join the"
             " body. But Palestinian Foreign Minister Riad al-Malki, speaking at Wednesday's ceremony, said it was a"
             " move toward greater justice. \"As Palestine formally becomes a State Party to the Rome Statute today, the"
             " world is also a step closer to ending a long era of impunity and injustice,\" he said, according to an"
             " ICC news release. \"Indeed, today brings us closer to our shared goals of justice and peace.\" Judge"
             " Kuniko Ozaki, a vice president of the ICC, said acceding to the treaty was just the first step for the"
             " Palestinians. \"As the Rome Statute today enters into force for the State of Palestine, Palestine"
             " acquires all the rights as well as responsibilities that come with being a State Party to the Statute."
             " These are substantive commitments, which cannot be taken lightly,\" she said. Rights group Human Rights"
             " Watch welcomed the development. \"Governments seeking to penalize Palestine for joining the ICC should"
             " immediately end their pressure, and countries that support universal acceptance of the court's treaty"
             " should speak out to welcome its membership,\" said Balkees Jarrah, international justice counsel for the"
             " group. \"What's objectionable is the attempts to undermine international justice, not Palestine's"
             " decision to join a treaty to which over 100 countries around the world are members.\" In January, when"
             " the preliminary ICC examination was opened, Israeli Prime Minister Benjamin Netanyahu described it as an"
             " outrage, saying the court was overstepping its boundaries. The United States also said it \"strongly\""
             " disagreed with the court's decision. \"As we have said repeatedly, we do not believe that Palestine is a"
             " state and therefore we do not believe that it is eligible to join the ICC,\" the State Department said in"
             " a statement. It urged the warring sides to resolve their differences through direct negotiations. \"We"
             " will continue to oppose actions against Israel at the ICC as counterproductive to the cause of peace,\""
             " it said. But the ICC begs to differ with the definition of a state for its purposes and refers to the"
             " territories as \"Palestine.\" While a preliminary examination is not a formal investigation, it allows the"
             " court to review evidence and determine whether to investigate suspects on both sides. Prosecutor Fatou"
             " Bensouda said her office would \"conduct its analysis in full independence and impartiality.\" The war"
             " between Israel and Hamas militants in Gaza last summer left more than 2,000 people dead. The inquiry"
             " will include alleged war crimes committed since June. The International Criminal Court was set up in"
             " 2002 to prosecute genocide, crimes against humanity and war crimes. CNN's Vasco Cotovio, Kareem Khadder"
             " and Faith Karimi contributed to this report."
         )
         _A      =			(
             " The Palestinian Authority becomes the 123rd member of the International Criminal Court . The move gives"
             " the court jurisdiction over alleged crimes in Palestinian territories . Israel and the United States"
             " opposed the Palestinians' efforts to join the court . Rights group Human Rights Watch welcomes the move,"
             " says governments seeking to penalize Palestine should end pressure ."
         )
         _A      =			summarizer(__SCREAMING_SNAKE_CASE	)
         self.assertEqual(result[0]["summary_text"]   ,   __SCREAMING_SNAKE_CASE	)













'''simple docstring'''




from typing import List, Optional, Union

import numpy as np

from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import PaddingStrategy, TensorType, logging


lowerCamelCase_							      =      logging.get_logger(__name__)

class      _UpperCAmelCase    (  snake_case_		):




   """simple docstring"""

   snake_case					       =		['''input_values''', '''attention_mask''']

   def __init__(	self				:			int   ,   __UpperCAmelCase				:			Optional[int]=1   ,   __UpperCAmelCase				:			List[str]=16000   ,   __UpperCAmelCase				:			Optional[Any]=0.0   ,   __UpperCAmelCase				:			Union[str, Any]=False   ,   __UpperCAmelCase				:			Dict=True   ,   **__UpperCAmelCase				:			int   ,   ):





         '''simple docstring'''





         super().__init__(feature_size=__UpperCAmelCase   ,   sampling_rate=__UpperCAmelCase   ,   padding_value=__UpperCAmelCase   ,   **__UpperCAmelCase	)
         _A      =			return_attention_mask
         _A      =			do_normalize

   @staticmethod
   def 		lowerCAmelCase				(	__UpperCAmelCase				:			List[np.ndarray]   ,   __UpperCAmelCase				:			List[np.ndarray]   ,   __UpperCAmelCase				:			float = 0.0	):





         '''simple docstring'''





         if attention_mask is not None:
               _A      =			np.array(__UpperCAmelCase   ,   np.intaa	)
               _A      =			[]

               for vector, length in zip(__UpperCAmelCase   ,   attention_mask.sum(-1	)	):
                     _A      =			(vector - vector[:length].mean()) / np.sqrt(vector[:length].var() + 1E-7	)
                     if length < normed_slice.shape[0]:
                           _A      =			padding_value

                     normed_input_values.append(__UpperCAmelCase	)
         else:
               _A      =			[(x - x.mean()) / np.sqrt(x.var() + 1E-7	) for x in input_values]

         return normed_input_values

   def __call__(	self				:			Dict   ,   __UpperCAmelCase				:			Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]   ,   __UpperCAmelCase				:			Union[bool, str, PaddingStrategy] = False   ,   __UpperCAmelCase				:			Optional[int] = None   ,   __UpperCAmelCase				:			bool = False   ,   __UpperCAmelCase				:			Optional[int] = None   ,   __UpperCAmelCase				:			Optional[bool] = None   ,   __UpperCAmelCase				:			Optional[Union[str, TensorType]] = None   ,   __UpperCAmelCase				:			Optional[int] = None   ,   **__UpperCAmelCase				:			str   ,   ):





         '''simple docstring'''





         if sampling_rate is not None:
               if sampling_rate != self.sampling_rate:
                     raise ValueError(
                         f'''The model corresponding to this feature extractor: {self} was trained using a sampling rate of'''
                         f''' {self.sampling_rate}. Please make sure that the provided `raw_speech` input was sampled with'''
                         f''' {self.sampling_rate} and not {sampling_rate}.'''	)
         else:
               logger.warning(
                   "It is strongly recommended to pass the ``sampling_rate`` argument to this function. "
                   "Failing to do so can result in silent errors that might be hard to debug."	)

         _A      =			isinstance(__UpperCAmelCase   ,   np.ndarray	) and len(raw_speech.shape	) > 1
         if is_batched_numpy and len(raw_speech.shape	) > 2:
               raise ValueError(f'''Only mono-channel audio is supported for input to {self}'''	)
         _A      =			is_batched_numpy or (
             isinstance(__UpperCAmelCase   ,   (list, tuple)	) and (isinstance(raw_speech[0]   ,   (np.ndarray, tuple, list)	))
         )

         # always return batch
         if not is_batched:
               _A      =			[raw_speech]

         # convert into correct format for padding
         _A      =			BatchFeature({"input_values": raw_speech}	)

         _A      =			self.pad(
             __UpperCAmelCase   ,   padding=__UpperCAmelCase   ,   max_length=__UpperCAmelCase   ,   truncation=__UpperCAmelCase   ,   pad_to_multiple_of=__UpperCAmelCase   ,   return_attention_mask=__UpperCAmelCase   ,   )

         # convert input values to correct format
         _A      =			padded_inputs["input_values"]
         if not isinstance(input_values[0]   ,   np.ndarray	):
               _A      =			[np.asarray(__UpperCAmelCase   ,   dtype=np.floataa	) for array in input_values]
         elif (
             not isinstance(__UpperCAmelCase   ,   np.ndarray	)
             and isinstance(input_values[0]   ,   np.ndarray	)
             and input_values[0].dtype is np.dtype(np.floataa	)
         ):
               _A      =			[array.astype(np.floataa	) for array in input_values]
         elif isinstance(__UpperCAmelCase   ,   np.ndarray	) and input_values.dtype is np.dtype(np.floataa	):
               _A      =			input_values.astype(np.floataa	)

         # convert attention_mask to correct format
         _A      =			padded_inputs.get("attention_mask"	)
         if attention_mask is not None:
               _A      =			[np.asarray(__UpperCAmelCase   ,   dtype=np.intaa	) for array in attention_mask]

         # zero-mean and unit-variance normalization
         if self.do_normalize:
               _A      =			(
                   attention_mask
                   if self._get_padding_strategies(__UpperCAmelCase   ,   max_length=__UpperCAmelCase	) is not PaddingStrategy.DO_NOT_PAD
                   else None
               )
               _A      =			self.zero_mean_unit_var_norm(
                   padded_inputs["input_values"]   ,   attention_mask=__UpperCAmelCase   ,   padding_value=self.padding_value	)

         if return_tensors is not None:
               _A      =			padded_inputs.convert_to_tensors(__UpperCAmelCase	)

         return padded_inputs





