

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


UpperCamelCase__					: Dict								=      -1 if torch_device == "cpu" else 0



@is_pipeline_test
class  __snake_case					(      unittest.TestCase    ):
  __lowerCAmelCase       :		Dict  	=					MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
  __lowerCAmelCase       :		List[Any]  	=					TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING

  def   lowerCAmelCase__       (   self  ,						_A  ,						_A  ,						_A):
      SCREAMING_SNAKE_CASE_													=  SummarizationPipeline(model=_A  ,						tokenizer=_A)
      return summarizer, ["(CNN)The Palestinian Authority officially became", "Some other text"]

  def   lowerCAmelCase__       (   self  ,						_A  ,						_A):
      SCREAMING_SNAKE_CASE_													=  summarizer.model

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

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

      # Some models (Switch Transformers, LED, T5, LongT5, etc) can handle long sequences.
      SCREAMING_SNAKE_CASE_													=  [
          """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(_A  ,						_A)
              and get_gpu_count() > 0
              and len(summarizer.model.trainable_weights) > 0
          ):
              with self.assertRaises(_A):
                  SCREAMING_SNAKE_CASE_													=  summarizer('This ' * 1000)
      SCREAMING_SNAKE_CASE_													=  summarizer('This ' * 1000  ,						truncation=TruncationStrategy.ONLY_FIRST)

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

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


  @require_torch
  @slow
  def   lowerCAmelCase__       (   self):
      SCREAMING_SNAKE_CASE_													=  pipeline(task='summarization'  ,						device=_A)
      SCREAMING_SNAKE_CASE_													=  (
          """ (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."""
      )
      SCREAMING_SNAKE_CASE_													=  (
          """ 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 ."""
      )
      SCREAMING_SNAKE_CASE_													=  summarizer(_A)
      self.assertEqual(result[0]['summary_text']  ,						_A)





from __future__ import annotations

import inspect
import unittest
from typing import List, Tuple

from transformers import ConvNextConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available

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


if is_tf_available():
       import tensorflow as tf

       from transformers import TFConvNextForImageClassification, TFConvNextModel


if is_vision_available():
       from PIL import Image

       from transformers import ConvNextImageProcessor



class  __snake_case					:

  def __init__(   self  ,						_A  ,						_A=13  ,						_A=32  ,						_A=3  ,						_A=4  ,						_A=[10, 20, 30, 40]  ,						_A=[2, 2, 3, 2]  ,						_A=True  ,						_A=True  ,						_A=37  ,						_A="gelu"  ,						_A=10  ,						_A=0.0_2  ,						_A=3  ,						_A=None  ,						):
      SCREAMING_SNAKE_CASE_													=  parent
      SCREAMING_SNAKE_CASE_													=  batch_size
      SCREAMING_SNAKE_CASE_													=  image_size
      SCREAMING_SNAKE_CASE_													=  num_channels
      SCREAMING_SNAKE_CASE_													=  num_stages
      SCREAMING_SNAKE_CASE_													=  hidden_sizes
      SCREAMING_SNAKE_CASE_													=  depths
      SCREAMING_SNAKE_CASE_													=  is_training
      SCREAMING_SNAKE_CASE_													=  use_labels
      SCREAMING_SNAKE_CASE_													=  intermediate_size
      SCREAMING_SNAKE_CASE_													=  hidden_act
      SCREAMING_SNAKE_CASE_													=  type_sequence_label_size
      SCREAMING_SNAKE_CASE_													=  initializer_range
      SCREAMING_SNAKE_CASE_													=  scope

  def   lowerCAmelCase__       (   self):
      SCREAMING_SNAKE_CASE_													=  floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])

      SCREAMING_SNAKE_CASE_													=  None
      if self.use_labels:
          SCREAMING_SNAKE_CASE_													=  ids_tensor([self.batch_size]  ,						self.type_sequence_label_size)

      SCREAMING_SNAKE_CASE_													=  self.get_config()

      return config, pixel_values, labels

  def   lowerCAmelCase__       (   self):
      return ConvNextConfig(
          num_channels=self.num_channels  ,						hidden_sizes=self.hidden_sizes  ,						depths=self.depths  ,						num_stages=self.num_stages  ,						hidden_act=self.hidden_act  ,						is_decoder=_A  ,						initializer_range=self.initializer_range  ,						)

  def   lowerCAmelCase__       (   self  ,						_A  ,						_A  ,						_A):
      SCREAMING_SNAKE_CASE_													=  TFConvNextModel(config=_A)
      SCREAMING_SNAKE_CASE_													=  model(_A  ,						training=_A)
      # expected last hidden states: B, C, H // 32, W // 32
      self.parent.assertEqual(
          result.last_hidden_state.shape  ,						(self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32)  ,						)

  def   lowerCAmelCase__       (   self  ,						_A  ,						_A  ,						_A):
      SCREAMING_SNAKE_CASE_													=  self.type_sequence_label_size
      SCREAMING_SNAKE_CASE_													=  TFConvNextForImageClassification(_A)
      SCREAMING_SNAKE_CASE_													=  model(_A  ,						labels=_A  ,						training=_A)
      self.parent.assertEqual(result.logits.shape  ,						(self.batch_size, self.type_sequence_label_size))


  def   lowerCAmelCase__       (   self):
      SCREAMING_SNAKE_CASE_													=  self.prepare_config_and_inputs()
      SCREAMING_SNAKE_CASE_							,     SCREAMING_SNAKE_CASE_							,     SCREAMING_SNAKE_CASE_													=  config_and_inputs
      SCREAMING_SNAKE_CASE_													=  {'pixel_values': pixel_values}
      return config, inputs_dict






@require_tf
class  __snake_case					(      lowerCAmelCase__       ,	lowerCAmelCase__       ,	unittest.TestCase    ):
  __lowerCAmelCase       :		str  	=					(TFConvNextModel, TFConvNextForImageClassification) if is_tf_available() else ()
  __lowerCAmelCase       :		List[str]  	=					(
      {'feature-extraction': TFConvNextModel, 'image-classification': TFConvNextForImageClassification}
      if is_tf_available()
      else {}
  )

  __lowerCAmelCase       :		Union[str, Any]  	=					False
  __lowerCAmelCase       :		Dict  	=					False
  __lowerCAmelCase       :		Optional[int]  	=					False
  __lowerCAmelCase       :		Optional[Any]  	=					False
  __lowerCAmelCase       :		List[Any]  	=					False

  def   lowerCAmelCase__       (   self):
      SCREAMING_SNAKE_CASE_													=  TFConvNextModelTester(self)
      SCREAMING_SNAKE_CASE_													=  ConfigTester(
          self  ,						config_class=_A  ,						has_text_modality=_A  ,						hidden_size=37  ,						)

  @unittest.skip(reason='ConvNext does not use inputs_embeds')
  def   lowerCAmelCase__       (   self):
      pass

  @unittest.skipIf(
      not is_tf_available() or len(tf.config.list_physical_devices('GPU')) == 0  ,						reason='TF does not support backprop for grouped convolutions on CPU.'  ,						)
  @slow
  def   lowerCAmelCase__       (   self):
      super().test_keras_fit()

  @unittest.skip(reason='ConvNext does not support input and output embeddings')
  def   lowerCAmelCase__       (   self):
      pass

  def   lowerCAmelCase__       (   self):
      SCREAMING_SNAKE_CASE_							,     SCREAMING_SNAKE_CASE_													=  self.model_tester.prepare_config_and_inputs_for_common()

      for model_class in self.all_model_classes:
          SCREAMING_SNAKE_CASE_													=  model_class(_A)
          SCREAMING_SNAKE_CASE_													=  inspect.signature(model.call)
          # signature.parameters is an OrderedDict => so arg_names order is deterministic
          SCREAMING_SNAKE_CASE_													=  [*signature.parameters.keys()]

          SCREAMING_SNAKE_CASE_													=  ['pixel_values']
          self.assertListEqual(arg_names[:1]  ,						_A)

  def   lowerCAmelCase__       (   self):
      SCREAMING_SNAKE_CASE_													=  self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_model(*_A)

  @unittest.skipIf(
      not is_tf_available() or len(tf.config.list_physical_devices('GPU')) == 0  ,						reason='TF does not support backprop for grouped convolutions on CPU.'  ,						)
  def   lowerCAmelCase__       (   self):
      super().test_dataset_conversion()

  def   lowerCAmelCase__       (   self):
      def check_hidden_states_output(_A  ,						_A  ,						_A):
          SCREAMING_SNAKE_CASE_													=  model_class(_A)

          SCREAMING_SNAKE_CASE_													=  model(**self._prepare_for_class(_A  ,						_A))
          SCREAMING_SNAKE_CASE_													=  outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

          SCREAMING_SNAKE_CASE_													=  self.model_tester.num_stages
          self.assertEqual(len(_A)  ,						expected_num_stages + 1)

          # ConvNext's feature maps are of shape (batch_size, num_channels, height, width)
          self.assertListEqual(
              list(hidden_states[0].shape[-2:])  ,						[self.model_tester.image_size // 4, self.model_tester.image_size // 4]  ,						)

      SCREAMING_SNAKE_CASE_							,     SCREAMING_SNAKE_CASE_													=  self.model_tester.prepare_config_and_inputs_for_common()

      for model_class in self.all_model_classes:
          SCREAMING_SNAKE_CASE_													=  True
          check_hidden_states_output(_A  ,						_A  ,						_A)

          # check that output_hidden_states also work using config
          del inputs_dict["output_hidden_states"]
          SCREAMING_SNAKE_CASE_													=  True

          check_hidden_states_output(_A  ,						_A  ,						_A)

  def   lowerCAmelCase__       (   self):
      SCREAMING_SNAKE_CASE_							,     SCREAMING_SNAKE_CASE_													=  self.model_tester.prepare_config_and_inputs_for_common()

      def check_equivalence(_A  ,						_A  ,						_A  ,						_A={}):
          SCREAMING_SNAKE_CASE_													=  model(_A  ,						return_dict=_A  ,						**_A)
          SCREAMING_SNAKE_CASE_													=  model(_A  ,						return_dict=_A  ,						**_A).to_tuple()

          def recursive_check(_A  ,						_A):
              if isinstance(_A  ,						(List, Tuple)):
                  for tuple_iterable_value, dict_iterable_value in zip(_A  ,						_A):
                      recursive_check(_A  ,						_A)
              elif tuple_object is None:
                  return
              else:
                  self.assertTrue(
                      all(tf.equal(_A  ,						_A))  ,						msg=(
                          'Tuple and dict output are not equal. Difference:'
                          f""" {tf.math.reduce_max(tf.abs(tuple_object - dict_object))}"""
                      )  ,						)

              recursive_check(_A  ,						_A)

      for model_class in self.all_model_classes:
          SCREAMING_SNAKE_CASE_													=  model_class(_A)

          SCREAMING_SNAKE_CASE_													=  self._prepare_for_class(_A  ,						_A)
          SCREAMING_SNAKE_CASE_													=  self._prepare_for_class(_A  ,						_A)
          check_equivalence(_A  ,						_A  ,						_A)

          SCREAMING_SNAKE_CASE_													=  self._prepare_for_class(_A  ,						_A  ,						return_labels=_A)
          SCREAMING_SNAKE_CASE_													=  self._prepare_for_class(_A  ,						_A  ,						return_labels=_A)
          check_equivalence(_A  ,						_A  ,						_A)

          SCREAMING_SNAKE_CASE_													=  self._prepare_for_class(_A  ,						_A)
          SCREAMING_SNAKE_CASE_													=  self._prepare_for_class(_A  ,						_A)
          check_equivalence(_A  ,						_A  ,						_A  ,						{'output_hidden_states': True})

          SCREAMING_SNAKE_CASE_													=  self._prepare_for_class(_A  ,						_A  ,						return_labels=_A)
          SCREAMING_SNAKE_CASE_													=  self._prepare_for_class(_A  ,						_A  ,						return_labels=_A)
          check_equivalence(_A  ,						_A  ,						_A  ,						{'output_hidden_states': True})

  def   lowerCAmelCase__       (   self):
      SCREAMING_SNAKE_CASE_													=  self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_image_classification(*_A)


  @slow
  def   lowerCAmelCase__       (   self):
      SCREAMING_SNAKE_CASE_													=  TFConvNextModel.from_pretrained('facebook/convnext-tiny-224')
      self.assertIsNotNone(_A)






def    _UpperCAmelCase    (					):





    """simple docstring"""


    SCREAMING_SNAKE_CASE_													=  Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'    )
    return image





@require_tf
@require_vision
class  __snake_case					(      unittest.TestCase    ):

  @cached_property
  def   lowerCAmelCase__       (   self):
      return ConvNextImageProcessor.from_pretrained('facebook/convnext-tiny-224') if is_vision_available() else None


  @slow
  def   lowerCAmelCase__       (   self):
      SCREAMING_SNAKE_CASE_													=  TFConvNextForImageClassification.from_pretrained('facebook/convnext-tiny-224')

      SCREAMING_SNAKE_CASE_													=  self.default_image_processor
      SCREAMING_SNAKE_CASE_													=  prepare_img()
      SCREAMING_SNAKE_CASE_													=  image_processor(images=_A  ,						return_tensors='tf')

      # forward pass
      SCREAMING_SNAKE_CASE_													=  model(**_A)

      # verify the logits
      SCREAMING_SNAKE_CASE_													=  tf.TensorShape((1, 1000))
      self.assertEqual(outputs.logits.shape  ,						_A)

      SCREAMING_SNAKE_CASE_													=  tf.constant([-0.0_2_6_0, -0.4_7_3_9, 0.1_9_1_1])

      tf.debugging.assert_near(outputs.logits[0, :3]  ,						_A  ,						atol=1E-4)


