"""simple docstring"""






import inspect
import unittest
from typing import List

import numpy as np

from transformers import EfficientFormerConfig
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 (
           TFEfficientFormerForImageClassification,
           TFEfficientFormerForImageClassificationWithTeacher,
           TFEfficientFormerModel,
       )
       from transformers.models.efficientformer.modeling_tf_efficientformer import (
           TF_EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
       )


if is_vision_available():
       from PIL import Image

       from transformers import EfficientFormerImageProcessor





class 					_A		:

      def __init__(		self						,			__lowerCAmelCase						,			__lowerCAmelCase = 13						,			__lowerCAmelCase = 64						,			__lowerCAmelCase = 2						,			__lowerCAmelCase = 3						,			__lowerCAmelCase = 3						,			__lowerCAmelCase = True						,			__lowerCAmelCase = True						,			__lowerCAmelCase = 128						,			__lowerCAmelCase=[16, 32, 64, 128]						,			__lowerCAmelCase = 7						,			__lowerCAmelCase = 4						,			__lowerCAmelCase = 37						,			__lowerCAmelCase = "gelu"						,			__lowerCAmelCase = 0.1						,			__lowerCAmelCase = 0.1						,			__lowerCAmelCase = 10						,			__lowerCAmelCase = 0.0_2						,			__lowerCAmelCase = 2						,			__lowerCAmelCase = 1						,			__lowerCAmelCase = 128						,			__lowerCAmelCase = [2, 2, 2, 2]						,			__lowerCAmelCase = 2						,			__lowerCAmelCase = 2						,			):







            """simple docstring"""



            lowercase								=      parent
            lowercase								=      batch_size
            lowercase								=      image_size
            lowercase								=      patch_size
            lowercase								=      num_channels
            lowercase								=      is_training
            lowercase								=      use_labels
            lowercase								=      hidden_size
            lowercase								=      num_hidden_layers
            lowercase								=      num_attention_heads
            lowercase								=      intermediate_size
            lowercase								=      hidden_act
            lowercase								=      hidden_dropout_prob
            lowercase								=      attention_probs_dropout_prob
            lowercase								=      type_sequence_label_size
            lowercase								=      initializer_range
            lowercase								=      encoder_stride
            lowercase								=      num_attention_outputs
            lowercase								=      embed_dim
            lowercase								=      embed_dim + 1
            lowercase								=      resolution
            lowercase								=      depths
            lowercase								=      hidden_sizes
            lowercase								=      dim
            lowercase								=      mlp_expansion_ratio

      def 							A__			(		self		):







            """simple docstring"""



            lowercase								=      floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]		)

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

            lowercase								=      self.get_config()

            return config, pixel_values, labels

      def 							A__			(		self		):







            """simple docstring"""



            return EfficientFormerConfig(
                image_size=self.image_size						,			patch_size=self.patch_size						,			num_channels=self.num_channels						,			hidden_size=self.hidden_size						,			num_hidden_layers=self.num_hidden_layers						,			num_attention_heads=self.num_attention_heads						,			intermediate_size=self.intermediate_size						,			hidden_act=self.hidden_act						,			hidden_dropout_prob=self.hidden_dropout_prob						,			attention_probs_dropout_prob=self.attention_probs_dropout_prob						,			is_decoder=__lowerCAmelCase						,			initializer_range=self.initializer_range						,			encoder_stride=self.encoder_stride						,			resolution=self.resolution						,			depths=self.depths						,			hidden_sizes=self.hidden_sizes						,			dim=self.dim						,			mlp_expansion_ratio=self.mlp_expansion_ratio						,			)

      def 							A__			(		self						,			__lowerCAmelCase						,			__lowerCAmelCase						,			__lowerCAmelCase		):







            """simple docstring"""



            lowercase								=      TFEfficientFormerModel(config=__lowerCAmelCase		)
            lowercase								=      model(__lowerCAmelCase						,			training=__lowerCAmelCase		)
            self.parent.assertEqual(result.last_hidden_state.shape						,			(self.batch_size, self.seq_length, self.hidden_size)		)

      def 							A__			(		self						,			__lowerCAmelCase						,			__lowerCAmelCase						,			__lowerCAmelCase		):







            """simple docstring"""



            lowercase								=      self.type_sequence_label_size
            lowercase								=      TFEfficientFormerForImageClassification(__lowerCAmelCase		)
            lowercase								=      model(__lowerCAmelCase						,			labels=__lowerCAmelCase						,			training=__lowerCAmelCase		)
            self.parent.assertEqual(result.logits.shape						,			(self.batch_size, self.type_sequence_label_size)		)

            # test greyscale images
            lowercase								=      1
            lowercase								=      TFEfficientFormerForImageClassification(__lowerCAmelCase		)

            lowercase								=      floats_tensor([self.batch_size, 1, self.image_size, self.image_size]		)
            lowercase								=      model(__lowerCAmelCase						,			labels=__lowerCAmelCase		)
            self.parent.assertEqual(result.logits.shape						,			(self.batch_size, self.type_sequence_label_size)		)







      def 							A__			(		self		):







            """simple docstring"""



            lowercase								=      self.prepare_config_and_inputs()
            lowercase				,				lowercase				,				lowercase								=      config_and_inputs
            lowercase								=      {"""pixel_values""": pixel_values}
            return config, inputs_dict






@require_tf
class 					_A		(			lowerCAmelCase    ,       lowerCAmelCase    ,       unittest.TestCase      ):
      snake_case__     :  Any				    =      (
          (
              TFEfficientFormerModel,
              TFEfficientFormerForImageClassificationWithTeacher,
              TFEfficientFormerForImageClassification,
          )
          if is_tf_available()
          else ()
      )
      snake_case__     :  str				    =      (
          {
              'feature-extraction': TFEfficientFormerModel,
              'image-classification': (
                  TFEfficientFormerForImageClassification,
                  TFEfficientFormerForImageClassificationWithTeacher,
              ),
          }
          if is_tf_available()
          else {}
      )

      snake_case__     :  int				    =      False

      snake_case__     :  Tuple				    =      False
      snake_case__     :  List[Any]				    =      False
      snake_case__     :  str				    =      False
      snake_case__     :  str				    =      False

      def 							A__			(		self		):







            """simple docstring"""



            lowercase								=      TFEfficientFormerModelTester(self		)
            lowercase								=      ConfigTester(
                self						,			config_class=__lowerCAmelCase						,			has_text_modality=__lowerCAmelCase						,			hidden_size=37		)

      def 							A__			(		self		):







            """simple docstring"""



            self.config_tester.run_common_tests()

      @unittest.skip(reason="""EfficientFormer does not use inputs_embeds"""		)
      def 							A__			(		self		):







            """simple docstring"""



            pass

      @unittest.skip(reason="""EfficientFormer does not support input and output embeddings"""		)
      def 							A__			(		self		):







            """simple docstring"""



            pass

      def 							A__			(		self		):







            """simple docstring"""



            lowercase				,				lowercase								=      self.model_tester.prepare_config_and_inputs_for_common()

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

                  lowercase								=      ["""pixel_values"""]
                  self.assertListEqual(arg_names[:1]						,			__lowerCAmelCase		)

      def 							A__			(		self		):







            """simple docstring"""



            def check_hidden_states_output(__lowerCAmelCase						,			__lowerCAmelCase						,			__lowerCAmelCase		):
                  lowercase								=      model_class(__lowerCAmelCase		)

                  lowercase								=      model(**self._prepare_for_class(__lowerCAmelCase						,			__lowerCAmelCase		)						,			training=__lowerCAmelCase		)
                  lowercase								=      outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

                  lowercase								=      getattr(
                      self.model_tester						,			"""expected_num_hidden_layers"""						,			self.model_tester.num_hidden_layers + 1		)
                  self.assertEqual(len(__lowerCAmelCase		)						,			__lowerCAmelCase		)

                  if hasattr(self.model_tester						,			"""encoder_seq_length"""		):
                        lowercase								=      self.model_tester.encoder_seq_length
                        if hasattr(self.model_tester						,			"""chunk_length"""		) and self.model_tester.chunk_length > 1:
                              lowercase								=      seq_length * self.model_tester.chunk_length
                  else:
                        lowercase								=      self.model_tester.seq_length

                  self.assertListEqual(
                      list(hidden_states[-1].shape[-2:]		)						,			[seq_length, self.model_tester.hidden_size]						,			)

                  if config.is_encoder_decoder:
                        lowercase								=      outputs.decoder_hidden_states

                        self.asseretIsInstance(__lowerCAmelCase						,			(list, tuple)		)
                        self.assertEqual(len(__lowerCAmelCase		)						,			__lowerCAmelCase		)
                        lowercase								=      getattr(self.model_tester						,			"""seq_length"""						,			__lowerCAmelCase		)
                        lowercase								=      getattr(self.model_tester						,			"""decoder_seq_length"""						,			__lowerCAmelCase		)

                        self.assertListEqual(
                            list(hidden_states[-1].shape[-2:]		)						,			[decoder_seq_length, self.model_tester.hidden_size]						,			)

            lowercase				,				lowercase								=      self.model_tester.prepare_config_and_inputs_for_common()

            for model_class in self.all_model_classes:
                  lowercase								=      True
                  check_hidden_states_output(__lowerCAmelCase						,			__lowerCAmelCase						,			__lowerCAmelCase		)

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

                  check_hidden_states_output(__lowerCAmelCase						,			__lowerCAmelCase						,			__lowerCAmelCase		)

      def 							A__			(		self						,			__lowerCAmelCase						,			__lowerCAmelCase						,			__lowerCAmelCase=False		):







            """simple docstring"""



            lowercase								=      super()._prepare_for_class(__lowerCAmelCase						,			__lowerCAmelCase						,			return_labels=__lowerCAmelCase		)

            if return_labels:
                  if model_class.__name__ == "TFEfficientFormerForImageClassificationWithTeacher":
                        del inputs_dict["labels"]

            return inputs_dict

      def 							A__			(		self		):







            """simple docstring"""



            lowercase								=      self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_model(*__lowerCAmelCase		)

      @unittest.skip(reason="""EfficientFormer does not implement masked image modeling yet"""		)
      def 							A__			(		self		):







            """simple docstring"""



            lowercase								=      self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_for_masked_image_modeling(*__lowerCAmelCase		)

      def 							A__			(		self		):







            """simple docstring"""



            lowercase								=      self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_for_image_classification(*__lowerCAmelCase		)

      @slow
      def 							A__			(		self		):







            """simple docstring"""



            for model_name in TF_EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                  lowercase								=      TFEfficientFormerModel.from_pretrained(__lowerCAmelCase		)
                  self.assertIsNotNone(__lowerCAmelCase		)

      def 							A__			(		self		):







            """simple docstring"""



            lowercase				,				lowercase								=      self.model_tester.prepare_config_and_inputs_for_common()
            lowercase								=      True

            lowercase								=      getattr(self.model_tester						,			"""seq_length"""						,			__lowerCAmelCase		)
            lowercase								=      getattr(self.model_tester						,			"""encoder_seq_length"""						,			__lowerCAmelCase		)
            lowercase								=      getattr(self.model_tester						,			"""key_length"""						,			__lowerCAmelCase		)
            lowercase								=      getattr(self.model_tester						,			"""chunk_length"""						,			__lowerCAmelCase		)

            if chunk_length is not None and hasattr(self.model_tester						,			"""num_hashes"""		):
                  lowercase								=      encoder_seq_length * self.model_tester.num_hashes

            for model_class in self.all_model_classes:
                  lowercase								=      True
                  lowercase								=      False
                  lowercase								=      True
                  lowercase								=      model_class(__lowerCAmelCase		)

                  lowercase								=      model(**self._prepare_for_class(__lowerCAmelCase						,			__lowerCAmelCase		)						,			training=__lowerCAmelCase		)
                  lowercase								=      outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
                  self.assertEqual(len(__lowerCAmelCase		)						,			self.model_tester.num_attention_outputs		)

                  # check that output_attentions also work using config
                  del inputs_dict["output_attentions"]
                  lowercase								=      True
                  lowercase								=      model_class(__lowerCAmelCase		)
                  lowercase								=      model(**self._prepare_for_class(__lowerCAmelCase						,			__lowerCAmelCase		)						,			training=__lowerCAmelCase		)

                  lowercase								=      outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
                  self.assertEqual(len(__lowerCAmelCase		)						,			self.model_tester.num_attention_outputs		)

                  if chunk_length is not None:
                        self.assertListEqual(
                            list(attentions[0].shape[-4:]		)						,			[self.model_tester.num_attention_heads, encoder_seq_length, chunk_length, encoder_key_length]						,			)
                  else:
                        self.assertListEqual(
                            list(attentions[0].shape[-3:]		)						,			[self.model_tester.num_attention_heads, encoder_seq_length, encoder_key_length]						,			)







      def 							A__			(		self		):







            """simple docstring"""



            lowercase				,				lowercase								=      self.model_tester.prepare_config_and_inputs_for_common()

            for model_class in self.all_model_classes:
                  # Prepare our model
                  lowercase								=      model_class(__lowerCAmelCase		)
                  # These are maximally general inputs for the model, with multiple None dimensions
                  # Hopefully this will catch any conditionals that fail for flexible shapes
                  lowercase								=      {
                      key: tf.keras.Input(shape=val.shape[1:]						,			dtype=val.dtype						,			name=__lowerCAmelCase		)
                      for key, val in model.input_signature.items()
                      if key in model.dummy_inputs
                  }
                  lowercase								=      model(__lowerCAmelCase		)
                  self.assertTrue(outputs_dict is not None		)






def      UpperCAmelCase__			(							)					->       Dict:
      '''simple docstring'''


      lowercase								=      Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""						)
      return image






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

      @cached_property
      def 							A__			(		self		):







            """simple docstring"""



            return (
                EfficientFormerImageProcessor.from_pretrained("""snap-research/efficientformer-l1-300"""		)
                if is_vision_available()
                else None
            )

      @slow
      def 							A__			(		self		):







            """simple docstring"""



            lowercase								=      TFEfficientFormerForImageClassification.from_pretrained("""snap-research/efficientformer-l1-300"""		)
            lowercase								=      self.default_image_processor
            lowercase								=      prepare_img()
            lowercase								=      image_processor(images=__lowerCAmelCase						,			return_tensors="""tf"""		)
            # forward pass
            lowercase								=      model(**__lowerCAmelCase						,			training=__lowerCAmelCase		)
            # verify the logits
            lowercase								=      tf.TensorShape((1, 1000)		)
            self.assertEqual(outputs.logits.shape						,			__lowerCAmelCase		)
            lowercase								=      tf.constant([-0.0_5_5_5, 0.4_8_2_5, -0.0_8_5_2]		)
            self.assertTrue(np.allclose(outputs.logits[0, :3]						,			__lowerCAmelCase						,			atol=1E-4		)		)







      @slow
      def 							A__			(		self		):







            """simple docstring"""



            lowercase								=      TFEfficientFormerForImageClassificationWithTeacher.from_pretrained(
                """snap-research/efficientformer-l1-300"""		)
            lowercase								=      self.default_image_processor
            lowercase								=      prepare_img()
            lowercase								=      image_processor(images=__lowerCAmelCase						,			return_tensors="""tf"""		)
            # forward pass
            lowercase								=      model(**__lowerCAmelCase						,			training=__lowerCAmelCase		)
            # verify the logits
            lowercase								=      tf.TensorShape((1, 1000)		)
            self.assertEqual(outputs.logits.shape						,			__lowerCAmelCase		)
            lowercase								=      tf.constant([-0.1_3_1_2, 0.4_3_5_3, -1.0_4_9_9]		)
            self.assertTrue(np.allclose(outputs.logits[0, :3]						,			__lowerCAmelCase						,			atol=1E-4		)		)




"""simple docstring"""






# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from ..models.auto import AutoModelForSeqaSeqLM, AutoTokenizer
from .base import PipelineTool





class 					_A		(			lowerCAmelCase      ):
      snake_case__     :  List[Any]				    =      'philschmid/bart-large-cnn-samsum'
      snake_case__     :  int				    =      (
          'This is a tool that summarizes an English text. It takes an input `text` containing the text to summarize, '
          'and returns a summary of the text.'
      )
      snake_case__     :  Tuple				    =      'summarizer'
      snake_case__     :  int				    =      AutoTokenizer
      snake_case__     :  Any				    =      AutoModelForSeqaSeqLM

      snake_case__     :  Optional[int]				    =      ['text']
      snake_case__     :  Optional[int]				    =      ['text']

      def 							A__			(		self						,			__lowerCAmelCase		):







            """simple docstring"""



            return self.pre_processor(__lowerCAmelCase						,			return_tensors="""pt"""						,			truncation=__lowerCAmelCase		)

      def 							A__			(		self						,			__lowerCAmelCase		):







            """simple docstring"""



            return self.model.generate(**__lowerCAmelCase		)[0]







      def 							A__			(		self						,			__lowerCAmelCase		):







            """simple docstring"""



            return self.pre_processor.decode(__lowerCAmelCase						,			skip_special_tokens=__lowerCAmelCase						,			clean_up_tokenization_spaces=__lowerCAmelCase		)



