




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 					__lowerCamelCase			:




    """simple docstring"""

    def __init__(			self	:	Tuple  ,      SCREAMING_SNAKE_CASE__	:	str  ,      SCREAMING_SNAKE_CASE__	:	int = 13  ,      SCREAMING_SNAKE_CASE__	:	int = 64  ,      SCREAMING_SNAKE_CASE__	:	int = 2  ,      SCREAMING_SNAKE_CASE__	:	int = 3  ,      SCREAMING_SNAKE_CASE__	:	int = 3  ,      SCREAMING_SNAKE_CASE__	:	bool = True  ,      SCREAMING_SNAKE_CASE__	:	bool = True  ,      SCREAMING_SNAKE_CASE__	:	int = 128  ,      SCREAMING_SNAKE_CASE__	:	int=[16, 32, 64, 128]  ,      SCREAMING_SNAKE_CASE__	:	int = 7  ,      SCREAMING_SNAKE_CASE__	:	int = 4  ,      SCREAMING_SNAKE_CASE__	:	int = 37  ,      SCREAMING_SNAKE_CASE__	:	str = "gelu"  ,      SCREAMING_SNAKE_CASE__	:	float = 0.1  ,      SCREAMING_SNAKE_CASE__	:	float = 0.1  ,      SCREAMING_SNAKE_CASE__	:	int = 10  ,      SCREAMING_SNAKE_CASE__	:	float = 0.02  ,      SCREAMING_SNAKE_CASE__	:	int = 2  ,      SCREAMING_SNAKE_CASE__	:	int = 1  ,      SCREAMING_SNAKE_CASE__	:	int = 128  ,      SCREAMING_SNAKE_CASE__	:	List[int] = [2, 2, 2, 2]  ,      SCREAMING_SNAKE_CASE__	:	int = 2  ,      SCREAMING_SNAKE_CASE__	:	int = 2  ,      ) ->			Tuple:
         lowerCAmelCase__            =					parent
         lowerCAmelCase__            =					batch_size
         lowerCAmelCase__            =					image_size
         lowerCAmelCase__            =					patch_size
         lowerCAmelCase__            =					num_channels
         lowerCAmelCase__            =					is_training
         lowerCAmelCase__            =					use_labels
         lowerCAmelCase__            =					hidden_size
         lowerCAmelCase__            =					num_hidden_layers
         lowerCAmelCase__            =					num_attention_heads
         lowerCAmelCase__            =					intermediate_size
         lowerCAmelCase__            =					hidden_act
         lowerCAmelCase__            =					hidden_dropout_prob
         lowerCAmelCase__            =					attention_probs_dropout_prob
         lowerCAmelCase__            =					type_sequence_label_size
         lowerCAmelCase__            =					initializer_range
         lowerCAmelCase__            =					encoder_stride
         lowerCAmelCase__            =					num_attention_outputs
         lowerCAmelCase__            =					embed_dim
         lowerCAmelCase__            =					embed_dim + 1
         lowerCAmelCase__            =					resolution
         lowerCAmelCase__            =					depths
         lowerCAmelCase__            =					hidden_sizes
         lowerCAmelCase__            =					dim
         lowerCAmelCase__            =					mlp_expansion_ratio


    def   a (			self	:	int  ) ->			Optional[Any]:
         lowerCAmelCase__            =					floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]  )

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

         lowerCAmelCase__            =					self.get_config()

         return config, pixel_values, labels


    def   a (			self	:	int  ) ->			List[str]:
         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=SCREAMING_SNAKE_CASE__  ,      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	:	Any  ,      SCREAMING_SNAKE_CASE__	:	Optional[Any]  ,      SCREAMING_SNAKE_CASE__	:	int  ,      SCREAMING_SNAKE_CASE__	:	Union[str, Any]  ) ->			Any:
         lowerCAmelCase__            =					TFEfficientFormerModel(config=SCREAMING_SNAKE_CASE__  )
         lowerCAmelCase__            =					model(SCREAMING_SNAKE_CASE__  ,      training=SCREAMING_SNAKE_CASE__  )
         self.parent.assertEqual(result.last_hidden_state.shape  ,      (self.batch_size, self.seq_length, self.hidden_size)  )


    def   a (			self	:	Tuple  ,      SCREAMING_SNAKE_CASE__	:	Union[str, Any]  ,      SCREAMING_SNAKE_CASE__	:	Union[str, Any]  ,      SCREAMING_SNAKE_CASE__	:	Optional[int]  ) ->			int:
         lowerCAmelCase__            =					self.type_sequence_label_size
         lowerCAmelCase__            =					TFEfficientFormerForImageClassification(SCREAMING_SNAKE_CASE__  )
         lowerCAmelCase__            =					model(SCREAMING_SNAKE_CASE__  ,      labels=SCREAMING_SNAKE_CASE__  ,      training=SCREAMING_SNAKE_CASE__  )
         self.parent.assertEqual(result.logits.shape  ,      (self.batch_size, self.type_sequence_label_size)  )

         # test greyscale images
         lowerCAmelCase__            =					1
         lowerCAmelCase__            =					TFEfficientFormerForImageClassification(SCREAMING_SNAKE_CASE__  )

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


    def   a (			self	:	Dict  ) ->			Optional[Any]:
         lowerCAmelCase__            =					self.prepare_config_and_inputs()
         lowerCAmelCase__      ,						lowerCAmelCase__      ,						lowerCAmelCase__            =					config_and_inputs
         lowerCAmelCase__            =					{"pixel_values": pixel_values}
         return config, inputs_dict






@require_tf
class 					__lowerCamelCase			(       UpperCamelCase__      ,							UpperCamelCase__      ,							unittest.TestCase       ):




    """simple docstring"""

    snake_case__   = (
        (
            TFEfficientFormerModel,
            TFEfficientFormerForImageClassificationWithTeacher,
            TFEfficientFormerForImageClassification,
        )
        if is_tf_available()
        else ()
    )
    snake_case__   = (
        {
            "feature-extraction": TFEfficientFormerModel,
            "image-classification": (
                TFEfficientFormerForImageClassification,
                TFEfficientFormerForImageClassificationWithTeacher,
            ),
        }
        if is_tf_available()
        else {}
    )

    snake_case__   = False

    snake_case__   = False
    snake_case__   = False
    snake_case__   = False
    snake_case__   = False


    def   a (			self	:	List[str]  ) ->			int:
         lowerCAmelCase__            =					TFEfficientFormerModelTester(self  )
         lowerCAmelCase__            =					ConfigTester(
             self  ,      config_class=SCREAMING_SNAKE_CASE__  ,      has_text_modality=SCREAMING_SNAKE_CASE__  ,      hidden_size=37  )


    def   a (			self	:	Any  ) ->			Union[str, Any]:
         self.config_tester.run_common_tests()


    @unittest.skip(reason="EfficientFormer does not use inputs_embeds"  )
    def   a (			self	:	str  ) ->			Dict:
         pass


    @unittest.skip(reason="EfficientFormer does not support input and output embeddings"  )
    def   a (			self	:	Optional[Any]  ) ->			List[str]:
         pass


    def   a (			self	:	Union[str, Any]  ) ->			Union[str, Any]:
         lowerCAmelCase__      ,						lowerCAmelCase__            =					self.model_tester.prepare_config_and_inputs_for_common()

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

              lowerCAmelCase__            =					["pixel_values"]
              self.assertListEqual(arg_names[:1]  ,      SCREAMING_SNAKE_CASE__  )


    def   a (			self	:	List[str]  ) ->			Union[str, Any]:
         def check_hidden_states_output(SCREAMING_SNAKE_CASE__	:	Optional[int]  ,      SCREAMING_SNAKE_CASE__	:	List[Any]  ,      SCREAMING_SNAKE_CASE__	:	List[str]  ):
              lowerCAmelCase__            =					model_class(SCREAMING_SNAKE_CASE__  )

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

              lowerCAmelCase__            =					getattr(
                  self.model_tester  ,      "expected_num_hidden_layers"  ,      self.model_tester.num_hidden_layers + 1  )
              self.assertEqual(len(SCREAMING_SNAKE_CASE__  )  ,      SCREAMING_SNAKE_CASE__  )

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

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

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

         lowerCAmelCase__      ,						lowerCAmelCase__            =					self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in self.all_model_classes:
              lowerCAmelCase__            =					True
              check_hidden_states_output(SCREAMING_SNAKE_CASE__  ,      SCREAMING_SNAKE_CASE__  ,      SCREAMING_SNAKE_CASE__  )

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

              check_hidden_states_output(SCREAMING_SNAKE_CASE__  ,      SCREAMING_SNAKE_CASE__  ,      SCREAMING_SNAKE_CASE__  )


    def   a (			self	:	Optional[Any]  ,      SCREAMING_SNAKE_CASE__	:	int  ,      SCREAMING_SNAKE_CASE__	:	Union[str, Any]  ,      SCREAMING_SNAKE_CASE__	:	Optional[int]=False  ) ->			Tuple:
         lowerCAmelCase__            =					super()._prepare_for_class(SCREAMING_SNAKE_CASE__  ,      SCREAMING_SNAKE_CASE__  ,      return_labels=SCREAMING_SNAKE_CASE__  )

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

         return inputs_dict


    def   a (			self	:	List[Any]  ) ->			List[Any]:
         lowerCAmelCase__            =					self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*SCREAMING_SNAKE_CASE__  )


    @unittest.skip(reason="EfficientFormer does not implement masked image modeling yet"  )
    def   a (			self	:	List[str]  ) ->			Union[str, Any]:
         lowerCAmelCase__            =					self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_masked_image_modeling(*SCREAMING_SNAKE_CASE__  )


    def   a (			self	:	str  ) ->			Any:
         lowerCAmelCase__            =					self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_image_classification(*SCREAMING_SNAKE_CASE__  )


    @slow
    def   a (			self	:	Optional[Any]  ) ->			str:
         for model_name in TF_EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
              lowerCAmelCase__            =					TFEfficientFormerModel.from_pretrained(SCREAMING_SNAKE_CASE__  )
              self.assertIsNotNone(SCREAMING_SNAKE_CASE__  )


    def   a (			self	:	Optional[int]  ) ->			Optional[Any]:
         lowerCAmelCase__      ,						lowerCAmelCase__            =					self.model_tester.prepare_config_and_inputs_for_common()
         lowerCAmelCase__            =					True

         lowerCAmelCase__            =					getattr(self.model_tester  ,      "seq_length"  ,      SCREAMING_SNAKE_CASE__  )
         lowerCAmelCase__            =					getattr(self.model_tester  ,      "encoder_seq_length"  ,      SCREAMING_SNAKE_CASE__  )
         lowerCAmelCase__            =					getattr(self.model_tester  ,      "key_length"  ,      SCREAMING_SNAKE_CASE__  )
         lowerCAmelCase__            =					getattr(self.model_tester  ,      "chunk_length"  ,      SCREAMING_SNAKE_CASE__  )

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

         for model_class in self.all_model_classes:
              lowerCAmelCase__            =					True
              lowerCAmelCase__            =					False
              lowerCAmelCase__            =					True
              lowerCAmelCase__            =					model_class(SCREAMING_SNAKE_CASE__  )

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

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

              lowerCAmelCase__            =					outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
              self.assertEqual(len(SCREAMING_SNAKE_CASE__  )  ,      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	:	Union[str, Any]  ) ->			Any:
         # We use a simplified version of this test for EfficientFormer because it requires training=False
         # and Keras refuses to let us force that during functional construction
         lowerCAmelCase__      ,						lowerCAmelCase__            =					self.model_tester.prepare_config_and_inputs_for_common()

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






def 				_A  (		):
     """simple docstring"""

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






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




    """simple docstring"""

    @cached_property
    def   a (			self	:	Dict  ) ->			Dict:
         return (
             EfficientFormerImageProcessor.from_pretrained("snap-research/efficientformer-l1-300"  )
             if is_vision_available()
             else None
         )


    @slow
    def   a (			self	:	str  ) ->			Optional[Any]:
         lowerCAmelCase__            =					TFEfficientFormerForImageClassification.from_pretrained("snap-research/efficientformer-l1-300"  )
         lowerCAmelCase__            =					self.default_image_processor
         lowerCAmelCase__            =					prepare_img()
         lowerCAmelCase__            =					image_processor(images=SCREAMING_SNAKE_CASE__  ,      return_tensors="tf"  )
         # forward pass
         lowerCAmelCase__            =					model(**SCREAMING_SNAKE_CASE__  ,      training=SCREAMING_SNAKE_CASE__  )
         # verify the logits
         lowerCAmelCase__            =					tf.TensorShape((1, 1_000)  )
         self.assertEqual(outputs.logits.shape  ,      SCREAMING_SNAKE_CASE__  )
         lowerCAmelCase__            =					tf.constant([-0.0_555, 0.4_825, -0.0_852]  )
         self.assertTrue(np.allclose(outputs.logits[0, :3]  ,      SCREAMING_SNAKE_CASE__  ,      atol=1e-4  )  )


    @slow
    def   a (			self	:	Tuple  ) ->			Union[str, Any]:
         lowerCAmelCase__            =					TFEfficientFormerForImageClassificationWithTeacher.from_pretrained(
             "snap-research/efficientformer-l1-300"  )
         lowerCAmelCase__            =					self.default_image_processor
         lowerCAmelCase__            =					prepare_img()
         lowerCAmelCase__            =					image_processor(images=SCREAMING_SNAKE_CASE__  ,      return_tensors="tf"  )
         # forward pass
         lowerCAmelCase__            =					model(**SCREAMING_SNAKE_CASE__  ,      training=SCREAMING_SNAKE_CASE__  )
         # verify the logits
         lowerCAmelCase__            =					tf.TensorShape((1, 1_000)  )
         self.assertEqual(outputs.logits.shape  ,      SCREAMING_SNAKE_CASE__  )
         lowerCAmelCase__            =					tf.constant([-0.1_312, 0.4_353, -1.0_499]  )
         self.assertTrue(np.allclose(outputs.logits[0, :3]  ,      SCREAMING_SNAKE_CASE__  ,      atol=1e-4  )  )




from __future__ import annotations

import requests

lowerCAmelCase       		=    set(
    """approved_at_utc approved_by author_flair_background_color
author_flair_css_class author_flair_richtext author_flair_template_id author_fullname
author_premium can_mod_post category clicked content_categories created_utc downs
edited gilded gildings hidden hide_score is_created_from_ads_ui is_meta
is_original_content is_reddit_media_domain is_video link_flair_css_class
link_flair_richtext link_flair_text link_flair_text_color media_embed mod_reason_title
name permalink pwls quarantine saved score secure_media secure_media_embed selftext
subreddit subreddit_name_prefixed subreddit_type thumbnail title top_awarded_type
total_awards_received ups upvote_ratio url user_reports""".split()
)




def     __SCREAMING_SNAKE_CASE							(       lowercase_      ,       lowercase_ = 1      ,       lowercase_ = "new"      ,       lowercase_ = None      )		->     dict:




						'''simple docstring'''







						__UpperCAmelCase			:							Tuple	    =	wanted_data or []
						if invalid_search_terms := ", ".join(sorted(set(lowercase_      ) - valid_terms      )      ):
												__UpperCAmelCase			:							List[Any]	    =	f"Invalid search term: {invalid_search_terms}"
												raise ValueError(lowercase_      )
						__UpperCAmelCase			:							Optional[int]	    =	requests.get(
						    f"https://reddit.com/r/{subreddit}/{age}.json?limit={limit}"      ,       headers={'''User-agent''': '''A random string'''}      ,       )
						if response.status_code == 429:
												raise requests.HTTPError

						__UpperCAmelCase			:							List[str]	    =	response.json()
						if not wanted_data:
												return {id_: data["data"]["children"][id_] for id_ in range(lowercase_      )}

						__UpperCAmelCase			:							List[Any]	    =	{}
						for id_ in range(lowercase_      ):
												__UpperCAmelCase			:							str	    =	{
												    item: data['''data''']['''children'''][id_]['''data'''][item] for item in wanted_data
												}
						return data_dict


if __name__ == "__main__":
				# If you get Error 429, that means you are rate limited.Try after some time
				print(get_subreddit_data("""learnpython""", wanted_data=["""title""", """url""", """selftext"""]))

