
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        lowercase      :



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



		def __UpperCamelCase				(				self )						->      List[str]:
				"""simple docstring"""
				UpperCamelCase						=  floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )

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

				UpperCamelCase						=  self.get_config()

				return config, pixel_values, labels



		def __UpperCamelCase				(				self )						->      List[Any]:
				"""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=A_       ,   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 __UpperCamelCase				(				self       ,   A_       ,   A_       ,   A_ )						->      Tuple:
				"""simple docstring"""
				UpperCamelCase						=  TFEfficientFormerModel(config=A_ )
				UpperCamelCase						=  model(A_       ,   training=A_ )
				self.parent.assertEqual(result.last_hidden_state.shape       ,   (self.batch_size, self.seq_length, self.hidden_size) )



		def __UpperCamelCase				(				self       ,   A_       ,   A_       ,   A_ )						->      Any:
				"""simple docstring"""
				UpperCamelCase						=  self.type_sequence_label_size
				UpperCamelCase						=  TFEfficientFormerForImageClassification(A_ )
				UpperCamelCase						=  model(A_       ,   labels=A_       ,   training=A_ )
				self.parent.assertEqual(result.logits.shape       ,   (self.batch_size, self.type_sequence_label_size) )

				# test greyscale images
				UpperCamelCase						=  1
				UpperCamelCase						=  TFEfficientFormerForImageClassification(A_ )

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



		def __UpperCamelCase				(				self )						->      List[Any]:
				"""simple docstring"""
				UpperCamelCase						=  self.prepare_config_and_inputs()
				UpperCamelCase			,						UpperCamelCase			,						UpperCamelCase						=  config_and_inputs
				UpperCamelCase						=  {'pixel_values': pixel_values}
				return config, inputs_dict






@require_tf
class        lowercase      (	_SCREAMING_SNAKE_CASE						,     _SCREAMING_SNAKE_CASE						,     unittest.TestCase     ):
		__lowercase : List[Any]			  =       (
		    (
		        TFEfficientFormerModel,
		        TFEfficientFormerForImageClassificationWithTeacher,
		        TFEfficientFormerForImageClassification,
		    )
		    if is_tf_available()
		    else ()
		)
		__lowercase : Optional[int]			  =       (
		    {
		        "feature-extraction": TFEfficientFormerModel,
		        "image-classification": (
		            TFEfficientFormerForImageClassification,
		            TFEfficientFormerForImageClassificationWithTeacher,
		        ),
		    }
		    if is_tf_available()
		    else {}
		)

		__lowercase : List[Any]			  =       False

		__lowercase : int			  =       False
		__lowercase : int			  =       False
		__lowercase : str			  =       False
		__lowercase : Dict			  =       False



		def __UpperCamelCase				(				self )						->      int:
				"""simple docstring"""
				UpperCamelCase						=  TFEfficientFormerModelTester(self )
				UpperCamelCase						=  ConfigTester(
				    self       ,   config_class=A_       ,   has_text_modality=A_       ,   hidden_size=37 )



		def __UpperCamelCase				(				self )						->      str:
				"""simple docstring"""
				self.config_tester.run_common_tests()



		@unittest.skip(reason='EfficientFormer does not use inputs_embeds' )
		def __UpperCamelCase				(				self )						->      Union[str, Any]:
				"""simple docstring"""
				pass



		@unittest.skip(reason='EfficientFormer does not support input and output embeddings' )
		def __UpperCamelCase				(				self )						->      Optional[Any]:
				"""simple docstring"""
				pass



		def __UpperCamelCase				(				self )						->      str:
				"""simple docstring"""
				UpperCamelCase			,						UpperCamelCase						=  self.model_tester.prepare_config_and_inputs_for_common()

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

						UpperCamelCase						=  ['pixel_values']
						self.assertListEqual(arg_names[:1]       ,   A_ )



		def __UpperCamelCase				(				self )						->      Dict:
				"""simple docstring"""
				def check_hidden_states_output(A_       ,   A_       ,   A_ ):
						UpperCamelCase						=  model_class(A_ )

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

						UpperCamelCase						=  getattr(
						    self.model_tester       ,   'expected_num_hidden_layers'       ,   self.model_tester.num_hidden_layers + 1 )
						self.assertEqual(len(A_ )       ,   A_ )

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

								self.asseretIsInstance(A_       ,   (list, tuple) )
								self.assertEqual(len(A_ )       ,   A_ )
								UpperCamelCase						=  getattr(self.model_tester       ,   'seq_length'       ,   A_ )
								UpperCamelCase						=  getattr(self.model_tester       ,   'decoder_seq_length'       ,   A_ )

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

				UpperCamelCase			,						UpperCamelCase						=  self.model_tester.prepare_config_and_inputs_for_common()

				for model_class in self.all_model_classes:
						UpperCamelCase						=  True
						check_hidden_states_output(A_       ,   A_       ,   A_ )

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

						check_hidden_states_output(A_       ,   A_       ,   A_ )



		def __UpperCamelCase				(				self       ,   A_       ,   A_       ,   A_=False )						->      Optional[Any]:
				"""simple docstring"""
				UpperCamelCase						=  super()._prepare_for_class(A_       ,   A_       ,   return_labels=A_ )

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

				return inputs_dict



		def __UpperCamelCase				(				self )						->      Optional[int]:
				"""simple docstring"""
				UpperCamelCase						=  self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_model(*A_ )



		@unittest.skip(reason='EfficientFormer does not implement masked image modeling yet' )
		def __UpperCamelCase				(				self )						->      Optional[int]:
				"""simple docstring"""
				UpperCamelCase						=  self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_for_masked_image_modeling(*A_ )



		def __UpperCamelCase				(				self )						->      Any:
				"""simple docstring"""
				UpperCamelCase						=  self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_for_image_classification(*A_ )



		@slow
		def __UpperCamelCase				(				self )						->      str:
				"""simple docstring"""
				for model_name in TF_EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
						UpperCamelCase						=  TFEfficientFormerModel.from_pretrained(A_ )
						self.assertIsNotNone(A_ )



		def __UpperCamelCase				(				self )						->      Tuple:
				"""simple docstring"""
				UpperCamelCase			,						UpperCamelCase						=  self.model_tester.prepare_config_and_inputs_for_common()
				UpperCamelCase						=  True

				UpperCamelCase						=  getattr(self.model_tester       ,   'seq_length'       ,   A_ )
				UpperCamelCase						=  getattr(self.model_tester       ,   'encoder_seq_length'       ,   A_ )
				UpperCamelCase						=  getattr(self.model_tester       ,   'key_length'       ,   A_ )
				UpperCamelCase						=  getattr(self.model_tester       ,   'chunk_length'       ,   A_ )

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

				for model_class in self.all_model_classes:
						UpperCamelCase						=  True
						UpperCamelCase						=  False
						UpperCamelCase						=  True
						UpperCamelCase						=  model_class(A_ )

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

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

						UpperCamelCase						=  outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
						self.assertEqual(len(A_ )       ,   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 __UpperCamelCase				(				self )						->      Dict:
				"""simple docstring"""
				# 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
				UpperCamelCase			,						UpperCamelCase						=  self.model_tester.prepare_config_and_inputs_for_common()

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






def    A (      )     ->						Union[str, Any]:
		'''simple docstring'''

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


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



		@cached_property
		def __UpperCamelCase				(				self )						->      int:
				"""simple docstring"""
				return (
				    EfficientFormerImageProcessor.from_pretrained('snap-research/efficientformer-l1-300' )
				    if is_vision_available()
				    else None
				)



		@slow
		def __UpperCamelCase				(				self )						->      Union[str, Any]:
				"""simple docstring"""
				UpperCamelCase						=  TFEfficientFormerForImageClassification.from_pretrained('snap-research/efficientformer-l1-300' )
				UpperCamelCase						=  self.default_image_processor
				UpperCamelCase						=  prepare_img()
				UpperCamelCase						=  image_processor(images=A_       ,   return_tensors='tf' )
				# forward pass
				UpperCamelCase						=  model(**A_       ,   training=A_ )
				# verify the logits
				UpperCamelCase						=  tf.TensorShape((1, 1_000) )
				self.assertEqual(outputs.logits.shape       ,   A_ )
				UpperCamelCase						=  tf.constant([-0.0555, 0.4825, -0.0852] )
				self.assertTrue(np.allclose(outputs.logits[0, :3]       ,   A_       ,   atol=1e-4 ) )



		@slow
		def __UpperCamelCase				(				self )						->      Any:
				"""simple docstring"""
				UpperCamelCase						=  TFEfficientFormerForImageClassificationWithTeacher.from_pretrained(
				    'snap-research/efficientformer-l1-300' )
				UpperCamelCase						=  self.default_image_processor
				UpperCamelCase						=  prepare_img()
				UpperCamelCase						=  image_processor(images=A_       ,   return_tensors='tf' )
				# forward pass
				UpperCamelCase						=  model(**A_       ,   training=A_ )
				# verify the logits
				UpperCamelCase						=  tf.TensorShape((1, 1_000) )
				self.assertEqual(outputs.logits.shape       ,   A_ )
				UpperCamelCase						=  tf.constant([-0.1312, 0.4353, -1.0499] )
				self.assertTrue(np.allclose(outputs.logits[0, :3]       ,   A_       ,   atol=1e-4 ) )


from scipy.stats import pearsonr, spearmanr
from sklearn.metrics import fa_score, matthews_corrcoef

import datasets


_UpperCAmelCase      :				Any          = "\\n@inproceedings{wang2019glue,\n  title={{GLUE}: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding},\n  author={Wang, Alex and Singh, Amanpreet and Michael, Julian and Hill, Felix and Levy, Omer and Bowman, Samuel R.},\n  note={In the Proceedings of ICLR.},\n  year={2019}\n}\n"

_UpperCAmelCase      :				str          = "\\nGLUE, the General Language Understanding Evaluation benchmark\n(https://gluebenchmark.com/) is a collection of resources for training,\nevaluating, and analyzing natural language understanding systems.\n"

_UpperCAmelCase      :				List[str]          = "\nCompute GLUE evaluation metric associated to each GLUE dataset.\nArgs:\n    predictions: list of predictions to score.\n        Each translation should be tokenized into a list of tokens.\n    references: list of lists of references for each translation.\n        Each reference should be tokenized into a list of tokens.\nReturns: depending on the GLUE subset, one or several of:\n    \"accuracy\": Accuracy\n    \"f1\": F1 score\n    \"pearson\": Pearson Correlation\n    \"spearmanr\": Spearman Correlation\n    \"matthews_correlation\": Matthew Correlation\nExamples:\n\n    >>> glue_metric = datasets.load_metric('glue', 'sst2')  # 'sst2' or any of [\"mnli\", \"mnli_mismatched\", \"mnli_matched\", \"qnli\", \"rte\", \"wnli\", \"hans\"]\n    >>> references = [0, 1]\n    >>> predictions = [0, 1]\n    >>> results = glue_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {'accuracy': 1.0}\n\n    >>> glue_metric = datasets.load_metric('glue', 'mrpc')  # 'mrpc' or 'qqp'\n    >>> references = [0, 1]\n    >>> predictions = [0, 1]\n    >>> results = glue_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {'accuracy': 1.0, 'f1': 1.0}\n\n    >>> glue_metric = datasets.load_metric('glue', 'stsb')\n    >>> references = [0., 1., 2., 3., 4., 5.]\n    >>> predictions = [0., 1., 2., 3., 4., 5.]\n    >>> results = glue_metric.compute(predictions=predictions, references=references)\n    >>> print({\"pearson\": round(results[\"pearson\"], 2), \"spearmanr\": round(results[\"spearmanr\"], 2)})\n    {'pearson': 1.0, 'spearmanr': 1.0}\n\n    >>> glue_metric = datasets.load_metric('glue', 'cola')\n    >>> references = [0, 1]\n    >>> predictions = [0, 1]\n    >>> results = glue_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {'matthews_correlation': 1.0}\n"


def    A (      lowercase					, lowercase				)     ->						List[str]:
		'''simple docstring'''

		return float((preds == labels).mean()				)


def    A (      lowercase					, lowercase				)     ->						Tuple:
		'''simple docstring'''

		UpperCamelCase						=  simple_accuracy(lowercase					, lowercase				)
		UpperCamelCase						=  float(fa_score(y_true=lowercase					, y_pred=lowercase				)				)
		return {
		    "accuracy": acc,
		    "f1": fa,
		}


def    A (      lowercase					, lowercase				)     ->						Optional[int]:
		'''simple docstring'''

		UpperCamelCase						=  float(pearsonr(lowercase					, lowercase				)[0]				)
		UpperCamelCase						=  float(spearmanr(lowercase					, lowercase				)[0]				)
		return {
		    "pearson": pearson_corr,
		    "spearmanr": spearman_corr,
		}


@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION						,     _KWARGS_DESCRIPTION     )
class        lowercase      (	datasets.Metric     ):



		def __UpperCamelCase				(				self )						->      Optional[Any]:
				"""simple docstring"""
				if self.config_name not in [
				    "sst2",
				    "mnli",
				    "mnli_mismatched",
				    "mnli_matched",
				    "cola",
				    "stsb",
				    "mrpc",
				    "qqp",
				    "qnli",
				    "rte",
				    "wnli",
				    "hans",
				]:
						raise KeyError(
						    'You should supply a configuration name selected in '
						    '["sst2", "mnli", "mnli_mismatched", "mnli_matched", '
						    '"cola", "stsb", "mrpc", "qqp", "qnli", "rte", "wnli", "hans"]' )
				return datasets.MetricInfo(
				    description=_DESCRIPTION       ,   citation=_CITATION       ,   inputs_description=_KWARGS_DESCRIPTION       ,   features=datasets.Features(
				        {
				            'predictions': datasets.Value('int64' if self.config_name != 'stsb' else 'float32' ),
				            'references': datasets.Value('int64' if self.config_name != 'stsb' else 'float32' ),
				        } )       ,   codebase_urls=[]       ,   reference_urls=[]       ,   format='numpy'       ,   )



		def __UpperCamelCase				(				self       ,   A_       ,   A_ )						->      Any:
				"""simple docstring"""
				if self.config_name == "cola":
						return {"matthews_correlation": matthews_corrcoef(A_       ,   A_ )}
				elif self.config_name == "stsb":
						return pearson_and_spearman(A_       ,   A_ )
				elif self.config_name in ["mrpc", "qqp"]:
						return acc_and_fa(A_       ,   A_ )
				elif self.config_name in ["sst2", "mnli", "mnli_mismatched", "mnli_matched", "qnli", "rte", "wnli", "hans"]:
						return {"accuracy": simple_accuracy(A_       ,   A_ )}
				else:
						raise KeyError(
						    'You should supply a configuration name selected in '
						    '["sst2", "mnli", "mnli_mismatched", "mnli_matched", '
						    '"cola", "stsb", "mrpc", "qqp", "qnli", "rte", "wnli", "hans"]' )
