
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       ,   )						->      str:
				"""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 )						->      int:
				"""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 )						->      Tuple:
				"""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=_lowercase       ,   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_ )						->      Optional[int]:
				"""simple docstring"""
				UpperCamelCase						=  TFEfficientFormerModel(config=_lowercase )
				UpperCamelCase						=  model(_lowercase       ,   training=_lowercase )
				self.parent.assertEqual(result.last_hidden_state.shape       ,   (self.batch_size, self.seq_length, self.hidden_size) )



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

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

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



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






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

		__lowercase : Dict			  =       False

		__lowercase : Optional[Any]			  =       False
		__lowercase : Dict			  =       False
		__lowercase : Any			  =       False
		__lowercase : Union[str, Any]			  =       False



		def __UpperCamelCase				(				self )						->      List[str]:
				"""simple docstring"""
				UpperCamelCase						=  TFEfficientFormerModelTester(self )
				UpperCamelCase						=  ConfigTester(
				    self       ,   config_class=_lowercase       ,   has_text_modality=_lowercase       ,   hidden_size=37 )



		def __UpperCamelCase				(				self )						->      List[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 )						->      int:
				"""simple docstring"""
				pass



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

				for model_class in self.all_model_classes:
						UpperCamelCase						=  model_class(_lowercase )
						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]       ,   _lowercase )



		def __UpperCamelCase				(				self )						->      List[str]:
				"""simple docstring"""

				def check_hidden_states_output(A_       ,   A_       ,   A_ ):
						UpperCamelCase						=  model_class(_lowercase )

						UpperCamelCase						=  model(**self._prepare_for_class(_lowercase       ,   _lowercase )       ,   training=_lowercase )
						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(_lowercase )       ,   _lowercase )

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

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

				UpperCamelCase						=  self.model_tester.prepare_config_and_inputs_for_common()

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

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

						check_hidden_states_output(_lowercase       ,   _lowercase       ,   _lowercase )



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

				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(*_lowercase )



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



		def __UpperCamelCase				(				self )						->      List[Any]:
				"""simple docstring"""
				UpperCamelCase						=  self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_for_image_classification(*_lowercase )



		@slow
		def __UpperCamelCase				(				self )						->      int:
				"""simple docstring"""

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



		def __UpperCamelCase				(				self )						->      Optional[Any]:
				"""simple docstring"""
				UpperCamelCase						=  self.model_tester.prepare_config_and_inputs_for_common()
				UpperCamelCase						=  True

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

				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(_lowercase )

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

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

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

				for model_class in self.all_model_classes:
						# Prepare our model
						UpperCamelCase						=  model_class(_lowercase )
						# 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=_lowercase )
						    for key, val in model.input_signature.items()
						    if key in model.dummy_inputs
						}
						UpperCamelCase						=  model(_lowercase )
						self.assertTrue(outputs_dict is not None )






def    A (      )     ->						Tuple:
		'''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 )						->      Union[str, Any]:
				"""simple docstring"""
				return (
				    EfficientFormerImageProcessor.from_pretrained('snap-research/efficientformer-l1-300' )
				    if is_vision_available()
				    else None
				)



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



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


from string import ascii_uppercase

_UpperCAmelCase      :				Dict          = {char: i for i, char in enumerate(ascii_uppercase)}
_UpperCAmelCase      :				Tuple          = dict(enumerate(ascii_uppercase))


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

		UpperCamelCase						=  len(lowercase				)
		UpperCamelCase						=  0
		while True:
				if x == i:
						UpperCamelCase						=  0
				if len(lowercase				) == len(lowercase				):
						break
				key += key[i]
				i += 1
		return key


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

		UpperCamelCase						=  ''
		UpperCamelCase						=  0
		for letter in message:
				if letter == " ":
						cipher_text += " "
				else:
						UpperCamelCase						=  (dicta[letter] - dicta[key_new[i]]) % 26
						i += 1
						cipher_text += dicta[x]
		return cipher_text


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

		UpperCamelCase						=  ''
		UpperCamelCase						=  0
		for letter in cipher_text:
				if letter == " ":
						or_txt += " "
				else:
						UpperCamelCase						=  (dicta[letter] + dicta[key_new[i]] + 26) % 26
						i += 1
						or_txt += dicta[x]
		return or_txt


def    A (      )     ->						None:
		'''simple docstring'''

		UpperCamelCase						=  'THE GERMAN ATTACK'
		UpperCamelCase						=  'SECRET'
		UpperCamelCase						=  generate_key(lowercase					, lowercase				)
		UpperCamelCase						=  cipher_text(lowercase					, lowercase				)
		print(f'''Encrypted Text = {s}'''				)
		print(f'''Original Text = {original_text(lowercase					, lowercase				)}'''				)


if __name__ == "__main__":
					import doctest

					doctest.testmod()
					main()
