
'''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 			SCREAMING_SNAKE_CASE__      :







				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     )->		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     )->		Optional[int]:







											'''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_     )->		int:







											'''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_     )->		Optional[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     )->		Union[str, 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 			SCREAMING_SNAKE_CASE__      (     snake_case_							,       snake_case_							,       unittest.TestCase):
				lowerCAmelCase_     			=							(
				    (
				        TFEfficientFormerModel,
				        TFEfficientFormerForImageClassificationWithTeacher,
				        TFEfficientFormerForImageClassification,
				    )
				    if is_tf_available()
				    else ()
				)
				lowerCAmelCase_     			=							(
				    {
				        """feature-extraction""": TFEfficientFormerModel,
				        """image-classification""": (
				            TFEfficientFormerForImageClassification,
				            TFEfficientFormerForImageClassificationWithTeacher,
				        ),
				    }
				    if is_tf_available()
				    else {}
				)

				lowerCAmelCase_     			=							False

				lowerCAmelCase_     			=							False
				lowerCAmelCase_     			=							False
				lowerCAmelCase_     			=							False
				lowerCAmelCase_     			=							False







				def 						UpperCAmelCase_    (       self     )->		Any:







											'''simple docstring'''






											UpperCamelCase   			=     TFEfficientFormerModelTester(self     )
											UpperCamelCase   			=     ConfigTester(
											    self						,				config_class=A_						,				has_text_modality=A_						,				hidden_size=37     )







				def 						UpperCAmelCase_    (       self     )->		Optional[Any]:







											'''simple docstring'''






											self.config_tester.run_common_tests()







				@unittest.skip(reason='EfficientFormer does not use inputs_embeds'     )
				def 						UpperCAmelCase_    (       self     )->		List[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     )->		Optional[int]:







											'''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     )->		Optional[int]:







											'''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     )->		int:







											'''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     )->		List[Any]:







											'''simple docstring'''






											UpperCamelCase   			=     self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_for_masked_image_modeling(*A_     )







				def 						UpperCAmelCase_    (       self     )->		Optional[int]:







											'''simple docstring'''






											UpperCamelCase   			=     self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_for_image_classification(*A_     )







				@slow
				def 						UpperCAmelCase_    (       self     )->		Any:







											'''simple docstring'''






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







				def 						UpperCAmelCase_    (       self     )->		List[Any]:







											'''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     )->		List[str]:







											'''simple docstring'''






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





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







				@cached_property
				def 						UpperCAmelCase_    (       self     )->		Optional[int]:







											'''simple docstring'''






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







				@slow
				def 						UpperCAmelCase_    (       self     )->		Dict:







											'''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, 1000)     )
											self.assertEqual(outputs.logits.shape						,				A_     )
											UpperCamelCase   			=     tf.constant([-0.0_555, 0.4_825, -0.0_852]     )
											self.assertTrue(np.allclose(outputs.logits[0, :3]						,				A_						,				atol=1e-4     )     )







				@slow
				def 						UpperCAmelCase_    (       self     )->		Optional[int]:







											'''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, 1000)     )
											self.assertEqual(outputs.logits.shape						,				A_     )
											UpperCamelCase   			=     tf.constant([-0.1_312, 0.4_353, -1.0_499]     )
											self.assertTrue(np.allclose(outputs.logits[0, :3]						,				A_						,				atol=1e-4     )     )






'''simple docstring'''
from collections import OrderedDict
from typing import Any, Mapping, Optional, Union

from ...configuration_utils import PretrainedConfig
from ...feature_extraction_utils import FeatureExtractionMixin
from ...onnx import OnnxConfig
from ...onnx.utils import compute_effective_axis_dimension
from ...tokenization_utils_base import PreTrainedTokenizerBase
from ...utils import TensorType, logging


lowerCAmelCase					:    Any           = logging.get_logger(__name__)

lowerCAmelCase					:    Optional[int]           = {
    'deepmind/language-perceiver': 'https://huggingface.co/deepmind/language-perceiver/resolve/main/config.json',
    # See all Perceiver models at https://huggingface.co/models?filter=perceiver
}


class 			SCREAMING_SNAKE_CASE__      (     snake_case_):
				lowerCAmelCase_     			=							"""perceiver"""







				def __init__(       self						,				A_=256						,				A_=1280						,				A_=768						,				A_=1						,				A_=26						,				A_=8						,				A_=8						,				A_=None						,				A_=None						,				A_="kv"						,				A_=1						,				A_=1						,				A_="gelu"						,				A_=0.1						,				A_=0.02						,				A_=1e-12						,				A_=True						,				A_=262						,				A_=2048						,				A_=56						,				A_=[368, 496]						,				A_=16						,				A_=1920						,				A_=16						,				A_=[1, 16, 224, 224]						,				**A_						,				)->		str:







											'''simple docstring'''






											super().__init__(**A_     )

											UpperCamelCase   			=     num_latents
											UpperCamelCase   			=     d_latents
											UpperCamelCase   			=     d_model
											UpperCamelCase   			=     num_blocks
											UpperCamelCase   			=     num_self_attends_per_block
											UpperCamelCase   			=     num_self_attention_heads
											UpperCamelCase   			=     num_cross_attention_heads
											UpperCamelCase   			=     qk_channels
											UpperCamelCase   			=     v_channels
											UpperCamelCase   			=     cross_attention_shape_for_attention
											UpperCamelCase   			=     self_attention_widening_factor
											UpperCamelCase   			=     cross_attention_widening_factor
											UpperCamelCase   			=     hidden_act
											UpperCamelCase   			=     attention_probs_dropout_prob
											UpperCamelCase   			=     initializer_range
											UpperCamelCase   			=     layer_norm_eps
											UpperCamelCase   			=     use_query_residual
											# masked language modeling attributes
											UpperCamelCase   			=     vocab_size
											UpperCamelCase   			=     max_position_embeddings
											# image classification attributes
											UpperCamelCase   			=     image_size
											# flow attributes
											UpperCamelCase   			=     train_size
											# multimodal autoencoding attributes
											UpperCamelCase   			=     num_frames
											UpperCamelCase   			=     audio_samples_per_frame
											UpperCamelCase   			=     samples_per_patch
											UpperCamelCase   			=     output_shape


class 			SCREAMING_SNAKE_CASE__      (     snake_case_):







				@property
				def 						UpperCAmelCase_    (       self     )->		Mapping[str, Mapping[int, str]]:







											'''simple docstring'''






											if self.task == "multiple-choice":
																		UpperCamelCase   			=     {0: 'batch', 1: 'choice', 2: 'sequence'}
											else:
																		UpperCamelCase   			=     {0: 'batch', 1: 'sequence'}
											return OrderedDict(
											    [
											        ('inputs', dynamic_axis),
											        ('attention_mask', dynamic_axis),
											    ]     )







				@property
				def 						UpperCAmelCase_    (       self     )->		float:







											'''simple docstring'''






											return 1e-4







				def 						UpperCAmelCase_    (       self						,				A_						,				A_ = -1						,				A_ = -1						,				A_ = -1						,				A_ = False						,				A_ = None						,				A_ = 3						,				A_ = 40						,				A_ = 40						,				)->		Mapping[str, Any]:







											'''simple docstring'''






											if isinstance(A_						,				A_     ):
																		# If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
																		UpperCamelCase   			=     compute_effective_axis_dimension(
																		    A_						,				fixed_dimension=OnnxConfig.default_fixed_batch						,				num_token_to_add=0     )
																		# If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
																		UpperCamelCase   			=     preprocessor.num_special_tokens_to_add(A_     )
																		UpperCamelCase   			=     compute_effective_axis_dimension(
																		    A_						,				fixed_dimension=OnnxConfig.default_fixed_sequence						,				num_token_to_add=A_     )
																		# Generate dummy inputs according to compute batch and sequence
																		UpperCamelCase   			=     [' '.join(['a']     ) * seq_length] * batch_size
																		UpperCamelCase   			=     dict(preprocessor(A_						,				return_tensors=A_     )     )
																		UpperCamelCase   			=     inputs.pop('input_ids'     )
																		return inputs
											elif isinstance(A_						,				A_     ) and preprocessor.model_input_names[0] == "pixel_values":
																		# If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
																		UpperCamelCase   			=     compute_effective_axis_dimension(A_						,				fixed_dimension=OnnxConfig.default_fixed_batch     )
																		UpperCamelCase   			=     self._generate_dummy_images(A_						,				A_						,				A_						,				A_     )
																		UpperCamelCase   			=     dict(preprocessor(images=A_						,				return_tensors=A_     )     )
																		UpperCamelCase   			=     inputs.pop('pixel_values'     )
																		return inputs
											else:
																		raise ValueError(
																		    'Unable to generate dummy inputs for the model. Please provide a tokenizer or a preprocessor.'     )




