



import argparse
import json
import os

import fairseq
import torch
from torch import nn

from transformers import (
    SpeechaTextaConfig,
    SpeechaTextaForCausalLM,
    SpeechaTextaTokenizer,
    SpeechEncoderDecoderConfig,
    SpeechEncoderDecoderModel,
    WavaVecaConfig,
    WavaVecaFeatureExtractor,
    WavaVecaModel,
    logging,
)


logging.set_verbosity_info()
_lowerCAmelCase	:   str    =   logging.get_logger(__name__)

_lowerCAmelCase	:   str    =   {
    "post_extract_proj": "feature_projection.projection",
    "encoder.pos_conv.0": "encoder.pos_conv_embed.conv",
    "self_attn.k_proj": "encoder.layers.*.attention.k_proj",
    "self_attn.v_proj": "encoder.layers.*.attention.v_proj",
    "self_attn.q_proj": "encoder.layers.*.attention.q_proj",
    "self_attn.out_proj": "encoder.layers.*.attention.out_proj",
    "self_attn_layer_norm": "encoder.layers.*.layer_norm",
    "fc1": "encoder.layers.*.feed_forward.intermediate_dense",
    "fc2": "encoder.layers.*.feed_forward.output_dense",
    "final_layer_norm": "encoder.layers.*.final_layer_norm",
    "encoder.layer_norm": "encoder.layer_norm",
    "w2v_model.layer_norm": "feature_projection.layer_norm",
    "quantizer.weight_proj": "quantizer.weight_proj",
    "quantizer.vars": "quantizer.codevectors",
    "project_q": "project_q",
    "final_proj": "project_hid",
    "w2v_encoder.proj": "lm_head",
    "mask_emb": "masked_spec_embed",
}
_lowerCAmelCase	:   List[str]    =   [
    "lm_head",
    "quantizer.weight_proj",
    "quantizer.codevectors",
    "project_q",
    "project_hid",
]







def     UpperCAmelCase_		(					snake_case__    ,			snake_case__    ,			snake_case__    ,			snake_case__    ,			snake_case__					)	->     Any:



				"""simple docstring"""





				for attribute in key.split('.'					):
								lowerCAmelCase__		  =  getattr(snake_case__    ,			snake_case__					)

				if weight_type is not None:
								lowerCAmelCase__		  =  getattr(snake_case__    ,			snake_case__					).shape
				else:
								lowerCAmelCase__		  =  hf_pointer.shape

				assert hf_shape == value.shape, (
				    f'Shape of hf {key + "." + weight_type if weight_type is not None else ""} is {hf_shape}, but should be'
				    f' {value.shape} for {full_name}'
				)

				if weight_type == "weight":
								lowerCAmelCase__		  =  value
				elif weight_type == "weight_g":
								lowerCAmelCase__		  =  value
				elif weight_type == "weight_v":
								lowerCAmelCase__		  =  value
				elif weight_type == "bias":
								lowerCAmelCase__		  =  value
				else:
								lowerCAmelCase__		  =  value

				logger.info(f'{key + "." + weight_type if weight_type is not None else ""} was initialized from {full_name}.'					)







def     UpperCAmelCase_		(					snake_case__    ,			snake_case__					)	->     List[Any]:



				"""simple docstring"""





				lowerCAmelCase__		  =  []
				lowerCAmelCase__		  =  fairseq_model.state_dict()

				lowerCAmelCase__		  =  hf_model.feature_extractor

				# if encoder has different dim to decoder -> use proj_weight
				lowerCAmelCase__		  =  None

				for name, value in fairseq_dict.items():
								lowerCAmelCase__		  =  False
								if "conv_layers" in name:
												load_conv_layer(
												    snake_case__    ,			snake_case__    ,			snake_case__    ,			snake_case__    ,			hf_model.config.feat_extract_norm == 'group'    ,			)
												lowerCAmelCase__		  =  True
								elif name.split('.'					)[0] == "proj":
												lowerCAmelCase__		  =  fairseq_model.proj
												lowerCAmelCase__		  =  True
								else:
												for key, mapped_key in MAPPING.items():
																if key in name or key.split('w2v_model.'					)[-1] == name.split('.'					)[0]:
																				lowerCAmelCase__		  =  True
																				if "*" in mapped_key:
																								lowerCAmelCase__		  =  name.split(snake_case__					)[0].split('.'					)[-2]
																								lowerCAmelCase__		  =  mapped_key.replace('*'    ,			snake_case__					)
																				if "weight_g" in name:
																								lowerCAmelCase__		  =  'weight_g'
																				elif "weight_v" in name:
																								lowerCAmelCase__		  =  'weight_v'
																				elif "bias" in name:
																								lowerCAmelCase__		  =  'bias'
																				elif "weight" in name:
																								lowerCAmelCase__		  =  'weight'
																				else:
																								lowerCAmelCase__		  =  None
																				set_recursively(snake_case__    ,			snake_case__    ,			snake_case__    ,			snake_case__    ,			snake_case__					)
																continue
								if not is_used:
												unused_weights.append(snake_case__					)

				logger.warning(f'Unused weights: {unused_weights}'					)

				return proj_weight







def     UpperCAmelCase_		(					snake_case__    ,			snake_case__    ,			snake_case__    ,			snake_case__    ,			snake_case__					)	->     List[Any]:



				"""simple docstring"""





				lowerCAmelCase__		  =  full_name.split('conv_layers.'					)[-1]
				lowerCAmelCase__		  =  name.split('.'					)
				lowerCAmelCase__		  =  int(items[0]					)
				lowerCAmelCase__		  =  int(items[1]					)

				if type_id == 0:
								if "bias" in name:
												assert value.shape == feature_extractor.conv_layers[layer_id].conv.bias.data.shape, (
												    f'{full_name} has size {value.shape}, but'
												    f' {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found.'
												)
												lowerCAmelCase__		  =  value
												logger.info(f'Feat extract conv layer {layer_id} was initialized from {full_name}.'					)
								elif "weight" in name:
												assert value.shape == feature_extractor.conv_layers[layer_id].conv.weight.data.shape, (
												    f'{full_name} has size {value.shape}, but'
												    f' {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found.'
												)
												lowerCAmelCase__		  =  value
												logger.info(f'Feat extract conv layer {layer_id} was initialized from {full_name}.'					)
				elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
								if "bias" in name:
												assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape, (
												    f'{full_name} has size {value.shape}, but {feature_extractor[layer_id].layer_norm.bias.data.shape} was'
												    " found."
												)
												lowerCAmelCase__		  =  value
												logger.info(f'Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'					)
								elif "weight" in name:
												assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape, (
												    f'{full_name} has size {value.shape}, but'
												    f' {feature_extractor[layer_id].layer_norm.weight.data.shape} was found.'
												)
												lowerCAmelCase__		  =  value
												logger.info(f'Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'					)
				else:
								unused_weights.append(snake_case__					)







def     UpperCAmelCase_		(					snake_case__					)	->     str:



				"""simple docstring"""





				lowerCAmelCase__  ,   lowerCAmelCase__		  =  emb.weight.shape
				lowerCAmelCase__		  =  nn.Linear(snake_case__    ,			snake_case__    ,			bias=snake_case__					)
				lowerCAmelCase__		  =  emb.weight.data
				return lin_layer







def     UpperCAmelCase_		(					snake_case__					)	->     Any:



				"""simple docstring"""





				with open(snake_case__    ,			'r'    ,			encoding='utf-8'					) as f:
								lowerCAmelCase__		  =  f.readlines()
								lowerCAmelCase__		  =  [line.split(' '					)[0] for line in lines]

				lowerCAmelCase__		  =  len(snake_case__					)

				lowerCAmelCase__		  =  {
				    '<s>': 0,
				    '<pad>': 1,
				    '</s>': 2,
				    '<unk>': 3,
				}

				vocab_dict.update(dict(zip(snake_case__    ,			range(4    ,			num_words + 4					)					)					)					)
				return vocab_dict







@torch.no_grad()
def     UpperCAmelCase_		(					snake_case__    ,			snake_case__    ,			snake_case__    ,			snake_case__    ,			snake_case__    ,			snake_case__    ,			snake_case__    ,			)	->     Any:



				"""simple docstring"""





				lowerCAmelCase__		  =  WavaVecaConfig.from_pretrained(snake_case__					)
				lowerCAmelCase__		  =  SpeechaTextaConfig.from_pretrained(
				    snake_case__    ,			vocab_size=snake_case__    ,			decoder_layers=snake_case__    ,			do_stable_layer_norm=snake_case__					)

				lowerCAmelCase__		  =  WavaVecaFeatureExtractor(
				    feature_size=1    ,			sampling_rate=16000    ,			padding_value=0    ,			do_normalize=snake_case__    ,			return_attention_mask=snake_case__    ,			)

				lowerCAmelCase__  ,   lowerCAmelCase__  ,   lowerCAmelCase__		  =  fairseq.checkpoint_utils.load_model_ensemble_and_task(
				    [checkpoint_path]    ,			arg_overrides={'data': '/'.join(dict_path.split('/'					)[:-1]					)}					)
				lowerCAmelCase__		  =  model[0].eval()

				# set weights for wav2vec2 encoder
				lowerCAmelCase__		  =  WavaVecaModel(snake_case__					)
				lowerCAmelCase__		  =  recursively_load_weights_wavaveca(model.encoder    ,			snake_case__					)

				lowerCAmelCase__		  =  SpeechaTextaForCausalLM(snake_case__					)
				lowerCAmelCase__  ,   lowerCAmelCase__		  =  hf_decoder.model.decoder.load_state_dict(model.decoder.state_dict()    ,			strict=snake_case__					)

				# set output linear layer
				unexpected_keys.remove('embed_out'					)
				lowerCAmelCase__		  =  nn.Parameter(model.decoder.embed_out.detach()					)

				# layer norm is init to identity matrix so leaving it is fine
				logger.warning(f'The following keys are missing when loading the decoder weights: {missing_keys}'					)
				logger.warning(f'The following keys are unexpected when loading the decoder weights: {unexpected_keys}'					)

				lowerCAmelCase__		  =  SpeechEncoderDecoderModel(encoder=snake_case__    ,			decoder=snake_case__					)
				lowerCAmelCase__		  =  False

				# add projection layer
				lowerCAmelCase__		  =  nn.Parameter(projection_layer.weight					)
				lowerCAmelCase__		  =  nn.Parameter(projection_layer.bias					)

				lowerCAmelCase__		  =  create_vocab_dict(snake_case__					)

				with open(os.path.join(snake_case__    ,			'vocab.json'					)    ,			'w'					) as fp:
								json.dump(snake_case__    ,			snake_case__					)

				lowerCAmelCase__		  =  SpeechaTextaTokenizer(os.path.join(snake_case__    ,			'vocab.json'					)					)
				tokenizer.save_pretrained(snake_case__					)

				lowerCAmelCase__		  =  hf_wavavec.config.to_dict()
				lowerCAmelCase__		  =  tokenizer.pad_token_id
				lowerCAmelCase__		  =  tokenizer.bos_token_id
				lowerCAmelCase__		  =  tokenizer.eos_token_id
				lowerCAmelCase__		  =  'speech_to_text_2'
				lowerCAmelCase__		  =  'wav2vec2'

				lowerCAmelCase__		  =  SpeechEncoderDecoderConfig.from_dict(snake_case__					)

				hf_wavavec.save_pretrained(snake_case__					)
				feature_extractor.save_pretrained(snake_case__					)


if __name__ == "__main__":
	_lowerCAmelCase	:   int    =   argparse.ArgumentParser()
	parser.add_argument("--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
	parser.add_argument("--checkpoint_path", default=None, type=str, help="Path to fairseq checkpoint")
	parser.add_argument("--dict_path", default=None, type=str, help="Path to dict of fine-tuned model")
	parser.add_argument(
	    "--encoder_config_path",
	    default="facebook/wav2vec2-large-lv60",
	    type=str,
	    help="Path to hf encoder wav2vec2 checkpoint config",
	)
	parser.add_argument(
	    "--decoder_config_path",
	    default="facebook/s2t-small-mustc-en-fr-st",
	    type=str,
	    help="Path to hf decoder s2t checkpoint config",
	)
	parser.add_argument("--vocab_size", default=1_0_2_2_4, type=int, help="Vocab size of decoder")
	parser.add_argument("--num_decoder_layers", default=7, type=int, help="Number of decoder layers")

	_lowerCAmelCase	:   Optional[int]    =   parser.parse_args()
	convert_wavaveca_checkpoint(
	    args.checkpoint_path,
	    args.pytorch_dump_folder_path,
	    args.dict_path,
	    encoder_config_path=args.encoder_config_path,
	    decoder_config_path=args.decoder_config_path,
	    vocab_size=args.vocab_size,
	    num_decoder_layers=args.num_decoder_layers,
	)





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 	__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    ,):







						"""simple docstring"""
						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 						SCREAMING_SNAKE_CASE_					(							self						):







						"""simple docstring"""
						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 						SCREAMING_SNAKE_CASE_					(							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=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 						SCREAMING_SNAKE_CASE_					(							self    ,a_    ,a_    ,a_						):







						"""simple docstring"""
						lowerCAmelCase__		  =  TFEfficientFormerModel(config=a_						)
						lowerCAmelCase__		  =  model(a_    ,training=a_						)
						self.parent.assertEqual(result.last_hidden_state.shape    ,(self.batch_size, self.seq_length, self.hidden_size)						)
		def 						SCREAMING_SNAKE_CASE_					(							self    ,a_    ,a_    ,a_						):







						"""simple docstring"""
						lowerCAmelCase__		  =  self.type_sequence_label_size
						lowerCAmelCase__		  =  TFEfficientFormerForImageClassification(a_						)
						lowerCAmelCase__		  =  model(a_    ,labels=a_    ,training=a_						)
						self.parent.assertEqual(result.logits.shape    ,(self.batch_size, self.type_sequence_label_size)						)

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

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



		def 						SCREAMING_SNAKE_CASE_					(							self						):







						"""simple docstring"""
						lowerCAmelCase__		  =  self.prepare_config_and_inputs()
						lowerCAmelCase__  ,   lowerCAmelCase__  ,   lowerCAmelCase__		  =  config_and_inputs
						lowerCAmelCase__		  =  {'pixel_values': pixel_values}
						return config, inputs_dict



@require_tf
class 	__snake_case   ( SCREAMING_SNAKE_CASE							, SCREAMING_SNAKE_CASE							, unittest.TestCase ):
		SCREAMING_SNAKE_CASE__								=	(
		    (
		        TFEfficientFormerModel,
		        TFEfficientFormerForImageClassificationWithTeacher,
		        TFEfficientFormerForImageClassification,
		    )
		    if is_tf_available()
		    else ()
		)
		SCREAMING_SNAKE_CASE__								=	(
		    {
		        'feature-extraction': TFEfficientFormerModel,
		        'image-classification': (
		            TFEfficientFormerForImageClassification,
		            TFEfficientFormerForImageClassificationWithTeacher,
		        ),
		    }
		    if is_tf_available()
		    else {}
		)

		SCREAMING_SNAKE_CASE__								=	False

		SCREAMING_SNAKE_CASE__								=	False
		SCREAMING_SNAKE_CASE__								=	False
		SCREAMING_SNAKE_CASE__								=	False
		SCREAMING_SNAKE_CASE__								=	False
		def 						SCREAMING_SNAKE_CASE_					(							self						):







						"""simple docstring"""
						lowerCAmelCase__		  =  TFEfficientFormerModelTester(self						)
						lowerCAmelCase__		  =  ConfigTester(
						    self    ,config_class=a_    ,has_text_modality=a_    ,hidden_size=37						)
		def 						SCREAMING_SNAKE_CASE_					(							self						):







						"""simple docstring"""
						self.config_tester.run_common_tests()
		@unittest.skip(reason='EfficientFormer does not use inputs_embeds'						)
		def 						SCREAMING_SNAKE_CASE_					(							self						):







						"""simple docstring"""
						pass
		@unittest.skip(reason='EfficientFormer does not support input and output embeddings'						)
		def 						SCREAMING_SNAKE_CASE_					(							self						):







						"""simple docstring"""
						pass
		def 						SCREAMING_SNAKE_CASE_					(							self						):







						"""simple docstring"""
						lowerCAmelCase__  ,   lowerCAmelCase__		  =  self.model_tester.prepare_config_and_inputs_for_common()

						for model_class in self.all_model_classes:
										lowerCAmelCase__		  =  model_class(a_						)
										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]    ,a_						)
		def 						SCREAMING_SNAKE_CASE_					(							self						):







						"""simple docstring"""
						def check_hidden_states_output(a_    ,a_    ,a_						):
										lowerCAmelCase__		  =  model_class(a_						)

										lowerCAmelCase__		  =  model(**self._prepare_for_class(a_    ,a_						)    ,training=a_						)
										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(a_						)    ,a_						)

										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(a_    ,(list, tuple)						)
														self.assertEqual(len(a_						)    ,a_						)
														lowerCAmelCase__		  =  getattr(self.model_tester    ,'seq_length'    ,a_						)
														lowerCAmelCase__		  =  getattr(self.model_tester    ,'decoder_seq_length'    ,a_						)

														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(a_    ,a_    ,a_						)

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

										check_hidden_states_output(a_    ,a_    ,a_						)
		def 						SCREAMING_SNAKE_CASE_					(							self    ,a_    ,a_    ,a_=False						):







						"""simple docstring"""
						lowerCAmelCase__		  =  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 						SCREAMING_SNAKE_CASE_					(							self						):







						"""simple docstring"""
						lowerCAmelCase__		  =  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 						SCREAMING_SNAKE_CASE_					(							self						):







						"""simple docstring"""
						lowerCAmelCase__		  =  self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_masked_image_modeling(*a_						)
		def 						SCREAMING_SNAKE_CASE_					(							self						):







						"""simple docstring"""
						lowerCAmelCase__		  =  self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_image_classification(*a_						)
		@slow
		def 						SCREAMING_SNAKE_CASE_					(							self						):







						"""simple docstring"""
						for model_name in TF_EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
										lowerCAmelCase__		  =  TFEfficientFormerModel.from_pretrained(a_						)
										self.assertIsNotNone(a_						)
		def 						SCREAMING_SNAKE_CASE_					(							self						):







						"""simple docstring"""
						lowerCAmelCase__  ,   lowerCAmelCase__		  =  self.model_tester.prepare_config_and_inputs_for_common()
						lowerCAmelCase__		  =  True

						lowerCAmelCase__		  =  getattr(self.model_tester    ,'seq_length'    ,a_						)
						lowerCAmelCase__		  =  getattr(self.model_tester    ,'encoder_seq_length'    ,a_						)
						lowerCAmelCase__		  =  getattr(self.model_tester    ,'key_length'    ,a_						)
						lowerCAmelCase__		  =  getattr(self.model_tester    ,'chunk_length'    ,a_						)

						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(a_						)

										lowerCAmelCase__		  =  model(**self._prepare_for_class(a_    ,a_						)    ,training=a_						)
										lowerCAmelCase__		  =  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"]
										lowerCAmelCase__		  =  True
										lowerCAmelCase__		  =  model_class(a_						)
										lowerCAmelCase__		  =  model(**self._prepare_for_class(a_    ,a_						)    ,training=a_						)

										lowerCAmelCase__		  =  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 						SCREAMING_SNAKE_CASE_					(							self						):







						"""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
						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(a_						)
										# 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=a_						)
										    for key, val in model.input_signature.items()
										    if key in model.dummy_inputs
										}
										lowerCAmelCase__		  =  model(a_						)
										self.assertTrue(outputs_dict is not None						)







def     UpperCAmelCase_		(					)	->     List[str]:



				"""simple docstring"""





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


@require_tf
@require_vision
class 	__snake_case   ( unittest.TestCase ):
		@cached_property
		def 						SCREAMING_SNAKE_CASE_					(							self						):







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







						"""simple docstring"""
						lowerCAmelCase__		  =  TFEfficientFormerForImageClassification.from_pretrained('snap-research/efficientformer-l1-300'						)
						lowerCAmelCase__		  =  self.default_image_processor
						lowerCAmelCase__		  =  prepare_img()
						lowerCAmelCase__		  =  image_processor(images=a_    ,return_tensors='tf'						)
						# forward pass
						lowerCAmelCase__		  =  model(**a_    ,training=a_						)
						# verify the logits
						lowerCAmelCase__		  =  tf.TensorShape((1, 1000)						)
						self.assertEqual(outputs.logits.shape    ,a_						)
						lowerCAmelCase__		  =  tf.constant([-0.0555, 0.4825, -0.0852]						)
						self.assertTrue(np.allclose(outputs.logits[0, :3]    ,a_    ,atol=1e-4						)						)



		@slow
		def 						SCREAMING_SNAKE_CASE_					(							self						):







						"""simple docstring"""
						lowerCAmelCase__		  =  TFEfficientFormerForImageClassificationWithTeacher.from_pretrained(
						    'snap-research/efficientformer-l1-300'						)
						lowerCAmelCase__		  =  self.default_image_processor
						lowerCAmelCase__		  =  prepare_img()
						lowerCAmelCase__		  =  image_processor(images=a_    ,return_tensors='tf'						)
						# forward pass
						lowerCAmelCase__		  =  model(**a_    ,training=a_						)
						# verify the logits
						lowerCAmelCase__		  =  tf.TensorShape((1, 1000)						)
						self.assertEqual(outputs.logits.shape    ,a_						)
						lowerCAmelCase__		  =  tf.constant([-0.1312, 0.4353, -1.0499]						)
						self.assertTrue(np.allclose(outputs.logits[0, :3]    ,a_    ,atol=1e-4						)						)
