






import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

# Register SEW's fairseq modules
from sew_asapp import tasks  # noqa: F401

from transformers import (
    SEWDConfig,
    SEWDForCTC,
    SEWDModel,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaProcessor,
    logging,
)


logging.set_verbosity_info()
__snake_case							      =						logging.get_logger(__name__)

__snake_case							      =						{
    """post_extract_proj""": """feature_projection""",
    """encoder.pos_conv.0""": """encoder.pos_conv_embed.conv""",
    """attention.self.query_proj""": """encoder.encoder.layer.*.attention.self.query_proj""",
    """attention.self.key_proj""": """encoder.encoder.layer.*.attention.self.key_proj""",
    """attention.self.value_proj""": """encoder.encoder.layer.*.attention.self.value_proj""",
    """attention.output.dense""": """encoder.encoder.layer.*.attention.output.dense""",
    """attention.output.LayerNorm""": """encoder.encoder.layer.*.attention.output.LayerNorm""",
    """intermediate.dense""": """encoder.encoder.layer.*.intermediate.dense""",
    """output.dense""": """encoder.encoder.layer.*.output.dense""",
    """output.LayerNorm""": """encoder.encoder.layer.*.output.LayerNorm""",
    """encoder.encoder.rel_embeddings""": """encoder.encoder.rel_embeddings""",
    """encoder.encoder.LayerNorm""": """encoder.encoder.LayerNorm""",
    """encoder.upsample.0""": """encoder.upsample.projection""",
    """encoder.layer_norm""": """encoder.layer_norm""",
    """w2v_model.layer_norm""": """layer_norm""",
    """w2v_encoder.proj""": """lm_head""",
    """mask_emb""": """masked_spec_embed""",
}


def 						_A     (       SCREAMING_SNAKE_CASE__						:			str  ,						SCREAMING_SNAKE_CASE__						:			Dict  ,						SCREAMING_SNAKE_CASE__						:			Optional[int]  ,						SCREAMING_SNAKE_CASE__						:			Dict  ,						SCREAMING_SNAKE_CASE__						:			Optional[int] ):
    for attribute in key.split('''.''' ):
        UpperCamelCase :List[Any]										=     getattr(SCREAMING_SNAKE_CASE__  ,						SCREAMING_SNAKE_CASE__ )

    if weight_type is not None:
        UpperCamelCase :Dict										=     getattr(SCREAMING_SNAKE_CASE__  ,						SCREAMING_SNAKE_CASE__ ).shape
    else:
        UpperCamelCase :int										=     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":
        UpperCamelCase :Any										=     value
    elif weight_type == "weight_g":
        UpperCamelCase :List[Any]										=     value
    elif weight_type == "weight_v":
        UpperCamelCase :str										=     value
    elif weight_type == "bias":
        UpperCamelCase :List[str]										=     value
    else:
        UpperCamelCase :Tuple										=     value

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


def 						_A     (       SCREAMING_SNAKE_CASE__						:			Optional[Any]  ,						SCREAMING_SNAKE_CASE__						:			List[str]  ,						SCREAMING_SNAKE_CASE__						:			Tuple ):
    UpperCamelCase :List[Any]										=     []
    UpperCamelCase :Tuple										=     fairseq_model.state_dict()

    UpperCamelCase :Tuple										=     hf_model.sew_d.feature_extractor if is_finetuned else hf_model.feature_extractor

    for name, value in fairseq_dict.items():
        UpperCamelCase :List[Any]										=     False
        if "conv_layers" in name:
            load_conv_layer(
                SCREAMING_SNAKE_CASE__  ,						SCREAMING_SNAKE_CASE__  ,						SCREAMING_SNAKE_CASE__  ,						SCREAMING_SNAKE_CASE__  ,						hf_model.config.feat_extract_norm == '''group'''  ,						)
            UpperCamelCase :List[str]										=     True
        else:
            for key, mapped_key in MAPPING.items():
                UpperCamelCase :Optional[int]										=     '''sew_d.''' + mapped_key if (is_finetuned and mapped_key != '''lm_head''') else mapped_key

                if key in name or key.split('''w2v_model.''' )[-1] == name.split('''.''' )[0]:
                    UpperCamelCase :List[str]										=     True
                    if "*" in mapped_key:
                        UpperCamelCase :Optional[Any]										=     name.split(SCREAMING_SNAKE_CASE__ )[0].split('''.''' )[-2]
                        if not layer_index.isnumeric():
                            continue
                        UpperCamelCase :Optional[Any]										=     mapped_key.replace('''*'''  ,						SCREAMING_SNAKE_CASE__ )
                    if "weight_g" in name:
                        UpperCamelCase :int										=     '''weight_g'''
                    elif "weight_v" in name:
                        UpperCamelCase :Union[str, Any]										=     '''weight_v'''
                    elif "weight" in name:
                        UpperCamelCase :Dict										=     '''weight'''
                    elif "bias" in name:
                        UpperCamelCase :Any										=     '''bias'''
                    else:
                        UpperCamelCase :str										=     None
                    set_recursively(SCREAMING_SNAKE_CASE__  ,						SCREAMING_SNAKE_CASE__  ,						SCREAMING_SNAKE_CASE__  ,						SCREAMING_SNAKE_CASE__  ,						SCREAMING_SNAKE_CASE__ )
                continue
        if not is_used:
            unused_weights.append(SCREAMING_SNAKE_CASE__ )

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


def 						_A     (       SCREAMING_SNAKE_CASE__						:			str  ,						SCREAMING_SNAKE_CASE__						:			str  ,						SCREAMING_SNAKE_CASE__						:			str  ,						SCREAMING_SNAKE_CASE__						:			Tuple  ,						SCREAMING_SNAKE_CASE__						:			Any ):
    UpperCamelCase :Tuple										=     full_name.split('''conv_layers.''' )[-1]
    UpperCamelCase :Union[str, Any]										=     name.split('''.''' )
    UpperCamelCase :Dict										=     int(items[0] )
    UpperCamelCase :Any										=     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.'''
            )
            UpperCamelCase :Optional[int]										=     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.'''
            )
            UpperCamelCase :Any										=     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."
            )
            UpperCamelCase :int										=     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.'''
            )
            UpperCamelCase :Optional[Any]										=     value
            logger.info(F'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.''' )
    else:
        unused_weights.append(SCREAMING_SNAKE_CASE__ )


def 						_A     (       SCREAMING_SNAKE_CASE__						:			str  ,						SCREAMING_SNAKE_CASE__						:			Any ):
    UpperCamelCase :Any										=     SEWDConfig()
    if is_finetuned:
        UpperCamelCase :List[Any]										=     model.wav_encoder.wav_model.cfg
    else:
        UpperCamelCase :Dict										=     model.cfg

    UpperCamelCase :Any										=     fs_config.conv_bias
    UpperCamelCase :List[Any]										=     eval(fs_config.conv_feature_layers )
    UpperCamelCase :Optional[int]										=     [x[0] for x in conv_layers]
    UpperCamelCase :List[str]										=     [x[1] for x in conv_layers]
    UpperCamelCase :Optional[Any]										=     [x[2] for x in conv_layers]
    UpperCamelCase :Any										=     '''gelu'''
    UpperCamelCase :Tuple										=     '''layer''' if fs_config.extractor_mode == '''layer_norm''' else '''group'''
    UpperCamelCase :Optional[int]										=     0.0
    UpperCamelCase :Tuple										=     fs_config.activation_fn.name
    UpperCamelCase :Optional[Any]										=     fs_config.encoder_embed_dim
    UpperCamelCase :Optional[Any]										=     0.02
    UpperCamelCase :Optional[Any]										=     fs_config.encoder_ffn_embed_dim
    UpperCamelCase :Optional[Any]										=     1e-5
    UpperCamelCase :Optional[Any]										=     fs_config.encoder_layerdrop
    UpperCamelCase :Dict										=     fs_config.encoder_attention_heads
    UpperCamelCase :List[str]										=     fs_config.conv_pos_groups
    UpperCamelCase :Union[str, Any]										=     fs_config.conv_pos
    UpperCamelCase :Tuple										=     len(SCREAMING_SNAKE_CASE__ )
    UpperCamelCase :List[Any]										=     fs_config.encoder_layers
    UpperCamelCase :Optional[Any]										=     fs_config.squeeze_factor
    # DeBERTa-specific parameters:
    UpperCamelCase :List[str]										=     fs_config.max_position_embeddings
    UpperCamelCase :str										=     fs_config.position_buckets
    UpperCamelCase :int										=     fs_config.share_att_key
    UpperCamelCase :Dict										=     fs_config.relative_attention
    UpperCamelCase :List[Any]										=     fs_config.position_biased_input
    UpperCamelCase :Optional[Any]										=     tuple(fs_config.pos_att_type.split('''|''' ) )
    UpperCamelCase :str										=     fs_config.norm_rel_ebd

    # take care of any params that are overridden by the Wav2VecCtc model
    if is_finetuned:
        UpperCamelCase :Tuple										=     model.cfg
        UpperCamelCase :Tuple										=     fs_config.final_dropout
        UpperCamelCase :Any										=     fs_config.layerdrop
    UpperCamelCase :List[Any]										=     fs_config.activation_dropout
    UpperCamelCase :Any										=     fs_config.mask_prob > 0 or fs_config.mask_channel_prob > 0
    UpperCamelCase :Optional[Any]										=     fs_config.attention_dropout
    UpperCamelCase :Dict										=     fs_config.dropout_input
    UpperCamelCase :int										=     fs_config.dropout
    UpperCamelCase :int										=     fs_config.mask_channel_length
    UpperCamelCase :Dict										=     fs_config.mask_channel_prob
    UpperCamelCase :Union[str, Any]										=     fs_config.mask_length
    UpperCamelCase :List[str]										=     fs_config.mask_prob

    UpperCamelCase :int										=     '''Wav2Vec2FeatureExtractor'''
    UpperCamelCase :int										=     '''Wav2Vec2CTCTokenizer'''

    return config


@torch.no_grad()
def 						_A     (       SCREAMING_SNAKE_CASE__						:			str  ,						SCREAMING_SNAKE_CASE__						:			Optional[int]  ,						SCREAMING_SNAKE_CASE__						:			Any=None  ,						SCREAMING_SNAKE_CASE__						:			List[str]=None  ,						SCREAMING_SNAKE_CASE__						:			Any=True ):

    if is_finetuned:
        UpperCamelCase							,     UpperCamelCase							,     UpperCamelCase :str										=     fairseq.checkpoint_utils.load_model_ensemble_and_task(
            [checkpoint_path]  ,						arg_overrides={'''data''': '''/'''.join(dict_path.split('''/''' )[:-1] )} )
    else:
        UpperCamelCase							,     UpperCamelCase							,     UpperCamelCase :Union[str, Any]										=     fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path] )

    if config_path is not None:
        UpperCamelCase :str										=     SEWDConfig.from_pretrained(SCREAMING_SNAKE_CASE__ )
    else:
        UpperCamelCase :Tuple										=     convert_config(model[0]  ,						SCREAMING_SNAKE_CASE__ )
    UpperCamelCase :str										=     model[0].eval()

    UpperCamelCase :Tuple										=     True if config.feat_extract_norm == '''layer''' else False
    UpperCamelCase :Dict										=     WavaVecaFeatureExtractor(
        feature_size=1  ,						sampling_rate=16000  ,						padding_value=0  ,						do_normalize=SCREAMING_SNAKE_CASE__  ,						return_attention_mask=SCREAMING_SNAKE_CASE__  ,						)

    if is_finetuned:
        if dict_path:
            UpperCamelCase :Tuple										=     Dictionary.load(SCREAMING_SNAKE_CASE__ )

            # important change bos & pad token id since CTC symbol is <pad> and
            # not <s> as in fairseq
            UpperCamelCase :int										=     target_dict.pad_index
            UpperCamelCase :Union[str, Any]										=     target_dict.bos_index
            UpperCamelCase :Dict										=     target_dict.pad_index
            UpperCamelCase :Optional[int]										=     target_dict.bos_index
            UpperCamelCase :Optional[Any]										=     target_dict.eos_index
            UpperCamelCase :List[str]										=     len(target_dict.symbols )
            UpperCamelCase :Dict										=     os.path.join(SCREAMING_SNAKE_CASE__  ,						'''vocab.json''' )
            if not os.path.isdir(SCREAMING_SNAKE_CASE__ ):
                logger.error('''--pytorch_dump_folder_path ({}) should be a directory'''.format(SCREAMING_SNAKE_CASE__ ) )
                return
            os.makedirs(SCREAMING_SNAKE_CASE__  ,						exist_ok=SCREAMING_SNAKE_CASE__ )
            with open(SCREAMING_SNAKE_CASE__  ,						'''w'''  ,						encoding='''utf-8''' ) as vocab_handle:
                json.dump(target_dict.indices  ,						SCREAMING_SNAKE_CASE__ )
            UpperCamelCase :Dict										=     WavaVecaCTCTokenizer(
                SCREAMING_SNAKE_CASE__  ,						unk_token=target_dict.unk_word  ,						pad_token=target_dict.pad_word  ,						bos_token=target_dict.bos_word  ,						eos_token=target_dict.eos_word  ,						word_delimiter_token='''|'''  ,						do_lower_case=SCREAMING_SNAKE_CASE__  ,						)
            UpperCamelCase :Optional[int]										=     WavaVecaProcessor(feature_extractor=SCREAMING_SNAKE_CASE__  ,						tokenizer=SCREAMING_SNAKE_CASE__ )
            processor.save_pretrained(SCREAMING_SNAKE_CASE__ )

        UpperCamelCase :Tuple										=     SEWDForCTC(SCREAMING_SNAKE_CASE__ )
    else:
        UpperCamelCase :List[str]										=     SEWDModel(SCREAMING_SNAKE_CASE__ )
        feature_extractor.save_pretrained(SCREAMING_SNAKE_CASE__ )

    recursively_load_weights(SCREAMING_SNAKE_CASE__  ,						SCREAMING_SNAKE_CASE__  ,						SCREAMING_SNAKE_CASE__ )

    hf_model.save_pretrained(SCREAMING_SNAKE_CASE__ )


if __name__ == "__main__":
 __snake_case							      =						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("""--config_path""", default=None, type=str, help="""Path to hf config.json of model to convert""")
 parser.add_argument(
     """--is_finetuned""", action="""store_true""", help="""Whether the model to convert is a fine-tuned model or not"""
 )
 __snake_case							      =						parser.parse_args()
 convert_sew_checkpoint(
     args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, args.is_finetuned
 )













from __future__ import annotations

import inspect
import unittest

import numpy as np

from transformers import SwinConfig
from transformers.testing_utils import require_tf, require_vision, slow, to_atuple
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.models.swin.modeling_tf_swin import (
     TF_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST,
     TFSwinForImageClassification,
     TFSwinForMaskedImageModeling,
     TFSwinModel,
 )


if is_vision_available():
 from PIL import Image

 from transformers import AutoImageProcessor







class     UpperCAmelCase_		:



      """simple docstring"""
      def __init__(       self					,       SCREAMING_SNAKE_CASE_					,       SCREAMING_SNAKE_CASE_=13					,       SCREAMING_SNAKE_CASE_=32					,       SCREAMING_SNAKE_CASE_=2					,       SCREAMING_SNAKE_CASE_=3					,       SCREAMING_SNAKE_CASE_=16					,       SCREAMING_SNAKE_CASE_=[1, 2, 1]					,       SCREAMING_SNAKE_CASE_=[2, 2, 4]					,       SCREAMING_SNAKE_CASE_=2					,       SCREAMING_SNAKE_CASE_=2.0					,       SCREAMING_SNAKE_CASE_=True					,       SCREAMING_SNAKE_CASE_=0.0					,       SCREAMING_SNAKE_CASE_=0.0					,       SCREAMING_SNAKE_CASE_=0.1					,       SCREAMING_SNAKE_CASE_="gelu"					,       SCREAMING_SNAKE_CASE_=False					,       SCREAMING_SNAKE_CASE_=True					,       SCREAMING_SNAKE_CASE_=0.02					,       SCREAMING_SNAKE_CASE_=1e-5					,       SCREAMING_SNAKE_CASE_=True					,       SCREAMING_SNAKE_CASE_=None					,       SCREAMING_SNAKE_CASE_=True					,       SCREAMING_SNAKE_CASE_=10					,       SCREAMING_SNAKE_CASE_=8					,       )       ->   None:
          UpperCamelCase :Optional[Any]										=     parent
          UpperCamelCase :Optional[Any]										=     batch_size
          UpperCamelCase :List[Any]										=     image_size
          UpperCamelCase :List[Any]										=     patch_size
          UpperCamelCase :List[Any]										=     num_channels
          UpperCamelCase :Optional[Any]										=     embed_dim
          UpperCamelCase :List[Any]										=     depths
          UpperCamelCase :List[Any]										=     num_heads
          UpperCamelCase :Tuple										=     window_size
          UpperCamelCase :Optional[int]										=     mlp_ratio
          UpperCamelCase :int										=     qkv_bias
          UpperCamelCase :str										=     hidden_dropout_prob
          UpperCamelCase :Dict										=     attention_probs_dropout_prob
          UpperCamelCase :Dict										=     drop_path_rate
          UpperCamelCase :Optional[Any]										=     hidden_act
          UpperCamelCase :int										=     use_absolute_embeddings
          UpperCamelCase :Any										=     patch_norm
          UpperCamelCase :str										=     layer_norm_eps
          UpperCamelCase :List[Any]										=     initializer_range
          UpperCamelCase :str										=     is_training
          UpperCamelCase :Union[str, Any]										=     scope
          UpperCamelCase :List[str]										=     use_labels
          UpperCamelCase :Union[str, Any]										=     type_sequence_label_size
          UpperCamelCase :int										=     encoder_stride
      def 					UpperCAmelCase		(       self				)       ->   Optional[int]:
          UpperCamelCase :Dict										=     floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]				)

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

          UpperCamelCase :List[str]										=     self.get_config()

          return config, pixel_values, labels
      def 					UpperCAmelCase		(       self				)       ->   int:
          return SwinConfig(
              image_size=self.image_size					,       patch_size=self.patch_size					,       num_channels=self.num_channels					,       embed_dim=self.embed_dim					,       depths=self.depths					,       num_heads=self.num_heads					,       window_size=self.window_size					,       mlp_ratio=self.mlp_ratio					,       qkv_bias=self.qkv_bias					,       hidden_dropout_prob=self.hidden_dropout_prob					,       attention_probs_dropout_prob=self.attention_probs_dropout_prob					,       drop_path_rate=self.drop_path_rate					,       hidden_act=self.hidden_act					,       use_absolute_embeddings=self.use_absolute_embeddings					,       path_norm=self.patch_norm					,       layer_norm_eps=self.layer_norm_eps					,       initializer_range=self.initializer_range					,       encoder_stride=self.encoder_stride					,       )
      def 					UpperCAmelCase		(       self					,       SCREAMING_SNAKE_CASE_					,       SCREAMING_SNAKE_CASE_					,       SCREAMING_SNAKE_CASE_				)       ->   List[Any]:
          UpperCamelCase :int										=     TFSwinModel(config=SCREAMING_SNAKE_CASE_				)
          UpperCamelCase :Tuple										=     model(SCREAMING_SNAKE_CASE_				)

          UpperCamelCase :str										=     ((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths				) - 1))
          UpperCamelCase :Union[str, Any]										=     int(config.embed_dim * 2 ** (len(config.depths				) - 1)				)

          self.parent.assertEqual(result.last_hidden_state.shape					,       (self.batch_size, expected_seq_len, expected_dim)				)
      def 					UpperCAmelCase		(       self					,       SCREAMING_SNAKE_CASE_					,       SCREAMING_SNAKE_CASE_					,       SCREAMING_SNAKE_CASE_				)       ->   Any:
          UpperCamelCase :int										=     TFSwinForMaskedImageModeling(config=SCREAMING_SNAKE_CASE_				)
          UpperCamelCase :List[str]										=     model(SCREAMING_SNAKE_CASE_				)
          self.parent.assertEqual(
              result.logits.shape					,       (self.batch_size, self.num_channels, self.image_size, self.image_size)				)

          # test greyscale images
          UpperCamelCase :Optional[int]										=     1
          UpperCamelCase :int										=     TFSwinForMaskedImageModeling(SCREAMING_SNAKE_CASE_				)

          UpperCamelCase :str										=     floats_tensor([self.batch_size, 1, self.image_size, self.image_size]				)
          UpperCamelCase :str										=     model(SCREAMING_SNAKE_CASE_				)
          self.parent.assertEqual(result.logits.shape					,       (self.batch_size, 1, self.image_size, self.image_size)				)
      def 					UpperCAmelCase		(       self					,       SCREAMING_SNAKE_CASE_					,       SCREAMING_SNAKE_CASE_					,       SCREAMING_SNAKE_CASE_				)       ->   List[str]:
          UpperCamelCase :Optional[Any]										=     self.type_sequence_label_size
          UpperCamelCase :Optional[int]										=     TFSwinForImageClassification(SCREAMING_SNAKE_CASE_				)
          UpperCamelCase :Tuple										=     model(SCREAMING_SNAKE_CASE_					,       labels=SCREAMING_SNAKE_CASE_				)
          self.parent.assertEqual(result.logits.shape					,       (self.batch_size, self.type_sequence_label_size)				)

          # test greyscale images
          UpperCamelCase :Union[str, Any]										=     1
          UpperCamelCase :Tuple										=     TFSwinForImageClassification(SCREAMING_SNAKE_CASE_				)
          UpperCamelCase :Any										=     floats_tensor([self.batch_size, 1, self.image_size, self.image_size]				)
          UpperCamelCase :Tuple										=     model(SCREAMING_SNAKE_CASE_				)
          self.parent.assertEqual(result.logits.shape					,       (self.batch_size, self.type_sequence_label_size)				)




      def 					UpperCAmelCase		(       self				)       ->   int:
          UpperCamelCase :List[str]										=     self.prepare_config_and_inputs()
          UpperCamelCase							,     UpperCamelCase							,     UpperCamelCase :Optional[Any]										=     config_and_inputs
          UpperCamelCase :Any										=     {'''pixel_values''': pixel_values}
          return config, inputs_dict







@require_tf
class     UpperCAmelCase_		(      lowercase,			lowercase,			unittest.TestCase				):



      """simple docstring"""
      UpperCamelCase_       :      int     =(
          (
              TFSwinModel,
              TFSwinForImageClassification,
              TFSwinForMaskedImageModeling,
          )
          if is_tf_available()
          else ()
      )
      UpperCamelCase_       :      Any     =(
          {'feature-extraction': TFSwinModel, 'image-classification': TFSwinForImageClassification}
          if is_tf_available()
          else {}
      )

      UpperCamelCase_       :      Optional[int]     =False
      UpperCamelCase_       :      int     =False
      UpperCamelCase_       :      Optional[int]     =False
      UpperCamelCase_       :      Any     =False
      def 					UpperCAmelCase		(       self				)       ->   Tuple:
          UpperCamelCase :str										=     TFSwinModelTester(self				)
          UpperCamelCase :str										=     ConfigTester(self					,       config_class=SCREAMING_SNAKE_CASE_					,       embed_dim=37				)
      def 					UpperCAmelCase		(       self				)       ->   Dict:
          self.create_and_test_config_common_properties()
          self.config_tester.create_and_test_config_to_json_string()
          self.config_tester.create_and_test_config_to_json_file()
          self.config_tester.create_and_test_config_from_and_save_pretrained()
          self.config_tester.create_and_test_config_with_num_labels()
          self.config_tester.check_config_can_be_init_without_params()
          self.config_tester.check_config_arguments_init()
      def 					UpperCAmelCase		(       self				)       ->   Union[str, Any]:
          return
      def 					UpperCAmelCase		(       self				)       ->   Optional[Any]:
          UpperCamelCase :Any										=     self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_model(*SCREAMING_SNAKE_CASE_				)
      def 					UpperCAmelCase		(       self				)       ->   Optional[int]:
          UpperCamelCase :Optional[int]										=     self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_masked_image_modeling(*SCREAMING_SNAKE_CASE_				)
      def 					UpperCAmelCase		(       self				)       ->   Optional[Any]:
          UpperCamelCase :Any										=     self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_image_classification(*SCREAMING_SNAKE_CASE_				)
      @unittest.skip(reason='''Swin does not use inputs_embeds'''				)
      def 					UpperCAmelCase		(       self				)       ->   Union[str, Any]:
          pass
      def 					UpperCAmelCase		(       self				)       ->   Optional[int]:
          UpperCamelCase							,     UpperCamelCase :int										=     self.model_tester.prepare_config_and_inputs_for_common()

          for model_class in self.all_model_classes:
              UpperCamelCase :Optional[int]										=     model_class(SCREAMING_SNAKE_CASE_				)
              self.assertIsInstance(model.get_input_embeddings()					,       tf.keras.layers.Layer				)
              UpperCamelCase :Union[str, Any]										=     model.get_output_embeddings()
              self.assertTrue(x is None or isinstance(SCREAMING_SNAKE_CASE_					,       tf.keras.layers.Dense				)				)
      def 					UpperCAmelCase		(       self				)       ->   str:
          UpperCamelCase							,     UpperCamelCase :str										=     self.model_tester.prepare_config_and_inputs_for_common()

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

              UpperCamelCase :Tuple										=     ['''pixel_values''']
              self.assertListEqual(arg_names[:1]					,       SCREAMING_SNAKE_CASE_				)
      def 					UpperCAmelCase		(       self				)       ->   List[Any]:
          UpperCamelCase							,     UpperCamelCase :Optional[int]										=     self.model_tester.prepare_config_and_inputs_for_common()
          UpperCamelCase :str										=     True

          for model_class in self.all_model_classes:
              UpperCamelCase :List[Any]										=     True
              UpperCamelCase :Optional[Any]										=     False
              UpperCamelCase :List[str]										=     True
              UpperCamelCase :Optional[int]										=     model_class(SCREAMING_SNAKE_CASE_				)
              UpperCamelCase :Union[str, Any]										=     model(**self._prepare_for_class(SCREAMING_SNAKE_CASE_					,       SCREAMING_SNAKE_CASE_				)				)
              UpperCamelCase :List[Any]										=     outputs.attentions
              UpperCamelCase :int										=     len(self.model_tester.depths				)
              self.assertEqual(len(SCREAMING_SNAKE_CASE_				)					,       SCREAMING_SNAKE_CASE_				)

              # check that output_attentions also work using config
              del inputs_dict["output_attentions"]
              UpperCamelCase :Optional[Any]										=     True
              UpperCamelCase :List[Any]										=     config.window_size**2
              UpperCamelCase :Dict										=     model_class(SCREAMING_SNAKE_CASE_				)
              UpperCamelCase :Dict										=     model(**self._prepare_for_class(SCREAMING_SNAKE_CASE_					,       SCREAMING_SNAKE_CASE_				)				)
              UpperCamelCase :List[Any]										=     outputs.attentions
              self.assertEqual(len(SCREAMING_SNAKE_CASE_				)					,       SCREAMING_SNAKE_CASE_				)

              self.assertListEqual(
                  list(attentions[0].shape[-3:]				)					,       [self.model_tester.num_heads[0], window_size_squared, window_size_squared]					,       )
              UpperCamelCase :List[Any]										=     len(SCREAMING_SNAKE_CASE_				)

              # Check attention is always last and order is fine
              UpperCamelCase :int										=     True
              UpperCamelCase :List[str]										=     True
              UpperCamelCase :List[str]										=     model_class(SCREAMING_SNAKE_CASE_				)
              UpperCamelCase :Tuple										=     model(**self._prepare_for_class(SCREAMING_SNAKE_CASE_					,       SCREAMING_SNAKE_CASE_				)				)

              if hasattr(self.model_tester					,       '''num_hidden_states_types'''				):
                  UpperCamelCase :Union[str, Any]										=     self.model_tester.num_hidden_states_types
              else:
                  # also another +1 for reshaped_hidden_states
                  UpperCamelCase :Optional[int]										=     2
              self.assertEqual(out_len + added_hidden_states					,       len(SCREAMING_SNAKE_CASE_				)				)

              UpperCamelCase :Optional[int]										=     outputs.attentions

              self.assertEqual(len(SCREAMING_SNAKE_CASE_				)					,       SCREAMING_SNAKE_CASE_				)

              self.assertListEqual(
                  list(self_attentions[0].shape[-3:]				)					,       [self.model_tester.num_heads[0], window_size_squared, window_size_squared]					,       )
      def 					UpperCAmelCase		(       self					,       SCREAMING_SNAKE_CASE_					,       SCREAMING_SNAKE_CASE_					,       SCREAMING_SNAKE_CASE_					,       SCREAMING_SNAKE_CASE_				)       ->   Dict:
          UpperCamelCase :Optional[int]										=     model_class(SCREAMING_SNAKE_CASE_				)
          UpperCamelCase :Any										=     model(**self._prepare_for_class(SCREAMING_SNAKE_CASE_					,       SCREAMING_SNAKE_CASE_				)				)
          UpperCamelCase :Union[str, Any]										=     outputs.hidden_states

          UpperCamelCase :Any										=     getattr(
              self.model_tester					,       '''expected_num_hidden_layers'''					,       len(self.model_tester.depths				) + 1				)
          self.assertEqual(len(SCREAMING_SNAKE_CASE_				)					,       SCREAMING_SNAKE_CASE_				)

          # Swin has a different seq_length
          UpperCamelCase :str										=     to_atuple(config.patch_size				)

          UpperCamelCase :Optional[int]										=     (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])

          self.assertListEqual(
              list(hidden_states[0].shape[-2:]				)					,       [num_patches, self.model_tester.embed_dim]					,       )

          UpperCamelCase :str										=     outputs.reshaped_hidden_states
          self.assertEqual(len(SCREAMING_SNAKE_CASE_				)					,       SCREAMING_SNAKE_CASE_				)

          UpperCamelCase							,     UpperCamelCase							,     UpperCamelCase							,     UpperCamelCase :Optional[int]										=     reshaped_hidden_states[0].shape

          UpperCamelCase :int										=     tf.reshape(reshaped_hidden_states[0]					,       (batch_size, num_channels, height * width)				)
          UpperCamelCase :Dict										=     tf.transpose(SCREAMING_SNAKE_CASE_					,       (0, 2, 1)				)

          self.assertListEqual(
              list(reshaped_hidden_states.shape[-2:]				)					,       [num_patches, self.model_tester.embed_dim]					,       )
      def 					UpperCAmelCase		(       self				)       ->   str:
          UpperCamelCase							,     UpperCamelCase :int										=     self.model_tester.prepare_config_and_inputs_for_common()

          UpperCamelCase :List[Any]										=     to_atuple(self.model_tester.image_size				)

          for model_class in self.all_model_classes:
              UpperCamelCase :List[Any]										=     True
              self.check_hidden_states_output(SCREAMING_SNAKE_CASE_					,       SCREAMING_SNAKE_CASE_					,       SCREAMING_SNAKE_CASE_					,       SCREAMING_SNAKE_CASE_				)

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

              self.check_hidden_states_output(SCREAMING_SNAKE_CASE_					,       SCREAMING_SNAKE_CASE_					,       SCREAMING_SNAKE_CASE_					,       SCREAMING_SNAKE_CASE_				)
      def 					UpperCAmelCase		(       self				)       ->   Optional[Any]:
          UpperCamelCase							,     UpperCamelCase :List[Any]										=     self.model_tester.prepare_config_and_inputs_for_common()
          UpperCamelCase :List[Any]										=     3

          UpperCamelCase :Any										=     to_atuple(self.model_tester.image_size				)
          UpperCamelCase :List[Any]										=     to_atuple(config.patch_size				)

          UpperCamelCase :Dict										=     image_size[0] + patch_size[0] - (image_size[0] % patch_size[0])
          UpperCamelCase :List[str]										=     image_size[1] + patch_size[1] - (image_size[1] % patch_size[1])

          for model_class in self.all_model_classes:
              UpperCamelCase :List[Any]										=     True
              self.check_hidden_states_output(SCREAMING_SNAKE_CASE_					,       SCREAMING_SNAKE_CASE_					,       SCREAMING_SNAKE_CASE_					,       (padded_height, padded_width)				)

              # check that output_hidden_states also work using config
              del inputs_dict["output_hidden_states"]
              UpperCamelCase :str										=     True
              self.check_hidden_states_output(SCREAMING_SNAKE_CASE_					,       SCREAMING_SNAKE_CASE_					,       SCREAMING_SNAKE_CASE_					,       (padded_height, padded_width)				)




      @slow
      def 					UpperCAmelCase		(       self				)       ->   List[Any]:
          for model_name in TF_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
              UpperCamelCase :Dict										=     TFSwinModel.from_pretrained(SCREAMING_SNAKE_CASE_				)
              self.assertIsNotNone(SCREAMING_SNAKE_CASE_				)







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



      """simple docstring"""
      @cached_property
      def 					UpperCAmelCase		(       self				)       ->   Union[str, Any]:
          return (
              AutoImageProcessor.from_pretrained('''microsoft/swin-tiny-patch4-window7-224'''				)
              if is_vision_available()
              else None
          )




      @slow
      def 					UpperCAmelCase		(       self				)       ->   Optional[int]:
          UpperCamelCase :Tuple										=     TFSwinForImageClassification.from_pretrained('''microsoft/swin-tiny-patch4-window7-224'''				)
          UpperCamelCase :int										=     self.default_image_processor

          UpperCamelCase :Optional[int]										=     Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''				)
          UpperCamelCase :List[str]										=     image_processor(images=SCREAMING_SNAKE_CASE_					,       return_tensors='''tf'''				)

          # forward pass
          UpperCamelCase :Optional[Any]										=     model(SCREAMING_SNAKE_CASE_				)

          # verify the logits
          UpperCamelCase :Tuple										=     tf.TensorShape((1, 1000)				)
          self.assertEqual(outputs.logits.shape					,       SCREAMING_SNAKE_CASE_				)
          UpperCamelCase :List[Any]										=     tf.constant([-0.0948, -0.6454, -0.0921]				)
          self.assertTrue(np.allclose(outputs.logits[0, :3]					,       SCREAMING_SNAKE_CASE_					,       atol=1e-4				)				)





