





"""simple docstring"""


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    __SCREAMING_SNAKE_CASE			:







  '''simple docstring'''


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

  def       __SCREAMING_SNAKE_CASE (						self      :				int		)						-> Dict:
        _UpperCamelCase					:		str  =				floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]		)

        _UpperCamelCase					:		Dict  =				None
        if self.use_labels:
              _UpperCamelCase					:		Tuple  =				ids_tensor([self.batch_size]    ,      self.type_sequence_label_size		)

        _UpperCamelCase					:		Dict  =				self.get_config()

        return config, pixel_values, labels

  def       __SCREAMING_SNAKE_CASE (						self      :				Union[str, Any]		)						-> Optional[Any]:
        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       __SCREAMING_SNAKE_CASE (						self      :				List[str]    ,      __a      :				Dict    ,      __a      :				Dict    ,      __a      :				Optional[int]		)						-> int:
        _UpperCamelCase					:		Optional[Any]  =				TFSwinModel(config=__a		)
        _UpperCamelCase					:		List[str]  =				model(__a		)

        _UpperCamelCase					:		Optional[int]  =				((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       __SCREAMING_SNAKE_CASE (						self      :				Optional[int]    ,      __a      :				Optional[int]    ,      __a      :				int    ,      __a      :				Optional[Any]		)						-> int:
        _UpperCamelCase					:		List[Any]  =				TFSwinForMaskedImageModeling(config=__a		)
        _UpperCamelCase					:		Dict  =				model(__a		)
        self.parent.assertEqual(
            result.logits.shape    ,      (self.batch_size, self.num_channels, self.image_size, self.image_size)		)

        # test greyscale images
        _UpperCamelCase					:		Tuple  =				1
        _UpperCamelCase					:		str  =				TFSwinForMaskedImageModeling(__a		)

        _UpperCamelCase					:		Optional[Any]  =				floats_tensor([self.batch_size, 1, self.image_size, self.image_size]		)
        _UpperCamelCase					:		Tuple  =				model(__a		)
        self.parent.assertEqual(result.logits.shape    ,      (self.batch_size, 1, self.image_size, self.image_size)		)

  def       __SCREAMING_SNAKE_CASE (						self      :				List[str]    ,      __a      :				Optional[int]    ,      __a      :				Union[str, Any]    ,      __a      :				List[str]		)						-> Dict:
        _UpperCamelCase					:		List[str]  =				self.type_sequence_label_size
        _UpperCamelCase					:		Tuple  =				TFSwinForImageClassification(__a		)
        _UpperCamelCase					:		Optional[int]  =				model(__a    ,      labels=__a		)
        self.parent.assertEqual(result.logits.shape    ,      (self.batch_size, self.type_sequence_label_size)		)

        # test greyscale images
        _UpperCamelCase					:		Dict  =				1
        _UpperCamelCase					:		int  =				TFSwinForImageClassification(__a		)
        _UpperCamelCase					:		Tuple  =				floats_tensor([self.batch_size, 1, self.image_size, self.image_size]		)
        _UpperCamelCase					:		Any  =				model(__a		)
        self.parent.assertEqual(result.logits.shape    ,      (self.batch_size, self.type_sequence_label_size)		)


  def       __SCREAMING_SNAKE_CASE (						self      :				Tuple		)						-> List[Any]:
        _UpperCamelCase					:		Dict  =				self.prepare_config_and_inputs()
        _UpperCamelCase,     _UpperCamelCase,     _UpperCamelCase					:		List[str]  =				config_and_inputs
        _UpperCamelCase					:		List[str]  =				{"pixel_values": pixel_values}
        return config, inputs_dict





@require_tf
class    __SCREAMING_SNAKE_CASE			(  _UpperCamelCase						,     _UpperCamelCase						,     unittest.TestCase      ):







  '''simple docstring'''


  SCREAMING_SNAKE_CASE__							:List[str]		   =    (
      (
          TFSwinModel,
          TFSwinForImageClassification,
          TFSwinForMaskedImageModeling,
      )
      if is_tf_available()
      else ()
  )
  SCREAMING_SNAKE_CASE__							:List[str]		   =    (
      {"feature-extraction": TFSwinModel, "image-classification": TFSwinForImageClassification}
      if is_tf_available()
      else {}
  )

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

  def       __SCREAMING_SNAKE_CASE (						self      :				List[str]		)						-> Optional[int]:
        _UpperCamelCase					:		Optional[Any]  =				TFSwinModelTester(self		)
        _UpperCamelCase					:		Optional[Any]  =				ConfigTester(self    ,      config_class=__a    ,      embed_dim=37		)

  def       __SCREAMING_SNAKE_CASE (						self      :				str		)						-> Optional[int]:
        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       __SCREAMING_SNAKE_CASE (						self      :				Optional[Any]		)						-> Optional[int]:
        return

  def       __SCREAMING_SNAKE_CASE (						self      :				int		)						-> Union[str, Any]:
        _UpperCamelCase					:		Any  =				self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*__a		)

  def       __SCREAMING_SNAKE_CASE (						self      :				Tuple		)						-> str:
        _UpperCamelCase					:		List[str]  =				self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_masked_image_modeling(*__a		)

  def       __SCREAMING_SNAKE_CASE (						self      :				Optional[int]		)						-> Any:
        _UpperCamelCase					:		Optional[int]  =				self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_image_classification(*__a		)

  @unittest.skip(reason="Swin does not use inputs_embeds"		)
  def       __SCREAMING_SNAKE_CASE (						self      :				str		)						-> List[str]:
        pass

  def       __SCREAMING_SNAKE_CASE (						self      :				List[Any]		)						-> List[Any]:
        _UpperCamelCase,     _UpperCamelCase					:		List[str]  =				self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
              _UpperCamelCase					:		str  =				model_class(__a		)
              self.assertIsInstance(model.get_input_embeddings()    ,      tf.keras.layers.Layer		)
              _UpperCamelCase					:		str  =				model.get_output_embeddings()
              self.assertTrue(x is None or isinstance(__a    ,      tf.keras.layers.Dense		)		)

  def       __SCREAMING_SNAKE_CASE (						self      :				Tuple		)						-> Any:
        _UpperCamelCase,     _UpperCamelCase					:		Optional[Any]  =				self.model_tester.prepare_config_and_inputs_for_common()

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

              _UpperCamelCase					:		Dict  =				["pixel_values"]
              self.assertListEqual(arg_names[:1]    ,      __a		)

  def       __SCREAMING_SNAKE_CASE (						self      :				Dict		)						-> List[str]:
        _UpperCamelCase,     _UpperCamelCase					:		Dict  =				self.model_tester.prepare_config_and_inputs_for_common()
        _UpperCamelCase					:		Union[str, Any]  =				True

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

              # check that output_attentions also work using config
              del inputs_dict["output_attentions"]
              _UpperCamelCase					:		str  =				True
              _UpperCamelCase					:		Dict  =				config.window_size**2
              _UpperCamelCase					:		Optional[int]  =				model_class(__a		)
              _UpperCamelCase					:		Tuple  =				model(**self._prepare_for_class(__a    ,      __a		)		)
              _UpperCamelCase					:		str  =				outputs.attentions
              self.assertEqual(len(__a		)    ,      __a		)

              self.assertListEqual(
                  list(attentions[0].shape[-3:]		)    ,      [self.model_tester.num_heads[0], window_size_squared, window_size_squared]    ,      )
              _UpperCamelCase					:		Optional[Any]  =				len(__a		)

              # Check attention is always last and order is fine
              _UpperCamelCase					:		Optional[int]  =				True
              _UpperCamelCase					:		Dict  =				True
              _UpperCamelCase					:		Union[str, Any]  =				model_class(__a		)
              _UpperCamelCase					:		List[str]  =				model(**self._prepare_for_class(__a    ,      __a		)		)

              if hasattr(self.model_tester    ,      "num_hidden_states_types"		):
                    _UpperCamelCase					:		Optional[int]  =				self.model_tester.num_hidden_states_types
              else:
                    # also another +1 for reshaped_hidden_states
                    _UpperCamelCase					:		int  =				2
              self.assertEqual(out_len + added_hidden_states    ,      len(__a		)		)

              _UpperCamelCase					:		Tuple  =				outputs.attentions

              self.assertEqual(len(__a		)    ,      __a		)

              self.assertListEqual(
                  list(self_attentions[0].shape[-3:]		)    ,      [self.model_tester.num_heads[0], window_size_squared, window_size_squared]    ,      )

  def       __SCREAMING_SNAKE_CASE (						self      :				Optional[int]    ,      __a      :				Dict    ,      __a      :				int    ,      __a      :				Optional[Any]    ,      __a      :				List[str]		)						-> str:
        _UpperCamelCase					:		Tuple  =				model_class(__a		)
        _UpperCamelCase					:		List[str]  =				model(**self._prepare_for_class(__a    ,      __a		)		)
        _UpperCamelCase					:		Optional[Any]  =				outputs.hidden_states

        _UpperCamelCase					:		List[Any]  =				getattr(
            self.model_tester    ,      "expected_num_hidden_layers"    ,      len(self.model_tester.depths		) + 1		)
        self.assertEqual(len(__a		)    ,      __a		)

        # Swin has a different seq_length
        _UpperCamelCase					:		List[Any]  =				to_atuple(config.patch_size		)

        _UpperCamelCase					:		str  =				(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					:		Union[str, Any]  =				outputs.reshaped_hidden_states
        self.assertEqual(len(__a		)    ,      __a		)

        _UpperCamelCase,     _UpperCamelCase,     _UpperCamelCase,     _UpperCamelCase					:		Optional[Any]  =				reshaped_hidden_states[0].shape

        _UpperCamelCase					:		Dict  =				tf.reshape(reshaped_hidden_states[0]    ,      (batch_size, num_channels, height * width)		)
        _UpperCamelCase					:		Optional[Any]  =				tf.transpose(__a    ,      (0, 2, 1)		)

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

  def       __SCREAMING_SNAKE_CASE (						self      :				List[str]		)						-> List[Any]:
        _UpperCamelCase,     _UpperCamelCase					:		Dict  =				self.model_tester.prepare_config_and_inputs_for_common()

        _UpperCamelCase					:		int  =				to_atuple(self.model_tester.image_size		)

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

              # check that output_hidden_states also work using config
              del inputs_dict["output_hidden_states"]
              _UpperCamelCase					:		Any  =				True

              self.check_hidden_states_output(__a    ,      __a    ,      __a    ,      __a		)

  def       __SCREAMING_SNAKE_CASE (						self      :				Optional[int]		)						-> Dict:
        _UpperCamelCase,     _UpperCamelCase					:		Dict  =				self.model_tester.prepare_config_and_inputs_for_common()
        _UpperCamelCase					:		Dict  =				3

        _UpperCamelCase					:		Optional[Any]  =				to_atuple(self.model_tester.image_size		)
        _UpperCamelCase					:		Optional[Any]  =				to_atuple(config.patch_size		)

        _UpperCamelCase					:		int  =				image_size[0] + patch_size[0] - (image_size[0] % patch_size[0])
        _UpperCamelCase					:		Optional[Any]  =				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(__a    ,      __a    ,      __a    ,      (padded_height, padded_width)		)

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


  @slow
  def       __SCREAMING_SNAKE_CASE (						self      :				Any		)						-> Union[str, Any]:
        for model_name in TF_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
              _UpperCamelCase					:		Dict  =				TFSwinModel.from_pretrained(__a		)
              self.assertIsNotNone(__a		)





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







  '''simple docstring'''


  @cached_property
  def       __SCREAMING_SNAKE_CASE (						self      :				str		)						-> int:
        return (
            AutoImageProcessor.from_pretrained("microsoft/swin-tiny-patch4-window7-224"		)
            if is_vision_available()
            else None
        )


  @slow
  def       __SCREAMING_SNAKE_CASE (						self      :				Tuple		)						-> Dict:
        _UpperCamelCase					:		Union[str, Any]  =				TFSwinForImageClassification.from_pretrained("microsoft/swin-tiny-patch4-window7-224"		)
        _UpperCamelCase					:		List[str]  =				self.default_image_processor

        _UpperCamelCase					:		str  =				Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"		)
        _UpperCamelCase					:		int  =				image_processor(images=__a    ,      return_tensors="tf"		)

        # forward pass
        _UpperCamelCase					:		Union[str, Any]  =				model(__a		)

        # verify the logits
        _UpperCamelCase					:		Dict  =				tf.TensorShape((1, 1000)		)
        self.assertEqual(outputs.logits.shape    ,      __a		)
        _UpperCamelCase					:		List[Any]  =				tf.constant([-0.09_48, -0.64_54, -0.09_21]		)
        self.assertTrue(np.allclose(outputs.logits[0, :3]    ,      __a    ,      atol=1e-4		)		)







"""simple docstring"""


import argparse

import torch

from transformers import ImageGPTConfig, ImageGPTForCausalLM, load_tf_weights_in_imagegpt
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging


logging.set_verbosity_info()




def     lowercase__			(    lowercase_						,lowercase_						,lowercase_	)    ->						Union[str, Any]:




      """simple docstring"""

      _UpperCamelCase					:		Optional[int]  =				{"small": (512, 8, 24), "medium": (1_024, 8, 36), "large": (1_536, 16, 48)}
      _UpperCamelCase,     _UpperCamelCase,     _UpperCamelCase					:		List[Any]  =				MODELS[model_size]  # set model hyperparameters
      _UpperCamelCase					:		str  =				ImageGPTConfig(n_embd=lowercase_						,n_layer=lowercase_						,n_head=lowercase_	)
      _UpperCamelCase					:		Dict  =				ImageGPTForCausalLM(lowercase_	)

      # Load weights from numpy
      load_tf_weights_in_imagegpt(lowercase_						,lowercase_						,lowercase_	)

      # Save pytorch-model
      _UpperCamelCase					:		str  =				pytorch_dump_folder_path + "/" + WEIGHTS_NAME
      _UpperCamelCase					:		Any  =				pytorch_dump_folder_path + "/" + CONFIG_NAME
      print(F'''Save PyTorch model to {pytorch_weights_dump_path}'''	)
      torch.save(model.state_dict()						,lowercase_	)
      print(F'''Save configuration file to {pytorch_config_dump_path}'''	)
      with open(lowercase_						,"w"						,encoding="utf-8"	) as f:
            f.write(config.to_json_string()	)


if __name__ == "__main__":
  lowerCamelCase__      =      argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      "--imagegpt_checkpoint_path",
      default=None,
      type=str,
      required=True,
      help="Path to the TensorFlow checkpoint path.",
  )
  parser.add_argument(
      "--model_size",
      default=None,
      type=str,
      required=True,
      help="Size of the model (can be either 'small', 'medium' or 'large').",
  )
  parser.add_argument(
      "--pytorch_dump_folder_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
  )
  lowerCamelCase__      =      parser.parse_args()
  convert_imagegpt_checkpoint_to_pytorch(
      args.imagegpt_checkpoint_path, args.model_size, args.pytorch_dump_folder_path
  )
