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 _a   :

	'''simple docstring'''



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

	def      __UpperCAmelCase(   self    ):
			__A							:    Any         =      floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]    )

			__A							:    Optional[Any]         =      None
			if self.use_labels:
					__A							:    List[str]         =      ids_tensor([self.batch_size]							, self.type_sequence_label_size    )

			__A							:    int         =      self.get_config()

			return config, pixel_values, labels

	def      __UpperCAmelCase(   self    ):
			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							, __UpperCAmelCase							, __UpperCAmelCase							, __UpperCAmelCase    ):
			__A							:    str         =      TFSwinModel(config=lowerCAmelCase__    )
			__A							:    str         =      model(lowerCAmelCase__    )

			__A							:    Union[str, Any]         =      ((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths    ) - 1))
			__A							:    Optional[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							, __UpperCAmelCase							, __UpperCAmelCase							, __UpperCAmelCase    ):
			__A							:    Optional[int]         =      TFSwinForMaskedImageModeling(config=lowerCAmelCase__    )
			__A							:    Any         =      model(lowerCAmelCase__    )
			self.parent.assertEqual(
			    result.logits.shape							, (self.batch_size, self.num_channels, self.image_size, self.image_size)    )

			# test greyscale images
			__A							:    Optional[int]         =      1
			__A							:    str         =      TFSwinForMaskedImageModeling(lowerCAmelCase__    )

			__A							:    List[str]         =      floats_tensor([self.batch_size, 1, self.image_size, self.image_size]    )
			__A							:    int         =      model(lowerCAmelCase__    )
			self.parent.assertEqual(result.logits.shape							, (self.batch_size, 1, self.image_size, self.image_size)    )

	def      __UpperCAmelCase(   self							, __UpperCAmelCase							, __UpperCAmelCase							, __UpperCAmelCase    ):
			__A							:    str         =      self.type_sequence_label_size
			__A							:    List[Any]         =      TFSwinForImageClassification(lowerCAmelCase__    )
			__A							:    Optional[Any]         =      model(lowerCAmelCase__							, labels=lowerCAmelCase__    )
			self.parent.assertEqual(result.logits.shape							, (self.batch_size, self.type_sequence_label_size)    )

			# test greyscale images
			__A							:    int         =      1
			__A							:    List[Any]         =      TFSwinForImageClassification(lowerCAmelCase__    )
			__A							:    List[str]         =      floats_tensor([self.batch_size, 1, self.image_size, self.image_size]    )
			__A							:    Optional[int]         =      model(lowerCAmelCase__    )
			self.parent.assertEqual(result.logits.shape							, (self.batch_size, self.type_sequence_label_size)    )







	def      __UpperCAmelCase(   self    ):
			__A							:    Optional[int]         =      self.prepare_config_and_inputs()
			__A							:    str         =      config_and_inputs
			__A							:    Any         =      {'pixel_values': pixel_values}
			return config, inputs_dict


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

	'''simple docstring'''



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

	lowerCamelCase_    :			List[str]	  =       False
	lowerCamelCase_    :			List[Any]	  =       False
	lowerCamelCase_    :			Tuple	  =       False
	lowerCamelCase_    :			List[Any]	  =       False

	def      __UpperCAmelCase(   self    ):
			__A							:    Optional[int]         =      TFSwinModelTester(self    )
			__A							:    Dict         =      ConfigTester(self							, config_class=lowerCAmelCase__							, embed_dim=37    )

	def      __UpperCAmelCase(   self    ):
			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    ):
			return

	def      __UpperCAmelCase(   self    ):
			__A							:    List[str]         =      self.model_tester.prepare_config_and_inputs()
			self.model_tester.create_and_check_model(*lowerCAmelCase__    )

	def      __UpperCAmelCase(   self    ):
			__A							:    List[str]         =      self.model_tester.prepare_config_and_inputs()
			self.model_tester.create_and_check_for_masked_image_modeling(*lowerCAmelCase__    )

	def      __UpperCAmelCase(   self    ):
			__A							:    List[str]         =      self.model_tester.prepare_config_and_inputs()
			self.model_tester.create_and_check_for_image_classification(*lowerCAmelCase__    )

	@unittest.skip(reason="Swin does not use inputs_embeds"    )
	def      __UpperCAmelCase(   self    ):
			pass

	def      __UpperCAmelCase(   self    ):
			__A							:    List[Any]         =      self.model_tester.prepare_config_and_inputs_for_common()

			for model_class in self.all_model_classes:
					__A							:    List[Any]         =      model_class(lowerCAmelCase__    )
					self.assertIsInstance(model.get_input_embeddings()							, tf.keras.layers.Layer    )
					__A							:    Optional[Any]         =      model.get_output_embeddings()
					self.assertTrue(x is None or isinstance(lowerCAmelCase__							, tf.keras.layers.Dense    )    )

	def      __UpperCAmelCase(   self    ):
			__A							:    List[str]         =      self.model_tester.prepare_config_and_inputs_for_common()

			for model_class in self.all_model_classes:
					__A							:    str         =      model_class(lowerCAmelCase__    )
					__A							:    str         =      inspect.signature(model.call    )
					# signature.parameters is an OrderedDict => so arg_names order is deterministic
					__A							:    int         =      [*signature.parameters.keys()]

					__A							:    Dict         =      ['pixel_values']
					self.assertListEqual(arg_names[:1]							, lowerCAmelCase__    )

	def      __UpperCAmelCase(   self    ):
			__A							:    List[str]         =      self.model_tester.prepare_config_and_inputs_for_common()
			__A							:    Dict         =      True

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

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

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

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

					if hasattr(self.model_tester							, "num_hidden_states_types"    ):
							__A							:    int         =      self.model_tester.num_hidden_states_types
					else:
							# also another +1 for reshaped_hidden_states
							__A							:    int         =      2
					self.assertEqual(out_len + added_hidden_states							, len(lowerCAmelCase__    )    )

					__A							:    Union[str, Any]         =      outputs.attentions

					self.assertEqual(len(lowerCAmelCase__    )							, lowerCAmelCase__    )

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

	def      __UpperCAmelCase(   self							, __UpperCAmelCase							, __UpperCAmelCase							, __UpperCAmelCase							, __UpperCAmelCase    ):
			__A							:    Tuple         =      model_class(lowerCAmelCase__    )
			__A							:    List[str]         =      model(**self._prepare_for_class(lowerCAmelCase__							, lowerCAmelCase__    )    )
			__A							:    str         =      outputs.hidden_states

			__A							:    Optional[Any]         =      getattr(
			    self.model_tester							, "expected_num_hidden_layers"							, len(self.model_tester.depths    ) + 1    )
			self.assertEqual(len(lowerCAmelCase__    )							, lowerCAmelCase__    )

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

			__A							:    Tuple         =      (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]							, )

			__A							:    Optional[Any]         =      outputs.reshaped_hidden_states
			self.assertEqual(len(lowerCAmelCase__    )							, lowerCAmelCase__    )

			__A							:    str         =      reshaped_hidden_states[0].shape

			__A							:    int         =      tf.reshape(reshaped_hidden_states[0]							, (batch_size, num_channels, height * width)    )
			__A							:    List[str]         =      tf.transpose(lowerCAmelCase__							, (0, 2, 1)    )

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

	def      __UpperCAmelCase(   self    ):
			__A							:    Tuple         =      self.model_tester.prepare_config_and_inputs_for_common()

			__A							:    List[str]         =      to_atuple(self.model_tester.image_size    )

			for model_class in self.all_model_classes:
					__A							:    Optional[int]         =      True
					self.check_hidden_states_output(lowerCAmelCase__							, lowerCAmelCase__							, lowerCAmelCase__							, lowerCAmelCase__    )

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

					self.check_hidden_states_output(lowerCAmelCase__							, lowerCAmelCase__							, lowerCAmelCase__							, lowerCAmelCase__    )

	def      __UpperCAmelCase(   self    ):
			__A							:    List[Any]         =      self.model_tester.prepare_config_and_inputs_for_common()
			__A							:    int         =      3

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

			__A							:    str         =      image_size[0] + patch_size[0] - (image_size[0] % patch_size[0])
			__A							:    Union[str, Any]         =      image_size[1] + patch_size[1] - (image_size[1] % patch_size[1])

			for model_class in self.all_model_classes:
					__A							:    Optional[int]         =      True
					self.check_hidden_states_output(lowerCAmelCase__							, lowerCAmelCase__							, lowerCAmelCase__							, (padded_height, padded_width)    )

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







	@slow
	def      __UpperCAmelCase(   self    ):




			for model_name in TF_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
					__A							:    Any         =      TFSwinModel.from_pretrained(lowerCAmelCase__    )
					self.assertIsNotNone(lowerCAmelCase__    )


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

	'''simple docstring'''



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







	@slow
	def      __UpperCAmelCase(   self    ):
			__A							:    int         =      TFSwinForImageClassification.from_pretrained("microsoft/swin-tiny-patch4-window7-224"    )
			__A							:    List[str]         =      self.default_image_processor

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

			# forward pass
			__A							:    Union[str, Any]         =      model(lowerCAmelCase__    )

			# verify the logits
			__A							:    Optional[Any]         =      tf.TensorShape((1, 1_000)    )
			self.assertEqual(outputs.logits.shape							, lowerCAmelCase__    )
			__A							:    List[str]         =      tf.constant([-0.09_48, -0.64_54, -0.09_21]    )
			self.assertTrue(np.allclose(outputs.logits[0, :3]							, lowerCAmelCase__							, atol=1e-4    )    )









# as due to their complexity multi-gpu tests could impact other tests, and to aid debug we have those in a separate module.

import os
import sys

from transformers.testing_utils import TestCasePlus, execute_subprocess_async, get_gpu_count, require_torch_gpu, slow

from .utils import load_json







class      __lowercase  (__SCREAMING_SNAKE_CASE   ):





							"""simple docstring"""







							@classmethod
							def  UpperCamelCase__			(	cls    ):





										"""simple docstring"""




										return cls






							@slow
							@require_torch_gpu
							def  UpperCamelCase__			(	self    ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_						:			List[str]       						= self.get_auto_remove_tmp_dir()
										SCREAMING_SNAKE_CASE_						:			Tuple       						= F'''
            --model_name Helsinki-NLP/opus-mt-en-ro
            --save_dir {output_dir}
            --data_dir {self.test_file_dir_str}/test_data/wmt_en_ro
            --num_beams 2
            --task translation
        '''.split()

										# we want this test to run even if there is only one GPU, but if there are more we use them all
										SCREAMING_SNAKE_CASE_						:			Dict       						= get_gpu_count()
										SCREAMING_SNAKE_CASE_						:			int       						= F'''
            -m torch.distributed.launch
            --nproc_per_node={n_gpu}
            {self.test_file_dir}/run_distributed_eval.py
        '''.split()
										SCREAMING_SNAKE_CASE_						:			Optional[Any]       						= [sys.executable] + distributed_args + args
										execute_subprocess_async(lowerCAmelCase__       ,		env=self.get_env()    )

										SCREAMING_SNAKE_CASE_						:			List[str]       						= os.path.join(lowerCAmelCase__       ,		'test_bleu.json'    )
										SCREAMING_SNAKE_CASE_						:			Optional[int]       						= load_json(lowerCAmelCase__    )
										# print(metrics)
										self.assertGreaterEqual(metrics['bleu']       ,		2_5    )





