
from __future__ import annotations

import inspect
import unittest
from typing import List, Tuple

from transformers import RegNetConfig
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 TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST, TFRegNetForImageClassification, TFRegNetModel


if is_vision_available():
					from PIL import Image

					from transformers import AutoImageProcessor





class 							UpperCAmelCase				:




	'''simple docstring'''





	def __init__(	self :			Any							,A :			Optional[int]							,A :			Optional[int]=3							,A :			str=32							,A :			Optional[int]=3							,A :			Tuple=10							,A :			Any=[10, 20, 30, 40]							,A :			List[str]=[1, 1, 2, 1]							,A :			Tuple=True							,A :			Dict=True							,A :			Union[str, Any]="relu"							,A :			List[Any]=3							,A :			List[str]=None							,):
					__A             =			parent
					__A             =			batch_size
					__A             =			image_size
					__A             =			num_channels
					__A             =			embeddings_size
					__A             =			hidden_sizes
					__A             =			depths
					__A             =			is_training
					__A             =			use_labels
					__A             =			hidden_act
					__A             =			num_labels
					__A             =			scope
					__A             =			len(A			)






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

					__A             =			None
					if self.use_labels:
									__A             =			ids_tensor([self.batch_size]							,self.num_labels			)

					__A             =			self.get_config()
					return config, pixel_values, labels






	def     UpperCamelCase_  (	self :			Optional[int]			):
					return RegNetConfig(
					    num_channels=self.num_channels							,embeddings_size=self.embeddings_size							,hidden_sizes=self.hidden_sizes							,depths=self.depths							,hidden_act=self.hidden_act							,num_labels=self.num_labels							,)






	def     UpperCamelCase_  (	self :			Tuple							,A :			Dict							,A :			int							,A :			Optional[int]			):
					__A             =			TFRegNetModel(config=A			)
					__A             =			model(A							,training=A			)
					# expected last hidden states: B, C, H // 32, W // 32
					self.parent.assertEqual(
					    result.last_hidden_state.shape							,(self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32)							,)






	def     UpperCamelCase_  (	self :			List[Any]							,A :			Tuple							,A :			List[Any]							,A :			Any			):
					__A             =			self.num_labels
					__A             =			TFRegNetForImageClassification(A			)
					__A             =			model(A							,labels=A							,training=A			)
					self.parent.assertEqual(result.logits.shape							,(self.batch_size, self.num_labels)			)







	def     UpperCamelCase_  (	self :			Optional[int]			):
					__A             =			self.prepare_config_and_inputs()
					__A      ,							__A      ,							__A             =			config_and_inputs
					__A             =			{"pixel_values": pixel_values}
					return config, inputs_dict





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




	'''simple docstring'''





	snake_case_    =					(TFRegNetModel, TFRegNetForImageClassification) if is_tf_available() else ()
	snake_case_    =					(
	    {"feature-extraction": TFRegNetModel, "image-classification": TFRegNetForImageClassification}
	    if is_tf_available()
	    else {}
	)

	snake_case_    =					False
	snake_case_    =					False
	snake_case_    =					False
	snake_case_    =					False
	snake_case_    =					False






	def     UpperCamelCase_  (	self :			Optional[int]			):
					__A             =			TFRegNetModelTester(self			)
					__A             =			ConfigTester(self							,config_class=A							,has_text_modality=A			)






	def     UpperCamelCase_  (	self :			Union[str, Any]			):
					return






	@unittest.skip(reason="RegNet does not use inputs_embeds"			)
	def     UpperCamelCase_  (	self :			List[str]			):
					pass






	@unittest.skipIf(
	    not is_tf_available() or len(tf.config.list_physical_devices("GPU"			)			) == 0							,reason="TF does not support backprop for grouped convolutions on CPU."							,)
	@slow
	def     UpperCamelCase_  (	self :			Optional[int]			):
					super().test_keras_fit()






	@unittest.skip(reason="RegNet does not support input and output embeddings"			)
	def     UpperCamelCase_  (	self :			str			):
					pass






	def     UpperCamelCase_  (	self :			Optional[int]			):
					__A      ,							__A             =			self.model_tester.prepare_config_and_inputs_for_common()

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

									__A             =			["pixel_values"]
									self.assertListEqual(arg_names[:1]							,A			)






	def     UpperCamelCase_  (	self :			Dict			):
					__A             =			self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_model(*A			)






	def     UpperCamelCase_  (	self :			Any			):
					def check_hidden_states_output(A :			int							,A :			List[Any]							,A :			int			):
									__A             =			model_class(A			)
									__A             =			model(**self._prepare_for_class(A							,A			)							,training=A			)

									__A             =			outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

									__A             =			self.model_tester.num_stages
									self.assertEqual(len(A			)							,expected_num_stages + 1			)

									# RegNet's feature maps are of shape (batch_size, num_channels, height, width)
									self.assertListEqual(
									    list(hidden_states[0].shape[-2:]			)							,[self.model_tester.image_size // 2, self.model_tester.image_size // 2]							,)

					__A      ,							__A             =			self.model_tester.prepare_config_and_inputs_for_common()
					__A             =			["basic", "bottleneck"]
					for model_class in self.all_model_classes:
									for layer_type in layers_type:
													__A             =			layer_type
													__A             =			True
													check_hidden_states_output(A							,A							,A			)

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

													check_hidden_states_output(A							,A							,A			)






	def     UpperCamelCase_  (	self :			str			):
					__A      ,							__A             =			self.model_tester.prepare_config_and_inputs_for_common()

					def check_equivalence(A :			Union[str, Any]							,A :			Union[str, Any]							,A :			List[Any]							,A :			int={}			):
									__A             =			model(A							,return_dict=A							,**A			)
									__A             =			model(A							,return_dict=A							,**A			).to_tuple()

									def recursive_check(A :			Union[str, Any]							,A :			str			):
													if isinstance(A							,(List, Tuple)			):
																	for tuple_iterable_value, dict_iterable_value in zip(A							,A			):
																					recursive_check(A							,A			)
													elif tuple_object is None:
																	return
													else:
																	self.assertTrue(
																	    all(tf.equal(A							,A			)			)							,msg=(
																	        "Tuple and dict output are not equal. Difference:"
																	        f''' {tf.math.reduce_max(tf.abs(tuple_object - dict_object			)			)}'''
																	    )							,)

													recursive_check(A							,A			)

					for model_class in self.all_model_classes:
									__A             =			model_class(A			)

									__A             =			self._prepare_for_class(A							,A			)
									__A             =			self._prepare_for_class(A							,A			)
									check_equivalence(A							,A							,A			)

									__A             =			self._prepare_for_class(A							,A							,return_labels=A			)
									__A             =			self._prepare_for_class(A							,A							,return_labels=A			)
									check_equivalence(A							,A							,A			)

									__A             =			self._prepare_for_class(A							,A			)
									__A             =			self._prepare_for_class(A							,A			)
									check_equivalence(A							,A							,A							,{"output_hidden_states": True}			)

									__A             =			self._prepare_for_class(A							,A							,return_labels=A			)
									__A             =			self._prepare_for_class(A							,A							,return_labels=A			)
									check_equivalence(A							,A							,A							,{"output_hidden_states": True}			)






	def     UpperCamelCase_  (	self :			Any			):
					__A             =			self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_for_image_classification(*A			)







	@slow
	def     UpperCamelCase_  (	self :			Tuple			):
					for model_name in TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
									__A             =			TFRegNetModel.from_pretrained(A			)
									self.assertIsNotNone(A			)





def 						UpperCAmelCase      (     )		->     Dict:







				"""simple docstring"""



				__A             =			Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"						)
				return image





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




	'''simple docstring'''





	@cached_property
	def     UpperCamelCase_  (	self :			Any			):
					return (
					    AutoImageProcessor.from_pretrained(TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]			)
					    if is_vision_available()
					    else None
					)







	@slow
	def     UpperCamelCase_  (	self :			Union[str, Any]			):
					__A             =			TFRegNetForImageClassification.from_pretrained(TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]			)

					__A             =			self.default_image_processor
					__A             =			prepare_img()
					__A             =			image_processor(images=A							,return_tensors="tf"			)

					# forward pass
					__A             =			model(**A							,training=A			)

					# verify the logits
					__A             =			tf.TensorShape((1, 10_00)			)
					self.assertEqual(outputs.logits.shape							,A			)

					__A             =			tf.constant([-0.41_80, -1.50_51, -3.48_36]			)

					tf.debugging.assert_near(outputs.logits[0, :3]							,A							,atol=1E-4			)


import json
import os
import unittest

from transformers.models.ctrl.tokenization_ctrl import VOCAB_FILES_NAMES, CTRLTokenizer

from ...test_tokenization_common import TokenizerTesterMixin





class 							UpperCAmelCase				(  __SCREAMING_SNAKE_CASE      ,      unittest.TestCase  ):




	'''simple docstring'''





	snake_case_    =					CTRLTokenizer
	snake_case_    =					False
	snake_case_    =					False






	def     UpperCamelCase_  (	self :			Dict			):
					super().setUp()

					# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
					__A             =			["adapt", "re@@", "a@@", "apt", "c@@", "t", "<unk>"]
					__A             =			dict(zip(A							,range(len(A			)			)			)			)
					__A             =			["#version: 0.2", "a p", "ap t</w>", "r e", "a d", "ad apt</w>", ""]
					__A             =			{"unk_token": "<unk>"}

					__A             =			os.path.join(self.tmpdirname							,VOCAB_FILES_NAMES["vocab_file"]			)
					__A             =			os.path.join(self.tmpdirname							,VOCAB_FILES_NAMES["merges_file"]			)
					with open(self.vocab_file							,"w"							,encoding="utf-8"			) as fp:
									fp.write(json.dumps(A			) + "\n"			)
					with open(self.merges_file							,"w"							,encoding="utf-8"			) as fp:
									fp.write("\n".join(A			)			)






	def     UpperCamelCase_  (	self :			List[str]							,**A :			List[str]			):
					kwargs.update(self.special_tokens_map			)
					return CTRLTokenizer.from_pretrained(self.tmpdirname							,**A			)






	def     UpperCamelCase_  (	self :			Optional[int]							,A :			Tuple			):
					__A             =			"adapt react readapt apt"
					__A             =			"adapt react readapt apt"
					return input_text, output_text







	def     UpperCamelCase_  (	self :			Any			):
					__A             =			CTRLTokenizer(self.vocab_file							,self.merges_file							,**self.special_tokens_map			)
					__A             =			"adapt react readapt apt"
					__A             =			"adapt re@@ a@@ c@@ t re@@ adapt apt".split()
					__A             =			tokenizer.tokenize(A			)
					self.assertListEqual(A							,A			)

					__A             =			tokens + [tokenizer.unk_token]

					__A             =			[0, 1, 2, 4, 5, 1, 0, 3, 6]
					self.assertListEqual(tokenizer.convert_tokens_to_ids(A			)							,A			)
