
import logging
from pathlib import Path

import numpy as np
import pytorch_lightning as pl
import torch
from pytorch_lightning.callbacks import EarlyStopping, ModelCheckpoint
from pytorch_lightning.utilities import rank_zero_only
from utils_rag import save_json



def      __lowerCAmelCase						(							UpperCAmelCase__				:      Optional[Any]       )  ->    Optional[int]:
							lowerCamelCase_							  =					filter(lambda UpperCAmelCase__       : p.requires_grad			,				model.parameters()       )
							lowerCamelCase_							  =					sum([np.prod(p.size()       ) for p in model_parameters]       )
							return params


lowercase        =  logging.getLogger(__name__)



def      __lowerCAmelCase						(							UpperCAmelCase__				:      int			,				UpperCAmelCase__				:      Union[str, Any]       )  ->    Any:
							if metric == "rouge2":
														lowerCamelCase_							  =					"""{val_avg_rouge2:.4f}-{step_count}"""
							elif metric == "bleu":
														lowerCamelCase_							  =					"""{val_avg_bleu:.4f}-{step_count}"""
							elif metric == "em":
														lowerCamelCase_							  =					"""{val_avg_em:.4f}-{step_count}"""
							elif metric == "loss":
														lowerCamelCase_							  =					"""{val_avg_loss:.4f}-{step_count}"""
							else:
														raise NotImplementedError(
														    F'''seq2seq callbacks only support rouge2 and bleu, got {metric}, You can make your own by adding to this'''
														    """ function."""       )

							lowerCamelCase_							  =					ModelCheckpoint(
							    dirpath=UpperCAmelCase__			,				filename=UpperCAmelCase__			,				monitor=F'''val_{metric}'''			,				mode="""max"""			,				save_top_k=1			,				every_n_epochs=1			,				)

							return checkpoint_callback



def      __lowerCAmelCase						(							UpperCAmelCase__				:      Optional[Any]			,				UpperCAmelCase__				:      str       )  ->    str:
							return EarlyStopping(
							    monitor=F'''val_{metric}'''			,				mode="""min""" if """loss""" in metric else """max"""			,				patience=UpperCAmelCase__			,				verbose=UpperCAmelCase__			,				)





class       __A(				pl.Callback       ):

					def    lowercase__      (	self					:     Optional[int]   ,					__UpperCamelCase					:     List[Any]   ,					__UpperCamelCase					:     Union[str, Any]   ):
												lowerCamelCase_							  =					{F'''lr_group_{i}''': param["""lr"""] for i, param in enumerate(pl_module.trainer.optimizers[0].param_groups   )}
												pl_module.logger.log_metrics(__UpperCamelCase   )

					@rank_zero_only
					def    lowercase__      (	self					:     Union[str, Any]   ,					__UpperCamelCase					:     pl.Trainer   ,					__UpperCamelCase					:     pl.LightningModule   ,					__UpperCamelCase					:     str   ,					__UpperCamelCase					:     List[str]=True   ):
												logger.info(F'''***** {type_path} results at step {trainer.global_step:05d} *****'''   )
												lowerCamelCase_							  =					trainer.callback_metrics
												trainer.logger.log_metrics({k: v for k, v in metrics.items() if k not in ["""log""", """progress_bar""", """preds"""]}   )
												# Log results
												lowerCamelCase_							  =					Path(pl_module.hparams.output_dir   )
												if type_path == "test":
																			lowerCamelCase_							  =					od / """test_results.txt"""
																			lowerCamelCase_							  =					od / """test_generations.txt"""
												else:
																			# this never gets hit. I prefer not to save intermediate generations, and results are in metrics.json
																			# If people want this it will be easy enough to add back.
																			lowerCamelCase_							  =					od / F'''{type_path}_results/{trainer.global_step:05d}.txt'''
																			lowerCamelCase_							  =					od / F'''{type_path}_generations/{trainer.global_step:05d}.txt'''
																			results_file.parent.mkdir(exist_ok=__UpperCamelCase   )
																			generations_file.parent.mkdir(exist_ok=__UpperCamelCase   )
												with open(__UpperCamelCase   ,					"""a+"""   ) as writer:
																			for key in sorted(__UpperCamelCase   ):
																										if key in ["log", "progress_bar", "preds"]:
																																	continue
																										lowerCamelCase_							  =					metrics[key]
																										if isinstance(__UpperCamelCase   ,					torch.Tensor   ):
																																	lowerCamelCase_							  =					val.item()
																										lowerCamelCase_							  =					F'''{key}: {val:.6f}\n'''
																										writer.write(__UpperCamelCase   )

												if not save_generations:
																			return

												if "preds" in metrics:
																			lowerCamelCase_							  =					"""\n""".join(metrics["""preds"""]   )
																			generations_file.open("""w+"""   ).write(__UpperCamelCase   )

					@rank_zero_only
					def    lowercase__      (	self					:     Optional[int]   ,					__UpperCamelCase					:     Optional[int]   ,					__UpperCamelCase					:     List[str]   ):
												try:
																			lowerCamelCase_							  =					pl_module.model.model.num_parameters()
												except AttributeError:
																			lowerCamelCase_							  =					pl_module.model.num_parameters()

												lowerCamelCase_							  =					count_trainable_parameters(__UpperCamelCase   )
												# mp stands for million parameters
												trainer.logger.log_metrics({"""n_params""": npars, """mp""": npars / 1E6, """grad_mp""": n_trainable_pars / 1E6}   )

					@rank_zero_only
					def    lowercase__      (	self					:     str   ,					__UpperCamelCase					:     pl.Trainer   ,					__UpperCamelCase					:     pl.LightningModule   ):
												save_json(pl_module.metrics   ,					pl_module.metrics_save_path   )
												return self._write_logs(__UpperCamelCase   ,					__UpperCamelCase   ,					"""test"""   )



					@rank_zero_only
					def    lowercase__      (	self					:     List[str]   ,					__UpperCamelCase					:     pl.Trainer   ,					__UpperCamelCase					:     int   ):
												save_json(pl_module.metrics   ,					pl_module.metrics_save_path   )
												# Uncommenting this will save val generations
												# return self._write_logs(trainer, pl_module, "valid")







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       __A:

					def __init__(	self					:     int   ,					__UpperCamelCase					:     Union[str, Any]   ,					__UpperCamelCase					:     Union[str, Any]=3   ,					__UpperCamelCase					:     int=3_2   ,					__UpperCamelCase					:     Any=3   ,					__UpperCamelCase					:     List[str]=1_0   ,					__UpperCamelCase					:     int=[1_0, 2_0, 3_0, 4_0]   ,					__UpperCamelCase					:     List[str]=[1, 1, 2, 1]   ,					__UpperCamelCase					:     str=True   ,					__UpperCamelCase					:     List[str]=True   ,					__UpperCamelCase					:     Dict="relu"   ,					__UpperCamelCase					:     int=3   ,					__UpperCamelCase					:     Dict=None   ,					):
												lowerCamelCase_							  =					parent
												lowerCamelCase_							  =					batch_size
												lowerCamelCase_							  =					image_size
												lowerCamelCase_							  =					num_channels
												lowerCamelCase_							  =					embeddings_size
												lowerCamelCase_							  =					hidden_sizes
												lowerCamelCase_							  =					depths
												lowerCamelCase_							  =					is_training
												lowerCamelCase_							  =					use_labels
												lowerCamelCase_							  =					hidden_act
												lowerCamelCase_							  =					num_labels
												lowerCamelCase_							  =					scope
												lowerCamelCase_							  =					len(__UpperCamelCase   )

					def    lowercase__      (	self					:     Any   ):
												lowerCamelCase_							  =					floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]   )

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

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

					def    lowercase__      (	self					:     str   ):
												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    lowercase__      (	self					:     Optional[int]   ,					__UpperCamelCase					:     int   ,					__UpperCamelCase					:     Tuple   ,					__UpperCamelCase					:     Union[str, Any]   ):
												lowerCamelCase_							  =					TFRegNetModel(config=__UpperCamelCase   )
												lowerCamelCase_							  =					model(__UpperCamelCase   ,					training=__UpperCamelCase   )
												# 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 // 3_2, self.image_size // 3_2)   ,					)

					def    lowercase__      (	self					:     Union[str, Any]   ,					__UpperCamelCase					:     int   ,					__UpperCamelCase					:     List[Any]   ,					__UpperCamelCase					:     List[str]   ):
												lowerCamelCase_							  =					self.num_labels
												lowerCamelCase_							  =					TFRegNetForImageClassification(__UpperCamelCase   )
												lowerCamelCase_							  =					model(__UpperCamelCase   ,					labels=__UpperCamelCase   ,					training=__UpperCamelCase   )
												self.parent.assertEqual(result.logits.shape   ,					(self.batch_size, self.num_labels)   )



					def    lowercase__      (	self					:     int   ):
												lowerCamelCase_							  =					self.prepare_config_and_inputs()
												lowerCamelCase_  ,   lowerCamelCase_  ,   lowerCamelCase_							  =					config_and_inputs
												lowerCamelCase_							  =					{"""pixel_values""": pixel_values}
												return config, inputs_dict





@require_tf
class       __A(				UpperCAmelCase     ,		UpperCAmelCase     ,		unittest.TestCase       ):
					SCREAMING_SNAKE_CASE			    =    (TFRegNetModel, TFRegNetForImageClassification) if is_tf_available() else ()
					SCREAMING_SNAKE_CASE			    =    (
					    {'''feature-extraction''': TFRegNetModel, '''image-classification''': TFRegNetForImageClassification}
					    if is_tf_available()
					    else {}
					)

					SCREAMING_SNAKE_CASE			    =    False
					SCREAMING_SNAKE_CASE			    =    False
					SCREAMING_SNAKE_CASE			    =    False
					SCREAMING_SNAKE_CASE			    =    False
					SCREAMING_SNAKE_CASE			    =    False

					def    lowercase__      (	self					:     Tuple   ):
												lowerCamelCase_							  =					TFRegNetModelTester(self   )
												lowerCamelCase_							  =					ConfigTester(self   ,					config_class=__UpperCamelCase   ,					has_text_modality=__UpperCamelCase   )

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

					@unittest.skip(reason="""RegNet does not use inputs_embeds"""   )
					def    lowercase__      (	self					:     Optional[int]   ):
												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    lowercase__      (	self					:     Tuple   ):
												super().test_keras_fit()

					@unittest.skip(reason="""RegNet does not support input and output embeddings"""   )
					def    lowercase__      (	self					:     Optional[Any]   ):
												pass

					def    lowercase__      (	self					:     Dict   ):
												lowerCamelCase_  ,   lowerCamelCase_							  =					self.model_tester.prepare_config_and_inputs_for_common()

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

																			lowerCamelCase_							  =					["""pixel_values"""]
																			self.assertListEqual(arg_names[:1]   ,					__UpperCamelCase   )

					def    lowercase__      (	self					:     Any   ):
												lowerCamelCase_							  =					self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_model(*__UpperCamelCase   )

					def    lowercase__      (	self					:     int   ):
												def check_hidden_states_output(__UpperCamelCase					:     Union[str, Any]   ,					__UpperCamelCase					:     str   ,					__UpperCamelCase					:     Any   ):
																			lowerCamelCase_							  =					model_class(__UpperCamelCase   )
																			lowerCamelCase_							  =					model(**self._prepare_for_class(__UpperCamelCase   ,					__UpperCamelCase   )   ,					training=__UpperCamelCase   )

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

																			lowerCamelCase_							  =					self.model_tester.num_stages
																			self.assertEqual(len(__UpperCamelCase   )   ,					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]   ,					)

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

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

																										check_hidden_states_output(__UpperCamelCase   ,					__UpperCamelCase   ,					__UpperCamelCase   )

					def    lowercase__      (	self					:     Optional[Any]   ):
												lowerCamelCase_  ,   lowerCamelCase_							  =					self.model_tester.prepare_config_and_inputs_for_common()

												def check_equivalence(__UpperCamelCase					:     List[str]   ,					__UpperCamelCase					:     Optional[Any]   ,					__UpperCamelCase					:     Tuple   ,					__UpperCamelCase					:     List[Any]={}   ):
																			lowerCamelCase_							  =					model(__UpperCamelCase   ,					return_dict=__UpperCamelCase   ,					**__UpperCamelCase   )
																			lowerCamelCase_							  =					model(__UpperCamelCase   ,					return_dict=__UpperCamelCase   ,					**__UpperCamelCase   ).to_tuple()

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

																										recursive_check(__UpperCamelCase   ,					__UpperCamelCase   )

												for model_class in self.all_model_classes:
																			lowerCamelCase_							  =					model_class(__UpperCamelCase   )

																			lowerCamelCase_							  =					self._prepare_for_class(__UpperCamelCase   ,					__UpperCamelCase   )
																			lowerCamelCase_							  =					self._prepare_for_class(__UpperCamelCase   ,					__UpperCamelCase   )
																			check_equivalence(__UpperCamelCase   ,					__UpperCamelCase   ,					__UpperCamelCase   )

																			lowerCamelCase_							  =					self._prepare_for_class(__UpperCamelCase   ,					__UpperCamelCase   ,					return_labels=__UpperCamelCase   )
																			lowerCamelCase_							  =					self._prepare_for_class(__UpperCamelCase   ,					__UpperCamelCase   ,					return_labels=__UpperCamelCase   )
																			check_equivalence(__UpperCamelCase   ,					__UpperCamelCase   ,					__UpperCamelCase   )

																			lowerCamelCase_							  =					self._prepare_for_class(__UpperCamelCase   ,					__UpperCamelCase   )
																			lowerCamelCase_							  =					self._prepare_for_class(__UpperCamelCase   ,					__UpperCamelCase   )
																			check_equivalence(__UpperCamelCase   ,					__UpperCamelCase   ,					__UpperCamelCase   ,					{"""output_hidden_states""": True}   )

																			lowerCamelCase_							  =					self._prepare_for_class(__UpperCamelCase   ,					__UpperCamelCase   ,					return_labels=__UpperCamelCase   )
																			lowerCamelCase_							  =					self._prepare_for_class(__UpperCamelCase   ,					__UpperCamelCase   ,					return_labels=__UpperCamelCase   )
																			check_equivalence(__UpperCamelCase   ,					__UpperCamelCase   ,					__UpperCamelCase   ,					{"""output_hidden_states""": True}   )

					def    lowercase__      (	self					:     Union[str, Any]   ):
												lowerCamelCase_							  =					self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_for_image_classification(*__UpperCamelCase   )



					@slow
					def    lowercase__      (	self					:     List[Any]   ):
												for model_name in TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																			lowerCamelCase_							  =					TFRegNetModel.from_pretrained(__UpperCamelCase   )
																			self.assertIsNotNone(__UpperCamelCase   )



def      __lowerCAmelCase						(							)  ->    Optional[int]:
							lowerCamelCase_							  =					Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""       )
							return image





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

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



					@slow
					def    lowercase__      (	self					:     List[str]   ):
												lowerCamelCase_							  =					TFRegNetForImageClassification.from_pretrained(TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]   )

												lowerCamelCase_							  =					self.default_image_processor
												lowerCamelCase_							  =					prepare_img()
												lowerCamelCase_							  =					image_processor(images=__UpperCamelCase   ,					return_tensors="""tf"""   )

												# forward pass
												lowerCamelCase_							  =					model(**__UpperCamelCase   ,					training=__UpperCamelCase   )

												# verify the logits
												lowerCamelCase_							  =					tf.TensorShape((1, 1_0_0_0)   )
												self.assertEqual(outputs.logits.shape   ,					__UpperCamelCase   )

												lowerCamelCase_							  =					tf.constant([-0.4180, -1.5051, -3.4836]   )

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





